Skip to main content
Download PDF

Genome-wide identification, classification and expression analysis of the heat shock transcription factor family in Garlic (Allium sativum L.)

BMC Plant Biology volume 24, Article number: 421 (2024) Cite this article

Abstract

Background

The heat shock transcription factor (HSF) plays a crucial role in the regulatory network by coordinating responses to heat stress as well as other stress signaling pathways. Despite extensive studies on HSF functions in various plant species, our understanding of this gene family in garlic, an important crop with nutritional and medicinal value, remains limited. In this study, we conducted a comprehensive investigation of the entire garlic genome to elucidate the characteristics of the AsHSF gene family.

Results

In this study, we identified a total of 17 AsHSF transcription factors. Phylogenetic analysis classified these transcription factors into three subfamilies: Class A (9 members), Class B (6 members), and Class C (2 members). Each subfamily was characterized by shared gene structures and conserved motifs. The evolutionary features of the AsHSF genes were investigated through a comprehensive analysis of chromosome location, conserved protein motifs, and gene duplication events. These findings suggested that the evolution of AsHSF genes is likely driven by both tandem and segmental duplication events. Moreover, the nucleotide diversity of the AsHSF genes decreased by only 0.0002% from wild garlic to local garlic, indicating a slight genetic bottleneck experienced by this gene family during domestication. Furthermore, the analysis of cis-acting elements in the promoters of AsHSF genes indicated their crucial roles in plant growth, development, and stress responses. qRT-PCR analysis, co-expression analysis, and protein interaction prediction collectively highlighted the significance of Asa6G04911. Subsequent experimental investigations using yeast two-hybridization and yeast induction experiments confirmed its interaction with HSP70/90, reinforcing its significance in heat stress.

Conclusions

This study is the first to unravel and analyze the AsHSF genes in garlic, thereby opening up new avenues for understanding their functions. The insights gained from this research provide a valuable resource for future investigations, particularly in the functional analysis of AsHSF genes.

Peer Review reports

Background

Plant growth and development depend significantly on a timely and efficient response to biotic and abiotic stresses. Transcription factors are essential in regulating the expression of stress-responsive genes, which play vital roles in plant responses to stresses [1,2,3]. Heat shock transcription factors (HSFs) play essential roles in the response to heat stress and other stresses such as chilling, salinity, drought, and heavy metal toxicity [4,5,6,7]. By binding to heat stress elements (HSEs) in the promoters of heat stress-responsive genes, HSFs regulate the expression of heat shock proteins (HSPs), which function as molecular chaperones to prevent protein denaturation [8, 9]. Additionally, HSPs regulate protein folding, accumulation, localization, and degradation, and are believed to play significant roles in coping with various environmental stresses [10]. Furthermore, HSFs can regulate reactive oxygen species (ROS)-scavenging enzymes, such as ascorbate peroxidase (APX) and catalase (CAT) [11]. Therefore, HSFs play crucial roles in elucidating the molecular mechanisms underlying plant responses and adaptations to stresses. Besides, HSFs also participate in plant growth and development. Further studies on HSFs are necessary to develop strategies for crop improvement, addressing the challenges posed by global environmental changes [12,13,14].

HSFs, like other transcription factors, consist of multiple structurally and functionally conserved regions. A typical HSF protein possesses a DNA-binding domain (DBD) near the N-terminus, an adjacent oligomerization domain (OD) consisting of heptad repeats of hydrophobic amino acid residues (HR-A/B) and specific motifs such as nuclear localization signal (NLS), nuclear export signal (NES), activator motif (AHA motif) and repressor domain [8, 15]. DBD is the most conserved ingredient of HSFs, comprising three helical bundles (α1, α2, and α3) and four antiparallel β sheets (β1, β2, β3, β4) [16]. Besides, DBD domain provides HSF proteins with the specific ability to recognize the HSEs (HSEs: 50-AGAAnnTTCT-30) which are conserved palindromic binding motifs present in the promoter of heat stress-inducible genes [15, 17, 18]. Additionally, the HR-A/B regions have the characteristic of predicted coiled-coil structure, which is vital for the formation of active trimers [19]. Under various stress conditions, HSF proteins can assemble into active trimers to activate the expression of target genes, such as HSP30, 70 and 90, by binding to cis-elements of these gene promoters [20,21,22,23]. Moreover, based on the variations in their HR-A/B domain, plant HSFs are categorized into three subfamilies: class A (subclasses A1, A2, A3, A4, A5, A6, A7, A8, and A9), class B (subclasses B1, B2, B3, and B4), and class C (subclasses C1 and C2) [24,25,26]. There are 21 or 7 amino acid residues inserted into HR-A/B region of Class A and Class C HSFs, respectively. However, class B HSFs have a heptad repeat pattern without insertion. Furthermore, the transport of HSFs into the nucleus can be assisted by NLS, and the NES regulates the distribution of HSFs between cytosol and nucleus. The AHA motif plays a crucial role as transcription activator in the class A HSFs, which is characterized by the presence of aromatic, large hydrophobic and acidic amino acid residues [27].

To date, the function of many members of HSF family have been analyzed in several plants including maize, rice, Chinese cabbage, wheat, carrot, soybean, Arabidopsis, cotton, legumes, poplar and barrel clover [28,29,30,31,32,33,34,35,36,37]. Experiments have confirmed that HSFs not only take part in resistance to heat but also are involved in response to abiotic or biotic stresses [38, 39]. For example, HSFA1a, HSFA1b, HSFA1d, HSFA1e and HSFA2 can act as the master regulators in the response to heat stress [40, 41]. As a typical representative of plant, AtHSFA2 not only confers heat and osmotic stress tolerance, but also is essential for plant growth and development [42,43,44].The transcriptional activation activity of AtHSFA6a significantly increased under salt stress [45]. Overexpression of GmHSFA1 was proved to improve heat stress tolerance of soybean [46]. The overexpression of HSFA4a can enhance Cd tolerance of wheat and rice [47]. By contrast, HSFBs are shown to be transcriptional repressors, for instance, AtHSFB1 and AtHSFB2b negatively regulate heat responsive genes [48]. OsHSFB2b overexpression significantly reduced plant drought and salt tolerance [49]. Moreover, OsHSFC1b expression was strongly unregulated by salt, mannitol and ABA, not by H2O2, and OsHSFC1b overexpression improved salt and osmotic tolerance [50]. Moreover, HSFA3 is necessary for the response to heat and drought stresses in Arabidopsis, but not in tomato [51], indicating that some HSFs have distinct functions in different species.

Garlic (Allium sativum L.), a perennial bulbous plant, is one of the most important economically vegetable, spice, and medicinal crops [51]. It originated from Central Asia 6000 years ago and had been cultivated for more than 5000 years all over the world [52]. Garlic is a diploid species (2n = 16), but the garlic genome is very large (16.9G) and complex, with high repetition rate (∼ 1.68%) and high heterozygosity of 80% [53]. The successful assembly of garlic chromosome-level genome provides basis for the comprehensive identification and characterization of the garlic HSFs families, which is crucial for exploring the molecular mechanism of garlic resistance to stresses. Here, we performed a comprehensive analysis of HSFs in garlic including physicochemical properties, phylogenetic relationships, motif distribution, intron-exon pattern, gene duplication events, expression profiles and preliminary functions. In addition, genomic variation, genetic diversity, and principal component analysis (PCA) were also analyzed. Our results provide valuable information for further studies of the biological functions of garlic HSFs.

Results

Identification and characterization analysis of HSFs in garlic

Totally, twenty-two genes encoding for HSFs proteins were identified in the whole genome using a range of HMM-based bioinformatics approaches. Among these genes, five genes (Asa1G01216, Asa7G01431, Asa7G07097, Asa6G06026, and Asa2G05473) were excluded due to the preservation of the DBD domain, coiled-coil structure, and gene integrity. Finally, 17 AsHSF genes were used for further analysis. The selected genes were ranked according to their physical locations (Table S1). The chromosome distribution displayed that the AsHSF genes were unevenly distributed on the chromosomes 1, 4, 5, 6, 7 and 8. Besides, Asa0G01036, Asa0G02991 and Asa0G03146 were discovered on unplaced scaffold. Chromosome 4 had the most abundant AsHSF genes with four, followed by chromosomes 6 and 8 with three genes. However, some chromosomes did not have the AsHSF genes such as chromosomes 2 and 3.

