Evaluation of Hepatotoxicity of the Extract of Green Walnut Husks ( Juglans regia L .) in Mice by a Metabonomic Approach

Evaluation of Hepatotoxicity of the Extract of Green (Juglans regia L.) in Mice by a Metabonomic Approach. Abstract This work was designed to delineate the comprehensive metabolic changes of hepatotoxicity in mice induced by the extract of green walnut husks ( Juglans regia L .) (GWHs). A metabonomic strategy based on high performance liquid chromatography (HPLC) is performed to characterize the metabolic profiling of mice feeding with GWHs extracts at a single dose of 40 mg/kg for ten consecutive days. Investigations on stability and precision of the established metabolic profiles indicated that the method was well controlled and reliable. Acquired chromatographic data sets were subjected to principal component analysis (PCA) and orthogonal partial least square-discriminant analysis (PLS-DA) for differentiating the feeding and the control mice. Six metabolites which have significant contributions to the classification were selected by the variable importance for the projection value. The results indicated that energy metabolism was decelerated, while the creatinine level in serum was significantly decreased. Histopathological slices of liver feeding with GWHs extract showed obviously vacuolization and dilation in the cell sap. We believe that metabonomic approach based on chromatography is helpful to further reveal the toxicity of some medicine.


Introduction
The green walnut husks (Juglans regia L.) are not only the byproduct of walnut production but also a kind of herbal medicine in Chinese for relieving inflammation, clearing away heat and inhibiting cancer [1,2]. Phytochemistry researchers have proved that the naphthoquinones, tetralone, flavonoids, and diarylheptanoids compounds from the Juglans and other natural products have exhibited a broad range of potent biological activities [3][4][5][6]. Besides that, the English walnut listed in the FDA Poisonous Plant Database (U.S. Food and Drug Administration) in the year 2000 was reported to be able to cause hyperpigmentation and dermatitis [7,8].
In toxicology study, much time and labor force will be consumed due to a limited number of biochemical indicators are available in general toxicologic evaluation. When the target protein and/or organ responses for toxic syndromes are not clear, some specific analysis cannot be performed. In addition, it has not clarified that the changing discipline of toxic materials, and the regular pattern of endogenous metabolites from the body biochemical process induced by natural products on the toxicity were still not clear [9]. Therefore, this study was designed to investigate the biochemical metabolic profile [11]. Metabolic profiling contains a mass of information concerning the interactions of organisms and natural products and offer promise for identifying early biomarkers that are specific indicators of damage to an organism [12]. In addition, high performance liquid chromatography (HPLC) is generally available to many labs and is of a relative high throughput and selectivity, which also makes it attractive for blood plasma profiling in the primary research [13,14].
The complex data sets obtained in metabonomics are difficult to summarize and interpret without appropriate statistical and visualization tools. The chemometric tools, such as principal component analysis (PCA) [15], partial least squares to latent structures (PLS) [16], and orthogonal PLS (OPLS) [17] are therefore of great importance as they provide efficient and robust methods for modeling, analysis, and interpretation of complex chemical and biological data. OPLS discriminant analysis (OPLS-DA) is an efficient approach for modeling of two or more classes, which can handle both the contribution and the correlation to the OPLS model. The present investigation was conducted with the objective of investigating the biochemical changes in mice plasma of GWHs induced hepatotoxicity by HPLC technique coupled with multivariate statistical methods.

Chemicals and Herbal materials
Uric acid and cytidine were purchased from Sigma-Aldrich Co.

Preparation and Characterization of the GWHs Extracts
Dried green walnut husks were extracted for three times with 95% hot EtOH for 6 hours with reflux. Then, the EtOH solution and purified from the green walnut husks in our laboratory. The purities of four compounds were determined to be more than 98% by normalization of the peak areas detected by HPLC-DAD methods. The four reference compounds were identified by various spectroscopic methods including intensive 2D NMR and HR-ESI-MS analysis [18,19].

