Abstract
Respiratory syncytial virus (RSV) is a major cause of lower respiratory tract infections in children, elderly, and immunocompromised individuals. Despite of advances in diagnosis and treatment, biomarkers of RSV infection are still unclear. To understand the host response and propose signatures of RSV infection, previous studies evaluated the transcriptional profile of the human bronchial epithelial cell line—BEAS-2B—infected with different strains of this virus. However, the evolution of statistical methods and functional analysis together with the large amount of expression data provide opportunities to uncover novel biomarkers of inflammation and infections. In view of those facts publicly available microarray datasets from RSV-infected BEAS-2B cells were analyzed with linear model-based statistics and the platform for functional analysis InnateDB. The results from those analyses argue for the reevaluation of previously reported transcription patterns and biological pathways in BEAS-2B cell lines infected with RSV. Importantly, this study revealed a biosignature constituted by genes such as ABCC4, ARMC8, BCLAF1, EZH1, FAM118A, FAM208B, FUS, HSPH1, KAZN, MAP3K2, N6AMT1, PRMT2, S100PBP, SERPINA1, TLK2, ZNF322, and ZNF337 which should be considered in the development of new molecular diagnosis tools.
1. Introduction
Respiratory syncytial virus (RSV) is a major etiologic agent causing acute lower respiratory infections that can progress to bronchiolitis and pneumonia in children, elderly, and immunocompromised individuals [1, 2]. RSV outbreaks are influenced by virus diversity and evolution [3, 4], environmental factors [5], and host immunity [6].
The epithelium is the primary site for host-virus interface, where cells recognize pathogen-associated patterns on microbes through innate immunity receptors [7, 8]. Indeed, epithelial cells constitute an important line of defense against RSV and other airborne pathogens [9]. They form a physical barrier and produce mucus to inhibit microbes from entering the body. Moreover, they express molecules with antimicrobial properties, as lysozyme, lactoferrin, collectins, and antimicrobial peptides [10]. Two human cell lines have been extensively used to understand the interaction between host and RSV, the alveolar epithelial cell, A549, and one from proximal airways, the bronchial epithelial cell, BEAS-2B.
Genome-wide microarrays are powerful tools to investigate host transcriptional response during infections in the pulmonary epithelium, including those induced by RSV [11, 12]. Indeed, two studies evaluated the patterns of gene expression from BEAS-2B cell lines infected with RSV [10, 13]. However, it is intriguing that after 4 h of infection Huang and collaborators (2008) found that RSV-modulated genes were only associated with the neuroactive ligand-receptor interaction pathway [13]; in contrast, Mayer and collaborators (2007) identified that the same time of RSV infection of BEAS-2B cells induced transcriptional changes similar to those found for other respiratory pathogens as Pseudomonas aeruginosa [10]. In spite of differences, publicly available microarray data offers an interesting opportunity to reveal common features of RSV induced transcriptional profiles to understand the early response of BEAS-2B cell lines and extend the knowledge on biomarkers of acute infections with this virus. Therefore, those datasets were evaluated in a meta-analysis by fitting linear models for each array probe and Empirical Bayesian approach to detect transcriptional changes that revealed significant associations with unreported pathways. Of importance, this strategy also rendered a biomarker signature of BEAS-2B cell lines infected with RSV that can be useful for the design of molecular diagnosis tools.
2. Materials and Methods
The datasets GSE3397 and GSE6802 were obtained from GEO database (http://www.ncbi.nlm.nih.gov/), which compared BEAS-2B cells infected with RSV with control experiments. Only arrays in which cells were infected with RSV for 4 h were selected for further analysis. Raw data were processed using the R Language and Environment for Statistical Computing (R) 3.2.0 [14] and Bioconductor 3.1 [15]. The affy package for R [16] was used to perform quality control when applicable. Data was log2 transformed and quantile normalization was applied for dataset GSE3397 due the absence of CEL files. The dataset GSE6802 was already RMA normalized. Batch effects were corrected with Combat( ) function [17] of sva package for R [18]. Expression data were weighted with the arrayWeights( ) function from limma package for R [19]. Differential gene expression was also evaluated with limma package for R [19], whereby differentially expressed genes (DEGs) were identified by a false discovery rate (FDR) <0.05. Hierarchical clustering was performed with Euclidian distance for metric calculations and the complete linkage method, which were displayed as heatmaps drawn with gplots package for R [20]. Pathway analyses were performed with the online platform for functional analysis InnateDB [21] and significant pathway overrepresentation was computed with hypergeometrical distribution and Benjamini-Hochberg correction for multiple comparisons. Significantly enriched pathways were determined by a P value < 0.05 and FDR < 0.1.
