Geert Geeven - Academia.edu (original) (raw)

Papers by Geert Geeven

Research paper thumbnail of YAP Drives Growth by Controlling Transcriptional Pause Release from Dynamic Enhancers

Molecular Cell, 2015

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Research paper thumbnail of Special Issue Evaluation of an Affymetrix High-density Oligonucleotide Microarray Platform as a Measurement System

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Research paper thumbnail of Characterization and dynamics of pericentromere-associated domains in mice

Genome Research, 2015

Despite recent progress in genome topology knowledge, the role of repeats, which make up the majo... more Despite recent progress in genome topology knowledge, the role of repeats, which make up the majority of mammalian genomes, remains elusive. Satellites repeats are highly abundant sequences that cluster around centromeres, attract pericentromeric heterochromatin and aggregate into nuclear chromocenters. These nuclear landmark structures are assumed to form a repressive compartment in the nucleus to which genes are recruited for silencing. Here we designed a strategy for genome-wide identification of pericentromere-associated domains (PADs) in different mouse cell types. The ~1000 PADs and non-PADs have similar chromatin states in embryonic stem cells, but during lineage commitment chromocenters progressively associate with constitutively inactive genomic regions at the nuclear periphery. This suggests that PADs are not actively recruited to chromocenters, but that chromocenters are themselves attracted to inactive chromatin compartments. However, we also found that experimentally induced proximity of an active locus to chromocenters was sufficient to cause gene repression. Collectively, our data suggests that rather than driving nuclear organization, pericentromeric satellite repeats mostly co-segregate with inactive genomic regions into nuclear compartments where they can contribute to stable maintenance of the repressed status of proximal chromosomal regions.

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Research paper thumbnail of Genome-wide profiling of p53-regulated enhancer RNAs uncovers a subset of enhancers controlled by a lncRNA

Nature Communications, 2015

p53 binds enhancers to regulate key target genes. Here, we globally mapped p53-regulated enhancer... more p53 binds enhancers to regulate key target genes. Here, we globally mapped p53-regulated enhancers by looking at enhancer RNA (eRNA) production. Intriguingly, while many p53-induced enhancers contained p53-binding sites, most did not. As long non-coding RNAs (lncRNAs) are prominent regulators of chromatin dynamics, we hypothesized that p53-induced lncRNAs contribute to the activation of enhancers by p53. Among p53-induced lncRNAs, we identified LED and demonstrate that its suppression attenuates p53 function. Chromatin-binding and eRNA expression analyses show that LED associates with and activates strong enhancers. One prominent target of LED was located at an enhancer region within CDKN1A gene, a potent p53-responsive cell cycle inhibitor. LED knockdown reduces CDKN1A enhancer induction and activity, and cell cycle arrest following p53 activation. Finally, promoter-associated hypermethylation analysis shows silencing of LED in human tumours. Thus, our study identifies a new layer of complexity in the p53 pathway and suggests its dysregulation in cancer.

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Research paper thumbnail of Large-Scale Identification of Coregulated Enhancer Networks in the Adult Human Brain

Cell Reports, 2014

Understanding the complexity of the human brain and its functional diversity remain a major chall... more Understanding the complexity of the human brain and its functional diversity remain a major challenge. Distinct anatomical regions are involved in an array of processes, including organismal homeostasis, cognitive functions, and susceptibility to neurological pathologies, many of which define our species. Distal enhancers have emerged as key regulatory elements that acquire histone modifications in a cell- and species-specific manner, thus enforcing specific gene expression programs. Here, we survey the epigenomic landscape of promoters and cis-regulatory elements in 136 regions of the adult human brain. We identify a total of 83,553 promoter-distal H3K27ac-enriched regions showing global characteristics of brain enhancers. We use coregulation of enhancer elements across many distinct regions of the brain to uncover functionally distinct networks at high resolution and link these networks to specific neuroglial functions. Furthermore, we use these data to understand the relevance of noncoding genomic variations previously linked to Parkinson's disease incidence.

