Identification of two novel HLA-DRB1 alleles, HLA-DRB1*08:03:13 and HLA-DRB1*08:119.

Compared with HLA-DRB1*08:03:02:01, the alleles HLA-DRB1*08:03:13 and HLA-DRB1*08:119 each show one nucleotide substitution, respectively.

The novel HLA-DRB1*03:210 allele characterised by two different sequencing-based typing techniques.

The novel allele HLA-DRB1*03:210 differs from HLA-DRB1*03:01:01:01 by one non-synonymous nucleotide substitution in exon 3.

Investigation of HLA susceptibility alleles and genotypes with hematological disease among Chinese Han population.

Several genes involved in the pathogenesis have been identified, with the human leukocyte antigen (HLA) system playing an essential role. However, the relationship between HLA and a cluster of hematological diseases has received little attention in China. Blood samples (n = 123913) from 43568 patients and 80345 individuals without known pathology were genotyped for HLA class I and II using sequencing-based typing. We discovered that HLA-A*11:01, B*40:01, C*01:02, DQB1*03:01, and DRB1*09:01 were prevalent in China. Furthermore, three high-frequency alleles (DQB1*03:01, DQB1*06:02, and DRB1*15:01) were found to be hazardous in malignant hematologic diseases when compared to controls. In addition, for benign hematologic disorders, 7 high-frequency risk alleles (A*01:01, B*46:01, C*01:02, DQB1*03:03, DQB1*05:02, DRB1*09:01, and DRB1*14:54) and 8 high-frequency susceptible genotypes (A*11:01-A*11:01, B*46:01-B*58:01, B*46:01-B*46:01, C*01:02-C*03:04, DQB1*03:01-DQB1*05:02, DQB1*03:03-DQB1*06:01, DRB1*09:01-DRB1*15:01, and DRB1*14:54-DRB1*15:01) were observed. To summarize, our findings indicate the association between HLA alleles/genotypes and a variety of hematological disorders, which is critical for disease surveillance.

The novel HLA-DRB1*11:323 allele characterized by two different sequencing-based typing techniques.

The novel allele HLA-DRB1*11:323 differs from HLA-DRB1*11:01:02:01 by one non-synonymous nucleotide substitution in exon 2.

Family-based association of HLA-DRB1 and DQB1 alleles and haplotypes in a group of Iranian Type 1 diabetes children.

This family-based study was conducted in a group of Iranians with Type 1 diabetes (T1D) to investigate the transmission from parents of risk and non-risk HLA alleles and haplotypes, and to estimate the genetic risk score for this disease within this population. A total of 240 T1D subjects including 111 parent-child trios (111 children with T1D, 133 siblings, and 222 parents) and 330 ethnically matched healthy individuals were recruited. High-resolution HLA typing for DRB1/DQB1 loci was performed for all study subjects (n = 925) using polymerase chain reaction-sequence-specific oligonucleotide probe method. The highest predisposing effect on developing T1D was conferred by the following haplotypes both in all subjects and in probands compared to controls: DRB1*04:05-DQB1*03:02 (Pc = 2.97e-06 and Pc = 6.04e-10, respectively), DRB1*04:02-DQB1*03:02 (Pc = 5.94e-17 and Pc = 3.86e-09, respectively), and DRB1*03:01-DQB1*02:01 (Pc = 8.26e-29 and Pc = 6.56e-16, respectively). Conversely, the major protective haplotypes included DRB1*13:01-DQB1*06:03 (Pc = 6.99e-08), DRB1*15:01-DQB1*06:02 (Pc = 2.97e-06) in the cases versus controls. Also, DRB1*03:01-DQB1*02:01/DRB1*04:02|05-DQB1*03:02 and DRB1*03:01-DQB1*02:01/DRB1*03:01-DQB1*02:01 diplotypes conferred the highest predisposing effect in the cases (Pc = 8.65e-17 and Pc = 6.26e-08, respectively) and in probands (Pc = 5.4e-15 and Pc = 0.001, respectively) compared to controls. Transmission disequilibrium test showed that the highest risk was conferred by DRB1*04:02-DQB1*03:02 (Pc = 3.26e-05) and DRB1*03:01-DQB1*02:01 (Pc = 1.78e-12) haplotypes and the highest protection by DRB1*14:01-DQB1*05:03 (Pc = 8.66e-05), DRB1*15:01-DQB1*06:02 (Pc = 0.002), and DRB1*11:01-DQB1*03:01 (Pc = 0.0003) haplotypes. Based on logistic regression analysis, carriage of risk haplotypes increased the risk of T1D development 24.5 times in the Iranian population (p = 5.61e-13). Also, receiver operating characteristic curve analysis revealed a high predictive power of those risk haplotypes in discrimination of susceptible from healthy individuals (area under curve: 0.88, p = 5.5e-32). Our study highlights the potential utility of genetic risk assessment based on HLA diplotypes for predicting T1D risk in individuals, particularly among family members of affected children in our population.

