Department of Biological Engineering and the Center for Cellular Decision Processes, Massachusetts Institute of Technology, 77 Mass Ave., Cambridge, MA 02139, USA.
Biochem Soc Trans. 2012 Feb;40(1):133-8. doi: 10.1042/BST20110633.
Inflammation is a key physiological response to infection and injury and, although usually beneficial, it can also be damaging to the host. The liver is a prototypical example in this regard because inflammation helps to resolve liver injury, but it also underlies the aetiology of pathologies such as fibrosis and hepatocellular carcinoma. Liver cells sense their environment, including the inflammatory environment, through the activities of receptor-mediated signal transduction pathways. These pathways are organized in a complex interconnected network, and it is becoming increasingly recognized that cellular adaptations result from the quantitative integration of multi-pathway network activities, rather than isolated pathways causing particular phenotypes. Therefore comprehending liver cell signalling in inflammation requires a scientific approach that is appropriate for studying complex networks. In the present paper, we review our application of systems analyses of liver cell signalling in response to inflammatory environments. Our studies feature broad measurements of cell signalling and phenotypes in response to numerous experimental perturbations reflective of inflammatory environments, the data from which are analysed using Boolean and fuzzy logic models and regression-based methods in order to quantitatively relate the phenotypic responses to cell signalling network states. Our principal biological insight from these studies is that hepatocellular carcinoma cells feature uncoupled inflammatory and growth factor signalling, which may underlie their immune evasion and hyperproliferative properties.
炎症是感染和损伤的一种关键生理反应，尽管通常是有益的，但它也可能对宿主造成损害。肝脏在这方面是一个典型的例子，因为炎症有助于解决肝损伤，但它也是纤维化和肝细胞癌等疾病的病因。肝细胞通过受体介导的信号转导途径的活动来感知其环境，包括炎症环境。这些途径以复杂的相互关联的网络形式组织，人们越来越认识到，细胞的适应性是多种途径网络活动的定量整合的结果，而不是孤立的途径导致特定的表型。因此，理解炎症中的肝细胞信号需要一种适合于研究复杂网络的科学方法。在本文中，我们回顾了我们在应用系统分析肝脏细胞信号对炎症环境的反应方面的工作。我们的研究以广泛测量细胞信号和表型为特色，这些表型是对反映炎症环境的许多实验扰动的反应，这些数据使用布尔逻辑和模糊逻辑模型以及基于回归的方法进行分析，以便定量地将表型反应与细胞信号网络状态联系起来。我们从这些研究中得到的主要生物学见解是，肝细胞癌具有解耦的炎症和生长因子信号，这可能是它们免疫逃避和过度增殖特性的基础。
