1. Introduction
Climate change is a major cause of abiotic and biotic stresses that may adversely affect agricultural productivity to irreversible levels, thereby limiting production growth and jeopardizing sustainable agriculture globally [1]. It is a major cause of biotic and abiotic stresses that negatively affect global agricultural production and productivity [2]. It exacerbates drought conditions around the world, with significant implications for water availability and agricultural systems. It affects the frequency, severity and duration of droughts [3]. Droughts have significant impacts on agricultural production and food security, but global trends in how droughts affect agricultural production remain poorly understood. With more severe droughts expected due to climate change, assessing the vulnerability of agricultural production to these phenomena has become an important area of research [4]. This is the main reason for decreased crop yields caused by abiotic factors globally. This issue leads to food shortages and poses significant challenges for small-scale farmers, who struggle to grow enough grain during periods of low and unpredictable rainfall [5]. Drought stress affects crops differently depending on the growth stage, with vegetative stage stress reducing light interception and grain-filling stage stress causing leaf senescence [6]. This significantly impacts cotton production by reducing growth, photosynthesis, and yield [7].
Cotton is a globally significant crop that is cultivated on more than 30 million hectares, with production exceeding 70 million tons of seed cotton [8]. Gossypium hirsutum, commonly known as upland cotton, is an important fiber crop that is sensitive to various environmental factors. Drought stress is a major concern in cotton cultivation, leading to reduced boll size, increased flower shedding, and impaired photosynthesis. It also causes oxidative damage through the production of reactive oxygen species (ROS), which can be mitigated by natural defense enzymes [9]. Cotton responds to water deficit through various mechanisms, including stomatal closure, osmoregulation, and accumulation of plant growth regulators. At the cellular level, drought stress triggers the overproduction of reactive oxygen species (ROS) and activates signaling pathways involving mitogen-activated protein kinases, Ca2+, and hormones [10]. These responses lead to the expression of stress-related transcription factors and genes, particularly those involved in ROS scavenging and ABA signaling [11]. To mitigate the effects of drought stress, researchers have suggested strategies such as developing drought-tolerant cultivars, applying nutrients and osmoprotectants, and utilizing plant growth-promoting rhizobacteria [10]. Additionally, identifying drought tolerance traits through QTL analysis and transgenic approaches may increase cotton resilience to water scarcity [11].
Aminotransferase genes are vital for improving stress tolerance in crops through the regulation of proline synthesis [12]. Classes of the aminotransferase family are involved in the metabolism of various amino acids, including aromatic amino acid aminotransferases (AAA-ATs) [13], tyrosine aminotransferase (TAT) [14], alanine aminotransferase (ALT) [15], aspartate aminotransferase (AST) [16], and histidinol-phosphate aminotransferase (HisC) [17]. Tyrosine aminotransferase (TAT) is essential for the growth and development of plants. The methyl jasmonate (MeJA)-induced TAT gene reacts to a variety of abiotic stressors. The TAT activity of poplar roots is increased by drought and low nitrogen stress, which alters the amount of RA [18]. TATs are essential for tyrosine metabolism and degradation, with TAT1 and TAT2 exhibiting both distinct and overlapping functions in A. thaliana. The majority of current research indicates that AtTAT1 aids in plant survival in the dark and is involved in tyrosine catabolism [19]. The first enzyme involved in the metabolic breakdown of tyrosine is tyrosine aminotransferase (TATN), which is crucial for tyrosine detoxification and assisting the body in fending off oxidative damage [20]. The TAT gene plays a crucial role in the synthesis of tocopherols in Arabidopsis. The study revealed that knockout mutants presented a substantial reduction in total TAT activity, leading to a significant accumulation of tyrosine and a marked decrease in tocopherol levels. These findings indicate that TAT is important for the utilization of tyrosine in metabolic pathways that produce tocopherols, which are vital for plant health and have significant nutritional value [21].
Multiple studies have highlighted the role of TAT genes across various crop species. SmTAT3-2 is involved in phenolic acid biosynthesis in Salvia miltiorrhiza [22], whereas the overexpression of MdTAT2 improves drought and osmotic stress tolerance in Malus domestica [13]. In Arabidopsis, AtTAT1 and AtTAT2 are linked to tyrosine metabolism and degradation, supporting plant survival [19,23]. Additionally, the AccTATN gene helps Apis cerana cerana respond to pesticide and heavy metal stress [20]. In this study, the GhTAT2 gene from G. hirsutum was cloned for gene knockdown, and the expression patterns of this gene family were analyzed. Recombinant GhTAT2 proteins were subjected to drought stress tests in cotton seedlings, and their role in tyrosine metabolism and other metabolic pathways was examined. This is the first functional characterization of TAT2 in G. hirsutum related to stress tolerance. These findings provide valuable insights into the role of TAT2 genes in the stress response and tyrosine metabolic pathways in cotton.
