Genome-wide maps of DNase I hypersensitive sites (DHSs) reveal that most human promoters contain perpetually active cis-regulatory elements between −150 bp and +50 bp (−150/+50 bp) relative to the transcription start site (TSS). Transcription factors (TFs) recruit cofactors (chromatin remodelers, histone/protein-modifying enzymes, and scaffold proteins) to these elements in order to organize the local chromatin structure and coordinate the balance of post-translational modifications nearby, contributing to the overall regulation of transcription. However, the rules of TF-mediated cofactor recruitment to the −150/+50 bp promoter regions remain poorly understood. Here, we provide evidence for a general model in which a series of cis-regulatory elements (here termed ‘cardinal’ motifs) prefer acting individually, rather than in fixed combinations, within the −150/+50 bp regions to recruit TFs that dictate cofactor signatures distinctive of specific promoter subsets. Subsequently, human promoters can be subclassified based on the presence of cardinal elements and their associated cofactor signatures. In this study, furthermore, we have focused on promoters containing the nuclear respiratory factor 1 (NRF1) motif as the cardinal cis-regulatory element and have identified the pervasive association of NRF1 with the cofactor lysine-specific demethylase 1 (LSD1/KDM1A). This signature might be distinctive of promoters regulating nuclear-encoded mitochondrial and other particular genes in at least some cells. Together, we propose that decoding a signature-based, expanded model of control at proximal promoter regions should lead to a better understanding of coordinated regulation of gene transcription.
BackgroundLysine-specific demethylase 1 (LSD1, also known as KDM1A and AOF2) is a chromatin-modifying activity that catalyzes the removal of methyl groups from lysine residues in histone and non-histone proteins, regulating gene transcription. LSD1 is overexpressed in several cancer types, and chemical inhibition of the LSD1 activity has been proposed as a candidate cancer therapy. Here, we examine the levels of LSD1 mRNA in human ovarian tumors and the cytotoxicity of several chemical LSD1 inhibitors in a panel of ovarian cancer cell lines.MethodsWe measured LSD1 mRNA levels in a cohort of n = 177 normal and heterogeneous tumor specimens by quantitative real time-PCR (qRT-PCR). Tumors were classified by FIGO stage, FIGO grade, and histological subtypes. We tested the robustness of our analyses in an independent cohort of n = 573 serous tumor specimens (source: TCGA, based on microarray). Statistical analyses were based on Kruskal-Wallis/Dunn’s and Mann Whitney tests. Changes in LSD1 mRNA levels were also correlated with transcriptomic alterations at genome-wide scale. Effects on cell viability (MTS/PMS assay) of six LSD1 inhibitors (pargyline, TCP, RN-1, S2101, CAS 927019-63-4, and CBB1007) were also evaluated in a panel of ovarian cancer cell lines (SKOV3, OVCAR3, A2780 and cisplatin-resistant A2780cis).ResultsWe found moderate but consistent LSD1 mRNA overexpression in stage IIIC and high-grade ovarian tumors. LSD1 mRNA overexpression correlated with a transcriptomic signature of up-regulated genes involved in cell cycle and down-regulated genes involved in the immune/inflammatory response, a signature previously observed in aggressive tumors. In fact, some ovarian tumors showing high levels of LSD1 mRNA are associated with poor patient survival. Chemical LSD1 inhibition induced cytotoxicity in ovarian cancer lines, which roughly correlated with their reported LSD1 inhibitory potential (RN-1,S2101 >> pargyline,TCP).ConclusionsOur findings may suggest a role of LSD1 in the biology of some ovarian tumors. It is of special interest to find a correlation of LSD1 mRNA overexpression with a transcriptomic signature relevant to cancer. Our findings, therefore, prompt further investigation of the role of LSD1 in ovarian cancer, as well as the study of its enzymatic inhibition in animal models for potential therapeutic purposes in the context of this disease.
Summary Genome-wide association studies (GWASs) are instrumental in identifying loci harboring common single-nucleotide variants (SNVs) that affect human traits and diseases. GWAS hits emerge in clusters, but the focus is often on the most significant hit in each trait- or disease-associated locus. The remaining hits represent SNVs in linkage disequilibrium (LD) and are considered redundant and thus frequently marginally reported or exploited. Here, we interrogate the value of integrating the full set of GWAS hits in a locus repeatedly associated with cardiac conduction traits and arrhythmia, SCN5A - SCN10A . Our analysis reveals 5 common 7-SNV haplotypes (Hap1–5) with 2 combinations associated with life-threatening arrhythmia—Brugada syndrome (the risk Hap 1/1 and protective Hap 2/3 genotypes). Hap1 and Hap2 share 3 SNVs; thus, this analysis suggests that assuming redundancy among clustered GWAS hits can lead to confounding disease-risk associations and supports the need to deconstruct GWAS data in the context of haplotype composition.
The Majorana Collaboration is constructing a system containing 40 kg of HPGe detectors to demonstrate the feasibility and potential of a future tonne-scale experiment capable of probing the neutrino mass scale in the inverted-hierarchy region. To realize this, a major goal of the Majorana Demonstrator is to demonstrate a path forward to achieving a background rate at or below 1 cnt/(ROI-t-y) in the 4 keV region of interest around the Q-value at 2039 keV. This goal is pursued through a combination of a significant reduction of radioactive impurities in construction materials with analytical methods for background rejection, for example using powerful pulse shape analysis techniques profiting from the p-type point contact HPGe detectors technology. The effectiveness of these methods is assessed using simulations of the different background components whose purity levels are constrained from radioassay measurements.
An important purpose of sequencing the human genome was to determine the number of human genes and to catalog them for future study. The final gene count matched the most conservative predictions, with gene sequences representing only 1.5% of the entire human genome. However, more recent transcriptomic analyses have revealed that the cumulative coverage of transcribed regions is at least 75‐85% of the human genome. Moreover, since these analyses did not investigate the process of transcription itself, but only the accumulation of RNAs, the range of cumulative transcribed genome is likely close to the full human genome. The question is how much of the human transcriptome (i.e. the transcribed genome) is functional and how much of the human transcriptome is transcriptional noise. Thus, we return to the question of how many genes (i.e. sequences coding for functional transcripts) are there in the human genome, and to the challenge of cataloging them. In this study, we catalog the full repertoire of long noncoding (lnc)RNAs (length >200 nucleotides) generated at enhancers, also known as enhancer (e)RNAs, in prostate (LNCaP), breast (MCF7), and ovarian (A2780) cancer cells. We use profiles of intergenic DNaseI hypersensitive sites (DNase‐seq) and H3K4me1/3 and H3K27ac regions (ChIP‐seq) to identify enhancers, combined with global run‐on next‐generation sequencing (GRO‐seq) and RNA‐seq to identify, characterize, and compare the eRNA transcriptomes in these three cancer lines. We expect that our analyses will facilitate the study of the functionality of the eRNA transcriptome, and will contribute to distinguishing the fraction of the human transcriptome that is functional from the fraction that could be regarded as noise.
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