ITMO scientists have developed an algorithm for deep analysis of bacterial genomes.
The tool allows researchers to automatically detect evolutionary events in genomes that are responsible for the adaptation of bacteria to a new environment, in particular, the pathogenic ones. This method streamlines the study of evolutionary mechanisms in bacterial genomes. It’s been tested on Streptococcus pneumoniae, Streptococcus pyogenes and Escherichia coli.
To unravel the evolutionary patterns and the underlying molecular mechanisms, comparative genomics studies the origins of genomes, their evolution and alterations.
The PaReBrick algorithm developed by bioinformatics specialists from ITMO University and researchers from the Institute of Science and Technology (IST Austria) allows to automatically identify parallel adaptation events in bacterial populations. At first, the tool takes a collection of strains represented as a sequence of common blocks and their phylogenetic tree, which demonstrates the evolutionary connections in them, as input data. Then, it detects evolutionary events in the genomes and visualizes information on the phylogenetic tree that represents a feature, such as the presence of a certain gene.
The algorithm was tested on about 200 strains of Streptococcus pyogenes, the cause of pneumonia, pharyngitis and skin infections.
According to Alexey Zabelkin, PhD student at ITMO University and co-author of this paper, the project was inspired by the work of their colleagues from the Institute for Information Transmission Problems of the Russian Academy of Sciences. They described a previously unknown evolutionary mechanism of antigenic variation that allows a pathogen to avoid immune system response. By comparing genomes of different Streptococcus pneumoniae strains, they’ve determined that one evolutionary event (alteration of gene order) takes place in different strains independently and changes the phenotype of the pathogen. Previously, such research was conducted manually and took quite a lot of time, but the tool developed by ITMO scientists will help automate this process.
“Our method can analyze the phylogenetic tree built for bacterial strains, study similar segments of genomes, look for the features we need, and mark strains on the tree with a specific color, depending on its state (for example, if there was an inversion or not). In this way, genomic data can be visualized as graphs,” says Alexey.
The method is universal and can be used for the analysis of not only streptococci but various bacterial species.
“Such a systematic analysis from the point of view of bioinformatics, building of trees, and studying the evolution of sequences for many strains has barely been done before. Usually researchers observe such phenomena in vitro. For example, a biologist can detect a pattern in one specific strain and describe it. But our project helps solve this problem by analyzing the data of many strains in one species and comparing them,” says Alexey.
The algorithm can be instrumental in fundamental and applied biological research; the results can be used in medicine, genetic engineering, agriculture. In particular, the study of evolutionary mechanisms of bacteria helps to reveal the potential reasons for their antibiotics resistance and describe evolutionary strategies. These findings can seriously aid in expediting the work of doctors, bioinformaticians, and biologists.
Source: ITMO