CHIT1 at diagnosis predicts faster disability progression and reflects early microglial activation in multiple sclerosis.
Journal
Nature communications
ISSN: 2041-1723
Titre abrégé: Nat Commun
Pays: England
ID NLM: 101528555
Informations de publication
Date de publication:
12 Jun 2024
12 Jun 2024
Historique:
received:
09
11
2023
accepted:
30
05
2024
medline:
13
6
2024
pubmed:
13
6
2024
entrez:
12
6
2024
Statut:
epublish
Résumé
Multiple sclerosis (MS) is characterized by heterogeneity in disease course and prediction of long-term outcome remains a major challenge. Here, we investigate five myeloid markers - CHIT1, CHI3L1, sTREM2, GPNMB and CCL18 - in the cerebrospinal fluid (CSF) at diagnostic lumbar puncture in a longitudinal cohort of 192 MS patients. Through mixed-effects and machine learning models, we show that CHIT1 is a robust predictor for faster disability progression. Integrative analysis of 11 CSF and 26 central nervous system (CNS) parenchyma single-cell/nucleus RNA sequencing samples reveals CHIT1 to be predominantly expressed by microglia located in active MS lesions and enriched for lipid metabolism pathways. Furthermore, we find CHIT1 expression to accompany the transition from a homeostatic towards a more activated, MS-associated cell state in microglia. Neuropathological evaluation in post-mortem tissue from 12 MS patients confirms CHIT1 production by lipid-laden phagocytes in actively demyelinating lesions, already in early disease stages. Altogether, we provide a rationale for CHIT1 as an early biomarker for faster disability progression in MS.
Identifiants
pubmed: 38866782
doi: 10.1038/s41467-024-49312-y
pii: 10.1038/s41467-024-49312-y
doi:
Substances chimiques
Biomarkers
0
chitotriosidase
EC 3.2.1.-
Hexosaminidases
EC 3.2.1.-
Chitinase-3-Like Protein 1
0
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
5013Subventions
Organisme : KU Leuven (Katholieke Universiteit Leuven)
ID : BOF-FKO
Organisme : National Multiple Sclerosis Society (National MS Society)
ID : PA-2002-36277
Organisme : Fonds Wetenschappelijk Onderzoek (Research Foundation Flanders)
ID : 11A0523N
Organisme : Fonds Wetenschappelijk Onderzoek (Research Foundation Flanders)
ID : 1S38023N
Informations de copyright
© 2024. The Author(s).
Références
Dendrou, C. A., Fugger, L. & Friese, M. A. Immunopathology of multiple sclerosis. Nat. Rev. Immunol. 15, 545–558 (2015).
pubmed: 26250739
doi: 10.1038/nri3871
Comabella, M. & Montalban, X. Body fluid biomarkers in multiple sclerosis. Lancet Neurol. 13, 113–126 (2014).
pubmed: 24331797
doi: 10.1016/S1474-4422(13)70233-3
Van Der Poel, M. et al. Transcriptional profiling of human microglia reveals grey–white matter heterogeneity and multiple sclerosis-associated changes. Nat. Commun. 10, 1139 (2019).
Consortium, I. M. S. G. Multiple sclerosis genomic map implicates peripheral immune cells and microglia in susceptibility. Science 365, eaav7188 (2019).
doi: 10.1126/science.aav7188
Distéfano-Gagné, F., Bitarafan, S., Lacroix, S. & Gosselin, D. Roles and regulation of microglia activity in multiple sclerosis: insights from animal models. Nat. Rev. Neurosci., 24, 397–415 (2023).
