Effects of Persistent Innate Immune Activation on Vaccine Efficacy Pilot Study: Gene Expression Profiling of Immune Response to HBV Vaccination in Healthy Volunteers
Overview
- Phase
- Not Applicable
- Intervention
- Not specified
- Conditions
- Hepatitis B
- Sponsor
- Rockefeller University
- Enrollment
- 10
- Locations
- 1
- Primary Endpoint
- Number of Differentially Expressed Genes at p < 0.05 (Without Multiple Testing Correction).
- Status
- Completed
- Last Updated
- 7 years ago
Overview
Brief Summary
Vaccines have been responsible for preventing millions of deaths and extending the average human lifespan. Effective vaccines stimulate the cells of the immune system to activate genes and associated functions that bring about protective immunity.This study aims to define cellular functions and genes important for the hepatitis B (HBV) vaccine immune response in healthy individuals. The investigators hypothesize that many genes associated with innate and adaptive immune functions are important for an effective HBV vaccine response.
Detailed Description
Vaccines have been responsible for preventing millions of deaths and extending the average human lifespan. Effective vaccines stimulate the cells of the immune system to activate genes and associated functions that bring about protective immunity. Knowledge of those genes and cellular functions activated by effective vaccination can improve our understanding of how the immune system works and define the features necessary for a successful vaccine response. This study aims to define cellular functions important for the hepatitis B (HBV) vaccine immune response in healthy individuals. The investigators will identify those genes that are activated or suppressed in immune cells at various times after each dose of the HBV vaccine. The investigators will explore these vaccine-induced "gene signatures" to characterize the cellular functions associated with an effective immune response to HBV vaccination. The investigators hypothesize that many genes associated with innate and adaptive immune functions are important for an effective HBV vaccine response.
Investigators
Brad Rosenberg
Whitehead Presidential Fellow
Rockefeller University
Eligibility Criteria
Inclusion Criteria
- •Healthy volunteer without significant medical problems
- •Willing to receive three doses of an FDA-approved Hepatitis B vaccine
Exclusion Criteria
- •Male or female \< 18 and \> 60 years of age
- •Received any vaccine within a month prior to study vaccine
- •History of Hepatitis B infection
- •History of previous Hepatitis B vaccination(s)
- •History of Hepatitis C virus (HCV) infection or positive HCV antibody test
- •Participation in another clinical study of an investigational product currently or within the past 90 days, or expected participation during this study
- •Positive serum antibody against Hep B surface antigen and/or core Hep B core antigen
- •human immunodeficiency virus (HIV) positive
- •In the opinion of the investigator, the volunteer is unlikely to comply with the study protocol
- •Any clinically significant abnormality or medical history or physical examination including history of immunodeficiency or autoimmune disease
Outcomes
Primary Outcomes
Number of Differentially Expressed Genes at p < 0.05 (Without Multiple Testing Correction).
Time Frame: Day 1, Day 3, Week 1, and Week 2
Number of differentially expressed genes at time point versus prevaccination baseline (p\<0.05). Following Principal Components Analysis, data from one participant series was identified as a technical outlier and excluded from downstream analyses. Differential gene expression analysis was conducted with the voom/limma tools in the R statistical framework.
Number of Significantly Differentially Expressed Genes at False Discovery Rate (FDR)< 0.05 (Upon Correction for Multiple Testing).
Time Frame: Day 1, Day 3, Week 1, and Week 2
Number of significantly differentially expressed genes at time point versus prevaccination baseline (FDR\<0.05). Following Principal Components Analysis, data from one participant series was identified as a technical outlier and excluded from downstream analyses. Differential gene expression analysis was conducted with the voom/limma tools in the R statistical framework.