Peptide Biomarkers for the Diagnosis of Dengue Infection

Abstract

In a world with an increasing population at risk of exposure to arthropod-borne flaviviruses, access to timely and accurate diagnostic tests would impact profoundly on the management of cases. Twenty peptides previously identified using a flavivirus proteome-wide microarray were evaluated to determine their discriminatory potential to detect dengue virus (DENV) infection. This included nine peptides recognized by IgM antibodies (PM peptides) and 11 peptides recognized by IgG antibodies (PG peptides). A bead-based multiplex peptide immunoassay (MPIA) using the Luminex technology was set-up to determine Ab binding levels to each of these peptides in a panel of 323 carefully selected human serum samples. Sera are derived from individuals either infected with different viruses, namely, the four DENV serotypes, Zika virus (ZIKV), yellow fever virus (YFV), chikungunya virus (CHIKV), West Nile virus (WNV) and Human immunodeficiency virus (HIV), or receiving vaccination against YFV, tick-borne encephalitis (TBEV), and Japanese encephalitis virus (JEV). Additionally, a set of healthy controls were included. We targeted a minimum specificity of 80% for all the analysis. The PG-9 peptide had the best sensitivity (73%) when testing DENV sera from acute patients (A-DENV; <8 days since symptom onset). With sera from convalescent DENV patients (C-DENV; >10 days since symptom onset) the FPG-1 peptide was the best seromarker with a sensitivity of 86%. When combining all A-DENV and C-DENV samples, peptides PM-22 and FPG-1 had the best-diagnostic performance with a sensitivity of 60 and 61.1%, and areas under the curve (AUC) of 0.7865 and 0.8131, respectively. A Random forest (RF) algorithm was used to select the best combination of peptides to classify DENV infection at a targeted specificity >80%. The best RF model for PM peptides that included A-DENV and C-DENV samples, reached a sensitivity of 72.3%, while for PG peptides, the best RF models for A-DENV only, C-DENV only and A-DENV + C-DENV reached a sensitivity of 88.9%, 89.1%, and 88.3%, respectively. In conclusion, the combination of multiple peptides constitutes a founding set of seromarkers for the discrimination of DENV infected individuals from other flavivirus infections.

Keywords: ROC analysis; arbovirus; dengue peptide; immunoassay; luminex; random forest; seromarkers.

Figure 1

Location of peptide biomarkers in the DENV proteome. Selected peptides are plotted relative to the polyprotein coordinates (start amino acid position) for DENV (P33478). Each blue vertical bar represents a single peptide. Top and bottom plots are IgM and IgG peptides respectively. Structural proteins: Capsid (C), Membrane (pr, M) and Envelope (E), and non-structural proteins: NS1, NS2A, NS2B, NS3, NS4A, NS4B, and NS5 are color coded. The peptide names are also indicated.

Figure 2

Antibody responses to peptide biomarkers. (A) Boxplot display of the IgM response against the peptides in median fluorescence intensity (MFI) values in serum samples for each virus group. Positive DENV samples (n = 137) where confirmed by RT-PCR in endemic patients and RT-PCR and/or IgM/IgG seroconversion in travelers. DENV-negative samples (n = 185) corresponded to ZIKV, YFV, TBEV, JEV, WNV, CHIKV, HIV, and negative controls from non-endemic healthy donors. Horizontal box boundaries and midline denote sample quartiles. (B) Boxplot display of the measured IgG against the biomarkers in MFI values in serum samples from each group. (C) Boxplot display of the measured IgM and IgG (MFI) against the viral lysates (entire proteome) of the four DENV serotypes in serum samples from each group. Significant differences (P<.05) between MFI values in DENV-infected individuals compared to the response from people with exposure to other flaviviruses was found for all peptides, except for those labeled with *.

Figure 3

Diagnostic performance of the peptide biomarkers. (A) ROC curves for IgM peptides and (B) IgG peptides. The three columns correspond to the separate analysis of the data considering as positive DENV samples: acute samples only (left graphs), convalescent samples only (center graphs) and acute + convalescent (right graphs). Colors correspond to Abs against the different antigens as shown in panel (C). (C) Area under the ROC curve (AUC) for individual biomarkers. Arrows in the top of each bar indicates an increase in the AUC of the peptide when only acute DENV or convalescent DENV samples are included as positive controls in the analysis.

Figure 4

Heatmap of the Spearman’s correlation coefficient between biomarkers. (A) IgM and (B) IgG Ab responses against the peptides were correlated. Correlation coefficients are indicated by the color scale. Blue indicates a negative correlation; red indicates a positive correlation.

Figure 5

ROC performance analysis combining multiple peptides using a random forest algorithm. ROC curves for (A) IgM and (C) IgG peptides. The peptides are added sequentially based on their classification accuracy. The axes have been rescaled to better differentiate between high values of sensitivity and specificity. (B) For a pre-defined assay specificity of 80%, the assay sensitivity increases with sequentially adding additional peptide biomarkers for detecting IgM (B) and IgG (D). Sensitivity was estimated using a random forest classifier. Points and whiskers denote the median and 95% CIs from repeat cross-validation.

Figure 6

Positive predictive values (PPV) and negative predictive values (NPV) for the RFM3 and RFG3 models. According to the Random Forest analysis for the combination of multiple peptides, the calculated specificity and sensitivity values were fixed at 80% and 74% for the RFM3 model, and 80% and 88% for the RFG3 model, respectively. The RFM3 model included the following peptides: PM-22, PM-2, PM30, PM-34, PM-23, and PM-12. The RFG3 model included the FPG-1, PG-40, PG15A, PG-33, PG-1, and PG-9 peptides. PPV and NPV calculated at a pre-set DENV prevalence of 15% and 30%, which corresponds prevalence which corresponds to the intersection of the horizontal bars with the curves.