Biomedical subjects

Gregory E Crawford

Publications and source records attributed to Gregory E Crawford.

7 recordsLinked to original sources

PubMed2026

Functional Annotation of the Major Histocompatibility Complex Locus.

The human major histocompatibility complex (MHC) locus has the greatest density of disease-associations in the human genome, including links to over 100 polygenic disorders. Its complex haplotype structure, rich gene density, and high degree of linkage disequilibrium combine to make deciphering the gene regulatory logic of the MHC locus extremely challenging. Employing complementary high-throughput CRISPR interference (CRISPRi) and activation (CRISPRa) epigenetic screens coupled with single-cell transcriptome profiling across three distinct human cell types, we identified hundreds of new connections between cis -regulatory elements (CREs) and their target genes in this locus. These CRE-gene links are largely cell type-specific and act as enhancers. Additionally, some CREs have complex features, including harboring both active and repressive histone marks, lacking chromatin accessibility, targeting multiple genes, or acting as silencers. Computational methods fail to predict a majority of these CRE-gene connections. These findings emphasize the potential for functional perturbation experiments to dissect complex loci and reveal shared and cell type-specific regulatory mechanisms relevant to genomics of complex diseases. Collectively, this study provides a unique resource for understanding the complex regulatory landscape within the MHC locus and supports the need for creating new models that encompass CRE-gene interactions, cell type-specific gene expression, and disease genetics in the noncoding genome.

Journal Article↗

PubMed2006

Distant conserved sequences flanking endothelial-specific promoters contain tissue-specific DNase-hypersensitive sites and over-represented motifs.

The transcriptional regulation of genes is a complex process, particularly for genes exhibiting a tissue-specific pattern of expression. We studied 28 genes that are expressed primarily in endothelial cells, another 28 genes that are expressed highly, but not exclusively, in cultured endothelial cells, and three control sets, consisting of genes not expressed in endothelium, genes expressed in neural tissues and housekeeping genes. For each gene, we identified conserved non-coding sequences (CNSs) of lengths 50 to >1000 nucleotides, located within the upstream intergenic region (from 500 to as far as 200 000 nucleotides upstream from the transcription start) or within the first intron. As a functional test, we assayed the CNSs from the set of endothelial cell-specific genes (EC-CNSs) for DNase hypersensitivity. Among 262 distant EC-CNSs, 33% are hypersensitive (HS) in endothelial cells, whereas only 16% are HS in control fibroblasts. A search for short sequence patterns revealed a number of motifs which are over-represented in EC-CNSs relative to CNSs from the control gene sets. In particular, the motif SAGGAAR is strongly and consistently over-represented among EC-CNSs, and is more over-represented in HS CNSs than in non-HS CNSs. CNSs which contain this motif are no closer to the promoter than an average CNS. This motif contains the core element of binding sites from the Ets family of transcription factors. Thus, one or several factors from this family may play a key role in the regulation of endothelial gene expression.

5' Flanking Region↗

PubMed2006

Genome-wide analysis of menin binding provides insights into MEN1 tumorigenesis.

Multiple endocrine neoplasia type I (MEN1) is a familial cancer syndrome characterized primarily by tumors of multiple endocrine glands. The gene for MEN1 encodes a ubiquitously expressed tumor suppressor protein called menin. Menin was recently shown to interact with several components of a trithorax family histone methyltransferase complex including ASH2, Rbbp5, WDR5, and the leukemia proto-oncoprotein MLL. To elucidate menin's role as a tumor suppressor and gain insights into the endocrine-specific tumor phenotype in MEN1, we mapped the genomic binding sites of menin, MLL1, and Rbbp5, to approximately 20,000 promoters in HeLa S3, HepG2, and pancreatic islet cells using the strategy of chromatin-immunoprecipitation coupled with microarray analysis. We found that menin, MLL1, and Rbbp5 localize to the promoters of thousands of human genes but do not always bind together. These data suggest that menin functions as a general regulator of transcription. We also found that factor occupancy generally correlates with high gene expression but that the loss of menin does not result in significant changes in most transcript levels. One exception is the developmentally programmed transcription factor, HLXB9, which is overexpressed in islets in the absence of menin. Our findings expand the realm of menin-targeted genes several hundred-fold beyond that previously described and provide potential insights to the endocrine tumor bias observed in MEN1 patients.

Cell Line, Tumor↗

PubMed2006

Statistics for ChIP-chip and DNase hypersensitivity experiments on NimbleGen arrays.