2. Results
2.1. Genome-Wide Identification of Aminotransferase Genes from Cotton Species
The 3 cotton species included a total of 203 genes, with 103 genes in G. hirsutum (47 in the GhAt subgenome and 53 in the GhDt subgenome) and 47 and 53 genes in the diploid species G. arboreum and G. raimondii, respectively. The A genome species (G. arboreum) and D genome species (G. raimondii) are regarded as progenitors of cultivated G. hirsutum [24]. The lengths of the TAT2 gene proteins ranged from 1068 to 3261 bp in the GhAt subgenome of G. hirsutum, 333 to 3378 bp in the GhDt subgenome, 537 to 3360 bp in G. arboreum, and 570 to 4083 bp in G. raimondii. The protein lengths ranged from 355 to 1086 aa in the G. hirsutum GhAt subgenome, 53 to 1125 aa in the GhDt subgenome, 178 to 1119 aa in G. arboreum, and 170 to 1084 aa in G. raimondii. The molecular weights varied from 38.734 to 121.045 kDa in the GhAt subgenome, 5.688 to 123.409 kDa in the GhDt subgenome, 20.782 to 122.993 kDa in G. arboreum, and 18.705 to 121.023 kDa in G. raimondii. The isoelectric points ranged from 5.03 to 9.495 in the GhAt subgenome, 4.324 to 10.748 in the GhDt subgenome, 5.418 to 9.575 in G. arboreum, and 4.826 to 10.43 in G. raimondii (Supplementary File S1).
2.2. Phylogenetic Tree Classification of Aminotransferase Proteins
This study systematically searched for aminotransferase genes in the genomes of diploid and tetraploid cotton, Arabidopsis, and tobacco via BLAST and identified 103, 47, 53, 28, and 34 aminotransferase genes in G. hirsutum, G. arboreum, G. raimondii, T. cacao, and A. thaliana, respectively. This study focused primarily on tetraploid G. hirsutum to explore the origin and evolution of polyploidy by analyzing aminotransferase genes. Additionally, comparisons with two other cotton species, A. thaliana and T. cacao, which are close relatives of upland cotton, were performed to elucidate their evolutionary history. To trace the evolutionary history of aminotransferase genes, we constructed a phylogenetic tree via protein sequences from multiple alignments of 103 G. hirsutum, 47 G. arboreum, 53 G. raimondii, 28 T. cacao, and 34 A. thaliana crop species. The alignment was performed with CLUSTALX (version 2.0) software, and an unrooted phylogenetic tree was generated via maximum likelihood (ML) via the online program iTOL (version 6) [25]. Among the 3 Gossypium species, the aminotransferase tree formed 5 clusters: 80 proteins encoding aromatic amino acid aminotransferases, 15 encoding tyrosine aminotransferases, 20 encoding alanine aminotransferases, 60 encoding aspartate aminotransferases, and 28 encoding histidine phosphate aminotransferases (Figure 1A). Similarly, of the 5 species, including A. thaliana and T. cacao, 94 proteins clustered in group I, 23 in group II, 26 in group III, 79 in group IV, and 43 in group V, each represented with different colors (Figure 1B).
2.3. Chromosomal Location, Gene Architecture and Conserved Domain Distribution
Chromosomal mapping of AT genes from G. hirsutum (AD)1 and two diploid cotton species, G. arboreum (AA) and G. raimondii (DD), was performed to investigate their structural and evolutionary dynamics related to genomic distribution. Among the 103 G. hirsutum genes, 43 were found on 12 chromosomes of the At subgenome (GhAt), with chromosomes 05 and 01 having the largest and lowest numbers of genes, with 7 and 1 genes, respectively. A total of 53 aminotransferase genes were irregularly distributed across 12 chromosomes in GhDt. The highest and lowest number of genes, 07 and 02, are found on chromosomes 05 and 01, respectively. Neither the GhAt nor the GhDt subgenomes contained any genes on chromosome number four. In both subgenomes, four GhAt genes and three GhDt genes were identified as scaffolds (Figure 2A,B,E). Similarly, in G. arboreum, 47 aminotransferase genes are dispersed at random among the A-genome’s 12 chromosomes, excluding chromosome 04. The greatest number of aminotransferase genes are found on chromosome A05 (8), and the fewest are found on chromosome A02 (1). Except for chromosome D12, all 12 chromosomes of the D-genome in G. raimondii contain 53 aminotransferase genes. D09 has the most genes (9), whereas D02 and D04 have the fewest (2 genes). Neither of the diploid species had scaffolds (Figure 2C,D).
