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Biodiversity continues to decline in the face of increasing anthropogenic pressures such as habitat destruction, exploitation, pollution and introduction of alien species. Existing global databases of species' threat status or population time series are dominated by charismatic species. The collation of datasets with broad taxonomic and biogeographic extents, and that support computation of a range of biodiversity indicators, is necessary to enable better understanding of historical declines and to project - and avert - future declines. We describe and assess a new database of more than 1.6 million samples from 78 countries representing over 28,000 species, collated from existing spatial comparisons of local-scale biodiversity exposed to different intensities and types of anthropogenic pressures, from terrestrial sites around the world. The database contains measurements taken in 208 (of 814) ecoregions, 13 (of 14) biomes, 25 (of 35) biodiversity hotspots and 16 (of 17) megadiverse countries. The database contains more than 1% of the total number of all species described, and more than 1% of the described species within many taxonomic groups - including flowering plants, gymnosperms, birds, mammals, reptiles, amphibians, beetles, lepidopterans and hymenopterans. The dataset, which is still being added to, is therefore already considerably larger and more representative than those used by previous quantitative models of biodiversity trends and responses. The database is being assembled as part of the PREDICTS project (Projecting Responses of Ecological Diversity In Changing Terrestrial Systems - ). We make site-level summary data available alongside this article. The full database will be publicly available in 2015.
Background
Disentangling the etiology of common, complex diseases is a major challenge in genetic research. For bipolar disorder (BD), several genome-wide association studies (GWAS) have been performed. Similar to other complex disorders, major breakthroughs in explaining the high heritability of BD through GWAS have remained elusive. To overcome this dilemma, genetic research into BD, has embraced a variety of strategies such as the formation of large consortia to increase sample size and sequencing approaches. Here we advocate a complementary approach making use of already existing GWAS data: a novel data mining procedure to identify yet undetected genotype–phenotype relationships. We adapted association rule mining, a data mining technique traditionally used in retail market research, to identify frequent and characteristic genotype patterns showing strong associations to phenotype clusters. We applied this strategy to three independent GWAS datasets from 2835 phenotypically characterized patients with BD. In a discovery step, 20,882 candidate association rules were extracted.
Results
Two of these rules—one associated with eating disorder and the other with anxiety—remained significant in an independent dataset after robust correction for multiple testing. Both showed considerable effect sizes (odds ratio ~ 3.4 and 3.0, respectively) and support previously reported molecular biological findings.
Conclusion
Our approach detected novel specific genotype–phenotype relationships in BD that were missed by standard analyses like GWAS. While we developed and applied our method within the context of BD gene discovery, it may facilitate identifying highly specific genotype–phenotype relationships in subsets of genome-wide data sets of other complex phenotype with similar epidemiological properties and challenges to gene discovery efforts.
Aims: We set out to investigate the antibacterial activity of a new Mn-based photoactivated carbon monoxide-releasing molecule (PhotoCORM, [Mn(CO)\(_3\)(tpa-kappa\(^3\)N)]\(^+\)) against an antibiotic-resistant uropathogenic strain (EC958) of Escherichia coli. Results: Activated PhotoCORM inhibits growth and decreases viability of E. coli EC958, but non-illuminated carbon monoxide-releasing molecule (CORM) is without effect. NADH-supported respiration rates are significantly decreased by activated PhotoCORM, mimicking the effect of dissolved CO gas. CO from the PhotoCORM binds to intracellular targets, namely respiratory oxidases in strain EC958 and a bacterial globin heterologously expressed in strain K-12. However, unlike previously characterized CORMs, the PhotoCORM is not significantly accumulated in cells, as deduced from the cellular manganese content. Activated PhotoCORM reacts avidly with hydrogen peroxide producing hydroxyl radicals; the observed peroxide-enhanced toxicity of the PhotoCORM is ameliorated by thiourea. The PhotoCORM also potentiates the effect of the antibiotic, doxycycline. Innovation: The present work investigates for the first time the antimicrobial activity of a light-activated PhotoCORM against an antibiotic-resistant pathogen. A comprehensive study of the effects of the PhotoCORM and its derivative molecules upon illumination is performed and mechanisms of toxicity of the activated PhotoCORM are investigated. Conclusion: The PhotoCORM allows a site-specific and time-controlled release of CO in bacterial cultures and has the potential to provide much needed information on the generality of CORM activities in biology. Understanding the mechanism(s) of activated PhotoCORM toxicity will be key in exploring the potential of this and similar compounds as antimicrobial agents, perhaps in combinatorial therapies with other agents.