Gut Microbiome and Metabolic and Immune Indices in Males with or without Evidence of Metabolic Dysregulation.

Background: The contributions of the gut microbiota to obesity and metabolic disease represent a potentially modifiable factor that may explain variation in risk between individuals. This study aimed to explore relationships among microbial composition and imputed functional attributes, a range of soluble metabolic and immune indices, and gene expression markers in males with or without evidence of metabolic dysregulation (MetDys). Methods: This case-control study included healthy males (n=15; 41.9±11.7 years; body mass index [BMI], 22.9±1.2 kg/m2) and males with evidence of MetDys (n=14; 46.6±10.0 years; BMI, 35.1±3.3 kg/m2) who provided blood and faecal samples for assessment of a range of metabolic and immune markers and microbial composition using 16S rRNA gene sequencing. Metagenomic functions were imputed from microbial sequence data for analysis. Results: In addition to elevated values in a range of traditional metabolic, adipokine and inflammatory indices in the MetDys group, 23 immunomodulatory genes were significantly altered in the MetDys group. Overall microbial diversity did not differ between groups; however, a trend for a higher relative abundance of the Bacteroidetes (P=0.06) and a lower relative abundance of the Verrucomicrobia (P=0.09) phyla was noted in the MetDys group. Using both family- and genera-level classifications, a partial least square discriminant analysis revealed unique microbial signatures between the groups. Conclusion: These findings confirm the need for ongoing investigations in human clinical cohorts to further resolve the relationships between the gut microbiota and metabolic and immune markers and risk for metabolic disease.

Selection of microbial biomarkers with genetic algorithm and principal component analysis.

BACKGROUND: Principal components analysis (PCA) is often used to find characteristic patterns associated with certain diseases by reducing variable numbers before a predictive model is built, particularly when some variables are correlated. Usually, the first two or three components from PCA are used to determine whether individuals can be clustered into two classification groups based on pre-determined criteria: control and disease group. However, a combination of other components may exist which better distinguish diseased individuals from healthy controls. Genetic algorithms (GAs) can be useful and efficient for searching the best combination of variables to build a prediction model. This study aimed to develop a prediction model that combines PCA and a genetic algorithm (GA) for identifying sets of bacterial species associated with obesity and metabolic syndrome (Mets). RESULTS: The prediction models built using the combination of principal components (PCs) selected by GA were compared to the models built using the top PCs that explained the most variance in the sample and to models built with selected original variables. The advantages of combining PCA with GA were demonstrated. CONCLUSIONS: The proposed algorithm overcomes the limitation of PCA for data analysis. It offers a new way to build prediction models that may improve the prediction accuracy. The variables included in the PCs that were selected by GA can be combined with flexibility for potential clinical applications. The algorithm can be useful for many biological studies where high dimensional data are collected with highly correlated variables.

MetaDEGalaxy: Galaxy workflow for differential abundance analysis of 16s metagenomic data.

Metagenomic sequencing is an increasingly common tool in environmental and biomedical sciences yet analysis workflows remain immature relative to other field such as DNASeq and RNASeq analysis pipelines.  While software for detailing the composition of microbial communities using 16S rRNA marker genes is constantly improving, increasingly researchers are interested in identifying changes exhibited within microbial communities under differing environmental conditions. In order to gain maximum value from metagenomic sequence data we must improve the existing analysis environment by providing accessible and scalable computational workflows able to generate reproducible results. Here we describe a complete end-to-end open-source metagenomics workflow running within Galaxy for 16S differential abundance analysis. The workflow accepts 454 or Illumina sequence data (either overlapping or non-overlapping paired end reads) and outputs lists of the operational taxonomic unit (OTUs) exhibiting the greatest change under differing conditions. A range of analysis steps and graphing options are available giving users a high-level of control over their data and analyses. Additionally, users are able to input complex sample-specific metadata information which can be incorporated into differential analysis and used for grouping / colouring within graphs.  Detailed tutorials containing sample data and existing workflows are available for three different input types: overlapping and non-overlapping read pairs as well as for pre-generated Biological Observation Matrix (BIOM) files. Using the Galaxy platform we developed MetaDEGalaxy, a complete metagenomics differential abundance analysis workflow. MetaDEGalaxy is designed for bench scientists working with 16S data who are interested in comparative metagenomics.  MetaDEGalaxy builds on momentum within the wider Galaxy metagenomics community with the hope that more tools will be added as existing methods mature.

