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1.  Non-CG DNA methylation is a biomarker for assessing endodermal differentiation capacity in pluripotent stem cells 
Nature Communications  2016;7:10458.
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 methylation of non-CpG residues is a poorly understood marker of pluripotent cells, gradually lost as cells differentiate. Here the authors show non-CG methylation can be used as a marker of differentiation potential.
PMCID: PMC4740175  PMID: 26822956
2.  Probe Lasso: A novel method to rope in differentially methylated regions with 450K DNA methylation data 
The speed and resolution at which we can scour the genome for DNA methylation changes has improved immeasurably in the last 10 years and the advent of the Illumina 450K BeadChip has made epigenome-wide association studies (EWAS) a reality. The resulting datasets are conveniently formatted to allow easy alignment of significant hits to genes and genetic features, however; methods that parse significant hits into discreet differentially methylated regions (DMRs) remain a challenge to implement. In this paper we present details of a novel DMR caller, the Probe Lasso: a flexible window based approach that gathers neighbouring significant-signals to define clear DMR boundaries for subsequent in-depth analysis. The method is implemented in the R package ChAMP (Morris et al., 2014) and returns sets of DMRs according to user-tuned levels of probe filtering (e.g., inclusion of sex chromosomes, polymorphisms) and probe-lasso size distribution. Using a sub-sample of colon cancer- and healthy colon-samples from TCGA we show that Probe Lasso shifts DMR calling away from just probe-dense regions, and calls a range of DMR sizes ranging from tens-of-bases to tens-of-kilobases in scale. Moreover, using TCGA data we show that Probe Lasso leverages more information from the array and highlights a potential role of hypomethylated transcription factor binding motifs not discoverable using a basic, fixed-window approach.
PMCID: PMC4304833  PMID: 25461817
Differentially methylated regions; DNA methylation; Epigenetics; EWAS; Illumina 450K BeadChip
3.  Genome-wide age-related changes in DNA methylation and gene expression in human PBMCs 
Age  2014;36(3):9648.
Aging is a progressive process that results in the accumulation of intra- and extracellular alterations that in turn contribute to a reduction in health. Age-related changes in DNA methylation have been reported before and may be responsible for aging-induced changes in gene expression, although a causal relationship has yet to be shown. Using genome-wide assays, we analyzed age-induced changes in DNA methylation and their effect on gene expression with and without transient induction with the synthetic transcription modulating agent WY14,643. To demonstrate feasibility of the approach, we isolated peripheral blood mononucleated cells (PBMCs) from five young and five old healthy male volunteers and cultured them with or without WY14,643. Infinium 450K BeadChip and Affymetrix Human Gene 1.1 ST expression array analysis revealed significant differential methylation of at least 5 % (ΔYO > 5 %) at 10,625 CpG sites between young and old subjects, but only a subset of the associated genes were also differentially expressed. Age-related differential methylation of previously reported epigenetic biomarkers of aging including ELOVL2, FHL2, PENK, and KLF14 was confirmed in our study, but these genes did not display an age-related change in gene expression in PBMCs. Bioinformatic analysis revealed that differentially methylated genes that lack an age-related expression change predominantly represent genes involved in carcinogenesis and developmental processes, and expression of most of these genes were silenced in PBMCs. No changes in DNA methylation were found in genes displaying transiently induced changes in gene expression. In conclusion, aging-induced differential methylation often targets developmental genes and occurs mostly without change in gene expression.
Electronic supplementary material
The online version of this article (doi:10.1007/s11357-014-9648-x) contains supplementary material, which is available to authorized users.
PMCID: PMC4082572  PMID: 24789080
Molecular aging; Epigenetics; DNA methylation; Gene expression; PBMCs; Epigenetic biomarkers of aging
4.  Human-specific CpG “beacons” identify loci associated with human-specific traits and disease 
Epigenetics  2012;7(10):1188-1199.
Regulatory change has long been hypothesized to drive the delineation of the human phenotype from other closely related primates. Here we provide evidence that CpG dinucleotides play a special role in this process. CpGs enable epigenome variability via DNA methylation, and this epigenetic mark functions as a regulatory mechanism. Therefore, species-specific CpGs may influence species-specific regulation. We report non-polymorphic species-specific CpG dinucleotides (termed “CpG beacons”) as a distinct genomic feature associated with CpG island (CGI) evolution, human traits and disease. Using an inter-primate comparison, we identified 21 extreme CpG beacon clusters (≥ 20/kb peaks, empirical p < 1.0 × 10−3) in humans, which include associations with four monogenic developmental and neurological disease related genes (Benjamini-Hochberg corrected p = 6.03 × 10−3). We also demonstrate that beacon-mediated CpG density gain in CGIs correlates with reduced methylation in these species in orthologous CGIs over time, via human, chimpanzee and macaque MeDIP-seq. Therefore mapping into both the genomic and epigenomic space the identified CpG beacon clusters define points of intersection where a substantial two-way interaction between genetic sequence and epigenetic state has occurred. Taken together, our data support a model for CpG beacons to contribute to CGI evolution from genesis to tissue-specific to constitutively active CGIs.
PMCID: PMC3469460  PMID: 22968434
epigenetics; epigenomics; CpG islands; gene regulation; evolution; human disease
5.  AutoMeDIP-seq: A high-throughput, whole genome, DNA methylation assay 
Methods (San Diego, Calif.)  2010;52(3-13):223-231.
DNA methylation is an epigenetic mark linking DNA sequence and transcription regulation, and therefore plays an important role in phenotypic plasticity. The ideal whole genome methylation (methylome) assay should be accurate, affordable, high-throughput and agnostic with respect to genomic features. To this end, the methylated DNA immunoprecipitation (MeDIP) assay provides a good balance of these criteria. In this Methods paper, we present AutoMeDIP-seq, a technique that combines an automated MeDIP protocol with library preparation steps for subsequent second-generation sequencing. We assessed recovery of DNA sequences covering a range of CpG densities using in vitro methylated λ-DNA fragments (and their unmethylated counterparts) spiked-in against a background of human genomic DNA. We show that AutoMeDIP is more reliable than manual protocols, shows a linear recovery profile of fragments related to CpG density (R2 = 0.86), and that it is highly specific (>99%). AutoMeDIP-seq offers a competitive approach to high-throughput methylome analysis of medium to large cohorts.
PMCID: PMC2977854  PMID: 20385236
DNA methylation; Automation; Whole genome; High-throughput sequencing; MeDIP

Results 1-5 (5)