Showing papers by "Alexander Meissner published in 2016"

LKB1 loss links serine metabolism to DNA methylation and tumorigenesis

Abstract: Intermediary metabolism generates substrates for chromatin modification, enabling the potential coupling of metabolic and epigenetic states Here we identify a network linking metabolic and epigenetic alterations that is central to oncogenic transformation downstream of the liver kinase B1 (LKB1, also known as STK11) tumour suppressor, an integrator of nutrient availability, metabolism and growth By developing genetically engineered mouse models and primary pancreatic epithelial cells, and employing transcriptional, proteomics, and metabolic analyses, we find that oncogenic cooperation between LKB1 loss and KRAS activation is fuelled by pronounced mTOR-dependent induction of the serine-glycine-one-carbon pathway coupled to S-adenosylmethionine generation At the same time, DNA methyltransferases are upregulated, leading to elevation in DNA methylation with particular enrichment at retrotransposon elements associated with their transcriptional silencing Correspondingly, LKB1 deficiency sensitizes cells and tumours to inhibition of serine biosynthesis and DNA methylation Thus, we define a hypermetabolic state that incites changes in the epigenetic landscape to support tumorigenic growth of LKB1-mutant cells, while resulting in potential therapeutic vulnerabilities

Molecular features of cellular reprogramming and development

Abstract: Differentiating somatic cells are progressively restricted to specialized functions during ontogeny, but they can be experimentally directed to form other cell types, including those with complete embryonic potential. Early nuclear reprogramming methods, such as somatic cell nuclear transfer (SCNT) and cell fusion, posed significant technical hurdles to precise dissection of the regulatory programmes governing cell identity. However, the discovery of reprogramming by ectopic expression of a defined set of transcription factors, known as direct reprogramming, provided a tractable platform to uncover molecular characteristics of cellular specification and differentiation, cell type stability and pluripotency. We discuss the control and maintenance of cellular identity during developmental transitions as they have been studied using direct reprogramming, with an emphasis on transcriptional and epigenetic regulation.

Transcriptional and Chromatin Dynamics of Muscle Regeneration After Severe Trauma

Abstract: Following injury, adult skeletal muscle undergoes a well-coordinated sequence of molecular and physiological events to promote repair and regeneration. However, a thorough understanding of the in vivo epigenomic and transcriptional mechanisms that control these reparative events is lacking. To address this, we monitored the in vivo dynamics of three histone modifications and coding and noncoding RNA expression throughout the regenerative process in a mouse model of traumatic muscle injury. We first illustrate how both coding and noncoding RNAs in tissues and sorted satellite cells are modified and regulated during various stages after trauma. Next, we use chromatin immunoprecipitation followed by sequencing to evaluate the chromatin state of cis-regulatory elements (promoters and enhancers) and view how these elements evolve and influence various muscle repair and regeneration transcriptional programs. These results provide a comprehensive view of the central factors that regulate muscle regeneration and underscore the multiple levels through which both transcriptional and epigenetic patterns are regulated to enact appropriate repair and regeneration.

Information recovery from low coverage whole-genome bisulfite sequencing.

Abstract: The cost of whole-genome bisulfite sequencing (WGBS) remains a bottleneck for many studies and it is therefore imperative to extract as much information as possible from a given dataset. This is particularly important because even at the recommend 30X coverage for reference methylomes, up to 50% of high-resolution features such as differentially methylated positions (DMPs) cannot be called with current methods as determined by saturation analysis. To address this limitation, we have developed a tool that dynamically segments WGBS methylomes into blocks of comethylation (COMETs) from which lost information can be recovered in the form of differentially methylated COMETs (DMCs). Using this tool, we demonstrate recovery of ∼30% of the lost DMP information content as DMCs even at very low (5X) coverage. This constitutes twice the amount that can be recovered using an existing method based on differentially methylated regions (DMRs). In addition, we explored the relationship between COMETs and haplotypes in lymphoblastoid cell lines of African and European origin. Using best fit analysis, we show COMETs to be correlated in a population-specific manner, suggesting that this type of dynamic segmentation may be useful for integrated (epi)genome-wide association studies in the future.

