# None-genetic metabolic heterogeneity and its influence on drug tolerance

> **NIH NIH R35** · WASHINGTON UNIVERSITY · 2020 · $393,750

## Abstract

PROJECT SUMMARY
Cellular heterogeneity is ubiquitous across all domains of life. Genetically identical cells can
display heterogeneous metabolic activities, even when grown in identical environments. In
bacteria, metabolic heterogeneity can shape the ensemble growth rate and affect antibiotic
tolerance. In higher organisms, metabolic activity is functionally related to tumor activity and drug
tolerance. While cellular heterogeneities in mRNA and protein abundance have been extensively
studied, many questions remain regarding metabolic heterogeneity. For instance, what
determines the size and frequency of metabolic fluctuation? How is metabolic heterogeneity
regulated? Can we control metabolic heterogeneity and therefore eliminate drug-tolerant cells?
The lack of fundamental understanding about this important topic has severely limited the
development of effective treatments for multiple diseases in which a small number of transiently
tolerant cells often cause disease recurrence. Over the past few years, the Zhang lab has
developed several methods to study metabolite heterogeneity, including metabolite-biosensor-
assisted single-cell imaging and metabolite quantification by cell sorting. Our work identified large,
non-genetic heterogeneity in fatty acid biosynthesis, and we exploited metabolic heterogeneity
for biotechnology applications (i.e. overproduction of chemicals). In this MIRA proposal, we aim
to obtain a systematic understanding of bacteria metabolic heterogeneity by using our
existing and novel methods for single-cell metabolic analysis. Using Escherichia coli as a
model, we will construct a tunable metabolic system that produces a unique fluorescent
metabolite with controlled flux and metabolite concentration. Microfluidics-assisted time-lapse
fluorescent microscopy will be used to simultaneously quantify the concentrations of this
metabolite and its biosynthetic enzyme, the cell growth rate, and the age of single cells. Data
analysis combined with modeling will be used to determine the origin, dynamics, and propagation
of metabolic heterogeneity. Furthermore, using the native fatty acid catabolic pathway as a model,
we will study how transcriptional and allosteric regulations affect metabolic heterogeneity. In
addition, we will explore the influence of metabolic heterogeneity on drug tolerance and seek to
reduce metabolic heterogeneity and multimodality. This project will reveal novel principles that
govern metabolic heterogeneity and provide a quantitative framework to explain various single-
cell phenomena. Understanding the regulation of metabolic heterogeneity and its influence on
drug tolerance will inform strategies to eliminate heterogeneous variants that are insensitive to
drugs, thus providing new treatments for recurrent diseases.

## Key facts

- **NIH application ID:** 9995537
- **Project number:** 5R35GM133797-02
- **Recipient organization:** WASHINGTON UNIVERSITY
- **Principal Investigator:** Fuzhong Zhang
- **Activity code:** R35 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $393,750
- **Award type:** 5
- **Project period:** 2019-09-01 → 2024-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9995537

## Citation

> US National Institutes of Health, RePORTER application 9995537, None-genetic metabolic heterogeneity and its influence on drug tolerance (5R35GM133797-02). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/9995537. Licensed CC0.

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