{"paper_id":"0fd8bc95-a1aa-459a-968f-2043da56af3f","body_text":"Genome-wide comparative analysis of structural features in fungal GATA transcription factors: Insights from 796 fungal species | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Genome-wide comparative analysis of structural features in fungal GATA transcription factors: Insights from 796 fungal species Mangi Kim This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9688441/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Fungi constitute a highly diverse and ecologically important kingdom with central roles in nutrient cycling, symbiosis, and pathogenesis. The regulation of these processes involves multiple layers of control, with transcription factors (TFs) playing key roles in shaping condition-specific gene expression within complex regulatory networks. In particular, fungal GATA TFs are a family of DNA-binding proteins characterized by a conserved GATA zinc finger motif that recognizes the consensus sequence WGATAR (W = A/T, R = A/G) in target gene promoters. They function as key regulators of light response, nitrogen and iron metabolism, secondary metabolism, and reproduction. Despite their functional importance, structural analyses of fungal GATA TFs across diverse species remain limited, hindering understanding of their structural diversity and evolutionary patterns. To address this gap, this study conducted a comprehensive genome-wide comparative analysis of the structural and molecular features of 7,846 fungal GATA TFs from 796 species across eleven divisions, sourced from EnsemblFungi and MycoCosm. Domain architecture, GATA motif diversity, and motif-domain associations of fungal GATA TFs were systematically characterized, and motif diversity was further compared with plant and animal GATA TFs to place fungal GATA evolution in a broader eukaryotic context. Phylogenetic relationships based on GATA domain sequences enabled the identification of putative orthologous groups and the inference of lineage-specific functional diversification. Structural predictions of representative fungal GATA TFs were performed to support functional interpretation. In addition, order-level analyses of Dikarya GATA TFs revealed lineage-specific architectural patterns. Taken together, this integrative analysis provides a comprehensive framework for understanding the evolutionary and functional diversification of fungal GATA TFs. Fungi GATA TFs Genome-wide Structural diversity Evolutionary patterns Integrative analysis. Figures Figure 1 Figure 2 Figure 3 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 1. Introduction Fungi are osmoheterotrophic eukaryotes with filamentous or unicellular vegetative structures, chitin- or polysaccharide-based cell walls, and asexual or sexual spores (Raghukumar 2017 ). As of 2024, the kingdom comprises approximately 140,000 formally described species (Hyde 2024 ). However, diversity estimates based on various approaches suggest that the actual number of fungal species may range from 2 to 11 million (Hawksworth and Lücking 2017 ; Lücking et al. 2021 ; Baldrian et al. 2022 ; Hyde 2022 ). Notably, species estimates from metabarcoding data are typically higher, with some studies reporting up to 11.7–13.2 million species (Wu et al. 2019 ). Collectively, this diversity supports the wide-ranging ecological functions and practical applications of fungi (Adnan et al. 2022 ; Wadhwa et al. 2024 ). Among these divisions, Ascomycota is the most species-rich fungal group, with ~ 98,000 described species (64% of all formally described fungi) (Wu et al. 2019 ; Hyde 2024 ), forming haploid spores in asci and ranging from yeast to filamentous forms (Dissanayake and Liu 2025 ). The biological functions of Ascomycota have been well studied, highlighting their ecological and biotechnological significance (Lorenzini et al. 2016 ). For example, Aspergillus species (e.g., A. oryzae, A. sojae ) are widely used in food fermentation and enzyme production (Orban et al. 2019 ), Penicillium species produce β-lactam antibiotics (Dutta and Phull 2021 ), Fusarium fujikuroi synthesizes growth-promoting gibberellins (Pfannmüller et al. 2017 ), and oleaginous fungi such as A. oryzae (Hui et al. 2010 )d niger (Beopoulos et al. 2009 ). Moreover, other fungal divisions, such as Basidiomycota and Mucoromycota , also play critical ecological, industrial, agricultural, and clinical roles (Coelho et al. 2017 ; He et al. 2022 ; Zhao et al. 2023 ). These examples demonstrate the functional and biotechnological importance of Ascomycota , as well as the critical ecological and diverse functional roles of other fungal divisions. Accordingly, understanding these diverse ecological and functional roles requires detailed analyses at the genomic and regulatory levels (Stajich 2017 ; Merényi et al. 2023 ; Xie et al. 2025 ). Building on this foundation, fungal transcription factors (TFs) function as central regulators of gene expression, underpinning the regulatory and evolutionary dynamics of fungal genomes (Shelest 2017 ). They are grouped into roughly 80 TF families (Shelest 2017 ), with representative examples including bZIP (Kong et al. 2015 ), MADS-box (Yang et al. 2015 ), p53-like (Katz et al. 2013 ), and GATA (Yu et al. 2019 ; Chen et al. 2021 ; Jiang et al. 2021 ). Among these, fungal GATA TFs typically contain one or two GATA domains characterized by a conserved Cys-X 2 -Cys-X 17−18 -Cys-X 2 -Cys motif, which enables specific recognition of the WGATAR element (W = A/T, R = A/G) in target gene promoters (Lowry and Atchley 2000 ). These TFs are particularly notable for regulating physiological and developmental processes, including light response, nitrogen and iron metabolism, secondary metabolism, and reproduction (Scazzocchio 2000 ). For example, in Neurospora crassa , WC-1 and WC-2 form a blue-light photoreceptor complex mediating circadian clock entrainment (He et al. 2002 ). At the same time, comparable light-regulatory roles are observed for Ltf1 in Botrytis cinerea (Schumacher et al. 2014 ) and its homolog, NsdD , in A. nidulans , which also promotes sexual development while repressing conidiation (Lee et al. 2014 ). Nitrogen-responsive GATA TFs AreA and AreB in F. fujikuroi regulate genes involved in nitrogen utilization (Pfannmüller et al. 2017 ), ASD4 in N. crassa governs ascus and ascospore differentiation (Feng et al. 2000 ), Urbs-1 in Ustilago maydis represses siderophore biosynthesis (An et al. 1997 ), and the divergent Ssams2 in Sclerotinia sclerotiorum contributes to appressorium formation and chromosome segregation (Liu et al. 2018a ). These roles highlight the need to understand both the regulatory and structural features of fungal GATA TFs. Given these roles, structural analysis of fungal GATA TFs provides important insights into functional diversification and evolutionary conservation (Lowry and Atchley 2000 ). To date, genome-wide structural identification of fungal GATA TFs has been performed in four single-species studies: six GATA TFs in Alternaria alternata (Chen et al. 2021 ), and seven each in Ustilaginoidea virens (Yu et al. 2019 ), A. oryzae (Jiang et al. 2021 ), and Tolypocladium guangdongense (Zhang et al. 2020 ). In addition, comparative structural analyses of fungal GATA TFs have been reported in three studies: a 2006 survey of 396 GATA TFs from 50 species (Park et al., 2006), a 2025 analysis of 83 GATA TFs from 19 species (Hu et al. 2025 ), and a 2026 study of 157 GATA TFs from 20 species (Virolainen et al. 2026 ). However, as of 2025, at least 21,848 fungal genomes are publicly available (Zaccaron and Stergiopoulos 2025 ) (e.g., NCBI (Kitts et al. 2016 ), MycoCosm (Grigoriev et al. 2014 ), and EnsemblFungi (Yates et al. 2026 )), highlighting a substantial gap between available genomic resources and current GATA TF analyses. To address this gap, this study systematically analyzed the structural and molecular features of 7,846 fungal GATA TFs from 796 species across eleven divisions, using genome-scale protein sequences sourced from EnsemblFungi (Yates et al. 2026 ) and MycoCosm (Grigoriev et al. 2014 ) (Fig. 1 ). Comprehensive analyses were performed to characterize domain architecture, GATA motif diversity, and motif-domain co-occurrence patterns of fungal GATA TFs. In particular, motif diversity was further compared with GATA TFs from plants and animals to place fungal GATA evolution in a broader eukaryotic context. Furthermore, phylogenetic relationships were reconstructed based on fungal GATA domain sequences, enabling the assignment of putative orthologous groups and the inference of functional diversification across lineages. Representative fungal GATA TFs, including AreA, NsdD, SreA, and WC-1 , were further subjected to structure prediction using AlphaFold2 to support functional interpretation. In addition, order-level analyses of Dikarya GATA TFs were conducted to resolve lineage-specific architectural patterns. This integrative analysis provides a framework for understanding the evolutionary and functional diversification of fungal GATA TFs. 2. Materials and methods 2 − 1. Fungal species selection and taxonomic information A total of 1,505 genomes were publicly available from EnsemblFungi ( https://fungi.ensembl.org/index.html ; Release 62) (Yates et al. 2026 ). Of these, 640 genomes were initially selected, one per species, representing a total of 7,243,130 predicted protein-coding genes (mean = 11,317, SD = 4,295). Subsequently, only species containing at least 5,000 predicted protein-coding genes were retained to ensure sufficient completeness and annotation quality. Based on these criteria, 40 Dikarya species with fewer than 5,000 predicted protein-coding genes were excluded, yielding a final set of 600 species for downstream analyses, which together contained 7,102,629 predicted protein-coding genes (mean = 11,838, SD = 3,901). However, EnsemblFungi predominantly provides genomes belonging to Dikarya , which comprises the phyla Ascomycota and Basidiomycota , resulting in limited representation of early-diverging fungal lineages. Accordingly, genomes representing early-diverging fungal lineages were obtained from MycoCosm ( https://mycocosm.jgi.doe.gov/ ) (Grigoriev et al. 2014 ). A total of 269 genomes were available in this database, from which 198 representative species were initially selected using the same filtering criteria applied to EnsemblFungi, excluding species already included in the EnsemblFungi dataset to avoid redundancy. Among these, 26 species contained fewer than 5,000 predicted protein-coding genes; however, 24 Microsporidia species were retained because their reduced gene content reflects extreme genome streamlining rather than annotation incompleteness (Peyretaillade et al. 2011 ), yielding a final set of 196 species comprising 2,120,090 predicted protein-coding genes (mean = 10,817, SD = 4,435). Finally, a total of 796 fungal species were compiled, comprising 9,222,719 predicted proteins (mean = 11,586, SD = 4,060; Supplementary Table 1). Their taxonomic information, including division, class, and order, was obtained from NCBI Taxonomy (Schoch et al. 2020 ). Furthermore, fungal divisions, classes, and orders were standardized within a phylogeny- and divergence time-informed framework, providing a consistent higher-level classification across species (Tedersoo et al. 2018 ). Based on this framework, taxonomic assignments for 796 fungal species were curated across 11 phyla: i) Ascomycota (n = 436), ii) Basidiomycota (n = 139), iii) Mucoromycota (n = 72), iv) Mortierellomycota (n = 60), v) Zoopagomycota (n = 5), vi) Kickxellomycota (n = 13), vii) Entomophthoromycota (n = 4), viii) Blastocladiomycota (n = 4), ix) Chytridiomycota (n = 26), x) Neocallimastigomycota (n = 12), and xi) Rozellomycota (n = 25). 2–2. Identification of fungal GATA TFs Protein sequences (FASTA format) from 796 selected fungal species were used to identify fungal GATA TFs. Accordingly, a total of 7,846 fungal GATA TFs were identified by screening protein sequences with InterProScan (v5.74) (Jones et al. 2014 ) against the Pfam database (v37.3) (Finn et al. 2014 ) for the presence of the GATA domain (PF00320) (Supplementary Table 2). For loci with multiple splicing isoforms, the longest protein was chosen as the representative GATA TF. To balance sensitivity and specificity, only domain hits with E-values ≤ 1e − 5 were considered, thereby capturing both canonical and moderately divergent GATA domains (Zhang et al. 2023 ; Zheng et al. 2024 ). As a result, approximately 1% of GATA domains were excluded from the dataset. In cases where proteins contained multiple GATA domains, each domain was manually inspected to ensure that no overlapping regions were present. In addition to the GATA domain, five major additional domains were also observed in fungal GATA TFs: PF08550 (Nitrogen regulatory protein AreA, GATA-like domain), PF08447 (PAS fold), PF13426 (PAS domain), PF25026 (Asd-4-like domain), and PF07573 (Nitrogen regulatory protein AreA N terminus). 2–3. GATA motif pattern analysis across eukaryotic GATA TFs To investigate patterns of the GATA zinc finger motif (hereafter, GATA motif) across eukaryotes, GATA TFs from plants and animals were additionally collected and analyzed alongside the fungal dataset. The PlantGATA ( https://plantgata.vercel.app/ ) (Kim 2026 ) provides 5,299 GATA TFs (5,416 GATA domains) from 174 plant species representing 12 taxonomic groups, and AnimalTFDB ( https://guolab.wchscu.cn/AnimalTFDB4/#/ ) (Shen et al. 2023 ) provides 2,835 GATA proteins (3,960 GATA domains) from 183 animal species representing 13 taxonomic groups (Supplementary Table 3). These counts were determined after removing alternative splicing isoforms, with the longest protein sequence selected as the representative GATA TF for each gene, consistent with the approach used in this fungal study. The phylogenetic relationships were based on previously published phylogenetic frameworks for plants and animals derived from large-scale transcriptomic and phylogenomic analyses (Irisarri et al. 2017 ; Puttick et al. 2018 ). In the animal lineage, the GATA family includes seven canonical members ( GATA1-6 (He et al. 2007 ) and TRPS1 (Gai et al. 2011 )), while at least 11 additional animal GATA proteins have been reported, including GATAD1/2A/2B/2AB (Liang et al. 2021 ), RERE/REREA-B (Plaster et al. 2007 ), MTA1-3 (Kumar and Wang 2016 ), and ZGLP1 (Dong et al. 2019 ), which contain GATA domains but do not function as classical GATA TFs. In this study, all of these GATA proteins were included to ensure comprehensive coverage of GATA domain-containing proteins. In particular, the identification of GATA TFs was harmonized across datasets. Plant GATA TFs were identified using the same domain-based pipeline applied to fungal GATA TFs in this study, ensuring methodological consistency across kingdoms. In contrast, animal GATA proteins, although initially obtained from a curated public database, were identified using slightly different criteria; therefore, they were re-identified using the same domain-based pipeline applied in this study to ensure consistency and comparability in GATA protein detection across all datasets. Through these procedures, GATA motif patterns of eukaryotic GATA TFs were systematically analyzed. To further characterize GATA motifs, eukaryotic GATA motifs were categorized into eight types based on the spacing of conserved cysteine residues (Kim 2024 ; Hu et al. 2025 ). This includes three major types: Type IVa (Cys-X 2 -Cys-X 17 -Cys-X 2 -Cys), Type IVb (Cys-X 2 -Cys-X 18 -Cys-X 2 -Cys), and Type IVc (Cys-X 2 -Cys-X 20 -Cys-X 2 -Cys); and five minor types: Type IV19 (Cys-X 2 -Cys-X 19 -Cys-X 2 -Cys), Type IV4 (Cys-X 4 -Cys-X 18 -Cys-X 2 -Cys), Type IVe (Cys-X 2 -Cys- −16,21− -Cys-X 2 -Cys, where n ≤ 16 or n ≥ 21), atypical forms (e.g., Cys-X-Cys-X 20 -Cys-X 2 -Cys), and Type IVp (partial motifs, e.g., Cys-X 2 -Cys-X 12 ). 2–4. Domain composition and co-occurrence of fungal GATA TFs To characterize the domain composition of fungal GATA TFs, Pfam-based domain annotations were used to identify domains present in each protein. Major co-occurring domains (PF08550, PF08447, PF13426, PF25026, and PF07573) were recorded, while all remaining low-frequency domains were grouped as “Minor domains”. For each division, the number of GATA TFs containing each domain was counted in a non-mutually exclusive manner, such that proteins with multiple domains contributed to multiple categories. To further investigate motif-domain relationships, co-occurrence patterns between GATA motifs and associated conserved domains were analyzed. For this purpose, each fungal GATA TF was decomposed into GATA motif-domain pairs within the same protein. Subsequently, redundant GATA motif-domain pairs were removed to eliminate duplication, yielding a non-redundant set of unique GATA motif-domain combinations. 2–5. Collection of functionally characterized fungal GATA TFs To assemble the dataset for this study, functionally validated fungal GATA TFs were collected from previously published literature (Schwechheimer et al. 2022 ; Virolainen et al. 2026 ). A total of 25 GATA TFs from four fungal species were obtained, all of which correspond exactly to the sequences used in prior studies of fungal GATA TFs (Supplementary Table 4). Specifically, six GATA TFs ( SreA, AreB, LreA, AreA, LreB, and NsdD ) were collected from A. oryzae , four ( NIT2, ASD4, WC1, and WC2 ) from N. crassa , ten ( SRD1, GAT4, GLN3, GAT1, GZF3, ASH1, DAL80, GAT3, GAT2, and ECM23 ) from Saccharomyces cerevisiae , and five ( AMS2, GAF1, FEP1, Fil1, and SFH1 ) from Schizosaccharomyces pombe . 2–6. Phylogenetic analysis of fungal GATA domains Phylogenetic analysis of fungal GATA domains was performed to investigate their evolutionary relationships. A total of 9,488 fungal GATA domains were collected and subjected to multiple sequence alignment using MAFFT (v7.505) (Katoh and Standley 2013 ). Subsequently, maximum-likelihood phylogenetic trees were constructed using IQ-TREE2 (v2.0.7) (Minh et al. 2020 ) with the Dayhoff model, and branch support was evaluated using 1,000 ultrafast bootstrap replicates (-bb option). The resulting phylogenetic tree files were visualized and annotated in the Interactive Tree of Life (iTOL) platform (Letunic and Bork 2024 ), enabling comprehensive interpretation of evolutionary relationships among the fungal GATA TFs. 2–7. Structural modeling of fungal GATA TFs Three-dimensional structures of 11 representative fungal GATA TFs, including AreA, NsdD, SreA, and WC-1 , were predicted using the AlphaFold2 framework (Bertoline et al. 2023 ). Protein sequences of the selected GATA TFs were used as input without truncation to preserve full-length structural context. AlphaFold2 produced a per-residue confidence score (pLDDT) ranging from 0 to 100. Some regions with low pLDDT may have been unstructured in isolation. Structural similarity searches were subsequently performed using Foldseek against the PDB100 database (Van Kempen et al. 2024 ; Kim et al. 2025 ) to identify experimentally resolved protein structures related to the predicted fungal GATA TF models. Only matches with HHpred probability values ≥ 0.95 were retained as high-confidence structural homologs for downstream interpretation. This filtering criterion was applied to minimize low-confidence or non-specific structural matches and to ensure reliable identification of conserved GATA-related structural features. 2–8. Physicochemical properties and protein feature predictions of fungal GATA TFs The molecular weight (MW), theoretical isoelectric point (pI), aliphatic index, and grand average of hydropathicity (GRAVY) of fungal GATA TFs were calculated using the ProtParam tool on the ExPASy server ( https://web.expasy.org/protparam/ ) (Gasteiger et al. 2005 ). To further characterize protein features, transmembrane helices (TMHs) were predicted using TMHMM v2.0 ( https://services.healthtech.dtu.dk/services/TMHMM-2.0/ ) with default parameters (Krogh et al. 2001 ). Subcellular localization of fungal GATA TFs was predicted using DeepLoc v2.1 ( https://services.healthtech.dtu.dk/services/DeepLoc-2.1/ ) (Ødum et al. 2024 ). 3. Results 3 − 1. Architectural features of fungal GATA TFs 3-1-1. Taxonomic distribution To characterize the lineage-specific distribution patterns of fungal GATA TFs, a genome-wide structural analysis of GATA TFs was conducted across 796 fungal species (Table 1 ). These species were classified into eleven divisions. Notably, the two divisions within Dikarya , Ascomycota and Basidiomycota , together accounted for 575 of 796 species, constituting the vast majority of the dataset (Fig. 2 a- 2 b). In contrast, the remaining nine early-diverging fungal divisions— Mucoromycota, Mortierellomycota, Zoopagomycota, Kickxellomycota, Entomophthoromycota, Blastocladiomycota, Chytridiomycota, Neocallimastigomycota, and Rozellomycota —collectively comprised 221 species, representing a comparatively minor proportion (Fig. 2 a- 2 b). From these species, a total of 9,222,719 predicted protein-coding gene sequences were obtained, from which 7,846 GATA TFs were identified (Table 1 ). These results established a comprehensive and taxonomically broad dataset of fungal GATA TFs, albeit with a strong representation bias toward Dikarya . Table 1 Distribution and abundance of GATA TFs across major fungal lineages Division Number of classes Number of orders Number of species Total number of proteins (average ± SD/species) Total number of GATA TFs (average ± SD/species) Ascomycota 12 38 436 4,924,537 (11,295 ± 3,039) 2,669 (6.1 ± 1.6) Basidiomycota 8 24 139 1,861,933 (13,395 ± 5,596) 1,015 (7.3 ± 2.4) Mucoromycota 3 3 72 865,591 (12,022 ± 2,126) 2,220 (30.8 ± 10.4) Mortierellomycota 1 1 60 716,421 (11,940 ± 1,413) 1,057 (17.6 ± 3.0) Zoopagomycota 1 1 5 35,767 (7,153 ± 1,301) 38 (7.6 ± 2.0) Kickxellomycota 3 4 13 106,794 (8,215 ± 1,431) 132 (10.2 ± 2.0) Entomophthoromycota 1 1 4 48,619 (12,155 ± 2,898) 49 (12.3 ± 4.0) Blastocladiomycota 2 2 4 52,541 (13,135 ± 4,429) 50 (12.5 ± 7.7) Chytridiomycota 2 8 26 303,673 (11,680 ± 3,356) 380 (14.6 ± 9.4) Neocallimastigomycota 1 1 12 236,171 (19,681 ± 6,242) 179 (14.9 ± 5.8) Rozellomycota 2 1 25 70,672 (2,827 ± 1,070) 57 (2.3 ± 1.1) Total 36 84 796 9,222,719 (11,586 ± 4,060) 7,846 (9.9 ± 8.5) * Taxa designated as incertae sedis were not included in the counts of classes and orders. To assess lineage-specific variation in the abundance and density of GATA TFs, pronounced variation was observed across the eleven fungal divisions (Fig. 2 c). Within the Dikarya , Ascomycota (436 species) contained 2,669 GATA TFs, ranging from 2 to 17 per species, with an average of 6.1 ± 1.6. By comparison, Basidiomycota comprised 1,015 GATA TFs across 139 species, with a slightly higher average of 7.3 ± 2.4 per species (range: 1–15). In contrast, early-diverging fungal divisions showed a wide range of GATA TF densities. Notably, Mucoromycota exhibited the highest average number of GATA TFs per species (30.8 ± 10.4), with counts ranging from 6 to 64 across 72 species. In addition, the other seven early-diverging divisions exhibited moderate GATA TF abundance, with average counts ranging from 7.6 ± 2.0 ( Zoopagomycota ) to 17.6 ± 3.0 ( Mortierellomycota ) per species. By contrast, Rozellomycota harbored only 2.3 ± 1.1 GATA TFs per species, representing the lowest abundance among the surveyed divisions. These observations highlighted the striking disparity in GATA TF distribution, highlighting both highly enriched and depauperate lineages among fungi. 3-1-2. Domain architecture of fungal GATA TFs Protein domains are discrete structural and functional modules that play a central role in protein function; thus, domain architecture was analyzed to elucidate the structural basis of functional diversity in fungal GATA TFs. In total, 12,768 domains from 7,846 fungal GATA TFs were identified and classified into 89 distinct types (Table 2 ). Five major domains were prominently observed to co-occur with the GATA domain (PF00320; n = 9,488) in fungal GATA TFs (Table 2 ): PF08550 (nitrogen regulatory protein AreA, GATA-like domain; 1,049 domains), PF08447 (PAS fold; 942 domains), PF13426 (PAS domain; 474 domains), PF25026 (Asd-4-like domain; 375 domains), and PF07573 (nitrogen regulatory protein AreA N terminus; 96 domains). The remaining 83 minor domain types collectively accounted for 344 domains, representing a small fraction of the total. Overall, fungal GATA TFs were characterized by the predominant co-occurrence of a limited set of major domains with the GATA domain, while most other domain types were rare. Table 2 Domain and GATA motif composition of GATA TFs across major fungal lineages Division Number of species Number of GATA TFs Number of domains Number of GATA motifs PF00320 PF08550 PF08447 PF13426 PF25026 PF07573 Minor domains IVa IVb IVe IVp IV19 IVc Ascomycota 436 2,669 3,065 375 671 360 374 96 54 1,704 1,311 5 25 9 11 Basidiomycota 139 1,015 1,238 194 30 20 1 0 85 655 548 2 19 13 1 Mucoromycota 72 2,220 2,790 366 214 82 0 0 105 1,612 1,139 31 8 0 0 Mortierellomycota 60 1,057 1,358 60 0 0 0 0 13 658 696 0 1 0 3 Zoopagomycota 5 38 45 2 1 0 0 0 6 20 19 0 1 0 5 Kickxellomycota 13 132 164 5 1 0 0 0 3 87 77 0 0 0 0 Entomophthoromycota 4 49 61 0 12 7 0 0 0 35 25 1 0 0 0 Blastocladiomycota 4 50 59 6 3 0 0 0 3 27 27 0 1 1 3 Chytridiomycota 26 380 424 21 10 5 0 0 21 202 200 10 5 4 3 Neocallimastigomycota 12 179 222 20 0 0 0 0 54 137 85 0 0 0 0 Rozellomycota 25 57 62 0 0 0 0 0 0 22 26 13 0 1 0 Total 796 7,846 9,488 1,049 942 474 375 96 344 5,159 4,153 62 60 28 26 * List of major domains: PF00320 (GATA zinc finger), PF08550 (Nitrogen regulatory protein areA, GATA-like domain), PF08447 (PAS fold), PF13426 (PAS domain), PF25026 (Asd-4-like domain), and PF07573 (Nitrogen regulatory protein AreA N terminus). * List of GATA motif types: IVa (CX 2 CX 17 CX 2 C), IVb (CX 2 CX 18 CX 2 C), IVp (partial motifs, e.g., CX 2 CX 12 ), IV19 (CX 2 CX 19 CX 2 C), IVe (CX 2 C − 16,21− CX 2 C), and IVc (CX 2 CX 20 CX 2 C). To assess lineage-specific differences in domain composition of fungal GATA TFs, the relative composition of protein domains revealed clear division-specific patterns (Table 2 and Fig. 2 d). Within Dikarya , PF08550 was the major domain in both Ascomycota and Basidiomycota . PF08447 and PF13426 were present in both, but more frequent in Ascomycota . PF25026 and PF07573 were predominantly observed in Ascomycota , with PF07573 exclusive to this division and PF25026 rarely occurring in Basidiomycota . Early-diverging divisions exhibited distinct patterns: Mortierellomycota and Neocallimastigomycota mainly contained PF08550 alone, whereas Mucoromycota included PF08550, PF08447, and PF13426. Rozellomycota displayed only the core GATA domain. These patterns reflected lineage-specific domain compositions, suggesting evolutionary divergence in functional diversification. Detailed domain occurrence patterns across fungal lineages, which provide quantitative support for lineage-specific domain distribution, are provided in Supplementary Table S5. To complement the domain-level analysis, protein domain architectures were examined (Table 3 ). Most GATA TFs consisted solely of the core GATA domain (PF00320 only; 5,277 GATA TFs). Among proteins with additional domains, PF00320 + PF08550 (921 GATA TFs) was most prevalent, followed by PF00320 + PF08447 (495 GATA TFs), PF00320 + PF08447 + PF13426 (441 GATA TFs), and PF00320 + PF25026 (385 GATA TFs). Lineage-specific differences were evident: in Dikarya , simpler architectures dominated, with PAS-related combinations more frequent in Ascomycota . Early-diverging lineages generally exhibited simpler architectures, whereas Mucoromycota showed relatively more complex PAS-related combinations. Rozellomycota displayed PF00320-only architectures exclusively. Collectively, fungal GATA TFs were predominantly composed of the core GATA domain, with the presence and complexity of additional domain combinations exhibiting clear lineage-specific patterns. Table 3 Domain architectures of fungal GATA TFs across major lineages Division Number of species Number of GATA TFs Number of domain architectures PF00320 only PF00320 + PF08550 PF00320 + PF08447 PF00320 + PF08447 + PF13426 PF00320 + PF25026 PF00320 + Minor domains PF00320 + PF07573 + PF08550 PF00320 + PF08550 + Minor domains PF00320 + PF13426 Other 4 minor architectures Ascomycota 436 2,669 1,188 279 336 331 373 35 95 1 25 6 Basidiomycota 139 1,015 763 171 10 20 1 27 0 23 0 0 Mucoromycota 72 2,220 1,593 359 130 82 0 47 0 7 0 2 Mortierellomycota 60 1,057 988 58 0 0 0 9 0 2 0 0 Zoopagomycota 5 38 30 2 1 0 0 5 0 0 0 0 Kickxellomycota 13 132 124 5 1 0 0 2 0 0 0 0 Entomophthoromycota 4 49 36 0 6 6 0 0 0 0 1 0 Blastocladiomycota 4 50 38 6 3 0 0 3 0 0 0 0 Chytridiomycota 26 380 329 21 8 2 0 17 0 0 3 0 Neocallimastigomycota 12 179 131 20 0 0 0 28 0 0 0 0 Rozellomycota 25 57 57 0 0 0 0 0 0 0 0 0 Total 796 7,846 5,277 921 495 441 374 173 95 33 29 8 * Repeated domains within a single GATA TF were counted once per domain type when defining domain architectures. * List of major domains: PF00320 (GATA zinc finger), PF08550 (Nitrogen regulatory protein areA, GATA-like domain), PF08447 (PAS fold), PF13426 (PAS domain), PF25026 (Asd-4-like domain), and PF07573 (Nitrogen regulatory protein AreA N terminus). 3-1-3. GATA domain multiplicity of fungal GATA TFs Multiplicity of GATA domains (PF00320) within a single GATA TF may affect regulatory properties, including DNA-binding versatility and transcriptional specificity. To investigate the potential role of GATA domain multiplicity in functional diversification, it was analyzed across fungal GATA TFs. In total, 9,488 GATA domains were identified among 7,846 fungal GATA TFs (Fig. 2 e), indicating that 1,642 GATA TFs possessed two GATA domains, whereas the remaining GATA TFs contained a single domain. Overall, the widespread occurrence of dual GATA-domain architectures suggested that GATA domain multiplicity represented an important structural feature contributing to functional diversification in fungal GATA TFs. Multiplicity of GATA domains in fungal GATA TFs occurred across all fungal divisions, although the prevalence varied slightly among lineages (Fig. 2 e). In Dikarya , GATA TFs in Ascomycota and Basidiomycota exhibited GATA domain multiplicity in 14.8% and 22.0% of cases, respectively. In early-diverging fungal divisions, i) Mucoromycota, Mortierellomycota, Kickxellomycota, Entomophthoromycota, and Neocallimastigomycota exhibited higher levels of GATA domain multiplicity, ranging from 24.0% to 28.5%, compared with Dikarya ; ii) Zoopagomycota and Blastocladiomycota displayed levels similar to those of Dikarya , ranging from 18.0% to 18.4%; and iii) Chytridiomycota and Rozellomycota showed lower levels, ranging from 8.8% to 11.6%. These results suggested that GATA domain multiplicity was widely distributed across fungal lineages but exhibited lineage-specific variation, potentially reflecting diversification of regulatory functions among fungal divisions. 3-1-4. Types of fungal GATA motifs The GATA zinc finger motif (hereafter, GATA motif) is a cysteine-rich DNA-binding domain in fungal GATA TFs that specifically recognizes WGATAR (W = A/T, R = A/G) sequences. Therefore, this study investigated structural variation in GATA motif architecture with respect to motif length and cysteine spacing. Accordingly, 9,488 fungal GATA domains were classified into six types (Table 2 and Fig. 2 f): Type IVa (CX 2 CX 17 CX 2 C) was the most prevalent with 5,159 GATA domains, followed by Type IVb (CX 2 CX 18 CX 2 C) with 4,153 GATA domains, whereas the remaining types were rare, including Type IVe (CX 2 CX − 16, 21− CX 2 C, where n ≤ 16 or n ≥ 21; 62 GATA domains), Type IVp (partial motifs; 60 GATA domains), Type IV19 (CX 2 CX 19 CX 2 C; 28 GATA domains), and Type IVc (CX 2 CX 20 CX 2 C; 26 GATA domains). These results indicated that most fungal GATA TFs possess the canonical CX 2 CX 17−18 CX 2 C zinc finger, with rare variants suggesting functional or evolutionary diversification. To investigate lineage-specific variation in GATA motif composition, the distribution of GATA motif types was analyzed across fungal divisions (Table 2 and Fig. 2 f). This analysis revealed that Type IVa and Type IVb (CX 2 CX 17−18 CX 2 C) were the predominant motifs across all divisions. Type IVa was generally slightly more abundant, whereas Type IVb was somewhat more prevalent in Mortierellomycota and Rozellomycota . Minor motifs, in contrast, showed division-specific combinations. Dikarya , including Ascomycota and Basidiomycota , possessed all four minor types (IVe, IVp, IV19, and IVc), whereas Mucoromycota displayed only two motifs (IVe and IVp). Some divisions, such as Kickxellomycota and Neocallimastigomycota , lacked minor motifs entirely. Notably, Rozellomycota exhibited a high proportion of Type IVe, which may have reflected a lineage-specific retention of this motif. Overall, fungal GATA motifs showed conserved Type IVa and Type IVb predominance alongside division-specific minor motif variation, reflecting lineage-dependent diversification. 3-1-5. Evolutionary patterns of eukaryotic GATA motifs Analyzing only fungal GATA motifs limited evolutionary insight, highlighting the need to examine GATA motifs across eukaryotes. Accordingly, GATA motifs were analyzed not only for the 9,488 fungal GATA domains but also for 5,416 GATA domains from plants and 3,960 GATA domains from animals (Fig. 3 ). A total of 18,864 eukaryotic GATA domains were classified into eight types: three major motifs: Type IVa (CX 2 CX 17 CX 2 C; 8,791 GATA domains), Type IVb (CX 2 CX 18 CX 2 C; 8,727 GATA domains), and Type IVc (CX 2 CX 20 CX 2 C; 855 GATA domains); and five minor motifs: Type IVp (partial motifs, e.g., CX 2 CX 12 ; 301 GATA domains), Type IVe (CX 2 C − 16,21− CX 2 C, where n ≤ 16 or n ≥ 21; 107 GATA domains), Type IV19 (CX 2 CX 19 CX 2 C; 75 GATA domains), an atypical form (e.g., CXCX 20 CX 2 C; 5 GATA domains), and Type IV4 (CX 4 CX 18 CX 2 C; 3 GATA domains) (Fig. 3 ). These results suggested that Type IVa, Type IVb, and Type IVc (CX 2 CX 17−20 CX 2 C) represented the evolutionarily conserved core GATA motifs across eukaryotes, whereas the remaining motif types occurred only as rare structural variants. The distribution of GATA motif types exhibited kingdom-specific patterns, reflecting distinct evolutionary trajectories and functional diversification in fungi, plants, and animals (Fig. 3 ). In plants, Type IVb and Type IVc (CX 2 CX 18−20 CX 2 C) constituted the predominant motifs, whereas Type IVa occurred at very low frequencies and was mainly limited to monocots and eudicots. Among the minor types, only Type IVp and Type IVe were present, with monocots and eudicots showing a relatively higher proportion of Type IVp. In contrast, late-diverging animals and fungi predominantly possessed Type IVa and Type IVb (CX 2 CX 17−18 CX 2 C), with Type IVc being rare. Notably, animals exhibited a high prevalence of Type IVa, exceeding 90%, whereas fungi displayed a more balanced distribution of Type IVa and Type IVb, representing lineage-specific characteristics. Among the minor types, Type IVp, Type IVe, and Type IV19 were observed in certain groups, and animals uniquely harbored the atypical form and Type IV4 motifs. Taken together, eukaryotic GATA motifs exhibited kingdom- and lineage-specific patterns, with plants dominated by CX 2 CX 18−20 CX 2 C motifs and animals and fungi by CX 2 CX 17−18 CX 2 C motifs, reflecting evolutionary and functional diversification. 3-1-6. Conservation and divergence of fungal GATA motifs The conservation and divergence of GATA motifs are critical for understanding their structural and functional constraints and for elucidating the evolutionary relationships. In this study, the analysis focused on the predominant motifs observed in fungal GATA TFs, including 5,159 instances of Type IVa (CX 2 CX 17 CX 2 C) and 4,153 instances of Type IVb (CX 2 CX 18 CX 2 C) (Fig. 4 ). In Type IVa, GATA motifs retained a highly conserved set of core cysteine residues and surrounding amino acids, forming a structural framework critical for DNA binding and reflecting strong evolutionary constraints (Fig. 4 a). Four core cysteine residues (C-1, C-4, C-22, and C-25) were fully conserved and formed the zinc-coordinating structure essential for WGATAR (W = T/A, R = G/A) binding. In addition to the core residues, several non-cysteine positions (N-3, T-6, T-9, P-10, L-11, W-12, R-13, R-14, G-18, N-23, and A-24) were highly conserved (≥ 80%), indicating roles in zinc finger stability and DNA interaction. A subset of residues, T-7, showed moderate but consistent conservation (≥ 60%) across both lineages, suggesting functional relevance with limited variability. Notably, lineage-specific differences were also evident: in Dikarya , T-7 was conserved in less than 60% of sequences, whereas in early-diverging fungal lineages, T-6 exhibited reduced conservation (from ≥ 80% to ≥ 60%), while N-23 showed stronger conservation, increasing from ≥ 80% to 100%. Overall, Type IVa were largely conserved across fungi, but specific residues showed lineage-dependent variation, reflecting both structural constraints and evolutionary divergence. Within this conserved framework, the conserved positions comprised residues with diverse functional and structural properties, including polar charged (e.g., R-13 and R-14), polar uncharged (e.g., N-3 and T-6), structurally important residues (e.g., P-10 and G-18), and hydrophobic residues (e.g., L-11 and W-12), suggesting position-specific functional constraints within the GATA motif. These results suggested that Type IVa motifs maintained a highly conserved structural framework across fungi, while lineage-specific residue variation may have contributed to functional diversification. In Type IVb, GATA motifs shared a conserved structural architecture, reflecting evolutionary constraints on DNA binding (Fig. 4 b). Four core cysteines (C-1, C-4, C-23, and C-26) were conserved and formed the zinc-coordinating structure essential for WGATAR binding. Several non-cysteine positions (P-10, W-12, R-13, G-15, P-16, G-18, L-22, N-24, and N-25) were highly conserved (≥ 80%), and T-9, E-11, R-14, and T-21 showed moderate conservation (≥ 60%), indicating roles in zinc finger stability and DNA interaction. Lineage-specific differences were also evident in Type IVb motifs. In Dikarya , E-11 showed increased conservation (≥ 60% to ≥ 80%), whereas R-14 decreased to < 60%, and A-25 decreased from ≥ 80% to ≥ 60%. In early-diverging fungal lineages, E-11 and T-21 showed reduced conservation (< 60%), W-12 became fully conserved (100%), and G-15 and G-18 decreased from ≥ 80% to ≥ 60%. Overall, these patterns suggested that while the core structural framework of Type IVb motifs was maintained, individual residues exhibited fungal lineage-dependent variability reflecting evolutionary adaptation. Within this conserved framework, conserved residues exhibited diverse functional and structural properties, reflecting position-specific constraints consistent with those observed in Type IVa. These results suggested that Type IVb motifs retained a conserved zinc finger framework across fungi, while lineage-specific variation in individual residues may have reflected adaptive diversification of regulatory functions. 3-1-7. Co-occurrence of fungal GATA motifs and domains Co-occurrence analysis was performed to investigate how protein domain composition was associated with GATA motif types across fungal lineages (Fig. 5 ). Unlike the domain multiplicity analysis (Section 3 - 1 - 3 ), which focuses on the copy number of GATA domains within individual proteins, this analysis examined preferential associations between domain identity and GATA motif types, aiming to reveal functional coupling patterns between domain architecture and motif evolution. From this analysis, a total of 11,504 domain-motif pairs were identified across fungal GATA TFs. Among these, multiple protein domains exhibited distinct biases toward specific GATA motif types, indicating that domain composition was closely linked to motif-type specification rather than being randomly associated. The canonical GATA domain (PF00320) displayed a balanced distribution between Type IVa (CX 2 CX 17 CX 2 C; 48.1%) and Type IVb (CX 2 CX 18 CX 2 C; 50.1%), suggesting that it served as a structurally flexible platform capable of accommodating both motif types. In contrast, several auxiliary domains showed strong motif-type specificity. Specifically, PF08550 (Nitrogen regulatory protein AreA-like domain), PF25026 (Asd-4-like domain), and PF07573 (AreA N-terminal domain) were almost exclusively associated with Type IVa motifs (98.5–100.0%), whereas PF08447 (PAS fold) and PF13426 (PAS domain) exhibited a near-complete preference for Type IVb motifs (99.3–100.0%). These patterns indicated that accessory domains imposed strong constraints on GATA motif type selection, suggesting coordinated evolution between domain architecture and DNA-binding motif structure. To further explore lineage-specific modulation of this coupling, the distribution of GATA motif types was compared across fungal divisions (Fig. 5 ). PF00320-associated GATA TFs showed moderate variation in Type IVa ratios across lineages, ranging from 34.4% in Rozellomycota to 52.5% in Neocallimastigomycota , indicating a relatively flexible association between the core zinc finger domain and motif type. In contrast, accessory domains exhibited highly conserved and strongly biased coupling patterns. Specifically, PF08550 was consistently associated with Type IVa across nearly all divisions, including complete association in multiple early-diverging lineages, whereas PF08447 and PF13426 showed the opposite pattern, maintaining near-exclusive associations with Type IVb motifs across both Dikarya and early-diverging fungi. Consistently, PF25026 and PF07573 were also minimally associated with Type IVb, where present, reinforcing this domain-dependent divergence. These results suggested that lineage-specific variation in GATA motif composition was relatively flexible in the core GATA domain but remained strongly constrained in accessory domain-associated motifs across fungal divisions. 3 − 2. Integrative functional and evolutionary analysis of fungal GATA TFs 3-2-1. Characterization of functionally validated fungal GATA TFs A curated set of 25 functionally validated GATA TFs from four representative Ascomycota species was assembled to establish a reference framework for integrative analyses (Table 4 ). All sequences were derived from previously characterized studies and correspond exactly to experimentally validated GATA TFs. Although restricted to Ascomycota , the dataset encompassed both filamentous fungi and yeasts, capturing substantial functional diversity within this lineage. The collected TFs collectively represented major regulatory roles, including nitrogen metabolism, light-responsive signaling, iron homeostasis, and developmental processes, as well as both transcriptional activation and repression mechanisms. This curated dataset served as a set of functional anchor points for interpreting evolutionary relationships and structural variation in subsequent phylogenetic and modeling analyses. Table 4 Functional and structural features of representative fungal GATA TFs Species GATA name Protein length domains GATA motifs Biological function References A. oryzae SreA 566 PF00320,PF00320 IVa, IVa regulates siderophore biosynthesis and iron homeostasis (Oberegger et al. 2001 ) A. oryzae AreB 313 PF00320,PF25026 IVa regulates nitrogen and secondary metabolism as an activator and a repressor (Pfannmüller et al. 2017 ) A. oryzae LreA 282 PF00320 IVb controls light response, conidiation, and pathogenicity (Park et al. 2025 ) A. oryzae AreA 866 PF07573,PF08550,PF00320 IVa regulates nitrogen and secondary metabolism as an activator and a repressor (Pfannmüller et al. 2017 ) A. oryzae LreB 508 PF08447,PF00320 IVb controls light response, conidiation, and pathogenicity (Park et al. 2025 ) A. oryzae NsdD 453 PF00320 IVb represses conidiation and promotes sexual development (Lee et al. 2014 ) N. crassa NIT2 1036 PF08550,PF00320 IVa governs nitrogen metabolism through transcriptional regulation (Bernardes et al. 2017 ) N. crassa ASD4 426 PF00320,PF25026 IVa promotes sexual development and ascospore formation (Feng et al. 2000 ) N. crassa WC1 1167 PF13426,PF08447,PF00320 IVb mediates light sensing and circadian rhythm regulation (Lewis et al. 2002 ) N. crassa WC2 530 PF08447,PF00320 IVb partners with WC1 to activate light-responsive gene expression (Linden 1997 ) S. cerevisiae SRD1 221 PF00320 IVb involved in pre-rRNA processing and ribosome biogenesis (Fabian et al. 1990 ) S. cerevisiae GAT4 121 PF00320 IV19 acts as a DNA-binding transcription factor in spore wall assembly (Lin et al. 2013 ) S. cerevisiae GLN3 730 PF00320 IVa activates nitrogen metabolism gene expression under nitrogen limitation (Courchesne and Magasanik 1988 ) S. cerevisiae GAT1 510 PF00320 IVa activates nitrogen metabolism and higher alcohol biosynthesis genes (Wang et al. 2021 ) S. cerevisiae GZF3 551 PF00320 IVa represses nitrogen-regulated gene expression under preferred nitrogen conditions (Soussi-Boudekou et al. 1997 ) S. cerevisiae ASH1 588 PF00320 IVc represses HO expression to control mating-type switching (Cosma 2004 ) S. cerevisiae DAL80 269 PF00320 IVa represses nitrogen catabolite gene expression under rich nitrogen conditions (Cunningham et al. 2000 ) S. cerevisiae GAT3 141 PF00320 IV19 acts as a DNA-binding transcription factor in spore wall assembly (Lin et al. 2013 ) S. cerevisiae GAT2 560 PF00320 IVb Regulates filamentous and invasive growth associated with morphological transitions (Du et al. 2012 ) S. cerevisiae ECM23 187 PF00320 IVb negatively regulates pseudohyphal growth and cell wall morphogenesis (Cañizares et al. 2002 ) S. pombe AMS2 697 PF00320 IVc activates the histone gene transcription during G1/S cell cycle (Takayama et al. 2010 ) S. pombe GAF1 855 PF08550,PF00320 IVa Negatively regulates mating and sporulation via repression of ste11 expression (Kim et al. 2012 ) S. pombe FEP1 564 PF00320,PF00320 IVa, IVa Regulates iron homeostasis by repressing genes involved in reductive iron uptake under iron-replete conditions (Pelletier et al. 2002 ) S. pombe Fil1 557 PF00320,PF00320 IVb, IVa Regulates cellular stress responses through activation of stress-induced transcriptional programmes (Rubio et al. 2021 ) S. pombe SFH1 418 PF04855,PF00320 IVc Regulates genome integrity by controlling centromere function, chromatin organization, and DNA damage repair (Kotomura et al. 2018 ) * Some GATA TFs included in this table were obtained from additional fungal species and are presented as representative orthologs across different fungal lineages. 3-2-2. Phylogenetic analysis of fungal GATA domains Phylogenetic analysis based on GATA domains was performed to investigate their evolutionary relationships. The distribution of 9,488 fungal GATA domains revealed extensive structural variation associated with motif types and domain architecture (Fig. 6 ). The distribution of GATA motif types across the phylogeny was non-random and exhibited distinct spatial patterns. Type IVa motifs (CX 2 CX 17 CX 2 C) were highly represented in the central region of the tree, whereas Type IVb motifs (CX 2 CX 18 CX 2 C) were more prevalent toward the peripheral branches. Intermediate regions displayed alternating clusters of Type IVa- and Type IVb-enriched clades, reflecting a structured but non-uniform distribution of motif types across the phylogeny. Overall, these results demonstrated that GATA motif types were non-randomly distributed and reflected evolutionarily structured patterns associated with phylogenetic divergence and structural variation. To understand the relationship between evolutionary divergence of GATA motifs and diversification of domain architectures, phylogenetic patterns and structural variation in GATA domains were analyzed (Fig. 6 ). Within the central region, where Type IVa motifs were predominant, multiple clades exhibited distinct domain architectures. Three major clades were characterized by the PF00320 + PF08550 domain combination. Between these clades, two clades composed of TFs containing only the PF00320 domain were observed, along with a smaller clade defined by the PF00320 + PF25026 architecture. In the peripheral region of the phylogeny, where Type IVb motifs were predominant, clades were distinguished based on domain architecture. Three recurrent structural configurations were observed: PF00320-only, PF00320 + PF08447, and PF00320 + PF08447 + PF13426. Each of these configurations appeared in two separate clades, resulting in a total of six distinct clades. In the intermediate regions, most clades were composed of GATA TFs containing only the PF00320 domain. In addition, a distinct subregion was observed in which Type IVa motifs were predominant, together with the frequent occurrence of the PF00320 + PF08550 domain combination. These results indicated that GATA motif divergence was closely associated with lineage-specific diversification and the repeated emergence of distinct domain architectures across phylogenetic regions. To assess whether identical domain architectures are lineage-restricted or broadly conserved across fungal divisions, the phylogenetic distribution of domain architecture-defined GATA TF clades was analyzed (Fig. 6 ). Across the phylogeny, clades defined by specific domain architectures included GATA TFs originating from multiple fungal divisions, indicating that identical structural configurations were not restricted to a single lineage. In several cases, individual clades contained TFs derived from both major and early-diverging fungal groups. Within clades, division-specific subclustering was consistently observed across multiple domain architecture-defined groups. Overall, these findings demonstrated that conserved domain architectures were widely distributed across fungal divisions, whereas lineage-specific subclustering within clades reflected deeper evolutionary differentiation. To provide functional context for the phylogenetic analysis, 25 functionally validated GATA TFs were mapped onto the phylogenetic tree (Fig. 6 ). These TFs were distributed across multiple clades, enabling partial annotation of functionally characterized groups. GATA TFs with similar biological roles were frequently located within the same or closely related clades. However, as only a limited number of functionally characterized GATA TFs were currently available, a large proportion of the phylogeny remained unannotated. Collectively, the limited availability of functionally characterized GATA TFs restricted comprehensive functional annotation across the phylogeny despite partial clustering of functionally similar proteins. 