Pradervand Sylvain, Maurya Mano R, Subramaniam Shankar
Bioinformatics and Data Coordination Laboratory, Alliance for Cellular Signaling, San Diego Supercomputer Center, University of California at San Diego, Gilman Drive, La Jolla, CA 92093, USA.
Genome Biol. 2006;7(2):R11. doi: 10.1186/gb-2006-7-2-r11. Epub 2006 Feb 20.
Release of immuno-regulatory cytokines and chemokines during inflammatory response is mediated by a complex signaling network. Multiple stimuli produce different signals that generate different cytokine responses. Current knowledge does not provide a complete picture of these signaling pathways. However, using specific markers of signaling pathways, such as signaling proteins, it is possible to develop a 'coarse-grained network' map that can help understand common regulatory modules for various cytokine responses and help differentiate between the causes of their release.
Using a systematic profiling of signaling responses and cytokine release in RAW 264.7 macrophages made available by the Alliance for Cellular Signaling, an analysis strategy is presented that integrates principal component regression and exhaustive search-based model reduction to identify required signaling factors necessary and sufficient to predict the release of seven cytokines (G-CSF, IL-1alpha, IL-6, IL-10, MIP-1alpha, RANTES, and TNFalpha) in response to selected ligands. This study provides a model-based quantitative estimate of cytokine release and identifies ten signaling components involved in cytokine production. The models identified capture many of the known signaling pathways involved in cytokine release and predict potentially important novel signaling components, like p38 MAPK for G-CSF release, IFNgamma- and IL-4-specific pathways for IL-1a release, and an M-CSF-specific pathway for TNFalpha release.
Using an integrative approach, we have identified the pathways responsible for the differential regulation of cytokine release in RAW 264.7 macrophages. Our results demonstrate the power of using heterogeneous cellular data to qualitatively and quantitatively map intermediate cellular phenotypes.
炎症反应期间免疫调节细胞因子和趋化因子的释放由一个复杂的信号网络介导。多种刺激产生不同的信号，进而产生不同的细胞因子反应。目前的知识并未完整呈现这些信号通路。然而，利用信号通路的特定标志物，如信号蛋白，有可能绘制出一张“粗粒度网络”图谱，这有助于理解各种细胞因子反应的共同调节模块，并有助于区分其释放的原因。
利用细胞信号转导联盟提供的RAW 264.7巨噬细胞中信号反应和细胞因子释放的系统分析，提出了一种分析策略，该策略整合主成分回归和基于穷举搜索的模型简化，以识别预测七种细胞因子（粒细胞集落刺激因子、白细胞介素-1α、白细胞介素-6、白细胞介素-10、巨噬细胞炎性蛋白-1α、调节激活正常T细胞表达和分泌因子、肿瘤坏死因子α）对选定配体反应释放所需的必要且充分的信号因子。本研究提供了基于模型的细胞因子释放定量估计，并确定了参与细胞因子产生的十个信号成分。所识别的模型涵盖了许多已知的参与细胞因子释放的信号通路，并预测了潜在重要的新信号成分，如参与粒细胞集落刺激因子释放的p38丝裂原活化蛋白激酶、参与白细胞介素-1α释放的γ干扰素和白细胞介素-4特异性通路，以及参与肿瘤坏死因子α释放的巨噬细胞集落刺激因子特异性通路。
采用综合方法，我们确定了RAW 264.7巨噬细胞中细胞因子释放差异调节的相关通路。我们的结果证明了利用异质细胞数据定性和定量绘制中间细胞表型图谱的能力。
