A New Liver Expression Quantitative Trait Locus Map From 1,183 Individuals Provides Evidence for Novel Expression Quantitative Trait Loci of Drug Response, Metabolic, and Sex-Biased Phenotypes.

Expression quantitative trait locus (eQTL) studies in human liver are crucial for elucidating how genetic variation influences variability in disease risk and therapeutic outcomes and may help guide strategies to obtain maximal efficacy and safety of clinical interventions. Associations between expression microarray and genome-wide genotype data from four human liver eQTL studies (n = 1,183) were analyzed. More than 2.3 million cis-eQTLs for 15,668 genes were identified. When eQTLs were filtered against a list of 1,496 drug response genes, 187,829 cis-eQTLs for 1,191 genes were identified. Additionally, 1,683 sex-biased cis-eQTLs were identified, as well as 49 and 73 cis-eQTLs that colocalized with genome-wide association study signals for blood metabolite or lipid levels, respectively. Translational relevance of these results is evidenced by linking DPYD eQTLs to differences in safety of chemotherapy, linking the sex-biased regulation of PCSK9 expression to anti-lipid therapy, and identifying the G-protein coupled receptor GPR180 as a novel drug target for hypertriglyceridemia.

Inferring statin-induced gene regulatory relationships in primary human hepatocytes.

MOTIVATION: Statins are the most widely used cholesterol-lowering drugs. The primary target of statins is HMG-CoA reductase, a key enzyme in cholesterol synthesis. However, statins elicit pleitropic responses including beneficial as well as adverse effects in the liver or other organs. Today, the regulatory mechanisms that cause these pleiotropic effects are not sufficiently understood. RESULTS: In this work, genome-wide RNA expression changes in primary human hepatocytes of six individuals were measured at up to six time points upon atorvastatin treatment. A computational analysis workflow was applied to reconstruct regulatory mechanisms based on these drug-response data and available knowledge about transcription factor (TF) binding specificities and protein-drug interactions. Several previously unknown TFs were predicted to be involved in atorvastatin-responsive gene expression. The novel relationships of nuclear receptors NR2C2 and PPARA on CYP3A4 were successfully validated in wet-lab experiments. AVAILABILITY: Microarray data are available at the Gene Expression Omnibus (GEO) database at www.ncbi.nlm.nih.gov/geo/, under accession number GSE29868. CONTACT: andreas.zell@uni-tuebingen.de; adrian.schroeder@uni-tuebingen.de SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Predicting DNA-binding specificities of eukaryotic transcription factors.

Today, annotated amino acid sequences of more and more transcription factors (TFs) are readily available. Quantitative information about their DNA-binding specificities, however, are hard to obtain. Position frequency matrices (PFMs), the most widely used models to represent binding specificities, are experimentally characterized only for a small fraction of all TFs. Even for some of the most intensively studied eukaryotic organisms (i.e., human, rat and mouse), roughly one-sixth of all proteins with annotated DNA-binding domain have been characterized experimentally. Here, we present a new method based on support vector regression for predicting quantitative DNA-binding specificities of TFs in different eukaryotic species. This approach estimates a quantitative measure for the PFM similarity of two proteins, based on various features derived from their protein sequences. The method is trained and tested on a dataset containing 1 239 TFs with known DNA-binding specificity, and used to predict specific DNA target motifs for 645 TFs with high accuracy.

ModuleMaster: a new tool to decipher transcriptional regulatory networks.

UNLABELLED: In this article we present ModuleMaster, a novel application for finding cis-regulatory modules (CRMs) in sets of co-expressed genes. The application comes with a newly developed method which not only considers transcription factor binding information but also multivariate functional relationships between regulators and target genes to improve the detection of CRMs. Given only the results of a microarray and a subsequent clustering experiment, the program includes all necessary data and algorithms to perform every step to find CRMs. This workbench possesses an easy-to-use graphical user interface, together with job-processing and command-line options, making ModuleMaster a sophisticated program for large-scale batch processing. The detected CRMs can be visualized and evaluated in various ways, i.e., generating GraphML- and R-based whole regulatory network visualizations or generating SBML files for subsequent analytical processing and dynamic modeling. AVAILABILITY: ModuleMaster is freely available to academics as a webstart application and for download at http://www.ra.cs.uni-tuebingen.de/software/ModuleMaster/, including comprehensive documentation.

