¹H NMR-based urine metabolomics for the evaluation of kidney injury in Wistar rats by 3-MCPD

The cause of toxicity induced by 3-chloro-1,2-propanediol (3-MCPD) remains under investigation, and progress towards understanding this toxicity has been limited by the lack of sensitive and reliable biomarkers.

Abstract

The cause of toxicity induced by 3-chloro-1,2-propanediol (3-MCPD) remains under investigation, and progress towards understanding this toxicity has been limited by the lack of sensitive and reliable biomarkers. Global metabolomics were analyzed to characterize the phenotypical biochemical perturbations and potential mechanisms of the 3-MCPD-induced toxicity. 3-MCPD was administered to Wistar rats (60 mg per kg bw, oral) for 7, 21, and 35 days and urine samples were collected at each time point. The urinary metabolomics was performed by ¹H NMR, and the NMR spectrum signals of the detected metabolites were normalized and analyzed by orthogonal pattern recognition methods (PCA and OPLS-DA). This analysis revealed a time- and dose-dependency of the biochemical perturbations induced by 3-MCPD toxicity. Several metabolites responsible for glycine, serine and threonine metabolism, taurine and hypotaurine metabolism and nicotinate and nicotinamide metabolism revealed that 3-MCPD produced serious kidney toxicity, consistent with clinical biochemistry and histopathology. Significant changes in seven identified metabolites were validated as phenotypic biomarkers of 3-MCPD toxicity. Overall, our work demonstrates the powerful use of metabolomics for improved detection of toxicity and biomarker discovery and highlights the powerful predictive potential of such analyses for understanding food toxicity.

1. Introduction

3-Chloro-1,2-propanediol (3-MCPD) is one of the chloropropanol chemicals formed by the reaction between hydrochloric acid and residual vegetable fat in the process of producing acid hydrolyzed vegetable protein (HVP).1 In recent years, HVP has been increasingly used as a common savory ingredient widely added to soy sauce and various composite seasonings.2 Corresponding to the increase in HVP addition, 3-MCPD has been detected in a wide range of blended soy sauces.3 3-MCPD has also been detected in various heat processed foods, including cereal-derived products like bread crust, toast and biscuits, malt-derived products, coffee, and grilled cheese, as well as smoke treated products.4 The toxicity of 3-MCPD is well-documented and includes neurotoxicity,5 nephrotoxicity,6 genotoxicity,7 immunotoxicity8 and reproductive toxicity.7 Recently, 3-MCPD was classified by the IARC as group 2B, “possibly carcinogenic to humans”. The European Union (EU) established a limit level of 20 μg kg^–1 for 3-MCPD in HVP and soy sauce.9

Metabolomics, attempting to profile the whole set of metabolites in a biological sample, holds enormous promise for the discovery of metabolite biomarkers to allow diagnosis of diseases (e.g., cancer).10 Although the sample size in a discovery study is usually small, analyzing a large number of samples is essential in the verification and validation phases to increase the statistical power and address heterogenesis of phenotypes for a disease.11 Changes in the concentration of metabolites may reflect both genetic and environmental factors, which together influence the susceptibility to chronic disease. Understanding metabolic changes has strong diagnostic potential and allows discovery of metabolic disturbance due to disease or other effects.11 Urine and serum or plasma are the most commonly studied biofluids and are easily obtained and prepared compared with intact or extracted tissues. Metabolomics can be used to evaluate metabolic changes in response to drugs, environmental stress or diseases, and could help both for improved diagnosis as well as for customization of treatment options.12

To assay the multitude of metabolites present in urine, analytical methods such as (¹H NMR) spectroscopy13 and mass spectrometry (MS)14 are often used in metabolomics analyses. When dealing with large population studies, rigorous analytical set-ups and data processing pipeline are required to allow: (1) unequivocal labeling of samples, (2) metabolite-rich information, (3) metabolite identification, (4) metabolite quantitation, (5) selectivity to detect biological differences, (6) high-throughput, (7) robustness, (8) reproducibility, (9) stability over time, and (10) minimal technical errors, so that relevant metabolic changes can be detected through various data mining strategies.15

