Metabolomics research on time-selected combination of Liuwei Dihuang and Jinkui Shenqi pills in treating kidney deficiency and aging by chemometric methods

Chemometrics and Intelligent Laboratory Systems 130 (2014) 50–57

Contents lists available at ScienceDirect

Chemometrics and Intelligent Laboratory Systems journal homepage: www.elsevier.com/locate/chemolab

Metabolomics research on time-selected combination of Liuwei Dihuang and Jinkui Shenqi pills in treating kidney deficiency and aging by chemometric methods Liangxiao Zhang a,b,⁎,1, Xiaofei Han a,c,1, Zhan Li d,1, Renhui Liu d, Wenjuan Xu a, Chunlan Tang a, Xiujuan Wang d,⁎⁎, Hongbin Xiao a,⁎⁎⁎ a

Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian 116023, China Oil Crops Research Institute of the Chinese Academy of Agricultural Sciences, Wuhan 430062, China c College of Environmental and Chemical Engineering, University of Dalian, Dalian 116622, China d School of Traditional Chinese Medicine, Capital Medical University, Beijing 100069, China b

a r t i c l e

i n f o

Article history: Received 28 June 2013 Received in revised form 17 August 2013 Accepted 1 September 2013 Available online 7 September 2013 Keywords: Metabolomics Kidney deficiency and aging Time-selected combination Metabolic pathway

a b s t r a c t Jinkui Shenqi pill (JKSQ) and Liuwei Dihuang (LWDH) pills are ancient traditional Chinese medicines (TCMs), which are usually used for the treatment of kidney deficiency for thousands of years in China. Time-selected combination of LWDH and JKSQ pills in treating kidney deficiency and aging is one of the features of traditional Chinese medicines (TCMs). However, the molecular mechanisms of timeselected combination remain unclear. In this work, UHPLC–QTOF/MS based metabolomics research was conducted to evaluate the therapeutic effect of LWDH, JKSQ pills and their combinations on kidney deficiency in Sprague–Dawley rats induced by D-galactose and Dexamethasone. Based on peak areas of serum extracts, analysis of variance (ANOVA) and graphical index of separation (GIOS) were employed to select the significant variables for kidney deficiency and aging and principal component projection (PCP) was subsequently applied to evaluate the influence of drugs on endogenous metabolites. 10 endogenous metabolites from 22 important ions were identified via database search. The score plot of PCA shows that nourishing Yang–nourishing Yin group shows the strongest rehabilitation for metabolic disorder induced by kidney deficiency and aging, which is consistent with the classic theories of traditional Chinese medicine. Moreover, metabolic pathway function analysis indicates that kidney deficiency and aging might possess closed relationships with lipid metabolism and energy metabolism. In this work, the change trends of potential biomarkers after administration provide molecular evidence for combined administration of Jinkui Shenqi pill in the morning and Liuwei Dihuang pill at night for the patients with kidney deficiency. The method proposed in this study may provide inspiration for evaluation of the therapeutic effect of Chinese medicines by comparing the rehabilitation of potential biomarkers. © 2013 Elsevier B.V. All rights reserved.

1. Introduction Aging is a natural process that leads to progressive and deleterious changes in organisms. Along with the increase of persons aged 60 and over at an unprecedented rate, anti-aging study becomes a hot research area. In traditional Chinese medicine (TCM), aging is regarded as a process of progressive decline of “vital energy” and subsequently leads to deterioration in functions and diseases [1–3]. Meanwhile, the kidney in TCM is an organ that stores and controls ⁎ Correspondence to: L. Zhang, Oil Crops Research Institute of the Chinese Academy of Agricultural Sciences, Wuhan 430062, China. Tel.: +86 27 86812943; fax: +86 27 86812862. ⁎⁎ Corresponding author. Tel./fax: +86 10 83911624. ⁎⁎⁎ Corresponding author. Tel./fax: +86 411 84379756. E-mail addresses: [email protected], [email protected] (L. Zhang), [email protected] (X. Wang), [email protected] (H. Xiao). 1 These authors contributed equally to this study. 0169-7439/$ – see front matter © 2013 Elsevier B.V. All rights reserved. http://dx.doi.org/10.1016/j.chemolab.2013.09.001

“essence” (Jing in Chinese), which is the essence of Qi and the basis for body matter and functional activities. Thus, aging is closely related with kidney deficiency and tonifying kidney is an important treatment method for anti-aging. Some herbs and prescriptions are employed for anti-aging in TCM. Most of them belong to tonifying kidney for example Liuwei Dihuang pill and Jinkui Shenqi pill. However, treatment based on syndrome differentiation is the basic principle of TCM. Different from Western medicine (WM), the different treatments are usually supplied for patients with different syndromes of the same disease. The kidney deficiency is divided into kidney Yang deficiency and kidney Yin deficiency according to main symptoms such as feeling cool (kidney Yang deficiency) or warm (kidney Yin deficiency); sleepy (kidney Yang deficiency) or active (kidney Yin deficiency) and humid (kidney Yang deficiency) or dry (kidney Yin deficiency), though they simultaneously exist in the same patient with kidney deficiency. Additionally,

