Unraveling the Dual-Functionality of Chickpea Methyltransferases in Selenium Homeostasis and Stress Resilience

Unraveling the Dual-Functionality of Chickpea Methyltransferases in Selenium Homeostasis and Stress Resilience | 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 Unraveling the Dual-Functionality of Chickpea Methyltransferases in Selenium Homeostasis and Stress Resilience Lalit Kharbikar, Sarmistha Nayak, Piyush Ghoshe, Shweta Nandanwar, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8305213/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 4 You are reading this latest preprint version Abstract Selenium is vital for plant defence against pathogens and oxidative stress but requires careful regulation to prevent toxicity. This study reveals a new role for dual-function methyltransferases in chickpeas, showing they possess phosphoglycerate kinase (PGK) activity linked to selenium homeostasis, glycolysis, and redox regulation, in addition to their known methylation functions. This finding suggests that methyltransferases contribute to both selenium detoxification and ATP production. Their interaction with PGK connects selenium metabolism to energy production, essential for adapting to oxidative stress. We characterized 32 methyltransferase proteins and found distinct evolutionary differences that separate selenium-related methyltransferases from conventional ones. The presence of PGK-like domains indicates adaptive evolution in selenium-rich environments, enhancing plant resilience. Additionally, we identified regulatory elements and miRNA interactions, emphasizing the importance of methyltransferase-PGK integration in stress resistance. These proteins play crucial roles in selenium detoxification, redox balance, selenoprotein synthesis, and energy regulation, making them key for adapting to stress. Overall, this research enhances our understanding of selenium metabolism in chickpeas and opens pathways for breeding selenium-efficient cultivars with improved stress tolerance. Selenium metabolism methyltransferases phosphoglycerate kinase stress adaptation bioinformatics chickpea Highlights • Employed a comprehensive in-silico approach to elucidate the diverse functions of methyltransferases in chickpeas, providing insights into their regulatory mechanisms. • Discovered methyltransferase proteins with key roles in selenium metabolism, aminotransferase activity, and phosphoglycerate kinase (PGK) activity, underscoring their vital contributions to stress adaptation and fungal disease resistance. • Identified a multifunctional PGK-like protein that bridges selenium metabolism and glycolysis, demonstrating its dual role in detoxification, redox regulation, and enhanced plant defence mechanisms. • Revealed that selenium-containing methyltransferases, strategically positioned in scaffold regions, exhibit functional patterns similar to those of their counterparts embedded within the genomic architecture, underscoring their coordinated role in plant stress response and defence mechanisms. • miRNA analysis identified interactions between the PGK-like methyltransferase and the EDM2 protein, suggesting a significant role in bolstering resistance against fungal pathogens. • This research advances cutting-edge genetic strategies by leveraging stress-responsive pathways to optimize trace element regulation and fortify plants against diseases. Introduction Chickpeas ( Cicer arietinum L .) are a crucial source of protein in the human diet, accounting for roughly 19% of global legume production (Food and Agriculture Organization of the United Nations 2024). Despite their significance, chickpeas are particularly susceptible to a wide range of biotic and abiotic stresses that severely impact both yield and productivity. Among the most serious threats are fungal diseases, with Fusarium wilt, caused by Fusarium oxysporum, ranking as one of the most destructive. Although fungicides are commonly used to control such pathogens, they carry substantial downsides, including increased production costs and negative environmental consequences (Jendoubi et al. 2017 ; Patra & Biswas 2017 ). This underscores the urgent need for sustainable, cost-effective alternatives that safeguard chickpea crops while reducing ecological harm. Identifying innovative, environmentally friendly solutions is essential to ensure the long-term viability of chickpea farming and global food security. Soil treatment with organic and inorganic compounds has long been used to control plant diseases, with varying degrees of success. Among these, selenium-based treatments have shown promising results. Selenium, a trace element naturally present in the environment, exists in both organic forms (selenocysteine (SeCys) and selenomethionine (SeMet) and inorganic forms (selenate (SeO4-2), selenide (Se-2), selenite (SeO3-2), and elemental selenium (Se). (Companioni et al. 2012 ) demonstrated that treating tomato plants with sodium selenite significantly reduced fusarium wilt by Fusarium oxysporum. This treatment enhanced the plants' total protein content, increased phenolic compounds, boosted antioxidant potential, and effectively diminished wilt symptoms. Further research by (Wu et al. 2015 ) corroborated these findings, showing that selenium-treated plants were not only more resistant to F. oxysporum but also exhibited reduced susceptibility to wilt disease overall. These results underscore selenium's potential as a powerful, eco-friendly alternative for disease management in crops, offering an effective means to enhance plant resilience while minimizing reliance on harmful chemicals. Selenium plays a vital role in plant health, but its concentration must be carefully regulated, as both excess and deficiency can negatively impact plant growth and development. At low concentrations, selenium strengthens plant defenses, enhances immunity, and promotes resistance to fungal diseases by modifying soil microbial communities (Li et al. 2023 ). However, when present at higher levels, selenium becomes toxic, damaging not only pathogens but also plant cells, underscoring the importance of precise regulation to harness its benefits while avoiding phytotoxicity (Hasanuzzaman et al. 2022 ; Li et al. 2023 ). Crucially, plants maintain optimal selenium levels through various methyltransferase enzymes, which facilitate the volatilization of excess selenium. Methionine S-methyltransferase (MMT), for instance, catalyzes the methylation of selenomethionine (Se-Met) into Se-methyl selenomethionine (SeMM), which is further processed into volatile selenium compounds like dimethyl selenide (DMSe), a key mechanism for reducing selenium toxicity (Tagmount et al. 2002 ; Schiavon & Pilon Smits 2017). In selenium hyperaccumulators, selenocysteine methyltransferase (SMT) performs a similar function by methylating selenocysteine (SeCys), detoxifying selenium into methyl-selenocysteine (MSC) and DMSe (Guignardi & Schiavon 2017 ; LeDuc et al. 2004 ). These processes ensure that selenium remains at non-toxic levels, safeguarding plant health while optimizing selenium's protective benefits against pathogens. Recent studies reveal that methyltransferases not only convert selenium-containing compounds but also influence the expression of other key enzymes in the family, including phosphoglycerate kinase (PGK) (Liu et al. 2024 ; Qiu et al. 2024 ). PGK, a crucial enzyme in glycolysis, catalyzes ATP production, the primary energy source for cellular functions, including protein synthesis. This energy is essential for the synthesis of selenoproteins, which contain the amino acid selenocysteine (Zhang et al. 2023 ). Selenoproteins play a critical role in maintaining redox homeostasis by acting as antioxidants, protecting cells from oxidative stress induced by reactive oxygen species (ROS) (He et al. 2017 ; Ye et al. 2022 ). Through its involvement in energy production and redox regulation, PGK supports the broader stress response, particularly selenium metabolism. This highlights a synergistic relationship between methyltransferases and PGK, where methyltransferases manage selenium detoxification and volatilization, while PGK provides the metabolic support needed for glycolysis, redox regulation, and selenoprotein synthesis. Together, these processes ensure optimal selenium homeostasis, enabling plants to balance the beneficial and toxic effects of selenium and adapt to selenium-induced stress effectively. Research shows that methyltransferases are essential for regulating selenium levels in plants. This study examines their role in selenium metabolism and stress responses, specifically identifying the methyltransferase proteins involved in chickpeas. The findings have important implications for genetic strategies to optimize trace element levels in crops. By highlighting the selenium-responsive pathways regulated by these enzymes, the research offers opportunities to enhance disease resistance in plants through better selenium metabolism. Additionally, understanding PGK-like methyltransferases could lead to breeding chickpea varieties with improved selenium utilization and stress tolerance. Strategies like genetic engineering and marker-assisted selection can target key genes responsible for selenium homeostasis, helping to maximize uptake while reducing toxicity. These insights provide a foundation for developing biofortified chickpeas with enhanced nutritional and agronomic qualities. Materials and methods 2.1. Retrieval of sequences and redundancy removal In our quest to investigate methyltransferases in chickpea (Cicer arietinum), we initially retrieved 98 protein sequences from the National Center for Biotechnology Information (NCBI) Protein database (https;// www.ncbi.nlm.nih.gov/protein ) in FASTA format using the keyword search “Cicer arietinum methyltransferase,” with a release date range from January 1, 2020, to December 31, 2025. To ensure the integrity and relevance of the dataset, sequences with uncertain functional annotations, specifically those labeled as “probable”, were excluded, resulting in a refined set of 46 high-confidence sequences for downstream in-silico analyses. All protein sequences were derived from the Cicer arietinum reference genome assembly (GCF_000331145.2; cultivar CDC Frontier) to maintain consistency and eliminate cross-genotype variation. Recognizing the importance of unique data, we then implemented redundancy removal to eliminate any sequence similarities that could compromise the accuracy of our findings. Using the skipredundant tool from EMBOSS version 6.6.0.0 (https;// www.bioinformatics.nl/cgi-bin/emboss/skipredundant ), we set a stringent threshold of 95% similarity, resulting in the exclusion of 14 redundant sequences. This crucial step not only streamlined our dataset but also enhanced the reliability of our subsequent analyses. Ultimately, we focused our investigation on a refined set of 32 distinct sequences of methyltransferases in chickpea plants. 2.2. Protein physicochemical properties To gain a comprehensive understanding of the methyltransferase sequences, we analyzed several critical physicochemical properties, including amino acid length, molecular weight, theoretical isoelectric point, instability index, aliphatic index, and Grand Average of Hydropathicity (GRAVY). Utilizing the ProtParam tool (https;//web.expasy.org/protparam/) on the Expasy server (Wilkins et al. 1999 ), we systematically inputted all methyltransferase sequences. 2.3. Identification of domains and conserved motifs To uncover the unique structural and functional units, known as domains, within methyltransferase proteins, we systematically submitted the amino acid sequences to the Pfam database (Mistry et al. 2021 ). Utilizing Hidden Markov Model (HMM) profiles (http;//pfam.xfam.org), we identified key domains that are critical for the functionality of these proteins. In addition, we conducted a thorough investigation of conserved motifs, regions of identical or similar amino acid sequences that indicate evolutionary significance, among chickpea methyltransferase proteins. For this purpose, we employed the Multiple Em for Motif Elicitation (MEME) suite (https;//meme-suite.org/meme/tools/meme), setting the total number of motif sets to 14 while keeping other parameters at their default values (Bailey et al. 2009 ). The identified motifs were further analyzed using the Find Individual Motif Occurrences (FIMO) tool (https;//meme-suite.org/meme/tools/fimo) (Grant et al. 2011 ) with standard parameters, enabling us to visualize the distribution of these motifs effectively using Toolkit Biologists Tools (TBtools) software (Chen et al. 2020 ). To explore the positional and functional relationships between conserved motifs and annotated domains, domain–motif overlap analysis was conducted. Stringent criteria were applied to ensure robust and reliable predictions, with a p-value threshold of 1e − 5 and a q-value below 0.05 for identifying high-confidence domain–motif overlap elements. For conservation assessment, motif occurrences were grouped based on their sequence patterns, and the number of distinct proteins (seq_id) containing each motif was calculated. Only motifs present in two or more proteins (≥ 2) were considered evolutionarily conserved. Finally, the domain–motif overlap relationships were visualized using Python scripts. 2.4 Phylogenetic tree construction To gain deeper insights into the evolutionary relationships among methyltransferase proteins in chickpea, a phylogenetic analysis was conducted. A total of 32 methyltransferase protein sequences were aligned using MAFFT v7 with the --auto strategy to ensure accurate and high-quality multiple sequence alignment (Katoh & Standley 2013 ). The resulting alignment was subsequently trimmed using trimAl v1.4 with the -automated1 option to remove poorly aligned or divergent regions (Capella-Gutiérrez et al. 2009 ). Phylogenetic inference was performed with IQ-TREE v2 using the Maximum Likelihood (ML) method (Minh et al. 2020 ). The best-fit substitution model was automatically determined by ModelFinder (Kalyaanamoorthy et al. 2017 ), and branch support values were estimated using 1,000 ultrafast bootstrap replicates (UFBoot) (Hoang et al. 2018 ) and 1,000 Shimodaira–Hasegawa approximate likelihood ratio tests (SH-aLRT) to ensure robust statistical confidence. The resulting consensus tree was midpoint-rooted and visualized using FigTree (Rambaut 2018 ) (http;//tree.bio.ed.ac.uk/software/figtree/). 2.5. Structural characterization and chromosomal localization of genes To elucidate the structural organization of genes encoding methyltransferases, both coding sequences (CDS) and genomic sequences were meticulously retrieved from the NCBI database. The Gene Structure Display Server (GSDS) v2.0 (Guo et al. 2007 ) (http;//gsds.cbi.pku.edu.cn/) was employed to predict the exon-intron structures of these methyltransferase genes, offering insights into their genetic architecture. To further refine the analysis, the start and end positions of each methyltransferase gene on chickpea chromosomes were retrieved from the Cicer arietinum reference genome assembly CDC Frontier_v2.0 (NCBI Assembly Accession; GCF_000331145.2) to determine their physical chromosomal locations. Information on the eight chromosomes, along with the sizes of the scaffold elements of the chickpea genome, was also sourced from NCBI. The CDC Frontier_v2.0 genome assembly served as the reference genome for this analysis. While other chickpea genome assemblies, such as ICCV 2 and ICC 4958, are available, this study focused exclusively on the CDC Frontier assembly to ensure consistency in chromosomal mapping. Using TBtools (Chen et al. 2020 ) with default parameters, a precise chromosomal map was generated, clearly illustrating the distribution of methyltransferase genes across the chickpea genome. This comprehensive approach unveiled the structural intricacies and genomic organization of methyltransferases. 2.6. Identification of cis-regulatory elements and transcription factor binding sites To gain insights into the regulatory mechanisms controlling the expression of methyltransferase genes, sequences spanning approximately − 1000 bp upstream to + 200 bp downstream of the transcription start site (TSS) were retrieved from the NCBI database. These promoter regions were analyzed using the PlantCARE server (Lescot et al. 2002 ) (http;//bioinformatics.psb.ugent.be/webtools/plantcare/html/) to identify key cis-regulatory elements. Elements without a clearly defined functional role, such as unknown or very short motifs, were excluded from the final results. Additionally, transcription factors (TFs) and their corresponding trans-regulatory elements were identified using the Plant Transcription Factor Database (PlantTFDB, V5.0) server (Jin et al. 2016) (http;//planttfdb.gao-lab.org/prediction.php). To ensure robust and reliable predictions, stringent criteria were applied, with a p-value threshold of 1e-5 and a q-value below 0.05 for identifying high-confidence trans-regulatory elements. The identified cis-regulatory elements were visualized through Python-based plotting scripts, while the transcription factor binding sites were visualized using TBtools. (Chen et al. 2020 ), offering a comprehensive view of the regulatory landscape governing methyltransferase gene expression. 2.7. Gene ontology (GO) analysis A two-tiered GO analysis was performed to gain deeper insights into the functional roles of methyltransferase proteins. First, a primary GO analysis was conducted using Blast2GO (Conesa & Götz 2008 ) (https;// www.blast2go.com/ ) by inputting the protein IDs of methyltransferases, providing an initial functional classification. This was followed by a more targeted GO enrichment analysis using PlantRegMap (Tian et al. 2020 ) (https;//plantregmap.gao-lab.org/) with methyltransferase gene symbols under default parameters, aiming to highlight significantly enriched biological processes, molecular functions, and cellular components. The resulting GO terms were visualized for clarity using the QuickGO server, offering a comprehensive graphical representation of the enriched terms of methyltransferases in chickpeas. 2.8. Identification of microRNAs (miRNAs) To identify miRNAs targeting methyltransferase genes in chickpeas, mature Cicer arietinum-specific miRNA sequences were retrieved from the Plant miRNAs Encyclopedia (https;//pmiren.com/download). These miRNAs, along with the coding sequences (CDS) of methyltransferases, were analyzed using the "small RNAs and targets" section of the psRNATarget server (https;// www.zhaolab.org/psRNATarget/analysis ) under default parameters (Dai et al. 2018 ). This analysis identified specific miRNA-methyltransferase interactions, which were subsequently visualized through custom Python scripts, providing a clear representation of the regulatory relationships between miRNAs and their methyltransferase targets. This approach ensured a robust and precise identification of miRNAs involved in regulating methyltransferase functions in chickpeas. 2.9. Prediction of secondary and tertiary structure and evaluation of the 3D Model To predict the secondary structure, including alpha-helices, beta-strands, and random coils, of the selected methyltransferase protein the SOPMA server (https;//npsa-prabi.ibcp.fr/cgi-bin/npsa_automat.pl?page=/NPSA/npsa_sopma.html) was utilized with default parameters (Geourjon & Deléage 1995 ) For a comprehensive three-dimensional (3D) structural prediction, comparative homology modelling was performed using the SWISS-Model Workspace (Waterhouse et al. 2018 ), where the protein’s FASTA sequence was uploaded. The resulting 3D model underwent rigorous evaluation using multiple validation tools, including QMEAN (Benkert et al. 2011 ) for structural quality assessment, ERRAT (Colovos & Yeates 1993 ) for analyzing non-bonded atomic interactions, verify3D (Bowie et al. 1991 ) for verifying the compatibility of the model’s 3D structure with its amino acid sequence, and PROVE (Pontius et al. 1996 ) for evaluating atomic packing quality. This multi-tiered approach ensured that the predicted structure was both accurate and reliable and gave valuable insights into the protein’s functional dynamics. 2.10. Protein-protein interaction A detailed analysis of protein-protein interactions was conducted to construct a comprehensive network of potential interacting partners for the selected protein. These interactions were explored to identify relationships between the selected protein and other proteins both within the same species and across different species. The investigation was carried out using the powerful STRING v11.5 server (https;//string-db.org/) (Szklarczyk et al. 2021 ), providing a high-confidence network that shed light on functional associations and potential biological pathways. Results 3.1. Protein physicochemical properties The physicochemical characteristics of methyltransferase proteins were evaluated using the ProtParam tool, revealing critical insights into their functional stability and behaviour. The protein lengths ranged from 666 to 3,279 amino acids, with molecular weights spanning from 53.769 to 266.282 kilodaltons (kDa). The isoelectric points (pI) of these proteins varied between 4.82 and 5.17, indicating their predominantly acidic nature. Importantly, the instability index, which ranged from 23.88 to 36.18, signified that these methyltransferases exhibit high stability, suggesting their robustness in physiological conditions. The aliphatic index, falling between 20.89 and 31.25, indicates moderate resistance to temperature fluctuations, while the GRAVY values, ranging from 0.687 to 0.847, highlight the hydrophobic nature of these proteins (Supplementary Table 1). 