Genome-wide characterization and expression analysis of the GATA transcription factor family in response to salt and drought stress in barley (Hordeum vulgare L.)

GATA transcription factors play a crucial role in regulating plant growth and development as well as stress responses. However, systematic analysis of GATA genes in barley remains uncharacterized, and their functional roles in salt and drought stress responses are poorly understood. This study conducted genome-wide identification and gene expression analysis of the GATA transcription factor family in barley through bioinformatics approaches. A total of 27 HvGATA genes were identified and divided into 4 subfamilies (I-IV), which were unevenly distributed on 7 chromosomes. Overall tertiary structural similarity of GATA proteins existed with differences, but similarity within the same subfamily was higher. Fragment repetition has been identified as the main driving factor for the expansion of the HvGATA family. Members of the same subfamily exhibit highly conserved exon-intron structures and motif compositions, indicating strong functional conservation among subfamilies. Cis-element analysis of the HvGATA promoter reveals potential regulatory complexity and enrichment of stress-responsive elements related to hormone signals (such as abscisic acid) and drought stress responses. qRT-PCR analysis showed that at 6 h after salt treatment, HvGATA1, HvGATA19, and HvGATA25, were significantly up-regulated (P < 0.05). Under drought treatment, at least three HvGATA genes (HvGATA1, HvGATA19, and HvGATA25) showed dynamic expression patterns, which were down-regulated and up-regulated at 2 h, 24 h, and 12 h, respectively, indicating that they have potential roles in regulating salt tolerance and drought tolerance of barley. This research provides new insights into the molecular mechanisms of salt and drought tolerance in barley and offers potential targets for enhancing the crop’s stress resistance under abiotic stresses.

1 Introduction

Transcription factors (TFs) play indispensable roles in plant growth and development, regulating processes such as cell differentiation (Stevanovic et al., 2022), physiological metabolism (Rueda-Lopez et al., 2015), signal transduction (Liu et al., 2022), stress responses (Darigh et al., 2022; Zha et al., 2022), and other biological functions. 64 transcription factor families have been identified in plants (Guo et al., 2008), including WRKY (Rushton et al., 2010; Jiang et al., 2017), MYB (Dubos et al., 2010), GRAS (Zeng et al., 2019), bHLH (Wang et al., 2018), and GATA (Kim, 2024). Among them, GATA is a transcription factor widely present in eukaryotes and an important regulatory protein that specifically binds to the DNA sequence WGATAR (W is T or A, R is G or A), thereby regulating the expression of downstream genes (Lowry and Atchley, 2000; Scazzocchio, 2000) and modulating plant growth and development. For instance, it plays a crucial role in regulating chlorophyll synthesis (Hudson et al., 2011) and stress responses (Huang et al., 2009). The GATA protein domain contains a class IV zinc finger structure characterized by CX₂CX₁₇-₂₀CX₂C, followed by a conserved region. Some members of this zinc finger structure can specifically bind to the DNA sequence (A/T) GATA (A/G) in the gene regulatory region, thereby modulating gene transcription level (Lowry and Atchley, 2000; Scazzocchio, 2000). Most GATA transcription factors in fungi and animals contain zinc finger domains characterized by CX₂CX₁₇CX₂C or CX₂CX₁₈CX₂C (Teakle and Gilmartin, 1998; Scazzocchio, 2000), while plant GATA transcription factors typically contain 18–20 residues. The zinc finger domain in plants is typically CX₂CX₁₈CX₂C or CX₂CX₂₀CX₂C, and it contains a single zinc finger motif (Lowry and Atchley, 2000; Behringer and Schwechheimer, 2015).

The GATA transcription factor NTL1, cloned from tobacco firstly, has been reported to participate in nitrogen metabolism pathways (Daniel-Vedele and Caboche, 1993). In plants, GATA transcription factors play crucial regulatory roles in nitrogen metabolism, stress responses, growth, development, and hormone signaling pathways (Richter et al., 2013). In Arabidopsis thaliana, GATA transcription factor family members, GNC and its homologous gene GNLI, have been shown to involve in chlorophyll biosynthesis, signal transduction, and regulation of developmental processes. Additionally, during plant growth, GNC and GNL regulate brassinosteroids, while GATA2 regulates brassinosteroids during Arabidopsis photomorphogenesis (Luo et al., 2010; Richter et al., 2013). In maize, GATA transcription factor family members GATA7 and GATA33 regulate the growth and development of the shoot apical meristem (Zhan et al., 2015). In rice, the GATA transcription factor family members NECKLEAF1 (Wang et al., 2009) and Cga1 (Hudson et al., 2013) have been found to regulate rice organ formation, chlorophyll synthesis, and chloroplast development. In wheat (Triticum aestivum), the transcription factor TaGATA1 enhances seed dormancy by directly regulating TaABI5 expression, thereby improving resistance to pre-harvest sprouting (Wei et al., 2023). To date, genome-wide analyses of GATA transcription factor families have been conducted in various crops. For instance, 30, 28, and 33 members have been identified in Arabidopsis thaliana (Manfield et al., 2007), Oryza sativa (Gupta et al., 2017), and Sorghum bicolor (Yao et al., 2023), respectively, providing valuable insights into their structure and biological functions in other crops.

Barley (Hordeum vulgare L.) is the fourth most important cereal crop globally, cultivated extensively across diverse agroecological systems. Barley is rich in protein, vitamins, trace elements, and dietary fiber and exhibits agronomically valuable traits, including a short growth cycle, high tolerance to drought and cold, and broad adaptability (Geng et al., 2022). The complete genome sequence of barley has been fully established, enabling comprehensive molecular biology studies on this species. However, systematic identification of barley GATA family genes and their functional roles in abiotic stress responses remains limited. In this study, systematically identified barley GATA family genes through bioinformatics approaches and analyzed their phylogeny, gene structure, conserved motifs, cis-acting elements, and chromosomal distribution. Furthermore, putative orthologs of barley GATA family genes in Arabidopsis and rice were identified for collinearity analysis to infer evolutionary relationships. Additionally, the barley cultivar Morex were subjected to salt stress (150 mM NaCl) and drought stress (20% PEG-6000) to evaluate abiotic stress responses. The transcriptional levels of four HvGATA genes were quantified using quantitative real-time PCR to abiotic stress provide insights into the functional characterization of barley GATA transcription factors.

