Breeding for Heat Stress Tolerance using Multi-Omics Approaches

Introduction

Heat Stress in Plants

Due to the increased rate of global warming, one of the most harmful abiotic stresses for agricultural output worldwideheat stressis becoming more prevalent. A number of studies [Hall (2001) [1] ; Wahid et al (2007) [2] ; Bita and Gerats (2013) [3] ] indicate food security is at odds with rising frequency, intensity and duration of heat waves. High temperatures upset many physiological, biochemical and molecular processes, such as photosynthesis, respiration, membrane integrity, enzyme activity, reproductive development, among others. Plants are organisms with adaptive processes that are developed as a result of stress in order to help them survive in high-temperature environments. These include: osmotic adjustment, antioxidant defence, sensing, signal transmission, transcriptional control and heat shock protein production (Vierling, 1991; Kotak et al., 2007) [4, 5].

Definition

Heat Stress in plant refers that during prolonged exposure to high temperature, the plant condition deviates from their optimum growth temperature and causes deterioration in physiological, biochemical and molecular processes which becomes irreparable (Wahid et al., 2007) [2] .

Heat stress is a longer time exposure than heat shock either to a high level or, more often, a fluctuating or constant level, and has a negative effect on plant development and yield; while heat shock is a short term exposure to extremely high temperature(s). The threshold temperature depends upon the environmental factor, developmental stage and the species. Thermotolerance is induced at temps. 10-15 above the optimal temperature for development, which causes damage to the cells (Vierling, 1991; Hasanuzzaman et al., 2013) [4, 6]. Heat stress can cause reduced crop yield and results in protein denaturation, destabilisation of membranes, metabolic imbalance, retardation of reproductive development and excessive production of reactive oxygen species (ROS) (Kotak et al., 2007; Bita & Gerats, 2013) [5, 3].

Types of Heat Stress Acute Heat Stress

Chronic heat stress occurs when plants are exposed to a high heat for an extended duration (minutes to hours). They often result in such conditions during heat waves or periods of skyheralding intense radiation from the sun. Rapid increase in temperature will result in unfolding of proteins, breakage of membranes, oxidative stress, inhibition of photosynthesis and increased cell death. Plants respond to high temperature by rapidly inducing heat shock factors (HSF) and synthesizing heat shock proteins (HSP) that help regulate the stability and equilibrium of proteins within the cell (Vierling, 1991; Kotak et al., 2007) [4, 5].

Chronic Heat Stress

Chronic heat stress can be caused by exposure to slightly higher temperatures for long periods of time (days or weeks). Over time, metabolic processes, morphology, and reproductive maturation are all impacted by chronic heat stress, in contrast to acute stress. A prolonged exposure to high temperature reduces growth of leaves, chlorophyll content, nitrogen uptake, biomass production and efficiency of photosynthesis, as well as senescence and grain filling length, (Wahid et al. 2007; Hasanuzzaman et al. 2013) [2, 6]. Acclimatation is a process in which plants adapt to the stress they have experienced by changing the way they function and use their metabolism but over time their ability to adapt is not as efficient.

Terminal Heat Stress

Terminal heat stress is caused by high temperatures at the end of the crop's cycle when it is in the reproductive and grain filling stages. It commonly occurs during periods of high temperature during late growth stages of winter crops like mustard, chickpea, wheat. Terminal heat stress has negative effects on grain filling time, assimilate translocation, starch synthesis, grain size, seed weight, and grain yield at harvest. Terminal heat stress is considered to be one of the most economically problemmatic types of agriculture heat stress as reproductive organs are particularly sensitive to heat stress (Hall, 2001; Wahid et al., 2007) [1, 2].

Critical Growth Stages Affected by Heat Stress

The sensitivity to heat stress also differs greatly as the plant grows, typically being highest at the reproductive phase.

Germination

High temperatures during germination period reduce germination percentage, delay time of radicle emergence, decrease the activity of hydrolytic enzymes and prevents mobilization of stored reserves for early seedling development (Hasanuzzaman et al. 2013) [6] . Heat stress will also reduce plant stand, inhibit seedling establishment, damage membranes, and increase oxidative damage.

Vegetative Stage

In vegetative growth heat has an inhibitory effect on leaf expansion and stem elongation, on the formation of chlorophyll and on root development. Under heat stress, the carbon consumed for respiration takes a larger share of the fixed carbon, hence biomass stayting will be lower. As a response to stress, plants often undergo adaptive processes like rolling the leaves, adjusting the leaf angle, improving water evaporation and other processes to keep cell homeosta?sis in check (Wahid et al., 2007; Bita & Gerats, 2013) [2, 3].

Flowering

Flowering is one of the most susceptible stages of growth to heat. Hot weather causes distortion of the developing flowers during the process of flower initiation, anther development, meiosis and pollen maturation, which all affect the fertility of flowers. Hot weather affects the fertility of flowers by interfering with the process of flower initiation, anther development, meiosis and pollen maturation, resulting in increased floral abortion. Heat stress during anthesis, even for a short period of time, can drastically lower grain yield and number of grains produced (Hall, 2001; Bita & Gerats, 2013) [1, 3].

Pollination

Pollen viability, pollen germination, pollen tube growth and fertility are reduced by heat stress. Male reproductive tissues are more sensitive than female tissues, because the development of pollen demands severly coordinated cellular/metabolic processes. Overexposure to high temperatures can often result in pollen sterility, lower seed-set and higher spikelet sterility in cereals like wheat and rice (Barnabás et al., 2008, Saini & Aspinall, 1982).

Grain Filling

High air temperatures in the grain filling period cause more leaf senescence, reduce grain filling time, inhibit the synthesis of starch as well as the moving of food from source to sink organs. This results in significant reductions in grain weight, grain quality and nutritional values. Heat stress in the terminal phase during grain filling is a significant factor contributing to the yield losses of cereals all over the world (Stone & Nicolas, 1995; Wahid et al., 2007) [7, 2].

Physiological Effects of Heat Stress Photosynthesis Inhibition

High temperatures are one of the first physiological processes to impact; photosynthesis. Heat stress harms and destabilizes photosystem II (PSII), harms thylakoid membrane organization, decreases Rubisco activity, decreases chlorophyll content, and decreases carbon assimilation. With high temperatures, stomatal closure further reduces the availability of CO₂ for photosynthesis, which consequently reduces photosynthetic efficiency and biomass production (Berry & Björkman, 1980; Allakhverdiev et al., 2008) [8, 9].

Increased Respiration

When the temperature is raised, metabolic reactions speed up and hence the respiration rate increases at an exponential rate. Respiration has been activated during stress, likening the supply of ATP needed for stress adaptation and repair processes, but high respiration during a prolonged period will result in excessive consumption of carbohydrates, depletion of energy resources in the body, accelerated senescence of plant tissues and reduced biomass growth (Wahid et al., 2007; Hasanuzzaman et al., 2013) [2, 6].

Altered Water Relations

Transpiration demand and VPD rise with temperature, thereby leading to accelerated transpiration, loss of water and decreased water potential of the leaf. A reduction in the relative water content will result in a drop in turgor, oppress the cell to growth and cause stomatal closure, which in turn will decrease photosynthesis and growth. Heat stress often coincides with drought stress, further aggravating the negative physiological effects (Blum, 2011, Bita & Gerats, 2013) [10] .

Membrane Stability

Hyperthermia causes increased permeability and fluidity of cell membranes, which function as its primary targets in a heat injury. Electrolytes are leaked, ions transported is disturbed and the integrity of the cell membrane is lost in the event of heatinduced lipid peroxidation. Therefore membrane thermostability is an important physiological parameter for screening heat-tolerant crop genotypes (Blum & Ebercon, 1981; Larkindale & Knight, 2002) [11, 12].

Pollen Sterility

The formation of pollen is one of the most sensitive reproductive stages in plants to temperature increase. Pollen wall formation, carbohydrate metabolism, function of the tapetum, and meiosis, all suffer disturbance at high temperatures, which in turn cause a decrease in viability and pollen fertility. Pollen thermotolerance is particularly important for breeding as raising it increases pollen sterility, which is associated with reduced seed-set and significant losses in cereal, legume and horticultural crops (Saini & Aspinall, 1982; Zinn et al., 2010) [13, 14].

