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Article

Drought Responses in Poaceae: Exploring the Core Components of the ABA Signaling Pathway in Setaria italica and Setaria viridis

by
Isabella Peres de Oliveira
,
Camila Schaaf
and
Nathalia de Setta
*
Centro de Ciências Naturais e Humanas, Universidade Federal do ABC, São Bernardo do Campo 09606-045, SP, Brazil
*
Author to whom correspondence should be addressed.
Plants 2024, 13(11), 1451; https://doi.org/10.3390/plants13111451
Submission received: 20 April 2024 / Revised: 14 May 2024 / Accepted: 21 May 2024 / Published: 23 May 2024
(This article belongs to the Special Issue The Role of Abscisic Acid (ABA) Machinery in Stress Response)
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Abstract

:
Drought severely impacts plant development and reproduction, reducing biomass and seed number, and altering flowering patterns. Drought-tolerant Setaria italica and Setaria viridis species have emerged as prominent model species for investigating water deficit responses in the Poaceae family, the most important source of food and biofuel biomass worldwide. In higher plants, abscisic acid (ABA) regulates environmental stress responses, and its signaling entails interactions between PYR/PYL/RCAR receptors and clade A PP2C phosphatases, which in turn modulate SnRK2 kinases via reversible phosphorylation to activate ABA-responsive genes. To compare the diversity of PYR/PYL/RCAR, PP2C, and SnRK2 between S. italica and S. viridis, and their involvement in water deficit responses, we examined gene and regulatory region structures, investigated orthology relationships, and analyzed their gene expression patterns under water stress via a meta-analysis approach. Results showed that coding and regulatory sequences of PYR/PYL/RCARs, PP2Cs, and SnRK2s are highly conserved between Setaria spp., allowing us to propose pairs of orthologous genes for all the loci identified. Phylogenetic relationships indicate which clades of Setaria spp. sequences are homologous to the functionally well-characterized Arabidopsis thaliana PYR/PYL/RCAR, PP2C, and SnRK2 genes. Gene expression analysis showed a general downregulation of PYL genes, contrasting with upregulation of PP2C genes, and variable expression modulation of SnRK2 genes under drought stress. This complex network implies that ABA core signaling is a diverse and multifaceted process. Through our analysis, we identified promising candidate genes for further functional characterization, with great potential as targets for drought resistance studies, ultimately leading to advances in Poaceae biology and crop-breeding strategies.

1. Introduction

Abscisic acid (ABA) phytohormone plays a crucial role in plant signaling. It has an ancient origin in evolutionary history [1], being present in all vascular plants, as well as some species of cyanobacteria, algae, bryophytes, fungi, and lichens [2]. ABA orchestrates various biochemical and physiological processes [3], such as vegetative growth, seed dormancy and germination, flowering and fruit ripening, leaf senescence, osmotic regulation, and stomatal closure [4,5,6]. ABA also governs responses to a wide range of environmental stresses, whether biotic or abiotic [7], through transcriptional and post-transcriptional mechanisms [8], modulating physiological changes at the cellular level, resulting in whole-system response and adaptation [9].
ABA signaling in higher plants involves the interplay between PYRABACTIN RESISTANCE 1/PYR1-LIKE/REGULATORY COMPONENTS OF ABA RECEPTORS (PYR/PYL/RCARs) receptors and class A of PROTEIN PHOSPHATASE TYPE 2Cs (PP2Cs), which, in turn, modulate SNF-related protein kinase 2s (SnRK2s). In the absence of ABA, PP2Cs are active and inhibit SnRK2s by the dephosphorylation of activation sites [6]. Conversely, in the presence of ABA, the structure of PYR/PYL/RCAR receptors changes, allowing these proteins to bind to ABA and PP2Cs, inhibiting PP2Cs and facilitating the accumulation of phosphorylated SnRK2s [6]. SnRK2 kinases can then activate its multiple downstream regulatory proteins, the ABA-responsive element binding factors (ABFs), which are essential for the activation of ABA-induced genes [4,6]. ABFs modulate ABA-dependent gene expression by binding to the cis-acting ABA-responsive elements (ABRE) located in the regulatory regions of the ABA-induced genes [10]. ABREs are extensively studied cis-elements involved in ABA-induced gene expression, functioning as transcription factor binding sites for ABA-responsive element binding factors (ABFs) [11]. Several ABREs, or combinations of ABREs with other cis-acting elements, are necessary for ABA-responsive transcription to take place [12,13].
The 14 Arabidopsis thaliana PYL receptors have different ABA-binding properties and selectively interact with PP2C co-receptors [14,15,16]. In general, PYL receptors are differentially expressed in multiple tissues and at different stages of plant development [17]. PP2C is a broad family of plant phosphatases divided into 12 subfamilies. Class A of PP2C phosphatases is especially responsive to ABA and ABA-related stress, consisting of nine protein-coding genes in A. thaliana [18]. The origin of the SnRK2 gene family predates the split between green algae and the ancestor of land plants, emphasizing its significance in the adaptation of plants to land environments [19]. Through duplication and subsequent exon-intron loss events, SnRK2 has diversified, acquiring distinct regulatory functions and responsiveness to ABA. Phylogenetic studies have categorized the 10 A. thaliana SnRK2 members into subclasses I, II, and III, which have different roles in the response to the abiotic stress response [20].
Drought is one of the most important abiotic stresses impacting plant development and reproduction, reducing biomass production and seed number, as well as altering the morphology and time of flowering patterns [21,22]. Severe and irregular drought events are growing worldwide as global temperatures persistently increase. Extreme temperatures and shifting precipitation patterns are a major concern for crop production and global food security [23]. Setaria italica and Setaria viridis (Setaria spp.) have emerged as plant models to study drought response in the plants of the Poaceae family due to their small diploid genome, short life cycle, and fully sequenced genome availability. The Poaceae family, which includes valuable world crops like rice, wheat, maize, sorghum, and sugarcane, is a key plant family to both human and animal food production, as well as a biomass source for biofuel production. Setaria viridis, known as green foxtail, first emerged near the 45th parallel north in Eurasia before spreading to various temperate, tropical, and subtropical habitats across the globe. Its remarkable history of colonization and adaptation to local conditions, coupled with its simple growth necessities, has led to its reputation as one of the most problematic weeds in agriculture [24]. Setaria italica, also known as foxtail millet, was domesticated from its wild sibling species, S. viridis, more than 8000 years ago in northern China [25]. It is commonly grown in arid and semi-arid regions of China and India, having natural tolerance to low-water environments and great water usage efficiency [26]. Exploring the mechanisms through which Setaria spp. regulate gene expression, particularly the ABA pathway, in response to drought is of significant value to understand its resistance mechanisms. Such insights can be applied in enhancing abiotic stress tolerance not only in Setaria spp. but also in other major crops belonging to the same family.
In Setaria spp., genome-wide analyses of PYR/PYL/RCAR, PP2C, and SnRK2 genes were exclusively conducted in S. viridis [4]. This study unveiled eight PYL receptor loci, divided into three subfamilies, as well as 12 PP2Cs and 11 SnRK2s loci. The SnRK2s loci were grouped according to the three A. thaliana subclasses. Expression patterns of PYL genes in S. viridis demonstrated either reduction or stability in shoots under ABA treatment, drought, and salt stress, contrasting with monocots such as O. sativa and T. aestivum [27]. In these monocots, at least half of the PYLs were found to be upregulated under ABA treatment [28,29]. Conversely, PP2C genes expression predominantly increased in shoots under drought, ABA treatment, and salt stress [4]. Regarding SnRK2 loci, gene expression profiles showed less uniformity under osmotic stress in S. viridis [4], with some being mostly upregulated (SvSnRK2.9, SvSnRK2.10, and SvSnRK2.11) and others downregulated (SvSnRK2.6, SvSnRK2.7, and SvSnRK2.8).
In this study, we characterized the genes of S. italica from the PYL/PYR/RCAR, PP2C, and SnRK2 gene families and identified their orthologues in S. viridis. The results showed a high conservation among sequences of these sibling species, with the most significant variation observed in their regulatory regions. Phylogenetic analyses enabled the clustering of Setaria spp. genes, Oryza sativa, and A. thaliana, species in which the function of PYL/PYR/RCAR, PP2C, and SnRK2 proteins have been more extensively studied. Lastly, we conducted a systematic meta-analysis of gene expression profiles in Setaria spp. under water stress conditions, enabling us to pinpoint genes modulated during water deficit responses.

