Portal de Periódicos da CAPES

Metabolomic Analysis of Key Regulatory Metabolites in Hepatitis C Virus–infected Tree Shrews

2012; Elsevier BV; Volume: 12; Issue: 3 Linguagem: Inglês

10.1074/mcp.m112.019141

1535-9484

Hui Sun, Aihua Zhang, Guangli Yan, Chengyu Piao, Weiyun Li, Chang Sun, Xiuhong Wu, Xinghua Li, Yun Chen, Xijun Wang,

Plant biochemistry and biosynthesis

Metabolomics is a powerful new technology that allows the assessment of global low-molecular-weight metabolites in a biological system and which shows great potential in biomarker discovery. Analysis of the key metabolites in body fluids has become an important part of improving the diagnosis, prognosis, and therapy of diseases. Hepatitis C virus (HCV) is a major leading cause of liver disease worldwide and a serious burden on public health. However, the lack of a small-animal model has hampered the analysis of HCV pathogenesis. We hypothesize that an animal model (Tupaia belangeri chinensis) of HCV would produce a unique characterization of metabolic phenotypes. Ultra-performance liquid-chromatography/electrospray ionization-SYNAPT-high-definition mass spectrometry (UPLC/ESI-SYNAPT-HDMS) coupled with pattern recognition methods and system analysis was carried out to obtain comprehensive metabolomics profiling and pathways of large biological data sets. Taurine, hypotaurine, ether lipid, glycerophospholipid, arachidonic acid, tryptophan, and primary bile acid metabolism pathways were acutely perturbed, and 38 differential metabolites were identified. More important, five metabolite markers were selected via the "significance analysis for microarrays" method as the most discriminant and interesting biomarkers that were effective for the diagnosis of HCV. Network construction has led to the integration of metabolites associated with the multiple perturbation pathways. Integrated network analysis of the key metabolites yields highly related signaling pathways associated with the differentially expressed proteins, which suggests that the creation of new treatment paradigms targeting and activating these networks in their entirety, rather than single proteins, might be necessary for controlling and treating HCV efficiently. Metabolomics is a powerful new technology that allows the assessment of global low-molecular-weight metabolites in a biological system and which shows great potential in biomarker discovery. Analysis of the key metabolites in body fluids has become an important part of improving the diagnosis, prognosis, and therapy of diseases. Hepatitis C virus (HCV) is a major leading cause of liver disease worldwide and a serious burden on public health. However, the lack of a small-animal model has hampered the analysis of HCV pathogenesis. We hypothesize that an animal model (Tupaia belangeri chinensis) of HCV would produce a unique characterization of metabolic phenotypes. Ultra-performance liquid-chromatography/electrospray ionization-SYNAPT-high-definition mass spectrometry (UPLC/ESI-SYNAPT-HDMS) coupled with pattern recognition methods and system analysis was carried out to obtain comprehensive metabolomics profiling and pathways of large biological data sets. Taurine, hypotaurine, ether lipid, glycerophospholipid, arachidonic acid, tryptophan, and primary bile acid metabolism pathways were acutely perturbed, and 38 differential metabolites were identified. More important, five metabolite markers were selected via the "significance analysis for microarrays" method as the most discriminant and interesting biomarkers that were effective for the diagnosis of HCV. Network construction has led to the integration of metabolites associated with the multiple perturbation pathways. Integrated network analysis of the key metabolites yields highly related signaling pathways associated with the differentially expressed proteins, which suggests that the creation of new treatment paradigms targeting and activating these networks in their entirety, rather than single proteins, might be necessary for controlling and treating HCV efficiently. Human hepatitis C virus (HCV) 1The abbreviations used are:HCVhepatitis C virusOPLS-DAorthogonal partial least-squares to latent structures discriminant analysisPCAprincipal component analysisSAMsignificance analysis for microarrays. 