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DNA methylation plays an important role in development and disease. The primary sites of DNA methylation in vertebrates are cytosines in the CpG dinucleotide context, which account for roughly three quarters of the total DNA methylation content in human and mouse cells. While the genomic distribution, inter-individual stability, and functional role of CpG methylation are reasonably well understood, little is known about DNA methylation targeting CpA, CpT, and CpC (non-CpG) dinucleotides. Here we report a comprehensive analysis of non-CpG methylation in 76 genome-scale DNA methylation maps across pluripotent and differentiated human cell types. We confirm non-CpG methylation to be predominantly present in pluripotent cell types and observe a decrease upon differentiation and near complete absence in various somatic cell types. Although no function has been assigned to it in pluripotency, our data highlight that non-CpG methylation patterns reappear upon iPS cell reprogramming. Intriguingly, the patterns are highly variable and show little conservation between different pluripotent cell lines. We find a strong correlation of non-CpG methylation and DNMT3 expression levels while showing statistical independence of non-CpG methylation from pluripotency associated gene expression. In line with these findings, we show that knockdown of DNMTA and DNMT3B in hESCs results in a global reduction of non-CpG methylation. Finally, non-CpG methylation appears to be spatially correlated with CpG methylation. In summary these results contribute further to our understanding of cytosine methylation patterns in human cells using a large representative sample set.

Microglia, the macrophages of the brain parenchyma, are key players in neurodegenerative diseases such as Alzheimer’s disease. These cells adopt distinct transcriptional subtypes known as states. Understanding state function, especially in human microglia, has been elusive owing to a lack of tools to model and manipulate these cells. Here, we developed a platform for modeling human microglia transcriptional states in vitro. We found that exposure of human stem-cell-differentiated microglia to synaptosomes, myelin debris, apoptotic neurons or synthetic amyloid-beta fibrils generated transcriptional diversity that mapped to gene signatures identified in human brain microglia, including disease-associated microglia, a state enriched in neurodegenerative diseases. Using a new lentiviral approach, we demonstrated that the transcription factor MITF drives a disease-associated transcriptional signature and a highly phagocytic state. Together, these tools enable the manipulation and functional interrogation of human microglial states in both homeostatic and disease-relevant contexts. Stevens and colleagues show that human stem-cell-differentiated microglia can be used to model the extensive transcriptional diversity of human brain microglia.

Chromatin profiling has emerged as a powerful means of genome annotation and detection of regulatory activity. The approach is especially well suited to the characterization of non-coding portions of the genome, which critically contribute to cellular phenotypes yet remain largely uncharted. Here we map nine chromatin marks across nine cell types to systematically characterize regulatory elements, their cell-type specificities and their functional interactions. Focusing on cell-type-specific patterns of promoters and enhancers, we define multicell activity profiles for chromatin state, gene expression, regulatory motif enrichment and regulator expression. We use correlations between these profiles to link enhancers to putative target genes, and predict the cell-type-specific activators and repressors that modulate them. The resulting annotations and regulatory predictions have implications for the interpretation of genome-wide association studies. Top-scoring disease single nucleotide polymorphisms are frequently positioned within enhancer elements specifically active in relevant cell types, and in some cases affect a motif instance for a predicted regulator, thus suggesting a mechanism for the association. Our study presents a general framework for deciphering cis -regulatory connections and their roles in disease. Genome annotation using chromatin profiles Large-scale chromatin profiling can be used to distinguish functional genomic elements. Here, a compendium of chromatin maps for various histone marks in multiple human cell types is presented. Using the resulting data it is possible to identify different chromatin states corresponding to distinct regulatory elements such as repressed and active promoters, enhancers and insulators. Several disease-associated single nucleotide polymorphisms are shown to overlap with regulatory elements. This work has implications for human disease, and in particular for interpreting genome-wide association studies.

