Shared bias in H chain V-J pairing in naive and memory B cells.

Introduction: H chain rearrangement in B cells is a two-step process where first DH binds JH, and only then VH is joined to the complex. As such, there is no direct rearrangement between VH and JH. Results: Nevertheless, we here show that the VHJH combinations frequency in humans deviates from the one expected based on each gene usage frequency. This bias is observed mainly in functional rearrangements, and much less in out-of-frame rearrangements. The bias cannot be explained by preferred binding for DH genes or a preferred reading frame. Preferred VH JH combinations are shared between donors. Discussion: These results suggest a common structural mechanism for these biases. Through development, thepreferred VH JH combinations evolve during peripheral selection to become stronger, but less shared. We propose that peripheral Heavy chain VH JH usage is initially shaped by a structural selection before the naive B cellstate, followed by pathogen-induced selection for host specific VH-JH pairs.

Autoencoder based local T cell repertoire density can be used to classify samples and T cell receptors.

Recent advances in T cell repertoire (TCR) sequencing allow for the characterization of repertoire properties, as well as the frequency and sharing of specific TCR. However, there is no efficient measure for the local density of a given TCR. TCRs are often described either through their Complementary Determining region 3 (CDR3) sequences, or theirV/J usage, or their clone size. We here show that the local repertoire density can be estimated using a combined representation of these components through distance conserving autoencoders and Kernel Density Estimates (KDE). We present ELATE-an Encoder-based LocAl Tcr dEnsity and show that the resulting density of a sample can be used as a novel measure to study repertoire properties. The cross-density between two samples can be used as a similarity matrix to fully characterize samples from the same host. Finally, the same projection in combination with machine learning algorithms can be used to predict TCR-peptide binding through the local density of known TCRs binding a specific target.

Maintenance of the human memory T cell repertoire by subset and tissue site.

BACKGROUND: Immune-mediated protection is mediated by T cells expressing pathogen-specific T cell antigen receptors (TCR) that are maintained at diverse sites of infection as tissue-resident memory T cells (TRM) or that disseminate as circulating effector-memory (TEM), central memory (TCM), or terminal effector (TEMRA) subsets in blood and tissues. The relationship between circulating and tissue resident T cell subsets in humans remains elusive, and is important for promoting site-specific protective immunity. METHODS: We analyzed the TCR repertoire of the major memory CD4+ and CD8+T cell subsets (TEM, TCM, TEMRA, and TRM) isolated from blood and/or lymphoid organs (spleen, lymph nodes, bone marrow) and lungs of nine organ donors, and blood of three living individuals spanning five decades of life. High-throughput sequencing of the variable (V) portion of individual TCR genes for each subset, tissue, and individual were analyzed for clonal diversity, expansion and overlap between lineage, T cell subsets, and anatomic sites. TCR repertoires were further analyzed for TRBV gene usage and CDR3 edit distance. RESULTS: Across blood, lymphoid organs, and lungs, human memory, and effector CD8+T cells exhibit greater clonal expansion and distinct TRBV usage compared to CD4+T cell subsets. Extensive sharing of clones between tissues was observed for CD8+T cells; large clones specific to TEMRA cells were present in all sites, while TEM cells contained clones shared between sites and with TRM. For CD4+T cells, TEM clones exhibited the most sharing between sites, followed by TRM, while TCM clones were diverse with minimal sharing between sites and subsets. Within sites, TRM clones exhibited tissue-specific expansions, and maintained clonal diversity with age, compared to age-associated clonal expansions in circulating memory subsets. Edit distance analysis revealed tissue-specific biases in clonal similarity. CONCLUSIONS: Our results show that the human memory T cell repertoire comprises clones which persist across sites and subsets, along with clones that are more restricted to certain subsets and/or tissue sites. We also provide evidence that the tissue plays a key role in maintaining memory T cells over age, bolstering the rationale for site-specific targeting of memory reservoirs in vaccines and immunotherapies.

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

Restoration 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) α and ß chains, and selection induced by binding of TCRs to MHC-peptide complexes. Multiple measures were proposed for this diversity. 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. Our analyses of the differences between donor and recipient complementarity determining region 3 (CDR3) beta composition and V-gene profile show that the CDR3 sequence composition does not change during restoration, implying its dependence on the HLA typing. In contrast, V-gene usage followed a time-dependent pattern, initially following the donor profile and then shifting back to the recipients' profile. The final long-term repertoire was more similar to that of the recipient's original one than the donor's; some recipients converged within months, while others took multiple years. Based on the results of our analyses, we propose that donor-recipient V-gene distribution differences may serve as clinical biomarkers for monitoring immune recovery.

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. We employ new Natural Language Processing (NLP) based methods to predict whether any TCR and peptide 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 reaches similar results to state of the art 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. ERGO is also available through a webserver at: http://tcr.cs.biu.ac.il/.

CDB-a database for protein heterodimeric complexes.

Crystallographic structures of protein complexes are essential to develop proteomic and structural biology methods, as prediction of protein-protein interaction (PPI) sites and protein-protein docking. Such structures can aid the development of protein complexation inhibitors. Complex DataBase (CDB), accessible at www.jct-bioinfo.com/cdb/search, is a database web application for heterodimeric protein crystallographic complexes along with the crystallographic structures of each individual unbound protein. Direct access to crystallographic structures of protein complexes, along with provided annotations, can serve as starting point for constructing new experimental protein complexes sets of any type, for protein binding studies, and the development and evaluation of PPIs prediction methods.