Cue-signal-response analysis of TNF-induced apoptosis by partial least squares regression of dynamic multivariate data
Cue-signal-response analysis of TNF-induced apoptosis by partial least squares regression of dynamic multivariate data
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DOI：
10.1089/cmb.2004.11.544
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发表时间：
2004-01-01
影响因子：
1.7
通讯作者：
Lauffenburger, DA
中科院分区：
文献类型：
--
作者：
Janes, KA;Kelly, JR;Lauffenburger, DA
生物信号网络处理细胞外信号以控制重要的细胞决定，例如死亡-存活、生长-静止和增殖-分化。受体激活后，细胞内信号蛋白的丰度、修饰状态和酶活性发生变化。信号网络中的许多蛋白质已经被鉴定出来，但尚不清楚信号分子如何共同控制细胞决策。为了开始解决这个问题，我们报告使用偏最小二乘回归作为一种分析方法，收集从HT-29人结肠癌细胞刺激进行程序性细胞死亡的异质性多变量信号数据的信号-响应关系。通过偏最小二乘建模，我们将20-30个细胞内信号的动态和定量测量与用肿瘤坏死因子α（死亡因子）和胰岛素（存活因子）治疗后的细胞存活相关联。我们发现，偏最小二乘模型可以区分高信息信号从冗余的无信息信号，以产生一个简化的模型，保留关键的信号特征和信号响应关系。在这些模型中，基于非常不同的技术（蛋白质印迹、激酶测定等）的生化特征的测量，作为协变量分组在一起，表明异质数据已被有效融合。重要的是，细胞反应的信息蛋白质预测因子总是多变量的，表明决策过程的多组分性质。
Biological signaling networks process extracellular cues to control important cell decisions such as death-survival, growth-quiescence, and proliferation-differentiation. After receptor activation, intracellular signaling proteins change in abundance, modification state, and enzymatic activity. Many of the proteins in signaling networks have been identified, but it is not known how signaling molecules work together to control cell decisions. To begin to address this issue, we report the use of partial least squares regression as an analytical method to glean signal-response relationships from heterogeneous multivariate signaling data collected from HT-29 human colon carcinoma cells stimulated to undergo programmed cell death. By partial least squares modeling, we relate dynamic and quantitative measurements of 20-30 intracellular signals to cell survival after treatment with tumor necrosis factor alpha (a death factor) and insulin (a survival factor). We find that partial least squares models can distinguish highly informative signals from redundant uninformative signals to generate a reduced model that retains key signaling features and signal-response relationships. In these models, measurements of biochemical characteristics, based on very different techniques (Western blots, kinase assays, etc.), are grouped together as covariates, showing that heterogenous data have been effectively fused. Importantly, informative protein predictors of cell responses are always multivariate, demonstrating the multicomponent nature of the decision process.