Establishment of DNA methylation patterns during mouse development

Methylation is the only known modification of DNA and in animals it mainly occurs at cytosines in a CpG context. The pattern of DNA methylation varies among organisms; some invertebrates are totally devoid of it, while others have densely methylated regions embedded in an otherwise unmethylated genome. The genome of mammals on the other hand, is very rich in DNA methylation with the exception of regions with high CpG frequency, known as CpG islands, that are often found devoid of methylation. Little is known about the factors that determine the genome-wide pattern of DNA methylation. Moreover, although there appears to be a specific developmental program for the establishment of methylation in specific genomic regions, the molecular events that lead to methylation establishment remain unknown. The establishment of methylation in the regulatory region of the murine Oct4 gene as well as the occurrence and establishment of methylation in mouse CpG islands are investigated in this study. The promoter of Oct4, which encodes an important developmental regulator, is known to gain methylation as the gene becomes silenced during early development. An in vitro model of murine early development has been used to recapitulate the events that lead to the gene’s silencing. In accordance to other reports, detailed methylation analysis of the gene’s entire upstream region and expression analysis showed that DNA methylation establishment follows the gene’s downregulation. Moreover, establishment of methylation at the Oct4 locus seems to start from the gene’s proximal enhancer and then spread towards the distal enhancer and the promoter. Although the initial establishment of methylation in the distal enhancer was not impaired in G9a -/- cells, methylation in these cells was unable to spread and accumulate. These findings demonstrate that the promoter of the gene is not the primary target for methylation as previously assumed and give rise to two possible mechanisms for DNA methylation establishment at this gene; one possibility is that methylation is actively targeted to the proximal enhancer, while the other is that the promoter and the distal enhancer are resistant to methylation, perhaps because of transcription factors bound to them. Moreover, the finding that G9a is not necessary for DNA methylation establishment but appears to have a role in methylation spreading, together with observations on the kinetics of the downregulation and the timing of methylation establishment, allowed the formation of a possible model for the role of DNA methylation in this gene’s downregulation. According to this model, DNA methylation acts to accelerate the gene’s downregulation ensuring its coordinated repression in the developing organism. For the study of methylation in CpG islands, first a novel algorithm was applied for the identification of CpG islands in the mouse genome. Approximately 21,000 CpG islands were identified in the mouse genome, half of which localised at the 5’ of genes, while the majority of the remaining was equally distributed in intragenic and intergenic regions. Only a very small proportion of the CpG islands localised at the 3’ of genes. When the gene ontology terms related with the CpG island-associated genes where interrogated, two main gene functions emerged as being preferentially associated with CpG islands, development and cell maintenance. Then, an affinity purification method, together with microarray hybridisation was applied for the identification of methylated CpG islands from mouse brain. Approximately 18% of all CpG islands were methylated in brain, with the big majority localised at 5’ and intragenic regions. When the gene ontology of the methylated CpG island-associated genes was analysed, developmental but not housekeeping genes were overrepresented in the methylated fraction. In order to further investigate the relationship of CpG islands with developmental genes, the same methodology was applied for the identification of CpG islands that become methylated after the in vitro induction of differentiation of ES cells. Although this approach failed to produce genome-wide data, it enforced the idea of a developmental program for CpG island methylation

Targeting of De Novo DNA Methylation Throughout the Oct-4 Gene Regulatory Region in Differentiating Embryonic Stem Cells

Differentiation of embryonic stem (ES) cells is accompanied by silencing of the Oct-4 gene and de novo DNA methylation of its regulatory region. Previous studies have focused on the requirements for promoter region methylation. We therefore undertook to analyse the progression of DNA methylation of the ∼2000 base pair regulatory region of Oct-4 in ES cells that are wildtype or deficient for key proteins. We find that de novo methylation is initially seeded at two discrete sites, the proximal enhancer and distal promoter, spreading later to neighboring regions, including the remainder of the promoter. De novo methyltransferases Dnmt3a and Dnmt3b cooperate in the initial targeted stage of de novo methylation. Efficient completion of the pattern requires Dnmt3a and Dnmt1, but not Dnmt3b. Methylation of the Oct-4 promoter depends on the histone H3 lysine 9 methyltransferase G9a, as shown previously, but CpG methylation throughout most of the regulatory region accumulates even in the absence of G9a. Analysis of the Oct-4 regulatory domain as a whole has allowed us to detect targeted de novo methylation and to refine our understanding the roles of key protein components in this process

