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Rumen fluid metabolomics of beef steers differing in feed efficiency

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Abstract

Introduction

Beef is the most consumed red meat in the United States, and the US is the largest producer and consumer of beef cattle globally. Feed is one of the largest input costs for the beef cattle industry, accounting for 40–60% of the total input costs. Identifying methods for improving feed efficiency in beef cattle herds could result in decreased cost to both producers and consumers, as well as increased animal protein available for global consumption.

Methods

In this study, rumen fluid was collected from low- (n = 14) and high-RFI (n = 15) steers. Rumen fluid was filtered through a 0.22 µM syringe filter, extracted using 0.1% formic acid in acetonitrile:water:methanol (2:2:1) and injected into the Dionex UltiMate 3000 UHPLC system with an Exactive Plus Orbitrap MS. Peaks were identified using MAVEN and analyzed using MetaboAnalyst 4.0 and SAS. Significance was determined using an α ≤ 0.05.

Results

Eight metabolites were greater in low-RFI steers compared to high-RFI steers, including 3,4-dihydroxyphenylacetate, 4-pyridoxate, citraconate, hypoxanthine, succinate/methylmalonate, thymine, uracil, and xylose (P ≤ 0.05). These metabolites were predominantly involved in amino acid and lipid metabolism.

Conclusions

Rumen fluid metabolomes differ in steers of varying feed efficiencies. These metabolites may be used as biomarkers of feed efficiency, and may provide insight as to factors contributing to differences in feed efficiency that may be exploited to improve feed efficiency in beef cattle herds.

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Data availability

The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.

References

  • Artegoitia, V. M., Foote, A. P., Lewis, R. M., & Freetly, H. C. (2017). Rumen fluid metabolomics analysis associated with feed efficiency on crossbred steers. Scientific Reports,7, 2864.

    PubMed  PubMed Central  Google Scholar 

  • Arthur, P., Renand, G., & Krauss, D. (2001). Genetic and phenotypic relationships among different measures of growth and feed efficiency in young Charolais bulls. Livestock Production Science,68, 131–139.

    Google Scholar 

  • Bechdel, S., Honeywell, H. E., Dutcher, R. A., & Knutsen, M. (1928). Synthesis of vitamin B in the rumen of the cow. Journal of Biological Chemistry,80, 231–238.

    CAS  Google Scholar 

  • Benjamini, Y. and Hochberg, Y. (1995) Controlling the false discovery rate: A practical and powerful approach to multiple testing. Journal of the Royal Statistical Society Series B (Methodological),57, 289–300.

    Google Scholar 

  • Berg, J. M., Tymoczko, J. L., & Stryer, L. (2002). Biochemistry (5th ed.). New York: WH Freeman.

    Google Scholar 

  • Blasi, P., Boyl, P. P., Ledda, M., Novelletto, A., Gibson, K. M., Jakobs, C., et al. (2002). Structure of human succinic semialdehyde dehydrogenase gene: Identification of promoter region and alternatively processed isoforms. Molecular Genetics and Metabolism,76, 348–362.

    CAS  PubMed  Google Scholar 

  • Chambers, M. C., Maclean, B., Burke, R., Amodei, D., Ruderman, D. L., Neumann, S., et al. (2012). A cross-platform toolkit for mass spectrometry and proteomics. Nature Biotechnology,30, 918.

    CAS  PubMed  PubMed Central  Google Scholar 

  • Chambliss, K. L., & Gibson, K. M. (1992). Succinic semialdehyde dehydrogenase from mammalian brain: Subunit analysis using polyclonal antiserum. International Journal of Biochemistry,24, 1493–1499.

    CAS  PubMed  Google Scholar 

  • Chong, J., Soufan, O., Caraus, I., Xia, J., Li, C., Wishart, D. S., et al. (2018). Metaboanalyst 4.0: Towards more transparent and integrative metabolomics analysis. Nucleic Acids Research,46, W486–W494.

    CAS  PubMed  PubMed Central  Google Scholar 

  • Clasquin, M.F., Melamud, E. and Rabinowitz, J.D. (2012) LC‐MS data processing with MAVEN: A metabolomic analysis and visualization engine. Current Protocols in Bioinformatics, 14.11. 1–14.11. 23.

