Accepted Manuscript Title: Changes in urinary metabolic profile after oral administration of curcuma extract in rats Author: Dall’Acqua Stefano Stocchero Matteo Clauser Maria Boschiero Irene Ndoum Emmanuel Schiavon Mariano Mammi Stefano Schievano Elisabetta PII: DOI: Reference:

S0731-7085(14)00368-9 http://dx.doi.org/doi:10.1016/j.jpba.2014.07.035 PBA 9671

To appear in:

Journal of Pharmaceutical and Biomedical Analysis

Received date: Revised date: Accepted date:

26-4-2014 23-7-2014 28-7-2014

Please cite this article as: D.A. Stefano, S. Matteo, C. Maria, B. Irene, N. Emmanuel, S. Mariano, M. Stefano, S. Elisabetta, Changes in urinary metabolic profile after oral administration of curcuma extract in rats, Journal of Pharmaceutical and Biomedical Analysis (2014), http://dx.doi.org/10.1016/j.jpba.2014.07.035 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

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Changes in urinary metabolic profile after oral administration of curcuma extract in rats

1Department

of Pharmaceutical and Pharmacological Sciences University of Padova, Via Marzolo 5 , 35131 Padova, Italy.

Soluzioni Informatiche, via Ferrari 14, 36100 Vicenza, Italy.

of Chemical Sciences University of Padova, Via Marzolo 1 , 35131 Padova, Italy.

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3Department

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Dall’Acqua Stefano1, Stocchero Matteo2, Clauser Maria1, Boschiero Irene1, Ndoum Emmanuel3, Schiavon Mariano1, Mammi Stefano3, Schievano Elisabetta3

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HPLC-MS

24-Hour Urine collection 1H-NMR

HPLC-MS Curcumin determination

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Multivariate analysis:

Oral treatment with curcuma extract

-PCA

-ASCA

Identification of metabolite changes correlated to ageing and treatment

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The effects of daily supplementation of Curcuma longa extract were studied in rats 24-hour urine samples were analyzed by 1H-NMR and HPLC-MS Multivariate methods, PCA and ASCA were applied on the two datasets. There is an effect on urinary composition of animals treated with curcuma extract Urinary Allantoin levels were reduced in treated group

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Highlights

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Changes in urinary metabolic profile after oral administration of curcuma extract in rats

10 Dall’Acqua Stefano1*, Stocchero Matteo2, Clauser Maria1, Boschiero Irene1, Ndoum Emmanuel3,

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Schiavon Mariano1, Mammi Stefano3, Schievano Elisabetta3

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5, 35131 Padova, Italy.

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S-IN Soluzioni Informatiche, Via Ferrari 14, 36100 Vicenza, Italy.

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Department of Chemical Sciences, University of Padova, Via Marzolo 1, 35131 Padova, Italy.

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Department of Pharmaceutical and Pharmacological Sciences, University of Padova, Via Marzolo

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* Corresponding author. Dall'Acqua Stefano, Department of Pharmaceutical and Pharmacological

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Sciences, Via Marzolo 5, 35121 Padova. E-mail [email protected] Tel +39 049-8275344

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Fax +39 049-8275366

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Abstract

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The diffusion of phytochemicals in health promoting products is growing, but studies related to

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their effects on healthy subjects are still lacking despite the large consumption of natural products as

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nutraceuticals or food supplements. In many cases, research supports the in vitro antioxidant

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activity of phytochemicals, but the health claims attributed to the final marketed nutraceutical

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products have dubious scientific foundation. Also, studies focussed on the definition of their

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biological targets and mechanisms of action can be useful to assess their efficacy and safety.

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In this study, the effect of oral administration of 80 mg/kg of Curcuma longa Linn. extract to 12

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healthy rats over 25 days was evaluated by monitoring the changes of urinary composition. 24-hour

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urine was collected during the animal experiment and the composition was analyzed by 1H-NMR

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and HPLC-MS. The two data sets were studied individually through a metabolomic approach and

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the multivariate analysis revealed significant differences between the control and the treated group.

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Curcumin levels were also measured in 24-hour urine samples by HPLC-MS. Both the 1H-NMR

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and the HPLC-MS data-set showed that the administration of 80 mg/kg of Curcuma longa extract to

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healthy animals induces changes in urinary composition. Decreased allantoin urinary levels can be

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considered a partial demonstration of the in vivo effect of curcumin on oxidative stress in a healthy

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animal model.

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1. Introduction

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Many epidemiological studies have shown the relationship between dietary habits and a reduced

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disease risk indicating the strong impact of food and nutrition on health. The consumption of plant-

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derived foods is correlated to health improvement and to the prevention of several degenerative

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diseases, especially age-related ones. There is an emerging use of so called “nutraceuticals” due to

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their supposed health promotion role. Such products are not therapeutics and are mainly used by

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healthy people at low doses and for relatively long periods of time [1-3]. To our knowledge, studies

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on long term supplementation to evaluate the biological effects after regular intake of these Page 4 of 37

supplements, are generally missing. The ability of such plant-derived foods to reduce the risk to

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develop chronic diseases has been associated to the occurrence of phytochemicals such as

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polyphenols [4], investigated for many years especially for their antioxidant properties [1, 4, 5].

