Ancestral absence of electron transport chains in Patescibacteria and DPANN

© The Author(s), 2020. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Beam, J. P., Becraft, E. D., Brown, J. M., Schulz, F., Jarett, J. K., Bezuidt, O., Poulton, N. J., Clark, K., Dunfield, P. F., Ravin, N. V., Spear, J. R., Hedlund, B. P., Kormas, K. A., Sievert, S. M., Elshahed, M. S., Barton, H. A., Stott, M. B., Eisen, J. A., Moser, D. P., Onstott, T. C., Woyke, T., & Stepanauskas, R. Ancestral absence of electron transport chains in Patescibacteria and DPANN. Frontiers in Microbiology, 11, (2020): 1848, doi:10.3389/fmicb.2020.01848.Recent discoveries suggest that the candidate superphyla Patescibacteria and DPANN constitute a large fraction of the phylogenetic diversity of Bacteria and Archaea. Their small genomes and limited coding potential have been hypothesized to be ancestral adaptations to obligate symbiotic lifestyles. To test this hypothesis, we performed cell–cell association, genomic, and phylogenetic analyses on 4,829 individual cells of Bacteria and Archaea from 46 globally distributed surface and subsurface field samples. This confirmed the ubiquity and abundance of Patescibacteria and DPANN in subsurface environments, the small size of their genomes and cells, and the divergence of their gene content from other Bacteria and Archaea. Our analyses suggest that most Patescibacteria and DPANN in the studied subsurface environments do not form specific physical associations with other microorganisms. These data also suggest that their unusual genomic features and prevalent auxotrophies may be a result of ancestral, minimal cellular energy transduction mechanisms that lack respiration, thus relying solely on fermentation for energy conservation.This work was funded by the USA National Science Foundation grants 1441717, 1826734, and 1335810 (to RS); and 1460861 (REU site at Bigelow Laboratory for Ocean Sciences). RS was also supported by the Simons Foundation grant 510023. TW, FS, and JJ were funded by the U.S. Department of Energy Joint Genome Institute, a DOE Office of Science User Facility supported under Contract No. DE-AC02-05CH11231. NR group was funded by the Russian Science Foundation (grant 19-14-00245). SS was funded by USA National Science Foundation grants OCE-0452333 and OCE-1136727. BH was funded by NASA Exobiology grant 80NSSC17K0548

Korarchaeota Diversity, Biogeography, and Abundance in Yellowstone and Great Basin Hot Springs and Ecological Niche Modeling Based on Machine Learning

Over 100 hot spring sediment samples were collected from 28 sites in 12 areas/regions, while recording as many coincident geochemical properties as feasible (>60 analytes). PCR was used to screen samples for Korarchaeota 16S rRNA genes. Over 500 Korarchaeota 16S rRNA genes were screened by RFLP analysis and 90 were sequenced, resulting in identification of novel Korarchaeota phylotypes and exclusive geographical variants. Korarchaeota diversity was low, as in other terrestrial geothermal systems, suggesting a marine origin for Korarchaeota with subsequent niche-invasion into terrestrial systems. Korarchaeota endemism is consistent with endemism of other terrestrial thermophiles and supports the existence of dispersal barriers. Korarchaeota were found predominantly in >55°C springs at pH 4.7–8.5 at concentrations up to 6.6×106 16S rRNA gene copies g−1 wet sediment. In Yellowstone National Park (YNP), Korarchaeota were most abundant in springs with a pH range of 5.7 to 7.0. High sulfate concentrations suggest these fluids are influenced by contributions from hydrothermal vapors that may be neutralized to some extent by mixing with water from deep geothermal sources or meteoric water. In the Great Basin (GB), Korarchaeota were most abundant at spring sources of pH<7.2 with high particulate C content and high alkalinity, which are likely to be buffered by the carbonic acid system. It is therefore likely that at least two different geological mechanisms in YNP and GB springs create the neutral to mildly acidic pH that is optimal for Korarchaeota. A classification support vector machine (C-SVM) trained on single analytes, two analyte combinations, or vectors from non-metric multidimensional scaling models was able to predict springs as Korarchaeota-optimal or sub-optimal habitats with accuracies up to 95%. To our knowledge, this is the most extensive analysis of the geochemical habitat of any high-level microbial taxon and the first application of a C-SVM to microbial ecology