Chapter 2 Multiomics Approach for Crop Improvement Under Climate Change

Abstract

Crop enhancement and safety is prerequisite to accomplish the requirement of developing scenario such as population explosion, climate change, and lack of productive land. Due to this, many agricultural scientist and researchers have been facing the challenges of improving crop productivity within a land where water supply is limited and use of chemicals is restricted for environmental protection. It is prerequisite to increase the crop variants i.e. Disease-resistance and stress tolerant crops. This transformation will helps in availing better variety of crops based on the quantity of the yield as well. Crop improvement includes raise nutritional value, yield and tolerance against several abiotic, biotic stresses under climate change scenario. Recent genomic scenario with advanced molecular techniques provides prospects for agricultural scientists to produce crops with innovative traits. The amalgamation of omics approaches (genomics, transcriptomics and metabolomics) enables a comprehensive understanding of the contrivances underlying the complex phenotypic traits of agricultural relevance. In this chapter recent trend in agricultural genomics is been highlighted for understanding plant phenotypic variation and their significance in genetic improvement of crops.

Full text

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Chapter 2
Multiomics Approach forCrop
Improvement Under Climate Change
ShaliniGupta, ReetaVerma, andRamanKumarRavi
Contents
Introduction 18
Climate Change Scenario and Its Impact on Crop Yield 18
Application of Advanced Omics Approaches 20
Transcriptomics 23
Proteomics 23
Metabolomics 24
Ionomics 24
Phenomics 25
Emerging Molecular Techniques 25
Zinc Finger Nuclease (ZFN) 25
Oligonucleotide-Directed Mutagenesis (ODM) 26
RNA-Dependent DNA Methylation (RdDM) 26
Reverse Breeding 26
Agro-Inltration 27
Synthetic Genomics 27
Widely Studied Crop 27
Conclusion and Future Prospects 28
References 29
S. Gupta (*)
Environment Studies, Delhi College of Arts and Commerce, University of Delhi, Delhi, India
R. Verma
School of Environment and Sustainable Development, Central University of Gujarat,
Gandhinagar, Gujarat, India
R. K. Ravi
Department of Environmental Science, Uttaranchal College of Science and Technology,
Dehradun, Uttarakhand, India
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2023
C. S. Prakash et al. (eds.), Sustainable Agriculture in the Era of the OMICs Revolution,
https://doi.org/10.1007/978-3-031-15568-0_2

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Introduction
Climate change is an outcome of the global rise in temperature due to anthropogenic
emanations of greenhouse gases and extensive shifts in weather phenomena. Climate
change affects the agricultural sector including crop yield, market price, and trading
and ultimately impacts human health. Food security is at greater threat with varia-
tion in the frequency and severity of droughts and oods. In developing countries,
adverse environmental conditions predominantly affect the agricultural yield; high
temperature and superuous CO2 accumulation phenomenon enforced researchers
to devise new strategies to address such challenges (Rosenzweig etal., 2014). Food
security and human health safety are vulnerable to grave weather conditions. The
development and yield of plants are signicantly inuenced by abiotic stresses such
as waterlogging, drought, heat, cold, UV-B, light intensity, ood, gas emissions, and
salinity (Ashraf etal., 2018; Benevenuto etal., 2017). Thus, new climate-smart crop
cultivars are a prerequisite in the current scenario (Wheeler & Von Braun 2013).
Technological approaches are vital to enable the crops to adapt to uctuating envi-
ronmental stress (Ali etal., 2019a, b). The amalgamation of biotechnologies into
crop improvement is a very dynamic eld. So, metabolomics, proteomics, transcrip-
tomics, and genomics transformation may act as supportive ways for crop improve-
ment. Metabolomics and proteomics are the study of cellular metabolites and
protein expressions, respectively. Transcriptomics provides detailed information
about RNA and genetic pathways under environmental stress. These techniques
commonly include structure, function, evolution, mapping of genomes, and genetic
manipulation (Fig.2.1). Resistance to herbicides and insects is an extensively used
genetically modied trait in crops (maize, soybean, cotton, and canola) with large
markets. Although herbicide and insect resistance traits greatly decrease soil tillage
and insecticide use, respectively, they require careful management to avoid the natu-
ral selection of weeds or pests resistance (Duke, 2015; Tabashnik etal., 2013; Ali
etal., 2019a, b). This chapter emphasizes on impact of climate change on crop yield
and improvement by multi-omics approaches and several recent applied molecular
techniques to meet the standard of market demand and food security.
Climate Change Scenario andIts Impact onCrop Yield
A climate scenario refers to a probable future climate that has been constructed for
categorical use in investigating the potential consequences of anthropogenic climate
change. It involves an array of possible environmental as well as socioeconomic
impacts (Fiaz etal., 2019a, b). More frequent and severe extreme weather events
such as heat waves, acute rainfall, oods, storms, droughts, and forest res are
anticipated outcomes of climate change. Several nations heralded a new epoch of
collaboration to preclude a climate disaster. Investments in renewable energy and
biotechnology produced an immediate decrease in emissions of greenhouse gases
S. Gupta et al.

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Fig. 2.1 Schematic representation genomic approaches could be employed to cultivate stress-
tolerant crop cultivars
and limited global warming to around 1.5°C above pre-industrial levels (O'Neill
etal., 2017). Carbon emissions are rising, setting the stage for 5°C of global warm-
ing by the end of the century. Here, climate scientists are required to explore diverse
issues that may appear with varying levels of global warming and climatic catastro-
phe (Hausfather & Peters, 2020). So the development of scenarios to represent a
range of upcoming future challenges would be faced by humanity. Although a
momentary drip in greenhouse gases emissions from the 2020 outbreak, countries
diverted to inexpensive fossil fuels to recuperate their economies postcrisis (IPCC,
2014). The major goal is to examine different environmental policies that may alter
carbon and other harmful gaseous emissions, also the response of the planet to heat-
trapping gas (Jeff, 2020; Hussain etal., 2018).
Prediction of climate change impact at a particular geographical location is not
easy, however general adaptations are evident based on plant physiology. It includes
the ability to yield and withstand: drought or ood, variations in temperature, pests,
and pathogenic diseases. Net positive impact on crop growth is anticipated with the
change in concentration of atmospheric CO2 as it is an essential nutrient (Lawlor &
Mitchell, 1991). Extreme weather disasters occurred during 1964–2007 reported
national cereal production losses worldwide (Corey et al., 2016). Droughts and
extreme heat owed a 9–10% reduction in national cereal production; however, the
effect of oods and extreme cold could not be identied. Droughts are accountable
2 Multiomics Approach forCrop Improvement Under Climate Change

20
for production losses due to constriction of harvested area and yields, whereas
extreme heat primarily reduced cereal yields. Furthermore, the developed countries
endured ~7% higher production damage from the latest droughts and 8–11% more
than the developing ones. Without CO2 fertilization, i.e., increased rate of photosyn-
thesis, effective adaptation, genetic improvement, and surge in earth’s mean tem-
perature would decrease crop yields: wheat by 6.0%, rice by 3.2%, maize by 7.4%,
and soybean by 3.1% world across. The outcomes are extremely heterogeneous
across crops and geographical areas. Considering the impacts of climate on major
crops on a worldwide scale, crop and region-specic adaptation strategies are sug-
gested to ensure food security for an increasing world population (Chuang etal.,
2017). A study at Maricopa, AZ, was conducted on wheat, wherein the crop was
planted for a total of 12 planting dates commencing September to May over 2years.
Infrared heaters overhead the crop catered supplemental heating only for six plant-
ing dates, increasing canopy temperature by 1.3°C and 2.7°C during day and night,
respectively. With every 1°C rise in seasonal temperature above 16.3°C, grain yield
reduced to 42 gm−2 (6.9%). Supplemental heating affected the early fall plantings
(Sept./Oct.) most, wherein the temperature was slightly below optimum (13.8°C).
Thus, a further rise in temperature may negatively impact the yield for late plantings
and alter optimum planting windows to earlier dates in geographical regions of the
world similar to the desert southwest of the USA (Ottman etal., 2012). C3 plants
(wheat and rice) are more affected by the increased CO2 than C4 plants (maize)
which have evolved mechanisms to optimize CO2. Higher CO2 concentrations
improve water utilization tendency as fewer stomatal openings are needed for the
exchange of gases. Due to climate change’s impact, many regions are facing recur-
rent or grievous droughts and oods. Thus, improved water consumption and the
ability to withstand yield under drought are vital to crop traits. Remarkable progress
has been made in the regions of ooding tolerance in rice with the introduction of
the Sub1 gene (Mackill etal., 2012; Xu et al., 2006; Shar etal., 2019). Escalated
plant development and reduced transpiration linked to a high level of atmospheric
CO2 are anticipated to inuence the nutritive content of food, with a decrease in
nitrogen content in certain plant species (Cotrufo etal., 1998). A major impact may
observe in developing nations wherein dietary nutrition is hitherto substandard. The
assortment of quality characteristics, maintenance of yield, and increase in viru-
lence of pests and diseases become more exigent underneath anticipated climate
change scenarios (Garrett et al., 2006; Gregory et al., 2009; Batley & Edwards,
2016). These past research studies may assist to determine agricultural primacy in
international disaster risk reduction and adaptation efforts.
Application ofAdvanced Omics Approaches
The current crop yield pattern is inadequate to fulll global nutritional requirements
by 2050. Superior and more stable crop production must be attained against a milieu
of climatic stress that restricts yields, caused by alterations in pests and pathogens,
S. Gupta et al.

