Physiogenomic resources for rat models of heart, lung and blood disorders.

Cardiovascular disorders are influenced by genetic and environmental factors. The TIGR rodent expression web-based resource (TREX) contains over 2,200 microarray hybridizations, involving over 800 animals from 18 different rat strains. These strains comprise genetically diverse parental animals and a panel of chromosomal substitution strains derived by introgressing individual chromosomes from normotensive Brown Norway (BN/NHsdMcwi) rats into the background of Dahl salt sensitive (SS/JrHsdMcwi) rats. The profiles document gene-expression changes in both genders, four tissues (heart, lung, liver, kidney) and two environmental conditions (normoxia, hypoxia). This translates into almost 400 high-quality direct comparisons (not including replicates) and over 100,000 pairwise comparisons. As each individual chromosomal substitution strain represents on average less than a 5% change from the parental genome, consomic strains provide a useful mechanism to dissect complex traits and identify causative genes. We performed a variety of data-mining manipulations on the profiles and used complementary physiological data from the PhysGen resource to demonstrate how TREX can be used by the cardiovascular community for hypothesis generation.

iBBiG: iterative binary bi-clustering of gene sets.

MOTIVATION: Meta-analysis of genomics data seeks to identify genes associated with a biological phenotype across multiple datasets; however, merging data from different platforms by their features (genes) is challenging. Meta-analysis using functionally or biologically characterized gene sets simplifies data integration is biologically intuitive and is seen as having great potential, but is an emerging field with few established statistical methods. RESULTS: We transform gene expression profiles into binary gene set profiles by discretizing results of gene set enrichment analyses and apply a new iterative bi-clustering algorithm (iBBiG) to identify groups of gene sets that are coordinately associated with groups of phenotypes across multiple studies. iBBiG is optimized for meta-analysis of large numbers of diverse genomics data that may have unmatched samples. It does not require prior knowledge of the number or size of clusters. When applied to simulated data, it outperforms commonly used clustering methods, discovers overlapping clusters of diverse sizes and is robust in the presence of noise. We apply it to meta-analysis of breast cancer studies, where iBBiG extracted novel gene set-phenotype association that predicted tumor metastases within tumor subtypes. AVAILABILITY: Implemented in the Bioconductor package iBBiG CONTACT: aedin@jimmy.harvard.edu.

nEASE: a method for gene ontology subclassification of high-throughput gene expression data.

UNLABELLED: High-throughput technologies can identify genes whose expression profiles correlate with specific phenotypes; however, placing these genes into a biological context remains challenging. To help address this issue, we developed nested Expression Analysis Systematic Explorer (nEASE). nEASE complements traditional gene ontology enrichment approaches by determining statistically enriched gene ontology subterms within a list of genes based on co-annotation. Here, we overview an open-source software version of the nEASE algorithm. nEASE can be used either stand-alone or as part of a pathway discovery pipeline. AVAILABILITY: nEASE is implemented within the Multiple Experiment Viewer software package available at http://www.tm4.org/mev. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

RNA-Seq analysis in MeV.

SUMMARY: RNA-Seq is an exciting methodology that leverages the power of high-throughput sequencing to measure RNA transcript counts at an unprecedented accuracy. However, the data generated from this process are extremely large and biologist-friendly tools with which to analyze it are sorely lacking. MultiExperiment Viewer (MeV) is a Java-based desktop application that allows advanced analysis of gene expression data through an intuitive graphical user interface. Here, we report a significant enhancement to MeV that allows analysis of RNA-Seq data with these familiar, powerful tools. We also report the addition to MeV of several RNA-Seq-specific functions, addressing the differences in analysis requirements between this data type and traditional gene expression data. These tools include automatic conversion functions from raw count data to processed RPKM or FPKM values and differential expression detection and functional annotation enrichment detection based on published methods.

Functional classification analysis of somatically mutated genes in human breast and colorectal cancers.