L. Zhang et al. / Chemometrics and Intelligent Laboratory Systems 130 (2014) 50–57

TCM emphasizes administration of herbs according to body status in different times of day, different seasons and ever different regions where patients localize [4]. Time-selected combination of two or more than two herbs or prescriptions is one of the characteristics of TCM [5]. For kidney deficiency, Jinkui Shenqi pill and Liuwei Dihuang pill are effectual to kidney Yang deficiency and kidney Yin deficiency, respectively. According to TCM theories, the patients in the morning often appear Yang deficiency, while the ones at night usually show Yin deficiency. Thus, combined administration of Jinkui Shenqi pill in the morning and Liuwei Dihuang pill at night could obtain the best curative effect for the patients with kidney deficiency. Though this time-selected combination was validated in the long time clinical practices, its mechanism at molecular level is still unknown. Obviously, systemic study of time-selected combination is necessary to reflect the influence of herbs on endogenous metabolites and subsequently disclose the mechanism. Metabolomics/metabonomics, an important area of systems biology, is the comprehensive profiling of dynamic metabolic response of living systems to pathophysiological stimuli, genetic modifications or environmental stress [6–8]. It attempts to capture global changes of low molecular-weight metabolites in biochemical networks and establish the model between these metabolites and overall physiological status in order to elucidate sites of perturbations and identify biomarkers of diseases [9,10]. The strategy of metabolomics is well accordant with the integrity and systemic feature of TCM. Metabolomics might play a key role in building the bridge between TCM and WM [10]. A number of analytical tools were employed including nuclear magnetic resonance (NMR) [11,12], gas chromatography–mass spectrometry (GC–MS) [13], liquid chromatography–mass spectrometry (LC–MS) [14,15] and capillary electrophoresis–mass spectrometry (CE–MS) [16]. Ultra high performance liquid chromatography (UHPLC) combined with the quadrupole time-of-flight mass spectrometry (QTOF-MS) possesses several dramatic advantages including high resolution, high selectivity and high sensitivity and could be considered to have a more bright future in the research of metabolomics[17,18]. In metabolomics studies, potential biomarker discovery is beneficial to establish a robust and precise model by eliminating the uninformative variables and interpretation to model and significant metabolites. To date, many different approaches were proposed for variable selection such as uninformative variable elimination (UVE) [19,20], Monte Carlo based UVE (MCUVE) [21], Bayesian variable selection [22], Hierarchical multi-block PLS and PC models [23] and Model population analysis (MPA) [24]. These methods evaluate the variables with the help of different statistical methods and then select the variables with higher or lower statistical value. Recently, graphical index of separation (GIOS) [25] was developed to straightforward visualize the distribution of all possible pairs of observations with one from each group. This method could effectively select the significantly different variables according to the influence to twoclass classifier [26]. In this study, UHPLC–QTOF/MS based metabolomics was designed to investigate time-selected combination of LWDH and JKSQ pills in treating kidney deficiency and aging. A comprehensive metabolic profilings by UHPLC–QTOF/MS combined with pattern recognition methods was employed to evaluate the therapeutic effect of nourishing Yin, nourishing Yang, nourishing Yin in the morning/nourishing Yang at night, and nourishing Yang in the morning/nourishing Yin at night and identify potential biomarkers and generate a better understanding of the pathophysiology. 2. Experimental 2.1. Material and animals 50 Sprague–Dawley rats (weighing 250 ± 10 g, 12 weeks of age) were purchased from Beijing Vital River Laboratories (certification

