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Updated: Jun 24, 2025

Generation of Enterobacter sp. YSU Auxotrophs Using Transposon Mutagenesis
Published on: October 31, 2014
Inducible transposon mutagenesis for genome-scale forward genetics
David W Basta1, Ian W Campbell2,3, Emily J Sullivan2,3
1Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
Inducible Transposon insertion sequencing (InducTn-seq) overcomes bacterial mutant diversity limits. This method enables sensitive detection of gene fitness defects and reveals pathogen metabolic plasticity during infection.
Area of Science:
- Microbiology
- Bacterial Genetics
- Infectious Disease
Background:
- Transposon insertion sequencing (Tn-seq) is vital for bacterial functional genetics.
- Traditional Tn-seq often suffers from limited mutant diversity due to inefficient delivery or population bottlenecks.
- This limits the detection of subtle gene fitness defects and accurate essentiality classification.
Purpose of the Study:
- To introduce InducTn-seq, a novel method for inducible transposition with temporal control.
- To enhance mutant diversity for sensitive fitness defect detection across all genes.
- To enable quantitative fitness measurements and overcome host bottlenecks in bacterial screens.
Main Methods:
- Developed InducTn-seq leveraging inducible mutagenesis for controlled transposition.
- Generated millions of transposon mutants from a single bacterial colony.
- Applied InducTn-seq in a mouse model of infectious colitis to assess bacterial pathogenesis.
Main Results:
- InducTn-seq generates highly diverse mutant populations, even after a restrictive host bottleneck.
- The method enables sensitive detection of subtle fitness defects in both essential and non-essential genes.
- Revealed the role of oxygen-related metabolic plasticity in bacterial pathogenesis during infection.
Conclusions:
- InducTn-seq overcomes limitations of traditional Tn-seq, enhancing genome-scale forward genetic screens.
- Provides a quantitative measure of gene fitness, transforming binary essentiality classifications.
- Unlocks new possibilities for studying bacterial adaptation and virulence in complex environments.
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