2009 pandemic H1N1 influenza virus elicits similar clinical course but differential host transcriptional response in mouse, macaque, and swine infection models
- Jennifer T Go†1Email author,
- Sarah E Belisle†1,
- Nicolas Tchitchek1,
- Terrence M Tumpey4,
- Wenjun Ma3,
- Juergen A Richt3,
- David Safronetz5,
- Heinz Feldmann5 and
- Michael G Katze1, 2
© Go et al.; licensee BioMed Central Ltd. 2012
Received: 21 July 2012
Accepted: 4 November 2012
Published: 15 November 2012
The 2009 pandemic H1N1 influenza virus emerged in swine and quickly became a major global health threat. In mouse, non human primate, and swine infection models, the pH1N1 virus efficiently replicates in the lung and induces pro-inflammatory host responses; however, whether similar or different cellular pathways were impacted by pH1N1 virus across independent infection models remains to be further defined. To address this we have performed a comparative transcriptomic analysis of acute phase responses to a single pH1N1 influenza virus, A/California/04/2009 (CA04), in the lung of mice, macaques and swine.
Despite similarities in the clinical course, we observed differences in inflammatory molecules elicited, and the kinetics of their gene expression changes across all three species. We found genes associated with the retinoid X receptor (RXR) signaling pathway known to control pro-inflammatory and metabolic processes that were differentially regulated during infection in each species, though the heterodimeric RXR partner, pathway associated signaling molecules, and gene expression patterns varied among the three species.
By comparing transcriptional changes in the context of clinical and virological measures, we identified differences in the host transcriptional response to pH1N1 virus across independent models of acute infection. Antiviral resistance and the emergence of new influenza viruses have placed more focus on developing drugs that target the immune system. Underlying overt clinical disease are molecular events that suggest therapeutic targets identified in one host may not be appropriate in another.
KeywordsPandemic, Influenza virus Genomics Inflammation Lipid metabolism Glucocorticoid receptor LXR/RXR
In 2009 a novel H1N1 influenza virus emerged and rapidly spread worldwide. Clinical disease among affected individuals ranged from mild infection to more severe pneumonia associated with acute respiratory distress syndrome[2, 3]. A recent study estimates that over 284,000 deaths occurred globally within the first year of the pandemic. Highly pathogenic influenza virus infection is characterized by a powerful and potentially destructive immune response[5, 6]. Enhanced cytokine production has been observed in cynomolgus macaques infected with 2009 pandemic H1N1 influenza (pH1N1) virus compared to former seasonal H1N1 influenza virus[7, 8], though pH1N1 virus does not precipitate a ‘cytokine storm’ seen with highly pathogenic influenza viruses (reviewed in).
The pH1N1 virus has been intensively studied in mice, macaques and swine, among other animal models of influenza infection. In the absence of prior host adaptation, inoculation of mice with pH1N1 virus generally results in nonlethal infection that is resolved around day 8 post-infection. There is low morbidity (<10% total body weight) and moderate bronchiolitis observed in the lung, despite efficient viral replication throughout infection. Alteration of viral PB1-F2 and NS1 proteins marginally impacted viral pathogenesis[11, 12] and did not affect viral control of innate immune responses during pH1N1 infection in mice. Cynomolgus macaques infected with pH1N1 virus have shown diffuse alveolar damage, more severe pulmonary lesions, and efficient replication of the virus in the lungs compared to seasonal H1N1 influenza virus[7, 8, 14]. Swine infected with pH1N1 virus develop mild respiratory disease characterized by coughing, sneezing, and acute bronchiolitis, with active replication in the lung. Recent studies have shown that the Eurasian-origin NA and M genes of pH1N1 virus are important for transmission among swine and can also confer transmission in ferrets and guinea pigs.
