Immunosequencing identifies signatures of cytomegalovirus exposure history and HLA-mediated effects on the T cell repertoire

Large clones of pre-existing T cells drive early immunity against SARS-COV-2 and LCMV infection

T cell responses precede antibody and may provide early control of infection. We analyzed the clonal basis of this rapid response following SARS-COV-2 infection. We applied T cell receptor (TCR) sequencing to define the trajectories of individual T cell clones immediately. In SARS-COV-2 PCR+ individuals, a wave of TCRs strongly but transiently expand, frequently peaking the same week as the first positive PCR test. These expanding TCR CDR3s were enriched for sequences functionally annotated as SARS-COV-2 specific. Epitopes recognized by the expanding TCRs were highly conserved between SARS-COV-2 strains but not with circulating human coronaviruses. Many expanding CDR3s were present at high frequency in pre-pandemic repertoires. Early response TCRs specific for lymphocytic choriomeningitis virus epitopes were also found at high frequency in the preinfection naive repertoire. High-frequency naive precursors may allow the T cell response to respond rapidly during the crucial early phases of acute viral infection.

T cell repertoire breadth is associated with the number of acute respiratory infections in the LoewenKIDS birth cohort

We set out to gain insight into peripheral blood B and T cell repertoires from 120 infants of the LoewenKIDS birth cohort to investigate potential determinants of early life respiratory infections. Low antigen-dependent somatic hypermutation of B cell repertoires, as well as low T and B cell repertoire clonality, high diversity, and high richness especially in public T cell clonotypes reflected the immunological naivety at 12 months of age when high thymic and bone marrow output are associated with relatively few prior antigen encounters. Infants with inadequately low T cell repertoire diversity or high clonality showed higher numbers of acute respiratory infections over the first 4 years of life. No correlation of T or B cell repertoire metrics with other parameters such as sex, birth mode, older siblings, pets, the onset of daycare, or duration of breast feeding was noted. Together, this study supports that-regardless of T cell functionality-the breadth of the T cell repertoire is associated with the number of acute respiratory infections in the first 4 years of life. Moreover, this study provides a valuable resource of millions of T and B cell receptor sequences from infants with available metadata for researchers in the field.

Prospects of Cytomegalovirus-Specific T-Cell Receptors in Clinical Diagnosis and Therapy

Human cytomegalovirus (HCMV) is responsible for widespread infections worldwide. In immunocompetent individuals it is typically latent, while infection or reactivation in immunocompromised individuals can result in severe clinical symptoms or even death. Although there has been significant progress in the treatment and diagnosis of HCMV infection in recent years, numerous shortcomings and developmental limitations persist. There is an urgent need to develop innovative, safe, and effective treatments, as well as to explore early and timely diagnostic strategies for HCMV infection. Cell-mediated immune responses are the primary factor controlling HCMV infection and replication, but the protective role of humoral immune responses remains controversial. T-cells, key effector cells of the cellular immune system, are critical for clearing and preventing HCMV infection. The T-cell receptor (TCR) lies at the heart of T-cell immune responses, and its diversity enables the immune system to differentiate between self and non-self. Given the significant influence of cellular immunity on human health and the indispensable role of the TCR in T-cell immune responses, we posit that the impact of TCR on the development of novel diagnostic and prognostic methods, as well as on patient monitoring and management of clinical HCMV infection, will be far-reaching and profound. High-throughput and single-cell sequencing technologies have facilitated unprecedented quantitative detection of TCR diversity. With these current sequencing technologies, researchers have already obtained a vast number of TCR sequences. It is plausible that in the near future studies on TCR repertoires will be instrumental in assessing vaccine efficacy, immunotherapeutic strategies, and the early diagnosis of HCMV infection.

Dynamics and survival associations of T cell receptor clusters in patients with pleural mesothelioma treated with immunotherapy

BACKGROUND

Immune checkpoint inhibitors (ICIs) are now a first-line treatment option for patients with pleural mesothelioma with the recent approval of ipilimumab and nivolumab. Mesothelioma has a low tumor mutation burden and no robust predictors of survival with ICI. Since ICIs enable adaptive antitumor immune responses, we investigated T-cell receptor (TCR) associations with survival in participants from two clinical trials treated with ICI.

METHODS

We included patients with pleural mesothelioma who were treated with nivolumab (NivoMes, NCT02497508) or nivolumab and ipilimumab (INITIATE, NCT03048474) after first-line therapy. TCR sequencing was performed with the ImmunoSEQ assay in 49 and 39 pretreatment and post-treatment patient peripheral blood mononuclear cell (PBMC) samples. These data were integrated with TCR sequences found in bulk RNAseq data by TRUST4 program in 45 and 35 pretreatment and post-treatment tumor biopsy samples and TCR sequences from over 600 healthy controls. The TCR sequences were clustered into groups of shared antigen specificity using GIANA. Associations of TCR clusters with overall survival were determined by cox proportional hazard analysis.

RESULTS

We identified 4.2 million and 12 thousand complementarity-determining region 3 (CDR3) sequences from PBMCs and tumors, respectively, in patients treated with ICI. These CDR3 sequences were integrated with 2.1 million publically available CDR3 sequences from healthy controls and clustered. ICI-enhanced T-cell infiltration and expanded T cell diversity in tumors. Cases with TCR clones in the top tertile in the pretreatment tissue or in circulation had significantly better survival than the bottom two tertiles (p<0.04). Furthermore, a high number of shared TCR clones between pretreatment tissue and in circulation was associated with improved survival (p=0.01). To potentially select antitumor clusters, we filtered for clusters that were (1) not found in healthy controls, (2) recurrent in multiple patients with mesothelioma, and (3) more prevalent in post-treatment than pretreatment samples. The detection of two-specific TCR clusters provided significant survival benefit compared with detection of 1 cluster (HR<0.001, p=0.026) or the detection of no TCR clusters (HR=0.10, p=0.002). These two clusters were not found in bulk tissue RNA-seq data and have not been reported in public CDR3 databases.

CONCLUSIONS

We identified two unique TCR clusters that were associated with survival on treatment with ICI in patients with pleural mesothelioma. These clusters may enable approaches for antigen discovery and inform future targets for design of adoptive T cell therapies.

Viral infection reveals hidden sharing of TCR CDR3 sequences between individuals

The T cell receptor is generated by a process of random and imprecise somatic recombination. The number of possible T cell receptors which this process can produce is enormous, greatly exceeding the number of T cells in an individual. Thus, the likelihood of identical TCRs being observed in multiple individuals (public TCRs) might be expected to be very low. Nevertheless such public TCRs have often been reported. In this study we explore the extent of TCR publicity in the context of acute resolving Lymphocytic choriomeningitis virus (LCMV) infection in mice. We show that the repertoire of effector T cells following LCMV infection contains a population of highly shared TCR sequences. This subset of TCRs has a distribution of naive precursor frequencies, generation probabilities, and physico-chemical CDR3 properties which lie between those of classic public TCRs, which are observed in uninfected repertoires, and the dominant private TCR repertoire. We have named this set of sequences "hidden public" TCRs, since they are only revealed following infection. A similar repertoire of hidden public TCRs can be observed in humans after a first exposure to SARS-COV-2. The presence of hidden public TCRs which rapidly expand following viral infection may therefore be a general feature of adaptive immunity, identifying an additional level of inter-individual sharing in the TCR repertoire which may form an important component of the effector and memory response.

Neural Network Models for Sequence-Based TCR and HLA Association Prediction

T cells rely on their T cell receptors (TCRs) to recognize foreign antigens presented by human leukocyte antigen (HLA) proteins. TCRs contain a record of an individual's past immune activities, and some TCRs are observed only in individuals with certain HLA alleles. As a result, characterising TCRs requires a thorough understanding of TCR-HLA associations. To this end, we propose a neural network method named Deep learning Prediction of TCR-HLA association (DePTH) to predict TCR-HLA associations based on their amino acid sequences. We show that the DePTH can be used to quantify the functional similarities of HLA alleles, and that these HLA similarities are associated with the survival outcomes of cancer patients who received immune checkpoint blockade treatment.