1The abbreviations used are:HCVhepatitis C virusOPLS-DAorthogonal partial least-squares to latent structures discriminant analysisPCAprincipal component analysisSAMsignificance analysis for microarrays. is a major pathogen that causes acute and chronic hepatitis, liver cirrhosis, and hepatocellular carcinoma (1Fellay J. Thompson A.J. Ge D. Gumbs C.E. Urban T.J. Shianna K.V. Little L.D. Qiu P. Bertelsen A.H. Watson M. Warner A. Muir A.J. Brass C. Albrecht J. Sulkowski M. McHutchison J.G. Goldstein D.B. ITPA gene variants protect against anaemia in patients treated for chronic hepatitis C.Nature. 2010; 464: 405-408Crossref PubMed Scopus (413) Google Scholar). An estimated total of 170 million individuals worldwide are believed to be infected with HCV and at risk of developing cirrhosis and hepatocellular carcinoma (2Hsu C.S. Hsu S.J. Chen H.C. Tseng T. C. Liu C.H. Niu W.F. Jeng J. Liu C.J. Lai M.Y. Chen P.J. Kao J.H. Chen D.S. Association of IL28B gene variations with mathematical modeling of viral kinetics in chronic hepatitis C patients with IFN plus ribavirin therapy.Proc. Natl. Acad. Sci. U.S.A. 2011; 108: 3719-3724Crossref PubMed Scopus (56) Google Scholar). Recently, a growing amount of evidence has shown that HCV-infection-induced alterations accumulate (3Mankouri J. Dallas M.L. Hughes M.E. Griffin S.D. Macdonald A. Peers C. Harris M. Suppression of a pro-apoptotic K+ channel as a mechanism for hepatitis C virus persistence.Proc. Natl. Acad. Sci. U.S.A. 2009; 106: 15903-15908Crossref PubMed Scopus (54) Google Scholar, 4Zhang P. Zhong L. Struble E.B. Watanabe H. Kachko A. Mihalik K. Virata-Theimer M.L. Alter H.J. Feinstone S. Major M. Depletion of interfering antibodies in chronic hepatitis C patients and vaccinated chimpanzees reveals broad cross-genotype neutralizing activity.Proc. Natl. Acad. Sci. U.S.A. 2009; 106: 7537-7541Crossref PubMed Scopus (121) Google Scholar). Although detailed analysis of the viral genetic organization has led to the identification of various elements, the study of HCV infection has been hampered by the inability to propagate the virus efficiently and the limited animal model of the virus. Fortunately, the tree shrew, Tupaia belangeri chinensis, a species closely related to primates, has been shown to be susceptible to a variety of human HCV (5Amako Y. Tsukiyama-Kohara K. Katsume A. Hirata Y. Sekiguchi S. Tobita Y. Hayashi Y. Hishima T. Funata N. Yonekawa H. Kohara M. Pathogenesis of hepatitis C virus infection in Tupaia belangeri.J. Virol. 2010; 84: 303-311Crossref PubMed Scopus (103) Google Scholar). It has been reported that tree shrews can be infected with HCV (6Xu X. Chen H. Cao X. Ben K. Efficient infection of tree shrew (Tupaia belangeri) with hepatitis C virus grown in cell culture or from patient plasma.J. Gen. Virol. 2007; 88: 2504-2512Crossref PubMed Scopus (61) Google Scholar, 7Tong Y. Zhu Y. Xia X. Liu Y. Feng Y. Hua X. Chen Z. Ding H. Gao L. Wang Y. Feitelson M.A. Zhao P. Qi Z.T. Tupaia CD81, SR-BI, claudin-1, and occludin support hepatitis C virus infection.J. Virol. 