Biology subjects

Baym, M.

Publications and source records attributed to Baym, M..

3 recordsLinked to original sources

bioRxiv2018

Lineage calling can identify antibiotic resistant clones within minutes

Surveillance of drug-resistant bacteria is essential for healthcare providers to deliver effective empiric antibiotic therapy. However, traditional molecular epidemiology does not typically occur on a timescale that could impact patient treatment and outcomes. Here we present a method called genomic neighbor typing for inferring the phenotype of a bacterial sample by identifying its closest relatives in a database of genomes with metadata. We show that this technique can infer antibiotic susceptibility and resistance for both S. pneumoniae and N. gonorrhoeae. We implemented this with rapid k-mer matching, which, when used on Oxford Nanopore MinION data, can run in real time. This resulted in determination of resistance within ten minutes (sens/spec 91%/100% for S. pneumoniae and 81%/100% N. gonorrhoeae from isolates with a representative database) of sequencing starting, and for clinical metagenomic sputum samples (75%/100% for S. pneumoniae), within four hours of sample collection. This flexible approach has wide application to pathogen surveillance and may be used to greatly accelerate appropriate empirical antibiotic treatment.

bioinformatics↗

bioRxiv2018

Stronger connectivity of the resident gut microbiome lends resistance to invading bacteria

Bacterial infection in the gut is often due to successful invasion of the host microbiome by an introduced pathogen. Ecological theory indicates that resident community members and their interactions should be strong determinants of whether an invading taxon can persist in a community. In the context of the gut microbiome, this suggests colonization resistance against newly introduced bacteria should depend on the instantaneous bacterial community composition within the gut and interactions between these constituent members. Here we develop a mathematical model of how metabolite-dependent biotic interactions between resident bacteria mediate invasion, and find that stronger biotic connectivity from metabolite cross-feeding and competition increases colonization resistance. We then introduce a statistical method for identifying invasive taxa in the human gut, and show empirically that greater connectivity of the resident gut microbiome is related to increased resistance to invading bacteria. Finally, we examined patient outcomes after fecal microbiota transplant (FMT) for recurring Clostridium difficile infection. Patients with lower connectivity of the gut microbiome after treatment were more likely to relapse, experiencing a later infection. Thus, simulation models and data from human subjects support the hypothesis that stronger interactions between bacteria in the gut repel invaders. These results demonstrate how ecological invasion theory can be applied to the gut microbiome, which might inform targeted microbiome manipulations and interventions. More broadly, this study provides evidence that low connectivity in gut microbial communities is a hallmark of community instability and susceptibility to invasion.

microbiology↗

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↗