IPD-IMGT/HLA Database

Recent developments in peptide vaccines against Glioblastoma, a review and update

Glioblastoma multiforme (GBM) is the most prevalent invasive CNS tumor, with a high incidence rate and a high likelihood of recurrence in most patients. Despite available treatments, recurrent glioblastoma (rGBM) exhibits growing resistance to chemotherapy and radiotherapy, which necessitates the development of newer methods of treatment. Peptide vaccines, a type of cancer immunotherapy, have recently attracted attention as a potentially practical therapeutic approach because they target tumor-associated or tumor-specific antigens to generate an effective immune response against cancer cells. These vaccines have been included in several clinical trials, demonstrating their safety and effectiveness by eliciting protective immune responses. However, peptide vaccines for glioblastoma face challenges due to the complex nature of intracranial brain tumors that require innovative approaches and in-depth research to increase their efficacy. The main topics covered in this article include immunological inhibitors and immune characteristics of the CNS and GBM, the basis of immunity, and the significant results of clinical trials of peptide vaccine therapy for GBM. Additionally, it examines the potential causes of the low effectiveness of these vaccines and recommends future research to address the specific challenges associated with immunotherapy in GBM. The evaluation of preliminary phase studies and phase III clinical trials will provide insights into potential immunological responses, biosecurity precautions, and clinical outcomes, guiding future vaccination initiatives to promote higher effectiveness.

HLA diversity and signatures of selection in the Maniq, a nomadic hunter-gatherer population in Southern Thailand

The Maniq, a small and isolated nomadic hunter-gatherer population from the rainforests of Southern Thailand, offer a unique context for investigating how demographic history, genetic drift, and pathogen-driven selection shape human leucocyte antigen (HLA) diversity. Using high-coverage whole-genome data from 21 individuals (12 unrelated), we genotyped HLA alleles with HLA-HD and T1K, identifying 32 alleles in classical and 14 in non-classical HLA genes. Although overall HLA diversity was comparatively low, a few alleles at each locus occurred at high frequency, mirroring patterns observed in other small, isolated populations. Principal-component analysis clustered the Maniq with other Austroasiatic-speaking Semang hunter-gatherers (Jehai, Kintaq) on the Malay Peninsula and, intriguingly, with the Austronesian-speaking Tao of Taiwan, indicating shared immunogenetic features across linguistic boundaries. Despite reduced diversity, multiple loci bore signatures of both long-term balancing and recent positive selection. Several SNPs under selection were in complete linkage disequilibrium with eQTLs known to influence responses to hepatitis B virus (HBV) and other pathogens, suggesting that pathogen-driven pressure-in particular HBV-may have contributed to recent HLA evolution in the Maniq. These findings provide critical insights into how demographic constraints and pathogen landscapes converge to shape HLA diversity and evolution. In light of increasing infectious disease burdens in indigenous communities, our results underscore the importance of studying small, isolated populations to better understand the adaptive significance of HLA genes.

Advances in neoantigen-based immunotherapy for head and neck squamous cell carcinoma: a comprehensive review

Head and Neck Squamous Cell Carcinoma (HNSCC), ranking among the six most prevalent malignancies worldwide, is characterized by significant heterogeneity. Conventional monotherapeutic approaches, including surgical intervention, radiotherapy, and chemotherapy, often fail to achieve complete tumor cell elimination, consequently leading to disease recurrence and metastatic progression. In this context, personalized immunotherapeutic strategies, particularly cancer vaccines and immune checkpoint inhibitors, have emerged as promising therapeutic modalities for patients with recurrent/metastatic (R/M) HNSCC. Neoantigens, which exhibit selective expression in tumor tissues while remaining absent in normal tissues, have garnered considerable attention as novel targets for HNSCC personalized immunotherapy. However, the marked heterogeneity of HNSCC, coupled with patient-specific HLA variations, necessitates precise technical identification and evaluation of neoantigens at the individual level-a significant contemporary challenge. This comprehensive review systematically explores the landscape of neoantigen-based immunotherapy in HNSCC, including neoantigen sources, screening strategies, identification methods, and their clinical applications. Additionally, it evaluates the therapeutic potential of combining neoantigen-based approaches with other immunotherapeutic modalities, particularly immune checkpoint inhibitors, providing valuable insights for future clinical practice and research directions in HNSCC treatment.

Long-read sequencing reveals novel genetic polymorphisms in the major histocompatibility complex region and their impacts on the Han Chinese population

Human leukocyte antigen (HLA) genes in the major histocompatibility complex (MHC) region are crucial for immunity and are associated with numerous diseases and phenotypes. The MHC region's complexity and high genetic diversity make it challenging to analyze using short-read sequencing (SRS) technology. We sequence the MHC region of 100 Han Chinese individuals using both long-read sequencing (LRS) and SRS platforms at approximately 30X coverage to study genetic alterations and their potential functional impacts. LRS provides significantly greater coverage of the MHC region and eight classical HLA genes, particularly at the HLA-DRB1 locus, compared with SRS. We detect 78,249 single nucleotide polymorphisms (SNPs) using LRS, with 26.0% undetectable by SRS. Based on SNP and inferred HLA allele types, we construct an LRS-based MHC reference panel for the Han Chinese, containing approximately 2.6 times more genetic variants than the SRS-based Han-MHC reference panel. A phenome-wide association study assessing 26,024 phenotypes across 15 categories identifies significant associations for 7,879 independent variants (including 809 LRS-specific SNPs) with 409 phenotypes in nine categories. This analysis reveals 24 unreported HLA allele associations in the bioelectric and cellular categories. The conditional analysis identifies 530 independent signals across the 409 phenotypes, including 28 previously unreported signals of eight classical HLA genes associated with 33 phenotypes. Of the top-associated SNPs, 191 are detected by LRS only. Fine-mapping identifies 126 independent candidate causal SNPs for three immune-related cellular phenotypes, with 17 detected exclusively by LRS. Our study reveals previously unreported variants and their functional impacts in the MHC region, enhancing our understanding of genetic diversity and its potential biological implications in the Han Chinese population.

MultiCook: A Tool That Improves Accuracy of HLA Imputation by Combining Probabilities From Multiple Reference Panels and Methods

HLA molecules are produced by genes within the Major Histocompatibility Complex (MHC). Although the identification of HLA genotype is costly, fortunately, recent computational methods have made it possible to impute HLA genotypes using inexpensive single nucleotide polymorphism (SNP) markers. These imputation methods perform well if ethnicity-matched reference panels are provided. However, the availability of large-sized panels specific to each ethnicity remains limited. As a result, to achieve better imputation for each population, we need to utilise available multiple reference panels together. In this study, we introduce MultiCook, which enables users to simultaneously utilise existing multiple HLA imputation methods with multiple reference panels. MultiCook is versatile in that panels typed with different SNP genotyping platforms can seamlessly be merged, outputs from multiple imputation methods can be combined and the output from the Michigan imputation server with a multiethnic reference panel can also be incorporated. We compared MultiCook to the existing single-reference-panel approaches and the Michigan HLA imputation server. In evaluation with a cohort of 413 Koreans, MultiCook reduced the imputation error rate by about one third, from 4.70% to 3.31%, by combining 1KG EAS (N = 504) and HAN Chinese (N = 9773) reference panels compared to the single-panel approach. Moreover, MultiCook achieved better accuracy for imputing low-frequency alleles in evaluation benchmarks.

Transcript-targeted antigen mapping reveals the potential of POSTN splicing junction epitopes in glioblastoma immunotherapy

Tumor antigens are crucial for T-cell mediated immunotherapy, but identified antigens for gliomas remain limited. Aberrant splicing variants are commonly expressed in tumors, resulting in unique tumor isoforms with potential antigenic properties. Herein, we analyzed multi-omics data from 587 glioma patients and assembled a library of putative tumor-enriched isoform antigens (TIA) and corresponding peptides presented on each HLA-I allele. We constructed an individual-specific TIA peptide candidate repertoire for each patient based on their TIA expression and HLA-I haplotypes. TIAs were highly expressed, enriched with glioma malignancy, and demonstrated strong HLA-binding affinity. We focused on periostin isoform-203 (POSTN-203), which was associated with poor survival of patients and contained multiple predicted HLA-restricted peptide epitopes. A selected HLA-A11-restricted peptide from POSTN-203 (POSTN-203A11) induced antigen-specific T-cell responses against both peptide-pulsed and POSTN-203-expressing glioma cells in an HLA-specific manner. Our findings highlight TIAs as a promising source of immunogenic antigens and POSTN-203 as a potential promising target for glioma immunotherapy.

