Mattia Pelizzola

Human DNA methylomes at base resolution show widespread epigenomic differences

DNA cytosine methylation is a central epigenetic modification that has essential roles in cellular processes including genome regulation, development and disease. Here we present the first genome-wide, single-base-resolution maps of methylated cytosines in a mammalian genome, from both human embryonic stem cells and fetal fibroblasts, along with comparative analysis of messenger RNA and small RNA components of the transcriptome, several histone modifications, and sites of DNA–protein interaction for several key regulatory factors. Widespread differences were identified in the composition and patterning of cytosine methylation between the two genomes. Nearly one-quarter of all methylation identified in embryonic stem cells was in a non-CG context, suggesting that embryonic stem cells may use different methylation mechanisms to affect gene regulation. Methylation in non-CG contexts showed enrichment in gene bodies and depletion in protein binding sites and enhancers. Non-CG methylation disappeared upon induced differentiation of the embryonic stem cells, and was restored in induced pluripotent stem cells. We identified hundreds of differentially methylated regions proximal to genes involved in pluripotency and differentiation, and widespread reduced methylation levels in fibroblasts associated with lower transcriptional activity. These reference epigenomes provide a foundation for future studies exploring this key epigenetic modification in human disease and development.

Hotspots of aberrant epigenomic reprogramming in human induced pluripotent stem cells

Induced pluripotent stem cells (iPSCs) offer immense potential for regenerative medicine and studies of disease and development. Somatic cell reprogramming involves epigenomic reconfiguration, conferring iPSCs with characteristics similar to embryonic stem (ES) cells. However, it remains unknown how complete the reestablishment of ES-cell-like DNA methylation patterns is throughout the genome. Here we report the first whole-genome profiles of DNA methylation at single-base resolution in five human iPSC lines, along with methylomes of ES cells, somatic cells, and differentiated iPSCs and ES cells. iPSCs show significant reprogramming variability, including somatic memory and aberrant reprogramming of DNA methylation. iPSCs share megabase-scale differentially methylated regions proximal to centromeres and telomeres that display incomplete reprogramming of non-CG methylation, and differences in CG methylation and histone modifications. Lastly, differentiation of iPSCs into trophoblast cells revealed that errors in reprogramming CG methylation are transmitted at a high frequency, providing an iPSC reprogramming signature that is maintained after differentiation.

Distinct Epigenomic Landscapes of Pluripotent and Lineage-Committed Human Cells

Human embryonic stem cells (hESCs) share an identical genome with lineage-committed cells, yet possess the remarkable properties of self-renewal and pluripotency. The diverse cellular properties in different cells have been attributed to their distinct epigenomes, but how much epigenomes differ remains unclear. Here, we report that epigenomic landscapes in hESCs and lineage-committed cells are drastically different. By comparing the chromatin-modification profiles and DNA methylomes in hESCs and primary fibroblasts, we find that nearly one-third of the genome differs in chromatin structure. Most changes arise from dramatic redistributions of repressive H3K9me3 and H3K27me3 marks, which form blocks that significantly expand in fibroblasts. A large number of potential regulatory sequences also exhibit a high degree of dynamics in chromatin modifications and DNA methylation. Additionally, we observe novel, context-dependent relationships between DNA methylation and chromatin modifications. Our results provide new insights into epigenetic mechanisms underlying properties of pluripotency and cell fate commitment.

Comparison of sequencing-based methods to profile DNA methylation and identification of monoallelic epigenetic modifications

