The therapeutic potential of gut microbiota-derived metabolite 4-phenylbutyric acid in Escherichia coli (E.coli)-induced colitis

Published: 17 September 2024| Version 1 | DOI: 10.17632/6crjmyww6r.1
Contributor:
chris wong

Description

Escherichia coli (E. coli) is a widely distributed highly infectious pathogen that can cause varying degrees of zoonotic diseases, harm farm livestock, and result in economic losses. It has a wide range of transmission routes and mainly harms the intestines of animals, causing intestinal inflammation and damage. Due to genetic characteristics and excessive use of antibiotics, many drug-resistant strains have emerged. According to reports, the internal environment of gut microbiota can regulate the occurrence and development of various diseases, and probiotics can effectively protect gut health and enhance resistance to the outside world. The mechanism by which gut microbiota and its metabolites regulate E. coli infection is still limited. Our scientific hypothesis is based on the close relationship between intestinal damage and dysbiosis caused by E. coli infection and uninfected gut and microbiota. The aim of this project is to use a multi omics approach combining 16S rRNA sequencing and metabolomics to analyze and identify key gut microbiota and metabolites that affect the process of E. coli infection, and further elucidate the mechanism by which gut microbiota and their metabolites regulate intestinal inflammation caused by E. coli infection through the TLR4/MyD88/NF - κ B pathway. This will provide important guidance for the development of drugs for the treatment of E. coli disease. The samples for this experiment are calf feces, including the diarrhea group and the healthy group (without obvious diarrhea), with 6 samples in each group. The samples was sourced from a large-scale dairy farm in Yunnan, China, and the sampling subjects were newborn cows aged 3-6 months. The collection method is as follows: after wearing personal protective equipment, use fully disinfected and cleaned tools (gloves, shovels, spoons, straws) to collect samples from fresh feces center, place them in a clean and sealed container, record the sampling date, time, calf number and other information, and record relevant data. After sampling, divide and store in the refrigerator for refrigeration.

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Based on the ultra-high efficiency liquid phase system Thermo Vanquish (Thermo Fisher Scientific, USA), using ACQUITY UPLC ® HSS T3 (2.1 × 100 mm, 1.8 µ m) (Waters, Milford, MA, USA) chromatographic column was used to separate fecal samples. The column temperature was maintained at 40 ℃, the flow rate was 0.3 mL/min, and the injection volume was 2 μ L each time. There are positive ion and negative ion modes respectively. 1、 The secondary spectrum acquisition was completed by a Thermo Q Exactive mass spectrometer (Thermo Fisher Scientific, USA). Use the Proteowizard software package (v3.0.8789) to process the raw data files generated by UPLC-MS/MS, and perform peak integration and quantification for each metabolite. Statistical analysis was conducted using the R software package Ropls platform, including principal component analysis (PCA), partial least squares discriminant analysis (PLS-DA), orthogonal partial least squares discriminant analysis (OPLS-DA), dimensionality reduction analysis, and single factor analysis. Metabolomics analysis used multivariate statistical analysis such as PCA and PLS-DA, as well as univariate statistical analysis (Student's t-test, Mann Whitney Wilcoxon U test, analysis of variance (ANOVA), and correlation analysis). The uniform calculation method adopts the statistical analysis package widely used in R studio( http://cran.r- Project. org/). Spearman correlation analysis was used to analyze the correlation between microbial communities and metabolites. Display the correlation between differential metabolites and differential microbial communities by calculating Spearman correlation coefficients. Metabolite information annotation KEGG pathway metabolite pathway analysis. If both the microbiota and metabolites are annotated onto the same KEGG pathway, it means they are closely related and can be analyzed for correlation.

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Untargeted Metabolomics

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