Meta-QTL and ortho analysis unravels the genetic architecture and key candidate genes for cold tolerance at seedling stage in rice

Abstract

Rice, a critical cereal crop, grapples with productivity challenges due to its inherent sensitivity to low temperatures, primarily during the seedling and booting stages. Recognizing the polygenic complexity of cold stress signaling in rice, a meta-analysis was undertaken, focusing on 20 physiological traits integral to cold tolerance. This initiative allowed the consolidation of genetic data from 242 QTLs into 58 meta-QTLs, thereby significantly constricting the genetic and physical intervals, with 84% of meta-QTLs (MQTLs) being reduced to less than 2 Mb. The list of 10,505 genes within these MQTLs, was further refined utilizing expression datasets to pinpoint 46 pivotal genes exhibiting noteworthy differential regulation during cold stress. The study underscored the presence of several TFs such as WRKY, NAC, CBF/DREB, MYB, and bHLH, known for their roles in cold stress response. Further, ortho-analysis involving maize, barley, and Arabidopsis identified OsWRKY71, among others, as a prospective candidate for enhancing cold tolerance in diverse crop plants. In conclusion, our study delineates the intricate genetic architecture underpinning cold tolerance in rice and propounds significant candidate genes, offering crucial insights for further research and breeding strategies focused on fortifying crops against cold stress, thereby bolstering global food resilience.

Supplementary Information

The online version contains supplementary material available at 10.1007/s12298-024-01412-1.

Introduction

As the world's population continues to surge and the threat of climate change looms large, the urgent quest to ensure food security intensifies. Worryingly, the recent perturbations in climate patterns suggest significant fragility within the so-called Asian 'rice bowls,' the global epicenters of rice cultivation (IRRI 2023). Moreover, both short-term and long-term climate forecasts point towards a potentially substantial impact on future rice productivity, which poses an undeniable risk to global food security. Rice, an essential staple carbohydrate and a predominantly tropical crop, is innately suited to warm climates. However, there is ample evidence indicating that roughly 15 million hectares of annual global rice yield is also impacted by low temperatures (Pradhan et al. 2019). Particularly in India, the predicament escalates as cold stress (CS) impacts around four million hectares of rice cultivation, causing significant delays in the life cycles of rice grown during the boro/rabi season (Pandit et al. 2017; Pradhan et al. 2016). Temperatures falling below 20 °C, categorized as low temperatures (LT), detrimentally impact rice productivity both directly and indirectly (Andaya and Tai 2006). The direct effects are most conspicuously observed during seedling establishment and encompass a range of negative outcomes: a slow-down in germination rates (Baruah et al. 2008), stunted growth evidenced by yellow leaves and higher mortality rates (da Cruz and Milach 2000; Andaya and Mackill 2003), reduced plant stature due to impaired root and shoot growth, leaf chlorosis resulting from the cold-induced suppression of chlorophyll synthesis and chloroplast formation, as well as delayed heading and reduced tillering (Zhang et al. 2014; Kong et al. 2020). Indirectly, these adverse conditions slow growth and delay heading, thereby exposing the plant's reproductive stages to the higher temperatures of subsequent seasons (da Cruz et al. 2013). This shift can result in increased spikelet sterility incidents and, ultimately, a reduction in yield.

Plants possess the remarkable ability to survive extended exposure to chilling or freezing temperatures by undergoing a process known as cold acclimation. This survival mechanism is triggered when they are first exposed to non-freezing temperatures, leading to a state of increased cold tolerance (Guo et al. 2019; Li et al. 2022). The development of this tolerance is a complex phenomenon, resulting from a series of morphological, physiological and biochemical changes that transpire during the cold acclimation process. At the heart of these transformations are intricate molecular changes initiated by CS. One of the primary sites for cellular injury during CS is the membranes, making changes in lipid composition essential for protecting against membrane damage. These alterations in lipid content act as a vital shield against the detrimental effects of cold, thereby preserving the membrane's integrity (Guo et al. 2019; Li et al. 2022). Furthermore, changes in the membrane structure often serve as the initial trigger for a downstream signaling cascade. This cascade involves a host of response regulators, protein kinases, and a multitude of transcription factors (TFs). The TFs, in turn, regulate the synthesis of proteins and metabolites vital for cellular survival during chilling stress (Guo et al. 2019; Li et al. 2022).

In the model dicot plant Arabidopsis, the signaling cascade of the low-temperature (LT) response has been thoroughly elucidated. The cellular response to low temperature (LT) is fundamentally regulated by TFs, with the CS inducible Dehydration Response Element Binding (DREB)/C-repeat binding factor (CBFs) group of TFs being the most prominent among them (Kidokoro et al. 2022). These TFs are members of the APETALA2/ethylene-response factor (AP2/ERF) family and are known to bind to DRE/C-repeat cis elements located in the promoters of numerous dehydration- and cold-inducible genes. In addition to DREB-mediated pathways, LT responses can also be facilitated by alternate pathways that are independent of DREBs (Kidokoro et al. 2022). As a result, the predominant cold response pathways are generally classified as either DREB-dependent or DREB-independent. The induction of DREBs in response to low temperature (LT) is under the hierarchical control of other TFs like Calmodulin-binding transcription activators (CAMTA) and circadian clock-associated MYB-like TFs (Kidokoro et al. 2022). These TFs play a pivotal role in modulating the transcriptional response, thereby establishing a layered regulatory network in the plant’s adaptation to low temperature.

