Genome-wide Ascertainment and Initial Functional Characterization and Expression Pattern Dissection of the GT92 Gene Family in Cotton

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In the plant genome, it is one of the numerous genes involved in sugar metabolism and glycosylation modification. Nonetheless, the GT92 sub-family assumes a vital function in facilitating plants' adaptation to adverse environments and modulating plant growth, development, and the processes of organogenesis. To date, comprehensive characterization and systematic investigation of GT92 in cotton remain underexplored. Results In this study, we systematically analyzed the structural features, phylogenetic tree, gene architecture, expression profiles, evolutionary relationships, and selective pressures of GT92 gene family members across four Gossypium species using bioinformatics approaches for the first time. Collectively, 44 GT92 genes were identified, including 14 in G. hirsutum . Based on the phylogenetic tree, GT92 protein sequences from the four cotton species were clustered into five distinct subfamilies. Chromosomal mapping of these genes was performed, and their structural details were visualized. We further predicted cis -acting elements in G. hirsutum GT92 genes and characterized duplication patterns across the four Gossypium species. Ka/Ks ratios of orthologous gene pairs were calculated to investigate selective pressures among the species. RNA-seq data from G. hirsutum and G. barbadense revealed GT92 expression patterns. WGCNA identified GhGT92_5 and GhGT92_6 as members of the MEtan module, which was significantly negatively correlated with the 6-hour time point post-drought stress. Mfuzz clustering classified GhGT92_5 and GhGT92_6 into Cluster13 and Cluster14, respectively. qRT-PCR validated their roles under drought and salt stress conditions. Subcellular localization showed GhGT92_5 primarily distributed in the plasma membrane and chloroplasts, while GhGT92_6 was localized in the cytoplasm and chloroplasts. Conclusions All of these findings have expanded our understanding of the GT92 family members, establishing a basis for more in-depth exploration of the stress-tolerance mechanisms of this gene in cotton. Cotton GT92 WGCNA Expression pattern Mfuzz Subcellular localization Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Introduction Plant cell walls are mainly composed of different polysaccharides, which can be divided into cellulose, hemicellulose, and pectin [1]. Pectin serves as a vital constituent of the primary cell walls in dicotyledonous plants, gymnosperms, and non-symbiotic monocotyledonous plants. It exists in comparatively small quantities within the secondary and primary walls of grasses. In dicotyledonous plants, like Arabidopsis thaliana , pectin constitutes 20%-35% of the primary cell wall [2–3]. Apart from being a structural element of the cell wall, pectin polysaccharides possess numerous distinctive functions. These functions involve intercellular cohesion [4], cell lengthening [5], as well as wall porosity and extensibility, which are of great significance for plant development [6]. Pectin polymers are fabricated in the Golgi apparatus by glycosyltransferases (GTs). The prevailing hypothesis posits that pectin undergoes synthesis, assembly, and processing as it traverses the Golgi apparatus and the trans-Golgi network. Ultimately, pectin is secreted into Golgi-derived vesicles and conveyed to the cell surface, where additional modification or remodeling takes place upon deposition [7]. It is estimated that the assembly of the pectin matrix requires the activities of more than 67 different transferases, yet only a few transferases have been clearly identified [8]. The first GT shown to play a role in pectin biosynthesis, especially in the formation of the HG backbone, is the HG galacturonosyltransferase GAUT1 [9]. Additional GAUT and GAUT-like (GATL) proteins are involved in pectin biosynthesis, but their exact functions and activities remain to be determined. Previously, the discovery of β-1,4-galactosylgalactosyltransferase galactan synthase 1 ( GALS1 ) in Arabidopsis thaliana has been documented [10]. β-1,4-galactan predominantly functions as a side-chain of RG-I in the primary cell wall and is also found in the secondary cell walls of trees, playing a role in the development of reaction wood as a response to gravitropic cues [11]. GALS1 and its two counterparts belong to the glycosyltransferase family 92 (GT92) [12–13], and every member of this family harbors a DUF23 motif. The presence of β-1,4-galactosylgalactosyltransferase activity was established as early as 50 years ago [14], and the synthesis of β-1,4-galactan in different plants has been confirmed in multiple in vitro studies [2]. However, it was not until recently that we discovered that the purified recombinant GALS1 protein can use UDP-Gal as a donor substrate to extend β-1,4-galactan acting as an acceptor, with GALS1 functioning as a β -1,4-galactosylgalactosyltransferase in vitro [10]. Recently, GALS1 has also been shown to possess arabinopyranosyltransferase activity, which can add arabinopyranose to the end of the growing galactan chain, thus preventing its further elongation [15]. Besides this, there are relatively few other reports on GT92 genes in plants. As an important fiber crop and a model for polyploid research, cotton frequently encounters abiotic stresses, including drought, salinity, cold injury, high temperature, and diverse biotic stresses throughout its growth and maturation processes [16–18]. Severe external surroundings can impinge on cotton growth, leading to a decline in both yield and fiber quality. Consequently, enhancing the stress-tolerance capacity of plants aids in boosting their adaptability to adverse environments. Genetic engineering techniques have emerged as a crucial approach to attain this objective [19–20]. With the advancements in cotton genome sequencing and assembly, a basis has been established for comprehensive investigations into cotton gene families [21–23]. In this study, by analyzing the publicly available transcriptome sequencing results [23], we found that the expression level of GH_A09G1400 ( GhGT92_5 ) showed a significant difference under drought stress. We hypothesized that this gene is related to drought resistance. Subsequently, we thoroughly recognized and examined the members of the GT92 gene family in two diploid Gossypium species ( G. arboreum and G. raimondii ), along with two tetraploid Gossypium species ( G. hirsutum and G. barbadense ). Employing bioinformatics approaches, via phylogenetic tree scrutiny, gene architecture, conserved motif, and sequence characteristic analysis, we ascertained the evolutionary associations among the cotton GT92 genes. Subsequently, we carried out collinearity assessment of the non-synonymous (Ka) and synonymous (Ks) substitution proportions (Ka/Ks ratios) among the four Gossypium species. Moreover, we investigated the expression of GT92 genes through promoter cis -element exploration and tissue-specific expression profile analysis. We also analyzed the co-expression network of GhGT92s and the gene set with the same expression trend through stress-resistant transcriptome data analysis. Through qRT-PCR analysis, we discovered that certain GhGT92s were triggered by drought and salt stresses. The findings of this research not only present a comprehensive analysis of the cotton GT92 gene family but also put forward novel concepts for cotton abiotic stress-resistant breeding. Results Discovery of GT92 family gene elements within four cotton varieties Candidate sequences were obtained by using the BLASTP program to perform alignment searches of the protein sequence of GH_A09G1400 in four cotton species. Following the validation of conserved domains with the help of Pfam and CDD software, those sequences lacking a complete GT92 domain were eliminated. Eventually, 44 objective genes were recognized across four distinct cotton species (as shown in Supplementary Table S1 ). Among them, there were eight genes in G. arboreum (A2), eight genes in G. raimondii (D5), 14 genes in G. hirsutum (AD1), and 14 genes in G. barbadense (AD2). Among these genes, the quantity of genes in G. raimondii (D5) is equal to that in G. arboreum (A2). Additionally, the number of GT92 genes in the two tetraploid cotton species, namely G. hirsutum (AD1) and G. barbadense (AD2), is identical, which is eight and 14 respectively. It is worth noting that the number of GT92 family members in the two tetraploid cotton species ( G. hirsutum , G. barbadense ) is two less than the sum of the number in the two diploid cotton species ( G. arboreum , G. raimondii ). We renamed these genes as GaGT92_1 - GaGT92_8 , GbGT92_1 - GbGT92_14 , GhGT92_1 - GhGT92_14 , and GrGT92_1-GrGT92_8 according to their locations on the cotton chromosomes (Supplementary Table S1 ). Subsequently, an analysis was carried out on the physicochemical properties of the amino acid sequences of the GT92 gene family members in the four cotton species. The length of the GT92 amino acids ranges from 454 to 605 amino acid residues, with an average sequence length of 537 amino acids. The molecular weight varies from 51.46 kDa to 70.40 kDa, with an average value of 61.33 kDa. The isoelectric point (pI) lies between 6.88 and 9.61, and the average pI is 9.04 (as shown in Supplementary Table S1 ). Construction of the phylogenetic tree of GT92 genes among four cotton species To delve into the evolutionary associations among the constituents of the GT92 family, the MEGA7 software was employed to erect a phylogenetic tree comprising 44 GT92 protein sequences derived from the four cotton species (Fig. 1 ). Eventually, the GT92 protein sequences were divided into five different subfamilies, namely Class1-Class5. Among them, Class3 had the largest number, encompassed 17 entities, succeeded by Class2 which held 11 components, and Class4 had the smallest quantity, having just four elements. Both Class1 and Class5 had six GT92 genes, with two genes in each of the two tetraploid species; and one gene in each of the two diploid species. The number of GT92 genes in Class4 was four, evenly distributed among the four cotton species. In Class3, the number of genes in the two diploid species was the same, both being three; the same was true in Class2, with a number of two. Interestingly, in Class3, the number of G. hirsutum was one more than that of G. barbadense ; while in Class2, the number of G. hirsutum was one less than that of G. barbadense . We found that the two diploid cotton species always clustered together with the two tetraploid cotton species, which also confirmed that the tetraploid G. hirsutum and G. barbadense evolved through hybridization of the diploid G. arboreum and G. raimondii [24]. Chromosomal location of genes in four cotton species Chromosomal location of GT92 genes in four cotton species To conduct a more in-depth exploration of the chromosomal distribution and gene duplication of GT92 genes in the four cotton species, we mapped the physical loci of these genes on the chromosomes. The 44 GT92 genes are haphazardly distributed across the chromosomes of the four cotton species. In the case of G. hirsutum (Fig. 2 A), 14 genes are distributed on 12 chromosomes, namely on chromosomes A01, A05, A09, A11, A12, A13; D01, D04, D09, D11, D12, D13. Among them, there are eight genes in the A subgenome and six genes in the D subgenome, and the number of genes in the A subgroup is two more than that in the D subgroup. It is worth noting that there are two GT92 genes on both chromosomes A05 and A09, and there is one gene on the remaining chromosomes. In G. barbadense (Fig. 2 B), the distribution pattern of GT92 genes is roughly the same as that in G. hirsutum , but the difference is that there are two GT92 genes on both chromosomes A09 and D09. In G. arboreum (Fig. 2 C), eight GT92 genes are distributed on seven chromosomes, namely Chr01, Chr04, Chr05, Chr09, Chr11, Chr12, Chr13, among which there are two GT92 genes on Chr09 and one gene on the remaining chromosomes. In G. raimondii (Fig. 2 D), eight GT92 genes are distributed on the same seven chromosomes as in G. arboreum , and like in G. arboreum , there are two GT92 genes on Chr09. The findings above provide further evidence that the two tetraploid cotton species originated from two diploid cotton species[24]. Gene structure, protein motif and -acting element analysis of Gene structure, protein motif and cis -acting element analysis of GhGT92s In an effort to gain deeper insights into the potential structural evolutionary correlations among GT92 family constituents, we generated a phylogenetic tree for 14 GT92 genes in G. hirsutum by utilizing the maximum-likelihood (ML) approach. Moreover, we carried out motif association analysis and gene structure analysis (Fig. 3 ). The protein sequences and annotation files of the 14 GT92 constituents were employed to construct the phylogenetic tree and gene structure information. The MEME and TBtools-II software (v2.150) were used to assess the conserved motifs within GT92 proteins. Within the 14 members of G. hirsutum , a total of 10 motifs were detected. GhGT92_3 , GhGT92_8 , GhGT92_10 and GhGT92_14 contained the most motifs, with eight motifs, namely motif1-motif8. Next, GhGT92_5 , GhGT92_6 , GhGT92_12 contained motif1, motif2, motif5, motif7, motif9, motif10, and GhGT92_7 , GhGT92_13 contained motif1, motif5, motif7, motif8, motif9, motif10, each of which had six motifs. Finally, the remaining five GhGT92 genes contained motif1, motif2, motif4, motif5, motif7, a total of five motifs. In addition, we analyzed the characteristics of the intron-exon structure. As shown in Fig. 3 , genes in the same group had similar intron-exon arrangements. Among them, GhGT92_3 , GhGT92_8 , GhGT92_10 and GhGT92_14 contained the most exons, with 11 exons and 10 introns. GhGT92_4 , GhGT92_7 , GhGT92_13 , GhGT92_5 , GhGT92_6 , GhGT92_11 , GhGT92_12 contained two exons and one intron, and GhGT92_1 , GhGT92_2 , GhGT92_9 had only one exon. To achieve a more profound understanding of the regulatory mechanism of GT92 genes, we made use of the PlantCARE database to predict the cis -acting elements in the 2000-base-pair promoter region ahead of the 14 GT92 genes in G. hirsutum . In the case of G. hirsutum (as illustrated in Fig. 3 ), these included the MYB binding site related to light responsiveness, along with the MYB binding site related to drought inducibility. The cis -acting elements connected with plant hormones consisted of abscisic acid-associated elements, salicylic acid-associated elements, MeJA-associated elements, and auxin-associated elements. Additionally, there were cis -acting elements engaged in defense and stress responses, cis -acting elements engaged in low-temperature responses, MYB binding sites involved in the regulation of flavonoid biosynthetic genes, cis -acting regulatory elements involved in seed-specific regulation, and cis -acting regulatory elements involved in zein metabolism regulation. The analysis of the promoter will help us verify the subsequent gene functions. Exploration of gene duplicative events and syntenic associations The evolutionary path of gene families mainly includes whole-genome duplication incidents, segmental duplication events, and tandem duplication happenings. With the intention of probing into the evolutionary models and the consequences of polyploidization, we identified the duplication types of GT92 genes among four Gossypium species (as shown in Supplementary Table S2). In diploid Gossypium species, six genes in G. arboreum were of the Dispersed type, and two genes were of the WGD or Segmental type. Four genes within G. raimondii fell into the Dispersed type, while the remaining four genes were classified under the WGD or Segmental type. When it comes to tetraploid Gossypium species, every gene in G. barbadense was assigned to the WGD or Segmental type. In G. hirsutum , a single gene pertained to the Dispersed type, and the rest of the genes were attributed to the WGD or Segmental type. First, we performed multiple synteny analyses of GT92 genes in G. hirsutum (AD1), G. barbadense (AD2), G. arboreum (A2), and G. raimondii (D5) (Fig. 4 E). We found that there were 19 orthologous gene pairs between G. barbadense and G. arboreum , 17 orthologous gene pairs between G. hirsutum and G. arboreum , 18 orthologous gene pairs between G. barbadense and G. hirsutum , 22 orthologous gene pairs between G. barbadense and G. raimondii , and 21 orthologous gene pairs between G. hirsutum and G. raimondii . Therefore, we speculated that during the evolution of the GT92 family genes, the main causes of gene amplification were whole-genome duplication events and segmental duplication events. Subsequently, we conducted synteny analyses of tetraploid G. hirsutum (Fig. 4 C), and a total of 24 orthologous/paralogous pairs were identified. In G. barbadense (Fig. 4 D), a total of 28 orthologous/paralogous pairs were identified. In addition, synteny analyses were also carried out in the two diploid Gossypium species. One orthologous/paralogous pair was found in G. arboreum (Fig. 4 A), and two orthologous/paralogous pairs were found in G. raimondii (Fig. 4 B). Calculation of selection pressure To study the differentiation mechanism of GT92 genes during cotton polyploid duplication events, we calculated the ratio of non-synonymous to synonymous substitutions (Ka/Ks ratio) to identify the types of selection pressure on these homologous gene pairs during evolution (Supplementary Table S3). We calculated the Ka/Ks ratios of 152 pairs of homologous genes in four Gossypium species respectively (Fig. 4 F). First, we analyzed the diploid and tetraploid Gossypium species. Between G. arboreum and G. barbadense , the Ka/Ks ratios of two pairs of homologous genes were greater than 0.5, and the ratios of the rest were less than 0.5. Between G. arboreum and G. hirsutum , the Ka/Ks ratios of three pairs of homologous genes were greater than 0.5, among which the ratio of one pair of homologous genes was greater than 1, and the ratios of the rest were less than 0.5. Between G. barbadense and G. raimondii , the Ka/Ks ratio of one pair of homologous genes was greater than 0.5, and the ratios of the rest were less than 0.5. Between G. hirsutum and G. raimondii , the Ka/Ks ratios of all homologous gene pairs were less than 0.5. Between diploid G. arboreum and G. arboreum , as well as between G. raimondii and G. raimondii , the Ka/Ks ratios of all homologous gene pairs were less than 0.5. Between tetraploid G. barbadense and G. barbadense , between G. hirsutum and G. hirsutum , and between G. hirsutum and G. barbadense , the Ka/Ks