Browsing (Epi)genomes: a guide to data resources and epigenome browsers for stem cell researchers

Toxicoepigenetics for Risk Assessment: Bridging the Gap Between Basic and Regulatory Science

Toxicoepigenetics examines the health effects of environmental exposure associated with, or mediated by, changes in the epigenome. Despite high expectations, toxicoepigenomic data and methods have yet to become significantly utilized in chemical risk assessment. This article draws on a social science framework to highlight hitherto overlooked structural barriers to the incorporation of toxicoepigenetics in risk assessment and to propose ways forward. The present barriers stem not only from the lack of maturity of the field but also from differences in constraints and standards between the data produced by toxicoepigenetics and the regulatory science data that risk assessment processes require. Criteria and strategies that frame the validation of knowledge used for regulatory purposes limit the application of basic research in toxicoepigenetics toward risk assessment. First, the need in regulatory toxicology for standardized methods that form a consensus between regulatory agencies, basic research, and the industry conflicts with the wealth of heterogeneous data in toxicoepigenetics. Second, molecular epigenetic data do not readily translate into typical toxicological endpoints. Third, toxicoepigenetics investigates new forms of toxicity, in particular low-dose and long-term effects, that do not align well with the traditional framework of regulatory toxicology. We propose that increasing the usefulness of epigenetic data for risk assessment will require deliberate efforts on the part of the toxicoepigenetics community in 4 areas: fostering the understanding of epigenetics among risk assessors, developing knowledge infrastructure to demonstrate applicability, facilitating the normalization and exchange of data, and opening the field to other stakeholders.

Using sound to understand protein sequence data: new sonification algorithms for protein sequences and multiple sequence alignments

BACKGROUND

The use of sound to represent sequence data-sonification-has great potential as an alternative and complement to visual representation, exploiting features of human psychoacoustic intuitions to convey nuance more effectively. We have created five parameter-mapping sonification algorithms that aim to improve knowledge discovery from protein sequences and small protein multiple sequence alignments. For two of these algorithms, we investigated their effectiveness at conveying information. To do this we focussed on subjective assessments of user experience. This entailed a focus group session and survey research by questionnaire of individuals engaged in bioinformatics research.

RESULTS

For single protein sequences, the success of our sonifications for conveying features was supported by both the survey and focus group findings. For protein multiple sequence alignments, there was limited evidence that the sonifications successfully conveyed information. Additional work is required to identify effective algorithms to render multiple sequence alignment sonification useful to researchers. Feedback from both our survey and focus groups suggests future directions for sonification of multiple alignments: animated visualisation indicating the column in the multiple alignment as the sonification progresses, user control of sequence navigation, and customisation of the sound parameters.

CONCLUSIONS

Sonification approaches undertaken in this work have shown some success in conveying information from protein sequence data. Feedback points out future directions to build on the sonification approaches outlined in this paper. The effectiveness assessment process implemented in this work proved useful, giving detailed feedback and key approaches for improvement based on end-user input. The uptake of similar user experience focussed effectiveness assessments could also help with other areas of bioinformatics, for example in visualisation.

Epigenetic Classification of Human Mesenchymal Stromal Cells

Standardization of mesenchymal stromal cells (MSCs) is hampered by the lack of a precise definition for these cell preparations; for example, there are no molecular markers to discern MSCs and fibroblasts. In this study, we followed the hypothesis that specific DNA methylation (DNAm) patterns can assist classification of MSCs. We utilized 190 DNAm profiles to address the impact of tissue of origin, donor age, replicative senescence, and serum supplements on the epigenetic makeup. Based on this, we elaborated a simple epigenetic signature based on two CpG sites to classify MSCs and fibroblasts, referred to as the Epi-MSC-Score. Another two-CpG signature can distinguish between MSCs from bone marrow and adipose tissue, referred to as the Epi-Tissue-Score. These assays were validated by site-specific pyrosequencing analysis in 34 primary cell preparations. Furthermore, even individual subclones of MSCs were correctly classified by our epigenetic signatures. In summary, we propose an alternative concept to use DNAm patterns for molecular definition of cell preparations, and our epigenetic scores facilitate robust and cost-effective quality control of MSC cultures.

