Cardiac safety of indacaterol in healthy subjects: a randomized, multidose, placebo- and positive-controlled, parallel-group thorough QT study

<p>Abstract</p> <p>Background</p> <p>Indacaterol is a novel once-daily ultra long-acting β₂-agonist for the treatment of chronic obstructive pulmonary disease. It is known that β₂-agonists, like other adrenergic compounds, can prolong the QT-interval. This thorough QT/QTc study (as per ICH E14 guideline) evaluated the effect of indacaterol on the QT interval in healthy subjects.</p> <p>Methods</p> <p>In this randomized, double-blind, parallel-group, placebo- and positive-controlled (open-label moxifloxacin) study, non-smoking healthy subjects (18-55 years, body mass index: 18.5-32.0 kg/m²) were randomized (4:4:2:4:1) to 14-day treatment with once-daily indacaterol (150 μg, 300 μg, or 600 μg), placebo, or placebo/moxifloxacin (double-blind 14-day treatment with placebo and a single open-label dose of 400 mg moxifloxacin on Day 14). The primary endpoint was the change from baseline on Day 14 in QTcF (QT interval corrected for heart rate using Fridericia's formula).</p> <p>Results</p> <p>In total, 404 subjects were randomized to receive indacaterol (150 [n = 108], 300 [n = 108], 600 μg [n = 54]), placebo (n = 107), or placebo/moxifloxacin (n = 27); 388 subjects completed the study. Maximal time-matched mean (90% confidence intervals) treatment differences from placebo in QTcF change from baseline on Day 14 were 2.66 (0.55, 4.77), 2.98 (1.02, 4.93) and 3.34 (0.86, 5.82) ms for indacaterol 150 μg, 300 μg and 600 μg, respectively. Study sensitivity was confirmed with moxifloxacin demonstrating a significant maximal time-matched QTcF prolongation of 13.90 (10.58, 17.22) ms compared to placebo. All indacaterol doses were well tolerated.</p> <p>Conclusion</p> <p>Indacaterol, at doses up to 600 μg once daily (2-4 times the therapeutic dose) does not have any clinically relevant effect on the QT interval.</p> <p>Trial Registration</p> <p>ClinicalTrials.gov: <a href="http://www.clinicaltrials.gov/ct2/show/NCT01263808">NCT01263808</a></p

Next-Generation Sequencing of Apoptotic DNA Breakpoints Reveals Association with Actively Transcribed Genes and Gene Translocations

DNA fragmentation is a well-recognized hallmark of apoptosis. However, the precise DNA sequences cleaved during apoptosis triggered by distinct mechanisms remain unclear. We used next-generation sequencing of DNA fragments generated in Actinomycin D-treated human HL-60 leukemic cells to generate a high-throughput, global map of apoptotic DNA breakpoints. These data highlighted that DNA breaks are non-random and show a significant association with active genes and open chromatin regions. We noted that transcription factor binding sites were also enriched within a fraction of the apoptotic breakpoints. Interestingly, extensive apoptotic cleavage was noted within genes that are frequently translocated in human cancers. We speculate that the non-random fragmentation of DNA during apoptosis may contribute to gene translocations and the development of human cancers

Genome-Scale Validation of Deep-Sequencing Libraries

Chromatin immunoprecipitation followed by high-throughput (HTP) sequencing (ChIP-seq) is a powerful tool to establish protein-DNA interactions genome-wide. The primary limitation of its broad application at present is the often-limited access to sequencers. Here we report a protocol, Mab-seq, that generates genome-scale quality evaluations for nucleic acid libraries intended for deep-sequencing. We show how commercially available genomic microarrays can be used to maximize the efficiency of library creation and quickly generate reliable preliminary data on a chromosomal scale in advance of deep sequencing. We also exploit this technique to compare enriched regions identified using microarrays with those identified by sequencing, demonstrating that they agree on a core set of clearly identified enriched regions, while characterizing the additional enriched regions identifiable using HTP sequencing

Extensive Evolutionary Changes in Regulatory Element Activity during Human Origins Are Associated with Altered Gene Expression and Positive Selection