Predicting pathogen evolution and immune evasion in the age of artificial intelligence

The genomic diversification of viral pathogens during viral epidemics and pandemics represents a major adaptive route for infectious agents to circumvent therapeutic and public health initiatives. Historically, strategies to address viral evolution have relied on responding to emerging variants after their detection, leading to delays in effective public health responses. Because of this, a long-standing yet challenging objective has been to forecast viral evolution by predicting potentially harmful viral mutations prior to their emergence. The promises of artificial intelligence (AI) coupled with the exponential growth of viral data collection infrastructures spurred by the COVID-19 pandemic, have resulted in a research ecosystem highly conducive to this objective. Due to the COVID-19 pandemic accelerating the development of pandemic mitigation and preparedness strategies, many of the methods discussed here were designed in the context of SARS-CoV-2 evolution. However, most of these pipelines were intentionally designed to be adaptable across RNA viruses, with several strategies already applied to multiple viral species. In this review, we explore recent breakthroughs that have facilitated the forecasting of viral evolution in the context of an ongoing pandemic, with particular emphasis on deep learning architectures, including the promising potential of language models (LM). The approaches discussed here employ strategies that leverage genomic, epidemiologic, immunologic and biological information.

Advancing risk stratification in kidney transplantation: integrating HLA-derived T-cell epitope and B-cell epitope matching algorithms for enhanced predictive accuracy of HLA compatibility

Introduction

The immune-mediated rejection of transplanted organs is a complex interplay between T cells and B cells, where the recognition of HLA-derived epitopes plays a crucial role. Several algorithms of molecular compatibility have been suggested, each focusing on a specific aspect of epitope immunogenicity.

Methods

Considering reported death-censored graft survival in the SRTR dataset, we evaluated four models of molecular compatibility: antibody-verified Eplets, Snow, PIRCHE-II and amino acid matching. We have statistically evaluated their co-dependency and synergistic effects between models systematically on 400,935 kidney transplantations using Cox proportional hazards and XGBoost models.

Results

Multivariable models of histocompatibility generally outperformed univariable predictors, with a combined model of HLA-A, -B, -DR matching, Snow and PIRCHE-II yielding highest AUC in XGBoost and lowest BIC in Cox models. Augmentation of a clinical prediction model of pre-transplant parameters by molecular compatibility metrics improved model performance particularly considering long-term outcomes.

Discussion

Our study demonstrates that the use of multiple specialized molecular HLA matching predictors improves prediction performance, thereby improving risk classification and supporting informed decision-making in kidney transplantation.

Deep learning enhances the prediction of HLA class I-presented CD8+ T cell epitopes in foreign pathogens

Accurate in silico determination of CD8+ T cell epitopes would greatly enhance T cell-based vaccine development, but current prediction models are not reliably successful. Here, motivated by recent successes applying machine learning to complex biology, we curated a dataset of 651,237 unique human leukocyte antigen class I (HLA-I) ligands and developed MUNIS, a deep learning model that identifies peptides presented by HLA-I alleles. MUNIS shows improved performance compared with existing models in predicting peptide presentation and CD8+ T cell epitope immunodominance hierarchies. Moreover, application of MUNIS to proteins from Epstein-Barr virus led to successful identification of both established and novel HLA-I epitopes which were experimentally validated by in vitro HLA-I-peptide stability and T cell immunogenicity assays. MUNIS performs comparably to an experimental stability assay in terms of immunogenicity prediction, suggesting that deep learning can reduce experimental burden and accelerate identification of CD8+ T cell epitopes for rapid T cell vaccine development.

HLA gene polymorphism is a modifier of age-related breast cancer penetrance in carriers of BRCA1 pathogenic alleles

PURPOSE

Female carriers of germline BRCA1 mutations almost invariably develop breast cancer (BC); however, the age at onset is a subject of variation. We hypothesized that the age-related penetrance of BRCA1 mutations may depend on inherited variability in the host immune system.

METHODS

Next-generation sequencing was utilized for genotyping of HLA class I/II genes (HLA-A, HLA-B, HLA-C, HLA-DPB1, HLA-DQB1, and HLA-DRB1/3/4/5) in patients with BRCA1-associated BC with early (< / = 38 years, n = 215) and late (> / = 58 years, n = 108) age at onset.

RESULTS

HLA-DQB1*06:03P prevalence was higher in the late-onset group due to the excess of allele carriers [25/108 (23.1%) vs. 22/215 (10.2%); OR 2.96, p < 0.001]. For all HLA-I loci, there was a trend toward an increase in the number of homozygotes in the early-onset group. This trend reached statistical significance for the HLA-A [14.4% vs. 6.5%, p = 0.037; OR 2.4, p = 0.042]. The frequencies of HLA-DPB1, HLA-DQB1, and HLA-DRB1/3/4/5 homozygous genotypes did not differ between young-onset and late-onset patients. The maximum degree of homozygosity detected in this study was 6 out of 7 HLA class I/II loci; all six carriers of these genotypes were diagnosed with BC at the age < / = 38 years [OR 6.97, p = 0.187].

CONCLUSION

HLA polymorphism may play a role in modifying the penetrance of BRCA1 pathogenic variants. Certain HLA alleles or HLA homozygosity may modify the risk of BC in BRCA1 carriers.

Genome-Wide Association Study Reveals a Causal Relationship Between Allergic Rhinitis and Hazelnut Allergy

BACKGROUND

Little is known about the genetics of food allergy (FA) to various allergens and the heterogeneity of FA in adults.

OBJECTIVE

We aimed to investigate genetic susceptibility to FA in an adult population and to assess the association between secondary FA and allergic rhinitis (AR).

METHODS

FA and allergen-specific FA were defined based on in-depth questionnaires and a previously published FA algorithm in the Lifelines. We performed a series of genome-wide association studies (GWAS) on FA and nine allergen-specific (e.g., hazelnut) FA in 21,353 adults in Lifelines. Single nucleotide polymorphisms (SNPs) (p < 1E-5) were replicated in a second independent set of 15,518 adults participating in the Lifelines followed by meta-analysis of the results of the two datasets. We subsequently investigated the causal relationship of AR to FA using Mendelian randomization (MR) analysis.

RESULTS

We observed co-occurrence of tree nuts and apple FA, with over 80% of this group also reporting AR. After meta-analysis, we identified one genome-wide significant locus near HLA-DPA1 associated with self-reported hazelnut allergy (hazelnutFA), of which the top SNP is rs5025825 (p = 2.51E-9, OR = 1.43). Two-sample MR indicated that AR is a significant causal risk factor for hazelnutFA (p-IVW = 5.27E-10, β = 5.90, p-pleiotropy = 0.46).

CONCLUSION

Our questionnaire enabled a large GWAS on self-reported FA in Dutch adults. We report one novel locus in the human leukocyte antigens (HLA) region associated with hazelnutFA, implying an association with antigen recognition. Our findings genetically link secondary FA to AR in adults.

Pharmacogenomic landscape of the Thai population from genome sequencing of 949 individuals

Inter-individual variability in drug responses is significantly influenced by genetic factors, underscoring the importance of population-specific pharmacogenomic studies to optimize clinical outcomes. In this study, we analyzed whole genome sequencing data from 949 unrelated Thai individuals and conducted an in-depth analysis of 3239 genes involved in drug pharmacokinetics, pharmacodynamics, or immune-mediated adverse drug reactions. We identified 43 single nucleotide polymorphisms (SNPs), 134 diplotypes, and 15 human leukocyte antigen (HLA) alleles, all with moderate to high clinical significance. On average, each Thai individual carried 14 SNPs, one to two HLA alleles, and six diplotypes with actionable phenotypic associations. Clinically important diplotypes were present in over 20% of individuals for seven genes (CYP2A6, CYP2B6, CYP2C19, CYP3A5, NAT2, SLCO1B1, and VKORC1). In addition, clinically significant SNPs with allele frequencies exceeding 20% were identified among 15 genes, including VKORC1, CYP4F2, and ABCG2. We also identified 21,211 potentially deleterious variants among 3239 genes. Of these variants, 3746 were novel. The comprehensive dataset from this study serves as a valuable resource of pharmacogenomic variants in the Thai population, which will facilitate the development of personalized drug therapies and enhance patient care in Thailand.

