Received: 16 August 2016 Revised: 30 October 2016 Accepted: 13 November 2016

DO

I: 10.1002/bmc.3899

R E S E A R CH AR T I C L E

Metabolic characterization of the early stage of hepatic fibrosisin rat using GC‐TOF/MS and multivariate data analyses

Hui Jiang1,2 | Jun‐mei Song1 | Peng‐fei Gao3 | Xiu‐juan Qin1 | Shuang‐zhi Xu1 |

Jia‐fu Zhang1

1Department of Pharmacy, The first affiliated

hospital of Anhui university of Chinese

medicine, Hefei, China

2College of Basic Medicine, Anhui Medical

University, Hefei, China

3College of Pharmacy, Dali University, Dali,

China

Correspondence

Jia‐fu Zhang, Department of Pharmacy, The

first affiliated hospital of Anhui university of

Chinese medicine, 117 Meishan Road, Hefei,

China.

Email: zyfyzjf@163.com

Funding information

National Natural Science Foundation of China,

Grant/Award Number: 81102874 Leading tal-

ents introduction and cultivation plan project

of colleges in Anhui province, Grant/Award

Number: gxfxZD2016118

Abbreviations used: AA, amino acid; B

trifluoroacetamide; GC‐TOF/MS, gas chromato

spectrometry; HF, hepatic fibrosis; PCA, principal

DA, orthogonal partial least squares–discrimina

least squares–discriminant analysis; ROS, free rad

cycle; TIC, total ion chromatogram; TMCS, trimet

importance projection.

Biomedical Chromatography. 2017;31:e3899.https://doi.org/10.1002/bmc.3899

AbstractThe aim of this study was to explore the changes in the urine metabolic spectrum in rats with the

early stage of liver fibrosis using gas chromatography–time of flight/mass spectrometry (GC‐

TOF/MS), try to search for potential biomarkers and elucidate the probably metabonomic patho-

genesis. The early stage of liver fibrosis was established with a single subcutaneous injection of

carbon tetrachloride twice each week for 4weeks continuously. At the end of the experiment,

GC‐TOF/MS technology with multivariate statistical approaches such as principal component

analysis, partial least squares‐discriminant analysis and orthogonal partial least squares‐

discriminant analysis was used to analyze the changes in the metabolic spectrum trajectory and

identify potential biomarkers. Twelve potential biomarkers in the model group, such as succinic

acid, threonine and lactose, were selected, which indicate that the metabonomic pathogenesis

of the early stage of liver fibrosis may be related to disorders of energy metabolism, amino acid

metabolism and fatty acid metabolism.

KEYWORDS

GC‐TOF/MS, metabolomics, potential biomarker, the early stage of liver fibrosis, urine

1 | INTRODUCTION

Hepatic fibrosis (HF), a reversible wound‐healing response following

liver injury, including chronic viral infection, immunological attack,

hereditary metal overload, parasitic disease and toxic damage, is a

pathological process characterized by the production and excessive

deposition of extracellular matrix (Arteaga et al., 2016; Liu et al.,

2016; Wu et al., 2016). As a result, some life‐threatening complications

such as cirrhosis, portal hypertension, hepatocellular carcinoma and

liver failure can develop in patients with HF (Zhang et al., 2016a, b).

Thus, early diagnosis and precise staging of liver fibrosis can help

predict the prognosis and select suitable treatment for patients (Zhang

et al., 2016a, b).

STFA, bis‐ (trimethylsilyl)

graphy–time of flight/mass

component analysis; OPLS‐nt analysis; PLS‐DA, partial

icals; TCA, tricaboxylic acid

hylchlorosilane; VIP, variable

wileyonlinelibrary.com/jo

To date, histology of hepatic parenchyma and histochemistry

based on special stains such Goldner or Masson trichrome stains is

regarded as a gold standard for a definitive diagnosis of HF, whereas

it was reported that sampling error and inter‐observer bias might limit

it in clinical use (Masserdotti & Bertazzolo, 2016; Zhu et al., 2011).

