Mammalian cells are used to produce valuable therapeutic and industrial proteins, such as monoclonal antibodies, enzymes, cytokines and vaccines. Many important proteins are difficult to produce because they are not easily secreted from cells. This project will decipher the molecular mechanisms controlling protein secretion. Understanding these mechanisms will enable strategies for engineering protein secretion and improving production of high-value proteins. Advanced machine learning approaches will also be developed to guide cell engineering. As part of this project, local high school students will be trained in a summer program focused on biological big data analytics.

This project will establish a novel method to identify and quantify the host cell secretory pathway machinery that directly regulates protein secretion. The primary focus will be on monoclonal antibodies (mAbs) and related Fc-fusion molecules of primary importance to the biotherapeutic industry. Protein-protein interactions (PPIs) will be measured in situ between the mAbs/related proteins and the cellular secretory pathway machinery. This will be done using proximity biotinylation with our Fc-mediated biotinylation by antibody recognition (FcBAR) method. This project will (1) establish a method for measuring essential PPIs for a secreted protein and identify PPIs correlating with secretion rate. This method will be applied to Rituximab-producing CHO (Chinese hamster ovary) cells. (2) A novel systems biology analysis approach will be applied to unravel how changes in PPI strength impact secretion. This computational framework will help define roles of interacting proteins and prioritize PPIs for further study. (3) Expression of these interaction factors will be modulated to validate their role in mAb secretion. (4) Finally, product-specific vs. more general PPIs used for protein secretion will be identified by quantifying PPIs for additional Fc-fusion proteins and comparing how much the secretory pathway needs differ across secreted proteins. The tools developed by this project will guide host cell engineering for biomanufacturing of diverse proteins and will provide deep insights into the functions of the mammalian secretory pathways.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

Project Start
Project End
Budget Start
2021-04-01
Budget End
2024-03-31
Support Year
Fiscal Year
2020
Total Cost
$670,036
Indirect Cost
Name
University of California San Diego
Department
Type
DUNS #
City
La Jolla
State
CA
Country
United States
Zip Code
92093