System-based proteomic analysis of the interferon response in human liver cells
© Yan et al.; licensee BioMed Central Ltd. 2004
Received: 21 January 2004
Accepted: 15 June 2004
Published: 22 July 2004
Interferons (IFNs) play a critical role in the host antiviral defense and are an essential component of current therapies against hepatitis C virus (HCV), a major cause of liver disease worldwide. To examine liver-specific responses to IFN and begin to elucidate the mechanisms of IFN inhibition of virus replication, we performed a global quantitative proteomic analysis in a human hepatoma cell line (Huh7) in the presence and absence of IFN treatment using the isotope-coded affinity tag (ICAT) method and tandem mass spectrometry (MS/MS).
In three subcellular fractions from the Huh7 cells treated with IFN (400 IU/ml, 16 h) or mock-treated, we identified more than 1,364 proteins at a threshold that corresponds to less than 5% false-positive error rate. Among these, 54 were induced by IFN and 24 were repressed by more than two-fold, respectively. These IFN-regulated proteins represented multiple cellular functions including antiviral defense, immune response, cell metabolism, signal transduction, cell growth and cellular organization. To analyze this proteomics dataset, we utilized several systems-biology data-mining tools, including Gene Ontology via the GoMiner program and the Cytoscape bioinformatics platform.
Integration of the quantitative proteomics with global protein interaction data using the Cytoscape platform led to the identification of several novel and liver-specific key regulatory components of the IFN response, which may be important in regulating the interplay between HCV, interferon and the host response to virus infection.
Interferons (IFNs) were originally discovered as antiviral proteins that inhibit virus replication . Upon virus infection, IFNs are induced in mammalian cells and thus mediate cellular homeostatic responses to virus infection. In addition to their antiviral properties, IFNs are involved in many other physiological processes including cell growth and proliferation, cell death, the immune response and other cellular defense mechanisms . The IFN signaling pathway has been extensively studied [3, 4]. On binding of IFNs to their cognate receptors, the JAK-STAT signal transduction pathway is triggered, culminating in the transcription of IFN-stimulated genes (ISGs) that mediate IFN function. The proteins encoded by ISGs include, but are not limited to, many antiviral effectors such as the double-stranded RNA-activated protein kinase PKR (which inhibits viral protein synthesis via eIF2α phosphorylation), the 2'-5' oligoadenylate synthetase (2'-5' OAS) (which activates RNase L to degrade viral RNA), and the Mx GTPases (which block viral transport inside the cell). Other ISGs include ISG56 (which inhibits translation via eIF3) and the P200 family (which impairs cell proliferation through cellular factors such as NFκB, E2F, P53, c-Myc, UBF-1, YY1, MyoD) .
Owing to its anti-growth and immune-response properties, IFNs have been successfully applied as therapeutics against several types of cancers and infectious diseases including multiple sclerosis, hepatitis and genital warts [2, 5]. One of the most prominent clinical applications of IFNs is treatment of patients infected by hepatitis C virus (HCV) . HCV has infected an estimated 3% of the world population . In the absence of a protective vaccine, the only useful therapeutic regimen to date has been treatment using interferon-alpha (IFN-α) together with ribavirin, a broad spectrum antiviral nucleoside . However, more than 50% of HCV-infected patients showed low rates of response to this therapy, in particular patients infected by genotype-1 HCV which is a more infectious sub-genotype among Americans and Europeans. Therefore, further elucidation of the mechanism of IFN response in liver cells could help development of more effective therapeutics against HCV.
Here we describe a quantitative proteomic analysis of the IFN response in human liver carcinoma Huh7 cells using the isotope-coded affinity tag (ICAT) method and mass spectrometry (MS). We compared the global protein expression profile in human liver cells in the presence or absence of IFN-α treatment. Protein identification and quantification were executed and statistically verified using a suite of software tools, including PeptideProphet , ProteinProphet  and ASAPRatio . By this analysis, we identified more than 1,300 proteins at a threshold that showed a false-positive rate of less than 5%. Of these proteins, 54 were IFN-induced proteins and 24 were IFN-repressed. These include previously well studied IFN-regulated proteins as well as novel proteins. The cellular functions of those proteins were analyzed on the basis of the Gene Ontology , using the GoMiner program  and the Cytoscape platform .
