Robust interlaboratory reproducibility of a gene expression signature measurement consistent with the needs of a new generation of diagnostic tools

The increasing use of DNA microarrays in biomedical research, toxicogenomics, pharmaceutical development, and diagnostics has focused attention on the reproducibility and reliability of microarray measurements. While the reproducibility of microarray gene expression measurements has been the subject of several recent reports, there is still a need for systematic investigation into what factors most contribute to variability of measured expression levels observed among different laboratories and different experimenters.SCOPUS: ar.jinfo:eu-repo/semantics/publishe

Diversity of human copy number variation and multicopy genes

Copy number variants affect both disease and normal phenotypic variation, but those lying within heavily duplicated, highly identical sequence have been difficult to assay. By analyzing short-read mapping depth for 159 human genomes, we demonstrated accurate estimation of absolute copy number for duplications as small as 1.9 kilobase pairs, ranging from 0 to 48 copies. We identified 4.1 million singly unique nucleotide positions informative in distinguishing specific copies and used them to genotype the copy and content of specific paralogs within highly duplicated gene families. These data identify human-specific expansions in genes associated with brain development, reveal extensive population genetic diversity, and detect signatures consistent with gene conversion in the human species. Our approach makes ∼1000 genes accessible to genetic studies of disease association

lincRNAs act in the circuitry controlling pluripotency and differentiation

Although thousands of large intergenic non-coding RNAs (lincRNAs) have been identified in mammals, few have been functionally characterized, leading to debate about their biological role. To address this, we performed loss-of-function studies on most lincRNAs expressed in mouse embryonic stem (ES) cells and characterized the effects on gene expression. Here we show that knockdown of lincRNAs has major consequences on gene expression patterns, comparable to knockdown of well-known ES cell regulators. Notably, lincRNAs primarily affect gene expression in trans. Knockdown of dozens of lincRNAs causes either exit from the pluripotent state or upregulation of lineage commitment programs. We integrate lincRNAs into the molecular circuitry of ES cells and show that lincRNA genes are regulated by key transcription factors and that lincRNA transcripts bind to multiple chromatin regulatory proteins to affect shared gene expression programs. Together, the results demonstrate that lincRNAs have key roles in the circuitry controlling ES cell state.Broad InstituteHarvard UniversityNational Human Genome Research Institute (U.S.)Merkin Family Foundation for Stem Cell Researc

PIASy, a nuclear matrix–associated SUMO E3 ligase, represses LEF1 activity by sequestration into nuclear bodies

The Wnt-responsive transcription factor LEF1 can activate transcription in association with β-catenin and repress transcription in association with Groucho. In search of additional regulatory mechanisms of LEF1 function, we identified the protein inhibitor of activated STAT, PIASy, as a novel interaction partner of LEF1. Coexpression of PIASy with LEF1 results in potent repression of LEF1 activity and in covalent modification of LEF1 with SUMO. PIASy markedly stimulates the sumoylation of LEF1 and multiple other proteins in vivo and functions as a SUMO E3 ligase for LEF1 in a reconstituted system in vitro. Moreover, PIASy binds to nuclear matrix–associated DNA sequences and targets LEF1 to nuclear bodies, suggesting that PIASy-mediated subnuclear sequestration accounts for the repression of LEF1 activity

Abstract

Constantly improving gene expression profiling technologies are expected to provide understanding and insight into cancer related cellular processes. Gene expression data is als

Pp. 559–583 Tissue Classi � cation with Gene Expression Pro � les

Constantly improving gene expression pro � ling technologies are expected to provide understanding and insight into cancer-related cellular processes. Gene expression data is also expected to signi � cantly aid in the development of ef � cient cancer diagnosis and classi � cation platforms. In this work we examine three sets of gene expression data measured across sets of tumor(s) and normal clinical samples: The � rst set consists of 2,000 genes, measured in 62 epithelial colon samples (Alon et al., 1999). The second consists of 100,000 clones, measured in 32 ovarian samples (unpublished extension of data set described in Schummer et al. (1999)). The third set consists of 7,100 genes, measured in 72 bone marrow and peripheral blood samples (Golub et al., 1999). We examine the use of scoring methods, measuring separation of tissue type (e.g., tumors from normals) using individual gene expression levels. These are then coupled with high-dimensional classi � cation methods to assess the classi � cation power of complete expression pro � les. We present results of performing leave-one-out cross validation (LOOCV) experiments on the three data sets, employing nearest neighbor classi � er, SVM (Cortes and Vapnik, 1995), AdaBoost (Freund and Schapire, 1997) and a novel clusteringbased classi � cation technique. As tumor samples can differ from normal samples in their cell-type composition, we also perform LOOCV experiments using appropriately modi � ed sets of genes, attempting to eliminate the resulting bias. We demonstrate success rate of at least 90 % in tumor versus normal classi � cation, using sets of selected genes, with, as well as without, cellular-contamination-related members. These results are insensitive to the exact selection mechanism, over a certain range. Key words: tissue classi � cation, gene expression analysis, ovarian cancer, colon cancer. 1

Use of complex oligonucleotide libraries for concurrent high-resolution fluorescence imaging of both DNA and RNA in various sample types

Fluorescence in situ Hybridization (FISH) is a powerful technique for determining the localization specific nucleic acid sequences within individual cells. Previously, the use of FISH has often been dependent upon access to cloned template DNA for the generation of probes, which can be difficult if clones for specific regions are unavailable, or if the genomic region of interest contains repetitive and/or other problematic sequences. We have developed the ability to chemically synthesize DNA in massively parallel reactions, which we have used to produce libraries of oligonucleotides up to 200 bases in length that can be utilized for the generation of FISH probes. The sequences of the oligonucleotides in these libraries are selected in silico using empirically determined criteria so as to avoid repetitive elements or regions homologous to other non-targeted loci. We have found that these oligonucleotide library-derived FISH probes can detect human genomic regions as small as 1.8 kb and as large as whole chromosomes in both metaphase and interphase cells, using the same simple assay protocol. Because of the inherent flexibility in our probe design methods, we can readily visualize regions rich in repeats and/or GC content. We have also used these oligonucleotide library-derived FISH probes to detect the localization of a variety of both coding and non-coding RNAs in fixed tissue culture cells and formalin-fixed paraffin-embedded tissue sections, using both conventional fluorescence and structured illumination microscopy. Simultaneous hybridization of FISH probes labeled with different fluorophores enables visualization of multiple sequences at once. Using probes designed specifically to transcribed vs. non-transcribed regions has enabled the simultaneously detect DNA and RNA from the same locus, or from two different loci, in the same FISH assay. The ability to generate high performance FISH probes using chemically synthesized oligo libraries that can simultaneously detect DNA and RNA yields a valuable tool for studies of how localization of specific nucleic acids impacts biological function