Computational epigenetics

Artificial intelligence and deep learning algorithms for epigenetic sequence analysis: A review for epigeneticists and AI experts

Epigenetics encompasses mechanisms that can alter the expression of genes without changing the underlying genetic sequence. The epigenetic regulation of gene expression is initiated and sustained by several mechanisms such as DNA methylation, histone modifications, chromatin conformation, and non-coding RNA. The changes in gene regulation and expression can manifest in the form of various diseases and disorders such as cancer and congenital deformities. Over the last few decades, high-throughput experimental approaches have been used to identify and understand epigenetic changes, but these laboratory experimental approaches and biochemical processes are time-consuming and expensive. To overcome these challenges, machine learning and artificial intelligence (AI) approaches have been extensively used for mapping epigenetic modifications to their phenotypic manifestations. In this paper we provide a narrative review of published research on AI models trained on epigenomic data to address a variety of problems such as prediction of disease markers, gene expression, enhancer-promoter interaction, and chromatin states. The purpose of this review is twofold as it is addressed to both AI experts and epigeneticists. For AI researchers, we provided a taxonomy of epigenetics research problems that can benefit from an AI-based approach. For epigeneticists, given each of the above problems we provide a list of candidate AI solutions in the literature. We have also identified several gaps in the literature, research challenges, and recommendations to address these challenges.

3D QSAR pharmacophore based lead identification of G9a lysine methyltransferase towards epigenetic therapeutics

The G9a, Lysine Methyltransferase that methylates the histone 3 lysine 9 (H3K9) of the nucleosome, is an excellent epigenetic target having no clinically passed inhibitor currently owing to adverse in vivo ADMET toxicities. In this work, we have carried out detailed computational investigations to find novel and safer lead against the target using advanced 3 D QSAR pharmacophore screening of databases containing more than 400000 entrees of natural compounds. The screening was conducted at different levels at increasing stringencies by employing pharmacophore mapping, druglikenesses and interaction profiles of the selected to identify potential hit compounds. The potential hits were further screened by advanced flexible docking, ADME and toxicity analysis to eight hit compounds. Based on the comparative analysis of the hits with the reference inhibitor, we identified one lead inhibitor against the G9a, having better binding efficacy and a safer ADMET profile than the reference inhibitor. Finally, the results were further verified using robust molecular dynamics simulation and MM-GBSA binding energy calculation. The natural compounds are generally considered benign due to their long human uses and this is the first attempt of in silico screening of a large natural compound library against G9a to our best knowledge. Therefore, the finding of this study may add value towards the development of epigenetic therapeutics against the G9a.Communicated by Ramaswamy H. Sarma.

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.

Identification and validation of a new pyroptosis-associated lncRNA signature to predict survival outcomes, immunological responses and drug sensitivity in patients with gastric cancer

BACKGROUND

Gastric cancer (GC) ranks fifth in prevalence among carcinomas worldwide. Both pyroptosis and long noncoding RNAs (lncRNAs) play crucial roles in the occurrence and development of gastric cancer. Therefore, we aimed to construct a pyroptosis-associated lncRNA model to predict the outcomes of patients with gastric cancer.

METHODS

Pyroptosis-associated lncRNAs were identified through co-expression analysis. Univariate and multivariate Cox regression analyses were performed using the least absolute shrinkage and selection operator (LASSO). Prognostic values were tested through principal component analysis, a predictive nomogram, functional analysis and Kaplan‒Meier analysis. Finally, immunotherapy and drug susceptibility predictions and hub lncRNA validation were performed.

RESULTS

Using the risk model, GC individuals were classified into two groups: low-risk and high-risk groups. The prognostic signature could distinguish the different risk groups based on principal component analysis. The area under the curve and the conformance index suggested that this risk model was capable of correctly predicting GC patient outcomes. The predicted incidences of the one-, three-, and five-year overall survivals exhibited perfect conformance. Distinct changes in immunological markers were noted between the two risk groups. Finally, greater levels of appropriate chemotherapies were required in the high-risk group. AC005332.1, AC009812.4 and AP000695.1 levels were significantly increased in gastric tumor tissue compared with normal tissue.

CONCLUSIONS

We created a predictive model based on 10 pyroptosis-associated lncRNAs that could accurately predict the outcomes of GC patients and provide a promising treatment option in the future.

Pan-cancer analysis of the DNA methylation patterns of long non-coding RNA

Long non-coding RNA (lncRNA) regulated by abnormal DNA methylation (ADM-lncRNA) emerges as a biomarker for cancer diagnosis and treatment. This study comprehensively described the methylation patterns of lncRNA in pan-cancer using the cancer data set in The Cancer Genome Atlas (TCGA). Based on the cancer heterogeneity of ADM-lncRNA in pan-cancer, we constructed a co-expression network of pan-cancer ADM-lncRNA (pADM-lncRNA) in 10 cancers, highlighting the combined action mode of abnormal DNA methylation, and indicating the internal connection among different cancers. Functional analysis revealed the pan-carcinogenic pathway of pADM-lncRNA and suggested potential factors for cancer heterogeneity and tumor immune microenvironment changes. Survival analysis showed the potential of pADM-lncRNA-mRNA co-expression pair as cancer biomarkers. Revealing the action mode of lncRNA and DNA methylation in cancer may help understand the key molecular mechanisms of cell carcinogenesis.

A review of machine learning approaches for drug synergy prediction in cancer

Combinational pharmacotherapy with the synergistic/additive effect is a powerful treatment strategy for complex diseases such as malignancies. Identifying synergistic combinations with various compounds and structures requires testing a large number of compound combinations. However, in practice, examining different compounds by in vivo and in vitro approaches is costly, infeasible and challenging. In the last decades, significant success has been achieved by expanding computational methods in different pharmacological and bioinformatics domains. As promising tools, computational approaches such as machine learning algorithms (MLAs) are used for prioritizing combinational pharmacotherapies. This review aims to provide the models developed to predict synergistic drug combinations in cancer by MLAs with various information, including gene expression, protein-protein interactions, metabolite interactions, pathways and pharmaceutical information such as chemical structure, molecular descriptor and drug-target interactions.

