Photo credit: www.sciencedaily.com

New Insights into Antibiotic Resistance through Bacterial Morphology

The discovery of penicillin marked a significant turning point in medicine, often referred to as “the silver bullet.” This groundbreaking antibiotic was unique in its ability to eradicate harmful bacteria while maintaining safety for human cells. However, the emergence of antibiotic-resistant strains has led to growing challenges in treating bacterial infections, as the extensive use of antibiotics diminishes our available treatments.

Recent research published in Frontiers in Microbiology by a team from Osaka University has uncovered that antibiotic-resistant bacteria display distinct physical characteristics. This finding could transform the approach to identifying resistant strains more swiftly.

Globally, antibiotic resistance poses a serious public health threat, limiting our options for effective treatment of bacterial infections. Efficiently identifying these resistant strains is critical to ensuring appropriate therapeutic interventions. Currently, the standard practice involves lengthy laboratory culture and testing, which can take several days and delay treatment.

According to Miki Ikebe, the lead author of the study, “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.” This became the focal point of the researchers’ inquiry, prompting them to explore whether these morphological changes could serve as indicators of antibiotic resistance, even in the absence of drug treatment.

The researchers conducted experiments with Escherichia coli by exposing them to fixed concentrations of various antibiotics to induce resistance. Once the bacteria developed resistance and the antibiotic treatment ceased, the researchers utilized machine learning techniques to analyze the bacteria’s morphology, including shape and size, through microscope imagery.

“The results were striking,” remarked Kunihiko Nishino, the senior author of the study. “The antibiotic-resistant strains presented as shorter or bulkier compared to their non-resistant counterparts, particularly in those resistant to quinolone and β-lactams.”

Furthermore, the study delved into the genetics of the antibiotic-resistant bacteria to assess any correlation between their shape and the underlying genetic factors. The findings indicated a relationship between alterations in morphology and genes associated with energy metabolism and antibiotic resistance.

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

The consistency in shape and size among bacteria resistant to quinolone, β-lactams, and chloramphenicol suggests that a common genetic mechanism might govern antibiotic resistance across these different strains. This insight raises the potential for developing machine learning tools capable of quickly analyzing bacterial samples from patients. Such innovation could lead to more precise treatment prescriptions, enhancing the overall quality of care in combating bacterial infections.

Source
www.sciencedaily.com