Probiotic Lacidofil® STRONG Mitigates the Antibiotic‐Induced Alteration of the Fecal microRNA Signature in Healthy Humans
Notice bibliographique
Résumé
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
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».