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Journal of Virology, February 2007, p. 1796-1812, Vol. 81, No. 4
0022-538X/07/$08.00+0     doi:10.1128/JVI.01936-06
Copyright © 2007, American Society for Microbiology. All Rights Reserved.

Adenovirus Infection Triggers a Rapid, MyD88-Regulated Transcriptome Response Critical to Acute-Phase and Adaptive Immune Responses In Vivo{triangledown} ,{dagger}

Zachary C. Hartman,1 Anne Kiang,1 Ruth S. Everett,1,{ddagger} Delila Serra,1 Xiao Y. Yang,2 Timothy M. Clay,2 and Andrea Amalfitano1,3*

Division of Medical Genetics, Department of Pediatrics,1 Department of Surgery, Duke University Medical Center, Durham, North Carolina 27710,2 Departments of Microbiology and Molecular Genetics, and Pediatrics, Michigan State University, East Lansing, Michigan 488243

Received 5 September 2006/ Accepted 14 November 2006


   ABSTRACT
Top
 Abstract
Introduction
 Materials and Methods
 Results
 Discussion
 References
 
Nearly 50 years ago, the discovery of interferon prompted the notion that host cells innately respond to viral invasion. Since that time, technological advances have allowed this response to be extensively characterized and dissected in vitro. However, these advances have only recently been applied to highly complex, in vivo biological systems. To this end, we exploited high-titer adenovirus (Ad) vectors to globally investigate the innate immune response to nonenveloped viral infection in vivo. Our results indicated a potent cellular transcriptome response shortly after infection, with global assessments revealing significant dysregulation in ~15% of the measured transcripts derived from Ad vector-transduced tissue. Bioinformatics-based transcriptome analysis revealed a complex innate response to Ad infection, with induction of proinflammatory responses (and suppression of metabolism and mitochondrial genes) akin to those observed when mice are challenged with lipopolysaccharide. Despite this commonality, there were many unique aspects of the Ad-dependent transcriptome response, including the upregulation of several RNA regulatory mechanisms and apoptosis-related pathways, accompanied by the suppression of lysosomal and endocytic genes. Our results also implicated the Toll-like receptors (TLRs) in these responses, prompting specific investigations into this pathway. By using MyD88KO mice, our results confirmed that Ad-induced dysregulation of five functionally related gene clusters are significantly dependent on this TLR adaptor gene. MyD88 deficiency also resulted in significantly diminished, although not abolished, adaptive and acute-phase immune responses to Ad, confirming the transcriptome data, as well as specifically identifying MyD88 as a significant Ad immunity amplifier and regulator in vivo.


   INTRODUCTION
Top
 Abstract
 Introduction
Materials and Methods
 Results
 Discussion
 References
 
For the past 100 years, a significant body of research has sought to understand the mammalian innate immune system, with recent efforts focusing on understanding its molecular workings (5). The discovery of a new family of "pathogen-sensing" genes, the Toll-like receptors (TLRs) and the deciphering of their role in mammalian innate immunity has been an especially robust area of recent investigation (2, 28). However, critical issues remain unanswered, chief among which is the intracellular innate response to viral infections in vivo. While recent studies have sought to investigate intracellular viral immunity and TLR involvement, almost all have relied on tissue culture systems, with little or no attention paid to in vivo models. The paucity of information regarding viral cellular immune activation in vivo is partially due to the difficulties in achieving efficient viral transduction in vivo, coupled with the complex interactions inherent to the mammalian innate and adaptive immune responses. To circumvent these obstacles, we took advantage of high-titer adenovirus (Ad) vectors, multiplexed enzyme-linked immunosorbent assay (ELISA), and comprehensive, array-based gene expression detection systems to globally delineate the immune responses to this virus in vivo.

Ads are nonenveloped icosahedral viruses that, as recombinant vectors, demonstrate a tremendous ability to effectively transduce liver cells in vivo. Recombinant Ads are widely used in current gene therapy trials, although mounting evidence of anti-Ad immunological responses (both innate and adaptive) prompted the development of improved Ad vectors (4, 7, 13, 34). The newer vectors have more viral genes deleted (in some cases all), thus greatly improving their ability to persist in vivo, facilitating the avoidance of adaptive immune responses (for a more in-depth review of this subject, see reference 4). However, the essential viral capsid and cellular entry mechanisms in these vectors remain unchanged, as do the innate responses invoked by the Ad capsid (6, 7, 30, 32).

In the present study, we used different human Ad vectors to transduce the livers of mice and compared the liver transcriptome response over increasing durations of time to ascertain the role of vector content and its time dependence on the cellular transcriptome response to infection. These studies confirmed that the most dramatic transcriptome responses to different types of Ad vector infection again occurred within hours (and not at later time points) of viral administration in vivo. To evaluate the viral specificity of this response, we compared the Ad liver transcriptome profile to those obtained after systemic lipopolysaccharide (LPS) treatments of mice. These studies implicated the involvement of the TLR pathogen recognition system in Ad innate immune responses. While a previous study had suggested a lack of MyD88 (a critical TLR adaptor gene) dependence in the Ad-induced upregulation of CD86 in mouse dendritic cells in vitro (35), a different in vivo study found a reduction in Ad-elicited cytokines using an Ad challenge model in TLR4-deficient mice (40). Based upon these considerations we sought to further validate the transcriptome results in general through investigation of a critical TLR system adaptor gene, MyD88. Our in vivo investigation with MyD88-deficient mice confirmed a significant MyD88-mediated effect on Ad innate immune responses at several levels. We further demonstrated that these early, innate MyD88-dependent changes have functional consequences in the subsequent development of several adaptive immune responses to Ad vectors, supporting the significance of the TLR system in orchestrating multiple components of in vivo antiviral immune responses.


