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Short Abstract: Chromatin organization, such as the deposition of active or repressive histone modifications, plays an important role in regulating gene expression. Advances in ChIP-seq and associated bioinformatics analytical techniques have enabled genome-wide analysis of histone modifications and transcription regulation dynamics. The chromatin landscapes of several model organisms have been widely studied, but other medically and biotechnologically important species are not yet fully studied. Here we present a genome-wide chromatin landscape of an important filamentous fungal model organism, Aspergillus nidulans.
Using ChIP-seq, we generated genome-wide profiles of H3K4me3, H3K4me2, H3K4me1, H3Ac, H3K9ac, H3K27ac, H3K36me3, H3K79me1, H3K79me2, H3K79me3, H3K9me3, H4K20me1, H2A.Z, Pol II and transcription factors in A. nidulans in the presence or absence of nitrogen source NH4. All sequences were mapped to the reference genome, and processed using the standard ChIP-seq analysis pipeline SPP. We used a recently developed hierarchically linked infinite hidden Markov model (hiHMM) to systematically discover and characterize the most prevalent combination of histone modifications, i.e., chromatin state.
Our analysis revealed interesting chromatin states that are associated with gene expression or repression. We investigated the association between these chromatin states with chromosomal location, gene ontology, and other genomic features that are important in fungal biology. Furthermore, we found that around 15% of the genome is marked by classical enhancer chromatin marks, suggesting that these previously uncharacterized non-coding regions may have potential regulatory functions.
Our work represents a valuable resource in A. nidulans that opens new avenues for investigation of the dynamic chromatin organization and gene regulation in fungi.
Short Abstract: Development of next generation sequencing (NGS) has revolutionized molecular biology by enhancing the ability to perform genome-wide studies. However, due to the need for bioinformatics expertise and the sheer size of resulting datasets, use of these technologies is still beyond the capabilities of many laboratories. Herein, we present the BioWardrobe platform, which allows users to store, visualize and analyze epigenomics and transcriptomics data using a biologist-friendly web interface, without the need for programming expertise. BioWardrobe can be installed on consumer class hardware within an institutional local network. Analysis capabilities include predefined pipelines that allow the user to download data from either institutional core facility or public databases, perform quality control, map reads, and visualize data on a built-in mirror of the UCSC genome browser. RPKMs are calculated for RNA-Seq, and islands of enrichment are identified for ChIP-Seq and similar datasets. Advanced analysis capabilities include differential gene expression and binding analysis, and creation of average tag density profiles and heatmaps. Since the original release, we have integrated a customized version of Airbnb’s Airflow workflow manager which enables us to run analysis pipelines written using Common Workflow Language (CWL). Use of CWL-based pipelines in BioWardrobe will facilitate integration of novel tools and increase portability and reproducibility of analysis. BioWardrobe can be found at http://biowardrobe.com.
Short Abstract: The most common form of congenital heart disease (CHD), namely ventricular septal defect (VSD), is a subfeature of Tetralogy of Fallot (TOF), which comprises the majority of cases of cyanotic CHD. The underlying causes of the bulk of CHDs are still unclear but most probably consist of a combination of genetic, epigenetic and environmental factors. DNA methylation is the most-widely studied epigenetic modification and, here, we present the first analysis of genome-wide DNA methylation data (MBD-seq) obtained from myocardial biopsies of TOF and VSD patients. We found clear methylation differences between cases and controls, and between patient groups. For TOF, we linked DNA methylation with genome-wide gene expression data (RNA-seq) and found a significant overlap for hypermethylated promoters and down-regulated genes, and vice versa. Interestingly, we found examples of methylation changes co-localized with novel, differential splicing events among sarcomeric genes. In addition to DNA methylation, short non-coding RNAs like microRNAs have been shown to play a role in gene silencing. Thus, we further analyzed genome-wide small RNA-seq data from TOF patients and controls. Subsequently, we combined the microRNA expression data with previously analyzed gene expression profiles. In summary, our data suggest DNA methylation and microRNAs likely contribute to the pathogenesis of CHD by modulating disease-specific gene expression profiles.
