Berberine and its structural analogs have differing effects on functional profiles of individual gut microbiomes
Bibliographic record
Abstract
The understanding of the effects of compounds on the gut microbiome is limited. In particular, it is unclear whether structurally similar compounds would have similar or distinct effects on the gut microbiome. Here, we selected berberine (BBR), an isoquinoline quaternary alkaloid, and 16 structural analogs and evaluated their effects on seven individual gut microbiomes cultured in vitro. The responses of the individual microbiomes were evaluated by metaproteomic profiles and by assessing butyrate production. We show that both interindividual differences and compound treatments significantly contributed to the variance of metaproteomic profiles. BBR and eight analogs led to changes in proteins involved in microbial defense and stress responses and enrichment of proteins from Verrucomicrobia, Proteobacteria, and Bacteroidetes phyla. It also led to a decrease in proteins from the Firmicutes phylum and its Clostridiales order which correlated to decrease proteins involved in the butyrate production pathway and butyrate concentration. Three of the compounds, sanguinarine, chelerythrine, and ethoxysanguinarine, activated bacterial protective mechanisms, enriched Proteobacteria, increased opacity proteins, and markedly reduced butyrate production. Dihydroberberine had a similar function to BBR in enriching the Akkermansia genus. In addition, it showed less overall adverse impacts on the functionality of the gut microbiome, including a better maintenance of the butyrate level. Our study shows that ex vivo microbiome assay can assess differential regulating effects of compounds with subtle differences and reveals that compound analogs can have distinct effects on the microbiome.
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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) | 0.002 | 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".