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

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

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.

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