Gene expression signatures diagnose influenza and other symptomatic respiratory viral infections in humans

Abstract

Acute respiratory infections (ARIs) are a common reason for seeking medical attention, and the threat of pandemic influenza will likely add to these numbers. Using human viral challenge studies with live rhinovirus, respiratory syncytial virus, and influenza A, we developed peripheral blood gene expression signatures that distinguish individuals with symptomatic ARIs from uninfected individuals with >95% accuracy. We validated this "acute respiratory viral" signature-encompassing genes with a known role in host defense against viral infections-across each viral challenge. We also validated the signature in an independently acquired data set for influenza A and classified infected individuals from healthy controls with 100% accuracy. In the same data set, we could also distinguish viral from bacterial ARIs (93% accuracy). These results demonstrate that ARIs induce changes in human peripheral blood gene expression that can be used to diagnose a viral etiology of respiratory infection and triage symptomatic individuals.

Figure 1

Consort diagram of study organization. Three unique cohorts of healthy volunteers were infected with one of three respiratory viruses (HRV, RSV or influenza A). Combined data was analyzed using sparse latent factor regression with leave-one-out cross validation. Subsequent validation occurred using a dataset available from the public domain.

Figure 2. An acute respiratory viral gene expression signature characterizes symptomatic respiratory viral infection

Experimentally infected adult subjects with symptomatic HRV, RSV or influenza A infection can be distinguished from uninfected individuals by a distinct group of genes (“factor”) demonstrating differential expression among symptomatic individuals as compared to asymptomatic individuals. For each viral challenge, peripheral blood was drawn for whole blood gene expression analysis at scheduled time points post intranasal inoculation of virus. Whole blood gene expression was determined pre-inoculation (baseline), at time of peak symptoms for each symptomatic individual and a matched timepoint for each asymptomatic individual. A) Heat map representing gene expression for genes contained in Factor 16. Columns represent subjects and correspond to points in Figure 1B, with the first 10 columns representing baseline gene expression of asymptomatic individuals in the HRV challenge, the next 10 columns representing timepoints matched to peak symptoms for the asymptomatic subjects in the HRV cohort and the following 10 columns representing time of peak symptoms for the 10 subjects who developed symptomatic HRV infection. A similar layout continues for the RSV and influenza cohorts. Blue and red represent extremes of gene expression, with visually apparent differences between baseline and matched timepoints in the asymptomatic individuals versus time of peak symptoms in symptomatic individuals. The initial models were built without label information for each subject (asymptomatic versus symptomatic, baseline timepoint versus infected/matched timepoint). This design allowed for the model to cluster individuals based on expression patterns alone, thus minimizing bias in factor organization. Bars underneath represent individual groups (black = baseline, red = asymptomatic, blue = symptomatic). P-value (ANOVA) for the difference in factor scores between symptomatic and asymptomatic subjects at time T for the combined dataset is < 1×10⁻¹⁶; for rhinovirus 2.5 × 10⁻⁵, for RSV is 2.3 × 10⁻⁷ and for influenza is 5.0 × 10⁻¹³). B) Factor plots representing categorization of asymptomatic and symptomatic subjects at baseline (black), matched timepoint to peak symptoms (asymptomatic, red) and peak symptoms (symptomatic, blue). C) Leave-one-out cross validation correctly identifies 97% of individuals with viral infection versus no infection (3/84 misclassified). Pd = probability of detection; Pf = probability of false discovery.

Figure 3

Acute respiratory viral factor derived from the three experimental cohorts (HRV, RSV, and influenza) predicts subjects with culture-proven influenza infection from an independent dataset with a high degree of accuracy. The acute respiratory viral classifier built on the combined three challenge datasets was used to predict disease state (uninfected versus influenza A infection) in the literature cohort. A) Predictive capability of the acute respiratory viral factor to classify subjects with no infection (red) versus influenza A infection (blue). X-axis represents the individual subjects and y-axis represents the decision threshold. 0.5 is chosen as the threshold for generation of the subsequent ROC curves. B) Prediction of influenza A infected versus healthy hospitalized control subjects using the acute respiratory viral classifier. Classification of subjects in the literature cohort was highly accurate [100% (23/23) for influenza infected versus no infection].

Figure 4

Acute respiratory viral factor derived from the three experimental cohorts (HRV, RSV, and influenza) distinguishes subjects from an independent dataset with culture-proven influenza infection versus bacterial infection (blue = influenza A; green = S. pneumoniae; yellow = S. aureus; turquoise = E. coli) with a high degree of accuracy. B) Prediction of bacterial infection (any) versus influenza A infection using the pan-respiratory viral classifier. Classification is accurate [80%, (73/91)] for influenza A infection versus any bacterial infection.

Figure 5

Detection of the acute respiratory viral factor occurs earlier than time of peak symptoms. Factor trajectory for the acute respiratory viral factor described in Figure 2 is shown for the symptomatic (blue) and asymptomatic (red) subjects from the influenza challenge study. Notably, factor 16 is detectable prior to the timing of peak symptoms. Each point represents the average factor score for the samples that fall into that group, with error bars representing the standard deviation. For example, the blue dot at Time 0 represents all samples from subjects immediately post inoculation who will subsequently become symptomatic (9 subjects). A t-test was performed at teach timepoint for difference in factor score from those who will become symptomatic from those who will remain asymptomatic. The difference between factor scores for symptomatic and asymptomatic became significant at P < 0.03 at 45.5 hours and continued through the end of the measurements. * = p <0.03.