Mock community taxonomic classification performance of publicly available shotgun metagenomics pipelines.

Shotgun metagenomic sequencing comprehensively samples the DNA of a microbial sample. Choosing the best bioinformatics processing package can be daunting due to the wide variety of tools available. Here, we assessed publicly available shotgun metagenomics processing packages/pipelines including bioBakery, Just a Microbiology System (JAMS), Whole metaGenome Sequence Assembly V2 (WGSA2), and Woltka using 19 publicly available mock community samples and a set of five constructed pathogenic gut microbiome samples. Also included is a workflow for labelling bacterial scientific names with NCBI taxonomy identifiers for better resolution in assessing results. The Aitchison distance, a sensitivity metric, and total False Positive Relative Abundance were used for accuracy assessments for all pipelines and mock samples. Overall, bioBakery4 performed the best with most of the accuracy metrics, while JAMS and WGSA2, had the highest sensitivities. Furthermore, bioBakery is commonly used and only requires a basic knowledge of command line usage. This work provides an unbiased assessment of shotgun metagenomics packages and presents results assessing the performance of the packages using mock community sequence data.

Phenotype-Based Threat Assessment.

Bacterial pathogen identification, which is critical for human health, has historically relied on culturing organisms from clinical specimens. More recently, the application of machine learning (ML) to whole-genome sequences (WGSs) has facilitated pathogen identification. However, relying solely on genetic information to identify emerging or new pathogens is fundamentally constrained, especially if novel virulence factors exist. In addition, even WGSs with ML pipelines are unable to discern phenotypes associated with cryptic genetic loci linked to virulence. Here, we set out to determine if ML using phenotypic hallmarks of pathogenesis could assess potential pathogenic threat without using any sequence-based analysis. This approach successfully classified potential pathogenetic threat associated with previously machine-observed and unobserved bacteria with 99% and 85% accuracy, respectively. This work establishes a phenotype-based pipeline for potential pathogenic threat assessment, which we term PathEngine, and offers strategies for the identification of bacterial pathogens.

Enhancing the antibacterial activity of polymyxins using a nonantibiotic drug.

Purpose: The rapid emergence of multidrug-resistant (MDR) bacteria and the lack of new therapies to eliminate them poses a major threat to global health. With the alarming rise in antimicrobial resistance (AMR), focus has now shifted to the use of the polymyxin class of antibiotics as the last line of defense for treatment of Gram-negative infections. Unfortunately, the growing resistance of bacteria against polymyxins is threatening the treatment of MDR infections, necessitating the need for novel strategies. The objective of this study was to determine if combination of polymyxin (polymyxin B or colistin) with a nonantibiotic small molecule AR-12, a celecoxib derivative that is devoid of cyclooxygenase 2 (COX-2) inhibitory activities, can be an effective strategy against polymyxin-resistant MDR bacteria. Methods: Growth inhibition studies, time-kill assays and permeability assays were conducted to investigate the effect of AR-12 on the antibacterial activity of polymyxins. Results: Growth studies were performed on a panel of polymyxin-resistant MDR strains using the combination of AR-12 with either colistin or polymyxin B. The combination treatment had no effect on strains that have inherent polymyxin resistance; however, AR-12 was effective in lowering the minimal inhibitory concentration (MIC) of polymyxins by 4-60-fold in several strains that had acquired polymyxin resistance. Time-kill assays using the combination of AR-12 and colistin with select MDR strains suggest rapid killing and bactericidal activity, while the permeability assays using fluorescently labeled dansylated polymyxin and 1-N-phenylnaphthylamine (NPN) in these MDR strains suggest that AR-12 can potentiate the antibacterial activity of polymyxins by possibly altering the bacterial outer membrane via modification of lipopolysaccharide and thereby improving the uptake of polymyxins. Conclusion: Our studies indicate that the combination of AR-12 and polymyxin is effective in targeting select Gram-negative bacteria that have acquired polymyxin resistance. Further understanding of the mechanism of action of AR-12 will provide new avenues for developing narrow-spectrum antibacterials to target select Gram-negative MDR bacteria. Importantly, our studies show that the use of nonantibiotic small molecules in combination with polymyxins is an attractive strategy to counter the growing resistance of bacteria to polymyxins.