PSVIII-29 Nutritional Epigenetic Modifications in Beef Cattle
Notice bibliographique
Résumé
Abstract Nutrition of the beef cow during pregnancy influences fetal development and potential changes in phenotype. On average 9 to 10% of the beef cattle herd are below the optimal body condition score of 2.5/5 at both pre-breeding and pregnancy tests, indicating potential nutritional stress during gestation. Epigenetic modifications are reported to regulate the changes in phenotype due to maternal nutrition during gestation. Our objective was to evaluate the influence of maternal nutrition during gestation on possible epigenetic mechanisms regulating development and biological function in Longissimus dorsi (LD) and Semimembranosus (SM) muscles, and liver (LV) at slaughter in steer progeny born from dams fed two different planes of nutrition during gestation. We also wanted to explore if the genetic potential of the offspring for residual feed intake (RFI) would interact with prenatal maternal diet to have differential effects on these epigenetic mechanisms. Purebred Angus steer calves (n = 23) were born from dams which were fed a diet formulated for an average daily gain (ADG) of either 0.5 or 0.7 kg/d from 30 to 150 days of gestation. Mating was designed so that calves were born from parents with differential genetic potential for high or low RFI. Calves were raised together as per normal industry standards and fed to be slaughtered as finished steers at approximately 512.1 ± 10.1 days of age. One approximately 10 g sample each of LV, LD, and SM muscles was aseptically collected within 30 to 45 min post-mortem and snap frozen in liquid nitrogen and subsequently stored at -80°C. Both DNA and RNA were isolated from the tissue samples and investigated for differences in methylation and gene expression, respectively. Fifteen potentially differently methylated regions (DMRs) in the DNA were measured using EpiTYPER MassARRAY technology, while expression of genes corresponding to the potential DMRs was measured by the nCounter Element Tagset by NanoString technologies. Average methylation across each DMR as well as gene expression within each tissue was profiled using principal component analyses (PCA). Distinct clustering was seen within both DNA methylation and gene expression PCAs such that the two muscles were clustered together and were separate from DNA methylation and gene expression measured in LV, representing expected functional variation due to tissue type. Between LD and SM, methylation patterns in the two muscles overlap, while there is much less overlap in gene expression patterns. This implies that the methylation pattern between the two muscle types is relatively similar compared with their patterns in gene expression, and that smaller differences in DNA methylation may lead to relatively larger differences in gene expression. These analyses are an important first step to interrogate the quality of our data that will be further analyzed for responses to maternal diet treatment and selection for genetic potential for RFI.
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 | 0,000 |
| 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 ».