Gd-DTPA-induced dynamic metabonomic changes in rat biofluids

    Gd-DTPA-induced dynamic metabonomic changes in rat biofluids Chuanling Wan, Youyang Zhan, Rong Xue, Yijie Wu, Xiaojing Li, Fengkui Pei PII: DOI: Reference:

S0730-725X(17)30009-7 doi:10.1016/j.mri.2017.01.009 MRI 8716

To appear in:

Magnetic Resonance Imaging

Received date: Revised date: Accepted date:

5 July 2016 9 January 2017 9 January 2017

Please cite this article as: Wan Chuanling, Zhan Youyang, Xue Rong, Wu Yijie, Li Xiaojing, Pei Fengkui, Gd-DTPA-induced dynamic metabonomic changes in rat biofluids, Magnetic Resonance Imaging (2017), doi:10.1016/j.mri.2017.01.009

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ACCEPTED MANUSCRIPT Gd-DTPA-Induced Dynamic Metabonomic Changes in Rat Biofluids Chuanling Wan a,b, Youyang Zhan a,b, Rong Xue a, Yijie Wu a, Xiaojing Li a *, Fengkui Pei a Changchun Institute of Applied Chemistry Chinese Academy of Sciences,No.5625, Renmin

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a

University of Chinese Academy of Sciences, No.19,Yuquan Road 19, Beijing 100049, China

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b

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Street, Changchun 130022, China

Abstract

Objectives: The purposes of this study were (1) to detect the dynamic metabonomic

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changes induced by gadopentetate dimeglumine (Gd-DTPA) and (2) to investigate the potential metabolic disturbances associated with the pathogenesis of nephrogenic

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systemic fibrosis (NSF) at the early stage.

Methods: A nuclear magnetic resonance (NMR)-based metabolomics approach was used to investigate the urinary and serum metabolic changes induced by a single tail

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vein injection of Gd-DTPA (dosed at 2 and 5 mmol/kg body weight) in rats. Urine and

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serum samples were collected on days 1, 2 and 7 after dosing. Results: Metabolic responses of rats to Gd-DTPA administration were systematic

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involving changes in lipid metabolism, glucose metabolism, TCA cycle, amino acid metabolism and gut microbiota functions. Urinary and serum metabonomic recovery could be observed in both the 2 and 5 mmol/kg body weight group, but the metabolic

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effects of high-dosed (5 mmol/kg body weight) Gd-DTPA lasted longer. It is worth noting that hyperlipidemia was observed after Gd-DTPA injection, and nicotinate might play a role in the subsequent self-recovery of lipid metabolism. The disturbance of tyrosine, glutamate and gut microbiota metabolism might associate with the progression of NSF. Conclusion: These findings offered essential information about the metabolic changes induced by Gd-DTPA, and could be potentially important for investigating the pathogenesis of NSF at the early stage. Moreover, the recovery of rats administrated with Gd-DTPA may have implications in the treatment of early stage NSF. Keywords: Gd-DTPA; Nephrogenic systemic fibrosis; Metabonomic; NMR; Multivariate statistical analysis

ACCEPTED MANUSCRIPT 1. Introduction Magnetic resonance imaging (MRI) contrast agents are widely used to add

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significant morphological and functional information to unenhanced magnetic

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resonance images [1]. With its large magnetic moment, the lanthanide ion Gd3+ is the most frequently chosen paramagnetic ion for contrast agents by now. Because of its high acute toxicity, the gadolinium (III) ion must be chelated with proper ligands

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without loss of magnetic properties [2]. Gadolinium chelates are normally excreted

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unvaried by passive glomerular filtration. Due to their high complex stability and rapid clearance, these gadolinium-based contrast agents (GBCAs) were regarded as

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secure and well-tolerated pharmaceuticals [3]. However, in 2000, Cowper et al.

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published the observations of a disease that was later known as nephrogenic systemic

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fibrosis(NSF) [4]. NSF is a rare but potentially lethal systemic disorder characterized by progressive multiple-organ fibrosis in patients with renal insufficiency. Increased

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tissue deposition of collagen is observed with thickening and hardening of the skin. Tissues such as heart, lung, kidney, diaphragm and other muscles can also be involved [1]. In 2006, Grobner first reported that GBCAs might be associated with NSF [5]. In that same year, Thomsen refined the association between NSF and gadolinium[6]. Subsequent investigations suggested that lowered renal clearance of GBCAs increased tissue exposure to gadolinium and their dissociation into toxic Gd3+ ions. This might result in an inflammatory reaction and NSF [7]. The mechanism of a reaction of the GBCAs resulting in tissue fibrosis is an area of active research. Various studies have suggested the role of transmetallation, oxidative

ACCEPTED MANUSCRIPT stress, chemokines, cytokines, apoptosis, angiotensin II AT1 receptors, mobilization of iron and competition of Gd3+ with Ca2+ for cellular processes[8]. There are also a lot

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of trials about the treatment for NSF [9, 10]. However, the pathogenesis of NSF

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remains unclear, and there is no effective treatment available for NSF when it is diagnosed. Therefore, the detection of systems changes at the early stage of pathogenesis is crucial in the prevention of NSF, which demands holistic approach.

