oPOSSUM: identification of over-represented transcription factor binding sites in co-expressed genes

Targeted transcript profiling studies can identify sets of co-expressed genes; however, identification of the underlying functional mechanism(s) is a significant challenge. Established methods for the analysis of gene annotations, particularly those based on the Gene Ontology, can identify functional linkages between genes. Similar methods for the identification of over-represented transcription factor binding sites (TFBSs) have been successful in yeast, but extension to human genomics has largely proved ineffective. Creation of a system for the efficient identification of common regulatory mechanisms in a subset of co-expressed human genes promises to break a roadblock in functional genomics research. We have developed an integrated system that searches for evidence of co-regulation by one or more transcription factors (TFs). oPOSSUM combines a pre-computed database of conserved TFBSs in human and mouse promoters with statistical methods for identification of sites over-represented in a set of co-expressed genes. The algorithm successfully identified mediating TFs in control sets of tissue-specific genes and in sets of co-expressed genes from three transcript profiling studies. Simulation studies indicate that oPOSSUM produces few false positives using empirically defined thresholds and can tolerate up to 50% noise in a set of co-expressed genes

Interleukin-13 in Asthma and Other Eosinophilic Disorders

Asthma is characterized by episodic, reversible airflow obstruction associated with variable levels of inflammation. Over the past several decades, there has been an increasing appreciation that the clinical presentation of asthma comprises a diverse set of underlying pathologies. Rather than being viewed as a single disease entity, asthma is now thought of as a clinical syndrome with the involvement of multiple pathological mechanisms. While it is appreciated that eosinophilia is present in only a subset of patients, it remains a key feature of asthma and other eosinophilic disorders such as atopic dermatitis, eosinophilic esophagitis, and chronic rhinosinusitis with nasal polyps. Eosinophils are bone marrow-derived leukocytes present in low numbers in health; however, during disease the type 2 cytokines [interleukins (IL)-4, -5, and -13] can induce rapid eosinophilopoiesis, prolonged eosinophil survival, and trafficking to the site of injury. In diseases such as allergic asthma there is an aberrant inflammatory response leading to eosinophilia, tissue damage, and airway pathology. IL-13 is a pleiotropic type 2 cytokine that has been shown to be integral in the pathogenesis of asthma and other eosinophilic disorders. IL-13 levels are elevated in animal models of eosinophilic inflammation and in the blood and tissue of patients diagnosed with eosinophilic disorders. IL-13 signaling elicits many pathogenic mechanisms including the promotion of eosinophil survival, activation, and trafficking. Data from preclinical models and clinical trials of IL-13 inhibitors in patients have revealed mechanistic insights into the role of this cytokine in driving eosinophilia. Promising results from clinical trials further support a key mechanistic role of IL-13 in asthma and other eosinophilic disorders. Here, we provide a perspective on the role of IL-13 in asthma and other eosinophilic disorders and describe ongoing clinical trials targeting this pathway in patients with significant unmet medical needs

Effects of dalcetrapib in patients with a recent acute coronary syndrome

In observational analyses, higher levels of high-density lipoprotein (HDL) cholesterol have been associated with a lower risk of coronary heart disease events. However, whether raising HDL cholesterol levels therapeutically reduces cardiovascular risk remains uncertain. Inhibition of cholesteryl ester transfer protein (CETP) raises HDL cholesterol levels and might therefore improve cardiovascular outcomes

Discovery and Expansion of Gene Modules by Seeking Isolated Groups in a Random Graph Process

BACKGROUND: A central problem in systems biology research is the identification and extension of biological modules-groups of genes or proteins participating in a common cellular process or physical complex. As a result, there is a persistent need for practical, principled methods to infer the modular organization of genes from genome-scale data. RESULTS: We introduce a novel approach for the identification of modules based on the persistence of isolated gene groups within an evolving graph process. First, the underlying genomic data is summarized in the form of ranked gene-gene relationships, thereby accommodating studies that quantify the relevant biological relationship directly or indirectly. Then, the observed gene-gene relationship ranks are viewed as the outcome of a random graph process and candidate modules are given by the identifiable subgraphs that arise during this process. An isolation index is computed for each module, which quantifies the statistical significance of its survival time. CONCLUSIONS: The Miso (module isolation) method predicts gene modules from genomic data and the associated isolation index provides a module-specific measure of confidence. Improving on existing alternative, such as graph clustering and the global pruning of dendrograms, this index offers two intuitively appealing features: (1) the score is module-specific; and (2) different choices of threshold correlate logically with the resulting performance, i.e. a stringent cutoff yields high quality predictions, but low sensitivity. Through the analysis of yeast phenotype data, the Miso method is shown to outperform existing alternatives, in terms of the specificity and sensitivity of its predictions

