# Core C: Proteomics Core

> **NIH NIH P01** · UNIVERSITY OF CALIFORNIA BERKELEY · 2020 · $284,485

## Abstract

Project Summary/Abstract (Core C, Krogan) 
Core C of this renewal of NIH 5P01AI063302-12 will apply innovative proteomics approaches to study the early 
immune response to bacterial infection. Using mass spectrometry-based approaches, we will analyze 
macrophages infected with three different bacteria (L. monocytogenes, L. pneumophila, and M. tuberculosis) to 
identify the host and bacterial proteins that are ubiquitylated or phosphorylated during infection. This work will 
provide a comprehensive, global view of the posttranslational landscape during early infection, how these 
events influence host and pathogen defenses, and how these events vary across bacterial species. These 
global studies will dovetail with other, more targeted studies that employ host and bacterial mutants to perturb 
specific pathways and proteins. We will employ innovative and powerful modeling approaches to map and 
integrate this data to uncover the complex functions and pathways that are a part of the immune system's 
ability to detect and thwart bacterial pathogenesis, and the specific ways in which the pathogens are able to 
disrupt these mechanisms. The core's analysis and modeling work will serve three experimental Projects and 
will integrate the effort of each, providing a unified vision of the Program's results.

## Key facts

- **NIH application ID:** 9977104
- **Project number:** 5P01AI063302-17
- **Recipient organization:** UNIVERSITY OF CALIFORNIA BERKELEY
- **Principal Investigator:** Nevan J Krogan
- **Activity code:** P01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $284,485
- **Award type:** 5
- **Project period:** 2004-12-01 → 2021-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9977104

## Citation

> US National Institutes of Health, RePORTER application 9977104, Core C: Proteomics Core (5P01AI063302-17). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/9977104. Licensed CC0.

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