Novel genetic variants of HLA gene associated with Thai Behcet's disease (BD) patients using next generation sequencing technology.

Behçet's disease (BD) manifests as an autoimmune disorder featuring recurrent ulcers and multi-organ involvement, influenced by genetic factors associated with both HLA and non-HLA genes, including TNF-α and ERAP1. The study investigated the susceptible alleles of both Class I and II molecules of the HLA gene in 56 Thai BD patients and 192 healthy controls through next-generation sequencing using a PacBio kit. The study assessed 56 BD patients, primarily females (58.9%), revealing diverse manifestations including ocular (41.1%), vascular (35.7%), skin (55.4%), CNS (5.4%), and GI system (10.7%) involvement. This study found associations between BD and HLA-A*26:01:01 (OR 3.285, 95% CI 1.135-9.504, P-value 0.028), HLA-B*39:01:01 (OR 6.176, 95% CI 1.428-26.712, P-value 0.015), HLA-B*51:01:01 (OR 3.033, 95% CI 1.135-8.103, P-value 0.027), HLA-B*51:01:02 (OR 6.176, 95% CI 1.428-26.712, P-value 0.015), HLA-C*14:02:01 (OR 3.485, 95% CI 1.339-9.065, P-value 0.01), HLA-DRB1*14:54:01 (OR 1.924, 95% CI 1.051-3.522, P-value 0.034), and HLA-DQB1*05:03:01 (OR 3.00, 95% CI 1.323-6.798, P-value 0.008). However, after Bonferroni correction none of these alleles were found to be associated with BD. In haplotype analysis, we found a strong linkage disequilibrium in HLA-B*51:01:01, HLA-C*14:02:01 (P-value 0.0, Pc-value 0.02). Regarding the phenotype, a significant association was found between HLA-DRB1*14:54:01 (OR 11.67, 95% CI 2.86-47.57, P-value 0.001) and BD with ocular involvement, apart from this, no distinct phenotype-HLA association was documented. In summary, our study identifies specific HLA associations in BD. Although limited by a small sample size, we acknowledge the need for further investigation into HLA relationships with CNS, GI, and neurological phenotypes in the Thai population.

Neutralizing IFN-γ autoantibodies are rare and pathogenic in HLA-DRB1*15:02 or 16:02 individuals.