3-2-3. Structural modeling and functional interpretation of fungal GATA TFs Structural prediction of fungal GATA TFs is essential to link sequence and domain variation with three-dimensional structural organization underlying functional diversity. Accordingly, to investigate structural features underlying functional diversity, three-dimensional models were generated for 11 representative GATA TFs ( GAT1, GAT3, NsdD, ASH1, SreA, Fil1, AreA, AreB, LreB, NIT2, and WC1 ) selected from the curated dataset (Fig. 7 ). These TFs were selected to systematically represent the full spectrum of domain organization in fungal GATA TFs, encompassing single GATA domain proteins, dual GATA domain architectures, and multi-domain configurations. Among the analyzed proteins, four fungal GATA TFs ( GAT1, GAT3, NsdD, and ASH1 ) were selected to represent the minimal structural unit of fungal GATA TFs, consisting of a single GATA domain. Despite differences in GATA motif types, all exhibited a conserved zinc finger structure within the GATA domain region. Notably, even TFs harboring relatively rare motif variants (e.g., IVc and IV19) maintained a well-defined zinc finger conformation, suggesting strong structural conservation of the DNA-binding module regardless of motif subtype. These results indicated that the core GATA zinc finger structure was highly conserved and robust to motif variation, preserving its DNA-binding architecture across fungal TFs. Two fungal GATA TFs ( SreA and Fil1 ) were selected to represent dual GATA domain architectures, a structural configuration that may enable cooperative DNA recognition through multiple zinc finger modules. These proteins displayed closely positioned domain architectures, in which both GATA domains independently formed canonical zinc finger structures (Fig. 7 ). Although the motif combinations differed (e.g., IVa + IVa and IVa + IVb), no substantial structural disruption was observed between motif types. Instead, the spatial proximity of the two zinc finger domains suggested a potential cooperative role in DNA recognition, possibly enhancing binding specificity or affinity. The remaining five fungal GATA TFs ( AreA, AreB, LreB, NIT2, and WC1 ) were selected to represent multi-domain architectures, in which additional auxiliary domains beyond the GATA zinc finger may contribute to expanded regulatory complexity and context-dependent functional specialization. In these proteins, the GATA domain consistently formed a stable zinc finger structure, indicating that its core DNA-binding function was structurally preserved even within more complex architectures. However, the presence of additional domains resulted in markedly different overall protein conformations compared to single-domain TFs, implying that these proteins may have performed more specialized or context-dependent regulatory functions. To further evaluate whether structural regions outside canonical GATA domains corresponded to previously known protein folds that may have escaped sequence-based domain annotation, Foldseek searches against the PDB100 database were additionally performed (Supplementary Table 6). The analysis revealed distinct patterns of structural conservation among fungal GATA TF architectures. Among single-domain GATA TFs, only GAT1 exhibited strong structural similarity to previously resolved GATA-related structures, whereas NsdD, ASH1, and GAT3 showed no significant matches under the applied confidence threshold. In contrast, both dual-domain TFs ( SreA and Fil1 ) displayed strong structural correspondence with experimentally resolved GATA zinc finger structures, supporting the structural conservation of tandem GATA domains. For multi-domain TFs, Foldseek primarily detected similarity within previously annotated conserved domains rather than identifying novel globular folds. Specifically, AreA, AreB, and NIT2 showed matches restricted to the canonical GATA zinc finger region, whereas LreB exhibited structural similarity only within the PAS-related region (PF08447). Notably, WC1 showed structural matches not only to known PAS-associated domains (PF13426 and PF08447) but also additional structurally conserved regions outside previously annotated domains, suggesting the possible presence of uncharacterized or highly diverged structural elements. Overall, these results indicated that Foldseek recovered only a subset of previously annotated conserved domains across fungal GATA TFs, while WC1 uniquely exhibited additional structurally conserved regions beyond conventional sequence-based domain annotation. 3–3. Physicochemical and cellular characteristics of fungal GATA TFs 3-3-1. Physicochemical properties Physicochemical profiling enabled a systematic comparison of fungal GATA TFs across divisions by capturing variations in size, charge, and hydrophobicity beyond sequence similarity. Accordingly, protein length, molecular weight (MW), isoelectric point (pI), aliphatic index, and hydropathy were examined across eleven fungal divisions. Fungal GATA TFs had an overall average protein length of 557.9 aa ± 347.4 aa and molecular weight (MW) of 60.8 kDa ± 37.3 kDa, with most proteins (6,661 of 7,846; 84.9%) ranging from 200 to 1,200 amino acids in length (Fig. 8 a- 8 b). In particular, Dikarya , represented by Ascomycota and Basidiomycota , showed broad length distributions, ranging from 50 to 2,341 aa (5.6-252.3 kDa) and 50 to 3,827 aa (5.6-402.6 kDa), respectively, with average lengths of 594.6 aa ± 277.0 aa (64.5 kDa ± 30.0 kDa) and 701.0 aa ± 471.9 aa (75.1 kDa ± 50.1 kDa). Early-diverging fungal divisions also displayed a wide range of protein lengths, spanning 50 to 3,172 aa (5.5-321.7 kDa) with an average of 499.5 aa ± 338.8 aa (54.9 kDa ± 36.7 kDa). Notably, Neocallimastigomycota exhibited the highest average length, at 777.5 aa ± 626.9 aa (87.5 kDa ± 87.5 kDa). Overall, fungal GATA TFs exhibited substantial variation in protein length and molecular weight, with Dikarya and certain early-diverging lineages, particularly Neocallimastigomycota , showing the largest average sizes. The isoelectric points (pI) of fungal GATA TFs ranged from 4.1 to 11.7, with most proteins (5,603 of 7,846; 71.4%) being neutral to weakly alkaline (pI ≥ 7) (Fig. 8 c). Similarly, the Dikarya divisions, Ascomycota and Basidiomycota , showed similar average pI values of 8.1 ± 1.5 and 8.2 ± 1.4, with overall ranges of 4.6–11.7 and 4.5–11.7, respectively. The remaining early-diverging fungal divisions also exhibited comparable pI values, with averages ranging from 7.7 ± 1.7 ( Mortierellomycota ) to 8.8 ± 1.4 ( Kickxellomycota ). Overall, fungal GATA TFs were predominantly neutral to weakly alkaline, with relatively consistent pI values across both Dikarya and early-diverging lineages. The aliphatic index of fungal GATA TFs varied across divisions, ranging from a minimum of 27.3 to a maximum of 125.6, with an overall average of 60.2 ± 9.9, generally falling within a moderate range (Fig. 8 d). Similarly, within Dikarya , Ascomycota and Basidiomycota had similar average aliphatic index values of 59.2 ± 9.3 and 55.4 ± 9.6, respectively, ranging from 32.0-100.9 and 30.2–86.7, respectively. The remaining early-diverging fungal divisions exhibited average aliphatic index values ranging from 51.9 ± 12.0 ( Rozellomycota ) to 65.6 ± 10.3 ( Neocallimastigomycota ). Overall, fungal GATA TFs displayed moderate aliphatic index values across divisions, with relatively consistent profiles in Dikarya and slightly broader variation in early-diverging lineages. Fungal GATA TFs showed consistently negative GRAVY (grand average of hydropathicity) values across all divisions, ranging from − 2.0 to 0.5, with an overall average of -0.7 ± 0.2, indicating a predominantly hydrophilic character (Fig. 8 e). Within Dikarya , Ascomycota exhibited an average GRAVY value of -0.7 ± 0.2 (range: -1.4 to 0.3); similarly, Basidiomycota showed an average of -0.7 ± 0.2 (range: -1.5 to 0.5). The early-diverging fungal divisions exhibited average GRAVY values ranging from − 1.1 ± 0.3 ( Rozellomycota ) to -0.5 ± 0.3 ( Blastocladiomycota ). Overall, fungal GATA TFs were predominantly hydrophilic across all divisions, with consistently negative GRAVY values reflecting a general preference for aqueous environments. 3-3-2. Transmembrane helices and subcellular localization prediction Analysis of transmembrane helices (TMHs) and subcellular localization revealed that TMHs or non-nuclear localization may reflect lineage-specific adaptations or functional divergence. Accordingly, these features were analyzed across eleven fungal divisions to assess GATA TF membrane association and intracellular distribution. The vast majority of fungal GATA TFs lacked predicted TMHs, with 7,796 of 7,846 TFs (99.4%) predicted to have none (Table 5 ). Only 50 fungal GATA TFs in specific divisions contained TMHs: 14 GATA TFs in Mucoromycota , 13 GATA TFs in Ascomycota , 12 GATA TFs in Mortierellomycota , 5 GATA TFs in Basidiomycota , 2 GATA TFs in Chytridiomycota and Neocallimastigomycota , and 1 GATA TF in Zoopagomycota and Blastocladiomycota . Most GATA TFs with predicted TMHs contained a single helix, although a subset exhibited multiple TMHs, with up to 11 helices detected in certain proteins. These results indicated that TMH-containing GATA TFs were rare and may represent lineage-specific adaptations. Table 5 Distribution of predicted TMHs and subcellular localization of GATA TFs across major fungal lineages Division Number of species Number of GATA TFs Number of TMHs Number of subcellular localizations 0 1 2 3 6 7 11 L1 L2 L3 L4 L5 L6 L7 L8 L9 L10 Ascomycota 436 2,669 2,656 9 3 0 0 0 1 2,465 189 11 2 1 1 0 0 0 0 Basidiomycota 139 1,015 1,010 5 0 0 0 0 0 891 93 25 0 2 0 2 1 0 1 Mucoromycota 72 2,220 2,206 14 0 0 0 0 0 1,940 269 10 0 0 1 0 0 0 0 Mortierellomycota 60 1,057 1,045 5 1 1 1 3 1 790 247 11 3 1 2 0 1 2 0 Zoopagomycota 5 38 37 1 0 0 0 0 0 34 2 2 0 0 0 0 0 0 0 Kickxellomycota 13 132 132 0 0 0 0 0 0 106 23 3 0 0 0 0 0 0 0 Entomophthoromycota 4 49 49 0 0 0 0 0 0 43 5 1 0 0 0 0 0 0 0 Blastocladiomycota 4 50 49 1 0 0 0 0 0 48 2 0 0 0 0 0 0 0 0 Chytridiomycota 26 380 378 2 0 0 0 0 0 308 57 15 0 0 0 0 0 0 0 Neocallimastigomycota 12 179 177 2 0 0 0 0 0 154 24 1 0 0 0 0 0 0 0 Rozellomycota 25 57 57 0 0 0 0 0 0 36 21 0 0 0 0 0 0 0 0 Total 796 7,846 7,796 39 4 1 1 3 2 6,815 932 79 5 4 4 2 2 2 1 * List of subcellular localization: L1 (Nucleus), L2 (Cytoplasm|Nucleus), L3 (Cytoplasm), L4 (Endoplasmic reticulum), L5 (Mitochondrion), L6 (Lysosome/Vacuole), L7 (Cytoplasm|Mitochondrion), L8 (Extracellular), L9 (Cell membrane), L10 (Golgi apparatus). Subcellular localization analysis indicated that 6,815 of 7,846 fungal GATA TFs (86.9%) were predicted to localize to the nucleus (Table 5 ). Predictions of Cytoplasm|Nucleus and Cytoplasm localization accounted for 932 (11.9%) and 79 (1.0%) GATA TFs, respectively, while the remaining seven subcellular localization categories were observed only in a few proteins. Division-specific patterns were generally similar, with these minor categories appearing combinatorially in Dikarya ( Ascomycota and Basidiomycota ) and the closely related early-diverging divisions Mucoromycota and Mortierellomycota . These results indicated that fungal GATA TFs were mostly predicted to localize to the nucleus, with a small fraction exhibiting dual or non-nuclear localization, potentially reflecting lineage-specific adaptations. 3–4. Order-level architectural profiling of Dikarya GATA TFs 3-4-1. Overview The 796 fungal species analyzed in this study spanned eleven divisions, comprising 36 classes and 84 orders (Table 1 ). Within Dikarya , the Ascomycota and Basidiomycota divisions accounted for the majority of taxonomic diversity, with 436 Ascomycota species encompassing 12 classes and 38 orders, and 139 Basidiomycota species comprising 8 classes and 24 orders. Given their extensive taxonomic representation, these two divisions provided sufficient resolution to assess whether structural characteristics of GATA TFs varied systematically across lower taxonomic ranks. Accordingly, structural analyses were conducted at the order level within Dikarya , specifically in the Ascomycota and Basidiomycota divisions. In contrast, the remaining nine early-diverging divisions collectively comprised only 16 classes and 22 orders and showed no substantial structural deviation from division-level patterns; therefore, they were excluded from further lower-taxonomic analyses. 3-4-2. Taxonomic distribution To assess whether GATA TF diversity and abundance are associated with taxonomic structure and lineage representation, their distribution was analyzed at the order level. The taxonomic distribution of GATA TFs was highly heterogeneous across Dikarya divisions (Fig. 9 a- 9 c). In Ascomycota , GATA TFs were distributed across 38 orders spanning 12 classes, with marked heterogeneity in both species representation and total GATA TF counts. Orders such as Eurotiales (109 species; 647 GATA TFs; 5.9 ± 0.7 per species), Hypocreales (71 species; 461 GATA TFs; 6.5 ± 1.8), Pleosporales (36 species; 212 GATA TFs; 5.9 ± 0.6), and Helotiales (28 species; 176 GATA TFs; 6.3 ± 1.2) accounted for a substantial fraction of the ascomycetous dataset. Several moderately represented orders, including Chaetothyriales (22 species; 133 GATA TFs; 6.0 ± 0.4) and Glomerellales (23 species; 165 GATA TFs; 7.2 ± 2.0), also contributed notable numbers of GATA TFs. In contrast, several orders comprised few species with low numbers of GATA TFs per species, such as Neolectales (1 species; 2 GATA TFs), Phaeomoniellales and Togniniales (1 species; 3 GATA TFs). These results indicated that GATA TFs in Ascomycota were highly unevenly distributed across orders, reflecting strong lineage and taxonomic biases. Similar to Ascomycota , to evaluate whether GATA TF diversity and abundance exhibit order-level taxonomic patterns within Basidiomycota , their distribution was analyzed across multiple fungal orders. In Basidiomycota , GATA TFs were identified from 24 orders across 8 classes, showing considerable diversity in species coverage and total GATA TF counts. Orders within Agaricomycetes dominated the dataset, including Agaricales (32 species; 298 GATA TFs; 9.0 ± 2.5 per species), Polyporales (24 species; 150 GATA TFs; 6.3 ± 1.6), and Boletales (9 species; 71 GATA TFs; 7.9 ± 2.7). Other orders, such as Tremellales (13 species; 65 GATA TFs; 5.0 ± 0.4) and Ustilaginales (9 species; 78 GATA TFs; 8.7 ± 2.1), also exhibited substantial GATA TF representation. Several orders had a limited number of species, including Leucosporidiales (1 species; 10 GATA TFs) and Mixiales (1 species; 6 GATA TFs). Overall, GATA TFs in Basidiomycota were unevenly distributed across orders, driven by the concentration of species within specific lineages. 3-4-3. Domain architecture of Dikarya GATA TFs To investigate whether GATA TF domain architectures are conserved or lineage-specific at the order level across Dikarya , the domain composition was examined (Fig. 9 d). In Ascomycota , most orders exhibited PF00320 (GATA zinc finger) domain frequencies of 50.0%-70.0%, whereas early-diverging orders—including Phaffomycetales, Saccharomycetales, Alaninales, Pichiales, Serinales, Lipomycetales, Schizosaccharomycetales, and Neolectales —displayed comparatively higher frequencies of 75.0%-100.0%. Consistently, while most orders contained additional major domains at moderate frequencies, the early-diverging orders exhibited low or absent occurrences of PF08447 (PAS fold), PF08550 (Nitrogen regulatory protein areA, GATA-like domain), and PF13426 (PAS domain). Uniquely, the PF07573 (Nitrogen regulatory protein AreA N terminus) domain was found exclusively in Eurotiales . Among the 38 orders, minor domains were generally detected at low frequencies in 13 orders. These patterns indicated that domain composition in Ascomycota GATA TFs was largely conserved, with early-diverging orders exhibiting distinctive deviations from the typical domain repertoire. In Basidiomycota , PF00320 (GATA zinc finger) frequencies were generally 70.0%-90.0%, but early-diverging orders— Leucosporidiales, Microbotryales, Sporidiobolales, and Mixiales —showed lower frequencies of 54.0–62.0%. This pattern was also reflected in the presence of additional major domains, which are absent in most orders but occurred exclusively in a few early-diverging orders, including PF08447 (PAS fold), PF13426 (PAS domain), and PF25026 (Asd-4-like domain). In contrast, early-diverging orders exhibited relatively lower frequencies of PF08550 (Nitrogen regulatory protein areA, GATA-like domain) compared with most other orders. Among the 24 orders, minor domains were generally detected at low frequencies in 13 orders, although some orders, such as Cantharellales (20.0%) and Sporidiobolales (18.3%), exhibit comparatively higher frequencies. Overall, these observations indicated that domain composition in Basidiomycota GATA TFs was largely conserved, with early-diverging orders displaying distinctive patterns of major and minor domain retention. 3-4-4. GATA domain multiplicity of Dikarya GATA TFs To examine whether GATA domain multiplicity varies across fungal orders and contributes to lineage-specific structural diversity, order-level variation in the number of Dikarya GATA domains was examined (Fig. 9 e). All GATA TFs across the orders contained either one or two GATA domains, with most of the 38 Ascomycota and 24 Basidiomycota orders exhibiting high proportions of GATA TFs containing a single domain (70.0%-79.9%: 11 orders; 80.0%-89.9%: 36 orders; 90.0%-100.0%: 12 orders). Exceptions included Schizosaccharomycetales (55.6%) in Ascomycota and Geastrales (0.0%) and Agaricales (66.8%) in Basidiomycota . Notably, as Geastrales contained only a single GATA TF, further sampling was required for robust comparative analysis. These findings suggested that, despite the predominance of single GATA domains across Dikarya GATA TFs, multi-domain configurations may have had functional implications in lineage-specific contexts. 3-4-5. Types of Dikarya GATA motifs To investigate whether GATA motif composition varies across fungal orders and exhibits lineage-specific distribution patterns in Dikarya , the distribution of GATA motif types was analyzed at the order level (Fig. 9 f). In Ascomycota , among 35 orders, the proportion of Type IVa (CX 2 CX 17 CX 2 C) motifs ranged from < 40.0% (3 orders) to 40.0%-49.9% (5 orders), 50.0%-59.9% (21 orders), and ≥ 60.0% (9 orders), with early-diverging orders exhibiting relatively higher frequencies. By contrast, Type IVb (CX 2 CX 18 CX 2 C) motifs were distributed as < 40.0% (13 orders), 40.0%-49.9% (18 orders), 50.0%-59.9% (4 orders), and ≥ 60.0% (3 orders), with early-diverging orders showing comparatively lower representation. Several minor GATA motif types were detected in only a limited number of orders, including IVp (10 orders), IV19 (1 order), IVc (4 orders), and IVe (3 orders). Overall, GATA motif composition in Ascomycota was highly order-specific, with dominant motifs exhibiting contrasting trends in early-diverging lineages and minor motifs restricted to a few orders. In Basidiomycota , among 24 orders, the proportion of Type IVa (CX 2 CX 17 CX 2 C) motifs was distributed as < 40.0% (4 orders), 40.0%-49.9% (4 orders), 50.0%-59.9% (13 orders), and ≥ 60.0% (3 orders), with early-diverging orders exhibiting comparatively lower frequencies. Type IVb (CX 2 CX 18 CX 2 C) motifs, in contrast, were distributed as < 40.0% (5 orders), 40.0%-49.9% (13 orders), 50.0%-59.9% (4 orders), and ≥ 60.0% (2 orders), with early-diverging orders displaying relatively higher representation. Minor GATA motif types were detected in a limited number of orders, including IVp (6 orders), IV19 (5 orders), IVc (1 order), and IVe (2 orders). Overall, GATA motif composition in Basidiomycota was highly order-specific, with dominant motifs showing opposing trends in early-diverging orders and minor motifs restricted to a few lineages. 4. Discussion 4 − 1. Importance of conserved structures in fungal GATA TFs Conserved structural features of fungal TFs constitute the molecular basis for functional stability and long-term evolutionary maintenance (Shelest 2017 ). Among them, fungal GATA TFs exhibit persistent core structural components, particularly the CX 2 CX 17−18 CX 2 C zinc finger motif and its associated domains, reflecting strong constraints linked to essential regulatory functions (Hu et al. 2025 ). These conserved structures are indispensable for specific binding to WGATAR (W = A/T, R = A/G) motifs in target gene promoters and coordinated regulation of essential physiological processes (Merika and Orkin 1993 ). Consequently, structural conservation in fungal GATA TFs reflects both shared evolutionary origin and the preservation of fundamental regulatory functions across fungal lineages. Based on this perspective, the study aimed to determine whether fungal GATA TFs retain a conserved structural core while exhibiting lineage-specific diversification. To test this hypothesis, a genome-wide analysis of 7,846 GATA TFs from 796 fungal species was conducted to characterize conserved and variable structural features across taxonomic scales. Beyond broad taxonomic comparisons, order-level analyses were incorporated to capture finer-scale structural variation among closely related lineages. This approach revealed that fundamental structural elements were conserved across fungi, while domain architectures and motif compositions exhibited order-level, lineage-specific patterns. Overall, these findings underscore the role of conserved structural cores in essential regulatory functions and of taxonomic context in shaping structural diversification of fungal GATA TFs. 4 − 2. Lineage-specific expansion of fungal GATA TFs Lineage-specific expansion of fungal GATA TFs was hypothesized to be driven by adaptive regulatory demands rather than neutral variation across fungal genomes (Shelest 2017 ). Within the Dikarya , both Ascomycota and Basidiomycota exhibited relatively conserved and limited GATA TF repertoires, typically comprising only a small number of TFs per genome, despite substantial variation in species diversity and overall proteome size. In contrast, early-diverging fungal lineages displayed pronounced heterogeneity in GATA TF repertoires, consistent with lineage-specific expansion and contraction dynamics (Shelest 2017 ). Notably, Rozellomycota showed a marked reduction in GATA TF repertoire, likely reflecting extreme genome streamlining in Microsporidia (Peyretaillade et al. 2011 ). Collectively, these patterns suggested that GATA TF evolution in fungi was shaped by lineage-specific selective pressures and associated regulatory demands, rather than by taxonomic breadth or overall proteome size (Nowick and Stubbs 2010 ; Shelest 2017 ). 4 − 3. Domain diversity of fungal GATA TFs Whether fungal GATA TFs in diverse lineages function in conjunction with auxiliary domains to expand their regulatory capacity remains an open question. In this context, the distribution of domain architectures suggests that auxiliary domain acquisition in fungal GATA TFs is associated with discrete functional rewiring beyond the ancestral DNA-binding role (Hu et al. 2025 ). For example, recurrent PF00320 + PF08550 and PF07573 combinations suggest integration of GATA-mediated transcription with AreA-type nitrogen metabolite repression pathways, linking nitrogen regulatory programs to environmental nutrient-responsive gene expression outputs (Pfannmüller et al. 2017 ). Similarly, architectures involving PAS-related domains (PF08447 and PF13426) suggest enhanced signal-integration capacity, consistent with PAS-mediated regulation in circadian and signaling systems, enabling the integration of diverse environmental cues such as redox state or oxygen availability (Ponting and Aravind 1997 ). The PF00320 + PF25026 architecture likely represents an ASD4-type GATA module associated with developmental regulation and oligomerization-mediated transcriptional control, supporting distinct regulatory axes across domain combinations (Feng et al. 2000 ). Additionally, minor domain architectures may contribute to lineage-specific regulatory fine-tuning by modulating transcriptional activity or protein interactions. Collectively, these results support a model in which auxiliary domain acquisition enables functional rewiring and regulatory expansion in fungal GATA TFs across diverse lineages. Clear lineage-specific patterns further suggest that domain diversification is linked to evolutionary adaptation. Within Dikarya , especially in Ascomycota , the enrichment and co-occurrence of nitrogen regulatory domains (PF08550 and PF07573) (Pfannmüller et al. 2017 ), PAS-related environmental sensing domains (PF08447 and PF13426) (Ponting and Aravind 1997 ), and Asd-4-like domain (PF25026) (Feng et al. 2000 ) reflect the expansion of regulatory complexity. In contrast, Basidiomycota and early-diverging lineages generally exhibit more restricted domain repertoires and simpler architectures, indicating more constrained functional capacities. Notably, the recurrent association of PAS-related domains (PF08447 and PF13426) supports a conserved role in environmental sensing (Ponting and Aravind 1997 ), while the widespread distribution of PF08550 underscores the central importance of nitrogen regulatory mechanisms (Pfannmüller et al. 2017 ). Exceptionally, the exclusive presence of PF00320-only architectures in Rozellomycota suggests the retention of a minimal functional unit in certain lineages. Taken together, these findings suggest that fungal GATA TF evolution is driven by domain turnover and combinatorial reuse, enabling lineage-specific regulatory diversification. Interestingly, approximately 21% of the fungal GATA TFs analyzed in this study contain two GATA domains. Variation in GATA domain multiplicity thus likely represents an additional mechanism contributing to functional diversification beyond conserved structural elements. The presence of multiple GATA domains within a single protein may enhance DNA-binding versatility and enable more complex regulatory interactions, thereby expanding transcriptional specificity. This interpretation is supported by studies of metazoan GATA TFs, where multiple zinc fingers cooperatively modulate DNA binding and transcriptional activity. For example, in GATA-1 , interactions between the N- and C-terminal zinc fingers enhance both DNA binding and transactivation (Merika and Orkin 1993 ), whereas GATA-3 exhibits alternative binding configurations, including cooperative binding between two molecules or simultaneous engagement of both zinc fingers within a single molecule, depending on motif spacing (Bates et al. 2008 ). Collectively, these findings suggest that fungal GATA TF evolution may be driven by multilayered modular innovation, involving both auxiliary domain acquisition and domain copy number variation, which together may expand regulatory input space and enable lineage-specific functional specialization. This modular architecture would allow a single TF scaffold to integrate diverse environmental, metabolic, and developmental signals, thereby potentially facilitating the emergence of highly adaptable and context-dependent regulatory systems across fungal lineages. 4–4. Evolutionary conservation and divergence of fungal GATA motifs To date, a systematic analysis of GATA motif diversity across multiple fungal lineages has not been comprehensively performed. The coexistence of multiple GATA motif types in fungal GATA TFs therefore reflects a balance between strict functional constraints and adaptive structural flexibility (Jiang et al. 2021 ). In this study, the predominance of canonical motif types (Type IVa and IVb; CX 2 CX 17−18 CX 2 C) across fungal divisions suggests that the core zinc finger architecture is under strong evolutionary constraint, as it is essential for stable zinc coordination and sequence-specific recognition of WGATAR elements (Hu et al. 2025 ). Although Type IVc (CX 2 CX 20 CX 2 C) was previously reported in 14.0% of fungi (Park et al., 2006), a large-scale analysis across 796 species shows they are extremely rare (0.3%), indicating that Type IVc is not a major GATA TF type in fungi. Overall, the strong evolutionary constraint on the canonical zinc finger structure likely limits extensive diversification of motif length or cysteine spacing, thereby preserving the fundamental DNA-binding function required for transcriptional regulation across diverse fungal lineages (Lowry and Atchley 2000 ). Building on these observations within fungi, the eukaryotic comparison reveals kingdom- and lineage-specific patterns in GATA motif distribution. Fungi display a relatively balanced representation of Type IVa and IVb motifs (CX 2 CX 17−18 CX 2 C), contrasting with the strong skew toward Type IVa (CX 2 CX 17 CX 2 C) in animals and the predominance of Type IVb and IVc (CX 2 CX 18−20 CX 2 C) in plants. This suggests that while the zinc finger core remains highly conserved in fungi, the distribution of minor motif types may reflect lineage-specific adaptations. Such controlled variation likely provides fungi with the flexibility to fine-tune regulatory interactions and respond to diverse ecological or developmental cues, without compromising the fundamental DNA-binding architecture. Within this conserved framework in fungal GATA motifs, divergence at non-core positions provides a mechanism for functional refinement without compromising structural integrity (Lowry and Atchley 2000 ). Conserved cysteine residues and highly conserved non-cysteine positions are likely indispensable for maintaining zinc finger stability and DNA-binding affinity. Within this conserved framework, diverse residue types—including polar charged, polar uncharged, structurally important, and hydrophobic residues—coexist in a position-specific manner, suggesting that each position within the GATA motif may be subject to distinct functional and structural constraints (Desantis et al. 2022 ). In contrast, moderately conserved or lineage-specific residues may modulate binding specificity, protein-protein interactions, or regulatory context (Lowry and Atchley 2000 ). Such peripheral variation may enable GATA TFs of the same motif type to participate in distinct regulatory networks, respond to different environmental cues, or interact with lineage-specific cofactors (Rest et al. 2012 ). The presence of rare or atypical GATA motif variants further suggests that limited structural innovation has occurred during fungal evolution, potentially through relaxed selective pressure or niche-specific adaptation (Jiang et al. 2021 ). However, the low frequency and restricted distribution of these variants indicate that extensive divergence of the GATA motif is generally disfavored. Collectively, these observations support a model in which fungal GATA TFs maintain a highly conserved DNA-binding core while permitting controlled, position-specific divergence that facilitates functional diversification within and across fungal lineages. 4–5. Insights into the co-occurrence of domains and GATA motifs Whether specific fungal GATA motif types are functionally associated with particular auxiliary domain architectures remains unclear. To explore this relationship, this study systematically investigated the co-occurrence patterns of GATA motifs and auxiliary domain architectures in fungal GATA TFs across diverse lineages. Based on these analyses, the coexistence of diverse auxiliary domains alongside the GATA zinc finger in fungal GATA TFs likely reflects an evolutionary strategy to expand regulatory versatility without compromising the conserved DNA-binding core (Scazzocchio 2000 ). While the GATA domain provides a stable framework for sequence-specific DNA recognition, additional domains may modulate transcriptional activity by mediating protein-protein interactions, subcellular localization, or responsiveness to environmental and developmental cues (Zhang et al. 2020 ). In this context, domain diversification enables fungal GATA TFs to operate within increasingly complex regulatory networks, particularly in lineages exposed to variable ecological niches or metabolic demands (Moon et al. 2025 ). The non-random co-occurrence between specific domains and GATA motif types further suggests coordinated structural and functional optimization rather than independent evolutionary events (Zhang et al. 2020 ). Distinct GATA motif types may fine-tune DNA-binding affinity, spacing tolerance, or cooperative binding behavior, but their functional potential is likely constrained or enhanced by the surrounding domain architecture (Trainor et al. 2000 ). Domains that preferentially associate with certain motif types may impose structural constraints that stabilize particular zinc finger conformations, or may facilitate selective interactions with cofactors that favor specific motif configurations (Vishwanath et al. 2018 ). Such coupling implies that motif diversification alone is insufficient for functional innovation; instead, effective regulatory specialization emerges from the integrated evolution of motif sequences and domain contexts. Collectively, these patterns support a model in which fungal GATA TFs evolve through domain–motif co-adaptation, preserving a conserved transcriptional foundation while enabling lineage-specific regulatory refinement (Moon et al. 2025 ). This coordinated evolution provides a mechanistic basis for how fungal GATA TFs balance structural conservation with functional diversification across phylogenetically and ecologically diverse lineages. 4–6. Phylogenetic insights into fungal GATA domains Phylogenetic analysis of fungal GATA domains was performed to elucidate how motif types and domain architectures are distributed across evolutionary lineages and how these patterns contribute to functional diversification. As a result, a structured and non-random distribution of motif types and domain architectures was observed across the fungal tree. Type IVa motifs are predominantly enriched in the central regions of the tree, while Type IVb motifs are more frequent in peripheral branches, and intermediate regions display alternating clusters of Type IVa- and IVb-enriched clades. This spatial organization suggests that motif type is closely linked to phylogenetic position, reflecting both evolutionary history and lineage-specific diversification (Schwechheimer et al. 2022 ). To further investigate whether specific domain architectures are conserved or have independently emerged across fungal lineages, the distribution of recurrent domain combinations was examined at the phylogenetic level. Within these regions, multiple domain architectures recur across distinct clades, including PF00320-only, PF00320 + PF08550, PF00320 + PF25026, and PF00320 + PF08447 ± PF13426 combinations. The repeated occurrence of these architectures in separate clades and across multiple fungal divisions indicates that certain motif-domain configurations have been evolutionarily favored and conserved. This pattern suggests that domain architecture may provide structural support or functional context for the zinc finger, allowing GATA TFs with identical motifs to adopt distinct regulatory roles depending on their domain composition (Schwechheimer et al. 2022 ). To assess whether the observed phylogenetic patterns are also reflected in known functional classifications, 25 functionally characterized fungal GATA TFs were mapped onto the phylogeny. Mapping of these TFs further supports this view, as TFs with similar biological functions tend to cluster within the same or closely related clades. However, many clades remain unannotated, highlighting the potential for undiscovered functional diversity within the fungal GATA family. Together, these findings indicate that fungal GATA TFs have evolved through a combination of conserved core motifs and modular domain arrangements, enabling both structural stability of the zinc finger and lineage-specific functional specialization (Schwechheimer et al. 2022 ). 4–7. Structural insights into functional diversity of fungal GATA TFs Structural conservation of the GATA zinc finger, despite sequence and domain variation, is hypothesized to underlie the functional diversification of fungal GATA TFs. To examine this hypothesis, three-dimensional structural modeling of 11 representative fungal GATA TFs was performed, providing mechanistic insight into how functional diversity is accommodated without compromising the conserved DNA-binding module (Bertoline et al. 2023 ). Across all analyzed fungal GATA TFs, including those with relatively rare GATA motif variants (e.g., IVc and IV19), the zinc finger fold within the GATA domain remains highly conserved, reinforcing the notion that structural integrity of the DNA-binding module is strongly constrained despite sequence variation. This observation aligns with previous analyses highlighting that the canonical zinc finger structure in fungal GATA TFs is under strict evolutionary conservation, ensuring reliable WGATAR recognition and transcriptional regulation (Merika and Orkin 1993 ; Bates et al. 2008 ). The presence of dual GATA domains is hypothesized to facilitate cooperative DNA binding through spatially coordinated zinc finger modules. Consistent with this hypothesis, fungal GATA TFs containing dual GATA domains show that both domains independently maintain canonical zinc finger conformations, and their close spatial arrangement suggests potential cooperative interactions during DNA binding. Such arrangements may enhance sequence specificity or binding affinity, illustrating how modular repetition of the GATA domain can expand regulatory potential without altering the fundamental fold (Merika and Orkin 1993 ; Bates et al. 2008 ). Auxiliary domains are hypothesized to drive functional diversification of fungal GATA TFs without disrupting the conserved DNA-binding core. In support of this hypothesis, fungal GATA TFs with additional auxiliary domains show that the GATA domain consistently preserves its zinc finger architecture, whereas the overall protein conformation varies significantly due to the presence of extra domains. This indicates that while the DNA-binding core is structurally stable, domain additions enable context-dependent or specialized regulatory functions, likely by mediating protein-protein interactions, subcellular localization, or responsiveness to environmental cues (Pfannmüller et al. 2017 ). Collectively, these structural observations suggest that fungal GATA TFs achieve functional diversification through conserved zinc finger structures combined with flexible domain architectures. While the core DNA-binding function remained structurally conserved, variation in domain composition likely contributed to lineage- or context-specific regulatory specialization. To further evaluate whether structural regions outside canonical GATA domains corresponded to previously unrecognized protein folds, additional Foldseek analyses were performed for 11 representative fungal GATA TFs. Most detected structural similarities corresponded to previously annotated conserved domains, supporting the overall reliability of sequence-based domain annotation approaches. However, WC1 additionally exhibited structurally conserved regions outside known domains, suggesting the possible presence of highly diverged or uncharacterized structural elements. These findings demonstrate that integrating structural similarity analyses with conventional sequence-based annotation approaches can provide additional insight into the structural and functional diversity of fungal GATA TFs. 4–8. Regulatory diversification of fungal GATA TFs To comprehensively understand the regulatory diversification of fungal GATA TFs beyond sequence and domain-level variation, their physicochemical properties and subcellular localization features were systematically analyzed. The results revealed that fungal GATA TFs exhibit notable physicochemical diversity across lineages. The physicochemical diversity observed among fungal GATA TFs underscores a nuanced balance between conserved functional constraints and lineage-specific adaptations (Hu et al. 2025 ). While hydrophilicity and aliphatic index remained largely conserved across divisions, substantial variation in protein length, molecular weight, and isoelectric point indicates structural flexibility extending beyond the core DNA-binding domain. Such variability likely enables divergent regulatory interactions, subcellular localization patterns, and protein-protein associations necessary to respond to distinct ecological and physiological contexts (Lambourne et al. 2025 ). Notably, Dikarya GATA TFs exhibited broad yet relatively consistent profiles, reflecting both the evolutionary conservation of DNA-binding functionality and the necessity for moderate structural adaptation. In contrast, early-diverging lineages, particularly Neocallimastigomycota , displayed extreme protein lengths and molecular weights, suggesting lineage-specific expansions or insertions that may facilitate specialized regulatory roles in unique environmental niches. Atypical physicochemical profiles, such as elevated aliphatic index or reduced hydrophilicity, appear to represent adaptive modifications rather than fundamental functional divergence (Smole et al. 2011 ), potentially enhancing stability or interactions under specific metabolic or environmental conditions. Collectively, these findings indicate that fungal GATA TFs maintain core physicochemical constraints necessary for DNA-binding activity while accommodating structural and regulatory diversification that may underpin functional innovation across evolutionary lineages. Extending beyond intrinsic physicochemical variation, analyses of TMHs and subcellular localization further highlight the regulatory complexity of fungal GATA TFs (Krogh et al. 2001 ; Ødum et al. 2024 ). The vast majority of GATA TFs were predicted to localize to the nucleus and lacked TMHs, consistent with their canonical role as soluble nuclear TFs (Lu et al. 2021 ). However, a small subset exhibited non-nuclear localization or contained one or more TMHs, including rare cases with multiple helices. These atypical features may reflect two complementary scenarios. On one hand, they could arise from annotation or prediction artefacts, such as incomplete gene models or inaccuracies in localization prediction (Krogh et al. 2001 ; Ødum et al. 2024 ). On the other hand, they may represent biologically meaningful regulatory strategies, including cytoplasmic retention, nucleocytoplasmic shuttling, or membrane-associated regulation coupled with signal-dependent activation, thereby expanding the functional versatility of these TFs beyond the nucleus (Cartwright and Helin* 2000 ; Liu et al. 2018b ). Notably, the rarity and lineage-specific distribution of TMH-containing and non-nuclear GATA TFs suggest that these features are unlikely to result solely from stochastic errors. Instead, they may reflect specialized adaptations in particular fungal lineages, potentially facilitating context-dependent regulatory interactions or environmental responsiveness. For example, TMH acquisition or dual localization may enable membrane-associated signaling or cross-compartmental regulatory control, complementing the canonical nuclear transcriptional activity (Seo 2014 ). Collectively, these observations indicate that fungal GATA TFs maintain the core physicochemical and structural constraints required for DNA binding. At the same time, they exhibit multilayered regulatory diversification across sequence composition, structural properties, and subcellular regulatory dynamics, which may underpin lineage-specific functional innovations. 4–9. Order-level architectural diversity of Dikarya GATA TFs Given that Dikarya encompasses the majority of fungal species diversity (Hyde 2024 ), order-level analyses were conducted to resolve fine-scale structural variation in GATA TFs. At this level, GATA TFs exhibit substantial heterogeneity in their taxonomic distribution, despite the overall conservation of the PF00320 (GATA zinc finger). In both Ascomycota and Basidiomycota , GATA TFs are unevenly distributed across orders, with a subset of major orders contributing disproportionately to total GATA TF counts, whereas many orders contain relatively few species and limited numbers of GATA TFs. These patterns indicate lineage-specific expansion and retention of GATA TFs at the order level within Dikarya (Nowick and Stubbs 2010 ). To elucidate whether domain architecture diversification in GATA TFs follows conserved or lineage-specific evolutionary patterns across Dikarya , domain architecture analyses were conducted. The results reveal contrasting patterns between the two Dikarya divisions. In Ascomycota , most orders show consistent PF00320 frequencies, while early-diverging orders exhibit relatively higher proportions of this domain and reduced or absent representation of auxiliary domains such as PF08447, PF08550, and PF13426. In contrast, Basidiomycota displays the opposite trend, where early-diverging orders show comparatively lower PF00320 frequencies and selective presence of auxiliary domains, including PF08447, PF13426, and PF25026. Additionally, certain domains are restricted to specific orders, such as PF07573 in Eurotiales , indicating order-specific innovations. These results demonstrate that domain composition does not follow a uniform pattern across Dikarya , but instead reflects division-specific evolutionary trajectories. To further determine whether motif composition patterns parallel the observed domain-level diversification across Dikarya , motif composition was analyzed. The results further reinforce these contrasting patterns. In Ascomycota , early-diverging orders tend to exhibit higher proportions of Type IVa motifs and lower proportions of Type IVb motifs. Conversely, in Basidiomycota , early-diverging orders show relatively higher representation of Type IVb motifs and lower proportions of Type IVa motifs. This opposing distribution of major motif types between the two divisions indicates that motif evolution cannot be generalized across Dikarya as a whole. Minor motif types, including IVp, IV19, IVc, and IVe, are restricted to a limited number of orders in both divisions, suggesting constrained diversification. Collectively, these findings indicate that Dikarya GATA TFs maintain a conserved DNA-binding core while exhibiting division- and order-specific diversification in both domain architecture and motif composition. 4–10. Integrative analysis and biological implications The integrative analysis presented in this study provides a conceptual framework for understanding how fungal GATA TFs have diversified at both structural and functional levels across major fungal lineages. By jointly examining GATA motif variation, domain architecture, physicochemical properties, and their co-occurrence patterns, this work moves beyond descriptive cataloging and offers mechanistic insights into how GATA TF families have evolved lineage-specific regulatory roles. From an evolutionary perspective, the observed diversification of GATA motif types and auxiliary domain compositions supports a model of modular evolution in fungal GATA TFs (Hu et al. 2025 ). The conservation of the core GATA zinc finger underscores its essential role in DNA binding and transcriptional regulation, while variation in motif residues and flanking domains contributes to functional innovation (Lowry and Atchley 2000 ). Subtle changes in zinc finger residues can alter DNA-binding affinity or sequence specificity, thereby reshaping target gene repertoires. Concurrently, auxiliary domains may mediate interactions with cofactors, chromatin modifiers, or signaling components, expanding regulatory capacity beyond core DNA recognition (Soto et al. 2022 ). Such modularity enables selective pressures to act independently on DNA-binding specificity and regulatory interaction capacity, facilitating functional diversification of GATA TFs without disrupting their fundamental transcriptional role (Lowry and Atchley 2000 ). This evolutionary strategy is particularly advantageous in fungi, where rapid adaptation to ecological niches, nutrient availability, and host-associated lifestyles is often required (Naranjo-Ortiz and Gabaldón 2019 ). Functionally, the lineage-dependent enrichment of specific domain-motif combinations suggests that fungal GATA TFs have undergone specialization tailored to distinct regulatory contexts (Moon et al. 2025 ). Domains associated with transcriptional activation, repression, or signal integration likely confer context-dependent regulatory behaviors, enabling GATA TFs to participate in diverse biological processes such as nitrogen metabolism, stress responses, morphogenesis, and pathogenicity (Hu et al. 2025 ). The preferential co-occurrence of certain domains with specific GATA motif types implies that transcriptional output is not solely dictated by DNA-binding capability but emerges from coordinated interactions between motif structure and domain-mediated regulatory mechanisms (Hu et al. 2025 ). This coupling may enhance regulatory precision, allowing fungi to fine-tune gene expression programs in response to environmental and developmental signals. At a mechanistic level, the integration of motif diversity with domain architecture provides insight into how transcriptional regulation is structurally encoded within GATA TFs. Variations in motif composition may alter zinc finger flexibility or DNA-contact geometry, while associated domains may stabilize these conformations or recruit specific cofactors (Cassandri et al. 2017 ). Such interactions could influence promoter selectivity, cooperative binding, or chromatin engagement, offering a plausible mechanistic explanation for the functional heterogeneity observed among fungal GATA TFs (Lu et al. 2012 ). Importantly, these features suggest that GATA TF function is best understood as an emergent property of the entire protein architecture rather than as an isolated domain function (Inukai et al. 2017 ). Beyond individual proteins, the results have broader implications for understanding the diversification of GATA TF families at the lineage and organismal levels. The lineage-specific patterns uncovered in this study provide a foundation for correlating domain architecture variation with fungal life-history traits, ecological strategies, and phenotypic adaptations. For example, expansion or contraction of specific GATA TF subtypes may be linked to metabolic flexibility, host interaction strategies, or environmental resilience (Hu et al. 2025 ). Such correlations open avenues for comparative analyses that connect TF evolution to organism-level traits and ecological success (Merényi et al. 2023 ). Finally, this study establishes a scalable framework for future integrative analyses of TF families in fungi and beyond. By combining motif-level resolution with domain architecture and physicochemical profiling, the approach enables systematic exploration of functional diversification across large genomic datasets. Future work integrating transcriptomic, chromatin accessibility, and phenotypic data could further refine the functional interpretations proposed here, allowing direct links between GATA TF structural variation and regulatory outcomes (Yang et al. 2019 ; Huang et al. 2021 ). Collectively, the findings underscore the importance of domain-motif integration in shaping the evolutionary and functional landscape of fungal GATA TFs and highlight their role as dynamic regulators within complex fungal regulatory networks (Hu et al. 2025 ). 