CS signaling in the model monocot, rice, mirrors the complexity observed in Arabidopsis. Like its dicot counterpart, rice experiences alterations in membrane fluidity, modifications in membrane-associated proteins, and cytoskeleton rearrangements under CS (Wei et al. 2021). The modifications in the plasma membrane-associated proteins such as calcium channels and receptor protein kinases instigate a significant influx of Ca²⁺ into the cytosol, marking an early pivotal response to CS. The resultant intracellular Ca²⁺ signature is perceived by Ca²⁺ sensor proteins, which in turn, initiate transcriptional regulation and trigger the activation of either the DREB-dependent or -independent signaling pathways (Zhang et al. 2014). In addition to the DREBs, many other TFs play an important role in rice as observed by enhanced CS tolerance by overexpression of TFs, such as OsWRKY71, OsbHLH1, OsICE1, OsICE2, OsDREB1, and OsMYB3R-2 (de Freitas et al. 2019). Overexpression of OsMYBS3 and OsMYB4 also contribute to cold tolerance via a DREB1/CBF-independent pathway (Park et al. 2010).

Given the intricate and polygenic nature of cold tolerance during the seedling stage, numerous QTL mapping studies have been executed to pinpoint the associated regions within the rice genome. These studies, leveraging molecular markers across varied genetic backgrounds from bi-parental mapping populations and a collection of rice accessions, have unearthed QTLs that house genes pivotal for cold tolerance. For instance, qCTS12, linked with resistance to cold-induced wilting and necrosis, contains two tandem zeta class glutathione S-transferases, known for their ROS detoxification role (Andaya and Tai 2006; Thom et al. 2001). Another QTL, qCTS4, associated with resistance to seedling yellowing and stunting has been fine-mapped to a potential peroxidase gene (Andaya and Tai 2007). The qRC10 QTL, tied to root conductivity, unveiled a terpene synthase and a pectin-degrading factor as its likely candidate genes (Xiao et al. 2014a). Concurrently, the overlapping QTLs, qLOP2 and qPSR2, attributed to chilling injury resistance, have been mapped to the cold-responsive CBF3/DREB1G gene (Xiao et al. 2014b). Furthermore, COLD1, a QTL, enhances chilling tolerance in japonica rice by influencing G-protein signaling on the plasma membrane, which in turn activates a Ca²⁺ channel (Ma et al. 2015). This assortment of QTLs and their associated genes provide insights into the intricate genetic landscape that underpins cold tolerance in rice.

Despite the existence of more than 30 QTL mapping studies, it is notable that fine mapping has only been pursued in a select few. Additionally, only three studies investigating cold-tolerance MQTLs, 2 during the reproductive stage and 1 involving seedling roots only have been documented (Yang et al. 2019; Leng et al. 2020; Kong et al. 2020). These MQTL studies used a lesser number of traits relevant to CS-mediated establishment of seedlings, which is a major concern in the Indian context. To address this gap, we embarked on a MQTL analysis with an extensive list of phenotypic attributes intimately linked to seedling health. This exploration unveiled 58 MQTLs, encapsulating a total of 10,505 genes. From this extensive list, 46 CGs were isolated, selected on the basis of their divergent expression patterns highlighted in various CS expression datasets. To widen our findings, we conducted an ortho-analysis of these 46 CGs in Zea mays, Hordeum vulgare, and Arabidopsis thaliana, and uncovered orthologs potentially linked to cold tolerance. Our research provides significant insights into the genetic architecture of cold tolerance in rice seedlings and sheds light on the prospective CGs crucial for future research and breeding initiatives.

Material and methods

Literature survey, QTL data collection, and input file preparation

A thorough literature survey up to 2022 was performed to compile QTLs and associated details like QTL name, trait, flanking markers, peak position, confidence interval (CI), mapping population details, LOD score, and phenotypic variance (R2) value from sources like Google Scholar and PubMed (Supplementary Table S1). If the peak position was absent, the mid-point between flanking marks was assumed as the peak. Missing LOD scores and R2/PVE values were substituted with 2.5 and 10%, respectively. Each study required a QTL input file with the mentioned data and a genetic map file containing marker genetic distances for each linkage group. QTLs from SNP marker studies were included by determining the physical location of flanking markers on the rice genome, using the closest markers on the reference map. Quantifiable traits related to cold tolerance identified from various studies include low-temperature germinability (LTG), growth ability of seedling at low temperature (GAS), seedling height (CSH), cold response index of seedling height (CRSH), cold response index of seedling fresh weight (CSFW), withering index (WI), cold induced withering tolerance (CIWI), seedling dry weight (SDW), survival percent (SP), non-death percent (NDP), root conductance (RC), root dry weight (RDW), root length (RL), root fresh weight (RFW), leaf osmotic potential (LOP), leaf dry weight (LDW), leaf conductivity (LC), leaf fresh weight (LFW) and plumule cold tolerance (PCT) (Fig. 1A).

Fig. 1.