Statistical inference reveals the role of length, GC content, and local sequence in V(D)J nucleotide trimming

To appropriately defend against a wide array of pathogens, humans somatically generate highly diverse repertoires of B cell and T cell receptors (BCRs and TCRs) through a random process called V(D)J recombination. Receptor diversity is achieved during this process through both the combinatorial assembly of V(D)J-genes and the junctional deletion and insertion of nucleotides. While the Artemis protein is often regarded as the main nuclease involved in V(D)J recombination, the exact mechanism of nucleotide trimming is not understood. Using a previously published TCRβ repertoire sequencing data set, we have designed a flexible probabilistic model of nucleotide trimming that allows us to explore various mechanistically interpretable sequence-level features. We show that local sequence context, length, and GC nucleotide content in both directions of the wider sequence, together, can most accurately predict the trimming probabilities of a given V-gene sequence. Because GC nucleotide content is predictive of sequence-breathing, this model provides quantitative statistical evidence regarding the extent to which double-stranded DNA may need to be able to breathe for trimming to occur. We also see evidence of a sequence motif that appears to get preferentially trimmed, independent of GC-content-related effects. Further, we find that the inferred coefficients from this model provide accurate prediction for V- and J-gene sequences from other adaptive immune receptor loci. These results refine our understanding of how the Artemis nuclease may function to trim nucleotides during V(D)J recombination and provide another step toward understanding how V(D)J recombination generates diverse receptors and supports a powerful, unique immune response in healthy humans.

Insights into anti-tumor immunity via the polyomavirus shared across human Merkel cell carcinomas

Understanding and augmenting cancer-specific immunity is impeded by the fact that most tumors are driven by patient-specific mutations that encode unique antigenic epitopes. The shared antigens in virus-driven tumors can help overcome this limitation. Merkel cell carcinoma (MCC) is a particularly interesting tumor immunity model because (1) 80% of cases are driven by Merkel cell polyomavirus (MCPyV) oncoproteins that must be continually expressed for tumor survival; (2) MCPyV oncoproteins are only ~400 amino acids in length and are essentially invariant between tumors; (3) MCPyV-specific T cell responses are robust and strongly linked to patient outcomes; (4) anti-MCPyV antibodies reliably increase with MCC recurrence, forming the basis of a standard clinical surveillance test; and (5) MCC has one of the highest response rates to PD-1 pathway blockade among all solid cancers. Leveraging these well-defined viral oncoproteins, a set of tools that includes over 20 peptide-MHC class I tetramers has been developed to facilitate the study of anti-tumor immunity across MCC patients. Additionally, the highly immunogenic nature of MCPyV oncoproteins forces MCC tumors to develop robust immune evasion mechanisms to survive. Indeed, several immune evasion mechanisms are active in MCC, including transcriptional downregulation of MHC expression by tumor cells and upregulation of inhibitory molecules including PD-L1 and immunosuppressive cytokines. About half of patients with advanced MCC do not persistently benefit from PD-1 pathway blockade. Herein, we (1) summarize the lessons learned from studying the anti-tumor T cell response to virus-positive MCC; (2) review immune evasion mechanisms in MCC; (3) review mechanisms of resistance to immune-based therapies in MCC and other cancers; and (4) discuss how recently developed tools can be used to address open questions in cancer immunotherapy. We believe detailed investigation of this model cancer will provide insight into tumor immunity that will likely also be applicable to more common cancers without shared tumor antigens.

Quantitative annotations of T-Cell repertoire specificity

The specificity of a T-cell receptor (TCR) repertoire determines personalized immune capacity. Existing methods have modeled the qualitative aspects of TCR specificity, while the quantitative aspects remained unaddressed. We developed a package, TCRanno, to quantify the specificity of TCR repertoires. We created deep-learning-based, epitope-aware vector embeddings to infer individual TCR specificity. Then we aggregated clonotype frequencies of TCRs to obtain a quantitative profile of repertoire specificity at epitope, antigen and organism levels. Applying TCRanno to 4195 TCR repertoires revealed quantitative changes in repertoire specificity upon infections, autoimmunity and cancers. Specifically, TCRanno found cytomegalovirus-specific TCRs in seronegative healthy individuals, supporting the possibility of abortive infections. TCRanno discovered age-accumulated fraction of severe acute respiratory syndrome coronavirus 2 specific TCRs in pre-pandemic samples, which may explain the aggressive symptoms and age-related severity of coronavirus disease 2019. TCRanno also identified the encounter of Hepatitis B antigens as a potential trigger of systemic lupus erythematosus. TCRanno annotations showed capability in distinguishing TCR repertoires of healthy and cancers including melanoma, lung and breast cancers. TCRanno also demonstrated usefulness to single-cell TCRseq+gene expression data analyses by isolating T-cells with the specificity of interest.

Analysis of CDR3 Sequences from T-Cell Receptor β in Acute Respiratory Distress Syndrome

Acute Respiratory Distress Syndrome (ARDS) is an illness that typically develops in people who are significantly ill or have serious injuries. ARDS is characterized by fluid build-up that occurs in the alveoli. T-cells are implicated as playing a role in the modulation of the aberrant response leading to excessive tissue damage and, eventually, ARDS. Complementarity Determining Region 3 (CDR3) sequences derived from T-cells are key players in the adaptive immune response. This response is governed by an elaborate specificity for distinct molecules and the ability to recognize and vigorously respond to repeated exposures to the same molecules. Most of the diversity in T-cell receptors (TCRs) is contained in the CDR3 regions of the heterodimeric cell-surface receptors. For this study, we employed the novel technology of immune sequencing to assess lung edema fluid. Our goal was to explore the landscape of CDR3 clonal sequences found within these samples. We obtained more than 3615 CDR3 sequences across samples in the study. Our data demonstrate that: (1) CDR3 sequences from lung edema fluid exhibit distinct clonal populations, and (2) CDR3 sequences can be further characterized based on biochemical features. Analysis of these CDR3 sequences offers insight into the CDR3-driven T-cell repertoire of ARDS. These findings represent the first step towards applications of this technology with these types of biological samples in the context of ARDS.

CD4+ T cell-mediated recognition of a conserved cholesterol-dependent cytolysin epitope generates broad antibacterial immunity

CD4⁺ T cell-mediated immunity against Streptococcus pneumoniae (pneumococcus) can protect against recurrent bacterial colonization and invasive pneumococcal diseases (IPDs). Although such immune responses are common, the pertinent antigens have remained elusive. We identified an immunodominant CD4⁺ T cell epitope derived from pneumolysin (Ply), a member of the bacterial cholesterol-dependent cytolysins (CDCs). This epitope was broadly immunogenic as a consequence of presentation by the pervasive human leukocyte antigen (HLA) allotypes DPB1^∗02 and DPB1^∗04 and recognition via architecturally diverse T cell receptors (TCRs). Moreover, the immunogenicity of Ply_427-444 was underpinned by core residues in the conserved undecapeptide region (ECTGLAWEWWR), enabling cross-recognition of heterologous bacterial pathogens expressing CDCs. Molecular studies further showed that HLA-DP4-Ply_427-441 was engaged similarly by private and public TCRs. Collectively, these findings reveal the mechanistic determinants of near-global immune focusing on a trans-phyla bacterial epitope, which could inform ancillary strategies to combat various life-threatening infectious diseases, including IPDs.

