Mitochondrial complex I activity in microglia sustains neuroinflammation.
Journal
Nature
ISSN: 1476-4687
Titre abrégé: Nature
Pays: England
ID NLM: 0410462
Informations de publication
Date de publication:
13 Mar 2024
13 Mar 2024
Historique:
received:
03
10
2022
accepted:
06
02
2024
medline:
14
3
2024
pubmed:
14
3
2024
entrez:
14
3
2024
Statut:
aheadofprint
Résumé
Sustained smouldering, or low-grade activation, of myeloid cells is a common hallmark of several chronic neurological diseases, including multiple sclerosis
Identifiants
pubmed: 38480879
doi: 10.1038/s41586-024-07167-9
pii: 10.1038/s41586-024-07167-9
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Informations de copyright
© 2024. The Author(s).
Références
Mrdjen, D. et al. High-dimensional single-cell mapping of central nervous system immune cells reveals distinct myeloid subsets in health, aging, and disease. Immunity 48, 380–395 (2018).
pubmed: 29426702
doi: 10.1016/j.immuni.2018.01.011
O’Neill, L. A., Kishton, R. J. & Rathmell, J. A guide to immunometabolism for immunologists. Nat. Rev. Immunol. 16, 553–565 (2016).
pubmed: 27396447
pmcid: 5001910
doi: 10.1038/nri.2016.70
Peruzzotti-Jametti, L. & Pluchino, S. Targeting mitochondrial metabolism in neuroinflammation: towards a therapy for progressive multiple sclerosis. Trends Mol. Med. 24, 838–855 (2018).
pubmed: 30100517
doi: 10.1016/j.molmed.2018.07.007
Jäckle, K. et al. Molecular signature of slowly expanding lesions in progressive multiple sclerosis. Brain 143, 2073–2088 (2020).
pubmed: 32577755
doi: 10.1093/brain/awaa158
Preziosa, P. et al. Slowly expanding lesions predict 9-year multiple sclerosis disease progression. Neurol. Neuroimmunol. Neuroinflamm. 9, e1139 (2022).
pubmed: 35105685
pmcid: 8808355
doi: 10.1212/NXI.0000000000001139
Hamzaoui, M. et al. Positron emission tomography with [
pubmed: 37039158
doi: 10.1002/ana.26657
Zrzavy, T. et al. Loss of ‘homeostatic’ microglia and patterns of their activation in active multiple sclerosis. Brain 140, 1900–1913 (2017).
pubmed: 28541408
pmcid: 6057548
doi: 10.1093/brain/awx113
Licht-Mayer, S. et al. Enhanced axonal response of mitochondria to demyelination offers neuroprotection: implications for multiple sclerosis. Acta Neuropathol. 140, 143–167 (2020).
pubmed: 32572598
pmcid: 7360646
doi: 10.1007/s00401-020-02179-x
Zambonin, J. L. et al. Increased mitochondrial content in remyelinated axons: implications for multiple sclerosis. Brain 134, 1901–1913 (2011).
pubmed: 21705418
pmcid: 3122369
doi: 10.1093/brain/awr110
Mahad, D., Ziabreva, I., Lassmann, H. & Turnbull, D. Mitochondrial defects in acute multiple sclerosis lesions. Brain 131, 1722–1735 (2008).
pubmed: 18515320
pmcid: 2442422
doi: 10.1093/brain/awn105
Campbell, G. & Mahad, D. J. Mitochondrial dysfunction and axon degeneration in progressive multiple sclerosis. FEBS Lett. 592, 1113–1121 (2018).
pubmed: 29453889
doi: 10.1002/1873-3468.13013
Mills, E. L. et al. Succinate dehydrogenase supports metabolic repurposing of mitochondria to drive inflammatory macrophages. Cell 167, 457–470 (2016).
