- Research article
- Open Access
Adaptive evolution and divergent expression of heat stress transcription factors in grasses
- Zefeng Yang†1,
- Yifan Wang†1,
- Yun Gao1,
- Yong Zhou1,
- Enying Zhang1,
- Yunyun Hu1,
- Yuan Yuan1,
- Guohua Liang1 and
- Chenwu Xu1Email author
© Yang et al.; licensee BioMed Central Ltd. 2014
Received: 26 July 2013
Accepted: 20 June 2014
Published: 30 June 2014
Heat stress transcription factors (Hsfs) regulate gene expression in response to heat and many other environmental stresses in plants. Understanding the adaptive evolution of Hsf genes in the grass family will provide potentially useful information for the genetic improvement of modern crops to handle increasing global temperatures.
In this work, we performed a genome-wide survey of Hsf genes in 5 grass species, including rice, maize, sorghum, Setaria, and Brachypodium, by describing their phylogenetic relationships, adaptive evolution, and expression patterns under abiotic stresses. The Hsf genes in grasses were divided into 24 orthologous gene clusters (OGCs) based on phylogeneitc relationship and synteny, suggesting that 24 Hsf genes were present in the ancestral grass genome. However, 9 duplication and 4 gene-loss events were identified in the tested genomes. A maximum-likelihood analysis revealed the effects of positive selection in the evolution of 11 OGCs and suggested that OGCs with duplicated or lost genes were more readily influenced by positive selection than other OGCs. Further investigation revealed that positive selection acted on only one of the duplicated genes in 8 of 9 paralogous pairs, suggesting that neofunctionalization contributed to the evolution of these duplicated pairs. We also investigated the expression patterns of rice and maize Hsf genes under heat, salt, drought, and cold stresses. The results revealed divergent expression patterns between the duplicated genes.
This study demonstrates that neofunctionalization by changes in expression pattern and function following gene duplication has been an important factor in the maintenance and divergence of grass Hsf genes.
With the rise in global temperatures and the rapid growth of the world’s population, the impact of heat stress on crop yield and quality has become increasingly significant. The genetic improvement of crops’ heat resistance through molecular manipulation has become extremely important. The expression levels of heat-shock genes increase rapidly when a plant is under conditions of heat stress, resulting in the rapid accumulation of heat-shock proteins (HSPs). HSPs function as molecular chaperones in preventing the accumulation of damaged proteins to maintain cellular homeostasis by protein refolding, stabilization, intracellular translocation and degradation [1, 2].
The expression of HSPs is regulated by multiple mechanisms, mainly on a transcriptional level. Heat shock transcription factor (Hsf) is the master regulator in this process, playing critical roles in high-temperature stress responses and thermal tolerance . The Hsf genes of animals and fungi play central roles in protecting cells from damage caused by various stress conditions, including heat, infection, inflammation, and pharmacological agents, via the activation of gene expression . Like other transcription factors, Hsf proteins have a particular modular structure with a central helix-turn-helix motif in the N-terminal region, an adjacent domain with heptad hydrophobic A/B repeats involved in oligomerization, a nuclear-localization signal, and a C-terminal activation domain [5, 6]. The Hsf genes can be divided into three structural classes: A, B and C. In the oligomerization domain, class A and C Hsf proteins possess an inserted sequence of 21 and 7 amino-acid residues, respectively, which is absent from class B Hsfs .
Plant Hsf genes have been isolated from various species. While other eukaryotes possess one to three Hsf genes, plants exhibit a dramatic expansion of this gene family . For example, Arabidopsis thaliana and rice (Oryza sativa) have 21 and 25 Hsf genes , respectively. Class A Hsfs are involved in activating Hsp gene expression, plant development and responses to a variety of environmental stresses . However, class B Hsfs mostly lack activator function, serving instead as repressors of gene expression . The Arabidopsis genes HsfA1d and HsfA1e control the expression of HsfA2, suggesting that plant Hsfs also function as regulators of other Hsf genes . In addition to heat-stress adaptation, many plant Hsf genes play important roles in responses to abiotic and biotic stresses, including drought, salt, cold, osmotic stress, pathogen attack, anoxia and submergence . In addition to stress responses, some evidence indicates that plant Hsfs play potential roles in plant development. Transgenic Arabidopsis plants over-expressing HsfA2 exhibit increased cell proliferation , while the rice gene OsHsfC1b is involved in ABA-mediated salt-stress tolerance, osmotic-stress response, and plant growth under non-stressed conditions .