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Metabonomics is a systematic approach in studying metabolite composition in

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integrated biological systems, which directly reflects the dynamic responses to both endogenous and exogenous perturbation [11, 12]. This approach has been widely

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applied in many areas, including drug discovery and development, disease processes,

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food chemistry, microbiology, plant biology and environmental monitoring [13]. In

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particular, metabonomics was used to investigate the metabolic responses of ultrasmall superparamagnetic particles of iron oxides (USPIO) – a kind of

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nanoparticles which had been developed as intravenous contrast agents to improve MRI in vivo [14, 15]. USPIO administration caused the disturbance of hepatic, renal and cardiac functions, which was reflected by changes in a number of metabolic pathways [14]. Previous study reported the metabonomic changes caused by intraperitoneal injection of GdCl3 [16]. Metabonomic changes showed that hepatorenal syndrome might occur because of acute severe liver damage caused by Gd3+ [16]. Although Gd3+ ions might release from GBCAs, the metabolic responses of gadolinium ion and gadolinium chelates are different. To the best of our knowledge, however, there is no literature reported focusing on the dynamic metabolic responses

ACCEPTED MANUSCRIPT of GBCAs with a metabonomics approach. In this study, we systematically investigated the Gd-DTPA-induced dynamic

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changes of the rat urinary and serum metabonome using the NMR-based

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metabonomics approach in conjunction with multivariate statistical analysis techniques. Gd-DPTA was chosen due to its wide application in clinical [1]. The main objectives of this work were (1) to understand the Gd-DTPA-induced dynamic

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metabonomic changes and (2) to investigate the potential metabolic disturbances

2. Materials and methods

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2.1. Chemicals

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associated with the pathogenesis of NSF at the early stage.

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Magnevist (Gd-DTPA, 0.5mol/L) was purchased from Bayer Schering Pharma AG

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(Berlin, Germany). Analytical grade Na2HPO4·12H2O and NaH2PO4·2H2O were purchased from Beijing Chemical Works. D2O (99.9%) was obtained from Cambridge

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Isotope Laboratories. Sodium 3-trimethylsilyl-1-(2, 2, 3, 3-d4) propionate (TSP, purity > 99.9%) and sodium azide (NaN3) were obtained from Sigma-Aldrich Inc. (St. Louis, MO, USA). Analytical grade GdCl3·6H2O was purchased from Alladdin (Shanghai, China). 2.2. Animal handling and sample collection All animal experimental procedures were conducted in accord with the National Guidelines for Experimental Animal Welfare (MOST of P.R. China, 2006). Eighty male Sprague Dawley rats (180-220g) obtained from Laboratory Animal Research Center, School of Basic Medicine Science, Jilin University, were allowed to

ACCEPTED MANUSCRIPT acclimatize for 7 days before drug administration. All animals were housed at a temperature of 20-24 ℃ and a humidity of 40-60% with a 12 h light-dark cycle.

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Standard rat chow and water were provided ad libitum throughout the study. The rats

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were kept in metabolism cages during urine collection periods.

Fifty-six animals were randomly divided into three groups: the control group (n=8), the low-dose group (LG, n=24) and the high-dose group (HG, n=24). Forty eight rats

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were received a single tail vein injection of Gd-DTPA (2mmol/kg body weight for the

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LG group, 5mmol/kg body weight for the HG group). Eight rats in the control group were injected with equivalent volume of 0.9% saline. Dose levels in this study were

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set according to the acute oral half lethal doses (LD50) for rats of Gd-DTPA [17] and

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the literature [18]. Urine samples were collected between 8 am and 12 am on days 0

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(before administration), 1, 2, and 7 post-injection into ice-cold vessels containing 1% sodium azide. Supernatant liquor was obtained by centrifugation (4000 rpm at 4 ℃

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for 10 min). Eight rats from each Gd-DTPA treated group were sacrificed on days 1, 2 and 7 after dosing. On the day 7 post-dose, eight rats in the control group were sacrificed. Blood samples were collected during the process. Twenty-four animals were randomly divided into two groups: the control group (n=8) and the GdCl3 group (n=16). Sixteen rats were received a single tail vein injection of GdCl3 (0.1mmol/kg body weight)[19]. Eight rats in the control group were injected with equivalent volume of 0.9% saline. Urine samples were collected between 8 am and 12 am on days 1 and 2 post-injection into ice-cold vessels containing 1% sodium azide. Eight rats from GdCl3 treated group were sacrificed on

ACCEPTED MANUSCRIPT days 1 and 2 after dosing. On the day 2 post-dose, eight rats in the control group were sacrificed. Blood samples were collected during the process.

