Whole-Genome Sequencing for Drug Resistance Profile Prediction in Mycobacterium tuberculosis.

Whole-genome sequencing allows rapid detection of drug-resistant Mycobacterium tuberculosis isolates. However, the availability of high-quality data linking quantitative phenotypic drug susceptibility testing (DST) and genomic data have thus far been limited. We determined drug resistance profiles of 176 genetically diverse clinical M. tuberculosis isolates from the Democratic Republic of the Congo, Ivory Coast, Peru, Thailand, and Switzerland by quantitative phenotypic DST for 11 antituberculous drugs using the BD Bactec MGIT 960 system and 7H10 agar dilution to generate a cross-validated phenotypic DST readout. We compared DST results with predicted drug resistance profiles inferred by whole-genome sequencing. Classification of strains by the two phenotypic DST methods into resistotype/wild-type populations was concordant in 73 to 99% of cases, depending on the drug. Our data suggest that the established critical concentration (5 mg/liter) for ethambutol resistance (MGIT 960 system) is too high and misclassifies strains as susceptible, unlike 7H10 agar dilution. Increased minimal inhibitory concentrations were explained by mutations identified by whole-genome sequencing. Using whole-genome sequences, we were able to predict quantitative drug resistance levels for the majority of drug resistance mutations. Predicting quantitative levels of drug resistance by whole-genome sequencing was partially limited due to incompletely understood drug resistance mechanisms. The overall sensitivity and specificity of whole-genome-based DST were 86.8% and 94.5%, respectively. Despite some limitations, whole-genome sequencing has the potential to infer resistance profiles without the need for time-consuming phenotypic methods.

Rapid disc diffusion antibiotic susceptibility testing for Pseudomonas aeruginosa, Acinetobacter baumannii and Enterococcus spp.

Background: We investigated the feasibility of rapid disc diffusion antibiotic susceptibility testing (rAST) with reading of inhibition zones after 6 and/or 8 h of incubation for Enterococcus faecalis, Enterococcus faecium, Pseudomonas aeruginosa and Acinetobacter baumannii. In addition, we evaluated discrimination of resistant populations from the WT populations at early timepoints and the requirement for clinical breakpoint adaptations for proper interpretation of rAST data. Methods: In total, 815 clinical strains [E. faecalis (n = 135), E. faecium (n = 227), P. aeruginosa (n = 295) and A. baumannii (n = 158)] were included in this study. Disc diffusion plates were streaked, incubated and imaged using the WASPLabTM automation system. WT populations and non-WT populations were defined using epidemiological cut-offs. Results and conclusions: rAST at 6 and 8 h was possible for A. baumannii and enterococci with readability of inhibition zones >90%. Overall categorical agreement of rAST at 6 h with AST at 18 h was 97.2%, 97.4% and 95.3% for E. faecalis, E. faecium and A. baumannii, respectively. With few exceptions, major categorization error rates were <1% for A. baumannii, and vancomycin-resistant E. faecium were clearly separated from the WT at 6 h. For P. aeruginosa the average readability of inhibition zones was 68.9% at 8 h and we found an overall categorical agreement of 94.8%. Adaptations of clinical breakpoints and/or introduction of technical buffer zones, preferably based on aggregated population data from various epidemiological settings, are required for proper interpretation of rAST.

MASTER: a model to improve and standardize clinical breakpoints for antimicrobial susceptibility testing using forecast probabilities.

