Quantitative control of phosphorylation and mechanistic links to immune cell decisions

ABSTRACT Cells integrate distinct stimuli through biochemical signaling pathways to induce the appropriate transcriptional programs. In these signaling-to-transcription networks, the rapid addition and removal of post-translational modifications impact the activity and specificity of transcription factors (TFs) to inform resultant cellular function. Understanding how extracellular cues are linked to gene expression is a fundamental challenge in biology. Existing signaling-to-transcription efforts are often constrained in scope, describing signaling events in detail with little transcriptional insights, or focusing on a few static signaling features while addressing more comprehensive genomic questions. My laboratory is addressing this problem in the context of signal transducers and activators of transcription (STATs), a family of TFs that integrate complex cytokine stimuli to inform a range of pro- to anti-inflammatory immune programs. We propose both data-driven and mechanistic modeling approaches to integrate TF dynamics, global phosphorylation, and transcriptomic data to 1) explore signaling mechanisms that shape stimulus-specific STAT phosphorylation dynamics and functions dependent on these dynamics, and 2) systematically identify phosphorylation events and STAT-cooperating TFs that predict specific gene sets. These efforts to link dynamic signaling to gene expression profiles are a step towards identifying and manipulating the biochemical events required for healthy versus pathology-associated gene expression.