HHMD: the human histone modification database

Comparison and benchmark of deep learning methods for non-coding RNA classification

The involvement of non-coding RNAs in biological processes and diseases has made the exploration of their functions crucial. Most non-coding RNAs have yet to be studied, creating the need for methods that can rapidly classify large sets of non-coding RNAs into functional groups, or classes. In recent years, the success of deep learning in various domains led to its application to non-coding RNA classification. Multiple novel architectures have been developed, but these advancements are not covered by current literature reviews. We present an exhaustive comparison of the different methods proposed in the state-of-the-art and describe their associated datasets. Moreover, the literature lacks objective benchmarks. We perform experiments to fairly evaluate the performance of various tools for non-coding RNA classification on popular datasets. The robustness of methods to non-functional sequences and sequence boundary noise is explored. We also measure computation time and CO2 emissions. With regard to these results, we assess the relevance of the different architectural choices and provide recommendations to consider in future methods.

CHHM: a Manually Curated Catalogue of Human Histone Modifications Revealing Hotspot Regions and Unique Distribution Patterns

Histone modification is one of the key elements in epigenetic control and plays important roles in regulation of biological processes and disease development. Currently, records of human histone modifications with various levels of confidence in evidence are scattered in various knowledgebases and databases. In the present study, a curated catalogue of human histone modifications, CHHM, was obtained by manual retrieval, evidence assessment, and integration of modification records from 10 knowledgebases/databases and 3 complementary articles. CHHM contains 6612 nonredundant modification entries covering 31 types of modifications (including 9 types of emerging modifications) and 2 types of histone-DNA crosslinks, that were identified in 11 H1 variants, 21 H2A variants, 21 H2B variants, 9 H3 variants, and 2 H4 variants. For ease of visualization and accessibility, modification entries are presented with aligned protein sequences in an Excel file. Confidence level in evidence is provided for each entry. Acylation modifications contribute to the highest number of modification entries in CHHM. This supports that cellular metabolic status plays a very important role in epigenetic control. CHHM reveals modification hotspot regions and uneven distribution of the modification entries across the histone families. Such uneven distribution may suggest that a particular histone family is more susceptible to certain types of modifications. CHHM not only serves as an important and user-friendly resource for biomedical and clinical researches involving histone modifications and transcriptional regulation, but also provides new insights for basic researches in the mechanism of human histone modifications and epigenetic control.

CancerMHL: the database of integrating key DNA methylation, histone modifications and lncRNAs in cancer

The discovery of key epigenetic modifications in cancer is of great significance for the study of disease biomarkers. Through the mining of epigenetic modification data relevant to cancer, some researches on epigenetic modifications are accumulating. In order to make it easier to integrate the effects of key epigenetic modifications on the related cancers, we established CancerMHL (http://www.positionprediction.cn/), which provide key DNA methylation, histone modifications and lncRNAs as well as the effect of these key epigenetic modifications on gene expression in several cancers. To facilitate data retrieval, CancerMHL offers flexible query options and filters, allowing users to access specific key epigenetic modifications according to their own needs. In addition, based on the epigenetic modification data, three online prediction tools had been offered in CancerMHL for users. CancerMHL will be a useful resource platform for further exploring novel and potential biomarkers and therapeutic targets in cancer. Database URL: http://www.positionprediction.cn/.

Role of Artificial Intelligence in Drug Discovery and Target Identification in Cancer

Drug discovery and development (DDD) is a highly complex process that necessitates precise monitoring and extensive data analysis at each stage. Furthermore, the DDD process is both timeconsuming and costly. To tackle these concerns, artificial intelligence (AI) technology can be used, which facilitates rapid and precise analysis of extensive datasets within a limited timeframe. The pathophysiology of cancer disease is complicated and requires extensive research for novel drug discovery and development. The first stage in the process of drug discovery and development involves identifying targets. Cell structure and molecular functioning are complex due to the vast number of molecules that function constantly, performing various roles. Furthermore, scientists are continually discovering novel cellular mechanisms and molecules, expanding the range of potential targets. Accurately identifying the correct target is a crucial step in the preparation of a treatment strategy. Various forms of AI, such as machine learning, neural-based learning, deep learning, and network-based learning, are currently being utilised in applications, online services, and databases. These technologies facilitate the identification and validation of targets, ultimately contributing to the success of projects. This review focuses on the different types and subcategories of AI databases utilised in the field of drug discovery and target identification for cancer.

Expanding adverse outcome pathways towards one health models for nanosafety

The ever-growing production of nano-enabled products has generated the need for dedicated risk assessment strategies that ensure safety for humans and the environment. Transdisciplinary approaches are needed to support the development of new technologies while respecting environmental limits, as also highlighted by the EU Green Deal Chemicals Strategy for Sustainability and its safe and sustainable by design (SSbD) framework. The One Health concept offers a holistic multiscale approach for the assessment of nanosafety. However, toxicology is not yet capable of explaining the interaction between chemicals and biological systems at the multiscale level and in the context of the One Health framework. Furthermore, there is a disconnect between chemical safety assessment, epidemiology, and other fields of biology that, if unified, would enable the adoption of the One Health model. The development of mechanistic toxicology and the generation of omics data has provided important biological knowledge of the response of individual biological systems to nanomaterials (NMs). On the other hand, epigenetic data have the potential to inform on interspecies mechanisms of adaptation. These data types, however, need to be linked to concepts that support their intuitive interpretation. Adverse Outcome Pathways (AOPs) represent an evolving framework to anchor existing knowledge to chemical risk assessment. In this perspective, we discuss the possibility of integrating multi-level toxicogenomics data, including toxicoepigenetic insights, into the AOP framework. We anticipate that this new direction of toxicogenomics can support the development of One Health models applicable to groups of chemicals and to multiple species in the tree of life.

Epigenetic targets in B- and T-cell lymphomas: latest developments

Non-Hodgkin's lymphomas (NHLs) comprise a diverse group of diseases, either of mature B-cell or of T-cell derivation, characterized by heterogeneous molecular features and clinical manifestations. While most of the patients are responsive to standard chemotherapy, immunotherapy, radiation and/or stem cell transplantation, relapsed and/or refractory cases still have a dismal outcome. Deep sequencing analysis have pointed out that epigenetic dysregulations, including mutations in epigenetic enzymes, such as chromatin modifiers and DNA methyltransferases (DNMTs), are prevalent in both B- cell and T-cell lymphomas. Accordingly, over the past decade, a large number of epigenetic-modifying agents have been developed and introduced into the clinical management of these entities, and a few specific inhibitors have already been approved for clinical use. Here we summarize the main epigenetic alterations described in B- and T-NHL, that further supported the clinical development of a selected set of epidrugs in determined diseases, including inhibitors of DNMTs, histone deacetylases (HDACs), and extra-terminal domain proteins (bromodomain and extra-terminal motif; BETs). Finally, we highlight the most promising future directions of research in this area, explaining how bioinformatics approaches can help to identify new epigenetic targets in B- and T-cell lymphoid neoplasms.

