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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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Principal components and hierarchical clustering plot. a The first three principal components are plotted with the proportion of variance explained by each component. The yellow oval highlights the samples taken from the four subjects with moderate/severe LCI 48 h post-challenge. N none or mild LCI, S moderate/severe LCI, V vaccinee, C control. b The hierarchical tree illustrates the relationship between clusters of samples. The height of the branches indicates the strength of the separation. The red oval highlights the samples taken from the four subjects with moderate/severe LCI 48 h post-challenge
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Fig2: Principal components and hierarchical clustering plot. a The first three principal components are plotted with the proportion of variance explained by each component. The yellow oval highlights the samples taken from the four subjects with moderate/severe LCI 48 h post-challenge. N none or mild LCI, S moderate/severe LCI, V vaccinee, C control. b The hierarchical tree illustrates the relationship between clusters of samples. The height of the branches indicates the strength of the separation. The red oval highlights the samples taken from the four subjects with moderate/severe LCI 48 h post-challenge

Mentions: Principal component analysis (PCA) and hierarchical clustering was performed to visualise variation in the dataset (Fig. 2). This demonstrated that four subjects shared a similar profile of variance in gene expression. These four subjects were the only cases of LCI whose symptom score was moderate/severe rather than mild. They also shared other virology outcomes as shown in Fig. 1.Fig. 2


Transcriptomic profiling facilitates classification of response to influenza challenge.

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

Principal components and hierarchical clustering plot. a The first three principal components are plotted with the proportion of variance explained by each component. The yellow oval highlights the samples taken from the four subjects with moderate/severe LCI 48 h post-challenge. N none or mild LCI, S moderate/severe LCI, V vaccinee, C control. b The hierarchical tree illustrates the relationship between clusters of samples. The height of the branches indicates the strength of the separation. The red oval highlights the samples taken from the four subjects with moderate/severe LCI 48 h post-challenge
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

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getmorefigures.php?uid=PMC4281383&req=5

Fig2: Principal components and hierarchical clustering plot. a The first three principal components are plotted with the proportion of variance explained by each component. The yellow oval highlights the samples taken from the four subjects with moderate/severe LCI 48 h post-challenge. N none or mild LCI, S moderate/severe LCI, V vaccinee, C control. b The hierarchical tree illustrates the relationship between clusters of samples. The height of the branches indicates the strength of the separation. The red oval highlights the samples taken from the four subjects with moderate/severe LCI 48 h post-challenge
Mentions: Principal component analysis (PCA) and hierarchical clustering was performed to visualise variation in the dataset (Fig. 2). This demonstrated that four subjects shared a similar profile of variance in gene expression. These four subjects were the only cases of LCI whose symptom score was moderate/severe rather than mild. They also shared other virology outcomes as shown in Fig. 1.Fig. 2

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