Approaches to Combat Biotic Stresses for Mungbean

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1		CHANDRA MOHAN SINGH	--	4201	2023-02-17 10:31:47	\|
2	format	Jason Zhu	Meta information modification	4201	2023-02-20 03:19:59	\|

This entry is adapted from the peer-reviewed paper 10.3390/agronomy12123000

Mungbean (Vigna radiata L. Wilczek) is an important short-duration grain legume of the genus Vigna that has wider adaptability across agro-climatic regions and soil types. Significant strides have been made towards the development of superior, high-yielding, and climate resilient cultivars in mungbean. A number of donors for various traits to have been deployed in introgression breeding. However, the use of common sources of resistance to different biotic stresses may lead to boom and bust cycles due to the appearance of new races or biotypes. Therefore, broadening the genetic base using wild and exotic plant genetic resources may offer a better quality of durable resistance. Many crop wild relatives (CWRs) confer a high degree of resistance against multiple diseases. Several agronomically important genes have been mapped using inter-specific populations, which are being deployed for the improvement of mungbean. In such a situation, tagging, mapping, and exploiting genes of interest from cross-species donors for stress tolerance will offer novel genetic variations. This will also provide increased opportunities for the selection of desirable types.

disease resistance gene flow green gram molecular markers

1. Pathogen Characterization and Screening

Extensive information on the causal organism of a disease, its pathotypes, mode of transmission, and inheritance pattern are of utmost significance since a single disease may be caused by many pathogen species. Sometimes, mixed infection may also lead to the appearance of common symptoms. For example, YMD, the most devastating mungbean disease ^[1]^[2], is caused by three different viruses with similar yellow mosaic symptoms. Even the inheritance of resistance of these pathogens is different. For example, MYMIV in black gram is governed by a single dominant gene ^[3], while the recessive monogenic inheritance pattern of MYMV was also reported by previous workers ^[4]^[5]. This suggests that different strategies must be adopted to improve disease resistance. Pathogens also change their host plants during the course of time for better survivability. However, the symptoms in alternate host may be changed. Haq et al. ^[6] studied the infectivity of MYMIV and MYMV in the black gram, green gram, French bean, and cowpea crops. They also studied the infectivity through recombinants of the virus. The MYMIV and MYMV caused yellow mosaic symptoms in black gram and green gram, whereas in cowpea and French bean, these caused stunting, mild leaf curl, and leaf deformation. These two pathogens are also able to cause stunting, downwards leaf curl, puckering, and necrotic symptoms in French bean. Similar symptoms were also recorded through agro-inoculation with recombinants of MYMIV–MYMV in French bean, whereas these recombinants were not able to cause any disease in mungbean and urdbean. The leaf curl disease caused by MYMIV in French bean was reported by Patwa et al. ^[7]. It was indicated that the pathogen characterization and screening of breeding materials against the specific pathogens are very important steps for tagging potential donors, which help in making appropriate strategies for enhancing the resistance. Secondly, screening techniques also play an important role. In most of the reports, diseases were screened under high disease pressure in the fields at hot spots.

2. Understanding the Genetics of Pathogen-Specific Biotic Stress Resistance

Knowledge about the inheritance of disease resistance greatly helps in improving the resistance status of crops. The simply inherited traits are easy to transfer from one background to another using morphological markers or classical breeding techniques. However, resistance to most diseases is governed by quantitatively inherited genes, and it is challenging to the breeders to pyramid them and improve the resistance status. A number of resistance sources in the secondary and tertiary gene pools were reported by various workers ^[8]^[9]^[10]. The genes from a cultivated background within the crop species are easy to transfer. However, the resistance breakdown is a very common phenomenon due to the pathogen evolution and narrow genetic base. To improve the durability of resistance against the pathogen or insect-pest, the pyramiding of target genes from different backgrounds is required. This is made possible through the exploitation of novel variations from cross-specific gene pools in breeding programs. The use of wild relatives in breeding programs also influences the inheritance of traits or resistance. Although the trait inheritance of cross-species members and CWRs is less studied, it needs to be explored for its potential use in mungbean breeding programs.

3. Exploring Cross-Specific Newer Gene Pools for Potential Donors

The genus Vigna is a highly diverse group comprising more than 200 species. Mungbean lies among the lowermost taxa and gene flow from other Vigna species to mungbean has already been reported ^[11], and while a few of the genes have been transferred from the wild species within the primary gene pool, the success is limited so far due to the selection bias against alien alleles. Most Vigna crops and their wild relatives are diploid and self-pollinated in nature with some exceptions (Creole bean). Considerable variability in wild relatives has been reported for yield-related and adaptive traits including biotic and abiotic stress resistance. The ploidy level also plays an important role in the success of alien gene transfer through distant hybridization ^[12]. The potential donors might be utilized in a breeding program for improving the biotic stress resistance in mungbean.

In Vigna species, the hybridization between mungbean and urdbean is routinely practiced for mungbean improvement. The derivatives obtained from such crosses exhibit many desirable features such as resistance to biotic and abiotic stresses, synchronous maturity, and improved seed quality traits ^[1]. “Meha” was the first interspecific mungbean variety which comprised MYMIV resistance transferred from V. mungo. Recently, “IPM 312-20” and “Tripura Mung-1” were developed through mungbean × urdbean interspecific hybridization. In similar ways, other wild species in addition to urdbean may also be utilized in breeding programs for improving the stress resistance and developing new mungbean varieties.