Results and discussion
Global proteomic analysis of IFN- and mock-treated Huh7 cells
To develop a systems-based understanding of the IFN response of liver cells we carried out a global proteomic analysis on human liver carcinoma cells, Huh7, under conditions of IFN or mock treatment. We chose the Huh7 cell line because it is a widely studied cell model and has been used to develop a unique cell system containing the HCV RNA replicon, which is the best cell model system to study HCV propagation and infection . We collected cells 16 hours after treatment with either IFN (400 IU/ml) or mock treatment. The time period and IFN dose were chosen on the basis of the maximal response in gene expression obtained from previous time-course studies on IFN responses using a microarray approach [16, 17]. To increase the coverage of protein identification, we fractionated the cell lysates into cytoplasmic, nuclear and membrane fractions and analyzed them individually by the ICAT method and tandem mass spectrometry (MS/MS). For each fraction, the IFN- or mock-treated samples were labeled with either isotopically light (12C) or heavy (13C) acid-cleavable ICAT reagents containing a biotin affinity tag. The light and heavy samples were then combined, proteolyzed to peptides, and fractionated by high-performance liquid chromatography (HPLC) cation-exchange chromatography followed by avidin-affinity chromatography. The purified isotopically labeled peptides were subject to microcapillary HPLC (μLC) followed by electrospray tandem mass spectrometry (ESI-MS/MS) using an ion trap [18, 19].
In addition to statistically validating peptide/protein identifications, we also applied statistical analysis to protein quantification. In quantitative proteomics, changes in protein abundance are determined by calculating the ratio of isotopically labeled peptides. The different isotopic peptides were introduced by ICAT labeling (light for IFN and heavy for mock in this study). The abundance ratios were calculated using the ASAPRatio software tool . The ratio for each quantified peptide was calculated from multiple measurements, including measurements of different charge states of the same peptide, measurements of repeat MS analyses of the same peptide from different cation-exchange chromatography fractions, and repeat measurements of the same peptides outside the dynamic exclusion windows of mass spectrometry. Protein abundance was subsequently calculated from the ratios of all the peptides corresponding to the same protein. Therefore, all protein ratios obtained in this study represent the average ± error calculated from numerous independent measurements. Such statistics-based data processing and presentation is particularly critical for global quantitative proteomics analysis, for which the possibility for multiple repeat experiments is technically and economically restricted at present.
Liver proteome representation
The 1,364 proteins identified in the study at a threshold of 5% error rate were examined for their biological association to Gene Ontology (GO) categories . With the help of the recently developed GoMiner program , we built a GO category structure based on 9,400 human genes currently carrying human GO annotations (used as Query Gene File). The 1,364 identified proteins from this study were then loaded as Query Changed Gene File into the GoMiner program to examine the distribution of these proteins in the GO category structure. Although only 815 of the 1,364 identified proteins have been currently annotated by the GO consortium, they already covered, at level 3 of the GO ontology, all seven GO 'biological_process' categories, six of the seven 'cellular_component' (except 'immunoglobulin complex') categories, and 27 of the 29 'molecular_function' (except 'ice nucleation activity' and 'regulator of establishment of competence for transformation activity') categories (Figure 1b). Therefore, the proteins identified in this study represent most, if not all, GO-categorized cellular processes. A human liver proteomic dataset consisting of more than 1,300 proteins, which is publicly accessible at , is expected to be useful for research on IFN signaling and liver diseases. The dataset, together with its enriched information on human liver protein/peptide identifications, GO categories, cellular fractionations, cation exchange and reverse-phase chromatography elution profiles, and so on, is also expected to be useful as a reference for future human proteomics analyses. In addition, it strongly suggests the feasibility of performing large-scale proteomics analyses on the Huh7 cell-based HCV replicon system to study cellular responses to HCV RNA replication.