2 Materials and methods

2.1 Identification of GATA members in barley

To identify members of the barley GATA family, the whole genome sequence, protein sequence, and gene annotation file of barley were obtained from the publicly released Ensembl Plant database (https://plants.ensembl.org/Hordeum_vulgare/Info/Index), and protein sequences of Arabidopsis and rice were retrieved from previously published data (Reyes et al., 2004). Putative HvGATA proteins were identified by performing a BLASTp search against the barley proteome (score ≥100, E-value ≤1e−10) (Altschul et al., 1997). Subsequently, the Pfam database (https://www.ebi.ac.uk/Tools/hmmer/) was used to screen candidate proteins for the presence of the GATA zinc finger domain (PF00320) using its corresponding hidden Markov model (HMM) profile with a significance threshold of 0.01 (Finn et al., 2008, 2011). To further validate conserved domains, CD-Search (https://www.ncbi.nlm.nih.gov/Structure/cdd/cdd.shtml) and SMART (http://smart.emblheidelberg.de/) were employed (Letunic and Bork, 2018; Yang et al., 2020). Proteins lacking the GATA domain were excluded from further analysis.

2.2 Bioinformatics analysis of HvGATA members

ExPASy (https://web.expasy.org/) was used to predict the amino acid length (aa), molecular weight (MW), isoelectric point (pI), instability index (II), aliphatic index, and grand average of hydropathicity (GRAVY). And use BUSCA (http://busca.biocomp.unibo.it/) for subcellular localization prediction.

2.3 Phylogenetic analysis of HvGATA proteins

Based on studies of GATA family genes in Arabidopsis and rice (Bi et al., 2005; He et al., 2018), zinc finger domain sequences of 30 Arabidopsis and 28 rice GATA proteins were downloaded. Multiple sequence alignment of barley, Arabidopsis, and rice GATA proteins was performed using Clustal W (Larkin et al., 2007) with default parameters in MEGA11 (Tamura et al., 2021). A phylogenetic tree was constructed using the maximum likelihood method with 1,000 bootstrap replicates.

2.4 Analysis of conserved motifs, gene structures, and Cis-acting elements

Conserved domains in barley GATA proteins were analyzed with DNAMAN. The MEME suite (https://meme-suite.org/meme/tools/meme) was employed to identify protein motifs in the full-length sequences of 27 HvGATAs (Bailey et al., 2009). Gene structures of HvGATAs were visualized using TBtools by aligning coding sequences (CDS) with their corresponding genomic DNA sequence (Chen et al., 2020). Cis-regulatory elements in the promoter regions (2,000 bp upstream) of HvGATA genes were predicted using PlantCare (http://bioinformatics.psb.ugent.be/webtools/plantcare/html/).

2.5 Detection and homology analysis of paralogous gene pairs

Homologous gene pairs and syntenic relationships in barley GATA genes were identified using the Multiple Collinearity Scan toolkit (MCScanX) with default parameters (Wang et al., 2012). To predict the function of barley GATA family genes, syntenic relationships of homologous GATA genes among barley, Arabidopsis, and rice were analyzed. Circos software (Krzywinski et al., 2009) was utilized to visualize the syntenic relationships of GATA genes across these three species.

2.6 Analysis of HvGATA genes expression profiles in barley transcriptome data

The expression profiles of barley GATA genes in different tissues and under salt/drought stress conditions were obtained from the BarleyExpDB database (http://barleyexp.com/index.html). These data were visualized using TBtools software to generate expression heatmaps of barley GATA genes.

2.7 Plant materials, stress treatment, and qRT-PCR analysis of 4 HvGATA genes

The barley variety Morex was used in this study. Seeds were placed on filter paper in germination boxes, germinated in darkness at 24°C for 48 h, transferred to Hoagland medium in 96-well plates, and cultivated in a growth chamber at 24°C (day) and 18°C (night) with a 14 h/10 h day/night cycle. When seedlings reached the three-leaf stage, they were subjected to two abiotic stress treatments, salt stress (150 mM NaCl) and drought stress (20% PEG-6000) (Zheng et al., 2021). Three biological replicates were performed for each sample, and leaves were collected at 0, 2, 6, 12, and 24 h following the initiation of each stress treatment. All samples were frozen in liquid nitrogen and stored at -80°C for RNA extraction.

Total RNA was extracted using the SteadyPure Plant RNA Extraction Kit (Accurate Biotechnology Co., Ltd., Hunan, China). RNA integrity was verified by 1% agarose gel electrophoresis, and concentration was measured using a UV-Vis spectrophotometer. First-strand cDNA was synthesized from total RNA with the Evo M-MLV RT Premix (Accurate Biotechnology, Hunan, China) for qPCR. Gene-specific primers for four HvGATA genes were designed with Primer 5.0 software (Supplementary Table S1), yielding amplicons of 80–150 bp. HvActin (HORVU6Hr1G054520) was used as the internal reference gene. qRT-PCR was performed on a Roche LightCycler 96 system using the SYBR Green Premix Pro Taq HS qPCR Kit (Accurate Biotechnology Co., Ltd., Hunan, China) with the following reaction components: 1.0 µL cDNA, 10.0 µL 2× SYBR mixture, 0.8 µL primer mix (forward/reverse), and 8.2 µL ddH₂O. Thermal cycling conditions were 95°C for 3 min; 40 cycles of 95°C for 5 s and 58°C for 30 s. Gene expression levels were calculated using the 2^−ΔΔCT method.

2.8 Prediction of structural features and protein interaction networks in HvGATA transcription factors

Secondary structure prediction for barley GATA proteins was performed using SOPMA (https://npsa.lyon.inserm.fr/cgi-bin/npsa_automat.pl?page=/NPSA/npsa_sopma.html). Tertiary structure modeling of identified GATA protein sequences was conducted via the SWISS-MODEL web server (https://swissmodel.expasy.org/interactive/). Protein-protein interaction (PPI) network analysis of barley GATA proteins was executed using the STRING database (https://cn.string-db.org/).

3 Results

3.1 Identification and physicochemical properties of HvGATA members

The hidden Markov model (HMM) of the GATA domain andthe BLASTp method were employed to identify 27 HvGATA genes in the barley genome, designated as HvGATA1-HvGATA27 according to their chromosomal locations (Supplementary Table S2). The encoded proteins ranged from 155 to 775 amino acids in length, with molecular weights ranging from 17343.27 Da to 88608.50 Da. Their isoelectric points varied from 4.66 to 10.22, with 13 members classified as acidic (pI < 7) and 14 as basic (pI > 7), indicating a mixture of acidic and basic proteins. Instability coefficient analysis showed that 26 HvGATA proteins were unstable (values > 40), with HvGATA21 protein exhibiting the highest instability coefficient (86.57). In contrast, HvGATA27 protein was relatively stable, with an instability index below 40. All HvGATA proteins displayed negative mean hydrophobicity coefficients, confirming their hydrophilic nature, though the extent of HvGATA proteins was localized to the nucleus, 3 to extracellular space, and HvGATA7 protein and HvGATA24 protein to chloroplasts.