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extreme precipitation, and heat waves. Plant sciences may address post-Green
Revolution agricultural challenges and search for emergent stratagems to enhance
sustainable crop production and resilience in uctuating climates (Bailey-Serres
etal., 2019; Barman etal., 2019). Aggrandized crop improvement affects the natu-
rally evolved characteristics and transformative engineering determined by mecha-
nistic understanding, to conrm future yields. Notwithstanding engineered
characters benecial to farmers and end-users inclusive of virus-resistant papaya
(Fitch etal., 1992), drought-tolerant corn (Castiglioni etal., 2008), rice (Potrykus,
2015) and pro-vitamin A fortied bananas (Paul et al., 2017), non-browning
apples(Murata etal., 2001) and low-acrylamide potatoes (Rommens etal., 2008),
the recognition of genetically modied characteristics is ambivalent in some nations,
and the cultivation of such crops is majorly prohibited in the European Union
(Bailey-Serres et al., 2019). The introduction of high-yielding crop varieties in
regions with advanced agricultural practices has resulted in the loss of genetic varia-
tion which provides exibility to suboptimal environments. Cultivation of crops
with a greater number of resistance genes and/or planting a diversity of varieties is
vital (Tabashnik etal., 2013). Evolutionary, genomic, and mechanistic ndings indi-
cate the requirement of comparatively fewer genetic components to confer nitrogen-
xation abilities. For instance, transference of nitrogenase to plants, the prerequisite
constraint of genetic components was accomplished by a combination of distin-
guishing bacterial genetic units to build a minimal set of three genes compulsory for
nitrogen xation (Yang etal., 2018). Furthermore, specic components of nitroge-
nase can be rmly expressed in yeast and plants (Buren & Rubio, 2018). Legumes
and arbuscular mycorrhizal fungi in association with the task of nitrogen xation in
legumes wherein many components are involved (Oldroyd, 2013) signies that
cereals have some essential fundamental units and the ability to rationalize engi-
neering efforts to transfer the nitrogen-xing association. This signicant engineer-
ing challenge will require accurate transcriptional and posttranslational regulation
of multiple heterologous genes in cereals (Julia etal., 2019).
Genomics deals with the study of genes and genomes and focuses on the struc-
ture, function, evolution, mapping, epigenomic, mutagenomic, and genome-editing
aspects (Muthamilarasan etal., 2019; Gaballah etal., 2021). It has a crucial role and
may explicate genetic variation, consequently improving crop breeding efcacy and
genetic improvement of crop species (Rehman etal., 2021). Genomics can be clas-
sied into structural genomics, mutagenomics, epigenomics, and pangenomics as
described below.
(a) Structural Genomics
Structural genomics includes sequence polymorphism and chromosomal organi-
zation and facilitates plant biologists to create physical and genetic maps to recog-
nize characteristics of concern (Fiaz etal., 2019a, b). Molecular markers are relied
extensively upon in structural genomics for concerned gene tagging and mapping
and its ensuing utilization in crop breeding programs. The marker techniques are
classied into different classes: (a) non-PCR-based techniques which involve
restriction fragment length polymorphisms (RFLP), enabling detection of DNA
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22
polymorphism through hybridizing labeled DNA probe to a Southern blot of DNA
digested by restriction enzymes and resulting in differential DNA fragment prole
(Agarwal etal., 2008) (b) PCR-based techniques for markers such as random ampli-
ed polymorphic DNA (RAPD), amplied fragment length polymorphisms (AFLP),
and single nucleotide polymorphisms (SNPs) (Vos etal., 1995; Li et al., 2021).
Genome-wide association studies have also identied the (drought resistance)
DR-related loci in rice crops (Guo etal., 2018). Moreover, frequent SNPs associated
with drought-responsive TFs have been recognized using GWAS of maize crops
(Shikha etal., 2017). Moreover, structural variants signicantly contribute to the
genetic control of agronomically essential traits in crops. The association of SVs
with agronomical characteristics has been recorded in GWAS of Bacillus napus
(Gabur etal., 2018), maize (Lu etal., 2015), and soybean (Zhou etal., 2015).
(b) Mutagenomics
Genetic modications in mutant characteristics are categorized under
mutagenomics. Erstwhile genome sequencing approaches, the identication of can-
didate genes involved strenuous techniques comprising suppression subtractive
hybridization (SSH), expressed sequence tag (EST), and cDNA-amplied fragment
length polymorphism (AFLP)-sequencing. Introduction of NGS eventually, has
eased the tiresome approaches (Muthamilarasan et al., 2019). Reverse genetic
approaches have enabled the investigation of gene function by silencing and inter-
rupting the candidate genes. Among the reverse genetic techniques, RNA
Interference (RNAi) is specically used to screen/induce mutations in crops.
Further, the approaches have been used in mutation screening in wheat, rice, maize,
barley, tomato, sunower, cotton, chickpea (Cicer arietinum L.), pea (Pisum sati-
vum L.), and soybean crops (Dwivedi etal., 2008; Gupta etal., 2008; Tomlekova
2010). Mutagenomics is reportedly benecial for improving crop growth, yield, and
stress resistance.
(c) Epigenomics
Implementation of epigenomics may also prove crucial in crop improvement
against abiotic/biotic stress. The unication of epigenetics and genomics known as
epigenomics has gained recognition as an emerging omics technique to comprehend
genetic regulation and its inuence on cellular growth and stress responses (Callinan
& Feinberg, 2006). Epigenetics is described as heritable changes apart from DNA
sequence as a result of DNA methylation and posttranslational modication (PTM)
of histones (Strahl & Allis, 2000; Novik etal., 2002). Lately, an epigenome study
ascertained MANTLED locus is accountable for the mantled phenotype (hypo-
methylation) in the oil palm (Elaeis guineensis) (Ong-Abdullah et al., 2015).
Whole-genome bisulte sequencing (WGBS) identied ncRNAs in cotton crops
under drought stress (Lu etal., 2017).
(d) Pangenomics
Mutagenomics and pangenomics are emerging omics approaches dedicated to
crop sciences (Golicz et al., 2016; Goh, 2018; Muthamilarasan et al., 2019).
S. Gupta et al.

