Biology subjects

Kishony, R.

Publications and source records attributed to Kishony, R..

6 recordsLinked to original sources

bioRxiv2024

De novo mutations mediate phenotypic switching in an opportunistic human lung pathogen

Bacteria evolving within human hosts encounter selective tradeoffs that render mutations adaptive in one context and deleterious in another. Here, we report that the cystic fibrosis-associated pathogen Burkholderia dolosa overcomes in-human selective tradeoffs by acquiring successive point mutations that alternate phenotypes. We sequenced the whole genomes of 931 respiratory isolates from two recently infected patients and an epidemiologically-linked, chronically-infected patient. These isolates are contextualized using 112 historical genomes from the same outbreak strain. Within both newly infected patients, diverse parallel mutations that disrupt O-antigen expression quickly arose, comprising 29% and 63% of their B. dolosa communities by 3 years. The selection for loss of O-antigen starkly contrasts with our previous observation of parallel O-antigen-restoring mutations after many years of chronic infection in the historical outbreak. Experimental characterization revealed that O-antigen loss increases uptake in immune cells while decreasing competitiveness in the mouse lung. We propose that the balance of these pressures, and thus whether O-antigen expression is advantageous, depends on tissue localization and infection duration. These results suggest that mutation-driven alternation during infection may be more frequent than appreciated and is underestimated without dense temporal sampling.

microbiology↗

bioRxiv2021

Host-parasite coevolution promotes innovation through deformations in fitness landscapes

During the struggle for survival, populations occasionally evolve new functions that give them access to untapped ecological opportunities. Theory suggests that coevolution between species can promote the evolution of such innovations by deforming fitness landscapes in ways that open new adaptive pathways. We directly tested this idea by using high throughput gene editing-phenotyping technology (MAGE-Seq) to measure the fitness landscape of a virus, bacteriophage {lambda}, as it coevolved with its host, the bacterium Escherichia coli. Through computer simulations of {lambda}s evolution on the empirical fitness landscape, we showed that {lambda} was more likely to evolve to use a new receptor if it experienced a shift in its fitness landscape caused by coevolution. This result was further validated by additional laboratory experiments. This study provides direct evidence for the role of coevolution in driving evolutionary novelty and provides a quantitative framework for predicting evolution in coevolving ecological communities.

evolutionary biology↗

bioRxiv2019

Escape mutations circumvent a tradeoff between resistance to beta-lactams and a beta-lactamase inhibitor

Beta-lactamase inhibitors are increasingly used to counteract microbial resistance to beta-lactam antibiotics mediated by beta-lactamase enzymes. These inhibitors compete with the beta-lactam drug for the same binding site of the beta-lactamase, thereby generating an inherent evolutionary tradeoff: enzyme mutations that increase its activity against the beta-lactam drug also increase its susceptibility towards the inhibitor. It is unclear how common and accessible are mutants that escape this adaptive tradeoff. Here, systematically constructing and phenotyping a deep mutant library of the ampC beta-lactamase gene of Escherichia coli, we identified escape mutations, which even in the presence of the enzyme inhibitor allow growth at beta-lactam concentrations far exceeding the native inhibitory levels of the wildtype strain. Importantly, while such escape mutations appear for combinations of avibactam with some beta-lactam drugs, for other drugs escape phenotypes are completely restricted. Amplicon sequencing of the selected mutant pool identified these escape mutations and showed that they are rare and drug specific. For the combination of avibactam with aztreonam, an escape phenotype was conferred via multiple substitutions in a single conserved amino acid (Tyr 150). In contrast, a different set of mutations showed an escape phenotype for cefepime, and no escape mutants appeared for piperacillin. The differential adaptive potential of ampC to combinations of avibactam and different beta-lactam drugs can help guide drug treatments that are more resilient to evolution of resistance.

microbiology↗

bioRxiv2018

Personal clinical history predicts antibiotic resistance in urinary tract infections

The prevalence of antibiotic resistance in urinary tract infections (UTIs) often renders the prescribed antimicrobial treatment ineffective, highlighting the need for personalized prediction of resistance at time of care. Here, crossing a 10-year longitudinal dataset of over 700,000 community-acquired UTIs with over 6,000,000 personally-linked records of antibiotic purchases, we show that the resistance profile of infections can be predicted based on patient-specific demographics and clinical history. Age, gender, and retirement home residence had strong, yet differential and even non-monotonic, associations with resistance to different antibiotics. Resistance profiles were also associated with the patients records of past urine samples and antibiotic usage, with these associations persisting for months and even longer than a year. Drug usage selected specifically for its own cognate resistance, which led indirectly, through genetic linkage, also to resistance to other, even mechanistically unrelated, drugs. Applying machine learning models, these association patterns allowed good personalized predictions of resistance, which could inform and better optimize empirical prescription of antibiotics.

epidemiology↗

bioRxiv2017

The null additivity of multi-drug combinations

From natural ecology 1-4 to clinical therapy 5-8, cells are often exposed to mixtures of multiple drugs. Two competing null models are used to predict the combined effect of drugs: response additivity (Bliss) and dosage additivity (Loewe) 9-11. Here, noting that these models diverge with increased number of drugs, we contrast their predictions with measurements of Escherichia coli growth under combinations of up to 10 different antibiotics. As the number of drugs increases, Bliss maintains accuracy while Loewe systematically loses its predictive power. The total dosage required for growth inhibition, which Loewe predicts should be fixed, steadily increases with the number of drugs, following a square root scaling. This scaling is explained by an approximation to Bliss where, inspired by RA Fishers classical geometric model 12, dosages of independent drugs adds up as orthogonal vectors rather than linearly. This dose-orthogonality approximation provides results similar to Bliss, yet uses the dosage language as Loewe and is hence easier to implement and intuit. The rejection of dosage additivity in favor of effect additivity and dosage orthogonality provides a framework for understanding how multiple drugs and stressors add up in nature and the clinic.

systems biology↗

bioRxiv2016

Population diversity jeopardizes the efficacy of antibiotic cycling

Treatment strategies that anticipate and respond to the evolution of pathogens are promising tools for combating the global rise of antibiotic resistance1-3. Mutations conferring resistance to one drug can confer positive or negative cross-resistance to other drugs4. The sequential use of drugs exhibiting negative cross-resistance has been proposed to prevent or slow down the evolution of resistance5-8, although factors affecting its efficacy have not been investigated. Here we show that population diversity can disrupt the efficacy of negative cross-resistance-based therapies. By testing 3317 resistant Staphylococcus aureus mutants against multiple antibiotics, we show that first-step mutants exhibit diverse cross-resistance profiles: even when the majority of mutants show negative cross-resistance, rare positive cross-resistant mutants can appear. Using a drug pair showing reciprocal negative cross-resistance, we found that selection for resistance to the first drug in small populations can decrease resistance to the second drug, but identical selection conditions in large populations can increases resistance to the second drug through the appearance of rare positive cross-resistant mutants. We further find that, even with small populations and strong bottlenecks, resistance to both drugs can increase through sequential steps of negative cross-resistance cycling. Thus, low diversity is necessary but not sufficient for effective cycling therapies. While evolutionary interventions are promising tools for controlling antibiotic resistance, they can be sensitive to population diversity and the accessibility of evolutionary paths, and so must be carefully designed to avoid harmful outcomes.

microbiology↗