Transcriptome-enabled network inference revealed the GmCOL1 feed-forward loop and its roles in photoperiodic flowering of soybean

Reference Report for AP20210708.7

Title:	Transcriptome-enabled network inference revealed the GmCOL1 feed-forward loop and its roles in photoperiodic flowering of soybean
Authors:	Wu, F., Kang, X., Wang, M., Haider, W., William B. W., Hajek, B., Hanzawa, Y.
Source:	Wu et al. 2019 Front. Plant. Sci., 10:1221
Abstract:	Photoperiodic flowering, a plant response to seasonal photoperiod changes in the control of reproductive transition, is an important agronomic trait that has been a central target of crop domestication and modern breeding programs. However, our understanding about the molecular mechanisms of photoperiodic flowering regulation in crop species is lagging behind. To better understand the regulatory gene networks controlling photoperiodic flowering of soybeans, we elucidated global gene expression patterns under different photoperiod regimes using the near isogenic lines (NILs) of maturity loci (Eloci). Transcriptome signatures identified the unique roles of theEloci in photoperiodic flowering and a set of genes controlled by these loci. To elucidate the regulatory gene networks underlying photoperiodic flowering regulation, we developed the network inference algorithmic package CausNet that integrates sparse linear regression and Granger causality heuristics, with Gaussian approximation of bootstrapping to provide reliability scores for predicted regulatory interactions. Using the transcriptome data, CausNet inferred regulatory interactions among soybean flowering genes. Published reports in the literature provided empirical verification for several of CausNet's inferred regulatory interactions. We further confirmed the inferred regulatory roles of the flowering suppressorsGmCOL1aandGmCOL1busingGmCOL1RNAi transgenic soybean plants. Combinations of the alleles ofGmCOL1and the major maturity locusE1demonstrated positive interaction between these genes, leading to enhanced suppression of flowering transition. Our work provides novel insights and testable hypotheses in the complex molecular mechanisms of photoperiodic flowering control in soybean and lays a framework forde novoprediction of biological networks controlling important agronomic traits in crops.

Funded by the USDA-ARS. Developed by the USDA-ARS SoyBase and Legume Clade Database group at the Iowa State University, Ames, IA

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