Cannabinoid receptor type 2 activation induces a microglial anti-inflammatory phenotype and reduces migration via MKP induction and ERK dephosphorylation

<p>Abstract</p> <p>Background</p> <p>Cannabinoid receptor type 2 (CBR2) inhibits microglial reactivity through a molecular mechanism yet to be elucidated. We hypothesized that CBR2 activation induces an anti-inflammatory phenotype in microglia by inhibiting extracellular signal-regulated kinase (ERK) pathway, via mitogen-activated protein kinase-phosphatase (MKP) induction. MKPs regulate mitogen activated protein kinases, but their role in the modulation of microglial phenotype is not fully understood.</p> <p>Results</p> <p>JWH015 (a CBR2 agonist) increased MKP-1 and MKP-3 expression, which in turn reduced p-ERK1/2 in LPS-stimulated primary microglia. These effects resulted in a significant reduction of tumor necrosis factor-α (TNF) expression and microglial migration. We confirmed the causative link of these findings by using MKP inhibitors. We found that the selective inhibition of MKP-1 by Ro-31-8220 and PSI2106, did not affect p-ERK expression in LPS+JWH015-treated microglia. However, the inhibition of both MKP-1 and MKP-3 by triptolide induced an increase in p-ERK expression and in microglial migration using LPS+JWH015-treated microglia.</p> <p>Conclusion</p> <p>Our results uncover a cellular microglial pathway triggered by CBR2 activation. These data suggest that the reduction of pro-inflammatory factors and microglial migration via MKP-3 induction is part of the mechanism of action of CBR2 agonists. These findings may have clinical implications for further drug development.</p

SSE: a nucleotide and amino acid sequence analysis platform

<p>Abstract</p> <p>Background</p> <p>There is an increasing need to develop bioinformatic tools to organise and analyse the rapidly growing amount of nucleotide and amino acid sequence data in organisms ranging from viruses to eukaryotes.</p> <p>Finding</p> <p>A simple sequence editor (SSE) was developed to create an integrated environment where sequences can be aligned, annotated, classified and directly analysed by a number of built-in bioinformatic programs. SSE incorporates a sequence editor for the creation of sequence alignments, a process assisted by integrated CLUSTAL/MUSCLE alignment programs and automated removal of indels. Sequences can be fully annotated and classified into groups and annotated of sequences and sequence groups and access to analytical programs that analyse diversity, recombination and RNA secondary structure. Methods for analysing sequence diversity include measures of divergence and evolutionary distances, identity plots to detect regions of nucleotide or amino acid homology, reconstruction of sequence changes, mono-, di- and higher order nucleotide compositional biases and codon usage.</p> <p>Association Index calculations, GroupScans, Bootscanning and TreeOrder scans perform phylogenetic analyses that reconcile group membership with tree branching orders and provide powerful methods for examining segregation of alleles and detection of recombination events. Phylogeny changes across alignments and scoring of branching order differences between trees using the Robinson-Fould algorithm allow effective visualisation of the sites of recombination events.</p> <p>RNA secondary and tertiary structures play important roles in gene expression and RNA virus replication. For the latter, persistence of infection is additionally associated with pervasive RNA secondary structure throughout viral genomic RNA that modulates interactions with innate cell defences. SSE provides several programs to scan alignments for RNA secondary structure through folding energy thermodynamic calculations and phylogenetic methods (detection of co-variant changes, and structure conservation between divergent sequences). These analyses complement methods based on detection of sequence constraints, such as suppression of synonymous site variability.</p> <p>For each program, results can be plotted in real time during analysis through an integrated graphics package, providing publication quality graphs. Results can be also directed to tabulated datafiles for import into spreadsheet or database programs for further analysis.</p> <p>Conclusions</p> <p>SSE combines sequence editor functions with analytical tools in a comprehensive and user-friendly package that assists considerably in bioinformatic and evolution research.</p

Inter-Observer Agreement on Subjects' Race and Race-Informative Characteristics

Health and socioeconomic disparities tend to be experienced along racial and ethnic lines, but investigators are not sure how individuals are assigned to groups, or how consistent this process is. To address these issues, 1,919 orthodontic patient records were examined by at least two observers who estimated each individual's race and the characteristics that influenced each estimate. Agreement regarding race is high for African and European Americans, but not as high for Asian, Hispanic, and Native Americans. The indicator observers most often agreed upon as important in estimating group membership is name, especially for Asian and Hispanic Americans. The observers, who were almost all European American, most often agreed that skin color is an important indicator of race only when they also agreed the subject was European American. This suggests that in a diverse community, light skin color is associated with a particular group, while a range of darker shades can be associated with members of any other group. This research supports comparable studies showing that race estimations in medical records are likely reliable for African and European Americans, but are less so for other groups. Further, these results show that skin color is not consistently the primary indicator of an individual's race, but that other characteristics such as facial features add significant information