4. Characterizing Vigna Diversity: From Conventional to Omics Approaches

A high yield coupled with enhanced stress resistance and added newer traits of adoption need to be a major focus for the genetic improvement of crops. The prime traits for improvement include durable resistance to diseases and insect-pests. Despite the continued yield improvement through conventional breeding, the new biotechnological techniques will be needed to maximize the probability of success. Molecular marker technology and genomic resources offer great promise for crop improvement. Owing to the genetic linkage, DNA markers can be used to detect the presence of allelic variation in the genes and increase the efficiency of breeders. The effective utilization of these markers as a tool in plant breeding accelerates marker-assisted selection (MAS). Several workers have utilized various marker systems to decipher the genetic variation in the Vigna gene pool ^[8]^[12]^[13]^[14]^[15]. Similarly, many of the marker systems display the recent trend of detecting genetic variations and accelerating molecular breeding. As mungbean is compatible to cross with many other Vigna species, cross-specific genomic resources are also very important for its improvement. Banni et al. ^[16] evaluated a panel of 178 adzukibean genotypes using 39 polymorphic SSRs and a gene flow study suggested their effective utilization for improving the related Vigna species. Kaewwongwal et al. ^[17] evaluated 520 cultivated and 14 wild accessions of black gram for diversity assessment using 22 SSR markers and found that black gram is closely related to the mungbean and ricebean, indicating that the useful genes from black gram and ricebean may be effectively utilized for mungbean improvement. Several researchers utilized cross-specific markers for the mapping and tagging of important traits in mungbean ^[8]^[18]. Zhao et al. ^[19] used the fluorescent-labeled SSR markers to detect genetic variations among 151 mungbean varieties. In the post-genome sequence era, SNP markers are now frequently utilized in understanding the linkage disequilibrium of the mungbean population. Noble et al. ^[20] characterized a mungbean panel comprising 466 cultivated genotypes and 16 wild accessions to demonstrate its utility through pilot genome-wide association study for seed coat color. They detected approximately 22,000 genome-wide SNPs and used them to understand the genetic diversity, population structure, and linkage disequilibrium (LD) of mungbean. Recently, Wu et al. ^[21] identified 6486 SNPs on a panel of 95 mungbean genotypes. These SNP markers will be utilized in marker-assisted breeding after its successful validation.

5. Highlights of Vigna Genomic Resources

Various Vigna crops have now been sequenced starting with mungbean as a model ^[22]. Before the decoding of genome sequence information, the researchers used transferable markers from other Vigna species in the analysis of mungbean ^[23] and other legumes ^[3]^[24]. Kang et al. ^[22] sequenced a mungbean pure line, namely VC1973A, and its relatives V. reflexo-pilosa var. glabra and V. radiata var. sublobata to construct a draft genome sequence. The estimated genome size ranged from 579 Mb to approximately 968 Mb (V. reflexo-pilosa var. glabra). In addition, they also sequenced a wild relative, namely TC1966, a V. radiata var. sublobata accession with the estimated genome size of 501 Mb. The available mungbean whole-genome sequence information will further boost genomics research in Vigna species and accelerate mungbean breeding programs. Jiao et al. ^[25] re-sequenced two accessions of mungbean, namely Salu and AL127, to map the lma locus and identify 236,998 single-nucleotide polymorphisms and 8896 insertion/deletions (InDels). Following the whole genome sequencing of mungbean, the genome sequencing of other Vigna crops was accelerated and many species have been sequenced to date. The genome size of adzukibean (V. angularis var. angularis) was estimated and approximately 75% genome assembly was drafted ^[26]. The assembly produced 3883 scaffolds with a proper read coverage statistics of the sequencing libraries, including the pseudo-library from the Newbler assembly and the N50 length of the scaffolds was 703 kb. The sum of the scaffold length was approximately 443 Mb. A total of 4524 segregating SNP sites were identified. Using the MAKER pipeline, 26,857 high-confidence genes were predicted in the adzukibean genome, among which 15,976 were located on 11 pseudo chromosomes. The 9196 orthologs between V. angularis var. angularis and V. radiata var. radiata showed persistent tissue specificity, suggesting that the gene functions were extensively retained. Lonardi et al. ^[27] presented the draft genome of cowpea cv. IT97K-499-35, which is approximately 519 Mbp against the estimated size of 640 Mbp. A total of 29,773 protein-coding loci were annotated, along with 12,514 alternatively spliced transcripts. Based on annotation, an estimated 49.5% of the genome is composed of transposable elements (39.2%), simple sequence repeats (4.0%), and unidentified low-complexity sequences (5.7%). Takahashi et al. ^[28] sequenced the whole genome of V. stipulacea cv. JP245503 and generated 19.6 Gbp of subreads using 52 SMRT cells with a coverage of approximately 387.7 Mbp (87.9%) of the estimated genome size of approximately 445.1 Mbp. Based on the gene models and transcript as well as protein alignments, a total of 26,038 protein coding genes was predicted. Kaul et al. ^[29] reported the ricebean (V. umbellata) draft genome sequences, and estimated there were approximately 31,276 highly confidential genes with a functional coverage of 96.08%. The genome assembly was found to be closer to adzukibean, followed by mungbean and cowpea. A draft genome sequence of V. marina cv. ANBp-14-03 was generated through the NGS platform and approximately 23.7 Gb of sequence data were generated. The assembly containing 68,731 scaffolds gave an N50 length of 10,272 bp and the assembled sequences totaled 365.6 Mb. A total of 35,448 SSRs, including 3574 compound SSRs, were identified. Genome analysis identified 50,670 genes with a mean coding sequence length of 1042 bp. Phylogenetic analysis revealed the highest sequence similarity with V. angularis, followed by V. radiata. The comparison with the V. angularis genome revealed 16,699 SNPs and 2253 InDels and the comparison with the V. radiata genome revealed 17,538 SNPs and 2300 InDels. Souframanien et al. ^[30] constructed the draft genome sequence of black gram (V. mungo L. Hepper) cv. PU-31 through hybrid genome assembly with Illumina reads and third-generation Oxford Nanopore sequencing technology. The final de-novo whole genome of black gram is ~ 475 Mb (82% of the genome) and has a maximum scaffold length of 6.3 Mb with a scaffold N50 of 1.42 Mb. A total of 42,115 genes were identified, among which approximately 80.60% of the predicted genes were annotated. Approximately 50% of the assembled sequence was composed of repetitive elements. A total of 166,014 SSRs, including 65,180 compound SSRs, were identified. Recently, Ambreen et al. ^[31] carried out the long read-based draft genome sequencing of black gram cv. Uttara and identified the disease and seed-related genes. Thy tagged 119 NBS-encoding genes which might be associated with various biotic stress resistance. The close resemblance of many of the Vigna species with mungbean will provide an insight into delineating the mechanism of stress tolerance and their effective utilization in mungbean breeding.