Differential gene expression induced by pH1N1 virus compared to concurrent human or swine influenza viruses has been investigated in mice, macaques and swine by microarray. In mice, the transcriptional program suggests that regulation of lipid metabolism genes plays a protective role, differentiating non-lethal wild-type and lethal mouse-adapted pH1N1 virus infection. Macaques infected with clinical pH1N1 isolates from Mexico showed increased expression of NFκB signaling molecule genes, cytokine and chemokine genes, as well as antigen presentation pathway genes compared to seasonal H1N1 influenza virus. In a separate study, macaques infected with pH1N1 virus also showed enhanced expression of antiviral and interferon (IFN)-regulated genes, such as IFIT2 and ISG15, compared to seasonal influenza virus. Transcriptomic analysis of swine host responses to pH1N1 virus revealed pronounced inflammatory response gene expression accompanied by increased expression of PPARG-associated lipid metabolism genes compared to the 1918-like classical swine influenza A/swine/Iowa/15/1930 (H1N1) virus. In contrast to in vivo models, transcriptomic profiling of infected type I alveolar epithelial cells showed comparable IFN-mediated antiviral and cytokine responses to pH1N1 and seasonal H1N1 influenza viruses.
Performing a comparative transcriptomic analysis across multiple model systems has revealed conserved responses during influenza infection, as demonstrated in a recent study by McDermott et al. that used multivariate modeling approaches to identify similarities in transcriptional responses to H5N1 virus in the lungs of mice and macaques, and human lung epithelial cells. To gain further insight into host responses to pH1N1 virus during acute infection, we examined lung gene expression from mice, macaques and swine infected with 2009 pandemic H1N1 influenza A/California/04/2009 (CA04) virus and compared the transcriptional response in each host. Our goals were to identify shared or differential gene expression patterns across species and to infer potential regulators mediating these changes during acute infection. Even though CA04 virus elicited a similar clinical outcome in each of these species, we found significant differences in the expression of inflammatory and lipid metabolism genes, likely impacted by nuclear hormone receptor signaling complexes including LXR/RXR that is known to regulate cholesterol homeostasis during inflammation. A greater understanding of the differences in acute responses from different hosts is important because it will aid in the design of tailored immunotherapies to influenza virus.
Results and discussion
We have previously shown 2009 pandemic H1N1 influenza A/California/04/2009 (CA04) virus efficiently replicates in the lung of mice and swine, inducing expression of pro-inflammatory genes and causing acute bronchiolitis[10, 15]. Infection of macaques with CA04 virus is marked by productive virus replication in the respiratory tract and moderately severe clinical symptoms peaking on day 6 post-infection (p.i.), with resolution by day 14 p.i. (aSafronetz and Feldmann, personal communications). Here, we examined mouse, macaque and swine responses to CA04 virus by microarray to further investigate transcriptional changes during acute pH1N1 infection. Mice were inoculated with 106 PFU virus and three animals were euthanized on days 1, 3, and 5 p.i. These three time points were selected for studying acute phase responses in the mouse. Cynomolgus macaques were inoculated with a total infectious dose of 7×106 TCID50 and two animals were euthanized on days 1 and 6 p.i. Day 1 macaque lung samples were collected for studying early host immune responses corresponding to early disease progression and day 6 lung samples were chosen for maximum pathology. Swine were inoculated intratracheally with 106 TCID50/animal and five animals were euthanized on days 3, 5 and 7 p.i. Swine lung samples were collected on days 3 and 5 to examine acute phase responses corresponding to maximal virus shedding and day 7 lung samples were chosen for recovery phase. Due to differences in the timing and kinetics of acute phase responses in each animal model, we examined gene expression changes within each host and then compared across species.
Viral mRNA expression was measured in each sample to verify CA04 infection prior to microarray analysis (Additional file1: Figure S1). In general, viral mRNA levels correlated with viral titers measured in the lung for each species. In mice, viral mRNA expression averaged between 3.6 – 4.5 log10RQ on day 1 to 5 p.i., corresponding to average lung virus titers between 5.2 – 6.1 log10 PFU/g of tissue on day 1 to 5 p.i.. In macaques, average viral mRNA expression was highest on day 1 and decreased by day 6 p.i., which was also reflected in the virus lung titers that reached around 107 and 104 TCID50(log10)/g on days 1 and 6 p.i., respectively (aSafronetz and Feldmann, personal communications). In swine, viral mRNA levels paralleled virus shedding (from days 3 to 5 p.i.), and the decreased viral mRNA expression observed on day 7 p.i. was concomitant with resolution of infection in these animals. Quantitation of viral mRNAs in infected samples used for microarray allowed us to correlate host gene expression changes with relative infection levels.