51

No. SCXK 2007-0001). The animals were housed in an air-conditioned room at a temperature of 23 ± 2 °C, with a relative humidity of 55 ± 10% and a frequency of air ventilation of 15–20 times/h. All animal procedures were approved by the Institutional Animal Care and Use Committee of China. 50 SD rats were randomly divided into 6 groups including 7 rats in healthy control group, 10 rats in model group, 10 rats in nourishing Yin group, 7 rats in nourishing Yang group, 7 rats in nourishing Yin-nourishing Yang group and 9 rats in nourishing Yang-nourishing Yin group. Jinkui Shenqi pill (JKSQ) and Liuwei Dihuang (LWDH) pills were purchased from Beijing Tongrentang and authenticated by Professor Shaoqing Cai in School of Pharmaceutical Sciences of Peking University (Beijing, China). 2.2. Oral administration The healthy control group rats were subcutaneously injected daily with 2 ml/kg normal saline in morning and intraperitoneally injected daily with 1 ml/kg normal saline at night; while the remaining rats were subcutaneously injected with 2 ml/kg 5% D-galactose saline solution in the morning and intraperitoneally with 1 ml/kg Dexamethasone body weight at night for 6 weeks to induce a rat model of kidney deficiency and aging [27]. After model establishment, rats in nourishing Yin group, nourishing Yang group, nourishing Yin-nourishing Yang group and nourishing Yang-nourishing Yin group were intragastrically administered with Jinkui Shenqi pill, Liuwei Dihuang pill and their time-selected combinations per day for 28 consecutive days, respectively. The detailed administration schemes were described as follows: 1) nourishing Yang group: administration of 1.16 g/kg Jinkui Shenqi pill suspension at 8:00 and 20:00; 2) nourishing Yin group: administration of 1.16 g/kg Liuwei Dihuang pill suspension at 8:00 and 20:00; 3) nourishing Yin-nourishing Yang group: administration of 1.16 g/kg Jinkui Shenqi pill suspension at 8:00 and administration of 1.16 g/kg Liuwei Dihuang pill suspension at 20:00; 4) nourishing Yang-nourishing Yin group: administration of 1.16 g/kg Liuwei Dihuang pill suspension at 8:00 and administration of 1.16 g/kg Jinkui Shenqi pill suspension at 20:00; and 5) healthy control and model groups: administration of the same volume of normal distilled water at 8:00 and 20:00. Herein, it is necessary to point out that the inverse time-selected administrations are employed for rates for model animals of rats possess inverse circannian rhythm to human being. Meanwhile, to keep consistency of terms, the nourishing Yang-nourishing Yin and nourishing Yinnourishing Yang used in the whole study refer to human being. At 16 h after the last administration and fasting, blood samples (2 mL) were collected from abdominal aorta under intraperitoneal anesthesia. After standing for 60 min, the samples were processed for plasma by centrifugation at 3500 rpm for 10 min and then were frozen and maintained at −80 °C until analysis. 2.3. Sample preparation and UHPLC–QTOF data acquisition Frozen serum was thawed at room temperature and 200 μL of serum was symmetrically mixed with 800 μL cool methanol and vortexed for 30 s. After standing for 60 min at 4 °C, the samples were processed for plasma by centrifugation at 12,000 rpm for 10 min. 800 μL supernatant organic solution was transferred, speed-vacuum-dried at room temperature and reconstituted in 200 μL of mobile phase (50% ACN/50% methanol) ready for injection. From each sample, 2 μL was injected onto Agilent Zorbax RRHD plus C18 (3 × 150 mm, 1.8 μm) using an UHPLC system (Agilent) with a gradient mobile phase consisting of 5% ACN in water containing 0.1% formic acid (A) and 2% water in ACN containing 0.1% formic acid (B). Each sample was resolved for 25 min at a flow rate of 0.5 mL/min. The gradient consisted of a ramp of curve 5 to 100% B.

52

L. Zhang et al. / Chemometrics and Intelligent Laboratory Systems 130 (2014) 50–57

The column eluent was introduced directly into the mass spectrometer by electrospray. Mass spectrometric analysis was performed on a QTOF Premier (Agilent) operating in positive mode. The capillary voltage was set to 3.5 kV, and the cone voltage of 150 V, respectively. The desolvation gas flow was set to 8 L/min and the temperature was set to 350 °C. The cone gas flow was 25 L/h, and the source temperature was 350 °C. UHPLC–QTOF data were acquired in centroid mode from 100 to 1000 mass-to-charge ratio (m/z) in MS scanning. 2.4. Data processing and analysis All data acquired in Agilent. d format were converted to mzXML using MassHunter Workstation software from Agilent (Santa Clara, CA). Then, the converted data were processed by XCMS (Scripps, La Jolla, CA) for peak picking, alignment, integration and extraction of the peak intensities [28]. Data analysis was conducted with multivariate statistical methods. 2.4.1. Graphical index of separation (GIOS) In metabolomics, biomarkers discovery (variable selection) is an important step to select a subset of relevant features for building a robust model, which could enhance the biological interpretation of model. From the viewpoint of optimization, variable selection could be regarded as an optimization problem to seek a subset of variables for a model with the best prediction rate. In this article, graphical index of separation (GIOS) was employed to rank variables and search for the variables that could separate SD rats of kidney deficiency from healthy controls to the most degree. The algorithms of GIOS were described in detail elsewhere [25,26]. Herein, just its general outline is shown for the brevity of the paper. GIOS is straightforward method to visualize value distribution of one variable between two classes in a bivariate plot. For each point in the GIOS plot of one variable, the value on x-axis is the value of this variable of an sample in class 1, while the value on y-axis equals the value of the same variable for an observation from class 2. Namely, there are m × n points in the plot, where m and n mean the number of samples in class 1 and class 2, respectively. Obviously, if the smallest value in class 1 is still bigger than the biggest one in class 2, all m × n points in bivariate plot appear under the diagonal (the curve: y = x), suggesting this variable is completely selective and therefore significantly important for classification. In GIOS, the fraction of number of points on majority side of the diagonal (frct %) and its χ2 test (chi2 norm) were taken as indicators for assessing the importance degree of variables. Moreover, the separation score (sep-t) combined by its t-test (t-statistic of sep-t) was proposed to measure the distance in the two-dimensional distribution from one of the median point to the diagonal running from the (min, min) to the (max, max) point. These two parameters are mainly used to further rank the importance of variables, whose frct % values equal 1. The advantage of GIOS is that it could be not influenced by the concentration of metabolites and find it could find the best variable making two classes as far as possible in model space. Meanwhile, t-test and the method of variance weight [29] were also performed as a reference method. The variance weight of the variable is calculated as follows: nC X nT X nC nT 2 ðxc −yt Þ 2 ðnC þ nT Þ c¼1 t¼1 Weight ¼ nC X nC  nT X nT  2 2 nC X nT X x 0 −xc þ y 0 −yt nC þ nT 0 c¼1 c nC þ nT 0 t¼1 t c ¼1