A recent study published by Sjoblom and colleagues [T. Sjoblom, S. Jones, L.D. Wood, D.W. Parsons, J. Lin, T.D. Barber, D. Mandelker, R.J. Leary, J. Ptak, N. Silliman, S. Szabo, P. Buckhaults, C. Farrell, P. Meeh, S.D. Markowitz, J. Willis, D. Dawson, J.K. Willson, A.F. Gazdar, J. Hartigan, L. Wu, C. Liu, G. Parmigiani, B.H. Park, K.E. Bachman, N. Papadopoulos, B. Vogelstein, K.W. Kinzler, V.E. Velculescu, The consensus coding sequences of human breast and colorectal cancers. Science 314 (2006) 268-274.] performed comprehensive sequencing of 13,023 human genes and identified mutations in genes specific to breast and colorectal tumors, providing insight into organ-specific tumor biology. Here we present a systematic analysis of the functional classifications of Sjoblom's "CAN" genes, a subset of these validated mutant genes, that identifies novel organ-specific biological themes and molecular pathways associated with disease-specific etiology. This analysis links four somatically mutated genes associated with diverse oncological types to colorectal and breast cancers through established TGF-beta1-regulated interactions, revealing mechanistic differences in these cancers and providing potential diagnostic and therapeutic targets.

Novel tricyclic glycal-based TRIB1 inducers that reprogram LDL metabolism in hepatic cells.

Increased expression of the Tribbles pseudokinase 1 gene (TRIB1) is associated with lower plasma levels of LDL cholesterol and triglycerides, higher levels of HDL cholesterol and decreased risk of coronary artery disease and myocardial infarction. We identified a class of tricyclic glycal core-based compounds that upregulate TRIB1 expression in human HepG2 cells and phenocopy the effects of genetic TRIB1 overexpression as they inhibit expression of triglyceride synthesis genes and ApoB secretion in cells. In addition to predicted effects related to downregulation of VLDL assembly and secretion these compounds also have unexpected effects as they upregulate expression of LDLR and stimulate LDL uptake. This activity profile is unique and favorably differs from profiles produced by statins or other lipoprotein targeting therapies. BRD8518, the initial lead compound from the tricyclic glycal class, exhibited stereochemically dependent activity and the potency far exceeding previously described benzofuran BRD0418. Gene expression profiling of cells treated with BRD8518 demonstrated the anticipated changes in lipid metabolic genes and revealed a broad stimulation of early response genes. Consistently, we found that BRD8518 activity is MEK1/2 dependent and the treatment of HepG2 cells with BRD8518 stimulates ERK1/2 phosphorylation. In agreement with down-regulation of genes controlling triglyceride synthesis and assembly of lipoprotein particles, the mass spectrometry analysis of cell extracts showed reduced rate of incorporation of stable isotope labeled glycerol into triglycerides in BRD8518 treated cells. Furthermore, we describe medicinal chemistry efforts that led to identification of BRD8518 analogs with enhanced potency and pharmacokinetic properties suitable for in vivo studies.

TM4 microarray software suite.

Powerful specialized software is essential for managing, quantifying, and ultimately deriving scientific insight from results of a microarray experiment. We have developed a suite of software applications, known as TM4, to support such gene expression studies. The suite consists of open-source tools for data management and reporting, image analysis, normalization and pipeline control, and data mining and visualization. An integrated MIAME-compliant MySQL database is included. This chapter describes each component of the suite and includes a sample analysis walk-through.

An unbiased approach to identify endogenous substrates of "histone" deacetylase 8.

Despite being extensively characterized structurally and biochemically, the functional role of histone deacetylase 8 (HDAC8) has remained largely obscure due in part to a lack of known cellular substrates. Herein, we describe an unbiased approach using chemical tools in conjunction with sophisticated proteomics methods to identify novel non-histone nuclear substrates of HDAC8, including the tumor suppressor ARID1A. These newly discovered substrates of HDAC8 are involved in diverse biological processes including mitosis, transcription, chromatin remodeling, and RNA splicing and may help guide therapeutic strategies that target the function of HDAC8.

MicroRNA paraffin-based studies in osteosarcoma reveal reproducible independent prognostic profiles at 14q32.

