Role of omics approaches in vegetable breeding for insect pest resistance

Abstract

Vegetables are usually herbaceous and succulent plants. This succulent nature of vegetable crops makes them susceptible to attack by a wide variety of pests. Traditionally insect resistance has not been as widely investigated as disease resistance due to relatively smaller economic losses caused by insects as compared to pathogens in earlier times. But owing to indiscriminate use of pesticides, population of natural enemies has declined which in turn has increased the frequency of insect epidemics. Outbreak of Tuta absoluta in tomato is a recent example. Intense selection of crops has reduced the genetic variability and has increased their genetic vulnerability to insects. Some pests like Meloidogyne spp., Helicoverpa and aphids are polyphagous and cannot be managed by chemical sprays. In such cases resorting to insect resistance is the best option for a breeder. But development of insect resistant variety takes considerable time and efforts. Recent advancements in omics approaches has accelerated the resistance breeding. Genetic markers permit effective indirect selection for insect resistant plants. They are effective tools for identifying genomic regions controlling pest resistance. Molecular markers permit transfer of precise DNA segments from disease resistant species to susceptible cultivars thus preventing the problem of linkage drag. This review highlights the achievements in recent years in vegetable resistance breeding via various omics-based approaches viz. genomics, transcriptomics, proteomics and metabolomics.

Article Highlights

Insect-pests cause substantial loss of crop and needs to be controlled timely. Development of robust varieties exhibiting strong and stable resistance against insect attack is need of the hour.

Traditional breeding using reductionist approach is resource consuming in terms of time and space.

Omics techniques provide understanding of molecular basis of resistance and provide a holistic solution for insect-pest management. But one needs to have expertise in data analysis for handling and interpreting huge amount of data generated through different omic technologies viz., genomics, transcriptomics, proteomics and metabolomics.

Introduction

Vegetable crops contribute significantly to agricultural economy. They are an indispensable part of human diet. Being rich in mineral nutrients and fibre, vegetables are considered to be protective foods. Vegetables are very sensitive to insect-pest attack. Inadequate plant protection measures and non-availability of resistant varieties causes huge economic losses. Pests like fruit borer are a nuisance in brinjal fields. Being polyphagous in nature, it’s control is often difficult. Yield loss of upto 95% has been observed in brinjal [1] and losses up to 73% and 22–91.6% has been witnessed in tomato and okra. Severe outbreak of Plutella xylostella in cole crops and fruit fly (Bacterocera cucurbitae) in cucurbits can cause complete crop failures. 100% crop failure occurred in cauliflower due to Plutella xylostella in province of Uttar Pradesh in India [2]. Hemipteran pests like aphids, mites, whitefly cause chlorosis of leaves thereby reducing the yield. Ram and co-workers reported a loss of 36.5% by cabbage aphid and mustard sawfly in cole crops [3]. Cabbage butterfly (Peiris rapae), a lepidopteran pest cause extensive damage to cruciferous crops and carrot [4, 5]. Chewing pests like leafhopper cause 40–88% loss in okra. Whitefly is another important pest of okra which serves as vector of most disastrous disease of okra i.e. YVMV. Thus pests owe a substantial loss of crop and need to be controlled timely.

Crop protection measures rely mainly on chemical pesticides. Repeated use of pesticides has triggered resistance in pests and new biotypes of pests resistant to chemical pesticides have evolved. Moreover, pesticides cause environmental pollution and pose serious health hazards to humans. Therefore, there is urgent need to shift from conventional chemical methods to more reliable methods like development of robust resistant varieties. Researchers have been trying to explore host plant resistance since decades. Planned selection for resistant plant types began around first half of the twentieth century when R H Painter grafted European grapevines onto resistant North American rootstocks [6]. Since then, hundreds of insect-resistant cultivars have been developed globally [7]. Inheritance of resistant genes have been studied in various crops. But conventional breeding is slow. Germplasm evaluation, identification of resistant sources and subsequent transfer of resistant genes into desirable crop cultivars takes a lot of time. In many cases resistance genes are found in wild species. Wide hybridization results in transfer of undesirable genes along with resistance genes from wild species into commercial cultivars. This limits the use of wild cultivars in resistance breeding programmes.

