Identification of Cellular Genes Induced in Human Cells After Activation of the OAS/RNaseL Pathway by Vaccinia Virus Recombinants Expressing These Antiviral Enzymes

Abstract

Interferon (IFN) type I induces the expression of antiviral proteins such as 2′,5′-oligoadenylate synthetases (OAS). The enzyme OAS is activated by dsRNA to produce 5′-phosphorylated, 2-5-linked oligoadenylates (2-5A) that activate RNaseL which, in turn, triggers RNA breakdown, leading to multiple biological functions. Although RNaseL is required for IFN antiviral function, there are many aspects of the molecular mechanisms that remain obscure. Here, we have used microarray analyses from human HeLa cells infected with vaccinia virus (VACV) recombinants expressing OAS-RNaseL enzymes (referred as 2-5A system) with the aim to identify host genes that are up- or down-regulated in the course of infection by the activation of this antiviral pathway. We found that activation of the 2-5A system from VACV recombinants produces a remarkable stimulation of transcription for genes that regulate many cellular processes, like those that promote cell growth arrest, GADD45B and KCTD11, apoptosis as CUL2, PDCD6, and TNFAIP8L2, IFN-stimulated genes as IFI6, and related to tumor suppression as PLA2G2A. The 2-5A system activation produces down-regulation of transcription of some genes that promote cell growth as RUNX2 and ESR2 and of genes in charge to maintain mitochondria homeostasis as MIPEP and COX5A. These results reveal new genes induced in response to the activation of the 2-5A system with roles in apoptosis, translational control, cell growth arrest, and tumor suppression.

Introduction

The interferons (IFNs) are cytokines that are released from animal cells in response to a variety of stimuli including viral infections (Isaacs and Lindenmann 1957). IFN regulates central processes in animal cells such as proliferation and differentiation, modulation of the immune response, and inhibition of virus replication by the induction of different genes (Der and others 1998; Geiss and others 2001; Samuel 2001). The best characterized IFN-induced proteins are the 68-kDa protein kinase (PKR), the 2-5A system composed by the 2′,5′-oligoadenylate synthetase-1 (2-5OAS1), and the 2-5A–dependent RNase (RNaseL) enzymes, and proteins that belong to the Mx family (Der and others 1998; Geiss and others 2001; for review Sadler and Williams 2008). Among the IFN-induced proteins, OAS1 and RNaseL constitute a powerful system in the antiviral defense. The OAS are dsRNA-dependent enzymes that, in the presence of ATP and dsRNA, synthesize a complex mixture of 5′-triphosphorylated oligoadenylic acid, which binds to and activates the endoribonuclease RNaseL, a ubiquitous 83-kDa protein that dimerizes into its catalytically active form (Dong and Silverman 1995; Marie and others 1999; Rebouillat and Hovanessian 1999). High levels of RNaseL suppressed the replication of diverse viruses as encephalomyocarditis virus, vesicular stomatitis virus, human parainfluenza virus-3, and vaccinia virus (VACV; Diaz-Guerra and others 1997b; Zhou and others 1998; Ghosh and others 2000). The antiviral function of RNaseL has been determined by in vivo studies with RNaseL^−/− mice, which are more susceptible to EMCV, coxsackievirus B4, herpes simplex virus 1, West Nile virus, and the alphavirus Sindbis infections (Zhou and others 1997; Zheng and others 2001; Ryman and others 2002; Flodstrom-Tullberg and others 2005; Samuel and others 2006).

The antiproliferative effect of RNaseL was demonstrated when cells expressing dominant mutant RNaseL were resistant to the antiproliferative effect of IFN-α, whereas those expressing wild-type RNaseL produced high sensitivity to IFN (Zhou and others 1997; Liang and others 2006). The antiproliferative action of RNaseL occurs by way of the IFN-α–induced down-regulation of mitochondrial mRNAs, which leads to a decrease in cellular ATP levels and suppression of mitochondrial function (Le Roy and others 2001; Le Roy and others 2007). Furthermore, RNaseL is considered a tumor suppressor from studies on the genetics of hereditary prostate cancer. Different mutations in RNaseL as R462Q and E265X could implicate higher risk on the onset of different types of pancreas and hereditary non-polyposis colorectal cancer (Silverman 2003; Bartsch and others 2005; Kruger and others 2005; Bisbal and Silverman 2007).

RNaseL mediates its functions regulating mRNA stability (Kerr and others 1982; Silverman 2007a). Therefore, activation of the 2-5A system triggers the cleavage of viral and cellular RNAs, which results in a general inhibition of protein synthesis and apoptosis (Diaz-Guerra and others 1997a, 1997b; Castelli and others 1998). Viral RNAs and rRNA are cleaved in intact ribosomes during some viral infections once OAS is induced and RNaseL is activated (Wreschner and others 1981).

Recently, it has been described that RNaseL is required for the optimal induction of proinflammatory cytokines that play essential roles in host defense from bacterial pathogens, regulating the expression of the endolysosomal protease cathepsin-E, and therefore endosome-associated activities that function to eliminate internalized bacteria and may contribute to RNaseL antimicrobial action (Li and others 2008). A previous microarray analysis in human cells transfected with 2-5A oligomers determined that a limited number of cellular mRNAs are regulated in a RNaseL-dependent manner, demonstrating its capacity to selectively regulate host transcripts (Malathi and others 2005).

In view of the multiple biological effects assigned to the OAS-RNaseL pathway, it is important to understand the molecular mechanisms that underlie the antiviral, antiproliferative, and tumor suppressor functions of the 2-5A system. To this aim we have performed a microarray analysis, using custom DNA chips that contained 19,256 human genes, with RNAs obtained from human HeLa cells infected with VACV recombinants that co-express OAS1 and RNaseL. The use of VACV as a tool of expression has been well developed and has many advantages over other vector systems, like the capacity of insertion of large number of genes (25 kb), the high levels of expression of the exogenous protein, and the possibility to express the target gene in an inducible manner (Gil and Esteban 2004). VACV infects in vitro many cell lines and in vivo different animal species, which allows expression of the desirable protein in many different systems. Moreover, the viral dsRNA produced during the VACV life cycle also constitutes an advantage, since it functions as the activator of the OAS/RNaseL system (Rice and others 1984; Paez and Esteban 1984; Silverman 2007b). Even though there are some VACV proteins that inhibit the IFN cascade and apoptosis (Seet and others 2003), the level of expression of exogenous proteins from recombinant VACV vectors overcomes their effect and produces their natural function as it has been corroborated in other expression systems for different proteins (Gil and Esteban 2004). With the VACV (OAS-RNaseL) cell system we showed here that between 600 and 890 different genes are modulated during infection, when the pattern of genes induced in cells infected with a control VACV are compared to cells infected with recombinant VACV (OAS-RNaseL). Of significance is that activation of RNaseL at 16 hpi still induces expression of many genes even though at this late time of infection there is a clear rRNA degradation, which points out a possible function for RNaseL as a transcription factor, as suggested by others (Malathi and others 2005). We have also verified that OAS-RNaseL is implicated in apoptosis and that this effect is related to mitochondria function, as the expression of genes involved in homeostasis and metabolism of mitochondria are down-regulated. In addition, we showed that during OAS-RNaseL expression there are genes that are up-regulated throughout infection, while other genes are induced differently with the time of infection. Overall, this study reveals that expression of OAS-RNaseL from VACV recombinants triggers many genes that are up- and down-regulated, some of which are likely to be implicated in the already assigned biological functions of OAS-RNaseL pathway, while other genes will be engaged in other functions. The new genes identified here help to explain different biological effects of the 2-5A system, like apoptosis, translational control, cell growth arrest, and tumor suppression, providing a framework for future studies.

Materials and Methods

Cells and viruses

African green monkey kidney cell line BSC-40 and HeLa G cells were grown in Dulbecco’s modified Eagle’s medium (DMEM) supplemented with 10% fetal calf serum (FCS), l-glutamine (2 mM; Sigma, St. Louis, MO), penicillin (100 U/mL; Sigma), streptomycin (0.1 mg/mL; Sigma), gentamicin (5 µg/mL; Sigma), and fungizone (0.5 Ug/mL; Sigma).

The recombinants of VACV, vvT7, WRluc, vvRL, and vv2-5AS were obtained as described (Rodriguez and Smith 1990; Diaz-Guerra and others 1997b). Expression of RNaseL produced by virus vvRL is conditional under the control of a T7 promoter and contains at the N-terminus a 6 histidine residues tag. Expression of RNaseL by VACV requires co-expression of T7 RNA polymerase from a VACV T7 polymerase recombinant. The expression of T7 polymerase and OAS1 produced by vvT7 and vv2-5AS, respectively, is constitutive under control of a VACV early/late promoter. The recombinant WRluc expresses constitutively luciferase. All VACV recombinants were grown and titrated in BSC-40 cells. All viruses were purified by ultracentrifugation through sucrose cushion gradients. Infections were performed in HeLa cells at a final multiplicity of 6 PFU/cell. Control VACV infections were performed with a mixture of vvT7+WRluc (referred throughout the text as vvMix), while infections with VACV recombinants expressing OAS and RNaseL were carried out with a mixture of vvRL, vv2-5AS, and vvT7 (referred as vv2-5ASystem). Under the conditions of infection used, all or nearly all cells are infected with each virus.

