Transcriptome-Enabled Network Inference Revealed the GmCOL1 Feed-Forward Loop and Its Roles in Photoperiodic Flowering of Soybean
Transcriptome-Enabled Network Inference Revealed the GmCOL1 Feed-Forward Loop and Its Roles in Photoperiodic Flowering of Soybean
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DOI：
10.3389/fpls.2019.01221
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发表时间：
2019-11
影响因子：
5.6
通讯作者：
Faqiang Wu;Xiaohan Kang;Minglei Wang;W. Haider;W. Price;B. Hajek;Y. Hanzawa
中科院分区：
文献类型：
--
作者：
Faqiang Wu;Xiaohan Kang;Minglei Wang;W. Haider;W. Price;B. Hajek;Y. Hanzawa
光周期开花是植物对季节性光周期变化的反应，是植物重要的农艺性状，也是作物驯化和现代育种的重要目标。然而，我们对作物光周期开花调控的分子机制的认识却相对滞后。为了更好地了解控制大豆光周期开花的调控基因网络，我们利用成熟位点（E loci）的近等基因系（NILs）阐明了不同光周期条件下大豆基因的表达模式。转录组签名确定了光周期开花的E位点的独特作用和由这些位点控制的一组基因。为了阐明光周期开花调控的调控基因网络，我们开发了网络推理算法包CausNet，该算法包集成了稀疏线性回归和格兰杰因果关系分析，并采用高斯近似自举法为预测的调控相互作用提供可靠性分数。利用转录组数据，CausNet推断了大豆开花基因之间的调控相互作用。文献中发表的报告为CausNet推断的几种监管相互作用提供了实证验证。利用转GmCOL1RNAi基因大豆植株进一步证实了开花抑制基因GmCOL1a和GmCOL1b的调控作用。GmCOL1和主要成熟位点E1的等位基因的组合表明这些基因之间的正相互作用，导致增强的开花转换的抑制。我们的工作为大豆光周期开花控制的复杂分子机制提供了新的见解和可检验的假设，并为控制作物重要农艺性状的生物网络的从头预测奠定了框架。
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 (E loci). Transcriptome signatures identified the unique roles of the E loci 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 suppressors GmCOL1a and GmCOL1b using GmCOL1 RNAi transgenic soybean plants. Combinations of the alleles of GmCOL1 and the major maturity locus E1 demonstrated 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 for de novo prediction of biological networks controlling important agronomic traits in crops.