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Serum samples were obtained from each blood sample by centrifugation (3000 rpm

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at 4 ℃ for 10 min). Urine and serum samples were snap-frozen in liquid nitrogen after collection and stored at -80 ℃ until required for analyses. 2.3. Clinical Chemistry Measurement

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Clinical chemistry analysis of serum samples was carried out on an Olympus 2700

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analyzer by standard procedures. These included total cholesterol (TC), aspartate aminotransferase (AST), alanine aminotransferase (ALT), glucose (GLU), total

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protein (TP), albumin (ALB), alkaline phosphatase (ALP), creatinine (CRN), blood

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urea nitrogen (BUN) and triglycerides (TG).

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2.4.Sample preparation and 1H-NMR spectroscopic analysis Urine samples for NMR spectroscopic analysis were prepared by mixing 400 μL of

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urine with 200 μL of phosphate buffer (0.25 M, pH 7.4, 20% D2O, 1 mM TSP). The D2O provided a deuterium lock signal for the NMR spectrometer. Phosphate in the buffer could precipitate gadolinium from Gd-DTPA[20]. The buffered urine samples were left to stand for 10 min and then centrifuged (10000 rpm at 4 ℃ for 10 min) to remove the precipitates. The supernatants of 550 μL were transferred into 5 mm NMR tubes for analysis. NMR spectra of urine samples were acquired at 600.13 MHz using a Bruker-Av600 spectrometer at 298 K. Water signals were suppressed by presaturation. Sixty-four free induction decays (FIDs) were collected into 32K data points using a spectral width of 6009.6 Hz with a relaxation delay of 2 s and an

ACCEPTED MANUSCRIPT acquisition time of 1.0224 s. Serum samples were prepared by mixing 400 μL of serum with 200 μL of

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phosphate buffer (0.25 M, pH 7.4, 50% D2O, 0.9% NaCl). The buffered serum

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samples were left to stand for 10 min before centrifuged at 10000 rpm at 4℃ for 10min. The supernatants of 550 μL were transferred into 5 mm NMR tubes before NMR analysis. NMR spectra of serum samples were recorded on a Bruker-Av600

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spectrometer at 298 K. The water-suppressed Carr-Purcell-Meiboom-Gill (CPMG)

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spinecho pulse sequence (RD-90°-(τ-180°-τ) n-ACQ) with a total spin-echo loop time (2nτ) of 64 ms was used to suppress signals from macromolecules and other

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molecules with constrained molecular motion. One hundred and twenty-eight FIDs

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were collected into 32 K data points over a spectral width of 6009.6 Hz with a

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relaxation delay of 2 s and an acquisition time of 1.0224 s. For metabolite identification purposes, standard

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H-1H total correlation

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spectroscopy (TOCSY) and 1H-13C heteronuclear multiple bond correlation (HMBC) NMR spectra were also recorded on selected urine and serum samples. 2.5.Data Processing and Multivariate Data Analysis The FIDs were multiplied by an exponential weighting function corresponding to a line-broadening of 0.3 Hz prior to Fourier transformation (FT). The spectra were referenced to the chemical shift of TSP (δ 0.00) for urine samples and to the internal lactate CH3 resonance (δ 1.33) for serum samples. Each 1H NMR spectrum of urine (δ 9.50-0.20) and serum (δ 9.00-0.50) was data-reduced using MestRe-C 2.3 (www.mestrec.com) to integrated regions 0.01ppm wide. For urine spectra, the

ACCEPTED MANUSCRIPT regions of water resonance (δ 5.30-4.50) and urea resonance (δ 6.00-5.50) were excluded to eliminate the variability in presaturation of the water resonance and

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cross-relaxation effects on the urea signal. The region corresponding to water

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resonance (δ 5.10-4.70) in the serum spectra was removed. The integrated data were probabilistic quotient normalized to compensate for the overall concentration differences.

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The resulting dataset was imported into the software SIMCA-P 11.0 (Umetrics,

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Sweden) for multivariate data analysis after mean-centering. The unsupervised pattern-recognition technique principal component analysis (PCA) was firstly used to

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discover intrinsic clusters and outliers within the dataset. Principal component (PC)

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score plots were constructed to visualize any inherent separation of different groups.

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Then, the supervised pattern-recognition method orthogonal projection to latent structure with discriminant analysis (OPLS-DA ) was employed with Pareto scaling to

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maximize separation between different groups using the class information as Y-matrix [21]. The contribution of variables to group discrimination can be evaluated by analyzing the corresponding loading plots [22]. The loadings in the corresponding loading plots were calculated using the back-transformation method. The corresponding loading plots were generated with an in-house modified Matlab script (www. mathworks.com) and were color-coded with the absolute value of coefficients (|r|). In this study, a correlation coefficient of |r| > 0.667 (for the degree of freedom=7) was used as the cutoff value for the statistical significance on the basis of the discrimination significance at the level of p < 0.05, which was determined according

ACCEPTED MANUSCRIPT to the test for the significance of the Pearson’s product-moment correlation The quality of each model was described by the parameters, R2 for the interpretability of

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the model and Q2 for the predictability of the model. All OPLS-DA models were

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further assessed with the CV-ANOVA (analysis of variance testing of cross-validated predictive residuals) tests at the level of p < 0.05. 2.6. Statistical Analysis

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One-way analysis of variance (ANOVA) was performed to evaluate the statistical

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significant difference among the groups, and a post hoc multiple analysis was followed using the Dunnett’s test. A value of p < 0.05 was considered significant.