ArachnoServer 3.0: an online resource for automated discovery, analysis and annotation of spider toxins.

Summary: ArachnoServer is a manually curated database that consolidates information on the sequence, structure, function and pharmacology of spider-venom toxins. Although spider venoms are complex chemical arsenals, the primary constituents are small disulfide-bridged peptides that target neuronal ion channels and receptors. Due to their high potency and selectivity, these peptides have been developed as pharmacological tools, bioinsecticides and drug leads. A new version of ArachnoServer (v3.0) has been developed that includes a bioinformatics pipeline for automated detection and analysis of peptide toxin transcripts in assembled venom-gland transcriptomes. ArachnoServer v3.0 was updated with the latest sequence, structure and functional data, the search-by-mass feature has been enhanced, and toxin cards provide additional information about each mature toxin. Availability and implementation: http://arachnoserver.org. Contact: support@arachnoserver.org. Supplementary information: Supplementary data are available at Bioinformatics online.

Coexpression networks identify brain region-specific enhancer RNAs in the human brain.

Despite major progress in identifying enhancer regions on a genome-wide scale, the majority of available data are limited to model organisms and human transformed cell lines. We have identified a robust set of enhancer RNAs (eRNAs) expressed in the human brain and constructed networks assessing eRNA-gene coexpression interactions across human fetal brain and multiple adult brain regions. Our data identify brain region-specific eRNAs and show that enhancer regions expressing eRNAs are enriched for genetic variants associated with autism spectrum disorders.

Global assessment of Antrodia cinnamomea-induced microRNA alterations in hepatocarcinoma cells.

Recent studies have demonstrated a potent anticancer potential of medicinal fungus Antrodia cinnamomea, especially against hepatocarcinoma. These studies, however, were performed with prolonged treatments, and the early anticancer events remain missing. To probe the early anticancer mechanisms of A. cinnamomea, we treated SK-Hep-1 liver cancer cell with A. cinnamomea fruiting body extract for 2 and 4 hours, sequenced RNA samples with next-generation sequencing approach, and profiled the genome-wide miRNA and mRNA transcriptomes. Results unmistakably associated the early anticancer effect of A. cinnamomea fruiting body extract with a global downregulation of miRNAs which occurred solely in the A. cinnamomea fruiting body extract-treated SK-Hep-1 cells. Moreover, the inhibitory effect of A. cinnamomea fruiting body extract upon cancer miRNAs imposed no discrimination against any particular miRNA species, with oncomirs miR-21, miR-191 and major oncogenic clusters miR-17-92 and miR-106b-25 among the most severely downregulated. Western blotting further indicated a decrease in Drosha and Dicer proteins which play a key role in miRNA biogenesis, together with an increase of XRN2 known to participate in miRNA degradation pathway. Transcriptome profiling followed by GO and pathway analyses indicated that A. cinnamomea induced apoptosis, which was tightly associated with a downregulation of PI3K/AKT and MAPK pathways. Phosphorylation assay further suggested that JNK and c-Jun were closely involved in the apoptotic process. Taken together, our data indicated that the anticancer effect of A. cinnamomea can take place within a few hours by targeting multiple proteins and the miRNA system. A. cinnamomea indiscriminately induced a global downregulation of miRNAs by simultaneously inhibiting the key enzymes involved in miRNA maturation and activating XRN2 protein involved in miRNA degradation. Collapsing of the miRNA system together with downregulation of cell growth and survival pathways and activation of JNK signaling unleash the extrinsic and intrinsic apoptosis pathways, leading to the cancer cell death.