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Virus Research 179 (2014) 187– 203

Contents lists available at ScienceDirect

Virus Research

jo ur nal home p age: www.elsev ier .com/ locate /v i rusres

epatocyte pathway alterations in response to in vitro Crimean Congoemorrhagic fever virus infection

hristophe Fraisiera,b,1, Raquel Rodriguesc,1, Vinh Vu Haia,b, Maya Belghazie,téphanie Bourdona, Glaucia Paranhos-Baccalac, Luc Camoinf,g, Lionel Almerasa,b,1,hristophe Nicolas Peyrefittec,d,h,∗,1

Institut de Recherche Biomédicale des Armées (Armed Forces Biomedical Research Institute, IRBA), antenne Marseille, Unité de Parasitologie, URMITE UM3, GSBdD de Marseille Aubagne, 111 avenue de la Corse BP 40026, 13568 Marseille Cedex 02, FranceAix-Marseille Université, Unité de Recherche en Maladies Infectieuses et Tropicales Emergentes (URMITE), UM 63, CNRS 7278, IRD 198, Inserm 1095, WHOollaborative Center for Rickettsioses and Other Arthropod-borne Bacterial Diseases, Faculté de Médecine, 27 boulevard Jean Moulin, 13385 Marseille cedex, FranceEmerging Pathogens Laboratory, Fondation Mérieux, 21 Avenue Tony Garnier, 69007 Lyon, FranceSFR Biosciences Gerland-Lyond Sud (UM53444/U58), FranceAix-Marseille Université, CNRS, CRN2M, UMR 7286, 13015 Marseille, FranceInserm, U1068, CNRS, UMR7258, CRCM, Aix-Marseille Univ, UM 105, Marseille F-13009, FranceInstitut Paoli-Calmettes, Marseille Protéomique, Marseille F-13009, FranceUnité de virologie, Institut de Recherche Biomédicale des Armées (IRBA) antenne de Lyon, tour CERVI, 21 avenue Tony Garnier, 69007 Lyon cedex, France

r t i c l e i n f o

rticle history:eceived 6 July 2013eceived in revised form 20 October 2013ccepted 21 October 2013vailable online 30 October 2013

eywords:unyaviridaeCHFVathways deregulated

a b s t r a c t

Crimean-Congo hemorrhagic fever virus (CCHFV) is a tick-borne virus responsible for hemorrhagicmanifestations and multiple organ failure, with a high mortality rate. In infected humans, damage toendothelial cells and vascular leakage may be a direct result of virus infection or an immune response-mediated indirect effect. The main target cells are mononuclear phagocytes, endothelial cells andhepatocytes; the liver being a key target for the virus, which was described as susceptible to inter-feron host response and to induce apoptosis. To better understand the early liver cell alterations due tovirus infection, the protein profile of in vitro CCHFV-infected HepG2 cells was analyzed using two quan-titative proteomic approaches, 2D-DIGE and iTRAQ. A set of 243 differentially expressed proteins wasidentified. Bioinformatics analysis (Ingenuity Pathways Analysis) revealed multiple host cell pathways

athogenesisD-DIGE

TRAQ

and functions altered after CCHFV infection, with notably 106 proteins related to cell death, including79 associated with apoptosis. Different protein networks emerged with associated pathways involvedin inflammation, oxidative stress and apoptosis, ubiquitination/sumoylation, regulation of the nucleo-cytoplasmic transport, and virus entry. Collectively, this study revealed host liver protein abundancesthat were modified at the early stages of CCHFV infection, offering an unparalleled opportunity of thedescription of the potential pathogenesis processes and of possible targets for antiviral research.

Abbreviations: CME, clathrin-mediated endocytosis; CCHFV, Crimean-Congoemorrhagic fever virus; FDR, false discovery rate; FC, fold-change; GO, genentology; IPG, immobilized pH gradient; IPA, Ingenuity Pathway Analysis; IFN-I,nterferon type I; IEF, isoelectric focusing; MS, mass spectrometry; MOI, multiplicityf infection; p.i., post infection.∗ Corresponding author at: Emerging Pathogens Laboratory, Fondation Mérieux,

1 Avenue Tony Garnier, 69007 Lyon, France. Tel.: +33 04 37 28 24 34;ax: +33 04 37 28 24 11.

E-mail address: cpeyrefitte2000@yahoo.fr (C.N. Peyrefitte).1 These authors contributed equally to this work.

168-1702/$ – see front matter © 2013 Elsevier B.V. All rights reserved.ttp://dx.doi.org/10.1016/j.virusres.2013.10.013

© 2013 Elsevier B.V. All rights reserved.

1. Introduction

Crimean-Congo hemorrhagic fever virus (CCHFV), an envelopedvirus, whose genome is tripartite single-stranded negative RNA, isa member of the Nairovirus genus inside the family Bunyaviridae(Elliott, 1990). Its circulation reflects the geographical distributionof its tick vectors (mostly of the Hyalomma genus) in many countriesthroughout south-east Europe, Africa, the Middle East and Asia(Chinikar et al., 2010; Christova et al., 2009; Ergonul et al., 2006;Papa et al., 2008, 2011; Rai et al., 2008; Xia et al., 2011). Recently,

CCHFV was detected in Iran (Fakoorziba et al., 2012), India (Mouryaet al., 2012) and Spain (Estrada-Pena et al., 2012). CCHFV is one ofthe most widespread virus of all medically important tick-borneviruses (Whitehouse, 2004). The mortality rate is high and can be

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p to 50% in humans (Ergonul et al., 2006; Swanepoel et al., 1987).mong clinical manifestations, severe hemorrhagic manifestationsnd multiple organ failure are some of the most common symp-oms (Ergonul et al., 2006; Swanepoel et al., 1987). In infectedumans, damage to endothelial cells and vascular leakage maye a direct result of the virus infection or an immune response-ediated indirect effect (Schnittler and Feldmann, 2003). In clinical

tudies, elevated serum levels of acute inflammatory markers suchs IL-6, TNF-�, IL-8, sICAM-1, sVCAM-1, and VEGF-A were corre-ated to CCHF severity in patients (Ergonul et al., 2006; Ozturkt al., 2010; Papa et al., 2010). A retrospective study pointed out theononuclear phagocytes, endothelial cells and hepatocytes as theain CCHFV infection targets (Burt et al., 1997). Nevertheless, theolecular mechanism behind the pathogenesis of CCHF is poorly

nderstood.The recent establishment of CCHFV in vitro infection in anti-

en presenting cells and endothelial cells enabled examination ofellular responses, including the production of soluble mediatorsConnolly-Andersen et al., 2009, 2011; Peyrefitte et al., 2010). Twonimal models were established – interferon type I (IFN-I) recep-or knockout mice and mice deficient in the signal transducer andctivator of transcription 1 (STAT-1) signaling, both susceptibleo CCHFV infection causing rapid onset of symptoms, includingiver damage and death (Bente et al., 2010; Bereczky et al., 2010).hese studies confirmed the susceptibility of CCHFV to the IFN-Iost response suggested by hepatocyte in vitro studies (Anderssont al., 2008, 2006) as well as the CCHFV-induced IFN I responseelay (Bowick et al., 2012). Recently, CCHFV was shown to induceellular apoptosis with the participation of the mitochondrial path-ay, likely due to ER-stress crosstalk in the Huh-7 hepatocyte cell

ine (Rodrigues et al., 2012). Thus, the liver appears as a key tar-et organ for many hemorrhagic fever viruses (Child et al., 1967;eisbert et al., 2003; Green et al., 2006; Jaax et al., 1996; Terrellt al., 1973; Walker et al., 1982; Winn and Walker, 1975) includingCHFV. CCHFV is known to feature extensive infection of hepato-ytes, with an increase in circulating liver enzymes, swelling andecrosis. The involvement of cellular liver functions in the outcomef CCHFV-induced disease can be studied through proteomic tech-iques as shown for Dengue virus-infected HepG2 cells (Higa et al.,008).

Thus, to better understand the first liver cell alterations ineaction to CCHFV infection, the response of HepG2 cells (i.e.,epatocyte derived cell-line) to CCHFV in vitro infection was ana-

yzed using two quantitative proteomic approaches (2D-DIGE andTRAQ). A total of 243 distinct differentially expressed proteins

ere successfully identified. Bioinformatics analysis indicated thatultiple host cell pathways and cellular functions were altered fol-

owing CCHFV infection. Possible roles for some of the identifiedroteins in CCHFV pathogenesis are presented.

. Material and methods

.1. Virus production and titration

All infectious work with CCHFV was carried out in a BSL-4.CHFV strain IbAr 10200, obtained from Institut Pasteur, was pas-aged as described elsewhere (Peyrefitte et al., 2010). Absencef Mycoplasma was confirmed using the Mycoalert kit (Lonza,erviers, Belgium). Virus titration was performed as described else-here (Peyrefitte et al., 2010).

.2. Cells and in vitro virus infection

HepG2 hepatocarcinoma cell line (CelluloNet, Cat N◦247,yon, France) was cultured in DMEM Glutamax (Invitrogen),

ch 179 (2014) 187– 203

supplemented with 10% FCS, 5 × 104 IU Penicillin and 50 �g Strep-tomycin (Invitrogen). Cells were cultivated at 37 ◦C, 5% CO2.Absence of Mycoplasma was confirmed using the Mycoalert kit(Lonza). HepG2 cells were infected in a 6-well plate (BD) at2.2 × 106 cells/well. The cells were then infected with CCHFV at amultiplicity of infection (MOI) of 0.1 and 1, or with UV-inactivatedCCHFV and supernatant from non-infected Vero E6 cells (Mock)at 37 ◦C, 5% CO2 for 45 min. This was designated as time 0, andthe time course started from this point. After the 45 min adsorp-tion period, residual or desorbed virus was eliminated by abundantwashing of the cell monolayer. The cells were incubated at 37 ◦C,5% CO2for 24 h. Cells were harvested in 1 mL 10% SDS sterile watersolution (Sigma, France) heated at 98 ◦C for 15 min and were storedat −20 ◦C until use. Supernatants were also harvested at 24 h postinfection (p.i.), centrifuged at 400 × g for 5 min, aliquoted and storedat −20 ◦C until use.

