Correlation of fecal metabolomics and gut microbiota in mice with endometriosis

From BugSigDB
Reviewed Marked as Reviewed by Claregrieve1 on 2022/12/23
study design
Citation
PMID PubMed identifier for scientific articles.
DOI Digital object identifier for electronic documents.
URI
Authors
Ni Z, Sun S, Bi Y, Ding J, Cheng W, Yu J, Zhou L, Li M, Yu C
Journal
American journal of reproductive immunology (New York, N.Y. : 1989)
Year
2020
Keywords:
endometriosis, intestines, metabolomics, microbiota
PROBLEM: Endometriosis (EMS) is a chronic inflammatory disease with unclear pathogenesis. Three studies have uncovered the influence of gut microbiota on mice with EMS, but no study has investigated the characteristics of fecal metabolomics to determine some important clues on EMS. This research aims to uncover the interaction between fecal metabolomics and gut microbiota in EMS mice. METHOD OF STUDY: Female C57BL/6J mice were used to construct the EMS model. Non-target metabolomics was applied to detect the fecal metabolites of EMS mice. The 16s rRNA sequencing was used for clarifying the composition of the gut microbiota. The functional characteristics of gut microbiota were analyzed using the PICRUSt. The receiver operator characteristic curve (ROC) analysis was utilized for determining the potential important differential metabolites, and the Spearman correlation coefficient was applied for expressing the correlation between the important differential metabolites and gut microbiota. RESULTS: A total of 156 named differential metabolites were screened. The diversity and the abundance of gut microbiota in EMS mice decreased. Eleven pathways were involved in the differential metabolites and the functional prediction of gut microbiota, among which the second bile acid biosynthesis and alpha-linolenic acid (ALA) metabolism were the significant enrichment pathways. The increased abundance of chenodeoxycholic and ursodeoxycholic acids and the decreased abundance of ALA and 12,13-EOTrE were found in the feces of EMS mice. CONCLUSION: The abnormal fecal metabolites, which are influenced by dysbacteriosis, may be the characteristics of EMS mice and can be the potential important indices to distinguish the disease.

Experiment 1


Reviewed Marked as Reviewed by Claregrieve1 on 2022/12/23

Curated date: 2021/08/09

Curator: Samara.Khan

Revision editor(s): Samara.Khan, WikiWorks, Peace Sandy

Subjects

Location of subjects
China
Host species Species from which microbiome was sampled. Contact us to have more species added.
Mus musculus
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
Endometriosis endometriosis,Endometriosis (clinical),endometriosis (disease),Endometriosis (disorder),Endometriosis (morphologic abnormality),ENDOMETRIOSIS NEC,Endometriosis NOS,Endometriosis NOS (disorder),Endometriosis of other specified sites,Endometriosis, site unspecified,Endometriosis
Group 0 name Corresponds to the control (unexposed) group for case-control studies
Non-endo mice
Group 1 name Corresponds to the case (exposed) group for case-control studies
Endo mice
Group 1 definition Diagnostic criteria applied to define the specific condition / phenotype represented in the case (exposed) group
Endometriosis was induced in female mice via transplantation of endometrial fragments.
Group 0 sample size Number of subjects in the control (unexposed) group
6
Group 1 sample size Number of subjects in the case (exposed) group
6
Antibiotics exclusion Number of days without antibiotics usage (if applicable) and other antibiotics-related criteria used to exclude participants (if any)
N/A

Lab analysis

Sequencing type
16S
16S variable region One or more hypervariable region(s) of the bacterial 16S gene
Not specified
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
Mann-Whitney (Wilcoxon)
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

Alpha Diversity

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

Signature 1

Reviewed Marked as Reviewed by Claregrieve1 on 2022/12/23

Curated date: 2021/08/09

Curator: Samara.Khan

Revision editor(s): Samara.Khan, Claregrieve1

Source: Figure 3 and Results section

Description: Differential microbial abundance of mice with simulated endometriosis compared to controls.

Abundance in Group 1: decreased abundance in Endo mice

NCBI Quality ControlLinks
Anaerostipes
Bacteroides
Blautia
Eisenbergiella
Faecalitalea
Bacillota
Gordonibacter
Lachnospiraceae bacterium NK4A136
Lactobacillus
Prevotellaceae
Roseburia
Tyzzerella

Revision editor(s): Samara.Khan, Claregrieve1

Signature 2

Reviewed Marked as Reviewed by Claregrieve1 on 2022/12/23

Curated date: 2021/08/09

Curator: Samara.Khan

Revision editor(s): Samara.Khan, Claregrieve1

Source: Figure 3

Description: Differential microbial abundance of mice with simulated endometriosis compared to controls.

Abundance in Group 1: increased abundance in Endo mice

NCBI Quality ControlLinks
Akkermansia
Allobaculum
Citrobacter
Parabacteroides
Parasutterella
Pseudomonadota
Rikenella
Verrucomicrobiota

Revision editor(s): Samara.Khan, Claregrieve1