Human Leukocyte Antigen Genes Research Articles

The Immune Modulation HLA-G*01:01:01 Full Allele Is Associated with Gastric Adenocarcinoma Development.

The Human Leukocyte Antigen (HLA) system contains a set of genes involved at many levels in the innate and adaptive immune response. Among the non-classical HLA class I genes, HLA-G stands out for the numerous studies about its pivotal role in regulating/modulating immune responses. Also, its involvement in extravillous cytotrophoblast function, viral infections, autoimmunity, and cancer has been extensively documented. The present study explores for the first time the relationship between natural alleles of HLA-G, rather than STSs, SNPs, or partial gene polymorphisms, and the development of gastric adenocarcinoma, by analyzing the genetic profile of a cohort of 40 Spanish patients with this type of tumor using DNA extracted from paired biopsies of tumoral and adjacent non-tumoral gastric tissue. Our results reveal a significant statistical relationship between the presence of the HLA-G*01:01:01 allele and the development of gastric cancer, while other common alleles such as -G*01:04 or -G*01:05N did not demonstrate a significant correlation. Studying the involvement of HLA genes in the development of many diseases is relevant to understanding their pathophysiology. However, the absence of specific mechanisms underlying these associations suggests that investigating complete HLA natural alleles' extended haplotypes or complotypes may offer a more precise and valuable approach to elucidating the association of HLA with the pathogenesis of disease.

Improving long-term kidney allograft survival by rethinking HLA compatibility: from molecular matching to non-HLA genes.

Optimizing immunologic compatibility in organ transplantation extends beyond the conventional approach of Human Leukocyte Antigen (HLA) antigen matching, which exhibits significant limitations. A broader comprehension of the roles of classical and non-classical HLA genes in transplantation is imperative for enhancing long-term graft survival. High-resolution molecular HLA genotyping, despite its inherent challenges, has emerged as the cornerstone for precise patient-donor compatibility assessment. Leveraging understanding of eplet biology and indirect immune activation, eplet mismatch calculators and the PIRCHE-II algorithm surpass traditional methods in predicting allograft rejection. Understanding minor histocompatibility antigens may also present an opportunity to personalize the compatibility process. While the application of molecular matching in deceased donor organ allocation presents multiple technical, logistical, and conceptual barriers, rendering it premature for mainstream use, several other areas of donor-recipient matching and post-transplant management are ready to incorporate molecular matching. Provision of molecular mismatch scores to physicians during potential organ offer evaluations could potentially amplify long-term outcomes. The implementation of molecular matching in living organ donation and kidney paired exchange programs is similarly viable. This article will explore the current understanding of immunologic matching in transplantation and the potential applications of epitope and non-epitope molecular biology and genetics in clinical transplantation.

No association between migraine and HLA alleles in a cohort of 13,210 individuals with migraine from the Danish Blood Donor Study.

To determine the association between human leukocyte antigen (HLA) alleles and migraine, migraine subtypes, and sex-specific factors. It has long been hypothesized that inflammation contributes to migraine pathophysiology. This study examined the association between migraine and alleles in the HLA system, a key player in immune response and genetic diversity. We performed a case-control study and included 13,210 individuals with migraine and 86,738 controls. All participants were part of the Danish Blood Donor Study Genomic Cohort. Participants were genotyped and 111 HLA alleles on 15 HLA genes were imputed. We examined the association between HLA alleles and migraine subtypes, considering sex-specific differences. We found no association between HLA alleles and migraine, neither overall, nor in the sex-specific analysis. In the migraine subtype analysis, three HLA alleles were associated with migraine without aura; however, these associations could not be replicated in an independent Icelandic cohort (2191 individuals with migraine without aura and 278,858 controls). Furthermore, we found no association between HLA alleles and migraine with aura or chronic migraine. We found no evidence of an association between the HLA system and migraine, suggesting that genetic factors related to the HLA system do not play a significant role in migraine susceptibility.