2011; 85: 2793-2802Crossref PubMed Scopus (65) Google Scholar, 8Tian Z.F. Shen H. Fu X.H. Chen Y.C. Blum H.E. Baumert T.F. Zhao X.P. Interaction of hepatitis C virus envelope glycoprotein E2 with the large extracellular loop of tupaia CD81.World J. Gastroenterol. 2009; 15: 240-244Crossref PubMed Scopus (11) Google Scholar). hepatitis C virus orthogonal partial least-squares to latent structures discriminant analysis principal component analysis significance analysis for microarrays. hepatitis C virus orthogonal partial least-squares to latent structures discriminant analysis principal component analysis significance analysis for microarrays. Deciphering the ways in which HCV can disrupt metabolic pathways for viral replication represents an important area for future therapeutic intervention. However, comprehensive metabolomics studies have not yet been reported. In recent years, there has been increasing interest in metabolomics technology, which offers the capability of associating changes in the global profile of low-molecular-weight metabolites (<1 kDa) in biofluids (9Cai Z. Zhao J.S. Li J.J. Peng D.N. Wang X.Y. Chen T.L. Qiu Y.P. Chen P.P. Li W.J. Xu L.Y. Li E.M. Tam J.P. Qi R.Z. Jia W. Xie D. A combined proteomics and metabolomics profiling of gastric cardia cancer reveals characteristic dysregulations in glucose metabolism.Mol. Cell. Proteomics. 2010; 9: 2617-2628Abstract Full Text Full Text PDF PubMed Scopus (102) Google Scholar). The metabolome can directly influence the phenotype, more than transcripts or proteins, and thus metabolomic analyses could offer a distinct advantage when trying to decipher disease pathogenesis. Metabolomics attempts to capture global changes and overall physiological status in biochemical networks and pathways in order to elucidate sites of perturbations, and it has shown great promise as a means to identify biomarkers of disease (10Arakaki A.K. Skolnick J. McDonald J.F. Marker metabolites can be therapeutic targets as well.Nature. 2008; 456: 443Crossref PubMed Scopus (96) Google Scholar, 11Stoop M.P. Coulier L. Rosenling T. Shi S. Smolinska A.M. Buydens L. Ampt K. Stingl C. Dane A. Muilwijk B. Luitwieler R.L. Sillevis Smitt P.A. Hintzen R.Q. Bischoff R. Wijmenga S.S. Hankemeier T. van Gool A.J. Luider T.M. Quantitative proteomics and metabolomics analysis of normal human cerebrospinal fluid samples.Mol. Cell. Proteomics. 2010; 9: 2063-2075Abstract Full Text Full Text PDF PubMed Scopus (122) Google Scholar, 12Zhang A. Sun H. Han Y. Yuan Y. Wang P. Song G. Yuan X. Zhang M. Xie N. Wang X. Exploratory urinary metabolic biomarkers and pathways using UPLC-Q-TOF-HDMS coupled with pattern recognition approach.Analyst. 2012; 137: 4200-4208Crossref PubMed Scopus (91) Google Scholar, 13Sreekumar A. Poisson L.M. Rajendiran T.M. Khan A.P. Cao Q. Yu J. Laxman B. Mehra R. Lonigro R.J. Li Y. Nyati M.K. Ahsan A. Kalyana-Sundaram S. Han B. Cao X. Byun J. Omenn G.S. Ghosh D. Pennathur S. Alexander D.C. Berger A. Shuster J.R. Wei J.T. Varambally S. Beecher C. Chinnaiyan A.M. Metabolomic profiles delineate potential role for sarcosine in prostate cancer progression.Nature. 2009; 457: 910-914Crossref PubMed Scopus (1748) Google Scholar). It enables the parallel assessment of a broad range of endogenous metabolites and has a great impact in the investigation of physiological status, the discovery of biomarkers, disease diagnosis, and the identification of perturbed pathways due to disease or treatment (14Wang X. Zhang A. Han Y. Wang P. Sun H. Song G. Dong T. Yuan Y. Yuan X. Zhang M. Ning X. Zhang H. Dong H. Dong W. Urine metabolomics analysis for biomarker discovery and detection of jaundice syndrome in patients with liver disease.Mol. Cell. Proteomics. 