Current Omics Advancement in Plant Abiotic Stress Biology

Applied Biotechnology Strategies to Combat Plant Abiotic Stress investigates the causal molecular factors underlying the respective mechanisms orchestrated by plants to help alleviate abiotic stress in which

Although knowledge of abiotic stresses in crop plants and high throughput tools and biotechnologies is avaiable, in this book, a systematic effort has been made for integrating omics interventions across major sorts of abiotic stresses with special emphasis to major food crops infused with detailed mechanistic understanding, which would furthermore help contribute in dissecting the interdisciplinary areas of omics-driven plant abiotic stress biology in a much better manner.

In 32 chapters Applied Biotechnology Strategies to Combat Plant Abiotic Stress focuses on the integration of multi-OMICS biotechnologies in deciphering molecular intricacies of plant abiotic stress namely drought, salt, cold, heat, heavy metals, in major C3 and C4 food crops. Together with this, the book provides updated knowledge of common and unique set of molecular intricacies playing a vital role in coping up severe abiotic stresses in plants deploying multi-OMICS approaches

This book is a valuable resource for early researchers, senior academicians, and scientists in the field of biotechnology, biochemistry, molecular biology, researchers in agriculture and, crops for human foods, and all those who wish to broaden their knowledge in the allied field.

• Describes biotechnological strategies to combat plant abiotic stress
• Covers the latest evidence based multipronged approaches in understanding omics perspective of stress tolerance
• Focuses on the integration of multi-OMICS technologies in deciphering molecular intricacies of plant abiotic stress

• Language: English
• Publisher: Academic Press
• Release date: May 7, 2024
• ISBN: 9780443216244

Book preview

Chapter 1

Advancement in the understanding of the different abiotic stresses using omics

Than Myint Htun¹, Myint Aye¹, Thu Zar² and Me Me Aung¹,    ¹Department of New Genetics, Advanced Center of Agricultural Research and Education, Yezin Agricultural University, Yezin, Myanmar,    ²Department of Agronomy, Yezin Agricultural University, Yezin, Myanmar

Abstract

To assure food supply for the unprecedented ever-growing world population, the crop improvement is extreme importance. Omics techniques have been widely utilized to solve the stress issue. The use of omics technology (genomics, proteomics, transcriptomics, and metabolomics), time saving, and less expense of producing better stress tolerance crops that are resistant to abiotic stress. The findings of genomics and transcriptomics were important to study on proteomics and metabolism. Omics enables a system biology approach toward understanding the complex interactions between genes, proteins, and metabolites. These technologies have made clear contributions in advancements in the understanding of general plant biology and plant abiotic stress tolerance leading to crop improvements.

Keywords

Drought stress; salinity stress; cold stress; omics; abiotic stress; system biology

1.1 Drought stress

Drought, an unavoidable factor encountered by plants under the current situation of global climate change, is one of the most restrictive factors for plant biomass production and yield worldwide (Nakashima et al., 2014; Singh et al., 2022). In 2021, the Food and Agricultural Organization reported that 34% of crop and livestock production suffered in least developed countries and lower-middle-income countries costing US$37 billion due to drought condition (FAO, 2021). More than 70% of the world’s accessible fresh water is used for agrarian purposes (Cooley et al., 2016). Globally the future global climate change scenarios emphasize the need to implement some sustainable resolutions to address the issue of drought for providing food safety and security (Zhang & Jia, 2013). Improvement of root architecture, growth, and water uptake has been proved to be effective to withstand drought pressure in several crops upholding the crop yield (Khan et al., 2022).

Drought stress has negative effects at the physiological, developmental, and molecular levels in plants, including photosynthesis inhibition, reactive oxygen species (ROS) generation, and cellular tissue and membrane damage (Golldack et al., 2014; Xu et al., 2010). Plants adapt several drought avoidances or tolerance mechanisms including biochemical, physiological, and gene regulatory networks, leading to their effective survival. Genomic technology modulates the defensive strategies of drought-related to phytohormones, signaling molecules, transcription factors (TFs), and translational modifications. Furthermore, proteomic modulation is allied with antioxidant defense, photosynthesis, respiration, stomatal conductance, cell signaling, and posttranslational modifications of proteins. These factors exhibit strong mitigation strategies related to the acclimatization of plants in response to water deficit.

In rice (Oryza sativa L.), several quantitative trait loci (QTLs) for grain yield have been identified and used in breeding program for developing drought-tolerant cultivars (Catolos et al., 2017; Vikram et al., 2016). Under drought conditions, a major grain yield QTL (qtl12.1/qDTY12.1), which was linked with decreased number of days to flowering, higher harvest index, increased biomass, and plant height, was identified between simple sequence repeats (SSR) markers, namely RM28048 and RM511 (Bernier et al., 2007). For grain yield under drought and plant height, qDTY1.1 was observed under drought stress condition (Ghimire et al., 2012; Venuprasad et al., 2012; Vikram et al., 2016). For grain yield trait, five QTLs qDTY2.1, qDTY3.1, qDTY2.2, qDTY9.1, and qDTY12.1 (Mishra et al., 2013; Swamy et al., 2011) were identified. Catolos et al. (2017) mined three QTLs qDTY1.1, qDTY1.3, and qDTY8.1 for grain yield as well as two major QTLs qRT9.1 and qRT5.1 for root trait. Yadav et al. (2019) reported high-density linkage map of rice constructed by genotyping-by-sequencing (GBS) and identified three QTLs (qDTY1.1, qDTY3.3, and qDTY6.3) linked to grain yield across the seasons under severe and moderate drought.

In hexaploidy bread wheat, three QTLs (QRdw.ccsu-2A.1, QRdw.ccsu-2A.2, and QRl.ccsu-2B.1) for deep root ratio, root dry weight, and root length were identified on chromosomes 2A and 2B. Christopher et al. (2013) mined the QTLs (QRA.qgw-2A, QRA.qgw-3D, and qRA.qgw-5D) for root angles and a QTL for root numbers (qRN.qgw-1B). Moreover, Sharma et al. (2014) reported QTLs for root anatomical characteristics, that is, characteristics of xylem vessels. Fine mapping for a major chromosome on 3B is in progress for durum wheat, which will affect the grain yield across a wide range of soil moisture regimes. In maize, 22 QTLs associated with drought-related traits were detected by a genetic linkage map developed using restriction fragment length polymorphism (RFLP) markers (Christopher et al., 2013). For leaf surface area, nine major QTLs on chromosomes 3 and 9 were detected, in maize, stay green is a desirable character for crop production. Mano et al. (2005) mapped QTLs on chromosomes 4 and 8 in maize for adventitious root formation in waterlogged conditions.

Joshi et al. (2016) reported that 5000 genes were upregulated and 6000 genes were downregulated upon drought exposure to rice. These genes are grouped into three main categories: membrane transport genes, signaling-related genes, and transcriptional regulatory genes (Kim et al., 2020; Upadhyaya & Panda, 2019). The expression of these genes in rice governs the biochemical, physiological, and molecular mechanisms under drought stress (Dash et al., 2018; Gupta, Rico-Medina, et al., 2020). The mode of regulation may be either abscisic acid (ABA)-dependent or ABA-independent (Gupta, Rico-Medina, et al., 2020). In an ABA-dependent manner, OsJAZ1 attenuates drought tolerance in rice (Fu et al., 2017). Similarly, late embryogenesis abundant (LEA) proteins and osmoregulatory genes confer drought tolerance to rice plants (Dash et al., 2018; Upadhyaya & Panda, 2019). OsPYL/RCAR5, EcNAC67 (Kim et al., 2014a; Rahman et al., 2016), OsDREB2B, CYP735A, and OsDREB1F (Kim et al., 2020) are also involved in morphological adjustments of rice upon drought exposures.