Gene expression analysis of mouse, macaque and swine lung infected with CA04 virus
Gene expression was profiled using species-specific commercial oligonucleotide arrays. Each array contained a different set of transcripts, with greater redundancy for macaque and swine compared to mouse. There are a total of 43,379 probes represented on the Mouse Whole Genome Gene Expression Microarray, a total of 20,217 probes represented on the Rhesus Macaque Gene Expression Microarray, and a total of 47,813 probes represented on the Porcine Gene Expression Microarray V1. Due to differences in gene annotation across species, differential gene expression analysis was performed for the transcripts associated with 4118 unique genes common to all three arrays using Ensembl gene identifiers associated with each array probe. This strategy accounted for differences in the number of probes with annotated transcripts and reduced the likelihood of falsely identifying differences in gene expression due to gaps in annotation or gene representation. Within this set of genes, there was a large representation of genes associated with Cell Death, Cancer, and Cellular Growth and Proliferation functional categories, as well as genes associated with Glucocorticoid Receptor Signaling, IL-12 Signaling and Production in Macrophages, and Acute Phase Response Signaling canonical pathways.
We investigated the functional classes of underrepresented gene sets present on each specie-specific array, as the focus on unique genes common to all three arrays may introduce bias in our analyses. There was enrichment of Molecular Mechanisms of Cancer, Axonal Guidance Signaling and G-Protein Coupled Receptor Signaling canonical pathways, and Gene Expression functional annotations related to transcription, organismal death and abnormal morphology of cells. Data integration and interpretation with a cross-species transcriptomic analysis brings its own challenges and although complete physical maps have been developed for mouse, macaque and swine genomes to support genome sequencing and comparative genomics, functional annotation to date is mostly based on human, mouse, and rat literature. As annotation improves, particularly for less characterized species such as swine, we will likely be able to more fully understand host responses to influenza virus using microarray and next-generation sequencing technologies.
Canonical Pathways enriched in mouse, macaque and swine CA04 infection models
B-H p- value
Acute Phase Response Signaling
Hepatic Fibrosis / Hepatic Stellate Cell Activation
IL-12 Signaling and Production in Macrophages
Type I Diabetes Mellitus Signaling
Clathrin-mediated Endocytosis Signaling
Dendritic Cell Maturation
Hematopoiesis from Pluripotent Stem Cells
Communication between Innate and Adaptive Immune Cells
Type I Diabetes Mellitus Signaling
Altered T Cell and B Cell Signaling in Rheumatoid Arthritis
Neuroprotective Role of THOP1 in Alzheimer's Disease
Hematopoiesis from Multipotent Stem Cells
Primary Immunodeficiency Signaling
Antigen Presentation Pathway
Communication between Innate and Adaptive Immune Cells
Hepatic Fibrosis / Hepatic Stellate Cell Activation
Type I Diabetes Mellitus Signaling
Primary Immunodeficiency Signaling
Role of Pattern Recognition Receptors in Recognition of Bacteria and Viruses
Differential inflammatory response gene expression distinguishes CA04 virus infection in mice, macaques and swine
Several models show an association between enhanced immune cell infiltrate and severe lung immunopathology. Excessive macrophage and neutrophils are observed in the lung of mice following H5N1 and 1918 infection, and in pregnant animals infected with pH1N1 virus. In mice, macaques, and swine infected with CA04 virus, leukocyte and lymphocyte responses were evident in all three species based on immune cell-specific gene expression changes detected in the lung, though the gene expression patterns varied across species. For example, there was greater upregulation of macrophage factor, SIGLEC1, observed in mice (days 3 and 5 p.i.) and macaques (days 1 and 6 p.i.) compared to swine on day 3 p.i. Enhanced expression of leukocyte associated immunoglobulin-like receptor 1, LAIR1, was observed in macaques and swine infected with CA04 virus, as compared to infected mice. In contrast to mice and macaques, swine exhibited strong expression of neutrophil factor, PLUNC, which we noted was downregulated during infection in the other species (Figure3). These results reflect a rapid inflammatory shift in the lungs of mice, macaques, and swine during CA04 infection that involves different immune cell responses, but unlike H5N1 and 1918 viruses, these immune cell responses do not cause immunopathology. A cross-species transcriptomic comparison of the host response to H5N1 and 1918 viruses would be necessary to further explore the potential role of specific immune cell responses to influenza pathogenesis.