2012; 11: 370-380Abstract Full Text Full Text PDF PubMed Scopus (210) Google Scholar, 15Suhre K. Shin S.Y. Petersen A.K. Mohney R.P. Meredith D. Wägele B. Altmaier E. CARDIoGRAM Deloukas P. Erdmann J. Grundberg E. Hammond C.J. de Angelis M.H. Kastenmüller G. Köttgen A. Kronenberg F. Mangino M. Meisinger C. Meitinger T. Mewes H.W. Milburn M.V. Prehn C. Raffler J. Ried J.S. Römisch-Margl W. Samani N.J. Small K.S. Wichmann H.E. Zhai G. Illig T. Spector T.D. Adamski J. Soranzo N. Gieger C. Assimes T.L. Deloukas P. Erdmann J. Holm H. Kathiresan S. König I.R. McPherson R. Reilly M.P. Roberts R. Samani N.J. Schunkert H. Stewart A.F. Human metabolic individuality in biomedical and pharmaceutical research.Nature. 2011; 477: 54-60Crossref PubMed Scopus (764) Google Scholar). Traditional markers of conventional clinical chemistry and histopathology methods are not region specific and increase significantly only after substantial disease injury. Therefore, more sensitive markers of disease are needed. Metabolomics has become a promising player in the disease arena, and its benefits have been demonstrated in diverse clinical areas (16Zhang A. Sun H. Wang X. Power of metabolomics in diagnosis and biomarker discovery of hepatocellular carcinoma.Hepatology. 2012; Google Scholar, 17Wang X. Yang B. Sun H. Zhang A. Pattern recognition approaches and computational systems tools for ultra performance liquid chromatography-mass spectrometry-based comprehensive metabolomic profiling and pathways analysis of biological data sets.Anal. Chem. 2012; 84: 428-439Crossref PubMed Scopus (143) Google Scholar, 18Wang X. Yang B. Zhang A. Sun H. Yan G. Potential drug targets on insomnia and intervention effects of Jujuboside A through metabolic pathway analysis as revealed by UPLC/ESI-SYNAPT-HDMS coupled with pattern recognition approach.J. Proteomics. 2012; 75: 1411-1427Crossref PubMed Scopus (88) Google Scholar). Aiming to gain better insight into HCV metabolism and identify biomarkers with potential diagnostic values for predicting HCV, we have developed a method based on metabolic networks to identify potential targets that might become an effective strategy for the discovery of new drugs for HCV. Metabolite data were analyzed to detect the enriched clusters, determine the perturbation pathways, and infer the biological processes. The specific and unique biochemical pathways can be identified when the approach is coupled with multivariate data analysis techniques and a machine learning algorithm. Thus, the present study was meant to identify the low-molecular-weight metabolites and pathways of HCV infection in tree shrews via the use of multivariate statistical data reduction tools. Acetonitrile and methanol (HPLC grade) were purchased from Merck (Darmstadt, Germany) and TEDIA (Tedia Company Inc, Ohio, USA), respectively. Distilled water was produced using a Milli-Q Ultra-pure water system (Millipore, Billerica, MA). Formic acid of HPLC grade was obtained from Honeywell Company (Morristown, NJ). Leucine enkephalin was purchased from Sigma-Aldrich (St. Louis, MO). All other reagents were of analytical grade. Adult male tree shrews (T. belangeri chinensis, n = 14) were supplied by Zhongkaitao Biotechnology Co., Ltd. (Guangzhou, China). Animals were housed individually in air-conditioned facilities. The room temperature was regulated at 25 °C ± 1 °C with 50% ± 5% humidity. A 12-h light/dark cycle was set, and all animals had free access to standard diet and water. All animals were allowed to acclimatize in metabolism cages for 1 week prior to treatment. The studies were approved by the Animal Experimental Ethical Committee of Heilongjiang University of Chinese Medicine. All efforts were made to ameliorate the suffering of the animals. Animals were divided into two groups, namely, the control group (n = 5) and the model group (n = 9). All animals were supplied with a standard laboratory diet and water ad libitum. The generation of HCV has been described previously (for details, see Ref. 19Xie Z.C. Riezu-Boj J.I. Lasarte J.J. Guillen J. Su J.H. Civeira M.P. Prieto J. Transmission of hepatitis C virus infection to tree shrews.Virology. 