Abstract
Most livestock metabolomic studies involve relatively small, homogenous populations of animals. However, livestock farming systems are non-homogenous, and large and more diverse datasets are required to ensure that biomarkers are robust. The aims of this study were therefore to (1) investigate the feasibility of using a large and diverse dataset for untargeted proton nuclear magnetic resonance (1H NMR) serum metabolomic profiling, and (2) investigate the impact of fixed effects (farm of origin, parity and stage of lactation) on the serum metabolome of early-lactation dairy cows. First, we used multiple linear regression to correct a large spectral dataset (707 cows from 13 farms) for fixed effects prior to multivariate statistical analysis with principal component analysis (PCA). Results showed that farm of origin accounted for up to 57% of overall spectral variation, and nearly 80% of variation for some individual metabolite concentrations. Parity and week of lactation had much smaller effects on both the spectra as a whole and individual metabolites (<3% and <20%, respectively). In order to assess the effect of fixed effects on prediction accuracy and biomarker discovery, we used orthogonal partial least squares (OPLS) regression to quantify the relationship between NMR spectra and concentrations of the current gold standard serum biomarker of energy balance, β-hydroxybutyrate (BHBA). Models constructed using data from multiple farms provided reasonably robust predictions of serum BHBA concentration (0.05 ≤ RMSE ≤ 0.18). Fixed effects influenced the results biomarker discovery; however, these impacts could be controlled using the proposed method of linear regression spectral correction.
Highlights
Modern metabolomic techniques such as proton nuclear magnetic resonance (1 H NMR)spectroscopy allow high-throughput, synchronous characterization of the small metabolites present in biological matrices [1]
Resultsofof principal component analysis spectra of serum obtained from dairy cows in early lactation; (a) principal component (PC)
Comparison of principal component analysis (PCA) results showed that herd of origin had a much greater impact on the serum metabolome than either parity or weeks in milk (WIM)
Summary
Modern metabolomic techniques such as proton nuclear magnetic resonance (1 H NMR)spectroscopy allow high-throughput, synchronous characterization of the small metabolites present in biological matrices [1]. 1 H NMR-based metabolomics offers exciting opportunities to better understand and characterize the complex physiological and biochemical challenges facing cows in the transition period (defined as the three weeks before and after calving [2,3]) which is the period of greatest disease risk [4] This in turn can facilitate identification of new molecular phenotypes (metabotypes) for genetic selection for improved animal health. Of particular interest are studies that have identified biomarkers that are predictive of transition period disorders, such as that by Hailemariam et al [12], who identified a panel of three metabolites that could predict the occurrence of peri-parturient disease up to four weeks before calving If robust, such predictive biomarkers would enable producers and veterinarians to implement preventive nutritional, management and/or veterinary interventions before the onset of disease.Unlike metabotype biomarkers used for genetic selection, the aim of biomarkers used for management purposes is to predict the external phenotype, and these must capture all sources of phenotypic variation (i.e., host genetics, rumen microbiome, and the environment)
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
