Identification of novel proteomics markers involved in ovarian endocrinology of riverine buffalo (Bubalus bubalis)

Not AvailableLong postpartum anestrus is one of the factors responsible for low reproductive efficiency. The present study was carried out to identify probable follicular fluid protein markers of postpartum anestrum buffaloes by proteomics approach. Ovum pick-up technique was used to collect follicular fluids from 23 buffaloes pertaining to postpartum estrus, anestrus and cystic follicular fluid. Pooled follicular fluids samples from each group were further divided into supernatant and cell lysate and subjected to twodimensional gel electrophoresis. Densitometric analyses revealed the presence of 767 total protein spot and out of which 34 were unique in the studied groups. With the aid of mass spectrometry, the 34 protein spots were identified and among them apolipoproteins (A-I, A-II, A-IV), serum amyloid A-4, adenylate kinase 2, alphafetoprotein, heat shock protein beta-1, protein kinase C-binding protein, complement C1q subcomponent, integrin beta-7 and cadherin-23 have been established to play one or other role in ovarian endocrinology. Moreover, using bioinformatics tool, gene ontology and protein–protein interactions among the identified protein were also analyzed. To our knowledge, this is the first report on identification of proteins involved in postpartum anestrus of buffaloes from India which are to be validated further to establish its relationship with postpartum anestrus.Not Availabl

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Not AvailableMachine learning algorithms were employed for predicting the feed conversion efficiency (FCE), using the blood parameters and average daily gain (ADG) as predictor variables in buffalo heifers. It was observed that isotonic regression outperformed other machine learning algorithms used in study. Further, we also achieved the best performance evaluation metrics model with additive regression as the meta learner and isotonic regression as the base learner on 10-fold cross-validation and leaving-one-out cross-validation tests. Further, we created three separate partial least square regression (PLSR) models using all 14 parameters of blood and ADG as independent (explanatory) variables and FCE as the dependent variable, to understand the interactions of blood parameters, ADG with FCE each by inclusion of all FCE values (i), only higher FCE values (negative RFI) (ii), and inclusion of only lower FCE (positive RFI) values (iii). The PLSR model including only the higher FCE values was concluded the best, based on performance evaluation metrics as compared to PLSR models developed by inclusion of the lower FCE values and all types of FCE values. IGF1 and its interactions with the other blood parameters were found highly influential for higher FCE measures. The strength of the estimated interaction effects of the blood parameter in relation to FCE may facilitate understanding of intricate dynamics of blood parameters for growth.Not Availabl

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Not AvailableMachine learning algorithms were employed for predicting the feed conversion efficiency (FCE), using the blood parameters and average daily gain (ADG) as predictor variables in buffalo heifers. It was observed that isotonic regression outperformed other machine learning algorithms used in study. Further, we also achieved the best performance evaluation metrics model with additive regression as the meta learner and isotonic regression as the base learner on 10-fold cross-validation and leaving-one-out cross-validation tests. Further, we created three separate partial least square regression (PLSR) models using all 14 parameters of blood and ADG as independent (explanatory) variables and FCE as the dependent variable, to understand the interactions of blood parameters, ADG with FCE each by inclusion of all FCE values (i), only higher FCE values (negative RFI) (ii), and inclusion of only lower FCE (positive RFI) values (iii). The PLSR model including only the higher FCE values was concluded the best, based on performance evaluation metrics as compared to PLSR models developed by inclusion of the lower FCE values and all types of FCE values. IGF1 and its interactions with the other blood parameters were found highly influential for higher FCE measures. The strength of the estimated interaction effects of the blood parameter in relation to FCE may facilitate understanding of intricate dynamics of blood parameters for growth.Not Availabl

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MOESM1 of Identification of potential protein biomarkers for early detection of pregnancy in cow urine using 2D DIGE and label free quantitation

Additional file 1: Figure S1. Supplimentary Table: Total identified proteins revelaed by Max quant Software