Association of HLA-DRB1 and -DQB1 Alleles with Susceptibility to IgA Nephropathy in Korean Patients

Single locus HLA sequencing with the nanopore technology for HLA disease association diagnosis

Associations between HLA genotype and disease susceptibility encompass almost all the classic HLA loci. The level of typing resolution enabling a correct identification of an HLA disease susceptibility gene depends on the disease itself and/or on the accumulated knowledge about the molecular involvement of the HLA allele(s) engaged. Therefore, the application of Next Generation Sequencing technologies to HLA disease association, which would improve typing resolution, could prove useful to better understand disease severity. In the present study, we tested a nanopore sequencing approach developed by Omixon Biocomputing Ltd, dedicated to on-demand locus typing for HLA and disease, as an alternative to the conventional widely used sequence specific oligoprobe (SSO) approach. A total of 145 DNA samples used in routine diagnosis by SSO were retrospectively analyzed with nanopore technology, for HLA-A*02 immunotherapy decision for A*29, B*27, B*51, B*57 identification in class I, and DRB1, DQA1, and DQB1 for bullous dermatosis, rheumatoid arthritis, diabetes, and celiac disease requests in class II. Each locus was typed in a separate experiment, except for DQB1 and DQA1, which were analyzed together. Concordance between typings reached 100% for all the loci tested. Ambiguities by nanopore were only found for missing exon coverage. This approach was found to be very well adapted to the routine flow imposed by the SSO technique. This study illustrates the use of the new NanoTYPE MONO kit for single locus HLA sequencing for HLA and disease association diagnosis.

Detection of N‑glycoprotein associated with IgA nephropathy in urine as a potential diagnostic biomarker using glycosylated proteomic analysis

The aim of the present study was to elucidate the potential diagnostic value of urinary N-glycoprotein in patients with IgA nephropathy (IgAN) using mass spectrometry (MS). All procedures were performed between June 2021 and June 2023 at Guangan People's Hospital (Guangan, China). Fresh mid-morning fasting midstream urine samples were collected from a total of 30 patients with IgAN and 30 sex- and age-matched healthy volunteers. Data acquired from 6 participants are available through ProteomeXchange with the identifier PXD041151. By comparison between the IgAN group (n=3) and healthy controls (n=3) and selection criteria of P<0.05 and |log fold-change|>2, a total of 11 upregulated and 22 downregulated glycoproteins in patients with IgAN were identified. The results of Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses suggested that glycoproteins are involved in various functions, such as the regulation of cell growth, cell adhesion, cellular component organization and protein binding, as well as multiple pathways, including p53, Notch and mTOR signaling pathways. The urine levels of afamin were further measured by ELISA in a validation cohort to assess the diagnostic performance of the single indicator model. In conclusion, MS-based proteomics of urinary glycoproteins may be an alternative option for diagnosing patients with IgAN. Biomarkers of IgAN may include, but are not limited to, CCL25, PD-L1, HLA-DRB1, IL7RD and WDR82. In addition, the levels of urinary AFM indicators are of diagnostic value for IgAN.

The Diagnostic and Predictive Significance of Immune-Related Genes and Immune Characteristics in the Occurrence and Progression of IgA Nephropathy

Objective

To investigate the potential diagnostic and predictive significance of immune-related genes in IgA nephropathy (IgAN) and discover the abnormal glomerular inflammation in IgAN.

Methods

GSE116626 was used as a training set to identify different immune-related genes (DIRGs) and establish machine learning models for the diagnosis of IgAN; then, a nomogram model was generated based on GSE116626, and GSE115857 was used as a test set to evaluate its clinical value. Short Time-Series Expression Miner (STEM) analysis was also performed to explore the changing trend of DIRGs with the progression of IgAN lesions. GSE141344 was used with DIRGs to establish the ceRNA network associated with IgAN progression. Finally, ssGSEA analysis was performed on the GSE141295 dataset to discover the abnormal inflammation in IgAN.

Results

Machine learning (ML) performed excellently in diagnosing IgAN using six DIRGs. A nomogram model was constructed to predict IgAN based on the six DIRGs. Three trends related to IgAN lesions were identified using STEM analysis. A ceRNA network associated with IgAN progression which contained 8 miRNAs, 14 lncRNAs, and 3 mRNAs was established. A higher macrophage ratio and lower CD4+ T cell ratio in IgAN compared to controls were observed, and the correlation between macrophages and monocytes in the glomeruli of IgAN patients was inverse compared to controls.

Conclusion

This study reveals the diagnostic and predictive significance of DIRGs in IgAN and finds that the imbalance between macrophages and CD4+ immune cells may be an important pathomechanism of IgAN. These results provide potential directions for the treatment and prevention of IgAN.