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<\/p>\n\t\t\t\t\t\tSingapore researchers map bacterial gene interactions to uncover drug targets<\/p>\n
October 6, 2025 | Monday | News<\/p>\n
\t\t\t\t\t\t\tUsing Dual transposon sequencing (Dual Tn-seq), researchers aim to tackle gene redundancy to identify functions and drug targets in bacterial genomes<\/p>\n
\t\t\t\t\t\t\t\t<\/p>\n
Image credit: Freepik<\/p>\n
Despite significant progress in decoding the genetic blueprints of living organisms, a major challenge remains: knowing a gene’s sequence does not automatically reveal its function. Even in well-studied bacteria like Escherichia coli (E. coli), approximately one-quarter of the genes have no known role. Traditional methods, such as turning off individual genes to observe their effects, are time-consuming, labor-intensive, and often inconclusive due to gene redundancy.<\/p>\n
Researchers from Singapore’s Yong Loo Lin School of Medicine, National University of Singapore\u00a0 (NUS Medicine) and collaborators from the University of California, Berkeley (UC Berkeley) have developed a new technique called Dual transposon sequencing (Dual Tn-seq), which\u00a0 allows for rapid identification of genetic interactions. It maps how bacterial genes work\u00a0 together, revealing vulnerabilities that could be targeted by future antibiotics.\u00a0<\/p>\n
Assistant Professor Chris\u00a0 Sham Lok To from the Infectious Diseases Translational Research Programme and the Department of Microbiology and Immunology, NUS Medicine, who led the study. explains, \u201cBy mapping, we can now\u00a0 see which genes depend on each other, and which pairs of genes bacteria can\u2019t live without. That\u2019s exactly the insight we need for next-generation antibiotics.\u201d\u00a0<\/p>\n
Researchers used tiny, mobile genetic elements called transposons, each\u00a0 tagged with a unique DNA \u201cbarcode\u201d to inactivate genes. By generating cells with two random\u00a0 transposon insertions and using a molecular \u201cmatchmaker\u201d enzyme called Cre recombinase, the team could read both barcodes together, identifying double mutants on a genome-wide\u00a0 scale. Applied to Streptococcus pneumoniae\u2014a major respiratory pathogen and an ideal\u00a0 genetic model because it is well-understood genetically and easy to manipulate in the lab\u2014\u00a0this method sampled 73% of the 1.3 million possible gene-pair deletions.\u00a0<\/p>\n
Some of the major finding were;\u00a0<\/p>\n
\nThe researchers found 244 important gene partnerships in the bacteria, including deadly\u00a0 combinations where removing both genes caused the bacterium to die.
\nThey discovered PyrJ, a new enzyme that produces the building blocks of DNA. This\u00a0 enzyme is present in several disease-causing bacteria, such as Clostridioides difficile, making it a promising target for new antibiotics.\u00a0
\nThey identified YjbK, a protein that acts like a \u201cstarter switch\u201d for building the bacterial\u00a0 cell wall, a vital structure that keeps the cell intact.\u00a0
\nThey also worked out possible functions for 67 mysterious proteins by revealing which\u00a0 known genes they interacted with.
<\/p>\n
Beyond solving fundamental mysteries of gene function, Dual Tn-seq has far-reaching\u00a0 implications. By mapping bacterial gene networks, scientists can pinpoint metabolic\u00a0 vulnerabilities that could be targeted by new drugs, which could help combat the global crisis\u00a0 of antimicrobial resistance. The method is highly adaptable, requiring no pre-constructed\u00a0 mutant collections, and can be applied to other pathogens or tested under different conditions,\u00a0 such as antibiotic exposure or host-like environments.\u00a0<\/p>\n
\u201cBy looking at pairs of genes instead of just one at a time, we can uncover hidden weaknesses\u00a0 in bacteria that would otherwise go unnoticed,\u201d said Professor Adam Deutschbauer,\u00a0 Department of Plant and Microbial Biology, UC Berkeley. \u201cThis kind of insight not only deepens\u00a0 our understanding of how bacteria work, but also points us toward new strategies for tackling\u00a0 drug-resistant infections.\u201d\u00a0\u00a0<\/p>\n
Looking ahead, the team aims to refine the method to study essential genes, apply it to other\u00a0 clinically important microbes, and generate large, high-quality datasets to train machine\u00a0 learning and artificial intelligence (AI) models for functional genomics. <\/p>\n
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