A total of 203 aminotransferase genes were classified into 3 subgroups according to their exon and intron counts. The intron-exon structure of aminotransferase genes was visualized to assess the evolutionary impact on the conservation or divergence of introns and exon numbers in cotton. The results revealed that exon counts ranged from 2 to 15 in the GhAt subgenome of G. hirsutum, 2 to 26 in the GhDt subgenome of G. hirsutum, 2 to 25 in G. arboreum, and 3 to 15 in the aminotransferase gene family of G. raimondii (Figure S1) The arrangement of exons and introns illuminates the evolutionary relationships among different gene family members. Notably, closely related genes within the same phylogenetic clade presented greater structural similarity in terms of intron and exon numbers than did those in other clades. A positive correlation was observed between phylogeny and exon-intron structure. Additionally, a one-intron-less structure was identified in the scaffold region of Gh_Sca206957G01. The absence of introns in these aminotransferase genes suggests a lower likelihood of alternative splicing.
Moreover, all cotton aminotransferase protein sequences were analyzed via MEME to identify coding motifs, revealing that aminotransferase proteins contain between one and ten conserved motifs. Among them, in the GhAt subgenome, motifs 4 and 7; in the GhDt subgenome, motifs 1, 4 and 5; in G. arboreum, motifs 1, 4 and 7; and in G. raimondii, motifs 4 and 5 remained almost conserved in all the proteins across the three species of cotton (Figure 3A–D). Specifically, we identified genes with no motif in the Gh_D10G0019 gene of the GhDt subgenome and the Gorai.009G362800, Gorai.002G090700, Gorai.011G002400 and Gorai.011G091200 genes in the G. raimondii genome (Figure 3D). Furthermore, all aminotransferase genes presented a conserved protein motif distribution pattern. For example, motif number 4 was observed in almost all aminotransferase proteins. Overall, aminotransferase genes show a strong ancestral link in their gene structure and phylogeny, maintaining consistent patterns of gene structure and protein motif distribution across subfamilies.
2.4. Analysis of Cis Regulatory Elements and Their Subcellular Localization
Analysis of the cis-acting regulatory elements in the promoter regions revealed that aminotransferase genes play various roles. ABRE, ARE, MBS, and MYB elements are associated with abscisic acid responsiveness, drought inducibility, and anaerobic induction. Elements such as the G-Box, GATA motif, LTR, and MRE are associated with light and low-temperature responsiveness. Similarly, motifs such as GARE, TGACG, TCA, and TGA are involved in hormone signaling related to auxin, gibberellin, MeJA, and salicylic acid (Figure 4A–D).
According to other prediction analyses, the three Gossypium species are expected to be present in various cellular compartments, including the chloroplast, cytoplasm, cytoskeleton, endoplasmic reticulum, extracellular space, Golgi apparatus, mitochondria, nucleus, peroxisome, plastids, and vacuole. Most genes are located predominantly in the chloroplast, cytoplasm, and nucleus, with fewer genes scattered in the other compartments (Figure 5A–D). The results indicated that most aminotransferase gene family members are highly expressed in the chloroplast, cytoplasm, and nucleus of plant cells, suggesting their significant role in food synthesis through photosynthesis, tolerance against stress, and regulation of cell functions.
2.5. Coexpression Network Analysis of Aminotransferases in G. hirsutum
A total of 25 genes from the aminotransferase gene family involved in various metabolic pathways were used to analyze gene coexpression networks via RNA-seq data from the leaf and root tissues of G. hirsutum (Figure 6A,B). The Cytohubba program in Cytoscape software (version 3.10.2) was used to analyze the regulatory network of aminotransferase gene coexpression, with a p value of 0.95 used as the threshold. Key genes were selected from leaf and root tissues on the basis of the range of topological coefficients associated with each node. Gh_A13G1261, Gh_D13G1562 and Gh_D10G1155 were identified as hub genes from both the leaf and root tissue network analyses (Figure 6C,D).
