RT Journal Article
SR Electronic
T1 From expression footprints to causal pathways: contextualizing large signaling networks with CARNIVAL
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 541888
DO 10.1101/541888
A1 Anika Liu
A1 Panuwat Trairatphisan
A1 Enio Gjerga
A1 Athanasios Didangelos
A1 Jonathan Barratt
A1 Julio Saez-Rodriguez
YR 2019
UL http://biorxiv.org/content/early/2019/06/07/541888.abstract
AB 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 contextualization 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 enables capturing a broad set of upstream cellular processes and regulators, leading to a higher accuracy when benchmarked against related tools. Implementation as an integer linear programming (ILP) problem guarantees efficient computation. As a case study, we applied CARNIVAL to contextualize signaling networks from gene expression data in IgA nephropathy, a condition that can lead to chronic kidney disease. CARNIVAL identified specific signaling pathways and associated mediators dysregulated in IgAN including WNT and TGF-β, that we subsequently validated experimentally. These results demonstrated how CARNIVAL generates hypotheses on potential upstream alterations that propagate through signaling networks, providing insights into diseases.