Population genetic differentiation of the hydrothermal vent crab Austinograea alayseae (Crustacea: Bythograeidae) in the Southwest Pacific Ocean.

To understand the origin, migration, and distribution of organisms across disjunct deep-sea vent habitats, previous studies have documented the population genetic structures of widely distributed fauna, such as gastropods, bivalves, barnacles, and squat lobsters. However, a limited number of investigations has been conducted in the Southwest Pacific Ocean, and many questions remain. In this study, we determined the population structure of the bythograeid crab Austinograea alayseae from three adjacent vent systems (Manus Basin, North Fiji Basin, and Tonga Arc) in the Southwest Pacific Ocean using the sequences of two mitochondrial genes (COI and 16S rDNA) and one nuclear gene (28S rDNA). Populations were divided into a Manus clade and a North Fiji-Tonga clade, with sequence divergence values in the middle of the barcoding gap for bythograeids. We inferred that hydrographic and/or physical barriers act on the gene flow of A. alayseae between the Manus and North Fiji basins. Austinograea alayseae individuals interact freely between the North Fiji Basin and the Lau Basin (Tonga Arc). Although further studies of genetic differentiation over a geological time scale, life-history attributes, and genome-based population genetics are needed to improve our understanding of the evolutionary history of A. alayseae, our results contribute to elucidating the phylogeny, evolution, and biogeography of bythograeids.

Complete mitochondrial genome of the hydrothermal vent shrimp Rimicaris variabilis (Decapoda: Caridea: Alvinocarididae) from the North Fiji basin.

The family Alvinocarididae is the monophyletic taxon which lives restrictively at chemosynthesis-based environments in the deep-sea. Here, for the first time, we report the complete mitogenome of the alvinocaridid vent shrimp Rimicaris variabilis from the North Fiji Basin. The mitogenome was 15,909 bp in length, with 65.6% AT content. Its protein-coding gene organization was typical of other alvinocaridid shrimps. Based on the phylogenetic tree, R. variabilis was most closely related to Shinkaicaris leurokolos, rather than with other Rimicaris species. To resolve this incongruence between traditional morphological classification and molecular analyses, further mitogenomic analysis of undetermined alvinocaridid taxa is necessary.

Complete mitochondrial genome of the catophragmid barnacle Catomerus polymerus (Cirripedia, Thoracica, Balanomorpha, Catophragmidae).

The family Catophragmidae is one of the lower balanomorphs from traditional and recent multiple mitochondrial and nuclear markers molecular analysis. Here, we characterized the first mitogenome of the catophragmid barnacle Catomerus polymerus, which was 15,446 bp in length with a 68.3% AT content. The mitogenome had the typical pancrustacean gene arrangement, which was identical to the mitogenome configurations of the chthamalid Octomeris sp. and pachylasmatoid Eochionelasmus ohtai. On the mitogenomic tree, the catophragmid Catomerus polymerus formed an independent branch that was basal to the members of the superfamilies Tetraclitoidea and Balanoidea, which was inconsistent with previous findings.

Induction of GD3/α1-adrenergic receptor/transglutaminase 2-mediated erythroid differentiation in chronic myelogenous leukemic K562 cells.

The disialic acid-containing glycosphingolipid GD3 recruited membrane transglutaminase 2 (TG2) as a signaling molecule for erythroid differentiation in human chronic myelogenous leukemia (CML) K562 cells. The α1-adrenergic receptor (α1-AR)/TG2-mediated signaling pathway regulated GD3 functions, including gene expression and production, to differentiate CML K562 cells into erythroid lineage cells. Epinephrine, an AR agonist, increased membrane recruitment as well as GTP-photoaffinity of TG2, inducing GD3 synthase gene expression. Epinephrine activated PI3K/Akt signaling and GTPase downstream of TG2 activated Akt. The coupling of TG2 and GD3 production was specifically suppressed by prazosin (α1-AR antagonist), but not by propranolol (ß-AR antagonist) or rauwolscine (α2-AR antagonist), indicating α1-AR specificity. Small interfering RNA (siRNA) experiment results indicated that the α1-AR/TG2-mediated signaling pathway activated PKCs α and δ to induce GD3 synthase gene expression. Transcription factors CREB, AP-1, and NF-κB regulated GD3 synthase gene expression during α1-AR-induced differentiation in CML K562 cells. In addition, GD3 synthase gene expression was upregulated in TG2-transfected cells via α1-AR with expression of erythroid lineage markers and benzidine-positive staining. α1-AR/TG2 signaling pathway-directed GD3 production is a crucial step in erythroid differentiation of K562 cells and GD3 interacts with α1-AR/TG2, inducing GD3/α1-AR/TG2-mediated erythroid differentiation. These results suggest that GD3, which acts as a membrane mediator of erythroid differentiation in CML cells, provides a therapeutic avenue for leukemia treatment.