Geometric deep learning improves generalizability of MHC-bound peptide predictions

The interaction between peptides and major histocompatibility complex (MHC) molecules is pivotal in autoimmunity, pathogen recognition and tumor immunity. Recent advances in cancer immunotherapies demand for more accurate computational prediction of MHC-bound peptides. We address the generalizability challenge of MHC-bound peptide predictions, revealing limitations in current sequence-based approaches. Our structure-based methods leveraging geometric deep learning (GDL) demonstrate promising improvement in generalizability across unseen MHC alleles. Further, we tackle data efficiency by introducing a self-supervised learning approach on structures (3D-SSL). Without being exposed to any binding affinity data, our 3D-SSL outperforms sequence-based methods trained on ~90 times more data points. Finally, we demonstrate the resilience of structure-based GDL methods to biases in binding data on an Hepatitis B virus vaccine immunopeptidomics case study. This proof-of-concept study highlights structure-based methods' potential to enhance generalizability and data efficiency, with possible implications for data-intensive fields like T-cell receptor specificity predictions.

Bioinformatic Tools for Studying the Cellular Immune Response to SARS-CoV-2, Vaccine Efficacy, and Future Pandemics at the Global Population Level

Antigen recognition by human leukocyte antigen (HLA) restriction is critical for an adequate antiviral response in both natural infection and vaccination. However, the overwhelming polymorphism of HLA, with nearly 40,000 alleles identified, is an important limitation for the global analysis of cellular immune responses and vaccine efficacy. In this narrative review, we included several immunoinformatics studies performed in our laboratory to circumvent this limitation. These analyses focused on studying the cellular immune responses restricted by the most common HLA alleles, and their role in vaccine efficacy. Computational studies validated experimentally, such as our laboratory has carried out, represent a useful, rapid, and cost-effective strategy to combat future pandemics.

COVID-19 infection and inactivated vaccination: Impacts on clinical and immunological profiles in Chinese children with type 1 diabetes

BACKGROUND

The coronavirus disease 2019 (COVID-19) pandemic has been linked to an increased incidence of diabetes and diabetic ketoacidosis (DKA). However, the relationship between COVID-19 infection and progression to type 1 diabetes (T1D) in children has not been well defined.

AIM

To evaluate the influence of COVID-19 infection and inactivated vaccine administration on the progression of T1D among Chinese children.

METHODS

A total of 197 newly diagnosed patients with T1D were retrospectively enrolled from Children's Hospital of Fudan University between September 2020 and December 2023. The patients were divided into three groups based on their history of COVID-19 infection and vaccination: the infection group, the vaccination-only group, and the non-infection/non-vaccination group. Comprehensive clinical assessments and detailed immunological evaluations were performed to delineate the characteristics and immune responses of these groups.

RESULTS

The incidence of DKA was significantly higher in the COVID-19 infection group (70.2%) compared to the non-infection/non-vaccination group (62.5%) and vacscination-only group (45.6%; P = 0.015). Prior COVID-19 infection was correlated with increased DKA risk (OR: 1.981, 95%CI: 1.026-3.825, P = 0.042), while vaccination was associated with a reduced risk (OR: 0.558, 95%CI: 0.312-0.998, P = 0.049). COVID-19 infection mildly altered immune profiles, with modest differences in autoantibody positivity, lymphocyte distribution, and immunoglobulin levels. Notably, HLA-DR3 positive children with a history of COVID-19 infection had an earlier T1D onset and lower fasting C-peptide levels than the HLA-DR3 negative children with a history of infection (both P < 0.05).

CONCLUSION

COVID-19 infection predisposes children to severe T1D, characterized by enhanced DKA risk. Inactivated vaccination significantly lowers DKA incidence at T1D onset. These findings are valuable for guiding future vaccination and T1D risk surveillance strategies in epidemic scenarios in the general pediatric population.

High KIR diversity in Uganda and Botswana children living with HIV

Killer-cell immunoglobulin-like receptors (KIRs) are essential components of the innate immune system found on the surfaces of natural killer (NK) cells. The KIRs encoding genes are located on chromosome 19q13.4 and are genetically diverse across populations. KIRs are associated with various disease states including HIV progression, and are linked to transplantation rejection and reproductive success. However, there is limited knowledge on the diversity of KIRs from Uganda and Botswana HIV-infected paediatric cohorts, with high endemic HIV rates. We used next-generation sequencing technologies on 312 (246 Uganda, 66 Botswana) samples to generate KIR allele data and employed customised bioinformatics techniques for allelic, allotype and disease association analysis. We show that these sample sets from Botswana and Uganda have different KIRs of different diversities. In Uganda, we observed 147 vs 111 alleles in the Botswana cohort, which had a more than 1 % frequency. We also found significant deviation towards homozygosity for the KIR3DL2 gene for both rapid (RPs) and long-term non-progressors (LTNPs)in the Ugandan cohort. The frequency of the bw4-80I ligand was also significantly higher among the LTNPs than RPs (8.9 % Vs 2.0%, P-value: 0.032). In the Ugandan cohort, KIR2DS4*001 (OR: 0.671, 95 % CI: 0.481-0.937, FDR adjusted Pc=0.142) and KIR2DS4*006 (OR: 2.519, 95 % CI: 1.085-5.851, FDR adjusted Pc=0.142) were not associated with HIV disease progression after adjustment for multiple testing. Our study results provide additional knowledge of the genetic diversity of KIRs in African populations and provide evidence that will inform future immunogenetics studies concerning human disease susceptibility, evolution and host immune responses.

Genetically modified and unmodified cellular approaches to enhance graft versus leukemia effect, without increasing graft versus host disease: the use of allogeneic cytokine-induced killer cells

Although allogeneic hematopoietic cell transplantation (HCT) represents a curative approach for many patients with hematological diseases, post-transplantation relapse occurs in 20-50% of cases, representing the primary cause of treatment failure and mortality. Alloreactive donor T cells are responsible for the graft versus leukemia (GvL) effect, which represents the key mechanism for the long-term curative effect of HCT. However, the downside is represented by graft versus host disease (GvHD), largely contributing to transplant-related mortality (TRM). Multiple factors play a role in regulating the delicate balance between GvL and GvHD, such as the optimization of the donor HLA and KIR match, the type of graft source, and the adaptive use of post-transplant cellular therapy. In addition to the standard donor lymphocyte infusion (DLI), several attempts were made to favor the GvL effect without increasing the GvHD risk. Selected DLI, NK DLI, activated DLI and more sophisticated genetically engineered cells can be employed. In this scenario, cytokine-induced killer (CIK) cells represent a suitable tool to boost GvL while minimizing GvHD. CIK cells are T lymphocytes activated in culture in the presence of monoclonal antibodies against CD3 (OKT3), interferon-gamma (IFN-g), and interleukin-2 (IL-2), characterized by the expression of markers typical of NK cells and T cells (CD3+, CD56+, with a prevalent CD8+ phenotype). CIK cells can mediate cytotoxicity through both MHC and non-MHC restricted recognition, which is the so-called "dual-functional capability" and display minimum alloreactivity. Allogeneic CIK cells showed a favorable rate of response, especially in the setting of minimal residual disease, with a rate of GvHD not exceeding 25%. Finally, the CIK cell platform can be adapted for chimeric antigen receptor (CAR) cell strategy, showing promising results in both preclinical and clinical settings. In this review, we describe the main immunological basis for the development of the GvL and the possible cellular therapy approaches used to boost it, with a particular focus on the use of CIK cells.

Polymorphic positions 349 and 725 of the autoimmunity-protective allotype 10 of ER aminopeptidase 1 are key in determining its unique enzymatic properties

Introduction

ER aminopeptidase 1 (ERAP1) is a polymorphic intracellular aminopeptidase with key roles in antigen presentation and adaptive immune responses. ERAP1 allotype 10 is highly protective toward developing some forms of autoimmunity and displays unusual functional properties, including very low activity versus some substrates.

