Developing Reduced Complexity Microbial Communities for Editing Across Scales
Authors:
Dawn Chiniquy1* ([email protected]), Mingfei Chen1, Spencer Diamond2, Archana Yadav1, Peter F. Andeer1, Shwetha Acharya1, Mon Oo Yee1, Kristine Cabugao1, Veronica Escalante1, Kateryna Zhalnina1, Romy Chakraborty1, Jillian Banfield2, Adam M. Deutschbauer1, Trent R. Northen1* ([email protected])
Institutions:
1Lawrence Berkeley National Laboratory; 2University of California–Berkeley
URLs:
Goals
The program’s goal is to understand the interactions, localization, and dynamics of grass rhizosphere microbial communities at the molecular level (e.g., genes, proteins, metabolites) to enable accurate predictions and interventions to effectively manage and harness microbes to achieve DOE missions in sustainable energy and carbon cycling.
Abstract
Synthetic communities are excellent tools in microbial ecology research to decipher the complexity in microbe-microbe and plant-microbe interactions. However, this approach often constructs these communities by pooling together individual isolates that are not known to interact or even inhabit the same environment, making the system less biologically relevant. By contrast, reduced complexity communities created using enrichment strategies from native environments can produce less complex mixtures of naturally occurring and interacting organisms. Using a combination of these natural enrichment communities, genome-resolved metagenomics and networking microbiome sequencing data, these projects have developed reduced complexity communities from both field soil and the plant rhizosphere. Communities were enriched on multiple carbon compounds in minimal media conditions to generate different taxa composition from the same soil inoculum and then subcultured over multiple months to generate a highly stable microbiome. These communities were further tested for freezing tolerance and reproducibility over multiple freeze-growth cycles to confirm community stability under cryogenic conditions and allow for higher predictability of community structure. This high predictability will enable the modeling and precision community editing of native but elusive members of the soil environment, expanding knowledge of biologically relevant interactions in this complex ecosystem. These reduced complexity communities will be tested in field simulated conditions to allow for testing microbiome editing capabilities across scales.
Funding Information
This material by m-CAFEs (Microbial Community Analysis and Functional Evaluation in Soils; [email protected]), a Science Focus Area led by Lawrence Berkeley National Laboratory, is based upon work supported by the U.S. DOE, Office of Science, BER program under contract number DE-AC02-05CH11231.