Metaproteomics Reveals Growth Phase-Dependent Responses of an <i>In Vitro</i> Gut Microbiota to Metformin
Bibliographic record
Abstract
Metaproteomics has been used in combination with in vitro gut microbiota models to study drug–microbiome interactions. However, it remains unexplored whether the metaproteomics profile of in vitro gut microbiota responds differently to a same stimulus added at different growth phases. In this study, we cultured a human gut microbiota in 96-deep well plates using a previously validated model. Metformin was added during the lag, log, and stationary phases. Microbiome samples, collected at different time points, were analyzed by optical density and function by metaproteomic. The in vitro gut microbiota growth curves, taxonomy, and functional responses were different depending whether metformin was added during the lag, log, or stationary phases. The addition of drugs at the log phase may lead to the greatest decline of bacterial growth. Metaproteomic analysis suggests that the strength of the metformin effect on the gut microbiome functional profile may be ranked as lag phase > log phase > stationary phase. Metformin added at the lag phase may result in a significantly reduced level of the Clostridiales order and an increased level of the Bacteroides genus, which is different from stimulations during the rest of the growth phases. Metformin may also result 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 .
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".