   MATERIALS AND METHODS
Top
 Abstract
 Introduction
 Materials and Methods
Results
 Discussion
 References
 
Vector preparation. Briefly, cytomegalovirus-driven LacZ (originally described in reference 19) and human carcinoembryonic antigen (CEA) (described in reference 44, 31) were independently subcloned into shuttle plasmids and used to generate [E1-E3-] and [E1-E2b-E3-] vectors using a previously described, bacterial-recombination-based system (20). Complementing C-7 cell lines (containing the Ad E2 polymerase and pTP genes) were used to produce high-titer stocks of these vectors, and cesium chloride double banding was performed to purify the vectors, as previously detailed (3). All Ad vector stocks were evaluated for replication-competent adenovirus (RCA) via a PCR-based RCA assay, essentially as described by Lochmuller et al. (26). Briefly, DNA isolated from the respective Ad vector preparations was subjected to PCR with primers located within the E1 region, and a known quantity of Ad2 wild-type genome (Invitrogen) was used as a positive control. The RCA assay confirmed that the viral preparations used were devoid of significant amounts of any replication-competent viral genomes, based on the sensitivity of the RCA assay being able to detect at least 7.8 x 104 [E1+] Ad genomes in a sample containing 2.75 x 1010 to 3 x 1010 vector particles. To ensure samples were not contaminated with LPS, purified vectors were tested by using an E-Toxate kit (Sigma, St. Louis, MO) according to the manufacturers' recommendations with all preparations containing <0.025EU per viral injection dose.

Assessment of viral transduction. Sections of snap-frozen liver tissue embedded in optimal-cutting-temperature fluid was sectioned at 7 µm and stained for bacterial ß-galactosidase (LacZ) expression with X-Gal (5-bromo-4-chloro-3-indolyl-ß-D-galactopyranoside) substrate. To quantitatively assess LacZ activity, snap-frozen samples were homogenized, and the LacZ activity was quantified by using a ß-galactosidase activity detection kit (Stratagene). To assess the number of Ad genome copies per liver cell, we utilized a previously described method, wherein frozen liver tissue from Ad-infected animals was homogenized, DNA extracted, and subsequently quantified by using real-time PCR (23).

Real-time PCR and quantitative real-time PCR. Real-time PCR was performed on an ABI 7900 system using the QIAGEN Quantitect SYBR green PCR kit (QIAGEN, Valencia, CA). The primers used for Ad amplification were 5'-CCACAGCTCGCGGTTGAGG-3' and 5'-GATCTAGCCCGCGCCC-3', and the primers for GAPDH (glyceraldehyde-3-phosphate dehydrogenase) were as described in Table S1 in the supplemental material. All PCRs were subjected to the following conditions: 95.0°C for 15 min, followed by 45 cycles of 95.0°C for 15 s and 58.0°C for 30 s, and an extension at 72.0°C for 30 s. To determine the absolute Ad genome copy numbers per cell, standard curves were run in duplicate and consisted of six half-log dilutions using total genomic DNA spiked with known concentrations of Ad2 DNA (Invitrogen). Melting curves corroborated the quality of each PCR. For the quantitation of cellular mRNA via real-time PCR, total RNA was isolated and reverse transcribed (after DNase treatment) by using iScript (Bio-Rad, Hercules, CA), and quantitative PCR performed using a Quantitect SYBR green PCR kit. The comparative cycle threshold (CT) method was used to determine differential gene expression using the geometric mean of five control housekeeping genes (GUS, Rpl13, HMBS, PMM1, and GAPDH) to standardize the expression levels across all samples (41).

Animal procedures and array RNA hybridization. Adult C57BL/6J mice (2 to 6 months of age) were purchased from Jackson Laboratory (Bar Harbor, ME). 129/C57BL/6J MyD88KO mice (1) (2 to 6 months of age) were kindly provided by S. Akira and were maintained in sibgroups, so that MyD88+/– and MyD88–/– siblings could be subjected to similar treatments (phosphate-buffered saline [PBS] vehicle or virus plus vehicle injections) to control against possible 129 background strain influences. Virus treatment consisted of 1.5 x 1011 total particles of the respective vectors intravenously injected (via the retro-orbital sinus) in a total volume of 200 µl in PBS, whereas mock-injected mice were injected with an equal volume of the identical virus vehicle buffer. Plasma and liver samples were obtained and processed at the indicated times postinjection using procedures approved by the Duke University (Durham, NC) Institutional Animal Care and Use Committee.

Platelets were measured from blood draws at various time points by using a Unopette device (Becton Dickinson, Franklin Lakes, NJ) in accordance with the manufacturer's recommendations. Statistically significant differences were determined by using a two-tailed homoscedastic Student t test.