t ¼1

Where, x and y are observation vector of the current variable in two classes, the nc and nT are the lengths of x and y, respectively. Subscripts c, c′, t and t′ denote the indices of x and y. After variable selection, we used principal component analysis (PCA) to build multivariate model to distinguish the SD rats of kidney

deficiency from healthy controls and then samples in four drug administration groups were projected onto the model space to evaluate the influence of drug on endogenous metabolites. The experiment data was log-transformed and mean-centered before GIOS and PCA. All programs used were coded in MATLAB7.6 for Windows and all calculations were performed on a personal computer. 2.4.2. Biomarker identification Potential markers of interest were discovered using GIOS. The exact masses of these markers were searched against METLIN (http://metlin. scripps.edu), the Human Metabolome Database (HMDB) (http://www. hmdb.ca) and Kyoto Encyclopedia of Genes and Genomes (KEGG) databases (http://www.genome.jp/kegg) to identify the biomarkers. 3. Results and discussion 3.1. Validation of model of kidney deficiency and aging In this study, the model of kidney deficiency and aging was established by injecting 5% D-galactose saline solution (2 ml/kg body weight) and intraperitoneally Dexamethasone (1 ml/kg body weight) for 6 weeks. This model was proposed by combining the D -galactose and Dexamethasone to induce kidney deficiency, which was proved to be more accordant to the symptom of kidney deficiency decried in TCM [27]. To evaluate the curative effect of four groups, it is necessary to validate the model of kidney deficiency and aging at first. Thus, MDA content SOD and GSH-PX activities were detected to validate the established kidney deficiency and aging model. From Table 1, it is found that three indices in model group are significantly different from ones in the group of healthy controls (p b 0.05). These results suggest that the kidney deficiency and aging model were established successfully and could be employed to evaluate the curative effect of LWDH, JKSQ pills and their time-selected combinations. The previous studies indicate that kidney deficiency possesses close relationship to hypothalamus-pituitary-adrenal-thymus (HPAT) axis, neuroendocrine-immune (NEI)., Cortisol (COR), Corticotropinreleasing hormone (CRH) and Adrenocorticotropic hormone (ACTH). Cortisol (COR) is a steroid hormone released in response to stress [30]. Corticotropin-releasing hormone (CRH) is a peptide hormone and neurotransmitter involved in the stress response for stimulation of the pituitary synthesis of Adrenocorticotropic hormone (ACTH), which is an important component of HPAT [31]. Thus, the serum COR, CRH and serum ACTH contents are selected as indicator to evaluate the model of kidney deficiency. After induced by D-galactose saline solution and Dexamethasone, as shown in Table 1, the serum COR content significantly decreases (p b 0.05), while other two values significantly increase (p b 0.05). These values indicate adrenal insufficiency occurs for production of ACTH in the pituitary and/or CRH in the hypothalamus significantly change. 3.2. Variable selection and unsupervised classification model Metabolomics emphasizes the complete set of untargeted smallmolecule metabolites. Thus, it is necessary to find the significantly different metabolites between two groups or among more than two groups. In this case, chemometric methods could select the most important variables and establish the classification model. In this study, Table 1 Comparison of the COR, ACTH and CRH contents between two groups. Group

COR(ng · ml–1)

ACTH(ng · ml–1)

CRH(ng · ml–1)

Health controls Model

9.30 ± 5.39 6.35 ± 1.65a

42.13 ± 7.19 53.00 ± 11.86a

5.73 ± 1.18 7.04 ± 1.37a

a

Significant difference (p b 0.05).

