A diagnostic technology that can read infection-causing bacteria with nearly 100% accuracy in 3 hours has emerged. It is expected to lower the mortality rate in diseases such as sepsis, where the timing of antibiotic administration is critical, as it is much faster and more accurate than bacterial culture or PCR analysis.
A research team from the Biomedical Engineering Department at the Ulsan National Institute of Science and Technology, led by Professors Kim Ha-jin, Kwon Tae-jun, and Kang Joo-heon, noted on the 12th that they developed a FISH diagnostic technology using the artificially designed molecule PNA as a probe. The FISH technology is a diagnostic method that reads fluorescent signals generated when probe molecules bind to specific gene sequences of bacteria.
The research team analyzed the genomic sequences of 20,000 bacterial species and designed PNA sequences to attach only to ribosomal RNA of specific species. PNA has a greater sensitivity to sequence mismatches compared to conventional DNA-based probes and also excels in penetrating bacterial cell walls. Since both PNA molecules must attach to the target site for a signal to occur, it can significantly reduce confusion caused by the probe molecules misidentifying binding sites.
In experiments detecting seven types of bacteria, including Escherichia coli, Pseudomonas aeruginosa, and Staphylococcus aureus from individual samples, all showed a detection accuracy of over 99% except for Staphylococcus aureus, which was detected with an accuracy of 96.3%. The performance in scenarios with mixed types of bacteria was also validated. In experiments mixing Streptococcus and Escherichia coli, both showed a detection accuracy of over 99%.
Professor Kim Ha-jin expects that this will help in the diagnosis of infectious diseases requiring immediate antibiotic treatment, such as sepsis, urinary tract infections, and pneumonia, and in reducing unnecessary antibiotic use. The research team added that they plan to seek clinical applications through additional experiments using blood samples taken from actual patients.
References
Biosensors and Bioelectronics(2025), DOI : https://doi.org/10.1016/j.bios.2024.116950