Distribution of QTLs and MQTLs associated with seedling cold tolerance on different chromosomes of rice. a Trait-wise distribution of initial QTLs used for the MQTL analysis. (CTS, Cold tolerance at seedling stage; LTG, low-temperature germinability; GAS, Growth Ability of Seedling at low temperature; CSH, Seedling height; CRSH, CRI of Seedling Height; CSFW, CRI of Seedling Fresh Weight; WI, Withering Index; CIWT, Cold Induced Withering Tolerance; SDW, Seedling Dry Weight, SP, Survival Percent; NDP, Non Death Percent; RC, Root Conductance; RDW, Root Dry Weight; RL, Root Length; RFW, Root Fresh Weight; LOP, Leaf Osmotic Potential; LDW, Leaf Dry Weight; LC, Leaf Conductivity; LFW, Leaf Fresh Weight; PCT, Plumule Cold Tolerance. b The distribution of QTLs, projected QTLs and meta-QTLs on twelve rice chromosomes

QTL projection on consensus map and meta-analysis of QTLs

Before the meta-analysis, a consensus map was created by merging genetic map information from all 32 studies with the rice reference map (Temnykh et al. 2001) using BioMercator v4.2.3 (Arcade et al. 2004). The phenotypic data comprised 20 major trait categories. QTLs with varying CI were placed onto the consensus map, and a meta-analysis was conducted on these QTLs, considering all traits as cold tolerance during the seedling stage. The two-step algorithm by Veyrieras et al. (2007) was employed. Initially, the best model with the lowest AIC value, indicating the estimated number of MQTLs, was calculated. Subsequently, suitable parameters for meta-analysis were established, including the number of MQTLs to be mapped, leading to the generation of MQTLs (Table S2).

Candidate genes identification and in silico promoter analysis

Genes within the MQTLs were identified using the physical locations of CI flanking markers from the Gramene Marker Database (http://archive.gramene.org/qtl/). If a marker's location was unavailable, genomic coordinates were determined using nearby genetic markers. The locus IDs were procured from the RAP database (Sakai et al. 2013). Genes were shortlisted based on similar expression patterns in a minimum of 8 out of 11 CS expression datasets (Table S3). The gene ontology information for the 46 differentially expressed genes (DEGs) was obtained from the ShinyGO 0.76 database (Ge et al. 2020). For the 46 CGs, 1.0 kb upstream sequence from the first codon was analyzed to identify cis-regulatory elements using the PlantCare database (Lescot et al. 2002).

Ortho analysis of candidate genes

Protein sequences of the 46 CGs underwent BLAST searches against maize, barley, and Arabidopsis protein databases available in Ensembl Plants (https://plants.ensembl.org) and TAIR (https://www.arabidopsis.org). The top hits, having the lowest e-value and significant high similarity, were shortlisted and subjected to reverse-BLAST at RGAP (http://rice.uga.edu/). Sequences identifying the same protein in reverse-BLAST were deemed orthologous. Protein sequences were aligned using MAAFT (https://mafft.cbrc.jp/alignment/server/) to formulate the phylogenetic tree through the ETE pipeline (https://www.genome.jp/tools-bin/ete). The tree was visualized using Evolview software (https://www.evolgenius.info/evolview/). The gene ontology data of the ortho-CGs were acquired from the ShinyGO v0.76 database, and the physical coordinates of the rice CGs and ortho-CGs were illustrated on the circos plot using the Shiny circos application (Yu et al. 2017), and Arabidopsis map was generated using Chromosome map viewer tool (https://www.arabidopsis.org/jsp/ChromosomeMap/tool.jsp).

Results

Seedling cold tolerance QTLs in rice

Numerous studies investigating QTLs related to rice seedling cold tolerance have employed a variety of phenotypic variation quantifiers (Table S1). However, a significant portion predominantly used “Cold tolerance at seedling stage” (CTS) as their primary trait metric. To perform MQTL analysis for cold tolerance traits in rice, we gathered data on 242 QTLs for all of these attributes from 32 independent mapping studies (Table S1). These QTLs were identified using 28 biparental crosses with populations ranging in size from 40 to 2723 lines. In these studies, 4740 Specific-locus amplified fragment sequencing (SLAF-seq), ~ 3428 Simple Sequence Repeats (SSRs), 2986 Single Nucleotide Polymorphisms (SNPs), 850 Restriction Fragment Length Polymorphisms (RFLPs), ~ 14 Indels and 1 single gene locus (SGL) molecular markers were used (Table S1). The number of QTLs per population ranged from 1 to 20 with chromosome 12 having the most (37) and chromosome 9 having the least number of QTLs (9). Confidence intervals (CI) of QTLs varied from 0.2 to 128 cM and the phenotypic variance ranged from 0.081 to 52.7%.

Distribution of seedling cold tolerance meta-QTLs on the rice genome

In our study, a consensus map was developed by integrating the 32 individual QTL maps generated from different studies with the rice reference map. The consensus map encompassed a total of 19,799 markers and spanned a genetic distance of 2,339.63 cM. Out of the initial 242 QTLs, 187 (77.27%) were successfully projected onto this consensus map (Fig. 1B). Subsequently, a meta-analysis of these 187 projected QTLs was conducted, leading to the identification of 58 MQTLs. This analysis was based on the Veyrieras algorithm within the BioMercator v4.2.3 software. We then selected the most representative MQTL model for each chromosome by considering criteria such as AIC, AICc, AIC3, BIC, and AWE values. These 58 MQTLs were distributed across all twelve rice chromosomes. Their distribution varied, with Chromosome 9 hosting the fewest (2 MQTLs) and Chromosomes 11 and 12 having the most (7 MQTLs each, as shown in Fig. 2). For further accuracy and precision, we considered only those MQTLs that were identified from at least two overlapping QTLs. MQTLs originating from a solitary QTL were disregarded, in line with the recommendation of Kumari et al. (2023). Consequently, MQTLs 4.1, 4.2, 4.3, 7.1, 7.4, 10.1, 10.2, and 11.5 were excluded from subsequent analyses (Table S2). The clustering of multiple QTLs, identified from separate mapping populations that were phenotyped under varied environmental conditions on the consensus map, underscores the significance of the MQTLs we pinpointed. The 95% confidence interval (CI) of these MQTLs ranged between 0.2 to 24.41 cM, with a median value of 3.78 cM. Physical positions were similarly refined, ranging from 0.04 to 4.2 Mb with a median value of 1 Mb. It's noteworthy that the CI of 28 MQTLs (52%) was under 5 cM, and 84% of the MQTLs displayed a physical span narrower than 2 Mb. Overall, our meta-analysis achieved a marked reduction in both the CIs and physical distances of the initial QTLs, enabling us to streamline the list of CGs for deeper analysis (Fig. 3a).