3. Results and Discussion
3.1. Dataset Selection and Preprocessing Analysis
To define a robust transcriptional signature of BEAS-2B acutely infected with RSV, two publicly available datasets, GSE3397 and GSE6802, were used to conduct a meta-analysis from which data were extracted for BEAS-2B cells infected with RSV for 4 h and controls. First, background subtracted expression data from GSE3397 (Figure 1(a)) were preprocessed and normalized (Figure 1(b)). However, in a first attempt to conduct differential gene expression analysis using limma [19], there were no statistically significant differences in gene expression. Therefore, principal component analysis (PCA) was used to evaluate the expression profiles of each array and, except for arrays named here Control2 and RSV2, the consistent pattern of clustering in Figure 1(c) suggests a batch effect. After normalization, this effect was even more evident (Figure 1(d)), which led to the speculation that Huang and collaborators (2008) [13] analyzed only three microarray experiments from this dataset based on the assumption that differences found for those microarrays were due to failures in experimental procedures; however they did not consider or correct for batch effects. In view of those facts, the datasets were adjusted with Combat function for R, which removed such effects from GSE3397 expression data (Figure 1(e)). Batch correction of GSE3397 did not change the profiles of arrays Control2 and RSV2; nevertheless, those arrays were included in further analysis because the variation observed in this experiment could have a substantial impact over the final result. Even adverse experimental variations that may change the overall expression patterns of a dataset could be useful to power up the identification of genes that are robustly modulated in BEAS-2B cells infected with RSV. The expression dataset GSE6802 (Figure 1(f)) was also included in the analysis. PCA from expression data extracted from GEO demonstrates that most of the variability between the arrays is explained (76.6%) by the infection with RSV, as the standardized PC1 separates RSV-infected from control arrays (Figure 1(g)), whereas standardized PC2 (11.4%) separates one pair of arrays (RSV_3 and ctrl2) and, although these arrays are supposedly from different batches, clustering features of this axis also suggested a batch effect (Figure 1(g)). log2 transformation of data impacted the profile of array RSV_1 however did not change the profiles from RSV_3 and ctrl_2 (Figure 1(h)). Combat( ) function was also applied to the expression dataset GSE6802; however, PCA shows that the adjustment did not to improve further clustering between specific arrays (Supplementary Figure 1; see Supplementary Material available online at http://dx.doi.org/10.1155/2016/3605302). In view of that, downstream analyses were carried out with normalized log2 transformed data.
Figure 1.
Preprocessing analysis of GEO datasets GSE3397 and GSE6802. (a) Boxplot of GSE3397, raw expression data. (b) Boxplot of GSE3397, normalized expression data. (c) Principal component analysis of GSE3397, raw expression data. (d) Principal component analysis of GSE3397, normalized expression data. (e) Principal component analysis of GSE3397, normalized and batch corrected expression data. (f) Boxplot of GSE6802, RMA normalized expression data. (g) Principal component analysis of GSE6802, normalized expression data. (h) Principal component analysis of GSE6802, log2 transformed RMA normalized expression data.
3.2. Differential Gene Expression
Next, linear model-based statistical analyses with a FDR < 0.05 were conducted to identify differentially expressed genes (DEGs). The dataset GSE3397 exhibited ninety-four DEGs (Figure 2(a) and Table 1). Those genes are highly discordant from DEGs previously reported by Huang and collaborators (2008) [13], which identified 277 DEGs based on different statistical analysis and assumptions. Fifty genes were downregulated and forty-four were upregulated (Table 1). The differences found in this study might reflect the inclusion of all microarray experiments from controls and 4 h after RSV infection; exclusion of expression data from 24 h after RSV infection; distinct preprocessing approaches as normalizing method and batch effect correction; and the assessment of statistical significance with a linear model-based method and corrected P values. In contrast, 1965 DEGs were identified for the dataset GSE6802. The top hundred DEGs ranked by fold changes (Figure 2(b) and Table 2) included genes such as JUNB, KLF4, CXCL1, CXCL2, and IL6, which are in agreement with those reported by Mayer and collaborators (2007) [10]. Several factors should account for the notable differences in expression analysis from both datasets. First, different RSV strains were used to stimulate BEAS-2B cells. Second, experimental conditions of controls were also different, as control experiments from GSE3397 were incubated with vehicle (not specified) and those from GSE6802 were not stimulated. Third, despite both datasets being generated with affymetrix microarray platform, those include distinct versions, HU133 plus 2.0 for GSE3397 and HU133A 2.0 for GSE6802.
Figure 2.
Transcriptional profiles of BEAS-2B cells infected with RSV for 4 h. (a) Hierarchical clustering of differentially expressed genes from dataset GSE3397. (b) Hierarchical clustering of differentially expressed genes from dataset GSE6802. Row Z-scores were calculated based on normalized expression data. The colors from green to red represent the transition of decreased to increased expression.
Table 1.