Aging can lead to cognitive decline associated with neural pathology and Alzheimer's disease (AD). Here the authors scan the methylation status of CpGs across the entire genome of brain samples from aged subjects in an epigenome-wide association study (EWAS). Several loci, including ANK1, were associated with AD pathology, gene expression and AD genetic risk networks. We used a collection of 708 prospectively collected autopsied brains to assess the methylation state of the brain's DNA in relation to Alzheimer's disease (AD). We found that the level of methylation at 71 of the 415,848 interrogated CpGs was significantly associated with the burden of AD pathology, including CpGs in the ABCA7 and BIN1 regions, which harbor known AD susceptibility variants. We validated 11 of the differentially methylated regions in an independent set of 117 subjects. Furthermore, we functionally validated these CpG associations and identified the nearby genes whose RNA expression was altered in AD: ANK1 , CDH23 , DIP2A , RHBDF2 , RPL13 , SERPINF1 and SERPINF2 . Our analyses suggest that these DNA methylation changes may have a role in the onset of AD given that we observed them in presymptomatic subjects and that six of the validated genes connect to a known AD susceptibility gene network.

Genome-wide association studies have identified loci underlying human diseases, but the causal nucleotide changes and mechanisms remain largely unknown. Here we developed a fine-mapping algorithm to identify candidate causal variants for 21 autoimmune diseases from genotyping data. We integrated these predictions with transcription and cis -regulatory element annotations, derived by mapping RNA and chromatin in primary immune cells, including resting and stimulated CD4 + T-cell subsets, regulatory T cells, CD8 + T cells, B cells, and monocytes. We find that ∼90% of causal variants are non-coding, with ∼60% mapping to immune-cell enhancers, many of which gain histone acetylation and transcribe enhancer-associated RNA upon immune stimulation. Causal variants tend to occur near binding sites for master regulators of immune differentiation and stimulus-dependent gene activation, but only 10–20% directly alter recognizable transcription factor binding motifs. Rather, most non-coding risk variants, including those that alter gene expression, affect non-canonical sequence determinants not well-explained by current gene regulatory models. Genome-wide association studies combined with data from epigenomic maps for immune cells have been used to fine-map causal variants for 21 autoimmune diseases; disease risk tends to be linked to single nucleotide polymorphisms in cell-type-specific enhancers, often in regions adjacent to transcription factor binding motifs. Gene variation in autoimmune diseases Hundreds of risk loci for autoimmunity have been identified previously in genome-wide association studies (GWASs), but the implicated loci comprise multiple variants in linkage disequilibrium and rarely alter protein-coding sequence, which complicates their interpretation. This study adopts a new approach for fine mapping causal genetic variants for 21 autoimmune diseases, applying a novel algorithm to GWAS-based loci and integrating genotypic data with epigenomic maps for specialized immune cells. The results implicate a very specific subset of enhancers involved in T-cell stimulation as causal determinants of autoimmune diseases.

Most pancreatic neuroendocrine tumors (PNETs) do not produce excess hormones and are therefore considered ‘non-functional’1–3. As clinical behaviors vary widely and distant metastases are eventually lethal2,4, biological classifications might guide treatment. Using enhancer maps to infer gene regulatory programs, we find that non-functional PNETs fall into two major subtypes, with epigenomes and transcriptomes that partially resemble islet α- and β-cells. Transcription factors ARX and PDX1 specify these normal cells, respectively5,6, and 84% of 142 non-functional PNETs expressed one or the other factor, occasionally both. Among 103 cases, distant relapses occurred almost exclusively in patients with ARX+PDX1− tumors and, within this subtype, in cases with alternative lengthening of telomeres. These markedly different outcomes belied similar clinical presentations and histology and, in one cohort, occurred irrespective of MEN1 mutation. This robust molecular stratification provides insight into cell lineage correlates of non-functional PNETs, accurately predicts disease course and can inform postoperative clinical decisions.

