Taylor, Ronald C | Search NASA

DOE OSTI2015

Genomic expansion of domain archaea highlights roles for organisms from new phyla in anaerobic carbon cycling.

Archaea represent a significant fraction of Earth's biodiversity, yet they remain much less well understood than Bacteria. Gene surveys, a few metagenomic studies, and some single-cell sequencing projects have revealed numerous little-studied archaeal phyla. Certain lineages appear to branch deeply and may be part of a major phylum radiation. The structure of this radiation and the physiology of the organisms remain almost unknown.We used genome-resolved metagenomic analyses to investigate the diversity, genomes sizes, metabolic capacities, and potential roles of Archaea in terrestrial subsurface biogeochemical cycles. We sequenced DNA from complex sediment and planktonic consortia from an aquifer adjacent to the Colorado River (USA) and reconstructed the first complete genomes for Archaea using cultivation-independent methods. To provide taxonomic context, we analyzed an additional 151 newly sampled archaeal sequences. We resolved two new phyla within a major, apparently deep-branching group of phyla (a superphylum). The organisms have small genomes, and metabolic predictions indicate that their primary contributions to Earth's biogeochemical cycles involve carbon and hydrogen metabolism, probably associated with symbiotic and/or fermentation-based lifestyles.The results dramatically expand genomic sampling of the domain Archaea and clarify taxonomic designations within a major superphylum. This study, in combination with recently published work on bacterial phyla lacking cultivated representatives, reveals a fascinating phenomenon of major radiations of organisms with small genomes, novel proteome composition, and strong interdependence in both domains.

Castelle, Cindy J↗

DOE OSTI2012

Modeling Dynamic Regulatory Processes in Stroke.

The ability to examine in silico the behavior of biological systems can greatly accelerate the pace of discovery in disease pathologies, such as stroke, where in vivo experimentation is lengthy and costly. In this paper we describe an approach to in silico examination of blood genomic responses to neuroprotective agents and subsequent stroke through the development of dynamic models of the regulatory processes observed in the experimental gene expression data. First, we identified functional gene clusters from these data. Next, we derived ordinary differential equations (ODEs) relating regulators and functional clusters from the data. These ODEs were used to develop dynamic models that simulate the expression of regulated functional clusters using system dynamics as the modeling paradigm. The dynamic model has the considerable advantage of only requiring an initial starting state, and does not require measurement of regulatory influences at each time point in order to make accurate predictions. The manipulation of input model parameters, such as changing the magnitude of gene expression, made it possible to assess the behavior of the networks through time under varying conditions. We report that an optimized dynamic model can provide accurate predictions of overall system behavior under several different preconditioning paradigms.

dynamic modeling↗