Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Esch-sur-Alzette L-4362, Luxembourg.
Integrated BioBank of Luxembourg, Dudelange L-3555, Luxembourg.
Nucleic Acids Res. 2019 Jul 9;47(12):e72. doi: 10.1093/nar/gkz232.
Induction of specific cellular transitions is of clinical importance, as it allows to revert disease cellular phenotype, or induce cellular reprogramming and differentiation for regenerative medicine. Signalling is a convenient way to accomplish such transitions without transfer of genetic material. Here we present the first general computational method that systematically predicts signalling molecules, whose perturbations induce desired cellular transitions. This probabilistic method integrates gene regulatory networks (GRNs) with manually-curated signalling pathways obtained from MetaCore from Clarivate Analytics, to model how signalling cues are received and processed in the GRN. The method was applied to 219 cellular transition examples, including cell type transitions, and overall correctly predicted experimentally validated signalling molecules, consistently outperforming other well-established approaches, such as differential gene expression and pathway enrichment analyses. Further, we validated our method predictions in the case of rat cirrhotic liver, and identified the activation of angiopoietins receptor Tie2 as a potential target for reverting the disease phenotype. Experimental results indicated that this perturbation induced desired changes in the gene expression of key TFs involved in fibrosis and angiogenesis. Importantly, this method only requires gene expression data of the initial and desired cell states, and therefore is suited for the discovery of signalling interventions for disease treatments and cellular therapies.
诱导特定的细胞转变具有重要的临床意义，因为它可以使疾病细胞表型逆转，或诱导细胞重编程和分化，用于再生医学。信号转导是一种在不转移遗传物质的情况下实现这些转变的便捷方式。在这里，我们提出了第一个通用的计算方法，可以系统地预测信号分子，其干扰可诱导所需的细胞转变。这种概率方法将基因调控网络 (GRN) 与从 Clarivate Analytics 的 MetaCore 中手动整理的信号通路集成在一起，以模拟信号线索如何在 GRN 中被接收和处理。该方法应用于 219 个细胞转变示例，包括细胞类型转变，并且总体上正确预测了实验验证的信号分子，始终优于其他成熟的方法，如差异基因表达和途径富集分析。此外，我们在大鼠肝硬化肝脏的情况下验证了我们方法的预测，并确定了血管生成素受体 Tie2 的激活作为逆转疾病表型的潜在靶点。实验结果表明，这种干扰诱导了关键 TF 参与纤维化和血管生成的基因表达的所需变化。重要的是，该方法仅需要初始和所需细胞状态的基因表达数据，因此适用于发现疾病治疗和细胞治疗的信号干预措施。
