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Elucidating biological aspects of feed efficiency in dairy cows using metabolomics
- Author / Creator
- Hashemiranjbar Sharifabad, Mohsen
Feed represents more than half of the costs of livestock production. Residual feed intake (RFI) is a phenotypic measure of feed efficiency that has been proposed as the best approach for genetic improvement of dairy cows. Although RFI is considered as a useful tool for feed efficiency, the application of RFI in dairy cows has been slow. The high cost of recording individual feed intake and production traits such as milk yield, body weight and milk composition traits has been the main reason for this slow rate of application. Keeping that in mind, the overall objectives of this study were: 1) identification of differences in metabolism of high and low RFI cows in early, mid, and late lactation stages and 2) assessment of the potential of metabolites as biomarkers for prediction of feed efficiency in lactating cows.
For RFI estimation of 75 lactating cows, feed intake and milk yield data were recorded on a daily basis, the concentrations of milk fat, protein and lactose were measured weekly and animals were weighed monthly. A random regression model (RRM) was used for prediction of daily values of component traits of RFI. Multiple linear regression was used to adjust actual energy intake for maintenance and production requirements. Values of RFI were obtained for each individual for day 3-240 of lactation and cows were grouped as high RFI (most inefficient, > +0.5 SD) and low RFI (least efficient, < -0.5 SD). The changes of RFI and its component traits for the RFI groups were visualized along lactation. RFI and actual energy intake (AEI) were significantly (P<0.05) different along lactation between groups. Whether these differences in feed efficiency have a basis in metabolism is investigated in three sampling days that are shown on RFI plots.
Targeted quantitative metabolomics using nuclear magnetic resonance (NMR) was employed to investigate the differences in metabolite profile of high and low RFI groups. Serum samples were taken at 50, 150 and 240 days in milk (DIM) from 75 cows and only high and low RFI samples were analyzed. Concentration of 4 (glycerol, urea, creatinine, dimethyl sulfone), 4 (creatinine, glycerol, L-ornithine, L-lysine), and 8 (glycerol, acetone, citric acid, 3-hydroxy butyric acid, choline, creatinine, glycine, formate) metabolites were significantly (P<0.05) different at 50, 150, and 240 DIM respectively. In addition, a small number of metabolites showed a tendency to significance (P < 0.10) at 150 (acetone) and 240 DIM (isoleucine, L-lysine, urea).
Biomarker discovery analysis was done using principal component analysis (PCA), partial least square discriminant analysis (PLS-DA) and receiver operating characteristic (ROC) curve. The result showed discrimination of high and low RFI in PCA and PLS-DA score plots. This analysis enabled us to identify metabolites with the most discriminatory power between high and low RFI groups. Area under the curve (AUC) in ROC plots represents how well the model is capable of distinguishing between classes where the closer the value to one, the better is the model in classification. The PLS-DA model had AUC of 0.936, 0.866, and 0.997 for 50, 150, and 240 DIM respectively. The metabolites were then used for multiple linear regression to estimate RFI for individual cows. The adjusted R-squared of the models were 0.62, 0.65, and 0.83 for metabolite profile of serum samples from early, mid, and late lactation. The relative contribution of each metabolite was estimated. The metabolites that are significantly different in RFI groups have biological roles in feed efficiency. They are involved in the citric acid and urea cycles that are associated with energy and protein turnover at cellular level. Glycerol and creatinine were differentially expressed consistently in early, mid, and late lactation stages. This may be an indication that these biological pathways explain variation between high and low RFI animals. This study provides evidence to support the potential use of serum metabolites as biomarkers of RFI for both classification into high or low RFI and prediction of the efficiency of individual cows, however, further investigation is warranted for validation of the results.
- Graduation date
- Spring 2021
- Type of Item
- Master of Science
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