Integrating Omics Approaches for Abiotic Stress Tolerance in Plants

Abstract

Plants are exposed to a variety of challenging abiotic stress pressures such as salt, drought, waterlogging, heat, oxidative stress, and heavy metals. An in-depth understanding of how plants respond to abiotic stress from the molecular side view is an important criterion for its actual management. Abiotic stress tolerance is a complicated phenomenon that includes many interacting steps such as signal recognition and an array of subsequent responses in a signal transduction pathway. This multitude of reactions necessitates evidence at the omics level to comprehend it properly. Enormous advance has been made in the field of omics in different areas such as genomics, transcriptomics, proteomics, metabolomics, phenomics, and ionomics. These advanced approaches generate multifaceted data that can shed some light on what is going on inside the plant cells. For instance, functional genomics deals with the relation between the genome and the phenotype, this relation is highly affected by environmental abiotic stress conditions. For effective analysis of the huge amount of data generated from the omics approaches, advancement in bioinformatics and computational tools have been exploited. This review summarizes the advances in omics tools, both traditional and recent, comprising QTL mapping for abiotic stress tolerance, genome-wide association studies (GWAS) and genomic selection (GS) used to examine the mechanisms of abiotic stress tolerance in plants.

1. Introduction

Abiotic stress tolerance is a complex molecular regulatory system. It is considered a multigenic response that involves stress-responsive gene expression, signal transduction, and sensing. This complex array of interplaying reactions happening inside the plant cells can be understood with the help of different omics tools. Plants, being sessile organisms, are continuously exposed to unavoidable abiotic stress factors, which highly affect plants, reduce productivity, and result in significant losses in crop yields of more than 60% [1]. Examples of the most influencing abiotic stress conditions on plants include salinity, drought, submergence, oxidative stress, and temperature change (heat and extreme cold) as well as environmental pollutants such as heavy metals, herbicides, and pesticides.

The phases of alarm, resistance, and exhaustion might be seen as the three primary phases of plant stress events and responses [2]. Each phase may induce the expression of a different set of genes.

Before the omics era, efforts were concentrated on the study of individual genes. Nowadays, advancement in omics tools, including genomics, transcriptomics, proteomics, and metabolomics enabled researchers to find the molecular interaction and relationships with signal cascades which connect specific signals obtained with specific molecules.

Each omics strategy has a limit that affects the approach’s specificity or sensitivity, but employing a multi-omics approach may help to get around this restriction by giving a full picture of the changes that happen in plants under abiotic stress conditions at the genomic, transcriptomic, proteomic, metabolomic, and phenomics side of view. In the following parts, we will be looking in some detail at each of these points of view.

Over the past 20 years, many plant genomes have been sequenced, thanks to advancements in genomics and next-generation sequencing. Modern genomics, including marker-assisted selection (MAS), genome-wide association studies (GWAS), genomic selection (GS), transgenic breeding, and gene editing, as well as transcriptomics, proteomics, and next-generation sequencing, offer a variety of novel, potent methods for the study of plant species. This review investigates the general effects of significant abiotic stresses on plants, as well as the adaptive mechanisms and the interplay of omics approaches including genomic, transcriptomic, and proteomic procedures employed by researchers to lessen these difficulties and address abiotic stresses. Understanding the response of a plant to an abiotic stress is the first step toward breeding stress-tolerant varieties. To provide a comprehensive image of the abiotic stress tolerance mechanism in plants, a detailed review of each omics technique and multi-omics integration has been covered in the following sections, thus helping in plant abiotic stress tolerance as well as breeding enhancement (Figure 1).

Figure 1.

Integrative multi-omics techniques to provide plants with abiotic stress tolerance. The diagram was produced using BioRender (https://biorender.com/).

1.1 Genomics approach

Scanning the genomes of many plant species for abiotic stress tolerance, specifically the crop species, allows us to predict stress-related genes. There are numerous opportunities for functional investigations of abiotic stress sensitive genes and tolerance mechanisms, thanks to RNA-Seq, random and targeted mutagenesis, gene shifting, complementation, and synthetic promoter trapping techniques [3].