2.6. Identification of Drought-Responsive Candidate Genes
We selected 12 DEGs from the aminotransferase family involved in metabolite pathway enrichment for real-time quantitative validation. After drought stress treatment, Gh_A13G1261, Gh_D13G1562, Gh_D10G1155, Gh_A10G1320, and Gh_D06G1003 were significantly upregulated in both materials, whereas Gh_A03G1375 and Gh_A13G0469 were significantly downregulated in both tissues. The expression of Gh_A07G0174 and Gh_D06G1069 did not significantly change in root tissue, whereas that of Gh_D02G1624 did significantly change in leaf tissue after drought stress. Gh_A13G1261, a member of the tyrosine aminotransferase class (GhTAT2), was chosen as the candidate gene for functional characterization (Figure 7). These results indicate that drought stress induces unique gene expression trends in cotton seedlings with different drought tolerances, influencing various pathways involved in the drought response and accounting for the observed tolerance variations. The RT-qPCR results for these genes aligned with the transcriptome expression trends. This shows a strong correlation between the transcriptome sequencing and RT-qPCR results, confirming the reliability and effectiveness of the data. In conclusion, the AT gene can rapidly adjust its expression through transcription within 48 h of drought stress to increase drought tolerance in cotton.
2.7. Metabolite Pathway Enrichment Analysis
KEGG enrichment analysis was conducted on G. hirsutum races to identify key and significantly enriched metabolites that are vital for drought stress tolerance. The GhTAT2 gene is involved in and regulates various metabolic pathways, namely, tyrosine metabolism; cysteine and methionine metabolism; isoquinoline alkaloid biosynthesis; phenylalanine metabolism; phenylalanine, tyrosine and tryptophan biosynthesis; and tropane, piperidine and pyridine alkaloid biosynthesis (Figure 8A–F). In cysteine and methionine metabolism, L-alanine, L-serine, L-homoserine, and L-methionine amino acids were upregulated, whereas homocystine was downregulated (Figure 8A). In tyrosine metabolism, phenylalanine, tyrosine and tryptophan biosynthesis amino acids, such as L-phenylalanine, L-DOPA, N-methyltyramine and succinic acid, were significantly upregulated, whereas maleic acid, p-coumaric acid, quinic acid, fructose 1-phosphate, phenylpyruvic acid and anthranilate were significantly downregulated (Figure 8C,D). In phenylalanine metabolism, isoquinoline alkaloid biosynthesis and tropane, piperidine and pyridine alkaloid biosynthesis metabolites such as 2-hydroxycinnamic acid, L-phenylalanine, benzoic acid, putrescine and L-DOPA were significantly upregulated, whereas papaverine, p-coumaric acid, vanillin, hyoscyamine and nicotine were downregulated after drought stress treatment (Figure 8B,F). A list of the metabolite data used for the enrichment analysis is provided in Supplementary File S2.
2.8. VIGS Agroinfiltration, Drought Stress Treatment and Relative Expression Analysis
To assess the role of the GhTAT2 gene (Gh_A13G1261) in drought stress tolerance, GhTAT2 gene silencing was performed on cotton seedlings. The experimental setup included TRV2:00, TRV2:GhTAT2, and TRV2:CLA1, with TRV2:CLA1 exhibiting an albino phenotype in leaf tissues from the second week onward (Figure 9A). Significant differences (p < 0.05) in morphological traits were noted among VIGS-treated plants and positive controls under drought conditions. Although plant height and root length remained similar, the VIGS-treated plants presented significantly lower shoot and root fresh weights than the positive controls did (Figure 9B,C). Samples were collected from the leaves and roots of TRV2:00 and TRV2:GhTAT2 plants to investigate the role of the GhTAT2 gene under drought conditions.
3. Discussion
Cotton (Gossypium hirsutum L.) is a commercially valuable fiber crop grown worldwide in various climates, with increasing demand driven by its use in the textile and oil industries [26]. This versatile crop is vulnerable to biotic and abiotic stresses, especially high temperatures and drought, which can considerably affect yield and quality [27]. Drought stress is a pressing global issue that significantly impacts cotton production. There is an increasing need to identify or breed drought-tolerant varieties to ensure sustainable cotton farming. This stress hinders cotton growth and development by altering metabolic pathways, lowering photosynthesis, and causing responses such as stomatal closure. It decreases photosynthesis and reduces the supply of photosynthates, which results in boll shedding and a low lint yield [10,28]. It impacts several physiological processes in plants, including photosynthesis, stomatal regulation, root–shoot growth ratio, leaf area expansion, transpiration, and osmoregulation. This stress triggers sensing and signaling pathways that activate various parallel responses, including physiological, molecular, and biochemical mechanisms [29,30]. Drought stress at the cellular level triggers excessive production of reactive oxygen species, mitogen-activated protein kinases, Ca2+, and hormone-mediated signaling. It also activates transcription factors involved in both abscisic acid-dependent and abscisic acid-independent stress signaling pathways in cotton. Cotton plants adapt to drought through mechanisms such as the accumulation of reactive oxygen species (ROS) and the activation of stress-responsive transcription factors [7,10].
