EnrichNet: network-based gene set enrichment analysis

in Bioinformatics (2012), 28(18), 451-457

Assessing functional associations between an experimentally derived gene or protein set of interest and a database of known gene/protein sets is a common task in the analysis of large-scale functional ... [more ▼]

Assessing functional associations between an experimentally derived gene or protein set of interest and a database of known gene/protein sets is a common task in the analysis of large-scale functional genomics data. For this purpose, a frequently used approach is to apply an over-representation-based enrichment analysis. However, this approach has four drawbacks: (i) it can only score functional associations of overlapping gene/proteins sets; (ii) it disregards genes with missing annotations; (iii) it does not take into account the network structure of physical interactions between the gene/protein sets of interest and (iv) tissue-specific gene/protein set associations cannot be recognized. RESULTS: To address these limitations, we introduce an integrative analysis approach and web-application called EnrichNet. It combines a novel graph-based statistic with an interactive sub-network visualization to accomplish two complementary goals: improving the prioritization of putative functional gene/protein set associations by exploiting information from molecular interaction networks and tissue-specific gene expression data and enabling a direct biological interpretation of the results. By using the approach to analyse sets of genes with known involvement in human diseases, new pathway associations are identified, reflecting a dense sub-network of interactions between their corresponding proteins. [less ▲]

A low protein diet during early gestation in sheep detrimentally impacts hepatic glucose metabolism in the adult offspring

in Proceedings of the Nutrition Society 2011 (2011)

Differences in maternal diet can account for variation in the metabolic competence of the subsequent individual as an adult. ‘Developmental programming’ may impair fetal organ development leading to a ... [more ▼]

Differences in maternal diet can account for variation in the metabolic competence of the subsequent individual as an adult. ‘Developmental programming’ may impair fetal organ development leading to a limitation in function as an adult and/or increase the rate of age-related organ decline for example under conditions of obesity. Here, we have tested the interaction between prenatal nutritional ‘thrift’ and postnatal nutritional excess on gluco-regulatory functions in an ovine model. Seventy-four Scottish Blackface ewes were randomly assigned to receive either a control protein diet with adequate energy (18% protein; CP, n 20) or low protein diet (9% protein) fed during early gestation (0–65 d, term ~147 d; LPE, n 37) or late gestation (65–147 d; LPL, n 17). At 65 d a proportion of ewes was euthanised for fetal sampling. At term, remaining ewes lambed naturally, were weaned at 10 weeks and a random sample of offspring studied longitudinally when lean (1.5 years of age) and after 6 months exposure to an obesogenic environment. Body composition was determined by dual-energy absorptiometry and glucose and insulin tolerance tests were conducted with appropriate sampling intervals. At post mortem, muscle and hepatic tissues were sampled for expression and abundance of relevant gluco-regulatory genes. The diets had little effect on maternal weight and body composition through gestation or on fetal weights at 65 d. Term weight was reduced by ~500 g (P = 0.001) in LPL v. other groups but, by weaning, body weight was similar between groups and growth rate to adulthood was not different. Homeostasis model assessment of baseline glucose and insulin concentrations indicated relative insulin resistance in male LPE . Indeed, when challenged with a GTT, the incremental insulin AUC was significantly greater in male LPE when obese but not when lean (unpublished results). Molecular quantification of glucose-insulin pathways in muscle and liver indicated specific down-regulation of the hepatic insulin, but not lipid, pathways in male liver only. Muscle insulin-signalling pathways were unaffected as determined by microarray (Affymetrix, U133 chip; www.arraymining.net). The data suggest that a maternal, low protein, diet during early gestation specifically impacts upon the function of the resulting adult liver, such that the offspring appear more susceptible to large excursions in plasma insulin during gluco-regulatory challenges. The insulin sensitivity of offspring muscle, the largest single source of insulin-stimulated glucose uptake, was largely unaffected. Thus, obesity appears to exacerbate any functional deficits inherent in low protein exposed offspring in sheep, but those offspring born of low birth weight were largely unaffected, illustrating that nutritional quality is far more important than nutritional quantity especially during sensitive developmental phases of growth. [less ▲]

RERG (Ras-like, oestrogen-regulated, growth-inhibitor) expression in breast cancer: a marker of ER-positive luminal-like subtype

in Breast Cancer Research and Treatment (2011), 128(2), 315-326

Global gene expression profiling studies have classified breast cancer into a number of distinct biological and molecular classes with clinical relevance. The heterogeneous luminal group, which is largely ... [more ▼]

