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Urinary metabolomics fingerprinting around parturition identifies metabolites that differentiate lame dairy cows from healthy ones

Published online by Cambridge University Press:  05 June 2020

E. F. Eckel ,
G. Zhang ,
E. Dervishi ,
G. Zwierzchowski Open the ORCID record for G. Zwierzchowski [Opens in a new window] ,
R. Mandal ,
D. S. Wishart  and
B. N. Ametaj
E. F. Eckel
Affiliation:
Department of Agricultural, Food and Nutritional Science, University of Alberta, Edmonton, AlbertaAB T6G 2P5, Canada
G. Zhang
Affiliation:
Department of Agricultural, Food and Nutritional Science, University of Alberta, Edmonton, AlbertaAB T6G 2P5, Canada
E. Dervishi
Affiliation:
Department of Agricultural, Food and Nutritional Science, University of Alberta, Edmonton, AlbertaAB T6G 2P5, Canada
G. Zwierzchowski
Affiliation:
Department of Agricultural, Food and Nutritional Science, University of Alberta, Edmonton, AlbertaAB T6G 2P5, Canada
R. Mandal
Affiliation:
Departments of Biological Sciences and Computing Science, University of Alberta, Edmonton, AlbertaT6G 2E9, Canada
D. S. Wishart
Affiliation:
Departments of Biological Sciences and Computing Science, University of Alberta, Edmonton, AlbertaT6G 2E9, Canada
B. N. Ametaj*
Affiliation:
Department of Agricultural, Food and Nutritional Science, University of Alberta, Edmonton, AlbertaAB T6G 2P5, Canada
*
†E-mail: [email protected]
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Abstract

Lameness is a very important disorder of periparturient dairy cows with implications on milk production and composition as well as with consequences on reproductive performance. The aetiology of lameness is not clear although there have been various hypotheses suggested over the years. The objective of this study was to metabotype the urine of dairy cows prior to, during and after the onset of lameness by evaluating at weeks −8, −4 pre-calving, the week of lameness diagnosis, and +4 and +8 weeks post-calving. We used a metabolomics approach to analyse urine samples collected from dairy cows around calving (6 cows with lameness v. 20 healthy control cows). A total of 153 metabolites were identified and quantified using an in-house MS library and classified into 6 groups including: 11 amino acids (AAs), 39 acylcarnitines (ACs), 3 biogenic amines (BAs), 84 glycerophospholipids, 15 sphingolipids and hexose. A total of 23, 36, 40, 23 and 49 metabolites were observed to be significantly different between the lame and healthy cows at −8 and −4 weeks pre-calving, week of lameness diagnosis as well as at +4 and +8 weeks post-calving, respectively. It should be noted that most of the identified metabolites were elevated; however, a few of them were also lower in lame cows. Overall, ACs and glycerophospholipids, specifically phosphatidylcholines (PCs), were the metabolite groups displaying the strongest differences in the urine of pre-lame and lame cows. Lysophosphatidylcholines (LysoPCs), although to a lesser extent than PCs, were altered at all time points. Alterations in urinary AA concentrations were also observed during the current study for four time points. During the pre-calving period, there was an observed elevation of arginine (−8 week), tyrosine (−8 week) and aspartate (−4 week), as well as a depression of urinary glutamate (−4 weeks). In the current study, it was additionally observed that concentrations of several sphingomyelins and one BA were altered in pre-lame and lame cows. Symmetric dimethylarginine was elevated at both −8 weeks pre-calving and the week of lameness diagnosis. Data showed that urinary fingerprinting might be a reliable methodology to be used in the future to differentiate lame cows from healthy ones.

Keywords

lamenesstransition dairy cowsurinemetabolomicsbiomarkers
Type
Research Article
Information
animal , Volume 14 , Issue 10 , October 2020 , pp. 2138 - 2149
Copyright
© The Author(s), 2020. Published by Cambridge University Press on behalf of The Animal Consortium

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Footnotes

a

Present address: Center for Renal Precision Medicine, Division of Nephrology, Department of Medicine, The University of Texas Health, San Antonio, TX 78229, USA; Audie L. Murphy Memorial VA Hospital, South Texas Veterans Health Care System, San Antonio, TX 78229, USA

b

Present address: Faculty of Biology and Biotechnology, University of Warmia and Mazury, 1a Oczapowskiego str., Olsztyn 10-719, Poland

