Microbial Guilds and Niches Enable Targeted Modifications of the Microbiome
Oriane Moyne* (firstname.lastname@example.org), Deepan Thiruppathy, Chloe Lieng, Manish Kumar, Eli Haddad, Grant Norton, and Karsten Zengler
University of California–San Diego
This project couples novel lab and field studies to develop the first predictive model of grass-microbiomes based on new mechanistic insights into dynamic plant-microbe interactions in the grasses Sorghum bicolor and Brachypodium distachyon that improve plant N use efficiency (NUE). The results will be used to predict plant mutants and microbial amendments that improve low-input biomass production for validation in lab and field studies. To achieve this goal, the team will determine the mechanistic basis of dynamic exudate exchange in the grass rhizosphere with a specific focus on the identification of plant transporters and proteins that regulate root exudate composition and how specific exudates select for beneficial microbes that increase plant biomass and NUE. The team will further develop a predictive plant-microbe model for advancing sustainable bioenergy crops and will predictively shift plant-microbe interactions to enhance plant biomass production and N acquisition from varied N forms.
Microbiome science has contributed greatly to understanding of microbial life and provided insights on the essential roles of microbial communities, from global elements cycling to human health. However, a comprehensive understanding of how these communities are assembled, maintained, and function as a system is still lacking. In particular, the nature of microbe-microbe interactions and how microbial communities respond to perturbations remains poorly understood. As a result, current microbiome science is largely descriptive and correlation-based, rather than predictive and based on mechanistic understanding.
To achieve predictive microbiome science, it is necessary to comprehensively elucidate the metabolic role of each microbe and its interactions with others. Such knowledge would enable the manipulation of a microbe’s trajectory within a community, for example by selectively promoting or limiting its growth.
In this study, the team presents a new method that integrates transcriptional and translational regulation measurements to reveal how each microbe allocates its resources for optimal proteome efficiency. Protein translation is the most energy-intensive process in a cell, and microbes closely regulate their resource allocation by prioritizing essential functions through differential translational efficiency (TE). Direct measurement of TE in a microbial community sample would provide insights into the metabolic role of each member of the community and allow for a better understanding of interactions with other members.
The team performed metatranscriptomics and metatranslatomics analysis to directly measure TE in situ, in a 16-member synthetic community (SynCom) composed of rhizosphere isolates grown in a complex culture medium. This approach allowed us to perform a guild-based microbiome classification, grouping microbes according to the metabolic pathways they prioritize independent of their taxonomic relationships. Team members demonstrated that guilds predicted competition between members of the same guild with 100% sensitivity and 74% specificity (77% accuracy) in the SynCom. Furthermore, gene-level analysis of TE allowed us to predict each microbe’s substrate preferences, i.e., their niche in the community. Such Microbial Niche Determination (MiND) predicted which particular microbes would benefit from substrate supplementation with 54% sensitivity and 83% specificity (78% accuracy) in the SynCom.
It is worth noting that as microbes adapt their translational regulation to community settings, such predictions could not be achieved using axenic culture approaches (e.g., phenotypic microarray or growth curves) or partially functional measurements (e.g., metagenomics or metatranscriptomics).
By combining TE-based MiND and guilds predictions, researchers were able to selectively manipulate the SynCom, by increasing or decreasing the abundance of targeted members, either by providing preferred substrates or by giving an advantage to their competitors. Importantly, this method is scalable to more complex, natural samples. Team members applied MiND to native soil samples and demonstrated its applicability in predicting changes and manipulating microorganisms in complex microbiomes.
In conclusion, the method presented in this study represents a significant step towards achieving predictive microbiome science by providing a comprehensive understanding of the metabolic role of each microbe and its interactions with others. The guild-based microbiome classification and MiND approach allows for the manipulation of microbial communities and has potential applications in various fields such as agriculture, biotechnology, and human health.
This material is based upon work supported by the U.S. Department of Energy, Office of Science, Office of Biological and Environmental Research under Awards DE- SC0021234 and DE-SC0022137. Furthermore, the development of the technologies described in this article were in part funded through Trial Ecosystem Advancement for Microbiome Science Program at Lawrence Berkeley National Laboratory funded by the U.S. Department of Energy, Office of Science, Office of Biological and Environmental Research Awards DE-AC02- 05CH11231. The work was also supported by the UC San Diego Center for Microbiome Innovation (CMI) through a Grand Challenge Award.