Microarray methods. Liver RNA was extracted by homogenizing snap-frozen samples in liquid nitrogen using TRI-Reagent (Molecular Reagents Center). After purification, the RNA (10 µg) was further purified by using an RNeasy kit (QIAGEN) and assessed for quality first by using UV spectroscopy and then by a more extensive analysis of degradation readings after electrophoresis on an Agilent Lab-on-a-Chip 2100 Bioanalyzer (Agilent Technologies, Palo Alto, CA) in accordance with the manufacturer's recommendations. After passing quality checks, the RNA from mouse liver was directly labeled with Cy5 dyes by using oligo(dT) primers, whereas mouse universal reference control RNA (Stratagene) was directly labeled with a Cy3 dye. The Cy-labeled cDNA probes (a mixture of Cy3 and Cy5) were then hybridized and placed onto a slide spotted with oligonucleotides (Operon 70-mer oligonucleotide sets), using either the MO20k or the MO30k 70-mer probe sets from Operon (mouse genome set, v. 2.0 and 3.0). The version 2.0 probe set represents 16,423 unique genes, while the v3.0 set represents 24,878 genes. Fluorescence array images were collected for both Cy3 and Cy5 with an Axon GenePix Pro 4000A scanner, and image intensity data were extracted and analyzed with Microarray Analysis Suite 5.0 software. Array datasets were deposited at NCBI's Gene Omnibus Express (GEO) in a MIAME-compliant form (along with complete details of all procedures) as accession numbers GSE3729 and GSE4339 using platforms GPL3222 and GPL3223.

Microarray analysis. Expression data from scanned microarray datasets were imported into Genespring 6.2 where intensity-dependent (Lowness) normalizations were applied, and microarray values below 0.01 were set to 0.01. After the data were normalized, the findings were filtered with respect to presence of flags (determined in Axon scanner analysis) so that those genes with a majority of samples showing absent calls were eliminated from further analysis. To ascertain differentially expressed genes, one-way parametric analysis of variance (ANOVA) tests were performed by using a Benjamini-Hochberg false discovery rate (BH FDR) for multiple testing correction, with a P value of 0.05 (theoretically minimizing the false-positive rate to an acceptable 5% of all genes identified as significantly dysregulated).

This list was further analyzed through use of Expression Analysis Systematic Explorer (EASE v2.1) by using standard protocols previously described (http://apps1.niaid.nih.gov/david/) (21). Gene lists derived from the one-way ANOVA (P = 0.05 using the BH FDR) statistically significant filtered gene groups were uploaded (using Unigene or GenBank numbers) and analyzed by using the Fisher exact test (using an EASE jackknife) to determine the over-representation of various pathways in the gene sets identified as significantly Ad induced or Ad repressed, relative to the same gene sets measured in mock-infected mice. In addition, these same lists were also analyzed by using hypergeometric tests against the nondysregulated genes through use of Gene Ontology Tree Machine (GOTM; http://genereg.ornl.gov/gotm) and the Gene Set Analysis Toolkit (WebGestalt) on the Oak Ridge National Laboratory Web site. These tests were performed to ascertain over-representation of various pathways in Ad induced or repressed genes and confirm the gene ontology (GO) functional groups over-represented in those Ad-affected genes, as had been previously analyzed using EASE (45).

qRT-PCR and cytokine/chemokine assays. To determine differential expression of selected mRNA targets, quantitative reverse transcription-PCR (qRT-PCR) was performed on extracted total liver RNA. Extracted RNA was DNase treated and reverse transcribed using iScript (Bio-Rad) in accordance with the manufacturer's recommendations. After RT, real-time PCR was performed as described previously, normalizing to the geometric mean of housekeeping genes and using the comparative CT method to calculate relative expression differences (25). Serum samples from retro-orbital bleeds were assayed by using a Bio-Rad 23-Plex mouse cytokine kit according to the manufacturer's recommendations. Similarly, interleukin-1ß (IL-1ß) and serum amyloid A (SAA) levels were measured by using a mouse IL-1ß ELISA kit (R&D Systems, Inc., Minneapolis, MN) and an SAA ELISA kit (BioSource, Inc., Camarillo, CA) in accordance with the manufacturers' recommendations. Statistics for ELISA data were performed by using a two-tailed homoscedastic Student t test using a Bonferroni correction.

Adenovirus antibody ELISA and enzyme-linked immunospot assay (ELISPOT) assay. To assess the effect of MyD88 on the adaptive immune response, mice were injected with 5 x 1010 particles of a CEA expressing [E1-,E3-] Ad vector via the footpad and sacrificed 14 days after injection. To quantify mouse anti-adenovirus antibodies, a previously described ELISA method was used (29). Briefly, 109 adenovirus particles per well were bound to a 96-well plate in a bicarbonate solution (200 mM NaHCO3, 81 mM Na2CO3 [pH 9.5]) overnight at 4°C. Wells were washed three times (PBS with 0.05% Tween 20) and blocked for an hour at room temperature by using a 1% bovine serum albumin-5% sucrose-0.05% NaN3 in PBS solution, rinsed in PBS, and incubated with serum (at the indicated dilution) for 60 min at 37°C. Wells were then washed three times with wash buffer, and 100 µl of a 1:2,500 dilution of sheep anti-mouse immunoglobulin G H+L antibody (Jackson Immunoresearch Laboratories) per well was added, followed by incubation for 60 min at 37°C. Finally, 100 µl per well of the substrate solution (5 mg of o-phenylenediamine dihydrochloride, 5 ml of H2O2, 12.5 ml of 0.2 M Na2HPO4, 0.1 M citric acid) was added to each well, and substrate conversion was measured at 405 nm after 45 min.