Complete mitochondrial genome of the deep-sea asymmetrical barnacle Altiverruca navicula (Cirripedia, Thoracica, Verrucumorpha).

The hitherto suborder Verrucomorpha contains asymmetrical barnacles of two groups: the true Verrucomorpha (Eoverruca + Verrucidae) and the Neoverrucidae. Here, we determined the mitochondrial genome (mitogenome) of Altiverruca navicula, a true Verrucomorpha species. The mitogenome was 15,976 base pairs in length and had the typical pancrustacean gene arrangement. Its protein-coding genes were very similar to those of other thoracican species in terms of length, AT content, and start and stop codons. In phylogenetic trees constructed with 13 protein-coding genes, A. navicula was positioned at an ancestral node of sessile barnacles, consistent with the findings of previous studies.

CLUSTOM: a novel method for clustering 16S rRNA next generation sequences by overlap minimization.

The recent nucleic acid sequencing revolution driven by shotgun and high-throughput technologies has led to a rapid increase in the number of sequences for microbial communities. The availability of 16S ribosomal RNA (rRNA) gene sequences from a multitude of natural environments now offers a unique opportunity to study microbial diversity and community structure. The large volume of sequencing data however makes it time consuming to assign individual sequences to phylotypes by searching them against public databases. Since ribosomal sequences have diverged across prokaryotic species, they can be grouped into clusters that represent operational taxonomic units. However, available clustering programs suffer from overlap of sequence spaces in adjacent clusters. In natural environments, gene sequences are homogenous within species but divergent between species. This evolutionary constraint results in an uneven distribution of genetic distances of genes in sequence space. To cluster 16S rRNA sequences more accurately, it is therefore essential to select core sequences that are located at the centers of the distributions represented by the genetic distance of sequences in taxonomic units. Based on this idea, we here describe a novel sequence clustering algorithm named CLUSTOM that minimizes the overlaps between adjacent clusters. The performance of this algorithm was evaluated in a comparative exercise with existing programs, using the reference sequences of the SILVA database as well as published pyrosequencing datasets. The test revealed that our algorithm achieves higher accuracy than ESPRIT-Tree and mothur, few of the best clustering algorithms. Results indicate that the concept of an uneven distribution of sequence distances can effectively and successfully cluster 16S rRNA gene sequences. The algorithm of CLUSTOM has been implemented both as a web and as a standalone command line application, which are available at http://clustom.kribb.re.kr.

Genome-wide analysis of redox reactions reveals metabolic engineering targets for D-lactate overproduction in Escherichia coli.

Most current metabolic engineering applications rely on the inactivation of unwanted reactions and the amplification of product-oriented reactions. All of the biochemical reactions involved with cellular metabolism are tightly coordinated with the electron flow, which depends on the cellular energy status. Thus, the cellular metabolic flux can be controlled either by modulation of the electron flow or the regulation of redox reactions. This study analyzed the genome-wide anaerobic fermentation products of 472 Escherichia coli single gene knockouts, which comprised mainly of dehydrogenases, oxidoreductases, and redox-related proteins. Many metabolic pathways that were located far from anaerobic mixed-acid fermentation significantly affected the profiles of lactic acid, succinic acid, acetic acid, formic acid, and ethanol. Unexpectedly, D-lactate overproduction was determined by a single gene deletion in dehydrogenases (e.g., guaB, pyrD, and serA) involved with nucleotide and amino acid metabolism. Furthermore, the combined knockouts of guaB, pyrD, serA, fnr, arcA, or arcB genes, which are involved with anaerobic transcription regulation, enhanced D-lactate overproduction. These results suggest that the anaerobic fermentation profiles of E. coli can be tuned via the disruption of peripheral dehydrogenases in anaerobic conditions.

Effect of weight-added regulatory networks on constraint-based metabolic models of Escherichia coli.

Though the traditional flux balance analysis (FBA) has successfully predicted intracellular fluxes using stoichiometry, linear programming, and metabolic pathways, it has not automatically reflected any potential genetic effects in response to the environmental changes in the metabolic pathways. Recently, attempts have been made to impose regulatory constraints described as a binary system, such as if-then rules using Boolean logic, on the traditional FBA. Yet this binary system has limited the representation of complex interactions between transcriptional factors and target genes. In addition, it is difficult to intuitively or visually recognize changes to the interactions among stimuli, sensors/regulatory proteins, and target genes due to the properties of the if-then rule systems. Thus, in the current work, in order to improve upon the previous approaches, we have (1) determined weight values after deducing from the inequality signs of the relative strengths of interactions between sensors/regulators and target genes based on the experimental data of gene expression, (2) divided expression level into eight levels, and (3) constructed and incorporated weight-added regulatory networks using the defined symbols within the FBA. Finally, a model system with the central metabolic pathway of Escherichia coli was examined under the aerobic batch culture with glucose and acetate reutilization and the aerobic and anaerobic batch culture with glucose only to demonstrate our suggested approach.