CA04 infection impacts glucocorticoid receptor signaling differently across species
We further sought to evaluate CA04 virus-induced host responses that were unique to each species by investigating non-overlapping gene sets shown in Figure1. The most significant canonical pathways represented in each of these three gene sets are summarized in Additional file5: Figure S2. The 207 DE gene set unique to swine was enriched for genes associated with Role of JAK1 and JAK3 in γc Cytokine Signaling canonical pathway that included JAK3 and STAT5A genes. Differential regulation of JAK3/STAT5 signaling in CA04 virus-infected swine may impact the homeostasis and activation of peripheral T lymphocytes. For example, STAT5A/B defects results in enhanced apoptosis of T lymphocytes in mice. Differential expression of JAK3 and STAT5A genes in swine during CA04 infection may also suggest a balance between inflammatory host defenses and glucocorticoid receptor (GR)-mediated cellular growth and survival. Closer inspection of each species inflammatory response DE gene set showed noteworthy representation of GR Signaling related genes, such as A2M, FGG, HSPA5, IL4, and MAPK13 genes in mice, FKBP4, JAK1, NCOA2 (also known as GRIP-1), and NRAS genes in macaques, and HSPA4 (also known as HSP70) in addition to JAK3, and STAT5A genes in swine. Alpha-2-macroglobulin (A2M) identified by mass spectrometry in human saliva was found to exhibit antiviral activity against pH1N1 virus.
Upstream Regulator analysis of mouse, macaque and swine CA04 infection models
p-value of overlap
Regulation z-score (dpi)
Predicted regulator status
2.747 (3); 2.124 (5)
ligand-dependent nuclear receptor
2.463 (3); 2.787 (5)
2.994 (3); 2.532 (5)
-3.166 (1); -3.743 (3); -3.035 (5)
-3.395 (1, 6)
4.284 (1, 6)
2.096 (1, 6)
3.232 (1); 3.57 (6)
2.823 (3); 3.157 (5)
ligand-dependent nuclear receptor
We have identified enriched TFs and targets from our dataset; however, the analyses reported here coordinately investigate the expression and sequence-specific TF DNA binding sites of the DE genes, and it does not investigate the combinatorial effects of multiple factors binding to the genomic regions or molecular determinants of transcriptional responses such as receptor ligation or histone acetylation that can collectively contribute to distinct transcriptional profiles. In addition, restriction of the search space to -450 to +50 nucleotides relative to the transcription start site (TSS) can preclude the identification of more distal TFs effecting expression, which may explain why GR is not identified as a predicted regulator (Table2), although many target genes are differentially expressed in the data set (Figure4). Future investigations of GR-mediated host responses to CA04 virus would need to take into account these considerations as well as the physical interactions between GR and STAT5, for example, and chromatin modifications known to occur to provide a more comprehensive view of transcriptional regulation during CA04 infection across different species.