Differentially expressed genes identified in dataset GSE3397.
| ProbeID | Gene symbol | Gene name | log2 fold change | FDR |
|---|---|---|---|---|
| 1560754_at | CMTM7 | CKLF like MARVEL transmembrane domain containing 7 | −1,54756 | 0,017104 |
| 239439_at | AFF4 | AF4/FMR2 family member 4 | −1,53581 | 0,023832 |
| 238929_at | SRSF8 | Serine/arginine-rich splicing factor 8 | −1,51887 | 0,018433 |
| 223142_s_at | UCK1 | Uridine-cytidine kinase 1 | −1,47939 | 0,017104 |
| 242636_at | PRCP | Prolylcarboxypeptidase | −1,45095 | 0,034358 |
| 228007_at | CEP85L | Centrosomal protein 85 kDa-like | −1,4103 | 0,017104 |
| 235573_at | HSPH1 | Heat shock protein family H (Hsp110) member 1 | −1,39959 | 0,0371 |
| 228391_at | CYP4V2 | Cytochrome P450 family 4 subfamily V member 2 | −1,38799 | 0,01671 |
| 219376_at | ZNF322 | Zinc finger protein 322 | −1,3491 | 0,046761 |
| 1553689_s_at | METTL6 | Methyltransferase like 6 | −1,34723 | 0,017104 |
| 242837_at | SRSF4 | Serine/arginine-rich splicing factor 4 | −1,34071 | 0,044693 |
| 237215_s_at | TFRC | Transferrin receptor | −1,32685 | 0,017104 |
| 208819_at | RAB8A | RAB8A, member RAS oncogene family | −1,32593 | 0,042264 |
| 236665_at | CCDC18 | Coiled-coil domain containing 18 | −1,31494 | 0,034201 |
| 206147_x_at | SCML2 | Sex comb on midleg-like 2 (Drosophila) | −1,30586 | 0,016454 |
| 229325_at | ZZZ3 | Zinc finger ZZ-type containing 3 | −1,30495 | 0,017104 |
| 1565716_at | FUS | FUS RNA binding protein | −1,29415 | 0,049505 |
| 205062_x_at | ARID4A | AT-rich interaction domain 4A | −1,28877 | 0,033039 |
| 1552312_a_at | MFAP3 | Microfibrillar associated protein 3 | −1,28521 | 0,046511 |
| 223223_at | ARV1 | ARV1 homolog, fatty acid homeostasis modulator | −1,27987 | 0,023832 |
| 232001_at | PRKCQ-AS1 | PRKCQ antisense RNA 1 | −1,27987 | 0,035983 |
| 233195_at | DNAI1 | Dynein axonemal intermediate chain 1 | −1,25963 | 0,047083 |
| 219094_at | ARMC8 | Armadillo repeat containing 8 | −1,25527 | 0,043392 |
| 235232_at | GMEB1 | Glucocorticoid modulatory element binding protein 1 | −1,2492 | 0,046511 |
| 218643_s_at | CRIPT | CXXC repeat containing interactor of PDZ3 domain | −1,24229 | 0,0371 |
| 1566851_at | TRIM42 | Tripartite motif containing 42 | −1,24057 | 0,042149 |
| 221821_s_at | KANSL2 | KAT8 regulatory NSL complex subunit 2 | −1,23799 | 0,017104 |
| 244115_at | FAM126A | Family with sequence similarity 126 member A | −1,23114 | 0,033039 |
| 215541_s_at | DIAPH1 | Diaphanous related formin 1 | −1,22774 | 0,033039 |
| 203196_at | ABCC4 | ATP binding cassette subfamily C member 4 | −1,22519 | 0,033039 |
| 225024_at | RPRD1B | Regulation of nuclear pre-mRNA domain containing 1B | −1,22264 | 0,043765 |
| 37860_at | ZNF337 | Zinc finger protein 337 | −1,22095 | 0,023832 |
| 212997_s_at | TLK2 | Tousled like kinase 2 | −1,21841 | 0,04814 |
| 225690_at | CDK12 | Cyclin-dependent kinase 12 | −1,21083 | 0,0371 |
| 232103_at | BPNT1 | 3′(2′), 5′-Bisphosphate nucleotidase 1 | −1,20748 | 0,0371 |
| 224848_at | CDK6 | Cyclin-dependent kinase 6 | −1,20247 | 0,0371 |
| 214962_s_at | NUP160 | Nucleoporin 160 kDa | −1,20247 | 0,046319 |
| 219629_at | FAM118A | Family with sequence similarity 118 member A | −1,19831 | 0,028374 |
| 212290_at | SLC7A1 | Solute carrier family 7 member 1 | −1,19748 | 0,042264 |
| 227187_at | CBLL1 | Cbl proto-oncogene like 1, E3 ubiquitin protein ligase | −1,19582 | 0,030047 |
| 233208_x_at | CPSF2 | Cleavage and polyadenylation specific factor 2 | −1,19334 | 0,046319 |
| 230566_at | MORC2-AS1 | MORC2 antisense RNA 1 | −1,17691 | 0,0371 |
| 238795_at | FAM208B | Family with sequence similarity 208 member B | −1,17609 | 0,0371 |
| 204980_at | CLOCK | Clock circadian regulator | −1,17283 | 0,0371 |
| 238653_at | LRIG2 | Leucine-rich repeats and immunoglobulin like domains 2 | −1,17202 | 0,048527 |
| 229939_at | ENDOV | Endonuclease V | −1,16878 | 0,041349 |
| 218185_s_at | ARMC1 | Armadillo repeat containing 1 | −1,16151 | 0,046319 |
