Differential Regulation of Host Genes Including Hepatic Fatty Acid

Article pubs.acs.org/jpr

Differential Regulation of Host Genes Including Hepatic Fatty Acid Synthase in HBV-Transgenic Mice Hongmin Zhang,† Hong Li,† Yixuan Yang, Sanglin Li, Hong Ren, Dazhi Zhang, and Huaidong Hu* Department of Infectious Diseases, Institute for Viral Hepatitis, Key Laboratory of Molecular Biology for Infectious Diseases, Ministry of Education, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, China S Supporting Information *

ABSTRACT: Hepatitis B virus (HBV) is the most common of the hepatitis viruses that cause chronic liver infections in humans, and it is considered to be a major global health problem. To gain a better understanding of HBV pathogenesis, and identify novel putative targets for anti-HBV therapy, this study was designed to elucidate the differential expression of host proteins in liver tissue from HBV-transgenic mice. Liver samples from two groups, (1) HBV-transgenic (Tg) mice, (2) corresponding background normal mice, wild-type (WT) mice, were collected and subjected to iTRAQ and mass spectrometry analysis. In total, 1950 unique proteins were identified, and 68 proteins were found to be differentially expressed in HBV-Tg mice as compared with that in WT mice. Several differentially expressed proteins were further validated by real-time quantitative RTPCR, Western blot and immunohistochemical analysis. Furthermore, the association of HBV replication with fatty acid synthase (FASN), one of the highly expressed proteins in HBV-Tg mice, was verified. Silencing of FASN expression in HepG2.2.15 cells suppressed viral replication through the IFN signaling pathway, and some downstream antiviral effectors. The implicated role of FASN in HBV replication provides an opportunity to test existing compounds against FASN for adjuvant therapy and/or treatment of HBV replication. KEYWORDS: HBV, FASN, iTRAQ, proteomics

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INTRODUCTION Hepatitis B virus (HBV) infection is one of the most common infectious diseases worldwide and is a major global health problem. Despite the availability of hepatitis B virus (HBV) vaccines, there are approximately 350 million chronic hepatitisB surface antigen (HBsAg) carriers in the world.1,2 Epidemiological studies on hepatitis B infection have revealed that it remains a main cause of chronic hepatitis, liver fibrosis, and hepatocellular carcinoma (HCC). Each year over 1 million persons die from HBV-related liver diseases, 30−50% of which are attributed to HCC.3 A persistently elevated serum HBV DNA level was reported to be an important risk factor for the development of hepatocellular carcinoma (HCC).4 Currently available antiviral drugs for chronic hepatitis B include interferon-alpha (INFa) and nucleos(t)ide analogue (NA) polymerase inhibitors (lamivudine, adefovir, entecavir, telbivudine and tenofovir).5 Unfortunately, the development of drugresistance to these drugs by the virus will most likely limit their long-term efficacy.6 Thus, it is crucial to gain a detailed understanding of both the host- and virus-based molecular mechanisms underlying HBV replication and pathogenesis. Studies on the pathogenesis of HBV infections have been hindered by the lack of an efficient cell culture system that supports HBV replication. However, the recent development of comparative proteomic analysis approaches holds promise for performing large-scale studies of protein profiles under various pathogenic conditions. This approach has been successfully applied to HepG2.2.15 and HepAD38 cultured cells lines, both © XXXX American Chemical Society

of which were derived from the HCC cell line HepG2 and contain greater than 1-unit length of the HBV genome integrated into the host genomes.7 These studies led to the identification and characterization of unique proteome profiles associated with HBV infection in immortalized hepatocytes.8,9 While these results provided important insights into the HBV activities during infection, they did not reflect the complexities of the system in vivo or the interactions between HBV and host factors. In this regard, liver specimens obtained from HBV-Tg mice which contain 1.3 copies of the complete HBV adr genome, offer a unique opportunity to investigate the host molecular mechanisms involved in HBV replication and pathogenesis. Such studies may be useful in identifying therapeutic targets against HBV. Over the past several years, there has been an increasing interest in applying isotope-based quantitative proteomics for research of diverse nature, ranging from biomarker discovery to analysis of post-translational modifications. Stable isotopes can be introduced in vitro by reacting proteins or peptides with isotope-coded reagents.10 These emerging technologies include isotope-coded affinity tags (ICAT), relative and absolute quantification (iTRAQ), and 18O, and stable isotope labeling with amino acids in cell culture (SILAC).11−13 Among them, the iTRAQ method is considered a particularly powerful tool, since it can facilitate simultaneous analysis of up to eight Received: March 19, 2013

A

dx.doi.org/10.1021/pr400247f | J. Proteome Res. XXXX, XXX, XXX−XXX

Journal of Proteome Research

Article

(7 M urea, 1 mg/mL of DNase I, 1 mM Na3VO4, and 1 mM PMSF) and separated by centrifugation at 15 000 rpm for 30 min at 4 °C. The supernatant was collected, and the concentration of the total proteins was determined by the 2D Quantification Kit (Amersham Biosciences). Protein samples were acetone precipitated overnight at −20 °C and dissolved in the lysis buffer, denatured, and treated for cysteine blocking as described in the iTRAQ protocol (Applied Biosystems). Each sample was digested with 20 mL of 0.1 mg/mL of trypsin solution (Promega) at 37 °C overnight, and then labeled with the iTRAQ tags as follows: pooled seven WT mice, 113 tag; HBV-Tg mouse 1, 114 tag; HBV-Tg mouse 2, 115 tag; HBVTg mouse 3, 116 tag; HBV-Tg mouse 4, 117 tag; HBV-Tg mouse 5, 118 tag; HBV-Tg mouse 6, 119 tag; HBV-Tg mouse 7, 121 tag. The labeled samples were pooled prior to further analysis.

samples in one experiment. The aim of the current study was to use iTRAQ to identify alterations in the proteome of HBV-Tg mice compared to WT mice in order to identify genes involved in host−viral interactions. In the current study, we employed iTRAQ to identify alterations in the proteome of HBV-Tg mice as compared to WT mice. We hypothesized that using the ultrasensitive nanoproteomics platform of iTRAQ-coupled 2D liquid chromatography electrospray tandem mass spectrometry (LC−MS/MS) technology would allow for the identification of not only a large panel of disease-related host proteins, but also novel proteins that represent critical virus−host mechanisms that may be useful targets of future HBV therapeutic strategies .

