Antibiotic-resistant bacteria exhibit shape differences

Japanese researchers have discovered that antibiotic-resistant bacteria exhibit characteristic morphological changes that can be detected microscopically, with the results of their study published in the journal Frontiers in Microbiology.

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. Now, researchers from Osaka University have found 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,” said Miki Ikebe, lead author of the new study. “We were interested in determining whether this feature could be used to detect antibiotic resistance without actually treating the bacteria with antibiotics.”

To do this, the researchers exposed E. coli to fixed concentrations of different antibiotics, prompting them to develop antibiotic resistance. They then removed the antibiotic treatment and used machine learning to assess the shapes, sizes and other physical features of the bacteria based on microscope images.

“The results were very clear,” said senior author Kunihiko Nishino. “The antibiotic-resistant strains were fatter or shorter than their parental strains, especially those that were resistant to quinolone and β-lactams.”

Next, the researchers explored the genetic makeup of the antibiotic-resistant bacteria to see whether there was any connection between bacterial shape and antibiotic resistance. The results showed that genes related to energy metabolism and antibiotic resistance were indeed associated with the shape changes that were observed in the antibiotic-resistant bacteria.

“Our findings show that drug-resistant bacteria can be identified from microscope images, in the absence of antibiotics, using machine learning,” Ikebe said.

Given that the bacteria that were resistant to quinolone, β-lactams and chloramphenicol all exhibited similar shapes and sizes, it seems likely that the same genetic mechanism is responsible for antibiotic resistance in all of these strains. In the future, a machine learning tool could be used to rapidly assess samples taken from patients to help prescribe the right drug to treat their infection.

Image credit: iStock.com/luismmolina