Guerrero, B. L. & Sicotte, N. L. Microglia in Multiple Sclerosis: Friend or Foe? Front. Immunol. 11, 374 (2020).
pubmed: 32265902
pmcid: 7098953
doi: 10.3389/fimmu.2020.00374
Oldoni, E. et al. CHIT1 at Diagnosis Reflects Long‐Term Multiple Sclerosis Disease Activity. Ann. Neurol. 87, 633–645 (2020).
pubmed: 31997416
pmcid: 7187166
doi: 10.1002/ana.25691
Novakova, L. et al. Cerebrospinal fluid biomarkers as a measure of disease activity and treatment efficacy in relapsing-remitting multiple sclerosis. J. Neurochem. 141, 296–304 (2017).
pubmed: 27787906
doi: 10.1111/jnc.13881
Mollgaard, M., Degn, M., Sellebjerg, F., Frederiksen, J. L. & Modvig, S. Cerebrospinal fluid chitinase-3-like 2 and chitotriosidase are potential prognostic biomarkers in early multiple sclerosis. Eur. J. Neurol. 23, 898–905 (2016).
pubmed: 26872061
doi: 10.1111/ene.12960
Hendrickx, D. A. E. et al. Gene Expression Profiling of Multiple Sclerosis Pathology Identifies Early Patterns of Demyelination Surrounding Chronic Active Lesions. Front. Immunol. 8, 1810 (2017).
pubmed: 29312322
pmcid: 5742619
doi: 10.3389/fimmu.2017.01810
Healy, L. M., Stratton, J. A., Kuhlmann, T. & Antel, J. The role of glial cells in multiple sclerosis disease progression. Nat. Rev. Neurol. 18, 237–248 (2022).
pubmed: 35190704
doi: 10.1038/s41582-022-00624-x
Jordão, M. J. C. et al. Single-cell profiling identifies myeloid cell subsets with distinct fates during neuroinflammation. Science 363, eaat7554 (2019).
pubmed: 30679343
doi: 10.1126/science.aat7554
Prinz, M., Masuda, T., Wheeler, M. A. & Quintana, F. J. Microglia and Central Nervous System–Associated Macrophages—From Origin to Disease Modulation. Annu. Rev. Immunol. 39, 251–277 (2021).
pubmed: 33556248
pmcid: 8085109
doi: 10.1146/annurev-immunol-093019-110159
Miedema, A. et al. Brain macrophages acquire distinct transcriptomes in multiple sclerosis lesions and normal appearing white matter. Acta Neuropathologica Commun. 10, 8 (2022).
doi: 10.1186/s40478-021-01306-3
Cantó, E. et al. Chitinase 3-like 1: prognostic biomarker in clinically isolated syndromes. Brain 138, 918–931 (2015).
pubmed: 25688078
doi: 10.1093/brain/awv017
Steinacker, P. et al. Chitotriosidase (CHIT1) is increased in microglia and macrophages in spinal cord of amyotrophic lateral sclerosis and cerebrospinal fluid levels correlate with disease severity and progression. J. Neurol. Neurosurg. Psychiatry 89, 239–247 (2018).
pubmed: 29142138
doi: 10.1136/jnnp-2017-317138
Dong, M.-H. et al. CSF sTREM2 in neurological diseases: a two-sample Mendelian randomization study. J. Neuroinflamm. 19, 79 (2022).
Ferreira-Atuesta, C., Reyes, S., Giovanonni, G. & Gnanapavan, S. The Evolution of Neurofilament Light Chain in Multiple Sclerosis. Front Neurosci. 15, 642384 (2021).
pubmed: 33889068
pmcid: 8055958
doi: 10.3389/fnins.2021.642384
D’hondt, R., Moylett, S., Goris, A. & Vens, C. A Binning Approach for Predicting Long-Term Prognosis in Multiple Sclerosis. In: Artificial Intelligence in Medicine (eds, Juarez, J.M., Marcos, M., Stiglic, G. & Tucker, A.) 25–34 (Springer Nature Switzerland, 2023).