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Although health benefits of many plant-derived polyphenols are ascribed to their antioxidant

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properties in vitro, evidence for in vivo antioxidant effects is limited: no validated in vivo

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biomarkers have been identified, no long-term studies are available, and knowledge of

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bioavailability is missing. The apparent discrepancy between in vitro and in vivo studies may be due

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to the poor plasma concentration and extensive metabolism of these molecules [6, 7].

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The pathogenesis of many diseases is rather multi-factorial and not due to a single cause; thus,

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multi-targeted antioxidant foods could be important in the area of preventing diseases. If the

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efficacy of nutraceuticals and plant-derived antioxidants is based on the combined action of

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different constituents acting with multi-targeted mechanisms, a holistic approach to study the

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efficacy of phytochemicals is necessary. "-Omics" technologies may be very useful to explain

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biological effects of different antioxidant supplements [8].

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Because phytochemicals introduced with the diet or with supplementation act on the whole

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metabolome, the evaluation of the entire metabolic changes induced by the phytoconstituents and

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the description of the complex connections between the various metabolites can provide new

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information about the mechanism of action of phytoconstituents and their health related effects. A

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metabolomic approach to the evaluation of phytochemicals has been adopted in several recent

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publications: An et coll. studied the effects of quercetin supplementation on rats [9]; Llorach et al.

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studied the change in urine profile after almond intake [10], and again Llorach and collaborators

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evaluated the influence of cocoa consumption on the urinary metabolome both in healthy [10, 11]

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and recenty in cardiopathic human subjects [12].

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One of the most studied and used extract is derived from Curcuma longa Linn., which is known for

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its content in the yellow-orange pigment curcumin, a hydrophobic polyphenol. This spice has been

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used for centuries both as food and as traditional remedy for many ailments. Curcumin and its

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derivatives possess several pharmacological effects [13-15], including anti-inflammatory [16],

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antimicrobial [17], immunomodulatory [18-20] and hypoglycemic [21-24] activities. Additionally,

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the cancer chemopreventive and anticarcinogenic [16, 25-27], as well as the anti Alzheimer [16, 28]

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effects of curcumin have been studied. One of the most critical aspects of the pharmacological

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activity of curcumin is its bioavaiability [29], but most recommended nutraceuticals in the market

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are formulated as dry extracts and/or powdered turmeric rhizome.

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The aim of this study is to evaluate the effects of a daily supplementation of Curcuma longa

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extracts in healthy rats through a metabolomic evaluation of the 24-hour urine composition.

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2.1 Materials

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Curcumin standard was obtained from Sigma Aldrich. Curcuma longa Linn. dried extract was

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purchased from a local seller and the amounts of curcumin, demethoxycurcumin and

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bisdemethoxycurcumin were determined by HPLC-MS and HPLC-DAD measurements as

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described in the Supplementary Information. The extract contains 71.0% curcumin, 20.5%

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demethoxycurcumin, and 2.5% bisdemethoxycurcumin, for a total curcuminoids content of 94%.

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2.2 Animals and urine collection

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All animal procedures were approved and conformed to the directives of the Ethical Committee for

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animal experiments of the University of Padova (CEASA). Six male and six female Sprague-

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Dawley rats, 10 ± 2 weeks of age and weighing 150-220 g, were used for the study. At the starting

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point of the experiments, male animals weighted 221.7 ± 5.2 g and female animals, 152.0 ± 4.8 g.

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Animals were fed standard laboratory diets and water ad libitum and were maintained in a

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temperature- and photoperiod-controlled (12 hr/day) room. Rats were divided randomly into a

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control group (three males and three females) and a curcumin-treated group (three males and three

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females). No differences were observed between the two different groups at the starting point of the

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experiment, based on HPLC-MS and NMR preliminary data (not shown). 300 mg of Curcuma

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longa extract were suspended in 12 mL of water. The treated group received a daily dose of 80

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mg/kg of Curcuma longa extract (corresponding to 56 mg/kg of curcumin) orally by gavage for 25

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days. An equal dose of water was given to the control group.

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Rats were housed individually in metabolic cages for the collection of the 24-h urine outputs. After

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collection, urine was stored at –80 °C until analysis. The urine of all rats was collected on day 1, 5,

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9, 14, 19, and 25 during the animal experiment. Urine samples were subjected to 1H-NMR and

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HPLC-MS based metabolomics analysis.

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2.3 HPLC-MS analysis of curcumin and its metabolites in the urine

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To measure the amount of curcumin and its metabolites in the collected urine samples, we modified

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an HPLC-MS method described by Marczylo et al. [30].

