Decay and damage of therapeutic phage OMKO1 by environmental stressors.

Antibiotic resistant bacterial pathogens are increasingly prevalent, driving the need for alternative approaches to chemical antibiotics when treating infections. One such approach is bacteriophage therapy: the use of bacteria-specific viruses that lyse (kill) their host cells. Just as the effect of environmental conditions (e.g. elevated temperature) on antibiotic efficacy is well-studied, the effect of environmental stressors on the potency of phage therapy candidates demands examination. Therapeutic phage OMKO1 infects and kills the opportunistic human pathogen Pseudomonas aeruginosa. Here, we used phage OMKO1 as a model to test how environmental stressors can lead to damage and decay of virus particles. We assessed the effects of elevated temperatures, saline concentrations, and urea concentrations. We observed that OMKO1 particles were highly tolerant to different saline concentrations, but decayed more rapidly at elevated temperatures and under high concentrations of urea. Additionally, we found that exposure to elevated temperature reduced the ability of surviving phage particles to suppress the growth of P. aeruginosa, suggesting a temperature-induced damage. Our findings demonstrate that OMKO1 is highly tolerant to a range of conditions that could be experienced inside and outside the human body, while also showing the need for careful characterization of therapeutic phages to ensure that environmental exposure does not compromise their expected potency, dosing, and pharmacokinetics.

miComplete: weighted quality evaluation of assembled microbial genomes.

SUMMARY: Metagenomics and single-cell genomics have revolutionized the study of microorganisms, increasing our knowledge of microbial genomic diversity by orders of magnitude. A major issue pertaining to metagenome-assembled genomes (MAGs) and single-cell amplified genomes (SAGs) is to estimate their completeness and redundancy. Most approaches rely on counting conserved gene markers. In miComplete, we introduce a weighting strategy, where we normalize the presence/absence of markers by their median distance to the next marker in a set of complete reference genomes. This approach alleviates biases introduced by the presence/absence of shorter DNA pieces containing many markers, e.g. ribosomal protein operons. AVAILABILITY AND IMPLEMENTATION: miComplete is written in Python 3 and released under GPLv3. Source code and documentation are available at https://bitbucket.org/evolegiolab/micomplete. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.