The grass family, a large and nearly ubiquitous family of monocotyledonous flowering plants, constitutes the most economically important plant family in modern times, providing forage, building materials, fuel, and food. Although genome-wide surveys have identified the members of the Hsf gene family in some plant species [3, 6, 8], a more detailed evolutionary history of grass Hsf genes, including a selective pattern profile, has not been described yet. Understanding the adaptive evolution of the Hsf gene family will provide potential useful information for the genetic improvement of modern crops to tolerate increasing global temperatures. Here, we examine the phylogenetic relationships, adaptive evolution, and expression patterns under abiotic stresses of the Hsf gene family in five grass species for which genome sequences are available.
Phylogenetic relationships of grass Hsfgenes
Thus, we identified 9 paralogous gene pairs formed after speciation from the common ancestor of the grass family. A search for contiguous Hsf genes in both the sharing region and neighboring regions revealed that only the paralogous pair SiHsf-09/SiHsf-10 was located adjacent to another Hsf gene (Figure 1), suggesting that tandem duplication contributed to the formation of this paralogous pair in Setaria. We found that all other paralogous Hsf pairs, except SiHsf-11/SiHsf-18, were formed by large-scale gene duplication events because the flanking regions for these pairs contained highly conserved genes.
Selective constraints on HsfOGCs
Detection of positive selection under site-specific models for each Hsf OGC in grasses
d N d /S (ω) under M0
2Δl, M3 vs. M0
2Δl, M8 vs. M7
2Δl, M8 vs. M8a
Parameter estimates under M8
Positively selected sites
p1 = 0.055, ω = 1.390
β (p = 0.241, q = 1.419)
p1 = 0.020, ω = 1.975
β (p = 0.185, q = 0.603)
p1 = 0.028, ω = 2.014
β (p = 0.537, q = 3.962)
p1 = 0.023, ω = 1.370
β (p = 0.326, q = 1.243)
p1 = 0.085, ω = 1.000
β (p = 1.347, q = 7.418)
p1 = 0.117, ω = 1.027
β (p = 0.940, q = 8.351)
p1 = 0.009, ω = 11.123
β (p = 0.190, q = 0.316)
p1 = 0.156, ω = 1.000
β (p = 1.149, q = 10.522)
p1 = 0.151, ω = 1.000
β (p = 1.903, q = 11.514)
p1 = 0.079, ω = 4.552
β (p = 0.400, q = 1.083)
p1 = 0.078, ω = 2.082
β (p = 0.602, q = 3.348)
p1 = 0.031, ω = 3.219
β (p = 0.422, q = 0.646)
p1 = 0.069, ω = 320.489
β (p = 0.354, q = 2.050)
p1 = 0.053, ω = 9.450
β (p = 0.287, q = 2.201)
p1 = 0.073, ω = 1.958
β (p = 1.077, q = 15.385)
p1 = 0.176, ω = 3.966
β (p = 0.204, q = 0.983)
p1 = 0.170, ω = 1.950
β (p = 0.729, q = 3.471)
p1 = 0.396, ω = 25.830
β (p = 0.256, q = 1.140)
p1 = 0.036, ω = 2.148
β (p = 0.574, q = 2.971)
p1 = 0.021, ω = 51.561
β (p = 0.781, q = 6.181)
p1 = 0.075, ω = 1.257
β (p = 2.719, q = 22.603)
p1 = 0.018, ω = 3.010
β (p = 0.370, q = 3.227)
p1 = 0.019, ω = 10.451
β (p = 0.153, q = 0.828)
p1 = 0.204, ω = 1.236
β (p = 3.990, q = 99.000)
To evaluate whether positive selection facilitated the evolution of each Hsf OGC in grasses, we compared the models M8 and M7. This analysis indicated that 11 OGCs had undergone positive selection during the evolution of grasses because they satisfied the following criteria: (1) an estimate of ω > 1 under M8, (2) sites found to be under positive selection, and (3) a statistically significant likelihood ratio test (LRT). In addition, 11 OGCs were further affirmed by the LRT of the models M8 and M8a, another comparison to detect positive selection. This result suggested that positive selection was an important contributor to the evolution of at least 11 Hsf OGCs in grasses. Among the 11 OGCs with no gene duplication and/or loss, 4 clusters showed signals of positive selection. 5 out of 8 OGCs with gene duplications were evidently influenced by positive selection, while 2 of the 4 OGCs with gene-loss events showed evidence of positive selection. Only one OGC contained both gene-duplication and gene-loss events, and this cluster showed no signature of positive selection.