| 201083_s_at | BCLAF1 | BCL2 associated transcription factor 1 | −1,15509 | 0,049505 |
| 227840_at | C2orf76 | Chromosome 2 open reading frame 76 | −1,15109 | 0,042264 |
| 201686_x_at | API5 | Apoptosis inhibitor 5 | −1,14076 | 0,046761 |
| 221699_s_at | DDX50 | DEAD-box helicase 50 | 1,140764 | 0,046511 |
| 1556178_x_at | TAF8 | TATA-box binding protein associated factor 8 | 1,159096 | 0,034358 |
| 205623_at | ALDH3A1 | Aldehyde dehydrogenase 3 family member A1 | 1,163927 | 0,049505 |
| 212495_at | KDM4B | Lysine demethylase 4B | 1,193336 | 0,044693 |
| 1569057_s_at | MIA3 | Melanoma inhibitory activity family member 3 | 1,193336 | 0,047866 |
| 222494_at | FOXN3 | Forkhead box N3 | 1,19582 | 0,048527 |
| 223311_s_at | MTA3 | Metastasis associated 1 family member 3 | 1,19582 | 0,041439 |
| 215424_s_at | SNW1 | SNW domain containing 1 | 1,196649 | 0,049505 |
| 213478_at | KAZN | Kazrin, periplakin interacting protein | 1,19914 | 0,025143 |
| 227864_s_at | MVB12A | Multivesicular body subunit 12A | 1,201636 | 0,030287 |
| 228674_s_at | EML4 | Echinoderm microtubule associated protein like 4 | 1,204137 | 0,040345 |
| 224196_x_at | DPH5 | Diphthamide biosynthesis 5 | 1,205808 | 0,025143 |
| 224652_at | CCNY | Cyclin Y | 1,207481 | 0,046761 |
| 212968_at | RFNG | RFNG O-fucosylpeptide 3-beta-N-acetylglucosaminyltransferase | 1,211673 | 0,0371 |
| 1555486_a_at | PRR5L | Proline rich 5 like | 1,212513 | 0,017104 |
| 232837_at | KIF13A | Kinesin family member 13A | 1,214195 | 0,042264 |
| 224320_s_at | MCM8 | Minichromosome maintenance 8 homologous recombination repair factor | 1,217566 | 0,033039 |
| 230131_x_at | ARSD | Arylsulfatase D | 1,221793 | 0,0371 |
| 218225_at | ECSIT | ECSIT signalling integrator | 1,224336 | 0,034358 |
| 222610_s_at | S100PBP | S100P binding protein | 1,226885 | 0,030047 |
| 32259_at | EZH1 | Enhancer of zeste 1 polycomb repressive complex 2 subunit | 1,229439 | 0,0371 |
| 203854_at | CFI | Complement factor I | 1,232852 | 0,042264 |
| 221600_s_at | AAMDC | Adipogenesis associated, Mth938 domain containing | 1,260503 | 0,0371 |
| 209558_s_at | HIP1R | Huntingtin interacting protein 1 related | 1,263127 | 0,042264 |
| 224814_at | DPP7 | Dipeptidyl peptidase 7 | 1,26488 | 0,016454 |
| 232280_at | SLC25A29 | Solute carrier family 25 member 29 | 1,277214 | 0,030047 |
| 228424_at | NAALADL1 | N-Acetylated alpha-linked acidic dipeptidase-like 1 | 1,286989 | 0,042264 |
| 203409_at | DDB2 | Damage specific DNA binding protein 2 | 1,288775 | 0,023832 |
| 229975_at | BMPR1B | Bone morphogenetic protein receptor type 1B | 1,297739 | 0,034358 |
| 227073_at | MAP3K2 | Mitogen-activated protein kinase kinase kinase 2 | 1,297739 | 0,017104 |
| 225347_at | ARL8A | ADP ribosylation factor like GTPase 8A | 1,298639 | 0,02672 |
| 221774_x_at | SUPT20H | SPT20 homolog, SAGA complex component | 1,308578 | 0,016454 |
| 223679_at | CTNNB1 | Catenin beta 1 | 1,318594 | 0,018043 |
| 227679_at | HDAC11 | Histone deacetylase 11 | 1,328686 | 0,044693 |
| 220020_at | XPNPEP3 | X-Prolyl aminopeptidase 3, mitochondrial | 1,342573 | 0,031097 |
| 203199_s_at | MTRR | 5-Methyltetrahydrofolate-homocysteine methyltransferase reductase | 1,360371 | 0,017104 |
| 228722_at | PRMT2 | Protein arginine methyltransferase 2 | 1,370783 | 0,016454 |
| 228951_at | SLC38A7 | Solute carrier family 38 member 7 | 1,431969 | 0,016454 |
| 217529_at | ORAI2 | ORAI calcium release-activated calcium modulator 2 | 1,453973 | 0,043775 |
| 220311_at | N6AMT1 | N-6 adenine-specific DNA methyltransferase 1 (putative) | 1,460032 | 0,017104 |
| 213402_at | ZNF787 | Zinc finger protein 787 | 1,469169 | 0,017104 |
| 226055_at | ARRDC2 | Arrestin domain containing 2 | 1,477338 | 0,017104 |
| 219756_s_at | POF1B | Premature ovarian failure, 1B | 1,580083 | 0,016454 |
| 202833_s_at | SERPINA1 | Serpin peptidase inhibitor, clade A (alpha-1 antiproteinase, antitrypsin), and member 1 | 2,488023 | 0,0371 |
Table 2.