Co-Expression Analysis of Airway Epithelial Transcriptome in Asthma Patients with Eosinophilic vs. Non-Eosinophilic Airway Infiltration

Asthma heterogeneity complicates the search for targeted treatment against airway inflammation and remodeling. We sought to investigate relations between eosinophilic inflammation, a phenotypic feature frequent in severe asthma, bronchial epithelial transcriptome, and functional and structural measures of airway remodeling. We compared epithelial gene expression, spirometry, airway cross-sectional geometry (computed tomography), reticular basement membrane thickness (histology), and blood and bronchoalveolar lavage (BAL) cytokines of n = 40 moderate to severe eosinophilic (EA) and non-eosinophilic asthma (NEA) patients distinguished by BAL eosinophilia. EA patients showed a similar extent of airway remodeling as NEA but had an increased expression of genes involved in the immune response and inflammation (e.g., KIR3DS1), reactive oxygen species generation (GYS2, ATPIF1), cell activation and proliferation (ANK3), cargo transporting (RAB4B, CPLX2), and tissue remodeling (FBLN1, SOX14, GSN), and a lower expression of genes involved in epithelial integrity (e.g., GJB1) and histone acetylation (SIN3A). Genes co-expressed in EA were involved in antiviral responses (e.g., ATP1B1), cell migration (EPS8L1, STOML3), cell adhesion (RAPH1), epithelial-mesenchymal transition (ASB3), and airway hyperreactivity and remodeling (FBN3, RECK), and several were linked to asthma in genome- (e.g., MRPL14, ASB3) or epigenome-wide association studies (CLC, GPI, SSCRB4, STRN4). Signaling pathways inferred from the co-expression pattern were associated with airway remodeling (e.g., TGF-β/Smad2/3, E2F/Rb, and Wnt/β-catenin).

Databases and prospects of dynamic gene regulation in eukaryotes: A mini review

In eukaryotes, dynamic regulation enables DNA polymerases to catalyze a variety of RNA products in spatial and temporal patterns. Dynamic gene expression is regulated by transcription factors (TFs) and epigenetics (DNA methylation and histone modification). The applications of biochemical technology and high-throughput sequencing enhance the understanding of mechanisms of these regulations and affected genomic regions. To provide a searchable platform for retrieving such metadata, numerous databases have been developed based on the integration of genome-wide maps (e.g., ChIP-seq, whole-genome bisulfite sequencing, RNA-seq, ATAC-seq, DNase-seq, and MNase-seq data) and functionally genomic annotation. In this mini review, we summarize the main functions of TF-related databases and outline the prevalent approaches used in inferring epigenetic regulations, their associated genes, and functions. We review the literature on crosstalk between TF and epigenetic regulation and the properties of non-coding RNA regulation, which are challenging topics that promise to pave the way for advances in database development.

Information enhanced model selection for Gaussian graphical model with application to metabolomic data

In light of the low signal-to-noise nature of many large biological data sets, we propose a novel method to learn the structure of association networks using Gaussian graphical models combined with prior knowledge. Our strategy includes two parts. In the first part, we propose a model selection criterion called structural Bayesian information criterion, in which the prior structure is modeled and incorporated into Bayesian information criterion. It is shown that the popular extended Bayesian information criterion is a special case of structural Bayesian information criterion. In the second part, we propose a two-step algorithm to construct the candidate model pool. The algorithm is data-driven and the prior structure is embedded into the candidate model automatically. Theoretical investigation shows that under some mild conditions structural Bayesian information criterion is a consistent model selection criterion for high-dimensional Gaussian graphical model. Simulation studies validate the superiority of the proposed algorithm over the existing ones and show the robustness to the model misspecification. Application to relative concentration data from infant feces collected from subjects enrolled in a large molecular epidemiological cohort study validates that metabolic pathway involvement is a statistically significant factor for the conditional dependence between metabolites. Furthermore, new relationships among metabolites are discovered which can not be identified by the conventional methods of pathway analysis. Some of them have been widely recognized in biological literature.

A systematic evaluation of the computational tools for ligand-receptor-based cell-cell interaction inference

Cell-cell interactions (CCIs) are essential for multicellular organisms to coordinate biological processes and functions. One classical type of CCI interaction is between secreted ligands and cell surface receptors, i.e. ligand-receptor (LR) interactions. With the recent development of single-cell technologies, a large amount of single-cell ribonucleic acid (RNA) sequencing (scRNA-Seq) data has become widely available. This data availability motivated the single-cell-resolution study of CCIs, particularly LR-based CCIs. Dozens of computational methods and tools have been developed to predict CCIs by identifying LR-based CCIs. Many of these tools have been theoretically reviewed. However, there is little study on current LR-based CCI prediction tools regarding their performance and running results on public scRNA-Seq datasets. In this work, to fill this gap, we tested and compared nine of the most recent computational tools for LR-based CCI prediction. We used 15 well-studied scRNA-Seq samples that correspond to approximately 100K single cells under different experimental conditions for testing and comparison. Besides briefing the methodology used in these nine tools, we summarized the similarities and differences of these tools in terms of both LR prediction and CCI inference between cell types. We provided insight into using these tools to make meaningful discoveries in understanding cell communications.

MIAOME: Human Microbiome Affect The Host Epigenome

Besides the genetic factors having tremendous influences on the regulations of the epigenome, the microenvironmental factors have recently gained extensive attention for their roles in affecting the host epigenome. There are three major types of microenvironmental factors: microbiota-derived metabolites (MDM), microbiota-derived components (MDC) and microbiota-secreted proteins (MSP). These factors can regulate host physiology by modifying host gene expression through the three highly interconnected epigenetic mechanisms (e.g. histone modifications, DNA modifications, and non-coding RNAs). However, no database was available to provide the comprehensive factors of these types. Herein, a database entitled 'Human Microbiome Affect The Host Epigenome (MIAOME)' was constructed. Based on the types of epigenetic modifications confirmed in the literature review, the MIAOME database captures 1068 (63 genus, 281 species, 707 strains, etc.) human microbes, 91 unique microbiota-derived metabolites & components (16 fatty acids, 10 bile acids, 10 phenolic compounds, 10 vitamins, 9 tryptophan metabolites, etc.) derived from 967 microbes; 50 microbes that secreted 40 proteins; 98 microbes that directly influence the host epigenetic modification, and provides 3 classifications of the epigenome, including (1) 4 types of DNA modifications, (2) 20 histone modifications and (3) 490 ncRNAs regulations, involved in 160 human diseases. All in all, MIAOME has compiled the information on the microenvironmental factors influence host epigenome through the scientific literature and biochemical databases, and allows the collective considerations among the different types of factors. It can be freely assessed without login requirement by all users at: http://miaome.idrblab.net/ttd/

