Virtual pathway explorer (viPEr) and pathway enrichment analysis tool (PEANuT): creating and analyzing focus networks to identify cross-talk between molecules and pathways

Background: Interpreting large-scale studies from microarrays or next-generation sequencing for further experimental testing remains one of the major challenges in quantitative biology. Combining expression with physical or genetic interaction data has already been successfully applied to enhance knowledge from all types of high-throughput studies. Yet, toolboxes for navigating and understanding even small gene or protein networks are poorly developed. Results: We introduce two Cytoscape plug-ins, which support the generation and interpretation of experiment-based interaction networks. The virtual pathway explorer viPEr creates so-called focus networks by joining a list of experimentally determined genes with the interactome of a specific organism. viPEr calculates all paths between two or more user-selected nodes, or explores the neighborhood of a single selected node. Numerical values from expression studies assigned to the nodes serve to score identified paths. The pathway enrichment analysis tool PEANuT annotates networks with pathway information from various sources and calculates enriched pathways between a focus and a background network. Using time series expression data of atorvastatin treated primary hepatocytes from six patients, we demonstrate the handling and applicability of viPEr and PEANuT. Based on our investigations using viPEr and PEANuT, we suggest a role of the FoxA1/A2/A3 transcriptional network in the cellular response to atorvastatin treatment. Moreover, we find an enrichment of metabolic and cancer pathways in the Fox transcriptional network and demonstrate a patient-specific reaction to the drug. Conclusions: The Cytoscape plug-in viPEr integrates -omics data with interactome data. It supports the interpretation and navigation of large-scale datasets by creating focus networks, facilitating mechanistic predictions from -omics studies. PEANuT provides an up-front method to identify underlying biological principles by calculating enriched pathways in focus networks

Coordinating Role of RXR alpha in Downregulating Hepatic Detoxification during Inflammation Revealed by Fuzzy-Logic Modeling

During various inflammatory processes circulating cytokines including IL-6, IL-1 beta, and TNF alpha elicit a broad and clinically relevant impairment of hepatic detoxification that is based on the simultaneous downregulation of many drug metabolizing enzymes and transporter genes. To address the question whether a common mechanism is involved we treated human primary hepatocytes with IL-6, the major mediator of the acute phase response in liver, and characterized acute phase and detoxification responses in quantitative gene expression and (phospho-)proteomics data sets. Selective inhibitors were used to disentangle the roles of JAK/STAT, MAPK, and PI3K signaling pathways. A prior knowledge-based fuzzy logic model comprising signal transduction and gene regulation was established and trained with perturbation-derived gene expression data from five hepatocyte donors. Our model suggests a greater role of MAPK/PI3K compared to JAK/STAT with the orphan nuclear receptor RXR alpha playing a central role in mediating transcriptional downregulation. Validation experiments revealed a striking similarity of RXRa gene silencing versus IL-6 induced negative gene regulation (r(s) = 0.79;P<0.0001). These results concur with RXRa functioning as obligatory heterodimerization partner for several nuclear receptors that regulate drug and lipid metabolism

Nutritional Systems Biology of Fat : integration and modeling of transcriptomics datasets related to lipid homeostasis

Fatty acids, in the form of triglycerides, are the main constituent of the class of dietary lipids. They not only serve as a source of energy but can also act as potent regulators of gene transcription. It is well accepted that an energy rich diet characterized by high intakes of dietary fat is linked to the dramatic increase in the prevalence of obesity in both developed and developing countries in the last several decades. Obese individuals are at increased risk of developing the metabolic syndrome, a cluster of metabolic abnormalities that ultimately increase the risk of developing vascular diseases and type 2 diabetes. Many studies have been performed to uncover the role of fatty acids on gene expression in different organs, but integrative studies in different organs over time driven by high throughput data are lacking. Therefore, we first aimed to develop integrative approaches on the level of individual genes but also pathways using genome-wide transcriptomics datasets of mouse liver and small intestine that are related to fatty acid sensing transcription factor peroxisome proliferator activated receptor alpha (PPARα). We also aimed to uncover the behavior of PPARαtarget genes and their corresponding biological functions in a short time series experiment, and integrated and modeled the influence of different levels of dietary fat and the time dependency on transcriptomics datasets obtained from several organs by developing system level approaches. We developed an integrative statistical approach that properly adjusted for multiple testing while integrating data from two experiments, and was driven by biological inference. By quantifying pathway activities in different mouse tissues over time and subsequent integration by partial least squares path model, we found that the induced pathways at early time points are the main drivers for the induced pathways at late time points. In addition, using a time course microarray study of rat hepatocytes, we found that most of the PPARα target genes at early stage are involved in lipid metabolism-related processes and their expression level could be modeled using a quadratic regression function. In this study, we also found that the transcription factorsNR2F, CREB, EREF and RXR might work together with PPARα in the regulation of genes involved in lipid metabolism. By integrating time and dose dependent gene expression data of mouse liver and white adipose tissue (WAT), we found a set of time-dose dependent genes in liver and WAT including potential signaling proteinssecreted from WAT that may induce metabolic changes in liver, thereby contributing to the pathogenesis of obesity. Taken together, in this thesis integrative statistical approaches are presented that were applied to a variety of datasets related to metabolism of fatty acids. Results that were obtained provide a better understanding of the function of the fatty acid-sensor PPARa, and identified a set of secreted proteins that may be important for organ cross talk during the development of diet induced obesity. </p