Top hundred differentially expressed genes identified in dataset GSE6802.
| ProbeID | Gene symbol | Gene name | log2 fold change | FDR |
|---|---|---|---|---|
| 212615_at | CHD9 | Chromodomain helicase DNA binding protein 9 | −3,69609 | 0,00131 |
| 221840_at | PTPRE | Protein tyrosine phosphatase, receptor type E | −3,56524 | 0,000195 |
| 220817_at | TRPC4 | Transient receptor potential cation channel subfamily C member 4 | −3,39168 | 0,001582 |
| 221703_at | BRIP1 | BRCA1 interacting protein C-terminal helicase 1 | −2,88786 | 0,021463 |
| 207012_at | MMP16 | Matrix metallopeptidase 16 | −2,82647 | 0,000119 |
| 219494_at | RAD54B | RAD54 homolog B (S. cerevisiae) | −2,81279 | 0,000177 |
| 207034_s_at | GLI2 | GLI family zinc finger 2 | −2,79723 | 0,005157 |
| 203518_at | LYST | Lysosomal trafficking regulator | −2,75872 | 5,90E − 05 |
| 205282_at | LRP8 | LDL receptor related protein 8 | −2,7549 | 0,000311 |
| 214440_at | NAT1 | N-Acetyltransferase 1 (arylamine N-acetyltransferase) | −2,68515 | 0,001777 |
| 219627_at | ZNF767P | Zinc finger family member 767, pseudogene | −2,67957 | 0,00024 |
| 218984_at | PUS7 | Pseudouridylate synthase 7 (putative) | −2,67586 | 0,001308 |
| 206554_x_at | SETMAR | SET domain and mariner transposase fusion gene | −2,63536 | 0,002432 |
| 219779_at | ZFHX4 | Zinc finger homeobox 4 | −2,62624 | 0,001411 |
| 213103_at | STARD13 | StAR related lipid transfer domain containing 13 | −2,57219 | 0,002525 |
| 210138_at | RGS20 | Regulator of G-protein signaling 20 | −2,55974 | 0,000415 |
| 204291_at | ZNF518A | Zinc finger protein 518A | −2,54383 | 9,70E − 05 |
| 204651_at | NRF1 | Nuclear respiratory factor 1 | −2,49147 | 0,003659 |
| 205408_at | MLLT10 | Myeloid/lymphoid or mixed-lineage leukemia; translocated to, 10 | −2,48975 | 5,10E − 05 |
| 219581_at | TSEN2 | tRNA splicing endonuclease subunit 2 | −2,45377 | 0,001774 |
| 218242_s_at | SUV420H1 | Lysine methyltransferase 5B | −2,44698 | 0,000754 |
| 203242_s_at | PDLIM5 | PDZ and LIM domain 5 | −2,43851 | 0,001699 |
| 203868_s_at | VCAM1 | Vascular cell adhesion molecule 1 | −2,43513 | 0,000761 |
| 220206_at | ZMYM1 | Zinc finger MYM-type containing 1 | −2,36362 | 0,008439 |
| 207616_s_at | TANK | TRAF family member associated NFKB activator | −2,34567 | 0,000424 |
| 218303_x_at | KRCC1 | Lysine-rich coiled-coil 1 | −2,34567 | 0,003187 |
| 218490_s_at | ZNF302 | Zinc finger protein 302 | −2,32785 | 0,001816 |
| 206876_at | SIM1 | Single-minded family bHLH transcription factor 1 | −2,32624 | 0,001681 |
| 219128_at | C2orf42 | Chromosome 2 open reading frame 42 | −2,28628 | 0,002926 |
| 212861_at | MFSD5 | Major facilitator superfamily domain containing 5 | −2,27048 | 0,000823 |
| 218653_at | SLC25A15 | Solute carrier family 25 member 15 | −2,25636 | 0,000562 |
| 206943_at | TGFBR1 | Transforming growth factor beta receptor I | −2,24856 | 0,025349 |
| 201995_at | EXT1 | Exostosin glycosyltransferase 1 | −2,247 | 0,000421 |
| 221430_s_at | RNF146 | Ring finger protein 146 | −2,23457 | 0,001084 |
| 212286_at | ANKRD12 | Ankyrin repeat domain 12 | −2,2253 | 0,00029 |
| 219544_at | BORA | Bora, aurora kinase A activator | −2,21914 | 0,000333 |
| 210455_at | R3HCC1L | R3H domain and coiled-coil containing 1 like | −2,2176 | 0,0039 |
| 219459_at | POLR3B | Polymerase (RNA) III subunit B | −2,2176 | 0,000832 |
| 219078_at | GPATCH2 | G-patch domain containing 2 | −2,19923 | 0,000723 |
| 204547_at | RAB40B | RAB40B, member RAS oncogene family | −2,17648 | 0,001741 |
| 209760_at | KIAA0922 | KIAA0922 | −2,17347 | 0,001048 |
| 218791_s_at | KATNBL1 | Katanin regulatory subunit B1 like 1 | −2,17347 | 0,001187 |
| 205173_x_at | CD58 | CD58 molecule | −2,17196 | 0,00022 |