Guo et al. (2014) reported that upregulation of S-adenosylmethionine synthetase under drought stress helps the plant to tolerate the drought stress and the increased abundance of actin isoforms involved in the creation of thick filaments to increase the density of actin filaments for providing mechanical strength to the cell structure (Kijima et al., 2018). Alpha-1, 4-glucan-protein synthase is also involved in drought stress and its inhibition has been observed during drought stress. In chickpea, (Gupta, Mishra, et al., 2020) reported that homocysteine methyltransferase, dihydro-lipoyl lysine residue succinyltransferase, aminoacylase-1, cysteine synthase, cinnamoyl-CoA reductase, chalcone-flavanone isomerase, elongation factor-1, protein disulfide isomerase-like, and 26S proteasome regulatory subunit 6A homolog also show higher accumulation under the drought condition. In rice under drought stress, proteins such as alpha-tubulin, putative elongation factor-2, putative thiamine biosynthesis protein, putative beta-alanine synthase, and cysteine synthase were found in high abundance. Some other proteins such as tricin synthase-1, triose phosphate isomerase, proteasome subunit alpha type-1, homocysteine methyltransferase, elongation factor-1 beta, proteasome subunit beta type 3, and ubiquitin-activating enzyme E1–2 showed mixed patterns of expression (Agrawal et al., 2016). Protein peptidyl-prolyl cis/trans isomerases showed high abundance in wheat and rice under drought, and their levels were reduced in Phaseolus vulgaris (Zadražnik et al., 2013). The H1 and H2B histone proteins are required for modification in chromatin structure and progression of the cell cycle. Proteomic studies showed decrease in the abundance of H2Bs in Brassica napus during drought stress (Koh et al., 2015), while, in the drought-tolerant variety of Zea mays, histone H1 got decreased during drought stress (Benešová et al., 2012).

1.2 Salt stress

Salinity in agricultural land is a major problem worldwide, placing a severe constraint on crop growth and productivity in many regions, and increased salinization of arable land is expected to have devastating global effects. Although plants vary in their sensitivity to salt stress, high salinity causes water deficit and ion toxicity in many plant species. Considerable efforts have, therefore, been made to investigate how genes respond to salt stress in various plants by using several omics approaches.

Salt stress is the main abiotic stress affecting plants, limiting their growth, and productivity (Parida & Das, 2005). Salt stress is an increasingly serious problem, and underscores the importance of identifying strategies for improving the salt tolerance of plants through, for example, the use of genetic engineering technologies. Salt stress causes molecular damage in plant cells that leads to hyperosmotic stress, homeostasis disruption, and ionic toxicity. The effects of mild cases of salt stress are primarily limited to plant growth, development, and crop productivity, but in extreme cases, salt stress can lead to plant death (Aoki et al., 2005). Salt stress causes a water deficit in the plant which then leads to secondary oxidative stress. Water deficit causes a reduction in the rate of photosynthesis, and exposure of chloroplasts to excess excitation energy limits carbon dioxide fixation, leading to the generation of ROS. Oxidative stress accelerates the production of ROS and subsequently alters the balance between the formation and removal of such species (Triantaphylidès & Havaux, 2009). The effect salt stress has on plant growth depends on the plant species. During the onset and development of salt stress, all major processes are affected, including photosynthesis, protein synthesis, energy production, and lipid metabolism (Parida & Das, 2005). Adaptation to salt stress is a complicated process, at both the whole plant and cellular levels, and requires alterations in gene expression that lead to changes in the protein profile (Parker et al., 2006).

Plant resistance to salinity stress is one of the main challenges of agriculture. The comprehension of the molecular and cellular mechanisms involved in plant tolerance to salinity can help to contrast crop losses due to high salt conditions in soil. Soil salinity is one of the primary causes of crop losses worldwide. Every year, about 1.5 million hectares of agriculture lands are affected by high salinity and rendered unsuitable for crop production (Carillo et al., 2011; Munns, 2005). Soil salinization severely affects agricultural productivity, because most crops cannot tolerate sodium ion (Na+) concentrations greater than 150–200 mM NaCl (Golldack et al., 2011; Hasegawa, 2013). Most of the plants can adapt to low or moderate saline conditions, but their growth is severely reduced above 200 mM NaCl. Salinity imposes an initial osmotic effect and a subsequent ionic effect, reducing the ability of plants to uptake water and micronutrients (Munns & Tester, 2008). Understanding the molecular and cellular mechanisms of salt tolerance are critical to help improve plant growth and productivity under saline conditions.

In this era, omics techniques have been comprehensively utilized to decipher the regulatory mechanisms and cellular intricacies in rice. Advancements in omics technologies have provided a strong platform for the reliable exploration of genetic resources. Omics disciplines such as genomics, transcriptomics, proteomics, and metabolomics have significantly contributed toward the achievement of desired improvements in rice under optimal and stressful environments (Iqbal et al., 2021).

Overexpression of Δ1-pyrroline-5-carboxylate synthetase, which is involved in proline production, confers salt tolerance in potatoes (Hmida-Sayari et al., 2005). Despite these studies, the molecular basis of potato responses to salt stress remains unknown. To produce salt-tolerant cultivars of potato, it is imperative that the genes conferring salt tolerance are identified. Analysis of changes to the potato proteome resulting from various environmental stresses is currently limited to the study of Rey et al. (1998), who found three microsequences of a protein induced by drought in potato chloroplasts that showed no similarity to known proteins.

The gain of function of salt-responsive genes permits the transgenic rice plants to have adequate osmoregulation and less oxidative damage. A recent study advocates that OsSTAP1 is an AP2/ERF transcriptional activator that positively controls salt tolerance. OsSTAP1 works by reducing the sodium/potassium ratio and sustaining cellular redox homeostasis (Wang et al., 2020b). High salinity–induced osmotic stress increases the biosynthesis of ABA. ABA biosynthesis via terpenoid pathway starting from isopentenyl pyrophosphate has been reviewed in rice (Ye et al., 2012b). Among many genes involved in this pathway, a phytoene synthase gene, OsPSY3, and 9-cis-epoxycarotenoid dioxygenases genes (OsNCED3, OsNCED4, and OsNCED5) are induced 1 hour after salt stress and their expression are well correlated to the level of ABA in rice roots (Welsch et al., 2008).

Salt, drought, H2O2, and ABA treatments induced the expression of a putative receptor-like kinases (RLK) gene, OsSIK1. Transgenic rice plants overexpressing OsSIK1 (OsSIK1-ox) show higher tolerance to salt and drought stresses than control plants and the knock-out mutants sik1 as well as RNA interference (RNAi) plants (Ouyang et al., 2018). Mitogen-activated protein (MAP) kinase cascades play a critical role in salt stress response in rice as well. At least two salt-inducible MEKKs have been reported in rice. One of them is OsEDR1 which is upregulated by various environmental stresses such as high salt, physical cutting, and hydrogen peroxide (Kim et al., 2003). A putative MEKK mutant, dsm1, showed sensitivity to salt stress as well as drought stress than wild-type plants (Ning et al., 2009). Although these genes are responsive to salt stress, there is no evidence that MEKKs are regulating any downstream MAPK kinase (MKK). Several salt-inducible MAPKs have been reported in rice. Transcriptional regulation of OsMAPK4 by salt, cold, and sugar starvation was reported although its ABA-dependency is not clear (Fu et al., 2002). Biotic and abiotic stress-inducible OsMAPK5 has been cloned and overexpressed in rice which subsequently exhibited increased tolerance to salt, drought, and cold stresses with increased kinase activity (Xiong & Yang, 2003). Expression of two novel MAPKs, OsMSRMK2 and OsMSRMK3, were induced by various environmental stresses suggesting their possible involvement in defense/stress response pathways (Agrawal et al., 2002; Agrawal et al., 2003).