NMR spectroscopy is less sensitive than MS, but it is high-throughput and can provide quantitative and qualitative information regarding nonoverlapping metabolites in a noninvasive, rapid and cost-effective manner.16 Additionally, NMR can be applied to biofluids such as urine to generate metabolic profiles.12 NMR is also well suited for metabolite fingerprinting, which involves the comprehensive and simultaneous analysis of a wide variety of compounds.17 Applications of NMR for metabolomics and metabolic profiling continue to grow rapidly as does the refinement of methods for the measurement, analysis, and interpretation of complex datasets. ¹H NMR spectroscopy can be used to generate comprehensive datasets comprising information of all the proton-containing metabolites present in a sample at micromolar levels, without prior knowledge of the compounds.18 NMR analysis can be performed with minimal sample preparation, thus minimizing sample alteration. ¹H NMR spectroscopy has been used for multivariate metabolic profiling of cells,19 tissues,20 and biological fluids21 since the 1980s. More recently, NMR-based applications of metabolomics have been published, including studies in experimental animals on male/female differences, age-related changes, dietary modulation, diurnal effects, and phenotyping of mutant and transgenic animals and toxicological applications to identify specific biomarkers of organ toxicity.22

The genotoxic potential of 3-MCPD has been found to be inconclusive and species-related, which can be further explained through the metabolism of 3-MCPD in physiological systems. Two predominant pathways of 3-MCPD metabolism were proposed: the microbial metabolic pathway and the mammalian metabolic pathway.23 When the physiological system is exposed to foreign substances, it will respond by releasing biomarkers, which are useful tools for disease detection, prevention of further exposure to contaminants and evaluation of the severity of exposure. Since the exact metabolic pathway of 3-MCPD is yet to be determined, there is no definitive way to prove that any of the metabolites reported is directly related to 3-MCPD exposure.

In this study, we propose a urinary NMR-based metabolomics approach for detecting metabolic changes as a result of 3-MCPD treatment. Urine was selected for the current study because: (1) the collecting method for urine samples is noninvasive, (2) urine samples enable analysis of metabolites in the same animal but at different time points and (3) urine sampling may be more feasible for monitoring drug efficacy in a clinical setting. This study also used body weight, clinical biochemistry and histopathology examination to understand how pathological states are reflected in abnormal metabolite profiles.

2. Materials and methods

2.1. Chemicals and reagents

3-Chloro-1,2-propanediol (3-MCPD, 97%) was purchased from J&K China Chemical Ltd. Anachro Certified 2,2-dimethyl-2-silapentane-5-sulphonate (DSS) standard solution was purchased from Anachro Technologies Inc. (Calgary, Canada). The phosphate buffer solution (0.1 M K₂HPO₄/NaH₂PO₄, pH 7.29) used in this study was obtained from Sigma. All other chemicals used were of HPLC grade. Deionized water used for all experiments was purified with a Milli-Q system (Millipore, USA).

2.2. Animal handling and treatment

A total of 28 male rats, 5 weeks of age (132 ± 5 g) were obtained from the animal facility of the institute and were acclimatized for 7 days in polypropylene cages at room temperature, 22 ± 2 °C, and relative humidity of 50 ± 10%. The light cycle was maintained as 12 h of light and 12 h of darkness. Food and water were provided ad libitum. Rats were randomly divided into four groups with an equal number of animals (n = 7 in each group) dosed (orally) with 15 mg kg^–1, 30 mg kg^–1 and 60 mg kg^–1 3-MCPD metal salt solution and metal salt solution without 3-MCPD.24 For urine collection, animals (n = 7 in each group) picked from the dosed groups and the control group were kept in metabolic cages for acclimatization for 3 days. All animal handling and experimental protocols were performed in strict accordance with the guidelines of the Institutional Animal Ethics Committee.

2.3. Collection of urine, serum and tissue

Urine samples from the dosed groups were collected in ice-cooled tubes containing 1% sodium azide at the 7^th day, 21^st day and 35^th day post dose (p.d.) whereas, for control (n = 7) urine samples were collected at the same time throughout the study. The supernatant was obtained by centrifugation, and then stored at –80 °C for NMR spectroscopic analysis. Animals (n = 7 in each group at each time point including the control) were sacrificed on the 35^th day by cervical dislocation and 1 ml of blood, and kidney tissue were collected. The blood sample was allowed to clot for about 30 min and serum was separated by centrifugation at 2700g for 10 min. The collected serum was used for serum biochemical analysis.