Liver X Receptor cis-Repression and Cholesterol Efflux Restrain Innate Immunity and Coronary Artery Disease

Atherosclerotic cardiovascular disease secondary to deposition of apolipoprotein B-containing lipoproteins in the artery wall is a leading cause of mortality. Therapies that reduce serum levels of atherogenic lipoprotein-cholesterol have been successful in reducing cardiovascular mortality, but this approach requires long-term treatment and substantial residual risk remains. Here, we investigate mechanistic determinants of atherosclerosis protection by two potential therapeutic approaches for lowering of residual cardiovascular risk. Using mouse models, we show that the nuclear receptor liver X receptor exerts an anti-inflammatory activity on innate immunity and atherosclerosis through both promotion of cholesterol efflux and a direct cis-repressive activity affecting neutrophil inflammation. We then assess the causal role of the cholesterol efflux pathway in human cardiovascular events by using genetic variants that modify high density lipoprotein-cholesterol in instrumental variable analysis. We show that this pathway is associated with protection from cardiovascular disease in a precise and robust Mendelian randomization analysis on an FDR-controlled set of variants, which suggests a causal effect. Thus, agents that target the cholesterol efflux and liver X receptor cis-repression pathways may be protective in atherosclerosis

An assessment of gene polymorphisms in young South African Indians with coronary artery disease and the effect of atorvastan in vitro.