Understanding the molecular basis for phenotypic differences between humans and other primates remains an outstanding challenge. Mutations in non-coding regulatory DNA that alter gene expression have been hypothesized as a key driver of these phenotypic differences. This has been supported by differential gene expression analyses in general, but not by the identification of specific regulatory elements responsible for changes in transcription and phenotype. To identify the genetic source of regulatory differences, we mapped DNaseI hypersensitive (DHS) sites, which mark all types of active gene regulatory elements, genome-wide in the same cell type isolated from human, chimpanzee, and macaque. Most DHS sites were conserved among all three species, as expected based on their central role in regulating transcription. However, we found evidence that several hundred DHS sites were gained or lost on the lineages leading to modern human and chimpanzee. Species-specific DHS site gains are enriched near differentially expressed genes, are positively correlated with increased transcription, show evidence of branch-specific positive selection, and overlap with active chromatin marks. Species-specific sequence differences in transcription factor motifs found within these DHS sites are linked with species-specific changes in chromatin accessibility. Together, these indicate that the regulatory elements identified here are genetic contributors to transcriptional and phenotypic differences among primate species

Nuclear Receptor HNF4α Binding Sequences are Widespread in Alu Repeats

<p>Abstract</p> <p>Background</p> <p>Alu repeats, which account for ~10% of the human genome, were originally considered to be junk DNA. Recent studies, however, suggest that they may contain transcription factor binding sites and hence possibly play a role in regulating gene expression.</p> <p>Results</p> <p>Here, we show that binding sites for a highly conserved member of the nuclear receptor superfamily of ligand-dependent transcription factors, hepatocyte nuclear factor 4alpha (HNF4α, NR2A1), are highly prevalent in Alu repeats. We employ high throughput protein binding microarrays (PBMs) to show that HNF4α binds > 66 unique sequences in Alu repeats that are present in ~1.2 million locations in the human genome. We use chromatin immunoprecipitation (ChIP) to demonstrate that HNF4α binds Alu elements in the promoters of target genes (<it>ABCC3, APOA4, APOM, ATPIF1, CANX, FEMT1A, GSTM4, IL32, IP6K2, PRLR, PRODH2, SOCS2, TTR</it>) and luciferase assays to show that at least some of those Alu elements can modulate HNF4α-mediated transactivation <it>in vivo </it>(<it>APOM, PRODH2, TTR, APOA4</it>). HNF4α-Alu elements are enriched in promoters of genes involved in RNA processing and a sizeable fraction are in regions of accessible chromatin. Comparative genomics analysis suggests that there may have been a gain in HNF4α binding sites in Alu elements during evolution and that non Alu repeats, such as Tiggers, also contain HNF4α sites.</p> <p>Conclusions</p> <p>Our findings suggest that HNF4α, in addition to regulating gene expression via high affinity binding sites, may also modulate transcription via low affinity sites in Alu repeats.</p

Global analysis of DNA methylation in early-stage liver fibrosis

<p>Abstract</p> <p>Background</p> <p>Liver fibrosis is caused by chemicals or viral infection. The progression of liver fibrosis results in hepatocellular carcinogenesis in later stages. Recent studies have revealed the importance of DNA hypermethylation in the progression of liver fibrosis to hepatocellular carcinoma (HCC). However, the importance of DNA methylation in the early-stage liver fibrosis remains unclear.</p> <p>Methods</p> <p>To address this issue, we used a pathological mouse model of early-stage liver fibrosis that was induced by treatment with carbon tetrachloride (CCl₄) for 2 weeks and performed a genome-wide analysis of DNA methylation status. This global analysis of DNA methylation was performed using a combination of methyl-binding protein (MBP)-based high throughput sequencing (MBP-seq) and bioinformatic tools, IPA and Oncomine. To confirm functional aspect of MBP-seq data, we complementary used biochemical methods, such as bisulfite modification and <it>in-vitro</it>-methylation assays.</p> <p>Results</p> <p>The genome-wide analysis revealed that DNA methylation status was reduced throughout the genome because of CCl₄treatment in the early-stage liver fibrosis. Bioinformatic and biochemical analyses revealed that a gene associated with fibrosis, <it>secreted phosphoprotein 1 </it>(<it>Spp1</it>), which induces inflammation, was hypomethylated and its expression was up-regulated. These results suggest that DNA hypomethylation of the genes responsible for fibrosis may precede the onset of liver fibrosis. Moreover, <it>Spp1 </it>is also known to enhance tumor development. Using the web-based database, we revealed that <it>Spp1 </it>expression is increased in HCC.</p> <p>Conclusions</p> <p>Our study suggests that hypomethylation is crucial for the onset of and in the progression of liver fibrosis to HCC. The elucidation of this change in methylation status from the onset of fibrosis and subsequent progression to HCC may lead to a new clinical diagnosis.</p