ratios of all homologous gene pairs were less than 1. In summary, among the four Gossypium species, the vast majority of GT92 genes have experienced strong purifying selection during evolution, while a few homologous gene pairs exhibit positive selection effects. Expression patterns of in Expression patterns of GbGT92s in G. barbadense We carried out an analysis of the expression levels of GT92 genes across various tissues and in the course of fiber development within G. barbadense [23]. It was found that the expression level of GbGT92_12 was higher than that of other genes in the bract and pental (Fig. 6 B); the expression level of GbGT92_13 was greater compared to that of other genes in the filament; the expression level of GbGT92_5 was superior to that of other genes in the leaf and stem. The expression level of GbGT92_4 was more elevated than that of other genes in the pistil; the expression level of GbGT92_5 was higher in comparison to that of other genes in the root; the expression level of GbGT92_10 was greater than that of other genes in the sepal and torus. At the same time, the expression levels of GbGT92_3 and GbGT92_4 in the ovule at different stages were higher than those in the fiber at the same stage. Intriguingly, the expression intensity of GbGT92_7 in the fiber was more substantial than that in the ovule. It is worthy of emphasis that when contrasted with other genes, the expression intensity of GbGT92_11 surged rapidly as time progressed in the early phase of ovule development, yet plummeted abruptly at 3 days post-anthesis (DPA), and then began to increase again at 10 DPA, suggesting that this gene might regulate the development of fuzz fiber in G. barbadense . The expression intensity of GbGT92_7 was greater than that of other genes during several stages of fiber development, attaining its peak value at 20 DPA (Fig. 6 C). To sum up, the expression levels of GbGT92 genes display a remarkable degree of specificity in the tissues of G. barbadense . Subsequently, we made utilization of the RNA-seq data of G. barbadense to examine the alterations in gene expression levels when subjected to cold, heat, salt, and PEG stresses [23]. We discovered (as shown in Fig. 6 D) that in the face of cold stress, the expression levels of GbGT92_4 , GbGT92_5 , and GbGT92_11 underwent notable changes in comparison to the control group. The changes in GbGT92_4 mainly occurred in the early and late stages, while those in GbGT92_5 and GbGT92_11 mainly occurred in the late stage. Under hot stress, the expression levels of GbGT92_4 , GbGT92_5 , and GbGT92_12 changed significantly compared with the control, and the changes mainly occurred at 1 h, 3 h, and 6 h, all reaching the highest at 3 h. Under PEG stress, the expression level of GbGT92_10 changed significantly compared with the control, mainly occurring in the middle and late stages of the stress. As the stress time increased, the expression level gradually increased and reached the highest at 24 h. Under salt stress, the expression levels of GbGT92_4 , GbGT92_11 , and GbGT92_12 changed significantly compared with the control, mainly occurring in the early stage of the stress. The expression level of GbGT92_11 was the highest among all genes in all stages of the stress, showing a trend of first decreasing and then increasing. The expression trends of the other two genes were the same as that of GbGT92_11 . Subsequently, We additionally employed the root transcriptome data of the disease-prone variety 3–79 and the disease-resistant variety S6 of G. barbadense , which were obtained 11 days subsequent to inoculation [30], to investigate the variations in the expression levels of GT92 family components. The findings indicated (as depicted in Fig. 6 E) that the expression levels of the majority of GT92 genes did not exhibit substantial changes prior to and following the infection. Nevertheless, the changes in the expression levels of GbGT92_5 , GbGT92_11 , GbGT92_12 , and GbGT92_13 were markedly distinct between the disease-resistant and disease-prone materials, signifying that these four genes play a crucial role in the FOV resistance of G. barbadense . WGCNA of GT92 members in G. hirsutum Although in-depth studies have been conducted on the evolutionary relationships, chromosomal locations, sequence structures, collinearity analyses, selection pressure analyses, and expression patterns of the GT92 gene family, their potential roles in the response of G. hirsutum to drought stress remain unclear. We collected publicly available transcriptome data on drought stress [23] (from G. hirsutum TM-1 materials before and after drought stress at 0 h, 1 h, 3 h, 6 h, 12 h, and 24 h). Finally, we sifted through and identified 15,127 differentially expressed genes with FPKM values above 1 for conducting WGCNA analysis. By using the dynamic shear tree method for the weight values to merge modules with similar expressions, a total of 24 modules were obtained in the G. hirsutum material TM-1 (Fig. 7 A). The turquoise module encompassed the greatest quantity of genes, consisting of 2,110 genes. The blue module ranked second, having 1,831 genes, and the darkturquoise module harbored the least number of genes, merely 35 genes. On average, each module contained 630 genes (Fig. 7 B). Within the TM-1 material, based on the standards of absolute correlation coefficient (|r|) greater than 0.55 and p -value less than 0.05, core modules were selected (Fig. 7 C). Among these, it was discovered that the MEtan module exhibited a significant negative correlation with the state 6 hours subsequent to drought stress; the MEbrown module showed a significant negative correlation with the condition 24 hours after drought stress. It is worthy of emphasis that among the 14 members of the GT92 family in G. hirsutum , the genes GhGT92_5 and GhGT92_6 are part of the MEtan module, while GhGT92_7 is a constituent of the MEbrown module. The MEtan module was predominantly enriched in pathways like Endocytosis, Amino sugar and nucleotide sugar metabolism, Inositol phosphate metabolism, Fructose and mannose metabolism, and Fatty acid degradation (Fig. 7 D). We uncovered that this core module might respond to drought stress in G. hirsutum via several metabolic pathways including Amino sugar and nucleotide sugar metabolism, Inositol phosphate metabolism, and Fructose and mannose metabolism. Several GT92 genes showed 2-fold upregulation under drought stress (e.g., GhGT92_5 , GhGT92_6 ). These genes are predicted to modify cell wall polysaccharides (xyloglucan and pectin), potentially maintaining cell wall integrity during water deficit. Next, we analyzed the interaction networks of GhGT92_5 and GhGT92_6 , with a weight value of 0.02 as the standard (Fig. 7 E). From this module, 121 genes interacting with GhGT92_5 , 124 genes interacting with GhGT92_6 , 28 genes interacting with GhGT92_12 , and one gene interacting with GhGT92_11 were selected to construct an interaction network (Supplementary Table S4). We conducted KEGG analyses on the genes interacting with GhGT92_5 and GhGT92_6 respectively (Fig. 7 F-G). We unearthed that these genes were principally abundant in certain metabolite production pathways, such as Glycan biosynthesis and metabolism, Carbohydrate metabolism, Metabolism of terpenoids and polyketides, Glycan biosynthesis and metabolism, Transport and catabolism, etc. We speculate that when subjected to drought stress, they may resist through the accumulation of some sugars. Analysis of GT92 gene sets based on Mfuzz trend Next, we utilized the Mfuzz clustering method to further analyze the 42,214 differential genes speciested from 18 samples at six time-points. The results showed (Fig. 8 A) that a total of 15 clusters were speciested. Cluster3 harbored the greatest quantity of genes, amounting to 7,878 genes, and Cluster12 encompassed the smallest number of genes, reaching 582 genes. Among them, the GT92 family members were divided into eight clusters. Specifically, GhGT92_13 was assigned to Cluster2, GhGT92_1 , GhGT92_3 , and GhGT92_9 were assigned to Cluster3, GhGT92_2 and GhGT92_7 were assigned to Cluster7, GhGT92_11 and GhGT92_10 were assigned to Cluster9 and Cluster11 respectively, GhGT92_5 and GhGT92_4 were assigned to Cluster13 and Cluster15 respectively, and GhGT92_6 and GhGT92_12 were assigned to Cluster14. It is worth noting that Cluster2, Cluster3, and Cluster11 had similar trends. Their expression trends were relatively gentle, without significant fluctuations. Cluster7 and Cluster15 showed a trend of first decreasing, then increasing, and finally decreasing. Cluster10 exhibited a tendency of initially rising and subsequently declining. Cluster13 and Cluster14 had similar expression patterns, both showing a trend of first decreasing and then increasing. Through GO enrichment analysis, for Cluster13, in terms of Cellular Component, it was mainly enriched in the extracellular region; in terms of Biological Process, it was mainly enriched in the oxidation-reduction process, sulfur utilization, and regulation of sulfur utilization; in terms of Molecular Function, it was mainly enriched in pathways such as oxaloacetate transmembrane transporter activity, hydrolase activity, hydrolyzing O-glycosyl compounds, and hydrolase activity, acting on ester bonds (Fig. 8 B). For Cluster14, in terms of Cellular Component, it was mainly enriched in the cell wall, external encapsulating structure, and intrinsic component of the membrane; in terms of Biological Process, it was mainly enriched in cell wall organization or biogenesis, cinnamic acid biosynthetic process, and cinnamic acid metabolic process; from the perspective of Molecular Function, it was predominantly enriched in pathways like catalytic prowess, hydrolase potency, operating on glycosyl linkages, and hydrolase potency, cleaving O-glycosyl substances (Fig. 8 C). Subcellular localization analysis of GhGT92s In order to determine the subcellular positioning of GhGT92_5 and GhGT92_6 , we transiently expressed green fluorescent protein (GFP)- GhGT92_5 , (GFP)- GhGT92_6 , red fluorescent signals, and isolated green fluorescent protein (GFP) in tobacco epidermal cells. The results showed (Fig. 9 ) that the green fluorescent protein (GFP) fluorescence of GhGT92_5 was mainly localized on the plasma membrane, and the red fluorescent signal was mainly localized in the chloroplasts; the green fluorescent protein (GFP) fluorescence of GhGT92_6 was mainly localized in the cytoplasm, and the red fluorescent signal was mainly localized in the chloroplasts. These results indicate that GhGT92_5 is mainly distributed on the plasma membrane and in the chloroplasts, and GhGT92_6 is mainly localized in the cytoplasm and on the chloroplasts. qRT-PCR analysis of GhGT92s under salt stress and drought stress Our prior investigation indicated that the expression of certain GhGT92s altered markedly under stress circumstances. Drawing upon the antecedent analysis, we postulated that GhGT92_5 and GhGT92_6 could be implicated in the response to abiotic stress situations. We carried out an expression pattern analysis on three G. hirsutum varieties featuring extreme traits to ascertain whether these genes are engaged in the stress response procedure. The results showed that after PEG-simulated drought stress (Fig. 10 A-B), the transcriptional levels of the two materials with extreme characteristics were stimulated at various time intervals, suggesting that GhGT92_5 and GhGT92_6 could be involved in the response of G. hirsutum to drought scenarios. GhGT92_5 demonstrated a pattern of initially being downregulated and then upregulated, and there was a notable disparity in expression levels at 6 hours and 12 hours between the drought-sensitive and drought- tolerant materials. GhGT92_6 likewise exhibited a significant difference in expression levels at 6 hours and 12 hours between the drought-sensitive and drought-tolerant materials. This implies that these two genes may have a certain function in the response of G. hirsutum to drought stress. In addition, we also studied the expression of the two genes under salt stress (Fig. 10 C-D). Both GhGT92_5 and GhGT92_6 showed a trend of first down-regulation, then up-regulation, then down-regulation, and subsequently up-regulation. In the salt-sensitive and salt-tolerant materials, GhGT92_5 had a significant difference in expression levels in the middle stage of stress, and GhGT92_6 had a significant difference in expression levels in the late stage of stress. This also indicates that these two genes may play a certain role in the response of G. hirsutum to salt stress. Discussion Drought significantly reduces the productivity of cotton crops. The impact of drought stress on the growth, development, and yield of cotton is worsening [31]. The development of drought-tolerant genotypes is an urgent need in the current era. To develop drought-tolerant genotypes, we must understand the drought resistance mechanisms of plants. Following the accomplishment of cotton genome sequencing, it has furnished substantial convenience for scientific investigators, allowing them to carry out profound explorations on the evolution and functional examination of diverse gene families. Over the past few years, numerous families in cotton have been investigated, for instance GhRab7 [32], GhMDVL [33], GhHIT4 [34], GbSOT [35], GhGT92 [36], GhCOBRA [37], GhRF2 [38], GhGATL [39], GhSMXL [40], and GhWOX [41]. Based on the previous transcriptome sequencing results [23], it was found that GH_A09G1400 ( GhGT92_5 ) had a significant difference in expression level when subjected to drought stress, and we speculate that this gene is related to drought resistance. Subsequently, we executed an all-encompassing study on this gene family within four Gossypium species, making use of a variety of techniques. These encompassed phylogenetic exploration, gene structure scrutiny, protein motif dissection, chromosomal mapping, gene duplication examination, and collinearity evaluation. Moreover, we additionally scrutinized the cis - element analysis, tissue-specific expression patterns, and the reactions of GT92 genes under abiotic stress conditions. Then, we conducted subcellular localization studies of GhGT92_5 and GhGT92_6 in tobacco and studies on their expression levels under drought and salt stress. Initial analysis of GT92 family components Based on the published reference genome information of four cotton species, we identified 44 GT92 genes in four cotton species. Subsequently, we conducted an assessment of the physicochemical properties of the amino-acid sequences of the GT92 gene family constituents in the four cotton species. The results indicated that the mean length of amino acids in GT92 family members was 537 amino acids, and the average molecular mass was 61.33 kDa. The average isoelectric point (pI) value was 9.04, which implies that these proteins are alkaline in nature. After that, we constructed a phylogenetic dendrogram, and the outcomes demonstrated that the diploid cotton invariably grouped together with the tetraploid cotton. This further validated the hypothesis that G. hirsutum and G. barbadense stemmed from the cross-breeding of G. arboreum and G. raimondii [24]. In terms of chromosomal apportionment, the distribution pattern of GT92 genes in diploid and tetraploid cotton species was approximately the same. Subsequently, we performed an extensive synteny analysis across the four cotton species. We speculated that the principal factors driving gene expansion during the evolution of GT92 family genes were whole-genome duplication instances and segmental duplication happenings. In the meantime, we determined the Ka/Ks values of GT92 homologous genes. Among the four cotton species, only a single pair of homologous genes had a value greater than 1, while the values of the others were less than 1. Among them, GH_A13G0136 (Ka/Ks = 1.23) was found to be twice as expressed as the control gene under 24 h cold stress, suggesting adaptive evolution under cold conditions. In addition, the ka/ks ratio between GH_A13G0136 and Ga13G0142 was 1.23. Meanwhile, the expression level of GH_A13G0136 in leaves was 14, and that of Ga13G0142 in leaves was 7. The expression level of GH_A13G0136 in leaves was twice that of Ga13G0142 . Positive selection may result in functional differentiation of GH_A13G0136 and Ga13G0142 to some extent. Higher expression levels may enable GH_A13G0136 to produce more protein products in the leaves, thus playing a more significant role in the physiological process of the leaves. The great majority of GT92 genes experienced robust purifying selection over the course of evolution, and a limited number of homologous gene pairs showed positive selection consequences [42]. Our collinearity analysis reveals the functional evolution pattern of the GT92 gene family: conserved collinearity regions (e.g., GhGT92_3 ) retain ancestral cell wall synthesis by maintaining gene neighborhood and dose balance; Non-collinear copies (e.g., GhGT92_2 ) acquire new regulatory elements through genome rearrangement, which may adapt to the special needs of cotton growth and development. In addition, we predicted the cis -regulatory elements of GT92 genes. In particular, these were cis -regulatory elements associated with phytohormones, for example, abscisic acid-responsive elements, salicylic acid-responsive elements, MeJA-responsive elements, and auxin-sensitive elements. Moreover, it also included cis -regulatory elements participating in defense and stress reactions as well as cis -regulatory elements related to cold-temperature responses. ABRE (ABA-responsive element) and MYB/MYC binding sites were significantly enriched in GT92 promoters (e.g., GhGT92_3 , GhGT92-5 and GhGT92_6 ), suggesting their roles in drought stress responses via ABA and JA signaling pathways. Via the analysis of promoters, this will aid us in validating subsequent gene functionalities [43]. Then, we used qRT-PCR technology to verify the expression patterns of GhGT92 genes under drought and salt stress, and the results indicated that GhGT92_5 and GhGT92_6 might be involved in the response to drought and salt stress conditions. Expression pattern analysis of GT92 family members The expression patterns of genes are intimately associated with their functionalities. First, we utilized the transcriptome data of tissues in G. hirsutum [23]. The results showed that GhGT92_6 was specifically expressed in the leaf, stamen, calycle, and torus; GhGT92_13 was specifically expressed in the pistil. In addition, the expression levels of GhGT92_2 , GhGT92_8 , GhGT92_13 , and GhGT92_14 fluctuated greatly during the development of the seed with the change of time. During the development of the cotyledon, the