Non-CG DNA methylation is a biomarker for assessing endodermal differentiation capacity in pluripotent stem cells

Non-CG methylation is an unexplored epigenetic hallmark of pluripotent stem cells. Here we report that a reduction in non-CG methylation is associated with impaired differentiation capacity into endodermal lineages. Genome-wide analysis of 2,670 non-CG sites in a discovery cohort of 25 phenotyped human induced pluripotent stem cell (hiPSC) lines revealed unidirectional loss (Δβ=13%, P<7.4 × 10(-4)) of non-CG methylation that correctly identifies endodermal differentiation capacity in 23 out of 25 (92%) hiPSC lines. Translation into a simplified assay of only nine non-CG sites maintains predictive power in the discovery cohort (Δβ=23%, P<9.1 × 10(-6)) and correctly identifies endodermal differentiation capacity in nine out of ten pluripotent stem cell lines in an independent replication cohort consisting of hiPSCs reprogrammed from different cell types and different delivery systems, as well as human embryonic stem cell (hESC) lines. This finding infers non-CG methylation at these sites as a biomarker when assessing endodermal differentiation capacity as a readout.

The effects of LSD1 inhibition on self-renewal and differentiation of human induced pluripotent stem cells

Human induced pluripotent stem cells (hiPSCs) are capable of unlimited self-renewal and can generate nearly all cells in the body. Changes induced by different LSD1 activities on the regulation of hiPSC self-renewal and differentiation and the mechanism underlying such changes were determined. We used two different LSD1 inhibitors (phenelzine sulfate and tranylcypromine) and RNAi technique to inhibit LSD1 activity, and we obtained hiPSCs showing 71.3%, 53.28%, and 31.33% of the LSD1 activity in normal hiPSCs. The cells still maintained satisfactory self-renewal capacity when LSD1 activity was at 71.3%. The growth rate of hiPSCs decreased and cells differentiated when LSD1 activity was at approximately 53.28%. The hiPSCs were mainly arrested in the G0/G1 phase and simultaneously differentiated into endodermal tissue when LSD1 activity was at 31.33%. Teratoma experiments showed that the downregulation of LSD1 resulted in low teratoma volume. When LSD1 activity was below 50%, pluripotency of hiPSCs was impaired, and the teratomas mainly comprised endodermal and mesodermal tissues. This phenomenon was achieved by regulating the critical balance between histone methylation and demethylation at regulatory regions of several key pluripotent and developmental genes.

Dynamic Changes in Occupancy of Histone Variant H2A.Z during Induced Somatic Cell Reprogramming

The development of induced pluripotent stem cells (iPSCs) has enabled study of the mechanisms underlying cellular reprogramming. Here, we have studied the dynamic distribution of H2A.Z during induced reprogramming with chromatin immunoprecipitation deep sequencing (ChIP-Seq). We found that H2A.Z tended to accumulate around transcription start site (TSS) and incorporate in genes with a high transcriptional activity. GO analysis with H2A.Z incorporated genes indicated that most genes are involved in chromatin assembly or disassembly and chromatin modification both in MEF and Day 7 samples, not in iPSCs. Furthermore, we detected the highest level of incorporation of H2A.Z around TSS in Day 7 samples compared to MEFs and iPSCs. GO analysis with only incorporated genes in Day 7 also displayed the function of chromatin remodeling. So, we speculate H2A.Z may be responsible for chromatin remodeling to enhance the access of transcription factors to genes important for pluripotency. This study therefore provides a deeper understanding of the mechanisms underlying induced reprogramming.

Publisher’s Note:Abstraction for data integration:Fusing mammalian molecular, cellular and phenotype big datasets for better knowledge extraction

With advances in genomics, transcriptomics, metabolomics and proteomics, and more expansive electronic clinical record monitoring, as well as advances in computation, we have entered the Big Data era in biomedical research. Data gathering is growing rapidly while only a small fraction of this data is converted to useful knowledge or reused in future studies. To improve this, an important concept that is often overlooked is data abstraction. To fuse and reuse biomedical datasets from diverse resources, data abstraction is frequently required. Here we summarize some of the major Big Data biomedical research resources for genomics, proteomics and phenotype data, collected from mammalian cells, tissues and organisms. We then suggest simple data abstraction methods for fusing this diverse but related data. Finally, we demonstrate examples of the potential utility of such data integration efforts, while warning about the inherit biases that exist within such data.