| 204352_at | TRAF5 | TNF receptor associated factor 5 | −2,16895 | 0,002659 |
| 212441_at | KIAA0232 | KIAA0232 | −2,16595 | 0,006084 |
| 204236_at | FLI1 | Fli-1 proto-oncogene, ETS transcription factor | −2,15397 | 0,005141 |
| 203072_at | MYO1E | Myosin IE | −2,15248 | 0,000154 |
| 219904_at | ZSCAN5A | Zinc finger and SCAN domain containing 5A | −2,14801 | 0,00144 |
| 219133_at | OXSM | 3-Oxoacyl-ACP synthase, mitochondrial | −2,12285 | 0,002424 |
| 205798_at | IL7R | Interleukin 7 receptor | −2,11257 | 0,00506 |
| 205476_at | CCL20 | C-C motif chemokine ligand 20 | 4,613942 | 9,50E − 05 |
| 213497_at | ABTB2 | Ankyrin repeat and BTB domain containing 2 | 4,623547 | 1,40E − 05 |
| 219179_at | DACT1 | Dishevelled-binding antagonist of beta-catenin 1 | 4,642816 | 9,00E − 06 |
| 219228_at | ZNF331 | Zinc finger protein 331 | 4,723971 | 6,00E − 06 |
| 213139_at | SNAI2 | Snail family zinc finger 2 | 4,76673 | 1,40E − 05 |
| 218177_at | CHMP1B | Charged multivesicular body protein 1B | 4,806544 | 1,00E − 05 |
| 203304_at | BAMBI | BMP and activin membrane-bound inhibitor | 4,826576 | 3,00E − 06 |
| 201631_s_at | IER3 | Immediate early response 3 | 4,833271 | 3,00E − 06 |
| 218559_s_at | MAFB | v-maf avian musculoaponeurotic fibrosarcoma oncogene homolog B | 4,870264 | 0,000468 |
| 220266_s_at | KLF4 | Kruppel-like factor 4 (gut) | 4,890561 | 0,00022 |
| 209211_at | KLF5 | Kruppel-like factor 5 (intestinal) | 4,924578 | 0,002036 |
| 209681_at | SLC19A2 | Solute carrier family 19 member 2 | 4,927992 | 5,90E − 05 |
| 205266_at | LIF | Leukemia inhibitory factor | 4,955395 | 2,20E − 05 |
| 204790_at | SMAD7 | SMAD family member 7 | 5,073566 | 0,000283 |
| 221667_s_at | HSPB8 | Heat shock protein family B (small) member 8 | 5,422657 | 2,90E − 05 |
| 212665_at | TIPARP | TCDD-inducible poly(ADP-ribose) polymerase | 5,525098 | 1,00E − 05 |
| 202935_s_at | SOX9 | SRY-box 9 | 5,971114 | 3,30E − 05 |
| 202023_at | EFNA1 | Ephrin-A1 | 6,164569 | 3,30E − 05 |
| 202393_s_at | KLF10 | Kruppel-like factor 10 | 6,194552 | 0,000195 |
| 213146_at | KDM6B | Lysine demethylase 6B | 6,203146 | 1,90E − 05 |
| 205193_at | MAFF | v-maf avian musculoaponeurotic fibrosarcoma oncogene homolog F | 6,2941 | 2,00E − 06 |
| 209457_at | DUSP5 | Dual specificity phosphatase 5 | 6,639157 | 1,30E − 05 |
| 206029_at | ANKRD1 | Ankyrin repeat domain 1 | 6,65759 | 0,008591 |
| 209283_at | CRYAB | Crystallin alpha B | 6,703897 | 0,000118 |
| 201693_s_at | EGR1 | Early growth response 1 | 7,056731 | 4,10E − 05 |
| 212099_at | RHOB | ras homolog family member B | 7,300524 | 0,000406 |
| 219682_s_at | TBX3 | T-box 3 | 7,722136 | 5,80E − 05 |
| 201473_at | JUNB | jun B proto-oncogene | 8,322402 | 7,00E − 06 |
| 200664_s_at | DNAJB1 | DnaJ heat shock protein family (Hsp40) member B1 | 8,586082 | 2,00E − 05 |
| 205828_at | MMP3 | Matrix metallopeptidase 3 | 8,711976 | 1,90E − 05 |
| 201169_s_at | BHLHE40 | Basic helix-loop-helix family member e40 | 8,870405 | 0,00011 |
| 203665_at | HMOX1 | Heme oxygenase 1 | 9,32433 | 0,000544 |
| 202643_s_at | TNFAIP3 | TNF alpha induced protein 3 | 9,573192 | 2,50E − 05 |
| 205207_at | IL6 | Interleukin 6 | 10,18236 | 3,00E − 06 |
| 202388_at | RGS2 | Regulator of G-protein signaling 2 | 10,25318 | 1,40E − 05 |
| 204472_at | GEM | GTP binding protein overexpressed in skeletal muscle | 10,8003 | 1,00E − 06 |
| 202149_at | NEDD9 | Neural precursor cell expressed, developmentally down-regulated 9 | 11,06553 | 2,50E − 05 |
| 219480_at | SNAI1 | Snail family zinc finger 1 | 11,70457 | 2,00E − 06 |
| 218839_at | HEY1 | hes related family bHLH transcription factor with YRPW motif 1 | 12,07541 | 6,00E − 06 |
