Comprehensive analysis of correlation coefficients estimated from pooling heterogeneous microarray data

Background The synthesis of information across microarray studies has been performed by combining statistical results of individual studies (as in a mosaic), or by combining data from multiple studies into a large pool to be analyzed as a single data set (as in a melting pot of data). Specific issues relating to data heterogeneity across microarray studies, such as differences within and between labs or differences among experimental conditions, could lead to equivocal results in a melting pot approach. Results We applied statistical theory to determine the specific effect of different means and heteroskedasticity across 19 groups of microarray data on the sign and magnitude of gene-to-gene Pearson correlation coefficients obtained from the pool of 19 groups. We quantified the biases of the pooled coefficients and compared them to the biases of correlations estimated by an effect-size model. Mean differences across the 19 groups were the main factor determining the magnitude and sign of the pooled coefficients, which showed largest values of bias as they approached ±1. Only heteroskedasticity across the pool of 19 groups resulted in less efficient estimations of correlations than did a classical meta-analysis approach of combining correlation coefficients. These results were corroborated by simulation studies involving either mean differences or heteroskedasticity across a pool of N \u3e 2 groups. Conclusions The combination of statistical results is best suited for synthesizing the correlation between expression profiles of a gene pair across several microarray studies

Multiple Determinants of Whole and Regional Brain Volume among Terrestrial Carnivorans

Mammalian brain volumes vary considerably, even after controlling for body size. Although several hypotheses have been proposed to explain this variation, most research in mammals on the evolution of encephalization has focused on primates, leaving the generality of these explanations uncertain. Furthermore, much research still addresses only one hypothesis at a time, despite the demonstrated importance of considering multiple factors simultaneously. We used phylogenetic comparative methods to investigate simultaneously the importance of several factors previously hypothesized to be important in neural evolution among mammalian carnivores, including social complexity, forelimb use, home range size, diet, life history, phylogeny, and recent evolutionary changes in body size. We also tested hypotheses suggesting roles for these variables in determining the relative volume of four brain regions measured using computed tomography. Our data suggest that, in contrast to brain size in primates, carnivoran brain size may lag behind body size over evolutionary time. Moreover, carnivore species that primarily consume vertebrates have the largest brains. Although we found no support for a role of social complexity in overall encephalization, relative cerebrum volume correlated positively with sociality. Finally, our results support negative relationships among different brain regions after accounting for overall endocranial volume, suggesting that increased size of one brain regions is often accompanied by reduced size in other regions rather than overall brain expansion

The unfolded protein response in immunity and inflammation.

The unfolded protein response (UPR) is a highly conserved pathway that allows the cell to manage endoplasmic reticulum (ER) stress that is imposed by the secretory demands associated with environmental forces. In this role, the UPR has increasingly been shown to have crucial functions in immunity and inflammation. In this Review, we discuss the importance of the UPR in the development, differentiation, function and survival of immune cells in meeting the needs of an immune response. In addition, we review current insights into how the UPR is involved in complex chronic inflammatory diseases and, through its role in immune regulation, antitumour responses.This work was supported by the Netherlands Organization for Scientific Research Rubicon grant 825.13.012 (J.G.); US National Institutes of Health (NIH) grants DK044319, DK051362, DK053056 and DK088199, and the Harvard Digestive Diseases Center (HDDC) grant DK034854 (R.S.B.); National Institutes of Health grants DK042394, DK088227, DK103183 and CA128814 (R.J.K.); and European Research Council (ERC) Starting Grant 260961, ERC Consolidator Grant 648889, and the Wellcome Trust Investigator award 106260/Z/14/Z (A.K.).This is the author accepted manuscript. The final version is available from Nature Publishing Group via http://dx.doi.org/10.1038/nri.2016.6

Phytoplankton responses to marine climate change – an introduction

Phytoplankton are one of the key players in the ocean and contribute approximately 50% to global primary production. They serve as the basis for marine food webs, drive chemical composition of the global atmosphere and thereby climate. Seasonal environmental changes and nutrient availability naturally influence phytoplankton species composition. Since the industrial era, anthropogenic climatic influences have increased noticeably – also within the ocean. Our changing climate, however, affects the composition of phytoplankton species composition on a long-term basis and requires the organisms to adapt to this changing environment, influencing micronutrient bioavailability and other biogeochemical parameters. At the same time, phytoplankton themselves can influence the climate with their responses to environmental changes. Due to its key role, phytoplankton has been of interest in marine sciences for quite some time and there are several methodical approaches implemented in oceanographic sciences. There are ongoing attempts to improve predictions and to close gaps in the understanding of this sensitive ecological system and its responses

Application of the biotic ligand model to explain potassium interaction with thallium uptake and toxicity to plankton

Competitive interaction between Tl(I) and K was successfully predicted by the biotic ligand model (BLM) for the microalga Chlorella sp. (Chlorophyta; University of Toronto Culture Collection strain 522) during 96-h toxicity tests. Because of a greater affinity of Tl(I) (log K = 7.3-7.4) as compared to K (log K = 5.3-6.3) for biologically sensitive sites, an excess of 40-to 160-fold of K is required to suppress Tl(I) toxic effects on Chlorella sp., regardless of [Tl(I)] in solution. Similar excess of K is required to suppress Tl(I) toxicity to Synechococcus leopoliensis (Cyanobacteria; University of Texas Culture Collection strain 625) and Brachionus calyciflorus (Rotifera; strain AB-R1F). The mechanism for the mitigating effect of K on Tl(I) toxicity was investigated by measuring 204Tl(I) cellular uptake flux and efflux in Chlorella sp. Potassium shows a competitive effect on Tl(I) uptake fluxes that could be modeled using the BLM-derived stability constants and a Michaelis-Menten relationship. A strong Tl efflux dependent only on the cellular Tl concentration was measured. Although Tl efflux does not explain the effect of K on Tl(I) toxicity and uptake, it is responsible for a high turnover of the cellular Tl pool (intracellular half-life = 12-13.5 min). No effect of Na+, Mg2+, or Ca2+ was observed on Tl+ uptake, whereas the absence of trace metals (Cu, Co, Mo, Mn, Fe, and Zn) significantly increased Tl uptake and decreased the mitigating effect of K+. The importance of K+ in determining the aquatic toxicity of Tl + underscores the use of ambient K+ concentration in the establishment of Tl water-quality guidelines and the need to consider K in predicting biogeochemical fates of Tl in the aquatic environment. © 2007 SETAC