Reich DS, Lucchinetti CF, Calabresi PA. Multiple sclerosis. N Engl J Med. 2018;378:169–80.

Article  CAS  Google Scholar 

Thompson AJ, Baranzini SE, Geurts J, Hemmer B, Ciccarelli O. Multiple sclerosis. Lancet. 2018;391:1622–36.

Article  Google Scholar 

Baecher-Allan C, Kaskow BJ, Weiner HL. Multiple sclerosis: mechanisms and immunotherapy. Neuron. 2018;97:742–68.

Article  CAS  Google Scholar 

Lublin FD, Reingold SC, Cohen JA, Cutter GR, Sørensen PS, Thompson AJ, et al. Defining the clinical course of multiple sclerosis: the 2013 revisions. Neurology. 2014;83:278–86.

Article  Google Scholar 

Comi G, Radaelli M, Soelberg SP. Evolving concepts in the treatment of relapsing multiple sclerosis. Lancet. 2017;389:1347–56.

Article  Google Scholar 

Soelberg SP. Safety concerns and risk management of multiple sclerosis therapies. Acta Neurol Scand. 2017;136:168–86.

Article  Google Scholar 

International Multiple Sclerosis Genetics Consortium, Patsopoulos NA, Baranzini SE, Santaniello A, Shoostari P, Cotsapas C, et al. Multiple sclerosis genomic map implicates peripheral immune cells and microglia in susceptibility. Science. 2019;365(6460):eaav7188.

Belbasis L, Bellou V, Evangelou E, Tzoulaki I. Environmental factors and risk of multiple sclerosis: findings from meta-analyses and Mendelian randomization studies. Mult Scler. 2020;26:397–404.

Article  Google Scholar 

Qin J, Li R, Raes J, Arumugam M, Burgdorf KS, Manichanh C, et al. A human gut microbial gene catalogue established by metagenomic sequencing. Nature. 2010;464:59–65.

Article  CAS  Google Scholar 

Cryan JF, O’Riordan KJ, Sandhu K, Peterson V, Dinan TG. The gut microbiome in neurological disorders. Lancet Neurol. 2020;19:179–94.

Article  CAS  Google Scholar 

Durack J, Lynch SV. The gut microbiome: relationships with disease and opportunities for therapy. J Exp Med. 2019;216:20–40.

Article  CAS  Google Scholar 

Ghezzi L, Cantoni C, Pinget G V, Zhou Y, Piccio L. Targeting the gut to treat multiple sclerosis. J Clin Invest. 2021;131(13):e143774.

Kadowaki A, Quintana FJ. The gut-CNS axis in multiple sclerosis. Trends Neurosci. 2020;43:622–34.

Article  CAS  Google Scholar 

Berer K, Mues M, Koutrolos M, Al RZ, Boziki M, Johner C, et al. Commensal microbiota and myelin autoantigen cooperate to trigger autoimmune demyelination. Nature. 2011;479:538–41.

Article  CAS  Google Scholar 

Berer K, Gerdes LA, Cekanaviciute E, Jia X, Xiao L, Xia Z, et al. Gut microbiota from multiple sclerosis patients enables spontaneous autoimmune encephalomyelitis in mice. Proc Natl Acad Sci U S A. 2017;114:10719–24.

Article  CAS  Google Scholar 

Ouyang W, Rutz S, Crellin NK, Valdez PA, Hymowitz SG. Regulation and functions of the IL-10 family of cytokines in inflammation and disease. Annu Rev Immunol. 2011;29:71–109.

Article  CAS  Google Scholar 

Shahi SK, Freedman SN, Murra AC, Zarei K, Sompallae R, Gibson-Corley KN, et al. Prevotella histicola, A human gut commensal, is as potent as COPAXONE® in an animal model of multiple sclerosis. Front Immunol. 2019;10:462.

Article  CAS  Google Scholar 

Shahi SK, Jensen SN, Murra AC, Tang N, Guo H, Gibson-Corley KN, et al. Human commensal Prevotella histicola ameliorates disease as effectively as interferon-beta in the experimental autoimmune encephalomyelitis. Front Immunol. 2020;11:578648.

Article  CAS  Google Scholar 

Haghikia A, Jörg S, Duscha A, Berg J, Manzel A, Waschbisch A, et al. Dietary fatty acids directly impact central nervous system autoimmunity via the small intestine. Immunity. 2015;43:817–29.

