Abstract
Antimicrobial resistance (AMR) in bacteria—the ability to survive despite the presence of drugs (antimicrobials) designed to kill them or inhibit their growth—is a growing concern worldwide. This resistance makes standard treatments ineffective in humans and animals, leading to persistent infections and the increased spread of resistant pathogens. AMR results from complex interactions between humans, food, animals, and environmental systems. The unit of AMR resistance is the strain, a dynamic entity that can evolve or acquire resistance genes. Despite significant research efforts, gaps remain in understanding how strains acquire AMR, persist, and spread across these interconnected systems, which hampers our ability to model and control AMR effectively. It is also challenging to effectively trace AMR-carrying strains across diverse ecosystems, limiting our capacity to predict the emergence and spread of AMR. In this project, we propose to develop a computational tool to predict the emergence of AMR and trace the transmission and persistence of bacteria carrying AMR at the strain level in different systems using publicly available metagenomics and whole genome sequencing data. Building on the fast-expanding metagenomic databases, we will focus on a key system such as the human, food, animal, soil and water microbiomes. This research will provide much-needed capabilities in source-tracking of AMR-carrying strains and significantly improve the models used to predict and control their spread.
Background
Metagenomics, the study of genetic material recovered directly from any type of samples, has revolutionized our ability to study microbial communities, including those that harbour antimicrobial resistance genes (AMR). It allows for the comprehensive analysis of microbial diversity and function without the need for culturing individual organisms. Metagenomics can provide a complete picture of the microbial community structure and the presence of AMR, offering insights into the resistome (the collection of all resistance genes) of a given environment (De Abreu et al., 2021). It enables the discovery of novel resistance genes and mobile genetic elements such as plasmids that could contribute to the spread of AMR and contributed to elucidate the ecological interactions and environmental factors that influence the distribution and abundance of AMR. It also has the potential to reveal and track specific strains carrying AMR. Tracking AMR at the strain level is crucial for understanding the transmission dynamics and persistence of resistant bacteria. Previous traditional methods, such as multilocus sequence typing (MLST) and whole-genome sequencing (WGS), have been employed to track specific strains and for outbreak investigations. However, these methods often rely on the isolation and culturing of bacteria, which can be labour-intensive and limited to cultivable strains. Combined with WGS, metagenomics is a powerful tool to characterize AMR at the community level in a given system. However, metagenomics also has limits. Because it relies on short-read sequencing technologies, it is difficult to assign AMR to specific bacterial strains. Nonetheless, advances in bioinformatics have led to the development of strain-level metagenomics, where algorithms can differentiate between closely related strains within a metagenomics sample (Beghini et al., 2021; Olm et al., 2021). The development of long-reads sequencing, sometimes combined with short-reads sequencing, has also improved the resolution to trace AMR-carrying strains in metagenomics data.
Despite these advances, comprehensive strain-level tracking of AMR in diverse microbiomes (human, food, animal, environmental) remains challenging. Our project aims to address these limitations by developing novel bioinformatics methods and integrating high-quality WGS data with metagenomics to enhance the resolution of strain-level analysis.
Specific aims
The specific aims will be the following:
- Benchmark the existing tools used for strain-level profiling in metagenomics data
- Develop a computational tool to trace bacteria at the strain level from metagenomics and whole-genome sequencing
- Validate this tool on a set of metagenomics data and determine the bacteria strain transmission between (and persistence within) samples.
Contact
Dr. Ghjuvan Grimaud (Division of Biotechnology and Applied Microbiology)
ghjuvan_micaelu.grimaud@ple.lth.se
References
Beghini, F., McIver, L. J., Blanco-Míguez, A., Dubois, L., Asnicar, F., Maharjan, S., … & Segata, N. (2021). Integrating taxonomic, functional, and strain-level profiling of diverse microbial communities with bioBakery 3. elife, 10, e65088.
De Abreu, V. A., Perdigão, J., & Almeida, S. (2021). Metagenomic approaches to analyze antimicrobial resistance: an overview. Frontiers in genetics, 11, 575592.
Olm, M. R., Crits-Christoph, A., Bouma-Gregson, K., Firek, B. A., Morowitz, M. J., & Banfield, J. F. (2021). inStrain profiles population microdiversity from metagenomic data and sensitively detects shared microbial strains. Nature Biotechnology, 39(6), 727-736.