Recent advances in omics technologies have made it possible to tag the genes conferring resistance against insects. Different omics approaches are represented in Fig. 1. Map based cloningaids in transfer of specific genomic regions into desired cultivars transgenically or through marker assisted backcross breeding. This not only accelerates the breeding process but also overcomes the problem of linkage drag. Marker assisted selection increase the selection efficiency thereby reducing time and space needed to screen resistant plants.

Fig. 1 [Images not available. See PDF.]

Different omics approaches

Molecular advancements in insect resistance breeding

Genomics

The sucking pests cause severe damage to fruits, flowers and leaves. 94 RILs of two Mesoamerican common bean lines, BAT 881 and G21212 were used to map QTLs for thrips resistance [8]. A major QTL, Tpr6.1 explaining 26.8% of genetic variation for thrips resistance was identified on linkage group b06 via Single interval mapping and Joint interval mapping. Three major QTLs viz., Fthp28, Fthp87 and Fthp129 in cowpea were identified on chromosome 2,4 and 6 at distance of 20.2, 19.2 and 40.3 cM respectively on linkage groups 2, 4 and 6 [9]. These QTLs accounted for 24.5, 12.2 and 6.5% variation for resistance to thrips. Mapping population comprised of 150 F₂ plants derived from cross of SANZI (resistant) and VYA (susceptible). Out of 1063 SNPs markers, a total of 232 SNPs markers showed polymorphism between parents. In pepper, a single major QTL was identified [10] for resistance to thrips; Frankliniella occidentalis in an F₂ population of cross C. annuum CGN16795 × C. chinense CGN17219 on chromosome 6. Later, QTL mapping was used in conjugation with untargeted metabolomic approach to detect metabolite QTLs (mQTLs) [11]. Four mQTLs were found to be co-located in the previously identified QTL region identified on chromosome 6. The QTL identified [10] to a region of 0.4 Mb pursing F₃ population of cross C. annuum CGN16795 × C. chinense CGN17219 were validated and fine mapped by [12]. Larval development was used as resistance parameter and three genes viz., acid phosphatase 1(APS1), an organic cation/carnitine transporter 7 (OCT7) and an uncharacterized locus LOC107874801 were identified as the candidate genes for thrips resistance using RNA-Seq approach. Summary of QTLs identified for insect resistance are presented in Table 1.

Table 1. Summary of QTLs identified for insect resistance

Insect/pest	Crop	QTLs	Markers used	Population used	Reference
Thrips	Common bean	4	RAPD, Microsattelite markers	F5:7	[8]
	Cowpea	3	SNPs	F2	[9]
	Pepper	1	57 SSR, 109 AFLP, 5 SNPs	F2	[10]
Aphids	Melon	6	88 SSR, 98 AFLP, 17 ISSR, 5 phenotypic, 5 RFLP and 3 RAPD	RILs	[13]
	Cucumber	1	SNP	F2	[14]
	Bell pepper	3	SNP	F2	[15]
White fly	Tomato	2	SNP	F2	[16]
	Cabbage	2	SNP	F2	[17]
	Tomato	4	SNP	F2	[19]
Red spider mite	Tomato	2	SSR	F4	[20]