23
Pangenomics is the summation of a core genome, common to all entities, plus a
dispensable genome partly shared or discrete (Tettelin etal., 2005). Various pange-
nomic studies on different crops including rice (Schatz etal., 2014; Wang et al.,
2018; Zhao etal., 2018), soybean (Li etal., 2014), wheat (Montenegro etal., 2017),
and Brassica napus (Hurgobin etal., 2018) have been done. The dispensable genes
of a pangenome are decided by structural variation (Xu etal., 2012; Mace et al.,
2013) and are enriched with genes associated with disease resistance in crops such
as maize (Zuo etal., 2015) and rice (Fukuoka etal., 2009) and abiotic stress in bar-
ley (Francia etal., 2016) and sorghum (Magalhaes etal., 2007). The technique sug-
gests the importance of dispensable genes in the maintenance of crop quality and
diversity.
Transcriptomics
Transcriptomics involves the transcriptome, which refers to the complete set of
RNA transcripts produced by the genome of an organism in a cell or tissue (Raza
etal., 2021). Transcriptome analysis is dynamic and has become a promising tech-
nique for analyzing any stimulated gene expression within a specic time period
(El-Metwally etal., 2014). Initially, traditional analyses like cDNAs-AFLP, differ-
ential display-PCR (DD-PCR), and SSH were used to analyze transcriptome
dynamics, but these techniques provide low resolution (Nataraja etal., 2017).
Since then, the introduction of powerful technologies has made it possible to
analyze RNA expression proles using microarrays and digital gene expression pro-
les (Duque etal., 2013). Microarray analysis reveals the differential expression of
soybean and barley genes at the developmental and reproductive stages under
drought stress (Le etal., 2012). Similarly, the Affymetrix gene chip array was used
to identify the differential expression of genes in soybeans under dehydration stress
(Khan etal., 2017). The novel TFs, Cys-2/His-2-type zinc nger (C2H2-ZF) TF and
drought and salt tolerance (DST) were found to control stomatal aperture in response
to salt and drought stress in rice crops (Huang etal., 2009). Another study proved
the function of WRKY TFs in response to wheat abiotic stress (Okay etal., 2014).
Proteomics
Proteomics is a technique involving the analysis of total expressed proteins in
organisms. It is divided into four different parts, including sequence, structure,
function, and phosphoproteomics (Aizat & Hassan, 2018).
(a) Structural proteomics deals with the structure of proteins to understand their
assumed functions. Structural proteomics can be analyzed by a variety of meth-
ods, such as computer-based modeling and experimental methods, including
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nuclear magnetic resonance (NMR), crystallization, electron microscopy, and
X-ray diffraction of protein crystals (Woolfson, 2018).
(b) Functional proteomics determines the functions of proteins. These functions
are examined by various methods, such as yeast one or two hybrids and protein
microarray analysis (Lueong etal., 2014).
(c) Phosphoproteomics aims to analyze protein phosphorylation by quantitatively
or qualitatively detecting phosphoproteins and their phosphorylated amino acid
residues (Mosa etal., 2017). In addition, proteomics and phosphoproteomics
have been combined to study different functions of crops [e.g., wheat and
grapevine (Vitis vinifera L.)] in response to phytoplasma and fungal pathogen
(Septoria tritici) (Yang etal., 2013). Both drought-resistant and phytoplasma-
resistant wheat varieties showed resistance (Zhang et al., 2014). Therefore,
phosphorylated proteomics helps to identify crop varieties that are resistant
and/or susceptible to various stresses.
Metabolomics
Metabolomics is dened as the comprehensive study of metabolites involved in dif-
ferent cellular events in biological systems. However, the metabolome refers to the
complete set of metabolites synthesized through metabolic pathways in the plant
system (Baharum & Azizan, 2018). Next-generation sequencing technology has
become a promising tool to understand the regulation of gene expression and the
molecular basis of cellular responses that occur in crops to deal with biotic and
abiotic stresses (Abdelrahman etal., 2018). Metabolomics is particularly important
in plant systems, because plants produce more metabolites than animals or microor-
ganisms. The secondary metabolites produced by plants help cope with environ-
mental pressures. Therefore, environmental metabolomics is a promising eld in
stress physiology during the response of plants to many abiotic stresses and changes
in their metabolites (Brunetti et al., 2013; Viant & Sommer, 2013). In addition,
many metabolite analyses related to drought, cold, and heat stress have been per-
formed in wheat, corn, tomato, and soybean crops (Witt etal., 2012; Sun et al.,
2016; Le etal., 2017; Paupiere etal., 2017).
Ionomics
Ionomics deals with the ion group, and the ion group refers to the composition of
total mineral nutrients and trace elements, representing the cellular inorganic com-
ponents of the plant system (Salt et al., 2008; Satismruti et al., 2013). Ionomics
includes the quantitative measurement of the elemental composition of organisms
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25
and the determination of changes in mineral composition caused by various physi-
ological stimuli, genetic modications, or developmental conditions. Elemental
analysis was also performed on tomato varieties to observe the concentration of
trace and macronutrients under water stress (Sanchez-Rodríguez et al., 2010).
Similarly, ionome analysis has been performed to analyze the nutritional balance of
certain fruit species, including kiwi, orange, mango, apple, and blueberry (Parent
etal., 2013). Therefore, these ionomics studies have shown the important role of
crop improvement and response to various nonbiological and biological stimuli.
Phenomics
Phenomic is dened as the characterization of phenotype by obtaining high-
dimensional phenotype data within the scope of the organism (Houle etal., 2010).
However, phenotype refers to the phenotype as a whole, and plant phenotype can be
determined by the interaction of genome, environment, and management (Gjuvsland
etal., 2013; Großkinsky etal., 2018), and hence phenomenon is also referred to as
genotype–phenotype–envirotype interactions (Zhao etal., 2019). Current research
work also proposes a CropSight system based on the Internet of Things (IoT), which
is used to expand and determine crop phenotypes and genotype–environment inter-
actions. The system can perform high-quality crop phenotyping analysis and moni-
tor the dynamics of microclimate conditions and has been applied to eld wheat
crop trials (Reynolds etal., 2019). In general, by combining phenomics with other
omics tools, phenomics plays an important role in the development of crop- breeding
strategies.
Emerging Molecular Techniques
Several developing molecular techniques are being listed here, and their applica-
tions with targeted crops are listed in Table2.1.
Zinc Finger Nuclease (ZFN)
Generate a single mutation or short indel or introduce a new gene into a predeter-
mined target site in the genome. Here we report a broadly applicable, versatile solu-
tion to this problem: the use of designed ZFNs that induce a double-stranded break
at their target locus (Rebar etal., 2002).
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Table 2.1 Emerging molecular techniques and its applications
Molecular techniques Application References
Zinc nger nuclease
(ZFN)
Modify endogenous loci in plants of the crop
species Zea mays
Shukla etal.
(2009)
Oligonucleotide-
directed mutagenesis
BFP transgene in Arabidopsis thaliana protoplasts
resulted in up to 0.05% precisely edited GFP loci
Noel etal.
(2015)
RNA-dependent DNA
methylation
It is best characterized in angiosperms, particularly
with Arabidopsis thaliana
Robert and
Colette (2020)
Reverse breeding Cucumber, onion, broccoli, cauliower, sugar beet,
maize, pea, sorghum, watermelon, rice, tomato, and
eggplant
Wijnker etal.
(2012)
Agroinltration Increase agroinltration-based transient gene
expression in Nicotiana benthamiana by improving
all levels of transgenesis
Norkunas etal.
(2018)
Synthetic genomics Design and construction of a synthetic
Saccharomyces cerevisiae genome, the Yeast 2.0
project
Sliva etal.
(2015)
Oligonucleotide-Directed Mutagenesis (ODM)
Targeted mutations of one or several nucleotides are induced to occur in Cis-genesis
and intra-genesis: genetically modied organisms (GMOs) are produced by insert-
ing genetic material derived from the species itself or cross-compatible species and
are contiguous and unchanged (cis-genesis) or the inserted DNA may be a new
combination of DNA fragments, but it must still come from the species itself or
from a cross-compatible species (Zhu etal., 2000).
RNA-Dependent DNA Methylation (RdDM)
Still improving, the modied gene expression is epigenetic, and the new phenotype
has only been inherited for a few generations.
Reverse Breeding
The combination of recombinant DNA technology and cell biology procedures is
used to quickly generate suitable homozygous parental lines without transgenes to
reconstruct excellent heterozygous genotypes.
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27
Agro-Inltration
The agro-inltration is mainly used in research environments, such as studying the
interaction between plants and pathogens in living tissues, selecting parental lines,
or evaluating the efcacy of transgenes. Liquid suspensions of Agrobacterium sp.
contain the required genes for inltrating plant tissues, mainly leaves so that the
genes can be expressed locally and instantaneously at high levels (Norkunas
etal., 2018).
Synthetic Genomics
Large functional DNA molecules synthesized without any natural templates are
used to construct the smallest viable genome, which can be used as a platform for
the biochemical production of chemicals such as biofuels and drugs pharmaceuti-
cals (Chikelu etal., 2012).
Widely Studied Crop
Rice is one of the most important food crops in the world, and it is also a model
system to study crop domestication (Islam etal., 2022). Though there are tons of
literature discussing rice origin and domestication, the origin and history of rice
domestication remain controversial. With the achievements of research on rice
domestication, their applications in modern rice breeding are impressive. Genome-
editing technology, which can efciently modify target genomes predictably and
precisely, is no doubt a revolutionary tool to perform molecular domestication to
obtain desirable traits in the laboratory (Wang etal., 2019; Hua etal., 2019). Wild
tomato has large genetic diversity and has been extensively studied to characterize
certain traits favorable for breeding (Rick & Chetelat, 1995; Larry & Joanne, 2007).
Potato cultivar varieties are autotetraploid and vegetatively propagated.
Consequently, breeding efforts for tuber yield and quality improvement are very
limited. Most potato germplasms bearing alleles controlling agronomically impor-
tant traits are diploids (Spooner etal., 2014) (Table2.2). The reinvention of inbred
diploid varieties has been proposed to overcome this limitation and accelerate
breeding (Jansky etal., 2016).
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28
Table 2.2 Widely studied crops by molecular tools for better outcome
Crops Molecular technique Outcome References
Rice (Oryza sativa) Genome-editing technology, by
editing qSH1 gene, broke down
seed dormancy by knockout
OsVP1, and developed superior
alleles of yield genes by editing
Gn1a and DEP1 genes
Reduced seed
shattering
Sheng etal.
(2020), Jung
etal. (2019) and
Huang etal.
(2018)
Tomato (Solanum
pimpinellifolium)
Edited six loci in wild tomato
by genome editing
Generated highly
productive progeny
that could serve to
breed improved
cultivars
Zsögön etal.
(2018)
Potato (Solanum
tuberosum)
Edit S-RNase genes Achieve self-
compatible diploid
potato varieties
Enciso-
Rodriguez etal.
(2019) and Ye
etal. (2018)
Maize (Zea mays) The endogenous maize gene
ZmIPK1 was disrupted by
insertion of PAT gene cassettes
Resulted in herbicide
tolerance and
alteration of the
inositol phosphate
prole of developing
maize seeds
Schreiber etal.
(2018)
Conclusion andFuture Prospects
There is a great challenge to deal with food security and climate change which may
lead to food scarcity, a rise in inequality of wealth, and well-being across the world.
Advances in multilevel genomics approaches have the potential to inuence the
production of stress-tolerant crops, improving world food security in the climate
change scenario. Multiomics analysis could play an integral role in the identica-
tion of genetic processes, growth, and stress tolerance in numerous crops.
Furthermore, transcriptomics and proteomics have proved to be potential tools to
explicate biochemical processes and abiotic stress tolerance in some model crops. A
combination of above-highlighted omics approaches might be benecial for identi-
fying potential candidate genes. With advances in molecular technologies, the amal-
gamation of such omics approaches has been promising in the agriculture eld.
Thus, it can be concluded that multiomic approaches with systematic biology by
phenotype to genotype and by genotype to phenotype model can be helpful in crop
improvement. It may improve the quality of agronomic traits for crop cultivars
under environmental abiotic or biotic stress conditions.
S. Gupta et al.