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MATERIALS AND METHODS

Fractionation of Peptides by Isoelectric Focusing (IEF) on Immobilized pH Gradient

Reagents

The iTRAQ 8-plex kits were purchased from Applied Biosystems (Foster City, CA). Sequence-grade modified trypsin was purchased from Promega (Madison, WI). Polyvinylidene fluoride (PVDF) membrane, IgG antibodies (goat antimouse, goat antirabbit or rabbit antigoat) conjugated with horseradish peroxidase (HRP), and the enhanced chemiluminescence (ECL) system were purchased from Amersham Biosciences (Uppsala, Sweden). Monoclonal antibodies or polyclonal antibodies against FASN, GSTP1, BHMT, ENO1 and actin were obtained from Abcam (Cambridge, MA). Specific siRNA oligonucleotides directed against human FASN (HSS103565, HSS176712, HSS176713) and a negative control (12935-400) were purchased from Invitrogen (Carlsbad, CA). Lipofectamine 2000 was purchased from Invitrogen (Carlsbad, CA).

iTRAQ-labeled tryptic peptide samples were dissolved in 300 μL of 8 M urea and 1% Pharmalyte (Amersham Biosciences). Samples were used to rehydrate IPG strips (pH 3−10, 18 cm long, Amersham Biosciences) for 14 h at 30 V. Subsequently peptides were focused successively for 1 h at 500 V, 1 h at 1000 V, 1 h at 3000 V and 8.5 h at 8000 V to give a total of 68 kV·h on IPGphor (Amersham Biosciences). The strips were removed and quickly cut into 36 0.5-cm pieces. Peptide extractions were performed by incubating the gel pieces in 100 μL of 2% acetonitrile, 0.1% formic acid for 1 h. These fractions were lyophilized in a vacuum concentrator and subjected to C18 cleanup using a C18 Discovery DSC-18 SPE column (100 mg capacity, Supelco, Sigma-Aldrich). The cleaned fractions were lyophilized again and stored at −20 °C prior to mass spectrometric analysis.

Tissue Samples and Cell Lines

Balb/c HBV-Tg mice and Balb/c WT mice (8 weeks old) were obtained from the Infectious Disease Center of No. 458 Hospital (Guangzhou, China).14,15 Mice were maintained under the pathogen-free conditions in a temperature-controlled animal facility at Chongqing Medical University with 12 h day/ night cycles and food and water provided ad libitum. Mice were sacrificed by cervical dislocation at the completion of experiments. The animal protocol was approved by the Institutional Animal Care and Use Committee of the Chongqing Medical University, and was performed in compliance with the Guide for the Care and Use of Laboratory Animals. As described previously,16 nontumor liver tissue samples used for tissue microarray were collected from 90 HBV-associated HCC patients and 70 non-HBV HCC patients who underwent hepatectomy at the Second Affiliated Hospital of Chongqing Medical University. Written informed consent was obtained from all participants prior to the collection of samples. All patients were verified negative for human immunodeficiency virus (HIV) and hepatitis virus C (HCV). Approval from the Institutional Research Board at the Chongqing Medical University was obtained prior to the start of the study. HepG2.2.15 and HepG2 cells, obtained from ATCC (Rockville, MD), were cultured at 37 °C, under 5.0% CO2, in DMEM (high glucose) containing 10% FBS, 100 IU/ mL of penicillin, 100 μg/mL of streptomycin, 2 mM glutamine, 0.1% nonessential amino acids and 1.0 mM sodium pyruvate.

Mass Spectrometry

Each cleaned-up peptide fraction was resuspended in 20 μL of Buffer A (0.1% formic acid in 2% acetonitrile). Ten microliters of sample were injected into a nano-LC−ESI−MS/MS system for each analysis. Mass spectrometry was performed using a QStar Elite Hybrid ESI Quadrupole time-of-flight tandem mass spectrometer (ESI-Q-TOF-MS/MS, Applied Biosystems, Framingham, MA; MDS-Sciex, Concord, Ontario, Canada), coupled to an online capillary liquid chromatography system (Dionex Ultimate 3000, Amsterdam, The Netherlands). The peptide mixture was separated on a PepMap C-18 RP capillary column (Dionex) at flow rate of 0.3 μL/min. A 125 min gradient was used, in which the gradient started with 4% Buffer B (0.1% formic acid in 98% acetonitrile) and 96% Buffer A for 3 min, followed by 3 ramping gradients of 4−10% Buffer B in 7 min, 10−35% Buffer B for 55 min and 35−100% Buffer B for 25 min. Subsequently, the proportion was held at 100% Buffer B for 15 min and finally at 96% Buffer A for 20 min. The mass spectrometer was set to perform data acquisition in the positive ion mode, with a selected mass range of 300−1800 m/z. The time of summation of MS/MS events was set to be 2 s. This refers to the amount of time allowed for the machine to accumulate MS/MS events before switching back to MS scan. The two most abundant charged peptides above a 20 count threshold were selected for MS/MS and dynamically excluded for 30 s with ±50 mDa mass tolerance. Protein identification and quantification for iTRAQ samples were carried out using ProteinPilot software (version 2.0; Applied Biosystems, MDS-Sciex). The search was performed

Protein Sample Preparation and iTRAQ Labeling

Total protein extracts were prepared by the Sample Grinding Kit from Amersham Biosciences using the iTRAQ lysis buffer B



Article

5 min with TBS-T buffer, the membranes were incubated with a horseradish peroxidase-conjugated (HRP) goat antirabbit IgG or rabbit antigoat IgG as the secondary antibody (1:5000 dilution) for 1 h at room temperature. The membranes were then washed three times in TBS-T buffer, and the reactions were visualized with the ECL detection system. All of the Western blotting analyses were repeated at least three times.19

against International protein index (IPI) MOUSE database (version 3.28, date of release: April 2007, 53 847 sequences). Database search was performed by setting cysteine modification by MMTS as a fixed modification. Other parameters include mass tolerance of up to 0.2 Da, maximum of one missed cleavage of trypsin, oxidation of methionine, N-terminal iTRAQ labeling and iTRAQ labeled-lysine. Relative quantification of proteins in the case of iTRAQ was performed on the MS/MS scans using ratios of the areas under the peaks at 113, 114, 115, 116, 117, 118, 119, and 121 Da, which were the masses of the tags corresponding to the iTRAQ reagents used to label the samples. Statistical calculation for iTRAQ-based detection and relative quantification was performed using the Paragon Algorithm embedded within the ProteinPilot software. Following data analysis by the ProteinPilot software, the protein summary results were exported into an Excel spreadsheet for manual data interpretation. Briefly, for protein identification and quantitative analysis, 95% confidence was used. Protein identification was based on at least three unique peptides. Protein hits that did not satisfy these criteria were not included in the analysis.

