Comparative analysis of CDR3 regions in paired human αβ CD8 T cells

Profiling the broad antibody diversity of lymphatic filariasis immune antibody repertoire by deep sequencing

Lymphatic filariasis is caused by infections of thread-like filarial worms, namely Wuchereria bancrofti, Brugia Malayi and Brugia timori. However, in-depth analysis of the antibody repertoire against Lymphatic filariasis is lacking. Using high-throughput sequencing of antibody repertoires, immunome analysis of IgG (LG) and IgM (LM) repertoires were studied. Despite significant differences between LG and LM in V(D)J gene usage, IGHV4-34, IGHV6-1, IGHD3-10 and IGHJ4 were preferred in both repertoires. The CDR3 in the LG repertoire showed a longer length than LM. Higher SHM level were observed in LG sequences and presence of oligoclonal sequences indicates the extent of clonal expansion. The prevalence of rare clonotypes in LM repertoire depicts the high clonal diversity when compared to LG repertoire. Monoclonal antibodies against closely related parasitic infections were present within the LG repertoire suggesting that immune repertoires may not be as exclusive and biased against the target infection as initially thought. The characterization of the immune repertoire can provide critical insight into the antibody response patterns in disease state, antibody generation process during infections and future antibody designs.

The T-cell receptor β chain CDR3 insights of bovine liver immune repertoire under heat stress

OBJECTIVE

The liver plays a dual role in regulating temperature and immune responses. Examining the influence of heat stress (HS) on liver T cells contributes significantly to understanding the intricate interplay between the immune system and hepatic tissues under thermal stress. This study focused on investigating the characteristics of the T-cell receptor (TCR) β chain CDR3 repertoire in bovine liver samples under both HS and pairfed (PF) environmental conditions.

METHODS

Sequencing data from six samples sourced from the GEO database underwent annotation. Utilizing immunarch and VDJtool software, the study conducted comprehensive analyses encompassing basic evaluation, clonality assessment, immune repertoire comparison, diversity estimation, gene usage profiling, VJ gene segment pairing scrutiny, clonal tracking, and Kmers analysis.

RESULTS

All four TCR chains, namely α, β, γ, and δ, were detected, with the α chains exhibiting the highest detection frequency, followed closely by the β chains. The prevalence of αβ TCRs in bovine liver samples underscored their crucial role in governing hepatic tissue's physiological functions. The TCR β CDR3 repertoire showcased substantial inter-individual variability, featuring diverse clonotypes exhibiting distinct amino acid lengths. Intriguingly, HS cattle displayed heightened diversity and clonality, suggesting potential peripheral T cell migration into the liver under environmental conditions. Notably, differential VJ gene pairings were observed in HS cattle compared to the PF, despite individual variations in V and J gene utilization. Additionally, while most high-frequency amino acid 5-mers remained consistent between the HS and PF, GELHF, and YDYHF were notably prevalent in the HS group. Across all samples, a prevalent trend of high-frequency 5mers skewed towards polar and hydrophobic amino acids was evident.

CONCLUSION

This study elucidates the characteristics of liver TCR β chain CDR3 repertoire under HS conditions, enhancing our understanding of HS implications.

Limits on inferring T cell specificity from partial information

A key challenge in molecular biology is to decipher the mapping of protein sequence to function. To perform this mapping requires the identification of sequence features most informative about function. Here, we quantify the amount of information (in bits) that T cell receptor (TCR) sequence features provide about antigen specificity. We identify informative features by their degree of conservation among antigen-specific receptors relative to null expectations. We find that TCR specificity synergistically depends on the hypervariable regions of both receptor chains, with a degree of synergy that strongly depends on the ligand. Using a coincidence-based approach to measuring information enables us to directly bound the accuracy with which TCR specificity can be predicted from partial matches to reference sequences. We anticipate that our statistical framework will be of use for developing machine learning models for TCR specificity prediction and for optimizing TCRs for cell therapies. The proposed coincidence-based information measures might find further applications in bounding the performance of pairwise classifiers in other fields.

Insights into cytomegalovirus-associated T cell receptors in recipients following allogeneic hematopoietic stem cell transplantation

BACKGROUND

Cytomegalovirus (CMV) reactivation is a serious problem in recipients of allogeneic hematopoietic stem cell transplantation. Long-term latency depends on specific T cell immune reconstitution, which identifies various pathogens by T cell receptors (TCRs). However, the mechanisms underlying the selection of CMV-specific TCRs in recipients after transplantation remain unclear.

