Influence of pharmacogenomic polymorphisms on allopurinol-induced cutaneous adverse drug reactions in Thai patients.

BACKGROUND: Allopurinol has been causing substantial morbidity and mortality particularly in Asian population by producing cutaneous adverse drug reactions (cADRs). Nonetheless, there are no data describing whether other genetics are a valid marker for prediction of allopurinol-induced cADRs patients in addition to HLA-B*58:01 allele. The goal of this study was to identify suitable single nucleotide polymorphisms (SNPs) for allopurinol induced cADRs among Thai patients. METHODS: We conducted a case-control association study after enrolling 57 Thai patients with allopurinol induced cADRs and 101 allopurinol-tolerant controls. The genetic biomarkers and associated SNPs located on chromosome 6p21 were examined by TaqMan® SNP genotyping assays in both the cases and the controls. RESULTS: Out of fifteen SNPs in nine genes, we found four combined SNPs (rs3099844 of HCP5, rs9263726 of PSORS1C1, rs9263733 of POLR2LP, and rs9263745 of CCHCR1) were significantly associated with allopurinol-induced cADRs compared to the tolerant controls (OR 73.2; 95% CI 24.2-266.8; P = 1.9 × 10- 24). The overall sensitivity, specificity, positive predictive value and negative predictive value of these combinations were 84%, 94%, 9%, and 100%, respectively. However, the variant alleles of these SNP combinations were detected in 89.5% (51/57) of the cases. Moreover, the HLA-B*58:01 allele was observed in 86.0% of patients with allopurinol-induced cADRs, but only in 4.0% of tolerant controls (OR: 137.2; 95% CI: 38.3-670.5 and p-value = 1.7 × 10- 27). CONCLUSIONS: Thus, this research confirms the association between the specific HLA-B*58:01 allele and all phenotypes of allopurinol-induced cADRs in Thais. Furthermore, there was found the combined four SNPs (rs3099844, rs9263726, rs9263733, and rs9263745) could be used as alternative novel biomarkers for predicting cADRs in patients taking allopurinol.

The novel HLA-B*44:48:02 allele characterised by two different sequencing-based typing techniques.

The novel allele HLA-B*44:48:02 differs from HLA-B*44:48:01 by one synonymous nucleotide substitution in exon 3.

Detection of the novel HLA-B*55:01:31, HLA-C*07:1113 alleles and confirmation of HLA-C*12:392.

Novel HLA-B*55:01:31, HLA-C*07:1113 alleles and confirmatory HLA-C*12:392 allele were detected during the HLA typing process.

The novel HLA-B*35:01:77 allele identified in a Russian volunteer bone marrow donor.

Nucleotide substitutions in the 5'UTR and exon 2 of HLA-B*35:01:01:05 result in a novel allele, HLA-B*35:01:77.

Identification of the novel HLA-B*13:191 allele by next-generation sequencing.

The novel HLA-B*13:191 allele was detected during the HLA typing for kidney transplantation.

Two novel HLA class I alleles: HLA-A*02:1148 and HLA-B*44:386.

Identification of the novel HLA-A*02:1148 and HLA-B*44:386 alleles by next-generation sequencing.

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.

Characterisation of the novel HLA-B*58:02:04 allele by next-generation sequencing.

HLA-B*58:02:04 differs from HLA-B*58:02:01 by one synonymous nucleotide in codon 215 in exon 4.

HLA molecular mismatches and induced donor-specific tolerance in combined living donor kidney and hematopoietic stem cell transplantation.

Introduction: We investigated the potential role of HLA molecular mismatches (MM) in achieving stable chimerism, allowing for donor-specific tolerance in patients undergoing combined living donor kidney and hematopoietic stem cell transplantation (HSCT). Methods: All patients with available DNA samples (N=32) who participated in a phase 2 clinical trial (NCT00498160) where they received an HLA mismatched co-transplantation of living donor kidney and facilitating cell-enriched HSCT were included in this study. High-resolution HLA genotyping data were used to calculate HLA amino acid mismatches (AAMM), Eplet MM, three-dimensional electrostatic mismatch scores (EMS-3D), PIRCHE scores, HLA-DPB1 T-cell epitope group MM, HLA-B leader sequence MM, and KIR ligands MM between the donor and recipient in both directions. HLA MM were analyzed to test for correlation with the development of chimerism, graft vs. host disease (GvHD), de novo DSA, and graft rejection. Results: Follow-up time of this cohort was 6-13.5 years. Of the 32 patients, 26 developed high-level donor or mixed stable chimerism, followed by complete withdrawal of immunosuppression (IS) in 25 patients. The remaining six of the 32 patients had transient chimerism or no engraftment and were maintained on IS (On-IS). In host versus graft direction, a trend toward higher median number of HLA-DRB1 MM scores was seen in patients On-IS compared to patients with high-level donor/mixed chimerism, using any of the HLA MM modalities; however, initial statistical significance was observed only for the EMS-3D score (0.45 [IQR, 0.30-0.61] vs. 0.24 [IQR, 0.18-0.36], respectively; p=0.036), which was lost when applying the Bonferroni correction. No statistically significant differences between the two groups were observed for AAMM, EMS-3D, Eplet MM, and PIRCHE-II scores calculated in graft versus host direction. No associations were found between development of chimerism and GvHD and non-permissive HLA-DPB1 T-cell epitope group MM, HLA-B leader sequence, and KIR ligands MM. Conclusion: Our results suggest an association between HLA-DRB1 molecular mismatches and achieving stable chimerism, particularly when electrostatic quality of the mismatch is considered. The non-permissive HLA-DPB1 T-cell epitope group, HLA-B leader sequence, and KIR ligands MM do not predict chimerism and GvHD in this combined kidney/HSCT transplant patient cohort. Further work is needed to validate our findings. Clinical trial registration: https://clinicaltrials.gov/study/NCT00498160, identifier NCT00498160.

