Idiotope-Driven T-Cell/B-Cell Collaboration-Based T-Cell Epitope Prediction Using B-Cell Receptor Repertoire Sequences in Infectious Diseases.

T-cell recognition of antigen epitopes is a crucial step for the induction of adaptive immune responses, and the identification of such T-cell epitopes is, therefore, important for understanding diverse immune responses and controlling T-cell immunity. A number of bioinformatic tools exist that predict T-cell epitopes; however, many of these methods highly rely on evaluating conventional peptide presentation by major histocompatibility complex (MHC) molecules, but they ignore epitope sequences recognized by T-cell receptor (TCR). Immunogenic determinant idiotopes are present on the variable regions of immunoglobulin molecules expressed on and secreted by B-cells. In idiotope-driven T-cell/B-cell collaboration, B-cells present the idiotopes on MHC molecules for recognition by idiotope-specific T-cells. According to the idiotype network theory formulated by Niels Jerne, such idiotopes found on anti-idiotypic antibodies exhibit molecular mimicry of antigens. Here, by combining these concepts and defining the patterns of TCR-recognized epitope motifs (TREMs), we developed a T-cell epitope prediction method that identifies T-cell epitopes derived from antigen proteins by analyzing B-cell receptor (BCR) sequences. This method allowed us to identify T-cell epitopes that contain the same TREM patterns between BCR and viral antigen sequences in two different infectious diseases caused by dengue virus and SARS-CoV-2 infection. The identified epitopes were among the T-cell epitopes detected in previous studies, and T-cell stimulatory immunogenicity was confirmed. Thus, our data support this method as a powerful tool for the discovery of T-cell epitopes from BCR sequences.

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 (TCRß). Based on the high-throughput sequencing data of 640 subjects from cohort 1, Fisher's exact test was performed to evaluate the relationship between TCRß 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.

SARS-CoV-2 epitope-specific T cells: Immunity response feature, TCR repertoire characteristics and cross-reactivity.

The devastating COVID-19 pandemic caused by SARS-CoV-2 and multiple variants or subvariants remains an ongoing global challenge. SARS-CoV-2-specific T cell responses play a critical role in early virus clearance, disease severity control, limiting the viral transmission and underpinning COVID-19 vaccine efficacy. Studies estimated broad and robust T cell responses in each individual recognized at least 30 to 40 SARS-CoV-2 antigen epitopes and associated with COVID-19 clinical outcome. Several key immunodominant viral proteome epitopes, including S protein- and non-S protein-derived epitopes, may primarily induce potent and long-lasting antiviral protective effects. In this review, we summarized the immune response features of immunodominant epitope-specific T cells targeting different SRAS-CoV-2 proteome structures after infection and vaccination, including abundance, magnitude, frequency, phenotypic features and response kinetics. Further, we analyzed the epitopes immunodominance hierarchy in combination with multiple epitope-specific T cell attributes and TCR repertoires characteristics, and discussed the significant implications of cross-reactive T cells toward HCoVs, SRAS-CoV-2 and variants of concern, especially Omicron. This review may be essential for mapping the landscape of T cell responses toward SARS-CoV-2 and optimizing the current vaccine strategy.

Hybrid and Traditional Cardiac Rehabilitation in a Rural Area: A RETROSPECTIVE STUDY.

PURPOSE: Cardiac rehabilitation is a prescribed exercise intervention that reduces cardiovascular mortality, secondary events, and hospitalizations. Hybrid cardiac rehabilitation (HBCR) is an alternative method that overcomes barriers to participation, such as travel distance and transportation issues. To date, comparisons of HBCR and traditional cardiac rehabilitation (TCR) are limited to randomized controlled trials, which may influence outcomes due to supervision associated with clinical research. Coincidental to the COVID-19 pandemic, we investigated HBCR effectiveness (peak metabolic equivalents [peak METs]), resting heart rate (RHR), resting systolic (SBP) and diastolic blood pressure (DBP), body mass index (BMI), and depression outcomes (Patient Health Questionnaire-9 [PHQ-9]). METHODS: Via retrospective analysis, TCR and HBCR were examined during the COVID-19 pandemic (October 1, 2020, and March 31, 2022). Key dependent variables were quantified at baseline (pre) and discharge (post). Completion was determined by participation in 18 monitored TCR exercise sessions and four monitored HBCR exercise sessions. RESULTS: Peak METs increased at post-TCR and HBCR ( P < .001); however, TCR resulted in greater improvements ( P = .034). The PHQ-9 scores were decreased in all groups ( P < .001), while post-SBP and BMI did not improve (SBP: P = .185, BMI: P = .355). Post-DBP and RHR increased (DBP: P = .003, RHR: P = .032), although associations between intervention and program completion were not observed ( P = .172). CONCLUSIONS: Peak METs and depression metric outcomes (PHQ-9) improved with TCR and HBCR. Improvements in exercise capacity were greater with TCR; however, HBCR did not produce inferior results by comparison, an outcome that may have been essential during the first 18 mo of the COVID-19 pandemic.

