An eQTL biological data visualization challenge and approaches from the visualization community

In 2011, the IEEE VisWeek conferences inaugurated a symposium on Biological Data Visualization. Like other domain-oriented Vis symposia, this symposium's purpose was to explore the unique characteristics and requirements of visualization within the domain, and to enhance both the Visualization and Bio/Life-Sciences communities by pushing Biological data sets and domain understanding into the Visualization community, and well-informed Visualization solutions back to the Biological community. Amongst several other activities, the BioVis symposium created a data analysis and visualization contest. Unlike many contests in other venues, where the purpose is primarily to allow entrants to demonstrate tour-de-force programming skills on sample problems with known solutions, the BioVis contest was intended to whet the participants' appetites for a tremendously challenging biological domain, and simultaneously produce viable tools for a biological grand challenge domain with no extant solutions. For this purpose expression Quantitative Trait Locus (eQTL) data analysis was selected. In the BioVis 2011 contest, we provided contestants with a synthetic eQTL data set containing real biological variation, as well as a spiked-in gene expression interaction network influenced by single nucleotide polymorphism (SNP) DNA variation and a hypothetical disease model. Contestants were asked to elucidate the pattern of SNPs and interactions that predicted an individual's disease state. 9 teams competed in the contest using a mixture of methods, some analytical and others through visual exploratory methods. Independent panels of visualization and biological experts judged entries. Awards were given for each panel's favorite entry, and an overall best entry agreed upon by both panels. Three special mention awards were given for particularly innovative and useful aspects of those entries. And further recognition was given to entries that correctly answered a bonus question about how a proposed "gene therapy" change to a SNP might change an individual's disease status, which served as a calibration for each approaches' applicability to a typical domain question. In the future, BioVis will continue the data analysis and visualization contest, maintaining the philosophy of providing new challenging questions in open-ended and dramatically underserved Bio/Life Sciences domains

Liver and Adipose Expression Associated SNPs Are Enriched for Association to Type 2 Diabetes

Genome-wide association studies (GWAS) have demonstrated the ability to identify the strongest causal common variants in complex human diseases. However, to date, the massive data generated from GWAS have not been maximally explored to identify true associations that fail to meet the stringent level of association required to achieve genome-wide significance. Genetics of gene expression (GGE) studies have shown promise towards identifying DNA variations associated with disease and providing a path to functionally characterize findings from GWAS. Here, we present the first empiric study to systematically characterize the set of single nucleotide polymorphisms associated with expression (eSNPs) in liver, subcutaneous fat, and omental fat tissues, demonstrating these eSNPs are significantly more enriched for SNPs that associate with type 2 diabetes (T2D) in three large-scale GWAS than a matched set of randomly selected SNPs. This enrichment for T2D association increases as we restrict to eSNPs that correspond to genes comprising gene networks constructed from adipose gene expression data isolated from a mouse population segregating a T2D phenotype. Finally, by restricting to eSNPs corresponding to genes comprising an adipose subnetwork strongly predicted as causal for T2D, we dramatically increased the enrichment for SNPs associated with T2D and were able to identify a functionally related set of diabetes susceptibility genes. We identified and validated malic enzyme 1 (Me1) as a key regulator of this T2D subnetwork in mouse and provided support for the association of this gene to T2D in humans. This integration of eSNPs and networks provides a novel approach to identify disease susceptibility networks rather than the single SNPs or genes traditionally identified through GWAS, thereby extracting additional value from the wealth of data currently being generated by GWAS

Molecular, Functional and Evolutionary Characterization of the Gene Encoding HMG-CoA Reductase in the Fission Yeast, Schizosaccharomyces pombe

