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Metabonomics Research Progress on Liver Diseases.

1. Introduction

Metabolomics is a new discipline of the "postgenomics" period, which constitutes the core of biology systems with genomics, transcriptomics, and proteomics together and is one of the world's most active fields of life science research. The definition of metabonomics was firstly proposed by the Imperial College London, UK, Dr. Nicholson in 1999 [1]. It is a technique by examining the dynamic changes of metabolites to study metabolomic networks of biological systems before and after stimulations or disturbances (such as a specific genetic mutation or environmental change). The object of metabolomics study is endogenous small molecule, of which the relative molecular mass is less than 1000.

Metabolomics has the following advantages [2]. (1) Small changes in gene and protein expression will be reflected and amplified by the metabolite. (2) Study of metabonomics will not need full genome sequencing and a large number of expressed sequence tags (EST). (3) Metabolites are far less than the number of genes and proteins. (4) The physiological or pathological condition of the body can be detected through the analysis of metabolites in biological fluids. (5) As the detected metabolites in different organisms are similar, the use of metabolomics technique is more common.

The commonly used analysis techniques of metabonomics include nuclear magnetic resonance (NMR), gas chromatography-mass spectrometry (GC-MS), and liquid chromatography-mass spectrometry (LC-MS).

The NMR is one of the earliest and most common used techniques [3]. The spectroscopic technique is based on a spin nuclear property that absorbs radiation at the external magnetic field to generate nuclear energy transitions. The advantages of NMR include the demand for small samples, no sample pretreatment, and nondestructive and noninvasive detection. Furthermore, it can obtain the NMR spectrum of a specific living body portion noninvasively and quickly. NMR is the only existing metabolomics analysis technique which can be used in vivo and in situ studies [4].

Currently the most widely used and most effective metabolomics technologies are GC-MS [5] and LC-MS [6]. The former is appropriate to analyze small molecules, thermally stable, volatile, and easily gasified compounds and the latter can analyze the compounds more polar, higher relative molecular mass and lower thermal stability. In clinical studies, due to the use of blood, urine, and tissue samples, these samples are rich in various polar, nonvolatile, and nonvaporized substances, so compared with GC-MS, the applications of LC-MS are more widely used.

The common pattern recognition methods of metabolomics include unsupervised and supervised ones. The unsupervised methods contain principal component analysis (PCA) [7], nonlinear mapping (NLM) [8], hierarchical cluster analysis (HCA) [9], and so on, while the supervised methods include partial least squares-discriminant analysis (PLS-DA) [10] and artificial neural networks (ANN) [11]. The PCA and PLS-DA are the most commonly used methods of pattern recognition.

2. The Application of Metabonomics in Liver Disease

Liver diseases are the worldwide public health problems. Many of them, such as DILI, ALD, NAFLD, and autoimmune liver diseases, lack specific clinical diagnosis markers and the diagnoses consist of history and relevant score, so they have the high subjectivity, inaccuracy, and limitation. With the development of metabolomics, they have become a hot research field and have the broad prospect to explore the sensitive and specific liver diseases' biomarkers.

2.1. Metabonomic Studies on DILI. In the United States, drug-induced acute liver failure accounted for over 50% of all acute liver failure [12]. In China, DILI accounted for about 10% of patients hospitalized with acute hepatitis [13]. Mild DILI only showed elevated transaminase; the severe one might cause liver failure leading to death [14]. Due to the lack of specific and sensitive diagnostic markers of DILI, it is difficult to make early diagnosis and treatment leading to poor prognosis.

Now there are several clinical and experimental metabolomic studies on DILI trying to find the specific and sensitive markers. The main information about those researches is shown in Table 1.

As mentioned in Table 1, these metabolites were involved in carbohydrates, lipids, amino acids, bile acids metabolism, and inflammatory response. Although the results varied, they provided the preliminary understanding on the pathophysiological process of DILI and were beneficial for the further study of DILI markers.

2.2. Metabonomics Studies on ALD. Alcoholic liver disease is divided into alcoholic fatty liver, alcoholic hepatitis, alcoholic liver fibrosis, alcoholic liver cirrhosis, and even alcoholic liver cancer. The diagnosis consists of history of drinking, liver function, medical imaging, and clinical symptoms. In general, alcoholic liver disease has no obvious symptoms and most patients will not go for physical examination. When the disease was found, it might have been in advanced stage. In order to find sensitive and specific biomarkers and make early diagnosis, there were several clinical and experimental metabolomic studies on ALD. The main findings are shown in Table 2.

As mentioned in Table 2, there were two LC-MS based urinary metabolomic studies which both showed the correlation between indole-3-lactic acid and ALD. This might mean that indole-3-lactic acid could be used as a potential diagnostic marker of ALD.

2.3. Metabonomics Studies on NAFLD. NAFLD ranges from simple nonalcoholic fatty liver (NAFL) and nonalcoholic steatohepatitis (NASH) to nonalcoholic cirrhosis. Although NAFL is a benign process and can be reversed through diet and exercise, it has the risk of progressing to NASH and nonalcoholic cirrhosis and causing type 2 diabetes (T2MD) [24]. NAFLD lacks sensitive and specific clinic diagnostic biomarkers. With the development of metabolomics, it provides a powerful tool for NAFLD research.

Now there are several clinical and experimental NAFLD metabolomic researches. However, there are still no uniform metabolites which could be used as the diagnostic markers of NAFLD. The details are shown in Table 3.

2.4. Metabonomics Studies of Hepatitis Virus Related Liver Diseases. According to the World Health Organization, about 2 billion people worldwide have been infected with HBV, of which 240 million people are chronically infected with HBV and each year about 650 thousand people died of liver failure, cirrhosis, and hepatocellular carcinoma caused by HBV.

2.4.1. Metabonomics Studies on Chronic Hepatitis B. Natural history of chronic hepatitis B infection is generally divided into immune tolerance, immune clearance, inactivation, and reactivation. The immune clearance phase is the best time of antiviral therapy. The clinical diagnosis requires a combination of virology, liver enzyme, prothrombin activity (PTA), history, and medical imaging to make a comprehensive assessment, which results the clinical work tedious, so it is of great significance to explore new sensitive and specific biomarkers to attain efficient antiviral treatment and good prognosis.

