Inferring B cell specificity for vaccines using a mixture model
Open accessPreprint — published as 10.1186/s12864-020-6571-7
Abstract
Abstract Vaccines have greatly reduced the burden of infectious disease, ranking in their impact on global health second only after clean water. Most vaccines confer protection by the production of antibodies with binding affinity for the antigen, which is the main effector function of B cells. This results in short term changes in the B Cell receptor (BCR) repertoire when an immune response is launched, and long term changes when immunity is conferred. Analysis of antibodies in the serum is usually used to evaluate vaccine response, however this is limited and therefore the investigation of the BCR repertoire provides far more detail for the analysis of vaccine response. Here, we introduce a novel Bayesian model to describe the observed distribution of BCR sequences and the pattern of sharing across time and between individuals, with the goal to identify vaccine-specific BCR sequences. We use data from two studies to assess the model and estimate that we can identify vaccine-specific sequences with 69% sensitivity.
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The selection of false positives can be tackled by more complex statistical models.
“The selection of false positives can be tackled by more complex statistical models as highlighted by Fowler and co-workers [217].”
From Computational approaches to therapeutic antibody design: established methods and emerging trends (2020) · cites this paper as [217]
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- Authors
- Fowler, A., Galson, J. D., Trück, J., Kelly, D. F., & Lunter, G.
- Affiliations
- University of Liverpool (GB)
- University of Zurich (CH)
- University of Oxford (GB)
- Centre for Human Genetics (GB)
- Corresponding author
- Fowler, Anna
- Venue
- bioRxiv (Cold Spring Harbor Laboratory)
- Publisher
- openRxiv
- Published
- 2018-11-07
- Volume
- 21
- Issue
- 1
- Pages
- 176-176
- ISSN
- 1471-2164 (ISSN-L)
- PMC
- PMC7036227
- OpenAlex
- W3009102617
- Cited by
- 12
- References
- 22
- Citations by year
- 2026: 2 · 2025: 1 · 2024: 3 · 2023: 1 · 2021: 1 · 2020: 3 · 2019: 1
- FWCI
- 0.41
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- BMC Genomics (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Zurich Open Repository and Archive (University of Zurich) (acceptedVersion, other-oa)
- Zurich Open Repository and Archive (University of Zurich) (acceptedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Zurich Open Repository and Archive (University of Zurich) (submittedVersion, other-oa)
- Zurich Open Repository and Archive (University of Zurich) (submittedVersion, cc-by)
- Zurich Open Repository and Archive (University of Zurich)
- Funders
- National Science Foundation (PZ00P3_161147)
- National Institute for Health and Care Research
- Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung (161147, 183777)
- NIHR Oxford Biomedical Research Centre
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › vaccines and immunoinformatics approaches
- Life Sciences › Immunology and Microbiology › Immunology › T-cell and B-cell Immunology
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Monoclonal and Polyclonal Antibodies Research
- Keywords
- B cell receptor repertoire, vaccine response, Bayesian mixture models, antibody affinity, B cell receptor sequences, sequence similarity
- MeSH terms
- B-Lymphocytes, Bayes Theorem, Hepatitis B, Humans, Influenza, Human, Vaccines, Models, Immunological
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