Scientists from ITMO University, the Federal Research and Clinical Centre of Physical-Chemical Medicine and MIPT have created a software program that allows them to quickly compare various sets of DNA of microorganisms living in different environments.
The researchers already have many plans as to how the new program can be applied in real-life practice. Using the algorithm to compare the microflora of a healthy person with the microflora of a patient, specialists are now able to detect pathogens that have previously been unknown and their strains, which can help in the development in personalized medications.
Each person on the planet has a genome, which is a specific sequence of genes according to how an individual develops. Any living organism has another gene sequence that is known as the metagenome. This is the total DNA content of the many different microorganisms that inhabit the same environment, including bacteria, fungi and viruses. The metagenome is studied in order to indicate the presence of various diseases or even predispositions to certain diseases. Studying microbiota plays a very crucial role in metagenomic research.
The software tool developed by scientists, known as MetaFast, conducts rapid and comparative analysis of large numbers of metagenomes. Vladimir Ulyantsev, lead developer of the algorithm says in the study of the intestinal microflora of patients, the team was able to detect many microorganisms associated with particular diseases, like diabetes, and even their predispositions. This allows for a basis to apply personalized medicine techniques and the development of new drugs. The results provided by the software allow biologists to draw up conclusions on how to continue the research because the algorithm grants them access to study environments that there is currently not any knowledge about.
One of the biggest benefits of the program is that it is able to work successfully within environments where genetic contents have not previously been studied. Dmitry Alexeev, leader of the project, says the new approach allows scientists to find all possible gene sequences (whether they were previously known or unknown) and also identify metagenomic patterns that distinguish patients from one another.
This allows the program to be used to conduct untargeted express analysis of markers indicated within certain diseases. Afterwards, targeted methods such as PCR can be used in order to verify results and make adjustments. The program may be able to greatly reduce the time it takes to create new medications.
Microorganisms do not always respond in vitro (within viruses, for example), but instead offer very abstract results in tests and collecting their DNA is not an option. The new program is able to detect these microorganisms. Alexeev says 90% of microbiota in skin alone is unknown organisms. The new approach lets the team work with unknown materials and still be able to receive results. Testing has been conducted in a wide range of environments, even those with massive amounts of viruses present. The new program can locate and collect single DNA strands.
MetaFast is able to be used in the comparison of distinct people in closed populations with people living in cities in order to help identify bacteria strains that are very useful to humans but have become lost during urbanization. Antibiotics, preservatives and colorants have gotten rid of many useful bacteria in our microflora but these bacteria may still be present in populations that are closed (such as in villages).
The MetaFast program has proven to be extremely effective in the study of rare and undiscovered metagenomes. During the study, scientists analyzed the metagenome of a handful of the largest lakes in the world. The program did not have any previous data about the samples of microbiota from the lakes and was still able to find genetic similarities between samples that were similar in terms of chemical composition.
The latest algorithm enables data to be processed at very high speeds. The program also saves RAM because it partially collects and at the same time partially compares genomes but does not go into an in-depth collection analysis of the data provided.
The complete study was published in Bioinformatics journal.