The landscape of sequence variations between resistant and susceptible hot peppers to predict functional candidate genes against bacterial wilt disease.

IF 5.4 3区 材料科学 Q2 CHEMISTRY, PHYSICAL ACS Applied Energy Materials Pub Date : 2024-11-01 DOI:10.1186/s12870-024-05742-w
Ji-Su Kwon, Junesung Lee, Jayabalan Shilpha, Hakgi Jang, Won-Hee Kang
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Abstract

Background: Bacterial wilt (BW), caused by Ralstonia solanacearum (Ral), results in substantial yield losses in pepper crops. Developing resistant pepper varieties through breeding is the most effective strategy for managing BW. To achieve this, a thorough understanding of the genetic information connected with resistance traits is essential. Despite identifying three major QTLs for bacterial wilt resistance in pepper, Bw1 on chromosome 8, qRRs-10.1 on chromosome 10, and pBWR-1 on chromosome 1, the genetic information of related BW pepper varieties has not been sufficiently studied. Here, we resequenced two pepper inbred lines, C. annuum 'MC4' (resistant) and C. annuum 'Subicho' (susceptible), and analyzed genomic variations through SNPs and Indels to identify candidate genes for BW resistance.

Results: An average of 139.5 Gb was generated among the two cultivars, with coverage ranging from 44.94X to 46.13X. A total of 8,815,889 SNPs was obtained between 'MC4' and 'Subicho'. Among them, 31,190 (0.35%) were non-synonymous SNPs (nsSNPs) corresponding to 10,926 genes, and these genes were assigned to 142 Gene Ontology (GO) terms across the two cultivars. We focused on three known BW QTL regions by identifying genes with sequence variants through gene set enrichment analysis and securing those belonging to high significant GO terms. Additionally, we found 310 NLR genes with nsSNP variants between 'MC4' (R) and 'Subicho' (S) within these regions. Also, we performed an Indel analysis on these genes. By integrating all this data, we identified eight candidate BW resistance genes, including two NLR genes with nsSNPs variations in qRRs-10.1 on chromosome 10.

Conclusion: We identified genomic variations in the form of SNPs and Indels by re-sequencing two pepper cultivars with contrasting traits for bacterial wilt. Specifically, the four genes associated with pBWR-1 and Bw1 that exhibit both nsSNP and Indel variations simultaneously in 'Subicho', along with the two NLR genes linked to qRRs-10.1, which are known for their direct involvement in immune responses, are identified as most likely BW resistance genes. These variants in leading candidate genes associated with BW resistance can be used as important markers for breeding pepper varieties.

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通过抗性辣椒和易感辣椒之间的序列变异来预测抗细菌性枯萎病的功能候选基因。
背景:由 Ralstonia solanacearum(Ral)引起的细菌性枯萎病(BW)给辣椒作物造成了巨大的产量损失。通过育种培育抗病辣椒品种是控制细菌性枯萎病的最有效策略。要实现这一目标,就必须全面了解与抗性性状相关的遗传信息。尽管发现了辣椒细菌性枯萎病抗性的三个主要 QTL,即 8 号染色体上的 Bw1、10 号染色体上的 qRRs-10.1 和 1 号染色体上的 pBWR-1,但相关 BW 辣椒品种的遗传信息尚未得到充分研究。在此,我们对两个辣椒近交系 C. annuum 'MC4'(抗性)和 C. annuum 'Subicho'(易感性)进行了重新测序,并通过 SNPs 和 Indels 分析了基因组变异,以确定 BW 抗性的候选基因:结果:两个栽培品种平均产生了 139.5 Gb 的基因组,覆盖率从 44.94 倍到 46.13 倍不等。在'MC4'和'Subicho'之间共获得 8,815,889 个 SNPs。其中,31,190个(0.35%)非同义SNPs(nsSNPs)对应于10,926个基因,这些基因被分配到两个品种的142个基因本体(GO)术语中。我们通过基因组富集分析确定了具有序列变异的基因,并确保这些基因属于高显著性的 GO 术语,从而重点研究了三个已知的 BW QTL 区域。此外,我们还发现在这些区域内,'MC4'(R)和'Subicho'(S)之间有 310 个 NLR 基因存在 nsSNP 变异。此外,我们还对这些基因进行了 Indel 分析。通过整合所有这些数据,我们确定了 8 个候选 BW 抗性基因,其中包括染色体 10.1 上 qRRs-10.1 中具有 nsSNPs 变异的两个 NLR 基因:我们通过对细菌性枯萎病性状截然不同的两个辣椒栽培品种进行重测序,发现了 SNPs 和 Indels 形式的基因组变异。具体来说,在'Subicho'中,与 pBWR-1 和 Bw1 相关的四个基因同时表现出 nsSNP 和 Indel 变异,与 qRRs-10.1 相关的两个 NLR 基因也因直接参与免疫反应而被确定为最有可能的 BW 抗性基因。这些与 BW 抗性相关的主要候选基因变异可作为培育辣椒品种的重要标记。
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来源期刊
ACS Applied Energy Materials
ACS Applied Energy Materials Materials Science-Materials Chemistry
CiteScore
10.30
自引率
6.20%
发文量
1368
期刊介绍: ACS Applied Energy Materials is an interdisciplinary journal publishing original research covering all aspects of materials, engineering, chemistry, physics and biology relevant to energy conversion and storage. The journal is devoted to reports of new and original experimental and theoretical research of an applied nature that integrate knowledge in the areas of materials, engineering, physics, bioscience, and chemistry into important energy applications.
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