Microarray-based analysis of microbial community RNAs by whole-community RNA amplification

A new approach, termed whole-community RNA amplification (WCRA), was developed to provide sufficient amounts of mRNAs from environmental samples for microarray analysis. This method employs fusion primers (six to nine random nucleotides with an attached T7 promoter) for the first-strand synthesis. The shortest primer (T7N6S) gave the best results in terms of the yield and representativeness of amplification. About 1,200- to 1,800-fold amplification was obtained with amounts of the RNA templates ranging from 10 to 100 ng, and very representative detection was obtained with 50 to 100 ng total RNA. Evaluation with a Shewanella oneidensis Deltafur strain revealed that the amplification method which we developed could preserve the original abundance relationships of mRNAs. In addition, to determine whether representative detection of RNAs can be achieved with mixed community samples, amplification biases were evaluated with a mixture containing equal quantities of RNAs (100 ng each) from four bacterial species, and representative amplification was also obtained. Finally, the method which we developed was applied to the active microbial populations in a denitrifying fluidized bed reactor used for denitrification of contaminated groundwater and ethanol-stimulated groundwater samples for uranium reduction. The genes expressed were consistent with the expected functions of the bioreactor and groundwater system, suggesting that this approach is useful for analyzing the functional activities of microbial communities. This is one of the first demonstrations that microarray-based technology can be used to successfully detect the activities of microbial communities from real environmental samples in a high-throughput fashion.     

Autophagy interplay with apoptosis and cell cycle regulation in the growth inhibiting effect of resveratrol in glioma cells

Prognosis of patients with glioblastoma (GBM) remains very poor, thus making the development of new drugs urgent. Resveratrol (Rsv) is a natural compound that has several beneficial effects such as neuroprotection and cytotoxicity for several GBM cell lines. Here we evaluated the mechanism of action of Rsv on human GBM cell lines, focusing on the role of autophagy and its crosstalk with apoptosis and cell cycle control. We further evaluated the role of autophagy and the effect of Rsv on GBM Cancer Stem Cells (gCSCs), involved in GBM resistance and recurrence. Glioma cells treated with Rsv was tested for autophagy, apoptosis, necrosis, cell cycle and phosphorylation or expression levels of key players of these processes. Rsv induced the formation of autophagosomes in three human GBM cell lines, accompanied by an upregulation of autophagy proteins Atg5, beclin-1 and LC3-II. Inhibition of Rsv-induced autophagy triggered apoptosis, with an increase in Bax and cleavage of caspase-3. While inhibition of apoptosis or autophagy alone did not revert Rsv-induced toxicity, inhibition of both processes blocked this toxicity. Rsv also induced a S-G2/M phase arrest, accompanied by an increase on levels of pCdc2(Y15), cyclin A, E and B, and pRb (S807/811) and a decrease of cyclin D1. Interestingly, this arrest was dependent on the induction of autophagy, since inhibition of Rsv-induced autophagy abolishes cell cycle arrest and returns the phosphorylation of Cdc2(Y15) and Rb(S807/811), and levels of cyclin A, and B to control levels. Finally, inhibition of autophagy or treatment with Rsv decreased the sphere formation and the percentage of CD133 and OCT4-positive cells, markers of gCSCs. In conclusion, the crosstalk among autophagy, cell cycle and apoptosis, together with the biology of gCSCs, has to be considered in tailoring pharmacological interventions aimed to reduce glioma growth using compounds with multiple targets such as Rsv.     

<Emphasis Type="Italic">Scoredist</Emphasis>: A simple and robust protein sequence distance estimator

Background  

Distance-based methods are popular for reconstructing evolutionary trees thanks to their speed and generality. A number of methods exist for estimating distances from sequence alignments, which often involves some sort of correction for multiple substitutions. The problem is to accurately estimate the number of true substitutions given an observed alignment. So far, the most accurate protein distance estimators have looked for the optimal matrix in a series of transition probability matrices, e.g. the Dayhoff series. The evolutionary distance between two aligned sequences is here estimated as the evolutionary distance of the optimal matrix. The optimal matrix can be found either by an iterative search for the Maximum Likelihood matrix, or by integration to find the Expected Distance. As a consequence, these methods are more complex to implement and computationally heavier than correction-based methods. Another problem is that the result may vary substantially depending on the evolutionary model used for the matrices. An ideal distance estimator should produce consistent and accurate distances independent of the evolutionary model used.     

Improved profile HMM performance by assessment of critical algorithmic features in SAM and HMMER

Background  

Profile hidden Markov model (HMM) techniques are among the most powerful methods for protein homology detection. Yet, the critical features for successful modelling are not fully known. In the present work we approached this by using two of the most popular HMM packages: SAM and HMMER. The programs' abilities to build models and score sequences were compared on a SCOP/Pfam based test set. The comparison was done separately for local and global HMM scoring.     

Host genetic and environmental effects on mouse intestinal microbiota

The mammalian gut harbors complex and variable microbial communities, across both host phylogenetic space and conspecific individuals. A synergy of host genetic and environmental factors shape these communities and account for their variability, but their individual contributions and the selective pressures involved are still not well understood. We employed barcoded pyrosequencing of V1-2 and V4 regions of bacterial small subunit ribosomal RNA genes to characterize the effects of host genetics and environment on cecum assemblages in 10 genetically distinct, inbred mouse strains. Eight of these strains are the foundation of the Collaborative Cross (CC), a panel of mice derived from a genetically diverse set of inbred founder strains, designed specifically for complex trait analysis. Diversity of gut microbiota was characterized by complementing phylogenetic and distance-based, sequence-clustering approaches. Significant correlations were found between the mouse strains and their gut microbiota, reflected by distinct bacterial communities. Cohabitation and litter had a reduced, although detectable effect, and the microbiota response to these factors varied by strain. We identified bacterial phylotypes that appear to be discriminative and strain-specific to each mouse line used. Cohabitation of different strains of mice revealed an interaction of host genetic and environmental factors in shaping gut bacterial consortia, in which bacterial communities became more similar but retained strain specificity. This study provides a baseline analysis of intestinal bacterial communities in the eight CC progenitor strains and will be linked to integrated host genotype, phenotype and microbiota research on the resulting CC panel.