Short Abstract: In several cancer types, aberrations of MLH1 gene showed strong association with microsatellite instability (MSI). MLH1 is silenced by promoter hypermethylation, also called ‘epigenetic silence’. From the fact that MLH1 silencing is caused by abnormal methylation status, TET enzyme which is key regulators of DNA demethylation may affect to expression of MLH1. When TET harbor nonsilent mutations that result in loss-of-function, methylation level will be increased. These flow might be impact to MLH1 silencing by promoter hypermethylation and inducing MSI. However, little is known about the relationship between nonsilent mutation of TET genes and MLH1 epigenetic silencing or MSI. In this study, we analyzed 208 colon and 232 gastric cancer patients sample using mRNA expression, DNA methylation, somatic mutation file and clinical data. All of the data was downloaded from The Cancer Genome Atlas (TCGA). We found significant relevance between TET mutation and MLH1 epigenetic silencing (Student’s t-test p<0.01, fold-change>0.4). Also, we identified significant relation between TET mutation and MSI (Chi-square or fisher’s exact test, p<0.01). From the result, we confirmed that MLH1 expression is significantly lower in TET mutated patient group than patients without mutations. Also, DNA methylation of MLH1 is significantly higher in TET mutated patient. In conclusion, our research shows TET mutation is highly associated with both epigenetic silencing of MLH1 and MSI, which shows the potential of TET genes as a positive prognostic marker in colon and gastric cancer.
Short Abstract: Although differential methylation caused by genetic variations at CpG dinucleotides (CpG-SNPs) or associated with nearby polymorphisms (cis-mQTLs) has been noted, their effects on the global methylation landscape have not been investigated. To investigate this, we obtained in vitro differentiated early and late myeloid cells from primary CD34+ bone marrow progenitor cells from multiple subjects. RNA-seq and WGBS (32.7× and 80% of CpGs ≥5×) were carried out for comparison of methylome alterations with transcriptome changes and genetic variations. While transcriptomic variations reflect changes of developmental stage, methylome variations are dominated by inter-subject differences, with 77.4% of the variance caused by CpG-SNPs. To overcome the effect of outlier CpG-SNPs, we developed a robust segmentation approach which allows for detection of differentially methylated regions (DMRs, average size 470 bp with 8.6 CpGs) associated with genetic background difference (g-DMRs) or developmental differentiation (d-DMRs). Interestingly, even though g-DMRs (0.94% of all CpGs) are more abundant than d-DMRs (0.66%), the latter were enriched for binding sites in known myeloid transcriptional regulators such as CEBPB (p<2.2E-16) and STAT3 (p=7.6E-9), and more d-DMRs are correlated with cis-gene expression. Furthermore, associations with adjacent SNPs were identified in 80% of g-DMRs. Initial exploration identified a cis-mQTL at chr6:29,647,831-29,649,135 (associated with rs365052) that overlapped CTCF binding sites and correlated with expression of ZFP57, an eQTL associated with the same SNP. Collectively, these findings demonstrate that segmentation analysis robustly identifies DMRs, d-DMRs are more likely to regulate cis-gene expression, and g-DMRs in transcription binding sites may lead into subject-specific gene expression.
Short Abstract: Integrative analysis of functional profiles from assays such as ChIP-seq, ATAC-seq and FAIRE-seq requires substantial effort in data pre-processing, normalization and downstream, higher-level analysis. These steps are typically performed in an ad-hoc manner using custom scripts developed by individual labs. Here, we present Integrative Processing of Sequence Tag Profiles (IPOST), a comprehensive bioinformatic pipeline that encapsulates widely used as well as novel analytical tasks. IPOST normalizes functional profiles for background signal biases, and reduces artifacts from copy-number variation even in the absence of control data. Peak calls from individual data sets are combined into a consensus set of peaks and then the matrix of GC-normalized peak heights (peaks x samples) is constructed. Multiple methods are included for higher-level analysis of the peak matrix, including finding combinatorial differential profiles of peaks between case and control. It is possible that disease samples (for example) may share the same aberrant molecular pathway, but not necessarily the same aberrant gene loci. To address such situations, IPOST translates the peak height matrix to different sets based on gene ontology or motif occurrence, and then tests for combinatorial differential profiles between case and control. The proposed set of methods can be used on any genome for comparing tag profiles in different kinds of applications such as finding differential peaks among two cell-types or among large cohorts of disease and control or even analyzing single cell epigenome profiles. IPOST methods will be packaged with next version of DFilter (Kumar et al. Nature biotechnology, 2013) .