Methods

To understand the molecular mechanisms that underlie the biology of allotype 10, we studied its enzymatic and biophysical properties focusing on its unique polymorphisms V349M and Q725R.

Results

Compared to ancestral allotype 1, allotype 10 is much less effective in trimming small substrates but presents allosteric kinetics that ameliorate activity differences at high substrate concentrations. Furthermore, it is inhibited by a transition-state analogue via a non-competitive mechanism and is much less responsive to an allosteric small-molecule modulator. It also presents opposite enthalpy, entropy, and heat capacity of activation compared to allotype 1, and its catalytic rate is highly dependent on viscosity. Polymorphisms V349M and Q725R significantly contribute to the lower enzymatic activity of allotype 10 for small substrates, especially at high substrate concentrations, influence the cooperation between the regulatory and active sites, and regulate viscosity dependence, likely by limiting product release.

Conclusions

Overall, our results suggest that allotype 10 is not just an inactive variant of ERAP1 but rather carries distinct enzymatic properties that largely stem from changes at positions 349 and 725. These changes affect kinetic and thermodynamic parameters that likely control rate-limiting steps in the catalytic cycle, resulting in an enzyme optimized for sparing small substrates and contributing to the homeostasis of antigenic epitopes in the ER.

HLA-B allele frequencies and implications for pharmacogenetics in the Kuwaiti population

Objective: This study explores the frequency of human leukocyte antigen (HLA) genes, particularly HLA-B alleles, within the Kuwaiti population. We aim to identify alleles with known associations to adverse drug reactions (ADRs) based on existing literature. We focus on the HLA-B gene due to its well-documented associations with severe cutaneous adverse reactions and the extensive pharmacogenetic research supporting its clinical relevance.

Methods

We utilized the HLA-HD tool to extract, annotate, and analyse HLA-B alleles from the exome data of 561 Kuwaiti individuals, sequenced on the Illumina HiSeq platform. HLA typing was conducted using the HLA-HD tool with a reference panel from the IPD-IMGT/HLA database. The major HLA-B pharmacogenetic markers were obtained from the HLA Adverse Drug Reaction Database, focusing on alleles with significant ADR associations in published literature.

Results

The distribution of HLA-B alleles in the Kuwaiti population revealed that the most frequent alleles were HLA-B*50:01 (10.52%), HLA-B*51:01 (9.89%), HLA-B*08:01 (6.06%), HLA-B*52:01 (4.55%), HLA-B*18:01 (3.92%), and HLA-B*41:01 (3.65%). Notably, alleles HLA-B*13:01, HLA-B*13:02, HLA-B*15:02, HLA-B*15:13, HLA-B*35:02, HLA-B*35:05, HLA-B*38:01, HLA-B*40:02, HLA-B*44:03, HLA-B*51:01, HLA-B*57:01 and HLA-B*58:01 were identified with known associations to various ADRs. For example, HLA-B*51:01 was associated with clindamycin, phenobarbital, and phenytoin, and was found in 18% of individuals.

Conclusion

Our study enriches the regional genetic landscape by delineating HLA-B allele variations within Kuwait and across the Arabian Peninsula. This genetic insight, along with the identification of markers previously linked to drug hypersensitivity, provides a foundation for future pharmacogenetic research and potential personalized medicine strategies in the region.

Impact of HLA Alleles on COVID-19 Severity in Kidney Transplant Recipients: A Single-Center Study

Introduction The coronavirus disease 2019 (COVID-19) pandemic has significantly impacted global health, particularly affecting vulnerable populations, such as organ transplant recipients. Human leukocyte antigens (HLAs) play a critical role in immune response regulation, and understanding their association with COVID-19 can provide insights into disease susceptibility and severity. This study aims to explore the association between HLA allele variability and COVID-19 susceptibility and severity among kidney transplant recipients. Methods In 2023, we conducted a study on 73 kidney transplant recipients who tested positive for COVID-19 via polymerase chain reaction. This study included assessments of clinical status, immunosuppressive drug levels, HLA allele frequencies, and donor-recipient tissue compatibility. Molecular analyses were performed using sequence-specific oligonucleotide typing, and statistical analysis was conducted using IBM SPSS Statistics, version 20.0 (IBM Corp., Armonk, NY). Results Among the participants, 31 were hospitalized and 42 were treated as outpatients. Significant differences were observed in HLA allele distributions, particularly the HLA-A*11 allele, which was more prevalent in outpatient-treated patients, suggesting a potential protective effect. No significant age differences were observed between hospitalized and outpatient groups. Serum tacrolimus levels were notably higher in outpatients. Statistical analyses revealed significant associations between certain HLA groups and the severity of COVID-19 infection. Conclusions This study highlights the importance of HLA allele compatibility in influencing the clinical outcomes of COVID-19 in kidney transplant recipients. The findings suggest that specific HLA alleles may reduce susceptibility or moderate the severity of COVID-19, indicating a need for broader genetic studies across diverse populations to validate these observations and improve management strategies for transplant recipients during pandemics.

Tumor Neoepitope-Based Vaccines: A Scoping Review on Current Predictive Computational Strategies

Therapeutic cancer vaccines have been considered in recent decades as important immunotherapeutic strategies capable of leading to tumor regression. In the development of these vaccines, the identification of neoepitopes plays a critical role, and different computational methods have been proposed and employed to direct and accelerate this process. In this context, this review identified and systematically analyzed the most recent studies published in the literature on the computational prediction of epitopes for the development of therapeutic vaccines, outlining critical steps, along with the associated program's strengths and limitations. A scoping review was conducted following the PRISMA extension (PRISMA-ScR). Searches were performed in databases (Scopus, PubMed, Web of Science, Science Direct) using the keywords: neoepitope, epitope, vaccine, prediction, algorithm, cancer, and tumor. Forty-nine articles published from 2012 to 2024 were synthesized and analyzed. Most of the identified studies focus on the prediction of epitopes with an affinity for MHC I molecules in solid tumors, such as lung carcinoma. Predicting epitopes with class II MHC affinity has been relatively underexplored. Besides neoepitope prediction from high-throughput sequencing data, additional steps were identified, such as the prioritization of neoepitopes and validation. Mutect2 is the most used tool for variant calling, while NetMHCpan is favored for neoepitope prediction. Artificial/convolutional neural networks are the preferred methods for neoepitope prediction. For prioritizing immunogenic epitopes, the random forest algorithm is the most used for classification. The performance values related to the computational models for the prediction and prioritization of neoepitopes are high; however, a large part of the studies still use microbiome databases for training. The in vitro/in vivo validations of the predicted neoepitopes were verified in 55% of the analyzed studies. Clinical trials that led to successful tumor remission were identified, highlighting that this immunotherapeutic approach can benefit these patients. Integrating high-throughput sequencing, sophisticated bioinformatics tools, and rigorous validation methods through in vitro/in vivo assays as well as clinical trials, the tumor neoepitope-based vaccine approach holds promise for developing personalized therapeutic vaccines that target specific tumor cancers.

Orthanq: transparent and uncertainty-aware haplotype quantification with application in HLA-typing

BACKGROUND

Identification of human leukocyte antigen (HLA) types from DNA-sequenced human samples is important in organ transplantation and cancer immunotherapy and remains a challenging task considering sequence homology and extreme polymorphism of HLA genes.

RESULTS

We present Orthanq, a novel statistical model and corresponding application for transparent and uncertainty-aware quantification of haplotypes. We utilize our approach to perform HLA typing while, for the first time, reporting uncertainty of predictions and transparently observing mutations beyond reported HLA types. Using 99 gold standard samples from 1000 Genomes, Illumina Platinum Genomes and Genome In a Bottle projects, we show that Orthanq can provide overall superior accuracy and shorter runtimes than state-of-the-art HLA typers.

CONCLUSIONS

Orthanq is the first approach that allows to directly utilize existing pangenome alignments and type all HLA loci. Moreover, it can be generalized for usages beyond HLA typing, e.g. for virus lineage quantification. Orthanq is available under https://orthanq.github.io .