1998; 244: 513-520Crossref PubMed Scopus (120) Google Scholar). Blood was collected from the hepatic portal vein, and serum was separated via centrifugation at 4500 rpm for 5 min at 4 °C, flash frozen in liquid nitrogen, and stored at −80 °C until metabolic experiment use. Proteins were precipitated from the defrosted serum samples (100 μl) via the addition of four volumes of methanol in 1.5-ml microtubes at room temperature. After brief vortex mixing, the samples were kept at 4 °C for 5 min. Supernatants were collected after centrifugation at 13,000 rpm for 15 min and transferred to vials for Ultra-performance liquid-chromatography (UPLC)/MS analysis. Chromatography was performed on a 2.1 mm inner diameter × 100 mm ACQUITY 1.8 μm HSS T3 column (Waters Corp., Milford, MA) using an ACQUITY UPLCTM system (Waters Corp., Milford, MA). A "purge-wash-purge" cycle was employed on the auto-sampler, with 90% aqueous formic acid used for the wash solvent and 0.1% aqueous formic acid used as the purge solvent; this ensured that the carry-over between injections was minimized. The column was maintained at 45 °C, and subsequently a gradient of 0.1% formic acid in acetonitrile (solvent A) and 0.1% formic acid in water (solvent B) was used as follows: a linear gradient of 5%–50% A over 0–2.0 min, 50%–55% A over 2.0–3.0 min, 55%–70% A over 3.0–4.0 min, 70%–80% A over 3.0–7.0 min, and 80%–99% A over 7.0–10.0 min. The flow rate was 0.40 ml/min, and a 5-μl aliquot of each sample was injected onto the column. The eluent was introduced to the mass spectrometry directly (i.e. without a split). Quality control samples were used to minimize the analytical variation, evaluate the compound stability, and monitor the sample preparation process. After every 10 sample injections, a pooled sample followed by a blank were injected in order to ensure consistent performance of the system. The eluent was introduced into the synapt high-definition mass spectrometer (Waters Corp., Milford, MA) analysis, and the optimal conditions were as follows: desolvation temperature of 350 °C, source temperature of 110 °C, sample cone voltage of 30 V, extraction cone voltage of 3.5 V, collision energy of 4 eV, microchannel plate voltage of 2400 V, cone gas flow of 50 l/h, desolvation gas flow of 600 l/h, and capillary voltage of 3.2 kV for positive ion mode and 2.8 kV for negative ion mode. Centroid data were acquired in the range of m/z 50–1000 using an accumulation time of 0.2 s per spectrum. For accurate mass acquisition, a lock-mass of leucine enkephalin at a concentration of 0.2 ng/ml was used via a lock spray interface at a flow rate of 100 μl · min−1 monitoring for positive ion mode ([M + H]+ = 556.2771) and negative ion mode ([M − H]− = 554.2615) to ensure accuracy during the MS analysis. The MassFragment™ application manager was used to facilitate the MS/MS fragment ion analysis process by way of chemically intelligent peak-matching algorithms. The identities of the specific metabolites were confirmed via comparison of their mass spectra and chromatographic retention times with those obtained using commercially available reference standards. This information was then submitted for database searching, either in-house or using the online ChemSpider database and MassBank data source. Centroided and integrated raw mass spectrometric data were processed using MassLynx V4.1 and MarkerLynx software (Waters Corp., Milford, MA). The