This study identified 103, 47, 53, 28, and 34 aminotransferase genes in G. hirsutum, G. arboreum, G. raimondii, A. thaliana, and T. cacao, respectively. The number of genes in cottons were greater than Arabidopsis, and tobacco. This is because of the polyploidy nature of cotton species due to interspecific hybridization. There are diploid and tetraploid sets of chromosomes in cotton, while Arabidopsis has only two sets. This extra genetic evolution in cotton gives it more genes for various functions [31]. The phylogenetic analysis grouped the aminotransferase class into 5 different classes, with 94, 23, 26, 79 and 43 aminotransferase proteins in the I, II, III, IV, and V groups, respectively. In the Jinjiang oyster, 18 aminotransferase class I and II genes were identified, and their expression varied under salinity stress [32]. Aminotransferases are categorized into five classes: I, II, III, IV, and V. Classes I and II include alanine aminotransferase, aromatic amino acid aminotransferase, aspartate aminotransferase, and histidine phosphate aminotransferase [32,33]. Aminotransferases are essential enzymes responsible for amino acid metabolism and transfer [33]. The aminotransferase gene family plays crucial roles in amino acid metabolism and osmotic regulation across various organisms [32]. Across species, aminotransferases are involved in crucial cellular processes, including osmotic pressure regulation, nitrogen metabolism, and stress response, highlighting their importance in cellular adaptation and homeostasis [32,34].
In the subcellular localization prediction analysis, most genes were found to be located in the chloroplast, cytoplasm, or nucleus. These cell components play crucial roles in photosynthesis, regulating cell functions, and enhancing stress tolerance. During stress, chloroplasts synthesize essential compounds such as amino acids, vitamins, phytohormones, lipids, nucleotides, and secondary metabolites [35]. Similarly, cytoplasmic stress granules and cytosolic pH homeostasis act as signaling hubs that influence cell viability and stress recovery and are essential for normal growth and stress responses in plants [36,37]. In addition, nucleolar proteins are essential for stress adaptation, affecting plant growth and tolerance to environmental stresses [38]. Analysis of cis-acting elements in the promoter regions of aminotransferase genes revealed several important motifs, including ABREs, AREs, MBSs, MYBs, G-boxes, GATA, LTRs, MREs, GAREs, TGACGs, TCAs, and TGATAs, which are associated with stress tolerance, light regulation, and hormone signaling. Various studies have identified motifs involved in hormonal regulation and phytohormonal responses, including ABREs, GARE motifs, GATA transcription factors [39,40,41], G-box, GT1 motifs, TCT motifs, MREs, and GATA motifs, which regulate development and responses to abiotic stresses [42]. These findings indicate that TAT2 is important for plant growth and abiotic stress resistance.
The aminotransferase gene plays a crucial role in abiotic stress tolerance, as revealed by KEGG enrichment analysis of pathways such as tyrosine metabolism, cysteine and methionine metabolism, and phenylalanine metabolism. Key metabolites, including N-methyltyramine, succinic acid, L-alanine, L-serine, L-homoserine, L-cysteine, L-methionine, 2-hydroxycinnamic acid, benzoic acid, and L-phenylalanine, were upregulated, indicating that aminotransferases are involved in drought stress tolerance. Tyrosine aminotransferases play a role in the oxidative stress response through m-tyrosine metabolism in Caenorhabditis elegans [43]. In Vigna radiata, tyrosine and lysine enhance growth under cadmium stress, highlighting their importance in plant nitrogen metabolism [44]. Additionally, tyrosine metabolism may improve drought tolerance by affecting carbon and nitrogen metabolism in okra [45]. The tyrosine metabolism pathway initiates the production of various structurally diverse natural compounds in plants, including tocopherols, betalains, salidroside, plastoquinone, ubiquinone, and benzylisoquinoline alkaloids. Notably, tyrosine-derived metabolites, tocopherols, ubiquinone and plastoquinone are vital for plant survival [46].
Plants generate several l-tyrosine (Tyr)-derived compounds essential for their adaptation, as well as having pharmaceutical and nutritional significance for human health. TAT catalyzes the reversible reaction between Tyr and 4-hydroxyphenylpyruvate, serving as the entry point for the biosynthesis of various natural products and the degradation of Tyr for energy and nutrient recycling [19]. Similar TAT genes have been found in other plants, including Salvia miltiorrhiza. In this species, three SmTAT genes exhibit various expression patterns and react to methyl jasmonate stimuli [47]. Methionine is crucial in the oxidative stress response and functions as a scavenger of reactive oxygen species [48]. It is central to the interconversion of sulfur-containing amino acids, with cysteine contributing to oxygen tolerance [49]. Both methionine and cysteine are highly sensitive to various reactive oxygen species, highlighting their antioxidant properties [50]. Furthermore, phenylalanine metabolism plays a crucial role in mitigating the negative effects of drought in Brassica campestris [51], cold stress in tartary buckwheat landraces [35], and heat stress in Cucumis sativus [52]. The enhanced production of aromatic amino acids in tobacco plants results in a significant increase in the levels of phenylpropanoid metabolites. This, in turn, contributes to improved tolerance against stresses that the plants may encounter [53]. Similarly, TAT enzymes are involved in various metabolic pathways, like amino acid metabolism, vitamin biosynthesis and secondary metabolites [33].