T-cell receptor diversity in minimal change disease in the NEPTUNE study

BACKGROUND

Minimal change disease (MCD) is the major cause of childhood idiopathic nephrotic syndrome, which is characterized by massive proteinuria and debilitating edema. Proteinuria in MCD is typically rapidly reversible with corticosteroid therapy, but relapses are common, and children often have many adverse events from the repeated courses of immunosuppressive therapy. The pathobiology of MCD remains poorly understood. Prior clinical observations suggest that abnormal T-cell function may play a central role in MCD pathogenesis. Based on these observations, we hypothesized that T-cell responses to specific exposures or antigens lead to a clonal expansion of T-cell subsets, a restriction in the T-cell repertoire, and an elaboration of specific circulating factors that trigger disease onset and relapses.

METHODS

To test these hypotheses, we sequenced T-cell receptors in fourteen MCD, four focal segmental glomerulosclerosis (FSGS), and four membranous nephropathy (MN) patients with clinical data and blood samples drawn during active disease and during remission collected by the Nephrotic Syndrome Study Network (NEPTUNE). We calculated several T-cell receptor diversity metrics to assess possible differences between active disease and remission states in paired samples.

RESULTS

Median productive clonality did not differ between MCD active disease (0.0083; range: 0.0042, 0.0397) and remission (0.0088; range: 0.0038, 0.0369). We did not identify dominant clonotypes in MCD active disease, and few clonotypes were shared with FSGS and MN patients.

CONCLUSIONS

While these data do not support an obvious role of the adaptive immune system T-cells in MCD pathogenesis, further study is warranted given the limited sample size. A higher resolution version of the Graphical abstract is available as Supplementary information.

Impact of BCG vaccination on the repertoire of human γδ T cell receptors

Introduction

Tuberculosis (TB) caused by Mycobacterium tuberculosis (Mtb) infection is a serious threat to human health. Vaccination with BCG prevents the development of the most severe forms of TB disease in infants and was recently shown to prevent Mtb infection in previously uninfected adolescents. γδ T cells play a major role in host defense at mucosal sites and are known to respond robustly to mycobacterial infection. However, our understanding of the effects of BCG vaccination on γδ T cell responses is incomplete.

Methods

In this study we performed γδ T cell receptor (TCR) repertoire sequencing of samples provided pre- and post-BCG vaccination from 10 individuals to identify specific receptors and TCR clones that are induced by BCG.

Results

Overall, there was no change in the diversity of γTCR or δTCR clonotypes in post- vs pre-BCG samples. Furthermore, the frequencies of TCR variable and joining region genes were minimally modulated by BCG vaccination at either the γTCR or δTCR loci. However, the γTCR and δTCR repertoires of individuals were highly dynamic; a median of ~1% of γTCR and ~6% of δTCR in the repertoire were found to significantly expand or contract in post- vs pre-BCG comparisons (FDR-q < 0.05). While many of the clonotypes whose frequency changed after BCG vaccination were not shared among multiple individuals in the cohort, several shared (i.e., "public") clonotypes were identified with a consistent increase or decrease in frequency across more than one individual; the degree of sharing of these clonotypes was significantly greater than the minimal sharing that would be expected among γTCR and δTCR repertoires. An in vitro analysis of Mtb antigen-reactive γδ T cells identified clonotypes that were similar or identical to the single-chain γTCRs and δTCRs that changed consistently after BCG vaccination; pairings of γTCRs and δTCRs that increased after BCG vaccination were significantly over-represented among the Mtb-reactive γδ T cells (p = 1.2e-6).

Discussion

These findings generate hypotheses about specific γδTCR clonotypes that may expand in response to BCG vaccination and may recognize Mtb antigens. Future studies are required to validate and characterize these clonotypes, with an aim to better understand the role of γδ T cells in Mtb immunity.

Applying T-classifier, binary classifiers, upon high-throughput TCR sequencing output to identify cytomegalovirus exposure history

With the continuous development of information technology and the running speed of computers, the development of informatization has led to the generation of increasingly more medical data. Solving unmet needs such as employing the constantly developing artificial intelligence technology to medical data and providing support for the medical industry is a hot research topic. Cytomegalovirus (CMV) is a kind of virus that exists widely in nature with strict species specificity, and the infection rate among Chinese adults is more than 95%. Therefore, the detection of CMV is of great importance since the vast majority of infected patients are in a state of invisible infection after the infection, except for a few patients with clinical symptoms. In this study, we present a new method to detect CMV infection status by analyzing high-throughput sequencing results of T cell receptor beta chains (TCRβ). Based on the high-throughput sequencing data of 640 subjects from cohort 1, Fisher's exact test was performed to evaluate the relationship between TCRβ sequences and CMV status. Furthermore, the number of subjects with these correlated sequences to different degrees in cohort 1 and cohort 2 were measured to build binary classifier models to identify whether the subject was CMV positive or negative. We select four binary classification algorithms: logistic regression (LR), support vector machine (SVM), random forest (RF), and linear discriminant analysis (LDA) for side-by-side comparison. According to the performance of different algorithms corresponding to different thresholds, four optimal binary classification algorithm models are obtained. The logistic regression algorithm performs best when Fisher's exact test threshold is 10^-5, and the sensitivity and specificity are 87.5% and 96.88%, respectively. The RF algorithm performs better at the threshold of 10^-5, with a sensitivity of 87.5% and a specificity of 90.63%. The SVM algorithm also achieves high accuracy at the threshold value of 10^-5, with a sensitivity of 85.42% and specificity of 96.88%. The LDA algorithm achieves high accuracy with 95.83% sensitivity and 90.63% specificity when the threshold value is 10^-4. This is probably because the two-dimensional distribution of CMV data samples is linearly separable, and linear division models such as LDA are more effective, while the division effect of nonlinear separable algorithms such as random forest is relatively inaccurate. This new finding may be a potential diagnostic method for CMV and may even be applicable to other viruses, such as the infectious history detection of the new coronavirus.

Time series clustering of T cell subsets dissects heterogeneity in immune reconstitution and clinical outcomes among MUD-HCT patients receiving ATG or PTCy

Introduction

Anti-T-lymphocyte globulin (ATG) or post-transplant cyclophosphamide (PTCy) prevent graft-versus-host disease (GVHD) after hematopoietic cell transplantation (HCT), yet individual patients benefit differentially.

Methods

Given the sparse comparative data on the impact of cellular immune reconstitution in this setting, we studied flow cytometry and clinical outcomes in 339 recipients of 10/10 matched-unrelated donor (MUD) HCT using either ATG (n=304) or PTCy (n=35) for in vivo T cell manipulation along with a haploidentical PTCy control cohort (n=45). Longitudinal cellular immune reconstitution data were analyzed conventionally and with a data science approach using clustering with dynamic time warping to determine the similarity between time-series of T cell subsets.

Results

Consistent with published studies, no significant differences in clinical outcomes were observed at the cohort level between MUD-ATG and MUD-PTCy. However, cellular reconstitution revealed preferences for distinct T cell subpopulations associating with GVHD protection in each setting. Starting early after HCT, MUD-PTCy patients had higher regulatory T cell levels after HCT (p &lt;0.0001), while MUD-ATG patients presented with higher levels of γδ T- or NKT cells (both p &lt;0.0001). Time-series clustering further dissected the patient population’s heterogeneity revealing distinct immune reconstitution clusters. Importantly, it identified phenotypes that reproducibly associated with impaired clinical outcomes within the same in vivo T cell manipulation platform. Exemplarily, patients with lower activated- and αβ T cell counts had significantly higher NRM (p=0.032) and relapse rates (p =0.01).

Discussion

The improved understanding of the heterogeneity of cellular reconstitution in MUD patients with T cell manipulation both at the cohort and individual level may support clinicians in managing HCT complications.