Selective constraints on duplicated genes
Expression patterns of rice Hsfgenes
We also analyzed the expression profiles of rice Hsf genes under control and heat-shock conditions. The rice Hsf genes exhibited two distinct expression patterns. Six rice Hsf genes were suppressed under heat-shock conditions compared to the control, while the remaining 19 genes were induced by heat-shock conditions (Figure 3B). When we used the program SAM to identify the genes with significant changes in expression between control and heat-shock conditions, we found that only 1 gene was significantly down-regulated by heat shock, while 7 genes were up-regulated by two-fold or greater (Additional file 4), respectively. We also observed that most of the rice Hsf genes were up-regulated under a variety of stresses, such as drought, salt, and cold. Among the 25 rice Hsf genes, 5, 6 and 4 genes were statistically up-regulated by drought, salt and cold, respectively (Figure 3C, Additional file 5). In addition, 2 genes were down-regulated by cold treatment.
Expression patterns of maize Hsfgenes in response to abiotic stresses
The maize genome contained 6 recently duplicated Hsf pairs. We further investigated the expression patterns of these paralogous pairs and found that each gene showed a differential expression pattern compared to its paralogous partner. For example, the gene ZmHsf-16 was strongly down-regulated by drought, while its paralog (ZmHsf-20) was strongly up-regulated by drought. These results indicate functional divergence between the members of maize Hsf duplicated pairs.
Gene duplication is a major mechanism through which new genetic material is generated during evolution. Among gene-duplication mechanisms, whole genome duplication (WGD) is the dominant mechanism for gene-family proliferation in plants because most plants are diploidized polyploids and retain numerous duplicated chromosomal blocks within their genomes [16, 17]. For example, the rice genome shows evidence for two rounds of ancient polyploidy events: one before the divergence of cereals and one before the split between monocots and dicots . In addition, it is generally believed that maize arose as a tetraploid [19, 20]. Here, we found 9 paralogous pairs of Hsf genes in 3 grass species. These paralogous pairs were formed after the divergence of the grasses, and at least 7 pairs were formed by large-scale gene duplication events. Among the surveyed grass genomes, maize possesses the largest number of Hsf genes, although its genome also shows gene-loss events. It is easier to infer that most recent duplicated maize Hsf genes formed through WGD because of the tetraploid process. The rice paralogous pair OsHsf-20/22 were also found to be formed through a large-scale duplication evnet. However, we also noticed that the rice chromosomes 8 and 9 formed through a whole-genome duplication event before the split of cereals [18, 21]. It was also suggested that reciprocal gene loss following a WGD can contribute to reproductive isolation through divergent resolution of duplicate copies, foreshadowing the diversification of species . Thus, the most acceptable duplicated pattern for the pair of OsHsf20/22 is that these two genes formed through a WGD event before the split of grasses, and have lost one partner in other cereal genomes.
Abiotic stresses have significant impacts on plants over the long term. Plants have successfully evolved enzymes and regulatory mechanisms to adapt to their environments, including abiotic stresses . However, global environments have changed tremendously during the long period of plant evolution. To adapt successfully, a plant must overcome deleterious new conditions without creating different but equally dangerous alterations in its ongoing successful metabolic relationship with its environment . Thus, stress-response genes are readily influenced by adaptive evolution. Adaptive evolution results from the spread of advantageous mutations through positive selection, which is thought to be the most important mechanism to generate new gene functions . Genes carrying signatures of selection may be involved in adaptation and functional innovation. The d N /d S ratio measures the selective pressure on amino-acid substitutions. A d N /d S ratio greater than 1 suggests positive selection, while a ratio less than 1 suggests purifying selection . The members of the Hsf family encode key regulators of physiological responses to heat and other abiotic stresses [5, 6, 8]. In an explicit evolutionary analysis of gains/losses, we show that the ancestor of grasses had 24 Hsf genes. As a result of evolution, modern grass species contain 24 Hsf OGCs. Positive selection has affected 11 of these OGCs during the evolution of the grass family. This result suggests that positive selection has played important roles in the evolution of the Hsf family in grasses. Interestingly, we observed that the OGCs with gene duplications and/or losses tended to show stronger evidence of positive selection. Because positive selection is believed to indicate the evolution of new functions, OGCs that contain only one member in each genome may have fewer chances to acquire new functions . However, gene duplication provides new genetic material to evolve new functions through positive selection, possibly explaining why most OGCs that contained duplicated genes were influenced by positive selection. The members of the OGCs that contained gene losses may not play housekeeping roles in grasses because the species that lack these genes have survived over long evolutionary periods. Thus, a possible explanation for the positive selection found in these OGCs is that these genes are subject to the relaxed constraints of purifying selection, and positive selection has helped them to fit the beneficial variants.