Active immunologic participation and metabolic shutdown of kidney structural cells during kidney transplant rejection

Contrary to immune cells, the response of the kidney structural cells in rejection is less established. We performed single-cell RNA sequencing of 18 kidney transplant biopsies from 14 recipients. Single-cell RNA sequencing identified cells from the major compartments of the kidney, next to infiltrated immune cells. Endothelial cells from the glomerulus, peritubular capillaries, and vasa recta showed upregulation of class I and II human leukocyte antigen genes, adhesion molecules, cytokines, and chemokines, suggesting active participation in the alloimmune process, with compartment-specific differences. Epithelial cells including proximal tubular, loop of Henle, and collecting duct cells, also showed increased expression of immune genes. Strikingly, in proximal tubule cells, a strong downregulation of energy metabolism upon inflammation was observed. There was a large overlap between the cell-specific expression changes upon alloimmune inflammation and those observed in 2 large microarray biopsy cohorts. In conclusion, the kidney structural cells, being the main target of the alloimmune process, appear to actively contribute herein, enhancing the damaging effects of the infiltrating immune cells. In epithelial cells, a profound shutdown of metabolism was seen upon inflammation, which is associated with poor kidney function. These observations highlight the critical role of the graft in triggering and sustaining rejection after transplantation.

A preliminary indication that HLA-A*03:01 may be associated with visceral leishmaniasis development in people living with HIV in Ethiopia.

Human immunodeficiency virus (HIV) co-infection is a major challenge for visceral leishmaniasis (VL) control, particularly in Ethiopia where the incidence of both pathogens is high. VL-HIV often leads to high rates of antileishmanial treatment failure and recurrent VL disease relapses. Considering the high prevalence of HIV and Leishmania in the Ethiopian population, preventing the progression of asymptomatic Leishmania infection to disease would be a valuable asset to VL disease control and to the clinical management of people living with HIV (PLWH). However, such a strategy requires good understanding of risk factors for VL development. In immunocompetent individuals living in Brazil, India, or Iran, the Human Leukocyte Antigen (HLA) gene region has been associated with VL development. We used NanoTYPE, an Oxford Nanopore Technologies sequencing-based HLA genotyping method, to detect associations between HLA genotype and VL development by comparing 78 PLWH with VL history and 46 PLWH that controlled a Leishmania infection, all living in a VL endemic region of North-West Ethiopia. We identified an association between HLA-A*03:01 and increased risk of VL development (OR = 3.89). These data provide candidate HLA alleles that can be further explored for inclusion in a potential Leishmania screen-and-treat strategy in VL endemic regions.

A review of the genetics of type I diabetes

Type I diabetes susceptibility is caused by both environmental and genetic factors, the latter comprising approximately half of the total risk as evidenced by the fact that identical twins have approximately 50% concordance, suggesting 50% of the disease risk is environmental. The human leukocyte antigen (HLA) genes account for approximately half of the genetic risk, as demonstrated by the concordance between HLA identical siblings. Because environmental and genetic differences vary between racial groups, the incidence of type 1 diabetes (TID) differs across the world, being highest in Caucasians. Recent GWAS (genome-wide association studies) studies have suggested there may be up to 50 genomic regions contributing to the non-major histocompatibility complex (MHC) genetic risk contribution. This review presents and discusses the latest research on the MHC and non-MHC genes. Only the non-MHC regions, which have been confirmed in multiple studies and which are considered definite regions of genetic susceptibility, are included in the review.

Variation within the non-coding genome influences genetic and epigenetic regulation of the human leukocyte antigen genes.

Variation within the non-coding genome may influence the regulation and expression of important genes involved in immune control such as the human leukocyte antigen (HLA) system. Class I and Class II HLA molecules are essential for peptide presentation which is required for T lymphocyte activation. Single nucleotide polymorphisms within non-coding regions of HLA Class I and Class II genes may influence the expression of these genes by affecting the binding of transcription factors and chromatin modeling molecules. Furthermore, an interplay between genetic and epigenetic factors may also influence HLA expression. Epigenetic factors such as DNA methylation and non-coding RNA, regulate gene expression without changing the DNA sequence. However, genetic variation may promote or allow genes to escape regulation by epigenetic factors, resulting in altered expression. The HLA system is central to most diseases, therefore, understanding the role of genetics and epigenetics on HLA regulation will tremendously impact healthcare. The knowledge gained from these studies may lead to novel and cost-effective diagnostic approaches and therapeutic interventions. This review discusses the role of non-coding variants on HLA regulation. Furthermore, we discuss the interplay between genetic and epigenetic factors on the regulation of HLA by evaluating literature based on polymorphisms within DNA methylation and miRNA regulatory sites within class I and Class II HLA genes. We also provide insight into the importance of the HLA non-coding genome on disease, discuss ethnic-specific differences across the HLA region and provide guidelines for future HLA studies.

