Nasopharyngeal Protein Biomarkers of Acute Respiratory Virus Infection

Abstract

Infection of respiratory mucosa with viral pathogens triggers complex immunologic events in the affected host. We sought to characterize this response through proteomic analysis of nasopharyngeal lavage in human subjects experimentally challenged with influenza A/H3N2 or human rhinovirus, and to develop targeted assays measuring peptides involved in this host response allowing classification of acute respiratory virus infection. Unbiased proteomic discovery analysis identified 3285 peptides corresponding to 438 unique proteins, and revealed that infection with H3N2 induces significant alterations in protein expression. These include proteins involved in acute inflammatory response, innate immune response, and the complement cascade. These data provide insights into the nature of the biological response to viral infection of the upper respiratory tract, and the proteins that are dysregulated by viral infection form the basis of signature that accurately classifies the infected state. Verification of this signature using targeted mass spectrometry in independent cohorts of subjects challenged with influenza or rhinovirus demonstrates that it performs with high accuracy (0.8623 AUROC, 75% TPR, 97.46% TNR). With further development as a clinical diagnostic, this signature may have utility in rapid screening for emerging infections, avoidance of inappropriate antibacterial therapy, and more rapid implementation of appropriate therapeutic and public health strategies.

Keywords: Diagnostic biomarker; Human rhinovirus; Infectious disease; Influenza; Proteomics.

Fig. 1

Study design and experimental workflow. A two-phased strategy was employed to identify and characterize candidate protein biomarkers of ARV infection from NPL samples collected from participants in four experimental ARV challenge cohorts. For phase 1 discovery analysis, four NPL pools were prepared from H3N2 #1 cohort and analyzed using unbiased 2D-LC-MS/MS. The numbers of subject (N) with samples included in each pool are shown (Uninf = uninfected individuals; Inf = infected individuals; BL = baseline; T = time of maximal symptoms). For phase 2, the original and three additional independent challenge cohorts were assayed by targeted MRM. Quantitative peptide expression data from 80 individuals and 156 total samples were used in the derivation of an NPL ARV classifier, and classification performance was assessed in independent challenge cohorts using LOOCV.

Fig. 2

Expression levels of candidate protein biomarkers as measured by unbiased LC/LC-MS/MS and targeted MRM. Four representative proteins (CFAB, TIG1, TBA1B, STATH) in H3N2 #1 challenge cohort were measured by (A) LC/LC-MS/MS unbiased discovery methods in sample pools (duplicate measures of multi-peptide meta-protein is shown), and (B) targeted MRM analysis of unique peptides (peptide sequence is shown above each plot) from individual participant samples from the same H3N2 #1 cohort. MRM data are expressed as relative ratio (endogenous “light” peptide to spiked SIL “heavy” peptide). Box-and-whisker plots for each group indicate intra-quartile range (box) with upper and lower (whisker) values representing 99% data coverage. Colored circles represent Uninf-BL (blue), Uninf-T (red), Inf-BL (yellow), and Inf-T (purple).

Fig. 3

Correlation of the peptide expression changes for all four H3N2 and HRV cohorts (r = 0.871). Graph shows peptides with significant expression changes (Benjamini-Hochberg FDR < 0.05) in H3N2 (red circle, 10 peptides), HRV (yellow circle, 14 peptides), both H3N2 and HRV (blue circle, 16 peptides), or neither cohort (purple circle) as measured by MRM.

Fig. 4

Performance of a 10-peptide classification model in independent ARV cohorts. The model was fit to discriminate Inf-T from Inf-BL and Uninf-T. (A) Probability of positive is plotted (x-axis) for individual participant samples from 4 cohorts, with threshold for positive classification set at > 0.5. Lines connect paired samples from individuals at baseline (left) and time T (right). Solid red line = infected; dashed blue line = uninfected; dashed green line = uninfected sham inoculation; open circles = classifier agrees with phenotype label; X = classifier disagrees with phenotype label. (B) Receiver operating curve for LOOCV with the 10 peptide (9 unique proteins) classifier. The optimal threshold on the curve (open red circle) produces 0.8623 AUROC, 75% TPR, 97.46% TNR, with the confusion matrix and peptide weights shown in Table 3A and B, respectively.