State Key Laboratory of Hybrid Rice, Key Laboratory for Research and Utilization of Heterosis in Indica Rice, the Ministry of Agriculture, College of Life Sciences, Wuhan University, Wuhan 430072, China.
Institute of Biological Chemistry, Washington State University, Pullman, Washington 99164-6414, USA.
Plant Physiol. 2021 Oct 5;187(2):1011-1025. doi: 10.1093/plphys/kiab273.
Understanding the molecular mechanisms underlying complex phenotypes requires systematic analyses of complicated metabolic networks and contributes to improvements in the breeding efficiency of staple cereal crops and diagnostic accuracy for human diseases. Here, we selected rice (Oryza sativa) heterosis as a complex phenotype and investigated the mechanisms of both vegetative and reproductive traits using an untargeted metabolomics strategy. Heterosis-associated analytes were identified, and the overlapping analytes were shown to underlie the association patterns for six agronomic traits. The heterosis-associated analytes of four yield components and plant height collectively contributed to yield heterosis, and the degree of contribution differed among the five traits. We performed dysregulated network analyses of the high- and low-better parent heterosis hybrids and found multiple types of metabolic pathways involved in heterosis. The metabolite levels of the significantly enriched pathways (especially those from amino acid and carbohydrate metabolism) were predictive of yield heterosis (area under the curve = 0.907 with 10 features), and the predictability of these pathway biomarkers was validated with hybrids across environments and populations. Our findings elucidate the metabolomic landscape of rice heterosis and highlight the potential application of pathway biomarkers in achieving accurate predictions of complex phenotypes.
理解复杂表型的分子机制需要对复杂的代谢网络进行系统分析，这有助于提高主要粮食作物的育种效率和人类疾病的诊断准确性。在这里，我们选择水稻杂种优势作为一种复杂表型，并采用非靶向代谢组学策略研究了营养生长和生殖生长性状的机制。鉴定出与杂种优势相关的分析物，并显示重叠分析物是六个农艺性状关联模式的基础。四个产量构成因子和株高的杂种优势相关分析物共同导致产量杂种优势，而五个性状之间的贡献程度不同。我们对高值和低值双亲杂种优势杂种进行了失调网络分析，发现涉及杂种优势的多种类型的代谢途径。显著富集途径（特别是来自氨基酸和碳水化合物代谢的途径）的代谢物水平可预测产量杂种优势（10 个特征的曲线下面积=0.907），并且这些途径生物标志物的可预测性已通过跨环境和群体的杂种进行了验证。我们的研究结果阐明了水稻杂种优势的代谢组学景观，并强调了途径生物标志物在实现复杂表型的准确预测中的潜在应用。