Recent advances in major genomic tools such as marker-assisted selection (MAS), single nucleotide polymorphisms (SNPs), next-generation sequencing (NGS), and genome-wide association studies (GWAS) have a great impact to study genetic diversity and emphasize it in case genetic diversity is low. The complicated biological mechanisms and pathways regulating abiotic stress tolerance in rapeseed have been extensively studied using omics, GE tools, and molecular breeding strategies. To reveal various molecular, physiologic, and metabolic pathways related to abiotic stress tolerance in rapeseed, multi-dimensional omics studies have gathered many datasets at the transcriptome, proteome, and metabolome level. This has opened new research avenues to understand complex traits such as abiotic stress tolerance [4].

Deoxyribonucleic acid (DNA) markers are applied to scan the genomes to screen desired loci and transgene; SNPs have been discovered in many plants using QTL mapping for the desired trait. Genes linked to various abiotic stresses were identified in rapeseed breeding programs. Finding SNPs for various abiotic stress-related QTLs is made possible by high-throughput sequencing techniques and rapeseed EST construction [5].

Stress tolerance results from multiple complex responses and mechanisms. Assessment of these mechanisms and stress-related QTLs has assisted plant breeders to develop elite genotypes with stress tolerance. Accurate depictions of the genetic pathways in crop plants that regulate abiotic stress responses will be possible with the utilization of numerous “omics data”. Combining this information with pan-genomics may improve the precision of genome-assisted breeding, boost crop breeding effectiveness, and open new opportunities for genome editing-based abiotic stress tolerance development [6].

1.2 Transcriptomic approach

Large-scale genomic sequence information has been revealed in many plants due to the advancements in transcriptome analysis methods. A comprehensive picture of the transcriptomes can be obtained by identifying partial or whole cDNA sequences. There are three primary databases that house the available ESTs.

These ESTs are organized by NCBI, TIGR, and Sputnik, which have fully characterized gene sequences. Microarrays and cDNA were frequently utilized to pinpoint gene expression profiles in abiotically stressed Arabidopsis, potato, rice, sorghum, maize, and wheat [7].

The introduction of genome sequencing technologies and the subsequent decline in their price have revolutionized the field of omics studies and hence the comprehension of plant metabolic pathway identification [8].

By using transcriptome investigations, it is possible to find molecular markers that are connected to widespread reactions in a variety of plant species; these molecular markers are utilized to comprehend the way of action of many agents such as bio-stimulants [9] and genes differentially expressed in each case. Alterations in gene expression have been studied as the intensity of RNA signals observed in plants in response to different treatments.

A comprehensive picture of the transcriptomes can be obtained by identifying partial or complete cDNA sequences. The available ESTs are arranged with fully characterized gene sequences in three major databases, namely NCBI, TIGR, and Sputnik. Researchers studying the gene expression profiles of stress tolerance-related traits in Arabidopsis and rice have greatly benefited from abiotic stress-related ESTs [7].

Transcriptome analysis is a dynamic method for assessing any increased gene expression over a given period [10]. Conventional techniques including cDNAs-AFLP, differential display-PCR (DD-PCR), and SSH were initially used to study transcriptome dynamics under drought conditions, but these techniques have poor resolution [11].

Digital gene expression (DGE) and microarray RNA expression profiles are also analyzed, thanks to advances in the relevant technology [12]. Microarray research demonstrated the differential expression of soybean and barley genes during the embryonic and reproductive stages of drought stress [13]. The Affymetrix gene chip array was used in a manner like this to identify the various gene expression levels in soybeans during dehydration stress [14].

1.3 Proteomic approach

Sequence, structural, functional, and expression proteomics are the four subcategories of proteomics. Proteomics is used to profile the total amount of expressed proteins in an organism [15]. Proteomics typically refers to two different sorts of studies: (1) the characterization of a proteome, which involves identifying all the proteins expressed in a particular cell, tissue, organ, organism, or population; and (2) other type of proteomics which is differential proteomics in which the proteome of a plant under control conditions is compared to the proteome of the same plant under study conditions, such as the exposure to a heavy metal or a water shortage, or in another example, the comparison of protein expression profiles between different varieties.

