Preprints (www.preprints.org) | NOT PEER-REVIEWED | Posted: 28 June 2018 doi:10.20944/preprints201806.0455.v1 Peer-reviewed version available at Int. J. Mol. Sci. 2018, 19, 2390; doi:10.3390/ijms19082390 Omics approaches for engineering wheat production under abiotic stresses Tariq Shah*1, Jinsong Xu1, Xiling Zou1 and Xuekun Zhang1 1Key Lab of Biology and Genetic Improvement of Oil Crops, Oil Crops Research Institute, Chinese Academy of Agricultural Sciences (CAAS), Wuhan District, Hubei 430062, China. Corresponding author: [email protected] © 2018 by the author(s). Distributed under a Creative Commons CC BY license. Preprints (www.preprints.org) | NOT PEER-REVIEWED | Posted: 28 June 2018 doi:10.20944/preprints201806.0455.v1 Peer-reviewed version available at Int. J. Mol. Sci. 2018, 19, 2390; doi:10.3390/ijms19082390 Abstract Abiotic stresses greatly influenced wheat productivity executed by environmental factors such as drought, salt, water submergence, and heavy metals. The effective management at molecular level is mandatory for thorough understanding of plant response to abiotic stress. The molecular mechanism of stress tolerance is complex and requires information at the omic level to understand it effectively. In this regard, enormous progress has been made in the omics field in the areas of genomics, transcriptomics, and proteomics. The emerging field of ionomics is also being employed for investigating abiotic stress tolerance in wheat. Omic approaches generate a huge amount of data, and adequate advancements in computational tools have been achieved for effective analysis. However, the integration of omic-scale information to address complex genetics and physiological questions is still a challenge. In this review, we have described advances in omic tools in the view of conventional and modern approaches being used to dissect abiotic stress tolerance in wheat. Emphasis was given to approaches such as quantitative trait loci (QTL) mapping, genome-wide association studies (GWAS), and genomic selection (GS). Comparative genomics and candidate gene approaches are also discussed considering identification of potential genomic loci, genes, and biochemical pathways involved in stress tolerance mechanism in wheat. This review also provides a comprehensive catalog of available online omic resources for wheat and its effective utilization. We have also addressed the significance of phenomics in the integrated approaches and recognized high-throughput multi-dimensional phenotyping as a major limiting factor for the improvement of abiotic stress tolerance in wheat. Keywords: Abiotic stresses, GWAS, Ionomics, Omics, Phenomics, QTL Introduction Wheat is one of the 3rd most cultivated cereal crop throughout the globe (Acevedo et al. 2002) which covers 22% of the cultivating land. It belongs to the family of Gramineae (Poacea). It mainly grows in the temperate zones with cool weather (12-25 0C temperature) and having 250-1750 mm annual precipitation [1]. It is widely planted at the end of autumn season as it needs the cold treatment for flower initiation, which is called vernalization process [2]. A recessive allele of Vrn genes on 5A, 5B and 5D genomes is responsible to control this growth habit of winter wheat, which requires 40 0C temperature prior to tillers and elongation stage [2, 3]. Both Genotypes and Preprints (www.preprints.org) | NOT PEER-REVIEWED | Posted: 28 June 2018 doi:10.20944/preprints201806.0455.v1 Peer-reviewed version available at Int. J. Mol. Sci. 2018, 19, 2390; doi:10.3390/ijms19082390 photoperiod are major factors to control the flowering pattern in wheat which lack photoperiod insensitivity genes (Ppd) need exposure to long growing days [4]. Abiotic stress is one of the key factors limiting the crop production. Environmental stresses like drought stress, salt stress, heavy metals stress as well as water submergence stress influenced the crop yield in different manner. To overcome such problems, local cultivars should be modified by making molecular changes in the gene [5]. Although the multi-selection field trials method has been widely used for direct selection of the tolerant varieties to any harsh environment however, this selection method doesn’t provide significant results for the abiotic stress related traits due to highly influenced by environmental condition and low heritability [6]. In addition, this direct selection approach is quite laborious as well as time consuming. The genetic variation in different yielding crops could lead to the development of the tolerant cultivars but there is a knowledge gap which needs strong effort to find the specific molecular marker [7]. The development of molecular markers will provide the new and latest sequenced genomes and organelles in crops [8]. Recently, the development of molecular markers to characterize the complete genome sequence plays a crucial role in marker-assisted breeding [9]. The availability of high density markers helps to identify different alleles involved in agronomics traits and also helps in haplotype analysis [10]. In addition, marker-assisted breeding has been accomplished for the simple