Cross-reactivity of T cells and its role in the immune system

Feature selection enhances peptide binding predictions for TCR-specific interactions

Introduction

T-cell receptors (TCRs) play a critical role in the immune response by recognizing specific ligand peptides presented by major histocompatibility complex (MHC) molecules. Accurate prediction of peptide binding to TCRs is essential for advancing immunotherapy, vaccine design, and understanding mechanisms of autoimmune disorders.

Methods

This study presents a theoretical approach that explores the impact of feature selection techniques on enhancing the predictive accuracy of peptide binding models tailored for specific TCRs. To evaluate our approach across different TCR systems, we utilized a dataset that includes peptide libraries tested against three distinct murine TCRs. A broad range of physicochemical properties, including amino acid composition, dipeptide composition, and tripeptide features, were integrated into the machine learning-based feature selection framework to identify key properties contributing to binding affinity.

Results

Our analysis reveals that leveraging optimized feature subsets not only simplifies the model complexity but also enhances predictive performance, enabling more precise identification of TCR peptide interactions. The results of our feature selection method are consistent with findings from hybrid approaches that utilize both sequence and structural data as input as well as experimental data.

Discussion

Our theoretical approach highlights the role of feature selection in peptide-TCR interactions, providing a quantitative tool for uncovering the molecular mechanisms of the T-cell response and assisting in the design of more advanced targeted therapeutics.

Determination of T cell response against XBB variants in adults who received either monovalent wild-type inactivated whole virus or mRNA vaccine or bivalent WT/BA.4-5 COVID-19 mRNA vaccine as the additional booster

OBJECTIVES

As the SARS-CoV-2 virus evolves more rapidly than vaccines are updated, T cell immunity potentially confers protection against disease progression and death from new variants. In this study, we aimed to assess whether the current boosting vaccination schemes offer sufficient T cell protection against new SARS-CoV-2 variants.

METHODS

A total of 292 adults who had received the second booster of either monovalent wild-type (WT) vaccines (inactivated virus or mRNA) (Cohort 1) or the second/third booster of bivalent WT/BA.4-5 mRNA vaccine (Cohort 2) were recruited in Hong Kong. All participants showed no serological evidence of recent infection of SARS-CoV-2. Blood samples of each participant were collected before and 1 month after receiving the booster. T cell and antibody responses were determined by flow cytometry and neutralization test, respectively.

RESULTS

Among all vaccination strategies, only the adults who had received the bivalent vaccine as the third booster dose significantly elicited T cell responses to the XBB variant. Either monovalent or bivalent mRNA but not inactivated virus vaccine as the second/third booster induced antibody against different XBB variants.

CONCLUSION

Receiving bivalent mRNA vaccine as the third booster is preferable to induce both T cell and antibody responses against XBB.

Unveiling cross-reactivity: implications for immune response modulation in cancer

Antigen recognition by CD8+ T-cell receptors (TCR) is crucial for immune responses to pathogens and tumors. TCRs are cross-reactive, a single TCR can recognize multiple peptide-Human Leukocyte Antigen (HLA) complexes. The study of cross-reactivity can support the development of therapies focusing on immune modulation, such as the expansion of pre-existing T-cell clones to fight pathogens and tumors. The peptide-HLA (pHLA) surface has previously been used to identify TCR cross-reactivities. In the present work, we sought to perform a comprehensive analysis of peptide-HLA by selecting thousands of human and viral epitopes. We profit from established docking models to identify features from different spatial perspectives of HLA-A*02:01, explore similarities between self and non-self epitopes, and list potential cross-reactive epitopes of therapeutic interest. A total of 2631 unique epitopes from representative viral proteins or human proteins were modeled. We were able to demonstrate that cross-reactive CDR3 sequences from public databases recognize epitopes with similar electrostatic potential, charge, and spatial location. Using data from published studies that measured T-cell reactivity to mutated epitopes, we observed a negative correlation between epitope dissimilarity and T-cell activation. Most analysed cancer epitopes were more similar to self epitopes, yet we identified features distinguishing those more similar to viral antigens. Finally, we enumerated potential cross-reactivities between tumoral and viral epitopes and highlighted some challenges in their identification for therapeutic use. Moreover, the thousands of peptide-HLA complexes generated in our work constitute a valuable resource to study T-cell cross-reactivity.

Feature Selection Enhances Peptide Binding Predictions for TCR-Specific Interactions

T-cell receptors (TCRs) play a critical role in the immune response by recognizing specific ligand peptides presented by major histocompatibility complex (MHC) molecules. Accurate prediction of peptide binding to TCRs is essential for advancing immunotherapy, vaccine design, and understanding mechanisms of autoimmune disorders. This study presents a novel theoretical method that explores the impact of feature selection techniques on enhancing the predictive accuracy of peptide binding models tailored for specific TCRs. To evaluate the universality of our approach across different TCR systems, we utilized a dataset that includes peptide libraries tested against three distinct murine TCRs. A broad range of physicochemical properties, including amino acid composition, dipeptide composition, and tripeptide features, were integrated into the machine learning-based feature selection framework to identify key features contributing to binding affinity. Our analysis reveals that leveraging optimized feature subsets not only simplifies the model complexity but also enhances predictive performance, enabling more precise identification of TCR-peptide interactions. The results of our feature selection method are consistent with findings from hybrid approaches that utilize both sequence and structural data as input as well as experimental data. Our theoretical approach highlights the role of feature selection in peptide-TCR interactions, providing a powerful tool for uncovering the molecular mechanisms of the T-cell response and assisting in the design of more advanced targeted therapeutics.

Unravelling the Oral-Gut Axis: Interconnection Between Periodontitis and Inflammatory Bowel Disease, Current Challenges, and Future Perspective

As the opposite ends of the orodigestive tract, the oral cavity and the intestine share anatomical, microbial, and immunological ties that have bidirectional health implications. A growing body of evidence suggests an interconnection between oral pathologies and inflammatory bowel disease [IBD], implying a shift from the traditional concept of independent diseases to a complex, reciprocal cycle. This review outlines the evidence supporting an 'oral-gut' axis, marked by a higher prevalence of periodontitis and other oral conditions in IBD patients and vice versa. We present an in-depth examination of the interconnection between oral pathologies and IBD, highlighting the shared microbiological and immunological pathways, and proposing a 'multi-hit' hypothesis in the pathogenesis of periodontitis-mediated intestinal inflammation. Furthermore, the review underscores the critical need for a collaborative approach between dentists and gastroenterologists to provide holistic oral-systemic healthcare.

Commensal HPVs Have Evolved to Be More Immunogenic Compared with High-Risk α-HPVs

Commensal human papillomaviruses (HPVs) are responsible for persistent asymptomatic infection in the human population by maintaining low levels of the episomal genome in the stratified epithelia. Herein, we examined the immunogenicity of cutaneotropic HPVs that are commonly found in the skin. Using an in silico platform to determine human leukocyte antigen (HLA)-peptide complex binding affinity, we observed that early genes of cutaneotropic HPV types within the same species can generate multiple conserved, homologous peptides that bind with high affinity to HLA class I alleles. Interestingly, we discovered that commensal β, γ, μ, and ν HPVs contain significantly more immunogenic peptides compared with α-HPVs, which include high-risk, oncogenic HPV types. Our findings indicate that commensal HPV proteins have evolved to generate peptides that better complement their host's HLA repertoire. Promoting higher control by host T cell immunity in this way could be a mechanism by which HPVs achieve widespread asymptomatic colonization in humans. This work supports the role of commensal HPVs as immunogenic targets within epithelial cells, which may contribute to the immune regulation of the skin and mucosa.

A journey to your self: The vague definition of immune self and its practical implications

The identification of immunogenic peptides has become essential in an increasing number of fields in immunology, ranging from tumor immunotherapy to vaccine development. The nature of the adaptive immune response is shaped by the similarity between foreign and self-protein sequences, a concept extensively applied in numerous studies. Can we precisely define the degree of similarity to self? Furthermore, do we accurately define immune self? In the current work, we aim to unravel the conceptual and mechanistic vagueness hindering the assessment of self-similarity. Accordingly, we demonstrate the remarkably low consistency among commonly employed measures and highlight potential avenues for future research.