Structural proteomics involves determining the protein amino acid sequence using High-Performance Liquid Chromatography (HPLC). Using computational-based methods to deal with the huge number of natural protein structures, like modeling, experimental techniques like crystallization, nuclear magnetic resonance (NMR), X-ray diffraction of protein crystals, and electron microscopy, structural proteomics studies protein structure to better understand potential activities of proteins [16].

Large-scale proteomic research is being done on how plants react to salinity and drought stressors. To understand the stress-response mechanism in plants, proteomic techniques have been used at the level of the entire plant, the organ, and the subcellular level. Key proteomic research on plant species under salinity and drought stress has been previously summarized [7].

Proteomic investigations revealed that heat-shock proteins, nucleoside diphosphate kinase, RuBisCO, Cu-Zn superoxide dismutase (SOD), and 2-Cys-peroxiredoxin were shown to be substantially increased in sugar beetroot under drought stress. With the aid of 2D gel electrophoresis and image analysis tools, the changes in leaf proteins were investigated. Certain proteins displayed genotype-specific patterns of up- or downregulation. Liquid chromatography-tandem mass spectrometry (LC-MS/MS) was used to examine relevant protein spots. Rubisco and other additional proteins involved in signal transduction, oxidative stress, redox control, and chaperone functions were identified. Some of these proteins may offer a physiological benefit in arid conditions, making them desirable MAS targets [17]. A proteome investigation of maize under drought stress [18] revealed proteins involved in metabolism, photosynthesis, and stress responses. Branched-chain amino acid amino transferase 3 protein and zinc finger transcription factor oxidative stress 2 proteins played a significant role in drought stress responses in the plants that overexpressed ethylene response factor, according to a proteomic analysis of Arabidopsis under drought stress [19].

2. Metabolomic approach

Under abiotic stress, the plant metabolome needs to be reprogrammed to maintain its essential metabolic homeostasis. This takes place by producing protective metabolites that enhance stress tolerance. Flavonoids, terpenes, phenols, and alkaloids are typical stress-induced specialized metabolites that are produced in certain plant species, organs, tissues, and cells [20].

If we want to rationally adjust plant systems to maximize plant tolerance to various abiotic stresses, we must first understand how key metabolites are regulated. We provide a brief overview of the modern metabolomics approaches used to study plant metabolism in the sections that follow. We must first comprehend how important metabolites are regulated, if we hope to rationally modify plant systems to increase plant tolerance to various abiotic stimuli. Metabolite profiling might be targeted, aiming to identify specific responses or non-targeted, aiming to classify as many metabolites as possible.

Using a non-targeted metabolome profiling, it has been demonstrated that both primary and secondary metabolites play significant roles in how plants react to abiotic stress such as drought and salinity. Primary metabolites with significant roles in photosynthetic failure and osmotic readjustment have been identified by gas chromatography coupled to mass spectrometry, including sugars, amino acids, and Krebs cycle intermediates. While the secondary metabolites, such as coenzymes, regulatory compounds, and antioxidant scavengers, responded to other specific stress circumstances. In addition to establishing the phenotypic response of the plant and identifying stress-tolerant lines, studies of metabolites in response to abiotic stress are useful for elucidating the genetic and biochemical mechanisms underlying the stress situation [21]. There is a complicated crosstalk between different stresses, as plants are continuously exposed to more than one stress rather than to individual stresses, resulting in significant effects on key metabolic processes like those involving sugars, their phosphates, and sulfur-containing substances.

Atomic and molecular mass spectrometry (MS) and nuclear magnetic resonance (NMR) spectroscopy are the two analytical techniques most frequently utilized in metabolomics research [22]. The non-destructive and effective NMR-based metabolomic study can reveal the structural details of the metabolites [22].

Previous research has reviewed a list of significantly changed central metabolites under cold, drought, flooding, heat, and salinity stress from published studies [23]. Specific metabolites were found to be impacted by cold stressors, others by drought, by flooding, by heat, and by salinity, among the summarized metabolites. Some metabolites associated with stress displayed common responses to many abiotic stresses, while others only responded to few stresses.