HLA-Cw6 Polymorphism in Autoimmune Blistering Diseases.

Autoimmune blistering diseases of the pemphigus and pemphigoid groups are immune-mediated disorders due to circulating pathogenetic autoantibodies. Multiple human leukocyte antigen (HLA) genes have been associated with predisposition to these disorders. HLA-Cw6 is involved in antigen presentation processes and has been linked to psoriasis. The aim of our study was to investigate the association between the presence of the HLA-Cw6 allele and susceptibility to pemphigus vulgaris and bullous pemphigoid. A genetic study in vitro with a cross-sectional design was performed enrolling forty patients with pemphigus vulgaris and forty patients with bullous pemphigoid. The detection of HLA-Cw6 was performed through the EUROArray test on DNA obtained from whole blood samples. The polymorphism was detected in 3/40 genotypes in the pemphigus vulgaris group and in 4/40 genotypes of patients with bullous pemphigoid, unveiling a non-statistically significant different frequency in pemphigus (p = 0.6368) and in pemphigoid (p = 0.62) compared to the reference frequency from the literature of 0.086. Further research is needed to better investigate the role of HLA-Cw6 in immune-mediated diseases and to identify novel genetic markers associated with susceptibility to autoimmune blistering diseases and with disease severity and response to immunosuppressive therapies.

Cluster of Differentiation Markers and Human Leukocyte Antigen Expression in Chronic Lymphocytic Leukemia Patients: Correlations and Clinical Relevance.

Chronic lymphocytic leukemia (CLL) is a distinct category of lymphoproliferative disorder characterized by the clonal expansion of mature B cells, followed by their accumulation in primary and secondary lymphoid organs. Cluster of differentiation (CD) markers such as CD79b, CD45, CD23, CD22 and CD81 serve as reliable prognostic indicators in CLL as well as the human leukocyte antigen (HLA) with its well-documented associations with various cancers. This study aims to investigate, for the first time, potential connections between HLA typing and CD marker expression in CLL. Although it is one of the most prevalent neoplasms, there is a need for biomarkers that can improve survival. This study included 66 CLL patients and 100 controls, with all samples analyzed using biochemical methods, flow cytometry, and cytomorphology. Next-generation sequencing was performed for HLA typing. The results indicate that several CD markers are statistically associated with different HLA alleles, specifically CD45 with HLA-C*07:01:01; CD79b with HLA-DPA1*02:01:02; CD23 with HLA-B*39:01:01; CD22 with HLA-B*49:01:01, HLA-C*07:01:01, HLA-DPB1*02:01:02, and HLA-DRB1*07:01:01; and CD81 with HLA-DPB1*04:02:01, HLA-DQA1*01:04:01, and HLA-DQB1*05:03:01. In conclusion, this research demonstrates significant statistical links between HLA genes and immunophenotypic markers in CLL patients, shedding new light on the immunological context of CLL.

Immunogenetic profiles of 9 human herpes virus envelope glycoproteins

Human herpes viruses (HHV) are ubiquitous and have been implicated in numerous long-term health conditions. Since the association between viral exposure and long-term health impacts is partially influenced by variation in human leukocyte antigen (HLA) genes, we evaluated in silico the binding affinities of 9 HHV envelope glycoproteins with 127 common HLA Class I and Class II molecules. The findings show substantial variability in HHV binding affinity across viruses, HLA Class, HLA genes, and HLA alleles. Specific findings were as follows: (1) the predicted binding affinities of HHVs were characterized by four distinct groupings—[HHV1, HHV2], [HHV3, HHV4, HHV5], [HHV6A], [HHV6B, HHV7, HHV8]—with relatively lower binding affinities for HHV1, HHV2, and HHV6a compared to other HHVs; (2) significantly higher binding affinity was found for HLA Class I relative to Class II; (3) analyses within each class demonstrated that alleles of the C gene (for Class I) and DRB1 gene (for Class II) had the highest binding affinities; and (4) for each virus, predicted binding affinity to specific alleles varied, with HHV6a having the lowest affinity for HHV-HLA complexes, and HHV3, HHV4, and HHV5 having the highest. Since HLA-antigen binding is the first step in initiating an immune response to foreign antigens, these relative differences in HHV binding affinities are likely to influence long-term health impacts such that the cells infected with viruses associated with higher binding affinities across common HLA alleles may be more reduced in numbers, thereby lowering the potential for long-term sequelae of their infections.