6. Tagging, Mapping, and Exploiting QTL

During the process of plant breeding, a high yield coupled with enhanced stress tolerance, yield stability, and sustainability should be a major focus for crop improvement. The prime traits include durable resistance to diseases, insect-pests and tolerance to abiotic stresses ^[8]. Despite the continued yield improvement through breeding and biotechnological interventions, desirable genes from wild genetic resources must be introduced to maximize the probability of success. Genomic resources and molecular marker technology offer great promise for plant breeding. Owing to genetic linkage, DNA markers can be used to detect the presence of allelic variations and increase the breeding efficiency. The effective utilization of these markers as a tool in plant breeding accelerates marker-assisted selection (MAS). Various types of plant populations are used for the mapping and tagging of genes/QTLs, thus governing various developmental traits as well as biotic and abiotic stresses. The nature of the mapping population is very important for detecting its power for mapping. In general, F₂ segregating populations originate from the extreme phenotype for a trait used for the mapping and tagging of loci governing the trait of interest. Additionally, backcross (BCF₂) populations and some fixed populations such as doubled haploids (DH), recombinant inbred lines (RILs), near isogenic lines (NILs), nested association mapping (NAM) population, and multiple advanced generations inter-cross (MAGIC) populations are frequently used for the purpose of mapping. However, these populations are developed using parents with extreme phenotypes, and therefore, only bi-parental segregation occurs, which is the major limitation of linkage mapping ^[32]. In recent years, exploring QTLs by association analysis has been one of the effective approaches in quantitative genetics, which performs the rapid and fine-mapping of the target locus ^[8]. Thomas ^[20] developed a mapping panel of mungbean, which consisted of 30 crosses including four interspecific crosses using V. sublobata and advanced to F₅ generation for mapping complex traits such as drought and heat tolerance.

Association mapping has the ability to detect more QTLs because it uses a diverse germplasm that has more allelic diversity and the occurrence of several random events because of its parental evolution history than bi-parental population. Many researchers used marker-trait association through association mapping studies for mapping various important traits, such as fiber quality in cotton ^[33], MYMIV resistance in soybean ^[34], agronomic and flowering traits in lentil ^[35], seed coat color in mungbean ^[20], MYMIV resistance in mungbean ^[8], and agronomic traits in an MYMIV-resistant panel of mungbean ^[2]. Previously, researchers believed that the identified markers associated with QTLs from preliminary mapping studies were directly used in MAS. However, in the recent past, it has become widely accepted that QTL confirmation and validation is required ^[36]. The use of donor parents in developing a mapping population and background of molecular markers also affects the detection of QTLs. Most studies showed that the use of cross-specific molecular markers in the identification of QTLs exhibited the efficiency of cross-specific resources. Kitsanachandee et al. ^[37] detected five QTLs for MYMIV resistance, explaining 6.24–27.93% phenotypic variations in mungbean. Kasettranan et al. ^[38] detected two QTLs for powdery mildew resistance in mungbean through cross-specific markers. Recently, Singh et al. ^[8] identified three linked loci through the AM-approach in mungbean using the cross-specific markers of adzukibean. The effectiveness of cross-specific genomics resources for mungbean improvement was evident. Mathivathana et al. ^[14] mapped the major QTL on LG-4 through genotyping by a sequencing (GBS) approach using the mapping population developed between mungbean × ricebean. Somta et al. ^[39] identified two QTLs such as qVmunBr6.1 and qVmunBr6.2. as new loci for C. maculatus resistance in Vigna mungo var. Silvestris that suggested that a lectin receptor kinase and chitinase are candidates for qVmunBr6.2. Mariyammal et al. ^[40] identified 12 QTLs in two environments through the mapping of an RIL population developed by crossing mungbean × ricebean. Venkataramana et al. ^[41] identified two major QTLs, namely Cmpd1.5 and Cmpd1.6, mapped within 11.9 cM and 13.0 cM of the flanking markers, which accounted for 67.3 and 77.4% of the variance for seed damage due to pulse beetle. Subramaniyan et al. ^[42] performed the linkage mapping in the population of urdbean for bruchid resistance and explained 17.01% of the genetic variation. This QTL was flanked with the SSR marker CEDG302 and GMES1248. Dhaliwal et al. ^[43] mapped a major QTL having 70% phenotypic variation for MYMIV resistance in the RILs of black gram × ricebean. They identified three competitive allele-specific (KASP) markers tightly linked to MYMIV that originated from serine threonine kinase, UBE2D2 and BAK1/BRI1-ASSOCIATED RECEPTOR KINASE genes. This indicates the possibility to identify and exploit cross-specific QTLs/genes. Previous studies suggested that V. umbellata, V. sylvistris and V. sublobata were less affected by the disease and insect-pests, and might prove as useful resource for improving biotic stress tolerance in mungbean.