Fig. 2.

Distribution of meta-QTLs on rice chromosomes. Rice chromosomes are represented by the vertical bars. The names of consensus markers along with their position (cM) are present on the right of the chromosomes. QTLs mapped in various studies are highlighted in blue color on the left of the chromosomes. Colored blocks on the chromosomes represent the identified meta-QTLs. Vertical bars on the left side of the chromosome represent the confidence interval (CI) of QTL and horizontal bars represent the phenotypic variance

Fig. 3.

Candidate genes underlying the MQTL regions. a Chromosome-wise distribution of genes underlying QTLs and the identified MQTLs in rice. b Upset plot highlighting the number of differentially regulated genes identified in eleven transcriptomic studies. c Distribution pattern of the gene density, QTLs, MQTLs, MQTL spanning genes and candidate genes identified on the rice genome. The outermost circle represents the chromosome position on the rice genome in Mb. The second circle with green color outlines the gene density on the rice genome. The third and fourth inner circles display the number of initial seedling cold tolerance QTLs and identified MQTLs, respectively. The fifth circle represents the MQTL underlying genes, while the innermost circle represents the heatmap of the identified candidate genes (red represents upregulation, while blue signifies downregulation)

Candidate gene mining within meta-QTL regions

In this study, we considered all the genes within MQTLs as potential CGs for cold tolerance in rice seedlings. This decision was made since many of the originating QTL regions haven't been fine-mapped yet. Using the Rice Annotation Project Database (RAP), we extracted gene IDs corresponding to each MQTL. In total, these 58 MQTL regions encompassed 10,505 unique genes. The distribution of these genes across the MQTLs was variable. For instance, while only 69 genes were found in the MQTL regions on chromosome 9, chromosome 11 was densely populated with 1,552 genes, with MQTL 11.1 alone housing 588 genes. The other gene dense MQTLs were MQTL 7.1 with 569 and MQTL 6.4 with 520 genes, respectively (Table S2). The analysis showed that some MQTLs, like 2.1, 6.1, and 11.7, contained the minimal number of genes, with 9, 10, and 11 genes, respectively. To narrow down our list of potential candidates, we utilized a well-adopted approach of selecting genes based on differential expression (Kumari et al. 2023). By comparing our MQTL genes with 11 previously published datasets on cold tolerance in rice seedlings (Supplementary File 1 and Table S3), we discerned that several genes were differentially expressed in response to CS. The highest number of differentially expressed MQTL genes was found in the study by Guan et al. (2019), accounting for 3,731 genes (Table S4). Similarly, 1931, 1663, 1551, 1411, 1003, 828, 827, 206, 137 and 6 MQTL genes were categorized as DEGs by other groups (Supplementary File 1 and Tables S5-14). To further make our analysis more robust, we focused on genes that were reported as differentially expressed in at least 8 of the 11 datasets. This criterion led to the identification of 46 core CGs spread across multiple MQTL regions (Fig. 3b, Table S15). Seven CGs from MQTL 3.5, four CGs each from MQTL 4.4, 5.1, 5.2 and 11.1; three CGs each from MQTL 1.2, 1.5 and 2.5, two CGs each from MQTL 6.2 and MQTL 12.7 were identified. One CG each from the remaining 10 MQTL regions (MQTL 1.4, 2.1, 5.3, 6.3, 7.2, 8.1, 8.2, 9.1.11.2 and 12.6) were identified (Fig. 3c). Notably, while chromosome 5 exhibited the highest number of CGs (9 in total), chromosome 9 had only one. Additionally, to decipher non-synonymous changes in these CGs we carried out a haplotype analysis in the 3 k rice genome dataset according to the pipeline described in Kumari et al. (2023). However, due to lack of cold tolerance phenotypic data correlating these variations was challenging. Furthermore, we investigated potential post-translational modifications (PTMs) to determine if they align with any of the identified haplotypes. Our analysis, however, did not reveal any significant correlation (Supplementary File1, Table S16). Our analysis thus provided a robust foundation for future studies aiming to functionally validate these CGs, providing a roadmap for enhancing cold tolerance in rice seedlings through molecular breeding.