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A Varian 212 HPLC system equipped with a Varian MS 500 IT detector was used for HPLC ion-

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trap mass spectrometry (HPLC-MSn). The detector was equipped with an electrospray (ESI)

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interface and was operating in positive ion mode. The operating parameters used were as follows:

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nebulizing gas, nitrogen; nebulizing pressure, 25.0 psi; needle voltage, 5000 V; capillary voltage,

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60.0 V; drying gas temperature, 380 °C; drying gas pressure, 15.0 psi. These parameters were used

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to detected curcumin derivatives and also for urine metabolites. An Agilent Eclipse XDB C-8

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column (2.1 x 150 mm 3.5 µm) was used as stationary phase. The mobile phase was composed of

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solvent A (acetonitrile with 0.5% acetic acid) and solvent B (water with 2% formic acid). Linear

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gradients of A and B were used, as follows: 0 min, 0% A; 7.3 min, 100% A; 11.20 min, 100% A,

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11.50 min, 0% A. The flow rate was 220 μL/min and the injection volume was 10 μL. The selective

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ion mode for the molecular ions at m/z 369 and 545 was used for curcumin and curcumin

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glucuronide, respectively. Quantitative measurements were obtained using curcumin (Sigma

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Aldrich) and curcumin glucuronide (synthesized in our laboratory as described elsewhere [31]) as

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reference compounds. Standard solutions of curcumin and curcumin glucuronide were prepared in

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the range 20-500 ng/mL and used for calibration.

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To obtain a metabolic profiling of urine, an HPLC-MS full scan method was used. An Agilent

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Eclipse XDB C-8 column (2.1 x 150 mm 3.5 µm) was used as stationary phase.

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The mobile phase was composed of solvent A (acetonitrile with 0.5% acetic acid) and solvent B

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(water with 2% formic acid). Linear gradients of A and B were used, as follows: 0 min, 10% A; 20

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min, 85% A; 21 min, 100% A, 21.30 min, 10% A; 27 min, 10% A. The flow rate was 200 μL/min

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and the injection volume was 10 μL. A chromatogram is reported in figure 1.

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The mass range explored was 50-1000 m/z. MS were recorded both in standard mode and in turbo

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depending data scanning (tdds) mode that allows the observation of fragmentation patterns of ions.

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Collected urine samples were centrifuged (4000 rpm for 10 minutes) and directly injected in the

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HPLC. Peaks corresponding to the most abundant species recorded for the control group were

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selected as descriptive variables and integrated. A data set composed of 25 variables was obtained.

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Median Fold Change normalization was applied before the data analysis, to take into account urine

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dilution effects [32].

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2.5 1H-NMR analysis of urine

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temperature. 700 μL of urine at pH 2.50 ± 0.05 (adjusted with 3 M HCl) were centrifuged at 13000

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rpm for 10 min; 70 μL of D2O was added to the supernatant and the solution was transferred into

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5-mm NMR tubes. One-dimensional spectra were acquired using the NOESYGPPR1D pulse

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sequence. Parameters used were: 64 scans; 32k data points; spectral width, 8389.26 Hz; relaxation

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delay, 2 s; mixing time, 50 ms; acquisition time, 1.95 s. Prior to Fourier transformation, the FIDs

H-NMR spectra were obtained on a Bruker Avance DMX 600 MHz spectrometer at room

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were zero-filled to 64k points and an exponential line broadening factor of 0.3 Hz was applied. All

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spectra were manually corrected for phase and baseline distortion and were referenced to the CH3

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resonance of creatinine at 3.13 ppm.

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Spectra were aligned using the CluPA algorithm (VU T.N., Laukens K., Valkenborg D. (2012).

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speaq: An R-package for NMR spectrum alignment and quantitation. R package version 1.1.) and

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intelligent bucketing was applied to the entire spectrum with the exclusion of the 4.7 - 5.0 ppm

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range, corresponding to the water signal. The data set obtained was normalized by total sum

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normalization.

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2.6 Identification of 8-hydroxy-3,4-dihydroquinolin-2(1H)-one (8HDHQ) in urine samples

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The pH of 15 mL of rat urine was adjusted at 2.50 ± 0.05 with 3 M HCl. The sample was then

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centrifuged at 7200 rpm for 10 min to remove any precipitate. Fifteen mL of chloroform were added

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to the supernatant. The mixture was mechanically stirred for 15 min and centrifuged for 15 min at

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7200 rpm. The chloroform layer was subsequently separated and evaporated under a gentle stream

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of nitrogen. The solid residue was dissolved in 1 mL of chloroform and eluted from a silica gel

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column with stepwise increments of methanol in chloroform (0%, 0.25%, 0.5%, 1%, 1.25%, 1.5%,

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2%, 3%). The fraction corresponding to each step was collected in 3 vials (approx. 8 mL) and

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evaporated under a gentle stream of nitrogen. The resulting solid residue was dissolved in 600 μL of

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deuterated chloroform and transferred into an NMR tube for the analysis. The desired marker

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compound eluted at 1.25% MeOH. The corresponding fraction was analyzed by 1D-1H-NMR, 2D-

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NMR (TOCSY, HMQC, HMBC) and positive and negative mass spectrometry.

