Multi-parameter approach to evaluate the timing of memory status after 17DD-YF primary vaccination

<div><p>In this investigation, machine-enhanced techniques were applied to bring about scientific insights to identify a minimum set of phenotypic/functional memory-related biomarkers for post-vaccination follow-up upon yellow fever (YF) vaccination. For this purpose, memory status of circulating T-cells (Naïve/early-effector/Central-Memory/Effector-Memory) and B-cells (Naïve/non-Classical-Memory/Classical-Memory) along with the cytokine profile (IFN/TNF/IL-5/IL-10) were monitored before-NV(day0) and at distinct time-points after 17DD-YF primary vaccination—PV(day30-45); PV(year1-9) and PV(year10-11). A set of biomarkers (eEfCD4; EMCD4; CMCD19; EMCD8; IFNCD4; IL-5CD8; TNFCD4; IFNCD8; TNFCD8; IL-5CD19; IL-5CD4) were observed in PV(day30-45), but not in NV(day0), with most of them still observed in PV(year1-9). Deficiencies of phenotypic/functional biomarkers were observed in NV(day0), while total lack of memory-related attributes was observed in PV(year10-11), regardless of the age at primary vaccination. Venn-diagram analysis pre-selected 10 attributes (eEfCD4, EMCD4, CMCD19, EMCD8, IFNCD4, IL-5CD8, TNFCD4, IFNCD8, TNFCD8 and IL-5CD4), of which the overall mean presented moderate accuracy to discriminate PV(day30-45)&PV(year1-9) from NV(day0)&PV(year10-11). Multi-parameter approaches and decision-tree algorithms defined the EMCD8 and IL-5CD4 attributes as the top-two predictors with moderated performance. Together with the PRNT titers, the top-two biomarkers led to a resultant memory status observed in 80% and 51% of volunteers in PV(day30-45) and PV(year1-9), contrasting with 0% and 29% found in NV(day0) and PV(year10-11), respectively. The deficiency of memory-related attributes observed at PV(year10-11) underscores the conspicuous time-dependent decrease of resultant memory following17DD-YF primary vaccination that could be useful to monitor potential correlates of protection in areas under risk of YF transmission.</p></div

Changes in neutralizing antibody titers and phenotypic/functional memory-related biomarkers at distinct time-points after primary 17DD-YF vaccination.

<p>Correlation analyses were performed to validate the time-dependent decline in (A) neutralizing antibody titers and 17DD-YF Memory-related (B) phenotypic and (C) functional features. Data are expressed as scattering distribution of individual values along distinct time-points after 17DD-YF primary vaccination against neutralizing antibody titers (PRNT) as well as phenotypic and functional features (YF-Ag/CC Index). Spearman’s correlation test was applied to identify significant time-dependent loss of memory-related biomarkers. Correlation indices (p and r) along with the 95% confidence band of the best-fit line are provided in the figure. Attributes with higher correlation indices (r values) are highlighted with gray background.</p

Overall 17DD-YF memory-related biomarker signatures at distinct time-points after primary vaccination.

<p>The phenotypic/functional biomarker signatures were built taking the proportion of subjects above the cut-off edges defined for each attribute, calculated as the median index value (17DD-YF/Control) for the study population. Diagrams were constructed for all study groups to calculate the proportion (%) of volunteers above the median cut-off indices for each biomarker (gray-shaded spots). (A) The PV(day30-45) group was used to construct the memory-related phenotypic and functional biomarker signatures and draw the reference curves, used for comparative analysis amongst the study groups, (B) NV(day0), (C) PV(year1-9) and (D) PV(year10-11). Hatched cells represent unavailable results. Data mining was carried out as proposed previously by Luiza-Silva et al., (2011), selecting from the PV(day30-45) reference curves, those biomarkers with more than 50% of volunteers above the cut-off index (surrounded by dashed rectangles). Comparative analysis amongst the study groups were carried out considering only the selected set of relevant biomarkers from the phenotypic and functional reference curves. Substantial change in the set of relevant biomarkers were highlighted by (*) when the proportion of subjects above the cut-off fell below 50%. The common set of relevant biomarkers on each study group was underscored in bold font.</p

17DD-YF memory-related biomarker signatures according to the age at primary vaccination.

<p>The memory-related biomarker signatures were constructed using the proportion of subjects above the cut-off edges defined for each attribute, calculated as the median index value (17DD-YF/Control) for the study population. The signatures were constructed for phenotypic/functional biomarkers, compiling the proportion (%) of volunteer above the median cut-off indices. The memory-related biomarker signatures, constructed for the PV(day30-45) group, were used as the reference curves for comparative analysis amongst the PV(year10-11) subgroups, categorized according to the age at primary vaccination, including (A) 20–30 years old, (B) 31–40 years old and (C) 41–74 years old. Comparative analysis were carried out considering only the set of relevant biomarkers pre-selected from the phenotypic/functional reference curves (surrounded by dashed rectangles), including those biomarkers with more than 50% of volunteers above the cut-off index in the PV(day30-45) reference curves (surrounded by dashed rectangles). Substantial changes in the set of relevant biomarkers were underscored by (*) when the proportion of subjects above the cut-off fell below 50%. The common set of relevant biomarkers on each study group was underscored in bold font.</p

Major phenotypic/functional biomarkers useful to monitor the memory status following primary 17DD-YF vaccination.

<p>(A) Decision tree analysis was carried out to identify root attributes [Ellipses] for phenotypic/functional (P&F) biomarkers amongst NV(day0)&PV(year10-11) [white rectangle] and PV(day30-45)&PV(year1-9) [black rectangle], considered biomarkers to discriminate unprotected from protected subjects. Leave-one-out-cross-validation analysis (LOOCV) was employed to minimize biased performance estimates by using all data set for decision tree model fitting. EMCD8 and IL-5CD4 were selected as major phenotypic and functional 17DD-YF Memory-related biomarkers, respectively. (B) Heatmaps were built, taking the mean index of the top-two phenotypic/functional biomarkers (EMCD8 & IL-5CD4) and demonstrating the proportion (%) of volunteers ranging from low (White) to high (Gray) YF-Ag/CC index. A scatter plot was constructed to show the sensitivity (Gray Circle) and specificity (White Circle) of the top-two biomarkers, employing the cut-off edge (Mean Index = 1.3) provided by the ROC curve analysis. (C) Resultant memory status was defined for each subject, considering the top-two biomarkers (Mean Index >1.3) and PRNT (>2.9 Log mIU/mL, according to Simões et al., 2012). Column statistics were used to calculate the proportion of subjects displaying differing categories of resultant memory, referred as none, top-two biomarkers—P&F, PRNT and both. (D) Pie charts illustrated the overall resultant memory status within each category, as determined by the top-two biomarkers and PRNT. Significant differences at p<0.05 (Chi-square test) of resultant memory status amongst study groups were represented by letters “a”, b”, “c” and “d” in comparison to NV(day0), PV(day30-45), PV(year1-9) and PV(year10-11), respectively.</p