BowTieBuilder: modeling signal transduction pathways.

BACKGROUND: Sensory proteins react to changing environmental conditions by transducing signals into the cell. These signals are integrated into core proteins that activate downstream target proteins such as transcription factors (TFs). This structure is referred to as a bow tie, and allows cells to respond appropriately to complex environmental conditions. Understanding this cellular processing of information, from sensory proteins (e.g., cell-surface proteins) to target proteins (e.g., TFs) is important, yet for many processes the signaling pathways remain unknown. RESULTS: Here, we present BowTieBuilder for inferring signal transduction pathways from multiple source and target proteins. Given protein-protein interaction (PPI) data signaling pathways are assembled without knowledge of the intermediate signaling proteins while maximizing the overall probability of the pathway. To assess the inference quality, BowTieBuilder and three alternative heuristics are applied to several pathways, and the resulting pathways are compared to reference pathways taken from KEGG. In addition, BowTieBuilder is used to infer a signaling pathway of the innate immune response in humans and a signaling pathway that potentially regulates an underlying gene regulatory network. CONCLUSION: We show that BowTieBuilder, given multiple source and/or target proteins, infers pathways with satisfactory recall and precision rates and detects the core proteins of each pathway.

SBML2L(A)T(E)X: conversion of SBML files into human-readable reports.

SUMMARY: The XML-based Systems Biology Markup Language (SBML) has emerged as a standard for storage, communication and interchange of models in systems biology. As a machine-readable format XML is difficult for humans to read and understand. Many tools are available that visualize the reaction pathways stored in SBML files, but many components, e.g. unit declarations, complex kinetic equations or links to MIRIAM resources, are often not made visible in these diagrams. For a broader understanding of the models, support in scientific writing and error detection, a human-readable report of the complete model is needed. We present SBML2L(A)T(E)X, a Java-based stand-alone program to fill this gap. A convenient web service allows users to directly convert SBML to various formats, including DVI, L(A)T(E)X and PDF, and provides many settings for customization. AVAILABILITY: Source code, documentation and a web service are freely available at (http://www.ra.cs.uni-tuebingen.de/software/SBML2LaTeX).

SBMLsqueezer: a CellDesigner plug-in to generate kinetic rate equations for biochemical networks.

BACKGROUND: The development of complex biochemical models has been facilitated through the standardization of machine-readable representations like SBML (Systems Biology Markup Language). This effort is accompanied by the ongoing development of the human-readable diagrammatic representation SBGN (Systems Biology Graphical Notation). The graphical SBML editor CellDesigner allows direct translation of SBGN into SBML, and vice versa. For the assignment of kinetic rate laws, however, this process is not straightforward, as it often requires manual assembly and specific knowledge of kinetic equations. RESULTS: SBMLsqueezer facilitates exactly this modeling step via automated equation generation, overcoming the highly error-prone and cumbersome process of manually assigning kinetic equations. For each reaction the kinetic equation is derived from the stoichiometry, the participating species (e.g., proteins, mRNA or simple molecules) as well as the regulatory relations (activation, inhibition or other modulations) of the SBGN diagram. Such information allows distinctions between, for example, translation, phosphorylation or state transitions. The types of kinetics considered are numerous, for instance generalized mass-action, Hill, convenience and several Michaelis-Menten-based kinetics, each including activation and inhibition. These kinetics allow SBMLsqueezer to cover metabolic, gene regulatory, signal transduction and mixed networks. Whenever multiple kinetics are applicable to one reaction, parameter settings allow for user-defined specifications. After invoking SBMLsqueezer, the kinetic formulas are generated and assigned to the model, which can then be simulated in CellDesigner or with external ODE solvers. Furthermore, the equations can be exported to SBML, LaTeX or plain text format. CONCLUSION: SBMLsqueezer considers the annotation of all participating reactants, products and regulators when generating rate laws for reactions. Thus, for each reaction, only applicable kinetic formulas are considered. This modeling scheme creates kinetics in accordance with the diagrammatic representation. In contrast most previously published tools have relied on the stoichiometry and generic modulators of a reaction, thus ignoring and potentially conflicting with the information expressed through the process diagram. Additional material and the source code can be found at the project homepage (URL found in the Availability and requirements section).