Testing for treatment effects on gene ontology

In studies that use DNA arrays to assess changes in gene expression, it is preferable to measure the significance of treatment effects on a group of genes from a pathway or functional category such as gene ontology terms (GO terms, ) because this facilitates the interpretation of effects and may markedly increase significance. A modified meta-analysis method to combine p-values was developed to measure the significance of an overall treatment effect on such functionally-defined groups of genes, taking into account the correlation structure among genes. For hypothesis testing that allows gene expression to change in both directions, p-values are calculated under the null distribution generated by a Monte Carlo method

SigTree: A Microbial Community Analysis Tool to Identify and Visualize Significantly Responsive Branches in a Phylogenetic Tree.

Microbial community analysis experiments to assess the effect of a treatment intervention (or environmental change) on the relative abundance levels of multiple related microbial species (or operational taxonomic units) simultaneously using high throughput genomics are becoming increasingly common. Within the framework of the evolutionary phylogeny of all species considered in the experiment, this translates to a statistical need to identify the phylogenetic branches that exhibit a significant consensus response (in terms of operational taxonomic unit abundance) to the intervention. We present the R software package SigTree, a collection of flexible tools that make use of meta-analysis methods and regular expressions to identify and visualize significantly responsive branches in a phylogenetic tree, while appropriately adjusting for multiple comparisons

Increased Expression of Cell-Cell Signaling Genes by Stimulated Mononuclear Leukocytes in Patients with Previous Atherothrombotic Stroke A Whole Genome Expression Profile Study

Background/Aims: Inflammation plays an important role in atherosclerosis and stroke. Acute infections are recognized as trigger factors for ischemic stroke. Methods: In this whole genome expression profile study of 15 patients and 15 control subjects, we tested the hypothesis that patients with a history of atherothrombotic stroke show enhanced transcription of inflammatory genes in circulating leukocytes. RNA from unstimulated or lipopolysaccharide (LPS)-stimulated peripheral blood mononuclear cells (PBMCs) was analyzed with Affymetrix U133A GeneChips using a pooling design. Expression of single genes and functional groups of genes was analyzed by global statistical tests. Results: A total of 10,197 probe sets showed positive calls. After correction for multiple testing no single probe set revealed significant differences either without or with LPS stimulation. However, significant global expression differences were found upon LPS stimulation for the group of genes that are involved in cell-cell signaling. Conclusion: LPS stimulation of PBMCs, a condition mimicking bacterial infection, induces differential expression of a group of cell-cell signaling genes in patients with previous atherothrombotic stroke. This finding can be caused by genetic differences between both groups, but acquired risk factors, medication and technical factors may also have contributed to the result. Copyright (C) 2009 S. Karger AG, Base

RegenBase: a knowledge base of spinal cord injury biology for translational research.

Spinal cord injury (SCI) research is a data-rich field that aims to identify the biological mechanisms resulting in loss of function and mobility after SCI, as well as develop therapies that promote recovery after injury. SCI experimental methods, data and domain knowledge are locked in the largely unstructured text of scientific publications, making large scale integration with existing bioinformatics resources and subsequent analysis infeasible. The lack of standard reporting for experiment variables and results also makes experiment replicability a significant challenge. To address these challenges, we have developed RegenBase, a knowledge base of SCI biology. RegenBase integrates curated literature-sourced facts and experimental details, raw assay data profiling the effect of compounds on enzyme activity and cell growth, and structured SCI domain knowledge in the form of the first ontology for SCI, using Semantic Web representation languages and frameworks. RegenBase uses consistent identifier schemes and data representations that enable automated linking among RegenBase statements and also to other biological databases and electronic resources. By querying RegenBase, we have identified novel biological hypotheses linking the effects of perturbagens to observed behavioral outcomes after SCI. RegenBase is publicly available for browsing, querying and download.Database URL:http://regenbase.org

Random-set methods identify distinct aspects of the enrichment signal in gene-set analysis

A prespecified set of genes may be enriched, to varying degrees, for genes that have altered expression levels relative to two or more states of a cell. Knowing the enrichment of gene sets defined by functional categories, such as gene ontology (GO) annotations, is valuable for analyzing the biological signals in microarray expression data. A common approach to measuring enrichment is by cross-classifying genes according to membership in a functional category and membership on a selected list of significantly altered genes. A small Fisher's exact test $p$-value, for example, in this $2\times2$ table is indicative of enrichment. Other category analysis methods retain the quantitative gene-level scores and measure significance by referring a category-level statistic to a permutation distribution associated with the original differential expression problem. We describe a class of random-set scoring methods that measure distinct components of the enrichment signal. The class includes Fisher's test based on selected genes and also tests that average gene-level evidence across the category. Averaging and selection methods are compared empirically using Affymetrix data on expression in nasopharyngeal cancer tissue, and theoretically using a location model of differential expression. We find that each method has a domain of superiority in the state space of enrichment problems, and that both methods have benefits in practice. Our analysis also addresses two problems related to multiple-category inference, namely, that equally enriched categories are not detected with equal probability if they are of different sizes, and also that there is dependence among category statistics owing to shared genes. Random-set enrichment calculations do not require Monte Carlo for implementation. They are made available in the R package allez.Comment: Published at http://dx.doi.org/10.1214/07-AOAS104 in the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

A transcriptomic investigation of handicap models in sexual selection

We are grateful to D. Calder and T. Helps for access to study sites, and G. Murray-Dickson and M. Oliver for help with fieldwork and comments on manuscript drafts. This work was funded by NERC grant NE/D000602/1 (SBP), a NERC advanced fellowship (FM) and a BBSRC studentship (MAW)Peer reviewedPostprin

Differential expression of skeletal muscle genes following administration of clenbuterol to exercised horses.

BackgroundClenbuterol, a beta2-adrenergic receptor agonist, is used therapeutically to treat respiratory conditions in the horse. However, by virtue of its mechanism of action it has been suggested that clenbuterol may also have repartitioning affects in horses and as such the potential to affect performance. Clenbuterol decreases the percent fat and increases fat-free mass following high dose administration in combination with intense exercise in horses. In the current study, microarray analysis and real-time PCR were used to study the temporal effects of low and high dose chronic clenbuterol administration on differential gene expression of several skeletal muscle myosin heavy chains, genes involved in lipid metabolism and the β2-adrenergic receptor. The effect of clenbuterol administration on differential gene expression has not been previously reported in the horse, therefore the primary objective of the current study was to describe clenbuterol-induced temporal changes in gene expression following chronic oral administration of clenbuterol at both high and low doses.ResultsSteady state clenbuterol concentrations were achieved at approximately 50 h post administration of the first dose for the low dose regimen and at approximately 18-19 days (10 days post administration of 3.2 μg/kg) for the escalating dosing regimen. Following chronic administration of the low dose (0.8 μg/kg BID) of clenbuterol, a total of 114 genes were differentially expressed, however, none of these changes were found to be significant following FDR adjustment of the p-values. A total of 7,093 genes were differentially expressed with 3,623 genes up regulated and 3,470 genes down regulated following chronic high dose administration. Of the genes selected for further study by real-time PCR, down-regulation of genes encoding myosin heavy chains 2 and 7, steroyl CoA desaturase and the β2-adrenergic receptor were noted. For most genes, expression levels returned towards baseline levels following cessation of drug administration.ConclusionThis study showed no evidence of modified gene expression following chronic low dose administration of clenbuterol to horses. However, following chronic administration of high doses of clenbuterol alterations were noted in transcripts encoding various myosin heavy chains, lipid metabolizing enzymes and the β2-adrenergic receptor