Promoter analysis

Transcriptional regulation of low-temperature responsive genes is at the core of cellular response to CS (Kidokoro et al. 2022). The molecular mechanism driving this regulation of gene expression in response to CS has earlier been examined by investigating the cis-acting elements present in the promoter of low temperature-responsive genes (Shinozaki and Shinozaki 2006). In this study, the cis regulatory elements were analyzed in the 1 kb putative promoter region of the 46 low temperature-responsive core CGs (Fig. 4). The hallmark cis-elements that are strongly associated with the CS response are Dehydration-responsive elements/C-repeats (DREs; GCCGAC) and Low temperature responsive element (LTRE; CCGAAA). Binding of DREBs/CBFs to DREs results in enhanced expression of downstream genes whose proteins play an important role in protection against CS. Promoters of 14 CGs had DRE/DRE1 while LTRE was found in the promoters of 8 CGs. Since the cold and dehydration stress responsive pathways share many components, a substantial number of CGs (33) had ABA-response elements (ABRE with the core sequence ACGTGG/T) in their promoters. The plant response to gradual cooling is mediated by circadian clock associated MYB TFs (Dong et al. 2011; Kidokoro et al. 2021) and we found that nearly 91% of the CGs (42 out of 46) had the cis-regulatory MYB-elements (Fig. 4). Evening element (EE) and EE-like cis regulatory sequences are present in the promoters of many cold-inducible genes (Dong et al. 2011; Kidokoro et al. 2021) and is also the binding site of circadian-clock regulated TFs. EE (AAAATATCT) and/or EE-like motif (AATATCT) were found in the promoters of 15 CGs. During rapid cooling or sudden drop in temperatures, CAMTA TFs bind to the CGCG box in the promoters of DREBs and other cold-inducible genes and strongly upregulate their expression. We found that CGCG-box (A/C/GCGCGG/T/C) was present in the promoters of 25 genes.

Fig. 4.

In silico identification of cis-regulatory elements in the putative promoter region of the 46 candidate genes. Promoter elements are represented by various shapes: CAMTA element (A/C/GCGCGG/T/C; blue star), CRT/DRE element (DRE: GCCGAC, DRE1: ACCGAGA; blue circle), ABRE element ( AGCTGG/T; red triangle), LTRE ( CCGAAA; grey square), four sequence variation of MYB (Myb; C/TAACTG; salmon square, MYB; C/TAACCA, CAACAG; double headed arrow and MYB recognition sequence; CCGTTG; maroon circle) and EE element ( AATATCT; light green arrow and AAAATATCT; dark green arrow). Scale is presented above in the figure

Ortho-Candidate gene mining

The close evolutionary relationship among grass genomes prompted us to conduct an ortho-analysis of the rice CGs with Hordeum vulgare and Zea mays (Fig. 5a and b). This analysis helped us expand our genetic understanding of CS response across these monocot species. Additionally, to delve deeper into the broader roles of cold-responsive genes, we also identified CG orthologs in the dicot model plant, Arabidopsis thaliana (Fig. 5c). This comparison allowed us to gain insights into the conservation and divergence of cold-responsive genes between monocots and dicots. Through BLASTP comparisons between rice and maize, we identified 69 ortho-CGs (Tables S17 and S18). Notably, these included TFs such as WRKY (WRKY15, 34, 48, 83, 92, and 125), bHLH (bHLH18, 103, and 137), MYB (MYB 5, 34, 37, 55, 56, and 79), and NAC (NAC5, 21, 71, and 86). In contrast, the rice-barley ortho-CG analysis revealed 46 ortho-CGs including several proteins of predicted or yet-to-be-characterized function (Tables S17 and S19). These comprised four proteins with the WRKY domain, three with the basic helix-loop-helix (bHLH) domain, and four with the NAC domain, two with AP2/ERF domain, four CGs belonging to the Homeobox-like domain superfamily, two CGs belonging to the Zinc finger C2H2 superfamily. These identified ortho-CGs not only underscore the utility of CGs among these closely related species but also serve as a testament to their evolutionary stability and reliability as potential candidates for enhancing cold tolerance. In the context of Arabidopsis, our analysis pinpointed 55 ortho-CGs (Tables S17 and S20) including bHLH (bHLH19, bHLH154, bHLH25, bHLH92), WRKY (WRKY40, WRKY70, WRKY26), MYB (MYB14, MYB15, MYB48), and NPF (NPF5.10, NPF5.16, NPF5.14, NPF5.13, NPF5.12). The gene ontology analysis for Zea, Hordeum, and Arabidopsis revealed that the ortho-CGs play roles in a myriad of processes. These include DNA-template transcription, cell wall biosynthesis, trehalose metabolism, cytokinesis, beta-glucan metabolism, GA-mediated signaling pathways, responses to fungi, and oligopeptide membrane transport, as shown in the Supplementary Fig. S1. The diverse range of processes in which ortho-CGs are involved provides valuable insights into the potential functions and regulatory mechanisms of ortho-CGs, paving the way for further research in understanding their significance in plant development and adaptation.

Fig. 5.

Chromosomal distribution of ortho-candidate genes. Rice, maize and barley chromosomes are arranged in a circle, rice chromosomes (1–12), barley chromosomes (HChr1-HChr7), maize chromosomes (ZChr1-ZChr10). The rice orthologous CGs are linked with their corresponding identified ortholog in maize and barley. Each CG is represented with one color. Chromosomes and gene loci on chromosomes are on scale. a Circos-like visualization of Ortho-candidate genes in Rice and maize, b Circos-like visualization of Ortho-candidate genes in Rice and barley. c Chromosome distribution of Arabidopsis CGs orthologs, d Phylogenetic tree of WRKY gene orthologs in Rice, Arabidopsis, Maize and Hordeum

Discussion

Cold stress (CS) tolerance in rice seedlings is polygenic, with nearly 250 QTLs identified from mapping populations. Interestingly, several studies pinpoint multiple QTLs within identical genetic regions. To minimize redundancy and identify overlapping regions, a meta-analysis was conducted, revealing 58 MQTL regions from 242 QTLs. To shortlist the CGs contributing to CS tolerance, expression of genes within these MQTLs was examined using available CS expression datasets, identifying 46 CGs. Additional analysis identified common cis-regulating elements in CGs and an ortho-analysis with Z. mays, H. vulgare, and A. thaliana further extended the understanding of potential protective genes against CS in both monocots and dicots. This research not only uncovers the genetic underpinnings of cold resistance in rice seedlings but also paves the way for enhancing cold tolerance in other crops.