Analysis of DNA methylation patterns relies increasingly on sequencing-based profiling methods. The four most frequently used sequencing-based technologies are the bisulfite-based methods MethylC-seq and reduced representation bisulfite sequencing (RRBS), and the enrichment-based techniques methylated DNA immunoprecipitation sequencing (MeDIP-seq) and methylated DNA binding domain sequencing (MBD-seq). We applied all four methods to biological replicates of human embryonic stem cells to assess their genome-wide CpG coverage, resolution, cost, concordance and the influence of CpG density and genomic context. The methylation levels assessed by the two bisulfite methods were concordant (their difference did not exceed a given threshold) for 82% for CpGs and 99% of the non-CpG cytosines. Using binary methylation calls, the two enrichment methods were 99% concordant and regions assessed by all four methods were 97% concordant. We combined MeDIP-seq with methylation-sensitive restriction enzyme (MRE-seq) sequencing for comprehensive methylome coverage at lower cost. This, along with RNA-seq and ChIP-seq of the ES cells enabled us to detect regions with allele-specific epigenetic states, identifying most known imprinted regions and new loci with monoallelic epigenetic marks and monoallelic expression.

Widespread dynamic DNA methylation in response to biotic stress

Regulation of gene expression by DNA methylation is crucial for defining cellular identities and coordinating organism-wide developmental programs in many organisms. In plants, modulation of DNA methylation in response to environmental conditions represents a potentially robust mechanism to regulate gene expression networks; however, examples of dynamic DNA methylation are largely limited to gene imprinting. Here we report an unexpected role for DNA methylation in regulation of the Arabidopsis thaliana immune system. Profiling the DNA methylomes of plants exposed to bacterial pathogen, avirulent bacteria, or salicylic acid (SA) hormone revealed numerous stress-induced differentially methylated regions, many of which were intimately associated with differentially expressed genes. In response to SA, transposon-associated differentially methylated regions, which were accompanied by up-regulation of 21-nt siRNAs, were often coupled to transcriptional changes of the transposon and/or the proximal gene. Thus, dynamic DNA methylation changes within repetitive sequences or transposons can regulate neighboring genes in response to SA stress.

Patterns of population epigenomic diversity.

Natural epigenetic variation provides a source for the generation of phenotypic diversity, but to understand its contribution to such diversity, its interaction with genetic variation requires further investigation. Here we report population-wide DNA sequencing of genomes, transcriptomes and methylomes of wild Arabidopsis thaliana accessions. Single cytosine methylation polymorphisms are not linked to genotype. However, the rate of linkage disequilibrium decay amongst differentially methylated regions targeted by RNA-directed DNA methylation is similar to the rate for single nucleotide polymorphisms. Association analyses of these RNA-directed DNA methylation regions with genetic variants identified thousands of methylation quantitative trait loci, which revealed the population estimate of genetically dependent methylation variation. Analysis of invariably methylated transposons and genes across this population indicates that loci targeted by RNA-directed DNA methylation are epigenetically activated in pollen and seeds, which facilitates proper development of these structures.

Selective transcriptional regulation by Myc in cellular growth control and lymphomagenesis

The c-myc proto-oncogene product, Myc, is a transcription factor that binds thousands of genomic loci. Recent work suggested that rather than up- and downregulating selected groups of genes, Myc targets all active promoters and enhancers in the genome (a phenomenon termed ‘invasion’) and acts as a general amplifier of transcription. However, the available data did not readily discriminate between direct and indirect effects of Myc on RNA biogenesis. We addressed this issue with genome-wide chromatin immunoprecipitation and RNA expression profiles during B-cell lymphomagenesis in mice, in cultured B cells and fibroblasts. Consistent with long-standing observations, we detected general increases in total RNA or messenger RNA copies per cell (hereby termed ‘amplification’) when comparing actively proliferating cells with control quiescent cells: this was true whether cells were stimulated by mitogens (requiring endogenous Myc for a proliferative response) or by deregulated, oncogenic Myc activity. RNA amplification and promoter/enhancer invasion by Myc were separable phenomena that could occur without one another. Moreover, whether or not associated with RNA amplification, Myc drove the differential expression of distinct subsets of target genes. Hence, although having the potential to interact with all active or poised regulatory elements in the genome, Myc does not directly act as a global transcriptional amplifier. Instead, our results indicate that Myc activates and represses transcription of discrete gene sets, leading to changes in cellular state that can in turn feed back on global RNA production and turnover.