New Insights for Biosensing: Lessons from Microbial Defense Systems

Microorganisms have gained defense systems during the lengthy process of evolution over millions of years. Such defense systems can protect them from being attacked by invading species (e.g., CRISPR-Cas for establishing adaptive immune systems and nanopore-forming toxins as virulence factors) or enable them to adapt to different conditions (e.g., gas vesicles for achieving buoyancy control). These microorganism defense systems (MDS) have inspired the development of biosensors that have received much attention in a wide range of fields including life science research, food safety, and medical diagnosis. This Review comprehensively analyzes biosensing platforms originating from MDS for sensing and imaging biological analytes. We first describe a basic overview of MDS and MDS-inspired biosensing platforms (e.g., CRISPR-Cas systems, nanopore-forming proteins, and gas vesicles), followed by a critical discussion of their functions and properties. We then discuss several transduction mechanisms (optical, acoustic, magnetic, and electrical) involved in MDS-inspired biosensing. We further detail the applications of the MDS-inspired biosensors to detect a variety of analytes (nucleic acids, peptides, proteins, pathogens, cells, small molecules, and metal ions). In the end, we propose the key challenges and future perspectives in seeking new and improved MDS tools that can potentially lead to breakthrough discoveries in developing a new generation of biosensors with a combination of low cost; high sensitivity, accuracy, and precision; and fast detection. Overall, this Review gives a historical review of MDS, elucidates the principles of emulating MDS to develop biosensors, and analyzes the recent advancements, current challenges, and future trends in this field. It provides a unique critical analysis of emulating MDS to develop robust biosensors and discusses the design of such biosensors using elements found in MDS, showing that emulating MDS is a promising approach to conceptually advancing the design of biosensors.

OUP accepted manuscript

DNA methylation, an important component of eukaryotic epigenetics, varies in pattern and function across Metazoa. Notably, bilaterian vertebrates and invertebrates differ dramatically in gene body methylation (GbM). Using the frequency of cytosine-phospho-guanines (CpGs), which are lost through mutation when methylated, we report the first broad survey of DNA methylation in Cnidaria, the ancient sister group to Bilateria. We find that: 1) GbM differentially relates to expression categories as it does in most bilaterian invertebrates, but distributions of GbM are less discretely bimodal. 2) Cnidarians generally have lower CpG frequencies on gene bodies than bilaterian invertebrates potentially suggesting a compensatory mechanism to replace CpG lost to mutation in Bilateria that is lacking in Cnidaria. 3) GbM patterns show some consistency within taxonomic groups such as the Scleractinian corals; however, GbM patterns variation across a range of taxonomic ranks in Cnidaria suggests active evolutionary change in GbM within Cnidaria. 4) Some but not all GbM variation is associated with life history change and genome expansion, whereas GbM loss is evident in endoparasitic cnidarians. 5) Cnidarian repetitive elements are less methylated than gene bodies, and methylation of both correlate with genome repeat content. 6) These observations reinforce claims that GbM evolved in stem Metazoa. Thus, this work supports overlap between DNA methylation processes in Cnidaria and Bilateria, provides a framework to compare methylation within and between Cnidaria and Bilateria, and demonstrates the previously unknown rapid evolution of cnidarian methylation.

Promising Epigenetic Biomarkers Associated With Cancer-Associated-Fibroblasts for Progression of Kidney Renal Clear Cell Carcinoma

Kidney renal clear cell carcinoma (KIRC) is the most common malignant kidney tumor as its characterization of highly metastatic potential. Patients with KIRC are associated with poor clinical outcomes with limited treatment options. Up to date, the underlying molecular mechanisms of KIRC pathogenesis and progression are still poorly understood. Instead, particular features of Cancer-Associated Fibroblasts (CAFs) are highly associated with adverse outcomes of patients with KIRC, while the precise regulatory mechanisms at the epigenetic level of KIRC in governing CAFs remain poorly defined. Therefore, explore the correlations between epigenetic regulation and CAFs infiltration may help us better understand the molecular mechanisms behind KIRC progression, which may improve clinical outcomes and patients quality of life. In the present study, we identified a set of clinically relevant CAFs-related methylation-driven genes, NAT8, TINAG, and SLC17A1 in KIRC. Our comprehensive in silico analysis revealed that the expression levels of NAT8, TINAG, and SLC17A1 are highly associated with outcomes of patients with KIRC. Meanwhile, their methylation levels are highly correlates with the severity of KIRC. We suggest that the biomarkers might contribute to CAFs infiltration in KIRC. Taken together, our study provides a set of promising biomarkers which could predict the progression and prognosis of KIRC. Our findings could have potential prognosis and therapeutic significance in the progression of KIRC.

The Female Snark Is Still a Boojum: Looking toward the Future of Studying Female Reproductive Biology

Philosophical truths are hidden in Lewis Carroll's nonsense poems, such as "The hunting of the snark." When the poem is used as a scientific allegory, a snark stands for the pursuit of scientific truth, while a boojum is a spurious discovery. In the study of female biology, boojums have been the result of the use of cultural stereotypes to frame hypotheses and methodologies. Although female reproduction is key for the continuation of sexually reproducing species, not only have females been understudied in many regards, but also data have commonly been interpreted in the context of now-outdated social mores. Spurious discoveries, boojums, are the result. In this article, we highlight specific gaps in our knowledge of female reproductive biology and provide a jumping-off point for future research. We discuss the promise of emerging methodologies (e.g., micro-CT scanning, high-throughput sequencing, proteomics, big-data analysis, CRISPR-Cas9, and viral vector technology) that can yield insights into previously cryptic processes and features. For example, in mice, deoxyribonucleic acid sequencing via chromatin immunoprecipitation followed by sequencing is already unveiling how epigenetics lead to sex differences in brain development. Similarly, new explorations, including microbiome research, are rapidly debunking dogmas such as the notion of the "sterile womb." Finally, we highlight how understanding female reproductive biology is well suited to the National Science Foundation's big idea, "Predicting Rules of Life." Studies of female reproductive biology will enable scholars to (1) traverse levels of biological organization from reproductive proteins at the molecular level, through anatomical details of the ovum and female reproductive tract, into physiological aspects of whole-organism performance, leading to behaviors associated with mating and maternal care, and eventually reaching population structure and ecology; (2) discover generalizable rules such as the co-evolution of maternal-offspring phenotypes in gestation and lactation; and (3) predict the impacts of changes to reproductive timing when the reliability of environmental cues becomes unpredictable. Studies in these key areas relative to female reproduction are sure to further our understanding across a range of diverse taxa.

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.

Ewastools: Infinium Human Methylation BeadChip pipeline for population epigenetics integrated into Galaxy

BACKGROUND

Infinium Human Methylation BeadChip is an array platform for complex evaluation of DNA methylation at an individual CpG locus in the human genome based on Illumina's bead technology and is one of the most common techniques used in epigenome-wide association studies. Finding associations between epigenetic variation and phenotype is a significant challenge in biomedical research. The newest version, HumanMethylationEPIC, quantifies the DNA methylation level of 850,000 CpG sites, while the previous versions, HumanMethylation450 and HumanMethylation27, measured >450,000 and 27,000 loci, respectively. Although a number of bioinformatics tools have been developed to analyse this assay, they require some programming skills and experience in order to be usable.

RESULTS

We have developed a pipeline for the Galaxy platform for those without experience aimed at DNA methylation analysis using the Infinium Human Methylation BeadChip. Our tool is integrated into Galaxy (http://galaxyproject.org), a web-based platform. This allows users to analyse data from the Infinium Human Methylation BeadChip in the easiest possible way.