The characteristic analysis of AsHSFs revealed that Asa7G06422 was the shortest protein with a length of 176 amino acids, whereas the longest one was Asa5G00955 with a length of 477 amino acids (Table S1). The molecular weight (MW) of these proteins ranged from 20.57 (Asa7G06422) to 53.23 (Asa5G00955) kDa and their computed theoretical isoelectric points (pI) ranged from 4.67 (Asa5G00955) to 9.41 (Asa7G06422). The grand average of hydropathicity (GRAVY) value, which is correlated with the protein hydrophilicity, ranged from − 0.845 (Asa4G01727) and − 0.515 (Asa0G01036) for AsHSFs, suggesting that all AsHSFs are hydrophilic. The result of instability index revealed that all the other proteins, except for Asa4G02336 and Asa8G01031, were found to be unstable. Additionally, all AsHSFs were predicted to localize in the nucleaus. These findings indicate variations in the physical and chemical properties of AsHSF proteins, potentially attributed to the dissimilarities in non-conserved regions.

Phylogenetic relationship, gene structure and conserved motif analysis

To investigate the phylogenetic relationships of HSF subfamilies among different species, we used the amino acid sequences of 17 proteins from A. sativum, 21 from A. thaliana, 25 from O. sativa, 8 from P. patens, and 30 from T. cacao to construct a phylogenetic tree using the maximum likelihood method (Fig. 1). The result showed that HSF proteins can be divided into three major subfamilies: classes A (pink), B (light green) and C (orange). The class A consisted of nine smaller clusters (A1-A9), representing the maximum of subclasses. Additionally, the class A subfamily had the highest number of HSF proteins, with 63 members across the five species, while the number of class C subfamily members was the smallest, only 8 members. The above result showed that the AsHSFs contained members of three subfamilies: 9 HSFAs, 6 HSFBs and 2 HSFCs.

Fig. 1
figure 1

Phylogenetic analysis of HSF family proteins from A. sativum (Asa), A. thaliana (AT), O. sativa (Os), P. patens (Pp) and T. cacao (EOY). The phylogenetic tree was constructed using the maximum likelihood (ML) with 1000 bootstrap replicates. Classes A, B and C are filled in pink, light green and orange, respectively. Red solid squares, green solid circles, blue solid stars, yellow solid hexagons and brown solid diamonds represent proteins from A. sativum, A. thaliana, O. sativa, P. patens, and T. cacao, respectively

Full size image

To further understand the structural variation of the AsHSFs, the intron and exon structures of AsHSF genes were investigated (Fig. 2a and b). The analysis showed that, except for Asa4G02336 and Asa4G01727, other AsHSFs contained two exons. Besides, to analyze the protein sequence features of AsHSFs, MEME was used to search for the conserved motifs of AsHSF proteins. As a result, ten motifs with lengths ranging from 8 to 50 amino acids were identified (Fig. 2a and c, Table S2). The distribution patterns of the motifs (motif 1, 2, 4 and 6) were similar for class B, except for Asa7G06422, which lacked motif 6. Besides, the same motifs (motif 1, 2, 3 and 4) were detected in class C. Motifs 1 and 4 were detected in all AsHSFs, and motif 6 was only present in the members of AsHSFBs. The number of conserved motifs in Asa6G04911 was highest (9), and Asa7G06422 had the fewest (3).

Fig. 2
figure 2

Architecture of phylogenetic tree, gene structure and conserved motifs of garlic HSFs. (a) Phylogenetic relationships of AsHSF proteins. Classes A, B and C are marked in black, light green and orange, respectively. (b) Exon-intron structures of garlic HSF genes. Green boxes and gray lines represent exons and introns, respectively. (c) The conserved motifs of AsHSF proteins. A total of ten motifs with the number 1–10 were identified and depicted in different colors. The sequence information of each motif is shown in Table S2. The protein length is indicated at the bottom of the figure

Full size image

Furthermore, the conserved domains in AsHSFs were analyzed (Supplementary Fig. 1, Table S3). The length of DBD domains and HR-A/B domains varied, with Asa0G02991 having the longest DBD domain (101 aa), Asa4G01727 having the smallest (87 aa), and most of DBD domains were 93 aa. Except for the DBD domain and HR-A/B domain, the other domains, like NLS domain, NES domain and AHA domain, were observed in AsHSF members. The majority of AsHSFs contained NLS domain whereas only four of seventeen AsHSFs possessed NES domain. In contrast, AHA motifs were present in the center of the C-terminal activation domains for the majority of class A members, while they were not detected in class B and C. Overall, despite variations in size and sequence, the members within a specific subfamily shared similar profiles of gene structure, conserved motif and domain, which confirmed the reliability of the phylogenetic tree.

Chromosome localization, gene duplication and synteny analysis of AsHSF genes

The analysis of chromosomal localization revealed the uneven arrangement of 14 AsHSF genes on 6 garlic chromosomes, and 3 AsHSF genes (Asa0G01036, Asa0G02991 and Asa0G03146) were present on 3 scaffolds, respectively (Fig. 3, Table S1). There were 4 AsHSF genes localized on chromosome 4, ranking as the most abundant chromosome, whereas no genes were detected on chromosomes 2 and 3. The other 5 chromosomes contained 3 (chromosomes 6 and 8) to 1 (chromosomes 1 and 7) AsHSF genes. Additionally, no significant correlation between the chromosome length and the number of AsHSF genes was observed (Pearson correlation r = 0.3408, p-value = 0.4088), implying that longer chromosomes do not inherently harbor a greater number of AsHSF genes.

Fig. 3
figure 3

Chromosomes distribution of AsHSF genes. Individual chromosomes are represented by white bars, and the chromosome numbers are marked on the left side of the bars. The scale on the left is in million bases (Mb)

Full size image

To investigate the gene expansion mechanism of AsHSFs, gene duplication event analysis was conducted. Two tandem duplicated regions including 4 AsHSF genes (Asa4G02336 and Asa4G02337, Asa8G02160 and Asa8G02166) were identified on chromosomes 4 and 8, implying hot spots of AsHSF gene distribution (Fig. 3, Table S4). Additionally, only one pair of segmentally duplicated genes (Asa1G03975 and Asa4G01727) was identified, which were related with chromosomes 1 and 4 (Fig. 4, Table S4). The above results indicated that both tandem and segmental duplication might be the key evolutionary driving force of AsHSF genes.

Fig. 4
figure 4

Interchromosomal relationships of AsHSF genes. Gray lines represent all syntenic relationships within the garlic genome, while collinear blocks of AsHSF genes are indicated by red lines

Full size image

To evaluate the evolutionary restrictions of AsHSF genes, the non-synonymous (Ka) vs. synonymous (Ks) substitution ratios of the above duplicated gene pairs were estimated. Interestingly, the Ka/Ks ratio between Asa4G02336 and Asa4G02337 was greater than 1, implying positive selection, whereas that between Asa4G01727 and Asa1G03975 was less than 1, indicating purifying selection (Table S4). Additionally, the Ka/Ks ratio between Asa8G02160 and Asa8G02166 was almost equal to 1, indicating neutral selection during gene expansion process.

Evolutionary analysis of HSF genes in garlic and several different species

To further analyze the evolutionary mechanisms of the AsHSF genes family, syntenic relationships with four representative plants, including two monocotyledonous plants (Oryza sativa and Zea mays) and two dicotyledonous plants (Arabidopsis thaliana and Theobroma cacao) were constructed (Fig. 5). A total of 4 HSF genes in garlic had syntenic relationships with those in four plants, of which 3, 3, 2 and 1 syntenic gene pairs were detected with two monocots (Zea mays and Oryza sativa) and two dicots (Arabidopsis thaliana and Theobroma cacao), respectively. The result is consistent with the evolutionary associations between garlic and these plants, indicating that these evolutionarily conserved genes may play vital roles during evolution. Additionally, the Ka/Ks ratios between garlic and the monocots had no significant difference with those between garlic and the dicots (Table S5).