Immunohistochemistry (IHC) and Tissue Microarray (TMA)

Four candidate proteins, FASN, GSTP1, BHMT and ENO1, were selected for further analysis by IHC on the basis of the fact that they had not been previously reported as abnormally expressed in HBV-Tg mice. Briefly, paraffin-embedded mouse liver sections were warmed in a 60 °C oven, deparaffinized in three changes of xylene, and passaged through a graded ethanol series (100, 95, and 70%) before a final wash in double distilled H2O.18 After quenching of endogenous peroxidase activity with 3% H2O2 for 10 min and blocking with bovine serum albumin (BSA) for 30 min, sections were incubated at 4 °C overnight with antibodies against FASN (1:200), GSTP1 (1:300), BHMT (1:200) and ENO1 (1:100). Detection was achieved with the Envision/horseradish peroxidase system (Dako-Cytomation, Glostrup, Denmark).18 All slides were counterstained with Gill’s hematoxylin for 1 min, dehydrated, and mounted for light microscope evaluation.18,19 FASN was selected for further analysis by tissue microarray on the basis of the fact that it had not been previously reported as aberrantly expressed in hepatitis patients. TMAs were baked at 60 °C, and immunohistochemical procedures were performed as mentioned above. Protein levels were assessed using a semiquantitative scoring consisting of an assessment of both staining intensity (scale: 0− 3) and the percentage of positive cells (0−100%), which when multiplied, generated a score ranging from 0 to 300.18 Interpretation of hematoxylin and eosin sections and analysis/scoring of IHC data were all done by the same certified pathologist to maintain consistency.

Real-Time Quantitative RT-PCR Analysis

Total RNA was extracted using Trizol reagent (Gibco BRL, Gaithersburg, MD) following the manufacturer’s instructions. Two micrograms of total RNA were reverse-transcribed into first-strand cDNA using the A3500 Reverse Transcription System (Promega). Quantitative RT-PCR was performed on the ABI 7900HT System using the TaqMan GeneExpression Assay Kit and gene-specific primers for GAPDH (Mm99999915_g1), Acss2 (Mm00480101_m1), Fdps (Mm00836315_g1), GSTP1 (Mm04213618_gH), Thrsp (Mm01273967_m1), Bhmt (Mm04210521_g1), Lss (Mm00461312_m1), Acly (Mm01302282_m1), Fasn (Mm00662319_m1), Pzp (Mm00431533_m1), SerpinA3K (Mm03058186_m1), Inmt (Mm00493537_m1), Ugt1a6a (Mm01967851_s1), and ENO1 (Mm01619597_g1). To detect the expression levels of type I IFN and the downstream IFNstimulated genes associated with FASN down-expression, genespecific primers for IFNa1 (Hs00855471_g1), IFNb1 (Hs01077958_s1), ISG15 (Hs01921425_s1), Mx1 (Hs00895608_m1), OAS1 (Hs00973637_m1), OAS2 (Hs00942643_m1), OAS3 (Hs00196324_m1), OASL (Hs00984390_m1), RNASEL (Hs00221692_m1), EIF2a (Hs00230684_m1), and GAPDH (Hs02758991_g1) were used. The relative quantification of gene expression was analyzed using the 2−ΔΔCT method.17 Real-time quantitative RT-PCR analysis was repeated at least three times.

FASN siRNA Transfection

For functional studies, three siRNA duplexes against human FASN (HSS103565, HSS176712, HSS176713) and a negative control (12935-400) were purchased from Invitrogen (Carlsbad, CA). As described previously,20 HepG2.2.15 cells were transfected with siRNA according to the protocol provided by the manufacturer. Briefly, cells were plated into 6-well plates at the density of 105 cells/ml medium. When the cells were 60− 80% confluent, they were transfected with 50 nmol/L of FASN siRNA, and control siRNA after a preincubation for 20 min with lipofectamine 2000 transfection reagent in Opti-MEM I medium (Gibco). After 4 h of transfection, the medium was replaced with DMEM medium containing 10% fetal calf serum, and continued in culture for additional 44 h. Cells were harvested 48 h after transfection for Western blot analysis. Effective knock-down of gene expression was verified by Western blotting analysis described above. Experiments were performed at least in triplicate.

Western Blotting Analysis

The tissues/cells were lysed at 4 °C with nonionic detergent (NID) lysis buffer (50 mM, pH 7.5 Tris-HCl, 0.5% IGEPAL, 150 mM NaCl, 1 mM, pH 8.0 EDTA, 0.5% Triton X, 50 mM sodium fluoride, 1 mM sodium orthovanadate, and protease inhibitors).18 The lysates were centrifuged at 15 000 rpm for 15 min at 4 °C. Protein concentration was determined using the 2D Quantification Kit (Amersham Biosciences). Twenty micrograms of proteins were separated by SDS-PAGE and transferred to PVDF membranes. The membranes were blocked 1 h with 1% BSA in Tris-buffered saline with Tween 20 (TBS-T) containing 20 mmol/L of Tris, pH 7.6, 100 mmol/ L of NaCl2, and 0.5% Tween-20. This was followed by 2 h of incubation with the primary antibodies (1:500−1:1000 dilution) at room temperature. After washing three times for

Supernatant HBV DNA Analysis

Culture media were collected 48 h after transfection for quantification of HBV DNA.16 Brief centrifugation was used to remove cell debris. Viral core particles were precipitated with 10% polyethylene glycol 8000 in 0.5 M NaCl at 4 °C overnight. After centrifugation at 15 000 rpm for 30 min, viral cores were pelleted and subsequently treated with DNase I (100 μg/mL in C



Article

Figure 1. (A) Flowchart of the iTRAQ proteomics approach used in this study. (B) Representative MS/MS spectrum showing the peptides from FASN (peptide sequence: ACVDTALENLSTLK). Pooled WT mouse samples were labeled with iTRAQ 113 tag; HBV-Tg mouse 1 was labeled with iTRAQ 114 tag; HBV-Tg mouse 2 was labeled with iTRAQ 115 tag; HBV-Tg mouse 3 was labeled with iTRAQ 116 tag; HBV-Tg mouse 4 was labeled with iTRAQ 117 tag; HBV-Tg mouse 5 was labeled with iTRAQ 118 tag; HBV-Tg mouse 6 was labeled with iTRAQ 119 tag; HBV-Tg mouse 7 was labeled with iTRAQ 121 tag. Thus, the ratio of 114:113, 115:113, 116:113, 117:113, 118:113, 119:113, and 121:113 indicated the relative abundance of FASN protein.

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50 mM Tris−HCl, pH 8.0, 10 mM MgCl2) at 37 °C for 3 h. The mixture was further digested with proteinase K (400 μg/ mL in 15 mM EDTA, 100 mM NaCl, 0.5% SDS) at 55 °C for 2 h, which was followed by phenol/chloroform extraction. HBV DNA was precipitated by ethanol, resuspended in TE buffer (10 mM Tris−HCl, pH 8.0, 1 mM EDTA) and digested with 100 ng/μL of Rnase A for 30 min at 37 °C. Purified DNA was then subjected to HBV DNA quantification using real-time RTPCR. Levels of HBsAg and HBeAg in the media of the transfected cells were determined by enzyme-linked immunosorbent assay (ELISA).16