2.3. Indirect immunofluorescence assay

In 8-well permanox slides, CCHFV-infected HepG2 cells werefixed with 3.7% PAF in PBS solution, washed three time in PBS solu-tion, permeabilised with 0.5% Triton X-100 in PBS solution, andthen incubated with primary and secondary specific antibodies asdescribed elsewhere (Peyrefitte et al., 2010). The cells were exam-ined using an Axio Observer Z.1 (Zeiss, France) and analyzed usingMetaMorph v7.6 software (Wellcome Trust, UK).

2.4. Quantification of CCHFV RNA

Briefly, total RNAs from CCHFV-infected cells were extractedfrom cell pellets using the RNeasy mini kit (Qiagen) according to themanufacturer’s instructions. The S genomes and antigenomes forCCHFV were then quantified using a quantitative RT-PCR previouslydescribed (Peyrefitte et al., 2010; Rodrigues et al., 2012).

2.5. Protein sample preparation

Six biological replicates of mock- and infected-HepG2 cells,stored in lysis buffer (SDS 10%, w/v), were disrupted by ultrasonica-tion (Vibracell 72412, Bioblock Scientific, Illkirch, France) five timesfor 60 s on ice at maximum amplitude. The resulting homogenatewas centrifuged for 15 min at 16,000 × g at 4 ◦C. Supernatant wascollected and an aliquot further precipitated with 100% cold ace-tone. Protein concentration for each sample was determined induplicate using the Lowry method (DC Protein assay Kit, Bio-Rad) according to the manufacturer’s instructions. Samples weresubjected to 2-D clean-up (2-D clean-up kit, GE healthcare) andthe protein pellet was resuspended in a standard cell lysis buffercontaining 8 M urea, 2 M thiourea, 4% (w/v) CHAPS and 30 mMTris, adjusted to pH 8.5 (UTC buffer) at a protein concentration of2.5 �g/�L. Sample quality and protein amount was checked out byloading 10 �g of each sample onto a 10% SDS-PAGE stained withImperialTM Protein Stain solution.

2.6. CyDye labeling

Proteins in each sample were minimally labeled with CyDyeaccording to the manufacturer’s recommended protocols and aspreviously described (Pastorino et al., 2009). Briefly, 50 �g of eachprotein sample (6 mock and 6 CCHF-infected HepG2 cells) werelabeled with 400 pmol of either Cy3 or Cy5, freshly dissolved inanhydrous DMF, and incubated on ice for 30 min in the dark. The

reaction was quenched with 1 �L of 10 mM free lysine by incu-bation 10 min on ice in the dark. An internal standard pool wasgenerated for each study by combining an equal amount (25 �g)of each sample included in the study and was labeled with Cy-2.

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y3-, Cy5- and Cy2-labeled samples were then pooled (Supplemen-ary Table S1), and an equal volume of UTC buffer containing 10 mMTT and 1% (v/v) immobilized pH gradient (IPG) buffer correspond-

ng to the IPG strips used, was added.

.7. Isoelectric focusing (IEF)

For the first dimension, labeled-samples were separated by IEFsing 6 precast 18-cm IPG strips pH 3-10 L, rehydrated for 8 h witheStreak buffer containing 1% (v/v) IPG buffer (pH = 3–10 L). The

amples were applied at the acidic end of the IPG strips using aup-loading technique. IEF was carried out at 20 ◦C for a total of5 kVh on an Ettan IPGphor 3 electrophoresis unit (GE Healthcare).

.8. Second-dimension electrophoresis

Prior to separation in the second dimension, the 6 IPG stripsere reduced in equilibration buffer (50 mM Tris–HCl, pH 8.6

uffer, 6 M urea, 2% SDS and 30% glycerol) supplemented with 1%w/v) DTT for 10 min and then alkylated in equilibration bufferontaining 2.5% (w/v) iodoacetamide for 10 min. Equilibrated IPGtrips were then deposed onto 10% SDS-PAGE gels using EttanALT six system (GE Healthcare UK). Strips were overlaid with 0.5%

ow-melting point agarose in 1× running buffer containing bro-ophenol blue and electrophoresis was run overnight at 1.5 W/gel

ntil the dye reached the bottom of the gel at 20 ◦C.

.9. Image analysis

After electrophoresis, the gels with Cydye-labeled proteins werecanned three times with a TyphoonTM Trio Image scanner (GEealthcare UK) each time at different excitation wavelengths (Cy3,80 BP 30/green (532 nm); Cy5, 670 BP 30/red (633 nm); Cy2,20 BP 40/blue (488)). Prescans were performed to adjust thehotomultiplier tube (PMT) voltage to obtain images with a max-

mum intensity of 60,000–80,000 U. Images were cropped withmageQuantTM software (GE Healthcare UK) and further analyzedsing the software package DeCyder v6.5 (GE Healthcare UK). Theifferent images from the six gels ran in parallel were included inhe analysis. Intra-gel spot detection and quantification was per-ormed using the differential in-gel analysis (DIA) mode whereasmages from different gels were matched using the biological vari-nce analysis (BVA) mode. Matching between gels was performedsing the in-gel standard from each image pair. The paired T-testas used for statistical analysis of the data and a false discovery

ate (FDR) correction was applied in order to eliminate false posi-ives (Benjamini and Hochberg, 2000). Protein spots which werexpressed differentially between two experimental groups (|fold-hange (FC)|≥1.5, p ≤ 0.05) were marked. After 2D-DIGE imagingnd image analysis, the protein spot pattern was visualized bymperialTM Protein Stain solution, according to the manufacturer’srotocol.

.10. In-gel digestion

Based on the DeCyder v6.5 analysis, protein spots of interestrom gels stained with ImperialTM Protein Stain solution werexcised and digested using a Shimadzu Xcise automated gelrocessing platform (Shimadzu Biotech, Kyoto, Japan) as describedreviously (Torrentino-Madamet et al., 2011) and stored at −20 ◦C

ntil their analysis by mass spectrometry (MS). To increase the pro-ein identification success and confidence, the same protein spot ofnterest from three of the six gels were accurately excised withhe Shimadzu Xcise automated robot and the three spots from the

ch 179 (2014) 187– 203 189

three gels were loaded in the same well from a 96-well plate priortreatments for MS analysis.

2.11. Mass spectrometry analysis of peptide mixture from gelelution

The samples were analyzed by nanoscale capillary liquidchromatography-tandem mass spectrometry (nano LC–MS/MS).Purification and analysis were performed on a C18 capillary col-umn using a CapLC system (Waters, Milford, MA) coupled to ahybrid quadrupole orthogonal acceleration time-of-flight tandemmass spectrometer (Q-TOF Ultima, Waters, MA). Chromatographicseparations were conducted on a reversed-phased capillary column(AtlantisTM dC18, 3 �m, 75 �m × 150 mm Nano EaseTM, Waters,MA) with a 180–200 nl min−1 flow. The gradient profile consistedin a linear gradient from 95% A (H2O, 0.1% HCOOH) to 60% B (80%acetonitrile, 0.1% HCOOH) in 60 min followed by a linear gradient to95% B in 10 min. Mass data acquisitions were piloted by MassLynx4.0 software using automatic switching between MS and MS/MSmodes. The internal parameters of Q-TOF were set as follows: theelectro-spray capillary voltage was set to 3.2 kV, the cone voltagewas set to 30 V, and the source temperature was set to 80 ◦C. The MSsurvey and MS/MS scan range were m/z 400–1300 and m/z 50–1500respectively with a scan time of 1 s and an interscan time of 0.1 s.When the intensity of a peak rose above a threshold of 15 counts,tandem mass spectra were acquired for 10 s. Normalized collisionenergies for peptide fragmentation were set using the charge-staterecognition files for +2 and +3 peptide ions. Fragmentation was per-formed using argon as the collision gas and with the collision energyprofile optimized for various mass ranges and charge of precursorions. Mass data collected during a nano LC–MS/MS analysis wereprocessed using ProteinLynx Global Server 2.2 software (Waters)with the following parameters: no background subtraction, smooth3/2 Savitzky Golay and no deisotoping to generate peak lists in themicromass pkl format. Pkl files were then fed into a local searchengine Mascot Daemon v2.2.2 (Matrix Science, London, UK).

2.12. MS data analysis

The data were searched using Mascot software, against Homosapiens and Crimean-Congo hemorrhagic fever virus with theSwissProt database version 51.6. (February 3rd, 2012). Searchparameters were set in order to allow one missed tryptic cleav-age site, the carbamidomethylation of cysteine, and the possibleoxidation of methionine; precursor and product ion mass error tol-erance was <0.2 Da. All identified proteins have a Mascot scoregreater than 31 (Mixed: H. sapiens + Crimean-Congo hemorrhagicfever virus, 20,336 sequences), corresponding to a statistically sig-nificant (p < 0.05) confident identification. Moreover, among thepositive matches, only protein identifications based on at least twodifferent non-overlapping peptide sequences with a mass tolerance<0.05 Da were accepted. These validation criteria were added inorder to limit the number of false positive matches without missingreal proteins of interest.

2.13. iTRAQ labeling

For iTRAQ labeling, 100 �g of 4 mock- and 4 CCHF-infectedsamples were precipitated with cold acetone for at least 2 h at−20 ◦C, centrifuged for 15 min at 16,000 × g, dissolved in 20 �L ofdissolution buffer, denatured, reduced, alkylated and digested with10 �g trypsin overnight at 37 ◦C, following manufacturer’s protocol

(iTRAQ® Reagent Multiplex Buffer kit, Applied Biosystems, FosterCity, CA, USA) and as previously described (Briolant et al., 2010).The resulting peptides were labeled with iTRAQ reagents (iTRAQ®

Reagent-8Plex multiplex kit, Applied Biosystems, Foster City, CA,

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SA) according to manufacturer’s instructions. Peptides from the 4ock samples were labeled with iTRAQ113, iTRAQ114, iTRAQ115,

TRAQ116, and peptides from the 4 CCHF-infected samples wereabeled with iTRAQ117, iTRAQ118, iTRAQ119, iTRAQ121 (Supple-

entary Table S2), at room temperature for 2 h and stored at -20 ◦C.efore combining the samples, a pre-mix containing an aliquot ofach sample, cleaned-up using a ZipTip® was analyzed by MS/MSo check out peptide labeling efficiency with iTRAQ Reagents andomogeneity of labeling between each samples. The content ofach iTRAQ Reagent-labeled sample was then pooled into one tubeccording to this previous test. The mixture was then cleanup usingn exchange chromatography (SCX/ICAT cation exchange cartridge,Bsciex, Foster City, USA) and reverse-phase chromatography C18artridge (C18 SpinTips, Proteabio, Nîmes, France), prior to beingeparated using the off-gel system (Agilent 3100 OFFGEL fraction-tor, Agilent Technologies).