SVA Regulation of Transposable Element Clustered Transcription within the Major Histocompatibility Complex Genomic Class II Region of the Parkinson's Progression Markers Initiative.

SINE-VNTR-Alu (SVA) retrotransposons can regulate expression quantitative trait loci (eQTL) of coding and noncoding genes including transposable elements (TEs) distributed throughout the human genome. Previously, we reported that expressed SVAs and human leucocyte antigen (HLA) class II genotypes on chromosome 6 were associated significantly with Parkinson's disease (PD). Here, our aim was to follow-up our previous study and evaluate the SVA associations and their regulatory effects on the transcription of TEs within the HLA class II genomic region. We reanalyzed the transcriptome data of peripheral blood cells from the Parkinson's Progression Markers Initiative (PPMI) for 1530 subjects for TE and gene RNAs with publicly available computing packages. Four structurally polymorphic SVAs regulate the transcription of 20 distinct clusters of 235 TE loci represented by LINES (37%), SINES (28%), LTR/ERVs (23%), and ancient transposon DNA elements (12%) that are located in close proximity to HLA genes. The transcribed TEs were mostly short length, with an average size of 389 nucleotides. The numbers, types and profiles of positive and negative regulation of TE transcription varied markedly between the four regulatory SVAs. The expressed SVA and TE RNAs in blood cells appear to be enhancer-like elements that are coordinated differentially in the regulation of HLA class II genes. Future work on the mechanisms underlying their regulation and potential impact is essential for elucidating their roles in normal cellular processes and disease pathogenesis.

Integrative analysis of the role of the DPH gene family in hepatocellular carcinoma and expression validation.

The diphthamide (DPH) gene family is a group of genes that encode a set of enzymes that specifically modify eukaryotic elongation factor 2 (eEF2). Although previous studies have shown a link between the DPH genes (DPHs) and carcinogenesis, it is still unknown how the DPHs affect hepatocellular carcinoma (HCC). This study aimed to describe the expression, clinical significance, and potential mechanisms of DPHs in HCC. Real-time quantitative polymerase chain reaction (RT-qPCR), Genotype-Tissue Expression (GTEx), and The Cancer Genome Atlas (TCGA) databases were utilized to research the expression of DPHs in HCC. The relationship between the expression of DPHs and the clinicopathological characteristics of HCC patients was investigated using TCGA data, and their diagnostic value was evaluated using receiver operating characteristic (ROC) curves and their prognostic value was analyzed using Kaplan-Meier curves and univariate and multivariate Cox regression analyses. Potential reasons for the upregulation of DPH2 and DPH3 (DPH2,3) expression in HCC were analyzed using multiple databases. Additionally, this study also explored the potential biological functions of DPH2,3 in HCC via gene sets enrichment analysis (GSEA). Correlation analysis of DPH2,3 expression with immune-related genes and immune checkpoints was performed using Spearman's correlation analysis, and single-sample GSEA was used to assess the distribution of tumor-infiltrating immune cell types. DPH1,7 expression was downregulated in tumor tissues while DPH2,3,5,6 expression was upregulated and showed a similar expression pattern in HCC. The results of the ROC analysis suggested that DPHs had valuable diagnostic properties in HCC. Kaplan-Meier analysis demonstrated that DPH2,3,7 had prognostic predictive value in HCC. Furthermore, univariate and multivariate Cox regression suggested that DPH2,3 was an independent predictive factor for HCC. GSEA analysis revealed that DPH2,3 might be tightly associated with Pathways in cancer, cell cycles, Fc gamma R mediated phagocytosis, etc. Additionally, DPH2,3 expression and numerous immune-related genes showed a positive connection, including chemokines receptor genes, immunosuppressive genes, chemokines genes, human leukocyte antigen (HLA) genes, and immunostimulatory genes. Further analysis of the association between 24 immune infiltrating cells and DPH2,3 revealed the greatest negative correlation between natural killer (NK) cells and Th17 cells, but the greatest positive correlation with Th2 cells. DPHs significantly influence the development and progression of HCC. DPH2,3 has significant diagnostic and prognostic potential and may be a promising target for immunotherapy.

