RESUMO
The objective of this study was to assess the protein profile of ovine seminal plasma using 2D-PAGE and verify if BSP A1/A2 are present in ovine seminal plasma. Seminal plasma was collected from three mature rams and pooled to eliminate individual differences. Seminal plasma samples were submitted to 2D-PAGE using 12% acrylamide gels. The image analysis software identified 21 protein spots on the air-dried gel, with molecular weight ranging from 15 to 115 kDa and pI 3.2 to 8.7. The most prominent spots were those <30 kDa. The most intensely stained spots were: 3 (18-19 kDa, pI 4.8-5.0), 5 (17-18 kDa, pI 5.0-5.2), 7 (15-16 kDa, pI 6.2-6.4), and 23 (105-108 kDa, pI 6.8-7.0). Three of these spots (spots 3, 5 and 7, respectively) accounted for 41.1% of the relative intensity of the spots of the gels, based on the intensity of the Comassie blue staining. Western blot analysis indicated that spots 3 and 5 were similar to BSP A1/A2 (16.5, pI 4.7-5.0 and 16 kDa, pI 4.9-5.2) identified in Manjunath's studies [Manjunath P, Sairam MR. Purification and biochimical characterization of three major acid proteins (BSP A1, BSP A2 and BSP A3) from bovine seminal plasma. Biochem J 7 (1987) 685-92.], based on the specific reaction of the polyclonal antibody to those spots.
Assuntos
Sêmen/metabolismo , Proteínas de Plasma Seminal/metabolismo , Proteínas Secretadas pela Vesícula Seminal/metabolismo , Ovinos/metabolismo , Animais , Western Blotting/veterinária , Eletroforese em Gel Bidimensional/veterinária , Ponto Isoelétrico , Masculino , Peso Molecular , Proteínas de Plasma Seminal/química , Proteínas Secretadas pela Vesícula Seminal/químicaRESUMO
The objective of this study was to evaluate the low weight (10-30 kDa) protein profile of bovine seminal plasma using two-dimensional polyacrylamide gel electrophoresis (2D-PAGE) and to determine if any of these proteins was associated with semen freezability. Seminal plasma was collected from 16 bulls of high or low semen freezability. Twelve protein spots were identified from the 2D gel (15%); six of these were present in all samples. Of the 12 proteins found, three spots, present in all samples, 3 (15-16 kDa), 5 (16-17 kDa), and 7 (10-12 kDa) had nonsignificant variation among bulls, regardless of their freezability classification. Four proteins were more abundant (P<0.05) in seminal plasma samples collected from bulls with high semen freezability than in samples of bulls with low semen freezability: the spots 3 (15-16 kDa, pI 4.7-5.2), 7 (11-12 kDa, pI 4.8-4.9), 11 (13-14 kDa, pI 4.0-4.5), and 23 (20-22 kDa, pI 4.8-5.2). On the other hand, spot 25 (25-26 kDa, pI 6.0-6.5) was more abundant (P<0.05) on seminal plasma samples from bulls with low semen freezability. The N-terminus sequence of protein 7 was identical to the acidic seminal fluid protein (aSFP). Protein 23 (after trypsin digestion) had structural similarity to bovine clusterin. We concluded that there were differences in the seminal plasma protein profile from bulls with low and high semen freezability; aSFP, clusterin, proteins 3 and 11 may be used as semen freezability markers; and protein 25 was related to low semen freezability.
Assuntos
Bovinos , Criopreservação , Eletroforese em Gel Bidimensional , Proteínas/análise , Preservação do Sêmen , Sêmen/química , Sequência de Aminoácidos , Animais , Ponto Isoelétrico , Masculino , Dados de Sequência Molecular , Peso Molecular , Proteínas/químicaRESUMO
1. Although the poultry industry uses state-of-the-art equipment and up-to-date services, in Brazil it generally makes decisions involving all its production variables based on purely subjective criteria. This paper reports the use of artificial neural networks to estimate performance in production birds belonging to a South Brazilian poultry farm. 2. Recorded data from 22 broiler production breeder flocks were obtained, from April, 1998 to December, 1999, which corresponded to 689 data lines of weekly recordings. 3. These data were processed by artificial neural networks using the software NeuroShell 2 version 4.0 (Ward Systems Group). The artificial neural network models generated were compared and selected based on their largest determination coefficient (R2), lowest Mean Squared Error (MSE), as well as on a uniform scatter in the residual plots. The authors conclude that it is possible to explain the performance variables of production birds, with the use of artificial neural networks. 4. The method allows the decisions made by the technical staff to be based on objective, scientific criteria, allows simulations of the consequences related to these decisions, and reports the contribution of each variable to the variables under study.