GeneTrail: A Framework for the Analysis of High-Throughput Profiles

Experimental high-throughput techniques, like next-generation sequencing or microarrays, are nowadays routinely applied to create detailed molecular profiles of cells. In general, these platforms generate high-dimensional and noisy data sets. For their analysis, powerful bioinformatics tools are required to gain novel insights into the biological processes under investigation. Here, we present an overview of the GeneTrail tool suite that offers rich functionality for the analysis and visualization of (epi-)genomic, transcriptomic, miRNomic, and proteomic profiles. Our framework enables the analysis of standard bulk, time-series, and single-cell measurements and includes various state-of-the-art methods to identify potentially deregulated biological processes and to detect driving factors within those deregulated processes. We highlight the capabilities of our web service with an analysis of a single-cell COVID-19 data set that demonstrates its potential for uncovering complex molecular mechanisms. GeneTrail can be accessed freely and without login requirements at http://genetrail.bioinf.uni-sb.de.

The impact of gene-body H3K36me3 patterns on gene expression level changes in chronic myelogenous leukemia

Chronic myelogenous leukemia (CML) is a malignant clonal disease of hematopoietic stem cells. Researches have exhibited that the progression of CML is related to histone modifications. Here, we perform the systematic analyses of H3K36me3 patterns and gene expression level changes. We observe that the genes with higher gene-body H3K36me3 levels in normal cells show fewer expression changes during leukemogenesis, while the genes with lower gene-body H3K36me3 levels in normal cells yield obvious expression changes during leukemogenesis (ρ = -0.98, P = 9.30 × 10^-8). These findings are conserved in human lung/breast cancers and mouse CML, regardless of gene expression levels and gene lengths. Regulatory element analysis and Random Forest regression display that Hoxd13, Rara, Scl, Smad3, Smad4 and Tgif1 induce the up-regulation of genes with lower H3K36me3 levels (ρ = 0.97, P = 2.35 × 10^-56). Enrichment analysis shows that the differentially expressed genes with lower H3K36me3 levels are involved in leukemia-related pathways, such as leukocyte migration and regulation of leukocyte activation. Finally, six driver genes (Tp53, Wt1, Dnmt3a, Cacna1b, Phactr1 and Gbp4) with lower H3K36me3 levels are identified. Our analyses indicate that lower gene-body H3K36me3 levels may serve as a biomarker for the progression of CML.

Incorporating K-mers Highly Correlated to Epigenetic Modifications for Bayesian Inference of Gene Interactions

Objective::

A gene interaction network, along with its related biological features, has an important role in computational biology. Bayesian network, as an efficient model, based on probabilistic concepts is able to exploit known and novel biological casual relationships between genes. The success of Bayesian networks in predicting the relationships greatly depends on selecting priors.

Methods::

K-mers have been applied as the prominent features to uncover the similarity between genes in a specific pathway, suggesting that this feature can be applied to study genes dependencies. In this study, we propose k-mers (4,5 and 6-mers) highly correlated with epigenetic modifications, including 17 modifications, as a new prior for Bayesian inference in the gene interaction network.

Result::

Employing this model on a network of 23 human genes and on a network based on 27 genes related to yeast resulted in F-measure improvements in different biological networks.

Conclusion::

The improvements in the best case are 12%, 36%, and 10% in the pathway, coexpression, and physical interaction, respectively.

Bioinformatic tools for DNA methylation and histone modification: A survey

Epigenetic inheritance occurs due to different mechanisms such as chromatin and histone modifications, DNA methylation and processes mediated by non-coding RNAs. It leads to changes in gene expressions and the emergence of new traits in different organisms in many diseases such as cancer. Recent advances in experimental methods led to the identification of epigenetic target sites in various organisms. Computational approaches have enabled us to analyze mass data produced by these methods. Next-generation sequencing (NGS) methods have been broadly used to identify these target sites and their patterns. By using these patterns, the emergence of diseases could be prognosticated. In this study, target site prediction tools for two major epigenetic mechanisms comprising histone modification and DNA methylation are reviewed. Publicly accessible databases are reviewed as well. Some suggestions regarding the state-of-the-art methods and databases have been made, including examining patterns of epigenetic changes that are important in epigenotypes detection.

LncRNA regulation: New frontiers in epigenetic solutions to drug chemoresistance

Long-noncoding RNAs (lncRNAs) have been shown to participate in sensitizing or de-sensitizing cancer cells to chemical drugs during cancer therapeutics. Notably, a plethora of lncRNAs have been confirmed to be associated with epigenetic controllers and regulate histone protein modification or DNA methylation states in the process of gene transcription. This correlation between lncRNAs and epigenetic regulators can induce the expression of core genes to trigger drug resistance. In addition, epigenetic signatures are considered to be effective and attractive biomarkers for monitoring drug therapeutic effects because they are inheritable, dynamic, and reversible. Therefore, the regulatory mechanism between lncRNAs and epigenetic machinery can serve as a novel indicator and target to overcome or reverse drug resistance in cancer therapy. In this review, we also presented a curated selection of computational tools (including online databases and network analysis) in the area of epigenetics. A classic workflow for lncRNA expression network analysis is presented, providing guidance for non-bioinformaticians to identify significant correlation between lncRNAs and other biomolecules.

HISTome2: a database of histone proteins, modifiers for multiple organisms and epidrugs

BACKGROUND

Epigenetics research is progressing in basic, pre-clinical and clinical studies using various model systems. Hence, updating the knowledge and integration of biological data emerging from in silico, in vitro and in vivo studies for different epigenetic factors is essential. Moreover, new drugs are being discovered which target various epigenetic proteins, tested in pre-clinical studies, clinical trials and approved by the FDA. It brings distinct challenges as well as opportunities to update the existing HIstome database for implementing and applying enormous data for biomedical research.

RESULTS

HISTome2 focuses on the sub-classification of histone proteins as variants and isoforms, post-translational modifications (PTMs) and modifying enzymes for humans (Homo sapiens), rat (Rattus norvegicus) and mouse (Mus musculus) on one interface for integrative analysis. It contains 232, 267 and 350 entries for histone proteins (non-canonical/variants and canonical/isoforms), PTMs and modifying enzymes respectively for human, rat, and mouse. Around 200 EpiDrugs for various classes of epigenetic modifiers, their clinical trial status, and pharmacological relevance have been provided in HISTome2. The additional features like 'Clustal omega' for multiple sequence alignment, link to 'FireBrowse' to visualize TCGA expression data and 'TargetScanHuman' for miRNA targets have been included in the database.