intensity of each ion was normalized with respect to the total ion count to generate a data matrix that consisted of the retention time, m/z value, and normalized peak area. The multivariate data matrix was analyzed using EZinfo software (Waters Corp., Milford, MA). The unsupervised segregation was checked via principal components analysis (PCA) using pareto-scaled data. PCA data were visualized by plotting the PCA scores such that each point in the score plot represented an individual sample and plotting the PCA loadings such that each point represented one mass/retention time pair. From the loading plots of orthogonal partial least-squares to latent structures discriminant analysis (OPLS-DA), various metabolites could be identified as responsible for the separation between control and model groups, and these were therefore viewed as potential biomarkers. Potential markers of interest were extracted from S-plots constructed following OPLS-DA, and markers were chosen based on their contribution to the variation and correlation within the data set. With the completion of the OPLS-DA, we were able to try computational systems analysis with MetaboAnalyst data annotation approach including a correlation analysis plot of the differential metabolites, VIP projection, and heatmap visualization. The construction, interaction, and pathway analysis of potential biomarkers was performed with MetPA based on database sources, including the Kyoto Encyclopedia of Genes and Genomes (http://www.genome.jp/kegg/) and the Human Metabolome Database (http://www.hmdb.ca/), to identify the affected metabolic pathway analysis and visualization. The possible biological roles were evaluated via enrichment analysis using MetaboAnalyst. Subsequently, signaling networks potentially involved in HCV-infected tree shrews were compared and merged using IPA software. In the process of IPA analysis, each network was assigned a P-score (P-score = −log10 (P-value)) reflecting the probability of the network's being generated at random; the p value was calculated as Fisher's exact test. SPSS 13.0 for Windows was used for the statistical analysis. The data were analyzed using the Wilcoxon Mann–Whitney Test, with p < 0.05 set as the level of statistical significance. A MetaboAnalyst data annotation approach was used for the hierarchical clustering analysis and significance analysis for microarrays (SAM). The SAM method, a well-established statistical method for metabolites, was used to select the most discriminant and interesting biomarkers. For UPLC-MS analysis, aliquots were separated using a Waters Acquity UPLC (Waters, Millford, MA) and analyzed using a Q-TOF/MS that consisted of an electrospray ionization source and a linear ion-trap mass analyzer. The UPLC-MS representative Basic Peak Intensity (BPI) profiles of consecutively injected samples of the same aliquot showed stable retention time with no drift in all of the peaks. The stable Basic Peak Intensity (BPI) profiles reflected the stability of UPLC-high-definition mass spectrometry (HDMS) analysis and the reliability of the metabolomic data. Low-molecular-mass metabolites could be separated well in the short time of 10 min because of the minor particles (less than 1.7 μm) of UPLC. Multivariate projection approaches such as PCA and OPLS-DA often can be used, because of their ability to cope with highly multivariate, noisy, collinear, and possibly incomplete data. With OPLS-DA, the identification of discriminatory variables proceeds from an analysis of the OPLS weights. The PCA score plots showed that the metabolic profiles of the control and model groups significantly changed as a result of HCV infection (Figs. 1A and 2A). Trajectory analysis of the serum samples