METHODS

Using high-throughput sequencing and bioinformatics analysis, the T cell immune repertoire of seven CMV reactivated recipients (CRRs) were analyzed and compared to those of seven CMV non-activated recipients (CNRs) at an early stage after transplant.

RESULTS

The counts of unique complementarity-determining region 3 (CDR3) were significantly higher in CNRs than in CRRs. The CDR3 clones in the CNRs exhibit higher homogeneity compared to the CRRs. With regard to T cell receptor β-chain variable region (TRBV) and joint region (TRBJ) genotypes, significant differences were observed in the frequencies of TRBV6, BV23, and BV7-8 between the two groups. In addition to TRBV29-1/BJ1-2, TRBV2/BJ2-2, and TRBV12-4/BJ1-5, 11 V-J combinations had significantly different expression levels between CRRs and CNRs.

CONCLUSIONS

The differences in TCR diversity, TRBV segments, and TRBV-BJ combinations observed between CNRs and CRRs might be associated with post-transplant CMV reactivation and could serve as a foundation for further research.

Local chicken breeds exhibit abundant TCR-V segments but similar repertoire diversity

The thymus-derived lymphocytes of jawed vertebrates have four T-cell receptor (TCR) chains that play a significant role in immunity. As chickens have commercial value, their immune systems require a great deal of attention. Local chicken breeds are an essential part of poultry genetic resources in China. Here, we used high-throughput sequencing to analyze the TCRα and TCRβ repertoires and their relative expression levels in the native chicken breeds Baier Buff, Longyou Partridge, Xiaoshan, and Xianju. We found that TCR Vα and TCR Vβ were expressed and included 17, 19, 17, and six segments of the Vα2, Vα3, Vβ1, and Vβ2 subgroups, respectively. V-J pairing was biased; Jα11 was utilized by nearly all Vα segments and was the most commonly used. Breed-specific V segments and V-J pairings were detected as well. The results of the principal coordinate analysis (PCoA) as well as the V-J pairing and CDR3 diversity analyses suggested that the four local chicken breeds did not significantly differ in terms of TCR diversity. Hence, they expressed not significant differentiation, and they are rich genetic resources for the development and utilization of immune-related poultry breeding.

HeteroTCR: A heterogeneous graph neural network-based method for predicting peptide-TCR interaction

Identifying interactions between T-cell receptors (TCRs) and immunogenic peptides holds profound implications across diverse research domains and clinical scenarios. Unsupervised clustering models (UCMs) cannot predict peptide-TCR binding directly, while supervised predictive models (SPMs) often face challenges in identifying antigens previously unencountered by the immune system or possessing limited TCR binding repertoires. Therefore, we propose HeteroTCR, an SPM based on Heterogeneous Graph Neural Network (GNN), to accurately predict peptide-TCR binding probabilities. HeteroTCR captures within-type (TCR-TCR or peptide-peptide) similarity information and between-type (peptide-TCR) interaction insights for predictions on unseen peptides and TCRs, surpassing limitations of existing SPMs. Our evaluation shows HeteroTCR outperforms state-of-the-art models on independent datasets. Ablation studies and visual interpretation underscore the Heterogeneous GNN module's critical role in enhancing HeteroTCR's performance by capturing pivotal binding process features. We further demonstrate the robustness and reliability of HeteroTCR through validation using single-cell datasets, aligning with the expectation that pMHC-TCR complexes with higher predicted binding probabilities correspond to increased binding fractions.

Blood transcriptomics analysis offers insights into variant-specific immune response to SARS-CoV-2

Bulk RNA sequencing (RNA-seq) of blood is typically used for gene expression analysis in biomedical research but is still rarely used in clinical practice. In this study, we propose that RNA-seq should be considered a diagnostic tool, as it offers not only insights into aberrant gene expression and splicing but also delivers additional readouts on immune cell type composition as well as B-cell and T-cell receptor (BCR/TCR) repertoires. We demonstrate that RNA-seq offers insights into a patient's immune status via integrative analysis of RNA-seq data from patients infected with various SARS-CoV-2 variants (in total 196 samples with up to 200 million reads sequencing depth). We compare the results of computational cell-type deconvolution methods (e.g., MCP-counter, xCell, EPIC, quanTIseq) to complete blood count data, the current gold standard in clinical practice. We observe varying levels of lymphocyte depletion and significant differences in neutrophil levels between SARS-CoV-2 variants. Additionally, we identify B and T cell receptor (BCR/TCR) sequences using the tools MiXCR and TRUST4 to show that-combined with sequence alignments and BLASTp-they could be used to classify a patient's disease. Finally, we investigated the sequencing depth required for such analyses and concluded that 10 million reads per sample is sufficient. In conclusion, our study reveals that computational cell-type deconvolution and BCR/TCR methods using bulk RNA-seq analyses can supplement missing CBC data and offer insights into immune responses, disease severity, and pathogen-specific immunity, all achievable with a sequencing depth of 10 million reads per sample.