Amino acids, the antioxidant phenolic compounds catechin and kaempferol, as well as the osmolytes raffinose and galactinol, exhibited increased abundance under drought stress, whereas metabolites involved in photorespiration, redox regulation, and carbon fixation showed decreased abundance under drought stress [24].

2.1 Ionomic approach

The term “ionome” refers to an organism’s composition of minerals, nutrients, and trace elements, which serves as the inorganic part of cellular and organismal systems [25]. Ionomics, considered as the functional genomics of elements, is thus the quantitative and simultaneous measurement of the elemental composition of living organisms (metal, non-metal, and metalloid) and changes in this composition in response to physiological stimuli, developmental state, and genetic modifications [25]. Due to their numerous uses, minerals (both essential and non-essential) have recently been recognized as important players in the biology of plant stress. ICP-AES and ICP-MS are two examples of high-throughput elemental analysis technologies that can be used to analyze the compositions of metal, non-metal, and metalloid elements in living organisms [26].

High-throughput elemental analysis technologies must be used for ionomics and must be integrated with both genetic and bioinformatics techniques. Ionomics has the capacity to record data regarding the functional status of an organism under abiotic stress. Samples must be digested in trace metal grade acid to reveal their fundamental elements. Compared to proteins, transcripts, and metabolites, elements have the advantages that they are relatively cheap to measure and the whole class of components can be measured in a single run.

2.2 Phenomics approach

In the context of the organism, phenomics is defined as the process of characterizing phenotype using high-dimensional phenotype data [27].

However, the term “phenotype” refers to the entire phenotype, and plant phenotype can be affected by the underlying interactions between the genome, environment, and management [28, 29]. For this reason, the phenomenon of phenomics is also known as genotype-phenotype-environotype interactions [30].

With the use of phenomics pipelines and advancement of non-destructing sensing and imaging techniques, researchers may turn vast amounts of image and sensor data into knowledge, necessitating the use of novel data processing and modeling techniques. Together, these breakthroughs are accelerating the development of the next generation of crops that are more climate change-resilient and sustainable, and whose advantages are expected to extend beyond physiology to breeding and have a real-world influence on ongoing efforts to ensure global food security. Advancements in technologies have a great impact in the phenomics area. One such technology is the WinRoots technology, which allows for both the immediate capture of high-throughput and high-quality whole-plant phenotypic data as well as the high-throughput growth of crops under controlled soil stress conditions. It would facilitate precision crop breeding by integrating phenotypes, genotypes, and other omics data sources in a comprehensive manner to unravel the genetic regulatory networks and mechanisms underpinning traits related to soil stress [31].

3. QTL mapping for abiotic stress tolerance

Plant breeders are now better able to understand how plants tolerate abiotic stress, thanks to the quantitative trait locus (QTL) discovery and mapping procedure. Molecular markers and stable QTL are crucial for molecular breeding.

Recent research has emphasized methods and developments for abiotic stress tolerance breeding programs in cereals, the difficulties in introducing advantageous QTL using conventional breeding techniques like mutation breeding and marker-assisted selection (MAS), and the advancements made by new breeding techniques like genome-wide association studies (GWASs), the clustered regularly interspaced short palindromic repeat, and others [32].

Conventional breeding, often known as traditional breeding, is the act of developing new plant varieties using traditional techniques and biological procedures [33]. Pyramiding several types of resistance, molecular markers have been utilized since the 1990s to select superior hybrids, thus creating multi-line cultivars with enduring resilience to abiotic stressors using many resistance genes [34].

SSRs (Single sequence repeats) and SNPs (Single nucleotide polymorphisms) can detect single nucleotide changes at the whole genome level as well as DNA variations in populations that are closely linked to one another [35]. The most well-known polymerase chain reaction (PCR)-based markers are microsatellites or SSR markers. Because they are co-dominant, hypervariable, locus sensitive, and multi-allelic, these markers are frequently utilized among plant species for screening, characterizing, and assessing genetic diversity [36].

SNP detection and application have become comparatively simple with the development of NGS and high-throughput genotyping technology [37]. The identification of genomic regions targeted for breeding programs for cold tolerance and the huge number of markers to find QTL with economically significant traits have both been made possible using high-density SNP iSelect assays, using 2 probes per SNP to determine the relative intensity ratio of the 2 possible target alleles for any given locus (9 and 90 K) in wheat (T. aestivum). The linkage markers produced in this study should help with molecular-assisted breeding of wheat with cold tolerance [38].