29
References
Ali, R., Razzaq, A., Mehmood, S.S., Zou, X., Zhang, X., & YanLv, J.X. (2019a). Impact of
climate change on crops adaptation and strategies to tackle its outcome: A review. Planning
Theory, 8(2). https://doi.org/10.3390/plants8020034
Ali, U., Zhong, M., Shar, T., Fiaz, S., Xie, L., Jiao, G., Ahmad, S., Sheng, Z., Tang, S., Wei, X.,
& Hu, P. (2019b). The inuence of Ph on cadmium accumulation in seedlings of rice (Oryza
sativa L.). Journal of Plant Growth Regulation, 1–11.
Aizat, W.M., & Hassan, M. (2018). Proteomics in systems biology. In W.Aizat, H. H. Goh, &
S.Baharum (Eds.), Omics applications for systems biology. Advances in experimental medi-
cine and biology (pp.31–49). Springer. https://doi.org/10.1007/978- 3- 319- 98758- 3_3
Ashraf, M. A., Akbar, A., Askari, S.H., Iqbal, M., Rasheed, R., & Hussain, I. (2018). Recent
advances in abiotic stress tolerance of plants through chemical priming: An overview. In
Advances in seed priming (pp.51–79). Springer
Agarwal, M., Shrivastava, N., & Padh, H. (2008). Advances in molecular marker techniques and
their applications in plant sciences. Plant Cell Reports, 27(4), 617–631. https://doi.org/10.1007/
s00299- 008- 0507- z. Epub 2008 Feb 2. PMID: 18246355.
Abdelrahman, M., El-Sayed, M.A., Hashem, A., Abd-Allah, E.F., Alqarawi, A.A., Burritt, D.J.,
et al. (2018). Metabolomics and transcriptomics in legumes under phosphate deciency in
relation to nitrogen xation by root nodules. Frontiers in Plant Science, 9, 922. https://doi.
org/10.3389/fpls.2018.00922
Brunetti, C., George, R. M., Tattini, M., Field, K., & Davey, M. P. (2013). Metabolomics in
plant environmental physiology. Journal of Experimental Botany, 64, 4011–4020. https://doi.
org/10.1093/jxb/ert244
Bailey-Serres, J., Parker, J.E., Ainsworth, E.A., Oldroyd, G., & Schroeder, J.I. (2019). Genetic
strategies for improving crop yields. Nature, 575(7781), 109–118. https://doi.org/10.1038/
s41586- 019- 1679- 0
Barman, H.N., Sheng, Z., Fiaz, S., Zhong, M., Wu, Y., Cai, Y., Wang, W., Jiao, G., Tang, S., Wei,
X., & Peisong, H. (2019). Generation of a new thermo-sensitive genic male sterile rice line
by targeted mutagenesis of TMS5 gene through CRISPR/Cas9 system. BMC Plant Biology,
19, 109.
Batley, J., & Edwards, D. (2016). The application of genomics and bioinformatics toaccelerate
crop improvement in a changing climate. Current Opinion in Plant Biology, 30, 78–81.
Benevenuto, R.F., Agapito-Tenfen, S.Z., Vilperte, V., Wikmark, O.-G., Van Rensburg, P.J., &
Nodari, R.O. (2017). Molecular responses of genetically modied maize to abiotic stresses
as determined through proteomic and metabolomic analyses. PLoS One, 2017(12), e0173069.
Buren, S., & Rubio, L.M. (2018). State of the art in eukaryotic nitrogenase engineering. FEMS
Microbiology Letters, 365, fnx274.
Baharum, S. N., & Azizan, K. A. (2018). Metabolomics in systems biology.
Advances in Experimental Medicine and Biology, 1102, 51–68. https://doi.
org/10.1007/978- 3- 319- 98758- 3_4
Callinan, P.A., & Feinberg, A.P. (2006). The emerging science of epigenomics. Human Molecular
Genetics, 15(Suppl_1), R95–R101. https://doi.org/10.1093/hmg/ddl095
Castiglioni, P., Warner, D., Bensen, R.J., Anstrom, D.C., Harrison, J., Stoecker, M., Abad, M.,
Kumar, G., Salvador, S., D'Ordine, R., Navarro, S., Back, S., Fernandes, M., Targolli, J.,
Dasgupta, S., Bonin, C., Luethy, M.H., & Heard, J. E. (2008). Bacterial RNA chaperones
confer abiotic stress tolerance in plants and improved grain yield in maize under water-limited
conditions. Plant Physiology, 147(2), 446–455. https://doi.org/10.1104/pp.108.118828
Chikelu, Guimaraes, E.P., Ghosh, K., & Mba. (2012). Re-orienting crop improvement for the
changing climatic conditions of the 21st century. Agriculture & Food Security, 1, 7. http://
www.agricultureandfoodsecurity.com/content/1/1/7
2 Multiomics Approach forCrop Improvement Under Climate Change