in the three-dimensional score plots corresponded to Fig. 1B in positive mode and Fig. 2B in negative mode. With regard to information analysis of PCA, the control and HCV-infected groups were significantly divided into two clusters, indicating that an HCV model was successfully reproduced. The ions that showed significant differences in abundance between the control and treated animals were contributed to the observed separation and selected from the respective S-plots and VIP-plots as potential markers in positive and negative modes (Figs. 1C, 1D, 2C, and 2D). Overall, 9237 retention-time-exact mass pairs were determined in metabolomic profiling of serum samples. The VIP-value threshold cutoff of the metabolites was set at 2.0; above this threshold, metabolites were filtered out as potential biomarkers. Finally, the number of markers making a significant contribution was 25 in positive mode and 13 in negative mode (Table I). Thirty-two differential metabolites were identified and verified via reference standards. Acquired data were subjected to computational systems analysis with MetaboAnalyst's data annotation tools in order to further investigate the HCV-infected metabolite profiles. The correlation analysis plot of the differential metabolites (Fig. 3A) and the heatmap visualization (Fig. 3B) for the HCV showed distinct segregation. These models were capable of distinguishing HCV-infected animals by adjusting multiple metabolic pathways from healthy subjects. The heatmap was constructed based on the potential candidates of importance, implemented in MetaboAnalyst, which is commonly used for unsupervised clustering. From the plots, various metabolites could be identified as responsible for the separation between control and model groups, and these were therefore viewed as potential biomarkers.Fig. 2A, PCA model results for control and model groups in negative mode. B, trajectory analysis of PCA score plots (three-dimensional) for the serum samples in negative mode. C, VIP-plot of OPLS-DA of samples in negative mode. D, S-plot of OPLS-DA of serum samples in negative mode. M, model group; K, control group.View Large Image Figure ViewerDownload Hi-res image Download (PPT)Table IPotential biomarkers identified in HCV-infected tree shrews in positive and negative modeNumberRate (min)m/z, determinedm/z, calculatedError (ppm)Ion formMolecular formulaMetabolite nameVIP valueTrendp value14.65520.3392520.3403−2.1[M+H]+C26H50NO7PLysoPC(18:2(9Z,12Z)/0:0)14.3↓0.0024.52520.3390520.3403−2.5[M+H]+C26H50NO7PLysoPC(0:0/18:2(9Z,12Z))7.3↓0.0034.93991.6738991.67281.0[2 m+H]+C24H50NO7PLysoPC(0:0/16:0)11.5↑0.0044.76496.3409496.34031.2[M+H]+C24H50NO7PLysoPC(16:0/0:0)8.5↑0.0056.02524.3699524.3716−3.2[M+H]+C26H54NO7PLysoPC(18:0/0:0)9.6↑0.0065.77524.3732524.37163.1[M+H]+C26H54NO7PLysoPC(0:0/18:0)6.8↑0.0171.41188.0713118.07120.5[M+H]+C11H9NO2Indoleacrylic acid5.1↓0.0182.15286.2013286.2018−1.7[M+H]+C15H27NO42-octenoylcarnitine5.0↑0.0192.35300.2160300.2175−5.0[M+H]+C16H29NO42-nonenoylcarnitine4.1↑0.01101.94270.0788270.0800−4.4[M+H]+C12H15NO4SUnknown5.4↓0.01115.44508.3661508.3672−2.2[M+H]+C26H54NO6PLysoPC(P-18:0)3.9↑0.01124.88454.2926454.2934−1.8[M+H]+C21H44NO7PLysoPE(16:0)3.9↑0.01135.96482.3231482.3247−3.4[M+H]+C23H48NO7PLysoPE(18:0)3.7↑0.01144.63568.3416568.34032.2[M+H]+C30H50NO7PLysoPC(22:6(4Z,7Z,10Z,13Z,16Z,19Z))3.6↑0.01154.24518.3245518.3247−0.4[M+H]+C26H48NO7PLysoPC(18:3(9Z,12Z,15Z))3.4↓0.02162.55314.2337314.23311.9[M+H]+C17H31NO49-decenoylcarnitine3.0↑0.02172.39312.2161312.2175−4.5[M+H]+C17H29NO42-trans,4-cis-decadienoylcarnitine2.9↑0.02184.61478.2950478.29343.3[M+H]+C23H44NO7PLysoPE(18:2(9Z,12Z))2.6↓0.02193.60358.2963358.29571.7[M+H]+C20H39NO4Unknown2.6↑0.02205.71482.3248482.32470.2[M+H]+C23H48NO7PLysoPE(18:0)2.5↑0.02216.42510.3928510.39240.8[M+H]+C26H56NO6PLysoPC(O-18:0)2.5↑0.03222.94464.2811464.28012.2[M+H]+C29H37NO4Unknown6.0↓0.03232.28464.2802464.28010.2[M+H]+C29H37NO4Unknown4.2↓0.03244.13468.3113468.30904.9[M+H]+C22H46NO7PLysoPC(14:0)2.1↑0.04252.30288.2174288.2175−0.3[M+H]+C15H29NO4l-octanoylcarnitine2.1↑0.04262.24245.0493245.04843.7[M−H]−C10H14O5SUnknown6.5↑0.00277.60327.2339327.23244.6[M−H]−C22H32O2Docosahexanoic