4–11. Annotation variability in fungal genomes and its implications for GATA TF analysis Fungal GATA TFs were identified based on protein sequences annotated in both the EnsemblFungi (Yates et al. 2026 ) and MycoCosm (Grigoriev et al. 2014 ). These resources have been widely employed in large-scale comparative genomic studies (Lim et al. 2020 ; Alouane et al. 2021 ; Cole et al. 2025 ) and are thus considered reliable sources for comprehensive fungal GATA TF identification. While these resources provide extensive coverage of fungal genomes for comparative genomics, the quality and consistency of gene annotations can vary across species and between databases. Many genomes included in EnsemblFungi are derived from diverse external sources and are not uniformly processed through a single standardized re-annotation pipeline (Yates et al. 2026 ). In contrast, MycoCosm genomes are annotated via JGI’s standardized pipelines with additional quality control and community-based curation (Grigoriev et al. 2014 ). Consequently, observed variation in GATA TF structure, domain composition, or abundance may partially reflect differences in annotation protocols rather than true biological divergence. Despite these potential limitations, the observed trends remain robust and well-supported, as evidenced by the consistent identification of canonical GATA domains across high-quality genomes from both databases. In addition, major additional domain associations (PF08550, PF08447, PF13426, PF25026, and PF07573) were consistently detected across multiple phylogenetically diverse species, further supporting the reliability of the observed patterns. By prioritizing domain-level evidence and applying stringent E-value thresholds, the impact of potential inconsistencies in gene model annotations on downstream functional classification was effectively minimized. Nevertheless, a small fraction of GATA domains (~ 1%) may have been excluded under these stringent filtering criteria, highlighting the need for cautious interpretation when assessing domain multiplicity and abundance patterns. In summary, while annotation variability across EnsemblFungi and MycoCosm represents a potential source of bias, the overall patterns reported—both in canonical GATA domain conservation and major additional domain associations—are supported by multiple genomes from both resources and are likely to reflect genuine biological trends. Incorporating re-annotated or experimentally validated fungal genomes in future studies would further strengthen these conclusions and reduce residual uncertainty arising from heterogeneous annotations. 4–12. Limitations and future perspectives This study provides a comprehensive analysis of the structural features and evolutionary conservation of fungal GATA TFs, highlighting their significance; however, several limitations exist. First, although efforts were made to reduce taxonomic bias by analyzing 796 species across 11 fungal divisions, a balanced representation of all 19 currently recognized fungal divisions could not be achieved (Wijayawardene et al. 2024 ). This limitation reflects the incomplete integration of publicly available genomic resources from diverse sources, including major repositories such as NCBI (Kitts et al. 2016 ) and integrated platforms such as FungiDB (Basenko et al. 2018 ). It suggests that structural patterns observed in underrepresented divisions should be interpreted with caution. Future studies incorporating more diverse and comprehensive genomic datasets will enable a deeper and more refined understanding of GATA TFs across the fungal kingdom (Li et al. 2021 ). Second, although the structural features of fungal GATA TFs have been comprehensively predicted, they have not been experimentally validated, leaving their functional relevance uncertain. Without direct evidence from mutational studies, DNA-binding assays, or in vivo characterization, it remains unclear whether the predicted motifs and domain architectures accurately reflect biological function. Consequently, while computational analyses provide valuable insights, caution is required in interpreting these predictions, and future experimental work is essential to confirm their functional significance. Third, the functional roles of minor GATA motifs within fungal GATA TFs remain largely hypothetical, as their contribution to transcriptional regulation has not been experimentally demonstrated. Although these motifs are predicted based on sequence analysis and domain co-occurrence patterns, their specific impact on DNA-binding specificity, protein-protein interactions, or regulatory activity is unclear. The lack of empirical validation limits the ability to determine whether these minor motifs play essential or auxiliary roles in transcriptional control, and it remains uncertain how variations in these motifs influence the functional diversity of GATA TFs across different fungal lineages. Therefore, targeted experimental studies, including site-directed mutagenesis and functional assays, will be necessary to clarify the biological significance of these motifs (Du et al. 2016 ). Fourth, although 25 well-known fungal GATA TFs (such as WC-1, WC-2, or NsdD (Zhang et al. 2020 )) were mapped onto phylogenetic clades to provide a functional framework, a substantial proportion of sequences could not be confidently assigned to any defined clade, limiting comprehensive functional interpretation. Consequently, fungal GATA TFs analyzed in this study could not be reliably functionally characterized, making it unclear whether they correspond to well-known regulators. This limitation is primarily due to the reliance on computational predictions and the lack of experimental validation, as many of the fungal species included have not been extensively studied at the molecular or functional level. Furthermore, the high diversity and lineage-specific expansion of GATA TFs across fungi complicate the identification of orthologous relationships, making functional assignment based solely on sequence similarity challenging. As a result, the precise regulatory roles of these GATA TFs remain largely unresolved, highlighting the need for targeted experimental assays to clarify their biological functions Lastly, although Foldseek-based structural analyses were performed for 11 representative fungal GATA TFs to assess whether structural regions outside canonical GATA domains correspond to previously unrecognized protein folds, a comprehensive fungal-wide evaluation of their prevalence was not conducted. In particular, systematic analysis across thousands of fungal GATA TFs would be required to determine how frequently such non-canonical or highly diverged structural elements occur within the fungal kingdom. However, such large-scale structural screening and comparative annotation were beyond the scope of the present study due to computational and resource constraints. Therefore, while this study provides evidence for additional structurally conserved regions beyond canonical domain annotations in specific cases (e.g., WC1 ), the extent to which these features are widespread or represent rare exceptions across fungi remains unresolved. Future large-scale structural prediction and database-level comparative analyses will be necessary to clarify the evolutionary prevalence and diversity of such structural features across fungal TFs. 5. Conclusion This study provides a comprehensive genome-wide comparative analysis of 7,846 GATA TFs across 796 fungal species, revealing a combination of strong structural conservation and lineage-specific diversification. The canonical GATA motifs are identified as a universally conserved core (CX 2 CX 17−18 CX 2 C), whereas variations in domain composition, motif architecture, and domain copy number exhibit pronounced order-level specificity, particularly within Dikarya . In addition, integrative analyses incorporating motif-domain associations, phylogenetic relationships based on GATA domain sequences, and cross-kingdom comparisons with plant and animal GATA TFs further contextualize the evolutionary patterns of fungal GATA TFs within a broader eukaryotic framework. Structural predictions of representative fungal GATA TFs provide additional support for the inferred structural organization and potential functional relevance of conserved and divergent features. These observations suggest that the evolutionary trajectory of fungal GATA TFs is driven by selective structural modifications rather than uniform expansion, enabling functional diversification while preserving fundamental regulatory roles. Overall, the findings establish a comprehensive structural framework that enhances understanding of the evolutionary and regulatory landscape of fungal GATA TFs and provides a robust reference for future functional and comparative studies. Declarations Statements & declarations Competing interests The author declares no conflict of interest, financial or otherwise. Funding This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. Author contributions M.K. conducted all the work until the manuscript was finalized and published. Data availability All fungal GATA TFs analyzed in this study are publicly available from EnsemblFungi (https://fungi.ensembl.org/index.html) (Yates et al. 2026) and MycoCosm (https://mycocosm.jgi.doe.gov/) (Grigoriev et al. 2014). Acknowledgements The author sincerely thanks the editor and the reviewers for their thorough review and valuable comments. Ethical approval Not applicable. Informed consent Not applicable. References Adnan M, Islam W, Gang L, Chen HYH (2022) Advanced research tools for fungal diversity and its impact on forest ecosystem. Environ Sci Pollut Res 29:45044–45062. https://doi.org/10.1007/s11356-022-20317-8 Alouane T, Rimbert H, Bormann J et al (2021) Comparative genomics of eight Fusarium graminearum strains with contrasting aggressiveness reveals an expanded open pangenome and extended effector content signatures. Int J Mol Sci 22:6257. https://doi.org/10.3390/ijms22126257 An Z, Zhao Q, McEvoy J et al (1997) The second finger of Urbs1 is required for iron-mediated repression of sid1 in Ustilago maydis . Proc Natl Acad Sci 94:5882–5887. https://doi.org/10.1073/pnas.94.11.5882 Baldrian P, Větrovský T, Lepinay C, Kohout P (2022) High-throughput sequencing view on the magnitude of global fungal diversity. Fungal Divers 114:539–547. https://doi.org/10.1007/s13225-021-00472-y Basenko EY, Pulman JA, Shanmugasundram A et al (2018) FungiDB: An integrated bioinformatic resource for fungi and Oomycetes . J Fungi 4:39. https://doi.org/10.3390/jof4010039 Bates DL, Chen Y, Kim G et al (2008) Crystal structures of multiple GATA zinc fingers bound to DNA reveal new insights into DNA recognition and self-association by GATA . J Mol Biol 381:1292–1306. https://doi.org/10.1016/j.jmb.2008.06.072 Beopoulos A, Cescut J, Haddouche R et al (2009) Yarrowia lipolytica as a model for bio-oil production. Prog Lipid Res 48:375–387. https://doi.org/10.1016/j.plipres.2009.08.005 Bernardes NE, Takeda AAS, Dreyer TR et al (2017) Nuclear transport of the Neurospora crassa NIT-2 transcription factor is mediated by importin-α. Biochem J 474:4091–4104. https://doi.org/10.1042/BCJ20170654 Bertoline LMF, Lima AN, Krieger JE, Teixeira SK (2023) Before and after AlphaFold2: An overview of protein structure prediction. Front Bioinforma 3:1120370. https://doi.org/10.3389/fbinf.2023.1120370 Cañizares JV, Pallotti C, Saínz-Pardo I et al (2002) The SRD2 gene is involved in Saccharomyces cerevisiae morphogenesis. Arch Microbiol 177:352–357. https://doi.org/10.1007/s00203-002-0400-z Cartwright P, Helin* K (2000) Nucleocytoplasmic shuttling of transcription factors. Cell Mol Life Sci 57:1193–1206. https://doi.org/10.1007/PL00000759 Cassandri M, Smirnov A, Novelli F et al (2017) Zinc-finger proteins in health and disease. Cell Death Discov 3:17071. https://doi.org/10.1038/cddiscovery.2017.71 Chen Y, Cao Y, Gai Y et al (2021) Genome-wide identification and functional characterization of GATA transcription factor gene family in Alternaria alternata . J Fungi 7:1013. https://doi.org/10.3390/jof7121013 Coelho MA, Bakkeren G, Sun S et al (2017) Fungal sex: The Basidiomycota . Microbiol Spectr 5. https://doi.org/10.1128/microbiolspec.FUNK-0046-2016 . 5.3.12 Cole J, Raguideau S, Abbaszadeh-Dahaji P et al (2025) Comparative genomic analysis of a metagenome-assembled genome reveals distinctive symbiotic traits in a Mucoromycotina fine root endophyte arbuscular mycorrhizal fungus. BMC Genomics 26:967. https://doi.org/10.1186/s12864-025-12149-w Cosma MP (2004) Daughter-specific repression of Saccharomyces cerevisiae HO : Ash1 is the commander. EMBO Rep 5:953–957. https://doi.org/10.1038/sj.embor.7400251 Courchesne WE, Magasanik B (1988) Regulation of nitrogen assimilation in Saccharomyces cerevisiae : roles of the URE2 and GLN3 genes. J Bacteriol 170:708–713. https://doi.org/10.1128/jb.170.2.708-713.1988 Cunningham TS, Rai R, Cooper TG (2000) The level of DAL80 expression down-regulates GATA factor-mediated transcription in Saccharomyces cerevisiae . J Bacteriol 182:6584–6591. https://doi.org/10.1128/JB.182.23.6584-6591.2000 Desantis F, Miotto M, Di Rienzo L et al (2022) Spatial organization of hydrophobic and charged residues affects protein thermal stability and binding affinity. Sci Rep 12:12087. https://doi.org/10.1038/s41598-022-16338-5 Dissanayake AJ, Liu J-K (2025) Ascomycota : Diversity, taxonomy and phylogeny, 2nd edition: editorial. J Fungi 11:419. https://doi.org/10.3390/jof11060419 Dong Z, Zhang N, Liu Y et al (2019) Expression analysis and characterization of zglp1 in the Chinese tongue sole ( Cynoglossus semilaevis ). Gene 683:72–79. https://doi.org/10.1016/j.gene.2018.10.003 Du H, Guan G, Xie J et al (2012) Roles of Candida albicans Gat2 , a GATA-type zinc finger transcription factor, in Biofilm Formation , filamentous growth and virulence. PLoS ONE 7:e29707. https://doi.org/10.1371/journal.pone.0029707 Du L, Tracy S, Rifkin SA (2016) Mutagenesis of GATA motifs controlling the endoderm regulator elt-2 reveals distinct dominant and secondary cis- regulatory elements. Dev Biol 412:160–170. https://doi.org/10.1016/j.ydbio.2016.02.013 Dutta AK, Phull PS (2021) Treatment of Helicobacter pylori infection in the presence of penicillin allergy. World J Gastroenterol 27:7661–7668. https://doi.org/10.3748/wjg.v27.i44.7661 Fabian GR, Hess SM, Hopper AK (1990) srd1 , a Saccharomyces cerevisiae suppressor of the temperature-sensitive pre-rRNA processing defect of rrp1-1 . Genetics 124:497–504. https://doi.org/10.1093/genetics/124.3.497 Feng B, Haas H, Marzluf GA (2000) ASD4 , a new GATA factor of Neurospora crassa , displays sequence-specific DNA binding and functions in Ascus and Ascospore Development. Biochemistry 39:11065–11073. https://doi.org/10.1021/bi000886j Finn RD, Bateman A, Clements J et al (2014) Pfam: the protein families database. Nucleic Acids Res 42:D222–D230. https://doi.org/10.1093/nar/gkt1223 Gai Z, Gui T, Muragaki Y (2011) The function of TRPS1 in the development and differentiation of bone, kidney, and hair follicles. Histol Histopathol 915–921. https://doi.org/10.14670/HH-26.915 Gasteiger E, Hoogland C, Gattiker A et al (2005) Protein identification and analysis tools on the ExPASy server. In: Walker JM (ed) The Proteomics Protocols Handbook. Humana, Totowa, NJ, pp 571–607 Grigoriev IV, Nikitin R, Haridas S et al (2014) MycoCosm portal: gearing up for 1000 fungal genomes. Nucleic Acids Res 42:D699–D704. https://doi.org/10.1093/nar/gkt1183 Hawksworth DL, Lücking R (2017) Fungal diversity revisited: 2.2 to 3.8 million species. Microbiol Spectr 5. https://doi.org/10.1128/microbiolspec.FUNK-0052-2016 . 5.4.10 He C, Cheng H, Zhou R (2007) GATA family of transcription factors of vertebrates: phylogenetics and chromosomal synteny. J Biosci 32:1273–1280. https://doi.org/10.1007/s12038-007-0136-7 He M-Q, Zhao R-L, Liu D-M et al (2022) Species diversity of Basidiomycota . Fungal Divers 114:281–325. https://doi.org/10.1007/s13225-021-00497-3 He Q, Cheng P, Yang Y et al (2002) White Collar-1 , a DNA binding transcription factor and a light sensor. Science 297:840–843. https://doi.org/10.1126/science.1072795 Hu D, Zhao R, Lin Y, Jiang C (2025) Evolution and functional diversity of GATA transcription factors in filamentous fungi: Structural characteristics, metabolic regulation and environmental response. Microbiol Res 16:120. https://doi.org/10.3390/microbiolres16060120 Huang L, Li X, Dong L et al (2021) Profiling of chromatin accessibility identifies transcription factor binding sites across the genome of Aspergillus species. BMC Biol 19:189. https://doi.org/10.1186/s12915-021-01114-0 Hui L, Wan C, Hai-tao D et al (2010) Direct microbial conversion of wheat straw into lipid by a cellulolytic fungus of Aspergillus oryzae A-4 in solid-state fermentation. Bioresour Technol 101:7556–7562. https://doi.org/10.1016/j.biortech.2010.04.027 Hyde K (2024) The 2024 Outline of Fungi and fungus-like taxa. Mycosphere 15:5146–6239. https://doi.org/10.5943/mycosphere/15/1/25 Hyde KD (2022) The numbers of fungi. Fungal Divers 114:1–1. https://doi.org/10.1007/s13225-022-00507-y Inukai S, Kock KH, Bulyk ML (2017) Transcription factor–DNA binding: beyond binding site motifs. Curr Opin Genet Dev 43:110–119. https://doi.org/10.1016/j.gde.2017.02.007 Irisarri I, Baurain D, Brinkmann H et al (2017) Phylotranscriptomic consolidation of the jawed vertebrate timetree. Nat Ecol Evol 1:1370–1378. https://doi.org/10.1038/s41559-017-0240-5 Jiang C, Lv G, Ge J et al (2021) Genome-wide identification of the GATA transcription factor family and their expression patterns under temperature and salt stress in Aspergillus oryzae . AMB Express 11:56. https://doi.org/10.1186/s13568-021-01212-w Jones P, Binns D, Chang H-Y et al (2014) InterProScan 5: genome-scale protein function classification. Bioinformatics 30:1236–1240. https://doi.org/10.1093/bioinformatics/btu031 Katoh K, Standley DM (2013) MAFFT multiple sequence alignment software version 7: Improvements in performance and usability. Mol Biol Evol 30:772–780. https://doi.org/10.1093/molbev/mst010 Katz ME, Braunberger K, Yi G et al (2013) A p53-like transcription factor similar to Ndt80 controls the response to nutrient stress in the filamentous fungus, Aspergillus nidulans . F1000Research 2:72. https://doi.org/10.12688/f1000research.2-72.v1 Kim L, Hoe K-L, Yu YM et al (2012) The fission yeast GATA factor, Gaf1 , modulates sexual development via direct down-regulation of ste11 + expression in response to nitrogen starvation. PLoS ONE 7:e42409. https://doi.org/10.1371/journal.pone.0042409 Kim M (2026) PlantGATA: a comprehensive database for plant GATA transcription factors. Funct Integr Genomics 26:90. https://doi.org/10.1007/s10142-026-01855-7 Kim M (2024) Comparative analysis of amino acid sequence level in plant GATA transcription factors. Sci Rep 14:29786. https://doi.org/10.1038/s41598-024-81159-7 Kim W, Mirdita M, Levy Karin E et al (2025) Rapid and sensitive protein complex alignment with Foldseek-Multimer. Nat Methods 22:469–472. https://doi.org/10.1038/s41592-025-02593-7 Kitts PA, Church DM, Thibaud-Nissen F et al (2016) Assembly: a resource for assembled genomes at NCBI. Nucleic Acids Res 44:D73–D80. https://doi.org/10.1093/nar/gkv1226 Kong S, Park S, Lee Y (2015) Systematic characterization of the bZIP transcription factor gene family in the rice blast fungus, M agnaporthe oryzae . Environ Microbiol 17:1425–1443. https://doi.org/10.1111/1462-2920.12633 Kotomura N, Tsunemine S, Kuragano M et al (2018) Sfh1 , an essential component of the RSC chromatin remodeling complex, maintains genome integrity in fission yeast. Genes Cells 23:738–752. https://doi.org/10.1111/gtc.12629 Krogh A, Larsson B, Von Heijne G, Sonnhammer ELL (2001) Predicting transmembrane protein topology with a hidden markov model: application to complete genomes. J Mol Biol 305:567–580. https://doi.org/10.1006/jmbi.2000.4315 Kumar R, Wang R-A (2016) Structure, expression and functions of MTA genes. Gene 582:112–121. https://doi.org/10.1016/j.gene.2016.02.012 Lambourne L, Mattioli K, Santoso C et al (2025) Widespread variation in molecular interactions and regulatory properties among transcription factor isoforms. Mol Cell 85:1445–1466e13. https://doi.org/10.1016/j.molcel.2025.03.004 Lee M-K, Kwon N-J, Choi JM et al (2014) NsdD is a key repressor of asexual development in Aspergillus nidulans . Genetics 197:159–173. https://doi.org/10.1534/genetics.114.161430 Letunic I, Bork P (2024) Interactive Tree of Life (iTOL) v6: recent updates to the phylogenetic tree display and annotation tool. Nucleic Acids Res 52:W78–W82. https://doi.org/10.1093/nar/gkae268 Lewis ZA, Correa A, Schwerdtfeger C et al (2002) Overexpression of White Collar-1 ( WC‐1 ) activates circadian clock‐associated genes, but is not sufficient to induce most light‐regulated gene expression in Neurospora crassa . Mol Microbiol 45:917–931. https://doi.org/10.1046/j.1365-2958.2002.03074.x Li Y, Steenwyk JL, Chang Y et al (2021) A genome-scale phylogeny of the kingdom Fungi. Curr Biol 31:1653–1665e5. https://doi.org/10.1016/j.cub.2021.01.074 Liang Y, Zhang X, Liu Y et al (2021) GATA zinc finger domain-containing protein 2A ( GATAD2A ) deficiency reactivates fetal haemoglobin in patients with β‐thalassaemia through impaired formation of methyl‐binding domain protein 2 ( MBD2 )‐containing nucleosome remodelling and deacetylation ( NuRD ) complex. Br J Haematol 193:1220–1227. https://doi.org/10.1111/bjh.17511 Lim JJJ, Koh J, Moo JR et al (2020) Fungi.guru: Comparative genomic and transcriptomic resource for the fungi kingdom. Comput Struct Biotechnol J 18:3788–3795. https://doi.org/10.1016/j.csbj.2020.11.019 Lin CP-C, Kim C, Smith SO, Neiman AM (2013) A Highly Redundant Gene Network Controls Assembly of the Outer Spore Wall in S. cerevisiae . PLoS Genet 9:e1003700. https://doi.org/10.1371/journal.pgen.1003700 Linden H (1997) White collar 2 , a partner in blue-light signal transduction, controlling expression of light-regulated genes in Neurospora crassa . EMBO J 16:98–109. https://doi.org/10.1093/emboj/16.1.98 Liu L, Wang Q, Zhang X et al (2018a) Ssams2 , a gene encoding GATA transcription factor, is required for appressoria formation and chromosome segregation in Sclerotinia sclerotiorum . Front Microbiol 9:3031. https://doi.org/10.3389/fmicb.2018.03031 Liu Y, Li P, Fan L, Wu M (2018b) The nuclear transportation routes of membrane-bound transcription factors. Cell Commun Signal 16:12. https://doi.org/10.1186/s12964-018-0224-3 Lorenzini M, Cappello MS, Logrieco A, Zapparoli G (2016) Polymorphism and phylogenetic species delimitation in filamentous fungi from predominant mycobiota in withered grapes. Int J Food Microbiol 238:56–62. https://doi.org/10.1016/j.ijfoodmicro.2016.08.039 Lowry JA, Atchley WR (2000) Molecular evolution of the GATA family of transcription factors: Conservation within the DNA-binding domain. J Mol Evol 50:103–115. https://doi.org/10.1007/s002399910012 Lu J, Wu T, Zhang B et al (2021) Types of nuclear localization signals and mechanisms of protein import into the nucleus. Cell Commun Signal 19:60. https://doi.org/10.1186/s12964-021-00741-y Lu Y, Su C, Liu H (2012) A GATA transcription factor recruits Hda1 in response to reduced Tor1 signaling to establish a hyphal chromatin state in Candida albicans . PLoS Pathog 8:e1002663. https://doi.org/10.1371/journal.ppat.1002663 Lücking R, Aime MC, Robbertse B et al (2021) Fungal taxonomy and sequence-based nomenclature. Nat Microbiol 6:540–548. https://doi.org/10.1038/s41564-021-00888-x Merényi Z, Krizsán K, Sahu N et al (2023) Genomes of fungi and relatives reveal delayed loss of ancestral gene families and evolution of key fungal traits. Nat Ecol Evol 7:1221–1231. https://doi.org/10.1038/s41559-023-02095-9 Merika M, Orkin SH (1993) DNA-binding specificity of GATA family transcription factors. Mol Cell Biol 13:3999–4010. https://doi.org/10.1128/mcb.13.7.3999-4010.1993 Minh BQ, Schmidt HA, Chernomor O et al (2020) IQ-TREE 2: New models and efficient methods for phylogenetic inference in the genomic era. Mol Biol Evol 37:1530–1534. https://doi.org/10.1093/molbev/msaa015 Moon H, Lee M-K, Shin J et al (2025) Species-specific gene regulatory network rewiring mediated by the GATA-type regulator. NsdD Aspergillus mBio 16:e01181–e01125. https://doi.org/10.1128/mbio.01181-25 Naranjo-Ortiz MA, Gabaldón T (2019) Fungal evolution: major ecological adaptations and evolutionary transitions. Biol Rev 94:1443–1476. https://doi.org/10.1111/brv.12510 Nowick K, Stubbs L (2010) Lineage-specific transcription factors and the evolution of gene regulatory networks. Brief Funct Genomics 9:65–78. https://doi.org/10.1093/bfgp/elp056 Oberegger H, Schoeser M, Zadra I et al (2001) SREA is involved in regulation of siderophore biosynthesis, utilization and uptake in Aspergillus nidulans . Mol Microbiol 41:1077–1089. https://doi.org/10.1046/j.1365-2958.2001.02586.x Ødum MT, Teufel F, Thumuluri V et al (2024) DeepLoc 2.1: multi-label membrane protein type prediction using protein language models. Nucleic Acids Res 52:W215–W220. https://doi.org/10.1093/nar/gkae237 Orban A, Fraatz MA, Rühl M (2019) Aroma profile analyses of filamentous fungi cultivated on solid substrates. Solid State Fermentation. Springer International Publishing, Cham, pp 85–107 Park H-M, Son Y-E, Cho H-J et al (2025) Characterization of blue light receptors LreA and LreB in Aspergillus flavus . J Microbiol Biotechnol 35:e2411054. https://doi.org/10.4014/jmb.2411.11054 Pelletier B, Beaudoin J, Mukai Y, Labbé S (2002) Fep1 , an iron sensor regulating iron transporter gene expression in Schizosaccharomyces pombe . J Biol Chem 277:22950–22958. https://doi.org/10.1074/jbc.M202682200 Peyretaillade E, El Alaoui H, Diogon M et al (2011) Extreme reduction and compaction of microsporidian genomes. Res Microbiol 162:598–606. https://doi.org/10.1016/j.resmic.2011.03.004 Pfannmüller A, Leufken J, Studt L et al (2017) Comparative transcriptome and proteome analysis reveals a global impact of the nitrogen regulators AreA and AreB on secondary metabolism in Fusarium fujikuroi . PLoS ONE 12:e0176194. https://doi.org/10.1371/journal.pone.0176194 Plaster N, Sonntag C, Schilling TF, Hammerschmidt M (2007) REREa/Atrophin-2 interacts with histone deacetylase and Fgf8 signaling to regulate multiple processes of zebrafish development. Dev Dyn 236:1891–1904. https://doi.org/10.1002/dvdy.21196 Ponting CP, Aravind L (1997) PAS: a multifunctional domain family comes to light. Curr Biol 7:R674–R677. https://doi.org/10.1016/S0960-9822(06)00352-6 Puttick MN, Morris JL, Williams TA et al (2018) The interrelationships of land plants and the nature of the ancestral embryophyte . Curr Biol 28:733–745e2. https://doi.org/10.1016/j.cub.2018.01.063 Raghukumar S (2017) Fungi: Characteristics and classification. Fungi in Coastal and Oceanic Marine Ecosystems. Springer International Publishing, Cham, pp 1–15 Rest JS, Bullaughey K, Morris GP, Li W-H (2012) Contribution of transcription factor binding site motif variants to condition-specific gene expression patterns in budding yeast. PLoS ONE 7:e32274. https://doi.org/10.1371/journal.pone.0032274 Rubio A, Ghosh S, Mülleder M et al (2021) Ribosome profiling reveals ribosome stalling on tryptophan codons and ribosome queuing upon oxidative stress in fission yeast. Nucleic Acids Res 49:383–399. https://doi.org/10.1093/nar/gkaa1180 Scazzocchio C (2000) The fungal GATA factors. Curr Opin Microbiol 3:126–131. https://doi.org/10.1016/S1369-5274(00)00063-1 Schoch CL, Ciufo S, Domrachev M et al (2020) NCBI Taxonomy: a comprehensive update on curation, resources and tools. Database 2020:baaa062. https://doi.org/10.1093/database/baaa062 Schumacher J, Simon A, Cohrs KC et al (2014) The transcription factor BcLTF1 regulates virulence and light responses in the necrotrophic plant pathogen Botrytis cinerea . PLoS Genet 10:e1004040. https://doi.org/10.1371/journal.pgen.1004040 Schwechheimer C, Schröder PM, Blaby-Haas CE (2022) Plant GATA factors: Their biology, phylogeny, and phylogenomics. Annu Rev Plant Biol 73:123–148. https://doi.org/10.1146/annurev-arplant-072221-092913 Seo PJ (2014) Recent advances in plant membrane-bound transcription factor research: Emphasis on intracellular movement. J Integr Plant Biol 56:334–342. https://doi.org/10.1111/jipb.12139 Shelest E (2017) Transcription factors in fungi: TFome dynamics, three major families, and dual-specificity TFs. Front Genet 8:53. https://doi.org/10.3389/fgene.2017.00053 Shen W-K, Chen S-Y, Gan Z-Q et al (2023) AnimalTFDB 4.0: a comprehensive animal transcription factor database updated with variation and expression annotations. Nucleic Acids Res 51:D39–D45. https://doi.org/10.1093/nar/gkac907 Smole Z, Nikolic N, Supek F et al (2011) Proteome sequence features carry signatures of the environmental niche of prokaryotes. BMC Evol Biol 11:26. https://doi.org/10.1186/1471-2148-11-26 Soto LF, Li Z, Santoso CS et al (2022) Compendium of human transcription factor effector domains. Mol Cell 82:514–526. https://doi.org/10.1016/j.molcel.2021.11.007 Soussi-Boudekou S, Vissers S, Urrestarazu A et al (1997) Gzf3p , a fourth GATA factor involved in nitrogen‐regulated transcription in Saccharomyces cerevisiae . Mol Microbiol 23:1157–1168. https://doi.org/10.1046/j.1365-2958.1997.3021665.x Stajich JE (2017) Fungal genomes and insights into the evolution of the kingdom. Microbiol Spectr 5. https://doi.org/10.1128/microbiolspec.FUNK-0055-2016 . 5.4.15 Takayama Y, Mamnun YM, Trickey M et al (2010) Hsk1- and SCFPof3- dependent proteolysis of S. pombe Ams2 ensures histone homeostasis and centromere function. Dev Cell 18:385–396. https://doi.org/10.1016/j.devcel.2009.12.024 Tedersoo L, Sánchez-Ramírez S, Kõljalg U et al (2018) High-level classification of the fungi and a tool for evolutionary ecological analyses. Fungal Divers 90:135–159. https://doi.org/10.1007/s13225-018-0401-0 Trainor CD, Ghirlando R, Simpson MA (2000) GATA zinc finger interactions modulate DNA binding and transactivation. J Biol Chem 275:28157–28166. https://doi.org/10.1074/jbc.M000020200 Van Kempen M, Kim SS, Tumescheit C et al (2024) Fast and accurate protein structure search with Foldseek. Nat Biotechnol 42:243–246. https://doi.org/10.1038/s41587-023-01773-0 Virolainen P, Pankova V, Nerezenko A, Chekunova E (2026) Structural features of algal and fungal GATA transcription factors may play a role in symbiosis. J Mol Evol. https://doi.org/10.1007/s00239-026-10310-x Vishwanath S, De Brevern AG, Srinivasan N (2018) Same but not alike: Structure, flexibility and energetics of domains in multi-domain proteins are influenced by the presence of other domains. PLOS Comput Biol 14:e1006008. https://doi.org/10.1371/journal.pcbi.1006008 Wadhwa K, Kapoor N, Kaur H et al (2024) A comprehensive review of the diversity of fungal secondary metabolites and their emerging applications in healthcare and environment. Mycobiology 52:335–387. https://doi.org/10.1080/12298093.2024.2416736 Wang Y-P, Liu L, Wang X-S et al (2021) GAT1 gene, the GATA transcription activator, regulates the production of higher alcohol during wheat beer fermentation by Saccharomyces cerevisiae . Bioengineering 8:61. https://doi.org/10.3390/bioengineering8050061 Wijayawardene NN, Hyde KD, Mikhailov KV et al (2024) Classes and phyla of the kingdom fungi. Fungal Divers 128:1–165. https://doi.org/10.1007/s13225-024-00540-z Wu B, Hussain M, Zhang W et al (2019) Current insights into fungal species diversity and perspective on naming the environmental DNA sequences of fungi. Mycology 10:127–140. https://doi.org/10.1080/21501203.2019.1614106 Xie M, Wang J, Wang F et al (2025) A review of genomic, transcriptomic, and proteomic applications in edible fungi biology: Current status and future directions. J Fungi 11:422. https://doi.org/10.3390/jof11060422 Yang C, Liu H, Li G et al (2015) The MADS-box transcription factor FgMcm1 regulates cell identity and fungal development in Fusarium graminearum . Environ Microbiol 17:2762–2776. https://doi.org/10.1111/1462-2920.12747 Yang C, Ma L, Xiao D et al (2019) Integration of ATAC-seq and RNA-seq identifies key genes in light-induced primordia formation of Sparassis latifolia . Int J Mol Sci 21:185. https://doi.org/10.3390/ijms21010185 Yates AD, Austine-Orimoloye O, Azov AG et al (2026) Ensembl 2026. Nucleic Acids Res 54:D1053–D1060. https://doi.org/10.1093/nar/gkaf1239 Yu M, Yu J, Cao H et al (2019) Genome-wide identification and analysis of the GATA transcription factor gene family in Ustilaginoidea virens . Genome 62:807–816. https://doi.org/10.1139/gen-2018-0190 Zaccaron AZ, Stergiopoulos I (2025) The dynamics of fungal genome organization and its impact on host adaptation and antifungal resistance. J Genet Genomics 52:628–640. https://doi.org/10.1016/j.jgg.2024.10.010 Zhang C, Wang G, Deng W, Li T (2020) Distribution, evolution and expression of GATA-TFs provide new insights into their functions in light response and fruiting body development of Tolypocladium guangdongense . PeerJ 8:e9784. https://doi.org/10.7717/peerj.9784 Zhang X, Ma J, Yang S et al (2023) Analysis of GATA transcription factors and their expression patterns under abiotic stress in grapevine ( Vitis vinifera L). BMC Plant Biol 23:611. https://doi.org/10.1186/s12870-023-04604-1 Zhao H, Nie Y, Zong T-K et al (2023) Species diversity, updated classification and divergence times of the phylum Mucoromycota . Fungal Divers 123:49–157. https://doi.org/10.1007/s13225-023-00525-4 Zheng Q, Huang Y, He X et al (2024) Genome-wide identification and expression pattern analysis of GATA gene family in Orchidaceae . Genes 15:915. https://doi.org/10.3390/genes15070915 Statements & declarations Additional Declarations The authors declare no competing interests. Supplementary Files SupplementaryTable16.xlsx Supplementary Table 1-6 Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {\"props\":{\"pageProps\":{\"initialData\":{\"identity\":\"rs-9688441\",\"acceptedTermsAndConditions\":true,\"allowDirectSubmit\":true,\"archivedVersions\":[],\"articleType\":\"Research Article\",\"associatedPublications\":[],\"authors\":[{\"id\":638815086,\"identity\":\"d2c49498-7c35-4b58-a7a3-4923fae586b2\",\"order_by\":0,\"name\":\"Mangi Kim\",\"email\":\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA3UlEQVRIiWNgGAWjYDACHiB+YGAjx9jMfECCeC0JFWnGzO1tCVAtzMRoOXM4sb3njAFxWvh7jj/7kNiWltg7I+fjjZ9t2xjM2fsP4NUicbbHeEZim43xzBm5my17224zWPYcJuCw8zzMDEBbZDfOyN0mwQvUYnAjGb8O+fPsj4FaDjPuv5HzTPIvSMv9x/i1GJxtMAZ5X7Gx5wybNMQWAt43PHPGGBzIjO1txtYy527zGJxJNsCrRe5M+mOGD5CofHjzTdltOYPjBx/gtwYd8JCmfBSMglEwCkYBVgAAQ3JN3Gp/vygAAAAASUVORK5CYII=\",\"orcid\":\"https://orcid.org/0009-0009-0227-4869\",\"institution\":\"Independent Researcher\",\"correspondingAuthor\":true,\"prefix\":\"\",\"firstName\":\"Mangi\",\"middleName\":\"\",\"lastName\":\"Kim\",\"suffix\":\"\"}],\"badges\":[],\"createdAt\":\"2026-05-12 07:48:54\",\"currentVersionCode\":1,\"declarations\":{\"humanSubjects\":false,\"vertebrateSubjects\":false,\"conflictsOfInterestStatement\":false,\"humanSubjectEthicalGuidelines\":false,\"humanSubjectConsent\":false,\"humanSubjectClinicalTrial\":false,\"humanSubjectCaseReport\":false,\"vertebrateSubjectEthicalGuidelines\":false},\"doi\":\"10.21203/rs.3.rs-9688441/v1\",\"doiUrl\":\"https://doi.org/10.21203/rs.3.rs-9688441/v1\",\"draftVersion\":[],\"editorialEvents\":[],\"editorialNote\":\"\",\"failedWorkflow\":false,\"files\":[{\"id\":109274828,\"identity\":\"a73813ea-4045-4a66-ac01-b909406692a1\",\"added_by\":\"auto\",\"created_at\":\"2026-05-14 14:52:27\",\"extension\":\"png\",\"order_by\":1,\"title\":\"Figure 1\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":313326,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eFig 1. Schematic overview of the study workflow. \\u003c/strong\\u003eOverview of the genome-wide collection, annotation, and comparative structural analysis of fungal GATA TFs.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Fig.1.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-9688441/v1/a5f8c5fd4457288a2c42a0b5.png\"},{\"id\":109274824,\"identity\":\"a02ec080-bcf5-4a9f-9bb4-cd60dc6f6ce6\",\"added_by\":\"auto\",\"created_at\":\"2026-05-14 14:52:27\",\"extension\":\"png\",\"order_by\":2,\"title\":\"Figure 2\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":153706,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eFig 2. Taxonomic distribution and architectural features of GATA TFs across major fungal lineages.\\u003c/strong\\u003e \\u003cstrong\\u003e(a)\\u003c/strong\\u003e Division-level phylogeny showing the taxonomic positions of the analyzed fungal divisions. \\u003cstrong\\u003e(b)\\u003c/strong\\u003e Total number of species in each fungal division. \\u003cstrong\\u003e(c)\\u003c/strong\\u003e Distribution of GATA TFs per species within each division, shown as boxplots indicating minimum, maximum, and interquartile ranges; a filled circle indicates the mean number of GATA TFs. \\u003cstrong\\u003e(d)\\u003c/strong\\u003e Distribution of protein domains identified in fungal GATA TFs. \\u003cstrong\\u003e(e)\\u003c/strong\\u003e Distribution of the number of GATA domains per GATA TF. \\u003cstrong\\u003e(f)\\u003c/strong\\u003e Distribution of types of GATA motifs observed across fungal GATA TFs.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Fig.2.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-9688441/v1/c0e21638b0ffbd0f1bd6f66f.png\"},{\"id\":109296391,\"identity\":\"287d8d1d-c0c6-4c99-a96e-3588a02bf1ac\",\"added_by\":\"auto\",\"created_at\":\"2026-05-15 08:46:47\",\"extension\":\"png\",\"order_by\":3,\"title\":\"Figure 3\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":155576,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eFig 3. Taxonomic distribution and GATA motifs of GATA TFs across major eukaryotic groups.\\u003c/strong\\u003e \\u003cstrong\\u003e(a) \\u003c/strong\\u003ePhylogenetic overview of 36 major eukaryotic groups (animals, plants, and fungi). \\u003cstrong\\u003e(b)\\u003c/strong\\u003e Number of species included in each group, shown as horizontal bars representing the total species count per group. \\u003cstrong\\u003e(c)\\u003c/strong\\u003e Distribution of GATA TFs across groups, shown as horizontal ranges (minimum-maximum) with mean values indicated by blue dots. \\u003cstrong\\u003e(d)\\u003c/strong\\u003e Heatmap illustrates the relative abundance (0-100%) of each GATA motif type within groups, with darker shades indicating higher abundance.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Fig.3.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-9688441/v1/eec77d5effc013452bf93aa0.png\"},{\"id\":109274826,\"identity\":\"7ec3805e-dbb1-462c-a775-ee33ee343e06\",\"added_by\":\"auto\",\"created_at\":\"2026-05-14 14:52:27\",\"extension\":\"png\",\"order_by\":5,\"title\":\"Figure 5\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":119177,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eFig 5. Co-occurrence patterns between GATA motif types and domains.\\u003c/strong\\u003e Heatmaps show the frequency of co-occurrence between each GATA motif type (y-axis) and major domains plus minor domains (x-axis) across nine fungal divisions. Color intensity represents the co-occurrence ratio, with white indicating 0% and red indicating 100%.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Fig.5.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-9688441/v1/e2858d09d6a2c8f4f6921d35.png\"},{\"id\":109296337,\"identity\":\"e70af0fe-7abd-44a3-9a57-43416a9884f6\",\"added_by\":\"auto\",\"created_at\":\"2026-05-15 08:46:32\",\"extension\":\"png\",\"order_by\":6,\"title\":\"Figure 6\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":721237,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eFig 6. Phylogenetic relationships of fungal GATA domains.\\u003c/strong\\u003e A maximum-likelihood phylogenetic tree of 9,488 fungal GATA domains was constructed using IQ-TREE2. Clades containing representative fungal GATA TFs with well-characterized biological functions are highlighted. Concentric annotation rings provide hierarchical information: Ring 1 indicates fungal divisions, Ring 2 represents domain architectures, and Ring 3 denotes GATA motif types. The outermost ring displays the dominant GATA motif within each clade. Black-outlined transparent boxes indicate clades characterized by dominant domain architectures.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Fig.6.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-9688441/v1/e5f81f68f44b738a6488ca3e.png\"},{\"id\":109296464,\"identity\":\"bc2afded-9a50-4e74-9961-f436a5e21c6e\",\"added_by\":\"auto\",\"created_at\":\"2026-05-15 08:47:09\",\"extension\":\"png\",\"order_by\":7,\"title\":\"Figure 7\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":245894,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eFig 7. Predicted structural models of representative fungal GATA TFs.\\u003c/strong\\u003e A total of 11 representative\\u003cstrong\\u003e \\u003c/strong\\u003efungal GATA TFs were subjected to structural prediction to examine domain organization and architectural diversity. The GATA domain is highlighted by a transparent blue box in each model. Among these, four GATA TFs (\\u003cem\\u003eGAT1, GAT3, NsdD, and ASH1\\u003c/em\\u003e) contain a single GATA domain but differ in motif types. Two GATA TFs (\\u003cem\\u003eSreA and Fil1\\u003c/em\\u003e) possess two GATA domains with distinct motif combinations. The remaining five GATA TFs (\\u003cem\\u003eAreA, AreB, LreB, NIT2, and WC1\\u003c/em\\u003e) harbor additional domains beyond the GATA domain.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Fig.7.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-9688441/v1/5984c6fc8064a7620818a185.png\"},{\"id\":109296406,\"identity\":\"52170913-f65c-41f8-86c1-6bed48d51bef\",\"added_by\":\"auto\",\"created_at\":\"2026-05-15 08:46:51\",\"extension\":\"png\",\"order_by\":8,\"title\":\"Figure 8\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":205152,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eFig 8. Physicochemical properties of fungal GATA TFs.\\u003c/strong\\u003e Distributions of key physicochemical properties across fungal GATA TFs: \\u003cstrong\\u003e(a)\\u003c/strong\\u003eprotein length, \\u003cstrong\\u003e(b)\\u003c/strong\\u003emolecular weight (MW, kDa), \\u003cstrong\\u003e(c)\\u003c/strong\\u003eisoelectric point (pI), \\u003cstrong\\u003e(d)\\u003c/strong\\u003ealiphatic index, and \\u003cstrong\\u003e(e)\\u003c/strong\\u003eGRAVY (grand average of hydropathicity). Boxplots represent the range and interquartile distribution, while the mean value is indicated by a thin yellow rectangle.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Fig.8.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-9688441/v1/c7646845a510600c6542fb0d.png\"},{\"id\":109274832,\"identity\":\"12410a82-81b7-4d03-9b01-8a532c326f06\",\"added_by\":\"auto\",\"created_at\":\"2026-05-14 14:52:28\",\"extension\":\"png\",\"order_by\":9,\"title\":\"Figure 9\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":308662,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eFig 9. Order-level architectural characteristics of \\u003c/strong\\u003e\\u003cem\\u003e\\u003cstrong\\u003eDikarya\\u003c/strong\\u003e\\u003c/em\\u003e\\u003cstrong\\u003e GATA TFs.\\u003c/strong\\u003e \\u003cstrong\\u003e(a)\\u003c/strong\\u003e Order-level phylogenetic relationships of \\u003cem\\u003eDikarya\\u003c/em\\u003especies included in this study, illustrating the evolutionary distribution of GATA TFs across major \\u003cem\\u003eDikarya\\u003c/em\\u003e classes. \\u003cstrong\\u003e(b)\\u003c/strong\\u003e Total number of species analyzed for each \\u003cem\\u003eDikarya\\u003c/em\\u003e order. \\u003cstrong\\u003e(c)\\u003c/strong\\u003e Minimum, maximum, and mean numbers of GATA TFs per species within each \\u003cem\\u003eDikarya\\u003c/em\\u003e order, highlighting interspecific variation in GATA TF abundance. \\u003cstrong\\u003e(d)\\u003c/strong\\u003e Proportional distribution of conserved protein domains identified in GATA TFs for each \\u003cem\\u003eDikarya\\u003c/em\\u003e order. \\u003cstrong\\u003e(e)\\u003c/strong\\u003e Relative proportions of GATA TFs containing different numbers of GATA domains across \\u003cem\\u003eDikarya\\u003c/em\\u003e orders. \\u003cstrong\\u003e(f)\\u003c/strong\\u003e Order-level distribution of GATA motif types, showing the relative abundance of distinct zinc finger configurations among \\u003cem\\u003eDikarya\\u003c/em\\u003eGATA TFs.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Fig.9.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-9688441/v1/b3c33b7c3b5153318ba0efd9.png\"},{\"id\":109296093,\"identity\":\"dd61c4fa-0323-424c-b207-62c06126bf87\",\"added_by\":\"auto\",\"created_at\":\"2026-05-15 08:45:27\",\"extension\":\"pdf\",\"order_by\":0,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"manuscript-pdf\",\"size\":1876458,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"manuscript.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-9688441/v1/d7c47b3a-1175-4302-8369-f10ade019a9a.pdf\"},{\"id\":109274823,\"identity\":\"2d3ffda4-83f8-4bf2-ad00-73dfe8d94570\",\"added_by\":\"auto\",\"created_at\":\"2026-05-14 14:52:27\",\"extension\":\"xlsx\",\"order_by\":1,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"supplement\",\"size\":5950981,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eSupplementary Table 1-6\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"SupplementaryTable16.xlsx\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-9688441/v1/77d05e8038b2c78d8757f4bb.xlsx\"}],\"financialInterests\":\"The authors declare no competing interests.\",\"formattedTitle\":\"\\u003cp\\u003e\\u003cstrong\\u003eGenome-wide comparative analysis of structural features in fungal GATA transcription factors: Insights from 796 fungal species\\u003c/strong\\u003e\\u003c/p\\u003e\",\"fulltext\":[{\"header\":\"1. Introduction\",\"content\":\"\\u003cp\\u003eFungi are osmoheterotrophic eukaryotes with filamentous or unicellular vegetative structures, chitin- or polysaccharide-based cell walls, and asexual or sexual spores (Raghukumar \\u003cspan citationid=\\\"CR87\\\" class=\\\"CitationRef\\\"\\u003e2017\\u003c/span\\u003e). As of 2024, the kingdom comprises approximately 140,000 formally described species (Hyde \\u003cspan citationid=\\\"CR38\\\" class=\\\"CitationRef\\\"\\u003e2024\\u003c/span\\u003e). However, diversity estimates based on various approaches suggest that the actual number of fungal species may range from 2 to 11\\u0026nbsp;million (Hawksworth and L\\u0026uuml;cking \\u003cspan citationid=\\\"CR31\\\" class=\\\"CitationRef\\\"\\u003e2017\\u003c/span\\u003e; L\\u0026uuml;cking et al. \\u003cspan citationid=\\\"CR70\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e; Baldrian et al. \\u003cspan citationid=\\\"CR4\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e; Hyde \\u003cspan citationid=\\\"CR39\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e). Notably, species estimates from metabarcoding data are typically higher, with some studies reporting up to 11.7\\u0026ndash;13.2\\u0026nbsp;million species (Wu et al. \\u003cspan citationid=\\\"CR110\\\" class=\\\"CitationRef\\\"\\u003e2019\\u003c/span\\u003e). Collectively, this diversity supports the wide-ranging ecological functions and practical applications of fungi (Adnan et al. \\u003cspan citationid=\\\"CR1\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e; Wadhwa et al. \\u003cspan citationid=\\\"CR107\\\" class=\\\"CitationRef\\\"\\u003e2024\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eAmong these divisions, \\u003cem\\u003eAscomycota\\u003c/em\\u003e is the most species-rich fungal group, with ~\\u0026thinsp;98,000 described species (64% of all formally described fungi) (Wu et al. \\u003cspan citationid=\\\"CR110\\\" class=\\\"CitationRef\\\"\\u003e2019\\u003c/span\\u003e; Hyde \\u003cspan citationid=\\\"CR38\\\" class=\\\"CitationRef\\\"\\u003e2024\\u003c/span\\u003e), forming haploid spores in asci and ranging from yeast to filamentous forms (Dissanayake and Liu \\u003cspan citationid=\\\"CR20\\\" class=\\\"CitationRef\\\"\\u003e2025\\u003c/span\\u003e). The biological functions of \\u003cem\\u003eAscomycota\\u003c/em\\u003e have been well studied, highlighting their ecological and biotechnological significance (Lorenzini et al. \\u003cspan citationid=\\\"CR66\\\" class=\\\"CitationRef\\\"\\u003e2016\\u003c/span\\u003e). For example, \\u003cem\\u003eAspergillus\\u003c/em\\u003e species (e.g., \\u003cem\\u003eA. oryzae, A. sojae\\u003c/em\\u003e) are widely used in food fermentation and enzyme production (Orban et al. \\u003cspan citationid=\\\"CR79\\\" class=\\\"CitationRef\\\"\\u003e2019\\u003c/span\\u003e), \\u003cem\\u003ePenicillium\\u003c/em\\u003e species produce β-lactam antibiotics (Dutta and Phull \\u003cspan citationid=\\\"CR24\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e), \\u003cem\\u003eFusarium fujikuroi\\u003c/em\\u003e synthesizes growth-promoting gibberellins (Pfannm\\u0026uuml;ller et al. \\u003cspan citationid=\\\"CR83\\\" class=\\\"CitationRef\\\"\\u003e2017\\u003c/span\\u003e), and oleaginous fungi such as \\u003cem\\u003eA. oryzae\\u003c/em\\u003e (Hui et al. \\u003cspan citationid=\\\"CR37\\\" class=\\\"CitationRef\\\"\\u003e2010\\u003c/span\\u003e)d \\u003cem\\u003eniger\\u003c/em\\u003e (Beopoulos et al. \\u003cspan citationid=\\\"CR7\\\" class=\\\"CitationRef\\\"\\u003e2009\\u003c/span\\u003e). Moreover, other fungal divisions, such as \\u003cem\\u003eBasidiomycota\\u003c/em\\u003e and \\u003cem\\u003eMucoromycota\\u003c/em\\u003e, also play critical ecological, industrial, agricultural, and clinical roles (Coelho et al. \\u003cspan citationid=\\\"CR14\\\" class=\\\"CitationRef\\\"\\u003e2017\\u003c/span\\u003e; He et al. \\u003cspan citationid=\\\"CR33\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e; Zhao et al. \\u003cspan citationid=\\\"CR119\\\" class=\\\"CitationRef\\\"\\u003e2023\\u003c/span\\u003e). These examples demonstrate the functional and biotechnological importance of \\u003cem\\u003eAscomycota\\u003c/em\\u003e, as well as the critical ecological and diverse functional roles of other fungal divisions. Accordingly, understanding these diverse ecological and functional roles requires detailed analyses at the genomic and regulatory levels (Stajich \\u003cspan citationid=\\\"CR100\\\" class=\\\"CitationRef\\\"\\u003e2017\\u003c/span\\u003e; Mer\\u0026eacute;nyi et al. \\u003cspan citationid=\\\"CR71\\\" class=\\\"CitationRef\\\"\\u003e2023\\u003c/span\\u003e; Xie et al. \\u003cspan citationid=\\\"CR111\\\" class=\\\"CitationRef\\\"\\u003e2025\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eBuilding on this foundation, fungal transcription factors (TFs) function as central regulators of gene expression, underpinning the regulatory and evolutionary dynamics of fungal genomes (Shelest \\u003cspan citationid=\\\"CR95\\\" class=\\\"CitationRef\\\"\\u003e2017\\u003c/span\\u003e). They are grouped into roughly 80 TF families (Shelest \\u003cspan citationid=\\\"CR95\\\" class=\\\"CitationRef\\\"\\u003e2017\\u003c/span\\u003e), with representative examples including bZIP (Kong et al. \\u003cspan citationid=\\\"CR51\\\" class=\\\"CitationRef\\\"\\u003e2015\\u003c/span\\u003e), MADS-box (Yang et al. \\u003cspan citationid=\\\"CR112\\\" class=\\\"CitationRef\\\"\\u003e2015\\u003c/span\\u003e), p53-like (Katz et al. \\u003cspan citationid=\\\"CR45\\\" class=\\\"CitationRef\\\"\\u003e2013\\u003c/span\\u003e), and GATA (Yu et al. \\u003cspan citationid=\\\"CR115\\\" class=\\\"CitationRef\\\"\\u003e2019\\u003c/span\\u003e; Chen et al. \\u003cspan citationid=\\\"CR13\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e; Jiang et al. \\u003cspan citationid=\\\"CR42\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e). Among these, fungal GATA TFs typically contain one or two GATA domains characterized by a conserved Cys-X\\u003csub\\u003e2\\u003c/sub\\u003e-Cys-X\\u003csub\\u003e17\\u0026minus;18\\u003c/sub\\u003e-Cys-X\\u003csub\\u003e2\\u003c/sub\\u003e-Cys motif, which enables specific recognition of the WGATAR element (W\\u0026thinsp;=\\u0026thinsp;A/T, R\\u0026thinsp;=\\u0026thinsp;A/G) in target gene promoters (Lowry and Atchley \\u003cspan citationid=\\\"CR67\\\" class=\\\"CitationRef\\\"\\u003e2000\\u003c/span\\u003e). These TFs are particularly notable for regulating physiological and developmental processes, including light response, nitrogen and iron metabolism, secondary metabolism, and reproduction (Scazzocchio \\u003cspan citationid=\\\"CR90\\\" class=\\\"CitationRef\\\"\\u003e2000\\u003c/span\\u003e). For example, in \\u003cem\\u003eNeurospora crassa\\u003c/em\\u003e, \\u003cem\\u003eWC-1\\u003c/em\\u003e and \\u003cem\\u003eWC-2\\u003c/em\\u003e form a blue-light photoreceptor complex mediating circadian clock entrainment (He et al. \\u003cspan citationid=\\\"CR34\\\" class=\\\"CitationRef\\\"\\u003e2002\\u003c/span\\u003e). At the same time, comparable light-regulatory roles are observed for \\u003cem\\u003eLtf1\\u003c/em\\u003e in \\u003cem\\u003eBotrytis cinerea\\u003c/em\\u003e (Schumacher et al. \\u003cspan citationid=\\\"CR92\\\" class=\\\"CitationRef\\\"\\u003e2014\\u003c/span\\u003e) and its homolog, \\u003cem\\u003eNsdD\\u003c/em\\u003e, in \\u003cem\\u003eA. nidulans\\u003c/em\\u003e, which also promotes sexual development while repressing conidiation (Lee et al. \\u003cspan citationid=\\\"CR56\\\" class=\\\"CitationRef\\\"\\u003e2014\\u003c/span\\u003e). Nitrogen-responsive GATA TFs \\u003cem\\u003eAreA\\u003c/em\\u003e and \\u003cem\\u003eAreB\\u003c/em\\u003e in \\u003cem\\u003eF. fujikuroi\\u003c/em\\u003e regulate genes involved in nitrogen utilization (Pfannm\\u0026uuml;ller et al. \\u003cspan citationid=\\\"CR83\\\" class=\\\"CitationRef\\\"\\u003e2017\\u003c/span\\u003e), \\u003cem\\u003eASD4\\u003c/em\\u003e in \\u003cem\\u003eN. crassa\\u003c/em\\u003e governs ascus and ascospore differentiation (Feng et al. \\u003cspan citationid=\\\"CR26\\\" class=\\\"CitationRef\\\"\\u003e2000\\u003c/span\\u003e), \\u003cem\\u003eUrbs-1\\u003c/em\\u003e in \\u003cem\\u003eUstilago maydis\\u003c/em\\u003e represses siderophore biosynthesis (An et al. \\u003cspan citationid=\\\"CR3\\\" class=\\\"CitationRef\\\"\\u003e1997\\u003c/span\\u003e), and the divergent \\u003cem\\u003eSsams2\\u003c/em\\u003e in \\u003cem\\u003eSclerotinia sclerotiorum\\u003c/em\\u003e contributes to appressorium formation and chromosome segregation (Liu et al. \\u003cspan citationid=\\\"CR64\\\" class=\\\"CitationRef\\\"\\u003e2018a\\u003c/span\\u003e). These roles highlight the need to understand both the regulatory and structural features of fungal GATA TFs.