Ortholog refer to the homologous genes where a gene is found in two different species, but the origin of the gene is a common ancestor. If a gene is duplicated in a species, the resulting duplicated genes are paralogs of each other. Orthologs generally retain the same function over the course of evolution. However, paralogs commonly evolve new functions, although these functions may be related to the original function, especially for those formed through lineage-specific duplication events [27–29]. During the gene-duplication process, paralogs commonly undergo a division of labor by retaining different parts (subfunctions) of their original ancestral function. This process is known as subfunctionalization. However, a gene may instead acquire a new function after gene duplication. This process is known as neofunctionalization . Gene duplication results in an additional copy that is free from selective pressure. If the duplicated pair does not undergo subfunctionalization, the additional copy may be lost due to the accumulation of natural mutations unless it acquires new functions through positive selection . Thus, the signal of positive selection indicates neofunctionalization for one of the duplicated genes . In this analysis, we tested for signals of positive selection in 9 duplicated pairs of Hsf genes in the surveyed grass genomes. We also tested the expression levels of 7 duplicated gene pairs in rice and maize and found that all of these duplicated pairs showed divergent expression patterns. This result suggests that subfunctionalization and/or neofunctionalization has occurred after duplication in response to different stresses. Our results also indicate that positive selection has acted on only one paralog within 8 pairs, while the other gene within each pair shows no evidence of positive selection. Thus, one gene in each pair likely retained the original function, while the other gene may have gained new functions through positive selection. The signatures of positive selection and divergent expression suggest that neofunctionalization has contributed to the evolution of duplicated Hsf genes. Our findings provide a novel reference for cloning the most promising candidate genes from the Hsf gene family for further functional detection.
Based on the phylogeny and syntenic information, the Hsf genes in five gramineous genomes were divided into 24 orthologous gene clusters (OGCs), suggesting that there were at least 24 Hsf genes in the common ancestor of these grass species and that the divergence in gene number in these species was the result of gene duplication and/or loss. In addition, 9 duplication and 5 gene-loss events were identified in the tested genomes. Among the 11 OGCs with no gene duplication and/or loss, 4 clusters showed signals of positive selection. However, 7 out of 13 OGCs with gene duplication and/or loss were evidently influenced by positive selection, suggesting that OGCs with duplicated or lost genes were more readily influenced by positive selection than other OGCs. When we used the improved branch-site model to test adaptive evolution for the recently duplicated Hsf genes, the results revealed that positive selection acted on only one of the duplicated genes in 8 of 9 paralogous pairs. Furth more, we also investigated the expression patterns of rice and maize Hsf genes under heat, salt, drought, and cold stresses, and the results revealed divergent expression patterns between the duplicated genes. This study demonstrates that neofunctionalization by changes in expression pattern and function following gene duplication has been an important factor in the maintenance and divergence of grass Hsf genes.
Identification of Hsfgenes in grasses
To identify the members of the Hsf gene family in rice (Oryza sativa), maize (Zea mays), Sorghum bicolor, Setaria italica and Brachypodium distachyon, Hsf gene sequences from Arabidopsis  were retrieved from the TAIR database  and used as queries to perform repetitive BLAST searches against the Phytozome database v9.1 . BLAST searches were also performed against the NCBI nucleic-acid sequence data repositories. All protein sequences derived from the BLAST searches were examined using domain-analysis programs, including Pfam  and SMART , with the default cut-off parameters.
Multiple sequence alignment and phylogenetic tree reconstruction
Multiple sequence alignment of Hsf proteins was performed using the program Clustal X  with the default parameters. Phylogenetic analyses were performed using neighbor joining (NJ), maximum parsimony (MP) in the program MEGA version 5.1  and using maximum likelihood (ML) in the program PhyML version 3.0 . Modelgenerator  analysis revealed that a JTT substitution matrix was the most appropriate parameter for the alignment dataset. The ML phylogenetic analyses used the following parameters: JTT model, estimated proportion of invariable sites, 4 rate categories, estimated gamma-distribution parameter, and BIONJ-optimized starting tree. The JTT model was also used for the construction of NJ trees. A total of 100 non-parametric bootstrap samplings were performed to estimate the support level for each internal branch in the ML, NJ and MP trees. The branch lengths and topologies of all phylogenies were calculated using PhyML. The phylogenetic trees were visualized using the explorer program in MEGA.