2.4. ¹H NMR spectroscopic measurement of urine

All of the ¹H NMR spectra were acquired at 298 K on a Bruker AV III-600 NMR spectrometer (Bruker BioSpin GmbH, Rheinstetten, Germany) equipped with an inverse cryoprobe operating at a proton NMR frequency of 600.13 MHz. For each sample, 32 scans were recorded with the following parameters: spectral width (SW) = 12019.23 Hz, pulse width (PW) = 10 μs, and relaxation delay = 1.0 s. The spectra were referenced to a DSS standard solution such that the peak height of DSS, which was set to 0 ppm, was equivalent for all of the spectra. To scale all of the NMR signals, 5 mM DSS was used as an internal chemical shift standard.25

2.5. Quantification of the metabolites

The quantification of the metabolite concentrations was performed using the profiler module of the Chenomx NMR Suite v.7.5 (Chenomx Inc., Edmonton, Canada). A reference compound (DSS) was used as an internal standard for the chemical shifts (set to 0 ppm) and a reference signal for the quantification. The quantification was performed by comparing the integral of a known reference signal (DSS-d₆) with the signals derived from a library of compounds containing chemical shifts and peak multiplicities. The identifiable metabolites were chosen for quantification by matching to the Chenomx 600 MHz Library.

2.6. Statistical analysis

The dataset was normalized to sample median and scaled to Pareto scaling, before performing principal component analysis (PCA) and orthogonal partial least squares-discrimination analysis (OPLS-DA) processing. In order to differentiate similarities in metabolic profiles of urine samples among all the groups, an unsupervised pattern recognition (PR) method, PCA, was applied to data obtained from the urine samples. Based on the results from the PCA, metabolites differentiating the control groups from each of the treated groups were identified and integrated. From the integrated data, the relative intensity of each of the metabolites was then calculated. Next, a supervised PR method, PLS-DA, was performed to maximize the separation of the control and treated groups based on metabolites identified from PCA. Due to the small number of animals (n = 7, less than 20), leave one-out cross validation was conducted to test the performance of the PLS-DA model. Pattern recognition of NMR data, statistical and pathway analyses were performed using the software Simca-P version 14.0.0.1359.26

3. Results and discussion

3.1. ¹H NMR spectra identification

All the urine samples provided highly resolved ¹H NMR spectra for quantitative analysis of metabolites. Comparative ¹H NMR spectra of urine from control and high dose of 3-MCPD groups on the 7^th day, 21^st day and 35^th day are presented in Fig. 1A. Through the visual inspection of the NMR spectra, more intense peaks were observed for dosed rat samples than control ones; this effect was even more obvious for high dose rat samples. This suggests potential metabolic changes in 3-MCPD treated rat groups, compared to controls. We identified and quantified 68 metabolites using the 600 MHz Library within the Chenomx software (see Fig. 1B). For identification of unambiguous peaks, spiking experiments were performed using several standard compounds. The concentrations of the identified metabolites were determined with respect to the internal standard DSS of a known concentration27 (Table S1,† the pool of 68 metabolites identified in rat urine by NMR).

Fig. 1. (A) ¹H NMR spectra of urine samples of the high-dosed group before and after 7 d, 21 d, and 35 d administration; (B) analytical spectrogram of ¹H NMR.

3.2. PCA, OPLS-DA and S-plot analysis of rat urine

Normalization of NMR-based metabolomics data is a mathematical operation that corrects for the different concentrations of samples to allow for the direct comparison of profiles. There are many different normalization approaches. Here, we employed a correction to the median, instead of a correction to the levels of creatinine, as 3-MCPD had toxic effects on the kidney.28

This scaling step is crucial to avoid disproportionate effects of the highest peaks which can occur in multivariate data analysis. Pareto scaling was used for the NMR-based metabolomics data, and the effects of this normalization are shown in Fig. S1.† 29