traits, which is controlled by a single or fewer loci [11; 12] however, such breeding also suffers due to unsought genetic strains [11]. The phenotypic expression of the newly developed gene (s) is controlled by the genetic makeup of the repeated parents, which is mainly due to epistatic interaction [13], and this epistatic interaction in mostly unpredictable in case of multiple complex traits, unless some proper evidence is available about the molecular processes involved during the developments of new traits. Current improvement in the genomics can easily predict the factors involved in their genetic variation, traits developments, distribution as well as interaction with the host environment [14]. Genetic Engineering is an advanced approach mainly used for the genetic enhancement of the plants. Interestingly, genetically modified (GM) plants has been proven to be successful for herbicide and insect tolerance, and widely used throughout the world [15]. A combination of multi- disciplinary knowledge is required for the development of an ideal plants, which could provide better yield even at adverse climatic conditions. This review has been written to explain the recent achievements in various omics approaches and to elaborate the future outcomes for the development of abiotic stress tolerant varieties. Preprints (www.preprints.org) | NOT PEER-REVIEWED | Posted: 28 June 2018 doi:10.20944/preprints201806.0455.v1 Peer-reviewed version available at Int. J. Mol. Sci. 2018, 19, 2390; doi:10.3390/ijms19082390 Technological advances have also provided a high-throughput, reliable, and quick array-based genotyping platforms. The SNP array development require initial information about SNPs, fortunately, information about millions of SNPs is already available in the public domain (Table 1). The Illumina Infinium array (SoySNP50K iSelect BeadChip) for ∼50,000 SNPs has been successfully developed and used for the genotyping of several soybean plant introduction (PI) lines [16]. Technological advances beyond this make it possible to resequence hundreds of lines in a cost effective manner and has started a new era of genotyping by re-sequencing [17]. Now, the challenge for plant biologists is how to effectively use these resources for marker-assisted applications. Omics approaches in technological era When genetics, nutritional or environmental conditions is changed, diverse omics technologies fulfil the understanding of all the changes that occur. Omics are useful in the understanding of species and thus providing insight into modification of the plant metabolism which results through contact with environment. The era of genomics had been started with the development of automated sequencing methods and led to first whole genome sequencing of Arabidopsis thaliana [122]. The genome sequencing has been stretched out to major crop plants such as rice [18; 19], soybean [20], maize [21]. The emergence of highly throughput “Omics” approaches has begun a successful period of plant molecular techniques for adjusting to changes in the environment. Recent development in ‘omics’ after post genomic era such as next generation sequencing, genome scale molecular analysis, modeling of different physiological and molecular understanding and correlation of these observations with physiology of the plant provides an accomplished move to adaptability and productivity under stress. Latest advent of next generation sequencing methods made possible sequencing the plant species quite useful [22]. Allohexaploid bread wheat (2n=6x=42, AABBDD genomes) is one of the complicated genomes in which homologous chromosomes having similar genes could complicates the reconstructing process of biological networks. A draft of wheat genome is completed which shows more than 124,000 gene loci which covers all the sub-genomes (A, B, and D) and proves useful in identifying genes which control biological process. Further, modern utilization of transcriptomics (RNA-seq) and proteomics (targeted vs non-targeted proteins) will help in defining their functions at gene and protein level respectively. As all genes are not always turned on at the same time therefore the metabolism becomes quite dynamic in phenotype which cannot be derived from the genotype. Thus, the Preprints (www.preprints.org) | NOT PEER-REVIEWED | Posted: 28 June 2018 doi:10.20944/preprints201806.0455.v1 Peer-reviewed version available at Int. J. Mol. Sci. 2018, 19, 2390; doi:10.3390/ijms19082390 successful integration of the transcriptomics (gene), proteomics (proteins), metabolomics (metabolite), ionomics (analysis of elemental compositions), epigenomics (inheritance), interactomics (protein-protein or protein-DNA interactions) will facilitate the breeder to select the potential candidates and best traits to generate and improve the crop productivity under abiotic stress (Fig. 1). Expression mRNA microarray proteomics miRNA microarray Functional mcRNA microarray proteomics RNAseq Genomics ProteomicsS tructural proteomics Omics Epigenome M e tabolomics DNA methylation Metabolite profiling profile Metabolic ChiP-Seq fingerprinting Genome sequencing Figure 1. Key