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

Abstract

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 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.

Clinical Pharmacokinetics and Pharmacodynamics of Monoclonal Antibodies Approved to Treat Rheumatoid Arthritis

“…This may explain the large variability of concentration-effect relationships to erroneous extrapolations. Multi-scale system models, which were previously developed in oncology, may be used to build models including signaling pathways (e.g., the MAPK [124], NF-jB [125], and IL-6 pathways [126]), the expression of genes of interest [127], the regulation of the immune system [126], inflammation [128], and clinical consequences. Multi-scale system biology modeling may allow the connecting of these phenomena and help to (i) optimize the dosing strategy of marketed mAbs, (ii) test and optimize diverse associations of biopharmaceuticals, e.g., adalimumab and tocilizumab, (iii) find new targets of clinical interest, and (iv) find new biomarkers of response to treatment.…”

Increased levels of inflammatory cytokines in the female reproductive tract are associated with altered expression of proteases, mucosal barrier proteins, and an influx of HIV-susceptible target cells

“…To identify the minimum multivariate protein profile of elevated mucosal cytokines, we used the LASSO (Least Absolute Shrinkage and Selection Operator) method for regression and shrinkage and partial least-squares discriminant analysis (PLSDA). 25,30 A profile of 16 proteins were best able to classify the women with elevated cytokines from controls. A PLSDA model of the 16 selected proteins provided excellent classification of study groups, with 88% calibration accuracy and 83% crossvalidation accuracy ( Figure 4a).…”

Models of signalling networks – what cell biologists can gain from them and give to them

“…For signal transduction, these combinations point to 'hidden dimensions' within a network, where multiple signalling proteins may be coordinately regulated to execute a common function (Jensen and Janes, 2012). Such models have proved to be remarkably versatile for signalling networks, capturing adaptors, effectors, cell-fate control and cytokine-release profiles in different settings (Beyer and MacBeath, 2012;Cosgrove et al, 2010;Gordus et al, 2009;Janes et al, 2005;Janes et al, 2008;Kemp et al, 2007;Kumar et al, 2007a;Kumar et al, 2007b;Lau et al, 2011;Lee et al, 2012;Miller-Jensen et al, 2007;Tentner et al, 2012). Therefore, the question is no longer whether these model-based simplifications of signalling networks are effective but, rather, why they work so well as often as they do.…”