Article  CAS  Google Scholar 

Melbye P, Olsson A, Hansen TH, Søndergaard HB, Bang OA. Short-chain fatty acids and gut microbiota in multiple sclerosis. Acta Neurol Scand. 2019;139:208–19.

Article  Google Scholar 

Horton MK, McCauley K, Fadrosh D, Fujimura K, Graves J, Ness J, et al. Gut microbiome is associated with multiple sclerosis activity in children. Ann Clin Transl Neurol. 2021;8:1867–83.

Article  CAS  Google Scholar 

Duscha A, Gisevius B, Hirschberg S, Yissachar N, Stangl GI, Eilers E, et al. Propionic acid shapes the multiple sclerosis disease course by an immunomodulatory mechanism. Cell. 2020;180:1067–1080.e16.

Article  CAS  Google Scholar 

Olsson A, Gustavsen S, Nguyen TD, Nyman M, Langkilde AR, Hansen TH, et al. Serum short-chain fatty acids and associations with inflammation in newly diagnosed patients with multiple sclerosis and healthy controls. Front Immunol. 2021;12:661493.

Article  CAS  Google Scholar 

Cantoni C, Lin Q, Dorsett Y, Ghezzi L, Liu Z, Pan Y, et al. Alterations of host-gut microbiome interactions in multiple sclerosis. EBioMedicine. 2022;76:103798.

Article  CAS  Google Scholar 

Mirza A, Forbes JD, Zhu F, Bernstein CN, Van Domselaar G, Graham M, et al. The multiple sclerosis gut microbiota: a systematic review. Mult Scler Relat Disord. 2020;37:101427.

Article  Google Scholar 

Schepici G, Silvestro S, Bramanti P, Mazzon E. The gut microbiota in multiple sclerosis: an overview of clinical trials. Cell Transplant. 2019;28:1507–27.

Article  Google Scholar 

Tremlett H, Bauer KC, Appel-Cresswell S, Finlay BB, Waubant E. The gut microbiome in human neurological disease: a review. Ann Neurol. 2017;81:369–82.

Article  Google Scholar 

Thompson AJ, Banwell BL, Barkhof F, Carroll WM, Coetzee T, Comi G, et al. Diagnosis of multiple sclerosis: 2017 revisions of the McDonald criteria. Lancet Neurol. 2018;17:162–73.

Article  Google Scholar 

Dantoft TM, Ebstrup JF, Linneberg A, Skovbjerg S, Madsen AL, Mehlsen J, et al. Cohort description: The Danish study of Functional Disorders. Clin Epidemiol. 2017;9:127–39.

Article  Google Scholar 

Costea PI, Zeller G, Sunagawa S, Pelletier E, Alberti A, Levenez F, et al. Towards standards for human fecal sample processing in metagenomic studies. Nat Biotechnol. 2017;35:1069–76. https://doi.org/10.1038/nbt.3960.

Article  CAS  Google Scholar 

International Human Microbiome Standards. Jouy-en-Josas: INRA - Département MICA; 2015. Available from: http://www.human-microbiome.org. Accessed 28 Mar 2022.

Pons N, Gauthier F, Batto J-M, Kennedy S, Almeida M, Boumezbeur F, et al. Meteor (Metagenomic Explorator), a software for profiling metagenomic data at gene level. Jouy-en-Josas: INRAe; 2021. Available from: https://forgemia.inra.fr/metagenopolis/meteor. Accessed 28 Mar 2022.

Criscuolo A, Brisse S. AlienTrimmer: a tool to quickly and accurately trim off multiple short contaminant sequences from high-throughput sequencing reads. Genomics. 2013;102:500–6.

Article  CAS  Google Scholar 

Wen C, Zheng Z, Shao T, Liu L, Xie Z, Le Chatelier E, et al. Quantitative metagenomics reveals unique gut microbiome biomarkers in ankylosing spondylitis. Genome Biol. 2017;18:142.

Article  Google Scholar 

Langmead B, Salzberg SL. Fast gapped-read alignment with Bowtie 2. Nat Methods. 2012;9:357–9.

Article  CAS  Google Scholar 

Le Chatelier E, Nielsen T, Qin J, Prifti E, Hildebrand F, Falony G, et al. Richness of human gut microbiome correlates with metabolic markers. Nature. 2013;500:541–6.