Fig. 5
figure 5

Syntenic analysis of HSF genes between garlic and four representative plant species. Grey lines represent all collinear blocks between the paired genomes, while red lines highlight syntenic HSF gene pairs

Full size image

Nucleotide variation and population structure of AsHSF genes

Garlic resequencing data was used to investigate AsHSF-associated single nucleotide polymorphisms (SNPs) which can reflect the sequence diversity of AsHSF genes. The SNP call pipeline identified 918 high confidence SNPs (Table S6). Most of SNPs associated with AsHSFs were enriched in the intergenic region, while others were present with in the genic regions, involving 1 missense, 5 intron, 7 downstream, and 4 upstream variants. Besides, the overall transition/transversion (Ts/Tv) ratio was 1.981, with C/T (21.02%) and G/A (21.46%) being the most prevalent allelic substitution profiles. These results implied a higher occurrence of purine to purine or pyrimidine to pyrimidine mutations compared to pyrimidine to purine or purine to pyrimidine mutations.

To further investigate the relatedness between the origin group and three different garlic cultivars, PCA using AsHSF-related SNPs was carried out (Fig. 6a). The first eigenvector, which explained 49.35% of the genetic variance, revealed divergence among these populations. The second and third eigenvector were used to distinguish wild-type and endemic garlic, accounting for 22.28% and 8.22% of the genetic variation, respectively. Similar population relationships were observed in the phylogenetic tree (Fig. 6b). ADMIXTURE analysis further confirmed the consistent group relatedness observed in the phylogenetic tree, exhibiting exact joining relationships. When K = 4, noticeable biological differences between wild garlic and local garlic were observed, and with an increase to K = 5, there was a clear separation according to geographical origin. The presence of genetic mixing between wild and landraces garlic indicates the underlying domestication of cultivated garlic and ongoing gene flow between wild and landraces garlic.

Fig. 6
figure 6

Population structure of wild garlic and local garlic according to AsHSF-related SNPs. (a) Principal component analysis plots of the first component (PC1), second component (PC2) and third component (PC3). The color of dots represents the population and location. (b) Phylogenetic tree and population structure with K ranging from 2 to 5. The rooted tree was constructed using neighbor-joining method. The orders and positions of sample accessions on the x-axis are consistent with those in the neighbor-joining tree

Full size image

Population-based nucleotide diversity was estimated to evaluate the emergence of previous genetic bottlenecks of AsHSF genes during garlic acclimation. The nucleotide diversity of AsHSF genes from wild garlic to local garlic decreased by only 0.0002% (Supplementary Fig. 2), suggesting a mild genetic bottleneck experienced by this gene family during domestication. Besides, Wright’s F-statistic (Fst) was used to assess population differentiation. The Fst index between wild and landrace garlic within the AsHSFs was 0.2121, suggesting that AsHSFs did not suffer from strong selective pressure during garlic domestication.

Cis-acting elements of garlic AsHSF genes

To predict the possible biological functions of the HSF family in garlic, cis-acting elements in the promoter of these genes were investigated. Owing to the limited assembly integrity of garlic genome, the promoter sequences of 4 genes (Asa0G01036, Asa0G02991, Asa7G06422 and Asa8G03146) were not retrieved. Therefore, we further investigated the cis-acting elements of 13 AsHSF genes (Fig. 7a). The result revealed that these predicted cis-acting elements can be divided into three classifications (developmental, hormone and stress process-related elements) (Fig. 7b and c). Among these, developmental-related elements were the most abundant, including the CAT-box associated with meristem expression, which were present in the promoter regions of most AsHSF genes. Furthermore, ARE element (essential for anaerobic induction), LTR element (related to low-temperature) and MBS (related to drought-inducibility) were detected in the promoters of 12, 6 and 5 AsHSF genes, respectively. All AsHSF gene promoters possessed the CGTCA-motif and TGACG motif (associated with methyl jasmonate (MeJA) responsiveness). Furthermore, the ABA-responsive element (ABRE), salicylic acid responsiveness (TCA-element) and the auxin-responsiveness element (TGA-element and AuxRR-core) were found in 10, 6, 4 and 4 AsHSFs, respectively. The promoter regions of 2, 5 and 4 genes contained elements associated with gibberellin responsiveness, such as GARE-motif, P-box and TATC-box. These results showed that transcriptional regulation of AsHSFs is related to hormones, development and stress.

Fig. 7
figure 7

Cis-acting elements in the promoter of the AsHSF genes. (a) Diagram illustrating the present of cis-acting elements in the promoter of the AsHSF genes. Each cis-acting elements is indicated by a rectangular box of a specific color. (b) Number of each cis-regulatory element in the promoter regions of the AsHSF genes. Red dots represent the number of genes containing the corresponding cis-acting element, while the total number of cis-acting elements is represented by gray boxes. (c) Frequency of each cis-regulatory element in the AsHSF gene promoters. The cis-acting elements are categorized into development-related, hormone-related and stress-related classes according to the feature notes

Full size image

Expression profile analysis of AsHSF genes under various abiotic stresses

To further analyze the potential physiological function of AsHSF genes under various abiotic stress, such as salt, heat and cold treatment, the expression patterns of these genes were detected via qRT-PCR (Fig. 8). For salt stress, all 17 AsHSF genes were detected in leaves and roots, of which 7 upregulated genes and 6 downregulated genes were found in leaves, and 10 upregulated and 3 downregulated AsHSF genes were detected in roots. Among them, in both the root and leaf, seven genes (Asa4G05278, Asa8G02166, Asa0G01036, Asa0G02991, Asa7G06422, Asa6G01673, Asa1G03975) exhibited induced expression, while three genes (Asa8G01031, Asa4G02336, Asa5G00486) displayed inhibited expression. For cold stress, 6 upregulated AsHSF genes and 10 downregulated AsHSF genes were identified in leaves, and 8 upregulated AsHSF genes and 7 downregulated AsHSF genes were found in roots. In both the root and leaf, six genes (Asa8G02166, Asa0G01036, Asa0G02991, Asa7G06422, Asa6G01673, Asa1G03975) were upregulated, and Asa0G01036 had the most significantly induced expression with the highest expression level at 12 h. In addition, Asa5G00955, Asa6G03712, Asa6G04911, Asa8G01031, Asa0G03146, Asa4G02336 and Asa5G00486 were inhibited in leaves and roots. For heat stress, the majority of genes were induced to express in both the root and leaf. Interestingly, Asa6G04911 exhibited the highest induced expression level in both the leaf and root, suggesting its potential key role in responding to heat stress. Moreover, the expression level of Asa6G03712, Asa6G04911, Asa8G01031, Asa4G02336 and Asa4G01727 were up-regulated under heat stress, and down-regulated under cold and salt stresses in leaves. Asa5G00955, Asa0G03146 and Asa8G02160 responded to temperature stress but not salt stress in leaf, whereas Asa6G03712 and Asa6G04911 responded to temperature stress but not salt stress in root. The expression level of Asa8G02166, Asa0G01036, Asa0G02991, Asa7G06422, Asa6G01673 and Asa1G03975 were up-regulated in leaf and root under three stresses (heat, cold and salt). The distinct expression patterns of individual AsHSF suggested the diverse roles of each AsHSF gene under different biotic stresses.

Fig. 8
figure 8

Expression analysis of AsHSF genes in response to salt, cold and heat using qRT-PCR. The expression data were normalized to 1 in unstressed plants (0 h). Error bars indicate standard deviations from the biological replicates. One asterisk (*) represents a significant difference at P < 0.05, determined by Student’s t-test

Full size image

Gene co-expression analysis

Co-expression network analysis can effectively annotate gene function [54]. The co-expression network of AsHSF genes was constructed based on a massive dataset of 185 RNA-Seq samples by using Weighted Gene Correlation Network Analysis (Table S7). A total of 8 co-expression networks were acquired (Fig. 9). The largest network was centered on Asa4G02336 (1156 genes), whereas the smallest network took Asa5G00955 as the core (46 genes) (Supplementary Fig. 3).

Fig. 9
figure 9

GO enrichment analysis of eight co-expressed gene sets. The size of each spot represents the number of genes enriched in specific term. The color of each spot indicates the enrichment significance level

Full size image

To investigate the potential biological processes in which these genes may be involved, GO enrichment analysis was further investigated. All of AsHSF genes contained the GO terms associated with stress response, such as response to stress, response to abiotic stimulus and response to osmotic stress, indicating that AsHSF genes might be crucial for stress response (Fig. 9). Particularly, among these genes, Asa6G04911 and Asa5G00955 were enriched in GO terms related to heat response, including response to heat and heat acclimation, suggesting key regulatory roles of these genes in heat response process. Additionally, except for Asa7G06422, the co-expressed networks of most AsHSF genes were enriched in developmental process, tissue development and organ development, suggesting that these genes might be associated with plant growth and development. Moreover, several GO terms related to hormone response, such as response to hormone stimulus, jasmonic acid mediated signaling pathway, and response to ethylene stimulus, were enriched in the co-expressed network of several AsHSF genes (Asa4G02336, Asa4G05278 and Asa7G06422), implying the potential vital function of these genes in the regulation of hormone response.