References

Adams, AE, Lombard, JE, Fossler, CP, Román-Muñiz, IN and Kopral, CA 2017. Associations between housing and management practices and the prevalence of lameness, hock lesions, and thin cows on US dairy operations. Journal of Dairy Science 100, 2119–2136.CrossRefGoogle Scholar
Baird, LG, O’Connell, NE, McCoy, MA, Keady, TW and Kilpatrick, DJ 2009. Effects of breed and production system on lameness parameters in dairy cattle. Journal of Dairy Science 92, 2174–2182.CrossRefGoogle Scholar
Bicalho, RC, Vokey, F, Erb, HN and Guard, CL 2007. Visual locomotion scoring in the first seventy days in milk: impact on pregnancy and survival. Journal of Dairy Science 90, 4586–4591.CrossRefGoogle ScholarPubMed
Bouatra, S, Aziat, F, Mandal, R, Guo, AC, Wilson, MR, Knox, C, Bjorndahl, TC, Krishnamurthy, R, Saleem, F, Liu, P, Dame, ZT, Poelzer, J, Huynh, J, Yallou, FS, Psychogios, N, Dong, E, Bogumil, R, Roehring, C and Wishart, DS 2013. The human urine metabolome. PLoS ONE 8, e73076.CrossRefGoogle ScholarPubMed
Canadian Dairy Information Centre 2018. Culling and replacement rates in dairy herds in Canada. Retrieved on 14 December 2019 from https://www.dairyinfo.gc.ca/index_e.php?s1=dff-fcil&s2=mrr-pcle&s3=cr-trGoogle Scholar
Ceciliani, F, Lecchi, C, Urh, C and Sauerwein, H 2018. Proteomics and metabolomics characterizing the pathophysiology of adaptive reactions to the metabolic challenges during the transition from late pregnancy to early lactation in dairy cows. Journal of Proteomics 178, 92–106.CrossRefGoogle ScholarPubMed
Cook, NB, Nordlund, KV, and Oetzel, GR 2004. Environmental influences on claw horn lesions associated with laminitis and subacute ruminal acidosis in dairy cows. Journal of Dairy Science 87, E36–E46.CrossRefGoogle Scholar
Costamagna, D, Costelli, P, Sampaolesi, M and Penna, F 2015. Role of inflammation in muscle homeostasis and myogenesis. Mediators of Inflammation 2015, 805172.CrossRefGoogle ScholarPubMed
Dervishi, E, Zhang, G, Dunn, SM, Mandal, R, Wishart, DS and Ametaj, BN 2017. GC-MS Metabolomics identifies metabolite alterations that precede subclinical mastitis in the blood of transition dairy cows. Journal of Proteome Research 16, 433–446.CrossRefGoogle ScholarPubMed
Dervishi, E, Zhang, G, Hailemariam, D, Mandal, R, Wishart, DSand Ametaj, BN 2018. Urine metabolic fingerprinting can be used to predict the risk of metritis and highlight the pathobiology of the disease in dairy cows. Metabolomics 14, 83.10.1007/s11306-018-1379-zCrossRefGoogle Scholar
Dharmashankar, K and Widlansky, ME 2010. Vascular endothelial function and hypertension: insights and directions. Current Hypertension Reports 12, 448–455.CrossRefGoogle ScholarPubMed
Dunstan, RH, Sparkes, DL, Macdonald, MM, De Jonge, XJ, Dascombe, BJ, Gottfries, J, Gottfries, CG and Roberts, TK 2017. Diverse characteristics of the urinary excretion of amino acids in humans and the use of amino acid supplementation to reduce fatigue and sub-health in adults. Nutrition Journal 16, 19.CrossRefGoogle ScholarPubMed
Ganeshan, K and Chawla, A 2014. Metabolic regulation of immune response. Annual Review of Immunology 32, 609–634.CrossRefGoogle Scholar
Garbarino, EJ, Hernandez, JA, Shearer, JK, Risco, CA and Thatcher, WW 2004. Effect of lameness on ovarian activity in postpartum Holstein cows. Journal of Dairy Science 87, 4123–4131.CrossRefGoogle ScholarPubMed
Gerspach, C, Imhasly, S, Gubler, M, Naegeli, H, Ruetten, M and Laczko, E 2017. Altered plasma lipidome profile of dairy cows with fatty liver disease. Research in Veterinary Science 110, 47–59.CrossRefGoogle ScholarPubMed
