The Evolution of X-ray Clusters of Galaxies

Considerable progress has been made over the last decade in the study of the evolutionary trends of the population of galaxy clusters in the Universe. In this review we focus on observations in the X-ray band. X-ray surveys with the ROSAT satellite, supplemented by follow-up studies with ASCA and Beppo-SAX, have allowed an assessment of the evolution of the space density of clusters out to z~1, and the evolution of the physical properties of the intra-cluster medium out to z~0.5. With the advent of Chandra and Newton-XMM, and their unprecedented sensitivity and angular resolution, these studies have been extended beyond redshift unity and have revealed the complexity of the thermodynamical structure of clusters. The properties of the intra-cluster gas are significantly affected by non-gravitational processes including star formation and Active Galactic Nucleus (AGN) activity. Convincing evidence has emerged for modest evolution of both the bulk of the X-ray cluster population and their thermodynamical properties since redshift unity. Such an observational scenario is consistent with hierarchical models of structure formation in a flat low density universe with Omega_m=0.3 and sigma_8=0.7-0.8 for the normalization of the power spectrum. Basic methodologies for construction of X-ray-selected cluster samples are reviewed and implications of cluster evolution for cosmological models are discussed.Comment: 40 pages, 15 figures. Full resolution figures can be downloaded from http://www.eso.org/~prosati/ARAA

Analysis and comparison of very large metagenomes with fast clustering and functional annotation

<p>Abstract</p> <p>Background</p> <p>The remarkable advance of metagenomics presents significant new challenges in data analysis. Metagenomic datasets (metagenomes) are large collections of sequencing reads from anonymous species within particular environments. Computational analyses for very large metagenomes are extremely time-consuming, and there are often many novel sequences in these metagenomes that are not fully utilized. The number of available metagenomes is rapidly increasing, so fast and efficient metagenome comparison methods are in great demand.</p> <p>Results</p> <p>The new metagenomic data analysis method Rapid Analysis of Multiple Metagenomes with a Clustering and Annotation Pipeline (<b>RAMMCAP</b>) was developed using an ultra-fast sequence clustering algorithm, fast protein family annotation tools, and a novel statistical metagenome comparison method that employs a unique graphic interface. RAMMCAP processes extremely large datasets with only moderate computational effort. It identifies raw read clusters and protein clusters that may include novel gene families, and compares metagenomes using clusters or functional annotations calculated by RAMMCAP. In this study, RAMMCAP was applied to the two largest available metagenomic collections, the "Global Ocean Sampling" and the "Metagenomic Profiling of Nine Biomes".</p> <p>Conclusion</p> <p>RAMMCAP is a very fast method that can cluster and annotate one million metagenomic reads in only hundreds of CPU hours. It is available from <url>http://tools.camera.calit2.net/camera/rammcap/</url>.</p

Binding to an unusual inactive kinase conformation by highly selective inhibitors of inositol-requiring enzyme 1a kinase-endoribonuclease

A series of imidazo[1,2-b]pyridazin-8-amine kinase inhibitors were discovered to allosterically inhibit the endoribonuclease function of the dual kinase-endoribonuclease inositol-requiring enzyme 1α (IRE1α), a key component of the unfolded protein response in mammalian cells and a potential drug target in multiple human diseases. Inhibitor optimization gave compounds with high kinome selectivity that prevented endoplasmic reticulum stress-induced IRE1α oligomerization and phosphorylation, and inhibited endoribonuclease activity in human cells. X-ray crystallography showed the inhibitors to bind to a previously unreported and unusually disordered conformation of the IRE1α kinase domain that would be incompatible with back-to-back dimerization of the IRE1α protein and activation of the endoribonuclease function. These findings increase the repertoire of known IRE1α protein conformations and can guide the discovery of highly selective ligands for the IRE1α kinase site that allosterically inhibit the endoribonuclease

Probing Metagenomics by Rapid Cluster Analysis of Very Large Datasets

BACKGROUND: The scale and diversity of metagenomic sequencing projects challenge both our technical and conceptual approaches in gene and genome annotations. The recent Sorcerer II Global Ocean Sampling (GOS) expedition yielded millions of predicted protein sequences, which significantly altered the landscape of known protein space by more than doubling its size and adding thousands of new families (Yooseph et al., 2007 PLoS Biol 5, e16). Such datasets, not only by their sheer size, but also by many other features, defy conventional analysis and annotation methods. METHODOLOGY/PRINCIPAL FINDINGS: In this study, we describe an approach for rapid analysis of the sequence diversity and the internal structure of such very large datasets by advanced clustering strategies using the newly modified CD-HIT algorithm. We performed a hierarchical clustering analysis on the 17.4 million Open Reading Frames (ORFs) identified from the GOS study and found over 33 thousand large predicted protein clusters comprising nearly 6 million sequences. Twenty percent of these clusters did not match known protein families by sequence similarity search and might represent novel protein families. Distributions of the large clusters were illustrated on organism composition, functional class, and sample locations. CONCLUSION/SIGNIFICANCE: Our clustering took about two orders of magnitude less computational effort than the similar protein family analysis of original GOS study. This approach will help to analyze other large metagenomic datasets in the future. A Web server with our clustering results and annotations of predicted protein clusters is available online at http://tools.camera.calit2.net/gos under the CAMERA project