Protein interaction network of AsHSFs

To further predict the functions of AsHSFs, STRING was used to analyze the functional and physical protein associations of the AsHSF proteins (Supplementary Fig. 4). The results revealed that the interaction network contain 17 AsHSF proteins and 10 known Arabidopsis proteins. The results of GO enrichment analysis revealed that the majority of AsHSFs exhibited enrichment in various biological processes. Among them, the most highly enriched processes were cellular response to heat, positive regulation of response to heat stress and response to temperature stimulus (Supplementary Fig. 5).

The functional analysis of Asa6G04911

The result of qRT-PCR analysis revealed that Asa6G04911 exhibited the most significant alteration in response to heat stress, and GO enrichment of co-expression analysis showed that Asa6G04911 was associated with heat-related processes. To gain deeper insights into the function of Asa6G04911 in heat stress, we introduced this gene into the S. cerevisiae BY4741 strain to assess its performance under heat stress. At 30 °C, there were no significant growth differences between the control strain (BY4741 transformed with an empty vector) and the recombinant strain harboring Asa6G04911. However, at 35 °C, the recombinant strain displayed a significantly accelerated growth rate compared to the control strain (Fig. 10a). These findings strongly suggested the key function of Asa6G04911 gene in the response to heat stress.

Fig. 10
figure 10

The functional analysis of Asa6G04911. (a) The control strain and the recombinant strain harboring Asa6G04911 were incubated on SG/-Ura plates at 30 °C and 35 °C for 3 days, respectively. (b) Yeast two-hybrid assay of Asa6G04911 and HSP70/90. The positive control (53-pGBKT7 + T-pGADT7) and negative control (53-pGBKT7 + lam-pGADT7) were employed for comparison

Full size image

Additionally, it is predicted that Asa6G04911 might interact with HSP70 and HSP90 using STRING (Supplementary Fig. 4). To verify these interactions, yeast two-hybrid (Y2H) assay was conducted. As depicted in Fig. 10b, both yeast transformants (Asa6G04911-BD and HSP70-AD) and yeast transformants (Asa6G04911-BD and HSP90-AD) exhibited blue color on SD/-Leu/-Trp/-His/-Ade/3-AT/x-α-gal, which verified the specific interactions between Asa6G04911 and HSP70/90.

Discussion

Global warming has caused serious abiotic stresses and an increasing number of reports support the crucial role of HSF genes in regulating the expression of genes related to biotic and abiotic stresses [34, 55]. Furthermore, HSF family members have been investigated and characterized in various plants, including maize [56], peach [57], wheat [58], potato [59], cotton [32], and Chinese cabbage [34]. However, a comprehensive study of AsHSF genes at the whole genome level in garlic has not been conducted so far. In this study, we conducted a comprehensive analysis of the AsHSF genes, resulting in the discovery of 17 full length HSF genes distributed across the garlic genome (Table S1). The number of HSF genes in garlic was lower than wheat (82) [60], rice (25) [61], tomato (26) [62], soybean (26) [28] and sorghum (25) [63]. Previous studies have demonstrated that different species harbor varying amounts of HSF transcription factors due to their distinct growth environments, and land plants generally possess a higher number of HSF genes compared to algae [36]. The diversification of HSFs in plants may be attributed to gene and genome-wide replication events, such as whole-genome duplication (WGD), occurring at distinct evolutionary stages, followed by gene loss [64]. Additionally, the result of theoretical isoelectric point implied that Asa4G02336, Asa7G06422, Asa8G02160 and Asa8G02166 might be basic protein, while the others were acidic proteins (Table S1), indicating their potential important roles in diverse microenvironments [65]. Furthermore, the GRAVY results showed that all the AsHSF proteins were hydrophilic, which is consistent with the findings in potato [59], Chinese cabbage [34] and carnation [66].

Phylogenetic analysis revealed that these identified garlic genes could be classified into three main classes, namely A, B and C, with 9, 6 and 2 members, respectively (Fig. 1). Similar to Arabidopsis thaliana, Oryza sativa and Theobroma cacao, class A was the most abundant subclass in garlic, surpassing class B and C in terms of member count. In comparison to Arabidopsis, subclass A6, A7, A8 and A9 were lost in the garlic’s HSF family, indicating that gene loss might have happened during the evolutionary process. Moreover, the modular structure of garlic HSF proteins was highly conserved, and the distribution of motifs showed that the majority of members within the same subclass exhibited similar protein motifs, providing additional insights into the evolutionary relationship of AsHSFs. Additionally, there are indications that gene intronic profiles are closely associated with gene function, and genes that require rapid activation in response to stress are inclined to evolve to gene with reduced intron density [67]. Although the intron number of most AsHSF genes were similar, their intron length varied, which is consistent with observations in potato [59] and carnation [66]. The differences in intron length and intron location of AsHSF genes indicate potential functional variations.

Various studies have reported that gene duplication, including tandem duplication and segmental duplication, might be the main driving force for the expansion of each family and the acquisition of novel gene functions [68]. Another report revealed that garlic has undergone three WGD events [53]. Here, only three pairs of AsHSFs were underwent gene duplication, including two pairs of tandem (Asa4G02336 and Asa4G02337, Asa8G02160 and Asa8G02166) and one segmental (Asa1G03975 and Asa4G01727) duplication events, implying that gene duplication events might play vital roles in the gene expansion of AsHSFs. Additionally, the collinearity between garlic HSFs and those of two monocots and two dicots was analyzed. More collinearity gene pairs were observed between garlic and monocots, compared with those between garlic and dicots, which is consistent with the relative phylogenetic relationships among these species. The results of Ka/Ks ratios between these gene pairs suggested that these genes might undergo different evolutionary selection pressures.

When wild species are subjected to a new selection environment related to human demands, it will slowly lead to distinct morphological and physical characteristics alterations in the species, eventually, making it separated from its wild ancestors. This is a process of domestication which is co-evolution of plants and animals [69, 70]. Currently, there is limited knowledge about the alterations in AsHSFs caused by garlic domestication. The genetic variations in AsHSF genes between wild and domesticated garlic populations were analyzed using whole-genome resequencing data. A total of 918 AsHSFs-related SNPs were identified, and we observed an uneven distribution of SNPs in the genome sequence, which is consistent with previous studies [71]. PCA, admixture and phylogenetic analysis effectively separated all accessions into two groups: wild garlic and local garlic. In addition, the assessment of nucleotide diversity in the population revealed that the AsHSF genes encountered a slight genetic bottleneck during domestication, and the result of Fst indicated that the AsHSF genes did not undergo strong selection pressure during domestication.

Compared with animals, plants are constantly exposed to environmental stresses, therefore a series of molecular mechanisms to cope with changes in the external environment have evolved in plants. Promoter analysis revealed significant differences in the number and types of cis-acting elements among AsHSF genes, suggesting that AsHSFs may play diverse roles in different types of stresses (Fig. 7). However, it is interesting that no cis-acting elements associated with heat were present in the promoter of AsHSF genes, which is consistent with studies in potato [59] and carnation [66]. The precise regulatory mechanisms of AsHSF genes need to be further investigated. The expression patterns of AsHSFs under different stresses were studied by qRT-PCR. The majority of AsHSFs responded to temperature stress, indicating that AsHSFs might play very important roles in the process of temperature stress response, and the different expression patterns of each gene under diverse stresses suggest that AsHSFs might have distinct functions in various abiotic stress processes. Thus, these genes hold significant potential as candidate genes for the study of stress resistance in garlic. In addition, the results showed that the majority of genes could respond to heat stress. Particularly, Asa6G04911 was the most significantly induced AsHSF gene in heat stress, suggesting that this gene might have specific functions for heat stress response and related signal transduction. Moreover, the GO enrichment result of co-expression analysis further predicted that Asa6G04911 might have a vital role in the heat response process, which was validated by the yeast-induced expression experiment. In addition, protein interaction network showed that Asa6G04911 might interact with HSP70/90, which was further proved by yeast two-hybrid experiment. Our study laid a foundation for further investigation on the molecular functional mechanism of Asa6G04911 in garlic.

