Periodic Reporting for period 4 - EVOMICROCOMM (Evolving interactions in microbial communities)
Berichtszeitraum: 2021-11-01 bis 2022-10-31
Microbes play an important role in various aspects of our lives, from our own health to the health of our environment. In almost all of their natural habitats, microbes live in dense communities composed of different strains and species that interact with each other. As these microbes evolve, so do the interactions between them, which alters the functioning of the community as a whole.
In this project, we have developed theoretical and experimental tools to study and control evolving interactions between cells and species living in microbial ecosystems. We have accomplished three main research objectives: first, we have coupled theory and experiments to disentangle and characterize the social interactions between four bacterial species that make up an ecosystem used to degrade industrial pollutants. Our second objective was to use this knowledge to control this same ecosystem, by directing it toward increased productivity and stability. Finally, our third objective was to "breed" novel communities from scratch using experimental evolution to promote cooperative interactions between community members and thereby increase productivity.
This interdisciplinary and ambitious research has allowed us to improve existing methods in pollution degradation, and to design new microbial communities for this and, in the future, for other purposes. More generally, our model system has provided an in-depth conceptual understanding of microbial ecosystems and their evolution, and the tools to investigate more complex microbial communities. Our ultimate vision is to possess the technology to use microbial communities to degrade waste, generate efficient biofuels, and design customized treatments for intestinal diseases. This project has laid the foundations needed to develop this technology, and open many exciting avenues for future research.
In this project, we have developed theoretical and experimental tools to study and control evolving interactions between cells and species living in microbial ecosystems. We have accomplished three main research objectives: first, we have coupled theory and experiments to disentangle and characterize the social interactions between four bacterial species that make up an ecosystem used to degrade industrial pollutants. Our second objective was to use this knowledge to control this same ecosystem, by directing it toward increased productivity and stability. Finally, our third objective was to "breed" novel communities from scratch using experimental evolution to promote cooperative interactions between community members and thereby increase productivity.
This interdisciplinary and ambitious research has allowed us to improve existing methods in pollution degradation, and to design new microbial communities for this and, in the future, for other purposes. More generally, our model system has provided an in-depth conceptual understanding of microbial ecosystems and their evolution, and the tools to investigate more complex microbial communities. Our ultimate vision is to possess the technology to use microbial communities to degrade waste, generate efficient biofuels, and design customized treatments for intestinal diseases. This project has laid the foundations needed to develop this technology, and open many exciting avenues for future research.
A first key result was that interactions are context-dependent and a toxic environment can lead to positive interactions, assuming that at least one of the species can reduce its toxicity. We later illustrated this principle in a simpler environment containing a single compound that became toxic at high concentration and showed that one could carefully change interaction sign by changing compound concentration.
We also studied how the four-species community co-evolved in this context and showed that positive interactions between species are maintained over time, although some become weaker. Genomic analysis has also shown that species evolve differently if they are part of a larger community. Co-evolved populations were more difficult for new species to invade.
Finally, we have show-cased a new method to design new communities from scratch. We first analysed this new method using computational models and showed why it works better than the state-of-the-art. We then applied it experimentally and could find a community that performs 20% better than our original community.
Our project has contributed to better understanding inter-species interactions and how they depend on the environment. We also present one of the first studies that has closely followed co-evolution between four species and how their interactions change over time. This will help develop our expectations on community co-evolution. Finally, our new approach to community design can potentially be applied to any microbial community with a practical purpose, from compost to probiotic communities.
We also studied how the four-species community co-evolved in this context and showed that positive interactions between species are maintained over time, although some become weaker. Genomic analysis has also shown that species evolve differently if they are part of a larger community. Co-evolved populations were more difficult for new species to invade.
Finally, we have show-cased a new method to design new communities from scratch. We first analysed this new method using computational models and showed why it works better than the state-of-the-art. We then applied it experimentally and could find a community that performs 20% better than our original community.
Our project has contributed to better understanding inter-species interactions and how they depend on the environment. We also present one of the first studies that has closely followed co-evolution between four species and how their interactions change over time. This will help develop our expectations on community co-evolution. Finally, our new approach to community design can potentially be applied to any microbial community with a practical purpose, from compost to probiotic communities.
In this project, we have developed theory and experiments into a powerful model system composed of just four bacterial species in a defined environment, which can be used to answer a number of important ecological and evolutionary questions. We have been successful at advancing the state of the art by showing how environmental toxicity can shape interactions between bacterial species, and how to change the environment to quantitatively predict and control species abundances. We have also provided important insights into making our existing community more productive, and how to evolve such productive communities from scratch.