3.2. Identification of domains and conserved motifs Domain analysis revealed the presence of 10 distinct protein domains across the 32 methyltransferase sequences analyzed, with the majority containing a methyltransferase domain responsible for the protein's catalytic activity (Supplementary Fig. 1). Notably, the methyltransferase protein XP_004516664.1 exhibited both PGK domain and a methyltransferase domain, underscoring its multifunctionality. Other key domains identified include Aminotran_1_2, LCM, PHD, Pox_MCEL, ribosomal protein methyltransferase (PrmA), Protein-L-isoaspartate (D-aspartate) O-methyltransferase (PCMT), PUA, and Sterol_MT_C (Supplementary Table 2). Conserved motif analysis identified 14 dominant motifs among the protein sequences, with Motifs 1, 2, and 3 highly conserved across all 32 methyltransferase genes in chickpeas (Supplementary Fig.s 2 and 3). FIMO analysis further revealed the predominant presence of motifs "GXGXG," "LVXXGGXI," and "GVXTGYS" within the sequences (Supplementary Fig. 4). These motifs were consistently found in several prominent proteins, underscoring their conserved function across various methyltransferase families (Table 1 ). The domain–motif overlap analysis revealed the presence of 9 motifs across 10 domains in a total of 23 proteins (Supplementary Table 3). Among these, ERF motifs were predominantly distributed across multiple domains, suggesting their broad regulatory significance. Notably, the PGK domain harbored the highest number of overlapping motifs (~ 7), indicating its potential role as a multi-functional regulatory hub involved in diverse biological processes (Fig. 1 ). Table 1 Summary of the occurrence and widespread conservation of predominant motifs and their associated proteins in chickpeas: Motif Protein Name Accession Number GXGXG Protein-lysine methyltransferase METTL21D XP_012570542.1 Histidine protein methyltransferase 1 homolog isoform X1 XP_004489174.1 Cycloartenol-C-24-methyltransferase XP_004499918.1 Protein N-lysine methyltransferase METTL21A XP_004506058.1 Methyltransferase-like protein 6 XP_004498868.1 Protein N-lysine methyltransferase METTL21A XP_004512683.1 tRNA (guanine-N(7))-methyltransferase-like XP_004510156.1 Methyltransferase-like protein 6 XP_004498868.1 LVXXGGXI Putative methyltransferase NSUN6 XP_004491318.1 Flavonoid 3'5'-methyltransferase-like XP_004504753.1 Caffeoyl-CoA O-methyltransferase NP_001351681.1 Tricin synthase 1 XP_004488701.1 Ribosomal RNA small subunit methyltransferase B XP_004491518.1 GVXTGYS Caffeoyl-CoA O-methyltransferase NP_001351681.1 Tricin synthase 1 XP_004488701.1 Flavonoid 3'5'-methyltransferase-like XP_004504753.1 Protein-L-isoaspartate O-methyltransferase 1 NP_001266141.1 3.3. Phylogenetic analysis The phylogenetic tree constructed using 32 Cicer arietinum methyltransferase protein sequences revealed three major clades (Fig. 2 ), each supported by moderate to high bootstrap values (61–100%), with this tripartite division indicating strong evolutionary divergence within the methyltransferase family. Clade I: Post-Translational Regulatory Methyltransferase Cluster Containing thirteen sequences, this clade showed moderate to strong bootstrap support (61–97%) and comprised members primarily annotated as post-translational (METTL/PRMT family) and SAM-dependent methyltransferases, displaying close sequence similarity among protein-modifying enzymes. Key subclusters: METTL family cluster: protein-lysine methyltransferase METTL21D (XP_012570542.1), protein N-lysine methyltransferase METTL21A (XP_004506058.1, XP_004512683.1) (bootstrap 95.2/96) Histidine/Lysine-related cluster: protein N-lysine methyltransferase METTL21A-like (XP_004509259.1) and histidine protein methyltransferase 1 homolog (XP_004489174.1) (61/73) SAM/PRMT cluster: S-adenosyl-L-methionine methyltransferase (XP_004516664.1), S-adenosyl-methionine-dependent methyltransferase (XP_004510156.1), and protein arginine N-methyltransferase PRMT10 (XP_004515598.1) (97.5/99)these three members are functionally associated with selenium-related methylation and detoxification processes. Clade II: Isoaspartyl and Small-Molecule Methyltransferase Cluster Containing twelve sequences, this clade exhibited strong bootstrap support (80–100%) and comprised primarily isoaspartyl repair and small-molecule methyltransferases, showing high within-group sequence conservation. Key subclusters: Isoaspartyl methyltransferase cluster: protein L-isoaspartyl methyltransferase 2 isoform 2 (NP_001265927.1) and protein L-isoaspartyl methyltransferase 1 (NP_001266141.1) (100/100) Sterol-related branch: cycloartenol-C-24-methyltransferase (XP_004499918.1) and 2-phytyl-1,4-beta-naphthoquinone methyltransferase, chloroplastic (XP_004489801.1) (86.1/56) mRNA cap methyltransferase branch: mRNA cap guanine-N7 methyltransferase 1 (XP_004515076.1) and mRNA cap guanine-N7 methyltransferase 2 isoform X1 (XP_004502631.1) (99/99) Clade III: RNA and Ribosomal Methyltransferase Cluster This clade included seven sequences representing RNA- and ribosome-associated methyltransferases, supported by moderate to strong bootstrap values (78–92%). Key subclusters: Pair cluster: protein ENHANCED DOWNY MILDEW 2-like (XP_004516997.1) and rRNA G2069 N7-methylase (XP_004510052.1) (17.3/64) RNA-processing branch: methyltransferase-like protein 13 isoform X1 (XP_004504352.1), rRNA (cytosine(967)-C(5))-methyltransferase (XP_004491518.1), putative methyltransferase NSUN6 (XP_004491318.1), and methionine S-methyltransferase isoform X1 (XP_004485405.1) (67.5/94) Independent branch: ribosomal RNA-processing protein 8-like (XP_004493600.1) 3.4. Structural characterization and chromosomal localizations of genes The structural characterization of the 32 methyltransferase genes revealed intriguing patterns in their exon-intron architecture, showcasing both simplicity and complexity in gene structure. Among these genes, 25 were intron-less, each containing a single exon distributed randomly across the gene structure. In contrast, the remaining seven genes contained introns with two exons each, separated by variable distances, indicating a more intricate regulation of gene expression. Most of these exons measured between 0 to 1500 base pairs (bp) in length, except one gene, XP_004485405.1, which had an exon spanning 2000 to 3000 bp, pointing to potential functional significance. The structural details of exons, introns, and their respective upstream and downstream regulatory regions are visually represented in Fig. 3 , providing a comprehensive overview of the gene organization. Chromosomal localization analysis further revealed the widespread distribution of methyltransferase genes across the chickpea genome. Out of the 32 genes analyzed, 26 were mapped to eight chromosomes, while the remaining six genes were associated with scaffolds, lacking specific chromosomal locations (Fig. 4 ). Chromosome 6 harboured the most methyltransferase genes, with seven genes mapped to this region, highlighting its potential as a hotspot for methyltransferase activity. Chromosomes 2 and 7 each contained four genes, while chromosomes 1 and 5 hosted three genes each. Two genes were located on chromosomes 3 and 8, and chromosome 4 contained just one gene (Supplementary Table 4). The chromosomal distribution of methyltransferases was notably diverse, with these genes dispersed across various genomic loci throughout the chickpea genome. 3.5. Identification of cis-regulatory elements and transcription factor binding sites The analysis of cis-regulatory elements in methyltransferase genes revealed 44 distinct types of cis-elements distributed across 28 genes (Supplementary Table 5). These cis-regulatory elements could be grouped into the following functional categories: Light-responsive elements : G-box, Sp1, chs-CMA1a, chs-CMA2a, GATA-motif, I-box, TCCC-motif, TCT-motif, AT1-motif, Box 4, ATC-motif, AE-box, GT1-motif, ACE, GA-motif, Gap-box, 4cl-CMA2b. Plant growth hormone regulatory elements : ABRE (abscisic acid responsiveness), TATC-box and GARE-motif (gibberellin responsiveness), TCA-element (salicylic acid responsiveness), TGA-element and AuxRR-core (auxin responsiveness), CGTCA-motif and TGACG-motif (MeJA responsiveness), and TC-rich repeats. Core promoter and protein-binding elements : TATA-box, CAAT-box, CCAAT-box, A-box, Box III, GC-motif, 3-AF1 binding site, and AT-rich element. Stress-responsive and environmental regulatory elements : ARE (anaerobic induction), MBS (drought-inducibility), DRE core (dehydration/cold response), LTR (low-temperature responsiveness), circadian, and as-1 element. Tissue-specific and developmental regulation elements : CAT-box, RY-element, GCN4_motif, AACA_motif, HD-Zip, O2-site, P-box, and LAMP-element. These cis-regulatory elements are visually represented in Fig. 5 , emphasizing their role in coordinating a range of gene expression responses. The transcription factor binding site (TFBS) analysis revealed an extensive network of binding sites corresponding to their trans-regulatory elements in the chickpea methyltransferases. A total of 33 transcription factors (TFs) were identified and grouped into five major functional categories including developmental regulation, hormone signalling, stress response, metabolic regulation, and cell cycle regulation (Table 2 ). Among these transcription factors, the following three key regulators stood out for their significant roles: Ethylene Responsive Factor (ERF) : Involved in stress and hormone signalling pathways. Lateral Organ Boundaries Domain (LBD) : Plays a critical role in developmental regulation and organ formation. Nin-like : Implicated in nitrogen metabolism and signaling pathways. Table 2 Categorization of transcription factor binding sites identified in the chickpea methyltransferase genes. Sl. No. Categories Transcription factors 1 Developmental regulation MIKC_MADS, AP2, TALE, MYB, Nin-like, TCP, Dof, SRS, bHLH, GATA, Trihelix, NAC, HD-ZIP, G2-like, MYB-related, SBP, and LFY 2 Hormonal signalling ERF, ARF, BES1, and CAMTA 3 Stress-responsive and environmental regulation BBR-BPC, ERF, C2H2, LBD, bZIP, WRKY, СЗН, HSF, and EIL 4 Metabolic regulation FAR 1 and B3 5 Cell cycle regulation E2F/DP, CPP, and ZF-HD These transcription factors were predominantly found in the 0-3000 bp upstream regions of the methyltransferase genes (Fig. 6 ). The statistical significance of these findings, reflected in p-values and q-values, further reinforces the relevance of these transcription factors in the regulation of methyltransferases in chickpeas (Supplementary Table 6). Overall, the complexity and precision of transcriptional regulation in chickpea methyltransferase genes were observed. 3.6. Gene ontology analysis The Gene Ontology (GO) analysis of methyltransferase proteins in chickpeas revealed a wide array of cellular functions, highlighting their importance across diverse biological processes. This analysis identified 12 cellular components, 22 molecular functions, and 26 biological processes (detailed in Supplementary Table 7). Although no direct GO terms associated with selenium metabolism in chickpeas were identified, the analysis offered a comprehensive overview of the functional roles of methyltransferases. The GO enrichment analysis further refined these findings, uncovering 17 potential GO terms, including 9 related to molecular functions and 8 to biological processes, linked to the 32 methyltransferase sequences analysed (Table 3 ). Interestingly, no GO terms related to cellular components were identified in this enrichment analysis. Among the enriched GO terms, three are particularly noteworthy due to their association with selenium metabolism (Fig. 7 ). These terms highlighted key biological processes and three methyltransferase proteins were identified as being linked to these GO terms are summarised in the Table 4 . Table 3 GO term enrichment output for 32 methyltransferase sequences of chickpea showing their involvement in various functions including biological processes (P) and, molecular functions (F) at highly significant p- and q-values. GO ID GO Term p-value q-value Aspect Genes GO:0003824 Catalytic activity 0.0049 1.000 + 00 F LOC101489113, LOC101489144, LOC101489289, LOC101495965, LOC101496374, LOC101498967, LOC101504126, LOC101504427, LOC101504483, LOC101508155, LOC101510447, LOC101511458, LOC101512163, LOC101512362, LOC101513216, LOC101515076, LOC101515626, LOC101515783 GO:0006396 RNA processing 0.00013 1.526E-01 P LOC101489144, LOC101495965, LOC101504483, LOC101515076, LOC101515626 GO:0006399 tRNA metabolic process 0.00072 4.828E-01 P LOC101489144, LOC101504483 GO:0006400 tRNA modification 5.4E-05 8.449E-02 P LOC101489144, LOC101504483 GO:0008033 tRNA processing 0.00023 2.159E-01 P LOC101489144, LOC101504483 GO:0008168 methyltransferase activity 3.8E-13 7.543E-10 F LOC101489113, LOC101489144, LOC101489289, LOC101495965, LOC101496374, LOC101498967, LOC101504126, LOC101504427, LOC101504483, LOC101508155, LOC101510447, LOC101511458, LOC101512163, LOC101512362, LOC101513216, LOC101515076, LOC101515626, LOC101515783 GO:0008173 RNA methyltransferase activity 4.6E-07 3.307E-04 F LOC101489144, LOC101495965, LOC101504483, LOC101515076, LOC101515626 GO:0008175 tRNA methyltransferase activity 1.5E-05 4.352E-03 F LOC101489144, LOC101504483 GO:0008176 tRNA (guanine-N7-)- methyltransferase activity 5.7E-07 3.307E-04 F LOC101489144, LOC101504483 GO:0008757 S-adenosylmethionine-dependent methyltransferase activity 5.80-06 1.923E-03 F LOC101489113, LOC101489144, LOC101489289, LOC101495965, LOC101504483, LOC101508155, LOC101515076, LOC101515626 GO:0009451 RNA modification 0.00059 4.616E-01 P LOC101489144, LOC101504483, LOC101515626 GO:0016423 tRNA (guanine) methyltransferase activity 4E-06 1.857E-03 F LOC101489144, LOC101504483 GO:0016740 Transferase activity 5.4E-06 1.923E-03 F LOC101489113, LOC101489144, LOC101489289, LOC101495965, LOC101496374, LOC101498967, LOC101504126, LOC101504427, LOC101504483, LOC101508155, LOC101510447, LOC101511458, LOC101512163, LOC101512362, LOC101513216, LOC101515076, LOC101515626, LOC101515783 GO:0016741 Transferase activity (transferring one-carbon group) 6.5E-13 7.543E-10 F LOC101489113, LOC101489144, LOC101489289, LOC101495965, LOC101496374, LOC101498967, LOC101504126, LOC101504427, LOC101504483, LOC101508155, LOC101510447, LOC101511458, LOC101512163, LOC101512362, LOC101513216, LOC101515076, LOC101515626, LOC101515783 GO:0034470 ncRNA processing 7.8€-06 3.661E-02 P LOC101489144, LOC101504483, LOC101515076, LOC101515626 GO:0034660 nRNA metabolic process 2.4E-05 5.633E-02 P LOC101489144, LOC101504483, LOC101515076, LOC101515626 GO:0043412 Macromolecule modification 0.00963 1.000E + 00 P Table 4 Key chickpea methyltransferase proteins, their associated GO terms, and their functional roles in biological processes relevant to tRNA and ncRNA modifications. Methyltransferase Protein Protein ID GO Terms Function LOC101489144 XP_004510156.1 GO:0006400 (tRNA modification), GO:0008033 (tRNA processing) tRNA (guanine-N(7)-)-methyltransferase; involved in tRNA modification and processing LOC101504483 XP_004516664.1 GO:0006400 (tRNA modification), GO:0008033 (tRNA processing) Phosphoglycerate kinase-like; plays a dual role in tRNA modification and processing LOC101515076 XP_004491518.1 GO:0034470 (ncRNA processing) Ribosomal RNA small subunit methyltransferase B; involved in ncRNA processing 3.7. Identification of miRNAs targeting methyltransferases A detailed analysis identified 18 miRNAs targeting 12 distinct methyltransferase genes, revealing intricate regulatory interactions. These miRNA-methyltransferase relationships formed four well-defined clusters, each illustrating varying miRNA targeting dynamics. Cluster I and Cluster II showed multiple miRNAs targeting individual genes, whereas Cluster III highlighted single miRNAs regulating multiple genes. Cluster IV (the PGK Cluster) presented a simpler interaction, where one miRNA targeted one gene. Notably, a single miRNA targeting multiple genes was also observed in Cluster II (Fig. 8 ). Cluster I featured XP_004512086.1, regulated by five distinct miRNAs: aly-miR162a-5p, aly-miR162b-5p, bra-miR162-5p, stu-miR162a-5p, and stu-miR162b-5p. Additionally, gma-miR9725 targets the methyltransferase XP_004510052.1. Cluster II focused on XP_004498868.1, which is targeted by another set of five miRNAs: gra-miR167a, gra-miR167b, mtr-miR169d-3p, mtr-miR169e-3p, and osa-miR2863a. Intriguingly, osa-miR2863a targets both XP_004498868.1 and XP_004513963.1, demonstrating a shared regulatory mechanism between two distinct proteins. Additionally, cca-miR6110-3p targets XP_004515598.1, further expanding the regulatory landscape in this cluster. Cluster III highlighted aly-miR822-5p, which regulates both NP_001265927.1 and NP_001266141.1, while sbi-miR5564c-5p targets XP_004492936.1 and cre-miR1144a.1 targeting XP_004510029.1, underscoring the complexity of single miRNA-multi-gene interactions. Cluster IV, or the PGK Cluster, included esi-miR3463-3p, which targets XP_004516997.1 (cytosine-specific DNA methyltransferase), tae-miR530 targeting XP_004504712.1 (ENHANCED DOWNY MILDEW 2-like isoform X1), and ath-miR837-5p targeting XP_004516664.1 (PGK). Notably, the PGK protein, LOC101504483 (XP_004516664.1), emerged as a key player, after both GO enrichment and miRNA analyses (Supplementary Table 8). 3.8. Prediction of secondary and tertiary structure and evaluation of the 3D Model of PGK The structural analysis of Phosphoglycerate Kinase (PGK) revealed key insights into its composition and functionality. The protein's secondary structure was dominated by alpha-helices, which constitute approximately 41%, suggesting their pivotal role in maintaining the protein's structural integrity. Meanwhile, random coils, making up 31.89% of the structure, contribute to the protein’s flexibility, allowing it to adapt dynamically to various functional states. The beta-strands, accounting for 18.11%, further enhance the protein’s stability, reinforcing its structural resilience (Fig. 9 ). In the process of constructing the PGK's 3D model through comparative homology modelling, a sequence identity analysis demonstrated a 28.61% similarity with the crystallographic structure of PGK from Thermotoga maritima , a hyper thermophilic bacterium (Fig. 10 ). The high-resolution structure (2.00 Å) of T. maritima PGK provided a strong foundation for modelling the chickpea PGK, confirming it to be a monomer. The quality of the model was evaluated using the Global Model Quality Estimate (GMQE) score, which yielded a value of 0.42, while the QMEANDisCo global score stood at 0.63 ± 0.05. These scores, both ranging from 0 to 1, reflected moderate accuracy and coverage of the model, indicating that while the model is reliable, there is room for refinement. Further evaluation using the Z-score, an indicator of the energy states in folded versus misfolded proteins, yielded a value of -2.80. This score suggests the model approaches native-like qualities, with values closer to 0 being ideal and values near − 4 indicating suboptimal quality. However, the model's overall structure was considered robust, as evidenced by the ERRAT score of 88.9182. This high score, which assesses non-bonded atomic interactions, is well above the threshold of 50, signalling strong overall quality. The model also achieved favourable results in the verification of its 3D structure, covering 81.65% of the residues with an average 3D-1D score of ≥ 0.2, meeting the benchmark for reliable models. Ramachandran plot analysis further confirmed the structural soundness, with approximately 86% of the residues located in the favoured regions, 12.3% in additionally allowed regions, and only 1.1% in disallowed regions. This distribution indicated a high-quality model, as favoured regions above 90% generally represent excellent accuracy. The near-90% favoured region coverage underscored the viability of this PGK model. These findings, illustrated in Fig.s 9 and 10, comprehensively validated the PGK structure, demonstrating its reliability for future functional studies. 