Department of Plant Biotechnology and Bioinformatics, Ghent University, Ghent, Belgium.
Center for Plant Systems Biology, VIB, Ghent, Belgium.
Methods Mol Biol. 2023;2698:323-349. doi: 10.1007/978-1-0716-3354-0_19.
Gene regulatory networks (GRNs) represent the regulatory links between transcription factors (TF) and their target genes. In plants, they are essential to understand transcriptional programs that control important agricultural traits such as yield or (a)biotic stress response. Although several high- and low-throughput experimental methods have been developed to map GRNs in plants, these are sometimes expensive, come with laborious protocols, and are not always optimized for tomato, one of the most important horticultural crops worldwide. In this chapter, we present a computational method that covers two protocols: one protocol to map gene identifiers between two different tomato genome assemblies, and another protocol to predict putative regulators and delineate GRNs given a set of functionally related or coregulated genes by exploiting publicly available TF-binding information. As an example, we applied the motif enrichment protocol on tomato using upregulated genes in response to jasmonate, as well as upregulated and downregulated genes in plants with genotypes OENAM1 and nam1, respectively. We found that our protocol accurately infers the expected TFs as top enriched regulators and identifies GRNs functionally enriched in biological processes related with the experimental context under study.
基因调控网络（GRNs）代表转录因子（TF）与其靶基因之间的调控关系。在植物中，它们对于理解控制重要农业性状（如产量或抗逆性）的转录程序至关重要。尽管已经开发了几种高通量和低通量的实验方法来绘制植物中的 GRN，但这些方法有时成本高昂，实验方案繁琐，并且并不总是针对番茄进行优化，番茄是全球最重要的园艺作物之一。在本章中，我们提出了一种计算方法，该方法涵盖了两个方案：一个方案用于在两个不同的番茄基因组组装之间映射基因标识符，另一个方案用于利用公开的 TF 结合信息，预测给定一组功能相关或共调控基因的潜在调节剂并描绘 GRN。例如，我们使用茉莉酸响应上调的基因以及 OENAM1 和 nam1 基因型植物中上调和下调的基因，在番茄上应用了 motif enrichment 方案。我们发现，我们的方案准确地推断出预期的 TF 作为顶级富集调节剂，并识别出与研究背景下的实验上下文相关的功能富集的 GRN。