Neutrophil estimation and prognosis analysis based on existing lung squamous cell carcinoma datasets: the development and validation of a prognosis prediction model.

Notwithstanding the rapid developments in precision medicine in recent years, lung cancer still has a low survival rate, especially lung squamous cell cancer (LUSC). The tumor microenvironment (TME) plays an important role in the progression of lung cancer, in which high neutrophil levels are correlated with poor prognosis, potentially due to their interactions with tumor cells via pro-inflammatory cytokines and chemokines. However, the precise mechanisms of how neutrophils influence lung cancer remain unclear. This study aims to explore these mechanisms and develop a prognosis predictive model in LUSC, addressing the knowledge gap in neutrophil-related cancer pathogenesis. LUSC datasets from the Xena Hub and Gene Expression Omnibus (GEO) databases were used, comprising 473 tumor samples and 195 tumor samples, respectively. Neutrophil contents in these samples were estimated using CIBERSORT, xCell, and microenvironment cell populations (MCP) counter tools. Differentially expressed genes (DEGs) were identified using DEseq2, and a weighted gene co-expression network analysis (WGCNA) was performed to identify neutrophil-related genes. A least absolute shrinkage and selection operator (LASSO) Cox regression model was constructed for prognosis prediction, and the model's accuracy was validated using Kaplan-Meier survival curves and time-dependent receiver operating characteristic (ROC) curves. Additionally, genomic changes, immune correlations, drug sensitivity, and immunotherapy response were analyzed to further validate the model's predictive power. Neutrophil content was significantly higher in adjacent normal tissue compared to LUSC tissue (P<0.001). High neutrophil content was associated with worse overall survival (OS) (P=0.02), disease-free survival (DFS) (P=0.02), and progression-free survival (PFS) (P=0.03) using different software estimates. Nine gene modules were identified, with blue and yellow modules showing strong correlations with neutrophil prognosis (P<0.001). Eight genes were selected for the prognostic model, which accurately predicted 1-, 3-, and 5-year survival in both the training set [area under the curve (AUC) value =0.60, 0.63, 0.66, respectively] and validation set (AUC value =0.58, 0.58, 0.59, respectively), with significant prognosis differences between high- and low-risk groups (P<0.001). The model's independent prognostic factors included risk group, pathologic M stage, and tumor stage (P<0.05). A further molecular mechanism analysis revealed differences between risk groups were revealed in immune checkpoint and human leukocyte antigen (HLA) gene expression, hallmark pathways, drug sensitivity, and immunotherapy responses. This study established a risk-score model that effectively predicts the prognosis of LUSC patients and sheds light on the molecular mechanisms involved. The findings enhance the understanding of neutrophil-tumor interactions, offering potential targets for personalized treatments. However, further experimental validation and clinical studies are required to confirm these findings and address study limitations, including reliance on public databases and focus on a specific lung cancer subtype.

Deciphering the Genetic Landscape: Exploring the Relationship Between HLA-DQA1, HLA-DQB1, and HLA-DRB1 Genes in Diabetes Mellitus

