Cluster of Excellence on Cellular Stress Responses in Aging-associated Diseases (CECAD), University of Cologne, Cologne, Germany.
Department II of Internal Medicine, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany.
Nat Rev Nephrol. 2024 Sep;20(9):616-633. doi: 10.1038/s41581-024-00849-7. Epub 2024 Jun 12.
The precise control of gene expression is required for the maintenance of cellular homeostasis and proper cellular function, and the declining control of gene expression with age is considered a major contributor to age-associated changes in cellular physiology and disease. The coordination of gene expression can be represented through models of the molecular interactions that govern gene expression levels, so-called gene regulatory networks. Gene regulatory networks can represent interactions that occur through signal transduction, those that involve regulatory transcription factors, or statistical models of gene-gene relationships based on the premise that certain sets of genes tend to be coexpressed across a range of conditions and cell types. Advances in experimental and computational technologies have enabled the inference of these networks on an unprecedented scale and at unprecedented precision. Here, we delineate different types of gene regulatory networks and their cell-biological interpretation. We describe methods for inferring such networks from large-scale, multi-omics datasets and present applications that have aided our understanding of cellular ageing and disease mechanisms.
精确控制基因表达是维持细胞内稳态和正常细胞功能所必需的,随着年龄的增长,基因表达的控制能力下降被认为是与年龄相关的细胞生理学和疾病变化的主要原因之一。基因表达的协调可以通过控制基因表达水平的分子相互作用模型来表示,即所谓的基因调控网络。基因调控网络可以表示通过信号转导发生的相互作用、涉及调节转录因子的相互作用,或者基于某些基因集在一系列条件和细胞类型下往往共表达的前提的基于基因-基因关系的统计模型。实验和计算技术的进步使得可以以前所未有的规模和精度推断这些网络。在这里,我们描述了不同类型的基因调控网络及其细胞生物学解释。我们描述了从大规模多组学数据集中推断这些网络的方法,并介绍了有助于我们理解细胞衰老和疾病机制的应用。