CiteULike: inesdesantiago's integration

Mapping the Genetic Architecture of Gene Expression in Human Liver.

Eric E Schadt — 2008-05-09T17:18:18-00:00

PLoS biology, Vol. 6, No. 5. (6 May 2008)

Genetic variants that are associated with common human diseases do not lead directly to disease, but instead act on intermediate, molecular phenotypes that in turn induce changes in higher-order disease traits. Therefore, identifying the molecular phenotypes that vary in response to changes in DNA and that also associate with changes in disease traits has the potential to provide the functional information required to not only identify and validate the susceptibility genes that are directly affected by changes in DNA, but also to understand the molecular networks in which such genes operate and how changes in these networks lead to changes in disease traits. Toward that end, we profiled more than 39,000 transcripts and we genotyped 782,476 unique single nucleotide polymorphisms (SNPs) in more than 400 human liver samples to characterize the genetic architecture of gene expression in the human liver, a metabolically active tissue that is important in a number of common human diseases, including obesity, diabetes, and atherosclerosis. This genome-wide association study of gene expression resulted in the detection of more than 6,000 associations between SNP genotypes and liver gene expression traits, where many of the corresponding genes identified have already been implicated in a number of human diseases. The utility of these data for elucidating the causes of common human diseases is demonstrated by integrating them with genotypic and expression data from other human and mouse populations. This provides much-needed functional support for the candidate susceptibility genes being identified at a growing number of genetic loci that have been identified as key drivers of disease from genome-wide association studies of disease. By using an integrative genomics approach, we highlight how the gene RPS26 and not ERBB3 is supported by our data as the most likely susceptibility gene for a novel type 1 diabetes locus recently identified in a large-scale, genome-wide association study. We also identify SORT1 and CELSR2 as candidate susceptibility genes for a locus recently associated with coronary artery disease and plasma low-density lipoprotein cholesterol levels in the process.

Identification of active transcriptional regulatory modules by the functional assay of DNA from nucleosome-free regions.

Mahesh Yaragatti — 2008-05-01T09:02:43-00:00

Genome research (25 April 2008)

The identification of transcriptional regulatory modules within mammalian genomes is a prerequisite to understanding the mechanisms controlling regulated gene expression. While high-throughput microarray- and sequencing-based approaches have been used to map the genomic locations of sites of nuclease hypersensitivity or target DNA sequences bound by specific protein factors, the identification of regulatory elements using functional assays, which would provide important complementary data, has been relatively rare. Here we present a method that permits the functional identification of active transcriptional regulatory modules using a simple procedure for the isolation and analysis of DNA derived from nucleosome-free regions (NFRs), the 2% of the cellular genome that contains these elements. The more than 100 new active regulatory DNAs identified in this manner from F9 cells correspond to both promoter-proximal and distal elements, and display several features predicted for endogenous transcriptional regulators, including localization within DNase-accessible chromatin and CpG islands, and proximity to expressed genes. Furthermore, comparison with published ChIP-seq data of ES-cell chromatin shows that the functional elements we identified correspond with genomic regions enriched for H3K4me3, a histone modification associated with active transcriptional regulatory elements, and that the correspondence of H3K4me3 with our promoter-distal elements is largely ES-cell specific. The majority of the distal elements exhibit enhancer activity. Importantly, these functional DNA fragments are an average 149 bp in length, greatly facilitating future applications to identify transcription factor binding sites mediating their activity. Thus, this approach provides a tool for the high-resolution identification of the functional components of active promoters and enhancers.

Finding noncoding RNA transcripts from low abundance expressed sequence tags

Chenghai Xue — 2008-08-04T17:54:57-00:00

Cell Res, Vol. 18, No. 6. (0000), pp. 695-700.

An Integrated Approach for the Analysis of Biological Pathways using Mixed Models

Lily Wang — 2008-07-07T18:45:50-00:00

PLoS Genet, Vol. 4, No. 7. (4 July 2008), e1000115.