Deep autoregressive generative models capture the intrinsics embedded in T-cell receptor repertoires

T-cell receptors (TCRs) play an essential role in the adaptive immune system. Probabilistic models for TCR repertoires can help decipher the underlying complex sequence patterns and provide novel insights into understanding the adaptive immune system. In this work, we develop TCRpeg, a deep autoregressive generative model to unravel the sequence patterns of TCR repertoires. TCRpeg largely outperforms state-of-the-art methods in estimating the probability distribution of a TCR repertoire, boosting the average accuracy from 0.672 to 0.906 measured by the Pearson correlation coefficient. Furthermore, with promising performance in probability inference, TCRpeg improves on a range of TCR-related tasks: profiling TCR repertoire probabilistically, classifying antigen-specific TCRs, validating previously discovered TCR motifs, generating novel TCRs and augmenting TCR data. Our results and analysis highlight the flexibility and capacity of TCRpeg to extract TCR sequence information, providing a novel approach for deciphering complex immunogenomic repertoires.

mRNA aggregates harness danger response for potent cancer immunotherapy

Messenger RNA (mRNA) has emerged as a remarkable tool for COVID-19 prevention but its use for induction of therapeutic cancer immunotherapy remains limited by poor antigenicity and a regulatory tumor microenvironment (TME). Herein, we develop a facile approach for substantially enhancing immunogenicity of tumor-derived mRNA in lipid-particle (LP) delivery systems. By using mRNA as a molecular bridge with ultrapure liposomes and foregoing helper lipids, we promote the formation of 'onion-like' multi-lamellar RNA-LP aggregates (LPA). Intravenous administration of RNA-LPAs mimics infectious emboli and elicits massive DC/T cell mobilization into lymphoid tissues provoking cancer immunogenicity and mediating rejection of both early and late-stage murine tumor models. Unlike current mRNA vaccine designs that rely on payload packaging into nanoparticle cores for toll-like receptor engagement, RNA-LPAs stimulate intracellular pathogen recognition receptors (RIG-I) and reprogram the TME thus enabling therapeutic T cell activity. RNA-LPAs were safe in acute/chronic murine GLP toxicology studies and immunologically active in client-owned canines with terminal gliomas. In an early phase first-in-human trial for patients with glioblastoma, we show that RNA-LPAs encoding for tumor-associated antigens elicit rapid induction of pro-inflammatory cytokines, mobilization/activation of monocytes and lymphocytes, and expansion of antigen-specific T cell immunity. These data support the use of RNA-LPAs as novel tools to elicit and sustain immune responses against poorly immunogenic tumors.

UcTCRdb: An unconventional T cell receptor sequence database with online analysis functions

Unlike conventional major histocompatibility complex (MHC) class I and II molecules reactive T cells, the unconventional T cell subpopulations recognize various non-polymorphic antigen-presenting molecules and are typically characterized by simplified patterns of T cell receptors (TCRs), rapid effector responses and ‘public’ antigen specificities. Dissecting the recognition patterns of the non-MHC antigens by unconventional TCRs can help us further our understanding of the unconventional T cell immunity. The small size and irregularities of the released unconventional TCR sequences are far from high-quality to support systemic analysis of unconventional TCR repertoire. Here we present UcTCRdb, a database that contains 669,900 unconventional TCRs collected from 34 corresponding studies in humans, mice, and cattle. In UcTCRdb, users can interactively browse TCR features of different unconventional T cell subsets in different species, search and download sequences under different conditions. Additionally, basic and advanced online TCR analysis tools have been integrated into the database, which will facilitate the study of unconventional TCR patterns for users with different backgrounds. UcTCRdb is freely available at http://uctcrdb.cn/.

Potential of TCR sequencing in graft-versus-host disease

Graft-versus-host disease (GvHD) remains one of the major complications following allogeneic haematopoietic stem cell transplantation (allo-HSCT). GvHD can occur in almost every tissue, with the skin, liver, and intestines being the mainly affected organs. T cells are implicated in initiating GvHD. T cells identify a broad range of antigens and mediate the immune response through receptors on their surfaces (T cell receptors, TCRs). The composition of TCRs within a T cell population defines the TCR repertoire of an individual, and this repertoire represents exposure to self and non-self proteins. Monitoring the changes in the TCR repertoire using TCR sequencing can provide an indication of the dynamics of a T cell population. Monitoring the frequency and specificities of specific TCR clonotypes longitudinally in different conditions and specimens (peripheral blood, GvHD-affected tissue samples) can provide insights into factors modulating immune reactions following allogeneic transplantation and will help to understand the underlying mechanisms mediating GvHD. This review provides insights into current studies of the TCR repertoire in GvHD and potential future clinical implications of TCR sequencing.

Risk HLA Variants Affect the T-Cell Repertoire in Multiple Sclerosis

BACKGROUND AND OBJECTIVES

The major histocompatibility complex (MHC) locus has a predominant role in the genetic predisposition to multiple sclerosis (MS), with 32 associations found to be involved. We aimed to investigate the impact of MHC MS-risk alleles on T-cell repertoire in patients with MS.

METHODS

We studied 161 untreated patients with relapsing-remitting MS for whom Class I and II human leukocyte antigen (HLA) alleles were inferred from whole-genome genotyping data, and T-cell receptor (TCR) CDR3 sequences were obtained through next-generation sequencing. T-cell repertoire features including diversity, public clones, and architecture were evaluated.

RESULTS

We identified 5 MS-risk loci associated with TCR diversity: HLA-DRB1*15:01 (7.65 × 10^-3), rs9271366 (1.96 × 10^-3), rs766848979 A (1.89 × 10^-2), rs9277626 (2.95 × 10^-2), and rs11751659 (1.92 × 10^-2), with evidence of expanded clonotypes in carriers of risk alleles. Moreover, HLA-DRB1*15:01 (4.99 × 10^-3), rs9271366 (6.54 × 10^-3), rs1049079 C (4.37 × 10^-2), AA DQΒ1 position -5 L (1.05 × 10^-3), and AA DQΒ1 position 221 Q (9.39 × 10^-4) showed an association with the CDR3 aminoacidic sequence architecture, suggesting an impact on the antigen recognition breadth as well. Evaluating the sharing of clones across MS-risk allele carrier individuals revealed the presence of highly shared clonotypes predicted to target viral antigens, including Epstein-Barr virus.

DISCUSSION

Our study supports the association between MHC-risk alleles and macrofeatures of the T-cell repertoire in the context of MS. Further studies are needed to understand the underlying molecular mechanisms.

Modeling and predicting the overlap of B- and T-cell receptor repertoires in healthy and SARS-CoV-2 infected individuals

Adaptive immunity's success relies on the extraordinary diversity of protein receptors on B and T cell membranes. Despite this diversity, the existence of public receptors shared by many individuals gives hope for developing population-wide vaccines and therapeutics. Using probabilistic modeling, we show many of these public receptors are shared by chance in healthy individuals. This predictable overlap is driven not only by biases in the random generation process of receptors, as previously reported, but also by their common functional selection. However, the model underestimates sharing between repertoires of individuals infected with SARS-CoV-2, suggesting strong specific antigen-driven convergent selection. We exploit this discrepancy to identify COVID-associated receptors, which we validate against datasets of receptors with known viral specificity. We study their properties in terms of sequence features and network organization, and use them to design an accurate diagnostic tool for predicting SARS-CoV-2 status from repertoire data.

Counting is almost all you need

The immune memory repertoire encodes the history of present and past infections and immunological attributes of the individual. As such, multiple methods were proposed to use T-cell receptor (TCR) repertoires to detect disease history. We here show that the counting method outperforms two leading algorithms. We then show that the counting can be further improved using a novel attention model to weigh the different TCRs. The attention model is based on the projection of TCRs using a Variational AutoEncoder (VAE). Both counting and attention algorithms predict better than current leading algorithms whether the host had CMV and its HLA alleles. As an intermediate solution between the complex attention model and the very simple counting model, we propose a new Graph Convolutional Network approach that obtains the accuracy of the attention model and the simplicity of the counting model. The code for the models used in the paper is provided at: https://github.com/louzounlab/CountingIsAlmostAllYouNeed.

