A host-based RT-PCR gene expression signature to identify acute respiratory viral infection

Abstract

Improved ways to diagnose acute respiratory viral infections could decrease inappropriate antibacterial use and serve as a vital triage mechanism in the event of a potential viral pandemic. Measurement of the host response to infection is an alternative to pathogen-based diagnostic testing and may improve diagnostic accuracy. We have developed a host-based assay with a reverse transcription polymerase chain reaction (RT-PCR) TaqMan low-density array (TLDA) platform for classifying respiratory viral infection. We developed the assay using two cohorts experimentally infected with influenza A H3N2/Wisconsin or influenza A H1N1/Brisbane, and validated the assay in a sample of adults presenting to the emergency department with fever (n = 102) and in healthy volunteers (n = 41). Peripheral blood RNA samples were obtained from individuals who underwent experimental viral challenge or who presented to the emergency department and had microbiologically proven viral respiratory infection or systemic bacterial infection. The selected gene set on the RT-PCR TLDA assay classified participants with experimentally induced influenza H3N2 and H1N1 infection with 100 and 87% accuracy, respectively. We validated this host gene expression signature in a cohort of 102 individuals arriving at the emergency department. The sensitivity of the RT-PCR test was 89% [95% confidence interval (CI), 72 to 98%], and the specificity was 94% (95% CI, 86 to 99%). These results show that RT-PCR-based detection of a host gene expression signature can classify individuals with respiratory viral infection and sets the stage for prospective evaluation of this diagnostic approach in a clinical setting.

Fig. 1. Symptom scores for experimental cohorts

Modified Jackson scores (15) were recorded for participants in the experimental cohorts, from preinoculation through 120 hours after inoculation. Scores were recorded twice daily, and the higher of the two scores was used to compile the aggregate score over 120 hours. (A) Daily modified Jackson scores for the nine participants who developed symptoms of acute respiratory viral illness in the H1N1 exposure study. (B) Daily modified Jackson scores for the nine participants who developed symptoms of acute respiratory viral illness in the H3N2 exposure study. Those who remained asymptomatic are not shown.

Fig. 2. Validation of RT-PCR expression in the H3N2 and H1N1 experimental cohorts

The performance of the RT-PCR assay for classification of symptomatic individuals was evaluated for H3N2 and H1N1 separately, using a training and leave-one-out cross-validation strategy on all participants in each cohort. (A and C) When a classifier was trained on H3N2/Wisconsin-exposed participants, the classification error was 0/17 (0%) (AUC = 1) with a cutoff score of 20. (B and D) When a classifier was trained on H1N1/Brisbane-exposed participants, the classification error was 3/23 (13%) (AUC = 0.77) (2 false negatives and 1 false positive) with a cutoff score of 20. When using only participants who met both the clinical and microbiological definitions of infection, classification error was 0/15 (0%; AUC = 1) and 1/15 (6.7%, one false negative; AUC = 0.93) for H3N2/Wisconsin and H1N1/Brisbane, respectively (figs. S1 and S2). The primary phenotype analyzed was based on clinical symptom scores, represented as symptomatic (blue) or asymptomatic (red). Symptomatic and microbiologically infected individuals are represented with blue dots, and asymptomatic and microbiologically uninfected individuals are represented with red dots. Asymptomatic but microbiologically infected individuals are represented with red diamonds. Pd, probability of detection; Pf, probability of false discovery.

Fig. 3. Cross-validation of RT-PCR expression among experimental cohorts

Classification of individuals with symptomatic RVI was validated across influenza subtypes. (A and B) When training on H3N2 participants and testing on H1N1 participants, the sensitivity of the test is 90% and specificity is 75% (1 false positive and 3 false negative results) (AUC = 0.83). (C and D) When training on H1N1 participants and testing on H3N2 participants, sensitivity and specificity are both 100% (AUC = 1). ENet score (y axis) represents the probability of having a viral infection, with a score of 20 indicative of 50% probability of detection. Symptomatic and microbiologically infected individuals are represented with blue dots, and asymptomatic and microbiologically uninfected individuals are represented with red dots. The primary phenotype analyzed was based on clinical symptom scores, represented as symptomatic (blue) or asymptomatic (red). Symptomatic but microbiologically uninfected individuals are represented with blue diamonds, and asymptomatic but microbiologically infected individuals are represented with red diamonds.

Fig. 4. External validation of the RT-PCR–based classifier in the emergency department cohort

(A) An RT-PCR–based gene expression classifier trained on a cohort experimentally inoculated with the H3N2/Wisconsin influenza virus accurately classifies individuals presenting to the emergency department with viral infection (blue) and distinguishes these individuals from those presenting with Gram-positive bacterial infection (red) and healthy controls (green). (B) The sensitivity of the RT-PCR test is 89% (95% CI, 72 to 98%), and the specificity is 94% (95% CI, 86 to 99%), with a positive predictive value of 84% (95% CI, 65 to 94%) and a negative predictive value of 96% (95% CI, 89 to 99%; AUC = 0.92). (C) ENet score (y axis) represents probability of having viral infection, with a score of 20 indicative of 50% probability of detection. “Classifier weight” refers to the regression coefficient for each gene in the model. “Gene index” represents each gene probe in the 48-probe assay.