2. Results

2.1. The Core ABA Signaling Genes of S. italica and S. viridis Are Highly Conserved

We identified 8, 12, and 11 encoding sequences for S. italica PYR/PYL/RCARs, PP2Cs, and SnRK2s, respectively (Table 1). The total S. viridis gene number validated earlier findings [4], with eight genes from the PYR/PYL/RCAR family, 13 genes from the PP2C family, and 11 genes from the SnRK2 family. The number of loci for the three gene families exhibited high conservation between the two species, apart from the duplication of the SvPP2C2 gene in S. viridis. SnRK2s showed longer transcripts (4418 ± 1018 bp), followed by PP2Cs (2986 ± 1149 bp) and PYR/PYL/RCARs (1686 ± 1065 bp). On the other hand, the longer amino acid sequences mean belonged to PP2C proteins (399 ± 64 aa), followed by SnRK2s (362 ± 31 aa) and PYR/PYL/RCARs (206 ± 17 aa).
A comparative analysis of the chromosomal locations was performed to assess orthology relationships between genes of S. viridis and S. italica. The genes from all three families were spread across the nine chromosomes in both species, with a significant conservation of the chromosomal locations between the Setaria spp. orthologous gene pairs (Figure 1). Major differences among chromosomal locations were identified for the PP2C phosphatases. Firstly, SvPP2C2 underwent tandem duplication (SvPP2C2.1: Sevir.6G004900 and SvPP2C2.2: Sevir.6G005000), with the genes positioned in inverted orientations. Secondly, the PP2C10 and PP2C11 genes were separated by about 3 Mb in S. italica, whereas the region separating these genes in S. viridis was considerably longer, spanning about 8 Mb in length. Lastly, on chromosome 7, the PP2C3 gene of S. italica was positioned approximately 4 Mbp from the beginning of the chromosome, while in S. viridis, it was located about 3 Mbp from the beginning of the chromosome.
In order to compare the structures of S. viridis and S. italica genes, we drew exon-intron structures using the Gene Structure Display Server (GSDS) tool. Most of S. viridis genes were longer than S. italica orthogues (Figure 2), with the exception of PYL2, PP2C5, and SnRK2.3. The differences in the length of the transcribed regions were mainly due to 5′ and 3′ UTR lengths. In S. italica, several PP2C genes lacked 5′ UTR regions, including SiPP2C1, SiPP2C2, SiPP2C3, SiPP2C11, and SiPP2C12. The SvPP2C2.1 duplicated gene had a closer gene structure to SiPP2C2 than its paralogue, SvPP2C2.2. We also evaluated the coding and protein sequence identities among the orthologous PYR/PYL/RCAR, PP2C, and SnRK2 sequences. As expected for sibling species, we observed a high degree of identity among orthologous pairs, especially for amino acid sequences (Table 2). The mean amino acid sequence identities ranged from 99.9 ± 0.2% for SnRK2 to 97.2 ± 9.0% for PP2C, while for nucleotide coding sequences, the mean values varied from 99.8 ± 0.2% for SnRK2 to 97.2 ± 8.8% for PP2C. Most of the substitutions within the coding sequences were synonymous, and the indels did not disrupt the open reading frames (ORFs), thus not leading to significant differences in the protein sequences. The most divergent orthologous sequence pair was SiPP2C2/SvPP2C2.2, which included one of the duplicated PP2C2 genes of S. viridis. This orthologous pair exhibited only 67.3% of amino acid and 67.9% of nucleotide identity. Most of these differences correspond to substitutions and indels in the first exon.
In an attempt to identify whether S. italica and S. viridis PYR/PYL/RCAR, PP2C, and SnRK2 genes can be differentially modulated in response to the ABA signaling pathway, the presence of cis-acting ABRE sites was surveyed in the putative regulatory regions. For this analysis, we defined as putative regulatory sequences DNA fragments of both species that encompass the longest 5′ UTR of each pair of orthologous genes, plus 2 kb upstream of the transcription start sites (TSSs) of each gene. Thus, the length of putative regulatory sequences analyzed varied from 2000 to 2665 bp for PYL receptors, from 2055 to 3160 bp for PP2C phosphatases, and from 2100 to 2595 bp for SnRK2 kinases (Table S1). PP2C genes exhibited the highest mean number of ABRE sites, followed by SnRK2s and PYLs, with significant variation among genes within each family (Figure 3A). For example, while PYL1s have only one ABRE site, PYL8s showed 11 ABREs in its regulatory regions. On the other hand, the number of ABRE sites was very similar for S. italica and S. viridis species, with few exceptions such as the SvSnRK2.6 and SvSnRK2.8 genes, which had four times as many ABRE sites as SiSnRK2.6 and SiSnRK2.8, respectively.
Transposable elements (TEs) are ubiquitous DNA sequences with the ability to transpose and replicate within the genomes of host species, thereby generating genetic variability linked to their activity. One way in which TEs generate genetic variability is by modulating gene expression, whether inserted within or near the genes, not only affecting the qualitative and quantitative production of proteins but also potentially impacting associated metabolic pathways [21,30]. To evaluate the genetic variability in the regulatory regions of Setaria spp. PYR/PYL/RCAR, PP2C, and SnRK2 genes associated with TE insertions, we conducted an analysis to determine the TE coverage. We observed a higher percentage of TEs in PP2C phosphatases and SnRK2 kinases than in PYL receptors, with two SnRK2 sequences (SnRK2.4 and SnRK2.6) showing more than 40% of TEs covering their putative regulatory sequences (Figure 3B). Despite some interesting patterns, there was no difference in the mean TE composition between the S. italica and S. viridis regulatory regions. However, among PP2C phosphatases, a higher frequency of genes containing more TEs was observed in S. viridis compared to S. italica (PP2C5, 6, 9, and 10). Finally, three PYL and three SnRK2 (PYL2, PYL3, PYL8, SnRK2.3, SnRK2.6, and SnRK2.11) sequences showed more TEs in S. italica than in S. viridis.

2.2. Phylogenetic Analyses Offer a Valuable Tool for Associating Setaria spp. Genes to Other Homologous Genes with Well-Defined Functions

To analyze the evolutionary history of the PYR/PYL/RCAR, PP2C, and SnRK2 gene families in Setaria spp., unrooted phylogenetic trees were constructed based on the alignment of coding nucleotide sequences. The sequences of A. thaliana and O. sativa, previously analyzed by Duarte and colleagues [4], were also included in the trees. All trees reinforced the orthology relationships between the Setaria spp. genes (Figure 4). Oryza sativa and A. thaliana genes grouped basally to Setaria spp. sequences, in accordance with the phylogenetic relationships of the species. PYL receptor phylogeny grouped Setaria spp. sequences in three distinct clades (Figure 4A), or subfamilies, as previously proposed for S. viridis sequences [4]. Subfamilies I, II, and III harbored Setaria spp. PYL7-8, PYL4-6, and PYL1-3, respectively. Subfamilies II and III exhibited a close phylogenetic relationship. Setaria spp. subfamilies clustered with A. thaliana PYLs as follows: subfamily I with AtPYL8-10, subfamily II with AtPYL4-6 and AtPYL11-13, and subfamily III with AtPYR1 and AtPYL1-3. Sequences of Setaria spp. PP2C clustered into two distinct groups, with PP2C8 positioned separately outside these clusters (Figure 4B). One cluster was basally connected by AtABI1, AtABI2, AtHAB1, and AtHAB2, while the other cluster was closer to AtHAI1-3. Finally, SnRK2 phylogeny evidenced the three subclasses of SnRK2 kinases (Figure 4C). Subclass I included SnRK2.6, SnRK2.7, SnRK2.8, SnRK2.10, and SnRK2.11; subclass II, SnRK2.4 and SnRK2.5; while subclass III grouped SnRK2.1, SnRK2.2, and SnRK2.3. Different from the tree presented by Duarte and colleagues [4], our phylogeny placed Setaria spp. SnRK2.9 basally.

