Introduction to Antibiotic Resistance
Levels of antibiotic resistance (AR), also often called Antimicrobial Resistance (AMR), increase when microbes are exposed to antibiotics through natural genetic mutations or by acquiring resistance genes from other bacteria. Microbial communities respond to antibiotics not only by changing composition, but also by evolving, optimizing and disseminating antibiotic resistance genes (ARGs), collectively termed resistomes. In humans, the gut microbiota is considered a reservoir for ARGs, where microbes exchange these genes thereby propagating resistance.
Development and spread of microbial antibiotic resistance are serious human health concerns given that once reliable antibiotics are now failing. Resistance is also a fast-growing problem in livestock, posing issues to animal health itself and also further issues for human health as animals enter the food chain. Urgent action is needed to halt the development of resistance in both humans and livestock, and to accelerate new treatments for bacterial infections.
Research is critical for applications including:
- Understanding how different antibiotic regimens affect antibiotic resistance genes levels
- Research and development of new antibiotic drugs and new classes of antibacterial compounds to treat infections
- New antibiotic drug development for livestock use
- Development of precision antibacterial compounds for treating multidrug resistant bacteria
- Supporting basic research into understanding how microbes develop resistance
At Clinical Microbiomics, we have broad experience supporting a wide range of partners with their antibiotic resistance and antimicrobial resistance studies. In one recent multi-partner study published in Nature Microbiology, we demonstrated that the effect of antibiotics on the microbiota depends heavily on initial microbiota status. Working with other collaborators, we showed that after a multidrug combination the human gut microbiota of healthy young adults recovered to the initial state ~ 42 days after treatment. However, some species were lost (e.g. Bifidobacterium spp.). Immediately after the antibiotic exposure, we observed an increase of opportunistic pathogens with colonization and virulence potential.
Sample Preparation - We offer professional DNA extraction for all types of sample, performed under highly controlled and traceable conditions in our advanced, Copenhagen-based, ISO17025 certified laboratory.
Sequencing - We offer Illumina-based shotgun metagenomic sequencing to support antibiotic resistance studies.
Shotgun metagenomics powered by our MGS bioinformatics platform provides detailed characterization with the highest taxonomical (species/strain) and functional resolution (genes) possible.
Data Analysis – We map sequence reads to our microbial gene catalogs annotated using curated ARG databases. This allows us to study the resistome of your study samples. Using the metagenomics species (MGS) concept we can link the ARGs to species and thus label these as resistant or non-resistant.
Resistome analysis can be used for investigating:
- How antibiotics impact the level of resistance genes during and after treatment
- Effects of antibiotics on the gut microbiome composition and specific species
- How other treatments and interventions impact the level of antibiotic resistance genes
Albert Pallejà Caro, PhD, Bioinformatics Specialist
Resistome analysis expert at Clinical Microbiomics.
Antibiotic Resistance Databases
To annotate our microbial gene catalogs we use the Comprehensive Antibiotic Resistance Database (CARD). This database is a manually curated reference based resource containing genes, proteins and mutations related to antibiotic resistance. It is based on the Antibiotic Resistance Ontology (ARO) - interconnected and hierarchical controlled vocabulary of antimicrobial molecules and their targets, resistance mechanisms, genes and mutations, and their relationships. CARD currently gathers 2540 antimicrobial resistance models and the database curation is updated monthly based on manual literature curation, computational text mining, and genome analysis