# Perinatal Origins of Asthma

> **NIH NIH F30** · UNIVERSITY OF ILLINOIS AT CHICAGO · 2020 · $43,205

## Abstract

Project Abstract
 Early in life many children develop wheezing which can be a sign of early-onset asthma. Yet, not all
children who experience wheezing episodes develop asthma. Predicting asthma that begins early in life is
important as those who develop early-onset asthma are more likely to have persistent symptoms and structural
changes to the lung. Identification and early intervention of high risk individuals could prevent long term lung
function abnormalities and provide an alternative to costly treatment. To date, risk prediction models for early-
onset asthma are limited mainly to clinical history and presentation. Additionally, no one model contains both
reliable sensitivity and specificity or clinically valid serological predictors for early-onset asthma.
 Our laboratory has focused previously on immunologic responses to common asthmatic triggers, house
dust mite (HMD) and cockroach allergens, between individuals and how these responses differ between
populations and outcomes. We have identified increased lymphoproliferation and cytokine response (IL-13) in
allergen-stimulated cord blood mononuclear cells (CBMC) derived from individuals who are atopic or African-
Americans. Bacterial products, such as lipopolysaccharide and peptidoglycan, stimulate the cytokine production
of IL-13 and IFN-γ. In this proposal our preliminary data indicate that the production of IL-13 after allergen
exposure in CBMCs is associated with exposure to different microbiota. In addition, early exposure to
Moraxellaceae greatly enhances IL-13 production by CBMCs after exposure to HDM. Taken together, our data
suggest that early-life bacterial exposure may enhance the production of cytokines that can alter host physiology
and immunity.
 In this translational medicine proposal, we will build a predictive model for early-onset asthma that will be
applicable to general and high risk populations. Our approach will include high-throughput techniques to assess
bacterial exposure and immunologic response in the perinatal time period. We will integrate the large amount of
data generated into a pipeline with machine learning algorithms that will be regulated specifically for the
prediction of later onset of disease. In addition, we will assess bacterial exposure in association with mothers
who carry risks for asthma development in their children. The proposed studies will address both the need of
predictive model for early-onset asthma and identify bacterial-host interactions associated with high risk
populations (e.g. maternal risk factors). Together, these studies form the platform for multidisciplinary training in
microbe- immune interactions, predictive risk for asthma, bioinformatics and form a solid foundation to launch a
successful career as a physician- scientist.

## Key facts

- **NIH application ID:** 10000960
- **Project number:** 5F30HL136001-04
- **Recipient organization:** UNIVERSITY OF ILLINOIS AT CHICAGO
- **Principal Investigator:** Benjamin Turturice
- **Activity code:** F30 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $43,205
- **Award type:** 5
- **Project period:** 2017-09-01 → 2021-05-16

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10000960

## Citation

> US National Institutes of Health, RePORTER application 10000960, Perinatal Origins of Asthma (5F30HL136001-04). Retrieved via AI Analytics 2026-05-21 from https://api.ai-analytics.org/grant/nih/10000960. Licensed CC0.

---

*[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*