Comprehensive multi-cohort transcriptional meta-analysis of muscle diseases identifies a signature of disease severity.

Muscle diseases share common pathological features suggesting common underlying mechanisms. We hypothesized there is a common set of genes dysregulated across muscle diseases compared to healthy muscle and that these genes correlate with severity of muscle disease. We performed meta-analysis of transcriptional profiles of muscle biopsies from human muscle diseases and healthy controls. Studies obtained from public microarray repositories fulfilling quality criteria were divided into six categories: (i) immobility, (ii) inflammatory myopathies, (iii) intensive care unit (ICU) acquired weakness (ICUAW), (iv) congenital muscle diseases, (v) chronic systemic diseases, (vi) motor neuron disease. Patient cohorts were separated in discovery and validation cohorts retaining roughly equal proportions of samples for the disease categories. To remove bias towards a specific muscle disease category we repeated the meta-analysis five times by removing data sets corresponding to one muscle disease class at a time in a "leave-one-disease-out" analysis. We used 636 muscle tissue samples from 30 independent cohorts to identify a 52 gene signature (36 up-regulated and 16 down-regulated genes). We validated the discriminatory power of this signature in 657 muscle biopsies from 12 additional patient cohorts encompassing five categories of muscle diseases with an area under the receiver operating characteristic curve of 0.91, 83% sensitivity, and 85.3% specificity. The expression score of the gene signature inversely correlated with quadriceps muscle mass (r = -0.50, p-value = 0.011) in ICUAW and shoulder abduction strength (r = -0.77, p-value = 0.014) in amyotrophic lateral sclerosis (ALS). The signature also positively correlated with histologic assessment of muscle atrophy in ALS (r = 0.88, p-value = 1.62 × 10-3) and fibrosis in muscular dystrophy (Jonckheere trend test p-value = 4.45 × 10-9). Our results identify a conserved transcriptional signature associated with clinical and histologic muscle disease severity. Several genes in this conserved signature have not been previously associated with muscle disease severity.

Disruption of Abi1/Hssh3bp1 expression induces prostatic intraepithelial neoplasia in the conditional Abi1/Hssh3bp1 KO mice.

Prostate cancer is one of the leading causes of cancer-related deaths in the United States and a leading diagnosed non-skin cancer in American men. Genetic mutations underlying prostate tumorigenesis include alterations of tumor suppressor genes. We tested the tumor suppressor hypothesis for ABI1/hSSH3BP1 by searching for gene mutations in primary prostate tumors from patients, and by analyzing the consequences of prostate-specific disruption of the mouse Abi1/Hssh3bp1 ortholog. We sequenced the ABI1/hSSH3BP1 gene and identified recurring mutations in 6 out of 35 prostate tumors. Moreover, complementation and anchorage-independent growth, proliferation, cellular adhesion and xenograft assays using the LNCaP cell line, which contains a loss-of-function Abi1 mutation, and a stably expressed wild-type or mutated ABI gene, were consistent with the tumor suppressor hypothesis. To test the hypothesis further, we disrupted the gene in the mouse prostate by breeding the Abi1 floxed strain with the probasin promoter-driven Cre recombinase strain. Histopathological evaluation of mice indicated development of prostatic intraepithelial neoplasia (PIN) in Abi1/Hssh3bp1 knockout mouse as early as the eighth month, but no progression beyond PIN was observed in mice as old as 12 months. Observed decreased levels of E-cadherin, ß-catenin and WAVE2 in mouse prostate suggest abnormal cellular adhesion as the mechanism underlying PIN development owing to Abi1 disruption. Analysis of syngeneic cell lines point to the possibility that upregulation of phospho-Akt underlies the enhanced cellular proliferation phenotype of cells lacking Abi1. This study provides proof-of-concept for the hypothesis that Abi1 downregulation has a role in the development of prostate cancer.

GeneMANIA Cytoscape plugin: fast gene function predictions on the desktop.

UNLABELLED: The GeneMANIA Cytoscape plugin brings fast gene function prediction capabilities to the desktop. GeneMANIA identifies the most related genes to a query gene set using a guilt-by-association approach. The plugin uses over 800 networks from six organisms and each related gene is traceable to the source network used to make the prediction. Users may add their own interaction networks and expression profile data to complement or override the default data. AVAILABILITY AND IMPLEMENTATION: The GeneMANIA Cytoscape plugin is implemented in Java and is freely available at http://www.genemania.org/plugin/.