At present, several metabolomic researches on chronic hepatitis B (CHB) have been made. However, there were no uniform biomarkers of CHB, soit might be necessary to make larger number of clinical studies. Now the relevant researches are shown as in Table 4.

2.4.2. Metabonomics Studies on Hepatitis B Virus-Induced Liver Cirrhosis. Hepatitis B virus- (HBV-) induced liver cirrhosis is often secondary to CHB. When it is found, liver usually has irreversible change and the prognosis is poor, so the detection of sensitive and specific biomarkers of early fibrosis has important clinical significance.

Till now, some researchers have made several studies on HBV-induced cirrhosis trying to find the new sensitive and specific biomarkers. However, due to some limitations, those results still could not be used as biomarkers of HBV-induced cirrhosis. Table 5 shows the contents of relevant researches.

2.4.3. Metabonomics Studies on HBV-Induced Liver Failure. Liver failure is a severe liver disease which progresses rapidly and has high mortality and poor prognosis and lacks the sensitive and specific biomarkers. We diagnose it by the combination of clinical symptoms, liver function, prothrombin activity (PTA), and medical imaging. Due to the lack of sensitive and specific markers, when liver failure was found, it might have been in progress, resulting in poor prognosis.

In China, liver failure is divided into four types: acute liver failure, subacute liver failure, acute-on-chronic liver failure, and chronic liver failure. The type determines the severity and prognosis of the disease. We usually distinguish the type by a detailed history; it means that there is certain subjectivity and limitation. So it has very important significance to detect sensitive and specific biomarkers of liver failure. At present, there are some scholars who have made some research trying to find the biomarkers. The main results will be shown in Table 6.

As shown in Table 6, the main metabolites were involved in lipid metabolism disorders. It has been reported that LPCs regulate cell proliferation, tumor cell invasiveness, and inflammation and play an important role in immunity adjustment. It is helpful to further understand the pathogenesis of liver failure. However, as the current researches are limited, the extensive researches are needed to verify these.

2.4.4. Metabonomics Studies on HBV Induced Hepatocellular Carcinoma. Hepatocellular carcinoma (HCC) is the fifth malignancy worldwide and is the third leading cause of cancer-related deaths worldwide. The diagnosis of HCC usually combines alpha-fetoprotein (AFP) with medical imaging. However, the specificity of AFP is poor and AFP can also increase in severe hepatitis, ovarian tumors, and embryonal tumors. Liver biopsy is the gold standard for the final diagnosis of HCC; however it is an invasive diagnostic method and brings great suffering to patients, so it is difficult to be accepted by patients. Therefore it is of importance to explore the sensitive and specific diagnostic markers.

At present, several metabolomic studies have been made to find the more sensitive and specific markers. Table 7 shows the main contents of these researches.

As shown in Table 7, the representative metabolic disorders included lipid metabolism, bile acid metabolism and amino acid metabolism. LPCs were the most significant changes among lipid metabolism. It has been reported that LPCs regulate cell proliferation, tumor cell invasiveness, and inflammation and play an important role in immunity adjustment. Some researchers have proved that bile acids play an important role in lipid and glucose metabolism, energy consumption, and even liver regeneration. The main changed amino acids in Table 7 have some functions, including providing one-carbon unit, as raw material of gluconeogenesis and antitumor immune response. These metabolites are helpful to further understand the pathogenesis of liver cancer. However, the metabolites as diagnostic biomarkers have not reached the consensus, so further studies might be needed.

2.5. Metabonomics Studies on Autoimmune Liver Diseases. Autoimmune liver diseases include autoimmune hepatitis (AIH), primary biliary cholangitis (PBC), and primary sclerosing cholangitis (PSC). The clinical diagnosis depends on liver-related antibodies, liver function, liver histology, and the excluding of hepatitis virus, drug, and alcohol to make the comprehensive score. However, because some items of the score system cannot be achieved, the accuracy decreases. Therefore, it is very necessary to explore the sensitive and specific biomarkers of autoimmune liver diseases.

Till now, there have been several researches about autoimmune liver diseases. However, there are no unified results because of the different methods and limited number. Now we will show the relevant researches results in Table 8.

3. Discussion

The main metabolites mentioned in Table 8 represented several metabolic disorders, including glucose metabolism, especially the citric acid cycle, lipid metabolism, amino acid metabolism, urea cycle, bile acid metabolism, bilirubin metabolism, gluconeogenesis, inflammation, immune response, cell regeneration, and apoptosis. These metabolisms interact with each other in liver disease and promote the development of liver disease together.

Among these metabolites, we found that PC and LPC decreased in all liver diseases and some studies found that FA reduced. At present, some studies have shown that PC is involved in lipoprotein composition [56] and plays an important role in the maintenance of cell membrane function [57]. PC could decompose into choline and LPC; choline could promote the synthesis of GSH which played the role of antioxidant [57]. In liver diseases, hepatocytes suffered an injury and oxygen free radicals increased. PC participated in the repair of cell membranes and promoted the generation of choline to synthesize GSH acting as antioxidant, so PC showed the downtrend and its degradation products showed the same trend. Some studies have found that FA is the major energy resource under pressure [58]. It could provide energy for hepatocyte to facilitate its function by [beta]-oxidation. Moreover, FA could be converted to ketones and glucose. In liver diseases, the citric acid cycle was blocked; FA broke down to produce energy, so it could explain the decreased level of FA.

In some researches above, we can see in liver diseases, except for liver cancer, that glucose, lactic acid, and citric acid increased. Glucose and citric acid may increase with the disorder of citric acid cycle. When hepatocyte was injured, it caused the mitochondrial dysfunction, resulting in the disorder of citric acid cycle. Furthermore, the increase of glucose was accompanied by the decreased glycerol and some amino acids such as glutamate, glutamine, and valine. These substances could be converted to glucose through gluconeogenesis and then enter the glucose metabolic pathways to produce energy. In liver diseases, the aerobic metabolism was blocked; glycolysis and pentose phosphate pathway became the main glucose metabolic pathways. Glycolysis could produce large amounts of lactic acid, leading to the high lactic acid level [59, 60].