3.9. Protein-protein interaction Protein-protein interaction analysis revealed a dynamic and intricate network of potential interacting partners for PGK, spanning both intra-species and inter-species connections. The analysis identified 22 key nodes and 97 edges, with a remarkably high average local clustering coefficient of 0.859, indicating a densely interconnected network (Fig. 11 ). Among the interactions, particularly strong associations were observed with key metabolic enzymes, reflecting PGK’s central role in glycolysis and related pathways. Notably, the interaction between glyceraldehyde-3-phosphate dehydrogenase (GAPDH) and erythrose-4-phosphate dehydrogenase showed an exceptionally high confidence score of 0.990, indicating near-certain functional collaboration. This was closely followed by interactions with triosephosphate isomerase (score = 0.987), fructose/tagatose bisphosphate aldolase (0.962), fructose-bisphosphate aldolase class 1 (0.962), pyruvate kinase (0.961), and enolase (0.945), the critical players in glycolysis and gluconeogenesis. Additional interactions with transketolase (0.937), glucose-6-phosphate isomerase (0.911), and two variants of phosphoglycerate mutase, BPG-dependent (0.910) and BPG-independent (AlkP superfamily, 0.866), further emphasized PGK’s extensive involvement in carbohydrate metabolism. The broad spectrum of interactions highlighted PGK’s essential role in regulating key biochemical processes across different species. Discussion In this study, we investigated the crucial role of methyltransferases in selenium metabolism and their impact on stress resistance in chickpeas. We analyzed the evolutionary and functional significance of these enzymes, revealing that while some methyltransferases, such as MMT, are directly involved in selenium metabolism, others may provide supportive or regulatory roles that enhance the plant’s resilience (Spechenkova et al. 2021 ; Watanabe et al. 2021 ). Notably, our analysis identified diverse amino acid residues in all methyltransferase sequences, including alanine (A), aspartic acid (D), cysteine (C), glycine (G), and glutamic acid (E). Each of these residues plays a vital role, as alanine contributes to enzyme stability, glycine facilitates substrate binding and catalysis, and cysteine aids in zinc binding, thereby boosting enzyme activity (Subbaramaiah et al. 1991 ; Wang et al. 2017 ). This process is driven by S-adenosylmethionine (SAM), a universal methyl donor that supplies methyl groups to these critical amino acid residues within methyltransferase proteins. By including all chickpea methyltransferases in our study, we capture a broader spectrum of these proteins, allowing for a comprehensive understanding of their collective contributions to the plant's defence mechanisms. This research not only deepens our insights into methyltransferases but also lays the groundwork for developing strategies to enhance disease resistance in chickpeas through targeted manipulation of selenium metabolism. The physicochemical analysis of methyltransferase proteins in chickpeas revealed significant variability in their lengths, ranging from 666 to 3,279 amino acids, and molecular weights, spanning 53.769 to 266.282 kDa. This variability can be attributed to differences in amino acid composition, which directly influences the functional specificity of the methyltransferases studied. For instance, proteins enriched with lighter residues like glycine or leucine exhibit lower molecular weights compared to those containing heavier residues such as arginine or methionine (Weeds & Frank 1974 ; Yang et al. 2018 ). This finding underscores the critical role of amino acid composition in determining the functional properties of methyltransferases, even among proteins derived from the same species. Recent research indicates that proteins with larger molecular weights and longer sequences tend to harbour more functional regions or domains, enhancing their versatility and regulatory capacity (Amaral & Devos 2024 ). Our study corroborated this trend; for instance, the protein XP_004485405.1, with the highest molecular weight of 266.282 kDa, contained three distinct domains (Methyltransferase, Aminotran_1_2, and PrmA) each contributing to selenium metabolism. Notably, the PrmA domain is associated with ribosomal protein methylation, which plays a vital role in managing selenium levels (Mazzoleni et al. 2015 ). In contrast, the protein XP_012570542.1, with the lowest molecular weight of 53.769 kDa, displayed only the Methyltransferase domain. These variations in molecular weight and length, driven by amino acid composition, not only reflect diverse methylation processes but also suggest that these methyltransferase proteins are integral to enhancing plant resilience, particularly in the context of selenium metabolism. The methyltransferase domain, identified through Pfam analysis, is crucial for managing cellular levels of methionine and S-adenosylmethionine (SAM). Acting as the enzyme's catalytic core, this domain specifically facilitates the methylation of the sulfur atom in methionine, highlighting its essential role in methylation processes (Peng et al. 2022 ). Likewise, protein domains like phosphoglycerate kinase (PGK) are highly conserved enzymes that play vital roles in glycolysis and photosynthesis, with different isoforms essential for carbon fixation and cellular metabolism, particularly in Arabidopsis thaliana (Li et al. 2019 ). This study reveals a previously overlooked dual functionality of methyltransferases in chickpeas, showcasing their PGK activity alongside their established role in methylation. This finding points to a new metabolic integration where methyltransferases are involved not only in selenium detoxification but also in ATP production through glycolysis. The connection between PGK and methyltransferases links selenium metabolism to energy generation, aiding cellular adaptation to oxidative stress. Additionally, the PrmA domain, which methylates ribosomal protein L11, enhances ribosome activity, supporting a wide range of metabolic processes. This includes crucial functions in selenium metabolism and stress responses in plants, underlining its significance in sustaining plant health during stressful conditions (Mazzoleni et al. 2015 ). Motif analysis revealed the presence of highly conserved motifs, such as “GXGXG,” “LVXXGGXI,” and “GVXTGYS”, across the methyltransferase sequences in chickpeas. These conserved motifs are crucial for binding the SAM cofactor, which is essential for the methylation process (Peng et al. 2022 ). Specifically, the conserved SAM-binding motif, common to many plant methyltransferases, interacts with the adenine moiety of SAM, facilitating the precise binding of the methionine portion and driving the enzymatic methylation reaction (Peng et al. 2022 ; Joshi & Chiang 1998 ). The identification of these motifs in the chickpea methyltransferase sequences strongly indicates their catalytic activity, confirming the functional capacity of these proteins to carry out methylation reactions. This insight reinforces the pivotal role of these methyltransferases in regulating plant metabolism and stress responses, particularly through their interaction with SAM, a key cofactor in numerous biological processes. Domain–motif overlap analysis showed that most motifs were located within the PGK domain, indicating its role as a multi-functional regulatory hub. Recent multi-omics studies corroborate the functional significance of methyltransferases and their associated hub motifs in activating metabolic networks that mediate drought tolerance and selenium homeostasis. These findings highlight the importance of galactose metabolism, antibiotic biosynthesis, and secondary metabolite production under stress conditions in chickpea (Kudapa et al. 2023 ; Apostolova 2024 ). Collectively, these insights suggest that the PGK domain may function as a molecular bridge integrating catalytic and regulatory mechanisms essential for environmental adaptation. Phylogenetic analysis of methyltransferase proteins in chickpea revealed a tripartite division, highlighting a sophisticated and multilayered methyltransferase system in Cicer arietinum. The three observed clades represent distinct groups of methyltransferases, each contributing differently to selenium homeostasis and stress tolerance through specialized molecular functions. Clade I, enriched for METTL and PRMT methyltransferases, comprises enzymes modulating protein function via lysine and arginine methylation. These post-translational modifications critically regulate protein stability and activity, including stress-related proteins and redox homeostasis factors, directly supporting selenium detoxification pathways and abiotic stress adaptation in chickpea (Verma et al. 2013 ; Lashley et al. 2022 ). Clade II includes isoaspartyl protein repair enzymes and various metabolic methyltransferases, emphasizing protein repair and metabolic regulation for maintaining cellular function under selenium-induced oxidative stress. Efficient protein turnover and specialized metabolic methylation in this clade reflect evolved strategies essential for plant stress resilience (Verma et al. 2013 ; Zhao et al. 2008 ; Lashley et al. 2022 ). Clade III highlights methylation-driven regulation of RNA processing and ribosome function, with RNA modifications controlling gene expression responses to stress, ensuring accurate translation, and possibly modulating redox balance during selenium exposure (Zhao et al. 2008 ; Kumar et al. 2021 ; Yadav et al. 2024 ). This reveals an additional transcriptional and translational regulatory layer critical for stress adaptation. The distribution across these three functional clades illustrates the complex molecular specialization that underpins selenium homeostasis and stress response mechanisms in chickpea. The gene structure analysis revealed that a substantial proportion (78%) of methyltransferase genes in Cicer arietinum are intron-less, a characteristic also observed in the WRKY gene family of the same species (Waqas et al. 2019 ). Intron-less genes are thought to facilitate rapid stress responses, making them crucial regulators of plant growth and developmental processes (77; Fan et al. 2021 ). In our study, key intron-less methyltransferases, such as XP_00451664.1 and XP_004491518.1, were specifically linked to selenium metabolism, as indicated by Gene Ontology (GO) analysis. This highlights their role in enhancing plant stress tolerance through efficient gene expression mechanisms. Particularly noteworthy is the methyltransferase XP_004485405.1, which possesses the longest exon, spanning 2000 to 3000 base pairs. This gene encodes the largest protein (266.282 kDa) identified in the study, containing three functional domains, Methyltransferase, Aminotran_1_2, and PrmA, all contributing to selenium metabolism. The presence of these distinct domains suggests a multifunctional role for this protein in selenium detoxification and overall plant resilience. This gene structure's efficiency and versatility further emphasize the critical role of methyltransferases in selenium-mediated defence mechanisms in chickpeas. Among all the chromosomes, the largest number of genes (four) clustered at positions from 10 Mb to 20 Mb on chromosome 6 indicates that this particular region could be a hotspot for methylation-related functions as the genes present there contain only methyltransferase domains. Chromosomes have regions exhibiting distinct evolutionary patterns where genes encoding specific functions cluster together (Jiao & Schneeberger 2020 ). Interestingly, scaffold proteins exhibit versatility in their functions and localization within cells, being present in various cellular compartments such as the plasma membrane, cytoplasm, endosomes, mitochondria, Golgi, and nucleus (Mukherjee & Low 2020 ). This diversity in localization enables scaffold proteins to regulate multiple signalling pathways and cellular processes simultaneously. In line with these findings, our study suggests that scaffold proteins may possess novel functions, as proteins located in scaffolds such as XP_004515076.1 and XP_004516664.1 were primarily involved in selenium metabolism, as indicated by the results of GO analysis with selenium metabolism. The identification of diverse cis-regulatory elements in the promoter regions of methyltransferase genes underscores the intricate regulation of these genes through a complex network of transcriptional and post-transcriptional mechanisms. Hence, these regulatory elements are essential in fine-tuning the expression of methyltransferases in response to various developmental, environmental, and physiological signals. Recent studies demonstrate that methyltransferases are involved in the biosynthesis of key stress-signaling compounds, such as S-methyl-methionine salicylate (MMS), which interacts with salicylic acid to boost plant stress tolerance (Peng et al. 2022 ; Balassa et al. 2022 ). This suggest that methyltransferases, essential for natural product biosynthesis, play a vital role in plant adaptation to stress conditions. Moreover, selenium-containing products synthesized via these signalling pathways contribute significantly to managing oxidative stress and enhancing fungal resistance (Kayrouz et al. 2022 ; Handa et al. 2016 ). In this study, we identified several critical light-responsive cis-regulatory elements, such as G-box and I-box, alongside hormone-responsive elements like ABRE and TCA-element, in the promoter regions of methyltransferase genes. This suggests that methyltransferase expression is influenced by both light and hormonal signalling pathways, positioning these enzymes at the nexus of stress response regulation. Specifically, ABRE elements, which are responsive to abscisic acid (ABA), play a pivotal role in regulating gene expression under high-light conditions and during exposure to biotic and abiotic stresses (Lim et al. 2015 ; Shi et al. 2022 ). This regulatory framework likely enhances the plant's adaptive capacity by coordinating methylation reactions that fortify biotic stress tolerance. This is evident in the case of MMT which has been recognized as a key enzyme in the production of dimethylsulfoniopropionate (DMSP), a critical anti-stress compound in marine ecosystems (Peng et al. 2022 ; Williams et al. 2019 ). Collectively, the presence of cis-regulatory elements in methyltransferase genes highlights their indispensable role in selenium metabolism, oxidative stress management and the broader defence mechanisms of chickpeas. Transcription factors are pivotal in regulating various stages of plant development and responses to environmental stimuli. In our study of chickpea methyltransferases, we identified several transcription factors that contribute to developmental regulation, hormone signalling, stress responses, metabolic processes, and cell cycle control. Methyltransferase proteins, particularly during developmental stages and under stress conditions, are central to plant resilience and growth (Ling et al. 2022 ). Among the key transcriptional regulators, MYB and MYB-related proteins are crucial for both primary and secondary metabolism, as well as for promoting methylation processes in plants (Pu et al. 2021 ; Cao et al. 2018 ). These proteins not only catalyze methylation reactions but also regulate the biosynthesis of selenium, which is essential for balancing growth and defence. WRKY transcription factors, known for their involvement in biological processes like metabolism and stress responses, are also key players in methylation reactions (Yu et al. 2022 ). Additionally, the ERF transcription factor is a critical component of the plant's innate immune system, fortifying its defences against fungal pathogens and contributing significantly to disease resistance (Son et al. 2012 ). Similarly, in selenium metabolism, the up-regulation of MYB, MYB-related, bZIP, ERF, and AP2 transcription factors, which support selenium uptake by replacing sulfur in proteins were observed (Cao et al. 2018 ; Wang et al. 2023 ). Our study also uncovered the significant roles of three highly efficient transcription factors, ERF, LBD, and Nin-like, in regulating methyltransferase genes. Recent research highlights that ERF and LBD genes respond to various environmental stresses and plant hormones, suggesting their involvement in plant metabolism and the management of both abiotic and biotic stresses (19; Hao et al. 2020 ; Yu et al. 2022 ). These insights emphasize the critical function of transcription factors in regulating methyltransferase activity, particularly in selenium metabolism, thereby enhancing disease resistance in chickpeas. The integration of these regulatory networks reflects a strategic adaptation in chickpeas, reinforcing their defence mechanisms and resilience to environmental challenges. The GO enrichment analysis conducted in this study revealed significant GO terms associated with selenium metabolism, such as GO;0006400 (tRNA modification), GO;0008033 (tRNA processing), and GO;0034470 (ncRNA processing), all of which are integral components of the RNA processing pathway (Sekulovski & Trowitzsch 2022 ). This finding aligns with a growing body of evidence highlighting selenium's profound influence on transcription and gene expression. For instance, in tea plants treated with selenite or selenate, key genes involved in amino acid and glutathione metabolism, along with those critical for DNA and RNA metabolism, were notably upregulated (Ren et al. 2022 ). This underscores selenium’s pivotal role in modulating metabolic pathways, linking selenium metabolism to RNA processing, and influencing key genes in DNA and RNA metabolism. Such insights emphasize selenium’s importance in plant biology and its broader implications for gene expression. Methyltransferases, through their involvement in RNA processing, are likely key regulators of selenium metabolism. RNA methyltransferases, specifically, are central to regulating RNA processing and metabolism, directly influencing selenium metabolism by facilitating selenoprotein synthesis (Li et al. 2022 ). Additionally, N6-methyladenosine (m6A) modification, catalyzed by methyltransferases like METTL3 and METTL14, affects various aspects of RNA metabolism, including splicing, processing, nuclear export, translation, and degradation (Lin & Gregory 2014 ; Shriwas et al. 2021 ). This modification also impacts the translation of genes encoding selenoproteins, which are essential for selenium metabolism. The regulation of this translation process is uniquely controlled by the selenocysteine insertion sequence (SECIS) element in the 3' untranslated region of selenoprotein mRNAs, alongside a specialized tRNA(Ser)Sec (Jameson & Diamond 2004 ). Methylation of this tRNA(Ser)Sec is crucial for its translational activity, with selenium-induced tRNA methylation serving as a critical regulatory mechanism for selenoprotein synthesis, thereby establishing a direct link between RNA methylation and selenium metabolism. Our research reveals that PGK-like methyltransferases play a crucial role in selenium detoxification and maintaining redox balance, a function that has not been previously identified in chickpeas. These proteins are instrumental in regulating the synthesis of selenoproteins, alleviating oxidative stress, and balancing energy, making them vital for adapting to abiotic stress. Furthermore, the connection between glycolysis and methylation processes introduces a new biochemical mechanism that may elucidate how chickpeas thrive in selenium-rich environments. The GO enrichment analysis also brought to light a biochemical mechanism related to RNA processing, associated with the PGK domain-containing methyltransferases studied here. This points to a potentially broader functional role for these proteins, possibly in selenium metabolism. Supporting research involving Rainbow trout indicates that exposure to selenium leads to the upregulation of genes linked to glycolysis, which is related to PGK proteins. Collectively, these findings suggest a significant relationship between RNA processing, methyltransferases, and selenium metabolism, reinforcing the complex role of RNA methylation in improving plant stress resilience and metabolic flexibility. The miRNAs play a pivotal role in the regulation of gene expression, despite being non-coding regions. They exert their influence by either cleaving target mRNAs or inhibiting their translation, directly affecting the levels of viable proteins within the plant. The expression of miRNAs, therefore, has a significant impact on plant biology, as it controls the suppression of key genes. In a previous study on Astragalus chrysochlorus, miRNAs were shown to modulate the expression of protein-coding genes in response to selenium stress, either upregulating or downregulating them based on the specific properties of the miRNAs involved (Cakir et al. 2016 ). Our findings are consistent with this previous research showing that selenium’s impact on miRNAs varies depending on concentration, influencing plant biological processes in various ways. Similarly, studies on rice revealed that selenium can have both beneficial and detrimental effects on plant growth, depending on its concentration (Pandey et al. 2015 ). At low levels, selenium can enhance growth by modulating miRNA-mediated processes, promoting plant development and stress tolerance. However, at higher concentrations, selenium can disrupt protein function and inhibit plant growth by interfering with miRNA regulation. These insights underscore the dual role of selenium, where controlled concentrations can harness miRNA pathways to boost plant resilience, while excessive selenium may impede critical biological functions. By understanding these miRNA mechanisms, it becomes possible to fine-tune selenium levels for optimal plant growth and stress tolerance. In our study, we uncovered the pivotal role of a PGK-like protein (LOC101504483) in selenium metabolism in chickpeas, as evidenced by GO analyses and miRNA investigations. This protein’s involvement in selenium metabolism is consistent with similar findings in other plant species, reinforcing its critical function in plant biology (Hofstee et al. 2020 ; Rojas-Pirela et al. 2020 ;28). Through miRNA analysis, LOC101504483 was identified as a target of ath-miR837-5p, a microRNA known to regulate essential genes involved in callus initiation, root formation, and cell division (Jatan et al. 2020 ). This connection suggests a potential link between selenium metabolism and key developmental processes in chickpeas, highlighting the versatile role of PGK in both growth regulation and stress response. Moreover, PGK was found to be functionally clustered with XP_004504712.1, an EDM2 protein that plays a crucial role in plant immunity, specifically in RPP7-mediated resistance to pathogens like downy mildew (Tsuchiya & Eulgem 2011 ). EDM2 stands out not only as a mediator of disease resistance but also as a regulator of gene expression and chromatin modification through its plant homeodomain (PHD)-finger-like domains (Eulgem et al. 2007 ). This positions EDM2 as a critical transcriptional regulator of plant defence, specifically targeting pathogen-responsive genes. The shared miRNA targets and functional clustering between PGK and EDM2-like proteins suggest a significant link between selenium metabolism and plant immune mechanisms. Selenium appears to exert its regulatory influence by enhancing PGK activity and related methyltransferases, contributing to improved plant immunity, especially in defending against fungal pathogens. EDM2’s ability to modulate chromatin and gene expression further reinforces the dual functionality of selenium metabolism, which supports both metabolic stability and enhanced immune responses. Our findings reveal that PGK and related methyltransferases are not only integral to selenium metabolism but also play a crucial role in strengthening plant immunity. This dual role of selenium metabolism offers promising new strategies for enhancing resistance in chickpeas and other crops. Optimizing selenium pathways and associated methylation processes, including the regulation of PGK, may represent a valuable approach to improving plant resilience against biotic stresses, positioning selenium as a key factor in sustainable agricultural practices. The high alpha-helix content of PGK, as revealed by our secondary structure prediction, indicates its robust structural conformation. This feature is probably essential for preserving its enzymatic function, particularly under stressful conditions. Selenium accumulation can lead to stress, triggering changes to histones. These