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Research paper thumbnail of Evaluation of an Affymetrix High-density Oligonucleotide Microarray Platform as a Measurement System

Quality and Reliability Engineering International, 2005

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Research paper thumbnail of LLM3D: a log-linear modeling-based method to predict functional gene regulatory interactions from genome-wide expression data

Nucleic Acids Research, 2011

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Research paper thumbnail of Deletion/Substitution/Addition (DSA) model selection algorithm applied to the study of archaeological settlement patterning

Journal of Archaeological Science, 2011

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Research paper thumbnail of Identification of context-specific gene regulatory networks with GEMULA--gene expression modeling using LAsso

Bioinformatics, 2012

Gene regulatory networks, in which edges between nodes describe interactions between transcriptio... more Gene regulatory networks, in which edges between nodes describe interactions between transcriptional regulators and their target genes, determine the coordinated spatiotemporal expression of genes. Especially in higher organisms, context-specific combinatorial regulation by transcription factors (TFs) is believed to determine cellular states and fates. TF-target gene interactions can be studied using high-throughput techniques such as ChIP-chip or ChIP-Seq. These experiments are time and cost intensive, and further limited by, for instance, availability of high affinity TF antibodies. Hence, there is a practical need for methods that can predict TF-TF and TF-target gene interactions in silico, i.e. from gene expression and DNA sequence data alone. We propose GEMULA, a novel approach based on linear models to predict TF-gene expression associations and TF-TF interactions from experimental data. GEMULA is based on linear models, fast and considers a wide range of biologically plausible models that describe gene expression data as a function of predicted TF binding to gene promoters. We show that models inferred with GEMULA are able to explain roughly 70% of the observed variation in gene expression in the yeast heat shock response. The functional relevance of the inferred TF-TF interactions in these models are validated by different sources of independent experimental evidence. We also have applied GEMULA to an in vitro model of neuronal outgrowth. Our findings confirm existing knowledge on gene regulatory interactions underlying neuronal outgrowth, but importantly also generate new insights into the temporal dynamics of this gene regulatory network that can now be addressed experimentally. The GEMULA R-package is available from http://www.few.vu.nl/~degunst/gemula_1.0.tar.gz.

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Research paper thumbnail of Targeted sequencing by proximity ligation for comprehensive variant detection and local haplotyping

Nature Biotechnology, 2014

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Research paper thumbnail of YAP Drives Growth by Controlling Transcriptional Pause Release from Dynamic Enhancers

Molecular Cell, 2015

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Research paper thumbnail of Special Issue Evaluation of an Affymetrix High-density Oligonucleotide Microarray Platform as a Measurement System

Bookmarks Related papers MentionsView impact

Research paper thumbnail of Characterization and dynamics of pericentromere-associated domains in mice

Genome Research, 2015

Despite recent progress in genome topology knowledge, the role of repeats, which make up the majo... more Despite recent progress in genome topology knowledge, the role of repeats, which make up the majority of mammalian genomes, remains elusive. Satellites repeats are highly abundant sequences that cluster around centromeres, attract pericentromeric heterochromatin and aggregate into nuclear chromocenters. These nuclear landmark structures are assumed to form a repressive compartment in the nucleus to which genes are recruited for silencing. Here we designed a strategy for genome-wide identification of pericentromere-associated domains (PADs) in different mouse cell types. The ~1000 PADs and non-PADs have similar chromatin states in embryonic stem cells, but during lineage commitment chromocenters progressively associate with constitutively inactive genomic regions at the nuclear periphery. This suggests that PADs are not actively recruited to chromocenters, but that chromocenters are themselves attracted to inactive chromatin compartments. However, we also found that experimentally induced proximity of an active locus to chromocenters was sufficient to cause gene repression. Collectively, our data suggests that rather than driving nuclear organization, pericentromeric satellite repeats mostly co-segregate with inactive genomic regions into nuclear compartments where they can contribute to stable maintenance of the repressed status of proximal chromosomal regions.