\\u003c/p\\u003e \\u003cp\\u003eGiven these roles, structural analysis of fungal GATA TFs provides important insights into functional diversification and evolutionary conservation (Lowry and Atchley \\u003cspan citationid=\\\"CR67\\\" class=\\\"CitationRef\\\"\\u003e2000\\u003c/span\\u003e). To date, genome-wide structural identification of fungal GATA TFs has been performed in four single-species studies: six GATA TFs in \\u003cem\\u003eAlternaria alternata\\u003c/em\\u003e (Chen et al. \\u003cspan citationid=\\\"CR13\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e), and seven each in \\u003cem\\u003eUstilaginoidea virens\\u003c/em\\u003e (Yu et al. \\u003cspan citationid=\\\"CR115\\\" class=\\\"CitationRef\\\"\\u003e2019\\u003c/span\\u003e), A. \\u003cem\\u003eoryzae\\u003c/em\\u003e (Jiang et al. \\u003cspan citationid=\\\"CR42\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e), and \\u003cem\\u003eTolypocladium guangdongense\\u003c/em\\u003e (Zhang et al. \\u003cspan citationid=\\\"CR117\\\" class=\\\"CitationRef\\\"\\u003e2020\\u003c/span\\u003e). In addition, comparative structural analyses of fungal GATA TFs have been reported in three studies: a 2006 survey of 396 GATA TFs from 50 species (Park et al., 2006), a 2025 analysis of 83 GATA TFs from 19 species (Hu et al. \\u003cspan citationid=\\\"CR35\\\" class=\\\"CitationRef\\\"\\u003e2025\\u003c/span\\u003e), and a 2026 study of 157 GATA TFs from 20 species (Virolainen et al. \\u003cspan citationid=\\\"CR105\\\" class=\\\"CitationRef\\\"\\u003e2026\\u003c/span\\u003e). However, as of 2025, at least 21,848 fungal genomes are publicly available (Zaccaron and Stergiopoulos \\u003cspan citationid=\\\"CR116\\\" class=\\\"CitationRef\\\"\\u003e2025\\u003c/span\\u003e) (e.g., NCBI (Kitts et al. \\u003cspan citationid=\\\"CR50\\\" class=\\\"CitationRef\\\"\\u003e2016\\u003c/span\\u003e), MycoCosm (Grigoriev et al. \\u003cspan citationid=\\\"CR30\\\" class=\\\"CitationRef\\\"\\u003e2014\\u003c/span\\u003e), and EnsemblFungi (Yates et al. \\u003cspan citationid=\\\"CR114\\\" class=\\\"CitationRef\\\"\\u003e2026\\u003c/span\\u003e)), highlighting a substantial gap between available genomic resources and current GATA TF analyses.\\u003c/p\\u003e \\u003cp\\u003eTo address this gap, this study systematically analyzed the structural and molecular features of 7,846 fungal GATA TFs from 796 species across eleven divisions, using genome-scale protein sequences sourced from EnsemblFungi (Yates et al. \\u003cspan citationid=\\\"CR114\\\" class=\\\"CitationRef\\\"\\u003e2026\\u003c/span\\u003e) and MycoCosm (Grigoriev et al. \\u003cspan citationid=\\\"CR30\\\" class=\\\"CitationRef\\\"\\u003e2014\\u003c/span\\u003e) (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e). Comprehensive analyses were performed to characterize domain architecture, GATA motif diversity, and motif-domain co-occurrence patterns of fungal GATA TFs. In particular, motif diversity was further compared with GATA TFs from plants and animals to place fungal GATA evolution in a broader eukaryotic context. Furthermore, phylogenetic relationships were reconstructed based on fungal GATA domain sequences, enabling the assignment of putative orthologous groups and the inference of functional diversification across lineages. Representative fungal GATA TFs, including \\u003cem\\u003eAreA, NsdD, SreA, and WC-1\\u003c/em\\u003e, were further subjected to structure prediction using AlphaFold2 to support functional interpretation. In addition, order-level analyses of \\u003cem\\u003eDikarya\\u003c/em\\u003e GATA TFs were conducted to resolve lineage-specific architectural patterns. This integrative analysis provides a framework for understanding the evolutionary and functional diversification of fungal GATA TFs.\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e\"},{\"header\":\"2. Materials and methods\",\"content\":\"\\u003cp\\u003e \\u003cb\\u003e2\\u0026thinsp;\\u0026minus;\\u0026thinsp;1. Fungal species selection and taxonomic information\\u003c/b\\u003e \\u003c/p\\u003e \\u003cp\\u003eA total of 1,505 genomes were publicly available from EnsemblFungi (\\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://fungi.ensembl.org/index.html\\u003c/span\\u003e\\u003cspan address=\\\"https://fungi.ensembl.org/index.html\\\" targettype=\\\"URL\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e; Release 62) (Yates et al. \\u003cspan citationid=\\\"CR114\\\" class=\\\"CitationRef\\\"\\u003e2026\\u003c/span\\u003e). Of these, 640 genomes were initially selected, one per species, representing a total of 7,243,130 predicted protein-coding genes (mean\\u0026thinsp;=\\u0026thinsp;11,317, SD\\u0026thinsp;=\\u0026thinsp;4,295). Subsequently, only species containing at least 5,000 predicted protein-coding genes were retained to ensure sufficient completeness and annotation quality. Based on these criteria, 40 \\u003cem\\u003eDikarya\\u003c/em\\u003e species with fewer than 5,000 predicted protein-coding genes were excluded, yielding a final set of 600 species for downstream analyses, which together contained 7,102,629 predicted protein-coding genes (mean\\u0026thinsp;=\\u0026thinsp;11,838, SD\\u0026thinsp;=\\u0026thinsp;3,901).\\u003c/p\\u003e \\u003cp\\u003eHowever, EnsemblFungi predominantly provides genomes belonging to \\u003cem\\u003eDikarya\\u003c/em\\u003e, which comprises the phyla \\u003cem\\u003eAscomycota\\u003c/em\\u003e and \\u003cem\\u003eBasidiomycota\\u003c/em\\u003e, resulting in limited representation of early-diverging fungal lineages. Accordingly, genomes representing early-diverging fungal lineages were obtained from MycoCosm (\\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://mycocosm.jgi.doe.gov/\\u003c/span\\u003e\\u003cspan address=\\\"https://mycocosm.jgi.doe.gov/\\\" targettype=\\\"URL\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e) (Grigoriev et al. \\u003cspan citationid=\\\"CR30\\\" class=\\\"CitationRef\\\"\\u003e2014\\u003c/span\\u003e). A total of 269 genomes were available in this database, from which 198 representative species were initially selected using the same filtering criteria applied to EnsemblFungi, excluding species already included in the EnsemblFungi dataset to avoid redundancy. Among these, 26 species contained fewer than 5,000 predicted protein-coding genes; however, 24 \\u003cem\\u003eMicrosporidia\\u003c/em\\u003e species were retained because their reduced gene content reflects extreme genome streamlining rather than annotation incompleteness (Peyretaillade et al. \\u003cspan citationid=\\\"CR82\\\" class=\\\"CitationRef\\\"\\u003e2011\\u003c/span\\u003e), yielding a final set of 196 species comprising 2,120,090 predicted protein-coding genes (mean\\u0026thinsp;=\\u0026thinsp;10,817, SD\\u0026thinsp;=\\u0026thinsp;4,435).\\u003c/p\\u003e \\u003cp\\u003eFinally, a total of 796 fungal species were compiled, comprising 9,222,719 predicted proteins (mean\\u0026thinsp;=\\u0026thinsp;11,586, SD\\u0026thinsp;=\\u0026thinsp;4,060; Supplementary Table\\u0026nbsp;1). Their taxonomic information, including division, class, and order, was obtained from NCBI Taxonomy (Schoch et al. \\u003cspan citationid=\\\"CR91\\\" class=\\\"CitationRef\\\"\\u003e2020\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eFurthermore, fungal divisions, classes, and orders were standardized within a phylogeny- and divergence time-informed framework, providing a consistent higher-level classification across species (Tedersoo et al. \\u003cspan citationid=\\\"CR102\\\" class=\\\"CitationRef\\\"\\u003e2018\\u003c/span\\u003e). Based on this framework, taxonomic assignments for 796 fungal species were curated across 11 phyla: i) \\u003cem\\u003eAscomycota\\u003c/em\\u003e (n\\u0026thinsp;=\\u0026thinsp;436), ii) \\u003cem\\u003eBasidiomycota\\u003c/em\\u003e (n\\u0026thinsp;=\\u0026thinsp;139), iii) \\u003cem\\u003eMucoromycota\\u003c/em\\u003e (n\\u0026thinsp;=\\u0026thinsp;72), iv) \\u003cem\\u003eMortierellomycota\\u003c/em\\u003e (n\\u0026thinsp;=\\u0026thinsp;60), v) \\u003cem\\u003eZoopagomycota\\u003c/em\\u003e (n\\u0026thinsp;=\\u0026thinsp;5), vi) \\u003cem\\u003eKickxellomycota\\u003c/em\\u003e (n\\u0026thinsp;=\\u0026thinsp;13), vii) \\u003cem\\u003eEntomophthoromycota\\u003c/em\\u003e (n\\u0026thinsp;=\\u0026thinsp;4), viii) \\u003cem\\u003eBlastocladiomycota\\u003c/em\\u003e (n\\u0026thinsp;=\\u0026thinsp;4), ix) \\u003cem\\u003eChytridiomycota\\u003c/em\\u003e (n\\u0026thinsp;=\\u0026thinsp;26), x) \\u003cem\\u003eNeocallimastigomycota\\u003c/em\\u003e (n\\u0026thinsp;=\\u0026thinsp;12), and xi) \\u003cem\\u003eRozellomycota\\u003c/em\\u003e (n\\u0026thinsp;=\\u0026thinsp;25).\\u003c/p\\u003e\\n\\u003ch3\\u003e2–2. Identification of fungal GATA TFs\\u003c/h3\\u003e\\n\\u003cp\\u003eProtein sequences (FASTA format) from 796 selected fungal species were used to identify fungal GATA TFs. Accordingly, a total of 7,846 fungal GATA TFs were identified by screening protein sequences with InterProScan (v5.74) (Jones et al. \\u003cspan citationid=\\\"CR43\\\" class=\\\"CitationRef\\\"\\u003e2014\\u003c/span\\u003e) against the Pfam database (v37.3) (Finn et al. \\u003cspan citationid=\\\"CR27\\\" class=\\\"CitationRef\\\"\\u003e2014\\u003c/span\\u003e) for the presence of the GATA domain (PF00320) (Supplementary Table\\u0026nbsp;2). For loci with multiple splicing isoforms, the longest protein was chosen as the representative GATA TF. To balance sensitivity and specificity, only domain hits with E-values\\u0026thinsp;\\u0026le;\\u0026thinsp;1e\\u003csup\\u003e\\u0026minus;\\u0026thinsp;5\\u003c/sup\\u003e were considered, thereby capturing both canonical and moderately divergent GATA domains (Zhang et al. \\u003cspan citationid=\\\"CR118\\\" class=\\\"CitationRef\\\"\\u003e2023\\u003c/span\\u003e; Zheng et al. \\u003cspan citationid=\\\"CR120\\\" class=\\\"CitationRef\\\"\\u003e2024\\u003c/span\\u003e). As a result, approximately 1% of GATA domains were excluded from the dataset. In cases where proteins contained multiple GATA domains, each domain was manually inspected to ensure that no overlapping regions were present.\\u003c/p\\u003e \\u003cp\\u003eIn addition to the GATA domain, five major additional domains were also observed in fungal GATA TFs: PF08550 (Nitrogen regulatory protein AreA, GATA-like domain), PF08447 (PAS fold), PF13426 (PAS domain), PF25026 (Asd-4-like domain), and PF07573 (Nitrogen regulatory protein AreA N terminus).\\u003c/p\\u003e\\n\\u003ch3\\u003e2–3. GATA motif pattern analysis across eukaryotic GATA TFs\\u003c/h3\\u003e\\n\\u003cp\\u003eTo investigate patterns of the GATA zinc finger motif (hereafter, GATA motif) across eukaryotes, GATA TFs from plants and animals were additionally collected and analyzed alongside the fungal dataset. The PlantGATA (\\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://plantgata.vercel.app/\\u003c/span\\u003e\\u003cspan address=\\\"https://plantgata.vercel.app/\\\" targettype=\\\"URL\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e) (Kim \\u003cspan citationid=\\\"CR47\\\" class=\\\"CitationRef\\\"\\u003e2026\\u003c/span\\u003e) provides 5,299 GATA TFs (5,416 GATA domains) from 174 plant species representing 12 taxonomic groups, and AnimalTFDB (\\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://guolab.wchscu.cn/AnimalTFDB4/#/\\u003c/span\\u003e\\u003cspan address=\\\"https://guolab.wchscu.cn/AnimalTFDB4/#/\\\" targettype=\\\"URL\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e) (Shen et al. \\u003cspan citationid=\\\"CR96\\\" class=\\\"CitationRef\\\"\\u003e2023\\u003c/span\\u003e) provides 2,835 GATA proteins (3,960 GATA domains) from 183 animal species representing 13 taxonomic groups (Supplementary Table\\u0026nbsp;3). These counts were determined after removing alternative splicing isoforms, with the longest protein sequence selected as the representative GATA TF for each gene, consistent with the approach used in this fungal study. The phylogenetic relationships were based on previously published phylogenetic frameworks for plants and animals derived from large-scale transcriptomic and phylogenomic analyses (Irisarri et al. \\u003cspan citationid=\\\"CR41\\\" class=\\\"CitationRef\\\"\\u003e2017\\u003c/span\\u003e; Puttick et al. \\u003cspan citationid=\\\"CR86\\\" class=\\\"CitationRef\\\"\\u003e2018\\u003c/span\\u003e). In the animal lineage, the GATA family includes seven canonical members (\\u003cem\\u003eGATA1-6\\u003c/em\\u003e (He et al. \\u003cspan citationid=\\\"CR32\\\" class=\\\"CitationRef\\\"\\u003e2007\\u003c/span\\u003e) and \\u003cem\\u003eTRPS1\\u003c/em\\u003e (Gai et al. \\u003cspan citationid=\\\"CR28\\\" class=\\\"CitationRef\\\"\\u003e2011\\u003c/span\\u003e)), while at least 11 additional animal GATA proteins have been reported, including \\u003cem\\u003eGATAD1/2A/2B/2AB\\u003c/em\\u003e (Liang et al. \\u003cspan citationid=\\\"CR60\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e), \\u003cem\\u003eRERE/REREA-B\\u003c/em\\u003e (Plaster et al. \\u003cspan citationid=\\\"CR84\\\" class=\\\"CitationRef\\\"\\u003e2007\\u003c/span\\u003e), \\u003cem\\u003eMTA1-3\\u003c/em\\u003e (Kumar and Wang \\u003cspan citationid=\\\"CR54\\\" class=\\\"CitationRef\\\"\\u003e2016\\u003c/span\\u003e), and \\u003cem\\u003eZGLP1\\u003c/em\\u003e (Dong et al. \\u003cspan citationid=\\\"CR21\\\" class=\\\"CitationRef\\\"\\u003e2019\\u003c/span\\u003e), which contain GATA domains but do not function as classical GATA TFs. In this study, all of these GATA proteins were included to ensure comprehensive coverage of GATA domain-containing proteins.\\u003c/p\\u003e \\u003cp\\u003eIn particular, the identification of GATA TFs was harmonized across datasets. Plant GATA TFs were identified using the same domain-based pipeline applied to fungal GATA TFs in this study, ensuring methodological consistency across kingdoms. In contrast, animal GATA proteins, although initially obtained from a curated public database, were identified using slightly different criteria; therefore, they were re-identified using the same domain-based pipeline applied in this study to ensure consistency and comparability in GATA protein detection across all datasets. Through these procedures, GATA motif patterns of eukaryotic GATA TFs were systematically analyzed.\\u003c/p\\u003e \\u003cp\\u003eTo further characterize GATA motifs, eukaryotic GATA motifs were categorized into eight types based on the spacing of conserved cysteine residues (Kim \\u003cspan citationid=\\\"CR48\\\" class=\\\"CitationRef\\\"\\u003e2024\\u003c/span\\u003e; Hu et al. \\u003cspan citationid=\\\"CR35\\\" class=\\\"CitationRef\\\"\\u003e2025\\u003c/span\\u003e). This includes three major types: Type IVa (Cys-X\\u003csub\\u003e2\\u003c/sub\\u003e-Cys-X\\u003csub\\u003e17\\u003c/sub\\u003e-Cys-X\\u003csub\\u003e2\\u003c/sub\\u003e-Cys), Type IVb (Cys-X\\u003csub\\u003e2\\u003c/sub\\u003e-Cys-X\\u003csub\\u003e18\\u003c/sub\\u003e-Cys-X\\u003csub\\u003e2\\u003c/sub\\u003e-Cys), and Type IVc (Cys-X\\u003csub\\u003e2\\u003c/sub\\u003e-Cys-X\\u003csub\\u003e20\\u003c/sub\\u003e-Cys-X\\u003csub\\u003e2\\u003c/sub\\u003e-Cys); and five minor types: Type IV19 (Cys-X\\u003csub\\u003e2\\u003c/sub\\u003e-Cys-X\\u003csub\\u003e19\\u003c/sub\\u003e-Cys-X\\u003csub\\u003e2\\u003c/sub\\u003e-Cys), Type IV4 (Cys-X\\u003csub\\u003e4\\u003c/sub\\u003e-Cys-X\\u003csub\\u003e18\\u003c/sub\\u003e-Cys-X\\u003csub\\u003e2\\u003c/sub\\u003e-Cys), Type IVe (Cys-X\\u003csub\\u003e2\\u003c/sub\\u003e-Cys-\\u003csub\\u003e\\u0026minus;16,21\\u0026minus;\\u003c/sub\\u003e-Cys-X\\u003csub\\u003e2\\u003c/sub\\u003e-Cys, where n\\u0026thinsp;\\u0026le;\\u0026thinsp;16 or n\\u0026thinsp;\\u0026ge;\\u0026thinsp;21), atypical forms (e.g., Cys-X-Cys-X\\u003csub\\u003e20\\u003c/sub\\u003e-Cys-X\\u003csub\\u003e2\\u003c/sub\\u003e-Cys), and Type IVp (partial motifs, e.g., Cys-X\\u003csub\\u003e2\\u003c/sub\\u003e-Cys-X\\u003csub\\u003e12\\u003c/sub\\u003e).\\u003c/p\\u003e\\n\\u003ch3\\u003e2–4. Domain composition and co-occurrence of fungal GATA TFs\\u003c/h3\\u003e\\n\\u003cp\\u003eTo characterize the domain composition of fungal GATA TFs, Pfam-based domain annotations were used to identify domains present in each protein. Major co-occurring domains (PF08550, PF08447, PF13426, PF25026, and PF07573) were recorded, while all remaining low-frequency domains were grouped as \\u0026ldquo;Minor domains\\u0026rdquo;. For each division, the number of GATA TFs containing each domain was counted in a non-mutually exclusive manner, such that proteins with multiple domains contributed to multiple categories.\\u003c/p\\u003e \\u003cp\\u003eTo further investigate motif-domain relationships, co-occurrence patterns between GATA motifs and associated conserved domains were analyzed. For this purpose, each fungal GATA TF was decomposed into GATA motif-domain pairs within the same protein. Subsequently, redundant GATA motif-domain pairs were removed to eliminate duplication, yielding a non-redundant set of unique GATA motif-domain combinations.\\u003c/p\\u003e\\n\\u003ch3\\u003e2–5. Collection of functionally characterized fungal GATA TFs\\u003c/h3\\u003e\\n\\u003cp\\u003eTo assemble the dataset for this study, functionally validated fungal GATA TFs were collected from previously published literature (Schwechheimer et al. \\u003cspan citationid=\\\"CR93\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e; Virolainen et al. \\u003cspan citationid=\\\"CR105\\\" class=\\\"CitationRef\\\"\\u003e2026\\u003c/span\\u003e). A total of 25 GATA TFs from four fungal species were obtained, all of which correspond exactly to the sequences used in prior studies of fungal GATA TFs (Supplementary Table\\u0026nbsp;4). Specifically, six GATA TFs (\\u003cem\\u003eSreA, AreB, LreA, AreA, LreB, and NsdD\\u003c/em\\u003e) were collected from \\u003cem\\u003eA. oryzae\\u003c/em\\u003e, four (\\u003cem\\u003eNIT2, ASD4, WC1, and WC2\\u003c/em\\u003e) from \\u003cem\\u003eN. crassa\\u003c/em\\u003e, ten (\\u003cem\\u003eSRD1, GAT4, GLN3, GAT1, GZF3, ASH1, DAL80, GAT3, GAT2, and ECM23\\u003c/em\\u003e) from \\u003cem\\u003eSaccharomyces cerevisiae\\u003c/em\\u003e, and five (\\u003cem\\u003eAMS2, GAF1, FEP1, Fil1, and SFH1\\u003c/em\\u003e) from \\u003cem\\u003eSchizosaccharomyces pombe\\u003c/em\\u003e.\\u003c/p\\u003e\\n\\u003ch3\\u003e2–6. Phylogenetic analysis of fungal GATA domains\\u003c/h3\\u003e\\n\\u003cp\\u003ePhylogenetic analysis of fungal GATA domains was performed to investigate their evolutionary relationships. A total of 9,488 fungal GATA domains were collected and subjected to multiple sequence alignment using MAFFT (v7.505) (Katoh and Standley \\u003cspan citationid=\\\"CR44\\\" class=\\\"CitationRef\\\"\\u003e2013\\u003c/span\\u003e). Subsequently, maximum-likelihood phylogenetic trees were constructed using IQ-TREE2 (v2.0.7) (Minh et al. \\u003cspan citationid=\\\"CR73\\\" class=\\\"CitationRef\\\"\\u003e2020\\u003c/span\\u003e) with the Dayhoff model, and branch support was evaluated using 1,000 ultrafast bootstrap replicates (-bb option). The resulting phylogenetic tree files were visualized and annotated in the Interactive Tree of Life (iTOL) platform (Letunic and Bork \\u003cspan citationid=\\\"CR57\\\" class=\\\"CitationRef\\\"\\u003e2024\\u003c/span\\u003e), enabling comprehensive interpretation of evolutionary relationships among the fungal GATA TFs.\\u003c/p\\u003e\\n\\u003ch3\\u003e2–7. Structural modeling of fungal GATA TFs\\u003c/h3\\u003e\\n\\u003cp\\u003eThree-dimensional structures of 11 representative fungal GATA TFs, including \\u003cem\\u003eAreA, NsdD, SreA, and WC-1\\u003c/em\\u003e, were predicted using the AlphaFold2 framework (Bertoline et al. \\u003cspan citationid=\\\"CR9\\\" class=\\\"CitationRef\\\"\\u003e2023\\u003c/span\\u003e). Protein sequences of the selected GATA TFs were used as input without truncation to preserve full-length structural context. AlphaFold2 produced a per-residue confidence score (pLDDT) ranging from 0 to 100. Some regions with low pLDDT may have been unstructured in isolation.\\u003c/p\\u003e \\u003cp\\u003eStructural similarity searches were subsequently performed using Foldseek against the PDB100 database (Van Kempen et al. \\u003cspan citationid=\\\"CR104\\\" class=\\\"CitationRef\\\"\\u003e2024\\u003c/span\\u003e; Kim et al. \\u003cspan citationid=\\\"CR49\\\" class=\\\"CitationRef\\\"\\u003e2025\\u003c/span\\u003e) to identify experimentally resolved protein structures related to the predicted fungal GATA TF models. Only matches with HHpred probability values\\u0026thinsp;\\u0026ge;\\u0026thinsp;0.95 were retained as high-confidence structural homologs for downstream interpretation. This filtering criterion was applied to minimize low-confidence or non-specific structural matches and to ensure reliable identification of conserved GATA-related structural features.\\u003c/p\\u003e\\n\\u003ch3\\u003e2–8. Physicochemical properties and protein feature predictions of fungal GATA TFs\\u003c/h3\\u003e\\n\\u003cp\\u003eThe molecular weight (MW), theoretical isoelectric point (pI), aliphatic index, and grand average of hydropathicity (GRAVY) of fungal GATA TFs were calculated using the ProtParam tool on the ExPASy server (\\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://web.expasy.org/protparam/\\u003c/span\\u003e\\u003cspan address=\\\"https://web.expasy.org/protparam/\\\" targettype=\\\"URL\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e) (Gasteiger et al. \\u003cspan citationid=\\\"CR29\\\" class=\\\"CitationRef\\\"\\u003e2005\\u003c/span\\u003e). To further characterize protein features, transmembrane helices (TMHs) were predicted using TMHMM v2.0 (\\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://services.healthtech.dtu.dk/services/TMHMM-2.0/\\u003c/span\\u003e\\u003cspan address=\\\"https://services.healthtech.dtu.dk/services/TMHMM-2.0/\\\" targettype=\\\"URL\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e) with default parameters (Krogh et al. \\u003cspan citationid=\\\"CR53\\\" class=\\\"CitationRef\\\"\\u003e2001\\u003c/span\\u003e). Subcellular localization of fungal GATA TFs was predicted using DeepLoc v2.1 (\\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://services.healthtech.dtu.dk/services/DeepLoc-2.1/\\u003c/span\\u003e\\u003cspan address=\\\"https://services.healthtech.dtu.dk/services/DeepLoc-2.1/\\\" targettype=\\\"URL\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e) (\\u0026Oslash;dum et al. \\u003cspan citationid=\\\"CR78\\\" class=\\\"CitationRef\\\"\\u003e2024\\u003c/span\\u003e).\\u003c/p\\u003e\"},{\"header\":\"3. Results\",\"content\":\"\\u003cp\\u003e \\u003cb\\u003e3\\u0026thinsp;\\u0026minus;\\u0026thinsp;1. Architectural features of fungal GATA TFs\\u003c/b\\u003e \\u003c/p\\u003e\\n\\u003ch3\\u003e3-1-1. Taxonomic distribution\\u003c/h3\\u003e\\n\\u003cp\\u003eTo characterize the lineage-specific distribution patterns of fungal GATA TFs, a genome-wide structural analysis of GATA TFs was conducted across 796 fungal species (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e). These species were classified into eleven divisions. Notably, the two divisions within \\u003cem\\u003eDikarya\\u003c/em\\u003e, \\u003cem\\u003eAscomycota and Basidiomycota\\u003c/em\\u003e, together accounted for 575 of 796 species, constituting the vast majority of the dataset (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003ea-\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003eb). In contrast, the remaining nine early-diverging fungal divisions\\u0026mdash;\\u003cem\\u003eMucoromycota, Mortierellomycota, Zoopagomycota, Kickxellomycota, Entomophthoromycota, Blastocladiomycota, Chytridiomycota, Neocallimastigomycota, and Rozellomycota\\u003c/em\\u003e\\u0026mdash;collectively comprised 221 species, representing a comparatively minor proportion (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003ea-\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003eb). From these species, a total of 9,222,719 predicted protein-coding gene sequences were obtained, from which 7,846 GATA TFs were identified (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e). These results established a comprehensive and taxonomically broad dataset of fungal GATA TFs, albeit with a strong representation bias toward \\u003cem\\u003eDikarya\\u003c/em\\u003e.\\u003c/p\\u003e \\u003cp\\u003e \\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab1\\\" border=\\\"1\\\"\\u003e \\u003ccaption language=\\\"En\\\"\\u003e \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 1\\u003c/div\\u003e \\u003cdiv class=\\\"CaptionContent\\\"\\u003e \\u003cp\\u003eDistribution and abundance of GATA TFs across major fungal lineages\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"6\\\"\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" class=\\\"colspec\\\" colname=\\\"c5\\\" colnum=\\\"5\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" class=\\\"colspec\\\" colname=\\\"c6\\\" colnum=\\\"6\\\"\\u003e\\u003c/div\\u003e \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eDivision\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eNumber of classes\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eNumber of\\u003c/p\\u003e \\u003cp\\u003eorders\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eNumber of species\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eTotal number of proteins (average\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;SD/species)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eTotal number of GATA TFs\\u003c/p\\u003e \\u003cp\\u003e(average\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;SD/species)\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cem\\u003eAscomycota\\u003c/em\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e12\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e38\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e436\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e4,924,537 (11,295\\u0026thinsp;\\u003cb\\u003e\\u0026plusmn;\\u003c/b\\u003e\\u0026thinsp;3,039)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e2,669 (6.1\\u0026thinsp;\\u003cb\\u003e\\u0026plusmn;\\u003c/b\\u003e\\u0026thinsp;1.6)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cem\\u003eBasidiomycota\\u003c/em\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e8\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e24\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e139\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e1,861,933 (13,395\\u0026thinsp;\\u003cb\\u003e\\u0026plusmn;\\u003c/b\\u003e\\u0026thinsp;5,596)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e1,015 (7.3\\u0026thinsp;\\u003cb\\u003e\\u0026plusmn;\\u003c/b\\u003e\\u0026thinsp;2.4)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cem\\u003eMucoromycota\\u003c/em\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e3\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e3\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e72\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e865,591 (12,022\\u0026thinsp;\\u003cb\\u003e\\u0026plusmn;\\u003c/b\\u003e\\u0026thinsp;2,126)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e2,220 (30.8\\u0026thinsp;\\u003cb\\u003e\\u0026plusmn;\\u003c/b\\u003e\\u0026thinsp;10.4)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cem\\u003eMortierellomycota\\u003c/em\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e60\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e716,421 (11,940\\u0026thinsp;\\u003cb\\u003e\\u0026plusmn;\\u003c/b\\u003e\\u0026thinsp;1,413)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e1,057 (17.6\\u0026thinsp;\\u003cb\\u003e\\u0026plusmn;\\u003c/b\\u003e\\u0026thinsp;3.0)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cem\\u003eZoopagomycota\\u003c/em\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e5\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e35,767 (7,153\\u0026thinsp;\\u003cb\\u003e\\u0026plusmn;\\u003c/b\\u003e\\u0026thinsp;1,301)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e38 (7.6\\u0026thinsp;\\u003cb\\u003e\\u0026plusmn;\\u003c/b\\u003e\\u0026thinsp;2.0)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cem\\u003eKickxellomycota\\u003c/em\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e3\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e4\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e13\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e106,794 (8,215\\u0026thinsp;\\u003cb\\u003e\\u0026plusmn;\\u003c/b\\u003e\\u0026thinsp;1,431)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e132 (10.2\\u0026thinsp;\\u003cb\\u003e\\u0026plusmn;\\u003c/b\\u003e\\u0026thinsp;2.0)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cem\\u003eEntomophthoromycota\\u003c/em\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e4\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e48,619 (12,155\\u0026thinsp;\\u003cb\\u003e\\u0026plusmn;\\u003c/b\\u003e\\u0026thinsp;2,898)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e49 (12.3\\u0026thinsp;\\u003cb\\u003e\\u0026plusmn;\\u003c/b\\u003e\\u0026thinsp;4.0)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cem\\u003eBlastocladiomycota\\u003c/em\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e2\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e2\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e4\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e52,541 (13,135\\u0026thinsp;\\u003cb\\u003e\\u0026plusmn;\\u003c/b\\u003e\\u0026thinsp;4,429)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e50 (12.5\\u0026thinsp;\\u003cb\\u003e\\u0026plusmn;\\u003c/b\\u003e\\u0026thinsp;7.7)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cem\\u003eChytridiomycota\\u003c/em\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e2\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e8\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e26\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e303,673 (11,680\\u0026thinsp;\\u003cb\\u003e\\u0026plusmn;\\u003c/b\\u003e\\u0026thinsp;3,356)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e380 (14.6\\u0026thinsp;\\u003cb\\u003e\\u0026plusmn;\\u003c/b\\u003e\\u0026thinsp;9.4)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cem\\u003eNeocallimastigomycota\\u003c/em\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e12\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e236,171 (19,681\\u0026thinsp;\\u003cb\\u003e\\u0026plusmn;\\u003c/b\\u003e\\u0026thinsp;6,242)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e179 (14.9\\u0026thinsp;\\u003cb\\u003e\\u0026plusmn;\\u003c/b\\u003e\\u0026thinsp;5.8)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cem\\u003eRozellomycota\\u003c/em\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e2\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e25\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e70,672 (2,827\\u0026thinsp;\\u003cb\\u003e\\u0026plusmn;\\u003c/b\\u003e\\u0026thinsp;1,070)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e57 (2.3\\u0026thinsp;\\u003cb\\u003e\\u0026plusmn;\\u003c/b\\u003e\\u0026thinsp;1.1)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eTotal\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e36\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e84\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e796\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e9,222,719 (11,586\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;4,060)\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e7,846 (9.9\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;8.5)\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003ctfoot\\u003e \\u003ctr\\u003e\\u003ctd colspan=\\\"6\\\"\\u003e\\u003cb\\u003e* Taxa designated as incertae sedis were not included in the counts of classes and orders.\\u003c/b\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e \\u003c/tfoot\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003cp\\u003eTo assess lineage-specific variation in the abundance and density of GATA TFs, pronounced variation was observed across the eleven fungal divisions (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003ec). Within the \\u003cem\\u003eDikarya\\u003c/em\\u003e, \\u003cem\\u003eAscomycota\\u003c/em\\u003e (436 species) contained 2,669 GATA TFs, ranging from 2 to 17 per species, with an average of 6.1\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;1.6. By comparison, \\u003cem\\u003eBasidiomycota\\u003c/em\\u003e comprised 1,015 GATA TFs across 139 species, with a slightly higher average of 7.3\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;2.4 per species (range: 1\\u0026ndash;15). In contrast, early-diverging fungal divisions showed a wide range of GATA TF densities. Notably, \\u003cem\\u003eMucoromycota\\u003c/em\\u003e exhibited the highest average number of GATA TFs per species (30.8\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;10.4), with counts ranging from 6 to 64 across 72 species. In addition, the other seven early-diverging divisions exhibited moderate GATA TF abundance, with average counts ranging from 7.6\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;2.0 (\\u003cem\\u003eZoopagomycota\\u003c/em\\u003e) to 17.6\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;3.0 (\\u003cem\\u003eMortierellomycota\\u003c/em\\u003e) per species. By contrast, \\u003cem\\u003eRozellomycota\\u003c/em\\u003e harbored only 2.3\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;1.1 GATA TFs per species, representing the lowest abundance among the surveyed divisions. These observations highlighted the striking disparity in GATA TF distribution, highlighting both highly enriched and depauperate lineages among fungi.\\u003c/p\\u003e\\n\\u003ch3\\u003e3-1-2. Domain architecture of fungal GATA TFs\\u003c/h3\\u003e\\n\\u003cp\\u003eProtein domains are discrete structural and functional modules that play a central role in protein function; thus, domain architecture was analyzed to elucidate the structural basis of functional diversity in fungal GATA TFs. In total, 12,768 domains from 7,846 fungal GATA TFs were identified and classified into 89 distinct types (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e). Five major domains were prominently observed to co-occur with the GATA domain (PF00320; n\\u0026thinsp;=\\u0026thinsp;9,488) in fungal GATA TFs (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e): PF08550 (nitrogen regulatory protein AreA, GATA-like domain; 1,049 domains), PF08447 (PAS fold; 942 domains), PF13426 (PAS domain; 474 domains), PF25026 (Asd-4-like domain; 375 domains), and PF07573 (nitrogen regulatory protein AreA N terminus; 96 domains). The remaining 83 minor domain types collectively accounted for 344 domains, representing a small fraction of the total. Overall, fungal GATA TFs were characterized by the predominant co-occurrence of a limited set of major domains with the GATA domain, while most other domain types were rare.\\u003c/p\\u003e \\u003cp\\u003e \\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab2\\\" border=\\\"1\\\"\\u003e \\u003ccaption language=\\\"En\\\"\\u003e \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 2\\u003c/div\\u003e \\u003cdiv class=\\\"CaptionContent\\\"\\u003e \\u003cp\\u003eDomain and GATA motif composition of GATA TFs across major fungal lineages\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"16\\\"\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c5\\\" colnum=\\\"5\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c6\\\" colnum=\\\"6\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c7\\\" colnum=\\\"7\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c8\\\" colnum=\\\"8\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c9\\\" colnum=\\\"9\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c10\\\" colnum=\\\"10\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c11\\\" colnum=\\\"11\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c12\\\" colnum=\\\"12\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c13\\\" colnum=\\\"13\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c14\\\" colnum=\\\"14\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c15\\\" colnum=\\\"15\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c16\\\" colnum=\\\"16\\\"\\u003e\\u003c/div\\u003e \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003eDivision\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003eNumber of species\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003eNumber of GATA TFs\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colspan=\\\"7\\\" nameend=\\\"c10\\\" namest=\\\"c4\\\"\\u003e \\u003cp\\u003eNumber of domains\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colspan=\\\"6\\\" nameend=\\\"c16\\\" namest=\\\"c11\\\"\\u003e \\u003cp\\u003eNumber of GATA motifs\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003ePF00320\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003ePF08550\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003ePF08447\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003ePF13426\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003ePF25026\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003ePF07573\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c10\\\"\\u003e \\u003cp\\u003eMinor domains\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c11\\\"\\u003e \\u003cp\\u003eIVa\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c12\\\"\\u003e \\u003cp\\u003eIVb\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c13\\\"\\u003e \\u003cp\\u003eIVe\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c14\\\"\\u003e \\u003cp\\u003eIVp\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c15\\\"\\u003e \\u003cp\\u003eIV19\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c16\\\"\\u003e \\u003cp\\u003eIVc\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cem\\u003eAscomycota\\u003c/em\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e436\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e2,669\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e3,065\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e375\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e671\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e360\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e374\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e96\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c10\\\"\\u003e \\u003cp\\u003e54\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c11\\\"\\u003e \\u003cp\\u003e1,704\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c12\\\"\\u003e \\u003cp\\u003e1,311\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c13\\\"\\u003e \\u003cp\\u003e5\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c14\\\"\\u003e \\u003cp\\u003e25\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c15\\\"\\u003e \\u003cp\\u003e9\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c16\\\"\\u003e \\u003cp\\u003e11\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cem\\u003eBasidiomycota\\u003c/em\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e139\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1,015\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1,238\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e194\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e30\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e20\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e1\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c10\\\"\\u003e \\u003cp\\u003e85\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c11\\\"\\u003e \\u003cp\\u003e655\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c12\\\"\\u003e \\u003cp\\u003e548\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c13\\\"\\u003e \\u003cp\\u003e2\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c14\\\"\\u003e \\u003cp\\u003e19\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c15\\\"\\u003e \\u003cp\\u003e13\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c16\\\"\\u003e \\u003cp\\u003e1\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cem\\u003eMucoromycota\\u003c/em\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e72\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e2,220\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e2,790\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e366\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e214\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e82\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c10\\\"\\u003e \\u003cp\\u003e105\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c11\\\"\\u003e \\u003cp\\u003e1,612\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c12\\\"\\u003e \\u003cp\\u003e1,139\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c13\\\"\\u003e \\u003cp\\u003e31\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c14\\\"\\u003e \\u003cp\\u003e8\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c15\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c16\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cem\\u003eMortierellomycota\\u003c/em\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e60\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1,057\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1,358\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e60\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c10\\\"\\u003e \\u003cp\\u003e13\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c11\\\"\\u003e \\u003cp\\u003e658\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c12\\\"\\u003e \\u003cp\\u003e696\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c13\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c14\\\"\\u003e \\u003cp\\u003e1\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c15\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c16\\\"\\u003e \\u003cp\\u003e3\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cem\\u003eZoopagomycota\\u003c/em\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e5\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e38\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e45\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e2\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e1\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c10\\\"\\u003e \\u003cp\\u003e6\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c11\\\"\\u003e \\u003cp\\u003e20\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c12\\\"\\u003e \\u003cp\\u003e19\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c13\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c14\\\"\\u003e \\u003cp\\u003e1\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c15\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c16\\\"\\u003e \\u003cp\\u003e5\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cem\\u003eKickxellomycota\\u003c/em\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e13\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e132\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e164\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e5\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e1\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c10\\\"\\u003e \\u003cp\\u003e3\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c11\\\"\\u003e \\u003cp\\u003e87\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c12\\\"\\u003e \\u003cp\\u003e77\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c13\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c14\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c15\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c16\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cem\\u003eEntomophthoromycota\\u003c/em\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e4\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e49\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e61\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e12\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e7\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c10\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c11\\\"\\u003e \\u003cp\\u003e35\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c12\\\"\\u003e \\u003cp\\u003e25\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c13\\\"\\u003e \\u003cp\\u003e1\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c14\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c15\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c16\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cem\\u003eBlastocladiomycota\\u003c/em\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e4\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e50\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e59\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e6\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e3\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c10\\\"\\u003e \\u003cp\\u003e3\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c11\\\"\\u003e \\u003cp\\u003e27\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c12\\\"\\u003e \\u003cp\\u003e27\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c13\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c14\\\"\\u003e \\u003cp\\u003e1\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c15\\\"\\u003e \\u003cp\\u003e1\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c16\\\"\\u003e \\u003cp\\u003e3\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cem\\u003eChytridiomycota\\u003c/em\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e26\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e380\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e424\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e21\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e10\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e5\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c10\\\"\\u003e \\u003cp\\u003e21\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c11\\\"\\u003e \\u003cp\\u003e202\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c12\\\"\\u003e \\u003cp\\u003e200\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c13\\\"\\u003e \\u003cp\\u003e10\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c14\\\"\\u003e \\u003cp\\u003e5\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c15\\\"\\u003e \\u003cp\\u003e4\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c16\\\"\\u003e \\u003cp\\u003e3\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cem\\u003eNeocallimastigomycota\\u003c/em\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e12\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e179\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e222\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e20\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c10\\\"\\u003e \\u003cp\\u003e54\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c11\\\"\\u003e \\u003cp\\u003e137\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c12\\\"\\u003e \\u003cp\\u003e85\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c13\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c14\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c15\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c16\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cem\\u003eRozellomycota\\u003c/em\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e25\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e57\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e62\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c10\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c11\\\"\\u003e \\u003cp\\u003e22\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c12\\\"\\u003e \\u003cp\\u003e26\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c13\\\"\\u003e \\u003cp\\u003e13\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c14\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c15\\\"\\u003e \\u003cp\\u003e1\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c16\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eTotal\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e796\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e7,846\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e9,488\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e1,049\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e942\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e474\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e375\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e96\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c10\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e344\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c11\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e5,159\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c12\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e4,153\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c13\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e62\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c14\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e60\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c15\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e28\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c16\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e26\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003ctfoot\\u003e \\u003ctr\\u003e\\u003ctd colspan=\\\"16\\\"\\u003e\\u003cb\\u003e* List of major domains: PF00320 (GATA zinc finger), PF08550 (Nitrogen regulatory protein areA, GATA-like domain), PF08447 (PAS fold), PF13426 (PAS domain), PF25026 (Asd-4-like domain), and PF07573 (Nitrogen regulatory protein AreA N terminus).