alterations can significantly affect protein synthesis and modify the protein structure (Huang et al. 2021 ). Even minor modifications during synthesis can impede protein production and induce conformational changes in the protein structure (Balog et al. 2007 ; Venyaminov & Yang 1996 ). However, the ability of PGK to maintain its high alpha-helix content may be indicative of its resilience against such stressors, ensuring its continued function in metabolic processes. This is consistent with recent findings that Group 4 LEA proteins in Arabidopsis thaliana adopt a similar high alpha-helix conformation under water deficiency, associated with their protective activity (Rendón-Luna et al. 2024 ). When plants are affected by fungi, ROS are generated, leading to oxidative stress. PGK plays a vital role in mitigating oxidative stress by facilitating the volatilization of excess selenium. Maintaining optimal selenium levels has been shown to enhance the antioxidant capacity of plants and strengthen their resistance to biotic stress (Zoidis et al. 2018 ; He et al. 2017 ; Ye et al. 2022 ). Our findings, demonstrating the high alpha-helix content of PGK, further support its role in both selenium metabolism and stress response. This suggests that the structural stability conferred by alpha-helices is essential for PGK-like methyltransferases to function effectively in challenging environmental conditions. Homology modelling analysis reveals that chickpea PGK shares a moderate sequence identity of 28.61% with Thermotoga maritima PGK. Despite this moderate similarity, studies have shown that proteins with conserved structural folds can exhibit functional similarities, even with low sequence identity, due to evolutionary divergence or convergence (Panchenko & Madej 2004 ). This suggests that, despite the differences in sequence, the chickpea PGK may retain similar functional capabilities to T. maritima PGK, given that key structural folds are conserved. Notably, T. maritima is recognized for its ability to produce hydrogen, a clean energy source, through carbohydrate fermentation in glycolysis (Merrill et al. 1995 ; Singh et al. 2018 ), a process where PGK plays a central catalytic role (Rojas-Pirela et al. 2020 ). As a hyper thermophilic bacterium, T. maritima produces proteins that are adapted to extreme temperatures, exhibiting optimal activity under heat stress, which aids in managing both biotic and abiotic stresses (Boileau et al. 2016 ; Yamini et al. 2022 ; Ul Haq et al. 2019 ). Moreover, thermotolerant proteins from T. maritima have been linked to improved iron intake and enhanced sulfur amino acid metabolism (Herve-Jimenez et al. 2008 ), a process closely related to selenium metabolism, as both sulfur and selenium share similar enzymatic transport pathways (Johnstone et al. 2021 ). These insights suggest that chickpea PGK, by sharing structural and functional similarities with T. maritima PGK, likely plays a significant role in maintaining selenium homeostasis and contributing to the plant's resilience against environmental stresses. In our comprehensive model analysis, despite a low Z-score, several key factors justify the model's acceptance, highlighting its alignment with known PGK properties. PGK, a ubiquitous enzyme across all living organisms, plays a fundamental role in crucial metabolic processes, making it an essential target for structural and functional studies. As additional crystallographic structures of PGK from various organisms, particularly plant species, become available, our model will be further refined, addressing current limitations due to the scarcity of high-quality plant protein data. This ongoing refinement strengthens the model's potential for advancing our understanding of PGK's role in plant metabolism and stress response. Protein-protein interaction analysis revealed a significant functional association between PGK, GAPDH, and erythrose-4-phosphate, suggesting that PGK forms a critical partnership with these proteins. In particular, PGK's role in selenium metabolism, as evidenced in chickpeas and other plants, further underscores its importance in cellular processes (Hofstee et al. 2020 ; Rojas-Pirela et al. 2020 ; Guo et al. 2020 ). The interaction between PGK and GAPDH is especially compelling, as GAPDH is not only a key enzyme in glycolysis but also a pivotal player in redox regulation and stress response (Hildebrandt et al. 2015 ). This dual functionality positions GAPDH as a critical mediator in balancing energy production and cellular protection under stress conditions. Selenoproteins, known for their antioxidant properties, safeguard cells against oxidative stress caused by reactive oxygen species (ROS) (He et al. 2017 ; Ye et al. 2022 ). The strong association between PGK and GAPDH suggests a coordinated effort between glycolytic energy pathways and selenium-dependent defence mechanisms, reinforcing the idea that selenium metabolism and glycolysis are intricately linked to enhancing redox homeostasis and stress resilience in plants. Our study also uncovered a significant interaction between PGK and erythrose-4-phosphate, a pivotal molecule in the pentose phosphate pathway (PPP), which is essential for generating NADPH, a key factor in maintaining cellular redox balance (Koletti et al. 2022 ; TeSlaa et al. 2023 ). The accumulation of erythrose-4-phosphate under normal conditions highlights its active role in critical metabolic processes like biosynthesis and NADPH production (Koletti et al. 2022 ). NADPH is vital for the function of selenium-dependent antioxidant enzymes, such as glutathione peroxidases (GPXs), which mitigate oxidative stress by neutralizing reactive oxygen species (ROS) and preventing cellular damage (Fontagné-Dicharry et al. 2015 ). Notably, our miRNA interaction analysis revealed PGK's link to EDM2, a protein crucial for plant immunity, particularly in defending against fungal pathogens like downy mildew (Rojas-Pirela et al. 2020 ). This interaction network suggests that PGK plays a central role in synchronizing glycolysis and the PPP with selenium-driven defence mechanisms. By facilitating redox balance through NADPH production and engaging selenium-based antioxidant systems, PGK emerges as a key player in chickpea’s resilience to fungal diseases. Its involvement in both metabolic and immune responses positions PGK as an integral component of the plant’s defence strategy, offering promising avenues for enhancing crop resistance. Conclusion This study examines the complex relationship between methyltransferases and phosphoglycerate kinase (PGK) in regulating selenium metabolism in chickpeas, which is vital for improving stress resistance. Through bioinformatics, we identified key motifs, domains, and phylogenetic connections that suggest these proteins are involved in plant stress responses. Understanding the role of PGK-like methyltransferases provides opportunities for breeding chickpea varieties that are more efficient in selenium uptake and have enhanced stress tolerance. Furthermore, PGK's interaction with essential enzymes involved in glycolysis and redox regulation highlights its importance in mitigating oxidative stress, which is critical for plant defence. This research not only enhances our understanding of selenium metabolism but also suggests that targeting PGK-like methyltransferases could lead to the development of more resilient chickpea varieties. By focusing on genes related to selenium homeostasis and energy metabolism, we can improve selenium uptake while reducing toxicity, paving the way for biofortified chickpeas with improved nutritional and agronomic traits. Declarations Author Contributions : Lalit Kharbikar was responsible for the conception and design of the study. Material preparation, data collection, and analysis were conducted by Shweta Nandanwar and Piyush Ghoshe. The first draft of the manuscript was written by Piyush Ghoshe, Sarmistha Nayak, and Shweta Nandanwar. Lalit Kharbikar and Sushma Rani Martha provided feedback on earlier versions of the manuscript. Both Lalit Kharbikar and Sushma Rani Martha contributed to data analysis and the interpretation of results. Data reanalysis was performed by Sarmistha Nayak, Sushma Rani Martha, and Lalit Kharbikar. Anil Dixit handled the statistical analysis, while Sushil K. Sharma contributed to the review, revision, and editing of the manuscript. All authors have read and approved the final version of the manuscript. Acknowledgement : The first author received funding from the Indian Council of Agricultural Research (ICAR) in New Delhi, India, through an institutional research project that contributed to this work. Additionally, this research was supported by the ICAR - National Institute of Biotic Stress Management (NIBSM) in Raipur, India. The first author expresses gratitude to the Director of ICAR – NIBSM, Raipur, India, for approving the research project. Conflict of Interests : The authors have no competing interests to declare that are relevant to the content of this article. Funding : This study was funded by the Indian Council of Agricultural Research, New Delhi. References Ailin Qiu, Xiaosha Wen, Qingshuang Zou, Lei Yin, Siqi Zhu, Yao Sheng, Yan He, Quan Liu, Dixian Luo, Zifen Guo. Phosphoglycerate Kinase 1: An Effective Therapeutic Target in Cancer. Front. Biosci. 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International Journal of Molecular Sciences, 23(6), 3272. https://doi.org/10.3390/ijms23063272. Zhang, M., Lu, W., Yang, X., Li, Q., Lin, X., Liu, K., ... & Wang, Z. (2023). Comprehensive analyses of the citrus WRKY gene family involved in the metabolism of fruit sugars and organic acids. Frontiers in Plant Science, 14, 1264283. https://doi.org/10.3389/fpls.2023.1264283. Zhao, N., Ferrer, J. L., Ross, J., Guan, J., Yang, Y., Pichersky, E., ... & Chen, F. (2008). Structural, biochemical, and phylogenetic analyses suggest that indole-3-acetic acid methyltransferase is an evolutionarily ancient member of the SABATH family. Plant physiology, 146(2), 455. Zoidis, E., Seremelis, I., Kontopoulos, N., & Danezis, G. P. (2018). Selenium-dependent antioxidant enzymes: Actions and properties of selenoproteins. https://doi.org/10.3390/antiox7050066. Figures Figures are available in the Supplementary Files section. Additional Declarations No competing interests reported. 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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-8305213","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":560122972,"identity":"25e9eff7-6edc-4118-bea5-30e83910be5f","order_by":0,"name":"Lalit Kharbikar","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABAUlEQVRIiWNgGAWjYBACPgbmhgMMBw4wMEgwNgP5NkDM2HgAnxY2BsaGAwcQWtJAWhoIamGAaGFgBvIPg0Xxa2E/2Hj4w5k78uazm5sNfu45b7e2/TDQlhqbaJxaeBKBDrvxzHDOnYPNiT3PbidvOwMUYTiWltuA02EgLR8OM86QSGw+wHPgdrLZAaAIY8Nh3Fr4H4K12IO0HPxz4Fyy2fmHBLRIgB12OBGkJZnnwAE7sxuEbJEAmnnmzOHkGTIHm41lDiQnmN0AiiTg8Qs/f/LhDxXHDtvOkG5/LPnmgJ292fn0hw8+1Njg1IIBEsEqE4hVDgL2pCgeBaNgFIyCkQEAKo50w5n/nnEAAAAASUVORK5CYII=","orcid":"","institution":"ICAR – National Institute of Biotic Stress Management","correspondingAuthor":true,"prefix":"","firstName":"Lalit","middleName":"","lastName":"Kharbikar","suffix":""},{"id":560122973,"identity":"10d2d45b-b7ae-4c3b-8961-4097b07d4c03","order_by":1,"name":"Sarmistha Nayak","email":"","orcid":"","institution":"ICAR – National Institute of Biotic Stress Management","correspondingAuthor":false,"prefix":"","firstName":"Sarmistha","middleName":"","lastName":"Nayak","suffix":""},{"id":560122974,"identity":"817f88a2-7544-41d8-8666-7809a617f860","order_by":2,"name":"Piyush Ghoshe","email":"","orcid":"","institution":"ICAR – National Institute of Biotic Stress Management","correspondingAuthor":false,"prefix":"","firstName":"Piyush","middleName":"","lastName":"Ghoshe","suffix":""},{"id":560122977,"identity":"014dbc4e-dc0f-4969-8798-d37e8e7cc74d","order_by":3,"name":"Shweta Nandanwar","email":"","orcid":"","institution":"ICAR – National Institute of Biotic Stress Management","correspondingAuthor":false,"prefix":"","firstName":"Shweta","middleName":"","lastName":"Nandanwar","suffix":""},{"id":560122978,"identity":"d23758d6-6e28-451c-ae3f-2d96ec379daf","order_by":4,"name":"Sushma Rani Martha","email":"","orcid":"","institution":"Odisha University of Agriculture and Technology","correspondingAuthor":false,"prefix":"","firstName":"Sushma","middleName":"Rani","lastName":"Martha","suffix":""},{"id":560122983,"identity":"cec03fe1-48f7-438f-82ef-eb9cba49e405","order_by":5,"name":"Sushil K. 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Despite their significance, chickpeas are particularly susceptible to a wide range of biotic and abiotic stresses that severely impact both yield and productivity. Among the most serious threats are fungal diseases, with Fusarium wilt, caused by Fusarium oxysporum, ranking as one of the most destructive. Although fungicides are commonly used to control such pathogens, they carry substantial downsides, including increased production costs and negative environmental consequences (Jendoubi et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Patra \u0026amp; Biswas \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). This underscores the urgent need for sustainable, cost-effective alternatives that safeguard chickpea crops while reducing ecological harm. Identifying innovative, environmentally friendly solutions is essential to ensure the long-term viability of chickpea farming and global food security.\u003c/p\u003e \u003cp\u003eSoil treatment with organic and inorganic compounds has long been used to control plant diseases, with varying degrees of success. Among these, selenium-based treatments have shown promising results. Selenium, a trace element naturally present in the environment, exists in both organic forms (selenocysteine (SeCys) and selenomethionine (SeMet) and inorganic forms (selenate (SeO4-2), selenide (Se-2), selenite (SeO3-2), and elemental selenium (Se). (Companioni et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) demonstrated that treating tomato plants with sodium selenite significantly reduced fusarium wilt by Fusarium oxysporum. This treatment enhanced the plants' total protein content, increased phenolic compounds, boosted antioxidant potential, and effectively diminished wilt symptoms. Further research by (Wu et al. \u003cspan citationid=\"CR100\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) corroborated these findings, showing that selenium-treated plants were not only more resistant to F. oxysporum but also exhibited reduced susceptibility to wilt disease overall. These results underscore selenium's potential as a powerful, eco-friendly alternative for disease management in crops, offering an effective means to enhance plant resilience while minimizing reliance on harmful chemicals.\u003c/p\u003e \u003cp\u003eSelenium plays a vital role in plant health, but its concentration must be carefully regulated, as both excess and deficiency can negatively impact plant growth and development. At low concentrations, selenium strengthens plant defenses, enhances immunity, and promotes resistance to fungal diseases by modifying soil microbial communities (Li et al. \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). However, when present at higher levels, selenium becomes toxic, damaging not only pathogens but also plant cells, underscoring the importance of precise regulation to harness its benefits while avoiding phytotoxicity (Hasanuzzaman et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Li et al. \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eCrucially, plants maintain optimal selenium levels through various methyltransferase enzymes, which facilitate the volatilization of excess selenium. Methionine S-methyltransferase (MMT), for instance, catalyzes the methylation of selenomethionine (Se-Met) into Se-methyl selenomethionine (SeMM), which is further processed into volatile selenium compounds like dimethyl selenide (DMSe), a key mechanism for reducing selenium toxicity (Tagmount et al. \u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Schiavon \u0026amp; Pilon Smits 2017). In selenium hyperaccumulators, selenocysteine methyltransferase (SMT) performs a similar function by methylating selenocysteine (SeCys), detoxifying selenium into methyl-selenocysteine (MSC) and DMSe (Guignardi \u0026amp; Schiavon \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; LeDuc et al. \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). These processes ensure that selenium remains at non-toxic levels, safeguarding plant health while optimizing selenium's protective benefits against pathogens.\u003c/p\u003e \u003cp\u003eRecent studies reveal that methyltransferases not only convert selenium-containing compounds but also influence the expression of other key enzymes in the family, including phosphoglycerate kinase (PGK) (Liu et al. \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Qiu et al. \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). PGK, a crucial enzyme in glycolysis, catalyzes ATP production, the primary energy source for cellular functions, including protein synthesis. This energy is essential for the synthesis of selenoproteins, which contain the amino acid\u003c/p\u003e \u003cp\u003eselenocysteine (Zhang et al. \u003cspan citationid=\"CR106\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Selenoproteins play a critical role in maintaining redox homeostasis by acting as antioxidants, protecting cells from oxidative stress induced by reactive oxygen species (ROS) (He et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Ye et al. \u003cspan citationid=\"CR104\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThrough its involvement in energy production and redox regulation, PGK supports the broader stress response, particularly selenium metabolism. This highlights a synergistic relationship between methyltransferases and PGK, where methyltransferases manage selenium detoxification and volatilization, while PGK provides the metabolic support needed for glycolysis, redox regulation, and selenoprotein synthesis. Together, these processes ensure optimal selenium homeostasis, enabling plants to balance the beneficial and toxic effects of selenium and adapt to selenium-induced stress effectively.\u003c/p\u003e \u003cp\u003eResearch shows that methyltransferases are essential for regulating selenium levels in plants. This study examines their role in selenium metabolism and stress responses, specifically identifying the methyltransferase proteins involved in chickpeas. The findings have important implications for genetic strategies to optimize trace element levels in crops. By highlighting the selenium-responsive pathways regulated by these enzymes, the research offers opportunities to enhance disease resistance in plants through better selenium metabolism. Additionally, understanding PGK-like methyltransferases could lead to breeding chickpea varieties with improved selenium utilization and stress tolerance. Strategies like genetic engineering and marker-assisted selection can target key genes responsible for selenium homeostasis, helping to maximize uptake while reducing toxicity. These insights provide a foundation for developing biofortified chickpeas with enhanced nutritional and agronomic qualities.\u003c/p\u003e"},{"header":"Materials and methods","content":"\n \u003ch2\u003e2.1. Retrieval of sequences and redundancy removal\u003c/h2\u003e\n \u003cp\u003eIn our quest to investigate methyltransferases in chickpea (Cicer arietinum), we initially retrieved 98 protein sequences from the National Center for Biotechnology Information (NCBI) Protein database (https;//\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ewww.ncbi.nlm.nih.gov/protein\u003c/span\u003e\u003c/span\u003e) in FASTA format using the keyword search \u0026ldquo;Cicer arietinum methyltransferase,\u0026rdquo; with a release date range from January 1, 2020, to December 31, 2025. To ensure the integrity and relevance of the dataset, sequences with uncertain functional annotations, specifically those labeled as \u0026ldquo;probable\u0026rdquo;, were excluded, resulting in a refined set of 46 high-confidence sequences for downstream in-silico analyses. All protein sequences were derived from the Cicer arietinum reference genome assembly (GCF_000331145.2; cultivar CDC Frontier) to maintain consistency and eliminate cross-genotype variation.\u003c/p\u003e\n \u003cp\u003eRecognizing the importance of unique data, we then implemented redundancy removal to eliminate any sequence similarities that could compromise the accuracy of our findings. Using the skipredundant tool from EMBOSS version 6.6.0.0 (https;//\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ewww.bioinformatics.nl/cgi-bin/emboss/skipredundant\u003c/span\u003e\u003c/span\u003e), we set a stringent threshold of 95% similarity, resulting in the exclusion of 14 redundant sequences. This crucial step not only streamlined our dataset but also enhanced the reliability of our subsequent analyses. Ultimately, we focused our investigation on a refined set of 32 distinct sequences of methyltransferases in chickpea plants.\u003c/p\u003e\n\n\n \u003ch2\u003e2.2. Protein physicochemical properties\u003c/h2\u003e\n \u003cp\u003eTo gain a comprehensive understanding of the methyltransferase sequences, we analyzed several critical physicochemical properties, including amino acid length, molecular weight, theoretical isoelectric point, instability index, aliphatic index, and Grand Average of Hydropathicity (GRAVY). Utilizing the ProtParam tool (https;//web.expasy.org/protparam/) on the Expasy server (Wilkins et al. \u003cspan class=\"CitationRef\"\u003e1999\u003c/span\u003e), we systematically inputted all methyltransferase sequences.