CONCLUSIONS

The pipeline provides a group of integrated analytical methods wrapped into an easy-to-use interface. Our tool is available from the Galaxy ToolShed, GitHub repository, and also as a Docker image. The aim of this project is to make Infinium Human Methylation BeadChip analysis more flexible and accessible to everyone.

DNA methyltransferase implicated in the recovery of conidiation, through successive plant passages, in phenotypically degenerated Metarhizium

Metarhizium robertsii is a fungus with two lifestyles; it is a plant root symbiont and an insect pathogen. A spontaneously phenotypically degenerated strain of M. robertsii strain ARSEF 2575 (M. robertsii lc-2575; lc = low conidiation) showed a reduction in conidiation and fungal virulence after successive subculturing on agar medium. In order to recover conidiation, we experimentally passaged M. robertsii lc-2575 through plant (soldier bean and switchgrass) root or insect (Galleria mellonella) larvae. After five passages, the resultant strains had significantly increased conidial yields on agar and increased virulence in insect bioassays. Concomitantly, DNA methyltransferase, MrDIM-2 expression was downregulated in BR5 (a strain after 5 bean root passages) and isolates after switchgrass and insect passages. Bisulfite sequencing showed little difference in overall genomic DNA methylation levels (~ 0.37%) between M. robertsii lc-2575 and BR5. However, a finer comparison of the different methylated regions (DMRs) showed that DMRs of BR5 were more abundant in the intergenic regions (69.32%) compared with that of M. robertsii lc-2575 (33.33%). The addition of DNA methyltransferase inhibitor, 5-azacytidine, to agar supported the role of DNA methyltransferases and resulted in an increase in conidiation of M. robertsii lc-2575. Differential gene expression was observed in selected DMRs in BR5 when compared with M. robertsii lc-2575. Here we implicated epigenetic regulation in the recovery of conidiation through the effects of DNA methyltransferase and that plant passage could be used as a method to recover fungal conidiation and virulence in a phenotypically degenerated M. robertsii. KEY POINTS: • Passage of Metarhizium through plant root or insect results in increased conidiation. • DNA methyltransferase is downregulated after host passage. • Bisulfite sequencing identified potentially methylated genes involved in conidiation.

Epigenetic silencing of heat shock protein 70 through DNA hypermethylation in pseudoexfoliation syndrome and glaucoma

This study is intended to investigate the epigenetic regulation of the most conserved molecular chaperone, HSP70 and its potential role in the pathophysiology of pseudoexfoliation syndrome (PEXS) and glaucoma (PEXG), a protein aggregopathy, contributing significantly to world blindness. Expression levels of HSP70 were significantly decreased in the lens capsule (LC) of PEXS but not in PEXG compared with that in control. Bisulfite sequencing of the LC of the study subjects revealed that the CpG islands (CGIs) located in the exonic region but not in the promoter region of HSP70 displayed hypermethylation only in PEXS individuals. There was a corresponding increase in DNA methyltransferase 3A (DNMT3A) expression in only PEXS individuals suggesting de novo methylation in this stage of the disease condition. On the other hand, peripheral blood of both PEXS and PEXG cases showed hypermethylation in the exonic region when compared with non-PEX controls displaying tissue-specific effects. Further, functional analyses of CGI spanning the exon revealed a decreased gene expression in the presence of methylated in comparison with unmethylated reporter gene vectors. Treatment of human lens epithelial B-3 (HLE B-3) cells with DNMT inhibitor restored the expression of HSP70 following depletion in methylation level at exonic CpG sites. In conclusion, a decreased HSP70 expression correlates with hypermethylation of a CGI of HSP70 in PEXS individuals. The present findings enhance our current understanding of the mechanism underlying HSP70 repression, contributing to the pathogenesis of PEX.

Epigenetics for the 21st-Century Biology Student

Epigenetics, a rapidly emerging biological science, investigates changes in gene expression without any change to the primary DNA sequence. Epigenetics plays an important role in diverse areas, including nutritional sciences, psychology, and environmental sciences. In addition, epigenetic phenomena are closely implicated in various diseases, including cancer and neurological disorders. Even though the importance of epigenetics has been widely discussed in the literature, there is no quantitative assessment on the development of epigenetics. In this paper, we show our metadata analysis of PubMed to quantitatively measure the temporal development of epigenetics. Our analysis indicates that the publication volume of epigenetics will reach 20.7% of all genetics paper in 10 years (year 2029). Based on our analysis, we suggest that epigenetics be added to the biology undergraduate curriculum.

Single-base-resolution methylome of giant panda's brain, liver and pancreatic tissue

The giant panda (Ailuropoda melanoleuca) is one of the most endangered mammals, and its conservation has significant ecosystem and cultural service value. Cytosine DNA methylation (5mC) is a stable epigenetic modification to the genome and has multiple functions such as gene regulation. However, DNA methylome of giant panda and its function have not been reported as of yet. Bisulfite sequencing was performed on a 4-day-old male giant panda's brain, liver and pancreatic tissues. We found that the whole genome methylation level was about 0.05% based on reads normalization and mitochondrial DNA was not methylated. Three tissues showed similar methylation tendency in the protein-coding genes of their genomes, but the brain genome had a higher count of methylated genes. We obtained 467 and 1,013 different methylation regions (DMR) genes in brain vs. pancreas and liver, while only 260 DMR genes were obtained in liver vs pancreas. Some lncRNA were also DMR genes, indicating that methylation may affect biological processes by regulating other epigenetic factors. Gene ontology and Kyoto Encyclopedia of Genes and Genomes analysis indicated that low methylated promoter, high methylated promoter and DMR genes were enriched at some important and tissue-specific items and pathways, like neurogenesis, metabolism and immunity. DNA methylation may drive or maintain tissue specificity and organic functions and it could be a crucial regulating factor for the development of newborn cubs. Our study offers the first insight into giant panda's DNA methylome, laying a foundation for further exploration of the giant panda's epigenetics.

Emerging epigenomic landscapes of pancreatic cancer in the era of precision medicine

Genetic studies have advanced our understanding of pancreatic cancer at a mechanistic and translational level. Genetic concepts and tools are increasingly starting to be applied to clinical practice, in particular for precision medicine efforts. However, epigenomics is rapidly emerging as a promising conceptual and methodological paradigm for advancing the knowledge of this disease. More importantly, recent studies have uncovered potentially actionable pathways, which support the prediction that future trials for pancreatic cancer will involve the vigorous testing of epigenomic therapeutics. Thus, epigenomics promises to generate a significant amount of new knowledge of both biological and medical importance.

In pancreatic cancer, the epigenomic landscape can strongly impact the disease phenotype. Here, the authors discuss recent advances in our understanding of pancreatic cancer epigenomics, and how this knowledge can integrate with precision medicine approaches in this lethal disease.

