Changes to collagen heat solubility with post mortem ageing of beef and validation of genetic markers for collagen characteristic
Notice bibliographique
Résumé
Consumer acceptability of meat is dependent on eating satisfaction, of which meat tenderness is a principal factor. While there has been much work done to improve meat quality, and grading systems are in place to evaluate meat and guarantee a superior eating experience, the characteristics of intramuscular connective tissue can still affect that experience. Intramuscular connective tissue limits the tenderness of beef that is cooked quickly due to it being principally composed of collagen, a protein which requires prolonged cooking times for its degradation. Additionally, the mature collagen cross-links pyridinoline and Ehrlich’s Chromogen further stabilize the collagen structure, leading to decreased collagen heat solubility, and have previously been associated with a marked increase in Warner Bratzler Shear Force (WBSF), an objective measure of meat toughness. Post mortem ageing of meat has been shown to decrease toughness, as the activity of enzymes responsible for the degradation of intramuscular collagen continues after slaughter. However, the use of growth promotants has been shown to have an adverse effect on meat quality and may limit the benefit of post mortem aging, and the effects of growth promotants on collagen characteristics is not well understood. In this thesis, collagen and other meat quality characteristics such as WBSF were analyzed for the longissimus thoracis muscle of 198 crossbred steers, the samples from which were aged 7 days prior to analysis. Of these animals, 149 steers were part of a steroid treatment to evaluate the effects of steroid use on meat quality and collagen characteristics. Cattle were implanted with 200 mg progesterone and 20 mg estradiol benzoate, and a terminal implant composed of 120 trenbolone acetate and 24 mg estradiol. One group received one backgrounding implant (n=16), a second group received two backgrounding implants (n=16), a third received two backgrounding implants and one terminal implant (n=16), a fourth group received one backgrounding implant and a terminal implant (n=16), and a fifth group received only a terminal implant (n=38). There were 47 animals in the control group (n=47) that received no implants. One part of each sample was refrigerated and aged for an additional 14 days for a total of 21 days to evaluate the effects of aging on collagen solubility. Results showed that steroid use was associated with reduced quality grade (P=0.01), improved yield grade (P<0.01), slower carcass cooling and pH decline, decreased carcass L* scores (lightness), and some increase in WBSF when multiple implants were used. However, there was no effect of steroids on collagen characteristics. There was some improvement of collagen solubility in the longissimus muscle if the carcass graded Canada Prime and had increased fat content, although collagen solubility decreased with animal age. WBSF was mainly associated with animal age and intramuscular fat content. There was no effect of aging on collagen solubility after 7 days post-mortem. Previous studies have identified single nucleotide polymorphisms (SNPs) within genes affecting total collagen, collagen cross-link synthesis, and collagenase activity. A genome-wide association study was designed to identify SNPs associated with collagen solubility and total collagen in 286 steers, and mature collagen crosslinks in 149 steers. In total there were 5 SNP windows associated with collagen solubility prior to aging the samples, 8 SNP windows associated with solubility after aging, and 5 windows relating to total collagen content. Further, 4 SNP windows were associated with Ehrlich’s Chromogen concentration and 10 windows were related to pyridinoline concentration. Functional analysis using Ingenuity Pathway Analysis (IPA) software identified genes within these SNP windows relating to cellular growth and proliferation, especially the proliferation of tumor cells and the metastasis of cancer. The observations suggest the possibility for marker-assisted genomic selection for collagen characteristics in beef cattle, however due to the small sample size of the study, a larger number of cattle will be needed to validate these results.
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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 | 0,001 | 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 ».