A novel bioinformatic approach for comprehensive genome scale analysis identifies key regulators of macrophage activation.

The initiation of inflammatory cytokine transcription by bacterial ligands is a central mechanism by which the immune system activates its first line of defense. Macrophage activation by the Toll-like Receptor 4 (TLR4) pathway is initiated with receptor binding of lipopolysaccharides (LPS) and culminates in a large-scale transcriptional response of the inflammatory gene program. Advancements in genome-wide screening technologies have made it possible to interrogate the regulatory landscape of signaling pathways such as those activated by TLR4. Utilizing these high-throughput methods for the comprehensive characterization of pathway components, particularly for regulators that are involved in critical cellular processes such as transcription and translation, however, requires an approach that goes beyond the top scoring and previously characterized hits of genome-scale studies. To address this challenge, I developed the Throughput Ranking by Iterative Analysis of Genomic Enrichment (TRIAGE) method, a bioinformatic analysis model that facilitates the comprehensive identification of likely regulators by iterative sampling of pathway and network databases. I validated the TRIAGE approach by analyzing three previously published genome-wide studies of regulators of early HIV infection and viral transcription. Analysis by TRIAGE showed significantly increased overlap and identified shared novel targets across the three studies. I further developed the TRIAGE analysis method as a globally accessible web-based resource. Applying TRIAGE analysis to three genome-scale studies of LPS treatment in macrophages of mouse and human cell lines, I identified an enrichment for regulators relating to alternative splicing and protein degradation. Using short read and long read RNA-seq of ligand-stimulated macrophages I further characterized the broad transcriptional variation induced by the LPS response and the novel and known transcript variants that define different macrophage activation states. These findings define an approach for comprehensive unbiased discovery of signaling pathway regulators from genome-scale datasets and suggest a model of macrophage activation involving proteasomal removal of negative regulators and remodeling of the macrophage state via a transcriptional shift in splice variant dynamics.