Conclusions

In this study, we conducted a comprehensive analysis of the garlic HSF gene family at the genome-wide level. We successfully identified a total of 17 genes classified into 3 classes. Additionally, we conducted analyses on various aspects, including gene structure, motif composition, chromosome localization, gene duplication events, nucleotide variations, and population structure of the AsHSF genes, providing insights into the evolutionary characteristics of AsHSF genes. Moreover, we investigated the functional aspects of the AsHSF genes by examining promoter cis-regulatory elements, expression patterns, co-expression analysis, and protein interaction prediction. Notably, our findings confirmed that Asa6G04911 plays a crucial role in response to heat stress and can interact with HSP70/90. These findings improve our understanding of the roles of AsHSF genes in heat stress response and provide a good foundation for the further investigation of the molecular regulatory mechanisms underlying the regulation of heat stress in garlic.

Methods

Identification and sequence analysis of the HSF members in garlic

Whole genome sequence and annotation data of garlic were retrieved from https://doi.org/10.6084/m9.figshare.12570947.v1, and those of the other five species, namely Arabidopsis thaliana, Oryza sativa, Physcomitrella patens, Zea mays and Theobroma cacao, were retrieved from the Phytozome (https://phytozome-next.jgi.doe.gov/) and Ensembl Plants database (http://plants.ensembl.org/index.html). HMMER 3.0 was used to conduct the HMM search with a threshold of E < 1e− 5 to explore the HSF domain (PF00447) which was obtained from the PFAM database (http://pfam.xfam.org/). Moreover, BLASTP was utilized to search the garlic proteins with a threshold of 1e− 5 for the e-value and 50% for the identity, using the HSF protein sequences of rice and Arabidopsis retrieved from PlantTFDB (http://planttfdb.gao-lab.org/prediction.php). Furthermore, NCBI-CDD (https://www.ncbi.nlm.nih.gov/cdd/) and SMART (http://smart.embl.de/) were used to determine the DBD domain of potential HSF genes. The coiled-coil structure was detected by MARCOIL (https://toolkit.tuebingen.mpg.de/tools/marcoil). The NLS domain in AsHSFs was detected by cNLS Mapper (http://nls-mapper.iab.keio.ac.jp/cgi-bin/NLS_Mapper_form.cgi) [72,73,74]. The candidate AsHSFs lacking DBD domain or coiled-coil structure were excluded. The theoretical pI MW, GRAVY, n.c.r (%, total number of negatively charged residues (Asp + Glu)), p.c.r (%, total number of positively charged residues (Arg + Lys)), instability index (I.I.) and aliphatic index (A.I.) of HSF proteins in garlic were estimated using the Expasy proteomics server (https://web.expasy.org/computepi/). Cell-PLoc 2.0 (http://www.csbio.sjtu.edu.cn/bioinf/plant-multi/) was used to predict subcellular localization of AsHSF proteins.

Phylogenetic relationship, gene structure and conserved motif analysis

MUSCLE [75] with default parameters was utilized to conduct multiple sequence alignment of retrieved AsHSF proteins. MEGA 7.0 was used to construct the phylogenetic tree of HSF proteins from Allium sativum, Arabidopsis thaliana, Oryza sativa, Physcomitrella patens and Theobroma cacao using the maximum likelihood method. The bootstrap value was 1000. The Gene Structure Display Server (GSDS: http://gsds.gao-lab.org/index.php) was utilized to elucidate the exon and intron structure of AsHSF genes. The conserved motifs of AsHSF proteins were analyzed by MEME (http://meme-suite.org/tools/meme)[76].

Chromosomal localization, gene duplication, nucleotide variation and population structure of AsHSFs

Chromosomal locations of AsHSF genes were acquired from the available genome annotation information (https://doi.org/10.6084/m9.figshare.12570947.v1). Mapchart was used to visualize chromosomal location maps. Multiple collinear scanning toolkits (MCScanX) was used to conduct gene replication events analysis of HSF genes with the criteria described [77]. In addition, the syntenic associations among HSF genes of various plants, such as Arabidopsis thaliana, Oryza sativa, Allium sativum, Zea mays and Theobroma cacao, were visualized by Dual Synteny Plotter software (https://github.com/CJChen/TBtools). The whole-genome resequencing data of 233 garlic samples were retrieved from the Genome Variation Map (accession: PRJCA006629), and detailed material information, such as geographic distribution, were showed in Table S8. The annotation of SNPs was performed using SnpEff v4.3 [78]. Furthermore, the population structure was estimated by ADMIXTURE v1.3.0 using K-values ranging from 2 to 5. Treebest v1.9.2 was used to construct the phylogenetic tree, and PCA was analyzed by the Smartpca implemented in EIGENSOFT v4.2. Vcftools v0.1.16 was used to calculate the nucleotide diversity (π) and Wright’s F-statistic (Fst).

Cis-acting elements and protein interaction network of AsHSFs

The promoter region was defined as the 2000-bp region located upstream of start codon of each gene and cis-acting elements were identified by PlantCARE (http://bioinformatics.psb.ugent.be/webtools/plantcare/html). The protein interaction network of AsHSFs was predicted by STRING (https://cn.string-db.org/) with a confidence threshold at 0.40.

RNA-seq data source and co-expression network analysis

A total of 185 RNA-Seq datasets were utilized to construct the co-expression network. These datasets were retrieved from the Gene Expression Omnibus (GEO) database with accession codes GSE211495, GSE186042, GSE145455, and from the Sequence Read Archive (SRA) database with accession codes PRJNA682570, PRJNA522648, PRJNA683607. Various quality parameters were used to assess the raw sequence data. Subsequently, the NGS QC Toolkit (v2.3) was employed to filter high-quality reads [79]. The filtered high-quality reads were aligned to the garlic genome using TopHat (v2.0.0) with default parameters. Then, Cufflinks (v2.0.2) was used to determine the FPKM values and read counts for each gene in the garlic. Weighted gene co-expression network analysis (WGCNA) was utilized to identify co-expressed genes with AsHSFs by constructing co-expression network, and co-expressed genes with the top 5% weighted values related to AsHSFs were selected for further investigation. To obtain the putative function of these genes, the BLAST alignment to Arabidopsis and rice proteins were implemented. Cytoscape v3.8.0 was used to visualize the co-expression networks. The GO enrichment analysis of gene sets was conducted using the clusterProfiler package in R [80].

Plant materials and treatments

Garlic cloves (cv. Ershuizao) were planted in pots and incubated in a greenhouse. The treatment experiments were conducted after 1 month. The treatment experiments were conducted using 200 mM NaCl solution for salt stress, 4 °C for cold stress, and 30 °C for heat stress. Leaf and root samples from three plants were collected in triplicates at 0, 6, 12, 24 and 48 h after each treatment and promptly frozen using liquid nitrogen and then stored in a −80 °C freezer. Plant RNA Extraction Kits (Vazyme, Nanjing, China) was used to extract total RNA. The cDNA was synthesized by using HiScript III RT SuperMix for qPCR (Vazyme, Nanjing, China) based on the manufacturer’s manual. Real-Time PCR Detection System (LightCycler480, Roche, USA) was used to perform qRT-PCR with the following procedures: 95℃ for 3 min; 95℃ for 10 s, 60℃ for the 30 s with 40 cycles; 95℃ for 15 s; 60℃ for 1 min; 95℃ for 15 s using ChamQ Universal SYBR qPCR Master Mix (Vazyme, Nanjing, China). Primer Express (v3.0) software was used to design primer (Table S9). Relative quantification was calculated based on the 2 −∆∆CT method [81]. Three independent biological and technical replicates were performed for each PCR assay. Statistical analysis was performed by Student’s t-test, using SPSS software, version 17 (SPSS Inc., Chicago, IL, USA). A significant level of P < 0.05 was considered statistically significant.

Yeast expression experiment and Y2H assay

To assess the high-temperature resistance of Asa6G04911 in yeast strain BY4741, Asa6G04911 was inserted into the pYES2 vector and transformed into the BY4741 strain. One single colony was randomly selected from the positive colonies and inoculated into SD-Ura liquid medium for cultivation until reaching an optical density (OD) of 1. The initial bacterial solution concentration was uniformly adjusted to an OD of 0.8. Subsequently, dilutions were prepared at ratios of 10− 1, 10− 2 and 10− 3. The diluted bacterial solution was spread onto SG/-Ura plates and incubated in incubators at temperatures of 30℃ and 35℃ for a duration of 3 days.