Profiling IFN-regulated protein expression
List of proteins up- or downregulated by two-fold or more by IFN in this study
ACACA: acetyl-coenzyme A carboxylase alpha
ADRM1: adhesion regulating molecule 1
AHCY: S-adenosylhomocysteine hydrolase
ANLN: anillin, actin binding protein (scraps homolog, Drosophila)
BAL: B aggressive lymphoma gene
CABC1: chaperone, ABC1 activity of bc1 complex like (S. pombe)
CD7: CD7 antigen (p41)
CH-TOG : Colonic and hepatic tumor overexpressed protein
CSA_PPIasePeptidyl prolyl cis trans isomerase
DCTN1: dynactin 1 (p150, glued homolog, Drosophila)
DKFZP564C186: DKFZP564C186 protein
DNAH11: dynein, axonemal, heavy polypeptide 11
DNAJB1: DnaJ (Hsp40) homolog, subfmaily B, member 1
DRG1: developmentally regulated GTP binding protein 1
EEF1A protein [Fragment]
ETFA: electron-transfer-flavoprotein, alpha polypeptide
FLJ32915: hypothetical protein FLJ32915
FOXA2: forkhead box A2, hepatic nuclear factor-3-beta
G1P2: IFN, alpha-inducible protein (clone IFI-15K)
GBP1: guanylate binding protein 1, IFN-inducible
GNB1:G protein, beta subunit 1
GPR111: G protein-coupled receptor 111
APG7L: ubiquitin activating enzyme E1-like protein, GSA7
IFI30: IFN, gamma-inducible protein 30
IFI35: IFN-induced protein 35
IFIT1: IFN-induced protein with tetratricopeptide repeats 1
IFIT4: IFN-induced protein with tetratricopeptide repeats 4
INPP5E: inositol polyphosphate-5-phosphatase, 72 kDa
ISG20: IFN stimulated gene 20 kDa
KIAA0186: KIAA0186 gene product
KIAA1276: KIAA1276 protein
KNS2: kinesin 2 60/70 kDa
LAMB1: laminin, beta 1
LOC151636: rhysin 2
M96:mouse metal response element binding transcription factor 2
MTP: microsomal triglyceride transfer protein (88 kDa)
MX1: myxovirus resistance 1, IFN-inducible protein p78
NMI: N-Myc (and STAT) interactor
NUDT2: nucleoside diphosphate linked moiety X type motif 2
OAS3: 2'-5'-oligoadenylate synthetase 3, 100 kDa
PASK PAS domain containing serine/threonine kinase
PCMT1: protein-L-isoaspartate (D-aspartate) O-methyltransferase
PLCD1: phospholipase C, delta 1
PKR protein kinase, IFN-inducible ds RNA dependent
RPLP0: ribosomal protein, large, P0
SART1: T-cell recognized squamous cell carcinoma antigen
STAT1: large peptide, 91 kDa
UBE2L6: ubiquitin-conjugating enzyme E2L 6
ENSEMBL:ENSP00000295676 Tax_Id = 9606
REFSEQ:XP_062729 hypothetical protein
REFSEQ:XP_058770 Hypothetical protein
REFSEQ:XP_167245 similar to seven-pass transmembrane receptor protein precursor and cyclophilin type peptidylprolyl isomerase A
CPSF4: cleavage and polyadenylation specific factor 4, 30 kDa
FABP: fatty acid binding protein
FACL4: fatty-acid-coenzyme A ligase, long-chain 4
FASN: fatty acid synthase
FXR2: fragile X mental retardation, autosomal homolog 2
G2AN: alpha glucosidase II alpha subunit
GPS1: G protein pathway suppressor 1
HIC1: hypermethylated in cancer 1
Hypothetical protein FLJ21140
IGLC3: Ig lambda chain C regions
KIAA0007: KIAA0007 protein
KRT10: keratin 10
KRT6: keratin 6
MAPRE1: RP/EB family, member 1
MGC3207: hypothetical protein MGC3207
MIG-6: Gene 33/Mig-6
OAT: ornithine aminotransferase
PPGB: protective protein for beta-galactosidase (galactosialidosis)
SARDH: sarcosine dehydrogenase
SRRM2: serine/arginine repetitive matrix 2
TRA1: tumor rejection antigen (gp96) 1
TUBA6: tubulin alpha 6