expression levels of GhGT92_6 and GhGT92_12 both reached the highest value at 72 h. In addition, some GhGT92 genes had specific expressions during the development of the root. Besides, some GhGT92 genes were also specifically expressed during the development of the fiber. For example, the expression level of GhGT92_8 in the ovule was lower than that in the fiber. Interestingly, we also found that GhGT92_8 might control the change in lint percentage (LP) during the fiber development of G. hirsutum . The expression level of GhGT92_5 in high oil content materials was significantly higher than that in low oil content materials, indicating that this gene might control the oil content of cotton materials. According to the transcriptome data, it was found that GhGT92_6 , GhGT92_14 , GhGT92_12 , and GhGT92_5 played important roles in the tolerance of cotton to flood abiotic stress and in controlling the plant height of G. hirsutum [27–28]. Moreover, under cold, polyethylene glycol (PEG), heat, and salt stress conditions, the expression intensities of GhGT92_5 and GhGT92_6 showed marked variations when compared with the control set. This implies that these two genes may be involved in the reaction to the previously mentioned four kinds of abiotic stresses. We also based on the transcriptome data of cotton roots after inoculation with Verticillium dahliae and found that after being infected by Verticillium dahliae , the expression levels of GhGT92_6 , GhGT92_12 , and GhGT92_13 changed significantly compared with the control, indicating that the above genes might be involved in the response of cotton to Verticillium dahliae treatment in the middle and late stages [23]. Subsequently, we found that nearly half of the GhGT92 genes were involved in the response of cotton to TDZ treatment. Then, using the transcriptome data of G. barbadense , we uncovered that certain GbGT92 genes were distinctly expressed in particular tissues of G. barbadense . For example, there were GT92 genes with exclusive expressions in bract, petal, filament, leaf, stem, pistil, sepal, and torus tissues. Additionally, we noticed that the expression levels of GbGT92_3 and GbGT92_4 in the ovule at various time points were higher than those in the fiber at the same time intervals; the expression level of GbGT92_7 in the fiber was greater than that in the ovule. Subsequently, we utilized the RNA-seq data of G. barbadense to examine the alterations in gene expression levels under cold, heat, salt, and polyethylene glycol (PEG) stress conditions [23]. We found that under the conditions of four abiotic stresses, GbGT92_4 , GbGT92_5 , GbGT92_10 , GbGT92_11 , and GbGT92_12 had specific expressions, indicating that the above genes, like G. hirsutum , participated in the response to the above four abiotic stresses. Finally, we also found that GbGT92_5 , GbGT92_11 , GbGT92_12 , and GbGT92_13 played important roles in the FOV resistance of G. barbadense . WGCNA and Mfuzz analysis of GhGT92s under drought stress We used differentially expressed genes for WGCNA analysis. We found that the genes in the MEtan module were significantly negatively correlated with the middle stage of drought stress, and the MEbrown module was significantly negatively correlated with the later stage of drought stress. It is worth noting that the genes GhGT92_5 and GhGT92_6 of G. hirsutum belonged to the MEtan module. Among them, the MEtan module might respond to the drought stress mechanism of G. hirsutum through some metabolic pathways such as Amino sugar and nucleotide sugar metabolism, Inositol phosphate metabolism, and Fructose and mannose metabolism. According to the results of KEGG enrichment analysis, the gene interacting with the core gene GhGT92_6 is related to Amino sugar and nucleotide sugar metabolism and Endocytosis pathway, and this core gene may catalyze glycosyl-transfer reaction. Participate in cell wall polysaccharide synthesis and indirectly affect the membrane localization of glycosylated products; In addition, the core GhGT92_5 interacting gene is also related to Inositol phosphate metabolism, and glycosylation modification of inositol derivatives by this gene may affect its signaling function. GH_A06G1824 is a member of the tan module and belongs to the Glycosyl transferase family 8. According to previous studies [44], the double mutation of GAUT10 and GAUT11 reduced pectin synthesis, promoted the demethylation of homogalacturonic acid (HG) and the degradation of demethylated HG, resulting in larger stomatal complexes, smaller stomatal areas, enhanced stomatal dynamics, and enhanced plant drought tolerance. In addition, among the 121 genes interacting with GhGT92_5 , GH_D05G1894 is a member of the Thaumatin family. The transgenic Arabidopsis lines overexpressing GhTLP19 showed higher proline content, thicker and longer trichomes, and stronger drought tolerance [45]. In addition, some transcription factors play important roles in the process of plant drought resistance. For example, GH_D08G1583 is a member of the MYB family, and GH_D08G0572 is a GRAS -type transcription factor. Under drought stress, MYB transcription factors can play roles in various ways. They can regulate the composition and structure of the plant cell wall, enhance the toughness of the cell wall, and reduce cell wall damage caused by cell water loss due to drought. Meanwhile, MYB transcription factors can also regulate the secondary metabolism of plants, such as regulating the synthesis of substances like anthocyanins. Anthocyanins have antioxidant effects and can help plants scavenge reactive oxygen species speciested by drought stress [46]. Abscisic acid (ABA) plays an important role in the plant's response to drought stress, and GRAS transcription factors are involved in the ABA signaling pathway. For example, ZmGRAS72 is involved in the biosynthesis and signaling of ABA. By regulating the expression of related genes, plants can better sense and respond to ABA signals under drought stress, thereby initiating a series of drought-resistant physiological responses [47]. In addition, among the genes interacting with GhGT92_6 , GH_A12G0623 belongs to the bZIP53 -type transcription factor. PgbZIP53 in ginseng can respond to drought stress signals. When ginseng is in a drought environment, the expression of the PgbZIP53 gene changes, indicating that it is involved in the perception and initial response process of plants to drought stress and may serve as a key factor in the drought signal transduction pathway to further transmit the drought signal and initiate the drought resistance mechanism in plants [48]. According to the analysis of the interaction network, GhGT92_5 interacts with the cellulose synthase gene GH_A09G2544 and the xyloglucosidase GH_A10G1801 , suggesting synergistic regulation of cell wall remodeling(Supplementary Table S4). Through Mfuzz analysis [49], genes or samples were clustered into different clusters based on expression patterns, and these clusters reflected different expression trends under specific time series. In this study, GhGT92_5 and GhGT92_6 were located in Cluster13 and Cluster14 respectively, and they had similar expression patterns, both showing a trend of first decreasing and then increasing. It means that the genes in these clusters involved in cell wall modification show a trend of first down-regulation and then up-regulation under drought stress, which may imply that there are dynamic changes in the cell wall during the process of plants adapting to drought stress. The down-regulation in the early stage may be to reduce the energy and material consumption required for cell wall synthesis, and the up-regulation in the later stage may be to enhance the strength and toughness of the cell wall to adapt to the water state changes of cells due to drought and prevent excessive water loss and mechanical damage of cells. Conclusion This research carried out an all- round analysis of the GT92 gene family in four cotton species. For the very first time, it executed bioinformatics investigations on the phylogenetic connections, gene architectures, expression profiles, evolutionary associations, and selection pressure assessments of GT92 members in G. hirsutum . Furthermore, the expression patterns of GT92 family genes were elucidated by means of RNA-seq data. Through the WGCNA analysis method, it was found that GhGT92_5 and GhGT92_6 belong to the MEtan module, and this module is significantly negatively correlated with 6 h after drought stress. Through Mfuzz analysis, it was found that GhGT92_5 and GhGT92_6 belong to Cluster13 and Cluster14 respectively, and they have similar expression patterns, both showing a trend of first decreasing and then increasing. Through qRT-PCR technology, it was found that GhGT92_5 and GhGT92_6 may play certain roles under drought and salt stress conditions. The subcellular localization results showed that GhGT92_5 is mainly distributed on the plasma membrane and in the chloroplasts, and GhGT92_6 is mainly localized in the cytoplasm and on the chloroplasts. These discoveries broaden our comprehension of GT92 family constituents and establish a basis for subsequent research into the stress resistance mechanism of this gene within cotton. Material and methods Identification of cotton GT92 gene family members The reference genome and genome annotation information files of G. arboreum (ICR), G. raimondii (JGI), G. hirsutum (ZJU), and G. barbadense (ZJU) sourced from the CottonFGD ( https://cottonfgd.org/ ) database [50]. The GT92 genes in the four cotton species were traced through Local BLASTP (v2.12.0), E-value cutoff (1e-5), applying the GT92 protein sequence of Arabidopsis thaliana . The detected GT92 genes were authenticated by means of the Hidden Markov Model (HMM) profiles retrieved from the Pfam (PF01697) database [51]. The domain details of the detected GT92 proteins were further validated via the NCBI Batch-CDD search. For the purpose of examining the physicochemical properties of GT92 genes, we conducted the analysis with the aid of TBtools-II software (v2.150) [52]. Investigation into the chromosomal localizations and gene replication of GT92s To probe into the chromosomal localities of GT92 genes among four cotton species, the GFF3 files encompassing cotton genome annotation details were obtained from the CottonFGD database [50]. The physical chromosomal whereabouts of all GT92 genes in the four cotton species were rendered visible via the utilization of TBtools-II software (v2.150) [52]. Moreover, the genomic collinearity segments and gene replication were scrutinized using MCScanX software [53]. Construction of a phylogenetic tree of GT92 family proteins To investigate the evolutionary interrelationships among the GT92 genes in four cotton species, we conducted multiple-sequence alignments on the 44 retrieved GT92 genes, employing MEGA (MEGA7) and ClustalW [54]. Drawing upon the comparison findings, a phylogenetic tree was assembled using the Maximum Likelihood (ML) technique, and the Bootstrap value was configured to 1000. Gene structure and conserved protein motif analysis of GT92 family genes Afterwards, we conducted a visual inspection by making use of TBtools-II software (version 2.150) on the MEME dataset, the NWK file derived from the phylogenetic tree scrutiny, and the GFF3 genome annotation dossier of G. hirsutum TM-1 [52]. Expression pattern and cis -element analysis of GT92 family genes To delve into the expression profiles of GT92s , we fetched the transcriptomic datasets of TM- 1 and Hai7124 from public data banks. These datasets were harnessed to assess the expression of GT92 genes in disparate tissues and at varying developmental phases of fibers and ovules. Thereafter, the RNA-seq datasets of TM-1 and Hai7124 under cold, hot, salt, and PEG stresses were also downloaded [23]. To conduct an initial inquiry into the role of gene expression modulation, we extracted the 2.0-kilobase sequence situated upstream of the initiation codon from the GT92 family genes of G. hirsutum . This sequence was utilized as the promoter sequence for an analysis of cis -regulatory elements. The cis -regulatory elements in the promoter zone of GT92 genes were further examined by means of PlantCARE. Then, the information about the identified cis -acting regulatory elements was made visually perceptible using TBtools-II software (version 2.150) [52]. Examination of collinearity and estimation of selective pressure in GT92 family genes To probe into the evolutionary associations and selection pressures of GT92 family genes across four cotton species and to identify collinear genes within the entire genome, all cotton protein sequences were subjected to a BLAST comparison using MCScanX software [53]. Eventually, a visual examination was carried out using TBtools-II software (v2.150) [52]. Moreover, the rates of non-synonymous substitution (Ka) and synonymous substitution (Ks) for duplicate genes were computed using TBtools-II software (v2.150) [52]. Weighted gene co-expression network analysis We retrieved the TM-1 drought stress transcriptomic dataset [23] for WGCNA exploration. Subsequently, we employed the WGCNA software suite within the R program [55] to build networks. Following threshold filtering, β = 16 was ultimately chosen to conduct power transformation on the original scaled relationship matrix, thereby obtaining a scale-free adjacency matrix, merge cut height (0.25). The least number of genes within a module was set at 30 (minModuleSize = 30). GO, KEGG enrichment analysis and interaction network construction The genes from the target module were put through KEGG and GO enrichment assays utilizing TBtools-II software (v2.150) [52]. The cut-off values were set as P < 0.01 and Q < 0.05. In the meantime, to obtain understandings of the potential interaction matrix of core genes, we calculated the Pearson's correlation index as the interaction intensity between target genes and candidate genes. The data fed from the outcomes of building the interaction network was visualized by means of Cytoscape (v.3.7.1) [56]. Trend analysis based on Mfuzz cluster A time-dependent kinetic clustering examination of gene expression was carried out by applying the Mfuzz package within the R programming environment [49]. Subcellular localization of Subcellular localization of GhGT92s To observe the subcellular localization of GhGT92_5 and GhGT92_6 , the constructed plasmids were transferred into Agrobacterium GV3101 and cultured at 30°C for two days. The bacterial suspension was injected into tobacco leaves, cultured under low light conditions for two days, and well-labeled. The tobacco leaves in the injection area were made into slides and observed and photographed under a confocal microscope. The GFP empty vector without GhGT92_5 and GhGT92_6 was used as a control. Quantitative RT-PCR technique We ascertained the expression levels of GhGT92_5 and GhGT92_6 subsequent to drought and salt stress exposures. Additionally, under drought stress and salt stress conditions, expression analysis was performed on KK1543 (drought-resistant), XLZ26 (drought-sensitive), XLZ30 (salt-sensitive), and XLZ26 (salt-tolerant) materials. Seeds of KK1543, XLZ26, and XLZ30 were germinated at 28°C under a 16-hour light/8-hour dark regime and then transplanted into a normal hydroponic solution. Hoagland nutrient solution was applied every two days. KK1543 and XLZ26 were subjected to drought treatment using 15% PEG6000 at the two-leaf stage, while XLZ26 and XLZ30 were subjected to salt stress treatment using 150 mmol/L − 1 NaCl. RNA was extracted from leaves using TRIzol (Invitrogen), with DNase I treatment. cDNA synthesis used 1 µg RNA and PrimeScript RT reagent Kit (Takara). The experiment was executed with three biological repetitions and three technical reiterations, and amplification was conducted on the real-time fluorescence quantitative apparatus 7500. The relative expression levels of the genes were scrutinized using the 2 −ΔΔt approach [57]. The primers utilized in this research are presented in Supplementary Table S5. Abbreviations WGCNA: Weighted Gene Co-expression Network Analysis; WGD: Whole-genome duplicati-on; DPA: days post-anthesis; LP: lint percentage; TDZ: Thidiazuron; Verticillium dahliae : V. dahliae ; FOV: Fusarium oxysporum f. sp. Vasinfectum ; FPKM: Fragments per kilobase of exon model per million mapped; MeJA: Methylj-asmonat-e; hpi: Hours post inoculation; Glycosyltransferases: GTs; Hidden Markov Model: HMM. Declarations Supplementary Information The online version contains supplementary material available at XXX. Acknowledgements All authors are grateful to the laboratory members for help, advice and discussion. Authors’ contributions JY and WX designed the experiments and wrote the manuscript.XHJ, ZDW, JY performed most of the experiments. JY, ZZP, YLY, LYY, Aerman, MQQ, ZTY, WMZ assisted in the experiments, analyzed the data and discussed the results. All authors read and approved the manuscript. Funding This work was funded by the Science and Technology Program Project of the Outstanding Youth Science Foundation in Xinjiang Uygur Autonomous Region (2022D01E58), the Xinjiang Academy of Agricultural Sciences Agricultural Science and technology innovation stability support project-The initial differentiation and regulatory network of G. hirsutum fuzz fiber were studied based on scRNA-seq(xjnkywdzc-2025001-37). Data Availability All GT92s sequence information is available in the Cotton Functional Genomics Database (CottonFGD) (https://cottonfgd.org/about/download.html). 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Functional analysis of maize GRAS transcription factor gene ZmGRAS72 in response to drought and salt stresses Agriculture Communications. Agriculture Communications. 2024;100054. Li HJ, Chen J, Zhao Q, Han YL, Li L, Sun CY, et al. Basic leucine zipper (bZIP) transcription factor genes and their responses to drought stress in ginseng, Panax ginseng C.A. Meyer. BMC Genomics. 2021;22(1):316. Kumar L, Futschik ME. Mfuzz: a software package for soft clustering of microarray data. Bioinformation. 2007;2(1):5–7. Zhu T, Liang CZ, Meng ZG, Sun GQ, Meng ZH, Guo SD, et al. CottonFGD: an integrated functional genomics database for cotton. BMC Plant Biology. 2017;17(1):101. Finn RD, Coggill P, Eberhardt RY, Eddy SR, Mistry J, Mitchell AL, et al. The pfam protein families database: Towards a more sustainable future. Nucleic Acids Res. 2016;44,D279-D285. Chen CJ, Wu Y, Li JW, Wang X, Zeng ZH, Xu J, et al. 