# Bioinformatics Core: Core C

> **NIH NIH U19** · DREXEL UNIVERSITY · 2020 · $228,855

## Abstract

Abstract: 
The microbiome is a key contributor to many physiologic parameters and diseases in mammals, but the effects 
on vaccine responses are less well defined. The microbiome has co-evolved with mammals over millions of 
years to include an estimated 100 trillion bacteria and perhaps 10-fold more viruses. We have recently found 
that anaerobic bacteria have profound effects on the generation of IL10-expressing CD4 Treg cells through 
autophagy gene-dependent mechanisms in both mice and humans. Importantly, it has been shown that 
preexisting antibodies to enteric bacteria can skew vaccine responses to cross-reacting HIV-1 antigen, 
arguably rendering a vaccine less protective and that enteric bacterial components regulate the vaccine 
response to influenza in mice through activation of Toll-like receptors. Helminth infections also have 
profound effects on host immune response. Limited study in human suggests that helminth infection 
may alter bacterial microbiome61. Helminths are documented to lower the efficacy of vaccination. 
The role of Core C is to provide centralized computational and technologic expertise, sequencing, and 
algorithms to generate an in depth analysis of the microbiome in subjects within the study cohorts that 
will be critical to understand both difference in the microbiome between subjects with and without 
helminth infection and the relationship between these descriptors and vaccine responses. Integrated 
analysis linking variations in the microbiome to study cohort metadata including measures of immune 
responses and other '-omic' signatures may lead to identification of digital and molecular signatures within the 
microbiome and host baseline immune characteristics that may be predictive of vaccine efficacy. Should the 
hypothesis of the U19 grant be validated (that the microbiome correlates with changes in basal immune 
parameters and vaccine responses), we will identify computational models that can be used as a component of 
analyses of new vaccines; these will be made accessible to the broader research community.

## Key facts

- **NIH application ID:** 9987494
- **Project number:** 5U19AI128910-04
- **Recipient organization:** DREXEL UNIVERSITY
- **Principal Investigator:** Rafick Pierre Sekaly
- **Activity code:** U19 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $228,855
- **Award type:** 5
- **Project period:** 2017-08-10 → 2022-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9987494

## Citation

> US National Institutes of Health, RePORTER application 9987494, Bioinformatics Core: Core C (5U19AI128910-04). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/9987494. Licensed CC0.

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