Distinct spectral phenotypes were readily observed between the ¹H NMR spectra of the urine samples collected from control and 3-MCPD treated rats. Two urine samples were collected from the untreated control rats and two urine samples were collected from the 3-MCPD treated rats after 7 days and 21 days. Principal component analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA) were performed and the score plots of the control and treatment groups are shown in Fig. 2. The PCA and OPLS-DA score plots for the 3-MCPD treated groups are also shown at different points in time. From Fig. 2a, b, d, e to g, h, the PCA and OPLS-DA score plots illustrated a significant distinction between the control groups and the 3-MCPD treated groups. However, on the first 7 days, an obvious fluctuation was observed, especially for the DO7-4 sample, which is out of the Hotelling's T2 scope (95%). Furthermore, the S-plots analysis were conducted for selecting significant metabolites, which were supposed as the potential biomarkers, as shown in Fig. 2c, f and g. After treated with 3-MCPD, the potential biomarkers, marked in red, were selected based on the principles of the variable importance for the projection (VIP) value larger than 1 and the t-test at the 95% level. From these selected biomarkers, some metabolites, such as 2-oxoglutarate, decreased at all the time points; some metabolites, such as 3-hydroxyisobutyrate and pyroglutamate, had an increasing trend in the first 21 days; but some metabolites had the same variation tendency, such as glycolate and lysine, from the 21^st day. These variation tendencies of metabolites indicated that the metabolic profiling of the 3-MCPD treated rats changed with the extension of dose time. The selected potential biomarkers were described with a more visual heat map, as shown in Fig. 2j, k and l, through which, the quantitative relationship of the selected metabolites were more unambiguous. The distinction of metabolic profiling at each time point is illustrated more obviously. So it is necessary to further analyze the distinction of metabolic profiling between long-term and short-term toxicity.

Fig. 2. PCA, OPLS-DA, S-plots and heat map analysis of the urine metabolites of rats treated with 3-MCPD for 7 days, 12 days and 35 days respectively.

The PCA time series score plot analysis was conducted to observe the distinction of the four time points (1^st day, 7^th day, 21^st day and 35^th day), as shown in Fig. 3A, which illustrated that the metabolites of the group treated with 3-MCPD for 35 days exhibited an observable difference compared to the groups treated with 3-MCPD for the first 21 days. The two-class separation with discrimination was implemented for a deeper study of the long-term and short-term toxicity of 3-MCPD on rats. The OPLS-DA analysis, described in Fig. 3B, showed that the two classes were distributed separately. The S-plot analysis of the mentioned two classes was conducted as shown in Fig. 3C; and after the VIP value and t-test analysis, five metabolites meeting the requirements were selected, one decreased biomarker 2-oxoglutarate, and four increased biomarkers allantoin, creatine, glycine and taurine.

Fig. 3. (A) Time series score plots generated from PCA analysis of ¹H NMR spectra of 3-MCPD treated group from the 1^st day to 35^th day. (B) The OPLS-DA and (C) the S-plot analysis of ¹H NMR spectra of 35-day groups treated with 3-MCPD and the short-term groups treated with 3-MCPD (the 1^st day, 7^th day and 21^st day).

3.3. Metabolic pathway analysis

The Rattus norvegicus pathway library was used to map the selected potential biomarkers using the web-based Metaboanalyst 3.0 program pathway analysis, which supports integrating enrichment and pathway topology analysis. The over-representation analysis is a hypergeometric test and the relative-betweenness centrality is used in the pathway topology analysis. The selected metabolites from the 7th day, 21st day, and 35th day treatment samples and the long-term metabolic profiling compared with short-term were used for the pathway analysis, and the results are shown in Fig. 4. In the first 7 days, the metabolomics profiling shows changes, mainly involving glycine, serine and threonine metabolism and alanine, aspartate and glutamate metabolism (Fig. 4A). After the 21^st day, the metabolism pathway affected is primarily glyoxylate and dicarboxylate metabolism and taurine and hypotaurine metabolism (Fig. 4B). However, after 35 days of 3-MCPD treatment, the most affected metabolism pathways are glycine, serine and threonine metabolism and synthesis and degradation of ketone bodies (Fig. 4C). Most of the involved metabolism pathways are involved in amino acid metabolism. The kidney is responsible for concentrating a variety of metabolites and excreting them in the urine, so urine metabolomics may directly affect biochemical pathways linked to kidney dysfunction.30 Amino acid metabolism was previously found to be significantly reduced in the urine of patients with kidney disease.31 The long-term metabolic profiling compared with short-term was conducted as shown in Fig. 4D, which illustrated that the glycine, serine and threonine metabolism and taurine and hypotaurine metabolism were two key pathways intervened by the 3-MCPD.

