Probiotic Lacidofil® STRONG Mitigates the Antibiotic‐Induced Alteration of the Fecal microRNA Signature in Healthy Humans
Bibliographic record
Abstract
Antibiotic treatments are often associated with disruption of the gut microbiota composition leading to adverse effects such as diarrhea and colitis. Probiotics can counteract these effects by stabilizing the altered intestinal microbiota. We and others previously showed that the gut microbiota is associated with a distinctive intestinal microRNA (miRNA) signature. MiRNAs originating from intestinal cells are recovered in the feces, their use as biomarkers of gut health is emerging. It is likely that fecal miRNAs could be used as tools for monitoring the microbiota‐dependent intestinal miRNAs. The objectives of this study were to assess: 1. If antibiotic treatment modifies the fecal miRNA signature and 2. If probiotic Lacidofil® STRONG positively impacts this response. Fecal samples were obtained from a double‐blind, randomized, placebo‐controlled trial where healthy adult participants received a placebo (n=80) or a multi‐strain probiotic Lacidofil STRONG® ( Lactobacillus rhamnosus R0011 and Lactobacillus helveticus R0052) (n=80) with 875 mg of amoxicillin and 125 mg of clavulanic acid twice a day for 7 days. A subset of 24 subjects (Body Mass Index between 18.5 and 24.9) (n=11 placebo group and n=13 probiotic group) were used here. Total RNA was extracted from feces collected before and after antibiotic treatment and used to profile the expression of 829 miRNAs with the nCounter human version 3 miRNA expression assay (NanoString Technologies). Statistics and hierarchical clustering were performed in R. Selected miRNAs were validated by qPCR. With threshold value set based on background subtracted‐normalized negative controls, 700 miRNAs were detected in the feces; this provides the first description of the fecal mirnome. Among these, 48 were changed after antibiotic treatment (p<0.05) in the placebo group, resulting in a clear separation between baseline and antibiotic treated samples based on hierarchical clustering. This shows that the fecal mirnome responds to antibiotics. Selected miRNA include down‐regulated miR‐378b (0.5 fold change, p=0.02) and up‐regulated miR‐574‐5p and miR‐612 (1.7 and 2.2 fold change, p= 0.01 and 0.04 respectively). No significant difference in miR‐378b expression was found in the probiotic group, suggesting that antibiotic‐dependent alteration of miR‐378b expression is mitigated by Lacidofil® STRONG. Previous studies showed that this antibiotic regimen modifies the gut microbiota composition. Moreover, the miR‐378 family was previously found to depend on the gut microbiota. Thus, it is likely that antibiotic‐dependent fecal miRNAs respond to alterations in the gut microbiota induced by the drug. Administration of Lacidofil ® STRONG was able to alleviate the alteration of the fecal miRNA signature. This implies that dietary manipulation of the gut microbiota can be a strategy to sustain intestinal health via miRNA. Moreover, microbiota‐associated fecal miRNAs, may be used as biomarkers of probiotic administration. Support or Funding Information NSERC, Lallemand Health Solutions, Lawson Family Chair in Microbiome Nutrition Research
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.001 | 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".