An algorithm for extracting the genomic environment of antibiotic resistance genes from metagenomic data has been developed. The algorithm was validated on a number of simulated and published datasets, as well as applied to new metagenomes of gut microbiota from patients with Helicobacter pylori who underwent antibiotic therapy.
According to scientists from ITMO University and the Research Center of Physical and Chemical Medicine, MetaCherchant makes it possible to explore drug resistance gene environment and see how it changes depending on bacteria species. Their research was published last month in Bioinformatics.
"We created a tool that enables scientists to have a closer look at the difference between gene surroundings in two or more samples of microbiota. We can analyze microbiota samples collected from different people or from the same person at different times, for example, before and after antibiotic treatment," says Vladimir Ulyantsev, associate professor of the computer technologies department at the ITMO. "Based on the obtained data, we can suggest how a particular resistance gene could spread from one microbial species to another."
Evgenii Olekhnovich, lead author and researcher at the Center of Physical and Chemical Medicine, explains that "Using MetaCherchant, we can analyze how microbiota contributes to the spread of resistance to a particular antibiotic class. Looking forward, it is possible to predict antibiotics, resistance to which is most likely to spread among pathogens. On the other hand, we can find drugs with low resistance risk. This, in turn, will help us adjust and tune specific therapies. This is the question of the next couple of years."
The team says that potential applications of the algorithm are not limited to gut microbiota genes analysis since the program can be also used to study genome samples from soil, water, or sewerage. "We can evaluate the spread of resistance within a single bacterial community, such as gut microbiota, as well as between different communities. This allows us, for example, to identify global pathways of antibiotic resistance spread through the environment," adds Olekhnovich.