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Malathy Krishnamurthy,1 Margaret M Lemmon,1 Evan M Falcinelli,1 Reuel A Sandy,1 Jennifer N Dootz,1 Tiffany M Mott,1 Sathish Rajamani,1,2 Kurt E Schaecher,3 Allen J Duplantier,1,4 Rekha G Panchal11Department of Target Discovery and Experimental Microbiology, Division of Molecular and Translational Sciences, United States Army Medical Research Institute of Infectious Diseases, Frederick, MD, USA; 2General Dynamics Information Technology, Frederick, MD, USA; 3Department of Pathology, Walter Reed National Military Medical Center, Bethesda, MD, USA; 4Cherokee Nation Assurance, Frederick, MD, USAPurpose: 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.Keywords: Gram-negative, MDR bacteria, potentiator, antimicrobial resistance

The experimental methods employed during metagenomic sequencing analyses of microbiome samples significantly impact the resulting data and typically vary substantially between laboratories. In this study, a full factorial experimental design was used to compare the effects of a select set of methodological choices (sample, operator, lot, extraction kit, variable region, and reference database) on the analysis of biologically diverse stool samples. For each parameter investigated, a main effect was calculated that allowed direct comparison both between methodological choices (bias effects) and between samples (real biological differences). Overall, methodological bias was found to be similar in magnitude to real biological differences while also exhibiting significant variations between individual taxa, even between closely related genera. The quantified method biases were then used to computationally improve the comparability of data sets collected under substantially different protocols. This investigation demonstrates a framework for quantitatively assessing methodological choices that could be routinely performed by individual laboratories to better understand their metagenomic sequencing workflows and to improve the scope of the datasets they produce.IMPORTANCEMethod-specific bias is a well-recognized challenge in metagenomic sequencing characterization of microbiome samples, but rigorous bias quantification is challenging. This report details a full factorial exploration of 48 experimental protocols by systematically varying microbiome sample, iterations of material production, laboratory personnel, DNA extraction kit, marker gene selection, and reference databases. Quantification of the biases associated with each parameter revealed similar magnitudes of variation arising from real biological differences and from varied analysis procedures. Furthermore, these measurement biases varied substantially with taxa, even between closely related genera. However, computational correction of method bias using a reference material was demonstrated that significantly harmonized metagenomic sequencing results collected using different analysis protocols.

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., (© 2024. This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply.)