The DOI matches its Crossref record; 21 of the 21 references it cites were found in a registry or an index.
Microglia and macrophage metabolism in CNS injury and disease: The role of immunometabolism in neurodegeneration and neurotrauma
Open accessAbstract
Innate immune responses, particularly activation of macrophages and microglia, are increasingly implicated in CNS disorders. It is now appreciated that the heterogeneity of functions adopted by these cells dictates neuropathophysiology. Research efforts to characterize the range of pro-inflammatory and anti-inflammatory phenotypes and functions adopted by microglia and macrophages are fueled by the potential for inflammatory cells to both exacerbate neurodegeneration and promote repair/disease resolution. The stimulation-based, M1/M2 classification system has emerged over the last decade as a common language to discuss macrophage and microglia heterogeneity across different fields. However, discontinuities between phenotypic markers and function create potential hurdles for the utility of the M1/M2 system in the development of effective immunomodulatory therapeutics for neuroinflammation. A framework to approach macrophage and microglia heterogeneity from a function-based phenotypic approach comes from rapidly emerging evidence that metabolic processes regulate immune cell activation. This concept of immunometabolism, however, is only beginning to unfold in the study of neurodegeneration and has yet to receive much focus in the context of neurotrauma. In this review, we first discuss the current views of macrophage and microglia heterogeneity and limitations of the M1/M2 classification system for neuropathological studies. We then review and discuss the current literature supporting metabolism as a regulator of microglia function in vitro. Lastly, we evaluate the evidence that metabolism regulates microglia and macrophage phenotype in vivo in models of Alzheimer's disease (AD), stroke, traumatic brain injury (TBI) and spinal cord injury (SCI).
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Alternative activation of macrophages
Gordon, S. (2003) · Nature Reviews Immunology · doi:10.1038/nri978 · Cited by 6,189
Meta-Analysis of 74,046 Individuals Identifies 11 New Susceptibility Loci for Alzheimer’s Disease
(EADI), E. A. D. I., Lambert, J.-C., (GERAD), G. A. E. R. I. A. D., Alzheimer's Disease Genetic Consortium (ADGC), (CHARGE), C. F. H. A. A. R. I. G. E., Ibrahim-Verbaas, C. A., Harold, D., Naj, A. C., Sims, R., Bellenguez, C., et al. (2013) · Nature Genetics · doi:10.1038/ng.2802 · Cited by 4,336
Variant of TREM2 Associated with the Risk of Alzheimer's Disease
Jonsson, T., Stefansson, H., Steinberg, S., Jonsdottir, I., Jonsson, P. V., Snaedal, J., Bjornsson, S., Huttenlocher, J., Levey, A. I., Lah, J. J., et al. (2013) · New England Journal of Medicine · doi:10.1056/nejmoa1211103 · Cited by 2,558
Microglial M1/M2 polarization and metabolic states
Orihuela, R., McPherson, C. A., & Harry, G. J. (2016) · British Journal of Pharmacology · doi:10.1111/bph.13139 · Cited by 2,070
TREM2 Maintains Microglial Metabolic Fitness in Alzheimer’s Disease
Ulland, T. K., Song, W. M., Huang, S. C.-C., Ulrich, J. D., Sergushichev, A., Beatty, W. L., Loboda, A. A., Zhou, Y., Cairns, N. J., Kambal, A., et al. (2017) · Cell · doi:10.1016/j.cell.2017.07.023 · Cited by 1,263 · cited 5×
“These metabolic impairments may hinder the response to Alzheimer’s pathology, as brain sections from TREM2−/−/5XFAD mice show decreased numbers of activated microglia and a drastic reduction in the number of microglia surrounding amyloid plaques compared to TREM2 sufficient 5XFAD controls (Ulland et al., 2017).”