3.2 Phylogeny, classification, and multiple sequence alignment of HvGATA proteins

Using MEGA11, a phylogenetic tree was constructed from 27 HvGATA, 28 OsGATA, and 30 AtGATA proteins (Figure 1, Supplementary Table S3). The analysis revealed four subfamilies. Subfamily I contained the highest number of HvGATA members (11), followed by Subfamily II (8), while Subfamilies III and IV each comprised four members, respectively. 10 HvGATA proteins clustered closely with AtGATA protein and OsGATA protein orthologs, suggesting conserved functions through evolution.

Figure 1

Figure 1. Phylogenetic tree of GATA proteins from barley, rice, and Arabidopsis. The MEGA 11 Maximum Likelihood (ML) method with 1,000 bootstrap replicates was used to generate the phylogenetic tree. Four subfamilies were categorized and indicated with colors.

Multiple sequence alignment of conserved regions (Figure 2) indicated that all barley GATA proteins harbor a single plant-specific GATA domain, Consistent with rice and Arabidopsis, 21 HvGATA proteins (from Subfamilies I, II, and IV) displayed the motif CX₂CX₁₈CX₂C, whereas 6 HvGATA proteins (four from Subfamily III and two from Subfamily IV) featured an extended variant (CX₂CX₂₀CX₂C). All subfamilies shared conserved sequences (e.g., T-7, P-8, G-13, P-14), highlighting both conservation and diversification within GATA domains.

Figure 2

Figure 2. Amino acid sequence alignment of HvGATAs. (A) GATA motifs and amino acid locations are marked with boxes and asterisks; (B) the sequence logo of the GATA pattern is displayed at the bottom.

3.3 Conserved motifs, gene structures, and Cis-acting elements of HvGATA genes

TBtools was used to construct a phylogenetic tree of 27 HvGATA proteins (Figure 3A), while the MEME online program was employed to analyze their conserved motifs (Figure 3B, Supplementary Table S4). Analysis revealed that the number of motifs among HvGATA members ranged from 1 to 8. Specifically, HvGATA1 contained the lowest motifs, whereas HvGATA6 and HvGATA16 had the highest number (8 motifs each). Notably, Motif 1 was conserved across all barley GATA proteins, suggesting its critical role in the evolution of this gene family. Subgroup-specific patterns were observed: Motif 2 and Motif 6 were exclusive to Subfamily I; Motif 4 was absent solely in HvGATA1; Motif 5 was restricted to Subfamily III; and Motif 7 was unique to Subfamily IV. Additionally, HvGATA6 and HvGATA16 uniquely contained Motifs 8-10, while HvGATA16 shared Motif 3 with all Subfamily III members. The widespread conservation of motifs among most HvGATA members implies functional similarities within this gene family.

Figure 3

Figure 3. Conserved motif and gene structure analysis of HvGATAs. (A) The Maximum Likelihood (ML) method with 1,000 bootstrap replicates was used to generate the phylogenetic tree. (B) The 1–10 amino acid motifs in the HvGATAs protein are represented by 10 different colored boxes, with black lines indicating relative protein lengths. (C) UTR (untranslated region), CDS (coding sequence), GATA domains, and introns are represented by green squares, yellow squares, pink squares, and black lines, respectively. The sizes of exons and introns are estimated using the scale line at the bottom.

Gene structure analysis (Figure 3C), revealed that HvGATA genes contain 0–7 introns. Among the 27 genes, two (HvGATA3 and HvGATA18) lacked introns, 10 harbored a single intron, seven contained two introns, one exhibited four introns, three possessed five introns, and two displayed six or seven introns, respectively. Subfamilies I and II exhibited minimal intron content (0–2 introns). In contrast, Subfamilies III and IV showed marked intron expansion: Subfamily III genes contained 6–7 introns, while Subfamily IV genes had 4–5 introns. These results highlight a significant increase in intron number in Subfamilies III and IV compared to Subfamilies I and II, suggesting divergent evolutionary dynamics among subfamilies.

To investigate the regulatory potential of promoters in the barley HvGATA gene family, cis-acting elements were computationally identified within the 2,000 bp upstream promoter regions of 27 HvGATA genes (Figure 4, Supplementary Table S5). Photoresponsive elements (187 total) and methyl jasmonate (MeJA)-responsive elements (140 total) were the most abundant, with Sp1 (36 occurrences) and I-box (29 occurrences) dominating the light-responsive category. Among hormone-related elements, MeJA-responsive motifs (CGTCA-motif and TGACG-motif) were present in 20 promoters, followed by gibberellin (GA)-responsive elements (31 total). Notably, 26 promoters harbored multiple hormone-responsive motifs, including MeJA, abscisic acid (ABA; ABRE, DRE1), auxin (IAA; TGA-element), and GA-responsive elements (GARE-motif, P-box, TATC-box), while HvGATA13 lacked these motifs.

Figure 4

Figure 4. Cis-acting element of the promoter region (upstream 2,000 bp) of HvGATA genes. Various types of cis-elements and their respective locations in each HvGATA gene. The numbers of different cis-acting elements in the initiation regions of HvGATA genes. Each cis-acting element and function was shown on the right, and the corresponding number of them was indicated by the color scale.

Stress-responsive cis-elements were also identified: drought-related motifs (as-1, MYC, and MBS) totaled 36, with as-1 restricted to HvGATA9 and HvGATA19, MYC to HvGATA1, HvGATA3, HvGATA5, HvGATA9, and HvGATA21; and MBS present in all 16 promoters analyzed. Additionally, 12 defense/stress motifs (e.g., STRE, CARE, and TC-rich repeats) were detected, with STRE in HvGATA9 and HvGATA25; and CARE in a single promoter. Elements linked to hypoxia (ARE, GC-motif), pathogens (WRE3, WUN), and drought (DRE core) occurred in 3, 1, and 19 promoters, respectively. These findings suggest that HvGATA genes may regulate not only plant growth but also hormone signaling and abiotic stress responses, particularly drought adaptation.

3.4 Chromosome distribution, gene replication, and collinearity analysis of HvGATA genes

To elucidate the chromosomal distribution of GATA genes in barley, 27 HvGATA genes were mapped to seven chromosomes (Figure 5A). The distribution frequency varied significantly across chromosomes: Chromosome 1 harbored the highest number of genes (5 genes, 18.52%), followed by Chromosome 4 (6 genes, 22.22%). Chromosomes 2 and 6 each contained 4 genes (14.81% per chromosome), while Chromosomes 3, 5, and 7 showed lower densities with 3 genes (11.11%) on Chromosome 3 (HvGATA10-HvGATA12), 3 genes (11.11%) on Chromosome 5 (HvGATA19-HvGATA21), and 2 genes (7.41%) on Chromosome 7 (HvGATA26 and HvGATA27).