The QTL for whitefly and aphid resistance in melon were mapped [13]. RIL population of cross Vedrantais × PI 161375 was used. Two additive QTLs were found to control resistance for whiteflies. Four additive and two epistatic QTLs harboured genes for aphid resistance. SLAF-Sequencing method in conjugation with bulk segregant analysis (BSA) was used to find candidate genes for aphid resistance in cucumber [14]. Bulks comprising of DNA from 50 plants with extreme phenotypes were prepared from F₂ population comprising of 1000 individuals. 0.31 Mb region was identified on chromosome 5 via SNP index analysis. In situ analysis led to identification of 16 potential genes associated with aphid resistance. Furthermore, gene expression analysis indicated down regulation in expression of 7 genes after aphid infestation whereas 5 genes were up-regulated after aphid infestation. Csa5M642150.1 was identified as the candidate gene for aphid resistance. A QTL on chromosome 2 was mapped for aphid resistance in capsicum [15]. Two major QTLs Rmpas-1 and Rmprp-1 and one minor QTL, Rmprp-2 affecting aphid survival and reproduction were identified via interval mapping on chromosome 2 and 4. They further narrowed down the region 96 kb. Four candidate genes encoding leucine-rich repeat domain (LRR-RLKs) were annotated in the identified locus. Besides, a major QTL, Wf-1 exhibiting resistance against whitefly infestation in a F₂ mapping population derived from cross between cultivated tomato and Solanum galapagense was also mapped [16] mapped. This major QTL located on chromosome 2 explained 54.1% of the variation in survival of adults and 81.5% of variation in occurrence of type IV trichomes. In addition, a minor QTL named Wf-2was identified on chromosome 9. Metabolomic analysis of 10 plants of extreme phenotypes revealed the role of acyl sugars in resistance against whitefly. Different approaches viz., genomics, transcriptomics and metabolomics were combined for mapping genes responsible for resistance against cabbage whitefly [17]. A F₂ population derived from cross of a resistant variety Rivera and susceptible white cabbage variety Christmas Drumhead. Two QTLs were mapped on chromosome 2 and 9 for whitefly survival and reproduction. The role of Wf-1 in F_2:7 RIL population derived for resistance to insects was confirmed [18]. Wf-1 regulated formation of glandular trichome-4. K-Seq technology was employed to map QTL for trichome IV density [19]. Trichomes provide resistance against many pests like whitefly. Two F₂ mapping populations derived by crossing S. pimpinellifolium with S. lycopersicum var. Cerasiformeand S. Lycopersicumvar. lycopersicum. An ultra-dense genetic map was built up with 1,47,326 SNP markers designed at 0.2 cm distance in the QTL region detected on chromosomes 5, 6, 9 and 11. Further, two QTLs were mapped using BSA-Seqapproach [20]. A F₄ population of wild tomato; S. Pimpinellifolium was employed to identify genomic region imparting resistance to red spider mite. Two flanking SSRs confined the QTL (Rtu2.1) to a region of 17 cM on chromosome 2. The region was further refined in advanced generations (RILs) and a more robust QTL named Rtu2.2 was detected in the same region. This QTL explained 30% of phenotypic variation.

Challenges of genomic technologies

Though genomics has proved to be fruitful in unravelling hotspot regions and key gene(s) conditioning insect resistance; still there are some lacunas that need to be addressed. Repetition and gaps in genomic sequences cause errors while assembling the genome assembly. Many crops have not been fully annotated which pose problem in functional annotation of genes. Poor quality of draft sequences also hinders the mapping process. Thus, availability of pan-genomes and sequencing at high depth will aid in better identification of variants in the genome of different strains of a species. Large scale phenotyping is often difficult and can add to phenotyping error. Thus, phenomic technologies needs to be improved to aid in high throughput phenotyping.

