Fluorescent metabolic labeling-based quick antibiotic susceptibility test for anaerobic bacteria

Abstract

Because of the advancements in medicine and science, the numbers of patients surviving complicated diseases are continuously increasing, which in turn leads to elevated chances of anaerobic infections by endogenous bacteria. Traditional growth yield-based antibiotic susceptibility tests (ASTs) against anaerobic bacteria are very time-consuming (≥48 h) and labor intensive, which delays the timely guidance of antibiotic prescription and increases the mortality of patients. Inspired by a fluorescent d-amino acid (FDAA) labeling-based AST (FaAST) that we recently developed for quick determination of aerobic bacteria's susceptibilities, here we report an accurate and fast AST method for anaerobic pathogens. Based on flow cytometry analysis of anaerobes that have been treated with various doses of antibiotics and metabolically labeled with FDAA, the intensities of which can reflect their affected metabolic status by the drugs, the MICs of each drug can then be determined. The whole process can be completed in 5 h. After testing 40 combinations of the representative anaerobic bacteria and antibiotics, our method demonstrates a high susceptibility category accuracy of 95.0%. This FaAST-based protocol is helpful in accurately and quickly guiding antibiotic decisions when treating critical infections caused by anaerobic bacteria.

Fig. 1. Working principle and workflow of FaAST for anaerobic bacteria. Anaerobic pathogens were incubated with FDAA probes and different doses of drugs in an anaerobic chamber for 4 h, and then the FDAA-labeling intensity of bacterial cells under the treatment of drugs was analyzed by flow cytometry. The resistant bacteria-antibiotic combinations showed unreduced FDAA-labeling until treating with a high dose of drugs; the FDAA-labeling of the susceptible combinations declined when treating with a low concentration of drugs. The MICs of different antibiotics were determined by the FDAA-labeling curves correspondingly.

Fig. 2. The labeling intensity of FDAA unveils the responses of bacterial metabolism to different doses of antibiotics. The bacterial cells were analyzed by flow cytometry after 4 h of anaerobic incubation with antibiotics and the FDAA probe. The x-axis represents the concentration of antibiotics and the y-axis shows the median fluorescence intensity values of each sample. The susceptible strains (top panel) showed relatively low MICs and the resistant isolates (bottom panel) showed high MICs. The MICs were determined by the standard agar dilution method (shown in each diagram). a.u., arbitrary units. N = 3. Symbols = mean; error bars = SD.

Fig. 3. Confocal fluorescence microscopy of anaerobic bacterial cells treated with different concentrations of antibiotics. The susceptible strain Cd ATCC700057 against metronidazole (A, scale bars = 10 μm) and resistant isolate against vancomycin (B, scale bars = 5 μm) are presented as examples, which showed agreeing FDAA labeling intensity changing trends with flow cytometry analysis results.

Fig. 4. The effects of incubation time and serum supplementation on FaAST analysis's accuracy. (A) For Cd ATCC700057 and Bf ATCC25285, 4 h co-culturing with drugs led to more accurate MICs. (B) Serum supplementation resulted in more accurate MICs of Cd ATCC700057 and Bf ATCC25285 against metronidazole by FaAST. FaAST-MICs are marked with arrows; ADM-MICs are shown in each diagram. N = 3; symbols = mean; error bars = SD.

Fig. 5. A correlation plot of the MICs determined by the traditional method and FaAST. The circle size represents the number of drug-bacteria combinations (not differentially shown) having the same MICs. MICs that were miscategorized by FaAST are shown in yellow.

Wei Wang