Polymorphic Cis- and Trans-Regulation of Human Gene Expression

Howard Hughes Medical Institute, Philadelphia, Pennsylvania, USA.
PLoS Biology (Impact Factor: 9.34). 09/2010; 8(9). DOI: 10.1371/journal.pbio.1000480
Source: PubMed


Author Summary
Cellular characteristics and functions are determined largely by gene expression and expression levels differ among individuals, however it is not clear how these levels are regulated. While many cis-acting DNA sequence variants in promoters and enhancers that influence gene expression have been identified, only a few polymorphic trans-regulators of human genes are known. Here, we used human B-cells from individuals belonging to large families and identified polymorphic trans-regulators for about 1,000 human genes. We validated these results by gene knockdown, metabolic perturbation studies and chromosome conformation capture assays. Although these regulatory relationships were identified in cultured B-cells, we show that some of the relationships were also found in primary fibroblasts. The large number of regulators allowed us to better understand gene expression regulation, to uncover new gene functions, and to identify their roles in disease processes. This study shows that genetic variation is a powerful tool not only for gene mapping but also to study gene interaction and regulation.

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Available from: Isabel Xiaorong Wang, May 14, 2014
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    • "Next, we study the relation between the genetic context and the structural connectivity of our colon cancer network GBC3Net in the following way. Interactions between genes on separate or the same chromosome can be seen as trans-interactions and cis-interactions, analogous to the trans- and cis-regulation of genes [45]. However, we would like to emphasize that there is a crucial difference between both types of connections. "
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    ABSTRACT: Cancer is a complex disease that has proven to be difficult to understand on the single-gene level. For this reason a functional elucidation needs to take interactions among genes on a systems-level into account. In this study, we infer a colon cancer network from a large-scale gene expression data set by using the method BC3Net. We provide a structural and a functional analysis of this network and also connect its molecular interaction structure with the chromosomal locations of the genes enabling the definition of cis- and trans-interactions. Furthermore, we investigate the interaction of genes that can be found in close neighborhoods on the chromosomes to gain insight into regulatory mechanisms. To our knowledge this is the first study analyzing the genome-scale colon cancer network.
    BMC Bioinformatics 05/2014; 15(Suppl 6):S6. DOI:10.1186/1471-2105-15-S6-S6 · 2.58 Impact Factor
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    • "Hence, 79.57% of the interactions connect genes on different chromosomes. Interactions between genes on separate or the same chromosome can be seen as trans-interactions and cis-interactions, in analogy to the trans- and cis-regulation of genes (Cheung et al., 2010). However, we would like to emphasize that there is a crucial difference between both types of connections. "
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    ABSTRACT: In this study, we infer the breast cancer gene regulatory network from gene expression data. This network is obtained from the application of the BC3Net inference algorithm to a large-scale gene expression data set consisting of 351 patient samples. In order to elucidate the functional relevance of the inferred network, we are performing a Gene Ontology (GO) analysis for its structural components. Our analysis reveals that most significant GO-terms we find for the breast cancer network represent functional modules of biological processes that are described by known cancer hallmarks, including translation, immune response, cell cycle, organelle fission, mitosis, cell adhesion, RNA processing, RNA splicing and response to wounding. Furthermore, by using a curated list of census cancer genes, we find an enrichment in these functional modules. Finally, we study cooperative effects of chromosomes based on information of interacting genes in the beast cancer network. We find that chromosome 21 is most coactive with other chromosomes. To our knowledge this is the first study investigating the genome-scale breast cancer network.
    Frontiers in Genetics 02/2014; 5:15. DOI:10.3389/fgene.2014.00015
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    • "Pickrell et al. and Montgomery et al. used low-coverage (4-25 million short reads per individual) single-end and paired-end RNA-seq to characterize gene expression and splicing in LCLs derived from 69 Nigerian [16] and 60 CEU (Utah residents of European descent from CEPH-Centre d'Etude du Polymporphisme Humain) [17] individuals. Cheung et al. independently generated an RNA-seq dataset on 41 CEU individuals at a deeper coverage of 28.4-66 million single-end reads per individual, although the authors restricted their data analysis to expression QTLs [18]. "
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    ABSTRACT: To characterize the genetic variation of alternative splicing, we develop GLiMMPS, a robust statistical method for detecting splicing quantitative trait loci (sQTLs) from RNA-seq data. GLiMMPS takes into account the individual variation in sequencing coverage and the noise prevalent in RNA-seq data. Analyses of simulated and real RNA-seq datasets demonstrate that GLiMMPS outperforms competing statistical models. Quantitative RT-PCR tests of 26 randomly selected GLiMMPS sQTLs yielded a validation rate of 100%. As population-scale RNA-seq studies become increasingly affordable and popular, GLiMMPS provides a useful tool for elucidating the genetic variation of alternative splicing in humans and model organisms.
    Genome biology 07/2013; 14(7):R74. DOI:10.1186/gb-2013-14-7-r74 · 10.81 Impact Factor
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