BACKGROUNDWeakly virulent environmental mycobacteria (EM) can cause severe disease in HLA-DRB1*15:02 or 16:02 adults harboring neutralizing anti-IFN-γ autoantibodies (nAIGAs). The overall prevalence of nAIGAs in the general population is unknown, as are the penetrance of nAIGAs in HLA-DRB1*15:02 or 16:02 individuals and the proportion of patients with unexplained, adult-onset EM infections carrying nAIGAs.METHODSThis study analyzed the detection and neutralization of anti-IFN-γ autoantibodies (auto-Abs) from 8,430 healthy individuals of the general population, 257 HLA-DRB1*15:02 or 16:02 carriers, 1,063 patients with autoimmune disease, and 497 patients with unexplained severe disease due to EM.RESULTSWe found that anti-IFN-γ auto-Abs detected in 4,148 of 8,430 healthy individuals (49.2%) from the general population of an unknown HLA-DRB1 genotype were not neutralizing. Moreover, we did not find nAIGAs in 257 individuals carrying HLA-DRB1* 15:02 or 16:02. Additionally, nAIGAs were absent in 1,063 patients with an autoimmune disease. Finally, 7 of 497 patients (1.4%) with unexplained severe disease due to EM harbored nAIGAs.CONCLUSIONThese findings suggest that nAIGAs are isolated and that their penetrance in HLA-DRB1*15:02 or 16:02 individuals is low, implying that they may be triggered by rare germline or somatic variants. In contrast, the risk of mycobacterial disease in patients with nAIGAs is high, confirming that these nAIGAs are the cause of EM disease.FUNDINGThe Laboratory of Human Genetics of Infectious Diseases is supported by the Howard Hughes Medical Institute, the Rockefeller University, the St. Giles Foundation, the National Institutes of Health (NIH) (R01AI095983 and U19AIN1625568), the National Center for Advancing Translational Sciences (NCATS), the NIH Clinical and Translational Science Award (CTSA) program (UL1 TR001866), the French National Research Agency (ANR) under the "Investments for the Future" program (ANR-10-IAHU-01), the Integrative Biology of Emerging Infectious Diseases Laboratory of Excellence (ANR-10-LABX-62-IBEID), ANR-GENMSMD (ANR-16-CE17-0005-01), ANR-MAFMACRO (ANR-22-CE92-0008), ANRSECTZ170784, the French Foundation for Medical Research (FRM) (EQU201903007798), the ANRS-COV05, ANR GENVIR (ANR-20-CE93-003), and ANR AI2D (ANR-22-CE15-0046) projects, the ANR-RHU program (ANR-21-RHUS-08-COVIFERON), the European Union's Horizon 2020 research and innovation program under grant agreement no. 824110 (EASI-genomics), the Square Foundation, Grandir - Fonds de solidarité pour l'enfance, the Fondation du Souffle, the SCOR Corporate Foundation for Science, the Battersea & Bowery Advisory Group, William E. Ford, General Atlantic's Chairman and Chief Executive Officer, Gabriel Caillaux, General Atlantic's Co-President, Managing Director, and Head of business in EMEA, and the General Atlantic Foundation, Institut National de la Santé et de la Recherche Médicale (INSERM) and of Paris Cité University. JR was supported by the INSERM PhD program for doctors of pharmacy (poste d'accueil INSERM). JR and TLV were supported by the Bettencourt-Schueller Foundation and the MD-PhD program of the Imagine Institute. MO was supported by the David Rockefeller Graduate Program, the Funai Foundation for Information Technology (FFIT), the Honjo International Scholarship Foundation (HISF), and the New York Hideyo Noguchi Memorial Society (HNMS).

Investigating Associations between HLA-DR Genotype, H. pylori Infection, and Anti-CagA IgA Seropositivity in a Turkish Gastritis Cohort.

Helicobacter pylori (H. pylori) is associated with gastric inflammation and mucosal antibodies against its cytotoxin-associated gene A (CagA) are protective. Vaccine-elicited immunity against H. pylori requires MHC class II expression, indicating that CD4+ T cells are protective. We hypothesized that the HLA-DR genotypes in human populations include protective alleles that more effectively bind immunogenic CagA peptide fragments and susceptible alleles with an impaired capacity to present CagA peptides. We recruited patients (n = 170) admitted for gastroendoscopy procedures and performed high-resolution HLA-DRB1 typing. Serum anti-CagA IgA levels were analyzed by ELISA (23.2% positive) and H. pylori classified as positive or negative in gastric mucosal tissue slides (72.9% positive). Pearson Chi-square analysis revealed that H. pylori infection was significantly increased in DRB1*11:04-positive individuals (p = 0.027). Anti-CagA IgA was significantly decreased in DRB1*11:04 positive individuals (p = 0.041). In contrast, anti-CagA IgA was significantly increased in DRB1*03:01 positive individuals (p = 0.030). For these HLA-DRB1 alleles of interest, we utilized two in silico prediction methods to compare their capacity to present CagA peptides. Both methods predicted increased numbers of peptides for DRB1*03:01 than DRB1*11:04. In addition, both alleles preferred distinctively different CagA 15mer peptide sequences for high affinity binding. These observations suggest that DRB1*11:04 is a susceptible genotype with impaired CagA immunity, whereas DRB1*03:01 is a protective genotype that promotes enhanced CagA immunity.