Figure 5

Figure 5. (A) The distribution of HvGATA genes on chromosomes. The four HvGATA subfamily members are represented by red, blue, orange, and green, respectively; each corresponding chromosome number is labeled on its left, the 0–700 Mb scale on the left represents the chromosome length, and the gene density is displayed in color depth. (B) Chromosome distribution and gene duplication relationship of HvGATA genes. The colored lines represent collinear regions in the barley genome, and the red lines represent large segments of replicated HvGATA gene pairs. (C) Synteny analysis of GATAs in Hordeum vulgare, Arabidopsis thaliana, and Oryza sativa. The red lines represent syntenic GATA gene pairs. The numbers on the chromosomes indicate chromosome numbers.

MCScanX based segmental duplication analysis of the 27 HvGATA genes identified seven duplication events involving 14 genes (Figure 5B, Supplementary Table S6), accounting for 51.85% (14/27) of the gene family. These duplicated pairs included HvGATA2/HvGATA18, HvGATA4/HvGATA11, HvGATA5/HvGATA10, HvGATA6/HvGATA16, HvGATA7/HvGATA24, HvGATA22/HvGATA27, and HvGATA23/HvGATA26. Subfamily classification revealed that four duplication pairs (57.14%) belonged to Subfamily I, two pairs (28.57%) to Subfamily II, and one pair each (14.29%) to Subfamilies III and IV. This uneven distribution suggests that segmental duplications may have been a major driver of HvGATA gene family expansion. Furthermore, HvGATA genes were asymmetrically distributed across seven barley linkage groups (LGs). LG1 and LG6 harbored the highest number of duplicated genes (3 genes each, 21.43% of total duplications), followed by LG2, LG3, LG4, and LG7 (2 genes each, 14.29% per LG).

To elucidate the evolutionary dynamics of HvGATA genes, synteny analysis was conducted between two monocotyledonous species (barley and rice) and one dicotyledonous species (Arabidopsis). The results revealed (Figure 5C, Supplementary Table S7), 23 OsGATA genes exhibiting orthologous relationships with 21 HvGATA genes, while 3 AtGATA genes showed orthology with 3 HvGATA genes. These findings indicate that barley, as a monocotyledonous poaceae species, shares a closer phylogenetic relationship with rice than with the dicotyledonous Arabidopsis. Furthermore, collinear gene pairs are often functionally conserved, suggesting that the 33 barley-rice collinear GATA gene pairs and 4 barley-Arabidopsis collinear GATA gene pairs may perform analogous roles in their respective species. This syntenic conservation underscores potential functional similarities driven by shared evolutionary origins.

3.5 Expression profile of HvGATA genes in different tissues and abiotic stresses

To investigate the tissue-specific expression patterns of HvGATA genes, transcript levels of 27 HvGATA genes were analyzed across eight barley tissues (Figure 6A, Supplementary Table S8). 15 genes (HvGATA1, HvGATA2, HvGATA4, HvGATA6-HvGATA9, HvGATA12, HvGATA13, HvGATA19-HvGATA22, HvGATA25, and HvGATA27) exhibiting ubiquitous and/or high expression across multiple tissues, suggesting their potential roles in barley growth and development, 6 genes (HvGATA3, HvGATA5, HvGATA10, HvGATA11, HvGATA15, HvGATA17) with low or undetectable expression in all tested tissues, and 6 genes (HvGATA14, HvGATA16, HvGATA18, HvGATA23, HvGATA24, HvGATA26) showing preferential expression in specific tissues. For instance, HvGATA2 was absent in senescent leaves and showed reduced expression in seeds at 15 days post-fertilization but was highly expressed in other tissues. In contrast, HvGATA26 displayed elevated expression in senescent leaves compared to other tissues, implicating its potential role in regulating leaf senescence during seedling development.

Figure 6

Figure 6. Expression profiles analysis of HvGATA genes across different tissues and salt/drought stress treatments. Different colors in the figure represent gene expression levels, with green indicating low gene expression and red indicating high gene expression. (A) The color scale represents the relative expression level from high (red) to low (green). CAR15 (seed 15 days after fertilization); LEA (10cm bud); ROO1 (root of 10 cm seedling); ROO2 (28-day root); SEN (senescent leaf); INF2 (1-1.5 cm inflorescence); INF1 (5 mm inflorescence); CAR5 (seed 5 days after fertilization). (B) Scarlett leaf control refers to the expression level of barley variety Scarlett leaves under normal treatment. Scarlett leaf drought refers to the expression of leaves of barley variety Scarlett under drought treatment. SBC073D leaf control was the expression level of barley leaves variety SBC073D under normal treatment. SBC073D leaf drought refers to the expression of barley leaf SBC073D under drought treatment. (C) Barcult leaf control is the expression level of leaves under normal treatment. Barcult leaf Salt is the expression of leaves under salt treatment.

To further explore the expression patterns of HvGATA genes in barley under abiotic stress, expression profiles were analyzed to characterize their responses to salt and drought treatments. The results revealed that under salt stress, HvGATA1 and HvGATA25 expression slightly decreased (Figures 6B, C), while HvGATA19 and HvGATA21 expression increased. Under normal and drought conditions, HvGATA1 and HvGATA19 expression declined, whereas HvGATA21 and HvGATA25 expression was up-regulated. A notable exception occurred under the SBC073D drought treatment, where HvGATA1, HvGATA21, and HvGATA25 expression decreased, contrasting with the elevated expression of the HvGATA19 gene.

3.6 HvGATA genes expression under salt and drought stress by qRT-PCR

In this study, qRT-PCR was used to detect the expression changes of HvGATAs under salt stress and drought stress conditions. Based on the expression profile of barley treated with salt and drought, four genes with high expression levels and significant changes in expression levels under salt and drought treatments (HvGATA1, HvGATA19, HvGATA21, and HvGATA25) were selected for qRT-PCR analysis. The results showed (Figure 7A, Supplementary Table S9), that under salt stress, the HvGATA1, HvGATA21, and HvGATA25 genes were significantly down-regulated at 2 h, reaching approximately 4.17 times, 2.63 times, and 1.51 times, respectively, while the HvGATA19 gene was significantly up-regulated by approximately 2.21 times. The HvGATA1, HvGATA19, and HvGATA25 genes were significantly up-regulated at 6 h under salt treatment, reaching 2.78 times, 3.15 times, and 1.44 times, respectively. Furthermore, the HvGATA21 gene was significantly down-regulated by approximately 2 times at 12 h under salt treatment. However, with the increase of salt treatment time, the HvGATA1, HvGATA19, HvGATA21, and HvGATA25 genes were up-regulated by 1.64 times, 1.40 times, 1.65 times, and 2.29 times, respectively, at 24 h under salt treatment. However, significant differences emerged in the HvGATA25 gene. The results show that these genes respond to salt stress.

Figure 7

Figure 7. Expression patterns of GATA genes in barley. (A) Expression analysis of GATA genes in barley salt stress. (B) Expression analysis of GATA genes in barley drought stress. Values represent the average and standard deviation of three biological replicates. ns indicates not statistically significant, *indicates P < 0.05, **indicates P < 0.01, ***indicates P < 0.001.