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2.7 Multivariate data analysis

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Multivariate methods based on projection were applied in our study. At first, Principal Component

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Analysis (PCA) was used for exploratory data analysis [33]. A coarse identification of the

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experimental variables that play a key role in explaining the effects of time and treatment on Page 9 of 37

metabolite variation was achieved. To better investigate the observed effects of time and treatment

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on the metabolome, ANOVA-Simultaneous Component Analysis (ASCA) [34, 35] and a new

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approach based on a suitable post-transformation of PLS2 were applied. ASCA is a version of

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analysis of variance for univariate data generalized to the multivariate case. The variation in the

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data induced by the factors under investigation is decomposed into independent blocks each

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corresponding to one single factor. Analyzing each block with simultaneous component analysis

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makes it possible to reveal the effects of the factors on the metabolic profile. Post-transformation of

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PLS2 follows the idea of Ergon [36] for post-processing by similarity transformation of PLS. The

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peculiarity of this technique is the capability to decompose the structured variation of the X-block

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into two main blocks corresponding to the variations correlated and orthogonal to the Y-block by a

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suitable rotation of the weight matrix of the PLS2 model. The first block will be called parallel part

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of the model while the second, orthogonal part. Post-transformation of PLS2 is a three steps

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approach. In the first step, a PLS2 regression model is built on the data; in the second step, the

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weight matrix of the model is rotated in order to obtain a new weight matrix able to focus the

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structured variation discovered by PLS2 in the two blocks of interest while in the third step a

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regression model is rebuilt by using the same framework of the PLS2 algorithm, but the new weight

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matrix specifies the weights for projecting the data. The relationships between X-block and Y-block

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can be discovered by exploring only the parallel part of the model. As a result, post-transformation

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of PLS2 model can be more easily interpreted than the unrotated PLS2 model because the number

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of components useful to interpret the model are usually reduced. The algorithm used to rotate the

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weight matrix of the PLS2 model is described in the Supplementary Information. Post-

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transformation of PLS2 can be used as an alternative approach to analysis of variance. Unlike

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ASCA, where the variation in the data is decomposed into independent contributions by considering

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a constrained minimization problem between the model chosen for the data and the collected data

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[34], in this approach the decomposition of the variance is based on regression. Indeed, a post-

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transformation of PLS2 model was built by considering as X-block the metabolic profile and as Y-

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block the design matrix representing our longitudinal study. By analysing the parallel part of the

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model it was possible to discover the relationships between experimental factors and the metabolic

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variations of the collected urines. Specifically, the rotation of the PLS2 model produced only 2

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components for the block explaining the behaviour of the metabolome with respect to time and

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treatment while PLS2 would have required 4 and 6 components for HPLC-MS and 1H-NMR data

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sets, respectively.

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Models were validated by cross-validation techniques and permutation tests according to

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standardized good practice to minimize false discoveries and to obtain robust statistical models. A

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small number of metabolites changing during the experiment was extracted and the behaviour of

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each single metabolite was studied by linear mixed-effects model for longitudinal studies [37]. In

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this step of our data analysis, the covariance structure was modelled by considering a first-order

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autoregressive structure with homogenous variances.

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PCA was performed using SIMCA 13 (Umetrics, Umea, Sweden) while the platform R 3.0.2 (R

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Foundation for Statistical Computing) was used for statistical analysis on single variable and to

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build ASCA models and post-transformation of PLS2.

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3. Results

222 3.1 Determination of curcumin and curcumin metabolites in 24-hour urine collections

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HPLC-MSn analysis was used to search for curcumin and its metabolites in the collected urines.

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Besides curcumin (m/z 369), the following metabolites were searched for using the selective ion

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method: demethoxycurcumin (m/z 339), bisdemethoxycurcumin (m/z 309), curcumin glucuronide

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(m/z 545), curcumin sulphate (m/z 449), dihydrocurcumin (m/z 371), tetrahydrocurcumin (m/z 373)

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and hexahydrocurcumin (m/z 375); none of these metabolites were identified in the collected urine

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samples. Chromatographic peaks related to curcumin were not present in any urine sample at day 1

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(24 h after the first dose) and curcumin started to appear in urine from day 5.

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Two peaks were detected working in selective ion mode with m/z 369 and both display an MS2

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spectrum similar to that of curcumin (MS2 m/z 285, 245 and 175) (see Supplementary Information).

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Payton et al. demonstrated that curcumin exists as keto-enol tautomer in solution also in mild acidic

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and alkaline conditions [38]. Shen et collaborators also confirmed the prevalence of the enol form in

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solution indicating that the enolic proton of curcumin is the most easily dissociable proton [39].