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

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

VitTCR: A deep learning method for peptide recognition prediction

This study introduces VitTCR, a predictive model based on the vision transformer (ViT) architecture, aimed at identifying interactions between T cell receptors (TCRs) and peptides, crucial for developing cancer immunotherapies and vaccines. VitTCR converts TCR-peptide interactions into numerical AtchleyMaps using Atchley factors for prediction, achieving AUROC (0.6485) and AUPR (0.6295) values. Benchmark analysis indicates VitTCR's performance is comparable to other models, with further comparative studies suggested to understand its effectiveness in varied contexts. Additionally, integrating a positional bias weight matrix (PBWM), derived from amino acid contact probabilities in structurally resolved pMHC-TCR complexes, slightly improves VitTCR's accuracy. The model's predictions show weak yet statistically significant correlations with immunological factors like T cell clonal expansion and activation percentages, underscoring the biological relevance of VitTCR's predictive capabilities. VitTCR emerges as a valuable computational tool for predicting TCR-peptide interactions, offering insights for immunotherapy and vaccine development.

Toward Consensus Epitopes B and T of Tropomyosin Involved in Cross-Reactivity across Diverse Allergens: An In Silico Study

Tropomyosin (TM) is a pan-allergen with cross-reactivity to arthropods, insects, and nematodes in tropical regions. While IgE epitopes of TM contribute to sensitization, T-cell (MHC-II) epitopes polarize the Th2 immune response. This study aimed to identify linear B and T consensus epitopes among house dust mites, cockroaches, Ascaris lumbricoides, shrimp, and mosquitoes, exploring the molecular basis of cross-reactivity in allergic diseases. Amino acid sequences of Der p 10, Der f 10, Blo t 10, Lit v 1, Pen a 1, Pen m 1, rAsc l 3, Per a 7, Bla g 7, and Aed a 10 were collected from Allergen Nomenclature and UniProt. B epitopes were predicted using AlgPred 2.0 and BepiPred 3.0. T epitopes were predicted with NetMHCIIpan 4.1 against 10 HLA-II alleles. Consensus epitopes were obtained through analysis and Epitope Cluster Analysis in the Immune Epitope Database. We found 7 B-cell epitopes and 28 linear T-cell epitopes binding to MHC II. A unique peptide (residues 160-174) exhibited overlap between linear B-cell and T-cell epitopes, highly conserved across tropomyosin sequences. These findings shed light on IgE cross-reactivity among the tested species. The described immuno-informatics pipeline and epitopes can inform in vitro research and guide synthetic multi-epitope proteins' design for potential allergology immunotherapies. Further in silico studies are warranted to confirm epitope accuracy and guide future experimental protocols.

Major Role of S-Glycoprotein in Providing Immunogenicity and Protective Immunity in mRNA Lipid Nanoparticle Vaccines Based on SARS-CoV-2 Structural Proteins

SARS-CoV-2 variants have evolved over time in recent years, demonstrating immune evasion of vaccine-induced neutralizing antibodies directed against the original S protein. Updated S-targeted vaccines provide a high level of protection against circulating variants of SARS-CoV-2, but this protection declines over time due to ongoing virus evolution. To achieve a broader protection, novel vaccine candidates involving additional antigens with low mutation rates are currently needed. Based on our recently studied mRNA lipid nanoparticle (mRNA-LNP) platform, we have generated mRNA-LNP encoding SARS-CoV-2 structural proteins M, N, S from different virus variants and studied their immunogenicity separately or in combination in vivo. As a result, all mRNA-LNP vaccine compositions encoding the S and N proteins induced excellent titers of RBD- and N-specific binding antibodies. The T cell responses were mainly specific CD4+ T cell lymphocytes producing IL-2 and TNF-alpha. mRNA-LNP encoding the M protein did not show a high immunogenicity. High neutralizing activity was detected in the sera of mice vaccinated with mRNA-LNP encoding S protein (alone or in combinations) against closely related strains, but was undetectable or significantly lower against an evolutionarily distant variant. Our data showed that the addition of mRNAs encoding S and M antigens to mRNA-N in the vaccine composition enhanced the immunogenicity of mRNA-N and induced a more robust immune response to the N protein. Based on our results, we suggested that the S protein plays a key role in enhancing the immune response to the N protein when they are both encoded in the mRNA-LNP vaccine.

Particle-Based Artificial Antigen-Presenting Cell Systems for T Cell Activation in Adoptive T Cell Therapy

T cell-based adoptive cell therapy (ACT) has emerged as a promising treatment for various diseases, particularly cancers. Unlike other immunotherapy modalities, ACT involves directly transferring engineered T cells into patients to eradicate diseased cells; hence, it necessitates methods for effectively activating and expanding T cells in vitro. Artificial antigen-presenting cells (aAPCs) have been widely developed based on biomaterials, particularly micro- and nanoparticles, and functionalized with T cell stimulatory antibodies to closely mimic the natural T cell-APC interactions. Due to their vast clinical utility, aAPCs have been employed as an off-the-shelf technology for T cell activation in FDA-approved ACTs, and the development of aAPCs is constantly advancing with the emergence of aAPCs with more sophisticated designs and additional functionalities. Here, we review the recent advancements in particle-based aAPCs for T cell activation in ACTs. Following a brief introduction, we first describe the manufacturing processes of ACT products. Next, the design and synthetic strategies for micro- and nanoparticle-based aAPCs are discussed separately to emphasize their features, advantages, and limitations. Then, the impact of design parameters of aAPCs, such as size, shape, ligand density/mobility, and stiffness, on their functionality and biomedical performance is explored to provide deeper insights into the design concepts and principles for more efficient and safer aAPCs. The review concludes by discussing current challenges and proposing future perspectives for the development of more advanced aAPCs.

A SARS-CoV-2 NSP7 homolog of a Treg epitope suppresses CD4+ and CD8+ T cell memory responses

Pathogens escape host defenses by T-cell epitope mutation or deletion (immune escape) and by simulating the appearance of human T cell epitopes (immune camouflage). We identified a highly conserved, human-like T cell epitope in non-structural protein 7 (NSP7) of SARS-CoV-2, RNA-dependent RNA polymerase (RdRp) hetero-tetramer complex. Remarkably, this T cell epitope has significant homology to a T regulatory cell epitope (Tregitope) previously identified in the Fc region of human immunoglobulin G (IgG) (Tregitope 289). We hypothesized that the SARS-CoV-2 NSP7 epitope (NSP7-289) may induce suppressive responses by engaging and activating pre-existing regulatory T cells. We therefore compared NSP7-289 and IgG Tregitopes (289 and 289z, a shorter version of 289 that isolates the shared NSP7 epitope) in vitro. Tregitope peptides 289, 289z and NSP7-289 bound to multiple HLA-DRB1 alleles in vitro and suppressed CD4+ and CD8+ T cell memory responses. Identification and in vitro validation of SARS-CoV-2 NSP7-289 provides further evidence of immune camouflage and suggests that pathogens can use human-like epitopes to evade immune response and potentially enhance host tolerance. Further exploration of the role of cross-conserved Tregs in human immune responses to pathogens such as SARS-CoV-2 is warranted.

Primary and Recall Immune Responses to SARS-CoV-2 in Breakthrough Infection

Breakthrough infections in SARS-CoV-2 vaccinated individuals are an ideal circumstance for the simultaneous exploration of both the vaccine-induced memory reaction to the spike (S) protein and the primary response to the membrane (M) and nucleocapsid (N) proteins generated by natural infection. We monitored 15 healthcare workers who had been vaccinated with two doses of Pfizer BioNTech BNT162b2 and were then later infected with the SARS-CoV-2 B.1.617.2. (Delta) variant, analysing the antiviral humoral and cellular immune responses. Natural infection determined an immediate and sharp rise in anti-RBD antibody titres and in the frequency of both S-specific antibody secreting cells (ASCs) and memory B lymphocytes. T cells responded promptly to infection by activating and expanding already at 2-5 days. S-specific memory and emerging M- and N-specific T cells both expressed high levels of activation markers and showed effector capacity with similar kinetics but with different magnitude. The results show that natural infection with SARS-CoV-2 in vaccinated individuals induces fully functional and rapidly expanding T and B lymphocytes in concert with the emergence of novel virus-specific T cells. This swift and punctual response also covers viral variants and captures a paradigmatic case of a healthy adaptive immune reaction to infection with a mutating virus.