Altered somatic hypermutation patterns in COVID-19 patients classifies disease severity

Introduction

The success of the human body in fighting SARS-CoV2 infection relies on lymphocytes and their antigen receptors. Identifying and characterizing clinically relevant receptors is of utmost importance.

Methods

We report here the application of a machine learning approach, utilizing B cell receptor repertoire sequencing data from severely and mildly infected individuals with SARS-CoV2 compared with uninfected controls.

Results

In contrast to previous studies, our approach successfully stratifies non-infected from infected individuals, as well as disease level of severity. The features that drive this classification are based on somatic hypermutation patterns, and point to alterations in the somatic hypermutation process in COVID-19 patients.

Discussion

These features may be used to build and adapt therapeutic strategies to COVID-19, in particular to quantitatively assess potential diagnostic and therapeutic antibodies. These results constitute a proof of concept for future epidemiological challenges.

Dynamic kernel matching for non-conforming data: A case study of T cell receptor datasets

Most statistical classifiers are designed to find patterns in data where numbers fit into rows and columns, like in a spreadsheet, but many kinds of data do not conform to this structure. To uncover patterns in non-conforming data, we describe an approach for modifying established statistical classifiers to handle non-conforming data, which we call dynamic kernel matching (DKM). As examples of non-conforming data, we consider (i) a dataset of T-cell receptor (TCR) sequences labelled by disease antigen and (ii) a dataset of sequenced TCR repertoires labelled by patient cytomegalovirus (CMV) serostatus, anticipating that both datasets contain signatures for diagnosing disease. We successfully fit statistical classifiers augmented with DKM to both datasets and report the performance on holdout data using standard metrics and metrics allowing for indeterminant diagnoses. Finally, we identify the patterns used by our statistical classifiers to generate predictions and show that these patterns agree with observations from experimental studies.

Unique and common TCR repertoire features of Ni2+ -, Co2+ -, and Pd2+ -specific human CD154 + CD4+ T cells

BACKGROUND

Apart from Ni²⁺ , Co²⁺ , and Pd²⁺ ions commonly trigger T cell-mediated allergic contact dermatitis. However, in vitro frequencies of metal-specific T cells and the mechanisms of antigen recognition remain unclear.

METHODS

Here, we utilized a CD154 upregulation assay to quantify Ni²⁺ -, Co²⁺ -, and Pd²⁺ -specific CD4+ T cells in peripheral blood mononuclear cells (PBMC). Involved αβ T cell receptor (TCR) repertoires were analyzed by high-throughput sequencing.

RESULTS

Peripheral blood mononuclear cells incubation with NiSO₄ , CoCl₂ , and PdCl₂ increased frequencies of CD154 + CD4+ memory T cells that peaked at ~400 μM. Activation was TCR-mediated as shown by the metal-specific restimulation of T cell clones. Most abundant were Pd²⁺ -specific T cells (mean 3.5%, n = 19), followed by Co²⁺ - and Ni²⁺ -specific cells (0.6%, n = 18 and 0.3%, n = 20) in both allergic and non-allergic individuals. A strong overrepresentation of the gene segment TRAV9-2 was unique for Ni²⁺ -specific TCR (28% of TCR) while Co²⁺ and Pd²⁺ -specific TCR favorably expressed TRAV2 (8%) and the TRBV4 gene segment family (21%), respectively. As a second, independent mechanism of metal ion recognition, all analyzed metal-specific TCR showed a common overrepresentation of a histidine in the complementarity determining region 3 (CDR3; 15% of α-chains, 34% of β-chains). The positions of the CDR3 histidine among metal-specific TCR mirrored those in random repertoires and were conserved among cross-reactive clonotypes.

CONCLUSIONS

Induced CD154 expression allows a fast and comprehensive detection of Ni²⁺ -, Co²⁺ -, and Pd²⁺ -specific CD4+ T cells. Distinct TCR repertoire features underlie the frequent activation and cross-reactivity of human metal-specific T cells.

T cell receptor repertoires associated with control and disease progression following Mycobacterium tuberculosis infection

Antigen-specific, MHC-restricted αβ T cells are necessary for protective immunity against Mycobacterium tuberculosis, but the ability to broadly study these responses has been limited. In the present study, we used single-cell and bulk T cell receptor (TCR) sequencing and the GLIPH2 algorithm to analyze M. tuberculosis-specific sequences in two longitudinal cohorts, comprising 166 individuals with M. tuberculosis infection who progressed to either tuberculosis (n = 48) or controlled infection (n = 118). We found 24 T cell groups with similar TCR-β sequences, predicted by GLIPH2 to have common TCR specificities, which were associated with control of infection (n = 17), and others that were associated with progression to disease (n = 7). Using a genome-wide M. tuberculosis antigen screen, we identified peptides targeted by T cell similarity groups enriched either in controllers or in progressors. We propose that antigens recognized by T cell similarity groups associated with control of infection can be considered as high-priority targets for future vaccine development.

simAIRR: simulation of adaptive immune repertoires with realistic receptor sequence sharing for benchmarking of immune state prediction methods

BACKGROUND

Machine learning (ML) has gained significant attention for classifying immune states in adaptive immune receptor repertoires (AIRRs) to support the advancement of immunodiagnostics and therapeutics. Simulated data are crucial for the rigorous benchmarking of AIRR-ML methods. Existing approaches to generating synthetic benchmarking datasets result in the generation of naive repertoires missing the key feature of many shared receptor sequences (selected for common antigens) found in antigen-experienced repertoires.

RESULTS

We demonstrate that a common approach to generating simulated AIRR benchmark datasets can introduce biases, which may be exploited for undesired shortcut learning by certain ML methods. To mitigate undesirable access to true signals in simulated AIRR datasets, we devised a simulation strategy (simAIRR) that constructs antigen-experienced-like repertoires with a realistic overlap of receptor sequences. simAIRR can be used for constructing AIRR-level benchmarks based on a range of assumptions (or experimental data sources) for what constitutes receptor-level immune signals. This includes the possibility of making or not making any prior assumptions regarding the similarity or commonality of immune state-associated sequences that will be used as true signals. We demonstrate the real-world realism of our proposed simulation approach by showing that basic ML strategies perform similarly on simAIRR-generated and real-world experimental AIRR datasets.

CONCLUSIONS

This study sheds light on the potential shortcut learning opportunities for ML methods that can arise with the state-of-the-art way of simulating AIRR datasets. simAIRR is available as a Python package: https://github.com/KanduriC/simAIRR.

Progress in kidney transplantation: The role for systems immunology

The development of systems biology represents an immense breakthrough in our ability to perform translational research and deliver personalized and precision medicine. A multidisciplinary approach in combination with use of novel techniques allows for the extraction and analysis of vast quantities of data even from the volume and source limited samples that can be obtained from human subjects. Continued advances in microfluidics, scalability and affordability of sequencing technologies, and development of data analysis tools have made the application of a multi-omics, or systems, approach more accessible for use outside of specialized centers. The study of alloimmune and protective immune responses after solid organ transplant offers innumerable opportunities for a multi-omics approach, however, transplant immunology labs are only just beginning to adopt the systems methodology. In this review, we focus on advances in biological techniques and how they are improving our understanding of the immune system and its interactions, highlighting potential applications in transplant immunology. First, we describe the techniques that are available, with emphasis on major advances that allow for increased scalability. Then, we review initial applications in the field of transplantation with a focus on topics that are nearing clinical integration. Finally, we examine major barriers to adapting these methods and discuss potential future developments.

TCRconv: predicting recognition between T cell receptors and epitopes using contextualized motifs

MOTIVATION

T cells use T cell receptors (TCRs) to recognize small parts of antigens, called epitopes, presented by major histocompatibility complexes. Once an epitope is recognized, an immune response is initiated and T cell activation and proliferation by clonal expansion begin. Clonal populations of T cells with identical TCRs can remain in the body for years, thus forming immunological memory and potentially mappable immunological signatures, which could have implications in clinical applications including infectious diseases, autoimmunity and tumor immunology.