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When curcumin is in urine at pH 7.8, there must be an equilibrium between the keto and the enolic

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form; thus, at the urine pH we observe the two forms (see Supplementary Information). We

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demonstrated this by adding a standard solution of curcumin to urine (pH 7.6) and following the

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solution by HPLC-MS both in light and in dark conditions. Results are summarized in

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Supplementary Information. Both in the dark and in the light, both isomers were detected by HPLC-

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MS after a storage of curcumin containing urines for more than 19 hours. For this reason, we

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quantified curcumin in urine considering the sum of the two observed peaks.

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3.2 Analysis of the 24-hour urine composition by HPLC-MS

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The method we developed allowed us to observe a relatively large number of ions in a relatively

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short analysis time (25 minutes with column re-equilibrium). An example chromatogram is shown

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in Figure 1.

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Positive ion mode was selected as working task; the chromatograms of all the sampled urines were

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recorded and the 25 most significant peaks detected (and corresponding MS spectra) for the control

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group were integrated and selected as attributes to perform statistical analysis. Obviously, no peak

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related to curcumin or its metabolites was used in this evaluation because these peaks are not

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present in the control group. Peak integrals were corrected using Median Fold Change

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normalization to take into account urine dilution effects; this choice was preferred over

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normalization using the creatinine peak because in ageing animals, changes in creatinine were

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observed [40]. The data set was log-transformed, mean centered and scaled by Pareto scaling. The

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data collected were then investigated using PCA (2 principal components, R2 = 0.51, Q2 = 0.26).

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The score scatter plot (Figure 2a) suggests the contribution of two main effects: the effect of time

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and of animals growing for both groups along the first component and the effect of treatment along

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the second component. From the beginning to the end of the experiment, there is a “double”

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evolution in urine composition, one related to the time passing and one related to the dietary

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intervention. The urinary modifications observed in the animals were mainly related to the “ageing”

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of the rats, but the treated group has a different behavior caused by the administration of Curcuma

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longa extract. We observed a more evident separation of the treated vs control group in the middle

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part of the experiment. To better characterize the two effects observed, we applied ASCA by

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considering an interaction model. The ASCA model (the score scatter plot is reported Fig. 2c)

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showed p-value < 0.01 for both the time and the treatment effects while the interaction term time ×

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treatment was insignificant (p-value = 0.53). In this way, we were able to prove the significance of

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the effects of time and treatment suggested by PCA. To confirm the robustness of our data analysis,

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we chose to apply an alternative approach able to take into account the design of the experiment as

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ASCA but based on PLS2. In this approach, that can be considered a post-transformation of the

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PLS2 model, the design matrix was explicitly calculated by considering the effects of treatment,

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time and the interaction term time × treatment and PLS2 was used to build a regression model

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pointing to the design matrix. The PLS2 model obtained was rotated on a subspace having the same

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dimension of the design matrix in order to focus the structured variation discovered by the model

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and related to the effects under investigation. The interaction term was insignificant while the

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effects of treatment and time were explained by using only 2 predictive components (the unrotated

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PLS2 model had 4 components, R2 = 0.58 and Q2 = 0.46). The score scatter plot of the model is

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reported in the supplementary information, Fig. S4. In the Supplementary Information, we report a

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short description of the algorithm used to rotate the PLS2 model.

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The analysis of the loadings of the PCA model (Figure 2b) allowed us to select seven variables, 4

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mainly related to ageing and 3 mainly related to the treatment. The value of the loadings of the

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ASCA model and that obtained by our post-transformation PLS2 approach confirmed the role

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played by these selected variables. Specifically, the ions corresponding to m/z 180, 126 and 162

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decreased while m/z 164 increased during the course of the experiment. The ions corresponding to

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m/z 114 and to m/z 159 are higher in the control group than in the treated group while m/z 281 is

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lower in the control than in the treated group. In Table 2, the effects of time and treatment estimated

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by linear mixed effect modelling are reported.

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A total of 6 metabolites were tentatively identified in the urine on the basis of their m/z value, which

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was compared to those registered in the Human Metabolome Database, Mass Bank Database and

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identification was confirmed using appropriate standards and mass fragmentation pattern: 2,4-

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dihydroxyquinoline ([M + H]+ m/z 162.05; MS2 m/z 144; MS3 m/z 116/89); taurine ([M + H]+ m/z

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126; MS2 m/z 108/85); 8-hydroxy-3,4-dihydroquinolin-2(1H)-one ([M + H]+ m/z 164; MS2 m/z

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146/122/98; MS3 m/z 94/83; MS4 m/z 77/46), hippuric acid ([M + H]+ m/z 180; MS2 m/z

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162/137/110; MS3 m/z 123/94), allantoin (([M + H]+ m/z 159, MS2 m/z 116/99/73). Compounds

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contributing to the discrimination of the different groups of rats were m/z 281 ([M + H]+ m/z 281;

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MS2 m/z 162/241; MS3 m/z 119; MS4 m/z 92/67), currently under investigation for identification,

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and creatinine ([M + H]+ m/z 114; MS2 m/z 86/44).