30
Chuang, Z., Liu, B., Piao, S., Wang, X., Lobell, D.B., Huang, Y., Huang, M., Yao, Y., Bassu, S.,
Ciais, P., Durand, J.-L., Elliott, J., Ewert, F., Janssens, I.A., Li, T., Lin, E., Liu, Q., Martre, P.,
Müller, C., Peng, S., Peñuelas, J., Ruane, A.C., Wallach, D., Wang, T., Wu, D., Liu, Z., Zhu, Y.,
Zhu, Z., & Asseng, S. (2017). Temperature increase reduces global yields of major crops in four
independent estimates. PNAS, 114(35), 9326–9331. https://doi.org/10.1073/pnas.1701762114
Corey, L., Rowhani, P., & Ramankutty, N. (2016). Inuence of extreme weather disasters on global
crop production. Nature, 529, 84–87.
Cotrufo, M.F., Ineson, P., & Scott, A. (1998). Elevated CO2 reduces thenitrogen concentration of
plant tissues. Global Change Biology, 4, 43–54.
Duque, A.S., Almeida, A.M., Bernardes, da Silva, A., Marques, da Silva, J., Farinha, A.P., Santos,
D., etal. (2013). Chapter 3: Abiotic stress responses in plants: Unraveling the complexity of
genes and networks to survive. In K.Vahdati & C.Leslie (Eds.), Abiotic stress: Plant responses
and applications in agriculture (pp.49–102). INTECH Open. https://doi.org/10.5772/45842
Dwivedi, S., Perotti, E., & Ortiz, R. (2008). Towards molecular breeding of reproduc-
tive traits in cereal crops. Plant Biotechnology Journal, 6, 529–559. https://doi.
org/10.1111/j.1467- 7652.2008.00343.x
Duke, S. O. (2015). Perspectives on transgenic, herbicide-resistant crops in the United States
almost 20 years after introduction. Pest Management Science, 71, 652–657.
Enciso-Rodriguez, F., Manrique-Carpintero, N., Nadakuduti, S.S., Buell, C. R., Zarka, D., &
Douches, D. (2019). Overcoming self-incompatibility in diploid potato using CRISPR-Cas9.
Frontiers in Plant Science, 10, 376.
El-Metwally, S., Ouda, O.M., & Helmy, M. (2014). Next generation sequencing technologies and
challenges in sequence assembly (1st ed.). Springer. https://doi.org/10.1007/978- 1- 4939- 0715- 1
Fiaz, S., Ahmad, S., Riaz, A., Noor, M. A., Wang, X., Younas, A., Riaz, A., Riaz, A., & Ali,
F. (2019a). Applications of the CRISPR/Cas9 system for rice grain quality improvement:
Perspectives and opportunities. International Journal of Molecular Sciences, 20(4), 888.
Fiaz, S., Lv, S., Barman, H.N., Sahr, T., Jiao, G., Wei, X., Sheng, Z., Tang, S., & Hu, P. (2019b).
Analysis of genomic regions governing cooking and eating quality traits using a Recombinant
Inbred population in Rice (Oryza sativa L.). International Journal of Agriculture and Biology,
22(4), 611–619.
Fitch, M.M. M., Manshardt, R.M., Gonsalves, D., Slightom, J.L., & Sanford, J.C. (1992). Virus
resistant papaya plants derived from tissues bombarded with the coat protein gene of papaya
rings pot virus. Bio/Technology, 10, 1466–1472.
Francia, E., Morcia, C., Pasquariello, M., Mazzamurro, V., Milc, J.A., Rizza, F., & etal. (2016).
Copy number variation at the HvCBF4-HvCBF2 genomic segment is a major component
of frost resistance in barley. Plant Molecular Biology, 92, 161–175. https://doi.org/10.1007/
s11103- 016- 0505- 4
Fukuoka, S., Saka, N., Koga, H., Ono, K., Shimizu, T., Ebana, K., etal. (2009). Loss of function of
a proline-containing protein confers durable disease resistance in rice. Science, 325, 998–1001.
https://doi.org/10.1126/science.1175550
Gaballah, M.M., Fiaz, S., Wang, X., Younas, A., Khan, S.A., Wattoo, F.M., & Shaq, M.R. (2021).
Identication of genetic diversity among some promising lines of rice under drought stress
using SSR markers. Journal of Taibah University for Science, 15(1), 468–478.
Gabur, I., Chawla, H.S., Liu, X., Kumar, V., Faure, S., von Tiedemann, S., etal. (2018). Finding
invisible quantitative trait loci with missing data. Plant Biotechnology Journal, 16, 2102–2112.
https://doi.org/10.1111/pbi.12942
Garrett, K.A., Dendy, S.P., Frank, E.E., Rouse, M.N., & Travers, S.E. (2006). Climate change
effects on plant disease: Genomes to ecosystems. Annual Review of Phytopathology, 44,
489–509.
Gregory, P.J., Johnson, S.N., & Newton, A.C. (2009). Ingram JSI: Integrating pests and pathogens
into the climate change/food security debate. Journal of Experimental Botany, 60, 2827–2838.
Guo, Z., Yang, W., Chang, Y., Ma, X., Tu, H., Xiong, F., etal. (2018). Genome- wide associa-
tion studies of image traits reveal genetic architecture of drought resistance in rice. Molecular
Plant, 11, 789–805. https://doi.org/10.1016/j.molp.2018.03.018
S. Gupta et al.

31
Gupta, P. K., Mir, R. R., Mohan, A., & Kumar, J. (2008). Wheat genomics: Present status
and future prospects. International Journal of Plant Genomics, 36, 896451. https://doi.
org/10.1155/2008/896451
Goh, H. H. (2018). “Integrative multi-omics through bioinformatics,” in Omics Applications for
Systems Biology. Advances in Experimental Medicine and Biology, eds W. Aizat, H. H. Goh,
and S. Baharum (Cham: Springer), 69–80. https://doi.org/10.1007/978-3-319-98758-3_5
Golicz, A.A., Batley, J., & Edwards, D. (2016). Towards plant pangenomics. Plant Biotechnology
Journal, 14, 1099–1105. https://doi.org/10.1111/pbi.12499
Großkinsky, D.K., Syaifullah, S.J., & Roitsch, T. (2018). Integration of multi- omics techniques
and physiological phenotyping within a holistic phenomics approachtostudysenescencein-
modelandcropplants. Journal of Experimental Botany, 69, 825–844. https://doi.org/10.1093/
jxb/erx333
Gjuvsland, A.B., Vik, J.O., Beard, D.A., Hunter, P.J., & Omholt, S.W. (2013). Bridging the
genotype- phenotype gap: What does it take? The Journal of Physiology, 591, 2055–2066.
https://doi.org/10.1113/jphysiol.2012.248864
Hausfather, Z., & Peters, G.P. (2020). Emissions–the ‘business as usual’ story is misleading.
Nature, 577, 618–620.
Hua, K., Zhang, J., Botella, J.R., Ma, C., Kong, F., Liu, B., & Zhu, J.K. (2019). Perspectives on the
application of genome-editing technologies in crop breeding. Molecular Plant, 12, 1047–1059.
Huang, L., Zhang, R., Huang, G., Li, Y., Melaku, G., Zhang, S., Chen, H., Zhao, Y., Zhang, J., &
Zhang, Y. (2018). Developing superior alleles of yield genes in rice by articial mutagenesis
using the CRISPR/Cas9 system. Crop Journal, 6, 475–481.
Hurgobin, B., Golicz, A.A., Bayer, P. E., Chan, C. K. K., Tirnaz, S., Dolatabadian, A., et al.
(2018). Parkin homoeologous exchange is a major cause of gene presence/absence variation
in the amphidiploid Brassica napus. Plant Biotechnology Journal, 16, 1265–1274. https://doi.
org/10.1111/pbi.12867
Huang, X.-Y., Chao, D.-Y., Gao, J.-P., Zhu, M.-Z., Shi, M., & Lin, H.-X. (2009). A previously
unknown zinc nger protein, DST, regulates drought and salt tolerance in rice via stomatal
aperture control. Genes & Development, 23, 1805–1817. https://doi.org/10.1101/gad.1812409
Hussain, S., Cao, X., Zhong, C., Zhu, L., Khaskheli, M.A., Fiaz, S., Zhamg, J., & Jin, Q. (2018).
Sodium chloride stress during early growth stages altered physiological and growth character-
istics of rice. Chilean Journal of Agricultural Research, 24(11), 111–118.
Houle, D., Govindaraju, D. R., & Omholt, S. (2010). Phenomics: The next challenge. Nature
Reviews. Genetics, 11, 855–866.
Intergovernmental Panel on Climate Change. Climate Change (2014). Synthesis Report.
Contribution of Working Groups I, II and III to the Fifth Assessment Report of the
Intergovernmental Panel on Climate Change.
Islam, M.R., Naveed, S.A., Zhang, Y., Li, Z., Zhao, X., Fiaz, S., Zhang, F., Wu, Z., Hu, Z., Fu, B.,
Shi, Y., Shah, S.M., Xu, J., & Wang, W. (2022). Identication of candidate genes for salinity
and anaerobic tolerance at the germination stage in rice by genome-wide association analyses.
Frontiers in Genetics, 13, 822516.
Jansky, S.H., Charkowski, A.O., Douches, D.S., Gusmini, G., Richael, C., Bethke, P.C., Spooner,
D.M., Novy, R.G., De Jong, H., & De Jong, W.S. (2016). Reinventing potato as a diploid
inbred line–based crop. Crop Science, 56, 1412.
Jeff Tollefson. (2020). How hot will earth get by 2100? Climate researchers are studying a fresh set
of scenarios to model the future of the planet. Nature, 580, 443–445. https://doi.org/10.1038/
d41586- 020- 01125- x
Julia, B.-S., Parker, J.E., Ainsworth, E.A., Oldroyd, G.E. D., & Schroeder, J.I. (2019). Genetic
strategies for improving crop yields. Nature, 575(7781), 109–118. https://doi.org/10.1038/
s41586- 019- 1679- 0
Jung, Y.J., Lee, H. J., Bae, S., Kim, J. H., Kim, D.H., Kim, H.K., Nam, K. H., Nogoy, F.M.,
Cho, Y.G., & Kang, K.K. (2019). Acquisition of seed dormancy breaking in rice (Oryza sativa
L.) via CRISPR/Cas9-targeted mutagenesis of OsVP1 gene. Plant Biotechnology Reports, 13,
511–520.
2 Multiomics Approach forCrop Improvement Under Climate Change