To quantify alloantigen-primed gamma interferon (IFN-{gamma})-producing T cells, an ELISPOT assay was used essentially as described previously (8, 18, 27, 31). Multiscreen-HA 96-well plates (Millipore, Bedford, MA) were coated overnight with capture anti-IFN-{gamma} antibody (AN-18, 1 mg/ml), washed five times with PBS, and then blocked for 2 h with RPMI 1640-25 mM HEPES-10% fetal bovine serum at 37°C. Capture and detection anti-IFN-{gamma} monoclonal antibodies were purchased from MABtech USA, Mariemont, OH. Donor splenocytes (5 x 105) were added to each well, followed by the addition of phorbol myristate acetate plus ionomicin (0.263 and 2.1 µg per well), CEA peptide (CAP-1, amino acids 571 to 579 [YLSGANLNL], 5.4 µg/well; a gift from J. Schlom, NCI, Bethesda, MD), pp65 peptide (BD Biosciences, 5.4 µg/well), or medium alone. Plates were incubated for 24 h at 37°C and then washed five times with PBS. Biotinylated detection anti-IFN-{gamma} antibody (R4-6A2; 1 mg/ml) was then added at a 1:1,000 concentration in PBS plus 1% bovine serum albumin, followed by incubation for 2 h at room temperature. The plates were then washed five times with PBS, after which, 100 µl of horseradish peroxidase-conjugated streptavidin (1/1,000 dilution; MABtech USA) was added to each well for 60 min at room temperature. The plates were again washed five times with PBS, developed with 3-amino-9-ethyl-carbazole (Sigma-Aldrich, St. Louis, MO), reconstituted in acetate buffer for 4 min in the dark, washed with H2O, and air dried. Membranes were attached to sealing tape (Millipore), and the number of spots per well was determined by using a KS ELISpot automated reader system with KS ELISpot 4.2 software (Carl Zeiss, Inc., Thornwood, NY).

NCBI accession numbers. Microarray data were submitted to the National Center for Biotechnology Information's GEO Web site (http://www.ncbi.nlm.nih.gov/geo/) under accession numbers GSE3729 and GSE4339 using the platforms GPL3222 and GPL3223.


   RESULTS
Top
 Abstract
 Introduction
 Materials and Methods
 Results
Discussion
 References
 
Systemic Ad vector injection causes a massive intracellular transcriptome response in vivo. Although mice are not the natural host for human Ads, past investigators have found that wild-type human Ads can efficiently infect and replicate in the liver, causing morbidity in these animals at high doses (11). In this same vein, we have previously shown that fully replication-incompetent [E1-,E2b-,E3-] Ad vectors persist for longer periods of time in vivo (beyond 3 weeks postinfection) and induce less inflammation and toxicity at 72 h postinfection (hpi) than do [E1-E3-] Ad vectors (13, 20). Our approach consisted of using either [E1-,E3-] or [E1-,E2b-,E3-] Ad vectors each expressing LacZ to ascertain the viral vector influence on transcriptome responses. C57BL/6J mice were mock infected or infected with 1.5 x 1011 particles of either [E1-,E3-] or [E1-,E2b-,E3-] Ad vectors, which resulted in nearly complete liver transduction at this dosage (see Fig. S1 in the supplemental material) (12). At 6 hpi, 72 hpi, and 3 weeks p.i. animals were sacrificed, and the transcriptomes derived from the transduced livers were assessed by using M20k oligonucleotide arrays (Operon MO20k).

Analysis of infected livers revealed that the most dramatic gene expression level differences between mock-infected and Ad livers occurred at 6 hpi, with ~12% showing a significant difference from mock-infected mice at this time point (864 of 7,062 gene probes). Using a Tukey post-hoc test, which measures significant differences between individual genes from different groups, we found that ~96% of these genes were similarly affected whether the mouse was infected by either an [E1-,E3-] or an [E1-,E2b-,E3-] vector (Fig. 1A). At later time points, the number of genes whose expression was significantly dysregulated by infection with either Ad vector was greatly diminished. Thus, we focused our efforts in the present study on early time points postinfection with an [E1-,E3-] Ad vector.


Figure 1
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  		FIG. 1. Expression pattern of genes significantly dysregulated by Ad. (A) One-way ANOVA after hierarchical clustering of expression patterns of Ad significantly affected genes (P < 0.01) using MO20k arrays across the full time course of infection. n = 3 for all groups except the [E1-E2b-E3-] 6 hpi group, for which n = 2. (B) Expression patterns of Ad significantly affected genes (P < 0.05 [BH FDR correction]) using MO30k arrays at 6 hpi. In both panels, columns represent independent infections of mice arranged by the infectious parameters listed above. Horizontal rows represent particular genes (colored according to expression level) clustered by using a Pearson correlation, as shown to the left of the columns. Transcriptionally high-expression genes are indicated in red, intermediately expressed genes are yellow, and minimally expressed genes are blue. Intermediate colors are indicated in the legend. Nonhybridized probes are gray.

 
Larger groups of mice were injected with the [E1-,E3-] Ad vector and compared to control mice (six experimental and six control mice, n = 12) by using a larger oligonucleotide array (MO30k), allowing for greater coverage of the mouse transcriptome. Using a one-way ANOVA with P = 0.05, using a BH FDR as a form of multiple testing correction, we observed ~15% of all gene probes as significantly dysregulated (expression upregulated or downregulated) by Ad infection, after we corrected for a 5% false-positive rate (Fig. 1B and Table 1) . This method relies on the statistical assessment of gene expression, providing a level of confidence in the designation of genes as differentially expressed or not differentially expressed. The expression levels of all of the significantly affected genes thus identified in this larger analysis are presented in Fig. 1B. When these same genes were filtered for only those with >3-fold differences in expression, approximately ~3.5% of all liver genes were found to be significantly affected by Ad infection.


