Host gene expression profiling in influenza A virus-infected lung epithelial (A549) cells: a comparative analysis between highly pathogenic and modified H5N1 viruses

Avian Influenza A (H5N1) virus, A/Chicken/India/WB-NIV2664/2008 (WB-NIV2664) isolated from West Bengal (India) outbreak in 2008 [15] and reverse genetics modified H5N1 vaccine virus A/India/NIV/2006(H5N1)-PR8-IBCDC-RG7 (IBCDC-RG7) were used in this study. The vaccine virus was constructed using modified hemagglutinin (HA) (deleting multiple basic amino acids at the cleavage site of HA) and neuraminidase (NA) of A/Chicken/India/NIV33487/06 (H5N1) in the background of A/PR/8/34 (H1N1) using reverse genetics technology at the Molecular Virology and Vaccine Laboratory, Influenza Division, Centers for Disease Control and Prevention, Atlanta. World Health Organization (WHO) has identified this strain as a H5N1 vaccine virus [16].

Human lung carcinoma (A549) cells were maintained in Dulbecco's modified Eagle's tissue culture medium (Invitrogen Life Technologies, Carlsbad, CA, USA) containing 10% fetal calf serum, 100units/ml penicillin, 100 units/ml streptomycin in tissue culture flasks (Corning, USA) at 37°C in a CO₂ incubator.

Monolayers of A549 cells at a concentration of 3 × 10⁶ cells/ml were infected with the viruses at a multiplicity of infection (MOI) of 3. After 1 hour the inoculum was removed; the cells were washed twice with phosphate buffer saline (PBS) and supplemented with growth media. For each virus four different sets of tissue culture flask were infected and harvested at four different time points. Mock infected cells at each time point served as controls. Infection of H5N1 viruses was performed in BSL-3+ facility.

Total RNA was extracted from the control and infected cells at 4, 8, 16 and 24 hpi using Trizol reagent (Invitrogen Life Technologies, Carlsbad, CA, USA) and purified by the RNeasy kit (Qiagen, Germany) following standard methodology. Amplification of RNA and indirect labeling of Cy-dye was done by Amino Allyl MessageAmp II aRNA amplification kit (Ambion, Austin, TX, USA) using manufacturer's instruction. One hundred nanograms of RNA from control and infected cells were used for the experiments. The RNA was reverse transcribed and amplified according to the manufacturer's protocol. The purified amino allyl aRNA was labeled with Cy3 and Cy5 (Amersham Biosciences, USA) for control and experimental samples respectively. Purified samples were lyophilized, resuspended in hybridization buffer (Pronto Universal Hybridization kit, Corning, USA) and hybridized on the Discover human chip (Arrayit Corporation, Sunnyvale, CA). Hybridization was carried out in a Hybstation (Genomic Solutions, Ann Arbor, MI) and the conditions used were 55°C for 6 h, 50°C for 6 h, and 42°C for 6 h. Scanning was performed at 5-mm resolutions with the Scan array express (PerkinElmer, Waltham, MI). Grid alignment was done using gene annotation files and raw data were extracted into MS EXCEL.

Data was analyzed using GENOWIZ Microarray and Pathway analysis tool (Ocimum Biosolutions, Hyderabad, India). Replicated values for genes were merged and median values of the expression ratios were considered for the dataset (2 slides per time point were used). Empty spots were removed by filtering. Dye bias was dealt with by applying loess normalization. Log transformation (log2) was done to stabilize the variation in dataset and median centering was performed to bring down data distribution of dataset close to zero. In order to detect highly expressed genes, fold change analysis was done. Genes with 1.5 folds up/down-regulation were considered as differentially expressed at a p-value < 0.05, Student's t-test. Functional classification of the genes was performed using gene ontology and pathway analysis.

The differential expression data was validated by quantitative RT-PCR. One hundred nanograms of total RNA from control and infected A549 cells were used for quantitative RT-PCR analysis. Reaction was performed using the QuantiTect SYBR Green RT-PCR kit (Qiagen, Germany) according to the manufacturer's instructions. Reaction efficiency was calculated by using serial 10-fold dilutions of the housekeeping gene- β-actin and sample genes. Reactions were carried out on an ABI 7300 real-time PCR system (Applied Biosystems, Foster City, CA, USA) and the thermal profile used was Stage 1: 50°C for 30 min; Stage 2: 95°C for 15 min; Stage 3: 94°C for 15 sec, 55°C for 30 sec; and 72°C for 30 sec, repeated for 30 cycles. Melting curve analysis was performed to verify product specificity. Reactions were performed in triplicates. All quantitations (threshold cycle [CT] values) were normalized to that of β-Actin to generate ΔCT, and the difference between the ΔCT value of the sample and that of the reference (uninfected sample) was calculated as ΔΔCT. The relative level of gene expression was expressed as 2^-ΔΔCT. Primer sequences for the genes of interest were designed using Primer Express 2.0 software (Applied Biosystems, Foster City, CA, USA). The primer sequences used in this study are as follows: JUN FP 5' TCGACATGGAGTCCCAGGA 3'; JUN RP 5' GGCGATTCTCTCCAGCTTCC 3'; STAT1 FP 5' CCATCCTTTGGTACAACATGC 3'; STAT1 RP 5'TGCACATGGTGGAGTCAGG 3'; CXCL10 FP 5' TTCAAGGAGTACCTCTCTCTAG 3'; CXCL10 RP 5' CTGGATTCAGACATCTCTTCTC 3'; CCL5 FP 5'TACCATGAAGGTCTCCGC 3'; CCL5 RP 5'GACAAAGACGACTGCTGG 3'; β-ACTIN FP 5' CATGAAGTGTGACGTGGACATCC 3'; β- ACTIN RP 5' GCTGATCCACATCTGGAAGG 3'; BCL2 FP 5' GATGTCCAGCCAGCTGCACCTG 3'; BCL2 RP 5' CACAAAGGCATCCCAGCCTCC 3'.