Amino acids can be divided into glucogenic amino acids, ketogenic amino acids, and glucogenic and ketogenic amino acids. In liver diseases, the aerobic metabolism was injured; glycolysis became mainly glucose metabolic pathways, producing less ATP and arousing gluconeogenesis. Glucogenic amino acids such as glutamic acid and glutamine converted into glucose, leading to the decrease of amino acids. In patients with cirrhosis, the branched-chain amino acid decreased, while the aromatic amino acids increased; the ratio of branched-chain amino acid and aromatic amino acid was out of balance. In addition, some studies have shown that high levels of aromatic amino acids, especially phenylalanine, are related to the impact of the gut microorganisms [61, 62].

The most metabolites declined in HCC, but the levels of glutamine and malic acid increased; this might be correlated with urea cycle disorders. When liver cells lost their normal function and urea cycle was injured, causing elevated blood ammonia. Glutamine as the detoxification products showed the elevated levels. Malic acid could be produced by fumaric acid and generate oxaloacetate which could promote the formation of aspartic acid in transamination process and aspartic acid was used in urea cycle. When urea cycle was disordered, aspartic acid consumption reduced, indirectly leading to malic acid accumulation.

Besides the metabolic pathways above, other metabolic pathways also involved bile acid metabolism and bilirubin metabolism. Bile acids synthesized in hepatocyte cytoplasm and microsomes and excreted into intestine through the bile duct. When liver cells were damaged, the bile acids in cytoplasm were released into blood, causing the elevated serum bile acid levels. Conjugated bilirubin came into being by unconjugated bilirubin in smooth endoplasmic reticulum of liver cells. When hepatocytes or bile duct cells swell, degeneration and necrosis and the secretion and excretion of bilirubin were in disorder, leading to the release and regurgitation of bilirubin into blood and resulting in the high serum bilirubin level.

4. Conclusion and Outlook

To date, metabolomics has had considerable progression, but as a novel technology, it also faces the challenges of methodology and application. From the methodological point, the current data analysis strongly depends on the relevant technical expertise and experience; the existed analysis instruments and techniques or data processing methods all need further development. From the application point, many liver-related metabolomics researches could only recognize the different metabolites between each group, while these metabolites had not been used as sensitive and specific markers for the diagnosis of diseases. Although metabonomics has some deficiency as mentioned above and is at the early stage of development, metabonomics application in liver disease has become a hot topic in the academic field. We believe that, with the further technological innovation, the application of metabolomics in the field of liver disease will have broad prospects in the aspects of finding the new early specific markers of different liver diseases and different stages of the same disease and providing new objective evidences for the diagnosis, treatment, and prognosis.

http://dx.doi.org/10.1155/2017/8467192

Competing Interests

There is no conflict of interests to declare.

Acknowledgments

This paper was financially supported by the National Natural Science Foundation of China (no. 81673728).

References

[1] T. L. Hai, L. Liu, H. Sun, W. Sun, and X. Wang, "The successful practice of metabolomics in medical research," Progress in Modern Biomedicine, vol. 10, pp. 1582-1584, 2007.

[2] J. Taylor, R. D. King, T. Altmann, and O. Fiehn, "Application of metabolomics to plant genotype discrimination using statistics and machine learning," Bioinformatics, vol. 18, no. 2, pp. S241-S248, 2002.

[3] C. Oakman, L. Tenori, L. Biganzoli et al., "Uncovering the metabolomic fingerprint of breast cancer," International Journal of Biochemistry and Cell Biology, vol. 43, no. 7, pp. 1010-1020, 2011.

[4] O. M. Rooney, J. Troke, J. K. Nicholson, and J. L. Griffin, "High-resolution diffusion and relaxation-edited magic angle spinning [sup.1]H NMR spectroscopy of intact liver tissue," Magnetic Resonance in Medicine, vol. 50, no. 5, pp. 925-930, 2003.

[5] N. Krone, B. A. Hughes, G. G. Lavery, P. M. Stewart, W. Arlt, and C. H. L. Shackleton, "Gas chromatography/mass spectrometry (GC/MS) remains a pre-eminent discovery tool in clinical steroid investigations even in the era of fast liquid chromatography tandem mass spectrometry (LC/MS/MS)," Journal of Steroid Biochemistry and Molecular Biology, vol. 121, no. 3-5, pp. 496-504, 2010.

[6] D. M. Drexler, M. D. Reily, and P. A. Shipkova, "Advances in mass spectrometry applied to pharmaceutical metabolomics," Analytical and Bioanalytical Chemistry, vol. 399, no. 8, pp. 2645-2653, 2011.

[7] J. Wei, G. Xie, Z. Zhou et al., "Salivary metabolite signatures of oral cancer and leukoplakia," International Journal of Cancer, vol. 129, no. 9, pp. 2207-2217, 2011.

[8] E. Holmes and H. Antti, "Chemometric contributions to the evolution of metabonomics: mathematical solutions to characterising and interpreting complex biological NMR spectra," Analyst, vol. 127, no. 12, pp. 1549-1557, 2002.

[9] H. C. Keun, T. M. D. Ebbels, M. E. Bollard et al., "Geometric trajectory analysis of metabolic responses to toxicity can define treatment specific profiles," Chemical Research in Toxicology, vol. 17, no. 5, pp. 579-587, 2004.

[10] C. Wang, H. Kong, Y. Guan et al., "Plasma phospholipid metabolic profiling and biomarkers of type 2 diabetes mellitus based on high-performance liquid chromatography/electrospray mass spectrometry and multivariate statistical analysis," Analytical Chemistry, vol. 77, no. 13, pp. 4108-4116, 2005.

[11] J. Sun, L. S. Von Tungeln, W. Hines, and R. D. Beger, "Identification of metabolite profiles of the catechol-O-methyl transferase inhibitor tolcapone in rat urine using LC/MS-based metabonomics analysis," Journal of Chromatography B: Analytical Technologies in the Biomedical and Life Sciences, vol. 877, no. 24, pp. 2557-2565, 2009.

[12] J. K. Nicholson and J. C. Lindon, "Systems biology: metabonomics," Nature, vol. 455, no. 7216, pp. 1054-1056, 2008.