Abstract: Diabetes mellitus (DM) is a complex and multifactorial metabolic disorder with a significant genetic component. The human leukocyte antigen (HLA) genes, specifically HLA-DQA1, HLA-DQB1, and HLA-DRB1, have been implicated in the susceptibility and pathogenesis of DM. This review delves into the intricate interplay of these HLA genes, seeking to unravel the genetic tapestry that contributes to the development and progression of diabetes. We begin by providing an overview of the HLA system and its critical role in immune regulation. Subsequently, we explore the current state of knowledge regarding the association between HLA-DQA1, HLA-DQB1, and HLADRB1 polymorphisms and susceptibility to both type 1 and type 2 diabetes. Emphasis is placed on recent advancements in genetic research methodologies, including genomewide association studies and next-generation sequencing, that have provided deeper insights into the genetic architecture of DM. The review also scrutinizes the functional implications of specific HLA alleles in modulating immune responses and the potential mechanisms by which they contribute to the autoimmune processes observed in type 1 diabetes. Additionally, we examine the role of HLA genes in the context of insulin resistance and beta-cell dysfunction in type 2 diabetes, shedding light on the shared and distinct genetic underpinnings of these two major forms of DM. Furthermore, we discuss the clinical implications of HLA genotyping in predicting disease risk, prognosis, and personalized treatment strategies. The integration of genetic information into clinical practice holds promise for precision medicine approaches in diabetes management.

Characteristics of two different immune infiltrating pyroptosis subtypes in ischemic stroke

BackgroundIschemic stroke (IS) is a serious health hazard and identified as the second leading cause of mortality around the world. However, the role of pyroptosis in the immune microenvironment regulation in IS is still unclear. Here, our study aims to elucidate the effect of pyroptosis on immune microenvironment in IS. MethodsThe regulation mode of pyroptosis in IS was systematically evaluated, and its effects on immune microenvironment were explored, including infiltration of immune cells, immune response gene sets, and human leukocyte antigen (HLA) gene. The genes and drugs related to pyroptosis phenotype were also identified. An MCAO rat model was constructed, and the mRNA expression levels in the classifier model were validated by qRT-PCR. ResultsThe separator is composed of 11 pyroptosis genes, out of which 10 genes could distinguish between ischemic stroke and control samples. CHMP2A, CHMP4A, and NAIP genes are significantly related to immune infiltrating cells, immune response gene sets, and HLA. However, two different pyroptosis subtypes mediated by 10 pyroptosis genes were identified, which were different in immune cell abundance, HLA genes, and immune response gene sets. Furthermore, 199 genes associated with pyroptosis phenotype was identified along with the analysis of biological functions. ConclusionThese findings reveal the potential mechanism of pyroptosis in the immune microenvironment of IS, indicating that pyroptosis functions as a vital component in the complexity and diversity of the immune microenvironment in patients with IS.

Machine learning developed a macrophage signature for predicting prognosis, immune infiltration and immunotherapy features in head and neck squamous cell carcinoma

Macrophages played an important role in the progression and treatment of head and neck squamous cell carcinoma (HNSCC). We employed weighted gene co-expression network analysis (WGCNA) to identify macrophage-related genes (MRGs) and classify patients with HNSCC into two distinct subtypes. A macrophage-related risk signature (MRS) model, comprising nine genes: IGF2BP2, PPP1R14C, SLC7A5, KRT9, RAC2, NTN4, CTLA4, APOC1, and CYP27A1, was formulated by integrating 101 machine learning algorithm combinations. We observed lower overall survival (OS) in the high-risk group and the high-risk group showed elevated expression levels in most of the immune checkpoint and human leukocyte antigen (HLA) genes, suggesting a strong immune evasion capacity. Correspondingly, TIDE score positively correlated with risk score, implying that high-risk tumors may resist immunotherapy more effectively. At the single-cell level, we noted macrophages in the tumor microenvironment (TME) predominantly stalled in the G2/M phase, potentially hindering epithelial-mesenchymal transition and playing a crucial role in the inhibition of tumor progression. Finally, the proliferation and migration abilities of HNSCC cells significantly decreased after the expression of IGF2BP2 and SLC7A5 reduced. It also decreased migration ability of macrophages and facilitated their polarization towards the M1 direction. Our study constructed a novel MRS for HNSCC, which could serve as an indicator for predicting the prognosis, immune infiltration and immunotherapy for HNSCC patients.