New Genetic Markers of Skin T-Cell Lymphoma Treatment.

AIM: Cutaneous T-cell lymphomas (CTCL) can be described as chronic skin inflammation lesions with the content of malignant T cells and they are considered to be T-cell-mediated skin diseases. CD147 is recognized as a 58-kDa cell surface glycoprotein of the immunoglobulin superfamily; it can induce the synthesis of MMPs (matrix metalloproteinases) on the surface of tumor cells where it was originally identified. It can also function in adjacent tumor fibroblasts using CD147-CD147 interactions. The polymorphism rs8259 T/A is situated in the untranslated region (3'UTR) of the CD147 gene. HLA DRB1*1501 takes part in the process of presentation and recognition of different antigens to T cells. It can be expressed by antigen-presenting cells-macrophages, dendritic cells, and B cells. The aim of the study is to test genotype-phenotype associations of both polymorphisms including therapy in a large cohort of CTCL patients. MATERIALS AND METHODS: A final total of 104 CTCL patients were enrolled in the study. For the first remission at the clinic department, they were treated by means of local skin-directed therapy, phototherapy, and systemic therapy. Genomic DNA was isolated from peripheral blood leukocytes. A standard technique using proteinase K was applied. The polymorphisms rs8259 T/A (CD147 gene) and rs3135388 (HLA DRB1*1501) were detected through standard PCR-restriction fragment length polymorphism methods. RESULTS: The severity of the disease (patients with parapsoriasis, stages IA and IB, vs patients with stages IIB, IIIA, and IIIB) was associated with the CD147 genotype: the AA variant was 3.38 times more frequent in more severe cases, which reflects the decision on systemic therapy (p = 0.02, specificity 0.965). The AA genotype in the CD147 polymorphism was 12 times more frequent in patients who underwent systemic therapy of CTCL compared to those not treated with this therapy (p = 0.009, specificity 0.976). The same genotype was also associated with radiotherapy-it was observed 14 times more frequently in patients treated with radiotherapy (p = 0.009, specificity 0.959). In patients treated with interferon α therapy, the AA genotype was observed to be 5.85 times more frequent compared to the patients not treated with interferon therapy (p = 0.03, specificity 0.963). The HLA DRB1*1501 polymorphism was associated with local skin-directed therapy of CTCL. The CC genotype of the polymorphism was observed to be 3.57 times more frequent in patients treated with local therapy (p = 0.008, specificity 0.948). When both polymorphisms had been calculated together, even better results were obtained: the AACC double genotype was 11 times more frequent in patients with severe CTCL (p = 0.009, specificity 0.977). The TACT double genotype was associated with local skin-directed therapy (0.09 times lower frequency, p = 0.007, sensitivity 0.982). The AACC genotype was 8.9 times more frequent in patients treated by means of systemic therapy (p = 0.02, specificity 0.976) and as many as 18.8 times more frequent in patients treated with radiotherapy (p = 0.005, specificity 0.969). Thus, the AACC double genotype of CD147 and DRB1*1501 polymorphisms seems to be a clinically highly specific marker of severity, systemic therapy and radiotherapy of patients with T-cell lymphoma. CONCLUSION: Although genotyping results were not known during the treatment decision and could not modify it, the clinical decision on severity and therapy reflected some aspects of the genetic background of this complicated T-cell-associated disease very well.

Pathologic Features of Anti-Ku Myositis.

