15 research outputs found

    Varied effects of algal symbionts on transcription factor NF-κB in a sea anemone and a coral: possible roles in symbiosis and thermotolerance

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    Many cnidarians, including the reef-building corals, undergo symbiotic mutualisms with photosynthetic dinoflagellate algae of the family Symbiodiniaceae. These partnerships are sensitive to temperature extremes, which cause symbiont loss and increased coral mortality. Previous studies have implicated host immunity and specifically immunity transcription factor NF-κB as having a role in the maintenance of the cnidarian-algal symbiosis. Here we have further investigated a possible role for NF-κB in establishment and loss of symbiosis in various strains of the anemone Exaiptasia (Aiptasia) and in the coral Pocillopora damicornis. Our results show that NF-κB expression is reduced in Aiptasia larvae and adults that host certain algae strains. Treatment of Aiptasia larvae with a known symbiosis-promoting cytokine, transforming growth factor β, also led to decreased NF-κB expression. We also show that aposymbiotic Aiptasia (with high NF-κB expression) have increased survival following infection with the pathogenic bacterium Serratia marcescens as compared to symbiotic Aiptasia (low NF-κB expression). Furthermore, a P. damicornis coral colony hosting Durusdinium spp. (formerly clade D) symbionts had higher basal NF-κB expression and decreased heat-induced bleaching as compared to two individuals hosting Cladocopium spp. (formerly clade C) symbionts. Lastly, genome-wide gene expression profiling and genomic promoter analysis identified putative NF-κB target genes that may be involved in thermal bleaching, symbiont maintenance, and/or immune protection in P. damicornis. Our results provide further support for the hypothesis that modulation of NF-κB and immunity plays a role in some, but perhaps not all, cnidarian-Symbiodiniaceae partnerships as well as in resistance to pathogens and bleaching.Accepted manuscrip

    A Mapping of Drug Space from the Viewpoint of Small Molecule Metabolism

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    Small molecule drugs target many core metabolic enzymes in humans and pathogens, often mimicking endogenous ligands. The effects may be therapeutic or toxic, but are frequently unexpected. A large-scale mapping of the intersection between drugs and metabolism is needed to better guide drug discovery. To map the intersection between drugs and metabolism, we have grouped drugs and metabolites by their associated targets and enzymes using ligand-based set signatures created to quantify their degree of similarity in chemical space. The results reveal the chemical space that has been explored for metabolic targets, where successful drugs have been found, and what novel territory remains. To aid other researchers in their drug discovery efforts, we have created an online resource of interactive maps linking drugs to metabolism. These maps predict the “effect space” comprising likely target enzymes for each of the 246 MDDR drug classes in humans. The online resource also provides species-specific interactive drug-metabolism maps for each of the 385 model organisms and pathogens in the BioCyc database collection. Chemical similarity links between drugs and metabolites predict potential toxicity, suggest routes of metabolism, and reveal drug polypharmacology. The metabolic maps enable interactive navigation of the vast biological data on potential metabolic drug targets and the drug chemistry currently available to prosecute those targets. Thus, this work provides a large-scale approach to ligand-based prediction of drug action in small molecule metabolism

    The Stata Journal

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    Estimating adjusted associations between random effects from multilevel models : the reffadjust package. Stata Journal, Volume 14 (Number 1). pp. 119-140. Permanent WRAP url: http://wrap.warwick.ac.uk/60030 Copyright and reuse: The Warwick Research Archive Portal (WRAP) makes this work of researchers of the University of Warwick available open access under the following conditions. Copyright © and all moral rights to the version of the paper presented here belong to the individual author(s) and/or other copyright owners. To the extent reasonable and practicable the material made available in WRAP has been checked for eligibility before being made available. Copies of full items can be used for personal research or study, educational, or not-forprofit purposes without prior permission or charge. Provided that the authors, title and full bibliographic details are credited, a hyperlink and/or URL is given for the original metadata page and the content is not changed in any way. Publisher's statement: http://www.stata-journal.com. A note on versions: The version presented in WRAP is the published version or, version of record, and may be cited as it appears here. The Stata Journal publishes reviewed papers together with shorter notes or comments, regular columns, book reviews, and other material of interest to Stata users. Examples of the types of papers include 1) expository papers that link the use of Stata commands or programs to associated principles, such as those that will serve as tutorials for users first encountering a new field of statistics or a major new technique; 2) papers that go "beyond the Stata manual" in explaining key features or uses of Stata that are of interest to intermediate or advanced users of Stata; 3) papers that discuss new commands or Stata programs of interest either to a wide spectrum of users (e.g., in data management or graphics) or to some large segment of Stata users (e.g., in survey statistics, survival analysis, panel analysis, or limited dependent variable modeling); 4) papers analyzing the statistical properties of new or existing estimators and tests in Stata; 5) papers that could be of interest or usefulness to researchers, especially in fields that are of practical importance but are not often included in texts or other journals, such as the use of Stata in managing datasets, especially large datasets, with advice from hard-won experience; and 6) papers of interest to those who teach, including Stata with topics such as extended examples of techniques and interpretation of results, simulations of statistical concepts, and overviews of subject areas. Copyright c 2014 by StataCorp LP Copyright Statement: The Stata Journal and the contents of the supporting files (programs, datasets, and help files) are copyright c by StataCorp LP. The contents of the supporting files (programs, datasets, and help files) may be copied or reproduced by any means whatsoever, in whole or in part, as long as any copy or reproduction includes attribution to both (1) the author and (2) the Stata Journal. The articles appearing in the Stata Journal may be copied or reproduced as printed copies, in whole or in part, as long as any copy or reproduction includes attribution to both (1) the author and (2) the Stata Journal. Written permission must be obtained from StataCorp if you wish to make electronic copies of the insertions. This precludes placing electronic copies of the Stata Journal, in whole or in part, on publicly accessible websites, fileservers, or other locations where the copy may be accessed by anyone other than the subscriber. Users of any of the software, ideas, data, or other materials published in the Stata Journal or the supporting files understand that such use is made without warranty of any kind, by either the Stata Journal, the author, or StataCorp. In particular, there is no warranty of fitness of purpose or merchantability, nor for special, incidental, or consequential damages such as loss of profits. Abstract. We describe a method to estimate associations between random effects from multilevel models. We provide two new postestimation commands, reffadjustsim and reffadjust4nlcom, which are distributed as the reffadjust package. These commands produce the estimates and their associated confidence intervals. The commands are used after official Stata multilevel model estimation commands mixed, meqrlogit, and meqrpoisson (formerly named xtmixed, xtmelogit, and xtmepoisson, respectively, before Stata 13) and with models fit in the MLwiN statistical software package via the runmlwin command. We demonstrate our commands with several simulated datasets and for a bivariate outcome model investigating the relationship between weight and mean arterial pressure in pregnant women using data from the Avon Longitudinal Study of Parents and Children. Our method and commands help to improve the interpretability of estimated random-effects variance components from multilevel models
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