Gene signature of children with severe respiratory syncytial virus infection.
Journal
Pediatric research
ISSN: 1530-0447
Titre abrégé: Pediatr Res
Pays: United States
ID NLM: 0100714
Informations de publication
Date de publication:
05 2021
05 2021
Historique:
received:
27
08
2020
accepted:
14
12
2020
revised:
15
11
2020
pubmed:
30
1
2021
medline:
15
1
2022
entrez:
29
1
2021
Statut:
ppublish
Résumé
The limited treatment options for children with severe respiratory syncytial virus (RSV) infection highlights the need for a comprehensive understanding of the host cellular response during infection. We aimed to identify host genes that are associated with severe RSV disease and to identify drugs that can be repurposed for the treatment of severe RSV infection. We examined clinical data and blood samples from 37 hospitalized children (29 mild and 8 severe) with RSV infection. We tested RNA from blood samples using next-generation sequencing to profile global mRNA expression and identify cellular processes. Retractions, decreased breath sounds, and tachypnea were associated with disease severity. We observed upregulation of genes related to neutrophil, inflammatory response, blood coagulation, and downregulation of genes related to T cell response in children with severe RSV. Using network-based approach, 43 drugs were identified that are predicted to interact with the gene products of these differentially expressed genes. These results suggest that the changes in the expression pattern in the innate and adaptive immune responses may be associated with RSV clinical severity. Compounds that target these cellular processes can be repositioned as candidate drugs in the treatment of severe RSV. Neutrophil, inflammation, and blood coagulation genes are upregulated in children with severe RSV infection. Expression of T cell response genes are suppressed in cases of severe RSV. Genes identified in this study can contribute in understanding the pathogenesis of RSV disease severity. Drugs that target cellular processes associated with severe RSV can be repositioned as potential therapeutic options.
Sections du résumé
BACKGROUND
The limited treatment options for children with severe respiratory syncytial virus (RSV) infection highlights the need for a comprehensive understanding of the host cellular response during infection. We aimed to identify host genes that are associated with severe RSV disease and to identify drugs that can be repurposed for the treatment of severe RSV infection.
METHODS
We examined clinical data and blood samples from 37 hospitalized children (29 mild and 8 severe) with RSV infection. We tested RNA from blood samples using next-generation sequencing to profile global mRNA expression and identify cellular processes.
RESULTS
Retractions, decreased breath sounds, and tachypnea were associated with disease severity. We observed upregulation of genes related to neutrophil, inflammatory response, blood coagulation, and downregulation of genes related to T cell response in children with severe RSV. Using network-based approach, 43 drugs were identified that are predicted to interact with the gene products of these differentially expressed genes.
CONCLUSIONS
These results suggest that the changes in the expression pattern in the innate and adaptive immune responses may be associated with RSV clinical severity. Compounds that target these cellular processes can be repositioned as candidate drugs in the treatment of severe RSV.
IMPACT
Neutrophil, inflammation, and blood coagulation genes are upregulated in children with severe RSV infection. Expression of T cell response genes are suppressed in cases of severe RSV. Genes identified in this study can contribute in understanding the pathogenesis of RSV disease severity. Drugs that target cellular processes associated with severe RSV can be repositioned as potential therapeutic options.
Identifiants
pubmed: 33510411
doi: 10.1038/s41390-020-01347-9
pii: 10.1038/s41390-020-01347-9
pmc: PMC8249238
doi:
Types de publication
Journal Article
Observational Study
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
1664-1672Références
Shi, T. et al. Global, regional, and national disease burden estimates of acute lower respiratory infections due to respiratory syncytial virus in young children in 2015: a systematic review and modelling study. Lancet 390, 946–958 (2017).
pubmed: 28689664
pmcid: 5592248
doi: 10.1016/S0140-6736(17)30938-8
Nair, H. et al. Global burden of acute lower respiratory infections due to respiratory syncytial virus in young children: a systematic review and meta-analysis. Lancet 375, 1545–1555 (2010).
pubmed: 20399493
pmcid: 2864404
doi: 10.1016/S0140-6736(10)60206-1
Janssen, R. et al. Host transcription profiles upon primary respiratory syncytial virus infection. J. Virol. 81, 5958–5967 (2007).
