In silico analysis of disease resistance and defence-related genes for a major sheath blight qShb 9-2 QTL in rice

Chanthran, Shyama Sundari Devi; Cheah, Boon Huat; Nadarajah, Kalaivani K.

doi:10.21161/mjm.1461812

MJM

2018

DOI: 10.21161/mjm.1461812

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In silico analysis of disease resistance and defence-related genes for a major sheath blight qShb 9-2 QTL in rice

Shyama Sundari Devi Chanthran

Boon Huat Cheah

Kalaivani K. Nadarajah³

Abstract: Aims: Sheath blight disease (Rhizoctonia solani) is an important rice disease that causes heavy yield losses in rice annually. To date, no rice variety has been found to be completely resistant to this disease. The most desirable approach for the management of sheath blight disease is to introgress genes with major and durable resistance into the rice genome. Therefore, this study aims to identify disease resistance and defence genes within qShb 9-2, a major QTL found within moderately resistant rice populatio… Show more

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Cited by 3 publications

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In silico Identification of Resistance and Defense Related Genes for Bacterial Leaf Blight (BLB) in Rice

Kumar¹,

Zaharin²,

Nadarajah³

2018

J Pure Appl Microbiol

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Bacterial leaf blight (BLB) disease is a rice disease caused by Xanthomonas oryzae pv.oryzae (Xoo). This disease causes devastating losses in the rice industry. To date research has been directed towards identifying QTLs and disease resistance genes that will afford resistance against Xoo. The rice plantations in different parts of the world are attacked by various strains of Xoo and thence the varieties that exhibit resistance to these strains may vary from one geographical location to another. In this paper we have analysed a QTL that has been recurrent in various rice backgrounds making it one of the three main QTLs responsible for BLB resistance in rice. qBBR11-1, a QTL found in chromosome eleven of rice is a good candidate for defense and resistance analysis towards BLB due to the various reports on the presence of multiple resistance genes in this QTL. Through the utilisation of bioinformatics tools we (1) identified and classified defense genes to functional groups, (2) identified and classified resistance genes according to the relevant domains, and finally, (3) mapped the relationship between defense related and resistance genes to build a defense model against BLB. A total of 21 defense genes have been classified according to functional groups such as chitinase activity, transduction signal and response to stress. A total of 37 putative resistance genes have been found and classified according to related domains such as nucleotide binding sites (NBS), trans membrane domains (Trd), WRKY domains and Ser / Thr Kin without LRR. Collectively all this information is used to provide a graphical representation of the interaction between the dense and the resistance gene systems. The key genes identified may be developed into markers for breeding or used in plant transformation works.

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In silico Identification of Resistance and Defense Related Genes for Bacterial Leaf Blight (BLB) in Rice

Kumar¹,

Zaharin²,

Nadarajah³

2018

J Pure Appl Microbiol

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Comparative Genomics: Insights on the Pathogenicity and Lifestyle of Rhizoctonia solani

Razali

Hisham

Kumar

et al. 2021

IJMS

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Proper management of agricultural disease is important to ensure sustainable food security. Staple food crops like rice, wheat, cereals, and other cash crops hold great export value for countries. Ensuring proper supply is critical; hence any biotic or abiotic factors contributing to the shortfall in yield of these crops should be alleviated. Rhizoctonia solani is a major biotic factor that results in yield losses in many agriculturally important crops. This paper focuses on genome informatics of our Malaysian Draft R. solani AG1-IA, and the comparative genomics (inter- and intra- AG) with four AGs including China AG1-IA (AG1-IA_KB317705.1), AG1-IB, AG3, and AG8. The genomic content of repeat elements, transposable elements (TEs), syntenic genomic blocks, functions of protein-coding genes as well as core orthologous genic information that underlies R. solani’s pathogenicity strategy were investigated. Our analyses show that all studied AGs have low content and varying profiles of TEs. All AGs were dominant for Class I TE, much like other basidiomycete pathogens. All AGs demonstrate dominance in Glycoside Hydrolase protein-coding gene assignments suggesting its importance in infiltration and infection of host. Our profiling also provides a basis for further investigation on lack of correlation observed between number of pathogenicity and enzyme-related genes with host range. Despite being grouped within the same AG with China AG1-IA, our Draft AG1-IA exhibits differences in terms of protein-coding gene proportions and classifications. This implies that strains from similar AG do not necessarily have to retain similar proportions and classification of TE but must have the necessary arsenal to enable successful infiltration and colonization of host. In a larger perspective, all the studied AGs essentially share core genes that are generally involved in adhesion, penetration, and host colonization. However, the different infiltration strategies will depend on the level of host resilience where this is clearly exhibited by the gene sets encoded for the process of infiltration, infection, and protection from host.

