Linking specific phosphorylation events to distinct transcriptional states - Abstract: Linking specific phosphorylation events to distinct transcriptional states The cellular behaviors that underlie health and disease are directed by signal transduction pathways that drive gene expression. Linking signaling to specific gene expression is a fundamental goal of biology. Our ability to identify causal links between specific phosphorylation events and the expression of certain genes would not only better our comprehension of cellular decision-making, but would reveal a breadth of novel points of therapeutic intervention for us to modulate cell functions and behaviors. We propose an innovative data-driven technology solution through integrating global, temporally-resolved transcriptomic and phosphoproteomic data with statistical modeling to systematically identify of signaling events predictive of context-specific gene sets. We will validate the predicted links between phosphosites and specific gene expression programs employing novel CRIPSR-mediated base editing technology to mutant individual phosphorylation sites at endogenous loci to assess their impact on gene expression at an unprecedented scale, as well as corroborate using conventional methods. As a model system, we will focus on cytokine induced signaling and gene expression in macrophages, which are critical modulators of inflammation. Specifically, we will interrogate the cytokines IL-6 and IL-10, which signal through common Janus kinases (JAKs) and signal transducers and activators of transcription (STAT) transcription factors to promote contrasting pro- and anti-inflammatory functions, respectively. We will explore the hypothesis that signaling events occurring outside of the JAK/STAT axis encode cytokine-induced transcriptional diversity and can be targeted to modulate context-specific cytokine induced genes. Successful completion of this proposal will provide a robust experimental and computational framework to identify and perturb key phosphorylation events, and measure the specificity of signaling-to-transcription links that shape the diversity of cellular responses. The workflow developed here will be broadly applied across a variety of stimulus, cellular, and disease contexts, improving efficiency of the therapeutic development pipeline.