The number of differentially expressed genes at different time-points after WB-NIV2664 infection is given in Table 1. Gene ontology analysis revealed that the genes involved in immune responses, translation and apoptosis were mostly up-regulated at all the time-points whereas genes involved in cell cycle and transcription were down-regulated. However, it was found that the number of genes was quantitatively different between the different time-points. A list of significantly up and down regulated genes at different post infection time points has been shown in Table S1 (Additional file 1). The 24 hpi time point showed maximum number of differentially expressed genes.

Cluster analysis of the differentially expressed genes was carried out using GENOWIZ software. K-means clustering and hierarchical clustering methods resulted in identification of 5 distinct patterns of gene expression at different time-points (Figure 1). A total of 189 genes were found common between all the time points. Expression pattern of most of the apoptotic genes was similar and formed a single cluster (Cluster 2 Figure 1). Apoptotic genes BAX, BAK1, TRADD and CASP1 were observed to be significantly up-regulated at 16 and 24 hpi but not at 4 and 8 hpi. Signaling molecules STAT1, IL15RA, GNBP1 were found to be up-regulated at all the time-points and showed a gradual increasing trend from 4 hpi to 24 hpi. Genes coding for ribosomal proteins and IRF1 (Interferon regulatory factor 1) were up-regulated at 4, 8 and 16 hpi but down-regulated at 24 hpi. Genes involved in cell cycle CDK4, CDK5, Cyclin E1, CKN2B, CDKN2D were down-regulated at all time-points post infection. Cytokines IL1, IL2-α and chemokines CXCL10, CCL5 were specifically up-regulated at 16 hpi.

The differentially expressed genes in cells infected with RG modified H5N1 (IBCDC-RG7) virus were involved in similar biological processes (GO analysis) as in response to H5N1 (WB-NIV2664) virus (Table 2). However, there was significant quantitative difference between the expression profiles of the two virus infections. Table S2 (Additional file 2) shows the list of significantly up and down regulated genes at different time point post infection. Contrastingly, genes coding for ribosomal proteins and other proteins involved in protein translation were down-regulated at early stages of (4 hpi) IBCDC-RG7 infection as compared to WB-NIV2664. Cell cycle regulator genes such as CDK5 and Cyclin B1 showed up-regulation only at 4 hpi but not at 8, 16 and 24 hpi. Apoptotic genes were significantly down-regulated at 16 hpi and 24 hpi. Genes involved in immune response such as IRF1, IL15R-α, small inducible cytokine subfamily B (Cys-X-Cys), IL1-β, MHC, class1c were down-regulated at 4 hpi and 16 hpi (Figure 2).

Gene expression data was compared separately at each post infection time points between the two virus infections (Figure 3). Significantly higher numbers of differentially expressed genes were observed at 16 hpi (Figure 3). A total of 44 genes were found to be common between the two virus infections at 16 hpi. Out of them 8 genes were up-regulated and 8 genes were down-regulated in response to both the virus infections. However, 28 genes were found to have differential expression pattern and were mainly involved in Cytokine-cytokine receptor interaction, Toll like-receptor mediated signaling and p53 signaling pathway [Figure S1 (Additional file 3) and Figure S2 (Additional file 4)]. Cytokines - CXCL10 and RANTES were up-regulated by 4 and 2 folds respectively in WB-NIV2664 infected cells but were down-regulated by 3 and 2 folds respectively in response to infection with IBCDC-RG7 (Table 3). Transcription factors v-JUN and NF-κB were up-regulated in response to WB-NIV2664 infection but were down-regulated in infection with recombinant RG modified H5N1 (Table 3). STAT1, which plays a significant role in JAK-STAT signaling pathway and has been reported to be involved in host immune response to virus infections, was found to be differentially expressing between the two virus infections in our study. Surprisingly, cell cycle regulator Cyclin B1 was down-regulated in WB-NIV2664 infected cells but up-regulated in IBCDC-RG7 infected cells. Pathway analysis using KEGG [Kyoto Encyclopedia of genes and genome http://​www.​genome.​jp/​kegg/​] tool reveled that these differences in expression profile between cells infected with two virus strains could manifest into differential cell cycle progression and immune response. Expression of selected genes was validated using Real-time PCR, which correlated with the microarray results (Figure 4).