32
Khan, F., Chai, H.H., Ajmera, I., Hodgman, C., Mayes, S., & Lu, C. (2017). A transcriptomic
comparison of two bambara groundnut landraces under dehydration stress. Genes, 8, 1–19.
https://doi.org/10.3390/genes8040121
Larry, R., & Joanne, L. (2007). Genetic resources of tomato. In M.K. Razdan & A.K. Mattoo
(Eds.), Genetic improvement of solanaceous crops. Science Publishers.
Lawlor, D.W., & Mitchell, R.A. C. (1991). The effects of increasing CO2 on cropphotosynthesis
and productivity—a review of eld studies. Plant, Cell & Environment, 14, 807–818.
Li, W., Huai, X., Li, P., Raza, A., Mubarik, M.S., Habib, M., Faiz, S., Zhang, B., Pan, J., & Khan,
R.S. A. (2021). Glutathione peroxidase (GPX) gene family in rapeseed (Brassica napus L.):
Genomics analysis and expression proling against different stresses. Antioxidant, 10(9), 1481.
Li, G.Y., Zhou, J.X., Ma, W.K., Jiang, L.G., Jin, Z.H., Zhang, Y., etal. (2014). De novo assembly
of soybean wild relatives for pan-genome analysis of diversity and agronomic traits. Nature
Biotechnology, 32, 1045–1052. https://doi.org/10.1038/nbt- 2979
Lueong, S.S., Hoheisel, J.D., & Alhamdani, M.S. S. (2014). Protein microarrays as tools for
functional proteomics: Achievements, promises and challenges. Journal of Proteomics &
Bioinformatics, 7, 1–10. https://doi.org/10.4172/jpb.S7- 004
Le, D.T., Nishiyama, R., Watanabe, Y., Tanaka, M., Seki, M., Le, H., etal. (2012). Differential gene
expression in soybean leaf tissues at late developmental stages under drought stress revealed
by genome-wide transcriptome analysis. PLoS One, 7, e49522. https://doi.org/10.1371/journal.
pone.0049522
Le, G.H., Fontaine, J. X., Molinie, R., Pelloux, J., Mesnard, F., Gillet, F., etal. (2017). NMR-
basedmetabolomicstostudythecold- acclimationstrategyoftwo Miscanthus genotypes.
Phytochemical Analysis, 28, 58–67. https://doi.org/10.1002/pca.2649
Lu, F., Romay, M.C., Glaubitz, J.C., Bradbury, P.J., Elshire, R.J., Wang, T., etal. (2015). High-
resolution genetic mapping of maize pan-genome sequence anchors. Nature Communications,
6, 6914. https://doi.org/10.1038/ncomms7914
Lu, X., Wang, X., Chen, X., Shu, N., Wang, J., Wang, D., etal. (2017). Single- base resolution meth-
ylomes of upland cotton (Gossypium hirsutum L.) reveal epigenome modications in response
to drought stress. BMC Genomics, 18, 297. https://doi.org/10.1186/s12864- 017- 3681- y
Mackill, D.J., Ismail, A.M., Singh, U.S., Labios, R.V., & Paris, T.R. (2012). Development and
rapid adoption of submergence-tolerant(Sub1) rice varieties. Advances in Agronomy, 115. An
excellent example of how a single gene can improve climate resiliencein a major crop.
Magalhaes, J.V., Liu, J., Guimaraes, C.T., Lana, U.G. P., Alves, V. M. C., Wang, Y. H., et al.
(2007). A gene in the multidrug and toxic compound extrusion (MATE) family confers alumi-
num tolerance in sorghum. Nature Genetics, 39, 1156–1161. https://doi.org/10.1038/ng2074
Mosa, K.A., Ismail, A., & Helmy, M. (2017). Omics and system biology approaches in plant stress
research. In K.A. Mosa, A.Ismail, & M.Helmy (Eds.), Plant stress tolerance: An integrated
omics approach (pp.21–34). Springer. https://doi.org/10.1007/978- 3- 319- 59379- 1_2
Montenegro, J.D., Golicz, A.A., Bayer, P.E., Hurgobin, B., Lee, H., Chan, C.K. K., etal. (2017).
The pangenome of hexaploid bread wheat. The Plant Journal, 90, 1007–1013. https://doi.
org/10.1111/tpj.13515
Murata, M., Nishimura, M., Murai, N., Haruta, M., Homma, S., & Itoh, Y. (2001). A trans-
genic apple callus showing reduced polyphenol oxidase activity and lower browning poten-
tial. Bioscience, Biotechnology, and Biochemistry, 65(2), 383–388. https://doi.org/10.1271/
bbb.65.383. PMID: 11302173.
Mace, E.S., Tai, S., Gilding, E.K., Li, Y., Prentis, P.J., Bian, L., etal. (2013). Whole- genome
sequencing reveals untapped genetic potential in Africa’s indigenous cereal crop sorghum.
Nature Communications, 4, 2320. https://doi.org/10.1038/ncomms3320
Muthamilarasan, M., Singh, N.K., & Prasad, M. (2019). Multi-omics approaches for strategic
improvement of stress tolerance in underutilized crop species: A climate change perspective.
Advances in Genetics, 103, 1–38. https://doi.org/10.1016/bs.adgen.2019.01.001
Nataraja Karaba N., Madhura Bhat G., Parvathi M. S. (2017) Omics: Modern tools for precise
understanding of drought adaptation in plants, chapter, Plant Omics and Crop Breeding, Ist
edition, Apple academic press, 32 pages, eBook-ISBN9781315365930
S. Gupta et al.