Under drought stress (Figure 7B), with the increase of 20% PEG solution treatment time, the HvGATA1, HvGATA19, and HvGATA21 genes were significantly down-regulated at 2 h, reaching approximately 1.47 times, 1.96 times, and 4.67 times, respectively. HvGATA21 gene was significantly up-regulated by 2.02 times at 6 h, and the HvGATA1 and HvGATA25 genes were significantly up-regulated at 12 h, reaching 1.86 times and 1.52 times, respectively. At 24 h of PEG treatment, the HvGATA19, HvGATA21, and HvGATA25 genes were significantly down-regulated, among which the expression level of HvGATA21 gene was down-regulated by approximately 5.6 times.

3.7 Structural prediction and protein interaction correlation analysis of HvGATA transcription factors

Secondary structure prediction of 27 barley GATA proteins was performed using the SOPMA online tool (Table 1). The results demonstrate that all 27 HvGATA proteins contain α-helices, β-turns, extended strands, and random coils in their secondary structures. Random coils constitute the predominant structural component among these proteins, with HvGATA5 protein exhibiting the highest proportion (91.24%) and HvGATA16 protein the lowest (40.93%). Within subfamily IV, HvGATA6 and HvGATA16 proteins displayed notably higher α-helix contents of 40.77% and 43.41%, respectively. This elevated α-helical composition may contribute to conformational stability in these proteins.

Table 1

Table 1. Secondary structure analysis of barley GATA transcription factors.

Tertiary structure prediction of barley GATA proteins revealed that these proteins adopt spatial conformations consisting primarily of α-helices and random coils (Figure 8). Comparative analysis demonstrated moderate structural complexity with significant variation in overall structural similarity. Notably, proteins within the same subfamily exhibited higher structural conservation corresponding to their phylogenetic relationships. For instance, in subfamily III, HvGATA13, HvGATA14, HvGATA22, and HvGATA27 proteins displayed strikingly similar tertiary architectures, suggesting evolutionary conservation of structural features within conserved clades.

Figure 8

Figure 8. Prediction of tertiary structure of HvGATA family proteins. The distinctively colored boxes in the figure denote the tertiary structures of HvGATA proteins from different subfamilies, with orange, blue, green, and black representing subfamilies I-IV, respectively.

Protein interaction network analysis of barley GATA transcription factors (Figure 9) revealed that 10 GATA proteins (HvGATA5, HvGATA10, HvGATA11, HvGATA15, HvGATA17, HvGATA18, HvGATA19, HvGATA23, HvGATA26, and HvGATA27) form a densely interconnected cluster. This topological configuration suggests potential functional homology among these proteins, indicating their cooperative regulation in physiological and developmental processes. Conversely, 17 GATA proteins (HvGATA1-4, HvGATA6-9, HvGATA12-14, HvGATA16, HvGATA20-22, and HvGATA24-25) showed no detectable interactions, revealing significant functional diversification within this transcription factor family.

Figure 9

Figure 9. HvGATA protein-protein interaction network.

4 Discussion

4.1 Conservation of HvGATA genes during evolution

GATA transcription factors are a class of DNA-binding proteins ubiquitous in eukaryotes, playing pivotal roles in plant biological processes such as chlorophyll biosynthesis, photomorphogenesis, and stress adaptation. In this study, 27 HvGATA genes were systematically identified from the barley genome through bioinformatic analysis. Among these, 21 HvGATA proteins harbor the canonical CX₂CX₁₈CX₂C zinc finger domain, while the remaining six possess a CX₂CX₂₀CX₂C zinc finger domain. This domain architecture aligns with reported GATA families in Arabidopsis (Kim et al., 2021), rice (He et al., 2018), and Brassica napus (Zhu et al., 2020), suggesting evolutionary conservation. Notably, the variation in the number of amino acid residues (18 or 20) within the CX₂CX_18-20CX₂C zinc finger domains of some GATA proteins may result from structural deletions or evolutionary divergence. Substantial variation was observed in HvGATA protein properties, including amino acid length (ranging from 155 to 775 aa), molecular weight (17343.27 Da-88608.50 Da), and isoelectric point (pI 4.66-10.22), potentially reflecting functional diversification. Prediction analysis of barley GATA protein structures and interaction relationships demonstrated that secondary structures were predominantly composed of random coils. Overall similarity of tertiary structures existed with differences, but the same subfamily exhibited higher similarity, indicating that members within the same subfamily possess high structural homology during evolution. Protein interaction correlation analysis also indicated functional similarity among family members, suggesting cooperative regulatory functions in certain plant growth and developmental processes. Phylogenetic clustering, supported by conserved motif and gene structure analyses, classified barley GATA members into four subfamilies, consistent with classifications in Arabidopsis (Reyes et al., 2004), rice (Reyes et al., 2004), and Sorghum bicolor (Yao et al., 2023). Functional predictions for barley GATA genes were inferred based on orthologous subfamily functions in Arabidopsis. Notably, members within the same subfamily exhibited highly similar motif compositions and exon-intron architectures, indicating strong evolutionary conservation and potential functional redundancy. Conversely, structural divergence across subfamilies likely arose from lineage-specific evolutionary pressures, such as domain shuffling or selective gene loss.

In higher plants, the GATA gene family exhibits significant variation in member numbers across species, even among closely related taxa. For instance, wheat harbors 79 GATA members (Buzby et al., 1990), compared to 30 in Arabidopsis (dicot) (Manfield et al., 2007) and 28 in rice (monocot) (Gupta et al., 2017). Such divergence likely reflects lineage-specific evolutionary trajectories, where gene duplication events-key drivers of family expansion play pivotal roles (Feng et al., 2022). In barley, we identified seven segmental duplication events among 27 HvGATA genes, with no tandem duplications observed. Notably, two duplication pairs localized to Subfamilies I and II, suggesting subfunctionalization or neofunctionalization within these clades. Synteny analysis further revealed stronger conservation between barley and rice than with Arabidopsis, corroborating closer phylogenetic relationships within the poaceae family.

4.2 HvGATA genes are involved in hormone signaling and abiotic stress response

Cis-regulatory elements are non-coding DNA sequences within gene promoters that orchestrate transcriptional regulation. Analyzing these elements in HvGATA promoters provides critical insights into their functional diversification in barley. Previous studies demonstrate that plant GATA transcription factors mediate light signaling by binding to GATA motifs in photoresponsive gene promoters (Cannon et al., 2004), exemplified by GATA2’s dual role in modulating light and brassinosteroid signaling pathways (Luo et al., 2010). This functional interplay suggests that GATA genes may both regulate and be regulated by light-responsive genes, underscoring the importance of HvGATA promoter analysis in barley. Plant GATA factors are also implicated in stress adaptation, developmental regulation, and hormone signaling (Manfield et al., 2007; Richter et al., 2013), consistent with our findings in the barley HvGATA family. Promoter analysis revealed an enrichment of stress-responsive cis-elements (e.g., drought-inducible MBS MYC) and hormone-related motifs (ABRE for abscisic acid, GARE for gibberellins, TGA-element for auxin, and CGTCA-motif for jasmonate) in HvGATA promoters. Additionally, specific HvGATA genes harbor elements associated with meristem expression (e.g., CAT-box), endosperm specificity (e.g., GCN4-motif), and hypoxia response (e.g., GC-motif), indicating their potential roles in diverse physiological processes. However, while these cis-element profiles suggest environmental sensitivity and multifunctional regulation, direct experimental evidence is required to confirm their binding efficacy and regulatory polarity (activation or repression) under specific conditions.