Frequency-dependent selection of neoantigens fosters tumor immune escape and predicts immunotherapy response

Cancer is an evolutionary process shaped by selective pressure from the microenvironments. However, recent studies reveal that certain tumors undergo neutral evolution where there is no detectable fitness difference amongst the cells following malignant transformation. Here, through computational modeling, we demonstrate that negative frequency-dependent selection (or NFDS), where the immune response against cancer cells depends on the clonality of neoantigens, can lead to an immunogenic landscape that is highly similar to neutral evolution. Crucially, NFDS promotes high antigenic heterogeneity and early immune evasion in hypermutable tumors, leading to poor responses to immune checkpoint blockade (ICB) therapy. Our model also reveals that NFDS is characterized by a negative association between average clonality and total burden of neoantigens. Indeed, this unique feature of NFDS is common in the whole-exome sequencing (WES) datasets (357 tumor samples from 275 patients) from four melanoma cohorts with ICB therapy and a non-small cell lung cancer (NSCLC) WES dataset (327 tumor samples from 100 patients). Altogether, our study provides quantitative evidence supporting the theory of NFDS in cancer, explaining the high prevalence of neutral-looking tumors. These findings also highlight the critical role of frequency-dependent selection in devising more efficient and predictive immunotherapies.

Long-read RNA-seq demarcates cis- and trans-directed alternative RNA splicing

Genetic regulation of alternative splicing constitutes an important link between genetic variation and disease. Nonetheless, RNA splicing is regulated by both cis-acting elements and trans-acting splicing factors. Determining splicing events that are directed primarily by the cis- or trans-acting mechanisms will greatly inform our understanding of the genetic basis of disease. Here, we show that long-read RNA-seq, combined with our new method isoLASER, enables a clear segregation of cis- and trans-directed splicing events for individual samples. The genetic linkage of splicing is largely individual-specific, in stark contrast to the tissue-specific pattern of splicing profiles. Analysis of long-read RNA-seq data from human and mouse revealed thousands of cis-directed splicing events susceptible to genetic regulation. We highlight such events in the HLA genes whose analysis was challenging with short-read data. We also highlight novel cis-directed splicing events in Alzheimer's disease-relevant genes such as MAPT and BIN1. Together, the clear demarcation of cis- and trans-directed splicing paves ways for future studies of the genetic basis of disease.

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.

A multi-ethnic reference panel to impute HLA classical and non-classical class I alleles in admixed samples: Testing imputation accuracy in an admixed sample from Brazil

The MHC class I region contains crucial genes for the innate and adaptive immune response, playing a key role in susceptibility to many autoimmune and infectious diseases. Genome-wide association studies have identified numerous disease-associated SNPs within this region. However, these associations do not fully capture the immune-biological relevance of specific HLA alleles. HLA imputation techniques may leverage available SNP arrays by predicting allele genotypes based on the linkage disequilibrium between SNPs and specific HLA alleles. Successful imputation requires diverse and large reference panels, especially for admixed populations. This study employed a bioinformatics approach to call SNPs and HLA alleles in multi-ethnic samples from the 1000 genomes (1KG) dataset and admixed individuals from Brazil (SABE), utilising 30X whole-genome sequencing data. Using HIBAG, we created three reference panels: 1KG (n = 2504), SABE (n = 1171), and the full model (n = 3675) encompassing all samples. In extensive cross-validation of these reference panels, the multi-ethnic 1KG reference exhibited overall superior performance than the reference with only Brazilian samples. However, the best results were achieved with the full model. Additionally, we expanded the scope of imputation by developing reference panels for non-classical, MICA, MICB and HLA-H genes, previously unavailable for multi-ethnic populations. Validation in an independent Brazilian dataset showcased the superiority of our reference panels over the Michigan Imputation Server, particularly in predicting HLA-B alleles among Brazilians. Our investigations underscored the need to enhance or adapt reference panels to encompass the target population's genetic diversity, emphasising the significance of multiethnic references for accurate imputation across different populations.

Transformers meets neoantigen detection: a systematic literature review

Cancer immunology offers a new alternative to traditional cancer treatments, such as radiotherapy and chemotherapy. One notable alternative is the development of personalized vaccines based on cancer neoantigens. Moreover, Transformers are considered a revolutionary development in artificial intelligence with a significant impact on natural language processing (NLP) tasks and have been utilized in proteomics studies in recent years. In this context, we conducted a systematic literature review to investigate how Transformers are applied in each stage of the neoantigen detection process. Additionally, we mapped current pipelines and examined the results of clinical trials involving cancer vaccines.

Comparison of human leukocyte antigen in patients with paroxysmal nocturnal hemoglobinuria of different clone sizes

Glycosylphosphatidylinositol-anchored protein-deficient hematopoietic stem and progenitor cell development caused by PIGA mutations cannot fully explain the pathogenesis of paroxysmal nocturnal hemoglobinuria (PNH). Herein, patients newly diagnosed with PNH at our hospital between April 2019 and April 2021 were recruited. The human leukocyte antigen (HLA) class I and II loci were analyzed, and patients were stratified by PNH clone sizes: small (< 50%) and large (≥ 50%). In 40 patients (29 males; 72.5%), the median PNH clone size was 72%. Thirteen (32.5%) and twenty-seven (67.5%) patients harbored small and large PNH clones, respectively. DRB1*15:01 and DQB1*06:02 had higher frequencies in patients with PNH than in healthy controls (adjusted P-value = 4.10 × 10-4 and 4.10 × 10-4, respectively). Whole HLA class I and II allele contributions differed (P = 0.046 and 0.065, not significant difference) when comparing patients with small and large PNH clones. B*13:01 and C*04:01 allelic frequencies were significantly higher in patients with small clones (P = 0.032 and P = 0.032, respectively). Patients with small clones had higher class II HLA evolutionary divergence (HED) (P = 0.041) and global class I and II HED (P = 0.019). In the entire cohort, 17 HLA aberrations were found in 11 (27.5%) patients. No significant differences in HLA aberrations were found between patients with small or large clones. In conclusion, patients with small clones tended to have a higher frequency of immune attack-associated alleles. A higher HED in patients with small clones may reflect a propensity for T cell-mediated autoimmunity. HLA aberrations were similar between patients with small and large clones.

Evaluation of HLA typing content of next-generation sequencing datasets from family trios and individuals of arab ethnicity

Introduction: HLA typing is a critical tool in both clinical and research applications at the individual and population levels. Benchmarking studies have indicated HLA-HD as the preferred tool for accurate and comprehensive HLA allele calling. The advent of next-generation sequencing (NGS) has revolutionized genetic analysis by providing high-throughput sequencing data. This study aims to evaluate, using the HLA-HD tool, the HLA typing content of whole exome, whole genome, and HLA-targeted panel sequence data from the consanguineous population of Arab ethnicity, which has been underrepresented in prior benchmarking studies. Methods: We utilized sequence data from family trios and individuals, sequenced on one or more of the whole exome, whole genome, and HLA-targeted panel sequencing technologies. The performance and resolution across various HLA genes were evaluated. We incorporated a comparative quality control analysis, assessing the results obtained from HLA-HD by comparing them with those from the HLA-Twin tool to authenticate the accuracy of the findings. Results: Our analysis found that alleles across 29 HLA loci can be successfully and consistently typed from NGS datasets. Clinical-grade whole exome sequencing datasets achieved the highest consistency rate at three-field resolution, followed by targeted HLA panel, research-grade whole exome, and whole genome datasets. Discussion: The study catalogues HLA typing consistency across NGS datasets for a large array of HLA genes and highlights assessments regarding the feasibility of utilizing available NGS datasets in HLA allele studies. These findings underscore the reliability of HLA-HD for HLA typing in underrepresented populations and demonstrate the utility of various NGS technologies in achieving accurate HLA allele calling.

Reassessing human MHC-I genetic diversity in T cell studies

The Major Histocompatibility Complex class I (MHC-I) system plays a vital role in immune responses by presenting antigens to T cells. Allele specific technologies, including recombinant MHC-I technologies, have been extensively used in T cell analyses for COVID-19 patients and are currently used in the development of immunotherapies for cancer. However, the immense diversity of MHC-I alleles presents challenges. The genetic diversity serves as the foundation of personalized medicine, yet it also poses a potential risk of exacerbating healthcare disparities based on MHC-I alleles. To assess potential biases, we analysed (pre)clinical publications focusing on COVID-19 studies and T cell receptor (TCR)-based clinical trials. Our findings reveal an underrepresentation of MHC-I alleles associated with Asian, Australian, and African descent. Ensuring diverse representation is vital for advancing personalized medicine and global healthcare equity, transcending genetic diversity. Addressing this disparity is essential to unlock the full potential of T cells for enhancing diagnosis and treatment across all individuals.