Total RNA isolation

Total RNA from MOCK-infected or infected HeLa cells at 8 and 16 hpi was isolated using Ultraspect-II RNA resin purification system (Biotecx, Houston, TX). RNA was denatured and analyzed in 1% formaldehyde–agarose gels and stained using ethidium bromide (Diaz-Guerra and others 1997b).

Northern blot

Total RNA (4.3 µg) was separated by electrophoresis in 1% formaldehyde–agarose gels.

After electrophoresis, the gel was washed for 1 h with SSC 20× and the RNA was transferred by capillarity onto a positively charged Nylon membrane (Hybond-N, Amersham) overnight following the procedures previously described by Brown and others (2004). The cDNA fragments were labeled with [³²P]dCTP specific for mitochondrial rRNA 16S (forward: CCGCCTGTTTACCAAAAACATC and reverse: GTCCTTTCGTACAGGGAGG) with the mix Random Prime labeling system (Amersham). Hybridization was performed in Clontech buffer 1 h at 68°C following the manufacturer’s instructions with the specific activity of 2 × 10⁶ cpm/mL. The filter was exposed for 24 h with an intensifying screen (Hypercassette, Amersham) and developed using a PhosphorImager system (Molecular Dynamics).

Analysis of genetic expression: two-color microarrays

HeLa cells were infected with the combination of viruses vvT7 (2 PFU/cell) + WRluc (4 PFU/cell) or with vvRL+vvT7+vv2-5AS (2 PFU/cell from each virus) during 8 and 16 h. Levels of mRNA expression of uninfected cells (MOCK) were compared with those of cells infected with vv2-5ASystem, while cells infected with vvMix were compared to cells infected with vv2-5ASystem. Once infected, total RNA was extracted as explained before and 35 µg were cleaned with columns of RNeasy kit (Qiagen, Hilden, Germany) following manufacturer’s instructions and integrity of RNA was confirmed using Bioanalyzer 2100 Agilent Technologies, Inc (Ambion, Foster City, CA). The staining of microarrays was done amplifying 1 µg RNA with Amino Allyl MessageAmp aRNA kit (Ambion) obtaining from 20 to 30 µg of amplified RNA (aRNA) with a medium size of 1,500 nucleotides. For each sample, 7.5 µg of aRNA were stained with Cy3 or Cy5 Mono NHS Ester (CyDye post-labeling reactive dye pack; GE Healthcare Amersham) and purified using Megaclear kit (Ambion). For each hybridization, the probes Cy3 or Cy5 (200 picomoles each) were mixed with 1 µL of polyA (20 µg/µL) and 1 µL of tRNA (20 µg/µL) for reducing unwanted hybridization. Incorporation of Cy3 or Cy5 was measured in Nanodrop (Nanodrop Technologies, Wilmington, DE). Stained aRNA was dried in high-speed vacuum and resuspended in 9 µL of RNase-free water, then fragmented with 1 µL of fragmentation 10× buffer (Ambion) during 15 min at 70°C and later on the reaction was stopped with 1 µL of stopping reaction (Ambion). Three replicates were done from each condition, staining for experiment 1 MOCK sample with Cy3 and vv2-5ASystem infection with Cy5, and for experiment 2, the RNA from cells infected with vvMix was stained with Cy3 and RNA from cells infected with vv2-5ASystem with Cy5.

The DNA microarray slides contained 22,264 spots (19,256 different oligonucleotides) corresponding to the Operon Human Genome Oligo set version 2.2 (Qiagen, Hilden, Germany) and were obtained from the Genomics and Microarray Laboratory (University of Cincinnati Medical Center, Cincinnati, OH). Information about the oligonucleotide set used can be found at http://microarray.uc.edu. The microarrays were prehybridized during 30 min at 42°C with SSC 6×, 1% BSA, and 10% SDS. They were cleaned with miliQ water and dried by centrifugation (5 min at 2,000 rpm). Stained aRNAs with fluorochromes were denatured with hybridization mixture that contained 50% formamide, SSC 6×, Denhardt’s solution 5×, and 0.5% SDS during 3 min at 95°C and later on cooled on ice. Stained and denatured aRNAs were hybridized on DNA chip at 37°C in hybridization chambers in dark in a water bath during 16 h. Chips were cleaned in dark, dried by centrifugation, and kept at room temperature until analysis of genetic expression.

The images of channels Cy3 and Cy5 were equilibrated and captured with scanner Axon 4000B and signals were quantified using GenePix 5.1 software, which provided the quantified images without normalization. Data obtained from three different replicates were normalized using Limma software (Bioconductor software, http://www.bioconductor.org) (Smyth 2004). Data within each chip were normalized by Loess and data between the three chips were normalized by quantiles. Data were visualized with Fiesta software (Oliveros 2007). ANOVA statistics test was applied after normalization to determine changes of expression. P values were adjusted by false discovery rate (FDR) to correct for multiple testing (Benjamini and Hochberg 1995). We selected as statistically significant data those genes with adjusted P value <0.05 for comparison of vvMix with MOCK-infected cells and adjusted P value <0.12 for comparison of vvMix with vv2-5ASystem infected cells at 8 hpi, and adjusted P value <0.05 for comparison of vvMix with MOCK-infected cells and adjusted P value <0.155 for comparison of vvMix with vv2-5ASystem infected cells at 16 hpi. We considered that genes were differently expressed when fold change in expression levels between two conditions was higher than 2 or lower than −2. These fold-change values correspond to M values higher than 1 and lower than −1, being M the base 2 logarithm of the ratio between Cy5 and Cy3 channel intensities (M = log2 Cy5/Cy3). For better comprehension ratios <1 (down-regulated genes) were transformed to their negative reciprocals in order to be represented in the same scale as ratios >1 (up-regulated genes), for example a ratio of 0.5 is equivalent to a fold change of −2.

The differently expressed genes were classified in functional categories following their associated Gene Ontology (GO) terms and annotations in UniProt and NCBI (PubMed and UniGene) databases. To analyze which functional terms and pathways were specifically overrepresented in the sets of regulated genes (at different times and conditions), we used Onto-Express and Pathway-Express, available as part of the Onto-Tools (Khatri and others 2004; Khatri and others 2005). The functional enrichment analysis was done with Onto-Express by comparing a list of regulated genes with the list of the whole gene set in our chip, and determining what GO functional terms were overrepresented in one of them with respect to the other. For the statistical significance test, we chose the hypergeometric distribution and FDR correction for multiple testing. Only GO terms with an adjusted P value <0.1 were selected. To analyze different pathways in which some of the regulated genes were involved, we used Pathway-Express (Draghici and others 2007), which performs a novel impact analysis method that incorporates the classical probabilistic component but also important biological factors like the position of the differentially expressed genes on the given pathways. Pathways were ranked according to impact factor and gamma P value derived from the analysis, and those with a gamma P value lower than 0.05 were selected.

Western blot analysis

Proteins were denatured by boiling in Laemmli buffer with β-mercaptoethanol. For immunoblots, protein samples were fractionated in 10% SDS-polyacrylamide gel electrophoresis (SDS-PAGE) and transferred to nitrocellulose membrane (Bio-Rad Laboratories, Hercules, CA) in a semi-dry blotting apparatus (Bio-Rad Laboratories) for 45 min at 200 mA. Membranes were blocked for 1 h in phosphate-buffered saline (PBS) containing 5% nonfat-dried milk and then probed with polyclonal anti-RNaseL (1/500) (Diaz-Guerra and others 1997b), polyclonal anti-2-5OAS (1/1,000) (Ilkka Julkunen), polyclonal anti-human MIPEP (Proteinch Group. Inc; 1/500), polyclonal anti-human eIF4B (Abcam Ltd; 1/100), monoclonal anti-human ESR2 (Abcam Ltd; 1/500), or monoclonal anti-β-actin (Sigma; ½,000) antibodies. Proteins were detected using HRP-labeled secondary antibodies and ECL (Amersham Biosciences).

Quantitative real-time RT-PCR

RT-PCR was performed with 250 ng of total RNA (obtained as described before for microarray hybridization) using Applied High Capacity Transcription kit (Applied Biosystems). RT was performed at 25°C 10 min, 37°C 120 min, and 85°C 5 s. After that, PCR amplification was performed with 0.5 µL of cDNA (10 ng/µL), 5 µL 2× TaqMan MasterMix (ABI), 250 nM TaqMan probe (FAM-labeled, MGB-NFQ modified; Applied Biosystems) in a total volume of 10 µL in 384 plates using thermocycler AB 7900HT. The reaction was initiated with incubation at 50°C 2 min, 95°C 10 min followed by 40 cycles of 95°C 15 s and 60°C 1 min. Data analysis were obtained using the software SDS 2.2.2 (Applied Biosystems). The primers used were purchased from Applied Biosystems: human potassium channel tetramerization domain-containing 11: KCTD11 (Hs00327145_s1, inventoried) and human housekeeping gene beta glucuronidase: GUSB (4333767T, inventoried). All the samples were assayed in triplicate.

