Rapid antibiotic susceptibility determination by fluorescence lifetime tracking of bacterial metabolism
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ORGANISM(S): Escherichia coli
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PROVIDER: S-BSST1723 | biostudies-other |
REPOSITORIES: biostudies-other
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Project description:Multidrug resistant organisms are a serious threat to human health1,2. Fast, accurate antibiotic susceptibility testing (AST) is a critical need in addressing escalating antibiotic resistance, since delays in identifying multidrug resistant organisms increase mortality3,4 and use of broad-spectrum antibiotics, further selecting for resistant organisms. Yet current growth-based AST assays, such as broth microdilution5, require several days before informing key clinical decisions. Rapid AST would transform the care of patients with infection while ensuring that our antibiotic arsenal is deployed as efficiently as possible. Growth-based assays are fundamentally constrained in speed by doubling time of the pathogen, and genotypic assays are limited by the ever-growing diversity and complexity of bacterial antibiotic resistance mechanisms. Here we describe a rapid assay for combined genotypic and phenotypic AST through RNA detection, GoPhAST-R, that classifies strains with 94-99% accuracy by coupling machine learning analysis of early antibiotic-induced transcriptional changes with simultaneous detection of key genetic resistance determinants to increase accuracy of resistance detection, facilitate molecular epidemiology and enable early detection of emerging resistance mechanisms. This two-pronged approach provides phenotypic AST 24-36 h faster than standard workflows, with <4 h assay time on a pilot instrument for hybridization-based multiplexed RNA detection implemented directly from positive blood cultures.
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Project description:Fluorescence lifetime imaging microscopy (FLIM) can assess cell's metabolism through the fluorescence of the co-enzymes NADH and FAD, which exhibit a double-exponential decay, with components related to free and protein-bound conditions. In vivo real time clinical imaging applications demand fast acquisition. As photodamage limits excitation power, this is best achieved using wide-field techniques, like time-gated FLIM, and algorithms that require few images to calculate the decay parameters. The rapid lifetime determination (RLD) algorithm requires only four images to analyze a double-exponential decay. Using computational simulations, we evaluated the accuracy and precision of RLD when measuring endogenous fluorescence lifetimes and metabolic free to protein-bound ratios, for total counts per pixel (TC) lower than 104. The simulations were based on a time-gated FLIM instrument, accounting for its instrument response function, gain and noise. While the optimal acquisition setting depends on the values being measured, the accuracy of the free to protein-bound ratio α2/α1 is stable for low gains and gate separations larger than 1000 ps, while its precision is almost constant for gate separations between 1500 and 2500 ps. For the gate separations and free to protein-bound ratios considered, the accuracy error can be as high as 30% and the precision error can reach 60%. Precision errors lower than 10% cannot be obtained. The best performance occurs for low camera gains and gate separations near 1800 ps. When considering the narrow physiological ranges for the free to protein-bound ratio, the precision errors can be confined to an interval between 10% and 20%. RLD is a valid option when for real time FLIM. The simulations and methodology presented here can be applied to any time-gated FLIM instrument and are useful to obtain the accuracy and precision limits for RLD in the demanding conditions of TC lower than 104.
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Project description:Rapid bacterial antibiotic susceptibility test (AST) and minimum inhibitory concentration (MIC) measurement are important to help reduce the widespread misuse of antibiotics and alleviate the growing drug-resistance problem. We discovered that, when a susceptible strain of Staphylococcus aureus or Escherichia coli is exposed to an antibiotic, the intensity of specific biomarkers in its surface-enhanced Raman scattering (SERS) spectra drops evidently in two hours. The discovery has been exploited for rapid AST and MIC determination of methicillin-susceptible S. aureus and wild-type E. coli as well as clinical isolates. The results obtained by this SERS-AST method were consistent with that by the standard incubation-based method, indicating its high potential to supplement or replace existing time-consuming methods and help mitigate the challenge of drug resistance in clinical microbiology.
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Project description:Fluorescence lifetime imaging (FLIM) is widely applied to obtain quantitative information from fluorescence signals, particularly using Förster Resonant Energy Transfer (FRET) measurements to map, for example, protein-protein interactions. Extracting FRET efficiencies or population fractions typically entails fitting data to complex fluorescence decay models but such experiments are frequently photon constrained, particularly for live cell or in vivo imaging, and this leads to unacceptable errors when analysing data on a pixel-wise basis. Lifetimes and population fractions may, however, be more robustly extracted using global analysis to simultaneously fit the fluorescence decay data of all pixels in an image or dataset to a multi-exponential model under the assumption that the lifetime components are invariant across the image (dataset). This approach is often considered to be prohibitively slow and/or computationally expensive but we present here a computationally efficient global analysis algorithm for the analysis of time-correlated single photon counting (TCSPC) or time-gated FLIM data based on variable projection. It makes efficient use of both computer processor and memory resources, requiring less than a minute to analyse time series and multiwell plate datasets with hundreds of FLIM images on standard personal computers. This lifetime analysis takes account of repetitive excitation, including fluorescence photons excited by earlier pulses contributing to the fit, and is able to accommodate time-varying backgrounds and instrument response functions. We demonstrate that this global approach allows us to readily fit time-resolved fluorescence data to complex models including a four-exponential model of a FRET system, for which the FRET efficiencies of the two species of a bi-exponential donor are linked, and polarisation-resolved lifetime data, where a fluorescence intensity and bi-exponential anisotropy decay model is applied to the analysis of live cell homo-FRET data. A software package implementing this algorithm, FLIMfit, is available under an open source licence through the Open Microscopy Environment.