RESULTS

iTRAQ Analysis of Differentially Expressed Proteins

To investigate the molecular characteristics of HBV replication, we adopted a quantitative proteomic approach with isobaric labeling (iTRAQ) using the liver tissues of HBV-Tg mice and WT mice. The experimental design showing how all seven independent HBV-Tg mice were analyzed using 8-plex iTRAQ is shown in Figure 1A. Baseline protein samples from all seven WT mice were pooled and used as an internal control so that data could be normalized and compared across all the samples. Hence, the relative quantification for each individual samples (seven HBV-Tg mice) was relative to this pooled sample. The MS/MS spectrum of FASN (peptide sequence: ACVDTALENLSTLK) is presented in Figure 1B. Seven WT mouse samples were pooled and labeled with iTRAQ 113 tag; HBVTg mouse 1 was labeled with iTRAQ 114 tag; HBV-Tg mouse 2 was labeled with iTRAQ 115 tag; HBV-Tg mouse 3 was labeled with iTRAQ 116 tag; HBV-Tg mouse 4 was labeled with iTRAQ 117 tag; HBV-Tg mouse 5 was labeled with

Statistical Analysis

Continuous variables are presented as mean ± standard deviation (SD); intergroup differences were compared by t tests. Categorical variables were compared by Pearson’s Chisquare (χ2) test or Fisher’s exact test, as appropriate. All statistical tests were two-sided, and a p-value < 0.05 was considered to be statistically significant. Statistical analysis was carried out with the Statistical Package for Social Science software, version 17.0 (SPSS, Chicago, IL, USA). D


accession

IPI:IPI00322530.1 IPI:IPI00117176.1 IPI:IPI00169958.3 IPI:IPI00330302.2 IPI:IPI00453826.2 IPI:IPI00113223.2 IPI:IPI00762047.1 IPI:IPI00120457.1 IPI:IPI00468859.3 IPI:IPI00319270.2 IPI:IPI00115850.1 IPI:IPI00662715.2 IPI:IPI00123449.1 IPI:IPI00225670.1 IPI:IPI00130950.1 IPI:IPI00338068.2

IPI:IPI00124225.3 IPI:IPI00262743.6 IPI:IPI00756386.1 IPI:IPI00129178.1 IPI:IPI00128692.1

IPI:IPI00228757.1 IPI:IPI00331707.1 IPI:IPI00315550.3 IPI:IPI00754489.2

IPI:IPI00751461.1

IPI:IPI00230649.1 IPI:IPI00758006.1

IPI:IPI00555023.2 IPI:IPI00469317.4 IPI:IPI00336362.2 IPI:IPI00128346.1 IPI:IPI00222515.5 IPI:IPI00114017.1 IPI:IPI00132397.1 IPI:IPI00760015.1

number

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16

17 18 19 20 21

22 23 24 25

E

26

27 28

29 30 31 32 33 34 35 36

Gstp1 Sars Aldh1a7 Zcd1 Psmd11 Anxa7 Sar1b Hsd3b7

Lgals9 2900073G15Rik

Pgd

Tmsb4x Hmgcs1 Txnl2 Papss2

Psme2b-ps;Psme2 Gpsn2 Dhtkd1 Oat Nsdhl

Scd1 Faah Lss Acss2 Matr3 Fasn Acly Fdps Fads1 M6prbp1 Idi1 EG638833 Thrsp Gphn Bhmt Fdft1

gene Acyl-CoA desaturase 1 Fatty-acid amide hydrolase Lanosterol synthase Acetyl-coenzyme A synthetase, cytoplasmic Matrin-3 Fatty acid synthase ATP-citrate synthase Farnesyl pyrophosphate synthetase delta-5 desaturase Mannose-6-phosphate receptor-binding protein 1 Isopentenyl-diphosphate Delta-isomerase 1 similar to Glyceraldehyde-3-phosphate dehydrogenase Thyroid hormone-inducible hepatic protein Gephyrin Betaine-homocysteine S-methyltransferase 1 farnesyl diphosphate farnesyl transferase 1, full insert sequence Proteasome activator complex subunit 2 Synaptic glycoprotein SC2 Dehydrogenase E1 and transketolase domain containing 1 Ornithine aminotransferase, mitochondrial precursor Sterol-4-alpha-carboxylate 3-dehydrogenase, decarboxylating Isoform Short of Thymosin beta-4 Hydroxymethylglutaryl-CoA synthase, cytoplasmic Thioredoxin-like protein 2 Bifunctional 3′-phosphoadenosine 5′-phosphosulfate synthetase 2 similar to 6-phosphogluconate dehydrogenase, decarboxylating isoform 9 Isoform Short of Galectin-9 Adult male hippocampus cDNA, RIKEN full-length enriched library, clone:2900073G15 product:myosin regulatory light chain A, smooth muscle homologue Glutathione S-transferase P 1 Seryl-tRNA synthetase, cytoplasmic Aldehyde dehydrogenase Ahd-2-like Zinc finger CDGSH domain-containing protein 1 26S proteasome non-ATPase regulatory subunit 11 Annexin A7 GTP-binding protein SAR1b hydroxy-delta-5-steroid dehydrogenase, 3 beta- and steroid delta-isomerase 7 isoform b

protein

23 59 56 12 47 53 22 41

40 20

53

5 58 37 69

26 36 103 48 41

41 63 83 79 95 272 120 41 52 47 26 36 17 81 45 48

protein molecular weight (kDa)

112 3 43 6 9 3 9 3

9 3

6

4 8 4 17

4 4 4 41 5

3 4 5 13 3 200 52 14 3 4 7 9 3 7 105 3

unique peptide (95%)

76.19 7.03 62.87 65.74 24.64 7.13 56.06 14.04

39.13 17.44

18.30

79.55 26.73 14.54 37.78

29.71 12.66 7.93 73.58 29.56

10.70 11.05 9.14 22.90 7.09 58.67 45.37 41.36 11.19 16.48 28.63 20.12 23.33 14.04 84.03 10.10

protein sequence % cov (95)

1.67↑ 1.45↑ 0.99 1.3↑ 1.41↑ 1.59↑ 1.44↑ 1.52↑

1.7↑ 1.64↑

1.53↑

1.76↑ 1.39↑ 1.39↑

1.2 1.39↑ 1.44↑ 1.03 1.72↑

2.67↑ 2.52↑ 1.76↑ 1.96↑ 2.49↑ 1.64↑ 1.84↑ 1.86↑ 1.63↑ 1.08 2.6↑

5.5↑ 2.55↑ 2.98↑ 1.89↑

HBVTg1

1.3 1.59↑ 1.33↑ 1.52↑ 1.55↑ 1.45↑ 1.29 1.07 1.27 1.04

1.13 1.33↑ 1.56↑ 1.31↑ 1.36↑ 1.08 1.24 0.99 1.59↑ 1.49↑ 1.59↑ 1.51↑ 1.51↑ 1.4↑ 1.43↑ 1.34↑ 1.31↑ 1.35↑ 1.85↑ 1.46↑ 1.41↑ 1.37↑ 1.37↑ 1.68↑ 1.43↑ 1.68↑ 1.46↑ 1.41↑ 1.28 1.42↑ 1.39↑ 1.5↑ 1.71↑ 1.32↑ 0.87 1.31↑ 1.39↑ 1.73↑ 1.5↑ 1.53↑