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3.3 Analysis of the 24-hour urine composition by 1H-NMR

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The data collected were mean centered and scaled by Pareto scaling. Pattern recognition was

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performed by PCA (2 principal components, R2 = 0.60, Q2 = 0.52). As observed in the previous

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investigation, two main effects seem to act on the metabolic content of the urine samples. Indeed,

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the analysis of the score scatter plot reported in Figure 3a shows that the first principal component

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of the model can be related to the time passing while the effects of treatment are more evident along

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the second component. The ASCA model (the score scatter plot is reported in Figure 3c) confirmed

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the presence and proved the significance of these two main effects (p-value < 0.01 both for the time

305

and the treatment effect) while the interaction term time × treatment was insignificant (p-value =

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0.52). Our post-transformation PLS2 approach confirmed the results of the ASCA model.

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Specifically, the unrotated PLS2 model had 6 components, R2 = 0.78 and Q2 = 0.60 while the

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investigation of the effects of ageing and treatment was performed by considering only 2 predictive

309

components built by rotating the PLS2 model (the score scatter plot is reported in Supplementary

310

Information Figure S5). Analysis of the loadings of the PCA model (Figure 3b), those of the ASCA

311

model, and the loadings of the post-transformed PLS2 model allowed us to reveal higher levels of

312

allantoin (bin “5.35”), creatinine (bin “3.12”) and taurine (bins “3.40, 3.24”) in control rats while in

313

treated rats significant changes were observed mainly in the aliphatic region indicating increased

314

amount of free organic acids. Specifically, the bins at 2.96 and 2.80 ppm were assigned to the

315

geminal doublets of citric acid, the spin system resonating at 1.28, 1.56 and 2.36 ppm (confirmed by

316

TOCSY experiment) was assigned to dicarboxylic acids such as suberic, sebacic or pimelic acid. In

317

the aromatic region, some significant resonances were associated with the treated group, but the

318

simple 1H-NMR measurements were not sufficient to indicate a possible structure. For this reason,

319

we decided to isolate the aromatic metabolite correlated with the clusterization of the model, to

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320

confirm the structure of the compound 8-hydroxy-3,4-dihydroquinolin-2(1H)-one (8HDHQ)

321

detected with MS measurements.

322 3.4 Identification of 8HDHQ

324

To confirm the presence of the 8HDHQ metabolite, urine samples were subjected to chloroform

325

partition and the extract obtained was resolved by semipreparative HPLC, isolating the metabolite.

326

The positive and negative ESI-MS of the compound were found to be 164 [M + H]+ and 162 [M -

327

H]-, respectively. The molecular mass of the compound corresponds to the chemical formula

328

C9H9NO2. The structure of the compound was confirmed using 1D-1H-NMR (Figure 4) and 2D-

329

NMR (TOCSY, HMQC, HMBC) (Table 3). The five signals in the 1H-NMR spectrum were

330

assigned as shown in Figure 4. The position of the amide and the hydroxyl groups was derived from

331

the HMBC spectrum. For the amide group, the correlation between C2 and both H3 and H4 was

332

diagnostic. The position of the hydroxyl group was determined because of the correlation between

333

H4 and C5, and the correlation between H5 and both C6 and C7.

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4. Discussion

336

The metabolic effects of polyphenols and other antioxidants are difficult to assess and, in clinical

337

trials or in vivo experiments, they are generally determined by measuring plasmatic concentrations

338

of lipids, glucose or searching for variations in the levels of some oxidative stress markers.

339

However, polyphenols are also able to modulate several cell signaling pathways and the complexity

340

of these effects can be explored by metabolomics [41]. During the 25 days of our experiment, no

341

significant differences between treated and control group were observed in 24-hours urine volume

342

and in body weight gain (+30.4% treated group; +33.0% control group at the end of the experiment

343

compared to the starting day, respectively). As a first result, the measurement of curcumin and its

344

metabolites in the 24-hour urine showed an accumulation of curcumin as the experiment proceeded.

345

In previously published papers about curcumin administration in vivo, this metabolite was rarely

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Page 16 of 37

determined in the 24-hour urine. Only in one human study [42], curcumin was measured as the

347

main metabolite present in the 24-hour urine after consumption of 3.6 g curcumin/day. On the other

348

hand, many papers dealing with the bioavailability and serum metabolites of curcumin report that

349

the most abundant metabolites were glucuronides while curcumin levels were very low, around 2

350

ng/mL [30, 43-45]. Further results obtained in this work regard the modifications in urinary

351

metabolome of curcumin-treated rats compared with the control group. HPLC-MS and 1H-NMR

352

data revealed that creatinine, dicarboxylic acids, taurine, allantoin and 8HDHQ levels were

353

significantly changed because of the treatment. We observed moderate creatinine increase and

354

hippuric acid decrease in the treated group compared to controls. Other authors indicated that

355

modifications in hippurate excretion during metabonomic studies are in general associated with

356

changes in feeding regimen [46, 47]. Hippuric acid concentration in urines can be increased with