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  		TABLE 1. Genes significantly dysregulated by Ad at 6 hpia

 
To delineate the biological significance of these results, Ad responsive genes were separated, filtered according to fold change, and then subjected to the Fisher exact (with a jackknifing correction) and hypergeometric tests to determine the statistically significant over-representation of specific gene pathways or functional gene clusters. For example, when the most highly upregulated genes (>3-fold upregulation) were investigated for GO function, over-representation of genes whose functions included defense response (P = 5.2 x 10–7), apoptosis (P = 3.6 x 10–2), inflammation (P = 3.5 x 10–2), and RNA binding (P = 3.5 x 10–2) categories were identified (Table 2). Many of these categories show great functional overlap and contain many of the same genes. For example, the 30 genes in the "defense response" category altered by Ad infection are all also found in the "response to biotic stimulus" category. When all significantly upregulated genes were similarly analyzed, the expanded number of genes resulted in the same groupings being generated, although genes with GO functions involved in binding and nucleotide metabolism were more over-represented (for a complete gene listing, see Tables 2 and 3). Gene pathway analysis of these genes revealed a similar immunodominance profile, identifying the Jak/STAT, TLR, apoptosis, focal adhesion, and mitogen-activated protein (MAPK) pathways (Table 4). The importance of the TLR pathway in particular was further confirmed after subsequent analyses in MyD88KO mice (see below).


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  		TABLE 2. Gene ontology categorization of significantly highly (>3-fold) upregulated genesa

 

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  		TABLE 3. Gene ontology categorization of significantly upregulated genesa

 

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  		TABLE 4. Ad significantly induced pathwaysa

 
Investigation of the functional significance of all genes whose expression was downregulated (between 1.3 and >60-fold) after Ad expression revealed over-representation of genes with GO functions involved in mitochondria (P = 5.6 x 10–6), metabolism (P = 1.6 x 10–6), oxidoreductase activity, lysosome-lytic vacuole (P = 1.7 x 10–6), endocytosis (P = 2.7 x 10–3), and cellular proliferation (P = 1.0 x 10–2) (Tables 5 and 6).


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  		TABLE 5. Gene ontology categorization of significantly highly (>3-fold) downregulated genesa

 

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  		TABLE 6. Gene ontology categorization of significantly downregulated genesa

 
Overall, Ad infection caused significant induction of transport, RNA metabolism, and endomembrane genes, while simultaneously causing repression of metabolic and growth genes (see Fig. 2). However, when only the more highly affected gene groups (significant and also >3-fold) were examined, Ad infection induced defensive and nucleotide binding gene groups of genes while repressing catalytic activity and membrane-related gene groups. Thus, whereas Ad infection caused significant broad changes in the transcription of transport, metabolism, and growth gene groups, the most potently (significant and also >3-fold) affected groups involved defensive and catalytic functions.


Figure 2
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  		FIG. 2. Gene groups dysregulated by Ad at 6 hpi. A proportional representation of each functional category among significantly Ad-affected GO gene groups whose expression significantly increased (top left), significantly increased and >3-fold (bottom left), significantly decreased (top right), or significantly decreased and >3-fold (bottom right) in response to Ad infection is shown. Significantly affected gene groups were determined by Ad infection at 6 hpi in an MO30k array experiment (one-way ANOVA, P = 0.05 [BH FDR correction]) and tested against all present genes on array for over-representation of GO functions by using EASE (P < 0.05) as described in Materials and Methods. Groups are listed proportionally as determined by gene number, with highly homologous groups of genes being merged together to help eliminate redundancy.

 
Transcriptome responses to Ad versus LPS. We next compared genes whose expression was significantly changed in the liver after Ad infection to those genes whose expression was also significantly changed after bacterial LPS exposures in a similar (mouse background) by utilizing publicly available data sets, as described in Materials and Methods (10, 43). While ca. 15% of genes were found to be significantly dysregulated by Ad at this time, an identical statistical analysis indicated that only 4.8% were significantly different between LPS- and mock-treated liver tissues at 6 hpi (using one-way ANOVA with P = 0.01), corroborating the original authors' findings.

Functional analysis of those liver genes that are responsive to both Ad and LPS indicated a significant common upregulation of defense and immune genes, accompanied by a significant common repression of mitochondrion-related and biosynthesis genes. However, in LPS-treated animals, there was a significant upregulation in the connexon channel (5.1 x 10–3), gap junction (5.7 x 10–3), and lipid transporter activity genes (1.3 x 10–2) concomitant with a downregulation in ligase activity genes (3.4 x 10–2); these gene network responses were not observed in Ad-exposed animals. Similarly, LPS-treated mice did not induce RNA regulation (RNA binding, splicing, transcription, etc.) or apoptosis, nor did they suppress lysosome, endocytic, and Wnt signaling activation gene groups, in contrast to Ad-infected animals (Table 2 to Table 6). The results suggest that Ads engage a more profound, unique innate immune response relative to LPS, a finding elaborated upon in the Discussion.

In vivo transcriptome responses after Ad infection of MyD88KO mice. Our studies in wild-type mice revealed that the TLR pathway was significantly induced (P = 2.0 x 10–4) in wild-type animals 6 hpi after Ad infection. Of particular interest, our studies revealed that MyD88, a critical TLR adaptor gene, was upregulated ~16-fold (P = 3.2 x 10–5) at 6 hpi after Ad infection compared to mock-infected counterparts (Table 7). In addition, in vitro findings have also revealed the TLR system to be significantly induced by Ad infection (17). To confirm the role of the TLR system in the Ad innate immune response in vivo, we injected MyD88–/– knockout (MyD88KO) mice with Ad vectors by using the same procedures previously described. Importantly, there was no statistical difference in hepatocyte transduction between MyD88KO mice and MyD88+ littermates as indicated by the levels of liver LacZ expression, in situ LacZ staining of hepatocytes, and the number of Ad genomes per cell (see Fig. S1 in the supplemental material). However, Ad-infected MyD88KO mice exhibited significant dysregulation of only ~4.4% (790 of 18,112 probes) of all assessable liver genes after Ad infection, whereas Ad infection of MyD88+ mice (MyD88+/+ and MyD88+/–) mice yielded at least a ~10.5% dysregulation (1,904 of 18,112 probes).