L. Zhang et al. / Chemometrics and Intelligent Laboratory Systems 130 (2014) 50–57

53

Fig. 1. The score plot of PCA for all variables from healthy controls and model group.

metabolomics was employed to investigate the influence of LWDH, JKSQ pills and their time-selected combinations on endogenous metabolites to compare the curative effect of four groups and explore the mystery of TCM time-selected combinations. A data set containing 4078 ion peaks was generated from XCMS processed data of metabolic profiling. Thereafter, the ion peak areas of the metabolites were log-transformed and mean-centered for further data processing. Because of long time drug administration, steady state plasma concentrations are achieved. If chemometric methods was conducted for all six groups including healthy controls, model and four drug administration groups together, drug molecules and their metabolites would make four drug administration groups far from healthy controls and model groups. Thus, the classification model for healthy

controls and model groups was firstly conducted after finding the model related biomarkers out at first. The samples from four drug administration groups were projected onto modeling space to show the influence of drugs on endogenous metabolites. In this study, a commonly used exploratory data analysis (EDA) method, principal component analysis (PCA), was employed to investigate the change trends of all metabolites from kidney deficiency and aging group to healthy controls. As shown in Fig. 1, there is no separation trend between two groups. This result suggests that the main different metabolites do not possess the largest possible variance. So, to eliminate uninformative variables and discover potential biomarkers for subsequent pathological researches, variable selection is necessary for establishment of accurate and comprehensive discrimination model.

Fig. 2. Bar plot of graphical index of separation (GIOS) for all variables from healthy controls and model group.

54

L. Zhang et al. / Chemometrics and Intelligent Laboratory Systems 130 (2014) 50–57

Herein, analysis of variance (ANOVA) was used to select the important variables (p b 0.05) at first. Then, graphical index of separation (GIOS) was employed to select the significant variables. As described above, GIOS shows bivariate plots of all values of the current variable from two classes, namely on its x-axis is plotted the value of variable for an observation of class 1, and on its y-axis the value of the same predictor for an observation from class 2. After rapid calculation, the fraction of number of counts on majority side of the diagonal in GIOS plot (frct %) with its χ2 test, the separation score (sep-t) with its t-test (t-statistic of sep-t) was computed for discriminating the kidney deficiency and aging and healthy controls (see Fig. 2). From frct % and its χ2 test in Fig. 2, it is found that most of variables, whose frct % values equal to 1, could individually separate the kidney deficiency/aging and healthy controls, suggesting the significant differences exist between two groups. To reflect the metabolites changing trends from kidney deficiency and aging to healthy after drug administration, the important variables that make two groups as far as possible were selected through setting an empirical threshold (1.5) for the separation score (sep-t) in Fig. 2. Finally, 20 ions possessing the dominative effect on discriminating the kidney deficiency and aging from healthy controls were selected. These metabolites corresponding to these ions could be therefore considered as potential biomarkers. Compared with t-test and F-test, GIOS

could directly reflect the distribution of all observations from two classes. Thus, it is relatively effective to select the significant variables out without considering whether the observations fit in some distribution or not. After important ions are selected out, they are employed to build a PCA model for discriminating the kidney deficiency and aging from healthy controls. As shown in Fig. 3, two groups could be clearly separated using 22 variables selected by GIOS, indicating these 20 ions are from potential biomarkers. After elimination of isotopic ions, 10 metabolites were exploringly identified by searching against the METLIN, HMDB and KEGG. 3.3. Principal component projection and evaluation of therapeutic effect After variable selection and model establishment of the kidney deficiency and aging, principal component projection was employed to evaluate the influence of drugs on endogenous metabolites. The samples in four drug administration groups were projected on the modeling space and shown in scores plot of PCA (Fig. 3). From Fig. 3, we can find that potential biomarkers of the kidney deficiency and aging show holistic change trend to healthy controls for all four administration groups. Among four administration groups, nourishing Yang-nourishing Yin group possesses nearest distance to the healthy controls in the scores

Fig. 3. (a) The score plot of PCA for 22 important ions selected according to GIOS; (b) partial enlarged drawing of Fig. 3a.

L. Zhang et al. / Chemometrics and Intelligent Laboratory Systems 130 (2014) 50–57

55

Table 2 Potential biomarkers identified of the kidney deficiency and aging. Anova

Formula

Actual-M (Da)

Metabolites

1

7.33

1.33E-09

C39H67O7P

678.4624

PA(P-16:0/20:5(5Z,8Z,11Z,14Z,17Z))

2

7.69

1.98E-05

C42H82NO10P

791.5676

PS(18:0/18:0)

3

7.978

5.42E-22

C59H100O6

904.7520

TG(18:0/18:3(9Z,12Z,15Z)/20:4(8Z,11Z,14Z,17Z))

No.

tR (min)