2.3. Meta-Analysis of Gene Expression Supports a Diversity of Roles of ABA Core Components during the Response to Water Stress

Aiming to investigate the expression patterns of core ABA signaling genes under drought stress, a meta-analysis was conducted using data from studies involving water deficit and PEG-induced drought stress in Setaria spp. Literature searches were performed from October 2020 to February 2024 using Google Scholar [31] and PubMed [32] websites (Table S2). We were able to track eight papers that had LogFC data available for download and analysis (Figure S1) [21,33,34,35,36,37,38,39]. LogFC was used to quantify the level of modulation in gene expression between experimental and control conditions (hereafter referred to as the sample). No studies with available LogFC data were identified for S. viridis, leading to its exclusion from the subsequent analysis. A total of 24 samples were identified for S. italica (Table S3).
We observed a general downregulation of PYL genes, with the exception of PYL7 (subfamily I), which displayed upregulation in three samples and downregulation in one, and PYL6 (subfamily II), which exhibited no significant differential expression (Figure 5). Notably, PYL4 (subfamily II) showed the most pronounced downregulation. Our analysis of seed tissue did not reveal upregulation in any PYL. Contrasting with the PYL expression profile, our meta-analysis unveiled a pattern of strong upregulation across PPC2 genes, with the exception of PP2C8 and PP2C1, which were downregulated in two samples and upregulated in only one sample, respectively (Figure 5). PP2C3 emerged as the most upregulated gene, being clustered with the other strongly upregulated PP2C2, PP2C4, PP2C6, PP2C7, and PP2C12. However, it is worth noting that the expression of PP2C7 in root tissue exhibited a contrasting downregulation. Lastly, the expression profiles of SnRK2 genes displayed the greatest variability (Figure 5). SnRK2.4, SnRK2.9, SnRK2.10, and SnRK2.11 showed an important upregulation. In contrast, SnRK2.8 and SnRK2.7 displayed expressive downregulation; SnRK2.1 was not differentially expressed and, SnRK2.2, SnRK2.3, SnRK2.5, and SnRK2.6 were weakly upregulated (Figure 5).

3. Discussion

The genome and transcriptome sequencing of plant species has offered invaluable insights into genome functionality and the adaptive mechanisms these species employ under diverse environmental conditions. Sibling species may manifest distinct phenotypes due to subtle variations in protein products, differences in gene expression profiles, and post-transcriptional and post-translational modifications. Previous studies have demonstrated that regardless of the phenotypic variation and the wide global distribution of S. viridis, its populations display low genetic diversity [33,34]. The genetic diversity in S. italica is even more restricted, a pattern commonly seen in domesticated species [35]. Therefore, the low genetic variability observed for the PYL/PYR/RCAR, PP2C, and SnRK2 gene families in S. italica and S. viridis was not unexpected. The main differences were associated with the presence and length of the UTRs, the number of ABRE elements, and the presence of TEs in the regulatory regions (Figure 2 and Figure 3). As a result, any variations in the functionality of the orthologous PYL/PYR/RCAR, PP2C, and SnRK2 genes of S. italica and S. viridis are more likely to manifest at the transcriptional, post-transcriptional, or post-translational levels.
PYL receptors of Setaria spp. were clustered into the three subfamilies, each containing sequences from both A. thaliana and O. sativa species (Figure 4). Among the 14 A. thaliana PYL receptors, representatives from subfamilies I (PYL1 and PYL3), II (PYL6), and III (PYL8, PYL9, PYL10, and PYL11) triggered the inhibition of PP2C activity in the presence of ABA [36]. Additionally, AtPYL13 inhibited PP2Cs in the ABA-independent pathway, and was able to interact with other PYLs, reducing their activity in the ABA-dependent pathway [15]. The absence of correlation between the tree topology and the function of PYL receptors indicates a complex evolutionary pattern of ABA dependency function. This pattern potentially involves neofunctionalization and functional convergence in the Angiosperm ancestral species.
Comprehending the expression patterns of PYL genes is a challenging task. Given that PYL proteins interact with PP2C to activate the SnRK2 signaling cascade, one would anticipate that the genes encoding PYL are upregulated under water stress. However, it has been demonstrated, at least in A. thaliana, that ABA decreases the expression of six PYLs receptors (AtPYR1, AtPYL1, AtPYL4, AtPYL5, AtPYL6, and AtPYL8), while the expression of the remaining receptors exhibits minimal changes [27]. This regulation profile helps prevent a prolonged ABA response, which could potentially harm plant performance. In monocots, several studies have indicated a reduction in the gene expression of PYL under ABA-related stresses, with some exceptions. PYL genes were reported to be downregulated in the leaves of S. viridis during water stress [4] and Sorghum bicolor under moisture-deficit stress, except for SbPYL1 and SbPYL7, which were upregulated [37]. Inversely, ABA led to the upregulation of mostly PYL loci in the shoots and roots of rice and wheat [28,29]. Our findings indicate that S. italica displays a PYL gene expression profile under osmotic stress that aligns more closely with other C4 Poaceae species, such as S. viridis and S. bicolor, as well as A. thaliana, than with C3 Poaceae species like rice and wheat.
The PP2C phylogenetic analysis enabled the differentiation of two groups of Setaria spp. genes: one closely related to ABIs, HABs, and AHGs, and the other more closely associated with HAIs from A. thaliana (Figure 4). ABI, HAB, and AHG PP2Cs are the canonical ABA-responsive PPC2s, with redundant functions in ABA signaling in the interaction with PYL and SnRK2 proteins [38]. On the other hand, HAI PP2Cs participate in the drought stress response by two complementary mechanisms. The primary function of HAI PP2Cs, particularly HAI1, lies in modulating the signaling pathway related to low water potential, which controls the accumulation of proline and osmoregulatory solutes [39,40]. Additionally, due to their restricted interaction with PYL, HAI PP2Cs can maintain their activity and inhibit SnRK2s in the presence of ABA [41]. Understanding the evolutionary relationships between Setaria spp. and A. thaliana PP2C genes can contribute in selecting candidate genes for additional functional studies on abiotic stress responses in Setaria spp. The upregulation of S. italica PP2Cs under water stress is in accordance with the findings on closely related species S. viridis [4] and sorghum [37], and also in the more distant eudicot species, among them, tomato [42], soybean [6] and grapevine [43]. The most highly expressed S. italica PP2C genes (Figure 5) are grouped closely with A. thaliana HAI genes in the phylogenetic analysis (Figure 4). Interestingly, the canonical A. thaliana PP2Cs, ABI, HAG, and HAB, are clustered with Setaria spp. PP2Cs that were less upregulated (Figure 4 and Figure 5). This clustering reinforced the function of Setaria spp. PP2C1, PP2C8, PP2C9, PP2C10, and PP2C11 on the ABA-dependent water deficit response.
SnRK2s are divided into three subclasses. Arabidopsis thaliana Subclass III SnRK2s (AtSnRK2.2, AtSnRK2.3, and AtSnRK2.6) are ABA-dependent kinases, which inhibit seed germination and seedling growth by the modulation of stress-responsive genes [44]. Subclass II SnRK2s (AtSnRK2.7 and AtSnRK2.8) are activated in response to osmotic stress, albeit to a very minimal extent in response to ABA [45]. Finally, subclass I SnRK2s (AtSnRK2.1, AtSnRK2.4, AtSnRK2.5, AtSnRK2.9, and AtSnRK2.10) contain ABA-unresponsive kinases that are activated under osmotic stress conditions, except for AtSnRK2.9, which is not modulated during water deficit response [44]. Using the topology of our SnRK2 phylogenetic tree, we can suggest categorizing S. italica gene clades into the three SnRK2 subclasses, providing an initial insight into their role in water deficit responses. In terms of gene expression modulation, SnRK2s displayed significant variability, which did not align directly with the identified SnRK2 subclasses in the phylogenetic analysis (Figure 4). However, these variations were in line with the expression patterns previously reported for S. viridis [4]. Multiple studies vary in their findings regarding expression modulation within each SnRK2 subclass. For example, subclass III genes were found to be downregulated in sorghum [37], rice [20], and grape [43]; however, subclass III exhibited increased expression in tomato and wheat under dehydration conditions [42,46]. These findings indicate variability in the gene expression modulation across various plant species, consistent with our own results. Alternatively, these differences in gene modulation patterns among species may stem from experimental designs and biological sampling specificities.
The expression of ABA core components during stress response is tightly regulated and is more intricate than previously understood [4,47]. A negative-feedback mechanism that attenuates the initial ABA response, leading to desensitization to the elevated levels of ABA, includes the downregulation of PYL and the upregulation of PP2C [18,48], and can explain, in part, our gene expression modulation profiles (Figure 5). The existence of multiple PYR/PYL/RCAR, PP2C, and SnRK2 proteins might indicate that different PP2Cs bind to different PYL receptors to fine-tune a response to the broad range of ABA concentrations and kinases, contributing to a precise and plastic response [49]. As potential target genes for future investigations, it would be interesting to obtain Setaria app. lines that either overexpress or knock down drought-responsive PP2Cs, PYLs, and SnRK2s, and evaluate the impact of these genetic modifications on the phenotype of plants under osmotic stress. Setaria spp. PP2C2, PP2C3, PP2C4, PP2C6, PP2C7, and PP2C12 present promising targets, as they were upregulated during drought and share close phylogenetic relationships with A. thaliana HAI phosphatases, which are known to be overexpressed under drought conditions and regulate the accumulation of proline and osmoregulatory solutes at low water potential [41]. Moreover, conducting transactivation assays could provide valuable insights into the interaction among ABA, PYL receptors, and PP2C phosphatases in Setaria spp. This approach would facilitate the identification of specific PP2Cs that modulate subclass III kinases (such as SiSnRK2.1, SiSnRK2.2, and SiSnRK2.3), which exhibit close phylogenetic relationships with AtSnRK2.2, AtSnRK2.3, and AtSnRK2.6 kinases and are primarily ABA-responsive proteins [44]. These represent only a few approaches that can help to unravel the intricate network underlying ABA signaling. In summary, our findings shed light on the genetic diversity, evolutionary relationships, and potential roles of the genes implicated in drought responses in S. italica and S. viridis species. Understanding the complexity of the ABA signaling network is paramount for deciphering the elaborate mechanisms through which drought-tolerant plants detect and respond to external stressors, ultimately contributing both plant biology and strategies for crop improvement.