7. Expanding Genomic Regions for Tagging New Candidate Genes

The identification of QTLs through various approaches for target traits is routine these days, although cloning and characterization remain limited to date ^[2]. Expanding the genomic regions associated with QTLs/loci will offer a means of tagging candidates for target traits. Mathivathanal et al. ^[14] identified five QTLs with phenotypic variation explained (PVE) from 10.11 to 20.04 for MYMV resistance using ricebean as a donor parent. The QTL qMYMV4-1 was found to be a major and stable QTL for MYMV. They also expanded the genomic regions of qMYMV4-1 and identified 16 candidate genes. Mariyammal et al. ^[40] also mapped the bruchid-resistant QTLs on chromosome 5 in mungbean × ricebean RILs population and identified 16 candidate genes. These candidate genes may have an important role in imparting resistance against MYMV and bruchid.

8. Comprehensive RNA-Seq Approach

The RNA-seq approach deals with the complete set of RNA transcripts produced by the genome of an individual in the cell/tissue under specific conditions. It is emerging as a promising technique to analyze the expression pattern of genes, which helps to understand the first layer function of a particular gene ^[44]. Various methods have been adopted earlier such as cDNAs-AFLP, differential display-PCR (DD-PCR), SSH, etc. However, these techniques provided low resolution. The introduction of advanced techniques such as microarrays, digital gene expression profiling, NGS, RNAseq, SAGE, etc. made it more effective to understand the candidate genes. The mapping of tagged candidate genes against the available decoded whole genome sequences of the crops and their relatives provided an insight into their structural and functional diversity. Wang et al. ^[45] emphasized single-molecule sequencing (SMS) as an emerging state-of-the-art technique for gene discovery and annotation. Baruah et al. ^[46] performed the expression of 20 defense related genes through qPCR, in which 12 genes showed up-regulation, whereas 8 showed down-regulation upon bruchid oviposition. Some major defense genes such as defensin, PR gene, and LOX were highly expressed in the oviposited population as compared with the non-oviposited ones. The Blast2GO analysis indicated the role of certain enzymes related to secondary metabolites, aromatic amino acid, and primary amino acid metabolism in activating defense mechanism. Lin et al. ^[47] performed transcriptome and proteome analysis and three DEGs/DPs, including resistant-specific protein (g39185), gag/pol polyprotein (g34458), and aspartic proteinase (g5551) were identified, which encode a protein containing a BURP domain. Liu et al. ^[48] compared the genomic and transcriptomic data in mungbean against bruchid resistance and 91 DEGs were identified upon bruchid infestation. They found 408 nucleotide variations (NVs) between bruchid-resistant and -susceptible lines in regions spanning 2 kb (kilo base pairs) of the promoters of 68 DEGs. Furthermore, 282 NVs were identified on exons of 148 sequence-changed-protein genes (SCPs). Raizada and Jegadeesan ^[30] performed the comparative transcriptome analysis of the developing seeds of wild and cultivated black gram with contrasting phenotypes for three traits, bruchids infestation, YMD, and seed size, in which 715 DEGs were re-annotated. Das et al. (2021) performed the transcriptomic analysis and stated that the bruchid ovipositioning-mediated defense response in black gram is induced by SA signaling pathways and defense genes, and such defensin genes could be potential candidates for resistance to bruchids ^[49]. These reports demonstrate the role of transcriptomics in terms of stress responses and development for crops. The RNA-seq approach also proved to be one of the powerful techniques of transcriptomics to develop genic-SSR or functional markers that can be linked to phenotypic trait variations. Kumar et al. ^[50] recently performed the transcriptomic analysis in lentils under heat stress and identified the DEGs and developed the genic SSR markers. To understand the differential expression profiles in response to specific stress in different crop species, the comparative transcriptomic approach is one of the viable options. Due to the close resemblance of Vigna species and their cross transferability of genes through classical and molecular approaches, the comparative transcriptomics will prove a way to understand the function of target genes. This approach will also help select the candidate genes from different cross-species to mungbean for their pyramiding. Collectively, all these transcriptomic techniques could be helpful for mungbean improvement.

9. Gene-Based Functional Markers

Functional markers can be developed through many approaches such as transcriptomics, NGS, TILLING, homologous recombinant (HR), association mapping, allele mining, etc. ^[51]. The transcriptome sequences available in databases provide a cost-effective and valuable source of the development of new molecular markers. Gupta and Gopalkrishna ^[52] developed 1071 SSRs in cowpea unigene sequences. Primer pairs were successfully designed for 803 SSR motifs and 102 SSR markers were finally characterized and validated. Gupta et al. ^[53] identified 12,596 EST sequences from mungbean and developed 1848 in silico EST-SSRs. One hundred randomly selected primer pairs were further used for characterization. These EST-SSRs might be useful for the QTL mapping of target traits and can be further utilized for the functional characterization and development of functional markers. This approach is more effective than utilizing the random markers in crop improvement. Baruah et al. ^[46] developed EST-SSRs associated with MYMV in black gram. However, the development of gene-based functional markers (FMs) is limited in Vigna species, and needs to be accelerated. The advantage of FMs over other molecular markers is the close genomic resemblance between an FM and a phenotypic trait. Therefore, FMs may facilitate the direct selection of genes associated with the phenotype, which serves to increase the selection efficiency for crop improvement. Furthermore, there is no need to use the flanking markers during gene introgression. Therefore, these markers would play an important role in marker-assisted selection strategies.