Persistent SARS-CoV-2-specific immune defects in kidney transplant recipients following third mRNA vaccine dose.

Kidney transplant recipients (KTRs) show poorer response to SARS-CoV-2 mRNA vaccination, yet response patterns and mechanistic drivers following third doses are ill-defined. We administered third monovalent mRNA vaccines to n = 81 KTRs with negative or low-titer anti-receptor binding domain (RBD) antibody (n = 39 anti-RBDNEG; n = 42 anti-RBDLO), compared with healthy controls (HCs, n = 19), measuring anti-RBD, Omicron neutralization, spike-specific CD8+%, and SARS-CoV-2-reactive T cell receptor (TCR) repertoires. By day 30, 44% anti-RBDNEG remained seronegative; 5% KTRs developed BA.5 neutralization (vs 68% HCs, P < .001). Day 30 spike-specific CD8+% was negative in 91% KTRs (vs 20% HCs; P = .07), without correlation to anti-RBD (rs = 0.17). Day 30 SARS-CoV-2-reactive TCR repertoires were detected in 52% KTRs vs 74% HCs (P = .11). Spike-specific CD4+ TCR expansion was similar between KTRs and HCs, yet KTR CD8+ TCR depth was 7.6-fold lower (P = .001). Global negative response was seen in 7% KTRs, associated with high-dose MMF (P = .037); 44% showed global positive response. Of the KTRs, 16% experienced breakthrough infections, with 2 hospitalizations; prebreakthrough variant neutralization was poor. Absent neutralizing and CD8+ responses in KTRs indicate vulnerability to COVID-19 despite 3-dose mRNA vaccination. Lack of neutralization despite CD4+ expansion suggests B cell dysfunction and/or ineffective T cell help. Development of more effective KTR vaccine strategies is critical. (NCT04969263).

Primary ChAdOx1 vaccination does not reactivate pre-existing, cross-reactive immunity.

Currently available COVID-19 vaccines include inactivated virus, live attenuated virus, mRNA-based, viral vectored and adjuvanted protein-subunit-based vaccines. All of them contain the spike glycoprotein as the main immunogen and result in reduced disease severity upon SARS-CoV-2 infection. While we and others have shown that mRNA-based vaccination reactivates pre-existing, cross-reactive immunity, the effect of vector vaccines in this regard is unknown. Here, we studied cellular and humoral responses in heterologous adenovirus-vector-based ChAdOx1 nCOV-19 (AZ; Vaxzeria, AstraZeneca) and mRNA-based BNT162b2 (BNT; Comirnaty, BioNTech/Pfizer) vaccination and compared it to a homologous BNT vaccination regimen. AZ primary vaccination did not lead to measurable reactivation of cross-reactive cellular and humoral immunity compared to BNT primary vaccination. Moreover, humoral immunity induced by primary vaccination with AZ displayed differences in linear spike peptide epitope coverage and a lack of anti-S2 IgG antibodies. Contrary to primary AZ vaccination, secondary vaccination with BNT reactivated pre-existing, cross-reactive immunity, comparable to homologous primary and secondary mRNA vaccination. While induced anti-S1 IgG antibody titers were higher after heterologous vaccination, induced CD4+ T cell responses were highest in homologous vaccinated. However, the overall TCR repertoire breadth was comparable between heterologous AZ-BNT-vaccinated and homologous BNT-BNT-vaccinated individuals, matching TCR repertoire breadths after SARS-CoV-2 infection, too. The reasons why AZ and BNT primary vaccination elicits different immune response patterns to essentially the same antigen, and the associated benefits and risks, need further investigation to inform vaccine and vaccination schedule development.

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.

Case report: Immunological characteristics of de novo ulcerative colitis in a child post COVID-19.

