Department of Biotechnology and Biomedicine, Technical University of Denmark, Kongens Lyngby, Denmark.
Department of Statistics, University of Oxford, Oxford, United Kingdom.
PLoS Comput Biol. 2022 Jun 22;18(6):e1009414. doi: 10.1371/journal.pcbi.1009414. eCollection 2022 Jun.
Gene expression is controlled by pathways of regulatory factors often involving the activity of protein kinases on transcription factor proteins. Despite this well established mechanism, the number of well described pathways that include the regulatory role of protein kinases on transcription factors is surprisingly scarce in eukaryotes. To address this, PhosTF was developed to infer functional regulatory interactions and pathways in both simulated and real biological networks, based on linear cyclic causal models with latent variables. GeneNetWeaverPhos, an extension of GeneNetWeaver, was developed to allow the simulation of perturbations in known networks that included the activity of protein kinases and phosphatases on gene regulation. Over 2000 genome-wide gene expression profiles, where the loss or gain of regulatory genes could be observed to perturb gene regulation, were then used to infer the existence of regulatory interactions, and their mode of regulation in the budding yeast Saccharomyces cerevisiae. Despite the additional complexity, our inference performed comparably to the best methods that inferred transcription factor regulation assessed in the DREAM4 challenge on similar simulated networks. Inference on integrated genome-scale data sets for yeast identified ∼ 8800 protein kinase/phosphatase-transcription factor interactions and ∼ 6500 interactions among protein kinases and/or phosphatases. Both types of regulatory predictions captured statistically significant numbers of known interactions of their type. Surprisingly, kinases and phosphatases regulated transcription factors by a negative mode or regulation (deactivation) in over 70% of the predictions.
基因表达受调控因子途径控制，这些途径通常涉及蛋白激酶对转录因子蛋白的活性。尽管这一机制已经得到很好的证实，但在真核生物中，包括蛋白激酶对转录因子的调控作用的描述良好的途径数量却出人意料地很少。为了解决这个问题，PhosTF 被开发出来，用于根据具有潜在变量的线性循环因果模型，推断模拟和真实生物网络中的功能调控相互作用和途径。GeneNetWeaverPhos 是 GeneNetWeaver 的扩展，它被开发出来允许模拟包括蛋白激酶和磷酸酶对基因调控的活性在内的已知网络中的扰动。然后，使用超过 2000 个全基因组基因表达谱，观察到调控基因的缺失或获得会扰乱基因调控，从而推断出调控相互作用的存在及其在酿酒酵母中的调控模式。尽管增加了复杂性，但我们的推断与在类似模拟网络上的 DREAM4 挑战中评估的推断转录因子调控的最佳方法相当。对酵母的综合基因组规模数据集的推断确定了约 8800 个蛋白激酶/磷酸酶-转录因子相互作用和约 6500 个蛋白激酶和/或磷酸酶之间的相互作用。这两种类型的调控预测都捕获了其类型的大量已知相互作用。令人惊讶的是，激酶和磷酸酶通过负调控模式（失活）调节转录因子的比例超过 70%。