Approaches to evaluate the specific immune responses to SARS-CoV-2

The SARS-CoV-2 pandemic has a huge impact on public health and global economy, meaning an enormous scientific, political, and social challenge. Studying how infection or vaccination triggers both cellular and humoral responses is essential to know the grade and length of protection generated in the population. Nowadays, scientists and authorities around the world are increasingly concerned about the arrival of new variants, which have a greater spread, due to the high mutation rate of this virus. The aim of this review is to summarize the different techniques available for the study of the immune responses after exposure or vaccination against SARS-CoV-2, showing their advantages and limitations, and proposing suitable combinations of different techniques to achieve extensive information in these studies. We wish that the information provided here will helps other scientists in their studies of the immune response against SARS-CoV-2 after vaccination with new vaccine candidates or infection with upcoming variants.

Neoantigen identification: Technological advances and challenges

Neoantigens have emerged as promising targets for cutting-edge immunotherapies, such as cancer vaccines and adoptive cell therapy. These neoantigens are unique to tumors and arise exclusively from somatic mutations or non-genomic aberrations in tumor proteins. They encompass a wide range of alterations, including genomic mutations, post-transcriptomic variants, and viral oncoproteins. With the advancements in technology, the identification of immunogenic neoantigens has seen rapid progress, raising new opportunities for enhancing their clinical significance. Prediction of neoantigens necessitates the acquisition of high-quality samples and sequencing data, followed by mutation calling. Subsequently, the pipeline involves integrating various tools that can predict the expression, processing, binding, and recognition potential of neoantigens. However, the continuous improvement of computational tools is constrained by the availability of datasets which contain validated immunogenic neoantigens. This review article aims to provide a comprehensive summary of the current knowledge as well as limitations in neoantigen prediction and validation. Additionally, it delves into the origin and biological role of neoantigens, offering a deeper understanding of their significance in the field of cancer immunotherapy. This article thus seeks to contribute to the ongoing efforts to harness neoantigens as powerful weapons in the fight against cancer.

Combining different bacteria in vaccine formulations enhances the chance for antiviral cross-reactive immunity: a detailed in silico analysis for influenza A virus

Bacteria are well known to provide heterologous immunity against viral infections through various mechanisms including the induction of innate trained immunity and adaptive cross-reactive immunity. Cross-reactive immunity from bacteria to viruses is responsible for long-term protection and yet its role has been downplayed due the difficulty of determining antigen-specific responses. Here, we carried out a systematic evaluation of the potential cross-reactive immunity from selected bacteria known to induce heterologous immunity against various viruses causing recurrent respiratory infections. The bacteria selected in this work were Bacillus Calmette Guerin and those included in the poly-bacterial preparation MV130: Streptococcus pneumoniae, Staphylococcus aureus, Staphylococcus epidermidis, Klebisella pneumoniae, Branhamella catarrhalis and Haemophilus influenzae. The virus included influenza A and B viruses, human rhinovirus A, B and C, respiratory syncytial virus A and B and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Through BLAST searches, we first identified the shared peptidome space (identity ≥ 80%, in at least 8 residues) between bacteria and viruses, and subsequently predicted T and B cell epitopes within shared peptides. Interestingly, the potential epitope spaces shared between bacteria in MV130 and viruses are non-overlapping. Hence, combining diverse bacteria can enhance cross-reactive immunity. We next analyzed in detail the cross-reactive T and B cell epitopes between MV130 and influenza A virus. We found that MV130 contains numerous cross-reactive T cell epitopes with high population protection coverage and potentially neutralizing B cell epitopes recognizing hemagglutinin and matrix protein 2. These results contribute to explain the immune enhancing properties of MV130 observed in the clinic against respiratory viral infections.

Immunological profile of mice immunized with a polyvalent virosome-based influenza vaccine

BACKGROUND

Influenza A virus (IAV) causes respiratory disease in pigs and is a major concern for public health. Vaccination of pigs is the most successful measure to mitigate the impact of the disease in the herds. Influenza-based virosome is an effective immunomodulating carrier that replicates the natural antigen presentation pathway and has tolerability profile due to their purity and biocompatibility.

METHODS

This study aimed to develop a polyvalent virosome influenza vaccine containing the hemagglutinin and neuraminidase proteins derived from the swine IAVs (swIAVs) H1N1, H1N2 and H3N2 subtypes, and to investigate its effectiveness in mice as a potential vaccine for swine. Mice were immunized with two vaccine doses (1 and 15 days), intramuscularly and intranasally. At 21 days and eight months later after the second vaccine dose, mice were euthanized. The humoral and cellular immune responses in mice vaccinated intranasally or intramuscularly with a polyvalent influenza virosomal vaccine were investigated.

RESULTS

Only intramuscular vaccination induced high hemagglutination inhibition (HI) titers. Seroconversion and seroprotection (> 4-fold rise in HI antibody titers, reaching a titer of ≥ 1:40) were achieved in 80% of mice (intramuscularly vaccinated group) at 21 days after booster immunization. Virus-neutralizing antibody titers against IAV were detected at 8 months after vaccination, indicating long-lasting immunity. Overall, mice immunized with the virosome displayed greater ability for B, effector-T and memory-T cells from the spleen to respond to H1N1, H1N2 and H3N2 antigens.

CONCLUSIONS

All findings showed an efficient immune response against IAVs in mice vaccinated with a polyvalent virosome-based influenza vaccine.

Unraveling the Link between Periodontitis and Inflammatory Bowel Disease: Challenges and Outlook

Periodontitis and Inflammatory Bowel Disease (IBD) are chronic inflammatory conditions, characterized by microbial dysbiosis and hyper-immunoinflammatory responses. Growing evidence suggest an interconnection between periodontitis and IBD, implying a shift from the traditional concept of independent diseases to a complex, reciprocal cycle. This review outlines the evidence supporting an "Oral-Gut" axis, marked by a higher prevalence of periodontitis in IBD patients and vice versa. The specific mechanisms linking periodontitis and IBD remain to be fully elucidated, but emerging evidence points to the ectopic colonization of the gut by oral bacteria, which promote intestinal inflammation by activating host immune responses. This review presents an in-depth examination of the interconnection between periodontitis and IBD, highlighting the shared microbiological and immunological pathways, and proposing a "multi-hit" hypothesis in the pathogenesis of periodontitis-mediated intestinal inflammation. Furthermore, the review underscores the critical need for a collaborative approach between dentists and gastroenterologists to provide holistic oral-systemic healthcare.

Determinants of tumor immune evasion: the role of T cell exposed motif frequency and mutant amino acid exposure

Few neoepitopes detected in tumor biopsies are immunogenic. Tumor-specific T cell responses require both the presentation of an epitope that differs from wildtype and the presence of T cells with neoepitope-cognate receptors. We show that mutations detected in tumor biopsies result in an increased frequency of rare amino acid combinations compared to the human proteome and gastrointestinal microorganisms. Mutations in a large data set of oncogene and tumor suppressor gene products were compared to wildtype, and to the count of corresponding amino acid motifs in the human proteome and gastrointestinal microbiome. Mutant amino acids in T cell exposed positions of potential neoepitopes consistently generated amino acid motifs that are less common in both proteome reference datasets. Approximately 10% of the mutant amino acid motifs are absent from the human proteome. Motif frequency does not change when mutants were positioned in the MHC anchor positions hidden from T cell receptors. Analysis of neoepitopes in GBM and LUSC cases showed less common T cell exposed motifs, and HLA binding preferentially placing mutant amino acids in an anchor position for both MHC I and MHC II. Cross-presentation of mutant exposed neoepitopes by MHC I and MHC II was particularly uncommon. Review of a tumor mutation dataset known to generate T cell responses showed immunogenic epitopes were those with mutant amino acids exposed to the T cell receptor and with exposed pentamer motifs present in the human and microbiome reference databases. The study illustrates a previously unrecognized mechanism of tumor immune evasion, as rare T cell exposed motifs produced by mutation are less likely to have cognate T cells in the T cell repertoire. The complex interactions of HLA genotype, binding positions, and mutation specific changes in T cell exposed motif underscore the necessity of evaluating potential neoepitopes in each individual patient.