\\u003c/b\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e \\u003ctr\\u003e\\u003ctd colspan=\\\"16\\\"\\u003e\\u003cb\\u003e* List of GATA motif types: IVa (CX\\u003c/b\\u003e\\u003csub\\u003e\\u003cb\\u003e2\\u003c/b\\u003e\\u003c/sub\\u003e\\u003cb\\u003eCX\\u003c/b\\u003e\\u003csub\\u003e\\u003cb\\u003e17\\u003c/b\\u003e\\u003c/sub\\u003e\\u003cb\\u003eCX\\u003c/b\\u003e\\u003csub\\u003e\\u003cb\\u003e2\\u003c/b\\u003e\\u003c/sub\\u003e\\u003cb\\u003eC), IVb (CX\\u003c/b\\u003e\\u003csub\\u003e\\u003cb\\u003e2\\u003c/b\\u003e\\u003c/sub\\u003e\\u003cb\\u003eCX\\u003c/b\\u003e\\u003csub\\u003e\\u003cb\\u003e18\\u003c/b\\u003e\\u003c/sub\\u003e\\u003cb\\u003eCX\\u003c/b\\u003e\\u003csub\\u003e\\u003cb\\u003e2\\u003c/b\\u003e\\u003c/sub\\u003e\\u003cb\\u003eC), IVp (partial motifs, e.g., CX\\u003c/b\\u003e\\u003csub\\u003e\\u003cb\\u003e2\\u003c/b\\u003e\\u003c/sub\\u003e\\u003cb\\u003eCX\\u003c/b\\u003e\\u003csub\\u003e\\u003cb\\u003e12\\u003c/b\\u003e\\u003c/sub\\u003e\\u003cb\\u003e), IV19 (CX\\u003c/b\\u003e\\u003csub\\u003e\\u003cb\\u003e2\\u003c/b\\u003e\\u003c/sub\\u003e\\u003cb\\u003eCX\\u003c/b\\u003e\\u003csub\\u003e\\u003cb\\u003e19\\u003c/b\\u003e\\u003c/sub\\u003e\\u003cb\\u003eCX\\u003c/b\\u003e\\u003csub\\u003e\\u003cb\\u003e2\\u003c/b\\u003e\\u003c/sub\\u003e\\u003cb\\u003eC), IVe (CX\\u003c/b\\u003e\\u003csub\\u003e\\u003cb\\u003e2\\u003c/b\\u003e\\u003c/sub\\u003e\\u003cb\\u003eC\\u003c/b\\u003e\\u003csub\\u003e\\u003cb\\u003e\\u0026minus;\\u0026thinsp;16,21\\u0026minus;\\u003c/b\\u003e\\u003c/sub\\u003e\\u003cb\\u003eCX\\u003c/b\\u003e\\u003csub\\u003e\\u003cb\\u003e2\\u003c/b\\u003e\\u003c/sub\\u003e\\u003cb\\u003eC), and IVc (CX\\u003c/b\\u003e\\u003csub\\u003e\\u003cb\\u003e2\\u003c/b\\u003e\\u003c/sub\\u003e\\u003cb\\u003eCX\\u003c/b\\u003e\\u003csub\\u003e\\u003cb\\u003e20\\u003c/b\\u003e\\u003c/sub\\u003e\\u003cb\\u003eCX\\u003c/b\\u003e\\u003csub\\u003e\\u003cb\\u003e2\\u003c/b\\u003e\\u003c/sub\\u003e\\u003cb\\u003eC).\\u003c/b\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e \\u003c/tfoot\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e \\u003cp\\u003eTo assess lineage-specific differences in domain composition of fungal GATA TFs, the relative composition of protein domains revealed clear division-specific patterns (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e and Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003ed). Within \\u003cem\\u003eDikarya\\u003c/em\\u003e, PF08550 was the major domain in both \\u003cem\\u003eAscomycota and Basidiomycota\\u003c/em\\u003e. PF08447 and PF13426 were present in both, but more frequent in \\u003cem\\u003eAscomycota\\u003c/em\\u003e. PF25026 and PF07573 were predominantly observed in \\u003cem\\u003eAscomycota\\u003c/em\\u003e, with PF07573 exclusive to this division and PF25026 rarely occurring in \\u003cem\\u003eBasidiomycota\\u003c/em\\u003e. Early-diverging divisions exhibited distinct patterns: \\u003cem\\u003eMortierellomycota and Neocallimastigomycota\\u003c/em\\u003e mainly contained PF08550 alone, whereas \\u003cem\\u003eMucoromycota\\u003c/em\\u003e included PF08550, PF08447, and PF13426. \\u003cem\\u003eRozellomycota\\u003c/em\\u003e displayed only the core GATA domain. These patterns reflected lineage-specific domain compositions, suggesting evolutionary divergence in functional diversification. Detailed domain occurrence patterns across fungal lineages, which provide quantitative support for lineage-specific domain distribution, are provided in Supplementary Table S5.\\u003c/p\\u003e \\u003cp\\u003eTo complement the domain-level analysis, protein domain architectures were examined (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003e). Most GATA TFs consisted solely of the core GATA domain (PF00320 only; 5,277 GATA TFs). Among proteins with additional domains, PF00320\\u0026thinsp;+\\u0026thinsp;PF08550 (921 GATA TFs) was most prevalent, followed by PF00320\\u0026thinsp;+\\u0026thinsp;PF08447 (495 GATA TFs), PF00320\\u0026thinsp;+\\u0026thinsp;PF08447\\u0026thinsp;+\\u0026thinsp;PF13426 (441 GATA TFs), and PF00320\\u0026thinsp;+\\u0026thinsp;PF25026 (385 GATA TFs). Lineage-specific differences were evident: in \\u003cem\\u003eDikarya\\u003c/em\\u003e, simpler architectures dominated, with PAS-related combinations more frequent in \\u003cem\\u003eAscomycota\\u003c/em\\u003e. Early-diverging lineages generally exhibited simpler architectures, whereas \\u003cem\\u003eMucoromycota\\u003c/em\\u003e showed relatively more complex PAS-related combinations. \\u003cem\\u003eRozellomycota\\u003c/em\\u003e displayed PF00320-only architectures exclusively. Collectively, fungal GATA TFs were predominantly composed of the core GATA domain, with the presence and complexity of additional domain combinations exhibiting clear lineage-specific patterns.\\u003c/p\\u003e \\u003cp\\u003e \\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab3\\\" border=\\\"1\\\"\\u003e \\u003ccaption language=\\\"En\\\"\\u003e \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 3\\u003c/div\\u003e \\u003cdiv class=\\\"CaptionContent\\\"\\u003e \\u003cp\\u003eDomain architectures of fungal GATA TFs across major lineages\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"13\\\"\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c5\\\" colnum=\\\"5\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c6\\\" colnum=\\\"6\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c7\\\" colnum=\\\"7\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c8\\\" colnum=\\\"8\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c9\\\" colnum=\\\"9\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c10\\\" colnum=\\\"10\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c11\\\" colnum=\\\"11\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c12\\\" colnum=\\\"12\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c13\\\" colnum=\\\"13\\\"\\u003e\\u003c/div\\u003e \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003eDivision\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003eNumber of species\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003eNumber of GATA TFs\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colspan=\\\"10\\\" nameend=\\\"c13\\\" namest=\\\"c4\\\"\\u003e \\u003cp\\u003eNumber of domain architectures\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003ePF00320 only\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003ePF00320\\u0026thinsp;+\\u0026thinsp;PF08550\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003ePF00320\\u0026thinsp;+\\u0026thinsp;PF08447\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003ePF00320\\u0026thinsp;+\\u0026thinsp;PF08447\\u0026thinsp;+\\u0026thinsp;PF13426\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003ePF00320\\u0026thinsp;+\\u0026thinsp;PF25026\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003ePF00320 +\\u003c/p\\u003e \\u003cp\\u003eMinor domains\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c10\\\"\\u003e \\u003cp\\u003ePF00320\\u0026thinsp;+\\u0026thinsp;PF07573\\u0026thinsp;+\\u0026thinsp;PF08550\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c11\\\"\\u003e \\u003cp\\u003ePF00320\\u0026thinsp;+\\u0026thinsp;PF08550\\u0026thinsp;+\\u0026thinsp;Minor domains\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c12\\\"\\u003e \\u003cp\\u003ePF00320\\u0026thinsp;+\\u0026thinsp;PF13426\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c13\\\"\\u003e \\u003cp\\u003eOther 4 minor architectures\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cem\\u003eAscomycota\\u003c/em\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e436\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e2,669\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1,188\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e279\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e336\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e331\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e373\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e35\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c10\\\"\\u003e \\u003cp\\u003e95\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c11\\\"\\u003e \\u003cp\\u003e1\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c12\\\"\\u003e \\u003cp\\u003e25\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c13\\\"\\u003e \\u003cp\\u003e6\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cem\\u003eBasidiomycota\\u003c/em\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e139\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1,015\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e763\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e171\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e10\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e20\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e1\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e27\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c10\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c11\\\"\\u003e \\u003cp\\u003e23\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c12\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c13\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cem\\u003eMucoromycota\\u003c/em\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e72\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e2,220\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1,593\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e359\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e130\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e82\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e47\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c10\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c11\\\"\\u003e \\u003cp\\u003e7\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c12\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c13\\\"\\u003e \\u003cp\\u003e2\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cem\\u003eMortierellomycota\\u003c/em\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e60\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1,057\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e988\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e58\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e9\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c10\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c11\\\"\\u003e \\u003cp\\u003e2\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c12\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c13\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cem\\u003eZoopagomycota\\u003c/em\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e5\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e38\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e30\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e2\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e1\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e5\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c10\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c11\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c12\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c13\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cem\\u003eKickxellomycota\\u003c/em\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e13\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e132\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e124\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e5\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e1\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e2\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c10\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c11\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c12\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c13\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cem\\u003eEntomophthoromycota\\u003c/em\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e4\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e49\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e36\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e6\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e6\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c10\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c11\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c12\\\"\\u003e \\u003cp\\u003e1\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c13\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cem\\u003eBlastocladiomycota\\u003c/em\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e4\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e50\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e38\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e6\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e3\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e3\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c10\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c11\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c12\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c13\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cem\\u003eChytridiomycota\\u003c/em\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e26\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e380\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e329\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e21\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e8\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e2\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e17\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c10\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c11\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c12\\\"\\u003e \\u003cp\\u003e3\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c13\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cem\\u003eNeocallimastigomycota\\u003c/em\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e12\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e179\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e131\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e20\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e28\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c10\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c11\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c12\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c13\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cem\\u003eRozellomycota\\u003c/em\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e25\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e57\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e57\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c10\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c11\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c12\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c13\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eTotal\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e796\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e7,846\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e5,277\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e921\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e495\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e441\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e374\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e173\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c10\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e95\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c11\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e33\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c12\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e29\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c13\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e8\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003ctfoot\\u003e \\u003ctr\\u003e\\u003ctd colspan=\\\"13\\\"\\u003e\\u003cb\\u003e* Repeated domains within a single GATA TF were counted once per domain type when defining domain architectures.\\u003c/b\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e \\u003ctr\\u003e\\u003ctd colspan=\\\"13\\\"\\u003e\\u003cb\\u003e* List of major domains: PF00320 (GATA zinc finger), PF08550 (Nitrogen regulatory protein areA, GATA-like domain), PF08447 (PAS fold), PF13426 (PAS domain), PF25026 (Asd-4-like domain), and PF07573 (Nitrogen regulatory protein AreA N terminus).\\u003c/b\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e \\u003c/tfoot\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e\\n\\u003ch3\\u003e3-1-3. GATA domain multiplicity of fungal GATA TFs\\u003c/h3\\u003e\\n\\u003cp\\u003eMultiplicity of GATA domains (PF00320) within a single GATA TF may affect regulatory properties, including DNA-binding versatility and transcriptional specificity. To investigate the potential role of GATA domain multiplicity in functional diversification, it was analyzed across fungal GATA TFs. In total, 9,488 GATA domains were identified among 7,846 fungal GATA TFs (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003ee), indicating that 1,642 GATA TFs possessed two GATA domains, whereas the remaining GATA TFs contained a single domain. Overall, the widespread occurrence of dual GATA-domain architectures suggested that GATA domain multiplicity represented an important structural feature contributing to functional diversification in fungal GATA TFs.\\u003c/p\\u003e \\u003cp\\u003eMultiplicity of GATA domains in fungal GATA TFs occurred across all fungal divisions, although the prevalence varied slightly among lineages (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003ee). In \\u003cem\\u003eDikarya\\u003c/em\\u003e, GATA TFs in \\u003cem\\u003eAscomycota and Basidiomycota\\u003c/em\\u003e exhibited GATA domain multiplicity in 14.8% and 22.0% of cases, respectively. In early-diverging fungal divisions, i) \\u003cem\\u003eMucoromycota, Mortierellomycota, Kickxellomycota, Entomophthoromycota, and Neocallimastigomycota\\u003c/em\\u003e exhibited higher levels of GATA domain multiplicity, ranging from 24.0% to 28.5%, compared with \\u003cem\\u003eDikarya\\u003c/em\\u003e; ii) \\u003cem\\u003eZoopagomycota and Blastocladiomycota\\u003c/em\\u003e displayed levels similar to those of \\u003cem\\u003eDikarya\\u003c/em\\u003e, ranging from 18.0% to 18.4%; and iii) \\u003cem\\u003eChytridiomycota and Rozellomycota\\u003c/em\\u003e showed lower levels, ranging from 8.8% to 11.6%. These results suggested that GATA domain multiplicity was widely distributed across fungal lineages but exhibited lineage-specific variation, potentially reflecting diversification of regulatory functions among fungal divisions.\\u003c/p\\u003e\\n\\u003ch3\\u003e3-1-4. Types of fungal GATA motifs\\u003c/h3\\u003e\\n\\u003cp\\u003eThe GATA zinc finger motif (hereafter, GATA motif) is a cysteine-rich DNA-binding domain in fungal GATA TFs that specifically recognizes WGATAR (W\\u0026thinsp;=\\u0026thinsp;A/T, R\\u0026thinsp;=\\u0026thinsp;A/G) sequences. Therefore, this study investigated structural variation in GATA motif architecture with respect to motif length and cysteine spacing. Accordingly, 9,488 fungal GATA domains were classified into six types (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e and Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003ef): Type IVa (CX\\u003csub\\u003e2\\u003c/sub\\u003eCX\\u003csub\\u003e17\\u003c/sub\\u003eCX\\u003csub\\u003e2\\u003c/sub\\u003eC) was the most prevalent with 5,159 GATA domains, followed by Type IVb (CX\\u003csub\\u003e2\\u003c/sub\\u003eCX\\u003csub\\u003e18\\u003c/sub\\u003eCX\\u003csub\\u003e2\\u003c/sub\\u003eC) with 4,153 GATA domains, whereas the remaining types were rare, including Type IVe (CX\\u003csub\\u003e2\\u003c/sub\\u003eCX\\u003csub\\u003e\\u0026minus;\\u0026thinsp;16, 21\\u0026minus;\\u003c/sub\\u003eCX\\u003csub\\u003e2\\u003c/sub\\u003eC, where n\\u0026thinsp;\\u0026le;\\u0026thinsp;16 or n\\u0026thinsp;\\u0026ge;\\u0026thinsp;21; 62 GATA domains), Type IVp (partial motifs; 60 GATA domains), Type IV19 (CX\\u003csub\\u003e2\\u003c/sub\\u003eCX\\u003csub\\u003e19\\u003c/sub\\u003eCX\\u003csub\\u003e2\\u003c/sub\\u003eC; 28 GATA domains), and Type IVc (CX\\u003csub\\u003e2\\u003c/sub\\u003eCX\\u003csub\\u003e20\\u003c/sub\\u003eCX\\u003csub\\u003e2\\u003c/sub\\u003eC; 26 GATA domains). These results indicated that most fungal GATA TFs possess the canonical CX\\u003csub\\u003e2\\u003c/sub\\u003eCX\\u003csub\\u003e17\\u0026minus;18\\u003c/sub\\u003eCX\\u003csub\\u003e2\\u003c/sub\\u003eC zinc finger, with rare variants suggesting functional or evolutionary diversification.\\u003c/p\\u003e \\u003cp\\u003eTo investigate lineage-specific variation in GATA motif composition, the distribution of GATA motif types was analyzed across fungal divisions (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e and Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003ef). This analysis revealed that Type IVa and Type IVb (CX\\u003csub\\u003e2\\u003c/sub\\u003eCX\\u003csub\\u003e17\\u0026minus;18\\u003c/sub\\u003eCX\\u003csub\\u003e2\\u003c/sub\\u003eC) were the predominant motifs across all divisions. Type IVa was generally slightly more abundant, whereas Type IVb was somewhat more prevalent in \\u003cem\\u003eMortierellomycota and Rozellomycota\\u003c/em\\u003e. Minor motifs, in contrast, showed division-specific combinations. \\u003cem\\u003eDikarya\\u003c/em\\u003e, including \\u003cem\\u003eAscomycota and Basidiomycota\\u003c/em\\u003e, possessed all four minor types (IVe, IVp, IV19, and IVc), whereas \\u003cem\\u003eMucoromycota\\u003c/em\\u003e displayed only two motifs (IVe and IVp). Some divisions, such as \\u003cem\\u003eKickxellomycota and Neocallimastigomycota\\u003c/em\\u003e, lacked minor motifs entirely. Notably, \\u003cem\\u003eRozellomycota\\u003c/em\\u003e exhibited a high proportion of Type IVe, which may have reflected a lineage-specific retention of this motif. Overall, fungal GATA motifs showed conserved Type IVa and Type IVb predominance alongside division-specific minor motif variation, reflecting lineage-dependent diversification.\\u003c/p\\u003e\\n\\u003ch3\\u003e3-1-5. Evolutionary patterns of eukaryotic GATA motifs\\u003c/h3\\u003e\\n\\u003cp\\u003eAnalyzing only fungal GATA motifs limited evolutionary insight, highlighting the need to examine GATA motifs across eukaryotes. Accordingly, GATA motifs were analyzed not only for the 9,488 fungal GATA domains but also for 5,416 GATA domains from plants and 3,960 GATA domains from animals (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003e). A total of 18,864 eukaryotic GATA domains were classified into eight types: three major motifs: Type IVa (CX\\u003csub\\u003e2\\u003c/sub\\u003eCX\\u003csub\\u003e17\\u003c/sub\\u003eCX\\u003csub\\u003e2\\u003c/sub\\u003eC; 8,791 GATA domains), Type IVb (CX\\u003csub\\u003e2\\u003c/sub\\u003eCX\\u003csub\\u003e18\\u003c/sub\\u003eCX\\u003csub\\u003e2\\u003c/sub\\u003eC; 8,727 GATA domains), and Type IVc (CX\\u003csub\\u003e2\\u003c/sub\\u003eCX\\u003csub\\u003e20\\u003c/sub\\u003eCX\\u003csub\\u003e2\\u003c/sub\\u003eC; 855 GATA domains); and five minor motifs: Type IVp (partial motifs, e.g., CX\\u003csub\\u003e2\\u003c/sub\\u003eCX\\u003csub\\u003e12\\u003c/sub\\u003e; 301 GATA domains), Type IVe (CX\\u003csub\\u003e2\\u003c/sub\\u003eC\\u003csub\\u003e\\u0026minus;\\u0026thinsp;16,21\\u0026minus;\\u003c/sub\\u003eCX\\u003csub\\u003e2\\u003c/sub\\u003eC, where n\\u0026thinsp;\\u0026le;\\u0026thinsp;16 or n\\u0026thinsp;\\u0026ge;\\u0026thinsp;21; 107 GATA domains), Type IV19 (CX\\u003csub\\u003e2\\u003c/sub\\u003eCX\\u003csub\\u003e19\\u003c/sub\\u003eCX\\u003csub\\u003e2\\u003c/sub\\u003eC; 75 GATA domains), an atypical form (e.g., CXCX\\u003csub\\u003e20\\u003c/sub\\u003eCX\\u003csub\\u003e2\\u003c/sub\\u003eC; 5 GATA domains), and Type IV4 (CX\\u003csub\\u003e4\\u003c/sub\\u003eCX\\u003csub\\u003e18\\u003c/sub\\u003eCX\\u003csub\\u003e2\\u003c/sub\\u003eC; 3 GATA domains) (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003e). These results suggested that Type IVa, Type IVb, and Type IVc (CX\\u003csub\\u003e2\\u003c/sub\\u003eCX\\u003csub\\u003e17\\u0026minus;20\\u003c/sub\\u003eCX\\u003csub\\u003e2\\u003c/sub\\u003eC) represented the evolutionarily conserved core GATA motifs across eukaryotes, whereas the remaining motif types occurred only as rare structural variants.\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003cp\\u003eThe distribution of GATA motif types exhibited kingdom-specific patterns, reflecting distinct evolutionary trajectories and functional diversification in fungi, plants, and animals (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003e). In plants, Type IVb and Type IVc (CX\\u003csub\\u003e2\\u003c/sub\\u003eCX\\u003csub\\u003e18\\u0026minus;20\\u003c/sub\\u003eCX\\u003csub\\u003e2\\u003c/sub\\u003eC) constituted the predominant motifs, whereas Type IVa occurred at very low frequencies and was mainly limited to monocots and eudicots. Among the minor types, only Type IVp and Type IVe were present, with monocots and eudicots showing a relatively higher proportion of Type IVp. In contrast, late-diverging animals and fungi predominantly possessed Type IVa and Type IVb (CX\\u003csub\\u003e2\\u003c/sub\\u003eCX\\u003csub\\u003e17\\u0026minus;18\\u003c/sub\\u003eCX\\u003csub\\u003e2\\u003c/sub\\u003eC), with Type IVc being rare. Notably, animals exhibited a high prevalence of Type IVa, exceeding 90%, whereas fungi displayed a more balanced distribution of Type IVa and Type IVb, representing lineage-specific characteristics. Among the minor types, Type IVp, Type IVe, and Type IV19 were observed in certain groups, and animals uniquely harbored the atypical form and Type IV4 motifs. Taken together, eukaryotic GATA motifs exhibited kingdom- and lineage-specific patterns, with plants dominated by CX\\u003csub\\u003e2\\u003c/sub\\u003eCX\\u003csub\\u003e18\\u0026minus;20\\u003c/sub\\u003eCX\\u003csub\\u003e2\\u003c/sub\\u003eC motifs and animals and fungi by CX\\u003csub\\u003e2\\u003c/sub\\u003eCX\\u003csub\\u003e17\\u0026minus;18\\u003c/sub\\u003eCX\\u003csub\\u003e2\\u003c/sub\\u003eC motifs, reflecting evolutionary and functional diversification.\\u003c/p\\u003e\\n\\u003ch3\\u003e3-1-6. Conservation and divergence of fungal GATA motifs\\u003c/h3\\u003e\\n\\u003cp\\u003eThe conservation and divergence of GATA motifs are critical for understanding their structural and functional constraints and for elucidating the evolutionary relationships. In this study, the analysis focused on the predominant motifs observed in fungal GATA TFs, including 5,159 instances of Type IVa (CX\\u003csub\\u003e2\\u003c/sub\\u003eCX\\u003csub\\u003e17\\u003c/sub\\u003eCX\\u003csub\\u003e2\\u003c/sub\\u003eC) and 4,153 instances of Type IVb (CX\\u003csub\\u003e2\\u003c/sub\\u003eCX\\u003csub\\u003e18\\u003c/sub\\u003eCX\\u003csub\\u003e2\\u003c/sub\\u003eC) (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig4\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003cp\\u003eIn Type IVa, GATA motifs retained a highly conserved set of core cysteine residues and surrounding amino acids, forming a structural framework critical for DNA binding and reflecting strong evolutionary constraints (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig4\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003ea). Four core cysteine residues (C-1, C-4, C-22, and C-25) were fully conserved and formed the zinc-coordinating structure essential for WGATAR (W\\u0026thinsp;=\\u0026thinsp;T/A, R\\u0026thinsp;=\\u0026thinsp;G/A) binding. In addition to the core residues, several non-cysteine positions (N-3, T-6, T-9, P-10, L-11, W-12, R-13, R-14, G-18, N-23, and A-24) were highly conserved (\\u0026ge;\\u0026thinsp;80%), indicating roles in zinc finger stability and DNA interaction. A subset of residues, T-7, showed moderate but consistent conservation (\\u0026ge;\\u0026thinsp;60%) across both lineages, suggesting functional relevance with limited variability. Notably, lineage-specific differences were also evident: in \\u003cem\\u003eDikarya\\u003c/em\\u003e, T-7 was conserved in less than 60% of sequences, whereas in early-diverging fungal lineages, T-6 exhibited reduced conservation (from \\u0026ge;\\u0026thinsp;80% to \\u0026ge;\\u0026thinsp;60%), while N-23 showed stronger conservation, increasing from \\u0026ge;\\u0026thinsp;80% to 100%. Overall, Type IVa were largely conserved across fungi, but specific residues showed lineage-dependent variation, reflecting both structural constraints and evolutionary divergence. Within this conserved framework, the conserved positions comprised residues with diverse functional and structural properties, including polar charged (e.g., R-13 and R-14), polar uncharged (e.g., N-3 and T-6), structurally important residues (e.g., P-10 and G-18), and hydrophobic residues (e.g., L-11 and W-12), suggesting position-specific functional constraints within the GATA motif. These results suggested that Type IVa motifs maintained a highly conserved structural framework across fungi, while lineage-specific residue variation may have contributed to functional diversification.\\u003c/p\\u003e \\u003cp\\u003eIn Type IVb, GATA motifs shared a conserved structural architecture, reflecting evolutionary constraints on DNA binding (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig4\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003eb). Four core cysteines (C-1, C-4, C-23, and C-26) were conserved and formed the zinc-coordinating structure essential for WGATAR binding. Several non-cysteine positions (P-10, W-12, R-13, G-15, P-16, G-18, L-22, N-24, and N-25) were highly conserved (\\u0026ge;\\u0026thinsp;80%), and T-9, E-11, R-14, and T-21 showed moderate conservation (\\u0026ge;\\u0026thinsp;60%), indicating roles in zinc finger stability and DNA interaction. Lineage-specific differences were also evident in Type IVb motifs. In \\u003cem\\u003eDikarya\\u003c/em\\u003e, E-11 showed increased conservation (\\u0026ge;\\u0026thinsp;60% to \\u0026ge;\\u0026thinsp;80%), whereas R-14 decreased to \\u0026lt;\\u0026thinsp;60%, and A-25 decreased from \\u0026ge;\\u0026thinsp;80% to \\u0026ge;\\u0026thinsp;60%. In early-diverging fungal lineages, E-11 and T-21 showed reduced conservation (\\u0026lt;\\u0026thinsp;60%), W-12 became fully conserved (100%), and G-15 and G-18 decreased from \\u0026ge;\\u0026thinsp;80% to \\u0026ge;\\u0026thinsp;60%. Overall, these patterns suggested that while the core structural framework of Type IVb motifs was maintained, individual residues exhibited fungal lineage-dependent variability reflecting evolutionary adaptation. Within this conserved framework, conserved residues exhibited diverse functional and structural properties, reflecting position-specific constraints consistent with those observed in Type IVa. These results suggested that Type IVb motifs retained a conserved zinc finger framework across fungi, while lineage-specific variation in individual residues may have reflected adaptive diversification of regulatory functions.\\u003c/p\\u003e\\n\\u003ch3\\u003e3-1-7. Co-occurrence of fungal GATA motifs and domains\\u003c/h3\\u003e\\n\\u003cp\\u003eCo-occurrence analysis was performed to investigate how protein domain composition was associated with GATA motif types across fungal lineages (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig5\\\" class=\\\"InternalRef\\\"\\u003e5\\u003c/span\\u003e). Unlike the domain multiplicity analysis (Section \\u003cspan refid=\\\"Sec10\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003e-\\u003cspan refid=\\\"Sec1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e-\\u003cspan refid=\\\"Sec10\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003e), which focuses on the copy number of GATA domains within individual proteins, this analysis examined preferential associations between domain identity and GATA motif types, aiming to reveal functional coupling patterns between domain architecture and motif evolution. From this analysis, a total of 11,504 domain-motif pairs were identified across fungal GATA TFs. Among these, multiple protein domains exhibited distinct biases toward specific GATA motif types, indicating that domain composition was closely linked to motif-type specification rather than being randomly associated.\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003cp\\u003eThe canonical GATA domain (PF00320) displayed a balanced distribution between Type IVa (CX\\u003csub\\u003e2\\u003c/sub\\u003eCX\\u003csub\\u003e17\\u003c/sub\\u003eCX\\u003csub\\u003e2\\u003c/sub\\u003eC; 48.1%) and Type IVb (CX\\u003csub\\u003e2\\u003c/sub\\u003eCX\\u003csub\\u003e18\\u003c/sub\\u003eCX\\u003csub\\u003e2\\u003c/sub\\u003eC; 50.1%), suggesting that it served as a structurally flexible platform capable of accommodating both motif types. In contrast, several auxiliary domains showed strong motif-type specificity. Specifically, PF08550 (Nitrogen regulatory protein AreA-like domain), PF25026 (Asd-4-like domain), and PF07573 (AreA N-terminal domain) were almost exclusively associated with Type IVa motifs (98.5\\u0026ndash;100.0%), whereas PF08447 (PAS fold) and PF13426 (PAS domain) exhibited a near-complete preference for Type IVb motifs (99.3\\u0026ndash;100.0%). These patterns indicated that accessory domains imposed strong constraints on GATA motif type selection, suggesting coordinated evolution between domain architecture and DNA-binding motif structure.\\u003c/p\\u003e \\u003cp\\u003eTo further explore lineage-specific modulation of this coupling, the distribution of GATA motif types was compared across fungal divisions (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig5\\\" class=\\\"InternalRef\\\"\\u003e5\\u003c/span\\u003e). PF00320-associated GATA TFs showed moderate variation in Type IVa ratios across lineages, ranging from 34.4% in \\u003cem\\u003eRozellomycota\\u003c/em\\u003e to 52.5% in \\u003cem\\u003eNeocallimastigomycota\\u003c/em\\u003e, indicating a relatively flexible association between the core zinc finger domain and motif type. In contrast, accessory domains exhibited highly conserved and strongly biased coupling patterns. Specifically, PF08550 was consistently associated with Type IVa across nearly all divisions, including complete association in multiple early-diverging lineages, whereas PF08447 and PF13426 showed the opposite pattern, maintaining near-exclusive associations with Type IVb motifs across both \\u003cem\\u003eDikarya\\u003c/em\\u003e and early-diverging fungi. Consistently, PF25026 and PF07573 were also minimally associated with Type IVb, where present, reinforcing this domain-dependent divergence. These results suggested that lineage-specific variation in GATA motif composition was relatively flexible in the core GATA domain but remained strongly constrained in accessory domain-associated motifs across fungal divisions.\\u003c/p\\u003e\\n\\u003ch3\\u003e3 − 2. Integrative functional and evolutionary analysis of fungal GATA TFs\\u003c/h3\\u003e\\n\\n\\u003ch3\\u003e3-2-1. Characterization of functionally validated fungal GATA TFs\\u003c/h3\\u003e\\n\\u003cp\\u003eA curated set of 25 functionally validated GATA TFs from four representative \\u003cem\\u003eAscomycota\\u003c/em\\u003e species was assembled to establish a reference framework for integrative analyses (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab4\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003e). All sequences were derived from previously characterized studies and correspond exactly to experimentally validated GATA TFs. Although restricted to \\u003cem\\u003eAscomycota\\u003c/em\\u003e, the dataset encompassed both filamentous fungi and yeasts, capturing substantial functional diversity within this lineage. The collected TFs collectively represented major regulatory roles, including nitrogen metabolism, light-responsive signaling, iron homeostasis, and developmental processes, as well as both transcriptional activation and repression mechanisms. This curated dataset served as a set of functional anchor points for interpreting evolutionary relationships and structural variation in subsequent phylogenetic and modeling analyses.\\u003c/p\\u003e \\u003cp\\u003e \\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab4\\\" border=\\\"1\\\"\\u003e \\u003ccaption language=\\\"En\\\"\\u003e \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 4\\u003c/div\\u003e \\u003cdiv class=\\\"CaptionContent\\\"\\u003e \\u003cp\\u003eFunctional and structural features of representative fungal GATA TFs\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"7\\\"\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c5\\\" colnum=\\\"5\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c6\\\" colnum=\\\"6\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c7\\\" colnum=\\\"7\\\"\\u003e\\u003c/div\\u003e \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eSpecies\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eGATA name\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eProtein length\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003edomains\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eGATA motifs\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eBiological function\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003eReferences\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cem\\u003eA. oryzae\\u003c/em\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e\\u003cem\\u003eSreA\\u003c/em\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e566\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003ePF00320,PF00320\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eIVa, IVa\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eregulates siderophore biosynthesis and iron homeostasis\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e(Oberegger et al. \\u003cspan citationid=\\\"CR77\\\" class=\\\"CitationRef\\\"\\u003e2001\\u003c/span\\u003e)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cem\\u003eA. oryzae\\u003c/em\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e\\u003cem\\u003eAreB\\u003c/em\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e313\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003ePF00320,PF25026\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eIVa\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eregulates nitrogen and secondary metabolism as an activator and a repressor\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e(Pfannm\\u0026uuml;ller et al. \\u003cspan citationid=\\\"CR83\\\" class=\\\"CitationRef\\\"\\u003e2017\\u003c/span\\u003e)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cem\\u003eA. oryzae\\u003c/em\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e\\u003cem\\u003eLreA\\u003c/em\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e282\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003ePF00320\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eIVb\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003econtrols light response, conidiation, and pathogenicity\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e(Park et al. \\u003cspan citationid=\\\"CR80\\\" class=\\\"CitationRef\\\"\\u003e2025\\u003c/span\\u003e)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cem\\u003eA. oryzae\\u003c/em\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e\\u003cem\\u003eAreA\\u003c/em\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e866\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003ePF07573,PF08550,PF00320\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eIVa\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eregulates nitrogen and secondary metabolism as an activator and a repressor\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e(Pfannm\\u0026uuml;ller et al. \\u003cspan citationid=\\\"CR83\\\" class=\\\"CitationRef\\\"\\u003e2017\\u003c/span\\u003e)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cem\\u003eA. oryzae\\u003c/em\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e\\u003cem\\u003eLreB\\u003c/em\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e508\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003ePF08447,PF00320\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eIVb\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003econtrols light response, conidiation, and pathogenicity\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e(Park et al. \\u003cspan citationid=\\\"CR80\\\" class=\\\"CitationRef\\\"\\u003e2025\\u003c/span\\u003e)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cem\\u003eA. oryzae\\u003c/em\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e\\u003cem\\u003eNsdD\\u003c/em\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e453\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003ePF00320\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eIVb\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003erepresses conidiation and promotes sexual development\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e(Lee et al. \\u003cspan citationid=\\\"CR56\\\" class=\\\"CitationRef\\\"\\u003e2014\\u003c/span\\u003e)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cem\\u003eN. crassa\\u003c/em\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e\\u003cem\\u003eNIT2\\u003c/em\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1036\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003ePF08550,PF00320\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eIVa\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003egoverns nitrogen metabolism through transcriptional regulation\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e(Bernardes et al. \\u003cspan citationid=\\\"CR8\\\" class=\\\"CitationRef\\\"\\u003e2017\\u003c/span\\u003e)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cem\\u003eN. crassa\\u003c/em\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e\\u003cem\\u003eASD4\\u003c/em\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e426\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003ePF00320,PF25026\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eIVa\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003epromotes sexual development and ascospore formation\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e(Feng et al. \\u003cspan citationid=\\\"CR26\\\" class=\\\"CitationRef\\\"\\u003e2000\\u003c/span\\u003e)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cem\\u003eN. crassa\\u003c/em\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e\\u003cem\\u003eWC1\\u003c/em\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1167\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003ePF13426,PF08447,PF00320\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eIVb\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003emediates light sensing and circadian rhythm regulation\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e(Lewis et al. \\u003cspan citationid=\\\"CR58\\\" class=\\\"CitationRef\\\"\\u003e2002\\u003c/span\\u003e)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cem\\u003eN. crassa\\u003c/em\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e\\u003cem\\u003eWC2\\u003c/em\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e530\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003ePF08447,PF00320\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eIVb\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003epartners with WC1 to activate light-responsive gene expression\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e(Linden \\u003cspan citationid=\\\"CR63\\\" class=\\\"CitationRef\\\"\\u003e1997\\u003c/span\\u003e)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cem\\u003eS. cerevisiae\\u003c/em\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e\\u003cem\\u003eSRD1\\u003c/em\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e221\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003ePF00320\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eIVb\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003einvolved in pre-rRNA processing and ribosome biogenesis\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e(Fabian et al. \\u003cspan citationid=\\\"CR25\\\" class=\\\"CitationRef\\\"\\u003e1990\\u003c/span\\u003e)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cem\\u003eS. cerevisiae\\u003c/em\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e\\u003cem\\u003eGAT4\\u003c/em\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e121\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003ePF00320\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eIV19\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eacts as a DNA-binding transcription factor in spore wall assembly\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e(Lin et al. \\u003cspan citationid=\\\"CR62\\\" class=\\\"CitationRef\\\"\\u003e2013\\u003c/span\\u003e)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cem\\u003eS. cerevisiae\\u003c/em\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e\\u003cem\\u003eGLN3\\u003c/em\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e730\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003ePF00320\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eIVa\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eactivates nitrogen metabolism gene expression under nitrogen limitation\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e(Courchesne and Magasanik \\u003cspan citationid=\\\"CR17\\\" class=\\\"CitationRef\\\"\\u003e1988\\u003c/span\\u003e)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cem\\u003eS. cerevisiae\\u003c/em\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e\\u003cem\\u003eGAT1\\u003c/em\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e510\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003ePF00320\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eIVa\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eactivates nitrogen metabolism and higher alcohol biosynthesis genes\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e(Wang et al. \\u003cspan citationid=\\\"CR108\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cem\\u003eS. cerevisiae\\u003c/em\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e\\u003cem\\u003eGZF3\\u003c/em\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e551\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003ePF00320\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eIVa\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003erepresses nitrogen-regulated gene expression under preferred nitrogen conditions\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e(Soussi-Boudekou et al. \\u003cspan citationid=\\\"CR99\\\" class=\\\"CitationRef\\\"\\u003e1997\\u003c/span\\u003e)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cem\\u003eS. cerevisiae\\u003c/em\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e\\u003cem\\u003eASH1\\u003c/em\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e588\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003ePF00320\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eIVc\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003erepresses HO expression to control mating-type switching\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e(Cosma \\u003cspan citationid=\\\"CR16\\\" class=\\\"CitationRef\\\"\\u003e2004\\u003c/span\\u003e)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cem\\u003eS. cerevisiae\\u003c/em\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e\\u003cem\\u003eDAL80\\u003c/em\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e269\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003ePF00320\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eIVa\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003erepresses nitrogen catabolite gene expression under rich nitrogen conditions\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e(Cunningham et al. \\u003cspan citationid=\\\"CR18\\\" class=\\\"CitationRef\\\"\\u003e2000\\u003c/span\\u003e)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cem\\u003eS. cerevisiae\\u003c/em\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e\\u003cem\\u003eGAT3\\u003c/em\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e141\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003ePF00320\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eIV19\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eacts as a DNA-binding transcription factor in spore wall assembly\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e(Lin et al. \\u003cspan citationid=\\\"CR62\\\" class=\\\"CitationRef\\\"\\u003e2013\\u003c/span\\u003e)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cem\\u003eS. cerevisiae\\u003c/em\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e\\u003cem\\u003eGAT2\\u003c/em\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e560\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003ePF00320\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eIVb\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eRegulates filamentous and invasive growth associated with morphological transitions\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e(Du et al. \\u003cspan citationid=\\\"CR22\\\" class=\\\"CitationRef\\\"\\u003e2012\\u003c/span\\u003e)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cem\\u003eS. cerevisiae\\u003c/em\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e\\u003cem\\u003eECM23\\u003c/em\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e187\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003ePF00320\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eIVb\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003enegatively regulates pseudohyphal growth and cell wall morphogenesis\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e(Ca\\u0026ntilde;izares et al. \\u003cspan citationid=\\\"CR10\\\" class=\\\"CitationRef\\\"\\u003e2002\\u003c/span\\u003e)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cem\\u003eS. pombe\\u003c/em\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e\\u003cem\\u003eAMS2\\u003c/em\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e697\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003ePF00320\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eIVc\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eactivates the histone gene transcription during G1/S cell cycle\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e(Takayama et al. \\u003cspan citationid=\\\"CR101\\\" class=\\\"CitationRef\\\"\\u003e2010\\u003c/span\\u003e)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cem\\u003eS. pombe\\u003c/em\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e\\u003cem\\u003eGAF1\\u003c/em\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e855\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003ePF08550,PF00320\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eIVa\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eNegatively regulates mating and sporulation via repression of ste11 expression\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e(Kim et al. \\u003cspan citationid=\\\"CR46\\\" class=\\\"CitationRef\\\"\\u003e2012\\u003c/span\\u003e)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cem\\u003eS. pombe\\u003c/em\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e\\u003cem\\u003eFEP1\\u003c/em\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e564\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003ePF00320,PF00320\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eIVa, IVa\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eRegulates iron homeostasis by repressing genes involved in reductive iron uptake under iron-replete conditions\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e(Pelletier et al. \\u003cspan citationid=\\\"CR81\\\" class=\\\"CitationRef\\\"\\u003e2002\\u003c/span\\u003e)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cem\\u003eS. pombe\\u003c/em\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e\\u003cem\\u003eFil1\\u003c/em\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e557\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003ePF00320,PF00320\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eIVb, IVa\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eRegulates cellular stress responses through activation of stress-induced transcriptional programmes\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e(Rubio et al. \\u003cspan citationid=\\\"CR89\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cem\\u003eS. pombe\\u003c/em\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e\\u003cem\\u003eSFH1\\u003c/em\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e418\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003ePF04855,PF00320\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eIVc\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eRegulates genome integrity by controlling centromere function, chromatin organization, and DNA damage repair\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e(Kotomura et al. \\u003cspan citationid=\\\"CR52\\\" class=\\\"CitationRef\\\"\\u003e2018\\u003c/span\\u003e)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003ctfoot\\u003e \\u003ctr\\u003e\\u003ctd colspan=\\\"7\\\"\\u003e\\u003cb\\u003e* Some GATA TFs included in this table were obtained from additional fungal species and are presented as representative orthologs across different fungal lineages.