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  		TABLE 7. Microarray and qRT-PCR assessment of Ad-mediated transcript dysregulationa

 
This difference was confirmed by direct comparisons between Ad-injected MyD88+ mice (MyD88+/+ and MyD88+/–) and Ad-injected MyD88KO mice, which revealed ~6.6% (1,203 of 18,112 probes) of the total transcripts as differentially expressed between these two groups of mice (Fig. 3). Of these, 22 genes were found to be differentially regulated in a comparison between mock-infected MyD88KO and mock-infected MyD88+ (MyD88+/+ and MyD88+/–) animals. These genes were removed from the analysis, leaving ~6.5% (1,181 of 18,112) of the total transcriptome response to Ad injection (or roughly half of all Ad-induced gene expression changes) as being modulated by MyD88 (Fig. 3). Although immune response genes were still upregulated after Ad injection of MyD88KO mice, this response was quantitatively diminished, highlighted by a complete absence of induction of many TLR-related genes, relative to identical infections in MyD88+ (MyD88+/+ and MyD88+/–) mice.


Figure 3
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  		FIG. 3. Identification of MyD88-dependent Ad-dysregulated genes. Heat maps of genes whose expression was found to be significantly different between Ad-infected MyD88+/+, MyD88+/–, or MyD88KO mice (one-way ANOVA, P = 0.05 [BH FDR correction]) were hierarchically clustered by using a Pearson correlation and subjected to QT clustering (correlation = 0.9, minimum group size = 50), forming five distinct clusters, whose functions are noted to the right. The MyD88 genotype is indicated at the top (with Mock data containing n = 4 of each MyD88+/+, MyD88+/–, and MyD88–/–genotype), with each column representing the microarray results from an individual mouse.

 
The slightly lower overall level of Ad-induced gene dysregulation in MyD88+ (MyD88+/– and MyD88+/+) mice compared to MyD88+/+ mice (~11% versus 15%) suggested that MyD88 haploinsufficiency may also have an effect upon Ad-induced transcriptome responses. To assess this, one-way ANOVA comparisons between the MyD88+/+ and MyD88+/– Ad-infected groups were performed. These analyses revealed that while there were no significant differences between their respective transcriptome responses after mock infection, there was a significant difference in ~2.5% (455 of the 18,112) of the Ad responsive genes between Ad-injected MyD88+/+ and Ad-injected MyD88+/– animals. These analyses suggest that MyD88 haploinsufficiency has a significant effect on innate responses to pathogens, such as Ad. These findings were confirmed by qRT-PCR (Table 7). Recent findings of diminished antiviral innate responses being detected in mitochondrial antiviral signaling gene (MAVS) heterozygous mice further suggest that the appropriate expression of innate immune pathway genes may impact upon innate immune responses to viruses specifically and pathogens in general (39). Despite our ability to detect this phenomenon, the majority of Ad-induced transcripts are similarly dysregulated in MyD88+/– or MyD88+/+ mice, a finding also confirmed by qRT-PCR (Table 7).

Functional categorization of Ad upregulated genes that are MyD88 modulated revealed over-representation of genes involved in immune response, nucleotide binding, RNA processing, complement activation, protein transport, and response to stress, while pathway analysis suggested that Ad-induced activation of the TLR, MAPK, apoptosis, and actin cytoskeletal regulation pathways are also MyD88 regulated. Similar analysis identified oxidoreductase activity, focal adhesion, oxidative phosphorylation, several different metabolic pathways, and mitochondrion-related genes as those whose expression is downregulated by Ad in a MyD88-dependent fashion.

To further analyze the coregulation of Ad-responsive, MyD88-modulated genes, we stratified all of the mice into three separate groups (Ad-infected MyD88+, Ad-infected MyD88KO, and mock-injected mice), and performed a quality threshold (QT) cluster analysis. This analysis revealed five well-grouped gene clusters that also showed grouping during hierarchical clustering (Fig. 3). Functional analysis of these coregulated, Ad-responsive, MyD88-regulated genes identified mitochondrial, RNA regulation, cell cycle and growth, extracellular, and immune response gene groupings (Fig. 3). We performed qRT-PCR on 17 total gene targets from among the groups identified in the array analysis to confirm the validity of our transcriptome findings (see Table 7 and Table S1 in the supplemental material).

Investigation of inflammatory cytokines and chemokines and of systemic immune responses. Our next, indirect validation of the transcriptome array results encompassed investigation of several acute inflammatory, as well as adaptive, immune responses in Ad-treated animals. Measurement of plasma inflammatory cytokines and chemokines in mock- or Ad-injected animals revealed no significant differences between identically treated MyD88+/+ C57BL/6 or MyD88+/– 129/C5BL/6 mice, in contrast to previously noted findings of minor differences in certain gene transcripts at 6 hpi between these same groups (data not shown and Table 7). However, we found that at 1 hpi, the levels of CXCL1 (KC), G-CSF, IL-6, and MCP-1 (CCL11) in plasma were all significantly lower for Ad-injected MyD88KO mice compared to Ad-injected MyD88+ mice (Fig. 4). At 6 hpi, MCP-1 (CCL11), MIP-1{alpha}, IL-5, IL-6, IL-12(p40), granulocyte colony-stimulating factor, granulocyte-macrophage colony-stimulating factor, and RANTES (CCL5) were all significantly lower in Ad-injected MyD88KO mice than in Ad-injected MyD88+ mice. A significant MyD88 dependence for these induced cytokines has not yet been demonstrated for Ad infection but is reminiscent of MyD88-dependent cytokine inductions by bacterial pathogens (14, 33, 36). Using both multiplex and traditional ELISAs, we were unable to detect early significant changes in the levels of IL-1{alpha} or IL-1ß in serum, suggesting that MyD88 involvement in Ad sensing was more likely mediated through TLRs than through IL-1 receptors. By 24 hpi, most cyto/chemokines approached baseline levels (Fig. 4).


