Han Xu, Zhang Ya-Wen, Liu Jin-Yang, Zuo Jian-Fang, Zhang Ze-Chang, Guo Liang, Zhang Yuan-Ming
College of Plant Science and Technology, Huazhong Agricultural University, Wuhan, 430070, China.
Institute of Industrial Crops, Jiangsu Academy of Agricultural Sciences, Nanjing, 210014, China.
Biotechnol Biofuels Bioprod. 2022 Sep 9;15(1):92. doi: 10.1186/s13068-022-02191-1.
The yield and quality of soybean oil are determined by seed oil-related traits, and metabolites/lipids act as bridges between genes and traits. Although there are many studies on the mode of inheritance of metabolites or traits, studies on multi-dimensional genetic network (MDGN) are limited.
In this study, six seed oil-related traits, 59 metabolites, and 107 lipids in 398 recombinant inbred lines, along with their candidate genes and miRNAs, were used to construct an MDGN in soybean. Around 175 quantitative trait loci (QTLs), 36 QTL-by-environment interactions, and 302 metabolic QTL clusters, 70 and 181 candidate genes, including 46 and 70 known homologs, were previously reported to be associated with the traits and metabolites, respectively. Gene regulatory networks were constructed using co-expression, protein-protein interaction, and transcription factor binding site and miRNA target predictions between candidate genes and 26 key miRNAs. Using modern statistical methods, 463 metabolite-lipid, 62 trait-metabolite, and 89 trait-lipid associations were found to be significant. Integrating these associations into the above networks, an MDGN was constructed, and 128 sub-networks were extracted. Among these sub-networks, the gene-trait or gene-metabolite relationships in 38 sub-networks were in agreement with previous studies, e.g., oleic acid (trait)-GmSEI-GmDGAT1a-triacylglycerol (16:0/18:2/18:3), gene and metabolite in each of 64 sub-networks were predicted to be in the same pathway, e.g., oleic acid (trait)-GmPHS-D-glucose, and others were new, e.g., triacylglycerol (16:0/18:1/18:2)-GmbZIP123-GmHD-ZIPIII-10-miR166s-oil content.
This study showed the advantages of MGDN in dissecting the genetic relationships between complex traits and metabolites. Using sub-networks in MGDN, 3D genetic sub-networks including pyruvate/threonine/citric acid revealed genetic relationships between carbohydrates, oil, and protein content, and 4D genetic sub-networks including PLDs revealed the relationships between oil-related traits and phospholipid metabolism likely influenced by the environment. This study will be helpful in soybean quality improvement and molecular biological research.
大豆油的产量和品质由种子油相关性状决定，代谢物/脂质充当基因与性状之间的桥梁。尽管关于代谢物或性状的遗传模式已有许多研究，但关于多维遗传网络（MDGN）的研究却很有限。
在本研究中，利用398个重组自交系中的6个种子油相关性状、59种代谢物和107种脂质，以及它们的候选基因和微小RNA（miRNA），构建了大豆的MDGN。此前已报道约175个数量性状基因座（QTL）、36个QTL与环境的互作以及302个代谢QTL簇，分别有70个和181个候选基因与这些性状和代谢物相关，其中包括46个和70个已知同源基因。利用共表达、蛋白质-蛋白质相互作用、转录因子结合位点以及候选基因与26个关键miRNA之间的miRNA靶标预测构建了基因调控网络。使用现代统计方法，发现463个代谢物-脂质、62个性状-代谢物和89个性状-脂质关联具有显著性。将这些关联整合到上述网络中，构建了MDGN，并提取了128个子网络。在这些子网络中，38个子网络中的基因-性状或基因-代谢物关系与先前研究一致，例如油酸（性状）-GmSEI-GmDGAT1a-三酰甘油（16:0/18:2/18:3），64个子网络中的每个子网络中的基因和代谢物被预测处于同一途径，例如油酸（性状）-GmPHS-D-葡萄糖，其他则是新发现的，例如三酰甘油（16:0/18:1/18:2）-GmbZIP123-GmHD-ZIPIII-10-miR166s-油含量。
本研究展示了MGDN在剖析复杂性状与代谢物之间遗传关系方面的优势。利用MGDN中的子网络，包括丙酮酸/苏氨酸/柠檬酸的3D遗传子网络揭示了碳水化合物、油和蛋白质含量之间的遗传关系，包括磷脂酶D（PLD）的4D遗传子网络揭示了油相关性状与可能受环境影响的磷脂代谢之间的关系。本研究将有助于大豆品质改良和分子生物学研究。