Blood transcriptomics analysis offers insights into variant-specific immune response to SARS-CoV-2

Bulk RNA sequencing (RNA-seq) of blood is typically used for gene expression analysis in biomedical research but is still rarely used in clinical practice. In this study, we argue that RNA-seq should be considered a routine diagnostic tool, as it offers not only insights into aberrant gene expression and splicing but also delivers additional readouts on immune cell type composition as well as B-cell and T-cell receptor (BCR/TCR) repertoires. We demonstrate that RNA-seq offers vital insights into a patient's immune status via integrative analysis of RNA-seq data from patients infected with various SARS-CoV-2 variants (in total 240 samples with up to 200 million reads sequencing depth). We compare the results of computational cell-type deconvolution methods (e.g., MCP-counter, xCell, EPIC, quanTIseq) to complete blood count data, the current gold standard in clinical practice. We observe varying levels of lymphocyte depletion and significant differences in neutrophil levels between SARS-CoV-2 variants. Additionally, we identify B and T cell receptor (BCR/TCR) sequences using the tools MiXCR and TRUST4 to show that - combined with sequence alignments and pBLAST - they could be used to classify a patient's disease. Finally, we investigated the sequencing depth required for such analyses and concluded that 10 million reads per sample is sufficient. In conclusion, our study reveals that computational cell-type deconvolution and BCR/TCR methods using bulk RNA-seq analyses can supplement missing CBC data and offer insights into immune responses, disease severity, and pathogen-specific immunity, all achievable with a sequencing depth of 10 million reads per sample.

The screening, identification, design and clinical application of tumor-specific neoantigens for TCR-T cells

Recent advances in neoantigen research have accelerated the development of tumor immunotherapies, including adoptive cell therapies (ACTs), cancer vaccines and antibody-based therapies, particularly for solid tumors. With the development of next-generation sequencing and bioinformatics technology, the rapid identification and prediction of tumor-specific antigens (TSAs) has become possible. Compared with tumor-associated antigens (TAAs), highly immunogenic TSAs provide new targets for personalized tumor immunotherapy and can be used as prospective indicators for predicting tumor patient survival, prognosis, and immune checkpoint blockade response. Here, the identification and characterization of neoantigens and the clinical application of neoantigen-based TCR-T immunotherapy strategies are summarized, and the current status, inherent challenges, and clinical translational potential of these strategies are discussed.

Analysis of tumor infiltrating CD4+ and CD8+ CDR3 sequences reveals shared features putatively associated to the anti-tumor immune response

Introduction

Tumor-infiltrating lymphocytes (TILs) have predictive and prognostic value in breast cancer (BC) and exert a protective function against tumor growth, indicating that it is susceptible to treatment using adoptive cell transfer of TILs or T cell receptor (TCR)-based therapies. TCR can be used to identify naturally tumor-reactive T cells, but little is known about the differences in the TCR repertoires of CD4+ and CD8+ TILs.

Methods

TCR high-throughput sequencing was performed using TILs derived from the initial cultures of 11 BC biopsies and expanded and sorted CD4+ and CD8+ TILs as well as using PBMCs from healthy donors expanded and sorted using the same methodology.

Results

Physicochemical TCR differences between T cell subsets were observed, as CD4+ TILs presented larger N(D)Nnt TRB sequences and with a higher usage of positively charged residues, although only the latest was also observed in peripheral T cells from healthy individuals. Moreover, in CD4+ TILs, a more restricted TCR repertoire with a higher abundance of similar sequences containing certain amino acid motifs was observed.

Discussion

Some differences between CD4+ and CD8+ TCRs were intrinsic to T cell subsets as can also be observed in peripheral T cells from healthy individuals, while other were only found in TILs samples and therefore may be tumor-driven. Notably, the higher similarity among CD4+ TCRs suggests a higher TCR promiscuity in this subset.