BACKGROUND: Although microRNAs (miRNAs) are implicated in osteosarcoma biology and chemoresponse, miRNA prognostic models are still needed, particularly because prognosis is imperfectly correlated with chemoresponse. Formalin-fixed, paraffin-embedded tissue is a necessary resource for biomarker studies in this malignancy with limited frozen tissue availability. METHODS: We performed miRNA and mRNA microarray formalin-fixed, paraffin-embedded assays in 65 osteosarcoma biopsy and 26 paired post-chemotherapy resection specimens and used the only publicly available miRNA dataset, generated independently by another group, to externally validate our strongest findings (n = 29). We used supervised principal components analysis and logistic regression for survival and chemoresponse, and miRNA activity and target gene set analysis to study miRNA regulatory activity. RESULTS: Several miRNA-based models with as few as five miRNAs were prognostic independently of pathologically assessed chemoresponse (median recurrence-free survival: 59 months versus not-yet-reached; adjusted hazards ratio = 2.90; P = 0.036). The independent dataset supported the reproducibility of recurrence and survival findings. The prognostic value of the profile was independent of confounding by known prognostic variables, including chemoresponse, tumor location and metastasis at diagnosis. Model performance improved when chemoresponse was added as a covariate (median recurrence-free survival: 59 months versus not-yet-reached; hazard ratio = 3.91; P = 0.002). Most prognostic miRNAs were located at 14q32 - a locus already linked to osteosarcoma - and their gene targets display deregulation patterns associated with outcome. We also identified miRNA profiles predictive of chemoresponse (75% to 80% accuracy), which did not overlap with prognostic profiles. CONCLUSIONS: Formalin-fixed, paraffin-embedded tissue-derived miRNA patterns are a powerful prognostic tool for risk-stratified osteosarcoma management strategies. Combined miRNA and mRNA analysis supports a possible role of the 14q32 locus in osteosarcoma progression and outcome. Our study creates a paradigm for formalin-fixed, paraffin-embedded-based miRNA biomarker studies in cancer.

Therapeutic implications of GIPC1 silencing in cancer.

GIPC1 is a cytoplasmic scaffold protein that interacts with numerous receptor signaling complexes, and emerging evidence suggests that it plays a role in tumorigenesis. GIPC1 is highly expressed in a number of human malignancies, including breast, ovarian, gastric, and pancreatic cancers. Suppression of GIPC1 in human pancreatic cancer cells inhibits in vivo tumor growth in immunodeficient mice. To better understand GIPC1 function, we suppressed its expression in human breast and colorectal cancer cell lines and human mammary epithelial cells (HMECs) and assayed both gene expression and cellular phenotype. Suppression of GIPC1 promotes apoptosis in MCF-7, MDA-MD231, SKBR-3, SW480, and SW620 cells and impairs anchorage-independent colony formation of HMECs. These observations indicate GIPC1 plays an essential role in oncogenic transformation, and its expression is necessary for the survival of human breast and colorectal cancer cells. Additionally, a GIPC1 knock-down gene signature was used to interrogate publically available breast and ovarian cancer microarray datasets. This GIPC1 signature statistically correlates with a number of breast and ovarian cancer phenotypes and clinical outcomes, including patient survival. Taken together, these data indicate that GIPC1 inhibition may represent a new target for therapeutic development for the treatment of human cancers.

MeSHer: identifying biological concepts in microarray assays based on PubMed references and MeSH terms.

UNLABELLED: MeSHer uses a simple statistical approach to identify biological concepts in the form of Medical Subject Headings (MeSH terms) obtained from the PubMed database that are significantly overrepresented within the identified gene set relative to those associated with the overall collection of genes on the underlying DNA microarray platform. As a demonstration, we apply this approach to gene lists acquired from a published study of the effects of angiotensin II (Ang II) treatment on cardiac gene expression and demonstrate that this approach can aid in the interpretation of the resulting 'significant' gene set. AVAILABILITY: The software is available at http://www.tm4.org. SUPPLEMENTARY INFORMATION: Results from the analysis of significant genes from the published Ang II study.