\u003c/p\u003e\n\n\n \u003ch2\u003e2.3. Identification of domains and conserved motifs\u003c/h2\u003e\n \u003cp\u003eTo uncover the unique structural and functional units, known as domains, within methyltransferase proteins, we systematically submitted the amino acid sequences to the Pfam database (Mistry et al. \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e). Utilizing Hidden Markov Model (HMM) profiles (http;//pfam.xfam.org), we identified key domains that are critical for the functionality of these proteins. In addition, we conducted a thorough investigation of conserved motifs, regions of identical or similar amino acid sequences that indicate evolutionary significance, among chickpea methyltransferase proteins. For this purpose, we employed the Multiple Em for Motif Elicitation (MEME) suite (https;//meme-suite.org/meme/tools/meme), setting the total number of motif sets to 14 while keeping other parameters at their default values (Bailey et al. \u003cspan class=\"CitationRef\"\u003e2009\u003c/span\u003e). The identified motifs were further analyzed using the Find Individual Motif Occurrences (FIMO) tool (https;//meme-suite.org/meme/tools/fimo) (Grant et al. \u003cspan class=\"CitationRef\"\u003e2011\u003c/span\u003e) with standard parameters, enabling us to visualize the distribution of these motifs effectively using Toolkit Biologists Tools (TBtools) software (Chen et al. \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eTo explore the positional and functional relationships between conserved motifs and annotated domains, domain\u0026ndash;motif overlap analysis was conducted. Stringent criteria were applied to ensure robust and reliable predictions, with a p-value threshold of 1e\u0026thinsp;\u0026minus;\u0026thinsp;5 and a q-value below 0.05 for identifying high-confidence domain\u0026ndash;motif overlap elements. For conservation assessment, motif occurrences were grouped based on their sequence patterns, and the number of distinct proteins (seq_id) containing each motif was calculated. Only motifs present in two or more proteins (\u0026ge;\u0026thinsp;2) were considered evolutionarily conserved. Finally, the domain\u0026ndash;motif overlap relationships were visualized using Python scripts.\u003c/p\u003e\n\n\n \u003ch2\u003e2.4 Phylogenetic tree construction\u003c/h2\u003e\n \u003cp\u003eTo gain deeper insights into the evolutionary relationships among methyltransferase proteins in chickpea, a phylogenetic analysis was conducted. A total of 32 methyltransferase protein sequences were aligned using MAFFT v7 with the --auto strategy to ensure accurate and high-quality multiple sequence alignment (Katoh \u0026amp; Standley \u003cspan class=\"CitationRef\"\u003e2013\u003c/span\u003e). The resulting alignment was subsequently trimmed using trimAl v1.4 with the -automated1 option to remove poorly aligned or divergent regions (Capella-Guti\u0026eacute;rrez et al. \u003cspan class=\"CitationRef\"\u003e2009\u003c/span\u003e). Phylogenetic inference was performed with IQ-TREE v2 using the Maximum Likelihood (ML) method (Minh et al. \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e). The best-fit substitution model was automatically determined by ModelFinder (Kalyaanamoorthy et al. \u003cspan class=\"CitationRef\"\u003e2017\u003c/span\u003e), and branch support values were estimated using 1,000 ultrafast bootstrap replicates (UFBoot) (Hoang et al. \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e) and 1,000 Shimodaira\u0026ndash;Hasegawa approximate likelihood ratio tests (SH-aLRT) to ensure robust statistical confidence. The resulting consensus tree was midpoint-rooted and visualized using FigTree (Rambaut \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e) (http;//tree.bio.ed.ac.uk/software/figtree/).\u003c/p\u003e\n\n\n \u003ch2\u003e2.5. Structural characterization and chromosomal localization of genes\u003c/h2\u003e\n \u003cp\u003eTo elucidate the structural organization of genes encoding methyltransferases, both coding sequences (CDS) and genomic sequences were meticulously retrieved from the NCBI database. The Gene Structure Display Server (GSDS) v2.0 (Guo et al. \u003cspan class=\"CitationRef\"\u003e2007\u003c/span\u003e) (http;//gsds.cbi.pku.edu.cn/) was employed to predict the exon-intron structures of these methyltransferase genes, offering insights into their genetic architecture.\u003c/p\u003e\n \n \u003cp\u003eTo further refine the analysis, the start and end positions of each methyltransferase gene on chickpea chromosomes were retrieved from the Cicer arietinum reference genome assembly CDC Frontier_v2.0 (NCBI Assembly Accession; GCF_000331145.2) to determine their physical chromosomal locations. Information on the eight chromosomes, along with the sizes of the scaffold elements of the chickpea genome, was also sourced from NCBI. The CDC Frontier_v2.0 genome assembly served as the reference genome for this analysis. While other chickpea genome assemblies, such as ICCV 2 and ICC 4958, are available, this study focused exclusively on the CDC Frontier assembly to ensure consistency in chromosomal mapping. Using TBtools (Chen et al. \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e) with default parameters, a precise chromosomal map was generated, clearly illustrating the distribution of methyltransferase genes across the chickpea genome. This comprehensive approach unveiled the structural intricacies and genomic organization of methyltransferases.\u003c/p\u003e\n \n \n\n\n \u003ch2\u003e2.6. Identification of cis-regulatory elements and transcription factor binding sites\u003c/h2\u003e\n \u003cp\u003eTo gain insights into the regulatory mechanisms controlling the expression of methyltransferase genes, sequences spanning approximately \u0026minus;\u0026thinsp;1000 bp upstream to +\u0026thinsp;200 bp downstream of the transcription start site (TSS) were retrieved from the NCBI database. These promoter regions were analyzed using the PlantCARE server (Lescot et al. \u003cspan class=\"CitationRef\"\u003e2002\u003c/span\u003e) (http;//bioinformatics.psb.ugent.be/webtools/plantcare/html/) to identify key cis-regulatory elements. Elements without a clearly defined functional role, such as unknown or very short motifs, were excluded from the final results. Additionally, transcription factors (TFs) and their corresponding trans-regulatory elements were identified using the Plant Transcription Factor Database (PlantTFDB, V5.0) server (Jin et al. 2016) (http;//planttfdb.gao-lab.org/prediction.php). To ensure robust and reliable predictions, stringent criteria were applied, with a p-value threshold of 1e-5 and a q-value below 0.05 for identifying high-confidence trans-regulatory elements. The identified cis-regulatory elements were visualized through Python-based plotting scripts, while the transcription factor binding sites were visualized using TBtools. (Chen et al. \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e), offering a comprehensive view of the regulatory landscape governing methyltransferase gene expression.\u003c/p\u003e\n\n\n \u003ch2\u003e2.7. Gene ontology (GO) analysis\u003c/h2\u003e\n \u003cp\u003eA two-tiered GO analysis was performed to gain deeper insights into the functional roles of methyltransferase proteins. First, a primary GO analysis was conducted using Blast2GO (Conesa \u0026amp; G\u0026ouml;tz \u003cspan class=\"CitationRef\"\u003e2008\u003c/span\u003e) (https;//\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ewww.blast2go.com/\u003c/span\u003e\u003c/span\u003e) by inputting the protein IDs of methyltransferases, providing an initial functional classification. This was followed by a more targeted GO enrichment analysis using PlantRegMap (Tian et al. \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e) (https;//plantregmap.gao-lab.org/) with methyltransferase gene symbols under default parameters, aiming to highlight significantly enriched biological processes, molecular functions, and cellular components. The resulting GO terms were visualized for clarity using the QuickGO server, offering a comprehensive graphical representation of the enriched terms of methyltransferases in chickpeas.\u003c/p\u003e\n\n\n \u003ch2\u003e2.8. Identification of microRNAs (miRNAs)\u003c/h2\u003e\n \u003cp\u003eTo identify miRNAs targeting methyltransferase genes in chickpeas, mature Cicer arietinum-specific miRNA sequences were retrieved from the Plant miRNAs Encyclopedia (https;//pmiren.com/download). These miRNAs, along with the coding sequences (CDS) of methyltransferases, were analyzed using the \u0026quot;small RNAs and targets\u0026quot; section of the psRNATarget server (https;//\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ewww.zhaolab.org/psRNATarget/analysis\u003c/span\u003e\u003c/span\u003e) under default parameters (Dai et al. \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e). This analysis identified specific miRNA-methyltransferase interactions, which were subsequently visualized through custom Python scripts, providing a clear representation of the regulatory relationships between miRNAs and their methyltransferase targets. This approach ensured a robust and precise identification of miRNAs involved in regulating methyltransferase functions in chickpeas.\u003c/p\u003e\n\n\n \u003ch2\u003e2.9. Prediction of secondary and tertiary structure and evaluation of the 3D Model\u003c/h2\u003e\n \u003cp\u003eTo predict the secondary structure, including alpha-helices, beta-strands, and random coils, of the selected methyltransferase protein the SOPMA server (https;//npsa-prabi.ibcp.fr/cgi-bin/npsa_automat.pl?page=/NPSA/npsa_sopma.html) was utilized with default parameters (Geourjon \u0026amp; Del\u0026eacute;age \u003cspan class=\"CitationRef\"\u003e1995\u003c/span\u003e) For a comprehensive three-dimensional (3D) structural prediction, comparative homology modelling was performed using the SWISS-Model Workspace (Waterhouse et al. \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e), where the protein\u0026rsquo;s FASTA sequence was uploaded. The resulting 3D model underwent rigorous evaluation using multiple validation tools, including QMEAN (Benkert et al. \u003cspan class=\"CitationRef\"\u003e2011\u003c/span\u003e) for structural quality assessment, ERRAT (Colovos \u0026amp; Yeates \u003cspan class=\"CitationRef\"\u003e1993\u003c/span\u003e) for analyzing non-bonded atomic interactions, verify3D (Bowie et al. \u003cspan class=\"CitationRef\"\u003e1991\u003c/span\u003e) for verifying the compatibility of the model\u0026rsquo;s 3D structure with its amino acid sequence, and PROVE (Pontius et al. \u003cspan class=\"CitationRef\"\u003e1996\u003c/span\u003e) for evaluating atomic packing quality. This multi-tiered approach ensured that the predicted structure was both accurate and reliable and gave valuable insights into the protein\u0026rsquo;s functional dynamics.\u003c/p\u003e\n\n\n \u003ch2\u003e2.10. Protein-protein interaction\u003c/h2\u003e\n \u003cp\u003eA detailed analysis of protein-protein interactions was conducted to construct a comprehensive network of potential interacting partners for the selected protein. These interactions were explored to identify relationships between the selected protein and other proteins both within the same species and across different species. The investigation was carried out using the powerful STRING v11.5 server (https;//string-db.org/) (Szklarczyk et al. \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e), providing a high-confidence network that shed light on functional associations and potential biological pathways.\u003c/p\u003e\n"},{"header":"Results","content":"\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Protein physicochemical properties\u003c/h2\u003e \u003cp\u003eThe physicochemical characteristics of methyltransferase proteins were evaluated using the ProtParam tool, revealing critical insights into their functional stability and behaviour. The protein lengths ranged from 666 to 3,279 amino acids, with molecular weights spanning from 53.769 to 266.282 kilodaltons (kDa). The isoelectric points (pI) of these proteins varied between 4.82 and 5.17, indicating their predominantly acidic nature. Importantly, the instability index, which ranged from 23.88 to 36.18, signified that these methyltransferases exhibit high stability, suggesting their robustness in physiological conditions. The aliphatic index, falling between 20.89 and 31.25, indicates moderate resistance to temperature fluctuations, while the GRAVY values, ranging from 0.687 to 0.847, highlight the hydrophobic nature of these proteins (Supplementary Table\u0026nbsp;1).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.2. Identification of domains and conserved motifs\u003c/h2\u003e \u003cp\u003eDomain analysis revealed the presence of 10 distinct protein domains across the 32 methyltransferase sequences analyzed, with the majority containing a methyltransferase domain responsible for the protein's catalytic activity (Supplementary Fig.\u0026nbsp;1). Notably, the methyltransferase protein XP_004516664.1 exhibited both\u003c/p\u003e \u003cp\u003ePGK domain and a methyltransferase domain, underscoring its multifunctionality. Other key domains identified include Aminotran_1_2, LCM, PHD, Pox_MCEL, ribosomal protein methyltransferase (PrmA), Protein-L-isoaspartate (D-aspartate) O-methyltransferase (PCMT), PUA, and Sterol_MT_C (Supplementary Table\u0026nbsp;2).\u003c/p\u003e \u003cp\u003eConserved motif analysis identified 14 dominant motifs among the protein sequences, with Motifs 1, 2, and 3 highly conserved across all 32 methyltransferase genes in chickpeas (Supplementary Fig.s 2 and 3). FIMO analysis further revealed the predominant presence of motifs \"GXGXG,\" \"LVXXGGXI,\" and \"GVXTGYS\" within the sequences (Supplementary Fig.\u0026nbsp;4). These motifs were consistently found in several prominent proteins, underscoring their conserved function across various methyltransferase families (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe domain\u0026ndash;motif overlap analysis revealed the presence of 9 motifs across 10 domains in a total of 23 proteins (Supplementary Table\u0026nbsp;3). Among these, ERF motifs were predominantly distributed across multiple domains, suggesting their broad regulatory significance. Notably, the PGK domain harbored the highest number of overlapping motifs (~\u0026thinsp;7), indicating its potential role as a multi-functional regulatory hub involved in diverse biological processes (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\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\u003eSummary of the occurrence and widespread conservation of predominant motifs and their associated proteins in chickpeas:\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\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=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMotif\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eProtein Name\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAccession Number\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"7\" rowspan=\"8\"\u003e \u003cp\u003e\u003cb\u003eGXGXG\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eProtein-lysine methyltransferase METTL21D\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eXP_012570542.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHistidine protein methyltransferase 1 homolog isoform X1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eXP_004489174.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCycloartenol-C-24-methyltransferase\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eXP_004499918.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eProtein N-lysine methyltransferase METTL21A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eXP_004506058.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMethyltransferase-like protein 6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eXP_004498868.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eProtein N-lysine methyltransferase METTL21A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eXP_004512683.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003etRNA (guanine-N(7))-methyltransferase-like\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eXP_004510156.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMethyltransferase-like protein 6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eXP_004498868.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e\u003cb\u003eLVXXGGXI\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePutative methyltransferase NSUN6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eXP_004491318.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFlavonoid 3'5'-methyltransferase-like\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eXP_004504753.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCaffeoyl-CoA O-methyltransferase\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNP_001351681.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTricin synthase 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eXP_004488701.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRibosomal RNA small subunit methyltransferase B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eXP_004491518.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cb\u003eGVXTGYS\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCaffeoyl-CoA O-methyltransferase\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNP_001351681.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTricin synthase 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eXP_004488701.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFlavonoid 3'5'-methyltransferase-like\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eXP_004504753.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eProtein-L-isoaspartate O-methyltransferase 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNP_001266141.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e3.3. Phylogenetic analysis\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThe phylogenetic tree constructed using 32 \u003cem\u003eCicer arietinum\u003c/em\u003e methyltransferase protein sequences revealed three major clades (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), each supported by moderate to high bootstrap values (61\u0026ndash;100%), with this tripartite division indicating strong evolutionary divergence within the methyltransferase family.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eClade I: Post-Translational Regulatory Methyltransferase Cluster\u003c/p\u003e \u003cp\u003eContaining thirteen sequences, this clade showed moderate to strong bootstrap support (61\u0026ndash;97%) and comprised members primarily annotated as post-translational (METTL/PRMT family) and SAM-dependent methyltransferases, displaying close sequence similarity among protein-modifying enzymes.\u003c/p\u003e \u003cp\u003eKey subclusters:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eMETTL family cluster: protein-lysine methyltransferase METTL21D (XP_012570542.1), protein N-lysine methyltransferase METTL21A (XP_004506058.1, XP_004512683.1) (bootstrap 95.2/96)\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eHistidine/Lysine-related cluster: protein N-lysine methyltransferase METTL21A-like (XP_004509259.1) and histidine protein methyltransferase 1 homolog (XP_004489174.1) (61/73)\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eSAM/PRMT cluster: S-adenosyl-L-methionine methyltransferase (XP_004516664.1), S-adenosyl-methionine-dependent methyltransferase (XP_004510156.1), and protein arginine N-methyltransferase PRMT10 (XP_004515598.1) (97.5/99)these three members are functionally associated with selenium-related methylation and detoxification processes.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eClade II: Isoaspartyl and Small-Molecule Methyltransferase Cluster\u003c/p\u003e \u003cp\u003eContaining twelve sequences, this clade exhibited strong bootstrap support (80\u0026ndash;100%) and comprised primarily isoaspartyl repair and small-molecule methyltransferases, showing high within-group sequence conservation.\u003c/p\u003e \u003cp\u003eKey subclusters:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eIsoaspartyl methyltransferase cluster: protein L-isoaspartyl methyltransferase 2 isoform 2 (NP_001265927.1) and protein L-isoaspartyl methyltransferase 1 (NP_001266141.1) (100/100)\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eSterol-related branch: cycloartenol-C-24-methyltransferase (XP_004499918.1) and 2-phytyl-1,4-beta-naphthoquinone methyltransferase, chloroplastic (XP_004489801.1) (86.1/56)\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003emRNA cap methyltransferase branch: mRNA cap guanine-N7 methyltransferase 1 (XP_004515076.1) and mRNA cap guanine-N7 methyltransferase 2 isoform X1 (XP_004502631.1) (99/99)\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eClade III: RNA and Ribosomal Methyltransferase Cluster\u003c/p\u003e \u003cp\u003eThis clade included seven sequences representing RNA- and ribosome-associated methyltransferases, supported by moderate to strong bootstrap values (78\u0026ndash;92%).\u003c/p\u003e \u003cp\u003eKey subclusters:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003ePair cluster: protein ENHANCED DOWNY MILDEW 2-like (XP_004516997.1) and rRNA G2069 N7-methylase (XP_004510052.1) (17.3/64)\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eRNA-processing branch: methyltransferase-like protein 13 isoform X1 (XP_004504352.1), rRNA (cytosine(967)-C(5))-methyltransferase (XP_004491518.1), putative methyltransferase NSUN6 (XP_004491318.1), and methionine S-methyltransferase isoform X1 (XP_004485405.1) (67.5/94)\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eIndependent branch: ribosomal RNA-processing protein 8-like (XP_004493600.1)\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e3.4. Structural characterization and chromosomal localizations of genes\u003c/h2\u003e \u003cp\u003eThe structural characterization of the 32 methyltransferase genes revealed intriguing patterns in their exon-intron architecture, showcasing both simplicity and complexity in gene structure. Among these genes, 25 were intron-less, each containing a single exon distributed randomly across the gene structure. In contrast, the remaining seven genes contained introns with two exons each, separated by variable distances, indicating a more intricate regulation of gene expression. Most of these exons measured between 0 to 1500 base pairs (bp) in length, except one gene, XP_004485405.1, which had an exon spanning 2000 to 3000 bp, pointing to potential functional significance. The structural details of exons, introns, and their respective upstream and downstream regulatory regions are visually represented in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, providing a comprehensive overview of the gene organization.