Gene Characterization Index: Assessing the Depth of Gene Annotation

We introduce the Gene Characterization Index, a bioinformatics method for scoring the extent to which a protein-encoding gene is functionally described. Inherently a reflection of human perception, the Gene Characterization Index is applied for assessing the characterization status of individual genes, thus serving the advancement of both genome annotation and applied genomics research by rapid and unbiased identification of groups of uncharacterized genes for diverse applications such as directed functional studies and delineation of novel drug targets.The scoring procedure is based on a global survey of researchers, who assigned characterization scores from 1 (poor) to 10 (extensive) for a sample of genes based on major online resources. By evaluating the survey as training data, we developed a bioinformatics procedure to assign gene characterization scores to all genes in the human genome. We analyzed snapshots of functional genome annotation over a period of 6 years to assess temporal changes reflected by the increase of the average Gene Characterization Index. Applying the Gene Characterization Index to genes within pharmaceutically relevant classes, we confirmed known drug targets as high-scoring genes and revealed potentially interesting novel targets with low characterization indexes. Removing known drug targets and genes linked to sequence-related patent filings from the entirety of indexed genes, we identified sets of low-scoring genes particularly suited for further experimental investigation.The Gene Characterization Index is intended to serve as a tool to the scientific community and granting agencies for focusing resources and efforts on unexplored areas of the genome. The Gene Characterization Index is available from http://cisreg.ca/gci/

Multimodal Communication in a Noisy Environment: A Case Study of the Bornean Rock Frog Staurois parvus

High background noise is an impediment to signal detection and perception. We report the use of multiple solutions to improve signal perception in the acoustic and visual modality by the Bornean rock frog, Staurois parvus. We discovered that vocal communication was not impaired by continuous abiotic background noise characterised by fast-flowing water. Males modified amplitude, pitch, repetition rate and duration of notes within their advertisement call. The difference in sound pressure between advertisement calls and background noise at the call dominant frequency of 5578 Hz was 8 dB, a difference sufficient for receiver detection. In addition, males used several visual signals to communicate with conspecifics with foot flagging and foot flashing being the most common and conspicuous visual displays, followed by arm waving, upright posture, crouching, and an open-mouth display. We used acoustic playback experiments to test the efficacy-based alerting signal hypothesis of multimodal communication. In support of the alerting hypothesis, we found that acoustic signals and foot flagging are functionally linked with advertisement calling preceding foot flagging. We conclude that S. parvus has solved the problem of continuous broadband low-frequency noise by both modifying its advertisement call in multiple ways and by using numerous visual signals. This is the first example of a frog using multiple acoustic and visual solutions to communicate in an environment characterised by continuous noise

Models for two-state disease processes with applications to relapsing-remitting multiple sclerosis

In diseases like relapsing-remitting multiple sclerosis (MS), patients experience repeated transitions between symptom-free and symptomatic disease states (the symptomatic state is called an exacerbation). Analyses for this kind of data commonly ignore the information available on the second state (the lengths of the exacerbations, for example). In this thesis, we consider models that incorporate the second state into the analyses. The basic stochastic models are Markov chains, alternating renewal processes and marked point processes. For the Markov chains and alternating renewal process models, we consider simple fixed effects models as well as random effects models where the random effects are introduced to allow for heterogeneity between patients and correlation of data on one patient. For these models, the statistical inference is based on maximum likelihood. For the marked point process model, we use a generalized estimating equation approach. We apply these models to a data set from a MS clinical trial. The aim of the analyses is to relate the available covariates to the disease process. We do not attempt a comprehensive analysis of the data set, rather the aim here is to see what can be achieved and which questions can be addressed with the different models.Science, Faculty ofStatistics, Department ofGraduat

Finding functional groups of genes using pairwise relational data : methods and applications

Genes, the fundamental building blocks of life, act together (often through their derived proteins) in modules such as protein complexes and molecular pathways to achieve a cellular function such as DNA repair and cellular transport. A current emphasis in genomics research is to identify gene modules from gene profiles, which are measurements (such as a mutant phenotype or an expression level), associated with the individual genes under conditions of interest; genes in modules often have similar gene profiles. Clustering groups of genes with similar profiles can hence deliver candidate gene modules. Pairwise similarity measures derived from these profiles are used as input to the popular hierarchical agglomerative clustering algorithms; however, these algorithms offer little guidance on how to choose candidate modules and how to improve a clustering as new data becomes available. As an alternative, there are methods based on thresholding the similarity values to obtain a graph; such a graph can be analyzed through (probabilistic) methods developed in the social sciences. However, thresholding the data discards valuable information and choosing the threshold is difficult. Extending binary relational analysis, we exploit ranked relational data as the basis for two distinct approaches for identifying modules from genomic data, both based on the theory of random graph processes. We propose probabilistic models for ranked relational data that allow candidate modules to be accompanied by objective confidence scores and that permit an elegant integration of external information on gene-gene relationships. We first followed theoretical work by Ling to objectively select exceptionally isolated groups as candidate gene modules. Secondly, inspired by stochastic block models used in the social sciences, we construct a novel model for ranked relational data, where all genes have hidden module parameters which govern the strength of all gene-gene relationships. Adapting a classical likelihood often used for the analysis of horse races, clustering is performed by estimating the module parameters using standard Bayesian methods. The method allows the incorporation of prior information on gene-gene relationships; the utility of using prior information in the form of protein-protein interaction data in clustering of yeast mutant phenotype profiles is demonstrated.Science, Faculty ofStatistics, Department ofGraduat

Evaluation of ambient air pollution in the Lower Mainland of British Columbia: Public health impacts, spatial variability, and temporal patterns

An assessment of public health impacts associated with ambient air pollution.Environmental Health (SOEH), School ofOccupational and Environmental Hygiene, School ofScience, Faculty ofStatistics, Department ofMedicine, Faculty ofPopulation and Public Health (SPPH), School ofUnreviewedFacultyGraduat