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An autoantibody signature predictive for multiple sclerosis

Nature Medicine (2024)Cite this article

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Abstract

Although B cells are implicated in multiple sclerosis (MS) pathophysiology, a predictive or diagnostic autoantibody remains elusive. In this study, the Department of Defense Serum Repository (DoDSR), a cohort of over 10 million individuals, was used to generate whole-proteome autoantibody profiles of hundreds of patients with MS (PwMS) years before and subsequently after MS onset. This analysis defines a unique cluster in approximately 10% of PwMS who share an autoantibody signature against a common motif that has similarity with many human pathogens. These patients exhibit antibody reactivity years before developing MS symptoms and have higher levels of serum neurofilament light (sNfL) compared to other PwMS. Furthermore, this profile is preserved over time, providing molecular evidence for an immunologically active preclinical period years before clinical onset. This autoantibody reactivity was validated in samples from a separate incident MS cohort in both cerebrospinal fluid and serum, where it is highly specific for patients eventually diagnosed with MS. This signature is a starting point for further immunological characterization of this MS patient subset and may be clinically useful as an antigen-specific biomarker for high-risk patients with clinically or radiologically isolated neuroinflammatory syndromes.

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Fig. 1: Overview of MS biomarker study.
Fig. 2: Profiling DoDSR MS cohort.
Fig. 3: IC peptide and cohort analysis.
Fig. 4: PhIP-Seq characterization of ORIGINS validation cohort in CSF and sera.
Fig. 5: Luminex validation assay of selected IC peptides.

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Data availability

The PROSITE database used for this study is freely available (https://prosite.expasy.org/). All data produced in the present study are available online at https://github.com/UCSF-Wilson-Lab/MS_DoD_and_ORIGINS_study_data.

Code availability

All analysis code can be found in the following publicly available repository: https://github.com/UCSF-Wilson-Lab/MS_DoD_and_ORIGINS_study_data. PairSeq package source code can be found in the following publicly available repository: https://github.com/cmbartley/PairSeq.

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Acknowledgements

This work was supported by the Valhalla Foundation (S.L.H., M.R.W., J.R.O. and B.A.C.); the Weill Neurohub (C.R.Z.); the Westridge Foundation (M.R.W., H.-C.v.B. and A.J.G.); National Institute of Neurological Disorders and Stroke R35NS122073 (S.L.H., M.R.W. and R.D.); National Institute of Allergy and Infectious Diseases R01AI170863-01A1 (S.S.Z.); National Multiple Sclerosis Society RFA-2104-37504 (M.R.W., M.T.W., J.H., B.A.C., C.R.Z., R.D. and R.D.B.); National Multiple Sclerosis Society RFA-2104-3747 (J.R.O.); the Department of Defense (A.A.); the German Society of Multiple Sclerosis (A.A.); the Water Cove Charitable Foundation (M.R.W., M.T.W., J.H., B.A.C. and C.R.Z.); Tim and Laura O’Shaughnessy (M.R.W.); the Littera Family (M.R.W.); the School of Medicine Dean’s Yearlong Fellowship, supported by residual funds from the Howard Hughes Medical Institute Medical Fellows at UCSF (G.M.S.); the Chan Zuckerberg Biohub San Francisco (J.L.D. and S.A.M.); the John A. Watson Scholar Program at UCSF (C.M.B.); the Hanna H. Gray Fellowship, Howard Hughes Medical Institute (C.M.B.); National Institutes of Health R01AI158861 (J.A.H. and K.J.W.); and the University of California President’s Postdoctoral Fellowship Program (C.M.B.). The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript. The authors would like to acknowledge the Chan Zuckerberg Biohub San Francisco and its sequencing team for assistance. The following members of the Walter Reed National Military Medical Center Neurology Department were collaborators on this project: A. Lewis and N. Tagg. Data were obtained from the Defense Medical Surveillance System, Armed Forces Health Surveillance Branch, Defense Health Agency (data from 1987–2007, released 2010). Figures 1 and 5a were created with BioRender. The authors would also like to thank the patients and their families for their contribution to this study.