PepSim: T-cell cross-reactivity prediction via comparison of peptide sequence and peptide-HLA structure

Introduction

Peptide-HLA class I (pHLA) complexes on the surface of tumor cells can be targeted by cytotoxic T-cells to eliminate tumors, and this is one of the bases for T-cell-based immunotherapies. However, there exist cases where therapeutic T-cells directed towards tumor pHLA complexes may also recognize pHLAs from healthy normal cells. The process where the same T-cell clone recognizes more than one pHLA is referred to as T-cell cross-reactivity and this process is driven mainly by features that make pHLAs similar to each other. T-cell cross-reactivity prediction is critical for designing T-cell-based cancer immunotherapies that are both effective and safe.

Methods

Here we present PepSim, a novel score to predict T-cell cross-reactivity based on the structural and biochemical similarity of pHLAs.

Results and discussion

We show our method can accurately separate cross-reactive from non-crossreactive pHLAs in a diverse set of datasets including cancer, viral, and self-peptides. PepSim can be generalized to work on any dataset of class I peptide-HLAs and is freely available as a web server at pepsim.kavrakilab.org.

Signatures of T cell immunity revealed using sequence similarity with TCRDivER algorithm

Changes in the T cell receptor (TCR) repertoires have become important markers for monitoring disease or therapy progression. With the rise of immunotherapy usage in cancer, infectious and autoimmune disease, accurate assessment and comparison of the "state" of the TCR repertoire has become paramount. One important driver of change within the repertoire is T cell proliferation following immunisation. A way of monitoring this is by investigating large clones of individual T cells believed to bind epitopes connected to the disease. However, as a single target can be bound by many different TCRs, monitoring individual clones cannot fully account for T cell cross-reactivity. Moreover, T cells responding to the same target often exhibit higher sequence similarity, which highlights the importance of accounting for TCR similarity within the repertoire. This complexity of binding relationships between a TCR and its target convolutes comparison of immune responses between individuals or comparisons of TCR repertoires at different timepoints. Here we propose TCRDivER algorithm (T cell Receptor Diversity Estimates for Repertoires), a global method of T cell repertoire comparison using diversity profiles sensitive to both clone size and sequence similarity. This approach allowed for distinction between spleen TCR repertoires of immunised and non-immunised mice, showing the need for including both facets of repertoire changes simultaneously. The analysis revealed biologically interpretable relationships between sequence similarity and clonality. These aid in understanding differences and separation of repertoires stemming from different biological context. With the rise of availability of sequencing data we expect our tool to find broad usage in clinical and research applications.

Targeting CD43 optimizes cancer immunotherapy through reinvigorating antitumor immune response in colorectal cancer

PURPOSE

Colorectal cancer (CRC) is one of the most common malignancies worldwide, with dramatically increasing incidence and mortality for decades. However, current therapeutic strategies for CRC, including chemotherapies and immunotherapies, have only demonstrated limited efficacy. Here, we report a novel immune molecule, CD43, that can regulate the tumor immune microenvironment (TIME) and serves as a promising target for CRC immunotherapy.

METHODS

The correlation of CD43 expression with CRC patient prognosis was revealed by public data analysis. CD43 knockout (KO) CRC cell lines were generated by CRISPR-Cas9 technology, and a syngenetic murine CRC model was established to investigate the in vivo function of CD43. The TIME was analyzed via immunohistochemical staining, flow cytometry and RNA-seq. Immune functions were investigated by depletion of immune subsets in vivo and T-cell functional assays in vitro, including T-cell priming, cytotoxicity, and chemotaxis experiments.

RESULTS

In this study, we found that high expression of CD43 was correlated with poor survival of CRC patients and the limited infiltration of CD8⁺ T cells in human CRC tissues. Importantly, CD43 expressed on tumor cells, rather than host cells, promoted tumor progression in a syngeneic tumor model. Loss of CD43 facilitated the infiltration of immune cells and immunological memory in the TIME of CRC tumors. Mechanistically, the protumor effect of CD43 depends on T cells, thereby attenuating T-cell-mediated cytotoxicity and cDC1-mediated antigen-specific T-cell activation. Moreover, targeting CD43 synergistically improved PD-L1 blockade immunotherapy for CRC.

CONCLUSION

Our findings revealed that targeting tumor-intrinsic CD43 could activate the antitumor immune response and provide particular value for optimized cancer immunotherapy by regulating the TIME in CRC patients.

Regulation of the tumor immune microenvironment by cancer-derived circular RNAs

Circular RNA (circRNAs) is a covalently closed circular non-coding RNA formed by reverse back-splicing from precursor messenger RNA. It is found widely in eukaryotic cells and can be released to the surrounding environment and captured by other cell types. This, circRNAs serve as connections between different cell types for the mediation of multiple signaling pathways. CircRNAs reshape the tumor microenvironment (TME), a key factor involved in all stages of cancer development, by regulating epithelial-stromal transformation, tumor vascularization, immune cell function, and inflammatory responses. Immune cells are the most abundant cellular TME components, and they have profound toxicity to cancer cells. This review summarizes circRNA regulation of immune cells, including T cells, natural killer cells, and macrophages; highlights the impact of circRNAs on tumor progression, treatment, and prognosis; and indicates new targets for tumor immunotherapy.

Checkpoint inhibitor immune-related adverse events: A focused review on autoantibodies and B cells as biomarkers, advancements and future possibilities

The use of immune checkpoint inhibitors (ICIs) has evolved rapidly with unprecedented treatment benefits being obtained for cancer patients, including improved patient survival. However, over half of the patients experience immune related adverse events (irAEs) or toxicities, which can be fatal, affect the quality of life of patients and potentially cause treatment interruption or cessation. Complications from these toxicities can also cause long term irreversible organ damage and other chronic health conditions. Toxicities can occur in various organ systems, with common observations in the skin, rheumatologic, gastrointestinal, hepatic, endocrine system and the lungs. These are not only challenging to manage but also difficult to detect during the early stages of treatment. Currently, no biomarker exists to predict which patients are likely to develop toxicities from ICI therapy and efforts to identify robust biomarkers are ongoing. B cells and antibodies against autologous antigens (autoantibodies) have shown promise and are emerging as markers to predict the development of irAEs in cancer patients. In this review, we discuss the interplay between ICIs and toxicities in cancer patients, insights into the underlying mechanisms of irAEs, and the involvement of the humoral immune response, particularly by B cells and autoantibodies in irAE development. We also provide an appraisal of the progress, key empirical results and advances in B cell and autoantibody research as biomarkers for predicting irAEs. We conclude the review by outlining the challenges and steps required for their potential clinical application in the future.

Durable spike-specific T cell responses after different COVID-19 vaccination regimens are not further enhanced by booster vaccination

Several COVID-19 vaccines are approved to prevent severe disease outcome after SARS-CoV-2 infection. Whereas induction and functionality of antiviral antibody response are largely studied, the induction of T cells upon vaccination with the different approved COVID-19 vaccines is less studied. Here, we report on T cell immunity 4 weeks and 6 months after different vaccination regimens and 4 weeks after an additional booster vaccination in comparison with SARS-CoV-2 T cell responses in convalescents and prepandemic donors using interferon-gamma ELISpot assays and flow cytometry. Increased T cell responses and cross-recognition of B.1.1.529 Omicron variant-specific mutations were observed ex vivo in mRNA- and heterologous-vaccinated donors compared with vector-vaccinated donors. Nevertheless, potent expandability of T cells targeting the spike protein was observed for all vaccination regimens, with frequency, diversity, and the ability to produce several cytokines of vaccine-induced T cell responses comparable with those in convalescent donors. T cell responses for all vaccinated donors significantly exceeded preexisting cross-reactive T cell responses in prepandemic donors. Booster vaccination led to a significant increase in anti-spike IgG responses, which showed a marked decline 6 months after complete vaccination. In contrast, T cell responses remained stable over time after complete vaccination with no significant effect of booster vaccination on T cell responses and cross-recognition of Omicron BA.1 and BA.2 mutations. This suggested that booster vaccination is of particular relevance for the amelioration of antibody response. Together, our work shows that different vaccination regimens induce broad and long-lasting spike-specific CD4⁺ and CD8⁺ T cell immunity to SARS-CoV-2.