Understanding and Therapeutic Application of Immune Response in Major Histocompatibility Complex (MHC) Diversity Using Multimodal Artificial Intelligence

Human Leukocyte Antigen (HLA) is like a device that monitors the internal environment of the body. T lymphocytes immediately recognize the HLA molecules that are expressed on the surface of the cells of the different individual, attacking it defeats microorganisms that is one of the causes of rejection in organ transplants performed between people with unmatched HLA types. Over 2850 and 3580 different polymorphisms have been reported for HLA-A and HLA-B respectively, around the world. HLA genes are associated with the risk of developing a variety of diseases, including autoimmune diseases, and play an important role in pathological conditions. By using a deep learning method called multi-task learning to simultaneously predict the gene sequences of multiple HLA genes, it is possible to improve accuracy and shorten execution time. Some new systems use a model called convolutional neural network (CNNs) in deep learning, which uses neural networks consisting of many layers and can learn complex correlations between SNP information and HLA gene sequences based on reference data for HLA imputation, which serves as training data. The learned model can output predicted values of HLA gene sequences with high accuracy using SNP information as input. To investigate which part of the input information surrounding the HLA gene is used to make learning predictions, predictions were made using not only a small number of nearby SNP information but also many SNP information distributed over a wider area by visualizing the learning information of the model. While conventional methods are strong at learning using nearly SNP information and not good at learning using SNP information located at distant locations, some new systems are thought that prediction accuracy may have improved because this problem was overcome. HLA genes are involved in the onset of a variety of diseases and are attracting attention. As an important area from the perspective of elucidating pathological conditions and realizing personalized medicine. The applied multi-task learning to two different HLA imputation reference panels—a Japanese panel (n = 1118) and type I diabetes genetics consortium panel (n = 5122). Through 10-fold cross-validation on these panels, the multi-task learning achieved higher imputation accuracy than conventional methods, especially for imputing low-frequency and rare HLA alleles. The increased prediction accuracy of HLA gene sequences is expected to increase the reliability of HLA analysis, including integrated analysis between different racial populations, and is expected to greatly contribute to the identification of HLA gene sequences associated with diseases and further elucidation of pathological conditions.

Unraveling pathogenesis, biomarkers and potential therapeutic agents for endometriosis associated with disulfidptosis based on bioinformatics analysis, machine learning and experiment validation

BackgroundEndometriosis (EMs) is an enigmatic disease of yet-unknown pathogenesis. Disulfidptosis, a novel identified form of programmed cell death resulting from disulfide stress, stands a chance of treating diverse ailments. However, the potential roles of disulfidptosis-related genes (DRGs) in EMs remain elusive. This study aims to thoroughly explore the key disulfidptosis genes involved in EMs, and probe novel diagnostic markers and candidate therapeutic compounds from the aspect of disulfidptosis based on bioinformatics analysis, machine learning, and animal experiments.ResultsEnrichment analysis on key module genes and differentially expressed genes (DEGs) of eutopic and ectopic endometrial tissues in EMs suggested that EMs was closely related to disulfidptosis. And then, we obtained 20 and 16 disulfidptosis-related DEGs in eutopic and ectopic endometrial tissue, respectively. The protein-protein interaction (PPI) network revealed complex interactions between genes, and screened nine and ten hub genes in eutopic and ectopic endometrial tissue, respectively. Furthermore, immune infiltration analysis uncovered distinct differences in the immunocyte, human leukocyte antigen (HLA) gene set, and immune checkpoints in the eutopic and ectopic endometrial tissues when compared with health control. Besides, the hub genes mentioned above showed a close correlation with the immune microenvironment of EMs. Furthermore, four machine learning algorithms were applied to screen signature genes in eutopic and ectopic endometrial tissue, including the binary logistic regression (BLR), the least absolute shrinkage and selection operator (LASSO), the support vector machine-recursive feature elimination (SVM-RFE), and the extreme gradient boosting (XGBoost). Model training and hyperparameter tuning were implemented on 80% of the data using a ten-fold cross-validation method, and tested in the testing sets which determined the excellent diagnostic performance of these models by six indicators (Sensitivity, Specificity, Positive Predictive Value, Negative Predictive Value, Accuracy, and Area Under Curve). And seven eutopic signature genes (ACTB, GYS1, IQGAP1, MYH10, NUBPL, SLC7A11, TLN1) and five ectopic signature genes (CAPZB, CD2AP, MYH10, OXSM, PDLIM1) were finally identified based on machine learning. The independent validation dataset also showed high accuracy of the signature genes (IQGAP1, SLC7A11, CD2AP, MYH10, PDLIM1) in predicting EMs. Moreover, we screened 12 specific compounds for EMs based on ectopic signature genes and the pharmacological impact of tretinoin on signature genes was further verified in the ectopic lesion in the EMs murine model.ConclusionThis study verified a close association between disulfidptosis and EMs based on bioinformatics analysis, machine learning, and animal experiments. Further investigation on the biological mechanism of disulfidptosis in EMs is anticipated to yield novel advancements for searching for potential diagnostic biomarkers and revolutionary therapeutic approaches in EMs.