pubmed: 27667687
pmcid: 5863951
doi: 10.1016/j.cell.2016.08.064
Peruzzotti-Jametti, L. et al. Macrophage-derived extracellular succinate licenses neural stem cells to suppress chronic neuroinflammation. Cell Stem Cell 22, 355–368 (2018).
pubmed: 29478844
pmcid: 5842147
doi: 10.1016/j.stem.2018.01.020
Scialo, F., Fernandez-Ayala, D. J. & Sanz, A. Role of mitochondrial reverse electron transport in ROS signaling: potential roles in health and disease. Front. Physiol. 8, 428 (2017).
pubmed: 28701960
pmcid: 5486155
doi: 10.3389/fphys.2017.00428
Jha, A. K. et al. Network integration of parallel metabolic and transcriptional data reveals metabolic modules that regulate macrophage polarization. Immunity 42, 419–430 (2015).
pubmed: 25786174
doi: 10.1016/j.immuni.2015.02.005
Lampropoulou, V. et al. Itaconate links inhibition of succinate dehydrogenase with macrophage metabolic remodeling and regulation of inflammation. Cell Metab. 24, 158–166 (2016).
pubmed: 27374498
pmcid: 5108454
doi: 10.1016/j.cmet.2016.06.004
Chouchani, E. T. et al. Ischaemic accumulation of succinate controls reperfusion injury through mitochondrial ROS. Nature 515, 431–435 (2014).
pubmed: 25383517
pmcid: 4255242
doi: 10.1038/nature13909
Brand, M. D. et al. Suppressors of Superoxide-H
pubmed: 27667666
pmcid: 5061631
doi: 10.1016/j.cmet.2016.08.012
Jordao, M. J. C. et al. Single-cell profiling identifies myeloid cell subsets with distinct fates during neuroinflammation. Science https://doi.org/10.1126/science.aat7554 (2019).
Hamel, R. et al. Time-resolved single-cell RNAseq profiling identifies a novel Fabp5
pubmed: 37636454
pmcid: 10450865
doi: 10.1016/j.heliyon.2023.e18339
Paolicelli, R. C. et al. Microglia states and nomenclature: a field at its crossroads. Neuron 110, 3458–3483 (2022).
pubmed: 36327895
pmcid: 9999291
doi: 10.1016/j.neuron.2022.10.020
Keren-Shaul, H. et al. A unique microglia type associated with restricting development of Alzheimer’s disease. Cell 169, 1276–1290 (2017).
pubmed: 28602351
doi: 10.1016/j.cell.2017.05.018
Deczkowska, A. et al. Disease-associated microglia: a universal immune sensor of neurodegeneration. Cell 173, 1073–1081 (2018).
pubmed: 29775591
doi: 10.1016/j.cell.2018.05.003
La Manno, G. et al. RNA velocity of single cells. Nature 560, 494–498 (2018).
pubmed: 30089906
pmcid: 6130801
doi: 10.1038/s41586-018-0414-6
Kahlhofer, F., Kmita, K., Wittig, I., Zwicker, K. & Zickermann, V. Accessory subunit NUYM (NDUFS4) is required for stability of the electron input module and activity of mitochondrial complex I. Biochim. Biophys. Acta Bioenerg. 1858, 175–181 (2017).
pubmed: 27871794
doi: 10.1016/j.bbabio.2016.11.010
Mendiola, A. S. et al. Transcriptional profiling and therapeutic targeting of oxidative stress in neuroinflammation. Nat. Immunol. 21, 513–524 (2020).
pubmed: 32284594
pmcid: 7523413
doi: 10.1038/s41590-020-0654-0
Schirmer, L. et al. Neuronal vulnerability and multilineage diversity in multiple sclerosis. Nature 573, 75–82 (2019).
pubmed: 31316211
pmcid: 6731122
doi: 10.1038/s41586-019-1404-z
Absinta, M. et al. A lymphocyte–microglia–astrocyte axis in chronic active multiple sclerosis. Nature 597, 709–714 (2021).