[13] V. Saxena, A. Gupta, G. A. N. Gowda, R. Saxena, S. K. Yachha, and C. L. Khetrapal, "[sup.1]H NMR spectroscopy for the prediction of therapeutic outcome in patients with fulminant hepatic failure," NMR in Biomedicine, vol. 19, no. 5, pp. 521-526, 2006.

[14] K. Yu, G. Sheng, J. Sheng et al., "A metabonomic investigation on the biochemical perturbation in liver failure patients caused by hepatitis B virus," Journal of Proteome Research, vol. 6, no. 7, pp. 2413-2419, 2007

[15] F. L. Xiao, S. L. Jiang, Y. W. Yin, and J. Huang, "The serum metabolomics research of drug induced liver injury based on ultra performance liquid chromatography-mass spectrometry," Chinese Journal Of Microecology, vol. 6, pp. 625-638, 2013.

[16] J. W. Kim, S. H. Ryu, S. Kim et al., "Pattern recognition analysis for hepatotoxicity induced by acetaminophen using plasma and urinary [sup.1]H NMR-based metabolomics in humans," Analytical Chemistry, vol. 85, no. 23, pp. 11326-11334, 2013.

[17] E. Gonzalez, S. van Liempd, J. Conde-Vancells et al., "Serum UPLC-MS/MS metabolic profiling in an experimental model for acute-liver injury reveals potential biomarkers for hepatotoxicity," Metabolomics, vol. 8, no. 6, pp. 997-1011, 2012.

[18] Y.-H. Xiong, Y. Xu, L. Yang, and Z.-T. Wang, "Gas chromatographymass spectrometry-based profiling of serum fatty acids in acetaminophen-induced liver injured rats," Journal of Applied Toxicology, vol. 34, no. 2, pp. 149-157, 2014.

[19] S. Harada, T. Takebayashi, A. Kurihara et al., "Metabolomic profiling reveals novel biomarkers of alcohol intake and alcohol-induced liver injury in community-dwelling men," Environmental Health and Preventive Medicine, vol. 21, no. 1,pp. 18-26, 2016.

[20] H. S. Chang, H. C. Bai, and M. Wang, "The urine metabolomics research of patients with alcoholic liver disease," Heilongjiang Medicine and Pharmacy, vol. 5, pp. 44-45, 2013.

[21] P. Nahon, R. Amathieu, M. N. Triba et al., "Identification of serum proton NMR metabolomic fingerprints associated with hepatocellular carcinoma in patients with alcoholic cirrhosis," Clinical Cancer Research, vol. 18, no. 24, pp. 6714-6722, 2012.

[22] S. K. Manna, M. D. Thompson, and F. J. Gonzalez, "Application of mass spectrometry-based metabolomics in identification of early noninvasive biomarkers of alcohol-induced liver disease using mouse model," Advances in Experimental Medicine and Biology, vol. 815, pp. 217-238, 2015.

[23] S. Li, H. Liu, Y. Jin, S. Lin, Z. Cai, and Y. Jiang, "Metabolomics study of alcohol-induced liver injury and hepatocellular carcinoma xenografts in mice," Journal of Chromatography B: Analytical Technologies

in the Biomedical and Life Sciences, vol. 879, no. 24, pp. 2369-2375, 2011.

[24] F. Nascimbeni, R. Pais, S. Bellentani et al., "From NAFLD in clinical practice to answers from guidelines," Journal of Hepatology, vol. 59, no. 4, pp. 859-871, 2013.

[25] E. Rodriguez-Gallego, M. Guirro, M. Riera-Borrull et al., "Mapping of the circulating metabolome reveals a-ketoglutarate as a predictor of morbid obesity-associated non-alcoholic fatty liver disease," International Journal of Obesity, vol. 39, no. 2, pp. 279-287, 2015.

[26] B. C. Ferslew, G. Xie, C. K. Johnston et al., "Altered Bile Acid Metabolome in Patients with Nonalcoholic Steatohepatitis," Digestive Diseases and Sciences, vol. 60, no. 11, pp. 3318-3328, 2015.

[27] K. Tokushige, E. Hashimoto, K. Kodama et al., "Serum metabolomic profile and potential biomarkers for severity of fibrosis in nonalcoholic fatty liver disease," Journal of Gastroenterology, vol. 48, no. 12, pp. 1392-1400, 2013.

[28] J. Barr, M. Vazquez-Chantada, C. Alonso et al., "Liquid chromatography-mass spectrometry-based parallel metabolic profiling of human and mouse model serum reveals putative biomarkers associated with the progression of nonalcoholic fatty liver disease," Journal of Proteome Research, vol. 9, no. 9, pp. 4501-4512, 2010.

[29] W. von Schonfels, E. Patsenker, R. Fahrner et al., "Metabolomic tissue signature in human non-alcoholic fatty liver disease identifies protective candidate metabolites," Liver International, vol. 35, no. 1, pp. 207-214, 2015.

[30] L. L. Xiao, N. M. Ya, Y. Z. Jing, D. Z. Min, and M. M. Yi, "Metabolomics research on nonalcoholic fatty liver disease in different phenotypes," Chinese Hepatology, vol. 2, pp. 95-100, 2015.

[31] H. Li, L. Wang, X. Yan et al., "A proton nuclear magnetic resonance metabonomics approach for biomarker discovery in nonalcoholic fatty liver disease," Journal of Proteome Research, vol. 10, no. 6, pp. 2797-2806, 2011.

[32] X. Shao, Metabolomics Research Ased on HPLC/TripleTOFMS/MS of Chronic Hepatitis B at Different Stages, Jilin University, Changchun, China, 2012.

[33] A. Zhang, H. Sun, Y. Han, G. Yan, and X. Wang, "Urinary metabolic biomarker and pathway study of hepatitis B virus infected patients based on UPLC-MS system," PLoS ONE, vol. 8, no. 5, Article ID e64381, 2013.

[34] Y. Hou, W. Zhu, C. Chen, Y. Wang, Z. Duan, and C. Yan, "Exploration on serum metabolic biomarkers of hepatitis B virus infected patients based on gas chromatography-mass spectrometry," Chinese Journal of Chromatography, vol. 33, no. 4, pp. 383-388, 2015.

