Investigating Bacteriophages Targeting the Opportunistic Pathogen Acinetobacter baumannii

The multi-drug resistance of the opportunistic pathogen Acinetobacter baumannii is of growing concern, with many clinical isolates proving to be resistant to last resort as well as front line antibiotic treatments. The use of bacteriophages is an attractive alternative to controlling and treating this emerging nosocomial pathogen. In this study, we have investigated bacteriophages collected from hospital wastewater in Thailand and we have explored their activity against clinical isolates of A. baumannii. Bacteriophage vB_AbaM_PhT2 showed 28% host range against 150 multidrug resistant (MDR) isolates and whole genome sequencing did not detect any known virulence factors or antibiotic resistance genes. Purified vB_AbaM_PhT2 samples had endotoxin levels below those recommended for preclinical trials and were not shown to be directly cytotoxic to human cell lines in vitro. The treatment of human brain and bladder cell lines grown in the presence of A. baumannii with this bacteriophage released significantly less lactate dehydrogenase compared to samples with no bacteriophage treatment, indicating that vB_AbaM_PhT2 can protect from A. baumannii induced cellular damage. Our results have also indicated that there is synergy between this bacteriophage and the end line antibiotic colistin. We therefore propose bacteriophage vB_AbaM_PhT2 as a good candidate for future research and for its potential development into a surface antimicrobial for use in hospitals.

Genomic analysis reveals high virulence and antibiotic resistance amongst phage susceptible Acinetobacter baumannii

In this study, we examined the association between antimicrobial resistance, CRISPR/Cas systems and virulence with phage susceptibility in Acinetobacter baumannii and investigated draft genomes of phage susceptible multidrug resistant A. baumannii strains from Thailand. We investigated 230 A. baumannii strains using 17 lytic A. baumannii phages and the phage susceptibility was 46.5% (107/230). Phage susceptibility was also associated with resistance to numerous antibiotics (p-value < 0.05). We also found association between biofilm formation and the presence of ompA gene among phage susceptible A. baumannii strains (p-value < 0.05). A. baumannii isolates carrying cas5 or combinations of two or three other cas genes, showed a significant increase in phage resistance. Whole-genome sequences of seven phage susceptible A. baumannii isolates revealed that six groups of antibiotic resistance genes were carried by all seven phage susceptible A. baumannii. All strains carried biofilm associated genes and two strains harbored complete prophages, acquired copper tolerance genes, and CRISPR-associated (cas) genes. In conclusion, our data exhibits an association between virulence determinants and biofilm formation among phage susceptible A. baumannii strains. These data help to understand the bacterial co-evolution with phages.

A Review of Using Mathematical Modeling to Improve Our Understanding of Bacteriophage, Bacteria, and Eukaryotic Interactions

Phage therapy, the therapeutic usage of viruses to treat bacterial infections, has many theoretical benefits in the ‘post antibiotic era.’ Nevertheless, there are currently no approved mainstream phage therapies. One reason for this is a lack of understanding of the complex interactions between bacteriophage, bacteria and eukaryotic hosts. These three-component interactions are complex, with non-linear or synergistic relationships, anatomical barriers and genetic or phenotypic heterogeneity all leading to disparity between performance and efficacy in in vivo versus in vitro environments. Realistic computer or mathematical models of these complex environments are a potential route to improve the predictive power of in vitro studies for the in vivo environment, and to streamline lab work. Here, we introduce and review the current status of mathematical modeling and highlight that data on genetic heterogeneity and mutational stochasticity, time delays and population densities could be critical in the development of realistic phage therapy models in the future. With this in mind, we aim to inform and encourage the collaboration and sharing of knowledge and expertise between microbiologists and theoretical modelers, synergising skills and smoothing the road to regulatory approval and widespread use of phage therapy.

Polymer-Mediated Cryopreservation of Bacteriophages

Bacteriophages (phages, bacteria-specific viruses) have biotechnological and therapeutic potential. To apply phages as pure or heterogeneous mixtures, it is essential to have a robust mechanism for transport and storage, with different phages having very different stability profiles across storage conditions. For many biologics, cryopreservation is employed for long-term storage and cryoprotectants are essential to mitigate cold-induced damage. Here, we report that poly(ethylene glycol) can be used to protect phages from cold damage, functioning at just 10 mg·mL-1 (∼1 wt %) and outperforms glycerol in many cases, which is a currently used cryoprotectant. Protection is afforded at both -20 and -80 °C, the two most common temperatures for frozen storage in laboratory settings. Crucially, the concentration of the polymer required leads to frozen solutions at -20 °C, unlike 50% glycerol (which results in liquid solutions). Post-thaw recoveries close to 100% plaque-forming units were achieved even after 2 weeks of storage with this method and kill assays against their bacterial host confirmed the lytic function of the phages. Initial experiments with other hydrophilic polymers also showed cryoprotection, but at this stage, the exact mechanism of this protection cannot be concluded but does show that water-soluble polymers offer an alternative tool for phage storage. Ice recrystallization inhibiting polymers (poly(vinyl alcohol)) were found to provide no additional protection, in contrast to their ability to protect proteins and microorganisms which are damaged by recrystallization. PEG's low cost, solubility, well-established low toxicity/immunogenicity, and that it is fit for human consumption at the concentrations used make it ideal to help translate new approaches for phage therapy.

Modeling the architecture of the regulatory system controlling methylenomycin production in Streptomyces coelicolor

Background

The antibiotic methylenomycin A is produced naturally by Streptomyces coelicolor A3(2), a model organism for streptomycetes. This compound is of particular interest to synthetic biologists because all of the associated biosynthetic, regulatory and resistance genes are located on a single cluster on the SCP1 plasmid, making the entire module easily transferable between different bacterial strains. Understanding further the regulation and biosynthesis of the methylenomycin producing gene cluster could assist in the identification of motifs that can be exploited in synthetic regulatory systems for the rational engineering of novel natural products and antibiotics.

Results

We identify and validate a plausible architecture for the regulatory system controlling methylenomycin production in S. coelicolor using mathematical modeling approaches. Model selection via an approximate Bayesian computation (ABC) approach identifies three candidate model architectures that are most likely to produce the available experimental data, from a set of 48 possible candidates. Subsequent global optimization of the parameters of these model architectures identifies a single model that most accurately reproduces the dynamical response of the system, as captured by time series data on methylenomycin production. Further analyses of variants of this model architecture that capture the effects of gene knockouts also reproduce qualitative experimental results observed in mutant S. coelicolor strains.

Conclusions

The mechanistic mathematical model developed in this study recapitulates current biological knowledge of the regulation and biosynthesis of the methylenomycin producing gene cluster, and can be used in future studies to make testable predictions and formulate experiments to further improve our understanding of this complex regulatory system.