pubmed: 34497421
pmcid: 8719282
doi: 10.1038/s41586-021-03892-7
Cunha, M. P. et al. Both creatine and its product phosphocreatine reduce oxidative stress and afford neuroprotection in an in vitro Parkinson’s model. ASN Neuro 6, 1759091414554945 (2014).
pubmed: 25424428
pmcid: 4357608
doi: 10.1177/1759091414554945
Mailloux, R. J. & Willmore, W. G. S-glutathionylation reactions in mitochondrial function and disease. Front. Cell Dev. Biol. 2, 68 (2014).
pubmed: 25453035
pmcid: 4233936
doi: 10.3389/fcell.2014.00068
Tannahill, G. M. et al. Succinate is an inflammatory signal that induces IL-1β through HIF-1α. Nature 496, 238–242 (2013).
pubmed: 23535595
pmcid: 4031686
doi: 10.1038/nature11986
Billingham, L. K. et al. Mitochondrial electron transport chain is necessary for NLRP3 inflammasome activation. Nat. Immunol. 23, 692–704 (2022).
pubmed: 35484407
pmcid: 9098388
doi: 10.1038/s41590-022-01185-3
Vilhardt, F., Haslund-Vinding, J., Jaquet, V. & McBean, G. Microglia antioxidant systems and redox signalling. Br. J. Pharmacol. 174, 1719–1732 (2017).
pubmed: 26754582
doi: 10.1111/bph.13426
Mander, P. & Brown, G. C. Activation of microglial NADPH oxidase is synergistic with glial iNOS expression in inducing neuronal death: a dual-key mechanism of inflammatory neurodegeneration. J. Neuroinflamm. 2, 20 (2005).
doi: 10.1186/1742-2094-2-20
Liu, L., Li, Y., Chen, G. & Chen, Q. Crosstalk between mitochondrial biogenesis and mitophagy to maintain mitochondrial homeostasis. J. Biomed. Sci. 30, 86 (2023).
pubmed: 37821940
pmcid: 10568841
doi: 10.1186/s12929-023-00975-7
Van de Sande, B. et al. A scalable SCENIC workflow for single-cell gene regulatory network analysis. Nat. Protoc. 15, 2247–2276 (2020).
pubmed: 32561888
doi: 10.1038/s41596-020-0336-2
Venkatesan, B., Mahimainathan, L., Das, F., Ghosh-Choudhury, N. & Ghosh Choudhury, G. Downregulation of catalase by reactive oxygen species via PI 3 kinase/Akt signaling in mesangial cells. J. Cell. Physiol. 211, 457–467 (2007).
pubmed: 17186497
doi: 10.1002/jcp.20953
Mukem, S., Thongbuakaew, T. & Khornchatri, K. Mito-Tempo suppresses autophagic flux via the PI3K/Akt/mTOR signaling pathway in neuroblastoma SH-SY5Y cells. Heliyon 7, e07310 (2021).
pubmed: 34195421
pmcid: 8239474
doi: 10.1016/j.heliyon.2021.e07310
Milliken, A. S., Nadtochiy, S. M. & Brookes, P. S. Inhibiting succinate release worsens cardiac reperfusion injury by enhancing mitochondrial reactive oxygen species generation. J. Am. Heart Assoc. 11, e026135 (2022).
pubmed: 35766275
pmcid: 9333399
doi: 10.1161/JAHA.122.026135
Yin, Z. et al. Structural basis for a complex I mutation that blocks pathological ROS production. Nat. Commun. 12, 707 (2021).
pubmed: 33514727
pmcid: 7846746
doi: 10.1038/s41467-021-20942-w
Young, K. & Morrison, H. Quantifying microglia morphology from photomicrographs of immunohistochemistry prepared tissue using ImageJ. J. Vis. Exp. https://doi.org/10.3791/57648 (2018).