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SPSS 18.0 (SPSS, Inc.) was employed for statistical evaluation.

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3. Results

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3.1. Serum clinical chemistry

The effects of Gd-DTPA on serum clinical biochemistry parameters are presented

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in Table 1. A significant increase in the levels of CRN, TC and TG was observed from the HG group. ALT values were significantly elevated in the LG group. Amounts of AST, ALP and BUN were shown to be lower in the two dose groups. The ALB level significantly decreased in rats exposed to the high dose of Gd-DTPA. 3.2. 1H-NMR spectroscopy of urine and serum Representative 1H-NMR spectra of urine and serum obtained from control and Gd-DTPA treated rats were shown in Fig. 1 and 2, respectively. The spectral signals were assigned according to the existing literature [14, 23, 24] and in-house developed databases with further confirmation from 2D NMR spectroscopy.

ACCEPTED MANUSCRIPT The detected metabolites in rat urine and serum included amino acids, choline metabolites

(choline

and

glycerophosphocholine),

ketone

bodies

(acetone,

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acetoacetate and β-hydroybutyrate), glucose and glycolysis products (lactate, acetate,

cis-aconitate,

α-ketoglutarate,

succinate,

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and pyruvate), tricarboxylic acid cycle (TCA cycle) intermediates (citrate, and

fumarate),

organic

acids

(p-hydroxyphenylacetate, o-hydroxyphenylacetate, and methylmalonate) and some

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waste metabolites (trimethylamine , trimethylamine-N-oxide , dimethylamine,

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creatine taurine, hippurate, N-methylnicotinamide, allantoin, urea, formate, and nicotinate). A number of perturbations in endogenous metabolites were observed in

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the 1H-NMR spectra. For example, the Gd-DTPA treated rats had higher taurine level

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in urine and lower lactate level in serum. In order to obtain more details about the

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Gd-DTPA induced metabolic changes, pattern recognition analysis was performed on the 1H-NMR spectra.

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3.3. Overview of metabolic effects of Gd-DTPA Fig. 3 and 4 showed the PCA score and trajectory plots of all samples. According to the trajectory plots of urine (Fig. 3A,B), Gd-DTPA treated groups moved away from pre-dose position at day 1 post-dose. Then the LG group started to move back to the pre-dose position at day 2. However, the HG group continued to move away at day 2, and moved back to the pre-dose position at day 7. The trajectories of serum (Fig. 4A,B) from LG group and HG group were similar in that they moved away from the control position at day 1, and started to recover at day 2 post-dose. Some differences could be seen from the PCA score plots of urine (Fig. 3C,D,E)

ACCEPTED MANUSCRIPT and serum (Fig. 4C,D,E) in a dose-dependent manner. The HG group showed more obvious metabolic changes than the LG group at day 1 and 2 post-injection. However,

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the profiles of dosed groups were similar to the control group at day 7 post-injection.

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3.4. Metabolic responses following Gd-DTPA administration

OPLS-DA was performed to obtain the detailed metabolites changes responsible for the separation between different groups. The score plots and corresponding loading

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plots of urine and serum were shown in Fig. 5 and 6. OPLS-DA models showed good

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qualities from the R2 and Q2 values (Fig. 5 and 6). Models that failed the CV-ANOVA tests (p > 0.05) were not shown in the figures. Significant class-discriminating

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metabolites (variable importance in projection (VIP) > 1, p < 0.05) and their

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coefficients for different groups were shown in Table S1 and S2 of the supplementary

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material.

The administration of low-dose Gd-DTPA caused significant elevation of urinary

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citrate, DMA, cis-aconitate, taurine and IAG at day 1 post-dose. More metabolic alterations were observed in the HG group, which were the elevation of DMA, cis-aconitate, TMAO, o-HPA, IAG and allantoin accompanied with level decreases for citrate and choline. After day 2 post-dose, the increase of pyruvate, α-ketoglutarate, citrate, TMA, PAG, IAG, hippurate, fumarate and nicotinate and the decrease of acetate, creatine, choline and glycine were observed for the HG group rats, but no obvious metabolic change was observed in the LG group at the same time point. Serum results (Fig. 6) indicated that low-dose Gd-DTPA exposure led to the higher concentration

of

isoleucine,

β-hydroxybutyrate,

glutamate,

lipid,

citrate,

ACCEPTED MANUSCRIPT triacylglycerol and UFA and the lower concentration of valine, threonine, alanine, glutamine, pyruvate, α-glucose, myo-inositol and tyrosine at day 1 post-dose. Rats in