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Project description:The current culture-based approach for the diagnosis of bloodstreams infection is incommensurate with timely treatment and curbing the prevalence of multi-drug resistant organisms (MDROs) due to its long time-to-result. Bloodstream infections typically involve extremely low (e.g., <10 colony-forming unit (CFU)/mL) bacterial concentrations that require a labor-intensive process and as much as 72 hours to yield a diagnosis. Here, we demonstrate a culture-free approach to achieve rapid diagnosis of bloodstream infections. An immuno-detection platform with intrinsic signal current amplification was developed for the ultrasensitive, rapid detection, identification (ID) and antibiotic susceptibility testing (AST) of infections. With its capability of monitoring short-term (1-2 hours) bacterial growth in blood, the platform is able to provide 84-minute simultaneous detection and ID in blood samples below the 10 CFU/mL level and 204-minute AST. The susceptible-intermediate-resistant AST capacity was demonstrated.
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Project description:The emergence and prevalence of novel plasmid-mediated tigecycline resistance genes, namely, tet(X) and their variants, pose a serious threat to public health worldwide. Rapid and accurate antibiotic susceptibility testing (AST) that can simultaneously detect the genotype and phenotype of tet(X)-positive bacteria may contribute to the deployment of an effective antibiotic arsenal, mortality reduction, and a decrease in the use of broad-spectrum antimicrobial agents. However, current bacterial growth-based AST methods, such as broth microdilution, are time consuming and delay the prompt treatment of infectious diseases. Here, we developed a rapid RNA-based AST (RBAST) assay to effectively distinguish tet(X)-positive and -negative strains. RBAST works by detecting specific mRNA expression signatures in bacteria after short-term tigecycline exposure. As a proof of concept, a panel of clinical isolates was characterized successfully by using the RBAST method, with a 3-h assay time and 87.9% accuracy (95% confidence interval [CI], 71.8% to 96.6%). Altogether, our findings suggest that RNA signatures upon antibiotic exposure are promising biomarkers for the development of rapid AST, which could inform early antibiotic choices. IMPORTANCE Infections caused by multidrug-resistant (MDR) Gram-negative pathogens are an increasing threat to global health. Tigecycline is one of the last-resort antibiotics for the treatment of these complicated infections; however, the emergence of plasmid-encoded tigecycline resistance genes, namely, tet(X), severely diminishes its clinical efficacy. Currently, there is a lack of rapid and accurate antibiotic susceptibility testing (AST) for the detection of tet(X)-positive bacteria. In this study, we developed a rapid and robust RNA-based antibiotic susceptibility determination (RBAST) assay to effectively distinguish tet(X)-negative and -positive strains using specific RNA biomarkers in bacteria after tigecycline exposure. Using this RBAST method, we successfully characterized a set of clinical strains in 3 h. Our data indicate that the RBAST assay is useful for identifying tet(X)-positive Escherichia coli.
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Project description:Antimicrobial resistance is a serious challenge facing human and veterinary health. Current methods of detecting resistance are limited in turn-around time or universal detection. In this work, a new antimicrobial susceptibility test is developed and validated, which utilizes deuterium labeling of membrane lipids to track the growth of bacterial cells. We hypothesize that deuterium uptake and subsequent labeling of lipids can be detected using matrix-assisted laser desorption/ionization mass spectrometry (MALDI-MS). Additionally, bacteria growth is performed on the MALDI target, minimizing sample preparation materials and time. When two Escherichia coli strains are grown in the presence of deuterium oxide, labeling can be detected in as little as 30 min to 2 h. The labeling efficiency, or the ratio of labeled to unlabeled lipid peaks, provides information about the growth rate of bacteria. This growth ratio can differentiate between resistant and susceptible strains of bacteria as a resistant strain will maintain ∼50% labeling efficiency between untreated and treated cultures. In comparison, a susceptible strain will see a decrease in fractional abundance of deuterium from ∼50% in the untreated to ∼10% in the treated. This approach is applied to measure the minimum inhibitory concentration (MIC) of the resistant and susceptible strains from on-target microdroplet culture in a range of antibiotic concentrations. The first antibiotic concentration with a significant decrease in fractional abundance of deuterium correlates well with a traditionally obtained MIC using broth dilution, indicating the clinical relevance of the results.
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