MetaDEGalaxy: Galaxy workflow for differential abundance analysis of 16s metagenomic data [version 2; peer review: 2 approved]

Metagenomic sequencing is an increasingly common tool in environmental and biomedical sciences.\ua0 While software for detailing the composition of microbial communities using 16S rRNA marker genes is relatively mature, 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.\ua0 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

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

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

Global Assessment of <i>Antrodia cinnamomea</i>-Induced MicroRNA Alterations in Hepatocarcinoma Cells

<div><p>Recent studies have demonstrated a potent anticancer potential of medicinal fungus <i>Antrodia cinnamomea</i>, 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 <i>A. cinnamomea</i>, we treated SK-Hep-1 liver cancer cell with <i>A. cinnamomea</i> 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 <i>A. cinnamomea</i> fruiting body extract with a global downregulation of miRNAs which occurred solely in the <i>A. cinnamomea</i> fruiting body extract-treated SK-Hep-1 cells. Moreover, the inhibitory effect of <i>A. cinnamomea</i> 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 <i>A. cinnamomea</i> 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 <i>A. cinnamomea</i> can take place within a few hours by targeting multiple proteins and the miRNA system. <i>A. cinnamomea</i> 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.</p></div

Expression of miRNAs encoded by miR-17-92 and miR-106b-25 oncogenic clusters.

<p>(#) miR-17-92 cluster; (*) miR-106b-25 cluster; 2U, 2-hr untreated; 2T, 2-hr treated; 4U, 4-hr untreated; 4T, 4-hr treated.</p

Western blot analysis of the phosphorylated and unphosphorylated forms of AKT, ERK, JNK, c-Fos, and c-Jun.

<p>SK-HEP1 liver cancer cells were treated with (at 0.5 mg/ml) or without AcFBE for 2 or 4 hrs. Immunoblotting experiments were repeated twice using GAPDH or Tubulin as controls. The duplicates were averaged and shown below each protein band, and the phosphorylated-to-unphosphorylated ratios were then calculated and shown below the treated samples. Protein bands were from the first set of immunoblotting experiment. The normalized protein level of untreated samples was set as 1.00 for comparing to their corresponding AcFBE-treated samples. 2U, untreated for 2 hrs; 2T, treated for 2 hrs; 4U, untreated for 4 hrs; 4T, treated for 4 hrs.</p

Cross-time Comparison (2 hr vs. 4 hr) of the top 10 most significantly upregulated miRNAs.

<p>(<b>A</b>) First batch of quality miRNAs (sequenced with SOLiD 3) from 2 hr AcFBE treated or untreated SK-HEP-1 cells were normalized by (actual reads/quality reads) ×1,000,000 and used to calculate the log₂ fold change. Values of the untreated were subtracted from the treated followed by a sorting to identify the top 10 miRNAs with highest positive values which were then compared with their levels at 4-h time point. (<b>B</b>) Same as A, but prepared from another batch of SK-HEP-1 cells (repeated samples sequenced with SOLiD 5500xl).</p

Boxplots showing cross-experiment comparison on miRNA expression between AcFBE-treated and –untreated samples.

<p>The distributions of miRNA expression level (inner boxes) and medians (horizontal line in each inner box, value shown in the parentheses right below the outer box) are presented by box plotting for each (untreated vs. treated) pair for each time point (either 2 hr and 4 hr time point and for both SK-HEP-1 and BNL CL.2 normal cells. Experiments of SK-HEP-1 cancer cells were repeated and the duplicates were sequenced separately by two types of sequencers (shown above). (<b>A</b>) SOLiD 3 miRNA dataset produced from SK-HEP-1 and SOLiD 5500 miRNA dataset produced from another batch of SK-HEP-1. (<b>B</b>) miRNA dataset produced from BNL CL.2 normal cells (control).</p

Expression profiles of miRNAs for SK-HEP-1 and BNL CL.2 by treated AcFBE.

<p>To compared the expression level of known miRNAs affected by treated AcFBE for SK-HEP-1 and BNL CL.2 cells at 2 hr and 4 hr.</p

GO analysis.

<p>GO analysis.</p