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Statistical inference of a convergent antibody repertoire response to influenza vaccine

BACKGROUND: Vaccines dramatically affect an individual’s adaptive immune system and thus provide an excellent means to study human immunity. Upon vaccination, the B cells that express antibodies (Abs) that happen to bind the vaccine are stimulated to proliferate and undergo mutagenesis at their Ab l...

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Autores principales: Strauli, Nicolas B., Hernandez, Ryan D.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4891843/
https://www.ncbi.nlm.nih.gov/pubmed/27255379
http://dx.doi.org/10.1186/s13073-016-0314-z
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author Strauli, Nicolas B.
Hernandez, Ryan D.
author_facet Strauli, Nicolas B.
Hernandez, Ryan D.
author_sort Strauli, Nicolas B.
collection PubMed
description BACKGROUND: Vaccines dramatically affect an individual’s adaptive immune system and thus provide an excellent means to study human immunity. Upon vaccination, the B cells that express antibodies (Abs) that happen to bind the vaccine are stimulated to proliferate and undergo mutagenesis at their Ab locus. This process may alter the composition of B cell lineages within an individual, which are known collectively as the antibody repertoire (AbR). Antibodies are also highly expressed in whole blood, potentially enabling RNA sequencing (RNA-seq) technologies to query this diversity. Less is known about the diversity of AbR responses across individuals to a given vaccine and if individuals tend to yield a similar response to the same antigenic stimulus. METHODS: Here we implement a bioinformatic pipeline that extracts the AbR information from a time-series RNA-seq dataset of five patients who were administered a seasonal trivalent influenza vaccine (TIV). We harness the detailed time-series nature of this dataset and use methods based in functional data analysis (FDA) to identify the Abs that respond to the vaccine. We then design and implement rigorous statistical tests in order to ask whether or not these patients exhibit a convergent AbR response to the same TIV. RESULTS: We find that high-resolution time-series data can be used to help identify the Abs that respond to an antigenic stimulus and that this response can exhibit a convergent nature across patients inoculated with the same vaccine. However, correlations in AbR diversity among individuals prior to inoculation can confound inference of a convergent signal unless it is taken into account. CONCLUSIONS: We developed a framework to identify the elements of an AbR that respond to an antigen. This information could be used to understand the diversity of different immune responses in different individuals, as well as to gauge the effectiveness of the immune response to a given stimulus within an individual. We also present a framework for testing a convergent hypothesis between AbRs; a hypothesis that is more difficult to test than previously appreciated. Our discovery of a convergent signal suggests that similar epitopes do select for antibodies with similar sequence characteristics. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s13073-016-0314-z) contains supplementary material, which is available to authorized users.
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spelling pubmed-48918432016-06-04 Statistical inference of a convergent antibody repertoire response to influenza vaccine Strauli, Nicolas B. Hernandez, Ryan D. Genome Med Research BACKGROUND: Vaccines dramatically affect an individual’s adaptive immune system and thus provide an excellent means to study human immunity. Upon vaccination, the B cells that express antibodies (Abs) that happen to bind the vaccine are stimulated to proliferate and undergo mutagenesis at their Ab locus. This process may alter the composition of B cell lineages within an individual, which are known collectively as the antibody repertoire (AbR). Antibodies are also highly expressed in whole blood, potentially enabling RNA sequencing (RNA-seq) technologies to query this diversity. Less is known about the diversity of AbR responses across individuals to a given vaccine and if individuals tend to yield a similar response to the same antigenic stimulus. METHODS: Here we implement a bioinformatic pipeline that extracts the AbR information from a time-series RNA-seq dataset of five patients who were administered a seasonal trivalent influenza vaccine (TIV). We harness the detailed time-series nature of this dataset and use methods based in functional data analysis (FDA) to identify the Abs that respond to the vaccine. We then design and implement rigorous statistical tests in order to ask whether or not these patients exhibit a convergent AbR response to the same TIV. RESULTS: We find that high-resolution time-series data can be used to help identify the Abs that respond to an antigenic stimulus and that this response can exhibit a convergent nature across patients inoculated with the same vaccine. However, correlations in AbR diversity among individuals prior to inoculation can confound inference of a convergent signal unless it is taken into account. CONCLUSIONS: We developed a framework to identify the elements of an AbR that respond to an antigen. This information could be used to understand the diversity of different immune responses in different individuals, as well as to gauge the effectiveness of the immune response to a given stimulus within an individual. We also present a framework for testing a convergent hypothesis between AbRs; a hypothesis that is more difficult to test than previously appreciated. Our discovery of a convergent signal suggests that similar epitopes do select for antibodies with similar sequence characteristics. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s13073-016-0314-z) contains supplementary material, which is available to authorized users. BioMed Central 2016-06-03 /pmc/articles/PMC4891843/ /pubmed/27255379 http://dx.doi.org/10.1186/s13073-016-0314-z Text en © Strauli and Hernandez. 2016 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Research
Strauli, Nicolas B.
Hernandez, Ryan D.
Statistical inference of a convergent antibody repertoire response to influenza vaccine
title Statistical inference of a convergent antibody repertoire response to influenza vaccine
title_full Statistical inference of a convergent antibody repertoire response to influenza vaccine
title_fullStr Statistical inference of a convergent antibody repertoire response to influenza vaccine
title_full_unstemmed Statistical inference of a convergent antibody repertoire response to influenza vaccine
title_short Statistical inference of a convergent antibody repertoire response to influenza vaccine
title_sort statistical inference of a convergent antibody repertoire response to influenza vaccine
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4891843/
https://www.ncbi.nlm.nih.gov/pubmed/27255379
http://dx.doi.org/10.1186/s13073-016-0314-z
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