2,955 research outputs found

    Genome-wide signatures of complex introgression and adaptive evolution in the big cats.

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    The great cats of the genus Panthera comprise a recent radiation whose evolutionary history is poorly understood. Their rapid diversification poses challenges to resolving their phylogeny while offering opportunities to investigate the historical dynamics of adaptive divergence. We report the sequence, de novo assembly, and annotation of the jaguar (Panthera onca) genome, a novel genome sequence for the leopard (Panthera pardus), and comparative analyses encompassing all living Panthera species. Demographic reconstructions indicated that all of these species have experienced variable episodes of population decline during the Pleistocene, ultimately leading to small effective sizes in present-day genomes. We observed pervasive genealogical discordance across Panthera genomes, caused by both incomplete lineage sorting and complex patterns of historical interspecific hybridization. We identified multiple signatures of species-specific positive selection, affecting genes involved in craniofacial and limb development, protein metabolism, hypoxia, reproduction, pigmentation, and sensory perception. There was remarkable concordance in pathways enriched in genomic segments implicated in interspecies introgression and in positive selection, suggesting that these processes were connected. We tested this hypothesis by developing exome capture probes targeting ~19,000 Panthera genes and applying them to 30 wild-caught jaguars. We found at least two genes (DOCK3 and COL4A5, both related to optic nerve development) bearing significant signatures of interspecies introgression and within-species positive selection. These findings indicate that post-speciation admixture has contributed genetic material that facilitated the adaptive evolution of big cat lineages

    A genetic algorithms-based approach for optimizing similarity aggregation in ontology matching

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    [Abstract] Ontology matching consists of finding the semantic relations between different ontologies and is widely recognized as an essential process to achieve an adequate interoperability between people, systems or organizations that use different, overlapping ontologies to represent the same knowledge. There are several techniques to measure the semantic similarity of elements from separate ontologies, which must be adequately combined in order to obtain precise and complete results. Nevertheless, combining multiple similarity measures into a single metric is a complex problem, which has been traditionally solved using weights determined manually by an expert, or through general methods that do not provide optimal results. In this paper, a genetic algorithms based approach to aggregate different similarity metrics into a single function is presented. Starting from an initial population of individuals, each one representing a combination of similarity measures, our approach allows to find the combination that provides the optimal matching quality.Instituto de Salud Carlos III; FISPI10/02180Programa Iberoamericano de Ciencia y Tecnología para el Desarrollo; 209RT0366Xunta de Galicia; CN2012/217Xunta de Galicia; CN2011/034Xunta de Galicia; CN2012/21

    Ancestral light and chloroplast regulation form the foundations for C4 gene expression.

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    C4 photosynthesis acts as a carbon concentrating mechanism that leads to large increases in photosynthetic efficiency. The C4 pathway is found in more than 60 plant lineages1 but the molecular enablers of this evolution are poorly understood. In particular, it is unclear how non-photosynthetic proteins in the ancestral C3 system have repeatedly become strongly expressed and integrated into photosynthesis gene regulatory networks in C4 leaves. Here, we provide clear evidence that in C3 leaves, genes encoding key enzymes of the C4 pathway are already co-regulated with photosynthesis genes and are controlled by both light and chloroplast-to-nucleus signalling. In C4 leaves this regulation becomes increasingly dependent on the chloroplast. We propose that regulation of C4 cycle genes by light and the chloroplast in the ancestral C3 state has facilitated the repeated evolution of the complex and convergent C4 trait.The work was funded by the European Union 3to4 project and Biotechnology and Biological Sciences Research Council (BBSRC) grant BB/J011754/1. I.G.-M. was supported by the Amgen Foundation. Research on chloroplast signalling by M.J.T. was supported by BBSRC grant (BB/J018139/1).This is the author accepted manuscript. The final version is available from the Nature Publishing Group via http://dx.doi.org/10.1038/nplants.2016.16

    The transcriptional repressor complex FRS7-FRS12 regulates flowering time and growth in Arabidopsis