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Additional Declarations No competing interests reported. Supplementary Files SupplementaryMaterial.zip Table S1.Physicochemical properties of GT92 genes Table S2.GT92 gene duplication type Table S3.Ka/Ks Values of all duplicated gene pairs from four Gossypium species Table S4.Interaction network of GhGT92 s in the MEtan module Table S5.Primers used for qRT-PCR Cite Share Download PDF Status: Published Journal Publication published 09 Oct, 2025 Read the published version in BMC Genomics → Version 1 posted Editorial decision: Revision requested 04 Jun, 2025 Reviews received at journal 04 Jun, 2025 Reviewers agreed at journal 01 Jun, 2025 Reviewers agreed at journal 01 Jun, 2025 Reviews received at journal 11 Apr, 2025 Reviewers agreed at journal 06 Apr, 2025 Reviewers invited by journal 06 Apr, 2025 Submission checks completed at journal 04 Apr, 2025 First submitted to journal 02 Apr, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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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-5984925","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":439924217,"identity":"158c5d1a-b0be-46cf-ba22-7f5651432720","order_by":0,"name":"Xin Wei","email":"","orcid":"","institution":"Cotton Research Institute of Xinjiang Academy of Agricultural Sciences (National Engineering Technology Research Center for Cotton)","correspondingAuthor":false,"prefix":"","firstName":"Xin","middleName":"","lastName":"Wei","suffix":""},{"id":439924218,"identity":"55ac80ad-381e-40b4-87d5-2e3e5b0419cd","order_by":1,"name":"Yang Jiao","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAtElEQVRIiWNgGAWjYBACxgYog5+Z+fAD4rUcSGBgkGxnSzMg3iqQFoPzPAoSRKlmnpH88PHHHzZ5xod5GAwYamyiCTtsRpqxwYGEtGKzw7wHHjAcS8ttIKwlh03iQMLhxG2H+RIMGBsOk6BlczOPgQRpWjYwE62l55mxwZm0tMQZh4GBnECMXwzbkx8+qLCxSezvP3z4wYcaGyK0TEhA4iXgUIUC5PkPEKNsFIyCUTAKRjQAACIxQZJJQjXfAAAAAElFTkSuQmCC","orcid":"","institution":"Cotton Research Institute of Xinjiang Academy of Agricultural Sciences (National Engineering Technology Research Center for Cotton)","correspondingAuthor":true,"prefix":"","firstName":"Yang","middleName":"","lastName":"Jiao","suffix":""},{"id":439924219,"identity":"4bc4647a-87d1-42a2-a079-7e5f080e5e3d","order_by":2,"name":"Zipiao Zheng","email":"","orcid":"","institution":"Cotton Research Institute of Xinjiang Academy of Agricultural Sciences (National Engineering Technology Research Center for Cotton)","correspondingAuthor":false,"prefix":"","firstName":"Zipiao","middleName":"","lastName":"Zheng","suffix":""},{"id":439924220,"identity":"b756d981-1ab8-4929-be49-c1c7dfb3f938","order_by":3,"name":"Aerman Abulimiti","email":"","orcid":"","institution":"Cotton Research Institute of Xinjiang Academy of Agricultural Sciences (National Engineering Technology Research Center for Cotton)","correspondingAuthor":false,"prefix":"","firstName":"Aerman","middleName":"","lastName":"Abulimiti","suffix":""},{"id":439924221,"identity":"6001652d-fac6-4bbb-bfcc-fbec788d59e2","order_by":4,"name":"Liyun Yang","email":"","orcid":"","institution":"Cotton Research Institute of Xinjiang Academy of Agricultural Sciences (National Engineering Technology Research Center for Cotton)","correspondingAuthor":false,"prefix":"","firstName":"Liyun","middleName":"","lastName":"Yang","suffix":""},{"id":439924222,"identity":"70891b56-4243-4394-bc53-0a2da25e8ca1","order_by":5,"name":"Yuanyuan Liu","email":"","orcid":"","institution":"Cotton Research Institute of Xinjiang Academy of Agricultural Sciences (National Engineering Technology Research Center for Cotton)","correspondingAuthor":false,"prefix":"","firstName":"Yuanyuan","middleName":"","lastName":"Liu","suffix":""},{"id":439924223,"identity":"90cbf91e-1087-4b38-a3fb-250e1fbfd6b9","order_by":6,"name":"Qingqian Ma","email":"","orcid":"","institution":"Cotton Research Institute of Xinjiang Academy of Agricultural Sciences (National Engineering Technology Research Center for Cotton)","correspondingAuthor":false,"prefix":"","firstName":"Qingqian","middleName":"","lastName":"Ma","suffix":""},{"id":439924224,"identity":"f1bc6126-0760-4ddd-8422-829fe4fe31d4","order_by":7,"name":"Tianyu Zhang","email":"","orcid":"","institution":"College of Agriculture, Xinjiang Agricultural University, 311 Nongda East Road, Urumqi, China.","correspondingAuthor":false,"prefix":"","firstName":"Tianyu","middleName":"","lastName":"Zhang","suffix":""},{"id":439924225,"identity":"aae36aa6-8e86-4f83-81f2-19ed09e63504","order_by":8,"name":"Mingzhe Wu","email":"","orcid":"","institution":"College of Agriculture, Xinjiang Agricultural University, 311 Nongda East Road, Urumqi, China.","correspondingAuthor":false,"prefix":"","firstName":"Mingzhe","middleName":"","lastName":"Wu","suffix":""},{"id":439924226,"identity":"ffbf8a0c-4b57-4fb7-9208-68f3a926b3dc","order_by":9,"name":"Dawei Zhang","email":"","orcid":"","institution":"Cotton Research Institute of Xinjiang Academy of Agricultural Sciences (National Engineering Technology Research Center for Cotton)","correspondingAuthor":false,"prefix":"","firstName":"Dawei","middleName":"","lastName":"Zhang","suffix":""},{"id":439924227,"identity":"4c51a817-6c08-4c41-a347-a2ef7ca985e5","order_by":10,"name":"Haijiang Xu","email":"","orcid":"","institution":"Cotton Research Institute of Xinjiang Academy of Agricultural Sciences (National Engineering Technology Research Center for Cotton)","correspondingAuthor":false,"prefix":"","firstName":"Haijiang","middleName":"","lastName":"Xu","suffix":""}],"badges":[],"createdAt":"2025-02-08 03:53:18","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5984925/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5984925/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12864-025-12034-6","type":"published","date":"2025-10-09T15:57:47+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":80327552,"identity":"6cc8dbfd-1ffa-45be-8b3a-9bde3d5bec56","added_by":"auto","created_at":"2025-04-10 14:42:21","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1073195,"visible":true,"origin":"","legend":"\u003cp\u003ePhylogenetic relationship of GT92 proteins in four cotton species.\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-5984925/v1/f248bc69e5e23f12921529d3.png"},{"id":80328182,"identity":"dfde2bf4-269e-4152-a1ff-3d5cfdff6cac","added_by":"auto","created_at":"2025-04-10 14:50:21","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1386225,"visible":true,"origin":"","legend":"\u003cp\u003eLocation of the GT92 genes on the chromosomes of the four cotton species.(A)\u003cem\u003eG. arboreum\u003c/em\u003e.(B)\u003cem\u003eG. hirsutum\u003c/em\u003e.(C)\u003cem\u003eG. raimondii\u003c/em\u003e.(D)\u003cem\u003eG. barbadense\u003c/em\u003e.\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-5984925/v1/58a2b2c4307b34d595c25afc.png"},{"id":80329410,"identity":"38568576-e43f-4831-ba0b-7b9d494aa2d9","added_by":"auto","created_at":"2025-04-10 14:58:21","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":800492,"visible":true,"origin":"","legend":"\u003cp\u003eAnalysis of evolutionary, gene structure, motif and \u003cem\u003ecis\u003c/em\u003e-acting elements of \u003cem\u003eGhGT92s\u003c/em\u003e.\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-5984925/v1/9ce8d12f475a3b20e6eee7f8.png"},{"id":80327559,"identity":"f9f1d860-508e-476f-ab5d-176ad0b5ad5d","added_by":"auto","created_at":"2025-04-10 14:42:21","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":3357829,"visible":true,"origin":"","legend":"\u003cp\u003eCollinearity analysis of GT92 genes.(A)Collinearity analysis of \u003cem\u003eG. arboreum\u003c/em\u003e.(B)Collinearity analysis of \u003cem\u003eG. raimondii\u003c/em\u003e.(C)Collinearity analysis of \u003cem\u003eG. hirsutum\u003c/em\u003e.(D)Collinearity analysis of \u003cem\u003eG. barbadense\u003c/em\u003e.(E)Multiple synteny analysis among cotton \u003cem\u003eGT92\u003c/em\u003egenes. Multiple synteny analysis was used to show the orthologous relationship among\u003cem\u003e G. arboreum\u003c/em\u003e, \u003cem\u003eG. hirsutum\u003c/em\u003e, \u003cem\u003eG. raimondii\u003c/em\u003e and \u003cem\u003eG. barbadense\u003c/em\u003e of\u003cem\u003e GT92\u003c/em\u003e genes. The chromosomes of different kinds of cotton are shown in different colors.(F)Selection pressure analysis of the GT92 gene family during evolution.\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-5984925/v1/6f1fd075b6436a30957b6bec.png"},{"id":80327555,"identity":"cad933b6-aa9e-4ff9-9ba7-942513300b27","added_by":"auto","created_at":"2025-04-10 14:42:21","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":2136704,"visible":true,"origin":"","legend":"\u003cp\u003eExpression patterns of \u003cem\u003eGhGT92s\u003c/em\u003e in diverse tissue types.(A) The expression patterns of \u003cem\u003eGhGT92s\u003c/em\u003ein diverse organs (Root, Stem, Leaf, Petal, Receptacle, Calycle, Stamen, Pistil).(B) Expression patterns of seed, cotyledon and root growth and development in upland cotton at different developmental stages. (C) Expression trends of \u003cem\u003eGhGT92s\u003c/em\u003ein ovules and fibers (-3, -1, 0, 1, 3, 5, 10, 20, 25, and 35 DPA). (D) Expression levels of \u003cem\u003eGhGT92s\u003c/em\u003e in ovule and fiber in higher lint percentage material and lower lint percentage material at different periods（5,10,15,20,25）DPA. (E) Expression patterns of \u003cem\u003eGhGT92s\u003c/em\u003ein high-oil and low-oil materials (10, 20, 30 DPA).(F) Expression patterns of \u003cem\u003eGhGT92s\u003c/em\u003ein CJ56 and CJ72 materials (0, 10, 20 Days).(G) Expression patterns of \u003cem\u003eGhGT92s\u003c/em\u003ein Ari971 (wild-type material) and Ari1327 (mutant material). (H) Expression footprints of \u003cem\u003eGhGT92s\u003c/em\u003e under cold, heat, salt and drought stressconditions (0, 1, 3, 6, 12, 24 h).(I) Expression footprints of \u003cem\u003eGhGT92s\u003c/em\u003efrom \u003cem\u003eG. hirsutum\u003c/em\u003e under\u003cem\u003e V. dahliae\u003c/em\u003e stress (0, 12, 24, 48, 72, 96, 120, 144 h).\u003c/p\u003e","description":"","filename":"image5.png","url":"https://assets-eu.researchsquare.com/files/rs-5984925/v1/f58907145b453502ac6d8e98.png"},{"id":80328186,"identity":"eb6bd9ec-d1aa-415e-8c30-83062933368c","added_by":"auto","created_at":"2025-04-10 14:50:21","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":1313003,"visible":true,"origin":"","legend":"\u003cp\u003eExpression patterns of GT92s in \u003cem\u003eG. hirsutum\u003c/em\u003e and \u003cem\u003eG. barbadense\u003c/em\u003e. (A) Expression modes of \u003cem\u003eGhGT92\u003c/em\u003egenes derived from \u003cem\u003eG. hirsutum\u003c/em\u003e upon TDZ exposure. (B) Expression profiles of \u003cem\u003eGbGT92s\u003c/em\u003e across divergent tissues (Bract, Filament, Leaf, Petal, Pistil, Root, Sepal, Stem, Torus). (C) Expression traits of \u003cem\u003eGbGT92s\u003c/em\u003e in ovule and fiber (-3, -1, 0, 1, 3, 5, 10, 20, 25 DPA). (D) Expression patterns of \u003cem\u003eGbGT92s\u003c/em\u003eunder the influence of cold, heat, salt, and drought stress(0, 1, 3, 6, 12, 24 h). (E) Expression behaviors of \u003cem\u003eGbGT92s\u003c/em\u003e under FOV stress conditions.\u003c/p\u003e","description":"","filename":"image6.png","url":"https://assets-eu.researchsquare.com/files/rs-5984925/v1/16d3292b8d018546037f72b5.png"},{"id":80329413,"identity":"e55e133f-c791-45c1-9001-9f9deb2c37b0","added_by":"auto","created_at":"2025-04-10 14:58:21","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":5352450,"visible":true,"origin":"","legend":"\u003cp\u003eWGCNA of drought stress in upland cotton. (A) Results of the gene grouping analysis carried out in WGCNA. (B) Number of genes within the 24 Co-expressed module elements. (C) Thermal map portraying the relationship between modules and traits. The numbers presented in squares indicate the correlation coefficients and \u003cem\u003ep\u003c/em\u003e-values among modules. (D) KEGG pathway enrichment appraisal of the MEtan module. (E) Complete network of \u003cem\u003eGhGT92s\u003c/em\u003e assembled using genome-scale transcriptome data. (F) KEGG enrichment analysis of 121 genes interacting with \u003cem\u003eGhGT92_5\u003c/em\u003e. (G) KEGG enrichment analysis of 124 genes interacting with\u003cem\u003e GhGT92_6\u003c/em\u003e.\u003c/p\u003e","description":"","filename":"image7.png","url":"https://assets-eu.researchsquare.com/files/rs-5984925/v1/7e5823e8da1232230e0fec31.png"},{"id":80327562,"identity":"5b043b13-d4e7-4ca4-a26f-ab5c40151859","added_by":"auto","created_at":"2025-04-10 14:42:21","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":4505830,"visible":true,"origin":"","legend":"\u003cp\u003eMfuzz trend analysis.(A) Mfuzz clustering results of 42,214 differentially expressed genes (DEGs).(B) GO pathway enrichment analysis of Cluster13.(C) GO pathway enrichment analysis of Cluster14.\u003c/p\u003e","description":"","filename":"image8.png","url":"https://assets-eu.researchsquare.com/files/rs-5984925/v1/f490366c19ba04f4478843b8.png"},{"id":80327566,"identity":"471ea7d5-9c36-43da-8fee-e26727fcc086","added_by":"auto","created_at":"2025-04-10 14:42:21","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":6584133,"visible":true,"origin":"","legend":"\u003cp\u003eSubcellular localization of \u003cem\u003eGhGT92_5\u003c/em\u003e and \u003cem\u003eGhGT92_6\u003c/em\u003e in epidermal cells of tobacco leaves was determined using green fluorescent protein (as a positive control) mediated by \u003cem\u003eAgrobacterium tumefaciens\u003c/em\u003e GV3101 or green fluorescent protein fused with \u003cem\u003eGhGT92_5\u003c/em\u003e and \u003cem\u003eGhGT92_6\u003c/em\u003e (\u003cem\u003eGhGT92_5\u003c/em\u003e, \u003cem\u003eGhGT92_6\u003c/em\u003e-green fluorescent protein, and red fluorescent signals). After 48 hours of \u003cem\u003eAgrobacterium\u003c/em\u003e infiltration, the fluorescence of the green fluorescent protein was observed using a confocal laser scanning microscope. Bars = 25 μm.\u003c/p\u003e","description":"","filename":"image9.png","url":"https://assets-eu.researchsquare.com/files/rs-5984925/v1/baf550940a20427fb6a0d03a.png"},{"id":80327563,"identity":"352a660d-ff1e-4af5-9b89-dd7817d0d6b5","added_by":"auto","created_at":"2025-04-10 14:42:21","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":308557,"visible":true,"origin":"","legend":"\u003cp\u003eExpression profiling of the \u003cem\u003eGhGT92s\u003c/em\u003e under salt stress and drought stress in \u003cem\u003eG. hirsutum\u003c/em\u003e. (A)Expression patterns of \u003cem\u003eGhGT92_5\u003c/em\u003e under drought stress(0、3、6、12、24 h). (B) Expression patterns of \u003cem\u003eGhGT92_6\u003c/em\u003e under drought stress(0、3、6、12、24 h). (C)Expression patterns of \u003cem\u003eGhGT92_5\u003c/em\u003eunder salt stress(0、3、6、12、24 h). (D) Expression patterns of \u003cem\u003eGhGT92_6\u003c/em\u003e under salt stress(0、3、6、12、24 h). The error bars represent the means of three technical replicates ± SEs. Statistically significant differences from the control group are indicated as *\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05; **\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01; ***\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001.\u003c/p\u003e","description":"","filename":"image10.png","url":"https://assets-eu.researchsquare.com/files/rs-5984925/v1/a45e8c4f4f94cefdfee76f3c.png"},{"id":93420054,"identity":"4b5772d3-7211-4697-aa40-9a593d6cdfd5","added_by":"auto","created_at":"2025-10-13 16:09:26","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":31664074,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5984925/v1/61110891-230a-4cbc-b936-73c0124969cb.pdf"},{"id":80329411,"identity":"51833aee-4902-41f1-a751-3f6dd27e9610","added_by":"auto","created_at":"2025-04-10 14:58:21","extension":"zip","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":65715,"visible":true,"origin":"","legend":"\u003cp\u003eTable S1.Physicochemical properties of GT92 genes\u003c/p\u003e\n\u003cp\u003eTable S2.GT92 gene duplication type\u003c/p\u003e\n\u003cp\u003eTable S3.Ka/Ks Values of all duplicated gene pairs from four \u003cem\u003eGossypium\u003c/em\u003especies\u003c/p\u003e\n\u003cp\u003eTable S4.Interaction network of \u003cem\u003eGhGT92\u003c/em\u003es in the MEtan module\u003c/p\u003e\n\u003cp\u003eTable S5.Primers used for qRT-PCR\u003c/p\u003e","description":"","filename":"SupplementaryMaterial.zip","url":"https://assets-eu.researchsquare.com/files/rs-5984925/v1/02ffca139606cc0b9fd17883.zip"}],"financialInterests":"No competing interests reported.","formattedTitle":"Genome-wide Ascertainment and Initial Functional Characterization and Expression Pattern Dissection of the GT92 Gene Family in Cotton","fulltext":[{"header":"Introduction","content":"\u003cp\u003ePlant cell walls are mainly composed of different polysaccharides, which can be divided into cellulose, hemicellulose, and pectin [1]. Pectin serves as a vital constituent of the primary cell walls in dicotyledonous plants, gymnosperms, and non-symbiotic monocotyledonous plants. It exists in comparatively small quantities within the secondary and primary walls of grasses. In dicotyledonous plants, like \u003cem\u003eArabidopsis thaliana\u003c/em\u003e, pectin constitutes 20%-35% of the primary cell wall [2\u0026ndash;3]. Apart from being a structural element of the cell wall, pectin polysaccharides possess numerous distinctive functions. These functions involve intercellular cohesion [4], cell lengthening [5], as well as wall porosity and extensibility, which are of great significance for plant development [6]. Pectin polymers are fabricated in the Golgi apparatus by glycosyltransferases (GTs). The prevailing hypothesis posits that pectin undergoes synthesis, assembly, and processing as it traverses the Golgi apparatus and the trans-Golgi network. Ultimately, pectin is secreted into Golgi-derived vesicles and conveyed to the cell surface, where additional modification or remodeling takes place upon deposition [7].\u003c/p\u003e \u003cp\u003eIt is estimated that the assembly of the pectin matrix requires the activities of more than 67 different transferases, yet only a few transferases have been clearly identified [8]. The first GT shown to play a role in pectin biosynthesis, especially in the formation of the HG backbone, is the HG galacturonosyltransferase GAUT1 [9]. Additional GAUT and GAUT-like (GATL) proteins are involved in pectin biosynthesis, but their exact functions and activities remain to be determined.