Improved ChIP-chip analysis by a mixture model approach

<p>Abstract</p> <p>Background</p> <p>Microarray analysis of immunoprecipitated chromatin (ChIP-chip) has evolved from a novel technique to a standard approach for the systematic study of protein-DNA interactions. In ChIP-chip, sites of protein-DNA interactions are identified by signals from the hybridization of selected DNA to tiled oligomers and are graphically represented as peaks. Most existing methods were designed for the identification of relatively sparse peaks, in the presence of replicates.</p> <p>Results</p> <p>We propose a data normalization method and a statistical method for peak identification from ChIP-chip data based on a mixture model approach. In contrast to many existing methods, including methods that also employ mixture model approaches, our method is more flexible by imposing less restrictive assumptions and allowing a relatively large proportion of peak regions. In addition, our method does not require experimental replicates and is computationally efficient. We compared the performance of our method with several representative existing methods on three datasets, including a spike-in dataset. These comparisons demonstrate that our approach is more robust and has comparable or higher power than the other methods, especially in the context of abundant peak regions.</p> <p>Conclusion</p> <p>Our data normalization and peak detection methods have improved performance to detect peak regions in ChIP-chip data.</p

SuRFing the genomics wave: an R package for prioritising SNPs by functionality

Identifying functional non-coding variants is one of the greatest unmet challenges in genetics. To help address this, we introduce an R package, SuRFR, which integrates functional annotation and prior biological knowledge to prioritise candidate functional variants. SuRFR is publicly available, modular, flexible, fast, and simple to use. We demonstrate that SuRFR performs with high sensitivity and specificity and provide a widely applicable and scalable benchmarking dataset for model training and validation. Website: http://www.cgem.ed.ac.uk/resources/ ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s13073-014-0079-1) contains supplementary material, which is available to authorized users

Bivalent-Like Chromatin Markers Are Predictive for Transcription Start Site Distribution in Human

Deep sequencing of 5′ capped transcripts has revealed a variety of transcription initiation patterns, from narrow, focused promoters to wide, broad promoters. Attempts have already been made to model empirically classified patterns, but virtually no quantitative models for transcription initiation have been reported. Even though both genetic and epigenetic elements have been associated with such patterns, the organization of regulatory elements is largely unknown. Here, linear regression models were derived from a pool of regulatory elements, including genomic DNA features, nucleosome organization, and histone modifications, to predict the distribution of transcription start sites (TSS). Importantly, models including both active and repressive histone modification markers, e.g. H3K4me3 and H4K20me1, were consistently found to be much more predictive than models with only single-type histone modification markers, indicating the possibility of “bivalent-like” epigenetic control of transcription initiation. The nucleosome positions are proposed to be coded in the active component of such bivalent-like histone modification markers. Finally, we demonstrated that models trained on one cell type could successfully predict TSS distribution in other cell types, suggesting that these models may have a broader application range

Computational analyses of eukaryotic promoters

Computational analysis of eukaryotic promoters is one of the most difficult problems in computational genomics and is essential for understanding gene expression profiles and reverse-engineering gene regulation network circuits. Here I give a basic introduction of the problem and recent update on both experimental and computational approaches. More details may be found in the extended references. This review is based on a summer lecture given at Max Planck Institute at Berlin in 2005