The synthesis of mevalonate, a molecule required for both sterol and isoprene biosynthesis in eukaryotes, is catalysed by 3-hydroxy-3-methylglutaryl coenzyme A (HMG-CoA) reductase. Using a gene dosage approach, we have isolated the gene encoding HMG-CoA reductase, hmg1+, from the fission yeast Schizosaccharomyces pombe (Accession Number L76979). Specifically, hmg1+ was isolated on the basis of its ability to confer resistance to lovastatin, a competitive inhibitor of HMG-CoA reductase. Gene disruption analysis showed that hmg1+ was an essential gene. This result provided evidence that, unlike Saccharomyces cerevisiae, S. pombe contained only a single functional HMG-CoA reductase gene. The presence of a single HMG-CoA reductase gene was confirmed by genomic hybridization analysis. As observed for the S. cerevisiae HMG1p, the hmg1+ protein induced membrane proliferations known as karmellae. A previously undescribed 'feed-forward' regulation was observed in which elevated levels of HMG-CoA synthase, the enzyme catalysing the synthesis of the HMG-CoA reductase substrate, induced elevated levels of hmg1+ protein in the cell and conferred partial resistance to lovastatin. The amino acid sequences of yeast and human HMG-CoA reductase were highly divergent in the membrane domains, but were extensively conserved in the catalytic domains. We tested whether the gene duplication that produced the two functional genes in S. cerevisiae occurred before or after S. pombe and S. cerevisiae diverged by comparing the log likelihoods of trees specified by these hypotheses. We found that the tree specifying post-divergence duplication had significantly higher likelihood. Moreover, phylogenetic analyses of available HMG-CoA reductase sequences also suggested that the lineages of S. pombe and S. cerevisiae diverged approximately 420 million years ago but that the duplication event that produced two HMG-CoA reductase genes in the budding yeast occurred only approximately 56 million years ago. To date, S. pombe is the only unicellular eukaryote that has been found to contain a single HMG-CoA reductase gene. Consequently, S. pombe may provide important opportunities to study aspects of the regulation of sterol biosynthesis that have been difficult to address in other organisms and serve as a test organism to identify novel therapies for modulating cholesterol synthesis.Organismic and Evolutionary Biolog

Development of the Digital Arthritis Index, a Novel Metric to Measure Disease Parameters in a Rat Model of Rheumatoid Arthritis

Despite a broad spectrum of anti-arthritic drugs currently on the market, there is a constant demand to develop improved therapeutic agents. Efficient compound screening and rapid evaluation of treatment efficacy in animal models of rheumatoid arthritis (RA) can accelerate the development of clinical candidates. Compound screening by evaluation of disease phenotypes in animal models facilitates preclinical research by enhancing understanding of human pathophysiology; however, there is still a continuous need to improve methods for evaluating disease. Current clinical assessment methods are challenged by the subjective nature of scoring-based methods, time-consuming longitudinal experiments, and the requirement for better functional readouts with relevance to human disease. To address these needs, we developed a low-touch, digital platform for phenotyping preclinical rodent models of disease. As a proof-of-concept, we utilized the rat collagen-induced arthritis (CIA) model of RA and developed the Digital Arthritis Index (DAI), an objective and automated behavioral metric that does not require human-animal interaction during the measurement and calculation of disease parameters. The DAI detected the development of arthritis similar to standard in vivo methods, including ankle joint measurements and arthritis scores, as well as demonstrated a positive correlation to ankle joint histopathology. The DAI also determined responses to multiple standard-of-care (SOC) treatments and nine repurposed compounds predicted by the SMarTRTM Engine to have varying degrees of impact on RA. The disease profiles generated by the DAI complemented those generated by standard methods. The DAI is a highly reproducible and automated approach that can be used in-conjunction with standard methods for detecting RA disease progression and conducting phenotypic drug screens

Diurnal variation of the human adipose transcriptome and the link to metabolic disease.

BackgroundCircadian (diurnal) rhythm is an integral part of the physiology of the body; specifically, sleep, feeding behavior and metabolism are tightly linked to the light-dark cycle dictated by earth's rotation.MethodsThe present study examines the effect of diurnal rhythm on gene expression in the subcutaneous adipose tissue of overweight to mildly obese, healthy individuals. In this well-controlled clinical study, adipose biopsies were taken in the morning, afternoon and evening from individuals in three study arms: treatment with the weight loss drug sibutramine/fasted, placebo/fed and placebo/fasted.ResultsThe results indicated that diurnal rhythm was the most significant driver of gene expression variation in the human adipose tissue, with at least 25% of the genes having had significant changes in their expression levels during the course of the day. The mRNA expression levels of core clock genes at a specific time of day were consistent across multiple subjects on different days in all three arms, indicating robust diurnal regulation irrespective of potential confounding factors. The genes essential for energy metabolism and tissue physiology were part of the diurnal signature. We hypothesize that the diurnal transition of the expression of energy metabolism genes reflects the shift in the adipose tissue from an energy-expending state in the morning to an energy-storing state in the evening. Consistent with this hypothesis, the diurnal transition was delayed by fasting and treatment with sibutramine. Finally, an in silico comparison of the diurnal signature with data from the publicly-available Connectivity Map demonstrated a significant association with transcripts that were repressed by mTOR inhibitors, suggesting a possible link between mTOR signaling, diurnal gene expression and metabolic regulation.ConclusionDiurnal rhythm plays an important role in the physiology and regulation of energy metabolism in the adipose tissue and should be considered in the selection of novel targets for the treatment of obesity and other metabolic disorders