MOLECULAR MIMICRY OF SARS-COV-2 SPIKE PROTEIN IN THE NERVOUS SYSTEM: A BIOINFORMATICS APPROACH

Introduction

The development of vaccines against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in record time to cope with the ongoing coronavirus disease 2019 (COVID-19) pandemic has led to uncertainty about their use and the appearance of adverse neurological reactions. The SARS-CoV-2 spike protein (SP) is used to produce neutralizing antibodies and stimulate innate immunity. However, considering the alterations in the nervous system (NS) caused by COVID- 19, cross-reactions are plausible.

Objective

To identify peptides in Homo sapiens SP-like proteins involved in myelin and axon homeostasis that may be affected due to molecular mimicry by antibodies and T cells induced by interaction with SP.

Materials and methods

A bioinformatics approach was used. To select the H. sapiens proteins to be studied, related biological processes categorized based on gene ontology were extracted through the construction of a protein–protein interaction network. Peripheral myelin protein 22, a major component of myelin in the peripheral nervous system, was used as the query protein. The extracellular domains and regions susceptible to recognition by antibodies were extracted from UniProt. In the study of T cells, linear sequence similarity between H. sapiens proteins and SP was assessed using BLASTp. This study considered the similarity in terms of biochemical groups per residue and affinity to the human major histocompatibility complex (human leukocyte antigen I), which were evaluated using Needle and NetMHCpan 4.1, respectively.

Results

A large number of shared pentapeptides between SP and H. sapiens proteins were identified. However, only a small group of 39 proteins was linked to axon and myelin homeostasis. In particular, some proteins, such as phosphacan, attractin, and teneurin-4, were susceptible targets of B and T cells. Other proteins closely related to myelin components in the NS, such as myelin-associated glycoprotein, were found to share at least one pentamer with SP in extracellular domains.

Conclusion

Proteins involved in the maintenance of nerve conduction in the central and peripheral NS were identified in H. sapiens. Based on these findings, re-evaluation of the vaccine composition is recommended to prevent possible neurological side effects.

Machine-Learning-Assisted Analysis of TCR Profiling Data Unveils Cross-Reactivity between SARS-CoV-2 and a Wide Spectrum of Pathogens and Other Diseases

During the last two years, the emergence of SARS-CoV-2 has led to millions of deaths worldwide, with a devastating socio-economic impact on a global scale. The scientific community's focus has recently shifted towards the association of the T cell immunological repertoire with COVID-19 progression and severity, by utilising T cell receptor sequencing (TCR-Seq) assays. The Multiplexed Identification of T cell Receptor Antigen (MIRA) dataset, which is a subset of the immunoACCESS study, provides thousands of TCRs that can specifically recognise SARS-CoV-2 epitopes. Our study proposes a novel Machine Learning (ML)-assisted approach for analysing TCR-Seq data from the antigens' point of view, with the ability to unveil key antigens that can accurately distinguish between MIRA COVID-19-convalescent and healthy individuals based on differences in the triggered immune response. Some SARS-CoV-2 antigens were found to exhibit equal levels of recognition by MIRA TCRs in both convalescent and healthy cohorts, leading to the assumption of putative cross-reactivity between SARS-CoV-2 and other infectious agents. This hypothesis was tested by combining MIRA with other public TCR profiling repositories that host assays and sequencing data concerning a plethora of pathogens. Our study provides evidence regarding putative cross-reactivity between SARS-CoV-2 and a wide spectrum of pathogens and diseases, with M. tuberculosis and Influenza virus exhibiting the highest levels of cross-reactivity. These results can potentially shift the emphasis of immunological studies towards an increased application of TCR profiling assays that have the potential to uncover key mechanisms of cell-mediated immune response against pathogens and diseases.

Contriving multi-epitope vaccine ensemble for monkeypox disease using an immunoinformatics approach

The current global outbreak of monkeypox (MPX) disease, caused by Monkeypox virus (MPXV), has resulted in 16 thousand infection cases, five deaths, and has been declared a global health emergency of international concern by the World Health Organization. Given current challenges in the safety of existing vaccines, a vaccine to prevent MPX infection and/or onset of symptoms would significantly advance disease management. In this context, a multi-epitope-based vaccine could be a well-suited approach. Herein, we searched a publicly accessible database (Virus Pathogen Database and Analysis Resource) for MPXV immune epitopes from various antigens. We prioritized a group of epitopes (10 CD8+ T cells and four B-cell epitopes) using a computer-aided technique based on desirable immunological and physicochemical properties, sequence conservation criteria, and non-human homology. Three multi-epitope vaccines were constructed (MPXV-1–3) by fusing finalized epitopes with the aid of appropriate linkers and adjuvant (beta-defensin 3, 50S ribosomal protein L7/L12, and Heparin-binding hemagglutinin). Codon optimization and in silico cloning in the pET28a (+) expression vector ensure the optimal expression of each construct in the Escherichia Coli system. Two and three-dimensional structures of the constructed vaccines were predicted and refined. The optimal binding mode of the construct with immune receptors [Toll-like receptors (TLR2, TLR3, and TLR4)] was explored by molecular docking, which revealed high docking energies of MPXV-1–TLR3 (–99.09 kcal/mol), MPXV-2–TLR3 (–98.68 kcal/mol), and MPXV-3–TLR2 (–85.22 kcal/mol). Conformational stability and energetically favourable binding of the vaccine-TLR2/3 complexes were assessed by performing molecular dynamics simulations and free energy calculations (Molecular Mechanics/Generalized Born Surface Area method). In silico immune simulation suggested that innate, adaptive, and humoral responses will be elicited upon administration of such potent multi-epitope vaccine constructs. The vaccine constructs are antigenic, non-allergen, non-toxic, soluble, topographically exposed, and possess favourable physicochemical characteristics. These results may help experimental vaccinologists design a potent MPX vaccine.

Role of cross-reactivity in cellular immune targeting of influenza A M158-66 variant peptide epitopes

The immunologic significance of cross-reactivity of TCR recognition of peptide:MHC complexes is still poorly understood. We have described TCR cross-reactivity in a system involving polyclonal CD8 T cell recognition of the well characterized influenza viral M158-66 epitope. While M158-66 is generally conserved between influenza A isolates, error-prone transcription generates stable variant RNA during infection which could act as novel epitopes. If packaged and viable, variant genomic RNA generates an influenza quasispecies. The stable RNA variants would generate a new transmissible epitope that can select a specific repertoire, which itself should have cross-reactive properties. We tested two candidate peptides in which Thr65 is changed to Ala (A65) or Ser (S65) using recall responses to identify responding T cell clonotypes. Both peptides generated large polyclonal T cell repertoires of their own with repertoire characteristics and cross-reactivity patterns like that observed for the M158-66 repertoire. Both substitutions could be present in viral genomes or mRNA at sufficient frequency during an infection to drive immunity. Peptides from the resulting protein would be a target for CD8 cells irrespective of virus viability or transmissibility. These data support the hypothesis that cross-reactivity is important for immunity against RNA virus infections.

