# Loss of resident donor-derived Tregs and the emergence of monocyte-derived alveolar macrophages leads to acute rejection and chronic dysfunction in lung transplant recipients

> **NIH NIH F30** · NORTHWESTERN UNIVERSITY · 2024 · $52,897

## Abstract

PROJECT SUMMARY: Chronic lung allograft dysfunction (CLAD) is the primary driver of morbidity and
mortality in lung transplant recipients. Currently there is a need to identify clinical and molecular biomarkers of
CLAD, where the latter has the potential to inform targetable pathways for intervention. There is growing
evidence to hypothesize that a key component of CLAD pathobiology is the recruitment of profibrotic
monocyte-derived alveolar macrophages (MoAM) upon injury to the lung epithelium. Profibrotic MoAMs
stimulate the activation, differentiation, and proliferation of myofibroblasts. With sustained injury, MoAMs are
continually maintained through colony stimulating factor 1 (CSF1) signaling through its cognate receptor
(CSF1R), leading to progressive fibrosis. T-regulatory cells dampen ongoing injury and have been shown to
mitigate CLAD in preclinical models and are also associated with more favorable lung transplant outcomes in
humans. Our group demonstrated that recipient-derived MoAMs express profibrotic genes in mice and humans
and that administering a CSF1R antagonist improved fibrosis in a murine model of CLAD. Translating these
findings in humans requires analyzing longitudinal data collected before CLAD diagnosis. Where most studies
focus on measurements made at one or two points in time, often when CLAD has significantly progressed, the
proposed work will leverage a machine learning approach developed by our group to determine clinical states
and their sequences that develop after lung transplantation. This work will examine associations between
these clinical states with flow cytometry, single cell and spatial transcriptomic analysis of BAL fluid across time
to identify early, predictive indicators of CLAD. Specifically, the proposed work will address the hypothesis that
the emergence of pathogenic MoAM and loss of tissue-resident donor-derived T-regs in serially sampled BAL
predict CLAD and ACR respectively. Aim 1 will determine whether the CSF1-driven maintenance of profibrotic
MoAMs precedes the clinical diagnosis of CLAD. Aim 2 will determine whether the paucity of tissue-resident
donor-derived T-regs is associated with CLAD after ACR. Both aims consist of 1. Combining flow cytometry
and single-cell transcriptomics to quantify cell abundances and ligand receptor analyses relevant to either aim
unique to CLAD BAL and 2. Integrating these molecular features with clinical data in machine learning models
for CLAD (aim 1) and ACR (aim 2) prediction. In leveraging these data-driven and machine learning
approaches, the long term goal of the proposed work is to reveal therapeutic targets and elucidate early signs
of ACR and CLAD for timelier intervention and to ultimately reduce lung transplant failure. The candidate and
her mentors have designed a detailed training plan that utilizes the support of diverse mentors and resources
in immunology, single-cell genomics, machine learning, and translational research. Ultimately, this train...

## Key facts

- **NIH application ID:** 10994449
- **Project number:** 1F30HL175886-01
- **Recipient organization:** NORTHWESTERN UNIVERSITY
- **Principal Investigator:** Lucy Luo
- **Activity code:** F30 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $52,897
- **Award type:** 1
- **Project period:** 2024-09-02 → 2026-09-01

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10994449

## Citation

> US National Institutes of Health, RePORTER application 10994449, Loss of resident donor-derived Tregs and the emergence of monocyte-derived alveolar macrophages leads to acute rejection and chronic dysfunction in lung transplant recipients (1F30HL175886-01). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10994449. Licensed CC0.

---

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