Meta-analysis supplemented with expression data uncovers key TFs that modulate cold response in rice seedlings

Plants' responses to CS are chiefly governed by the transcriptional regulation of cold-regulated genes. Out of the 46 identified CGs, 19 were TFs, from families like CBF/DREB, MYB, bHLH, NAC, WRKY, AUX/IAA, and bZIP, all known for their pivotal roles in plant response to CS. Particularly noteworthy is the OsDREB2B from rice, identified as a CG in MQTL 5.2. Its constitutive overexpression has been observed to enhance chilling and other abiotic stress tolerances in Arabidopsis plants (Matsukura et al. 2010). Moreover, OsDREB2 exhibits genotype-specific differential regulation by CS in rice, accumulating more rapidly in the cold-tolerant rice genotype LTH compared to the cold-sensitive IR29 (Zhang et al. 2012a). This suggests OsDREB2 as a significant factor in enhancing CS tolerance in rice, emphasizing its potential role in developing cold-resistant rice varieties.

In this study, three bHLH TFs, OsbHLH018, OsbHLH65, and OsbHLH148, were identified. Notably, OsbHLH148, located in MQTL3.5, stands out due to its induction by low temperatures and various other abiotic stresses (Seo et al. 2011; Buti et al. 2019). This TF interacts with jasmonate ZIM domain proteins, OsJAZ, and is normally sequestered by OsJAZ members under normal conditions, preventing it from acting as a transcriptional activator. However, upon exposure to abiotic stress, OsJAZ1 protein undergoes degradation via the 26S proteasome, releasing OsbHLH148 and allowing it to play its role as a transcriptional activator of DREB genes (Seo et al. 2011). Overexpression of OsbHLH148 leads to the elevated expression of several OsDREB1 genes (DREB1A, B, C, E, and G), which is associated with enhanced drought tolerance in rice (Seo et al. 2011). Even though the original study did not specifically investigate chilling stress, given the analogous responses between drought and CS in plants, it’s reasonable to hypothesize that OsbHLH148 may also contribute to cold tolerance in rice. Further supporting this, a subsequent study showed that OsbHLH148 interacts with OsWRKY76, enhancing the expression of OsDREB1B and, in turn, improving chilling tolerance in rice (Zhang et al. 2022). This convergence of evidence suggests that OsbHLH148 not only plays a multifaceted role in managing responses to various abiotic stresses but also holds significant potential in the development of stress-resistant rice varieties.

The WRKY TF family is instrumental in defining plant response to diverse abiotic stresses. Our study identified 4 CGs of the WRKY TF family-OsWRKY71, OsWRKY53, OsWRKY45 and OsWRKY67. OsWRKY71, situated in MQTL 2.1, is recognized as a positive regulator of rice's cold tolerance and is induced specifically by CS, highlighting its potential role in an ABA-independent cold response pathway. The overexpression of OsWRKY71 elevates cold tolerance in rice by modulating the expression of downstream targets, OsTGFR and WSI76, the rice homologs of Arabidopsis DREB1A and DREB2A target genes (Kim et al. 2016). Conversely, expression of OsWRKY45, found in MQTL 5.2 and a known negative regulator of cold tolerance, is controlled by OsGATA16, a GATA-type zinc-finger TF (Tao et al. 2011). By binding to the promoter region of WRKY45-1, OsGATA16 suppresses its expression, thereby enhancing cold tolerance in rice at the seedling stage (Zhang et al. 2021). Further, protein-protein interaction analysis revealed interaction between OsWRKY71 and OsWRKY45 (Supplementary Fig. S2). We found three XGATAY elements (− 1335, − 1479, and − 1862) in the promoter region, as reported previously (Zhang et al. 2021). OsWRKY53, found in MQTL 5.2, has been indicated to play a role in regulating oxidative responses and is a target of the novel-m0586-5p miRNA (Van Eck et al. 2014; Fang et al. 2017). While there isn’t direct evidence of OsWRKY53 enhancing chilling tolerance, some indirect links have been made. Transgenic rice seedlings overexpressing the Low silicon gene 1 (Lsi1), a silicon uptake gene, exhibited higher CS tolerance, and this was associated with increased expression of OsWRKY53 (Fang et al. 2017). However, the role of OsWRKY53 in CS is complex. It is downregulated during CS in wild-type plants and has been identified as a negative regulator of cold tolerance at the booting stage in rice (Tang et al. 2022). Thus, the indirect role observed by Fang et al. (2017) in improving chilling tolerance comes into question, given these conflicting observations. Nevertheless, understanding the dual nature of OsWRKY53, as both an indirect enhancer and a direct negative regulator of CS, could be pivotal.