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eChromosomal localization analysis further revealed the widespread distribution of methyltransferase genes across the chickpea genome. Out of the 32 genes analyzed, 26 were mapped to eight chromosomes, while the remaining six genes were associated with scaffolds, lacking specific chromosomal locations (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Chromosome 6 harboured the most methyltransferase genes, with seven genes mapped to this region, highlighting its potential as a hotspot for methyltransferase activity. Chromosomes 2 and 7 each contained four genes, while chromosomes 1 and 5 hosted three genes each. Two genes were located on chromosomes 3 and 8, and chromosome 4 contained just one gene (Supplementary Table\u0026nbsp;4). The chromosomal distribution of methyltransferases was notably diverse, with these genes dispersed across various genomic loci throughout the chickpea genome.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e3.5. Identification of cis-regulatory elements and transcription factor binding sites\u003c/h2\u003e \u003cp\u003eThe analysis of cis-regulatory elements in methyltransferase genes revealed 44 distinct types of cis-elements distributed across 28 genes (Supplementary Table\u0026nbsp;5).\u003c/p\u003e \u003cp\u003eThese cis-regulatory elements could be grouped into the following functional categories:\u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eLight-responsive elements\u003c/b\u003e: G-box, Sp1, chs-CMA1a, chs-CMA2a, GATA-motif, I-box, TCCC-motif, TCT-motif, AT1-motif, Box 4, ATC-motif, AE-box, GT1-motif, ACE, GA-motif, Gap-box, 4cl-CMA2b.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003ePlant growth hormone regulatory elements\u003c/b\u003e: ABRE (abscisic acid responsiveness), TATC-box and GARE-motif (gibberellin responsiveness), TCA-element (salicylic acid responsiveness), TGA-element and AuxRR-core (auxin responsiveness), CGTCA-motif and TGACG-motif (MeJA responsiveness), and TC-rich repeats.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eCore promoter and protein-binding elements\u003c/b\u003e: TATA-box, CAAT-box, CCAAT-box, A-box, Box III, GC-motif, 3-AF1 binding site, and AT-rich element.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eStress-responsive and environmental regulatory elements\u003c/b\u003e: ARE (anaerobic induction), MBS (drought-inducibility), DRE core (dehydration/cold response), LTR (low-temperature responsiveness), circadian, and as-1 element.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eTissue-specific and developmental regulation elements\u003c/b\u003e: CAT-box, RY-element, GCN4_motif, AACA_motif, HD-Zip, O2-site, P-box, and LAMP-element.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003cp\u003eThese cis-regulatory elements are visually represented in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, emphasizing their role in coordinating a range of gene expression responses.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe transcription factor binding site (TFBS) analysis revealed an extensive network of binding sites corresponding to their trans-regulatory elements in the chickpea methyltransferases. A total of 33 transcription factors (TFs) were identified and grouped into five major functional categories including developmental regulation, hormone signalling, stress response, metabolic regulation, and cell cycle regulation (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Among these transcription factors, the following three key regulators stood out for their significant roles:\u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eEthylene Responsive Factor (ERF)\u003c/b\u003e: Involved in stress and hormone signalling pathways.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eLateral Organ Boundaries Domain (LBD)\u003c/b\u003e: Plays a critical role in developmental regulation and organ formation.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eNin-like\u003c/b\u003e: Implicated in nitrogen metabolism and signaling pathways.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \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\u003eCategorization of transcription factor binding sites identified in the chickpea methyltransferase genes.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\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=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSl. No.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCategories\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTranscription factors\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDevelopmental regulation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMIKC_MADS, AP2, TALE, MYB, Nin-like, TCP, Dof, SRS, bHLH, GATA, Trihelix, NAC, HD-ZIP, G2-like, MYB-related, SBP, and LFY\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHormonal signalling\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eERF, ARF, BES1, and CAMTA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStress-responsive and environmental regulation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBBR-BPC, ERF, C2H2, LBD, bZIP, WRKY, СЗН, HSF, and EIL\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMetabolic regulation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFAR 1 and B3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCell cycle regulation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eE2F/DP, CPP, and ZF-HD\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThese transcription factors were predominantly found in the 0-3000 bp upstream regions of the methyltransferase genes (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). The statistical significance of these findings, reflected in p-values and q-values, further reinforces the relevance of these transcription factors in the regulation of methyltransferases in chickpeas (Supplementary Table\u0026nbsp;6). Overall, the complexity and precision of transcriptional regulation in chickpea methyltransferase genes were observed.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e3.6. Gene ontology analysis\u003c/h2\u003e \u003cp\u003eThe Gene Ontology (GO) analysis of methyltransferase proteins in chickpeas revealed a wide array of cellular functions, highlighting their importance across diverse biological processes. This analysis identified 12 cellular components, 22 molecular functions, and 26 biological processes (detailed in Supplementary Table\u0026nbsp;7). Although no direct GO terms associated with selenium metabolism in chickpeas were identified, the analysis offered a comprehensive overview of the functional roles of methyltransferases.\u003c/p\u003e \u003cp\u003eThe GO enrichment analysis further refined these findings, uncovering 17 potential GO terms, including 9 related to molecular functions and 8 to biological processes, linked to the 32 methyltransferase sequences analysed (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Interestingly, no GO terms related to cellular components were identified in this enrichment analysis. Among the enriched GO terms, three are particularly noteworthy due to their association with selenium metabolism (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). These terms highlighted key biological processes and three methyltransferase proteins were identified as being linked to these GO terms are summarised in the Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e.\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\u003eGO term enrichment output for 32 methyltransferase sequences of chickpea showing their involvement in various functions including biological processes (P) and, molecular functions (F) at highly significant p- and q-values.\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=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" 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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGO ID\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGO Term\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eq-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAspect\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eGenes\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGO:0003824\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCatalytic activity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0049\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.000\u0026thinsp;+\u0026thinsp;00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLOC101489113, LOC101489144, LOC101489289, LOC101495965, LOC101496374, LOC101498967, LOC101504126, LOC101504427, LOC101504483, LOC101508155, LOC101510447, LOC101511458, LOC101512163, LOC101512362, LOC101513216, LOC101515076, LOC101515626, LOC101515783\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGO:0006396\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRNA processing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.00013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.526E-01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLOC101489144, LOC101495965, LOC101504483, LOC101515076, LOC101515626\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGO:0006399\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003etRNA metabolic process\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.00072\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.828E-01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLOC101489144, LOC101504483\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGO:0006400\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003etRNA modification\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.4E-05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.449E-02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLOC101489144, LOC101504483\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGO:0008033\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003etRNA processing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.00023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.159E-01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLOC101489144, LOC101504483\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGO:0008168\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emethyltransferase activity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.8E-13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.543E-10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLOC101489113, LOC101489144, LOC101489289, LOC101495965, LOC101496374, LOC101498967, LOC101504126, LOC101504427, LOC101504483, LOC101508155, LOC101510447, LOC101511458, LOC101512163, LOC101512362, LOC101513216, LOC101515076, LOC101515626, LOC101515783\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGO:0008173\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRNA methyltransferase activity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.6E-07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.307E-04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLOC101489144, LOC101495965, LOC101504483, LOC101515076, LOC101515626\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGO:0008175\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003etRNA methyltransferase activity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.5E-05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.352E-03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLOC101489144, LOC101504483\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGO:0008176\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003etRNA (guanine-N7-)-\u003c/p\u003e \u003cp\u003emethyltransferase activity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.7E-07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.307E-04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLOC101489144, LOC101504483\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGO:0008757\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eS-adenosylmethionine-dependent methyltransferase activity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.80-06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.923E-03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLOC101489113, LOC101489144, LOC101489289, LOC101495965, LOC101504483, LOC101508155, LOC101515076, LOC101515626\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGO:0009451\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRNA modification\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.00059\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.616E-01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLOC101489144, LOC101504483, LOC101515626\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGO:0016423\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003etRNA (guanine)\u003c/p\u003e \u003cp\u003emethyltransferase activity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4E-06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.857E-03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLOC101489144, LOC101504483\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGO:0016740\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTransferase activity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.4E-06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.923E-03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLOC101489113, LOC101489144, LOC101489289, LOC101495965, LOC101496374, LOC101498967, LOC101504126, LOC101504427, LOC101504483, LOC101508155, LOC101510447, LOC101511458, LOC101512163, LOC101512362, LOC101513216, LOC101515076, LOC101515626, LOC101515783\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGO:0016741\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTransferase activity (transferring one-carbon group)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.5E-13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.543E-10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLOC101489113, LOC101489144, LOC101489289, LOC101495965, LOC101496374, LOC101498967, LOC101504126, LOC101504427, LOC101504483, LOC101508155, LOC101510447, LOC101511458, LOC101512163, LOC101512362, LOC101513216, LOC101515076, LOC101515626, LOC101515783\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGO:0034470\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003encRNA processing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.8\u0026euro;-06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.661E-02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLOC101489144, LOC101504483, LOC101515076, LOC101515626\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGO:0034660\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003enRNA metabolic process\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.4E-05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.633E-02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLOC101489144, LOC101504483, LOC101515076, LOC101515626\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGO:0043412\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMacromolecule modification\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.00963\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.000E\u0026thinsp;+\u0026thinsp;00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \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\u003eKey chickpea methyltransferase proteins, their associated GO terms, and their functional roles in biological processes relevant to tRNA and ncRNA modifications.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\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=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMethyltransferase Protein\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eProtein ID\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGO Terms\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFunction\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLOC101489144\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eXP_004510156.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGO:0006400 (tRNA modification), GO:0008033 (tRNA processing)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003etRNA (guanine-N(7)-)-methyltransferase; involved in tRNA modification and processing\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLOC101504483\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eXP_004516664.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGO:0006400 (tRNA modification), GO:0008033 (tRNA processing)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePhosphoglycerate kinase-like; plays a dual role in tRNA modification and processing\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLOC101515076\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eXP_004491518.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGO:0034470 (ncRNA processing)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRibosomal RNA small subunit methyltransferase B; involved in ncRNA processing\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e3.7. Identification of miRNAs targeting methyltransferases\u003c/h2\u003e \u003cp\u003eA detailed analysis identified 18 miRNAs targeting 12 distinct methyltransferase genes, revealing intricate regulatory interactions. These miRNA-methyltransferase relationships formed four well-defined clusters, each illustrating varying miRNA targeting dynamics. Cluster I and Cluster II showed multiple miRNAs targeting individual genes, whereas Cluster III highlighted single miRNAs regulating multiple genes. Cluster IV (the PGK Cluster) presented a simpler interaction, where one miRNA targeted one gene. Notably, a single miRNA targeting multiple genes was also observed in Cluster II (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eCluster I featured XP_004512086.1, regulated by five distinct miRNAs: aly-miR162a-5p, aly-miR162b-5p, bra-miR162-5p, stu-miR162a-5p, and stu-miR162b-5p. Additionally, gma-miR9725 targets the methyltransferase XP_004510052.1.\u003c/p\u003e \u003cp\u003eCluster II focused on XP_004498868.1, which is targeted by another set of five miRNAs: gra-miR167a, gra-miR167b, mtr-miR169d-3p, mtr-miR169e-3p, and osa-miR2863a. Intriguingly, osa-miR2863a targets both XP_004498868.1 and XP_004513963.1, demonstrating a shared regulatory mechanism between two distinct proteins. Additionally, cca-miR6110-3p targets XP_004515598.1, further expanding the regulatory landscape in this cluster.\u003c/p\u003e \u003cp\u003eCluster III highlighted aly-miR822-5p, which regulates both NP_001265927.1 and NP_001266141.1, while sbi-miR5564c-5p targets XP_004492936.1 and cre-miR1144a.1 targeting XP_004510029.1, underscoring the complexity of single miRNA-multi-gene interactions.\u003c/p\u003e \u003cp\u003eCluster IV, or the PGK Cluster, included esi-miR3463-3p, which targets XP_004516997.1 (cytosine-specific DNA methyltransferase), tae-miR530 targeting XP_004504712.1 (ENHANCED DOWNY MILDEW 2-like isoform X1), and ath-miR837-5p targeting XP_004516664.1 (PGK). Notably, the PGK protein, LOC101504483 (XP_004516664.1), emerged as a key player, after both GO enrichment and miRNA analyses (Supplementary Table\u0026nbsp;8).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003e3.8. Prediction of secondary and tertiary structure and evaluation of the 3D Model of PGK\u003c/h2\u003e \u003cp\u003eThe structural analysis of Phosphoglycerate Kinase (PGK) revealed key insights into its composition and functionality. The protein's secondary structure was dominated by alpha-helices, which constitute approximately 41%, suggesting their pivotal role in maintaining the protein's structural integrity. Meanwhile, random coils, making up 31.89% of the structure, contribute to the protein\u0026rsquo;s flexibility, allowing it to adapt dynamically to various functional states. The beta-strands, accounting for 18.11%, further enhance the protein\u0026rsquo;s stability, reinforcing its structural resilience (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn the process of constructing the PGK's 3D model through comparative homology modelling, a sequence identity analysis demonstrated a 28.61% similarity with the crystallographic structure of PGK from \u003cem\u003eThermotoga maritima\u003c/em\u003e, a hyper thermophilic bacterium (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003e). The high-resolution structure (2.00 \u0026Aring;) of \u003cem\u003eT. maritima\u003c/em\u003e PGK provided a strong foundation for modelling the chickpea PGK, confirming it to be a monomer. The quality of the model was evaluated using the Global Model Quality Estimate (GMQE) score, which yielded a value of 0.42, while the QMEANDisCo global score stood at 0.63\u0026thinsp;\u0026plusmn;\u0026thinsp;0.05. These scores, both ranging from 0 to 1, reflected moderate accuracy and coverage of the model, indicating that while the model is reliable, there is room for refinement.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFurther evaluation using the Z-score, an indicator of the energy states in folded versus misfolded proteins, yielded a value of -2.80. This score suggests the model approaches native-like qualities, with values closer to 0 being ideal and values near \u0026minus;\u0026thinsp;4 indicating suboptimal quality. However, the model's overall structure was considered robust, as evidenced by the ERRAT score of 88.9182. This high score, which assesses non-bonded atomic interactions, is well above the threshold of 50, signalling strong overall quality.