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Research paper thumbnail of Genome-wide profiling of p53-regulated enhancer RNAs uncovers a subset of enhancers controlled by a lncRNA

Nature Communications, 2015

p53 binds enhancers to regulate key target genes. Here, we globally mapped p53-regulated enhancer... more p53 binds enhancers to regulate key target genes. Here, we globally mapped p53-regulated enhancers by looking at enhancer RNA (eRNA) production. Intriguingly, while many p53-induced enhancers contained p53-binding sites, most did not. As long non-coding RNAs (lncRNAs) are prominent regulators of chromatin dynamics, we hypothesized that p53-induced lncRNAs contribute to the activation of enhancers by p53. Among p53-induced lncRNAs, we identified LED and demonstrate that its suppression attenuates p53 function. Chromatin-binding and eRNA expression analyses show that LED associates with and activates strong enhancers. One prominent target of LED was located at an enhancer region within CDKN1A gene, a potent p53-responsive cell cycle inhibitor. LED knockdown reduces CDKN1A enhancer induction and activity, and cell cycle arrest following p53 activation. Finally, promoter-associated hypermethylation analysis shows silencing of LED in human tumours. Thus, our study identifies a new layer of complexity in the p53 pathway and suggests its dysregulation in cancer.

Bookmarks Related papers MentionsView impact

Research paper thumbnail of Large-Scale Identification of Coregulated Enhancer Networks in the Adult Human Brain

Cell Reports, 2014

Understanding the complexity of the human brain and its functional diversity remain a major chall... more Understanding the complexity of the human brain and its functional diversity remain a major challenge. Distinct anatomical regions are involved in an array of processes, including organismal homeostasis, cognitive functions, and susceptibility to neurological pathologies, many of which define our species. Distal enhancers have emerged as key regulatory elements that acquire histone modifications in a cell- and species-specific manner, thus enforcing specific gene expression programs. Here, we survey the epigenomic landscape of promoters and cis-regulatory elements in 136 regions of the adult human brain. We identify a total of 83,553 promoter-distal H3K27ac-enriched regions showing global characteristics of brain enhancers. We use coregulation of enhancer elements across many distinct regions of the brain to uncover functionally distinct networks at high resolution and link these networks to specific neuroglial functions. Furthermore, we use these data to understand the relevance of noncoding genomic variations previously linked to Parkinson's disease incidence.

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Research paper thumbnail of Evaluation of an Affymetrix High-density Oligonucleotide Microarray Platform as a Measurement System

Quality and Reliability Engineering International, 2005

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Research paper thumbnail of LLM3D: a log-linear modeling-based method to predict functional gene regulatory interactions from genome-wide expression data

Nucleic Acids Research, 2011

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Research paper thumbnail of Deletion/Substitution/Addition (DSA) model selection algorithm applied to the study of archaeological settlement patterning

Journal of Archaeological Science, 2011

Bookmarks Related papers MentionsView impact

Research paper thumbnail of Identification of context-specific gene regulatory networks with GEMULA--gene expression modeling using LAsso

Bioinformatics, 2012

Gene regulatory networks, in which edges between nodes describe interactions between transcriptio... more Gene regulatory networks, in which edges between nodes describe interactions between transcriptional regulators and their target genes, determine the coordinated spatiotemporal expression of genes. Especially in higher organisms, context-specific combinatorial regulation by transcription factors (TFs) is believed to determine cellular states and fates. TF-target gene interactions can be studied using high-throughput techniques such as ChIP-chip or ChIP-Seq. These experiments are time and cost intensive, and further limited by, for instance, availability of high affinity TF antibodies. Hence, there is a practical need for methods that can predict TF-TF and TF-target gene interactions in silico, i.e. from gene expression and DNA sequence data alone. We propose GEMULA, a novel approach based on linear models to predict TF-gene expression associations and TF-TF interactions from experimental data. GEMULA is based on linear models, fast and considers a wide range of biologically plausible models that describe gene expression data as a function of predicted TF binding to gene promoters. We show that models inferred with GEMULA are able to explain roughly 70% of the observed variation in gene expression in the yeast heat shock response. The functional relevance of the inferred TF-TF interactions in these models are validated by different sources of independent experimental evidence. We also have applied GEMULA to an in vitro model of neuronal outgrowth. Our findings confirm existing knowledge on gene regulatory interactions underlying neuronal outgrowth, but importantly also generate new insights into the temporal dynamics of this gene regulatory network that can now be addressed experimentally. The GEMULA R-package is available from http://www.few.vu.nl/~degunst/gemula_1.0.tar.gz.

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Research paper thumbnail of Targeted sequencing by proximity ligation for comprehensive variant detection and local haplotyping

Nature Biotechnology, 2014

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