1.53↑

1.72↑ 1.22 1.1 1.53↑

2.05↑ 1.14 2.23↑ 2.36↑ 1.58↑

1.42↑ 1.43↑

1.68↑

1.34↑ 1.71↑ 1.63↑ 1.43↑

1.31↑ 1.77↑ 1.78↑ 1.32↑ 1.56↑

1.71↑ 1.79↑

1.57↑

1.83↑ 1.55↑ 1.95↑ 1.65↑

2.05↑ 2.57↑ 1.46↑ 1.81↑ 1.57↑

1.5↑ 1.04

1.52↑

1.65↑ 1.36↑ 1.66↑ 1.81↑

1.87↑ 1.12 1.23 2↑ 1.8↑

1.24 1.17

1.27

1.63↑ 1.12 1.58↑ 1.6↑

2.32↑ 1.32↑ 1.88↑ 1.94↑ 1.44↑

1.3 1.97↑ 2.06↑ 1.76↑ 1.52↑ 1.34↑ 1.57↑ 1.98↑ 1.47↑ 1.67↑ 1.78↑ 1.47↑ 1.32↑ 1.69↑ 1.68↑ 1.6↑ 1.35↑ 1.69↑ 2.08↑ 2.07↑ 1.4↑ 1.71↑ 1.84↑ 1.55↑ 1.56↑ 1.5↑ 1.64↑ 2.05↑ 1.49↑ 1.36↑ 1.69↑ 1.43↑ 2.21↑ 1.85↑ 2.7↑ 1.62↑ 1.71↑ 1.81↑ 2.21↑ 1.71↑ 1.62↑ 2.75↑ 1.94↑ 1.63↑ 1.64↑ 1.94↑ 2.25↑

HBVTg7

HBVTg6

HBVTg5

1.45↑ 1.58↑

1.52↑

1.21 2.23↑ 1.51↑ 1.37↑

0.81 2.2↑ 1.37↑ 0.88 1.44↑

1.79↑ 2.75↑ 2.93↑ 2.82↑

1.04 2.54↑ 1.02 1.31↑ 1.37↑ 1.09 1.07 1.37↑ 2.27↑ 1.27 1.37↑ 1.7↑ 1.48↑ 1.85↑ 2.12↑ 1 6.05↑ 2.1↑ 2.74↑ 2.18↑ 4.28↑ 2.76↑ 2.45↑ 1.69↑ 1.64↑ 2.69↑ 1.16 1.49↑ 1.82↑ 1.78↑ 1 1.55↑ 1.93↑ 1.92↑ 2.45↑ 2.13↑ 1.35↑ 2.21↑ 1.88↑ 2.25↑ 1.9↑ 2.3↑ 1.31↑

HBVTg4

HBVTg3

HBVTg2

Table 1. A Partial List of Genes Found to Be Differentially Expressed between HBV-Tg Mice and WT Mice by iTRAQ Analysis

Journal of Proteome Research Article


F

IPI:IPI00131830.1 IPI:IPI00322218.4 IPI:IPI00331361.2 IPI:IPI00621538.2 IPI:IPI00623951.3 IPI:IPI00282848.1

IPI:IPI00110588.4 IPI:IPI00125460.1 IPI:IPI00785343.2 IPI:IPI00828649.1 IPI:IPI00323571.1 IPI:IPI00337893.2

47 48 49 50 51 52

63 64 65 66 67 68

IPI:IPI00125658.5 IPI:IPI00110658.1 IPI:IPI00798455.1

44 45 46

IPI:IPI00135915.1 IPI:IPI00624663.3 IPI:IPI00756027.1 IPI:IPI00753303.2 IPI:IPI00776264.1 IPI:IPI00308885.5 IPI:IPI00468203.3 IPI:IPI00132958.1 IPI:IPI00114628.1 IPI:IPI00229008.2

IPI:IPI00320459.2 IPI:IPI00153317.3 IPI:IPI00469114.5 IPI:IPI00480429.2 IPI:IPI00454201.1 IPI:IPI00127172.3 IPI:IPI00653643.2

37 38 39 40 41 42 43

53 54 55 56 57 58 59 60 61 62

accession

number

Table 1. continued

Serpina3k Cyp4a10 Mybbp1a Hnrpf Hist2h2ab Hist2h3c1

Glb1 Pzp Eno1 0610010D20Rik Retsat Hspd1 Anxa2 Them2 Inmt Ndufs4

Msn Atp5j;LOC674583 H3f3a;H3f3b Dnajc11 Apoe Pdha1

Gclc Hba-a1;Hba-a2 Idh2

Eppk1 Aldh1l1 Hba-a1;Hba-a2 Ugt1a6a;Ugt1a1 Ccbl2 Ddx1 Hnrpl

gene Epiplakin 10-formyltetrahydrofolate dehydrogenase Hemoglobin subunit alpha UDP glucuronosyltransferase 1 family, polypeptide A1 Kynurenine aminotransferase III ATP-dependent RNA helicase DDX1 heterogeneous nuclear ribonucleoprotein L, full insert sequence Glutamate–cysteine ligase catalytic subunit hemoglobin, beta adult major chain, full insert sequence Isoform 1 of Isocitrate dehydrogenase [NADP], mitochondrial precursor Moesin ATP synthase coupling factor 6, mitochondrial precursor Histone H3.3 DnaJ (Hsp40) homologue, subfamily C, member 11 Apolipoprotein E precursor Pyruvate dehydrogenase E1 component alpha subunit, somatic form, mitochondrial precursor Beta-galactosidase precursor Alpha-2-macroglobulin precursor enolase 1, alpha non-neuron Dihydrodipicolinate synthase-like, mitochondrial precursor all-trans-13,14-dihydroretinol saturase 60 kDa heat shock protein, mitochondrial precursor Annexin A2 Thioesterase superfamily member 2 Indolethylamine N-methyltransferase NADH dehydrogenase [ubiquinone] iron−sulfur protein 4, mitochondrial precursor Serine protease inhibitor A3K precursor Cytochrome P450 4A10 Myb-binding protein 1A Isoform 1 of Heterogeneous nuclear ribonucleoprotein F Histone H2A type 2-B H3 histone, family 2 isoform 2

protein

48 58 149 46 14 15

76 164 47 35 67 61 39 15 29 20

68 13 15 63 36 43

73 16 51

556 99 15 60 51 82 64

protein molecular weight (kDa)

5 6 3 4 37 13

3 10 50 7 5 67 3 8 21 3

7 3 12 3 9 4

4 71 15

16 95 41 40 6 8 3

unique peptide (95%)

16.51 10.22 2.90 12.53 58.46 29.28

3.71 13.67 66.82 45.79 10.02 63.70 7.67 56.43 48.48 20.00

6.93 43.52 32.35 8.23 35.37 11.28

9.73 78.87 32.63

15.82 74.72 78.87 35.80 14.29 15.14 7.90

protein sequence % cov (95)