RESULTS

We introduce TCRconv, a deep learning model for predicting recognition between TCRs and epitopes. TCRconv uses a deep protein language model and convolutions to extract contextualized motifs and provides state-of-the-art TCR-epitope prediction accuracy. Using TCR repertoires from COVID-19 patients, we demonstrate that TCRconv can provide insight into T cell dynamics and phenotypes during the disease.

AVAILABILITY AND IMPLEMENTATION

TCRconv is available at https://github.com/emmijokinen/tcrconv.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Control of naive and effector CD4 T cell receptor repertoires by rheumatoid-arthritis-risk HLA alleles

OBJECTIVE

Human Leukocyte Antigen (HLA) alleles regulate susceptibility to rheumatoid arthritis (RA) and immune-mediated diseases. This study aims to elucidate the impact of HLA alleles to T cell subsets.

METHODS

We performed genome-wide and HLA allele association analysis for T cell receptor (TCR) beta chain repertoire in 13 purified T cell subsets from the ImmuNexUT database, consisting of 407 donors with ten immune-mediated diseases and healthy controls.

RESULTS

HLA class II alleles were associated with TRBV gene usage and the public clones of CD4 T cells, while HLA class I alleles were associated with CD8 T cells. RA-risk and immune-mediated diseases-risk HLA alleles were associated with TRBV gene usage of naive and effector CD4 T cell subsets and public clones accumulating in Th17. Clonal diversity was independent of HLA alleles and was correlated with transcriptome changes that reflect TCR signaling.

CONCLUSION

This study revealed in vivo evidence that both HLA alleles and environmental factors shape naive and effector TCR repertoires in RA and immune-mediated diseases patients.

Perturbations of the T-cell immune repertoire in kidney transplant rejection

In this cross-sectional and longitudinal analysis of mapping the T-cell repertoire in kidney transplant recipients, we have investigated and validated T-cell clonality, immune repertoire chronology at rejection, and contemporaneous allograft biopsy quantitative tissue injury, to better understand the pathobiology of acute T-cell fraction, T-cell repertoire and antibody-mediated kidney transplant rejection. To follow the dynamic evolution of T-cell repertoire changes before and after engraftment and during biopsy-confirmed acute rejection, we sequenced 323 peripheral blood samples from 200 unique kidney transplant recipients, with (n=100) and without (n=100) biopsy-confirmed acute rejection. We report that patients who develop acute allograft rejection, have lower (p=0.01) T-cell fraction even before transplantation, followed by its rise after transplantation and at the time of acute rejection accompanied by high TCR repertoire turnover (p=0.004). Acute rejection episodes occurring after the first 6 months post-transplantation, and those with a component of antibody-mediated rejection, had the highest turnover; p=0.0016) of their T-cell repertoire. In conclusion, we validated that detecting repertoire changes in kidney transplantation correlates with post-transplant rejection episodes suggesting that T-cell receptor sequencing may provide recipient pre-transplant and post-transplant predictors of rejection risk.

T cell receptor convergence is an indicator of antigen-specific T cell response in cancer immunotherapies

T cells are potent at eliminating pathogens and playing a crucial role in the adaptive immune response. T cell receptor (TCR) convergence describes T cells that share identical TCRs with the same amino acid sequences but have different DNA sequences due to codon degeneracy. We conducted a systematic investigation of TCR convergence using single-cell immune profiling and bulk TCRβ-sequence (TCR-seq) data obtained from both mouse and human samples and uncovered a strong link between antigen-specificity and convergence. This association was stronger than T cell expansion, a putative indicator of antigen-specific T cells. By using flow-sorted tetramer+ single T cell data, we discovered that convergent T cells were enriched for a neoantigen-specific CD8+ effector phenotype in the tumor microenvironment. Moreover, TCR convergence demonstrated better prediction accuracy for immunotherapy response than the existing TCR repertoire indexes. In conclusion, convergent T cells are likely to be antigen-specific and might be a novel prognostic biomarker for anti-cancer immunotherapy.

Broader Epstein-Barr virus-specific T cell receptor repertoire in patients with multiple sclerosis

Epstein-Barr virus (EBV) infection precedes multiple sclerosis (MS) pathology and cross-reactive antibodies might link EBV infection to CNS autoimmunity. As an altered anti-EBV T cell reaction was suggested in MS, we queried peripheral blood T cell receptor β chain (TCRβ) repertoires of 1,395 MS patients, 887 controls, and 35 monozygotic, MS-discordant twin pairs for multimer-confirmed, viral antigen-specific TCRβ sequences. We detected more MHC-I-restricted EBV-specific TCRβ sequences in MS patients. Differences in genetics or upbringing could be excluded by validation in monozygotic twin pairs discordant for MS. Anti-VLA-4 treatment amplified this observation, while interferon β- or anti-CD20 treatment did not modulate EBV-specific T cell occurrence. In healthy individuals, EBV-specific CD8+ T cells were of an effector-memory phenotype in peripheral blood and cerebrospinal fluid. In MS patients, cerebrospinal fluid also contained EBV-specific central-memory CD8+ T cells, suggesting recent priming. Therefore, MS is not only preceded by EBV infection, but also associated with broader EBV-specific TCR repertoires, consistent with an ongoing anti-EBV immune reaction in MS.

All that glitters is not LGL Leukemia

LGL disorders are rare hematological neoplasias with remarkable phenotypic, genotypic and clinical heterogeneity. Despite these constraints, many achievements have been recently accomplished in understanding the aberrant pathways involved in the LGL leukemogenesis. In particular, compelling evidence implicates STAT signaling as a crucial player of the abnormal cell survival. As interest increases in mapping hematological malignancies by molecular genetics, the relevance of STAT gene mutations in LGL disorders has emerged thanks to their association with discrete clinical features. STAT3 and STAT5b mutations are recognized as the most common gain-of-function genetic lesions up to now identified in T-LGL leukemia (T-LGLL) and are actually regarded as the hallmark of this disorder, also contributing to further refine its subclassification. However, from a clinical perspective, the relationships between T-LGLL and other borderline and overlapping conditions, including reactive cell expansions, clonal hematopoiesis of indeterminate potential (CHIP) and unrelated clonopathies are not fully established, sometimes making the diagnosis of T cell malignancy challenging. In this review specifically focused on the topic of clonality of T-LGL disorders we will discuss the rationale of the appropriate steps to aid in distinguishing LGLL from its mimics, also attempting to provide new clues to stimulate further investigations designed to move this field forward.

Amino acids at position 5 in the peptide/MHC binding region of a public virus-specific TCR are completely inter-changeable without loss of function

Anti-viral T-cell responses are usually directed against a limited set of antigens, but often contain many T cells expressing different T-cell receptors (TCRs). Identical TCRs found within virus-specific T-cell populations in different individuals are known as public TCRs, but also TCRs highly-similar to these public TCRs, with only minor variations in amino acids on specific positions in the Complementary Determining Regions (CDRs), are frequently found. However, the degree of freedom at these positions was not clear. In this study, we used the HLA-A*02:01-restricted EBV-LMP2^FLY -specific public TCR as model and modified the highly-variable position 5 of the CDR3β sequence with all 20 amino acids. Our results demonstrate that amino acids at this particular position in the CDR3β region of this TCR are completely inter-changeable, without loss of TCR function. We show that the inability to find certain variants in individuals is explained by their lower recombination probability rather than by steric hindrance.

pyTCR: A comprehensive and scalable solution for TCR-Seq data analysis to facilitate reproducibility and rigor of immunogenomics research

T cell receptor (TCR) studies have grown substantially with the advancement in the sequencing techniques of T cell receptor repertoire sequencing (TCR-Seq). The analysis of the TCR-Seq data requires computational skills to run the computational analysis of TCR repertoire tools. However biomedical researchers with limited computational backgrounds face numerous obstacles to properly and efficiently utilizing bioinformatics tools for analyzing TCR-Seq data. Here we report pyTCR, a computational notebook-based solution for comprehensive and scalable TCR-Seq data analysis. Computational notebooks, which combine code, calculations, and visualization, are able to provide users with a high level of flexibility and transparency for the analysis. Additionally, computational notebooks are demonstrated to be user-friendly and suitable for researchers with limited computational skills. Our tool has a rich set of functionalities including various TCR metrics, statistical analysis, and customizable visualizations. The application of pyTCR on large and diverse TCR-Seq datasets will enable the effective analysis of large-scale TCR-Seq data with flexibility, and eventually facilitate new discoveries.

