ICEO, a biological ontology for representing and analyzing bacterial integrative and conjugative elements

Enriching the FIDEO ontology with food-drug interactions from online knowledge sources

The increasing number of articles on adverse interactions that may occur when specific foods are consumed with certain drugs makes it difficult to keep up with the latest findings. Conflicting information is available in the scientific literature and specialized knowledge bases because interactions are described in an unstructured or semi-structured format. The FIDEO ontology aims to integrate and represent information about food-drug interactions in a structured way. This article reports on the new version of this ontology in which more than 1700 interactions are integrated from two online resources: DrugBank and Hedrine. These food-drug interactions have been represented in FIDEO in the form of precompiled concepts, each of which specifies both the food and the drug involved. Additionally, competency questions that can be answered are reviewed, and avenues for further enrichment are discussed.

Microbial evolution through horizontal gene transfer by mobile genetic elements

Mobile genetic elements (MGEs) are crucial for horizontal gene transfer (HGT) in bacteria and facilitate their rapid evolution and adaptation. MGEs include plasmids, integrative and conjugative elements, transposons, insertion sequences and bacteriophages. Notably, the spread of antimicrobial resistance genes (ARGs), which poses a serious threat to public health, is primarily attributable to HGT through MGEs. This mini-review aims to provide an overview of the mechanisms by which MGEs mediate HGT in microbes. Specifically, the behaviour of conjugative plasmids in different environments and conditions was discussed, and recent methodologies for tracing the dynamics of MGEs were summarised. A comprehensive understanding of the mechanisms underlying HGT and the role of MGEs in bacterial evolution and adaptation is important to develop strategies to combat the spread of ARGs.

Impacts of net cages on pollutant accumulation and its consequence on antibiotic resistance genes (ARGs) dissemination in freshwater ecosystems: Insights for sustainable urban water management

There has been increasing interest in the role of human activities in disseminating antibiotic-resistance genes (ARGs) in aquatic ecosystems. However, the influence of pollutant accumulation on anthropogenic pollutant-ARG synergistic actions is limited. This study explored the association of net cages with the propagation of anthropogenic pollutants and their consequences for influencing the enrichment of ARGs using high-throughput metagenomic sequencing. We showed that net cages could substantially impact the ecology of freshwater systems by enhancing i) ARG diversity and the tendency for ARG-horizontal gene transfer and ii) the overlap of mobile genetic elements (MGEs) with biocide-metal resistance genes (BMRGs) and ARGs. These findings suggested that the cotransfer of these three genetic determinants would be favored in net cage plots and that nonantibiotic factors such as metal(loid)s, particularly iron (Fe), displayed robust selective pressures on ARGs exerted by the net cage. The resistome risk scores of net cage sediments and biofilms were higher than those from off-net cage plots, indicating that the net cage-origin antibiotic resistome should be of great concern. The combination of deterministic and stochastic processes acting on bacterial communities could explain the higher ARG variations in cage plots (8.2%) than in off-cage plots (3.4%). Moreover, MGEs and pollutants together explained 43.3% of the total variation in ARG communities, which was higher than that of off-cage plots (8.8%), considering pollutants, environmental variables, MGEs, and assembly processes. These findings will inform the development of policies and guidelines to more effectively limit the spread of antimicrobial resistance and achieve the goal of sustainability in freshwater systems in urban areas.

Translational informatics for human microbiota: data resources, models and applications

With the rapid development of human intestinal microbiology and diverse microbiome-related studies and investigations, a large amount of data have been generated and accumulated. Meanwhile, different computational and bioinformatics models have been developed for pattern recognition and knowledge discovery using these data. Given the heterogeneity of these resources and models, we aimed to provide a landscape of the data resources, a comparison of the computational models and a summary of the translational informatics applied to microbiota data. We first review the existing databases, knowledge bases, knowledge graphs and standardizations of microbiome data. Then, the high-throughput sequencing techniques for the microbiome and the informatics tools for their analyses are compared. Finally, translational informatics for the microbiome, including biomarker discovery, personalized treatment and smart healthcare for complex diseases, are discussed.