Results

Activation of OAS/RNaseL pathway in human cells infected with VACV recombinants

We have previously described a virus cell system for expression of OAS1 and RNaseL by VACV recombinants (Diaz-Guerra and others 1997b). To show the characteristics of this virus cell system prior to microarray analyses, we first examined the expression levels of OAS1 and RNaseL from VACV recombinants expressing those enzymes with time of infection and their consequences in host RNA stability. Thus, HeLa cells were MOCK-infected, infected with vvMix or with vv2-5ASystem and at 8 and 16 hpi expression of OAS1 and RNaseL were revealed by Western blot with specific antibodies. As shown in Figure 1, panel A, both RNaseL and OAS1 were efficiently co-expressed in the human cells, while the endogenous levels of these enzymes were undetectable in MOCK- and in vvMix-infected cells. To reveal the degree of activation of the OAS/RNaseL pathway, we measured cleavage of rRNA from these cell cultures in two independent experiments. As shown in Figure 1, panel B, rRNA cleavage in cells infected with the vv2-5ASystem was not observed at 8 hpi while it was evident at 16 hpi where rRNA breakdown produced the characteristic 28S and 18S cleavage products. These findings showed that rRNA breakdown is a late event when the OAS/RNaseL enzymes are co-expressed from VACV recombinants.

FIG. 1.

Expression of OAS1 and RNaseL from vaccinia virus (VACV) vectors and activation of the 2-5A system. HeLa cells were MOCK-infected, double infected with vvT7 (2 PFU/cell) + WRluc (4 PFU/cell) called here vvMix or triple infected with vvRL+vvT7+vv2-5AS (2 PFU/cell from each virus) called here vv2-5ASystem, during 8 and 16 hpi. (A) Protein extracts were resolved in 10% SDS-PAGE polyacrylamide gel electrophoresis followed by Western blot using polyclonal antibody against human RNaseL, polyclonal antibody against rat 2–5OAS and monoclonal anti β-actin. (B) One microgram of total RNA from duplicated samples was loaded in 1% agarose–formaldehyde gels.

Profiling of the host genes induced in human cells expressing OAS/RNaseL pathway from VACV recombinants

Since the 2-5A system exerts multiple biological effects on cells, we were interested in obtaining a global view of transcriptional changes following activation of OAS1 and RNaseL under conditions where the two enzymes are simultaneously produced in cells from viral vectors. This situation was obtained by infecting HeLa cells with VACV recombinants that co-express OAS1 and RNaseL as in Figure 1. The question raised was whether activation of the OAS/RNaseL system during infection modulated the expression of a specific subset of cellular genes, which could be related with the biological functions assigned to the 2-5A system, like antiviral, antiproliferative, and tumor suppressor or of other unknown functions. Therefore, we chose the times shown in Figure 1 for microarray analyses in order to detect induction and repression of specific host mRNAs before and during rRNA degradation.

HeLa cells were MOCK-infected or infected with either vvMix or vv2-5ASystem and total RNAs were extracted at 8 and 16 hpi. Three independent assays were performed in duplicate with the aim to obtain homogeneous samples with low experimental variability. Transcriptional levels of cellular genes were analyzed with DNA microarrays. Three different hybridizations were performed: in the first one, vv2-5ASystem-infected cells were compared with MOCK-infected cells; in the second one, vvMix-infected cells were compared with MOCK-infected cells, while in the third hybridization, vv2-5ASystem-infected cells were compared with vvMix-infected cells. In this latter case, the transcriptional modifications induced by vvMix in the host cells were substracted from those induced by vv2-5ASystem-infected cells and thus, the changes observed in gene transcription were specifically attributed to activation of the 2-5A system and not to virus infection. It should be pointed out that in the comparison of levels of gene expression between RNA samples, the mRNA from RNASEL was produced from the recombinant virus, and was considered an internal control.

Pattern of host gene expression at 8 hpi

In the comparison of expression profiling of vv2-5ASystem-infected cells with uninfected cells, only genes with FDR-adjusted P values lower than 0.05 were selected for analysis. However, in the direct comparison of vv2-5ASystem-infected cells with vvMix-infected cells, we did not obtain any regulated gene with a P value lower than 0.05. Since we had found that RNaseL gene transcription was increased in vv2-5ASystem-infected cells and its expression has been confirmed (Fig. 1A), we raised the P value thresholds up to those of RNASEL gene at 8 and 16 hpi (0.12 and 0.155, respectively). We were confident about these results because we considered RNASEL gene our positive control.

At 8 hpi the comparison on gene expression between cells infected with vvMix and cells infected with vv2-5ASystem showed 634 genes with differences in expression using an adjusted P value <0.12. From these genes, 87 (14%) were up-regulated with a fold change more than 2 and 93 (15%) were down-regulated with fold change less than −2. These differences in expression were due to the 2-5A system activation because the genes induced in cells infected with control virus (vvMix) were substracted from those genes induced in cells expressing the OAS/RNaseL pathway. RNaseL gene transcription level was 9.45-fold higher in vv2-5ASystem-infected cells than in vvMix-infected cells.

Selected up-regulated and down-regulated genes upon activation of the 2-5A system have been classified in functional categories (Table 1; Supplementary Table 1). (Supplementary materials are available online at http://www.liebertpub.com/). Among the up-regulated genes, we found many genes included in signaling and cell cycle category, mainly GTPases as GNAT2, and other genes as PLRG1, necessary for spliceosome assembly and for pre-mRNA splicing (Ajuh and others 2001), and KCTD11. Many genes with up-regulated expression were involved in immune and inflammatory response as CTSG, IFI6, GDF3, and TNFAIP6. We observed high up-regulation in the expression of the genes CUL2, involved in G1/S transition of mitotic cell cycle (Kipreos and others 1996), and tumor markers as TIAM1, included in signaling category. In the category of replication and transcription we found up-regulation of some transcription factors as CREB5, and genes as SMG5 and EIF4B in protein synthesis. Some genes included in metabolism category were highly up-regulated, as PLA2G2A, PLA2G12A, and HS3ST1.

Table 1.

Differentially Expressed Genes in vv2-5ASystem-Infected Cells in Comparison with vvMix-Infected Cells at 8 hpi

GenBank	Gene symbol	Description	Fold change
Immune response, inflammatory response
NM_001442	FABP4	Fatty acid-binding protein 4, adipocyte	7.89
NM_001911	CTSG	Cathepsin G	6.92
NM_020634	GDF3	Growth differentiation factor 3	4.63
NM_007115	TNFAIP6	Tumor necrosis factor, alpha-induced protein 6	4.44
NM_022873	IFI6	Interferon, alpha-inducible protein 6	2.43
Apoptosis, cell death
NM_021133	RNASEL	Ribonuclease L (2′,5′-oligoisoadenylate synthetase-dependent)	9.45
NM_003591	CUL2	Cullin 2	2.50
NM_024085	ATG9A	ATG9 autophagy-related 9 homolog A	−2.66
AK024486	GLTSCR2	Glioma tumor suppressor candidate region gene 2	−2.11
NM_004972	JAK2	Janus kinase 2 (a protein tyrosine kinase)	−2.08
Signaling, cell cycle
AK056227	KCTD11	Potassium channel tetramerization domain-containing 11	17.63
NM_002669	PLRG1	Pleiotropic regulator 1	10.93
BC000233	GNAT2	Guanine nucleotide-binding protein (G protein), alpha-transducing activity polypeptide 2	6.11
X65550	MKI67	Antigen identified by monoclonal antibody Ki-67	4.82
U90902	TIAM1	T-cell lymphoma invasion and metastasis 1	4.76
AL353944	RUNX2	Runt-related transcription factor 2	−6.92
AB006589	ESR2	Estrogen receptor 2 (ER beta)	−4.86
NM_002969	MAPK12	Mitogen-activated protein kinase 12	−2.15
Cytoskeleton, transport
NM_000259	MYO5A	Myosin VA (heavy chain 12, myoxin)	7.26
AK022326	CTNNA1	Catenin (cadherin-associated protein), alpha 1, 102 kDa	−3.39
Replication, transcription
AK057151	CREB5	CAMP-responsive element-binding protein 5	11.31
NM_015725	RDH8	Homo sapiens retinol dehydrogenase 8 (all-trans)	7.31
BC009628	FOXP4	Forkhead box P4	4.63
BC003525	MAX	MYC-associated factor X	4.32
NM_016539	SIRT6	Sirtuin (silent mating type information regulation 2 homolog) 6	−7.11
Protein synthesis
AB029012	SMG5	Smg-5 homolog, nonsense-mediated mRNA decay factor	7.89
NM_006853	KLK11	Kallikrein-related peptidase 11	7.46
NM_001417	EIF4B	Eukaryotic translation initiation factor 4B	2.33
Metabolism related to mitochondria
NM_015533	DAK	Dihydroxyacetone kinase 2 homolog	4.50
NM_000663	ABAT	4-Aminobutyrate aminotransferase	2.60
NM_005932	MIPEP	Mitochondrial intermediate peptidase	−6.41
NM_001082	CYP4F2	Cytochrome P450, family 4, subfamily F, polypeptide 2	−2.14
NM_004074	COX8A	Cytochrome c oxidase subunit 8A (ubiquitous)	−2.07
Metabolism
NM_000300	PLA2G2A	Phospholipase A2, group IIA (platelets, synovial fluid)	15.24
NM_005114	HS3ST1	Heparan sulfate (glucosamine) 3-O-sulfotransferase 1	12.38
NM_030821	PLA2G12A	Phospholipase A2, group XIIA	8.06

Among the 93 genes whose expression was repressed, the genes with higher differences were: ESR2, which is a natural promoter of breast cancer (Schubert and others 1999; Imamov and others 2005), RUNX2, and MAPK12. The gene that codes for SIRT6, which is involved in repairing the production of reactive oxygen species (ROS) preventing cellular aging (Lim 2007), was highly down-regulated after activation of the 2-5A system (−7.11-folds). ATG9A and some genes that code for proteins that had been described as tumor suppressors as GLTSCR2 were down-regulated, as Table 1 shows. Finally, showing the important role of the 2-5A system in the mitochondria (Le Roy and others 2001; Domingo-Gil and Esteban 2006; Le Roy and others 2007), we found several down-regulated genes related to mitochondrial homeostasis, and therefore, apoptosis, as the mitochondrial intermediate peptidase (MIPEP) that processes the signal peptide from proteins that are imported to mitochondria whose functions are the electron transport chain and mitochondrial DNA synthesis (Chew and others 1997). Other genes that code for proteins involved in mitochondrial electron transport, as COX8A and CYP4F2, were also down-regulated.

