Biology subjects

Teraguchi, S.

Publications and source records attributed to Teraguchi, S..

5 recordsLinked to original sources

bioRxiv2024

Th17 cells with a pathogenic signature in joints of ZAP-70 mutant arthritic mice harbor unique TCR repertoires that share features with WT Tregs

Mutations in the ZAP-70 gene that cause moderate attenuation of T cell receptor (TCR) signaling in mice can result in autoimmune manifestations. One explanation for this pathology is a shift in the regulatory-conventional (Treg-Tconv) T cell repertoire composition. To test this hypothesis, we characterized the single-cell gene expression profiles and TCR repertoires of Tconv and Treg CD4+ T cells of arthritic (ZAC), poised (SKG) ZAP-70 mutant, and wild-type (WT) mice. We identified a group of Th17 cells which exhibited a pathogenic signature and occurred exclusively in inflamed joints of ZAC mice. Such pathogenic signature was uniquely detected in CD4+ T cells obtained from inflamed joints of RA patients. Overall, the Tconv repertoires of ZAP-70 mutant mice were increasingly similar to the repertoires of WT Tregs, and this effect was most notable in the subset of pathogenic Th17 cells. Our results support a model where, upon moderate ZAP-70-mediated signal weakening, T cells that would normally develop into Tregs, instead develop into self-reactive Tconvs, resulting in a breakdown in self-tolerance and susceptibility to autoimmune arthritis.

immunology↗

bioRxiv2023

Generalizable features for the diagnosis of infectious disease, autoimmunity and cancer from adaptive immune receptor repertoires

Liquid biopsies based on peripheral blood offer a minimally invasive alternative to solid tissue biopsies for the detection of diseases, primarily cancers. However, such tests currently consider only the serum component of blood, overlooking a potentially rich source of biomarkers: adaptive immune receptors (AIRs) expressed on circulating B and T cells. Machine learning-based classifiers trained on AIRs have been reported to accurately identify not only cancers, but also autoimmune and infectious diseases as well. However, when using the conventional "clonotype cluster" representation of AIRs, donors within a disease or healthy cohort exhibit vastly different features, limiting the generalizability of these classifiers. This paper addresses the challenge of classifying specific diseases from circulating B or T cells by developing a novel representation of AIRs based on similarity networks constructed from their antigen-binding regions (paratopes). Features based on this novel representation, paratope cluster occupancies (PCOs), significantly improved disease classification performance for infectious disease, autoimmunity and cancer. Under identical methodological conditions, classifiers trained on PCOs achieved a mean ROC AUC of 0.893 when applied to new donors, compared to clonotype cluster-based classifiers (0.714) or the best-performing published classifier (0.777). Surprisingly, for cancer patients, we observed that some of the AIRs that were important for classification were significantly more abundant in healthy controls than in individuals with disease. These "healthy-biased" AIRs were predicted to target known cancer-associated antigens at dramatically higher rates than healthy AIRs as a whole (Z scores > 75), suggesting the existence of an overlooked reservoir of cancer-targeting immune cells that are diagnostic and identifiable from a routine blood test. Consequently, PCOs not only enhance classification of a broad range of diseases but also identify immune cells with therapeutic potential.

bioinformatics↗

bioRxiv2022

InterClone: Store, Search and Cluster Adaptive Immune Receptor Repertoires

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.

bioinformatics↗

bioRxiv2022

Improved antibody-specific epitope prediction using AlphaFold and AbAdapt

Antibodies recognize their cognate antigens with high affinity and specificity, but the prediction of binding sites on the antigen (epitope) corresponding to a specific antibody remains a challenging problem. To address this problem, we developed AbAdapt, a pipeline that integrates antibody and antigen structural modeling with rigid docking in order to derive antibody-antigen specific features for epitope prediction. In this study, we assess the impact of integrating the state-of-the-art protein modeling method AlphaFold with the AbAdapt pipeline and demonstrate significant improvement in the prediction of antibody-specific epitope residues.

bioinformatics↗

bioRxiv2021

Unbiased integration of single cell transcriptomes using a linear hybrid method

Single cell transcriptomic approaches are becoming mainstream, with replicate experiments commonly performed with the same single cell technology. Methods that enable integration of these datasets by removing batch effects while preserving biological information are required for unbiased data interpretation. Here we introduce Canek for this purpose. Canek leverages information from mutual nearest neighbor to combine local linear corrections with cell-specific non-linear corrections within a fuzzy logic framework. Using a combination of real and synthetic datasets, we show that Canek corrects batch effects while introducing the least amount of bias compared with competing methods. Canek is computationally efficient and can easily integrate thousands of single-cell transcriptomes from replicated experiments.

bioinformatics↗