OBJECTIVE: Characteristics of myositis with anti-Ku antibodies are poorly understood. The purpose of this study was to elucidate the pathologic features of myositis associated with anti-Ku antibodies, compared with immune-mediated necrotizing myopathy (IMNM) with anti-signal recognition particle (SRP) and anti-3-hydroxy-3-methylglutaryl-coenzyme A reductase (HMGCR) antibodies, in muscle biopsy-oriented registration cohorts in Japan and Germany. METHODS: We performed a retrospective pathology review of patients with anti-Ku myositis samples diagnosed in the Japanese and German cohorts. We evaluated histologic features and performed HLA phenotyping. RESULTS: Fifty biopsied muscle samples in the Japanese cohort and 10 in the German cohort were obtained. After exclusion of myositis-specific autoantibodies or other autoimmune connective tissue diseases, 26 samples (43%) of anti-Ku antibody-positive myositis were analyzed. All the samples shared some common features with IMNM, whereas they showed expression of MHC class II and clusters of perivascular inflammatory cells more frequently than the anti-SRP/HMGCR IMNM samples (71% vs 7%/16%; p < 0.005/<0.005; 64% vs 0%/0%; p < 0.005/<0.005). Anti-Ku myositis biopsies could be divided into 2 subgroups based on the extent of necrosis and regeneration. The group with more abundant necrosis and regeneration showed a higher frequency of MHC class II expression and perivascular inflammatory cell clusters. HLA phenotyping in the 44 available patients showed possible associations of HLA-DRB1*03:01, HLA-DRB1*11:01, and HLA-DQB1*03:01 (p = 0.0045, 0.019, and 0.027; odds ratio [OR] 50.2, 4.6, and 2.8; 95% CI 2.6-2942.1, 1.1-14.5, and 1.0-7.0) in the group with less conspicuous necrosis and regeneration. On the contrary, in the group of more abundant necrosis and regeneration, the allele frequencies of HLA-A*24:02, HLA-B*52:01, HLA-C*12:02, and HLA-DRB1*15:02 were lower than those of healthy controls (p = 0.0036, 0.027, 0.016, and 0.026; OR = 0.27, 0, 0, and 0; 95% CI 0.1-0.7, 0-0.8, 0-0.8, and 0-0.8). However, these HLA associations did not remain significant after statistical correction for multiple testing. DISCUSSION: While anti-Ku myositis shows necrotizing myopathy features, they can be distinguished from anti-SRP/HMGCR IMNM by their MHC class II expression and clusters of perivascular inflammatory cells. The HLA analyses suggest that anti-Ku myositis may have different subsets associated with myopathologic subgroups.

Identification of four novel HLA alleles: HLA-B*27:265, -B*35:569, -DRB1*08:117 and -DPB1*1435:01.

Identification of four new HLA alleles (B*27:265, B*35:569, DRB1*08:117, and DPB1*1435:01) in Brazilian bone marrow donors.

Association of HLA-DRB1 locus with treatment response to abatacept or TNF inhibitors in patients with seropositive rheumatoid arthritis.

The strongest genetic risk factor for rheumatoid arthritis (RA) has been known as HLA-DRB1 based on amino acid positions 11, 71, and 74. This study analyzed the association between specific HLA-DRB1 locus and treatment response to abatacept or TNF inhibitors (TNFi) in patients with seropositive RA. A total of 374 Korean RA patients were treated with abatacept (n = 110) or TNFi (n = 264). Associations between HLA-DRB1 and treatment response after 6 months were analyzed using multivariable logistic regression. Seropositive RA patients with HLA-DRB1 shared epitope (SE) had a favorable response to abatacept (OR = 3.67, P = 0.067) and an inversely associated response to TNFi (OR 0.57, P = 0.058) based on EULAR response criteria, but the difference was not statistically significant in comparison to those without SE. In analyses using amino acid positions of HLA-DRB1, a significant association was found between valine at amino acid position 11 of SE and good response to abatacept (OR = 6.46, P = 5.4 × 10-3). The VRA haplotype also showed a good response to abatacept (OR = 4.56, P = 0.013), but not to TNFi. Our results suggest that treatment response to abatacept or TNFi may differ depending on HLA-DRB1 locus in seropositive RA, providing valuable insights for selecting optimal therapy.

Machine learning-based prediction of rheumatoid arthritis with development of ACPA autoantibodies in the presence of non-HLA genes polymorphisms.