4. Materials and Methods

4.1. Mining and Gene Structure Analyses

PYR/PYL/RCAR, PP2C, and SnRK2 coding sequences were identified using the BLASTn tool from the Phytozome 13 database [50], S. italica v2.2 and S. viridis v4.1 genomes, and previously characterized S. viridis sequences [4] as queries. Gene and coding sequences, the chromosomal location, presence and location of PFAM domains, the number of amino acids, and transcription orientation were extracted from Phytozome for subsequent analyses. The genomic sequences and CDSs were used to draw schematic diagrams of the exon–intron structures using the Gene Structure Display Server 2.0 platform (GSDS, [51]). Orthology maps were drawn using the PhenoGram v1 tool [52] and the coordinates of the transcription start site (TSS) of each locus. The sequence identity between the nucleotide and amino acid sequences was evaluated using the p-distance method, calculated by the BioEdit v7.2.6.1 tool [53]. Putative promoter sequences were obtained from Phytozome and utilized to annotate ABRE binding sites using PlantCARE v1 [54] and TE insertion profiles using the Censor v2 tool [55], with a cut-off score ≥ 250. In order to compare the putative regulatory regions of each pair of orthologous genes, we established that the putative regulatory sequences correspond to the longest 5′ UTR region of each orthologous pair plus a 2 kb fragment upstream of the TSS of the same gene.

4.2. Phylogenetic Reconstructions

Coding sequences were aligned using the Clustal W v1.4 program [56], as implemented by the BioEdit v7.2.6.1 tool [53]. Phylogenetic relationships were reconstructed using the maximum likelihood method and the PhyML v3 program [57], as implemented in the ATGC Montpellier Bioinformatics Platform [57], according to the best distance method evaluated with the ‘Find the Best Model’ tool of the MEGA X v10.2 program [58]. The phylogenetic trees were reconstructed using the General Time Reversible model and five discrete gamma categories. The robustness of the topology was tested using the aLRT SH-like method [57]. Sequences from A. thaliana and O. sativa [4] were also included in the phylogenetic reconstructions. The tree was visualized and edited in Fig Tree v.1.4.3 [59].

4.3. Meta-Analysis of Gene Expression Profile

To understand gene expression patterns of the genes involved in the ABA signaling pathway during water deficit, a title literature search was performed in Google Scholar [31] and PubMed [32] using the keywords “water deficit” OR “drought”, “tolerance” OR “stress” and “setaria” OR “foxtail”. Previously published studies with available S. italica and S. viridis RNA sequencing data were compiled in a database of expression values (LogFC). Loci were considered as differentially expressed if they met the criteria of FDR-adjusted p ≤ 0.05 and the screening cutoff of LogFC ≤ −1.0 or ≥ 1.0. The resulting database was filtered to show genes belonging to PP2C, SnRK2, and PYL/PYR/RCAR gene families. Heatmaps were generated with the ComplexHeatmap package [60] of R Studio v4.3.1 [61], utilizing the Euclidean method for clusterization and the complete method for distance calculation.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/plants13111451/s1, Table S1: Genomic locations and length of regulatory sequences, Table S2: Metadata of papers included in meta-analysis, Table S3: LogFC values of meta-analysis samples. Figure S1. PRISMA flow diagram summarizing the steps and results of paper selection on drought stress in Setaria spp. The funnel represents the last step of filtering. References [62,63,64,65,66,67,68] are cited in Supplementary Materials.

Author Contributions

Conceptualization: I.P.d.O., C.S. and N.d.S.; Formal analysis: I.P.d.O. and C.S.; Writing—original draft preparation: all authors; Writing—review and editing: all authors; Funding acquisition: N.d.S. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP, Grant Number: 2023/02414-9).

Data Availability Statement

Data are contained within the article and Supplementary Materials.

