Isolation and characterization of highly pathogenic avian influenza virus subtype H5N1 from donkeys

Background  

The highly pathogenic H5N1 is a major avian pathogen that crosses species barriers and seriously affects humans as well as some mammals. It mutates in an intensified manner and is considered a potential candidate for the possible next pandemic with all the catastrophic consequences.     

<Emphasis Type="Italic">TRAF1/C5</Emphasis>polymorphism is not associated with increased mortality in rheumatoid arthritis: two large longitudinal studies

Introduction  

Recently an association between a genetic variation in TRAF1/C5 and mortality from sepsis or cancer was found in rheumatoid arthritis (RA). The most prevalent cause of death, cardiovascular disease, may have been missed in that study, since patients were enrolled at an advanced disease stage. Therefore, we used an inception cohort of RA patients to investigate the association between TRAF1/C5 and cardiovascular mortality, and replicate the findings on all-cause mortality. As TRAF1/C5 associated mortality may not be restricted to RA, we also studied a large cohort of non-RA patients.     

Non-native ecosystem engineer alters estuarine communities

Many ecosystems are created by the presence of ecosystem engineers that play an important role in determining species' abundance and species composition. Additionally, a mosaic environment of engineered and non-engineered habitats has been shown to increase biodiversity. Non-native ecosystem engineers can be introduced into environments that do not contain or have lost species that form biogenic habitat, resulting in dramatic impacts upon native communities. Yet, little is known about how non-native ecosystem engineers interact with natives and other non-natives already present in the environment, specifically whether non-native ecosystem engineers facilitate other non-natives, and whether they increase habitat heterogeneity and alter the diversity, abundance, and distribution of benthic species. Through sampling and experimental removal of reefs, we examine the effects of a non-native reef-building tubeworm, Ficopomatus enigmaticus, on community composition in the central Californian estuary, Elkhorn Slough. Tubeworm reefs host significantly greater abundances of many non-native polychaetes and amphipods, particularly the amphipods Monocorophium insidiosum and Melita nitida, compared to nearby mudflats. Infaunal assemblages under F. enigmaticus reefs and around reef's edges show very low abundance and taxonomic diversity. Once reefs are removed, the newly exposed mudflat is colonized by opportunistic non-native species, such as M. insidiosum and the polychaete Streblospio benedicti, making removal of reefs a questionable strategy for control. These results show that provision of habitat by a non-native ecosystem engineer may be a mechanism for invasional meltdown in Elkhorn Slough, and that reefs increase spatial heterogeneity in the abundance and composition of benthic communities.     

A unified approach for quantifying, testing and correcting population stratification in case-control association studies

The HapMap project has given case-control association studies a unique opportunity to uncover the genetic basis of complex diseases. However, persistent issues in such studies remain the proper quantification of, testing for, and correction for population stratification (PS). In this paper, we present the first unified paradigm that addresses all three fundamental issues within one statistical framework. Our unified approach makes use of an omnibus quantity (delta), which can be estimated in a case-control study from suitable null loci. We show how this estimated value can be used to quantify PS, to statistically test for PS, and to correct for PS, all in the context of case-control studies. Moreover, we provide guidelines for interpreting values of delta in association studies (e.g., at alpha = 0.05, a delta of size 0.416 is small, a delta of size 0.653 is medium, and a delta of size 1.115 is large). A novel feature of our testing procedure is its ability to test for either strictly any PS or only 'practically important' PS. We also performed simulations to compare our correction procedure with Genomic Control (GC). Our results show that, unlike GC, it maintains good Type I error rates and power across all levels of PS.     

Positional Information,Positional Error,and Readout Precision in Morphogenesis: A Mathematical Framework

The concept of positional information is central to our understanding of how cells determine their location in a multicellular structure and thereby their developmental fates. Nevertheless, positional information has neither been defined mathematically nor quantified in a principled way. Here we provide an information-theoretic definition in the context of developmental gene expression patterns and examine the features of expression patterns that affect positional information quantitatively. We connect positional information with the concept of positional error and develop tools to directly measure information and error from experimental data. We illustrate our framework for the case of gap gene expression patterns in the early Drosophila embryo and show how information that is distributed among only four genes is sufficient to determine developmental fates with nearly single-cell resolution. Our approach can be generalized to a variety of different model systems; procedures and examples are discussed in detail.     

