title: Statistical inference of a convergent antibody repertoire response to influenza vaccine.
abstract: 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.
  
  
  
  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.
  
  
  
  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.
  
  
  
  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.
authors.affiliation: Biomedical Sciences Graduate Program, University of California, San Francisco, CA, USA.
authors.forename: Nicolas B
authors.initials: NB
authors.lastname: Strauli
authors.affiliation: Department of Bioengineering and Therapeutic Sciences, University of California, Byers Hall, 1700 4th Street, San Francisco, CA, 94158, USA. ryan.hernandez@ucsf.edu.|Institute for Human Genetics, University of California, San Francisco, CA, USA. ryan.hernandez@ucsf.edu.|Institute for Quantitative Biosciences (QB3), University of California, San Francisco, CA, USA. ryan.hernandez@ucsf.edu.
authors.forename: Ryan D
authors.initials: RD
authors.lastname: Hernandez
mesh_terms: D000914:Antibodies, Viral / Q000097:blood / Q000235:genetics*; D019295:Computational Biology / Q000379:methods*; D030541:Databases, Genetic; D006801:Humans; D053118:Influenza A Virus, H1N1 Subtype / Q000276:immunology*; D007252:Influenza Vaccines / Q000008:administration & dosage* / Q000276:immunology; D015233:Models, Statistical; D017423:Sequence Analysis, RNA
chemical_list: D000914:Antibodies, Viral; D007252:Influenza Vaccines