Characterizing the Effect of Amylase Inhibitors on Maltodextrin Metabolism by Gut Bacteria Using Fluorescent Glycan Labeling
Bibliographic record
Abstract
Diet-derived polysaccharides are an important carbon source for gut bacteria and shape the human gut microbiome. Acarbose, a compound used clinically to treat type 2 diabetes, is known to inhibit the growth of some bacteria on starches based on its activity as an inhibitor of α-glucosidases and α-amylases. In contrast to acarbose, montbretin A, a new drug candidate for the treatment of type 2 diabetes, has been reported to be more specific for the inhibition of α-amylase, notably human pancreatic α-amylase. However, the effects of both molecules on glycan metabolism across a larger diversity of human gut bacteria remain to be characterized. Here, we used ex vivo metabolic labeling of a human microbiota sample with fluorescent maltodextrin to identify gut bacteria affected by amylase inhibitors. Metabolic labeling was performed in the presence and absence of amylase inhibitors, and the fluorescently labeled bacteria were identified by fluorescence-activated cell sorting coupled with 16S rDNA amplicon sequencing. We validated the labeling results in cultured isolates and identified four gut bacteria species whose metabolism of maltodextrin is inhibited by acarbose. In contrast, montbretin A slowed the growth of only one species, supporting the fact that it is more selective. Metabolic labeling is a valuable tool to characterize glycan metabolism in microbiota samples and could help understand the untargeted impact of drugs on the human gut microbiota.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".