Divergent methylation pattern in adult stage between two forms of Tetranychus urticae (Acari: Tetranychidae)

The two-spotted spider mite, Tetranychus urticae Koch has two forms: green form and red form. Understanding the molecular basis of how these two forms established without divergent genetic background is an intriguing area. As a well-known epigenetic process, DNA methylation has particularly important roles in gene regulation and developmental variation across diverse organisms that do not alter genetic background. Here, to investigate whether DNA methylation could be associated with different phenotypic consequences in the two forms of T. urticae, we surveyed the genome-wide cytosine methylation status and expression level of DNA methyltransferase 3 (Tudnmt3) throughout their entire life cycle. Methylation-sensitive amplification polymorphism (MSAP) analyses of 585 loci revealed variable methylation patterns in the different developmental stages. In particular, principal coordinates analysis (PCoA) indicates a significant epigenetic differentiation between female adults of the two forms. The gene expression of Tudnmt3 was detected in all examined developmental stages, which was significantly different in the adult stage of the two forms. Together, our results reveal the epigenetic distance between the two forms of T. urticae, suggesting that DNA methylation might be implicated in different developmental demands, and contribute to different phenotypes in the adult stage of these two forms.

Predominance of N6-Methyladenine-Specific DNA Fragments Enriched by Multiple Immunoprecipitation

N⁶-methyladenine (6mA) is a rediscovered DNA modification in eukaryotic genomes. To explore the distribution and functions of 6mA, it is of paramount option to use immunoprecipitation to select 6mA-containing DNA fragments for genome-wide sequencing. Presumably, most of the 6mA-free fragments are removed, and the copulling down of the residual is stochastic and sequence-independent and thus they should not be called as peaks by computation. Surprisingly, here we show the predominance of 6mA-free fragments in the pulled-down fractions. By taking advantage of the submicromolar affinity of the antibodies, we further develop an elegant, multiple-round immunoprecipitation (MrIP) approach and show that 6mA-containing fragments can be enriched over 9100-fold and dominate in the final pulled-down fractions. This biochemical approach would greatly reduce the peak calling bias, which is caused by handling of dominated 6mA-free DNA fragments with an assumption-based algorithm computation and facilitates 6mA-pertinent data mining. The MrIP concept is extendable for the genome-wide sequencing of diverse DNA modifications.

Epigenetic Applications in Adverse Outcome Pathways and Environmental Risk Evaluation

BACKGROUND

The epigenome may be an important interface between environmental chemical exposures and human health. However, the links between epigenetic modifications and health outcomes are often correlative and do not distinguish between cause and effect or common-cause relationships. The Adverse Outcome Pathway (AOP) framework has the potential to demonstrate, by way of an inference- and science-based analysis, the causal relationship between chemical exposures, epigenome, and adverse health outcomes.

OBJECTIVE

The objective of this work is to discuss the epigenome as a modifier of exposure effects and risk, perspectives for integrating toxicoepigenetic data into an AOP framework, tools for the exploration of epigenetic toxicity, and integration of AOP-guided epigenetic information into science and risk-assessment processes.

DISCUSSION

Organizing epigenetic information into the topology of a qualitative AOP network may help describe how a system will respond to epigenetic modifications caused by environmental chemical exposures. However, understanding the biological plausibility, linking epigenetic effects to short- and long-term health outcomes, and including epigenetic studies in the risk assessment process is met by substantive challenges. These obstacles include understanding the complex range of epigenetic modifications and their combinatorial effects, the large number of environmental chemicals to be tested, and the lack of data that quantitatively evaluate the epigenetic effects of environmental exposure.

CONCLUSION

We anticipate that epigenetic information organized into AOP frameworks can be consistently used to support biological plausibility and to identify data gaps that will accelerate the pace at which epigenetic information is applied in chemical evaluation and risk-assessment paradigms. https://doi.org/10.1289/EHP2322.

Global DNA Methylation in the Chestnut Blight Fungus Cryphonectria parasitica and Genome-Wide Changes in DNA Methylation Accompanied with Sectorization

Mutation in CpBck1, an ortholog of the cell wall integrity mitogen-activated protein kinase kinase kinase (MAPKKK) of Saccharomyces cerevisiae, in the chestnut blight fungus Cryphonectria parasitica resulted in a sporadic sectorization as culture proceeded. The progeny from the sectored area maintained the characteristics of the sector, showing a massive morphogenetic change, including robust mycelial growth without differentiation. Epigenetic changes were investigated as the genetic mechanism underlying this sectorization. Quantification of DNA methylation and whole-genome bisulfite sequencing revealed genome-wide DNA methylation of the wild-type at each nucleotide level and changes in DNA methylation of the sectored progeny. Compared to the wild-type, the sectored progeny exhibited marked genome-wide DNA hypomethylation but increased methylation sites. Expression analysis of two DNA methyltransferases, including two representative types of DNA methyltransferase (DNMTase), demonstrated that both were significantly down-regulated in the sectored progeny. However, functional analysis using mutant phenotypes of corresponding DNMTases demonstrated that a mutant of CpDmt1, an ortholog of RID of Neurospora crassa, resulted in the sectored phenotype but the CpDmt2 mutant did not, suggesting that the genetic basis of fungal sectorization is more complex. The present study revealed that a mutation in a signaling pathway component resulted in sectorization accompanied with changes in genome-wide DNA methylation, which suggests that this signal transduction pathway is important for epigenetic control of sectorization via regulation of genes involved in DNA methylation.

Integrated genetic and epigenetic prediction of coronary heart disease in the Framingham Heart Study

An improved method for detecting coronary heart disease (CHD) could have substantial clinical impact. Building on the idea that systemic effects of CHD risk factors are a conglomeration of genetic and environmental factors, we use machine learning techniques and integrate genetic, epigenetic and phenotype data from the Framingham Heart Study to build and test a Random Forest classification model for symptomatic CHD. Our classifier was trained on n = 1,545 individuals and consisted of four DNA methylation sites, two SNPs, age and gender. The methylation sites and SNPs were selected during the training phase. The final trained model was then tested on n = 142 individuals. The test data comprised of individuals removed based on relatedness to those in the training dataset. This integrated classifier was capable of classifying symptomatic CHD status of those in the test set with an accuracy, sensitivity and specificity of 78%, 0.75 and 0.80, respectively. In contrast, a model using only conventional CHD risk factors as predictors had an accuracy and sensitivity of only 65% and 0.42, respectively, but with a specificity of 0.89 in the test set. Regression analyses of the methylation signatures illustrate our ability to map these signatures to known risk factors in CHD pathogenesis. These results demonstrate the capability of an integrated approach to effectively model symptomatic CHD status. These results also suggest that future studies of biomaterial collected from longitudinally informative cohorts that are specifically characterized for cardiac disease at follow-up could lead to the introduction of sensitive, readily employable integrated genetic-epigenetic algorithms for predicting onset of future symptomatic CHD.

