Lineages of acidophilic archaea revealed by community genomic analysis

Novel, low-abundance microbial species can be easily overlooked in standard polymerase chain reaction (PCR)-based surveys. We used community genomic data obtained without PCR or cultivation to reconstruct DNA fragments bearing unusual 16S ribosomal RNA ( rRNA) and protein-coding genes from organisms belonging to novel archaeal lineages. The organisms are minor components of all biofilms growing in pH 0.5 to 1.5 solutions within the Richmond Mine, California. Probes specific for 16S rRNA showed that the fraction less than 0.45 micrometers in diameter is dominated by these organisms. Transmission electron microscope images revealed that the cells are pleomorphic with unusual folded membrane protrusions and have apparent volumes of < 0.006 cubic micrometer.

Modeling hybridoma cell metabolism using a generic genome-scale metabolic model of Mus musculus

The reconstructed cellular metabolic network of Mus musculus, based on annotated genomic data, pathway databases, and currently available biochemical and physiological information, is presented. Although incomplete, it represents the first attempt to collect and characterize the metabolic network of a mammalian cell on the basis of genomic data. The reaction network is generic in nature and attempts to capture the carbon, energy, and nitrogen metabolism of the cell. The metabolic reactions were compartmentalized between the cytosol and the mitochondria, including transport reactions between the compartments and the extracellular medium. The reaction list consists of 872 internal metabolites involved in a total of 1220 reactions, whereof 473 relate to known open reading frames. Initial in silico analysis of the reconstructed model is presented.

Genetics and genomics of osteoclast differentiation: Integrating cell signaling pathways and gene networks

The regulation of osteoclast differentiation in the bone microenvironment is critical for normal bone remodeling, as well as for various human bone diseases. Over the last decade, our knowledge of how osteoclast differentiation occurs has progressed rapidly. We highlight some of the major advances in understanding how cell signaling and transcription are integrated to direct the differentiation of this cell type. These studies used genetic, molecular, and biochemical approaches. Additionally, we summarize data obtained from studies of osteoclast differentiation that used the functional genomic approach of global gene profiling applied to osteoclast differentiation. This genomic data confirms results from studies using the classical experimental approaches and also may suggest new modes by which osteoclast differentiation and function can be modulated. Two conclusions that emerge are that osteoclast differentiation depends on a combination of fairly ubiquitously expressed transcription factors rather than unique osteoclast factors, and that the overlay of cell signaling pathways on this set of transcription factors provides a powerful mechanism to fine tune the differentiation program in response to the local bone microenvironment.

Heuristic algorithm for computing reversal distance with multigene families via binary integer programming

Hannenhalli and Pevzner developed the first polynomial-time algorithm for the combinatorial problem of sorting of signed genomic data. Their algorithm solves the minimum number of reversals required for rearranging a genome to another when gene duplication is nonexisting. In this paper, we show how to extend the Hannenhalli-Pevzner approach to genomes with multigene families. We propose a new heuristic algorithm to compute the reversal distance between two genomes with multigene families via the concept of binary integer programming without removing gene duplicates. The experimental results on simulated and real biological data demonstrate that the proposed algorithm is able to find the reversal distance accurately. ©2005 IEEE

Evolving discriminative motifs for recognizing proteins imported to the peroxisome via the PTS2 pathway

Peroxisomes are small subcellular compartments that utilize proteins manufactured in the cytoplasm. Proteins use one of two peroxisomal import pathways. This paper presents a simple evolutionary search for a motif that describes the signal used by one of the two pathways: PTS2. The evolved motif has a discriminative accuracy exceeding previously manually curated motifs and can be used to screen genomic data for putative peroxisomal proteins.

Chloroplast and total genomic diversity in the endemic Costa Rican tree Lonchocarpus costaricensis (J.D. Smith) Pittier (Papilionaceae).

In Mesoamerica, tropical dry forest is a highly threatened habitat, and species endemic to this environment are under extreme pressure. The tree species, Lonchocarpus costaricensis is endemic to the dry northwest of Costa Rica and southwest Nicaragua. It is a locally important species but, as land has been cleared for agriculture, populations have experienced considerable reduction and fragmentation. To assess current levels and distribution of genetic diversity in the species, a combination of chloroplast-specific (cpDNA) and whole genome DNA markers (amplified fragment length polymorphism, AFLP) were used to fingerprint 121 individual trees in 6 populations. Two cpDNA haplotypes were identified, distributed among populations such that populations at the extremes of the distribution showed lowest diversity. A large number (487) of AFLP markers were obtained and indicated that diversity levels were highest in the two coastal populations (Cobano, Matapalo, H = 0.23, 0.28 respectively). Population differentiation was low overall, F-ST = 0.12, although Matapalo was strongly differentiated from all other populations (F-ST = 0.16-0.22), apart from Cobano (F., = 0.11). Spatial genetic structure was present in both datasets at different scales: cpDNA was structured at a range-wide distribution scale, whilst AFLP data revealed genetic neighbourhoods on a population scale. In general, the habitat degradation of recent times appears not to have yet impacted diversity levels in mature populations. However, although no data on seed or saplings were collected, it seems likely that reproductive mechanisms in the species will have been affected by land clearance. It is recommended that efforts should be made to conserve the extant genetic resource base and further research undertaken to investigate diversity levels in the progeny generation.

GONOME: measuring correlations between GO terms and genomic positions

Background: Current methods to find significantly under- and over-represented gene ontology (GO) terms in a set of genes consider the genes as equally probable balls in a bag, as may be appropriate for transcripts in micro-array data. However, due to the varying length of genes and intergenic regions, that approach is inappropriate for deciding if any GO terms are correlated with a set of genomic positions. Results: We present an algorithm - GONOME - that can determine which GO terms are significantly associated with a set of genomic positions given a genome annotated with (at least) the starts and ends of genes. We show that certain GO terms may appear to be significantly associated with a set of randomly chosen positions in the human genome if gene lengths are not considered, and that these same terms have been reported as significantly over-represented in a number of recent papers. This apparent over-representation disappears when gene lengths are considered, as GONOME does. For example, we show that, when gene length is taken into account, the term development is not significantly enriched in genes associated with human CpG islands, in contradiction to a previous report. We further demonstrate the efficacy of GONOME by showing that occurrences of the proteosome-associated control element (PACE) upstream activating sequence in the S. cerevisiae genome associate significantly to appropriate GO terms. An extension of this approach yields a whole-genome motif discovery algorithm that allows identification of many other promoter sequences linked to different types of genes, including a large group of previously unknown motifs significantly associated with the terms 'translation' and 'translational elongation'. Conclusion: GONOME is an algorithm that correctly extracts over-represented GO terms from a set of genomic positions. By explicitly considering gene size, GONOME avoids a systematic bias toward GO terms linked to large genes. Inappropriate use of existing algorithms that do not take gene size into account has led to erroneous or suspect conclusions. Reciprocally GONOME may be used to identify new features in genomes that are significantly associated with particular categories of genes.