Animal experiments
Male Kunming mice (20.0±2.0 g) purchased from Guangdong Medical Laboratory Animal Center. The animals were housed in cages with wood chip bedding in an animal room with a 12 h lightdark cycle at room temperature (24±2°C) and allowed free access to standard laboratory diet. Forty healthy male mice were divided into four groups. The control group (CG) had been intragastric administrated (i.g.) by 0.9% saline solution for successive 10 days (n = 10). The other three groups had been i.g. fraction A, B and C (group 1, 2 and 3) at dosage of 40 mg/kg for successive 10 days (n = 10), respectively. During the research, the abnormal behaviors, skin and hairs, and the assumption of water and food of mice were observed and recorded.

Preparation of the Plasma samples
The mice administrated with fraction A, B and C were sacrificed on the 11th day. Anticoagulative (Heparin Sodium) blood samples were collected via eyeballs veniplex. After centrifuging, the supernatant was separated and stored at -20 o C until pretreatment.
Acetonitrile (900 μL) were added to 300 μL plasma sample. Then, the mixture was vortex-mixed for 30s and centrifuged (1,369 × g, 5 min). The supernatants (1 mL) were transferred to Eppendorf tubes and vacuum dehydrated at 37 o C for 6 hours. The samples from each group were reconstituted in 130 μL water and were filtered through 0.22 μm millipore filter before injection. All the samples were kept at 4°C during the experiment.

Metabolic profiling analysis of plasma samples
The HPLC-DAD instrument and analytical column for metabolic profiling were the same as in the analysis of the extracts. The The wavelength of DAD detector was 260 nm. In a metabolic fingerprint analysis, the signal response of a metabolite depends on both the concentration and its detection sensitivity. Generally, it is not the absolute concentration change but the relative concentration change of a metabolite that is crucial for SVs discovery. Unit variance (UV) scaling [20] is commonly applied and uses the standard deviation as the scaling factor. All variables have a standard deviation of one and therefore the data is analyzed on the basis of correlations instead of covariance. The UV scaling is expressed as:

Data preprocessing and statistical analysis
where mn x  represents the data after UV scaling, n is the sample number.
PCA was used for dimensionality reduction and cross validation is used to decide how many principle components (PCs) will reproduce the data with "sufficient accuracy". In addition, groupings, trends and outliers of the data can be readily found by projecting the raw data onto the first 2 or 3 PCs. OPLS-DA was introduced as an improvement of the PLS-DA method to discriminate two or more groups using multivariate data [21]. The advantage of OPLS-DA is that single component is used as a predictor for the class, while the other components describe the variation orthogonal to the first predictive component [22]. Cross validation [23] was used to calculate the number of significant PCs. The first M significant components can be used to modeling the elements of X by the formula as: To ensure the robustness to perturbations of the model, crossvalidation predicted variance (Q2) [24] is the commonly used criterion, which is defined as: where VIP is the sum over all model dimensions of the variable influence, SSY is the sum of squares of the Y matrix and the summation is made over a = 1 to a = A.

Histopathology
After fixed in 10% formalin for 12 h and embedded in paraffin, the liver samples were processed to small sections. Tissue sections were subsequently stained with hematoxylin-eosin (H-E) for light microscope examination.

Characterization of the GWHs extracts
The GWHs extracts exhibited both anti-tumor activity and toxicity. Further isolated by eluting from MAR AB-8 was performed to try to find some specific compounds related to toxicity. The four fractions were analyzed at the same level. It can be observed from

The gradient elution buffers were A (water and 2% acetic acid)
and B (acetonitrile), and the flow rate was at 1 mL/min. Gradient

