Department of Crop Genetics, John Innes Centre, Norwich Research Park, Norwich, NR4 7UH, United Kingdom.
Computational and Analytical Sciences, Rothamsted Research, Harpenden, Hertfordshire, AL5 2JQ, United Kingdom.
G3 (Bethesda). 2020 Oct 5;10(10):3675-3686. doi: 10.1534/g3.120.401436.
Gene regulatory networks are powerful tools which facilitate hypothesis generation and candidate gene discovery. However, the extent to which the network predictions are biologically relevant is often unclear. Recently a GENIE3 network which predicted targets of wheat transcription factors was produced. Here we used an independent RNA-Seq dataset to test the predictions of the wheat GENIE3 network for the senescence-regulating transcription factor (TraesCS6A02G108300). We re-analyzed the RNA-Seq data against the RefSeqv1.0 genome and identified a set of differentially expressed genes (DEGs) between the wild-type and mutant which recapitulated the known role of in senescence and nutrient remobilisation. We found that the GENIE3-predicted target genes of overlap significantly with the DEGs, more than would be expected by chance. Based on high levels of overlap between GENIE3-predicted target genes and the DEGs, we identified candidate senescence regulators. We then explored genome-wide trends in the network related to polyploidy and found that only homeologous transcription factors are likely to share predicted targets in common. However, homeologs which vary in expression levels across tissues are less likely to share predicted targets than those that do not, suggesting that they may be more likely to act in distinct pathways. This work demonstrates that the wheat GENIE3 network can provide biologically-relevant predictions of transcription factor targets, which can be used for candidate gene prediction and for global analyses of transcription factor function. The GENIE3 network has now been integrated into the KnetMiner web application, facilitating its use in future studies.
基因调控网络是生成假说和发现候选基因的有力工具。然而，网络预测在多大程度上具有生物学相关性通常不清楚。最近，生成了一个预测小麦转录因子靶标的 GENIE3 网络。在这里，我们使用独立的 RNA-Seq 数据集来测试小麦 GENIE3 网络对衰老调节转录因子（TraesCS6A02G108300）的预测。我们针对 RefSeqv1.0 基因组重新分析了 RNA-Seq 数据，并确定了一组在野生型和突变体之间差异表达的基因（DEGs），这些基因重现了 在衰老和养分再动员中的已知作用。我们发现，GENIE3 预测的 靶基因与 DEGs 显著重叠，超过了随机预期的程度。基于 GENIE3 预测的靶基因和 DEGs 之间的高度重叠，我们确定了候选衰老调节剂。然后，我们探索了与多倍体相关的网络的全基因组趋势，并发现只有同源转录因子可能具有共同的预测靶标。然而，在不同组织中表达水平不同的同源物比那些不具有共同预测靶标的同源物不太可能共享预测靶标，这表明它们可能更有可能在不同的途径中发挥作用。这项工作表明，小麦 GENIE3 网络可以提供具有生物学相关性的转录因子靶标的预测，可用于候选基因预测和转录因子功能的全局分析。GENIE3 网络现已集成到 KnetMiner 网络应用程序中，便于在未来的研究中使用。