Cross-reactivity of SARS-CoV-2- and influenza A-specific T cells in individuals exposed to SARS-CoV-2

Cross-reactive immunity between SARS-CoV-2 and other related coronaviruses has been well-documented, and it may play a role in preventing severe COVID-19. Epidemiological studies early in the pandemic showed a geographical association between high influenza vaccination rates and lower incidence of SARS-CoV-2 infection. We, therefore, analyzed whether exposure to influenza A virus (IAV) antigens could influence the T cell repertoire in response to SARS-CoV-2, indicating a heterologous immune response between these 2 unrelated viruses. Using artificial antigen-presenting cells (aAPCs) combined with real-time reverse-transcription PCR (RT-qPCR), we developed a sensitive assay to quickly screen for antigen-specific T cell responses and detected a significant correlation between responses to SARS-CoV-2 epitopes and IAV dominant epitope (M158-66). Further analysis showed that some COVID-19 convalescent donors exhibited both T cell receptor (TCR) specificity and functional cytokine responses to multiple SARS-CoV-2 epitopes and M158-66. Utilizing an aAPC-based stimulation/expansion assay, we detected cross-reactive T cells with specificity to SARS-CoV-2 and IAV. In addition, TCR sequencing of the cross-reactive and IAV-specific T cells revealed similarities between the TCR repertoires of the two populations. These results indicate that heterologous immunity shaped by our exposure to other unrelated endemic viruses may affect our immune response to novel viruses such as SARS-CoV-2.

Computer-Aided Analysis of West Sub-Saharan Africa Snakes Venom towards the Design of Epitope-Based Poly-Specific Antivenoms

Snakebite envenomation is a neglected tropical disease that causes over 100,000 deaths each year. The only effective treatment consists of antivenoms derived from animal sera, but these have been deemed with highly variable potency and are usually inaccessible and too costly for victims. The production of antivenoms by venom-independent techniques, such as the immunization with multi-epitope constructs, could circumvent those drawbacks. Herein, we present a knowledge-based pipeline to prioritize potential epitopes of therapeutic relevance from toxins of medically important snakes in West Sub-Saharan Africa. It is mainly based on sequence conservation and protein structural features. The ultimately selected 41 epitopes originate from 11 out of 16 snake species considered of highest medical importance in the region and 3 out of 10 of those considered as secondary medical importance. Echis ocellatus, responsible for the highest casualties in the area, would be covered by 12 different epitopes. Remarkably, this pipeline is versatile and customizable for the analysis of snake venom sequences from any other region of the world.

Cellular Immunity Is Critical for Assessing COVID-19 Vaccine Effectiveness in Immunocompromised Individuals

COVID-19 vaccine clinical development was conducted with unprecedented speed. Immunity measurements were concentrated on the antibody response which left significant gaps in our understanding how robust and long-lasting immune protection develops. Better understanding the cellular immune response will fill those gaps, especially in the elderly and immunocompromised populations which not only have the highest risk for severe infection, but also frequently have inadequate antibody responses. Although cellular immunity measurements are more logistically complex to conduct for clinical trials compared to antibody measurements, the feasibility and benefit of doing them in clinical trials has been demonstrated and so should be more widely adopted. Adding significant cellular response metrics will provide a deeper understanding of the overall immune response to COVID-19 vaccination, which will significantly inform vaccination strategies for the most vulnerable populations. Better monitoring of overall immunity will also substantially benefit other vaccine development efforts, and indeed any therapies that involve the immune system as part of the therapeutic strategy.

Immunosenescence and Altered Vaccine Efficiency in Older Subjects: A Myth Difficult to Change

Organismal ageing is associated with many physiological changes, including differences in the immune system of most animals. These differences are often considered to be a key cause of age-associated diseases as well as decreased vaccine responses in humans. The most often cited vaccine failure is seasonal influenza, but, while it is usually the case that the efficiency of this vaccine is lower in older than younger adults, this is not always true, and the reasons for the differential responses are manifold. Undoubtedly, changes in the innate and adaptive immune response with ageing are associated with failure to respond to the influenza vaccine, but the cause is unclear. Moreover, recent advances in vaccine formulations and adjuvants, as well as in our understanding of immune changes with ageing, have contributed to the development of vaccines, such as those against herpes zoster and SARS-CoV-2, that can protect against serious disease in older adults just as well as in younger people. In the present article, we discuss the reasons why it is a myth that vaccines inevitably protect less well in older individuals, and that vaccines represent one of the most powerful means to protect the health and ensure the quality of life of older adults.

Immunogenic epitope panel for accurate detection of non-cross-reactive T cell response to SARS-CoV-2

The ongoing COVID-19 pandemic calls for more effective diagnostic tools. T cell response assessment serves as an independent indicator of prior COVID-19 exposure while also contributing to a more comprehensive characterization of SARS-CoV-2 immunity. In this study, we systematically assessed the immunogenicity of 118 epitopes with immune cells collected from multiple cohorts of vaccinated, convalescent, healthy unexposed, and SARS-CoV-2–exposed donors. We identified 75 immunogenic epitopes, 24 of which were immunodominant. We further confirmed HLA restriction for 49 epitopes and described association with more than 1 HLA allele for 14 of these. Exclusion of 2 cross-reactive epitopes that generated a response in prepandemic samples left us with a 73-epitope set that offered excellent diagnostic specificity without losing sensitivity compared with full-length antigens, and this evoked a robust cross-reactive response. We subsequently incorporated this set of epitopes into an in vitro diagnostic Corona-T-test, which achieved a diagnostic accuracy of 95% in a clinical trial. In a cohort of asymptomatic seronegative individuals with a history of prolonged SARS-CoV-2 exposure, we observed a complete absence of T cell response to our epitope panel. In combination with strong reactivity to full-length antigens, this suggests that a cross-reactive response might protect these individuals.

Immune mechanisms in cancer patients that lead to poor outcomes of SARS-CoV-2 infection

Patients with cancers have been severely affected by the COVID-19 pandemic. This is highlighted by the adverse outcomes in cancer patients with COVID-19 as well as by the impact of the COVID-19 pandemic on cancer care. Patients with cancer constitute a heterogeneous population that exhibits distinct mechanisms of immune dysfunction, associated with distinct systemic features of hot (T-cell-inflamed/infiltrated) and cold (Non-T-cell-inflamed and/or infiltrated) tumors. The former show hyper immune activated cells and a highly inflammatory environment while, contrastingly, the latter show the profile of a senescent and/or quiescent immune system. Thus, the evolution of SARS-CoV-2 infection in different types of cancers can show distinct trajectories which could lead to a variety of clinical and pathophysiological outcomes. The altered immunological environment including cytokines that characterizes hot and cold tumors will lead to different mechanisms of immune dysfunction, which will result in downstream effects on the course of SARS-CoV-2 infection. This review will focus on defining the known contributions of soluble pro- and anti-inflammatory mediators on immune function including altered T-cells and B-cells responses and as well on how these factors modulate the expression of SARS-CoV-2 receptor ACE2, TMPRSS2 expression, and lymph node fibrosis in cancer patients. We will propose immune mechanisms that underlie the distinct courses of SARS-CoV-2 infection in cancer patients and impact on the success of immune based therapies that have significantly improved cancer outcomes. Better understanding of the immune mechanisms prevalent in cancer patients that are associated to the outcomes of SARS-CoV-2 infection will help to identify the high-risk cancer patients and develop immune-based approaches to prevent significant adverse outcomes by targeting these pathways.

Immunometabolic Mechanisms of Heart Failure with Preserved Ejection Fraction

Heart failure with preserved ejection fraction (HFpEF) is increasing in prevalence worldwide, already accounting for at least half of all heart failure (HF). As most patients with HFpEF are obese with metabolic syndrome, metabolic stress has been implicated in syndrome pathogenesis. Recently, compelling evidence for bidirectional crosstalk between metabolic stress and chronic inflammation has emerged, and alterations in systemic and cardiac immune responses are held to participate in HFpEF pathophysiology. Indeed, based on both preclinical and clinical evidence, comorbidity-driven systemic inflammation, coupled with metabolic stress, have been implicated together in HFpEF pathogenesis. As metabolic alterations impact immune function(s) in HFpEF, major changes in immune cell metabolism are also recognized in HFpEF and in HFpEF-predisposing conditions. Both arms of immunity - innate and adaptive - are implicated in the cardiomyocyte response in HFpEF. Indeed, we submit that crosstalk among adipose tissue, the immune system, and the heart represents a critical component of HFpEF pathobiology. Here, we review recent evidence in support of immunometabolic mechanisms as drivers of HFpEF pathogenesis, discuss pivotal biological mechanisms underlying the syndrome, and highlight questions requiring additional inquiry.