0.41↓ 0.71↓ 0.48↓ 0.62↓ 0.53↓ 0.58↓

0.47↓ 0.44↓ 0.64↓

1.04 0.82 0.66↓ 0.72↓ 0.82 0.59↓

0.67↓ 0.93 0.61↓ 0.87 0.84

0.72↓ 1.28 0.72↓

1.45↑ 0.97 1.42 0.76↓ 0.66↓ 0.8 0.92

HBVTg1

0.51↓ 0.67↓ 0.59↓ 0.62↓ 0.64↓ 0.91 0.46↓ 0.72↓ 0.45↓ 0.63↓ 0.74↓ 0.55↓ 0.51↓ 0.48↓ 1.07 0.45↓

0.7↓ 0.65↓ 0.93 0.61↓ 0.47↓ 0.79 0.78 0.72↓ 0.97 0.42↓ 0.74↓ 0.8 0.57↓ 0.46↓ 0.11↓ 0.55↓

0.63↓ 0.75↓ 0.69↓ 0.6↓ 0.45↓ 0.51↓ 0.73↓ 0.68↓ 0.54↓ 0.65↓ 0.5↓ 0.58↓ 0.5↓ 0.62↓ 0.22↓ 0.56↓

0.53↓ 0.67↓ 0.69↓ 0.78 0.64↓ 0.57↓ 0.36↓ 0.65↓ 0.61↓ 0.51↓ 0.6↓ 0.51↓ 0.73↓ 0.59↓ 0.93 0.74↓

0.56↓ 0.62↓ 0.63↓ 0.77 0.52↓ 0.52↓ 0.65↓ 0.65↓ 0.78 0.7↓ 0.7↓ 0.59↓ 0.59↓ 0.57↓ 0.68↓ 0.35↓ 0.43↓ 0.27↓ 0.62↓ 0.63↓ 0.39↓ 0.6↓

0.85 0.78 0.53↓ 0.68↓ 0.69↓ 0.71↓ 0.72↓ 0.66↓ 0.53↓ 0.73↓ 0.53↓ 0.72↓ 0.7↓ 0.69↓ 0.69↓ 0.86 0.7↓ 0.62↓ 0.68↓ 0.69↓ 0.96 0.59↓ 0.74↓ 0.7↓ 0.72↓ 0.7↓ 0.72↓ 0.51↓ 0.76↓ 0.59↓ 0.98 0.67↓ 0.66↓ 0.41↓ 0.95 0.56↓ 0.78 0.46↓ 0.42↓ 0.6↓

0.71↓ 0.86 0.68↓ 1.04 0.69↓ 0.77

0.64↓ 0.55↓ 0.75↓

0.89 0.58↓ 0.71↓

1.46↑ 1.33↑ 0.56↓ 0.84 0.72↓ 0.76↓ 0.66↓ 1.42↑ 1.41↑ 0.53↓ 0.73↓ 1.05 0.84 0.53↓ 0.77 0.55↓ 0.74↓

HBVTg7

HBVTg6

0.72↓ 0.52↓ 0.81

1.44↑ 1.53↑ 0.58↓ 0.76↓ 0.76↓ 0.76↓ 0.76↓

HBVTg5

0.73↓ 0.39↓ 0.7↓

1.17 1.6↑ 0.61↓ 0.77↓ 0.62↓ 0.73↓ 0.95

1.03 1.43↑ 0.41↓ 0.74↓ 0.8 0.7↓ 0.68↓

1.41↑ 1.02 1.33 0.78 0.73↓ 0.69↓ 0.73↓ 0.77 1.24 0.67↓

HBVTg4

HBVTg3

HBVTg2

Journal of Proteome Research Article



Article

Figure 2. Pie chart showing representation (%) of the various functional categories for the 68 differentially expressed proteins. Categories were identified on the basis of the PANTHER classification system.

Figure 3. Evaluation of the differentially expressed proteins in WT mice and HBV-Tg mice. (A) Real-time RT-PCR detected the relative mRNA expression levels of Acss2, Acly, Bhmt, Fasn, Fdps, Gstp1, Lss, Thrsp, Eno1, Inmt, Pzp, Serpina3K and Ugt1a6a. GADPH was used as the normalization standard. Bars indicate SD. *p < 0.05 differ from control by t test. (B) A representative Western blot for ACSS2, BHMT, FASN, GSTP1and Eno1 expression in the two groups of mice.

G



Article

Figure 4. (A) Representative images of the immmunohistochemical analysis of FASN, GSTP1, BHMT and ENO1 in 7 matched HBV-Tg mice and WT mice. (B) Representative immunohistochemical analyses of FASN in HBV (−) and HBV (+) patients’ liver tissues from tissue microarrays. (C) IHC score values of FASN are significantly higher in HBV (+) tissues than in HBV (−) tissues (p < 0.05, independent sample t test).

These 68 proteins were classified into 19 functional categories by using the PANTHER classification system (www.pantherdb.org) (Figure 2). The top five molecular functions categories were oxidoreductase (22.5%), transferase (14.1%), nucleic acid binding (9.9%), lyase (7.0%), and ligase (7.0%).

iTRAQ 118 tag; HBV-Tg mouse 6 was labeled with iTRAQ 119 tag; HBV-Tg mouse 7 was labeled with iTRAQ 121 tag. Thus, the ratio of 114:113, 115:113, 116:113, 117:113, 118:113, 119:113, and 121:113 indicated the relative abundance of FASN protein (Figure 1B) in HBV-Tg mice compared to that in control group. For ProteinPilot-based database searching and identification, the protein threshold [unused protscore (conf)] was set to achieve 95% confidence at 5% false discovery rate (FDR). Although relative quantification analysis by ProteinPilot 2.0 software comes with statistical analysis and since most methods are prone to technical variation, we included an additional 1.3fold cut off for all iTRAQ ratios to add stringency when classifying proteins as up- or down-regulated. This cutoff value was guided from an analysis of a duplicate set of iTRAQ experiments that determined the technical variation to be less than 30% (Supporting Information 1). This value is wellaccepted and had been employed in other large scale protein identification and quantification studies using iTRAQ approach.21−24 Therefore, the upper and lower range worked out to be 1.3 (1 × 1.3) and 0.77 (1/1.3), respectively.19 In other words, proteins with iTRAQ ratios below the lower range were considered to be underexpressed, while those above the higher range were considered overexpressed.19 Although there is drawback in this method, which applies a general benchmark to every protein, subsequent verification step using immunoblotting and IHC will validate the key findings. A total of 1950 proteins were identified with 95% confidence (Supporting Information 2). To short-list the proteins, we selected proteins that displayed the same expression trend (over- or underexpression) in at least five HBV-Tg mice. This resulted in a total of 68 candidate proteins which are presented in Table 1. Of these, 38 proteins were overexpressed in HBV-Tg mice, whereas 30 were under-expressed in HBV-Tg compared to WT mice.

