Genome-wide single-molecule analysis of long-read DNA methylation reveals heterogeneous patterns at heterochromatin that reflect nucleosome organisation

High-throughput sequencing technology is central to our current understanding of the human methylome. The vast majority of studies use chemical conversion to analyse bulk-level patterns of DNA methylation across the genome from a population of cells. While this technology has been used to probe single-molecule methylation patterns, such analyses are limited to short reads of a few hundred basepairs. DNA methylation can also be directly detected using Nanopore sequencing which can generate reads measuring megabases in length. However, thus far these analyses have largely focused on bulk-level assessment of DNA methylation. Here, we analyse DNA methylation in single Nanopore reads from human lymphoblastoid cells, to show that bulk-level metrics underestimate large-scale heterogeneity in the methylome. We use the correlation in methylation state between neighbouring sites to quantify single-molecule heterogeneity and find that heterogeneity varies significantly across the human genome, with some regions having heterogeneous methylation patterns at the single-molecule level and others possessing more homogeneous methylation patterns. By comparing the genomic distribution of the correlation to epigenomic annotations, we find that the greatest heterogeneity in single-molecule patterns is observed within heterochromatic partially methylated domains (PMDs). In contrast, reads originating from euchromatic regions and gene bodies have more ordered DNA methylation patterns. By analysing the patterns of single molecules in more detail, we show the existence of a nucleosome-scale periodicity in DNA methylation that accounts for some of the heterogeneity we uncover in long single-molecule DNA methylation patterns. We find that this periodic structure is partially masked in bulk data and correlates with DNA accessibility as measured by nanoNOMe-seq, suggesting that it could be generated by nucleosomes. Our findings demonstrate the power of single-molecule analysis of long-read data to understand the structure of the human methylome

DNMT3B PWWP mutations cause hypermethylation of heterochromatin

The correct establishment of DNA methylation patterns is vital for mammalian development and is achieved by the de novo DNA methyltransferases DNMT3A and DNMT3B. DNMT3B localises to H3K36me3 at actively transcribing gene bodies via its PWWP domain. It also functions at heterochromatin through an unknown recruitment mechanism. Here we find that knockout of DNMT3B causes loss of methylation predominantly at H3K9me3-marked heterochromatin and that DNMT3B PWWP domain mutations or deletion result in striking increases of methylation in H3K9me3-marked heterochromatin. Removal of the N-terminal region of DNMT3B affects its ability to methylate H3K9me3-marked regions. This region of DNMT3B directly interacts with HP1 and facilitates the bridging of DNMT3B with H3K9me3-marked nucleosomes in vitro. Our results suggest that DNMT3B is recruited to H3K9me3 marked heterochromatin in a PWWP-independent mannerthat is facilitated by the protein’s N-terminal region through an interaction with a key heterochromatin protein. More generally, we suggest that DNMT3B plays a role in DNA methylation homeostasis at heterochromatin, a process which is disrupted in cancer, aging and Immunodeficiency, Centromeric Instability and Facial Anomalies (ICF) syndrome

DNMT3B PWWP mutations cause hypermethylation of heterochromatin

The correct establishment of DNA methylation patterns is vital for mammalian development and is achieved by the de novo DNA methyltransferases DNMT3A and DNMT3B. DNMT3B localises to H3K36me3 at actively transcribing gene bodies via its PWWP domain. It also functions at heterochromatin through an unknown recruitment mechanism. Here, we find that knockout of DNMT3B causes loss of methylation predominantly at H3K9me3-marked heterochromatin and that DNMT3B PWWP domain mutations or deletion result in striking increases of methylation in H3K9me3-marked heterochromatin. Removal of the N-terminal region of DNMT3B affects its ability to methylate H3K9me3-marked regions. This region of DNMT3B directly interacts with HP1α and facilitates the bridging of DNMT3B with H3K9me3-marked nucleosomes in vitro. Our results suggest that DNMT3B is recruited to H3K9me3-marked heterochromatin in a PWWP-independent manner that is facilitated by the protein’s N-terminal region through an interaction with a key heterochromatin protein. More generally, we suggest that DNMT3B plays a role in DNA methylation homeostasis at heterochromatin, a process which is disrupted in cancer, aging and Immunodeficiency, Centromeric Instability and Facial Anomalies (ICF) syndrome