The synteny relationships of Hsf genes in one OGC were analyzed by reciprocal BLASTP. 5 protein-coding genes upstream and downstream of each Hsf gene were obtained from the Phytozome database . The genes flanking one Hsf gene were used to match the genes flanking other Hsf gene in the same OGC using reciprocal BLASTP. Therefore, we considered Hsf genes in the same OGC to share syntenic region if they resided within a region of other conserved protein-coding genes. The detection of conserved protein-coding gene used the tool of BLASTP with the E-value ≤ 1E - 10.
Detection of positive selection
The program PAL2NAL  was used to convert the protein sequence alignment into the corresponding codon-based nucleotide alignment, which was input into the codeml program in PAML . Using the program codeml, we detected variation in the ω parameter among sites by employing likelihood-ratio tests (LRTs) for M0 vs. M3 and M7 vs. M8. The LRT for the M0 vs. M3 comparison was used to test the heterogeneity in ω between codon positions, where M0 model assumes one ω among all sites and M8 model uses an unconstrained discrete distribution of ω with a set number of classes. The M7 vs. M8 comparison was used to detect the role of positive selection. M7 is the null model assumes a beta distribution of ω values between ω = 0 and ω = 1 among the sites, while the alternative model M8 adds a free parameter to the null model and allows positive selection to occur. In each LRT, twice the difference of the log likelihood of the two models was compared to the chi-squared (χ2) statistic, with the degrees of freedom (DFs) being equal to the difference in the number of parameters. In our analyses, the DFs were 4 for the M0 vs. M3 test and 2 for the M7 vs. M8 test . Additionally, to stringently test for evidence of positive selection and to remove the potential identification of relaxed purifying selection, we conducted a comparison of M8 model (where a single class of sites is allowed with ω > 1) to M8a, which is specified using ω = 1 .
An improved branch-site model  was also used to detect the impact of positive selection upon one gene in each duplicated pair. For this analysis, we compared the null hypothesis (ω fixed to 1) with the alternative hypothesis (free ω) to test whether positive selection acted upon the genes in duplicated pairs. In this analysis, each gene in a duplicated pair was used as the foreground, while the other genes in the same OGC were used as the background. The BEB procedure  in codeml was used to calculate the posterior probability that each site in the foreground branch was subject to positive selection.
Microarray data analysis
The microarray data publicly available from the GEO database under the series accession numbers GSE7951 (expression profiling of 9 rice tissues), GSE6901 (expression data for heat, cold and salt treatments), and GSE14275 (expression data for heat-shock conditions) were used in an expression analysis of rice Hsf genes. The program dChip version 2010  was used to perform the cluster analysis and to display the expression patterns of rice Hsf genes based on the microarray data. Gene-expression values were compared using the program Significance Analysis of Microarrays (SAM)  in Microsoft Excel based on the criterion of more than two-fold change. In this analysis, SAM two-class unpaired analysis was used to calculate p-values, q-values and fold changes in expression levels.
Plant materials and stress treatment
The maize inbred line Huangzaosi was used to check the gene-expression levels of maize Hsf genes. The maize plants were grown until the four-leaf stage under natural light and environmental conditions in soil-filled pots that were watered every 2 d. To induce drought stress, watering was stopped for each pot for 6 d. To induce heat stress, the pots were placed in an incubator at 42°C. To induce cold stress, the pots were placed in an incubator at 4°C. To induce salt stress, the pots were watered with 200 mM NaCl in water. The leaves were sampled after 4 h of heat, salt or cold treatment.
RNA isolation and quantitative real-Time PCR (qrt-PCR) analyses
Total RNA was extracted from Huangzaosi maize plants subjected to four stress treatments using an RNAsimple Total RNA Kit (Tiangen). The RNA was stored at -72°C and reverse-transcribed into cDNA using PrimeScript RT Master Mix Perfect Real Time (TaKaRa). Real-time quantitative PCR was performed using 2 μl of cDNA in a 25-μl reaction volume with SYBR Premix Ex Taq (TaKaRa), utilizing the Applied Biosystems 7500 Real-Time PCR System. Gene-specific primers were designed using the program Primer 5.0 (Additional file 5). The Zea mays Actin gene was used as an internal reference for all qRT–PCR analyses. Each treatment was repeated three times independently. The reaction profile consisted of an initial incubation at 50°C for 2 min and 95°C for 5 min, followed by 40 cycles at 95°C for 30 s, 54°C for 30 s and 72°C for 40 s. The relative quantification of Hsfs transcript levels was achieved using the comparative C T method (also known as the method) . The independent-samples t test was employed to compare the significant difference of all stress treatments against their controls using SAS v9.1.3 (SAS Institute Inc., USA). In this analysis, a total of 112 independent comparisons were performed, and the experiment-wide significance level were set to . According to multiple testing of Šidák correction, the significance level for per comparison was defined as . Thus, if the P value < α for an independent-samples t test, the significant difference between stress treatment and its control is suggested.