Therefore, there has been an increasing need for a noninvasive,

reliable, accurate, important and acceptable tool for HF diagnosis

(Chang et al., 2008). Recently, multiple noninvasive techniques

including serological and urinary markers as well as imaging have been

explored as possible alternatives to percutaneous biopsy, although

there are some problems in these diagnostic methods (Rustogi et al.,

2012; Sebastiani et al., 2011; Yu et al., 2016).

Metabolomics is one of the major emerging fields of systems biol-

ogy, and provides a powerful platform for simultaneously monitoring

endogenous metabolite levels in biological fluid samples (including

those derived from blood, urine and tissues) combined with a variety

of pattern‐analysis methods to reveal potential biomarkers of interest

and identify related metabolic pathways (Bouatra et al., 2013). As an

important analytical technology, metabonomics has been increasingly

applied to find potential biomarkers for various diseases (Ressom

et al., 2012; Wu et al., 2011; Wang et al., 2016a–c).

Copyright © 2016 John Wiley & Sons, Ltd.urnal/bmc 1 of 9

2 of 9 JIANG ET AL.

In our previous experiments, we have studied the changes in

serum and urine metabolic spectrum in rats with HF using gas chroma-

tography–time of flight/mass spectrometry (GC‐TOF/MS). The results

confirmed that there was some dysfunction of glucose metabolism,

amino acid metabolism, energy metabolism and fatty acid metabolism

in the model group (Jiang, Wu, Gao, & Chen, 2014b; Jiang, Wu, Gao,

& Chen, 2015). Many studies have shown that, after a short period

(about 4weeks) of subcutaneous injection of carbon tetrachloride

(CCl4), pathological changes of liver tissue such as fat vacuoles, inflam-

matory cell infiltration and a number of hyperplastic collagen fibers will

occur. This phase was called the early stage of HF (Jeong et al., 2005;

Du, Zhang, Zhai, & Zhou, 1999; Jiang, Wu, Gao, & Chen, 2014a,

2014b). To our knowledge, little attention has been placed on the

endogenous metabolites in the early stage of HF.

In this study, we identified urine metabolic profile changes

associated with CCl4‐induced the early stage of HF in rats based on

GC‐TOF/MSwithmultivariate statistical techniques, including principal

component analysis (PCA), partial least squares–discriminate analysis

(PLS‐DA), orthogonal partial least squares–discriminant analysis

(OPLS‐DA) and defined potential biomarkers and elucidated the proba-

ble metabonomic pathogenesis.

2 | EXPERIMENTAL

2.1 | Chemicals and reagents

Carbon tetrachloride was purchased from Shantou Xilong Chemical

Factory, China (registered number 0811152); bis‐(trimethylsilyl)

trifluoroacetamide (BSTFA, including 1% trimethylchlorosilane, TMCS,

v/v) was purchased from Regis Technologies Inc., USA; methanol,

chloroform, L‐2‐chlorophenylalanine and pyridine were purchased

from Shanghai Heng Bo Biological Technology Co., China.

2.2 | Instruments and equipment

The GC chromatograph was an Agilent 7890A, Agilent, USA. The

mass spectrometer was an LECO Chroma TOF PEGASUS 4D,

LECO, USA. A GL‐20A automatic high‐speed refrigerated centrifuge

was used (Hunan Instruments Plant centrifuge plant), as well as a

37°C incubator (Hubei Huangshi Medical Instruments Factory) and

a − 80°C ultra‐low temperature freezer, upright type (Sanyo

Company).

2.3 | Experimental animal

Male Sprague–Dawley rats (180–200 g), specified pathogen‐free, were

purchased from the Laboratory Animal Center of Anhui Medical Uni-

versity. All rats were housed in standard cages under set temperature

18–22°C and humidity 40–60% with a 12 h day/night cycle. The rats

were supplied with standard animal food and water. The protocol

was approved by the Committee on the Ethics of Animal Experiments

of The First Affiliated Hospital of Anhui University of Chinese Medi-

cine. All surgeries were performed under isoflurane, and all efforts

were made to minimize suffering.

2.4 | Duplicating model and samples collection

After a feeding adaptation of 2weeks, animals were divided randomly

into the control group and model group (n = 5). The early stage of liver

fibrosis was established by a single subcutaneous injection of CCl4

twice each week for four consecutive weeks (1.0mL/kg 50% CCl4,

diluted in olive oil; Jiang et al., 2014a, 2014b). The control group

was injected with olive oil for comparison at the same time. After

4weeks, 24 h urine was collected from all rats and stored at

−80°C until analysis.

