Search

Summary: Macrophages exhibit diverse phenotypes and respond flexibly to environmental cues through metabolic remodeling. In this study, we present a comprehensive multi-omics dataset integrating intra- and extracellular metabolomes with transcriptomic data to investigate the metabolic impact on human macrophage function. Our analysis establishes a metabolite-gene correlation network that characterizes macrophage activation. We find that the concurrent inhibition of tryptophan catabolism by IDO1 and IL4I1 inhibitors suppresses the macrophage pro-inflammatory response, whereas single inhibition leads to pro-inflammatory activation. We find that a subset of anti-inflammatory macrophages activated by Fc receptor signaling promotes glycolysis, challenging the conventional concept of reduced glycolysis preference in anti-inflammatory macrophages. We demonstrate that cholesterol accumulation suppresses macrophage IFN-γ responses. Our integrated network enables the discovery of immunometabolic features, provides insights into macrophage functional metabolic reprogramming, and offers valuable resources for researchers exploring macrophage immunometabolic characteristics and potential therapeutic targets for immune-related disorders.

Abstract Complex disease phenotypes often span multiple molecular processes. Functional characterization of these processes can shed light on disease mechanisms and drug effects. Thermal Proteome Profiling (TPP) is a mass-spectrometry (MS) based technique assessing changes in thermal protein stability that can serve as proxies of functional protein changes. These unique insights of TPP can complement those obtained by other omics technologies. Here, we show how TPP can be integrated with phosphoproteomics and transcriptomics in a network-based approach using COSMOS, a multi-omics integration framework, to provide an integrated view of transcription factors, kinases and proteins with altered thermal stability. This allowed us to recover consequences of Poly (ADP-ribose) polymerase (PARP) inhibition in ovarian cancer cells on cell cycle and DNA damage response as well as interferon and hippo signaling. We found that TPP offers a complementary perspective to other omics data modalities, and that its integration allowed us to obtain a more complete molecular overview of PARP inhibition. We anticipate that this strategy can be used to integrate functional proteomics with other omics to study molecular processes.