Compression and thermal expansion of nanocrystalline TiN

Abstract. TiN nanopowders synthesized by the application of an anaerobic "imide" route and aerosol synthesis with 5 and 26 nm average size were examined by in situ diffraction at high pressure up to 6 GPa and high temperature up to 800 o C. Overall compressibilities and thermal expansion coefficients were determined for the examined pressure and temperature intervals. Nanocrystals of TiN show core-shell type structure where elastic properties of interior and surface shell are different; surface is softer and shows larger thermal expansion than crystalline TiN. Core-shell model was confirmed by analysis of large Q powder diffraction at room temperature with application of alp-Q and PDF analysis

Habitable Zones in the Universe

Habitability varies dramatically with location and time in the universe. This was recognized centuries ago, but it was only in the last few decades that astronomers began to systematize the study of habitability. The introduction of the concept of the habitable zone was key to progress in this area. The habitable zone concept was first applied to the space around a star, now called the Circumstellar Habitable Zone. Recently, other, vastly broader, habitable zones have been proposed. We review the historical development of the concept of habitable zones and the present state of the research. We also suggest ways to make progress on each of the habitable zones and to unify them into a single concept encompassing the entire universe.Comment: 71 pages, 3 figures, 1 table; to be published in Origins of Life and Evolution of Biospheres; table slightly revise

Comparison of the protein-coding genomes of three deep-sea, sulfur-oxidising bacteria: “Candidatus Ruthia magnifica”, “Candidatus Vesicomyosocius okutanii” and Thiomicrospira crunogena

Abstract Objective “ Candidatus Ruthia magnifica”, “Candidatus Vesicomyosocius okutanii” and Thiomicrospira crunogena are all sulfur-oxidising bacteria found in deep-sea vent environments. Recent research suggests that the two symbiotic organisms, “Candidatus R. magnifica” and “Candidatus V. okutanii”, may share common ancestry with the autonomously living species T. crunogena. We used comparative genomics to examine the genome-wide protein-coding content of all three species to explore their similarities. In particular, we used the OrthoMCL algorithm to sort proteins into groups of putative orthologs on the basis of sequence similarity. Results The OrthoMCL inflation parameter was tuned using biological criteria. Using the tuned value, OrthoMCL delimited 1070 protein groups. 63.5% of these groups contained one protein from each species. Two groups contained duplicate protein copies from all three species. 123 groups were unique to T. crunogena and ten groups included multiple copies of T. crunogena proteins but only single copies from the other species. “Candidatus R. magnifica” had one unique group, and had multiple copies in one group where the other species had a single copy. There were no groups unique to “Candidatus V. okutanii”, and no groups in which there were multiple “Candidatus V. okutanii” proteins but only single proteins from the other species. Results align with previous suggestions that all three species share a common ancestor. However this is not definitive evidence to make taxonomic conclusions and the possibility of horizontal gene transfer was not investigated. Methodologically, the tuning of the OrthoMCL inflation parameter using biological criteria provides further methods to refine the OrthoMCL procedure

Structure and evolution of the mouse pregnancy-specific glycoprotein (Psg) gene locus