\u003c/p\u003e \u003cp\u003ePreviously, the discovery of \u003cem\u003eβ-1,4-galactosylgalactosyltransferase galactan synthase 1\u003c/em\u003e (\u003cem\u003eGALS1\u003c/em\u003e) in \u003cem\u003eArabidopsis thaliana\u003c/em\u003e has been documented [10]. β-1,4-galactan predominantly functions as a side-chain of RG-I in the primary cell wall and is also found in the secondary cell walls of trees, playing a role in the development of reaction wood as a response to gravitropic cues [11]. GALS1 and its two counterparts belong to the glycosyltransferase family 92 (GT92) [12\u0026ndash;13], and every member of this family harbors a DUF23 motif. The presence of \u003cem\u003eβ-1,4-galactosylgalactosyltransferase\u003c/em\u003e activity was established as early as 50 years ago [14], and the synthesis of \u003cem\u003eβ-1,4-galactan\u003c/em\u003e in different plants has been confirmed in multiple in vitro studies [2]. However, it was not until recently that we discovered that the purified recombinant GALS1 protein can use UDP-Gal as a donor substrate to extend \u003cem\u003eβ-1,4-galactan\u003c/em\u003e acting as an acceptor, with \u003cem\u003eGALS1\u003c/em\u003e functioning as a \u003cem\u003eβ -1,4-galactosylgalactosyltransferase\u003c/em\u003e in vitro [10]. Recently, \u003cem\u003eGALS1\u003c/em\u003e has also been shown to possess arabinopyranosyltransferase activity, which can add arabinopyranose to the end of the growing galactan chain, thus preventing its further elongation [15]. Besides this, there are relatively few other reports on \u003cem\u003eGT92\u003c/em\u003e genes in plants.\u003c/p\u003e \u003cp\u003eAs an important fiber crop and a model for polyploid research, cotton frequently encounters abiotic stresses, including drought, salinity, cold injury, high temperature, and diverse biotic stresses throughout its growth and maturation processes [16\u0026ndash;18]. Severe external surroundings can impinge on cotton growth, leading to a decline in both yield and fiber quality. Consequently, enhancing the stress-tolerance capacity of plants aids in boosting their adaptability to adverse environments. Genetic engineering techniques have emerged as a crucial approach to attain this objective [19\u0026ndash;20]. With the advancements in cotton genome sequencing and assembly, a basis has been established for comprehensive investigations into cotton gene families [21\u0026ndash;23].\u003c/p\u003e \u003cp\u003eIn this study, by analyzing the publicly available transcriptome sequencing results [23], we found that the expression level of \u003cem\u003eGH_A09G1400\u003c/em\u003e (\u003cem\u003eGhGT92_5\u003c/em\u003e) showed a significant difference under drought stress. We hypothesized that this gene is related to drought resistance. Subsequently, we thoroughly recognized and examined the members of the GT92 gene family in two diploid \u003cem\u003eGossypium\u003c/em\u003e species (\u003cem\u003eG. arboreum\u003c/em\u003e and \u003cem\u003eG. raimondii\u003c/em\u003e), along with two tetraploid \u003cem\u003eGossypium\u003c/em\u003e species (\u003cem\u003eG. hirsutum\u003c/em\u003e and \u003cem\u003eG. barbadense\u003c/em\u003e). Employing bioinformatics approaches, via phylogenetic tree scrutiny, gene architecture, conserved motif, and sequence characteristic analysis, we ascertained the evolutionary associations among the cotton GT92 genes. Subsequently, we carried out collinearity assessment of the non-synonymous (Ka) and synonymous (Ks) substitution proportions (Ka/Ks ratios) among the four \u003cem\u003eGossypium\u003c/em\u003e species. Moreover, we investigated the expression of GT92 genes through promoter \u003cem\u003ecis\u003c/em\u003e-element exploration and tissue-specific expression profile analysis. We also analyzed the co-expression network of \u003cem\u003eGhGT92s\u003c/em\u003e and the gene set with the same expression trend through stress-resistant transcriptome data analysis. Through qRT-PCR analysis, we discovered that certain \u003cem\u003eGhGT92s\u003c/em\u003e were triggered by drought and salt stresses. The findings of this research not only present a comprehensive analysis of the cotton GT92 gene family but also put forward novel concepts for cotton abiotic stress-resistant breeding.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eDiscovery of GT92 family gene elements within four cotton varieties\u003c/h2\u003e \u003cp\u003eCandidate sequences were obtained by using the BLASTP program to perform alignment searches of the protein sequence of \u003cem\u003eGH_A09G1400\u003c/em\u003e in four cotton species. Following the validation of conserved domains with the help of Pfam and CDD software, those sequences lacking a complete GT92 domain were eliminated. Eventually, 44 objective genes were recognized across four distinct cotton species (as shown in Supplementary Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). Among them, there were eight genes in \u003cem\u003eG. arboreum\u003c/em\u003e (A2), eight genes in \u003cem\u003eG. raimondii\u003c/em\u003e (D5), 14 genes in \u003cem\u003eG. hirsutum\u003c/em\u003e (AD1), and 14 genes in \u003cem\u003eG. barbadense\u003c/em\u003e (AD2). Among these genes, the quantity of genes in \u003cem\u003eG. raimondii\u003c/em\u003e (D5) is equal to that in \u003cem\u003eG. arboreum\u003c/em\u003e (A2). Additionally, the number of \u003cem\u003eGT92\u003c/em\u003e genes in the two tetraploid cotton species, namely \u003cem\u003eG. hirsutum\u003c/em\u003e (AD1) and \u003cem\u003eG. barbadense\u003c/em\u003e (AD2), is identical, which is eight and 14 respectively. It is worth noting that the number of GT92 family members in the two tetraploid cotton species (\u003cem\u003eG. hirsutum\u003c/em\u003e, \u003cem\u003eG. barbadense\u003c/em\u003e) is two less than the sum of the number in the two diploid cotton species (\u003cem\u003eG. arboreum\u003c/em\u003e, \u003cem\u003eG. raimondii\u003c/em\u003e).\u003c/p\u003e \u003cp\u003eWe renamed these genes as \u003cem\u003eGaGT92_1\u003c/em\u003e-\u003cem\u003eGaGT92_8\u003c/em\u003e, \u003cem\u003eGbGT92_1\u003c/em\u003e-\u003cem\u003eGbGT92_14\u003c/em\u003e, \u003cem\u003eGhGT92_1\u003c/em\u003e-\u003cem\u003eGhGT92_14\u003c/em\u003e, and \u003cem\u003eGrGT92_1-GrGT92_8\u003c/em\u003e according to their locations on the cotton chromosomes (Supplementary Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSubsequently, an analysis was carried out on the physicochemical properties of the amino acid sequences of the GT92 gene family members in the four cotton species. The length of the GT92 amino acids ranges from 454 to 605 amino acid residues, with an average sequence length of 537 amino acids. The molecular weight varies from 51.46 kDa to 70.40 kDa, with an average value of 61.33 kDa. The isoelectric point (pI) lies between 6.88 and 9.61, and the average pI is 9.04 (as shown in Supplementary Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eConstruction of the phylogenetic tree of GT92 genes among four cotton species\u003c/h3\u003e\n\u003cp\u003eTo delve into the evolutionary associations among the constituents of the GT92 family, the MEGA7 software was employed to erect a phylogenetic tree comprising 44 GT92 protein sequences derived from the four cotton species (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Eventually, the GT92 protein sequences were divided into five different subfamilies, namely Class1-Class5. Among them, Class3 had the largest number, encompassed 17 entities, succeeded by Class2 which held 11 components, and Class4 had the smallest quantity, having just four elements. Both Class1 and Class5 had six GT92 genes, with two genes in each of the two tetraploid species; and one gene in each of the two diploid species. The number of \u003cem\u003eGT92\u003c/em\u003e genes in Class4 was four, evenly distributed among the four cotton species. In Class3, the number of genes in the two diploid species was the same, both being three; the same was true in Class2, with a number of two. Interestingly, in Class3, the number of \u003cem\u003eG. hirsutum\u003c/em\u003e was one more than that of \u003cem\u003eG. barbadense\u003c/em\u003e; while in Class2, the number of \u003cem\u003eG. hirsutum\u003c/em\u003e was one less than that of \u003cem\u003eG. barbadense\u003c/em\u003e.\u003c/p\u003e \u003cp\u003eWe found that the two diploid cotton species always clustered together with the two tetraploid cotton species, which also confirmed that the tetraploid \u003cem\u003eG. hirsutum\u003c/em\u003e and \u003cem\u003eG. barbadense\u003c/em\u003e evolved through hybridization of the diploid \u003cem\u003eG. arboreum\u003c/em\u003e and \u003cem\u003eG. raimondii\u003c/em\u003e [24].\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003eChromosomal location of genes in four cotton species\u003c/h3\u003e\n\u003cdiv class=\"Heading\"\u003eChromosomal location of \u003cem\u003eGT92\u003c/em\u003e genes in four cotton species\u003c/div\u003e \u003cp\u003eTo conduct a more in-depth exploration of the chromosomal distribution and gene duplication of \u003cem\u003eGT92\u003c/em\u003e genes in the four cotton species, we mapped the physical loci of these genes on the chromosomes. The 44 \u003cem\u003eGT92\u003c/em\u003e genes are haphazardly distributed across the chromosomes of the four cotton species. In the case of \u003cem\u003eG. hirsutum\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA), 14 genes are distributed on 12 chromosomes, namely on chromosomes A01, A05, A09, A11, A12, A13; D01, D04, D09, D11, D12, D13. Among them, there are eight genes in the A subgenome and six genes in the D subgenome, and the number of genes in the A subgroup is two more than that in the D subgroup. It is worth noting that there are two \u003cem\u003eGT92\u003c/em\u003e genes on both chromosomes A05 and A09, and there is one gene on the remaining chromosomes.\u003c/p\u003e \u003cp\u003eIn \u003cem\u003eG. barbadense\u003c/em\u003e(Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB), the distribution pattern of \u003cem\u003eGT92\u003c/em\u003e genes is roughly the same as that in \u003cem\u003eG. hirsutum\u003c/em\u003e, but the difference is that there are two \u003cem\u003eGT92\u003c/em\u003e genes on both chromosomes A09 and D09. In \u003cem\u003eG. arboreum\u003c/em\u003e(Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC), eight \u003cem\u003eGT92\u003c/em\u003e genes are distributed on seven chromosomes, namely Chr01, Chr04, Chr05, Chr09, Chr11, Chr12, Chr13, among which there are two \u003cem\u003eGT92\u003c/em\u003e genes on Chr09 and one gene on the remaining chromosomes. In \u003cem\u003eG. raimondii\u003c/em\u003e(Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD), eight \u003cem\u003eGT92\u003c/em\u003e genes are distributed on the same seven chromosomes as in \u003cem\u003eG. arboreum\u003c/em\u003e, and like in \u003cem\u003eG. arboreum\u003c/em\u003e, there are two \u003cem\u003eGT92\u003c/em\u003e genes on Chr09. The findings above provide further evidence that the two tetraploid cotton species originated from two diploid cotton species[24].\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003eGene structure, protein motif and -acting element analysis of \u003c/h3\u003e\n\u003cdiv class=\"Heading\"\u003eGene structure, protein motif and \u003cem\u003ecis\u003c/em\u003e-acting element analysis of \u003cem\u003eGhGT92s\u003c/em\u003e\u003c/div\u003e \u003cp\u003eIn an effort to gain deeper insights into the potential structural evolutionary correlations among GT92 family constituents, we generated a phylogenetic tree for 14 \u003cem\u003eGT92\u003c/em\u003e genes in \u003cem\u003eG. hirsutum\u003c/em\u003e by utilizing the maximum-likelihood (ML) approach. Moreover, we carried out motif association analysis and gene structure analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The protein sequences and annotation files of the 14 GT92 constituents were employed to construct the phylogenetic tree and gene structure information. The MEME and TBtools-II software (v2.150) were used to assess the conserved motifs within GT92 proteins. Within the 14 members of \u003cem\u003eG. hirsutum\u003c/em\u003e, a total of 10 motifs were detected. \u003cem\u003eGhGT92_3\u003c/em\u003e, \u003cem\u003eGhGT92_8\u003c/em\u003e, \u003cem\u003eGhGT92_10\u003c/em\u003e and \u003cem\u003eGhGT92_14\u003c/em\u003e contained the most motifs, with eight motifs, namely motif1-motif8. Next, \u003cem\u003eGhGT92_5\u003c/em\u003e, \u003cem\u003eGhGT92_6\u003c/em\u003e, \u003cem\u003eGhGT92_12\u003c/em\u003e contained motif1, motif2, motif5, motif7, motif9, motif10, and \u003cem\u003eGhGT92_7\u003c/em\u003e, \u003cem\u003eGhGT92_13\u003c/em\u003e contained motif1, motif5, motif7, motif8, motif9, motif10, each of which had six motifs. Finally, the remaining five \u003cem\u003eGhGT92\u003c/em\u003e genes contained motif1, motif2, motif4, motif5, motif7, a total of five motifs.\u003c/p\u003e \u003cp\u003eIn addition, we analyzed the characteristics of the intron-exon structure. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, genes in the same group had similar intron-exon arrangements. Among them, \u003cem\u003eGhGT92_3\u003c/em\u003e, \u003cem\u003eGhGT92_8\u003c/em\u003e, \u003cem\u003eGhGT92_10\u003c/em\u003e and \u003cem\u003eGhGT92_14\u003c/em\u003e contained the most exons, with 11 exons and 10 introns. \u003cem\u003eGhGT92_4\u003c/em\u003e, \u003cem\u003eGhGT92_7\u003c/em\u003e, \u003cem\u003eGhGT92_13\u003c/em\u003e, \u003cem\u003eGhGT92_5\u003c/em\u003e, \u003cem\u003eGhGT92_6\u003c/em\u003e, \u003cem\u003eGhGT92_11\u003c/em\u003e, \u003cem\u003eGhGT92_12\u003c/em\u003e contained two exons and one intron, and \u003cem\u003eGhGT92_1\u003c/em\u003e, \u003cem\u003eGhGT92_2\u003c/em\u003e, \u003cem\u003eGhGT92_9\u003c/em\u003e had only one exon.\u003c/p\u003e \u003cp\u003eTo achieve a more profound understanding of the regulatory mechanism of GT92 genes, we made use of the PlantCARE database to predict the \u003cem\u003ecis\u003c/em\u003e-acting elements in the 2000-base-pair promoter region ahead of the 14 \u003cem\u003eGT92\u003c/em\u003e genes in \u003cem\u003eG. hirsutum\u003c/em\u003e. In the case of \u003cem\u003eG. hirsutum\u003c/em\u003e (as illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e), these included the \u003cem\u003eMYB\u003c/em\u003e binding site related to light responsiveness, along with the \u003cem\u003eMYB\u003c/em\u003e binding site related to drought inducibility. The \u003cem\u003ecis\u003c/em\u003e-acting elements connected with plant hormones consisted of abscisic acid-associated elements, salicylic acid-associated elements, MeJA-associated elements, and auxin-associated elements. Additionally, there were \u003cem\u003ecis\u003c/em\u003e-acting elements engaged in defense and stress responses, \u003cem\u003ecis\u003c/em\u003e-acting elements engaged in low-temperature responses, \u003cem\u003eMYB\u003c/em\u003e binding sites involved in the regulation of flavonoid biosynthetic genes, \u003cem\u003ecis\u003c/em\u003e-acting regulatory elements involved in seed-specific regulation, and \u003cem\u003ecis\u003c/em\u003e-acting regulatory elements involved in zein metabolism regulation. The analysis of the promoter will help us verify the subsequent gene functions.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003eExploration of gene duplicative events and syntenic associations\u003c/h3\u003e\n\u003cp\u003eThe evolutionary path of gene families mainly includes whole-genome duplication incidents, segmental duplication events, and tandem duplication happenings. With the intention of probing into the evolutionary models and the consequences of polyploidization, we identified the duplication types of GT92 genes among four \u003cem\u003eGossypium\u003c/em\u003e species (as shown in Supplementary Table S2). In diploid \u003cem\u003eGossypium\u003c/em\u003e species, six genes in \u003cem\u003eG. arboreum\u003c/em\u003e were of the Dispersed type, and two genes were of the WGD or Segmental type. Four genes within \u003cem\u003eG. raimondii\u003c/em\u003e fell into the Dispersed type, while the remaining four genes were classified under the WGD or Segmental type. When it comes to tetraploid \u003cem\u003eGossypium\u003c/em\u003e species, every gene in \u003cem\u003eG. barbadense\u003c/em\u003e was assigned to the WGD or Segmental type. In \u003cem\u003eG. hirsutum\u003c/em\u003e, a single gene pertained to the Dispersed type, and the rest of the genes were attributed to the WGD or Segmental type.\u003c/p\u003e \u003cp\u003eFirst, we performed multiple synteny analyses of \u003cem\u003eGT92\u003c/em\u003e genes in \u003cem\u003eG. hirsutum\u003c/em\u003e (AD1), \u003cem\u003eG. barbadense\u003c/em\u003e (AD2), \u003cem\u003eG. arboreum\u003c/em\u003e (A2), and \u003cem\u003eG. raimondii\u003c/em\u003e (D5) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eE). We found that there were 19 orthologous gene pairs between \u003cem\u003eG. barbadense\u003c/em\u003e and \u003cem\u003eG. arboreum\u003c/em\u003e, 17 orthologous gene pairs between \u003cem\u003eG. hirsutum\u003c/em\u003e and \u003cem\u003eG. arboreum\u003c/em\u003e, 18 orthologous gene pairs between \u003cem\u003eG. barbadense\u003c/em\u003e and \u003cem\u003eG. hirsutum\u003c/em\u003e, 22 orthologous gene pairs between \u003cem\u003eG. barbadense\u003c/em\u003e and \u003cem\u003eG. raimondii\u003c/em\u003e, and 21 orthologous gene pairs between \u003cem\u003eG. hirsutum\u003c/em\u003e and \u003cem\u003eG. raimondii\u003c/em\u003e. Therefore, we speculated that during the evolution of the GT92 family genes, the main causes of gene amplification were whole-genome duplication events and segmental duplication events.\u003c/p\u003e \u003cp\u003eSubsequently, we conducted synteny analyses of tetraploid \u003cem\u003eG. hirsutum\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC), and a total of 24 orthologous/paralogous pairs were identified. In \u003cem\u003eG. barbadense\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD), a total of 28 orthologous/paralogous pairs were identified. In addition, synteny analyses were also carried out in the two diploid \u003cem\u003eGossypium\u003c/em\u003e species. One orthologous/paralogous pair was found in \u003cem\u003eG. arboreum\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA), and two orthologous/paralogous pairs were found in \u003cem\u003eG. raimondii\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB).\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eCalculation of selection pressure\u003c/h2\u003e \u003cp\u003eTo study the differentiation mechanism of GT92 genes during cotton polyploid duplication events, we calculated the ratio of non-synonymous to synonymous substitutions (Ka/Ks ratio) to identify the types of selection pressure on these homologous gene pairs during evolution (Supplementary Table S3). We calculated the Ka/Ks ratios of 152 pairs of homologous genes in four \u003cem\u003eGossypium\u003c/em\u003e species respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eF). First, we analyzed the diploid and tetraploid \u003cem\u003eGossypium\u003c/em\u003e species. Between \u003cem\u003eG. arboreum\u003c/em\u003e and \u003cem\u003eG. barbadense\u003c/em\u003e, the Ka/Ks ratios of two pairs of homologous genes were greater than 0.5, and the ratios of the rest were less than 0.5. Between \u003cem\u003eG. arboreum\u003c/em\u003e and \u003cem\u003eG. hirsutum\u003c/em\u003e, the Ka/Ks ratios of three pairs of homologous genes were greater than 0.5, among which the ratio of one pair of homologous genes was greater than 1, and the ratios of the rest were less than 0.5. Between \u003cem\u003eG. barbadense\u003c/em\u003e and \u003cem\u003eG. raimondii\u003c/em\u003e, the Ka/Ks ratio of one pair of homologous genes was greater than 0.5, and the ratios of the rest were less than 0.5. Between \u003cem\u003eG. hirsutum\u003c/em\u003e and \u003cem\u003eG. raimondii\u003c/em\u003e, the Ka/Ks ratios of all homologous gene pairs were less than 0.5.\u003c/p\u003e \u003cp\u003eBetween diploid \u003cem\u003eG. arboreum\u003c/em\u003e and \u003cem\u003eG. arboreum\u003c/em\u003e, as well as between \u003cem\u003eG. raimondii\u003c/em\u003e and \u003cem\u003eG. raimondii\u003c/em\u003e, the Ka/Ks ratios of all homologous gene pairs were less than 0.5. Between tetraploid \u003cem\u003eG. barbadense\u003c/em\u003e and \u003cem\u003eG. barbadense\u003c/em\u003e, between \u003cem\u003eG. hirsutum\u003c/em\u003e and \u003cem\u003eG. hirsutum\u003c/em\u003e, and between \u003cem\u003eG. hirsutum\u003c/em\u003e and \u003cem\u003eG. barbadense\u003c/em\u003e, the Ka/Ks ratios of all homologous gene pairs were less than 1.\u003c/p\u003e \u003cp\u003eIn summary, among the four \u003cem\u003eGossypium\u003c/em\u003e species, the vast majority of GT92 genes have experienced strong purifying selection during evolution, while a few homologous gene pairs exhibit positive selection effects.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eExpression patterns of in \u003c/h3\u003e\n\u003cdiv class=\"Heading\"\u003eExpression patterns of \u003cem\u003eGbGT92s\u003c/em\u003e in \u003cem\u003eG. barbadense\u003c/em\u003e\u003c/div\u003e \u003cp\u003eWe carried out an analysis of the expression levels of \u003cem\u003eGT92\u003c/em\u003e genes across various tissues and in the course of fiber development within \u003cem\u003eG. barbadense\u003c/em\u003e [23]. It was found that the expression level of \u003cem\u003eGbGT92_12\u003c/em\u003e was higher than that of other genes in the bract and pental (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB); the expression level of \u003cem\u003eGbGT92_13\u003c/em\u003e was greater compared to that of other genes in the filament; the expression level of \u003cem\u003eGbGT92_5\u003c/em\u003e was superior to that of other genes in the leaf and stem. The expression level of \u003cem\u003eGbGT92_4\u003c/em\u003e was more elevated than that of other genes in the pistil; the expression level of \u003cem\u003eGbGT92_5\u003c/em\u003e was higher in comparison to that of other genes in the root; the expression level of \u003cem\u003eGbGT92_10\u003c/em\u003e was greater than that of other genes in the sepal and torus. At the same time, the expression levels of \u003cem\u003eGbGT92_3\u003c/em\u003e and \u003cem\u003eGbGT92_4\u003c/em\u003e in the ovule at different stages were higher than those in the fiber at the same stage. Intriguingly, the expression intensity of \u003cem\u003eGbGT92_7\u003c/em\u003e in the fiber was more substantial than that in the ovule. It is worthy of emphasis that when contrasted with other genes, the expression intensity of \u003cem\u003eGbGT92_11\u003c/em\u003e surged rapidly as time progressed in the early phase of ovule development, yet plummeted abruptly at 3 days post-anthesis (DPA), and then began to increase again at 10 DPA, suggesting that this gene might regulate the development of fuzz fiber in \u003cem\u003eG. barbadense\u003c/em\u003e. The expression intensity of \u003cem\u003eGbGT92_7\u003c/em\u003e was greater than that of other genes during several stages of fiber development, attaining its peak value at 20 DPA (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eC). To sum up, the expression levels of \u003cem\u003eGbGT92\u003c/em\u003e genes display a remarkable degree of specificity in the tissues of \u003cem\u003eG. barbadense\u003c/em\u003e.\u003c/p\u003e \u003cp\u003eSubsequently, we made utilization of the RNA-seq data of \u003cem\u003eG. barbadense\u003c/em\u003e to examine the alterations in gene expression levels when subjected to cold, heat, salt, and PEG stresses [23]. We discovered (as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eD) that in the face of cold stress, the expression levels of \u003cem\u003eGbGT92_4\u003c/em\u003e, \u003cem\u003eGbGT92_5\u003c/em\u003e, and \u003cem\u003eGbGT92_11\u003c/em\u003e underwent notable changes in comparison to the control group. The changes in \u003cem\u003eGbGT92_4\u003c/em\u003e mainly occurred in the early and late stages, while those in \u003cem\u003eGbGT92_5\u003c/em\u003e and \u003cem\u003eGbGT92_11\u003c/em\u003e mainly occurred in the late stage. Under hot stress, the expression levels of \u003cem\u003eGbGT92_4\u003c/em\u003e, \u003cem\u003eGbGT92_5\u003c/em\u003e, and \u003cem\u003eGbGT92_12\u003c/em\u003e changed significantly compared with the control, and the changes mainly occurred at 1 h, 3 h, and 6 h, all reaching the highest at 3 h. Under PEG stress, the expression level of \u003cem\u003eGbGT92_10\u003c/em\u003e changed significantly compared with the control, mainly occurring in the middle and late stages of the stress. As the stress time increased, the expression level gradually increased and reached the highest at 24 h. Under salt stress, the expression levels of \u003cem\u003eGbGT92_4\u003c/em\u003e, \u003cem\u003eGbGT92_11\u003c/em\u003e, and \u003cem\u003eGbGT92_12\u003c/em\u003e changed significantly compared with the control, mainly occurring in the early stage of the stress. The expression level of \u003cem\u003eGbGT92_11\u003c/em\u003e was the highest among all genes in all stages of the stress, showing a trend of first decreasing and then increasing. The expression trends of the other two genes were the same as that of \u003cem\u003eGbGT92_11\u003c/em\u003e. Subsequently, We additionally employed the root transcriptome data of the disease-prone variety 3\u0026ndash;79 and the disease-resistant variety S6 of \u003cem\u003eG. barbadense\u003c/em\u003e, which were obtained 11 days subsequent to inoculation [30], to investigate the variations in the expression levels of GT92 family components. The findings indicated (as depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eE) that the expression levels of the majority of \u003cem\u003eGT92\u003c/em\u003e genes did not exhibit substantial changes prior to and following the infection. Nevertheless, the changes in the expression levels of \u003cem\u003eGbGT92_5\u003c/em\u003e, \u003cem\u003eGbGT92_11\u003c/em\u003e, \u003cem\u003eGbGT92_12\u003c/em\u003e, and \u003cem\u003eGbGT92_13\u003c/em\u003e were markedly distinct between the disease-resistant and disease-prone materials, signifying that these four genes play a crucial role in the FOV resistance of \u003cem\u003eG. barbadense\u003c/em\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eWGCNA of \u003cem\u003eGT92\u003c/em\u003e members in \u003cem\u003eG. hirsutum\u003c/em\u003e\u003c/h2\u003e \u003cp\u003eAlthough in-depth studies have been conducted on the evolutionary relationships, chromosomal locations, sequence structures, collinearity analyses, selection pressure analyses, and expression patterns of the GT92 gene family, their potential roles in the response of \u003cem\u003eG. hirsutum\u003c/em\u003e to drought stress remain unclear. We collected publicly available transcriptome data on drought stress [23] (from \u003cem\u003eG. hirsutum\u003c/em\u003e TM-1 materials before and after drought stress at 0 h, 1 h, 3 h, 6 h, 12 h, and 24 h). Finally, we sifted through and identified 15,127 differentially expressed genes with FPKM values above 1 for conducting WGCNA analysis.\u003c/p\u003e \u003cp\u003eBy using the dynamic shear tree method for the weight values to merge modules with similar expressions, a total of 24 modules were obtained in the \u003cem\u003eG. hirsutum\u003c/em\u003e material TM-1 (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eA). The turquoise module encompassed the greatest quantity of genes, consisting of 2,110 genes. The blue module ranked second, having 1,831 genes, and the darkturquoise module harbored the least number of genes, merely 35 genes. On average, each module contained 630 genes (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eB).\u003c/p\u003e \u003cp\u003eWithin the TM-1 material, based on the standards of absolute correlation coefficient (|r|) greater than 0.55 and \u003cem\u003ep\u003c/em\u003e-value less than 0.05, core modules were selected (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eC). Among these, it was discovered that the MEtan module exhibited a significant negative correlation with the state 6 hours subsequent to drought stress; the MEbrown module showed a significant negative correlation with the condition 24 hours after drought stress.\u003c/p\u003e \u003cp\u003eIt is worthy of emphasis that among the 14 members of the GT92 family in \u003cem\u003eG. hirsutum\u003c/em\u003e, the genes \u003cem\u003eGhGT92_5\u003c/em\u003e and \u003cem\u003eGhGT92_6\u003c/em\u003e are part of the MEtan module, while \u003cem\u003eGhGT92_7\u003c/em\u003e is a constituent of the MEbrown module. The MEtan module was predominantly enriched in pathways like Endocytosis, Amino sugar and nucleotide sugar metabolism, Inositol phosphate metabolism, Fructose and mannose metabolism, and Fatty acid degradation (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eD). We uncovered that this core module might respond to drought stress in \u003cem\u003eG. hirsutum\u003c/em\u003e via several metabolic pathways including Amino sugar and nucleotide sugar metabolism, Inositol phosphate metabolism, and Fructose and mannose metabolism. Several GT92 genes showed 2-fold upregulation under drought stress (e.g., \u003cem\u003eGhGT92_5\u003c/em\u003e, \u003cem\u003eGhGT92_6\u003c/em\u003e). These genes are predicted to modify cell wall polysaccharides (xyloglucan and pectin), potentially maintaining cell wall integrity during water deficit.\u003c/p\u003e \u003cp\u003eNext, we analyzed the interaction networks of \u003cem\u003eGhGT92_5\u003c/em\u003e and \u003cem\u003eGhGT92_6\u003c/em\u003e, with a weight value of 0.02 as the standard (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eE). From this module, 121 genes interacting with \u003cem\u003eGhGT92_5\u003c/em\u003e, 124 genes interacting with \u003cem\u003eGhGT92_6\u003c/em\u003e, 28 genes interacting with \u003cem\u003eGhGT92_12\u003c/em\u003e, and one gene interacting with \u003cem\u003eGhGT92_11\u003c/em\u003e were selected to construct an interaction network (Supplementary Table S4). We conducted KEGG analyses on the genes interacting with \u003cem\u003eGhGT92_5\u003c/em\u003e and \u003cem\u003eGhGT92_6\u003c/em\u003e respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eF-G). We unearthed that these genes were principally abundant in certain metabolite production pathways, such as Glycan biosynthesis and metabolism, Carbohydrate metabolism, Metabolism of terpenoids and polyketides, Glycan biosynthesis and metabolism, Transport and catabolism, etc. We speculate that when subjected to drought stress, they may resist through the accumulation of some sugars.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eAnalysis of \u003cem\u003eGT92\u003c/em\u003e gene sets based on Mfuzz trend\u003c/h2\u003e \u003cp\u003eNext, we utilized the Mfuzz clustering method to further analyze the 42,214 differential genes speciested from 18 samples at six time-points. The results showed (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eA) that a total of 15 clusters were speciested. Cluster3 harbored the greatest quantity of genes, amounting to 7,878 genes, and Cluster12 encompassed the smallest number of genes, reaching 582 genes. Among them, the GT92 family members were divided into eight clusters. Specifically, \u003cem\u003eGhGT92_13\u003c/em\u003e was assigned to Cluster2, \u003cem\u003eGhGT92_1\u003c/em\u003e, \u003cem\u003eGhGT92_3\u003c/em\u003e, and \u003cem\u003eGhGT92_9\u003c/em\u003e were assigned to Cluster3, \u003cem\u003eGhGT92_2\u003c/em\u003e and \u003cem\u003eGhGT92_7\u003c/em\u003e were assigned to Cluster7, \u003cem\u003eGhGT92_11\u003c/em\u003e and \u003cem\u003eGhGT92_10\u003c/em\u003e were assigned to Cluster9 and Cluster11 respectively, \u003cem\u003eGhGT92_5\u003c/em\u003e and \u003cem\u003eGhGT92_4\u003c/em\u003e were assigned to Cluster13 and Cluster15 respectively, and \u003cem\u003eGhGT92_6\u003c/em\u003e and \u003cem\u003eGhGT92_12\u003c/em\u003e were assigned to Cluster14.\u003c/p\u003e \u003cp\u003eIt is worth noting that Cluster2, Cluster3, and Cluster11 had similar trends. Their expression trends were relatively gentle, without significant fluctuations. Cluster7 and Cluster15 showed a trend of first decreasing, then increasing, and finally decreasing. Cluster10 exhibited a tendency of initially rising and subsequently declining. Cluster13 and Cluster14 had similar expression patterns, both showing a trend of first decreasing and then increasing. Through GO enrichment analysis, for Cluster13, in terms of Cellular Component, it was mainly enriched in the extracellular region; in terms of Biological Process, it was mainly enriched in the oxidation-reduction process, sulfur utilization, and regulation of sulfur utilization; in terms of Molecular Function, it was mainly enriched in pathways such as oxaloacetate transmembrane transporter activity, hydrolase activity, hydrolyzing O-glycosyl compounds, and hydrolase activity, acting on ester bonds (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eB). For Cluster14, in terms of Cellular Component, it was mainly enriched in the cell wall, external encapsulating structure, and intrinsic component of the membrane; in terms of Biological Process, it was mainly enriched in cell wall organization or biogenesis, cinnamic acid biosynthetic process, and cinnamic acid metabolic process; from the perspective of Molecular Function, it was predominantly enriched in pathways like catalytic prowess, hydrolase potency, operating on glycosyl linkages, and hydrolase potency, cleaving O-glycosyl substances (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eC).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eSubcellular localization analysis of \u003cem\u003eGhGT92s\u003c/em\u003e\u003c/h2\u003e \u003cp\u003eIn order to determine the subcellular positioning of \u003cem\u003eGhGT92_5\u003c/em\u003e and \u003cem\u003eGhGT92_6\u003c/em\u003e, we transiently expressed green fluorescent protein (GFP)-\u003cem\u003eGhGT92_5\u003c/em\u003e, (GFP)-\u003cem\u003eGhGT92_6\u003c/em\u003e, red fluorescent signals, and isolated green fluorescent protein (GFP) in tobacco epidermal cells. The results showed (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e) that the green fluorescent protein (GFP) fluorescence of \u003cem\u003eGhGT92_5\u003c/em\u003e was mainly localized on the plasma membrane, and the red fluorescent signal was mainly localized in the chloroplasts; the green fluorescent protein (GFP) fluorescence of \u003cem\u003eGhGT92_6\u003c/em\u003e was mainly localized in the cytoplasm, and the red fluorescent signal was mainly localized in the chloroplasts. These results indicate that \u003cem\u003eGhGT92_5\u003c/em\u003e is mainly distributed on the plasma membrane and in the chloroplasts, and \u003cem\u003eGhGT92_6\u003c/em\u003e is mainly localized in the cytoplasm and on the chloroplasts.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eqRT-PCR analysis of \u003cem\u003eGhGT92s\u003c/em\u003e under salt stress and drought stress\u003c/h2\u003e \u003cp\u003eOur prior investigation indicated that the expression of certain \u003cem\u003eGhGT92s\u003c/em\u003e altered markedly under stress circumstances. Drawing upon the antecedent analysis, we postulated that \u003cem\u003eGhGT92_5\u003c/em\u003e and \u003cem\u003eGhGT92_6\u003c/em\u003e could be implicated in the response to abiotic stress situations. We carried out an expression pattern analysis on three \u003cem\u003eG. hirsutum\u003c/em\u003e varieties featuring extreme traits to ascertain whether these genes are engaged in the stress response procedure.\u003c/p\u003e \u003cp\u003eThe results showed that after PEG-simulated drought stress (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003eA-B), the transcriptional levels of the two materials with extreme characteristics were stimulated at various time intervals, suggesting that \u003cem\u003eGhGT92_5\u003c/em\u003e and \u003cem\u003eGhGT92_6\u003c/em\u003e could be involved in the response of \u003cem\u003eG. hirsutum\u003c/em\u003e to drought scenarios. \u003cem\u003eGhGT92_5\u003c/em\u003e demonstrated a pattern of initially being downregulated and then upregulated, and there was a notable disparity in expression levels at 6 hours and 12 hours between the drought-sensitive and drought- tolerant materials. \u003cem\u003eGhGT92_6\u003c/em\u003e likewise exhibited a significant difference in expression levels at 6 hours and 12 hours between the drought-sensitive and drought-tolerant materials. This implies that these two genes may have a certain function in the response of \u003cem\u003eG. hirsutum\u003c/em\u003e to drought stress.\u003c/p\u003e \u003cp\u003eIn addition, we also studied the expression of the two genes under salt stress (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003eC-D). Both \u003cem\u003eGhGT92_5\u003c/em\u003e and \u003cem\u003eGhGT92_6\u003c/em\u003e showed a trend of first down-regulation, then up-regulation, then down-regulation, and subsequently up-regulation. In the salt-sensitive and salt-tolerant materials, \u003cem\u003eGhGT92_5\u003c/em\u003e had a significant difference in expression levels in the middle stage of stress, and \u003cem\u003eGhGT92_6\u003c/em\u003e had a significant difference in expression levels in the late stage of stress. This also indicates that these two genes may play a certain role in the response of \u003cem\u003eG. hirsutum\u003c/em\u003e to salt stress.