CA04 virus significantly alters expression of genes involved in cholesterol homeostasis in mice and swine, and vitamin D receptor genes in macaques
In human macrophages and respiratory epithelial cells, vitamin D plays a role in innate immune responses by increasing TLR coreceptor, CD14, and stimulating expression of antimicrobial peptides, such as cathelicidin during respiratory infection[35, 36]. Vitamin D has also been investigated in epidemiological studies of reduced risk of influenza infection in different human patient cohorts. In contrast to mice and swine, IL2 gene expression was more strongly induced in infected macaques on both days. This may indicate macaques mount enhanced Th1 cell responses during CA04 virus infection compared to mice and swine (Figure6, lower panel). As shown in Figure6, we also found increased, expression of VDR/RXR Activation associated genes such as CYP24A1, encoding a hydroxylase involved in vitamin D catabolism, RXRG, and HR, a transcriptional corepressor of vitamin D receptor. Intriguingly, antimicrobial peptide, CAMP (also known as LL-37), was found to be downregulated in mouse and macaques infected with CA04 virus, and the decreased expression of CAMP in CA04 virus-infected macaques may be explained in part by the increased expression of APOA1 (Figure6, top panel; Additional file7: Table S4), which has been shown to bind and inhibit CAMP. While there are several genes associated with VDR/RXR Activation that are differentially expressed in macaques infected with CA04 virus, vitamin D control of innate immune responses does not appear to play a major role during infection.
We have shown differences in transcriptional responses to a single 2009 pandemic H1N1 influenza virus in three independent animal models. Our goal in performing a cross-species transcriptomic comparison was to identify shared and differential gene expression patterns to better evaluate the acute phase in different hosts. We found significant differences in expression of inflammatory response and lipid metabolism genes, which were likely impacted by glucocorticoid receptor and retinoid X receptor signaling complexes, such as LXR/RXR that is known to regulate cholesterol homeostasis during inflammation. The timing and magnitude of the host response is critical in determining disease outcome and the success of immunomodulatory therapy (reviewed in). For example, corticosteroid therapy in patients infected with pH1N1 virus was found to increase the risk of developing severe disease. Metagenomic studies using clinical samples will be necessary to further understand the host response to pH1N1 virus and other etiological agents within the human population, as recently explored by Greninger et al. using a pan-viral microarray and deep sequencing to characterize pH1N1 virus from human nasopharyngeal aspirates. Despite similar clinical outcomes, differences in the host transcriptional response could suggest that therapeutic targets identified in one host may not be relevant in another.
The gene expression differences elicited by CA04 virus within these three independent animal models demonstrate the disparate nature of the host response. There are particular advantages and disadvantages in modeling disease and immune responses to influenza infection in mice, macaques and swine. Mice, though largely resistant to infection with most human virus isolates, provide a tractable system for genetic manipulation to study key genes in the regulation of host responses to influenza virus. Non human primates are thought to more closely model the human response to influenza virus given their genetic and physiological similarities, while swine serve as a natural reservoir for influenza A viruses and have been linked to the emergence of some of the most notable influenza pandemics in recent history, including the H1N1 pandemic in 2009. There has been interest in determining the extent to which influenza viruses are able to cause disease in swine, particularly with regard to 1918 pandemic influenza virus that does not cause severe disease in swine like it does in mice and macaques. This makes swine a unique model for future systems biology analyses that may help to uncover host responses contributing toward the emergence or maintenance of novel influenza viruses with pandemic potential.
2009 pandemic H1N1 influenza virus A/California/04/2009 (CA04) was isolated from a nasal swab of a <18 y.o. boy from San Diego, California .
Female BALB/c mice (Mus musculus), 6 to 8-week-old, were intranasally inoculated with 106 plaque-forming units of CA04 virus in 50 μl (n = 9) or inoculated with 50 μl of phosphate-buffered saline (control; n = 8). Whole lungs were harvested from infected animals at days 1, 3 and 5 post-inoculation (n = 3 per time point) and from time-matched control animals (n = 3 on days 1 and 3 and n = 2 on day 5) for extraction of total RNA as previously described. Crossbred pigs (Sus Scrofa), 4-week-old, were inoculated intratracheally with either 106 TCID50/pig egg-derived CA04 virus (n = 15) or mock inoculated with non-infectious cell culture supernatant (control; n = 15) as described elsewhere. Animals were euthanized on 3, 5, and 7 dpi (n = 5 per time point). Cynomolgus macaques (Macaca fascicularis), 4 to 15 y.o., weighing 3.0-8.7 kg, were infected with CA04 virus (n = 6) under anesthesia through a combination of intratracheal (4 ml), intranasal (0.5 ml per nostril), conjunctival (0.5 ml per eyelid) and oral (1 ml) routes with a suspension containing 1×106 TCID50/ml (total infectious dose was 7×106 TCID50) as described elsewhere. Animals were euthanized on 1 and 6 dpi (n = 2 per time point).
