Antibiotic combinations reduce Staphylococcus aureus clearance.

The spread of antibiotic resistance is attracting increased attention to combination-based treatments. Although drug combinations have been studied extensively for their effects on bacterial growth1-11, much less is known about their effects on bacterial long-term clearance, especially at cidal, clinically relevant concentrations12-14. Here, using en masse microplating and automated image analysis, we systematically quantify Staphylococcus aureus survival during prolonged exposure to pairwise and higher-order cidal drug combinations. By quantifying growth inhibition, early killing and longer-term population clearance by all pairs of 14 antibiotics, we find that clearance interactions are qualitatively different, often showing reciprocal suppression whereby the efficacy of the drug mixture is weaker than any of the individual drugs alone. Furthermore, in contrast to growth inhibition6-10 and early killing, clearance efficacy decreases rather than increases as more drugs are added. However, specific drugs targeting non-growing persisters15-17 circumvent these suppressive effects. Competition experiments show that reciprocal suppressive drug combinations select against resistance to any of the individual drugs, even counteracting methicillin-resistant Staphylococcus aureus both in vitro and in a Galleria mellonella larva model. As a consequence, adding a ß-lactamase inhibitor that is commonly used to potentiate treatment against ß-lactam-resistant strains can reduce rather than increase treatment efficacy. Together, these results underscore the importance of systematic mapping the long-term clearance efficacy of drug combinations for designing more-effective, resistance-proof multidrug regimes.

Minimizing treatment-induced emergence of antibiotic resistance in bacterial infections.

Treatment of bacterial infections currently focuses on choosing an antibiotic that matches a pathogen's susceptibility, with less attention paid to the risk that even susceptibility-matched treatments can fail as a result of resistance emerging in response to treatment. Combining whole-genome sequencing of 1113 pre- and posttreatment bacterial isolates with machine-learning analysis of 140,349 urinary tract infections and 7365 wound infections, we found that treatment-induced emergence of resistance could be predicted and minimized at the individual-patient level. Emergence of resistance was common and driven not by de novo resistance evolution but by rapid reinfection with a different strain resistant to the prescribed antibiotic. As most infections are seeded from a patient's own microbiota, these resistance-gaining recurrences can be predicted using the patient's past infection history and minimized by machine learning-personalized antibiotic recommendations, offering a means to reduce the emergence and spread of resistant pathogens.

Ubiquitous selection for mecA in community-associated MRSA across diverse chemical environments.

Community-associated methicillin-resistant Staphylococcus aureus (CA-MRSA) is threatening public health as it spreads worldwide across diverse environments. Its genetic hallmark, the mecA gene, confers resistance to many ß-lactam antibiotics. Here, we show that, in addition, mecA provides a broad selective advantage across diverse chemical environments. Competing fluorescently labelled wild-type and mecA-deleted CA-MRSA USA400 strains across ~57,000 compounds supplemented with subinhibitory levels of the ß-lactam drug cefoxitin, we find that mecA provides a widespread advantage across ß-lactam and non ß-lactam antibiotics, non-antibiotic drugs and even diverse natural and synthetic compounds. This advantage depends on the presence of cefoxitin and is strongly associated with the compounds' physicochemical properties, suggesting that it may be mediated by differential compounds permeability into the cell. Indeed, mecA protects the bacteria against increased cell-envelope permeability under subinhibitory cefoxitin treatment. Our findings suggest that CA-MRSA success might be driven by a cell-envelope mediated selective advantage across diverse chemical compounds.

The spectrum of bacteria and mechanisms of resistance identified from the casualties treated in the Israeli field hospital after the earthquake in Nepal, 2015: A retrospective analysis.

BACKGROUND: On the April 25, 2015, a 7.8 magnitude earthquake struck Nepal. Soon-after, the Israel Defense Force (IDF) dispatched a tertiary field-hospital to Kathmandu. The field-hospital was equipped with a clinical laboratory with microbiology capabilities. Limited data exists regarding the spectrum of bacteria isolated from earthquake casualties. We aimed to identify the spectrum of bacteria and their mechanisms of resistance in-order to allow preparedness of antibiotic treatment protocols for future disaster scenarios. METHODS: - The field-laboratory phenotypically processed cultures from sterile and non-sterile sites as needed clinically. Later-on, the isolates were brought to Israel for quality control, definite identification and molecular characterization including mechanisms of resistance. RESULTS: A total of 82 clinical pathogens were isolated from 56 patients; 68% of them were Gram negative bacilli. The most common isolates were Enterobacteriaceae (55%) -36% carried bla-NDM and 33% produced Extended-spectrum beta-lactamase (ESBL), mostly blaCTX-M-15. Enterococcus spp were the main Gram positive bacteria isolated (22 isolates), yet, none were vancomycin resistant. The overall level of resistance was 27% MDR and 23% extensively drug resistant (XDR) bacteria. CONCLUSIONS: - Gram negative bacteria were the predominant organism cultured from the casualties, of them 77% were MDR or XDR. NDM was the most common resistance mechanism. The Antibiotic inventory of a field-hospital should be set to cover a wide and unexpected spectrum of bacteria, including resistant organisms. This report adds important information to the scarce reports of bacterial resistance in Nepal.

Personal clinical history predicts antibiotic resistance of urinary tract infections.

Antibiotic resistance is prevalent among the bacterial pathogens causing urinary tract infections. However, antimicrobial treatment is often prescribed 'empirically', in the absence of antibiotic susceptibility testing, risking mismatched and therefore ineffective treatment. Here, linking a 10-year longitudinal data set of over 700,000 community-acquired urinary tract infections with over 5,000,000 individually resolved records of antibiotic purchases, we identify strong associations of antibiotic resistance with the demographics, records of past urine cultures and history of drug purchases of the patients. When combined together, these associations allow for machine-learning-based personalized drug-specific predictions of antibiotic resistance, thereby enabling drug-prescribing algorithms that match an antibiotic treatment recommendation to the expected resistance of each sample. Applying these algorithms retrospectively, over a 1-year test period, we find that they greatly reduce the risk of mismatched treatment compared with the current standard of care. The clinical application of such algorithms may help improve the effectiveness of antimicrobial treatments.

Evolthon: A community endeavor to evolve lab evolution.

In experimental evolution, scientists evolve organisms in the lab, typically by challenging them to new environmental conditions. How best to evolve a desired trait? Should the challenge be applied abruptly, gradually, periodically, sporadically? Should one apply chemical mutagenesis, and do strains with high innate mutation rate evolve faster? What are ideal population sizes of evolving populations? There are endless strategies, beyond those that can be exposed by individual labs. We therefore arranged a community challenge, Evolthon, in which students and scientists from different labs were asked to evolve Escherichia coli or Saccharomyces cerevisiae for an abiotic stress-low temperature. About 30 participants from around the world explored diverse environmental and genetic regimes of evolution. After a period of evolution in each lab, all strains of each species were competed with one another. In yeast, the most successful strategies were those that used mating, underscoring the importance of sex in evolution. In bacteria, the fittest strain used a strategy based on exploration of different mutation rates. Different strategies displayed variable levels of performance and stability across additional challenges and conditions. This study therefore uncovers principles of effective experimental evolutionary regimens and might prove useful also for biotechnological developments of new strains and for understanding natural strategies in evolutionary arms races between species. Evolthon constitutes a model for community-based scientific exploration that encourages creativity and cooperation.