ArticleBMC microbiology2018
Taxonomic classification for microbiome analysis, which correlates well with the metabolite milieu of the gut.
Article in BMC microbiology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers, 2 of them syntheses that pooled it.
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Who cites it
28 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Genetic correlations between Alzheimer's disease and gut microbiome genera.Scientific reports · 2023Pooled it
- Microbiome changes associated with acute and chronic pancreatitis: A systematic review.Pancreatology : official journal of the International Association of Pancreatology (IAP) ... [et al.] · 2021Pooled it
- Enhancing the understanding of environmental microbiomes through topic modeling: a quantitative and qualitative analysis.Environmental microbiome · 2026Article
- Consequences ofMicroorganisms · 2026Article
- Identification of disease ameliorated metabolite candidates from Gut Microbes and their interacting targets based on a novel estimating function.Frontiers in microbiology · 2026Article
- Development of a multi-targeted metabolomics platform for semi-quantification of faecal metabolites: a proof-of-concept analysis in human faeces.Microbiome research reports · 2026Article
- Testing for Consistency in Co-occurrence Patterns Among Bacterial Taxa Across the Microbiomes of Four Different Trematode Parasites.Microbial ecology · 2025Article
- The Gut Microbiome in Anxiety Disorders.Current psychiatry reports · 2025Review
- Prospecting cellulolytic bacteria from white grubs (Holotrichia serrata (F.) and Leucopholis coneophora Burmeister) native to Karnataka region.BMC microbiology · 2025Article
- Use of fecal microbiome to understand the impact of housing conditions on metabolic stress responses in farmed saltwater crocodiles (Frontiers in veterinary science · 2025Article
- Large-scale genetic correlation studies explore the causal relationship and potential mechanism between gut microbiota and COVID-19-associated risks.BMC microbiology · 2024Article
- Artificial Intelligence: A Promising Tool in Exploring the Phytomicrobiome in Managing Disease and Promoting Plant Health.Plants (Basel, Switzerland) · 2023Review
- Role of the Gut Microbiota in Children with Kidney Disease.Children (Basel, Switzerland) · 2023Article
- Unlocking the Potential of the Human Microbiome for Identifying Disease Diagnostic Biomarkers.Diagnostics (Basel, Switzerland) · 2022Review
- Impact of Co-Delivery of EGCG and Tuna Oil within a Broccoli Matrix on Human Gut Microbiota, Phenolic Metabolites and Short Chain Fatty Acids In Vitro.Molecules (Basel, Switzerland) · 2022Article
- Inter-breeder differences in prepulse inhibition deficits of C57BL/6J mice in a maternal immune activation model.Neuropsychopharmacology reports · 2021Article
- Beneficial effect of dietary geranylgeraniol on glucose homeostasis and bone microstructure in obese mice is associated with suppression of proinflammation and modification of gut microbiome.Nutrition research (New York, N.Y.) · 2021Article
- In Vitro Modulatory Effect of Stevioside, as a Partial Sugar Replacer in Sweeteners, on Human Child Microbiota.Microorganisms · 2021Article
- Agricultural Soil Management Practices Differentially Shape the Bacterial and Fungal Microbiome ofApplied and environmental microbiology · 2021Article
- Alzheimer's Disease and Diabetes: Role of Diet, Microbiota and Inflammation in Preclinical Models.Biomolecules · 2021Review
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
background16S rRNA gene amplicon sequencing analysis (16S amplicon sequencing) has provided considerable information regarding the ecology of the intestinal microbiome. Recently, metabolomics has been used for investigating the crosstalk between the intestinal microbiome and the host via metabolites. In the present study, we determined the accuracy with which 16S rRNA gene data at different classification levels correspond to the metabolome data for an in-depth understanding of the intestinal environment.
resultsOver 200 metabolites were identified using capillary electrophoresis and time-of-flight mass spectrometry (CE-TOFMS)-based metabolomics in the feces of antibiotic-treated and untreated mice. 16S amplicon sequencing, followed by principal component analysis (PCA) of the intestinal microbiome at each taxonomic rank, revealed differences between the antibiotic-treated and untreated groups in the first principal component in the family-, genus, and species-level analyses. These differences were similar to those observed in the PCA of the metabolome. Furthermore, a strong correlation between principal component (PC) scores of the metabolome and microbiome was observed in family-, genus-, and species-level analyses.
conclusionsLower taxonomic ranks such as family, genus, or species are preferable for 16S amplicon sequencing to investigate the correlation between the microbiome and metabolome. The correlation of PC scores between the microbiome and metabolome at lower taxonomic levels yield a simple method of integrating different "-omics" data, which provides insights regarding crosstalk between the intestinal microbiome and the host.
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