John Wooley

A Primer on Metagenomics

Metagenomics is a discipline that enables the genomic study of uncultured microorganisms. Faster, cheaper sequencing technologies and the ability to sequence uncultured microbes sampled directly from their habitats are expanding and transforming our view of the microbial world. Distilling meaningful information from the millions of new genomic sequences presents a serious challenge to bioinformaticians. In cultured microbes, the genomic data come from a single clone, making sequence assembly and annotation tractable. In metagenomics, the data come from heterogeneous microbial communities, sometimes containing more than 10,000 species, with the sequence data being noisy and partial. From sampling, to assembly, to gene calling and function prediction, bioinformatics faces new demands in interpreting voluminous, noisy, and often partial sequence data. Although metagenomics is a relative newcomer to science, the past few years have seen an explosion in computational methods applied to metagenomic-based research. It is therefore not within the scope of this article to provide an exhaustive review. Rather, we provide here a concise yet comprehensive introduction to the current computational requirements presented by metagenomics, and review the recent progress made. We also note whether there is software that implements any of the methods presented here, and briefly review its utility. Nevertheless, it would be useful if readers of this article would avail themselves of the comment section provided by this journal, and relate their own experiences. Finally, the last section of this article provides a few representative studies illustrating different facets of recent scientific discoveries made using metagenomics.

Structural Genomics of the Thermotoga maritima Proteome Implemented in a High-throughput Structure Determination Pipeline

Structural genomics is emerging as a principal approach to define protein structure–function relationships. To apply this approach on a genomic scale, novel methods and technologies must be developed to determine large numbers of structures. We describe the design and implementation of a high-throughput structural genomics pipeline and its application to the proteome of the thermophilic bacterium Thermotoga maritima. By using this pipeline, we successfully cloned and attempted expression of 1,376 of the predicted 1,877 genes (73%) and have identified crystallization conditions for 432 proteins, comprising 23% of the T. maritima proteome. Representative structures from TM0423 glycerol dehydrogenase and TM0449 thymidylate synthase-complementing protein are presented as examples of final outputs from the pipeline.

Community cyberinfrastructure for Advanced Microbial Ecology Research and Analysis: the CAMERA resource

The Community Cyberinfrastructure for Advanced Microbial Ecology Research and Analysis (CAMERA, http://camera.calit2.net/) is a database and associated computational infrastructure that provides a single system for depositing, locating, analyzing, visualizing and sharing data about microbial biology through an advanced web-based analysis portal. CAMERA collects and links metadata relevant to environmental metagenome data sets with annotation in a semantically-aware environment allowing users to write expressive semantic queries against the database. To meet the needs of the research community, users are able to query metadata categories such as habitat, sample type, time, location and other environmental physicochemical parameters. CAMERA is compliant with the standards promulgated by the Genomic Standards Consortium (GSC), and sustains a role within the GSC in extending standards for content and format of the metagenomic data and metadata and its submission to the CAMERA repository. To ensure wide, ready access to data and annotation, CAMERA also provides data submission tools to allow researchers to share and forward data to other metagenomics sites and community data archives such as GenBank. It has multiple interfaces for easy submission of large or complex data sets, and supports pre-registration of samples for sequencing. CAMERA integrates a growing list of tools and viewers for querying, analyzing, annotating and comparing metagenome and genome data.

Three-Dimensional Structural View of the Central Metabolic Network of Thermotoga maritima

Now Shown in 3D With the advent of systems-wide technologies and the development of analytical methods, data produced by analyzing individual or small groups of molecular components can be integrated to reassemble whole biological systems. Zhang et al. (p. 1544) have undertaken a major technological challenge: to integrate biochemical data with experimentally determined or predicted three-dimensional structures of all proteins involved in the central metabolism of a bacterial cell. This integration of large-scale data sets provides evolutionary and functional insights and furthers our understanding of the molecular assembly of complex biological networks. Protein structure and biochemical data generate a three-dimensional view of the metabolic network of a bacterial cell. Metabolic pathways have traditionally been described in terms of biochemical reactions and metabolites. With the use of structural genomics and systems biology, we generated a three-dimensional reconstruction of the central metabolic network of the bacterium Thermotoga maritima. The network encompassed 478 proteins, of which 120 were determined by experiment and 358 were modeled. Structural analysis revealed that proteins forming the network are dominated by a small number (only 182) of basic shapes (folds) performing diverse but mostly related functions. Most of these folds are already present in the essential core (~30%) of the network, and its expansion by nonessential proteins is achieved with relatively few additional folds. Thus, integration of structural data with networks analysis generates insight into the function, mechanism, and evolution of biological networks.

Exploration of Uncharted Regions of the Protein Universe

Determination of first protein structures, from hundreds of families of unknown function, have shown that divergence, rather than novelty, is the dominant force that shapes the evolution of the protein universe.

The JCSG High-throughput Structural Biology Pipeline

The Joint Center for Structural Genomics high-throughput structural biology pipeline has delivered more than 1000 structures to the community over the past ten years and has made a significant contribution to the overall goal of the NIH Protein Structure Initiative (PSI) of expanding structural coverage of the protein universe.

