# Immunogenetic Profiling for Risk of Primary Graft Dysfunction after Heart Transplantation

> **NIH NIH R21** · DUKE UNIVERSITY · 2022 · $201,250

## Abstract

Immunogenetic Profiling for Risk of Primary Graft Dysfunction after Heart Transplantation
For the approximately 250,000 people in the United States living with end-stage heart failure, heart
transplantation (HT) remains the gold standard therapy. Despite excellent long-term survival, early mortality
remains high as a result of infection, rejection, and primary graft dysfunction (PGD). PGD, in particular, is a
devastating complication of HT in which acute failure of the new allograft leads to hemodynamic instability and
end-organ hypoperfusion. Thus, we propose herein to build upon interesting preliminary data suggesting that
plasma levels of CLEC4C, a protein that serves as a cell surface receptor for plasmacytoid dendritic cells
(pDCs), are higher prior to transplant in patients who subsequently develop PGD after HT. Specifically, we will
integrate immune profiling, single cell sequencing of pDCs and PBMCs, and single cell proteomics in
isolated pDCs to test the hypothesis that pDCS are quantitatively and functionally enriched in HT
recipient who develop PGD. This study has the potential to identify clinically relevant biomarkers in the care
of HT recipients and may help to elucidate the underlying molecular mechanisms of PGD.

## Key facts

- **NIH application ID:** 10391731
- **Project number:** 1R21AI167366-01
- **Recipient organization:** DUKE UNIVERSITY
- **Principal Investigator:** Svati H. Shah
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $201,250
- **Award type:** 1
- **Project period:** 2021-11-01 → 2023-10-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10391731

## Citation

> US National Institutes of Health, RePORTER application 10391731, Immunogenetic Profiling for Risk of Primary Graft Dysfunction after Heart Transplantation (1R21AI167366-01). Retrieved via AI Analytics 2026-06-02 from https://api.ai-analytics.org/grant/nih/10391731. Licensed CC0.

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