Office Space Bacterial Abundance and Diversity in Three Metropolitan Areas

People in developed countries spend approximately 90% of their lives indoors, yet we know little about the source and diversity of microbes in built environments. In this study, we combined culture-based cell counting and multiplexed pyrosequencing of environmental ribosomal RNA (rRNA) gene sequences to investigate office space bacterial diversity in three metropolitan areas. Five surfaces common to all offices were sampled using sterile double-tipped swabs, one tip for culturing and one for DNA extraction, in 30 different offices per city (90 offices, 450 total samples). 16S rRNA gene sequences were PCR amplified using bar-coded “universal” bacterial primers from 54 of the surfaces (18 per city) and pooled for pyrosequencing. A three-factorial Analysis of Variance (ANOVA) found significant differences in viable bacterial abundance between offices inhabited by men or women, among the various surface types, and among cities. Multiplex pyrosequencing identified more than 500 bacterial genera from 20 different bacterial divisions. The most abundant of these genera tended to be common inhabitants of human skin, nasal, oral or intestinal cavities. Other commonly occurring genera appeared to have environmental origins (e.g., soils). There were no significant differences in the bacterial diversity between offices inhabited by men or women or among surfaces, but the bacterial community diversity of the Tucson samples was clearly distinguishable from that of New York and San Francisco, which were indistinguishable. Overall, our comprehensive molecular analysis of office building microbial diversity shows the potential of these methods for studying patterns and origins of indoor bacterial contamination. “[H]umans move through a sea of microbial life that is seldom perceived except in the context of potential disease and decay.” – Feazel et al. (2009)

A comparison of four clustering methods for brain expression microarray data

Background DNA microarrays, which determine the expression levels of tens of thousands of genes from a sample, are an important research tool. However, the volume of data they produce can be an obstacle to interpretation of the results. Clustering the genes on the basis of similarity of their expression profiles can simplify the data, and potentially provides an important source of biological inference, but these methods have not been tested systematically on datasets from complex human tissues. In this paper, four clustering methods, CRC, k-means, ISA and memISA, are used upon three brain expression datasets. The results are compared on speed, gene coverage and GO enrichment. The effects of combining the clusters produced by each method are also assessed. Results k-means outperforms the other methods, with 100% gene coverage and GO enrichments only slightly exceeded by memISA and ISA. Those two methods produce greater GO enrichments on the datasets used, but at the cost of much lower gene coverage, fewer clusters produced, and speed. The clusters they find are largely different to those produced by k-means. Combining clusters produced by k-means and memISA or ISA leads to increased GO enrichment and number of clusters produced (compared to k-means alone), without negatively impacting gene coverage. memISA can also find potentially disease-related clusters. In two independent dorsolateral prefrontal cortex datasets, it finds three overlapping clusters that are either enriched for genes associated with schizophrenia, genes differentially expressed in schizophrenia, or both. Two of these clusters are enriched for genes of the MAP kinase pathway, suggesting a possible role for this pathway in the aetiology of schizophrenia. Conclusion Considered alone, k-means clustering is the most effective of the four methods on typical microarray brain expression datasets. However, memISA and ISA can add extra high-quality clusters to the set produced by k-means, so combining these three methods is the method of choice

EMIRGE: reconstruction of full-length ribosomal genes from microbial community short read sequencing data

Recovery of ribosomal small subunit genes by assembly of short read community DNA sequence data generally fails, making taxonomic characterization difficult. Here, we solve this problem with a novel iterative method, based on the expectation maximization algorithm, that reconstructs full-length small subunit gene sequences and provides estimates of relative taxon abundances. We apply the method to natural and simulated microbial communities, and correctly recover community structure from known and previously unreported rRNA gene sequences. An implementation of the method is freely available at https://github.com/csmiller/EMIRGE

Increased incidence of rare codon clusters at 5' and 3' gene termini:implications for function