pubmed: 17376894
pmcid: 1900269
doi: 10.1128/JVI.02220-06
Martinez, I., Lombardia, L., Garcia-Barreno, B., Dominguez, O. & Melero, J. A. Distinct gene subsets are induced at different time points after human respiratory syncytial virus infection of A549 cells. J. Gen. Virol. 88, 570–581 (2007).
pubmed: 17251576
doi: 10.1099/vir.0.82187-0
Hastie, M. L. et al. The human respiratory syncytial virus nonstructural protein 1 regulates type I and type II interferon pathways. Mol. Cell Proteomics 11, 108–127 (2012).
pubmed: 22322095
pmcid: 3418853
doi: 10.1074/mcp.M111.015909
Mejias, A. et al. Whole blood gene expression profiles to assess pathogenesis and disease severity in infants with respiratory syncytial virus infection. PLoS Med. 10, e1001549 (2013).
pubmed: 24265599
pmcid: 3825655
doi: 10.1371/journal.pmed.1001549
Brand, H. K. et al. Olfactomedin 4 serves as a marker for disease severity in pediatric respiratory syncytial virus (RSV) infection. PLoS ONE 10, e0131927 (2015).
pubmed: 26162090
pmcid: 4498630
doi: 10.1371/journal.pone.0131927
Mariani, T. J. et al. Association of dynamic changes in the CD4 T-cell transcriptome with disease severity during primary respiratory syncytial virus infection in young infants. J. Infect. Dis. 216, 1027–1037 (2017).
pubmed: 28962005
doi: 10.1093/infdis/jix400
Do, L. A. H. et al. Host transcription profle in nasal epithelium and whole blood of hospitalized children under 2 years of age with respiratory syncytial virus infection. J. Infect. Dis. 217, 134–146 (2018).
doi: 10.1093/infdis/jix519
Modjarrad, K., Giersing, B., Kaslow, D. C., Smith, P. G. & Moorthy, V. S. WHO consultation on respiratory syncytial virus vaccine development report from a World Health Organization meeting held on 23-24 March 2015. Vaccine 34, 190–197 (2016).
pubmed: 26100926
doi: 10.1016/j.vaccine.2015.05.093
Okamoto, M. et al. Molecular characterization of respiratory syncytial virus in children with repeated infections with subgroup B in the Philippines. J. Infect. Dis. 218, 1045–1053 (2018).
pubmed: 29722817
pmcid: 6107742
doi: 10.1093/infdis/jiy256
Malasao, R. et al. Molecular characterization of human respiratory syncytial virus in the Philippines, 2012-2013. PLoS ONE 10, e0142192 (2015).
pubmed: 26540236
pmcid: 4635013
doi: 10.1371/journal.pone.0142192
Beccuti, M. et al. SeqBox: RNAseq/ChIPseq reproducible analysis on a consumer game computer. Bioinformatics 34, 871–872 (2018).
pubmed: 29069297
doi: 10.1093/bioinformatics/btx674
Jiang, H., Lei, R., Ding, S. W. & Zhu, S. Skewer: a fast and accurate adapter trimmer for next-generation sequencing paired-end reads. BMC Bioinformatics 15, 182 (2014).
pubmed: 24925680
pmcid: 4074385
doi: 10.1186/1471-2105-15-182
Dobin, A. et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29, 15–21 (2013).
doi: 10.1093/bioinformatics/bts635
pubmed: 23104886
Li, B. et al. RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome. BMC Bioinformatcs 12, 323 (2011).
doi: 10.1186/1471-2105-12-323
Marini, F. & Binder, H. PcaExplorer: an R/bioconductor package for interacting with RNA-seq principal components. BMC Bioinformatics 20, 331 (2019).
pubmed: 31195976
pmcid: 6567655
doi: 10.1186/s12859-019-2879-1
Anders, S. & Huber, W. Differential expression analysis for sequence count data. Genome Biol. 11, R106 (2010).
pubmed: 20979621
pmcid: 3218662
doi: 10.1186/gb-2010-11-10-r106
Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 550 (2014).
