Cue-signal-response analysis of TNF-induced apoptosis by partial least squares regression of dynamic multivariate data.
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.
Janes
Kevin A
KA
Biological Engineering Division, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.
Kelly
Jason R
JR
Gaudet
Suzanne
S
Albeck
John G
JG
Sorger
Peter K
PK
Lauffenburger
Douglas A
DA
D017209:Apoptosis / Q000187:drug effects*; D053181:Caspase 8; D020169:Caspases / Q000378:metabolism; D002470:Cell Survival / Q000187:drug effects; D019295:Computational Biology; D019073:HT29 Cells; D006801:Humans; D007328:Insulin / Q000494:pharmacology; D016018:Least-Squares Analysis; D015999:Multivariate Analysis; D017346:Protein Serine-Threonine Kinases / Q000378:metabolism; D011518:Proto-Oncogene Proteins / Q000378:metabolism; D051057:Proto-Oncogene Proteins c-akt; D047888:Receptors, Tumor Necrosis Factor, Type I / Q000378:metabolism; D015398:Signal Transduction / Q000187:drug effects; D014409:Tumor Necrosis Factor-alpha / Q000494:pharmacology*
D007328:Insulin; D011518:Proto-Oncogene Proteins; D047888:Receptors, Tumor Necrosis Factor, Type I; D014409:Tumor Necrosis Factor-alpha; D017346:Protein Serine-Threonine Kinases; D051057:Proto-Oncogene Proteins c-akt; C505530:CASP8 protein, human; D053181:Caspase 8; D020169:Caspases