Data obtained from high-density oligonucleotide tiling arrays present new computational challenges for users. This chapter presents ACME (Algorithm for Capturing Microarray Enrichment), a computer program developed for the analysis of data obtained using NimbleGen-tiled microarrays. ACME identifies signals or "peaks" in tiled array data using a simple sliding window and threshold strategy and assigns a probability value (p value) to each and every probe on the array. We present data indicating that this approach can be applied successfully to at least two different genomic applications involving tiled arrays: ChIP-chip and DNase-chip. In addition to highlighting previously described methods for analyzing tiled array data, we provide recommendations for assessing the quality of ChIP-chip and DNase-chip data, suggestions for optimizing the use of ACME, and descriptions of several of ACME features designed to facilitate interpretation of processed tiled array data. ACME is written in R language and is freely available upon request or through Bioconductor.

Algorithms↗

PubMed2006

DNase-chip: a high-resolution method to identify DNase I hypersensitive sites using tiled microarrays.

Mapping DNase I hypersensitive sites is an accurate method of identifying the location of gene regulatory elements, including promoters, enhancers, silencers and locus control regions. Although Southern blots are the traditional method of identifying DNase I hypersensitive sites, the conventional manual method is not readily scalable to studying large chromosomal regions, much less the entire genome. Here we describe DNase-chip, an approach that can rapidly identify DNase I hypersensitive sites for any region of interest, or potentially for the entire genome, by using tiled microarrays. We used DNase-chip to identify DNase I hypersensitive sites accurately from a representative 1% of the human genome in both primary and immortalized cell types. We found that although most DNase I hypersensitive sites were present in both cell types studied, some of them were cell-type specific. This method can be applied globally or in a targeted fashion to any tissue from any species with a sequenced genome.

Cells, Cultured↗

PubMed2005

Genome-wide mapping of DNase hypersensitive sites using massively parallel signature sequencing (MPSS).

A major goal in genomics is to understand how genes are regulated in different tissues, stages of development, diseases, and species. Mapping DNase I hypersensitive (HS) sites within nuclear chromatin is a powerful and well-established method of identifying many different types of regulatory elements, but in the past it has been limited to analysis of single loci. We have recently described a protocol to generate a genome-wide library of DNase HS sites. Here, we report high-throughput analysis, using massively parallel signature sequencing (MPSS), of 230,000 tags from a DNase library generated from quiescent human CD4+ T cells. Of the tags that uniquely map to the genome, we identified 14,190 clusters of sequences that group within close proximity to each other. By using a real-time PCR strategy, we determined that the majority of these clusters represent valid DNase HS sites. Approximately 80% of these DNase HS sites uniquely map within one or more annotated regions of the genome believed to contain regulatory elements, including regions 2 kb upstream of genes, CpG islands, and highly conserved sequences. Most DNase HS sites identified in CD4+ T cells are also HS in CD8+ T cells, B cells, hepatocytes, human umbilical vein endothelial cells (HUVECs), and HeLa cells. However, approximately 10% of the DNase HS sites are lymphocyte specific, indicating that this procedure can identify gene regulatory elements that control cell type specificity. This strategy, which can be applied to any cell line or tissue, will enable a better understanding of how chromatin structure dictates cell function and fate.

Cell Differentiation↗

PubMed2004

Identifying gene regulatory elements by genome-wide recovery of DNase hypersensitive sites.

Analysis of the human genome sequence has identified approximately 25000-30000 protein-coding genes, but little is known about how most of these are regulated. Mapping DNase I hypersensitive (HS) sites has traditionally represented the gold-standard experimental method for identifying regulatory elements, but the labor-intensive nature of this technique has limited its application to only a small number of human genes. We have developed a protocol to generate a genome-wide library of gene regulatory sequences by cloning DNase HS sites. We generated a library of DNase HS sites from quiescent primary human CD4(+) T cells and analyzed approximately 5600 of the resulting clones. Compared to sequences from randomly generated in silico libraries, sequences from these clones were found to map more frequently to regions of the genome known to contain regulatory elements, such as regions upstream of genes, within CpG islands, and in sequences that align between mouse and human. These cloned sites also tend to map near genes that have detectable transcripts in CD4(+) T cells, demonstrating that transcriptionally active regions of the genome are being selected. Validation of putative regulatory elements was achieved by repeated recovery of the same sequence and real-time PCR. This cloning strategy, which can be scaled up and applied to any cell line or tissue, will be useful in identifying regulatory elements controlling global expression differences that delineate tissue types, stages of development, and disease susceptibility.

CD4-Positive T-Lymphocytes↗