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Olds, C.L.; Glennon, E.K.K.; Luckhart, S. Abscisic Acid: New Perspectives on an Ancient Universal Stress Signaling Molecule. Microbes Infect. 2018, 20, 484–492. [Google Scholar] [CrossRef] [PubMed]
  2. Hartung, W. The Evolution of Abscisic Acid (ABA) and ABA Function in Lower Plants, Fungi and Lichen. Funct. Plant Biol. 2010, 37, 806–812. [Google Scholar] [CrossRef]
  3. Trivedi, D.K.; Gill, S.S.; Tuteja, N. Abscisic Acid (ABA): Biosynthesis, Regulation, and Role in Abiotic Stress Tolerance. In Abiotic Stress Response in Plants; Tuteja, N., Gill, S.S., Eds.; Wiley-VCH Verlag GmbH & Co.: Weinheim, Germany, 2016; pp. 311–322. [Google Scholar] [CrossRef]
  4. Duarte, K.E.; de Souza, W.R.; Santiago, T.R.; Sampaio, B.L.; Ribeiro, A.P.; Cotta, M.G.; da Cunha, B.A.D.B.; Marraccini, P.R.R.; Kobayashi, A.K.; Molinari, H.B.C. Identification and Characterization of Core Abscisic Acid (ABA) Signaling Components and Their Gene Expression Profile in Response to Abiotic Stresses in Setaria viridis. Sci. Rep. 2019, 9, 4028. [Google Scholar] [CrossRef] [PubMed]
  5. Addicott, F.T. Abscisic Acid: Discovery, and Exploration of Properties. In Discoveries in Plant Biology; Kung, S.-D., Yang, S.-F., Eds.; World Scientific Publishing: Singapore, 1998; Volume I, pp. 33–46. [Google Scholar] [CrossRef]
  6. Shen, X.; Nan, H.; Jiang, Y.; Zhou, Y.; Pan, X. Genome-Wide Identification, Expression and Interaction Analysis of GmSnRK2 and Type A PP2C Genes in Response to Abscisic Acid Treatment and Drought Stress in Soybean Plant. Int. J. Mol. Sci. 2022, 23, 13166. [Google Scholar] [CrossRef] [PubMed]
  7. Finkelstein, R. Abscisic Acid Synthesis and Response. Arab. Book 2013, 11, e0166. [Google Scholar] [CrossRef]
  8. Chen, K.; Li, G.J.; Bressan, R.A.; Song, C.P.; Zhu, J.K.; Zhao, Y. Abscisic Acid Dynamics, Signaling, and Functions in Plants. J. Integr. Plant Biol. 2020, 62, 25–54. [Google Scholar] [CrossRef]
  9. Hu, B.; Cao, J.; Ge, K.; Li, L. The Site of Water Stress Governs the Pattern of ABA Synthesis and Transport in Peanut. Sci. Rep. 2016, 6, 32143. [Google Scholar] [CrossRef]
  10. Fidler, J.; Graska, J.; Gietler, M.; Nykiel, M.; Prabucka, B.; Rybarczyk-Płońska, A.; Muszyńska, E.; Morkunas, I.; Labudda, M. PYR/PYL/RCAR Receptors Play a Vital Role in the Abscisic-Acid-Dependent Responses of Plants to External or Internal Stimuli. Cells 2022, 11, 1352. [Google Scholar] [CrossRef]
  11. Kim, J.S.; Mizoi, J.; Yoshida, T.; Fujita, Y.; Nakajima, J.; Ohori, T.; Todaka, D.; Nakashima, K.; Hirayama, T.; Shinozaki, K.; et al. An ABRE Promoter Sequence Is Involved in Osmotic Stress-Responsive Expression of the DREB2A Gene, Which Encodes a Transcription Factor Regulating Drought-Inducible Genes in Arabidopsis. Plant Cell Physiol. 2011, 52, 2136–2146. [Google Scholar] [CrossRef]
  12. Gómez-Porras, J.L.; Riaño-Pachón, D.; Dreyer, I.; Mayer, J.E.; Mueller-Roeber, B. Genome-Wide Analysis of ABA-Responsive Elements ABRE and CE3 Reveals Divergent Patterns in Arabidopsis and Rice. BMC Genom. 2007, 8, 260. [Google Scholar] [CrossRef]
  13. Narusaka, Y.; Nakashima, K.; Shinwari, Z.K.; Sakuma, Y.; Furihata, T.; Abe, H.; Narusaka, M.; Shinozaki, K.; Yamaguchi-Shinozaki, K. Interaction between Two Cis-acting Elements, ABRE and DRE in ABA-dependent Expression of Arabidopsis Rd29A Gene in Response to Dehydration and High-Salinity Stresses. Plant J. 2003, 34, 137–148. [Google Scholar] [CrossRef] [PubMed]
  14. Hao, Q.; Yin, P.; Li, W.; Wang, L.; Yan, C.; Lin, Z.; Wu, J.Z.; Wang, J.; Yan, S.F.; Yan, N. The Molecular Basis of ABA-Independent Inhibition of PP2Cs by a Subclass of PYL Proteins. Mol. Cell 2011, 42, 662–672. [Google Scholar] [CrossRef] [PubMed]
  15. Zhao, Y.; Chan, Z.; Xing, L.; Liu, X.; Hou, Y.J.; Chinnusamy, V.; Wang, P.; Duan, C.; Zhu, J.K. The Unique Mode of Action of a Divergent Member of the ABA-Receptor Protein Family in ABA and Stress Signaling. Cell Res. 2013, 23, 1380–1395. [Google Scholar] [CrossRef] [PubMed]
  16. Zhao, Y.; Zhang, Z.; Gao, J.; Wang, P.; Hu, T.; Wang, Z.; Hou, Y.J.; Wan, Y.; Liu, W.; Xie, S.; et al. Arabidopsis Duodecuple Mutant of PYL ABA Receptors Reveals PYL Repression of ABA-Independent SnRK2 Activity. Cell Rep. 2018, 23, 3340–3351.e5. [Google Scholar] [CrossRef] [PubMed]
  17. Gonzalez-Guzman, M.; Pizzio, G.A.; Antoni, R.; Vera-Sirera, F.; Merilo, E.; Bassel, G.W.; Fernández, M.A.; Holdsworth, M.J.; Perez-Amador, M.A.; Kollist, H.; et al. Arabidopsis PYR/PYL/RCAR Receptors Play a Major Role in Quantitative Regulation of Stomatal Aperture and Transcriptional Response to Abscisic Acid. Plant Cell 2012, 24, 2483–2496. [Google Scholar] [CrossRef] [PubMed]
  18. Fuchs, S.; Grill, E.; Meskiene, I.; Schweighofer, A. Type 2C Protein Phosphatases in Plants. FEBS J. 2012, 280, 681–693. [Google Scholar] [CrossRef]
  19. Saha, J.; Chatterjee, C.; Sengupta, A.; Gupta, K.; Gupta, B. Genome-Wide Analysis and Evolutionary Study of Sucrose Non-Fermenting 1-Related Protein Kinase 2 (SnRK2) Gene Family Members in Arabidopsis and Oryza. Comput. Biol. Chem. 2013, 49, 59–70. [Google Scholar] [CrossRef] [PubMed]
  20. Kobayashi, Y.; Yamamoto, S.; Minami, H.; Kagaya, Y.; Hattori, T. Differential Activation of the Rice Sucrose Nonfermenting1-Related Protein Kinase2 Family by Hyperosmotic Stress and Abscisic Acid. Plant Cell 2004, 16, 1163–1177. [Google Scholar] [CrossRef]
  21. Suguiyama, V.F.; Rodriguez, J.D.P.; dos Santos, T.C.N.; Lira, B.S.; de Haro, L.A.; Silva, J.P.N.; Borba, E.L.; Purgatto, E.; da Silva, E.A.; Bellora, N.; et al. Regulatory Mechanisms behind the Phenotypic Plasticity Associated with Setaria Italica Water Deficit Tolerance. Plant Mol. Biol. 2022, 109, 761–780. [Google Scholar] [CrossRef]
  22. Sun, C.; Gao, X.; Chen, X.; Fu, J.; Zhang, Y. Metabolic and Growth Responses of Maize to Successive Drought and Re-Watering Cycles. Agric. Water Manag. 2016, 172, 62–73. [Google Scholar] [CrossRef]
  23. Wang, Q.; Wu, J.; Lei, T.; He, B.; Wu, Z.; Liu, M.; Mo, X.; Geng, G.; Li, X.; Zhou, H.; et al. Temporal-Spatial Characteristics of Severe Drought Events and Their Impact on Agriculture on a Global Scale. Quat. Int. 2014, 349, 10–21. [Google Scholar] [CrossRef]
  24. Dekker, J. The Foxtail (Setaria) Species-Group. Weed Sci. 2003, 51, 641–656. [Google Scholar] [CrossRef]
  25. Diao, X.; Jia, G. Origin and Domestication of Foxtail Millet. In Genetics and Genomics of Setaria; Doust, A.N., Diao, X., Eds.; Springer International Publishing: Beijing, China, 2017; pp. 61–72. ISBN 9783319451053. [Google Scholar]
  26. Lata, C.; Gupta, S.; Prasad, M. Foxtail Millet: A Model Crop for Genetic and Genomic Studies in Bioenergy Grasses. Crit. Rev. Biotechnol. 2012, 33, 328–343. [Google Scholar] [CrossRef]