CARD8 and NLRP1 Undergo Autoproteolytic Processing through a ZU5-Like Domain

The “Function to Find Domain” (FIIND)-containing proteins CARD8 (Cardinal; Tucan) and NLRP1 (NALP1; NAC) are well known components of inflammasomes, multiprotein complexes responsible for activation of caspase-1, a regulator of inflammation and innate immunity. Although identified many years ago, the role of the FIIND is unknown. Here, we report that CARD8 and NLRP1 undergo autoproteolytic cleavage at a conserved SF/S motif within the FIIND. Using bioinformatics and computational modeling approaches, we detected striking structural similarity between the FIIND and the ZU5-UPA domain present in the autoproteolytic protein PIDD. This allowed us to generate a three-dimensional model and to gain insights in the molecular mechanism of the cleavage. Site-directed mutagenesis experiments revealed that the second serine of the SF/S motif is required for CARD8 and NLRP1 autoproteolysis. Furthermore, we discovered an important function for conserved glutamic acid and histidine residues, located in proximity of the cleavage site in regulating the autoprocessing efficiency. Altogether, these results identify a function for the FIIND and show that CARD8 and NLRP1 are ZU5-UPA domain-containing autoproteolytic proteins, thus suggesting a novel mechanism for regulating innate immune responses.

Semantics in Support of Biodiversity Knowledge Discovery: An Introduction to the Biological Collections Ontology and Related Ontologies

The study of biodiversity spans many disciplines and includes data pertaining to species distributions and abundances, genetic sequences, trait measurements, and ecological niches, complemented by information on collection and measurement protocols. A review of the current landscape of metadata standards and ontologies in biodiversity science suggests that existing standards such as the Darwin Core terminology are inadequate for describing biodiversity data in a semantically meaningful and computationally useful way. Existing ontologies, such as the Gene Ontology and others in the Open Biological and Biomedical Ontologies (OBO) Foundry library, provide a semantic structure but lack many of the necessary terms to describe biodiversity data in all its dimensions. In this paper, we describe the motivation for and ongoing development of a new Biological Collections Ontology, the Environment Ontology, and the Population and Community Ontology. These ontologies share the aim of improving data aggregation and integration across the biodiversity domain and can be used to describe physical samples and sampling processes (for example, collection, extraction, and preservation techniques), as well as biodiversity observations that involve no physical sampling. Together they encompass studies of: 1) individual organisms, including voucher specimens from ecological studies and museum specimens, 2) bulk or environmental samples (e.g., gut contents, soil, water) that include DNA, other molecules, and potentially many organisms, especially microbes, and 3) survey-based ecological observations. We discuss how these ontologies can be applied to biodiversity use cases that span genetic, organismal, and ecosystem levels of organization. We argue that if adopted as a standard and rigorously applied and enriched by the biodiversity community, these ontologies would significantly reduce barriers to data discovery, integration, and exchange among biodiversity resources and researchers.

Crystal structures of two novel dye-decolorizing peroxidases reveal a beta-barrel fold with a conserved heme-binding motif.

BtDyP from Bacteroides thetaiotaomicron (strain VPI‐5482) and TyrA from Shewanella oneidensis are dye‐decolorizing peroxidases (DyPs), members of a new family of heme‐dependent peroxidases recently identified in fungi and bacteria. Here, we report the crystal structures of BtDyP and TyrA at 1.6 and 2.7 Å, respectively. BtDyP assembles into a hexamer, while TyrA assembles into a dimer; the dimerization interface is conserved between the two proteins. Each monomer exhibits a two‐domain, α+β ferredoxin‐like fold. A site for heme binding was identified computationally, and modeling of a heme into the proposed active site allowed for identification of residues likely to be functionally important. Structural and sequence comparisons with other DyPs demonstrate a conservation of putative heme‐binding residues, including an absolutely conserved histidine. Isothermal titration calorimetry experiments confirm heme binding, but with a stoichiometry of 0.3:1 (heme:protein). Proteins 2007.

Crystal structure of thy1, a thymidylate synthase complementing protein from Thermotoga maritima at 2.25 Å resolution

Peter Kuhn, Scott A. Lesley, Irimpan I. Mathews, Jaume M. Canaves, Linda S. Brinen, Xiaoping Dai, Ashley M. Deacon, Marc A. Elsliger, Said Eshaghi, Ross Floyd, Adam Godzik, Carina Grittini, Slawomir K. Grzechnik, Chittibabu Guda, Keith O. Hodgson, Lukasz Jaroszewski, Cathy Karlak, Heath E. Klock, Eric Koesema, John M. Kovarik, Andreas T. Kreusch, Daniel McMullan, Timothy M. McPhillips, Mark A. Miller, Mitchell Miller, Andrew Morse, Kin Moy, Jie Ouyang, Alyssa Robb, Kevin Rodrigues, Thomas L. Selby, Glen Spraggon, Raymond C. Stevens, Susan S. Taylor, Henry van den Bedem, Jeff Velasquez, Juli Vincent, Xianhong Wang, Bill West, Guenter Wolf, John Wooley, and Ian A. Wilson* The Joint Center for Structural Genomics Stanford Synchrotron Radiation Laboratory, Stanford University, Menlo Park, California The Genomics Institute of Novartis Foundation, San Diego, California The San Diego Supercomputer Center, La Jolla, California The University of California, San Diego, La Jolla, California The Scripps Research Institute, La Jolla, California