Back

Growth phase-dependent responses of an in vitro gut microbiome to metformin

Hao, Z.; Li, L.; Ning, Z.; Zhang, X.; Mayne, J.; Cheng, K.; Walker, K.; Liu, H.; Figeys, D.

2020-02-06 microbiology
10.1101/2020.02.05.936500 bioRxiv
Show abstract

In vitro gut microbiota models are often used to study drug-microbiome interaction. Similar to culturing individual microbial strains, the biomass accumulation of in vitro gut microbiota follows a logistic growth curve. Current studies on in vitro gut microbiome responses introduce drug stimulation during different growth stages, e.g. lag phase or stationary phase. However, in vitro gut microbiota in different growth phases may respond differently to same stimuli. Therefore, in this study, we used a 96-deep well plate-based culturing model (MiPro) to culture the human gut microbiota. Metformin, as the stimulus, was added at the lag, log and stationary phases of growth. Microbiome samples were collected at different time points for optical density and metaproteomic functional analysis. Results show that in vitro gut microbiota responded differently to metformin added during different growth phases, in terms of the growth curve, alterations of taxonomic and functional compositions. The addition of drugs at log phase leads to the greatest decline of bacterial growth. Metaproteomic analysis suggested that the strength of the metformin effect on the gut microbiome functional profile was ranked as lag phase > log phase > stationary phase. Our results showed that metformin added at lag phase resulted in a significantly reduced abundance of the Clostridiales order as well as an increased abundance of the Bacteroides genus, which was different from stimulation during the rest of the growth phase. Metformin also resulted in alterations of several pathways, including energy production and conversion, lipid transport and metabolism, translation, ribosomal structure and biogenesis. Our results indicate that the timing for drug stimulation should be considered when studying drug-microbiome interactions in vitro.

Matching journals

The top 5 journals account for 50% of the predicted probability mass.

1
mSystems
361 papers in training set
Top 0.3%
14.6%
2
Frontiers in Microbiology
375 papers in training set
Top 0.3%
12.2%
3
mSphere
281 papers in training set
Top 0.2%
10.0%
4
BMC Microbiology
35 papers in training set
Top 0.1%
10.0%
5
Scientific Reports
3102 papers in training set
Top 19%
6.3%
50% of probability mass above
6
PLOS ONE
4510 papers in training set
Top 29%
6.3%
7
npj Biofilms and Microbiomes
56 papers in training set
Top 0.5%
3.6%
8
Gut Microbes
70 papers in training set
Top 0.5%
2.1%
9
Frontiers in Cellular and Infection Microbiology
98 papers in training set
Top 2%
1.9%
10
Microbiology Spectrum
435 papers in training set
Top 2%
1.8%
11
Antibiotics
32 papers in training set
Top 0.7%
1.8%
12
Microbiome
139 papers in training set
Top 2%
1.7%
13
Microbial Biotechnology
29 papers in training set
Top 0.4%
1.7%
14
Current Microbiology
18 papers in training set
Top 0.2%
1.6%
15
Microorganisms
101 papers in training set
Top 0.9%
1.6%
16
PeerJ
261 papers in training set
Top 8%
1.5%
17
Microbial Ecology
28 papers in training set
Top 0.2%
1.2%
18
mBio
750 papers in training set
Top 11%
0.8%
19
Genomics, Proteomics & Bioinformatics
171 papers in training set
Top 6%
0.7%
20
ISME Communications
103 papers in training set
Top 2%
0.7%
21
PLOS Computational Biology
1633 papers in training set
Top 26%
0.7%
22
Viruses
318 papers in training set
Top 6%
0.6%
23
Environmental Microbiology
119 papers in training set
Top 3%
0.6%
24
Microbial Genomics
204 papers in training set
Top 3%
0.6%
25
Communications Biology
886 papers in training set
Top 29%
0.6%