Molecular Basis of Heat Stress Response

Plants have an extraordinarily tightly organised molecular network that is able to sense, translate and react to an increased temperature. The heat stress leads to a disruption of cellular homeostasis through protein denaturation, membrane fluidization, oxidative damage and metabolic imbalance. Plants trigger many signaling pathways that involve calcium ions (Ca²⁺), reactive oxygen species (ROS), mitogen-activated protein kinases (MAPK) network, phytohormone responses, transcription factors, and other heat shock proteins (HSPs). These intertwined pathways affect the expression of genes that respond to stress, the stability of proteins, the protection of cell structure, and thermotolerance (Vierling, 1991; Kotak et al., 2007; Bita & Gerats, 2013) [4, 5, 3].

Heat Sensing and Signal Perception

Change in the fluidity of the plasma membrane, change in protein conformation, and change in cytoskeletal organisation are early events in the perception of heat stress. When the fluidity of the membrane is increased, these membrane associated protein receptors and ion channels are activated and intracellular signaling occurs very quickly. The unfolding of proteins into the cytoplasm and endoplasmic reticulum serve as another important signal to initiate the heat shock response. The primary sensing events trigger downstream signaling pathways which control gene expression and protection of the cell (Saidi et al., 2011; Mittler et al., 2012) [15, 16].

Calcium (Ca²⁺) Signaling

Calcium ions function as universal second-messengers in case of heat stress. During elevated temperatures, cytosolic Ca²⁺ levels temporarily rise as a result of the opening of Ca²⁺permeable channels. Ca is taken up by sensor proteins like calmodulin (CaM), calcium-dependent protein kinases (CDPKs) and calcineurin B-like proteins (CBLs) that later activate proteins involved in stress responses and signal transduction cascades. The regulation of antioxidant defense, heat shock protein expression and acclimation to stress are well known to be regulated by calcium signaling (Liu et al., 2008; Reddy et al., 2011) [17] .

Reactive Oxygen Species (ROS) Signaling

During heat stress, excessive production of ROS can occur in the form of superoxide radicals (O₂•⁻), hydrogen peroxide (H₂O₂), hydroxyl radicals (•OH) and singlet oxygen (¹O₂). While excessive ROS cause lipid, protein and nucleic acid damage, a regulated increase in ROS is important as a signal to activate antioxidant enzymes, HSPs and defense related genes. As a result ROS play a dual role in thermotolerance: they can be detrimental or as mediators of signaling (Mittler et al., 2004; Miller et al., 2010) [18] .

MAPK Signaling Pathway

The signal from membrane receptors that occurs upon cooling is carried to the nucleus via a series of phosphorylation of mitogen-activated protein kinase (MAPK), MAPKK and MAPKKK. MAPKs can control transcription factors that regulate genes that regulate antioxidant defence, hormone signalling, and the production of heat shock proteins. Also, MAPK signaling coordinates the activities of calcium and ROS pathways to create a integrated stress signaling network (Rodriguez et al., 2010; Xu & Zhang, 2015) [19] . [22] .  Fourthly, cytokinins maintain a constant level of chlorophyll and delay leaf senescence and ensure that photosynthesis continues to occur despite the increase in temperature. Several different crop species have been successfully engineered to become more tolerant to heat and also to gain in cytokinin signaling (Hare et al., 1997) [23] .  Jasmonic acid (JA): JA is involved in enhancing the antioxidant defense mechanisms, helps stabilization of cellular membranes and activates stress-responsive genes and consequently increases the potential for plant survival at high temperatures (Wasternack & Hause, 2013).  An antioxidant named salicylic acid (SA) can increase photosynthetic efficiency in heat stressed plant and decrease the oxidative damage (Khan et al., 2015) [24] .  Brassinosteroids (BRs): Brassinosteroids have been seen

Hormonal Regulation

to mediate thermotolerance through regulation of photosynthesis, stability of membranes, antioxidant metabolism and heat-sensitive genes transcription (Vardhini and Anjum, 2015) [25] .

Heat Shock Factors (HSFs)

The transcriptional responses to heat stress are controlled by Heat Shock Factors (HSFs). As a result of their connection with molecular chaperones, HSFs stay inactive under typical circumstances. Heat stress-induced up-regulation of heat shock factors (HSFs) can induce the transcription (transactivation) of heat stress genes by binding to heat shock elements (HSEs) present in the promoter regions. Of these, HSFA2 appears to be involved in development of thermotolerance and in storage of stress responses, whereas HSFA1 seems to primarily initiate the heat stress response (Nover et al. 2001; Scharf et al. 2012) [26, 27].

Heat Shock Proteins (HSPs)

Proteins are help kept from denaturing by molecular chaperones called heat shock proteins (HSPs) and will help them fold, refold proteins if they have been broken and facilitate protein degradation if they are completely damaged. Huge HSP families are made up of HSP100, HSP90, HSP70 and HSP60, and smaller HSP families only include proteins of this type. known as sHSPs). The speedy accumulation of thermotolerance at elevated temperatures represents one of the major common pathways to thermotolerance found in several plant species (Vierling, 1991; Wang et al., 2004) [4, 28].

Antioxidant Defense System

Plants have a powerful antioxidant defence mechanism that includes both enzymatic and nonenzymatic antioxidants. In fact this defence system is only activated if ROS stores up.

Antioxidants can be enzymatic and/or non-enzymatic, such as peroxidases (POD), glutathione reductase (GR), ascorbate peroxidase (APX), catalase (CAT) and superoxide dismutase (SOD). Mittler, 2002 and Gill and Tuteja, 2010 [29] showed that antioxidants are involved in maintaining the safety of cellular components from oxidative damage and maintaining redox homeostasis.

Osmolytes

When exposed to high heat, osmolytes (compatible solutes) like proline, glycine betaine, trehalose, soluble sugars, polyols and even some amino acids are stimulated to build up. Such compounds are able to neutralize reactive oxygen species (ROS), help stabilize membranes and proteins, and regulate the osmotic equilibrium of cells. In many crop species, improvement of their heat tolerance was achieved with enhanced osmolyte accumulation (Ashraf & Foolad, 2007; Szabados & Savouré, 2010) [30, 31].

Transcription Factors Regulating Heat Stress Response

Many heat-responsive genes are regulated by transcription factors which are able to bind to DNA and regulate gene transcription by integrating many signalling pathways.

 DREB (Dehydration-Responsive Element Binding):

The DREB proteins lead to increased salt, heat, and drought tolerance by controlling the expression of abiotic stressresponsive genes, which contain the DRE/CRT promoter elements (Sakuma et al., 2006) [32] .  WRKY transcription factors promote thermotolerance through the modulation of ROS homeostasis, hormone signalling and gene expression of defense-related genes (Rushton et al., 2010) [33] .  Thirdly, NAC proteins regulate the expression of Antiaging, antioxidant metabolism, osmotic regulation and Stress-response genes at high heat (Olsen et al., 2005) [34] .  The fourth function of MYB transcription factors is in regulating secondary metabolism, antioxidant biosynthesis and accumulation of osmoprotectants in plants, which enhances their tolerance of heat stress (Dubos et al., 2010) [35] .  The bZIP proteins regulate stress-adaptation related genes, antioxidant-defence genes, protein-protection genes, and are also involved in ABA-dependent signalling pathways, as suggested by Jakoby et al. (2002) [36] .  The basic helix-loop-helix (bHLH) transcription factors play roles in thermotolerance by regulating the balance of reactive oxygen species (ROS), the activation of hormone signalling and adaptation of the embryo to high temperatures (Pires & Dolan, 2010) [37] .

A complex web of signalling molecules, phytohormones, transcription factors, molecular chaperones, antioxidants, and osmoprotectants works together in plants' molecular response to heat stress to keep cells from overheating and increase their chances of survival. In order to create heat-tolerant cultivars using cutting-edge multi-omics-assisted breeding techniques, it is necessary to have a firm grasp of these processes.

Conventional Breeding for Heat Tolerance

Heat-tolerant crop types have been vitally developed using traditional breeding methods to harness the genetic variation of domesticated and wild relatives. Processing crops before molecular breeding involved the primary plant improvement techniques of phenotypic selection, hybridisation and repeated selection under hot conditions. Despite the fact that heat tolerance is a complicated quantitative trait, influenced by numerous gene and a lot of environmental factors, several crops have been equipped with thermotolerance through conventional breeding, such as wheat, rice, maize, sorghum, soybean, chickpea and tomato (Hall, 2001; Reynolds et al., 2010; Bita & Gerats, 2013) [1, 38, 3]. Success of conventional breeding is reliant on genetic diversity, phenotyping tools, and good selection.