Breiman, L. Random Forests. Mach. Learn. 45, 5–32 (2001).
doi: 10.1023/A:1010933404324
Van Hove, H. et al. A single-cell atlas of mouse brain macrophages reveals unique transcriptional identities shaped by ontogeny and tissue environment. Nat. Neurosci. 22, 1021–1035 (2019).
pubmed: 31061494
doi: 10.1038/s41593-019-0393-4
Sankowski, R. et al. Multiomic spatial landscape of innate immune cells at human central nervous system borders. Nat. Med. 30, 186–198 (2023).
pubmed: 38123840
pmcid: 10803260
doi: 10.1038/s41591-023-02673-1
Masuda, T. et al. Spatial and temporal heterogeneity of mouse and human microglia at single-cell resolution. Nature 566, 388–392 (2019).
pubmed: 30760929
doi: 10.1038/s41586-019-0924-x
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
Ostkamp, P. et al. A single-cell analysis framework allows for characterization of CSF leukocytes and their tissue of origin in multiple sclerosis. Sci. Transl. Med. 14, eadc9778 (2022).
pubmed: 36449599
doi: 10.1126/scitranslmed.adc9778
Pinteac, R., Montalban, X. & Comabella, M. Chitinases and chitinase-like proteins as biomarkers in neurologic disorders. Neurol. Neuroimmunol. Neuroinflamm. 8, e921 (2021).
pubmed: 33293459
doi: 10.1212/NXI.0000000000000921
Russo, C., Valle, M. S., Casabona, A. & Malaguarnera, L. Chitinase Signature in the Plasticity of Neurodegenerative Diseases. Int. J. Mol. Sci. 24, 6301 (2023).
pubmed: 37047273
pmcid: 10094409
doi: 10.3390/ijms24076301
Gille, B. et al. Inflammatory markers in cerebrospinal fluid: independent prognostic biomarkers in amyotrophic lateral sclerosis? J. Neurol. Neurosurg. Psychiatry 90, 1338–1346 (2019).
pubmed: 31175169
Hollak, C. E., van Weely, S., van Oers, M. H. & Aerts, J. M. Marked elevation of plasma chitotriosidase activity. A novel hallmark of Gaucher disease. J. Clin. Invest. 93, 1288–1292 (1994).
pubmed: 8132768
pmcid: 294082
doi: 10.1172/JCI117084
van Dussen, L. et al. Value of plasma chitotriosidase to assess non-neuronopathic Gaucher disease severity and progression in the era of enzyme replacement therapy. J. Inherit. Metab. Dis. 37, 991–1001 (2014).
pubmed: 24831585
doi: 10.1007/s10545-014-9711-x
Brinkman, J. et al. Plasma chitotriosidase and CCL18: early biochemical surrogate markers in type B Niemann-Pick disease. J. Inherit. Metab. Dis. 28, 13–20 (2005).
pubmed: 15702402
doi: 10.1007/s10545-005-4416-9
Vedder, A. C. et al. Plasma chitotriosidase in male Fabry patients: a marker for monitoring lipid-laden macrophages and their correction by enzyme replacement therapy. Mol. Genet Metab. 89, 239–244 (2006).
pubmed: 16765076
doi: 10.1016/j.ymgme.2006.04.013
Kanneganti, M., Kamba, A. & Mizoguchi, E. Role of chitotriosidase (chitinase 1) under normal and disease conditions. J. Epithel. Biol. Pharm. 5, 1–9 (2012).
doi: 10.2174/1875044301205010001
Czartoryska, B., Fiszer, U. & Lugowska, A. Chitotriosidase Activity in Cerebrospinal Fluid as a Marker of Inflammatory Processes in Neurological Diseases. Chitotriosidase-Aktivität in Liquor Cerebrospinalis als ein Marker des Entzündungsprozesses bei Nervenkrankheiten. LaboratoriumsMedizin 25, 77–81 (2001).
doi: 10.1515/labm.2001.25.3-4.77
Sotgiu, S. et al. Intrathecal chitotriosidase and the outcome of multiple sclerosis. Mult. Scler. J. 12, 551–557 (2006).