WNT9A: wingless-type MMTV integration site family, member 9A
The antiviral defense and immune response
We then investigated the individual cellular pathways with which the IFN-regulated proteins were associated. IFNs are best known for their antiviral defense properties. Years of IFN research have discovered several well known IFN-induced proteins such as the protein kinase PKR, 2'-5' OAS, Mx, RNA-specific adenosine deaminase (ADAR) and interferon regulatory factors (IRF) [2–4, 24]. From this study, we identified all these classical IFN-induced proteins except the IRFs. Missing the IRFs is possibly due to the very low abundance of these transcription factors inside the liver cell, resulting in a concentration that is below the sensitivity of our MS detection. All these IFN-induced proteins were shown to be induced at least two-fold upon IFN treatment with the exception of ADAR (1.4-fold) (Table 1). Identification of these IFN-induced proteins is consistent with a previous microarray study performed by Williams and colleagues at the Cleveland Clinic Foundation (Table 1, proteins in bold) [17, 25]. We also identified/quantified two IFN-induced proteins, STAT1 and NMI, which are involved in the upstream IFN-related JAK/STAT signaling pathways. We performed western blot analysis to confirm the protein expression patterns detected by quantitative proteomics analysis (Figure 1c). We examined the expression patterns of several IFN-induced proteins such as PKR, NMI and STAT1 at multiple doses (0, 50, 200, 400 IU/ml) and multiple time points (8, 16, 40 and 72 hours). These data indicate that the patterns observed by immunoblot analysis are consistent with the data obtained by quantitative mass spectrometry and that the conditions used for the proteomics analysis (16-hour treatment of IFN at 400 IU/ml concentration) represented conditions of which an optimal IFN response was observed (Figure 1c, boxed in blue).
Cell metabolism and growth
IFN treatment has previously been shown to have pleiotropic effects on many aspects of cell physiology, including cell growth, proliferation, reorganization and death [2, 4]. The cellular response to stress conditions such as virus infection, which induces high IFN levels, might be to reduce levels of cellular metabolism. In support of this notion, we observed that IFN suppressed expression of proteins involved in metabolic pathways including fatty-acid metabolism (fatty acid synthase (FASN); long-chain fatty-acid-coenzyme A ligase 4 (FACL4); and fatty-acid-binding proteins (FABP)), amino-acid metabolism (ornithine aminotransferase (OAT); sarcosine dehydrogenase (SARDH); and FASN), nitrogen metabolism (OAT), and DNA metabolism (tumor rejection antigen 1 (TRA1) involved in histone acetylation) (Figure 3).
IFN was previously reported to inhibit cell growth, possibly through regulation of cell-cycle control . In support of this, we observed an induction of PKR by IFN treatment; PKR has been reported to negatively regulate cell growth and proliferation . Interestingly, information provided from the GO analysis also revealed repression of protein MAPRE1, which is an EB1 family member binding to APC (adenomatous polyposis coli), and is associated with the mitotic apparatus and cell-cycle regulation [27, 28].