Epigenetic Aberration of FMR1 Gene in Infertile Women with Diminished Ovarian Reserve

OBJECTIVES

The diminished ovarian reserve (DOR) is a condition characterized by a reduction in the number and/or quality of oocytes. This primary infertility disorder is usually accompanied with an increase in the follicle-stimulating hormone (FSH) levels and regular menses. Although there are many factors contributing to the DOR situation, it is likely that many of idiopathic cases have genetic/epigenetic bases. The association between the FMR1 premutation (50-200 CGG repeats) and the premature ovarian failure (POF) suggests that epigenetic disorders of FMR1 can act as a risk factor for the DOR as well. The aim of this study was to analyze the mRNA expression and epigenetic alteration (histone acetylation/methylation) of the FMR1 gene in blood and granulosa cells of 20 infertile women.

MATERIALS AND METHODS

In this case-control study, these women were referred to the Royan Institute, having been clinically diagnosed as DOR patients. Our control group consisted of 20 women with normal antral follicle numbers and serum FSH level. All these women had normal karyotype and no history of genetic disorders. The number of CGG triplet repeats in the exon 1 of the FMR1 gene was analyzed in all samples.

RESULTS

Results clearly demonstrated significantly higher expression of the FMR1 gene in blood and granulosa cells of the DOR patients with the FMR1 premutation compared to the control group. In addition, epigenetic marks of histone 3 lysine 9 acetylation (H3K9ac) and di-metylation (H3K9me2) showed significantly higher incorporations in the regulatory regions of the FMR1 gene, including the promoter and the exon 1, whereas tri-metylation (H3K9me3) mark showed no significant difference between two groups.

CONCLUSIONS

Our data demonstrates, for the first time, the dynamicity of gene expression and histone modification pattern in regulation of FMR1 gene, and implies the key role played by epigenetics in the development of the ovarian function.

Bioinformatics Tools for Genome-Wide Epigenetic Research

Epigenetics play a central role in the regulation of many important cellular processes, and dysregulations at the epigenetic level could be the source of serious pathologies, such as neurological disorders affecting brain development, neurodegeneration, and intellectual disability. Despite significant technological advances for epigenetic profiling, there is still a need for a systematic understanding of how epigenetics shapes cellular circuitry, and disease pathogenesis. The development of accurate computational approaches for analyzing complex epigenetic profiles is essential for disentangling the mechanisms underlying cellular development, and the intricate interaction networks determining and sensing chromatin modifications and DNA methylation to control gene expression. In this chapter, we review the recent advances in the field of "computational epigenetics," including computational methods for processing different types of epigenetic data, prediction of chromatin states, and study of protein dynamics. We also discuss how "computational epigenetics" has complemented the fast growth in the generation of epigenetic data for uncovering the main differences and similarities at the epigenetic level between individuals and the mechanisms underlying disease onset and progression.

DMAK: A curated pan-cancer DNA methylation annotation knowledgebase

Pan-cancer analysis can identify cell- and tissue-specific genomic loci and regions with underlying biological functions. Here we present an online curated DNA Methylation Annotation Knowledgebase, DMAK, which includes the pan-cancer analysis results for differentially-methylated loci and regions by the Reduced Representation Bisulfite Sequencing profiling technology. DMAK contains 3 modules of curated information and analysis results on 688,445 CpG sites across 19 cancer and embryonic stem cell lines from ENCODE, and further analysis of survival associations with clinical sources retrieved from TCGA. The knowledgebase covers all identified differentially-methylated CpG sites and regions of interest, further annotated genomic information, together with tumor suppressor genes information and calculated methylation level. DMAK provides meaningful clues for deriving functional association network and related clinical association results based on protein-coding genes, including tumor suppressor genes, identified from differentially methylated regions of interest. Thus DMAK constitutes a comprehensive reference source for the current epigenetic research and clinical study.

Intrauterine growth restriction inhibits expression of eukaryotic elongation factor 2 kinase, a regulator of protein translation

Nutrient deprivation suppresses protein synthesis by blocking peptide elongation. Transcriptional upregulation and activation of eukaryotic elongation factor 2 kinase (eEF2K) blocks peptide elongation by phosphorylating eukaryotic elongation factor 2. Previous studies examining placentas from intrauterine growth restricted (IUGR) newborn infants show decreased eEF2K expression and activity despite chronic nutrient deprivation. However, the effect of IUGR on hepatic eEF2K expression in the fetus is unknown. We, therefore, examined the transcriptional regulation of hepatic eEF2K gene expression in a Sprague-Dawley rat model of IUGR. We found decreased hepatic eEF2K mRNA and protein levels in IUGR offspring at birth compared with control, consistent with previous placental observations. Furthermore, the CpG island within the eEF2K promoter demonstrated increased methylation at a critical USF 1/2 transcription factor binding site. In vitro methylation of this binding site caused near complete loss of eEF2K promoter activity, designating this promoter as methylation sensitive. The eEF2K promotor in IUGR offspring also lost the protective histone covalent modifications associated with unmethylated CGIs. In addition, the +1 nucleosome was displaced 3' and RNA polymerase loading was reduced at the IUGR eEF2K promoter. Our findings provide evidence to explain why IUGR-induced chronic nutrient deprivation does not result in the upregulation of eEF2K gene transcription.

Analysis of cis-acting regulatory elements of Respiratory burst oxidase homolog (Rboh) gene families in Arabidopsis and rice provides clues for their diverse functions

NADPH oxidase (NOX) is a critical enzyme in the production of reactive oxygen species (ROS). It catalyzes the production of apoplastic superoxide (O2(-)), that regulates a wide array of biological functions in different organisms. Plant Noxes are homologs of catalytic subunit of mammalian NADPH oxidase and are well-known as Respiratory burst oxidase homologs (Rbohs). In recent years, there has been growing interest to study plant Noxes due to their versatile roles in plant systems. In the present work, comprehensive analysis on upstream regions from 10 Rbohs from Arabidopsis thaliana and 9 from Oryza sativa japonica was conducted. The distribution of various cis-elements, CpG islands and tandem repeats were analyzed to uncover the 5' regulatory region in wide array of functions from Rbohs. Information retrieved from cis-elements analysis was also correlated with the microarray data. Present study which involves uncovering transcription regulatory elements provided vital clues for diverse functions of plant Rbohs.