WebCell: a web-based environment for kinetic modeling and dynamic simulation of cellular networks.

SUMMARY: WebCell is a web-based environment for managing quantitative and qualitative information on cellular networks and for interactively exploring their steady-state and dynamic behaviors in response to systemic perturbations. It is designed as a user-friendly web interface, allowing users to efficiently construct, visualize, analyze and store reaction network models, thereby facilitating kinetic modeling and in silico simulation of biological systems of interest. A collected model library is also available to provide comprehensive implications for cellular dynamics of the published models.

Encoding microbial metabolic logic: predicting biodegradation.

Prediction of microbial metabolism is important for annotating genome sequences and for understanding the fate of chemicals in the environment. A metabolic pathway prediction system (PPS) has been developed that is freely available on the world wide web (http://umbbd.ahc.umn.edu/predict/), recognizes the organic functional groups found in a compound, and predicts transformations based on metabolic rules. These rules are designed largely by examining reactions catalogued in the University of Minnesota Biocatalysis/Biodegradation Database (UM-BBD) and are generalized based on metabolic logic. The predictive accuracy of the PPS was tested: (1) using a 113-member set of compounds found in the database, (2) against a set of compounds whose metabolism was predicted by human experts, and (3) for consistency with experimental microbial growth studies. First, the system correctly predicted known metabolism for 111 of the 113 compounds containing C and H, O, N, S, P and/or halides that initiate existing pathways in the database, and also correctly predicted 410 of the 569 known pathway branches for these compounds. Second, computer predictions were compared to predictions by human experts for biodegradation of six compounds whose metabolism was not described in the literature. Third, the system predicted reactions liberating ammonia from three organonitrogen compounds, consistent with laboratory experiments showing that each compound served as the sole nitrogen source supporting microbial growth. The rule-based nature of the PPS makes it transparent, expandable, and adaptable.

BioSilico: an integrated metabolic database system.

BioSilico is a web-based database system that facilitates the search and analysis of metabolic pathways. Heterogeneous metabolic databases including LIGAND, ENZYME, EcoCyc and MetaCyc are integrated in a systematic way, thereby allowing users to efficiently retrieve the relevant information on enzymes, biochemical compounds and reactions. In addition, it provides well-designed view pages for more detailed summary information. BioSilico is developed as an extensible system with a robust systematic architecture.

Microbial pathway prediction: a functional group approach.

We have developed a system to predict microbial catabolism, using the University of Minnesota Biocatalysis/Biodegradation Database (UM-BBD, http://umbbd.ahc.umn.edu/) as a knowledge base. The present system, available on the Web (http://umbbd.ahc.umn.edu/predict/), can predict biodegradation of most of the major aliphatic and aromatic organic functional groups containing C, H, N, O, and halogens. It can duplicate at least one known biodegradation pathway for 60% of the compounds in a 84-member validation set; most pathways that did not completely duplicate known metabolism could plausibly occur in nature. Users are encouraged, and have begun, to submit additional biotransformation rules and comment on existing rules; the system will further develop under the direction of the scientific community.

The University of Minnesota Biocatalysis/Biodegradation Database: post-genomic data mining.

The University of Minnesota Biocatalysis/Biodegradation Database (UM-BBD, http://umbbd.ahc.umn.edu/) provides curated information on microbial catabolism and related biotransformations, primarily for environmental pollutants. Currently, it contains information on over 130 metabolic pathways, 800 reactions, 750 compounds and 500 enzymes. In the past two years, it has increased its breath to include more examples of microbial metabolism of metals and metalloids; and expanded the types of information it includes to contain microbial biotransformations of, and binding interactions with many chemical elements. It has also increased the ways in which this data can be accessed (mined). Structure-based searching was added, for exact matches, similarity, or substructures. Analysis of UM-BBD reactions has lead to a prototype, guided, pathway prediction system. Guided prediction means that the user is shown all possible biotransformations at each step and guides the process to its conclusion. Mining the UM-BBD's data provides a unique view into how the microbial world recycles organic functional groups. UM-BBD users are encouraged to comment on all aspects of the database, including the information it contains and the tools by which it can be mined. The database and prediction system develop under the direction of the scientific community.