10. Developing Potential SCARs

A variety of molecular markers have been developed and utilized in crop improvement. PCR-based molecular markers are believed to be cost-effective and require less DNA. Pratap et al. ^[54], Kumari et al. ^[55], and many more workers assessed the molecular diversity through cross-specific molecular markers in the efficiency and effectiveness of these markers in crop breeding. Singh et al. ^[2]^[8] used the molecular markers from adzukibean in mapping loci for MYMIV and agronomic traits in the population of mungbean. The development of SCAR markers is one of the ways to develop the tightly linked markers to the trait of species. Souframanien and Gopalakrishna ^[56] used the RAPD markers and the marker ISSR8111357 was sequenced and the SCAR-YMV1 linked to MYMV was designed. Dhole and Reddy ^[57] developed the marker OPB-07600 which was closely linked (6.8 cM) with an MYMV resistance gene. Recently, Zhang and Panthee ^[58] developed the codominant SCAR marker linked to the resistant genes in tomato for gene pyramiding. Feng et al. ^[59] developed SCoT-based SCAR markers to authenticate the species of the genus Physalis. These SCAR markers were also found to be effective in discriminating the genotypes of the related species, which might be utilized in cross-specific alien gene transfer. Some of the wild species are closely related to mungbean as well as each other, which require robust taxonomy-based phenotyping for their identification. Gore et al. ^[60] identified two genotypes of the section Aconitifoliae (V. trilobata and V. stipulacea) using 47 descriptive traits. The SCAR marker can help easily identify those species and accelerate their effective utilization. Zheng et al. ^[61] developed the SCAR markers for discriminating the toxic genotypes to non-toxic ones in Dendrobium officinale. This indicated the potential of SCAR markers in Vigna improvement.

11. Marker-Assisted Breeding

Marker-assisted selection (MAS) refers to use of DNA markers to assist phenotypic selection towards crop improvement. The markers, such as RAPD, SCoT, ISSR, SSR, and SNPs were effectively utilized in breeding programs. The identification and exploitation of tightly linked markers to the trait of interest has led to practical achievements in terms of varietal development with enhanced efficiency and accuracy in shorter time periods. NGS technologies also led to remarkable advancements providing ultra-throughput sequences for plant genotyping. To further broaden the usages of sequencing technologies to large crop genomes or the non-availability of reference sequences, GBS has been developed for marker discovery. In recent days, the bioinformatic pipelines were deployed to identify the candidate genes ^[62]. These candidates might be useful for developing the functional markers for MAS programs for mungbean improvement. Many QTLs/genes from cross-specific donors using the RIL population for biotic stress resistance were identified. However, their cloning, characterization, and introgression in high-yielding varieties for improving the biotic stress tolerance are rather limited. Wu et al. ^[63] successfully introgressed the bruchid resistant gene VrPGIP-2 in KPS-1 from V2802 using VrBR-SSR013 and DMB-SSR-158 as foreground markers. Mariyammal et al. ^[40] identified 15 candidate genes for bruchid resistance in mungbean × ricebean population. Recently Chen et al. ^[64] identified a bruchid-resistant gene Vradi05g03810 encoding the probable resistant-specific protein from TC1966 in the background of mungbean. Mathivathana et al. ^[14] identified 19 candidate genes on chr.04 in the mungbean × ricebean population for MYMV resistance. After mapping these genes, the genes can be tagged and introgressed in mungbean to improve the bruchid resistance. The identification of resistant