Validation of Differentially Expressed Proteins

The differential expression levels of the proteins identified by iTRAQ were validated by using Western blotting and real-time quantitative RT-PCR analysis. Figure 3A shows the relative mRNA expression levels of Acss2, Acly, Bhmt, Fasn, Fdps, Gstp1, Lss, Thrsp, Eno1, Inmt, Pzp, Serpina3K, and Ugt1a6a as normalized to GADPH. The mRNA levels of Acss2, Acly, Bhmt, Fasn, Fdps, Gstp1, Lss, and Thrsp were found to be upregulated in the HBV-Tg mice, whereas the mRNA levels of Eno1, Inmt, Pzp, Serpina3K and Ugt1a6a were down-regulated, compared to WT mice. This trend was similar to the protein expression level determined by the iTRAQ approach. Figure 3B shows representative Western blot analysis results of ACSS2, BHMT, FASN, GSTP1 and Eno1 expression in the HBV-Tg mice. Compared with WT mice, HBV-Tg mice had an obvious up-regulation of FASN, GSTP1, ACSS2 and BHMT, and a marked down-regulation of ENO1. The expression of FASN, GSTP1, BHMT and ENO1 was further assessed in 15 matched HBV-Tg and wild-type mice using IHC. FASN, GSTP1 and BHMT were up-regulated in HBV-Tg mice compared with WT mice in 9 out of 15 (60%), 8 out of 15 (53%), and 6 out of 15 (40%) matched cases, respectively. Conversely, ENO1 was down-regulated in 8 out of 15 (53%) matched HBV-Tg and WT mice tissues. Figure 4A shows the representative IHC images of the candidates tested. The relationship between HBV infection and FASN has been investigated previously in vitro demonstrating that HBV induces FASN transcription through the activation of the H



Article

Figure 5. Evaluation of the FASN differential expression in HepG2 and HepG2.2.15 cells.

nuclear receptor LXRalpha.25 To assess the clinical relevance of the observations, we examined the expression of FASN in tissue microarrays containing nontumor normal tissues from 90 HBV (+) and 70 HBV (−) HCC patients by IHC. The ages of these patients ranged from 25 to 98 years old, and the majority of the cohort (77%) was male. The grade and stage of most nontumor tissues (71%) indicated liver cirrhosis. Representative images of the IHC results for FASN HBV (−) and HBV (+) liver tissues are shown in Figure 4B. The results showed that the highest levels of signal intensity were detected in specimens from HBV (+) tissues. As shown in Figure 4C, IHC score values of FASN are significantly higher in HBV (+) tissues than in HBV (−) tissues. Specifically, 32/70 (46%) of HBV (−) samples expressed FASN, as opposed to 68/90 (76%) of HBV (+) samples. The results are very similar to those generated by the proteomic analysis.

FASN-Mediated Alteration of IFN-Induced Downstream Signaling Pathways

We measured the expression levels of type I IFN and downstream IFN-stimulated genes in response to FASN down-expression. As shown in Figure 6C, transfection of the FASN-siRNA caused increased mRNA levels of type I IFN and five downstream IFN-stimulated genes (ISG15, OAS1, OAS3, RNASEL, and EIF2a). However, three other effectors of the type I IFN-mediated antiviral signal, Mx1, OAS2, and OASL, showed only minimally altered mRNA levels in response to FASN down-expression.

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DISCUSSION Chronic HBV infection not only results in serious liver diseases, such as cirrhosis and liver failure, but also noticeably increases the risk of liver cancer by over 100-fold. Understanding the molecular mechanisms of HBV infection and replication pathogenesis are critical measures not only to improve patient screening and treatment regimens but also to provide new way for drug and vaccine development. So far, there were some proteomics studies on transgenic mice with different HBV gene transfer.27−29 In the present study, we used the iTRAQ proteomic approach to identify proteins with differential expression between HBV-Tg mice and the corresponding background normal mice, WT mice. As a result, 68 proteins were found with significant alterations in expression between the two groups of mice. Many of them, including Acss2, Fdps, GSTP1, Thrsp, Bhmt, Lss, Acly, Fasn, Pzp, SerpinA3K, Inmt, Ugt1a6a and ENO1, were confirmed using real-time RT-PCR analysis, Western blot analysis, and IHC. In addition, IHC on TMAs demonstrated the upregulation of FASN expression in HBV (+) patients. Taken together, these data provide evidence that the iTRAQ reagents labeling method for large scale protein quantification is powerful and reliable for in vitro and in vivo HBV-related investigations. On the basis of the PANTHER classification system, all 68 proteins were classified into 20 functional categories. We discuss some of the key proteins discovered in this study in the following text. The expression levels of FASN were found to be significantly increased not only in the HBV-Tg mice, but also in HBV DNAtransfected HepG2.2.15 cell line, compared with their nonHBV counterparts. These findings indicate an association between FASN and HBV infection, which is supported by previous findings in the literature. Serum FASN concentration

Relevance of Aberrant FASN Expression in Cells

To further verify the relationship between FASN expression and HBV infection, we measured FASN differential expression in the persistently HBV expressing cell line HepG2.2.15 and its parent cell line, a non-HBV expressing cell line HepG2 by Western blot.26 Figure 5 revealed that FASN protein was also overexpressed in HepG2.2.15 cells compared with paired HepG2 cells. Association of FASN with HBV Replication

Bioinformatic analysis and literature search of studies assessing the function of FASN in viral infection and effects of its upregulated expression in vitro and in vivo allowed us to propose the hypothesis that FASN plays a role in HBV replication. To this end, the HepG2.2.15 hepatoma cell line, containing an integrated greater than 1-unit length HBV genome, was transfected with three FASN-specific siRNA sequences and one control siRNA. siRNA-induced inhibition of FASN expression was then determined by Western blot analysis. As shown in Figure 6A, transfection of HepG2.2.15 cells with FASN siRNA significantly reduced the FASN protein levels, whereas FASN protein expression was not significantly suppressed by the control siRNA. We next tested the effect of FASN siRNA transfection on HBV replication. After 48 h of incubation, the FASN siRNA-transfected HepG2.2.15 cells showed reduced HBV DNA, HBsAg and HBeAg levels. The decrease in HBV DNA production was almost 2-fold higher than in the controls, Figure 6B. I



Article

Figure 6. Functional studies to assess the role of FASN in HBV replication in HepG2.2.15 cells. (A) Western blotting analysis showed that FASNspecific siRNAs significantly reduced FASN protein levels in cell lysates. Compared to controls, silencing of FASN expression by three different genespecific siRNA sequences. (B) It caused a nearly 2-fold decrease of HBV production and significantly decreased the secreted HBsAg and HBeAg levels, significantly different compared to control (p < 0.05). (C) Real-time RT-PCR detected the relative mRNA expression levels of type I interferon and downstream interferon-stimulated genes after down-expression of FASN. The relative mRNA expression levels of type I IFN and five downstream IFN-stimulated genes were increased, while no obvious changes were detected for the other three IFN-stimulated genes. *(p < 0.05,).

