Dataset Information

Analysis of urine differential proteins in patients with allergic rhinitis.

ABSTRACT:

Background

Allergic rhinitis (AR) is one of the most common clinical allergic diseases. Early diagnosis and medical intervention will benefit patients with allergic rhinitis. In this study, we focused on changes in urine proteomics in AR patients to investigate their potential clinical utility in AR diagnosis and evaluation.

Material and methods

TMT-labeled mass spectrometry-based proteomics was carried out to identify differentially expressed proteins (DEPs) in urine between allergic rhinitis patients and normal control groups. The molecular biological role of DEPs was investigated by Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis, and protein-protein interaction (PPI) network analysis.

Results

Enrichment analysis showed that the differentially expressed proteins were mainly related to cell-cell adhesion, complement and coagulation cascades, peptidase activity regulation, MAP kinase activity, etc. Compared with the NC group, HLA-DRB1, WFDC12, and DEFA4, among the top ten up-regulated proteins in the urine of the AR group, were related to the biological process of the humoral immune response. Among the top 10 down-regulated proteins, GUSB, SQSTM1, and KIT are related to protein domain-specific binding in terms of molecular function.

Conclusions

We found differential protein changes between AR patients and normal subjects may be related to the pathophysiological changes of AR, which provides the possibility for further exploration of urinary proteomics biomarkers in the future.

SUBMITTER: Liu N

PROVIDER: S-EPMC10329111 | biostudies-literature | 2023 Jun

REPOSITORIES: biostudies-literature

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Publications

Analysis of urine differential proteins in patients with allergic rhinitis.

Liu Na N Wang Jitu J Wang Xueyan X Zhang Man M

Heliyon 20230617 6

<h4>Background</h4>Allergic rhinitis (AR) is one of the most common clinical allergic diseases. Early diagnosis and medical intervention will benefit patients with allergic rhinitis. In this study, we focused on changes in urine proteomics in AR patients to investigate their potential clinical utility in AR diagnosis and evaluation.<h4>Material and methods</h4>TMT-labeled mass spectrometry-based proteomics was carried out to identify differentially expressed proteins (DEPs) in urine between alle ...[more]

PMID: 37426793

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Project description:IntroductionAlthough recent studies have shown that the human microbiome is involved in the pathogenesis of allergic diseases, the impact of microbiota on allergic rhinitis (AR) and non-allergic rhinitis (nAR) has not been elucidated. The aim of this study was to investigate the differences in the composition of the nasal flora in patients with AR and nAR and their role in the pathogenesis.MethodFrom February to September 2022, 35 AR patients and 35 nAR patients admitted to Harbin Medical University's Second Affiliated Hospital, as well as 20 healthy subjects who underwent physical examination during the same period, were subjected to 16SrDNA and metagenomic sequencing of nasal flora.ResultsThe microbiota composition of the three groups of study subjects differs significantly. The relative abundance of Vibrio vulnificus and Acinetobacter baumanni in the nasal cavity of AR patients was significantly higher when compared to nAR patients, while the relative abundance of Lactobacillus murinus, Lactobacillus iners, Proteobacteria, Pseudomonadales, and Escherichia coli was lower. In addition, Lactobacillus murinus and Lacttobacillus kunkeei were also negatively correlated with IgE, while Lacttobacillus kunkeei was positively correlated with age. The relative distribution of Faecalibacterium was higher in moderate than in severe AR patients. According to KEGG functional enrichment annotation, ICMT(protein-S-isoprenylcysteine O-methyltransferase,ICMT) is an AR microbiota-specific enzyme that plays a role, while glycan biosynthesis and metabolism are more active in AR microbiota. For AR, the model containing Parabacteroides goldstemii, Sutterella-SP-6FBBBBH3, Pseudoalteromonas luteoviolacea, Lachnospiraceae bacterium-615, and Bacteroides coprocola had the highest the area under the curve (AUC), which was 0.9733(95%CI:0.926-1.000) in the constructed random forest prediction model. The largest AUC for nAR is 0.984(95%CI:0.949-1.000) for the model containing Pseudomonas-SP-LTJR-52, Lachnospiraceae bacterium-615, Prevotella corporis, Anaerococcus vaginalis, and Roseburia inulinivorans.ConclusionIn conclusion, patients with AR and nAR had significantly different microbiota profiles compared to healthy controls. The results suggest that the nasal microbiota may play a key role in the pathogenesis and symptoms of AR and nAR, providing us with new ideas for the treatment of AR and nAR.

Dataset Information

Analysis of urine differential proteins in patients with allergic rhinitis.

Background

Material and methods

Results

Conclusions

Publications

Analysis of urine differential proteins in patients with allergic rhinitis.

Similar Datasets

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