The pathological mechanisms of de novo inflammatory bowel disease (IBD) following SARS-CoV-2 infection are unknown. However, cases of coexisting IBD and multisystem inflammatory syndrome in children (MIS-C), which occurs 2-6 weeks after SARS-CoV-2 infection, have been reported, suggesting a shared underlying dysfunction of immune responses. Herein, we conducted the immunological analyses of a Japanese patient with de novo ulcerative colitis following SARS-CoV-2 infection based on the pathological hypothesis of MIS-C. Her serum level of lipopolysaccharide-binding protein, a microbial translocation marker, was elevated with T cell activation and skewed T cell receptor repertoire. The dynamics of activated CD8+ T cells, including T cells expressing the gut-homing marker α4ß7, and serum anti-SARS-CoV-2 spike IgG antibody titer reflected her clinical symptoms. These findings suggest that SARS-CoV-2 infection may trigger the de novo occurrence of ulcerative colitis by impairing intestinal barrier function, T cell activation with a skewed T cell receptor repertoire, and increasing levels of anti-SARS-CoV-2 spike IgG antibodies. Further research is needed to clarify the association between the functional role of the SARS-CoV-2 spike protein as a superantigen and ulcerative colitis.

An optimized flow cytometry protocol for simultaneous detection of T cell activation induced markers and intracellular cytokines: Application to SARS-CoV-2 immune individuals.

Antigen (ag)-specific T cell analysis is an important step for investigation of cellular immunity in many settings, such as infectious diseases, cancer and vaccines. Multiparameter flow cytometry has advantages in studying both the rarity and heterogeneity of these cells. In the cellular immunologist's toolbox, the expression of activation-induced markers (AIM) following antigen exposure has made possible the study and sorting of ag-specific T cells without using human leukocyte antigen (HLA)-multimers. In parallel, assessing the cytokine profile of responding T cells would support a more comprehensive description of the ongoing immune response by providing information related to cell function, such as polarization and effector activity. Here, a method and flow cytometry panel were optimized to combine the detection of activated CD4+ and CD8+ T cells in a TCR-dependent manner with the evaluation of cytokine production by intracellular staining, without affecting the positivity of activation markers. In particular, the expression of CD134 (OX40) and CD69 have been tested in conjunction with intracellular (ic) CD137 (4-1BB) to detect SARS-CoV-2 Spike protein-specific activated T cells. In our setting, CD134 provided minimal contribution to detect the pool of AIM+ T cells, whereas a key role was described for ic-CD69 which was co-expressed with ic-CD137 in both CD4+ and CD8+ lymphocytes. Moreover, the analysis of TCR-triggered cytokine-producing T cells (IFNγ, TNFα and IL-2 were assessed) further confirmed the capacity of ic-CD69 to identify functionally responsive antigen-specific T cells which were often largely negative or weakly positive for CD134 expression. In parallel, the use of CD45RA, CCR7 and CXCR5 allowed us to describe the T cell matuarion curve and detect T follicular helper (Tfh) CD4+ cells, including the antigen specific activated subsets. In conclusion, we optimized a method and flow cytometry panel combining assessment of activation induced markers and intracellular cytokines that will be useful for measuring TCR stimulation-dependent activation of CD4+ and CD8+ T cells.

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 αßTCRs 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.

Structural insights into protection against a SARS-CoV-2 spike variant by T cell receptor diversity.

T cells play a crucial role in combatting SARS-CoV-2 and forming long-term memory responses to this coronavirus. The emergence of SARS-CoV-2 variants that can evade T cell immunity has raised concerns about vaccine efficacy and the risk of reinfection. Some SARS-CoV-2 T cell epitopes elicit clonally restricted CD8+ T cell responses characterized by T cell receptors (TCRs) that lack structural diversity. Mutations in such epitopes can lead to loss of recognition by most T cells specific for that epitope, facilitating viral escape. Here, we studied an HLA-A2-restricted spike protein epitope (RLQ) that elicits CD8+ T cell responses in COVID-19 convalescent patients characterized by highly diverse TCRs. We previously reported the structure of an RLQ-specific TCR (RLQ3) with greatly reduced recognition of the most common natural variant of the RLQ epitope (T1006I). Opposite to RLQ3, TCR RLQ7 recognizes T1006I with even higher functional avidity than the WT epitope. To explain the ability of RLQ7, but not RLQ3, to tolerate the T1006I mutation, we determined structures of RLQ7 bound to RLQ-HLA-A2 and T1006I-HLA-A2. These complexes show that there are multiple structural solutions to recognizing RLQ and thereby generating a clonally diverse T cell response to this epitope that assures protection against viral escape and T cell clonal loss.

