The evolution of Runx genes I. A comparative study of sequences from phylogenetically diverse model organisms

BACKGROUND: Runx genes encode proteins defined by the highly conserved Runt DNA-binding domain. Studies of Runx genes and proteins in model organisms indicate that they are key transcriptional regulators of animal development. However, little is known about Runx gene evolution. RESULTS: A phylogenetically broad sampling of publicly available Runx gene sequences was collected. In addition to the published sequences from mouse, sea urchin, Drosophila melanogaster and Caenorhabditis elegans, we collected several previously uncharacterised Runx sequences from public genome sequence databases. Among deuterostomes, mouse and pufferfish each contain three Runx genes, while the tunicate Ciona intestinalis and the sea urchin Strongylocentrotus purpuratus were each found to have only one Runx gene. Among protostomes, C. elegans has a single Runx gene, while Anopheles gambiae has three and D. melanogaster has four, including two genes that have not been previously described. Comparative sequence analysis reveals two highly conserved introns, one within and one just downstream of the Runt domain. All vertebrate Runx genes utilize two alternative promoters. CONCLUSIONS: In the current public sequence database, the Runt domain is found only in bilaterians, suggesting that it may be a metazoan invention. Bilaterians appear to ancestrally contain a single Runx gene, suggesting that the multiple Runx genes in vertebrates and insects arose by independent duplication events within those respective lineages. At least two introns were present in the primordial bilaterian Runx gene. Alternative promoter usage arose prior to the duplication events that gave rise to three Runx genes in vertebrates

Temperature-dependent benefits of bacterial exposure in embryonic development of Daphnia magna resting eggs

The environments in which animals develop and evolve are profoundly shaped by bacteria, which affect animals both indirectly through their role in biogeochemical processes and directly through antagonistic or beneficial interactions. The outcomes of these activities can differ according to environmental context. In a series of laboratory experiments with diapausing eggs of the water flea Daphnia magna, we manipulated two environmental parameters, temperature and presence of bacteria, and examined their effect on development. At elevated temperatures (≥ 26 °C), resting eggs developing without live bacteria had reduced hatching success and correspondingly higher rates of severe morphological abnormalities compared with eggs with bacteria in their environment. The beneficial effect of bacteria was strongly reduced at 20 °C. Neither temperature nor the presence of bacteria affected directly developing parthenogenetic eggs. The mechanistic basis of this effect of bacteria on development is unclear, but these results highlight the complex interplay of biotic and abiotic factors influencing animal development after diapause

Characterization of the large (L) RNA of peanut bud necrosis tospovirus

The nucleocapsids purified from groundnut plants systemically infected with peanut bud necrosis tospovirus (PBNV) contained both viral (v) and viral complementary (vc) sense L RNAs. Defective forms of L RNA containing core polymerase region were observed. The full length L RNA of PBNV was sequenced using overlapping cDNA clones. The 8911 nucleotide L RNA contains a single open reading frame (ORF) in the vc strand, and encodes a protein of 330 kDa. At the 5' and 3' termini of the v sense RNA there were 247 and 32 nt untranslated regions, respectively, containing an 18 nt complementary sequence with 1 mismatch. Comparisons of the predicted amino acid sequence of the L protein of PBNV with other members of Bunyaviridae suggest that the L protein of PBNV is a viral polymerase. The L protein had highest identity in the core-polymerase domain with the corresponding regions of tospoviruses, tomato spotted wilt tospovirus and impatiens necrotic spot tospovirus

Comparison of Pattern Detection Methods in Microarray Time Series of the Segmentation Clock

While genome-wide gene expression data are generated at an increasing rate, the repertoire of approaches for pattern discovery in these data is still limited. Identifying subtle patterns of interest in large amounts of data (tens of thousands of profiles) associated with a certain level of noise remains a challenge. A microarray time series was recently generated to study the transcriptional program of the mouse segmentation clock, a biological oscillator associated with the periodic formation of the segments of the body axis. A method related to Fourier analysis, the Lomb-Scargle periodogram, was used to detect periodic profiles in the dataset, leading to the identification of a novel set of cyclic genes associated with the segmentation clock. Here, we applied to the same microarray time series dataset four distinct mathematical methods to identify significant patterns in gene expression profiles. These methods are called: Phase consistency, Address reduction, Cyclohedron test and Stable persistence, and are based on different conceptual frameworks that are either hypothesis- or data-driven. Some of the methods, unlike Fourier transforms, are not dependent on the assumption of periodicity of the pattern of interest. Remarkably, these methods identified blindly the expression profiles of known cyclic genes as the most significant patterns in the dataset. Many candidate genes predicted by more than one approach appeared to be true positive cyclic genes and will be of particular interest for future research. In addition, these methods predicted novel candidate cyclic genes that were consistent with previous biological knowledge and experimental validation in mouse embryos. Our results demonstrate the utility of these novel pattern detection strategies, notably for detection of periodic profiles, and suggest that combining several distinct mathematical approaches to analyze microarray datasets is a valuable strategy for identifying genes that exhibit novel, interesting transcriptional patterns