We checked that many genes that appeared up-regulated and down-regulated by substracting the genes triggered after infection with VACV recombinants (OAS/RNaseL) from those after infection with VACV control (vvMix) were also up-regulated and down-regulated, respectively, when host gene expression was compared between cells infected with vv2-5ASystem versus MOCK-infected cells (Table 2). Significantly, the genes CTSG, TNFAIP8L2, and IFI6 in the immune response category were highly up-regulated, and also genes involved in signaling as GNAT2 and KCTD11 were up-regulated. In transcription and protein synthesis category, we found the same genes that had been described before with up-regulated expression: CREB5 and SMG5, and in the metabolism category PLA2G2A and H3ST1. In both experiments there was down-regulation of genes that code for proteins related to mitochondria metabolism and homeostasis as COX5B, COX8A, MRPL20, CYP4F2, NDUFA2, ATP6V0E1, CYC1, and UQCRC1. There were no changes in those genes indicated in Table 2 after comparison of cells infected with vvMix versus MOCK-infected cells. The findings of Tables 1 and 2 revealed a specific subset of host genes induced or repressed upon 2-5A system activation, when there is little effect on RNA breakdown in cells.

Table 2.

Differentially Expressed Genes in vv2-5ASystem-Infected Cells and in vvMix-Infected Cells That Are Equally Regulated at 8 hpi

GenBank	Gene symbol	Description	Fold change
GenBank	Gene symbol	Description	Mock vs. vv2-5ASystem	vvMix vs. vv2-5ASystem
Immune response, inflammatory response
NM_024575	TNFAIP8L2	Tumor necrosis factor, alpha-induced protein 8-like 2	9.92	5.24
NM_001911	CTSG	Cathepsin G	7.67	6.92
NM_022873	IFI6	Interferon, alpha-inducible protein 6	2.2	2.43
Apoptosis, cell death
NM_021133	RNASEL	Ribonuclease L (2′,5′-oligoisoadenylate synthetase-dependent)	32	9.45
NM_003591	CUL2	Cullin 2	3.68	2.5
Signaling, cell cycle
AK056227	KCTD11	Potassium channel tetramerization domain-containing 11	18.38	17.63
NM_002669	PLRG1	Pleiotropic regulator 1 (PRL1 homolog, Arabidopsis)	10.34	10.93
BC000233	GNAT2	Guanine nucleotide-binding protein (G protein), alpha-transducing activity polypeptide 2	6.92	6.11
X65550	MKI67	Antigen identified by monoclonal antibody Ki-67	5.46	4.82
U90902	TIAM1	T-cell lymphoma invasion and metastasis 1	4.99	4.76
Cytoskeleton, transport
NM_000259	MYO5A	Myosin VA (heavy chain 12, myoxin)	7.41	7.26
Replication, transcription
AK057151	CREB5	CAMP-responsive element-binding protein 5	14.22	11.31
Protein synthesis
AB029012	SMG5	Smg-5 homolog, nonsense-mediated mRNA decay factor	6.19	7.89
Metabolism
NM_005114	HS3ST1	Heparan sulfate (glucosamine) 3-O-sulfotransferase 1	16.56	12.38
NM_000300	PLA2G2A	Phospholipase A2, group IIA (platelets, synovial fluid)	9.92	15.24
Metabolism related to mitochondria
NM_015533	DAK	Dihydroxyacetone kinase 2 homolog	9.92	4.5
NM_001862	COX5B	Cytochrome c oxidase subunit Vb	−3.72	−1.71
NM_004074	COX8A	Cytochrome c oxidase subunit 8A (ubiquitous)	−3.46	−2.07
NM_017971	MRPL20	Mitochondrial ribosomal protein L20	−3.2	−1.79
NM_001082	CYP4F2	Cytochrome P450, family 4, subfamily F, polypeptide 2	−2.83	−2.14
NM_002488	NDUFA2	NADH dehydrogenase (ubiquinone) 1 alpha subcomplex, 2, 8 kDa	−2.55	−1.64
NM_003945	ATP6V0E1	ATPase, H+ transporting, lysosomal 9 kDa, V0 subunit e1	−2.39	−1.68
NM_001916	CYC1	Cytochrome c-1	−2.29	−1.97
NM_003365	UQCRC1	Ubiquinol-cytochrome c reductase core protein I	−2	−1.77

Pattern of host gene expression at 16 hpi

At 16 hpi the comparison between cells infected with vvMix and cells infected with vv2-5ASystem showed 898 genes with differences in expression from which 303 (34%) were up-regulated and 380 (42%) down-regulated at least 2-fold using an adjusted P value <0.155. The levels of gene expression were obtained by raising the P value threshold up to those of RNASEL at 16 hpi.

Up-regulated and down-regulated selected genes have been included in functional categories and are shown in Table 3 (Supplementary Table 2). Among up-regulated genes, TNFAIP8L2 in the immune response category, which is an essential negative regulator of Toll-like receptor and T-cell receptor function, appeared clearly induced (6.33-fold) as well as STAT6. Among the most up-regulated genes, there were genes involved in signaling and cell cycle as KCTD11 and ADAMTS10. We also found many up-regulated genes included in apoptosis, mainly CYCS, PDCD6, and ATG9B. There were up-regulated genes involved in transcription regulation and splicing as SP3 and STAU2, in protein synthesis as EEF1A1 and EIF4A2, in cytoskeleton (MYO5A), and in metabolism (DAK, UGT2B15, and ABHD5).

Table 3.

Differentially Expressed Genes in vv2-5ASystem-Infected Cells in Comparison with vvMix-Infected Cells at 16 hpi

