# Spindle Assembly Checkpoint Silencing

> **NIH NIH R35** · UNIV OF NORTH CAROLINA CHAPEL HILL · 2022 · $388,750

## Abstract

ABSTRACT
The Spindle Assembly Checkpoint (SAC) is a signaling pathway responsible for the fidelity of chromosome
segregation. Disruption of this process leads to catastrophic cellular consequences, such as aneuploidy and
cancer. The primary effector of the SAC is the inhibitory complex known as Mitotic Checkpoint Complex
(MCC). MCC is responsible for binding and inhibiting the 1.2 MDa ubiquitin ligase Anaphase-Promoting
Complex/Cyclosome (APC). Once all of the sister chromatids achieve proper bipolar orientation, MCC is
dismantled. This restores APC activity to trigger the ubiquitin-mediated proteasomal destruction of key mitotic
regulators, e.g. Cyclin B and Securin, permitting mitotic exit. The mechanisms of release and disassembly of
APC-bound MCC (BUBR1, MAD2, BUB3, and CDC20- an APC coactivator) by a triad of large multiprotein
enzymes remain poorly understood. I aim to dissect this process using an innovative technological approach
involving enzyme kinetics, chemical crosslinking, protein engineering, electron microscopy, NMR,
crystallography, and cell-based assays. Information generated from the proposed research will have a long-
lasting impact on the cell cycle field and may enable the development of novel cancer therapeutics.

## Key facts

- **NIH application ID:** 10456802
- **Project number:** 5R35GM128855-05
- **Recipient organization:** UNIV OF NORTH CAROLINA CHAPEL HILL
- **Principal Investigator:** Nicholas Gene Brown
- **Activity code:** R35 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $388,750
- **Award type:** 5
- **Project period:** 2018-08-01 → 2023-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10456802

## Citation

> US National Institutes of Health, RePORTER application 10456802, Spindle Assembly Checkpoint Silencing (5R35GM128855-05). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10456802. Licensed CC0.

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