4.3 HvGATA genes play an important role in the response mechanism of barley salt and drought stress

Gene expression patterns across tissues often reflect functional specialization. For instance, OsGATA23a in rice exhibits broad stress responsiveness, with significant up-regulation under salt and drought conditions (Gupta et al., 2017), underscoring the conserved regulatory role of GATA transcription factors in stress signaling. In barley, tissue-specific expression profiling of four HvGATA genes revealed conserved expression patterns within subfamilies, indicative of functional conservation. qRT-PCR analysis further demonstrated that HvGATA genes are dynamically regulated under abiotic stress. Under salt stress, at 2 h, only HvGATA19 was significantly up-regulated, while the other three genes were significantly down-regulated. HvGATA1 and HvGATA19 were transiently up-regulated at 6 h post-treatment, whereas HvGATA21 and HvGATA25 peaked at 12 h. Conversely, drought stress triggered pronounced down-regulation of HvGATA1, HvGATA19, and HvGATA21 at 2h. At 12 h, HvGATA1 and HvGATA25 genes were significantly up-regulated. The HvGATA19, HvGATA21, and HvGATA25 genes were significantly down-regulated by approximately 2.63 times, 5.56 times, and 3.57 times, respectively, at 24 h. These temporal and stress-specific expression dynamics suggest functional diversification within the HvGATA family, enabling coordinated regulation of developmental processes and stress adaptation.

5 Conclusion

In this study, 27 GATA genes were identified in the barley genome using bioinformatics approaches and classified into four subfamilies. Members within the same GATA subfamily exhibit higher sequence similarity and conservation, suggesting potential functional similarities. Analysis of promoter regions indicated that the cis-acting elements associated with HvGATA genes may be subject to complex regulation, particularly elements responsive to hormone signaling pathways and abiotic stress. Furthermore, the expression patterns of HvGATA1, HvGATA19, HvGATA21, and HvGATA25 genes differed following salt and drought stress treatments applied over different time courses, indicating their potential involvement in barley’s salt and drought tolerance mechanisms.

Data availability statement

The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/Supplementary Material.

Author contributions

HZ: Conceptualization, Data curation, Formal Analysis, Investigation, Methodology, Writing – original draft, Writing – review & editing. PY: Conceptualization, Data curation, Formal Analysis, Methodology, Software, Supervision, Validation, Visualization, Writing – original draft, Writing – review & editing. HX: Data curation, Formal Analysis, Writing – original draft. CW: Methodology, Software, Writing – review & editing. XY: Software, Writing – review & editing, Formal Analysis. XZ: Writing – review & editing, Data curation, Supervision.

Funding

The author(s) declare that financial support was received for the research and/or publication of this article. This research was supported by the Interdisciplinary Research Project of Hangzhou Normal University (2025JCXK01) and the HZNU scientific research and innovation team project (TD2025005).

Acknowledgments

We thank the public database for the downloaded raw data. We are very grateful to the editor and reviewers for critically evaluating the manuscript and providing constructive comments for its improvement.

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Generative AI statement

The author(s) declare that no Generative AI was used in the creation of this manuscript.

Any alternative text (alt text) provided alongside figures in this article has been generated by Frontiers with the support of artificial intelligence and reasonable efforts have been made to ensure accuracy, including review by the authors wherever possible. If you identify any issues, please contact us.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Supplementary material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fpls.2025.1661591/full#supplementary-material

References

Altschul, S. F., Madden, T. L., Schaffer, A. A., Zhang, J., Zhang, Z., Miller, W., et al. (1997). Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucleic Acids Res. 25, 3389–3402. doi: 10.1093/nar/25.17.3389

PubMed Abstract | Crossref Full Text | Google Scholar

Bailey, T. L., Boden, M., Buske, F. A., Frith, M., Grant, C. E., Clementi, L., et al. (2009). MEME SUITE: tools for motif discovery and searching. Nucleic Acids Res. 37, W202–W208. doi: 10.1093/nar/gkp335

PubMed Abstract | Crossref Full Text | Google Scholar

Behringer, C. and Schwechheimer, C. (2015). B-GATA transcription factors-insights into their structure, regulation, and role in plant development. Front. Plant Sci. 6. doi: 10.3389/fpls.2015.00090

PubMed Abstract | Crossref Full Text | Google Scholar

Bi, Y. M., Zhang, Y., Signorelli, T., Zhao, R., Zhu, T., and Rothstein, S. (2005). Genetic analysis of Arabidopsis GATA transcription factor gene family reveals a nitrate-inducible member important for chlorophyll synthesis and glucose sensitivity. Plant J. 44, 680–692. doi: 10.1111/j.1365-313X.2005.02568.x

PubMed Abstract | Crossref Full Text | Google Scholar

Buzby, J. S., Yamada, T., and Tobin, E. M. (1990). A light-regulated DNA-binding activity interacts with a conserved region of a Lemna gibba rbcS promoter. Plant Cell. 2, 805–814. doi: 10.1105/tpc.2.8.805

PubMed Abstract | Crossref Full Text | Google Scholar

Cannon, S. B., Mitra, A., and Baumgarten, A. (2004). The roles of segmental and tandem gene duplication in the evolution of large gene families in Arabidopsis thaliana. BMC Plant Biol. 4, 10. doi: 10.1186/1471-2229-4-10

PubMed Abstract | Crossref Full Text | Google Scholar

Chen, C., Chen, H., Zhang, Y., Thomas, H. R., Frank, M. H., He, Y., et al. (2020). TBtools: an integrative toolkit developed for interactive analyses of big biological data. Mol. Plant 13, 1194–1202. doi: 10.1016/j.molp.2020.06.009

PubMed Abstract | Crossref Full Text | Google Scholar

Daniel-Vedele, F. and Caboche, M. (1993). A tobacco cDNA clone encoding a GATA-1 zinc finger protein homologous to regulators of nitrogen metabolism in fungi. Mol. Gen. Genet. 240, 365–373. doi: 10.1007/BF00280388