GenBank	Gene symbol	Description	Fold change
Immune response, inflammatory response
NM_024575	TNFAIP8L2	Tumor necrosis factor, alpha-induced protein 8-like 2	6.33
NM_004833	AIM2	Absent in melanoma 2	2.89
NM_003153	STAT6	Signal transducer and activator of transcription 6, interleukin-4–induced	2.83
NM_000882	IL12A	Interleukin 12A (natural killer cell stimulatory factor 1, cytotoxic lymphocyte maturation factor 1, p35)	2.65
BC009864	TBK1	TANK-binding kinase 1	2.30
NM_016388	TRAT1	T-cell receptor-associated transmembrane adaptor 1	−6.73
Apoptosis, cell death
NM_021133	RNASEL	Ribonuclease L (2′,5′-oligoisoadenylate synthetase-dependent)	6.90
AK056360	CYCS	Cytochrome c, somatic	2.77
AK024175	PDCD6	Programmed cell death 6	2.53
AK027791	ATG9B	ATG9 autophagy-related 9 homolog B (Saccharomyces cerevisiae)	2.53
NM_015675	GADD45B	Growth arrest and DNA-damage-inducible, beta	2.32
NM_021138	TRAF2	TNF receptor-associated factor 2	−2.68
NM_006597	HSPA8	Heat shock 70 kDa protein 8	−2.55
NM_021631	FKSG2	Apoptosis inhibitor	−2.31
Cell cycle, signaling
AK056227	KCTD11	Potassium channel tetramerization domain-containing 11	12.03
AF163762	ADAMTS10	ADAM metallopeptidase with thrombospondin type 1 motif, 10	6.90
X65550	MKI67	Antigen identified by monoclonal antibody Ki-67	2.92
BC016285	PRKACB	Protein kinase, cAMP-dependent, catalytic, beta	2.92
NM_002228	JUN	Jun oncogene	2.63
NM_005354	JUND	Jun D proto-oncogene	2.37
AB006589	ESR2	Estrogen receptor 2 (ER beta)	−8.99
NM_000871	HTR6	5-Hydroxytryptamine (serotonin) receptor 6	−7.30
X60188	MAPK3	Mitogen-activated protein kinase 3	−5.12
NM_001350	DAXX	Death-associated protein 6	−3.09
NM_001320	CSNK2B	Casein kinase 2, beta polypeptide	−2.38
NM_002467	MYC	V-myc myelocytomatosis viral oncogene homolog (avian)	−2.04
Cytoskeleton, transport
NM_006579	TBC1D25	TBC1 domain family, member 25	3.85
NM_000259	MYO5A	Myosin VA (heavy chain 12, myoxin)	3.62
NM_005125	CCS	Copper chaperone for superoxide dismutase	−6.05
NM_000424	KRT6A	Keratin 6A	−5.90
AK022326	CTNNA1	Catenin (cadherin-associated protein), alpha 1, 102 kDa	−4.29
NM_001614	ACTG1	Actin, gamma 1	−2.97
Replication, transcription
AK021546	SP3	Sp3 transcription factor	3.18
NM_014393	STAU2	Staufen, RNA-binding protein, homolog 2 (Drosophila)	3.16
NM_001356	DDX3X	DEAD (Asp-Glu-Ala-Asp) box polypeptide 3, X-linked	2.74
NM_005902	SMAD3	SMAD family member 3	2.04
AB051485	INTS5	Integrator complex subunit 5	−12.60
NM_016539	SIRT6	Sirtuin silent mating type information regulation 2 homolog 6 (S. cerevisiae)	−10.08
AL353944	RUNX2	Runt-related transcription factor 2	−7.45
Protein synthesis
NM_032817	ITIH5	Inter-alpha (globulin) inhibitor H5	4.58
AJ420488	EEF1A1	Eukaryotic translation elongation factor 1 alpha 1	3.11
NM_001967	EIF4A2	Eukaryotic translation initiation factor 4A, isoform 2	2.90
NM_003750	EIF3A	Eukaryotic translation initiation factor 3, subunit A	2.22
AK023419	RPL37A	Ribosomal protein L37a	−4.32
NM_007104	RPL10A	Ribosomal protein L10a	−2.78
NM_001005	RPS3	Ribosomal protein S3	−2.72
NM_000994	RPL32	Ribosomal protein L32	−2.49
NM_000980	RPL18A	Ribosomal protein L18a	−2.48
Metabolism related to mitochondria
NM_015533	DAK	Dihydroxyacetone kinase 2 homolog (S. cerevisiae)	7.00
NM_005932	MIPEP	Mitochondrial intermediate peptidase	−5.89
NM_004255	COX5A	Cytochrome c oxidase subunit Va	−3.37
NM_005333	HCCS	Holocytochrome c synthase (cytochrome c heme-lyase)	−3.30
NM_007253	CYP4F8	Cytochrome P450, family 4, subfamily F, polypeptide 8	−2.76
NM_004074	COX8A	Cytochrome c oxidase subunit 8A (ubiquitous)	−2.75
NM_017840	MRPL16	Mitochondrial ribosomal protein L16	−2.63
NM_017971	MRPL20	Mitochondrial ribosomal protein L20	−2.33
Metabolism
NM_001076	UGT2B15	UDP glucuronosyltransferase 2 family, polypeptide B15	5.46
NM_016006	ABHD5	Abhydrolase domain-containing 5	5.06
NM_004418	DUSP2	Dual specificity phosphatase 2	−3.82

Among the 380 genes whose expression was repressed, the category that contained more genes and with higher differences of expression was signaling and cell cycle as occurred at 8 hpi. Again, we found the gene ESR2 highly down-regulated (−8.99-fold) and HTR6 as well. RUNX2 was severely down-regulated as well as INTS5 (−12.60-fold) and the gene for sirtuin 6. There were many down-regulated genes coding for ribosomal proteins as RPL37A, RPL10A, RPS3, RPL32, and RPL18A. At 16 hpi, we also found many down-regulated genes related to mitochondrial homeostasis and therefore, apoptosis, as MIPEP, COX5A, HCCS, CYP4F8, and COX8A, and also two mitochondrial ribosomal proteins (MRPL16 and MRPL20).

At 16 hpi many genes that appeared regulated in the comparison of vvMix infection with vv2-5ASystem infection were evenly regulated in the comparison of vv2-5ASystem-infected cells with MOCK-infected cells (Table 4). Significantly, the gene for IL12A was up-regulated in both cases, as well as PDGFD, P53AIP1, and ATG9B. The genes RAB38, member of the RAS oncogene family (RAB38) and KCTD11 were highly up-regulated in both cases and many genes were found down-regulated as SUMO3 and CCNB1. In transcription and protein synthesis category, the SP3 gene was up-regulated and some genes that code for RNA polymerase II subunits as POLR2E and POLR2G were down-regulated. Genes involved in protein synthesis were down-regulated in both comparisons, like some eukaryotic initiation factors of protein synthesis as EIF3K and EIF4A3 and genes for ribosomal proteins as RPL37A, as well as some genes related to proteasome as PSMA5. In both cases the gene that codes for UGT2B15 was highly up-regulated. In the mitochondria metabolism, ABAT was highly up-regulated and many subunits of cytochrome c oxidase as COX5A, COX6A1, COX8A, COX5B, and COX7A2L were down-regulated. There were fewer changes in those genes indicated in Table 4 after comparison of cells infected with vvMix versus MOCK-infected cells. The findings of Tables 3 and 4 further demonstrate a specific subset of host genes induced or repressed upon 2-5A system activation, in spite of significant RNA breakdown in cells.

Table 4.

Differentially Expressed Genes in vv2-5ASystem-Infected Cells and in vvMix-Infected Cells that are Equally Regulated at 16 hpi

GenBank	Gene symbol	Description	Fold change
GenBank	Gene symbol	Description	Mock vs. vvMix	Mock vs. vv2-5ASystem	vvMix vs. vv2-5ASystem
Immune response, inflammatory response
NM_000882	IL12A	Interleukin 12A (natural killer cell stimulatory factor 1, cytotoxic lymphocyte maturation factor 1, p35)	1.83	4.19	2.65
BC009864	TBK1	TANK-binding kinase 1	—	2.29	2.30
Apoptosis, cell death
NM_021133	RNASEL	Ribonuclease L (2′,5′-oligoisoadenylate synthetase-dependent)	−2.15	24.21	6.90
NM_025208	PDGFD	Platelet-derived growth factor D	2.10	5.20	2.33
NM_015675	GADD45B	Growth arrest and DNA-damage-inducible, beta	1.47	2.96	2.32
NM_022112	P53AIP1	P53-regulated apoptosis-inducing protein 1	2.11	2.57	2.08
AK027791	ATG9B	ATG9 autophagy-related 9 homolog B (Saccharomyces cerevisiae)	—	2.12	2.53
NM_001350	DAXX	Death-associated protein 6	−3.65	−6.23	−3.09
NM_006597	HSPA8	Heat shock 70 kDa protein 8	—	−2.32	−2.55
Signaling, cell cycle
NM_022337	RAB38	RAB38, member RAS oncogene family	5.73	12.92	2.74
BC016285	PRKACB	Protein kinase, cAMP-dependent, catalytic, beta	5.30	9.97	2.92
AK056227	KCTD11	Potassium channel tetramerization domain-containing 11	−1.50	9.04	12.03
AF163762	ADAMTS10	ADAM metallopeptidase with thrombospondin type 1 motif, 10	1.82	6.97	6.90
NM_031966	CCNB1	Cyclin B1	−2.88	−4.41	−2.20
NM_006936	SUMO3	SMT3 suppressor of mif two 3 homolog 3 (S. cerevisiae)	−3.13	−4.02	−2.44
X60188	MAPK3	Mitogen-activated protein kinase 3	—	−2.43	−5.12
Cytoskeleton, transport
NM_000259	MYO5A	Myosin VA (heavy chain 12, myoxin)	—	4.82	3.62
Replication, transcription
NM_005902	SMAD3	SMAD family member 3	2.17	4.08	2.04
NM_002228	JUN	Jun oncogene	2.31	3.31	2.63
AK021546	SP3	Sp3 transcription factor	—	2.58	3.18
NM_002695	POLR2E	Polymerase (RNA) II (DNA-directed) polypeptide E, 25 kDa	−3.07	−3.22	−2.55
NM_002696	POLR2G	Polymerase (RNA) II (DNA-directed) polypeptide G	−2.45	−2.42	−2.31
Protein synthesis
NM_001967	EIF4A2	Eukaryotic translation initiation factor 4A, isoform 2	—	3.56	2.90
NM_001005	RPS3	Ribosomal protein S3	−3.27	−4.29	−2.72
NM_013234	EIF3K	Eukaryotic translation initiation factor 3, subunit K	−3.58	−4.09	−2.45
AK023419	RPL37A	Ribosomal protein L37a	−1.98	−3.76	−4.32
NM_014740	EIF4A3	Eukaryotic translation initiation factor 4A, isoform 3	−2.07	−3.57	−2.15
NM_002790	PSMA5	Proteasome (prosome, macropain) subunit, alpha type, 5	−2.57	−3.31	−1.79
Metabolism
NM_001076	UGT2B15	UDP glucuronosyltransferase 2 family, polypeptide B15	5.02	17.51	5.46
Metabolism related to mitochondria
NM_000663	ABAT	4-Aminobutyrate aminotransferase	4.91	14.83	3.08
AK056360	CYCS	Cytochrome c, somatic	—	3.07	2.77
NM_004255	COX5A	Cytochrome c oxidase subunit Va	−4.92	−6.46	−3.37
NM_004718	COX7A2L	Cytochrome c oxidase subunit VIIa polypeptide 2-like	−3.06	−4.87	−2.69
NM_004074	COX8A	Cytochrome c oxidase subunit 8A (ubiquitous)	−2.95	−4.31	−2.75
NM_001862	COX5B	Cytochrome c oxidase subunit Vb	−3.86	−3.84	−2.24
NM_004373	COX6A1	Cytochrome c oxidase subunit VIa polypeptide 1	−3.34	−3.62	−2.48
NM_017840	MRPL16	Mitochondrial ribosomal protein L16	−2.09	−3.09	−2.63

Common genes up-regulated and down-regulated at both times: 8 and 16 hpi

Several genes were either up-regulated or down-regulated at both 8 and 16 hpi (Table 5). In addition to RNASEL, we remark KCTD11, TNFAIP8L2, DOK2, MKI67, ABAT, and DAK among the up-regulated genes, and BACE2, CTNNA1, SIRT6, RUNX2, EIF4A3, ESR2, MIPEP, COX8A, and ACOT7 among the down-regulated ones, some of them, clearly implicated in maintaining the homeostasis of mitochondria.