CONCLUSION

The information for multiple organisms and EpiDrugs on a common platform will accelerate the understanding and future development of drugs. Overall, HISTome2 has significantly increased the extent and diversity of its content which will serve as a 'knowledge Infobase' for biologists, pharmacologists, and clinicians. HISTome2: The HISTone Infobase is freely available on http://www.actrec.gov.in/histome2/ .

Exome sequencing identifies new somatic alterations and mutation patterns of tongue squamous cell carcinoma in a Chinese population

Tongue squamous cell carcinoma (TSCC) is an aggressive group of tumors characterized by high rates of regional lymph node metastasis and local recurrence. Emerging evidence has revealed genetic variations of TSCC across different geographical regions due to the impact of multiple risk factors such as chewing betel-quid. However, we know little of the mutational processes of TSCC in the Chinese population without the history of chewing betel-quid/tobacco. To explore the mutational spectrum of this disease, we performed whole-exome sequencing of sample pairs, comprising tumors and normal tissue, from 82 TSCC patients. In addition to identifying seven previously known TSCC-associated genes (TP53, CDKN2A, PIK3CA, NOTCH1, ASXL1, USH2A, and CSMD3), the analysis revealed six new genes (GNAQ, PRG4, RP1, ZNF16, NBEA, and PTPRC) that had not been reported previously in TSCC. Our in vitro experiments identified ZNF16 for the first time as a solid tumor associated gene to promote malignancy of TSCC cells. We also identified a microRNA (miR-585-5p) encoded by the 5q35.1 region and characterized it as a tumor suppressor by targeting SOX9. At least one non-silent mutation of genes involved in the 10 canonical oncogenic pathways (Notch, RTK-RAS, PI3K, Wnt, Cell cycle, p53, Myc, Hippo, TGFβ, and Nrf2) was found in 82.9% of cases. Collectively, our data extend the spectrum of TSCC mutations and define novel diagnosis markers and potential clinical targets for TSCC. © 2020 Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd.

A Novel Integrative Approach for Non-coding RNA Classification Based on Deep Learning

Background:

Molecular biomarkers show new ways to understand many disease processes. Noncoding RNAs as biomarkers play a crucial role in several cellular activities, which are highly correlated to many human diseases especially cancer. The classification and the identification of ncRNAs have become a critical issue due to their application, such as biomarkers in many human diseases.

Objective:

Most existing computational tools for ncRNA classification are mainly used for classifying only one type of ncRNA. They are based on structural information or specific known features. Furthermore, these tools suffer from a lack of significant and validated features. Therefore, the performance of these methods is not always satisfactory.

Methods:

We propose a novel approach named imCnC for ncRNA classification based on multisource deep learning, which integrates several data sources such as genomic and epigenomic data to identify several ncRNA types. Also, we propose an optimization technique to visualize the extracted features pattern from the multisource CNN model to measure the epigenomics features of each ncRNA type.

Results:

The computational results using a dataset of 16 human ncRNA classes downloaded from RFAM show that imCnC outperforms the existing tools. Indeed, imCnC achieved an accuracy of 94,18%. In addition, our method enables to discover new ncRNA features using an optimization technique to measure and visualize the features pattern of the imCnC classifier.

Variant discovery using next-generation sequencing and its future role in pharmacogenetics

Next-generation sequencing (NGS) has enabled the discovery of a multitude of novel and mostly rare variants in pharmacogenes that may alter a patient’s therapeutic response to drugs. In addition to single nucleotide variants, structural variation affecting the number of copies of whole genes or parts of genes can be detected. While current guidelines concerning clinical implementation mostly act upon well-documented, common single nucleotide variants to guide dosing or drug selection, in silico and large-scale functional assessment of rare variant effects on protein function are at the forefront of pharmacogenetic research to facilitate their clinical integration. Here, we discuss the role of NGS in variant discovery, paving the way for more comprehensive genotype-guided pharmacotherapy that can translate to improved clinical care.

Identification of molecular signatures and pathways to identify novel therapeutic targets in Alzheimer's disease: Insights from a systems biomedicine perspective

Alzheimer's disease (AD) is a progressive neurodegenerative disease characterized by the accumulation of amyloid plaques and neurofibrillary tangles in the brain. However, there are no peripheral biomarkers available that can detect AD onset. This study aimed to identify the molecular signatures in AD through an integrative analysis of blood gene expression data. We used two microarray datasets (GSE4226 and GSE4229) comparing peripheral blood transcriptomes of AD patients and controls to identify differentially expressed genes (DEGs). Gene set and protein overrepresentation analysis, protein-protein interaction (PPI), DEGs-Transcription Factors (TFs) interactions, DEGs-microRNAs (miRNAs) interactions, protein-drug interactions, and protein subcellular localizations analyses were performed on DEGs common to the datasets. We identified 25 common DEGs between the two datasets. Integration of genome scale transcriptome datasets with biomolecular networks revealed hub genes (NOL6, ATF3, TUBB, UQCRC1, CASP2, SND1, VCAM1, BTF3, VPS37B), common transcription factors (FOXC1, GATA2, NFIC, PPARG, USF2, YY1) and miRNAs (mir-20a-5p, mir-93-5p, mir-16-5p, let-7b-5p, mir-708-5p, mir-24-3p, mir-26b-5p, mir-17-5p, mir-193-3p, mir-186-5p). Evaluation of histone modifications revealed that hub genes possess several histone modification sites associated with AD. Protein-drug interactions revealed 10 compounds that affect the identified AD candidate biomolecules, including anti-neoplastic agents (Vinorelbine, Vincristine, Vinblastine, Epothilone D, Epothilone B, CYT997, and ZEN-012), a dermatological (Podofilox) and an immunosuppressive agent (Colchicine). The subcellular localization of molecular signatures varied, including nuclear, plasma membrane and cytosolic proteins. In the present study, it was identified blood-cell derived molecular signatures that might be useful as candidate peripheral biomarkers in AD. It was also identified potential drugs and epigenetic data associated with these molecules that may be useful in designing therapeutic approaches to ameliorate AD.

