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Transcriptomic profiling facilitates classification of response to influenza challenge.

Davenport EE, Antrobus RD, Lillie PJ, Gilbert S, Knight JC - J. Mol. Med. (2014)

Bottom Line: By combining the dynamic transcriptomic data with the clinical parameters this variability can be reduced.We have further reduced this profile to six genes (CCL2, SEPT4, LAMP3, RTP4, MT1G and OAS3) that can be used to define these subjects.We have used this gene set to predict symptomatic infection from an independent study.

View Article: PubMed Central - PubMed

Affiliation: Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, OX3 7BN, UK.

ABSTRACT

Unlabelled: Despite increases in vaccination coverage, reductions in influenza-related mortality have not been observed. Better vaccines are therefore required and influenza challenge studies can be used to test the efficacy of new vaccines. However, this requires the accurate post-challenge classification of subjects by outcome, which is limited in current methods that use artificial thresholds to assign 'symptomatic' and 'asymptomatic' phenotypes. We present data from an influenza challenge study in which 22 healthy adults (11 vaccinated) were inoculated with H3N2 influenza (A/Wisconsin/67/2005). We generated genome-wide gene expression data from peripheral blood taken immediately before the challenge and at 12, 24 and 48 h post-challenge. Variation in symptomatic scoring was found amongst those with laboratory confirmed influenza. By combining the dynamic transcriptomic data with the clinical parameters this variability can be reduced. We identified four subjects with severe laboratory confirmed influenza that show differential gene expression in 1103 probes 48 h post-challenge compared to the remaining subjects. We have further reduced this profile to six genes (CCL2, SEPT4, LAMP3, RTP4, MT1G and OAS3) that can be used to define these subjects. We have used this gene set to predict symptomatic infection from an independent study. This analysis gives further insight into host-pathogen interactions during influenza infection. However, the major potential value is in the clinical trial setting by providing a more quantitative method to better classify symptomatic individuals post influenza challenge.

Key message: Differential gene expression signatures are seen following influenza challenge. Expression of six predictive genes can classify response to influenza challenge. The genomic influenza response classification replicates in an independent dataset.

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Pathway analysis of differential expression in LCI. The differentially expressed probes between the four moderate/severe LCI samples 48 h post-challenge and the remaining samples were used to determine pathway enrichment with IPA. a Pathways with significant enrichment based on Benjamini-Hochberg (B-H) multiple testing corrected p value <0.01 are shown. The blue bars show the −log(p value). Differentially expressed genes are plotted for each enriched pathway (red upregulated in moderate/severe LCI individuals, green downregulated) with numbers of enriched genes shown in brackets below x-axis labels. b The most significantly enriched pathway, interferon signalling, is shown with enriched genes shaded in colour
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Fig4: Pathway analysis of differential expression in LCI. The differentially expressed probes between the four moderate/severe LCI samples 48 h post-challenge and the remaining samples were used to determine pathway enrichment with IPA. a Pathways with significant enrichment based on Benjamini-Hochberg (B-H) multiple testing corrected p value <0.01 are shown. The blue bars show the −log(p value). Differentially expressed genes are plotted for each enriched pathway (red upregulated in moderate/severe LCI individuals, green downregulated) with numbers of enriched genes shown in brackets below x-axis labels. b The most significantly enriched pathway, interferon signalling, is shown with enriched genes shaded in colour

Mentions: To assess the functional significance of the differentially expressed genes in subjects with moderate/severe LCI at 48 h post-challenge, we performed pathway and network analysis (Fig. 4, Online Resource Figure S2). This showed significant enrichment for genes involved in a number of pathways notably interferon signalling (Benjamini-Hochberg corrected p value = 6.4 × 10−10), role of pattern recognition receptors (PRR) in recognition of bacteria and viruses (p = 4.0 × 10−6), activation of IRF by cytosolic PRR (p = 7.7 × 10−4), eIF2 signalling (p = 8.1 × 10−3) and death receptor signalling (p = 8.1 × 10−3) (Fig. 4). The most significant upstream regulators identified were type III, II and I interferons (IFNL1 p = 2.2 × 10−59, IFNA2 2.7 × 10−53, IFNG 3.4 × 10−42). Network analysis further highlighted the significant upregulation of genes involved in the IFN response seen in these patients (Fig. S2). IFNs play a critical role in host defence to viral infection with the significance of IFNλ recently recognised [26–28]. The eIF2 signalling pathway plays a role in host defence by reducing translation in the host cell thereby reducing the rate at which new viral particles are generated [29] while death receptor signalling involving apoptosis has been related to disease severity in influenza A infection [30]. In terms of diseases and functions related to the differentially expressed genes at 48 h post-challenge, infectious disease was most significant, specifically replication of virus (P = 9.3 × 10−17).Fig. 4


Transcriptomic profiling facilitates classification of response to influenza challenge.