Author information

Author notes

  1. These authors contributed equally: Colin R. Zamecnik, Gavin M. Sowa.

Authors and Affiliations

  1. Department of Neurology, UCSF Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, USA

    Colin R. Zamecnik, Ahmed Abdelhak, Ravi Dandekar, Rebecca D. Bair, Kristen J. Wade, Kerry Kizer, Danillo G. Augusto, Asritha Tubati, Refujia Gomez, Camille Fouassier, Chloe Gerungan, Colette M. Caspar, Jessica Alexander, Anne E. Wapniarski, Rita P. Loudermilk, Erica L. Eggers, Kirtana Ananth, Nora Jabassini, Nicholas R. Ragan, Adam Santaniello, Roland G. Henry, Sergio E. Baranzini, Scott S. Zamvil, Joseph J. Sabatino Jr., Riley M. Bove, Chu-Yueh Guo, Jeffrey M. Gelfand, Richard Cuneo, H.-Christian von Büdingen, Jorge R. Oksenberg, Bruce A. C. Cree, Jill A. Hollenbach, Ari J. Green, Stephen L. Hauser & Michael R. Wilson

  2. University of California, San Francisco School of Medicine, San Francisco, CA, USA

    Gavin M. Sowa

  3. Department of Medicine, McGaw Medical Center of Northwestern University, Chicago, IL, USA

    Gavin M. Sowa

  4. Department of Psychiatry and Behavioral Sciences, UCSF Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, USA

    Christopher M. Bartley

  5. Department of Biological Sciences, University of North Carolina at Charlotte, Charlotte, NC, USA

    Danillo G. Augusto

  6. Department of Biochemistry and Biophysics, University of California, San Francisco, San Francisco, CA, USA

    Kelsey C. Zorn, Sabrina A. Mann & Joseph L. DeRisi

  7. Chan Zuckerberg Biohub San Francisco, San Francisco, CA, USA

    Sabrina A. Mann & Joseph L. DeRisi

  8. Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, CA, USA

    Jill A. Hollenbach

  9. Department of Veterans Affairs, Multiple Sclerosis Center of Excellence, Washington, DC, USA

    Mitchell T. Wallin

  10. University of Maryland School of Medicine, Baltimore, MD, USA

    Mitchell T. Wallin

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  1. Colin R. Zamecnik
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Contributions

C.R.Z., G.M.S., M.R.W., M.T.W. and J.L.D. designed the experiments. C.R.Z. performed all PhIP-Seq and Luminex assays. R.D.B. and C.M.C. performed the Luminex assays, and G.M.S. and S.A.M. performed PhIP-Seq immunoprecipitations and NGS library preparation. S.A.M., C.F. and A.E.W. assisted with DoDSR sample preparation. A.A., N.J. and K.A. performed the sNfL studies, and A.A. analyzed the data. R.D. and C.M.B. built the analysis code and pipeline for PhIP-Seq data and analyzed the PhIP-Seq data, with G.M.S., as well as generated figures. K.C.Z., A.T., C.F., J.A., C.G., E.B.T., H.-C.v.B., R.P.L., R.G. and E.L.E. curated and collected patient samples and clinical data for the UCSF ORIGINS cohort. K.J.W., J.R.O., D.G.A., K.K. and J.A.H. performed the HLA sequencing, genotyping and association. M.T.W. generated the DoDSR cohort and epidemiologic analysis, and A.J.G. contributed valuable sNfL analysis interpretation. S.L.H., B.A.C.C. and J.R.O., together with the UCSF MS-EPIC Team and J.M.G., C.-Y.G. and R.C., led the recruitment and phenotyping of the UCSF ORIGINS cohort. S.L.H., B.A.C.C. and J.L.D. provided valuable experimental guidance and manuscript editing. All authors contributed to study design. C.R.Z., G.M.S. and M.R.W. wrote the manuscript. All authors provided editorial comments.

Corresponding author

Correspondence to Michael R. Wilson.

