Tickotsky-Moskovitz, N. | bioRxiv Science

bioRxiv2020

CDR3 and V- genes show distinct reconstitution patterns in T-cell repertoire post allogenic bone marrow transplantation

BackgroundRestoration of T-cell repertoire diversity after allogeneic bone marrow transplantation (allo-BMT) is crucial for immune recovery. T-cell diversity is produced by rearrangements of germline gene segments (V (D) and J) of the T-cell receptor (TCR) and {beta} chains, and selection induced by binding of TCRs to MHC-peptide complexes. This diversity can be measured by many measures. We here focus on the V gene usage and the CDR3 sequences of the beta chain. We compared multiple T-cell repertoires to follow T cell repertoire changes post allo-BMT in HLA-matched related donor and recipient pairs. ResultsOur analyses of the differences between donor and recipient complementarity determining region 3 (CDR3) beta composition and V-gene profile show that the CDR sequence composition does not change during restoration, implying its dependence on the HLA typing. In contrast, V gene usage followed a time-dependent pattern, got following the donor profile post transplant and then shifting back to the recipients profile. The final long-term repertoire was more similar to that of the recipients original one than the donors.; some recipients converged within months while others took multiple years. ConclusionBased on the results of our analyses, we propose that donor-recipient V-gene distribution differences may serve as clinical biomarkers for monitoring immune recovery.

immunology↗

bioRxiv2019

Prediction of specific TCR-peptide binding from large dictionaries of TCR-peptide pairs

Current sequencing methods allow for detailed samples of T cell receptors (TCR) repertoires. To determine from a repertoire whether its host had been exposed to a target, computational tools that predict TCR-epitope binding are required. Currents tools are based on conserved motifs and are applied to peptides with many known binding TCRs. Given any TCR and peptide, we employ new NLP-based methods to predict whether they bind. We combined large-scale TCR-peptide dictionaries with deep learning methods to produce ERGO (pEptide tcR matchinG predictiOn), a highly specific and generic TCR-peptide binding predictor. A set of standard tests are defined for the performance of peptide-TCR binding, including the detection of TCRs binding to a given peptide/antigen, choosing among a set of candidate peptides for a given TCR and determining whether any pair of TCR-peptide bind. ERGO significantly outperforms current methods in these tests even when not trained specifically for each test. The software implementation and data sets are available at https://github.com/louzounlab/ERGO

immunology↗