A suite of conditional mouse models for secretome labeling

NIH RePORTER · NIH · R21 · $198,226 · view on reporter.nih.gov ↗

Abstract

PROJECT SUMMARY. Secreted proteins and polypeptides mediate fundamental axes of cell and tissue crossltalk. In recent years, there has been a renewed interest in profiling the entire collection of secreted proteins and proteins (e.g., the secretome) from cells and organs in vivo. To address this challenge, we have recently described a high-sensitivity proximity labeling methodology using adeno-associated viruses (AAVs) to delivery the proximity labeling enzyme TurboID into distinct subcellular compartments for cell type-specific secretome profiling in vivo. Our methodology provides a direct biochemical readout of secretome composition and dynamics in a cell type-specific manner, directly in a living animal. Here, we will generate and validate a suite of conditional mouse models based on this secretome profiling methodology. This application and our proposed suite of conditional secretome labeling mice directly responds to the Funding Opportunity Announcement PAR-19-369 which encourages the development of improved animal models and novel technologies to better understand health and disease. These conditional mice are important to develop because many cell types still cannot be transduced efficiently or quantitatively with AAVs, or are rapidly turned over such that transient AAV infection does not result in stable labeling of those cellular populations. In Specific Aim 1, we will generate three conditional secretome labeling mouse lines with TurboID localized to the secretory pathway, cytosol, or membrane and use shotgun proteomics to benchmark their performance relative to our first-generation viral reagents. In Specific Aim 2, we will use these conditional mice to address a fundamental and well-defined question that had previously remained inaccessible experimentally: to what extent are the levels of secreted proteins determined transcriptionally in vitro? Successful completion of this proposal will provide investigators with a conditional animal model to answer previously inaccessible and fundamental questions regarding the identity of secreted protein and polypeptide factors mediating cell and tissue crosstalk in their experimental system of interest.

Key facts

NIH application ID
10640784
Project number
1R21OD034455-01
Recipient
STANFORD UNIVERSITY
Principal Investigator
Jonathan Z Long
Activity code
R21
Funding institute
NIH
Fiscal year
2023
Award amount
$198,226
Award type
1
Project period
2023-04-15 → 2025-03-31