AURKB promotes immunogenicity and immune infiltration in clear cell renal cell carcinoma

BackgroundChromatin regulators (CRs) are capable of causing epigenetic alterations, which are significant features of cancer. However, the function of CRs in controlling Clear Cell Renal Cell Carcinoma (ccRCC) is not well understood. This research aims to discover a CRs prognostic signature in ccRCC and to elucidate the roles of CRs-related genes in tumor microenvironment (TME).MethodsExpression profiles and relevant clinical annotations were retrieved from the Cancer Genome Atlas (TCGA) and UCSC Xena platform for progression-free survival (PFS) data. The R package "limma" was used to identify differentially expressed CRs. A predictive model based on five CRs was developed using LASSO-Cox analysis. The model's predictive power and applicability were validated using K-M curves, ROC curves, nomograms, comparisons with other models, stratified survival analyses, and validation with the ICGC cohort. GO and GSEA analyses were performed to investigate mechanisms differentiating low and high riskScore groups. Immunogenicity was assessed using Tumor Mutational Burden (TMB), immune cell infiltrations were inferred, and immunotherapy was evaluated using immunophenogram analysis and the expression patterns of human leukocyte antigen (HLA) and checkpoint genes. Differentially expressed CRs (DECRs) between low and high riskScore groups were identified using log2|FC|> 1 and FDR < 0.05. AURKB, one of the high-risk DECRs and a component of our prognostic model, was selected for further analysis.ResultsWe constructed a 5 CRs signature, which demonstrated a strong capacity to predict survival and greater applicability in ccRCC. Elevated immunogenicity and immune infiltration in the high riskScore group were associated with poor prognosis. Immunotherapy was more effective in the high riskScore group, and certain chemotherapy medications, including cisplatin, docetaxel, bleomycin, and axitinib, had lower IC50 values. Our research shows that AURKB is critical for the immunogenicity and immune infiltration of the high riskScore group.ConclusionOur study produced a reliable prognostic prediction model using only 5 CRs. We found that AURKB promotes immunogenicity and immune infiltration. This research provides crucial support for the development of prognostic biomarkers and treatment strategies for ccRCC.

Association between human leukocyte antigen alleles and COVID-19 disease severity

Backgroundthe human leukocyte antigen (HLA) loci have been widely characterized to be associated with viral infectious diseases. Several studies including various ethnic groups and populations suggested associations between certain HLA alleles and SARS-CoV-2 infection. Despite the numerous associations identified, the role of HLA polymorphisms in determining the individual response to SARS-CoV-2 infection is controversial among different Saudi populations. MethodHere, we performed HLA typing by next-generation sequencing to investigate if variations in polymorphic HLA genes are linked to COVID-19 severity in the Saudi population. Namely, we analyzed HLA loci at allele level in 575 Saudi patients with SARS-CoV-2 infection. HLA class I and class II frequencies in patients were compared with allele frequency data from healthy Saudi population. Resultsin our cohort HLA-A* 02:01:01 G was associated with mild disease but was not associated with moderate and severe disease. HLA-B* 51:01:01 G was protective from severe disease while HLA-B* 50:01:01 G, HLA-C* 06:02:01 G and HLA-DRB1 * 07:01:01 G were associated with risk to severe disease as well as the total COVID-19 cohort. HLA-DRB1 * 15:01:01 G was associated with risk to all severity groups. Conclusionin conclusion, we found significant associations between HLA alleles and COVID-19 disease severity in Saudis. Further studies are warranted to include HLA typing in the workup for any new COVID-19 patients.