Targeted bisulfite sequencing of the dynamic DNA methylome

Abstract: The ability to measure DNA methylation precisely and efficiently continues to drive our understanding of this modification in development and disease. Whole genome bisulfite sequencing has the advantage of theoretically capturing all cytosines in the genome at single-nucleotide resolution, but it has a number of significant practical drawbacks that become amplified with increasing sample numbers. All other technologies capture only a fraction of the cytosines that show dynamic regulation across cell and tissue types. Here, we present a novel hybrid selection design focusing on loci with dynamic methylation that captures a large number of differentially methylated gene-regulatory elements. We benchmarked this assay against matched whole genome data and profiled 25 human tissue samples to explore its ability to detect differentially methylated regions. Our target capture design fills a major gap left by all other assays that exist to map DNA methylation. It maintains the ability to link cytosine methylation to genetic differences, the single-base resolution and the analysis of neighboring cytosines while notably reducing the cost per sample by focusing the sequencing effort on the most informative and relevant regions of the genome.

Saturation analysis for whole-genome bisulfite sequencing data

Abstract: To the Editor: Whole-genome bisulfite sequencing (WGBS) has become an integral part of basic and clinical research and has been widely used to generate reference methylomes since 2010 (refs. 1,2). However, because of the initial high cost of a 30× WGBS methylome3, no saturation analysis has been performed to assess the information that can be harnessed from individual methylome features at different sequencing coverage. Consequently, the International Human Epigenome Consortium (IHEC; http://ihec-epigenomes. org/research/reference-epigenomestandards/) decided to sequence reference methylomes to 30× coverage, which was believed to adequately capture the majority of the methylation signal for subsequent analyses. Here, we report the first saturation analysis for WGBS. We assessed the effect of coverage on the identification of five features that reveal key aspects of the methylome, including informative CpG sites (iCGs), differentially methylated positions (DMPs), differentially methylated regions (DMRs), blocks of comethylation (COMETs) and differentially methylated COMETs (DMCs). We carried out a downsampling analysis by sequentially removing random WGBS reads—thereby reducing coverage—to assess the loss of information for each of the above features related to coverage, resolution and complexity. Individual CpG methylation states, defined by iCGs, and methylation changes, defined by DMPs, exhibited the highest (single base) level of resolution and lowest level of complexity. In contrast, COMETs and DMCs had the lowest resolution and highest levels of feature complexity, whereas DMRs had medium resolution and complexity. On the basis of this analysis, we show that the current reference methylome coverage (30×) results in ~50% loss of DMPs and is therefore only of limited use for high-resolution feature analysis (e.g., DMPs). We analyzed 13 WGBS methylomes (M1–13), which are summarized in Supplementary Table 1 and Supplementary Methods4. Except for M13, all methylomes were generated by the Roadmap Epigenomics5 (http:// www.roadmapepigenomics.org/) and BLUEPRINT6 (http://www.blueprintepigenome.eu/) projects. The same methylomes were also used in a parallel study4 describing the COMET, DMC and information recovery analyses. To our knowledge, M1–3 are the deepest methylomes reported to date and thus constitute particularly valuable references for future studies. Downsampling is the method of choice for saturation analysis and assessing coverage-dependent information loss. It requires a static reference methylome against which to downsample a deepcoverage test methylome. Better results are obtained if both methylomes are available in multiple replicates as described below. For the static reference, we evaluated two preIHEC (i.e., created before the consortium and its guidelines were established) (M4 (ref. 7), M13 (ref. 8) and four IHEC (M7– 10) methylomes (Fig. 1) and selected the superior IHEC replicates M7–10 (derived from human embryonic stem cells and generated by the Roadmap Epigenomics Project) against which to downsample deepcoverage test replicates M1–2 (derived from purified human monocytes and generated by the BLUEPRINT Project). For each of the five features described above, the test methylomes (M1–2) were randomly downsampled to different read-coverage levels and assessed for information loss by comparison to the static reference methylomes (M7–10). For the analysis of iCGs, DMPs and DMRs, we used BSmooth9 and RADmeth10, and we used COMETgazer4 and COMETvintage4 for the analysis of COMETs and DMCs (https:// github.com/rifathamoudi/COMETgazer). Figure 2a shows the saturation analysis of iCGs, DMPs, DMRs, COMETs and DMCs for M1–2 by downsampling from 83× or 91× to 5× sequence coverage. For each coverage and feature, the respective percentages of retained information are plotted on the y axis. The total number of M1–2 features