The carboxy-terminus, a key regulator of protein function

All proteins end with a carboxyl terminus that has unique biophysical properties and is often disordered. Although there are examples of important C-termini functions, a more global role for the C-terminus is not yet established. In this review, we summarize research on C-termini, a unique region in proteins that cells exploit. Alternative splicing and proteolysis increase the diversity of proteins and peptides in cells with unique C-termini. The C-termini of proteins contain minimotifs, short peptides with an encoded function generally characterized as binding, posttranslational modifications, and trafficking. Many of these activities are specific to minimotifs on the C-terminus. Approximately 13% of C-termini in the human proteome have a known minimotif, and the majority, if not all of the remaining termini have conserved motifs inferring a function that remains to be discovered. C-termini, their predictions, and their functions are collated in the C-terminome, Proteus, and Terminus Oriented Protein Function INferred Database (TopFIND) database/web systems. Many C-termini are well conserved, and some have a known role in health and disease. We envision that this summary of C-termini will guide future investigation of their biochemical and physiological significance.

Prolactin Receptor Signaling Regulates a Pregnancy-Specific Transcriptional Program in Mouse Islets

Pancreatic β-cells undergo profound hyperplasia during pregnancy to maintain maternal euglycemia. Failure to reprogram β-cells into a more replicative state has been found to underlie susceptibility to gestational diabetes mellitus (GDM). We recently identified a requirement for prolactin receptor (PRLR) signaling in the metabolic adaptations to pregnancy, where β-cell-specific PRLR knockout (βPRLRKO) mice exhibit a metabolic phenotype consistent with GDM. However, the underlying transcriptional program that is responsible for the PRLR-dependent metabolic adaptations during gestation remains incompletely understood. To identify PRLR signaling gene regulatory networks and target genes within β-cells during pregnancy, we performed a transcriptomic analysis of pancreatic islets isolated from either βPRLRKO mice or littermate controls in late gestation. Gene set enrichment analysis identified forkhead box protein M1 and polycomb repressor complex 2 subunits, Suz12 and enhancer of zeste homolog 2 (Ezh2), as novel candidate regulators of PRLR-dependent β-cell adaptation. Gene ontology term pathway enrichment revealed both established and novel PRLR signaling target genes that together promote a state of increased cellular metabolism and/or proliferation. In contrast to the requirement for β-cell PRLR signaling in maintaining euglycemia during pregnancy, PRLR target genes were not induced following high-fat diet feeding. Collectively, the current study expands our understanding of which transcriptional regulators and networks mediate gene expression required for islet adaptation during pregnancy. The current work also supports the presence of pregnancy-specific adaptive mechanisms distinct from those activated by nutritional stress.

Novel genetic and epigenetic factors of importance for inter-individual differences in drug disposition, response and toxicity

Individuals differ substantially in their response to pharmacological treatment. Personalized medicine aspires to embrace these inter-individual differences and customize therapy by taking a wealth of patient-specific data into account. Pharmacogenomic constitutes a cornerstone of personalized medicine that provides therapeutic guidance based on the genomic profile of a given patient. Pharmacogenomics already has applications in the clinics, particularly in oncology, whereas future development in this area is needed in order to establish pharmacogenomic biomarkers as useful clinical tools. In this review we present an updated overview of current and emerging pharmacogenomic biomarkers in different therapeutic areas and critically discuss their potential to transform clinical care. Furthermore, we discuss opportunities of technological, methodological and institutional advances to improve biomarker discovery. We also summarize recent progress in our understanding of epigenetic effects on drug disposition and response, including a discussion of the only few pharmacogenomic biomarkers implemented into routine care. We anticipate, in part due to exciting rapid developments in Next Generation Sequencing technologies, machine learning methods and national biobanks, that the field will make great advances in the upcoming years towards unlocking the full potential of genomic data.

DNAmod: the DNA modification database

Covalent DNA modifications, such as 5-methylcytosine (5mC), are increasingly the focus of numerous research programs. In eukaryotes, both 5mC and 5-hydroxymethylcytosine (5hmC) are now recognized as stable epigenetic marks, with diverse functions. Bacteria, archaea, and viruses contain various other modified DNA nucleobases. Numerous databases describe RNA and histone modifications, but no database specifically catalogues DNA modifications, despite their broad importance in epigenetic regulation. To address this need, we have developed DNAmod: the DNA modification database. DNAmod is an open-source database ( https://dnamod.hoffmanlab.org ) that catalogues DNA modifications and provides a single source to learn about their properties. DNAmod provides a web interface to easily browse and search through these modifications. The database annotates the chemical properties and structures of all curated modified DNA bases, and a much larger list of candidate chemical entities. DNAmod includes manual annotations of available sequencing methods, descriptions of their occurrence in nature, and provides existing and suggested nomenclature. DNAmod enables researchers to rapidly review previous work, select mapping techniques, and track recent developments concerning modified bases of interest.

Computational Methods for the Pharmacogenetic Interpretation of Next Generation Sequencing Data

Up to half of all patients do not respond to pharmacological treatment as intended. A substantial fraction of these inter-individual differences is due to heritable factors and a growing number of associations between genetic variations and drug response phenotypes have been identified. Importantly, the rapid progress in Next Generation Sequencing technologies in recent years unveiled the true complexity of the genetic landscape in pharmacogenes with tens of thousands of rare genetic variants. As each individual was found to harbor numerous such rare variants they are anticipated to be important contributors to the genetically encoded inter-individual variability in drug effects. The fundamental challenge however is their functional interpretation due to the sheer scale of the problem that renders systematic experimental characterization of these variants currently unfeasible. Here, we review concepts and important progress in the development of computational prediction methods that allow to evaluate the effect of amino acid sequence alterations in drug metabolizing enzymes and transporters. In addition, we discuss recent advances in the interpretation of functional effects of non-coding variants, such as variations in splice sites, regulatory regions and miRNA binding sites. We anticipate that these methodologies will provide a useful toolkit to facilitate the integration of the vast extent of rare genetic variability into drug response predictions in a precision medicine framework.

PTMD: A Database of Human Disease-associated Post-translational Modifications

Various posttranslational modifications (PTMs) participate in nearly all aspects of biological processes by regulating protein functions, and aberrant states of PTMs are frequently implicated in human diseases. Therefore, an integral resource of PTM–disease associations (PDAs) would be a great help for both academic research and clinical use. In this work, we reported PTMD, a well-curated database containing PTMs that are associated with human diseases. We manually collected 1950 known PDAs in 749 proteins for 23 types of PTMs and 275 types of diseases from the literature. Database analyses show that phosphorylation has the largest number of disease associations, whereas neurologic diseases have the largest number of PTM associations. We classified all known PDAs into six classes according to the PTM status in diseases and demonstrated that the upregulation and presence of PTM events account for a predominant proportion of disease-associated PTM events. By reconstructing a disease–gene network, we observed that breast cancers have the largest number of associated PTMs and AKT1 has the largest number of PTMs connected to diseases. Finally, the PTMD database was developed with detailed annotations and can be a useful resource for further analyzing the relations between PTMs and human diseases. PTMD is freely accessible at http://ptmd.biocuckoo.org.