[35] D. Yue, Y. Zhang, L. Cheng et al., "Hepatitis B virus X protein (HBx)-induced abnormalities of nucleic acid metabolism revealed by [sup.1]H-NMR-based metabonomics," Scientific Reports, vol. 6, Article ID 24430, 2016.

[36] Z. Du, L. Zhang, and S. Liu, "Application of liquid chromatographymass spectrometry in the study of metabolic profiling of cirrhosis in different grades," Chinese Journal of Chromatography (Se Pu), vol. 29, no. 4, pp. 314-319, 2011.

[37] P. Yin, D. Wan, C. Zhao et al., "A metabonomic study of hepatitis B-induced liver cirrhosis and hepatocellular carcinoma by using RP-LC and HILIC coupled with mass spectrometry," Molecular BioSystems, vol. 5, no. 8, pp. 868-876, 2009.

[38] R. Xue, L. Dong, H. Wu, T. Liu, J. Wang, and X. Shen, "Gas chromatography/mass spectrometry screening of serum metabolomic biomarkers in hepatitis B virus infected cirrhosis patients," Clinical Chemistry and Laboratory Medicine, vol. 47, no. 3, pp. 305-310, 2009.

[39] W. Q. Su, The Serum Metabolomics Study of HBV Cirrhosis at Different Stages, Chong Qing Medical University, 2012.

[40] C. Y. Nie, T. Han, L. Zhang et al., "Cross-sectional and dynamic change of serum metabolite profiling for Hepatitis B-related acute-on-chronic liver failure by UPLC/MS," Journal of Viral Hepatitis, vol. 21, no. 1, pp. 53-63, 2014.

[41] F. L. Xiao, The Serum Metabolomics Preliminary Study of Acuteon-Chronic Liver Failure, Chronic Liver Failure and Pre-Liver Failure, Zhejiang University, Hangzhou, China, 2014.

[42] L. Zhang, X. Jia, X. Peng et al., "Development and validation of a liquid chromatography-mass spectrometry metabonomic platform in human plasma of liver failure caused by hepatitis B virus," Acta Biochimica et Biophysica Sinica, vol. 42, no. 10, pp. 688-698, 2010.

[43] L. Zhou, Q. Wang, P. Yin et al., "Serum metabolomics reveals the deregulation of fatty acids metabolism in hepatocellular carcinoma and chronic liver diseases," Analytical and Bioanalytical Chemistry, vol. 403, no. 1, pp. 203-213, 2012.

[44] Y. Shao, B. Zhu, R. Zheng et al., "Development of urinary pseudotargeted LC-MS-based metabolomics method and its application in hepatocellular carcinoma biomarker discovery," Journal of Proteome Research, vol. 14, no. 2, pp. 906-916, 2015.

[45] Q. Liang, H. Liu, C. Wang, and B. Li, "Phenotypic characterization analysis of human hepatocarcinoma by urine metabolomics approach," Scientific Reports, vol. 6, Article ID 19763, 2016.

[46] Q. Huang, Y. Tan, P. Yin et al., "Metabolic characterization of hepatocellular carcinoma using nontargeted tissue metabolomics," Cancer Research, vol. 73, no. 16, pp. 4992-5002, 2013.

[47] H. W. Ressom, J. F. Xiao, L. Tuli et al., "Utilization of metabolomics to identify serum biomarkers for hepatocellular carcinoma in patients with liver cirrhosis," Analytica Chimica Acta, vol. 743, pp. 90-100, 2012.

[48] T. Chen, G. Xie, X. Wang et al., "Serum and urine metabolite profiling reveals potential biomarkers of human hepatocellular carcinoma," Molecular & cellular proteomics : MCP, vol. 10, no. 7, 2011.

[49] S.-Y. Liu, R.-L. Zhang, H. Kang, Z.-J. Fan, and Z. Du, "Human liver tissue metabolic profiling research on hepatitis B virus-related hepatocellular carcinoma," World Journal of Gastroenterology, vol. 19, no. 22, pp. 3423-3432, 2013.

[50] J. Zeng, P. Yin, Y. Tan et al., "Metabolomics study of hepatocellular carcinoma: discovery and validation of serum potential biomarkers by using capillary electrophoresis-mass spectrometry," Journal of Proteome Research, vol. 13, no. 7, pp. 3420-3431, 2014.

[51] Q. Lu, H. H. Yi, H. Cong et al., "The serum metabolomic studies in patients with hepatocellular carcinoma and cirrhosis," Chinese Journal of Analytical Chemistry, vol. 2, pp. 194-198, 2009.

[52] Y. Tan, P. Yin, L. Tang et al., "Metabolomics study of stepwise hepatocarcinogenesis from the model rats to patients: potential biomarkers effective for small hepatocellular carcinoma diagnosis," Molecular & Cellular Proteomics, vol. 11, no. 2, Article ID M111.010694, 2012.

[53] J.-S. Lian, W. Liu, S.-R. Hao et al., "A serum metabolomic analysis for diagnosis and biomarker discovery of primary biliary cirrhosis and autoimmune hepatitis," Hepatobiliary and Pancreatic Diseases International, vol. 14, no. 4, pp. 413-421, 2015.

[54] J. Trottier, A. Bialek, P. Caron et al., "Metabolomic profiling of 17 bile acids in serum from patients with primary biliary cirrhosis and primary sclerosing cholangitis: a pilot study," Digestive and Liver Disease, vol. 44, no. 4, pp. 303-310, 2012.

[55] J.-B. Wang, S.-B. Pu, Y. Sun et al., "Metabolomic profiling of autoimmune hepatitis: the diagnostic utility of nuclear magnetic resonance spectroscopy," Journal of Proteome Research, vol. 13, no. 8, pp. 3792-3801, 2014.

[56] M. Ascha, Z. Wang, M. S. Ascha et al., "Metabolomics studies identify novel diagnostic and prognostic indicators in patients with alcoholic hepatitis," World Journal of Hepatology, vol. 8, no. 10, pp. 499-508, 2016.