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eDrought significantly reduces the productivity of cotton crops. The impact of drought stress on the growth, development, and yield of cotton is worsening [31]. The development of drought-tolerant genotypes is an urgent need in the current era. To develop drought-tolerant genotypes, we must understand the drought resistance mechanisms of plants. Following the accomplishment of cotton genome sequencing, it has furnished substantial convenience for scientific investigators, allowing them to carry out profound explorations on the evolution and functional examination of diverse gene families. Over the past few years, numerous families in cotton have been investigated, for instance \u003cem\u003eGhRab7\u003c/em\u003e [32], \u003cem\u003eGhMDVL\u003c/em\u003e [33], \u003cem\u003eGhHIT4\u003c/em\u003e [34], \u003cem\u003eGbSOT\u003c/em\u003e [35], \u003cem\u003eGhGT92\u003c/em\u003e [36], \u003cem\u003eGhCOBRA\u003c/em\u003e [37], \u003cem\u003eGhRF2\u003c/em\u003e [38], \u003cem\u003eGhGATL\u003c/em\u003e [39], \u003cem\u003eGhSMXL\u003c/em\u003e [40], and \u003cem\u003eGhWOX\u003c/em\u003e [41].\u003c/p\u003e \u003cp\u003eBased on the previous transcriptome sequencing results [23], it was found that \u003cem\u003eGH_A09G1400\u003c/em\u003e (\u003cem\u003eGhGT92_5\u003c/em\u003e) had a significant difference in expression level when subjected to drought stress, and we speculate that this gene is related to drought resistance. Subsequently, we executed an all-encompassing study on this gene family within four \u003cem\u003eGossypium\u003c/em\u003e species, making use of a variety of techniques. These encompassed phylogenetic exploration, gene structure scrutiny, protein motif dissection, chromosomal mapping, gene duplication examination, and collinearity evaluation. Moreover, we additionally scrutinized the \u003cem\u003ecis\u003c/em\u003e- element analysis, tissue-specific expression patterns, and the reactions of \u003cem\u003eGT92\u003c/em\u003e genes under abiotic stress conditions. Then, we conducted subcellular localization studies of \u003cem\u003eGhGT92_5\u003c/em\u003e and \u003cem\u003eGhGT92_6\u003c/em\u003e in tobacco and studies on their expression levels under drought and salt stress.\u003c/p\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eInitial analysis of GT92 family components\u003c/h2\u003e \u003cp\u003eBased on the published reference genome information of four cotton species, we identified 44 \u003cem\u003eGT92\u003c/em\u003e genes in four cotton species. Subsequently, we conducted an assessment of the physicochemical properties of the amino-acid sequences of the GT92 gene family constituents in the four cotton species. The results indicated that the mean length of amino acids in GT92 family members was 537 amino acids, and the average molecular mass was 61.33 kDa. The average isoelectric point (pI) value was 9.04, which implies that these proteins are alkaline in nature. After that, we constructed a phylogenetic dendrogram, and the outcomes demonstrated that the diploid cotton invariably grouped together with the tetraploid cotton. This further validated the hypothesis that \u003cem\u003eG. hirsutum\u003c/em\u003e and \u003cem\u003eG. barbadense\u003c/em\u003e stemmed from the cross-breeding of \u003cem\u003eG. arboreum\u003c/em\u003e and \u003cem\u003eG. raimondii\u003c/em\u003e [24]. In terms of chromosomal apportionment, the distribution pattern of \u003cem\u003eGT92\u003c/em\u003e genes in diploid and tetraploid cotton species was approximately the same.\u003c/p\u003e \u003cp\u003eSubsequently, we performed an extensive synteny analysis across the four cotton species. We speculated that the principal factors driving gene expansion during the evolution of GT92 family genes were whole-genome duplication instances and segmental duplication happenings. In the meantime, we determined the Ka/Ks values of \u003cem\u003eGT92\u003c/em\u003e homologous genes. Among the four cotton species, only a single pair of homologous genes had a value greater than 1, while the values of the others were less than 1. Among them, \u003cem\u003eGH_A13G0136\u003c/em\u003e (Ka/Ks\u0026thinsp;=\u0026thinsp;1.23) was found to be twice as expressed as the control gene under 24 h cold stress, suggesting adaptive evolution under cold conditions. In addition, the ka/ks ratio between \u003cem\u003eGH_A13G0136\u003c/em\u003e and \u003cem\u003eGa13G0142\u003c/em\u003e was 1.23. Meanwhile, the expression level of \u003cem\u003eGH_A13G0136\u003c/em\u003e in leaves was 14, and that of \u003cem\u003eGa13G0142\u003c/em\u003e in leaves was 7. The expression level of \u003cem\u003eGH_A13G0136\u003c/em\u003e in leaves was twice that of \u003cem\u003eGa13G0142\u003c/em\u003e. Positive selection may result in functional differentiation of \u003cem\u003eGH_A13G0136\u003c/em\u003e and \u003cem\u003eGa13G0142\u003c/em\u003e to some extent. Higher expression levels may enable \u003cem\u003eGH_A13G0136\u003c/em\u003e to produce more protein products in the leaves, thus playing a more significant role in the physiological process of the leaves. The great majority of \u003cem\u003eGT92\u003c/em\u003e genes experienced robust purifying selection over the course of evolution, and a limited number of homologous gene pairs showed positive selection consequences [42]. Our collinearity analysis reveals the functional evolution pattern of the GT92 gene family: conserved collinearity regions (e.g., \u003cem\u003eGhGT92_3\u003c/em\u003e) retain ancestral cell wall synthesis by maintaining gene neighborhood and dose balance; Non-collinear copies (e.g., \u003cem\u003eGhGT92_2\u003c/em\u003e) acquire new regulatory elements through genome rearrangement, which may adapt to the special needs of cotton growth and development. In addition, we predicted the \u003cem\u003ecis\u003c/em\u003e-regulatory elements of \u003cem\u003eGT92\u003c/em\u003e genes. In particular, these were \u003cem\u003ecis\u003c/em\u003e-regulatory elements associated with phytohormones, for example, abscisic acid-responsive elements, salicylic acid-responsive elements, MeJA-responsive elements, and auxin-sensitive elements. Moreover, it also included \u003cem\u003ecis\u003c/em\u003e-regulatory elements participating in defense and stress reactions as well as \u003cem\u003ecis\u003c/em\u003e-regulatory elements related to cold-temperature responses. ABRE (ABA-responsive element) and MYB/MYC binding sites were significantly enriched in GT92 promoters (e.g., \u003cem\u003eGhGT92_3\u003c/em\u003e, \u003cem\u003eGhGT92-5\u003c/em\u003e and \u003cem\u003eGhGT92_6\u003c/em\u003e), suggesting their roles in drought stress responses via ABA and JA signaling pathways. Via the analysis of promoters, this will aid us in validating subsequent gene functionalities [43].\u003c/p\u003e \u003cp\u003eThen, we used qRT-PCR technology to verify the expression patterns of \u003cem\u003eGhGT92\u003c/em\u003e genes under drought and salt stress, and the results indicated that \u003cem\u003eGhGT92_5\u003c/em\u003e and \u003cem\u003eGhGT92_6\u003c/em\u003e might be involved in the response to drought and salt stress conditions.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eExpression pattern analysis of GT92 family members\u003c/h2\u003e \u003cp\u003eThe expression patterns of genes are intimately associated with their functionalities. First, we utilized the transcriptome data of tissues in \u003cem\u003eG. hirsutum\u003c/em\u003e [23]. The results showed that \u003cem\u003eGhGT92_6\u003c/em\u003e was specifically expressed in the leaf, stamen, calycle, and torus; \u003cem\u003eGhGT92_13\u003c/em\u003e was specifically expressed in the pistil. In addition, the expression levels of \u003cem\u003eGhGT92_2\u003c/em\u003e, \u003cem\u003eGhGT92_8\u003c/em\u003e, \u003cem\u003eGhGT92_13\u003c/em\u003e, and \u003cem\u003eGhGT92_14\u003c/em\u003e fluctuated greatly during the development of the seed with the change of time. During the development of the cotyledon, the expression levels of \u003cem\u003eGhGT92_6\u003c/em\u003e and \u003cem\u003eGhGT92_12\u003c/em\u003e both reached the highest value at 72 h. In addition, some \u003cem\u003eGhGT92\u003c/em\u003e genes had specific expressions during the development of the root. Besides, some \u003cem\u003eGhGT92\u003c/em\u003e genes were also specifically expressed during the development of the fiber. For example, the expression level of \u003cem\u003eGhGT92_8\u003c/em\u003e in the ovule was lower than that in the fiber. Interestingly, we also found that \u003cem\u003eGhGT92_8\u003c/em\u003e might control the change in lint percentage (LP) during the fiber development of \u003cem\u003eG. hirsutum\u003c/em\u003e. The expression level of \u003cem\u003eGhGT92_5\u003c/em\u003e in high oil content materials was significantly higher than that in low oil content materials, indicating that this gene might control the oil content of cotton materials. According to the transcriptome data, it was found that \u003cem\u003eGhGT92_6\u003c/em\u003e, \u003cem\u003eGhGT92_14\u003c/em\u003e, \u003cem\u003eGhGT92_12\u003c/em\u003e, and \u003cem\u003eGhGT92_5\u003c/em\u003e played important roles in the tolerance of cotton to flood abiotic stress and in controlling the plant height of \u003cem\u003eG. hirsutum\u003c/em\u003e [27\u0026ndash;28]. Moreover, under cold, polyethylene glycol (PEG), heat, and salt stress conditions, the expression intensities of \u003cem\u003eGhGT92_5\u003c/em\u003e and \u003cem\u003eGhGT92_6\u003c/em\u003e showed marked variations when compared with the control set. This implies that these two genes may be involved in the reaction to the previously mentioned four kinds of abiotic stresses. We also based on the transcriptome data of cotton roots after inoculation with \u003cem\u003eVerticillium dahliae\u003c/em\u003e and found that after being infected by \u003cem\u003eVerticillium dahliae\u003c/em\u003e, the expression levels of \u003cem\u003eGhGT92_6\u003c/em\u003e, \u003cem\u003eGhGT92_12\u003c/em\u003e, and \u003cem\u003eGhGT92_13\u003c/em\u003e changed significantly compared with the control, indicating that the above genes might be involved in the response of cotton to \u003cem\u003eVerticillium dahliae\u003c/em\u003e treatment in the middle and late stages [23]. Subsequently, we found that nearly half of the \u003cem\u003eGhGT92\u003c/em\u003e genes were involved in the response of cotton to TDZ treatment.\u003c/p\u003e \u003cp\u003eThen, using the transcriptome data of \u003cem\u003eG. barbadense\u003c/em\u003e, we uncovered that certain \u003cem\u003eGbGT92\u003c/em\u003e genes were distinctly expressed in particular tissues of \u003cem\u003eG. barbadense\u003c/em\u003e. For example, there were \u003cem\u003eGT92\u003c/em\u003e genes with exclusive expressions in bract, petal, filament, leaf, stem, pistil, sepal, and torus tissues. Additionally, we noticed that the expression levels of \u003cem\u003eGbGT92_3\u003c/em\u003e and \u003cem\u003eGbGT92_4\u003c/em\u003e in the ovule at various time points were higher than those in the fiber at the same time intervals; the expression level of \u003cem\u003eGbGT92_7\u003c/em\u003e in the fiber was greater than that in the ovule. Subsequently, we utilized the RNA-seq data of \u003cem\u003eG. barbadense\u003c/em\u003e to examine the alterations in gene expression levels under cold, heat, salt, and polyethylene glycol (PEG) stress conditions [23]. We found that under the conditions of four abiotic stresses, \u003cem\u003eGbGT92_4\u003c/em\u003e, \u003cem\u003eGbGT92_5\u003c/em\u003e, \u003cem\u003eGbGT92_10\u003c/em\u003e, \u003cem\u003eGbGT92_11\u003c/em\u003e, and \u003cem\u003eGbGT92_12\u003c/em\u003e had specific expressions, indicating that the above genes, like \u003cem\u003eG. hirsutum\u003c/em\u003e, participated in the response to the above four abiotic stresses. Finally, we also found that \u003cem\u003eGbGT92_5\u003c/em\u003e, \u003cem\u003eGbGT92_11\u003c/em\u003e, \u003cem\u003eGbGT92_12\u003c/em\u003e, and \u003cem\u003eGbGT92_13\u003c/em\u003e played important roles in the FOV resistance of \u003cem\u003eG. barbadense\u003c/em\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eWGCNA and Mfuzz analysis of \u003cem\u003eGhGT92s\u003c/em\u003e under drought stress\u003c/h2\u003e \u003cp\u003eWe used differentially expressed genes for WGCNA analysis. We found that the genes in the MEtan module were significantly negatively correlated with the middle stage of drought stress, and the MEbrown module was significantly negatively correlated with the later stage of drought stress. It is worth noting that the genes \u003cem\u003eGhGT92_5\u003c/em\u003e and \u003cem\u003eGhGT92_6\u003c/em\u003e of \u003cem\u003eG. hirsutum\u003c/em\u003e belonged to the MEtan module. Among them, the MEtan module might respond to the drought stress mechanism of \u003cem\u003eG. hirsutum\u003c/em\u003e through some metabolic pathways such as Amino sugar and nucleotide sugar metabolism, Inositol phosphate metabolism, and Fructose and mannose metabolism. According to the results of KEGG enrichment analysis, the gene interacting with the core gene \u003cem\u003eGhGT92_6\u003c/em\u003e is related to Amino sugar and nucleotide sugar metabolism and Endocytosis pathway, and this core gene may catalyze glycosyl-transfer reaction. Participate in cell wall polysaccharide synthesis and indirectly affect the membrane localization of glycosylated products; In addition, the core \u003cem\u003eGhGT92_5\u003c/em\u003e interacting gene is also related to Inositol phosphate metabolism, and glycosylation modification of inositol derivatives by this gene may affect its signaling function.\u003c/p\u003e \u003cp\u003e\u003cem\u003eGH_A06G1824\u003c/em\u003e is a member of the tan module and belongs to the Glycosyl transferase family 8. According to previous studies [44], the double mutation of \u003cem\u003eGAUT10\u003c/em\u003e and \u003cem\u003eGAUT11\u003c/em\u003e reduced pectin synthesis, promoted the demethylation of homogalacturonic acid (HG) and the degradation of demethylated HG, resulting in larger stomatal complexes, smaller stomatal areas, enhanced stomatal dynamics, and enhanced plant drought tolerance. In addition, among the 121 genes interacting with \u003cem\u003eGhGT92_5\u003c/em\u003e, \u003cem\u003eGH_D05G1894\u003c/em\u003e is a member of the Thaumatin family. The transgenic \u003cem\u003eArabidopsis\u003c/em\u003e lines overexpressing \u003cem\u003eGhTLP19\u003c/em\u003e showed higher proline content, thicker and longer trichomes, and stronger drought tolerance [45]. In addition, some transcription factors play important roles in the process of plant drought resistance. For example, \u003cem\u003eGH_D08G1583\u003c/em\u003e is a member of the MYB family, and \u003cem\u003eGH_D08G0572\u003c/em\u003e is a \u003cem\u003eGRAS\u003c/em\u003e-type transcription factor. Under drought stress, \u003cem\u003eMYB\u003c/em\u003e transcription factors can play roles in various ways. They can regulate the composition and structure of the plant cell wall, enhance the toughness of the cell wall, and reduce cell wall damage caused by cell water loss due to drought. Meanwhile, \u003cem\u003eMYB\u003c/em\u003e transcription factors can also regulate the secondary metabolism of plants, such as regulating the synthesis of substances like anthocyanins. Anthocyanins have antioxidant effects and can help plants scavenge reactive oxygen species speciested by drought stress [46]. Abscisic acid (ABA) plays an important role in the plant's response to drought stress, and \u003cem\u003eGRAS\u003c/em\u003e transcription factors are involved in the ABA signaling pathway. For example, \u003cem\u003eZmGRAS72\u003c/em\u003e is involved in the biosynthesis and signaling of ABA. By regulating the expression of related genes, plants can better sense and respond to ABA signals under drought stress, thereby initiating a series of drought-resistant physiological responses [47]. In addition, among the genes interacting with \u003cem\u003eGhGT92_6\u003c/em\u003e, \u003cem\u003eGH_A12G0623\u003c/em\u003e belongs to the \u003cem\u003ebZIP53\u003c/em\u003e-type transcription factor. \u003cem\u003ePgbZIP53\u003c/em\u003e in ginseng can respond to drought stress signals. When ginseng is in a drought environment, the expression of the \u003cem\u003ePgbZIP53\u003c/em\u003e gene changes, indicating that it is involved in the perception and initial response process of plants to drought stress and may serve as a key factor in the drought signal transduction pathway to further transmit the drought signal and initiate the drought resistance mechanism in plants [48]. According to the analysis of the interaction network, \u003cem\u003eGhGT92_5\u003c/em\u003e interacts with the cellulose synthase gene \u003cem\u003eGH_A09G2544\u003c/em\u003e and the xyloglucosidase \u003cem\u003eGH_A10G1801\u003c/em\u003e, suggesting synergistic regulation of cell wall remodeling(Supplementary Table S4).\u003c/p\u003e \u003cp\u003eThrough Mfuzz analysis [49], genes or samples were clustered into different clusters based on expression patterns, and these clusters reflected different expression trends under specific time series. In this study, \u003cem\u003eGhGT92_5\u003c/em\u003e and \u003cem\u003eGhGT92_6\u003c/em\u003e were located in Cluster13 and Cluster14 respectively, and they had similar expression patterns, both showing a trend of first decreasing and then increasing. It means that the genes in these clusters involved in cell wall modification show a trend of first down-regulation and then up-regulation under drought stress, which may imply that there are dynamic changes in the cell wall during the process of plants adapting to drought stress. The down-regulation in the early stage may be to reduce the energy and material consumption required for cell wall synthesis, and the up-regulation in the later stage may be to enhance the strength and toughness of the cell wall to adapt to the water state changes of cells due to drought and prevent excessive water loss and mechanical damage of cells.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis research carried out an all- round analysis of the GT92 gene family in four cotton species. For the very first time, it executed bioinformatics investigations on the phylogenetic connections, gene architectures, expression profiles, evolutionary associations, and selection pressure assessments of \u003cem\u003eGT92\u003c/em\u003e members in \u003cem\u003eG. hirsutum\u003c/em\u003e. Furthermore, the expression patterns of GT92 family genes were elucidated by means of RNA-seq data. Through the WGCNA analysis method, it was found that \u003cem\u003eGhGT92_5\u003c/em\u003e and \u003cem\u003eGhGT92_6\u003c/em\u003e belong to the MEtan module, and this module is significantly negatively correlated with 6 h after drought stress. Through Mfuzz analysis, it was found that \u003cem\u003eGhGT92_5\u003c/em\u003e and \u003cem\u003eGhGT92_6\u003c/em\u003e belong to Cluster13 and Cluster14 respectively, and they have similar expression patterns, both showing a trend of first decreasing and then increasing. Through qRT-PCR technology, it was found that \u003cem\u003eGhGT92_5\u003c/em\u003e and \u003cem\u003eGhGT92_6\u003c/em\u003e may play certain roles under drought and salt stress conditions. The subcellular localization results showed that \u003cem\u003eGhGT92_5\u003c/em\u003e is mainly distributed on the plasma membrane and in the chloroplasts, and \u003cem\u003eGhGT92_6\u003c/em\u003e is mainly localized in the cytoplasm and on the chloroplasts. These discoveries broaden our comprehension of GT92 family constituents and establish a basis for subsequent research into the stress resistance mechanism of this gene within cotton.