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Bovine In Vitro Fertilization Embryo Development Predicted by Capacitation-Induced Zinc Ion Flux and Acrosome
Kourtney Jimmerson1, Marianna Jahnke2, Emma Keller1
1Department of Animal Science, Iowa State University, Ames, IA 50011, USA.
None:
Sire-dependent variation in bovine in vitro fertilization (IVF) success is poorly explained by conventional semen parameters once samples meet standard quality thresholds. Sperm capacitation is required for acquisition of fertilization competence, yet its contribution to sire-dependent variation in bovine IVF outcomes remains unclear. Fifteen bulls (Bos taurus) were evaluated across five IVF replicates using 30 abattoir-derived oocytes per bull per replicate. Sperm were analyzed before (0 h) and after 3 h of in vitro capacitation using image-based flow cytometry to quantify zinc ion localization patterns (zinc signatures; Fluo-Zin 3 AM), acrosome remodeling (peanut agglutinin conjugated to Alexa Fluor 647; PNA-AF647), and plasma membrane integrity (propidium iodide). Motility parameters were assessed using computer-aided sperm analysis (CASA). Cleavage rate was assessed on Day 3 and blastocyst development on Day 7. Substantial bull-to-bull variation was observed for both cleavage (31.5-76.3%) and blastocyst development (7.3-40%). Bull effects on cleavage were significant (p < 0.0001). Predictive modeling revealed that CASA or biomarker variables alone explained limited variance in embryo development. In contrast, models incorporating capacitation-induced changes in zinc signatures and acrosome remodeling significantly improved prediction of IVF outcomes. Integration of biomarker and motility parameters across time points yielded the highest explanatory power, with zinc signature dynamics among the most consistently informative predictors. These findings demonstrate that sperm capacitation-associated zinc flux and acrosomal remodeling are biologically relevant determinants of bull-specific variation in bovine IVF embryo development and provide mechanistic biomarkers that improve prediction beyond conventional motility assessment alone.
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