TNF-insulin crosstalk at the transcription factor GATA6 is revealed by a model that links signaling and transcriptomic data tensors.
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 Ser(37) on the long form of the transcription factor GATA6. Phosphorylation of GATA6 on Ser(37) 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.
Department of Biomedical Engineering, University of Virginia, Charlottesville, VA 22908, USA. Department of Pharmacology, University of Virginia, Charlottesville, VA 22908, USA.
Zeinab
Z
Chitforoushzadeh
Department of Biomedical Engineering, University of Virginia, Charlottesville, VA 22908, USA.
Zi
Z
Ye
Department of Biomedical Engineering, University of Virginia, Charlottesville, VA 22908, USA.
Ziran
Z
Sheng
Department of Biomedical Engineering, University of Virginia, Charlottesville, VA 22908, USA.
Silvia
S
LaRue
Department of Environmental Sciences and Engineering, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA.
Rebecca C
RC
Fry
Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.
Douglas A
DA
Lauffenburger
Department of Biomedical Engineering, University of Virginia, Charlottesville, VA 22908, USA. kjanes@virginia.edu.
Kevin A
KA
Janes
D045744:Cell Line, Tumor; D016000:Cluster Analysis; D019295:Computational Biology; D003198:Computer Simulation; D050993:GATA6 Transcription Factor / Q000378:metabolism*; D057809:HEK293 Cells; D006801:Humans; D007249:Inflammation; D007328:Insulin / Q000378:metabolism; D016018:Least-Squares Analysis; D008024:Ligands; D008954:Models, Biological; D015233:Models, Statistical; D015999:Multivariate Analysis; D009154:Mutation; D016011:Normal Distribution; D020411:Oligonucleotide Array Sequence Analysis; D010766:Phosphorylation; D040901:Proteomics / Q000379:methods; D015398:Signal Transduction; D059467:Transcriptome; D014409:Tumor Necrosis Factor-alpha / Q000378:metabolism*
D050993:GATA6 Transcription Factor; C494759:GATA6 protein, human; D007328:Insulin; D008024:Ligands; D014409:Tumor Necrosis Factor-alpha