Genome wide classification and characterisation of CpG sites in cancer and normal cells

This study identifies common methylation patterns across different cancer types in an effort to identify common molecular events in diverse types of cancer cells and provides evidence for the sequence surrounding a CpG to influence its susceptibility to aberrant methylation. CpG sites throughout the genome were divided into four classes: sites that either become hypo or hyper-methylated in a variety cancers using all the freely available microarray data (HypoCancer and HyperCancer classes) and those found in a constant hypo (Never methylated class) or hyper-methylated (Always methylated class) state in both normal and cancer cells. Our data shows that most CpG sites included in the HumanMethylation450K microarray remain unmethylated in normal and cancerous cells; however, certain sites in all the cancers investigated become specifically modified. More detailed analysis of the sites revealed that majority of those in the never methylated class were in CpG islands whereas those in the HyperCancer class were mostly associated with miRNA coding regions. The sites in the Hypermethylated class are associated with genes involved in initiating or maintaining the cancerous state, being enriched for processes involved in apoptosis, and with transcription factors predicted to bind to these genes linked to apoptosis and tumourgenesis (notably including E2F). Further we show that more LINE elements are associated with the HypoCancer class and more Alu repeats are associated with the HyperCancer class. Motifs that classify the classes were identified to distinguish them based on the surrounding DNA sequence alone, and for the identification of DNA sequences that could render sites more prone to aberrant methylation in cancer cells. This provides evidence that the sequence surrounding a CpG site has an influence on whether a site is hypo or hyper methylated.

methylPipe and compEpiTools: a suite of R packages for the integrative analysis of epigenomics data

BACKGROUND

Numerous methods are available to profile several epigenetic marks, providing data with different genome coverage and resolution. Large epigenomic datasets are then generated, and often combined with other high-throughput data, including RNA-seq, ChIP-seq for transcription factors (TFs) binding and DNase-seq experiments. Despite the numerous computational tools covering specific steps in the analysis of large-scale epigenomics data, comprehensive software solutions for their integrative analysis are still missing. Multiple tools must be identified and combined to jointly analyze histone marks, TFs binding and other -omics data together with DNA methylation data, complicating the analysis of these data and their integration with publicly available datasets.

RESULTS

To overcome the burden of integrating various data types with multiple tools, we developed two companion R/Bioconductor packages. The former, methylPipe, is tailored to the analysis of high- or low-resolution DNA methylomes in several species, accommodating (hydroxy-)methyl-cytosines in both CpG and non-CpG sequence context. The analysis of multiple whole-genome bisulfite sequencing experiments is supported, while maintaining the ability of integrating targeted genomic data. The latter, compEpiTools, seamlessly incorporates the results obtained with methylPipe and supports their integration with other epigenomics data. It provides a number of methods to score these data in regions of interest, leading to the identification of enhancers, lncRNAs, and RNAPII stalling/elongation dynamics. Moreover, it allows a fast and comprehensive annotation of the resulting genomic regions, and the association of the corresponding genes with non-redundant GeneOntology terms. Finally, the package includes a flexible method based on heatmaps for the integration of various data types, combining annotation tracks with continuous or categorical data tracks.

CONCLUSIONS

methylPipe and compEpiTools provide a comprehensive Bioconductor-compliant solution for the integrative analysis of heterogeneous epigenomics data. These packages are instrumental in providing biologists with minimal R skills a complete toolkit facilitating the analysis of their own data, or in accelerating the analyses performed by more experienced bioinformaticians.

Environmental epigenetics: A promising venue for developing next-generation pollution biomonitoring tools in marine invertebrates

Environmental epigenetics investigates the cause-effect relationships between specific environmental factors and the subsequent epigenetic modifications triggering adaptive responses in the cell. Given the dynamic and potentially reversible nature of the different types of epigenetic marks, environmental epigenetics constitutes a promising venue for developing fast and sensible biomonitoring programs. Indeed, several epigenetic biomarkers have been successfully developed and applied in traditional model organisms (e.g., human and mouse). Nevertheless, the lack of epigenetic knowledge in other ecologically and environmentally relevant organisms has hampered the application of these tools in a broader range of ecosystems, most notably in the marine environment. Fortunately, that scenario is now changing thanks to the growing availability of complete reference genome sequences along with the development of high-throughput DNA sequencing and bioinformatic methods. Altogether, these resources make the epigenetic study of marine organisms (and more specifically marine invertebrates) a reality. By building on this knowledge, the present work provides a timely perspective highlighting the extraordinary potential of environmental epigenetic analyses as a promising source of rapid and sensible tools for pollution biomonitoring, using marine invertebrates as sentinel organisms. This strategy represents an innovative, groundbreaking approach, improving the conservation and management of natural resources in the oceans.

The Role of Epigenetic Change in Autism Spectrum Disorders

Autism spectrum disorders (ASD) are a heterogeneous group of neurodevelopmental disorders characterized by problems with social communication, social interaction, and repetitive or restricted behaviors. ASD are comorbid with other disorders including attention deficit hyperactivity disorder, epilepsy, Rett syndrome, and Fragile X syndrome. Neither the genetic nor the environmental components have been characterized well enough to aid diagnosis or treatment of non-syndromic ASD. However, genome-wide association studies have amassed evidence suggesting involvement of hundreds of genes and a variety of associated genetic pathways. Recently, investigators have turned to epigenetics, a prime mediator of environmental effects on genomes and phenotype, to characterize changes in ASD that constitute a molecular level on top of DNA sequence. Though in their infancy, such studies have the potential to increase our understanding of the etiology of ASD and may assist in the development of biomarkers for its prediction, diagnosis, prognosis, and eventually in its prevention and intervention. This review focuses on the first few epigenome-wide association studies of ASD and discusses future directions.

Using epigenomics data to predict gene expression in lung cancer

Background

Epigenetic alterations are known to correlate with changes in gene expression among various diseases including cancers. However, quantitative models that accurately predict the up or down regulation of gene expression are currently lacking.

Methods

A new machine learning-based method of gene expression prediction is developed in the context of lung cancer. This method uses the Illumina Infinium HumanMethylation450K Beadchip CpG methylation array data from paired lung cancer and adjacent normal tissues in The Cancer Genome Atlas (TCGA) and histone modification marker CHIP-Seq data from the ENCODE project, to predict the differential expression of RNA-Seq data in TCGA lung cancers. It considers a comprehensive list of 1424 features spanning the four categories of CpG methylation, histone H3 methylation modification, nucleotide composition, and conservation. Various feature selection and classification methods are compared to select the best model over 10-fold cross-validation in the training data set.

Results

A best model comprising 67 features is chosen by ReliefF based feature selection and random forest classification method, with AUC = 0.864 from the 10-fold cross-validation of the training set and AUC = 0.836 from the testing set. The selected features cover all four data types, with histone H3 methylation modification (32 features) and CpG methylation (15 features) being most abundant. Among the dropping-off tests of individual data-type based features, removal of CpG methylation feature leads to the most reduction in model performance. In the best model, 19 selected features are from the promoter regions (TSS200 and TSS1500), highest among all locations relative to transcripts. Sequential dropping-off of CpG methylation features relative to different regions on the protein coding transcripts shows that promoter regions contribute most significantly to the accurate prediction of gene expression.

Conclusions

By considering a comprehensive list of epigenomic and genomic features, we have constructed an accurate model to predict transcriptomic differential expression, exemplified in lung cancer.

Characterization of age signatures of DNA methylation in normal and cancer tissues from multiple studies

Background

DNA methylation (DNAm) levels can be used to predict the chronological age of tissues; however, the characteristics of DNAm age signatures in normal and cancer tissues are not well studied using multiple studies.