Gore, MO, Lüneburg, N, Schwedhelm, E, Ayers, CR, Anderssohn, M, Khera, A, Atzler, D, de Lemos, JA, Grant, PJ, McGuire, DK and Böger, RH 2013. Symmetrical dimethylarginine predicts mortality in the general population: observations from the Dallas heart study. Arteriosclerosis, Thrombosis, and Vascular Biology 33, 2682–2688.CrossRefGoogle ScholarPubMed
Emmanuel, DG, Dunn, SM and Ametaj, BN 2008. Feeding high proportions of barley grain stimulates an inflammatory response in dairy cows. Journal of Dairy Science 91, 606–614.CrossRefGoogle ScholarPubMed
Hailemariam, D, Mandal, R, Saleem, F, Dunn, SM, Wishart, DS and Ametaj, BN 2014. Identification of predictive biomarkers of disease state in transition dairy cows. Journal of Dairy Science 97, 2680–2693.CrossRefGoogle Scholar
Imhasly, S, Naegeli, H, Baumann, S, von Bergen, M, Luch, A, Jungnickel, H, Potratz, S and Gerspach, C 2014. Metabolomic biomarkers correlating with hepatic lipidosis in dairy cows. BMC Veterinary Research 10, 122.CrossRefGoogle ScholarPubMed
Jewell, MT, Cameron, M, Spears, J, McKenna, SL, Cockram, MS, Sanchez, J, Keefe, GP 2019. Prevalence of hock, knee, and neck skin lesions and associated risk factors in dairy herds in the Maritime Provinces of Canada. Journal of Dairy Science 102, 3376–3391.CrossRefGoogle Scholar
Kenéz, Á, Dänicke, S, Rolle-Kampczyk, U, Von Bergen, M and Huber, K 2016. A metabolomics approach to characterize phenotypes of metabolic transition from late pregnancy to early lactation in dairy cows. Metabolomics 12, 165.CrossRefGoogle Scholar
King, MTM, LeBlanc, SJ, Pajor, EA and DeVries, TJ 2017. Cow-level associations of lameness, behavior, and milk yield of cows in automated systems. Journal of Dairy Science 100, 4818–4828.CrossRefGoogle ScholarPubMed
Kloosterman, P 2007. Laminitis - Prevention, diagnosis and treatment, Advanced. Dairy Science and Technology 19, 157–166.Google Scholar
Mihalik, SJ, Goodpaster, BH, Kelley, DE, Chace, DH, Vockley, J, Toledo, FG and DeLany, JP 2010. Increased levels of plasma acylcarnitines in obesity and type 2 diabetes and identification of a marker of glucolipotoxicity. Obesity 18, 1695–1700.CrossRefGoogle ScholarPubMed
National Research Council 2001. Nutrient requirements of dairy cattle, 7th revised edition. The National Academies Press, Washington, DC, USA.Google Scholar
Nocek, JE 1997. Bovine acidosis: implications on laminitis. Journal of Dairy Science 80, 1005–1028.CrossRefGoogle ScholarPubMed
R Core Team 2008. R: A language and environment for statistical computing. R Foundation for statistical computing, Vienna, Austria. Retrieved from http://www.R-project.org/Google Scholar
Refaai, W, Gad, M and Mahmmod, Y 2017. Association of claw disorders with subclinical intramammary infections in Egyptian dairy cows. Veterinary World 10, 358–362.CrossRefGoogle ScholarPubMed
Rico, JE, Zang, Y, Haughey, N, Rius, AG and McFadden, JW 2018. Short communication: circulating fatty acylcarnitines are elevated in overweight periparturient dairy cows in association with sphingolipid biomarkers of insulin resistance. Journal of Dairy Science 101, 812–819.CrossRefGoogle ScholarPubMed
Sprecher, DJ, Hostetler, DE and Kaneene, JB 1997. A Lameness scoring system that uses posture and gait to predict dairy cattle reproductive performance. Theriogenology 47, 1179–1187.CrossRefGoogle ScholarPubMed