Short Abstract: A common goal in epigenomic sequencing studies is to identify differences between conditions, i.e differential enrichment. Strategies to do so fall in two general categories: peak and window based. While window based strategies risk testing too many regions in which there is no signal, peak based strategies can introduce biases if peak calling is not done properly. An additional challenge for differential enrichment is encountered when the location of the epigenetic signal varies between replicates, as is the case for histone modification and chromatin accessibility data. Here we introduce DEScan, an R based integrated peak and differential caller, specifically designed for broad epigenomic signals. DEScan first calls peaks on individual replicates using an adaptive window scan and the surrounding 10kb as background. It then integrates peak calls among replicates by requiring a user-defined number of carriers (2 minimum) among replicates. The resulting reproducible peaks are tested for differential enrichment using RNA-seq based strategies. We use DEScan to analyze chromatin accessibility sequencing data following contextual fear conditioning (FC) in the mouse hippocampus. We show that FC increases activation of 2,101 regulatory regions. These regions are disproportionally associated with known ASD genes (p-value<0.004) and enriched in CHD8 binding sites. Using genotyping we show that one of those regions, promoter 6 within the Shank3 gene, contains a genetic variant significantly associated with ASD (SNP rs6010065, 422 ASD cases, 182 controls, p-value=0.03). Our results suggest that DEScan can identify relevant regulatory regions for genetic association studies in clinical populations.
Short Abstract: Introduction: Aging is associated with a plethora of diseases such as cancer, cardiovascular disorders and neurodegeneration. Aberrant DNA-methylation is a key process in many age-related diseases; however there is limited understanding of how DNA methylation changes with the aging process. In this study we investigated age-related changes in DNA methylation in the hippocampus in a base, strand and sex specific manner.
Methods: To determine age-related alterations to the hippocampal methylome two independent sets of male and female mice (Young – 3M and Aged - 24M) were collected. One set of animals was subjected to whole-genome bisulfite sequencing while the other was subjected to bisulfite oligonucleotide capture sequencing for 110Mb of gene regulatory regions.
Results: There was no evidence of genome-wide hyper- or hypo- methylation with age. Differentially methylated cytosines (DMCs) and differentially methylated regions (DMRs) with were evident throughout the genome. Sex-specific, age-related DMCs and DMRs were also observed. Changes in methylation were dispersed across the genome with no preference to a particular genomic element. Additionally, variance in DNA methylation changes in a sex-specific manner.
Conclusions: The longstanding hypothesis of genomic hypomethylation with aging is not supported by base-specific quantitation of DNA methylation. Instead, DNA methylation at specific genomic loci is regulated according to age and sex in both CpG and non-CpG context. Males, but not females, show an increase in methylation variance suggesting a loss of epigenetic regulation in the aged male methylome. The significant differences between males and females may underlie known sex-differences in the aging process.
Short Abstract: The Illumina Human Methylation 450k BeadChip (450k) is a widely used microarray that measures the methylation level of more than 480,000 CpG sites on the human genome. Of note, The Cancer Genome Atlas (TCGA) uses the 450k extensively in tumor profiling. With increased use, there is a concomitant increase in normalization methods. It is less clear the metrics that are most appropriate for analysis and quality control.
The skewr Bioconductor package is a tool for visualizing the 450k data. Methylation signals in log2 intensity space are modeled using mixtures of skew-normal distributions. The expected signal distributions and empirical distributions are visualized, allowing for identification of artifacts and poor quality samples. The chip consists of two colors and two distinct assay types. Thus, there are six subsets of signal intensities: Type I-red methylated and unmethylated, Type I-green methylated and unmethylated, Type II methylated and unmethylated. For each sample, skewr creates a panel plot with nine frames. Six of the frames display the density distributions of the log2 intensity data while the remaining three frames contain the density plots for the Type I-red, Type I-green, and Type II beta-values. A recent method for visualizing and classifying the shapes of the beta distributions has been developed to better understand the effects that some of the more popular normalization methods have on the beta-value distribution.
Short Abstract: The thyroid, necessary for normal growth and development, is essential for the regulation of metabolism in every cell of the human body. Its function -- to produce and secrete appropriate levels of thyroid hormone -- is simple; however, the incidence of thyroid abnormalities is increasing and accurate assessment of abnormal thyroids for different individuals is challenging. A fundamental understanding of the normal thyroid is therefore needed. One way to characterize the normal thyroid is to study its epigenome and matched transcriptome. In this study we are analyzing grossly uninvolved tumour-adjacent thyroids from four human individuals using ChIP-seq, RNA-seq, and bisulfite-seq. We examine 4 activating (H3K4me1, H3K4me3, H3K27ac, H3K36me3) and 2 repressing (H3K9me3, H3K27me3) histone post-translational modifications, identify chromatin states using a hidden Markov model, establish maps of regulatory elements, and compare DNA methylation and RNA expression profiles between samples. The goals of this study are (1) to understand and characterize regions of regulation which are consistent and regions of regulation which are variable between the thyroids of different individuals and (2) to produce an available reference thyroid epigenome as a resource and reference of human epigenomic data for comparison and integration of future studies.