2.5 | Histological examination

At the end of the experimental period, the animals were anesthetized

with isoflurane. A portion of each rat liver from the same lobe was

excised and fixed in 10% formalin solution. Hematoxylin and eosin

and Masson staining for histopathological examination were per-

formed according to standard procedures.

2.6 | Sample preparation

A 100 μL aliquot from each sample was taken into 1.5mL EP tubes;

10 μL of urease suspension (160mg/mL in water) was added, then

vortexed for 10 s and incubated at 37°C for 1 h to decompose and

remove excess urea. Then 0.35mL of the extraction liquid

(Vmethanol–Vchloroform, 3:1) and 50 μL of L‐2‐chlorophenylalanine

(0.2mg/mL stock in dH2O)were added as an internal standard, vortexed

for 10 s and centrifuged for 10min at 12,000 rpm, 4°C. The supernatant

(~0.35mL) was transferred into new 2mL GC‐TOF/MS glass vials. The

extracts were dried in a vacuum concentrator without heating, and

80 μLmethoxymethyl amine salt (dissolved in pyridine, the final concen-

tration of 20mg/mL) was added to the dried metabolites followed by

incubation at 37°C for 2 h in an oven after mixing and sealing. Then

100 μL BSTFA (containing 1% TMCS, v/v) was added into each sample,

sealed again and incubated at 70°C for 1 h. A 10 μL aliquot of FAMEs

(standard mixture of fatty acid methyl esters; C8–C16, 1mg/mL; C18–

C30, 0.5mg/mL in chloroform) was added to the mixed sample after

cooling to room temperature and mixed well for GC‐TOF/MS analysis

(Jiang et al., 2016).

2.7 | GC/MS analysis

GC‐TOF/MS analysis was performed using an Agilent 7890 gas chro-

matograph system coupled with a Pegasus HT time‐of‐flight mass

spectrometer. The system utilized a DB‐5MScapillary column coated

with 5% diphenyl cross‐linked with 95% dimethyl polysiloxane

(30m × 250 μm inner diameter, 0.25 μm film thickness; J&W Scientific,

Folsom, CA, USA). A 1 μL aliquot of the analyte was injected in splitless

mode. Heliumwas used as the carrier gas, the front inlet purge flowwas

3mL/min and the gas flow rate through the column was 20mL/min.

The initial temperature was kept at 50°C for 1min, then raised to

330°C at a rate of 10°C/min, then kept for 5min at 330°C. The injec-

tion, transfer line and ion source temperatures were 280, 280 and

220°C, respectively. The energy was −70 eV in electron impact mode.

The mass spectrometry data was acquired in the full‐scan mode with

JIANG ET AL. 3 of 9

the m/z range of 85–600 at a rate of 20 spectra per second after a sol-

vent delay of 366 s (Jiang et al., 2016).

2.8 | Data analysis

The GC‐TOF/MS raw data was first processed by Chroma TOF4.3X

software and LECO‐Fiehn Rtx5 database for compound detection, peak

alignment, identification, deconvolution analysis and integration of the

peak area. The peaks were analyzed by the multivariate statistical anal-

ysis using the SIMCA‐P+11.5, including PCA, PLS‐DA, and OPLS‐DA

(Umetrics AB, Umea, Sweden). The biochemical data was introduced

into SPSS 17.0 software (SPSS Inc., Chicago, IL, USA) for statistical anal-

ysis, and statistical analyses were handled using the Student's t‐test.

3 | RESULTS

3.1 | The histopathologic changes of liver tissue

In order to assess the histopathologic changes, hematoxylin and eosin

and Masson staining were performed on liver tissues from all rats.