Quintana, A., Kruse, S. E., Kapur, R. P., Sanz, E. & Palmiter, R. D. Complex I deficiency due to loss of Ndufs4 in the brain results in progressive encephalopathy resembling Leigh syndrome. Proc. Natl Acad. Sci. USA 107, 10996–11001 (2010).
pubmed: 20534480
pmcid: 2890717
doi: 10.1073/pnas.1006214107
Padmanabhan, K. et al. Thymosin beta4 is essential for adherens junction stability and epidermal planar cell polarity. Development https://doi.org/10.1242/dev.193425 (2020).
Spiteri, A. G., Wishart, C. L., Pamphlett, R., Locatelli, G. & King, N. J. C. Microglia and monocytes in inflammatory CNS disease: integrating phenotype and function. Acta Neuropathol. 143, 179–224 (2022).
pubmed: 34853891
doi: 10.1007/s00401-021-02384-2
Masuda, T., Sankowski, R., Staszewski, O. & Prinz, M. Microglia heterogeneity in the single-cell era. Cell Rep. 30, 1271–1281 (2020).
pubmed: 32023447
doi: 10.1016/j.celrep.2020.01.010
Proto, J. D. et al. Disrupted microglial iron homeostasis in progressive multiple sclerosis. Preprint at bioRxiv https://doi.org/10.1101/2021.05.09.443127 (2021).
Singh, S. et al. Relationship of acute axonal damage, Wallerian degeneration, and clinical disability in multiple sclerosis. J. Neuroinflamm. 14, 57 (2017).
doi: 10.1186/s12974-017-0831-8
Wheaton, W. W. et al. Metformin inhibits mitochondrial complex I of cancer cells to reduce tumorigenesis. eLife 3, e02242 (2014).
pubmed: 24843020
pmcid: 4017650
doi: 10.7554/eLife.02242
Liao, S. T. et al. 4-Octyl itaconate inhibits aerobic glycolysis by targeting GAPDH to exert anti-inflammatory effects. Nat. Commun. 10, 5091 (2019).
pubmed: 31704924
pmcid: 6841710
doi: 10.1038/s41467-019-13078-5
Ackermann, W. W. & Potter, V. R. Enzyme inhibition in relation to chemotherapy. Proc. Soc. Exp. Biol. Med. 72, 1–9 (1949).
pubmed: 15391660
doi: 10.3181/00379727-72-17313
Peace, C. G. & O’Neill, L. A. The role of itaconate in host defense and inflammation. J. Clin. Invest. 132, e148548 (2022).
pubmed: 35040439
pmcid: 8759771
doi: 10.1172/JCI148548
Parkhurst, C. N. et al. Microglia promote learning-dependent synapse formation through brain-derived neurotrophic factor. Cell 155, 1596–1609 (2013).
pubmed: 24360280
pmcid: 4033691
doi: 10.1016/j.cell.2013.11.030
Madisen, L. et al. A robust and high-throughput Cre reporting and characterization system for the whole mouse brain. Nat. Neurosci. 13, 133–140 (2010).
pubmed: 20023653
doi: 10.1038/nn.2467
Rubino, S. J. et al. Acute microglia ablation induces neurodegeneration in the somatosensory system. Nat. Commun. 9, 4578 (2018).
pubmed: 30385785
pmcid: 6212411
doi: 10.1038/s41467-018-05929-4
Ewels, P., Magnusson, M., Lundin, S. & Kaller, M. MultiQC: summarize analysis results for multiple tools and samples in a single report. Bioinformatics 32, 3047–3048 (2016).
pubmed: 27312411
pmcid: 5039924
doi: 10.1093/bioinformatics/btw354
Zheng, G. X. et al. Massively parallel digital transcriptional profiling of single cells. Nat. Commun. 8, 14049 (2017).
pubmed: 28091601
pmcid: 5241818
doi: 10.1038/ncomms14049
Stuart, T. et al. Comprehensive Integration of Single-Cell Data. Cell 177, 1888–1902 (2019).
pubmed: 31178118
pmcid: 6687398
doi: 10.1016/j.cell.2019.05.031
Korsunsky, I. et al. Fast, sensitive and accurate integration of single-cell data with Harmony. Nat. Methods 16, 1289–1296 (2019).
pubmed: 31740819
pmcid: 6884693
doi: 10.1038/s41592-019-0619-0
Shahsavari, A., Munteanu, A. & Mohorianu, I. ClustAssess: tools for assessing the robustness of single-cell clustering. Preprint at bioRxiv https://doi.org/10.1101/2022.01.31.478592 (2022).