VIGS analyses revealed phenotype differences in TRV2:00 and TRV2:GhTAT2 before and after drought stress treatment, highlighting its potential role in cotton drought tolerance (Figure 10A,B). RT-qPCR analysis disclosed that GhTAT2 expression in VIGS-treated plants was lower than that in positive control plants, indicating that TRV2:GhTAT2 is sensitive to drought stress. Additionally, the morphological traits of VIGS-treated seedlings were notably different from those of TRV:00-treated seedlings. Silencing the GhTAT2 gene reduced both shoot and root fresh weight. Furthermore, after drought, the relative expression of the GhTAT2 gene significantly increased in the leaves and roots of positive control plants but notably decreased in both tissues of VIGS-treated plants (Figure 10C,D). Previous reports indicate that the TAT2 gene enhances stress responses in various crop species. In Populus simonii, TAT2 genes improve drought tolerance and low nitrogen tolerance [18]. The overexpression of MdTAT2 in Malus domestica enhances resistance to osmotic and drought stress [13], whereas the overexpression of AccTATN in Apis cerana cerana increases heavy metal stress tolerance and antioxidant capacity [20]. An ornithine δ-aminotransferase gene OsOAT gene, plays a crucial role in the metabolism of proline and arginine. This gene has been found to enhance drought tolerance as well as provide protection against oxidative stress in rice plants [54].
4. Materials and Methods
4.1. Aminotransferase Gene Identification in Cotton
Cotton species genome data for G. hirsutum (NAU assembly), G. arboreum (CRI assembly) and G. raimondii (JGI assembly) were retrieved from CottonFGD (
4.2. Protein Sequence Alignment and Phylogenetic Tree Construction
The ClustalX tool (Ver. 2.1) was used to fully align all of the aminotransferase protein sequences identified from the five species and perform bootstrap N-J tree analysis with the default settings [58]. A Newick file was produced after manual modification in MEGA 7.0 software via the maximum likelihood approach with 1000 bootstrap repetitions [59]. The Newick file from MEGA 7.0 was used to construct the final tree via the iTOL online tool [25]. Comparative phylogenetic analysis was also performed to identify the evolutionary relationships of cotton aminotransferase with A. thaliana and T. cacao. All relevant list of proteins associated with aminotransferases are given in Supplementary File S3.
4.3. Chromosomal Mapping, Gene Structure and Conserved Domain Analysis
General feature format (GFF3) files and genome assembly sequences for the three cotton species were obtained from CottonFGD [55]. TBtools software [60] was used to visualize the chromosomal locations of aminotransferase genes in the cotton genomes. Gene structure analysis involved the retrieval of detailed coding sequence (CDS) and genomic sequences from CottonFGD. The intron/exon arrangement of the cotton aminotransferase genes was illustrated via the gene structure display server GSDS 2.0 (
4.4. Promoter Region Analysis and Prediction of Subcellular Localization
We utilized the PlantCARE (
4.5. Expression Profiling, Coexpression Network and RT-qPCR Analysis
RNA-seq data (NCBI accession number: PRJNA663204) were used to examine the relative expression patterns of aminotransferases under drought stress for 0 h, 24 h and 48 h for each treatment and two tissues, namely, roots and leaves, from three cotton races [65]. To verify the sequencing results, the identified candidate genes were analyzed via real-time quantitative PCR. The primers for the target genes (Supplementary File S4) were designed via NCBI Primer Blast (
4.6. Cotton Seedlings and Growth Conditions
The experiment took place in seedling growth chamber at Nantong University’s School of Life Sciences. For VIGS experiment, CRI variety, a G. hirsutum drought-tolerant cotton variety released by the Institute of cotton research, Chinese Academy of Agricultural Sciences (ICR, CAAS) was used [68]. Seedlings were planted in pots containing equal parts vermiculite and humus and were grown under the same conditions. A total of 12 pot cotton seedlings for the TRV2:00, TRV2:GhTAT2 and TRV2:CLA1 treatment sets were used for the experiment. When the seedlings grew to three true leaves, drought stress treatment was applied by adding the nutrient solution with PEG-6000 [69].