Germplasm Screening

A basic approach to breeding heat tolerance is to look for seeds with a diverse gene pool that have shown an extreme level of heat tolerance. Large germplasm collections, landraces,

conventional cultivars, breeding lines and exotic accessions are tested for heat stress, both natural and artificiated. Phenological and yield parameters are included, as are features like yield stability, chlorophyll content, stay-green characteristics, pollen viability, grain filling time and canopy temperature depression. By applying high throughput phenotyping and controlled environment screening, Blom (1988) [39] , Reynolds et al. (2010) [38] and Pinto et al. (2010) [40] each suggest it will be more efficient to identify heat tolerant genotypes.

Utilization of Wild Relatives

The majority of elite cultivars do not contain any special heat tolerance genes, but these can be provided by wild relatives and crop progenitors. The increased antioxidant capacity, stronger roots, more stable membranes and reproductive tolerance to high temperatures are an example of how these species have evolved to thrive in a variety of different environments. With introgression of useful genes from the wild relatives of wheat, rice, tomatoes, barley, and chickpeas, heat tolerant versions of all have emerged. Where linkage drag occurs for desirable gene transfers (Tanksley & McCouch, 1997; Hajjar & Hodgkin, 2007) [41, 42], backcrossing may be needed to restore the desirable gene combinations to the crop pedigree.

Hybridization

Hybridisation consists of crossing different genetic lines of parents and traits to improve the ways that complement each other may be used for parent selection, including early maturity, pollen fertility, canopy cooling, and heat stress grain yield. The segregated populations are subjected to strict selection over many generations to isolate the best recombinants. Hybrid breeding has proved very effective in a number of crops, such as vegetable crops like sunflower, sorghum, rice and maize plants, where hybrid breeding has been very well applied to improve stress tolerance and yield (Hallauer et al., 2010; Acquaah, 2012) [43] .

Selection

Selection is the basis for traditional breeding. Many different kinds of selection methods including bulk population breeding, pedigree selection and mass selection, recurrent selection and single-seed descent are used depending upon variety of crop and/or breeding objectives. Direct selection of the genotype under heat-stressed conditions has been found more effective than indirect selection; it allows the identification of useful genotypes which can carry out a larger production in higher temperatures. The physiological traits chlorophyll fluorescence, pollen viability, photosynthetic efficiency, canopy temperature and membrane thermostability are important traits for selection because of their close relation with heat tolerance (Blum, 1988; Reynolds et al., 2010) [44] .

Backcross Breeding

A frequent use of backcross breeding is to introduce some desirable heat tolerance genes into a top cultivar, and preserve the excellent agronomic background of the top cultivars. Repeat crosses of the elite with the young that get the desired characteristic will speed up the recovery of the elite genome further. This is a superb method in cases the place several genes or examined QTLs are known to help heat tolerance. Some of the traditional backcross breeding techniques have been effective, but are time-consuming due to the number of generations needed for breeding and the rigorous phenotypic selection applied (Allard, 1999) [45] .

Mutation Breeding

The process of mutation breeding is to expose seeds or plant tissues to physical or chemical stressors like gamma rays, Xrays or ethyl methanesulfonate (EMS) so as to produce new variations of the genetic material. The development of heattolerant varieties through induced mutations in various cereals including rice, wheat, barley and legumes has been successful in providing both agronomic as well as physiological advances.

In instances where there is limited genetic difference, mutation breeding can be useful, but the screening of the induced mutants with desirable traits has to be very high because most of the mutations induced are not desirable (Ahloowalia et al., 2004; Shu et al., 2012) [46, 47].

Advantages of Conventional Breeding

The advantages of conventional breeding are that cultivars can be developed to withstand heat. As they harness natural variation, the outcome is generally accepted and non-regulatory problems don't exist because it's not transmitted through transgenic genes. The technique requires no complex molecular structures, is inexpensive, and has Developed a wide range of heat tolerant cultivars which are now grown commercially worldwide. Continuous recombination and selection of their desired agronomical traits and heat tolerance can be achieved. Both Acquaah (2012) [43] and Reynolds et al. (2010) [38] have concluded that traditional breeding is the genetic foundation of molecular breeding and multiomics approaches.

Limitations of Conventional Breeding

Even though traditional breeding has proven to be successful, the method has its limitations with regard to raising heat tolerance. The complicated polygenic characteristic of heat tolerance is controlled by many genes, each of which has a limited individual effect, and is heavily impacted by interactions between genes and the environment. Trait assessment is difficult to make in field situations due to heat stress that may occur at the same time as drought and other environmental difficulties, which make it unpredictable. It takes some time to produce and document a new variety, which can be as long as 8-12 years. In addition, the following problems arise which will decrease breeding efficiency: limited amount of genetic diversity within elite germplasm, linkage drag during introgression from wild relatives, and inability to accurately identify causative genes. [52][53][54] .

Transcriptomics

Transcriptomics is the postgenomics study on the expression patterns of all the genes in the genome in response to heat stress. [55] have suggested that using transcriptome and genomic information could facilitate heat tolerance breeding, while Jha et al. (2019) [56] has reported that genomic and transcriptome data could be integrated to develop heat tolerance tomato seeds. This enables identification of potential genes and their specific functional confirmation

Proteomics

Proteomics affects the abundance of proteins, post-translational modification of proteins, enzyme activities, and protein-protein interactions that all impact plant response to heat stress. [57][58][59] have shown that metabolic indicators can be very useful for identifying heattolerant genotypes and the way the body reacts during stressful periods.

Epigenomics

Epigenomics is the science of studying changes in the expression of genes that are heritable but not due to changes in the nucleotide sequences. Chromatin remodelling, epigenetic alterations (such as DNA methylation, histone acetylation, and methylation), and non-coding RNA regulation are all brought about by heat stress. These changes not only change the chromatin accessibility but also the transcriptional activation of susceptible genes to stress. Significantly, epigenetic alterations aid in stress memory, allowing plants to better react to future heat stress. Recent research by Chinnusamy & Zhu (2009) [60] ,

Lämke & Bäurle (2017), and Ashapkin et al. (2020) [61] reveals new possibilities in how epigenetic regulation can be leveraged for breeding crop plants with greater tolerance to climate change conditions via an epigenetic regulation pathway and targeted genome editing.

Phenomics

To be able to properly evaluate plant reactions under both controlled conditions and in the field, phenomics also involves high throughput phenotyping technology. Modern phenomics platforms acquire morphological, physiological and biochemical parameters associated with heat tolerance using thermal, hyperspectral and RGB cameras as well as unmanned aerial vehicles (UAVs) and automated greenhouse systems and LiDAR. Canopy temperature, NDVI, Fv/Fm, stomatal conductance, plant architecture, biomass accumulation, growth dynamics and stay-green phenotype may be quickly evaluated in large breeding populations. Furbank and Tester (2011) [62] , Araus and Cairns (2014) [63] and Yang et al. (2020) [64] reported that high-throughput phenotyping has greatly improved phenotyping accuracy and is adopted with genomic and transcriptome analysis in precision breeding.

Ionomics

The focus in ionomics is studying the distribution of elemental homeostasis and the mineral nutrient content in plants. Of the essentials that are impacted by heat stress with respect to absorption, transportation, and distribution, some of the most notable elements are Ca, K, Mg, Zn, Fe, Mn, P, and Si. Ionic balance maintenance is necessary under temperature stress for enzyme activity, cellular signalling, antioxidant defence, osmotic management and membrane integrity. Several crop species have had ionomic features linked to thermotolerance and nutrient-use efficiency discovered using high-throughput elemental profiling with inductively coupled plasma-mass spectrometry (ICP-MS) and X-ray fluorescence spectroscopy (Salt et al., 2008; Baxter, 2015) [65, 66].

Microbiomics

Whether the function of the plant microbiome (endophytic, phyllosphere and rhizospheric microbes) for better heat stress tolerance is non-ignorable more and more pieces of evidence are emerging. All of this serves to regulate gene expression in the presence of stress, nutrient uptake, phytohormones and antioxidant metabolism by the cooperation of the endophytic microorganisms, arbuscular mycorrhizal fungi (AMF) and beneficial plant growth-promoting rhizobacteria (PGPR). In recent years, progress has been made in understanding the complex plant-soil-microbiome interactions that contribute to thermotolerance through microbiome profiling, amplicon sequencing, and metagenomics. The new generation of studies on the symbiosis of plants and microbes through microbiome profiling, amplicon sequencing and metagenomics has shed light on the complex microbiota-plants-soil interactions which contribute to thermotolerance. One ecologically friendly solution to improve Crops climate change resistant is beneficial microbial populations use according Berendsen et al. (2012) [67] , Compant et al. (2019) [68] and Niu et al. (2020) [69] .