Global gene expression profiling studies have classified breast cancer into a number of distinct biological and molecular classes with clinical relevance. The heterogeneous luminal group, which is largely characterised by oestrogen receptor (ER) expression, appears to contain distinct subgroups with differing behaviour. In this study, we analysed 47,293 gene transcripts in 128 invasive breast carcinomas (BC) using Artificial Neural Networks and a cross-validation analysis in combination with an ensemble sample classification to identify genes that can be used to subclassify ER+ luminal tumours. The results were validated using immunohistochemistry on TMAs containing 1,140 invasive breast cancers. Our results showed that the RERG gene is one of the highest ranked genes to differentiate between ER+ luminal-like and ER- non-luminal cancers based on a 10-fold external cross-validation analysis with an average classification accuracy of 89%. This was confirmed in our protein expression studies that showed RERG positive associations with markers of luminal differentiation including ER, luminal cytokeratins (CK19, CK18 and CK7/8) and FOXA1 (P = 0.004) and other markers of good prognosis in BC including small size, lower histologic grade and positive expression of androgen receptor, nuclear BRCA1, FHIT and cell cycle inhibitors p27 and p21. RERG expression was inversely associated with the proliferation marker MIB1 (P = 0.005) and p53. Strong RERG expression showed an association with longer breast cancer specific survival and distant metastasis free interval in the whole series as well as in the ER+ luminal group and these associations were independent of other prognostic variables. In conclusion, we used novel bioinformatics methods to identify candidate genes to characterise ER+ luminal-like breast cancer. RERG gene is a key marker of the luminal BC class and can be used to separate distinct prognostic subgroups. [less ▲]

Combining chondrocyte gene expression, literature mining and pathway/network analysis to extract biological insights from small-scale microarray data

in Osteoarthritis and Cartilage (2010), 18(2), 169

Luminal-like oestrogen receptor-positive breast cancer: identification of prognostic biological subclasses

in European Journal of Cancer Supplements (2010), 8(3), 91

TopoGSA: network topological gene set analysis

in Bioinformatics (2010), 26(9), 1271-1272

TopoGSA (Topology-based Gene Set Analysis) is a web-application dedicated to the computation and visualization of network topological properties for gene and protein sets in molecular interaction networks ... [more ▼]

TopoGSA (Topology-based Gene Set Analysis) is a web-application dedicated to the computation and visualization of network topological properties for gene and protein sets in molecular interaction networks. Different topological characteristics, such as the centrality of nodes in the network or their tendency to form clusters, can be computed and compared with those of known cellular pathways and processes. [less ▲]

ArrayMining: a modular web-application for microarray analysis combining ensemble and consensus methods with cross-study normalization

in BMC Bioinformatics (2009), 10(1), 358-358

Background: Statistical analysis of DNA microarray data provides a valuable diagnostic tool for the investigation of genetic components of diseases. To take advantage of the multitude of available data ... [more ▼]

Background: Statistical analysis of DNA microarray data provides a valuable diagnostic tool for the investigation of genetic components of diseases. To take advantage of the multitude of available data sets and analysis methods, it is desirable to combine both different algorithms and data from different studies. Applying ensemble learning, consensus clustering and cross-study normalization methods for this purpose in an almost fully automated process and linking different analysis modules together under a single interface would simplify many microarray analysis tasks. Results: We present ArrayMining.net, a web-application for microarray analysis that provides easy access to a wide choice of feature selection, clustering, prediction, gene set analysis and cross-study normalization methods. In contrast to other microarray-related web-tools, multiple algorithms and data sets for an analysis task can be combined using ensemble feature selection, ensemble prediction, consensus clustering and cross-platform data integration. By interlinking different analysis tools in a modular fashion, new exploratory routes become available, e.g. ensemble sample classification using features obtained from a gene set analysis and data from multiple studies. The analysis is further simplified by automatic parameter selection mechanisms and linkage to web tools and databases for functional annotation and literature mining. Conclusion: ArrayMining.net is a free web-application for microarray analysis combining a broad choice of algorithms based on ensemble and consensus methods, using automatic parameter selection and integration with annotation databases. [less ▲]