Availability of supporting data
The data sets supporting the results of this article are included within the article and its additional files. Alignment and Phylogenetic tree which support the findings presented in this research article are available online in the Dryad Digital Repository (doi:10.5061/dryad.11243) .
This work was supported by grants from the National Basic Research Program of China (2011CB100100), the Priority Academic Program Development of Jiangsu Higher Education Institutions, the National Natural Science Foundation of China (31200943, 31391632 and 31171187), the Natural Science Foundation of Jiangsu Province (BK2012261), and the Innovative Research Team of Universities in Jiangsu Province.
- Wang W, Vinocur B, Shoseyov O, Altman A: Role of plant heat-shock proteins and molecular chaperones in the abiotic stress response. Trends Plant Sci. 2004, 9 (5): 244-252.PubMedView ArticleGoogle Scholar
- Sangster TA, Queitsch C: The HSP90 chaperone complex, an emerging force in plant development and phenotypic plasticity. Curr Opin Plant Biol. 2005, 8 (1): 86-92.PubMedView ArticleGoogle Scholar
- 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-PubMedPubMed CentralView ArticleGoogle Scholar
- 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): 4031-4043.PubMedPubMed CentralView ArticleGoogle Scholar
- Jin GH, Gho HJ, Jung KH: A systematic view of rice heat shock transcription factor family using phylogenomic analysis. J Plant Physiol. 2013, 170 (3): 321-329.PubMedView ArticleGoogle Scholar
- 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): 104-119.PubMedView ArticleGoogle Scholar
- 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-PubMedPubMed CentralView ArticleGoogle Scholar
- 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): 105-118.PubMedView ArticleGoogle Scholar
- 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): 1243-1254.PubMedPubMed CentralView ArticleGoogle Scholar
- Nishizawa-Yokoi A, Nosaka R, Hayashi H, Tainaka H, Maruta T, Tamoi M, Ikeda M, Ohme-Takagi M, Yoshimura K, Yabuta Y, Shigeoka S: HsfA1d and HsfA1e involved in the transcriptional regulation of HsfA2 function as key regulators for the Hsf signaling network in response to environmental stress. Plant Cell Physiol. 2011, 52 (5): 933-945.PubMedView ArticleGoogle Scholar
- 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): 3373-3383.PubMedView ArticleGoogle Scholar
- Alexandrov NN, Brover VV, Freidin S, Troukhan ME, Tatarinova TV, Zhang H, Swaller TJ, Lu YP, Bouck J, Flavell RB, Feldmann KA: Insights into corn genes derived from large-scale cDNA sequencing. Plant Mol Biol. 2009, 69 (1–2): 179-194.PubMedPubMed CentralView ArticleGoogle Scholar
- Zhang J, Nielsen R, Yang Z: Evaluation of an improved branch-site likelihood method for detecting positive selection at the molecular level. Mol Biol Evol. 2005, 22 (12): 2472-2479.PubMedView ArticleGoogle Scholar
- Yang Z, Wong WS, Nielsen R: Bayes empirical bayes inference of amino acid sites under positive selection. Mol Biol Evol. 2005, 22 (4): 1107-1118.PubMedView ArticleGoogle Scholar
- Whittle CA, Krochko JE: Transcript profiling provides evidence of functional divergence and expression networks among ribosomal protein gene paralogs in Brassica napus. Plant Cell. 2009, 21 (8): 2203-2219.PubMedPubMed CentralView ArticleGoogle Scholar
- Cannon SB, Mitra A, Baumgarten A, Young ND, May G: The roles of segmental and tandem gene duplication in the evolution of large gene families in Arabidopsis thaliana. BMC Plant Biol. 2004, 4: 10-PubMedPubMed CentralView ArticleGoogle Scholar
