# A Mechanistic and Translational Research Program Linking Impaired Resolution, Defective Efferocytosis, and Clonal Hematopoiesis to the Formation of Clinically Dangerous Atherosclerotic Plaques

> **NIH NIH R35** · COLUMBIA UNIVERSITY HEALTH SCIENCES · 2020 · $979,027

## Abstract

This R35 proposal represents a comprehensive, long-term program that explores new mechanisms and
therapeutic concepts related to the formation of the unique types of atherosclerotic plaques that cause acute
cardiovascular disease (CVD). The training and mentoring of young scientists is also a key part of this program.
The PI has held multiple NIH grants without interruption for many years, publishes on atherosclerosis and
cardiometabolic disease in the highest impact journals, and has a highly successful record of training young
scientists to be independent academic researchers. The program will explore new, highly interrelated concepts
related to four key areas in which major gaps exist: (i) inflammation resolution and efferocytosis (clearance of
dead cells); (ii) pathophysiology of the minority of atherosclerotic lesions that are most clinically important; (iii)
amino acid metabolism in Ms as it relates to high-burden efferocytosis; and (iv) aging-related clonal
hematopoiesis (CH). Processes that impair inflammation resolution, which are distinct from those that promote
inflammation per se, and defective efferocytosis promote the formation of clinically relevant necrotic, thin-capped
plaques. The lab's new work indicates that (i) the ability of Ms to internalize multiple apoptotic cells (high-burden
efferocytosis) is critical to avoid necrotic plaques (Cell 2017); and (ii) a pathway related to M metabolism of
apoptotic cell-derived amino acids is critical for high-burden efferocytosis. Another exciting new concept
supported by preliminary data is that impaired efferocytosis and resolution are exacerbated by CH, which is
emerging as a major age-related risk factor for atherosclerotic CVD. The overall vision of the program is to study
these new areas by first using (i) mouse and human Ms to elucidate in-depth mechanisms; and (ii) genetically
altered mice to test causation in advanced plaque progression. The R35 will also explore the therapeutic potential
of these ideas in pre-clinical models, with the hypothesis that resolution mediator therapy will have efficacy and
safety advantages over conventional anti-inflammatory therapy. The PI will then use the flexibility and continuity
of the R35 program to move into new directions related to human studies. Through a rich network of collaborators
at Columbia and elsewhere—including Columbia's Cardiovascular and Metabolic Precision Medicine program—
the program will apply the new discoveries to (i) analyses of human atheroma; (ii) human genetics, including
subjects with KOs of genes in resolution/efferocytosis pathways through a collaboration with the PROMIS study;
and (iii) studies with human monocyte- and iPSC-derived Ms that are amenable to CRISPR/Cas9-mediated
genetic engineering, as guided by the program's mechanistic and human genetic data. Through the flexibility
and forward-looking nature of the R35 program, the combination of the proposed mechanistic work and human
studies will provide a p...

## Key facts

- **NIH application ID:** 9889165
- **Project number:** 5R35HL145228-02
- **Recipient organization:** COLUMBIA UNIVERSITY HEALTH SCIENCES
- **Principal Investigator:** Ira A Tabas
- **Activity code:** R35 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $979,027
- **Award type:** 5
- **Project period:** 2019-03-07 → 2026-02-28

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9889165

## Citation

> US National Institutes of Health, RePORTER application 9889165, A Mechanistic and Translational Research Program Linking Impaired Resolution, Defective Efferocytosis, and Clonal Hematopoiesis to the Formation of Clinically Dangerous Atherosclerotic Plaques (5R35HL145228-02). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/9889165. Licensed CC0.

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