Inter-species interconnections in acid mine drainage microbial communities.

Metagenomic studies are revolutionizing our understanding of microbes in the biosphere. They have uncovered numerous proteins of unknown function in tens of essentially unstudied lineages that lack cultivated representatives. Notably, few of these microorganisms have been visualized, and even fewer have been described ultra-structurally in their essentially intact, physiologically relevant states. Here, we present cryogenic transmission electron microscope (cryo-TEM) 2D images and 3D tomographic datasets for archaeal species from natural acid mine drainage (AMD) microbial communities. Ultrastructural findings indicate the importance of microbial interconnectedness via a range of mechanisms, including direct cytoplasmic bridges and pervasive pili. The data also suggest a variety of biological structures associated with cell-cell interfaces that lack explanation. Some may play roles in inter-species interactions. Interdependences amongst the archaea may have confounded prior isolation efforts. Overall, the findings underline knowledge gaps related to archaeal cell components and highlight the likely importance of co-evolution in shaping microbial lineages.

Prokaryotic Super Program Advisory Committee DOE Joint Genome Institute, Walnut Creek, CA, March 27, 2013.

The Prokaryotic Super Program Advisory Committee met on March 27, 2013 for their annual review the Prokaryotic Super Program at the DOE Joint Genome Institute. As is the case with any site visit or program review, the objective is to evaluate progress in meeting organizational objectives, provide feedback to from the user-community and to assist the JGI in formulating plans for the coming year. The advisors want to commend the JGI for its central role in developing new technologies and capabilities, and for catalyzing the formation of new collaborative user communities. Highlights of the post-meeting exchanges among the advisors focused on the importance of programmatic initiatives including: • GEBA, which serves as a phylogenetic "base-map" on which our knowledge of functional diversity can be layered. • FEBA, which promises to provide new insights into the physiological capabilities of prokaryotes under highly standardized conditions. • Single-cell genomics technology, which is seen to significantly enhance our ability to interpret genomic and metagenomic data and broaden the scope of the GEBA program to encompass at least a part of the microbial "dark-matter". • IMG, which is seen to play a central role in JGI programs and is viewed as a strategically important asset in the JGI portfolio. On this latter point, the committee encourages the formation of a strategic relationship between IMG and the Kbase to ensure that the intelligence, deep knowledge and experience captured in the former is not lost. The committee strongly urges the DOE to continue its support for maintaining this critical resource.

NIBBS-search for fast and accurate prediction of phenotype-biased metabolic systems.

Understanding of genotype-phenotype associations is important not only for furthering our knowledge on internal cellular processes, but also essential for providing the foundation necessary for genetic engineering of microorganisms for industrial use (e.g., production of bioenergy or biofuels). However, genotype-phenotype associations alone do not provide enough information to alter an organism's genome to either suppress or exhibit a phenotype. It is important to look at the phenotype-related genes in the context of the genome-scale network to understand how the genes interact with other genes in the organism. Identification of metabolic subsystems involved in the expression of the phenotype is one way of placing the phenotype-related genes in the context of the entire network. A metabolic system refers to a metabolic network subgraph; nodes are compounds and edges labels are the enzymes that catalyze the reaction. The metabolic subsystem could be part of a single metabolic pathway or span parts of multiple pathways. Arguably, comparative genome-scale metabolic network analysis is a promising strategy to identify these phenotype-related metabolic subsystems. Network Instance-Based Biased Subgraph Search (NIBBS) is a graph-theoretic method for genome-scale metabolic network comparative analysis that can identify metabolic systems that are statistically biased toward phenotype-expressing organismal networks. We set up experiments with target phenotypes like hydrogen production, TCA expression, and acid-tolerance. We show via extensive literature search that some of the resulting metabolic subsystems are indeed phenotype-related and formulate hypotheses for other systems in terms of their role in phenotype expression. NIBBS is also orders of magnitude faster than MULE, one of the most efficient maximal frequent subgraph mining algorithms that could be adjusted for this problem. Also, the set of phenotype-biased metabolic systems output by NIBBS comes very close to the set of phenotype-biased subgraphs output by an exact maximally-biased subgraph enumeration algorithm ( MBS-Enum ). The code (NIBBS and the module to visualize the identified subsystems) is available at http://freescience.org/cs/NIBBS.