  • Clemmons, B. A., Mihelic, R. I., Beckford, R. C., Powers, J. B., Melchior, E. A., McFarlane, Z. D., et al. (2017). Serum metabolites associated with feed efficiency in black angus steers. Metabolomics,13, 147.

    Google Scholar 

  • Clemmons, B. A., Martino, C., Powers, J. B., Campagna, S. R., Voy, B. H., Donohoe, D. R., et al. (2019a). Rumen bacteria and serum metabolites predictive of feed efficiency phenotypes in beef cattle. Scientific Reports,9, 19265.

    PubMed  PubMed Central  Google Scholar 

  • Clemmons, B. A., Martino, C., Schneider, L. G., Lefler, J., Embree, M. M., & Myer, P. R. (2019b). Temporal stability of the ruminal bacterial communities in beef steers. Scientific Reports,9, 9522.

    PubMed  PubMed Central  Google Scholar 

  • ERS, U. (2019) Cattle and Beef Sector at a Glance in Knight, R. (Ed).

  • Fan, Z., Deng, J., Liu, G., Cai, H., He, J., Wu, M., et al. (2007). Effects of γ-aminobutyric acid on the performance and internal hormone levels in growing pigs. Chinese Journal of Animal Nutrition,19, 350–356.

    CAS  Google Scholar 

  • Fan, H., Wu, Y., Zhou, X., Xia, J., Zhang, W., Song, Y., et al. (2015). Pathway-based genome-wide association studies for two meat production traits in Simmental cattle. Scientific Reports,5, 18389.

    CAS  PubMed  PubMed Central  Google Scholar 

  • Ferrell, C., & Jenkins, T. (1984). Energy utilization by mature, nonpregnant, nonlactating cows of different types. Journal of Animal Science,58, 234–243.

    CAS  PubMed  Google Scholar 

  • Fontanesi, L. (2016). Metabolomics and livestock genomics: Insights into a phenotyping frontier and its applications in animal breeding. Animal Frontiers,6, 73–79.

    Google Scholar 

  • Gnegy, M. E. (2012). Catecholamines. In S. T. Brady (Ed.), Basic neurochemistry (pp. 283–299). San Diego: Elsevier.

    Google Scholar 

  • Goodrich, R., Garrett, J., Gast, D., Kirick, M., Larson, D., & Meiske, J. (1984). Influence of monensin on the performance of cattle. Journal of Animal Science,58, 1484–1498.

    CAS  PubMed  Google Scholar 

  • Henderson, G., Cox, F., Ganesh, S., Jonker, A., Young, W., Abecia, L., et al. (2015). Rumen microbial community composition varies with diet and host, but a core microbiome is found across a wide geographical range. Scientific Reports,5, 14567.

    CAS  PubMed  PubMed Central  Google Scholar 

  • Kamphorst, J. J., Fan, J., Lu, W., White, E., & Rabinowitz, J. D. (2011). Liquid chromatography–high resolution mass spectrometry analysis of fatty acid metabolism. Analytical Chemistry,83, 9114–9122.

    CAS  PubMed  PubMed Central  Google Scholar 

  • Kanehisa, M., Goto, S., Sato, Y., Kawashima, M., Furumichi, M., & Tanabe, M. (2013). Data, information, knowledge and principle: Back to metabolism in KEGG. Nucleic Acids Research,42, D199–D205.

    PubMed  PubMed Central  Google Scholar 

  • Koch, R. M., Swiger, L. A., Chambers, D., & Gregory, K. E. (1963). Efficiency of feed use in beef cattle. Journal of Animal Science,22, 486–494.

    Google Scholar 

  • Kong, R. S. G., Liang, G., Chen, Y., Stothard, P., & Guan, L. L. (2016). Transcriptome profiling of the rumen epithelium of beef cattle differing in residual feed intake. BMC Genomics,17, 592.

    PubMed  PubMed Central  Google Scholar 

  • Krishnamachar, V., Subramanian, S., & Rao, M. R. (1964). Microbiological oxidation of the branched C 5-dicarboxylic acids. Archiv für Mikrobiologie,47, 338–343.