[57] P. Zhou, L. Shao, L. Zhao et al., "Efficacy of fluidized bed bioartificial liver in treating fulminant hepatic failure in pigs: a metabolomics study," Scientific Reports, vol. 6, Article ID 26070, 2016.

[58] V. Y. Serebrov, D. I. Kuzmenko, P. G. Burov, and O. B. Sapugoltseva, "Activity of enzymes of a sphingomyelin cycle and concentration of products of sphingomyelin degradation in the liver of rats in dynamics of the acute toxic hepatitis," Biomeditsinskaya Khimiya, vol. 56, no. 2, pp. 283-289, 2010.

[59] M. J. McPhail, D. L. Shawcross, M. R. Lewis et al., "Multivariate metabotyping of plasma predicts survival in patients with decompensated cirrhosis," Journal of Hepatology, vol. 64, no. 5, pp. 1058-1067, 2016.

[60] N. Embade, Z. Marino, T. Diercks et al., "Metabolic characterization of advanced liver fibrosis in HCV patients as studied by serum 'H-NMR spectroscopy," PLoS ONE, vol. 11, no. 5, Article ID e0155094, 2016.

[61] S. Ferolla, G. Armiliato, C. Couto, and T. Ferrari, "The role of intestinal bacteria overgrowth in obesity-related nonalcoholic fatty liver disease," Nutrients, vol. 6, no. 12, pp. 5583-5599,2014.

[62] V.Giannelli, V.Di Gregorio, V. Iebba et al., "Microbiota and the gut-liver axis: bacterial translocation, inflammation and infection in cirrhosis," World Journal of Gastroenterology, vol. 20, no. 45, pp. 16795-16810, 2014.

Mengqian Yu, Ying Zhu, Qingwei Cong, and Chunyan Wu

Department of Infectious Diseases, The First Affiliated Hospital of Dalian Medical University, Dalian, Liaoning 116000, China

Correspondence should be addressed to Ying Zhu; zhuyingsh52@126.com

Received 23 September 2016; Revised 18 December 2016; Accepted 9 February 2017; Published 21 February 2017

Academic Editor: Thomas D. Boyer
Table 1: The main information about clinical and
experimental metabolomic studies on DILI.

Reference   Methods           Sample

[15]        UPLC-TOF-MS       Serum

[16]        [sup.1]H-NMR      Serum and urine

[17]        UPLC-TOF- MS/MS   Serum

[18]        GC-MS             Serum

Reference   Main findings

[15]        LPC 16:0, LPC 18:0, LPC 18:2, and LPC
            18:3[down arrow]; glycylchenodeoxycholic
            acid, glycocholate, bilirubin, stearic acid
            amide, oleic acid amide, myristyl amide,
            and hypoxanthine [up arrow]

[16]        Serum metabolites: lactate, glucose, 3-
            hydroxyisovalerate, isoleucine,
            acetylglycine, acetone, acetate, glutamine,
            ethanol, and isobutyrate [up arrow]; urine
            metabolites: citrate, glycine, hippurate
            [down arrow]; 3-chlorotyrosine,
            phenylalanine, and glutarate [up arrow]

[17]        sn-1 monoacylglycerophosphocholine, sn-2
            arachidonyl diacylglycerophosphocholine,
            and sphingolipids [down arrow];
            diacylglycerophosphocholines,
            monoacylglycerophosphoethanolamines, and
            amino acidsf

[18]        NEFAs: C18:1n9, C18:1n7, C18:2n6, C20:3n6,
            C20:4n6, C20:5n3, and C22:6n3[up arrow];
            all EFAs except C12:0[up arrow]

UPLC-TOF-MS: ultraperformance liquid chromatography/time-of-flight
mass spectrometry.

UPLC-TOF-MS/MS: ultraperformance liquid chromatography/time-of-flight
tandem mass spectrometry.

[sup.1]H-NMR: a proton nuclear magnetic resonance.

GC-MS: gas chromatography-mass spectrometry.

Table 2: The main results of clinical and
experimental metabolomic studies on ALD.

Reference   Methods             Sample

[19]        CE-TOF-MS           Serum

[20]        HPLC-ion trap-MS    Urine

[21]        [sup.1]H-NMR        Serum

[22]        UPLC-ESI-QTOF- MS   Urine

[23]        UPLC-QTOF-MS        Serum

Reference   Main findings

[19]        19 metabolites associated with alcohol
            intake, and three biomarker candidates
            (threonine, guanidinosuccinate, and
            glutamine) were related to alcohol/induced
            liver injury. Glutamate/glutamine ratio
            might also be good biomarker.

[20]        Seven metabolites were identified,
            including creatinine, indole-3-carboxylic
            acid, indole-3-lactic acid, L-tryptophan,
            L-serine, L-leucine, and glutathione.

[21]        Metabolites significantly increased with
            large HCC were glutamate, acetate, and N-
            acetyl glycoproteins; metabolites that
            correlated with cirrhosis were lipids and
            glutamine. Metabolomic profiles of small
            HCC patients were similar to those of large
            HCC group.

[22]        Indole-3-lactic acid and phenyllactic acid
            may serve as robust noninvasive biomarkers
            for early stages of ALD.

[23]        Five metabolic pathways were identified,
            namely, phenylalanine and tyrosine
            metabolism, leucine degradation, tryptophan
            metabolism, sphingolipid metabolism, and
            glycerophospholipid metabolism. Serum LPCs
            showed disease-specific changes, probably
            reflecting metabolic difference between
            liver injury and HCCX in nude mice.

CE-TOF-MS: capillary electrophoresis time-of-flight mass
spectrometry. UPLC-ESI-QTOF-MS: ultraperformance liquid
chromatography coupled with electrospray ionization quadrupole
time-of-flight mass spectrometry.

Table 3: The findings of metabolomic researches on NAFLD.

Reference   Methods                     Sample

[25]        LC-QTOF-MS and GC-QTOF-MS   Serum

[26]        UPLC-MS/MS                  Serum and urine

[27]        CE-TOF-MS and LC-TOF-MS     Serum

[28]        UPLC-QTOF- MS/MS            Serum

[29]        GC-MS                       Tissue

[30]        HPLC-QTOF-MS                Tissue and serum

[31]        [sup.1]H-NMR                Serum

Reference   Main findings

[25]        [alpha]-Ketoglutarate levels are
            significantly increased in obese patients
            compared with lean controls.

