Feline urine metabolomic signature: characterization of low-molecular-weight substances in urine from domestic cats

Correlation between urine anion gap and urine ammonia-creatinine ratio in healthy cats and cats with kidney disease

BACKGROUND

Ammonium excretion decreases as kidney function decreases in several species, including cats, and may have predictive or prognostic value in patients with chronic kidney disease (CKD). Urine ammonia measurement is not readily available in clinical practice, and urine anion gap (UAG) has been proposed as a surrogate test.

OBJECTIVES

Evaluate the correlation between urine ammonia-to-creatinine ratio (UACR) and UAG in healthy cats and those with CKD and determine if a significant difference exists between UAG of healthy cats and cats with CKD.

ANIMALS

Urine samples collected from healthy client-owned cats (n = 59) and those with stable CKD (n = 17).

METHODS

Urine electrolyte concentrations were measured using a commercial chemistry analyzer and UAG was calculated as ([sodium] + [potassium]) - [chloride]. Urine ammonia and creatinine concentrations had been measured previously using commercially available enzymatic assays and used to calculate UACR. Spearman's rank correlation coefficient between UAG and UACR was calculated for both groups. The UAG values of healthy cats and cats with CKD were assessed using the Mann-Whitney test (P < .05).

RESULTS

The UAG was inversely correlated with UACR in healthy cats (P < .002, r0  = -0.40) but not in cats with CKD (P = .55; r0  = -0.15). A significant difference was found between UAG in healthy cats and those with CKD (P < .001).

CONCLUSIONS AND CLINICAL IMPORTANCE

The UAG calculation cannot be used as a substitute for UACR in cats. The clinical relevance of UAG differences between healthy cats and those with CKD remains unknown.

The effects of a hydrolyzed protein diet on the plasma, fecal and urine metabolome in cats with chronic enteropathy

Hydrolyzed protein diets are extensively used to treat chronic enteropathy (CE) in cats. However, the biochemical effects of such a diet on feline CE have not been characterized. In this study an untargeted 1H nuclear magnetic resonance spectroscopy-based metabolomic approach was used to compare the urinary, plasma, and fecal metabolic phenotypes of cats with CE to control cats with no gastrointestinal signs recruited at the Royal Veterinary College (RVC). In addition, the biomolecular consequences of a hydrolyzed protein diet in cats with CE was also separately determined in cats recruited from the RVC (n = 16) and the University of Bristol (n = 24) and whether these responses differed between dietary responders and non-responders. Here, plasma metabolites related to energy and amino acid metabolism significantly varied between CE and control cats in the RVC cohort. The hydrolyzed protein diet modulated the urinary metabolome of cats with CE (p = 0.005) in both the RVC and Bristol cohort. In the RVC cohort, the urinary excretion of phenylacetylglutamine, p-cresyl-sulfate, creatinine and taurine at diagnosis was predictive of dietary response (p = 0.025) although this was not observed in the Bristol cohort. Conversely, in the Bristol cohort plasma betaine, glycerol, glutamine and alanine at diagnosis was predictive of outcome (p = 0.001), but these same results were not observed in the RVC cohort. The biochemical signature of feline CE in the RVC cohort was consistent with that identified in human and animal models of inflammatory bowel disease. The hydrolyzed protein diet had the same effect on the urinary metabolome of cats with CE at both sites. However, biomarkers that were predictive of dietary response at diagnosis differed between the 2 sites. This may be due to differences in disease severity, disease heterogeneity, factors unrelated to the disease or small sample size at both sites. As such, further studies utilizing larger number of cats are needed to corroborate these findings.

