Methods, Models, and Machine Learning Approaches for Understanding Pathogen-Specific Humoral Immunity

Abstract

The humoral immune response is comprised of vast libraries of polyclonal antibodies capable of recognizing a myriad of targets and directing a spectrum of innate immune functions. The complex heterogeneity in antibody profiles across both populations and diseases makes defining mechanisms of protection difficult. Understanding these mechanisms and the factors that influence them is essential to defining immunity and helps inform the design of vaccines and therapeutics. Thus, in this thesis, I describe five studies that present the development of experimental and computational methods, and machine learning approaches for investigating the mechanisms, dynamics, and determinants of pathogen-specific humoral immunity.

The first study introduces an assay for probing antigen-specific antibody mediated primary monocyte phagocytosis, that is capable of capturing subsequent downstream functions. The second study describes a machine learning approach for defining the correlates of upper and lower respiratory protection against RSV and methods for evaluating vaccine designs. The third study uses machine learning methods to uncover signatures of humoral protection against SARS-CoV-2. The fourth study presents a method for longitudinally modelling humoral immunity that was used to investigate the temporal dynamics of antibody features across individuals with varying COVID-19 severity. Finally, the last study describes a genome-wide association screen of pathogen-specific polyclonal antibody characteristics and functions that was then validated with transcriptomics data. Ultimately, the methods described in this thesis present new approaches for investigating underlying phenomena related to pathogen-specific humoral immunity.