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Design of a tunable bacterial gene expression system using engineered σ factors
Twinkal Patel1, Amit Dinda1, Sankar Mahesh1
1Molecular Biophysics Unit, Indian Institute of Science, Bangalore, Karnataka, India.
Applied and Environmental Microbiology
|April 12, 2024
Summary
Engineered bacterial transcription factors (σ factors) can control gene expression by altering flexible loops. This method enhances biosynthetic pathways in E. coli without genetic modification.
Area of Science:
- Bacterial transcription regulation
- Synthetic microbiology
- Protein engineering
Background:
- Extracytoplasmic function (ECF) σ factors regulate specific gene expression in bacteria.
- Promoter specificity and transcription initiation efficiency are governed by dynamic polypeptide segments (L3 loop and linker) within σ factors.
- Limited conservation of these segments hinders correlation between protein-DNA interactions and transcription efficiency.
Purpose of the Study:
- To characterize features governing gene expression dynamic range using chimeric Escherichia coli σE.
- To engineer σ factors for bespoke gene expression levels.
- To improve efficiency of microbial biosynthetic pathways.
Main Methods:
- Created chimeric σE by replacing L3 loop and linker with segments from 10 Mycobacterium tuberculosis ECF σ factors.
- Performed in vitro and in vivo measurements to assess effects of polypeptide replacements on gene expression.
- Utilized the chimeric σE library to modulate enzyme levels in a two-enzyme biosynthetic pathway in E. coli.
Main Results:
- Generated an experimentally validated dataset of gene expression levels for σE chimeras.
- Demonstrated increased desired product levels in a biosynthetic pathway by altering intracellular enzyme concentrations.
- Showed that modifying flexible loops of σE modulates target gene expression without altering the genomic makeup.
Conclusions:
- Engineering σ factors via modification of flexible loops offers a route to control gene expression.
- Chimeric σE library enables pre-determined conditional gene expression for synthetic microbiology applications.
- This approach facilitates pathway optimization without significant genetic engineering, improving microbial cell fitness.
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