doi: 10.1177/1352458506070614
Comabella, M. et al. Plasma chitotriosidase activity in multiple sclerosis. Clin. Immunol. 131, 216–222 (2009).
pubmed: 19176289
doi: 10.1016/j.clim.2008.12.004
Verbeek, M. M., Notting, E. A., Faas, B., Claessens-Linskens, R. & Jongen, P. J. H. Increased cerebrospinal fluid chitotriosidase index in patients with multiple sclerosis. Acta Neurologica Scand. 121, 309–314 (2010).
doi: 10.1111/j.1600-0404.2009.01242.x
Boot, R. G. et al. The Human Chitotriosidase Gene. J. Biol. Chem. 273, 25680–25685 (1998).
pubmed: 9748235
doi: 10.1074/jbc.273.40.25680
Piras, I. et al. Human CHIT1 gene distribution: new data from Mediterranean and European populations. J. Hum. Genet 52, 110 (2007).
pubmed: 17106626
doi: 10.1007/s10038-006-0086-1
Pagliardini, V. et al. Chitotriosidase and lysosomal enzymes as potential biomarkers of disease progression in amyotrophic lateral sclerosis: a survey clinic-based study. J. Neurol. Sci. 348, 245–250 (2015).
pubmed: 25563799
doi: 10.1016/j.jns.2014.12.016
Irún, P., Alfonso, P., Aznarez, S., Giraldo, P. & Pocovi, M. Chitotriosidase variants in patients with Gaucher disease. Implications for diagnosis and therapeutic monitoring. Clin. Biochem. 46, 1804–1807 (2013).
pubmed: 24060732
doi: 10.1016/j.clinbiochem.2013.09.006
Dardis, A. et al. Patient centered guidelines for the laboratory diagnosis of Gaucher disease type 1. Orphanet J. Rare Dis. 17, 442 (2022).
pubmed: 36544230
pmcid: 9768924
doi: 10.1186/s13023-022-02573-6
Castellani, R. et al. Chitin-like Polysaccharides in Alzheimers Disease Brains. Curr. Alzheimer Res. 2, 419–423 (2005).
pubmed: 16248847
doi: 10.2174/156720505774330555
Stefano, S. et al. Chitotriosidase and Alzheimers Disease. Curr. Alzheimer Res. 4, 295–296 (2007).
doi: 10.2174/156720507781077232
Sotgiu, S., Musumeci, S., Marconi, S., Gini, B. & Bonetti, B. Different content of chitin-like polysaccharides in multiple sclerosis and Alzheimer’s disease brains. J. Neuroimmunol. 197, 70–73 (2008).
pubmed: 18485490
doi: 10.1016/j.jneuroim.2008.03.021
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
Zrzavy, T. et al. Dominant role of microglial and macrophage innate immune responses in human ischemic infarcts. Brain Pathol. 28, 791–805 (2018).
pubmed: 29222823
doi: 10.1111/bpa.12583
van Wageningen, T. A. et al. Regulation of microglial TMEM119 and P2RY12 immunoreactivity in multiple sclerosis white and grey matter lesions is dependent on their inflammatory environment. Acta Neuropathol. Commun. 7, 206 (2019).
pubmed: 31829283
pmcid: 6907356
doi: 10.1186/s40478-019-0850-z
Lier, J., Streit, W. J. & Bechmann, I. Beyond Activation: Characterizing Microglial Functional Phenotypes. Cells 10, 2236 (2021).
pubmed: 34571885
pmcid: 8464670
doi: 10.3390/cells10092236
Keren-Shaul, H. et al. A Unique Microglia Type Associated with Restricting Development of Alzheimer’s Disease. Cell 169, 1276–1290.e1217 (2017).
