miR-33 Silencing Reprograms the Immune Cell Landscape in Atherosclerotic Plaques
Open accessAbstract
Rationale: MicroRNA-33 (miR-33 [post-transcriptionally in the rationale]) post-transcriptionally represses genes involved in lipid metabolism and energy homeostasis. Targeted inhibition of miR-33 increases plasma HDL (high-density lipoprotein) cholesterol and promotes atherosclerosis regression, in part, by enhancing reverse cholesterol transport and dampening plaque inflammation. However, how miR-33 reshapes the immune microenvironment of plaques remains poorly understood. Objective: To define how miR-33 inhibition alters the dynamic balance and transcriptional landscape of immune cells in atherosclerotic plaques. Methods and Results: We used single-cell RNA-sequencing of aortic CD45 + cells, combined with immunohistologic, morphometric, and flow cytometric analyses to define the changes in plaque immune cell composition, gene expression, and function following miR-33 inhibition. We report that anti–miR-33 treatment of Ldlr –/– mice with advanced atherosclerosis reduced plaque burden and altered the plaque immune cell landscape by shifting the balance of proatherosclerotic and antiatherosclerotic macrophage and T-cell subsets. By quantifying the kinetic processes that determine plaque macrophage burden, we found that anti–miR-33 reduced levels of circulating monocytes and splenic myeloid progenitors, decreased macrophage proliferation and retention, and promoted macrophage attrition by apoptosis and efferocytotic clearance. Single-cell RNA-sequencing of aortic arch plaques showed that anti–miR-33 reduced the frequency of MHCII hi (major histocompatibility complex II) inflammatory and Trem2 hi (Triggering Receptor Expressed On Myeloid Cells 2) metabolic macrophages, but not tissue-resident macrophages. Furthermore, anti–miR-33 led to derepression of distinct miR-33 target genes in the different macrophage subsets: in resident and Trem2 hi macrophages, anti–miR-33 relieved repression of miR-33 target genes involved in lipid metabolism (eg, Abca1 , Ncoa1, Ncoa2 , Crot ), whereas in MHCII hi macrophages, anti–miR-33 upregulated target genes involved in chromatin remodeling and transcriptional regulation. Anti–miR-33 also reduced the accumulation of aortic CD8+ T cells and CD4+ T-helper 1 cells, and increased levels of FoxP3+ (Forkhead Box P3) regulatory T cells in plaques, consistent with an immune-dampening effect on plaque inflammation. Conclusions: Our results provide insight into the immune mechanisms and cellular players that execute anti–miR-33’s atheroprotective actions in the plaque.
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Single-cell analyses have confirmed anti-inflammatory and profibrotic effects in macrophages, which can contribute to atheroprotective effects and plaque stabilization.
“Single-cell analyses have confirmed their anti-inflammatory and profibrotic effects in macrophages, which can contribute to atheroprotective effects and plaque stabilization 30 , 31) .”
From Critical Role of microRNA-33a/b in Cardiovascular and Metabolic Disease: Molecular Mechanisms and Therapeutic Perspectives (2026) · cites this paper as [30]
The Results and Discussion (pages unavailable) link single-cell analysis of anti-miR-33-treated mouse plaques to reduced inflammatory macrophage signatures, increased tissue-reparative macrophage markers, and atheroprotective effects. They do not establish a profibrotic effect or demonstrate plaque stabilization; the reported outcomes include increased efferocytosis and reduced necrotic area. (LLM, conf=0.86)
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Gui, Y., Zheng, H., & Cao, R. Y. (2022) · Frontiers in Cardiovascular Medicine · doi:10.3389/fcvm.2022.845942 · Cited by 258 · see citing works
Horie, T., Baba, O., Nishino, T., Yamashita, Y., Tsujisaka, Y., & Ono, K. (2026) · Journal of Atherosclerosis and Thrombosis · doi:10.5551/jat.rv22045 · Cited by 5 · see citing works
- Authors
- Afonso, M. S., Sharma, M., Schlegel, M., van Solingen, C., Koelwyn, G. J., Shanley, L. C., Beckett, L., Peled, D., Rahman, K., Giannarelli, C., Li, H., Brown, E. J., Khodadadi-Jamayran, A., Fisher, E. A., & Moore, K. J.
- Affiliations
- New York University (US)
- Department of Medicine, Leon H. Charney Division of Cardiology, New York University Grossman School of Medicine (M.S.A., M. Sharma, M. Schlegel, C.v.S., G.J.K., L.C.S., L.B., D.P., K.R., E.J.B., E.A.F., K.J.M.).
- Icahn School of Medicine at Mount Sinai (US)
- Applied Bioinformatics Laboratories (A.K.-J.), New York University School of Medicine.
- ORCID
- Venue
- Circulation Research
- Published
- 2021-04-16
- Volume
- 128
- Issue
- 8
- Pages
- 1122-1138
- ISSN
- 0009-7330 (ISSN-L)
- 1524-4571
- PMC
- PMC8049965
- OpenAlex
- W3129455797
- Cited by
- 53
- References
- 14
- Citations by year
- 2026: 8 · 2025: 5 · 2024: 12 · 2023: 11 · 2022: 17
- FWCI
- 3.73
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Circulation Research (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- mediaTUM – the media and publications repository of the Technical University Munich (Technical University Munich) (submittedVersion)
- Funders
- HHS | NIH | National Heart, Lung, and Blood Institute (R35HL135799, R01HL084312, P01HL131481, P01HL131478, T32HL098129, R01HL153712)
- American Heart Association (19POST34380010, 14Post20180018, 19CDA34630066, 20SFRN35210252)
- Topics
- Life Sciences › Immunology and Microbiology › Immunology › Atherosclerosis and Cardiovascular Diseases
- Health Sciences › Medicine › Surgery › Cholesterol and Lipid Metabolism
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Cancer Research › MicroRNA in disease regulation
- Keywords
- miR-33, atherosclerosis, macrophage polarization, TREM2, MHC class II, CD8+ T cells, Th1 cells, CD4+ T cells, efferocytosis, monocyte recruitment, Ldlr-deficient mice, lipid metabolism, chromatin remodeling
- MeSH terms
- Animals, Macrophages, Male, Mice, Inbred C57BL, Monocytes, T-Lymphocytes, MicroRNAs, Lipid Metabolism, Mice, Plaque, Atherosclerotic, Transcriptome
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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