Transcriptomics

Transcriptomics focuses on transcriptomes, which are the entire collection of RNA transcripts generated by an organism's genetic code in a cell or tissue [21]. Transcriptome sequencing has gained popularity for examining how genes are expressed in response to various events over an extended period of time [22]. This approach assists the scientist in understanding the first layer function of a given gene by observing the differential gene expression in vitro. Initially, transcriptomic analysis was done through conventional profiling, cDNAs-AFLP, differential display-PCR (DD-PCR), although these methods had low resolution [23]. Soon after, the development of advanced methods enabled the use of microarrays, digital gene expression profiling, RNAseq, and SAGE for RNA expression profiling. [24–26]. In tomato, microarray analysis has revealed the 290 genes are significantly expressed in case of viruliferous whitefly [27]. Similarly, genes associated with bruchid resistance and a new QTL in common bean were identified by constructing high genetic linkage through whole genome sequencing [28] identified. This helps in understanding the molecular mechanism of bruchid resistance and the resistant genes (Table 2). Tomato is prone to Tuta absoluta damage causing heavy yield losses in crop production. Transcriptomic analysis has been done in tomato, revealing the presence of different signaling genes associated with Tuta infestation. A total of 1072 differently expressed genes in tomato which were significant in the prevention of Tuta infestation were detected [29]. Sweet potato weevil is the most destructive pest worldwide, causing economic damage to farmers. Therefore, there is a need to develop weevil-resistant cultivars to alleviate the problem. Transcriptomic analysis was performed in sweet potato to study the regulation of different resistant genes involved and the mechanism associated with the weevil resistance [30]. Another transcriptomic study in cucurbits revealed the presence of sex-specific transcripts and different genes for molecular biological control of the striped cucumber beetle [31]. Leptinotarsa decemlineata, a potato pest, has the potential to significantly destroy potato crops all over the world. This pest has been managed using a number of strategies, including the use of various insecticides. However, information regarding the molecular impact associated with the management is sparse in this pest.

Table 2. Various transcriptomics approaches for insect resistance in vegetable crops

Insect/pest	Platform	Crop	DEG	Result	Reference
Tuta absoluta	Illumina HiSeq Ten X	Tomato and Brinjal	1072 genes and 2834 genes	Control strategies against Tuta absoluta	[29]
Striped Cucumber beetle	BLAST x	Cucurbits	2898 genes	For using in biocontrol techniques	[31]
Colorado potato beetle	Universite Laval sequencing platform	Potato	13,281 genes	Pest control	[32]
Nematode	Illumina HiSeq 2000 platform	Tomato	25 genes	Management	[35]
Tuta absoluta	Illumina sequencing	Tomato	1577 genes	For the control of leaf miner (Tuta absoluta)	[36]
Aphid	Illumina Genome Analyzer	Cucumber	49 genes	Aphid resistance	[37]
Whitefly	BLAST p	Sweet potato	1338 genes	Resistance	[38]
Aphid	Blast2GO software	Tomato	899 genes	Plant response mechanism to aphid infestation	[39]

Transcriptomic studies revealed the presence of differently expressed genes in Spinosad-treated Leptinotarsa decemlineata. This approach can be used for the control of the pest [32]. Basic work flow of transcriptomics is represented in Fig. 2. Numerous RNA sequence studies have been done in vegetable crops that revealed the tissue-specific gene expression in response to various biological and abiotic stress factors [33, 34].

Fig. 2 [Images not available. See PDF.]

Basic work flow of transcriptomics

Challenges of transcriptomic technologies

Transcriptome analysis is much more complex than genomic analysis. It requires good infrastructure and assistance from data analysists to handle the large amount of data generated. Moreover, the quality of data is sensitive to stage of sampling and quality of samples. Lack of dense markers in the exonic regions make it difficult to map the gene locus. Thus, cost effective sequencing, bioinformatic analysis and genotyping technologies are required for effective transcriptome analysis.

Metabolomics

Metabolomics comes under functional genomics which includes the extensive understanding of metabolitesin various cellular processes in a biological system [40]. This is an innovative method for performing qualitative and quantitative studies on plant metabolism throughout the world [41]. The production of defense metabolites along with the activation of signal transduction pathways in plants is governed by differential gene expression in response to phytopathogens. The most examined defense-related proteins in plants, proteinase inhibitors (PIs), are biomarkers for JA and have been discovered to be increased in tomato or pepper plant responses to various pests [42]. The mode-of-action (MoA) of JA, the JA receptor, and other components that activate downstream signal transduction pathways and induce defense-related genes have been clarified by research on Arabidopsis thaliana and tomato [43]. Initially, defense responses occur only in the part this is actually targeted by the pest but simultaneously it is present in the other plant parts (systemic response). For this, knowledge of systemic effects is required for controlling pests in the future [44]. Metabolites have a significant contribution to the systemic effects (Table 3). Basic work flow of metabolomics is represented in Fig. 3.

