Speciation as a positive feedback loop between postzygotic and prezygotic barriers to gene flow

Speciation is intimately associated with the evolution of sex-and-reproduction-related traits, including those affecting hybrid incompatibility (postzygotic isolation) and species recognition (prezygotic isolation). Genes controlling such traits are not randomly distributed in the genome but are particularly abundant on the sex chromosomes. However, the evolutionary consequences of the sex linkage of genes involved in speciation have been little explored. Here, we present simulations of a continent-island diploid model that examines the effects of reduced recombination using both autosomal and sex-linked inheritance. We show first that linkage between genes affecting postzygotic and prezygotic isolation leads to a positive feedback loop in which both are strengthened. As species recognition evolves, genes causing hybrid incompatibility will hitchhike along with those improving premating isolation, leading to stronger hybrid incompatibility and thus increased pressure for further preference divergence. Second, we show that this loop effect is generally enhanced by sex linkage, because recombination is eliminated in the heterogametic sex, leading to tighter effective linkage between the two classes of genes and because natural selection is more efficient at sex-linked loci, as recessive alleles are not masked by dominance in the heterogametic sex. Accordingly, hitchhiking can be important in promoting speciation and can also lead to increased postzygotic isolation through adaptive evolution

Demography and disorders of German Shepherd Dogs under primary veterinarycare in the UK

The German Shepherd Dog (GSD) has been widely used for a variety of working roles. However, concerns for the health and welfare of the GSD have been widely aired and there is evidence that breed numbers are now in decline in the UK. Accurate demographic and disorder data could assist with breeding and clinical prioritisation. The VetCompassTM Programme collects clinical data on dogs under primary veterinary care in the UK. This study included all VetCompassTM dogs under veterinary care during 2013. Demographic, mortality and clinical diagnosis data on GSDs were extracted and reported

GIFtS: annotation landscape analysis with GeneCards

<p>Abstract</p> <p>Background</p> <p>Gene annotation is a pivotal component in computational genomics, encompassing prediction of gene function, expression analysis, and sequence scrutiny. Hence, quantitative measures of the annotation landscape constitute a pertinent bioinformatics tool. GeneCards^®is a gene-centric compendium of rich annotative information for over 50,000 human gene entries, building upon 68 data sources, including Gene Ontology (GO), pathways, interactions, phenotypes, publications and many more.</p> <p>Results</p> <p>We present the GeneCards Inferred Functionality Score (GIFtS) which allows a quantitative assessment of a gene's annotation status, by exploiting the unique wealth and diversity of GeneCards information. The GIFtS tool, linked from the GeneCards home page, facilitates browsing the human genome by searching for the annotation level of a specified gene, retrieving a list of genes within a specified range of GIFtS value, obtaining random genes with a specific GIFtS value, and experimenting with the GIFtS weighting algorithm for a variety of annotation categories. The bimodal shape of the GIFtS distribution suggests a division of the human gene repertoire into two main groups: the high-GIFtS peak consists almost entirely of protein-coding genes; the low-GIFtS peak consists of genes from all of the categories. Cluster analysis of GIFtS annotation vectors provides the classification of gene groups by detailed positioning in the annotation arena. GIFtS also provide measures which enable the evaluation of the databases that serve as GeneCards sources. An inverse correlation is found (for GIFtS>25) between the number of genes annotated by each source, and the average GIFtS value of genes associated with that source. Three typical source prototypes are revealed by their GIFtS distribution: genome-wide sources, sources comprising mainly highly annotated genes, and sources comprising mainly poorly annotated genes. The degree of accumulated knowledge for a given gene measured by GIFtS was correlated (for GIFtS>30) with the number of publications for a gene, and with the seniority of this entry in the HGNC database.</p> <p>Conclusion</p> <p>GIFtS can be a valuable tool for computational procedures which analyze lists of large set of genes resulting from wet-lab or computational research. GIFtS may also assist the scientific community with identification of groups of uncharacterized genes for diverse applications, such as delineation of novel functions and charting unexplored areas of the human genome.</p

Kepler eclipsing binary stars. VII. the catalogue of eclipsing binaries found in the entire Kepler data set

The primary Kepler Mission provided nearly continuous monitoring of ~200,000 objects with unprecedented photometric precision. We present the final catalog of eclipsing binary systems within the 105 deg2 Kepler field of view. This release incorporates the full extent of the data from the primary mission (Q0-Q17 Data Release). As a result, new systems have been added, additional false positives have been removed, ephemerides and principal parameters have been recomputed, classifications have been revised to rely on analytical models, and eclipse timing variations have been computed for each system. We identify several classes of systems including those that exhibit tertiary eclipse events, systems that show clear evidence of additional bodies, heartbeat systems, systems with changing eclipse depths, and systems exhibiting only one eclipse event over the duration of the mission. We have updated the period and galactic latitude distribution diagrams and included a catalog completeness evaluation. The total number of identified eclipsing and ellipsoidal binary systems in the Kepler field of view has increased to 2878, 1.3% of all observed Kepler targets

Transcriptomic comparison of the retina in two mouse models of diabetes

Mouse models of type I diabetes offer the potential to combine genetic approaches with other pharmacological or physiological manipulations to investigate the pathophysiology and treatment of diabetic retinopathy. Type I diabetes is induced in mice through chemical toxins or can arise spontaneously from genetic mutations. Both models are associated with retinal vascular and neuronal changes. Retinal transcriptomic responses in C57BL/6J mice treated with streptozotocin and Ins2Akita/+ were compared after 3 months of hyperglycemia. Specific gene expression changes suggest a neurovascular inflammatory response in diabetic retinopathy. Genes common to the two models may represent the response of the retina to hyperglycemia, while changes unique to each model may represent time-dependent disease progression differences in the various models. Further investigation of the commonalities and differences between mouse models of type I diabetes may define cause and effect events in early diabetic retinopathy disease progression

Context-Specific Protein Network Miner – An Online System for Exploring Context-Specific Protein Interaction Networks from the Literature

Background: Protein interaction networks (PINs) specific within a particular context contain crucial information regarding many cellular biological processes. For example, PINs may include information on the type and directionality of interaction (e.g. phosphorylation), location of interaction (i.e. tissues, cells), and related diseases. Currently, very few tools are capable of deriving context-specific PINs for conducting exploratory analysis. Results: We developed a literature-based online system, Context-specific Protein Network Miner (CPNM), which derives context-specific PINs in real-time from the PubMed database based on a set of user-input keywords and enhanced PubMed query system. CPNM reports enriched information on protein interactions (with type and directionality), their network topology with summary statistics (e.g. most densely connected proteins in the network; most densely connected protein-pairs; and proteins connected by most inbound/outbound links) that can be explored via a user-friendly interface. Some of the novel features of the CPNM system include PIN generation, ontology-based PubMed query enhancement, real-time, user-queried, up-to-date PubMed document processing, and prediction of PIN directionality. Conclusions: CPNM provides a tool for biologists to explore PINs. It is freely accessible at http://www.biotextminer.com/CPNM/.Statistic