Biological networks in Parkinson's disease: an insight into the epigenetic mechanisms associated with this disease

BACKGROUND

Parkinson's disease (PD) is the second most prevalent neurodegenerative disorders in the world. Studying PD from systems biology perspective involving genes and their regulators might provide deeper insights into the complex molecular interactions associated with this disease.

RESULT

We have studied gene co-expression network obtained from a PD-specific microarray data. The co-expression network identified 11 hub genes, of which eight genes are not previously known to be associated with PD. Further study on the functionality of these eight novel hub genes revealed that these genes play important roles in several neurodegenerative diseases. Furthermore, we have studied the tissue-specific expression and histone modification patterns of the novel hub genes. Most of these genes possess several histone modification sites those are already known to be associated with neurodegenerative diseases. Regulatory network namely mTF-miRNA-gene-gTF involves microRNA Transcription Factor (mTF), microRNA (miRNA), gene and gene Transcription Factor (gTF). Whereas long noncoding RNA (lncRNA) mediated regulatory network involves miRNA, gene, mTF and lncRNA. mTF-miRNA-gene-gTF regulatory network identified a novel feed-forward loop. lncRNA-mediated regulatory network identified novel lncRNAs of PD and revealed the two-way regulatory pattern of PD-specific miRNAs where miRNAs can be regulated by both the TFs and lncRNAs. SNP analysis of the most significant genes of the co-expression network identified 20 SNPs. These SNPs are present in the 3' UTR of known PD genes and are controlled by those miRNAs which are also involved in PD.

CONCLUSION

Our study identified eight novel hub genes which can be considered as possible candidates for future biomarker identification studies for PD. The two regulatory networks studied in our work provide a detailed overview of the cellular regulatory mechanisms where the non-coding RNAs namely miRNA and lncRNA, can act as epigenetic regulators of PD. SNPs identified in our study can be helpful for identifying PD at an earlier stage. Overall, this study may impart a better comprehension of the complex molecular interactions associated with PD from systems biology perspective.

iHMS: a database integrating human histone modification data across developmental stages and tissues

Background

Differences in chromatin states are critical to the multiplicity of cell states. Recently genome-wide histone modification maps of diverse human developmental stages and tissues have been charted.

Description

To facilitate the investigation of epigenetic dynamics and regulatory mechanisms in cellular differentiation processes, we developed iHMS, an integrated human histone modification database that incorporates massive histone modification maps spanning different developmental stages, lineages and tissues (http://www.tongjidmb.com/human/index.html). It also includes genome-wide expression data of different conditions, reference gene annotations, GC content and CpG island information. By providing an intuitive and user-friendly query interface, iHMS enables comprehensive query and comparative analysis based on gene names, genomic region locations, histone modification marks and cell types. Moreover, it offers an efficient browser that allows users to visualize and compare multiple genome-wide histone modification maps and related expression profiles across different developmental stages and tissues.

Conclusion

iHMS is of great helpfulness to understand how global histone modification state transitions impact cellular phenotypes across different developmental stages and tissues in the human genome. This extensive catalog of histone modification states thus presents an important resource for epigenetic and developmental studies.

Confident gene activity prediction based on single histone modification H2BK5ac in human cell lines

BACKGROUND

The histones in the core of nucleosomes may be subject to covalent post-transcriptional modifications. These modifications are thought to correlate with and possibly affect various genomic functions, including transcription. Each modification may alone or in combination with other modifications influence or be influenced by transcription. We aimed to identify correlations between single modifications or combinations of modifications at specific nucleosome sized gene regions with transcription activity based on global histone modification and transcription data of human CD4+ T cells and three other human cell lines. Transcription activity was defined in a binary fashion as either on or off. The analysis was done using the Classification and Regression Tree (CART) data mining protocol, and the Multifactorial Dimensionality Reduction (MDR) method was performed to confirm the CART results. These powerful methods have not previously been used for analysis of histone modification data.

RESULTS

We showed that analysis of the single histone modification H2BK5ac at only four gene regions correctly predicted transcription activity status of over 75% of genes in CD4+ T-cells. The H2BK5ac modification status also had high power for prediction of gene transcription activity in the three other cell lines studied. The informative gene regions with the H2BK5ac modification were all positioned proximal to transcription initiation sites. The CART and MDR methods were appropriate tools for the analysis performed. In the study, we also developed a non-arbitrary protocol for binary classification of genes as transcriptionally active or inactive.

CONCLUSIONS

The importance of H2BK5ac modification with regards to transcription control has not previously been emphasized. Analysis of this single modification at only four nucleosome sized gene regions, all of which are at or proximal to transcription initiation, has high power for prediction of gene transcription activity.

HEDD: the human epigenetic drug database

Epigenetic drugs are chemical compounds that target disordered post-translational modification of histone proteins and DNA through enzymes, and the recognition of these changes by adaptor proteins. Epigenetic drug-related experimental data such as gene expression probed by high-throughput sequencing, co-crystal structure probed by X-RAY diffraction and binding constants probed by bio-assay have become widely available. The mining and integration of multiple kinds of data can be beneficial to drug discovery and drug repurposing. HEMD and other epigenetic databases store comprehensively epigenetic data where users can acquire segmental information of epigenetic drugs. However, some data types such as high-throughput datasets are not provide by these databases and they do not support flexible queries for epigenetic drug-related experimental data. Therefore, in reference to HEMD and other epigenetic databases, we developed a relatively comprehensive database for human epigenetic drugs. The human epigenetic drug database (HEDD) focuses on the storage and integration of epigenetic drug datasets obtained from laboratory experiments and manually curated information. The latest release of HEDD incorporates five kinds of datasets: (i) drug, (ii) target, (iii) disease, (vi) high-throughput and (v) complex. In order to facilitate data extraction, flexible search options were built in HEDD, which allowed an unlimited condition query for specific kinds of datasets using drug names, diseases and experiment types.

Database URL:http://hedds.org/

Integrating Epigenomics into the Understanding of Biomedical Insight

Epigenetics is one of the most rapidly expanding fields in biomedical research, and the popularity of the high-throughput next-generation sequencing (NGS) highlights the accelerating speed of epigenomics discovery over the past decade. Epigenetics studies the heritable phenotypes resulting from chromatin changes but without alteration on DNA sequence. Epigenetic factors and their interactive network regulate almost all of the fundamental biological procedures, and incorrect epigenetic information may lead to complex diseases. A comprehensive understanding of epigenetic mechanisms, their interactions, and alterations in health and diseases genome widely has become a priority in biological research. Bioinformatics is expected to make a remarkable contribution for this purpose, especially in processing and interpreting the large-scale NGS datasets. In this review, we introduce the epigenetics pioneering achievements in health status and complex diseases; next, we give a systematic review of the epigenomics data generation, summarize public resources and integrative analysis approaches, and finally outline the challenges and future directions in computational epigenomics.

