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

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.

The extracellular matrix as a key regulator of intracellular signalling networks

“…These modelling approaches differ in their level of abstraction, data requirements and predictive power but can be broadly separated into two categories: descriptive statistical models and predictive mechanistic models (Halasz et al, ). Statistical models are derived to create relationships between signalling nodes that best describe the available experimental data (Terfve et al, ; Chitforoushzadeh et al, ; Hill et al, ). Conceptually, these models are essentially maps of signalling transduction networks that allow direct comparison between healthy cells and those with aberrant signalling (Krogan et al, ).…”

Mathematically universal and biologically consistent astrocytoma genotype encodes for transformation and predicts survival phenotype

“…This makes the GSVD sensitive to robust genotype-phenotype relations in small discovery sets of only, e.g., 251, 59, and 85 patients, and possibly imbalanced validation sets of, e.g., 184 and 74 patients, with large genomic profiles of, e.g., 213K, 934K, and 2.8M probes or bins each. This is possible because the GSVD uses the structure of the tumor and normal datasets, of two column-matched but row-independent matrices, in the blind source separation (BSS) 52–64 of the tumor-exclusive from the normal genotype-phenotype relations and from experimental batch effects. Patient-matched normal CNVs are often missing from other analyses of tumor CNAs, even though CNVs overlap ≈12% of the normal human genome, 65 where they are 10 2 –10 4 times more frequent than point mutations, 66 and are associated with both tumor and normal development.…”

Profiling Subcellular Protein Phosphatase Responses to Coxsackievirus B3 Infection of Cardiomyocytes

“…Furthermore, it should be easier to hone in on detailed mechanisms of phosphosubstrate regulation (29) by focusing on the specific phosphatases localized to where activity changes were measured (5,22). In this way, systematic experiments performed with high-throughput methods set the stage for more in-depth mechanistic hypotheses (120,121).…”