Research ArticleCOMPUTATIONAL BIOLOGY

TNF-insulin crosstalk at the transcription factor GATA6 is revealed by a model that links signaling and transcriptomic data tensors

See allHide authors and affiliations

Sci. Signal.  07 Jun 2016:
Vol. 9, Issue 431, pp. ra59
DOI: 10.1126/scisignal.aad3373

You are currently viewing the abstract.

View Full Text

Identifying integrators of multiple signals

Cells are never exposed to only one signal at a time. They are bathed in a complex, dynamically changing milieu of growth factors, nutrients, cytokines, and hormones, which creates enormous complexity for studying cellular regulation. Chitforoushzadeh et al. applied a statistical modeling approach called “tensor partial least squares regression,” which maintains data structures as multidimensional elements called tensors. Application of tensor modeling to proteomic signaling data and transcriptomic data revealed a specific phosphorylation event on the long form of the transcription factor GATA6 that enabled the growth factor insulin to inhibit the expression of genes targeted by the proinflammatory cytokine TNF. The computational analysis revealed information not readily obvious in the large data sets and provided a molecular explanation for the specific patterns of gene expression that occurred when the cells experienced growth factors in the presence or absence of a proinflammatory cytokine.

Abstract

Signal transduction networks coordinate transcriptional programs activated by diverse extracellular stimuli, such as growth factors and cytokines. Cells receive multiple stimuli simultaneously, and mapping how activation of the integrated signaling network affects gene expression is a challenge. We stimulated colon adenocarcinoma cells with various combinations of the cytokine tumor necrosis factor (TNF) and the growth factors insulin and epidermal growth factor (EGF) to investigate signal integration and transcriptional crosstalk. We quantitatively linked the proteomic and transcriptomic data sets by implementing a structured computational approach called tensor partial least squares regression. This statistical model accurately predicted transcriptional signatures from signaling arising from single and combined stimuli and also predicted time-dependent contributions of signaling events. Specifically, the model predicted that an early-phase, AKT-associated signal downstream of insulin repressed a set of transcripts induced by TNF. Through bioinformatics and cell-based experiments, we identified the AKT-repressed signal as glycogen synthase kinase 3 (GSK3)–catalyzed phosphorylation of Ser37 on the long form of the transcription factor GATA6. Phosphorylation of GATA6 on Ser37 promoted its degradation, thereby preventing GATA6 from repressing transcripts that are induced by TNF and attenuated by insulin. Our analysis showed that predictive tensor modeling of proteomic and transcriptomic data sets can uncover pathway crosstalk that produces specific patterns of gene expression in cells receiving multiple stimuli.

View Full Text