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Competing interests

M.R.W. receives unrelated research grant funding from Roche/Genentech and Novartis and has received speaking honoraria from Genentech, Takeda, WebMD and Novartis. M.R.W. and J.L.D. receive licensing fees from CDI Labs. C.M.B. serves as a paid consultant for the Neuroimmune Foundation. J.J.S. has unrelated research grant funding from Roche/Genentech and Novartis and advisory board honoraria from IgM Biosciences. C.-Y.G. has received financial compensation from serving on advisory boards for Genentech and Horizon. R.G.H. has unrelated research funding from Roche/Genentech and Atara Bio; consulting fees from Roche/Genentech, Novartis and QIA Consulting; and discussion leader fees from Sanofi/Genzyme. The remaining authors declare no competing interests.

Peer review

Peer review information

Nature Medicine thanks the anonymous reviewers for their contribution to the peer review of this work. Primary Handling Editor: Jerome Staal, in collaboration with the Nature Medicine team

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Extended data

Extended Data Fig. 1

Heatmap of logRPK values from whole proteome. PhIP-Seq data on MS patient sera from DoDSR cohort. Top peptides shown with peptide enrichment. blowout to demonstrate similarity between pre and post onset sample enrichments over time.

Extended Data Fig. 2

We investigated the similarity between different timepoints from the from the DoDSR cohort (n = 500). (Top Left) Unfiltered normalized read counts (rpK) were log transformed, and a correlation matrix was computed using the Euclidean distance measure. The similarity between any two samples was defined by their n-dimensional Euclidean distance, where each peptide in the library was treated as a dimension. The rows and columns of this matrix are ordered such that the early timepoints are followed by the late timepoints. The central diagonal stripe in the heatmap represents a distance of zero where the distance was computed between the same sample. However, the diagonal stripes represent distances calculated between different timepoints of the same sample. (Top Right) Box plots of the Euclidean distance between timepoints, with a trend towards increasing distance at longer intervals. However, patterns of self-reactivity remain stable for more than 15 years. Notches approximate 95% confidence intervals. (Bottom) A boxplot demonstrates the significant difference (two-sided t-test) in Euclidean distance between two timepoints of the same sample versus the distance between two unrelated samples. Notches approximate 95% confidence intervals.

Extended Data Fig. 3

A subset of the library capturing relevant motifs from previously identified putative targets of molecular mimicry such as GlialCAM and anoctamin-2. (Top) MS patient samples are ordered so as to preserve the IC clusters. Although individual samples can be seen to enrich peptides of interest, they did not meet the predefined cutoffs for this analysis. (Bottom) HC samples demonstrating background levels of enrichment.

Extended Data Fig. 4

Time point specific NfL data from DoD cohort. (1st time point: HC n = 236, MSno-IC n = 204, MSIC n = 13. 2nd time point: HC n = 234, MSno-IC n = 191, MSIC n = 21. Data represented as geometric mean and standard factors for each box and whisker.).

Extended Data Fig. 5

Additional OND CSF controls (n = 20), lacking enrichment of the IC motif.

Extended Data Fig. 6

Correlation of sum of MFI from all six peptides of interest in Table 1 from Luminex assay with their sum of respective normalized read counts from PhIP-Seq data in both CSF and serum samples from ORIGINS cohort. Shaded area represents 95% confidence intervals.

Extended Data Fig. 7

Luminex assay for antibodies against listed pathogen peptides in ORIGINS cohort a) MS patient CSF (n = 104), b) MS patient serum (n = 104), c) OND patient CSF(n = 42) and d) OND patient serum (n = 22). e) Same patient populations respresnted as sum of MFI combining signal across pathogen peptides with highlighted patients having normalized sumMFI values > 3 in CSF.

Extended Data Table 1 DoDSR cohort demographics
Full size table
Extended Data Table 2 ORIGINS cohort demographics
Full size table
Extended Data Table 3 MSIC demographic analysis
Full size table

Supplementary information

Reporting Summary

Supplementary Tables 1 and 2

Supplementary Table 1: HLA data for the ORIGINS cohort; Supplementary Table 2: HLA allele frequency assessment.

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Zamecnik, C.R., Sowa, G.M., Abdelhak, A. et al. An autoantibody signature predictive for multiple sclerosis. Nat Med (2024). https://doi.org/10.1038/s41591-024-02938-3

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