IDENTIFICATION OF FERTILITY MARKERS IN EQUINE SEMEN USING PROTEOMICS TECHNIQUES
Notice bibliographique
Résumé
Current semen evaluation techniques are useful in identifying subfertile animals, however they are not reliable indicators of fertility. As sperm and seminal plasma proteins have known effects on both in vivo and in vitro fertilization, their potential as markers of semen quality and stallion fertility are important areas of research. The objectives of this study were to determine if specific proteins in the stallion semen could be related to semen quality characteristics and in vivo fertility using proteomics techniques. Seven fertile stallions were regularly collected and bred to a total of 142 mares for the 2006 breeding season. Fresh semen for the study was collected on three occasions in the middle of the breeding season (Sandy Ridge Stallion Station, Bassano, Alberta), each two weeks apart, and the sperm and seminal plasma were immediately separated by centrifugation and frozen until subjected to 2D gel electrophoresis. The sperm and seminal plasma proteins were separated on 24 cm 12 % acrylamide gels, and pH 3–10 in the first dimension (GE Healthcare, Baie d'Urfé, QC). All protein species were analyzed and matched across stallions using Progenesis software (Non-Linear Dynamics, Durham, NC). The quantified proteins were compared to visual semen quality characteristics and in vivo fertility using the mixed models procedure with repeated measures as well as correlation analysis in SAS (SAS Institute, Cary, NC). The stallions ranged from 50 % to 100 % for first cycle pregnancy rate, and overall pregnancy rate ranged from 75 to 100 %. When semen quality was assessed, there were differences (P<0.05) in sperm concentration, total number of sperm and semen volume. There were no differences reported in progressively motile sperm (PMS), however there were replicates where stallions showed large variation in their motility, down to 35 % PMS. Only preliminary data has been obtained for the proteomics analysis at this time. Over 3000 proteins for each gel were visualized and quantified using the Progenesis software, and 32 proteins in the spermatozoa and 8 proteins in seminal plasma differed across stallions (P<0.05). For the seminal plasma, 3 proteins were positively correlated with PMS, and 5 were negatively correlated with PMS (P<0.05). Interestingly, none of these proteins were correlated with fertility, however 14 different proteins were either positively or negatively correlated to fertility. For sperm proteins, 3 proteins were positively correlated with PMS and 9 were negatively correlated with PMS (P<0.05). All proteins of interest will be identified and verified using mass spectrometry techniques. Candidate protein markers reported in previous studies, such as osteopontin, will also be characterized in these samples. Subjecting equine sperm and seminal plasma to proteomics techniques has revealed many potential candidates of fertility in both sperm and seminal plasma in preliminary results. Determination of a reliable marker that is associated with stallion fertility would be very useful for screening stallions for breeding potential and also for the improvement of reproductive technologies. (poster)
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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 | 0,001 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 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 ».