Biology subjects

Johnning, A.

Publications and source records attributed to Johnning, A..

4 recordsLinked to original sources

bioRxiv2025

Community-promoted antibiotic resistance genes show increased dissemination among pathogens

Antibiotic resistance is increasing among bacterial pathogens, posing one of the most severe threats to future public health. A major contributor to the increasing resistance is the dissemination of mobile antibiotic resistance genes (ARGs) among bacterial communities. These genes are ubiquitously present in various environments and are especially diverse in the human gut and wastewater. Despite this, the clinical implications of the prevalence of ARGs in these bacterial communities remain unclear. In this study, we aimed to investigate how the prevalence of ARGs in human gut and wastewater microbiomes reflects their dissemination among important bacterial pathogens. To do this, we estimated the prevalence of >30,000 ARGs, including both well-known (established) and computationally predicted (latent) genes, in >6,000 metagenomic samples. From their prevalence in the human gut and wastewater, we identified four categories of ARGs: co-promoted, human gut (HG)-promoted, wastewater (WW)- promoted, and non-promoted. Our results showed that co-promoted ARGs were by far the most promiscuous, being more frequently found across multiple bacterial phyla, and more often co-localized with broad host range conjugative elements. Co-promoted ARGs were also found to be overrepresented among genes identified in multiple pathogenic species and exhibited an overall higher genetic compatibility with both pathogens and other typical residents of the human gut and wastewater microbiomes. Taken together, our results highlight the link between the promotion of ARGs in the human gut and wastewater microbiomes and their presence in human pathogens, and, thereby, the genes potential risk to human health.

bioinformatics↗

bioRxiv2024

The transfer of antibiotic resistance genes between evolutionary distant bacteria

Infections from antibiotic-resistant bacteria threaten human health globally. Resistance is often caused by mobile antibiotic resistance genes (ARGs) shared horizontally between bacterial genomes. Many ARGs originate from environmental and commensal bacteria and are transferred between divergent bacterial hosts before they reach pathogens. This process remains, however, poorly understood, which complicates the development of countermeasures that reduce the spread of ARGs. In this study, we aimed to systematically analyze the ARGs transferred between the most evolutionary distant bacteria, here defined based on their phylum. We implemented an algorithm that identified inter-phyla transfers (IPTs) by combining ARG-specific phylogenetic trees with the taxonomy of the bacterial hosts. From the analysis of almost 1 million resistance genes identified in >400,000 bacterial genomes, we identified 661 IPTs, which included transfers between all major bacterial phyla. The frequency of IPTs varies substantially between ARG classes and was highest for the aminoglycoside resistance gene AAC(3) while the levels for beta-lactamases were, generally, lower. ARGs involved in IPTs also differed between phyla where, for example, tetracycline resistance genes were commonly transferred between Firmicutes and Proteobacteria, but rarely between Actinobacteria and Proteobacteria. The results, furthermore, show that conjugative systems are seldom shared between bacterial phyla, suggesting that other mechanisms drive the dissemination of ARGs between divergent hosts. We also show that bacterial genomes involved in IPTs of ARGs are either over- or under-represented in specific environments. These IPTs were also found to be more recent compared to transfers associated with bacteria isolated from water, soil, and sediment. While macrolide and tetracycline resistance genes involved in ITPs almost always were +95% identical between phyla, corresponding {beta}-lactamases showed a median identity of < 60%. We conclude that inter-phyla transfer is recurrent and our results offer new insights into how resistance genes are disseminated between evolutionary distant bacteria.

microbiology↗

bioRxiv2024

Genetic compatibility and ecological connectivity drive the dissemination of antibiotic resistance genes

The dissemination of mobile antibiotic resistance genes (ARGs) via horizontal gene transfer is a significant threat to public health globally. The flow of ARGs into and between pathogens, however, remains poorly understood, limiting our ability to develop strategies for managing the antibiotic resistance crisis. Therefore, we aimed to identify genetic and ecological factors that are fundamental for successful horizontal ARG transfer. From the analysis of [~]1 million bacterial genomes and >20,000 metagenomes, we developed random forest models that could reliably predict horizontal ARG transfer between bacteria. Our results suggest that genetic incompatibility, measured as nucleotide composition dissimilarity, negatively influences the likelihood of transfer of ARGs between evolutionarily divergent bacteria. Conversely, environmental co-occurrence increased the likelihood, especially in humans and wastewater, in which several environment-specific dissemination patterns were observed. This study provides new ways to predict the spread of ARGs and provides new insights into the mechanisms governing this evolutionary process.

microbiology↗

bioRxiv2023

Confidence-based Prediction of Antibiotic Resistance at the Patient-level Using Transformers

Rapid and accurate diagnostics of bacterial infections are necessary for efficient treatment of antibiotic-resistant pathogens. Cultivation-based methods, such as antibiotic susceptibility testing (AST), are limited by bacterial growth rates and seldom yield results before treatment needs to start, increasing patient risk and contributing to antibiotic overprescription. Here, we present a deep-learning method that leverages patient data and available AST results to predict antibiotic susceptibilities that have not yet been measured. After training on three million AST results from 30 European countries, the method achieved an average accuracy of 93% across bacterial species and antibiotics. It predicted susceptibility with an average major error rate below 5% for quinolones, cephalosporins, and carbapenems, and below 8% and 14% for aminoglycosides and penicillins, respectively. Furthermore, the model predicted resistance with an average very major rate below 10% for cephalosporins, carbapenems, and aminoglycosides, but with higher very major error rates for penicillins and quinolones. We combined the method with conformal prediction and demonstrated accurate estimation of the predictive uncertainty at the patient level. Our results suggest that AI-based decision support may offer new means to meet the growing burden of antibiotic resistance. IMPORTANCEImproved diagnostic tools are vital for maintaining efficient treatment of antibiotic-resistant bacteria and for reducing antibiotic overconsumption. In our research, we introduce a new deep learning-based method capable of predicting untested antibiotic resistance phenotypes. The method uses transformers, a powerful AI technique that efficiently leverages both antibiotic susceptibility tests (AST) and patient data simultaneously. The model produces predictions that can be used as time- and cost-efficient alternatives to results from cultivation-based diagnostic assays. Significantly, our study highlights the potential of AI technologies to address the increasing prevalence of antibiotic-resistant bacterial infections.

microbiology↗