Analysis of the control group demonstrated that the liver cell struc-

ture was clear and the cell nucleus was large and round. The nucleo-

lus was obvious, the cytoplasm was abundant and there was only

minimal collagen deposition (Figure 1A.a and B.a). The model group

displayed intact lobular architecture, with liver cells becoming slightly

larger. Also at the portal area appeared fat vacuoles, inflammatory

cell infiltration, dissolved nucleus and a number of hyperplastic colla-

gen fibers (Figure 1A.b and B.b), which was in accordance with our

previous research and indicated that the early stage of liver fibrosis

was successfully established (Jiang et al., 2014a, 2014b).

FIGURE 1 Histological examination of liver tissue in rats of the early sta(magnification ×200). (A.a and A.b) Hematoxylin and eosin staining. (B.a anmodel group

3.2 | Total ion chromatogram chromatogram of urinesamples

The GC‐TOF/MS total ion chromatograms of urine samples from the

control and model groups at 4weeks after the first dose of CCl4

administration are shown in Figure 2. The horizontal axis represents

the time at which metabolites occur, and the vertical axis represents

abundance. Each peak corresponds to a compound, and the figures

above them represent their retention times. The area under a peak rep-

resents the relative abundance of the metabolite (Feng et al., 2013).

Based on the LECO‐Fiehn Rtx5 database, ~520 metabolites were iden-

tified. After using Chroma TOF4.3 X software to correct for blank

values, eliminate noise and correct to an internal standard, 486 metab-

olites were identified. To illustrate the differences in metabolic pro-

files, GC‐TOF/MS spectra were pre‐treated further, and multivariate

statistical analysis was performed.

3.3 | Principal component analysis of urine samples

To illustrate the general trend, PCA was performed to explain as much

variation with as few components as possible. PCA displays the inter-

nal structure of datasets in an unprejudiced manner and decreases the

dimensionality of the data (Fujimura et al., 2011). In the PCA score plot,

each data point represents one sample, and the distance between

points in the score plot is an indication of similarity between samples.

Figure 3 indicates that the control and model groups clustered and

separated, respectively. However, some sample points were over-

lapped with the other group. To confirm separation between the two

groups, PCA‐class models were established, then their receiver operat-

ing characteristic (ROC) curves and Cooman's plots were examined

ge of hepatic fibrosis by hematoxylin and eosin and Masson stainingd B.b) Masson staining. (A.a and B.a) Control group; (A.b and B.b)

FIGURE 2 Typical GC‐TOF/MS total ion chromatogramchromatograms of rat urine samples at the early stage of hepaticfibrosis obtained from the control group and model group. The blackline represents the control group and the red line represents the modelgroup.

FIGURE 3 Principal component analysis score plots of urine metabolicprofiling of control group and model group at the early stage of hepaticfibrosis. The dark dots represent control group and the red dotsrepresent model group.

4 of 9 JIANG ET AL.

(Bengtsson et al., 2016). As expected, the AUC of the control vs model

and the DModX values shown in the Cooman's plot indicate a very

good discrimination of the two groups, where the AUC of 1, a

sensitivity of 100% and a specificity of 100% were obtained. The

Fisher probability was 0.004 between the two groups. Thus, besides

the histopathologic changes of liver tissue, significant differences

between the control and model groups were also observed in the urine

metabolites using the multi‐variation analysis method.

3.4 | Partial least squares discriminant analysis ofurine samples

To obtain a higher level of group separation and enhance recognition

of variables responsible for classification, PLS‐DA was used. PLS‐DA

is a mathematical model, which was used to classify the samples cor-

rectly (Yang et al., 2006). The validity of the model was obtained by

the use of a number of evaluation values, such as the explanatory

power of the supervised model (R2Y) and the predictive power of the

model (Q2). The PLS‐DA score plot is shown in Figure 4(a); the exper-

imental results showed that metabolic data for the control and model

groups were completely separated by PLS‐DA analysis. Values of R2Y

and Q2 were 0.998 and 0.503, respectively, indicating that the model

has good stability and predictive ability. In order to ensure the validity

of the PLS‐DA model, the experiment was arranged randomly n = 200

times to change the sort order of classification variables R2 and Q2.

Permutation tests of urine samples obtained from the two groups are

shown in Figure 4(b); R2 and Q2 were 0.991 and 0.126, respectively,

which indicates that the model does not have a fitting phenomenon,

and it is more reliable.

