Dataset Information

Association between human leukocyte antigens (HLAs) and human neutrophil antigens (HNAs) and autoimmune neutropenia of infancy in Danish patients.

ABSTRACT:

Background

Autoimmune neutropenia of infancy (AIN) is a frequent cause of neutropenia in children. The disease is caused by antibodies against epitopes on the immunoglobulin G (IgG) Fc receptor type 3b (FcγIIIb). We investigated the possible association of human neutrophil antigens (HNA), human leukocyte antigen (HLA)-DR, and HLA-DQ alleles with AIN and the association of these genotypes with the presence of autoantibodies.

Methods

Eighty AIN cases with a median age of 13.5 months were included. Controls were healthy unrelated Danish blood donors. Anti-HNA-1a autoantibodies were detected using a flow cytometric granulocyte immunofluorescence test (Flow-GIFT) with phenotyped donor cells for detection of antibody specificity. Molecular determination of HNA genotypes was determined using real-time polymerase chain reaction (q-PCR). High-resolution HLA-DRB1 and HLA-DQB1 were determined by next-generation sequencing.

Results

Antibodies against HNA-1a were detected in 51% (n = 41) of AIN patients, and anti-HNA-1b was detected in 3% (n = 2) of cases. In 46% of cases, the antibodies were anti-FcγIIIb-reactive. FCGR3B*01+,*02-,*03- was more common (odds ratio, 6.70; P < .0001), and FCGR3B*01-,*02+,*03- was less common (odds ratio, 0.30; P < .0001) among AIN cases. HNA-1a antibodies were significantly more frequent among AIN cases with the FCGR3B*01+,*02-,*03- genotype (odds ratio, 3.86; P < .007). The HLA-DRB1*14 - HLA-DQB1*05:03 haplotype was significantly more common (odds ratio, 7.44; P < .0001) in AIN patients.

Conclusion

The HLA haplotype HLA-DRB1*14 - DQB1*05:03 is associated with Danish AIN cases. Among Danish AIN patients, anti-HNA-1a is the most common autoantibody, and the antibody is more common in cases with the FCGR3B*01+,*02-,*03- genotype.

SUBMITTER: Nielsen KR

PROVIDER: S-EPMC8248029 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:As the major histocompatibility complex (MHC), human leukocyte antigens (HLAs) are one of the most polymorphic genes in humans. Patients carrying certain HLA alleles may develop adverse drug reactions (ADRs) after taking specific drugs. Peptides play an important role in HLA related ADRs as they are the necessary co-binders of HLAs with drugs. Many experimental data have been generated for understanding HLA-peptide binding. However, efficiently utilizing the data for understanding and accurately predicting HLA-peptide binding is challenging. Therefore, we developed a network analysis based method to understand and predict HLA-peptide binding.Qualitative Class I HLA-peptide binding data were harvested and prepared from four major databases. An HLA-peptide binding network was constructed from this dataset and modules were identified by the fast greedy modularity optimization algorithm. To examine the significance of signals in the yielded models, the modularity was compared with the modularity values generated from 1,000 random networks. The peptides and HLAs in the modules were characterized by similarity analysis. The neighbor-edges based and unbiased leverage algorithm (Nebula) was developed for predicting HLA-peptide binding. Leave-one-out (LOO) validations and two-fold cross-validations were conducted to evaluate the performance of Nebula using the constructed HLA-peptide binding network.Nine modules were identified from analyzing the HLA-peptide binding network with a highest modularity compared to all the random networks. Peptide length and functional side chains of amino acids at certain positions of the peptides were different among the modules. HLA sequences were module dependent to some extent. Nebula archived an overall prediction accuracy of 0.816 in the LOO validations and average accuracy of 0.795 in the two-fold cross-validations and outperformed the method reported in the literature.Network analysis is a useful approach for analyzing large and sparse datasets such as the HLA-peptide binding dataset. The modules identified from the network analysis clustered peptides and HLAs with similar sequences and properties of amino acids. Nebula performed well in the predictions of HLA-peptide binding. We demonstrated that network analysis coupled with Nebula is an efficient approach to understand and predict HLA-peptide binding interactions and thus, could further our understanding of ADRs.