Protein kinase-mediated phosphorylation plays a critical role in many biological processes. However, the identification of key regulatory kinases is still a great challenge. Here, we develop a trans-omics-based method, central kinase inference, to predict potentially key kinases by integrating quantitative transcriptomic and phosphoproteomic data. Using known kinases associated with anti-cancer drug resistance, the accuracy of our method denoted by the area under the curve is 5.2% to 29.5% higher than Kinase-Substrate Enrichment Analysis. We further use this method to analyze trans-omic data in hepatocyte maturation and hepatic reprogramming of human dermal fibroblasts, uncovering 5 kinases as regulators in the two processes. Further experiments reveal that a serine/threonine kinase, PIM1, promotes hepatic conversion and protects human dermal fibroblasts from reprogramming-induced ferroptosis and cell cycle arrest. This study not only reveals new regulatory kinases, but also provides a helpful method that might be extended to predict central kinases involved in other biological processes.

蛋白激酶介导的磷酸化在许多生物过程中发挥着关键作用。然而，关键调节激酶的鉴定仍然是一个巨大的挑战。在这里，我们开发了一种基于跨组学的方法，即中心激酶推断，通过整合定量转录组和磷酸蛋白质组数据来预测潜在的关键激酶。使用与抗癌药物耐药性相关的已知激酶，我们用曲线下面积表示的方法的准确性比激酶底物富集分析高 5.2% 至 29.5%。我们进一步使用这种方法来分析人真皮成纤维细胞的肝细胞成熟和肝重编程的跨组学数据，发现 5 个激酶作为这两个过程的调节因子。进一步的实验表明，丝氨酸/苏氨酸激酶 PIM1 可促进肝脏转化并保护人类真皮成纤维细胞免受重编程诱导的铁死亡和细胞周期停滞的影响。这项研究不仅揭示了新的调节激酶，而且还提供了一种有用的方法，可以扩展到预测参与其他生物过程的中心激酶。