Classification of calves by the metabolic profile without previous hypothesis

Abstract

In order to assess whether Metabolomics methodology could be applied to predict metabolic differences within a given set of animals without previous hypothesis for their classification, we applied multivariate statistical methods to data from UPLC-MS analysis of 16 calf plasma. Blood samples were taken at the age of 60 – 80 days. For UPLC-MS analysis, metabolites were extracted with cold acetonitrile. An Acquity UPLC HSS T3 1.8 µm, 2.1 × 100 mm column with a pre-column (VanGuard 2.1 mm × 5 mm, 1.8 m particle size) was used for the liquid-chromatography analysis (LC), which was performed in an Acquity™ Ultraperformance LC (UPLC®) from WATERS (Barcelona, Spain). Analyte detection was conducted in a mass spectrometer SYNAPT HDMS G2 (WATERS, Manchester, UK) fitted with an ESI source and time of flight analyser (ESI-QToF-MS) under positive ionization mode. The XS application of the MarkerLynx® software (WATERS, Manchester, UK) and the Metaboanalyst software (https://www.metaboanalyst.ca/) were used for statistical analysis of UPLC-MS data. PLS-DA showed three separated groups could be considered. One group (A1) included only two animals, a second group (A2) included six animals, and a third group (B) included eight animals. R 2Y=0.72 and Q2= 0.62 for component 1, and R 2Y=0.98 and Q2=0.86 for component 2. Sample clustering with the Metaboanalyst software reported the same sample classification as with PLS-DA. According to the VIP score, several lysophosphatidylcholines and bile acids were shown as differential biomarkers. Further biochemical analysis showed that there were substantial differences between the animals of groups A (A1 + A2) and B in regard to the lipid metabolism. Further research will aim at determining whether the animal classification according to metabolomics may derive from genetic and dietetic factors regarding lipid metabolism. Ultimately, the lipid differences will be validated by Lipidomics and evaluated whether this feature has relevance for milk quality.