.14. Off-gel separation

The resulting peptides were dried and separated into 12 frac-ions in-solution with an Agilent 3100 OFFGEL fractionator (Agilentechnologies). Separation of peptides was based on their isoelectricoint on 11 cm IPG strips pH 3–10 using IPG buffer, pH 3–10 (Agi-

ent Technologies). The IPG strips and paper wicks were rehydratedith 40 �l of 2.44% glycerol (v/v), 1% IPG buffer for 15 min. While

he strips were rehydrating, the sample was solubilized in 1.8 mL ofhe same rehydration buffer. After complete rehydration, 150 �l ofample was added to each well, the wells were sealed, and mineralil was added to each end of the strip. The strips were focused until0 kV h was reached with a max voltage of 8000 V, 50 �A, 200 mW,nd a hold setting of 500 V. After 24 h of run time the paper wicksere removed and new wicks wetted with deionised water applied.

he runs took approximately 35–40 h.

.15. Mass spectrometry analysis of peptide fractions from off-geleparation

For nanoLC mass spectrometry measurements, 5 �g of peptideample was injected onto a nanoliquid chromatography systemUltiMate® 3000 Rapid Separation LC (RSLC) systems, Dionex, Sun-yvale, CA). After pre-concentration and washing of the sample on aionex Acclaim PepMap 100 C18 column (2 cm × 100 �m i.d. 100 A,

�m particle size), peptides were separated on a Dionex AcclaimepMap RSLC C18 column (15 cm × 75 �m i.d., 100 A, 2 mm particleize) (Dionex, Amsterdam) using a linear 90 min gradient (4–40%cetonitrile/H20; 0.1% formic acid) at a flow rate of 300 nL/min. Theeparation of the peptides was monitored by a UV detector (absorp-ion at 214 nm). The nanoLC was coupled to a nanospray source of

linear ion trap Orbitrap mass spectrometer (LTQ Orbitrap Velos,hermo Electron, Bremen, Germany). The LTQ spray voltage was.4 kV and the capillary temperature was set at 275 ◦C. All samplesere measured in a data dependent acquisition mode. Each runas preceded by a blank MS run in order to monitor system back-

round. The peptide masses were measured in a survey full scanscan range 300–1700 m/z, with 30 K FWHM resolution at m/z = 400,arget AGC value of 1.00 × 106 and maximum injection time of00 ms). In parallel to the high-resolution full scan in the Orbitrap,he data-dependent CID scans of the 10 most intense precursor ionsere fragmented and measured in the linear ion trap (normalized

ollision energy of 35%, activation time of 10 ms target AGC valuef 1.00 × 104, maximum injection time 100 ms, isolation window

Da and wideband activation enabled). The fragment ion masses

ere measured in the linear ion trap to have a maximum sensitiv-

ty and the maximum amount of MS/MS data. Dynamic exclusionas implemented with a repeat count of 1 and exclusion duration

f 37 s.

ch 179 (2014) 187– 203

2.16. Data analysis

Raw files generated from mass spectrometry analysis werecombined and processed with Proteome Discoverer 1.1 (ThermoFisher Scientific). This software was used for extraction of MGFfiles. Protein identification and quantification were carried outusing ProteinPilot version 4.0 (Applied Biosytems). The searchwas performed against the mixed database containing 58,843sequences (H. sapiens extracted from Uniprot the 24th November2011 + Crimean-Congo hemorrhagic fever virus, and some classicalcontaminants proteins). Data were processed as described previ-ously (Auer et al., 2010). Briefly the following criteria were used;trypsin cleavage specificity, carbamidomethylated cysteins, biolog-ical modifications for the ID focus settings and thorough searcheffort. A protein was considered to be significantly identified when2 or more high confidence (>95%) unique peptides were assigned,the protein identification had to have a 95% confidence (unusedprotein score >1.3). The average ratio of identified peptides wascalculated based on the iTRAQ reporter ion intensities. The accu-racy of each protein ratio is given by a calculated “error factor” inthe software, and a p value is given to assess whether the proteinis significantly differentially expressed. The results were exportedinto excel for manual data analysis. While relative quantificationand statistical analysis were provided by ProteinPilot version 4.0software, an additional 1.5-fold change cutoff for all iTRAQ ratioswas used to categorize proteins, and were selected only proteinspresenting both |FC| >1.5 and p < 0.05.

2.17. Ingenuity Pathway Analysis

A dataset of selected proteins and their gene/protein ID numberswas uploaded to the Ingenuity Pathway Analysis (IPA) softwareto investigate the biological networks and functions associatedwith these proteins (http://www.ingenuity.com). The IPA programuses a knowledgebase derived from the scientific literature torelate genes or proteins based on their interactions and func-tions. IPA generates biological networks, canonical pathways andfunctions relevant to the uploaded dataset. Highly regulated biolog-ical networks and functions are identified using association rulesamong focus proteins in a particular experiment. Networks werealgorithmically generated and IPA computes a score for each indi-vidual network. The scores are derived from a p-value (score = −log(p-value)) and indicate the likelihood that focus proteins (i.e., theidentified proteins within a network) are clustered together. Aright-tailed Fisher’s exact test is used for calculating p-values todetermine if the probability that the association between the pro-teins in the dataset and the functional and canonical pathway can beexplained by chance alone. The final scores are expressed as nega-tive log of p-values or by p-values and used for ranking. Probabilities(p value) of less than 0.05 were considered significant. IPA also cal-culates a ratio which indicates the strength of association with acanonical pathway. From these two numbers, IPA determines themost significant canonical pathways associated with the dataset.

2.18. SDS-PAGE, blotting, and analysis procedures

Immunoblotting with fluorescence-based methods was usedto detect both the total protein expression profile and the spe-cific immunoreactive proteins as described previously (Pastorinoet al., 2009). The same protein samples used for 2D DIGE wereminimally labeled with Cy3 cyanine dye as described above (seeSection 2.6). Labeled samples were separated by 4–15% SDS-

PAGE in a Mini-PROTEAN Cell (Bio-Rad). Gels were transferredto a nitrocellulose membrane (0.2 �m; GE Healthcare) using asemidry blotting system at 200 mA for 40 min. Blots were sat-urated with 5% nonfat dried milk in PBS containing 0.1% (v/v)

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ween 20 (PBS-T-milk) for 1 h. Western blot (WB) analyses werearried out with rabbit mono- or polyclonal antibodies directedgainst prelamin A/C (1:2000, LMNA, no. sc-20681, Santa Cruziotechnology, Inc., Santa Cruz, CA), apolipoprotein E (1:100,poE, no. sc-98573, Santa Cruz), Ran GTPase-activating protein

(1:50, Ran GAP1, no. sc-25630, Santa Cruz), 14-3-3-� tyrosine-monooxygenase/tryptophan 5-monooxygenase activation pro-ein, zeta polypeptide (1:400, YWHAZ, no. sc-1019, Santa Cruz),ukaryotic elongation factor 2 (1:250, eEF2, clone EP723Y, Epito-ics, Burlingame, CA), annexin V (1:250, ANXA5, clone EPR3979,

pitomics), manganese superoxide dismutase (1:250, SOD2, clonePR2560Y, Epitomics), signal transducer and activator of tran-cription 1 (1:1000, STAT1, no. 9172, Cell Signaling Technology,anvers, MA), diluted in PBS-T-milk and incubated overnight at◦C. After three washes in PBS-T, primary rabbit antibodies were

evealed with ECL Plex goat anti-rabbit IgG Cy5-conjugated sec-ndary antibody (1:1000, GE Healthcare), diluted in PBS-T-milk.ll manipulations were protected from light. The gel electrophore-is and immunoblots were scanned using a Typhoon Trio imagecanner as mentioned above (see Section 2.9). Immunoreactiveands were analyzed using TotalLab Quant v12.2 software (Non-

inear Dynamics). To evaluate the expression level of the differentroteins, immunoreactive band intensities were normalized to the

ntensities of a global protein pattern labeled with Cy3 as describedreviously (Pastorino et al., 2009). Band intensities were also cor-ected for the adjacent background. Differences in the relativebundance of each protein between two independent groups wereetermined using Student’s t-test. All differences were consideredignificant at p < 0.05 and statistical analysis was performed usingraphPad Prism v5.01 statistical software (GraphPad Software Inc.,a Jolla, CA). Standard molecular weight markers were loaded ninach gel (Bio-Rad).

.19. Reagents

N-hydroxy succinimide ester Cy2, Cy3 and Cy5, urea, glycerol,ineral oil, immobiline DryStrip gel (18 cm, pH 3–10 L) and IPG

uffer solutions (pH 3–10 L) were purchased from GE HealthcarePiscataway, NJ). Acrylamide, DTT, Tris, glycine and SDS were pur-hased from Bio-Rad (Hercules, CA, USA). Dimethyl formamideDMF), CHAPS, l-lysine, ammonium persulfate, iodoacetamide,garose, bromophenol blue and TFA were purchased from AldrichPoole, Dorset, UK). Thiourea, TEMED, acetone, acetonitrile (ACN)nd ethanol were purchased from Fluka (Buchs, Switzerland).rypsin (sequencing grade) was purchased from Promega (Madi-on, WI). ImperialTM Protein Stain solution was purchased fromhermo Scientific (Rockford, IL, USA). All buffers were preparedith Milli-Q water (Millipore, Belford, MA, USA).

. Results

.1. Virus infection conditions

Hepatocytes are considered to be important target cells alongith endothelial and mononuclear phagocyte cells (Ergonul, 2012).CHFV kinetic studies in HepG2 cells displayed an optimum repli-ation peak at 24 h p.i. and MOI 1 (2.25 × 106 FFU/mL, 2.36 × 108

enomic copy number and 7.87 × 105antigenomic copy numberespectively shown in Fig. 1C–E) while 22.3% of HepG2 werenfected by CCHFV (Fig. 1A and B). In these conditions no nuclear

ragmentation was detected using a TUNEL assay (data not shown).hese conditions represent the exponential viral replication phasef CCHFV-HepG2 infected cells and were selected to study the earlyhase of CCHFV modulation in HepG2 cells.

ch 179 (2014) 187– 203 191

3.2. Detection of altered protein abundance following CCHFVinfection by 2D-DIGE analysis

To determine consequences of CCHFV infections to the hostcellular proteome, 2D-DIGE experiments were performed. Sixindependent cultures of mock- and CCHFV-infected HepG2 cellscultivated for 24 h with CCHFV at a MOI of 1 were included in thisanalysis. Following protein separation using pH 3–10 range IPGstrip (18 cm) and homogeneous 10% SDS-PAGE, 1724 protein spotswere matched. Among them, 49 spots were significantly modifiedbetween the mock- and CCHFV-infected HepG2 cells (32 spots wereup-regulated, and 17 spots were down-regulated; |fold-change(FC)|≥1.5, p ≤ 0.05 after FDR correction; spots shown in Fig. 2).