Accurate Genome Relative Abundance Estimation Based on Shotgun Metagenomic Reads

Accurate estimation of microbial community composition based on metagenomic sequencing data is fundamental for subsequent metagenomics analysis. Prevalent estimation methods are mainly based on directly summarizing alignment results or its variants; often result in biased and/or unstable estimates. We have developed a unified probabilistic framework (named GRAMMy) by explicitly modeling read assignment ambiguities, genome size biases and read distributions along the genomes. Maximum likelihood method is employed to compute Genome Relative Abundance of microbial communities using the Mixture Model theory (GRAMMy). GRAMMy has been demonstrated to give estimates that are accurate and robust across both simulated and real read benchmark datasets. We applied GRAMMy to a collection of 34 metagenomic read sets from four metagenomics projects and identified 99 frequent species (minimally 0.5% abundant in at least 50% of the data- sets) in the human gut samples. Our results show substantial improvements over previous studies, such as adjusting the over-estimated abundance for Bacteroides species for human gut samples, by providing a new reference-based strategy for metagenomic sample comparisons. GRAMMy can be used flexibly with many read assignment tools (mapping, alignment or composition-based) even with low-sensitivity mapping results from huge short-read datasets. It will be increasingly useful as an accurate and robust tool for abundance estimation with the growing size of read sets and the expanding database of reference genomes

Metagenomics - a guide from sampling to data analysis

Metagenomics applies a suite of genomic technologies and bioinformatics tools to directly access the genetic content of entire communities of organisms. The field of metagenomics has been responsible for substantial advances in microbial ecology, evolution, and diversity over the past 5 to 10 years, and many research laboratories are actively engaged in it now. With the growing numbers of activities also comes a plethora of methodological knowledge and expertise that should guide future developments in the field. This review summarizes the current opinions in metagenomics, and provides practical guidance and advice on sample processing, sequencing technology, assembly, binning, annotation, experimental design, statistical analysis, data storage, and data sharing. As more metagenomic datasets are generated, the availability of standardized procedures and shared data storage and analysis becomes increasingly important to ensure that output of individual projects can be assessed and compared

Compton Thick AGN: the dark side of the X-ray background

The spectrum of the hard X-ray background records the history of accretion processes integrated over the cosmic time. Several pieces of observational and theoretical evidence indicate that a significant fraction of the energy density is obscured by large columns of gas and dust. The absorbing matter is often very thick, with column densities exceeding N_H > 1.5 10^24 cm-2, the value corresponding to unity optical depth for Compton scattering. These sources are called ``Compton thick'' and appear to be very numerous, at least in the nearby universe. Although Compton thick Active Galactic Nuclei (AGN) are thought to provide an important contribution to the overall cosmic energy budget, their space density and cosmological evolution are poorly known. The properties of Compton thick AGN are reviewed here, with particular emphasis on their contributions to the extragalactic background light in the hard X-ray and infrared bands.Comment: 28 pages, 10 figures. Review for "Supermassive Black Holes in the Distant Universe", Ed. A. J. Barger, Kluwer Academi

Achievements and new knowledge unraveled by metagenomic approaches

Metagenomics has paved the way for cultivation-independent assessment and exploitation of microbial communities present in complex ecosystems. In recent years, significant progress has been made in this research area. A major breakthrough was the improvement and development of high-throughput next-generation sequencing technologies. The application of these technologies resulted in the generation of large datasets derived from various environments such as soil and ocean water. The analyses of these datasets opened a window into the enormous phylogenetic and metabolic diversity of microbial communities living in a variety of ecosystems. In this way, structure, functions, and interactions of microbial communities were elucidated. Metagenomics has proven to be a powerful tool for the recovery of novel biomolecules. In most cases, functional metagenomics comprising construction and screening of complex metagenomic DNA libraries has been applied to isolate new enzymes and drugs of industrial importance. For this purpose, several novel and improved screening strategies that allow efficient screening of large collections of clones harboring metagenomes have been introduced