# Uncovering cell factors with aggregate clearance activity by scalable induced proximity

> **NIH NIH K99** · CHILDREN'S HOSP OF PHILADELPHIA · 2022 · $129,627

## Abstract

Summary
 Toxic aggregation of proteins is a pathogenic mechanism in neurodegenerative disease (ND). Proteins
such as TDP-43 in frontotemporal dementia (FTD) or Alzheimer’s disease (AD) misfold into aggregates, which
both disrupts endogenous protein functions and confers toxic new functions that lead to neuronal cell death.
There are no cures for AD or FTD, but current research suggests aggregate clearance is a promising therapeutic
strategy. Aggregates of TDP-43 are influenced by the proteostasis network consisting of chaperones and
degradation machinery, though which factors can mediate aggregate clearance and how they do so is not known.
Prior work using small molecule ligands called PROTACs to induce proximity of target proteins to components
of the ubiquitin-proteasome system has proven a promising approach for clearing aberrant protein species.
Analogously, this proposal aims to systematically screen the large, uninterrogated portion of the proteostasis
network for its ability to clear aggregates of TDP-43 by ligand-induced proximity. In addition to proteins that may
function as degraders or disaggregases, RNAs will also be screened for aggregate clearance activity. This work
will reveal new quality control factors and mechanisms involved in aggregate clearance and will provide a
platform for translational development of multispecific drugs for ND.
 This work will be accomplished in three Aims, providing me with critical training for transition into
independent research. In Aim 1, I will develop an experimental workflow to image aggregate-prone TDP-43
expressed in a multiplexed tag cell library. I will write computational pipelines to characterize mechanisms of
aggregate clearance and integrate them with automated in-situ sequencing to reveal the identity of the putative
effector in each cell. This aim will optimize reagents and develop analysis methods for induced proximity screens,
training me in models of ND and pooled image analysis. In Aim 2, I will focus on screening proteostasis network
components by scalable induced proximity and validate factors mediating aggregate clearance in neurons and
in vitro, gaining training in cell biology and biochemical techniques to characterize degraders and disaggregases.
This work will uncover recruitable factors inducing aggregate clearance. In Aim 3, as an independent
investigator, I will develop pooled tagging of transcripts and use the resulting multiplexed cell libraries to screen
for RNAs with induced proximity-based aggregate clearance activity. The outcome of this work will systematically
characterize the proteostatic potential of RNAs in modulating aggregation and greatly expand the space of
recruitable effectors with potential therapeutic benefit. The expert mentoring team I have assembled, as well as
the excellent training environment at CHOP and Penn, will greatly facilitate my research and training during the
mentored phase of this proposal and provide me with the skills necessary to begi...

## Key facts

- **NIH application ID:** 10524896
- **Project number:** 1K99AG075256-01A1
- **Recipient organization:** CHILDREN'S HOSP OF PHILADELPHIA
- **Principal Investigator:** Yevgeniy Vladimirovich Serebrenik
- **Activity code:** K99 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $129,627
- **Award type:** 1
- **Project period:** 2022-09-15 → 2024-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10524896

## Citation

> US National Institutes of Health, RePORTER application 10524896, Uncovering cell factors with aggregate clearance activity by scalable induced proximity (1K99AG075256-01A1). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10524896. Licensed CC0.

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