Abstract
Most microorganisms from all taxonomic levels are uncultured. Single-cell genomes and metagenomes continue to increase the known diversity of Bacteria and Archaea; however, while ’omics can be used to infer physiological or ecological roles for species in a community, most of these hypothetical roles remain unvalidated. Here, we report an approach to capture specific microorganisms from complex communities into pure cultures using genome-informed antibody engineering. We apply our reverse genomics approach to isolate and sequence single cells and to cultivate three different species-level lineages of human oral Saccharibacteria (TM7). Using our pure cultures, we show that all three Saccharibacteria species are epibionts of diverse Actinobacteria. We also isolate and cultivate human oral SR1 bacteria, which are members of a lineage of previously uncultured bacteria. Reverse-genomics-enabled cultivation of microorganisms can be applied to any species from any environment and has the potential to unlock the isolation, cultivation and characterization of species from as-yet-uncultured branches of the microbial tree of life.
Original language | English |
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Pages (from-to) | 1314-1321 |
Number of pages | 8 |
Journal | Nature Biotechnology |
Volume | 37 |
Issue number | 11 |
DOIs | |
State | Published - Nov 1 2019 |
Funding
We thank S. Allman, S. Kauffman, S. Lebreux, L. Sukharnikov and M. Robeson for technical assistance. Support for this work was provided by grants (nos. R56DE021567 and R01DE024463) from the National Institute of Dental and Craniofacial Research of the US National Institutes of Health. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. J.M.P. was supported by the Laboratory Directed Research and Development program at Oak Ridge National Laboratory, which is managed by UT-Battelle, LLC for the US Department of Energy (contract no. DE-AC05-00OR22725). S.J.C. was supported by a National Science Foundation Graduate Research Fellowship (grant no. 2017219379). This work used resources of the Compute and Data Environment for Science at Oak Ridge National Laboratory.