  27. Ruiz-Partida, R.; Rosario, S.M.; Lozano-Juste, J. An Update on Crop ABA Receptors. Plants 2021, 10, 1087. [Google Scholar] [CrossRef] [PubMed]
  28. Mega, R.; Abe, F.; Kim, J.S.; Tsuboi, Y.; Tanaka, K.; Kobayashi, H.; Sakata, Y.; Hanada, K.; Tsujimoto, H.; Kikuchi, J.; et al. Tuning Water-Use Efficiency and Drought Tolerance in Wheat Using Abscisic Acid Receptors. Nat. Plants 2019, 5, 153–159. [Google Scholar] [CrossRef]
  29. Yadav, S.K.; Venkata, V.; Kumar, S.; Verma, R.K.; Yadav, P.; Saroha, A.; Wankhede, D.P.; Chaudhary, B.; Chinnusamy, V. Genome-Wide Identification and Characterization of ABA Receptor PYL Gene Family in Rice. BMC Genom. 2020, 21, 676. [Google Scholar] [CrossRef]
  30. Quadrana, L.; Almeida, J.; Asís, R.; Duffy, T.; Dominguez, P.G.; Bermúdez, L.; Conti, G.; Corrêa Da Silva, J.V.; Peralta, I.E.; Colot, V.; et al. Natural Occurring Epialleles Determine Vitamin e Accumulation in Tomato Fruits. Nat. Commun. 2014, 5, 4027. [Google Scholar] [CrossRef]
  31. n.d. Google Scholar. Available online: https://scholar.google.com/ (accessed on 1 October 2020).
  32. National Library of Medicine of PubMed. Available online: https://pubmed.ncbi.nlm.nih.gov/ (accessed on 1 October 2020).
  33. Wang, R.-L.; Wendel, J.F.; Dekker, J.H. Weedy Adaptation in Setaria spp. i. Isozyme Analysis of Genetic Diversity and Population Genetic Structure in Setaria viridis. Am. J. Bot. 1995, 82, 308–317. [Google Scholar] [CrossRef]
  34. D’Ennequin, M.L.T.; Panaud, O.; Toupance, B.; Sarr, A. Assessment of Genetic Relationships between Setaria italica and Its Wild Relative S. viridis Using AFLP Markers. Theor. Appl. Genet. 2000, 100, 1061–1066. [Google Scholar] [CrossRef]
  35. Wang, C.; Chen, J.; Zhi, H.; Yang, L.; Li, W.; Wang, Y.; Li, H.; Zhao, B.; Chen, M.; Diao, X. Population Genetics of Foxtail Millet and Its Wild Ancestor. BMC Genet. 2010, 11, 90. [Google Scholar] [CrossRef]
  36. Raghavendra, A.S.; Gonugunta, V.K.; Christmann, A.; Grill, E. ABA Perception and Signalling. Trends Plant Sci. 2010, 15, 395–401. [Google Scholar] [CrossRef]
  37. Dalal, M.; Inupakutika, M. Transcriptional Regulation of ABA Core Signaling Component Genes in Sorghum (Sorghum bicolor L. Moench). Mol. Breed. 2014, 34, 1517–1525. [Google Scholar] [CrossRef]
  38. Rubio, S.; Rodrigues, A.; Saez, A.; Dizon, M.B.; Galle, A.; Kim, T.H.; Santiago, J.; Flexas, J.; Schroeder, J.I.; Rodriguez, P.L. Triple Loss of Function of Protein Phosphatases Type 2C Leads to Partial Constitutive Response to Endogenous Abscisic Acid. Plant Physiol. 2009, 150, 1345–1355. [Google Scholar] [CrossRef]
  39. Fujita, Y.; Nakashima, K.; Yoshida, T.; Katagiri, T.; Kidokoro, S.; Kanamori, N.; Umezawa, T.; Fujita, M.; Maruyama, K.; Ishiyama, K.; et al. Three SnRK2 Protein Kinases Are the Main Positive Regulators of Abscisic Acid Signaling in Response to Water Stress in Arabidopsis. Plant Cell Physiol. 2009, 50, 2123–2132. [Google Scholar] [CrossRef]
  40. Antoni, R.; Gonzalez-Guzman, M.; Rodriguez, L.; Rodrigues, A.; Pizzio, G.A.; Rodriguez, P.L. Selective Inhibition of Clade A Phosphatases Type 2C by PYR/PYL/RCAR Abscisic Acid Receptors. Plant Physiol. 2012, 158, 970–980. [Google Scholar] [CrossRef]
  41. Bhaskara, G.B.; Nguyen, T.T.; Verslues, P.E. Unique Drought Resistance Functions of the Highly ABA-Induced Clade a Protein Phosphatase 2Cs. Plant Physiol. 2012, 160, 379–395. [Google Scholar] [CrossRef]
  42. Sun, L.; Wang, Y.P.; Chen, P.; Ren, J.; Ji, K.; Li, Q.; Li, P.; Dai, S.J.; Leng, P. Transcriptional Regulation of SlPYL, SlPP2C, and SlSnRK2 Gene Families Encoding ABA Signal Core Components during Tomato Fruit Development and Drought Stress. J. Exp. Bot. 2011, 62, 5659–5669. [Google Scholar] [CrossRef]
  43. Boneh, U.; Biton, I.; Zheng, C.; Schwartz, A.; Ben-Ari, G. Characterization of Potential ABA Receptors in Vitis vinifera. Plant Cell Rep. 2012, 31, 311–321. [Google Scholar] [CrossRef] [PubMed]
  44. Hasan, M.M.; Liu, X.D.; Waseem, M.; Guang-Qian, Y.; Alabdallah, N.M.; Jahan, M.S.; Fang, X.W. ABA Activated SnRK2 Kinases: An Emerging Role in Plant Growth and Physiology. Plant Signal. Behav. 2022, 17, e2071024. [Google Scholar] [CrossRef] [PubMed]
  45. Mizoguchi, M.; Umezawa, T.; Nakashima, K.; Kidokoro, S.; Takasaki, H.; Fujita, Y.; Yamaguchi-Shinozaki, K.; Shinozaki, K. Two Closely Related Subclass II SnRK2 Protein Kinases Cooperatively Regulate Drought-Inducible Gene Expression. Plant Cell Physiol. 2010, 51, 842–847. [Google Scholar] [CrossRef] [PubMed]
  46. Zhang, H.; Li, W.; Mao, X.; Jing, R.; Jia, H. Differential Activation of the Wheat SnRK2 Family by Abiotic Stresses. Front. Plant Sci. 2016, 7, 420. [Google Scholar] [CrossRef]
  47. Zhang, J.; Jia, W.; Yang, J.; Ismail, A.M. Role of ABA in Integrating Plant Responses to Drought and Salt Stresses. Field Crop. Res. 2006, 97, 111–119. [Google Scholar] [CrossRef]
  48. Santiago, J.; Dupeux, F.; Round, A.; Antoni, R.; Park, S.Y.; Jamin, M.; Cutler, S.R.; Rodriguez, P.L.; Márquez, J.A. The Abscisic Acid Receptor PYR1 in Complex with Abscisic Acid. Nature 2009, 462, 665–668. [Google Scholar] [CrossRef] [PubMed]
  49. Hirayama, T.; Umezawa, T. The PP2c-SnRK2 Complex: The Central Regulator of an Abscisic Acid Signaling Pathway. Plant Signal. Behav. 2010, 5, 160–163. [Google Scholar] [CrossRef] [PubMed]
  50. Goodstein, D.M.; Shu, S.; Howson, R.; Neupane, R.; Hayes, R.D.; Fazo, J.; Mitros, T.; Dirks, W.; Hellsten, U.; Putnam, N.; et al. Phytozome: A Comparative Platform for Green Plant Genomics. Nucleic Acids Res. 2012, 40, D1178–D1186. [Google Scholar] [CrossRef] [PubMed]
  51. Hu, B.; Jin, J.; Guo, A.Y.; Zhang, H.; Luo, J.; Gao, G. GSDS 2.0: An Upgraded Gene Feature Visualization Server. Bioinformatics 2015, 31, 1296–1297. [Google Scholar] [CrossRef] [PubMed]
  52. Wolfe, D.; Dudek, S.; Ritchie, M.D.; Pendergrass, S.A. Visualizing Genomic Information across Chromosomes with PhenoGram. BioData Min. 2013, 6, 18. [Google Scholar] [CrossRef] [PubMed]
  53. Hall, T.A. BioEdit: A User-Friendly Biological Sequence Alignment Editor and Analysis Program for Windows 95/98/NT. Nucleic Acids Symp. Ser. 1999, 41, 95–98. [Google Scholar]
  54. Lescot, M.; Déhais, P.; Thijs, G.; Marchal, K.; Moreau, Y.; Van De Peer, Y.; Rouzé, P.; Rombauts, S. PlantCARE, a Database of Plant Cis-Acting Regulatory Elements and a Portal to Tools for in Silico Analysis of Promoter Sequences. Nucleic Acids Res. 2002, 30, 325–327. [Google Scholar] [CrossRef]
  55. Kohany, O.; Gentles, A.J.; Hankus, L.; Jurka, J. Annotation, Submission and Screening of Repetitive Elements in Repbase: RepbaseSubmitter and Censor. BMC Bioinform. 2006, 7, 474. [Google Scholar] [CrossRef]