    CAS  PubMed  Google Scholar 

  • Leklem, J., & Machlin, L. (1991). Handbook of vitamins. New York: Marcel Decker Inc.

    Google Scholar 

  • Leng, R., & Nolan, J. (1984). Nitrogen metabolism in the rumen. Journal of Dairy Science,67, 1072–1089.

    CAS  PubMed  Google Scholar 

  • Linkswiler, H., & Reynolds, M. S. (1950). Urinary and fecal elimination of B6 and 4-pyridoxic acid on three levels of intake. The Journal of Nutrition,41, 523–532.

    CAS  PubMed  Google Scholar 

  • Lu, W., Clasquin, M. F., Melamud, E., Amador-Noguez, D., Caudy, A. A., & Rabinowitz, J. D. (2010). Metabolomic analysis via reversed-phase ion-pairing liquid chromatography coupled to a stand alone orbitrap mass spectrometer. Analytical Chemistry,82, 3212–3221.

    CAS  PubMed  PubMed Central  Google Scholar 

  • McAllan, A., & Smith, R. (1973). Degradation of nucleic acid derivatives by rumen bacteria in vitro. British Journal of Nutrition,29, 467–474.

    CAS  PubMed  Google Scholar 

  • McAllan, A. B., Williams, A. P., Merry, R. J., & Smith, R. H. (1982). Effect of different levels of casein, with or without formaldehyde treatment, on carbohydrate metabolism between mouth and duodenum of steers. Journal of the Science of Food and Agriculture,33, 722–728.

    CAS  PubMed  Google Scholar 

  • McCormick, D. B. (2006). Vitamin B6. Present Knowledge in Nutrition,1, 269–277.

    Google Scholar 

  • Merrill, A., & Burnham, F. (1990) Vitamin B-6. Present knowledge in nutrition (pp. 155–162). New York: Nutrition Foundation.

  • Montaño-Bermudez, M., Nielsen, M. K., & Deutscher, G. H. (1990). Energy requirements for maintenance of crossbred beef cattle with different genetic potential for milk. Journal of Animal Science,68, 2279–2288.

    PubMed  Google Scholar 

  • Myer, P. R., Smith, T. P. L., Wells, J. E., Kuehn, L. A., & Freetly, H. C. (2015). Rumen microbiome from steers differing in feed efficiency. PLoS ONE,10, e0129174.

    PubMed  PubMed Central  Google Scholar 

  • Myer, P., Clemmons, B., Schneider, L., & Ault, T. (2019). Microbiomes in ruminant protein production and food security. CAB Reviews,14, 1–11.

    Google Scholar 

  • Nafikov, R. A., & Beitz, D. C. (2007). Carbohydrate and lipid metabolism in farm animals. The Journal of Nutrition,137, 702–705.

    CAS  PubMed  Google Scholar 

  • Novais, F. J., Pires, P. R. L., Alexandre, P. A., Dromms, R. A., Iglesias, A. H., Ferraz, J. B. S., et al. (2019). Identification of a metabolomic signature associated with feed efficiency in beef cattle. BMC Genomics,20, 8.

    PubMed  PubMed Central  Google Scholar 

  • Ogunade, I., & Schweickart, H. (2018). Effect of dietary monensin on rumen fluid metabolomic profile of beef cattle. Journal of Animal Science,96, 441–441.

    PubMed Central  Google Scholar 

  • Ogunade, I., Schweickart, H., Andries, K., Lay, J., & Adeyemi, J. (2018). Monensin alters the functional and metabolomic profile of rumen microbiota in beef cattle. Animals,8, 211.

    PubMed Central  Google Scholar 

  • Pearson, E., & Baldwin, B. (1981). D-xylose absorption in the adult bovine. The Cornell Veterinarian,71, 288–296.

    CAS  PubMed  Google Scholar 

  • Rabinowitz, J. D., & Kimball, E. (2007). Acidic acetonitrile for cellular metabolome extraction from Escherichia coli. Analytical Chemistry,79, 6167–6173.