The ability to regulate the amount of salt translocated to the shoot through the transpiration stream might be a determining factor in salt tolerance (Munns & Tester, 2008). Halophytes can attain Na+ and Cl− (chloride) exclusion under high saline conditions (Flowers & Colmer, 2008). Proteins act as a major stimulator of plant stress response. Proteins not only act as enzymes but also as a key component in the transcription and translation processes, thereby plant stress response genes are regulated both at RNA and protein level (Kosová et al., 2011). However, stress response genes expressed at mRNA and protein level cannot be correlated due to the posttranscriptional and posttranslational modifications in the former (Hossain et al., 2013). Several salt stress-responsive proteins are expressed at different cellular functions such as signal transduction, regulation of carbohydrate, nitrogen and energy metabolism, RNA and protein synthesis, ROS regulation, and redox homeostasis. Hence, quantitative proteomics is a prominent tool to be applied to study the salinity stress tolerance in plants. Such studies have been carried out in halophytes such as Thellungiella halophila (Gao et al., 2008), Suaeda aegyptiaca (Askari et al., 2006), Bruguiera gymnorhiza (Sugihara et al., 2000), and Sesuvium portulacastrum (Yi et al., 2014).

In the salinity experiment of Salicornia brachiate and Salicornia maritima, the concentration of 200–500 mM NaCl was tested and the various proteins of the shoot were analyzed. In S. maritima, cytochrome b6 that up-accumulated at both NaCl concentrations, the most of photosynthesis-related proteins (ATP synthase subunit alpha, apocytochrome f, cytochrome b6-f complex subunit 4) pointed out an up-accumulation at the lowest concentrations and a down-accumulation at the highest. In S. maritima, the level of cytochrome b6, apocytochrome f, and photosystem I iron-sulfur center was up- and down-accumulation at 200 and 500 mM NaCl, respectively. In contrast, ribulose bisphosphate carboxylase large chains and ATP synthase subunits were down-accumulated at both salt conditions. The protein chloroplastic glyceraldehyde-3-phosphate dehydrogenase A, involved in energy metabolism, was down-accumulated at both NaCl concentrations, while cytosolic glyceraldehyde-3-phosphate dehydrogenase 2 showed a contrasting behavior being down-accumulated at the lowest NaCl concentration and up-accumulated at the highest. Finally, the level of pyruvate decarboxylase 1 was up-accumulated at both NaCl concentrations. Acetolactate synthase-1, the protein, involved the amino acid metabolism, showed up- and down-accumulation at 200 and 500 mM NaCl treatment in S. maritima. The oxidative stress-related proteins such as heat shock protein (HSP) 82, catalase isozyme 1, and 1-Cys peroxiredoxin PER1 were down-accumulated in S. brachiata in both concentrations. On the contrary, the level of peroxidase 42 was up-accumulated under both salt conditions, whereas chaperonin CPN60-1 was up-accumulated at 200 mM NaCl but down-accumulated at 500 mM NaCl. In S. brachiata, the protein expansin-B1 was down-accumulated at both salt treatments, while the leucine-rich repeat receptor-like kinase protein, a protein promoting vegetative meristem, was down-accumulated. However, the protein terminal ear1 (regulating leaf initiation rate and shoot development) was up-accumulated at 200 mM NaCl pointing out an opposite trend at the highest concentrations. In S. maritima, the level of Actin-1 was up- and down-accumulated at 200 and 500 mM NaCl, respectively. Furthermore, an upregulation of the gene encoding Retinoblastoma (required for cell-cycle progression, endoreplication, transcriptional regulation, chromatin remodeling, and cell growth) was observed. Significant differences in signaling-related proteins were only observed in S. brachiata. Calmodulin, a Ca2+-dependent signaling molecule involved in the signaling network that mediates Na+ homeostasis and salt tolerance, was down- and up-accumulated at 200 and 500 mM NaCl, respectively. On the contrary, indole-3-acetaldehyde oxidase, a protein involved in auxin biosynthesis, was up-accumulated at lowest concentration assayed and down-accumulated at the highest. In S. brachiata, the proteins related to transcriptional regulation, homeobox protein HOX1A, and ADP-ribosylation factor were up-accumulated at both salt treatments. Nonetheless, the proteins related to protein synthesis, turnover were also altered by salinity. The level of eukaryotic initiation factor 4A and luminal-binding protein 3 was up-accumulated at 200 mM NaCl but down-accumulated at 500 mM salinity, while the expression of histone H2A was down- and up-accumulated at 200 mM and 500 mM NaCl, respectively. In S. maritima, the proteins related to transcription such as DNA-directed RNA polymerase subunit beta were up-accumulated at both concentrations. The level of histone H4 was up- (200 mM) and down-accumulated (500 mM) while protein histone regulator A (HIRA) was down- (200 mM) and up-accumulated (500 mM) by NaCl treatments. Moreover, ribosomal proteins altered their abundance during salinity stress. In particular, at both NaCl concentrations, the 60S acidic ribosomal protein P0 was up-accumulated, whereas the Ubiquitin-40S ribosomal protein S27a was down-accumulated (Benjamin et al., 2020).

1.3 Heat stress

Ideal temperatures are required for better productivity in crop plants. The developmental progressions of plants can severely harmed and terminated due to the variation of temperature. Plant growth, development, and productivity are significantly affected by a rise in ambient temperatures due to climate change causing global food insecurity. The accumulation of atmospheric concentrations of greenhouse gases, such as CO2, N2O, and CH4, increases the global surface temperature by 0.3°C during the next decade and is expected to reach 1.8°C–4.0°C by 2100 according to the report of the Intergovernmental Panel on Climate Change (IPCC). Globally, heat stress (HS) including high day temperature and high night temperature is one of the major abiotic factors for many crops. HS harmfully disturbs photosynthesis, primary and secondary metabolisms, lipid and hormonal signaling (Raza et al., 2019, 2020; Youldash et al., 2020), pollen viability, root growth (Sehgal et al., 2017; Valdés-López et al., 2016), grain number and weight yield, and quality of crops among different cultivars and genotypes. Cellular oxidative injury can be induced by extended HS due to ROS production (Raza et al., 2020; Youldash et al., 2020). Plants have several adaptive, escaping, and acclimation mechanisms to deal with HS conditions. Cellular damage, cell death, the constancy of numerous proteins, membranes, RNA species, cytoskeleton assemblies, and the productivity of enzymatic responses in the cell are differentially affected by elevated HS (Hasanuzzaman et al., 2013, 2020).

HS is a complex trait which is controlled by many genes that impart heat-stress tolerance. Genomic techniques such as high-throughput analyses of expressed sequence tags (ESTs) and the different types of molecular markers including SSRs, diversity arrays technology markers, single nucleotide polymorphism (SNP) markers, different SNP platforms, microarray-based markers, next-generation sequencing (NGS)-based markers, GBS, InDel markers, etc. have greatly improved our current understanding on plant responses to HS to identify the heat-responsive genes in plants.

Genomic developments made it feasible to dissect genetic architecture of underlying such complex traits through QTL/gene mapping. Molecular markers help to tag the genomic regions (i.e., QTLs) controlling the phenotype of a complex trait, which are distributed throughout the genome. Each QTL comprises many genes, which are investigated as potential candidate genes for a trait. Mapping populations derived from two parents (i.e., biparental QTL mapping) and multiparents/association panel (i.e., association mapping) are usually used to map the QTLs/genes controlling a trait of interest (Kitony et al., 2021; Samantara et al., 2021). However, markers linked a QTL controlling complex traits have not been used in marker-assisted breeding program due to poor markers density within QTL regions. However, next-generation sequencing-based approaches especially GBS have provided many evenly distributed SNP and gene SSR markers over genome (Reyes et al., 2021; Spindel & Iwata, 2018). This led to the development of high-resolution linkage maps in crops and are used to map several traits including traits imparting in heat tolerance.

Rice varieties tolerant to high temperature and more than 30 QTLs associated with heat tolerance have been reported in different studies (Cheng et al., 2012; Matsui et al., 1997, 2001; Tenorio et al., 2013; Ye et al., 2012a, 2015a). Several QTLs for heat tolerance at flowering stage have been identified and used in breeding program for developing heat-tolerant cultivars of rice (O. sativa L.) (Kilasi et al., 2018; Ye et al., 2012a, 2015a). In rice, qHTSF4.1 for HS was consistently identified in different populations at the flowering stage (Xiao et al., 2011; Ye et al., 2012a, 2015b). In maize (Z. mays L.), QTL hot spots for grain yield under HS have been identified (Frey et al., 2016). QTLs for heat tolerance have also been mapped using genome-wide association study (GWAS) in wheat (Triticum aestivum L.) (Chopra et al., 2017; Maulana et al., 2018; Paliwal et al., 2012; Sharma et al., 2017; Shirdelmoghanloo et al., 2016; Talukder et al., 2014; Vijayalakshmi et al., 2010) and jowar or sorghum (Sorghum bicolor L.) (Chopra et al., 2017).