TCRpair: prediction of functional pairing between HLA-A*02:01-restricted T cell receptor α and β chains

SUMMARY

The ability of a T cell to recognize foreign peptides is defined by a single α and a single β hypervariable complementarity determining region (CDR3), which together form the T cell receptor (TCR) heterodimer. In ∼30%-35% of T cells, two α chains are expressed at the mRNA level but only one α chain is part of the functional TCR. This effect can also be observed for β chains, although it is less common. The identification of functional α/β chain pairs is instrumental in high-throughput characterization of therapeutic TCRs. TCRpair is the first method that predicts whether an α and β chain pair forms a functional, HLA-A*02:01 specific TCR without requiring the sequence of a recognized peptide. By taking additional amino acids flanking the CDR3 regions into account, TCRpair achieves an AUC of 0.71.

AVAILABILITY

TCRpair is implemented in Python using TensorFlow 2.0 and is freely available at https://www.github.com/amoesch/TCRpair.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Characterization of amino acid residues of T-cell receptors interacting with HLA-A*02-restricted antigen peptides

Background

The present study aimed to explore residues’ properties interacting with HLA-A*02-restricted peptides on T-cell receptors (TCRs) and their effects on bond types of interaction and binding free energy.

Methods

We searched the crystal structures of HLA-A*02-restricted peptide-TCR complexes from the Protein Data Bank (PDB) database and subsequently collected relevant parameters. We then employed Schrodinger to analyze the bond types of interaction and Gromacs 2019 to evaluate the TCR-antigen peptide complex’s molecular dynamics simulation. Finally, we compared the changes of bond types of interaction and binding free energy before and after residue substitution to ensure consistency of the conditions before and after residue substitution.

Results

The main sites on the antigen peptides that formed the intermolecular interaction [hydrogen bond (HB) and pi stack] with TCRs were P4, P8, P2, and P6. The hydrophobicity of the amino acids inside or outside the disulfide bond of TCRs may be related to the intermolecular interaction and binding free energy between TCRs and peptides. Residues located outside the disulfide bond of TCR α or β chains and forming pi stack force played favorable roles in the complex intermolecular interaction and binding free energy. The residues of the TCR α or β chains that interacted with peptides were replaced by alanine (Ala) or glycine (Gly), and their intermolecular binding free energy of the complex had been improved. However, it had nothing to do with the formation of HB.

Conclusions

The findings of this study suggest that the hydrophobic nature of the amino acids inside or outside the disulfide bonds on the TCR may be associated with the intermolecular interaction and binding between the TCR and polypeptide. The residues located outside the TCR α or β single-chain disulfide bond and forming the pi-stack force showed a beneficial effect on the intermolecular interaction and binding of the complex. In addition, the part of the residues on the TCR α or β single chain that produced bond types of interaction with the polypeptide after being replaced by Ala or Gly, the intermolecular binding free energy of the complex was increased, regardless of whether HB was formed.

T-Cell Receptor CDR3 Loop Conformations in Solution Shift the Relative Vα-Vβ Domain Distributions

T-cell receptors are an important part in the adaptive immune system as they are responsible for detecting foreign proteins presented by the major histocompatibility complex (MHC). The affinity is predominantly determined by structure and sequence of the complementarity determining regions (CDRs), of which the CDR3 loops are responsible for peptide recognition. We present a kinetic classification of T-cell receptor CDR3 loops with different loop lengths into canonical and non-canonical solution structures. Using molecular dynamics simulations, we do not only sample available X-ray structures, but we also observe a substantially broader CDR3 loop ensemble with various distinct kinetic minima in solution. Our results strongly imply, that for given CDR3 loop sequences several canonical structures have to be considered to characterize the conformational diversity of these loops. Our suggested dominant solution structures could extend the repertoire of available canonical clusters by including kinetic minimum structures present in solution. Thus, the CDR3 loops need to be characterized as conformational ensembles in solution. Furthermore, the conformational changes of the CDR3 loops follow the paradigm of conformational selection, because the experimentally determined binding competent state is present within this ensemble of pre-existing conformations without the presence of the antigen. We also identify strong correlations between the CDR3 loops and include combined state descriptions. Additionally, we observe a strong dependency of the CDR3 loop conformations on the relative Vα-Vβ interdomain orientations, revealing that certain CDR3 loop states favor specific interface orientations.