Modeling the dynamics and composition of T-cell receptor repertoires in post-acute sequelae of COVID-19 (PASC)

Project Summary Post-acute sequelae of COVID-19 (PASC), colloquially known as long covid, has been reported in 31%-69% of COVID-19 patients. The prevalence of PASC among patients, even those with mild infection, and those with prior vaccination, underscores the importance of principled approaches for uncovering and addressing the cause of these issues. We propose to conduct a detailed analysis of the immune repertoire dynamics of PASC patients and compare them to both COVID-19 patients without PASC, and healthy individuals. These will be secondary analyses, conducted on existing data from a previous study with the Institute of Systems Biology and Swedish Health Services, which includes longitudinal deep immunophenotyping with single cell and plasma multiomics, repertoire sequencing, electronic health records, viremia measurements, and antibody titers for 209 COVID-19 patients. In Aim 1, we will conduct inference, analysis, and comparison of T-cell receptor repertoire dynamics in PASC. Using interpretable statistical, biophysical, and machine learning approaches, we will conduct a detailed analysis of T-cell repertoires aimed at finding PASC specific clonotypes and their corresponding receptor features. This involves building cohort specific models of thymic selection, examining how these models differ between cohorts and over time, inferring the dynamics of repertoire size, sharing and diversity, and uncovering the receptor features which drive these differences. In Aim 2, we will develop new methods for the integration of T-cell repertoire and single cell dynamics. The existing breadth of multiomics data allows us to explore new methodologies for integrating different modalities of longitudinal data. For T-cell repertoires in particular, we plan to extend existing models of thymic selection to include gene expression of relevant T-cell specific genes and to study how the expansion and contraction of clonotypes affects the dynamics of T-cells in gene expression space. By using interpretable biophysical and machine learning methods, we can construct generative models of TCRs including gene expression values and study how these distributions change in time. Results have strong potential to accelerate our understanding of the etiology of immune-based PASC responses, which is essential for prioritizing potential therapeutic targets for prevention and treatment. Further, results will advance methods for future research across a wide range of infectious diseases and immune-mediated medical conditions.