Predictive modelling using pathway scores: robustness and significance of pathway collections.

BACKGROUND: Transcriptomic data is often used to build statistical models which are predictive of a given phenotype, such as disease status. Genes work together in pathways and it is widely thought that pathway representations will be more robust to noise in the gene expression levels. We aimed to test this hypothesis by constructing models based on either genes alone, or based on sample specific scores for each pathway, thus transforming the data to a 'pathway space'. We progressively degraded the raw data by addition of noise and examined the ability of the models to maintain predictivity. RESULTS: Models in the pathway space indeed had higher predictive robustness than models in the gene space. This result was independent of the workflow, parameters, classifier and data set used. Surprisingly, randomised pathway mappings produced models of similar accuracy and robustness to true mappings, suggesting that the success of pathway space models is not conferred by the specific definitions of the pathway. Instead, predictive models built on the true pathway mappings led to prediction rules with fewer influential pathways than those built on randomised pathways. The extent of this effect was used to differentiate pathway collections coming from a variety of widely used pathway databases. CONCLUSIONS: Prediction models based on pathway scores are more robust to degradation of gene expression information than the equivalent models based on ungrouped genes. While models based on true pathway scores are not more robust or accurate than those based on randomised pathways, true pathways produced simpler prediction rules, emphasizing a smaller number of pathways.

KLB is associated with alcohol drinking, and its gene product ß-Klotho is necessary for FGF21 regulation of alcohol preference.

Excessive alcohol consumption is a major public health problem worldwide. Although drinking habits are known to be inherited, few genes have been identified that are robustly linked to alcohol drinking. We conducted a genome-wide association metaanalysis and replication study among >105,000 individuals of European ancestry and identified ß-Klotho (KLB) as a locus associated with alcohol consumption (rs11940694; P = 9.2 × 10-12). ß-Klotho is an obligate coreceptor for the hormone FGF21, which is secreted from the liver and implicated in macronutrient preference in humans. We show that brain-specific ß-Klotho KO mice have an increased alcohol preference and that FGF21 inhibits alcohol drinking by acting on the brain. These data suggest that a liver-brain endocrine axis may play an important role in the regulation of alcohol drinking behavior and provide a unique pharmacologic target for reducing alcohol consumption.

diXa: a data infrastructure for chemical safety assessment.

MOTIVATION: The field of toxicogenomics (the application of '-omics' technologies to risk assessment of compound toxicities) has expanded in the last decade, partly driven by new legislation, aimed at reducing animal testing in chemical risk assessment but mainly as a result of a paradigm change in toxicology towards the use and integration of genome wide data. Many research groups worldwide have generated large amounts of such toxicogenomics data. However, there is no centralized repository for archiving and making these data and associated tools for their analysis easily available. RESULTS: The Data Infrastructure for Chemical Safety Assessment (diXa) is a robust and sustainable infrastructure storing toxicogenomics data. A central data warehouse is connected to a portal with links to chemical information and molecular and phenotype data. diXa is publicly available through a user-friendly web interface. New data can be readily deposited into diXa using guidelines and templates available online. Analysis descriptions and tools for interrogating the data are available via the diXa portal. AVAILABILITY AND IMPLEMENTATION: http://www.dixa-fp7.eu CONTACT: d.hendrickx@maastrichtuniversity.nl; info@dixa-fp7.eu SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

A rodent model for Dirofilaria immitis, canine heartworm: parasite growth, development, and drug sensitivity in NSG mice.

Heartworm disease, caused by Dirofilaria immitis, remains a significant threat to canines and felines. The development of parasites resistant to macrocyclic lactones (ML) has created a significant challenge to the control of the infection. The goal of this study was to determine if mice lacking a functional immune response would be susceptible to D. immitis. Immunodeficient NSG mice were susceptible to the infection, sustaining parasites for at least 15 weeks, with infective third-stage larvae molting and developing into the late fourth-stage larvae. Proteomic analysis of host responses to the infection revealed a complex pattern of changes after infection, with at least some of the responses directed at reducing immune control mechanisms that remain in NSG mice. NSG mice were infected with isolates of D. immitis that were either susceptible or resistant to MLs, as a population. The susceptible isolate was killed by ivermectin whereas the resistant isolate had improved survivability, while both isolates were affected by moxidectin. It was concluded that D. immitis survives in NSG mice for at least 15 weeks. NSG mice provide an ideal model for monitoring host responses to the infection and for testing parasites in vivo for susceptibility to direct chemotherapeutic activity of new agents.