Correlation of the cellular pathways
Acquisition of a large global proteomics dataset provides us with a unique opportunity to analyze the IFN-mediated cellular pathways at a systems-based level. The results discussed above suggest cross-talk between activation of the antiviral defense pathways and suppression of pathways involved with cell growth and proliferation. When applying GO analysis to investigate the distribution of the 78 IFN-regulated (induced and repressed) proteins in multiple cellular pathways, we observed that many IFN-induced and repressed proteins may be functionally related and react in a coordinated manner. As shown in Figure 2, at the 'biological process' GO category (level 2) and its subcategories, IFN-induced proteins were enriched in several cellular defense-related pathways including immune response, stress response and signal transductions (Figure 2a, red nodes) and were relatively depleted in the categories of metabolism (Figure 2a, blue nodes). On the other hand, the IFN-repressed proteins are more likely to be enriched in the metabolic pathways (Figure 2b, red nodes) but not in pathways such as those involved in cellular defense mechanisms.
Taking advantage of the global proteomics approach together with systems-based data mining, we also successfully identified and quantified multiple components of the IFN signaling pathways, including STAT1 and NMI in the JAK-STAT pathway, Mx1 (a GTPase involved in the immune response, cellular organization and apoptosis), PASK (PAS-domain-containing serine/threonine kinase in phosphate metabolism), GPR111 and PLCD1 (G-protein signaling), GNB1 (G-protein signaling and Ras signal transduction), GPS1 (G-protein, MAPK, and JNK signaling), WNT9A (Wnt signaling) (Figure 3). The IFN-mediated differential expression of many signaling molecules within the signal transduction network strongly suggests a crosstalk among multiple signaling pathways.
Of particular interest is the activation of the G-protein-coupled receptor (GPCR) signaling pathway during the IFN response. The GPCR signaling pathway has been widely studied. More than 1,000 GPCRs have been cloned, which regulate, at a minimum, the adenylate cyclase, phospholipase C, and phosphodiesterase-mediated signal transduction pathways [29, 30]. However, little is known about the regulation of the IFN-stimulated G-protein signaling pathways. Interestingly, we observed a number of IFN-induced proteins involved in the G-protein signaling pathways: GPR111 (upregulated 21.3-fold), GNB1 (5.8-fold), and PLCD1 (4.6-fold) (Figure 3). GPR111 is the G-protein-coupled receptor 111. GNB1 is the β-subunit of G proteins and has crucial roles in both inositol trisphosphate (IP3)-coupled G-protein signaling and Ras protein signal transduction. The phospholipase C family member PLCD1 is a PIP2 phosphodiesterase (delta subunit) and is also involved in IP3-coupled G-protein signaling. Curiously, a G-protein pathway suppressor, GPS1 was downregulated by IFN treatment (2.2-fold) in our study. Therefore, induction of G-protein-coupled signaling molecules appeared to be coordinated with downregulation of the G-protein pathway suppressor protein. Observation of these multiple signaling molecules involved with the GPCR pathways strongly suggests an IFN-stimulated and GPCR-mediated signal transduction. It is unclear whether this enrichment of the series of G-protein signaling components is reflective of a novel IFN-mediated signaling loop or just involves identification of several components of the G-protein signaling pathways.
In addition to the G-protein signaling, we also observed a decreased expression of the Wnt signaling molecule WNT9A on IFN treatment (downregulated 3.2-fold). The Wnt signaling pathway is essential in development. Whether this downregulation of WNT9A is related to a stress-mediated attenuation of cellular development awaits further study. The Wnt signaling was also reported to regulate cell polarity, in parallel with Cdc42 . The regulation of the cell polarity requires participation of APC, EB1 and activation of cell organization involved with the dynein proteins of the cytoskeleton . Interestingly, we identified multiple components of the cell-polarity signaling pathway using our proteomic approach, including WNT9A, EB1 (MAPRE1) and dynein (DNAH1, a large subunit of dynactin; and DCTN1, ciliary dynein heavy chain 11), suggesting an IFN-mediated regulation of cell polarity.
