Proteomics Laboratory, Division of Plant Biotechnology, Sher-E-Kashmir University of Agricultural Sciences and Technology of Kashmir, Shalimar, Srinagar, Jammu and Kashmir, India.
Division of Animal Biotechnology, Sher-E-Kashmir University of Agricultural Sciences and Technology of Kashmir, Srinagar, India.
BMC Plant Biol. 2023 Jul 28;23(1):373. doi: 10.1186/s12870-023-04381-x.
Buckwheat (Fagopyrum spp.), belonging to the Polygonaceae family, is an ancient pseudo-cereal with high nutritional and nutraceutical properties. Buckwheat proteins are gluten-free and show balanced amino acid and micronutrient profiles, with higher content of health-promoting bioactive flavonoids that make it a golden crop of the future. Plant metabolome is increasingly gaining importance as a crucial component to understand the connection between plant physiology and environment and as a potential link between the genome and phenome. However, the genetic architecture governing the metabolome and thus, the phenome is not well understood. Here, we aim to obtain a deeper insight into the genetic architecture of seed metabolome in buckwheat by integrating high throughput metabolomics and genotyping-by-sequencing applying an array of bioinformatics tools for data analysis.
High throughput metabolomic analysis identified 24 metabolites in seed endosperm of 130 diverse buckwheat genotypes. The genotyping-by-sequencing (GBS) of these genotypes revealed 3,728,028 SNPs. The Genome Association and Prediction Integrated Tool (GAPIT) assisted in the identification of 27 SNPs/QTLs linked to 18 metabolites. Candidate genes were identified near 100 Kb of QTLs, providing insights into several metabolic and biosynthetic pathways.
We established the metabolome inventory of 130 germplasm lines of buckwheat, identified QTLs through marker trait association and positions of potential candidate genes. This will pave the way for future dissection of complex economic traits in buckwheat.
荞麦（Fagopyrum spp.）属于蓼科，是一种古老的伪谷物，具有很高的营养价值和营养保健特性。荞麦蛋白不含麸质，且氨基酸和微量营养素比例均衡，含有更高含量的促进健康的生物活性类黄酮，因此被誉为未来的黄金作物。植物代谢组学作为理解植物生理与环境之间联系的关键组成部分，以及连接基因组和表型组的潜在纽带，其重要性日益增加。然而，控制代谢组进而控制表型的遗传结构尚不清楚。在这里，我们旨在通过整合高通量代谢组学和基于测序的基因型分析（应用一系列生物信息学工具进行数据分析），深入了解荞麦种子代谢组的遗传结构。
高通量代谢组学分析鉴定了 130 个不同荞麦基因型种子胚乳中的 24 种代谢物。对这些基因型进行基于测序的基因型分析（GBS）揭示了 3728028 个 SNPs。基因组关联和预测综合工具（GAPIT）协助鉴定了与 18 种代谢物相关的 27 个 SNP/QTL。在 QTL 附近发现了 100 Kb 左右的候选基因，为多种代谢和生物合成途径提供了深入了解。
我们建立了 130 份荞麦种质资源的代谢组图谱，通过标记-性状关联和潜在候选基因的位置鉴定了 QTL。这将为未来荞麦复杂经济性状的剖析铺平道路。