Integration of Multi-Omics

The prominent benefit of multi-omics is the possibility of a systems-level knowledge of a heat stress response acquired by integration of multiple biological information. Combining the various disciplines (microbiomics, epigenomics, phenomics, transcriptomics, proteomics and genomics) will enable us to identify molecular pathways, protein networks, metabolites and phenotypes, which regulate the thermotolerance. The integration and understanding of these complex datasets have become easier thanks to the modern computational tools, coupled with AI, ML, and systems biology, which allow for the identification of candidate genes' faster, creation of biomarkers, and the possibility of breeding for a more precise purpose. The authors Hasin et al. (2017) [50] , Yang et al. (2021) [51] and Varshney et al. (2021) [49] predict that such approaches will prove essential in producing novel crop varieties that will be resistant to climate change and sustain yields of agricultural products even as the global climate warms.

Integration of Multi-Omics for Heat Stress Tolerance Breeding

The innovations brought by the use of different omics technologies have turned the plant breeding approach from the so called gene-based approach to a framework based on systems biology that would allow to understand the complex molecular pathways of heat stress tolerance. Individual omics technologies only tell a part of the story as many different genes, proteins, metabolites, epigenetic modifications, etc. interact and modulate heat tolerance. To scientifically link plants with the heat stress challenges requires several existing knowledge areas like Microbiomics, Genomics, Epigenomics, Phenomics, Transcriptomics, Proteomics, and Metabolomics. All of the papers discussed by Hasin et al., 2017 [50] ; Misra et al., 2019 [70] and Yang et al., 2021 [51] conclude that integrated analyses increase the speed by which novel and climate-resilient crop varieties can be developed through the better accuracy with which candidate genes can be discovered, biomarkers can be identified and breeding decisions can be made.

Systems Biology Approach

Systems biologists use a variety of different biological data for a comprehensive understanding of the reaction of the cells during the heat stress. As an alternative to looking at certain genes or This approach is based on understanding the interaction between genes, transcripts, proteins, metabolites and the phenotype, rather than between the proteins and the genotype. The regulatory mechanisms and biological processes that govern thermotolerance may be more easily reconstructed with the use of computational modelling. Kitano (2002) [71] and Rajasundaram & Selbig (2016) [72] point out that the premise of systems biology could be employed to identify key molecular regulators and understand their roles in complex stress adaptation processes.

Multi-Omics Data Integration

Multi-omics data integration combines variations in the genome with the abundance of transcripts, abundance of proteins, metabolite accumulation, epigenetic changes and phenotypical measurements. It is a method that builds a connection between changes in the DNA sequence and then the reaction that occurs; therefore the causal link that the genotype will lead to the phenotype can be uncovered. Discovering new regulatory pathways involved in heat stress adaptation is made easier using integrated studies, which also increase the reliability of candidate gene identification when compared to separate omics techniques (Hasin et al., 2017; Misra et al., 2019) [50, 70].

Gene Regulatory Networks

A systems analysis of gene regulatory networks (GRNs) provides insights into the interactions between transcription factors that regulate the heat stress response and signalling molecules and downstream target genes. Heat Shock Factors (HSFs), DREB, WRKY, NAC, MYB, bZIP and bHLH transcription factors are coordinators of expression of numerous stress-responsive genes involved in antioxidant defence, osmotic adjustment and protein protection. Reconstructing regulatory circuits controlling thermotolerance and stress memory is made possible by integrating transcriptomic and epigenomic datasets (Scharf et al., 2012; Zandalinas et al., 2021) [27, 73].

Protein-Protein Interaction Networks

The interactions between proteins can lend insights into the functional relationship between the heat stress signalling proteins. At elevated temperatures, increased stability to protein homeostasis and integrity of the cell is maintained by interactions of heat shock proteins, molecular chaperones, antioxidants, kinases, phosphatases, AND transcription factors. The findings of both transcriptome and proteome can be combined for the identification of proteins which are involved in thermotolerance, that could be candidates for genome editing and/or molecular breeding (Wang et al., 2017; Kosová et al., 2011).

Metabolic Network Analysis

Extensive metabolic rewiring via carbohydrates, amino acids, lipids, phytochemicals (flavonoids), and phytohormones happens in response to heat stress. The integration of transcriptome, proteome, and metabolome information makes it possible to reconstruct metabolic pathways related to energy metabolism, osmotic adjustment and antioxidant defence. Metabolic indicators that differentiate between tolerant and susceptible genotypes are uncovered by these investigations, which in turn provide useful targets for crop development (Obata & Fernie, 2012; Arbona et al., 2013) [57, 58].

Candidate Gene and Biomarker Discovery

The discovery of heat tolerance-related molecular biomarkers and high-confidence candidate genes is a key benefit of integrated multi-omics. Genes that appear in multiple data sets, like genomic, transcriptomic, proteomic, or metabolomic, have a higher probability of representing a significant biological function than genes which are discovered in a single data set.

Heattolerant cultivars can also be rapidly created using these proven candidates for marker-assisted selection, genomic selection, functional genomics and genome editing (Varshney et

Artificial Intelligence and Machine Learning in Multi-Omics-Assisted Heat Stress Breeding

Tailored on a massive scale, AI and ML have revolutionized the plant breeding sector, as they enable the study of large, complex and. Multi-omics systems that deliver datasets which are multidimensional. It is difficult to integrate environmental, phenomic, transcriptomic, proteomic and metabolomic data with genetic data-traditional statistical approaches often fall short. AI-driven algorithms can quickly and accurately streamline breeding decisions, reveal patterns, and predict complex properties. By harnessing the power of AI to integrate data from various sources, Libbrecht and Noble (2015) [74] and Washburn et al. (2022) also suggest that AI could be used to identify candidate genes, forecast heat tolerance in specific crops, and streamline agricultural practices for these.

AI-Based Multi-Omics Data Integration

This is because sophisticated computational methods and tools are needed to deal with the vast amount of data generated by modern breeding operations, which comes in many different forms. Some machine learning algorithms that use phenotypic/genomic/transcriptomic, proteomic and metabolomic data to identify heat tolerance biomarkers are Deep Learning (DL), Extreme Gradient Boosting (XGBoost), Random Forest (RF), Support Vector Machine (SVM), Artificial Neural Networks (ANN), and Random Forest (RF). These techniques are better suited to candidate gene prioritisation than traditional analytical techniques and are more able to reveal regulatory networks (Libbrecht & Noble, 2015; Eraslan et al., 2019) [74, 76].

Genomic Prediction and AI-Assisted Breeding

To determine an individual's breeding value without doing lengthy field evaluations, genomic prediction integrates genome-wide genetic markers with AI algorithms. In cases of complex traits such as heat tolerance, the ability of machine learning algorithms to generate more accurate predictions is enhanced by their capacity to detect and recognize non-linear interactions between the genes and environmental factors. Genomic selection, with the assistance of AI, can help identify superior breeding lines earlier in the breeding cycle, reducing cycle time, phenotyping costs, and also enhancing genetic gains, as stated by Crossa et al. (2017) [54] and Montesinos-López et al. (2021).

High-Throughput Phenotyping and Computer Vision AI has revolutionized computer vision and image processing, ushering in the era of high throughput phenotyping. With the help of deep learning models, based on image captured by an RGB camera, a thermal camera, a hyperspectral sensor, a drone, or a satellite platform, features such as canopy temperature, chlorophyll content, plant senescence, biomass, plant architecture, or stress symptoms can be quantified. These techniques enable rapid (Aurus & Cairns, 2014) and non destructive (Singh et al., 2018) [77] screening of breeding population in large scale under heat stress.

Digital Twins and Predictive Breeding

By combining crop simulation models with artificial intelligence, digital twins can be created that simulate the development of a crop in a number of different settings. These models are used to predict the likely performance of crops under various climates, to determine ideal parent plants to utilize and to evaluate the effectiveness of breeding operations prior to implementation. Because it would be desirable to reduce the risk of heat stress (HS) in breeds in hot environments, using predictive breeding to select more heat-stress resistant genotypes could be time-saving for breeders and conserve livestock resources (Hammer et al. 2020) [78] .

Challenges and Future Perspectives

While AI breeding has made remarkable advances, it still faces challenges in areas such as data quality, computing infrastructure, interpretability of the models, and the standardisation of multi-omics datasets. Emerging advancements in explainable AI, cloud computing, federated learning, and digital agriculture are expected to drive further progress towards model transparency and the use of AI in crop improvement. Use of AI with genome editing, speed breeding, robot bionics and precision agriculture will speed the development of heat resistant crop varieties (Varshney et al., 2021; Washburn et al., 2021) [49, 75].