<p>Abstract</p> <p>Background</p> <p>The process of translation can be affected by the use of rare versus common codons within the mRNA transcript.</p> <p>Results</p> <p>Here, we show that rare codons are enriched at the 5' and 3' termini of genes from <it>E. coli </it>and other prokaryotes. Genes predicted to be secreted show significant enrichment in 5' rare codon clusters, but not 3' rare codon clusters. Surprisingly, no correlation between 5' mRNA structure and rare codon usage was observed.</p> <p>Conclusions</p> <p>Potential functional roles for the enrichment of rare codons at terminal positions are explored.</p

Search for rare quark-annihilation decays, B --> Ds(*) Phi

We report on searches for B- --> Ds- Phi and B- --> Ds*- Phi. In the context of the Standard Model, these decays are expected to be highly suppressed since they proceed through annihilation of the b and u-bar quarks in the B- meson. Our results are based on 234 million Upsilon(4S) --> B Bbar decays collected with the BABAR detector at SLAC. We find no evidence for these decays, and we set Bayesian 90% confidence level upper limits on the branching fractions BF(B- --> Ds- Phi) Ds*- Phi)<1.2x10^(-5). These results are consistent with Standard Model expectations.Comment: 8 pages, 3 postscript figues, submitted to Phys. Rev. D (Rapid Communications

The Populus holobiont: dissecting the effects of plant niches and genotype on the microbiome

Background: Microorganisms serve important functions within numerous eukaryotic host organisms. An understanding of the variation in the plant niche-level microbiome, from rhizosphere soils to plant canopies, is imperative to gain a better understanding of how both the structural and functional processes of microbiomes impact the health of the overall plant holobiome. Using Populus trees as a model ecosystem, we characterized the archaeal/bacterial and fungal microbiome across 30 different tissue-level niches within replicated Populus deltoides and hybrid Populus trichocarpa × deltoides individuals using 16S and ITS2 rRNA gene analyses. Results: Our analyses indicate that archaeal/bacterial and fungal microbiomes varied primarily across broader plant habitat classes (leaves, stems, roots, soils) regardless of plant genotype, except for fungal communities within leaf niches, which were greatly impacted by the host genotype. Differences between tree genotypes are evident in the elevated presence of two potential fungal pathogens, Marssonina brunnea and Septoria sp., on hybrid P. trichocarpa × deltoides trees which may in turn be contributing to divergence in overall microbiome composition. Archaeal/bacterial diversity increased from leaves, to stem, to root, and to soil habitats, whereas fungal diversity was the greatest in stems and soils. Conclusions: This study provides a holistic understanding of microbiome structure within a bioenergy relevant plant host, one of the most complete niche-level analyses of any plant. As such, it constitutes a detailed atlas or map for further hypothesis testing on the significance of individual microbial taxa within specific niches and habitats of Populus and a baseline for comparisons to other plant species

Quality of life assessment as a predictor of survival in non-small cell lung cancer

<p>Abstract</p> <p>Background</p> <p>There are conflicting and inconsistent results in the literature on the prognostic role of quality of life (QoL) in cancer. We investigated whether QoL at admission could predict survival in lung cancer patients.</p> <p>Methods</p> <p>The study population consisted of 1194 non-small cell lung cancer patients treated at our institution between Jan 2001 and Dec 2008. QoL was evaluated using EORTC-QLQ-C30 prior to initiation of treatment. Patient survival was defined as the time interval between the date of first patient visit and the date of death from any cause/date of last contact. Univariate and multivariate Cox regression evaluated the prognostic significance of QoL.</p> <p>Results</p> <p>Mean age at presentation was 58.3 years. There were 605 newly diagnosed and 589 previously treated patients; 601 males and 593 females. Stage of disease at diagnosis was I, 100; II, 63; III, 348; IV, 656; and 27 indeterminate. Upon multivariate analyses, global QoL as well as physical function predicted patient survival in the entire study population. Every 10-point increase in physical function was associated with a 10% increase in survival (95% CI = 6% to 14%, p < 0.001). Similarly, every 10-point increase in global QoL was associated with a 9% increase in survival (95% CI = 6% to 11%, p < 0.001). Furthermore, physical function, nausea/vomiting, insomnia, and diarrhea (p < 0.05 for all) in newly diagnosed patients, but only physical function (p < 0.001) in previously treated patients were predictive of survival.</p> <p>Conclusions</p> <p>Baseline global QoL and physical function provide useful prognostic information in non-small cell lung cancer patients.</p