357

the ingestion of fruits and vegetables due to the influence of plants-derived phenolic constituents

358

present in blueberries, cranberries or apples [48]. Specifically, the degradation of such polyphenols

359

by gut microflora can produce also hippuric acid [49]. In our experiment with curcuma extract, we

360

observed a moderate decrease of this metabolite in both groups (Table 2) suggesting that the doses

361

of curcuminoids used do not increase urinary levels of hippuric acid. The increased urinary

362

excretion of dicarboxylic acids is related both to ageing and to treatment (Table 3). Dicarboxylic

363

acids such as suberic acid are mainly metabolized in the liver by the ϖ –oxidation pathway [50-52];

364

thus, their increase in urine can indicate an influence of ageing and of the treatment on liver

365

function. Specifically, the increase of the urinary levels of such compounds may indicate an

366

increased fatty acid ϖ-oxidation. Other authors observed decreased levels of suberic, citric and

367

sebacic acids in rats treated with DDVP (an organophosphorus insecticide) and considered the

368

reduction of suberic and citric acid together with modified levels of other metabolites, such as

369

hippuric, cholic, and orotic acid, as an indication of the DDVP negative influence on liver

370

functionality [51, 52]. Previously published studies reported that variations in urinary excretion of

371

dicarboxylic acids and urinary dicarboxylic aciduria is related to defective fatty acid metabolism

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[47]. Citric acid is an intermediate in the tricarboxylic acid (TCA) cycle and increased urinary

373

levels of this compound in the treated group indicates also an influence of curcuma extract on the

374

TCA cycle. Thus, changes in dicarboxylic and citric acids may indicate an influence of curcumin

375

treatment in the glucose and fatty acid metabolism. Urinary taurine levels were decreased during the

376

experiments due to time and treatment. Taurine has been previously reported to decline in a number

377

of tissues with advancing age and also in rats, the urinary levels were significantly reduced with

378

ageing [53]. Recently, lowered brain taurine concentrations in curcumin-treated rats were observed

379

[54]. On the other hand, increased urinary taurine levels have been indicated as a specific marker of

380

liver toxicity [54]. We cannot find any rational explanation for the presence and changes of 8HDHQ

381

in the treated group versus the control group.

382

Allantoin urinary levels were higher in controls compared to curcumin-treated rats. Allantoin is the

383

predominant product of the non-enzymatic oxidation of uric acid by different types of radicals, so

384

that urinary allantoin levels are considered as a biomarker of oxidative stress in humans [56]. Thus,

385

the observation that allantoin levels are reduced in the treated group can be in part indicative of a

386

reduced oxidative stress in the group receiving curcuma extract.

387

Studies with a larger number of animals and different doses are in progress. Time-related

388

modification were also observed indicating that this type of approach can also be useful for ageing

389

studies.

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5. Conclusions

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Multivariate data analysis of the HPLC-MS and 1H-NMR data of treated vs control groups indicate

393

that the urinary composition of healthy animals is modified by supplementation with 80 mg/kg of

394

dried curcuma extract. Decreased allantoin urinary levels can be considered a partial demonstration

395

of an in vivo effect of curcumin on oxidative stress in the healthy animal model. Modifications in

396

the urinary metabolome due to animal growing and ageing were also observed and thus the

Page 18 of 37

modified metabolic profile may be considered as a new starting point for future studies of ageing or

398

age-associated diseases.

399

In addition, this work wants to point out some of the several caveats present in the study of

400

nutraceuticals. First, the in vitro antioxidant effect of a certain compound may not reflect its in vivo

401

activity, because of poor bioavailability and in vivo transformation into other metabolites. It is time

402

to rethink the design of in vitro and in vivo studies to resolve these gaps, at least in part. Also,

403

phenols are complex molecules and are likely to have multiple potential biological activities;

404

therefore, it is not sufficient to study the change of only one biomarker. We looked at the change in

405

the whole metabolome and not only at one antioxidant biomarker.

406

The application of a 1H-NMR and HPLC-MS-based metabolomic approach can be a powerful

407

instrument for future research on multi-targeted phytochemical-derived products.

408

Acknowledgments

409

The authors gratefully acknowledge funding from the University of Padova (PRAT CPDA118080).

412

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Figure 1. Representative HPLC-MS chromatogram of the analyzed urines.

M

565 566 567 568

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C day 1 C day 5 C day 9 C day 14 C day 19 C day 25

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t[2]

treatment

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-2

time -8

-6

-4

-2

t[1]

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2

d

Figure 2a. PCA model for the HPLC-MS data: score scatter plot. The plot clearly shows a modification of the urine

te

composition along time. A difference between treated (blue symbols) and control (green symbols) rats can be observed in the middle of the experimental period (day 9 and day 14). We used different lines (dashed and solid lines for treated and control rats respectively) to indicate the trend of the metabolic variation.