3.3. Identification of altered protein abundance following CCHFVinfection detected by 2D-DIGE analysis

Based on the DeCyder v6.5 analysis, the 49 protein spots ofinterest were excised from gels, subjected to in-gel digestion, andsubmitted to MS for identification. The resulting fragment ionspectra were searched against Homo sapiens and Crimean-Congohemorrhagic fever virus protein databases (SwissProt). Thirty-nine(79.6%) protein spots were identified with a high degree of confi-dence (Table 1). Mixtures of distinct proteins were identified for 7spots and 2 protein spots showed a combination of both species(i.e., virus and host proteins). Twenty four distinct cellular pro-teins were identified according to their accession number, 16 and 8were up- and down-regulated, respectively (Table 1). Several pro-teins such as serotransferrin (TF), elongation factor 2 (EEF2), ATPsynthase subunit alpha (ATP5A1), nuclear migration protein nudC(NUDC) and X-ray repair cross-complementing protein 6 (XRCC6)for example, were detected in more than one differentially regu-lated protein spot, suggesting that the abundance variations mayconcern different isoforms of a same protein. As expected, viralproteins including the CCHFV nucleoprotein (n = 11) were identi-fied displaying an important average fold-change (Table 1). Finally,10 protein spots (20.4%) were not identified probably because ofinsufficient amounts of protein or low MS spectra qualities.

3.4. Identification of altered protein abundance following CCHFVinfection by iTRAQ-labeling analysis

Lysate samples from 4 mock-and 4 CCHFV-infected HepG2 cellswere digested with trypsin and the resulting peptides of eachsample were labeled with a specific iTRAQ reagent (Supplemen-tary Table S2). Samples were mixed, separated by off-gel systemin 12 fractions and each fraction was submitted to MS analysis.Data generated were analyzed with the Protein Pilot software.Although a total of 3847 proteins were identified, after applyinga global False Discovery Rate (FDR) cut-off at 1%, 3071 unique pro-teins were selected for the next analysis steps. Among them, 3043were quantified comprising 11 contaminant proteins (e.g., keratin,trypsin) which were excluded from the analysis. The comparison ofthe four biological replicates between mock- and CCHFV-infectedsamples allowed to highlight 225 proteins presenting a signifi-cant expression fold-change (|FC|≥1.5, p ≤ 0.05), including 3 CCHFviral proteins (nucleoprotein, envelope glycoprotein and L protein).Among the cellular proteins differentially regulated, 164 and 58were significantly up- and down-regulated, respectively (Supple-mentary Table S3).

3.5. Combination of in-gel (2D-DIGE) and off-gel

(iTRAQ-labeling) analyses

Six host proteins (i.e., superoxide dismutase (SOD2), UTP-glucose-1-phosphate uridylyltransferase (UGP2), elongation factor

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179 (2014) 187– 203Table 1Proteins identified from the differential 2-D DIGE (pH 3–10) analysis of HepG2 cells after CCHFV infection.

Protein name ID (SwissProt) Entry name Molecularweight (kDa)

pI Spot ID Number of MS/MSpeptide sequences

SequenceCoverage (%)

Mascot score CCHF/Mock

Averagevolume ratio

(p value)

Viral proteins

Nucleoprotein OS = Crimean-Congo hemorrhagicfever virus (strain Nigeria/IbAr10200/1970) GN = NPE = 3 SV = 1]

P89522 NCAP CCHFI 54.199 8.47

967 5 11.2 323 2.85 0.0002971 4 8.9 848 5.87 6,30E-05978 9 19.5 149 3.05 6.3 E-5984 3 7.3 246 8.70 6,10E-06999 6 13.1 1281 11.41 6.1 E-61000 6 12.9 954 5.76 0.000871004 12 26.1 811 3.6 0.000151005 1 2.3 65 8.86 6.1 E-61010 4 9.5 228 7.68 6,10E-061014 11 23.2 364 4.57 0.000751024 5 11.2 595 3.34 0.0000491169 6 14.1 140 1.50 0.0092

Host proteinsTranslation–transcription regulationElongation factor 2 OS = homo sapiens GN = EEF2

PE = 1 SV = 4P13639 EF2 HUMAN 96.246 6.41 331 20 19.8 408 -1.83 6.3 E-5

333 24 23.8 776 -1.95 0.0035335 25 25.3 634 -1.82 0.0011

Eukaryotic initiation factor 4A-I OS = homo sapiensGN = EIF4A1 PE = 1 SV = 1

P60842 IF4A1 HUMAN 46.353 5.32 1226 11 25.4 282 1.53 0.0011

Elongation factor 1-delta OS = homo sapiensGN = EEF1D PE = 1 SV = 5

P29692 EF1D HUMAN 31.2172 4.90 1741 2 7.5 86 1.67 0.0026

Interleukin enhancer-binding factor 2 OS = homosapiens GN = ILF2 PE = 1 SV = 2

Q12905 ILF2 HUMAN 42.263 5.19 1320 4 16.7 140 3.92 0.00012

X-ray repair cross-complementing protein 6OS = homo sapiens GN = XRCC6 PE = 1 SV = 2

P12956 XRCC6 HUMAN 70.084 6.23 589 5 8.9 216 -1.66 0.00087

593 9 15.6 421 -1.57 0.0049GTP-binding nuclear protein Ran OS = homo

sapiens GN = RAN PE = 1 SV = 3P62826 RAN HUMAN 24.579 7.01 2370 2 11.1 48 1.61 6.3 E-5

ATP-dependent RNA helicase DDX3X OS = homosapiens GN = DDX3X PE = 1 SV = 3

O00571 DDX3X HUMAN 73.597 6.73 502 2 3.9 94 -1.54 0.0035

506 7 11.6 186 -1.56 0.0022511 3 5.4 90 -1.54 0.0086

Metabolic pathwaysGlutamate dehydrogenase 1, mitochondrial

OS = homo sapiens GN = GLUD1 PE = 1 SV = 2P00367 DHE3 HUMAN 61.701 7.66 975 7 13.3 175 1.66 0.0039

UTP – glucose-1-phosphate uridylyltransferaseOS = Homo sapiens GN = UGP2 PE = 1 SV = 5]

Q16851 UGPA HUMAN 57.0765 8.16 998 6 12.2 1586 5.87 6.3 E-5

1005 7 14.2 165 8.86 6.1 E-6Pyruvate kinase isozymes M1/M2 OS = Homo

sapiens GN = PKM2 PE = 1 SV = 4P14618 KPYM HUMAN 58.470 7.96 833 16 33.0 909 -1.51 0.0015

Succinate-semialdehyde dehydrogenase,mitochondrial OS = homo sapiens GN = ALDH5A1PE = 1 SV = 2

P51649 SSDH HUMAN 58.034 8.62 1005 3 6.5 123 8.86 6.1 E-6

Serine hydroxymethyltransferase, mitochondrialOS = homo sapiens GN = SHMT2 PE = 1 SV = 3

P34897 GLYM HUMAN 56.414 8.76 1007 9 19.2 91 1.8 0.0026

TransporterSerum albumin OS = homo sapiens GN = ALB PE = 1

SV = 2P02768 ALBU HUMAN 71.317 5.92 579 10 16.6 289 -1.56 0.00069

582 10 18.9 3740 -1.72 0.0016

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193

ATP synthase subunit alpha, mitochondrialOS = homo sapiens GN = ATP5A1 PE = 1 SV = 1

P25705 ATPA HUMAN 59.828 9.16 1016 8 13.4 308 1.76 0.0093

1169 9 24.1 1176 1.50 0.00921024 7 16.6 1550 3.34 0.000049

ATP synthase subunit beta, mitochondrialOS = homo sapiens GN = ATP5B PE = 1 SV = 3]

P06576 ATPB HUMAN 56.525 5.26 1017 9 20.8 550 1.68 0.001

Vesicle-fusing ATPase OS = homo sapiens GN = NSFPE = 1 SV = 3

P46459 NSF HUMAN 83.055 6.52 502 9 12.9 147 -1.54 0.0035

506 12 16.9 221 -1.56 0.0022CytoskeletonActin, cytoplasmic 1 OS = homo sapiens GN = ACTB

PE = 1 SV = 1P60709 ACTB HUMAN 42.052 5.29 1251 7 20.3 157 1.56 0.00087

Caldesmon OS = homo sapiens GN = CALD1 PE = 1SV = 3

Q05682 CALD1 HUMAN 93,232 5.62 501 2 3.4 75 -1.56 0.0044

502 4 5.8 95 -1.54 0.0035Platelet degranulation/blood coagulationCalumenin OS = homo sapiens GN = CALU PE = 1

SV = 2]O43852 CALU HUMAN 37.198 4.47 1234 3 9.8 80 1.59 0.0015

Serotransferrin OS = homo sapiens GN = TF PE = 1SV = 3

P02787 TRFE HUMAN 79.294 6.81 492 16 25.4 556 -1.63 0.0016

499 15 24.8 4380 -1.6 0.0056511 10 16.6 299 -1.54 0.0086506 6 9.5 131 -1.56 0.0022

Cell redox/oxygen homeostasisSuperoxide dismutase [Mn], mitochondrial

OS = homo sapiens GN = SOD2 PE = 1 SV = 2P04179 SODM HUMAN 24.878 8.35 2408 2 10.4 244 1.54 0.00072

Glutathione reductase, mitochondrial OS = homosapiens GN = GSR PE = 1 SV = 2

P00390 GSHR HUMAN 56.791 8.74 975 2 4.6 105 1.66 0.0039

ApoptosisAnnexin A5 OS = homo sapiens GN = ANXA5 PE = 1

SV = 2P08758 ANXA5 HUMAN 35.971 4.94 2072 5 15.3 204 1.52 0.00035

Cell proliferationNuclear migration protein nudC OS = homo sapiens

GN = NUDC PE = 1 SV = 1]Q9Y266 NUDC HUMAN 38.276 5.27 1251 5 14.5 118 1.56 0.00087

1258 2 6.0 62 1.6 0.00097No identification (n.i)n.i 635 -1.69 0.00037n.i 1011 1.62 0.015n.i 1398 1.62 6.3 E-5n.i 1606 -1.7 0.0049n.i 1607 -1.64 0.0039n.i 1736 1.56 0.0066n.i 1815 1.52 0.0017n.i 1871 1.52 0.0062n.i 1898 2.68 7.0 E-5n.i 2259 1.51 0.0026

n.i.: no identified.The proteins were identified by mass spectrometry following in-gel trypsin digestion. The spot numbers correspond to the same numbers as indicated in Fig. 2. The identity of the spots, their SwissProt accession numbers, thetheoretical MW and pI values, as well as the number of peptides sequences, the corresponding percentage sequence coverage and the Mascot score are listed for MS/MS analysis (protein scores greater than 31 are considered assignificant (p < 0.05)). Paired average volume ratio and p values (t-test) between HepG2 cells infected with CCHFV versus uninfected cells (mock) were quantified using Decyder software.