T Cell Receptor Sequences Amplified during Severe COVID-19 and Multisystem Inflammatory Syndrome in Children Mimic SARS-CoV-2, Its Bacterial Co-Infections and Host Autoantigens.

Published hypervariable region V-beta T cell receptor (TCR) sequences were collected from people with severe COVID-19 characterized by having various autoimmune complications, including blood coagulopathies and cardiac autoimmunity, as well as from patients diagnosed with the Kawasaki disease (KD)-like multisystem inflammatory syndrome in children (MIS-C). These were compared with comparable published v-beta TCR sequences from people diagnosed with KD and from healthy individuals. Since TCR V-beta sequences are supposed to be complementary to antigens that induce clonal expansion, it was surprising that only a quarter of the TCR sequences derived from severe COVID-19 and MIS-C patients mimicked SARS-CoV-2 proteins. Thirty percent of the KD-derived TCR mimicked coronaviruses other than SARS-CoV-2. In contrast, only three percent of the TCR sequences from healthy individuals and those diagnosed with autoimmune myocarditis displayed similarities to any coronavirus. In each disease, significant increases were found in the amount of TCRs from healthy individuals mimicking specific bacterial co-infections (especially Enterococcus faecium, Staphylococcal and Streptococcal antigens) and host autoantigens targeted by autoimmune diseases (especially myosin, collagen, phospholipid-associated proteins, and blood coagulation proteins). Theoretical explanations for these surprising observations and implications to unravel the causes of autoimmune diseases are explored.

Racial and ethnic disparities in access to primary care during COVID-19.

Context: Early evidence suggests that many patients chose to forgo or delay necessary medical care during the COVID-19 pandemic. Existing and well-documented racial and ethnic disparities in access to care were exacerbated by the pandemic for many reasons, potentially including the additional barriers involved in a rapid shift to telehealth for certain groups of patients. Objectives: 1) Examine changes in primary care visit volume and telehealth during the COVID-19 pandemic. 2) Test for racial and ethnic differences in primary care in-person and telehealth visits during the pandemic relative to pre-pandemic levels. Study design: Longitudinal. Datasets: EHR data including patient visits, procedures, and demographics captured in the American Board of Family Medicine's PRIME Registry. Population studied: 2,966,859 patients seeing 1,477 primary care clinicians enrolled in the PRIME Registry. Outcome measures: 7-day average of weekly visits per clinician, both in-person and telehealth, tracking trends in the volume of care provided before and during the pandemic by patient race/ethnicity. We defined telehealth conversion ratio (TCR) as the number of telehealth visits during the pandemic divided by the total number of pre-pandemic visits. We calculated TCR and visit volume changes from March 15 through the end of 2020 relative to the same period in 2019. Results: During the pandemic we observed decreases of 12% and 22% in the average number of total and in-person visits, respectively, as well as a 10% TCR. Total visits reached a nadir in April 2020 with a 29% decrease from the same point in 2019. Telehealth visits peaked the following week with 23% of that week's total visits, and 139 times more than 2019. Total visits decreased and telehealth visits increased for patients of all races/ethnicities. The magnitude of these changes differed, with Black (5% decline, 15% in-person decline, 10% TCR) and Hispanic (9%, 24%, 15%) patients seeing less of a decrease in total visits than White (12%, 21%, 9%) and Asian (16%, 30%, 14%) patients. Conclusion: Declines in primary care visits during the pandemic were partially offset by an increase in telehealth use. Utilization in our sample suggests less decline in Black and Hispanic patient primary care utilization during the pandemic than expected, in contrast to Asian patients, who demonstrated the largest declines. This metric and these results are novel and foundational for ongoing & further study using other data sources.

Multimodal single-cell analyses of peripheral blood mononuclear cells of COVID-19 patients in Japan.

SARS-CoV-2 continues to spread worldwide. Patients with COVID-19 show distinct clinical symptoms. Although many studies have reported various causes for the diversity of symptoms, the underlying mechanisms are not fully understood. Peripheral blood mononuclear cells from COVID-19 patients were collected longitudinally, and single-cell transcriptome and T cell receptor repertoire analysis was performed. Comparison of molecular features and patients' clinical information revealed that the proportions of cells present, and gene expression profiles differed significantly between mild and severe cases; although even among severe cases, substantial differences were observed among the patients. In one severely-infected elderly patient, an effective antibody response seemed to have failed, which may have caused prolonged viral clearance. Naïve T cell depletion, low T cell receptor repertoire diversity, and aberrant hyperactivation of most immune cell subsets were observed during the acute phase in this patient. Through this study, we provided a better understanding of the diversity of immune landscapes and responses. The information obtained from this study can help medical professionals develop personalized optimal clinical treatment strategies for COVID-19.