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the HG group resulted in significant elevation of LDL, VLDL, leucine,

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β-hydroxybutyrate, threonine, glutamate, lipid, acetone, triacylglycerol and UFA but decline of lactate, alanine, citrate, creatine and tyrosine at day 1 post-dose. Similar metabolic changes were observed at day 2, which were the promotion of LDL, VLDL,

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isoleusine, β-hydroxybutyrate, glutamate, lipid, NAG, triacylglycerol and UFA and

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accompanied with the depression of HDL, valine, alanine, pyruvate, GPC, inositol and tyrosine in the HG group. There was no significant metabolic change in the LG

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group at day 2 and 7 post-dose. Moreover, dose-dependent metabolic changes were

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observed between the LG group and the HG group, which were the higher levels of

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VLDL, lactate, threonine, glutamate, lipid and NAG at day 2 post-dose in the HG group. After day 7 post-dose, the contents of isoleusine, valine, lysine, acetate, citrate,

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β-glucose, α-glucose and tyrosine were significantly increased, whereas the levels of VLDL and NAG were significantly decreased in the HG group. To visualize the dynamic alterations of metabolites induced by Gd-DTPA and GdCl3 administration, the signals of metabolites having the least overlap with others were selected to calculate their changes relative to control group for each time point. The alterations (Fig. 7) induced by Gd-DTPA were consistent with the results of multivariate statistical analysis. Several compounds (Fig. 8) in GdCl3-infected samples showed similar variation patterns with the results of Gd-DTPA, including urinary pyruvate, allantoin, choline, α-ketoglutarate and glycine accompanied with

ACCEPTED MANUSCRIPT serum β-hydroybutyrate, glutamate, lysine, tyrosine, β-glucose and BCAAs.

4. Discussions

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In this work, an NMR-based metabonomics approach was applied to identify the

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distinguishing metabolites under Gd-DTPA administration. Urinary and serum metabonomic recovery could be observed in both the LG and HG group, but the metabolic effects of high-dosed Gd-DTPA lasted longer.

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The relative abundance of serum lipids, TC, TG, LDL and VLDL were increased

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but that of HDL decreased in the HG group. These changes were consistent with the symptoms of hyperlipidemia [25]. To the best of our knowledge, this is the first study

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to report the association of Gd-DTPA and hyperlipidemia. It is interesting to note that

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the administration of GdCl3 caused significant decrease of serum TG, LDL and VLDL

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accompanied with the elevation of HDL. The release of gadolinium ion (Gd3+) is thought to be an important factor of acute toxicity associated with GBCAs in rodents.

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However, it might not be the reason of metabolic changes correlated with hyperlipidemia in this study. Acute hyperlipidemia could induce a proinfammatory and proatherogenic response in blood [26]. Acute phase proteins were secreted by hepatocytes in answer to tissue impairment and act as inflammatory mediators. Therefore, the disturbance of serum N-acetyl glycoprotein in the HG group might be an inflammatory response to Gd-DTPA [27, 28]. Nicotinate has been widely used as lipid-modifying agent to prevent atherosclerotic cardiovascular disease. As is well known, Nicotinate can decrease TG and LDL cholesterol and increase HDL cholesterol [29]. In this work, urinary nicotinate was significantly inceased at day 2

ACCEPTED MANUSCRIPT post-dose, which was consistent with the subsequent self-recovery of lipids, TC, TG, LDL, VLDL, HDL and fatty acids.

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Most ketone bodies are produced in liver [30]. In hepatic ketogenesis, when

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acetyl-CoA derived from fatty acids β-oxidation exceeds the capacity of the TCA cycle, it will be converted into ketone bodies [14]. In this study, the elevation of serum acetone and β-hydroxybutyrate was possibly related to Gd-DTPA-induced

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lipids and fatty acids accumulation. The increased fatty acids β-oxidation was also

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reflected in the level elevation of urinary TCA cycle intermediates including fumarate, α-ketoglutarate and cis-aconitate. Lysine is an important precursor of carnitine

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synthesis. Carnitine is required for the transfer of long-chain fatty acids across the

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inner mitochondrial membrane for subsequent β-oxidation [31]. The accumulation of

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lysine was observed at day 2 post-dose, which might subsequently lead to the reduction of fatty acids β-oxidation. This was further confirmed by the recovery of

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serum ketone bodies at day 2 and 7 post-dose. Glucose is the main source of energy in cells. The concentrations of glucose were reduced in serum after Gd-DTPA administration. This result was consistent with the consequence of GdCl3 study. Moreover, some other intermediates and end products of glycolysis including pyruvate, lactate and acetate also showed a trend of decrease. Previous study reported that the Omniscan® (Gadolinium-based MRI contrast agent) was associated with increased serum visfatin level [32]. Visfatin, a proinflammatory adipocytokine, is highly enriched in visceral adipose tissue. Visfatin exerts insulin-mimetic effects and decreases plasma glucose levels in animal [33]. Therefore,

ACCEPTED MANUSCRIPT the decline of serum glucose, pyruvate, lactate and acetate might be related to the increased visfatin level. myo-Inositol can be synthesized endogenously from glucose

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[34]. So, the decline of serum myo-inositol could be related to the reduction of

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glucose.