4.7. VIGS Experiment for Drought Stress Tolerance
The VIGS experiment used the G. hirsutum variety “CRI-12” to study the role of GhTAT2 genes in drought stress tolerance and tissue-specific expression profiling via gene silencing genetic transformation. The functional analysis focused on significantly upregulated expression of the GhTAT2 gene (Gh_A13G1261) in G. hirsutum. The candidate GhTAT2 with a 271 bp fragment was amplified via specific forward and reverse primers. The amplified primers were subsequently cloned and inserted into the vector plasmid tobacco rattle virus (pTRV2) via XbaI and BamHI enzymes. A. tumefaciens “GV3101” served as the recombinant vector carrier via the freeze-thaw method, with TRV2:GhTAT2 as the silenced gene, TRV2:CLA1 as the positive control and TRV2:00 as the negative control [70]. The cotton seedlings’ cotyledons underwent agroinfiltration when the cotyledons were fully expanded and before the first true leaf had just emerged. After infiltration, the plants were kept in darkness for 24 h before being transferred to a growth chamber at 25 °C with a 16/8 h light/dark cycle. Two weeks post-infection, plants transformed with the positive control gene CLA1 presented an albino phenotype in their newly emerged leaves. 15% PEG-6000 was used to induce drought stress at the three-leaf stage, and leaf and root samples were collected at 0, 24 and 48 h in liquid nitrogen and stored at −80 °C for expression analysis in three replications from before to after treatment [71].
4.8. Statistical and Graphic Analysis
The data analysis was performed using one-way analysis of variance (ANOVA) at 5% probability. The significant differences between treatment means were estimated through t-test at the 5% and 1% (p ≤ 0.05 and p ≤ 0.01) confidence levels. GraphPad Prism (version 8.4.3) was used to display significant differences. The data are presented as the means ± SDs from three separate experiments, with * and ** indicating significance at p ≤ 0.05 and p ≤ 0.01, respectively [72].
5. Conclusions
A total of 203 aminotransferase genes were identified across 3 Gossypium species, with 103 in G. hirsutum, 47 in G. arboreum, and 53 in G. raimondii, through gene family analysis. Genome-wide identification, KEGG enrichment analysis, VIGS, and RT-qPCR profiling suggest that the GhTAT2 gene cluster plays a role in drought stress tolerance through its involvement in tyrosine, cysteine and methionine, and phenylalanine metabolism. Silencing of GhTAT2 via VIGS and subsequent RT-qPCR revealed downregulation and reduced relative expression under drought stress. GhTAT2 has significant potential for drought stress tolerance in cotton. Thus, further gene transformation and editing are required to enhance our understanding of their roles in drought stress tolerance and metabolic mechanisms at the genetic and molecular levels.
Conceptualization and writing—original draft preparation and visualization, T.G.M. and J.T.; methodology and data curation, H.G.; software and formal analysis, H.F. and J.H.; validation and resources, J.Z. and F.L.; investigation and writing—review and editing, K.W.; supervision, project administration and funding acquisition, D.Y. and B.W. All authors have read and agreed to the published version of the manuscript.
Not applicable.
Not applicable.
Data are contained within this article and
The authors declare no conflicts of interest.
| CLA | Cloroplastos alterados |
| CottonFGD | Cotton functional genomics database |
| TAT | Tyrosine aminotransferases |
| TRV | Tobacco rattle virus |
| VIGS | Virus-induced gene silencing |
Footnotes
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Enlarge this image.Figure 1. Phylogenetic tree analysis of aminotransferase gene family members. (A) Tree constructed using the protein sequences of G. hirsutum, G. arboreum and G. raimondii. (B) Tree constructed using the protein sequences of G. hirsutum, G. arboreum, G. raimondii, A. thaliana and T. cacao. Different colors represent different clusters, and AT stands for aminotransferase.
Enlarge this image.Figure 2. Chromosome mapping of aminotransferase genes. (A) G. hirsutum GhAt subgenome; (B) G. hirsutum GhDt subgenome; (C) G. arboreum; (D) G. raimondii; (E) genes located in scaffold regions.
Enlarge this image.Figure 3. Phylogenetics, gene structure and motif analysis of the aminotransferase genes in cotton species: (A) the G. hirsutum GhAt subgenome (B), the G. hirsutum GhDt subgenome, (C) G. arboreum and (D) G. raimondii. The phylogenetic tree was created with MEGA 7 via the neighbor-joining method and 1000 bootstrap replicates, while conserved motifs in aminotransferase proteins were visualized with TBtools (version 2.154) software. The exon-intron structures of the aminotransferase genes reflect their evolutionary relationships, with yellow circles representing exons and gray lines indicating introns. Each motif is marked by a colored box in the legend, and the lengths of the motifs in each protein are shown proportionally.
