Corrigendum: Rapid antibiotic-resistance predictions from genome sequence data for Staphylococcus aureus and Mycobacterium tuberculosis

Antimicrobial Resistance in Rivers: A Review of the Genes Detected and New Challenges

River ecosystems are very important parts of the water cycle and an excellent habitat, food, and drinking water source for many organisms, including humans. Antibiotics are emerging contaminants which can enter rivers from various sources. Several antibiotics and their related antibiotic resistance genes (ARGs) have been detected in these ecosystems by various research programs and could constitute a substantial problem. The presence of antibiotics and other resistance cofactors can boost the development of ARGs in the chromosomes or mobile genetic elements of natural bacteria in rivers. The ARGs in environmental bacteria can also be transferred to clinically important pathogens. However, antibiotics and their resistance genes are both not currently monitored by national or international authorities responsible for controlling the quality of water bodies. For example, they are not included in the contaminant list in the European Water Framework Directive or in the US list of Water-Quality Benchmarks for Contaminants. Although ARGs are naturally present in the environment, very few studies have focused on non-impacted rivers to assess the background ARG levels in rivers, which could provide some useful indications for future environmental regulation and legislation. The present study reviews the antibiotics and associated ARGs most commonly measured and detected in rivers, including the primary analysis tools used for their assessment. In addition, other factors that could enhance antibiotic resistance, such as the effects of chemical mixtures, the effects of climate change, and the potential effects of the coronavirus disease 2019 pandemic, are discussed. Environ Toxicol Chem 2022;41:687-714. © 2022 SETAC.

A machine learning framework to predict antibiotic resistance traits and yet unknown genes underlying resistance to specific antibiotics in bacterial strains

Recently, the frequency of observing bacterial strains without known genetic components underlying phenotypic resistance to antibiotics has increased. There are several strains of bacteria lacking known resistance genes; however, they demonstrate resistance phenotype to drugs of that family. Although such strains are fewer compared to the overall population, they pose grave emerging threats to an already heavily challenged area of antimicrobial resistance (AMR), where death tolls have reached ~700 000 per year and a grim projection of ~10 million deaths per year by 2050 looms. Considering the fact that development of novel antibiotics is not keeping pace with the emergence and dissemination of resistance, there is a pressing need to decipher yet unknown genetic mechanisms of resistance, which will enable developing strategies for the best use of available interventions and show the way for the development of new drugs. In this study, we present a machine learning framework to predict novel AMR factors that are potentially responsible for resistance to specific antimicrobial drugs. The machine learning framework utilizes whole-genome sequencing AMR genetic data and antimicrobial susceptibility testing phenotypic data to predict resistance phenotypes and rank AMR genes by their importance in discriminating the resistance from the susceptible phenotypes. In summary, we present here a bioinformatics framework for training machine learning models, evaluating their performances, selecting the best performing model(s) and finally predicting the most important AMR loci for the resistance involved.

Tuning the Bandgap of Photo-Sensitive Polydopamine/Ag3PO4/Graphene Oxide Coating for Rapid, Noninvasive Disinfection of Implants

Bacterial infection and associated complications are threats to human health especially when biofilms form on biomedical devices and artificial implants. Herein, a hybrid polydopamine (PDA)/Ag3PO4/graphene oxide (GO) coating is designed and constructed to achieve rapid bacteria killing and eliminate biofilms in situ. By varying the amount of GO in the hybrid coating, the bandgap can be tuned from 2.52 to 2.0 eV so that irradiation with 660 nm visible light produces bacteria-killing effects synergistically in concert with reactive oxygen species (ROS). GO regulates the release rate of Ag+ to minimize the cytotoxicity while maintaining high antimicrobial activity, and a smaller particle size enhances the yield of ROS. After irradiation with 660 nm visible light for 15 min, the antimicrobial rates of the PDA/Ag3PO4/GO hybrid coating against Escherichia coli and Staphylococcus aureus are 99.53% and 99.66%, respectively. In addition, this hybrid coating can maintain a repeatable and sustained antibacterial efficacy. The released Ag+ and photocatalytic Ag3PO4 produce synergistic antimicrobial effects in which the ROS increases the permeability of the bacterial membranes to increase the probability of Ag+ to enter the cells to kill them together with ROS synergistically.