33
Novik, K. L., Nimmrich, I., Genc, B., Maier, S., Piepenbrock, C., Olek, A., et al. (2002).
Epigenomics: Genome-wide study of methylation phenomena. Current Issues in Molecular
Biology, 4, 111–128.
Noel, J.S., Mozoruk, J., Miller, R.B., Warburg, Z.J., Walker, K.A., Beetham, P.R., Schöpke,
C.R., & Gocal, G.F. W. (2015). Oligonucleotide-directed mutagenesis for precision gene edit-
ing. Plant Biotechnology Journal, 14(2), 496–502. https://doi.org/10.1111/pbi.12496
Norkunas, K., Harding, R., Dale, J., & Dugdale, B. (2018). Improving agroinltration-based
transient gene expression in Nicotiana benthamiana. Plant Methods, 14, 71. https://doi.
org/10.1186/s13007- 018- 0343- 2
Ong-Abdullah, M., Ordway, J.M., Jiang, N., Ooi, S.E., Kok, S.Y., Sarpan, N., etal. (2015). Loss
of karma transposon methylation underlies the mantled somaclonal variant of oil palm. Nature,
525, 533–537. https://doi.org/10.1038/nature15365
O'Neill, B.C., Kriegler, E., Ebi, K.L., Kemp-Benedict, E., Riahi, K., Rothman, D.S., van Ruijven,
B.J., & van Vuuren, D.P. (2017). The roads ahead: Narratives for shared socioeconomic path-
ways describing world futures in the 21st century. Global Environmental Change, 42, 169–180.
https://doi.org/10.1016/j.gloenvcha.2015.01.004
Oldroyd. (2013). Signalling systems that promote benecial symbiotic associations in plants.
Nature Reviews. Microbiology, 11, 252–263.
Ottman, M.J., Kimball, B.A., White, J.W., & Wall, G.W. (2012). Wheat growth response to
increased temperature from varied planting dates and supplemental infrared heating. Crop
Ecology Physiological. https://doi.org/10.2134/agronj2011.0212
Okay, S., Derelli, E., & Unver, T. (2014). Transcriptome-wide identication of bread wheat
WRKY transcription factors in response to drought stress. Molecular Genetics and Genomics,
289, 765–781. https://doi.org/10.1007/s00438- 014- 0849- x
Paupiere, M. J., Muller, F., Li, H.J., Rieu, I., Tikunov, Y. M., Visser, R. G. F., etal. (2017).
Untargeted metabolomic analysis of tomato pollen development and heat stress response. Plant
Reproduction, 30, 81–94. https://doi.org/10.1007/s00497- 017- 0301- 6
Paul, J.Y., Khanna, H., Kleidon, J., Hoang, P., Geijskes, J., Daniells, J., Zaplin, E., Rosenberg,
Y., James, A., Mlalazi, B., Deo, P., Arinaitwe, G., Namanya, P., Becker, D., Tindamanyire, J.,
Tushemereirwe, W., Harding, R., & Dale, J. (2017). Golden bananas in the eld: Elevated fruit
pro-vitamin A from the expression of a single banana transgene. Plant Biotechnology Journal,
15(4), 520–532. https://doi.org/10.1111/pbi.12650. Epub 2016 Dec 20. PMID: 27734628;
PMCID: PMC5362681.
Potrykus, I. (2015). From the concept of totipotency to biofortied cereals. Annual Review of Plant
Biology, 66, 1–22.
Parent, S.E., Parent, L.E., Egozcue, J.J., Rozane, D.E., Hernandes, A., Lapointe, L., etal. (2013).
The plant ionome revisited by the nutrient balance concept. Frontiers in Plant Science, 4, 39.
https://doi.org/10.3389/fpls.2013.00039
Rebar, E.J., Huang, Y., Hickey, R., Nath, A.K., Meoli, D., Nath, S., Chen, B., Xu, L., Liang, Y.,
& Jamieson, A.C. (2002). Induction of angiogenesis in a mouse model using engineering tran-
scription factors. Nature Medicine, 8, 1427–1432.; PMID:12415262. https://doi.org/10.1038/
nm1202- 795
Raza, A., Tabassum, J., Kudapa, H., & Varshney, R.K. (2021). Can omics deliver temperature
resilient ready-to-grow crops? Critical Reviews in Biotechnology, 2021, 1–24. https://doi.org/1
0.1080/07388551.2021.1898332
Rehman, S.U., Qanmber, G., Tahir, M.H. N., Irshad, A., Fiaz, S., Ahmad, F., Ali, Z., Sajjad, M.,
Shees, M., Usman, M., & Geng, Z. (2021). Characterization of vascular plant one-zinc nger
in soybean (Glycine max and Glycine soja) and their expression analyses under drought condi-
tion. PLoS One, 16(7), e0253836.
Reynolds, D., Ball, J., Bauer, A., Davey, R., Grifths, S., & Zhou, J. (2019). CropSight: A scal-
able and open-source information management system for distributed plant phenotyping and
IoT-based crop management. Gigascience, 8, 1–35. https://doi.org/10.1093/gigascience/giz009
Rick, C.M., & Chetelat, R.T. (1995). Utilization of related wild species for tomato improvement.
Acta Horticulturae, 412, 21–38.
2 Multiomics Approach forCrop Improvement Under Climate Change

34
Robert, M.E., & Colette, L.P. (2020). RNA-directed DNA methylation. PLoS Genetics, 16(10),
e1009034. https://doi.org/10.1371/journal.pgen.1009034
Rommens, C.M., Yan, H., Swords, K., Richael, C., & Ye, J. (2008). Low-acrylamide French fries
and potato chips. Plant Biotechnology Journal, 6, 843–853.
Rosenzweig, C., Elliott, J., Deryng, D., Ruane, A.C., Müller, C., Arneth, A., Boote, K.J., Folberth,
C., Glotter, M., & Khabarov, N. (2014). Assessing agricultural risks of climate change in the
21st century in a global gridded crop model intercomparison. Proceedings of the National
Academy of Sciences of the United States of America, 111, 3268–3273.
Schatz, M.C., Maron, L.G., Stein, J.C., Wences, A.H., Gurtowski, J., Biggers, E., etal. (2014).
Whole genome de novo assemblies of three divergent strains of rice, Oryza sativa, docu-
ment novel gene space of aus and indica. Genome Biology, 15, 506. https://doi.org/10.1186/
PREACCEPT- 2784872521277375
Shar, T., Sheng, Z., Ali, U., Fiaz, S., Wei, X., Xie, L., Jiao, G., Ali, F., Shao, G., Hu, S., Hu, P.,
& Tang, S. (2019). Mapping quantitative trait loci associated with paste viscosity attributes
in double haploid population of rice (Oryza sativa L.). Journal of Integrative Agriculture,
18(0), 2–14.
Shikha, M., Kanika, A., Rao, A.R., Mallikarjuna, M.G., Gupta, H. S., & Nepolean, T. (2017).
Genomicselectionfordroughttoleranceusinggenome-wideSNPsin maize. Frontiers in Plant
Science, 8, 550. https://doi.org/10.3389/fpls.2017.00550
Salt, D.E., Baxter, I., & Lahner, B. (2008). Ionomics and the study of the plant ionome. Annual Review
of Plant Biology, 59, 709–733. https://doi.org/10.1146/annurev.arplant.59.032607.092942
Satismruti, K., Senthil, N., Vellaikumar, S., Ranjani, R.V., & Raveendran, M. (2013). Plant
Ionomics: A platform for identifying novel gene regulating plant mineral nutrition. American
Journal of Plant Sciences, 4, 1309–1315.
Sanchez-Rodríguez, E., Md, M., Rubio-Wilhelmi, Cervilla, L.M., Blasco, B., Rios, J. J., et al.
(2010). Study of the ionome and uptake uxes in cherry tomato plants under moderate water
stress conditions. Plant and Soil, 335, 339–347. https://doi.org/10.1007/s11104- 010- 0422- 2
Sun, C. X., Gao, X. X., Li, M. Q., Fu, J. Q., & Zhang, Y. L. (2016). Plasticresponses in the
metabolome and functional traits of maize plants to temperature variations. Plant Biology, 18,
249–261. https://doi.org/10.1111/plb.12378
Schreiber, M., Stein, N., & Mascher, M. (2018). Genomic approaches for studying crop evolution.
Genome Biology, 19, 140.
Strahl, B., & Allis, C. (2000). The language of covalent histone modications. Nature, 403, 41–45.
https://doi.org/10.1038/47412
Sheng, X., Sun, Z., Wang, X., Tan, Y., Yu, D., Yuan, G., Yuan, D., & Duan, M. (2020). Improvement
of the rice “easy-to-shatter” trait via CRISPR/Cas9-mediated mutagenesis of the qSH1 gene.
Frontiers in Plant Science, 11, 1–14.
Shukla, V. K., Doyon, Y., Miller, J.C., DeKelver, R.C., Moehle, E.A., Worden, S.E., Mitchell,
J.C., Arnold, N.L., Gopalan, S., Meng, X., Choi, V.M., Rock, J.M., Wu, Y.Y., Katibah, G.E.,
Zhifang, G., McCaskill, D., Simpson, M.A., Blakeslee, B., Greenwalt, S.A., Butler, H. J.,
Hinkley, S.J., Zhang, L., Rebar, E.J., Gregory, P.D., & Urnov, F.D. (2009). Precise genome
modication in the crop species Zea mays using zinc-nger nucleases. Nature 21, 459(7245),
437–441. https://doi.org/10.1038/nature07992. Epub 2009 Apr 29. PMID: 19404259.
Sliva, A., Yang, H., Boeke, J.D., & Mathews, D.J. H. (2015). Freedom and responsibility in syn-
thetic genomics: The synthetic yeast project. Genetics, 200, 1021–1028.
Spooner, D.M., Ghislain, M., Simon, R., Jansky, S.H., & Gavrilenko, T. (2014). Systematics,
diversity, genetics, and evolution of wild and cultivated potatoes. The Botanical Review, 80,
283–383.
Tabashnik, B.E., Brévault, T., & Carrière, Y. (2013). Insect resistance to Bt crops: Lessons from
the rst billion acres. Nature Biotechnology, 31, 510–521.
Tettelin, H., Masignani, V., Cieslewicz, M. J., Donati, C., Medini, D., Ward, N. L., et al. (2005).
Genome analysis of multiple pathogenic isolates of Streptococcus agalactiae: implications
S. Gupta et al.