TCR repertoire profiling revealed antigen-driven CD8+ T cell clonal groups shared in synovial fluid of patients with spondyloarthritis

Spondyloarthritis (SpA) comprises a number of inflammatory rheumatic diseases with overlapping clinical manifestations. Strong association with several HLA-I alleles and T cell infiltration into an inflamed joint suggest involvement of T cells in SpA pathogenesis. In this study, we performed high-throughput T cell repertoire profiling of synovial fluid (SF) and peripheral blood (PB) samples collected from a large cohort of SpA patients. We showed that synovial fluid is enriched with expanded T cell clones that are shared between patients with similar HLA genotypes and persist during recurrent synovitis. Using an algorithm for identification of TCRs involved in immune response we discovered several antigen-driven CD8+ clonal groups associated with risk HLA-B*27 or HLA-B*38 alleles. We further show that these clonal groups were enriched in SF and had higher frequency in PB of SpA patients vs healthy donors, implying their relevance to SpA pathogenesis. Several of the groups were shared among patients with different SpAs that suggests a common immunopathological mechanism of the diseases. In summary, our results provide evidence for the role of specific CD8+ T cell clones in pathogenesis of SpA.

Perturbations in the T cell receptor β repertoire during malaria infection in children: A preliminary study

The changes occurring in the T cell repertoire during clinical malaria infection in children remain unknown. In this study, we undertook the first detailed comparative study of the T cell repertoire in African children with and without clinical malaria to test the hypothesis that clonotypic expansions that occur during P. falciparum infection will contribute to the generation of a T cell repertoire that is unique to each disease state. We profiled the complementarity-determining region 3 (CDR3) of the TCRβ chain sequences from children with Plasmodium falciparum infections (asymptomatic, uncomplicated and severe malaria) and compared these with sequences from healthy children. Interestingly, we discovered that children with symptomatic malaria have a lower TCR diversity and frequency of shared (or “public”) TCR sequences compared to asymptomatic children. Also, TCR diversity was inversely associated with parasitemia. Furthermore, by clustering TCR sequences based on their predicted antigen specificities, we identified a specificity cluster, with a 4-mer amino acid motif, that is overrepresented in the asymptomatic group compared to the diseased groups. Further investigations into this finding may help in delineating important antigenic targets for vaccine and therapeutic development. The results show that the T cell repertoire in children is altered during malaria, suggesting that exposure to P. falciparum antigens disrupts the adaptive immune response, which is an underlying feature of the disease.

Evolution and modulation of antigen-specific T cell responses in melanoma patients

Analyzing antigen-specific T cell responses at scale has been challenging. Here, we analyze three types of T cell receptor (TCR) repertoire data (antigen-specific TCRs, TCR-repertoire, and single-cell RNA + TCRαβ-sequencing data) from 515 patients with primary or metastatic melanoma and compare it to 783 healthy controls. Although melanoma-associated antigen (MAA) -specific TCRs are restricted to individuals, they share sequence similarities that allow us to build classifiers for predicting anti-MAA T cells. The frequency of anti-MAA T cells distinguishes melanoma patients from healthy and predicts metastatic recurrence from primary melanoma. Anti-MAA T cells have stem-like properties and frequent interactions with regulatory T cells and tumor cells via Galectin9-TIM3 and PVR-TIGIT -axes, respectively. In the responding patients, the number of expanded anti-MAA clones are higher after the anti-PD1(+anti-CTLA4) therapy and the exhaustion phenotype is rescued. Our systems immunology approach paves the way for understanding antigen-specific responses in human disorders.

Previous studies have characterized the diversity and dynamics of the T cell receptor (TCR) repertoire in patients with solid cancer. Here, by analyzing TCR repertoire data from multiple datasets, the authors report that melanoma-associated antigen-specific TCRs can be used to separate metastatic melanoma patients from healthy controls and to follow anti-tumor responses in patients treated with immunotherapy.

T cell Repertoire Profiling and the Mechanism by which HLA-B27 Causes Ankylosing Spondylitis

Purpose of Review

Ankylosing spondylitis (AS) is strongly associated with the HLA-B27 gene. The canonical function of HLA-B27 is to present antigenic peptides to CD8 lymphocytes, leading to adaptive immune responses. The ‘arthritogenic peptide’ theory as to the mechanism by which HLA-B27 induces ankylosing spondylitis proposes that HLA-B27 presents peptides derived from exogenous sources such as bacteria to CD8 lymphocytes, which subsequently cross-react with antigens at the site of inflammation of the disease, causing inflammation. This review describes findings of studies in AS involving profiling of T cell expansions and discusses future research opportunities based on these findings.

Recent Findings

Consistent with this theory, there is an expanding body of data showing that expansion of a restricted pool of CD8 lymphocytes is found in most AS patients yet only in a small proportion of healthy HLA-B27 carriers.

Summary

These exciting findings strongly support the theory that AS is driven by presentation of antigenic peptides to the adaptive immune system by HLA-B27. They point to new potential approaches to identify the exogenous and endogenous antigens involved and to potential therapies for the disease.

Attenuated T cell activation and rearrangement of T cell receptor β repertoire in silica nanoparticle-induced pulmonary fibrosis of mice

Silica nanoparticles (SiNPs) cause pulmonary fibrosis through a complex immune response, but the underlying mechanisms by which SiNPs interact with T cells and affect their functions remain unclear. The T cell receptor (TCR) repertoire is closely related to T cell activation and proliferation and mediates innate and adaptive immunity. High-throughput sequencing of the TCR enables comprehensive monitoring of the immune microenvironment. Here, the role of the TCRβ repertoire was explored using a mouse model of SiNP-induced pulmonary fibrosis and a co-culture of RAW264.7 and CD4⁺ T cells. Our results demonstrated increased TCRβ expression and decreased CD25 and CD69 expression in CD4⁺ T cells from peripheral blood and lung collected 14 days after the induction of pulmonary fibrosis by SiNPs. Simultaneously, SiNPs significantly decreased CD25 and CD69 expression in CD4⁺ T cells in vitro via RAW264.7 cell presentation. Mechanistically, pLCK and pZap70 expression, involved in mediating T cell activation, were also decreased in the lung of mice with SiNP-induced pulmonary fibrosis. Furthermore, the profile of the TCRβ repertoire in mice with SiNP-induced pulmonary fibrosis showed that SiNPs markedly altered the usage of V genes, VJ gene combinations, and CDR3 amino acids in lung tissue. Collectively, our data suggested that SiNPs could interfere with T cell activation by macrophage presentation via the LCK/Zap70 pathway and rearrange the TCRβ repertoire for adaptive immunity and the pulmonary microenvironment.