Proposed structure

4

11.08

2.89E-08

C11H19O13P

390.0563

1-Phosphatidyl-D-myo-inositol

5

12.90

2.47E-07

C5H13N2O6P

228.0511

L-2-Aminoethyl seryl phosphate

6

17.16

2.93E-06

C25H44NO7P

501.2855

LysoPE(20:4(8Z,11Z,14Z,17Z)/0:0)

7

17.93

0.009127

C21H44NO7P

453.2855

LysoPE(16:0/0:0)

8

18.16

2.51E-12

C24H50NO7P

495.3325

LysoPC(16:0)

9

19.67

0.000214

C14H26O4

258.1831

Tetradecanedioic acid

10

20.20

3.40E-06

C26H54NO7P

523.3638

LysoPC(18:0)

plot of PCA, suggesting this group has strongest rehabilitation for metabolic disorder induced by the kidney deficiency and aging. These results are accordant with theories of traditional Chinese medicine descried in the selection of Introduction and also the same with change tendency of clinical indices in Ref. [27]. Additionally, nourishing Yang-nourishing Yin group shows significant difference from other three groups, while nourishing Yang group and nourishing Yin-nourishing Yang group possess no significant difference in almost all metabolites. The above results indicate that the change trends of potential biomarkers after administration provide molecular evidence for combined administration of Jinkui Shenqi pill in the morning and Liuwei Dihuang pill at night for the patients with kidney deficiency. 3.4. Potential biomarkers As pointed in the above section, 22 ions were selected by GIOS. The UHPLC–QTOF/MS analysis platform provides the retention time and precise molecular mass for the structural elucidation to potential

biomarkers. 22 important ions were checked in original data to eliminate isotopic and adduct ions according to extracted ion chromatograms and determine precise molecular mass. The precise molecular mass was searched in Metabolite and Tandem MS Database (METLIN) and Human Metabolome Database and other databases to identify the possible chemical structure. As a result, 10 endogenous metabolites contributing to the separation of the model group and the healthy control group were exploringly identified in the samples (Table 2). The significantly upregulated 1-Phosphatidyl-D-myo-inositol, L-2Aminoethyl seryl phosphate, LysoPE (20:4(8Z, 11Z, 14Z, 17Z)/0:0), LysoPE (16:0/0:0), LysoPC (16:0), Tetradecanedioic acid, LysoPC (18:0) and the downregulated PA(P-16:0/20:5(5Z, 8Z, 11Z, 14Z, 17Z)), PS(18:0/18:0), TG(18:0/18:3(9Z,12Z,15Z)/20:4(8Z,11Z,14Z,17Z)) were observed in the model group compared with the healthy control group. These metabolites could be employed to monitor development of kidney deficiency and aging. From Table 2, it is found that most of biomarkers are related to fatty acids, which are important sources of fuel. This result indicates

56

L. Zhang et al. / Chemometrics and Intelligent Laboratory Systems 130 (2014) 50–57

that lipid metabolism and energy metabolism are involved in metabolic disorder induced by kidney deficiency and aging. In this study, the high-quality KEGG metabolic pathway was employed to identify the most relevant pathways involved in the conditions under study. The significantly changed metabolites are responsible for glycerophospholipid metabolism (map00564), glycerolipid metabolism (map00561), phosphatidylethanolamine (PE) biosynthesis (M00093) and phosphatidylcholine (PC) biosynthesis (M00090). Among the potential biomarkers, lysophospholipids have a role in lipid signaling by acting on lysophospholipid receptors (LPL-R), which are members of the G protein-coupled receptor family of integral membrane proteins. Triglycerides are major components of very low density lipoprotein (VLDL) and chylomicrons, play an important role in metabolism as energy sources and transporters of dietary fat. The significant decrease of triglyceride in blood sample of kidney deficiency and aging indicates that this disease possesses closed relationships with lipid metabolism and energy metabolism. Aging has closed relationships with kidney deficiency, which is outward manifestation of the age-related degenerative process. Thus, kidney deficiency reflects holistic, comprehensive, dynamic change processing of body function [32]. The changes in macroand micro-nutrient diet content paired with scientific evidences that point to an important role of lipids in diet, makes a strong case in favor of lipid metabolism as a longevity determinant. Lipids are candidates to modulate longevity through various mechanisms, from signaling that activates stress resistance, to regulators of cell membrane-associated protein activities [33]. Meanwhile, the aging is related to the energy metabolism in the human organs and tissues according to the energy metabolism hypothesis for kidney deficiency and aging. Thus, the selected biomarkers in this study could reflect the metabolic disorder induced by aging and kidney deficiency. Subsequently, the comeback tendency of these biomarkers could be also employed to reflect the therapeutic effect of Chinese herbal medicines. In this study, the change trends of potential biomarkers after administration show rehabilitation for metabolic disorder induced by kidney deficiency and aging, especially for nourishing Yang-nourishing Yin group. These results provide an evidence for time-selected combination of LWDH and JKSQ capsules.