35
for the microbial ‘pan-genome’. Proc. Natl. Acad. Sci. U.S.A. 102, 13950–13955. https://doi.
org/10.1073/pnas.0506758102
Tomlekova, N.B. (2010). Induced mutagenesis for crop improvement in Bulgaria. Plant Mutation
Reports, 2, 4–27.
Vos, P., Hogers, R., Bleeker, M., Reijans, M., Lee, T., Hornes, M., Frijters, A., Pot, J., Peleman,
J., Kuiper, M., & Zabeau, M. (1995). AFLP:a new technique for DNA ngerprinting. Nucleic
Acids Research, 23, No. 214407-441.
Viant, M.R., & Sommer, U. (2013). Mass spectrometry based environmental metabolomics: A
primer and review. Metabolomics, 9, 144–158. https://doi.org/10.1007/s11306- 012- 0412- x
Woolfson, M. (2018). The development of structural x-ray crystallography. Physica Scripta, 93,
1–32. https://doi.org/10.1088/1402- 4896/aa9c30
Wang, T., Zhang, H., & Zhu, H. (2019). CRISPR technology is revolutionizing the improvement
of tomato and other fruit crops. Hortic Res, 6, 1–13.
Witt, S., Galicia, L., Lisec, J., Cairns, J., Tiessen, A., Araus, J.L., etal. (2012). Metabolic and phe-
notypic responses of greenhouse-grown maize hybrids to experimentally controlled drought
stress. Molecular Plant, 5, 401–417. https://doi.org/10.1093/mp/ssr102
Wang, W.S., Mauleon, R., Hu, Z.Q., Chebotarov, D., Tai, S.S., Wu, Z.C., etal. (2018). Genomic
variation in 3,010 diverse accessions of Asian cultivated rice. Nature, 557, 43–49. https://doi.
org/10.1038/s41586- 018- 0063- 9
Wheeler, T., & Von Braun, J. (2013). Climate change impacts on global food security. Science,
341, 508–513.
Wijnker, E., van Dun, K., de Snoo, C., etal. (2012). Reverse breeding in Arabidopsis thaliana gen-
erates homozygous parental lines from a heterozygous plant. Nature Genetics, 44, 467–470.
https://doi.org/10.1038/ng.2203
Xu, K. N., Xu, X., Fukao, T., Canlas, P., Maghirang-Rodriguez, R., Heuer, S., Ismail, A. M.,
Bailey-Serres, J., Ronald, P.C., & Mackill, D.J. (2006). Sub1A is anethylene-response-factor-
like gene that conferssubmergence tolerance to rice. Nature (London), 442.
Xu, X., Liu, X., Ge, S., Jensen, J.D., Hu, F.Y., Li, X.Y., etal. (2012). Resequencing 50 acces-
sions of cultivated and wild rice yields markers for identifying agronomically important genes.
Nature Biotechnology, 30, 105–111. https://doi.org/10.1038/nbt.2050
Yang, J., Xie, X., Xiang, N., Zhe-Xian, T., Dixon, R., & Wang, Y.-P. (2018). Polyprotein strategy
for stoichiometric assembly of nitrogen xation components for synthetic biology. Proceedings
of the National Academy of Sciences of the United States of America, 115, E8509–E8517.
Yang, F., Melo-Braga, M.N., Larsen, M.R., Jørgensen, H.J. L., & Palmisano, G. (2013). Battle
through signaling between wheat and the fungal pathogen Septoria tritici revealed by pro-
teomics and phosphoproteomics. Molecular & Cellular Proteomics, 12, 2497–2508. https://
doi.org/10.1074/mcp.M113.027532
Ye, M., Peng, Z., Tang, D., Yang, Z., Li, D., Xu, Y., Zhang, C., & Huang, S. (2018). Generation of
self-compatible diploid potato by knockout of S-RNase. Nat Plants, 4, 651–654.
Zhang, M., Lv, D., Ge, P., Bian, Y., Chen, G., & Zhu, G. (2014). Phosphoproteome analysis reveals
new drought response and defense mechanisms of seedling leaves in bread wheat (Triticum
aestivum L.). Journal of Proteomics, 109, 290–308. https://doi.org/10.1016/j.jprot.2014.07.010
Zhu, T., Mettenburg, K., Peterson, D.J., Tagliani, L., & Baszczynski, C.L. (2000). Engineering
herbicide-resistant maize using chimeric RNA/DNA oligonucleotides. Nature Biotechnology,
18, 555–558. https://doi.org/10.1038/75435
Zsögön, A., Cermak, T., Naves, E.R., Notini, M.M., Edel, K.H., Weinl, S., Freschi, L., Voytas,
D.F., Kudla, J., & Peres, L.E. P. (2018). De novo domestication of wild tomato using genome
editing. Nature Biotechnology.
Zhao, C., Zhang, Y., Du, J., Guo, X., Wen, W., Gu, S., etal. (2019). Crop phenomics: Current status
and perspectives. Frontiers in Plant Science, 10, 714. https://doi.org/10.3389/fpls.2019.00714
Zhao, Q., Feng, Q., Lu, H.Y., Li, Y., Wang, A., Tian, Q.L., et al. (2018). Pan-genome analysis
highlights the extent of genomic variation in cultivated and wild rice. Nature Genetics, 50,
278–284. https://doi.org/10.1038/s41588- 018- 0041- z
2 Multiomics Approach forCrop Improvement Under Climate Change

36
Zhou, Z., Jiang, Y., Wang, Z., Gou, Z., Lyu, J., Li, W., etal. (2015). Resequencing 302 wild and
cultivated accessions identies genes related to domestication and improvement in soybean.
Nature Biotechnology, 33, 408–414. https://doi.org/10.1038/nbt.3096
Zuo, W.L., Chao, Q., Zhang, N., Ye, J. R., Tan, G.Q., Li, B.L., et al. (2015). A maize wall-
associated kinase confers quantitative resistance to head smut. Nature Genetics, 47, 151–157.
https://doi.org/10.1038/ng.3170
S. Gupta et al.