# Defining neutrophil pathobiology in pediatric Long COVID

> **NIH NIH R01** · MASSACHUSETTS GENERAL HOSPITAL · 2024 · $828,233

## Abstract

Over 15.6 million children in the U.S. alone have been infected by SARS-CoV2. While most recover, roughly 1
million children suffer from Long COVID. Neutrophils have been shown to be hyperactivated in Long COVID,
which is concerning because they can be quite inflammatory, causing vascular and tissue damage and
contributing to disease. We aim is to define the neutrophil profiles driving Long COVID in order to ultimately offer
novel strategies for diagnosing and treating this new disease. To achieve this goal, we will use both single-cell
RNA sequencing technology to define neutrophil activation profiles and microfluidics to test neutrophil
functionality Long COVID, compared to healthy controls. Our central hypothesis is that neutrophil activation in
Long COVID carries a distinct neutrophilic gene expression and functional profile, which contributes to
pathogenicity. Importantly, we aim to partner with an existing clinical trial of larazotide for Long COVID
(ClinicalTrials.gov Identifier: NCT05747534) to test reversibility of neutrophil activation by targeting sources of
Spike antigenemia. Ultimately, mechanisms driving the pathogenesis of this newly emerged post-COVID-19-
related illness must be defined to establish diagnostics and effective therapies.

## Key facts

- **NIH application ID:** 10853960
- **Project number:** 1R01HL173059-01
- **Recipient organization:** MASSACHUSETTS GENERAL HOSPITAL
- **Principal Investigator:** Lael Yonker
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $828,233
- **Award type:** 1
- **Project period:** 2024-05-01 → 2029-02-28

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10853960

## Citation

> US National Institutes of Health, RePORTER application 10853960, Defining neutrophil pathobiology in pediatric Long COVID (1R01HL173059-01). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10853960. Licensed CC0.

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