Table 3. Metabolomics approaches for insect resistance in vegetable crops

Insect/pest	Techniques	Crop	Metabolites	Result	References
Thrips	Gas chromatography–mass spectrometry (GC–MS) approach	Brinjal	Quinic acid	Thrips resistance	[52]
Leaf miner	GC/EI/MS Metabolite Profiling	Tomato	Jasmonic Acid (JA) biosynthesis pathway (α-linolenic acid)	Leaf miner resistance	[54]
Tuta absoluta	Ultraperformance liquid chromatography–tandem mass spectrometry (UPLC–MS/MS)	Brinjal	aldehyde, alcohols, alkanes, amine, phenol, and terpenes	Resistance to T. absoluta	[53]
Thrips	Nuclear magnetic resonance spectroscopy (NMR)	Carrot	luteolin, sinapic acid and b-alanine	Thrips resistant carrot	[55]
Thrips	Liquid-chromatography–mass spectrophotometry	Pepper	acyclic diterpene glycosides (capsianosides)	Resistance to thrips	[56]

Fig. 3 [Images not available. See PDF.]

Basic work flow of metabolomics

Additionally, the relation between gene expression and metabolite levels in pierced vs. unexposed plant portions will allow for the development of pest management control methods viz., through traditional breeding approaches or gene editing [45–48] or by treatment of seed or metabolite spray in the plants [49, 50]. Secondary metabolites serve as a feasible source for host resistance and developing biopesticides concerning the rapid spread of pests in crops throughout the world. In order to implement an intregated pest management (IPM) program, numerous pest control strategies are required. The key approach in integrated management is the use of crop plant resilience. Till now, technical crop resilience studies are being limited to the identification and analysis of specific volatile substances. But in biochemical processes, typically there is the involvement of many compounds. Multiple compounds can be detected at a time with the metabolomics approach. Nuclear magnetic resonance spectroscopy is one of the most utilized metabolomic methods (NMR). NMR has been used as a proof of concept to demonstrate how metabolomics can substantially advance the assessment of resistance among host plants. NMR-based metabolomics has a huge potential and will drive future resistance breeding and biopesticide development [51]. Liu and co-workers used a gas chromatography–mass spectrometry (GC–MS)-based approach, to examine and compare the metabolites in brinjal plants. It was discovered that quinic acid was more in resistant brinjal plants [52].

In brinjal, the effect of volatiles extracted from different plant parts has been reported on the behavior of fruit and shoot borer. Further, the spectrometry analysis revealed the production of various secondary metabolites in the plant which would be associated with the control of pest. One of the most destructive pests in solanaceous crops is the tomato pinworm or Tuta absoluta. Although the current management techniques have shown some promise, a more effective management approach is still needed to reduce the damage it can do to crop productivity. Chen and co-workers identified the organic molecules and phytochemicals which causes variations in tomato and eggplant responses to T. absoluta infestation. They performed ultra-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) for the extraction of metabolites [53]. It was revealed that 35 differentially accumulated VOCs might cause a reaction in female T. absoluta, suggesting that these substances had a major impact on this pest's behaviour. Further, this can guide in pest management practices by adopting effective control measures against T. absoluta.

Challenges of metabolomic technologies

Involvement of metabolites in multiple biosynthetic pathways makes it somewhat difficult to trace the specific pathway. This limits the development and use of these secondary metabolites as biochemical markers. The cost of chromatographic columns is high and hi-tech facilities are required to process the metabolomic data. More powerful hyphenated techniques should be developed and sampling and analysis should be done with acute precision.

Proteomics

Proteomics is the scientific, extensive examination of proteins. Basic work flow of proteomics is represented in Fig. 4. It relies on the idea that a proteome as a complete protein set is produced by the cell or organism under a specific set of circumstances. As proteins are involved practically in every biological function a thorough examination of the proteins in the cell gives a distinctive global viewpoint on how these molecules collaborate and interact to build and maintain a functional biological system. Various proteomics approaches for insect resistance in vegetable crops are presented in Table 4. The response of the cell to external and internal changes is regulated by the level and activity of proteins, so a proteome change can provide an image of this regulatory change. The diversity of metabolic processes as well as how plants respond to stress are investigated using the proteome approach [57, 58].