4. Genome-wide association studies (GWAS)

Abiotic stress has an impact on agricultural production. Investigations are typically based on single genes/traits. However, plant stress is controlled not by one trait but rather by multiple traits. GWASs are an effective method for examining a variety of traits linked to a single or multiple stresses. New gene candidates or quantitative trait loci, responsible for abiotic stress, have been discovered through GWAS on a variety of plants and crops. Future crop breeders and designers will benefit greatly from the findings of these investigations to understand the genetics underlying complex phenotypic traits.

In this field, network maps have been developed to reveal these complex traits and how genes interact and coordinate. A network map developed in maize shows that duplicated genes may have functional divergence at various layers. The integrative network map provides a potential that can be used to predict gene function, analyze cross-regulating interactions, and identify the molecular processes underlying complex traits [39].

The development of suitable cultivars may be facilitated and accelerated by the discovery of molecular markers linked to important genes that regulate tolerance to many abiotic stress factors and thus improve the plant yield and quality. Studies of multigene-associated traits, particularly GWASs, make extensive use of improved statistical methods and increased computational efficiency. The findings of these studies on plants have given breeders and future breeding studies useful information on the interactions between genotype, phenotype, and environment [38].

Through GWAS SNPs, selected genotypes were genotyped using the Barley 9 K iSelect SNP Array and several likely candidates for genes that underlie cold acclimation stress and/or chlorophyll were found. The candidate genes for cold-related traits were found in nearby regions of significant SNPs, primarily on chromosomes 1H, 3H, and 6H. For example, abscisic acid (ABA) signaling, hydrolase activity, protein kinase, and transduction of environmental signal transduction at the post-translational modification levels are a few examples of candidate genes that participate in plant response to abiotic stress at the post-transcriptional modification level [40].

5. Genomic selection (GS)

Genomic selection (GS) is a relatively straightforward, reliable, and powerful approach that has been used to solve the problem of breeding complex polygenic traits such as abiotic stress. In GS, the breeding values of lines are predicted using their phenotypes and marker genotypes [41, 42]. GS is more efficient than Marker-assisted breeding (MAB), because it builds a prediction model using all available marker information simultaneously, preventing the effects of skewed markers. Another advantage is that the majority of the variation’s small-effect QTL, including epistatic interaction effects, are captured by GS.

By using precise, high-throughput phenotyping and a wide base of selection, genomic selection can be used to select for genotypes that are tolerant to abiotic stress. The development of genomics, transcriptomics, and proteomics has led to the identification of numerous target genes that may reduce the impact of abiotic stress. Additionally, mutant loci that facilitate complex properties, such as yield, have been found, which are otherwise very challenging to understand.

Genomic selection can be helpful to precisely exercise genetic gain and selection needed for improvement of any plant trait [43] and hence has become a crucial tool that can use this data to model crop yield for quick and efficient selection under different stress conditions to meet the production challenges in a world, where the climate is continuously changing, thus causing many abiotic stresses affecting plant growth and yield. GS is currently utilized in the breeding of numerous crops reviewed in [44]. Genetic and environmental (GE) interactions play a major role in the accuracy of GS, but many studies have only attempted to estimate the main effects for each marker. In addition to studying G and E, these multi-environmental trials are crucial for plant breeding, because they will increase the number of breeding cycles per year.

Acknowledgments

This research has received no external funding. The authors would like to extend their gratitude to the reviewers and the editors.

Funding

The authors declare that there is no funding provided for this review.

Authors’ contributions

A. Morsy conceived the paper and the gap in the literature that this research aims to address, N. El-Sherif (corresponding author) wrote the paper and revised the related literature related to the manuscript. Both authors revised the final manuscript.

Competing interests

The authors declare that there is no conflicting interest of any type that could influence this research.

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The manuscript has not included any data or material and ethics issues. The authors declare that they have no personal relationships that could have appeared to influence the work in this manuscript and that their participation is completely voluntary.

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