Identification and characterisation of tomato torrado virus, a new plant picorna-like virus from tomato

A new virus was isolated from tomato plants from the Murcia region in Spain which showed symptoms of ‘torrado disease’ very distinct necrotic, almost burn-like symptoms on leaves of infected plants. The virus particles are isometric with a diameter of approximately 28 nm. The viral genome consists of two (+)ssRNA molecules of 7793 (RNA1) and 5389 nts (RNA2). RNA1 contains one open reading frame (ORF) encoding a predicted polyprotein of 241 kDa that shows conserved regions with motifs typical for a protease-cofactor, a helicase, a protease and an RNA-dependent RNA polymerase. RNA2 contains two, partially overlapping ORFs potentially encoding proteins of 20 and 134 kDa. These viral RNAs are encapsidated by three proteins with estimated sizes of 35, 26 and 23 kDa. Direct protein sequencing mapped these coat proteins to ORF2 on RNA2. Phylogenetic analyses of nucleotide and derived amino acid sequences showed that the virus is related to but distinct from viruses belonging to the genera Sequivirus, Sadwavirus and Cheravirus. This new virus, for which the name tomato torrado virus is proposed, most likely represents a member of a new plant virus genus

The sea urchin kinome: A first look

AbstractThis paper reports a preliminary in silico analysis of the sea urchin kinome. The predicted protein kinases in the sea urchin genome were identified, annotated and classified, according to both function and kinase domain taxonomy. The results show that the sea urchin kinome, consisting of 353 protein kinases, is closer to the Drosophila kinome (239) than the human kinome (518) with respect to total kinase number. However, the diversity of sea urchin kinases is surprisingly similar to humans, since the urchin kinome is missing only 4 of 186 human subfamilies, while Drosophila lacks 24. Thus, the sea urchin kinome combines the simplicity of a non-duplicated genome with the diversity of function and signaling previously considered to be vertebrate-specific. More than half of the sea urchin kinases are involved with signal transduction, and approximately 88% of the signaling kinases are expressed in the developing embryo. These results support the strength of this nonchordate deuterostome as a pivotal developmental and evolutionary model organism

Comparing biological networks via graph compression

<p>Abstract</p> <p>Background</p> <p>Comparison of various kinds of biological data is one of the main problems in bioinformatics and systems biology. Data compression methods have been applied to comparison of large sequence data and protein structure data. Since it is still difficult to compare global structures of large biological networks, it is reasonable to try to apply data compression methods to comparison of biological networks. In existing compression methods, the uniqueness of compression results is not guaranteed because there is some ambiguity in selection of overlapping edges.</p> <p>Results</p> <p>This paper proposes novel efficient methods, CompressEdge and CompressVertices, for comparing large biological networks. In the proposed methods, an original network structure is compressed by iteratively contracting identical edges and sets of connected edges. Then, the similarity of two networks is measured by a compression ratio of the concatenated networks. The proposed methods are applied to comparison of metabolic networks of several organisms, <it>H. sapiens, M. musculus, A. thaliana, D. melanogaster, C. elegans, E. coli, S. cerevisiae,</it> and <it>B. subtilis,</it> and are compared with an existing method. These results suggest that our methods can efficiently measure the similarities between metabolic networks.</p> <p>Conclusions</p> <p>Our proposed algorithms, which compress node-labeled networks, are useful for measuring the similarity of large biological networks.</p

Algorithm of OMA for large-scale orthology inference

Since the publication of our article (Roth, Gonnet, and Dessimoz: BMC Bioinformatics 2008 9: 518), we have noticed several errors, which we correct in the following

Genome Sizes and the Benford Distribution

BACKGROUND: Data on the number of Open Reading Frames (ORFs) coded by genomes from the 3 domains of Life show the presence of some notable general features. These include essential differences between the Prokaryotes and Eukaryotes, with the number of ORFs growing linearly with total genome size for the former, but only logarithmically for the latter. RESULTS: Simply by assuming that the (protein) coding and non-coding fractions of the genome must have different dynamics and that the non-coding fraction must be particularly versatile and therefore be controlled by a variety of (unspecified) probability distribution functions (pdf's), we are able to predict that the number of ORFs for Eukaryotes follows a Benford distribution and must therefore have a specific logarithmic form. Using the data for the 1000+ genomes available to us in early 2010, we find that the Benford distribution provides excellent fits to the data over several orders of magnitude. CONCLUSIONS: In its linear regime the Benford distribution produces excellent fits to the Prokaryote data, while the full non-linear form of the distribution similarly provides an excellent fit to the Eukaryote data. Furthermore, in their region of overlap the salient features are statistically congruent. This allows us to interpret the difference between Prokaryotes and Eukaryotes as the manifestation of the increased demand in the biological functions required for the larger Eukaryotes, to estimate some minimal genome sizes, and to predict a maximal Prokaryote genome size on the order of 8-12 megabasepairs. These results naturally allow a mathematical interpretation in terms of maximal entropy and, therefore, most efficient information transmission