194 C. Fraisier et al. / Virus Research 179 (2014) 187– 203

Fig. 1. Detection of CCHFV antigens and RNAs in infected HepG2 cells. (A) fluorescent photomicrography of CCHFV-infected HepG2 monolayers (MOI 1), incubated withs percp e cell

g SD th

2aepptgtt(M1t12e(c

3p

C

pecific anti-CCHFV mouse polyclonal antibodies. The magnification was 20×. Theerformed from 3 through 48 h p.i. CCHF-infected HepG2 were assayed for (C) thenomic or the (E) antigenomic strand copy number from cellular extract. Means ±

(EEF2), annexin A5 (ANXA5), ATP synthase subunit beta (ATP5B)nd serotransferrin (TF)) were found significantly differentiallyxpressed either by 2D-DIGE or iTRAQ analysis. These last six hostroteins were differentially regulated in the same way by bothroteomic approaches and represented 25.0% of the proteins iden-ified by 2D-DIGE. Thus, the two quantitative proteomic approachesenerated a total of 240 unique host proteins that were foundo be differentially expressed after CCHFV infection. These pro-eins were classified according to their subcellular localizationFig. 3A) and their Gene Ontology (GO) biological function (Fig. 3B).

ost of the proteins were associated to metabolic process (n = 39;6%), transcription/translation regulation (n = 35; 15%), cytoskele-on maintenance (n = 31; 13%) and molecular transport (n = 27;1%). A representation of signal intensities combining data fromD-DIGE and iTRAQ are showed in Supplementary Fig. S1. Inter-stingly, among the proteins above the threshold values define|FC|≥1.5 and p < 0.05), the majority (73%; 176/240) were signifi-antly up-regulated.

.6. Networks, biological pathways and functions associated with

roteins differentially expressed after CCHFV infection

To further understand the biological mechanisms involved inCHFV infection of HepG2 cells, a bioinformatics analysis was

entage of infected cells observed using IFA is represented (B). Observations weresupernatant titers determined in plaque assay and expressed in FFU/mL; (D) theree independent experiments are represented.

performed using IPA on the dataset of the differentially expressedhost proteins identified by 2D-DIGE (24 host proteins; 16 up and8 down) and iTRAQ (222 host proteins; 164 up and 58 down).The IPA tool builds networks to identify potentially co-regulatedpartner proteins and to establish their interactions in knownnetworks. This software enabled us to highlight the most rele-vant canonical pathways, to determine proteins involved in thesepathways and also to classify the most significant biological func-tions involving the uploaded proteins. The bioinformatics analysisof the differentially expressed proteins in CCHFV-infected cells gen-erated 4 top-networks with highly significant scores (i.e., upperthan 40) and including at least 25 molecules per network fromthe dataset (Table 2). To provide a greater degree of moleculardetail, network 1 showed the direct interaction between proteinsrelated to the following functions: cellular assembly and organiza-tion, gene expression, protein synthesis (score = 43, 26 molecules,Fig. 4). In this network, tyrosine 3-monooxygenase/tryptophan5-monooxygenase activation protein, zeta polypeptide (YWAHZ)seemed to be the central part with the highest number of directinteractions with the other groups of proteins. This protein belongs

to the 14-3-3 protein family and is involved in Protein Kinase Asignaling pathway including FLNA, AKAP1 and HIST1H1E proteins.Three main groups of proteins linked to YWAHZ emerged fromthis network, associated with significant canonical pathways (i)

C. Fraisier et al. / Virus Research 179 (2014) 187– 203 195

Fig. 2. 2D-DIGE analysis of mock- or CCHFV-infected HepG2 cells at 24 h postinfection (MOI = 1). (A) Representative data from a 2D-DIGE experiment using a 10% SDS-polyacrylamide gel with the pH range from 3 to 10 is shown. Proteins from mock- and CCHFV-infected HepG2 cells were labeled with Cy5 and Cy3 cyanine dyes, respectively.As determined by DeCyder software, protein spots that were differentially expressed between two experimental conditions (|FC| ≥1.5, p ≤ 0.05 after an FDR correction) andidentified by mass spectrometry were annotated on the gel and corresponded to master gel numbers. Spots identified as Homo sapiens (italic numbers) and CCHFV (boldnumbers) proteins are listed in Table 1. Spots identified as a mixture of both species were indicated in bold, italic numbers. Representative images correspond to mock- (B)and CCHFV-infected (C) HepG2 protein profiles. Red dots indicated in B and C corresponded to down- and up-regulated protein spots following CCHFV infection, respectively.(For interpretation of references to colour in this figure legend, the reader is referred to the web version of this article.)

Table 2Top-4 networks with associated functions generated from IPA of differentially expressed proteins in CCHFV-infected HepG2 cells, determined by 2D-DIGE and iTRAQ analyses.Up- and down-regulated proteins from the uploaded dataset are indicated.

ID Molecules in network Score Focus molecules Top functions

1 Up-regulatedAARS, AKAP1, ANXA6, DSP, EIF3A, EIF3B, EIF4A1, EIF4A2, FLNA,HIST1H1E, KRT8, LMNA, LMNB1, LMNB2, PLEC, RAN, RANGAP1,SPTAN1, TKT, YWHAZDown-regulatedCSE1L, DDX3X, KPNB1, PKM2, TNPO1, XPO1

Not in dataset: EIF4A, Eif4 g, Histone H1, Importin alpha, Importinbeta, Lamin, Lamin b, RAN-GTP, Rnr,

43 26 Cellular assembly and organization,gene expression, protein synthesis

2 Up-regulatedACTB, ACTG1, ACTN1, C4B (includes others), CANX, CAP1, CFL1, CTTN,GGT7, NUP214, P4HB, PA2G4, PDIA3, RNF213, RPN1, SERPINA1,SERPINA3, STAT1, Down-regulatedALB, ANXA4, APOB, APOH, ASGR1, PTBP1, RDX, TF

Not in dataset: CaMKII, Cofilin, G-Actin, Ggt, HDL, LDL, MHC Class I(complex), STAT, Tap

43 26 Amino acid metabolism, drugmetabolism, molecular transport

3 Up-regulatedATP5A1, ATP5B, ATPase, CDC37, DNAJB1, HDLBP HSP90B1, HSPA4,HSPA5, HSPA9, HSPB1, HSPD1, ILF2, MACF1, MDH2 (includesEG:17448), MYH9, NRCAM, PC, PSMC2, PSMD2, RNH1, TOP2A, XRCC6Down-regulatedHSPA8, NSF, PRKDC

Not in dataset:26 s Proteasome, Akt, ATP synthase, Ck2, Hsp27, Hsp70,Hsp90, HSP, NFkB (complex)

41 25 Protein degradation, protein synthesis,post-translational modification

4 Up-regulatedAFP, Cbp/p300, CCNE1, CCT4, CCT6A, ECHDC1GTF2A2, GTF2H1, NCL,NUDC, PARP1, PGK1, PNN, RPL12SND1, SOD2, SSB, TAGLN,Down-regulatedCCT2, FASN, GCN1L1, MCM2, MCM3, MCM4, RBPJ, RRM2, Not indataset Alpha tubulin, Beta Tubulin, Creb, Cyclin E, Histone h3, Histoneh4, Mcm, RNA polymerase II, Vegf

40 25 Cellular assembly and organization,DNA replication, recombination, andrepair

196 C. Fraisier et al. / Virus Resear

Fig. 3. Pie chart representing up- and down-regulated proteins identified by massspectrometry in CCHFV infected HepG2 cells (2D-DIGE and iTRAQ approaches).Proteins have been classified according to their subcellular location (A) and theirbiological function (B). The number and the percentage of proteins associated withe

RX(Sses(iopp

faiArSHpmT0tXi

for SOD2 (FCWB = 1.78; FC2D-DIGE = 1.54; FCiTRAQ = 18.20). These vari-ations could be attributed to the mode of protein/peptide detection,

ach category are indicated into brackets.

AN signaling pathway (RAN, RANGAP1, KPNB1, CSE1L, TNPO1,PO1, Importin; p < 1.10−7, ratio = 0.261, Supplementary Table S4),

ii) mechanism of viral exit from host cells (Lamin, LMNB1, LMNB2,PTAN1, XPO1; p < 0.0001, ratio = 0.111), and (iii) initiation factorignaling (Regulation of eIF4 and p70S6 K signaling) (EIF3A, EIF3B,IF4A1, EIF4A2; p < 0.02, ratio = 0.035). RAN signaling was the firstignificant canonical pathway emerging from this analysis. RANras-related nuclear protein) is a small GTP binding protein belong-ng to the RAS superfamily that is essential for the translocationf RNA and proteins through the nuclear pore complex. The RANrotein is also involved in control of DNA synthesis and cell cyclerogression (Joseph, 2006).