Fig. 4 [Images not available. See PDF.]

Basic work flow of proteomics

Table 4. Proteomics approaches for insect resistance in vegetable crops

Insect/pest	Techniques	Crop	Proteins	Result	References
Colorado potato beetle	Tandem mass spectrometry	Potato	21 proteins	Beetle resistance	[62]
Green peach aphid	relative and absolute quantification (iTRAQ)	Cucumber	744 proteins	To know about aphid transmission mechanism	[63]
Thrips	relative and absolute quantification (iTRAQ)	Pepper	37 proteins	Proteins within resistant	[61]
Lamprosema indicate (leaf feeding pest)	relative and absolute quantification (iTRAQ)	Soybean	53 proteins	Reduced damage to bean	[64]
whitefly	relative and absolute quantification (iTRAQ)	Sweet potato	52 proteins	Proteins between resistant and susceptible genotypes	[38]
Thrips	454-Titanium sequencing of cDNA	Tomato	37 proteins	Response of identified proteins	[60]
Cotton worm	Matrix-assisted laser desorption/ionization time of flight (MALDI-TOF MS)	Soybean	11 proteins	Response to worm resistance	[65]

With the use of proteomics, it is possible to find prospective candidate genes that might be used to genetically modify crops to withstand challenges and improve quality [59]. Vargas and co-workers identified the proteins present in the larvae that result in variable responses to viruses. It was revealed that there were 37 proteins that were differentially expressed in their response to the tomato spotted wilt virus [60]. They found that 26 spots containing 37 proteins were significantly altered in response to TSWV. Wu and co-workers identified 37 differentially expressed proteins in the resistant genotype and 17 proteins in the susceptible genotype in pepper in response to thrips [61]. In cabbage, Brassica oleracea L. var. capitata, glycosinolates (GLS) are significant anionic secondary metabolites that are abundant in thiocyanin. GLS significantly affects human anti-cancer effects, plant antibacterial activity, food flavour, and pest and disease resistance. Yang and co-workers identified the presence of 52 proteins among sweet potato genotypes with respect to attack by thrips [38].

Challenges of proteomic technologies

Proteome of an organism is much more variable than genome and transcriptome. This makes study of structure and function of proteins difficult. Classical proteomic techniques like 2-D gel electrophoresis are less accurate. Therefore, there is need to develop improved techniques for high resolution mapping of peptides.

Conclusion

Different omics techniques have been used to understand the molecular inheritance underlying response of plants. Plants modulate themselves by controlling metabolites, gene regulation and proteins and thus adapt the existing stress conditions. Advancements in omics technologies helps to understand the behaviour of plants to deal with existing environmental conditions. Omics approaches viz., genomics, transcriptomics, proteomics and metabalomics plays an important role in order to understand the tolerance mechanisms against various insect-pests. These advances are expected to enhance breeding efforts and to identify candidate genotypes on the basis of their breeding value. Besides developing new genotypes, it also unravel the complex traits of plants in response to extreme environmental conditions, diseases and insect-pests. However, because transcriptome, proteome, and metabolome data are highly inconsistent through time, stages, and environmental conditions, sample collection and analysis must be done with extreme caution.

Acknowledgements

The authors are highly grateful to Dr. Yashwant Singh Parmar University of Horticulture and Forestry, Nauni, Solan, Himachal Pradesh, India for the necessary support regarding the preparation of this review.

Author contributions

All the authors contributed to the conceptual framework, preparation, writing as well as editing of the manuscript. Dr. Dharminder Kumar designed the theme of the review and edited the manuscript. Jagmeet Singh collected the data studies along with writing the review of literature. Shivani Chauhan, Harnoor Kaur Dhillon, Dr. Sandeep Kumar, Dr. Vikas Kumar and Dr. Renu Kapoor regularly helped in the editing as well as refinement of the manuscript.

Declarations

Conflict of interest

The authors declare that there is no conflict of interest regarding publication of this paper.

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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