The branched-chain amino acids (BCAAs, valine, leucine, and isoleucine) play important roles in stimulating the immune system and in the synthesis of glutamine

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[35]. BCAAs are nitrogen donors for moving nitrogen from muscle amino acid

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oxidation through glutamate into alanine and glutamine to the liver [36]. Gd-DTPA-induced decline of serum BCAAs might disorder the metabolism of

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glutamate and glutamine. A significant decrease of serum BCAAs was also observed

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in the GdCl3 study. Previous study reported that gadolinium exposure caused

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accumulation of intracellular reactive oxygen species (ROS) [37]. Oxidative stress followed by ROS generation might induce impairment to mitochondria. Glutamate

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metabolism was affected by the mitochondrial membrane potential because its principal genes were functionalized as mitochondrial matrix enzymes [38]. The elevation of serum glutamate was possibly related to Gd-DTPA-induced oxidative stress which was then affected the key enzymes (e.g. AST, ALT and glutamine synthetase) of glutamate metabolism. This opinion was further supported by significant elevation of urinary allantoin – an indicator for oxidative stress [39]. Meanwhile, taurine as an antioxidant was also found to be elevated (not statistically significant) in urine [40], which might be an intrinsic self-defense against the oxidative damage induced by Gd-DTPA. N-acetylglutamate, a metabolite produced

ACCEPTED MANUSCRIPT from glutamate, is the rate limiting enzyme of the urea cycle [41]. Therefore, the accumulation of glutamate might cause the urea cycle disorder, which was confirmed

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with the decline of serum BUN. The glutamine-to-glutamate ratio is positively

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associated with HDL and negatively associated with insulin and log TG. Furthermore, glutamine is inversely associated with lots of metabolic traits, including hypertension and hypertriglyceridemia [42]. In this study, the decline of serum glutamine (not

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statistically significant) and the elevation of serum glutamate were consistent with the

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changes of TG, HDL and glucose. In addition, Gd-DTPA-caused elevation of serum ALT indicated that the conversion from α-ketoglutarate to glutamate was promoted.

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However, an obvious reversal was observed in the concentration of serum ALT,

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glutamate and alanine together with urinary α-ketoglutarate and proline (a

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glutamate-derived amino acid). This reversal reflected the metabonomic recovery of rats at day 1 and 2 post-dose.

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The relative abundance of serum tyrosine was significantly increased in the HG group at day 7 post-dose. The level of tyrosine was also significant increased after the administration of GdCl3, which means that the release of gadolinium ion from Gd-DTPA might lead to the elevation of serum tyrosine. Previous study reported that reduced levels of tyrosine were correlated with increased levels of gelsolin [43] [44]. Gelsolin is a calcium-dependent multifunctional actin regulatory protein. It may inhibit the fibrosis of the amyloid beta-protein [45] and decrease actin toxicity and inflammation in multiple sclerosis [46]. Further studies are required to address the question if gelsolin is related to the nephrogenic systemic fibrosis (NSF).

ACCEPTED MANUSCRIPT Choline is catabolized by gut microbiota to TMA [47], which is subsequently either metabolized by liver to TMAO or decomposed to DMA prior to excretion [48].

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Benzoate is also produced by gut microbiota, which is then conjugated with glycine to

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form hippurate [49]. A significant decrease of urinary choline and glycine was also observed in the GdCl3 study. Gd-DTPA-caused decline of serum GPC, urinary choline and glycine together with the elevation of urinary TMA, TMAO, DMA and hippurate

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indicated a disturbance of gut microbiota metabolism. The elevation of TMAO was

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associated with renal tubulointerstitial fibrosis and renal functional impairment [50]. It remains to be determined whether TMAO plays a role in progression of NSF. The

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transmetallation occured with endogenous Zn2+ ions could lead to the decline of

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serum alkaline phosphatase (ALP) – a zinc-dependent metallo-enzyme [51]. Intestinal

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alkaline phosphatase is a member of the alkaline phosphatase (AP) family, and plays an important role in gut mucosal defense [52]. Therefore, the disturbance of gut

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microbiota metabolism might be due to the decline of ALP. Creatinine is an important indicator that relates to kidney function. Therefore, the elevation of serum creatinine also indicated renal dysfunction induced by Gd-DTPA. Previous study reported that gadoteric acid caused higher concentration of creatine kinase [53]. Creatine kinase is an essential enzyme that can catalyze the reversible interconversion of creatine and phosphocreatine [54]. The creatine-phosphocreatine system plays an important role in energy metabolism by reconstruction and buffering of adenosine triphosphate (ATP) [55]. The decline of creatine indicated that Gd-DTPA might disrupt the homeostasis of creatine-phosphocreatine system.