The Y2H assays were carried out following the recommended protocol from the manufacturer (Clontech Laboratories, Inc., Palo Alto, CA, USA). The coding sequence (CDS) of Asa6G04911 was cloned into the PGBKT7 vector as the bait, while CDS of HSP70/90 was inserted into the PGADT7 vector as the prey. Both plasmids were introduced into yeast cells (strain AH109) through transformation. The resulting yeast transformants were cultivated on SD medium supplemented with 3-aminotriazole and X-α-gal, but deficient in Trp, Leu, His, and Ade, at 30 °C for 2–3 days.

Data availability

All data generated or analyzed during this study are involved in both this published article and its supplementary information files. The 185 RNA-Seq data used to construct co-expression network are publicly available from GEO under accession codes GSE211495, GSE186042, GSE145455 and SRA under accession codes PRJNA682570, PRJNA522648, PRJNA683607. The data supporting our findings can be obtained from the corresponding author upon reasonable request.

References

  1. Hu H, Xiong L. Genetic engineering and breeding of drought-resistant crops. Annu Rev Plant Biol. 2014;65:715–41.

    Article  CAS  PubMed  Google Scholar 

  2. Song L, Huang SC, Wise A, Castanon R, Nery JR, Chen H, Watanabe M, Thomas J, Bar-Joseph Z, Ecker JR. A transcription factor hierarchy defines an environmental stress response network. Science 2016, 354(6312).

  3. Singh K, Foley RC, Onate-Sanchez L. Transcription factors in plant defense and stress responses. Curr Opin Plant Biol. 2002;5(5):430–6.

    Article  CAS  PubMed  Google Scholar 

  4. Wang P, Song H, Li C, Li P, Li A, Guan H, Hou L, Wang X. Genome-wide dissection of the heat shock transcription factor family genes in Arachis. Front Plant Sci. 2017;8:106.

    PubMed  PubMed Central  Google Scholar 

  5. Cai SY, Zhang Y, Xu YP, Qi ZY, Li MQ, Ahammed GJ, Xia XJ, Shi K, Zhou YH, Reiter RJ et al. HsfA1a upregulates melatonin biosynthesis to confer cadmium tolerance in tomato plants. J Pineal Res 2017, 62(2).

  6. Kotak S, Larkindale J, Lee U, von Koskull-Doring P, Vierling E, Scharf KD. Complexity of the heat stress response in plants. Curr Opin Plant Biol. 2007;10(3):310–6.

    Article  CAS  PubMed  Google Scholar 

  7. Swindell WR, Huebner M, Weber AP. Transcriptional profiling of Arabidopsis heat shock proteins and transcription factors reveals extensive overlap between heat and non-heat stress response pathways. BMC Genomics. 2007;8:125.

    Article  PubMed  PubMed Central  Google Scholar 

  8. Nover L, Bharti K, Doring P, Mishra SK, Ganguli A, Scharf KD. Arabidopsis and the heat stress transcription factor world: how many heat stress transcription factors do we need? Cell Stress Chaperones. 2001;6(3):177–89.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Cicero MP, Hubl ST, Harrison CJ, Littlefield O, Hardy JA, Nelson HC. The wing in yeast heat shock transcription factor (HSF) DNA-binding domain is required for full activity. Nucleic Acids Res. 2001;29(8):1715–23.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Feder ME, Hofmann GE. Heat-shock proteins, molecular chaperones, and the stress response: evolutionary and ecological physiology. Annu Rev Physiol. 1999;61:243–82.

    Article  CAS  PubMed  Google Scholar 

  11. Ohama N, Sato H, Shinozaki K, Yamaguchi-Shinozaki K. Transcriptional Regulatory Network of Plant Heat Stress Response. Trends Plant Sci. 2017;22(1):53–65.

    Article  CAS  PubMed  Google Scholar 

  12. Wei Y, Hu W, Xia F, Zeng H, Li X, Yan Y, He C, Shi H. Heat shock transcription factors in banana: genome-wide characterization and expression profile analysis during development and stress response. Sci Rep. 2016;6:36864.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Zhang J, Jia H, Li J, Li Y, Lu M, Hu J. Molecular evolution and expression divergence of the Populus Euphratica Hsf genes provide insight into the stress acclimation of desert poplar. Sci Rep. 2016;6:30050.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Chidambaranathan P, Jagannadham PTK, Satheesh V, Kohli D, Basavarajappa SH, Chellapilla B, Kumar J, Jain PK, Srinivasan R. Genome-wide analysis identifies chickpea (Cicer arietinum) heat stress transcription factors (Hsfs) responsive to heat stress at the pod development stage. J Plant Res. 2018;131(3):525–42.

    Article  CAS  PubMed  Google Scholar 

  15. Scharf KD, Berberich T, Ebersberger I, Nover L. The plant heat stress transcription factor (hsf) family: structure, function and evolution. Biochim Biophys Acta. 2012;1819(2):104–19.

    Article  CAS  PubMed  Google Scholar 

  16. Damberger FF, Pelton JG, Harrison CJ, Nelson HC, Wemmer DE. Solution structure of the DNA-binding domain of the heat shock transcription factor determined by multidimensional heteronuclear magnetic resonance spectroscopy. Protein Sci. 1994;3(10):1806–21.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Peteranderl R, Rabenstein M, Shin YK, Liu CW, Wemmer DE, King DS, Nelson HC. Biochemical and biophysical characterization of the trimerization domain from the heat shock transcription factor. Biochemistry. 1999;38(12):3559–69.

    Article  CAS  PubMed  Google Scholar 

  18. von Koskull-Doring P, Scharf KD, Nover L. The diversity of plant heat stress transcription factors. Trends Plant Sci. 2007;12(10):452–7.

    Article  Google Scholar 

  19. Hartl FU, Hayer-Hartl M. Molecular chaperones in the cytosol: from nascent chain to folded protein. Science. 2002;295(5561):1852–8.

    Article  CAS  PubMed  Google Scholar 

  20. Morimoto RI. Dynamic remodeling of transcription complexes by molecular chaperones. Cell. 2002;110(3):281–4.

    Article  CAS  PubMed  Google Scholar 

  21. Krishna P, Gloor G. The Hsp90 family of proteins in Arabidopsis thaliana. Cell Stress Chaperones. 2001;6(3):238–46.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Pratt WB, Toft DO. Regulation of signaling protein function and trafficking by the hsp90/hsp70-based chaperone machinery. Exp Biol Med (Maywood). 2003;228(2):111–33.

    Article  CAS  PubMed  Google Scholar 

  23. Sato Y, Yokoya S. Enhanced tolerance to drought stress in transgenic rice plants overexpressing a small heat-shock protein, sHSP17.7. Plant Cell Rep. 2008;27(2):329–34.

    Article  CAS  PubMed  Google Scholar 

  24. Singh A, Mittal D, Lavania D, Agarwal M, Mishra RC, Grover A. OsHsfA2c and OsHsfB4b are involved in the transcriptional regulation of cytoplasmic OsClpB (Hsp100) gene in rice (Oryza sativa L). Cell Stress Chaperones. 2012;17(2):243–54.

    Article  CAS  PubMed  Google Scholar 

  25. Cheng Q, Zhou Y, Liu Z, Zhang L, Song G, Guo Z, Wang W, Qu X, Zhu Y, Yang D. An alternatively spliced heat shock transcription factor, OsHSFA2dI, functions in the heat stress-induced unfolded protein response in rice. Plant Biol (Stuttg). 2015;17(2):419–29.

    Article  CAS  PubMed  Google Scholar 

  26. Giesguth M, Sahm A, Simon S, Dietz KJ. Redox-dependent translocation of the heat shock transcription factor AtHSFA8 from the cytosol to the nucleus in Arabidopsis thaliana. FEBS Lett. 2015;589(6):718–25.

    Article  CAS  PubMed  Google Scholar 

  27. Kotak S, Port M, Ganguli A, Bicker F, von Koskull-Doring P. Characterization of C-terminal domains of Arabidopsis heat stress transcription factors (Hsfs) and identification of a new signature combination of plant class A hsfs with AHA and NES motifs essential for activator function and intracellular localization. Plant J. 2004;39(1):98–112.