WERAM: a database of writers, erasers and readers of histone acetylation and methylation in eukaryotes

In this work, we developed a database WERAM (http://weram.biocuckoo.org/) for histone acetyltransferases, histone deacetylases, histone methyltransferases, histone demethylases and acetyl- or methyl-binding proteins, which catalyze, remove and recognize histone acetylation and methylation sites as 'writers', 'erasers' and 'readers', and synergistically determine the 'histone code'. From the scientific literature, we totally collected over 580 experimentally identified histone regulators from eight model organisms, including Homo sapiens, Mus musculus, Rattus norvegicus, Drosophila melanogaster, Caenorhabditis elegans, Arabidopsis thaliana, Schizosaccharomyces pombe and Saccharomyces cerevisiae We also collected ∼900 site-specific regulator-histone relations from the eight species. According to the experimental evidence, known histone regulators were classified into distinct families. To computationally detect more proteins in eukaryotes, we constructed hidden Markov model (HMM) profiles for histone regulator families. For families without HMM profiles, we also conducted orthologous searches. Totally, WERAM database contained more than 20 thousand non-redundant histone regulators from 148 eukaryotes. The detailed annotations and classification information of histone regulators were provided, together with site-specific histone substrates if available.

Combinations of Histone Modifications for Pattern Genes

Histone post-translational modifications play important roles in transcriptional regulation. It is known that multiple histone modifications can act in a combinatorial manner. In this study, we investigated the effects of multiple histone modifications on expression levels of five gene categories (four kinds of pattern genes and non-pattern genes) in coding regions. The combinatorial patterns of modifications for the five gene categories were also studied in the regions. Our results indicated that the differences in the expression levels between any two gene categories were significant. There were some corresponding differences in multiple histone modification levels among the five gene categories. Multiple histone modifications jointly impacted expression levels of every gene category. Four mutual combinations of histone modifications were found and analyzed.

Influence factors on the correlations between expression levels of neighboring pattern genes

Some genes tend to cluster and be co-expressed. Multiple factors affect gene co-expression. In this study, we investigated the relationships between multiple factors and the correlations of expression levels of neighboring genes, which were divided into four kinds of pattern genes and one type of non-pattern gene. Our results indicate that the correlation between expression levels of neighboring non-pattern genes is related to multiple factors with the exception of transcriptional orientations of neighboring genes. The correlation between expression levels of neighboring specific genes or neighboring repressed genes is likely to be dependent on the co-functions of neighboring genes. The correlation between expression levels of neighboring housekeeping genes is associated with histone modifications in intergenic regions.

EpiFactors: a comprehensive database of human epigenetic factors and complexes

Epigenetics refers to stable and long-term alterations of cellular traits that are not caused by changes in the DNA sequence per se. Rather, covalent modifications of DNA and histones affect gene expression and genome stability via proteins that recognize and act upon such modifications. Many enzymes that catalyse epigenetic modifications or are critical for enzymatic complexes have been discovered, and this is encouraging investigators to study the role of these proteins in diverse normal and pathological processes. Rapidly growing knowledge in the area has resulted in the need for a resource that compiles, organizes and presents curated information to the researchers in an easily accessible and user-friendly form. Here we present EpiFactors, a manually curated database providing information about epigenetic regulators, their complexes, targets and products. EpiFactors contains information on 815 proteins, including 95 histones and protamines. For 789 of these genes, we include expressions values across several samples, in particular a collection of 458 human primary cell samples (for approximately 200 cell types, in many cases from three individual donors), covering most mammalian cell steady states, 255 different cancer cell lines (representing approximately 150 cancer subtypes) and 134 human postmortem tissues. Expression values were obtained by the FANTOM5 consortium using Cap Analysis of Gene Expression technique. EpiFactors also contains information on 69 protein complexes that are involved in epigenetic regulation. The resource is practical for a wide range of users, including biologists, pharmacologists and clinicians.

Database URL: http://epifactors.autosome.ru

Insights into the miRNA regulations in human disease genes

BACKGROUND

MicroRNAs are a class of short non-coding RNAs derived from either cellular or viral transcripts that act post-transcriptionally to regulate mRNA stability and translation. In recent days, increasing numbers of miRNAs have been shown to be involved in the development and progression of a variety of diseases. We, therefore, intend to enumerate miRNA targets in several known disease classes to explore the degree of miRNA regulations on them which is unexplored till date.

RESULTS

Here, we noticed that miRNA hits in cancer genes are remarkably higher than other diseases in human. Our observation suggests that UTRs and the transcript length of cancer related genes have a significant contribution in higher susceptibility to miRNA regulation. Moreover, gene duplication, mRNA stability, AREScores and evolutionary rate were likely to have implications for more miRNA targeting on cancer genes. Consequently, the regression analysis have confirmed that the AREScores plays most important role in detecting miRNA targets on disease genes. Interestingly, we observed that epigenetic modifications like CpG methylation and histone modification are less effective than miRNA regulations in controlling the gene expression of cancer genes.

CONCLUSIONS

The intrinsic properties of cancer genes studied here, for higher miRNA targeting will enhance the knowledge on cancer gene regulation.

No more non-model species: the promise of next generation sequencing for comparative immunology

Next generation sequencing (NGS) allows for the rapid, comprehensive and cost effective analysis of entire genomes and transcriptomes. NGS provides approaches for immune response gene discovery, profiling gene expression over the course of parasitosis, studying mechanisms of diversification of immune receptors and investigating the role of epigenetic mechanisms in regulating immune gene expression and/or diversification. NGS will allow meaningful comparisons to be made between organisms from different taxa in an effort to understand the selection of diverse strategies for host defence under different environmental pathogen pressures. At the same time, it will reveal the shared and unique components of the immunological toolkit and basic functional aspects that are essential for immune defence throughout the living world. In this review, we argue that NGS will revolutionize our understanding of immune responses throughout the animal kingdom because the depth of information it provides will circumvent the need to concentrate on a few "model" species.