Network 2 (score = 43, 26 molecules) included molecules whoseunctions were related to amino acid metabolism, drug metabolism,nd molecular transport. Some of them were particularly involvedn (i) clathrin-mediated endocytosis (CME) pathway (HSPA8, APOE,LB, APOB, TF, ACTB, TFRC, SERPINA1, ACTG1, CTTN; p < 0.0002,atio = 0.051, Supplementary Table S4), (ii) Acute Phase Responseignaling pathway (FN1, C3, APOH, SERPINA3, STAT3, FGG, ALB,P, SOD2, TF, C4B (includes others), SERPINA1, FGA, SERPINE1;

< 0.0001, ratio = 0.079, Supplementary Table S4) or (iii) lipidetabolism via LXR/RXR activation pathway (APOE, ALB, APOB, C3,

F, APOH, FASN, C4B (includes others), SERPINA1, FGA; p = 2.88E-6, Supplementary Table S4). Liver X receptor (LXR) belongs tohe nuclear receptor family and forms heterodimers with retinoid

receptors (RXR), that regulates transcription of several genesnvolved in cholesterol metabolism (Murthy et al., 2002).

ch 179 (2014) 187– 203

Network 3 (score = 41, 25 molecules) contained proteins relatedto protein degradation, protein synthesis, post-translational mod-ification, with most of them involved in the protein ubiquitinationpathway (HSPA8, HSPA4, HSP90B1, PSMD2, HSPA9, UBA1, DNAJB1,HSPD1, PSMC2, HSPA5, UBE3A, HSPB1; p < 0.0002, ratio = 0.044,Supplementary Table S4). Network 4 (score = 40, 25 molecules)included molecules related to cellular assembly and organization,DNA replication, recombination and repair, with some involved inglucocorticoid receptor signaling pathway (ICAM1, HSPA9, STAT3,HSPA5, FGG, GTF2A2, HSPA8, HSPA4, HSP90B1, PCK2, GTF2H1,STAT1, SERPINE1; p < 0.0001, ratio = 0.04, Supplementary Table S4).

In addition, the main molecules associated to biological and tox-icological functions generated by IPA were indicated in the Table 3.Interestingly, most of the cellular and molecular functions alteredfollowing CCHFV infection were related to cell death with the high-est number of proteins differentially regulated (n = 106), including79 molecules involved in apoptosis.

3.7. Verification of protein abundance variations from selectedcandidates

To verify the 2D DIGE and iTRAQ results, WB analyses wereperformed. Eight candidates were selected as being representa-tive of the significant networks, biological and toxic functions,and pathways emerging from IPA: YWHAZ in the center of net-work1, and involved in cell death; RanGAP1 in network 1, involvedin Ran signaling pathway; STAT1 in network 2, involved in hostresponse; SOD2 in network 4 involved in acute phase responseand oxidative stress; APOE involved in CME and LXR/RXR acti-vation; LMNA associated with cell death/apoptosis, involved inmechanisms of viral exit and linked to other lamin proteins in net-work 1; ANXA5 involved in apoptosis signaling pathway and EEF2involved in protein synthesis. Moreover, three of these target pro-teins (EEF2, ANXA5 and SOD2) were found differentially expressedby both DIGE and iTRAQ analyses. For all WB, each protein samplewas labeled with cyanine-3 dye to reveal the variations in sampleloading that were taken into account for the normalization and thecalculation of the average band volume ratio that was detected byeach selected antibody and revealed by fluorescence-conjugatedsecondary antibody.

The differential regulation of SOD2, ANXA5 (up) and EEF2(down) in CCHFV-infected cells, determined by both DIGE andiTRAQ analyses, was confirmed statistically by WB using specificantibodies, highlighting the increase of oxidative stress and apo-ptosis, and the alteration of protein synthesis after CCHV infection,leading to cell death. The up-regulation of RanGAP1 and STAT1observed by iTRAQ analysis was also significantly validated by WB.Fig. 5 shows that both forms of RanGAP1 (cytoplasmic and SUMO1-modified) were detected and increased in CCHFV-infected samples,suggesting an increase in the nucleo-cytoplasmic transport. The up-regulation of STAT1, together with that of SOD2 mentioned above,also suggests a cell response to viral infection. Despite lower fold-change values obtained by WB, the up-regulation of YWHAZ, LMNA(Lamins A and C) and APOE was also observed. However, theseprotein abundance variations were not significant for YWHAZ andAPOE. Finally, WB confirmed statistically a majority of the selectedprotein abundance variations observed by proteomic approaches.Interestingly, FC-values obtained by WB were lower than thosedetermined by iTRAQ and for proteins whose abundance were verylow prior CCHFV infection (i.e., mock-samples), differences in FCvalues could occur when a proteomic method is compared to WB:

quantification and normalization between 2D-DIGE, iTRAQ and WBmethods.

C. Fraisier et al. / Virus Research 179 (2014) 187– 203 197

Table 3Molecules associated to biological and toxicological functions generated by IPA from differentially expressed proteins in CCHFV-infected HepG2 cells, determined by 2D-DIGEand iTRAQ analyses. Up- and down-regulated proteins from the uploaded dataset are indicated.

Biological functions

Category Functions annotation p-Value # Mol. Molecules

Cell death Apoptosis 2.65E−12 79 Up-regulatedAARS, AKAP1 ANXA5, ANXA7, APOE, ATP1A1, C3, CCNE1, CFL1,CTSB, CTTN, DNAJB1, DSP, EEF1D, EIF3B, EZRFLNB, FN1, GLUD1,GSR, HSD17B10, HSP90B1, HSPA4, HSPA5, HSPA9, HSPB1,HSPD1, ICAM1, KRT8, LGALS1, LGALS3BP, LMNA, LMNB1, LTF,MAP4, NAMPT, NCL, P4HB, PA2G4, PARP1, PDIA3, PHB, PON2,PPIA, PSAP, SERPINA1, SERPINA3, SERPINE1, SND1, SOD2, SON,STAT1, STAT3, TF, TFRC, TGM2, TOP2A, VCL, XPO1, YWHAZDown-regulatedAFP, ALB, ANXA4, ASAH1, CCT2, CSE1L, DDX3X, FASN, GCLC,HELLS, HSPA8, INPPL1, MCM2, PEBP1, PKM2, PPM1F, PRDX6,PRKDC, XRCC6

Cellular growth and proliferation Proliferation of cells 3.56E−07 68 Up-regulatedAFP, ANXA7, APOE, ASAH1, ATP5A1, C3, CCNE1, CDC37, CFL1,CTSB, EIF3A, EZR, FGA, FN1, FSCN1, HSP90B1, HSPA5, HSPD1,ICAM1, ISG15, ITGA2, KRT8, LGALS1, LTF, MVP, MYH10,NAMPT, NCL, NUDC, PA2G4, PARP1, PGK1, PHB, PPIA, RAN,SERPINA1, SERPINE1, SHMT2, SOD2, SPTAN1, SPTBN1, STAT1,STAT3, TAX1BP3, TGM2, UBA1, UBE3ADown-regulatedAFP, APOH, CCT2, CSE1L, DDX3X, FASN, GPC3, INPPL1, LAMC1,MCM2, MCM4, NASP, PEBP1, PEG10, PRKDC, RBPJ, RRM2,SLC9A3R1, TF, TFRC, XRCC6

Cellular movement Cell movement 1.73E−09 58 Up-regulatedACTB, ANXA5, APOE, C3, C4B (includes others), CAP1, CFL1,CTSB, CTTN, EZR, FGA, FLNA, FLNB, FN1, FSCN1, HP, HSP90B1,HSPA5, HSPD1, ICAM1, ITGA2, KRT8, LAMC1, LGALS1, LMNA,LTF, MYH10, MYH9, NCL, NRCAM, PA2G4, PARP1, PHB, PLEC,PON2, PPIA, PPIB, ROCK2, SERPINA1, SERPINA3, SERPINE1,SOD2, STAT1, STAT3, TGM2, TLN1, VCLDown-regulatedALB, APOB, CSE1L, DDX3X, GPC3, INPPL1, MCM2, MCM3,SLC9A3R1, SORD, TRIP10

Protein synthesis Metabolism of protein 3.95E−09 32 Up-reguatedAPOE, C3, C4B (includes others), CANX, CDC37, CTSB, EIF3B,EIF4A1, EIF4A2, FLNA, FN1, HSP90B1, HSPA5, HSPB1, HSPD1,ITGA2, NCL, NPEPPS, PSMC2, PSMD2, RRBP1, SERPINE1, SOD2,SSB, TGM2, TUFM, UBE3ADown-regulatedAPOB, EEF2, GPC3, PTBP1, XPO1

Molecular transport Transport of molecule 3.44E−04 33 Up-regulatedANXA6, APOE, ARF4, ATP1A1, ATP5B, C3, CFL1, FLNA, GDI2,HSPA9, KPNB1, MYH9, NUP214, PDIA3, PLIN3, PSAP, RAN,RANGAP1, SPTBN1, STAT3, Down-regulatedALB, APOB, APOH, GPC3, HSPA8, NSF, PRDX6, SLC25A17,SLC5A3, TF, TFRC, TNPO1, XPO1

Toxicological functionsFunctions p-Value Ratio # Mol. MoleculesPositive acute phase response 1.06E−10 9/30 (0.3) 9 Up-regulated

C3, C4B (includes others,) FGA,FGG, HP, SERPINA1, SERPINA3,SERPINE1, SOD2

LXR/RXR activation 3.62E−06 10/119(0.084) 10 Up-regulatedAPOE, C3, C4B (includes otrhes), FGA, SERPINA1, Down-regulatedALB, APOB, APOH, FASN, TF

Oxidative stress 9.62E−05 6/57 (0.105) 6 Up-regulatedGSR, ICAM1, SOD2, STAT3Down-regulatedGCLC, PRDX6

NRF2-mediated oxidative stress response 1.28E−02 8/237 (0.034) 8 Up-regulatedACTB, ACTG1, DNAJB1, GSR, HSP90B1, PPIB, SOD2Down-regulatedGCLC

198 C. Fraisier et al. / Virus Research 179 (2014) 187– 203

Fig. 4. Graphical representation of network 1. This network was associated with “Cellular Assembly and Organization Gene expression, Protein Synthesis” generated byIngenuity Pathway Analysis (IPA) for differentially expressed proteins in HepG2 cells infected with CCHF virus, determined after 2D-DIGE and iTRAQ analyses. On thisnetwork, nodes (proteins) shown in red (up) and green (down) -regulated proteins, while the nodes (proteins) in white have been added by IPA to maximize the networkconnectivity. The edges with arrowheads described the direct (solid lines) nature of the interaction between these proteins. Proteins involved in significant canonical pathwaysw rent sht

4

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ere encircled and the title of the corresponding pathway was also indicated. Diffehe legend.

