---
title: "MGST3 marks a mitochondrial-health–linked M2-like macrophage state in atherosclerosis"
id: "frontiers-in-immunology-7-mgst3-defines-a-mitochondrial-health-associated-m2-macrophage-state-in"
canonical_url: "https://medichelpline.com/clinical-feed/frontiers-in-immunology-7-mgst3-defines-a-mitochondrial-health-associated-m2-macrophage-state-in"
content_type: "clinical_feed_article"
specialty: "Cardiology"
source_name: "Frontiers in Immunology"
source_url: "https://www.frontiersin.org/articles/10.3389/fimmu.2026.1864957"
published_at: "2026-09-17T00:00:00.000Z"
evidence_level: "Journal Feed"
license: "CC-BY-NC-4.0 / Informational Use"
---
# MGST3 marks a mitochondrial-health–linked M2-like macrophage state in atherosclerosis
## Provenance & Clinical Metadata
- **Canonical URL:** https://medichelpline.com/clinical-feed/frontiers-in-immunology-7-mgst3-defines-a-mitochondrial-health-associated-m2-macrophage-state-in
- **Specialty:** [Cardiology](https://medichelpline.com/clinical-feed/cardiology.md)
- **Primary Source:** Frontiers in Immunology
- **Source URL:** [Original Journal Publication](https://www.frontiersin.org/articles/10.3389/fimmu.2026.1864957)
- **Published At:** 2026-09-17T00:00:00.000Z
- **Evidence Rating:** Journal Feed
## Executive GIST (TL;DR)
- The study integrated WGCNA, machine learning, two-sample **Mendelian randomization**, bulk transcriptomics, single-cell RNA-seq, disease-level validation, single-cell mediation, computational perturbation, and in vitro knockdown to identify mitochondrial regulators of macrophage states in atherosclerosis (AS). - Eight candidate mitochondrial genes were consistently prioritized by both machine-learning methods; six had eligible blood cis-eQTL instruments for MR, with **MGST3** showing the strongest protective genetic signal for coronary disease risk. - Two-sample MR found genetically predicted higher **MGST3** expression associated with lower CAD/CHD risk (training set OR 0.989, 95% CI 0.978–0.999, P = 0.042; test set OR 0.977, 95% CI 0.960–0.995, P = 0.013). - Bulk carotid plaque validation confirmed reduced MGST3 at mRNA and protein levels in whole plaques despite an expanded MGST3-high **M2-like** macrophage subpopulation within plaques. - Single-cell analyses (GSE159677) showed MGST3-high M2-like macrophages had higher **OXPHOS**, antioxidant programs, and composite **MitoHealth** scores and lower inflammatory signatures compared with MGST3-low counterparts. - Single-cell mediation indicated that **MitoHealth** statistically links higher MGST3 expression to lower inflammatory activity, reduced M1-like polarization, and less atherogenic macrophage programming. - Virtual knockdown predictions and in vitro MGST3 knockdown in M2-polarized macrophages reduced M2- and mitochondrial/antioxidant transcripts and increased pro-inflammatory transcripts, supporting functional relevance at the transcript level. - PheWAS detected no significant phenotype associations at the predefined threshold. The MGST3–MitoHealth axis is proposed as a compensatory, mitochondrial-preserving macrophage program warranting further diagnostic and therapeutic investigation in AS.