Aibar, S. et al. SCENIC: single-cell regulatory network inference and clustering. Nat. Methods 14, 1083–1086 (2017).
pubmed: 28991892
pmcid: 5937676
doi: 10.1038/nmeth.4463
Wolf, F. A., Angerer, P. & Theis, F. J. SCANPY: large-scale single-cell gene expression data analysis. Genome Biol. 19, 15 (2018).
pubmed: 29409532
pmcid: 5802054
doi: 10.1186/s13059-017-1382-0
Guttikonda, S. R. et al. Fully defined human pluripotent stem cell-derived microglia and tri-culture system model C3 production in Alzheimer’s disease. Nat. Neurosci. 24, 343–354 (2021).
pubmed: 33558694
pmcid: 8382543
doi: 10.1038/s41593-020-00796-z
Chen, S. W. et al. Efficient conversion of human induced pluripotent stem cells into microglia by defined transcription factors. Stem Cell Rep. 16, 1363–1380 (2021).
doi: 10.1016/j.stemcr.2021.03.010
Shen, W., Le, S., Li, Y. & Hu, F. SeqKit: a cross-platform and ultrafast toolkit for FASTA/Q file manipulation. PLoS ONE 11, e0163962 (2016).
pubmed: 27706213
pmcid: 5051824
doi: 10.1371/journal.pone.0163962
Mohorianu, I. et al. Comparison of alternative approaches for analysing multi-level RNA-seq data. PLoS ONE 12, e0182694 (2017).
pubmed: 28792517
pmcid: 5549751
doi: 10.1371/journal.pone.0182694
Dobin, A. et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29, 15–21 (2013).
pubmed: 23104886
doi: 10.1093/bioinformatics/bts635
Liao, Y., Smyth, G. K. & Shi, W. featureCounts: an efficient general purpose program for assigning sequence reads to genomic features. Bioinformatics 30, 923–930 (2014).
pubmed: 24227677
doi: 10.1093/bioinformatics/btt656
Moutsopoulos, I. et al. noisyR: enhancing biological signal in sequencing datasets by characterizing random technical noise. Nucleic Acids Res. 49, e83 (2021).
pubmed: 34076236
pmcid: 8373073
doi: 10.1093/nar/gkab433
Bolstad, B. M., Irizarry, R. A., Astrand, M. & Speed, T. P. A comparison of normalization methods for high density oligonucleotide array data based on variance and bias. Bioinformatics 19, 185–193 (2003).
pubmed: 12538238
doi: 10.1093/bioinformatics/19.2.185
Robinson, M. D., McCarthy, D. J. & Smyth, G. K. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26, 139–140 (2010).
pubmed: 19910308
doi: 10.1093/bioinformatics/btp616
Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 550 (2014).
pubmed: 25516281
pmcid: 4302049
doi: 10.1186/s13059-014-0550-8
Raudvere, U. et al. g:Profiler: a web server for functional enrichment analysis and conversions of gene lists (2019 update). Nucleic Acids Res. 47, W191–W198 (2019).
pubmed: 31066453
pmcid: 6602461
doi: 10.1093/nar/gkz369
Moutsopoulos, I., Williams, E. C. & Mohorianu, I. I. bulkAnalyseR: an accessible, interactive pipeline for analysing and sharing bulk multi-modal sequencing data. Brief. Bioinform. 24, bbac591 (2023).