Fig. 4. Summary of metabolic pathways analyzed with MetaboAnalyst 3.0 of urine treated with 3-MCPD for (A) 7 days, (B) 21 days, (C) 35 days and (D) the long-term metabolic profiling compared with short-term.

The integrated metabolism pathway of the urine from samples after 35 days of treatment with 3-MCPD is shown in Fig. 5, based on the KEGG pathway database. The three metabolism pathways, including the glycine, serine and threonine metabolism, taurine and hypotaurine metabolism and nicotinate and nicotinamide metabolism, were influenced by the 3-MCPD. In Fig. 5A, the four up-regulated metabolites, creatine, glycine, threonine and betaine, were involved in the glycine, serine and threonine metabolism. Creatine was regarded as a marker of kidney function, especially for chronic kidney disease.32,33 As seen in Fig. 5A, creatine plays an important role in upstream and downstream metabolites; the other three up-regulated metabolites, glycine, threonine and betaine, would be affected by kidney injury caused by 3-MCPD. As a methyl donor, betaine participates in the methionine cycle—primarily in the human liver and kidneys.34 Taurine is a β-amino acid naturally found in the kidneys,35 and it could be speculated that the kidney injury would lead to the increase of taurine. As taurine is a significant metabolite in the taurine and hypotaurine metabolism, the downstream metabolite, glutamate, decreased in the urine of 3-MCPD treated rats, as shown in Fig. 5B. 1-Methylnicotinamide is a remarkable metabolite down-regulated from the first 7 days to the end, which indicated that the toxicity of 3-MCPD must be related to the nicotinate and nicotinamide metabolism. According to the report,36 the nicotinate and nicotinamide metabolism has a close relationship with kidney cancer.

Fig. 5. Integrated pathway analysis of (A) glycine, serine and threonine metabolism, (B) taurine and hypotaurine metabolism and (C) nicotinate and nicotinamide metabolism based on the KEGG pathway database (http://www.kegg.jp/kegg/pathway.html), bubble area is proportional to the impact of each pathway, with color denoting the significance from highest in red to lowest in white.

We speculated that variation in the levels of these and associated metabolites in urine spectra could be due to physiological perturbation/functional damage in the kidney upon exposure to 3-MCPD. The kidney coefficient result revealed that the kidney coefficient increased significantly by the 35^th day for the treated group, as shown in Fig. S2.† Kidney histopathology of the high-dose 3-MCPD treated rat revealed many small vesicas, in an elongated radiated or cystic arrangement, salient features of hydropic degeneration. Residual glomeruli with abnormal shape and incomplete form were observed in the renal cortex, surrounded by disordered granulosa cells and capillaries, as shown in Fig. S3.† Furthermore, serum biochemical parameters GAL (β-galactosidase) and NAG (N-acetyl β-d-amino glycosidase enzymes) levels, related to kidney function,37 were significantly increased in all the treated groups, most obviously in the high-dose treated rats, Fig. S4.† Altogether, these findings indicate that 3-MCPD has significant toxic effects on rat kidney.

4. Conclusion

The toxicity evaluation of 3-MCPD on rats based on the index of physiological states, clinical chemistry, and histopathology revealed that 3-MCPD has a significant toxic effect on the kidney. The integrated separation approach by combining ¹H NMR allowed metabolomic analysis of rat urine and identified potential biomarkers for the early diagnosis of 3-MCPD. The results demonstrated that of 7 potential biomarkers having a close relationship with 3-MCPD toxicity, 4 potential biomarkers were up-regulated and 3 were down-regulated, which demonstrate a deregulation of the glycine metabolism, taurine and nicotinate metabolism, leading to toxicological effects in rat kidney. Future research is warranted to clinically validate these potential biomarkers in large patient cohorts before they can be used in real clinical diagnostics.

Conflict of interest

All the authors declare no competing financial interest.

Compliance with ethical requirements

All the procedures performed in studies involving animals were in accordance with the ethical committee named “Jiangnan University Ethics Committee for the Management of Laboratory Animals and Animal Welfare”. And the institutional guideline of “Jiangnan University Laboratory Animal Management” was followed to implement the experimentation with human subjects in this manuscript. The committee was operated with the law of “Jiangnan University Laboratory Animal Welfare Ethics Committee procedures”.