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A Meta-Analysis of Quantitative Trait Loci Associated with Multiple Disease Resistance in Rice (Oryza sativa L.)

Kumar

Nadarajah

2020

Plants

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Rice blast, sheath blight and bacterial leaf blight are major rice diseases found worldwide. The development of resistant cultivars is generally perceived as the most effective way to combat these diseases. Plant disease resistance is a polygenic trait where a combinatorial effect of major and minor genes affects this trait. To locate the source of this trait, various quantitative trait loci (QTL) mapping studies have been performed in the past two decades. However, investigating the congruency between the reported QTL is a daunting task due to the heterogeneity amongst the QTLs studied. Hence, the aim of our study is to integrate the reported QTLs for resistance against rice blast, sheath blight and bacterial leaf blight and objectively analyze and consolidate the location of QTL clusters in the chromosomes, reducing the QTL intervals and thus identifying candidate genes within the selected meta-QTL. A total of twenty-seven studies for resistance QTLs to rice blast (8), sheath blight (15) and bacterial leaf blight (4) was compiled for QTL projection and analyses. Cumulatively, 333 QTLs associated with rice blast (114), sheath blight (151) and bacterial leaf blight (68) resistance were compiled, where 303 QTLs could be projected onto a consensus map saturated with 7633 loci. Meta-QTL analysis on 294 QTLs yielded 48 meta-QTLs, where QTLs with membership probability lower than 60% were excluded, reducing the number of QTLs within the meta-QTL to 274. Further, three meta-QTL regions (MQTL2.5, MQTL8.1 and MQTL9.1) were selected for functional analysis on the basis that MQTL2.5 harbors the highest number of QTLs; meanwhile, MQTL8.1 and MQTL9.1 have QTLs associated with all three diseases mentioned above. The functional analysis allows for determination of enriched gene ontology and resistance gene analogs (RGAs) and other defense-related genes. To summarize, MQTL2.5, MQTL8.1 and MQTL9.1 have a considerable number of R-genes that account for 10.21%, 4.08% and 6.42% of the total genes found in these meta-QTLs, respectively. Defense genes constitute around 3.70%, 8.16% and 6.42% of the total number of genes in MQTL2.5, MQTL8.1 and MQTL9.1, respectively. This frequency is higher than the total frequency of defense genes in the rice genome, which is 0.0096% (167 defense genes/17,272 total genes). The integration of the QTLs facilitates the identification of QTL hotspots for rice blast, sheath blight and bacterial blight resistance with reduced intervals, which helps to reduce linkage drag in breeding. The candidate genes within the promising regions could be utilized for improvement through genetical engineering.

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In silico analysis of disease resistance and defence-related genes for a major sheath blight qShb 9-2 QTL in rice

Cited by 3 publications

References 52 publications

In silico Identification of Resistance and Defense Related Genes for Bacterial Leaf Blight (BLB) in Rice

In silico Identification of Resistance and Defense Related Genes for Bacterial Leaf Blight (BLB) in Rice

Comparative Genomics: Insights on the Pathogenicity and Lifestyle of Rhizoctonia solani

A Meta-Analysis of Quantitative Trait Loci Associated with Multiple Disease Resistance in Rice (Oryza sativa L.)

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