Davenport EE, Antrobus RD, Lillie PJ, Gilbert S, Knight JC - J. Mol. Med. (2014)

Pathway analysis of differential expression in LCI. The differentially expressed probes between the four moderate/severe LCI samples 48 h post-challenge and the remaining samples were used to determine pathway enrichment with IPA. a Pathways with significant enrichment based on Benjamini-Hochberg (B-H) multiple testing corrected p value <0.01 are shown. The blue bars show the −log(p value). Differentially expressed genes are plotted for each enriched pathway (red upregulated in moderate/severe LCI individuals, green downregulated) with numbers of enriched genes shown in brackets below x-axis labels. b The most significantly enriched pathway, interferon signalling, is shown with enriched genes shaded in colour
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

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getmorefigures.php?uid=PMC4281383&req=5

Fig4: Pathway analysis of differential expression in LCI. The differentially expressed probes between the four moderate/severe LCI samples 48 h post-challenge and the remaining samples were used to determine pathway enrichment with IPA. a Pathways with significant enrichment based on Benjamini-Hochberg (B-H) multiple testing corrected p value <0.01 are shown. The blue bars show the −log(p value). Differentially expressed genes are plotted for each enriched pathway (red upregulated in moderate/severe LCI individuals, green downregulated) with numbers of enriched genes shown in brackets below x-axis labels. b The most significantly enriched pathway, interferon signalling, is shown with enriched genes shaded in colour
Mentions: To assess the functional significance of the differentially expressed genes in subjects with moderate/severe LCI at 48 h post-challenge, we performed pathway and network analysis (Fig. 4, Online Resource Figure S2). This showed significant enrichment for genes involved in a number of pathways notably interferon signalling (Benjamini-Hochberg corrected p value = 6.4 × 10−10), role of pattern recognition receptors (PRR) in recognition of bacteria and viruses (p = 4.0 × 10−6), activation of IRF by cytosolic PRR (p = 7.7 × 10−4), eIF2 signalling (p = 8.1 × 10−3) and death receptor signalling (p = 8.1 × 10−3) (Fig. 4). The most significant upstream regulators identified were type III, II and I interferons (IFNL1 p = 2.2 × 10−59, IFNA2 2.7 × 10−53, IFNG 3.4 × 10−42). Network analysis further highlighted the significant upregulation of genes involved in the IFN response seen in these patients (Fig. S2). IFNs play a critical role in host defence to viral infection with the significance of IFNλ recently recognised [26–28]. The eIF2 signalling pathway plays a role in host defence by reducing translation in the host cell thereby reducing the rate at which new viral particles are generated [29] while death receptor signalling involving apoptosis has been related to disease severity in influenza A infection [30]. In terms of diseases and functions related to the differentially expressed genes at 48 h post-challenge, infectious disease was most significant, specifically replication of virus (P = 9.3 × 10−17).Fig. 4

Bottom Line: By combining the dynamic transcriptomic data with the clinical parameters this variability can be reduced.We have further reduced this profile to six genes (CCL2, SEPT4, LAMP3, RTP4, MT1G and OAS3) that can be used to define these subjects.We have used this gene set to predict symptomatic infection from an independent study.

View Article: PubMed Central - PubMed

Affiliation: Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, OX3 7BN, UK.

ABSTRACT

Unlabelled: Despite increases in vaccination coverage, reductions in influenza-related mortality have not been observed. Better vaccines are therefore required and influenza challenge studies can be used to test the efficacy of new vaccines. However, this requires the accurate post-challenge classification of subjects by outcome, which is limited in current methods that use artificial thresholds to assign 'symptomatic' and 'asymptomatic' phenotypes. We present data from an influenza challenge study in which 22 healthy adults (11 vaccinated) were inoculated with H3N2 influenza (A/Wisconsin/67/2005). We generated genome-wide gene expression data from peripheral blood taken immediately before the challenge and at 12, 24 and 48 h post-challenge. Variation in symptomatic scoring was found amongst those with laboratory confirmed influenza. By combining the dynamic transcriptomic data with the clinical parameters this variability can be reduced. We identified four subjects with severe laboratory confirmed influenza that show differential gene expression in 1103 probes 48 h post-challenge compared to the remaining subjects. We have further reduced this profile to six genes (CCL2, SEPT4, LAMP3, RTP4, MT1G and OAS3) that can be used to define these subjects. We have used this gene set to predict symptomatic infection from an independent study. This analysis gives further insight into host-pathogen interactions during influenza infection. However, the major potential value is in the clinical trial setting by providing a more quantitative method to better classify symptomatic individuals post influenza challenge.

Key message: Differential gene expression signatures are seen following influenza challenge. Expression of six predictive genes can classify response to influenza challenge. The genomic influenza response classification replicates in an independent dataset.

Show MeSH
Related in: MedlinePlus