PubMed Abstract | Crossref Full Text | Google Scholar

Darigh, F., Iranbakhsh, A., Oraghi Ardebili, Z., Ebadi, M., and Hassanpour, H. (2022). Simulated microgravity contributed to modification of callogenesis performance and secondary metabolite production in Cannabis Indica. Plant Physiol. Biochem. 186, 157–168. doi: 10.1016/j.plaphy.2022.07.012

PubMed Abstract | Crossref Full Text | Google Scholar

Dubos, C., Stracke, R., Grotewold, E., Weisshaar, B., Martin, C., Lepiniec, L., et al. (2010). MYB transcription factors in Arabidopsis. Trends Plant Sci. 15, 573–581. doi: 10.1016/j.tplants.2010.06.005

PubMed Abstract | Crossref Full Text | Google Scholar

Feng, X., Yu, Q., Zeng, J., He, X., and Liu, W. (2022). Genome-wide identification and characterization of GATA family genes in wheat. BMC Plant Biol. 22, 372. doi: 10.1186/s12870-022-03733-3

PubMed Abstract | Crossref Full Text | Google Scholar

Finn, R. D., Clements, J., and Eddy, S. R. (2011). HMMER web server: interactive sequence similarity searching. Nucleic Acids Res. 39, W29–W37. doi: 10.1093/nar/gkr367

PubMed Abstract | Crossref Full Text | Google Scholar

Finn, R. D., Tate, J., Mistry, J., Coggill, P. C., Sammut, S. J., Hotz, H. R., et al. (2008). The Pfam protein families database. Nucleic Acids Res. 36, D281–D288. doi: 10.1093/nar/gkp985

PubMed Abstract | Crossref Full Text | Google Scholar

Geng, L., Li, M., Zhang, G., and Ye, L. Z. (2022). Barley: a potential cereal for producing healthy and functional foods. Food Qual. safety. 6, 142–154. doi: 10.1093/fqsafe/fyac012

Crossref Full Text | Google Scholar

Guo, A. Y., Chen, G. G., Zhang, H., Zhu, Q. H., Liu, X. C., Zhong, Y. F., et al. (2008). Plant TFDB: a comprehensive plant transcription factor database. Nucleic Acids Res. 36, D966–D969. doi: 10.1093/nar/gkm841

PubMed Abstract | Crossref Full Text | Google Scholar

Gupta, P., Nutan, K. K., Singla-Pareek, S. L., and Pareek, A. (2017). Abiotic stresses cause differential regulation of alternative splice forms of GATA transcription factor in rice. Front. Plant Sci. 8. doi: 10.3389/fpls.2017.01944

PubMed Abstract | Crossref Full Text | Google Scholar

He, P., Wang, X., Zhang, X., Jiang, Y., Tian, W., Zhang, X., et al. (2018). Short and narrow flag leaf1, a GATA zinc finger domain-containing protein, regulates flag leaf size in rice (Oryza sativa). BMC Plant Biol. 18, 273–273. doi: 10.1186/s12870-018-1452-9

PubMed Abstract | Crossref Full Text | Google Scholar

Huang, X. Y., Chao, D. Y., Gao, J. P., Zhu, M. Z., Shi, M., and Lin, H. X. (2009). A previously unknown zinc finger protein, DST, regulates drought and salt tolerance in rice via stomatal aperture control. Genes Dev. 23, 1805–1817. doi: 10.1101/gad.1812409

PubMed Abstract | Crossref Full Text | Google Scholar

Hudson, D., Guevara, D. R., Hand, A. J., Xu, Z., Hao, L., Chen, X., et al. (2013). Rice cytokinin GATA transcription Factor1 regulates chloroplast development and plant architecture. Plant Physiol. 162, 132–144. doi: 10.1104/pp.113.217265

PubMed Abstract | Crossref Full Text | Google Scholar

Hudson, D., Guevara, D., Yaish, M. W., Hannam, C., Long, N., Clarke, J. D., et al. (2011). GNC and CGA1 modulate chlorophyll biosynthesis and glutamate synthase (GLU1/Fd-GOGAT) expression in Arabidopsis. PloS One 6, e26765. doi: 10.1371/journal.pone.0026765

PubMed Abstract | Crossref Full Text | Google Scholar

Jiang, J., Ma, S., Ye, N., Jiang, M., Cao, J., and Zhang, J. (2017). WRKY transcription factors in plant responses to stresses. J. Integr. Plant Biol. 59, 86–101. doi: 10.1111/jipb.12513

PubMed Abstract | Crossref Full Text | Google Scholar

Kim, M. (2024). Comparative analysis of amino acid sequence level in plant GATA transcription factors. Sci. Rep. 14, 29786. doi: 10.1038/s41598-024-81159-7

PubMed Abstract | Crossref Full Text | Google Scholar

Kim, M., Xi, H., Park, S., Yun, Y., and Park, J. (2021). Genome-wide comparative analyses of GATA transcription factors among seven Populus genomes. Sci. Rep. 11, 16578. doi: 10.1038/s41598-021-95940-5

PubMed Abstract | Crossref Full Text | Google Scholar

Krzywinski, M., Schein, J., Birol, I., Connors, J., Gascoyne, R., Horsman, D., et al. (2009). Circos: an information aesthetic for comparative genomics. Genome Res. 19, 1639–1645. doi: 10.1101/gr.092759.109

PubMed Abstract | Crossref Full Text | Google Scholar

Larkin, M. A., Blackshields, G., Brown, N. P., Chenna, R., McGettigan, P. A., McWilliam, H., et al. (2007). Clustal W and clustal X version 2.0. Bioinformatics 23, 2947–2948. doi: 10.1093/bioinformatics/btm404

PubMed Abstract | Crossref Full Text | Google Scholar

Letunic, I. and Bork, P. (2018). 20 years of the SMART protein domain annotation resource. Nucleic Acids Res. 46, D493–D496. doi: 10.1093/nar/gkx922

PubMed Abstract | Crossref Full Text | Google Scholar

Liu, H., Yuan, L., Guo, W., and Wu, W. (2022). Transcription factor TERF1 promotes seed germination under osmotic conditions by activating gibberellin acid signaling. Plant Sci. 322, 111350. doi: 10.1016/j.plantsci.2022.111350

PubMed Abstract | Crossref Full Text | Google Scholar

Lowry, J. A. and Atchley, W. R. (2000). Molecular evolution of the GATA family of transcription factors: conservation within the DNA-binding domain. J. Mol. Evol. 50, 103–115. doi: 10.1007/s002399910012

PubMed Abstract | Crossref Full Text | Google Scholar

Luo, X. M., Lin, W. H., Zhu, S., Zhu, J. Y., Sun, Y., Fan, X. Y., et al. (2010). Integration of light- and brassinosteroid-signaling pathways by a GATA transcription factor in Arabidopsis. Dev. Cell. 19, 872–883. doi: 10.1016/j.devcel.2010.10.023