\\u003c/b\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e \\u003c/tfoot\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e\\n\\u003ch3\\u003e3-2-2. Phylogenetic analysis of fungal GATA domains\\u003c/h3\\u003e\\n\\u003cp\\u003ePhylogenetic analysis based on GATA domains was performed to investigate their evolutionary relationships. The distribution of 9,488 fungal GATA domains revealed extensive structural variation associated with motif types and domain architecture (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig6\\\" class=\\\"InternalRef\\\"\\u003e6\\u003c/span\\u003e). The distribution of GATA motif types across the phylogeny was non-random and exhibited distinct spatial patterns. Type IVa motifs (CX\\u003csub\\u003e2\\u003c/sub\\u003eCX\\u003csub\\u003e17\\u003c/sub\\u003eCX\\u003csub\\u003e2\\u003c/sub\\u003eC) were highly represented in the central region of the tree, whereas Type IVb motifs (CX\\u003csub\\u003e2\\u003c/sub\\u003eCX\\u003csub\\u003e18\\u003c/sub\\u003eCX\\u003csub\\u003e2\\u003c/sub\\u003eC) were more prevalent toward the peripheral branches. Intermediate regions displayed alternating clusters of Type IVa- and Type IVb-enriched clades, reflecting a structured but non-uniform distribution of motif types across the phylogeny. Overall, these results demonstrated that GATA motif types were non-randomly distributed and reflected evolutionarily structured patterns associated with phylogenetic divergence and structural variation.\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003cp\\u003eTo understand the relationship between evolutionary divergence of GATA motifs and diversification of domain architectures, phylogenetic patterns and structural variation in GATA domains were analyzed (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig6\\\" class=\\\"InternalRef\\\"\\u003e6\\u003c/span\\u003e). Within the central region, where Type IVa motifs were predominant, multiple clades exhibited distinct domain architectures. Three major clades were characterized by the PF00320\\u0026thinsp;+\\u0026thinsp;PF08550 domain combination. Between these clades, two clades composed of TFs containing only the PF00320 domain were observed, along with a smaller clade defined by the PF00320\\u0026thinsp;+\\u0026thinsp;PF25026 architecture. In the peripheral region of the phylogeny, where Type IVb motifs were predominant, clades were distinguished based on domain architecture. Three recurrent structural configurations were observed: PF00320-only, PF00320\\u0026thinsp;+\\u0026thinsp;PF08447, and PF00320\\u0026thinsp;+\\u0026thinsp;PF08447\\u0026thinsp;+\\u0026thinsp;PF13426. Each of these configurations appeared in two separate clades, resulting in a total of six distinct clades. In the intermediate regions, most clades were composed of GATA TFs containing only the PF00320 domain. In addition, a distinct subregion was observed in which Type IVa motifs were predominant, together with the frequent occurrence of the PF00320\\u0026thinsp;+\\u0026thinsp;PF08550 domain combination. These results indicated that GATA motif divergence was closely associated with lineage-specific diversification and the repeated emergence of distinct domain architectures across phylogenetic regions.\\u003c/p\\u003e \\u003cp\\u003eTo assess whether identical domain architectures are lineage-restricted or broadly conserved across fungal divisions, the phylogenetic distribution of domain architecture-defined GATA TF clades was analyzed (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig6\\\" class=\\\"InternalRef\\\"\\u003e6\\u003c/span\\u003e). Across the phylogeny, clades defined by specific domain architectures included GATA TFs originating from multiple fungal divisions, indicating that identical structural configurations were not restricted to a single lineage. In several cases, individual clades contained TFs derived from both major and early-diverging fungal groups. Within clades, division-specific subclustering was consistently observed across multiple domain architecture-defined groups. Overall, these findings demonstrated that conserved domain architectures were widely distributed across fungal divisions, whereas lineage-specific subclustering within clades reflected deeper evolutionary differentiation.\\u003c/p\\u003e \\u003cp\\u003eTo provide functional context for the phylogenetic analysis, 25 functionally validated GATA TFs were mapped onto the phylogenetic tree (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig6\\\" class=\\\"InternalRef\\\"\\u003e6\\u003c/span\\u003e). These TFs were distributed across multiple clades, enabling partial annotation of functionally characterized groups. GATA TFs with similar biological roles were frequently located within the same or closely related clades. However, as only a limited number of functionally characterized GATA TFs were currently available, a large proportion of the phylogeny remained unannotated. Collectively, the limited availability of functionally characterized GATA TFs restricted comprehensive functional annotation across the phylogeny despite partial clustering of functionally similar proteins.\\u003c/p\\u003e\\n\\u003ch3\\u003e3-2-3. Structural modeling and functional interpretation of fungal GATA TFs\\u003c/h3\\u003e\\n\\u003cp\\u003eStructural prediction of fungal GATA TFs is essential to link sequence and domain variation with three-dimensional structural organization underlying functional diversity. Accordingly, to investigate structural features underlying functional diversity, three-dimensional models were generated for 11 representative GATA TFs (\\u003cem\\u003eGAT1, GAT3, NsdD, ASH1, SreA, Fil1, AreA, AreB, LreB, NIT2, and WC1\\u003c/em\\u003e) selected from the curated dataset (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig7\\\" class=\\\"InternalRef\\\"\\u003e7\\u003c/span\\u003e). These TFs were selected to systematically represent the full spectrum of domain organization in fungal GATA TFs, encompassing single GATA domain proteins, dual GATA domain architectures, and multi-domain configurations.\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003cp\\u003eAmong the analyzed proteins, four fungal GATA TFs (\\u003cem\\u003eGAT1, GAT3, NsdD, and ASH1\\u003c/em\\u003e) were selected to represent the minimal structural unit of fungal GATA TFs, consisting of a single GATA domain. Despite differences in GATA motif types, all exhibited a conserved zinc finger structure within the GATA domain region. Notably, even TFs harboring relatively rare motif variants (e.g., IVc and IV19) maintained a well-defined zinc finger conformation, suggesting strong structural conservation of the DNA-binding module regardless of motif subtype. These results indicated that the core GATA zinc finger structure was highly conserved and robust to motif variation, preserving its DNA-binding architecture across fungal TFs.\\u003c/p\\u003e \\u003cp\\u003eTwo fungal GATA TFs (\\u003cem\\u003eSreA and Fil1\\u003c/em\\u003e) were selected to represent dual GATA domain architectures, a structural configuration that may enable cooperative DNA recognition through multiple zinc finger modules. These proteins displayed closely positioned domain architectures, in which both GATA domains independently formed canonical zinc finger structures (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig7\\\" class=\\\"InternalRef\\\"\\u003e7\\u003c/span\\u003e). Although the motif combinations differed (e.g., IVa\\u0026thinsp;+\\u0026thinsp;IVa and IVa\\u0026thinsp;+\\u0026thinsp;IVb), no substantial structural disruption was observed between motif types. Instead, the spatial proximity of the two zinc finger domains suggested a potential cooperative role in DNA recognition, possibly enhancing binding specificity or affinity.\\u003c/p\\u003e \\u003cp\\u003eThe remaining five fungal GATA TFs (\\u003cem\\u003eAreA, AreB, LreB, NIT2, and WC1\\u003c/em\\u003e) were selected to represent multi-domain architectures, in which additional auxiliary domains beyond the GATA zinc finger may contribute to expanded regulatory complexity and context-dependent functional specialization. In these proteins, the GATA domain consistently formed a stable zinc finger structure, indicating that its core DNA-binding function was structurally preserved even within more complex architectures. However, the presence of additional domains resulted in markedly different overall protein conformations compared to single-domain TFs, implying that these proteins may have performed more specialized or context-dependent regulatory functions.\\u003c/p\\u003e \\u003cp\\u003eTo further evaluate whether structural regions outside canonical GATA domains corresponded to previously known protein folds that may have escaped sequence-based domain annotation, Foldseek searches against the PDB100 database were additionally performed (Supplementary Table\\u0026nbsp;6). The analysis revealed distinct patterns of structural conservation among fungal GATA TF architectures. Among single-domain GATA TFs, only \\u003cem\\u003eGAT1\\u003c/em\\u003e exhibited strong structural similarity to previously resolved GATA-related structures, whereas \\u003cem\\u003eNsdD, ASH1, and GAT3\\u003c/em\\u003e showed no significant matches under the applied confidence threshold. In contrast, both dual-domain TFs (\\u003cem\\u003eSreA and Fil1\\u003c/em\\u003e) displayed strong structural correspondence with experimentally resolved GATA zinc finger structures, supporting the structural conservation of tandem GATA domains. For multi-domain TFs, Foldseek primarily detected similarity within previously annotated conserved domains rather than identifying novel globular folds. Specifically, \\u003cem\\u003eAreA, AreB, and NIT2\\u003c/em\\u003e showed matches restricted to the canonical GATA zinc finger region, whereas \\u003cem\\u003eLreB\\u003c/em\\u003e exhibited structural similarity only within the PAS-related region (PF08447). Notably, \\u003cem\\u003eWC1\\u003c/em\\u003e showed structural matches not only to known PAS-associated domains (PF13426 and PF08447) but also additional structurally conserved regions outside previously annotated domains, suggesting the possible presence of uncharacterized or highly diverged structural elements. Overall, these results indicated that Foldseek recovered only a subset of previously annotated conserved domains across fungal GATA TFs, while \\u003cem\\u003eWC1\\u003c/em\\u003e uniquely exhibited additional structurally conserved regions beyond conventional sequence-based domain annotation.\\u003c/p\\u003e\\n\\u003ch3\\u003e3–3. Physicochemical and cellular characteristics of fungal GATA TFs\\u003c/h3\\u003e\\n\\n\\u003ch3\\u003e3-3-1. Physicochemical properties\\u003c/h3\\u003e\\n\\u003cp\\u003ePhysicochemical profiling enabled a systematic comparison of fungal GATA TFs across divisions by capturing variations in size, charge, and hydrophobicity beyond sequence similarity. Accordingly, protein length, molecular weight (MW), isoelectric point (pI), aliphatic index, and hydropathy were examined across eleven fungal divisions.\\u003c/p\\u003e \\u003cp\\u003eFungal GATA TFs had an overall average protein length of 557.9 aa\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;347.4 aa and molecular weight (MW) of 60.8 kDa\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;37.3 kDa, with most proteins (6,661 of 7,846; 84.9%) ranging from 200 to 1,200 amino acids in length (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig8\\\" class=\\\"InternalRef\\\"\\u003e8\\u003c/span\\u003ea-\\u003cspan refid=\\\"Fig8\\\" class=\\\"InternalRef\\\"\\u003e8\\u003c/span\\u003eb). In particular, \\u003cem\\u003eDikarya\\u003c/em\\u003e, represented by \\u003cem\\u003eAscomycota and Basidiomycota\\u003c/em\\u003e, showed broad length distributions, ranging from 50 to 2,341 aa (5.6-252.3 kDa) and 50 to 3,827 aa (5.6-402.6 kDa), respectively, with average lengths of 594.6 aa\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;277.0 aa (64.5 kDa\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;30.0 kDa) and 701.0 aa\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;471.9 aa (75.1 kDa\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;50.1 kDa). Early-diverging fungal divisions also displayed a wide range of protein lengths, spanning 50 to 3,172 aa (5.5-321.7 kDa) with an average of 499.5 aa\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;338.8 aa (54.9 kDa\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;36.7 kDa). Notably, \\u003cem\\u003eNeocallimastigomycota\\u003c/em\\u003e exhibited the highest average length, at 777.5 aa\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;626.9 aa (87.5 kDa\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;87.5 kDa). Overall, fungal GATA TFs exhibited substantial variation in protein length and molecular weight, with \\u003cem\\u003eDikarya\\u003c/em\\u003e and certain early-diverging lineages, particularly \\u003cem\\u003eNeocallimastigomycota\\u003c/em\\u003e, showing the largest average sizes.\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003cp\\u003eThe isoelectric points (pI) of fungal GATA TFs ranged from 4.1 to 11.7, with most proteins (5,603 of 7,846; 71.4%) being neutral to weakly alkaline (pI\\u0026thinsp;\\u0026ge;\\u0026thinsp;7) (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig8\\\" class=\\\"InternalRef\\\"\\u003e8\\u003c/span\\u003ec). Similarly, the \\u003cem\\u003eDikarya\\u003c/em\\u003e divisions, \\u003cem\\u003eAscomycota and Basidiomycota\\u003c/em\\u003e, showed similar average pI values of 8.1\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;1.5 and 8.2\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;1.4, with overall ranges of 4.6\\u0026ndash;11.7 and 4.5\\u0026ndash;11.7, respectively. The remaining early-diverging fungal divisions also exhibited comparable pI values, with averages ranging from 7.7\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;1.7 (\\u003cem\\u003eMortierellomycota\\u003c/em\\u003e) to 8.8\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;1.4 (\\u003cem\\u003eKickxellomycota\\u003c/em\\u003e). Overall, fungal GATA TFs were predominantly neutral to weakly alkaline, with relatively consistent pI values across both \\u003cem\\u003eDikarya\\u003c/em\\u003e and early-diverging lineages.\\u003c/p\\u003e \\u003cp\\u003eThe aliphatic index of fungal GATA TFs varied across divisions, ranging from a minimum of 27.3 to a maximum of 125.6, with an overall average of 60.2\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;9.9, generally falling within a moderate range (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig8\\\" class=\\\"InternalRef\\\"\\u003e8\\u003c/span\\u003ed). Similarly, within \\u003cem\\u003eDikarya\\u003c/em\\u003e, \\u003cem\\u003eAscomycota and Basidiomycota\\u003c/em\\u003e had similar average aliphatic index values of 59.2\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;9.3 and 55.4\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;9.6, respectively, ranging from 32.0-100.9 and 30.2\\u0026ndash;86.7, respectively. The remaining early-diverging fungal divisions exhibited average aliphatic index values ranging from 51.9\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;12.0 (\\u003cem\\u003eRozellomycota\\u003c/em\\u003e) to 65.6\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;10.3 (\\u003cem\\u003eNeocallimastigomycota\\u003c/em\\u003e). Overall, fungal GATA TFs displayed moderate aliphatic index values across divisions, with relatively consistent profiles in \\u003cem\\u003eDikarya\\u003c/em\\u003e and slightly broader variation in early-diverging lineages.\\u003c/p\\u003e \\u003cp\\u003eFungal GATA TFs showed consistently negative GRAVY (grand average of hydropathicity) values across all divisions, ranging from \\u0026minus;\\u0026thinsp;2.0 to 0.5, with an overall average of -0.7\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.2, indicating a predominantly hydrophilic character (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig8\\\" class=\\\"InternalRef\\\"\\u003e8\\u003c/span\\u003ee). Within \\u003cem\\u003eDikarya\\u003c/em\\u003e, \\u003cem\\u003eAscomycota\\u003c/em\\u003e exhibited an average GRAVY value of -0.7\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.2 (range: -1.4 to 0.3); similarly, \\u003cem\\u003eBasidiomycota\\u003c/em\\u003e showed an average of -0.7\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.2 (range: -1.5 to 0.5). The early-diverging fungal divisions exhibited average GRAVY values ranging from \\u0026minus;\\u0026thinsp;1.1\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.3 (\\u003cem\\u003eRozellomycota\\u003c/em\\u003e) to -0.5\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.3 (\\u003cem\\u003eBlastocladiomycota\\u003c/em\\u003e). Overall, fungal GATA TFs were predominantly hydrophilic across all divisions, with consistently negative GRAVY values reflecting a general preference for aqueous environments.\\u003c/p\\u003e\\n\\u003ch3\\u003e3-3-2. Transmembrane helices and subcellular localization prediction\\u003c/h3\\u003e\\n\\u003cp\\u003eAnalysis of transmembrane helices (TMHs) and subcellular localization revealed that TMHs or non-nuclear localization may reflect lineage-specific adaptations or functional divergence. Accordingly, these features were analyzed across eleven fungal divisions to assess GATA TF membrane association and intracellular distribution.\\u003c/p\\u003e \\u003cp\\u003eThe vast majority of fungal GATA TFs lacked predicted TMHs, with 7,796 of 7,846 TFs (99.4%) predicted to have none (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab5\\\" class=\\\"InternalRef\\\"\\u003e5\\u003c/span\\u003e). Only 50 fungal GATA TFs in specific divisions contained TMHs: 14 GATA TFs in \\u003cem\\u003eMucoromycota\\u003c/em\\u003e, 13 GATA TFs in \\u003cem\\u003eAscomycota\\u003c/em\\u003e, 12 GATA TFs in \\u003cem\\u003eMortierellomycota\\u003c/em\\u003e, 5 GATA TFs in \\u003cem\\u003eBasidiomycota\\u003c/em\\u003e, 2 GATA TFs in \\u003cem\\u003eChytridiomycota and Neocallimastigomycota\\u003c/em\\u003e, and 1 GATA TF in \\u003cem\\u003eZoopagomycota and Blastocladiomycota\\u003c/em\\u003e. Most GATA TFs with predicted TMHs contained a single helix, although a subset exhibited multiple TMHs, with up to 11 helices detected in certain proteins. These results indicated that TMH-containing GATA TFs were rare and may represent lineage-specific adaptations.\\u003c/p\\u003e \\u003cp\\u003e \\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab5\\\" border=\\\"1\\\"\\u003e \\u003ccaption language=\\\"En\\\"\\u003e \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 5\\u003c/div\\u003e \\u003cdiv class=\\\"CaptionContent\\\"\\u003e \\u003cp\\u003eDistribution of predicted TMHs and subcellular localization of GATA TFs across major fungal lineages\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"20\\\"\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c5\\\" colnum=\\\"5\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c6\\\" colnum=\\\"6\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c7\\\" colnum=\\\"7\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c8\\\" colnum=\\\"8\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c9\\\" colnum=\\\"9\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c10\\\" colnum=\\\"10\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c11\\\" colnum=\\\"11\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c12\\\" colnum=\\\"12\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c13\\\" colnum=\\\"13\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c14\\\" colnum=\\\"14\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c15\\\" colnum=\\\"15\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c16\\\" colnum=\\\"16\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c17\\\" colnum=\\\"17\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c18\\\" colnum=\\\"18\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c19\\\" colnum=\\\"19\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c20\\\" colnum=\\\"20\\\"\\u003e\\u003c/div\\u003e \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003eDivision\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003eNumber of species\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003eNumber of GATA TFs\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colspan=\\\"7\\\" nameend=\\\"c10\\\" namest=\\\"c4\\\"\\u003e \\u003cp\\u003eNumber of TMHs\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colspan=\\\"10\\\" nameend=\\\"c20\\\" namest=\\\"c11\\\"\\u003e \\u003cp\\u003eNumber of subcellular localizations\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e1\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e2\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e3\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e6\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e7\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c10\\\"\\u003e \\u003cp\\u003e11\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c11\\\"\\u003e \\u003cp\\u003eL1\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c12\\\"\\u003e \\u003cp\\u003eL2\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c13\\\"\\u003e \\u003cp\\u003eL3\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c14\\\"\\u003e \\u003cp\\u003eL4\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c15\\\"\\u003e \\u003cp\\u003eL5\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c16\\\"\\u003e \\u003cp\\u003eL6\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c17\\\"\\u003e \\u003cp\\u003eL7\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c18\\\"\\u003e \\u003cp\\u003eL8\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c19\\\"\\u003e \\u003cp\\u003eL9\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c20\\\"\\u003e \\u003cp\\u003eL10\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cem\\u003eAscomycota\\u003c/em\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e436\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e2,669\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e2,656\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e9\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e3\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c10\\\"\\u003e \\u003cp\\u003e1\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c11\\\"\\u003e \\u003cp\\u003e2,465\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c12\\\"\\u003e \\u003cp\\u003e189\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c13\\\"\\u003e \\u003cp\\u003e11\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c14\\\"\\u003e \\u003cp\\u003e2\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c15\\\"\\u003e \\u003cp\\u003e1\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c16\\\"\\u003e \\u003cp\\u003e1\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c17\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c18\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c19\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c20\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cem\\u003eBasidiomycota\\u003c/em\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e139\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1,015\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1,010\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e5\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c10\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c11\\\"\\u003e \\u003cp\\u003e891\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c12\\\"\\u003e \\u003cp\\u003e93\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c13\\\"\\u003e \\u003cp\\u003e25\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c14\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c15\\\"\\u003e \\u003cp\\u003e2\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c16\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c17\\\"\\u003e \\u003cp\\u003e2\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c18\\\"\\u003e \\u003cp\\u003e1\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c19\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c20\\\"\\u003e \\u003cp\\u003e1\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cem\\u003eMucoromycota\\u003c/em\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e72\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e2,220\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e2,206\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e14\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c10\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c11\\\"\\u003e \\u003cp\\u003e1,940\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c12\\\"\\u003e \\u003cp\\u003e269\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c13\\\"\\u003e \\u003cp\\u003e10\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c14\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c15\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c16\\\"\\u003e \\u003cp\\u003e1\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c17\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c18\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c19\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c20\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cem\\u003eMortierellomycota\\u003c/em\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e60\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1,057\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1,045\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e5\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e1\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e1\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e1\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e3\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c10\\\"\\u003e \\u003cp\\u003e1\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c11\\\"\\u003e \\u003cp\\u003e790\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c12\\\"\\u003e \\u003cp\\u003e247\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c13\\\"\\u003e \\u003cp\\u003e11\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c14\\\"\\u003e \\u003cp\\u003e3\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c15\\\"\\u003e \\u003cp\\u003e1\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c16\\\"\\u003e \\u003cp\\u003e2\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c17\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c18\\\"\\u003e \\u003cp\\u003e1\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c19\\\"\\u003e \\u003cp\\u003e2\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c20\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cem\\u003eZoopagomycota\\u003c/em\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e5\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e38\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e37\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e1\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c10\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c11\\\"\\u003e \\u003cp\\u003e34\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c12\\\"\\u003e \\u003cp\\u003e2\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c13\\\"\\u003e \\u003cp\\u003e2\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c14\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c15\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c16\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c17\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c18\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c19\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c20\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cem\\u003eKickxellomycota\\u003c/em\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e13\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e132\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e132\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c10\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c11\\\"\\u003e \\u003cp\\u003e106\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c12\\\"\\u003e \\u003cp\\u003e23\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c13\\\"\\u003e \\u003cp\\u003e3\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c14\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c15\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c16\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c17\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c18\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c19\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c20\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cem\\u003eEntomophthoromycota\\u003c/em\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e4\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e49\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e49\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c10\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c11\\\"\\u003e \\u003cp\\u003e43\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c12\\\"\\u003e \\u003cp\\u003e5\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c13\\\"\\u003e \\u003cp\\u003e1\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c14\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c15\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c16\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c17\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c18\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c19\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c20\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cem\\u003eBlastocladiomycota\\u003c/em\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e4\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e50\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e49\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e1\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c10\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c11\\\"\\u003e \\u003cp\\u003e48\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c12\\\"\\u003e \\u003cp\\u003e2\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c13\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c14\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c15\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c16\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c17\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c18\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c19\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c20\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cem\\u003eChytridiomycota\\u003c/em\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e26\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e380\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e378\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e2\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c10\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c11\\\"\\u003e \\u003cp\\u003e308\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c12\\\"\\u003e \\u003cp\\u003e57\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c13\\\"\\u003e \\u003cp\\u003e15\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c14\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c15\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c16\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c17\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c18\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c19\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c20\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cem\\u003eNeocallimastigomycota\\u003c/em\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e12\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e179\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e177\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e2\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c10\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c11\\\"\\u003e \\u003cp\\u003e154\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c12\\\"\\u003e \\u003cp\\u003e24\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c13\\\"\\u003e \\u003cp\\u003e1\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c14\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c15\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c16\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c17\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c18\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c19\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c20\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cem\\u003eRozellomycota\\u003c/em\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e25\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e57\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e57\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c10\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c11\\\"\\u003e \\u003cp\\u003e36\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c12\\\"\\u003e \\u003cp\\u003e21\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c13\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c14\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c15\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c16\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c17\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c18\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c19\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c20\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eTotal\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e796\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e7,846\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e7,796\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e39\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e4\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e1\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e1\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e3\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c10\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e2\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c11\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e6,815\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c12\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e932\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c13\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e79\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c14\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e5\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c15\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e4\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c16\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e4\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c17\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e2\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c18\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e2\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c19\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e2\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c20\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e1\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003ctfoot\\u003e \\u003ctr\\u003e\\u003ctd colspan=\\\"20\\\"\\u003e\\u003cb\\u003e* List of subcellular localization: L1 (Nucleus), L2 (Cytoplasm|Nucleus), L3 (Cytoplasm), L4 (Endoplasmic reticulum), L5 (Mitochondrion), L6 (Lysosome/Vacuole), L7 (Cytoplasm|Mitochondrion), L8 (Extracellular), L9 (Cell membrane), L10 (Golgi apparatus).\\u003c/b\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e \\u003c/tfoot\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e \\u003cp\\u003eSubcellular localization analysis indicated that 6,815 of 7,846 fungal GATA TFs (86.9%) were predicted to localize to the nucleus (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab5\\\" class=\\\"InternalRef\\\"\\u003e5\\u003c/span\\u003e). Predictions of Cytoplasm|Nucleus and Cytoplasm localization accounted for 932 (11.9%) and 79 (1.0%) GATA TFs, respectively, while the remaining seven subcellular localization categories were observed only in a few proteins. Division-specific patterns were generally similar, with these minor categories appearing combinatorially in \\u003cem\\u003eDikarya\\u003c/em\\u003e (\\u003cem\\u003eAscomycota and Basidiomycota\\u003c/em\\u003e) and the closely related early-diverging divisions \\u003cem\\u003eMucoromycota and Mortierellomycota\\u003c/em\\u003e. These results indicated that fungal GATA TFs were mostly predicted to localize to the nucleus, with a small fraction exhibiting dual or non-nuclear localization, potentially reflecting lineage-specific adaptations.\\u003c/p\\u003e\\n\\u003ch3\\u003e3–4. Order-level architectural profiling of Dikarya GATA TFs\\u003c/h3\\u003e\\n\\n\\u003ch3\\u003e3-4-1. Overview\\u003c/h3\\u003e\\n\\u003cp\\u003eThe 796 fungal species analyzed in this study spanned eleven divisions, comprising 36 classes and 84 orders (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e). Within \\u003cem\\u003eDikarya\\u003c/em\\u003e, the \\u003cem\\u003eAscomycota and Basidiomycota\\u003c/em\\u003e divisions accounted for the majority of taxonomic diversity, with 436 \\u003cem\\u003eAscomycota\\u003c/em\\u003e species encompassing 12 classes and 38 orders, and 139 \\u003cem\\u003eBasidiomycota\\u003c/em\\u003e species comprising 8 classes and 24 orders. Given their extensive taxonomic representation, these two divisions provided sufficient resolution to assess whether structural characteristics of GATA TFs varied systematically across lower taxonomic ranks. Accordingly, structural analyses were conducted at the order level within \\u003cem\\u003eDikarya\\u003c/em\\u003e, specifically in the \\u003cem\\u003eAscomycota and Basidiomycota\\u003c/em\\u003e divisions. In contrast, the remaining nine early-diverging divisions collectively comprised only 16 classes and 22 orders and showed no substantial structural deviation from division-level patterns; therefore, they were excluded from further lower-taxonomic analyses.\\u003c/p\\u003e\\n\\u003ch3\\u003e3-4-2. Taxonomic distribution\\u003c/h3\\u003e\\n\\u003cp\\u003eTo assess whether GATA TF diversity and abundance are associated with taxonomic structure and lineage representation, their distribution was analyzed at the order level. The taxonomic distribution of GATA TFs was highly heterogeneous across \\u003cem\\u003eDikarya\\u003c/em\\u003e divisions (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig9\\\" class=\\\"InternalRef\\\"\\u003e9\\u003c/span\\u003ea-\\u003cspan refid=\\\"Fig9\\\" class=\\\"InternalRef\\\"\\u003e9\\u003c/span\\u003ec). In \\u003cem\\u003eAscomycota\\u003c/em\\u003e, GATA TFs were distributed across 38 orders spanning 12 classes, with marked heterogeneity in both species representation and total GATA TF counts. Orders such as \\u003cem\\u003eEurotiales\\u003c/em\\u003e (109 species; 647 GATA TFs; 5.9\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.7 per species), \\u003cem\\u003eHypocreales\\u003c/em\\u003e (71 species; 461 GATA TFs; 6.5\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;1.8), \\u003cem\\u003ePleosporales\\u003c/em\\u003e (36 species; 212 GATA TFs; 5.9\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.6), and \\u003cem\\u003eHelotiales\\u003c/em\\u003e (28 species; 176 GATA TFs; 6.3\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;1.2) accounted for a substantial fraction of the ascomycetous dataset. Several moderately represented orders, including \\u003cem\\u003eChaetothyriales\\u003c/em\\u003e (22 species; 133 GATA TFs; 6.0\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.4) and \\u003cem\\u003eGlomerellales\\u003c/em\\u003e (23 species; 165 GATA TFs; 7.2\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;2.0), also contributed notable numbers of GATA TFs. In contrast, several orders comprised few species with low numbers of GATA TFs per species, such as \\u003cem\\u003eNeolectales\\u003c/em\\u003e (1 species; 2 GATA TFs), \\u003cem\\u003ePhaeomoniellales and Togniniales\\u003c/em\\u003e (1 species; 3 GATA TFs). These results indicated that GATA TFs in \\u003cem\\u003eAscomycota\\u003c/em\\u003e were highly unevenly distributed across orders, reflecting strong lineage and taxonomic biases.\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003cp\\u003eSimilar to \\u003cem\\u003eAscomycota\\u003c/em\\u003e, to evaluate whether GATA TF diversity and abundance exhibit order-level taxonomic patterns within \\u003cem\\u003eBasidiomycota\\u003c/em\\u003e, their distribution was analyzed across multiple fungal orders. In \\u003cem\\u003eBasidiomycota\\u003c/em\\u003e, GATA TFs were identified from 24 orders across 8 classes, showing considerable diversity in species coverage and total GATA TF counts. Orders within \\u003cem\\u003eAgaricomycetes\\u003c/em\\u003e dominated the dataset, including \\u003cem\\u003eAgaricales\\u003c/em\\u003e (32 species; 298 GATA TFs; 9.0\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;2.5 per species), \\u003cem\\u003ePolyporales\\u003c/em\\u003e (24 species; 150 GATA TFs; 6.3\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;1.6), and \\u003cem\\u003eBoletales\\u003c/em\\u003e (9 species; 71 GATA TFs; 7.9\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;2.7). Other orders, such as \\u003cem\\u003eTremellales\\u003c/em\\u003e (13 species; 65 GATA TFs; 5.0\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.4) and \\u003cem\\u003eUstilaginales\\u003c/em\\u003e (9 species; 78 GATA TFs; 8.7\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;2.1), also exhibited substantial GATA TF representation. Several orders had a limited number of species, including \\u003cem\\u003eLeucosporidiales\\u003c/em\\u003e (1 species; 10 GATA TFs) and \\u003cem\\u003eMixiales\\u003c/em\\u003e (1 species; 6 GATA TFs). Overall, GATA TFs in \\u003cem\\u003eBasidiomycota\\u003c/em\\u003e were unevenly distributed across orders, driven by the concentration of species within specific lineages.\\u003c/p\\u003e\\n\\u003ch3\\u003e3-4-3. Domain architecture of Dikarya GATA TFs\\u003c/h3\\u003e\\n\\u003cp\\u003eTo investigate whether GATA TF domain architectures are conserved or lineage-specific at the order level across \\u003cem\\u003eDikarya\\u003c/em\\u003e, the domain composition was examined (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig9\\\" class=\\\"InternalRef\\\"\\u003e9\\u003c/span\\u003ed). In \\u003cem\\u003eAscomycota\\u003c/em\\u003e, most orders exhibited PF00320 (GATA zinc finger) domain frequencies of 50.0%-70.0%, whereas early-diverging orders\\u0026mdash;including \\u003cem\\u003ePhaffomycetales, Saccharomycetales, Alaninales, Pichiales, Serinales, Lipomycetales, Schizosaccharomycetales, and Neolectales\\u003c/em\\u003e\\u0026mdash;displayed comparatively higher frequencies of 75.0%-100.0%. Consistently, while most orders contained additional major domains at moderate frequencies, the early-diverging orders exhibited low or absent occurrences of PF08447 (PAS fold), PF08550 (Nitrogen regulatory protein areA, GATA-like domain), and PF13426 (PAS domain). Uniquely, the PF07573 (Nitrogen regulatory protein AreA N terminus) domain was found exclusively in \\u003cem\\u003eEurotiales\\u003c/em\\u003e. Among the 38 orders, minor domains were generally detected at low frequencies in 13 orders. These patterns indicated that domain composition in \\u003cem\\u003eAscomycota\\u003c/em\\u003e GATA TFs was largely conserved, with early-diverging orders exhibiting distinctive deviations from the typical domain repertoire.\\u003c/p\\u003e \\u003cp\\u003eIn \\u003cem\\u003eBasidiomycota\\u003c/em\\u003e, PF00320 (GATA zinc finger) frequencies were generally 70.0%-90.0%, but early-diverging orders\\u0026mdash;\\u003cem\\u003eLeucosporidiales, Microbotryales, Sporidiobolales, and Mixiales\\u003c/em\\u003e\\u0026mdash;showed lower frequencies of 54.0\\u0026ndash;62.0%. This pattern was also reflected in the presence of additional major domains, which are absent in most orders but occurred exclusively in a few early-diverging orders, including PF08447 (PAS fold), PF13426 (PAS domain), and PF25026 (Asd-4-like domain). In contrast, early-diverging orders exhibited relatively lower frequencies of PF08550 (Nitrogen regulatory protein areA, GATA-like domain) compared with most other orders. Among the 24 orders, minor domains were generally detected at low frequencies in 13 orders, although some orders, such as \\u003cem\\u003eCantharellales\\u003c/em\\u003e (20.0%) and \\u003cem\\u003eSporidiobolales\\u003c/em\\u003e (18.3%), exhibit comparatively higher frequencies. Overall, these observations indicated that domain composition in \\u003cem\\u003eBasidiomycota\\u003c/em\\u003e GATA TFs was largely conserved, with early-diverging orders displaying distinctive patterns of major and minor domain retention.\\u003c/p\\u003e\\n\\u003ch3\\u003e3-4-4. GATA domain multiplicity of Dikarya GATA TFs\\u003c/h3\\u003e\\n\\u003cp\\u003eTo examine whether GATA domain multiplicity varies across fungal orders and contributes to lineage-specific structural diversity, order-level variation in the number of \\u003cem\\u003eDikarya\\u003c/em\\u003e GATA domains was examined (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig9\\\" class=\\\"InternalRef\\\"\\u003e9\\u003c/span\\u003ee). All GATA TFs across the orders contained either one or two GATA domains, with most of the 38 \\u003cem\\u003eAscomycota\\u003c/em\\u003e and 24 \\u003cem\\u003eBasidiomycota\\u003c/em\\u003e orders exhibiting high proportions of GATA TFs containing a single domain (70.0%-79.9%: 11 orders; 80.0%-89.9%: 36 orders; 90.0%-100.0%: 12 orders). Exceptions included \\u003cem\\u003eSchizosaccharomycetales\\u003c/em\\u003e (55.6%) in \\u003cem\\u003eAscomycota and Geastrales\\u003c/em\\u003e (0.0%) and \\u003cem\\u003eAgaricales\\u003c/em\\u003e (66.8%) in \\u003cem\\u003eBasidiomycota\\u003c/em\\u003e. Notably, as Geastrales contained only a single GATA TF, further sampling was required for robust comparative analysis. These findings suggested that, despite the predominance of single GATA domains across \\u003cem\\u003eDikarya\\u003c/em\\u003e GATA TFs, multi-domain configurations may have had functional implications in lineage-specific contexts.\\u003c/p\\u003e\\n\\u003ch3\\u003e3-4-5. Types of Dikarya GATA motifs\\u003c/h3\\u003e\\n\\u003cp\\u003eTo investigate whether GATA motif composition varies across fungal orders and exhibits lineage-specific distribution patterns in \\u003cem\\u003eDikarya\\u003c/em\\u003e, the distribution of GATA motif types was analyzed at the order level (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig9\\\" class=\\\"InternalRef\\\"\\u003e9\\u003c/span\\u003ef). In \\u003cem\\u003eAscomycota\\u003c/em\\u003e, among 35 orders, the proportion of Type IVa (CX\\u003csub\\u003e2\\u003c/sub\\u003eCX\\u003csub\\u003e17\\u003c/sub\\u003eCX\\u003csub\\u003e2\\u003c/sub\\u003eC) motifs ranged from \\u0026lt;\\u0026thinsp;40.0% (3 orders) to 40.0%-49.9% (5 orders), 50.0%-59.9% (21 orders), and \\u0026ge;\\u0026thinsp;60.0% (9 orders), with early-diverging orders exhibiting relatively higher frequencies. By contrast, Type IVb (CX\\u003csub\\u003e2\\u003c/sub\\u003eCX\\u003csub\\u003e18\\u003c/sub\\u003eCX\\u003csub\\u003e2\\u003c/sub\\u003eC) motifs were distributed as \\u0026lt;\\u0026thinsp;40.0% (13 orders), 40.0%-49.9% (18 orders), 50.0%-59.9% (4 orders), and \\u0026ge;\\u0026thinsp;60.0% (3 orders), with early-diverging orders showing comparatively lower representation. Several minor GATA motif types were detected in only a limited number of orders, including IVp (10 orders), IV19 (1 order), IVc (4 orders), and IVe (3 orders). Overall, GATA motif composition in \\u003cem\\u003eAscomycota\\u003c/em\\u003e was highly order-specific, with dominant motifs exhibiting contrasting trends in early-diverging lineages and minor motifs restricted to a few orders.\\u003c/p\\u003e \\u003cp\\u003eIn \\u003cem\\u003eBasidiomycota\\u003c/em\\u003e, among 24 orders, the proportion of Type IVa (CX\\u003csub\\u003e2\\u003c/sub\\u003eCX\\u003csub\\u003e17\\u003c/sub\\u003eCX\\u003csub\\u003e2\\u003c/sub\\u003eC) motifs was distributed as \\u0026lt;\\u0026thinsp;40.0% (4 orders), 40.0%-49.9% (4 orders), 50.0%-59.9% (13 orders), and \\u0026ge;\\u0026thinsp;60.0% (3 orders), with early-diverging orders exhibiting comparatively lower frequencies. Type IVb (CX\\u003csub\\u003e2\\u003c/sub\\u003eCX\\u003csub\\u003e18\\u003c/sub\\u003eCX\\u003csub\\u003e2\\u003c/sub\\u003eC) motifs, in contrast, were distributed as \\u0026lt;\\u0026thinsp;40.0% (5 orders), 40.0%-49.9% (13 orders), 50.0%-59.9% (4 orders), and \\u0026ge;\\u0026thinsp;60.0% (2 orders), with early-diverging orders displaying relatively higher representation. Minor GATA motif types were detected in a limited number of orders, including IVp (6 orders), IV19 (5 orders), IVc (1 order), and IVe (2 orders). Overall, GATA motif composition in \\u003cem\\u003eBasidiomycota\\u003c/em\\u003e was highly order-specific, with dominant motifs showing opposing trends in early-diverging orders and minor motifs restricted to a few lineages.\\u003c/p\\u003e\"},{\"header\":\"4. Discussion\",\"content\":\"\\u003cp\\u003e \\u003cb\\u003e4\\u0026thinsp;\\u0026minus;\\u0026thinsp;1. Importance of conserved structures in fungal GATA TFs\\u003c/b\\u003e \\u003c/p\\u003e \\u003cp\\u003eConserved structural features of fungal TFs constitute the molecular basis for functional stability and long-term evolutionary maintenance (Shelest \\u003cspan citationid=\\\"CR95\\\" class=\\\"CitationRef\\\"\\u003e2017\\u003c/span\\u003e). Among them, fungal GATA TFs exhibit persistent core structural components, particularly the CX\\u003csub\\u003e2\\u003c/sub\\u003eCX\\u003csub\\u003e17\\u0026minus;18\\u003c/sub\\u003eCX\\u003csub\\u003e2\\u003c/sub\\u003eC zinc finger motif and its associated domains, reflecting strong constraints linked to essential regulatory functions (Hu et al. \\u003cspan citationid=\\\"CR35\\\" class=\\\"CitationRef\\\"\\u003e2025\\u003c/span\\u003e). These conserved structures are indispensable for specific binding to WGATAR (W\\u0026thinsp;=\\u0026thinsp;A/T, R\\u0026thinsp;=\\u0026thinsp;A/G) motifs in target gene promoters and coordinated regulation of essential physiological processes (Merika and Orkin \\u003cspan citationid=\\\"CR72\\\" class=\\\"CitationRef\\\"\\u003e1993\\u003c/span\\u003e). Consequently, structural conservation in fungal GATA TFs reflects both shared evolutionary origin and the preservation of fundamental regulatory functions across fungal lineages.