The MYB TF family in plants is a large family and categorized into R2R3, R1R2R3 and MYB-related proteins, with the R2R3 type being predominant in plants (Katiyar et al. 2012). Notably, OsMYB4, located in MQTL 4.4, is documented to augment cold tolerance in rice. Its action is noteworthy as it operates in a DREB- and ABA-independent pathway (Vannini et al. 2004; Pasquali et al. 2008; Park et al. 2010). The manner in which OsMYB4 enhances the levels of proline/compatible solutes and upregulates the expression of ROS-detoxification enzymes implies its significant role as a central regulator in CS response. Our study also revealed a no apical meristem (NAC) TF, OsNAC45, from MQTL 11.1. This TF has demonstrated a vital role in mitigating the abiotic stress effects on rice, particularly focusing on cold and salt stress. The overexpression of OsNAC45 has shown promising results, alleviating the restriction of root growth imposed by abiotic stresses and augmenting peroxidase activity, which is crucial in stress response (Yu et al. 2017). Given the importance of root length in the absorption of water and nutrients, the role of OsNAC45 becomes pivotal, especially for young rice seedlings dealing with CS. This TF can potentially be a key candidate in developing cold-resistant rice varieties due to its positive influence on root growth and stress tolerance. In this study, another noteworthy TF identified was AUX/IAA23, located in MQTL 6.3, which modulates auxin signaling, a crucial process in plant development and response to environmental stimuli. The role of auxin is highly multifaceted and operates in conjunction with other hormones, namely ABA and JA, to regulate the expression of WRKY TFs, which are known regulators of CS tolerance in rice (Yang et al. 2015). The expression levels of this gene were found to be notably high in the roots of the cold-tolerant rice genotype TNG67, contrasting with its low expression in the cold-sensitive genotype TCN1 (Yang et al. 2015), indicating that auxin levels play an important role in cold tolerance through IAA23. The identification of IAA23 in cold meta-analysis expression studies from the booting stage and seedling root further emphasizes its importance in CS signaling at different developmental stages in rice (Fig. S3).

Candidate non-TF genes crucial for seedling level cold tolerance in rice

Alongside pivotal TFs, our investigation brought to light 27 other CGs. The potential roles of the majority of these genes in CS remain mostly uncharted. One such CG, Trehalose-6-phosphate synthase 5 (OsTPS5), situated in MQTL 2.5, contributes to the synthesis of trehalose, an integral osmolyte. The enhancement of trehalose levels within plants has been linked to elevated tolerance to not only CS but also other abiotic stresses. Recent revelations indicate that trehalose seed priming augments seed vigor, seedling growth, and fortifies chilling tolerance in rice. The interaction of OsTPS5 regulates the pivotal OsTPS1 enzyme, assembling a 360 kDa complex to modulate trehalose levels during stress conditions (Zhang et al. 2011). Aligning with this, the overexpression of OsTPS5 has been demonstrated to bolster tolerance to cold, drought, and salinity stresses in rice (Li et al. 2011). Additionally, our study revealed a significant protein phosphatase, OsPP2C27, located in MQTL2.5, as another crucial CG. This phosphatase serves as a negative regulator within the intricate OsMAPK3-OsbHLH002-OsTPP1 signaling network. Interestingly, OsPP2C27, induced by cold, establishes a feedback inhibition loop, mitigating sustained activation of the OsMAPK3-OsbLH002-OsTPP1 pathway. It accomplishes this by interacting with and dephosphorylating phospho-OsMAPK3 and its target, phospho-OsbHLH002, in response to CS (Xia et al. 2021). Moreover, investigations show a decrease in the expression levels of OsTPP1 and OsDREBs in OsPP2C27-overexpression lines and an increase in OsPP2C27-RNAi lines (Xia et al. 2020).

In our exploration for finding additional promising cold-inducible non-TF CGs, three genes were found to have distinctive features worth noting. Firstly, a gene encoding calcium-binding EF-hand protein identified from MQTL 9.1 caught our attention. Remarkably, this gene experiences up-regulation within just five minutes of CS initiation in rice seedlings (Nah et al. 2016). This swift response is hypothesized to manage the influx of Ca²⁺, a known secondary messenger in the cold tolerance response, elucidating its potentially pivotal role in modulating cellular reaction to CS. Secondly, we spotlighted a two-component A-type response regulator 9 (ARR9) gene located in MQTL 11.1. Intriguingly, this gene's expression was noted to be downregulated under CS across three cold-tolerant genotypes (Xie et al. 2022). This pattern indicates that the signaling mediated by ARR9 likely plays an important role in LT responses in rice, making it a promising focal point for further research into cold tolerance mechanisms. Next, we uncovered an allyl alcohol dehydrogenase gene from MQTL 4.4. This gene, a NADP-dependent oxidoreductase, showed a proportional increase in expression in correlation with the degree of CS, with severity of stress (Chawade et al. 2013). Notably, its expression was predominantly higher in P427, a cultivar known for its low-temperature tolerance, compared to the more sensitive cultivars, Nip, and 9311 (Guo et al. 2019). Furthermore, the OsHOX22 gene, hailing from MQTL 4.4, exhibited a gradual up-regulation under various stress conditions, including cold, desiccation, salinity, and osmotic stress (Bhattacharjee et al. 2016). Its expression levels were notably different between cold-tolerant and sensitive rice cultivars (Zhang et al. 2012; Agalou et al. 2007), accentuating its potential role in stress response mechanisms.