In chickpea, HS tolerance major QTLs qfpod02.5, qts02.5, qgy02.5, and q%podset06.5 were observed for filled pods/plot, total number of seeds/plot, grain yield/plot, and pod setting percentage (Jha et al., 2021; Paul et al., 2018). A linkage map spanning 529.11 cM and comprising 271 GBS-based SNP markers identified major QTL for number of filled pods per plot, total number of seeds per plot, grain yield per plot and % pod setting under HS (Paul et al., 2018). Moreover, GWASs allow narrowing down the candidate regions controlling associated quantitative traits to explore specific haplotypes in natural populations and even wild species (Bhat et al., 2016; George & Cavanagh, 2015; Verdeprado et al., 2018). To investigate the marker-trait association for heat tolerance, a recent GWAS was conducted in a panel of 300 accessions (Thudi et al., 2014).

In pea, GWAS using 16,877 SNPs identified association of genomic regions with chlorophyll concentration (6 QTLs), with photochemical reflectance index and canopy temperature (2 QTLs); reproductive stem length (7 QTLs), internode length (6 QTLs), and pod number (9 QTLs) and also identified 48 candidate genes responsible for these traits under HS (Tafesse et al., 2020).

In cowpea (Vigna unguiculata L.), two dominant genes have been identified for HS tolerance (Marfo & Hall, 1992) and QTLs for pod set number per peduncle under HS (Lucas et al., 2013; Pottorff et al., 2014). In soybean (Glycine max L.), HSPs and heat shock factors (HSFs) in these QTL regions were identified by further comparative genomic analysis (Liu et al., 2019). In azuki bean (Vigna angularis var. angularis (Willd.) Ohwi and Ohashi), QTL mapping was conducted and identified two QTLs (i.e., HQTL1 and HQTL2) for pollen viability trait under HS (Kaga et al., 2003; Vaughan et al., 2005). In lentil, HS tolerance QTLs (qHT.ss and qHT.ps) were observed for seedling survival and pod set traits (Singh et al., 2019). In field pea, six minor QTLs for chlorophyll concentration, phytochemical reflectance index, canopy temperature, reproductive stem length, internode length, and pod number were observed on chromosome 2, 6, and 9 (Tafesse et al., 2020).

To develop heat-tolerant or high-temperature-stress-resistant cultivars, transcriptomics provides a better understanding of the molecular mechanisms underlying HS and help to identify key candidate genes which can be further utilized in genetic engineering and molecular breeding experiments. Transcriptome analysis at seedling and anthesis stages showed the expression of genes encoding several TF families while induction of the expression of genes encoding HSPs and HSFs was observed in rice (González-Schain et al., 2016; Jung et al., 2012). Several genes were mainly expressed at the late stages of anther development in rice. The correlation of heat tolerance with the induction levels of a set of heat-responsive genes was observed in reproductive tissues. Seedlings of heat-susceptible and heat-tolerant rice breeding lines were exposed to 45°C at three leaf stages (Fang et al., 2018) revealing a total of 32,391 unique genes. When plants exposed to the HS for 12 hours, a sum of 2275 (heat susceptible) and 2603 (heat tolerant) genes was upregulated, and 2270 genes (heat susceptible) and 2900 (heat tolerant) were downregulated when compared with control (0 hour). As compared to control, HS treatment for 24 hours showed a sum of 1438 genes (heat susceptible) and 1599 genes (heat tolerant) with enhanced regulation and 1237 genes (heat susceptible) and 1754 (heat tolerant) with inhibited regulation.

Heat-responsive genes analysis between heat susceptible and heat tolerant showed that HSP family was dominant in heat tolerant among differentially expressed genes (DEGs). Rice HSFs play a vital role in the regulation of heat stress response (HSR) by controlling HSPs (Guo et al., 2008). In rice seedlings, thermotolerance can be affected by the interplay between HSP101 and HSA32 (Lin et al., 2014). HSFs can be explored into GWAS for the identification of heat-tolerant QTLs, which will provide a strong basis for breeding heat-tolerant rice cultivars (Lafarge et al., 2017). During reproductive development MAD box, TFs, which determine seed size and thermal sensitivity in rice and several other genes identified in response to HS, could be utilized for improving thermotolerant rice cultivars specifically (Chen et al., 2016).

Transcriptome profiling from leaves of wheat seedlings exposed to HS (40°C for 1 and 6 hours) showed up- and downregulation of several genes and TFs (Liu et al., 2015, 2019). High induction of HSPs and other chaperones were showed in HS-treated leaves (Qin et al., 2008). In a heat-sensitive wheat cv. HD2329 exposed to HS during the anthesis stage, RNA sequencing analysis generated a wheat transcript dataset for de novo assembly (Kumar et al., 2015). At 93 days old, Vishwakarma et al. (2018) constructed suppression subtractive hybridization library during grain filling stage from leaf samples in an HS tolerant Indian bread wheat cultivar C306. HS tolerant gene TaHsp90 can be identified and utilized for developing heat-tolerant transgenic wheat.

In response to HS, significantly three pathways, carbon metabolism, starch and sucrose metabolism, and carbon fixation, were enriched in leaves of maize seedling. Moreover, significant upregulation of the expression of genes involved in abscisic acid biosynthesis (β-carotene hydroxylase, 9-cis-epoxycarotenoid dioxygenase, abscisic acid, and Arabidopsis aldehyde oxidase) has also been noted (Li et al., 2017). Improved plant growth and performance under high temperature stress were observed when maize plants were overexpressed with OsMYB55. When transgenic maize lines were compared with wild type, they exhibited higher plant biomass and reduced leaf damage (Casaretto et al., 2016). Comparative transcriptome profiling showed DEGs in the leaves of two heat tolerant and heat-susceptible maize genotypes. A few genes were up- and downregulated in response to HS when exposed to 42°C. To identify potential candidate genes involved in HS resistance, DEGs were subjected to pathway analysis. Whereas upregulated genes were found to be involved in 36 different metabolic pathways (biosynthesis of secondary metabolites, flavonoid biosynthesis, fatty acid biosynthesis, photosynthesis, etc.), downregulated genes were mainly involved in 23 different metabolic pathways (phenylpropanoid biosynthesis, spliceosome, cytosolic DNA-sensing pathway, glutathione metabolism, etc.). New insights into molecular mechanisms involved in heat tolerant and heat-susceptible maize genotypes were provided by these comparative transcriptomic studies (Shi et al., 2017).

In many legumes, transcriptome analyses have been conducted for heat tolerance. In cowpea, the expression of various thermotolerant genes has been analyzed using cDNA-amplified fragment length polymorphism (AFLP) (Simões-Araújo et al., 2002). In many crop species, efforts have been made to understand the genetic mechanism underlying HSFs which play a vital role for survival under HS. HSF-ESTs have been identified in Lotus japonicas (19), Medicago truncatula (21), and soybean (25) (Kotak et al., 2007). Transcript expression of VfHsp17.9CII gene in faba bean showed its increased accumulation and made 620-fold changes under high temperature (Kumar et al., 2015). HSP genes (HSP 20 and GmHsfA1) and their role in thermotolerance have been evaluated in soybean (Chen et al., 2006; Lopes-Caitar et al., 2013; Zhu et al., 2006).

