Gut microbiota composition and functional prediction in diarrhea-predominant irritable bowel syndrome

Study information

Reviewed Marked as Reviewed by Claregrieve1 on 2022/12/31

study design

case-control

Citation

PMID PubMed identifier for scientific articles.

33663411

DOI Digital object identifier for electronic documents.

10.1186/s12876-021-01693-w

URI

Authors

Mei L, Zhou J, Su Y, Mao K, Wu J, Zhu C, He L, Cui Y

Journal

BMC gastroenterology

Year

2021

Keywords:

Diarrhea-predominant irritable bowel syndrome, Functional prediction, Gut microbiota

BACKGROUND: Irritable bowel syndrome (IBS) is common and difficult to treat and its pathogenesis is closely related to gut microbiota. However, differences in gut microbiota of patients in different regions make it more difficult to elucidate the mechanism of IBS. We performed an analysis of gut microbiota composition and functional prediction in Chinese patients with diarrhea-predominant IBS (IBS-D). METHODS: Fecal samples were obtained from 30 IBS-D patients and 30 healthy controls (HCs) in Nanchang, China. Using 16S gene sequence profiles, we analyzed the abundance of dominant microbiota at different taxonomy levels. Based on 16S information, Phylogenetic Investigation of Communities by Reconstruction of Unobserved States (PICRUSt) was used to predicting the function of gut microbiota. RESULTS: Compared to HCs, gut microbiota richness but not diversity was decreased in IBS-D patients. The abundant phyla Firmicutes, Fusobacteria and Actinobacteria decreased significantly, and Proteobacteria increased significantly in IBS-D patients. PICRUSt indicated that function expression of gut microbiota in IBS-D patients was up-regulated in metabolism of cofactors and vitamins, xenobiotics biodegradation and metabolism, and down-regulated in environmental adaptation, cell growth and death. CONCLUSIONS: Compared with the normal population in China, IBS-D patients are characterized by complex and unstable gut microbiota, which may influence inflammation and metabolism of the host.

Experiment 1

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Reviewed Marked as Reviewed by Claregrieve1 on 2022/12/31

Curated date: 2021/07/04

Curator: Kwekuamoo

Revision editor(s): WikiWorks, LGeistlinger, Claregrieve1, Kwekuamoo, Victoria

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: Feces Cow dung,Cow pat,Droppings,Dung,Excrement,Excreta,Faeces,Fecal material,Fecal matter,Fewmet,Frass,Guano,Matières fécales@fr,Merde@fr,Ordure,Partie de la merde@fr,Piece of shit,Porción de mierda@es,Portion of dung,Portion of excrement,Portion of faeces,Portion of fecal material,Portion of fecal matter,Portion of feces,Portion of guano,Portion of scat,Portionem cacas,Scat,Spoor,Spraint,Stool,Teil der fäkalien@de,Feces,feces

Condition The experimental condition / phenotype studied according to the Experimental Factor Ontology

Group 0 name Corresponds to the control (unexposed) group for case-control studies: Healthy Control

Group 1 name Corresponds to the case (exposed) group for case-control studies: IBS-D

Group 1 definition Diagnostic criteria applied to define the specific condition / phenotype represented in the case (exposed) group: Individuals aged from 20 to 64 meeting the Rome IV diagnostic criteria [19] for IBS-D

Group 0 sample size Number of subjects in the control (unexposed) group: 30

Group 1 sample size Number of subjects in the case (exposed) group: 30

Antibiotics exclusion Number of days without antibiotics usage (if applicable) and other antibiotics-related criteria used to exclude participants (if any): 4 weeks

Lab analysis

Sequencing type: 16S

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

Sequencing platform Manufacturer and experimental platform used for quantifying microbial abundance: Illumina

Statistical Analysis

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

Statistical test: LEfSe

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)?: No

LDA Score above Threshold for the linear discriminant analysis (LDA) score for studies using the popular LEfSe tool: 4

Alpha Diversity

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

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 Claregrieve1 on 2022/12/31

Curated date: 2021/07/04

Curator: Kwekuamoo

Revision editor(s): Kwekuamoo, Claregrieve1, WikiWorks

Source: Figure 5

Description: Differential microbial abundance between IBS-D patients and controls by LefSe

Abundance in Group 1: increased abundance in IBS-D

NCBI	Quality Control	Links
Actinomycetota
Alloprevotella
Bifidobacteriaceae
Bifidobacterium
Bifidobacterium pseudocatenulatum
Clostridia
Faecalibacterium
Bacillota
Ruminococcus
Clostridiales bacterium
Prevotellamassilia timonensis
Phocaeicola plebeius

Revision editor(s): Kwekuamoo, Claregrieve1, WikiWorks

Signature 2

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Reviewed Marked as Reviewed by Claregrieve1 on 2022/12/31

Curated date: 2021/07/04

Curator: Kwekuamoo

Revision editor(s): Kwekuamoo, Claregrieve1, WikiWorks

Source: Figure 5

Description: Differential microbial abundance between IBS-D patients and controls by LefSe

Abundance in Group 1: decreased abundance in IBS-D

NCBI	Quality Control	Links
Bacilli
Bacteroides fragilis
Enterobacteriaceae
Escherichia coli
Gammaproteobacteria
Lactobacillales
Pseudomonadota
Streptococcaceae
Streptococcus
Enterobacterales

Revision editor(s): Kwekuamoo, Claregrieve1, WikiWorks