In Vivo Systems Analysis Identifies Spatial and Temporal Aspects of the Modulation of TNF-#- Induced Apoptosis and Proliferation by MAPKs

Cellular responses to external stimuli depend on dynamic features of multi-pathway network signaling; thus, the behavior is of a cell is influenced in a complex manner by its environment and by intrinsic properties. Methods of multi-variate systems analysis have provided an understanding of these convoluted effects, but to date this has been only for relatively simplified in vitro cell culture examples. An unaddressed question is whether such approaches can be successfully brought to bear on in vivo conditions. We have analyzed the in vivo signaling network that determines the response of intestinal epithelial cells to the pro-inflammatory cytokine tumor necrosis factor α (TNF-α). We built data-driven, partial least-squares discriminant analysis (PLSDA) models based on signaling, apoptotic, and proliferative responses in the mouse small intestinal epithelium after systemic exposure to TNF-α. We identified the extracellular signal– regulated kinase (ERK) signaling axis as a critical modulator of the temporal variation in apoptosis at different doses of TNF-α, as well as the spatial variation in proliferative responses in distinct intestinal regions. Pharmacologic inhibition of MEK, a mitogen-activated protein kinase kinase upstream of ERK, integrally altered the signaling network and changed the temporal and spatial phenotypes in accordance with a priori model predictions. Our results demonstrate the dynamic, adaptive nature of in vivo signaling networks and identify natural, tissue-level variation in response phenotypes that can be deconvoluted only with quantitative, multi-variate computational modeling. To our knowledge, this is the first study to apply computational *To whom correspondence should be addressed. khaigis@partners.org. SUPPLEMENTARY MATERIALS Fig. S1. Basal levels of proliferation and apoptosis in the small intestinal epithelium. Fig. S2. Effects of loss of TNFR1 or TNFR2 on TNF-α-induced signaling. Fig. S3. A representative TNF-α signaling network. Fig. S4. Time courses of protein phosphorylation signals activated after i.v. administration of TNF-α in vivo. Fig. S5. Total protein abundance and localization of activation in vivo. Fig. S6. Statistical modeling of the TNF-α signaling network in vivo. Fig. S7. Signaling data obtained after treatment with PD325901 or vehicle. Fig. S8. Statistical modeling of TNF-α-induced apoptosis in the context of MEK inhibition. Fig. S9. Statistical modeling of TNF-α-induced proliferation in the context of MEK inhibition. Author contributions: K.S. L. , D.A. L. , and K.M. H. designed experiments; K.S. L. , A.M.J., K.R. C. , and S.R. P. conducted experiments; K.S. L. performed computational analyses; K.S. L. , D.A. L. , K.M. H. wrote the manuscript; all of the authors discussed the results and edited the manuscript; and K.M. H. coordinated all aspects of the project. Competing interests: The authors declare no competing financial interests. NIH Public Access Author Manuscript Sci Signal. Author manuscript; available in PMC 2014 March 24. Published in final edited form as: Sci Signal. ; 4(165): ra16. doi:10.1126/scisignal.2001338. N IH PA Athor M anscript N IH PA Athor M anscript N IH PA Athor M anscript modeling of signaling networks to a bona fide in vivo system, and it lays a foundation for the use of systems-based approaches to understand how dysregulation of the cellular network state underlies complex disease.