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NMR-based metabolomics of plasma from dairy calves infected with two primary causal agents of bovine respiratory disease (BRD)
NMR-based metabolomics of plasma from dairy calves infected with two primary causal agents of bovine respiratory disease (BRD)
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NMR-based metabolomics of plasma from dairy calves infected with two primary causal agents of bovine respiratory disease (BRD)
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NMR-based metabolomics of plasma from dairy calves infected with two primary causal agents of bovine respiratory disease (BRD)
NMR-based metabolomics of plasma from dairy calves infected with two primary causal agents of bovine respiratory disease (BRD)

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NMR-based metabolomics of plasma from dairy calves infected with two primary causal agents of bovine respiratory disease (BRD)
NMR-based metabolomics of plasma from dairy calves infected with two primary causal agents of bovine respiratory disease (BRD)
Journal Article

NMR-based metabolomics of plasma from dairy calves infected with two primary causal agents of bovine respiratory disease (BRD)

2023
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Overview
Each year, bovine respiratory disease (BRD) results in significant economic loss in the cattle sector, and novel metabolic profiling for early diagnosis represents a promising tool for developing effective measures for disease management. Here, 1 H-nuclear magnetic resonance ( 1 H-NMR) spectra were used to characterize metabolites from blood plasma collected from male dairy calves (n = 10) intentionally infected with two of the main BRD causal agents, bovine respiratory syncytial virus (BRSV) and Mannheimia haemolytica (MH), to generate a well-defined metabolomic profile under controlled conditions. In response to infection, 46 metabolites (BRSV = 32, MH = 33) changed in concentration compared to the uninfected state. Fuel substrates and products exhibited a particularly strong effect, reflecting imbalances that occur during the immune response. Furthermore, 1 H-NMR spectra from samples from the uninfected and infected stages were discriminated with an accuracy, sensitivity, and specificity ≥ 95% using chemometrics to model the changes associated with disease, suggesting that metabolic profiles can be used for further development, understanding, and validation of novel diagnostic tools.