Novel IFN-regulated proteins and potential targets for future study
Among the IFN-regulated proteins we identified, proteins hitherto not known to be regulated by IFN are of particular interest as they may reveal important new functions of the IFN response. For example, we discovered two new IFN-induced proteins, ADRM1 and LAMB, that are cell adhesion molecules and may be involved in IFN-mediated cell migration, attachment and cell-cell interactions. Interestingly, these two proteins were both recently found to be downregulated in microarray studies of liver biopsies from HCV-infected patients (M. Smith, D. Thomas, and M.G.K., unpublished data). Two keratin proteins, KRT6 (keratin 6) and KRT10 (keratin 10) were found to be repressed by IFN treatment in our study. Keratins are important components of the cytoskeleton and have important roles in cell migration and invasion. Furthermore, one family member, keratin 19, has previously been shown to be correlated with hepatocellular carcinoma metastasis .
Three molecular chaperones, CABC1, CSA PPIase, and DNAJB1, were found to be induced by IFN, while another protein with a protein-protective role, PPGB (protective protein for beta-galactosidase), was repressed by IFN. In addition, four IFN-induced proteins - G1P2, IFI30, PCMT1 and UBE2L6 - were involved in ubiquitination and protein degradation in response to stress signals. It is reasonable to speculate that protein degradation might be activated as a cellular survival response.
Importantly, four IFN-regulated proteins were liver specific, including CH-TOG (overexpressed in hepatomas), FOXA2 (also known as hepatocyte nuclear forkhead transcription factor), MTP (lipid transfer to apoB in the endoplasmic reticulum of human hepatoma cells), and FASN (a fatty-acid synthase whose expression is regulated by liver X receptors). Consistent with the antiviral role of IFN, we found that IFN suppressed expression of two proteins, CPSF4 and TRA1, which are known to be regulated during viral infection. CPSF4 is a cleavage and polyadenylation-specific factor which forms a complex with the influenza virus NS1 protein to selectively block the nuclear export of cellular but not viral mRNA . TRA1, a stress-induced tumor rejection antigen (gp96) 1, is involved in histone acetylation and chromatin modification and associates with the RNA polymerase of the hepatitis B virus .
Genome analysis of the IFN-regulated proteins
Partial list of cis-DNA elements of IFN-induced proteins
The protein-interaction network of the IFN-regulated proteins
To gain systematic insights into the novel functions of IFN-regulated proteins, we examined the interactions between these proteins using the software package Cytoscape as our bioinformatics analysis platform. Cytoscape provides a unique in silico approach to examine and display protein-interaction networks based on available protein-protein interactions characterized from previous biological studies [36–38]. We loaded the more than 1,300 identified and quantified proteins from our SBEAMS database into Cytoscape, and identified 219 interactions. Of the total 219 interactions, 157 were directly obtained from publicly available protein interaction databases including HPRD  and PreBIND . Expanding the search to additional databases such as BIND  and a privately curated compiled 'yeast reference network' [40, 42–44], an extra 57 interactions were detected as occurring between orthologs (NCBI HomoloGene database ) of the reference proteins in Mus musculus, Saccharomyces cerevisiae and Drosophila melanogaster.
In summary, we performed large-scale proteomic analysis on IFN-treated human liver cells and analyzed the large dataset using a systems-biology approach. Data presented here both confirmed earlier work and, more importantly, identified novel IFN-regulated proteins and pathways. We propose a general picture of IFN-mediated regulation involving complex novel pathways and cross-talk between various cellular pathways in responses to multiple environmental signals (Figure 3). Such a systems-based proteomic study will extend our understanding about the IFN-mediated liver physiology and may ultimately lead to development of more efficient therapies against virus infections such as HCV.
Materials and methods
Cell culture and IFN treatment
Human liver carcinoma cells (Huh7) were cultured in DMEM containing 10% FBS, 100 U/ml penicillin and 100 U/ml streptomycin at 37°C. In proteomics analysis, cells were treated for 16 h with 400 IU/ml recombinant IFN-α2b (Intron-A, Schering-Plough). In immunoblot experiments, cells were treated for 8, 16, 40 or 72 h with 0, 50, 200 or 400 IU/ml IFN before harvest.