Figure 4
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  		FIG. 4. Significantly elevated plasma cytokine and chemokine concentrations in Ad-infected MyD88+ (WT) mice compared to Ad-infected MyD88KO mice. n = 28 for mock injected mice (all time points); n = 10 for MyD88KO mice (all time points). n = 12, 12, and 10 at 1, 6, and 24 hpi, respectively, for MyD88+ mice; n = 12, 12, and 6 at the same respective time points for MyD88KO mice. Error bars indicate the standard deviations. These results were obtained over the course of five independent experiments. "*" or "**" indicate time points when the levels of the respective cytokine or chemokine were significantly different (P < 0.05 or P < 0.01, respectively) between Ad-injected and mock-injected mice; "#" or "##" indicate time points when the levels of cytokine or chemokine were significantly different (P < 0.05 or P < 0.01, respectively) between Ad-injected MyD88+ and MyD88KO mice.

 
Our microarray analysis had also implicated SAA, a multifunctional acute-phase protein produced primarily by hepatocytes, as an Ad-responsive, MyD88-dependent gene (data not shown). The levels of SAA in plasma at 24 and 48 hpi were confirmed to be significantly lower in Ad-injected MyD88KO mice relative to Ad-injected MyD88+/– mice, confirming the downstream functional effects of earlier transcriptional differences (Fig. 5A).


Figure 5
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  		FIG. 5. Significantly diminished acute-phase and adaptive immune responses in Ad-infected MyD88KO mice compared to MyD88 heterozygous mice. "*" or "**" indicate time points when the levels of the respective cytokine or chemokine were significantly different (P < 0.05 or P < 0.01, respectively) between Ad-injected and mock-injected mice; "#" or "##" indicate time points when the levels of cytokine or chemokine were significantly different (P < 0.05 or P < 0.01, respectively) between Ad-injected MyD88Het (MyD88+/–) and MyD88KO mice. (A) SAA concentration after Ad infection. n = 6 and 11 at the 24- and 48-hpi time points, respectively, for mock-injected mice; n = 7 and 14 for the respective time points in Ad-infected wild-type mice. n = 6 and 10 for these time points in Ad-injected MyD88KO mice. Error bars indicate the standard deviations. (B) Anti-Ad antibody ELISA. Serum from immunized mice (14 days after Ad injection) was quantified by using an Ad-specific ELISA as described in Materials and Methods. The data are shown as absorbance values at appropriate dilutions and are representative of multiple experiments using triplicate groups. (C) Day 14 CEA-specific ELISPOT IFN-{gamma} responses of MyD88Het (MyD88+/–) and MyD88KO mice after exposure to a CEA-transducing Ad. Splenocytes derived from the respectively treated mice (14 days after Ad injection) were cultured and exposed to the respective peptides (antigen-specific CEA or nonspecific pp65) as described in Materials and Methods. The results represent the triplicate average of three mice per group ± the standard deviation.

 
Previous investigations into Ad adaptive responses indicated robust responses at 14 days postinfection (dpi), leading us to investigate the role that MyD88 has on adaptive immune responses to Ad vectors or their encoded transgenes at these time points. These studies revealed that anti-Ad immunoglobulin G antibody concentrations were significantly diminished in Ad-injected MyD88KO mice compared to Ad-injected MyD88+/– mice at 14 dpi (Fig. 5B). Also, ELISPOT assays were performed to measure the magnitude of T-cell responses to a defined antigen, human CEA transduced by an [E1-,E3-] Ad vector (44). As predicted by the transcriptome data, Ad CEA-infected MyD88+/– mice have significantly more CEA-specific IFN-{gamma}-secreting T cells in their spleens relative to identically infected MyD88KO mice (Fig. 5C). Thus, diminished antibody and T-cell responses at 14 dpi suggest that MyD88 is a crucial component in the anti-Ad adaptive immune response.

MyD88 ablation did not affect all innate immune responses that we investigated. For example, Ad injections of MyD88KO mice induced thrombocytopenia no different than that noted in Ad-infected MyD88+/– mice (data not shown). In addition, while the elaboration of several plasma chemokines and cytokines was blunted by MyD88 ablation, others, such as RANTES and MCP-1, were still noted to be elevated to levels no different than those noted in Ad-injected MyD88+ mice at 24 hpi (Fig. 4).


   DISCUSSION
Top
 Abstract
 Introduction
 Materials and Methods
 Results
 Discussion
References
 
A global view of the immune response to Ad-mediated gene transfer in vivo. Our investigation of the in vivo response to Ad vector infection revealed a massive dysregulation of the mouse liver transcriptome occurring rapidly after systemic Ad vector injections. Use of [E1-,E2-,E3-] Ad vectors did not significantly change this response, reinforcing the notion (corroborated by previous studies investigating specific immune transcripts using UV-ablated vectors) that it is the nature of the Ad5 capsid that is the primary determinant of these early responses (24). Furthermore, our use of a replication-defective Ad likely facilitated our ability to detect these responses, since infection with a wild-type virus typically results in viral usurping of most cellular functions and/or blunting of host innate responses (16, 46).

