The DOI matches its Crossref record; 92 of the 92 references it cites were found in a registry or an index.
Mapping macrophage polarization over the myocardial infarction time continuum
Open accessAbstract
In response to myocardial infarction (MI), cardiac macrophages regulate inflammation and scar formation. We hypothesized that macrophages undergo polarization state changes over the MI time course and assessed macrophage polarization transcriptomic signatures over the first week of MI. C57BL/6 J male mice (3–6 months old) were subjected to permanent coronary artery ligation to induce MI, and macrophages were isolated from the infarct region at days 1, 3, and 7 post-MI. Day 0, no MI resident cardiac macrophages served as the negative MI control. Whole transcriptome analysis was performed using RNA-sequencing on n = 4 pooled sets for each time. Day 1 macrophages displayed a unique pro-inflammatory, extracellular matrix (ECM)-degrading signature. By flow cytometry, day 0 macrophages were largely F4/80 high Ly6C low resident macrophages, whereas day 1 macrophages were largely F4/80 low Ly6C high infiltrating monocytes. Day 3 macrophages exhibited increased proliferation and phagocytosis, and expression of genes related to mitochondrial function and oxidative phosphorylation, indicative of metabolic reprogramming. Day 7 macrophages displayed a pro-reparative signature enriched for genes involved in ECM remodeling and scar formation. By triple in situ hybridization, day 7 infarct macrophages in vivo expressed collagen I and periostin mRNA. Our results indicate macrophages show distinct gene expression profiles over the first week of MI, with metabolic reprogramming important for polarization. In addition to serving as indirect mediators of ECM remodeling, macrophages are a direct source of ECM components. Our study is the first to report the detailed changes in the macrophage transcriptome over the first week of MI.
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MetaboAnalyst 3.0—making metabolomics more meaningful
Xia, J., Sinelnikov, I. V., Han, B., & Wishart, D. S. (2015) · Nucleic Acids Research · doi:10.1093/nar/gkv380 · Cited by 2,676
Nahrendorf, M., Swirski, F. K., Aikawa, E., Stangenberg, L., Wurdinger, T., Figueiredo, J.-L., Libby, P., Weissleder, R., & Pittet, M. J. (2007) · The Journal of Experimental Medicine · doi:10.1084/jem.20070885 · Cited by 2,300
The Biological Basis for Cardiac Repair After Myocardial Infarction: From Inflammation to Fibrosis
Prabhu, S. D., & Frangogiannis, N. G. (2016) · Circulation Research · doi:10.1161/circresaha.116.303577 · Cited by 2,224
Molecular Mechanisms That Influence the Macrophage M1–M2 Polarization Balance
Wang, N., Liang, H., & Zen, K. (2014) · Frontiers in Immunology · doi:10.3389/fimmu.2014.00614 · Cited by 1,980
Understanding the Mysterious M2 Macrophage through Activation Markers and Effector Mechanisms
Rőszer, T. (2015) · Mediators of Inflammation · doi:10.1155/2015/816460 · Cited by 1,762
Immunometabolism governs dendritic cell and macrophage function
O’Neill, L. A. J., & Pearce, E. J. (2016) · Journal of Experimental Medicine · doi:10.1084/jem.20151570 · Cited by 1,694
Using MetaboAnalyst 3.0 for Comprehensive Metabolomics Data Analysis
Xia, J., & Wishart, D. S. (2016) · Current Protocols in Bioinformatics · doi:10.1002/cpbi.11 · Cited by 1,544
Epelman, S., Lavine, K. J., Beaudin, A. E., Sojka, D. K., Carrero, J. A., Calderon, B., Brija, T., Gautier, E. L., Ivanov, S., Satpathy, A. T., et al. (2014) · Immunity · doi:10.1016/j.immuni.2013.11.019 · Cited by 1,494
Novel Markers to Delineate Murine M1 and M2 Macrophages
Jablonski, K. A., Amici, S. A., Webb, L. M., Ruiz-Rosado, J. D. D., Popovich, P. G., Partida-Sanchez, S., & Guerau-de-Arellano, M. (2015) · PLOS ONE · doi:10.1371/journal.pone.0145342 · Cited by 978
Mitochondria in the Regulation of Innate and Adaptive Immunity
Weinberg, S. E., Sena, L. A., & Chandel, N. S. (2015) · Immunity · doi:10.1016/j.immuni.2015.02.002 · Cited by 960
Cited by
Bajpai, G., Bredemeyer, A., Li, W., Zaitsev, K., Koenig, A. L., Lokshina, I., Mohan, J., Ivey, B., Hsiao, H.-M., Weinheimer, C., et al. (2019) · Circulation Research · doi:10.1161/circresaha.118.314028 · Cited by 751
Cardiac monocytes and macrophages after myocardial infarction
Peet, C., Ivetic, A., Bromage, D. I., & Shah, A. M. (2020) · Cardiovascular Research · doi:10.1093/cvr/cvz336 · Cited by 600
Metabolism of tissue macrophages in homeostasis and pathology
Wculek, S. K., Dunphy, G., Heras-Murillo, I., Mastrangelo, A., & Sancho, D. (2022) · Cellular & Molecular Immunology · doi:10.1038/s41423-021-00791-9 · Cited by 501
Role of Cardiac Macrophages on Cardiac Inflammation, Fibrosis and Tissue Repair
Lafuse, W. P., Wozniak, D. J., & Rajaram, M. V. S. (2020) · Cells · doi:10.3390/cells10010051 · Cited by 401
Inflammation and fibrosis in murine models of heart failure
Bacmeister, L., Schwarzl, M., Warnke, S., Stoffers, B., Blankenberg, S., Westermann, D., & Lindner, D. (2019) · Basic Research in Cardiology · doi:10.1007/s00395-019-0722-5 · Cited by 382
Metadata
- Authors
- Mouton, A. J., DeLeon-Pennell, K. Y., Rivera Gonzalez, O. J., Flynn, E. R., Freeman, T. C., Saucerman, J. J., Garrett, M. R., Ma, Y., Harmancey, R., & Lindsey, M. L.
- Venue
- Basic Research in Cardiology
- Published
- 2018-06-04
- Volume
- 113
- Issue
- 4
- Pages
- 26-26
- ISSN
- 0003-9217 (ISSN-L)
- 0300-8428
- 1435-1803
- OpenAlex
- W2805446818
- Cited by
- 309
- References
- 92
- FWCI
- 20.64
- Type
- journal article
- Language
- en
- Open access
- hybrid
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- Mouton, A. J., DeLeon-Pennell, K. Y., Rivera Gonzalez, O. J., Flynn, E. R., Freeman, T. C., Saucerman, J. J., Garrett, M. R., Ma, Y., Harmancey, R., & Lindsey, M. L. (2018). Mapping macrophage polarization over the myocardial infarction time continuum. Basic Research in Cardiology. https://doi.org/10.1007/s00395-018-0686-x
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