Serum Metabolites Characterization Produced by Cats CKD Affected, at the 1 and 2 Stages, before and after Renal Diet

Utilizing metabolomics, a tool for measuring and characterizing low-molecular-weight substances (LMWs), to identify eventual changes in response to dietary intervention is novel in cats with chronic kidney disease (CKD), a condition characterized by retention of uremic solutes. This study aims to assess the serum metabolomic profile of cats in early stages of CKD and to compare the serum metabolomic of CKD cats after 60 days of a renal diet to evaluate the effect of dietary intervention on these metabolites. Twenty-five domestic cats were included in the study. Fifteen cats with CKD stages 1 (n = 6) and 2 (n = 9) according to the International Renal Interest Society (IRIS) were included in the renal groups, and a control group consisting of 10 cats was included. All animals were enrolled on a maintenance diet for 30 days before the experimental period. The metabolomics analysis was performed by gas chromatography-mass spectrometry (GC-MS). Partial least squares discriminant analysis (PLS-DA) was performed on Metaboanalyst 4.0 software. Forty-three metabolites were identified. Citric acid and monostearin were altered in the CKD2 group when compared to CKD1 and the control group at T0. A total of seven serum metabolites differed after 60 days of the renal diet: glycine, fructose, glutamic acid, arachidonic acid, stearic acid, creatinine, and urea. Changes were seen in the serum metabolomic profile after 60 days of the renal diet, and some of the metabolites that changed in response to the diet have beneficial effects on health. Overall, metabolomics markers have the potential to identify early stages of CKD, providing insights into the possible pathophysiologic processes that contribute to the development and progression of CKD.

In-depth characterisation of the urine metabolome in cats with and without urinary tract diseases

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.

Untargeted metabolomic analysis in cats with naturally occurring inflammatory bowel disease and alimentary small cell lymphoma

Feline chronic enteropathy (CE) is a common gastrointestinal disorder in cats and mainly comprises inflammatory bowel disease (IBD) and small cell lymphoma (SCL). Differentiation between IBD and SCL can be diagnostically challenging. We characterized the fecal metabolome of 14 healthy cats and 22 cats with naturally occurring CE (11 cats with IBD and 11 cats with SCL). Principal component analysis and heat map analysis showed distinct clustering between cats with CE and healthy controls. Random forest classification revealed good group prediction for healthy cats and cats with CE, with an overall out-of-bag error rate of 16.7%. Univariate analysis indicated that levels of 84 compounds in cats with CE differed from those in healthy cats. Polyunsaturated fatty acids held discriminatory power in differentiating IBD from SCL. Metabolomic profiles of cats with CE resembled those in people with CE with significant alterations of metabolites related to tryptophan, arachidonic acid, and glutathione pathways.

Differences in metabolic profiles between the Burmese, the Maine coon and the Birman cat-Three breeds with varying risk for diabetes mellitus

Feline diabetes mellitus shares many features with type 2 diabetes in people, regarding clinical presentation, physiology, and pathology. A breed predisposition for type 2 diabetes has been identified, with the Burmese breed at a fivefold increased risk of developing the condition compared to other purebred cats. We aimed to characterize the serum metabolome in cats (n = 63) using nuclear magnetic resonance metabolomics, and to compare the metabolite pattern of Burmese cats with that of two cat breeds of medium or low risk of diabetes, the Maine coon (MCO) and Birman cat, respectively. Serum concentrations of adiponectin, insulin and insulin-like growth factor-1 were also measured (n = 94). Burmese cats had higher insulin and lower adiponectin concentrations than MCO cats. Twenty one metabolites were discriminative between breeds using a multivariate statistical approach and 15 remained significant after adjustment for body weight and body condition score. Burmese cats had higher plasma levels of 2-hydroxybutyrate relative to MCO and Birman cats and increased concentrations of 2-oxoisocaproic acid, and tyrosine, and lower concentrations of dimethylglycine relative to MCO cats. The metabolic profile of MCO cats was characterized by high concentrations of arginine, asparagine, methionine, succinic acid and low levels of acetylcarnitine while Birman cats had the highest creatinine and the lowest taurine plasma levels, compared with MCO and Burmese. The pattern of metabolites in Burmese cats is similar to that in people with insulin resistance. In conclusion, the metabolic profile differed between healthy cats of three breeds. Detection of an abnormal metabolome might identify cats at risk of developing diabetes.

Applying metabolomics to veterinary pharmacology and therapeutics

Metabolomics is the large-scale study of low-molecular-weight substances in a biological system in a given physiological state at a given time point. Metabolomics can be applied to identify predictors of inter-individual variability in drug response, provide clinicians with data useful for decision-making processes in drug selection, and inform about the pharmacokinetics and pharmacodynamics of a drug. It is, therefore, an exceptional approach for gaining new understanding effects in the field of comparative veterinary pharmacology. However, the incorporation of metabolomics into veterinary pharmacology and toxicology is not yet widespread, and this is probably, at least in part, a result of its highly multidisciplinary nature. This article reviews the potential applications of metabolomics in veterinary pharmacology and therapeutics. It integrates key concepts for designing metabolomics studies and analyzing and interpreting metabolomics data, providing solid foundations for applying metabolomics to the study of drugs in all veterinary species.

Urinary proteome and metabolome in dogs (Canis lupus familiaris): The effect of chronic kidney disease

Chronic kidney disease (CKD) is a progressive and irreversible disease. Although urine is an ideal biological sample for proteomics and metabolomics studies, sensitive and specific biomarkers are currently lacking in dogs. This study characterised dog urine proteome and metabolome aiming to identify and possibly quantify putative biomarkers of CKD in dogs. Twenty-two healthy dogs and 28 dogs with spontaneous CKD were selected and urine samples were collected. Urinary proteome was separated by SDS-PAGE and analysed by mass spectrometry, while urinary metabolome was analysed in protein-depleted samples by 1D ¹H NMR spectra. The most abundant proteins in urine samples from healthy dogs were uromodulin, albumin and, in entire male dogs, arginine esterase. In urine samples from CKD dogs, the concentrations of uromodulin and albumin were significantly lower and higher, respectively, than in healthy dogs. In addition, these samples were characterised by a more complex protein pattern indicating mixed glomerular (protein bands ≥65 kDa) and tubular (protein bands <65 kDa) proteinuria. Urine spectra acquired by NMR allowed the identification of 86 metabolites in healthy dogs, belonging to 49 different pathways mainly involved in amino acid metabolism, purine and aminoacyl-tRNA biosynthesis or tricarboxylic acid cycle. Seventeen metabolites showed significantly different concentrations when comparing healthy and CKD dogs. In particular, carnosine, trigonelline, and cis-aconitate, might be suggested as putative biomarkers of CKD in dogs. SIGNIFICANCE: Urine is an ideal biological sample, however few proteomics and metabolomics studies investigated this fluid in dogs and in the context of CKD (chronic kidney disease). In this research, applying a multi-omics approach, new insights were gained regarding the molecular changes triggered by this disease in canine urinary proteome and metabolome. In particular, the involvement of the tubular component was highlighted, suggesting uromodulin, trigonelline and carnosine as possible biomarkers of CKD in dogs.

Urinary chemical fingerprint left behind by repeated NSAID administration: Discovery of putative biomarkers using artificial intelligence

Prediction and early detection of kidney damage induced by nonsteroidal anti-inflammatories (NSAIDs) would provide the best chances of maximizing the anti-inflammatory effects while minimizing the risk of kidney damage. Unfortunately, biomarkers for detecting NSAID-induced kidney damage in cats remain to be discovered. To identify potential urinary biomarkers for monitoring NSAID-based treatments, we applied an untargeted metabolomics approach to urine collected from cats treated repeatedly with meloxicam or saline for up to 17 days. Applying multivariate analysis, this study identified a panel of seven metabolites that discriminate meloxicam treated from saline treated cats. Combining artificial intelligence machine learning algorithms and an independent testing urinary metabolome data set from cats with meloxicam-induced kidney damage, a panel of metabolites was identified and validated. The panel of metabolites including tryptophan, tyrosine, taurine, threonic acid, pseudouridine, xylitol and lyxitol, successfully distinguish meloxicam-treated and saline-treated cats with up to 75–100% sensitivity and specificity. This panel of urinary metabolites may prove a useful and non-invasive diagnostic tool for monitoring potential NSAID induced kidney injury in feline patients and may act as the framework for identifying urine biomarkers of NSAID induced injury in other species.