Plant Physiology and BiochemistryPlant BiologyPeer-reviewed journal article
Multiomic analysis of genes related to oil traits in legumes provide insights into lipid metabolism and oil richness in soybean
Dayana K. Turquetti-MoraesiD, Cláudio Benício Cardoso-SilvaiD, Fabricio Almeida‐SilvaiD, Thiago M. VenâncioiD
Abstract
Soybean (Glycine max) and common bean (Phaseolus vulgaris) diverged approximately 19 million years ago. While these species share a whole-genome duplication (WGD), the Glycine lineage experienced a second, independent WGD. Despite the significance of these WGDs, their impact on gene families related to oil-traits remains poorly understood. Here, we report an in-depth investigation of oil-related gene families in soybean, common bean, and twenty-eight other legume species. We adopted a systematic approach that included transcriptome and co-expression analysis, identification of orthologous groups, gene duplication modes and evolutionary rates, and family expansions and contractions. We curated a list of oil candidate genes and found that 91.5% of the families containing these genes expanded in soybean in comparison to common bean. Notably, we observed an expansion of triacylglycerol (TAG) biosynthesis ([~]3:1) and an erosion of TAG degradation ([~]1.4:1) families in soybean in comparison to common bean. In addition, TAG degradation genes were two-fold more expressed in common bean than in soybean, suggesting that oil degradation is also important for the sharply contrasting seed oil contents in these species. We found 17 transcription factor hub genes that are likely regulators of lipid metabolism. Finally, we inferred expanded and contracted families and correlated these patterns with oil content found in different legume species. In summary, our results do not only shed light on the evolution of oil metabolism genes in soybean, but also present multifactorial evidence supporting the prioritization of candidates for crop improvement.
- organism: plant (other)
- assay: bulk RNA-seq
- research focus: gene regulatory network inference
- research focus: clustering
- approach: statistical model
- Citations
- 6
- FWCI
- 0.97
- Field percentile
- top 32%
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