Genome Editing and Functional Validation

Genome editing can precisely modify the genes involved in important agronomic traits which are influenced by the heat stress, and this has tremendous implications in plant breeding. Unlike conventional breeding, genome editing can focus on specific regions of the genome to improve cultivars' development, while maintaining the rapid speed. By using multi-omics techniques for identifying potential genes for enhancing thermotolerance, the approach to genome editing has been much extended (Jaganathan et al., 2018) [79] .

CRISPR/Cas Systems CRISPR/Cas9 -Gen bewusst, so extremely convenient, extremely precise, extremely efficient -has unwittingly emerged as the genome Editing tool of choice. It makes it possible to activate or delete particular genes or to modify their sequence in those genes whose function is connected with the heat shock proteins, antioxidants, hormones, heat stress signalling, or heat shock signalling. Along with the CRISPR/Cas9 system (Jinek et al., 2012; Anzalone et al., 2020) [80, 81], sophisticated systems like CRISPR/Cas12, base editing and prime editing also achieve superior accuracy of editing without off-target effects.

Functional Validation of Candidate Genes

In the context of breeding, candidates genes identified through genomics, transcriptomics and proteomics need to be functional validated prior to their use in programs. A set of functional genomics approaches are routinely employed, such as T-DNA insertion mutants, RNA interference, overexpression, genome CRISPR mediated gene silencing, and viral induced gene silencing (VIGS) to gain insight into the response created by genes to heat stress. Validation studies have indicated that the genes for heat shock proteins (HSPs), heat shock factors (HSFs), DREB transcription factors, NAC proteins and antioxidant enzymes play an important role in making rice plants thermotolerant (Kotak et al., 2007; Scharf et al., 2012) [5, 27].

Applications in Crop Improvement

Genome editing has been successfully demonstrated in a range of crops to improve their tolerance to heat and other abiotic stresses, such as drought, flooding, heat, and high temperatures. Gene editing that regulates flowering time, pollen production, scavenging of reactive oxygen species, signalling of hormones, and maintaining protein homeostasis improved the survival and yield of the plants under hot conditions. New possibilities for the quick creation of cultivars that are resistant to climate change have emerged with the combination of genome editing, genomic selection, and speed breeding (Chen et al., 2019; Gao, 2021) [82, 83].

Crop-Wise Progress in Multi-Omics-Assisted Breeding for Heat Stress Tolerance

Multi-omics technologies have enabled accelerated progression towards understanding the discovery of genes, proteins, metabolites, and pathways associated with traits across key crops, facilitated by genetic editing, marker-assisted breeding, and genomic selection, and led to the generation of heatresilient cultivars. The extent of application is different across different crops, but there has been success (Varshney et al., 2021; Yang et al., 2021) [49, 51] in cereals, legumes, oilseeds, vegetables and fibre crops.

Wheat (Triticum aestivum L.)

Wheat production and quality are compromised if the plant suffers from severe heat stress at anthesis and grain filling. It has been revealed through genetic analysis that there are numerous QTLs involved in the Grain Weight, Chlorophyll Content, Stay Green traits and Canopy temperature depression. Transcriptomic analyses indicated that the expression of heat shock proteins (HSPs), heat shock factors (HSFs), DREB, nuclear factor kappa B (NF-κ) and WRKY transcription factors was all found to change with heat stress. Proteomic and metabolomic analyses identified a number of additional proteins involved in thermotolerance, including photosynthetic proteins, osmolytes and antioxidant enzymes. The results have paved the way for genomic selection and marker-assisted selection (MAS) to breed wheat cultivars with heat tolerance (Pinto et al., 2010; Reynolds et al., 2010; Varshney et al., 2021) [40, 38, 49].

Rice (Oryza sativa L.)

Rice is most vulnerable to heat stress, but pollen sterility and spikelet abortion are signs of heat stress in this crop. Through multi-omics studies, the genes regulating the processes of flowering, pollen viability, glucose metabolism and detoxification of reactive oxygen species have been identified. The genome-wide association (GWAS) and RNA-Seq analysis have resulted in the discovery of several potential genes, including OsHsfA2d, OsHSPs and SNAC family genes related to thermotolerance. Jagadish et al. (2015) [84] , Li et al. (2015) [85] and Gao (2021) [83] have reported metabolomic studies that found that proline, sugars, and antioxidant metabolites are increased during heat stress, and that CRISPR/Cas-mediated editing has confirmed several genes that are responsive to heat.

Maize (Zea mays L.)

Pollen viability, kernel set, and grain production are all drastically reduced when maize plants are subjected to heat stress during tasseling and silking. Genetic mapping has identified a number of quantitative trait loci (QTLs) that control reproductive resistance to heat in the hot climate, plant canopy temperature, and bloom initiation time. Proteins linked to photosynthesis, energy metabolism, and antioxidant defence have been discovered via proteomic investigations, whereas heat-induced expression of heat-shock proteins (HSPs), calcium signalling components, and ABA-responsive genes has been uncovered through transcriptomic studies. Cairns et al. (2013) [86] , Cooper et al. (2014) [87] , determined that genomic selection and high throughput phenotype were used to increase the efficiency of breeding heat-tolerant maize hybrids. Soybean (Glycine max L.) During the blooming and pod development stages of soybeans, high temperatures have a significant impact on production. Genes associated in heat shock response, hormone control, antioxidant metabolism, and seed development have been discovered via multi-omics investigations. In the heat stress treatment conditions it was revealed that NAC transcription factors, DREB, HSFs and HSP70 were all higher expressed in the transcriptomic analysis. Metabolic studies indicate build-up of a series of osmoprotectants (proline, raffinose, flavonoids and others) which help provide thermotolerance. These are results that lend support to the notion that Genome Editing and Genomic-assisted breeding could assist soybeans in adapting more readily to warmer climates as proposed by Prince et al., (2015) and Wang et al., (2020).

Chickpea (Cicer arietinum L.)

Terminal heat stress is a frequent occurrence during reproductive development of the chickpea plant, leading to loss of flowers, reduced pod set and seed yield. In the genomic studies, Quantitative trait genes (QTLs) and candidate genes associated with pollen viability, quantity of seeds filled under hot conditions, and flowering time were identified. Transcriptomic studies have shed light on the function of proteins that suppress heat, nuclear anti-oxidant gen transcription factor (NAC) proteins, anti-oxidant enzymes and hormonal signalling pathways. The combination of genomes and phenomics has aided by making the process of identifying breeding lines more suitable for heat more efficient (Varshney et al., 2013; Devasirvatham & Tan, 2018) [88, 89].

Tomato (Solanum lycopersicum L.)

During the blooming and fruit development stages, tomatoes are very susceptible to high temperatures. Heat stress has negative effects on fruit set, fruit quality and pollen viability. Using multi-omics research, we now understand the genes for the control of hormone production, reactions to heat stress, glucose metabolism and fertilisation. A proteomic analysis revealed increases in more HSPs, as well as antioxidant enzymes, while a metabolomic analysis identified increases in more carbohydrates, amino acids, secondary metabolites that protect cells. Bita and Gerats (2013) [3] and Fragkostefanakis et al.

(2015) [90] identified the thermotolerance of tomato further with the aid of genome modifying of the heat-responsive genes.

Cotton (Gossypium hirsutum L.)

It is a common occurrence in cotton where heat stress causes a reduction in fibre quality and lint yields in boll development and blooming stage. Genomic-and transcriptome-based studies have identified genes important for pollen production, glucose metabolism, scavenging of reactive oxygen species and hot environment responses to hormones. Studies on proteinomics (modification changes to proteins) have revealed that heat modifies protein structure for protein molecules such as molecular chaperones, antioxidant enzymes and proteins involved in photosynthesis. These findings have made it easier to find molecular markers that can be used to develop cotton varieties that can withstand higher temperatures (Wang et al., 2019; Snider et al., 2010) [82, 91].

Brassica Crops

Seed production and quality of oil, in plants of Brassica species (mustard, rape), are drastically reduced in response to heat stress during flowering and seed filling. Multi-omics analysis has led to the identification of candidate genes that play a role in heat shock responses, antioxidant defence, lipid metabolism and flowering control. Incorporating genomics, metabolomics and transcriptomics The Mayan plant responses to heat have been better understood and breeding for them to withstand heat is now easier thanks to research (Angadi et al., 2000; Singh et al., 2021) [92] . Millets Millets (sorghum, pearl millet, foxtail millet, finger millet) are important climate-resilient crops due to their inherent heat and drought tolerant. Genes that enable plants to survive in hot climates, to conserve water, to resist attack by antioxidants, and to ensure the stability of their photosynthesis have been discovered. Generating the benefits of other cereal crops through improvements in their heat tolerance has been demonstrated in research with millets (Varshney et al., 2021; Muthamilarasan & Prasad, 2021) [49, 93].