NetMatch: a Cytoscape plugin for searching biological networks.

UNLABELLED: NetMatch is a Cytoscape plugin which allows searching biological networks for subcomponents matching a given query. Queries may be approximate in the sense that certain parts of the subgraph-query may be left unspecified. To make the query creation process easy, a drawing tool is provided. Cytoscape is a bioinformatics software platform for the visualization and analysis of biological networks. AVAILABILITY: The full package, a tutorial and associated examples are available at the following web sites: http://alpha.dmi.unict.it/~ctnyu/netmatch.html, http://baderlab.org/Software/NetMatch.

Systematic genetic analysis with ordered arrays of yeast deletion mutants.

In Saccharomyces cerevisiae, more than 80% of the approximately 6200 predicted genes are nonessential, implying that the genome is buffered from the phenotypic consequences of genetic perturbation. To evaluate function, we developed a method for systematic construction of double mutants, termed synthetic genetic array (SGA) analysis, in which a query mutation is crossed to an array of approximately 4700 deletion mutants. Inviable double-mutant meiotic progeny identify functional relationships between genes. SGA analysis of genes with roles in cytoskeletal organization (BNI1, ARP2, ARC40, BIM1), DNA synthesis and repair (SGS1, RAD27), or uncharacterized functions (BBC1, NBP2) generated a network of 291 interactions among 204 genes. Systematic application of this approach should produce a global map of gene function.

BIND--The Biomolecular Interaction Network Database.

The Biomolecular Interaction Network Database (BIND; http://binddb. org) is a database designed to store full descriptions of interactions, molecular complexes and pathways. Development of the BIND 2.0 data model has led to the incorporation of virtually all components of molecular mechanisms including interactions between any two molecules composed of proteins, nucleic acids and small molecules. Chemical reactions, photochemical activation and conformational changes can also be described. Everything from small molecule biochemistry to signal transduction is abstracted in such a way that graph theory methods may be applied for data mining. The database can be used to study networks of interactions, to map pathways across taxonomic branches and to generate information for kinetic simulations. BIND anticipates the coming large influx of interaction information from high-throughput proteomics efforts including detailed information about post-translational modifications from mass spectrometry. Version 2.0 of the BIND data model is discussed as well as implementation, content and the open nature of the BIND project. The BIND data specification is available as ASN.1 and XML DTD.

BIND--a data specification for storing and describing biomolecular interactions, molecular complexes and pathways.

MOTIVATION: Proteomics is gearing up towards high-throughput methods for identifying and characterizing all of the proteins, protein domains and protein interactions in a cell and will eventually create more recorded biological information than the Human Genome Project. Each protein expressed in a cell can interact with various other proteins and molecules in the course of its function. A standard data specification is required that can describe and store this information in all its detail and allow efficient cross-platform transfer of data. A complete specification must be the basis for any database or tool for managing and analysing this information. RESULTS: We have defined a complete data specification in ASN.1 that can describe information about biomolecular interactions, complexes and pathways. Our group is using this data specification in our database, the Biomolecular Interaction Network Database (BIND). An interaction record is based on the interaction between two objects. An object can be a protein, DNA, RNA, ligand, molecular complex or an interaction. Interaction description encompasses cellular location, experimental conditions used to observe the interaction, conserved sequence, molecular location, chemical action, kinetics, thermodynamics, and chemical state. Molecular complexes are defined as collections of more than two interactions that form a complex, with extra descriptive information such as complex topology. Pathways are defined as collections of more than two interactions that form a pathway, with additional descriptive information such as cell cycle stage. A request for proposal of a human readable flat-file format that mirrors the BIND data specification is also tendered for interested parties. AVAILABILITY: The ASN.1 data specification for biomolecular interaction, molecular complex and pathway data is available at ftp://bioinfo.mshri.on.ca/pub/BIND/Spec/bind.asn. An interactive browser for this document is available through our homepage at http://bioinfo.mshri.on.ca/BIND/asn-browser/.