\\u003c/p\\u003e \\u003cp\\u003eBased on this perspective, the study aimed to determine whether fungal GATA TFs retain a conserved structural core while exhibiting lineage-specific diversification. To test this hypothesis, a genome-wide analysis of 7,846 GATA TFs from 796 fungal species was conducted to characterize conserved and variable structural features across taxonomic scales. Beyond broad taxonomic comparisons, order-level analyses were incorporated to capture finer-scale structural variation among closely related lineages. This approach revealed that fundamental structural elements were conserved across fungi, while domain architectures and motif compositions exhibited order-level, lineage-specific patterns. Overall, these findings underscore the role of conserved structural cores in essential regulatory functions and of taxonomic context in shaping structural diversification of fungal GATA TFs.\\u003c/p\\u003e\\n\\u003ch3\\u003e4 − 2. Lineage-specific expansion of fungal GATA TFs\\u003c/h3\\u003e\\n\\u003cp\\u003eLineage-specific expansion of fungal GATA TFs was hypothesized to be driven by adaptive regulatory demands rather than neutral variation across fungal genomes (Shelest \\u003cspan citationid=\\\"CR95\\\" class=\\\"CitationRef\\\"\\u003e2017\\u003c/span\\u003e). Within the \\u003cem\\u003eDikarya\\u003c/em\\u003e, both \\u003cem\\u003eAscomycota and Basidiomycota\\u003c/em\\u003e exhibited relatively conserved and limited GATA TF repertoires, typically comprising only a small number of TFs per genome, despite substantial variation in species diversity and overall proteome size. In contrast, early-diverging fungal lineages displayed pronounced heterogeneity in GATA TF repertoires, consistent with lineage-specific expansion and contraction dynamics (Shelest \\u003cspan citationid=\\\"CR95\\\" class=\\\"CitationRef\\\"\\u003e2017\\u003c/span\\u003e). Notably, \\u003cem\\u003eRozellomycota\\u003c/em\\u003e showed a marked reduction in GATA TF repertoire, likely reflecting extreme genome streamlining in \\u003cem\\u003eMicrosporidia\\u003c/em\\u003e (Peyretaillade et al. \\u003cspan citationid=\\\"CR82\\\" class=\\\"CitationRef\\\"\\u003e2011\\u003c/span\\u003e). Collectively, these patterns suggested that GATA TF evolution in fungi was shaped by lineage-specific selective pressures and associated regulatory demands, rather than by taxonomic breadth or overall proteome size (Nowick and Stubbs \\u003cspan citationid=\\\"CR76\\\" class=\\\"CitationRef\\\"\\u003e2010\\u003c/span\\u003e; Shelest \\u003cspan citationid=\\\"CR95\\\" class=\\\"CitationRef\\\"\\u003e2017\\u003c/span\\u003e).\\u003c/p\\u003e\\n\\u003ch3\\u003e4 − 3. Domain diversity of fungal GATA TFs\\u003c/h3\\u003e\\n\\u003cp\\u003eWhether fungal GATA TFs in diverse lineages function in conjunction with auxiliary domains to expand their regulatory capacity remains an open question. In this context, the distribution of domain architectures suggests that auxiliary domain acquisition in fungal GATA TFs is associated with discrete functional rewiring beyond the ancestral DNA-binding role (Hu et al. \\u003cspan citationid=\\\"CR35\\\" class=\\\"CitationRef\\\"\\u003e2025\\u003c/span\\u003e). For example, recurrent PF00320\\u0026thinsp;+\\u0026thinsp;PF08550 and PF07573 combinations suggest integration of GATA-mediated transcription with AreA-type nitrogen metabolite repression pathways, linking nitrogen regulatory programs to environmental nutrient-responsive gene expression outputs (Pfannm\\u0026uuml;ller et al. \\u003cspan citationid=\\\"CR83\\\" class=\\\"CitationRef\\\"\\u003e2017\\u003c/span\\u003e). Similarly, architectures involving PAS-related domains (PF08447 and PF13426) suggest enhanced signal-integration capacity, consistent with PAS-mediated regulation in circadian and signaling systems, enabling the integration of diverse environmental cues such as redox state or oxygen availability (Ponting and Aravind \\u003cspan citationid=\\\"CR85\\\" class=\\\"CitationRef\\\"\\u003e1997\\u003c/span\\u003e). The PF00320\\u0026thinsp;+\\u0026thinsp;PF25026 architecture likely represents an ASD4-type GATA module associated with developmental regulation and oligomerization-mediated transcriptional control, supporting distinct regulatory axes across domain combinations (Feng et al. \\u003cspan citationid=\\\"CR26\\\" class=\\\"CitationRef\\\"\\u003e2000\\u003c/span\\u003e). Additionally, minor domain architectures may contribute to lineage-specific regulatory fine-tuning by modulating transcriptional activity or protein interactions. Collectively, these results support a model in which auxiliary domain acquisition enables functional rewiring and regulatory expansion in fungal GATA TFs across diverse lineages.\\u003c/p\\u003e \\u003cp\\u003eClear lineage-specific patterns further suggest that domain diversification is linked to evolutionary adaptation. Within \\u003cem\\u003eDikarya\\u003c/em\\u003e, especially in \\u003cem\\u003eAscomycota\\u003c/em\\u003e, the enrichment and co-occurrence of nitrogen regulatory domains (PF08550 and PF07573) (Pfannm\\u0026uuml;ller et al. \\u003cspan citationid=\\\"CR83\\\" class=\\\"CitationRef\\\"\\u003e2017\\u003c/span\\u003e), PAS-related environmental sensing domains (PF08447 and PF13426) (Ponting and Aravind \\u003cspan citationid=\\\"CR85\\\" class=\\\"CitationRef\\\"\\u003e1997\\u003c/span\\u003e), and Asd-4-like domain (PF25026) (Feng et al. \\u003cspan citationid=\\\"CR26\\\" class=\\\"CitationRef\\\"\\u003e2000\\u003c/span\\u003e) reflect the expansion of regulatory complexity. In contrast, \\u003cem\\u003eBasidiomycota\\u003c/em\\u003e and early-diverging lineages generally exhibit more restricted domain repertoires and simpler architectures, indicating more constrained functional capacities. Notably, the recurrent association of PAS-related domains (PF08447 and PF13426) supports a conserved role in environmental sensing (Ponting and Aravind \\u003cspan citationid=\\\"CR85\\\" class=\\\"CitationRef\\\"\\u003e1997\\u003c/span\\u003e), while the widespread distribution of PF08550 underscores the central importance of nitrogen regulatory mechanisms (Pfannm\\u0026uuml;ller et al. \\u003cspan citationid=\\\"CR83\\\" class=\\\"CitationRef\\\"\\u003e2017\\u003c/span\\u003e). Exceptionally, the exclusive presence of PF00320-only architectures in \\u003cem\\u003eRozellomycota\\u003c/em\\u003e suggests the retention of a minimal functional unit in certain lineages. Taken together, these findings suggest that fungal GATA TF evolution is driven by domain turnover and combinatorial reuse, enabling lineage-specific regulatory diversification.\\u003c/p\\u003e \\u003cp\\u003eInterestingly, approximately 21% of the fungal GATA TFs analyzed in this study contain two GATA domains. Variation in GATA domain multiplicity thus likely represents an additional mechanism contributing to functional diversification beyond conserved structural elements. The presence of multiple GATA domains within a single protein may enhance DNA-binding versatility and enable more complex regulatory interactions, thereby expanding transcriptional specificity. This interpretation is supported by studies of metazoan GATA TFs, where multiple zinc fingers cooperatively modulate DNA binding and transcriptional activity. For example, in \\u003cem\\u003eGATA-1\\u003c/em\\u003e, interactions between the N- and C-terminal zinc fingers enhance both DNA binding and transactivation (Merika and Orkin \\u003cspan citationid=\\\"CR72\\\" class=\\\"CitationRef\\\"\\u003e1993\\u003c/span\\u003e), whereas \\u003cem\\u003eGATA-3\\u003c/em\\u003e exhibits alternative binding configurations, including cooperative binding between two molecules or simultaneous engagement of both zinc fingers within a single molecule, depending on motif spacing (Bates et al. \\u003cspan citationid=\\\"CR6\\\" class=\\\"CitationRef\\\"\\u003e2008\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eCollectively, these findings suggest that fungal GATA TF evolution may be driven by multilayered modular innovation, involving both auxiliary domain acquisition and domain copy number variation, which together may expand regulatory input space and enable lineage-specific functional specialization. This modular architecture would allow a single TF scaffold to integrate diverse environmental, metabolic, and developmental signals, thereby potentially facilitating the emergence of highly adaptable and context-dependent regulatory systems across fungal lineages.\\u003c/p\\u003e\\n\\u003ch3\\u003e4–4. Evolutionary conservation and divergence of fungal GATA motifs\\u003c/h3\\u003e\\n\\u003cp\\u003eTo date, a systematic analysis of GATA motif diversity across multiple fungal lineages has not been comprehensively performed. The coexistence of multiple GATA motif types in fungal GATA TFs therefore reflects a balance between strict functional constraints and adaptive structural flexibility (Jiang et al. \\u003cspan citationid=\\\"CR42\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e). In this study, the predominance of canonical motif types (Type IVa and IVb; CX\\u003csub\\u003e2\\u003c/sub\\u003eCX\\u003csub\\u003e17\\u0026minus;18\\u003c/sub\\u003eCX\\u003csub\\u003e2\\u003c/sub\\u003eC) across fungal divisions suggests that the core zinc finger architecture is under strong evolutionary constraint, as it is essential for stable zinc coordination and sequence-specific recognition of WGATAR elements (Hu et al. \\u003cspan citationid=\\\"CR35\\\" class=\\\"CitationRef\\\"\\u003e2025\\u003c/span\\u003e). Although Type IVc (CX\\u003csub\\u003e2\\u003c/sub\\u003eCX\\u003csub\\u003e20\\u003c/sub\\u003eCX\\u003csub\\u003e2\\u003c/sub\\u003eC) was previously reported in 14.0% of fungi (Park et al., 2006), a large-scale analysis across 796 species shows they are extremely rare (0.3%), indicating that Type IVc is not a major GATA TF type in fungi. Overall, the strong evolutionary constraint on the canonical zinc finger structure likely limits extensive diversification of motif length or cysteine spacing, thereby preserving the fundamental DNA-binding function required for transcriptional regulation across diverse fungal lineages (Lowry and Atchley \\u003cspan citationid=\\\"CR67\\\" class=\\\"CitationRef\\\"\\u003e2000\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eBuilding on these observations within fungi, the eukaryotic comparison reveals kingdom- and lineage-specific patterns in GATA motif distribution. Fungi display a relatively balanced representation of Type IVa and IVb motifs (CX\\u003csub\\u003e2\\u003c/sub\\u003eCX\\u003csub\\u003e17\\u0026minus;18\\u003c/sub\\u003eCX\\u003csub\\u003e2\\u003c/sub\\u003eC), contrasting with the strong skew toward Type IVa (CX\\u003csub\\u003e2\\u003c/sub\\u003eCX\\u003csub\\u003e17\\u003c/sub\\u003eCX\\u003csub\\u003e2\\u003c/sub\\u003eC) in animals and the predominance of Type IVb and IVc (CX\\u003csub\\u003e2\\u003c/sub\\u003eCX\\u003csub\\u003e18\\u0026minus;20\\u003c/sub\\u003eCX\\u003csub\\u003e2\\u003c/sub\\u003eC) in plants. This suggests that while the zinc finger core remains highly conserved in fungi, the distribution of minor motif types may reflect lineage-specific adaptations. Such controlled variation likely provides fungi with the flexibility to fine-tune regulatory interactions and respond to diverse ecological or developmental cues, without compromising the fundamental DNA-binding architecture.\\u003c/p\\u003e \\u003cp\\u003eWithin this conserved framework in fungal GATA motifs, divergence at non-core positions provides a mechanism for functional refinement without compromising structural integrity (Lowry and Atchley \\u003cspan citationid=\\\"CR67\\\" class=\\\"CitationRef\\\"\\u003e2000\\u003c/span\\u003e). Conserved cysteine residues and highly conserved non-cysteine positions are likely indispensable for maintaining zinc finger stability and DNA-binding affinity. Within this conserved framework, diverse residue types\\u0026mdash;including polar charged, polar uncharged, structurally important, and hydrophobic residues\\u0026mdash;coexist in a position-specific manner, suggesting that each position within the GATA motif may be subject to distinct functional and structural constraints (Desantis et al. \\u003cspan citationid=\\\"CR19\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e). In contrast, moderately conserved or lineage-specific residues may modulate binding specificity, protein-protein interactions, or regulatory context (Lowry and Atchley \\u003cspan citationid=\\\"CR67\\\" class=\\\"CitationRef\\\"\\u003e2000\\u003c/span\\u003e). Such peripheral variation may enable GATA TFs of the same motif type to participate in distinct regulatory networks, respond to different environmental cues, or interact with lineage-specific cofactors (Rest et al. \\u003cspan citationid=\\\"CR88\\\" class=\\\"CitationRef\\\"\\u003e2012\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eThe presence of rare or atypical GATA motif variants further suggests that limited structural innovation has occurred during fungal evolution, potentially through relaxed selective pressure or niche-specific adaptation (Jiang et al. \\u003cspan citationid=\\\"CR42\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e). However, the low frequency and restricted distribution of these variants indicate that extensive divergence of the GATA motif is generally disfavored. Collectively, these observations support a model in which fungal GATA TFs maintain a highly conserved DNA-binding core while permitting controlled, position-specific divergence that facilitates functional diversification within and across fungal lineages.\\u003c/p\\u003e\\n\\u003ch3\\u003e4–5. Insights into the co-occurrence of domains and GATA motifs\\u003c/h3\\u003e\\n\\u003cp\\u003eWhether specific fungal GATA motif types are functionally associated with particular auxiliary domain architectures remains unclear. To explore this relationship, this study systematically investigated the co-occurrence patterns of GATA motifs and auxiliary domain architectures in fungal GATA TFs across diverse lineages. Based on these analyses, the coexistence of diverse auxiliary domains alongside the GATA zinc finger in fungal GATA TFs likely reflects an evolutionary strategy to expand regulatory versatility without compromising the conserved DNA-binding core (Scazzocchio \\u003cspan citationid=\\\"CR90\\\" class=\\\"CitationRef\\\"\\u003e2000\\u003c/span\\u003e). While the GATA domain provides a stable framework for sequence-specific DNA recognition, additional domains may modulate transcriptional activity by mediating protein-protein interactions, subcellular localization, or responsiveness to environmental and developmental cues (Zhang et al. \\u003cspan citationid=\\\"CR117\\\" class=\\\"CitationRef\\\"\\u003e2020\\u003c/span\\u003e). In this context, domain diversification enables fungal GATA TFs to operate within increasingly complex regulatory networks, particularly in lineages exposed to variable ecological niches or metabolic demands (Moon et al. \\u003cspan citationid=\\\"CR74\\\" class=\\\"CitationRef\\\"\\u003e2025\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eThe non-random co-occurrence between specific domains and GATA motif types further suggests coordinated structural and functional optimization rather than independent evolutionary events (Zhang et al. \\u003cspan citationid=\\\"CR117\\\" class=\\\"CitationRef\\\"\\u003e2020\\u003c/span\\u003e). Distinct GATA motif types may fine-tune DNA-binding affinity, spacing tolerance, or cooperative binding behavior, but their functional potential is likely constrained or enhanced by the surrounding domain architecture (Trainor et al. \\u003cspan citationid=\\\"CR103\\\" class=\\\"CitationRef\\\"\\u003e2000\\u003c/span\\u003e). Domains that preferentially associate with certain motif types may impose structural constraints that stabilize particular zinc finger conformations, or may facilitate selective interactions with cofactors that favor specific motif configurations (Vishwanath et al. \\u003cspan citationid=\\\"CR106\\\" class=\\\"CitationRef\\\"\\u003e2018\\u003c/span\\u003e). Such coupling implies that motif diversification alone is insufficient for functional innovation; instead, effective regulatory specialization emerges from the integrated evolution of motif sequences and domain contexts.\\u003c/p\\u003e \\u003cp\\u003eCollectively, these patterns support a model in which fungal GATA TFs evolve through domain\\u0026ndash;motif co-adaptation, preserving a conserved transcriptional foundation while enabling lineage-specific regulatory refinement (Moon et al. \\u003cspan citationid=\\\"CR74\\\" class=\\\"CitationRef\\\"\\u003e2025\\u003c/span\\u003e). This coordinated evolution provides a mechanistic basis for how fungal GATA TFs balance structural conservation with functional diversification across phylogenetically and ecologically diverse lineages.\\u003c/p\\u003e\\n\\u003ch3\\u003e4–6. Phylogenetic insights into fungal GATA domains\\u003c/h3\\u003e\\n\\u003cp\\u003ePhylogenetic analysis of fungal GATA domains was performed to elucidate how motif types and domain architectures are distributed across evolutionary lineages and how these patterns contribute to functional diversification. As a result, a structured and non-random distribution of motif types and domain architectures was observed across the fungal tree. Type IVa motifs are predominantly enriched in the central regions of the tree, while Type IVb motifs are more frequent in peripheral branches, and intermediate regions display alternating clusters of Type IVa- and IVb-enriched clades. This spatial organization suggests that motif type is closely linked to phylogenetic position, reflecting both evolutionary history and lineage-specific diversification (Schwechheimer et al. \\u003cspan citationid=\\\"CR93\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eTo further investigate whether specific domain architectures are conserved or have independently emerged across fungal lineages, the distribution of recurrent domain combinations was examined at the phylogenetic level. Within these regions, multiple domain architectures recur across distinct clades, including PF00320-only, PF00320\\u0026thinsp;+\\u0026thinsp;PF08550, PF00320\\u0026thinsp;+\\u0026thinsp;PF25026, and PF00320\\u0026thinsp;+\\u0026thinsp;PF08447\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;PF13426 combinations. The repeated occurrence of these architectures in separate clades and across multiple fungal divisions indicates that certain motif-domain configurations have been evolutionarily favored and conserved. This pattern suggests that domain architecture may provide structural support or functional context for the zinc finger, allowing GATA TFs with identical motifs to adopt distinct regulatory roles depending on their domain composition (Schwechheimer et al. \\u003cspan citationid=\\\"CR93\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eTo assess whether the observed phylogenetic patterns are also reflected in known functional classifications, 25 functionally characterized fungal GATA TFs were mapped onto the phylogeny. Mapping of these TFs further supports this view, as TFs with similar biological functions tend to cluster within the same or closely related clades. However, many clades remain unannotated, highlighting the potential for undiscovered functional diversity within the fungal GATA family. Together, these findings indicate that fungal GATA TFs have evolved through a combination of conserved core motifs and modular domain arrangements, enabling both structural stability of the zinc finger and lineage-specific functional specialization (Schwechheimer et al. \\u003cspan citationid=\\\"CR93\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e).\\u003c/p\\u003e\\n\\u003ch3\\u003e4–7. Structural insights into functional diversity of fungal GATA TFs\\u003c/h3\\u003e\\n\\u003cp\\u003eStructural conservation of the GATA zinc finger, despite sequence and domain variation, is hypothesized to underlie the functional diversification of fungal GATA TFs. To examine this hypothesis, three-dimensional structural modeling of 11 representative fungal GATA TFs was performed, providing mechanistic insight into how functional diversity is accommodated without compromising the conserved DNA-binding module (Bertoline et al. \\u003cspan citationid=\\\"CR9\\\" class=\\\"CitationRef\\\"\\u003e2023\\u003c/span\\u003e). Across all analyzed fungal GATA TFs, including those with relatively rare GATA motif variants (e.g., IVc and IV19), the zinc finger fold within the GATA domain remains highly conserved, reinforcing the notion that structural integrity of the DNA-binding module is strongly constrained despite sequence variation. This observation aligns with previous analyses highlighting that the canonical zinc finger structure in fungal GATA TFs is under strict evolutionary conservation, ensuring reliable WGATAR recognition and transcriptional regulation (Merika and Orkin \\u003cspan citationid=\\\"CR72\\\" class=\\\"CitationRef\\\"\\u003e1993\\u003c/span\\u003e; Bates et al. \\u003cspan citationid=\\\"CR6\\\" class=\\\"CitationRef\\\"\\u003e2008\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eThe presence of dual GATA domains is hypothesized to facilitate cooperative DNA binding through spatially coordinated zinc finger modules. Consistent with this hypothesis, fungal GATA TFs containing dual GATA domains show that both domains independently maintain canonical zinc finger conformations, and their close spatial arrangement suggests potential cooperative interactions during DNA binding. Such arrangements may enhance sequence specificity or binding affinity, illustrating how modular repetition of the GATA domain can expand regulatory potential without altering the fundamental fold (Merika and Orkin \\u003cspan citationid=\\\"CR72\\\" class=\\\"CitationRef\\\"\\u003e1993\\u003c/span\\u003e; Bates et al. \\u003cspan citationid=\\\"CR6\\\" class=\\\"CitationRef\\\"\\u003e2008\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eAuxiliary domains are hypothesized to drive functional diversification of fungal GATA TFs without disrupting the conserved DNA-binding core. In support of this hypothesis, fungal GATA TFs with additional auxiliary domains show that the GATA domain consistently preserves its zinc finger architecture, whereas the overall protein conformation varies significantly due to the presence of extra domains. This indicates that while the DNA-binding core is structurally stable, domain additions enable context-dependent or specialized regulatory functions, likely by mediating protein-protein interactions, subcellular localization, or responsiveness to environmental cues (Pfannm\\u0026uuml;ller et al. \\u003cspan citationid=\\\"CR83\\\" class=\\\"CitationRef\\\"\\u003e2017\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eCollectively, these structural observations suggest that fungal GATA TFs achieve functional diversification through conserved zinc finger structures combined with flexible domain architectures. While the core DNA-binding function remained structurally conserved, variation in domain composition likely contributed to lineage- or context-specific regulatory specialization. To further evaluate whether structural regions outside canonical GATA domains corresponded to previously unrecognized protein folds, additional Foldseek analyses were performed for 11 representative fungal GATA TFs. Most detected structural similarities corresponded to previously annotated conserved domains, supporting the overall reliability of sequence-based domain annotation approaches. However, \\u003cem\\u003eWC1\\u003c/em\\u003e additionally exhibited structurally conserved regions outside known domains, suggesting the possible presence of highly diverged or uncharacterized structural elements. These findings demonstrate that integrating structural similarity analyses with conventional sequence-based annotation approaches can provide additional insight into the structural and functional diversity of fungal GATA TFs.\\u003c/p\\u003e\\n\\u003ch3\\u003e4–8. Regulatory diversification of fungal GATA TFs\\u003c/h3\\u003e\\n\\u003cp\\u003eTo comprehensively understand the regulatory diversification of fungal GATA TFs beyond sequence and domain-level variation, their physicochemical properties and subcellular localization features were systematically analyzed. The results revealed that fungal GATA TFs exhibit notable physicochemical diversity across lineages. The physicochemical diversity observed among fungal GATA TFs underscores a nuanced balance between conserved functional constraints and lineage-specific adaptations (Hu et al. \\u003cspan citationid=\\\"CR35\\\" class=\\\"CitationRef\\\"\\u003e2025\\u003c/span\\u003e). While hydrophilicity and aliphatic index remained largely conserved across divisions, substantial variation in protein length, molecular weight, and isoelectric point indicates structural flexibility extending beyond the core DNA-binding domain. Such variability likely enables divergent regulatory interactions, subcellular localization patterns, and protein-protein associations necessary to respond to distinct ecological and physiological contexts (Lambourne et al. \\u003cspan citationid=\\\"CR55\\\" class=\\\"CitationRef\\\"\\u003e2025\\u003c/span\\u003e). Notably, \\u003cem\\u003eDikarya\\u003c/em\\u003e GATA TFs exhibited broad yet relatively consistent profiles, reflecting both the evolutionary conservation of DNA-binding functionality and the necessity for moderate structural adaptation.\\u003c/p\\u003e \\u003cp\\u003eIn contrast, early-diverging lineages, particularly \\u003cem\\u003eNeocallimastigomycota\\u003c/em\\u003e, displayed extreme protein lengths and molecular weights, suggesting lineage-specific expansions or insertions that may facilitate specialized regulatory roles in unique environmental niches. Atypical physicochemical profiles, such as elevated aliphatic index or reduced hydrophilicity, appear to represent adaptive modifications rather than fundamental functional divergence (Smole et al. \\u003cspan citationid=\\\"CR97\\\" class=\\\"CitationRef\\\"\\u003e2011\\u003c/span\\u003e), potentially enhancing stability or interactions under specific metabolic or environmental conditions. Collectively, these findings indicate that fungal GATA TFs maintain core physicochemical constraints necessary for DNA-binding activity while accommodating structural and regulatory diversification that may underpin functional innovation across evolutionary lineages.\\u003c/p\\u003e \\u003cp\\u003eExtending beyond intrinsic physicochemical variation, analyses of TMHs and subcellular localization further highlight the regulatory complexity of fungal GATA TFs (Krogh et al. \\u003cspan citationid=\\\"CR53\\\" class=\\\"CitationRef\\\"\\u003e2001\\u003c/span\\u003e; \\u0026Oslash;dum et al. \\u003cspan citationid=\\\"CR78\\\" class=\\\"CitationRef\\\"\\u003e2024\\u003c/span\\u003e). The vast majority of GATA TFs were predicted to localize to the nucleus and lacked TMHs, consistent with their canonical role as soluble nuclear TFs (Lu et al. \\u003cspan citationid=\\\"CR68\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e). However, a small subset exhibited non-nuclear localization or contained one or more TMHs, including rare cases with multiple helices. These atypical features may reflect two complementary scenarios. On one hand, they could arise from annotation or prediction artefacts, such as incomplete gene models or inaccuracies in localization prediction (Krogh et al. \\u003cspan citationid=\\\"CR53\\\" class=\\\"CitationRef\\\"\\u003e2001\\u003c/span\\u003e; \\u0026Oslash;dum et al. \\u003cspan citationid=\\\"CR78\\\" class=\\\"CitationRef\\\"\\u003e2024\\u003c/span\\u003e). On the other hand, they may represent biologically meaningful regulatory strategies, including cytoplasmic retention, nucleocytoplasmic shuttling, or membrane-associated regulation coupled with signal-dependent activation, thereby expanding the functional versatility of these TFs beyond the nucleus (Cartwright and Helin* \\u003cspan citationid=\\\"CR11\\\" class=\\\"CitationRef\\\"\\u003e2000\\u003c/span\\u003e; Liu et al. \\u003cspan citationid=\\\"CR65\\\" class=\\\"CitationRef\\\"\\u003e2018b\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eNotably, the rarity and lineage-specific distribution of TMH-containing and non-nuclear GATA TFs suggest that these features are unlikely to result solely from stochastic errors. Instead, they may reflect specialized adaptations in particular fungal lineages, potentially facilitating context-dependent regulatory interactions or environmental responsiveness. For example, TMH acquisition or dual localization may enable membrane-associated signaling or cross-compartmental regulatory control, complementing the canonical nuclear transcriptional activity (Seo \\u003cspan citationid=\\\"CR94\\\" class=\\\"CitationRef\\\"\\u003e2014\\u003c/span\\u003e). Collectively, these observations indicate that fungal GATA TFs maintain the core physicochemical and structural constraints required for DNA binding. At the same time, they exhibit multilayered regulatory diversification across sequence composition, structural properties, and subcellular regulatory dynamics, which may underpin lineage-specific functional innovations.\\u003c/p\\u003e\\n\\u003ch3\\u003e4–9. Order-level architectural diversity of Dikarya GATA TFs\\u003c/h3\\u003e\\n\\u003cp\\u003eGiven that \\u003cem\\u003eDikarya\\u003c/em\\u003e encompasses the majority of fungal species diversity (Hyde \\u003cspan citationid=\\\"CR38\\\" class=\\\"CitationRef\\\"\\u003e2024\\u003c/span\\u003e), order-level analyses were conducted to resolve fine-scale structural variation in GATA TFs. At this level, GATA TFs exhibit substantial heterogeneity in their taxonomic distribution, despite the overall conservation of the PF00320 (GATA zinc finger). In both \\u003cem\\u003eAscomycota and Basidiomycota\\u003c/em\\u003e, GATA TFs are unevenly distributed across orders, with a subset of major orders contributing disproportionately to total GATA TF counts, whereas many orders contain relatively few species and limited numbers of GATA TFs. These patterns indicate lineage-specific expansion and retention of GATA TFs at the order level within \\u003cem\\u003eDikarya\\u003c/em\\u003e (Nowick and Stubbs \\u003cspan citationid=\\\"CR76\\\" class=\\\"CitationRef\\\"\\u003e2010\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eTo elucidate whether domain architecture diversification in GATA TFs follows conserved or lineage-specific evolutionary patterns across \\u003cem\\u003eDikarya\\u003c/em\\u003e, domain architecture analyses were conducted. The results reveal contrasting patterns between the two \\u003cem\\u003eDikarya\\u003c/em\\u003e divisions. In \\u003cem\\u003eAscomycota\\u003c/em\\u003e, most orders show consistent PF00320 frequencies, while early-diverging orders exhibit relatively higher proportions of this domain and reduced or absent representation of auxiliary domains such as PF08447, PF08550, and PF13426. In contrast, \\u003cem\\u003eBasidiomycota\\u003c/em\\u003e displays the opposite trend, where early-diverging orders show comparatively lower PF00320 frequencies and selective presence of auxiliary domains, including PF08447, PF13426, and PF25026. Additionally, certain domains are restricted to specific orders, such as PF07573 in \\u003cem\\u003eEurotiales\\u003c/em\\u003e, indicating order-specific innovations. These results demonstrate that domain composition does not follow a uniform pattern across \\u003cem\\u003eDikarya\\u003c/em\\u003e, but instead reflects division-specific evolutionary trajectories.\\u003c/p\\u003e \\u003cp\\u003eTo further determine whether motif composition patterns parallel the observed domain-level diversification across \\u003cem\\u003eDikarya\\u003c/em\\u003e, motif composition was analyzed. The results further reinforce these contrasting patterns. In \\u003cem\\u003eAscomycota\\u003c/em\\u003e, early-diverging orders tend to exhibit higher proportions of Type IVa motifs and lower proportions of Type IVb motifs. Conversely, in \\u003cem\\u003eBasidiomycota\\u003c/em\\u003e, early-diverging orders show relatively higher representation of Type IVb motifs and lower proportions of Type IVa motifs. This opposing distribution of major motif types between the two divisions indicates that motif evolution cannot be generalized across \\u003cem\\u003eDikarya\\u003c/em\\u003e as a whole. Minor motif types, including IVp, IV19, IVc, and IVe, are restricted to a limited number of orders in both divisions, suggesting constrained diversification. Collectively, these findings indicate that \\u003cem\\u003eDikarya\\u003c/em\\u003e GATA TFs maintain a conserved DNA-binding core while exhibiting division- and order-specific diversification in both domain architecture and motif composition.\\u003c/p\\u003e\\n\\u003ch3\\u003e4–10. Integrative analysis and biological implications\\u003c/h3\\u003e\\n\\u003cp\\u003eThe integrative analysis presented in this study provides a conceptual framework for understanding how fungal GATA TFs have diversified at both structural and functional levels across major fungal lineages. By jointly examining GATA motif variation, domain architecture, physicochemical properties, and their co-occurrence patterns, this work moves beyond descriptive cataloging and offers mechanistic insights into how GATA TF families have evolved lineage-specific regulatory roles.\\u003c/p\\u003e \\u003cp\\u003eFrom an evolutionary perspective, the observed diversification of GATA motif types and auxiliary domain compositions supports a model of modular evolution in fungal GATA TFs (Hu et al. \\u003cspan citationid=\\\"CR35\\\" class=\\\"CitationRef\\\"\\u003e2025\\u003c/span\\u003e). The conservation of the core GATA zinc finger underscores its essential role in DNA binding and transcriptional regulation, while variation in motif residues and flanking domains contributes to functional innovation (Lowry and Atchley \\u003cspan citationid=\\\"CR67\\\" class=\\\"CitationRef\\\"\\u003e2000\\u003c/span\\u003e). Subtle changes in zinc finger residues can alter DNA-binding affinity or sequence specificity, thereby reshaping target gene repertoires. Concurrently, auxiliary domains may mediate interactions with cofactors, chromatin modifiers, or signaling components, expanding regulatory capacity beyond core DNA recognition (Soto et al. \\u003cspan citationid=\\\"CR98\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e). Such modularity enables selective pressures to act independently on DNA-binding specificity and regulatory interaction capacity, facilitating functional diversification of GATA TFs without disrupting their fundamental transcriptional role (Lowry and Atchley \\u003cspan citationid=\\\"CR67\\\" class=\\\"CitationRef\\\"\\u003e2000\\u003c/span\\u003e). This evolutionary strategy is particularly advantageous in fungi, where rapid adaptation to ecological niches, nutrient availability, and host-associated lifestyles is often required (Naranjo-Ortiz and Gabald\\u0026oacute;n \\u003cspan citationid=\\\"CR75\\\" class=\\\"CitationRef\\\"\\u003e2019\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eFunctionally, the lineage-dependent enrichment of specific domain-motif combinations suggests that fungal GATA TFs have undergone specialization tailored to distinct regulatory contexts (Moon et al. \\u003cspan citationid=\\\"CR74\\\" class=\\\"CitationRef\\\"\\u003e2025\\u003c/span\\u003e). Domains associated with transcriptional activation, repression, or signal integration likely confer context-dependent regulatory behaviors, enabling GATA TFs to participate in diverse biological processes such as nitrogen metabolism, stress responses, morphogenesis, and pathogenicity (Hu et al. \\u003cspan citationid=\\\"CR35\\\" class=\\\"CitationRef\\\"\\u003e2025\\u003c/span\\u003e). The preferential co-occurrence of certain domains with specific GATA motif types implies that transcriptional output is not solely dictated by DNA-binding capability but emerges from coordinated interactions between motif structure and domain-mediated regulatory mechanisms (Hu et al. \\u003cspan citationid=\\\"CR35\\\" class=\\\"CitationRef\\\"\\u003e2025\\u003c/span\\u003e). This coupling may enhance regulatory precision, allowing fungi to fine-tune gene expression programs in response to environmental and developmental signals.\\u003c/p\\u003e \\u003cp\\u003eAt a mechanistic level, the integration of motif diversity with domain architecture provides insight into how transcriptional regulation is structurally encoded within GATA TFs. Variations in motif composition may alter zinc finger flexibility or DNA-contact geometry, while associated domains may stabilize these conformations or recruit specific cofactors (Cassandri et al. \\u003cspan citationid=\\\"CR12\\\" class=\\\"CitationRef\\\"\\u003e2017\\u003c/span\\u003e). Such interactions could influence promoter selectivity, cooperative binding, or chromatin engagement, offering a plausible mechanistic explanation for the functional heterogeneity observed among fungal GATA TFs (Lu et al. \\u003cspan citationid=\\\"CR69\\\" class=\\\"CitationRef\\\"\\u003e2012\\u003c/span\\u003e). Importantly, these features suggest that GATA TF function is best understood as an emergent property of the entire protein architecture rather than as an isolated domain function (Inukai et al. \\u003cspan citationid=\\\"CR40\\\" class=\\\"CitationRef\\\"\\u003e2017\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eBeyond individual proteins, the results have broader implications for understanding the diversification of GATA TF families at the lineage and organismal levels. The lineage-specific patterns uncovered in this study provide a foundation for correlating domain architecture variation with fungal life-history traits, ecological strategies, and phenotypic adaptations. For example, expansion or contraction of specific GATA TF subtypes may be linked to metabolic flexibility, host interaction strategies, or environmental resilience (Hu et al. \\u003cspan citationid=\\\"CR35\\\" class=\\\"CitationRef\\\"\\u003e2025\\u003c/span\\u003e). Such correlations open avenues for comparative analyses that connect TF evolution to organism-level traits and ecological success (Mer\\u0026eacute;nyi et al. \\u003cspan citationid=\\\"CR71\\\" class=\\\"CitationRef\\\"\\u003e2023\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eFinally, this study establishes a scalable framework for future integrative analyses of TF families in fungi and beyond. By combining motif-level resolution with domain architecture and physicochemical profiling, the approach enables systematic exploration of functional diversification across large genomic datasets. Future work integrating transcriptomic, chromatin accessibility, and phenotypic data could further refine the functional interpretations proposed here, allowing direct links between GATA TF structural variation and regulatory outcomes (Yang et al. \\u003cspan citationid=\\\"CR113\\\" class=\\\"CitationRef\\\"\\u003e2019\\u003c/span\\u003e; Huang et al. \\u003cspan citationid=\\\"CR36\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e). Collectively, the findings underscore the importance of domain-motif integration in shaping the evolutionary and functional landscape of fungal GATA TFs and highlight their role as dynamic regulators within complex fungal regulatory networks (Hu et al. \\u003cspan citationid=\\\"CR35\\\" class=\\\"CitationRef\\\"\\u003e2025\\u003c/span\\u003e).\\u003c/p\\u003e\\n\\u003ch3\\u003e4–11. Annotation variability in fungal genomes and its implications for GATA TF analysis\\u003c/h3\\u003e\\n\\u003cp\\u003eFungal GATA TFs were identified based on protein sequences annotated in both the EnsemblFungi (Yates et al. \\u003cspan citationid=\\\"CR114\\\" class=\\\"CitationRef\\\"\\u003e2026\\u003c/span\\u003e) and MycoCosm (Grigoriev et al. \\u003cspan citationid=\\\"CR30\\\" class=\\\"CitationRef\\\"\\u003e2014\\u003c/span\\u003e). These resources have been widely employed in large-scale comparative genomic studies (Lim et al. \\u003cspan citationid=\\\"CR61\\\" class=\\\"CitationRef\\\"\\u003e2020\\u003c/span\\u003e; Alouane et al. \\u003cspan citationid=\\\"CR2\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e; Cole et al. \\u003cspan citationid=\\\"CR15\\\" class=\\\"CitationRef\\\"\\u003e2025\\u003c/span\\u003e) and are thus considered reliable sources for comprehensive fungal GATA TF identification. While these resources provide extensive coverage of fungal genomes for comparative genomics, the quality and consistency of gene annotations can vary across species and between databases. Many genomes included in EnsemblFungi are derived from diverse external sources and are not uniformly processed through a single standardized re-annotation pipeline (Yates et al. \\u003cspan citationid=\\\"CR114\\\" class=\\\"CitationRef\\\"\\u003e2026\\u003c/span\\u003e). In contrast, MycoCosm genomes are annotated via JGI\\u0026rsquo;s standardized pipelines with additional quality control and community-based curation (Grigoriev et al. \\u003cspan citationid=\\\"CR30\\\" class=\\\"CitationRef\\\"\\u003e2014\\u003c/span\\u003e). Consequently, observed variation in GATA TF structure, domain composition, or abundance may partially reflect differences in annotation protocols rather than true biological divergence.\\u003c/p\\u003e \\u003cp\\u003eDespite these potential limitations, the observed trends remain robust and well-supported, as evidenced by the consistent identification of canonical GATA domains across high-quality genomes from both databases. In addition, major additional domain associations (PF08550, PF08447, PF13426, PF25026, and PF07573) were consistently detected across multiple phylogenetically diverse species, further supporting the reliability of the observed patterns. By prioritizing domain-level evidence and applying stringent E-value thresholds, the impact of potential inconsistencies in gene model annotations on downstream functional classification was effectively minimized. Nevertheless, a small fraction of GATA domains (~\\u0026thinsp;1%) may have been excluded under these stringent filtering criteria, highlighting the need for cautious interpretation when assessing domain multiplicity and abundance patterns.\\u003c/p\\u003e \\u003cp\\u003eIn summary, while annotation variability across EnsemblFungi and MycoCosm represents a potential source of bias, the overall patterns reported\\u0026mdash;both in canonical GATA domain conservation and major additional domain associations\\u0026mdash;are supported by multiple genomes from both resources and are likely to reflect genuine biological trends. Incorporating re-annotated or experimentally validated fungal genomes in future studies would further strengthen these conclusions and reduce residual uncertainty arising from heterogeneous annotations.\\u003c/p\\u003e\\n\\u003ch3\\u003e4–12. Limitations and future perspectives\\u003c/h3\\u003e\\n\\u003cp\\u003eThis study provides a comprehensive analysis of the structural features and evolutionary conservation of fungal GATA TFs, highlighting their significance; however, several limitations exist. First, although efforts were made to reduce taxonomic bias by analyzing 796 species across 11 fungal divisions, a balanced representation of all 19 currently recognized fungal divisions could not be achieved (Wijayawardene et al. \\u003cspan citationid=\\\"CR109\\\" class=\\\"CitationRef\\\"\\u003e2024\\u003c/span\\u003e). This limitation reflects the incomplete integration of publicly available genomic resources from diverse sources, including major repositories such as NCBI (Kitts et al. \\u003cspan citationid=\\\"CR50\\\" class=\\\"CitationRef\\\"\\u003e2016\\u003c/span\\u003e) and integrated platforms such as FungiDB (Basenko et al. \\u003cspan citationid=\\\"CR5\\\" class=\\\"CitationRef\\\"\\u003e2018\\u003c/span\\u003e). It suggests that structural patterns observed in underrepresented divisions should be interpreted with caution. Future studies incorporating more diverse and comprehensive genomic datasets will enable a deeper and more refined understanding of GATA TFs across the fungal kingdom (Li et al. \\u003cspan citationid=\\\"CR59\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eSecond, although the structural features of fungal GATA TFs have been comprehensively predicted, they have not been experimentally validated, leaving their functional relevance uncertain. Without direct evidence from mutational studies, DNA-binding assays, or in vivo characterization, it remains unclear whether the predicted motifs and domain architectures accurately reflect biological function. Consequently, while computational analyses provide valuable insights, caution is required in interpreting these predictions, and future experimental work is essential to confirm their functional significance.\\u003c/p\\u003e \\u003cp\\u003eThird, the functional roles of minor GATA motifs within fungal GATA TFs remain largely hypothetical, as their contribution to transcriptional regulation has not been experimentally demonstrated. Although these motifs are predicted based on sequence analysis and domain co-occurrence patterns, their specific impact on DNA-binding specificity, protein-protein interactions, or regulatory activity is unclear. The lack of empirical validation limits the ability to determine whether these minor motifs play essential or auxiliary roles in transcriptional control, and it remains uncertain how variations in these motifs influence the functional diversity of GATA TFs across different fungal lineages. Therefore, targeted experimental studies, including site-directed mutagenesis and functional assays, will be necessary to clarify the biological significance of these motifs (Du et al. \\u003cspan citationid=\\\"CR23\\\" class=\\\"CitationRef\\\"\\u003e2016\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eFourth, although 25 well-known fungal GATA TFs (such as \\u003cem\\u003eWC-1, WC-2, or NsdD\\u003c/em\\u003e (Zhang et al. \\u003cspan citationid=\\\"CR117\\\" class=\\\"CitationRef\\\"\\u003e2020\\u003c/span\\u003e)) were mapped onto phylogenetic clades to provide a functional framework, a substantial proportion of sequences could not be confidently assigned to any defined clade, limiting comprehensive functional interpretation. Consequently, fungal GATA TFs analyzed in this study could not be reliably functionally characterized, making it unclear whether they correspond to well-known regulators. This limitation is primarily due to the reliance on computational predictions and the lack of experimental validation, as many of the fungal species included have not been extensively studied at the molecular or functional level. Furthermore, the high diversity and lineage-specific expansion of GATA TFs across fungi complicate the identification of orthologous relationships, making functional assignment based solely on sequence similarity challenging. As a result, the precise regulatory roles of these GATA TFs remain largely unresolved, highlighting the need for targeted experimental assays to clarify their biological functions\\u003c/p\\u003e \\u003cp\\u003eLastly, although Foldseek-based structural analyses were performed for 11 representative fungal GATA TFs to assess whether structural regions outside canonical GATA domains correspond to previously unrecognized protein folds, a comprehensive fungal-wide evaluation of their prevalence was not conducted. In particular, systematic analysis across thousands of fungal GATA TFs would be required to determine how frequently such non-canonical or highly diverged structural elements occur within the fungal kingdom. However, such large-scale structural screening and comparative annotation were beyond the scope of the present study due to computational and resource constraints. Therefore, while this study provides evidence for additional structurally conserved regions beyond canonical domain annotations in specific cases (e.g., \\u003cem\\u003eWC1\\u003c/em\\u003e), the extent to which these features are widespread or represent rare exceptions across fungi remains unresolved. Future large-scale structural prediction and database-level comparative analyses will be necessary to clarify the evolutionary prevalence and diversity of such structural features across fungal TFs.\\u003c/p\\u003e\"},{\"header\":\"5. Conclusion\",\"content\":\"\\u003cp\\u003eThis study provides a comprehensive genome-wide comparative analysis of 7,846 GATA TFs across 796 fungal species, revealing a combination of strong structural conservation and lineage-specific diversification. The canonical GATA motifs are identified as a universally conserved core (CX\\u003csub\\u003e2\\u003c/sub\\u003eCX\\u003csub\\u003e17\\u0026minus;18\\u003c/sub\\u003eCX\\u003csub\\u003e2\\u003c/sub\\u003eC), whereas variations in domain composition, motif architecture, and domain copy number exhibit pronounced order-level specificity, particularly within \\u003cem\\u003eDikarya\\u003c/em\\u003e. In addition, integrative analyses incorporating motif-domain associations, phylogenetic relationships based on GATA domain sequences, and cross-kingdom comparisons with plant and animal GATA TFs further contextualize the evolutionary patterns of fungal GATA TFs within a broader eukaryotic framework. Structural predictions of representative fungal GATA TFs provide additional support for the inferred structural organization and potential functional relevance of conserved and divergent features. These observations suggest that the evolutionary trajectory of fungal GATA TFs is driven by selective structural modifications rather than uniform expansion, enabling functional diversification while preserving fundamental regulatory roles. Overall, the findings establish a comprehensive structural framework that enhances understanding of the evolutionary and regulatory landscape of fungal GATA TFs and provides a robust reference for future functional and comparative studies.\\u003c/p\\u003e\"},{\"header\":\"Declarations\",\"content\":\"\\u003cp\\u003eStatements \\u0026amp; declarations\\u003c/p\\u003e\\n\\u003cp\\u003eCompeting interests\\u003c/p\\u003e\\n\\u003cp\\u003eThe author declares no conflict of interest, financial or otherwise.\\u003c/p\\u003e\\n\\u003cp\\u003eFunding\\u003c/p\\u003e\\n\\u003cp\\u003eThis research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.\\u003c/p\\u003e\\n\\u003cp\\u003eAuthor contributions\\u003c/p\\u003e\\n\\u003cp\\u003eM.K. conducted all the work until the manuscript was finalized and published.\\u003c/p\\u003e\\n\\u003cp\\u003eData availability\\u003c/p\\u003e\\n\\u003cp\\u003eAll fungal GATA TFs analyzed in this study are publicly available from EnsemblFungi (https://fungi.ensembl.org/index.html) (Yates et al. 2026) and MycoCosm (https://mycocosm.jgi.doe.gov/) (Grigoriev et al. 2014).\\u003c/p\\u003e\\n\\u003cp\\u003eAcknowledgements\\u003c/p\\u003e\\n\\u003cp\\u003eThe author sincerely thanks the editor and the reviewers for their thorough review and valuable comments.\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003eEthical\\u0026nbsp;approval\\u003c/p\\u003e\\n\\u003cp\\u003eNot applicable.