Table 5.

Common Differentially Expressed Genes in vv2-5ASystem-Infected Cells in Comparison with vvMix-Infected Cells at 8 and 16 hpi

GenBank	Gene symbol	Description	Fold change
GenBank	Gene symbol	Description	8 hpi	16 hpi
Immune response, inflammatory response
NM_024575	TNFAIP8L2	Tumor necrosis factor, alpha-induced protein 8-like 2	5.24	6.33
Apoptosis, cell death
NM_021133	RNASEL	Ribonuclease L (2′,5′-oligoisoadenylate synthetase-dependent)	9.45	6.90
Cell cycle, signaling
AK056227	KCTD11	Potassium channel tetramerization domain-containing 11	17.63	12.03
X65550	MKI67	Antigen identified by monoclonal antibody Ki-67	4.82	2.92
NM_003974	DOK2	Docking protein 2, 56 kDa	3.43	5.50
AF178532	BACE2	Beta-site APP-cleaving enzyme 2	−5.54	−6.46
NM_003144	SSR1	Signal sequence receptor, alpha (translocon-associated protein alpha)	−2.31	−6.03
NM_033131	WNT3A	Wingless-type MMTV integration site family, member 3A	−2.13	−2.79
NM_018101	CDCA8	Cell division cycle-associated 8	−2.06	−2.41
Cytoskeleton, transport
AK022326	CTNNA1	Catenin (cadherin-associated protein), alpha 1, 102 kDa	−3.39	−4.29
NM_054033	FKBP1B	FK506-binding protein 1B, 12.6 kDa	−3.16	−3.83
NM_000033	ABCD1	ATP-binding cassette, subfamily D (ALD), member 1	−2.58	−2.10
Replication, transcription
NM_016539	SIRT6	Sirtuin silent mating type information regulation 2 homolog 6 (Saccharomyces cerevisiae)	−7.11	−10.08
AL353944	RUNX2	Runt-related transcription factor 2	−6.92	−7.45
AB006589	ESR2	Estrogen receptor 2 (ER beta)	−4.86	−8.99
NM_003440	ZNF140	Zinc finger protein 140	−2.22	−3.11
Protein synthesis
NM_014740	EIF4A3	Eukaryotic translation initiation factor 4A, isoform 3	−1.69	−2.15
Metabolism related to mitochondria
NM_015533	DAK	Dihydroxyacetone kinase 2 homolog	4.50	7.00
NM_000663	ABAT	4-Aminobutyrate aminotransferase	2.60	3.08
NM_005932	MIPEP	Mitochondrial intermediate peptidase	−6.41	−5.89
NM_004074	COX8A	Cytochrome c oxidase subunit 8A (ubiquitous)	−2.07	−2.75
Metabolism
NM_001491	GCNT2	Glucosaminyl (N-acetyl) transferase 2, I-branching enzyme (I blood group)	−2.45	−3.09
NM_007274	ACOT7	Acyl-CoA thioesterase 7	−2.17	−2.51

Functional enrichment analysis of regulated genes

Although we had already observed that regulated genes belong to a number of functional categories, we wanted to find out what cellular functions were specifically affected by the expression of the 2-5A system. Thus, we performed an enrichment analysis in functional terms on the lists of differentially expressed genes. A significantly overrepresented functional term means that the proportion of genes associated with that functionality among the regulated genes is significantly higher than expected by chance, that is, the proportion of genes associated with that functionality among all genes in the chip. However, it does not necessarily mean that most regulated genes belong to that functional category. Onto-Express was used to select GO terms significantly overrepresented in genes up-regulated or down-regulated in vv2-5ASystem-infected cells in comparison with MOCK- or vvMix-infected cells. In both cases, no term was significantly overrepresented in up-regulated gene lists at any time. However, at 8 and 16 hpi a large number of functional terms were overrepresented in the down-regulated gene list in the comparison with MOCK-infected cells. These included “mitochondrial electron transport,” “transcription,” “RNA splicing,” “translation,” and many other related to these biological processes. In the microarray experiment where RNAs from vvMix-infected and vv2-5ASystem-infected cells were compared, overrepresented terms were found only in the down-regulated gene list at 16 hpi. Most functional terms were related to mitochondria and ribosomes, like “cytochrome c oxidase activity,” “translation,” and “structural constituent of ribosome” (Table 6). At least 18 genes encoding ribosomal proteins, including nine mitochondrial ribosomal proteins, were down-regulated at 16 hpi (Supplementary Table 3) while none was found up-regulated, suggesting a tendency to impair translation at 16 hpi, as previously demonstrated for 2-5A system activation (Diaz-Guerra and others 1997b; Bisbal and Silverman 2007). Similarly 48 genes that code for proteins that can be located in mitochondria were down-regulated at 16 hpi. Most of them are related to metabolism (oxidative phosphorylation and catabolic pathways) or constituent of the mitochondrial ribosomes, and include the mitochondrial intermediate peptidase MIPEP, necessary for protein import. These data are indicative of diminished expression of both mitochondrial-encoded and nuclear-encoded mitochondrial proteins and therefore of mitochondrial dysfunction.

Table 6.

Gene Ontology Terms Overrepresented in Down-Regulated Genes in vv2-5ASystem-Infected Cells in Comparison with vvMix-Infected Cells at 16 hpi

Category	Gene ontology	Total input genes	Reference input genes	Corrected P value
Biological process
	Glycolysis	6	30	0.045
	Translational elongation	8	67	0.057
	Translation	11	141	0.099
Molecular function
	Structural constituent of ribosome	15	123	3.63E-05
	Protein binding	125	4286	0.008
	Cytochrome c oxidase activity	5	20	0.008
	Protein N-terminus binding	6	43	0.034
	RNA binding	22	470	0.055
Cellular component
	Mitochondrion	48	758	4.00E-09
	Ribosome	18	146	3.58E-07
	Cytoplasm	106	3139	3.79E-05
	Mitochondrial inner membrane	12	144	0.004
	Cytosol	33	744	0.004

Significantly represented pathways

Another way of predicting the functional effects of transcriptional changes, complementary to the enrichment analysis in functional terms, is to determine how regulated genes can affect known cellular pathways such as those included in KEGG database (Kanehisa and others 2008). We used Pathway-Express to find what KEGG cellular pathways were significantly overrepresented in the sets of genes regulated by the 2-5A system (vv2-5ASystem-infected cells compared to vvMix-infected cells) at 8 and 16 hpi (Table 7). The adherens junction pathway was one of the best scored at 8 and 16 hpi, together with the tight junction pathway at 16 hpi. Both pathways are involved in cytoskeleton organization and cell–cell communication, and can be related with cell growth and differentiation. Their high impact factors were mainly due to down-regulation of several genes, among them actin gamma 1 (ACGT1), alpha catenin 1 (CTNNA1), and casein kinase 2 beta (CSNK2B). This could indicate some degree of cytoskeleton disorganization after 2-5A system activation.

Table 7.