acid2.8↑0.00284.87452.2791452.27773.1[M−H]−C21H44NO7PLysoPE(16:0)4.5↑0.00297.82303.2310303.2324−4.6[M−H]−C20H32O2Arachidonic acid3.8↑0.01306.00568.3594568.3614−3.5[M−HCOO]−C26H54NO7PLysoPC(18:0)2.9↑0.01315.44552.3693552.36704.2[M−HCOO]−C26H54NO6PLysoPC(P-18:0)2.1↑0.01325.93480.3078480.3090−2.5[M−H]−C23H48NO7PLysoPE(18:0)4.5↑0.02334.13512.2972512.2993−3.1[M−HCOO]−C22H46NO7PLysoPC(14:0)2.2↑0.02342.28674.3250674.32105.9[M−H]−C32H53NO12PSTaurodeoxycholic acid glucuronide4.7↓0.02352.94498.2899498.28892.0[M−H]−C26H45NO6STaurodeoxycholic acid6.5↓0.02363.09498.2917498.28895.6[M−H]−C26H45NO6STauroursodeoxycholic acid3.0↓0.02372.55514.2844514.28391.0[M−H]−C26H45NO7STaurocholic acid5.4↓0.03381.95268.0636268.0644−3.0[M−H]−C12H15NO4SUnknown3.3↓0.03 Open table in a new tab Fig. 3Computational systems analysis with MetaboAnalyst's data annotation tools. A, correlation analysis plot of the differential metabolites. B, heatmap visualization for the HCV-infected tree shrews. The heatmaps were constructed based on the potential candidates of importance and implemented in MetaboAnalyst, and they are commonly used for unsupervised clustering. Rows: samples; columns: metabolites.View Large Image Figure ViewerDownload Hi-res image Download (PPT) The robust UPLC-HDMS analysis platform provides the retention time, precise molecular mass, and MS/MS data for the structural identification of biomarkers. The molecular mass was determined within measurement errors via Q-TOF, and the potential elemental composition, degree of unsaturation, and fractional isotope abundance of compounds were also obtained. The presumed molecular formula was searched in Chemspider, the Human Metabolome Database, and other databases in order to identify the possible chemical constitutions, and MS/MS data were screened to determine the potential structures of the ions. According to the protocol detailed above, 38 endogenous metabolites contributing to the separation of the model group and the control group were detected in the samples (Table I). Monitoring changes in these metabolites might aid predictions of the development of HCV. Therefore, these metabolites were selected as candidate markers for further validation. The SAM method was used to select the most discriminant and interesting biomarkers. The results indicated that lysoPC(0:0/16:0), 2-octenoylcarnitine, lysoPE(16:0), arachidonic acid, and taurocholic acid were the most significant differential metabolites for the classification of the HCV model and the controls (Fig. 4). More detailed analyses of pathways and networks influenced by HCV infection were performed using MetPA, which is a free web-based tool that combines results from powerful pathway enrichment analysis with the topology analysis. Metabolic pathway analysis with MetPA revealed that metabolites that were identified together were important for the host response to HCV and were responsible for taurine and hypotaurine metabolism, ether lipid metabolism, glycerophospholipid metabolism, primary bile acid, arachidonic acid metabolism, and tryptophan metabolism (supplemental Fig. S1 and supplemental Table S1). Potential biomarkers were also identified from these relevant pathways. Some significantly changed metabolites have been found and used to explain the arachidonic acid metabolism. Th