Abstract：
BACKGROUND: 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.
RESULTS: 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.
CONCLUSIONS: 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.
RESULTS: 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.
CONCLUSIONS: 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.
摘要：
背景：大豆油的产量和品质由种子油相关性状决定，和代谢物/脂质充当基因和性状之间的桥梁。尽管有许多关于代谢物或性状遗传方式的研究，关于多维遗传网络(MDGN)的研究有限。
结果：在这项研究中，六个种子油相关性状，59种代谢物，和398个重组自交系中的107个脂质，以及它们的候选基因和miRNAs，用于在大豆中构建MDGN。约175个数量性状基因座(QTLs)，36个QTL与环境的相互作用，和302个代谢QTL簇，70和181个候选基因，包括46和70个已知的同源物，以前报道与性状和代谢物有关，分别。使用共表达构建基因调控网络，蛋白质-蛋白质相互作用，以及候选基因和26个关键miRNA之间的转录因子结合位点和miRNA靶标预测。利用现代统计方法，463代谢物-脂质，62性状代谢物，发现89个性状-脂质关联是显着的。将这些关联整合到上述网络中，建立了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-代谢物关系与以前的研究一致，例如，油酸（性状）-GmSEI-GmDGAT1a-三酰甘油（16：0/18：2/18：3），预测64个子网络中的每个基因和代谢物都在相同的途径中，例如，油酸（性状）-GmPHS-D-葡萄糖，其他人是新的，例如，三酰基甘油(16:0/18:1/18:2)-GmbZIP123-GmHD-ZIPIII-10-miR166s-油含量。
结论：本研究显示了MGDN在解剖复杂性状与代谢产物之间的遗传关系方面的优势。在MGDN中使用子网络，包括丙酮酸/苏氨酸/柠檬酸在内的3D遗传子网络揭示了碳水化合物之间的遗传关系，油,和蛋白质含量，和包括PLD的4D遗传子网络揭示了可能受环境影响的油相关性状和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。
结果：在这项研究中，六个种子油相关性状，59种代谢物，和398个重组自交系中的107个脂质，以及它们的候选基因和miRNAs，用于在大豆中构建MDGN。约175个数量性状基因座(QTLs)，36个QTL与环境的相互作用，和302个代谢QTL簇，70和181个候选基因，包括46和70个已知的同源物，以前报道与性状和代谢物有关，分别。使用共表达构建基因调控网络，蛋白质-蛋白质相互作用，以及候选基因和26个关键miRNA之间的转录因子结合位点和miRNA靶标预测。利用现代统计方法，463代谢物-脂质，62性状代谢物，发现89个性状-脂质关联是显着的。将这些关联整合到上述网络中，建立了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-代谢物关系与以前的研究一致，例如，油酸（性状）-GmSEI-GmDGAT1a-三酰甘油（16：0/18：2/18：3），预测64个子网络中的每个基因和代谢物都在相同的途径中，例如，油酸（性状）-GmPHS-D-葡萄糖，其他人是新的，例如，三酰基甘油(16:0/18:1/18:2)-GmbZIP123-GmHD-ZIPIII-10-miR166s-油含量。
结论：本研究显示了MGDN在解剖复杂性状与代谢产物之间的遗传关系方面的优势。在MGDN中使用子网络，包括丙酮酸/苏氨酸/柠檬酸在内的3D遗传子网络揭示了碳水化合物之间的遗传关系，油,和蛋白质含量，和包括PLD的4D遗传子网络揭示了可能受环境影响的油相关性状和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。
