Highly Targeted Metabolomics Coupled With Gene Expression Analysis by RT–qPCR Improves Beef Separation Based on Grass, Grain, or Grape Supplemented Diet

The objective of this study was to use a multi-omics (i.e., gene expression quantification, metabolomics, and fatty acid [FA] profiling) approach to separate and authenticate beef from three different dietary groups. In this 2-year study, Red Angus steers (n = 54) were randomly allocated to one of three treatments: (1) complex biodiverse pasture (GRASS), (2) total mixed ration (TMR) in feedlot (GRAIN), or (3) TMR in feedlot supplemented with 5% (dry matter) grapeseed extract for the last 30 days (GRAPE). FAs were measured by gas chromatography-mass spectrometry (GC–MS), secondary metabolites were identified using ultra-high-performance liquid chromatography tandem mass spectrometry (UPLC–MS/MS), and gene expression analysis was performed using quantitative reverse transcription polymerase chain reaction (RT–qPCR). All target genes were upregulated in beef from GRASS compared to the other two groups. Multivariate analyses showed that long-chain n-3 polyunsaturated FAs, the n-6:n-3 ratio, vitamin E, organic acids, amino acid derivatives, and the nephronectin isoform X1 (NPNT-1) gene were the most important compounds for group separation. These compounds, considered to be beneficial for human health, showed higher concentrations in beef from GRASS. The success of beef separation by dietary treatment was highlighted by the 90.4% prediction accuracy of the random forest model, with beef from GRASS being 100% accurately predicted and beef from GRAPE being 94.4% accurately predicted. Beef from GRAIN was 76.5% accurately predicted. In conclusion, coupling gene expression analysis to metabolomics and FA profiling allowed for the separation of beef samples from varying dietary backgrounds with a high degree of confidence.