branches of omics and their major components being used in different integrated approaches in wheat. Genomics progresses for abiotic stress tolerance in wheat Genomics emphasis on the genome physical structure, aiming to recognize, detect, and order genomic structures along chromosomes. Here we discuss some of genomic progresses to understand an abiotic stress tolerance in wheat. Molecular marker resources The emergence of genomic technology has opened a new window in genetic enhancement of more complicated traits such as salt and drought tolerance. The amalgam of genomic approaches along with marker assisted selection (MAS) can be helpful in the identification of specific genes at a much fast rate in breeding population as compared to classical breeding [23, 24, 25, 26]. Preprints (www.preprints.org) | NOT PEER-REVIEWED | Posted: 28 June 2018 doi:10.20944/preprints201806.0455.v1 Peer-reviewed version available at Int. J. Mol. Sci. 2018, 19, 2390; doi:10.3390/ijms19082390 Developments in MAS for wheat was lagged behind due to limited genomic data but recent progress in DNA sequencing and genotyping techniques have developed genome datasets which are much useful in designing sequence based simple sequence repeats (SSRs) and SNP markers [27, 28, 29]. SNPs are frequently used for genome mapping and germplasm characterization as compared to other molecular markers. As SNPs are highly-throughput, rapid, cost-effective, co- dominant, sequence tagged and highly abundant, they are appropriate for division of complex traits using highly multiplexed marker microarrays such as the Affymetrix GeneChip [30, 29]. For instance Axiom Wheat Breeders’ genotyping array, robust system for screening large wheat population, is developed recently. It is a cost effective and efficient genotyping method having 35,143 pre-validated SNPs which covered all wheat chromosomes and have ability to genotype 384 samples at once. In duram wheat, it has been applied recently in the development linkage map of high density and also in identifying the genomic areas of complex traits such as drought [31]. QTL Mapping for abiotic stress For agronomically important traits, linkage maps are necessary for mapping the QTLs as they are constructed from genotypic data from multiplexed marker assays [32-35]. High-density linkage maps also offers a genomic resource for positional cloning of significant genes. They can also be applied in comparative genomics to assess chromosomal organization and evolution as they are constructed from sequence tagged markers. In the linkage map, markers are helpful in identifying regions having QTLs of selected traits and several QTLs have mapped previously for salt tolerance [36, 17, 35] and drought tolerance [37-39] in wheat cultivars (Table 1). Advances in genomics and phenomics provide us more precise and broad characterization of the QTLs that control a targeted trait known as QTLome. The vast knowledge on QTLome put a responsibility on breeders to utilize this knowledge in effective way. Enhanced QTL meta- analysis, valuation of QTL effects and upgraded crop modeling will allow an actual utilization of the QTLome [40]. Preprints (www.preprints.org) | NOT PEER-REVIEWED | Posted: 28 June 2018 doi:10.20944/preprints201806.0455.v1 Peer-reviewed version available at Int. J. Mol. Sci. 2018, 19, 2390; doi:10.3390/ijms19082390 Table 1: Major and stable QTL with PVE ranging from 19% to 59% for agronomic and physiological traits S.No Linked . QTL markers Position Env. a PVE (R2 ) b References Agronomic traits 1. Grain yield a qGYWD.3B.2 Xgpw7774 97.6 7/4 19.6 [41] b 4A Xwmc420 90.4 Mean/2 20 [42] c 4A-a Xgwm397 6 7/5 23.9 [43] d Qyld.csdh.7AL Xgwm322 155.9 21/11 20.0* [44] 2. 1000-grain weight a 3B Xbarc101 86.1 Mean/2 45.2 [45] b QTgw-7D-b XC29-P13 12.5 11/10 21.9 [46] 3. Days to heading a QDh-7D.b XC29-P13 12.5 11/11 22.7 [47] b QHd.idw-2A.2 Xwmc177 46.1 13/16 32.2 [46] 4. Days to maturity X7D-acc/cat- a QDm-7D.b 10 2.7 11/10 22.7 [48] Physiological traits 1. Stem reserve mobilization a QSrm.ipk-2D Xgwm249a 142 2/2 42.2 [48] b QSrm.ipk-5D Xfbb238b 19 2/2 37.5 [48] c QSrm.ipk-7D Xfbb189b 338 2/2 21 [48] 2. Water Soluble Carbohydrate QWsc-c.aww- a 3A Xwmc0388A 64.9 2/2 19 [49] 3. SPAD/Chlorophyll Content a Qchl.ksu-3B Xbarc68 67.2 3/2 59.1 [50] aNumber of environments in which QTL was detected/number of total environments; bhighest PVE (R2 ) values under drought/water stress, * with >20% higher yield per ear. Genome wide association studies Alternative strategy to resist the shortcomings is association mapping based on traditional meiosis in the divergence analysis and gives accurate results [51]. It is quite feasible and cost effective to establish association mapping in comparison to RIL development. In association mapping, experimental structures and statistical evaluation is constantly fluctuating to diminish the results of confounding factors, decrease false positives and also control minor allele effects (Fig. 2). Genetic interaction and population designs confuse marker-trait relations results in the disequilibrium without true linkages [52]. To decrease false positives and minor allele QTL