References

Pratap, A.; Gupta, S.; Nair, R.M.; Schafleitner, R.; Basu, P.S.; Singh, C.M.; Prajapati, U.; Gupta, A.K.; Nayyar, H.; Mishra, A.K.; et al. Using Plant Phenomics to Exploit the Gains of Genomics. Agronomy 2019, 9, 126.
Singh, C.M.; Singh, P.; Tiwari, C.; Purwar, S.; Kumar, M.; Pratap, A.; Singh, S.; Chugh, V.; Mishra, A.K. Improving drought tolerance in Mungbean (Vigna radiata L. Wilczek): Morpho-physiological, biochemical and molecular Perspectives. Agronomy 2021, 11, 1534.
Gupta, S.; Gupta, D.S.; Anjum, T.K.; Pratap, A.; Kumar, J. Inheritance and molecular tagging of MYMIV resistance gene in blackgram (Vigna mungo L. Hepper). Euphytica 2013, 193, 27–37.
Sudha, M.; Anusuya, P.; Mahadev, N.G.; Karthikeyan, A.; Nagarajan, P.; Raveendran, M.; Senthil, N.; Pandiyan, M.; Angappan, K.; Balasubramanian, P. Molecular studies on mungbean (Vigna radiata (L.) Wilczek) and ricebean (Vigna umbellata (Thunb.)) interspecific hybridisation for Mungbean yellow mosaic virus resistance and development of species-specific SCAR marker for ricebean. Arch. Phytopathol. Plant Prot. 2013, 46, 503–517.
Sai, C.B.; Nagarajan, P.; Raveendran, M.; Rabindran, R.; Kannan Bapu, J.R.; Senthil, N. Understanding the inheritance of mungbean yellow mosaic virus (MYMV) resistance in mungbean (Vigna radiata L. Wilczek). Mol. Breed. 2017, 37, 1–15.
Haq, Q.M.I.; Rouhibakhsh, A.; Ali, A.; Malathi, V.G. Infectivity analysis of a blackgram isolate of Mungbean yellow mosaic virus and genetic assortment with MYMIV in selective hosts. Virus Genes 2011, 42, 429–439.
Patwa, N.; Chatterjee, C.; Basak, J. Differential responses of Phaseolus vulgaris cultivars following mungbean yellow mosaic India virus infection. Physiol. Mol. Biol. Plants 2020, 26, 817–828.
Singh, C.M.; Pratap, A.; Gupta, S.; Biradar, R.S.; Singh, N.P. Association mapping for mungbean yellow mosaic India virus resistance in mungbean (Vigna radiata L. Wilczek). 3 Biotech 2020, 10, 33.
Akram, M.; Kamaal, N.; Pratap, A.; Singh, N.P. Resistance status of mungbean (Vigna radiata (L.) Wilczek) advanced breeding materials against mungbean yellow mosaic India virus. Arch. Phytopathol. Plant Prot. 2021, 54, 2533–2546.
Nair, R.M.; Pandey, A.K.; War, A.R.; Hanumantharao, B.; Shwe, T.; Alam, A.; Pratap, A.; Malik, S.R.; Karimi, R.; Mbeyagala, E.K. Biotic and abiotic constraints in mungbean production—Progress in genetic improvement. Front. Plant Sci. 2019, 10, 1340.
Bhanu, A.N.; Singh, M.N.; Srivastava, K. Crossability studies of interspecific hybridization among Vigna species. Biomed. J. 2018, 2, 1–7.
Pratap, A.; Gupta, S.; Malviya, N.; Tomar, R.; Maurya, R.; Joseph John, K.; Madhavan, L.; Singh, N.P. Genome scanning of Asiatic Vigna species for discerning population genetic structure based on microsatellite variation. Mol. Breed. 2015, 35, 178.
Singh, I.; Sandhu, J.S.; Gupta, S.K.; Singh, S. Introgression of productivity and other desirable traits from ricebean (Vigna umbellata) into black gram (Vigna mungo). Plant Breed. 2013, 132, 401–406.
Mathivathana, M.K.; Murukarthick, J.; Karthikeyan, A.; Jang, W.; Dhasarathan, M.; Jagadeeshselvam, N.; Sudha, M.; Vanniarajan, C.; Karthikeyan, G.; Yang, T.-J.; et al. Detection of QTLs associated with mungbean yellow mosaic virus (MYMV) resistance using the interspecific cross of Vigna radiata × Vigna umbellata. J. Appl. Genet. 2019, 60, 255–268.
Suman, S.; Rani, B.; Sharma, V.K.; Kumar, H.; Shahi, V.K. SSR marker based profiling and diversity analysis of mungbean genotypes. Legume Res. An. Int. J. 2019, 42, 585–594.
Banni, K.; Moe, K.T.; Park, Y.-J. Assessing genetic diversity, population structure and gene flow in the Korean red bean using SSR markers. Plant Genet. Resour. 2012, 10, 74–82.
Kaewwongwal, A.; Kongjaimun, A.; Somta, P.; Chankaew, S.; Yimram, T.; Srinives, P. Genetic diversity of the black gram gene pool as revealed by SSR markers. Breed. Sci. 2015, 65, 127–137.
Isemura, T.; Kaga, A.; Tabata, S.; Somta, P.; Srinives, P.; Shimizu, T.; Jo, U.; Vaughan, D.A.; Tomooka, N. Construction of a genetic linkage map and genetic analysis of domestication related traits in mungbean (Vigna radiata). PLoS ONE 2012, 7, e41304.
Zhao, Y.; Wang, Y.; Wang, L.; Zhang, D. Molecular identification of mung bean accessions (Vigna radiata L.) from Northeast China using capillary electrophoresis with fluorescence-labeled SSR markers. Food Energy Secur. 2020, 9, e182.
Noble, T.J.; Tao, Y.; Mace, E.S.; Williams, B.; Jordan, D.R.; Douglas, C.A.; Mundree, S.G. Characterization of linkage disequilibrium and population structure in a mungbean diversity panel. Front. Plant Sci. 2018, 8, 2102.
Wu, X.; Islam, A.S.M.F.; Limpot, N.; Mackasmiel, L.; Mierzwa, J.; Cortés, A.J.; Blair, M.W. Genome-wide Snp identification and association mapping for seed mineral concentration in mung bean (Vigna Radiata L.). Front. Genet. 2020, 11, 656.
