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

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journal contribution
posted on 2020-05-14, 13:53 authored by Anika Liu, Panuwat Trairatphisan, Enio Gjerga, Athanasios Didangelos, Jonathan Barratt, Julio Saez-Rodriguez
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 (IgAN), 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, which we subsequently validated experimentally. These results demonstrated how CARNIVAL generates hypotheses on potential upstream alterations that propagate through signaling networks, providing insights into diseases.

Funding

This work was supported by the European Union’s Horizon 2020 program [675585 Marie-Curie ITN “SymBioSys”] and JRC for Computational Biomedicine, which is partially funded by Bayer, for EG; and the Innovative Medicines Initiative 2 Joint Undertaking [116030 “TransQST”] for PT. This Joint Undertaking receives support from the European Union’s Horizon 2020 research and innovation programme and EFPIA.

History

Citation

npj Systems Biology and Applications (2019) 5:40

Author affiliation

Department of Respiratory Sciences

Version

  • VoR (Version of Record)

Published in

npj Systems Biology and Applications

Volume

5

Issue

1

Publisher

Nature Research

eissn

2056-7189

Acceptance date

2019-10-09

Copyright date

2019

Publisher version

https://www.nature.com/articles/s41540-019-0118-z#Abs1

Language

English

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