Great efforts are made to understand what makes particular areas of a pathogen protein attractive to the immune system. A better understanding of the biophysical and structural features that underpin antibody-recognition of antigens may allow us to infer the relative importance of the different areas (epitopes) recognised by antibodies and to predict which mutations are most likely to result in new pathogen strains able to evade pre-existing immunity.
We’ve uploaded a new manuscript to the arXiv describing an improved model for identifying antigenic sites titled “Improving the identification of antigenic sites in the H1N1 Influenza virus through accounting for the experimental structure in a sparse hierarchical Bayesian model”.
For a while, I’ve been interested in the threshold model in phylogenetics and the opportunity it allows for performing comparative methods analyses that combine both discrete and continuous characters. Here I’ve had a go at performing a simple analysis involving phenotypic trait reconstruction of Darwin’s finches.
We’ve published a paper in Computational Statistics describing “A sparse hierarchical Bayesian model for detecting relevant antigenic sites in virus evolution”. This work, led by Vinny Davies and Dirk Husmeier (Glasgow Uni, school of Mathematics and Statistics), involved the development of a Bayesian model for identifying amino acid positions where substitutions are responsible for antigenic change. These substitutions enable viruses to escape existing immunity in the host population that arises following infection or vaccination.
A collaboration I’m involved in with the NHS West of Scotland Specialist Virology Centre (WoSSVC) and the MRC-University of Glasgow Centre for Virus Research (CVR) have been working on an analysis of seasonal influenza A(H3N2) full genomes. We’ve put a manuscript on “Integrating patient and whole genome sequencing data to provide insights into the epidemiology of seasonal influenza A(H3N2) viruses” up on bioRxiv.
This work led by Emily Goldstein and Rory Gunson (WoSSVC) examines the potential benefits of whole genome sequencing (WGS) for surveillance of human influenza. Genetic surveillance of seasonal flu viruses currently remains focused on the haemagglutinin (HA) gene, which is understandable given the importance of HA mutations that reduce vaccine effectiveness. Continue reading “Analysis of flu full genomes with linked patient data”