3.5 | Orthogonal partial least‐squares discriminantanalysis of urine samples

To better show the differences between the control and model groups,

we used an orthogonal model and analyzed the first and second prin-

cipal components based on OPLS‐DA (Jin et al., 2014). As shown in

Figure 5(a), the two groups were separated completely after OPLS‐

DA, which suggests that several potential biomarkers in the urine were

clearly changed in the early stage of liver fibrosis compared with the

control group.

Loading plots show the distribution of variables and identify

differences in the compound present in the samples or groups based

on those variables (Ji et al., 2014). Each dot in the loading plot

represents a metabolite, and the dots near the center indicate smaller

differences between the groups than those shown by dots far away

from the center. The red dots represent the 12 potential biomarkers

found in the present experiment (Figure 5b).

3.6 | Potential biomarkers of urine samples

Potential biomarkers are more reliable after analysis with OPLS‐DA.

In order to further investigate the potential biomarkers on the

metabolite profiles of the early stage of liver fibrosis, we calculated

the variable importance for the projection (VIP) values. Variables with

VIP values >1.0 were first selected. In the second step, the remaining

variables were then assessed by t‐test, and variables with p > 0.05

between the model and control groups were discarded. The structure

of potential biomarkers was established according to retention time,

the LECO‐Fiehn Rtx5 and database construction laboratory. Based

on these analyses, 12 different metabolites were identified and are

listed in Table 1.

In order to better visualize changes in the potential biomarkers,

box‐plots were created. The box‐plots method is commonly used in

statistics, and it can be relatively intuitive to see the data distribution

characteristics and compare their differences (Blydt‐Hansen, Sharma,

Gibson, Mandal, & Wishart, 2014). The height of the box represents

the interquartile range, the horizontal line represents the median and

the extensions up and down at the ends of the thread represent the

maximum and the minimum. Compared with the control group, the

FIGURE 4 Score plot (a) and plot of the permutation test (200 times) of the PLS‐DA model obtained from the control group, model group. Twohundred permutations were performed, plotted according to the resulting R2 and Q2 values. Green triangle representative R2, blue squarerepresentative Q2, and R2=0.991, Q2=0.126 (b).

FIGURE 5 Orthogonal projections to latent structures‐discriminant analysis score plot (a) and loading plot of rat urine samples obtained from thecontrol group and model group. Each dot in loading plot represents a metabolic substances that manifests the most influential variables accordingto their respective contributions to the discrimination; red dots are the potential biomarkers (b).

TABLE 1 Potential biomarkers selected and change trend in each group

No. Compound Formula Retention time (min) VIP p‐Value Trend Related pathway

1 Arbutin C12H16O7 34.9851,0 1.90913 0.01792 ↑ Glycolysis

2 Fructose C6H12O6 25.6894,0 1.75372 0.04101 ↓ Galactose metabolism

3 Fumaric acid C4H4O4 15.8661,0 1.65621 0.04763 ↓ Citrate cycle

4 Lactic acid C3H6O3 22.6395,0 1.57768 0.04472 ↓ Pyruvate metabolism

5 Lactose C12H22O11 35.9367,0 1.88884 0.02066 ↓ Galactose metabolism

6 N‐Acetyl‐L‐glutamic acid C7H11NO5 25.8478,0 2.07567 0.00356 ↑ 2‐Oxocarboxylic acid metabolism

7 Oxalic acid C2H2O4 23.4238,0 1.81854 0.03539 ↑ Glyoxylate and dicarboxylate etabolism

8 Oxamide C18H21N3O4 15.3767,0 1.92499 0.01566 ↓ Purine metabolism

9 Palmitic acid C16H32O2 31.3118,0 1.9037 0.01641 ↑ Fatty acid biosynthesis

10 Pipecolinic acid C6H11NO2 10.5919,0 1.80853 0.03302 ↑ Lysine degradation

11 Succinic acid C4H6O4 15.4204,0 1.75516 0.03245 ↑ Citrate cycle

12 Threonine C4H9NO3 15.1812,0 1.89734 0.02354 ↑ Glycine, serine and threonine etabolism

Note:p‐value by control group vs model group;↑ refers to the content being relatively high in the model group; ↓ refers to the content being relatively low inthe model group, compared with the control group.