Comparative Perspective

Comparative multi-omics analyses of thermotolerant crops have identified various conserved mechanisms underlying toleration, such as Biosynthesis of heat shock factors (HSFs) and heat shock proteins (HSPs), calcium signalling, detoxification of reactive oxygen species (ROS), and accumulation of compatible osmolytes like proline and glycine betaine. It is important to employ accepted methods, however, for the species involved as mammals can respond differently to these depending on the developmental needs and regulation of various crops. Furthermore, the amalgamation of Multi-omics, Artificial Intelligence, Genomic Selection, and Genome Editing is expected to speed up the crop development for increased heat tolerance under future climatic scenarios (Yang et al., 2021; Varshney et al., 2021) [51, 49].

Success Stories of Multi-Omics-Assisted Heat Stress Breeding

The use of multi-omics technology has greatly sped up the process of finding heat tolerance genes and incorporating them into crop breeding initiatives. Stable QTLs and candidate genes regulating canopy temperature, stay-green characteristics, and grain filling have been discovered in wheat using genomic mapping and transcriptome investigations. This has enabled marker-assisted selection of heat-tolerant cultivars to be achieved (Reynolds et al., 2010) [38] . Genomes and transcriptomics have allowed researchers to discover rice genes, including OsHsfA2d and SNAC as well as several HSP genes. These genes are also linked to improvements in reproductive thermotolerance and maintaining grain production at high temperatures (Jagadish et al., 2015) [84] . The use of genomic prediction and high throughput phenotyping (HTP) has improved the selection of hybrids for reproductive resilience under heat stress in maize (Cairns et al., 2013) [86] . Genome editing has been used to validate a number of genes found in multi-omics studies that are related to temperature. CRISPR/Cas technology has been applied to thermotolerate several crops such as tomato, rice, and others (Gao, 2021) [83] . Likewise, by using the proteomic and metabolomic approaches, early selection has become more feasible in breeding programs to find chemical markers associated with heat tolerance. The genomic, phenomic approaches, along with AI and speed breeding approaches have speeded up breeding cycles and improved accuracy in the selection process (Crossa et al., 2017; Varshney et al., 2021) [54, 49]. The above accomplishments demonstrate that selective breeding has transitioned from natural selection to knowledgebased breeding with the help of multi-omics approaches. Continued The development of genome edited crops that can remain resilient to the impacts of climate change and ensure food security on a global scale is dependent upon planting together genome editing, computational biology and modern omics technologies.

Conclusion

With climate change, it becomes increasingly common and is a potential risk to good agricultural output, affecting food production and supplies on a global scale. Heat stress has negative effects on plant development, physiology, reproduction and yield that occur through several cellular, molecular and biochemical pathways. Traditional breeding methods have helped create heat-tolerant varieties, but they are limited in their advancement due to factors such as the quantitative aspect of thermotolerance, robust interactions between genotype and environment, and long breeding cycles. The era of multi-omics technologies has started, which has offered insights into the molecular and genetic basis of response to heat stress in crop development. A wide variety of genomic, transcriptomic, proteomic, metabolic, epigenetic, phenomenological, ionic, and microbiomic approaches have made it possible to pinpoint potential thermotolerance-related genes, regulatory networks, proteins, metabolites, and physiological features. The integration of these omics data by applying systems biology, artificial intelligence and machine learning methods has improved the accuracy of the identification of these genes, as well as the genomic prediction, identification of markers and decision on breeding. Additionally, new varieties of crops are emerging that also have traits suited to hot climate, such as tolerance to increased temperatures, due to the innovations on multi-omics, genome editing technology, genomic selection, high-throughput phenotyping, and speed breeding. Despite recent progress, integration of separate datasets from the different omics technologies, harmonisation of laboratory dispatches, largevolume biological data handling and the translation of laboratory results into field applications remain a problem. Future studies combining precision genome editing, single cell and spatial "omics", digital phenotyping and AI-based prediction modelling by pangenomics will improve understanding of the regulatory networks that control heat tolerance. The implementation of multi-omics research into real breeding outcomes means that the close collaboration of plant breeders, molecular biologists, bioinformaticians, physiologists and computer scientists is essential. Precision breeding is a radical transformation of crop improvement approaches as it is based on knowledge-driven rather than the phenotypic selection system. The current efforts in sustaining agriculture and ensuring long-term global security in food and nutrition in the context of the rising challenge of climate change can be realised by sustaining the development of the omics technologies and their application in contemporary breeding practices. These will be directed towards breeding heat-tolerant high yielding and climate-smart crops.