\u003c/p\u003e"},{"header":"Material and methods","content":"\u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003eIdentification of cotton GT92 gene family members\u003c/h2\u003e \u003cp\u003eThe reference genome and genome annotation information files of \u003cem\u003eG. arboreum\u003c/em\u003e (ICR), \u003cem\u003eG. raimondii\u003c/em\u003e (JGI), \u003cem\u003eG. hirsutum\u003c/em\u003e (ZJU), and \u003cem\u003eG. barbadense\u003c/em\u003e (ZJU) sourced from the CottonFGD (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://cottonfgd.org/\u003c/span\u003e\u003cspan address=\"https://cottonfgd.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) database [50]. The GT92 genes in the four cotton species were traced through Local BLASTP (v2.12.0), E-value cutoff (1e-5), applying the GT92 protein sequence of \u003cem\u003eArabidopsis thaliana\u003c/em\u003e. The detected GT92 genes were authenticated by means of the Hidden Markov Model (HMM) profiles retrieved from the Pfam (PF01697) database [51]. The domain details of the detected GT92 proteins were further validated via the NCBI Batch-CDD search. For the purpose of examining the physicochemical properties of GT92 genes, we conducted the analysis with the aid of TBtools-II software (v2.150) [52].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003eInvestigation into the chromosomal localizations and gene replication of \u003cem\u003eGT92s\u003c/em\u003e\u003c/h2\u003e \u003cp\u003eTo probe into the chromosomal localities of \u003cem\u003eGT92\u003c/em\u003e genes among four cotton species, the GFF3 files encompassing cotton genome annotation details were obtained from the CottonFGD database [50]. The physical chromosomal whereabouts of all \u003cem\u003eGT92\u003c/em\u003e genes in the four cotton species were rendered visible via the utilization of TBtools-II software (v2.150) [52]. Moreover, the genomic collinearity segments and gene replication were scrutinized using MCScanX software [53].\u003c/p\u003e \u003cdiv id=\"Sec23\" class=\"Section3\"\u003e \u003ch2\u003eConstruction of a phylogenetic tree of GT92 family proteins\u003c/h2\u003e \u003cp\u003eTo investigate the evolutionary interrelationships among the \u003cem\u003eGT92\u003c/em\u003e genes in four cotton species, we conducted multiple-sequence alignments on the 44 retrieved \u003cem\u003eGT92\u003c/em\u003e genes, employing MEGA (MEGA7) and ClustalW [54]. Drawing upon the comparison findings, a phylogenetic tree was assembled using the Maximum Likelihood (ML) technique, and the Bootstrap value was configured to 1000.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003eGene structure and conserved protein motif analysis of GT92 family genes\u003c/h2\u003e \u003cp\u003eAfterwards, we conducted a visual inspection by making use of TBtools-II software (version 2.150) on the MEME dataset, the NWK file derived from the phylogenetic tree scrutiny, and the GFF3 genome annotation dossier of \u003cem\u003eG. hirsutum\u003c/em\u003e TM-1 [52].\u003c/p\u003e \u003cdiv id=\"Sec25\" class=\"Section3\"\u003e \u003ch2\u003eExpression pattern and \u003cem\u003ecis\u003c/em\u003e-element analysis of GT92 family genes\u003c/h2\u003e \u003cp\u003eTo delve into the expression profiles of \u003cem\u003eGT92s\u003c/em\u003e, we fetched the transcriptomic datasets of TM- 1 and Hai7124 from public data banks. These datasets were harnessed to assess the expression of \u003cem\u003eGT92\u003c/em\u003e genes in disparate tissues and at varying developmental phases of fibers and ovules. Thereafter, the RNA-seq datasets of TM-1 and Hai7124 under cold, hot, salt, and PEG stresses were also downloaded [23].\u003c/p\u003e \u003cp\u003eTo conduct an initial inquiry into the role of gene expression modulation, we extracted the 2.0-kilobase sequence situated upstream of the initiation codon from the GT92 family genes of \u003cem\u003eG. hirsutum\u003c/em\u003e. This sequence was utilized as the promoter sequence for an analysis of \u003cem\u003ecis\u003c/em\u003e-regulatory elements. The \u003cem\u003ecis\u003c/em\u003e-regulatory elements in the promoter zone of \u003cem\u003eGT92\u003c/em\u003e genes were further examined by means of PlantCARE. Then, the information about the identified \u003cem\u003ecis\u003c/em\u003e-acting regulatory elements was made visually perceptible using TBtools-II software (version 2.150) [52].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec26\" class=\"Section3\"\u003e \u003ch2\u003eExamination of collinearity and estimation of selective pressure in GT92 family genes\u003c/h2\u003e \u003cp\u003eTo probe into the evolutionary associations and selection pressures of GT92 family genes across four cotton species and to identify collinear genes within the entire genome, all cotton protein sequences were subjected to a BLAST comparison using MCScanX software [53]. Eventually, a visual examination was carried out using TBtools-II software (v2.150) [52]. Moreover, the rates of non-synonymous substitution (Ka) and synonymous substitution (Ks) for duplicate genes were computed using TBtools-II software (v2.150) [52].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec27\" class=\"Section3\"\u003e \u003ch2\u003eWeighted gene co-expression network analysis\u003c/h2\u003e \u003cp\u003eWe retrieved the TM-1 drought stress transcriptomic dataset [23] for WGCNA exploration. Subsequently, we employed the WGCNA software suite within the R program [55] to build networks. Following threshold filtering, \u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;16 was ultimately chosen to conduct power transformation on the original scaled relationship matrix, thereby obtaining a scale-free adjacency matrix, merge cut height (0.25). The least number of genes within a module was set at 30 (minModuleSize\u0026thinsp;=\u0026thinsp;30).\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec28\" class=\"Section2\"\u003e \u003ch2\u003eGO, KEGG enrichment analysis and interaction network construction\u003c/h2\u003e \u003cp\u003eThe genes from the target module were put through KEGG and GO enrichment assays utilizing TBtools-II software (v2.150) [52]. The cut-off values were set as \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01 and Q\u0026thinsp;\u0026lt;\u0026thinsp;0.05. In the meantime, to obtain understandings of the potential interaction matrix of core genes, we calculated the Pearson's correlation index as the interaction intensity between target genes and candidate genes. The data fed from the outcomes of building the interaction network was visualized by means of Cytoscape (v.3.7.1) [56].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec29\" class=\"Section2\"\u003e \u003ch2\u003eTrend analysis based on Mfuzz cluster\u003c/h2\u003e \u003cp\u003eA time-dependent kinetic clustering examination of gene expression was carried out by applying the Mfuzz package within the R programming environment [49].\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eSubcellular localization of \u003c/h3\u003e\n\u003cdiv class=\"Heading\"\u003eSubcellular localization of \u003cem\u003eGhGT92s\u003c/em\u003e\u003c/div\u003e \u003cp\u003eTo observe the subcellular localization of \u003cem\u003eGhGT92_5\u003c/em\u003e and \u003cem\u003eGhGT92_6\u003c/em\u003e, the constructed plasmids were transferred into \u003cem\u003eAgrobacterium\u003c/em\u003e GV3101 and cultured at 30\u0026deg;C for two days. The bacterial suspension was injected into tobacco leaves, cultured under low light conditions for two days, and well-labeled. The tobacco leaves in the injection area were made into slides and observed and photographed under a confocal microscope. The GFP empty vector without \u003cem\u003eGhGT92_5\u003c/em\u003e and \u003cem\u003eGhGT92_6\u003c/em\u003e was used as a control.\u003c/p\u003e \u003cdiv id=\"Sec31\" class=\"Section2\"\u003e \u003ch2\u003eQuantitative RT-PCR technique\u003c/h2\u003e \u003cp\u003eWe ascertained the expression levels of \u003cem\u003eGhGT92_5\u003c/em\u003e and \u003cem\u003eGhGT92_6\u003c/em\u003e subsequent to drought and salt stress exposures. Additionally, under drought stress and salt stress conditions, expression analysis was performed on KK1543 (drought-resistant), XLZ26 (drought-sensitive), XLZ30 (salt-sensitive), and XLZ26 (salt-tolerant) materials. Seeds of KK1543, XLZ26, and XLZ30 were germinated at 28\u0026deg;C under a 16-hour light/8-hour dark regime and then transplanted into a normal hydroponic solution. Hoagland nutrient solution was applied every two days. KK1543 and XLZ26 were subjected to drought treatment using 15% PEG6000 at the two-leaf stage, while XLZ26 and XLZ30 were subjected to salt stress treatment using 150 mmol/L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003eNaCl.\u003c/p\u003e \u003cp\u003eRNA was extracted from leaves using TRIzol (Invitrogen), with DNase I treatment. cDNA synthesis used 1 \u0026micro;g RNA and PrimeScript RT reagent Kit (Takara). The experiment was executed with three biological repetitions and three technical reiterations, and amplification was conducted on the real-time fluorescence quantitative apparatus 7500. The relative expression levels of the genes were scrutinized using the 2\u003csup\u003e\u0026minus;ΔΔt\u003c/sup\u003e approach [57]. The primers utilized in this research are presented in Supplementary Table S5.\u003c/p\u003e \u003c/div\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eWGCNA: Weighted Gene Co-expression Network Analysis; WGD: Whole-genome duplicati-on; DPA: days post-anthesis; LP: lint percentage; TDZ: Thidiazuron;\u0026nbsp;\u003cem\u003eVerticillium dahliae\u003c/em\u003e:\u0026nbsp;\u003cem\u003eV. dahliae\u003c/em\u003e; FOV: \u003cem\u003eFusarium oxysporum\u0026nbsp;\u003c/em\u003ef. sp.\u003cem\u003e\u0026nbsp;Vasinfectum\u003c/em\u003e; FPKM: Fragments per kilobase of exon model per million mapped; MeJA: Methylj-asmonat-e; hpi: Hours post inoculation; Glycosyltransferases: GTs; Hidden Markov Model: HMM.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eSupplementary Information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe online version contains supplementary material available at XXX.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors are grateful to the laboratory members for help, advice and discussion.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eJY and WX designed the experiments and wrote the manuscript.XHJ, ZDW, JY performed most of the experiments. JY, ZZP, YLY, LYY, Aerman,\u0026nbsp;MQQ, ZTY, WMZ\u0026nbsp;assisted in the experiments, analyzed the data and discussed the results. All authors read and approved the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was funded by the Science and Technology Program Project of the Outstanding Youth Science Foundation in Xinjiang Uygur Autonomous Region (2022D01E58), the Xinjiang Academy of Agricultural Sciences Agricultural Science and technology innovation stability support project-The initial differentiation and regulatory network of \u003cem\u003eG. hirsutum\u003c/em\u003e fuzz fiber were studied based on scRNA-seq(xjnkywdzc-2025001-37).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll \u003cem\u003eGT92s\u003c/em\u003e sequence information is available in the Cotton Functional Genomics Database (CottonFGD) (https://cottonfgd.org/about/download.html). The data generated or analyzed during the current study are included in this published article and its supplemental data files and available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no conflict of interest.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003e Cosgrove DJ. Structure and growth of plant cell walls. Nat Rev Mol Cell Biol. 2024;25:340\u0026ndash;358.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e Atmodjo MA, Hao ZY, Mohnen D. Evolving Views of Pectin Biosynthesis. 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MEGA7: Molecular Evolutionary Genetics Analysis Version 7.0 for Bigger Datasets. Molecular Biology and Evolution. 2016;33(7):1870-4.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e Langfelder P, Horvath S. WGCNA: an R package for weighted correlation network analysis. BMC Bioinformatics. 2008;9:559.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT, Ramage D, et al. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 2003;13(11):2498\u0026thinsp;\u0026minus;\u0026thinsp;504.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e Livak KJ, Schmittgen TD. Analysis of relative gene expression data using real-time quantitative PCR and the 2\u003csup\u003e\u0026minus;∆∆CT\u003c/sup\u003e method. Methods. 2001;25(4): 402\u0026ndash;408.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-genomics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"gics","sideBox":"Learn more about [BMC Genomics](http://bmcgenomics.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/gics","title":"BMC Genomics","twitterHandle":"#BMCGenomics","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Cotton, GT92, WGCNA, Expression pattern, Mfuzz, Subcellular localization","lastPublishedDoi":"10.21203/rs.3.rs-5984925/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5984925/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e The \u003cem\u003eglycosyltransferase\u003c/em\u003e92 (GT92) gene belongs to the glycosyltransferase gene family. In the plant genome, it is one of the numerous genes involved in sugar metabolism and glycosylation modification. Nonetheless, the GT92 sub-family assumes a vital function in facilitating plants' adaptation to adverse environments and modulating plant growth, development, and the processes of organogenesis. To date, comprehensive characterization and systematic investigation of GT92 in cotton remain underexplored.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e \u0026nbsp;In this study, we systematically analyzed the structural features, phylogenetic tree, gene architecture, expression profiles, evolutionary relationships, and selective pressures of GT92 gene family members across four \u003cem\u003eGossypium\u003c/em\u003e species using bioinformatics approaches for the first time. Collectively, 44 GT92 genes were identified, including 14 in \u003cem\u003eG. hirsutum\u003c/em\u003e. Based on the phylogenetic tree, GT92 protein sequences from the four cotton species were clustered into five distinct subfamilies. Chromosomal mapping of these genes was performed, and their structural details were visualized. We further predicted \u003cem\u003ecis\u003c/em\u003e-acting elements in \u003cem\u003eG. hirsutum\u003c/em\u003eGT92 genes and characterized duplication patterns across the four \u003cem\u003eGossypium\u003c/em\u003especies. Ka/Ks ratios of orthologous gene pairs were calculated to investigate selective pressures among the species. RNA-seq data from \u003cem\u003eG. hirsutum\u003c/em\u003e and \u003cem\u003eG. barbadense\u003c/em\u003e revealed GT92 expression patterns. WGCNA identified \u003cem\u003eGhGT92_5\u003c/em\u003eand \u003cem\u003eGhGT92_6\u003c/em\u003e as members of the MEtan module, which was significantly negatively correlated with the 6-hour time point post-drought stress. Mfuzz clustering classified \u003cem\u003eGhGT92_5\u003c/em\u003e and \u003cem\u003eGhGT92_6\u003c/em\u003e into Cluster13 and Cluster14, respectively. qRT-PCR validated their roles under drought and salt stress conditions. Subcellular localization showed \u003cem\u003eGhGT92_5\u003c/em\u003e primarily distributed in the plasma membrane and chloroplasts, while \u003cem\u003eGhGT92_6\u003c/em\u003e was localized in the cytoplasm and chloroplasts.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions\u003c/strong\u003e All of these findings have expanded our understanding of the GT92 family members, establishing a basis for more in-depth exploration of the stress-tolerance mechanisms of this gene in cotton.\u003c/p\u003e","manuscriptTitle":"Genome-wide Ascertainment and Initial Functional Characterization and Expression Pattern Dissection of the GT92 Gene Family in Cotton","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-10 14:42:16","doi":"10.21203/rs.3.rs-5984925/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-06-04T15:15:26+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-06-04T10:03:26+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"83744461690647234157640128064637142098","date":"2025-06-01T10:28:50+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"43290603991763180179506531424793302340","date":"2025-06-01T07:42:00+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-04-11T08:13:42+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"196703346009975015404956772678320868561","date":"2025-04-06T06:55:43+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-04-06T06:38:26+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-04-04T05:16:12+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Genomics","date":"2025-04-02T11:45:35+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-genomics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"gics","sideBox":"Learn more about [BMC Genomics](http://bmcgenomics.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/gics","title":"BMC Genomics","twitterHandle":"#BMCGenomics","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"a77d2603-b21f-4054-a356-297bad437d7c","owner":[],"postedDate":"April 10th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-10-13T16:06:43+00:00","versionOfRecord":{"articleIdentity":"rs-5984925","link":"https://doi.org/10.1186/s12864-025-12034-6","journal":{"identity":"bmc-genomics","isVorOnly":false,"title":"BMC Genomics"},"publishedOn":"2025-10-09 15:57:47","publishedOnDateReadable":"October 9th, 2025"},"versionCreatedAt":"2025-04-10 14:42:16","video":"","vorDoi":"10.1186/s12864-025-12034-6","vorDoiUrl":"https://doi.org/10.1186/s12864-025-12034-6","workflowStages":[]},"version":"v1","identity":"rs-5984925","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5984925","identity":"rs-5984925","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}