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URINARY METABOLIC PROFILING OF RATS
TREATED WITH CURCUMIN USING HPLC-MS
Matteo Stocchero#, Elisabetta Schievano°, Stefano Dall’Acqua*
INTRODUCTIONThere is a growing interest towards so-called “nutraceuticals”, namely
food or plant-derived mixtures that possess health promoting effects.Curcuma extracts, derived from the rhizome of Curcuma longa, are
# S-IN Soluzioni Informatiche, Via G. Salvemini 9, 36100, Vicenza, Italy°Dipartimento di Scienze Chimiche, Università di Padova, Via Marzolo, 1, 35131, Italy
* Dipartimento di Scienze del Farmaco, Università di Padova, Via Marzolo, 5, 35131, Italy
AIM OF THE STUDYIn the present study, the effects of the supplementation
of Curcuma extract on the metabolic status of healthyrats was investigated. Untargeted metabolomics basedCurcuma extracts, derived from the rhizome of Curcuma longa, are
used as spices and as traditional medicines in several asiatic countries.Many studies dealing with Curcuma were published showing severalpharmacological effects including anti-inflammatory, antimicrobial,
antioxidant and chemopreventive. Despite being one of the most widelystudied plant extracts, its mode of action in vivo remains still unclear.
Experimental design
Six male and six female Sprague-Dawley rats, (10±2 week of age and weighing 150-220 g), were fed with
rats was investigated. Untargeted metabolomics basedon HPLC–MS data in conjunction with statistical dataanalysis was applied to rat urine.
Six male and six female Sprague-Dawley rats, (10±2 week of age and weighing 150-220 g), were fed witha standard laboratory diet and water ad libitum and were kept in a temperature- and photoperiod-controlled(12 hr/day) room. Rats were randomly divided into a control group and a curcumin-treated group (three
males and three females each). 300 mg of Curcuma longa extract (containing 95% of curcuminoids) weresuspended in 12 mL of water and the treated group received 80 mg/kg*day of Curcuma longa extract(corresponding to 56 mg/kg*day of curcumin) orally for 25 days. An equal dose of water was given to thecontrol group. On day 1, 5, 9, 14, 19, and 25, rats were individually housed in metabolic cages for thecollection of the 24-h urine outputs. Urines were stored at -80°C until HPLC-MS analysis.
RAW DATA
DATA EXTRACTIONPrior to perform data analysis, HPLC-MS low resolutionraw data have to be transformed into a data table whereonly real metabolites should be extracted from the raw
data. For this step of data processing we chose to applythe componentization procedure implemented in
COMPONENTIZATION
DATA TABLE
104 (time x mass) variables
ACD/IntelliXtract[1] (Advanced Chemistry Development,Inc.). With a small number of parameters to set,ACD/IntelliXtract is able to extract chemical relevant
peaks by an efficient noise reduction. As a result smallpeaks can be accurately detected and quantified and afterpeak matching data tables containing high qualityinformation can be produced. The peak lists were filteredon the basis of the calculated origin of the peaks and
matched by using a simple heuristic approach.
PEAK MATCHING
matched by using a simple heuristic approach.
DATA MODELLING
To investigate the effects of time and treatment on themetabolome, ANOVA-Simultaneous ComponentAnalysis (ASCA)[2] and a new approach based on asuitable post-transformation of PLS2 were applied.Models were validated by cross-validation techniques
C day 1
C day 5
C day 9
C day 14
T day 1
T day 5
T day 9
T day 14
ASCA: score scatter plot Post-transformation PLS2: score scatter plot
Models were validated by cross-validation techniques
and permutation tests according to standardized goodpractice to obtain reliable and robust statisticalmodels[3]. A small number of metabolites changingduring the experiment was extracted by exploring theloading structure of the models and the behaviour of
each single metabolite was studied by linear mixed-effects model for longitudinal studies[4]. Platform R3.0.2 (R Foundation for Statistical Computing) was
C day 14
C day 19
C day 25
T day 14
T day 19
T day 25
tre
atm
ent
time
tre
atm
ent
time 3.0.2 (R Foundation for Statistical Computing) wasused for data modelling.
CONCLUSION
The preliminary results obtained studying the treatment of healthy rats with curcumin showed significant modification of urinary metabolic profiles as well as significantcurcumin accumulation in the 24 hours collected urines. HPLC-MS based data set allowed the discriminatiion of treated vs control group and data analysis revealedchanges in urinary composition due to time and treatment. Allantoin, taurin and creatinin are decreased in treated group, allantoin and creatinin increased levels can bein part related to oxidative stress suggesting a protective role of Curcuma supplementation. NMR and HPLC-MS approaches (data not shown) lead to similar results but
time
[1] http://www.acdlabs.com/products/spectrus/workbooks/ms/msworkbooksuite
[2] Smilde A.K., Jansen J.J., Hoefsloot H.C.J., Lamers R.-J.A.N., van der Greef J., Timmerman M.E., ANOVA-simultaneous component analysis (ASCA): A new tool for analyzing designed metabolomics data. Bioinformatics, 21(13) (2005) 3043-3048.
[3] Broadhurst D.I., Kell D.B., Statistical strategies for avoiding false discoveries in metabolomics and related experiments, Metabolomics 2 (2006) 171-196.
[4] Laird N.M., Ware J.H., Random-effects models for longitudinal data. Biometrics, 38(4) (1982) 963-974.
in part related to oxidative stress suggesting a protective role of Curcuma supplementation. NMR and HPLC-MS approaches (data not shown) lead to similar results butallow to identify different metabolites that are modified with the treatment.
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