References

1. Acquaah G. Principles of plant genetics and breeding. 2nd ed. Wiley-Blackwell; 2012.
2. Ahloowalia BS, Maluszynski M, Nichterlein K. Global impact of mutation-derived varieties. Euphytica. 2004;135(2):187-204. doi:10.1023/B:EUPH.0000014914.85465.4f
3. Allakhverdiev SI, Kreslavski VD, Klimov VV, Los DA, Carpentier R, Mohanty P. Heat stress: An overview of molecular responses in photosynthesis. Photosynth Res. 2008;98(1-3):541-550. doi:10.1007/s11120-008-9331-0
4. Allard RW. Principles of plant breeding. 2nd ed. John Wiley & Sons; 1999.
5. Angadi SV, Cutforth HW, Miller PR, McConkey BG, Entz MH, Brandt SA, Volkmar KM. Response of three Brassica species to high temperature stress during reproductive growth. Can J Plant Sci. 2000;80(4):693-701. doi:10.4141/P99-152
6. Anzalone AV, Randolph PB, Davis JR, Sousa AA, Koblan LW, Levy JM, et al. Search-and-replace genome editing without double-strand breaks or donor DNA. Nature. 2019;576(7785):149-157. doi:10.1038/s41586-019-17114
7. Araus JL, Cairns JE. Field high-throughput phenotyping: The new crop breeding frontier. Trends Plant Sci. 2014;19(1):52-61. doi:10.1016/j.tplants.2013.09.008
8. Arbona V, Manzi M, Ollas C, Gómez-Cadenas A. Metabolomics as a tool to investigate abiotic stress tolerance in plants. Int J Mol Sci. 2013;14(3):4885-4911. doi:10.3390/ijms14034885
9. Ashapkin VV, Kutueva LI, Aleksandrushkina NI, Vanyushin BF. Epigenetic mechanisms of plant adaptation to biotic and abiotic stresses. Int J Mol Sci. 2020;21(20):7457. doi:10.3390/ijms21207457
10. Ashraf M, Foolad MR. Roles of glycine betaine and proline in improving plant abiotic stress tolerance. Environ Exp Bot. 2007;59(2):206-216. doi:10.1016/j.envexpbot.2005.12.006
11. Barnabás B, Jäger K, Fehér A. The effect of drought and heat stress on reproductive processes in cereals. Plant Cell Environ. 2008;31(1):11-38. doi:10.1111/j.13653040.2007.01727.x
12. Baxter I. Should we treat the ionome as a combination of individual elements, or should we be deriving novel combined traits? J Exp Bot. 2015;66(8):2127-2131. doi:10.1093/jxb/erv040
13. Berendsen RL, Pieterse CMJ, Bakker PAHM. The rhizosphere microbiome and plant health. Trends Plant Sci. 2012;17(8):478-486. doi:10.1016/j.tplants.2012.04.001
14. Berry J, Björkman O. Photosynthetic response and adaptation to temperature in higher plants. Annu Rev Plant Physiol. 1980;31:491-543. doi:10.1146/annurev.pp.31.060180.002423
15. Bita CE, Gerats T. Plant tolerance to high temperature in a changing environment: Scientific fundamentals and production of heat stress-tolerant crops. Front Plant Sci. 2013;4:273. doi:10.3389/fpls.2013.00273
16. Blum A. Plant breeding for stress environments. CRC Press; 1988.
17. Blum A. Plant breeding for water-limited environments. Springer; 2011. doi:10.1007/978-1-4419-7491-4
18. Blum A, Ebercon A. Cell membrane stability as a measure of drought and heat tolerance in wheat. Crop Sci. 1981;21(1):43-47. doi:10.2135/cropsci1981.0011183X002100010013x
19. Cairns JE, Sonder K, Zaidi PH, Verhulst N, Mahuku G, Babu R, et al. Maize production in a changing climate: Impacts, adaptation, and mitigation strategies. Adv Agron. 2012;114:1-58. doi:10.1016/B978-0-12-394275-3.000067
20. Chen K, Wang Y, Zhang R, Zhang H, Gao C. CRISPR/Cas genome editing and precision plant breeding in agriculture. Annu Rev Plant Biol. 2019;70:667-697. doi:10.1146/annurev-arplant-050718-100049
21. Chinnusamy V, Zhu JK. Epigenetic regulation under plant abiotic stress. Curr Opin Plant Biol. 2009;12(2):133-139. doi:10.1016/j.pbi.2008.12.006
22. Collard BCY, Mackill DJ. Marker-assisted selection: An approach for precision plant breeding in the twenty-first century. Philos Trans R Soc Lond B Biol Sci. 2008;363(1491):557-572. doi:10.1098/rstb.2007.2170
23. Collins NC, Tardieu F, Tuberosa R. Quantitative trait loci and crop performance under abiotic stress. Plant Physiol. 2008;147(2):469-486. doi:10.1104/pp.108.118117
24. Compant S, Samad A, Faist H, Sessitsch A. A review on the plant microbiome: Ecology, functions and emerging trends. Microbiome. 2019;7(1):29. doi:10.1186/s40168019-0667-2
25. Cooper M, Gho C, Leafgren R, Tang T, Messina C. Breeding drought-tolerant maize hybrids for the US Corn Belt. Crop Sci. 2014;54(3):1039-1049. doi:10.2135/cropsci2013.09.0622
26. Crossa J, Pérez-Rodríguez P, Cuevas J, Montesinos-López O, Jarquín D, de Los Campos G, et al. Genomic selection in plant breeding: Methods, models, and perspectives. Trends Plant Sci. 2017;22(11):961-975. doi:10.1016/j.tplants.2017.08.011
27. Devasirvatham V, Tan DKY. Impact of high temperature and drought stresses on chickpea production. Agronomy. 2018;8(8):145. doi:10.3390/agronomy8080145
28. Dubos C, Stracke R, Grotewold E, Weisshaar B, Martin C, Lepiniec L. MYB transcription factors in Arabidopsis. Trends Plant Sci. 2010;15(10):573-581. doi:10.1016/j.tplants.2010.06.005
29. Eraslan G, Avsec Ž, Gagneur J, Theis FJ. Deep learning: New computational modelling techniques for genomics. Nat Rev Genet. 2019;20(7):389-403. doi:10.1038/s41576019-0122-6
30. Fragkostefanakis S, Röth S, Schleiff E, Scharf KD. Prospects of engineering thermotolerance in crops through modulation of heat stress transcription factor and heat shock protein networks. Plant Cell Environ. 2015;38(9):1881-1895. doi:10.1111/pce.12396
31. Furbank RT, Tester M. Phenomics-Technologies to relieve the phenotyping bottleneck. Trends Plant Sci. 2011;16(12):635-644. doi:10.1016/j.tplants.2011.09.005
32. Gao C. Genome engineering for crop improvement and future agriculture. Cell. 2021;184(6):1621-1635. doi:10.1016/j.cell.2021.01.005
33. Gill SS, Tuteja N. Reactive oxygen species and antioxidant machinery in abiotic stress tolerance in crop plants. Plant Physiol Biochem. 2010;48(12):909-930. doi:10.1016/j.plaphy.2010.08.016
34. Hajjar R, Hodgkin T. The use of wild relatives in crop improvement. Crop Sci. 2007;47(S3):S-56-S-61. doi:10.2135/cropsci2007.04.0005IPBS
35. Hall AE. Crop responses to environment. CRC Press; 2001.
36. Hammer GL, McLean G, Doherty A, van Oosterom E, Jordan D, Messina C. Designing crops for adaptation to the future environment using crop models and genomics. Nat Rev Genet. 2020;21(11):684-696. doi:10.1038/s41576020-0253-0
37. Hare PD, Cress WA, Van Staden J. The involvement of cytokinins in plant responses to environmental stress. Plant Growth Regul. 1997;23(1-2):79-103. doi:10.1023/A:1005954029393
38. Hasanuzzaman M, Nahar K, Alam MM, Roychowdhury R, Fujita M. Physiological, biochemical, and molecular mechanisms of heat stress tolerance in plants. Int J Mol Sci. 2013;14(5):9643-9684. doi:10.3390/ijms14059643
39. Hasin Y, Seldin M, Lusis A. Multi-omics approaches to disease. Genome Biol. 2017;18(1):83. doi:10.1186/s13059-017-1215-1
40. Iqbal N, Khan NA, Ferrante A, Trivellini A, Francini A, Khan MIR. Ethylene role in plant growth, development and senescence: Interaction with other phytohormones. Front Plant Sci. 2017;8:475. doi:10.3389/fpls.2017.00475
41. Jagadish SVK, Murty MVR, Quick WP. Rice responses to rising temperatures-Challenges, perspectives and future directions. Plant Cell Environ. 2015;38(9):1686-1698. doi:10.1111/pce.12430
42. Jaganathan D, Ramasamy K, Sellamuthu G, Jayabalan S, Venkataraman G. CRISPR for crop improvement: An update review. Front Plant Sci. 2018;9:985. doi:10.3389/fpls.2018.00985
43. Jakoby M, Weisshaar B, Dröge-Laser W, VicenteCarbajosa J, Tiedemann J, Kroj T, Parcy F. bZIP transcription factors in Arabidopsis. Trends Plant Sci. 2002;7(3):106-111. doi:10.1016/S1360-1385(01)02223-3
44. Jha UC, Bohra A, Singh NP, Jha R. Integrating omics approaches for improving abiotic stress tolerance in legumes. Plant Cell Rep. 2019;38(10):1251-1267. doi:10.1007/s00299-019-02453-3
45. Jinek M, Chylinski K, Fonfara I, Hauer M, Doudna JA, Charpentier E. A programmable dual-RNA-guided DNA endonuclease in adaptive bacterial immunity. Science. 2012;337(6096):816-821. doi:10.1126/science.1225829
46. Khan MIR, Fatma M, Per TS, Anjum NA, Khan NA. Salicylic acid-induced abiotic stress tolerance and underlying mechanisms in plants. Front Plant Sci. 2015;6:462. doi:10.3389/fpls.2015.00462
47. Kosová K, Vítámvás P, Prášil IT, Renaut J. Plant proteome changes under abiotic stress-Contribution of proteomics studies to understanding plant stress response. J Proteomics. 2011;74(8):1301-1322. doi:10.1016/j.jprot.2011.02.006