Gene class, ontology, or pathway testing analysis has become increasingly popular in microarray data analysis. Such approaches allow the integration of gene annotation databases, such as Gene Ontology and KEGG Pathway, to formally test for subtle but coordinated changes at a system level. Higher power in gene class testing is gained by combining weak signals from a number of individual genes in each pathway. We propose an alternative approach for gene-class testing based on mixed models, a class of statistical models that: : provides the ability to model and borrow strength across genes that are both up and down in a pathway, operates within a well-established statistical framework amenable to direct control of false positive or false discovery rates, exhibits improved power over widely used methods under normal location-based alternative hypotheses, and handles complex experimental designs for which permutation resampling is difficult. We compare the properties of this mixed models approach with nonparametric method GSEA and parametric method PAGE using a simulation study, and illustrate its application with a diabetes data set and a dose-response data set.

The Insulator Binding Protein CTCF Positions 20 Nucleosomes around Its Binding Sites across the Human Genome

Yutao Fu — 2008-07-25T22:24:36-00:00

PLoS Genet, Vol. 4, No. 7. (25 July 2008), e1000138.

Chromatin structure plays an important role in modulating the accessibility of genomic DNA to regulatory proteins in eukaryotic cells. We performed an integrative analysis on dozens of recent datasets generated by deep-sequencing and high-density tiling arrays, and we discovered an array of well-positioned nucleosomes flanking sites occupied by the insulator binding protein CTCF across the human genome. These nucleosomes are highly enriched for the histone variant H2A.Z and 11 histone modifications. The distances between the center positions of the neighboring nucleosomes are largely invariant, and we estimate them to be 185 bp on average. Surprisingly, subsets of nucleosomes that are enriched in different histone modifications vary greatly in the lengths of DNA protected from micrococcal nuclease cleavage (106–164 bp). The nucleosomes enriched in those histone modifications previously implicated to be correlated with active transcription tend to contain less protected DNA, indicating that these modifications are correlated with greater DNA accessibility. Another striking result obtained from our analysis is that nucleosomes flanking CTCF sites are much better positioned than those downstream of transcription start sites, the only genomic feature previously known to position nucleosomes genome-wide. This nucleosome-positioning phenomenon is not observed for other transcriptional factors for which we had genome-wide binding data. We suggest that binding of CTCF provides an anchor point for positioning nucleosomes, and chromatin remodeling is an important component of CTCF function.

Multivariate proteomic analysis of murine embryonic stem cell self-renewal versus differentiation signaling

Wendy Prudhomme — 2007-09-28T01:32:28-00:00

Proceedings of the National Academy of Sciences, Vol. 101, No. 9. (2 March 2004), pp. 2900-2905.

A number of extracellular stimuli, including soluble cytokines and insoluble matrix factors, are known to influence murine embryonic stem cell self-renewal and differentiation behavioral responses via intracellular signaling pathways, but their net effects in combination are difficult to understand. To gain insight concerning key intracellular signals governing these behavioral responses, we employ a multivariate systems analysis of proteomic data generated from combinatorial stimulation of mouse embryonic stem cells by fibronectin, laminin, leukemia-inhibitory factor, and fibroblast growth factor 4. Phosphorylation states of 31 intracellular signaling network components were obtained across 16 different stimulus conditions at three time points by quantitative Western blotting, and partial-least-squares modeling was used to determine which components were most strongly correlated with cell proliferation and differentiation rate constants obtained from flow cytometry measurements of Oct-4 expression levels. This data-driven, multivariate (16 conditions x 31 components x 3 time points = approx1,500 values) proteomic approach identified a set of signaling network components most critically associated (positively or negatively) with differentiation (Stat3, Raf1, MEK, and ERK), proliferation of undifferentiated cells (MEK and ERK), and proliferation of differentiated cells (PKBalpha, Stat3, Src, and PKCepsilon). These predictions were found to be consistent with previous in vivo literature, along with direct in vitro test here by a peptide inhibitor of PKCepsilon. Our results demonstrate how a computational systems biology approach can elucidate key sets of intracellular signaling protein activities that combine to govern cell phenotypic responses to extracellular cues. 10.1073/pnas.0308768101

Integrated Genomic and Proteomic Analyses of Gene Expression in Mammalian Cells

Qiang Tian — 2008-07-24T15:17:07-00:00

Mol Cell Proteomics, Vol. 3, No. 10. (1 October 2004), pp. 960-969.