- Yang Z, Zhou Y, Wang X, Gu S, Yu J, Liang G, Yan C, Xu C: Genomewide comparative phylogenetic and molecular evolutionary analysis of tubby-like protein family in Arabidopsis, rice, and poplar. Genomics. 2008, 92 (4): 246-253.PubMedView ArticleGoogle Scholar
- Tang H, Bowers JE, Wang X, Paterson AH: Angiosperm genome comparisons reveal early polyploidy in the monocot lineage. Proc Natl Acad Sci U S A. 2010, 107 (1): 472-477.PubMedPubMed CentralView ArticleGoogle Scholar
- Swigonova Z, Lai J, Ma J, Ramakrishna W, Llaca V, Bennetzen JL, Messing J: Close split of sorghum and maize genome progenitors. Genome Res. 2004, 14 (10A): 1916-1923.PubMedPubMed CentralView ArticleGoogle Scholar
- Wei F, Coe E, Nelson W, Bharti AK, Engler F, Butler E, Kim H, Goicoechea JL, Chen M, Lee S, Fuks G, Sanchez-Villeda H, Schroeder S, Fang Z, McMullen M, Davis G, Bowers JE, Paterson AH, Schaeffer M, Gardiner J, Cone K, Messing J, Soderlund C, Wing RA: Physical and genetic structure of the maize genome reflects its complex evolutionary history. PLoS Genet. 2007, 3 (7): e123-PubMedPubMed CentralView ArticleGoogle Scholar
- Yu J, Wang J, Lin W, Li S, Li H, Zhou J, Ni P, Dong W, Hu S, Zeng C, Zhang J, Zhang Y, Li R, Xu Z, Li S, Li X, Zheng H, Cong L, Lin L, Yin J, Geng J, Li G, Shi J, Liu J, Lv H, Li J, Wang J, Deng Y, Ran L, Shi X, et al: The Genomes of Oryza sativa: a history of duplications. PLoS Biol. 2005, 3 (2): e38-PubMedPubMed CentralView ArticleGoogle Scholar
- Chen L, Song Y, Li S, Zhang L, Zou C, Yu D: The role of WRKY transcription factors in plant abiotic stresses. Biochim Biophys Acta. 2012, 1819 (2): 120-128.PubMedView ArticleGoogle Scholar
- Wright BE: Stress-directed adaptive mutations and evolution. Mol Microbiol. 2004, 52 (3): 643-650.PubMedView ArticleGoogle Scholar
- Gossmann TI, Song BH, Windsor AJ, Mitchell-Olds T, Dixon CJ, Kapralov MV, Filatov DA, Eyre-Walker A: Genome wide analyses reveal little evidence for adaptive evolution in many plant species. Mol Biol Evol. 2010, 27 (8): 1822-1832.PubMedPubMed CentralView ArticleGoogle Scholar
- Cao J, Huang J, Yang Y, Hu X: Analyses of the oligopeptide transporter gene family in poplar and grape. BMC Genomics. 2011, 12: 465-PubMedPubMed CentralView ArticleGoogle Scholar
- Kosiol C, Vinar T, da Fonseca RR, Hubisz MJ, Bustamante CD, Nielsen R, Siepel A: Patterns of positive selection in six Mammalian genomes. PLoS Genet. 2008, 4 (8): e1000144-PubMedPubMed CentralView ArticleGoogle Scholar
- Yang Z, Wang X, Gu S, Hu Z, Xu H, Xu C: Comparative study of SBP-box gene family in Arabidopsis and rice. Gene. 2008, 407 (1–2): 1-11.PubMedView ArticleGoogle Scholar
- Wang J, Marowsky NC, Fan C: Divergent evolutionary and expression patterns between lineage specific new duplicate genes and their parental paralogs in Arabidopsis thaliana. PLoS One. 2013, 8 (8): e72362-PubMedPubMed CentralView ArticleGoogle Scholar
- Corbi J, Dutheil JY, Damerval C, Tenaillon MI, Manicacci D: Accelerated evolution and coevolution drove the evolutionary history of AGPase sub-units during angiosperm radiation. Ann Bot. 2012, 109 (4): 693-708.PubMedPubMed CentralView ArticleGoogle Scholar
- Rastogi S, Liberles DA: Subfunctionalization of duplicated genes as a transition state to neofunctionalization. BMC Evol Biol. 2005, 5: 28-PubMedPubMed CentralView ArticleGoogle Scholar
- Innan H: Population genetic models of duplicated genes. Genetica. 2009, 137 (1): 19-37.PubMedView ArticleGoogle Scholar
- Liu SL, Baute GJ, Adams KL: Organ and cell type-specific complementary expression patterns and regulatory neofunctionalization between duplicated genes in Arabidopsis thaliana. Genome Biol Evol. 2011, 3: 1419-1436.PubMedPubMed CentralView ArticleGoogle Scholar