In all terrestrial vertebrates, the parathyroid glands are critical regulators of calcium homeostasis and the sole source of parathyroid hormone (PTH). Hyperparathyroidism and hypoparathyroidism are clinically important disorders affecting multiple organs. However, our knowledge regarding regulatory mechanisms governing the parathyroids has remained limited. Here, we present the comprehensive maps of the chromatin landscape of the human parathyroid glands, identifying active regulatory elements and chromatin interactions. These data allow us to define regulatory circuits and previously unidentified genes that play crucial roles in parathyroid biology. We experimentally validate candidate parathyroid-specific enhancers and demonstrate their integration with GWAS SNPs for parathyroid-related diseases and traits. For instance, we observe reduced activity of a parathyroid-specific enhancer of the Calcium Sensing Receptor gene, which contains a risk allele associated with higher PTH levels compared to the wildtype allele. Our datasets provide a valuable resource for unraveling the mechanisms governing parathyroid gland regulation in health and disease. Parathyroid glands are crucial for balancing blood calcium levels. Here, the authors generate comprehensive maps of the chromatin landscape of human parathyroids, linking identified regulatory elements to key functions in calcium homeostasis.

As the number of genomics datasets grows rapidly, sample mislabeling has become a high stakes issue. We present CrosscheckFingerprints (Crosscheck), a tool for quantifying sample-relatedness and detecting incorrectly paired sequencing datasets from different donors. Crosscheck outperforms similar methods and is effective even when data are sparse or from different assays. Application of Crosscheck to 8851 ENCODE ChIP-, RNA-, and DNase-seq datasets enabled us to identify and correct dozens of mislabeled samples and ambiguous metadata annotations, representing ~1% of ENCODE datasets. Parallelized analysis in clinical genomics can lead to sample or data mislabelling, and could have serious downstream consequences. Here the authors present a tool to quantify sample genetic relatedness and detect such mistakes, and apply it to thousands of datasets from the ENCODE consortium.

A critical goal in functional genomics is evaluating which non-coding elements contribute to gene expression, cellular function, and disease. Functional characterization remains a challenge due to the abundance and complexity of candidate elements. Here, we develop a CRISPRi-based approach for multi-locus screening of putative transcription factor binding sites with a single truncated guide. A truncated guide with hundreds of sequence match sites can reliably disrupt enhancer activity, which expands the targeting scope of CRISPRi while maintaining repressive efficacy. We screen over 13,000 possible CTCF binding sites with 24 guides at 10 nucleotides in spacer length. These truncated guides direct CRISPRi-mediated deposition of repressive H3K9me3 marks and disrupt transcription factor binding at most sequence match target sites. This approach can be a valuable screening step for testing transcription factor binding motifs or other repeated genomic sequences and is easily implemented with existing tools. A critical goal in functional genomics is evaluating which non-coding elements contribute to gene expression, cellular function, and disease. Here the authors present a CRISPRi-based method using truncated guides disrupts transcription factor binding and enhancer activity across thousands of sites, expanding CRISPRi targeting scope for functional genomics and enabling efficient screening of repeated genomic elements

Insulin resistance is a cardinal feature of Type 2 diabetes (T2D) and a frequent complication of multiple clinical conditions, including obesity, ageing and steroid use, among others. How such a panoply of insults can result in a common phenotype is incompletely understood. Furthermore, very little is known about the transcriptional and epigenetic basis of this disorder, despite evidence that such pathways are likely to play a fundamental role. Here, we compare cell autonomous models of insulin resistance induced by the cytokine tumour necrosis factor-α or by the steroid dexamethasone to construct detailed transcriptional and epigenomic maps associated with cellular insulin resistance. These data predict that the glucocorticoid receptor and vitamin D receptor are common mediators of insulin resistance, which we validate using gain- and loss-of-function studies. These studies define a common transcriptional and epigenomic signature in cellular insulin resistance enabling the identification of pathogenic mechanisms. Rosen and colleagues perform epigenomic and transcriptomic analyses of insulin-resistant cells, and report that the vitamin D receptor and glucocorticoid receptor mediate transcriptional responses that promote insulin resistance.