Novel insights into TCR-T cell therapy in solid neoplasms: optimizing adoptive immunotherapy

Adoptive immunotherapy in the T cell landscape exhibits efficacy in cancer treatment. Over the past few decades, genetically modified T cells, particularly chimeric antigen receptor T cells, have enabled remarkable strides in the treatment of hematological malignancies. Besides, extensive exploration of multiple antigens for the treatment of solid tumors has led to clinical interest in the potential of T cells expressing the engineered T cell receptor (TCR). TCR-T cells possess the capacity to recognize intracellular antigen families and maintain the intrinsic properties of TCRs in terms of affinity to target epitopes and signal transduction. Recent research has provided critical insight into their capability and therapeutic targets for multiple refractory solid tumors, but also exposes some challenges for durable efficacy. In this review, we describe the screening and identification of available tumor antigens, and the acquisition and optimization of TCRs for TCR-T cell therapy. Furthermore, we summarize the complete flow from  laboratory to clinical applications of TCR-T cells. Last, we emerge future prospects for improving therapeutic efficacy in cancer world with combination therapies or TCR-T derived products. In conclusion, this review depicts our current understanding of TCR-T cell therapy in solid neoplasms, and provides new perspectives for expanding its clinical applications and improving therapeutic efficacy.

Resolving unknown nucleotides in the IPD-IMGT/HLA database by extended and full-length sequencing of HLA class I and II alleles

In the past, identification of HLA alleles was limited to sequencing the region of the gene coding for the peptide binding groove, resulting in a lack of sequence information in the HLA database, challenging HLA allele assignment software programs. We investigated full-length sequences of 19 HLA class I and 7 HLA class II alleles, and we extended another 47 HLA class I alleles with sequences of 5' and 3' UTR regions that were all not yet available in the IPD-IMGT/HLA database. We resolved 8638 unknown nucleotides in the coding sequence of HLA class I and 2139 of HLA class II. Furthermore, with full-length sequencing of the 26 alleles, more than 90 kb of sequence information was added to the non-coding sequences, whereas extension of the 47 alleles resulted in the addition of 5.5 kb unknown nucleotides to the 5' UTR and > 31.7 kb to the 3' UTR region. With this information, some interesting features were observed, like possible recombination events and lineage evolutionary origins. The continuing increase in the availability of full-length sequences in the HLA database will enable the identification of the evolutionary origin and will help the community to improve the alignment and assignment accuracy of HLA alleles.

Identification of apolipoprotein B-reactive CDR3 motifs allows tracking of atherosclerosis-related memory CD4+T cells in multiple donors

Introduction

Atherosclerosis is a major pathological condition that underlies many cardiovascular diseases (CVDs). Its etiology involves breach of tolerance to self, leading to clonal expansion of autoreactive apolipoprotein B (APOB)-reactive CD4+T cells that correlates with clinical CVD. The T-cell receptor (TCR) sequences that mediate activation of APOB-specific CD4+T cells are unknown.

Methods

In a previous study, we had profiled the hypervariable complementarity determining region 3 (CDR3) of CD4+T cells that respond to six immunodominant APOB epitopes in most donors. Here, we comprehensively analyze this dataset of 149,065 APOB-reactive and 199,211 non-reactive control CDR3s from six human leukocyte antigen-typed donors.

Results

We identified 672 highly expanded (frequency threshold > 1.39E-03) clones that were significantly enriched in the APOB-reactive group as compared to the controls (log10 odds ratio ≥1, Fisher's test p < 0.01). Analysis of 114,755 naïve, 91,001 central memory (TCM) and 29,839 effector memory (TEM) CDR3 sequences from the same donors revealed that APOB+ clones can be traced to the complex repertoire of unenriched blood T cells. The fraction of APOB+ clones that overlapped with memory CDR3s ranged from 2.2% to 46% (average 16.4%). This was significantly higher than their overlap with the naïve pool, which ranged from 0.7% to 2% (average 1.36%). CDR3 motif analysis with the machine learning-based in-silico tool, GLIPHs (grouping of lymphocyte interactions by paratope hotspots), identified 532 APOB+ motifs. Analysis of naïve and memory CDR3 sequences with GLIPH revealed that ~40% (209 of 532) of these APOB+ motifs were enriched in the memory pool. Network analysis with Cytoscape revealed extensive sharing of the memory-affiliated APOB+ motifs across multiple donors. We identified six motifs that were present in TCM and TEM CDR3 sequences from >80% of the donors and were highly enriched in the APOB-reactive TCR repertoire.

Discussion

The identified APOB-reactive expanded CD4+T cell clones and conserved motifs can be used to annotate and track human atherosclerosis-related autoreactive CD4+T cells and measure their clonal expansion.

Race, ethnicity, ancestry, and aspects that impact HLA data and matching for transplant

Race, ethnicity, and ancestry are terms that are often misinterpreted and/or used interchangeably. There is lack of consensus in the scientific literature on the definition of these terms and insufficient guidelines on the proper classification, collection, and application of this data in the scientific community. However, defining groups for human populations is crucial for multiple healthcare applications and clinical research. Some examples impacted by population classification include HLA matching for stem-cell or solid organ transplant, identifying disease associations and/or adverse drug reactions, defining social determinants of health, understanding diverse representation in research studies, and identifying potential biases. This article describes aspects of race, ethnicity and ancestry information that impact the stem-cell or solid organ transplantation field with particular focus on HLA data collected from donors and recipients by donor registries or transplant centers.

Advancements in HLA Typing Techniques and Their Impact on Transplantation Medicine

HLA typing serves as a standard practice in hematopoietic stem cell transplantation to ensure compatibility between donors and recipients, preventing the occurrence of allograft rejection and graft-versus-host disease. Conventional laboratory methods that have been widely employed in the past few years, including sequence-specific primer PCR and sequencing-based typing (SBT), currently face the risk of becoming obsolete. This risk stems not only from the extensive diversity within HLA genes but also from the rapid advancement of next-generation sequencing and third-generation sequencing technologies. Third-generation sequencing systems like single-molecule real-time (SMRT) sequencing and Oxford Nanopore (ONT) sequencing have the capability to analyze long-read sequences that span entire intronic-exonic regions of HLA genes, effectively addressing challenges related to HLA ambiguity and the phasing of multiple short-read fragments. The growing dominance of these advanced sequencers in HLA typing is expected to solidify further through ongoing refinements, cost reduction, and error rate minimization. This review focuses on hematopoietic stem cell transplantation (HSCT) and explores prospective advancements and application of HLA DNA typing techniques. It explores how the adoption of third-generation sequencing technologies can revolutionize the field by offering improved accuracy, reduced ambiguity, and enhanced assessment of compatibility in HSCT. Embracing these cutting-edge technologies is essential to advancing the success rates and outcomes of hematopoietic stem cell transplantation. This review underscores the importance of staying at the forefront of HLA typing techniques to ensure the best possible outcomes for patients undergoing HSCT.

MR1 antigen presentation to MAIT cells and other MR1-restricted T cells

MHC antigen presentation plays a fundamental role in adaptive and semi-invariant T cell immunity. Distinct MHC molecules bind antigens that differ in chemical structure, origin and location and present them to specialized T cells. MHC class I-related protein 1 (MR1) presents a range of small molecule antigens to MR1-restricted T (MR1T) lymphocytes. The best studied MR1 ligands are derived from microbial metabolism and are recognized by a major class of MR1T cells known as mucosal-associated invariant T (MAIT) cells. Here, we describe the MR1 antigen presentation pathway: the known types of antigens presented by MR1, the location where MR1-antigen complexes form, the route followed by the complexes to the cell surface, the mechanisms involved in termination of MR1 antigen presentation and the accessory cellular proteins that comprise the MR1 antigen presentation machinery. The current road map of the MR1 antigen presentation pathway reveals potential strategies for therapeutic manipulation of MR1T cell function and provides a foundation for further studies that will lead to a deeper understanding of MR1-mediated immunity.