Results

We studied approximately 4000 normal and cancer samples with multiple tissue types from diverse studies, and using linear and nonlinear regression models identified reliable tissue type-invariant DNAm age signatures. A normal signature comprising 127 CpG loci was highly enriched on the X chromosome. Age-hypermethylated loci were enriched for guanine–and-cytosine-rich regions in CpG islands (CGIs), whereas age-hypomethylated loci were enriched for adenine–and-thymine-rich regions in non-CGIs. However, the cancer signature comprised only 26 age-hypomethylated loci, none on the X chromosome, and with no overlap with the normal signature. Genes related to the normal signature were enriched for aging-related gene ontology terms including metabolic processes, immune system processes, and cell proliferation. The related gene products of the normal signature had more than the average number of interacting partners in a protein interaction network and had a tendency not to interact directly with each other. The genomic sequences of the normal signature were well conserved and the age-associated DNAm levels could satisfactorily predict the chronological ages of tissues regardless of tissue type. Interestingly, the age-associated DNAm increases or decreases of the normal signature were aberrantly accelerated in cancer samples.

Conclusion

These tissue type-invariant DNAm age signatures in normal and cancer can be used to address important questions in developmental biology and cancer research.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2164-15-997) contains supplementary material, which is available to authorized users.

Exact distribution of a maximally selected Wilcoxon and a new hybrid test of symmetry

Recently, a maximally selected normalized Wilcoxon, whose asymptotic distribution is a Brownian Bridge, was proposed for testing symmetry of a distribution about zero. The test sequentially discards observations whose absolute value is below increasing thresholds. The Wilcoxon is obtained at each threshold, and the maximum is the test statistic. We develop a recursive function for the exact distribution of a modification of the Max Wilcoxon test (MW) and provide critical values and a program for computing the p-value for a sample. A new hybrid test that combines the sign and MW tests is introduced. The power of MW and the new hybrid test are compared with Modarres and Gastwirth's hybrid test (MGH) and the Max McNemar (MM), under the generalized lambda distributions (GLD) family and two normal mixture models. The MW and the new hybrid test outperform the MGH, which is superior to the MM test in the GLD family. In one mixture model, MM is the least powerful test and the remaining three are essentially equivalent. In the second mixture model, when the zero median assumption is nearly valid, the MW test does well; its performance degrades when this assumption is violated. In the latter case, the MM performs better than MW for the same degree of skewness because the MM simultaneously tests both symmetry and zero median. Data from a genetic study of monozygotic twins discordant for major depressive disorder is used to illustrate the new tests.

jNMFMA: a joint non-negative matrix factorization meta-analysis of transcriptomics data

MOTIVATION

Tremendous amount of omics data being accumulated poses a pressing challenge of meta-analyzing the heterogeneous data for mining new biological knowledge. Most existing methods deal with each gene independently, thus often resulting in high false positive rates in detecting differentially expressed genes (DEG). To our knowledge, no or little effort has been devoted to methods that consider dependence structures underlying transcriptomics data for DEG identification in meta-analysis context.

RESULTS

This article proposes a new meta-analysis method for identification of DEGs based on joint non-negative matrix factorization (jNMFMA). We mathematically extend non-negative matrix factorization (NMF) to a joint version (jNMF), which is used to simultaneously decompose multiple transcriptomics data matrices into one common submatrix plus multiple individual submatrices. By the jNMF, the dependence structures underlying transcriptomics data can be interrogated and utilized, while the high-dimensional transcriptomics data are mapped into a low-dimensional space spanned by metagenes that represent hidden biological signals. jNMFMA finally identifies DEGs as genes that are associated with differentially expressed metagenes. The ability of extracting dependence structures makes jNMFMA more efficient and robust to identify DEGs in meta-analysis context. Furthermore, jNMFMA is also flexible to identify DEGs that are consistent among various types of omics data, e.g. gene expression and DNA methylation. Experimental results on both simulation data and real-world cancer data demonstrate the effectiveness of jNMFMA and its superior performance over other popular approaches.

AVAILABILITY AND IMPLEMENTATION

R code for jNMFMA is available for non-commercial use via http://micblab.iim.ac.cn/Download/.

CONTACT

hqwang@ustc.edu

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

The return of metabolism: biochemistry and physiology of the pentose phosphate pathway

The pentose phosphate pathway (PPP) is a fundamental component of cellular metabolism. The PPP is important to maintain carbon homoeostasis, to provide precursors for nucleotide and amino acid biosynthesis, to provide reducing molecules for anabolism, and to defeat oxidative stress. The PPP shares reactions with the Entner–Doudoroff pathway and Calvin cycle and divides into an oxidative and non-oxidative branch. The oxidative branch is highly active in most eukaryotes and converts glucose 6-phosphate into carbon dioxide, ribulose 5-phosphate and NADPH. The latter function is critical to maintain redox balance under stress situations, when cells proliferate rapidly, in ageing, and for the ‘Warburg effect’ of cancer cells. The non-oxidative branch instead is virtually ubiquitous, and metabolizes the glycolytic intermediates fructose 6-phosphate and glyceraldehyde 3-phosphate as well as sedoheptulose sugars, yielding ribose 5-phosphate for the synthesis of nucleic acids and sugar phosphate precursors for the synthesis of amino acids. Whereas the oxidative PPP is considered unidirectional, the non-oxidative branch can supply glycolysis with intermediates derived from ribose 5-phosphate and vice versa, depending on the biochemical demand. These functions require dynamic regulation of the PPP pathway that is achieved through hierarchical interactions between transcriptome, proteome and metabolome. Consequently, the biochemistry and regulation of this pathway, while still unresolved in many cases, are archetypal for the dynamics of the metabolic network of the cell. In this comprehensive article we review seminal work that led to the discovery and description of the pathway that date back now for 80 years, and address recent results about genetic and metabolic mechanisms that regulate its activity. These biochemical principles are discussed in the context of PPP deficiencies causing metabolic disease and the role of this pathway in biotechnology, bacterial and parasite infections, neurons, stem cell potency and cancer metabolism.

The dynamic DNA methylation cycle from egg to sperm in the honey bee Apis mellifera

In honey bees (Apis mellifera), the epigenetic mark of DNA methylation is central to the developmental regulation of caste differentiation, but may also be involved in additional biological functions. In this study, we examine the whole genome methylation profiles of three stages of the haploid honey bee genome: unfertilised eggs, the adult drones that develop from these eggs and the sperm produced by these drones. These methylomes reveal distinct patterns of methylation. Eggs and sperm show 381 genes with significantly different CpG methylation patterns, with the vast majority being more methylated in eggs. Adult drones show greatly reduced levels of methylation across the genome when compared with both gamete samples. This suggests a dynamic cycle of methylation loss and gain through the development of the drone and during spermatogenesis. Although fluxes in methylation during embryogenesis may account for some of the differentially methylated sites, the distinct methylation patterns at some genes suggest parent-specific epigenetic marking in the gametes. Extensive germ line methylation of some genes possibly explains the lower-than-expected frequency of CpG sites in these genes. We discuss the potential developmental and evolutionary implications of methylation in eggs and sperm in this eusocial insect species.