Characterization of the novel HLA-B*40:538 allele by next-generation sequencing.

The HLA-B*40:538 allele differs from HLA-B*40:01:02:01 at position 905 C→T in exon 5.

[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.

Full-length sequence of the novel allele, HLA-B*58:01:40, identified in a Chinese individual.

HLA-B*58:01:40 differs from HLA-B*58:01:01 by a single nucleotide change in exon 3, 507 C- > T (codon 145.3 CGC- > CGT).

Genomic sequence of the novel HLA-B*41:02:01:11, and -C*08:266 alleles identified in a solid organ recipient.

HLA-B*41:02:01:11 and -C*08:266 were detected in a solid organ recipient during the HLA typing process.

Detection of the HLA-B*40:01:35 allele in a Taiwanese individual.

Nucleotide substitutions in codons -1 and 84 of HLA-B*40:01:01 result in a novel allele, HLA-B*40:01:35.

Identification of the novel HLA-B*40:06:01:18 allele in a North Indian individual.

HLA-B*40:06:01:18 differs from HLA-B*40:06:01:02 by one nucleotide change in the 5'UTR (T > C).

Discovery of the novel HLA-B*47:01:01:07 allele in a Greek volunteer bone marrow donor.

HLA-B*47:01:01:07 differs from the HLA-B*47:01:01:03 allele by one nucleotide deletion in the 3'UTR.

Rapid and Reliable HLA-B*59:01 Genotyping to Prevent Carbonic Anhydrase Inhibitor-Induced Severe Cutaneous Adverse Reactions.

OBJECTIVE: Carbonic anhydrase inhibitors (CAIs) are intraocular pressure-reducing medications used in ophthalmology. Human leukocyte antigen-B*59:01 (HLA-B*59:01) is strongly associated with CAI-induced severe cutaneous adverse reactions (SCARs). This study aimed to develop and validate a rapid and economical screening method for HLA-B*59:01 to prevent carbonic anhydrase inhibitor-induced SCARs. METHODS: Duplex allele-specific polymerase chain reaction (PCR) with an internal control was performed for HLA-B*59:01 genotyping. The accuracy of duplex allele-specific PCR for HLA-B*59:01 genotyping was evaluated in 200 blood samples, using sequence-based typing (SBT) as the reference method. RESULTS: In total, 50 HLA-B*59:01-positive and 150 HLA-B*59:01-negative results obtained using duplex allele-specific PCR were in complete agreement with the SBT results. CONCLUSION: Duplex allele-specific PCR is a rapid, reliable, and economical assay for screening the HLA-B*59:01 allele.

Platelet transfusions in haploidentical haematopoietic stem cell allograft candidates: Protecting HLA-A and HLA-B antigens through eplet analysis.

In patients awaiting an allogeneic haematopoietic stem cell transplantation, platelet transfusion is a risk factor for anti-HLA class I immunization because the resulting donor-specific antibodies complicate the allograft process. The objective of the present study was to determine the feasibility of a novel eplet-based strategy for identifying HLA class I mismatches between potential donors and the recipient when pre-allograft platelet transfusions were required. We included 114 recipient/haploidentical relative pairs. For each pair, we entered HLA-class I typing data into the HLA Eplet Mismatch calculator, defined the list of mismatched eplets (for the recipient versus donor direction) and thus identified the shared HLAs to be avoided. Using this list of HLAs, we defined the theoretical availability of platelet components (PCs) by calculating the virtual panel-reactive antibody (vPRA). We also determined the number of PCs actually available in France by querying the regional transfusion centre's database. The mean ± standard deviation number of highly/moderately exposed eplets to be avoided in platelet transfusions was 5.8 ± 3.3, which led to the prohibition of 38.5 ± 2 HLAs-A and -B. Taking into account the mismatched antigens and the eplet load, the mean ± standard deviation theoretical availability of PCs (according to the vPRA) was respectively 34.49% ± 1.95% for HLA-A and 80% ± 2.3% for HLA-B. A vPRA value below 94.9% for highly or moderately exposed eplets would predict that 10 PCs were actually available nationally. Although epitope protection of HLA molecules is feasible, it significantly restricts the choice of PCs.

Detection of two HLA-B*35 variants: HLA-B*35:01:01:39 and -B*35:03:01:32.

Two nucleotide substitutions in intronic regions give rise to the novel alleles: HLA-B*35:01:01:39 and -B*35:03:01:32.