Reducing Immunogenicity by Design: Approaches to Minimize Immunogenicity of Monoclonal Antibodies

Monoclonal antibodies (mAbs) have transformed therapeutic strategies for various diseases. Their high specificity to target antigens makes them ideal therapeutic agents for certain diseases. However, a challenge to their application in clinical practice is their potential risk to induce unwanted immune response, termed immunogenicity. This challenge drives the continued efforts to deimmunize these protein therapeutics while maintaining their pharmacokinetic properties and therapeutic efficacy. Because mAbs hold a central position in therapeutic strategies against an array of diseases, the importance of conducting comprehensive immunogenicity risk assessment during the drug development process cannot be overstated. Such assessment necessitates the employment of in silico, in vitro, and in vivo strategies to evaluate the immunogenicity risk of mAbs. Understanding the intricacies of the mechanisms that drive mAb immunogenicity is crucial to improving their therapeutic efficacy and safety and developing the most effective strategies to determine and mitigate their immunogenic risk. This review highlights recent advances in immunogenicity prediction strategies, with a focus on protein engineering strategies used throughout development to reduce immunogenicity.

An immunogenetic basis for lung cancer risk

Cancer risk is influenced by inherited mutations, DNA replication errors, and environmental factors. However, the influence of genetic variation in immunosurveillance on cancer risk is not well understood. Leveraging population-level data from the UK Biobank and FinnGen, we show that heterozygosity at the human leukocyte antigen (HLA)-II loci is associated with reduced lung cancer risk in smokers. Fine-mapping implicated amino acid heterozygosity in the HLA-II peptide binding groove in reduced lung cancer risk, and single-cell analyses showed that smoking drives enrichment of proinflammatory lung macrophages and HLA-II+ epithelial cells. In lung cancer, widespread loss of HLA-II heterozygosity (LOH) favored loss of alleles with larger neopeptide repertoires. Thus, our findings nominate genetic variation in immunosurveillance as a critical risk factor for lung cancer.

Forum on immune digital twins: a meeting report

Medical digital twins are computational models of human biology relevant to a given medical condition, which are tailored to an individual patient, thereby predicting the course of disease and individualized treatments, an important goal of personalized medicine. The immune system, which has a central role in many diseases, is highly heterogeneous between individuals, and thus poses a major challenge for this technology. In February 2023, an international group of experts convened for two days to discuss these challenges related to immune digital twins. The group consisted of clinicians, immunologists, biologists, and mathematical modelers, representative of the interdisciplinary nature of medical digital twin development. A video recording of the entire event is available. This paper presents a synopsis of the discussions, brief descriptions of ongoing digital twin projects at different stages of progress. It also proposes a 5-year action plan for further developing this technology. The main recommendations are to identify and pursue a small number of promising use cases, to develop stimulation-specific assays of immune function in a clinical setting, and to develop a database of existing computational immune models, as well as advanced modeling technology and infrastructure.

Canine major histocompatibility complex class I (MHC-I) diversity landscape

The genes of the Major Histocompatibility Complex class I (MHC-I) are among the most diverse in the mammalian genome, playing a crucial role in immunology. Understanding the diversity landscape of MHC-I is therefore of paramount importance. The dog is a key translational model in various biomedical fields. However, our understanding of the canine MHC-I diversity landscape lags significantly behind that of humans. To address this deficiency, we used our newly developed software, KPR de novo assembler and genotyper, to genotype 1,325 samples from 1,025 dogs with paired-end RNA-seq data from 43 BioProjects, after extensive quality control. Among 926 dogs that pass the QC, 591 dogs (64%) have at least one allele genotyped, and a total of 97 known alleles and 52 putative new alleles were identified. Further analysis reveals that DLA-I gene expression levels vary among the tissues, with lowest for testis and brain tissues and highest for blood, corpus luteum, and spleen. We identified dominant alleles in each of the 17 canine breeds, as well as among the entire canine population. Furthermore, our analysis also identifies breed-specific alleles and mutually co-occurred/exclusive alleles. Our study indicates that canine DLA-88 is as diversified as human HLA-A/B/C genes within the entire population, but less diversified within a breed than with HLA-A/B/C within an ethnic group. Lastly, we examined the hypervariable regions (HVR) within or across human/canine MHC-I alleles and found that 80% of the HVRs overlap between the two species. We further noted that 80% of the HVRs are within 4A contact with the peptides, and that the dog-human difference overlaps with only 20% HVRs. Our research offers valuable insights for immunological studies involving dogs.

T-cell virtuosity in ''knowing thyself"

Major Histocompatibility Complex (MHC) I and II and the αβ T-cell antigen receptor (TCRαβ) govern fundamental traits of adaptive immunity. They form a membrane-borne ligand-receptor system weighing host proteome integrity to detect contamination by nonself proteins. MHC-I and -II exhibit the "MHC-fold", which is able to bind a large assortment of short peptides as proxies for self and nonself proteins. The ensuing varying surfaces are mandatory ligands for Ig-like TCRαβ highly mutable binding sites. Conserved molecular signatures guide TCRαβ ligand binding sites to focus on the MHC-fold (MHC-restriction) while leaving many opportunities for its most hypervariable determinants to contact the peptide. This riveting molecular strategy affords many options for binding energy compatible with specific recognition and signalling aimed to eradicated microbial pathogens and cancer cells. While the molecular foundations of αβ T-cell adaptive immunity are largely understood, uncertainty persists on how peptide-MHC binding induces the TCRαβ signals that instruct cell-fate decisions. Solving this mystery is another milestone for understanding αβ T-cells' self/nonself discrimination. Recent developments revealing the innermost links between TCRαβ structural dynamics and signalling modality should help dissipate this long-sought-after enigma.

Large-scale integrative analysis of juvenile idiopathic arthritis for new insight into its pathogenesis

BACKGROUND

Juvenile idiopathic arthritis (JIA) is one of the most prevalent rheumatic disorders in children and is classified as an autoimmune disease (AID). While a robust genetic contribution to JIA etiology has been established, the exact pathogenesis remains unclear.

METHODS

To prioritize biologically interpretable susceptibility genes and proteins for JIA, we conducted transcriptome-wide and proteome-wide association studies (TWAS/PWAS). Then, to understand the genetic architecture of JIA, we systematically analyzed single-nucleotide polymorphism (SNP)-based heritability, a signature of natural selection, and polygenicity. Next, we conducted HLA typing using multi-ethnicity RNA sequencing data. Additionally, we examined the T cell receptor (TCR) repertoire at a single-cell level to explore the potential links between immunity and JIA risk.

RESULTS

We have identified 19 TWAS genes and two PWAS proteins associated with JIA risks. Furthermore, we observe that the heritability and cell type enrichment analysis of JIA are enriched in T lymphocytes and HLA regions and that JIA shows higher polygenicity compared to other AIDs. In multi-ancestry HLA typing, B*45:01 is more prevalent in African JIA patients than in European JIA patients, whereas DQA1*01:01, DQA1*03:01, and DRB1*04:01 exhibit a higher frequency in European JIA patients. Using single-cell immune repertoire analysis, we identify clonally expanded T cell subpopulations in JIA patients, including CXCL13+BHLHE40+ TH cells which are significantly associated with JIA risks.

CONCLUSION

Our findings shed new light on the pathogenesis of JIA and provide a strong foundation for future mechanistic studies aimed at uncovering the molecular drivers of JIA.

Investigating the genetic makeup of the major histocompatibility complex (MHC) in the United Arab Emirates population through next-generation sequencing

The Human leukocyte antigen (HLA) molecules are central to immune response and have associations with the phenotypes of various diseases and induced drug toxicity. Further, the role of HLA molecules in presenting antigens significantly affects the transplantation outcome. The objective of this study was to examine the extent of the diversity of HLA alleles in the population of the United Arab Emirates (UAE) using Next-Generation Sequencing methodologies and encompassing a larger cohort of individuals. A cohort of 570 unrelated healthy citizens of the UAE volunteered to provide samples for Whole Genome Sequencing and Whole Exome Sequencing. The definition of the HLA alleles was achieved through the application of the bioinformatics tools, HLA-LA and xHLA. Subsequently, the findings from this study were compared with other local and international datasets. A broad range of HLA alleles in the UAE population, of which some were previously unreported, was identified. A comparison with other populations confirmed the current population's unique intertwined genetic heritage while highlighting similarities with populations from the Middle East region. Some disease-associated HLA alleles were detected at a frequency of > 5%, such as HLA-B*51:01, HLA-DRB1*03:01, HLA-DRB1*15:01, and HLA-DQB1*02:01. The increase in allele homozygosity, especially for HLA class I genes, was identified in samples with a higher level of genome-wide homozygosity. This highlights a possible effect of consanguinity on the HLA homozygosity. The HLA allele distribution in the UAE population showcases a unique profile, underscoring the need for tailored databases for traditional activities such as unrelated transplant matching and for newer initiatives in precision medicine based on specific populations. This research is part of a concerted effort to improve the knowledge base, particularly in the fields of transplant medicine and investigating disease associations as well as in understanding human migration patterns within the Arabian Peninsula and surrounding regions.

