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Metabolic Fingerprinting of Blood and Urine of Dairy Cows Affected by Bovine Leukemia Virus: A Mass Spectrometry Approach. | LitMetric

AI Article Synopsis

  • * Out of 145 cows tested, 42 were found to be BLV-seropositive, with many experiencing health issues, but there was no significant difference in milk production between infected and non-infected cows.
  • * Metabolomic analysis showed notable differences in cow metabolism linked to BLV, identifying specific disrupted metabolites related to energy metabolism, immune function, and potential early cancer indicators, which could help in detecting infected cows and understanding the disease better.

Article Abstract

Objectives: This study investigated metabolic changes associated with bovine leukemia virus (BLV) infection in dairy cows, focusing on pre-parturition alterations.

Methods: Metabolite identification in serum and urine samples was performed using a targeted metabolomics method, employing the TMIC Prime kit in combination with flow injection analysis and liquid chromatography-tandem mass spectrometry.

Results: Of 145 cows examined, 42 (28.9%) were BLV-seropositive. Around 38% of infected cows showed high somatic cell counts indicative of subclinical mastitis, with 15 experiencing additional health issues such as ketosis, milk fever, and lameness. Despite these conditions, no significant differences in milk yield or composition were observed between the infected and control groups. Metabolomic analysis conducted at -8 and -4 weeks prepartum revealed significant metabolic differences between BLV-infected and healthy cows. At -8 weeks, 30 serum metabolites were altered, including sphingomyelins, lysophosphatidylcholines, amino acids, and acylcarnitines, suggesting disruptions in membrane integrity, energy metabolism, and immune function indicative of early neoplastic transformations. By -4 weeks, the number of altered metabolites decreased to 17, continuing to reflect metabolic disruptions in cows with leukemia. Multivariate analysis highlighted distinct metabolic profiles between infected and control cows, identifying key discriminating metabolites such as choline, aspartic acid, phenylalanine, and arginine. Urine metabolomics revealed significant prepartum shifts in metabolites related to glucose, asymmetric dimethylarginine, and pyruvic acid, among others.

Conclusions: The research confirmed metabolomics' efficacy in defining a BLV infection metabolic profile, elucidating leukosis-associated metabolic disruptions. This approach facilitates the identification of BLV-infected cows and enhances understanding of infection pathophysiology, providing a foundation for advanced management and intervention strategies in dairy herds. The study underscores the profound impact of leukosis on metabolic processes and highlights urine metabolomics' utility in non-invasively detecting BLV infection, offering the potential for improved herd health management.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11596772PMC
http://dx.doi.org/10.3390/metabo14110624DOI Listing

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