Vaccines save millions of lives each year but the risk of infection remains high early in life. Improvement of early life immunization requires a better understanding of vaccine-induced molecular pathways that underlie protection and immunogenicity in the form of Correlates of Protection (CoP). Systems biology approaches (?OMICs?) applied to vaccinology have provided critical insights into vaccine-mediated protection, but have not yet been applied to the youngest, despite their great need for improved immunization. Immunization with Hepatitis B vaccine (HBV) starting at birth is highly effective resulting in protection of > 90%. HBV is one of the few vaccines that has a well characterized and quantifiable CoP (anti-Hep B surface antigen antigen (anti-HBs) antibody levels). Importantly, while there is an established minimal protective threshold (anti-HBs > 10 mIU/ml), the absolute titer reached correlates directly with protection (the higher the titer, the higher and the more durable the protection). Such high protective efficacy, coupled with a quantifiable CoP yet significant response-variability in the neonatal and infant population makes HBV an ideal model to define mechanisms underlying successful neonatal immunization. Accordingly, our HIPC proposal, focuses on ?Systems Biology to Identify Biomarkers of Neonatal Vaccine Immunogenicity? using HBV as the model. To this end, newborns will be immunized with nothing (delayed), HBV, BCG or (HBV + BCG) and peripheral blood pre-/post-immunization collected for transcriptomic and proteomic analyses to identify pathways associated with CoP. In our Project 2 (?Immune status as a predictor of neonatal vaccine immunogenicity?; PI Tobias Kollmann; Co-Lead Ryan Brinkman) we will analyze the exact same samples interrogated by OMIC approaches via detailed immune phenotyping to translate the derived OMICs signatures to host immune parameters. This will not only help to de-convolute the OMIC message, but generate the fine-granular detailed view necessary to identify biomarkers predicting a protective immune response following neonatal HBV vaccination.
In Aim 1 we will determine cell composition in blood samples by high-end flow cytometry in pre- and post immunization samples from newborns and correlate with vaccine outcome. To this end we have developed a novel automated unsupervised gating platform that is the equivalent of unbiased systems biology discovery approaches but for flow cytometry.
In Aim 2 we will determine the concentration of soluble immune modulators including cytokines, chemokines, and purine metabolizing enzymes in plasma pre- and post- immunization and correlate with vaccine outcome.
In Aim 3 we will develop novel tools to further advance high-end immune phenotyping, and apply them to the data generated here on the newborn vaccine response to HBV. Overall our efforts will provide key insight into immunophenotypes associated with protective neonatal immunization thereby informing future development of early life vaccines.
|Scheid, Annette; Borriello, Francesco; Pietrasanta, Carlo et al. (2018) Adjuvant Effect of Bacille Calmette-Guérin on Hepatitis B Vaccine Immunogenicity in the Preterm and Term Newborn. Front Immunol 9:29|
|Lux, Markus; Brinkman, Ryan Remy; Chauve, Cedric et al. (2018) flowLearn: fast and precise identification and quality checking of cell populations in flow cytometry. Bioinformatics 34:2245-2253|
|Borriello, Francesco; van Haren, Simon D; Levy, Ofer (2018) First International Precision Vaccines Conference: Multidisciplinary Approaches to Next-Generation Vaccines. mSphere 3:|