. Discussion

Despite the clinical importance of CCHF (Ergonul, 2012), theechanisms underlying CCHFV-induced disease are complex and

till need further investigation. A common pathogenic featuref hemorrhagic fever viruses is their ability to disable the hostmmune response by attacking and manipulating the cells initi-ting the antiviral response (Baize et al., 2004; Bosio et al., 2003;ukashevich et al., 1999). Their induced damage is characterized byapid replication of the virus along with deregulation of the vascu-ar system and lymphoid organs (Ergonul, 2012). It further leads toaemostatic failure by stimulating platelet aggregation and degra-ulation, with subsequent activation of the intrinsic coagulationascade (Appannanavar and Mishra, 2011). Several organs, includ-ng the liver, were identified as main targets for CCHFV (Anderssont al., 2006; Bente et al., 2010; Bereczky et al., 2010; Bowick et al.,012). The hepatocyte responses to CCHFV infection are likely toave a key role in the pathogenicity of the disease, due to theirigh infectivity and sensitivity to the virus (Andersson et al., 2006;ente et al., 2010; Bereczky et al., 2010; Rodrigues et al., 2012).

In this regard, to obtain a broader picture of the molecular mech-nisms underlying CCHFV pathogenesis at the early time point,

proteomic analysis of CCHFV-infected HepG2 cells at a MOI of collected 24 h p.i was performed. Such experimental conditionettings allowed preventing apoptotic phenomenon which couldnduce non-specific protein changes, but, it is possible that the lowate of infected cells (22.3%) could failed to detect low abundantroteins with a weak abundance variation change in the wholeell lysate samples. However, in the present study, a total of 243

roteins differentially regulated were identified by two compre-ensive quantitative proteomic approaches, 2D-DIGE and iTRAQ

ollowed by tandem MS analysis. The detection of viral proteinsy proteomic methods, confirmed the infection of HepG2 cells by

apes of the nodes represent functional classification of the proteins as indicated in

CCHFV at this early time point. Interestingly, for the large majorityof the proteins, their abundance was positively regulated follow-ing viral infection (73%). According to the high number of proteinsdifferentially regulated upon CCHFV infection, the protein datasetwas submitted to a bioinformatic tool (Ingenuity Pathway Analy-sis) generating functional networks based on known interactions,and categorized major altered pathways. Numerous significant spe-cific cellular functions, pathways and networks were identified andsome of them are discussed below.

4.1. Modulation of the inflammatory response following CCHFVinfection

Several studies have demonstrated the critical role played byinterferon pathways (e.g., IFN-�, IFN-�) to control viral infections,and particularly against bunyaviruses (Le May and Bouloy, 2012).The two recently established animal models for CCHFV infectionconfirmed the key function of IFN response. The first one reportedthat mice knockout (KO) for IFN receptor were highly susceptibleto CCHFV infection compared to wild-type, presented liver damage(Bereczky et al., 2010) and displayed a marked proinflammatoryhost responses (Zivcec et al., 2013). The second one, KO for the sig-nal transducer and activator of transcription 1 (STAT1), a moleculethat has a crucial function in the IFN signaling pathway, led to acomplete abolishment of the intracellular IFN response and a highersusceptibility of STAT1 KO mice to CCHFV infection (Akira, 1999;Bente et al., 2010). Thus, the activation of the IFN-I pathways bythe host in response to viral infection, represents a major antivi-ral defence mechanism to protect host from clinical symptoms of

CCHFV infection. Here, an increase of STAT1 protein abundancewas detected following CCHFV infection and was confirmed by WB.However, as the STAT1 phosphorylation states was not considered,it is not possible to determine whether the accumulation of STAT1

C. Fraisier et al. / Virus Research 179 (2014) 187– 203 199

Fig. 5. Western blot validations of differentially regulated proteins identified by 2D-DIGE and/or iTRAQ analyses. (A) Protein samples from each group used for proteomicanalysis were minimally labeled with cyanine-3 dye. At the top, a representative protein profile of six biological replicates from mock- and CCHF-infected HepG2 cellsseparated by 4–15% SDS-PAGE is shown. Western Bloting with fluorescence-based methods was used to detect an overlaid fluorescent scan of the general protein patterns(Cy3 dye; green) and the specific immunoreactive proteins (FITC or Cy5 dye; red). To better visualize protein detection signals observed with each specific antibody used,corresponding cropped WB images are presented in gray levels. (B) The graphs correspond to the mean ± S.D. of protein quantity measured by densitometry of the antigenicbands. Densitometry analyses were performed using TotalLab Quant v12.2 software (Nonlinear Dynamics), and data were normalized to levels of global protein patternintensity. The values indicated under each graph correspond to fold-changes from comparisons between mock- and CCHFV-infected samples. The significance of the differentialprotein expression are indicated *p < 0.05; **p < 0.01; ***p < 0.001. A.U., arbitrary units. �-, antibody anti SOD2, manganese superoxide dismutase; Ran GAP1, Ran GTPase-activating protein 1 (a, SUMO 1-modified Ran GAP1; b, cytoplasmic Ran GAP1); YWHAZ, 14–3-3-�; ANXA5, annexin V; LMNA; prelamin A/C (a; Lamin A; b, Lamin C); APOE,a eukarl

pttirSn

tSciuiSgoe6lrb

polipoprotein E; STAT1, signal transducer and activator of transcription 1; EEF2,

egend, the reader is referred to the web version of this article.)

rotein could be attributed to a cell response against CCHFV infec-ion as an attempt to control the viral replication or correspondo an inhibition of the Jak/STAT signaling pathway due to CCHFVnfection. Nevertheless, the recent demonstration of unphospho-ylated STAT1 proteins in gene expression regulation (Yang andtark, 2008), indicated the high complexity of this protein function,ecessitating to be explored more precisely.

Interestingly, in addition to STAT1, an increase of STAT3 pro-ein abundance was detected following CCHFV infection. Like otherTAT proteins, STAT3-dependent signaling is tightly controlled, andan be activated by the phosphorylated STAT3 homodimers, formedn response to the activation of the gp130 common receptor sub-nit by different ligands such as IL-6 (Cheon et al., 2011). Thus,

n contrast to STAT1 which was shown to attenuate IL-6-inducedTAT3 activity and to increase the expression of pro-apoptoticenes (Dimberg et al., 2012), it is possible that the up-regulationf STAT3 protein could correspond to a viral activation in order tovade cell death and to succeed its multiplication. Interestingly, IL-

production was not detected in CCHFV infected hepatocytes celline (i.e., Huh7) (Rodrigues et al., 2012), suggesting that STAT3 up-egulation could result from another pathway. Other factors haveeen described to activate STAT3 such as growth receptor tyrosine

yotic elongation factor 2. (For interpretation of references to colour in this figure

kinases (e.g., EGFR family), non-receptor tyrosine kinases (e.g., Srcand Abl) or in response to stimulation of G-protein-coupled recep-tors (Raptis et al., 2011). Here, the up-regulation of two Rho GTPaseproteins (i.e., TAX1BP3 and ROCK2) could be involved in the regula-tion of STAT3 activity (Ehrenreiter et al., 2009). To our knowledge,it is the first time that STAT3 protein was shown to be up-regulatedfollowing CCHFV infection. Complementary experiments would beneeded to evaluate the level of STAT3 phosphorylation and to sup-port the role of STAT3 protein in viral cycle which, in this case, couldbe an excellent target to develop medical countermeasures.

Here, interleukin-2 enhancer binding factor 2 (ILF-2) proteinabundance was increased in response to CCHFV infection. AlthoughILF-2, also referred as NF45, is a component of the nuclear factorassociated with dsRNA (NFAR) complex that is responsible for thecell type-specific expression of IL-2 gene (Wang et al., 2006), recentin vitro and in vivo experiments performed in hepatocyte cell line orin wild-type mouse model, respectively, failed to detect increasedsecretion of IL-2 in response to CCHFV infection (Rodrigues et al.,

2012; Zivcec et al., 2013). These data suggest that ILF-2 should actdifferently. Several works provided evidences that distinct mem-bers of NFAR complex such as NF45 are recruited by RNA virusto promote replication process (Gomila et al., 2011; Isken et al.,

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007). In hepatitis C virus, an interaction of viral RNA with NF45as demonstrated and this contact seems to be a key factor for

iral translation and RNA replication (Isken et al., 2007; Lee et al.,011). The up-regulation of ILF-2/NF45 protein observed in theresent study could then promote CCHF viral cycle. In addition, theotential NF45 recruitment by CCHFV could weaken host defenseechanisms by inhibiting indirectly IL-2 gene expression and pro-

ein secretion. However, Stricker and collaborators established thathe association of NF45 with infectious bursal disease virus proteinsesulted in a decrease of virus replication (Stricker et al., 2010), sug-esting that the involvement of NF45 in virus cycle is more complexhan expected. Indeed, the precise evaluation of the involvementf NF45 in CCHFV replication and/or in antiviral response would beelpful for the development of an interesting strategy that inter-

eres with RNA viral replication.

.2. Oxidative stress and CCHFV infection

It is well known that a disturbance of the cellular redox equi-ibrium contributes to the pathogenesis of viral infections, wheret may trigger apoptosis, necrosis, inflammation and immuneesponse (Higa et al., 2008), helping to control viral replicationPan et al., 2012). Among the oxidative stress-response proteinsnduced by CCHFV, ACTB, ACTG1, DNAJB1, GSR, PPIB and SOD2

ere shown to be up-regulated, whereas GCLC and PRDX6 wereown-regulated. Our results revealed substantial modifications ineveral of the early response-, oxidative stress- and damage-relatedroteins which are similar to previously published findings display-

ng increase in reactive oxygen species (ROS) after treatment withrugs (Gomez-Sucerquia et al., 2012). In this regard, the increasef Mn-superoxide dismutase (SOD2) expression level found inCHFV-infected hepatocytes using DIGE, iTRAQ and confirmed byB is of high interest. SOD2 is one of the most important antiox-

dant enzymes, protecting cells against the detrimental effects ofOS (Miao and St Clair, 2009). The significant increase of SOD2 pro-ein in CCHFV infected HepG2 cells, as also observed in Dengueirus (DV)-infected cells, may correspond to a defence mechanismgainst CCHFV-induced oxidative stress (Higa et al., 2008). The pro-uction of SOD could then participate in the prevention of oxidative

iver damage during CCHFV infection. The serological detection ofntioxidant enzymes like SOD or glutathione reductase (GSR), alsoound to be up-regulated in this study, could be useful as a pro-nostic biomarker of disease evolution as previously described forther hepatic viral infections (Gil et al., 2004; Khan et al., 2011).

