Integrated gene expression profiling and linkage analysis in the rat

The combined application of genome-wide expression profiling from microarray experiments with genetic linkage analysis enables the mapping of expression quantitative trait loci (eQTLs) which are primary control points for gene expression across the genome. This approach allows for the dissection of primary and secondary genetic determinants of gene expression. The cis-acting eQTLs in practice are easier to investigate than the trans-regulated eQTLs because they are under simpler genetic control and are likely to be due to sequence variants within the gene itself or its neighboring regulatory elements. These genes are therefore candidates both for variation in gene expression and for contributions to whole-body phenotypes, particularly when these are located within known and relevant physiologic QTLs. Multiple trans-acting eQTLs tend to cluster to the same genetic location, implying shared regulatory control mechanisms that may be amenable to network analysis to identify gene clusters within the same metabolic pathway. Such clusters may ultimately underlie development of individual complex, whole-body phenotypes. The combined expression and linkage approach has been applied successfully in several mammalian species, including the rat which has specific features that demonstrate its value as a model for studying complex traits.     

Genomic profiling of cortical neurons following exposure to beta-amyloid

In vitro and in vivo studies have shown that beta-amyloid peptide induces neuronal cell death. To explore the molecular basis underlying beta-amyloid-induced toxicity, we analyzed gene expression profiles of cultured rat cortical neurons treated for 24 and 48 h with synthetic beta-amyloid peptide. From the 8740 genes interrogated by oligonucleotide microarray analysis, 241 genes were found to be differentially expressed and segregated into distinct clusters. Functional clustering based on gene ontologies showed coordinated expression of genes with common biological functions and metabolic pathways. The comparison with genes differentially expressed in cerebellar granule neurons following serum and potassium deprivation indicates the existence of common regulatory mechanisms underlying neuronal cell death. Our results offer a genomic view of the changes that accompany beta-amyloid-induced neurodegeneration.     

Identifying subset of genes that have influential impacts on cancer progression: a new approach to analyze cancer microarray data

Cancer is a complex genetic disease, resulting from defects of multiple genes. Development of microarray techniques makes it possible to survey the whole genome and detect genes that have influential impacts on the progression of cancer. Statistical analysis of cancer microarray data is challenging because of the high dimensionality and cluster nature of gene expressions. Here, clusters are composed of genes with coordinated pathological functions and/or correlated expressions. In this article, we consider cancer studies where censored survival endpoint is measured along with microarray gene expressions. We propose a hybrid clustering approach, which uses both pathological pathway information retrieved from KEGG and statistical correlations of gene expressions, to construct gene clusters. Cancer survival time is modeled as a linear function of gene expressions. We adopt the clustering threshold gradient directed regularization (CTGDR) method for simultaneous gene cluster selection, within-cluster gene selection, and predictive model building. Analysis of two lymphoma studies shows that the proposed approach - which is composed of the hybrid gene clustering, linear regression model for survival, and clustering regularized estimation with CTGDR - can effectively identify gene clusters and genes within selected clusters that have satisfactory predictive power for censored cancer survival outcomes.     

Co-clustering of biological networks and gene expression data

MOTIVATION: Large scale gene expression data are often analysed by clustering genes based on gene expression data alone, though a priori knowledge in the form of biological networks is available. The use of this additional information promises to improve exploratory analysis considerably. RESULTS: We propose constructing a distance function which combines information from expression data and biological networks. Based on this function, we compute a joint clustering of genes and vertices of the network. This general approach is elaborated for metabolic networks. We define a graph distance function on such networks and combine it with a correlation-based distance function for gene expression measurements. A hierarchical clustering and an associated statistical measure is computed to arrive at a reasonable number of clusters. Our method is validated using expression data of the yeast diauxic shift. The resulting clusters are easily interpretable in terms of the biochemical network and the gene expression data and suggest that our method is able to automatically identify processes that are relevant under the measured conditions.     

Genetic and biochemical characterization of the galactose gene cluster in Kluyveromyces lactis.

