Abstract:
Haris Antypas, Keira Melican, Helene Andersson-Svahn, Agneta Richter-Dahlfors
Swedish Medical Nanoscience Center, Department of Neuroscience, Karolinska
Institutet, Stockholm, Sweden.
Diagnosis of bacterial infection and antimicrobial susceptibility is a bottleneck in clinical settings when a patient is in urgent need of optimal antibiotic treatment. Since current diagnostic techniques take 1-3 days, the clinician is left with no alternative, but to empirically prescribe broad-spectrum antibiotics. Despite the short-term benefit, such therapy shifts the patient's commensal flora and selects for resistant strains. In our lab we are working on developing new techniques and devices to greatly enhance the specificity and speed of pathogen identification and antibiotic sensitivity. By combining microbiology, nanotechnology and mathematical analysis, we have developed a high-throughput nano well antibiotic susceptibility testing (AST) device providing diagnostic results within 2-4 hours. Each of the 672 wells serves as an AST nano-incubator, where bacteria are exposed to a range of concentrations of multiple antibiotics. Optical recordings collected in real-time are processed by a mathematical algorithm that precisely defines the time point when bacterial growth shifts from lag to early logarithmic phase (Tlag). By translating Tlag to minimum inhibitory concentration (MIC), the antibiotic susceptibility profiles for clinical Uropathogenic E. coli (UPEC) isolates were detected within 2-4 h. With a possibility to enhance multiplexing capacity, this device serves as a high-throughput diagnostic tool that rapidly aids clinicians in prescribing the optimal antibiotic therapy. Additionally, this device may serve as a useful tool for clinical microbiologists to capture emerging susceptibility patterns of bacteria. Beyond this initial device we are investigating new methodologies both for pathogen identification but also biomimetic devices to better mimic the in vivo environment of infection. I will present a biomimetic device that mimics the kidney environment by introducing the important parameter of the urine flow. Using this device, we monitor the infection in real-time under a fluorescence microscope, using GFP- expressing UPEC strains, as well as probes that visualize the response of the host cells to the infection. Ultimately, we expect to increase our understanding of the molecular mechanisms during urinary tract infections, which may contribute to the development of new prevention and treatment strategies.