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Airway Microbiome and Serum Metabolomics Analysis Identify Differential Candidate Biomarkers in Allergic Rhinitis

Allergic rhinitis (AR) is a common heterogeneous chronic disease with a high prevalence and a complex pathogenesis influenced by numerous factors, involving a combination of genetic and environmental factors. To gain insight into the pathogenesis of AR and to identity diagnostic biomarkers, we combi...

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Autores principales: Yuan, Yuze, Wang, Chao, Wang, Guoqiang, Guo, Xiaoping, Jiang, Shengyu, Zuo, Xu, Wang, Xinlei, Hsu, Alan Chen-Yu, Qi, Mingran, Wang, Fang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8766840/
https://www.ncbi.nlm.nih.gov/pubmed/35069544
http://dx.doi.org/10.3389/fimmu.2021.771136
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author Yuan, Yuze
Wang, Chao
Wang, Guoqiang
Guo, Xiaoping
Jiang, Shengyu
Zuo, Xu
Wang, Xinlei
Hsu, Alan Chen-Yu
Qi, Mingran
Wang, Fang
author_facet Yuan, Yuze
Wang, Chao
Wang, Guoqiang
Guo, Xiaoping
Jiang, Shengyu
Zuo, Xu
Wang, Xinlei
Hsu, Alan Chen-Yu
Qi, Mingran
Wang, Fang
author_sort Yuan, Yuze
collection PubMed
description Allergic rhinitis (AR) is a common heterogeneous chronic disease with a high prevalence and a complex pathogenesis influenced by numerous factors, involving a combination of genetic and environmental factors. To gain insight into the pathogenesis of AR and to identity diagnostic biomarkers, we combined systems biology approach to analyze microbiome and serum composition. We collected inferior turbinate swabs and serum samples to study the microbiome and serum metabolome of 28 patients with allergic rhinitis and 15 healthy individuals. We sequenced the V3 and V4 regions of the 16S rDNA gene from the upper respiratory samples. Metabolomics was used to examine serum samples. Finally, we combined differential microbiota and differential metabolites to find potential biomarkers. We found no significant differences in diversity between the disease and control groups, but changes in the structure of the microbiota. Compared to the HC group, the AR group showed a significantly higher abundance of 1 phylum (Actinobacteria) and 7 genera (Klebsiella, Prevotella and Staphylococcus, etc.) and a significantly lower abundance of 1 genus (Pelomonas). Serum metabolomics revealed 26 different metabolites (Prostaglandin D2, 20-Hydroxy-leukotriene B4 and Linoleic acid, etc.) and 16 disrupted metabolic pathways (Linoleic acid metabolism, Arachidonic acid metabolism and Tryptophan metabolism, etc.). The combined respiratory microbiome and serum metabolomics datasets showed a degree of correlation reflecting the influence of the microbiome on metabolic activity. Our results show that microbiome and metabolomics analyses provide important candidate biomarkers, and in particular, differential genera in the microbiome have also been validated by random forest prediction models. Differential microbes and differential metabolites have the potential to be used as biomarkers for the diagnosis of allergic rhinitis.
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spelling pubmed-87668402022-01-20 Airway Microbiome and Serum Metabolomics Analysis Identify Differential Candidate Biomarkers in Allergic Rhinitis Yuan, Yuze Wang, Chao Wang, Guoqiang Guo, Xiaoping Jiang, Shengyu Zuo, Xu Wang, Xinlei Hsu, Alan Chen-Yu Qi, Mingran Wang, Fang Front Immunol Immunology Allergic rhinitis (AR) is a common heterogeneous chronic disease with a high prevalence and a complex pathogenesis influenced by numerous factors, involving a combination of genetic and environmental factors. To gain insight into the pathogenesis of AR and to identity diagnostic biomarkers, we combined systems biology approach to analyze microbiome and serum composition. We collected inferior turbinate swabs and serum samples to study the microbiome and serum metabolome of 28 patients with allergic rhinitis and 15 healthy individuals. We sequenced the V3 and V4 regions of the 16S rDNA gene from the upper respiratory samples. Metabolomics was used to examine serum samples. Finally, we combined differential microbiota and differential metabolites to find potential biomarkers. We found no significant differences in diversity between the disease and control groups, but changes in the structure of the microbiota. Compared to the HC group, the AR group showed a significantly higher abundance of 1 phylum (Actinobacteria) and 7 genera (Klebsiella, Prevotella and Staphylococcus, etc.) and a significantly lower abundance of 1 genus (Pelomonas). Serum metabolomics revealed 26 different metabolites (Prostaglandin D2, 20-Hydroxy-leukotriene B4 and Linoleic acid, etc.) and 16 disrupted metabolic pathways (Linoleic acid metabolism, Arachidonic acid metabolism and Tryptophan metabolism, etc.). The combined respiratory microbiome and serum metabolomics datasets showed a degree of correlation reflecting the influence of the microbiome on metabolic activity. Our results show that microbiome and metabolomics analyses provide important candidate biomarkers, and in particular, differential genera in the microbiome have also been validated by random forest prediction models. Differential microbes and differential metabolites have the potential to be used as biomarkers for the diagnosis of allergic rhinitis. Frontiers Media S.A. 2022-01-05 /pmc/articles/PMC8766840/ /pubmed/35069544 http://dx.doi.org/10.3389/fimmu.2021.771136 Text en Copyright © 2022 Yuan, Wang, Wang, Guo, Jiang, Zuo, Wang, Hsu, Qi and Wang https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Immunology
Yuan, Yuze
Wang, Chao
Wang, Guoqiang
Guo, Xiaoping
Jiang, Shengyu
Zuo, Xu
Wang, Xinlei
Hsu, Alan Chen-Yu
Qi, Mingran
Wang, Fang
Airway Microbiome and Serum Metabolomics Analysis Identify Differential Candidate Biomarkers in Allergic Rhinitis
title Airway Microbiome and Serum Metabolomics Analysis Identify Differential Candidate Biomarkers in Allergic Rhinitis
title_full Airway Microbiome and Serum Metabolomics Analysis Identify Differential Candidate Biomarkers in Allergic Rhinitis
title_fullStr Airway Microbiome and Serum Metabolomics Analysis Identify Differential Candidate Biomarkers in Allergic Rhinitis
title_full_unstemmed Airway Microbiome and Serum Metabolomics Analysis Identify Differential Candidate Biomarkers in Allergic Rhinitis
title_short Airway Microbiome and Serum Metabolomics Analysis Identify Differential Candidate Biomarkers in Allergic Rhinitis
title_sort airway microbiome and serum metabolomics analysis identify differential candidate biomarkers in allergic rhinitis
topic Immunology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8766840/
https://www.ncbi.nlm.nih.gov/pubmed/35069544
http://dx.doi.org/10.3389/fimmu.2021.771136
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