Our microarray-based results revealed that liver transduction by Ad vectors was marked by a profound upregulation of genes in the Jak/STAT, MAPK, focal adhesion, TLR, proteasome, and apoptosis-related pathways, a result we have also found in mouse embryonic fibroblast in vitro analyses (17). Interestingly, pathway analysis also revealed an Ad-induced downregulation of Wnt, calcium signaling, and actin cytoskeleton regulatory pathways in vivo. While the Wnt pathway is typically associated with cell proliferation and developmental functions, Ad-mediated repression of Wnt signaling genes (Camk2g, Ctbp2, Lrp5, Prkaca, Ppard, Skp1a, and Nfat5) parallels findings in Drosophila melanogaster of Wnt suppression after viral infection (15) and suggests that the repression of Wnt signaling may facilitate the innate immune response to viral infections in mammals. Furthermore, Ad-induced repression of Wnt pathway gene expression is attenuated in Ad-injected MyD88KO mice, suggesting that MyD88-dependent pathways modulate Wnt genes, indirectly implicating their importance in innate antiviral immunity.

GO investigations revealed Ad upregulation of RNA-related genes, including RNA binding, RNA processing, RNA metabolism, and RNA splicing gene groups (see Table 2). These results suggest that a major response to viral infection may require heightened RNA processing by the infected cell, a state that may be potentially usurped by the virus at later times during infection. Furthermore, MyD88 is required to fully induce these RNA regulatory genes, a response that could be potentially linked to the recently identified MyD88-mediated stabilization of IFN-induced chemokine mRNAs (38).

GO gene clusters identified as having their gene expression levels downregulated after Ad transduction encompassed various metabolic functions. Downregulation of metabolic pathways and reduction of cellular proliferation are likely due to the tremendous IFN-related response, precipitated by a massive in vivo infection. Transcriptional repression of lysosome-related genes and siderochrome transport genes could reflect a cellular response to limit additional caveola-related viral endocytosis (9).

The highly significant, Ad-induced repression of mitochondrion-related gene expression suggests the mitochondria to play a significant role in the response to Ad infection in vivo, a finding consistent with a global repression of mitochondrial genes noted after Ad infection in vitro (17). Indeed, our array-based analysis also revealed that the mouse homolog of the recently discovered mitochondrial antiviral signaling gene (MAVS, IPS-1, and VISA), RIKEN cDNA D430028G21, was significantly downregulated after Ad infection in vivo (determined using one-way ANOVA [P = 0.05 with a BH FDR correction]). These observations suggest that this adaptor could play a significant role in antiadenoviral responses (22, 37, 42).

Since our array-based results implicated the TLR system in the Ad-mediated innate immune response, we used MyD88KO mice to probe the role of multiple TLRs during an Ad-mediated gene transfer attempt in vivo. Statistical (one-way ANOVA) and cluster analyses (QT) revealed that several Ad responsive gene groups with dominant functional profiles (i.e., mitochondrial, cell cycle, RNA regulatory, cell adhesion, and defense gene networks) were regulated in a MyD88-dependent fashion. Ad-infected MyD88KO mice also have significant deficiencies in the Ad-induced elaboration of many Th1 and Th2 cytokines and chemokines. Despite this, the continued induction of many IFN-related genes, the weak or delayed induction of several cytokines and chemokines [MCP-1, MIP-1ß, G-CSF, IL-12(p40), and RANTES], and the continued SAA production and virus-induced thrombocytopenia in MyD88KO-Ad-infected mice indicates that not all anti-Ad innate immune responses are entirely dependent upon MyD88 in vivo. Although we have previously shown that Ad interactions with the complement system may be responsible for some of these latter responses (23), other TLR systems may play a role in these MyD88 independent antiviral responses (i.e., TRIF-dependent TLR pathways).

Finally, the generation of antigen specific T-cell responses to Ad encoded transgenes and anti-Ad antibodies at 2 weeks postinfection was also confirmed to be, at least in part, MyD88 dependent. Thus, our findings not only provide the first evidence for the importance of the MyD88 adaptor in initiating proper acute and adaptive immune responses after Ad-mediated gene transfer in vivo but also provide insights into the functional implications of numerous genes, whose expression are rapidly dysregulated after Ad vector infections in vivo.


   ACKNOWLEDGMENTS
 
We thank Greg Sempowski and Holly Dressman for their expertise and support. We thank S. Akira for granting access to the MyD88KO mice and David Schwartz and Donald Cook for supplying them.

A.A. and Z.C.H. were supported in part by a grant from the Children's Miracle Network (Durham, NC). A.A. was also supported by grants from the NIH (RO1DK-069884 and P01 CA078673) and the Osteopathic Heritage Foundation.


   FOOTNOTES
 
* Corresponding author. Mailing address: 4194 Biomedical and Physical Sciences Bldg., Michigan State University, East Lansing, MI 48823. Phone: (517) 355-6463. Fax: (517) 353-8957. E-mail: amalfit1{at}msu.edu. Back

{triangledown} Published ahead of print on 22 November 2006. Back

{dagger} Supplemental material for this article may be found at http://jvi.asm.org/. Back

{ddagger} Present address: Division of Pulmonary and Critical Care Medicine, University of Arkansas for the Medical Sciences, Little Rock, AR 72205. Back


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Journal of Virology, February 2007, p. 1796-1812, Vol. 81, No. 4
0022-538X/07/$08.00+0     doi:10.1128/JVI.01936-06
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