  56. Thompson, J.D.; Higgins, D.G.; Gibson, T.J. CLUSTAL W: Improving the Sensitivity of Progressive Multiple Sequence Alignment through Sequence Weighting, Position-Specific Gap Penalties and Weight Matrix Choice. Nucleic Acids Res. 1994, 22, 4673–4680. [Google Scholar] [CrossRef]
  57. Guindon, S.; Dufayard, J.F.; Lefort, V.; Anisimova, M.; Hordijk, W.; Gascuel, O. New Algorithms and Methods to Estimate Maximum-Likelihood Phylogenies: Assessing the Performance of PhyML 3.0. Syst. Biol. 2010, 59, 307–321. [Google Scholar] [CrossRef]
  58. Kumar, S.; Stecher, G.; Li, M.; Knyaz, C.; Tamura, K. MEGA X: Molecular Evolutionary Genetics Analysis across Computing Platforms. Mol. Biol. Evol. 2018, 35, 1547–1549. [Google Scholar] [CrossRef]
  59. Rambaut, A. FigTree v1.3.1. Available online: http://tree.bio.ed.ac.uk/software/figtree/ (accessed on 1 February 2024).
  60. Gu, Z.; Eils, R.; Schlesner, M. Complex Heatmaps Reveal Patterns and Correlations in Multidimensional Genomic Data. Bioinformatics 2016, 32, 2847–2849. [Google Scholar] [CrossRef]
  61. Posit Team. RStudio: Integrated Development for R; RStudio, Inc.: Boston, MA, USA, 2023. [Google Scholar]
  62. Qi, X.; Xie, S.; Liu, Y.; Yi, F.; Yu, J. Genome-Wide Annotation of Genes and Noncoding RNAs of Foxtail Millet in Response to Simulated Drought Stress by Deep Sequencing. Plant Mol. Biol. 2013, 83, 459–473. [Google Scholar] [CrossRef]
  63. Tang, S.; Li, L.; Wang, Y.; Chen, Q.; Zhang, W.; Jia, G.; Zhi, H.; Zhao, B.; Diao, X. Genotype-Specific Physiological and Transcriptomic Responses to Drought Stress in Setaria italica (an Emerging Model for Panicoideae Grasses). Sci. Rep. 2017, 7, 10009. [Google Scholar] [CrossRef]
  64. Qin, L.; Chen, E.; Li, F.; Yu, X.; Liu, Z.; Yang, Y.; Wang, R.; Zhang, H.; Wang, H.; Liu, B.; et al. Genome-Wide Gene Expression Profiles Analysis Reveal Novel Insights into Drought Stress in Foxtail Millet (Setaria italica L.). Int. J. Mol. Sci. 2020, 21, 8520. [Google Scholar] [CrossRef]
  65. Yu, A.; Zhao, J.; Wang, Z.; Cheng, K.; Zhang, P.; Tian, G.; Liu, X.; Guo, E.; Du, Y.; Wang, Y. Transcriptome and Metabolite Analysis Reveal the Drought Tolerance of Foxtail Millet Significantly Correlated with Phenylpropanoids-Related Pathways during Germination Process under PEG Stress. BMC Plant Biol. 2020, 20, 274. [Google Scholar] [CrossRef]
  66. Guo, Y.; Hao, D.; Wang, X.; Wang, H.; Wu, Z.; Yang, P.; Zhang, B. Comparative Transcriptomics Reveals Key Genes Contributing to the Differences in Drought Tolerance among Three Cultivars of Foxtail Millet (Setaria italica). Plant Growth Regul. 2023, 99, 45–64. [Google Scholar] [CrossRef]
  67. Zhang, R.; Zhi, H.; Li, Y.; Guo, E.; Feng, G.; Tang, S.; Guo, W.; Zhang, L.; Jia, G.; Diao, X. Response of Multiple Tissues to Drought Revealed by a Weighted Gene Co-Expression Network Analysis in Foxtail Millet [Setaria italica (L.) P. Beauv.]. Front. Plant Sci. 2022, 12, 746166. [Google Scholar] [CrossRef]
  68. Cui, X.; Wang, B.; Chen, Z.; Guo, J.; Zhang, T.; Zhang, W.; Shi, L. Comprehensive Physiological, Transcriptomic, and Metabolomic Analysis of the Key Metabolic Pathways in Millet Seedling Adaptation to Drought Stress. Physiol. Plant. 2023, 175, e14122. [Google Scholar] [CrossRef] [PubMed]
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Figure 1. Chromosomal location analysis of the S. italica (above) and S. viridis (below) PYR/PYL/RCAR, PP2C, and SnRK2 genes. The physical mapping of the genes was based on data retrieved from Phytozome. Chromosomes were drawn to scale and chromosome numbers were indicated below each chromosome ideogram. Pink, blue, and green circles represent PYR/PYL/RCAR, PP2C, and SnRK2 gene families, respectively. Arrows indicate major differences between S. italica and S. viridis gene locations.
Figure 1. Chromosomal location analysis of the S. italica (above) and S. viridis (below) PYR/PYL/RCAR, PP2C, and SnRK2 genes. The physical mapping of the genes was based on data retrieved from Phytozome. Chromosomes were drawn to scale and chromosome numbers were indicated below each chromosome ideogram. Pink, blue, and green circles represent PYR/PYL/RCAR, PP2C, and SnRK2 gene families, respectively. Arrows indicate major differences between S. italica and S. viridis gene locations.
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Plants 13 01451 g002
Figure 2. Exon–intron structures of the S. italica and S. viridis genes PYR/PYL/RCAR (A), PP2C (B), and SnRK2 (C) coding sequences. Pink, blue, and green boxes represent PYR/PYL/RCAR, PP2C, and SnRK2 coding sequences, respectively. Yellow boxes represent 5′ and 3′ UTRs, and black lines indicate introns. Gene structures were drawn to scale and oriented from 5′ to 3′ UTRs.
Figure 2. Exon–intron structures of the S. italica and S. viridis genes PYR/PYL/RCAR (A), PP2C (B), and SnRK2 (C) coding sequences. Pink, blue, and green boxes represent PYR/PYL/RCAR, PP2C, and SnRK2 coding sequences, respectively. Yellow boxes represent 5′ and 3′ UTRs, and black lines indicate introns. Gene structures were drawn to scale and oriented from 5′ to 3′ UTRs.
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Plants 13 01451 g003
Figure 3. Analysis of the number of ABRE sites (A) and the proportion of transposable elements coverage (B) in the putative regulatory regions of Setaria spp. PYR/PYL/RCAR, PP2C, and SnRK2 genes. Setaria italica and S. viridis orthologous gene pairs are grouped in the same bars. Purple and orange bars represent S. italica and S. viridis values, respectively.
Figure 3. Analysis of the number of ABRE sites (A) and the proportion of transposable elements coverage (B) in the putative regulatory regions of Setaria spp. PYR/PYL/RCAR, PP2C, and SnRK2 genes. Setaria italica and S. viridis orthologous gene pairs are grouped in the same bars. Purple and orange bars represent S. italica and S. viridis values, respectively.
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Plants 13 01451 g004
Figure 4. Phylogenetic analysis of PYR/PYL/RCAR (A), PP2C (B), and SnRK2 (C) gene families. The unrooted trees were reconstructed using the PhyML tool and the maximum likelihood method. At: A. thaliana; Os: O. sativa; Si: S. italica; Sv: S. viridis. The numbers at each branch are the percentage occurrence values of the clusters calculated with the aLTR SH-like method. The PYR/PYL/RCARs, PP2Cs, and SnRK2s alignments were 498, 1203, and 1155 characters long, respectively. We indicated PYL subfamilies and SnRK2 subclasses in pink and green, respectively.