Objectives: The procedure for setting clinical breakpoints (CBPs) for antimicrobial susceptibility has been poorly standardized with respect to population data, pharmacokinetic parameters and clinical outcome. Tools to standardize CBP setting could result in improved antibiogram forecast probabilities. We propose a model to estimate probabilities for methodological categorization errors and defined zones of methodological uncertainty (ZMUs), i.e. ranges of zone diameters that cannot reliably be classified. The impact of ZMUs on methodological error rates was used for CBP optimization. Methods: The model distinguishes theoretical true inhibition zone diameters from observed diameters, which suffer from methodological variation. True diameter distributions are described with a normal mixture model. The model was fitted to observed inhibition zone diameters of clinical Escherichia coli strains. Repeated measurements for a quality control strain were used to quantify methodological variation. Results: For 9 of 13 antibiotics analysed, our model predicted error rates of < 0.1% applying current EUCAST CBPs. Error rates were > 0.1% for ampicillin, cefoxitin, cefuroxime and amoxicillin/clavulanic acid. Increasing the susceptible CBP (cefoxitin) and introducing ZMUs (ampicillin, cefuroxime, amoxicillin/clavulanic acid) decreased error rates to < 0.1%. ZMUs contained low numbers of isolates for ampicillin and cefuroxime (3% and 6%), whereas the ZMU for amoxicillin/clavulanic acid contained 41% of all isolates and was considered not practical. Conclusions: We demonstrate that CBPs can be improved and standardized by minimizing methodological categorization error rates. ZMUs may be introduced if an intermediate zone is not appropriate for pharmacokinetic/pharmacodynamic or drug dosing reasons. Optimized CBPs will provide a standardized antibiotic susceptibility testing interpretation at a defined level of probability.

Fully automated disc diffusion for rapid antibiotic susceptibility test results: a proof-of-principle study.

Background: Antibiotic resistance poses a significant threat to patients suffering from infectious diseases. Early readings of antibiotic susceptibility test (AST) results could be of critical importance to ensure adequate treatment. Disc diffusion is a well-standardized, established and cost-efficient AST procedure; however, its use in the clinical laboratory is hampered by the many manual steps involved, and an incubation time of 16-18 h, which is required to achieve reliable test results. Methods: We have evaluated a fully automated system for its potential for early reading of disc diffusion diameters after 6-12 h of incubation. We assessed availability of results, methodological precision, categorical agreement and interpretation errors as compared with an 18 h standard. In total, 1028 clinical strains (291 Escherichia coli , 272 Klebsiella pneumoniae , 176 Staphylococcus aureus and 289 Staphylococcus epidermidis ) were included in this study. Disc diffusion plates were streaked, incubated and imaged using the WASPLab TM automation system. Results and conclusions: Our results demonstrate that: (i) early AST reading is possible for important pathogens; (ii) methodological precision is not hampered at early timepoints; and (iii) species-specific reading times must be selected. As inhibition zone diameters change over time and are phenotype/drug combination dependent, specific cut-offs and expert rules will be essential to ensure reliable interpretation and reporting of early susceptibility testing results.

Rapid detection of ESBL, carbapenemases, MRSA and other important resistance phenotypes within 6-8 h by automated disc diffusion antibiotic susceptibility testing.

BACKGROUND: In principle, automated systems allow rapid reading of disc diffusion AST (rAST) within 6-8 h. OBJECTIVES: This study analysed whether rAST can discriminate resistance phenotypes such as ESBL, carbapenemases and MRSA/methicillin-resistant Staphylococcus epidermidis from WT populations. We describe species-drug combinations that may require clinical breakpoint adaptions for early reading due to zone diameter changes during the incubation period. METHODS: In total, 1852 clinical strains [Escherichia coli (n = 475), Klebsiella pneumoniae (n = 375), Enterobacter cloacae (n = 301), Staphylococcus aureus (n = 407) and S. epidermidis (n = 294)] were included in this study comprising WT populations and important resistance phenotypes, e.g. ESBL, carbapenemases and MRSA. We assessed (i) separation of resistance phenotypes and WT populations after 6, 8 and 12 h as compared with the 18 h standard, and (ii) diameter changes of WT populations and associated putative epidemiological cut-offs during the incubation period. Disc diffusion plates were automatically streaked, incubated and imaged using the WASPLabTM system. RESULTS AND CONCLUSIONS: We demonstrated that important resistance phenotypes could reliably be separated from WT populations at early reading times for the most prevalent bacterial pathogens encountered in the clinical laboratory. Current AST expert rules and algorithms for identification of resistance mechanisms can readily be applied for rAST, e.g. EUCAST recommended rules for detection of ESBL, AmpC, carbapenemases and MRSA/methicillin-resistant S. epidermidis. However, several species-drug combinations may require clinical breakpoint adaptations when using rAST as the diameter, and hence the epidemiological cut-off, changes during the incubation period.

Kinetic response of a photoperturbed allosteric protein.

By covalently linking an azobenzene photoswitch across the binding groove of a PDZ domain, a conformational transition, similar to the one occurring upon ligand binding to the unmodified domain, can be initiated on a picosecond timescale by a laser pulse. The protein structures have been characterized in the two photoswitch states through NMR spectroscopy and the transition between them through ultrafast IR spectroscopy and molecular dynamics simulations. The binding groove opens on a 100-ns timescale in a highly nonexponential manner, and the molecular dynamics simulations suggest that the process is governed by the rearrangement of the water network on the protein surface. We propose this rearrangement of the water network to be another possible mechanism of allostery.

Peptide Binding to a PDZ Domain by Electrostatic Steering via Nonnative Salt Bridges.

We have captured the binding of a peptide to a PDZ domain by unbiased molecular dynamics simulations. Analysis of the trajectories reveals on-pathway encounter complex formation, which is driven by electrostatic interactions between negatively charged carboxylate groups in the peptide and positively charged side chains surrounding the binding site. In contrast, the final stereospecific complex, which matches the crystal structure, features completely different interactions, namely the burial of the hydrophobic side chain of the peptide C-terminal residue and backbone hydrogen bonds. The simulations show that nonnative salt bridges stabilize kinetically the encounter complex during binding. Unbinding follows the inverse sequence of events with the same nonnative salt bridges in the encounter complex. Thus, in contrast to protein folding, which is driven by native interactions, the binding of charged peptides can be steered by nonnative interactions, which might be a general mechanism, e.g., in the recognition of histone tails by bromodomains.

Weighted Distance Functions Improve Analysis of High-Dimensional Data: Application to Molecular Dynamics Simulations.

Data mining techniques depend strongly on how the data are represented and how distance between samples is measured. High-dimensional data often contain a large number of irrelevant dimensions (features) for a given query. These features act as noise and obfuscate relevant information. Unsupervised approaches to mine such data require distance measures that can account for feature relevance. Molecular dynamics simulations produce high-dimensional data sets describing molecules observed in time. Here, we propose to globally or locally weight simulation features based on effective rates. This emphasizes, in a data-driven manner, slow degrees of freedom that often report on the metastable states sampled by the molecular system. We couple this idea to several unsupervised learning protocols. Our approach unmasks slow side chain dynamics within the native state of a miniprotein and reveals additional metastable conformations of a protein. The approach can be combined with most algorithms for clustering or dimensionality reduction.

High-resolution visualisation of the states and pathways sampled in molecular dynamics simulations.

We have recently developed a scalable algorithm for ordering the instantaneous observations of a dynamical system evolving continuously in time. Here, we apply the method to long molecular dynamics trajectories. The procedure requires only a pairwise, geometrical distance as input. Suitable annotations of both structural and kinetic nature reveal the free energy basins visited by biomolecules. The profile is supplemented by a trace of the temporal evolution of the system highlighting the sequence of events. We demonstrate that the resultant SAPPHIRE (States And Pathways Projected with HIgh REsolution) plots provide a comprehensive picture of the thermodynamics and kinetics of complex, molecular systems exhibiting dynamics covering a range of time and length scales. Information on pathways connecting states and the level of recurrence are quickly inferred from the visualisation. The considerable advantages of our approach are speed and resolution: the SAPPHIRE plot is scalable to very large data sets and represents every single snapshot. This minimizes the risk of missing states because of overlap or prior coarse-graining of the data.