vCOMBAT: a novel tool to create and visualize a computational model of bacterial antibiotic target-binding

Background As antibiotic resistance creates a significant global health threat, we need not only to accelerate the development of novel antibiotics but also to develop better treatment strategies using existing drugs to improve their efficacy and prevent the selection of further resistance. We require new tools to rationally design dosing regimens from data collected in early phases of antibiotic and dosing development. Mathematical models such as mechanistic pharmacodynamic drug-target binding explain mechanistic details of how the given drug concentration affects its targeted bacteria. However, there are no available tools in the literature that allow non-quantitative scientists to develop computational models to simulate antibiotic-target binding and its effects on bacteria. Results In this work, we have devised an extension of a mechanistic binding-kinetic model to incorporate clinical drug concentration data. Based on the extended model, we develop a novel and interactive web-based tool that allows non-quantitative scientists to create and visualize their own computational models of bacterial antibiotic target-binding based on their considered drugs and bacteria. We also demonstrate how Rifampicin affects bacterial populations of Tuberculosis bacteria using our vCOMBAT tool. Conclusions The vCOMBAT online tool is publicly available at https://combat-bacteria.org/.

A Degeneration Gradient of Poplar Trees Contributes to the Taxonomic, Functional, and Resistome Diversity of Bacterial Communities in Rhizosphere Soils

Bacterial communities associated with roots influence the health and nutrition of the host plant. However, the microbiome discrepancy are not well understood under different healthy conditions. Here, we tested the hypothesis that rhizosphere soil microbial diversity and function varies along a degeneration gradient of poplar, with a focus on plant growth promoting bacteria (PGPB) and antibiotic resistance genes. Comprehensive metagenomic analysis including taxonomic investigation, functional detection, and ARG (antibiotics resistance genes) annotation revealed that available potassium (AK) was correlated with microbial diversity and function. We proposed several microbes, Bradyrhizobium, Sphingomonas, Mesorhizobium, Nocardioides, Variovorax, Gemmatimonadetes, Rhizobacter, Pedosphaera, Candidatus Solibacter, Acidobacterium, and Phenylobacterium, as candidates to reflect the soil fertility and the plant health. The highest abundance of multidrug resistance genes and the four mainly microbial resistance mechanisms (antibiotic efflux, antibiotic target protection, antibiotic target alteration, and antibiotic target replacement) in healthy poplar rhizosphere, corroborated the relationship between soil fertility and microbial activity. This result suggested that healthy rhizosphere soil harbored microbes with a higher capacity and had more complex microbial interaction network to promote plant growing and reduce intracellular levels of antibiotics. Our findings suggested a correlation between the plant degeneration gradient and bacterial communities, and provided insight into the role of high-turnover microbial communities as well as potential PGPB as real-time indicators of forestry soil quality, and demonstrated the inner interaction contributed by the bacterial communities.

vCOMBAT: a Novel Tool to Create and Visualize a COmputational Model of Bacterial Antibiotic Target-binding

MotivationAs antibiotic resistance creates a significant global health threat, we need not only to accelerate the development of novel antibiotics but also to develop better treatment strategies using existing drugs to improve their efficacy and prevent the selection of further resistance. We require new tools to rationally design dosing regimens to from data collected in early phases of antibiotic and dosing development. Mathematical models such as mechanistic pharmacodynamic drug-target binding explain mechanistic details of how the given drug concentration affects its targeted bacteria. However, there are no available tools in the literature that allows non-quantitative scientists to develop computational models to simulate antibiotic-target binding and its effects on bacteria.ResultsIn this work, we have devised an extension of a mechanistic binding-kinetic model to incorporate clinical drug concentration data. Based on the extended model, we develop a novel and interactive web-based tool that allows non-quantitative scientists to create and visualize their own computational models of bacterial antibiotic target-binding based on their considered drugs and bacteria. We also demonstrate how Rifampicin affects bacterial populations of Tuberculosis (TB) bacteria using our vCOMBAT tool.AvailabilityvCOMBAT online tool is publicly available at https://combat-bacteria.org/.

Cell division protein FtsZ: from structure and mechanism to antibiotic target

Antimicrobial resistance to virtually all clinically applied antibiotic classes severely limits the available options to treat bacterial infections. Hence, there is an urgent need to develop and evaluate new antibiotics and targets with resistance-breaking properties. Bacterial cell division has emerged as a new antibiotic target pathway to counteract multidrug-resistant pathogens. New approaches in antibiotic discovery and bacterial cell biology helped to identify compounds that either directly interact with the major cell division protein FtsZ, thereby perturbing the function and dynamics of the cell division machinery, or affect the structural integrity of FtsZ by inducing its degradation. The impressive antimicrobial activities and resistance-breaking properties of certain compounds validate the inhibition of bacterial cell division as a promising strategy for antibiotic intervention.