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    Most living organisms developed systems to efficiently time environmental changes. The plant-clock acts in coordination with external signals to generate output responses determining seasonal growth and flowering time. Here, we show that two Arabidopsis thaliana transcription factors, FAR1 RELATED SEQUENCE 7 (FRS7) and FRS12, act as negative regulators of these processes. These proteins accumulate particularly in short-day conditions and interact to form a complex. Loss-of-function of FRS7 and FRS12 results in early flowering plants with overly elongated hypocotyls mainly in short days. We demonstrate by molecular analysis that FRS7 and FRS12 affect these developmental processes in part by binding to the promoters and repressing the expression of GIGANTEA and PHYTOCHROME INTERACTING FACTOR 4 as well as several of their downstream signalling targets. Our data reveal a molecular machinery that controls the photoperiodic regulation of flowering and growth and offer insight into how plants adapt to seasonal changes

    Alcohol-Induced Histone Acetylation Reveals a Gene Network Involved in Alcohol Tolerance

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    Alfredo Ghezzi, Harish R. Krishnan, Linda Lew, Francisco J. Prado III, Darryl S. Ong, Nigel S. Atkinson, Section of Neurobiology and Waggoner Center for Alcohol and Addiction Research, The University of Texas at Austin, Austin, Texas, United States of AmericaSustained or repeated exposure to sedating drugs, such as alcohol, triggers homeostatic adaptations in the brain that lead to the development of drug tolerance and dependence. These adaptations involve long-term changes in the transcription of drug-responsive genes as well as an epigenetic restructuring of chromosomal regions that is thought to signal and maintain the altered transcriptional state. Alcohol-induced epigenetic changes have been shown to be important in the long-term adaptation that leads to alcohol tolerance and dependence endophenotypes. A major constraint impeding progress is that alcohol produces a surfeit of changes in gene expression, most of which may not make any meaningful contribution to the ethanol response under study. Here we used a novel genomic epigenetic approach to find genes relevant for functional alcohol tolerance by exploiting the commonalities of two chemically distinct alcohols. In Drosophila melanogaster, ethanol and benzyl alcohol induce mutual cross-tolerance, indicating that they share a common mechanism for producing tolerance. We surveyed the genome-wide changes in histone acetylation that occur in response to these drugs. Each drug induces modifications in a large number of genes. The genes that respond similarly to either treatment, however, represent a subgroup enriched for genes important for the common tolerance response. Genes were functionally tested for behavioral tolerance to the sedative effects of ethanol and benzyl alcohol using mutant and inducible RNAi stocks. We identified a network of genes that are essential for the development of tolerance to sedation by alcohol.This work was supported by National Institute of Health grant R01 AA018037 to NSA (http://www.nih.gov/). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.NeuroscienceWaggoner Center for Alcohol and Addiction ResearchEmail: [email protected] (AG)Email: [email protected] (NSA

    Robust Inference of Genetic Exchange Communities from Microbial Genomes Using TF-IDF

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    Bacteria and archaea can exchange genetic material across lineages through processes of lateral genetic transfer (LGT). Collectively, these exchange relationships can be modeled as a network and analyzed using concepts from graph theory. In particular, densely connected regions within an LGT network have been defined as genetic exchange communities (GECs). However, it has been problematic to construct networks in which edges solely represent LGT. Here we apply term frequency-inverse document frequency (TF-IDF), an alignment-free method originating from document analysis, to infer regions of lateral origin in bacterial genomes. We examine four empirical datasets of different size (number of genomes) and phyletic breadth, varying a key parameter (word length k) within bounds established in previous work. We map the inferred lateral regions to genes in recipient genomes, and construct networks in which the nodes are groups of genomes, and the edges natively represent LGT. We then extract maximum and maximal cliques (i.e., GECs) from these graphs, and identify nodes that belong to GECs across a wide range of k. Most surviving lateral transfer has happened within these GECs. Using Gene Ontology enrichment tests we demonstrate that biological processes associated with metabolism, regulation and transport are often over-represented among the genes affected by LGT within these communities. These enrichments are largely robust to change of k
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