\u003c/p\u003e \u003cp\u003eThe model also achieved favourable results in the verification of its 3D structure, covering 81.65% of the residues with an average 3D-1D score of \u0026ge;\u0026thinsp;0.2, meeting the benchmark for reliable models. Ramachandran plot analysis further confirmed the structural soundness, with approximately 86% of the residues located in the favoured regions, 12.3% in additionally allowed regions, and only 1.1% in disallowed regions. This distribution indicated a high-quality model, as favoured regions above 90% generally represent excellent accuracy. The near-90% favoured region coverage underscored the viability of this PGK model. These findings, illustrated in Fig.s 9 and 10, comprehensively validated the PGK structure, demonstrating its reliability for future functional studies.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003e3.9. Protein-protein interaction\u003c/h2\u003e \u003cp\u003eProtein-protein interaction analysis revealed a dynamic and intricate network of potential interacting partners for PGK, spanning both intra-species and inter-species connections. The analysis identified 22 key nodes and 97 edges, with a remarkably high average local clustering coefficient of 0.859, indicating a densely interconnected network (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003e). Among the interactions, particularly strong associations were observed with key metabolic enzymes, reflecting PGK\u0026rsquo;s central role in glycolysis and related pathways. Notably, the interaction between glyceraldehyde-3-phosphate dehydrogenase (GAPDH) and erythrose-4-phosphate dehydrogenase showed an exceptionally high confidence score of 0.990, indicating near-certain functional collaboration. This was closely followed by interactions with triosephosphate isomerase (score\u0026thinsp;=\u0026thinsp;0.987), fructose/tagatose bisphosphate aldolase (0.962), fructose-bisphosphate aldolase class 1 (0.962), pyruvate kinase (0.961), and enolase (0.945), the critical players in glycolysis and gluconeogenesis.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAdditional interactions with transketolase (0.937), glucose-6-phosphate isomerase (0.911), and two variants of phosphoglycerate mutase, BPG-dependent (0.910) and BPG-independent (AlkP superfamily, 0.866), further emphasized PGK\u0026rsquo;s extensive involvement in carbohydrate metabolism. The broad spectrum of interactions highlighted PGK\u0026rsquo;s essential role in regulating key biochemical processes across different species.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study, we investigated the crucial role of methyltransferases in selenium metabolism and their impact on stress resistance in chickpeas. We analyzed the evolutionary and functional significance of these enzymes, revealing that while some methyltransferases, such as MMT, are directly involved in selenium metabolism, others may provide supportive or regulatory roles that enhance the plant\u0026rsquo;s resilience (Spechenkova et al. \u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Watanabe et al. \u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Notably, our analysis identified diverse amino acid residues in all methyltransferase sequences, including alanine (A), aspartic acid (D), cysteine (C), glycine (G), and glutamic acid (E). Each of these residues plays a vital role, as alanine contributes to enzyme stability, glycine facilitates substrate binding and catalysis, and cysteine aids in zinc binding, thereby boosting enzyme activity (Subbaramaiah et al. \u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e1991\u003c/span\u003e; Wang et al. \u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). This process is driven by S-adenosylmethionine (SAM), a universal methyl donor that supplies methyl groups to these critical amino acid residues within methyltransferase proteins. By including all chickpea methyltransferases in our study, we capture a broader spectrum of these proteins, allowing for a comprehensive understanding of their collective contributions to the plant's defence mechanisms. This research not only deepens our insights into methyltransferases but also lays the groundwork for developing strategies to enhance disease resistance in chickpeas through targeted manipulation of selenium metabolism.\u003c/p\u003e \u003cp\u003eThe physicochemical analysis of methyltransferase proteins in chickpeas revealed significant variability in their lengths, ranging from 666 to 3,279 amino acids, and molecular weights, spanning 53.769 to 266.282 kDa. This variability can be attributed to differences in amino acid composition, which directly influences the functional specificity of the methyltransferases studied. For instance, proteins enriched with lighter residues like glycine or leucine exhibit lower molecular weights compared to those containing heavier residues such as arginine or methionine (Weeds \u0026amp; Frank \u003cspan citationid=\"CR97\" class=\"CitationRef\"\u003e1974\u003c/span\u003e; Yang et al. \u003cspan citationid=\"CR103\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). This finding underscores the critical role of amino acid composition in determining the functional properties of methyltransferases, even among proteins derived from the same species. Recent research indicates that proteins with larger molecular weights and longer sequences tend to harbour more functional regions or domains, enhancing their versatility and regulatory capacity (Amaral \u0026amp; Devos \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Our study corroborated this trend; for instance, the protein XP_004485405.1, with the highest molecular weight of 266.282 kDa, contained three distinct domains (Methyltransferase, Aminotran_1_2, and PrmA) each contributing to selenium metabolism. Notably, the PrmA domain is associated with ribosomal protein methylation, which plays a vital role in managing selenium levels (Mazzoleni et al. \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). In contrast, the protein XP_012570542.1, with the lowest molecular weight of 53.769 kDa, displayed only the Methyltransferase domain. These variations in molecular weight and length, driven by amino acid composition, not only reflect diverse methylation processes but also suggest that these methyltransferase proteins are integral to enhancing plant resilience, particularly in the context of selenium metabolism.\u003c/p\u003e \u003cp\u003eThe methyltransferase domain, identified through Pfam analysis, is crucial for managing cellular levels of methionine and S-adenosylmethionine (SAM). Acting as the enzyme's catalytic core, this domain specifically facilitates the methylation of the sulfur atom in methionine, highlighting its essential role in methylation processes (Peng et al. \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Likewise, protein domains like phosphoglycerate kinase (PGK) are highly conserved enzymes that play vital roles in glycolysis and photosynthesis, with different isoforms essential for carbon fixation and cellular metabolism, particularly in Arabidopsis thaliana (Li et al. \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). This study reveals a previously overlooked dual functionality of methyltransferases in chickpeas, showcasing their PGK activity alongside their established role in methylation. This finding points to a new metabolic integration where methyltransferases are involved not only in selenium detoxification but also in ATP production through glycolysis. The connection between PGK and methyltransferases links selenium metabolism to energy generation, aiding cellular adaptation to oxidative stress. Additionally, the PrmA domain, which methylates ribosomal protein L11, enhances ribosome activity, supporting a wide range of metabolic processes. This includes crucial functions in selenium metabolism and stress responses in plants, underlining its significance in sustaining plant health during stressful conditions (Mazzoleni et al. \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eMotif analysis revealed the presence of highly conserved motifs, such as \u0026ldquo;GXGXG,\u0026rdquo; \u0026ldquo;LVXXGGXI,\u0026rdquo; and \u0026ldquo;GVXTGYS\u0026rdquo;, across the methyltransferase sequences in chickpeas. These conserved motifs are crucial for binding the SAM cofactor, which is essential for the methylation process (Peng et al. \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Specifically, the conserved SAM-binding motif, common to many plant methyltransferases, interacts with the adenine moiety of SAM, facilitating the precise binding of the methionine portion and driving the enzymatic methylation reaction (Peng et al. \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Joshi \u0026amp; Chiang \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e1998\u003c/span\u003e). The identification of these motifs in the chickpea methyltransferase sequences strongly indicates their catalytic activity, confirming the functional capacity of these proteins to carry out methylation reactions. This insight reinforces the pivotal role of these methyltransferases in regulating plant metabolism and stress responses, particularly through their interaction with SAM, a key cofactor in numerous biological processes.\u003c/p\u003e \u003cp\u003eDomain\u0026ndash;motif overlap analysis showed that most motifs were located within the PGK domain, indicating its role as a multi-functional regulatory hub. Recent multi-omics studies corroborate the functional significance of methyltransferases and their associated hub motifs in activating metabolic networks that mediate drought tolerance and selenium homeostasis. These findings highlight the importance of galactose metabolism, antibiotic biosynthesis, and secondary metabolite production under stress conditions in chickpea (Kudapa et al. \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Apostolova \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Collectively, these insights suggest that the PGK domain may function as a molecular bridge integrating catalytic and regulatory mechanisms essential for environmental adaptation.\u003c/p\u003e \u003cp\u003ePhylogenetic analysis of methyltransferase proteins in chickpea revealed a tripartite division, highlighting a sophisticated and multilayered methyltransferase system in Cicer arietinum. The three observed clades represent distinct groups of methyltransferases, each contributing differently to selenium homeostasis and stress tolerance through specialized molecular functions. Clade I, enriched for METTL and PRMT methyltransferases, comprises enzymes modulating protein function via lysine and arginine methylation. These post-translational modifications critically regulate protein stability and activity, including stress-related proteins and redox homeostasis factors, directly supporting selenium detoxification pathways and abiotic stress adaptation in chickpea (Verma et al. \u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Lashley et al. \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Clade II includes isoaspartyl protein repair enzymes and various metabolic methyltransferases, emphasizing protein repair and metabolic regulation for maintaining cellular function under selenium-induced oxidative stress. Efficient protein turnover and specialized metabolic methylation in this clade reflect evolved strategies essential for plant stress resilience (Verma et al. \u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Zhao et al. \u003cspan citationid=\"CR107\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Lashley et al. \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Clade III highlights methylation-driven regulation of RNA processing and ribosome function, with RNA modifications controlling gene expression responses to stress, ensuring accurate translation, and possibly modulating redox balance during selenium exposure (Zhao et al. \u003cspan citationid=\"CR107\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Kumar et al. \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Yadav et al. \u003cspan citationid=\"CR101\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). This reveals an additional transcriptional and translational regulatory layer critical for stress adaptation. The distribution across these three functional clades illustrates the complex molecular specialization that underpins selenium homeostasis and stress response mechanisms in chickpea.\u003c/p\u003e \u003cp\u003eThe gene structure analysis revealed that a substantial proportion (78%) of methyltransferase genes in Cicer arietinum are intron-less, a characteristic also observed in the WRKY gene family of the same species (Waqas et al. \u003cspan citationid=\"CR95\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Intron-less genes are thought to facilitate rapid stress responses, making them crucial regulators of plant growth and developmental processes (77; Fan et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). In our study, key intron-less methyltransferases, such as XP_00451664.1 and XP_004491518.1, were specifically linked to selenium metabolism, as indicated by Gene Ontology (GO) analysis. This highlights their role in enhancing plant stress tolerance through efficient gene expression mechanisms. Particularly noteworthy is the methyltransferase XP_004485405.1, which possesses the longest exon, spanning 2000 to 3000 base pairs. This gene encodes the largest protein (266.282 kDa) identified in the study, containing three functional domains, Methyltransferase, Aminotran_1_2, and PrmA, all contributing to selenium metabolism. The presence of these distinct domains suggests a multifunctional role for this protein in selenium detoxification and overall plant resilience. This gene structure's efficiency and versatility further emphasize the critical role of methyltransferases in selenium-mediated defence mechanisms in chickpeas.\u003c/p\u003e \u003cp\u003eAmong all the chromosomes, the largest number of genes (four) clustered at positions from 10 Mb to 20 Mb on chromosome 6 indicates that this particular region could be a hotspot for methylation-related functions as the genes present there contain only methyltransferase domains. Chromosomes have regions exhibiting distinct evolutionary patterns where genes encoding specific functions cluster together (Jiao \u0026amp; Schneeberger \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Interestingly, scaffold proteins exhibit versatility in their functions and localization within cells, being present in various cellular compartments such as the plasma membrane, cytoplasm, endosomes, mitochondria, Golgi, and nucleus (Mukherjee \u0026amp; Low \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). This diversity in localization enables scaffold proteins to regulate multiple signalling pathways and cellular processes simultaneously. In line with these findings, our study suggests that scaffold proteins may possess novel functions, as proteins located in scaffolds such as XP_004515076.1 and XP_004516664.1 were primarily involved in selenium metabolism, as indicated by the results of GO analysis with selenium metabolism.\u003c/p\u003e \u003cp\u003eThe identification of diverse cis-regulatory elements in the promoter regions of methyltransferase genes underscores the intricate regulation of these genes through a complex network of transcriptional and post-transcriptional mechanisms. Hence, these regulatory elements are essential in fine-tuning the expression of methyltransferases in response to various developmental, environmental, and physiological signals. Recent studies demonstrate that methyltransferases are involved in the biosynthesis of key stress-signaling compounds, such as S-methyl-methionine salicylate (MMS), which interacts with salicylic acid to boost plant stress tolerance (Peng et al. \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Balassa et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). This suggest that methyltransferases, essential for natural product biosynthesis, play a vital role in plant adaptation to stress conditions. Moreover, selenium-containing products synthesized via these signalling pathways contribute significantly to managing oxidative stress and enhancing fungal resistance (Kayrouz et al. \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Handa et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn this study, we identified several critical light-responsive cis-regulatory elements, such as G-box and I-box, alongside hormone-responsive elements like ABRE and TCA-element, in the promoter regions of methyltransferase genes. This suggests that methyltransferase expression is influenced by both light and hormonal signalling pathways, positioning these enzymes at the nexus of stress response regulation. Specifically, ABRE elements, which are responsive to abscisic acid (ABA), play a pivotal role in regulating gene expression under high-light conditions and during exposure to biotic and abiotic stresses (Lim et al. \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Shi et al. \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). This regulatory framework likely enhances the plant's adaptive capacity by coordinating methylation reactions that fortify biotic stress tolerance. This is evident in the case of MMT which has been recognized as a key enzyme in the production of dimethylsulfoniopropionate (DMSP), a critical anti-stress compound in marine ecosystems (Peng et al. \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Williams et al. \u003cspan citationid=\"CR98\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Collectively, the presence of cis-regulatory elements in methyltransferase genes highlights their indispensable role in selenium metabolism, oxidative stress management and the broader defence mechanisms of chickpeas.\u003c/p\u003e \u003cp\u003eTranscription factors are pivotal in regulating various stages of plant development and responses to environmental stimuli. In our study of chickpea methyltransferases, we identified several transcription factors that contribute to developmental regulation, hormone signalling, stress responses, metabolic processes, and cell cycle control. Methyltransferase proteins, particularly during developmental stages and under stress conditions, are central to plant resilience and growth (Ling et al. \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Among the key transcriptional regulators, MYB and MYB-related proteins are crucial for both primary and secondary metabolism, as well as for promoting methylation processes in plants (Pu et al. \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Cao et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). These proteins not only catalyze methylation reactions but also regulate the biosynthesis of selenium, which is essential for balancing growth and defence. WRKY transcription factors, known for their involvement in biological processes like metabolism and stress responses, are also key players in methylation reactions (Yu et al. \u003cspan citationid=\"CR105\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Additionally, the ERF transcription factor is a critical component of the plant's innate immune system, fortifying its defences against fungal pathogens and contributing significantly to disease resistance (Son et al. \u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Similarly, in selenium metabolism, the up-regulation of MYB, MYB-related, bZIP, ERF, and AP2 transcription factors, which support selenium uptake by replacing sulfur in proteins were observed (Cao et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Wang et al. \u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOur study also uncovered the significant roles of three highly efficient transcription factors, ERF, LBD, and Nin-like, in regulating methyltransferase genes. Recent research highlights that ERF and LBD genes respond to various environmental stresses and plant hormones, suggesting their involvement in plant metabolism and the management of both abiotic and biotic stresses (19; Hao et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Yu et al. \u003cspan citationid=\"CR105\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). These insights emphasize the critical function of transcription factors in regulating methyltransferase activity, particularly in selenium metabolism, thereby enhancing disease resistance in chickpeas. The integration of these regulatory networks reflects a strategic adaptation in chickpeas, reinforcing their defence mechanisms and resilience to environmental challenges.\u003c/p\u003e \u003cp\u003eThe GO enrichment analysis conducted in this study revealed significant GO terms associated with selenium metabolism, such as GO;0006400 (tRNA modification), GO;0008033 (tRNA processing), and GO;0034470 (ncRNA processing), all of which are integral components of the RNA processing pathway (Sekulovski \u0026amp; Trowitzsch \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). This finding aligns with a growing body of evidence highlighting selenium's profound influence on transcription and gene expression. For instance, in tea plants treated with selenite or selenate, key genes involved in amino acid and glutathione metabolism, along with those critical for DNA and RNA metabolism, were notably upregulated (Ren et al. \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). This underscores selenium\u0026rsquo;s pivotal role in modulating metabolic pathways, linking selenium metabolism to RNA processing, and influencing key genes in DNA and RNA metabolism. Such insights emphasize selenium\u0026rsquo;s importance in plant biology and its broader implications for gene expression.