Top-down Mass Spectrometry Analysis of Human Serum Autoantibody Antigen-Binding Fragments View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2019-12

AUTHORS

Zhe Wang, Xiaowen Liu, Jennifer Muther, Judith A. James, Kenneth Smith, Si Wu

ABSTRACT

Detecting autoimmune diseases at an early stage is crucial for effective treatment and disease management to slow disease progression and prevent irreversible organ damage. In many autoimmune diseases, disease-specific autoantibodies are produced by B cells in response to soluble autoantigens due to defects in B cell tolerance mechanisms. Autoantibodies accrue early in disease development, and several are so disease-specific they serve as classification criteria. In this study, we established a high-throughput, sensitive, intact serum autoantibody analysis platform based on the optimization of a one dimensional ultra-high-pressure liquid chromatography top-down mass spectrometry platform (1D UPLC-TDMS). This approach has been successfully applied to a 12 standard monoclonal antibody antigen-binding fragment (Fab) mixture, demonstrating the feasibility to separate and sequence intact antibodies with high sequence coverage and high sensitivity. We then applied the optimized platform to characterize total serum antibody Fabs in a systemic lupus erythematosus (SLE) patient sample and compared it to healthy control samples. From this analysis, we show that the SLE sample has many dominant antibody Fab-related mass features unlike the healthy controls. To our knowledge, this is the first top-down demonstration of serum autoantibody pool analysis. Our proposed approach holds great promise for discovering novel serum autoantibody biomarkers that are of interest for diagnosis, prognosis, and tolerance induction, as well as improving our understanding of pathogenic autoimmune processes. More... »

PAGES

2345

Identifiers

URI

http://scigraph.springernature.com/pub.10.1038/s41598-018-38380-y

DOI

http://dx.doi.org/10.1038/s41598-018-38380-y

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1112223007

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/30787393


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Download the RDF metadata as:  json-ld nt turtle xml License info

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Turtle is a human-readable linked data format.

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RDF/XML is a standard XML format for linked data.

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