A99 IMPACT OF OSMOTIC PERTURBATIONS ON THE GUT MICROBIOTA AND HOST HEALTH
Notice bibliographique
Résumé
Abstract Background Osmotic diarrhea is a prevalent condition concurrent with diverse pathologies such as Crohn’s disease and is due to unabsorbed solutes in the gastrointestinal contents inhibiting normal water absorption. Osmotic laxatives such as polyethylene glycol (PEG) take advantage of this process to counteract constipation. However, short-term PEG treatment impacts the gut microbial community, and in humanized and conventional mice leads to disappearance of the abundant and prevalent bacterial family S24-7, while increasing the relative abundance of a related family, Bacteroidaceae. Excitingly, despite community re-equilibration over weeks, S24-7 is capable of superseding other bacterial members and reach its original levels when reintroduced into the environment. My central hypothesis is that the depletion of S24-7 during osmotic diarrhea is due to its deficiency of stress response pathways required to counteract increased osmolality. However, in the absence of this stress, S24-7 is capable of exploiting a unique interaction with the immune system to successfully recolonize. Aims To shed light on this, I will pursue the following two aims: Aim 1: Compare the growth, survival and expression profiles of S24-7 and B. thetaiotaomicron in a bi-colonized gnotobiotic mouse model before, during and after osmotic perturbation to identify candidate genes involved in response to osmotic perturbation. Aim 2: Identify changes in S24-7 gene expression during recolonization and evaluate the host immune response. Methods Gnotobiotic mice were first colonized with S24-7 and then B. thetaiotaomicron, treated with PEG for 6 days and monitored during recovery and recolonization for 20 days. Fecal pellets were used to quantify bacterial abundances via qPCR and bacterial gene expression analysis through metatranscriptomics. Mouse serum was used for ELISA immunoassays to compare adaptive immune responses through serum IgG as well as to quantify glycine betaine levels. Results During PEG treatment S24-7 became undetectable and recolonized to pre-treatment levels during recovery. Using genomic and metagenomic annotation we have identified that S24-7 isolates do not possess the glycine betaine transport system, which is present in B. thetaiotaomicron (Figure 1). B. thetaiotaomicron reduced host serum glycine betaine levels, unlike S24-7. In S24-7 mono-colonized mice, S24-7 specific serum IgG was not detectable. However, once mice were co-colonized with B. thetaiotaomicron, both S24-7 and B. thetaiotaomicron specific IgG is detected, identifying an interesting relationship between S24-7 and the adaptive immune system that may shed light on its unique recolonization. Conclusions By studying S24-7 sensitivity to osmotic stress and its robust colonization abilities we are shedding light on the mechanisms of microbiota response to perturbations that are commonly experienced by the human gut. Funding Agencies None
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) | 0,002 | 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 ».