University of Connecticut

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CCAM Seminar Series - Kevin Janes

Thursday, June 2, 2016
4:00pm – 5:00pm

UConn Health
CCAM Conference Room, R1673, 400 Farmington Ave

CCAM Seminar Series Speaker: Kevin Janes, Associate Professor, Department of Biomedical Engineering, University of Virginia Title: “From statistics to molecular mechanisms—linking proteomic and transcriptomic measurements by data-driven modeling” Host: Dr. Bruce Mayer

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 datasets 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 inhibiting the ability of GATA6 to act as a repressor of transcripts that are induced by TNF and attenuated by insulin. Our analysis showed that predictive tensor modeling of proteomic and transcriptomic datasets can uncover pathway crosstalk that produces specific patterns of gene expression in cells receiving multiple stimuli.


Center for Cell Analysis and Modeling (primary), School of Medicine

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