Plant systems biology: insights, advances and challenges

Plants dwelling at the base of biological food chain are of fundamental significance in providing solutions to some of the most daunting ecological and environmental problems faced by our planet. The reductionist views of molecular biology provide only a partial understanding to the phenotypic knowledge of plants. Systems biology offers a comprehensive view of plant systems, by employing a holistic approach integrating the molecular data at various hierarchical levels. In this review, we discuss the basics of systems biology including the various 'omics' approaches and their integration, the modeling aspects and the tools needed for the plant systems research. A particular emphasis is given to the recent analytical advances, updated published examples of plant systems biology studies and the future trends.

Imbalanced class learning in epigenetics

In machine learning, one of the important criteria for higher classification accuracy is a balanced dataset. Datasets with a large ratio between minority and majority classes face hindrance in learning using any classifier. Datasets having a magnitude difference in number of instances between the target concept result in an imbalanced class distribution. Such datasets can range from biological data, sensor data, medical diagnostics, or any other domain where labeling any instances of the minority class can be time-consuming or costly or the data may not be easily available. The current study investigates a number of imbalanced class algorithms for solving the imbalanced class distribution present in epigenetic datasets. Epigenetic (DNA methylation) datasets inherently come with few differentially DNA methylated regions (DMR) and with a higher number of non-DMR sites. For this class imbalance problem, a number of algorithms are compared, including the TAN+AdaBoost algorithm. Experiments performed on four epigenetic datasets and several known datasets show that an imbalanced dataset can have similar accuracy as a regular learner on a balanced dataset.

Applications of alignment-free methods in epigenomics

Epigenetic mechanisms play an important role in the regulation of cell type-specific gene activities, yet how epigenetic patterns are established and maintained remains poorly understood. Recent studies have supported a role of DNA sequences in recruitment of epigenetic regulators. Alignment-free methods have been applied to identify distinct sequence features that are associated with epigenetic patterns and to predict epigenomic profiles. Here, we review recent advances in such applications, including the methods to map DNA sequence to feature space, sequence comparison and prediction models. Computational studies using these methods have provided important insights into the epigenetic regulatory mechanisms.

Comparative (computational) analysis of the DNA methylation status of trinucleotide repeat expansion diseases

Previous studies have examined DNA methylation in different trinucleotide repeat diseases. We have combined this data and used a pattern searching algorithm to identify motifs in the DNA surrounding aberrantly methylated CpGs found in the DNA of patients with one of the three trinucleotide repeat (TNR) expansion diseases: fragile X syndrome (FRAXA), myotonic dystrophy type I (DM1), or Friedreich's ataxia (FRDA). We examined sequences surrounding both the variably methylated (VM) CpGs, which are hypermethylated in patients compared with unaffected controls, and the nonvariably methylated CpGs which remain either always methylated (AM) or never methylated (NM) in both patients and controls. Using the J48 algorithm of WEKA analysis, we identified that two patterns are all that is necessary to classify our three regions CCGG∗ which is found in VM and not in AM regions and AATT∗ which distinguished between NM and VM + AM using proportional frequency. Furthermore, comparing our software with MEME software, we have demonstrated that our software identifies more patterns than MEME in these short DNA sequences. Thus, we present evidence that the DNA sequence surrounding CpG can influence its susceptibility to be de novo methylated in a disease state associated with a trinucleotide repeat.

A computational model for genetic and epigenetic signals in colon cancer

Cancer, a class of diseases, characterized by abnormal cell growth, has one of the highest overall death rates world-wide. Its development has been linked to aberrant genetic and epigenetic events, affecting the regulation of key genes that control cellular mechanisms. However, a major issue in cancer research is the lack of precise information on tumour pathways; therefore, the delineation of these and of the processes underlying disease proliferation is an important area of investigation. A computational approach to modelling malignant system events can help to improve understanding likely "triggers", i.e. initiating abnormal micro-molecular signals that occur during cancer development. Here, we introduce a network-based model for genetic and epigenetic events observed at different stages of colon cancer, with a focus on the gene relationships and tumour pathways. Additionally, we describe a case study on tumour progression recorded for two gene networks on colon cancer, carcinoma in situ. Our results to date showed that tumour progression rate is higher for a small, closely-associated network of genes than for a larger, less-connected set; thus, disease development depends on assessment of network properties. The current work aims to provide improved insight on the way in which aberrant modifications characterize cancer initiation and progression. The framework dynamics are described in terms of interdependencies between three main layers: genetic and epigenetic events, gene relationships and cancer stage levels.

Epigenetic biomarkers in laboratory diagnostics: emerging approaches and opportunities

Epigenetics has emerged as a new and promising field in recent years. Lifestyle, stress, drugs, physiopathological situations and pharmacological interventions have a great impact on the epigenetic code of the cells by altering the methylome, miRNA expression and the covalent histone modifications. Since there exists a need to find new biomarkers and improve diagnosis for several diseases, the research on epigenetic biomarkers for molecular diagnostics encourages the translation of this field from the bench to clinical practice. In this context, deciphering intricate epigenetic modifications involved in several molecular processes is a challenge that will be solved in the near future. In this review, the authors present an overview of the high-throughput technologies and laboratory techniques available for epigenetic studies, and also discuss which of them are more reliable to be used in a clinical diagnostic laboratory. In addition, the authors describe the most promising epigenetic biomarkers in lung, colorectal and prostate cancer, in which most advances have been achieved. Finally, the authors describe epigenetic biomarkers in some rare diseases; these rare syndromes are paradigms for a specific impaired molecular pathway, thus providing valuable information on the discovery of new epigenetic biomarkers.

Combining position weight matrices and document-term matrix for efficient extraction of associations of methylated genes and diseases from free text

Background

In a number of diseases, certain genes are reported to be strongly methylated and thus can serve as diagnostic markers in many cases. Scientific literature in digital form is an important source of information about methylated genes implicated in particular diseases. The large volume of the electronic text makes it difficult and impractical to search for this information manually.

Methodology

We developed a novel text mining methodology based on a new concept of position weight matrices (PWMs) for text representation and feature generation. We applied PWMs in conjunction with the document-term matrix to extract with high accuracy associations between methylated genes and diseases from free text. The performance results are based on large manually-classified data. Additionally, we developed a web-tool, DEMGD, which automates extraction of these associations from free text. DEMGD presents the extracted associations in summary tables and full reports in addition to evidence tagging of text with respect to genes, diseases and methylation words. The methodology we developed in this study can be applied to similar association extraction problems from free text.

Conclusion

The new methodology developed in this study allows for efficient identification of associations between concepts. Our method applied to methylated genes in different diseases is implemented as a Web-tool, DEMGD, which is freely available at http://www.cbrc.kaust.edu.sa/demgd/. The data is available for online browsing and download.