    CAS  PubMed  Google Scholar 

  • Ryzlak, M. T., & Pietruszko, R. (1988). Human brain "high Km" aldehyde dehydrogenase: purification, characterization, and identification as NAD+ -dependent succinic semialdehyde dehydrogenase. Archives of Biochemistry and Biophysics,266, 386–396.

    CAS  PubMed  Google Scholar 

  • Thanh, V. T. K., & Ørskov, E. (2006). Causes of differences in urinary excretion of purine derivatives in buffaloes and cattle. Animal Science,82, 355–358.

    CAS  Google Scholar 

  • Ushida, K., Miyazaki, A., & Kawashima, R. (1985). Effect of monensin on ruminal VFA and gas [methane] production of sheep fed high concentrate diet. Japanese Journal of Zootechnical Sciencem, 56, 822–826. https://doi.org/10.2508/chikusan.56.822.

    Article  CAS  Google Scholar 

  • Van Gylswyk, N. (1995). Succiniclasticum ruminis gen. nov., sp. nov., a ruminal bacterium converting succinate to propionate as the sole energy-yielding mechanism. International Journal of Systematic and Evolutionary Microbiology,45, 297–300.

    Google Scholar 

  • Wang, D., Wang, C., Liu, H., Liu, J., & Ferguson, J. D. (2013). Effects of rumen-protected γ-aminobutyric acid on feed intake, lactation performance, and antioxidative status in early lactating dairy cows. Journal of Dairy Science,96, 3222–3227.

    CAS  PubMed  Google Scholar 

  • Weimer, P. J. (1998). Manipulating ruminal fermentation: A microbial ecological perspective. Journal of Animal Science,76, 3114–3122.

    CAS  PubMed  Google Scholar 

  • Weiss, W.P. and Ferreira, G. (2006). Water soluble vitamins for dairy cattle. Proceedings of the Tri-State Dairy Nutrition Conference Fort, Wayne, IN, pp. 51–63.

  • Westerhuis, J. A., Hoefsloot, H. C., Smit, S., Vis, D. J., Smilde, A. K., van Velzen, E. J., et al. (2008). Assessment of PLSDA cross validation. Metabolomics,4, 81–89.

    CAS  Google Scholar 

  • Wirth, R., Kádár, G., Kakuk, B., Maróti, G., Bagi, Z., Szilágyi, Á., et al. (2018). The planktonic core microbiome and core functions in the cattle rumen by next generation sequencing. Frontiers in Microbiology,9, 2285.

    PubMed  PubMed Central  Google Scholar 

  • Yost, W. M., Young, J. W., Schmidt, S. P., & McGilliard, A. D. (1977). Gluconeogenesis in ruminants: Propionic acid production from a high-grain diet fed to cattle. The Journal of Nutrition,107, 2036–2043.

    CAS  PubMed  Google Scholar 

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Acknowledgements

This study was supported by Ascus Biosciences, Inc. (Grant No. A17-0146–003) and USDA-NIFA Hatch/Multistate Project W4177—TEN00538—Enhancing the Competitiveness and Value of U.S. Beef; Accession Number: 1016984. The authors thank the staff at the Plateau Research and Education Center in Crossville, TN for their technical assistance.

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Contributions

BAC performed research, analyzed data, and wrote the paper. JBP contributed analytical tools and analyzed data. SRC contributed analytical tools and analyzed data. TBS analyzed data and wrote segments of the paper. MME conceived of or designed study, performed research, and analyzed data. PRM conceived of or designed study, performed research, analyzed data, and wrote the paper.

Corresponding author

Correspondence to Phillip R. Myer.

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Conflict of interest

BA Clemmons, JB Powers, SR Campagna, TB Seay, and PR Myer declare that they have no conflict of interest. MM Embree is the Co-Founder and Chief Science Officer at Ascus Biosciences, Inc.

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All applicable international, national, and/or institutional guidelines for the care and use of animals were followed.

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Clemmons, B.A., Powers, J.B., Campagna, S.R. et al. Rumen fluid metabolomics of beef steers differing in feed efficiency. Metabolomics 16, 23 (2020). https://doi.org/10.1007/s11306-020-1643-x

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