# Mapping genetic variation in enzyme velocity to growth rate phenotype

> **NIH NIH R01** · UT SOUTHWESTERN MEDICAL CENTER · 2024 · $342,695

## Abstract

SUMMARY
Metabolic enzyme velocity (the product of activity and abundance) is shaped by the need of growing cells to
maintain metabolic flux while avoiding the accumulation of toxic intermediates and limiting inefficient
biosynthesis. Imbalances in enzyme velocity play a significant role in metabolic disease, and changes in enzyme
expression are often associated with drug resistance. Nonetheless, the connection between metabolic enzyme
expression, intracellular abundance, and cell growth rate is poorly characterized. Our long term goal is to create
a genome-scale model that quantitatively relates E. coli gene expression to growth rate phenotypes across both
common laboratory and host-associated conditions. We envision using this model for antibiotic discovery,
biosynthetic pathway engineering, and to interpret the effect of mutations in clinical isolates. Recently, we
developed a new modeling approach that predicts the growth rate effects of combinatorial variation in E. coli
gene expression and environment from sparsely sampled experimental training data. The basic strategy is to
first quantify the growth rate effects of gradated changes in gene expression across multiple environments and
genetic backgrounds of interest. Then, we use these data to parameterize a machine-learning model describing
the connection between gene expression and growth rate. We found that the model can predict the effects of at
least four combinatorial perturbations in gene expression and environment when trained on experimental data
considering only pairwise perturbations. The central goal of this grant is to now apply and extend this approach
to quantitatively understand the connection between enzyme abundance, growth rate, and antibiotic resistance
in E. coli. More specifically, we will: (1) identify and model the metabolic factors influencing trimethoprim
resistance in E. coli, (2) quantify the stoichiometric constraints on relative enzyme expression and abundance in
16 central metabolic pathways, and (3) apply new sequencing-based tools to simultaneously quantify gene
knockdown effects and growth rate across core metabolism. Together, this work will test hypotheses about the
modular organization of metabolism and yield a deeper understanding of the connection between metabolism
and antibiotic resistance. Moreover, our work in Aim 3 will generate a genome-scale collection of barcoded
strains for interrogating the dynamic connection between gene expression and growth rate across environments.

## Key facts

- **NIH application ID:** 10881154
- **Project number:** 2R01GM136842-05
- **Recipient organization:** UT SOUTHWESTERN MEDICAL CENTER
- **Principal Investigator:** Kimberly Ann Reynolds
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $342,695
- **Award type:** 2
- **Project period:** 2020-04-20 → 2025-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10881154

## Citation

> US National Institutes of Health, RePORTER application 10881154, Mapping genetic variation in enzyme velocity to growth rate phenotype (2R01GM136842-05). Retrieved via AI Analytics 2026-06-24 from https://api.ai-analytics.org/grant/nih/10881154. Licensed CC0.

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