JIANG ET AL. 5 of 9

levels of arbutin, N‐Acetyl‐L‐glutamic acid, oxalic acid, palmitic acid,

pipecolinic acid, succinic acid and threonine were significantly

increased, whereas the levels of fructose, fumaric acid, lactic acid,

lactose and oxamide were significantly decreased in the model group

(Figure 6).

3.7 | Correlation network analysis of potentialbiomarkers

To identify potential biomarkers that may be involved in metabolic

pathways and visualize the connections between the various

FIGURE 6 Box–whisker plots of uccinic acid, threonine, lactose, etc. The height of the box represents the interquartile range, the horizontal linerepresents the median, and the extensions up and down at the ends of the thread represent the maximum and the minimum.

6 of 9 JIANG ET AL.

pathways, we applied physiological, pathophysiological and biochemi-

cal databases, such as the Kyoto Encyclopedia of Genes and Genomes

(http://www.kegg.jp/) and Human the Metabolome Database (http://

www.hmdb.ca). Together, these resources, which are consulted both

domestically and overseas, provided quantitative and metabolic infor-

mation on the organism metabolites. We determined that the metabo-

lites were primarily involved in energy metabolism, amino acid

metabolism, fatty acid metabolism and so on. Based on this informa-

tion, we constructed a network of metabolic pathways involving the

potential biomarkers of the early stage of HF (Figure 7).

4 | DISCUSSION

Liver fibrosis results from chronic damage to the liver in conjunction

with the accumulation of extracellular matrix proteins, which is a com-

mon stage of most chronic liver diseases regardless of the etiology, and

its progression may lead to hepatic cirrhosis or hepatoceluar carcinoma

(Kitano & Bloomston, 2016; Wang et al., 2016a–c). Because the early

stage of liver fibrosis can be reversed, active screening of new diagno-

sis and treatment methods for early interventions for HF can effec-

tively reduce the incidence of hepatic cirrhosis, or even hepatic

FIGURE 7 Global metabolomic network analysis of disturbed metabolic pathways in the control group and model group. Note: the different colorsof words denote the endogenous metabolites used in explaining the possible metabolic pathways. Red words refer to the content being relativelyhigh in the model group; green words refer to the content being relatively low in the model group, which are compared with the control group.

JIANG ET AL. 7 of 9

carcinoma, improving the quality of life for patients with HF (Feng

et al., 2016).

Metabolomics, a powerful approach in systems biology, is a disci-

pline used to qualitatively and quantitatively analyze the small mole-

cule metabolites of cells at specific times and under certain

conditions to describe the changes in the endogenous biological

metabolites as a whole and their response to internal and external

stimuli (Wang et al., 2016a–c). Metabolite profiling has enormous

potential for the characterization of pathological states in animals

and humans, as well as providing diagnostic information and present-

ing mechanistic insight into biochemical effects of drugs (Yao et al.,

2014). Currently, a number of analytical methods, including NMR,

HPLC/MS and GC/MS, are used to complete metabonomic research

(Gou et al., 2013). Among these analytical techniques, the GC/MS

method has been proved to be a robust, unbiased method for identify-

ing and quantifying metabolites with high sensitivity, reproducibility,

simplicity and National Institute of Standards and Technology data-

base accessibility, and has gained increased implementation recently

in performing the global metabolic profiles (Xiong et al., 2015). In the

present study, we used GC‐TOF/MS combined with multivariate data

analyses from the perspective of metabonomics to search the potential

biomarkers and explore the pathogenesis of the early stage of liver

fibrosis. Through these methods, we identified 12 potential bio-

markers, including arbutin, succinic acid, fructose and lactic acid,

among others, which are primarily involved in energy metabolism,

amino acid metabolism and fatty acid metabolism.

The CCl4‐induced animal model of liver fibrosis is a classical model

because of its high success rate, high stability, time‐saving and espe-

cially the pathological changes that are similar to the evolution of

human chronic hepatitis and HF (Jin, Cao, Wang, Li, & Bai, 2015).