Machine learning (ML) algorithms can handle complex genomic data and identify predictive patterns that may not be apparent through traditional statistical methods. They become popular tools for medical applications including prediction, diagnosis or treatment of complex diseases like rheumatoid arthritis (RA). RA is an autoimmune disease in which genetic factors play a major role. Among the most important genetic factors predisposing to the development of this disease and serving as genetic markers are HLA-DRB and non-HLA genes single nucleotide polymorphisms (SNPs). Another marker of RA is the presence of anticitrullinated peptide antibodies (ACPA) which is correlated with severity of RA. We use genetic data of SNPs in four non-HLA genes (PTPN22, STAT4, TRAF1, CD40 and PADI4) to predict the occurrence of ACPA positive RA in the Polish population. This work is a comprehensive comparative analysis, wherein we assess and juxtapose various ML classifiers. Our evaluation encompasses a range of models, including logistic regression, k-nearest neighbors, naïve Bayes, decision tree, boosted trees, multilayer perceptron, and support vector machines. The top-performing models demonstrated closely matched levels of accuracy, each distinguished by its particular strengths. Among these, we highly recommend the use of a decision tree as the foremost choice, given its exceptional performance and interpretability. The sensitivity and specificity of the ML models is about 70% that are satisfying. In addition, we introduce a novel feature importance estimation method characterized by its transparent interpretability and global optimality. This method allows us to thoroughly explore all conceivable combinations of polymorphisms, enabling us to pinpoint those possessing the highest predictive power. Taken together, these findings suggest that non-HLA SNPs allow to determine the group of individuals more prone to develop RA rheumatoid arthritis and further implement more precise preventive approach.

Characterization of the novel HLA-DRB1*04:04:20 allele by sequencing-based typing.

HLA-DRB1*04:04:20 differs from HLA-DRB1*04:04:01:04 by one nucleotide substitution in codon 135 in exon 3.

[Establishment and validation of prediction models for human leukocyte antigen haplotypes and human leukocyte antigen genotypes].

Objective: To establish prediction models for human leukocyte antigen (HLA) haplotypes and HLA genotypes, and verify the prediction accuracy. Methods: The prediction models were established based on the characteristic of HLA haplotype inheritance and linkage disequilibrium (LD), as well as the invention patents and software copyrights obtained. The models include algorithm and reference databases such as HLA A-C-B-DRB1-DQB1 high-resolution haplotypes database, B-C and DRB1-DQB1 LD database, G group alleles table, and NMDP Code alleles table. The prediction algorithm involves data processing, comparison with reference data, filtering results, probability calculation and ranking, confidence degree estimation, and output of prediction results. The accuracy of the predictions was verified by comparing them with the correct results, and the relationship between prediction accuracy and the probability distribution and confidence degree of the predicted results was analyzed. Results: The HLA haplotypes and genotypes prediction models were established. The prediction algorithm included the prediction of A-C-B-DRB1-DQB1 haplotypes according to HLA-A, B, DRB1, C, DQB1 genotypes, the prediction of C and DQB1 high-resolution results according to A, B and DRB1 high-resolution results, and the prediction of A, B, DRB1, C and DQB1 high resolution results according to the A, B and DRB1 intermediate or low resolution results. Validation results of "Predicting A-C-B-DRB1-DQB1 haplotypes basing on HLA-A, B, DRB1, C, DQB1 genotypes" model: for 787 data, the accuracy was 94.0% (740/787) with 740 correct predictions, 34 incorrect predictions, and 13 instances with no predicted results. For 847 data, the accuracy was 100% (847/847). The 2 411 and 2 594 haplotype combinations predicted from 787 and 847 data were grouped according to confidence degree, the accuracy was 100% (48/48, 114/114) for a confidence degree of 1, 96.2% (303/315) and 97.8% (409/418) for a confidence degree of 2 respectively. Validation results of "Predicting A, B, DRB1 and C, DQB1 high-resolution genotypes basing on HLA-A, B, DRB1 high, intermediate, or low resolution genotypes" model: when predicting C and DQB1 high resolution genotypes basing on A, B, and DRB1 high resolution genotypes, 89.3% (1 459/1 634) of the predictions were correct. The accuracy for the top 2 predicted probability (GPP) ranking was 79.2% (1 156/1 459), and for the top 10, it was 95.0% (1 386/1 459). Furthermore, when GPP≥90% and GPP 50%-90%, the prediction accuracy was 81.3% (209/257) and 72.8% (447/614) respectively. The accuracy of predicting C and DQB1 high resolution genotypes basing on the results of A, B, and DRB1 high resolution genotypes from the China Marrow Donor Program was 87.0% (20/23). The accuracy of predicting A, B, DRB1, C, and DQB1 high resolution genotypes basing on the results of A, B, and DRB1 intermediate or low-resolution genotypes was 70.0% (7/10) and 52.5% (21/40) respectively. When predicting whether the patient is likely to have a HLA 10/10 matched donor, the accuracy of the top 2 GPP combinations with a proportion of ≥50% was 85.7% (6/7). Conclusions: When using A, B, DRB1, C, DQB1 genotypes to predict A-C-B-DRB1-DQB1 haplotype combinations, the results with a confidence degree of 1 and 2 are reliable. When predicting C and DQB1 genotypes according to A, B and DRB1 genotypes, the top 10 results ranked by GPP are reliable, and the top 2 results with GPP≥50% are more reliable.