[26]        Serum and urine DCA concentrations were
            statistically significantly increased.
            Patients with NASH exhibited a greater
            postprandial increase in all bile acid
            groups except LCA and 6[alpha]-
            hydroxylated bile acids. Patients with NASH
            had decreased glycine-and taurine-
            conjugated LCA exposure over the
            postprandial period.

[27]        Etiocholanolone/S, DHEA/2S, and DHEA/S
            decreased with the progression of fibrosis,
            while 16/OH/DHEA/S increased with the
            progression of fibrosis. The ratio of 16/
            OH/DHEA/S to DHEA/S (16/D) and the ratio of
            16/OH/DHEA/S to etiocholanolone/S (16/E)
            were more clearly associated with the
            fibrosis grade.

[28]        Strong overlap of the animal model samples
            and human NAFLD patients is observed in the
            sn/1 monoacylglycerophosphocholine profile.
            Seven sphingomyelin type lipids: (SM 36:3),
            (d18:2/16:0), (d18:2/14:0), (d18:1/18:0),
            (d18:1/16:0), (d18:1/12:0), and (d18:0/
            16:0) were found to be significantly
            altered in the human NAFLD patients
            compared to normal liver subjects; similar
            tendencies were also found in the animal
            model samples. Deoxycholic acid was found
            significantly higher both in the animal
            model samples and in the human NAFLD
            patients.

[29]        Hydroquinone (HQ) and nicotinic acid (NA)
            suggest a protective effect against NAFLD.
            However, only NA showed marked effects on
            both steatosis and transaminase levels
            suggesting its potential as a therapeutic
            or preventive agent in NALFD.

[30]        Stearoylcarnitine increased notably in both
            rat liver tissue and serum. However, the
            potential as biomarker of NAFLD still needs
            to be confirmed by extensive studies.

[31]        Four potential biomarkers for diagnosis of
            NAFLD stages were selected: serum glucose,
            lactate, glutamate/glutamine, and taurine.
            A specific combination of spectroscopic
            changes in glucose, lactate, glutamate/
            glutamine, and taurine levels may prove to
            be an accurate means of noninvasively
            diagnosing various stages of NAFLD.

Table 4: The relevant information about researches on CHB.

Reference   Methods                  Sample

[32]        UPLC- TripleTOF- MS/MS   Serum

[33]        UPLC- QTOF-MS            Urine

[34]        GC-TOF-MS                Serum

[35]        Th-NMR                   Cell fluid

Reference   Main findings

[32]        Lysophosphatidylcholine, 3-oxodecanoic
            acid, and bile acids (TCA, GCA, and TCDCA)
            had high diagnostic value on immune
            clearance; oleamide amide was elevated in
            the three groups of CHB but was more
            notable in the inactive phase and had high
            diagnostic value on inactive phase.

[33]        Biotin sulfone, 5-oxo-heneicosanoic acid,
            d-Glucosaminide, and 2-methylhippuric acid
            were the most significant differential
            metabolites for the classification of the
            HBV and the control.

[34]        Citric acid, aconitic acid, glutamine, N,N-
            dimethyl glycine, and malonic acid had good
            correlation with hepatitis B and had high
            sensitivity and specificity to
            differentiate CHB from control.

[35]        Uridine, inosine, guanosine, uracil, and
            xanthine were all significantly decreased.

Table 5: The main contents of metabolomic studies on
HBV-induced cirrhosis.

Reference   Methods                             Sample

[36]        UPLC- LTQOrbitrap-MS                Serum

[37]        RP-HPLC-QTOF-MS and HILIC-QTOF-MS   Serum

[38]        GC/MS                               Serum

[39]        1 H-NMR                             Serum

Reference   Main findings

[36]        The nine metabolites differentiated A-
            grade from the control group, including
            nicotinamide, aminoadipic acid, glutamine,
            tyramine, dodecenoic acid,
            lysophosphatidylcholine, glycol-cysteine,
            cysteine amino acid, and octenoic acid;
            three metabolites that distinguished grade
            A from B, including ethanolamine, glycine,
            glycosylchenodeoxycholic acid; 10
            metabolites that differentiated grade B
            from C, including aminoadipic acid,
            taurine, aminoacetone, glycine, pyruvate,
            glycolcholodeoxycholic acid, alanine,
            pipecolic acid, methionine, and serine.

[37]        Oleic acid, bilirubin, acetylcarnitine, and
            GCDCA were significantly increased in
            cirrhosis patients and distinguished
            cirrhosis from control.

[38]        Acetic acid, sorbitol, D-lactic acid,
            hexanoic acid, 1-naph-thalenamine, butanoic
            acid, phosphoric acid, D-glucitol, and
            glucose were the strongest segregation
            between cirrhosis and CHB.

[39]        Compared with the compensatory period, some
            metabolites increased significantly,
            including glucose, citrate, succinate,
            phenylalanine, tyrosine, lysine, glutamine,
            and creatine, whereas some decreased
            notably, namely, LDL, VLDL, N-acyl
            glycoprotein (NAG), choline, acetone,
            isoleucine, and valine in the
            decompensation period.

UPLC-LTQOrbitrap-MS: ultraperformance liquid chromatography coupled
with linear trap quadrupole Orbitrap mass spectrometry.
RP-HPLC-QTOF-MS and HILIC-QTOF-MS: reversed-phased (RP) high liquid
chromatographyand hydrophilic interaction chromatography(HILIC)
coupled with quadrupole time-of-flight mass spectrometry.

Table 6: The main findings of studies on liver failure.

Reference   Methods                Sample

[40]        UPLC-LTQ Orbitrap-MS   Serum

[41]        UPLC-QTOF-MS           Serum

[42]        HPLC-ion trapTOF-MS    Serum

Reference   Main findings

[40]        LysoPC(18:0), lysoPC(17:0), lysoPC(16:0),
            lysoPC(15:0), PA(20:4(5Z,8Z,11Z,14Z)e-
            2:0), phenylalanyl phenylalanine, bilirubin
            glucuronide, acetoacetic acid, L-
            threonine, and DHAP(18:0) showed
            significant differences between the
            survival group and nonsurvival group;
            lysoPC(14:0), phenylalanyl phenylalanine,
            and bilirubin glucuronide improved with
            treatment and had potential disease-
            monitoring capability.

