Developmental Dynamic Transcriptome and Systematic Analysis Reveal the Major Genes Underlying Isoflavone Accumulation in Soybean

Technical Route

Soy isoflavone, a sub–group of flavonoids derived from phenylalanine, is a class of polyphenolic compounds exclusively occurred in legumes. This secondary metabolite is usually referred as phytoestrogen, since it is structurally and functionally similar to estrogen. Currently, isoflavones have been widely used in health product and bio–pharmaceutical, for it has the functions of preventing breast cancer, relieving Alzheimer disease, resisting menopause syndrome, et cetera. Isoflavones also play a vital role in the system of plant defense responses. It could be further synthesized into major phytoalexins, and therefore exhibiting significant effect in plant insect–resistance, disease–resistance, and various other biotic stresses.

Because of the irreplaceable role of isoflavone in human health and plant growth, many related studies have been carried out, including its structure, ingredient, and genetic background. Up to now, a total of 12 kind of isoflavones are found, which are generally divided into 4 main categories based on their structure. They are: 1) aglycones (including daidzein, glycitein and genistein); 2) glycosides (including daidzin, glycitin and genistin); 3) acetyl–glycosides (including acetyl-daidzin, acetyl-glycitin and acetyl-genistin); 4) malonyl–glycosides (including malonyl-daidzin, malonyl-glycitin and malonyl-genistin) (Dixon, 2004). The glucosides are the hinges of isoflavone biosynthesis pathway in vivo, for they are the products of aglycones and the precursors of other two categories. Since the aglycones are the mainly active form in soybean seed while the glycosides are the primary storage form in soybean seed, the aglycones and glycosides are the most important concerned categories among these 12 isoflavone.

Isoflavone content is a typical quantitative trait, which is regulated by both genetic and environmental factors. The approaches of quantitative genetics have been the major methods for investigating the genetic background of isoflavone, such as QTL mapping. Based on the high-density genetic map, a total of 63 stable QTL (containing 52 meta-QTL and 11 novel QTL) were mapped by our proceeding study. However, although these QTL have been narrowed down to a short interval length, major genes influencing the accumulation of isoflavones are required for the quantitative trait genes (QTG), rather than QTL, regulate the phenotype in vivo.

Transcriptome analysis is a good way to obtain QTG from QTL, as well as an efficient and useful approach in exploring the major genes of complex quantitative trait. For instance, CHS7 and cytochrome P450 have been identified, which influenced the accumulation of isoflavone. Generally, candidate genes could be obtained from RNA–seq data by differential gene expression analysis (DE analysis). By comparing the transcriptional changes, the differential expression genes (DEG) among several significantly different samples could be obtained. However, the internal relationships between candidate genes could not be clarified in this way. The weighted gene co–expression network analysis (WGCNA) to make up the inadequacy of DE analysis, for it could assist to describe the internal relationships among candidate genes by analyzing the degree of association between genes, finding molecular modules (clusters) and mapping hub genes. The time series analysis is also applied to analyze RNA–seq data, which generally require a time series expression profile and could give the temporal expression variation of several important clusters or genes.

The outline of isoflavones biosynthesis pathway has been basically revealed for decades. However, the accumulation pattern in different developing stages of isoflavones at both molecular and physiological level is still unclear. Actually, there is a significant difference between the variation trend of different components, and a quite close internal relationship among different components of isoflavone. The main factor influencing isoflavones variation might not be same in different developing stages. Consequently, clarifying the accumulation patterns of isoflavone components at physiological and molecular level is quite essential for comprehensively and deeply understanding the biosynthesis and regulation mechanisms of isoflavone in soybean.

The aims of current study were: 1) to investigate the accumulation patterns of isoflavones at physiological and molecular level; 2) to map the major genes and QTG associated with isoflavones accumulation; 3) to explore the dynamic regulation network regulating isoflavones accumulation in developing seed. The results could facilitate elucidating the molecular mechanism of isoflavones biosynthesis and regulation, as well as breeding elite soybean cultivars with high isoflavone content.

Heng Chen
Heng Chen
PhD Student

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