Periodic Reporting for period 4 - Antibodyomics (Vaccine profiling and immunodiagnostic discovery by high-throughput antibody repertoire analysis)
Reporting period: 2020-12-01 to 2021-05-31
Vaccines and immunodiagnostics have been vital for public health and medicine, however a quantitative molecular understanding of vaccine-induced antibody responses is lacking. Antibody research is currently going through a big-data driven revolution, largely due to progress in nextgeneration
sequencing (NGS) and bioinformatic analysis of antibody repertoires. A main advantage of high-throughput antibody repertoire analysis is that it provides a wealth of quantitative information not possible with other classical methods of antibody analysis (i.e. serum titers) The overall goal of this proposal will be to apply high-throughput antibody repertoire analysis for quantitative vaccine profiling and discovery of next-generation immunodiagnostics. Using mouse subunit vaccination as our model system, we will answer for the first time, a fundamental biological question within the context of antibody responses - what is the link between genotype (antibody repertoire) and phenotype (serum antibodies)? We will expand upon this approach for improved rational vaccine design by quantitatively determining the impact of a comprehensive set of subunit vaccination parameters on complete antibody landscapes.Finally we will develop advanced bioinformatic methods to discover immunodiagnostics based on antibody repertoire sequences. In summary, this proposal lays the foundation for fundamentally new approaches in the quantitative analysis of antibody responses, which long-term will promote the development of next-generation vaccines and immunodiagnostics.
sequencing (NGS) and bioinformatic analysis of antibody repertoires. A main advantage of high-throughput antibody repertoire analysis is that it provides a wealth of quantitative information not possible with other classical methods of antibody analysis (i.e. serum titers) The overall goal of this proposal will be to apply high-throughput antibody repertoire analysis for quantitative vaccine profiling and discovery of next-generation immunodiagnostics. Using mouse subunit vaccination as our model system, we will answer for the first time, a fundamental biological question within the context of antibody responses - what is the link between genotype (antibody repertoire) and phenotype (serum antibodies)? We will expand upon this approach for improved rational vaccine design by quantitatively determining the impact of a comprehensive set of subunit vaccination parameters on complete antibody landscapes.Finally we will develop advanced bioinformatic methods to discover immunodiagnostics based on antibody repertoire sequences. In summary, this proposal lays the foundation for fundamentally new approaches in the quantitative analysis of antibody responses, which long-term will promote the development of next-generation vaccines and immunodiagnostics.
Here we report on the in-depth genotypic and phenotypic characterization of clonally expanded PC antibody repertoires following protein immunization. We find that there is only a strong correlation with antigen-specificity among the most expanded clones (top ~ 10), whereas among the rest of the clonal repertoire antigen-specificity is stochastic. Furthermore, we report both on a polyclonal repertoire and clonal lineage level that antibody-antigen binding affinity does not correlate with clonal expansion or somatic hypermutation. Lastly, we provide evidence for convergence towards dominant epitopes despite clonal sequence diversity among the most expanded clones. Our results highlight the extent to which clonal expansion can be ascribed to antigen binding, affinity and epitope specificity and they have implications for the assessment of effective vaccines.
Here we report that there is extensive convergent selection in antibody repertoires of mice for a range of protein antigens and immunization conditions. We employed a deep learning approach utilizing variational autoencoders (VAEs) to model the underlying process of B cell receptor (BCR) recombination and assume that the data generation follows a Gaussian mixture model (GMM) in latent space. This provides both a latent embedding and cluster labels that group similar sequences, thus enabling the discovery of a multitude of convergent, antigen-associated sequence patterns. Using a linear, one-versus-all support vector machine (SVM), we confirm that the identified sequence patterns are predictive of antigenic exposure and outperform predictions based on the occurrence of public clones. Recombinant expression of both natural and in silico-generated antibodies possessing convergent patterns confirms their binding specificity to target antigens. Our work highlights to which extent convergence in antibody repertoires can occur and shows how deep learning can be applied for immunodiagnostics and antibody discovery and engineering.
Here we report that there is extensive convergent selection in antibody repertoires of mice for a range of protein antigens and immunization conditions. We employed a deep learning approach utilizing variational autoencoders (VAEs) to model the underlying process of B cell receptor (BCR) recombination and assume that the data generation follows a Gaussian mixture model (GMM) in latent space. This provides both a latent embedding and cluster labels that group similar sequences, thus enabling the discovery of a multitude of convergent, antigen-associated sequence patterns. Using a linear, one-versus-all support vector machine (SVM), we confirm that the identified sequence patterns are predictive of antigenic exposure and outperform predictions based on the occurrence of public clones. Recombinant expression of both natural and in silico-generated antibodies possessing convergent patterns confirms their binding specificity to target antigens. Our work highlights to which extent convergence in antibody repertoires can occur and shows how deep learning can be applied for immunodiagnostics and antibody discovery and engineering.
We have developed advanced machine learning and deep learning tools which are helping to uncover high-dimensional sequence properties that are able to predict functionality of antibodies. This is a major development and lays the foundation for using antibody sequencing as a powerful tool for immunodiagnostics, vaccine development, and antibody engineering. One publication is completed, another two are in preparation, and a patent application is in preparation.