- Lamesch P, Berardini TZ, Li D, Swarbreck D, Wilks C, Sasidharan R, Muller R, Dreher K, Alexander DL, Garcia-Hernandez M, Karthikeyan AS, Lee CH, Nelson WD, Ploetz L, Singh S, Wensel A, Huala E: The Arabidopsis Information Resource (TAIR): improved gene annotation and new tools. Nucleic Acids Res. 2012, 40 (Database issue): D1202-D1210.PubMedPubMed CentralView ArticleGoogle Scholar
- Goodstein DM, Shu S, Howson R, Neupane R, Hayes RD, Fazo J, Mitros T, Dirks W, Hellsten U, Putnam N, Rokhsar DS: Phytozome: a comparative platform for green plant genomics. Nucleic Acids Res. 2012, 40 (Database issue): D1178-D1186.PubMedPubMed CentralView ArticleGoogle Scholar
- Punta M, Coggill PC, Eberhardt RY, Mistry J, Tate J, Boursnell C, Pang N, Forslund K, Ceric G, Clements J, Heger A, Holm L, Sonnhammer EL, Eddy SR, Bateman A, Finn RD: The Pfam protein families database. Nucleic Acids Res. 2012, 40 (Database issue): D290-D301.PubMedPubMed CentralView ArticleGoogle Scholar
- Letunic I, Doerks T, Bork P: SMART 7: recent updates to the protein domain annotation resource. Nucleic Acids Res. 2012, 40 (Database issue): D302-D305.PubMedPubMed CentralView ArticleGoogle Scholar
- Larkin MA, Blackshields G, Brown NP, Chenna R, McGettigan PA, McWilliam H, Valentin F, Wallace IM, Wilm A, Lopez R, Thompson JD, Gibson TJ, Higgins DG: Clustal W and Clustal X version 2.0. Bioinformatics. 2007, 23 (21): 2947-2948.PubMedView ArticleGoogle Scholar
- Tamura K, Peterson D, Peterson N, Stecher G, Nei M, Kumar S: MEGA5: molecular evolutionary genetics analysis using maximum likelihood, evolutionary distance, and maximum parsimony methods. Mol Biol Evol. 2011, 28 (10): 2731-2739.PubMedPubMed CentralView ArticleGoogle Scholar
- Guindon S, Delsuc F, Dufayard JF, Gascuel O: Estimating maximum likelihood phylogenies with PhyML. Methods Mol Biol. 2009, 537: 113-137.PubMedView ArticleGoogle Scholar
- Keane TM, Creevey CJ, Pentony MM, Naughton TJ, McLnerney JO: Assessment of methods for amino acid matrix selection and their use on empirical data shows that ad hoc assumptions for choice of matrix are not justified. BMC Evol Biol. 2006, 6: 29-PubMedPubMed CentralView ArticleGoogle Scholar
- Suyama M, Torrents D, Bork P: PAL2NAL: robust conversion of protein sequence alignments into the corresponding codon alignments. Nucleic Acids Res. 2006, 34 (Web Server issue): W609-W612.PubMedPubMed CentralView ArticleGoogle Scholar
- Yang Z: PAML 4: phylogenetic analysis by maximum likelihood. Mol Biol Evol. 2007, 24 (8): 1586-1591.PubMedView ArticleGoogle Scholar
- Yang Z, Bielawski JP: Statistical methods for detecting molecular adaptation. Trends Ecol Evol. 2000, 15 (12): 496-503.PubMedView ArticleGoogle Scholar
- Wong WS, Yang Z, Goldman N, Nielsen R: Accuracy and power of statistical methods for detecting adaptive evolution in protein coding sequences and for identifying positively selected sites. Genetics. 2004, 168 (2): 1041-1051.PubMedPubMed CentralView ArticleGoogle Scholar
- Li C: Automating dChip: toward reproducible sharing of microarray data analysis. BMC Bioinformatics. 2008, 9: 231-PubMedPubMed CentralView ArticleGoogle Scholar
- Tusher VG, Tibshirani R, Chu G: Significance analysis of microarrays applied to the ionizing radiation response. Proc Natl Acad Sci U S A. 2001, 98 (9): 5116-5121.PubMedPubMed CentralView ArticleGoogle Scholar
- 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): 402-408.PubMedView ArticleGoogle Scholar
- Yang Z, Wang Y, Gao Y, Zhou Y, Zhang E, Hu Y, Yuan Y, Liang G, Xu C: Adaptive evolution and divergent expression of heat stress transcription factors in grasses. Dryad Digital Repository. doi:10.5061/dryad.11243Google Scholar
This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited.