Development of a non-targeted metabolomics method to investigate urine in a rat model of polycystic kidney disease

Temporal Profile of Kynurenine Pathway Metabolites in a Rodent Model of Autosomal Recessive Polycystic Kidney Disease

Autosomal recessive polycystic kidney disease (ARPKD) is an early onset genetic disorder characterized by numerous renal cysts resulting in end stage renal disease. Our study aimed to determine if metabolic reprogramming and tryptophan (Trp) metabolism via the kynurenine pathway (KP) is a critical dysregulated pathway in PKD. Using the Lewis polycystic kidney (LPK) rat model of PKD and Lewis controls, we profiled temporal trends for KP metabolites in plasma, urine, and kidney tissues from 6- and 12-week-old mixed sex animals using liquid and gas chromatography, minimum n = 5 per cohort. A greater kynurenine (KYN) concentration was observed in LPK kidney and plasma of 12-week rats compared to age matched Lewis controls (P ⩽ .05). LPK kidneys also showed an age effect (P ⩽ .05) with KYN being greater in 12-week versus 6-week LPK. The metabolites xanthurenic acid (XA), 3-hydroxykynurenine (3-HK), and 3-hydroxyanthranilic acid (3-HAA) were significantly greater in the plasma of 12-week LPK rats compared to age matched Lewis controls (P ⩽ .05). Plasma XA and 3-HK also showed an age effect (P ⩽ .05) being greater in 12-week versus 6-week LPK. We further describe a decrease in Trp levels in LPK plasma and kidney (strain effect P ⩽ .05). There were no differences in KP metabolites in urine between cohorts. Using the ratio of product and substrates in the KP, a significant age-strain effect (P ⩽ .05) was observed in the activity of the KYN/Trp ratio (tryptophan-2,3-dioxygenase [TDO] or indoleamine-2,3-dioxygenase [IDO] activity), kynurenine 3-monooxygenase (KMO), KAT A (kynurenine aminotransferase A), KAT B, total KAT, total KYNU (kynureninase), KYNU A, KYNU B, and total KYNU within LPK kidneys, supporting an activated KP. Confirmation of the activation of these enzymes will require verification through orthogonal techniques. In conclusion, we have demonstrated an up-regulation of the KP in alignment with progression of renal impairment in the LPK rat model, suggesting that KP activation may be a critical contributor to the pathobiology of PKD.

Metabolomics Profiling of Cystic Renal Disease towards Biomarker Discovery

Simple Summary

Cystic renal disease (CRD) is a group of diseases characterized by abnormal sacs, or cysts, in the kidneys. CRD can be detected using certain imaging modalities (i.e., ultrasound). Patients with CRD might be symptoms-free, while others can show symptoms long after cysts development. Although these cysts represent structural changes, we hypothesized that they have an underlying biochemical alteration. If so, this would open the floor for potential biomarker discovery, which would aid in CRD diagnosis and, possibly, treatment. On that basis, this study focuses on identifying biomarkers for CRD. To achieve that, we employed a metabolomics-based approach to identify intermediate molecules inside the cells that are byproducts of biochemical reactions. We used dry blood spots and serum samples of CRD patients and healthy controls to study the differences in their metabolomic profile. Our results suggest that certain metabolites, including uridine diphosphate, cystine-5-diphosphate, and morpholine, are potential biomarkers for CRD. The affected biochemical pathways in CRD include aminoacyl-tRNA biosynthesis, purine, pyrimidine, glutathione, TCA cycle, and some amino acid metabolism. These preliminary results could be the starting point for possible diagnostic and therapeutic approaches for CRD in the future.

Abstract

Cystic renal disease (CRD) comprises a heterogeneous group of genetic and acquired disorders. The cystic lesions are detected through imaging, either incidentally or after symptoms develop, due to an underlying disease process. In this study, we aim to study the metabolomic profiles of CRD patients for potential disease-specific biomarkers using unlabeled and labeled metabolomics using low and high-resolution mass spectrometry (MS), respectively. Dried-blood spot (DBS) and serum samples, collected from CRD patients and healthy controls, were analyzed using the unlabeled and labeled method. The metabolomics profiles for both sets of samples and groups were collected, and their data were processed using the lab’s standard protocol. The univariate analysis showed (FDR p < 0.05 and fold change 2) was significant to show a group of potential biomarkers for CRD discovery, including uridine diphosphate, cystine-5-diphosphate, and morpholine. Several pathways were involved in CRD patients based on the metabolic profile, including aminoacyl-tRNA biosynthesis, purine and pyrimidine, glutathione, TCA cycle, and some amino acid metabolism (alanine, aspartate and glutamate, arginine and tryptophan), which have the most impact. In conclusion, early CRD detection and treatment is possible using a metabolomics approach that targets alanine, aspartate, and glutamate pathway metabolites.

Urinary metabolites associate with the rate of kidney function decline in patients with autosomal dominant polycystic kidney disease

Background

The variable course of autosomal dominant polycystic kidney disease (ADPKD), and the advent of renoprotective treatment require early risk stratification. We applied urinary metabolomics to explore differences associated with estimated glomerular filtration rate (eGFR; CKD-EPI equation) and future eGFR decline.

Methods

Targeted, quantitative metabolic profiling (¹H NMR-spectroscopy) was performed on baseline spot urine samples obtained from 501 patients with ADPKD. The discovery cohort consisted of 338 patients (56% female, median values for age 46 [IQR 38 to 52] years, eGFR 62 [IQR 45 to 85] ml/min/1.73m², follow-up time 2.5 [range 1 to 3] years, and annual eGFR slope –3.3 [IQR –5.3 to –1.3] ml/min/1.73m²/year). An independent cohort (n = 163) was used for validation. Multivariate modelling and linear regression were used to analyze the associations between urinary metabolites and eGFR, and eGFR decline over time.

Results

Twenty-nine known urinary metabolites were quantified from the spectra using a semi-automatic quantification routine. The model optimization routine resulted in four metabolites that most strongly associated with actual eGFR in the discovery cohort (F = 128.9, P = 7×10⁻⁵⁴, R² = 0.724). A model using the ratio of two other metabolites, urinary alanine/citrate, showed the best association with future annual change in eGFR (F = 51.07, P = 7.26×10⁻¹², R² = 0.150). This association remained significant after adjustment for clinical risk markers including height-adjusted total kidney volume (htTKV). Results were confirmed in the validation cohort.

Conclusions

Quantitative NMR profiling identified urinary metabolic markers that associated with actual eGFR and future rate of eGFR decline. The urinary alanine/citrate ratio showed additional value beyond conventional risk markers.

Metabolomics Approaches for the Diagnosis and Understanding of Kidney Diseases

Diseases of the kidney are difficult to diagnose and treat. This review summarises the definition, cause, epidemiology and treatment of some of these diseases including chronic kidney disease, diabetic nephropathy, acute kidney injury, kidney cancer, kidney transplantation and polycystic kidney diseases. Numerous studies have adopted a metabolomics approach to uncover new small molecule biomarkers of kidney diseases to improve specificity and sensitivity of diagnosis and to uncover biochemical mechanisms that may elucidate the cause and progression of these diseases. This work includes a description of mass spectrometry-based metabolomics approaches, including some of the currently available tools, and emphasises findings from metabolomics studies of kidney diseases. We have included a varied selection of studies (disease, model, sample number, analytical platform) and focused on metabolites which were commonly reported as discriminating features between kidney disease and a control. These metabolites are likely to be robust indicators of kidney disease processes, and therefore potential biomarkers, warranting further investigation.

Untargeted Metabolomic Analysis of Rat Neuroblastoma Cells as a Model System to Study the Biochemical Effects of the Acute Administration of Methamphetamine

Methamphetamine is an illicit psychostimulant drug that is linked to a number of diseases of the nervous system. The downstream biochemical effects of its primary mechanisms are not well understood, and the objective of this study was to investigate whether untargeted metabolomic analysis of an in vitro model could generate data relevant to what is already known about this drug. Rat B50 neuroblastoma cells were treated with 1 mM methamphetamine for 48 h, and both intracellular and extracellular metabolites were profiled using gas chromatography–mass spectrometry. Principal component analysis of the data identified 35 metabolites that contributed most to the difference in metabolite profiles. Of these metabolites, the most notable changes were in amino acids, with significant increases observed in glutamate, aspartate and methionine, and decreases in phenylalanine and serine. The data demonstrated that glutamate release and, subsequently, excitotoxicity and oxidative stress were important in the response of the neuronal cell to methamphetamine. Following this, the cells appeared to engage amino acid-based mechanisms to reduce glutamate levels. The potential of untargeted metabolomic analysis has been highlighted, as it has generated biochemically relevant data and identified pathways significantly affected by methamphetamine. This combination of technologies has clear uses as a model for the study of neuronal toxicology.

Repetitive low intensity magnetic field stimulation in a neuronal cell line: a metabolomics study

Low intensity repetitive magnetic stimulation of neural tissue modulates neuronal excitability and has promising therapeutic potential in the treatment of neurological disorders. However, the underpinning cellular and biochemical mechanisms remain poorly understood. This study investigates the behavioural effects of low intensity repetitive magnetic stimulation (LI-rMS) at a cellular and biochemical level. We delivered LI-rMS (10 mT) at 1 Hz and 10 Hz to B50 rat neuroblastoma cells in vitro for 10 minutes and measured levels of selected metabolites immediately after stimulation. LI-rMS at both frequencies depleted selected tricarboxylic acid (TCA) cycle metabolites without affecting the main energy supplies. Furthermore, LI-rMS effects were frequency-specific with 1 Hz stimulation having stronger effects than 10 Hz. The observed depletion of metabolites suggested that higher spontaneous activity may have led to an increase in GABA release. Although the absence of organised neural circuits and other cellular contributors (e.g., excitatory neurons and glia) in the B50 cell line limits the degree to which our results can be extrapolated to the human brain, the changes we describe provide novel insights into how LI-rMS modulates neural tissue.

Metabolomics for clinical use and research in chronic kidney disease

Chronic kidney disease (CKD) has a high prevalence in the general population and is associated with high mortality; a need therefore exists for better biomarkers for diagnosis, monitoring of disease progression and therapy stratification. Moreover, very sensitive biomarkers are needed in drug development and clinical research to increase understanding of the efficacy and safety of potential and existing therapies. Metabolomics analyses can identify and quantify all metabolites present in a given sample, covering hundreds to thousands of metabolites. Sample preparation for metabolomics requires a very fast arrest of biochemical processes. Present key technologies for metabolomics are mass spectrometry and proton nuclear magnetic resonance spectroscopy, which require sophisticated biostatistic and bioinformatic data analyses. The use of metabolomics has been instrumental in identifying new biomarkers of CKD such as acylcarnitines, glycerolipids, dimethylarginines and metabolites of tryptophan, the citric acid cycle and the urea cycle. Biomarkers such as c-mannosyl tryptophan and pseudouridine have better performance in CKD stratification than does creatinine. Future challenges in metabolomics analyses are prospective studies and deconvolution of CKD biomarkers from those of other diseases such as metabolic syndrome, diabetes mellitus, inflammatory conditions, stress and cancer.

The potential of metabolomic analysis techniques for the characterisation of α1-adrenergic receptors in cultured N1E-115 mouse neuroblastoma cells

Several studies of neuropathic pain have linked abnormal adrenergic signalling to the development and maintenance of pain, although the mechanisms underlying this are not yet fully understood. Metabolomic analysis is a technique that can be used to give a snapshot of biochemical status, and can aid in the identification of the mechanisms behind pathological changes identified in cells, tissues and biological fluids. This study aimed to use gas chromatography-mass spectrometry-based metabolomic profiling in combination with reverse transcriptase-polymerase chain reaction and immunocytochemistry to identify functional α1-adrenergic receptors on cultured N1E-115 mouse neuroblastoma cells. The study was able to confirm the presence of mRNA for the α1D subtype, as well as protein expression of the α1-adrenergic receptor. Furthermore, metabolomic data revealed changes to the metabolite profile of cells when exposed to adrenergic pharmacological intervention. Agonist treatment with phenylephrine hydrochloride (10 µM) resulted in altered levels of several metabolites including myo-inositol, glucose, fructose, alanine, leucine, phenylalanine, valine, and n-acetylglutamic acid. Many of the changes observed in N1E-115 cells by agonist treatment were modulated by additional antagonist treatment (prazosin hydrochloride, 100 µM). A number of these changes reflected what is known about the biochemistry of α1-adrenergic receptor activation. This preliminary study therefore demonstrates the potential of metabolomic profiling to confirm the presence of functional receptors on cultured cells.

Systems biology of polycystic kidney disease: a critical review

The proliferation and diminishing costs of 'omics' approaches have finally opened the doors for small and medium laboratories to enter the 'systems biology era'. This is a welcome evolution that requires a new framework to design, interpret, and validate studies. Here, we highlight some of the challenges, contributions, and prospects of the 'cyst-ems biology' of polycystic kidney disease.

Alterations of urinary metabolite profile in model diabetic nephropathy

Countering the diabetes pandemic and consequent complications, such as nephropathy, will require better understanding of disease mechanisms and development of new diagnostic methods. Animal models can be versatile tools in studies of diabetic renal disease when model pathology is relevant to human diabetic nephropathy (DN). Diabetic models using endothelial nitric oxide synthase (eNOS) knock-out mice develop major renal lesions characteristic of human disease. However, it is unknown whether they can also reproduce changes in urinary metabolites found in human DN. We employed Type 1 and Type 2 diabetic mouse models of DN, i.e. STZ-eNOS(-/-) C57BLKS and eNOS(-/-) C57BLKS db/db, with the goal of determining changes in urinary metabolite profile using proton nuclear magnetic resonance (NMR). Six urinary metabolites with significantly lower levels in diabetic compared to control mice have been identified. Specifically, major changes were found in metabolites from tricarboxylic acid (TCA) cycle and aromatic amino acid catabolism including 3-indoxyl sulfate, cis-aconitate, 2-oxoisocaproate, N-phenyl-acetylglycine, 4-hydroxyphenyl acetate, and hippurate. Levels of 4-hydroxyphenyl acetic acid and hippuric acid showed the strongest reverse correlation to albumin-to-creatinine ratio (ACR), which is an indicator of renal damage. Importantly, similar changes in urinary hydroxyphenyl acetate and hippurate were previously reported in human renal disease. We demonstrated that STZ-eNOS(-/-) C57BLKS and eNOS(-/-) C57BLKS db/db mouse models can recapitulate changes in urinary metabolome found in human DN and therefore can be useful new tools in metabolomic studies relevant to human pathology.

Standardizing the experimental conditions for using urine in NMR-based metabolomic studies with a particular focus on diagnostic studies: a review

The metabolic composition of human biofluids can provide important diagnostic and prognostic information. Among the biofluids most commonly analyzed in metabolomic studies, urine appears to be particularly useful. It is abundant, readily available, easily stored and can be collected by simple, noninvasive techniques. Moreover, given its chemical complexity, urine is particularly rich in potential disease biomarkers. This makes it an ideal biofluid for detecting or monitoring disease processes. Among the metabolomic tools available for urine analysis, NMR spectroscopy has proven to be particularly well-suited, because the technique is highly reproducible and requires minimal sample handling. As it permits the identification and quantification of a wide range of compounds, independent of their chemical properties, NMR spectroscopy has been frequently used to detect or discover disease fingerprints and biomarkers in urine. Although protocols for NMR data acquisition and processing have been standardized, no consensus on protocols for urine sample selection, collection, storage and preparation in NMR-based metabolomic studies have been developed. This lack of consensus may be leading to spurious biomarkers being reported and may account for a general lack of reproducibility between laboratories. Here, we review a large number of published studies on NMR-based urine metabolic profiling with the aim of identifying key variables that may affect the results of metabolomics studies. From this survey, we identify a number of issues that require either standardization or careful accounting in experimental design and provide some recommendations for urine collection, sample preparation and data acquisition.

Isotope cluster-based compound matching in gas chromatography/mass spectrometry for non-targeted metabolomics

Gas chromatography coupled to mass spectrometry (GC/MS) has emerged as a powerful tool in metabolomics studies. A major bottleneck in current data analysis of GC/MS-based metabolomics studies is compound matching and identification, as current methods generate high rates of false positive and false-negative identifications. This is especially true for data sets containing a high amount of noise. In this work, a novel spectral similarity measure based on the specific fragmentation patterns of electron impact mass spectra is proposed. An important aspect of these algorithmic methods is the handling of noisy data. The performance of the proposed method compared to the dot product, the current gold standard, was evaluated on a complex biological data set. The analysis results showed significant improvements of the proposed method in compound matching and chromatogram alignment compared to the dot product.

Network analysis of a Pkd1-mouse model of autosomal dominant polycystic kidney disease identifies HNF4α as a disease modifier

Autosomal Dominant Polycystic Kidney Disease (ADPKD; MIM ID's 173900, 601313, 613095) leads to end-stage kidney disease, caused by mutations in PKD1 or PKD2. Inactivation of Pkd1 before or after P13 in mice results in distinct early- or late-onset disease. Using a mouse model of ADPKD carrying floxed Pkd1 alleles and an inducible Cre recombinase, we intensively analyzed the relationship between renal maturation and cyst formation by applying transcriptomics and metabolomics to follow disease progression in a large number of animals induced before P10. Weighted gene co-expression network analysis suggests that Pkd1-cystogenesis does not cause developmental arrest and occurs in the context of gene networks similar to those that regulate/maintain normal kidney morphology/function. Knowledge-based Ingenuity Pathway Analysis (IPA) software identifies HNF4α as a likely network node. These results are further supported by a meta-analysis of 1,114 published gene expression arrays in Pkd1 wild-type tissues. These analyses also predict that metabolic pathways are key elements in postnatal kidney maturation and early steps of cyst formation. Consistent with these findings, urinary metabolomic studies show that Pkd1 cystic mutants have a distinct profile of excreted metabolites, with pathway analysis suggesting altered activity in several metabolic pathways. To evaluate their role in disease, metabolic networks were perturbed by inactivating Hnf4α and Pkd1. The Pkd1/Hnf4α double mutants have significantly more cystic kidneys, thus indicating that metabolic pathways could play a role in Pkd1-cystogenesis.

Autosomal Dominant Polycystic Kidney Disease (ADPKD) is the most common genetic cause of polycystic kidney disease and is responsible for 4.6% of the end-stage renal disease (ESRD) cases in the United States. It is most often caused by mutation in the PKD1 gene. To understand this disease, we made a mouse model in which we could delete the Pkd1 gene and study the animal as its kidney becomes cystic. Using this model, we had previously found that the maturation status of the animal determines whether cysts form within days or within months, and we had narrowed down this switch to a two-day interval. In the current study, we used the rapid cyst-forming model to analyze the expression pattern of thousands of genes in mutant and control kidneys, and metabolites excreted in the urine. Our results identify a number of genes that may be involved in cyst formation and suggest that metabolic changes may play a role in ADPKD and could alter disease progression. These analyses also predict that metabolic pathways are key elements in normal postnatal kidney maturation.

Development of an untargeted metabolomics method for the analysis of human faecal samples using Cryptosporidium-infected samples

Faecal metabolite profiling, though in its infancy, allows for investigation of complex metabolic interactions between gastrointestinal infections or diseases and host health. In the present study, we describe a faecal metabolite extraction method for untargeted gas chromatography-mass spectrometry (GC-MS) analysis using Cryptosporidium positive and negative human faecal samples. The extraction method takes into account the varying faecal consistencies and quantities received for clinical diagnosis. Optimisation was carried out using different extraction solvents and on three different faecal quantities to determine the minimum amount of faecal sample required. The method was validated by untargeted GC-MS analysis on 8 Cryptosporidium positive and 8 Cryptosporidium negative human faecal samples, extracted using the optimised conditions. The method showed good extraction reproducibility with a relative standard deviation of 9.14%. Multivariate analysis of the GC-MS generated dataset showed distinct differences between profiles of Cryptosporidium positive and Cryptosporidium negative samples. The most notable differences included changes in amino acid, nitrogen and energy metabolism, demonstrating the association of infection with Cryptosporidium and altered permeability of the small intestine.