HLA Class II (DR, DQ, DP) Genes Were Separately Associated With the Progression From Seroconversion to Onset of Type 1 Diabetes Among Participants in Two Diabetes Prevention Trials (DPT-1 and TN07).

OBJECTIVE: To explore associations of HLA class II genes (HLAII) with the progression of islet autoimmunity from asymptomatic to symptomatic type 1 diabetes (T1D). RESEARCH DESIGN AND METHODS: Next-generation targeted sequencing was used to genotype eight HLAII genes (DQA1, DQB1, DRB1, DRB3, DRB4, DRB5, DPA1, DPB1) in 1,216 participants from the Diabetes Prevention Trial-1 and Randomized Diabetes Prevention Trial with Oral Insulin sponsored by TrialNet. By the linkage disequilibrium, DQA1 and DQB1 are haplotyped to form DQ haplotypes; DP and DR haplotypes are similarly constructed. Together with available clinical covariables, we applied the Cox regression model to assess HLAII immunogenic associations with the disease progression. RESULTS: First, the current investigation updated the previously reported genetic associations of DQA1*03:01-DQB1*03:02 (hazard ratio [HR] = 1.25, P = 3.50*10-3) and DQA1*03:03-DQB1*03:01 (HR = 0.56, P = 1.16*10-3), and also uncovered a risk association with DQA1*05:01-DQB1*02:01 (HR = 1.19, P = 0.041). Second, after adjusting for DQ, DPA1*02:01-DPB1*11:01 and DPA1*01:03-DPB1*03:01 were found to have opposite associations with progression (HR = 1.98 and 0.70, P = 0.021 and 6.16*10-3, respectively). Third, DRB1*03:01-DRB3*01:01 and DRB1*03:01-DRB3*02:02, sharing the DRB1*03:01, had opposite associations (HR = 0.73 and 1.44, P = 0.04 and 0.019, respectively), indicating a role of DRB3. Meanwhile, DRB1*12:01-DRB3*02:02 and DRB1*01:03 alone were found to associate with progression (HR = 2.6 and 2.32, P = 0.018 and 0.039, respectively). Fourth, through enumerating all heterodimers, it was found that both DQ and DP could exhibit associations with disease progression. CONCLUSIONS: These results suggest that HLAII polymorphisms influence progression from islet autoimmunity to T1D among at-risk subjects with islet autoantibodies.

Contribution of HLA class I (A, B, C) and HLA class II (DRB1, DQA1, DQB1) alleles and haplotypes in exploring ethnic origin of central Tunisians.

