In-depth characterisation of the urine metabolome in cats with and without urinary tract diseases
-
Published:2022-03-17
Issue:4
Volume:18
Page:
-
ISSN:1573-3890
-
Container-title:Metabolomics
-
language:en
-
Short-container-title:Metabolomics
Author:
Kim YounjungORCID, Xu Wei, Barrs Vanessa, Beatty Julia, Kenéz Ákos
Abstract
Abstract
Introduction
Our understanding of the urine metabolome and its association with urinary tract disease is limited in cats.
Objectives
We conducted a case–control study to characterise the feline urine metabolome, investigate its association with chronic kidney disease (CKD) and feline idiopathic cystitis (FIC), and assess its compositional relationship with the urine microbiome.
Methods
The urine metabolome of 45 owned cats, including 23 controls, 16 CKD, and 6 FIC cases, was characterised by an untargeted metabolomics approach using high-performance chemical isotope labelling liquid chromatography–mass spectrometry.
Results
We detected 9411 unique compounds in the urine of controls and cases and identified 1037 metabolites with high confidence. Amino acids, peptides, and analogues dominated these metabolites (32.2%), followed by carbonyl compounds (7.1%) and carbohydrates (6.5%). Seven controls from one household showed a significant level of metabolome clustering, with a distinct separation from controls from other households (p value < 0.001). Owner surveys revealed that this cluster of cats was fed dry food only, whereas all but one other control had wet food in their diet. Accordingly, the diet type was significantly associated with the urine metabolome composition in our multivariate model (p value = 0.001). Metabolites significantly altered in this cluster included taurine, an essential amino acid in cats. Urine metabolome profiles were not significantly different in CKD and FIC cases compared with controls, and no significant compositional relationship was detected between the urine metabolome and microbiome.
Conclusion
Our study reveals in-depth diversity of the feline urine metabolome composition, and suggests that it can vary considerably depending on environmental factors.
Publisher
Springer Science and Business Media LLC
Subject
Clinical Biochemistry,Biochemistry,Endocrinology, Diabetes and Metabolism
Reference46 articles.
1. Alonso, A., Julià, A., Vinaixa, M., Domènech, E., Fernández-Nebro, A., Cañete, J. D., Ferrándiz, C., Tornero, J., Gisbert, J. P., Nos, P., Casbas, A. G., Puig, L., González-Álvaro, I., Pinto-Tasende, J. A., Blanco, R., Rodríguez, M. A., Beltran, A., Correig, X., Marsal, S., et al. (2016). Urine metabolome profiling of immune-mediated inflammatory diseases. BMC Medicine, 14, 133. 2. Bouatra, S., Aziat, F., Mandal, R., Guo, A. C., Wilson, M. R., Knox, C., Bjorndahl, T. C., Krishnamurthy, R., Saleem, F., Liu, P., Dame, Z. T., Poelzer, J., Huynh, J., Yallou, F. S., Psychogios, N., Dong, E., Bogumil, R., Roehring, C., & Wishart, D. S. (2013). The human urine metabolome. PLoS ONE, 8, e73076. 3. Broughton-Neiswanger, L. E., Rivera-Velez, S. M., Suarez, M. A., Slovak, J. E., Piñeyro, P. E., Hwang, J. K., & Villarino, N. F. (2020). Urinary chemical fingerprint left behind by repeated NSAID administration: Discovery of putative biomarkers using artificial intelligence. PLoS ONE, 15, e0228989. 4. Chen, D.-Q., Cao, G., Chen, H., Argyopoulos, C. P., Yu, H., Su, W., Chen, L., Samuels, D. C., Zhuang, S., Bayliss, G. P., Zhao, S., Yu, X.-Y., Vaziri, N. D., Wang, M., Liu, D., Mao, J.-R., Ma, S.-X., Zhao, J., Zhang, Y., et al. (2019). Identification of serum metabolites associating with chronic kidney disease progression and anti-fibrotic effect of 5-methoxytryptophan. Nature Communications, 10, 1476. 5. Conroy, M., Brodbelt, D. C., O'Neill, D., Chang, Y. M., & Elliott, J. (2019). Chronic kidney disease in cats attending primary care practice in the UK: a VetCompass(TM) study. Veterinary Record, 184(17), 526.
Cited by
2 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献
|
|