BACKGROUND: The pregnancy-specific glycoprotein (Psg) genes encode proteins of unknown function, and are members of the carcinoembryonic antigen (Cea) gene family, which is a member of the immunoglobulin gene (Ig) superfamily. In rodents and primates, but not in artiodactyls (even-toed ungulates / hoofed mammals), there have been independent expansions of the Psg gene family, with all members expressed exclusively in placental trophoblast cells. For the mouse Psg genes, we sought to determine the genomic organisation of the locus, the expression profiles of the various family members, and the evolution of exon structure, to attempt to reconstruct the evolutionary history of this locus, and to determine whether expansion of the gene family has been driven by selection for increased gene dosage, or diversification of function. RESULTS: We collated the mouse Psg gene sequences currently in the public genome and expressed-sequence tag (EST) databases and used systematic BLAST searches to generate complete sequences for all known mouse Psg genes. We identified a novel family member, Psg31, which is similar to Psg30 but, uniquely amongst mouse Psg genes, has a duplicated N1 domain. We also identified a novel splice variant of Psg16 (bCEA). We show that Psg24 and Psg30 / Psg31 have independently undergone expansion of N-domain number. By mapping BAC, YAC and cosmid clones we described two clusters of Psg genes, which we linked and oriented using fluorescent in situ hybridisation (FISH). Comparison of our Psg locus map with the public mouse genome database indicates good agreement in overall structure and further elucidates gene order. Expression levels of Psg genes in placentas of different developmental stages revealed dramatic differences in the developmental expression profile of individual family members. CONCLUSION: We have combined existing information, and provide new information concerning the evolution of mouse Psg exon organization, the mouse Psg genomic locus structure, and the expression patterns of individual Psg genes. This information will facilitate functional studies of this complex gene family

GO4genome: A Prokaryotic Phylogeny Based on Genome Organization

Determining the phylogeny of closely related prokaryotes may fail in an analysis of rRNA or a small set of sequences. Whole-genome phylogeny utilizes the maximally available sample space. For a precise determination of genome similarity, two aspects have to be considered when developing an algorithm of whole-genome phylogeny: (1) gene order conservation is a more precise signal than gene content; and (2) when using sequence similarity, failures in identifying orthologues or the in situ replacement of genes via horizontal gene transfer may give misleading results. GO4genome is a new paradigm, which is based on a detailed analysis of gene function and the location of the respective genes. For characterization of genes, the algorithm uses gene ontology enabling a comparison of function independent of evolutionary relationship. After the identification of locally optimal series of gene functions, their length distribution is utilized to compute a phylogenetic distance. The outcome is a classification of genomes based on metabolic capabilities and their organization. Thus, the impact of effects on genome organization that are not covered by methods of molecular phylogeny can be studied. Genomes of strains belonging to Escherichia coli, Shigella, Streptococcus, Methanosarcina, and Yersinia were analyzed. Differences from the findings of classical methods are discussed

The CanOE Strategy: Integrating Genomic and Metabolic Contexts across Multiple Prokaryote Genomes to Find Candidate Genes for Orphan Enzymes

Of all biochemically characterized metabolic reactions formalized by the IUBMB, over one out of four have yet to be associated with a nucleic or protein sequence, i.e. are sequence-orphan enzymatic activities. Few bioinformatics annotation tools are able to propose candidate genes for such activities by exploiting context-dependent rather than sequence-dependent data, and none are readily accessible and propose result integration across multiple genomes. Here, we present CanOE (Candidate genes for Orphan Enzymes), a four-step bioinformatics strategy that proposes ranked candidate genes for sequence-orphan enzymatic activities (or orphan enzymes for short). The first step locates “genomic metabolons”, i.e. groups of co-localized genes coding proteins catalyzing reactions linked by shared metabolites, in one genome at a time. These metabolons can be particularly helpful for aiding bioanalysts to visualize relevant metabolic data. In the second step, they are used to generate candidate associations between un-annotated genes and gene-less reactions. The third step integrates these gene-reaction associations over several genomes using gene families, and summarizes the strength of family-reaction associations by several scores. In the final step, these scores are used to rank members of gene families which are proposed for metabolic reactions. These associations are of particular interest when the metabolic reaction is a sequence-orphan enzymatic activity. Our strategy found over 60,000 genomic metabolons in more than 1,000 prokaryote organisms from the MicroScope platform, generating candidate genes for many metabolic reactions, of which more than 70 distinct orphan reactions. A computational validation of the approach is discussed. Finally, we present a case study on the anaerobic allantoin degradation pathway in Escherichia coli K-12

A Computational Study of Elongation Factor G (EFG) Duplicated Genes: Diverged Nature Underlying the Innovation on the Same Structural Template

