# Phenotypic profiling of bacterial stress response networks: A transformative framework for characterizing and predicting antibiotic targets and interactions

> **NIH NIH F32** · STANFORD UNIVERSITY · 2020 · $28,321

## Abstract

Project Abstract/Summary
The Wellcome Trust estimates the death toll due to microbial pathogenesis to be 700,000/year. This number is
expected to rapidly increase in the next decade if the rise of antimicrobial resistance remains unaddressed. As
a first step to understanding the mechanisms of antibiotic resistance emergence, recent studies have explored
the biological processes affected by antibiotics from a holistic cellular perspective. Results from these studies
have challenged the traditional notion of each antibiotic eliciting a specific stress, revealing communication
between bacterial responses that highlight the importance of probing systems-level cellular physiology and
exploiting multi-dimensional phenotypes.
 Although many attempts have been made to characterize cellular response to antibiotics on a
comprehensive scale, most of these studies suffer from the significant disadvantage of measuring bulk
population-level responses. As most resistant mutants are a sub-population that dominates after selective
antibiotic bottlenecks have been applied, bulk measurements that fail to account for single-cell behavior do not
capture the entire spectrum of responses to antibiotic stress.
 I will leverage two key technological developments: 1) a high-throughput imaging and image analysis
pipeline, and 2) a CRISPR interference library of essential gene knockdowns in the model organism
Escherichia coli to answer fundamental questions about the bacterial response to antibiotics. I propose to use
a combination of high-throughput microscopy and plate reader-based bulk measurements of fluorescent
stress-response reporters to map response dynamics in E. coli under both oxygen-rich and anoxic conditions. I
will combine morphological parameters and stress response information to build a rich landscape for
phenotypic profiling that can be utilized to identify targets of novel antibiotics, predict antagonism in
combinatorial therapies, and probe the fundamental wiring between pathways. To investigate the molecular
mechanisms underlying the network architecture, I will employ CRISPRi genetic tools to alter drug-target
expression and drug efflux. My overarching goal is to eliminate a key bottleneck in drug discovery and drug
administration approaches–the identification of cellular targets for antibiotics with unknown mechanisms of
action and prediction of combinatorial therapeutics with improved efficacy from the vantage point of stress-
response activation. This study should accelerate the antibiotic discovery pipeline through rapid target
identification while also contributing deep understanding of bacterial physiology to guide future research across
a wide range of organisms.

## Key facts

- **NIH application ID:** 9898254
- **Project number:** 5F32AI133917-03
- **Recipient organization:** STANFORD UNIVERSITY
- **Principal Investigator:** Manohary Rajendram
- **Activity code:** F32 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $28,321
- **Award type:** 5
- **Project period:** 2018-04-01 → 2020-08-21

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9898254

## Citation

> US National Institutes of Health, RePORTER application 9898254, Phenotypic profiling of bacterial stress response networks: A transformative framework for characterizing and predicting antibiotic targets and interactions (5F32AI133917-03). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/9898254. Licensed CC0.

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