Autoregulation of CCL26 synthesis and secretion in A549 cells: a possible mechanism by which alveolar epithelial cells modulate airway inflammation

Eotaxins (CCL11, CCL24, CCL26) originating from airway epithelial cells and leukocytes have been detected in bronchoalveolar lavage of asthmatics. Although the alveolar epithelium is the destination of uncleared allergens and other inflammatory products, scanty information exists on their contribution to the generation and regulation of the eotaxins. We envisioned a state whereby alveolar type II cells, a known source of other inflammatory proteins, could be involved in both the production and regulation of CCL24 and CCL26. Herein, we demonstrated that all three eotaxins are constitutively expressed in A549 cells. IL-4 and IL-13 stimulated a concentration-dependent secretion of CCL24 and CCL26. The cytokines did not act synergistically. Cycloheximide and actinomycin D abrogated IL-4- and IL-13-dependent CCL26 but not CCL24 secretion. Both IL-13 and IL-4 stimulated CCL26 synthesis that was inhibited in a concentration-dependent manner by CCL26 but not CCL24. Only CCL26 reduced expression of CCR3 receptors by 30-40%. On the other hand, anti-CCR3 pretreatment reduced IL-4+IL-13-dependent CCL26 secretion, implying autoregulation. A CCR3-specific antagonist (SB-328437) significantly decreased IL-4-dependent synthesis and release of CCL26. Eosinophils treated with medium from IL-4-stimulated A549 cells preincubated with anti-CCL26 showed a marked decrease of superoxide anion production compared with anti-CCL24 treated. These results suggest that CCL26 is a major eotaxin synthesized and released by alveolar epithelial cells and is involved in autoregulation of CCR3 receptors and other eotaxins. This CCL26-CCR3 ligand-receptor system may be an attractive target for development of therapeutics that limits progress of inflammation in airway disease.     

Effect of population stratification on case-control association studies. I. Elevation in false positive rates and comparison to confounding risk ratios (a simulation study)

OBJECTIVES: This is the first of two articles discussing the effect of population stratification on the type I error rate (i.e., false positive rate). This paper focuses on the confounding risk ratio (CRR). It is accepted that population stratification (PS) can produce false positive results in case-control genetic association. However, which values of population parameters lead to an increase in type I error rate is unknown. Some believe PS does not represent a serious concern, whereas others believe that PS may contribute to contradictory findings in genetic association. We used computer simulations to estimate the effect of PS on type I error rate over a wide range of disease frequencies and marker allele frequencies, and we compared the observed type I error rate to the magnitude of the confounding risk ratio. METHODS: We simulated two populations and mixed them to produce a combined population, specifying 160 different combinations of input parameters (disease prevalences and marker allele frequencies in the two populations). From the combined populations, we selected 5000 case-control datasets, each with either 50, 100, or 300 cases and controls, and determined the type I error rate. In all simulations, the marker allele and disease were independent (i.e., no association). RESULTS: The type I error rate is not substantially affected by changes in the disease prevalence per se. We found that the CRR provides a relatively poor indicator of the magnitude of the increase in type I error rate. We also derived a simple mathematical quantity, Delta, that is highly correlated with the type I error rate. In the companion article (part II, in this issue), we extend this work to multiple subpopulations and unequal sampling proportions. CONCLUSION: Based on these results, realistic combinations of disease prevalences and marker allele frequencies can substantially increase the probability of finding false evidence of marker disease associations. Furthermore, the CRR does not indicate when this will occur.     

The accuracy of several multiple sequence alignment programs for proteins

Background  

There have been many algorithms and software programs implemented for the inference of multiple sequence alignments of protein and DNA sequences. The "true" alignment is usually unknown due to the incomplete knowledge of the evolutionary history of the sequences, making it difficult to gauge the relative accuracy of the programs.     

On single and multiple models of protein families for the detection of remote sequence relationships

Background  

The detection of relationships between a protein sequence of unknown function and a sequence whose function has been characterised enables the transfer of functional annotation. However in many cases these relationships can not be identified easily from direct comparison of the two sequences. Methods which compare sequence profiles have been shown to improve the detection of these remote sequence relationships. However, the best method for building a profile of a known set of sequences has not been established. Here we examine how the type of profile built affects its performance, both in detecting remote homologs and in the resulting alignment accuracy. In particular, we consider whether it is better to model a protein superfamily using a single structure-based alignment that is representative of all known cases of the superfamily, or to use multiple sequence-based profiles each representing an individual member of the superfamily.