4D genetic networks reveal the genetic basis of metabolites and seed oil-related traits in 398 soybean RILs

Xu Han; Ya-Wen Zhang; Jin-Yang Liu; Jian-Fang Zuo; Ze-Chang Zhang; Liang Guo; Yuan-Ming Zhang

doi:10.1186/s13068-022-02191-1

4D genetic networks reveal the genetic basis of metabolites and seed oil-related traits in 398 soybean RILs

Biotechnol Biofuels Bioprod. 2022 Sep 9;15(1):92. doi: 10.1186/s13068-022-02191-1.

Authors

Xu Han¹, Ya-Wen Zhang¹, Jin-Yang Liu², Jian-Fang Zuo¹, Ze-Chang Zhang¹, Liang Guo¹, Yuan-Ming Zhang³

Affiliations

¹ College of Plant Science and Technology, Huazhong Agricultural University, Wuhan, 430070, China.
² Institute of Industrial Crops, Jiangsu Academy of Agricultural Sciences, Nanjing, 210014, China.
³ College of Plant Science and Technology, Huazhong Agricultural University, Wuhan, 430070, China. soyzhang@mail.hzau.edu.cn.

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.

Keywords: Lipid; Metabolite; Multi-dimension genetic network; Recombinant inbred line; Seed oil-related trait; Soybean; miRNA.

Abstract

Grants and funding