pubmed: 28602351
doi: 10.1016/j.cell.2017.05.018
Kuhlmann, T. et al. Multiple sclerosis progression: time for a new mechanism-driven framework. Lancet Neurol. 22, 78–88 (2023).
pubmed: 36410373
doi: 10.1016/S1474-4422(22)00289-7
Yong, V. W. Microglia in multiple sclerosis: Protectors turn destroyers. Neuron 110, 3534–3548 (2022).
pubmed: 35882229
doi: 10.1016/j.neuron.2022.06.023
Lassmann, H., Van Horssen, J. & Mahad, D. Progressive multiple sclerosis: pathology and pathogenesis. Nat. Rev. Neurol. 8, 647–656 (2012).
pubmed: 23007702
doi: 10.1038/nrneurol.2012.168
Iliff, J. J. et al. A Paravascular Pathway Facilitates CSF Flow Through the Brain Parenchyma and the Clearance of Interstitial Solutes, Including Amyloid β. Sci. Transl. Med. 4, 147ra111–147ra141 (2012).
pubmed: 22896675
pmcid: 3551275
doi: 10.1126/scitranslmed.3003748
Munro, D. A. D., Movahedi, K. & Priller, J. Macrophage compartmentalization in the brain and cerebrospinal fluid system. Sci. Immunol. 7, eabk0391 (2022).
Oh, J., Vidal-Jordana, A. & Montalban, X. Multiple sclerosis: clinical aspects. Curr. Opin. Neurol. 31, 752–759 (2018).
pubmed: 30300239
doi: 10.1097/WCO.0000000000000622
Krämer, J., Bar-Or, A., Turner, T. J. & Wiendl, H. Bruton tyrosine kinase inhibitors for multiple sclerosis. Nat. Rev. Neurol. 19, 289–304 (2023).
pubmed: 37055617
pmcid: 10100639
doi: 10.1038/s41582-023-00800-7
Kappos, L. et al. Contribution of Relapse-Independent Progression vs Relapse-Associated Worsening to Overall Confirmed Disability Accumulation in Typical Relapsing Multiple Sclerosis in a Pooled Analysis of 2 Randomized Clinical Trials. JAMA Neurol. 77, 1132–1140 (2020).
pubmed: 32511687
doi: 10.1001/jamaneurol.2020.1568
Thompson, A. J. et al. Diagnosis of multiple sclerosis: 2017 revisions of the McDonald criteria. Lancet Neurol. 17, 162–173 (2018).
pubmed: 29275977
doi: 10.1016/S1474-4422(17)30470-2
Manouchehrinia, A. et al. Age Related Multiple Sclerosis Severity Score: Disability ranked by age. Mult. Scler. 23, 1938–1946 (2017).
pubmed: 28155580
pmcid: 5700773
doi: 10.1177/1352458517690618
Roxburgh, R. H. S. R. et al. Multiple Sclerosis Severity Score: Using disability and disease duration to rate disease severity. Neurology 64, 1144–1151 (2005).
pubmed: 15824338
doi: 10.1212/01.WNL.0000156155.19270.F8
Hilven, K. et al. Genetic basis for relapse rate in multiple sclerosis: Association with LRP2 genetic variation. Mult. Scler. J. 24, 1773–1775 (2018).
doi: 10.1177/1352458517749894
Trobisch, T. et al. Cross-regional homeostatic and reactive glial signatures in multiple sclerosis. Acta Neuropathol. 144, 987–1003 (2022).
pubmed: 36112223
pmcid: 9547805
doi: 10.1007/s00401-022-02497-2
Morsey, B. et al. Cryopreservation of microglia enables single-cell RNA sequencing with minimal effects on disease-related gene expression patterns. iScience 24, 102357 (2021).
pubmed: 33870145
pmcid: 8044433
doi: 10.1016/j.isci.2021.102357
Touil, H. et al. A structured evaluation of cryopreservation in generating single-cell transcriptomes from cerebrospinal fluid. Cell Rep. Methods 3, 100533 (2023).