ACCEPTED MANUSCRIPT 5. Conclusions In conclusion, Gd-DTPA administration effects were reflected in a number of

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metabolic pathways including lipid metabolism, glucose metabolism, TCA cycle,

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amino acid metabolism and gut microbiota functions. Multivariate data analysis of H-NMR spectra showed that both the LG and HG groups were recovered with

time-dependence, but the metabonomic recovery in the HG group was not completed

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at day 7 post-dose. It is worth noting that hyperlipidemia was observed after

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Gd-DTPA administration. Moreover, nicotinate might play a role in the subsequent self-recovery of lipid metabolism. The disturbance of tyrosine, glutamate and gut

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microbiota metabolism might concern with the released gadolinium ions. Whether

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these changes may associate with the progression of NSF deserve to be further

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investigated. The metabonomic strategy offers a promising approach to investigate the potential adverse effects of Gd-DTPA and to detect the pathogenesis of NSF at the

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early stage.

Acknowledgments The authors thank the financial support of National Natural Science Foundation of China (21305134) and Natural Science Foundation of Jilin (20160101074JC) for this work.

ACCEPTED MANUSCRIPT References [1]

Villaraza AJL, Bumb A, Brechbiel MW. Macromolecules, Dendrimers, and

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ACCEPTED MANUSCRIPT Figure 1. 600 MHz 1H-NMR spectra (δ 9.50-5.30 and δ 4.50-0.70) of rat urine (A) in the control group and (B) 1 day post-dose in the high-dose group. The region of δ 9.50-5.30 was vertically expanded 3 times. Keys: 1: valerate; 2: α-hydroxy-n-butyrate; 3: α-hydroxy-n-valerate;

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4:β-hydroxy isobutyrate; 5: methylmalonate; 6: lactate; 7: alanine; 8: acetate; 9: acetamide; 10: N-acetylglutamate; 11: glycoprotein; 12: acetone; 13: acetoacetate; 14: proline; 15: pyruvate; 16:

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succinate; 17: α-ketoglutarate; 18: citrate; 19: dimethylamine (DMA); 20: Gd-DTPA; 21: trimethylamine (TMA); 22: dimethylglycine; 23: creatine; 24: malonate; 25: cis-aconitate; 26: choline;

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trimethylamine

N-oxide

(TMAO);

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taurine;

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glycine;

30:

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o-hydroxyphenylacetate (o-HPA); 31: phenylacetyl-glycine (PAG); 32: indoleacetyl-glycine (IAG); 33: p-hydroxyphenylacetate (p-HPA); 34: hippurate; 35: N-methylnicotinamide (N-MN); 36:

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allantoin; 37: urea; 38: fumarate; 39: formate; 40: nicotinate. Figure 2. 600 MHz 1H-NMR spectra (δ 9.00-5.10 and δ 4.70-0.70) of rat serum (A) in the control

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group and (B) 1 day post-dose in the high-dose group. The dotted region (δ 9.00-6.00) was

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vertically expanded 16 times. Keys: 1: high density lipoprotein (HDL); 2: low density lipoprotein (LDL); 3: very low-density lipoprotein (VLDL); 4: isoleucine; 5: leucine; 6: valine; 7:

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β-hydroxybutyrate; 8: lactate; 9: threonine; 10: alanine; 11: arginine; 12: lysine; 13: acetate; 14: glutamate; 15: lipid; 16: N-acetyl glycoprotein (NAG); 17: glutamine; 18: acetone; 19: pyruvate; 20: citrate; 21: Gd-DTPA; 22: trimethylamine (TMA); 23: creatine; 24: choline; 25:

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glycerophosphocholine (GPC); 26: β-glucose; 27: trimethylamine N-oxide (TMAO); 28: α-glucose; 29: glycine; 30: myo-inositol; 31: tyrosine; 32: triacylglycerol; 33: unsaturated fatty acid (UFA); 34: allantoin; 35: urea; 36: histidine; 37: formate. Figure 3. PCA score and trajectory plots of urine 1H-NMR spectra. Trajectory plots of urine from LG group (A) and HG group (B) at day 0, 1, 2 and 7 post-dose. PCA score plots of urine at day 1 (C), 2 (D) and 7 (E) post-dose. Percentage of variation in the NMR data explained by the first two principal components were 75.05%, 70.47%, 49.37%, 69.25% and 74.73%, respectively. Ellipses represent means ± S.D. for the various groups. Figure 4. PCA score and trajectory plots of serum 1H-NMR spectra. Trajectory plots of serum from LG group (A) and HG group (B) at day 0, 1, 2 and 7 post-dose. PCA score plots of serum from treated rats at day 1 (C), 2 (D) and 7 (E) post-dose and serum from control rats. Percentage of variation in the NMR data explained by the first two principal components were 88.81%,