Ortho-candidate gene identification among closely related species expands their utility, validates their stability and boosts the confidence of related CGs

Comparative genomic studies serve as a valuable tool to detect information across species, enabling the identification of conserved genes. Our ortho-CG analysis led to the identification of 69 rice orthologs in maize. Among the important ones were WRKY and DREB TF family members. The mutant of ZmWRKY48 gene, an ortholog of OsWRKY71 has lower tolerance against CS as compared to the wild-type plants (Zhang et al. 2022), suggesting that OsWRKY71 and its orthologs may play a crucial role in cold tolerance across different plant species. Additionally, orthologs of OsWRKY53 and OsWRKY67, namely ZmWRKY92 and ZmWRKY125, showed upregulation under both drought and salt stress, pointing towards their involvement in the abiotic stress response (Hu et al. 2021). ZmDREB2.1, an ortholog of OsDREB2b, is also a potential candidate for cold tolerance; it is upregulated in response to both CS and exogenous ABA application and is believed to play a role in maize stress resistance (Filyushin et al. 2022). The conservation of these genes in maize, as well as their relationship with cold tolerance, indicates that the mechanisms underlying cold tolerance in rice and maize may share some common genetic components. Further research is needed to fully understand the extent of this conservation and its implications for improving cold tolerance in both crops.

A homology search in the barley identified forty-six putative barley CGs for CS tolerance. Several of these CGs were involved in the abiotic stress response in barley, i.e., cold, drought, and salinity stress. HvWRKY38 (HORVU6Hr1G028790), an ortholog of OsWRKY71, is a group II type WRKY TF required for cold and drought tolerance in barley (Mare et al. 2004). The TF is induced early and transiently upon exposure to low non-freezing temperatures in an ABA-independent manner and early but not transiently upon drought stress (Mare et al. 2004). This suggests that HvWRKY38 might be a part of the ABA-independent signaling pathway and a putative CG for cold and drought stress in barley. HvHox22 (HORVU2Hr1G092710), a class I HD-Zip family member, is an ortholog of OsHOX22. It is induced by cold, drought, and salt stress and ABA treatments and is one of the promising CGs for the improvement of stress responses in barley (Zhang et al. 2012). Furthermore, many identified orthologs were reported to be involved in either salinity or drought stress response, including HORVU6Hr1G067760 (a protein kinase superfamily protein) and HORVU5Hr1G097060 (a basic-leucine zipper, bZIP), which were identified in GWAS studies to be associated with drought and salt tolerance at seedling stage in barley (Xiong et al. 2023). The conservation of these genes in barley as well as their involvement in different stressors suggest that these CGs have the potential to improve stress tolerance in barley via their engagement in cold, drought, and salt tolerance.

We also identified 55 orthologs of rice CGs in Arabidopsis. The Arabidopsis orthologs of OsbHLH148 and OsbHLH65, AtbHLH92 and AtbHLH154, respectively, show increased transcript abundance in response to a wide range of abiotic stresses, including cold treatments, NaCl, dehydration, and mannitol (Kreps et al. 2002; Jiang et al. 2009). This suggests that these orthologs may play a role in stress responses in Arabidopsis as well and highlights the complexity and redundancy of stress signaling networks in plants. Another important ortholog identified was AtMYB15, a known negative regulator of the CBF-dependent cold signaling pathway in Arabidopsis (Agarwal et al. 2006). The Arabidopsis myb15 mutant exhibited freezing stress tolerance, whereas transgenic lines overexpressing AtMYB15 showed reduced freezing tolerance (Agarwal et al. 2006; Kim et al 2017), indicating that AtMYB15 is involved in regulating the plant's response to CS. AtMYB15’s expression and activity are regulated by ICE1, OST1-PUB25-PUB25, and MPK6 signaling (Wang et al. 2019). This intricate regulation, together with the responses of mutant and overexpression lines of AtMYB15 to CS, points to a highly complex network involved in the regulation of cold tolerance in plants. AtWRK40, an ortholog of OsWRKY71, is involved in the glutathione (GSH)-mediated transcriptional activation of AtMPK3 (Mitogen-activated protein kinase 3), which is one of the key regulators of the MPK cascade and whose expression is induced in response to cold and salinity stress (Boro et al. 2022). This suggests that AtWRK40 plays a crucial role in the plant's response to both cold and salinity stress by regulating redox signaling in response to environmental stresses. Our ortho-CG analysis also identified two important plant metabolism-associated genes, AtTSP11 and AtCESA1, which are targets of all three AtCBFs (Song et al. 2021), suggesting their role in early cold response. The conservation of the above-mentioned putative CGs involved with cold tolerance in Arabidopsis underlines the shared genetic components essential for cold tolerance in monocots and dicots. The conservation of OsWRKY71 orthologs, together with earlier studies, indicates that they have a role in cold tolerance in the monocot model crops maize and barley, as well as the dicot model plant Arabidopsis. We propose OsWRKY71 as potentially one of the most important CGs for introducing cold tolerance in plants.

Conclusions

MQTL analysis surpasses single QTL mapping studies, effectively identifying precise QTLs and contributing significantly to the discovery of CGs and pertinent flanking markers for MAS in breeding initiatives. Employing trait-specific transcriptome datasets further refines the process of selecting potential CGs. Additionally, exploring ortho-analysis is valuable in uncovering the CGs impacting complex quantitative traits in related crop species. Our study revealed several CGs in various crops linked with cold tolerance at the seedling stage, laying the foundation for further investigations into the genetic bases of cold tolerance in crop plants. These findings pave the way for more in-depth research into the intricate gene interactions responsible for crops' cold resilience.

Supplementary Information

Below is the link to the electronic supplementary material.

Declarations

Conflict of interest

The authors declare no conflict of interest.