Using DNA microarrays together with quantitative proteomic techniques (ICAT reagents, two-dimensional DIGE, and MS), we evaluated the correlation of mRNA and protein levels in two hematopoietic cell lines representing distinct stages of myeloid differentiation, as well as in the livers of mice treated for different periods of time with three different peroxisome proliferative activated receptor agonists. We observe that the differential expression of mRNA (up or down) can capture at most 40% of the variation of protein expression. Although the overall pattern of protein expression is similar to that of mRNA expression, the incongruent expression between mRNAs and proteins emphasize the importance of posttranscriptional regulatory mechanisms in cellular development or perturbation that can be unveiled only through integrated analyses of both proteins and mRNAs. 10.1074/mcp.M400055-MCP200

The human transcriptome map reveals extremes in gene density, intron length, GC content, and repeat pattern for domains of highly and weakly expressed genes.

R Versteeg — 2006-12-20T01:59:30-00:00

Genome Res, Vol. 13, No. 9. (September 2003), pp. 1998-2004.

The chromosomal gene expression profiles established by the Human Transcriptome Map (HTM) revealed a clustering of highly expressed genes in about 30 domains, called ridges. To physically characterize ridges, we constructed a new HTM based on the draft human genome sequence (HTMseq). Expression of 25,003 genes can be analyzed online in a multitude of tissues (http://bioinfo.amc.uva.nl/HTMseq). Ridges are found to be very gene-dense domains with a high GC content, a high SINE repeat density, and a low LINE repeat density. Genes in ridges have significantly shorter introns than genes outside of ridges. The HTMseq also identifies a significant clustering of weakly expressed genes in domains with fully opposite characteristics (antiridges). Both types of domains are open to tissue-specific expression regulation, but the maximal expression levels in ridges are considerably higher than in antiridges. Ridges are therefore an integral part of a higher order structure in the genome related to transcriptional regulation.

Combinatorial patterns of histone acetylations and methylations in the human genome

Zhibin Wang — 2008-06-26T19:38:09-00:00

Nat Genet, Vol. 40, No. 7. (July 2008), pp. 897-903.

Dissecting direct reprogramming through integrative genomic analysis

Tarjei Mikkelsen — 2008-05-29T14:02:31-00:00

Nature (28 May 2008)

Genomic Maps and Comparative Analysis of Histone Modifications in Human and Mouse

Bradley Bernstein — 2007-11-11T21:04:42-00:00

Cell, Vol. 120, No. 2. (28 January 2005), pp. 169-181.

We mapped histone H3 lysine 4 di- and trimethylation and lysine 9/14 acetylation across the nonrepetitive portions of human chromosomes 21 and 22 and compared patterns of lysine 4 dimethylation for several orthologous human and mouse loci. Both chromosomes show punctate sites enriched for modified histones. Sites showing trimethylation correlate with transcription starts, while those showing mainly dimethylation occur elsewhere in the vicinity of active genes. Punctate methylation patterns are also evident at the cytokine and IL-4 receptor loci. The Hox clusters present a strikingly different picture, with broad lysine 4-methylated regions that overlay multiple active genes. We suggest these regions represent active chromatin domains required for the maintenance of Hox gene expression. Methylation patterns at orthologous loci are strongly conserved between human and mouse even though many methylated sites do not show sequence conservation notably higher than background. This suggests that the DNA elements that direct the methylation represent only a small fraction of the region or lie at some distance from the site.

CpG Island Methylation in Human Lymphocytes Is Highly Correlated with DNA Sequence, Repeats, and Predicted DNA Structure

Christoph Bock — 2006-12-20T21:34:15-00:00

PLoS Genetics, Vol. 2, No. 3. (1 March 2006), e26.

CpG island methylation plays an important role in epigenetic gene control during mammalian development and is frequently altered in disease situations such as cancer. The majority of CpG islands is normally unmethylated, but a sizeable fraction is prone to become methylated in various cell types and pathological situations. The goal of this study is to show that a computational epigenetics approach can discriminate between CpG islands that are prone to methylation from those that remain unmethylated. We develop a bioinformatics scoring and prediction method on the basis of a set of 1,184 DNA attributes, which refer to sequence, repeats, predicted structure, CpG islands, genes, predicted binding sites, conservation, and single nucleotide polymorphisms. These attributes are scored on 132 CpG islands across the entire human Chromosome 21, whose methylation status was previously established for normal human lymphocytes. Our results show that three groups of DNA attributes, namely certain sequence patterns, specific DNA repeats, and a particular DNA structure, are each highly correlated with CpG island methylation (correlation coefficients of 0.64, 0.66, and 0.49, respectively). We predicted, and subsequently experimentally examined 12 CpG islands from human Chromosome 21 with unknown methylation patterns and found more than 90% of our predictions to be correct. In addition, we applied our prediction method to analyzing Human Epigenome Project methylation data on human Chromosome 6 and again observed high prediction accuracy. In summary, our results suggest that DNA composition of CpG islands (sequence, repeats, and structure) plays a significant role in predisposing CpG islands for DNA methylation. This finding may have a strong impact on our understanding of changes in CpG island methylation in development and disease.