Article  Google Scholar 

Plaza Oñate F, Le Chatelier E, Almeida M, Cervino ACL, Gauthier F, Magoulès F, et al. MSPminer: abundance-based reconstitution of microbial pan-genomes from shotgun metagenomic data. Bioinformatics. 2019;35:1544–52.

Article  Google Scholar 

Plaza Onate F, Pons N, Gauthier F, Almeida M, Ehrlich SD, Le Chatelier E. Updated Metagenomic Species Pan-genomes (MSPs) of the human gastrointestinal microbiota. Paris: Recherche Data Gouv; 2021. Available from: https://doi.org/10.15454/FLANUP. Accessed 28 Mar 2022.

Kanehisa M, Sato Y, Kawashima M, Furumichi M, Tanabe M. KEGG as a reference resource for gene and protein annotation. Nucleic Acids Res. 2016;44:D457–62.

Article  CAS  Google Scholar 

Huerta-Cepas J, Szklarczyk D, Forslund K, Cook H, Heller D, Walter MC, et al. EGGNOG 4.5: a hierarchical orthology framework with improved functional annotations for eukaryotic, prokaryotic and viral sequences. Nucleic Acids Res. 2016;44:D286–93.

Article  CAS  Google Scholar 

Haft DH. TIGRFAMs: a protein family resource for the functional identification of proteins. Nucleic Acids Res. 2001;29:41–3.

Article  CAS  Google Scholar 

Buchfink B, Xie C, Huson DH. Fast and sensitive protein alignment using DIAMOND. Nat Methods. 2015;12:59–60.

Article  CAS  Google Scholar 

Eddy S. HMMER user’s guide: biological sequence analysis using prole hidden Markov models. Chevy Chase: Howard Hughes Medical Institute; 1998 [updated 2020 November]. Available from: http://hmmer.org/. Accessed 28 Mar 2022.

Vieira-Silva S, Falony G, Darzi Y, Lima-Mendez G, Garcia Yunta R, Okuda S, et al. Species-function relationships shape ecological properties of the human gut microbiome. Nat Microbiol. 2016;1:16088.

Article  CAS  Google Scholar 

Valles-Colomer M, Falony G, Darzi Y, Tigchelaar EF, Wang J, Tito RY, et al. The neuroactive potential of the human gut microbiota in quality of life and depression. Nat Microbiol. 2019;4:623–32.

Article  CAS  Google Scholar 

LaPierre N, Mangul S, Alser M, Mandric I, Wu NC, Koslicki D, et al. MiCoP: microbial community profiling method for detecting viral and fungal organisms in metagenomic samples. BMC Genomics. 2019;20:423. https://doi.org/10.1186/s12864-019-5699-9.

Article  CAS  Google Scholar 

Brister JR, Ako-Adjei D, Bao Y, Blinkova O. NCBI viral genomes resource. Nucleic Acids Res. 2015;43:D571–7.

Article  CAS  Google Scholar 

R Core Team. The R Project for Statistical Computing. Vienna: R Foundation; 2017. Available from: https://www.r-project.org/. Accessed 28 Mar 2022.

Torchiano M. effsize: Efficient Effect Size Computation. Vienna: Institute for Statistics and Mathematics; 2018. Available from: https://cran.r-project.org/package=effsize. Accessed 28 Mar 2022.

Oksanen J, Blanchet FG, Friendly M, Kindt R, Legendre P, McGlinn D, et al. Vegan: community ecology package. Vienna: Institute for Statistics and Mathematics; 2019. Available from: http://cran.rproject.org/package=vegan. Accessed 28 Mar 2022.

Thioulouse J, Dray S, Dufour A-B, Siberchicot A, Jombart T, Pavoine S. Multivariate Analysis of Ecological Data with ade4. New-York:Springer; 2018. p. 329. Available from: https://doi.org/10.1007/978-1-4939-8850-1. Accessed 28 Mar 2022.

Forslund SK, Chakaroun R, Zimmermann-Kogadeeva M, Markó L, Aron-Wisnewsky J, Nielsen T, et al. Combinatorial, additive and dose-dependent drug-microbiome associations. Nature. 2021;600:500–5.

Article  CAS  Google Scholar 

TillBirkner. TillBirkner/metadeconfoundR: MetadeconfoundR Release for Documentation of the MetaDrugs Analysis as Part of the MetaCardis Consortium. San Francisco: Github; 2021. Available from: https://github.