To understand the mechanisms of heat tolerance in crops, proteomic study is crucial in response to high temperature. The matrix-assisted laser desorption ionization–time of flight mass spectrometry (MALDI-TOF/MS) has been used to study the crucial pathway for elevated temperature stress responsiveness and tolerance (Huang et al., 2012). Rapid and reliable protein identification were allowed by two-dimensional electrophoresis (2DE) in combination with MS (Klose, 2009). Several genes and proteins that respond to elevated temperature have already been identified (Liao et al., 2012; Mittal et al., 2012). During the ripening period, rice grain quality was affected by elevated temperature due to impaired deposition and the transformation of starch content and protein accumulation (Zhong et al., 2012). Accumulation of HSPs under the control of HSFs is known to play a major role in HSR and in acquiring thermotolerance in plants and other organisms. HSPs functionally act as molecular chaperons to repair as well as aid in the renaturation of stress-damaged proteins (Hasanuzzaman et al., 2013). Chaperones that are responsible for stabilizing proteins and membranes, protein folding can assist in protein refolding under elevated temperature (Wang et al., 2004). 2DE- and MALDI-TOF/MS-based proteomics approaches revealed a total of 73 protein spots in leaf of heat-sensitive rice genotype, IET 21405 (Kumar et al., 2017).

Rizhsky et al. (2004) studied response of plant to HS in Arabidopsis by analysis transcriptome, in which some genes appeared upregulated. The products of these genes included HSP100/ClpB, HSP90/HtpG, HSP70/DnaK, HSP60/GroEL, and small HSPs. In protein quality control, these HSPs are proposed to act as molecular chaperones. Lee et al. (2007) investigated the responses of abundant and low-abundant proteins of rice leaves upon HS, performed 2DE on polyethylene glycol (PEG)-fractionated supernatant and pellet samples of rice leaf proteome. A total of 48 differentially expressed proteins in samples taken after 12 or 24 hours of heat exposure compared to controls were revealed, 18 of which were HSP (HSP70, Dnak-type molecular chaperone BiP, HSP100, Cpn60, and sHSPs). During seedling and anthesis stages in response to HS, Chen et al. (2014) studied HSP26.7, HSP23.2, HSP17.9, HSP17.4, and HSP16.9 in rice, which were upregulated in Nipponbare cultivar. The expressing levels of these five sHSPs in the heat-tolerant rice cultivar Co39 were all significantly higher than that in the heat-susceptible rice cultivar Azucena. This indicated that the expressive level of these five sHSPs was positively related to the ability of rice plants to avoid HS. Thus the expression level of these five sHSPs can be considered as biomarkers for screening rice cultivars to avoid HS.

Abou-Deif et al. (2019) investigated the responses of abundant proteins of maize leaves, in an Egyptian inbred line K1, upon HS through 2DE on samples of maize leaf proteome. In 2D analysis of proteins from plants treated at 45°C for 2 hours, 59 protein spots were manifested which were reproducibly detected as new spots. In 2D for treated plants for 4 hours, 104 protein spots were expressed only under HS. Quantification of spot intensities derived from heat treatment showed that 20 protein spots were involved. Nine spots appeared with more intensity after heat treatments than the control, while 4 spots appeared only after heat treatments. Five spots were clearly induced after heat treatment either at 2 or 4 hours and were chosen for more analysis by liquid chromatography with tandem mass spectrometry (LC–MS-MS). They were identified as ATPase beta subunit, HSP26, HSP16.9, and unknown HSP/chaperonin.

Metabolic redeployment can be caused by HS on the way to homeostasis, sustaining vital metabolism, and producing metabolites with HS-defensive and signaling features. A large set of stress-related metabolites and metabolic pathways were provided by comprehensive metabolomic investigations, advancing crops under HS conditions. Metabolomics-assisted breeding (MAB), including mQTL and mGWAS boosted the improving numerous quantitative traits under HS. In Arabidopsis, the metabolome profile responded inversely toward different HS levels, that is, control, prolong warming, and heat shock. Under prolong warming, decreased stomatal conductance and suppressed tricarboxylic acid (TCA) cycle were detected, while transpiration and glycolysis pathways were improved by heat shock which limits the biosynthesis of acetyl-coenzyme-A. HSFA1s, dehydration-responsive element bindings (DREBs), and bZIPs were upregulated under all stress levels (Wang et al., 2020a). The picoPPESI-Ms approach was used to reveal the metabolites in response to HS-treated single pollen grains of heat-tolerant (N22) and heat-sensitive rice cultivars (Koshihikari). Overall, 106 differently produced metabolites (DPMs) (ribose, deoxyribose, gluconate, xylose, xylitol, lysine, alanine, methionine, and isoleucine) were detected in both cultivars along with the variations in phosphateidylinositol (PI) (34:3) in mature pollen. More PI contents were noticed in N22 pollen, but not for Koshihikari pollen (Wada et al., 2020).

Dhatt et al. (2019) studied HS influencing rice seed by metabolic profling. Masses of sugars (sucrose, glucose, and fructose), TCA cycle, and starch biosynthesis were strongly linked with the HS tolerance in rice. In another cluster of genes, the physical deterioration of starch granules, modification of mature seed, and accumulation of aspartate under HS were observed. Moreover, under HS, three rice cultivars were used to perform a gas chromatography–mass spectrometry–based metabolome analysis of rice organs at several developmental stages, that is, fag leaves, flowering spikelets, and developing seeds (Lawas et al., 2019). In the flag leaves, the identified metabolites (>50%) at the flowering phase were expressively different in the two cultivars. In the heat-tolerant cultivar (N22), the upregulation of the polyols, Myo-inositol, and glycerol was observed in the flowering spikelets. In the developing seeds, putrescine level was upregulated in N22; some other metabolites, for example, vanillic acid, arbutin, arabitol, 4-hydroxy-benzoic acid, and hydroquinone were upregulated in Dular (heat sensitive) cultivar, and only erythritol and Myo-inositol were upregulated in Anjali cultivar. Nine DPMs were expressed in all three cultivars during the developmental stages of flag leaves. These DPMs were then well-thought-out to be precise to the overall response to HS (Lawas et al., 2019).

In soybean (G. max L.), untargeted metabolome profiling of leaf was achieved under HS. In response to HS, DPMs (carbohydrates, lipids, amino acids, peptides, cofactors, nucleotides, and secondary metabolites) were detected in leaves. Numerous DPMs (ribose, deoxyribose, gluconate, xylose, xylitol, lysine, alanine, methionine, and isoleucine) were involved in cellular processes pathways. HS affected glycolysis, the pentose phosphate pathway, TCA cycle, and starch biosynthesis. Significantly the upregulation of sugar and nitrogen metabolisms can help to cope with the HS (Das et al., 2017). Thomason et al. (2018) reported the untargeted LC–MS-based metabolome analysis of wheat (T. aestivum L.) plants under postanthesis HS. Among several DPMs, l-tryptophan and pipecolate which exhibited a negative association with yield-related traits under HS were significantly upregulated while two metabolites (drummondol and anthranilate) which positively associated yield traits under HS were downregulated. In tomato (Solanum lycopersicum L.) pollen, several putatively notorious secondary metabolites went to three major sets, that is, alkaloids, flavonoids, and polyamines, in response to HS (Paupière et al., 2017). In wheat, 98 DPMs induced by HS were identified at the grain filling phase. Carbohydrate-related metabolic contents were significantly reduced, whereas amino acids and starch biosynthesis-related contents were increased under HS (Wang et al., 2018). Similarly, some vital compounds such as malate, valine, isoleucine, glucose, starch, sucrose, proline, glycine, and serine were effectively produced in response to CO2 and HS in maize (Z. mays L.) plants (Qu et al., 2018).

Researches that combine metabolomics with other omics systems, such as epigenomic QTL (eQTL), proteomic QTL (pQTL), and metabolic QTL (mQTL), for the mapping for quantitative traits and dismembering genetic differences at the mRNA, protein, and metabolic stages, due to the availability of all-inclusive datasets of several omics approaches. Interestingly, GWAS together with metabolomics (mGWAS), mQTLs, metabolome-wide association studies (MWAS), and genome–phenome wide association studies (GPWAS) are powerful platforms for the investigation of genetic differences related to metabolic characters in plants (Fang et al., 2019; Templer et al., 2017). Understanding the metabolic systems directing the multifaceted machines in metabolomics has a significant impact on MAB to develop improved cultivars that can withstand numerous stresses. Besides, data attained from mQTL studies lead to further inclusive information about quantifiable genetics (Acuña-Galindo et al., 2015).