Enlarge this image.Figure 4. Cis-regulatory element analysis of the promoter regions of the aminotransferase gene family. (A) G. hirsutum GhAt subgenome; (B) G. hirsutum GhDt subgenome; (C) G. arboreum; (D) G. raimondii.
Enlarge this image.Figure 5. Prediction of the subcellular localization of aminotransferase genes in Gossypium species. (A) G. hirsutum in the GhAt subgenome; (B) G. hirsutum in the GhDt subgenome; (C) G. arboreum; (D) G. raimondii.
Enlarge this image.Figure 6. Transcriptome and coexpression network analysis of aminotransferase genes. (A) Transcriptome analysis of aminotransferase genes in G. hirsutum leaf tissue; (B) transcriptome analysis of aminotransferase genes in G. hirsutum root tissue; (C) coexpression analysis and candidate gene identification in leaf expression; (D) coexpression analysis and candidate gene identification in root expression. Genes in red have positive correlation values and are considered hub genes.
Enlarge this image.Figure 7. The relative expression levels of aminotransferase genes after drought stress were analyzed via a t-test, and the results are presented as the means ± SDs from three independent experiments. Significance is indicated by *, **, and *** for p ≤ 0.05, p ≤ 0.01, and p ≤ 0.001, respectively, ns stands for non-significant.
Enlarge this image.Figure 8. Metabolite enrichment analyses of GhTAT2 genes under drought stress. (A) Cysteine and methionine metabolism; (B) phenylalanine metabolism; (C) tyrosine metabolism; (D) phenylalanine, tyrosine and tryptophan biosynthesis; (E) tropane, piperidine and pyridine alkaloid biosynthesis; (F) isoquinoline alkaloid biosynthesis. The heatmap was generated in TBtools via log2-transformed metabolite expression data.
![View Image - Figure 9. Phenotypic variations in cotton seedlings after agroinfiltration (A) TRV2:00, empty vector, TRV2:GhTAT2, VIGS plant, TRV2:CLA1, positive control, (B) plant height and root length measurement, and (C) shoot fresh weight and root fresh weight measurement. ns stands for non-significant; * indicates a significant difference at p [less than] 0.05.](https://media.proquest.com/media/hms/THUM/18/FuiYb?_a=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%2BgIBWYIDA1dlYooDHENJRDoyMDI1MDkxMzExNTI1NTU2NTo3NDI5MDE%3D&_s=CFUEUoRSLENdDUuqnwDw%2Bens%2Fhw%3D "View Image - Figure 9. Phenotypic variations in cotton seedlings after agroinfiltration (A) TRV2:00, empty vector, TRV2:GhTAT2, VIGS plant, TRV2:CLA1, positive control, (B) plant height and root length measurement, and (C) shoot fresh weight and root fresh weight measurement. ns stands for non-significant; * indicates a significant difference at p [less than] 0.05.")
Enlarge this image.Figure 9. Phenotypic variations in cotton seedlings after agroinfiltration (A) TRV2:00, empty vector, TRV2:GhTAT2, VIGS plant, TRV2:CLA1, positive control, (B) plant height and root length measurement, and (C) shoot fresh weight and root fresh weight measurement. ns stands for non-significant; * indicates a significant difference at p [less than] 0.05.
Enlarge this image.Figure 10. Relative expression analysis of positive control and silenced tissues. (A) Phenotypic images of TRV2:00 and TRV2:GhTAT2 before treatment. (B) Phenotypic images of TRV2:00 and TRV2:GhTAT2 after PEG-6000 treatment. (C) Expression profiling of the empty vector and TRV2:GhTAT2 before drought stress treatment in leaf and root tissues. (D) Expression profiling of the empty vector and TRV2:GhTAT2 after drought stress treatment in leaf and root tissues. Bars with different letters indicate significant differences at * for p ≤ 0.05, ns stands for non-significant.
Supplementary Materials
The following supporting information can be downloaded at:
References
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; Tang, Jungfeng 1 ; Gu, Haijing 1 ; Fang, Hui 1
; Han, Jinlei 1
; Zheng, Jie 2
; Liu, Fang 2
; Wang, Kai 1 ; Yao, Dengbing 1 ; Wang, Baohua 1 1 School of Life Sciences, Nantong University, Nantong 226019, China;
2 State Key Laboratory of Cotton Bio-Breeding and Integrated Utilization, Institute of Cotton Research of Chinese Academy of Agricultural Sciences, Anyang 455000, China;