Immune-driven clonal cell selection at the intersection among cancer, infections, autoimmunity and senescence

Immune surveillance mechanisms play a crucial role in maintaining lifelong immune homeostasis in response to pathologic stimuli and aberrant cell states. However, their persistence, especially in the context of chronic antigenic exposure, can create a fertile ground for immune evasion. These escaping cell phenotypes, harboring a variety of genomic and transcriptomic aberrances, chiefly in human leukocyte antigen (HLA) and antigen presentation machinery genes, may survive and proliferate, featuring a scenario of clonal cell expansion with immune failure characteristics. While well characterized in solid and, to some extent, hematological malignancies, little is known about their occurrence and significance in other disease contexts. Historical literature highlights the role for escaping HLA-mediated recognition as a strategy adopted by virus to evade from the immune system, hinting at the potential for immune aberrant cell expansion in the context of chronic infections. Additionally, unmasked in idiopathic aplastic anemia as a mechanism able to rescue failing hematopoiesis, HLA clonal escape may operate in autoimmune disorders, particularly in tissues targeted by aberrant immune responses. Furthermore, senescent cell status emerging as immunogenic phenotypes stimulating T cell responses, may act as a bottleneck for the selection of such immune escaping clones, blurring the boundaries between neoplastic transformation, aging and inflammation. Here we provide a fresh overview and perspective on this immune-driven clonal cell expansion, linking pathophysiological features of neoplastic, autoimmune, infectious and senescence processes exposed to immune surveillance.

A rigorous benchmarking of alignment-based HLA typing algorithms for RNA-seq data

Accurate identification of human leukocyte antigen (HLA) alleles is essential for various clinical and research applications, such as transplant matching and drug sensitivities. Recent advances in RNA-seq technology have made it possible to impute HLA types from sequencing data, spurring the development of a large number of computational HLA typing tools. However, the relative performance of these tools is unknown, limiting the ability for clinical and biomedical research to make informed choices regarding which tools to use. Here we report the study design of a comprehensive benchmarking of the performance of 12 HLA callers across 682 RNA-seq samples from 8 datasets with molecularly defined gold standard at 5 loci, HLA-A, -B, -C, -DRB1, and -DQB1. For each HLA typing tool, we will comprehensively assess their accuracy, compare default with optimized parameters, and examine for discrepancies in accuracy at the allele and loci levels. We will also evaluate the computational expense of each HLA caller measured in terms of CPU time and RAM. We also plan to evaluate the influence of read length over the HLA region on accuracy for each tool. Most notably, we will examine the performance of HLA callers across European and African groups, to determine discrepancies in accuracy associated with ancestry. We hypothesize that RNA-Seq HLA callers are capable of returning high-quality results, but the tools that offer a good balance between accuracy and computational expensiveness for all ancestry groups are yet to be developed. We believe that our study will provide clinicians and researchers with clear guidance to inform their selection of an appropriate HLA caller.

In Silico Tools for Predicting Novel Epitopes

Identifying antigens within a pathogen is a critical task to develop effective vaccines and diagnostic methods, as well as understanding the evolution and adaptation to host immune responses. Historically, antigenicity was studied with experiments that evaluate the immune response against selected fragments of pathogens. Using this approach, the scientific community has gathered abundant information regarding which pathogenic fragments are immunogenic. The systematic collection of this data has enabled unraveling many of the fundamental rules underlying the properties defining epitopes and immunogenicity, and has resulted in the creation of a large panel of immunologically relevant predictive (in silico) tools. The development and application of such tools have proven to accelerate the identification of novel epitopes within biomedical applications reducing experimental costs. This chapter introduces some basic concepts about MHC presentation, T cell and B cell epitopes, the experimental efforts to determine those, and focuses on state-of-the-art methods for epitope prediction, highlighting their strengths and limitations, and catering instructions for their rational use.

A novel HLA-B*58:141 allele identified by next-generation sequencing in a potential hematopoietic cell recipient

We report a novel HLA-B*58 allele, now named B*58:141, identified by next-generation sequencing.

NanoHLA: A Method for Human Leukocyte Antigen Class I Genes Typing Without Error Correction Based on Nanopore Sequencing Data

Human leukocyte antigen (HLA) typing is of great importance in clinical applications such as organ transplantation, blood transfusion, disease diagnosis and treatment, and forensic analysis. In recent years, nanopore sequencing technology has emerged as a rapid and cost-effective option for HLA typing. However, due to the principles and data characteristics of nanopore sequencing, there was a scarcity of robust and generalizable bioinformatics tools for its downstream analysis, posing a significant challenge in deciphering the thousands of HLA alleles present in the human population. To address this challenge, we developed NanoHLA as a tool for high-resolution typing of HLA class I genes without error correction based on nanopore sequencing. The method integrated the concepts of HLA type coverage analysis and the data conversion techniques employed in Nano2NGS, which was characterized by applying nanopore sequencing data to NGS-liked data analysis pipelines. In validation with public nanopore sequencing datasets, NanoHLA showed an overall concordance rate of 84.34% for HLA-A, HLA-B, and HLA-C, and demonstrated superior performance in comparison to existing tools such as HLA-LA. NanoHLA provides tools and solutions for use in HLA typing related fields, and look forward to further expanding the application of nanopore sequencing technology in both research and clinical settings. The code is available at https://github.com/langjidong/NanoHLA .

Repeated local ellipsoid protrusion supplements HLA surface characterization

Allorecognition of donor HLA is a major risk factor for long-term kidney graft survival. Although several molecular matching algorithms have been proposed that compare physiochemical and structural features of the donors' and recipients' HLA proteins in order to predict their compatibility, the exact underlying mechanisms are still not fully understood. We hypothesized that the ElliPro approach of single ellipsoid fitting and protrusion ranking lacks sensitivity for the characteristic shape of HLA molecules and developed a prediction pipeline named Snowball that is fitting smaller ellipsoids iteratively to substructures. Aggregated protrusion ranks of locally fitted ellipsoids were calculated for 712 publicly available HLA structures and 78 predicted structures using AlphaFold 2. Amino-acid sequence and protrusion ranks were used to train deep neural network predictors to infer protrusion ranks for all known HLA sequences. Snowball protrusion ranks appear to be more sensitive than ElliPro scores in fine parts of the HLA such as the helix structures forming the HLA binding groove in particular when the ellipsoids are fitted to substructures considering atoms within a 15 Å radius. A cloud-based web service was implemented based on amino-acid matching considering both protein- and position-specific surface area and protrusion ranks extending the previously presented Snowflake prediction pipeline.

Allele Frequency Net Database

The allele frequency net database (AFND, http://www.allelefrequencies.net ) is an online web-based repository that contains information on the frequencies of immune-related genes and their corresponding alleles in worldwide human populations. At present, the website contains data from 1784 population samples in more than 14 million individuals from 129 countries on the frequency of genes from different polymorphic regions including data for the human leukocyte antigen (HLA) system. In addition, over the last four years, AFND has also incorporated genotype raw data from 85,000 individuals comprising 215 population samples from 39 countries. Moreover, more population data sets containing next generation sequencing data spanning >3 million individuals have been added. This resource has been widely used in a variety of contexts such as histocompatibility, immunology, epidemiology, pharmacogenetics, epitope prediction algorithms for population coverage in vaccine development, population genetics, among many others. In this chapter, we present an update of the most used searching mechanisms as described in a previous volume and some of the latest developments included in AFND.