Module networks: identifying regulatory modules and their condition-specific regulators from gene expression data

Much of a cell's activity is organized as a network of interacting modules: sets of genes coregulated to respond to different conditions. We present a probabilistic method for identifying regulatory modules from gene expression data. Our procedure identifies modules of coregulated genes, their regulators and the conditions under which regulation occurs, generating testable hypotheses in the form 'regulator X regulates module Y under conditions W'. We applied the method to a Saccharomyces cerevisiae expression data set, showing its ability to identify functionally coherent modules and their correct regulators. We present microarray experiments supporting three novel predictions, suggesting regulatory roles for previously uncharacterized proteins.     

Clustering of housekeeping genes provides a unified model of gene order in the human genome

It is often supposed that, except for tandem duplicates, genes are randomly distributed throughout the human genome. However, recent analyses suggest that when all the genes expressed in a given tissue (notably placenta and skeletal muscle) are examined, these genes do not map to random locations but instead resolve to clusters. We have asked three questions: (i) is this clustering true for most tissues, or are these the exceptions; (ii) is any clustering simply the result of the expression of tandem duplicates and (iii) how, if at all, does this relate to the observed clustering of genes with high expression rates? We provide a unified model of gene clustering that explains the previous observations. We examined Serial Analysis of Gene Expression (SAGE) data for 14 tissues and found significant clustering, in each tissue, that persists even after the removal of tandem duplicates. We confirmed clustering by analysis of independent expressed-sequence tag (EST) data. We then tested the possibility that the human genome is organized into subregions, each specializing in genes needed in a given tissue. By comparing genes expressed in different tissues, we show that this is not the case: those genes that seem to be tissue-specific in their expression do not, as a rule, cluster. We report that genes that are expressed in most tissues (housekeeping genes) show strong clustering. In addition, we show that the apparent clustering of genes with high expression rates is a consequence of the clustering of housekeeping genes.     

A module map showing conditional activity of expression modules in cancer

DNA microarrays are widely used to study changes in gene expression in tumors, but such studies are typically system-specific and do not address the commonalities and variations between different types of tumor. Here we present an integrated analysis of 1,975 published microarrays spanning 22 tumor types. We describe expression profiles in different tumors in terms of the behavior of modules, sets of genes that act in concert to carry out a specific function. Using a simple unified analysis, we extract modules and characterize gene-expression profiles in tumors as a combination of activated and deactivated modules. Activation of some modules is specific to particular types of tumor; for example, a growth-inhibitory module is specifically repressed in acute lymphoblastic leukemias and may underlie the deregulated proliferation in these cancers. Other modules are shared across a diverse set of clinical conditions, suggestive of common tumor progression mechanisms. For example, the bone osteoblastic module spans a variety of tumor types and includes both secreted growth factors and their receptors. Our findings suggest that there is a single mechanism for both primary tumor proliferation and metastasis to bone. Our analysis presents multiple research directions for diagnostic, prognostic and therapeutic studies.     

A tiling resolution DNA microarray with complete coverage of the human genome

We constructed a tiling resolution array consisting of 32,433 overlapping BAC clones covering the entire human genome. This increases our ability to identify genetic alterations and their boundaries throughout the genome in a single comparative genomic hybridization (CGH) experiment. At this tiling resolution, we identified minute DNA alterations not previously reported. These alterations include microamplifications and deletions containing oncogenes, tumor-suppressor genes and new genes that may be associated with multiple tumor types. Our findings show the need to move beyond conventional marker-based genome comparison approaches, that rely on inference of continuity between interval markers. Our submegabase resolution tiling set for array CGH (SMRT array) allows comprehensive assessment of genomic integrity and thereby the identification of new genes associated with disease.