4. Conclusion Metabolomics for the screening of biomarkers and elucidation of biochemical processes is important part of global systems biology. In this study, metabolomics was employed to research the time-selected combination of LWDH and JKSQ pills in treating kidney deficiency and aging. After experimental optimization, the metabolic profiles were obtained to investigate the metabolic changes among the healthy controls, kidney deficiency and aging and four administration groups. Analysis of variance (ANOVA) and graphical index of separation (GIOS) were employed to select the significant variables for kidney deficiency and aging. As a result, 10 endogenous metabolites from 22 important ions were exploringly identified via database search. Principal component projection was subsequently applied to evaluate the influence of drugs on endogenous metabolites. The results indicate that nourishing Yangnourishing Yin group shows strongest rehabilitation for metabolic disorder induced by the kidney deficiency and aging, which is consistent with the classic theories of traditional Chinese medicine. The change trends of potential biomarkers in this study after administration provide molecular evidence for combined administration of Jinkui Shenqi pill in the morning and Liuwei Dihuang pill at night for the patients with kidney deficiency. Metabolic pathway function analysis indicates kidney deficiency and aging might possess closed relationships with lipid metabolism and energy metabolism. The method proposed in this study may provide inspiration for evaluation of the curative effect for Chinese medicines by comparing the rehabilitation of potential biomarkers.

Acknowledgments This article is supported financially by Capital Traditional Chinese Medicine and Nursing Research Special Subject (10ZYH03) and the National Nature Foundation Committee of P.R. China (Grants No. 21205118 and 20875104). References [1] T.S. Gao, H. Bai, S.Q. Zheng, Traditional Chinese Internal Medicine, China Press of Traditional Chinese Medicine, Beijing, 2004. [2] Y.S. Ho, K.F. So, R.C. Chang, Anti-aging herbal medicine-How and why can they be used in aging-associated neurodegenerative diseases, Ageing Res. Rev. 9 (2010) 354–362. [3] Y.F. Xue, The treatment of Parkinson's disease by acupuncture and herbal medicine, Chin. Med. J-Peking 73 (2003) 9–12. [4] X.M. Lu, Z.L. Xiong, J.J. Li, S.N. Zheng, T.G. Huo, F.M. Li, Metabonomic study on “Kidney-Yang Deficiency syndrome” and intervention effects of Rhizoma drynariae extracts in rats using ultra performance liquid chromatography coupled with mass spectrometry, Talanta 83 (2011) 700–708. [5] Y.Z. Wang, Q.S. Wang, Y. Liu, L. Wang, Research progress on medicine use in treating kidney deficiency syndrome at selected time, Anhui Med. Pharm. J. 13 (2009) 1009–1011. [6] O. Fiehn, Metabolomics-the link between genotype and phenotype, Plant Mol. Biol. 48 (2002) 155–171. [7] J.K. Nicholson, J.C. Lindon, Systems biology: Metabonomics, Nature 455 (2008) 1054–1056. [8] J.K. Nicholson, J.C. Lindon, E. Holmes, “Metabonomics”: understanding the metabolic responses of living systems to pathophysiological stimuli via multivariate statistical analysis of biological NMR spectroscopic data, Xenobiotica 29 (1999) 1181–1189. [9] K. Kim, P. Aronov, S.O. Zakharkin, D. Anderson, B. Perroud, I.M. Thompson, R.H. Weiss, Urine metabolomics analysis for kidney cancer detection and biomarker discovery, Mol. Cell. Proteomics 8 (2009) 558–570. [10] X. Wang, H. Sun, A. Zhang, W. Sun, P. Wang, Z. Wang, Potential role of metabolomics approaches in the area of traditional Chinese medicine: As pillars of the bridge between Chinese and Western medicine, J. Pharm. Biomed. 55 (2011) 859–868. [11] L. Li, B. Sun, Q. Zhang, J.J. Fang, K.P. Ma, U. Li, H.B. Chen, F.T. Dong, Y. Gao, F.M. Li, X.Z. Yan, Metabonomic study on the toxicity of Hei-Shun-Pian, the processed lateral root of Aconitum carmichaelii Debx. (Ranunculaceae), J. Ethnopharmacol. 116 (2008) 561–568. [12] L.C. Zhao, H.C. Gao, Y.X. Zhao, D.H. Lin, Metabonomic analysis of the therapeutic effect of Zhibai Dihuang Pill in treatment of streptozotocin-induced diabetic nephropathy, J. Ethnopharmacol. 142 (2012) 647–656. [13] X.X. Gao, X.Y. Zheng, Z.Y. Li, Y.Z. Zhou, H.F. Sun, L.Z. Zhang, X.Q. Guo, G.H. Du, X.M. Qin, Metabonomic study on chronic unpredictable mild stress and intervention effects of Xiaoyaosan in rats using gas chromatography coupled with mass spectrometry, J. Ethnopharmacol. 137 (2011) 690–699. [14] P. Jiang, W.X. Dai, S.K. Yan, Z.L. Chen, R.L. Xu, J.M. Ding, L. Xiang, S.P. Wang, R.H. Liu, W.D. Zhang, Biomarkers in the early period of acute myocardial infarction in rat serum and protective effects of Shexiang Baoxin Pill using a metabolomic method, J. Ethnopharmacol. 138 (2011) 530–536. [15] J.F. Xiao, B. Zhou, H.W. Ressom, Metabolite identification and quantitation in LC-MS/MS-based metabolomics, TrAC, Trends Anal. Chem. 32 (2012) 1–14. [16] C. Barbasa, E.P. Moraesa, A. Villaseñor, Capillary electrophoresis as a metabolomics tool for non-targeted fingerprinting of biological samples, J. Pharm. Biomed. 55 (2011) 823–831. [17] S. Becker, L. Kortz, C. Helmschrodt, J. Thiery, U. Ceglarek, LC–MS-based metabolomics in the clinical laboratory, J. Chromatogr. B 883–884 (2012) 68–75. [18] H.M. Zhang, S.L. Li, H. Zhang, Y. Wang, Z.L. Zhao, S.L. Chen, H.X. Xu, Holistic quality evaluation of commercial white and red ginseng using a UHPLC– QTOF–MS/MS-based metabolomics approach, J. Pharm. Biomed. 62 (2012) 258–273. [19] V. Centner, D.L. Massart, O.E. de Noord, S. de Jong, B.M. Vandeginste, C. Sterna, Elimination of uninformative variables for multivariate calibration, Anal. Chem. 68 (1996) 3851–3858. [20] X. Shao, F. Wang, D. Chen, Q. Su, A method for near-infrared spectral calibration of complex plant samples with wavelet transform and elimination of uninformative variables, Anal. Bioanal. Chem. 378 (2004) 1382–1387. [21] W. Cai, Y. Li, X. Shao, A variable selection method based on uninformative variable elimination for multivariate calibration of near-infrared spectra, Chemometr. Intell. Lab. 90 (2008) 188–194. [22] T. Chen, E. Martin, Bayesian linear regression and variable selection for spectroscopic calibration, Anal. Chim. Acta 631 (2009) 13–21. [23] S. Wold, N. Kettaneh, K. Tjessem, Hierarchical multiblock PLS and PC models for easier model interpretation and as an alternative to variable selection, J. Chemometr. 10 (1996) 463–482. [24] H.D. Li, Y.Z. Liang, Q.S. Xu, D.S. Cao, Model population analysis for variable selection, J. Chemometr. 24 (2010) 418–423. [25] L. Eriksson, S. Wold, A graphical index of separation (GIOS) in multivariate modeling, J. Chemometr. 24 (2010) 779–789. [26] W.J. Xu, L.X. Zhang, Y.H. Huang, Q.X. Yang, H.B. Xiao, D.Q. Zhang, Fatty acid metabolic profiles and biomarker discovery for type 2 diabetes mellitus using graphical index of separation combined with principal component analysis and partial least squares-discriminant analysis, Chemometr. Intell. Lab. 118 (2012) 173–179.