Templer et al. (2017) performed a metabolome profiling of 81 barley (Hordeum vulgare L.) accessions under a combination of drought and HS for the identification of mQTLs related to stress tolerance. A total of 57 metabolites were found to be linked with antioxidant defense metabolism under HS. Identified mQTLs related to the pathways (γ-tocopherol, glutathione, and succinate) generated antioxidant enzymatic metabolites in response to stress. These mQTLs-based antioxidant defense help barley to cope with a stressful environment (Templer et al., 2017). Wheat mQTL analysis was performed under both drought and HS. About 234 QTLs were linked with HS and 66 mQTL distributed all over the wheat genome. About 43 mQTL were correlated with both stresses, while only 2 were specific for HS. A combination of 137 SNP markers for heat and drought-related candidate genes recognized 50 SNPs inside mQTL and these genes elaborated in sugar metabolism, ROS scavenging, and ABA-induced stomatal opening and closing. Recognized mQTL and genes could be considered for future investigations and genetic advancement of wheat under HS (Acuña-Galindo et al., 2015). Rice mQTL investigation has been completed, and among 12 chromosomes, numerous mQTLs have been perceived in fag leaf and evolving seeds (Gong et al., 2013). Previously, Feng et al. (2012) performed the metabolic and genetic analysis dependent on glucosinolate biosynthesis in rapeseed (B. napus L.).

1.4 Nutrient stress

Nutrient imbalances will lead to growth damages and yield losses. Currently significant attempt has been made to the enhancement of the nutritional caliber of rice grain through genetic engineering and other breeding techniques (Bao, 2014). Using mapping population of doubled haploid, many QTLs were fine mapped for Fe, Mn, P, Cu, and Zn contents (Stangoulis et al., 2007). Some other reports showed 41 QTLs for 17 mineral elements content (Norton et al., 2010). Similarly, the concentration of Ca, Fe, Mn, Cu, and Zn was supposed to regulated by 10 QTLs and 28 interactions of digenic QTLs (Lu et al., 2008). Likewise, another report on analysis of introgression lines derived from the cross between the Oryza rufipogon (wild rice) and Teqing an indica elite variety manifested 31 putative QTLs for K, Mg, P, Zn, Ca, Mn, Fe, and Cu contents and among them many QTLs for these attributes were contributed by the wild rice types (Garcia-Oliveira et al., 2009). In addition, QTLs for various minerals were discovered the same position; these congregate QTLs also contribute valuable knowledge for concurrent upgrade of content of various minerals in rice kernel through molecular breeding (Ishikawa et al., 2010). Zhang et al. (2014a) mapped about 134 trait loci (QTLs) associated with 16 minerals using two mapping populations of rice which were distributed into 39 genomic parts. In another report, 14 QTLs were identified for Zn and Fe content of rice seed, while the genes (OsNAS1, OsARD2, OsNAS2, OsIRT1, OsMTP1, and OsYSL1) were reported as high priority candidate genes for Zn and Fe accumulation (Anuradha et al., 2012). Elucidating the molecular markers and its expression and regulation systems for production and accumulation of essential mineral elements are obligatory for improvement of mineral elements in rice through biofortification techniques (Masuda et al., 2013).

QTLs for Zn and Fe mineral accumulations have been reported on chromosome 7 and 12 in rice, where all of Zn QTLs were colocated with the Fe QTLs except qZn7.3, suggesting possibility of selection of high Zn lines with high Fe lines using molecular (DNA) markers as selection criteria in these two regions (Bao, 2014; Masuda et al., 2013). Garcia-Oliveira et al. (2009) found 17 colocations of eight distinct minerals (K, P, Mg, Ca, Fe, Zn, Cu, and Mn), using several (85) introgression lines developed from a cross between the wild rice (O. rufipogon) and an elite indica cultivar Teqing.

Plants employ various nutrient responsive genes/TFs to maintain nutrient homeostasis under nutrient stress (Schachtman & Shin, 2007). With the use of RNA-seq analysis by Gao et al. (2022), the expression level of photosynthetic genes is changed under phosphate starvation. Chilling temperature reduces the rate of photosynthesis in plants, which is more pronounced in association with phosphate (Pi) starvation. Previous studies showed that Pi resupply improves recovery of the rate of photosynthesis in plants much better under combination of dual stresses than in nonchilled samples. During evolution, plants have developed mechanisms to sense nutrient scarcity. Deficiencies in essential nutrients, such as nitrogen, phosphorus, and potassium (NPK), affect plant growth and development. Deficiencies in nitrogen activate several genes involved in phytohormone biosynthesis (abscisic acid and jasmonic acid), amino acid metabolism, and the phenylpropanoid pathway.

Under nitrate deficiency, lateral root growth gets inhibited in Arabidopsis, as revealed by studies on nitrate transporter NTR1.1. This nitrate sensor NRT1.1B interacts with a phosphate signaling repressor SPX4. Nitrate perception strengthens the NRT1.1B–SPX4 interaction and promotes the ubiquitination and degradation of SPX4 by recruiting NRT1.1B interacting protein 1 (NBIP1), an E3 ubiquitin ligase (Hu et al., 2019). The STOP1 (Sensitive to Protein Rhizotoxicity) TF regulates the expression of the plasma membrane localized aluminum activated malate transporter 1 (ALMT1). Balzergue et al. (2017) showed that phosphate depletion activates STOP1-ALMT1, which rapidly results in inhibition of root cell elongation.

Genes responsible for the phenylpropanoid metabolism pathway were also induced during phosphorus deficiency. Phosphocholine phosphatase, a high-affinity phosphate transporter and glycolipid biosynthesis encoding gene, gets upregulated during phosphorus deficiency. Early root growth in a wheat variety depicted phosphorus-deficiency tolerance, which was conferred by phosphorus-deficiency tolerance (PSTOL1) protein kinase (Neelam et al., 2017). A deficiency in potassium also induces the expression of protein kinases, transporters, and phytohormones such as jasmonic acid, auxin, and ethylene (Ashley et al., 2006).

Under an Fe-deficient condition, a mutant of bHLH TF POPEYE, or a bHLH34 and bHLH104 interaction, restricts primary root growth (Liang et al., 2017). Balzergue et al. (2017) ALMT1 promotes Fe accumulation in the root meristem and resulted in a reduction in cell expansion. Under P deficiency, Fe gets accumulated in root tips that lead to root apical meristem differentiation, through callose deposition in the symplastic pathway. Callose deposition and Fe accumulation in the root meristem and elongation zone are determined by the PDR2-LPR1 module under P starvation.

1.5 Cold stress

Plants experience cold or chilling stress at temperatures from 0 to 15°C. Under such situations, plants try to maintain homeostasis to acquire freezing tolerance and this involves extensive reprograming of gene expression and metabolism (Cook et al., 2004; Thomashow, 1998). Cold temperatures are very harmful to plants. They induce changes in proteins involved in carbohydrate metabolism, photosynthesis, stress-related proteins among other processes, protein folding and degradation, as well as ROS scavenging and biosynthesis of compatible solutes (Shi et al., 2014).