pubmed: 25516281
pmcid: 4302049
doi: 10.1186/s13059-014-0550-8
Leek, J. T., Johnson, W. E., Parker, H. S., Jaffe, A. E. & Storey, J. D. The SVA package for removing batch effects and other unwanted variation in high-throughput experiments. Bioinformatics 28, 882–883 (2012).
pubmed: 22257669
pmcid: 3307112
doi: 10.1093/bioinformatics/bts034
Ignatiadis, N., Klaus, B., Zaugg, J. B. & Huber, W. Data-driven hypothesis weighting increases detection power in genome-scale multiple testing. Nat. Methods 13, 577–580 (2016).
pubmed: 27240256
pmcid: 4930141
doi: 10.1038/nmeth.3885
Weiner, J. & Domaszewska, T. tmod: an R package for general and multivariate enrichment analysis. PeerJ. 4, 1–9 (2016).
Ashburner, M. et al. Gene ontology: tool for the unification of biology. Nat. Genet. 25, 25–29 (2000).
pubmed: 10802651
pmcid: 3037419
doi: 10.1038/75556
Consoritium, G. O. The Gene Ontology Resource: 20 years and still going strong. Nucleic Acids Res. 47, D330–D338 (2019).
doi: 10.1093/nar/gky1055
Kanehisa, M. & Goto, S. KEGG: Kyoto encyclopedia of genes and genomes. Nucleic Acids Res. 28, 27–30 (2000).
pubmed: 10592173
pmcid: 102409
doi: 10.1093/nar/28.1.27
Szklarczyk, D. et al. STRING v11: protein-protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets. Nucleic Acids Res. 47, 607–613 (2019).
doi: 10.1093/nar/gky1131
Shannon, P. et al. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 13, 2498–2504 (2003).
pubmed: 14597658
pmcid: 403769
doi: 10.1101/gr.1239303
Bader, G. D. & Hogue, C. W. V. An automated method for finding molecular complexes in large protein interaction networks. BMC Bioinformatics 4, 2 (2003).
pubmed: 12525261
pmcid: 149346
doi: 10.1186/1471-2105-4-2
Maere, S., Heymans, K. & Kuiper, M. BiNGO: a Cytoscape plugin to assess overrepresentation of gene ontology categories in biological networks. Bioinformatics 21, 3448–3449 (2005).
pubmed: 15972284
doi: 10.1093/bioinformatics/bti551
Wishart, D. S. et al. DrugBank 5.0: a major update to the DrugBank database for 2018. Nucleic Acids Res. 46, 1074–1082 (2018).
doi: 10.1093/nar/gkx1037
Benjamini, Y. & Hochberg, Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. R. Stat. Soc. B 57, 289–300 (1995).
R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, Vienna, Austria, 2020).
Everard, M. L. et al. Analysis of cells obtained by bronchial lavage of infants with respiratory syncytial virus infection. Arch. Dis. Child. 71, 428–432 (1994).
pubmed: 7826113
pmcid: 1030058
doi: 10.1136/adc.71.5.428
Smith, P. K., Wang, S. Z., Dowling, K. D. & Forsyth, K. D. Leucocyte populations in respiratory syncytial virus-induced bronchiolitis. J. Paediatr. Child Health 37, 146–151 (2001).
pubmed: 11328469
doi: 10.1046/j.1440-1754.2001.00618.x
Wang, S. Z. & Forsyth, K. D. The interaction of neutrophils with respiratory epithelial cells in viral infection. Respirology 5, 1–9 (2000).
pubmed: 10728725
doi: 10.1046/j.1440-1843.2000.00219.x
Fjaerli, H. O. et al. Whole blood gene expression in infants with respiratory syncytial virus bronchiolitis. BMC Infect. Dis. 6, 1–7 (2006).
doi: 10.1186/1471-2334-6-175
Jong, V. L. et al. Transcriptome assists prognosis of disease severity in respiratory syncytial virus infected infants. Sci. Rep. 6, 1–12 (2016).