| 206115_at | EGR3 | Early growth response 3 | 14,19194 | 1,20E − 05 |
| 204470_at | CXCL1 | C-X-C motif chemokine ligand 1 | 17,61827 | 2,00E − 06 |
| 204621_s_at | NR4A2 | Nuclear receptor subfamily 4 group A member 2 | 18,77837 | 0 |
| 209774_x_at | CXCL2 | C-X-C motif chemokine ligand 2 | 19,02731 | 9,00E − 06 |
| 202859_x_at | CXCL8 | C-X-C motif chemokine ligand 8 | 19,89039 | 1,00E − 06 |
| 202340_x_at | NR4A1 | Nuclear receptor subfamily 4 group A member 1 | 20,74943 | 1,00E − 06 |
| 209189_at | FOS | FBJ murine osteosarcoma viral oncogene homolog | 23,36051 | 1,00E − 06 |
| 202672_s_at | ATF3 | Activating transcription factor 3 | 24,18432 | 0 |
| 202768_at | FOSB | FBJ murine osteosarcoma viral oncogene homolog B | 32,92245 | 0 |
| 207978_s_at | NR4A3 | Nuclear receptor subfamily 4 group A member 3 | 43,80428 | 1,00E − 06 |
| 117_at | HSPA6 | Heat shock protein family A (Hsp70) member 6 | 90,82389 | 0 |
3.3. Functional Analysis
To obtain a biological interpretation of the transcriptional signature of RSV-infected BEAS-2B cells and compare with those reported by previous studies, enrichment analysis was performed with the online platform for functional analysis InnateDB [21]. Based on a FDR < 0.1, DEGs identified for GSE3397 were enriched in pathways related to Chromatin organization, histone acetylation, signaling by NOTCH, IL1, Integrin-linked kinase signaling, EPO signaling pathway, VEGF signaling pathway, platelet degranulation, p73 transcription factor network, IL-7 signaling, p53 signaling pathway, and others (Figure 3(a) and Supplementary Data 1). Of interest, Huang and collaborators (2008) [13] reported gene overrepresentation within p53 signaling pathway, but only after 24 h following RSV infection of BEAS-2B cells. After 4 h following RSV infection, Huang and collaborators (2008) [13] only found a significant association with neuroactive ligand-receptor interaction pathway, which was not overrepresented in the present analysis. In contrast, DEGs resultant from dataset GSE6802 were enriched in pathways related to AP-1 transcription factor, ATF-2 transcription factor, IL-6 signaling, SMAD function, signaling by TGFBR, HIF-1α transcription factor, signaling by CD40/CD40L, signaling by MAPK, signaling by innate immune receptors, and others (Figure 3(b) and Supplementary Data 1). Some of those pathways as CD40 signaling are indeed commonly induced by a variety of viral respiratory infections [22], whereas several of those pathways could indicate novel directions for studying the host response against RSV. Six pathways were enriched by DEGs from both datasets, the EPO signaling pathway, FBXW7 Mutants and NOTCH1 in Cancer, IL1, p53 signaling pathway, p73 transcription factor network, and signaling by NOTCH1. The erythropoietin (EPO) gene is a primary target of HIF-1α transcription factor, whereas binding of HIF-1α to the EPO enhancer promoter region induces transcriptional programs that influence inflammation and infection processes [23]. In addition, expression of Dll4, a major NOTCH ligand, is upregulated in dendritic cells infected with RSV, whereas blockage of Dll4 in vivo increased hyperreactivity of airways and mucus secretion that impacted the pathology of the disease, showing a key role of signaling by NOTCH in the regulation of immunity against RSV [24]. Moreover, besides modulations of the p53 signaling pathway by infection of RSV in vitro [10, 13], this pathway was found to be upregulated in whole blood of children with lower respiratory tract infection by RSV [25]. Taken together, those data point to key pathways which can impact infections of human bronchial epithelial cells with RSV.