    Article  CAS  PubMed  Google Scholar 

  28. Chung E, Kim KM, Lee JH. Genome-wide analysis and molecular characterization of heat shock transcription factor family in Glycine max. J Genet Genomics. 2013;40(3):127–35.

    Article  CAS  PubMed  Google Scholar 

  29. Lin YX, Jiang HY, Chu ZX, Tang XL, Zhu SW, Cheng BJ. Genome-wide identification, classification and analysis of heat shock transcription factor family in maize. BMC Genomics. 2011;12:76.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Lin Y, Cheng Y, Jin J, Jin X, Jiang H, Yan H, Cheng B. Genome duplication and gene loss affect the evolution of heat shock transcription factor genes in legumes. PLoS ONE. 2014;9(7):e102825.

    Article  PubMed  PubMed Central  Google Scholar 

  31. Mittal D, Chakrabarti S, Sarkar A, Singh A, Grover A. Heat shock factor gene family in rice: genomic organization and transcript expression profiling in response to high temperature, low temperature and oxidative stresses. Plant Physiol Biochem. 2009;47(9):785–95.

    Article  CAS  PubMed  Google Scholar 

  32. Wang J, Sun N, Deng T, Zhang L, Zuo K. Genome-wide cloning, identification, classification and functional analysis of cotton heat shock transcription factors in cotton (Gossypium hirsutum). BMC Genomics. 2014;15:961.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Xue GP, Sadat S, Drenth J, McIntyre CL. The heat shock factor family from Triticum aestivum in response to heat and other major abiotic stresses and their role in regulation of heat shock protein genes. J Exp Bot. 2014;65(2):539–57.

    Article  CAS  PubMed  Google Scholar 

  34. Song X, Liu G, Duan W, Liu T, Huang Z, Ren J, Li Y, Hou X. Genome-wide identification, classification and expression analysis of the heat shock transcription factor family in Chinese cabbage. Mol Genet Genomics. 2014;289(4):541–51.

    Article  CAS  PubMed  Google Scholar 

  35. Guo J, Wu J, Ji Q, Wang C, Luo L, Yuan Y, Wang Y, Wang J. Genome-wide analysis of heat shock transcription factor families in rice and Arabidopsis. J Genet Genomics. 2008;35(2):105–18.

    Article  CAS  PubMed  Google Scholar 

  36. Huang Y, Li MY, Wang F, Xu ZS, Huang W, Wang GL, Ma J, Xiong AS. Heat shock factors in carrot: genome-wide identification, classification, and expression profiles response to abiotic stress. Mol Biol Rep. 2015;42(5):893–905.

    Article  CAS  PubMed  Google Scholar 

  37. Wang F, Dong Q, Jiang H, Zhu S, Chen B, Xiang Y. Genome-wide analysis of the heat shock transcription factors in Populus trichocarpa and Medicago truncatula. Mol Biol Rep. 2012;39(2):1877–86.

    Article  PubMed  Google Scholar 

  38. Guo M, Liu JH, Ma X, Luo DX, Gong ZH, Lu MH. The Plant Heat Stress Transcription Factors (HSFs): structure, regulation, and function in response to Abiotic stresses. Front Plant Sci. 2016;7:114.

    Article  PubMed  PubMed Central  Google Scholar 

  39. Yu XY, Yao Y, Hong YH, Hou PY, Li CX, Xia ZQ, Geng MT, Chen YH. Differential expression of the Hsf family in cassava under biotic and abiotic stresses. Genome. 2019;62(8):563–9.

    Article  CAS  PubMed  Google Scholar 

  40. Busch W, Wunderlich M, Schoffl F. Identification of novel heat shock factor-dependent genes and biochemical pathways in Arabidopsis thaliana. Plant J. 2005;41(1):1–14.

    Article  CAS  PubMed  Google Scholar 

  41. Nishizawa A, Yabuta Y, Yoshida E, Maruta T, Yoshimura K, Shigeoka S. Arabidopsis heat shock transcription factor A2 as a key regulator in response to several types of environmental stress. Plant J. 2006;48(4):535–47.

    Article  CAS  PubMed  Google Scholar 

  42. Charng YY, Liu HC, Liu NY, Chi WT, Wang CN, Chang SH, Wang TT. A heat-inducible transcription factor, HsfA2, is required for extension of acquired thermotolerance in Arabidopsis. Plant Physiol. 2007;143(1):251–62.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Liu HC, Charng YY. Common and distinct functions of Arabidopsis class A1 and A2 heat shock factors in diverse abiotic stress responses and development. Plant Physiol. 2013;163(1):276–90.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Ogawa D, Yamaguchi K, Nishiuchi T. High-level overexpression of the Arabidopsis HsfA2 gene confers not only increased themotolerance but also salt/osmotic stress tolerance and enhanced callus growth. J Exp Bot. 2007;58(12):3373–83.

    Article  CAS  PubMed  Google Scholar 

  45. Miller G, Mittler R. Could heat shock transcription factors function as hydrogen peroxide sensors in plants? Ann Bot. 2006;98(2):279–88.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Zhu B, Ye C, Lu H, Chen X, Chai G, Chen J, Wang C. Identification and characterization of a novel heat shock transcription factor gene, GmHsfA1, in soybeans (Glycine max). J Plant Res. 2006;119(3):247–56.

    Article  CAS  PubMed  Google Scholar 

  47. Shim D, Hwang JU, Lee J, Lee S, Choi Y, An G, Martinoia E, Lee Y. Orthologs of the class A4 heat shock transcription factor HsfA4a confer cadmium tolerance in wheat and rice. Plant Cell. 2009;21(12):4031–43.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Ikeda M, Mitsuda N, Ohme-Takagi M. Arabidopsis HsfB1 and HsfB2b act as repressors of the expression of heat-inducible hsfs but positively regulate the acquired thermotolerance. Plant Physiol. 2011;157(3):1243–54.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Xiang J, Ran J, Zou J, Zhou X, Liu A, Zhang X, Peng Y, Tang N, Luo G, Chen X. Heat shock factor OsHsfB2b negatively regulates drought and salt tolerance in rice. Plant Cell Rep. 2013;32(11):1795–806.

    Article  CAS  PubMed  Google Scholar 

  50. Schmidt R, Schippers JH, Welker A, Mieulet D, Guiderdoni E, Mueller-Roeber B. Transcription factor OsHsfC1b regulates salt tolerance and development in Oryza sativa ssp. japonica. AoB Plants. 2012;2012:pls011.

    Article  PubMed  PubMed Central  Google Scholar 

  51. Kamenetsky R, Faigenboim A, Shemesh Mayer E, Ben Michael T, Gershberg C, Kimhi S, Esquira I, Rohkin Shalom S, Eshel D, Rabinowitch HD, et al. Integrated transcriptome catalogue and organ-specific profiling of gene expression in fertile garlic (Allium sativum L). BMC Genomics. 2015;16:12.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Bystrická J, Kovarovič Jn, Lenková M, Horváthová J, Končeková L, Halmová D, Lidiková A. The content of polyphenols, antioxidant activity and macroelements in selected garlic varieties. J Microbiol Biotechnol food Sci. 2018;8(1):738–40.

    Article  Google Scholar 

  53. Sun X, Zhu S, Li N, Cheng Y, Zhao J, Qiao X, Lu L, Liu S, Wang Y, Liu C, et al. A Chromosome-Level Genome Assembly of Garlic (Allium sativum) provides insights into genome evolution and Allicin Biosynthesis. Mol Plant. 2020;13(9):1328–39.

    Article  CAS  PubMed  Google Scholar 

  54. Wei K, Chen H. Comparative functional genomics analysis of bHLH gene family in rice, maize and wheat. BMC Plant Biol. 2018;18(1):309.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Morimoto RI. Regulation of the heat shock transcriptional response: cross talk between a family of heat shock factors, molecular chaperones, and negative regulators. Genes Dev. 1998;12(24):3788–96.

    Article  CAS  PubMed  Google Scholar 

  56. Jiang L, Hu W, Qian Y, Ren Q, Zhang J. Genome-wide identification, classification and expression analysis of the Hsf and Hsp70 gene families in maize. Gene. 2021;770:145348.

    Article  CAS  PubMed  Google Scholar 

  57. Tan B, Yan L, Li H, Lian X, Cheng J, Wang W, Zheng X, Wang X, Li J, Ye X, et al. Genome-wide identification of HSF family in peach and functional analysis of PpHSF5 involvement in root and aerial organ development. PeerJ. 2021;9:e10961.