\u003c/p\u003e \u003cp\u003eMethyltransferases, through their involvement in RNA processing, are likely key regulators of selenium metabolism. RNA methyltransferases, specifically, are central to regulating RNA processing and metabolism, directly influencing selenium metabolism by facilitating selenoprotein synthesis (Li et al. \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Additionally, N6-methyladenosine (m6A) modification, catalyzed by methyltransferases like METTL3 and METTL14, affects various aspects of RNA metabolism, including splicing, processing, nuclear export, translation, and degradation (Lin \u0026amp; Gregory \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Shriwas et al. \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). This modification also impacts the translation of genes encoding selenoproteins, which are essential for selenium metabolism. The regulation of this translation process is uniquely controlled by the selenocysteine insertion sequence (SECIS) element in the 3' untranslated region of selenoprotein mRNAs, alongside a specialized tRNA(Ser)Sec (Jameson \u0026amp; Diamond \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). Methylation of this tRNA(Ser)Sec is crucial for its translational activity, with selenium-induced tRNA methylation serving as a critical regulatory mechanism for selenoprotein synthesis, thereby establishing a direct link between RNA methylation and selenium metabolism.\u003c/p\u003e \u003cp\u003eOur research reveals that PGK-like methyltransferases play a crucial role in selenium detoxification and maintaining redox balance, a function that has not been previously identified in chickpeas. These proteins are instrumental in regulating the synthesis of selenoproteins, alleviating oxidative stress, and balancing energy, making them vital for adapting to abiotic stress. Furthermore, the connection between glycolysis and methylation processes introduces a new biochemical mechanism that may elucidate how chickpeas thrive in selenium-rich environments. The GO enrichment analysis also brought to light a biochemical mechanism related to RNA processing, associated with the PGK domain-containing methyltransferases studied here. This points to a potentially broader functional role for these proteins, possibly in selenium metabolism. Supporting research involving Rainbow trout indicates that exposure to selenium leads to the upregulation of genes linked to glycolysis, which is related to PGK proteins. Collectively, these findings suggest a significant relationship between RNA processing, methyltransferases, and selenium metabolism, reinforcing the complex role of RNA methylation in improving plant stress resilience and metabolic flexibility.\u003c/p\u003e \u003cp\u003eThe miRNAs play a pivotal role in the regulation of gene expression, despite being non-coding regions. They exert their influence by either cleaving target mRNAs or inhibiting their translation, directly affecting the levels of viable proteins within the plant. The expression of miRNAs, therefore, has a significant impact on plant biology, as it controls the suppression of key genes. In a previous study on Astragalus chrysochlorus, miRNAs were shown to modulate the expression of protein-coding genes in response to selenium stress, either upregulating or downregulating them based on the specific properties of the miRNAs involved (Cakir et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Our findings are consistent with this previous research showing that selenium\u0026rsquo;s impact on miRNAs varies depending on concentration, influencing plant biological processes in various ways. Similarly, studies on rice revealed that selenium can have both beneficial and detrimental effects on plant growth, depending on its concentration (Pandey et al. \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). At low levels, selenium can enhance growth by modulating miRNA-mediated processes, promoting plant development and stress tolerance. However, at higher concentrations, selenium can disrupt protein function and inhibit plant growth by interfering with miRNA regulation. These insights underscore the dual role of selenium, where controlled concentrations can harness miRNA pathways to boost plant resilience, while excessive selenium may impede critical biological functions. By understanding these miRNA mechanisms, it becomes possible to fine-tune selenium levels for optimal plant growth and stress tolerance.\u003c/p\u003e \u003cp\u003eIn our study, we uncovered the pivotal role of a PGK-like protein (LOC101504483) in selenium metabolism in chickpeas, as evidenced by GO analyses and miRNA investigations. This protein\u0026rsquo;s involvement in selenium metabolism is consistent with similar findings in other plant species, reinforcing its critical function in plant biology (Hofstee et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Rojas-Pirela et al. \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2020\u003c/span\u003e;28). Through miRNA analysis, LOC101504483 was identified as a target of ath-miR837-5p, a microRNA known to regulate essential genes involved in callus initiation, root formation, and cell division (Jatan et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). This connection suggests a potential link between selenium metabolism and key developmental processes in chickpeas, highlighting the versatile role of PGK in both growth regulation and stress response.\u003c/p\u003e \u003cp\u003eMoreover, PGK was found to be functionally clustered with XP_004504712.1, an EDM2 protein that plays a crucial role in plant immunity, specifically in RPP7-mediated resistance to pathogens like downy mildew (Tsuchiya \u0026amp; Eulgem \u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). EDM2 stands out not only as a mediator of disease resistance but also as a regulator of gene expression and chromatin modification through its plant homeodomain (PHD)-finger-like domains (Eulgem et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). This positions EDM2 as a critical transcriptional regulator of plant defence, specifically targeting pathogen-responsive genes. The shared miRNA targets and functional clustering between PGK and EDM2-like proteins suggest a significant link between selenium metabolism and plant immune mechanisms.\u003c/p\u003e \u003cp\u003eSelenium appears to exert its regulatory influence by enhancing PGK activity and related methyltransferases, contributing to improved plant immunity, especially in defending against fungal pathogens. EDM2\u0026rsquo;s ability to modulate chromatin and gene expression further reinforces the dual functionality of selenium metabolism, which supports both metabolic stability and enhanced immune responses. Our findings reveal that PGK and related methyltransferases are not only integral to selenium metabolism but also play a crucial role in strengthening plant immunity. This dual role of selenium metabolism offers promising new strategies for enhancing resistance in chickpeas and other crops. Optimizing selenium pathways and associated methylation processes, including the regulation of PGK, may represent a valuable approach to improving plant resilience against biotic stresses, positioning selenium as a key factor in sustainable agricultural practices.\u003c/p\u003e \u003cp\u003eThe high alpha-helix content of PGK, as revealed by our secondary structure prediction, indicates its robust structural conformation. This feature is probably essential for preserving its enzymatic function, particularly under stressful conditions. Selenium accumulation can lead to stress, triggering changes to histones. These alterations can significantly affect protein synthesis and modify the protein structure (Huang et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Even minor modifications during synthesis can impede protein production and induce conformational changes in the protein structure (Balog et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Venyaminov \u0026amp; Yang \u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e1996\u003c/span\u003e). However, the ability of PGK to maintain its high alpha-helix content may be indicative of its resilience against such stressors, ensuring its continued function in metabolic processes. This is consistent with recent findings that Group 4 LEA proteins in Arabidopsis thaliana adopt a similar high alpha-helix conformation under water deficiency, associated with their protective activity (Rend\u0026oacute;n-Luna et al. \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). When plants are affected by fungi, ROS are generated, leading to oxidative stress. PGK plays a vital role in mitigating oxidative stress by facilitating the volatilization of excess selenium. Maintaining optimal selenium levels has been shown to enhance the antioxidant capacity of plants and strengthen their resistance to biotic stress (Zoidis et al. \u003cspan citationid=\"CR108\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; He et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Ye et al. \u003cspan citationid=\"CR104\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Our findings, demonstrating the high alpha-helix content of PGK, further support its role in both selenium metabolism and stress response. This suggests that the structural stability conferred by alpha-helices is essential for PGK-like methyltransferases to function effectively in challenging environmental conditions.\u003c/p\u003e \u003cp\u003eHomology modelling analysis reveals that chickpea PGK shares a moderate sequence identity of 28.61% with Thermotoga maritima PGK. Despite this moderate similarity, studies have shown that proteins with conserved structural folds can exhibit functional similarities, even with low sequence identity, due to evolutionary divergence or convergence (Panchenko \u0026amp; Madej \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). This suggests that, despite the differences in sequence, the chickpea PGK may retain similar functional capabilities to T. maritima PGK, given that key structural folds are conserved. Notably, T. maritima is recognized for its ability to produce hydrogen, a clean energy source, through carbohydrate fermentation in glycolysis (Merrill et al. \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e1995\u003c/span\u003e; Singh et al. \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), a process where PGK plays a central catalytic role (Rojas-Pirela et al. \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). As a hyper thermophilic bacterium, T. maritima produces proteins that are adapted to extreme temperatures, exhibiting optimal activity under heat stress, which aids in managing both biotic and abiotic stresses (Boileau et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Yamini et al. \u003cspan citationid=\"CR102\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Ul Haq et al. \u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Moreover, thermotolerant proteins from T. maritima have been linked to improved iron intake and enhanced sulfur amino acid metabolism (Herve-Jimenez et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2008\u003c/span\u003e), a process closely related to selenium metabolism, as both sulfur and selenium share similar enzymatic transport pathways (Johnstone et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). These insights suggest that chickpea PGK, by sharing structural and functional similarities with T. maritima PGK, likely plays a significant role in maintaining selenium homeostasis and contributing to the plant's resilience against environmental stresses.\u003c/p\u003e \u003cp\u003eIn our comprehensive model analysis, despite a low Z-score, several key factors justify the model's acceptance, highlighting its alignment with known PGK properties. PGK, a ubiquitous enzyme across all living organisms, plays a fundamental role in crucial metabolic processes, making it an essential target for structural and functional studies. As additional crystallographic structures of PGK from various organisms, particularly plant species, become available, our model will be further refined, addressing current limitations due to the scarcity of high-quality plant protein data. This ongoing refinement strengthens the model's potential for advancing our understanding of PGK's role in plant metabolism and stress response.\u003c/p\u003e \u003cp\u003eProtein-protein interaction analysis revealed a significant functional association between PGK, GAPDH, and erythrose-4-phosphate, suggesting that PGK forms a critical partnership with these proteins. In particular, PGK's role in selenium metabolism, as evidenced in chickpeas and other plants, further underscores its importance in cellular processes (Hofstee et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Rojas-Pirela et al. \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Guo et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The interaction between PGK and GAPDH is especially compelling, as GAPDH is not only a key enzyme in glycolysis but also a pivotal player in redox regulation and stress response (Hildebrandt et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). This dual functionality positions GAPDH as a critical mediator in balancing energy production and cellular protection under stress conditions. Selenoproteins, known for their antioxidant properties, safeguard cells against oxidative stress caused by reactive oxygen species (ROS) (He et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Ye et al. \u003cspan citationid=\"CR104\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The strong association between PGK and GAPDH suggests a coordinated effort between glycolytic energy pathways and selenium-dependent defence mechanisms, reinforcing the idea that selenium metabolism and glycolysis are intricately linked to enhancing redox homeostasis and stress resilience in plants.\u003c/p\u003e \u003cp\u003eOur study also uncovered a significant interaction between PGK and erythrose-4-phosphate, a pivotal molecule in the pentose phosphate pathway (PPP), which is essential for generating NADPH, a key factor in maintaining cellular redox balance (Koletti et al. \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; TeSlaa et al. \u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The accumulation of erythrose-4-phosphate under normal conditions highlights its active role in critical metabolic processes like biosynthesis and NADPH production (Koletti et al. \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). NADPH is vital for the function of selenium-dependent antioxidant enzymes, such as glutathione peroxidases (GPXs), which mitigate oxidative stress by neutralizing reactive oxygen species (ROS) and preventing cellular damage (Fontagn\u0026eacute;-Dicharry et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Notably, our miRNA interaction analysis revealed PGK's link to EDM2, a protein crucial for plant immunity, particularly in defending against fungal pathogens like downy mildew (Rojas-Pirela et al. \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). This interaction network suggests that PGK plays a central role in synchronizing glycolysis and the PPP with selenium-driven defence mechanisms. By facilitating redox balance through NADPH production and engaging selenium-based antioxidant systems, PGK emerges as a key player in chickpea\u0026rsquo;s resilience to fungal diseases. Its involvement in both metabolic and immune responses positions PGK as an integral component of the plant\u0026rsquo;s defence strategy, offering promising avenues for enhancing crop resistance.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study examines the complex relationship between methyltransferases and phosphoglycerate kinase (PGK) in regulating selenium metabolism in chickpeas, which is vital for improving stress resistance. Through bioinformatics, we identified key motifs, domains, and phylogenetic connections that suggest these proteins are involved in plant stress responses. Understanding the role of PGK-like methyltransferases provides opportunities for breeding chickpea varieties that are more efficient in selenium uptake and have enhanced stress tolerance. Furthermore, PGK's interaction with essential enzymes involved in glycolysis and redox regulation highlights its importance in mitigating oxidative stress, which is critical for plant defence. This research not only enhances our understanding of selenium metabolism but also suggests that targeting PGK-like methyltransferases could lead to the development of more resilient chickpea varieties. By focusing on genes related to selenium homeostasis and energy metabolism, we can improve selenium uptake while reducing toxicity, paving the way for biofortified chickpeas with improved nutritional and agronomic traits.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e:\u0026nbsp;Lalit Kharbikar was responsible for the conception and design of the study. Material preparation, data collection, and analysis were conducted by Shweta Nandanwar and Piyush Ghoshe. The first draft of the manuscript was written by Piyush Ghoshe, Sarmistha Nayak, and Shweta Nandanwar. Lalit Kharbikar and Sushma Rani Martha provided feedback on earlier versions of the manuscript. Both Lalit Kharbikar and Sushma Rani Martha contributed to data analysis and the interpretation of results. Data reanalysis was performed by Sarmistha Nayak, Sushma Rani Martha, and Lalit Kharbikar. Anil Dixit handled the statistical analysis, while Sushil K. Sharma contributed to the review, revision, and editing of the manuscript. All authors have read and approved the final version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgement\u003c/strong\u003e: The first author received funding from the Indian Council of Agricultural Research (ICAR) in New Delhi, India, through an institutional research project that contributed to this work. Additionally, this research was supported by the ICAR - National Institute of Biotic Stress Management (NIBSM) in Raipur, India. The first author expresses gratitude to the Director of ICAR \u0026ndash; NIBSM, Raipur, India, for approving the research project.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of Interests\u003c/strong\u003e: The authors have no competing interests to declare that are relevant to the content of this article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e: This study was funded by the Indian Council of Agricultural Research, New Delhi.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAilin Qiu, Xiaosha Wen, Qingshuang Zou, Lei Yin, Siqi Zhu, Yao Sheng, Yan He, Quan Liu, Dixian Luo, Zifen Guo. Phosphoglycerate Kinase 1: An Effective Therapeutic Target in Cancer. Front. Biosci. (Landmark Ed) 2024, 29(3), 92. https://doi.org/10.31083/j.fbl2903092.\u003c/li\u003e\n\u003cli\u003eAmaral, A. S., \u0026amp; Devos, D. P. (2024). The neglected giants: Uncovering the prevalence and functional groups of huge proteins in proteomes. 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Selenium-dependent antioxidant enzymes: Actions and properties of selenoproteins. https://doi.org/10.3390/antiox7050066.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Figures","content":"\u003cp\u003eFigures are available in the Supplementary Files section.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"tropical-plant-biology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"trpb","sideBox":"Learn more about [Tropical Plant Biology](http://link.springer.com/journal/12042)","snPcode":"12042","submissionUrl":"https://submission.nature.com/new-submission/12042/3","title":"Tropical Plant Biology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Selenium metabolism, methyltransferases, phosphoglycerate kinase, stress adaptation, bioinformatics, chickpea","lastPublishedDoi":"10.21203/rs.3.rs-8305213/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8305213/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eSelenium is vital for plant defence against pathogens and oxidative stress but requires careful regulation to prevent toxicity. This study reveals a new role for dual-function methyltransferases in chickpeas, showing they possess phosphoglycerate kinase (PGK) activity linked to selenium homeostasis, glycolysis, and redox regulation, in addition to their known methylation functions. This finding suggests that methyltransferases contribute to both selenium detoxification and ATP production. Their interaction with PGK connects selenium metabolism to energy production, essential for adapting to oxidative stress. We characterized 32 methyltransferase proteins and found distinct evolutionary differences that separate selenium-related methyltransferases from conventional ones. The presence of PGK-like domains indicates adaptive evolution in selenium-rich environments, enhancing plant resilience. Additionally, we identified regulatory elements and miRNA interactions, emphasizing the importance of methyltransferase-PGK integration in stress resistance. These proteins play crucial roles in selenium detoxification, redox balance, selenoprotein synthesis, and energy regulation, making them key for adapting to stress. Overall, this research enhances our understanding of selenium metabolism in chickpeas and opens pathways for breeding selenium-efficient cultivars with improved stress tolerance.\u003c/p\u003e","manuscriptTitle":"Unraveling the Dual-Functionality of Chickpea Methyltransferases in Selenium Homeostasis and Stress Resilience","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-17 14:48:47","doi":"10.21203/rs.3.rs-8305213/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-12-14T18:25:41+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-12-14T18:20:53+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-12-08T23:30:05+00:00","index":"","fulltext":""},{"type":"submitted","content":"Tropical Plant Biology","date":"2025-12-08T08:01:07+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"tropical-plant-biology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"trpb","sideBox":"Learn more about [Tropical Plant Biology](http://link.springer.com/journal/12042)","snPcode":"12042","submissionUrl":"https://submission.nature.com/new-submission/12042/3","title":"Tropical Plant Biology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"707569ce-d626-4374-a18a-0b6ef3cb872f","owner":[],"postedDate":"February 17th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-02-17T14:48:47+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-17 14:48:47","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8305213","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8305213","identity":"rs-8305213","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}