doi: 10.1038/srep36603
Brand, K. H. et al. Use of MMP-8 and MMP-9 to assess disease severity in children with viral lower respiratory tract infections. J. Med. Virol. 84, 1471–1480 (2012).
pubmed: 22825827
pmcid: 7167016
doi: 10.1002/jmv.23301
Mariani, T. J. et al. Association of dynamic changes in the CD4 T-cell transcriptome with disease severity during primary respiratory syncytial virus infection in young infants. J. Infect. Dis. 216, 1027–1037 (2017).
pubmed: 28962005
doi: 10.1093/infdis/jix400
Kuhlicke, J., Frick, J. S., Morote-Garcia, J. C., Rosenberger, P. & Eltzschig, H. K. Hypoxia inducible factor (HIF)-1 coordinates induction of toll-like receptors TLR2 and TLR6 during hypoxia. PLoS ONE 2, e1364 (2007).
pubmed: 18159247
pmcid: 2147045
doi: 10.1371/journal.pone.0001364
Antoniak, S. The coagulation system in host defense. Res. Pract. Thromb. Haemost. 2, 549–557 (2018).
pubmed: 30046760
pmcid: 6046589
doi: 10.1002/rth2.12109
Gibbins, J. M. Platelet adhesion signalling and the regulation of thrombus formation. J. Cell Sci. 117, 3415–3425 (2004).
pubmed: 15252124
doi: 10.1242/jcs.01325
Greiller, C. L. et al. Vitamin D attenuates rhinovirus-induced expression of intercellular adhesion molecule-1 (ICAM-1) and platelet-activating factor receptor (PAFR) in respiratory epithelial cells. J. Steroid Biochem. Mol. Biol. 187, 152–159 (2019).
pubmed: 30476590
doi: 10.1016/j.jsbmb.2018.11.013
Cundell, D. R., Gerard, N. P., Gerard, C., Idanpaan-Heikkila, I. & Tuomanen, E. I. Streptococcus pneumoniae anchor to activated human cells by the receptor for platelet-activating factor. Nature 377, 435–438 (1995).
pubmed: 7566121
doi: 10.1038/377435a0
De Steenhuijsen Piters, W. A. A. et al. Nasopharyngeal microbiota, host transcriptome, and disease severity in children with respiratory syncytial virus infection. Am. J. Respir. Crit. Care Med. 194, 1104–1111 (2016).
pubmed: 27135599
pmcid: 5114450
doi: 10.1164/rccm.201602-0220OC
De Weerd, W., Twilhaar, W. N. & Kimpen, J. L. L. T cell subset analysis in peripheral blood of children with RSV bronchiolitis. Scand. J. Infect. Dis. 30, 77–78 (1998).
pubmed: 9670363
doi: 10.1080/003655498750002349
Bont, L. et al. Peripheral blood cytokine responses and disease severity in respiratory syncytial virus bronchiolitis. Eur. Respir. J. 14, 144–149 (1999).
pubmed: 10489842
doi: 10.1034/j.1399-3003.1999.14a24.x
Welliver, T. P. et al. Severe human lower respiratory tract illness caused by respiratory syncytial virus and influenza virus is characterized by the absence of pulmonary cytotoxic lymphocyte responses. J. Infect. Dis. 195, 1126–1136 (2007).
pubmed: 17357048
doi: 10.1086/512615
DeVincenzo, J. P. Factors predicting childhood respiratory syncytial virus severity: what they indicate about pathogenesis. Pediatr. Infect. Dis. J. 24, 177–183 (2005).
doi: 10.1097/01.inf.0000187274.48387.42
Tamura, T. et al. Early activation signal transduction pathways of Th1 and Th2 cell clones stimulated with anti-Cd3: roles of protein tyrosine kinases in the signal for IL-2 and IL-4 production. J. Immunol. 155, 4692–4701 (1995).
pubmed: 7594469
doi: 10.4049/jimmunol.155.10.4692
Graham, B. S. Biological challenges and technological opportunities for respiratory syncytial virus vaccine development. Immunol. Rev. 239, 149–166 (2011).
pubmed: 21198670
pmcid: 3023887
doi: 10.1111/j.1600-065X.2010.00972.x