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Size matters: Bioinformatics accurately detects short, fat antibiotic-resistant bacteria

Penicillin was hailed as "the silver bullet" when it was discovered, as it had the unprecedented quality of being able to kill disease-causing bacteria without harming the human body. Since then, a multitude of other antibiotics have been developed that specifically target a wide range of bacteria; but the more often they are used, the greater the risk that antibiotic-resistant strains will arise.

In a study recently published in Frontiers in Microbiology, researchers from Osaka University have revealed that bacteria exhibit characteristic shape differences when they are resistant to drug treatment.

Antibiotic resistance is a major public health problem worldwide, as it means that we have fewer and fewer options for treating bacterial infections. Identifying antibiotic-resistant bacteria quickly is important for ensuring that patients receive effective treatment; but the most readily available method for doing this involves several days of growing the bacteria in a lab and treating them with drugs to see how they respond.

"There is some evidence that antibiotic resistance reveals itself in other ways; for example, the morphology of Gram-negative rod-shaped bacteria changes when they are exposed to antibiotics," says lead author of the study Miki Ikebe. "We were interested in determining whether this feature could be used to detect antibiotic resistance without actually treating the bacteria with antibiotics."

Upper left panel for each cluster: The pie charts indicate the proportion of the cluster occupied by each bacterial strain. Upper right panel for each cluster: The average shape of the cells in the cluster is shown; the solid line represents the mean and the dotted lines represent the standard deviation. The vertical and horizontal axes are in μm. Bottom panel for each cluster: The results of the PCA for each cluster are shown. Credit: Frontiers in Microbiology

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