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Reverse Ecology and Comparative Genomics of Uncultured Freshwater Actinobacteria

Undergraduate #237
Discipline: Ecology Environmental and Earth Sciences
Subcategory: Water

Brittany Brown - Georgia Institute of Technology
Co-Author(s): Joshua Hamilton and Katherine McMahon, University of Wisconsin-Madison, Madison, WI



Uncultured microbes are directly affected by the drivers of natural and engineered systems. These organisms are essential players in ecosystem functions such as nutrient cycling. By understanding the role microbes play in particularly ecosystems, this knowledge can be manipulated to design ecosystem scale predictive models of natural systems. Trait-based approaches are currently being explored as a foundation for this knowledge. In this study, phylogenetic patterns are being analyzed through comparative genomics and reverse ecology to achieve a better understanding of microbial community dynamics in order to build trait libraries for the Actinobacteria phylum. Phylosift was used to build phylogenetic trees, KBase was used to build metabolic models to extract seed compounds, and the US Department of Energy Joint Genome Institute’s system: Integrated Microbial Genomes (IMG) was used for genome sequencing. By connecting metabolic models to seed compounds, niches within the acI, acIV, and acV lineages of the Actinobacteria phylum as well as the acI-A and acI-B clades were discovered. The lineages acI and acV have a preference for branched chain amino acids as a carbon and nitrogen source, while acIV uses particular polysaccharides as its carbon source. These conclusions were drawn based upon the high frequency (greater than 70%) of certain groups of metabolites within each lineages metabolic model. In addition to discovering niches amongst Actinobacteria phylogeny, opportunities for competition are being evaluated in ongoing research to identify seed differentiations between competing tribes. There is also opportunity in future research to look at cooperation between tribes and to expand the trait libraries to include other lineages within the Actinobacteria phylum.

References: Borenstein, E., Kupiec, M., Feldman, M. W., & Ruppin, E. (2008). Large-scale reconstruction and phylogenetic analysis of metabolic environments. Proceedings of the National Academy of Sciences, 105(38), 14482-14487.
Borenstein, E., & Feldman, M. W. (2009). Topological signatures of species interactions in metabolic networks. Journal of Computational Biology, 16(2), 191-200.
Darling, A. E., Jospin, G., Lowe, E., Matsen, F. A., Bik, H. M., & Eisen, J. A. (2014). PhyloSift: phylogenetic analysis of genomes and metagenomes. PeerJ, 2, e243.
Stamatakis, A. (2014). RAxML version 8: a tool for phylogenetic analysis and post-analysis of large phylogenies. Bioinformatics, 30(9), 1312-1313.
Henry, C. S., DeJongh, M., Best, A. A., Frybarger, P. M., Linsay, B., & Stevens, R. L. (2010). High-throughput generation, optimization and analysis of genome-scale metabolic models. Nature biotechnology, 28(9), 977-982.

Funder Acknowledgement(s): I present this research with the approval of Dr. Katherine McMahon, and I acknowledge the support of Stefan Bertilsson, Rex Malmstrom, Ramunas Stepanauskas, Susannah Tringe, Tanja Woyke and their laboratories; the University of Wisconsin Madison College of Engineering and Graduate School; the National Science Foundation; the Long Term Ecological Research (LTER) Network; and 3M.

Faculty Advisor: Katherine McMahon,

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This material is based upon work supported by the National Science Foundation (NSF) under Grant No. DUE-1930047. Any opinions, findings, interpretations, conclusions or recommendations expressed in this material are those of its authors and do not represent the views of the AAAS Board of Directors, the Council of AAAS, AAAS’ membership or the National Science Foundation.

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