\\u003c/p\\u003e\\n\\u003cp\\u003eInformed\\u0026nbsp;consent\\u003c/p\\u003e\\n\\u003cp\\u003eNot\\u0026nbsp;applicable.\\u003cbr clear=\\\"all\\\"\\u003e \\u003c/p\\u003e\"},{\"header\":\"References\",\"content\":\"\\u003col\\u003e\\u003cli\\u003e\\u003cspan\\u003eAdnan M, Islam W, Gang L, Chen HYH (2022) Advanced research tools for fungal diversity and its impact on forest ecosystem. Environ Sci Pollut Res 29:45044\\u0026ndash;45062. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1007/s11356-022-20317-8\\u003c/span\\u003e\\u003cspan address=\\\"10.1007/s11356-022-20317-8\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eAlouane T, Rimbert H, Bormann J et al (2021) Comparative genomics of eight \\u003cem\\u003eFusarium graminearum\\u003c/em\\u003e strains with contrasting aggressiveness reveals an expanded open pangenome and extended effector content signatures. Int J Mol Sci 22:6257. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.3390/ijms22126257\\u003c/span\\u003e\\u003cspan address=\\\"10.3390/ijms22126257\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eAn Z, Zhao Q, McEvoy J et al (1997) The second finger of \\u003cem\\u003eUrbs1\\u003c/em\\u003e is required for iron-mediated repression of \\u003cem\\u003esid1\\u003c/em\\u003e in \\u003cem\\u003eUstilago maydis\\u003c/em\\u003e. Proc Natl Acad Sci 94:5882\\u0026ndash;5887. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1073/pnas.94.11.5882\\u003c/span\\u003e\\u003cspan address=\\\"10.1073/pnas.94.11.5882\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eBaldrian P, Větrovsk\\u0026yacute; T, Lepinay C, Kohout P (2022) High-throughput sequencing view on the magnitude of global fungal diversity. Fungal Divers 114:539\\u0026ndash;547. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1007/s13225-021-00472-y\\u003c/span\\u003e\\u003cspan address=\\\"10.1007/s13225-021-00472-y\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eBasenko EY, Pulman JA, Shanmugasundram A et al (2018) FungiDB: An integrated bioinformatic resource for fungi and \\u003cem\\u003eOomycetes\\u003c/em\\u003e. J Fungi 4:39. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.3390/jof4010039\\u003c/span\\u003e\\u003cspan address=\\\"10.3390/jof4010039\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eBates DL, Chen Y, Kim G et al (2008) Crystal structures of multiple GATA zinc fingers bound to DNA reveal new insights into DNA recognition and self-association by \\u003cem\\u003eGATA\\u003c/em\\u003e. J Mol Biol 381:1292\\u0026ndash;1306. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1016/j.jmb.2008.06.072\\u003c/span\\u003e\\u003cspan address=\\\"10.1016/j.jmb.2008.06.072\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eBeopoulos A, Cescut J, Haddouche R et al (2009) \\u003cem\\u003eYarrowia lipolytica\\u003c/em\\u003e as a model for bio-oil production. Prog Lipid Res 48:375\\u0026ndash;387. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1016/j.plipres.2009.08.005\\u003c/span\\u003e\\u003cspan address=\\\"10.1016/j.plipres.2009.08.005\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eBernardes NE, Takeda AAS, Dreyer TR et al (2017) Nuclear transport of the \\u003cem\\u003eNeurospora crassa\\u003c/em\\u003e NIT-2 transcription factor is mediated by importin-α. Biochem J 474:4091\\u0026ndash;4104. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1042/BCJ20170654\\u003c/span\\u003e\\u003cspan address=\\\"10.1042/BCJ20170654\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eBertoline LMF, Lima AN, Krieger JE, Teixeira SK (2023) Before and after AlphaFold2: An overview of protein structure prediction. Front Bioinforma 3:1120370. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.3389/fbinf.2023.1120370\\u003c/span\\u003e\\u003cspan address=\\\"10.3389/fbinf.2023.1120370\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eCa\\u0026ntilde;izares JV, Pallotti C, Sa\\u0026iacute;nz-Pardo I et al (2002) The \\u003cem\\u003eSRD2\\u003c/em\\u003e gene is involved in \\u003cem\\u003eSaccharomyces cerevisiae\\u003c/em\\u003e morphogenesis. Arch Microbiol 177:352\\u0026ndash;357. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1007/s00203-002-0400-z\\u003c/span\\u003e\\u003cspan address=\\\"10.1007/s00203-002-0400-z\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eCartwright P, Helin* K (2000) Nucleocytoplasmic shuttling of transcription factors. Cell Mol Life Sci 57:1193\\u0026ndash;1206. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1007/PL00000759\\u003c/span\\u003e\\u003cspan address=\\\"10.1007/PL00000759\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eCassandri M, Smirnov A, Novelli F et al (2017) Zinc-finger proteins in health and disease. Cell Death Discov 3:17071. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1038/cddiscovery.2017.71\\u003c/span\\u003e\\u003cspan address=\\\"10.1038/cddiscovery.2017.71\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eChen Y, Cao Y, Gai Y et al (2021) Genome-wide identification and functional characterization of GATA transcription factor gene family in \\u003cem\\u003eAlternaria alternata\\u003c/em\\u003e. J Fungi 7:1013. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.3390/jof7121013\\u003c/span\\u003e\\u003cspan address=\\\"10.3390/jof7121013\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eCoelho MA, Bakkeren G, Sun S et al (2017) Fungal sex: The \\u003cem\\u003eBasidiomycota\\u003c/em\\u003e. Microbiol Spectr 5. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1128/microbiolspec.FUNK-0046-2016\\u003c/span\\u003e\\u003cspan address=\\\"10.1128/microbiolspec.FUNK-0046-2016\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e. 5.3.12\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eCole J, Raguideau S, Abbaszadeh-Dahaji P et al (2025) Comparative genomic analysis of a metagenome-assembled genome reveals distinctive symbiotic traits in a \\u003cem\\u003eMucoromycotina\\u003c/em\\u003e fine root endophyte arbuscular mycorrhizal fungus. BMC Genomics 26:967. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1186/s12864-025-12149-w\\u003c/span\\u003e\\u003cspan address=\\\"10.1186/s12864-025-12149-w\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eCosma MP (2004) Daughter-specific repression of \\u003cem\\u003eSaccharomyces cerevisiae HO\\u003c/em\\u003e: \\u003cem\\u003eAsh1\\u003c/em\\u003e is the commander. EMBO Rep 5:953\\u0026ndash;957. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1038/sj.embor.7400251\\u003c/span\\u003e\\u003cspan address=\\\"10.1038/sj.embor.7400251\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eCourchesne WE, Magasanik B (1988) Regulation of nitrogen assimilation in \\u003cem\\u003eSaccharomyces cerevisiae\\u003c/em\\u003e: roles of the \\u003cem\\u003eURE2 and GLN3\\u003c/em\\u003e genes. J Bacteriol 170:708\\u0026ndash;713. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1128/jb.170.2.708-713.1988\\u003c/span\\u003e\\u003cspan address=\\\"10.1128/jb.170.2.708-713.1988\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eCunningham TS, Rai R, Cooper TG (2000) The level of \\u003cem\\u003eDAL80\\u003c/em\\u003e expression down-regulates GATA factor-mediated transcription in \\u003cem\\u003eSaccharomyces cerevisiae\\u003c/em\\u003e. J Bacteriol 182:6584\\u0026ndash;6591. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1128/JB.182.23.6584-6591.2000\\u003c/span\\u003e\\u003cspan address=\\\"10.1128/JB.182.23.6584-6591.2000\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eDesantis F, Miotto M, Di Rienzo L et al (2022) Spatial organization of hydrophobic and charged residues affects protein thermal stability and binding affinity. Sci Rep 12:12087. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1038/s41598-022-16338-5\\u003c/span\\u003e\\u003cspan address=\\\"10.1038/s41598-022-16338-5\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eDissanayake AJ, Liu J-K (2025) \\u003cem\\u003eAscomycota\\u003c/em\\u003e: Diversity, taxonomy and phylogeny, 2nd edition: editorial. J Fungi 11:419. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.3390/jof11060419\\u003c/span\\u003e\\u003cspan address=\\\"10.3390/jof11060419\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eDong Z, Zhang N, Liu Y et al (2019) Expression analysis and characterization of \\u003cem\\u003ezglp1\\u003c/em\\u003e in the Chinese tongue sole (\\u003cem\\u003eCynoglossus semilaevis\\u003c/em\\u003e). Gene 683:72\\u0026ndash;79. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1016/j.gene.2018.10.003\\u003c/span\\u003e\\u003cspan address=\\\"10.1016/j.gene.2018.10.003\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eDu H, Guan G, Xie J et al (2012) Roles of \\u003cem\\u003eCandida albicans Gat2\\u003c/em\\u003e, a GATA-type zinc finger transcription factor, in \\u003cem\\u003eBiofilm Formation\\u003c/em\\u003e, filamentous growth and virulence. PLoS ONE 7:e29707. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1371/journal.pone.0029707\\u003c/span\\u003e\\u003cspan address=\\\"10.1371/journal.pone.0029707\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eDu L, Tracy S, Rifkin SA (2016) Mutagenesis of GATA motifs controlling the endoderm regulator elt-2 reveals distinct dominant and secondary cis- regulatory elements. Dev Biol 412:160\\u0026ndash;170. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1016/j.ydbio.2016.02.013\\u003c/span\\u003e\\u003cspan address=\\\"10.1016/j.ydbio.2016.02.013\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eDutta AK, Phull PS (2021) Treatment of \\u003cem\\u003eHelicobacter pylori\\u003c/em\\u003e infection in the presence of penicillin allergy. World J Gastroenterol 27:7661\\u0026ndash;7668. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.3748/wjg.v27.i44.7661\\u003c/span\\u003e\\u003cspan address=\\\"10.3748/wjg.v27.i44.7661\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eFabian GR, Hess SM, Hopper AK (1990) \\u003cem\\u003esrd1\\u003c/em\\u003e, a \\u003cem\\u003eSaccharomyces cerevisiae\\u003c/em\\u003e suppressor of the temperature-sensitive pre-rRNA processing defect of \\u003cem\\u003errp1-1\\u003c/em\\u003e. Genetics 124:497\\u0026ndash;504. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1093/genetics/124.3.497\\u003c/span\\u003e\\u003cspan address=\\\"10.1093/genetics/124.3.497\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eFeng B, Haas H, Marzluf GA (2000) \\u003cem\\u003eASD4\\u003c/em\\u003e, a new GATA factor of \\u003cem\\u003eNeurospora crassa\\u003c/em\\u003e, displays sequence-specific DNA binding and functions in Ascus and Ascospore Development. Biochemistry 39:11065\\u0026ndash;11073. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1021/bi000886j\\u003c/span\\u003e\\u003cspan address=\\\"10.1021/bi000886j\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eFinn RD, Bateman A, Clements J et al (2014) Pfam: the protein families database. Nucleic Acids Res 42:D222\\u0026ndash;D230. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1093/nar/gkt1223\\u003c/span\\u003e\\u003cspan address=\\\"10.1093/nar/gkt1223\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eGai Z, Gui T, Muragaki Y (2011) The function of \\u003cem\\u003eTRPS1\\u003c/em\\u003e in the development and differentiation of bone, kidney, and hair follicles. Histol Histopathol 915\\u0026ndash;921. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.14670/HH-26.915\\u003c/span\\u003e\\u003cspan address=\\\"10.14670/HH-26.915\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eGasteiger E, Hoogland C, Gattiker A et al (2005) Protein identification and analysis tools on the ExPASy server. In: Walker JM (ed) The Proteomics Protocols Handbook. Humana, Totowa, NJ, pp 571\\u0026ndash;607\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eGrigoriev IV, Nikitin R, Haridas S et al (2014) MycoCosm portal: gearing up for 1000 fungal genomes. Nucleic Acids Res 42:D699\\u0026ndash;D704. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1093/nar/gkt1183\\u003c/span\\u003e\\u003cspan address=\\\"10.1093/nar/gkt1183\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eHawksworth DL, L\\u0026uuml;cking R (2017) Fungal diversity revisited: 2.2 to 3.8 million species. Microbiol Spectr 5. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1128/microbiolspec.FUNK-0052-2016\\u003c/span\\u003e\\u003cspan address=\\\"10.1128/microbiolspec.FUNK-0052-2016\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e. 5.4.10\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eHe C, Cheng H, Zhou R (2007) GATA family of transcription factors of vertebrates: phylogenetics and chromosomal synteny. J Biosci 32:1273\\u0026ndash;1280. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1007/s12038-007-0136-7\\u003c/span\\u003e\\u003cspan address=\\\"10.1007/s12038-007-0136-7\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eHe M-Q, Zhao R-L, Liu D-M et al (2022) Species diversity of \\u003cem\\u003eBasidiomycota\\u003c/em\\u003e. Fungal Divers 114:281\\u0026ndash;325. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1007/s13225-021-00497-3\\u003c/span\\u003e\\u003cspan address=\\\"10.1007/s13225-021-00497-3\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eHe Q, Cheng P, Yang Y et al (2002) \\u003cem\\u003eWhite Collar-1\\u003c/em\\u003e, a DNA binding transcription factor and a light sensor. Science 297:840\\u0026ndash;843. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1126/science.1072795\\u003c/span\\u003e\\u003cspan address=\\\"10.1126/science.1072795\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eHu D, Zhao R, Lin Y, Jiang C (2025) Evolution and functional diversity of GATA transcription factors in filamentous fungi: Structural characteristics, metabolic regulation and environmental response. Microbiol Res 16:120. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.3390/microbiolres16060120\\u003c/span\\u003e\\u003cspan address=\\\"10.3390/microbiolres16060120\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eHuang L, Li X, Dong L et al (2021) Profiling of chromatin accessibility identifies transcription factor binding sites across the genome of \\u003cem\\u003eAspergillus\\u003c/em\\u003e species. BMC Biol 19:189. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1186/s12915-021-01114-0\\u003c/span\\u003e\\u003cspan address=\\\"10.1186/s12915-021-01114-0\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eHui L, Wan C, Hai-tao D et al (2010) Direct microbial conversion of wheat straw into lipid by a cellulolytic fungus of \\u003cem\\u003eAspergillus oryzae\\u003c/em\\u003e A-4 in solid-state fermentation. Bioresour Technol 101:7556\\u0026ndash;7562. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1016/j.biortech.2010.04.027\\u003c/span\\u003e\\u003cspan address=\\\"10.1016/j.biortech.2010.04.027\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eHyde K (2024) The 2024 Outline of Fungi and fungus-like taxa. Mycosphere 15:5146\\u0026ndash;6239. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.5943/mycosphere/15/1/25\\u003c/span\\u003e\\u003cspan address=\\\"10.5943/mycosphere/15/1/25\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eHyde KD (2022) The numbers of fungi. Fungal Divers 114:1\\u0026ndash;1. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1007/s13225-022-00507-y\\u003c/span\\u003e\\u003cspan address=\\\"10.1007/s13225-022-00507-y\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eInukai S, Kock KH, Bulyk ML (2017) Transcription factor\\u0026ndash;DNA binding: beyond binding site motifs. Curr Opin Genet Dev 43:110\\u0026ndash;119. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1016/j.gde.2017.02.007\\u003c/span\\u003e\\u003cspan address=\\\"10.1016/j.gde.2017.02.007\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eIrisarri I, Baurain D, Brinkmann H et al (2017) Phylotranscriptomic consolidation of the jawed vertebrate timetree. Nat Ecol Evol 1:1370\\u0026ndash;1378. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1038/s41559-017-0240-5\\u003c/span\\u003e\\u003cspan address=\\\"10.1038/s41559-017-0240-5\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eJiang C, Lv G, Ge J et al (2021) Genome-wide identification of the GATA transcription factor family and their expression patterns under temperature and salt stress in \\u003cem\\u003eAspergillus oryzae\\u003c/em\\u003e. AMB Express 11:56. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1186/s13568-021-01212-w\\u003c/span\\u003e\\u003cspan address=\\\"10.1186/s13568-021-01212-w\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eJones P, Binns D, Chang H-Y et al (2014) InterProScan 5: genome-scale protein function classification. Bioinformatics 30:1236\\u0026ndash;1240. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1093/bioinformatics/btu031\\u003c/span\\u003e\\u003cspan address=\\\"10.1093/bioinformatics/btu031\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eKatoh K, Standley DM (2013) MAFFT multiple sequence alignment software version 7: Improvements in performance and usability. Mol Biol Evol 30:772\\u0026ndash;780. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1093/molbev/mst010\\u003c/span\\u003e\\u003cspan address=\\\"10.1093/molbev/mst010\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eKatz ME, Braunberger K, Yi G et al (2013) A p53-like transcription factor similar to \\u003cem\\u003eNdt80\\u003c/em\\u003e controls the response to nutrient stress in the filamentous fungus, \\u003cem\\u003eAspergillus nidulans\\u003c/em\\u003e. F1000Research 2:72. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.12688/f1000research.2-72.v1\\u003c/span\\u003e\\u003cspan address=\\\"10.12688/f1000research.2-72.v1\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eKim L, Hoe K-L, Yu YM et al (2012) The fission yeast GATA factor, \\u003cem\\u003eGaf1\\u003c/em\\u003e, modulates sexual development via direct down-regulation of \\u003cem\\u003este11\\u0026thinsp;+\\u003c/em\\u003e\\u0026thinsp;expression in response to nitrogen starvation. PLoS ONE 7:e42409. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1371/journal.pone.0042409\\u003c/span\\u003e\\u003cspan address=\\\"10.1371/journal.pone.0042409\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eKim M (2026) PlantGATA: a comprehensive database for plant GATA transcription factors. Funct Integr Genomics 26:90. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1007/s10142-026-01855-7\\u003c/span\\u003e\\u003cspan address=\\\"10.1007/s10142-026-01855-7\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eKim M (2024) Comparative analysis of amino acid sequence level in plant GATA transcription factors. Sci Rep 14:29786. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1038/s41598-024-81159-7\\u003c/span\\u003e\\u003cspan address=\\\"10.1038/s41598-024-81159-7\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eKim W, Mirdita M, Levy Karin E et al (2025) Rapid and sensitive protein complex alignment with Foldseek-Multimer. Nat Methods 22:469\\u0026ndash;472. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1038/s41592-025-02593-7\\u003c/span\\u003e\\u003cspan address=\\\"10.1038/s41592-025-02593-7\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eKitts PA, Church DM, Thibaud-Nissen F et al (2016) Assembly: a resource for assembled genomes at NCBI. Nucleic Acids Res 44:D73\\u0026ndash;D80. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1093/nar/gkv1226\\u003c/span\\u003e\\u003cspan address=\\\"10.1093/nar/gkv1226\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eKong S, Park S, Lee Y (2015) Systematic characterization of the bZIP transcription factor gene family in the rice blast fungus, \\u003cem\\u003eM agnaporthe oryzae\\u003c/em\\u003e. Environ Microbiol 17:1425\\u0026ndash;1443. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1111/1462-2920.12633\\u003c/span\\u003e\\u003cspan address=\\\"10.1111/1462-2920.12633\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eKotomura N, Tsunemine S, Kuragano M et al (2018) \\u003cem\\u003eSfh1\\u003c/em\\u003e, an essential component of the RSC chromatin remodeling complex, maintains genome integrity in fission yeast. Genes Cells 23:738\\u0026ndash;752. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1111/gtc.12629\\u003c/span\\u003e\\u003cspan address=\\\"10.1111/gtc.12629\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eKrogh A, Larsson B, Von Heijne G, Sonnhammer ELL (2001) Predicting transmembrane protein topology with a hidden markov model: application to complete genomes. J Mol Biol 305:567\\u0026ndash;580. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1006/jmbi.2000.4315\\u003c/span\\u003e\\u003cspan address=\\\"10.1006/jmbi.2000.4315\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eKumar R, Wang R-A (2016) Structure, expression and functions of \\u003cem\\u003eMTA\\u003c/em\\u003e genes. Gene 582:112\\u0026ndash;121. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1016/j.gene.2016.02.012\\u003c/span\\u003e\\u003cspan address=\\\"10.1016/j.gene.2016.02.012\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eLambourne L, Mattioli K, Santoso C et al (2025) Widespread variation in molecular interactions and regulatory properties among transcription factor isoforms. Mol Cell 85:1445\\u0026ndash;1466e13. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1016/j.molcel.2025.03.004\\u003c/span\\u003e\\u003cspan address=\\\"10.1016/j.molcel.2025.03.004\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eLee M-K, Kwon N-J, Choi JM et al (2014) \\u003cem\\u003eNsdD\\u003c/em\\u003e is a key repressor of asexual development in \\u003cem\\u003eAspergillus nidulans\\u003c/em\\u003e. Genetics 197:159\\u0026ndash;173. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1534/genetics.114.161430\\u003c/span\\u003e\\u003cspan address=\\\"10.1534/genetics.114.161430\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eLetunic I, Bork P (2024) Interactive Tree of Life (iTOL) v6: recent updates to the phylogenetic tree display and annotation tool. Nucleic Acids Res 52:W78\\u0026ndash;W82. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1093/nar/gkae268\\u003c/span\\u003e\\u003cspan address=\\\"10.1093/nar/gkae268\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eLewis ZA, Correa A, Schwerdtfeger C et al (2002) Overexpression of White Collar-1 (\\u003cem\\u003eWC‐1\\u003c/em\\u003e) activates circadian clock‐associated genes, but is not sufficient to induce most light‐regulated gene expression in \\u003cem\\u003eNeurospora crassa\\u003c/em\\u003e. Mol Microbiol 45:917\\u0026ndash;931. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1046/j.1365-2958.2002.03074.x\\u003c/span\\u003e\\u003cspan address=\\\"10.1046/j.1365-2958.2002.03074.x\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eLi Y, Steenwyk JL, Chang Y et al (2021) A genome-scale phylogeny of the kingdom Fungi. Curr Biol 31:1653\\u0026ndash;1665e5. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1016/j.cub.2021.01.074\\u003c/span\\u003e\\u003cspan address=\\\"10.1016/j.cub.2021.01.074\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eLiang Y, Zhang X, Liu Y et al (2021) GATA zinc finger domain-containing protein 2A (\\u003cem\\u003eGATAD2A\\u003c/em\\u003e) deficiency reactivates fetal haemoglobin in patients with β‐thalassaemia through impaired formation of methyl‐binding domain protein 2 (\\u003cem\\u003eMBD2\\u003c/em\\u003e)‐containing nucleosome remodelling and deacetylation (\\u003cem\\u003eNuRD\\u003c/em\\u003e) complex. Br J Haematol 193:1220\\u0026ndash;1227. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1111/bjh.17511\\u003c/span\\u003e\\u003cspan address=\\\"10.1111/bjh.17511\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eLim JJJ, Koh J, Moo JR et al (2020) Fungi.guru: Comparative genomic and transcriptomic resource for the fungi kingdom. Comput Struct Biotechnol J 18:3788\\u0026ndash;3795. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1016/j.csbj.2020.11.019\\u003c/span\\u003e\\u003cspan address=\\\"10.1016/j.csbj.2020.11.019\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eLin CP-C, Kim C, Smith SO, Neiman AM (2013) A Highly Redundant Gene Network Controls Assembly of the Outer Spore Wall in \\u003cem\\u003eS. cerevisiae\\u003c/em\\u003e. PLoS Genet 9:e1003700. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1371/journal.pgen.1003700\\u003c/span\\u003e\\u003cspan address=\\\"10.1371/journal.pgen.1003700\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eLinden H (1997) \\u003cem\\u003eWhite collar 2\\u003c/em\\u003e, a partner in blue-light signal transduction, controlling expression of light-regulated genes in \\u003cem\\u003eNeurospora crassa\\u003c/em\\u003e. EMBO J 16:98\\u0026ndash;109. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1093/emboj/16.1.98\\u003c/span\\u003e\\u003cspan address=\\\"10.1093/emboj/16.1.98\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eLiu L, Wang Q, Zhang X et al (2018a) \\u003cem\\u003eSsams2\\u003c/em\\u003e, a gene encoding GATA transcription factor, is required for appressoria formation and chromosome segregation in \\u003cem\\u003eSclerotinia sclerotiorum\\u003c/em\\u003e. Front Microbiol 9:3031. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.3389/fmicb.2018.03031\\u003c/span\\u003e\\u003cspan address=\\\"10.3389/fmicb.2018.03031\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eLiu Y, Li P, Fan L, Wu M (2018b) The nuclear transportation routes of membrane-bound transcription factors. Cell Commun Signal 16:12. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1186/s12964-018-0224-3\\u003c/span\\u003e\\u003cspan address=\\\"10.1186/s12964-018-0224-3\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eLorenzini M, Cappello MS, Logrieco A, Zapparoli G (2016) Polymorphism and phylogenetic species delimitation in filamentous fungi from predominant mycobiota in withered grapes. Int J Food Microbiol 238:56\\u0026ndash;62. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1016/j.ijfoodmicro.2016.08.039\\u003c/span\\u003e\\u003cspan address=\\\"10.1016/j.ijfoodmicro.2016.08.039\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eLowry JA, Atchley WR (2000) Molecular evolution of the GATA family of transcription factors: Conservation within the DNA-binding domain. J Mol Evol 50:103\\u0026ndash;115. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1007/s002399910012\\u003c/span\\u003e\\u003cspan address=\\\"10.1007/s002399910012\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eLu J, Wu T, Zhang B et al (2021) Types of nuclear localization signals and mechanisms of protein import into the nucleus. Cell Commun Signal 19:60. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1186/s12964-021-00741-y\\u003c/span\\u003e\\u003cspan address=\\\"10.1186/s12964-021-00741-y\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eLu Y, Su C, Liu H (2012) A GATA transcription factor recruits \\u003cem\\u003eHda1\\u003c/em\\u003e in response to reduced \\u003cem\\u003eTor1\\u003c/em\\u003e signaling to establish a hyphal chromatin state in \\u003cem\\u003eCandida albicans\\u003c/em\\u003e. PLoS Pathog 8:e1002663. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1371/journal.ppat.1002663\\u003c/span\\u003e\\u003cspan address=\\\"10.1371/journal.ppat.1002663\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eL\\u0026uuml;cking R, Aime MC, Robbertse B et al (2021) Fungal taxonomy and sequence-based nomenclature. Nat Microbiol 6:540\\u0026ndash;548. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1038/s41564-021-00888-x\\u003c/span\\u003e\\u003cspan address=\\\"10.1038/s41564-021-00888-x\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eMer\\u0026eacute;nyi Z, Krizs\\u0026aacute;n K, Sahu N et al (2023) Genomes of fungi and relatives reveal delayed loss of ancestral gene families and evolution of key fungal traits. Nat Ecol Evol 7:1221\\u0026ndash;1231. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1038/s41559-023-02095-9\\u003c/span\\u003e\\u003cspan address=\\\"10.1038/s41559-023-02095-9\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eMerika M, Orkin SH (1993) DNA-binding specificity of GATA family transcription factors. Mol Cell Biol 13:3999\\u0026ndash;4010. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1128/mcb.13.7.3999-4010.1993\\u003c/span\\u003e\\u003cspan address=\\\"10.1128/mcb.13.7.3999-4010.1993\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eMinh BQ, Schmidt HA, Chernomor O et al (2020) IQ-TREE 2: New models and efficient methods for phylogenetic inference in the genomic era. Mol Biol Evol 37:1530\\u0026ndash;1534. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1093/molbev/msaa015\\u003c/span\\u003e\\u003cspan address=\\\"10.1093/molbev/msaa015\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eMoon H, Lee M-K, Shin J et al (2025) Species-specific gene regulatory network rewiring mediated by the GATA-type regulator. NsdD Aspergillus mBio 16:e01181\\u0026ndash;e01125. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1128/mbio.01181-25\\u003c/span\\u003e\\u003cspan address=\\\"10.1128/mbio.01181-25\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eNaranjo-Ortiz MA, Gabald\\u0026oacute;n T (2019) Fungal evolution: major ecological adaptations and evolutionary transitions. Biol Rev 94:1443\\u0026ndash;1476. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1111/brv.12510\\u003c/span\\u003e\\u003cspan address=\\\"10.1111/brv.12510\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eNowick K, Stubbs L (2010) Lineage-specific transcription factors and the evolution of gene regulatory networks. Brief Funct Genomics 9:65\\u0026ndash;78. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1093/bfgp/elp056\\u003c/span\\u003e\\u003cspan address=\\\"10.1093/bfgp/elp056\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eOberegger H, Schoeser M, Zadra I et al (2001) \\u003cem\\u003eSREA\\u003c/em\\u003e is involved in regulation of siderophore biosynthesis, utilization and uptake in \\u003cem\\u003eAspergillus nidulans\\u003c/em\\u003e. Mol Microbiol 41:1077\\u0026ndash;1089. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1046/j.1365-2958.2001.02586.x\\u003c/span\\u003e\\u003cspan address=\\\"10.1046/j.1365-2958.2001.02586.x\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003e\\u0026Oslash;dum MT, Teufel F, Thumuluri V et al (2024) DeepLoc 2.1: multi-label membrane protein type prediction using protein language models. Nucleic Acids Res 52:W215\\u0026ndash;W220. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1093/nar/gkae237\\u003c/span\\u003e\\u003cspan address=\\\"10.1093/nar/gkae237\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eOrban A, Fraatz MA, R\\u0026uuml;hl M (2019) Aroma profile analyses of filamentous fungi cultivated on solid substrates. Solid State Fermentation. Springer International Publishing, Cham, pp 85\\u0026ndash;107\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003ePark H-M, Son Y-E, Cho H-J et al (2025) Characterization of blue light receptors \\u003cem\\u003eLreA and LreB\\u003c/em\\u003e in \\u003cem\\u003eAspergillus flavus\\u003c/em\\u003e. J Microbiol Biotechnol 35:e2411054. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.4014/jmb.2411.11054\\u003c/span\\u003e\\u003cspan address=\\\"10.4014/jmb.2411.11054\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003ePelletier B, Beaudoin J, Mukai Y, Labb\\u0026eacute; S (2002) \\u003cem\\u003eFep1\\u003c/em\\u003e, an iron sensor regulating iron transporter gene expression in \\u003cem\\u003eSchizosaccharomyces pombe\\u003c/em\\u003e. J Biol Chem 277:22950\\u0026ndash;22958. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1074/jbc.M202682200\\u003c/span\\u003e\\u003cspan address=\\\"10.1074/jbc.M202682200\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003ePeyretaillade E, El Alaoui H, Diogon M et al (2011) Extreme reduction and compaction of microsporidian genomes. Res Microbiol 162:598\\u0026ndash;606. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1016/j.resmic.2011.03.004\\u003c/span\\u003e\\u003cspan address=\\\"10.1016/j.resmic.2011.03.004\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003ePfannm\\u0026uuml;ller A, Leufken J, Studt L et al (2017) Comparative transcriptome and proteome analysis reveals a global impact of the nitrogen regulators \\u003cem\\u003eAreA and AreB\\u003c/em\\u003e on secondary metabolism in \\u003cem\\u003eFusarium fujikuroi\\u003c/em\\u003e. PLoS ONE 12:e0176194. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1371/journal.pone.0176194\\u003c/span\\u003e\\u003cspan address=\\\"10.1371/journal.pone.0176194\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003ePlaster N, Sonntag C, Schilling TF, Hammerschmidt M (2007) \\u003cem\\u003eREREa/Atrophin-2\\u003c/em\\u003e interacts with histone deacetylase and \\u003cem\\u003eFgf8\\u003c/em\\u003e signaling to regulate multiple processes of zebrafish development. Dev Dyn 236:1891\\u0026ndash;1904. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1002/dvdy.21196\\u003c/span\\u003e\\u003cspan address=\\\"10.1002/dvdy.21196\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003ePonting CP, Aravind L (1997) PAS: a multifunctional domain family comes to light. Curr Biol 7:R674\\u0026ndash;R677. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1016/S0960-9822(06)00352-6\\u003c/span\\u003e\\u003cspan address=\\\"10.1016/S0960-9822(06)00352-6\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003ePuttick MN, Morris JL, Williams TA et al (2018) The interrelationships of land plants and the nature of the ancestral \\u003cem\\u003eembryophyte\\u003c/em\\u003e. Curr Biol 28:733\\u0026ndash;745e2. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1016/j.cub.2018.01.063\\u003c/span\\u003e\\u003cspan address=\\\"10.1016/j.cub.2018.01.063\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eRaghukumar S (2017) Fungi: Characteristics and classification. Fungi in Coastal and Oceanic Marine Ecosystems. Springer International Publishing, Cham, pp 1\\u0026ndash;15\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eRest JS, Bullaughey K, Morris GP, Li W-H (2012) Contribution of transcription factor binding site motif variants to condition-specific gene expression patterns in budding yeast. PLoS ONE 7:e32274. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1371/journal.pone.0032274\\u003c/span\\u003e\\u003cspan address=\\\"10.1371/journal.pone.0032274\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eRubio A, Ghosh S, M\\u0026uuml;lleder M et al (2021) Ribosome profiling reveals ribosome stalling on tryptophan codons and ribosome queuing upon oxidative stress in fission yeast. Nucleic Acids Res 49:383\\u0026ndash;399. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1093/nar/gkaa1180\\u003c/span\\u003e\\u003cspan address=\\\"10.1093/nar/gkaa1180\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eScazzocchio C (2000) The fungal GATA factors. Curr Opin Microbiol 3:126\\u0026ndash;131. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1016/S1369-5274(00)00063-1\\u003c/span\\u003e\\u003cspan address=\\\"10.1016/S1369-5274(00)00063-1\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eSchoch CL, Ciufo S, Domrachev M et al (2020) NCBI Taxonomy: a comprehensive update on curation, resources and tools. Database 2020:baaa062. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1093/database/baaa062\\u003c/span\\u003e\\u003cspan address=\\\"10.1093/database/baaa062\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eSchumacher J, Simon A, Cohrs KC et al (2014) The transcription factor \\u003cem\\u003eBcLTF1\\u003c/em\\u003e regulates virulence and light responses in the necrotrophic plant pathogen \\u003cem\\u003eBotrytis cinerea\\u003c/em\\u003e. PLoS Genet 10:e1004040. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1371/journal.pgen.1004040\\u003c/span\\u003e\\u003cspan address=\\\"10.1371/journal.pgen.1004040\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eSchwechheimer C, Schr\\u0026ouml;der PM, Blaby-Haas CE (2022) Plant GATA factors: Their biology, phylogeny, and phylogenomics. Annu Rev Plant Biol 73:123\\u0026ndash;148. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1146/annurev-arplant-072221-092913\\u003c/span\\u003e\\u003cspan address=\\\"10.1146/annurev-arplant-072221-092913\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eSeo PJ (2014) Recent advances in plant membrane-bound transcription factor research: Emphasis on intracellular movement. J Integr Plant Biol 56:334\\u0026ndash;342. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1111/jipb.12139\\u003c/span\\u003e\\u003cspan address=\\\"10.1111/jipb.12139\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eShelest E (2017) Transcription factors in fungi: TFome dynamics, three major families, and dual-specificity TFs. Front Genet 8:53. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.3389/fgene.2017.00053\\u003c/span\\u003e\\u003cspan address=\\\"10.3389/fgene.2017.00053\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eShen W-K, Chen S-Y, Gan Z-Q et al (2023) AnimalTFDB 4.0: a comprehensive animal transcription factor database updated with variation and expression annotations. Nucleic Acids Res 51:D39\\u0026ndash;D45. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1093/nar/gkac907\\u003c/span\\u003e\\u003cspan address=\\\"10.1093/nar/gkac907\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eSmole Z, Nikolic N, Supek F et al (2011) Proteome sequence features carry signatures of the environmental niche of prokaryotes. BMC Evol Biol 11:26. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1186/1471-2148-11-26\\u003c/span\\u003e\\u003cspan address=\\\"10.1186/1471-2148-11-26\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eSoto LF, Li Z, Santoso CS et al (2022) Compendium of human transcription factor effector domains. Mol Cell 82:514\\u0026ndash;526. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1016/j.molcel.2021.11.007\\u003c/span\\u003e\\u003cspan address=\\\"10.1016/j.molcel.2021.11.007\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eSoussi-Boudekou S, Vissers S, Urrestarazu A et al (1997) \\u003cem\\u003eGzf3p\\u003c/em\\u003e, a fourth GATA factor involved in nitrogen‐regulated transcription in \\u003cem\\u003eSaccharomyces cerevisiae\\u003c/em\\u003e. Mol Microbiol 23:1157\\u0026ndash;1168. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1046/j.1365-2958.1997.3021665.x\\u003c/span\\u003e\\u003cspan address=\\\"10.1046/j.1365-2958.1997.3021665.x\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eStajich JE (2017) Fungal genomes and insights into the evolution of the kingdom. Microbiol Spectr 5. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1128/microbiolspec.FUNK-0055-2016\\u003c/span\\u003e\\u003cspan address=\\\"10.1128/microbiolspec.FUNK-0055-2016\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e. 5.4.15\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eTakayama Y, Mamnun YM, Trickey M et al (2010) \\u003cem\\u003eHsk1- and SCFPof3-\\u003c/em\\u003edependent proteolysis of \\u003cem\\u003eS. pombe Ams2\\u003c/em\\u003e ensures histone homeostasis and centromere function. Dev Cell 18:385\\u0026ndash;396. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1016/j.devcel.2009.12.024\\u003c/span\\u003e\\u003cspan address=\\\"10.1016/j.devcel.2009.12.024\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eTedersoo L, S\\u0026aacute;nchez-Ram\\u0026iacute;rez S, K\\u0026otilde;ljalg U et al (2018) High-level classification of the fungi and a tool for evolutionary ecological analyses. Fungal Divers 90:135\\u0026ndash;159. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1007/s13225-018-0401-0\\u003c/span\\u003e\\u003cspan address=\\\"10.1007/s13225-018-0401-0\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eTrainor CD, Ghirlando R, Simpson MA (2000) GATA zinc finger interactions modulate DNA binding and transactivation. J Biol Chem 275:28157\\u0026ndash;28166. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1074/jbc.M000020200\\u003c/span\\u003e\\u003cspan address=\\\"10.1074/jbc.M000020200\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eVan Kempen M, Kim SS, Tumescheit C et al (2024) Fast and accurate protein structure search with Foldseek. Nat Biotechnol 42:243\\u0026ndash;246. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1038/s41587-023-01773-0\\u003c/span\\u003e\\u003cspan address=\\\"10.1038/s41587-023-01773-0\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eVirolainen P, Pankova V, Nerezenko A, Chekunova E (2026) Structural features of algal and fungal GATA transcription factors may play a role in symbiosis. J Mol Evol. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1007/s00239-026-10310-x\\u003c/span\\u003e\\u003cspan address=\\\"10.1007/s00239-026-10310-x\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eVishwanath S, De Brevern AG, Srinivasan N (2018) Same but not alike: Structure, flexibility and energetics of domains in multi-domain proteins are influenced by the presence of other domains. PLOS Comput Biol 14:e1006008. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1371/journal.pcbi.1006008\\u003c/span\\u003e\\u003cspan address=\\\"10.1371/journal.pcbi.1006008\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eWadhwa K, Kapoor N, Kaur H et al (2024) A comprehensive review of the diversity of fungal secondary metabolites and their emerging applications in healthcare and environment. Mycobiology 52:335\\u0026ndash;387. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1080/12298093.2024.2416736\\u003c/span\\u003e\\u003cspan address=\\\"10.1080/12298093.2024.2416736\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eWang Y-P, Liu L, Wang X-S et al (2021) \\u003cem\\u003eGAT1\\u003c/em\\u003e gene, the GATA transcription activator, regulates the production of higher alcohol during wheat beer fermentation by \\u003cem\\u003eSaccharomyces cerevisiae\\u003c/em\\u003e. Bioengineering 8:61. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.3390/bioengineering8050061\\u003c/span\\u003e\\u003cspan address=\\\"10.3390/bioengineering8050061\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eWijayawardene NN, Hyde KD, Mikhailov KV et al (2024) Classes and phyla of the kingdom fungi. Fungal Divers 128:1\\u0026ndash;165. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1007/s13225-024-00540-z\\u003c/span\\u003e\\u003cspan address=\\\"10.1007/s13225-024-00540-z\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eWu B, Hussain M, Zhang W et al (2019) Current insights into fungal species diversity and perspective on naming the environmental DNA sequences of fungi. Mycology 10:127\\u0026ndash;140. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1080/21501203.2019.1614106\\u003c/span\\u003e\\u003cspan address=\\\"10.1080/21501203.2019.1614106\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eXie M, Wang J, Wang F et al (2025) A review of genomic, transcriptomic, and proteomic applications in edible fungi biology: Current status and future directions. J Fungi 11:422. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.3390/jof11060422\\u003c/span\\u003e\\u003cspan address=\\\"10.3390/jof11060422\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eYang C, Liu H, Li G et al (2015) The MADS-box transcription factor \\u003cem\\u003eFgMcm1\\u003c/em\\u003e regulates cell identity and fungal development in \\u003cem\\u003eFusarium graminearum\\u003c/em\\u003e. Environ Microbiol 17:2762\\u0026ndash;2776. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1111/1462-2920.12747\\u003c/span\\u003e\\u003cspan address=\\\"10.1111/1462-2920.12747\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eYang C, Ma L, Xiao D et al (2019) Integration of ATAC-seq and RNA-seq identifies key genes in light-induced primordia formation of \\u003cem\\u003eSparassis latifolia\\u003c/em\\u003e. Int J Mol Sci 21:185. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.3390/ijms21010185\\u003c/span\\u003e\\u003cspan address=\\\"10.3390/ijms21010185\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eYates AD, Austine-Orimoloye O, Azov AG et al (2026) Ensembl 2026. Nucleic Acids Res 54:D1053\\u0026ndash;D1060. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1093/nar/gkaf1239\\u003c/span\\u003e\\u003cspan address=\\\"10.1093/nar/gkaf1239\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eYu M, Yu J, Cao H et al (2019) Genome-wide identification and analysis of the GATA transcription factor gene family in \\u003cem\\u003eUstilaginoidea virens\\u003c/em\\u003e. Genome 62:807\\u0026ndash;816. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1139/gen-2018-0190\\u003c/span\\u003e\\u003cspan address=\\\"10.1139/gen-2018-0190\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eZaccaron AZ, Stergiopoulos I (2025) The dynamics of fungal genome organization and its impact on host adaptation and antifungal resistance. J Genet Genomics 52:628\\u0026ndash;640. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1016/j.jgg.2024.10.010\\u003c/span\\u003e\\u003cspan address=\\\"10.1016/j.jgg.2024.10.010\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eZhang C, Wang G, Deng W, Li T (2020) Distribution, evolution and expression of GATA-TFs provide new insights into their functions in light response and fruiting body development of \\u003cem\\u003eTolypocladium guangdongense\\u003c/em\\u003e. PeerJ 8:e9784. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.7717/peerj.9784\\u003c/span\\u003e\\u003cspan address=\\\"10.7717/peerj.9784\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eZhang X, Ma J, Yang S et al (2023) Analysis of GATA transcription factors and their expression patterns under abiotic stress in grapevine (\\u003cem\\u003eVitis vinifera\\u003c/em\\u003e L). BMC Plant Biol 23:611. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1186/s12870-023-04604-1\\u003c/span\\u003e\\u003cspan address=\\\"10.1186/s12870-023-04604-1\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eZhao H, Nie Y, Zong T-K et al (2023) Species diversity, updated classification and divergence times of the phylum \\u003cem\\u003eMucoromycota\\u003c/em\\u003e. Fungal Divers 123:49\\u0026ndash;157. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1007/s13225-023-00525-4\\u003c/span\\u003e\\u003cspan address=\\\"10.1007/s13225-023-00525-4\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eZheng Q, Huang Y, He X et al (2024) Genome-wide identification and expression pattern analysis of GATA gene family in \\u003cem\\u003eOrchidaceae\\u003c/em\\u003e. Genes 15:915. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.3390/genes15070915\\u003c/span\\u003e\\u003cspan address=\\\"10.3390/genes15070915\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eStatements \\u0026amp; declarations\\u003c/span\\u003e\\u003c/li\\u003e\\u003c/ol\\u003e\"}],\"fulltextSource\":\"\",\"fullText\":\"\",\"funders\":[],\"hasAdminPriorityOnWorkflow\":false,\"hasManuscriptDocX\":true,\"hasOptedInToPreprint\":true,\"hasPassedJournalQc\":\"\",\"hasAnyPriority\":true,\"hideJournal\":true,\"highlight\":\"\",\"institution\":\"Independent Researcher\",\"isAcceptedByJournal\":false,\"isAuthorSuppliedPdf\":false,\"isDeskRejected\":\"\",\"isHiddenFromSearch\":false,\"isInQc\":false,\"isInWorkflow\":false,\"isPdf\":false,\"isPdfUpToDate\":true,\"isWithdrawnOrRetracted\":false,\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"researchsquare\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":true,\"externalIdentity\":\"\",\"sideBox\":\"\",\"snPcode\":\"\",\"submissionUrl\":\"/submission\",\"title\":\"Research Square\",\"twitterHandle\":\"researchsquare\",\"acdcEnabled\":true,\"dfaEnabled\":false,\"editorialSystem\":\"\",\"reportingPortfolio\":\"\",\"inReviewEnabled\":false,\"inReviewRevisionsEnabled\":true},\"keywords\":\"Fungi, GATA TFs, Genome-wide, Structural diversity, Evolutionary patterns, Integrative analysis.\",\"lastPublishedDoi\":\"10.21203/rs.3.rs-9688441/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-9688441/v1\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"\\u003cp\\u003eFungi constitute a highly diverse and ecologically important kingdom with central roles in nutrient cycling, symbiosis, and pathogenesis. The regulation of these processes involves multiple layers of control, with transcription factors (TFs) playing key roles in shaping condition-specific gene expression within complex regulatory networks. In particular, fungal GATA TFs are a family of DNA-binding proteins characterized by a conserved GATA zinc finger motif that recognizes the consensus sequence WGATAR (W\\u0026thinsp;=\\u0026thinsp;A/T, R\\u0026thinsp;=\\u0026thinsp;A/G) in target gene promoters. They function as key regulators of light response, nitrogen and iron metabolism, secondary metabolism, and reproduction. Despite their functional importance, structural analyses of fungal GATA TFs across diverse species remain limited, hindering understanding of their structural diversity and evolutionary patterns. To address this gap, this study conducted a comprehensive genome-wide comparative analysis of the structural and molecular features of 7,846 fungal GATA TFs from 796 species across eleven divisions, sourced from EnsemblFungi and MycoCosm. Domain architecture, GATA motif diversity, and motif-domain associations of fungal GATA TFs were systematically characterized, and motif diversity was further compared with plant and animal GATA TFs to place fungal GATA evolution in a broader eukaryotic context. Phylogenetic relationships based on GATA domain sequences enabled the identification of putative orthologous groups and the inference of lineage-specific functional diversification. Structural predictions of representative fungal GATA TFs were performed to support functional interpretation. In addition, order-level analyses of \\u003cem\\u003eDikarya\\u003c/em\\u003e GATA TFs revealed lineage-specific architectural patterns. Taken together, this integrative analysis provides a comprehensive framework for understanding the evolutionary and functional diversification of fungal GATA TFs.\\u003c/p\\u003e\",\"manuscriptTitle\":\"Genome-wide comparative analysis of structural features in fungal GATA transcription factors: Insights from 796 fungal species\",\"msid\":\"\",\"msnumber\":\"\",\"nonDraftVersions\":[{\"code\":1,\"date\":\"2026-05-14 14:52:21\",\"doi\":\"10.21203/rs.3.rs-9688441/v1\",\"editorialEvents\":[{\"type\":\"communityComments\",\"content\":0}],\"status\":\"published\",\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"researchsquare\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":true,\"externalIdentity\":\"\",\"sideBox\":\"\",\"snPcode\":\"\",\"submissionUrl\":\"/submission\",\"title\":\"Research Square\",\"twitterHandle\":\"researchsquare\",\"acdcEnabled\":true,\"dfaEnabled\":false,\"editorialSystem\":\"\",\"reportingPortfolio\":\"\",\"inReviewEnabled\":false,\"inReviewRevisionsEnabled\":true}}],\"origin\":\"\",\"ownerIdentity\":\"c1661f75-8007-43d7-acec-418c335c27fb\",\"owner\":[],\"postedDate\":\"May 14th, 2026\",\"published\":true,\"recentEditorialEvents\":[],\"rejectedJournal\":[],\"revision\":\"\",\"amendment\":\"\",\"status\":\"posted\",\"subjectAreas\":[],\"tags\":[],\"updatedAt\":\"2026-05-14T14:52:21+00:00\",\"versionOfRecord\":[],\"versionCreatedAt\":\"2026-05-14 14:52:21\",\"video\":\"\",\"vorDoi\":\"\",\"vorDoiUrl\":\"\",\"workflowStages\":[]},\"version\":\"v1\",\"identity\":\"rs-9688441\",\"journalConfig\":\"researchsquare\"},\"__N_SSP\":true},\"page\":\"/article/[identity]/[[...version]]\",\"query\":{\"redirect\":\"/article/rs-9688441\",\"identity\":\"rs-9688441\",\"version\":[\"v1\"]},\"buildId\":\"XKTyCvWXoU3ODBz1xrDgd\",\"isFallback\":false,\"isExperimentalCompile\":false,\"dynamicIds\":[84888],\"gssp\":true,\"scriptLoader\":[]}","source_license":"CC-BY-4.0","license_restricted":false}