Reiterative de novo methylation maintainsmethylation levels in somatic cells

DNA methylation is a pervasive epigenetic mark in normal cells. DNA methylation abnormalities are a fundamental hallmark of cancer that can promote carcinogenesis. DNA methylation is lost specifically in heterochromatic regions in tumours. These hypomethylated regions are termed partially methylated domains (PMDs) and replicate during late S-phase. The late replication of PMDs has been proposed to play a key role in their hypomethylation. Specifically, it has been suggested that PMDs passively lose methylation due to incomplete maintenance of methylation after consecutive cell divisions. This model directly implicates DNMT1 as the maintenance methyltransferase and suggests that it does not have enough time to fully methylate late replicating regions. I aimed to elucidate how PMDs become hypomethylated during tumorigenesis and address this ‘passive loss’ model. I investigated the levels and patterns of DNA methylation in HCT116 colorectal cancer cells and their DNMT1 Knock-Out (DNMT1KO) derivatives. I identified that PMDs show distinct hypomethylation in HCT116 cells, depending on their heterochromatic state. Constitutive heterochromatic PMDs, marked by H3K9me3, showed more pronounced hypomethylation than facultative ones, marked by H3K27me3. In DNMT1KO cells, I observed global loss of methylation levels. However, hypomethylation was particularly prominent within PMDs, suggesting that hindering DNMT1 activity led to poorer maintenance of methylation, in agreement with the model. I also observed a subgroup of PMDs that were predominantly marked by H3K9me3 and bordered by H3K27me3 in HCT116 cells, which unexpectedly showed increased methylation levels in DNMT1KO cells. These hypermethylated PMDs remain late replicating in DNMT1KO cells despite their high methylation. However, these regions were no longer marked by H3K9me3 and H3K27me3 in DNMT1KO cells, indicating the loss of their heterochromatic state. Finally, using ChIP, I identified that DNMT3A and DNMT3B were not recruited in constitutive and facultative heterochromatic regions. DNMT3A, but not DNMT3B, recruitment was detected in these hypermethylated PMDs in DNMT1KO but not HCT116 cells, aligning with the loss of the heterochromatic marks in the hypermethylated PMDs. Taken together, my results suggested that hypermethylated PMDs in DNMT1KO cells could maintain high methylation levels, despite their late replication timing, due to the recruitment of DNMT3A. More generally, this suggested that de novo DNMTs play an important role in maintenance of methylation levels via reiterative de novo methylation, while highlighting that chromatin environment and its role in DNMT recruitment might play a more important role than replication timing in the hypomethylation observed in cancers

Comparative Transcriptome Analysis of Two Olive Cultivars in Response to NaCl-Stress

Background: Olive (Olea europaea L.) cultivation is rapidly expanding and low quality saline water is often used for irrigation. The molecular basis of salt tolerance in olive, though, has not yet been investigated at a system level. In this study a comparative transcriptomics approach was used as a tool to unravel gene regulatory networks underlying salinity response in olive trees by simulating as much as possible olive growing conditions in the field. Specifically, we investigated the genotype-dependent differences in the transcriptome response of two olive cultivars, a salt-tolerant and a salt-sensitive one. Methodology/Principal Findings: A 135-day long salinity experiment was conducted using one-year old trees exposed to NaCl stress for 90 days followed by 45 days of post-stress period during the summer. A cDNA library made of olive seedling mRNAs was sequenced and an olive microarray was constructed. Total RNA was extracted from root samples after 15, 45 and 90 days of NaCl-treatment as well as after 15 and 45 days of post-treatment period and used for microarray hybridizations. SAM analysis between the NaCl-stress and the post-stress time course resulted in the identification of 209 and 36 differentially expressed transcripts in the salt–tolerant and salt–sensitive cultivar, respectively. Hierarchical clustering revealed two major, distinct clusters for each cultivar. Despite the limited number of probe sets, transcriptional regulatory networks were constructed for both cultivars while several hierarchically-clustered interacting transcription factor regulators such as JERF and bZIP homologues were identified. Conclusions/Significance: A systems biology approach was used and differentially expressed transcripts as well as regulatory interactions were identified. The comparison of the interactions among transcription factors in olive with those reported for Arabidopsis might indicate similarities in the response of a tree species with Arabidopsis at the transcriptional level under salinity stress

Visual symptoms of Na-Cl stress.

<p>One-year-old olive trees cvs Chondrolia Chalkidikis [A] and Kalamon [B] throughout the experimental timepoints: 15 days, 45 days and 90 days of stress and 15 days and 45 days post-stress.</p

Pie chart of the EST annotation of olive seedling cDNA library.

<p>Biological process level 3 of 1956 transcripts according to Blast2GO database.</p

GO annotation of the differentially expressed transcripts.

<p>[A] 22/51 of the differentially expressed transcripts (2-class paired SAM) of cv. Kalamon and 5/6 of the differentially expressed transcripts (2-class paired SAM) transcripts of cv. Chondrolia Chalkidikis were annotated using the 1e-6 threshold of blastx in the Blast2GO software. [B] K-means clustering of the 51 Kalamon differentially expressed transcripts. Log₂ (Treated/Control) gene expression data were best represented by four k-means clusters. Time and fold change are indicated on the x- and y-axes, respectively. In each cluster, the number of transcripts is indicated. A pseudoline in magenta colour superimposed on each cluster represents the general pattern of expression. [C] Pie chart of the cv. Chondrolia Chalkidikis annotated transcripts. [D] Pie chart of the cv. Kalamon annotated transcripts.</p

Expression panel of selected regulatory networks.

<p>The upper panel represents the regulatory TFs of a module and the lower panel the transcripts it consists of. Each column represents a time-point. The expression of the transcripts and TFs is color-coded with the dark blue representing lower expression whereas the bright yellow represents higher expression. [A] module-9 in cv. Kalamon; [B] module-7 in cv. Chondrolia Chalkidikis. Both modules are enriched in GO annotations related to stimulus response.</p