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

Study information

Reviewed Marked as Reviewed by Svetlana up on 2024-4-8

study design

case-control

Citation

PMID PubMed identifier for scientific articles.

35069544

DOI Digital object identifier for electronic documents.

10.3389/fimmu.2021.771136

URI Uniform resource identifier for web resources.

Authors

Yuan Y, Wang C, Wang G, Guo X, Jiang S, Zuo X, Wang X, Hsu AC, Qi M, Wang F

Journal

Frontiers in immunology

Year

2021

Keywords:

allergic rhinitis, biomarkers, metabolomics, microbiome, multiomics

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.

Experiment 1

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Reviewed Marked as Reviewed by Svetlana up on 2024-4-8

Curated date: 2024/03/30

Curator: Ayibatari

Revision editor(s): WikiWorks, Ayibatari

Subjects

Location of subjects: China

Host species Species from which microbiome was sampled. Contact us to have more species added.: Homo sapiens

Body site Anatomical site where microbial samples were extracted from according to the Uber Anatomy Ontology: Inferior nasal concha Concha nasalis inferior,Concha nasi inferior,Inferior concha,Inferior nasal turbinate,Inferior nasal turbinate bone,Inferior turbinate,Inferior turbinated,Maxilloturbinal,Inferior nasal concha,inferior nasal concha

Condition The experimental condition / phenotype studied according to the Experimental Factor Ontology: Allergic rhinitis allergic form of rhinitis,allergic rhinitis,Alrh,atopic rhinitis,non-seasonal allergic rhinitis,Perenial allergic rhinitis,perennial allergic rhinitis,pollenosis,seasonal allergic rhinitis,Allergic rhinitis

Group 0 name Corresponds to the control (unexposed) group for case-control studies: healthy controls (HC)

Group 1 name Corresponds to the case (exposed) group for case-control studies: Allergic rhinitis (AR)

Group 1 definition Diagnostic criteria applied to define the specific condition / phenotype represented in the case (exposed) group: Patients with allergic rhinitis (AR)

Group 0 sample size Number of subjects in the control (unexposed) group: 15

Group 1 sample size Number of subjects in the case (exposed) group: 28

Lab analysis

Sequencing type: 16S

16S variable region One or more hypervariable region(s) of the bacterial 16S gene: V3-V4

Sequencing platform Manufacturer and experimental platform used for quantifying microbial abundance: Ion Torrent

Statistical Analysis

Data transformation Data transformation applied to microbial abundance measurements prior to differential abundance testing (if any).: relative abundances

Statistical test: T-Test

Significance threshold p-value or FDR threshold used for differential abundance testing (if any): 0.05

MHT correction Have statistical tests be corrected for multiple hypothesis testing (MHT)?: Yes

Matched on Factors on which subjects have been matched on in a case-control study: age, sex

Alpha Diversity

Chao1 Abundance-based estimator of species richness: unchanged

Simpson Estimator of species richness and species evenness: more weight on species evenness: unchanged

Richness Number of species: unchanged

Signature 1

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Reviewed Marked as Reviewed by Svetlana up on 2024-4-8

Curated date: 2024/03/30

Curator: Ayibatari

Revision editor(s): Omojokunoluwatomisin, Ayibatari, WikiWorks

Source: Figure 3. C, D.

Description: The distribution of taxa in phylum and genus levels of AR and HC groups. (C) the statistical results of top 10 phyla. (D) the statistical results of top 35 genera.

Abundance in Group 1: increased abundance in Allergic rhinitis (AR)

NCBI	Quality Control	Links
Actinomycetota
Bacillota
Bacteroidia
Delftia
Finegoldia
Klebsiella
Prevotella
Staphylococcus
Vibrio
uncultured cyanobacterium
unidentified_Corynebacteriaceaeunidentified_Corynebacteriaceae