Repertoire-scale measures of antigen binding

Antibodies and T cell receptors (TCRs) are the fundamental building blocks of adaptive immunity. Repertoire-scale functionality derives from their epitope-binding properties, just as macroscopic properties like temperature derive from microscopic molecular properties. However, most approaches to repertoire-scale measurement, including sequence diversity and entropy, are not based on antibody or TCR function in this way. Thus, they potentially overlook key features of immunological function. Here we present a framework that describes repertoires in terms of the epitope-binding properties of their constituent antibodies and TCRs, based on analysis of thousands of antibody-antigen and TCR-peptide-major-histocompatibility-complex binding interactions and over 400 high-throughput repertoires. We show that repertoires consist of loose overlapping classes of antibodies and TCRs with similar binding properties. We demonstrate the potential of this framework to distinguish specific responses vs. bystander activation in influenza vaccinees, stratify cytomegalovirus (CMV)-infected cohorts, and identify potential immunological "super-agers." Classes add a valuable dimension to the assessment of immune function.

Reference-based comparison of adaptive immune receptor repertoires

B and T cell receptor (immune) repertoires can represent an individual's immune history. While current repertoire analysis methods aim to discriminate between health and disease states, they are typically based on only a limited number of parameters. Here, we introduce immuneREF: a quantitative multidimensional measure of adaptive immune repertoire (and transcriptome) similarity that allows interpretation of immune repertoire variation by relying on both repertoire features and cross-referencing of simulated and experimental datasets. To quantify immune repertoire similarity landscapes across health and disease, we applied immuneREF to >2,400 datasets from individuals with varying immune states (healthy, [autoimmune] disease, and infection). We discovered, in contrast to the current paradigm, that blood-derived immune repertoires of healthy and diseased individuals are highly similar for certain immune states, suggesting that repertoire changes to immune perturbations are less pronounced than previously thought. In conclusion, immuneREF enables the population-wide study of adaptive immune response similarity across immune states.

Resolving SARS-CoV-2 CD4+ T cell specificity via reverse epitope discovery

The current strategy to detect immunodominant T cell responses focuses on the antigen, employing large peptide pools to screen for functional cell activation. However, these approaches are labor and sample intensive and scale poorly with increasing size of the pathogen peptidome. T cell receptors (TCRs) recognizing the same epitope frequently have highly similar sequences, and thus, the presence of large sequence similarity clusters in the TCR repertoire likely identify the most public and immunodominant responses. Here, we perform a meta-analysis of large, publicly available single-cell and bulk TCR datasets from severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)-infected individuals to identify public CD4+ responses. We report more than 1,200 αβTCRs forming six prominent similarity clusters and validate histocompatibility leukocyte antigen (HLA) restriction and epitope specificity predictions for five clusters using transgenic T cell lines. Collectively, these data provide information on immunodominant CD4+ T cell responses to SARS-CoV-2 and demonstrate the utility of the reverse epitope discovery approach.

Immunocompromised host section: Adoptive T-cell therapy for dsDNA viruses in allogeneic hematopoietic cell transplant recipients

PURPOSE OF REVIEW

Double-stranded DNA (dsDNA) viruses remain important causes of morbidity and mortality after allogeneic hematopoietic cell transplantation (HCT). As treatment options are limited, adoptive therapy with virus-specific T cells (VST) is promising in restoring immunity and thereby preventing and treating virus infections. Here we review current evidence and recent advances in the field of VST for dsDNA viruses in allogeneic HCT recipients.

RECENT FINDINGS

Four different protocols for VST generation are currently used in clinical trials, and various products including multivirus-specific and off-the-shelf products are under investigation for prophylaxis, preemptive therapy or treatment. Data from nearly 1400 dsDNA-VST applications in allogeneic HCT patients have been published and demonstrated its safety. Although Epstein-Barr virus, cytomegalovirus, and adenovirus-specific T-cell therapy studies have predominated over the past 25 years, additional human herpes viruses were added to multivirus-specific T cells over the last decade and clinical evidence for polyomavirus-specific VST has just recently emerged. Response rates of around 70-80% have been reported, but cautious interpretation is warranted as data are predominantly from phase 1/2 studies and clinical efficacy needs to be confirmed in phase 3 studies.

SUMMARY

Investigation on the 'ideal' composition of VST is ongoing. Several products recently entered phase 3 trials and may allow widespread clinical use in the near future.

Two types of human TCR differentially regulate reactivity to self and non-self antigens

Based on analyses of TCR sequences from over 1,000 individuals, we report that the TCR repertoire is composed of two ontogenically and functionally distinct types of TCRs. Their production is regulated by variations in thymic output and terminal deoxynucleotidyl transferase (TDT) activity. Neonatal TCRs derived from TDT-negative progenitors persist throughout life, are highly shared among subjects, and are reported as disease-associated. Thus, 10%–30% of most frequent cord blood TCRs are associated with common pathogens and autoantigens. TDT-dependent TCRs present distinct structural features and are less shared among subjects. TDT-dependent TCRs are produced in maximal numbers during infancy when thymic output and TDT activity reach a summit, are more abundant in subjects with AIRE mutations, and seem to play a dominant role in graft-versus-host disease. Factors decreasing thymic output (age, male sex) negatively impact TCR diversity. Males compensate for their lower repertoire diversity via hyperexpansion of selected TCR clonotypes.

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Over 10⁸ TCR CDR3 sequences from ∼10³ individuals and 7 cohorts were analyzed

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The TCR repertoire is composed of two layers: neonatal and TDT-dependent layer

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∼70% of frequent cord blood TCRs are associated with common pathogens

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Acute graft-vs-host disease correlates with a high proportion of TDT-dependent TCRs

Comparative Study of Repertoire Classification Methods Reveals Data Efficiency of k-mer Feature Extraction

The repertoire of T cell receptors encodes various types of immunological information. Machine learning is indispensable for decoding such information from repertoire datasets measured by next-generation sequencing (NGS). In particular, the classification of repertoires is the most basic task, which is relevant for a variety of scientific and clinical problems. Supported by the recent appearance of large datasets, efficient but data-expensive methods have been proposed. However, it is unclear whether they can work efficiently when the available sample size is severely restricted as in practical situations. In this study, we demonstrate that their performances can be impaired substantially below critical sample sizes. To complement this drawback, we propose MotifBoost, which exploits the information of short k-mer motifs of TCRs. MotifBoost can perform the classification as efficiently as a deep learning method on large datasets while providing more stable and reliable results on small datasets. We tested MotifBoost on the four small datasets which consist of various conditions such as Cytomegalovirus (CMV), HIV, α-chain, β-chain and it consistently preserved the stability. We also clarify that the robustness of MotifBoost can be attributed to the efficiency of k-mer motifs as representation features of repertoires. Finally, by comparing the predictions of these methods, we show that the whole sequence identity and sequence motifs encode partially different information and that a combination of such complementary information is necessary for further development of repertoire analysis.

CompAIRR: ultra-fast comparison of adaptive immune receptor repertoires by exact and approximate sequence matching

MOTIVATION

Adaptive immune receptor (AIR) repertoires (AIRRs) record past immune encounters with exquisite specificity. Therefore, identifying identical or similar AIR sequences across individuals is a key step in AIRR analysis for revealing convergent immune response patterns that may be exploited for diagnostics and therapy. Existing methods for quantifying AIRR overlap scale poorly with increasing dataset numbers and sizes. To address this limitation, we developed CompAIRR, which enables ultra-fast computation of AIRR overlap, based on either exact or approximate sequence matching.

RESULTS

CompAIRR improves computational speed 1000-fold relative to the state of the art and uses only one-third of the memory: on the same machine, the exact pairwise AIRR overlap of 104 AIRRs with 105 sequences is found in ∼17 min, while the fastest alternative tool requires 10 days. CompAIRR has been integrated with the machine learning ecosystem immuneML to speed up commonly used AIRR-based machine learning applications.

AVAILABILITY AND IMPLEMENTATION

CompAIRR code and documentation are available at https://github.com/uio-bmi/compairr. Docker images are available at https://hub.docker.com/r/torognes/compairr. The code to replicate the synthetic datasets, scripts for benchmarking and creating figures, and all raw data underlying the figures are available at https://github.com/uio-bmi/compairr-benchmarking.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.