KEGG Pathways Significantly Overrepresented in Differentially Expressed Genes in vv2-5ASystem-Infected Cells in Comparison with vvMix-Infected Cells at 8 and 16 hpi

Pathway name	Impact factor	Gamma P value	Input/total genes in pathway
8 hpi
Phosphatidylinositol-signaling system	27.23	4.22E-11	1/77
Long-term depression	11.46	1.32E-04	3/76
Adherens junction	9.54	7.57E-04	1/75
GnRH-signaling pathway	8.09	0.0028	4/97
VEGF-signaling pathway	6.99	0.0074	3/71
Fc epsilon RI-signaling pathway	6.68	0.0096	3/77
Renin–angiotensin system	6.58	0.0105	2/17
Prostate cancer	6.46	0.0117	1/89
Alzheimer’s disease	6.20	0.0146	1/22
MAPK-signaling pathway	6.17	0.0150	5/265
Regulation of autophagy	6.15	0.0153	2/30
Regulation of actin cytoskeleton	4.99	0.0408	4/211
16 hpi
Adherens junction	23.55	1.45E-09	6/75
Wnt-signaling pathway	10.81	2.38E-04	12/148
Ribosome	9.79	6.04E-04	9/91
Tight junction	9.40	8.59E-04	10/135
Amyotrophic lateral sclerosis	9.11	0.0011	4/17
MAPK-signaling pathway	8.50	0.0019	14/265
Colorectal cancer	7.38	0.0052	7/84
Cell cycle	7.23	0.0059	8/112
TGF-beta–signaling pathway	7.23	0.0060	7/89
Type II diabetes mellitus	7.03	0.0071	1/44
Pathogenic Escherichia coli infection	6.88	0.0081	5/51
Melanoma	6.73	0.0093	3/71
Alzheimer’s disease	6.37	0.0126	2/22
Long-term depression	6.25	0.0140	5/76
Notch-signaling pathway	6.09	0.0160	1/46
Thyroid cancer	5.50	0.0265	3/29
Gap junction	5.39	0.0291	5/96
Long-term potentiation	5.12	0.0365	3/69
RNA polymerase	4.98	0.0410	3/25
p53-signaling pathway	4.71	0.0516	4/68

Other pathway with best scores and more genes involved at both times was the MAPK-signaling pathway, in which both up- and down-regulated genes were found. At 8 hpi 5 genes were regulated, including 2 up-regulated members of the phospholipase A2 family and the repressed MAPK12 (Supplementary Table 4). At 16 hpi 13 genes were differentially expressed, with 2 JUN family genes and the growth suppressor GADD45B being up-regulated, and MAPK3, DUSP2, TRAF2, and MYC among the down-regulated genes. At 16 hpi other relevant pathways included cell cycle pathway and TGF-beta–signaling pathway. Among the 8 genes involved in cell cycle several genes that control progression were up-regulated (SMAD3, GADD45B, and MAD2L2) while others that promote mitosis like cyclin B and ANAPC11 were down-regulated. The up-regulated gene SMAD3 is also a transcriptional modulator in TGF-beta–signaling pathway where it mediates TGF-beta–induced apoptosis and cell cycle arrest. The combination of these results could point toward functions related to cell growth arrest and IFN-signaling pathways.

Effect of RNaseL activation on mitochondrial rRNA 16S

Due to the observed down-regulation of many genes involved in oxidative phosphorylation and mitochondria pathways, and related with previous results in which RNaseL was involved with mitochondria induction of apoptosis and was also localized in this organelle (Le Roy and others 2001; Domingo-Gil and Esteban 2006), we analyzed if RNaseL could degrade the mitochondria ribosomal RNA 16S as it does with the cellular rRNAs 28S and 18S. For this purpose, we performed a Northern blot to detect the 16S ribosomal RNA with a specific probe after infection with the recombinant viruses that express the 2-5A system during 48 h. A major band of 1,558 bp was seen in the lanes of uninfected or VACV-infected cells corresponding to the size of amplified 16S rRNA, while expression of RNaseL and its activation with 2-5OAS induced a clear, almost complete degradation of the 16S rRNA (Fig. 2). This degrading effect was not observed with a non-catalytic mutant of RNaseL, indicating a specific activity of RNaseL (data not shown).

FIG. 2.

Mitochondrial 16S ribosomal RNA degradation during expression of the 2-5A system from VACV vectors. HeLa cells were MOCK-infected, single infected with VACV (6 PFU/cell) or triple infected with vvRL+vvT7+vv2-5AS (2 PFU/cell from each virus) called here vv2-5ASystem, 48 hpi. Total RNA was extracted and Northern blot was performed using a specific probe for mitochondrial 16S rRNA.

Validation of microarray data by Western blot and RT-PCR

We have previously shown that the expression pattern and relative mRNA abundance of selected genes evaluated by quantitative RT-PCR correlated well with microarray data (Guerra and others 2007). In order to validate some of the genes that were found differentially expressed in the microarray experiments, we checked the expression of two down-regulated genes: Mitochondrial intermediate peptidase (MIPEP) and the estrogen receptor 2 (ESR2) and the up-regulated translation factor EIF4B by Western blot with cells collected at 8, 16, and 24 hpi. As Figure 3A shows, there was a protein down-regulation of MIPEP at 16 and 24 hpi when cells were infected with vv2-5ASystem, correlating with the mRNA down-regulation found at 8 and 16 hpi when cells infected with vvMix were compared to vv2-5ASystem (−6.41 and −5.89, respectively). There was also an increase of MIPEP levels when cells were infected with vvMix compared to MOCK at 16 hpi (column 2 related to 1) that correlates with the microarray results (data not shown). The levels of ESR2 were decreased when cells were infected with vv2-5ASystem compared to vvMix and MOCK-infected cells at 16 and 24 hpi mainly (Fig. 3A), which correlates with the down-regulated mRNA levels of ESR2 found at 8 and 16 hpi in the microarray experiments (−4.86 and −8.99, respectively).

FIG. 3.

Validation at the protein level of some genes with differences of expression. (A) HeLa cells were double infected with vvT7+WRluc (2 PFU/cell) called here vvMix, triple infected with vvRL+vvT7+vv2-5AS (2 PFU/cell from each virus) called here vv2-5ASystem, during 8, 16, and 24 hpi. (B)Protein extracts were resolved in 10% SDS-PAGE polyacrylamide gel electrophoresis followed by Western blot using polyclonal antibody against human EIF4B (1/100 dilution), human MIPEP (1/500 dilution), and monoclonal antibody against human ESR2 (1/500 dilution) and anti-β-actin (1/2,000). (C) RT-PCR was used to validate the changes in KCTD11 mRNA levels detected by microarray analysis of total RNA from infected cells at 16 hpi as described in Materials and Methods. The data presented are relative to MOCK sample and were previously normalized to the GUSB endogenous mRNA levels. Each experiment was performed in triplicate.

The eukaryotic EIF4B factor, which is required for mRNA binding to ribosomes and functions in close association with other translation factors as EIF4-F and EIF4-A (Holcik and Sonenberg 2005), was also found up-regulated when cells were infected with vv2-5 ASystem compared to vvMix at 16 hpi (Fig. 3B), which correlates with the data obtained by the microarray experiment at 8 hpi when vvMix was compared with vv2-5ASystem (2.33-fold increase).

It should be pointed out the difficulty in evaluating protein expression by Western blot due to the activation of the 2-5A system, that while it can up-regulate expression of some cellular genes it can also trigger a translational block (Diaz-Guerra and others 1997b).

We also validated the microarray analysis by measuring the mRNA levels of KCTD11 gene by quantitative RT-PCR using a specific TaqMan probe, and we found that cells infected with vv2-5ASystem had a 4.5-fold increase compared to vvMix-infected cells and a 50-fold increase compared to MOCK-infected cells (Fig. 3C).

Discussion

The 2-5A system (OAS-RNaseL) has been related with inhibition of viral replication, enhanced transcription of IFN-stimulated genes, apoptosis, and tumor suppression in prostate cancer (Li and others 2000; Xiang and others 2003; Silverman 2007b). In previous microarray experiments adding purified 2-5A to cells, RNaseL activation induced transcription of genes that suppress virus replication like several IFN-stimulated genes (IFIT1, IFIT2, IL8, ISG15), and of an apoptotic suppressor of prostate cancer (MIC-1/NAG-1). MIC-1/NAG-1 required RNaseL catalytic function and the activation of the MAPK-signaling pathway through JNK and ERK (Malathi and others 2005).

The aim of the work reported here was to identify cellular genes specifically induced in response to OAS/RNaseL system when both enzymes were produced in cells by VACV recombinants. In our experiments the effects specifically derived from the poxvirus-based expression of the 2-5A system included the enhanced transcription of several genes available in an IFN-stimulated gene database (http://www.lerner.ccf.org/labs/williams/xchip-html.cgi), like IFI6 at 8 hpi and DDX3X, EIF3A, JUN, and MKI67 at 16 hpi. IFI6 is an IFN-induced survival factor, while DDX3X is a critical component of the TBK1-dependent IFN type I induction in the innate immune response (Soulat and others 2008). TBK1 gene was also up-regulated at 8 hpi.

On the other hand, we found that several genes involved in cell growth arrest were up-regulated (KCTD11, GADD45B) while other genes promoting cell growth were down-regulated (MYC, RUNX2). In the analysis of relevant pathways in our data with Pathway-Express, the MAPK-signaling pathway is one of the most significantly altered at 8 and 16 hpi. At 8 hpi MAPK12, which stimulates cell differentiation, was down-regulated and MAPK3 (ERK1) appeared down-regulated at 16 hpi although one of its inhibitors, DUSP2, was down-regulated as well. Another down-regulated gene at 16 hpi was TRAF2, which has a possible anti-apoptotic role. In fact, TRAF molecules that are integral to TLR-signaling pathways have been found to interact with PKR (Gil and others 2004). Interestingly, the gene GADD45B, which activates the MAPK-signaling pathway and promotes cell growth arrest, was up-regulated at 16 hpi. GADD45B transcription is enhanced by SMAD3 (Yoo and others 2003), which was also up-regulated at 16 hpi. These results are related with the impact of RNaseL overexpression on cellular growth previously described (Zhou and others 1998). RNaseL and JNK use the same signaling mechanism to induce apoptosis after virus infection. MAP kinases need RNaseL to produce apoptosis after viral infection and JNK proteins are essential for the apoptosis mediated by RNaseL after different stimuli (Li and others 2004), therefore the genes of MAPK pathway found up-regulated in our results confirm the relationship between these two enzymes.