Cellular Responses to Membrane and Nucleocapsid Viral Proteins Are Also Boosted After SARS-CoV-2 Spike mRNA Vaccination in Individuals With Either Past Infection or Cross-Reactivity

SARS-CoV-2 spike mRNA vaccines have shown remarkable clinical efficacy in the general population, although the nature of T-cell priming is not fully understood. We performed longitudinal spike-, membrane-, and nucleocapsid-specific T-cell analysis in individuals with past infection and infection-naïve individuals with cross-reactivity. We found an additional enhancement of T-cell response to the structural membrane (M) and nucleocapsid (N) SARS-CoV-2 proteins after mRNA vaccine in these individuals. Thus, despite the spike-specific response, we found that the first dose of the vaccine boosted a significant CD8 cell response to M and N proteins, whereas no cellular response to those proteins was found in infection-naïve individuals without pre-existing cross-reactivity who were tested for eventual asymptomatic infection. These findings highlight the additional benefit of mRNA vaccines as broad boosters of cellular responses to different viral epitopes in these individuals and suggest extended protection to other viral variants.

Mechanistic diversity in MHC class I antigen recognition

Throughout its evolution, the human immune system has developed a plethora of strategies to diversify the antigenic peptide sequences that can be targeted by the CD8+ T cell response against pathogens and aberrations of self. Here we provide a general overview of the mechanisms that lead to the diversity of antigens presented by MHC class I complexes and their recognition by CD8+ T cells, together with a more detailed analysis of recent progress in two important areas that are highly controversial: the prevalence and immunological relevance of unconventional antigen peptides; and cross-recognition of antigenic peptides by the T cell receptors of CD8+ T cells.

The role of polyspecific T-cell exhaustion in severe outcomes for COVID-19 patients having latent pathogen infections such as Toxoplasmagondii

Various categories of coronavirus disease 19 (COVID-19) patients have exhibited major mortality rate differences and symptoms. Some papers have recently explained these differences in mortality rates and symptoms as a consequence of this virus infection acting in synergy with one or more latent pathogen infections in some patients. A latent pathogen infection likely to be involved in millions of these patients is the protozoan parasite Toxoplasma gondii, which infects approximately one third of the global human population. However, other papers have concluded that latent protozoan parasite infections can reduce the severity of viral infections. The aims and purposes of this paper include providing explanations for the contradictions between these studies and introducing a significant new category of T-cell exhaustion. Latent pathogens can have different genetic strains with great differences in their effects on a second pathogen infection. Furthermore, depending on the timing and effectiveness of drug treatments, pathogen infections that become latent may or may not later induce immune cell dysfunctions, including T-cell exhaustion. Concurrent multiple pathogen T-cell exhaustion is herein called "polyspecific T-cell exhaustion."

A structural-based machine learning method to classify binding affinities between TCR and peptide-MHC complexes

The activation of T cells is triggered by the interactions of T cell receptors (TCRs) with their epitopes, which are peptides presented by major histocompatibility complex (MHC) on the surfaces of antigen presenting cells (APC). While each TCR can only recognize a specific subset from a large repertoire of peptide-MHC (pMHC) complexes, it is very often that peptides in this subset share little sequence similarity. This is known as the specificity and cross-reactivity of T cells, respectively. The binding affinities between different types of TCRs and pMHC are the major driving force to shape this specificity and cross-reactivity in T cell recognition. The binding affinities, furthermore, are determined by the sequence and structural properties at the interfaces between TCRs and pMHC. Fortunately, a wealth of data on binding and structures of TCR-pMHC interactions becomes publicly accessible in online resources, which offers us the opportunity to develop a random forest classifier for predicting the binding affinities between TCR and pMHC based on the structure of their complexes. Specifically, the structure and sequence of a given complex were projected onto a high-dimensional feature space as the input of the classifier, which was then trained by a large-scale benchmark dataset. Based on the cross-validation results, we found that our machine learning model can predict if the binding affinity of a given TCR-pMHC complex is stronger or weaker than a predefined threshold with an overall accuracy approximately around 75 %. The significance of our prediction was estimated by statistical analysis. Moreover, more than 60 % of binding affinities in the ATLAS database can be successfully classified into groups within the range of 2 kcal/mol. Additionally, we show that TCR-pMHC complexes with strong binding affinity prefer hydrophobic interactions between amino acids with large aromatic rings instead of electrostatic interactions. Our results therefore provide insights to design engineered TCRs which enhance the specificity for their targeted epitopes. Taken together, this method can serve as a useful addition to a suite of existing approaches which study binding between TCR and pMHC.

Recurrence of Primary Sclerosing Cholangitis After Liver Transplant in Children: An International Observational Study

BACKGROUND AND AIMS

Recurrent primary sclerosing cholangitis (rPSC) following liver transplant (LT) has a negative impact on graft and patient survival; little is known about risk factors for rPSC or disease course in children.

APPROACH AND RESULTS

We retrospectively evaluated risk factors for rPSC in 140 children from the Pediatric PSC Consortium, a multicenter international registry. Recipients underwent LT for PSC and had >90 days of follow-up. The primary outcome, rPSC, was defined using Graziadei criteria. Median follow-up after LT was 3 years (interquartile range 1.1-6.1). rPSC occurred in 36 children, representing 10% and 27% of the subjects at 2 years and 5 years following LT, respectively. Subjects with rPSC were younger at LT (12.9 vs. 16.2 years), had faster progression from PSC diagnosis to LT (2.5 vs. 4.1 years), and had higher alanine aminotransferase (112 vs. 66 IU/L) at LT (all P < 0.01). Inflammatory bowel disease was more prevalent in the rPSC group (86% vs. 66%; P = 0.025). After LT, rPSC subjects had more episodes of biopsy-proved acute rejection (mean 3 vs. 1; P < 0.001), and higher prevalence of steroid-refractory rejection (41% vs. 20%; P = 0.04). In those with rPSC, 43% developed complications of portal hypertension, were relisted for LT, or died within 2 years of the diagnosis. Mortality was higher in the rPSC group (11.1% vs. 2.9%; P = 0.05).

CONCLUSIONS

The incidence of rPSC in this cohort was higher than previously reported, and was associated with increased morbidity and mortality. Patients with rPSC appeared to have a more aggressive, immune-reactive phenotype. These findings underscore the need to understand the immune mechanisms of rPSC, to lay the foundation for developing new therapies and improve outcomes in this challenging population.

The Effects of Trained Innate Immunity on T Cell Responses; Clinical Implications and Knowledge Gaps for Future Research

The burgeoning field of innate immune training, also called trained immunity, has given immunologists new insights into the role of innate responses in protection against infection and in modulating inflammation. Moreover, it has led to a paradigm shift in the way we think about immune memory and the interplay between innate and adaptive immune systems in conferring immunity against pathogens. Trained immunity is the term used to describe the medium-term epigenetic and metabolic reprogramming of innate immune cells in peripheral tissues or in the bone marrow stem cell niche. It is elicited by an initial challenge, followed by a significant period of rest that results in an altered response to a subsequent, unrelated challenge. Trained immunity can be associated with increased production of proinflammatory mediators, such as IL-1β, TNF and IL-6, and increased expression of markers on innate immune cells associated with antigen presentation to T cells. The microenvironment created by trained innate immune cells during the secondary challenge may have profound effects on T cell responses, such as altering the differentiation, polarisation and function of T cell subtypes, including Th17 cells. In addition, the Th1 cytokine IFN-γ plays a critical role in establishing trained immunity. In this review, we discuss the evidence that trained immunity impacts on or can be impacted by T cells. Understanding the interplay between innate immune training and how it effects adaptive immunity will give insights into how this phenomenon may affect the development or progression of disease and how it could be exploited for therapeutic interventions or to enhance vaccine efficacy.