Epigenetic biomarker to support classification into pluripotent and non-pluripotent cells

Quality control of human induced pluripotent stem cells (iPSCs) can be performed by several methods. These methods are usually relatively labor-intensive, difficult to standardize, or they do not facilitate reliable quantification. Here, we describe a biomarker to distinguish between pluripotent and non-pluripotent cells based on DNA methylation (DNAm) levels at only three specific CpG sites. Two of these CpG sites were selected by their discriminatory power in 258 DNAm profiles – they were either methylated in pluripotent or non-pluripotent cells. The difference between these two β-values provides an Epi-Pluri-Score that was validated on independent DNAm-datasets (264 pluripotent and 1,951 non-pluripotent samples) with 99.9% specificity and 98.9% sensitivity. This score was complemented by a third CpG within the gene POU5F1 (OCT4), which better demarcates early differentiation events. We established pyrosequencing assays for the three relevant CpG sites and thereby correctly classified DNA of 12 pluripotent cell lines and 31 non-pluripotent cell lines. Furthermore, DNAm changes at these three CpGs were tracked in the course of differentiation of iPSCs towards mesenchymal stromal cells. The Epi-Pluri-Score does not give information on lineage-specific differentiation potential, but it provides a simple, reliable, and robust biomarker to support high-throughput classification into either pluripotent or non-pluripotent cells.

Community resources and technologies developed through the NIH Roadmap Epigenomics Program

This chapter describes resources and technologies generated by the NIH Roadmap Epigenomics Program that may be useful to epigenomics researchers investigating a variety of diseases including cancer. Highlights include reference epigenome maps for a wide variety of human cells and tissues, the development of new technologies for epigenetic assays and imaging, the identification of novel epigenetic modifications, and an improved understanding of the role of epigenetic processes in a diversity of human diseases. We also discuss future needs in this area including exploration of epigenomic variation between individuals, single-cell epigenomics, environmental epigenomics, exploration of the use of surrogate tissues, and improved technologies for epigenome manipulation.

chroGPS, a global chromatin positioning system for the functional analysis and visualization of the epigenome

Development of tools to jointly visualize the genome and the epigenome remains a challenge. chroGPS is a computational approach that addresses this question. chroGPS uses multidimensional scaling techniques to represent similarity between epigenetic factors, or between genetic elements on the basis of their epigenetic state, in 2D/3D reference maps. We emphasize biological interpretability, statistical robustness, integration of genetic and epigenetic data from heterogeneous sources, and computational feasibility. Although chroGPS is a general methodology to create reference maps and study the epigenetic state of any class of genetic element or genomic region, we focus on two specific kinds of maps: chroGPS(factors), which visualizes functional similarities between epigenetic factors, and chroGPS(genes), which describes the epigenetic state of genes and integrates gene expression and other functional data. We use data from the modENCODE project on the genomic distribution of a large collection of epigenetic factors in Drosophila, a model system extensively used to study genome organization and function. Our results show that the maps allow straightforward visualization of relationships between factors and elements, capturing relevant information about their functional properties that helps to interpret epigenetic information in a functional context and derive testable hypotheses.

Functional interpretation of non-coding sequence variation: concepts and challenges

Understanding the functional mechanisms underlying genetic signals associated with complex traits and common diseases, such as cancer, diabetes and Alzheimer's disease, is a formidable challenge. Many genetic signals discovered through genome-wide association studies map to non-protein coding sequences, where their molecular consequences are difficult to evaluate. This article summarizes concepts for the systematic interpretation of non-coding genetic signals using genome annotation data sets in different cellular systems. We outline strategies for the global analysis of multiple association intervals and the in-depth molecular investigation of individual intervals. We highlight experimental techniques to validate candidate (potential causal) regulatory variants, with a focus on novel genome-editing techniques including CRISPR/Cas9. These approaches are also applicable to low-frequency and rare variants, which have become increasingly important in genomic studies of complex traits and diseases. There is a pressing need to translate genetic signals into biological mechanisms, leading to prognostic, diagnostic and therapeutic advances.

The future of neuroepigenetics in the human brain

Complex mechanisms shape the genome of brain cells into transcriptional units, clusters of condensed chromatin, and many other features that distinguish between various cell types and developmental stages sharing the same genetic material. Only a few years ago, the field's focus was almost entirely on a single mark, CpG methylation; the emerging complexity of neuronal and glial epigenomes now includes multiple types of DNA cytosine methylation, more than 100 residue-specific posttranslational histone modifications and histone variants, all of which superimposed by a dynamic and highly regulated three-dimensional organization of the chromosomal material inside the cell nucleus. Here, we provide an update on the most innovative approaches in neuroepigenetics and their potential contributions to approach cognitive functions and disorders unique to human. We propose that comprehensive, cell type-specific mappings of DNA and histone modifications, chromatin-associated RNAs, and chromosomal "loopings" and other determinants of three-dimensional genome organization will critically advance insight into the pathophysiology of the disease. For example, superimposing the epigenetic landscapes of neuronal and glial genomes onto genetic maps for complex disorders, ranging from Alzheimer's disease to schizophrenia, could provide important clues about neurological function for some of the risk-associated noncoding sequences in the human genome.