    Article  PubMed  PubMed Central  Google Scholar 

  58. Zhou M, Zheng S, Liu R, Lu J, Lu L, Zhang C, Liu Z, Luo C, Zhang L, Yant L, et al. Genome-wide identification, phylogenetic and expression analysis of the heat shock transcription factor family in bread wheat (Triticum aestivum L). BMC Genomics. 2019;20(1):505.

    Article  PubMed  PubMed Central  Google Scholar 

  59. Tang R, Zhu W, Song X, Lin X, Cai J, Wang M, Yang Q. Genome-wide identification and function analyses of heat shock transcription factors in Potato. Front Plant Sci. 2016;7:490.

    Article  PubMed  PubMed Central  Google Scholar 

  60. Duan S, Liu B, Zhang Y, Li G, Guo X. Genome-wide identification and abiotic stress-responsive pattern of heat shock transcription factor family in Triticum aestivum L. BMC Genomics. 2019;20(1):257.

    Article  PubMed  PubMed Central  Google Scholar 

  61. Agarwal G, Garg V, Kudapa H, Doddamani D, Pazhamala LT, Khan AW, Thudi M, Lee SH, Varshney RK. Genome-wide dissection of AP2/ERF and HSP90 gene families in five legumes and expression profiles in chickpea and pigeonpea. Plant Biotechnol J. 2016;14(7):1563–77.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  62. Yang X, Zhu W, Zhang H, Liu N, Tian S. Heat shock factors in tomatoes: genome-wide identification, phylogenetic analysis and expression profiling under development and heat stress. PeerJ. 2016;4:e1961.

    Article  PubMed  PubMed Central  Google Scholar 

  63. Nagaraju M, Reddy PS, Kumar SA, Srivastava RK, Kishor PB, Rao DM. Genome-wide scanning and characterization of Sorghum bicolor L. Heat Shock transcription factors. Curr Genomics. 2015;16(4):279–91.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  64. Chalhoub B, Denoeud F, Liu S, Parkin IA, Tang H, Wang X, Chiquet J, Belcram H, Tong C, Samans B, et al. Plant genetics. Early allopolyploid evolution in the post-neolithic Brassica napus oilseed genome. Science. 2014;345(6199):950–3.

    Article  CAS  PubMed  Google Scholar 

  65. Kiraga J, Mackiewicz P, Mackiewicz D, Kowalczuk M, Biecek P, Polak N, Smolarczyk K, Dudek MR, Cebrat S. The relationships between the isoelectric point and: length of proteins, taxonomy and ecology of organisms. BMC Genomics. 2007;8:163.

    Article  PubMed  PubMed Central  Google Scholar 

  66. Li W, Wan XL, Yu JY, Wang KL, Zhang J. Genome-wide identification, classification, and expression analysis of the Hsf Gene Family in Carnation (Dianthus caryophyllus). Int J Mol Sci 2019, 20(20).

  67. Guo M, Lu JP, Zhai YF, Chai WG, Gong ZH, Lu MH. Genome-wide analysis, expression profile of heat shock factor gene family (CaHsfs) and characterisation of CaHsfA2 in pepper (Capsicum annuum L). BMC Plant Biol. 2015;15:151.

    Article  PubMed  PubMed Central  Google Scholar 

  68. Kawahara Y, de la Bastide M, Hamilton JP, Kanamori H, McCombie WR, Ouyang S, Schwartz DC, Tanaka T, Wu J, Zhou S, et al. Improvement of the Oryza sativa nipponbare reference genome using next generation sequence and optical map data. Rice (N Y). 2013;6(1):4.

    Article  PubMed  Google Scholar 

  69. Purugganan MD. Evolutionary insights into the Nature of Plant Domestication. Curr Biol. 2019;29(14):R705–14.

    Article  CAS  PubMed  Google Scholar 

  70. Purugganan MD, Fuller DQ. The nature of selection during plant domestication. Nature. 2009;457(7231):843–8.

    Article  CAS  PubMed  Google Scholar 

  71. Lu X, Fang Y, Tian B, Tong T, Wang J, Wang H, Cai S, Hu J, Zeng D, Xu H, et al. Genetic variation of HvXYN1 associated with endoxylanase activity and TAX content in barley (Hordeum vulgare L). BMC Plant Biol. 2019;19(1):170.

    Article  PubMed  PubMed Central  Google Scholar 

  72. Kosugi S, Hasebe M, Tomita M, Yanagawa H. Systematic identification of cell cycle-dependent yeast nucleocytoplasmic shuttling proteins by prediction of composite motifs. Proc Natl Acad Sci U S A. 2009;106(25):10171–6.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  73. Kosugi S, Hasebe M, Matsumura N, Takashima H, Miyamoto-Sato E, Tomita M, Yanagawa H. Six classes of nuclear localization signals specific to different binding grooves of importin alpha. J Biol Chem. 2009;284(1):478–85.

    Article  CAS  PubMed  Google Scholar 

  74. Kosugi S, Hasebe M, Entani T, Takayama S, Tomita M, Yanagawa H. Design of peptide inhibitors for the Importin alpha/beta nuclear import pathway by activity-based profiling. Chem Biol. 2008;15(9):940–9.

    Article  CAS  PubMed  Google Scholar 

  75. Edgar RC. MUSCLE: multiple sequence alignment with high accuracy and high throughput. Nucleic Acids Res. 2004;32(5):1792–7.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  76. Bailey TL, Elkan C. The value of prior knowledge in discovering motifs with MEME. Proc Int Conf Intell Syst Mol Biol. 1995;3:21–9.

    CAS  PubMed  Google Scholar 

  77. Chen L, Hu W, Tan S, Wang M, Ma Z, Zhou S, Deng X, Zhang Y, Huang C, Yang G, et al. Genome-wide identification and analysis of MAPK and MAPKK gene families in Brachypodium distachyon. PLoS ONE. 2012;7(10):e46744.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  78. Cingolani P, Platts A, Wang le L, Coon M, Nguyen T, Wang L, Land SJ, Lu X, Ruden DM. A program for annotating and predicting the effects of single nucleotide polymorphisms, SnpEff: SNPs in the genome of Drosophila melanogaster strain w1118; iso-2; iso-3. Fly (Austin). 2012;6(2):80–92.

    Article  CAS  PubMed  Google Scholar 

  79. Patel RK, Jain M. NGS QC Toolkit: a toolkit for quality control of next generation sequencing data. PLoS ONE. 2012;7(2):e30619.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  80. Yu G, Wang LG, Han Y, He QY. clusterProfiler: an R package for comparing biological themes among gene clusters. OMICS. 2012;16(5):284–7.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  81. Livak KJ, Schmittgen TD. Analysis of relative gene expression data using real-time quantitative PCR and the 2(-Delta Delta C(T)) method. Methods. 2001;25(4):402–8.

    Article  CAS  PubMed  Google Scholar 

Download references

Acknowledgements

Not applicable.

Funding

This research was funded by Natural Science Foundation of Shandong Province (Grant No. ZR2023QC312), research initiation project of Jining Medical University (Grant No. 600993001) and research initiation project of Beijing Academy of Science and Technology (Grant No. 0420239352KF001-05).

Author information

Authors and Affiliations

  1. Institute of Neurobiology, Jining Medical University, Jining, China

    Xiaomeng Hao

  2. Institute of Biotechnology and Health, Beijing Academy of Science and Technology, Beijing, China

    Shutao He

Authors
  1. Xiaomeng Hao
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Shutao He
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Shutao He designed and supervised the project. Xiaomeng Hao performed and analysis the experiments. Xiaomeng Hao and Shutao He performed the data analysis and drafted the manuscript. All authors contributed to and approved the final manuscript.

Corresponding author

Correspondence to Shutao He.

Ethics declarations

Ethics approval and consent to participate

The experimental study on plants in this study were conducted in complicance with relevant institutional, national, and international guidelines.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary Material 1

Supplementary Material 2

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Hao, X., He, S. Genome-wide identification, classification and expression analysis of the heat shock transcription factor family in Garlic (Allium sativum L.). BMC Plant Biol 24, 421 (2024). https://doi.org/10.1186/s12870-024-05018-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s12870-024-05018-3

Keywords

BMC Plant Biology

ISSN: 1471-2229

Contact us