Cell lysis and fractionation
Cells were collected in a hypotonic lysis buffer (50 mM Tris pH 7.5, 5 mM MgCl2, 5 mM CaCl2, 1 mM DTT, 1 mM EDTA, and 1× Protease Inhibitor Cocktail (Roche) and then lysed by Dounce homogenization followed by centrifugation at 3,000g for 15 min. The pellet obtained (P-3) was washed by buffer containing 600 mM KCl to produce the nucleus fraction. The supernatant (S-3) was subsequently centrifuged at 100,000g for 2 h and generated the cytoplasm fraction (S-100) and the membrane fraction (P-100).
ICAT labeling and mass spectrometry
The cytoplasm, membrane and nuclear fractions were acetone-precipitated and resuspended in ICAT labeling buffer (0.05% SDS, 50 mM Tris pH 8.3, 5 mM EDTA, and 6 M urea). Each fractionated sample was labeled with the second generation ICAT reagents (acid cleavable), either in light (12C for IFN-treated) or in heavy (13C for mock-treated) isotopes (Applied Biosystems). Equal amounts of the two labeled samples were combined and digested into peptides by trypsin (Promega). ICAT-labeled peptides were subsequently purified by cation-exchange chromatography and avidin-affinity chromatography. Peptide mixtures were then analyzed by microcapillary HPLC-electrospray ionization (ESI)-MS/MS using an ion-trap mass spectrometer (LCQ-DecaXP, ThermoFinnigan) as previously described .
Immunoblot analysis and antibodies
Immunoblot analysis was performed as previously described . The anti-PKR antibody was a gift from Ara Hovanessian . The anti-NMI antibody was generously provided by Louie Naumovski at Stanford University (California, USA). Antibodies against STAT1 (monoclonal antibody 9H2) and β-actin (ab6276) were purchased from Cell Signaling Technology and Abcam, respectively.
Data analysis and software
SEQUEST matches peptide tandem mass spectra to sequence in the human IPI database ; PeptideProphet assigns a probability to the identified peptide sequence , and ProteinProphet assigns an overall probability to the protein identification . To quantify protein ratio between IFN- and mock-treated samples, the program ASAPRatio was used to calculate the ICAT ratios . To classify the identified proteins into cellular pathways, GO  analysis was performed using the GoMiner program . All proteomics data were stored and systematically analyzed at the Systems Biology Experiment Analytical Management System (SBEAMS) at the Institute for Systems Biology (ISB) . The data were visualized using Cytoscape, which was co-developed by ISB and MIT [14, 36].
Additional data files
The following additional data is available with the online version of this paper: the original data files of the ICAT analyses on the cytoplasmic (Additional data 1), membrane (Additional data 2) and nuclear (Additional data 3) fractions of Huh7 cells. Each file contains all proteins identified in individual experiment and only those with ProteinProphet probability score ≥ 0.4 (with an error rate ≤ 5%) are included for data analysis in this article. The descriptions of each column in these three data files are given in Additional data 4; the figure in this file indicates the distribution of the protein ratio (ASAPRatio) of the proteins analyzed in this paper (ProteinProphet probability score ≥ 0.4) (y-axis: ASAPRatio ratio; x-axis: protein number). A normalization factor of 0.8 was obtained from this figure and used to normalize individual protein ratios as shown in columns 'O' and 'P' of the three data files. These data are also available at .
We thank Kelly Cooke at ISB for technical help. We are grateful to Maria Smith and Eric Chen at the University of Washington for helpful discussions and critical reading of the manuscript and to Maria Smith for sharing unpublished data. This investigation is supported by grant 1P30DA01562501 to M.G.K. and R.A. from the National Institute on Drug Abuse. This project has also been funded in part with Federal funds from the National Heart, Lung and Blood Institute, National Institutes of Health, under contract number N01-HV-28179.
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