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Oncogene-induced senescence (OIS) is an inherent and important tumor suppressor mechanism. However, if not removed timely via immune surveillance, senescent cells also have detrimental effects. Although this has mostly been attributed to the senescence-associated secretory phenotype (SASP) of these cells, we recently proposed that “escape” from the senescent state is another unfavorable outcome. The mechanism underlying this phenomenon remains elusive. Here, we exploit genomic and functional data from a prototypical human epithelial cell model carrying an inducible CDC6 oncogene to identify an early-acquired recurrent chromosomal inversion that harbors a locus encoding the circadian transcription factor BHLHE40. This inversion alone suffices for BHLHE40 activation upon CDC6 induction and driving cell cycle re-entry of senescent cells, and malignant transformation. Ectopic overexpression of BHLHE40 prevented induction of CDC6-triggered senescence. We provide strong evidence in support of replication stress-induced genomic instability being a causative factor underlying “escape” from oncogene-induced senescence.

Spatial organization and gene expression of mammalian chromosomes are maintained and regulated in conjunction with cell cycle progression. This link is perturbed once cells enter senescence. The highly abundant HMGB1 protein, known to associate with bent and looped DNA, is actively depleted from senescent cell nuclei to act as an extracellular proinflammatory stimulus. Despite its physiological importance, we still lack understanding of the positioning and functional roles of HMGB1 on chromatin in vivo . To address this, we mapped HMGB1 binding genome-wide in different primary cells using a tailored protocol. We then integrated ChIP-seq and Hi-C data with a knot theory approach to uncover HMGB1 demarcation at the boundaries of particular topologically-associating domains (TADs). These TADs harbor genes involved in the key proinflammatory leg of the senescent transcriptional program. Moreover, we used sCLIP and siRNA-mediated knockdown to show that HMGB1 is a bona fide RNA-binding protein also affecting splicing choices. Together, our findings highlight a broader than hitherto assumed role for HMGB1 in chromatin homeostasis connected to cell cycle potency, and allow us to postulate a “rheostat” model for HMGB function with implications in cancer biology.

RWTH Aachen University, Diss., 2016; 128 Seiten(2016)., Transcriptional regulation orchestrates the proper temporal and spatial expression of genes. The identification of transcriptional regulatory elements, such as transcription factor binding sites (TFBSs), is crucial to understand regulatory networks driving cellular processes such as cell development and the onset of diseases.The standard computational approach is to use sequence-based methods, which search over the genome’s DNA for sequences representing the DNA binding affinity sequence of transcription factors (TFs). However, this approach is not able to predict active binding sites, i.e. binding sites that are being currently bound by TFs at a particular cell state. This happens as the sequence-based methods do not account for the fact that the chromatin dynamically changes its state between an open form (and accessible to TF binding) and closed (not accessible by TFs).Advances in next-generation sequencing techniques have enabled the measurement of such open chromatin regions in a genome-wide manner with assays such as the chromatin immunoprecipitation followed by massive sequencing (ChIP-seq) and DNase I digestion followed by massive sequencing (DNase-seq). Current research has proven that such open chromatin genome-wide assays improve sequence-based detection of active TFBSs. The rationale is to restrict the sequence-based search of binding sites to genomic regions where these assays indicate the chromatin is open and accessible for TF binding, in a cell-specific manner.We propose the first computational framework which integrates both DNase-seq and ChIP-seq data to perform predictions of active TFBSs. We have previously observed that there is a distinctive pattern at active TFBSs regarding both DNase-seq and ChIP-seq data. Our framework treats these data using signal normalization strategies and searches for these distinctive patterns, the so-called “footprints”, by segmenting the genome using hidden Markov models (HMMs). Given that, our framework - termed HINT (HMM-based identification of TF footprints) - is categorized as a “computational footprinting method”.We evaluate our computational footprinting method by comparing the footprint predictions to experimentally verified active TFBSs. Our evaluation approach creates statistics which enables the comparison between our method and competing computational footprinting methods. Our comparative experiment is the most complete so far, with a total of 14 computational footprinting methods and 233 TFs evaluated.Furthermore, we successfully applied our computational footprinting method HINT in two different biological studies to identify regulatory elements involved in specific biological conditions. HINT has proven to be a useful computational framework in biological studies involving regulatory genomics., Published by Aachen