The ability of microbes to metabolize a variety of compounds, including man-made industrial pollutants, offers significant potential for solving environmental problems. However, existing approaches based on bacteria are still not very effective. One of the biggest challenges is to find the right selection of different bacteria that together in a microbial community have the greatest desired effect.
In a study published in Nature Communications, researchers led by Sara Mitri, Associate Professor at the Department of Basic Microbiology of the Faculty of Biology and Medicine of the University of Lausanne, with the participation of the BIH Center for Regenerative Therapies (BCRT), have developed a new method of artificial selection to identify the groups of bacteria that are most efficient in degrading an industrial pollutant. “The approach, inspired by genetic algorithms, is based on 'selection by disassembly': a process in which random communities are generated, the most efficient ones are selected and then disassembled to create new communities with slightly different compositions. Starting from 29 random communities subjected to 18 cycles of selection and rearrangement, we managed to create a group of microbes that were significantly more efficient than the original ones,” explains Sara Mitri.
The study also shows that this strategy, also known as 'directed evolution', not only improves the overall performance of a community, but also promotes cooperative species. Thus, the success of a community does not only depend on the strongest bacteria. In fact, some microorganisms, although less efficient on their own, improve degradation when they occur in a group. These results show the potential of artificial selection to optimize microbial communities. In particular, they pave the way for more efficient applications in various fields beyond environmental remediation, such as biogas production and the development of food probiotics.