PubMed Abstract | Crossref Full Text | Google Scholar

Manfield, I. W., Devlin, P. F., Jen, C. H., Westhead, D. R., and Gilmartin, P. M. (2007). Conservation, convergence, and divergence of light-responsive, circadian-regulated, and tissue-specific expression patterns during evolution of the Arabidopsis GATA gene family. Plant Physiol. 143, 941–958. doi: 10.1104/pp.106.090761

PubMed Abstract | Crossref Full Text | Google Scholar

Reyes, J. C., Muro-Pastor, M. I., and Florencio, F. J. (2004). The GATA family of transcription factors in Arabidopsis and rice. Plant Physiol. 134, 1718–1732. doi: 10.1104/pp.103.037788

PubMed Abstract | Crossref Full Text | Google Scholar

Richter, R., Behringer, C., Zourelidou, M., and Schwechheimer, C. (2013). Convergence of auxin and gibberellin signaling on the regulation of the GATA transcription factors GNC and GNL in Arabidopsis thaliana. Proc. Natl. Acad. Sci. 110, 13192–13197. doi: 10.1073/pnas.1304250110

PubMed Abstract | Crossref Full Text | Google Scholar

Rueda-Lopez, M., Canas, R. A., Canales, J., Canovas, F. M., and Avila, C. (2015). The overexpression of the pine transcription factor PpDof5 in Arabidopsis leads to increased lignin content and affects carbon and nitrogen metabolism. Physiol. Plant 155, 369–383. doi: 10.1111/ppl.12381

PubMed Abstract | Crossref Full Text | Google Scholar

Rushton, P. J., Somssich, I. E., Ringler, P., and Shen, Q. J. (2010). WRKY transcription factors. Trends Plant Sci. 15, 247–258. doi: 10.1016/j.tplants.2010.02.006

PubMed Abstract | Crossref Full Text | Google Scholar

Scazzocchio, C. (2000). The fungal GATA factors. Curr. Opin. Microbiol. 3, 126–131. doi: 10.1016/s1369-5274(00)00063-1

PubMed Abstract | Crossref Full Text | Google Scholar

Stevanovic, M., Stanisavljevic Ninkovic, D., Mojsin, M., Drakulic, D., and Schwirtlich, M. (2022). Interplay of SOX transcription factors and microRNAs in the brain under physiological and pathological conditions. Neural Regener. Res. 17, 2325–2334. doi: 10.4103/1673-5374.338990

PubMed Abstract | Crossref Full Text | Google Scholar

Tamura, K., Stecher, G., and Kumar, S. (2021). MEGA11: molecular evolutionary genetics analysis version 11. Mol. Biol. Evol. 38, 3022–3027. doi: 10.1093/molbev/msab120

PubMed Abstract | Crossref Full Text | Google Scholar

Teakle, G. R. and Gilmartin, P. M. (1998). Two forms of type IV zinc-finger motif and their kingdom-specific distribution between the flora, fauna and fungi. Trends Biochem. Sci. 23, 100–102. doi: 10.1016/s0968-0004(98)01174-8

PubMed Abstract | Crossref Full Text | Google Scholar

Wang, Y., Tang, H., Debarry, J. D., Tan, X., Li, J., Wang, X., et al. (2012). MCScanX: a toolkit for detection and evolutionary analysis of gene synteny and collinearity. Nucleic Acids Res. 40, e49. doi: 10.1093/nar/gkr1293

PubMed Abstract | Crossref Full Text | Google Scholar

Wang, R., Zhao, P., Kong, N., Lu, R., Pei, Y., Huang, C., et al. (2018). Genome-wide identification and characterization of the potato bHLH transcription factor family. Genes 9, 54. doi: 10.3390/genes9010054

PubMed Abstract | Crossref Full Text | Google Scholar

Wang, F., Zhu, D., Huang, X., Li, S., Gong, Y., Yao, Q., et al. (2009). Biochemical insights on degradation of Arabidopsis DELLA proteins gained from a cell-free assay system. Plant Cell. 21, 2378–2390. doi: 10.1105/tpc.108.065433

PubMed Abstract | Crossref Full Text | Google Scholar

Wei, X., Li, Y., Zhu, X., Liu, X., Ye, X., Zhou, M., et al. (2023). The GATA transcription factor TaGATA1 recruits demethylase TaELF6-A1 and enhances seed dormancy in wheat by directly regulating TaABI5. J. Integr. Plant Biol. 65, 1262–1276. doi: 10.1111/jipb.13437

PubMed Abstract | Crossref Full Text | Google Scholar

Yang, M., Derbyshire, M. K., Yamashita, R. A., and Marchler-Bauer, A. (2020). NCBI’s conserved domain database and tools for protein domain analysis. Curr. Protoc. Bioinf. 69, e90. doi: 10.1002/cpbi.90

PubMed Abstract | Crossref Full Text | Google Scholar

Yao, X., Lai, D., Zhou, M., Ruan, J., Ma, C., Wu, W., et al. (2023). Genome-wide identification, evolution and expression pattern analysis of the GATA gene family in Sorghum bicolor. Front. Plant Sci. 14. doi: 10.3389/fpls.2023.1163357

PubMed Abstract | Crossref Full Text | Google Scholar

Zeng, X., Ling, H., Chen, X., and Guo, S. (2019). Genome-wide identification, phylogeny and function analysis of GRAS gene family in Dendrobium catenatum (Orchidaceae). Gene 705, 5–15. doi: 10.1016/j.gene.2019.04.038

PubMed Abstract | Crossref Full Text | Google Scholar

Zha, Q., Xi, X., He, Y., Yin, X., and Jiang, A. (2022). Interaction of VvbZIP60s and VvHSP83 in response to high-temperature stress in grapes. Gene 810, 146053. doi: 10.1016/j.gene.2021.146053

PubMed Abstract | Crossref Full Text | Google Scholar

Zhan, J., Thakare, D., Ma, C., Lloyd, A., Nixon, N. M., Arakaki, A. M., et al. (2015). RNA sequencing of laser-capture microdissected compartments of the maize kernel identifies regulatory modules associated with endosperm cell differentiation. Plant Cell. 27, 513–531. doi: 10.1105/tpc.114.135657

PubMed Abstract | Crossref Full Text | Google Scholar

Zheng, J., Zhang, Z., Tong, T., Fang, Y., and Zhang, X. (2021). Genome-wide identification of WRKY gene family and expression analysis under abiotic stress in barley. Agronomy 11, 521. doi: 10.3390/agronomy11030521

Crossref Full Text | Google Scholar

Zhu, W., Guo, Y., Chen, Y., Wu, D., and Jiang, L. (2020). Genome-wide identification, phylogenetic and expression pattern analysis of GATA family genes in Brassica napus. BMC Plant Biol. 20, 543. doi: 10.1186/s12870-020-02752-2

PubMed Abstract | Crossref Full Text | Google Scholar