Kang, Y.J.; Kim, S.K.; Kim, M.Y.; Lestari, P.; Kim, K.H.; Ha, B.-K.; Jun, T.H.; Hwang, W.J.; Lee, T.; Lee, J. Genome sequence of mungbean and insights into evolution within Vigna species. Nat. Commun. 2014, 5, 5443.
Pratap, A.; Gupta, S.; Tomar, R.; Malviya, N.; Maurya, R.; Pandey, V.R.; Mehandi, S.; Singh, N.P. Cross-genera amplification of informative microsatellite markers from common bean and scarlet runner bean for assessment of genetic diversity in mungbean (Vigna radiata). Plant Breed. 2016, 135, 499–505.
He, G.; Woullard, F.E.; Marong, I.; Guo, B.Z. Transferability of soybean SSR markers in peanut (Arachis hypogaea L.). Peanut Sci. 2006, 33, 22–28.
Jiao, K.; Li, X.; Guo, W.; Yuan, X.; Cui, X.; Chen, X. Genome re-sequencing of two accessions and fine mapping the locus of lobed leaflet margins in mungbean. Mol. Breed. 2016, 36, 128.
Kang, Y.J.; Satyawan, D.; Shim, S.; Lee, T.; Lee, J.; Hwang, W.J.; Kim, S.K.; Lestari, P.; Laosatit, K.; Kim, K.H. Draft genome sequence of adzuki bean, Vigna angularis. Sci. Rep. 2015, 5, 8069.
Lonardi, S.; Muñoz-Amatriaín, M.; Liang, Q.; Shu, S.; Wanamaker, S.I.; Lo, S.; Tanskanen, J.; Schulman, A.H.; Zhu, T.; Luo, M. The genome of cowpea (Vigna unguiculata Walp.). Plant J. 2019, 98, 767–782.
Takahashi, Y.; Sakai, H.; Yoshitsu, Y.; Muto, C.; Anai, T.; Pandiyan, M.; Senthil, N.; Tomooka, N.; Naito, K. Domesticating Vigna stipulacea: A potential legume crop with broad resistance to biotic stresses. Front. Plant Sci. 2019, 10, 1607.
Kaul, T.; Eswaran, M.; Thangaraj, A.; Meyyazhagan, A.; Nehra, M.; Raman, N.M.; Bharti, J.; Badapanda, C.; Balamurali, B. Rice Bean (Vigna umbellata) draft genome sequence: Unravelling the late flowering and unpalatability related genomic resources for efficient domestication of this underutilized crop. bioRxiv 2019.
Jegadeesan, S.; Raizada, A.; Dhanasekar, P.; Suprasanna, P. Draft genome sequence of the pulse crop blackgram reveals potential R-genes. Sci. Rep. 2021, 11, 11247.
Ambreen, H.; Oraon, P.K.; Wahlang, D.R.; Satyawada, R.R.; Katiyar-Agarwal, S.; Agarwal, M.; Jagannath, A.; Kumar, A.; Budhwar, R.; Shukla, R.N.; et al. Long-read-based draft genome sequence of Indian black gram IPU-94-1 ‘Uttara’: Insights into disease resistance and seed storage protein genes. Plant Genome 2022, 15, e20234.
Jaiswal, V.; Mir, R.R.; Mohan, A.; Balyan, H.S.; Gupta, P.K. Association mapping for pre-harvest sprouting tolerance in common wheat (Triticum aestivum L.). Euphytica 2012, 188, 89–102.
Nie, X.; Huang, C.; You, C.; Li, W.; Zhao, W.; Shen, C.; Zhang, B.; Wang, H.; Yan, Z.; Dai, B. Genome-wide SSR-based association mapping for fiber quality in nation-wide upland cotton inbreed cultivars in China. BMC Genom. 2016, 17, 352.
Rani, A.; Kumar, V.; BS, G.; Rathi, P.; Shukla, S.; RK, S. Linkage mapping of Mungbean yellow mosaic India virus (MYMIV) resistance gene in soybean. Breed. Sci. 2017, 67, 95–100.
Kumar, J.; Gupta, D.S.; Gupta, S.; Dubey, S.; Gupta, P.; Kumar, S. Quantitative trait loci from identification to exploitation for crop improvement. Plant Cell Rep. 2017, 36, 1187–1213.
Langridge, P.; Lagudah, E.S.; Holton, T.A.; Appels, R.; Sharp, P.J.; Chalmers, K.J. Trends in genetic and genome analyses in wheat: A review. Aust. J. Agric. Res. 2001, 52, 1043–1077.
Kitsanachandee, R.; Somta, P.; Chatchawankanphanich, O.; Akhtar, K.P.; Shah, T.M.; Nair, R.M.; Bains, T.S.; Sirari, A.; Kaur, L.; Srinives, P. Detection of quantitative trait loci for mungbean yellow mosaic India virus (MYMIV) resistance in mungbean (Vigna radiata (L.) Wilczek) in India and Pakistan. Breed. Sci. 2013, 63, 367–373.
Kasettranan, W.; Somta, P.; Srinives, P. Mapping of quantitative trait loci controlling powdery mildew resistance in mungbean (Vigna radiata (L.) Wilczek). J. Crop Sci. Biotechnol. 2010, 13, 155–161.
Somta, P.; Chen, J.; Yundaeng, C.; Yuan, X.; Yimram, T.; Tomooka, N.; Chen, X. Development of an SNP-based high-density linkage map and QTL analysis for bruchid (Callosobruchus maculatus F.) resistance in black gram (Vigna mungo (L.) Hepper). Sci. Rep. 2019, 9, 3930.
Mariyammal, I.; Seram, D.; Samyuktha, S.M.; Karthikeyan, A.; Dhasarathan, M.; Murukarthick, J.; Kennedy, J.S.; Malarvizhi, D.; Yang, T.-J.; Pandiyan, M. QTL mapping in Vigna radiata × Vigna umbellata population uncovers major genomic regions associated with bruchid resistance. Mol. Breed. 2019, 39, 110.
Venkataramana, P.B.; Gowda, R.; Somta, P.; Ramesh, S.; Mohan Rao, A.; Bhanuprakash, K.; Srinives, P.; Gireesh, C.; Pramila, C.K. Mapping QTL for bruchid resistance in rice bean (Vigna umbellata). Euphytica 2016, 207, 135–147.