effects, Preprints (www.preprints.org) | NOT PEER-REVIEWED | Posted: 28 June 2018 doi:10.20944/preprints201806.0455.v1 Peer-reviewed version available at Int. J. Mol. Sci. 2018, 19, 2390; doi:10.3390/ijms19082390 several diagnostic and precise statistical analyses have been developed. Studies have been carried out on the correct genomic locations of developing genes such as reduced height (Rht), vernalization (Vrn), and photoperiod responsiveness (Ppd) and used as standard measure in order to incorporate phenotypic diversity and markers present in the study [3]. As genetic reference, these genes possess the stress adaptive capability by changes heading date, plant height, maturity and other physiological processes [53]. Preprints (www.preprints.org) | NOT PEER-REVIEWED | Posted: 28 June 2018 doi:10.20944/preprints201806.0455.v1 Peer-reviewed version available at Int. J. Mol. Sci. 2018, 19, 2390; doi:10.3390/ijms19082390 QTL mapping Genomic selection GWAS Statistical models RILs/DH Training population Cultivars/Landraces Multi environment testing Major QTLs Model evaluation GWAS Loci Backcrossing with MAS Integration of QTLs Model evaluation and GS GWAS Loci Improved lines with major QTLs/GWAS loci along with optimized genetic background Figure 2. Combined approach of QTL mapping/Genome-wide association study (GWAS) and Genomic selection (GS). Genomic selection The emergence of model based association and easy availability of molecular markers, a correlated concept known as genomic selection has emerged to assess genotypic breeding value [54]. This technique is utilized to minimize the shortcomings of map based genetic analysis that identify very few QTL to explicate the divergence in targeted traits [54, 123]. The concrete significance of evaluated QTL effect and linkage disequilibrium based on the genetic relatedness and divergence of the population under study. Populations that shows more allelic variants of targeted traits display more precise evaluation of QTL effect compared to populations which are more closely related. Linkage disequilibrium is often overvalued in closely related inter-mating individuals and diminish Preprints (www.preprints.org) | NOT PEER-REVIEWED | Posted: 28 June 2018 doi:10.20944/preprints201806.0455.v1 Peer-reviewed version available at Int. J. Mol. Sci. 2018, 19, 2390; doi:10.3390/ijms19082390 in further meiotic events [55]. Genomic breeding value can be predicted by genomic selection by transforming marker assisted selection with the help of markers. A model that is established and evaluated using genotypic and phenotypic data of study population will be utilized to assess phenotypic variation of sample population based on their genetic composition only. This will enhance genetic gain in comparison to both QTL and phenotype-based selection [56]. Statistical methods are used to develop genomic selection models that explains the properties of various markers and traits [57, 123]. The distribution of marker effects and random sampling of germplasm from selected population is being considered by the multiple regression models [58]. Genomic best linear unbiased prediction evaluates genetic relatedness among individuals on the basis of molecular marker composition similarity and also assess their phenotypic performance. Such a model is similar to estimation of breeding value (BV) from heritability and phenotypic performance of related genotypes in a pedigree [54, 123]. Statistical analyses shows that forward and mixed type regression models have ability to remove markers on the basis of their relative significance effect. Ridge regression have an additional feature of incorporating penalty parameter in the design for the markers in excess of statistically accepted number (> number of genotypes) [59]. Transcriptome profiling for abiotic stress tolerance In wheat, expressed sequence tag databases showed that homologous genes can show expression in one but remain silent in one or both of the remaining genomes in the analysis of gene expression [60]. For the cereals, various microarray and macroarray platforms have been developed. There are various significant arrays like 10000 cDNA array reported by Leader [61] and Affymatrix arrays have been developed for wheat and barley recently [62]. In abiotic stress conditions in variety of plants, microarray is one of the successful method for the genome wide transcript expression profiling and is being widely used to generate transcriptional profiles (Fig. 3). Studies have been carried out in barely in response to salt stress [63, 64] and the model cereal Brachypodium [65] but there are limited research on wheat due to polyploidy genome [66]. The high-throughput analyses of gene expression is being enabled by deep genome sequencing technology (RNA-Seq) which proves to be successful technique to identify precise changes in the genome. Next-generation sequencing technology is in the emerging phase in plant studies but it is predicted to replace microarray technique due to its accurate results. Due to lack of fully sequenced and complex genome (hexaploid) in wheat present many difficulties to “OMICS” studies. Meng