Temporal transcriptional response to ethylene gas drives growth hormone cross-regulation in Arabidopsis

The gaseous plant hormone ethylene regulates a multitude of growth and developmental processes. How the numerous growth control pathways are coordinated by the ethylene transcriptional response remains elusive. We characterized the dynamic ethylene transcriptional response by identifying targets of the master regulator of the ethylene signaling pathway, ETHYLENE INSENSITIVE3 (EIN3), using chromatin immunoprecipitation sequencing and transcript sequencing during a timecourse of ethylene treatment. Ethylene-induced transcription occurs in temporal waves regulated by EIN3, suggesting distinct layers of transcriptional control. EIN3 binding was found to modulate a multitude of downstream transcriptional cascades, including a major feedback regulatory circuitry of the ethylene signaling pathway, as well as integrating numerous connections between most of the hormone mediated growth response pathways. These findings provide direct evidence linking each of the major plant growth and development networks in novel ways. DOI: http://dx.doi.org/10.7554/eLife.00675.001

Genome-wide screen of promoter methylation identifies novel markers in melanoma

DNA methylation is an important component of epigenetic modifications, which influences the transcriptional machinery aberrant in many human diseases. In this study we present the first genome-wide integrative analysis of promoter methylation and gene expression for the identification of methylation markers in melanoma. Genome-wide promoter methylation and gene expression of eight early-passage human melanoma cell strains were compared with newborn and adult melanocytes. We used linear mixed effect models (LME) in combination with a series of filters based on the localization of promoter methylation relative to the transcription start site, overall promoter CpG content, and differential gene expression to discover DNA methylation markers. This approach identified 76 markers, of which 68 were hyper- and eight hypomethylated (LME, P < 0.05). Promoter methylation and differential gene expression of five markers (COL1A2, NPM2, HSPB6, DDIT4L, MT1G) were validated by sequencing of bisulfite-modified DNA and real-time reverse transcriptase PCR, respectively. Importantly, the incidence of promoter methylation of the validated markers increased moderately in early and significantly in advanced-stage melanomas, using early-passage cell strains and snap-frozen tissues (n = 18 and n = 24, respectively) compared with normal melanocytes and nevi (n = 11 and n = 9, respectively). Our approach allows robust identification of methylation markers that can be applied to other studies involving genome-wide promoter methylation. In conclusion, this study represents the first unbiased systematic effort to determine methylation markers in melanoma and revealed several novel genes regulated by promoter methylation that were not described in cancer cells before.

Statistical Similarities between Transcriptomics and Quantitative Shotgun Proteomics Data

If the large collection of microarray-specific statistical tools was applicable to the analysis of quantitative shotgun proteomics datasets, it would certainly foster an important advancement of proteomics research. Here we analyze two large multidimensional protein identification technology datasets, one containing eight replicates of the soluble fraction of a yeast whole-cell lysate and one containing nine replicates of a human immunoprecipitate, to test whether normalized spectral abundance factor (NSAF) values share substantially similar statistical properties with transcript abundance values from Affymetrix GeneChip data. First we show similar dynamic range and distribution properties of these two types of numeric values. Next we show that the standard deviation (S.D.) of a proteins NSAF values was dependent on the average NSAF value of the protein itself, following a power law. This relationship can be modeled by a power law global error model (PLGEM), initially developed to describe the variance-versus-mean dependence that exists in GeneChip data. PLGEM parameters obtained from NSAF datasets proved to be surprisingly similar to the typical parameters observed in GeneChip datasets. The most important common feature identified by this approach was that, although in absolute terms the S.D. of replicated abundance values increases as a function of increasing average abundance, the coefficient of variation, a relative measure of variability, becomes progressively smaller under the same conditions. We next show that PLGEM parameters were reasonably stable to decreasing numbers of replicates. We finally illustrate one possible application of PLGEM in the identification of differentially abundant proteins that might potentially outperform standard statistical tests. In summary, we believe that this body of work lays the foundation for the application of microarray-specific tools in the analysis of NSAF datasets.