Subramaniyan, R.; Narayana, M.; Krishnamoorthy, I.; Natarajan, G.; Gandhi, K. Novel and stable QTL regions conferring resistance to MYMV disease and its inheritance in blackgram (Vigna mungo (L.) Hepper). J. Genet. 2022, 101, 18.
Dhaliwal, S.S.; Sharma, V.; Shukla, A.K. Impact of micronutrients in mitigation of abiotic stresses in soils and plants—A progressive step toward crop security and nutritional quality. Adv. Agron. 2022, 173, 1–78.
Agarwal, P.; Parida, S.K.; Mahto, A.; Das, S.; Mathew, I.E.; Malik, N.; Tyagi, A.K. Expanding frontiers in plant transcriptomics in aid of functional genomics and molecular breeding. Biotechnol. J. 2014, 9, 1480–1492.
Wang, B.; Kumar, V.; Olson, A.; Ware, D. Reviving the transcriptome studies: An insight into the emergence of single-molecule transcriptome sequencing. Front. Genet. 2019, 10, 384.
Baruah, I.K.; Panda, D.; M.V., J.; Das, D.J.; Acharjee, S.; Sen, P.; Sarmah, B.K. Bruchid egg induced transcript dynamics in developing seeds of black gram (Vigna mungo). PLoS ONE 2017, 12, e0176337.
Lin, W.-J.; Ko, C.-Y.; Liu, M.-S.; Kuo, C.-Y.; Wu, D.-C.; Chen, C.-Y.; Schafleitner, R.; Chen, L.-F.O.; Lo, H.-F. Transcriptomic and proteomic research to explore bruchid-resistant genes in mungbean isogenic lines. J. Agric. Food Chem. 2016, 64, 6648–6658.
Liu, M.-S.; Kuo, T.C.-Y.; Ko, C.-Y.; Wu, D.-C.; Li, K.-Y.; Lin, W.-J.; Lin, C.-P.; Wang, Y.-W.; Schafleitner, R.; Lo, H.-F. Genomic and transcriptomic comparison of nucleotide variations for insights into bruchid resistance of mungbean (Vigna radiata R. Wilczek). BMC Plant Biol. 2016, 16, 46.
Das, D.; Baruah, I.K.; Panda, D.; Paswan, R.R.; Acharjee, S.; Sarmah, B.K. Bruchid beetle ovipositioning mediated defense responses in black gram pods. BMC Plant Biol. 2021, 21, 38.
Kumar, J.; Gupta, D.S.; Kesari, R.; Verma, R.; Murugesan, S.; Basu, P.S.; Soren, K.R.; Gupta, S.; Singh, N.P. Comprehensive RNAseq analysis for identification of genes expressed under heat stress in lentil. Physiol. Plant. 2021, 173, 1785–1807.
Salgotra, R.K.; Stewart, C.N. Functional markers for precision plant breeding. Int. J. Mol. Sci. 2020, 21, 4792.
Gupta, S.K.; Gopalakrishna, T. Development of unigene-derived SSR markers in cowpea (Vigna unguiculata) and their transferability to other Vigna species. Genome 2010, 53, 508–523.
Gupta, S.K.; Bansal, R.; Gopalakrishna, T. Development and characterization of genic SSR markers for mungbean (Vigna radiata (L.) Wilczek). Euphytica 2014, 195, 245–258.
Pratap, A.; Basu, P.S.; Gupta, S.; Malviya, N.; Rajan, N.; Tomar, R.; Madhavan, L.; Nadarajan, N.; Singh, N.P. Identification and characterization of sources for photo- and thermo-insensitivity in Vigna species. Plant Breed. 2014, 133, 756–764.
Kumari, G.; Pratap, A.; Lavanya, R.G.; Akram, M.; Rathore, M.; Madhavan, L.; Singh, Y.; Singh, N.P. Potential resistant donors for yellow mosaic disease identified from endemic wild Vigna species. J. Food Legumes 2021, 34, 10–16.
Souframanien, J.; Gopalakrishna, T. ISSR and SCAR markers linked to the mungbean yellow mosaic virus (MYMV) resistance gene in blackgram . Plant Breed. 2006, 125, 619–622.
Dhole, V.J.; Reddy, K.S. Genetic analysis of resistance to mungbean yellow mosaic virus in mungbean (Vigna radiata). Plant Breed. 2012, 131, 414–417.
Zhang, J.; Panthee, D.R. Next-generation sequencing-based bulked segregant analysis without sequencing the parental genomes. G3 2022, 12, jkab400.
Feng, S.; Zhu, Y.; Yu, C.; Jiao, K.; Jiang, M.; Lu, J.; Shen, C.; Ying, Q.; Wang, H. Development of species-specific SCAR markers, based on a SCoT analysis, to authenticate Physalis (Solanaceae) species. Front. Genet. 2018, 9, 192.
Gore, P.G.; Tripathi, K.; Pratap, A.; Bhat, K.V.; Umdale, S.D.; Gupta, V.; Pandey, A. Delineating taxonomic identity of two closely related Vigna species of section Aconitifoliae: V. trilobata (L.) Verdc. and V. stipulacea (Lam.) Kuntz in India. Genet. Resour. Crop Evol. 2019, 66, 1155–1165.
Zheng, K.; Cai, Y.; Chen, W.; Gao, Y.; Jin, J.; Wang, H.; Feng, S.; Lu, J. Development, identification, and application of a germplasm specific SCAR Marker for Dendrobium officinale Kimura et Migo. Front. Plant Sci. 2021, 12, 879.
Singh, P.; Mishra, A.K.; Singh, C.M. Genome-wide identification and characterization of Lectin receptor-like kinase (LecRLK) genes in mungbean (Vigna radiata L. Wilczek). J. Appl. Genet. 2021, 62, 223–234.
Wu, R.; Zhang, Q.; Lin, Y.; Chen, J.; Somta, P.; Yan, Q.; Xue, C.; Liu, J.; Chen, X.; Yuan, X. Marker-Assisted Backcross Breeding for Improving Bruchid (Callosobruchus spp.) Resistance in Mung Bean (Vigna radiata L.). Agronomy 2022, 12, 1271.
Chen, T.; Hu, L.; Wang, S.; Wang, L.; Cheng, X.; Chen, H. Construction of High-Density Genetic Map and Identification of a Bruchid Resistance Locus in Mung Bean (Vigna radiata L.). Front. Genet. 2022, 13, 903267.

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