A complete characterization of the proteome of seminal plasma (SP) is an essential step to understand how SP influences sperm function and fertility after artificial insemination (AI). The purpose of this study was to identify which among characterized proteins in boar SP were differently expressed among AI boars with significantly different fertility outcomes. A total of 872 SP proteins, 390 of them belonging specifically to Sus Scrofa taxonomy, were identified (Experiment 1) by using a novel proteomic approach that combined size exclusion chromatography and solid-phase extraction as prefractionation steps prior to Nano LC-ESI-MS/MS analysis. The SP proteomes of 26 boars showing significant differences in farrowing rate (n = 13) and litter size (n = 13) after the AI of 10 526 sows were further analyzed (Experiment 2). A total of 679 SP proteins were then quantified by the SWATH approach, where the penalized linear regression LASSO revealed differentially expressed SP proteins for farrowing rate (FURIN, AKR1B1, UBA1, PIN1, SPAM1, BLMH, SMPDL3A, KRT17, KRT10, TTC23, and AGT) and litter size (PN-1, THBS1, DSC1, and CAT). This study extended our knowledge of the SP proteome and revealed some SP proteins as potential biomarkers of fertility in AI boars.
A method for the determination of fatty acids in vegetable oils by capillary electrophoresis with indirect UV-vis detection has been developed. The separation of fatty acids was optimized in terms of Brij surfactant nature and concentration and organic modifier (2-propanol) percentage. The optimal background electrolyte consisted of 10 mM p-hydroxybenzoate, 5 mM Tris at pH 8.8, 80 mM Brij 98, 40% acetonitrile, and 10% 2-propanol. Under these conditions, vegetable oils from five botanical origins (avocado, corn, extra virgin olive, hazelnut, and soybean) were analyzed and the fatty acid contents established. Linear discriminant analysis (LDA) models were constructed using fatty acid peak areas as predictors. An excellent resolution among all category pairs was obtained, and all samples were correctly classified with assignment probabilities of >95%.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.