4.7 Article

Latent Biochemical Relationships in the Blood-Milk Metabolic Axis of Dairy Cows Revealed by Statistical Integration of 1H NMR Spectroscopic Data

Journal

JOURNAL OF PROTEOME RESEARCH
Volume 12, Issue 3, Pages 1428-1435

Publisher

AMER CHEMICAL SOC
DOI: 10.1021/pr301056q

Keywords

metabolomics; metabonomics; nuclear magnetic resonance (NMR); statistical heterospectroscopy (SHY); partial least-squares regression (PLS); dairy; bovine; milk

Funding

  1. Dairy Futures Cooperative Research Council
  2. Gardiner Foundation, Victoria
  3. Department of Primary Industries, Victoria, Australia

Ask authors/readers for more resources

A detailed understanding of the relationships between the distinct metabolic compartments of blood and milk would be of potential benefit to our understanding of the physiology of lactation, and potentially for development of biomarkers for health and commercially relevant traits in dairy cattle. NMR methods were used to measure metabolic profiles from blood and milk samples from Holstein cows. Data were analyzed using PLS regression to identify quantitative relationships between metabolic profiles and important traits. Statistical Heterospectroscopy (SHY), a powerful approach to recovering latent biological information in NMR spectroscopic data sets from multiple complementary samples, was employed to explore the metabolic relationships between blood and milk from these animals. The study confirms milk is a distinct metabolic compartment with a metabolite composition largely not influenced by plasma composition under normal circumstances. However, several significant relationships were identified, including a high correlation for trimethylamine (TMA) and climethylsulfone (DMSO2) across plasma and milk compartments, and evidence plasma valine levels are linked to differences in amino acid catabolism in the mammary gland. The findings provide insights into the physiological mechanisms underlying lactation and identification of links between key metabolites and milk traits such as the protein and fat content of milk. The approach has the potential to enable measurement of health, metabolic status and other important phenotypes with milk sampling.

Authors

I am an author on this paper
Click your name to claim this paper and add it to your profile.

Reviews

Primary Rating

4.7
Not enough ratings

Secondary Ratings

Novelty
-
Significance
-
Scientific rigor
-
Rate this paper

Recommended

No Data Available
No Data Available