# Effectors of retrotransposon movement

> **NIH NIH R01** · UNIVERSITY OF GEORGIA · 2021 · $442,517

## Abstract

PROJECT SUMMARY/ABSTRACT
Effectors of retrotransposon movement
 The retrovirus-like transposon Ty1 of Saccharomyces is an informative model for understanding how
retrotransposons and retroviruses mobilize, shape the genome, and interact with host and element encoded
factors. The additive nature of the retroelement replication cycle can result in massive increases in copy
number if left unchecked. Host cells have countered with several defense systems, including domestication of
retroelement genes that now act as restriction factors to minimize propagation. We discovered a truncated
form of the Ty1 capsid protein that inhibits virus-like particle assembly and function. This self-encoded
restriction factor expands the repertoire of defense proteins targeting the capsid. Our work also highlights an
intriguing host-parasite strategy. Instead of inhibiting all transposon movement by domesticating the restriction
gene as a distinct locus, yeast and Ty1 may have coevolved a relationship that allows high levels of
transposition when copy numbers are low and progressively less transposition as copy numbers rise. Our goal
is to understand the mechanism and evolution of Ty1 copy number control by a combination of genetic,
genomic, cell biological, biochemical and structural approaches.

## Key facts

- **NIH application ID:** 10224748
- **Project number:** 5R01GM124216-04
- **Recipient organization:** UNIVERSITY OF GEORGIA
- **Principal Investigator:** David J. Garfinkel
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $442,517
- **Award type:** 5
- **Project period:** 2018-09-01 → 2024-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10224748

## Citation

> US National Institutes of Health, RePORTER application 10224748, Effectors of retrotransposon movement (5R01GM124216-04). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10224748. Licensed CC0.

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