Metadata: 
In this folder are the data to reproduce all figures & data analysis.

1) About the Dataset

Title: Data set for in vitro investigation into high protein diets on microbiota composition and metabolic output
Organisaton: University of Reading
Rights-holder: University of Reading
Publication year: 2023

Description:
The data was produced by in-vitro gut modelling fermentation of different protein sources. Analysed were different bacterial clusters and metabolites present at different time points (0, 8, 24, 48 h) from the fermentation protein in faecal microbiota from 10 donors (5 male and 5 females).

Cite as: Daniel James (2023): Data set for in vitro investigation into high protein diets on microbiota composition and metabolic output. University of Reading. Dataset. 10.17864/1947.000504

Related publication: Do high-protein diets have the potential to reduce gut barrier function in a sex-dependent manner?

Contant: Daniel James; d.james3@pgr.reading.ac.uk.

Acknowledgements: Associate professor Marie Lewis for supervising the project.

2. Terms of Use

Copyright 2023 University of Reading. This dataset is licensed under a Creative Commons Attribution 4.0 International Licence: https://creativecommons.org/licenses/by/4.0/

3. Project and Funding Information

Title of the project: Diet and Chronic Disease: mechanistic studies on the impact of dietary protein on gut barrier function and the microbiota. PhD project.

Dates: 09/2020-09/2024

Funding organisation: BBSRC Case Studentship co-funded by Feed and Food Innovations Ltd.

Grant no: BB/T008776/1


4. Contents

File name: Data_for_protein_microbome_invitro.csv


The different conditions include
- individual proteins: milk, whey, egg, fish meal, pea, soy, mycoprotein
-animal proteins: milk, whey, egg, fish meal.
-non animal proteins: pea, soy, mycoprotein.
-not protein: negative control (no substrate), positive control (P95 prebiotic inulin)
-additional protein: A combination of all proteins together.
-no additional protein: the negative control (no substrate) in isolation.

5. Methods:
After in-vitro gut modelling systems were used to create the samples. The data collection outcomes and methods were as follows:


Ammonia (mM) in supernatant: as measured by ELISA (Sigma-Aldrich Co Ltd, AA0100). 

short chain fatty acids (mM) concentration in supernatant: Butyrate, Acetate, Propionate, valerate, iso-valerate, iso-butyrate: 
as measured by Gas chromatography (Agilent 7890B gas chromatograph, Hewlett Packard, UK) with a HP-5ms column.

Phenolics (mM)
phenol, indole, p-cresol, skatol: as measured by SPME-GCMS (Agilent 110PAL injection system mounted on an Agilent 7890 GC connected to a 5975 mass selective detector (MSD).The SPME fibre stationary phase was composed of 75um divinylbenzene/Carboxen.

Bacteria present in the gut vessels: (Cells/ml)
Total bacteria (EUB), Bifidobacterium genus (BIF), Lactobacillus-Enterococcus group (LAB), Bacteroides (BAC), Clostridium coccoides group (EREC), Roseburia (RREC), Atopobium cluster (ATO), Clostridium cluster IX (PRO), Faecalibacterium prausnitzii subgroup (FPRAU), Desulphovibrionaceae (DSV), Geobactaraceae and Clostridium histolyticum group (CHIS). 
As measured by Fluorescent in-situ hybridisation coupled with flow cytometry (Accuri C6 flow cytometer)