BACKGROUND: Estimation of HLA (Human leukocyte Antigen) alleles' frequencies in populations is essential to explore their ethnic origin. Anthropologic studies of central Tunisian population were rarely reported. Then, in this work, we aimed to explore the origin of central Tunisian population using HLA alleles and haplotypes frequencies. METHODS: HLA class I (A, B, C) and HLA class II (DRB1, DQA1, DQB1) loci genotyping of 272 healthy unrelated organ donors was performed by Polymerase Chain Reaction-Sequence Specific Oligonucleotide (PCR-SSO). We compared central Tunisians with other populations (Arabs, Berbers, Mediterraneans, Europeans, Africans, etc.) using alleles and haplotypes frequencies, genetic distances, Neighbour-Joining dendrogram and correspondence analysis. RESULTS: Among the 19 HLA A alleles, the 26 HLA B alleles, the 13 HLA C alleles, the 15 HLA DRB1 alleles, the 6 HLA DQA1 alleles and the 5 HLA DQB1 alleles identified in the studied population, HLA A*02 (22.8%), HLA B*50 (13.1%), HLA C*06 (21.8%), HLA DRB1*07 (17.8%), HLA DQA1*01 (32.1%) and HLA DQB1*03 (31.6%) were the most frequent alleles. The extended haplotypes HLA A*02-B*50-C*06-DRB1*07-DQA1*02-DQB1*02 (1.97%) was the most frequent HLA six-loci haplotype. CONCLUSION: Central Tunisians were very close to other Tunisian populations, to Iberians and North Africans. They were rather distant from sub-Saharan populations and eastern Mediterraneans especially Arabs although the strong cultural and religious impact of Arabs in this population.

Bioinformatics and Systems Biology Approaches to Identify the Synergistic Effects of Alcohol Use Disorder on the Progression of Neurological Diseases.

Clinical investigations showed that individuals with Alcohol Use Disorder (AUD) have worse Neurological Disease (ND) development, pointing to possible pathogenic relationships between AUD and NDs. It remains difficult to identify risk factors that are predisposing between AUD and NDs. In order to fix these issues, we created the bioinformatics pipeline and network-based approaches for employing unbiased methods to discover genes abnormally stated in both AUD and NDs and to pinpoint some of the common molecular pathways that might underlie AUD and ND interaction. We found 100 differentially expressed genes (DEGs) in both the AUD and ND patient's tissue samples. The most important Gene Ontology (GO) terms and metabolic pathways, including positive control of cytotoxicity caused by T cells, proinflammatory responses, antigen processing and presentation, and platelet-triggered interactions with vascular and circulating cell pathways were then extracted using the overlapped DEGs. Protein-protein interaction analysis was used to identify hub proteins, including CCL2, IL1B, TH, MYCN, HLA-DRB1, SLC17A7, and HNF4A, in the pathways that have been reported as playing a function in these disorders. We determined several TFs (HNF4A, C4A, HLA-B, SNCA, HLA-DMB, SLC17A7, HLA-DRB1, HLA-C, HLA-A, and HLA-DPB1) and potential miRNAs (hsa-mir-34a-5p, hsa-mir-34c-5p, hsa-mir-449a, hsa-mir-155-5p, and hsa-mir-1-3p) were crucial for regulating the expression of AUD and ND which could serve as prospective targets for treatment. Our methodologies discovered unique putative biomarkers that point to the interaction between AUD and various neurological disorders, as well as pathways that could one day be the focus of therapeutic intervention.

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.

Shared genomic segments analysis identifies MHC class I and class III molecules as genetic risk factors for juvenile idiopathic arthritis.

Juvenile idiopathic arthritis (JIA) is a complex rheumatic disease encompassing several clinically defined subtypes of varying severity. The etiology of JIA remains largely unknown, but genome-wide association studies (GWASs) have identified up to 22 genes associated with JIA susceptibility, including a well-established association with HLA-DRB1. Continued investigation of heritable risk factors has been hindered by disease heterogeneity and low disease prevalence. In this study, we utilized shared genomic segments (SGS) analysis on whole-genome sequencing of 40 cases from 12 multi-generational pedigrees significantly enriched for JIA. Subsets of cases are connected by a common ancestor in large extended pedigrees, increasing the power to identify disease-associated loci. SGS analysis identifies genomic segments shared among disease cases that are likely identical by descent and anchored by a disease locus. This approach revealed statistically significant signals for major histocompatibility complex (MHC) class I and class III alleles, particularly HLA-A∗02:01, which was observed at a high frequency among cases. Furthermore, we identified an additional risk locus at 12q23.2-23.3, containing genes primarily expressed by naive B cells, natural killer cells, and monocytes. The recognition of additional risk beyond HLA-DRB1 provides a new perspective on immune cell dynamics in JIA. These findings contribute to our understanding of JIA and may guide future research and therapeutic strategies.