Researchers at Auburn University in Alabama and Keesler Air Force Base in Mississippi developed a bio-based sensor that can discriminate between strains of staph bacteria resistant and sensitive to antibiotics. The team led by Auburn veterinary medicine professor Vitaly Vodyanoy published its findings in the May 2013 issue of the online Journal of Visualized Experiments (institutional subscription required).
Staphylococcus bacteria can cause an irritating skin infection that is treatable with antibiotics, but subsequent mutations of the pathogen have made these later strains resistant to antibiotics. Patients with compromised immne systems are perhaps the most susceptible to complications from antibiotic-resistant infections in the lungs or respiratory system.
Staph bacteria resistant to antibiotics, known as methicillin resistant Staphylococcus aureus or MRSA have become a particular problem in health care facilities, particularly those operated by the military, where hygenic conditions cannot be guaranteed (The U.S. Air Force funded the study.). A 2011 report by the Centers for Disease Control and Prevention cites the growing publc health burden caused by some 90,000 invasive MRSA infections each year in the U.S.
Vodyanoy and colleagues devised a sensor to indicate the presence of MRSA bacteria using bacteriophages, which are viruses that invade bacterial cells and disrupt the bacteria’s metabolism. The bacteriophages in this research are benign to humans and placed on the sensor surface, which then bind to the bacteria. The sensor is also fitted with penicillin-binding protein antibody latex beads, which make it possible to discriminate between methicillin sensitive and resistant staph bacteria.
The sensor works by the bacteriophages, spiked with penicillin-binding protein, interacting with the suspect bacteria. Tests of the sensor showed the sensor could indicate the presence of staph bacteria, and through a change in color, discriminate between MRSA strains and those sensitive to antibiotics.
Vodyanoy notes that the Auburn-Keesler team’s sensor also works faster — 10 to 12 minutes — than current methods that can take hours.
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