Current challenges for unseen-epitope TCR interaction prediction and a new perspective derived from image classification

The prediction of epitope recognition by T-cell receptors (TCRs) has seen many advancements in recent years, with several methods now available that can predict recognition for a specific set of epitopes. However, the generic case of evaluating all possible TCR-epitope pairs remains challenging, mainly due to the high diversity of the interacting sequences and the limited amount of currently available training data. In this work, we provide an overview of the current state of this unsolved problem. First, we examine appropriate validation strategies to accurately assess the generalization performance of generic TCR-epitope recognition models when applied to both seen and unseen epitopes. In addition, we present a novel feature representation approach, which we call ImRex (interaction map recognition). This approach is based on the pairwise combination of physicochemical properties of the individual amino acids in the CDR3 and epitope sequences, which provides a convolutional neural network with the combined representation of both sequences. Lastly, we highlight various challenges that are specific to TCR-epitope data and that can adversely affect model performance. These include the issue of selecting negative data, the imbalanced epitope distribution of curated TCR-epitope datasets and the potential exchangeability of TCR alpha and beta chains. Our results indicate that while extrapolation to unseen epitopes remains a difficult challenge, ImRex makes this feasible for a subset of epitopes that are not too dissimilar from the training data. We show that appropriate feature engineering methods and rigorous benchmark standards are required to create and validate TCR-epitope predictive models.

CD8+ T cells specific for conserved coronavirus epitopes correlate with milder disease in COVID-19 patients

A central feature of the SARS-CoV-2 pandemic is that some individuals become severely ill or die, whereas others have only a mild disease course or are asymptomatic. Here we report development of an improved multimeric αβ T cell staining reagent platform, with each maxi-ferritin "spheromer" displaying 12 peptide-MHC complexes. Spheromers stain specific T cells more efficiently than peptide-MHC tetramers and capture a broader portion of the sequence repertoire for a given peptide-MHC. Analyzing the response in unexposed individuals, we find that T cells recognizing peptides conserved amongst coronaviruses are more abundant and tend to have a "memory" phenotype, compared to those unique to SARS-CoV-2. Significantly, CD8+ T cells with these conserved specificities are much more abundant in COVID-19 patients with mild disease versus those with a more severe illness, suggesting a protective role.

Deconvoluting the T Cell Response to SARS-CoV-2: Specificity Versus Chance and Cognate Cross-Reactivity

SARS-CoV-2 infection takes a mild or clinically inapparent course in the majority of humans who contract this virus. After such individuals have cleared the virus, only the detection of SARS-CoV-2-specific immunological memory can reveal the exposure, and hopefully the establishment of immune protection. With most viral infections, the presence of specific serum antibodies has provided a reliable biomarker for the exposure to the virus of interest. SARS-CoV-2 infection, however, does not reliably induce a durable antibody response, especially in sub-clinically infected individuals. Consequently, it is plausible for a recently infected individual to yield a false negative result within only a few months after exposure. Immunodiagnostic attention has therefore shifted to studies of specific T cell memory to SARS-CoV-2. Most reports published so far agree that a T cell response is engaged during SARS-CoV-2 infection, but they also state that in 20-81% of SARS-CoV-2-unexposed individuals, T cells respond to SARS-CoV-2 antigens (mega peptide pools), allegedly due to T cell cross-reactivity with Common Cold coronaviruses (CCC), or other antigens. Here we show that, by introducing irrelevant mega peptide pools as negative controls to account for chance cross-reactivity, and by establishing the antigen dose-response characteristic of the T cells, one can clearly discern between cognate T cell memory induced by SARS-CoV-2 infection vs. cross-reactive T cell responses in individuals who have not been infected with SARS-CoV-2.

Functional characterization of CD4+ T cell receptors crossreactive for SARS-CoV-2 and endemic coronaviruses

BACKGROUNDRecent studies have reported T cell immunity to the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in unexposed donors, possibly due to crossrecognition by T cells specific for common cold coronaviruses (CCCs). True T cell crossreactivity, defined as the recognition by a single TCR of more than one distinct peptide-MHC ligand, has never been shown in the context of SARS-CoV-2.METHODSWe used the viral functional expansion of specific T cells (ViraFEST) platform to identify T cell responses crossreactive for the spike (S) glycoproteins of SARS-CoV-2 and CCCs at the T cell receptor (TCR) clonotype level in convalescent COVID-19 patients (CCPs) and SARS-CoV-2-unexposed donors. Confirmation of SARS-CoV-2/CCC crossreactivity and assessments of functional avidity were performed using a TCR cloning and transfection system.RESULTSMemory CD4+ T cell clonotypes that crossrecognized the S proteins of SARS-CoV-2 and at least one other CCC were detected in 65% of CCPs and unexposed donors. Several of these TCRs were shared among multiple donors. Crossreactive T cells demonstrated significantly impaired SARS-CoV-2-specific proliferation in vitro relative to monospecific CD4+ T cells, which was consistent with lower functional avidity of their TCRs for SARS-CoV-2 relative to CCC.CONCLUSIONSOur data confirm, for what we believe is the first time, the existence of unique memory CD4+ T cell clonotypes crossrecognizing SARS-CoV-2 and CCCs. The lower avidity of crossreactive TCRs for SARS-CoV-2 may be the result of antigenic imprinting, such that preexisting CCC-specific memory T cells have reduced expansive capacity upon SARS-CoV-2 infection. Further studies are needed to determine how these crossreactive T cell responses affect clinical outcomes in COVID-19 patients.FUNDINGNIH funding (U54CA260492, P30CA006973, P41EB028239, R01AI153349, R01AI145435-A1, R21AI149760, and U19A1088791) was provided by the National Institute of Allergy and Infectious Diseases, the National Cancer Institute, and the National Institute of Biomedical Imaging and Bioengineering. The Bloomberg~Kimmel Institute for Cancer Immunotherapy, The Johns Hopkins University Provost, and The Bill and Melinda Gates Foundation provided funding for this study.

Preexisting and Post-COVID-19 Immune Responses to SARS-CoV-2 in Patients with Cancer

Patients with cancer, in particular patients with hematologic malignancies, are at increased risk for critical illness upon COVID-19. We here assessed antibody as well as CD4⁺ and CD8⁺ T-cell responses in unexposed and SARS-CoV-2-infected patients with cancer to characterize SARS-CoV-2 immunity and to identify immunologic parameters contributing to COVID-19 outcome. Unexposed patients with hematologic malignancies presented with reduced prevalence of preexisting SARS-CoV-2 cross-reactive CD4⁺ T-cell responses and signs of T-cell exhaustion compared with patients with solid tumors and healthy volunteers. Whereas SARS-CoV-2 antibody responses did not differ between patients with COVID-19 and cancer and healthy volunteers, intensity, expandability, and diversity of SARS-CoV-2 T-cell responses were profoundly reduced in patients with cancer, and the latter associated with a severe course of COVID-19. This identifies impaired SARS-CoV-2 T-cell immunity as a potential determinant for dismal outcome of COVID-19 in patients with cancer. SIGNIFICANCE: This first comprehensive analysis of SARS-CoV-2 immune responses in patients with cancer reports on the potential implications of impaired SARS-CoV-2 T-cell responses for understanding pathophysiology and predicting severity of COVID-19, which in turn might allow for the development of therapeutic measures and vaccines for this vulnerable patient population.See related commentary by Salomé and Horowitz, p. 1877.This article is highlighted in the In This Issue feature, p. 1861.

Predicting TCR-Epitope Binding Specificity Using Deep Metric Learning and Multimodal Learning

Understanding the recognition of specific epitopes by cytotoxic T cells is a central problem in immunology. Although predicting binding between peptides and the class I Major Histocompatibility Complex (MHC) has had success, predicting interactions between T cell receptors (TCRs) and MHC class I-peptide complexes (pMHC) remains elusive. This paper utilizes a convolutional neural network model employing deep metric learning and multimodal learning to perform two critical tasks in TCR-epitope binding prediction: identifying the TCRs that bind a given epitope from a TCR repertoire, and identifying the binding epitope of a given TCR from a list of candidate epitopes. Our model can perform both tasks simultaneously and reveals that inconsistent preprocessing of TCR sequences can confound binding prediction. Applying a neural network interpretation method identifies key amino acid sequence patterns and positions within the TCR, important for binding specificity. Contrary to common assumption, known crystal structures of TCR-pMHC complexes show that the predicted salient amino acid positions are not necessarily the closest to the epitopes, implying that physical proximity may not be a good proxy for importance in determining TCR-epitope specificity. Our work thus provides an insight into the learned predictive features of TCR-epitope binding specificity and advances the associated classification tasks.