From expression footprints to causal pathways: contextualizing large signaling networks with CARNIVAL

Abstract

While gene expression profiling is commonly used to gain an overview of cellular processes, the identification of upstream processes that drive expression changes remains a challenge. To address this issue, we introduce CARNIVAL, a causal network building tool which derives network architectures from gene expression footprints. CARNIVAL (CAusal Reasoning pipeline for Network identification using Integer VALue programming) integrates different sources of prior knowledge, including signed and directed protein-protein interactions, transcription factor targets, and pathway signatures. The use of prior knowledge in CARNIVAL allows the capture of a broad set of upstream cellular processes and regulators, which in turn delivered results with higher accuracy when benchmarked against related tools. Implementation as an integer linear programming (ILP) problem also guarantees efficient computation. As a case study, we applied CARNIVAL to contextualize signaling networks from gene expression data in IgA nephropathy, a chronic kidney disease. CARNIVAL identified specific signaling pathways and associated mediators with important bioactivity in IgAN including WNT and TGF-{beta}, that we subsequently validated experimentally. In summary, we demonstrated how CARNIVAL generates hypotheses on potential upstream alterations that propagate through signaling networks, providing valuable insights into disease processes. CARNIVAL, freely available as an R-package at https://saezlab.github.io/CARNIVAL, can be applied to any field of biomedical research to contextualize signaling networks and identify the causal relationships between downstream gene expression and upstream regulators.