## Clinical Analysis & Structured Key Points
Frontiers | MGST3 defines a mitochondrial-health-associated M2 macrophage state in atherosclerosis: integrative multi-omics, single-cell mediation, and functional validation ORIGINAL RESEARCH article Front. Immunol. , 17 September 2026 Sec. Inflammation Volume 17 - 2026 | https://doi.org/10.3389/fimmu.2026.1864957 Published in Frontiers in Immunology Inflammation 7 impact factor 11.3 citescore Part of a Research Topic Frontiers in Atherosclerosis Research 2026: From Emerging Molecular Mechanisms to Clinical Innovations Submission open 19k views 14 articles Editor & Reviewers Edited by G C Guoyun Chen Reviewed by L Z Longbin Zheng X Z Xiaoliang Zhu Outline Figures and Tables Figure 1 View in article Figure 2 View in article Figure 3 View in article Figure 4 View in article Figure 5 View in article Figure 6 View in article Figure 7 View in article Figure 8 View in article Figure 9 View in article Figure 10 View in article Figure 11 View in article ORIGINAL RESEARCH article Front. Immunol. , 17 September 2026 Sec. Inflammation Volume 17 - 2026 | https://doi.org/10.3389/fimmu.2026.1864957 MGST3 defines a mitochondrial-health-associated M2 macrophage state in atherosclerosis: integrative multi-omics, single-cell mediation, and functional validation Q Z Qiong-Chao Zou 1,2,3 † Y Y Yong-Hong Yi 3,4,5 † J H Jin He 1,2,3 J R Jing-Jing Rong 1,2,3 H P Hong-Wei Pan 1,2,3 ‡ * P Z Peng-Fei Zheng 1,2,3 ‡ * 1. Cardiology Department, Hunan Provincial People’s Hospital, Changsha Hunan, China 2. Clinical Research Center for Heart Failure in Hunan Province, Changsha, Hunan, China 3. Institute of Cardiovascular Epidemiology, Hunan Provincial People’s Hospital, Changsha, Hunan, China 4. Department of Cardiology, The First Affiliated Hospital of Hunan Normal University (Hunan Provincial People’s Hospital), Changsha, Hunan, China 5. School of Medicine, Hunan Normal University, Changsha, China See more Article metrics View details Abstract Abstract Background: Mitochondrial dysfunction is increasingly recognized as a driver of maladaptive macrophage polarization in atherosclerosis (AS), but the mitochondrial genes that shape disease-relevant macrophage states remain poorly defined. Methods: We integrated weighted gene co-expression network analysis (WGCNA), machine learning, two-sample Mendelian randomization (two-sample MR), bulk transcriptomics, single-cell analysis, disease-level validation, single-cell mediation, and in vitro perturbation to identify macrophage-relevant mitochondrial regulators in AS. Results: Eight candidate genes were consistently prioritized by WGCNA and machine learning; among them, microsomal glutathione S-transferase 3 (MGST3) showed the strongest protective genetic signal. Two-sample MR supported a protective association between genetically predicted MGST3 expression and coronary artery disease/coronary heart disease risk (training set OR = 0.989, 95% CI 0.978-0.999, P = 0.042; test set OR = 0.977, 95% CI 0.960-0.995, P = 0.013). Human carotid plaque validation confirmed reduced MGST3 at both mRNA and protein levels in AS lesions. Notably, despite this whole-plaque downregulation, single-cell and disease-level analyses revealed an expanded MGST3-high M2-like macrophage state in AS, suggesting a compensatory protective macrophage program within diseased plaques. MGST3-high M2-like cells displayed an OXPHOS-oriented, antioxidant, and less inflammatory transcriptional state, acted as an active signaling population, and showed higher composite MitoHealth scores. Single-cell mediation analysis further indicated that MitoHealth statistically links higher MGST3 expression to lower inflammatory activity, lower M1-like polarization, and lower atherogenic macrophage programming. In M2-polarized macrophages, MGST3 knockdown reduced M2-associated and mitochondrial/antioxidant transcripts while increasing pro-inflammatory transcripts, providing transcript-level functional support. PheWAS analysis detected no significant phenotype associations at the predefined threshold. Conclusions: These findings define MGST3 as a marker of a mitochondrial-health-associated M2-like macrophage state that appears expanded within AS plaques despite reduced whole-plaque MGST3 expression. The MGST3-MitoHealth axis may represent a compensatory macrophage program linked to mitochondrial preservation and lower pro-atherogenic activation, warranting further diagnostic and therapeutic investigation in AS. Introduction Atherosclerosis (AS) is a chronic inflammatory disease of the arterial wall in which lipid deposition, immune-cell