CCl4 is believed to act through an experimental toxin and free radicals

(ROS) such as trichloromethyl free radical produced by the cytochrome

P450 enzyme and further converted to trichloromethyl peroxy radical

(Zira et al., 2013). Under oxidative stress, the tricarboxylic acid (TCA)

cycle is slowed down in cellular regulation to reduce the natural pro-

duction of ROS, which is related to liver disease caused by various

mechanisms (Srinivasan et al., 2014). As the important intermediates

of theTCA cycle, fumaric acid and succinic acid are the energy supplies

for the body. In this study, we observed that the levels of fumaric acid

and succinic acid increased in the model group, caused by disturbances

in the TCA cycle.

Studies have shown that ~80% of the amino acids (AA) absorbed

from the digestive tract are used in the processes of protein synthesis,

deamination and transamination in liver (Yu et al., 2007). It has already

been proposed that CCl4 intoxication can decay AAs uptake and pro-

teins synthesis. Once the liver is injured, owing to the weak absorbing

ability for glucose, protein and lipid, AAs will be generated by the

decomposition of endogenous proteins for energy metabolism, which

also leads to the increase of AAs. In the present study, some amino

acids, including arbutin, N‐acetyl‐L‐glutamic and threonine, were found

to be potential metabolites, whose concentration were increased in

the early stage of HF.

It is known that arbutin can be converted to glucose by the glyco-

lytic pathway and then through pentose phosphate pathway, glucose

can change into pyruvate, which further transforms into lactic acid.

Meanwhile fructose and lactose can be generated via glucose by the

metabolism of galactose in liver. Some studies have shown that there

is some connection between the disorder of glucose metabolism and

liver diseases, such as early‐stage HF (Bechmann et al., 2012). In our

research, the model group displayed higher arbutin level and lower lac-

tic acid, fructose and lactose levels compared with those of the control

group, which was in accordance with the literature (Lu et al., 2013).

Pipecolic acid is an imidic acid, and is related to lysine metabolism

in living organisms. Lysine, a structural component of carnitine, can

promote the synthesis of fatty acid, then the latter can inhibit the oxi-

dation of cells, and thus plays a protective role against cell damage.

8 of 9 JIANG ET AL.

Palmitic acid belongs to the free fatty acids, and biosynthesis of ROS

and reactive nitrogen species will increase when the level of free fatty

acids elevates, and then oxidative stress reaction will be activated

(Gyamfi, Everitt, Tewfik, Clemens, & Patel, 2012). Oxidative stress

causes damage to the cell membrane and organelle membrane and

can lead to liver cell injury, so chronic liver diseases such as liver fibro-

sis occur if the injury is persistent. In the present experiment, the

contents of pipecolic acid and palmitic acid were increased in the

early‐stage HF group, suggesting a possible association with the disor-

der of fatty acid metabolism.

The previous study also reported that the content of alanine ami-

notransferase is deficient in some patients with liver diseases, which

will reduces the production of glycine by glyoxylic acid, and increases

the level of oxalic acid. The result is consistent with the findings of

our study (Zhu, Zhang, Xu, & Xing, 2005).

5 | CONCLUSION

In conclusion, a metabonomics method based on GC‐TOF/MS com-

bined with multivariate statistical technique has been successfully used

to study the early stage of HF. Furthermore, 12 urine potential bio-

markers with a significant contribution to classification were selected,

which were involved in energy metabolism, amino acid metabolism and

fatty acid metabolism. Relevant metabolic networks can help to further

understand the possible pathogenesis of the early stage of HF, and

provide a basis for early disease prediction and diagnosis.

ACKNOWLEDGMENTS

This study was financially supported by National Natural Science

Foundation of China (81102874) and Leading Talents Introduction

and Cultivation Plan Project of Colleges in Anhui Province

(gxfxZD2016118). We are grateful to Dr Junliang Deng (Biotree Bio‐

technology Co. Ltd, Shanghai, China) for providing help with data

analysis.

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How to cite this article: Jiang H, Song J‐m, Gao P‐f, Qin X‐j,

Xu S‐z, Zhang J‐f. Metabolic characterization of the early stage

of hepatic fibrosis in rat using GC‐TOF/MS and multivariate

data analyses. Biomedical Chromatography. 2017;31:e3899.

https://doi.org/10.1002/bmc.3899

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