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T day 1 T day 5 T day 9 T day 14 T day 19 T day 25

Page 28 of 37

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166 0.3

253b

661 0.2

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372 144

123

170 0 180

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-0.1

252 281

-0.3

-0.2

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-0.3

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-0.4 -0.4

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p[2]

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-0.1

0

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279 269 285

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0.1

0.2

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580 581

Figure 2b. PCA model for the HPLC-MS data: loading scatter plot. The variables taken into account to explain the variation of urine composition with time and treatment are reported as filled triangles.

Ac ce p

579

d

p[1]

Page 29 of 37

3

2

C day 1 C day 5 C day 9 C day 14 C day 19 C day 25

ip t

cr

0

-1

-2

an

-3

-5 -5

-4

-3

-2

-1

0

t[1] time

1

2

3

d

584

M

-4

Figure 2c. ASCA model for HPLC-MS data: score scatter plot showing the first component of the model for the block

te

related to the time effect and that related to the block of the treatment effect.

Ac ce p

585 586

T day 1 T day 5 T day 9 T day 14 T day 19 T day 25

us

t[1] treatment

1

Page 30 of 37

C day 1 C day 5 C day 9 C day 14 C day 19 C day 25

10

time

ip t

5

us

treatment

-10

cr

t[2]

0

-5

-15

-10

-5

0

t[1]

587

10

15

Figure 3a. PCA model for the 1H-NMR data: score scatter plot. The plot shows a modification of the urine composition

d

along time. A difference between treated (blue symbols) and control (green symbols) rats can be observed from day 9 to

te

the end of the experimental period. We used different lines (dashed and solid lines for treated and control rats respectively) to indicate the trend of the metabolic variation.

Ac ce p

588 589 590 591 592

5

M

-20

an

-15

-20 -25

T day 1 T day 5 T day 9 T day 14 T day 19 T day 25

Page 31 of 37

2.96 2.80

2.68

0.3

2.92 2.64

3.00

-0.1

2.12 1.48 2.88 3.08 1.52 8.72 4.40 3.80 4.68 2.76 1.28 6.91 3.56 6.95 3.76 2.40 5.23 8.08 8.84 8.48 2.84 1.56 4.16 2.32 6.67 4.20 6.31 4.00 9.12 8.68 8.64 7.90 7.57 7.864.52 5.39 1.44 4.64 4.60 8.20 7.43 7.03 3.52 1.24 1.36 1.84 7.94 8.44 4.99 4.56 8.12 8.40 6.75 6.47 9.20 4.32 7.77 9.32 8.56 6.59 2.36 6.71 6.43 5.71 5.31 0.64 2.72 7.61 8.96 8.16 1.320.84 6.63 6.11 5.11 0.80 3.04 7.23 6.19 5.67 5.59 3.48 3.16 4.363.84 4.24 9.08 5.75 8.36 5.63 9.04 9.36 9.40 1.12 9.28 5.99 6.07 5.91 9.48 1.00 6.51 6.15 9.00 6.55 6.39 9.44 7.73 5.07 5.15 7.47 5.51 0.60 4.95 8.764.48 8.80 5.79 9.16 8.60 5.27 5.55 0.68 3.36 6.79 5.95 3.44 7.11 6.23 4.04 1.04 8.32 3.32 6.03 1.80 4.08 3.88 5.87 3.72 6.27 5.03 2.16 8.88 8.92 7.27 6.83 5.83 3.20 1.60 1.64 8.24 5.47 0.72 0.96 1.08 2.60 2.48 0.76 2.20 2.52 1.68 9.24 6.35 5.19 1.20 1.76 2.56 1.92 1.88 4.44 7.15 5.43 4.12 1.72 2.28 7.35 8.52 2.24 2.04 0.88 6.99 7.07 8.28 7.19 1.16 3.60 6.87 0.92

1.40

us

p[2] 0

3.28

cr

2.44

0.1

ip t

0.2

-0.2

3.24 3.40 2.08 4.28 3.12

an

3.92 3.96 7.39 1.96 7.31 2.00

5.35 -0.3 -0.3

-0.1

0

0.1

0.2

M

p[1]

d

Figure 3b. PCA model for the 1H-NMR data: loading scatter plot. The variables taken into account to explain the

te

variation of urine composition with time and treatment are reported as filled triangles.

Ac ce p

592 593 594 595 596 597

-0.2

Page 32 of 37

C day 1 C day 5 C day 9 C day 14 C day 19 C day 25

4

ip t cr

0

-2

us

t[1] treatment

2

-6

-6

-4

-2

0

t[1] time

4

6

d

Figure 3c. ASCA model for 1H-NMR data: score scatter plot showing the first component of the model for the block

te

related to the time effect and that related to the block of the treatment effect.

Ac ce p

598 599 600

2

M

-8

an

-4

-8 -10

T day 1 T day 5 T day 9 T day 14 T day 19 T day 25

Page 33 of 37

ip t cr

Figure 4. Molecular structure and 1H-NMR spectrum of 8HDHQ.

us

601 602 603

an

604 605 606

M

607

Ac ce p

te

d

608

Page 34 of 37

608 1