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 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.

Dysregulated mesenchymal PDGFR‐β drives kidney fibrosis

“…The fold changes in gene expression of Foxd1Cre::Pdgfrb +/ J mice compared to controls were applied to estimate the activities of regulatory transcriptional factors and of signaling pathway using DoRothEA (Garcia‐Alonso et al , ) and PROGENy (Schubert et al , ), respectively. The activities of top 50 regulatory transcription factors and signaling pathways were integrated into a signed‐directed human protein–protein interaction network obtained from OmniPath (Turei et al , ) to infer upstream regulatory network structures using CARNIVAL (Liu et al , ). Over‐representation analyses of the nodes that were predicted to be upregulated were performed using the Biocarta gene sets in the curated (C2) branch from MSigDB (Subramanian et al , ).…”

“…Comparison to the results generated from public microarray data sets of CKD patients (Tajti et al, 2019) showed that 9 out of 11 pathways (82%) were similarly regulated in our mice compared to patients. We next generated regulatory causal networks from both murine and human data sets with the tool CARNIVAL (Liu et al, 2019). Then, we performed an enrichment analysis using the Biocarta gene set from the MSigDB database on the pathways obtained from CARNIVAL ( Fig 8C).…”

Transcription factors: Bridge between cell signaling and gene regulation

“…One group of approaches uses causal reasoning to integrate transcriptomics or on occasion phosphoproteomics data with prior knowledge-based protein interaction networks and pathways to infer upstream signaling regulation of the gene networks that are represented in the transcriptomics data [121][122][123][124][125][126][127]. Common to the cited studies is the dependence on a reliable annotation of TFs with their target genes, highlighting once more the pivotal role of TFs in understanding the connection of signaling and gene regulation.…”

Causal integration of multi-omics data with prior knowledge to generate mechanistic hypotheses

“…Recently, we proposed the CARNIVAL tool 20 to systematically generate mechanistic hypotheses connecting TFs through global causal reasoning supported by Integer Linear Programming. CARNIVAL connects activity perturbed nodes such as drug targets with deregulated TFs activities by contextualizing a signed and directed Prior Knowledge Network (PKN).…”