MetaImprint: an information repository of mammalian imprinted genes

Genomic imprinting is a complex genetic and epigenetic phenomenon that plays important roles in mammalian development and diseases. Mammalian imprinted genes have been identified widely by experimental strategies or predicted using computational methods. Systematic information for these genes would be necessary for the identification of novel imprinted genes and the analysis of their regulatory mechanisms and functions. Here, a well-designed information repository, MetaImprint (http://bioinfo.hrbmu.edu.cn/MetaImprint), is presented, which focuses on the collection of information concerning mammalian imprinted genes. The current version of MetaImprint incorporates 539 imprinted genes, including 255 experimentally confirmed genes, and their detailed research courses from eight mammalian species. MetaImprint also hosts genome-wide genetic and epigenetic information of imprinted genes, including imprinting control regions, single nucleotide polymorphisms, non-coding RNAs, DNA methylation and histone modifications. Information related to human diseases and functional annotation was also integrated into MetaImprint. To facilitate data extraction, MetaImprint supports multiple search options, such as by gene ID and disease name. Moreover, a configurable Imprinted Gene Browser was developed to visualize the information on imprinted genes in a genomic context. In addition, an Epigenetic Changes Analysis Tool is provided for online analysis of DNA methylation and histone modification differences of imprinted genes among multiple tissues and cell types. MetaImprint provides a comprehensive information repository of imprinted genes, allowing researchers to investigate systematically the genetic and epigenetic regulatory mechanisms of imprinted genes and their functions in development and diseases.

Quantitative research of histone H3 acetylation levels of human hepatocellular carcinoma cells

BACKGROUND

Core histone H3 is a highly conserved protein in the cell nucleus, it goes through various post-translational modifications easily, and the state of the acetylation has clinical diagnostic significance in prostate cancer, breast cancer, lung cancer and other diseases.

RESULTS

In this work, the combinatorial method of chromatographic separation, methylation isotope labeling and LTQ-Orbitrap(®) MS was employed to quantify the acetylation sites of histone H3 separately within normal liver cells L02 and hepatocellular carcinoma (HCC) cells HepG2, HCC metastasis cells 97H and HCC cells HepG2, high HCC metastasis potential cells LM3 and low HCC metastasis potential cells 97L. In comparison with the quantitative results of HepG2 and L02, the amounts of five acetylated and methylated peptides were found decreased. Similarly, when comparing the 97H with HepG2, the amounts of eight acetylated and methylated peptides were found decreased, and when comparing the LM3 with 97L, the amounts of six acetylated and methylated peptides were found decreased.

CONCLUSION

These results provide some fundamental reference information for the research into post-translational modifications of histones in human liver cancer and other related diseases.

EPITRANS: a database that integrates epigenome and transcriptome data

Epigenetic modifications affect gene expression and thereby govern a wide range of biological processes such as differentiation, development and tumorigenesis. Recent initiatives to define genome-wide DNA methylation and histone modification profiles by microarray and sequencing methods have led to the construction of databases. These databases are repositories for international epigenetic consortiums or provide mining results from PubMed, but do not integrate the epigenetic information with gene expression changes. In order to overcome this limitation, we constructed EPITRANS, a novel database that visualizes the relationships between gene expression and epigenetic modifications. EPITRANS uses combined analysis of epigenetic modification and gene expression to search for cell function-related epigenetic and transcriptomic alterations (Freely available on the web at http://epitrans.org ).

Balancing reactive oxygen species in the epigenome: NADPH oxidases as target and perpetrator

SIGNIFICANCE

NADPH oxidases are important sources for regulated generation of reactive oxygen species (ROS). The main ROS produced are superoxide and hydrogen peroxide, both of which are redox signaling molecules in the context of various cellular functions. Redox imbalance due to excessive or insufficient ROS is a hallmark of pathophysiological aspects, including cancer development and progression.

RECENT ADVANCES

Epigenetic silencing of NADPH oxidases by hypermethylation of their promoter region or of the genes required for their assembly and activity occurs in diseases, such as lung cancer, and may represent an early stage of neoplastic transformation.

CRITICAL ISSUES

Loss of ROS-mediated signaling by epigenetic silencing may promote tumorigenesis. Conversely, increased oxidative stress caused by oncogene-induced overexpression of NADPH oxidases may also drive epigenetic instability. Thus, the cellular redox balance is likely vital in carcinogenesis.

FUTURE DIRECTIONS

NADPH oxidases may serve as prognostic tumor biomarker, especially when their individual expression is confined to accessible tissues, such as mucosal epithelia or blood. Further validation of NADPH oxidase/dual oxidase enzymes as candidate markers will require well controlled, large-scale clinical data sets. This review is focused on NADPH oxidases as targets of epigenetic changes in cancer and on the emerging role of ROS as inducers of epigenetic changes.

PEpiD: a prostate epigenetic database in mammals

Epigenetic mechanisms play key roles in initiation and progression of prostate cancer by changing gene expression. The Prostate Epigenetic Database (PEpiD: http://wukong.tongji.edu.cn/pepid) archives the three extensively characterized epigenetic mechanisms DNA methylation, histone modification, and microRNA implicated in prostate cancer of human, mouse, and rat. PEpiD uses a distinct color scheme to present the three types of epigenetic data and provides a user-friendly interface for flexible query. The retrieved information includes Refseq ID, gene symbol, gene alias, genomic loci of epigenetic changes, tissue source, experimental method, and supportive references. The change of histone modification (hyper or hypo) and the corresponding gene expression change (up or down) are also indicated. A graphic view of DNA methylation with exon-intron structure and predicted CpG islands is provided as well. Moreover, the prostate-related ENCODE tracks (DNA methylation, histone modifications, chromatin remodelers), and other key transcription factors with reported roles in prostate are displayed in the browser as well. The reversibility of epigenetic aberrations has made them potential markers for diagnosis and prognosis, and targets for treatment of cancers. This curated information will improve our understanding of epigenetic mechanisms of gene regulation in prostate cancer, and serve as an important resource for epigenetic research in prostate cancer.

Online tools for bioinformatics analyses in nutrition sciences

Recent advances in "omics" research have resulted in the creation of large datasets that were generated by consortiums and centers, small datasets that were generated by individual investigators, and bioinformatics tools for mining these datasets. It is important for nutrition laboratories to take full advantage of the analysis tools to interrogate datasets for information relevant to genomics, epigenomics, transcriptomics, proteomics, and metabolomics. This review provides guidance regarding bioinformatics resources that are currently available in the public domain, with the intent to provide a starting point for investigators who want to take advantage of the opportunities provided by the bioinformatics field.

The chromatin fingerprint of gene enhancer elements

Different cell types within a single organism are generally distinguished by strikingly different patterns of gene expression, which are dynamic throughout development and adult life. Distal enhancer elements are key drivers of spatiotemporal specificity in gene regulation. Often located tens of kilobases from their target promoters and functioning in an orientation-independent manner, the identification of bona fide enhancers has proved a formidable challenge. With the development of ChIP-seq, global cataloging of putative enhancers has become feasible. Here, we review the current understanding of the chromatin landscape at enhancers and how these chromatin features enable robust identification of tissue-specific enhancers.