Oxidative DNA damage is important to the evolution of antibiotic resistance: evidence of mutation bias and its medicinal implications

Triclosan weakens the nitrification process of activated sludge and increases the risk of the spread of antibiotic resistance genes

The usage of triclosan (TCS) may rise rapidly due to the COVID-19 pandemic. TCS usually sinks in the activated sludge. However, the effects of TCS in activated sludge remain largely unknown. The changes in nitrogen cycles and the abundances of antibiotic resistance genes (ARGs) caused by TCS were investigated in this study. The addition of 1000 μg/L TCS significantly inhibited nitrification since the ammonia conversion rate and the abundance of nitrification functional genes decreased by 12.14%. The other nitrogen cycle genes involved in nitrogen fixation and denitrification were also suppressed. The microbial community shifted towards tolerance and degradation of phenols. The addition of 100 μg/L TCS remarkably increased the total abundance of ARGs and mobile genetic elements by 33.1%, and notably, the tetracycline and multidrug resistance genes increased by 54.75% and 103.42%, respectively. The co-occurrence network revealed that Flavobacterium might have played a key role in the spread of ARGs. The abundance of this genus increased 92-fold under the addition of 1000 μg/L TCS, indicating that Flavobacterium is potent in the tolerance and degradation of TCS. This work would help to better understand the effects of TCS in activated sludge and provide comprehensive insight into TCS management during the pandemic era.

MutVis: Automated framework for analysis and visualization of mutational signatures in pathogenic bacterial strains

In recent years, mutational signature analysis has become a routine practice in cancer genomics for classification and diagnosis. Characterizing mutational signatures across species or within genomes of a bacteria helps in understanding their evolution and adaptation. However, an integrated framework for analysis and visualization of mutational signatures in bacterial genome is lacking. Hence, we aim to develop an integrated, automated, open-source and user-friendly framework called MutVis to analyze mutational signatures from bacterial whole genome next generation sequencing data. The current framework integrates various publicly available packages using Snakemake workflow management software, Python and R scripting. MutVis supports variant calling, transition (Ti) and transversion (Tv) graphical representation, generation of mutational count matrix, graphical visualization of base-pair substitution spectrum (BPSs) and mutation signatures extraction. TvTi plots provide the 6 base substitution classification for both genome and gene level. Further resolution of base pair substitution classification is provided as 96-profile BPSs plot. Mutation signatures is derived based on the characteristic pattern observed in BPSs using non-negative matrix factorization. Relative contribution of signatures is given as hierarchically clustered heatmap. This provides information on active signatures in the individual given sample and classify samples according to signature contributions. We demonstrated the MutVis framework using geographically different strains of Mycobacterium tuberculosis, downloaded from PATRIC TB-ARC Antibiotic Resistance Catalog (n = 963). The current framework can be used to study mutation biases and characteristic mutational signatures in bacterial genomes and is freely available at https://github.com/AkshathaPrasanna/MutVis.

Reactive Oxygen Species-Related Nanoparticle Toxicity in the Biomedical Field

The unique physicochemical characteristics of nanoparticles have recently gained increasing attention in a diverse set of applications, particularly in the biomedical field. However, concerns about the potential toxicological effects of nanoparticles remain, as they have a higher tendency to generate excessive amounts of reactive oxygen species (ROS). Due to the strong oxidation potential, the excess ROS induced by nanoparticles can result in the damage of biomolecules and organelle structures and lead to protein oxidative carbonylation, lipid peroxidation, DNA/RNA breakage, and membrane structure destruction, which further cause necrosis, apoptosis, or even mutagenesis. This review aims to give a summary of the mechanisms and responsible for ROS generation by nanoparticles at the cellular level and provide insights into the mechanics of ROS-mediated biotoxicity. We summarize the literature on nanoparticle toxicity and suggest strategies to optimize nanoparticles for biomedical applications.

Transition bias influences the evolution of antibiotic resistance in Mycobacterium tuberculosis

Transition bias, an overabundance of transitions relative to transversions, has been widely reported among studies of the rates and spectra of spontaneous mutations. However, demonstrating the role of transition bias in adaptive evolution remains challenging. In particular, it is unclear whether such biases direct the evolution of bacterial pathogens adapting to treatment. We addressed this challenge by analyzing adaptive antibiotic-resistance mutations in the major human pathogen Mycobacterium tuberculosis (MTB). We found strong evidence for transition bias in two independently curated data sets comprising 152 and 208 antibiotic-resistance mutations. This was true at the level of mutational paths (distinct adaptive DNA sequence changes) and events (individual instances of the adaptive DNA sequence changes) and across different genes and gene promoters conferring resistance to a diversity of antibiotics. It was also true for mutations that do not code for amino acid changes (in gene promoters and the 16S ribosomal RNA gene rrs) and for mutations that are synonymous to each other and are therefore likely to have similar fitness effects, suggesting that transition bias can be caused by a bias in mutation supply. These results point to a central role for transition bias in determining which mutations drive adaptive antibiotic resistance evolution in a key pathogen.

Some types of mutations occur more frequently than expected. This study shows that such bias —an excess of transitions over transversions—influences the evolution of antibiotic resistance in a key global pathogen, Mycobacterium tuberculosis.

Chronic Exposure to an Environmentally Relevant Triclosan Concentration Induces Persistent Triclosan Resistance but Reversible Antibiotic Tolerance in Escherichia coli

The major concern regarding the biocide triclosan (TCS) stems from its potential coselection for antibiotic resistance. However, environmental impacts are often investigated using high concentrations and acute exposure, while predicted releases are typified by chronic low concentrations. Moreover, little information is available regarding the reversibility of TCS and derived antibiotic resistance with diminishing TCS usage. Here, the model Gram-negative bacterium Escherichia coli was exposed to 0.01 mg/L TCS continuously for more than 100 generations. The adapted cells gained considerable resistance to TCS as indicated by a significant increase in the minimal inhibitory concentration (MIC₅₀) from 0.034 to 0.581 mg/L. This adaptive evolution was attributed to overexpression and mutation of target genes (i.e., fabI) as evidenced by transcriptomic and genomic analyses. However, only mild tolerance to various antibiotics was observed, possibly due to reduced membrane permeability and biofilm formation. After TCS exposure ceased, the adapted cells showed persistent resistance to TCS due to inheritable genetic mutations, whereas their antibiotic tolerance declined over time. Our results suggest that extensive use of TCS may promote the evolution and persistence of TCS-resistant bacterial pathogens. A quantitative definition of the conditions under which TCS selects for multidrug resistance in the environment is crucially needed.