Thesis (M.Med.)-University of KwaZulu-Natal, Durban, 2012.The global burden of heart disease increases every year. It has been estimated that by the year 2020, coronary artery disease (CAD) will be the number one cause of death worldwide. Indian populations throughout the world have the highest prevalence of CAD and early onset of the disease compared to other ethnic groups. Glutathione S-transferases (GSTs) detoxify environmental agents which influence the onset and progression of disease. Dysfunctional detoxification enzymes are responsible for prolonged exposure to reactive molecules and can contribute to endothelial damage, an underlying factor in CAD. Uncoupling proteins (UCPs) 2 and 3 play an important role in the regulation of oxidative stress which contributes to chronic inflammation. Coronary artery disease is a chronic inflammatory disorder characterized by elevated levels of C-reactive protein (CRP) and pro-inflammatory cytokines such as interleukin 6 (IL-6). Polymorphisms of these genes have been linked to CAD and other chronic diseases. Statins, metabolised in the liver, are the most commonly used drug to control atherosclerosis progression in CAD patients. The pleiotropic effects of statins have been attributed to both favourable and adverse outcomes in CAD patients particularly related to myopathy and hepatotoxicity. All patients (n=102) recruited into this study were South African Indian males. A corresponding age-, gender- and ethnicity-matched control group (n=100) was also recruited. The frequency of the GSTM1 +/0, GSTP1 A105/G105, IL6 -174G/C and CRP -390C/A/T genotypes was assessed by polymerase chain reaction (PCR) and PCR restriction fragment length polymorphism (PCR-RFLP). For the in vitro study, the biological effect of atorvastatin on HepG2 cells was assessed. The metabolic activity, cytotoxicity, oxidative stress and nitric oxide production was assessed by the ATP, lactate dehydrogenase (LDH), thiobarbituric acid reactive substance (TBARS) and Griess assays, respectively. The profile of 84 microRNA (miRNA) species was evaluated using the miRNA Pathway Finder PCR SuperArray. The predicted targets of up-regulated miRNAs were determined using the online software, Targetscan. The mRNA levels of guanidinoacetoacetate (GAMT), arginine glycine aminotransferase (AGAT) and spermine oxidase (SMO) were determined using quantitative PCR. Western blotting was used to determine GAMT and phosphorylated p53 levels in treated cells. The GSTM1 0/0 and GSTP1 A105/A105 genotypes occurred at higher frequencies in CAD patients compared with the control group (36% vs. 18% and 65% vs. 48%, respectively). A significant association with CAD was observed in GSTM1 0/0 (odds ratio (OR)=2.593; 95% confidence interval (CI) 1.353 - 4.971; p=0.0043) and GSTP1 A105/A105 OR=0.6011; 95% CI 0.3803 - 0.9503; p=0.0377). We found a significant association between smoking and CAD; the presence of either of the respective genotypes together with smoking increased the CAD risk (GSTP1 A105 relative risk (RR)=1.382; 95% CI 0.958 - 1.994; p=0.0987 and GSTM1 null RR=1.725; 95% CI 1.044 - 2.851; p=0.0221). The UCP2 -866G/A and UCP3 -55C/C genotypes occurred at highest frequency in CAD patients (59% vs. 52% and 66% vs. controls: 63% respectively) and did not influence the risk of CAD. Homozygous UCP3 -55T/T genotype was associated with highest fasting glucose (11.87±3.7mmol/L vs. C/C:6.11±0.27mmol/L and C/T:6.48±0.57mmol/L, p=0.0025), HbA1c (10.05±2.57% vs. C/C:6.44±0.21% and C/T:6.76±0.35%, p=0.0006) and triglycerides (6.47±1.7mmol/Lvs. C/C:2.33±0.17mmol/L and C/T:2.06±0.25mmol/L, p<0.0001) in CAD patients. A significant association between the G allele of the IL6 -174 polymorphism and non-diabetic CAD patients was found (p=0.0431 odds ratio: 1.307, 95% CI: 1.047-1.632). A significant association with the C allele of the -390 CRP triallelic variants and CAD (p=0.021 odds ratio: 1.75, 95% CI: 1.109-2.778) was also found using a contingency of the C allele vs. the minor A and T allele frequencies. The strength of the association of the C allele with non- diabetic CAD subjects was much higher (p=0.0048 odds ratio: 2.634, 95% CI: 1.350-5.138). Circulating median levels of IL-6 (0.9 (0.90, 0.91) pg/ml and 0.9 (0.87, 0.92) pg/ml) and CRP (5.65 (1.9, 8.2) mg/l and 2.90 (1.93, 8.35) mg/l) were similar between CAD patients and controls, respectively. A similar finding was observed between controls and non-diabetic CAD subjects. Levels of IL-6 and CRP in CAD subjects were not significantly influenced by polymorphic variants of IL-6 and CRP. In the control group, the level of IL-6 was significantly influenced by the IL6 -174 G allele (p=0.0002) and the CRP -390 C allele (p=0.0416), where subjects with the homozygous GG (0.9 (0.9, 1,78) pg/ml) and CC (0.9 (0.9, 0.95) pg/ml) genotype had higher levels than the C allele carriers (0.9 (0.64, 0.91) pg/ml) or A and T carriers (0.9 (0.69, 0.91) pg/ml) combined. The lowest measure of proliferation/metabolism in HepG2 cells was observed at 20μM atorvastatin, with 82±9.8% viability. The level of cytotoxicity was increased in statin treated cells from 0.95±0.02 units to 1.11±0.03 units (p=0.001) and malondialdehyde levels was reduced from 0.133±0.003 units to 0.126±0.005 units (p=0.009) whilst nitrite levels were elevated (0.0312±0.003 units vs. control: 0.027±0.001 units, p=0.044). MicroRNAs most significantly upregulated by atorvastatin included miR-302a-3p (3.05-fold), miR-302c-3p (3.61-fold), miR-124-3p (3.90-fold) and miR-222-3p (4.4-fold); miR-19a-3p, miR-101-3p and let-7g were downregulated (3.63-fold, 2.92-fold, 2.81-fold, respectively). A list of miRNA targets identified included those with a role in metabolism and inflammation. The miR-124a specifically targets the mRNA of GAMT and SMO

Health Sciences Research Day, Thursday, November 11, 2010

The 2010 Health Sciences Research Day booklet contains the program for the day, biographies of the 2010 Dorsett L. Spurgeon, MD Distinguished Medical Research Award Recipient and invited speakers, and abstracts of the research posters presented.Sponsored by: MU School of Medicine, MU Sinclair School of Nursing, MU School of Health Professions, Truman Veterans Hospital, MU Institute for Clinical and Translational Science ; Supported by: School of Medicine Research Council, Office of Medical Research

Methods in Computational Biology

Modern biology is rapidly becoming a study of large sets of data. Understanding these data sets is a major challenge for most life sciences, including the medical, environmental, and bioprocess fields. Computational biology approaches are essential for leveraging this ongoing revolution in omics data. A primary goal of this Special Issue, entitled “Methods in Computational Biology”, is the communication of computational biology methods, which can extract biological design principles from complex data sets, described in enough detail to permit the reproduction of the results. This issue integrates interdisciplinary researchers such as biologists, computer scientists, engineers, and mathematicians to advance biological systems analysis. The Special Issue contains the following sections:•Reviews of Computational Methods•Computational Analysis of Biological Dynamics: From Molecular to Cellular to Tissue/Consortia Levels•The Interface of Biotic and Abiotic Processes•Processing of Large Data Sets for Enhanced Analysis•Parameter Optimization and Measuremen