We isolated and identified mutant strains of Kluyveromyces lactis that are defective for the Leloir pathway enzymes galactokinase, transferase, and epimerase, and we termed these loci GAL1 , GAL7 , and GAL10 , respectively. Genetic data indicate that these three genes are tightly linked, having an apparent order of GAL7 - GAL10 - GAL1 . This same gene order has been observed in Saccharomyces cerevisiae. Strains harboring gal7 mutations have elevated levels of beta-galactosidase, coded by an unlinked gene, galactokinase, and epimerase activities under uninduced conditions. We investigated the genetic basis of this constitutive gene expression and found no recombinants between the constitutive and Gal- phenotypes among 76 tetrads, suggesting that either GAL7 or a tightly linked gene codes for a regulatory function. This is the second gene that has been shown to specifically coregulate expression of the genes coding for beta-galactosidase and the Leloir pathway enzymes.     

GAL4 enhancer traps that can be used to drive gene expression in developing Drosophila spermatocytes

The Drosophila testis has proven to be a valuable model organ for investigation of germline stem cell (GSC) maintenance and differentiation as well as elucidation of the genetic programs that regulate differentiation of daughter spermatogonia. Development of germ cell specific GAL4 driver transgenes has facilitated investigation of gene function in GSCs and spermatogonia but specific GAL4 tools are not available for analysis of postmitotic spermatogonial differentiation into spermatocytes. We have screened publically available pGT1 strains, a GAL4‐encoding gene trap collection, to identify lines that can drive gene expression in late spermatogonia and early spermatocytes. While we were unable to identify any germline‐specific drivers, we did identify an insertion in the chiffon locus, which drove expression specifically in early spermatocytes within the germline along with the somatic cyst cells of the testis. genesis 50:914–920, 2012. © 2012 Wiley Periodicals, Inc.     

Activation of a human chromosomal replication origin by protein tethering

The specification of mammalian chromosomal replication origins is incompletely understood. To analyze the assembly and activation of prereplicative complexes (pre-RCs), we tested the effects of tethered binding of chromatin acetyltransferases and replication proteins on chromosomal c-myc origin deletion mutants containing a GAL4-binding cassette. GAL4^DBD (DNA binding domain) fusions with Orc2, Cdt1, E2F1 or HBO1 coordinated the recruitment of the Mcm7 helicase subunit, the DNA unwinding element (DUE)-binding protein DUE-B and the minichromosome maintenance (MCM) helicase activator Cdc45 to the replicator, and restored origin activity. In contrast, replication protein binding and origin activity were not stimulated by fusion protein binding in the absence of flanking c-myc DNA. Substitution of the GAL4-binding site for the c-myc replicator DUE allowed Orc2 and Mcm7 binding, but eliminated origin activity, indicating that the DUE is essential for pre-RC activation. Additionally, tethering of DUE-B was not sufficient to recruit Cdc45 or activate pre-RCs formed in the absence of a DUE. These results show directly in a chromosomal background that chromatin acetylation, Orc2 or Cdt1 suffice to recruit all downstream replication initiation activities to a prospective origin, and that chromosomal origin activity requires singular DNA sequences.     

Network constrained clustering for gene microarray data

Many bioinformatics problems can be tackled from a fresh angle offered by the network perspective. Directly inspired by metabolic network structural studies, we propose an improved gene clustering approach for inferring gene signaling pathways from gene microarray data. Based on the construction of co-expression networks that consists of both significantly linear and non-linear gene associations together with controlled biological and statistical significance, our approach tends to group functionally related genes into tight clusters despite their expression dissimilarities. We illustrate our approach and compare it to the traditional clustering approaches on a yeast galactose metabolism dataset and a retinal gene expression dataset. Our approach greatly outperforms the traditional approach in rediscovering the relatively well known galactose metabolism pathway in yeast and in clustering genes of the photoreceptor differentiation pathway. AVAILABILITY: The clustering method has been implemented in an R package "GeneNT" that is freely available from: http://www.cran.org.