Steelman, SM, Johnson, P, Jackson, A, Schulze, J and Chowdhary, BP 2014. Serum metabolomics identifies citrulline as a predictor of adverse outcomes in an equine model of gut-derived sepsis. Physiological Genomics 46, 339–347.CrossRefGoogle Scholar
Tain, YL and Hsu, CN 2017. Toxic Dimethylarginines: asymmetric dimethylarginine (ADMA) and symmetric dimethylarginine (SDMA). Toxins 9, 92.CrossRefGoogle Scholar
Tojo, A, Welch, WJ, Bremer, V, Kimoto, M, Kimura, K, Omata, M, Ogawa, T, Vallance, P and Wilcox, CS 1997. Colocalization of demethylating enzymes and NOS and functional effects of methylarginines in rat kidney. Kidney International 52, 1593–160.CrossRefGoogle ScholarPubMed
van der Linde, C, de Jong, G, Koenen, EP and Eding, H 2010. Claw health index for Dutch dairy cattle based on claw trimming and conformation data. Journal of Dairy Science 93, 4883–4891.CrossRefGoogle Scholar
von Keyserlingk, MAG, Barrientos, A, Ito, K, Galo, E and Weary, DM 2012. Benchmarking cow comfort on North American freestall dairies: lameness, leg injuries, lying time, facility design, and management for high-producing Holstein dairy cows. Journal of Dairy Science 95, 7399–7408.CrossRefGoogle ScholarPubMed
Westin, R, Vaughan, A, de Passillé, AM, DeVries, TJ, Pajor, EA, Pellerin, D, Siegford, JM, Witaifi, A, Vasseur, E and Rushen, J 2016. Cow- and farm-level risk factors for lameness on dairy farms with automated milking systems. Journal of Dairy Science 99, 3732–3743.CrossRefGoogle ScholarPubMed
Wu, G 2009. Amino acids: metabolism, functions, and nutrition. Amino Acids 37, 1–17.CrossRefGoogle ScholarPubMed
Xia, J, Psychogios, N, Young, N and Wishart, DS 2009. MetaboAnalyst: a web server for metabolomic data analysis and interpretation. Nucleic Acids Research 37, W652–W660.CrossRefGoogle ScholarPubMed
Xia, J and Wishart, DS 2011. Metabolomic data processing, analysis, and interpretation using MetaboAnalyst. Current Protocols in Bioinformatics 34, Chapter 14, Unit 14.10.CrossRefGoogle Scholar
Xia, J, Sinelnikov, IV, Han, B and Wishart, DS 2015. MetaboAnalyst 3.0 - making metabolomics more meaningful. Nucleic Acids Research 43, W251–W257.CrossRefGoogle ScholarPubMed
Xia, J, and Wishart, DS 2016. Using MetaboAnalyst 3.0 for comprehensive metabolomics data analysis. Current Protocols in Bioinformatics 55, 14.10.1–14.10.91.CrossRefGoogle Scholar
Yea, K, Kim, J, Yoon, JH, Kwon, T, Kim, JH, Lee, BD, Lee, HJ, Lee, SJ, Kim, JI, Lee, TG, Baek, MC, Park, HS, Park, KS, Ohba, M, Suh, PG and Ryu, SH 2009. Lysophosphatidylcholine activates adipocyte glucose uptake and lowers blood glucose levels in murine models of diabetes. Journal of Biological Chemistry 284, 33833–43380.CrossRefGoogle ScholarPubMed
Zhang, G, Hailemariam, D, Dervishi, E, Deng, Q, Goldansaz, SA, Dunn, SM and Ametaj, B 2015. Alterations of innate immunity reactants in transition dairy cows before clinical signs of lameness. Animals 5, 717–747.CrossRefGoogle ScholarPubMed
Zhang, G, Dervishi, E, Dunn, S, Mandal, R, Liu, P, Han, B, Wishart, DSand Ametaj, BN 2017a. Metabotyping reveals distinct metabolic alterations in ketotic cows and identifies early predictive serum biomarkers for the risk of disease. Metabolomics 13, 43–57.CrossRefGoogle Scholar
Zhang, G, Deng, Q, Mandal, R, Wishart, DS and Ametaj, BN 2017b. DI/LC-MS/MS-based metabolic profiling for identification of early predictive serum biomarkers of metritis in transition dairy cows. Journal of Agricultural and Food Chemistry 65, 8510–8521.CrossRefGoogle ScholarPubMed
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