ArticleeLife2018
Context-enriched interactome powered by proteomics helps the identification of novel regulators of macrophage activation.
Article in eLife, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
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Who cites it
8 citing papers in PubMed.
- Delineation of signaling routes that underlie differences in macrophage phenotypic states.NAR molecular medicine · 2025Article
- Precision cardiovascular medicine: shifting the innovation paradigm.Frontiers in science · 2025Article
- Guf1 overexpression improves pancreatic β cell functions in type 2 diabetes mellitus rats with Roux-en-Y gastric bypass (RYGB) surgery.Journal of physiology and biochemistry · 2023Article
- Disease-specific interactome alterations via epichaperomics: the case for Alzheimer's disease.The FEBS journal · 2022Review
- Decoding communication patterns of the innate immune system by quantitative proteomics.Journal of leukocyte biology · 2019Review
- Exploring the cross-phenotype network region of disease modules reveals concordant and discordant pathways between chronic obstructive pulmonary disease and idiopathic pulmonary fibrosis.Human molecular genetics · 2019Article
- Network Medicine in the Age of Biomedical Big Data.Frontiers in genetics · 2019Review
- Article
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The role of pro-inflammatory macrophage activation in cardiovascular disease (CVD) is a complex one amenable to network approaches. While an indispensible tool for elucidating the molecular underpinnings of complex diseases including CVD, the interactome is limited in its utility as it is not specific to any cell type, experimental condition or disease state. We introduced context-specificity to the interactome by combining it with co-abundance networks derived from unbiased proteomics measurements from activated macrophage-like cells. Each macrophage phenotype contributed to certain regions of the interactome. Using a network proximity-based prioritization method on the combined network, we predicted potential regulators of macrophage activation. Prediction performance significantly increased with the addition of co-abundance edges, and the prioritized candidates captured inflammation, immunity and CVD signatures. Integrating the novel network topology with transcriptomics and proteomics revealed top candidate drivers of inflammation. In vitro loss-of-function experiments demonstrated the regulatory role of these proteins in pro-inflammatory signaling.
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