hepatitis B virus replication remains unknown. Subsequently, we examined its effect on the HepG2.2.15 hepatoma cell line, which contains a greater than 1-unit-length HBV DNA.16 Surprisingly, we demonstrated that suppression of FASN expression in HepG2.2.15 by siRNA caused a nearly 2-fold decrease of HBV production, compared to two control groups (control siRNA and lipo). In addition, the levels of secreted HBsAg and HBeAg were also decreased. FASN is the key lipogenic enzyme responsible for the endogenous synthesis of fatty acids.35 It has been reported that intracellular free fatty acids (FFA) accumulation in HCV cell culture induces endoplasmic reticulum (ER) stress response and down-regulates the IFNAR1 chain of the type I IFN receptor leading to defective Jak-Stat signaling and impaired antiviral response.36 Ultimately, we focused on whether IFN-

was found to be remarkably increased in patients with chronic hepatitis viral infections and levels correlated with the degree of liver steatosis.30 More recently, Aragones et al. found that serum FASN concentration was significantly increased in HIVinfected individuals, and that the release of FASN into the circulation was further enhanced in patients who were coinfected with HCV.31 In addition, overexpression of FASN has been found to be associated with poor prognosis and a higher risk of relapse of human cancers.32 FASN gene expression and enzymatic activity are primarily regulated by metabolic signals in the liver. It is a key enzyme in the synthesis of palmitate, the precursor of major nutritional, energetic and signaling lipids.32,33 Reduction of FASN by ShRNA suppressed viral replication in both replicon and infection systems.34 However, whether FASN can also affect J



Article

mediated antiviral response.53 The correlation between each of the differentially expressed proteins and HBV replication will be a focus of future study. Using proteomic methods on a similar system, we have found that cathepsin S mediates gastric cancer cell migration and invasion via a putative network of metastasis-associated proteins, and that SLC3A2 is a potential biomarker that could be exploited for molecular imaging-based detection of gastric cancer.19,54 Those results in addition to the current ones indicate that a proteomic approach is a powerful tool in the analysis of complex signaling systems. In conclusion, we focused attention on those proteins with altered expression levels in the HBV-Tg mice. As a result, 68 differentially expressed proteins possibly associated with HBV replication were identified, and the correlation of FASN with HBV replication was confirmed. We demonstrated, for the first time, that reduction of FASN by siRNA suppressed viral replication through IFN signaling pathway, specifically inducing type I interferon and some of its downstream antiviral effectors. The role of each of these proteins in the HBV replication should be investigated in future studies. We believe that FASN may represent a novel therapeutic target for pharmacologic intervention of HBV replication.

induced antiviral signaling pathways were altered by FASN silence in HepG2.2.15 cells. As a result, increased mRNA levels of type I IFN and five downstream IFN-stimulated genes were observed, indicating an activation of these genes induced by FASN siRNA. However, three other effectors of the type I IFNmediated antiviral signal, Mx1, OAS2, and OASL, showed teeny changes in the mRNA levels of related genes. Thus, our data showed that reduction of FASN by siRNA suppressed viral replication possibly involving the IFN signaling pathway. Up-regulation of GSTP1 protein was also observed in HBVTg mice. GSTP1 belongs to the GSTs family of phase II detoxification enzymes that catalyze the conjugation of a wide variety of endogenous and exogenous toxins, including aflatoxin B1, with glutathione; GSTs defend cells against damage mediated by oxidant and electrophilic carcinogens and may be crucial determinants of cancer pathogenesis.37 Previous studies have shown that the HBV infection probably increased expression of GST-pi.38 Patients positive for HBV DNA were reported to have had significantly lower GST activity than those who were HBV negative.39 One of such tumor suppressor gene, GSTP1, was found to be down-expressed in many human cancers and in HBV integrated HepG2.2.15 cells because of the hypermethylation in its promoter region. HBx in genotype D HBV has been shown to play a key role in inhibiting GSTP1 expression.40−42 In the current study, we found that GSTP1 was increased dramatically in HBV-Tg mice, compared to levels detected in WT mice. The role of GSTP1 in viral replication needs further validation. Apolipoprotein E (ApoE), which plays an important role in regulating lipid and lipoprotein metabolism, was found to be suppressed in HBV-Tg mice. There are three alleles of the ApoE gene (Sigma 2, Sigma 3, and Sigma 4) resulting in three different isoforms (E2, E3, and E4) and six different genotypes (2,2; 2,3; 2,4; 3,3; 3,4; and 4,4).43 Apolipoproteins may influence the course of HBV infection by modulating transport and entry of the hepatitis B virus into hepatocytes and reducing binding of the LDL receptor. These properties may explain the more benign course of hepatitis B among carriers of ApoE2E4.44 Certain genetic polymorphisms can lead to differences in immune function, resulting in different outcomes for HBV patients. The ε2 allele showed a positive correlation with HBV infection. The ApoE ε3/3 genotype frequency was found to be higher in patients with HBV-associated liver cirrhosis than in the controls.45,46 Therefore, the effect of ApoE on HBV-Tg mice and patients with hepatitis B may be variable. Another protein found in our study that was up-regulated in HBV-Tg is phosphogluconate dehydrogenase, PGD. It has been reported that PGD-related pathways might be involved in the pathogenesis of HBV-related HCC.47 Moreover, IHC measurement of PGD expression in HCC tumors and normal adjacent tissue in 20 chronic HBV carriers showed that PDG was overexpressed significantly in nontumor tissues.47,48 Collectively, these data have revealed that PGD may play an important role in HBV-related HCC. Finally, the current study identified several other proteins, including Aldob, Dak, Ddx1, Eno1, and Matr3, which had not been previously associated with HBV replication. These proteins have been implicated in other viral infections, such as HCV,49 dengue fever (DF) and dengue hemorrhagic fever (DHF),50 coronaviruses,51 HIV.52 A recent study reported that DAK interacted and down-regulated the activity of the MDA5, a cellular RNA helicase that, together with RIG-I, functioned as a viral sensor and was in charge of the activation of the IFN-

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ASSOCIATED CONTENT

S Supporting Information *

This material is available free of charge via the Internet at http://pubs.acs.org.

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AUTHOR INFORMATION

Corresponding Author

*Tel: 86 23 63726663. Fax: 86 23 63711527. E-mail: huaidong. [email protected]. Author Contributions †

H. Zhang and H. Li contributed equally to this work.

Notes

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS This work was supported by grants from the National Natural Science Foundation of China (No. 30972584, 30930082, 81171560, and 81171561), the National Science and Technology Major Project of China (No. 2008ZX10002006), and the Program for Changjiang Scholars and Innovative Research Team in University (No. IRT08720).

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ABBREVIATIONS iTRAQ, isotope tagging for relative and absolute quantification; SCX, strong cation exchange; ESI, electrospray ionization; LC− MS/MS, liquid chromatography−tandem mass spectrometry

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REFERENCES

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