A few up-regulated genes in previous results from Malathi and colleagues (2005) appeared also up-regulated in our experiments: IFI6 at 8 hpi, and JUN and IDI1 at 16 hpi. These are interesting findings since IFI6 is an IFN-induced survival factor that antagonizes mitochondrial-mediated apoptosis.

Other genes related to tumor generation and cell cycle arrest have been found up-regulated at 8 hpi when 2-5A system was activated: TNFAIP6, which is involved in cell–cell and cell–matrix interactions during inflammation and tumor genesis (Wisniewski and others 1994) and CUL2, which is implicated in cell cycle arrest regulating negatively cell proliferation and also related to the induction of apoptosis by intracellular signals (Kipreos and others 1996). A gene that can be involved in prostate cancer, CREB5, was highly up-regulated at 8 hpi; the gene product binds to the cAMP response element and activates transcription, promoting cell growth and transformation (Zu and others 1993; Dong and others 2007).

Among the genes that showed a sustained up-regulation at both 8 and 16 hpi (Table 5), we highlight KCTD11 and TNFAIP8L2. The gene KCTD11 codes for potassium channel tetramerization domain-containing protein 11, which induces apoptosis, growth arrest, and plays a role as a tumor repressor (Di Marcotullio and others 2004). The protein coded by TNFAIP8L2 (TIPE2) is an essential negative regulator of Toll-like receptor and T-cell receptor function; it also prevents hyperresponsiveness of the immune system and maintains immune homeostasis (Sun and others 2008). It promotes Fas-induced apoptosis and inhibits AP1 and NF-κB activation. Other genes that had been described as tumor markers and also have antibacterial functions have been found highly up-regulated after 2-5A system activation at 8 hpi, such as PLA2G2A and PLA2G12A (Huhtinen and others 2006; Belinsky and others 2007). Induction of genes involved in cell growth arrest and apoptosis points toward the 2-5A system as playing an important role in tumor suppressor function through the induction of specific group of genes (Casey and others 2002; Xiang and others 2003).

Our results show up-regulated expression of genes that code for proteins involved in transcription, translation, and splicing of cellular mRNAs, which correlates to the regulation of stability of some mRNAs and the role of RNaseL in translation (Le Roy and others 2005; Le Roy and others 2007). We found the translation factor EIF4B up-regulated (2.33-fold) at mRNA and also at the protein level (Fig. 3B) providing support for a role of RNaseL in translational control. We also found alterations at the protein level of some subunits of the translation factor eIF3 when cells were infected with vv2-5ASystem compared to vvMix and MOCK that supports this role of RNaseL in translational control and also correlates with the data obtained by microarray (data not shown).

In microarray studies performed on IFN-treated cells many genes related to cellular signaling, G proteins, growth factors, transcription, splicing, mRNA translation, and apoptosis were found up-regulated (de Veer and others 2001). In our study with two IFN antiviral enzymes (OAS/RNaseL), and at 8 hpi, there were few genes up-regulated that are related to apoptosis, probably because at this time post-infection vv2-5ASystem does not induce apoptosis as has been previously described (Domingo-Gil and Esteban 2006). However, at 16 hpi more genes involved in apoptosis were found, such as PDCD6 (up-regulated), which induces apoptosis, and the inhibitor of apoptosis FKSG2 (down-regulated).

Among the genes that showed a sustained down-regulation at both 8 and 16 hpi, we found RUNX2, coding for Runt-related transcription factor 2. This protein is known to promote transformation in vivo and cell proliferation in vitro by associating with specific coactivators or corepressors (Blyth and others 2006). Other strongly down-regulated genes are SIRT6 and ESR2. The gene that codes sirtuin 6 (SIRT6) is involved in repairing the production of ROS preventing cellular aging (Lim 2007) that correlates with previous published results in which activation of the 2-5A system from vv2-5ASystem-infected cells produced reactive oxygen species (ROS) (Domingo-Gil and Esteban 2006). The estrogen receptor 2 (ESR2) is a natural promoter of breast cancer (Schubert and others 1999; Imamov and others 2005) and the down-regulation of this protein suggests that it could prevent the malignancy of some cells, corroborating the involvement of RNaseL in cancer (Xiang and others 2003; Liu and others 2007) and its role as a tumor suppressor (Imamov and others 2005). Interestingly, the estrogen receptor 2 is expressed in mitochondria and functions as a mitochondrial vulnerability factor involved in membrane potential maintenance (Yang and others 2009), so repression of this gene could have harmful effects on mitochondrial stability.

Other down-regulated genes are related to maintaining homeostasis of mitochondria as MIPEP (mitochondrial intermediate peptidase) and different subunits of cytochrome c oxidase and ATPase. The product of MIPEP performs the final step in processing a specific class of nuclear-encoded proteins targeted to the mitochondrial matrix or inner membrane. This protein is primarily involved in the maturation of proteins related with oxidative phosphorylation (Chew and others 1997). Its down-regulation supports the observed diminished transcription of genes implicated in oxidative phosphorylation in our results. Down-regulation of these genes would imply a mitochondrial destabilization that could end up in the induction of apoptosis. Related to this, it has been described that activation of RNaseL produces destabilization of cytochrome c oxidase subunit II mRNA (Le Roy and others 2001; Le Roy and others 2007).

It has been reported that the activation of 2-5A system causes inhibition of protein synthesis due to mRNA and rRNA degradation (Diaz-Guerra and others 1997b). Our functional enrichment analysis showed an outstanding overrepresentation of genes encoding ribosomal proteins within the genes down-regulated by the 2-5A system at 16 hpi, which could constitute an additional mechanism responsible for that inhibition of translation. Moreover, the functional enrichment analysis revealed that a significant proportion of genes encoding proteins that can be located in mitochondria were also down-regulated at 16 hpi. These genes are mainly related to different metabolic pathways like the oxidative phosphorylation, and to maintenance of mitochondrial homeostasis and membrane potential. Thus the down-regulation of these genes could have the same effect that the IFN-α–induced down-regulation of mitochondrial mRNAs, which leads to suppression of mitochondrial function (Le Roy and others 2001; Le Roy and others 2007) and finally apoptosis.

The results obtained by Northern blot indicate that mitochondrial 16S rRNA was degraded when active RNaseL was expressed and activated by the vv2-5ASystem, whereas 16S was maintained intact when cells were infected with control VACV (Fig. 2). It is possible that degradation of mitochondrial rRNA produces a blockade in the synthesis of mitochondrial proteins, such as ATP synthase complex and electron transport chain, which could contribute to mitochondrial dysfunction and thereby to apoptosis (Crawford and others 1997). A mitochondrial translation factor has been described that interacts with RNaseL and this could explain the regulation that RNaseL produces on the stability of some mitochondrial mRNAs (Le Roy and others 2005). This result corroborates the localization of RNaseL inside mitochondria, which will affect RNA stability, and supports our previous findings about the function that RNaseL is playing in the cell cytoplasm and inside the mitochondria (Domingo-Gil and Esteban 2006).

Overall, the transcriptional changes that we have observed in the VACV system co-expressing OAS-RNaseL provided evidence for a subset of cellular genes induced when the 2-5A system is activated during infection. These findings complement other microarray studies using purified 2-5A and while there are similarities in gene expression, clear differences are observed in the pattern of other host genes up- or down-regulated with the VACV system. We found specific regulation of some of the host genes involved in translational control, cell growth arrest, tumor suppressor function, promotion of apoptosis, and destabilization of mitochondria. A simplified OAS/RNaseL pathway with indications where some of the genes identified in this work could modulate the 2-5A function is represented in Figure 4. These findings provide a framework to further characterize the function of the identified new genes on the biological effects of the 2-5A system, and in turn, help to explain the multiple effects of IFN.

FIG. 4.

Global effects of transcriptional changes induced by activation of the 2-5A system. Activation of the 2-5A system, an important antiviral and anticellular response of the cells, results in RNA degradation, protein synthesis inhibition, mitochondrial dysfunction, and changes in gene expression that contribute to different biological effects. Specific functions of the activated 2-5A system are shown in the scheme along with some of the regulated genes that could be responsible for the multiple effects. In fact, down-regulation of genes encoding ribosomal proteins could affect the inhibition of protein synthesis; repression of mitochondrion-related genes could have an important effect on mitochondrial dysfunction; induction of proapoptotic genes would enhance cellular apoptosis; regulation of genes involved in cell cycle could lead to cell growth arrest and possibly tumor suppressor activity; and induction of genes related to innate immune response could lead to antiviral response.

Footnotes

Acknowledgments

We are very grateful to Ilkka Julkunen (National Public Health Institute, Helsinki, Finland) for providing the polyclonal antibody that recognizes rat 2-5 OAS1 protein and Jesús de Celis (Centro Nacional de Biotecnología, CSIC, Madrid, Spain) for help with microarray design and analysis. This investigation was supported by the Spanish Ministry of Education and Science (grant BIO2005-06264) and by Fundación Marcelino Botín.

Author Disclosure Statement

No competing financial interests exist.

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