Mouse infection experiments were completed at the CDC under the guidance of the CDC’s Institutional Animal Care and Use Committee in an Association for Assessment and Accreditation of Laboratory Animal Care International-(AAALAC)-accredited animal facility. Swine infection experiments were completed at the Central States Research Center (CSRC), Inc BSL-3 facility (Oakland, NE) in compliance with the CSRC’s Institutional Animal Care and Use Committee studies. Macaque infection experiments were approved by the RML Institutional Animal Care and Use Committee (IACUC), and performed following the guidelines of AAALAC by certified staff in an AALAC approved facility.
Real-time PCR was performed using a Custom TaqMan Gene Expression Assay (Applied Biosystems) designed for CA04 HA sequence (forward primer: AGCTCAGTGTCATCATTTGAAAGGT; reverse primer: GGACATGCTGCCGTTACAC; reporter: TTGGGCCATGAACTTG). cDNAs were generated using a QuantiTect reverse transcription kit (Qiagen). Samples from individual animals were run in quadruplicate. rRNA (18S) was used to normalize quantification of the target and quantification of normalized target was performed using the 2-ΔΔCt calculation. HA expression was quantified relative to expression of an endogenous control for each specie sample set that did not change with infection and expression in an uninfected lung sample. The following TaqMan Gene Expression Assays (Applied Biosystems) were used: Mfap1a (Assay ID Mm00849648_gH) served as the endogenous control for mouse samples, B2M (Assay ID Rh02847368_m1) served as the endogenous control for macaque samples, and RPS6 (Assay ID Ss03374061_g1) served as the endogenous control for swine samples. Average log10RQ expression is shown for each species at each time point ± standard deviation.
Total RNA isolated from lung tissue from individual animals on each day of euthanasia was used for oligonucleotide array experiments. For swine and mice, RNA isolated from mock-infected animals at each time point served as an uninfected reference. For cynomolgus macaques, a pool of RNA from the lungs of eight uninfected animals matched for age and sex was used as the uninfected reference. NanoDrop ND-1000 and Agilent 2100 Bioanalyzer instrumentation was used to determine the concentration and quality of all RNA samples. A total of 1500 ng Cy3-labeled probe was used for microarray slide hybridizations, thereby normalizing for the input RNA amount. Mouse, macaque and swine samples were measured with 4×44K commercial arrays from Agilent Technologies designed for each species, Mouse Whole Genome Gene Expression Microarray (G4122F), Rhesus Macaque Gene Expression Microarray (G2519F; Design ID: V2: 026806), and Porcine Gene Expression Microarray V1 (G2519F; Design ID: V1: 020109).
Data normalization and accessibility
The background corrected data from the Feature Extraction output were normalized across replicates within each species using central tendency normalization (75% percentile, target 1000) within Genedata (Analyst 7.0). All primary microarray data have been deposited in NCBI's Gene Expression Omnibus (GEO) under GEO Series (GSE) accession number GSE40092. The primary microarray data are also available at the University of Washington’s Public Microarray Data Download site (http://expression.microslu.washington.edu).
Statistical analysis of microarray data
For mice and swine, Student’s t-test was performed on background corrected, normalized log-intensity data comparing CA04 virus-infected lung gene expression to time- and species-matched mock-infected lung gene expression at each time point (Unadjusted P-value < 0.05). For cynomologus macaques, Student’s t-test was performed on background corrected, normalized log-intensity data comparing CA04 virus-infected lung gene expression to an uninfected lung reference pool (n = 8) matched for age and sex at each time point (Unadjusted P-value < 0.05). Differentially expressed genes were then filtered to include only genes that changed at least two-fold compared to mock on at least one day within each species. Statistical comparison using Student’s t-test with the Benjamini-Hochberg multiple testing correction (Adjusted P-value < 0.05) and no fold-change parameter resulted in very few macaque genes that were statistically significant, though there was considerable overlap in the DE gene sets identified from the two tests for each species.