Low temperature stress is a common problem in rice cultivation and it is commonly evaluated on the spikelet fertility under cold stress. Globally, one-third of the total land area is potentially suitable for arable agriculture. Due to abiotic stresses, only approximately one-ninth of the potentially arable land is ideal for crop production (Bruinsma, 2003). Severe weather conditions such as extreme cold, and substantial and extended precipitation, hailstorms, and heatwaves and droughts limit agricultural productivity worldwide (Ciais et al., 2005; Li et al., 2019b; Rosenzweig et al., 2002; Sánchez et al., 1996; Van der Velde et al., 2010). Abiotic stresses affect the farming of existing crop species and act as a significant barrier for the introduced new crops. Abiotic stresses adversely affect growth, productivity and cause a series of morphological, physiological, biochemical, and molecular changes in plants. Worldwide recent research has clearly shown that osmoprotectants significantly increase cold stress tolerance in plants. Cold stress is a significant abiotic stress that adversely affects plants’ growth and development and restricts crop plants’ geographical distribution. Plants are classified as either chilling (0°C–15°C) or freezing (<0°C) tolerant. These two classes are not mutually exclusive, as chilling tolerant plants in a temperate climate can induce their freezing resilience after exposure to chilling or nonfreezing temperatures during cold acclimation (Lyons & Breidenbach, 1981). Cold acclimation in plants is linked to biochemical and physiological changes resulting from altered gene expression as well as bio-membrane lipid composition and accumulation of small molecules (Sanghera et al., 2011; Thomashow, 1998; Yamaguchi-Shinozaki & Shinozaki, 2006). Plants from tropical and subtropical regions lack such cold acclimation machinery and are sensitive to chilling stress. The molecular basis of cold acclimation and acquired freezing tolerance has been investigated extensively in plants like Arabidopsis and winter cereals. Cold stress can be just as lethal as HS. A cell freezes cause water expansion that cause the cell membrane breakage lead to cell death. Plants respond to cold temperatures by activating metabolic pathways that protect their cells from cold and freezing conditions. One of the protection strategies is to produce proteins that stabilize membranes to help them resist rupture. In cereal, such as rice is sensitive to low temperature at germination, seedling, and booting stages, and cold stress leads to reduced yield (Pan et al., 2020; Yang et al., 2020).

Cold tolerance is a very complex trait controlled by many genes and regulated by chill in atmosphere (Sanghera et al., 2011). Tomato is sensitive to both chilling and freezing temperatures, and low temperature (10°C or below) that inhibits tomato growth. QTLs for cold tolerance at the booting stage was identified on chromosomes 1, 4, 5, 10, and 11 by using strongly cold-tolerant japonica landrace, Kunmingxiaobaigu to cold-sensitive japonica cultivar, Towada (Xu et al., 2008). Andaya and Mackill (2003) reported QTLs on chromosomes 1, 2, 3, 5, 6, 7, 9, and 12 were identified to confer cold tolerance at the booting stage with temperate japonica, M-202, and a tropical indica, IR50. Liu et al. (2003) detected QTLs for cold tolerance was found on chromosome 2, 4, 5, 8, and 10 in common wild rice O. rufipogon and indica cultivar Guichao 2. Takeuchi et al. (2001) identified QTLs on chromosomes 1, 7, and 11 with the use of doubled-haploid lines derived from a cross of Akihikari (moderately cool-temperature susceptible) and Koshihikari (cool-temperature tolerance) rice cultivars. Many cold-tolerance-related QTL have been identified in the past 20 years. Many QTLs related to cold tolerance at the reproductive stage have been identified in recent years. Saito and Miura detected two QTLs, Ctb1 and Ctb2, on chromosome 4 using a set of near-isogenic lines derived from the backcross Kirara397/Norin-PL8/Kirara397 (Saito et al., 2001). The QTLs Ctb1, qCTB2a, qPSST-3, and qLTB3 are related to cold tolerance at the reproductive stage; qCTP11 is related to cold tolerance at the germination stage; and qCtss11 and qCTS4a are related to cold tolerance at the seedling stage (Zhang et al., 2014b).

Cold stress adversely affects the growth and development of plants and limits the geographical distribution of crop plants. GWAS of the phenotypic variation of Arabidopsis natural variants with that of the single nucleotide polymorphic loci followed by T-DNA insertion mutant analyses of 29 candidate genes led to assigning cold tolerance function including three nucleotide-binding sites leucine repeat region protein genes.

Fujino et al. (2004) identified three QTLs on chromosomes 3 and 4 (qLTG-3-1, qLTG-3-2, and qLTG-4) with a total phenotypic variance of 58% for low-temperature germinability (LTG). qLTG-3-1 was a major QTL with 35% of the total phenotypic variation. During the seedling stage of rice, many QTLs/genes related to cold tolerance have been isolated, including qCTS12 (Andaya & Tai, 2006), qCTS4 (Andaya & Tai, 2007), qCtss11(Koseki et al., 2010), qSCT1 and qSCT11 (Kim et al., 2014b), qLOP2 and qPSR2-1 (Xiao et al., 2015), COLD1 (Ma et al., 2015), and qCTS-9 (Zhao et al., 2017). Among them, qCTS12 was the first cold-tolerance gene identified at the seedling stage. COLD1 is another cold-tolerance gene identified to be involved in signal transduction at the seedling stage. At the booting stage of rice, several genes have been isolated, including Ctb1 (Saito et al., 2004), qCT8 (Kuroki et al., 2007), qCTB7 (Zhou et al., 2010), qCTB3 (Shirasawa et al., 2012), and qCT-3-2 (Zhu et al., 2015). Ctb1 is the first gene to be linked to cold tolerance at the booting stage in rice (Andaya & Tai, 2006).

A major QTL for cold tolerance at the seedling stage, qCtss11, was fine mapped to a 60 kb candidate region defined by markers AK24 and GP0030 on chromosome 11, in which six genes have been annotated. Expression analyses and resequencing of these six candidate genes indicated that the Os11g0615600 gene is expressed only from the GLA4 allele, and that the Os11g0615600 gene has a premature stop codon in the GLA4 haplotype. This suggests that either Os11g0615600 or Os11g0615900, or both, might control seedling cold tolerance in this population derived from the cross between W1943 and GLA4 (Koseki et al., 2010). Two major QTL (qCTP11 and qCTP12) for cold tolerance at the plumule stage were identified in genetic stocks derived from wild and cultivated rice (Baruah et al., 2009). Cold tolerance at the bud burst stage (CTB) was evaluated at 5°C in a set of 95 chromosome-segment substitution lines, derived from indica rice accession 9311 and japonica rice cultivar Nipponbare, which has the genetic background of 9311. A set of QTLs, that is, qCTB-5-1, qCTB-5-2, and qCTB-5-3, were mapped on rice chromosome 5 and qCTB-7 mapped rice chromosome 7 (Lin et al., 2010).

Pan et al. (2015) found 51 cold tolerance QTLs in 174 Chinese rice accessions at the germination and booting stages by using GWASs. RILs of highly-tolerant-to-low temperature indica rice H335 and sensitive-to-low-temperature indica rice CHA-1 to detect 11 QTLs on chromosome 9 on the basis of a high-density genetic map; six QTLs explained 5.13%–9.42% of the total phenotypic variation during the germination stage (Yang et al., 2020). Borjas et al. (2016) demonstrated weedy rice can be valuable alleles to improve germination and seedling stage cold tolerance in rice along with three major QTLs associated with coleoptile length and seedling shoot length under low temperature. Yang et al. (2016) mapped two cold-tolerant QTLs (qCTBB-5 and qCTBB-6) at the bud bursting in single segment substitution lines derived from cold-tolerant japonica variety Nan-yang-zhan/indica variety Huajing-xian 74. Twelve QTLs for LTG were identified, and they could explain greater than 10% of the phenotypical variation (Yang et al., 2018).

Comparison of low-temperature germination in the population (DX-BILs) for SLAF-seq showed that 94 BILs (BC1F7) derived from a hybrid between DXWR and Xieqingzao B, five QTLs qLTG2, qLTG5, qLTG10.1, qLTG10.2, and qLTG12 were separated, while qLTG5, qLTG10.1, and qLTG10.2 could explain 19.7%, 14.2%, and 12.1% of the phenotypical variation, respectively (Li et al., 2019a). A total of 200 traditional rice cultivars were evaluated using low-temperature germination, 1672 SNP markers were detected in QTL to be associated with LTG, and two wide regions of chromosomes 3 and 6 were consistently associated with rice LTG (Sales et al., 2017). Wang et al. (2016) used cold-tolerant phenotypes of temperate and tropical japonica rice cultivars and 44K SNP chip dataset of rice diversity panel 1, and identified 67 QTLs for CT on chromosome 11.