pubmed: 36583521
doi: 10.1093/bib/bbac591
Kolberg, L., Raudvere, U., Kuzmin, I., Vilo, J. & Peterson, H. gprofiler2—an R package for gene list functional enrichment analysis and namespace conversion toolset g:Profiler. F1000Res 9, ELIXIR-709 (2020).
pubmed: 33564394
pmcid: 7859841
doi: 10.12688/f1000research.24956.2
Nemkov, T., Reisz, J. A., Gehrke, S., Hansen, K. C. & D’Alessandro, A. High-throughput metabolomics: isocratic and gradient mass spectrometry-based methods. Methods Mol. Biol. 1978, 13–26 (2019).
Nemkov, T., D’Alessandro, A. & Hansen, K. C. Three-minute method for amino acid analysis by UHPLC and high-resolution quadrupole orbitrap mass spectrometry. Amino Acids 47, 2345–2357 (2015).
pubmed: 26058356
pmcid: 4624008
doi: 10.1007/s00726-015-2019-9
Inglese, P., Correia, G., Pruski, P., Glen, R. C. & Takats, Z. Colocalization features for classification of tumors using desorption electrospray ionization mass spectrometry imaging. Anal. Chem. 91, 6530–6540 (2019).
pubmed: 31013058
pmcid: 6533599
doi: 10.1021/acs.analchem.8b05598
Wishart, D. S. et al. HMDB: a knowledgebase for the human metabolome. Nucleic Acids Res. 37, D603–D610 (2009).
pubmed: 18953024
doi: 10.1093/nar/gkn810
Li, J. et al. Minimum volume simplex analysis: a fast algorithm for linear hyperspectral unmixing. IEEE Trans. Geosci. Remote Sens. 53, 5067–5082 (2015).
doi: 10.1109/TGRS.2015.2417162
Saura, J., Tusell, J. M. & Serratosa, J. High-yield isolation of murine microglia by mild trypsinization. Glia 44, 183–189 (2003).
pubmed: 14603460
doi: 10.1002/glia.10274
Brownjohn, P. W. et al. Functional studies of missense TREM2 mutations in human stem cell-derived microglia. Stem Cell Rep. 10, 1294–1307 (2018).
doi: 10.1016/j.stemcr.2018.03.003
Haenseler, W. et al. A highly efficient human pluripotent stem cell microglia model displays a neuronal-co-culture-specific expression profile and inflammatory response. Stem Cell Rep. 8, 1727–1742 (2017).
doi: 10.1016/j.stemcr.2017.05.017
Prag, H. A. et al. Ester prodrugs of malonate with enhanced intracellular delivery protect against cardiac ischemia-reperfusion injury in vivo. Cardiovasc. Drugs Ther. 36, 1–13 (2022).
pubmed: 32648168
doi: 10.1007/s10557-020-07033-6
Magliozzi, R. et al. Inflammatory intrathecal profiles and cortical damage in multiple sclerosis. Ann. Neurol. 83, 739–755 (2018).
pubmed: 29518260
doi: 10.1002/ana.25197
van Waesberghe, J. H. et al. Axonal loss in multiple sclerosis lesions: magnetic resonance imaging insights into substrates of disability. Ann. Neurol. 46, 747–754 (1999).
pubmed: 10553992
doi: 10.1002/1531-8249(199911)46:5<747::AID-ANA10>3.0.CO;2-4
van der Valk, P. & De Groot, C. J. Staging of multiple sclerosis (MS) lesions: pathology of the time frame of MS. Neuropathol. Appl. Neurobiol. 26, 2–10 (2000).
pubmed: 10736062
doi: 10.1046/j.1365-2990.2000.00217.x
Wilkerson, M. D. & Hayes, D. N. ConsensusClusterPlus: a class discovery tool with confidence assessments and item tracking. Bioinformatics 26, 1572–1573 (2010).
pubmed: 20427518
pmcid: 2881355
doi: 10.1093/bioinformatics/btq170