[41]        Phosphatidylcholine,
            lysophosphatidylcholine, nonconjugated bile
            acids, and conjugated bile acids were
            considered as common biomarkers of ACLF and
            CLF group, while linoleyl carnitine showed
            significant increase in CLF compared with
            ACLF; it was considered as the differential
            marker of the diagnosis of CLF and ACLF.

[42]        1-Linoleoylglycerophosphocholine or 1-
            linoleoylphosphatidyl choline was found
            significantly different between healthy
            controls and liver failure group.

Table 7: The main results of relevant metabolomic studies on HCC.

Reference   Methods                              Sample

[43]        RRLC-QTOF-MS                         Serum

[44]        UPLC-linear ion trap Q-Orbitrap-MS   Urine

[45]        UPLC-QTOF-MS                         Urine

[46]        UHPLC-LTQ- Orbitrap-MS               Tissue and serum

[47]        UPLC-QTOF-MS and UPLC-QqQLIT-MS      Serum
            and UPLC-triple quadrupole-MS

[48]        GC-TOF-MS UPLC-QTOF-MS               Serum and urine

[49]        UPLC-LTQ- Orbitrap-MS                Tissue

[50]        CE-TOF-MS                            Serum

[51]        [sup.1]H-NMR                         Serum

[52]        RRLC-QTOF-MS UPLC-LTQ- Orbitrap-MS   Serum

Reference   Main findings

[43]        Tryptophan decreased in the sera of CHB,
            CIR, and HCC patients; C16:1-CN, as one of
            the long-chain acylcarnitines, increased
            with severity of chronic liver diseases.

[44]        Adenosine, inosine, cyclic AMP, and citric
            acid increased significantly, while
            xanthine, MTA, 6-methyladenosinie, CA, GCA,
            and GCDCS decreased notably in liver
            diseases than healthy control group.
            Carnitine C4:0 and hydantoin-5-propionic
            acid were defined as a combinational marker
            to distinguish HCC from CIR.

[45]        Palmitic acid, alpha-N-phenylacetyl-L-
            glutamine, phytosphingosine, indoleacetyl
            glutamine, and glycocholic acid were the
            most significant differential metabolites
            for the classification ofthe HCC and the
            control.

[46]        In liver tissue, there were 880 metabolites
            that could differentiate hepatocellular
            carcinoma tissue from distal noncancerous
            tissue, while in serum, betaine and
            propionylcarnitine were selected as the
            optimal combination for diagnosis of
            hepatocellular carcinoma.

[47]        GCA, GDCA, TCA, and TCDCA were
            significantly downregulated in sera of HCC
            versus those with cirrhosis, while lysoPC
            17:0 and S-1-P showed a marginally
            significant upregulation.

[48]        Five differential metabolites are found
            both in serum and urine samples in HCC
            patients, including glycocholic acid,
            cysteine, cystine, taurine, and
            phenylalanine. These metabolites represent
            bile acid metabolism, methionine
            metabolism, and phenylalanine and tyrosine
            metabolism.

[49]        Betasitosterol, L-phenylalanine, lysoPCs,
            glycerol-phosphocholine, lysoPEs,
            enodeoxycholic acid glycine conjugate, and
            quinaldic acid were significantly lower in
            central tumor tissue group compared with
            the distant tissue group. Arachidyl
            carnitine, tetradecanal, and oleamide were
            significantly high in the central tumor
            tissue group compared with the distant
            tissue group. A comparison of the levels of
            these metabolites in the adjacent tissue
            group showed relative closeness to those in
            the central tumor tissue group.

[50]        Four metabolites including tryptophan,
            glutamine, arginine, and 2-hydroxybutyric
            acid fulfilled the demand of small HCC
            discrimination. The combination of Trp,
            Gln, and 2-hydroxybutyric acid was better
            to establish the discrimination model for
            the validation set (including small HCC
            subjects).

[51]        LDL, VLDL, choline, and acetoacetic acid
            decreased in liver cirrhosis and
            hepatocellular carcinoma patients, while
            the contents of glutamine, pyruvate,
            phenylalanine, and tyrosine increased.
            These metabolites could differentiate
            patients from healthy people, but liver
            cirrhosis and hepatocellular carcinoma
            partially overlap.

[52]        Six major clusters were observed, the
            representative characteristic metabolites
            were selected from each cluster including
            LPC 22:5, palmitoyl-L-carnitine, LPC 22:6,
            LPE 16:0, LPC O-16:0, and TCA. Three
            metabolites including LPC 22:5, LPE 16:0,
            and TCA were selected as candidate markers
            for the classification of HCC and chronic
            liver diseases.

RRLC-QTOF-MSapid-resolution liquid chromatography quadrupole
time-of-flight mass spectrometry. : ultraperformance liquid
chromatography coupled with triple quadrupole linear ion
trap mass spectrometry.

Table 8: The main results of studies on autoimmune
liver diseases.

Reference   Methods       Sample

[53]        UPLC-MS       Serum

[54]        UPLC- MS/MS   Serum

[55]        1H-NMR        Serum

Reference   Main findings

[53]        Glycochenodeoxycholic acid, FFA, LPC/16:0,
            PC-16:0/16:0, and SM could be used to
            diagnose AIH and PBC accurately.

[54]        LCA-S, TDCA, and GDCA were significantly
            different between PSC and PBC samples.

[55]        Citrate, glutamine, acetone, pyruvate,
            [beta]-hydroxyisobutyrate, acetoacetate,
            histidine, dimethylamine, and creatinine
            had a high diagnostic accuracy for the
            discrimination of AIH from PBC.
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Author:Yu, Mengqian; Zhu, Ying; Cong, Qingwei; Wu, Chunyan
Publication:Canadian Journal of Gastroenterology and Hepatology
Article Type:Report
Geographic Code:9CHIN
Date:Jan 1, 2017
Words:6747
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