A compendium of signals and responses triggered by prodeath and prosurvival cytokines.
Cell-signaling networks consist of proteins with a variety of functions (receptors, adaptor proteins, GTPases, kinases, proteases, and transcription factors) working together to control cell fate. Although much is known about the identities and biochemical activities of these signaling proteins, the ways in which they are combined into networks to process and transduce signals are poorly understood. Network-level understanding of signaling requires data on a wide variety of biochemical processes such as posttranslational modification, assembly of macromolecular complexes, enzymatic activity, and localization. No single method can gather such heterogeneous data in high throughput, and most studies of signal transduction therefore rely on series of small, discrete experiments. Inspired by the power of systematic datasets in genomics, we set out to build a systematic signaling dataset that would enable the construction of predictive models of cell-signaling networks. Here we describe the compilation and fusion of approximately 10,000 signal and response measurements acquired from HT-29 cells treated with tumor necrosis factor-alpha, a proapoptotic cytokine, in combination with epidermal growth factor or insulin, two prosurvival growth factors. Nineteen protein signals were measured over a 24-h period using kinase activity assays, quantitative immunoblotting, and antibody microarrays. Four different measurements of apoptotic response were also collected by flow cytometry for each time course. Partial least squares regression models that relate signaling data to apoptotic response data reveal which aspects of compendium construction and analysis were important for the reproducibility, internal consistency, and accuracy of the fused set of signaling measurements. We conclude that it is possible to build self-consistent compendia of cell-signaling data that can be mined computationally to yield important insights into the control of mammalian cell responses.
Gaudet
Suzanne
S
Department of Biology, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA.
Janes
Kevin A
KA
Albeck
John G
JG
Pace
Emily A
EA
Lauffenburger
Douglas A
DA
Sorger
Peter K
PK
D000595:Amino Acid Sequence; D015153:Blotting, Western; D016923:Cell Death / Q000187:drug effects; D002470:Cell Survival / Q000187:drug effects; D002478:Cells, Cultured; D004815:Epidermal Growth Factor / Q000494:pharmacology*; D066246:ErbB Receptors / Q000737:chemistry; D019073:HT29 Cells; D006801:Humans; D007328:Insulin / Q000494:pharmacology*; D055504:Insulin Receptor Substrate Proteins; D008969:Molecular Sequence Data; D010750:Phosphoproteins / Q000737:chemistry; D010766:Phosphorylation; D040081:Protein Array Analysis; D011494:Protein Kinases / Q000378:metabolism; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D015398:Signal Transduction / Q000187:drug effects*; D013379:Substrate Specificity; D013997:Time Factors; D014409:Tumor Necrosis Factor-alpha / Q000494:pharmacology*
C526292:IRS1 protein, human; D007328:Insulin; D055504:Insulin Receptor Substrate Proteins; D010750:Phosphoproteins; D014409:Tumor Necrosis Factor-alpha; D004815:Epidermal Growth Factor; D011494:Protein Kinases; D066246:ErbB Receptors