recruitment, and vascular remodeling progressively generate plaques that may rupture and trigger acute ischemic events ( 1 – 3 ). Although early lesions can remain clinically silent, advanced plaques may undergo fibrotic, calcified, and necrotic remodeling before precipitating myocardial infarction or cerebral ischemia, both major contributors to global mortality ( 4 , 5 ). Even with effective management of dyslipidemia, hypertension, diabetes, and other classical risk factors, substantial residual cardiovascular risk persists ( 6 , 7 ). This residual burden underscores the need to define local, cell-type-specific molecular programs that operate within plaques and may inform earlier detection or more selective intervention. Macrophage dysfunction is central to AS pathogenesis ( 8 ). Although plaque macrophages exist along a continuum of highly plastic states, the M1/M2 framework remains useful for studying polarization-dependent gene function. In this framework, lipopolysaccharide or interferon-gamma promotes inflammatory M1-like activation, whereas IL-4-induced M2-like macrophages are associated with lipid handling, efferocytosis, and tissue repair ( 9 , 10 ). Monocytes and macrophages dominate the immune landscape across AS stages ( 10 ). After internalizing atherogenic lipoproteins, including low-density lipoprotein and lipoprotein(a), macrophages can become foam cells and shift toward inflammatory cytokine production, thereby reinforcing oxidative lipoprotein modification, lipid accumulation, and local inflammation ( 11 , 12 ). Although immune infiltration and mitochondrial dysfunction have both been implicated in AS progression ( 13 ), the mitochondrial genes that shape macrophage polarization within plaques remain insufficiently resolved. Mitochondria govern macrophage polarization and effector function. Inflammatory M1-like macrophages rely predominantly on glycolysis and generate abundant reactive oxygen species, whereas reparative M2-like macrophages are more dependent on oxidative phosphorylation and fatty acid oxidation ( 14 ). Because M2-like polarization is metabolically coupled to oxidative metabolism, mitochondrial genes are likely to exert especially relevant effects within this compartment, making polarization-resolved analysis essential ( 14 ). Failure of mitochondrial quality control, whether through excessive mitochondrial ROS, defective mitophagy, or altered fission-fusion dynamics, can push macrophages toward inflammatory activation and tissue injury ( 15 – 18 ). However, which mitochondrial genes couple redox metabolism, lipid stress, and macrophage polarization within AS plaques remains unclear. Most bioinformatics studies have prioritized mitochondrial genes from bulk-tissue correlations ( 19 ), an approach that cannot determine whether a candidate gene acts within a specific macrophage state or whether its association with disease is genetically supported. This limitation is particularly important in AS plaques, where whole-tissue expression profiles average signals across vascular, stromal, and immune populations and may therefore obscure adaptive or compensatory macrophage subpopulations that emerge within diseased lesions. To address this gap, we integrated WGCNA, machine learning, and two-sample Mendelian randomization to prioritize mitochondrial genes and test whether genetically predicted expression was associated with CAD/CHD risk. This unbiased framework prioritized microsomal glutathione S-transferase 3 (MGST3), a MitoCarta-annotated member of the membrane-associated proteins in eicosanoid and glutathione metabolism (MAPEG) family, as the most consistent candidate ( 20 , 21 ). The biological position of MGST3 at the intersection of glutathione-dependent redox buffering and lipid-mediator metabolism ( 22 ) made it particularly plausible in the lipid-rich and oxidative plaque environment. We then used single-cell transcriptomics ( 23 ) and disease-level signature validation to map the cellular context of MGST3, followed by computational perturbation, single-cell mediation, human carotid plaque validation, and in vitro knockdown to evaluate its functional relevance in M2-like macrophages. This framework defined an MGST3-high M2-like macrophage state linked to mitochondrial integrity, lower inflammatory activation, and potential plaque-protective function in AS, while highlighting the need for future activity-based and in vivo validation. Materials and methods Study datasets, preprocessing, and genetic instruments Datasets GSE28829, GSE41571, GSE43292, and GSE163154 (147 samples in total) were obtained from the