Figure 4. Phylogenetic analysis of PYR/PYL/RCAR (A), PP2C (B), and SnRK2 (C) gene families. The unrooted trees were reconstructed using the PhyML tool and the maximum likelihood method. At: A. thaliana; Os: O. sativa; Si: S. italica; Sv: S. viridis. The numbers at each branch are the percentage occurrence values of the clusters calculated with the aLTR SH-like method. The PYR/PYL/RCARs, PP2Cs, and SnRK2s alignments were 498, 1203, and 1155 characters long, respectively. We indicated PYL subfamilies and SnRK2 subclasses in pink and green, respectively.
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Plants 13 01451 g005
Figure 5. Heatmap of LogFC values for core ABA signaling genes. PYR/PYL/RCAR, PP2C, and SnRK2 gene names are written in pink, blue, and green, respectively. Boxplots indicate the LogFC profiles for each gene. Dendrograms on the left and above the heatmap show the clustering patterns of genes and samples, respectively.
Figure 5. Heatmap of LogFC values for core ABA signaling genes. PYR/PYL/RCAR, PP2C, and SnRK2 gene names are written in pink, blue, and green, respectively. Boxplots indicate the LogFC profiles for each gene. Dendrograms on the left and above the heatmap show the clustering patterns of genes and samples, respectively.
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Table 1. Identification of S. italica and S. viridis PYL/PYR/RCAR, PP2C, and SnRK2 genes.
Table 1. Identification of S. italica and S. viridis PYL/PYR/RCAR, PP2C, and SnRK2 genes.
LociScaffoldTranscription SitesProtein LengthPFAM Domain *Phytozome Gene Identifier
StartStop
SiPYL19360038713600547820742–192Seita.9G311900
SiPYL212860240286267520138–183Seita.1G030500
SiPYL34363385813633920220647–198Seita.4G239500
SiPYL49489930154899420622067–213Seita.9G437300
SiPYL53162010291620268420453–197Seita.3G207900
SiPYL65405925744059319820748–195Seita.5G369100
SiPYL734851938485430220546–192Seita.3G076200
SiPYL811185260118898121153–198Seita.1G013900
SvPYL19347614133476353820748–192Sevir.9G318000
SvPYL212710051271342920143–180Sevir.1G031000
SvPYL34358213533582197320652–198Sevir.4G251800
SvPYL49463218204632330022070–213Sevir.9G441100
SvPYL53159499571595167520456–197Sevir.3G213000
SvPYL65394679693946859220751–194Sevir.5G374800
SvPYL735043188504559714147–131Sevir.3G077900
SvPYL811156602116032521155–198Sevir.1G013800
SiPP2C1382173008220607451133–434Seita.3G121900
SiPP2C26375937378923444107–433Seita.6G005300
SiPP2C3740055064010019453116–442Seita.7G021400
SiPP2C42267899852679162737476–362Seita.2G177500
SiPP2C55412978704130000840187–390Seita.5G379400
SiPP2C63177143341771653442285–411Seita.3G218800
SiPP2C79505842475058634039771–330Seita.9G460200
SiPP2C8112180941222395358105–345Seita.1G014100
SiPP2C93120490661205212638158–364Seita.3G164700
SiPP2C1053202784932033321479176–462Seita.5G257700
SiPP2C115350331973503475822644–226Seita.5G294000
SiPP2C1239730764973321639989–376Seita.3G139000
SvPP2C1384817338485238451176–434Sevir.3G124100
SvPP2C2.16387891391296444113–396Sevir.6G004900
SvPP2C2.26387891391296328132–281Sevir.6G005000
SvPP2C37785451790598451119–410Sevir.7G004500
SvPP2C42257031962570526237678–364Sevir.2G184100
SvPP2C55401424494014471640187–390Sevir.5G384400
SvPP2C63169225451692477842285–411Sevir.3G223900
SvPP2C79479064424790843539771–330Sevir.9G463500
SvPP2C8111904191194718358105–345Sevir.1G014100
SvPP2C93119446911194794538158–364Sevir.3G168300
SvPP2C1052583898725843846479176–462Sevir.5G211600
SvPP2C115339330943393478222644–226Sevir.5G296900
SvPP2C1239748111975088739889–376Sevir.3G141300
SiSnRK2.194716245472122036628–284Seita.9G079800
SiSnRK2.29114734831147649436223–279Seita.9G169200
SiSnRK2.33473859884738981237437–292Seita.3G369900
SiSnRK2.4943841286438457043444–260Seita.9G379000
SiSnRK2.5245956775459617253394–260Seita.2G394500
SiSnRK2.61272518172725618045494–350Seita.1G190000
SiSnRK2.7720312277203172763584–260Seita.7G100500
SiSnRK2.831572691592763804–260Seita.3G003200
SiSnRK2.9936623245366264683335–261Seita.9G318200
SiSnRK2.10319092667190979293604–260Seita.3G230400
SiSnRK2.11542440115424447313624–260Seita.5G395400
SvSnRK2.194714220471922436628–284Sevir.9G078200
SvSnRK2.29112631051126622936223–279Sevir.9G167300
SvSnRK2.33464848354648755337537–293Sevir.3G387400
SvSnRK2.4941982274419866783444–260Sevir.9G384100
SvSnRK2.5244725651447306883394–260Sevir.2G405700
SvSnRK2.61267049972670945345494–350Sevir.1G194000
SvSnRK2.7719178334191834173584–260Sevir.7G108300
SvSnRK2.832742092772593794–260Sevir.3G004100
SvSnRK2.9935394567353978343335–261Sevir.9G323900
SvSnRK2.10318332249183375323604–260Sevir.3G235900
SvSnRK2.11541261960412668663624–260Sevir.5G400900
* PF10604 (Polyketide_cyc2) for PYL/PYR/RCAR, PF00481 (PP2C) for PP2C, and PF00069 (Pkinase) for SnRK2 proteins.
Table 2. Sequence identity between S. italica and S. viridis orthologues.
Table 2. Sequence identity between S. italica and S. viridis orthologues.
LociNucleotideAmino Acids
IdentitySubstitutionsIndels *IdentitySubstitutionsIndels *
SiPP2C1/SvPP2C199.7%3-99.7%1-
SiPP2C2/SvPP2C2.-199.2%9-99.5%2-
SiPP2C2/SvPP2C2.267.9%8034867.3%31116
SiPP2C3/SvPP2C399.4%2699.5%-2
SiPP2C4/SvPP2C499.2%3699.2%12
SiPP2C5/SvPP2C599.9%1-100%--
SiPP2C6/SvPP2C699.7%3-100%--
SiPP2C7/SvPP2C7100%--100%--
SiPP2C8/SvPP2C899.8%2-100%--
SiPP2C9/SvPP2C999.4%6-99.2%3-
SiPP2C10/SvPP2C1099.9%1-100%--
SiPP2C11/SvPP2C1199.7%2-99.1%2-
SiPP2C12/SvPP2C1299.4%4399.7%-1
SiPYL1/SvPYL1100%--100%--
SiPYL2/SvPYL298.6%2699.0%-2
SiPYL3/SvPYL399.8%10100%--
SiPYL4/SvPYL499.6%2-99.5%1-
SiPYL5/SvPYL599.1%5-100%--
SiPYL6/SvPYL699.8%1-100%--
SiPYL7/SvPYL7100%--100%--
SiPYL8/SvPYL899.6%2-100%--
SiSnRK2.1/SvSnRK2.199.9%1-100%--
SiSnRK2.2/SvSnRK2.299.6%4-100%--
SiSnRK2.3/SvSnRK2.399.3%4399.4%11
SiSnRK2.4/SvSnRK2.499.8%2-100%--
SiSnRK2.5/SvSnRK2.599.7%3-100%--
SiSnRK2.6/SvSnRK2.699.7%4-99.7%1-
SiSnRK2.7/SvSnRK2.7100%--100%--
SiSnRK2.8/SvSnRK2.899.6%1399.7%-1
SiSnRK2.9/SvSnRK2.999.9%1-99.7%1-
SiSnRK2.10/SvSnRK2.10100%--100%--
SiSnRK2.11/SvSnRK2.11100%--100%--
* Number of coding nucleotides and amino acids involved in indels.
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de Oliveira, I.P.; Schaaf, C.; de Setta, N. Drought Responses in Poaceae: Exploring the Core Components of the ABA Signaling Pathway in Setaria italica and Setaria viridis. Plants 2024, 13, 1451. https://doi.org/10.3390/plants13111451

AMA Style

de Oliveira IP, Schaaf C, de Setta N. Drought Responses in Poaceae: Exploring the Core Components of the ABA Signaling Pathway in Setaria italica and Setaria viridis. Plants. 2024; 13(11):1451. https://doi.org/10.3390/plants13111451

Chicago/Turabian Style

de Oliveira, Isabella Peres, Camila Schaaf, and Nathalia de Setta. 2024. "Drought Responses in Poaceae: Exploring the Core Components of the ABA Signaling Pathway in Setaria italica and Setaria viridis" Plants 13, no. 11: 1451. https://doi.org/10.3390/plants13111451

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