GOexpress: an R/Bioconductor package for the identification and visualisation of robust gene ontology signatures through supervised learning of gene expression data

Background: Identification of gene expression profiles that differentiate experimental groups is critical for discovery and analysis of key molecular pathways and also for selection of robust diagnostic or prognostic biomarkers. While integration of differential expression statistics has been used to refine gene set enrichment analyses, such approaches are typically limited to single gene lists resulting from simple two-group comparisons or time-series analyses. In contrast, functional class scoring and machine learning approaches provide powerful alternative methods to leverage molecular measurements for pathway analyses, and to compare continuous and multi-level categorical factors. Results: We introduce GOexpress, a software package for scoring and summarising the capacity of gene ontology features to simultaneously classify samples from multiple experimental groups. GOexpress integrates normalised gene expression data (e.g., from microarray and RNA-seq experiments) and phenotypic information of individual samples with gene ontology annotations to derive a ranking of genes and gene ontology terms using a supervised learning approach. The default random forest algorithm allows interactions between all experimental factors, and competitive scoring of expressed genes to evaluate their relative importance in classifying predefined groups of samples. Conclusions: GOexpress enables rapid identification and visualisation of ontology-related gene panels that robustly classify groups of samples and supports both categorical (e.g., infection status, treatment) and continuous (e.g., time-series, drug concentrations) experimental factors. The use of standard Bioconductor extension packages and publicly available gene ontology annotations facilitates straightforward integration of GOexpress within existing computational biology pipelines.Department of Agriculture, Food and the MarineEuropean Commission - Seventh Framework Programme (FP7)Science Foundation IrelandUniversity College Dubli

Prioritising between direct observation of therapy and case-finding interventions for tuberculosis: use of population impact measures

BACKGROUND: Population impact measures (PIMs) have been developed as tools to help policy-makers with locally relevant decisions over health risks and benefits. This involves estimating and prioritising potential benefits of interventions in specific populations. Using tuberculosis (TB) in India as an example, we examined the population impact of two interventions: direct observation of therapy and increasing case-finding. METHODS: PIMs were calculated using published literature and national data for India, and applied to a notional population of 100 000 people. Data included the incidence or prevalence of smear-positive TB and the relative risk reduction from increasing case finding and the use of direct observation of therapy (applied to the baseline risks over the next year), and the incremental proportion of the population eligible for the proposed interventions. RESULTS: In a population of 100 000 people in India, the directly observed component of the Directly Observed Treatment, Short-course (DOTS) programme may prevent 0.188 deaths from TB in the next year compared with 1.79 deaths by increasing TB case finding. The costs of direct observation are (in international dollars) I$5960 and of case finding are I$4839 or I$31702 and I$2703 per life saved respectively. CONCLUSION: Increasing case-finding for TB will save nearly 10 times more lives than will the use of the directly observed component of DOTS in India, at a smaller cost per life saved. The demonstration of the population impact, using simple and explicit numbers, may be of value to policy-makers as they prioritise interventions for their populations

Insulin Production and Signaling in Renal Tubules of Drosophila Is under Control of Tachykinin-Related Peptide and Regulates Stress Resistance

The insulin-signaling pathway is evolutionarily conserved in animals and regulates growth, reproduction, metabolic homeostasis, stress resistance and life span. In Drosophila seven insulin-like peptides (DILP1-7) are known, some of which are produced in the brain, others in fat body or intestine. Here we show that DILP5 is expressed in principal cells of the renal tubules of Drosophila and affects survival at stress. Renal (Malpighian) tubules regulate water and ion homeostasis, but also play roles in immune responses and oxidative stress. We investigated the control of DILP5 signaling in the renal tubules by Drosophila tachykinin peptide (DTK) and its receptor DTKR during desiccative, nutritional and oxidative stress. The DILP5 levels in principal cells of the tubules are affected by stress and manipulations of DTKR expression in the same cells. Targeted knockdown of DTKR, DILP5 and the insulin receptor dInR in principal cells or mutation of Dilp5 resulted in increased survival at either stress, whereas over-expression of these components produced the opposite phenotype. Thus, stress seems to induce hormonal release of DTK that acts on the renal tubules to regulate DILP5 signaling. Manipulations of S6 kinase and superoxide dismutase (SOD2) in principal cells also affect survival at stress, suggesting that DILP5 acts locally on tubules, possibly in oxidative stress regulation. Our findings are the first to demonstrate DILP signaling originating in the renal tubules and that this signaling is under control of stress-induced release of peptide hormone

LC-MS/MS Confirms That COX-1 Drives Vascular Prostacyclin Whilst Gene Expression Pattern Reveals Non-Vascular Sites of COX-2 Expression

This research was supported by a program grant from the Wellcome Trust (0852551Z108/Z to JAM) and NIH-NCI P50 award CA086306 (to HRH). Copyright: © 2013 Kirkby et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.There are two schools of thought regarding the cyclooxygenase (COX) isoform active in the vasculature. Using urinary prostacyclin markers some groups have proposed that vascular COX-2 drives prostacyclin release. In contrast, we and others have found that COX-1, not COX-2, is responsible for vascular prostacyclin production. Our experiments have relied on immunoassays to detect the prostacyclin breakdown product, 6-keto-PGF1α and antibodies to detect COX-2 protein. Whilst these are standard approaches, used by many laboratories, antibodybased techniques are inherently indirect and have been criticized as limiting the conclusions that can be drawn. To address this question, we measured production of prostanoids, including 6-keto-PGF1α, by isolated vessels and in the circulation in vivo using liquid chromatography tandem mass spectrometry and found values essentially identical to those obtained by immunoassay. In addition, we determined expression from the Cox2 gene using a knockin reporter mouse in which luciferase activity reflects Cox2 gene expression. Using this we confirm the aorta to be essentially devoid of Cox2 driven expression. In contrast, thymus, renal medulla, and regions of the brain and gut expressed substantial levels of luciferase activity, which correlated well with COX-2-dependent prostanoid production. These data are consistent with the conclusion that COX-1 drives vascular prostacyclin release and puts the sparse expression of Cox2 in the vasculature in the context of the rest of the body. In doing so, we have identified the thymus, gut, brain and other tissues as target organs for consideration in developing a new understanding of how COX-2 protects the cardiovascular systemPeer reviewedFinal Published versio