B and T cell receptor repertoire data has the potential to fundamentally change the way we diagnose and treat a wide range of diseases. However, there are few resources for storing or analyzing repertoire data. InterClone provides tools for storing, searching, and clustering repertoire datasets. Efficiency is achieved by encoding the complementarity-determining regions of sequences as mmseqs2 databases. Single chain search or cluster results can be merged into paired (alpha-beta or heavy-light) results for analysis of single-cell sequencing data. We illustrate the use of InterClone with two recently reported examples: 1) searching for SARS-CoV-2 infection-enhancing antibodies in bulk COVID-19 and healthy donor repertoires; 2) identification of SARS-CoV-2 specific TCRs by clustering paired and bulk sequences from COVID-19, BNT162b2 vaccinated and healthy unvaccinated donors. The core functions of InterClone have been implemented as a web server and integrated database (https://sysimm.org/interclone). All source code is available upon request.

The mechanism of T cell triggering upon engagement with a peptide-MHC (pMHC) complex remains a challenging problem. In order to observe structural and dynamics changes in the T cell receptor (TCR) upon pMHC binding, we carried out coarse grained molecular dynamics simulations of TCR-only and TCR-pMHC systems starting from a recently solved cryo-EM structure of the TCR and CD3 co-receptors. The simulations were performed in biological membranes for an aggregated length of 2 ms. We observed that, while unengaged TCRs adopted conformations that bent and restricted the dynamics of the CD3 co-receptors, the pMHC-bound TCRs adopted elongated conformations that allowed CD3 co-receptors to diffuse more freely. In this way, the TCR-pMHC pair acted as a "drawbridge", licensing the dynamics of the CD3 co-receptors, resulting in signal transmission across the plasma membrane.

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Recent advances in single cell technology now allow researchers to simultaneously profile the transcriptional program and chromatin accessibility from individual cells, providing access to both the characterization of cell types and states, and the exploration of gene regulatory programs at the same time and in the same cells.Here, we present an analysis of single cell transcriptomes and chromatin accessibility profiles in PBMCs from a group of SARS-COV-2 infected subjects with a range of disease severities. We were able to identify several immune cell types at a coarse level based on transcriptomic profiles. By additional subclustering, we found 4 clusters of CD8T cells which show differential distribution across COVID-19 severities. Analysis of DEGs revealed higher expression of genes associated with CD8 T cell terminal differentiation and effector functions in the cluster enriched in mild patients. Chromatin accessibility analysis of the selected DEGs in CD8T cells confirms higher accessibility in patients with mild disease vs severe patients. Interestingly, the transcripion factor ZEB2 was identified as one of the top markers of the mild-severity cluster. Further motif analysis will clarify the importance of this TF in CD8T cell differentiation and outcome in SARS-COV-2 infected subjects.