ACCEPTED MANUSCRIPT 96.16%, 92.95%, 94.21% and 92.11%, respectively. Ellipses represent means ± S.D. for the various groups. Figure 5. OPLS-DA score plots (left) and corresponding loading plots (right) derived from 1

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H-NMR spectra of urine at day 1 (A and B) and 2 (C) post-dose. In the corresponding loading

plots, positive peaks indicate a increase in the group shown in the positive direction of

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corresponding score plots. The color map on the right-hand side of the corresponding loading plot shows the significance of metabolite variations between the two classes. Models that failed the CV-ANOVA tests were not shown in the figure. Metabolite keys are shown in Fig. 1.

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Figure 6. OPLS-DA score plots (left) and corresponding loading plots (right) derived from 1

H-NMR spectra of serum at day 1 (A and B), 2 (C and D) and 7 (E) post-dose. In the

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corresponding loading plots, positive peaks indicate an increase in the group shown in the positive direction of corresponding score plots. The color map on the right-hand side of the corresponding

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loading plot shows the significance of metabolite variations between the two classes. Models that

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failed the CV-ANOVA tests were not shown in the figure. Metabolite keys are shown in Fig. 2. Figure 7. Dynamic urinary (A) and serum (B) metabonomic changes for rats administrated with

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Gd-DTPA.

Ratios were calculated in the form of (CG-CC)/CC , where CG and CC stood for the metabolite concentration in Gd-DTPA-treated group and control group, respectively. The concentration of

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each metabolite was determined from the normalized integral data of selected signals (least overlapping ones). Increased and decreased metabolite levels are depicted by red and blue colors, respectively.

* Significantly different from the control at P < 0.05. * * Significantly different from the control at P < 0.01. Figure 8. Dynamic urinary (A) and serum (B) metabonomic changes for rats administrated with GdCl3. Ratios were calculated in the form of (CG-CC)/CC , where CG and CC stood for the metabolite concentration in GdCl3-treated group and control group, respectively. The concentration of each metabolite was determined from the normalized integral data of selected signals (least overlapping ones). Increased and decreased metabolite levels are depicted by red and blue colors, respectively. * Significantly different from the control at P < 0.05.

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ACCEPTED MANUSCRIPT Table 1. Summary of serum clinical biochemistry parameters Parameters

Control

LG (2mmol/kg body weight Gd-DTPA)

HG (5mmol/kg body weight Gd-DTPA)

Day 1

Day 2

Day 7

Day 1

Day 2

Day 7

179.21 ± 21.01

152.61 ± 24.29*

97.81 ± 11.81**

163.43 ± 20.87

116.82 ± 13.54**

118.19 ± 16.29**

173.96 ± 13.00

ALT (U/L)

63.12 ± 7.63

84.26 ± 15.15**

45.19 ± 8.61**

53.99 ± 9.43

69.80 ± 16.89

59.10 ± 6.86

61.38 ± 3.00

ALP (U/L)

385.89 ± 49.98

346.94 ± 84.50

232.63 ± 66.53**

306.20 ± 47.97

294.34 ± 71.35*

215.09 ± 63.89**

366.56 ± 37.16

ALB (g/L)

26.80 ± 2.41

24.03 ± 2.11

26.33 ± 1.05

29.14 ± 4.29

25.85 ± 0.98

27.48 ± 2.11

21.67 ± 2.75**

BUN (mmol/L)

8.00 ± 0.47

5.67 ± 0.67**

4.36 ± 0.81**

7.47 ± 0.53

6.74 ± 1.00*

5.23 ± 0.89**

7.44 ± 1.17

CRN (mmol/L)

77.37 ± 6.78

81.25 ± 19.88

89.93 ± 10.39

76.47 ± 4.73

130.81 ± 12.31**

122.17 ± 18.36**

78.69 ± 6.24

GLU (mmol/L)

11.39 ± 1.36

9.80 ± 2.38

10.57 ± 1.21

11.27 ± 1.17

10.94 ± 1.16

10.04 ± 1.62

12.02 ± 1.42

TC (mmol/L)

0.41 ± 0.15

0.63 ± 0.36

0.61 ± 0.22

0.32 ± 0.09

0.74 ± 0.36*

0.55 ± 0.19

0.47 ± 0.18

TG (mmol/L)

0.87 ± 0.25

1.24 ± 0.72

1.12 ± 0.57

0.38 ± 0.35

1.52 ± 0.71

2.07 ± 0.63**

0.47 ± 0.28

* Significantly different from the control at p < 0.05.

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* * Significantly different from the control at p < 0.01.

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Data were presented as mean ± S.D. of eight rats per groups.

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AST (U/L)