Machine learning identifies T cell receptor repertoire signatures associated with COVID-19 severity.

T cell receptor (TCR) repertoires are critical for antiviral immunity. Determining the TCR repertoire composition, diversity, and dynamics and how they change during viral infection can inform the molecular specificity of host responses to viruses such as SARS-CoV-2. To determine signatures associated with COVID-19 disease severity, here we perform a large-scale analysis of over 4.7 billion sequences across 2130 TCR repertoires from COVID-19 patients and healthy donors. TCR repertoire analyses from these data identify and characterize convergent COVID-19-associated CDR3 gene usages, specificity groups, and sequence patterns. Here we show that T cell clonal expansion is associated with the upregulation of T cell effector function, TCR signaling, NF-kB signaling, and interferon-gamma signaling pathways. We also demonstrate that machine learning approaches accurately predict COVID-19 infection based on TCR sequence features, with certain high-power models reaching near-perfect AUROC scores. These analyses provide a systems immunology view of T cell adaptive immune responses to COVID-19.

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.

T cell repertoire profiling in allografts and native tissues in recipients with COVID-19 after solid organ transplantation: Insight into T cell-mediated allograft protection from viral infection.

Introduction: The effects of the SARS-CoV-2 virus on the body, and why the effects are more severe in certain patients, remain incompletely understood. One population of special interest is transplant recipients because of their immunosuppressed state. Understanding the pathophysiology of graft dysfunction in transplant patients with the COVID-19 viral syndrome is important for prognosticating the risk to the graft as well as understanding how best to prevent and, if necessary, treat graft injury in these patients. Methods: We analyzed multiple types of solid organ transplant recipients (liver, kidney, heart or lung) at our institution who died from SARS-CoV-2 and underwent autopsy (n = 6) or whose grafts were biopsied during active SARS-CoV-2 infection (n = 8). Their serum inflammatory markers were examined together with the histological appearance, viral load, and TCR repertoire of their graft tissue and, for autopsy patients, several native tissues. Results: Histology and clinical lab results revealed a systemic inflammatory pattern that included elevated inflammatory markers and diffuse tissue damage regardless of graft rejection. Virus was detected throughout all tissues, although most abundant in lungs. The TCR repertoire was broadly similar throughout the tissues of each individual, with greater sharing of dominant clones associated with more rapid disease course. There was no difference in viral load or clonal distribution of overall, COVID-associated, or putative SARS-CoV-2-specific TCRs between allograft and native tissue. We further demonstrated that SARSCoV-2-specific TCR sequences in transplant patients lack a donor HLArestricted pattern, regardless of distribution in allograft or native tissues,suggesting that recognition of viral antigens on infiltrating recipient cells can effectively trigger host T cell anti-viral responses in both the host and graft. Discussion: Our findings suggest a systemic immune response to the SARS-CoV-2 virus in solid organ transplant patients that is not associated with rejection and consistent with a largely destructive effect of recipient HLA-restricted T cell clones that affects donor and native organs similarly.

Improved predictions of antigen presentation and TCR recognition with MixMHCpred2.2 and PRIME2.0 reveal potent SARS-CoV-2 CD8+ T-cell epitopes.

The recognition of pathogen or cancer-specific epitopes by CD8+ T cells is crucial for the clearance of infections and the response to cancer immunotherapy. This process requires epitopes to be presented on class I human leukocyte antigen (HLA-I) molecules and recognized by the T-cell receptor (TCR). Machine learning models capturing these two aspects of immune recognition are key to improve epitope predictions. Here, we assembled a high-quality dataset of naturally presented HLA-I ligands and experimentally verified neo-epitopes. We then integrated these data in a refined computational framework to predict antigen presentation (MixMHCpred2.2) and TCR recognition (PRIME2.0). The depth of our training data and the algorithmic developments resulted in improved predictions of HLA-I ligands and neo-epitopes. Prospectively applying our tools to SARS-CoV-2 proteins revealed several epitopes. TCR sequencing identified a monoclonal response in effector/memory CD8+ T cells against one of these epitopes and cross-reactivity with the homologous peptides from other coronaviruses.

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.