Transcriptomics is a prominent field of study related to functional genome of an organism. It deals with quantification of the total set of transcripts or a specific subset of it present in a particular cell type and transcript abundance in a specific developmental stage (Imadi et al., 2015). Transcriptomics can be better employed to study cold stress responses in plants. Nineteen microRNA genes of 11 microRNA families in Arabidopsis thaliana were identified that were upregulated in response to cold stress. A further analysis of their promoter sequence shows the prevalence of some stress regulatory cis-elements (Gupta et al., 2013).

Cold stress induces the expression of APETALA2/ETHYLENE RESPONSE FACTOR family of TFs, that is, C-repeat binding factors (CBFs, also known as dehydration-responsive element-binding protein 1s or DREB1s), which can bind to cis-elements in the promoters of COR genes and activate their expression (Phukan et al., 2017).

In rice, numerous TFs have been found to play important roles in response to cold stress. The best characterized regulon of cold stress responses in plant contains TF CBF/DREB and its cold-inducible target genes, known as COR (cold-regulated gene), KIN (cold-induced gene), RD (responsive gene to dehydration), or LTI (low-temperature-induced gene). The promoters of RD29A (also known as COR78/LTI78) genes contain both ABRE and dehydration-responsive element (DRE)/CRT factors. TFs belonging to the EREBP/AP2 family that bind to DRE/CRT were termed CBF1/DREB1B, CBF2/DREBC, and BF3/DREB1A. These TF genes are induced early and transiently by cold stress and, in turn, activate the expression of target genes. Similar TFs DREB2A and DREB2B are activated by osmotic stress and may confer osmotic stress induction of target stress-responsive genes (Shi et al., 2018).

Three DREB1-type TFs act as master switches in cold-inducible gene expression in a number of plant species. The DREB1 genes that encode these TFs are themselves induced by cold; thus, cold triggers the production of the DREB1 TFs, which then act to turn on the expression of a large number of cold-induced genes encoding proteins that increase cold stress tolerance in plants. Other TFs known as CAMTAs contribute to the cold-induced activation of the DREB1 TF (Dubouzet et al., 2003).

The work of Kidokoro et al. (2017) revealed that plants recognize cold stress as two different signals. One signal is caused by a rapid temperature drop during both the day and night. Another signal is caused by both rapid and more gradual temperature decreases, but this signaling pathway functions only during the day. CAMTA TFs induce DREB1 gene expression in response to a rapid temperature decrease during the day and night, whereas another type of TF involved in circadian rhythms regulates DREB1 expression in response to both rapid and slow temperature decreases only during the day.

Transcriptome analysis in cold-tolerant rice has identified 121 genes that were induced by treatment with cold stress, and found that an ROS-bZIP1 regulon plays an important role in early responses to cold stress (Wang et al., 2017). The TFs of TNG67, such as OsMyb, OsbHLH, OsIAA23, SNAC2, and OsWRKY1v2, may be good candidates for cold stress tolerance-related genes in rice (Yang et al., 2015).

The enhanced stress tolerance of 35s; OsMYB3R-2 Arabidopsis plants reveals that OsMYB3R-2 could mediate signal transduction, regulating some stress-responsive genes involved in CBF-dependent or CBF-independent pathways. OsMYB3R-2 transgenic plants showed enhanced tolerance to freezing, drought, and salt stress and decreased sensitivity to ABA (Dai et al., 2007). Ma et al. (2009) showed that OsMYB3R-2 functions in both stress and developmental processes in rice. Cold treatment greatly induced the expression of OsMYB3R-2, which encodes an active TF.

LAC genes, such as LAC3, LAC12, and LAC13, were repressed by microRNAs (miRNA408) under NAC, confirming that a fine-tuning expression of laccases is important to determine cold tolerance in cmm1T (Esposito et al., 2020). Elevated cytosolic Ca²+ induces the CBF, an Apetela 2 domain-containing transcriptional factor that regulates many cold-induced genes (Gopal & Kundu, 2012).

The expression of TF families showed strong responses to low-temperature treatment included AP2/ERF-ERF, B3, bHLH, bZIP, C2C2-CO-like, C2H2, C3H, B-box zinc finger protein, GARP-G2-like, GRAS, HB-BELL, HB-HD-ZIP, HMG, HSF, LOB, MADS-MIKC, MYB, MYB-related, NAC, SBP, NF-YC, and zf-HD. Most of these TF families had been reported to play an important role in abiotic stress including cold stress (Lindemose et al., 2013) and some of these TFs had bene utilized to improve plant abiotic stress tolerance by genetic transformation technology. Basic Helix-Loop-Helix (bHLH) proteins are the second largest TF families in plants and play an important role in both plant development and abiotic stress responses (Penfield et al., 2005).

The NAC (NAM, ATAF, and CUC) proteins constitute a large TF family with more than 150 members in rice and a number of them have been demonstrated to play crucial roles in plant abiotic stress response (Huang et al., 2016). Low temperature is first perceived by the temperature sensor COLD1/RGA1 complex on the plasma membrane, then the complex triggers calcium influx, ROS production, ABA accumulation, and an MAPK cascade (OsMKK6-OsMPK3) leading to active downstream TFs responses in the nucleus (Guo et al., 2018; Ma et al., 2015; Manishankar & Kudla, 2015). Recently, the vitamin E-vitamin K1 subnetwork of the COLD1 downstream pathway was found to be responsible for chilling tolerance divergence (Luo et al., 2021). Other components of cold tolerance have been identified recently, such as CTB4a, which interacts with a beta subunit of ATP synthase AtpB to mediate the ATP supply in rice plant cells to improve cold tolerance (Zhang et al., 2017). The standing variation of cold tolerance gene CTB2 and de novo mutation of CTB4a facilitate cold adaptation of rice cultivation from high altitude to high latitude areas (Li et al., 2021); rice OsMADS57 interacts with OsTB1 and both directly target OsWRKY94 and D14 for adaptation to cold (Chen et al., 2018).

Cold-induced changes in expression of specific proteins have been observed in cold-tolerant plant species. Using proteomic analysis, an investigation aimed at a better understanding of the molecular adaptation mechanisms of cold stress was carried out in rice. By fractionation, approximately 1700 protein spots were separated and visualized on CBB-stained 2D gels. Sixty protein spots were found to be upregulated in responding to the progressively low temperature stress and displayed different dynamic patterns. These cold-responsive proteins, besides two proteins of unknown function, include four factors of protein biosynthesis, four molecular chaperones, two proteases, and eight enzymes involved in biosynthesis of cell wall components, seven antioxidative/detoxifying enzymes, and proteins linked to energy pathway, as well as a protein involved in signal transduction.

LEA proteins are water-soluble proteins that accumulate during seed desiccation and under different stresses such as low temperature, drought, or salinity (Liu et al., 2016). Pathogenesis-related (PR) proteins induced by pathological conditions are a large group of proteins that are classified in 17 families (Almeida-Silva & Venancio, 2022). The increased expression of certain PR proteins, such as PR-2 (β-1,3-glucanase), PR-3, PR-4, PR-5 (thaumatin-like protein), PR-8, PR-10, PR-11 (chitinase), and PR-14 (lipid transport protein), can also be caused by low temperature stress (Janská et al., 2010). Two PR-10 proteins (ZmPR-10 and ZmPR-10.1) are known to be induced by various stresses, including cold stress (Jain et al., 2012). Four starch-degrading proteins were identified as cold-responsive proteins, including GWD1, GWD3, DPE2, and PHS2, supporting the hypothesis that activation of starch degradation plays an important role in increased freezing tolerance in early cold acclimation (Kaplan et al., 2007; Yano et al., 2005). OsSEC14-NODULIN DOMAIN-CONTAINING 517 PROTEIN 1 (OsSNDP1) gene, encoding a phosphatidylinositol transfer protein (PITP), promotes root hair elongation via phospholipid signaling and metabolism, suggesting that the mediation of these processes by PITP is required for root hair elongation in rice (Huang et al.,