CpG Island Mapping by Epigenome Prediction.

Christoph Bock — 2007-06-17T06:58:42-00:00

PLoS Comput Biol, Vol. 3, No. 6. (8 June 2007)

CpG islands were originally identified by epigenetic and functional properties, namely, absence of DNA methylation and frequent promoter association. However, this concept was quickly replaced by simple DNA sequence criteria, which allowed for genome-wide annotation of CpG islands in the absence of large-scale epigenetic datasets. Although widely used, the current CpG island criteria incur significant disadvantages: (1) reliance on arbitrary threshold parameters that bear little biological justification, (2) failure to account for widespread heterogeneity among CpG islands, and (3) apparent lack of specificity when applied to the human genome. This study is driven by the idea that a quantitative score of "CpG island strength" that incorporates epigenetic and functional aspects can help resolve these issues. We construct an epigenome prediction pipeline that links the DNA sequence of CpG islands to their epigenetic states, including DNA methylation, histone modifications, and chromatin accessibility. By training support vector machines on epigenetic data for CpG islands on human Chromosomes 21 and 22, we identify informative DNA attributes that correlate with open versus compact chromatin structures. These DNA attributes are used to predict the epigenetic states of all CpG islands genome-wide. Combining predictions for multiple epigenetic features, we estimate the inherent CpG island strength for each CpG island in the human genome, i.e., its inherent tendency to exhibit an open and transcriptionally competent chromatin structure. We extensively validate our results on independent datasets, showing that the CpG island strength predictions are applicable and informative across different tissues and cell types, and we derive improved maps of predicted "bona fide" CpG islands. The mapping of CpG islands by epigenome prediction is conceptually superior to identifying CpG islands by widely used sequence criteria since it links CpG island detection to their characteristic epigenetic and functional states. And it is superior to purely experimental epigenome mapping for CpG island detection since it abstracts from specific properties that are limited to a single cell type or tissue. In addition, using computational epigenetics methods we could identify high correlation between the epigenome and characteristics of the DNA sequence, a finding which emphasizes the need for a better understanding of the mechanistic links between genome and epigenome.

Statistical analysis of the genomic distribution and correlation of regulatory elements in the ENCODE regions

Zhengdong Zhang — 2007-06-14T15:33:33-00:00

Genome Res., Vol. 17, No. 6. (1 June 2007), pp. 787-797.

The comprehensive inventory of functional elements in 44 human genomic regions carried out by the ENCODE Project Consortium enables for the first time a global analysis of the genomic distribution of transcriptional regulatory elements. In this study we developed an intuitive and yet powerful approach to analyze the distribution of regulatory elements found in many different ChIP-chip experiments on a 10[~]100-kb scale. First, we focus on the overall chromosomal distribution of regulatory elements in the ENCODE regions and show that it is highly nonuniform. We demonstrate, in fact, that regulatory elements are associated with the location of known genes. Further examination on a local, single-gene scale shows an enrichment of regulatory elements near both transcription start and end sites. Our results indicate that overall these elements are clustered into regulatory rich "islands" and poor "deserts." Next, we examine how consistent the nonuniform distribution is between different transcription factors. We perform on all the factors a multivariate analysis in the framework of a biplot, which enhances biological signals in the experiments. This groups transcription factors into sequence-specific and sequence-nonspecific clusters. Moreover, with experimental variation carefully controlled, detailed correlations show that the distribution of sites was generally reproducible for a specific factor between different laboratories and microarray platforms. Data sets associated with histone modifications have particularly strong correlations. Finally, we show how the correlations between factors change when only regulatory elements far from the transcription start sites are considered. 10.1101/gr.5573107