BackgroundInfluentialMitochondrial dysfunction prevents repolarization of inflammatory macrophages
Van den Bossche, J., Baardman, J., Otto, N. A., van der Velden, S., Neele, A. E., van den Berg, S. M., Luque-Martin, R., Chen, H.-J., Boshuizen, M. C. S., Ahmed, M., et al. (2016) · Cell Reports · doi:10.1016/j.celrep.2016.09.008 · Cited by 1,027
Inflammation after trauma: Microglial activation and traumatic brain injury
Ramlackhansingh, A. F., Brooks, D. J., Greenwood, R. J., Bose, S. K., Turkheimer, F. E., Kinnunen, K. M., Gentleman, S., Heckemann, R. A., Gunanayagam, K., Gelosa, G., et al. (2011) · Annals of Neurology · doi:10.1002/ana.22455 · Cited by 999
Functional brain abnormalities in young adults at genetic risk for late-onset Alzheimer's dementia
Reiman, E. M., Chen, K., Alexander, G. E., Caselli, R. J., Bandy, D., Osborne, D., Saunders, A. M., & Hardy, J. (2004) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2635903100 · Cited by 977
Microglia in Alzheimer's disease
Sarlus, H., & Heneka, M. T. (2017) · Journal of Clinical Investigation · doi:10.1172/jci90606 · Cited by 911
Neuroimmunology of Traumatic Brain Injury: Time for a Paradigm Shift
Jassam, Y. N., Izzy, S., Whalen, M., McGavern, D. B., & El Khoury, J. (2017) · Neuron · doi:10.1016/j.neuron.2017.07.010 · Cited by 799
Shechter, R., Miller, O., Yovel, G., Rosenzweig, N., London, A., Ruckh, J., Kim, K.-W., Klein, E., Kalchenko, V., Bendel, P., et al. (2013) · Immunity · doi:10.1016/j.immuni.2013.02.012 · Cited by 683
Tan, H.-Y., Wang, N., Li, S., Hong, M., Wang, X., & Feng, Y. (2016) · Oxidative Medicine and Cellular Longevity · doi:10.1155/2016/2795090 · Cited by 657
Kroner, A., Greenhalgh, A. D., Zarruk, J. G., Passos dos Santos, R., Gaestel, M., & David, S. (2014) · Neuron · doi:10.1016/j.neuron.2014.07.027 · Cited by 655
NADPH oxidase in brain injury and neurodegenerative disorders
Ma, M. W., Wang, J., Zhang, Q., Wang, R., Dhandapani, K. M., Vadlamudi, R. K., & Brann, D. W. (2017) · Molecular Neurodegeneration · doi:10.1186/s13024-017-0150-7 · Cited by 432
Takahashi, K., Prinz, M., Stagi, M., Chechneva, O., & Neumann, H. (2007) · PLoS Medicine · doi:10.1371/journal.pmed.0040124 · Cited by 414
Microglial/Macrophage Polarization Dynamics following Traumatic Brain Injury
Kumar, A., Alvarez-Croda, D.-M., Stoica, B. A., Faden, A. I., & Loane, D. J. (2016) · Journal of Neurotrauma · doi:10.1089/neu.2015.4268 · Cited by 339
Kumar, A., Stoica, B. A., Sabirzhanov, B., Burns, M. P., Faden, A. I., & Loane, D. J. (2013) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2012.11.013 · Cited by 244
Meiser, J., Krämer, L., Sapcariu, S. C., Battello, N., Ghelfi, J., D'Herouel, A. F., Skupin, A., & Hiller, K. (2016) · Journal of Biological Chemistry · doi:10.1074/jbc.m115.676817 · Cited by 243
Call off the dog(ma): M1/M2 polarization is concurrent following traumatic brain injury
Morganti, J. M., Riparip, L.-K., & Rosi, S. (2016) · PLoS ONE · doi:10.1371/journal.pone.0148001 · Cited by 229
Fenn, A. M., Hall, J. C. E., Gensel, J. C., Popovich, P. G., & Godbout, J. P. (2014) · Journal of Neuroscience · doi:10.1523/jneurosci.1146-14.2014 · Cited by 207
Self-citationPSEN1ΔE9, APPswe, and APOE4 Confer Disparate Phenotypes in Human iPSC-Derived Microglia
Konttinen, H., Cabral-da-Silva, M. E. C., Ohtonen, S., Wojciechowski, S., Shakirzyanova, A., Caligola, S., Giugno, R., Ishchenko, Y., Hernández, D., Fazaludeen, M. F., et al. (2019) · Stem Cell Reports · doi:10.1016/j.stemcr.2019.08.004 · Cited by 191
Cited by
Microglia states and nomenclature: A field at its crossroads
Paolicelli, R. C., Sierra, A., Stevens, B., Tremblay, M.-E., Aguzzi, A., Ajami, B., Amit, I., Audinat, E., Bechmann, I., Bennett, M., et al. (2022) · Neuron · doi:10.1016/j.neuron.2022.10.020 · Cited by 1,973
Lee, S., Devanney, N. A., Golden, L. R., Smith, C. T., Schwartz, J. L., Walsh, A. E., Clarke, H. A., Goulding, D. S., Allenger, E. J., Morillo-Segovia, G., et al. (2023) · Cell Reports · doi:10.1016/j.celrep.2023.112196 · Cited by 203
Self-citationFarmer, B. C., Williams, H. C., Devanney, N. A., Piron, M. A., Nation, G. K., Carter, D. J., Walsh, A. E., Khanal, R., Young, L. E. A., Kluemper, J. C., et al. (2021) · Molecular Neurodegeneration · doi:10.1186/s13024-021-00483-y · Cited by 87
Self-citationYin, H., Ju, Z., Zheng, M., Zhang, X., Zuo, W., Wang, Y., Ding, X., Zhang, X., Peng, Y., Li, J., et al. (2023) · PLOS Biology · doi:10.1371/journal.pbio.3002017 · Cited by 62
Huang, Q., Wang, Y., Chen, S., & Liang, F. (2023) · Aging and disease · doi:10.14336/ad.2023.0807 · Cited by 62
Metadata
- Authors
- Devanney, N. A., Stewart, A. N., & Gensel, J. C.
- Venue
- Experimental Neurology
- Publisher
- Elsevier BV
- Published
- 2020-07
- Volume
- 329
- Pages
- 113310
- ISSN
- 0014-4886 (ISSN-L)
- OpenAlex
- W3015747477
- Cited by
- 306
- References
- 21
- FWCI
- 13.35
- Type
- journal article
- Language
- en
- Open access
- hybrid
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 ›
Are you an author, or do you see a problem with this page? Request a review
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