Gene Expression Omnibus (GEO; http://www.ncbi.nlm.nih.gov/geo ), integrated into a discovery expression matrix, and used for candidate screening, immune infiltration, and disease-level analyses. These cohorts represent related but non-identical AS phenotypes, including advanced versus early lesions, ruptured versus stable plaques, plaque versus macroscopically intact arterial tissue, and intraplaque hemorrhage versus non-hemorrhage. Accordingly, the integrated matrix was treated as a discovery framework rather than a single homogeneous clinical cohort. Three additional GEO datasets (GSE97320, 3 acute myocardial infarction and 3 control samples; GSE226790, 3 advanced atherosclerotic plaques and 3 healthy samples; GSE104140, 13 control and 19 AS samples) were combined with the discovery cohorts in a seven-cohort meta-analysis of the MGST3-high M2 signature. Single-cell RNA-seq data (GSE159677; 3 healthy and 3 AS samples) were used for macrophage polarization and mechanistic analyses, and GSE111782 (9 atherosclerotic and 9 control carotid samples) served as an independent cohort for signature and mitochondrial-module co-expression validation. Bulk datasets were processed according to data type before integration. For bulk microarray datasets, probe-level expression matrices were processed using limma ( 24 ); probes mapping to multiple genes were excluded, and multiple probes mapping to the same gene were averaged. For bulk RNA-seq datasets, gene-level expression matrices were processed using normalized expression values or count-based library-size normalization before downstream analysis. Batch effects across the four discovery bulk datasets were corrected using ComBat from the sva package before matrix integration. Single-cell RNA-seq data were processed separately using a Seurat-based workflow and were not included in the ComBat-corrected bulk matrix. Robustness across heterogeneous bulk datasets was assessed using cohort-stratified analyses, random-effects meta-analysis, leave-one-out sensitivity analysis, and independent validation in GSE111782 where appropriate. All statistical analyses were performed in R (version 4.5.2). Two-sided P 0 were considered MGST3-expressing; when >85% of AS M2-like cells expressed MGST3, the top and bottom tertiles defined MGST3-high and MGST3-low cells, and otherwise expressing versus non-expressing cells were compared. Functional module scores (OXPHOS, TCA cycle, mitochondrial translation, antioxidant/ROS, mitophagy, and inflammation) were calculated with AddModuleScore and compared between MGST3-high and MGST3-low cells by paired t-tests across the three AS samples; cell-level Spearman correlations with MGST3 were also computed. Expression of curated mitophagy-related genes (PINK1, BNIP3, BNIP3L, FUNDC1, OPTN, SQSTM1, MAP1LC3B, ATG5, ATG7, MFN1, MFN2, OPA1, PRKN, CALCOCO2) was also compared between MGST3-high and MGST3-low M2-like cells. A composite MitoHealth score was defined within AS M2-like cells as the mean of z-scored OXPHOS, TCA, mitochondrial translation, and antioxidant scores minus the z-scored inflammatory score, and was compared between groups and correlated with MGST3. The MGST3-high M2 signature was constructed from genes differentially expressed between MGST3-high and MGST3-low M2 cells (Wilcoxon rank-sum test; genes with adjusted P 2 and adjusted P < 0.05 were subjected to GO biological process enrichment. These virtual knockdown results were treated as computational predictions of network perturbation rather than as direct loss-of-function evidence. Cell-cell communication was inferred with CellChat (v2) ( 38 ) using the triMean method and interactions with P < 0.05; signaling strengths, top ligand-receptor interactions, and pathway-level differences between MGST3-high and MGST3-low groups were summarized, and a group-label permutation analysis was performed as a sensitivity check ( Supplementary Table S10 ). As a complementary bulk analysis, MGST3 co-expression was assessed in the 147-sample integrated discovery matrix using Spearman correlation with Benjamini-Hochberg FDR correction; co-expression with top OXPHOS genes was visualized, the top 50 co-expressed genes were tested for hypergeometric enrichment within MitoCarta-derived mitochondrial modules, and bootstrap resampling (100 iterations) was used to assess the stability of the top-50 co-expression network. Disease-level evidence was evaluated through complementary bulk analyses. M2-like scores were estimated by ssGSEA ( 38 ) (alpha = 0.25) in the four discovery datasets. BisqueRNA d
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