Short-chain fatty acids influence host immunity, mucus secretion and microbial community structure to reduce enteritis
Notice bibliographique
Résumé
Optimal intestinal health is critical to overall host well-being, and acute and chronic enteric inflammatory diseases impart a significant detrimental impact on Canadians. The consumption of dietary fibre (DF) has long been associated with providing a health benefit to individuals, and regulatory organizations including Health Canada have legislated that scientific evidence be required to validate health claims. Short-chain fatty acids (SCFA) are produced by the microbial fermentation of DF in the colon, with acetate, propionate and butyrate being the most abundantly produced in the colon. Studies analyzing the effect of DF fermentation in pathogen-challenge models are limited. Thus, the overarching goal of this research was to determine how DFs, and in particular the by-products of DF fermentation, impact enteric inflammation and overall host health using an intestinal pathogen to incite inflammation. Two studies were conducted using Citrobacter rodentium to incite acute Th1/Th17 inflammation (i.e. enteritis). In the first study, the impact of butyrate on the host-microbiota relationship was examined in mice ± enteritis. Rectal administration of 140 mM butyrate to mice increased fecal concentrations of butyrate, and increased food consumption and weight gain in mice with enteritis. Histological scores of colonic inflammation 14 and 21 days post-infection (p.i.) were lower in infected mice administered 140 mM butyrate. In mice without enteritis, butyrate administration elevated the expression of IL-10, TGFβ, and Muc2 in comparison to mice not administered butyrate. Infected mice administered butyrate displayed elevated expression of genes necessary for pathogen clearance (i.e. IL-17A, IL-1β), and epithelial barrier repair and restoration (i.e. Relmβ, Tff3, Myd88). Butyrate supplemented to inflamed colons increased Proteobacteria and Lachnospiraceae, and reduced the abundance of Clostridiaceae species. Mice with enteritis that were administered butyrate exhibited increased accumulation of mucus in the colonic lumen and within goblet cells. In the second study, the impacts of the DFs, WB and RS on host enteric health were measured in mice ± enteritis. Diets enriched for RS increased weight gain in mice inoculated with C. rodentium compared to mice consuming a conventional control (CN) diet. Cecal and distal colonic SCFA quantities were higher in mice consuming DFs, and DF consumption increased butyrate concentrations in the distal colons of mice with enteritis. Histopathologic sores of inflammation in the proximal colon on day 14 (peak infection) and 21 p.i. (late infection) were lower in mice consuming DF-enriched diets compared to the CN diet. Consumption of WB reduced the expression of Th1/Th17 cytokines. Alternatively, the expression of bacterial recognition and response genes such as Relmβ, RegIIIγ, and TLR4 increased in mice consuming the RS-enriched diets. Furthermore, each diet selected for different bacterial communities in the cecum, proximal and distal colon, suggesting a link between DF fermentation, SCFA concentrations, and inflammation in the murine colon. Collectively, study two data indicated that the consumption of DF-rich diets ameliorate the effects of C. rodentium-induced enteritis by modifying the host microbiota to increase SCFA production, and bacterial recognition and response mechanisms to promote host health. In conclusion, SCFA are important to colonic health, and when administered directly to colons primarily influence host immunity through the activation of innate factors, while SCFA derived from DFs in diets modify the host microbiome to effectively maintain intestinal homeostasis and promote host health. Notably, this research provides foundational information to ascertain the effects of functional foods on intestinal health and host well-being.
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 | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 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 | 0,001 |
| 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 ».