BACKGROUND: Elongation factor G (EFG) is a core translational protein that catalyzes the elongation and recycling phases of translation. A more complex picture of EFG's evolution and function than previously accepted is emerging from analyzes of heterogeneous EFG family members. Whereas the gene duplication is postulated to be a prominent factor creating functional novelty, the striking divergence between EFG paralogs can be interpreted in terms of innovation in gene function. METHODOLOGY/PRINCIPAL FINDINGS: We present a computational study of the EFG protein family to cover the role of gene duplication in the evolution of protein function. Using phylogenetic methods, genome context conservation and insertion/deletion (indel) analysis we demonstrate that the EFG gene copies form four subfamilies: EFG I, spdEFG1, spdEFG2, and EFG II. These ancient gene families differ by their indispensability, degree of divergence and number of indels. We show the distribution of EFG subfamilies and describe evidences for lateral gene transfer and recent duplications. Extended studies of the EFG II subfamily concern its diverged nature. Remarkably, EFG II appears to be a widely distributed and a much-diversified subfamily whose subdivisions correlate with phylum or class borders. The EFG II subfamily specific characteristics are low conservation of the GTPase domain, domains II and III; absence of the trGTPase specific G2 consensus motif "RGITI"; and twelve conserved positions common to the whole subfamily. The EFG II specific functional changes could be related to changes in the properties of nucleotide binding and hydrolysis and strengthened ionic interactions between EFG II and the ribosome, particularly between parts of the decoding site and loop I of domain IV. CONCLUSIONS/SIGNIFICANCE: Our work, for the first time, comprehensively identifies and describes EFG subfamilies and improves our understanding of the function and evolution of EFG duplicated genes

Genomic Analysis of Individual Differences in Ethanol Drinking: Evidence for Non-Genetic Factors in C57BL/6 Mice

Genetic analysis of factors affecting risk to develop excessive ethanol drinking has been extensively studied in humans and animal models for over 20 years. However, little progress has been made in determining molecular mechanisms underlying environmental or non-genetic events contributing to variation in ethanol drinking. Here, we identify persistent and substantial variation in ethanol drinking behavior within an inbred mouse strain and utilize this model to identify gene networks influencing such “non-genetic” variation in ethanol intake. C57BL/6NCrl mice showed persistent inter-individual variation of ethanol intake in a two-bottle choice paradigm over a three-week period, ranging from less than 1 g/kg to over 14 g/kg ethanol in an 18 h interval. Differences in sweet or bitter taste susceptibility or litter effects did not appreciably correlate with ethanol intake variation. Whole genome microarray expression analysis in nucleus accumbens, prefrontal cortex and ventral midbrain region of individual animals identified gene expression patterns correlated with ethanol intake. Results included several gene networks previously implicated in ethanol behaviors, such as glutamate signaling, BDNF and genes involved in synaptic vesicle function. Additionally, genes functioning in epigenetic chromatin or DNA modifications such as acetylation and/or methylation also had expression patterns correlated with ethanol intake. In verification for the significance of the expression findings, we found that a histone deacetylase inhibitor, trichostatin A, caused an increase in 2-bottle ethanol intake. Our results thus implicate specific brain regional gene networks, including chromatin modification factors, as potentially important mechanisms underlying individual variation in ethanol intake

Operons

Operons (clusters of co-regulated genes with related functions) are common features of bacterial genomes. More recently, functional gene clustering has been reported in eukaryotes, from yeasts to filamentous fungi, plants, and animals. Gene clusters can consist of paralogous genes that have most likely arisen by gene duplication. However, there are now many examples of eukaryotic gene clusters that contain functionally related but non-homologous genes and that represent functional gene organizations with operon-like features (physical clustering and co-regulation). These include gene clusters for use of different carbon and nitrogen sources in yeasts, for production of antibiotics, toxins, and virulence determinants in filamentous fungi, for production of defense compounds in plants, and for innate and adaptive immunity in animals (the major histocompatibility locus). The aim of this article is to review features of functional gene clusters in prokaryotes and eukaryotes and the significance of clustering for effective function