Airway Microbiome and Serum Metabolomics Analysis Identify Differential Candidate Biomarkers in Allergic Rhinitis
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Study information
-
Quality control
- Retracted paper
- Contamination issues suspected
- Batch effect issues suspected
- Uncontrolled confounding suspected
- Results are suspect (various reasons)
- Tags applied
study design
Citation
PMID PubMed identifier for scientific articles.
DOI Digital object identifier for electronic documents.
URI
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
Reviewed Marked as Reviewed by Svetlana up on 2024-4-8
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
Reviewed Marked as Reviewed by Svetlana up on 2024-4-8
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)
Revision editor(s): Omojokunoluwatomisin, Ayibatari
Signature 2
Reviewed Marked as Reviewed by Svetlana up on 2024-4-8
Source: Figure 3 D.
Description: The distribution of taxa in phylum and genus levels of AR and HC groups. (D) the statistical results of top 35 genera.
Abundance in Group 1: decreased abundance in Allergic rhinitis (AR)
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Revision editor(s): Ayibatari, Svetlana up
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