Functional analysis of differential gene expression data
Functional analysis of DE genes was performed using Ingenuity Pathways Analysis (Ingenuity Systems), which analyzes the experimental dataset in the context of known biological response and regulatory networks in the Ingenuity Pathways Knowledge Base (IPKB). Ensembl human gene annotations were used for functional analyses of macaque and swine gene sets (Additional file8: Table S5). The right-tailed Fisher’s Exact test was used to determine the statistical significance of each biological function assigned to the gene expression data, and the Benjamini-Hochberg (B-H) Multiple Testing Correction was applied to p-values to reduce the likelihood that statistical associations were due to random chance.
Transcription factor enrichment analysis
Upstream Regulator Analysis in IPA incorporates expression of downstream target genes from the experimental dataset and compiled knowledge of reported relationships between regulators and their known target genes within IPKB. This analytical tool was used to predict upstream regulators and infer their activation state by calculating a z-score that determines whether gene expression changes for known targets of each regulator (z > 2, regulator predicted as “activated” and z < -2, regulator predicted as “inhibited”. Transcription factor binding motif enrichment was performed with PSCAN using Position Weight Matrices (PWM) for human and mouse species obtained from JASPAR CORE database and the promoter of each target gene defined from -450 to +50 nucleotides relative to the TSS. PSCAN computes a z-test P-value for each regulator, which is an assessment of whether there is significant representation (P < 0.001) of the regulator DNA-binding motif in promoters of the queried genes.
Transcription factor DNA-binding promoter analysis
Transcription factor (TF) DNA-binding analysis was performed using STAT1 (human MA0137.2) and HNF1A (mouse MA153.1) PWMs obtained from the JASPAR CORE database and STAT1 (mouse M00224) and HNF1A (human M00206) PWMs obtained from the TRANSFAC database 7.0. Promoters sequences of genes analyzed were defined from -450 to +50 nucleotides relative to the TSS and were retrieved from the Ensembl database (release 66). Macaca mulatta sequences were used instead of Macaca fascicularis sequences for the TF DNA-binding analysis. P-values associated for each target gene were calculated using the TFM-Pvalue analytical tool described in and distribution of nucleotides amongst the promoter sequences have been taken into account for background correction. Significant target genes have been identified as having a P-value < 10-4.
aSafronetz and Feldmann, personal communications Laboratory of Virology, Division of Intramural Research, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Rocky Mountain Laboratories, Hamilton, MT 59840, USA.
Ingenuity Pathways Analysis
Ingenuity Pathway Knowledge Database
Retinoid X receptor
Vitamin D receptor
2009 pandemic H1N1 influenza virus
Influenza A/California/04/2009 virus
Transcription Start Site
Position Weight Matrix.
We thank Elizabeth Rosenzweig, Sara Kelly, and Jean Chang for microarray technical support. We are grateful to Marcus Korth, Lynn Law, and Janine Bryan for their critical review of the manuscript. This work was supported by federal funds from the National Institute of Allergy and Infectious Diseases, National Institutes of Health, Department of Health and Human Services, under contract HHSN272200800060C (M.G.K.) and contract HHSN266200700005C (J.A.R.). We thank the staff of the Rocky Mountain Veterinary Branch (Division of Intramural Research (DIR), NIAID, NIH) for their assistance with the nonhuman primate work. In addition, we thank Friederike Feldmann (DIR, NIAID, NIH) for technical assistance in biocontainment. The study was partially funded by the Intramural research Program of the NIAID, NIH. We thank Deborah Clouser, Darlene Sheffer, Xi Li, Qinfang Liu, and Chuanling Qiao of Kansas State University for their assistance with the pig work.
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