L. Zhang et al. / Chemometrics and Intelligent Laboratory Systems 130 (2014) 50–57 [27] Z. Li, R.H. Liu, X. Kang, X.J. Wang, Study on establishment of kidney deficient aging model and comparison with D-galactose induced aging model, China J. Chin. Mater. Med. 37 (2012) 2435–2438. [28] C.A. Smith, E.J. Want, G.C. Tong, R. Abagyan, G. Siuzdak, XCMS: processing mass spectrometry data for metabolite profiling using nonlinear peak alignment, matching, and identification, Anal. Chem. 78 (2006) 779–787. [29] L.Z. Yi, J. He, Y.Z. Liang, D.L. Yuan, F.T. Chau, Plasma fatty acid metabolic profiling and biomarkers of type 2 diabetes mellitus based on GC/MS and PLS–LDA, FEBS Lett. 580 (2006) 6837–6845.

57

[30] J.P. Desborough, The stress response to trauma and surgery, Br. J. Anaesth. 85 (2000) 109–117. [31] F. Holsboer, The rationale for corticotropin-releasing hormone receptor (CRH-R) antagonists to treat depression and anxiety, J. Psychiatr. Res. 33 (1999) 181–214. [32] W.L. Lu, Z.Q. Fang, Appraisal of the modeling methods of Yang-deficiency syndrome in animals, Acta Univ. Tradit. Medicalis Sin. Pharmacol. Shanghai 18 (2004) 44–47. [33] A.A. Puca, C. Chatgilialoglu, C. Ferreri, Lipid metabolism and diet: possible mechanisms of slow aging, Int. J. Biochem. Cell Biol. 40 (2008) 324–333.