48. Kotak S, Larkindale J, Lee U, von Koskull-Döring P, Vierling E, Scharf KD. Complexity of the heat stress response in plants. Curr Opin Plant Biol. 2007;10(3):310316. doi:10.1016/j.pbi.2007.04.011
49. Kumar R, Sharma SK, Sharma V. Plant metabolomics: Current advances and future perspectives for crop improvement under abiotic stress. Plants. 2022;11(8):1045. doi:10.3390/plants11081045
50. Lämke J, Bäurle I. Epigenetic and chromatin-based mechanisms in environmental stress adaptation and stress memory in plants. Genome Biol. 2017;18(1):124. doi:10.1186/s13059-017-1263-6
51. Larkindale J, Knight MR. Protection against heat stressinduced oxidative damage in Arabidopsis by calcium, abscisic acid, and salicylic acid. Plant Physiol. 2002;128(2):682-695. doi:10.1104/pp.010320
52. Libbrecht MW, Noble WS. Machine learning applications in genetics and genomics. Nat Rev Genet. 2015;16(6):321332. doi:10.1038/nrg3920
53. Liu HT, Li B, Shang ZL, Li XZ, Mu RL, Sun DY, Zhou RG. Calmodulin is involved in heat shock signal transduction in Arabidopsis. Plant Physiol. 2003;132(3):1186-1195. doi:10.1104/pp.102.018838
54. Liu J, Feng L, Li J, He Z. Genetic and epigenetic control of plant heat responses. Front Plant Sci. 2015;6:267. doi:10.3389/fpls.2015.00267
55. Miller G, Suzuki N, Ciftci-Yilmaz S, Mittler R. Reactive oxygen species homeostasis and signalling during drought and salinity stresses. Plant Cell Environ. 2010;33(4):453467. doi:10.1111/j.1365-3040.2009.02041.x
56. Misra BB, Langefeld C, Olivier M, Cox LA. Integrated omics: Tools, advances and future approaches. J Mol Endocrinol. 2019;62(1):R21-R45. doi:10.1530/JME-180055
57. Mittler R. Oxidative stress, antioxidants and stress tolerance. Trends Plant Sci. 2002;7(9):405-410. doi:10.1016/S1360-1385(02)02312-9
58. Mittler R, Finka A, Goloubinoff P. How do plants feel the heat? Trends Biochem Sci. 2012;37(3):118-125. doi:10.1016/j.tibs.2011.11.007
59. Montesinos-López OA, Montesinos-López A, Crossa J, Gianola D, Hernández-Suárez CM, Martín-Vallejo J, et al. A review of deep learning applications for genomic prediction. Comput Electron Agric. 2021;184:106071. doi:10.1016/j.compag.2021.106071
60. Muthamilarasan M, Prasad M. Small millets for enduring food security amid pandemics. Trends Plant Sci. 2021;26(1):33-40. doi:10.1016/j.tplants.2020.08.008
61. Niu B, Paulson JN, Zheng X, Kolter R. Simplified and representative bacterial community of maize roots. Proc Natl Acad U S A. 2017;114(12):E2450-E2459. doi:10.1073/pnas.1616148114
62. Nover L, Bharti K, Döring P, Mishra SK, Ganguli A, Scharf KD. Arabidopsis and the heat stress transcription factor world. Cell Stress Chaperones. 2001;6(3):177-189. doi:10.1379/1466-1268(2001)006<0177:aathst>2.0.co;2
63. Obata T, Fernie AR. The use of metabolomics to dissect plant responses to abiotic stresses. Cell Mol Life Sci. 2012;69(19):3225-3243. doi:10.1007/s00018-012-1091-5
64. Olsen AN, Ernst HA, Leggio LL, Skriver K. NAC transcription factors: Structurally distinct, functionally diverse. Trends Plant Sci. 2005;10(2):79-87. doi:10.1016/j.tplants.2004.12.010
65. Pinto RS, Reynolds MP, Mathews KL, McIntyre CL, Olivares-Villegas JJ, Chapman SC. Heat and drought adaptive QTL in wheat. Funct Integr Genomics. 2010;10(4):481-496. doi:10.1007/s10142-010-0183-9
66. Pires N, Dolan L. Origin and diversification of basic helixloop-helix proteins in plants. Mol Biol Evol. 2010;27(4):862-874. doi:10.1093/molbev/msp288
67. Prince SJ, Song L, Qiu D, dos Santos JV, Chai C, Joshi T, et al. Genetic variants in root architecture-related genes improve soybean adaptation to abiotic stress. Plant Physiol. 2015;167(4):1530-1545. doi:10.1104/pp.114.252841
68. Quint M, Delker C, Franklin KA, Wigge PA, Halliday KJ, Van Zanten M. Molecular and genetic control of plant thermomorphogenesis. Nat Plants. 2016;2(1):15190. doi:10.1038/nplants.2015.190
69. Rajasundaram D, Selbig J. More effort-More results: Recent advances in integrating omics data. Curr Opin Plant Biol. 2016;30:57-61. doi:10.1016/j.pbi.2016.02.010
70. Reddy ASN, Ali GS, Celesnik H, Day IS. Coping with stresses: Roles of calcium-and calcium/calmodulinregulated gene expression. Plant Cell. 2011;23(6):20102032. doi:10.1105/tpc.111.084988
71. Reynolds MP, Pask AJD, Mullan DM, editors. Physiological breeding I: Interdisciplinary approaches to improve crop adaptation. CIMMYT; 2012.
72. Rodriguez MCS, Petersen M, Mundy J. Mitogen-activated protein kinase signaling in plants. Annu Rev Plant Biol. 2010;61:621-649. doi:10.1146/annurev-arplant-042809112252
73. Rushton PJ, Somssich IE, Ringler P, Shen QJ. WRKY transcription factors. Trends Plant Sci. 2010;15(5):247258. doi:10.1016/j.tplants.2010.02.006
74. Saidi Y, Finka A, Goloubinoff P. Heat perception and signalling in plants. Plant Signal Behav. 2011;6(11):17311733. doi:10.4161/psb.6.11.17972
75. Saini HS, Aspinall D. Sterility in wheat induced by water deficit or high temperature. Aust J Plant Physiol. 1982;9(5):529-537. doi:10.1071/PP9820529
76. Sakuma Y, Maruyama K, Qin F, Osakabe Y, Shinozaki K, Yamaguchi-Shinozaki K. Dual function of DREB2A in water and heat stress-responsive gene expression. Plant Cell. 2006;18(5):1292-1309. doi:10.1105/tpc.105.035865
77. Salt DE, Baxter I, Lahner B. Ionomics and the study of the plant ionome. Annu Rev Plant Biol. 2008;59:709-733. doi:10.1146/annurev.arplant.59.032607.092942
78. Scharf KD, Berberich T, Ebersberger I, Nover L. The plant heat stress transcription factor (HSF) family. Biochim Biophys Acta. 2012;1819(2):104-119. doi:10.1016/j.bbagrm.2011.10.002
79. Shu QY, Forster BP, Nakagawa H, editors. Plant mutation breeding and biotechnology. CABI; 2012.
80. Singh A, Ganapathysubramanian B, Singh AK, Sarkar S. Machine learning for high-throughput stress phenotyping in plants. Trends Plant Sci. 2018;23(10):883-898. doi:10.1016/j.tplants.2018.07.015
81. Snider JL, Oosterhuis DM, Kawakami EM. Genotypic differences in reproductive thermotolerance of cotton. Crop Sci. 2010;50(1):240-249. doi:10.2135/cropsci2009.04.0212
82. Szabados L, Savouré A. Proline: A multifunctional amino acid. Trends Plant Sci. 2010;15(2):89-97. doi:10.1016/j.tplants.2009.11.009
83. Tanksley SD, McCouch SR. Seed banks and molecular maps: Unlocking genetic potential from the wild. Science. 1997;277(5329):1063-1066. doi:10.1126/science.277.5329.1063
84. Vardhini BV, Anjum NA. Brassinosteroids make plant life easier under abiotic stresses. Acta Physiol Plant. 2015;37(2):25. doi:10.1007/s11738-014-1760-3
85. Varshney RK, Bohra A, Roorkiwal M, Sharma R, Ojiewo CO, Joshi PK, et al. Fast-forward breeding for climate resilience. Trends Plant Sci. 2021;26(11):1124-1140. doi:10.1016/j.tplants.2021.06.014
86. Varshney RK, Song C, Saxena RK, Azam S, Fan G, Chitikineni A, et al. Draft genome sequence of chickpea (Cicer arietinum). Nat Biotechnol. 2013;31(3):240-246. doi:10.1038/nbt.2491
87. Varshney RK, Terauchi R, McCouch SR. Harvesting the promising fruits of genomics. Nat Rev Genet. 2014;15(8):529-545. doi:10.1038/nrg3743
88. Vierling E. The roles of heat shock proteins in plants. Annu Rev Plant Physiol Plant Mol Biol. 1991;42:579-620. doi:10.1146/annurev.pp.42.060191.003051
89. Vishwakarma K, Upadhyay N, Kumar N, Yadav G, Singh VP, Mishra RK, et al. Abscisic acid signaling and abiotic stress tolerance in plants: A review on current knowledge and future prospects. Front Plant Sci. 2017;8:161. doi:10.3389/fpls.2017.00161
90. Wahid A, Gelani S, Ashraf M, Foolad MR. Heat tolerance in plants: An overview. Environ Exp Bot. 2007;61(3):199223. doi:10.1016/j.envexpbot.2007.05.011
91. Wang W, Vinocur B, Shoseyov O, Altman A. Role of plant heat shock proteins and molecular chaperones in abiotic stress. Trends Plant Sci. 2004;9(5):244-252. doi:10.1016/j.tplants.2004.03.006
92. Washburn JD, Burch MB, Ramstein GP. Machine learning for plant breeding and biotechnology. Plant Genome. 2021;14(1):e20050. doi:10.1002/tpg2.20050
93. Yang W, Duan L, Chen G, Xiong L, Liu Q. Plant phenomics and multi-omics integration for crop improvement. Mol Plant. 2021;14(7):1141-1158. doi:10.1016/j.molp.2021.03.005
94. Yang W, Feng H, Zhang X, Zhang J, Doonan JH, Batchelor WD, et al. Crop phenomics and high-throughput phenotyping. Mol Plant. 2020;13(2):187-214. doi:10.1016/j.molp.2019.12.008
95. Zandalinas SI, Fritschi FB, Mittler R. Global warming, climate change, and environmental stress. Plant Physiol. 2021;182(1):53-64. doi:10.1093/plphys/kiaa001
96. Zinn KE, Tunc-Ozdemir M, Harper JF. Temperature stress and plant sexual reproduction. J Exp Bot. 2010;61(1):195204. doi:10.1093/jxb/erp242