pubmed: 37533636
pmcid: 10391561
doi: 10.1016/j.crmeth.2023.100533
Young, M. D. & Behjati, S. SoupX removes ambient RNA contamination from droplet-based single-cell RNA sequencing data. GigaScience 9, 1–10 (2020).
doi: 10.1093/gigascience/giaa151
Germain, P.-L., Lun, A., Garcia Meixide, C., Macnair, W. & Robinson, M. D. Doublet identification in single-cell sequencing data using scDblFinder. F1000Research 10, 979 (2022).
pmcid: 9204188
doi: 10.12688/f1000research.73600.2
Hao, Y. et al. Integrated analysis of multimodal single-cell data. Cell 184, 3573–3587 (2021).
pubmed: 34062119
pmcid: 8238499
doi: 10.1016/j.cell.2021.04.048
Osorio, D. & Cai, J. J. Systematic determination of the mitochondrial proportion in human and mice tissues for single-cell RNA-sequencing data quality control. Bioinformatics 37, 963–967 (2021).
pubmed: 32840568
doi: 10.1093/bioinformatics/btaa751
Hafemeister, C. & Satija, R. Normalization and variance stabilization of single-cell RNA-seq data using regularized negative binomial regression. Genome Biol. 20, 296 (2019).
pubmed: 31870423
pmcid: 6927181
doi: 10.1186/s13059-019-1874-1
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
Zappia, L. & Oshlack, A. Clustering trees: a visualization for evaluating clusterings at multiple resolutions. GigaScience 7, 1–9 (2018).
doi: 10.1093/gigascience/giy083
Aran, D. et al. Reference-based analysis of lung single-cell sequencing reveals a transitional profibrotic macrophage. Nat. Immunol. 20, 163–172 (2019).
pubmed: 30643263
pmcid: 6340744
doi: 10.1038/s41590-018-0276-y
Novershtern, N. et al. Densely Interconnected Transcriptional Circuits Control Cell States in Human Hematopoiesis. Cell 144, 296–309 (2011).
pubmed: 21241896
pmcid: 3049864
doi: 10.1016/j.cell.2011.01.004
Tirosh, I. et al. Dissecting the multicellular ecosystem of metastatic melanoma by single-cell RNA-seq. Science 352, 189–196 (2016).
pubmed: 27124452
pmcid: 4944528
doi: 10.1126/science.aad0501
Yu, G., Wang, L. G., Han, Y. & He, Q. Y. clusterProfiler: an R package for comparing biological themes among gene clusters. OMICS 16, 284–287 (2012).
pubmed: 22455463
pmcid: 3339379
doi: 10.1089/omi.2011.0118
Street, K. et al. Slingshot: cell lineage and pseudotime inference for single-cell transcriptomics. BMC Genomics 19, 477 (2018).
pubmed: 29914354
pmcid: 6007078
doi: 10.1186/s12864-018-4772-0
Van den Berge, K. et al. Trajectory-based differential expression analysis for single-cell sequencing data. Nat. Commun. 11, 1201 (2020).
pubmed: 32139671
pmcid: 7058077
doi: 10.1038/s41467-020-14766-3
Frischer, J. M. et al. Clinical and pathological insights into the dynamic nature of the white matter multiple sclerosis plaque. Ann. Neurol. 78, 710–721 (2015).
pubmed: 26239536
pmcid: 4623970
doi: 10.1002/ana.24497
Brown, V. A. An Introduction to Linear Mixed-Effects Modeling in R. Adv. Methods Pract. Psychol. Sci. 4, 1–19 (2021).
Spyromitros-Xioufis, E., Tsoumakas, G., Groves, W. & Vlahavas, I. Multi-target regression via input space expansion: treating targets as inputs. Mach. Learn. 104, 55–98 (2016).
doi: 10.1007/s10994-016-5546-z