Surveillance Of mobiLome meDiated aNtibiotic rEsiStance Spread (SOLIDNESS)
MGEs are one of the main players in antibiotic resistance dissemination and can be shared by different bacterial strains, species or even genera, which makes them one of the biggest concerns to healthcare stakeholders. MGEs are challenging to characterise through sequencing, due to their chimeric, modular and repetitive nature. Our main objective is to establish a network of excellence for surveillance of MGE-mediated antibiotic resistance spread. This network will improve the access to high-quality and curated MGEs sequencing data that will be shared at the international level. It will result in the production of documents (standard operating procedures, SOPs) by the network detailing 1) harmonisation of high-quality sequencing standards and protocols for MGEs detection; 2) definition of a bioinformatics workflow for MGEs sequence analysis from next-generation sequencing data; 3) definition of new sequence-based typing methods of plasmids for both Gram-positive and Gram-negative bacteria.
Since the network includes a wide range of stakeholders with diverse expertise, including expertise in classical typing methods, “-omics”, bioinformatics and plasmid-detection, the combination of backgrounds will contribute to the creation of the high-quality and curated MGEs database from different sources. This proposal aims to track the evolution and spread of antimicrobial resistance and virulence in bacteria, mediated by MGEs, and in the future, find ways to prevent it.
- John Rossen, University of Groningen, Netherlands (Coordinator)
This network includes 21 partners, please click on the following link to see complete network composition: Network composition Surveillance Of mobiLome meDiated aNtibiotic rEsiStance Spread (SOLIDNESS)
Mobile genetic elements (MGEs) are DNA molecules containing genes that are important for a microorganism’s fitness. These include resistance and virulence genes, which can provide the
bacteria with an adaptive advantage over other bacteria. MGEs play a central role in horizontal gene transfer and are among the main drivers of antibiotic resistance and virulence spread. Such
dissemination is a significant concern for healthcare stakeholders and a challenge for those concerned with preventing the dissemination of antibiotic-resistant and / or virulent bacteria,
such as carbapenemase-producing Enterobacteriaceae and Shiga-toxin-producing Escherichia coli (STEC). Molecular characterization of MGEs is essential for a better understanding of these
molecules and their dissemination. Both are required for optimal surveillance of bacteria containing resistance and virulence genes. Sequence analysis techniques are often used for this
purpose. The sequencing protocols and subsequent bioinformatics tools used to characterize MGEs may significantly influence the final results and their interpretation.
To map this out, several studies were performed within the consortium, including a proficiency test. Significant differences were found in the quality of the sequences obtained from different
laboratories, in which the sequencing process and the sequencing technique played an important role. This had an impact on the subsequent data analysis, leading to variable results.
Small MGEs, abundant in Gram-positive bacteria, could be characterized by using a technique that generates short sequence fragments (≤ 300 base pairs). This method was not suitable for the
characterization of larger MGEs, typical for Gram-negative bacteria. Using a sequencing technology that produces longer sequencing fragments facilitated the characterization of large
MGEs, but some smaller MGEs could not be analyzed. Besides, this technology resulted in lower accuracy, which could be partially overcome by using additional polishing steps. The best results were obtained by combining the data generated by the two different sequencing technologies, which resulted in the correct characterization of small and large MGEs.
In summary, when characterizing MGEs, researchers must weigh the costs/turnaround time and the accuracy of the results. However, only by combined analyzes of the data generated by both short- and long fragment sequencing technologies will they produce results that will include all the MGEs of a specific strain.