Figure 3.
Pathway enrichment analysis with InnateDB. Differentially expressed genes from (a) GSE3397 or (b) GSE6802 were evaluated for overrepresentation in pathways annotated in databases as INOH, KEGG, NETPATH, PID NIC, and REACTOME.
3.4. Meta-Analysis Based Biomarker Signature of RSV-Infected BEAS-2B Cells
To determine a unique transcriptional signature of BEAS-2B cells induced by early infection with RSV, common DEGs for both datasets were further identified. The analysis retrieved a list of seventeen common genes: ABCC4, ARMC8, BCLAF1, EZH1, FAM118A, FAM208B, FUS, HSPH1, KAZN, MAP3K2, N6AMT1, PRMT2, S100PBP, SERPINA1, TLK2, ZNF322, and ZNF337 (Figure 4). Despite particular features in expression data from both datasets, unsupervised hierarchical clustering analysis based on this signature revealed the formation of robust clusters between RSV-infected or uninfected BEAS-2B cells (Figure 4). Of note, human airway epithelial cells were shown to express ABCC4/MRP4, a transporter for uric acid and cAMP [26]. Mucosal production of uric acid was recently linked to particulate matter-induced allergic sensitization [26]; therefore RSV infection could trigger such a response and contribute to the development and severity of allergic responses to particulate matter [27]. Moreover, both ABCC4 and SERPINA1 are annotated into the platelet degranulation pathway (Figure 3(a)), suggesting a role in antiviral mechanisms from bronchial epithelial cells. After an initial encounter with RSV, the transcriptional activity of human bronchial epithelial cells is reprogrammed to counteract viruses and other pathogens [10], whereas MAP3K2 and ZNF322 are clearly involved on the activation and regulation of MAP kinase signaling pathway [28, 29]. Indeed, RSV infection leads to the activation of p38 MAPK [30] and c-JUN kinase pathway, which negatively regulates the production of TNF-α in human epithelial cells [31] and might contribute to virus evasion from an early immune response. Interestingly, the biosignature also included BCLAF1, a molecule involved in processes as apoptosis, transcription and processing of RNA, and export of mRNA from the nucleus [32]. However, this nuclear protein was also implicated as a viral restriction factor targeted to degradation by human cytomegalovirus [32]. Moreover, EZH1 was shown to be involved in the methylation of histone 3 at lysine 27 (H3K27) of the HIV provirus in resting cells [33] and could thus exert a significant function in infections with RSV, whereby other genes such as N6AMT1, FUS, and PRMT2 are also involved in protein methylation. Indeed, using coimmunoprecipitation and mass spectrometry, recent work demonstrated that RSV nucleoprotein (N) interacts with protein arginine N-methyltransferase 5 (PRMT5) [34], suggesting that PRMT2 could also interact with RSV proteins and play an important role during infections of human bronchial epithelial cells. Several of the genes identified in this study have been poorly studied in the context of RSV infection, whereby none of them was previously reported as a biomarker of infections by this virus. Of note, except for FAM208B and KAZN, analysis conducted by Smith and collaborators (2012) [22] which included both datasets (GSE3397 and GSE6802) also identified the significant modulation of the genes included in the biomarker signature identified herein.
Figure 4.
Biomarker signature of BEAS-2B cells infected with RSV for 4 h. Hierarchical clustering of expression data for ABCC4, ARMC8, BCLAF1, EZH1, FAM118A, FAM208B, FUS, HSPH1, KAZN, MAP3K2, N6AMT1, PRMT2, S100PBP, SERPINA1, TLK2, ZNF322, and ZNF337 from (a) dataset GSE3397 and (b) dataset GSE6802. Row Z-scores were calculated based on normalized expression data. The colors from blue to red represent the transition of decreased to increased expression.
4. Conclusions
The combined analysis of distinct datasets from BEAS-2B cells infected with RSV retrieved intriguing results, whereby using powerful statistical methods and assumptions this study identified a new set of biomarkers of early infection with RSV composed by seventeen genes: ABCC4, ARMC8, BCLAF1, EZH1, FAM118A, FAM208B, FUS, HSPH1, KAZN, MAP3K2, N6AMT1, PRMT2, S100PBP, SERPINA1, TLK2, ZNF322, and ZNF337. This transcriptional signature could be useful for the development of molecular diagnosis tools as well as future investigations of processes involved in host-pathogen interactions.
Supplementary Material
Pathway enrichment analysis with the web-based platform InnateDB.
Acknowledgments
The author is grateful to Dr. Fátima Pereira de Souza for critical comments on the paper. Luiz Gustavo Gardinassi was supported by scholarships from Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP).
Competing Interests
The author declares that there are no competing interests regarding the publication of this paper.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Pathway enrichment analysis with the web-based platform InnateDB.