Short Abstract: We are currently in the golden age of epigenetics. Chromatin immunoprecipitation followed by DNA sequencing (ChIP-seq) has become the mainstream approach for mapping protein interactions with chromatin as well as localisation of histone post-translational modifications (PTMs) across the genome. These experiments are however limited to studying one chromatin binding protein (CBP) or histone PTM at a time, which has the effect of averaging out the exact chromatin dynamics across thousands of cells. Mass spectrometry (MS)-based proteomic approaches, on the other hand, are capable of detecting histone PTMs on a given peptide in a combinatorial fashion, thus enabling the study of their colocalisation on a single molecule. Current MS technology is mostly limited to breaking down the histone protein into smaller peptides containing up to four PTMs each to reduce signal complexity and maintain sensitivity. This work proposes that publicly available ChIP-seq data could be combined with MS proteomics to statistically reconstruct the complete combinatorial PTM state for the entire nucleosome. We demonstrate how this approach can be applied to characterise the specific combinatorial PTM patterns recognised by epigenetic reader proteins spanning multiple histone proteins within the nucleosome core.
Short Abstract: The Encyclopedia of DNA elements (ENCODE) project is an ongoing collaborative effort to
create a comprehensive catalog of functional elements. The current database exceeds 5500
experiments across more than 350 cell lines and tissues using a wide array of experimental
techniques to study the chromatin structure, regulatory and transcriptional landscape of the H.
sapiens and M. musculus genomes. All ENCODE experimental data, metadata, and associated
computational analyses are submitted to the ENCODE Data Coordination Center (DCC) for
validation, tracking, storage, and distribution to community resources and the scientific
community. As the volume of data increases, the identification and organization of experimental
details becomes increasingly intricate and demands careful curation. The ENCODE DCC has
created a general purpose software system, known as SnoVault, that supports metadata and
file submission, a database used for metadata storage, web pages for displaying the metadata
and a robust API for querying the metadata. The software is fully opensource,
code and
installation instructions can be found at: http://github.com/ENCODEDCC/
snovaultThe core database engine, SnoVault (which is completely independent of
ENCODE, genomic data, or bioinformatic data) has been released as a separate Python
package.
Short Abstract: Mixed results have been obtained in studies of complex disease prediction through the combination of multiple disease-associated single nucleotide polymorphisms into a count-based genotype risk score (GRS). Our recent simulation study showed that a count-based GRS method under the additive risk effect model has low power in predicting dichotomous outcomes; however, a GRS method under the multiplicative model can be useful for predicting such outcomes. A count-based GRS method is unlikely to be clinically useful for predicting the risk of a binary outcome. Therefore, we propose a tree-based recursive partitioning (RP) statistical model as an alternative approach for identifying a combined multiplicative effect of interactions. We performed simulation studies demonstrating that our RP tree method is useful for identifying biologically meaningful pathways of gene-gene multiplicative effects that may not be identified by a count-based GRS or traditional logistic regression methods. Finally, receiver operating characteristic (ROC) analysis was used to compare the risk discrimination capabilities of count-based risk score model, a parametric logistic regression model, and an RP tree-based method.
Short Abstract: Environmental changes elicit responses at different organismal levels, often involving structural transitions in the DNA within the cell nucleus. Such modifications are modulated by epigenetic mechanisms, including chromatin-associated proteins (i.e., histone variants and histone-modifying complexes, among others). Variations in the expression of genes encoding these proteins have the potential of mirroring environmental stress, thus representing promising biomarker candidates. In order to enhance the sensitivity and specificity of these biomarkers, gene expression dynamics must be considered.
Co-expression network analysis offers a valuable approach to fulfill this requirement. Accordingly, the analysis of expression profiles obtained from data series (time series or dose-response studies) facilitates the pairwise calculation of correlation values between genes. Results are graphically represented as networks, comprising information about individual gene expression levels (nodes) and the correlation between gene expression profiles (edges). The comparison of networks can pinpoint patterns that are unique for different stressors, thus conveying potential as biomarkers. To test this idea, we have used RNA-Seq data series from Pacific oysters (Crassostrea gigas) challenged with abiotic factors such as temperature, salinity and heavy metal pollution.
Overall, this work describes and compares the gene co-expression networks activated in oysters under abiotic stress, identifying transcriptional patterns associated with epigenetic mechanisms involved in the response to specific stressors. In addition to its biomarker potential, this information will contribute to a better understanding of the role of chromosomal proteins and their modifications in the epigenetic regulation of gene expression.
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