Influence of the Extracts on Mice Metabolism
To investigate whether the extracts have some influence on the metabolism of mice, some observations were performed. The results showed that two mice in the group 1 died in the 5 th h and 72 th h after the i.g. administration, respectively. After dissecting, it was observed that two mice died not from an operation mistake. As shown in Figure 3, the consumption of food and water in group 2 are higher than that of control. However, all the fractions have little influence on the increase of body weight compared with control.
In addition, these mice had no significant abnormal behavior and physical signs in the cardiovascular, gastrointestinal, urogenital systems during the research.  Figure 4 (a) shows the resulting PCA score plot (t [1] vs. t [2]) of the four groups and the contribution rate R 2 X(cum) of the first two PCs in PCA is 0.422. The goal in metabonomics is to distinguish classes of samples and identify the differences, but the four groups showed an overlapped clustering and could not be discriminated completely by the first two components. To obtain improved model transparency and interpretability, the supervised multivariate projection method OPLS-DA was applied to highlight differences between the control and treated groups. Figure 4 (b) is the score plot which clearly shows the overall distribution of the four groups. The classification of the four groups resulted in one predictive and one orthogonal (1+1) components with the crossvalidated predictive ability Q 2 (Y) was 0.163 based on 7-fold cross validation. A value of 0.355 of the variance R 2 (X) is used to account for 0.291 of the variance R 2 (Y) and the variance related to class separation R 2 p(X) was 0.19.
To identify SVs, the VIP values and loading plot were used. The preferred selection of metabolites has a high covariance combined with a high correlation and a small confidence interval. Figure 4 (c) shows the loadings where each point represents one peak area/ retention time pair. The loading plot gives an indication of the metabolites that most strongly influence the patterns in the score plot. Six variables with relatively high covariance and correlation regions were selected as SVs. In Figure 4 (d), the VIP values of the six endogenous metabolites are greater than 1, which could reflect the metabolic changes, significantly. and adenosine in plasma were below the limit of quantification, only creatinine and uric acid were quantified under the optimum condition. The established regression equations, correlation coefficients, linear ranges and detection limits for the two analytes are listed in Table 1. In addition, the level of creatinine in plasma induced by the fraction A is lower than that of control (P < 0.05).
According to the assessed metabolic pathway, creatinine is a waste product formed by the slow spontaneous degradation of creatine phosphate. While creatine is charged with energy by the enzyme creatine kinase which transfer the high-energy (~) phosphate bond of ATP to make creatine ~ phosphate in vivo. Creatine and creatine ~ phosphate exist in a reversible equilibrium and creatine ~ phosphate functions as a "battery" that stores the energy of excess ATP [25]. In this study, the GWHs extracts disturb the energy metabolism in mice and the level of creatinine in plasma decreases.
In addition, the level of uric acid in plasma is relative higher than that of control in group 1. Systemic administration of uric acid is known to increase serum antioxidant capacity and it can reduce oxidative stress [26,27]. Therefore, the increased level of uric acid in biofluid might be useful to eliminate reactive oxygen species in this research.
The levels of endogenous metabolites have altered as a consequence of GWHs treatment. The fraction A, containing compounds JA, JB, RE, SD and other compounds have a greater influence on the mice metabolism than the other extracts. The pharmacokinetics study proved that JB eliminated rapidly from rats after i.v. administration [28]. RE and SD are dihydroxy-tetralone compounds and the compounds of diastereoisomeric bicyclic ketals showed noticeable antifungal and antibacterial activities [29]. The value of IC50 for the cytotoxicity of RE is 1.16 μmol/L [30]. In this study, the mice plasma presented abnormal level of the endogenous metabolites and morphology in liver induced by fraction A.
The results indicate that the biochemical changes induced by GWHs extracts have some relationships with the tetralone and diarylheptanoids compounds.

Conclusion
In this study, we reported an investigation of hepatotoxicity of mice induced by GWHs extracts based on the method of plasma metabolic profiling and chemometric analysis. The biochemical changes are associated with liver damage and the results suggest the involvement of some specific pathways. The formation of creatinine was decelerated, while UA was accelerated in organism. This study indicated that metabolic profiling combined with chemometrics is a promising tool for identifying and characterizing biochemical responses to toxicity.

Conflict of Interest
The authors have declared that there is no conflict of interest.