.3. CCHFV-induced modulation of ubiquitination pathwayroteins and ubiquitin-like modifiers

The protein ubiquitination pathway plays a role in a varietyf cellular mechanisms including the control of protein stabil-ty, the regulation of signal transduction pathways but also inntiviral responses (Nandi et al., 2006; Sadowski et al., 2012). Kan-aya and collaborators have observed, by a proteomic approach,n up-regulation of proteasome proteins following dengue virusnfection of human endothelial cells (Kanlaya et al., 2010). Theseuthors demonstrated that the ubiquitin-activating enzyme E1UBE1) could limit viral protein synthesis and its replication, sup-orting the crucial function of ubiquitin-proteasome pathways inhe regulation of viral infections. However, others reported thatertain viruses could influence the ubiquitin pathway to facilitateheir own replication, preventing viral clearance and promoting theutcome of infection (Isaacson and Ploegh, 2009). The E6 viral pro-

ein from human papilloma virus (HPV) can activate the host E3igase which induces a specific ubiquitination of the p53 transcrip-ion factor leading to uncontrolled cellular proliferation withoutnducing apoptosis (Mammas et al., 2008). Here, an increase of the

ch 179 (2014) 187– 203

proteins involved in protein ubiquitination pathway, includingUBE3A, UBA1 and several HSPs was noted. The up-regulation ofUBE3A (E3) ligase could result from activation of this ubiquitinmachinery protein by CCHF infection, to escape cell from apoptosis,ensuring viral replicative success (Kerscher et al., 2006). A furtherconfirmation of the involvement of host E3 ligase in viral cell cycleachievement, could lead to new areas of investigation for the dis-covery of specific drug inhibitors of this ubiquitin protein.

The participation of ubiquitin-like modifiers, such as the smallubiquitin-related modifier (SUMO) and the interferon-stimulatedgene 15 (ISG15), that were found differentially regulated uponCCHFV infection, have already been considered in viral replicationand egress (Wimmer et al., 2012). Sumoylation is an ubiquitin-likedynamic and reversible post-translational modification process,performed by enzymatic cascade, leading to the covalent attach-ment of SUMO to its target proteins. The consequences of proteinsumoylation are difficult to predict since SUMO attachment to itstarget alters protein–protein interactions, but could also modifyits subcellular location, enhance its stability or change its bio-logical activity and regulate gene transcription. Although nuclearinteractions of SUMO-1 with viral nucleocapsid proteins fromBunyavirus have been observed, viral proteins appear not to besumoylated (Kaukinen et al., 2003). This non-covalent interactioncould play a role in the relocation of viral nucleocapsid proteinsin the perinuclear region and could contribute to viral replicationand virion assembly. Here, an up-regulation of SUMO-E1 enzyme(SAE2/UBA2), an enzyme involved in the first step of the sumoy-lation machinery, was observed in infected samples. Although theexact role of sumoylation regulation in CCHFV infection remainsto be clarified, the discovery of new pharmaceutical agents whichcould block SUMO-E1 protein, may improve our understanding inthe CCHFV replication and in the control of viral infection (Boggioet al., 2004).

Interferon stimulated gene 15 (ISG15) was the first ubiquitin-like protein modifier identified (Haas et al., 1987; Korant et al.,1984). Similarly to the ubiquitination pathway, ISG15 conjugates totarget proteins (ISGylation) through a cascade of enzymatic reac-tions involving E1, E2 and E3 enzymes, modifying the function ofISGylated proteins (Harty et al., 2009). ISG15 was shown to modu-late the immune response, to be strongly induced after interferontreatment and to act as an antiviral effector (Sadler and Williams,2008). Numerous previous works underlined the antiviral effect ofthe IGS15 expression in protection from virus-induced death andlimiting viral replication (for review: (Harty et al., 2009)), eitherby blocking the ubiquitination of viral proteins disrupting virionassembly and budding (Okumura et al., 2006, 2008); or by a directconjugation of ISG15 to target proteins (Lai et al., 2009). Inversely,viruses have developed strategies to counteract the antiviral effectsof ISG15 and ISGylation. For example, the nonstructural protein-1 (NS1) protein from influenza B virus binds to IGS15, inhibitingthe activity of E1 enzyme and thus preventing ISGylation of tar-get proteins (Yuan and Krug, 2001). Interestingly, in the presentstudy, in addition to the increase of E1 enzyme (UBA1) involved inthe ubiquitination and ISGylation pathways, the ISG15 protein wasfound to be up-regulated in CCHV-infected HepG2 cells. The up-regulation of UBA1 and ISG15 which have a major function in thecontrol of virus replication and spreading could possibly representa strategy used by the host to circumvent viral infection. However,the discovery of viral ovarian tumor (OUT)-domain protease in the Lprotein from CCHFV, which deconjugates protease activity and thenregulates ubiquitination and ISGylation, seems to be a response ofnairoviruses to evade this host antiviral response (Bergeron et al.,

2010; Frias-Staheli et al., 2007). The development of specific block-ing agonist to deISGylation activity from CCHFV L protein could behelpful for the host to counter the viral anti-ubiquitin-like proteinmodifier response.

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.4. RAN signaling canonical pathway

Ras-related nuclear (Ran) proteins are involved in diverseellular processes like the regulation of the nucleo-cytoplasmicransport of macromolecules in conjunction with importinsnd exportins and cell cycle progression (Joseph, 2006). Here,PA analysis underlined that the RAN signaling was the firstanonical pathway altered including six proteins. RAN andan GTPase activity protein-1 (RANGAP1), which activates theTPase activity of RAN promoting nuclear transport, were bothp-regulated; in contrast four proteins involved in the RAN nucleo-ytoplasmic recycling (KPNB1/Importin-beta, TNPO1/Transportin, XPO1/Exportin-1 and CSE1L/Exportin-2) were found to be down-egulated in CCHFV-infected cells. Several RNA viruses have beenhown to modulate this pathway for their own benefit and canranslocate viral proteins into the host cell nucleus to perturb thentiviral response (Alvisi et al., 2008). Porter and colleagues demon-trated the interaction of a picornavirus protein with RAN-GTPaseesulting in the inhibition of the nuclear translocation of factorsegulating synthesis of antiviral proteins which consequently pro-oted viral replication (Porter et al., 2006). Even if currently no

CHFV proteins were shown to be transported to the nucleus,he alteration of several proteins involved in the protein nuclearrafficking following CCHFV infection needs to be clarified. Thentriguing observation of the RAN pathway transport manipulationy viral proteins led Caly and collaborators to propose a therapeu-ic strategy aiming to inhibit the viral proteins nuclear traffickingCaly et al., 2012). Recently, the use of Ivermectin, an inhibitor ofmportin �/�1-dependent transport, gave promising results for theontrol of viral protein nuclear import (Wagstaff et al., 2012). Thessessment of such antiviral agents offers an attractive possibilityo limit CCHFV replication.

.5. Other proteins, biological functions or canonical pathwaysisturbed by CCHFV-infection

Among the other main biological functions and pathways mod-fied following CCHFV infection, numerous proteins involved inhe virus circulation, including entry and exit were highlighted.ere, an alteration of the expression level of proteins involved in

he CME canonical pathway (HSPA8, APOE, ALB, APOB, TF, ACTB,FRC, SERPINA1, ACTG1 and CTTN), which was demonstrated asne mechanism of CCHFV cell entry (Garrison et al., 2013; Simont al., 2009), was detected. Interestingly, another group of proteins,nvolved in the caveolar-mediated endocytosis signaling pathway,

as shown to be altered (ARCN1, ALB, ACTB, ACTG1, FLNA, FLNB,TGA2, COPA and COPG) suggesting another possible entry processor CCHFV. However, the inhibition of the caveolar-mediated endo-ytosis mechanism seems not to be essential for CCHFV infectionSimon et al., 2009).

The receptor for CCHFV entry has not yet to be clearly char-cterized. Recently, the nucleolin protein was shown to interactith a CCHFV surface protein (Gc) (Xiao et al., 2011). Although

he primary cellular location of nucleolin is the nucleus (Tajrishit al., 2011), the detection of this protein at the surface of differentell types, including HepG2 (Christian et al., 2003; Seddiki et al.,999; Semenkovich et al., 1990), and also other CCHFV-susceptibleells support the hypothesis of nucleolin as the putative host cellCHFV receptor entry (Xiao et al., 2011). Here, the nucleolin proteinas found positively regulated after CCHFV infection. It would be

nteresting to determine whether the increase of nucleolin proteinevel occurs at the cell surface. In the case of nucleolin being showns the receptor for CCHFV entry, an augmentation of this proteint the membrane would likely facilitate CCHFV cell infection andpreading.

ch 179 (2014) 187– 203 201

5. Conclusion

Due to the limited protein encoding ability of the viral genome,viruses are obligate parasites requiring host cell machinery andthen hijacking several host proteins to succeed its complete replica-tion cycle. The present study provides for the first time an overviewof qualitative and quantitative host proteome changes at the liverlevel in response to CCHFV infection. Several altered biological pro-cesses were highlighted such as inflammation, oxidative stress,ubiquitination/sumoylation, regulation of the nucleo-cytoplasmictransport, and virus entry. Altogether, the differential regulationof host proteins gives a new insight of CCHFV infection mecha-nisms to better understand the early events of virus pathogenesis.This work also offers the description of proteins involved in thesepathways, such as STAT3, NF45, SOD2, RAN and nucleolin, whichmay present a particular interest and could be used as biomarkerof CCHFV infection or as therapeutic target for the prevention ofvirus spreading. Further investigations are nevertheless needed tocomfort the host-virus interaction presented and to more preciselydetermine the role of the modified proteins in virus replication orantiviral response.

Acknowledgements

Financial support: This study was supported by DirectionGénérale pour l’Armement (DGA, ArthroSer 10Ca 401) and Servicede Santé des Armées. We thank Yann Gouriou (UMR 7286, Mar-seille, France) for its involvement in MS sample submission andSamuel Granjeaud (Inserm, IPC, CRCM, Marseille, France) for it crit-ical manuscript reading.

Appendix A. Supplementary data

Supplementary data associated with this article can befound, in the online version, at http://dx.doi.org/10.1016/j.virusres.2013.10.013.

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