Establishment of DNA methylation patterns during mouse development

Methylation is the only known modification of DNA and in animals it mainly occurs at cytosines in a CpG context. The pattern of DNA methylation varies among organisms; some invertebrates are totally devoid of it, while others have densely methylated regions embedded in an otherwise unmethylated genome. The genome of mammals on the other hand, is very rich in DNA methylation with the exception of regions with high CpG frequency, known as CpG islands, that are often found devoid of methylation. Little is known about the factors that determine the genome-wide pattern of DNA methylation. Moreover, although there appears to be a specific developmental program for the establishment of methylation in specific genomic regions, the molecular events that lead to methylation establishment remain unknown. The establishment of methylation in the regulatory region of the murine Oct4 gene as well as the occurrence and establishment of methylation in mouse CpG islands are investigated in this study. The promoter of Oct4, which encodes an important developmental regulator, is known to gain methylation as the gene becomes silenced during early development. An in vitro model of murine early development has been used to recapitulate the events that lead to the gene’s silencing. In accordance to other reports, detailed methylation analysis of the gene’s entire upstream region and expression analysis showed that DNA methylation establishment follows the gene’s downregulation. Moreover, establishment of methylation at the Oct4 locus seems to start from the gene’s proximal enhancer and then spread towards the distal enhancer and the promoter. Although the initial establishment of methylation in the distal enhancer was not impaired in G9a -/- cells, methylation in these cells was unable to spread and accumulate. These findings demonstrate that the promoter of the gene is not the primary target for methylation as previously assumed and give rise to two possible mechanisms for DNA methylation establishment at this gene; one possibility is that methylation is actively targeted to the proximal enhancer, while the other is that the promoter and the distal enhancer are resistant to methylation, perhaps because of transcription factors bound to them. Moreover, the finding that G9a is not necessary for DNA methylation establishment but appears to have a role in methylation spreading, together with observations on the kinetics of the downregulation and the timing of methylation establishment, allowed the formation of a possible model for the role of DNA methylation in this gene’s downregulation. According to this model, DNA methylation acts to accelerate the gene’s downregulation ensuring its coordinated repression in the developing organism. For the study of methylation in CpG islands, first a novel algorithm was applied for the identification of CpG islands in the mouse genome. Approximately 21,000 CpG islands were identified in the mouse genome, half of which localised at the 5’ of genes, while the majority of the remaining was equally distributed in intragenic and intergenic regions. Only a very small proportion of the CpG islands localised at the 3’ of genes. When the gene ontology terms related with the CpG island-associated genes where interrogated, two main gene functions emerged as being preferentially associated with CpG islands, development and cell maintenance. Then, an affinity purification method, together with microarray hybridisation was applied for the identification of methylated CpG islands from mouse brain. Approximately 18% of all CpG islands were methylated in brain, with the big majority localised at 5’ and intragenic regions. When the gene ontology of the methylated CpG island-associated genes was analysed, developmental but not housekeeping genes were overrepresented in the methylated fraction. In order to further investigate the relationship of CpG islands with developmental genes, the same methodology was applied for the identification of CpG islands that become methylated after the in vitro induction of differentiation of ES cells. Although this approach failed to produce genome-wide data, it enforced the idea of a developmental program for CpG island methylation.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

A complete statistical model for calibration of RNA-seq counts using external spike-ins and maximum likelihood theory.

A fundamental assumption, common to the vast majority of high-throughput transcriptome analyses, is that the expression of most genes is unchanged among samples and that total cellular RNA remains constant. As the number of analyzed experimental systems increases however, different independent studies demonstrate that this assumption is often violated. We present a calibration method using RNA spike-ins that allows for the measurement of absolute cellular abundance of RNA molecules. We apply the method to pooled RNA from cell populations of known sizes. For each transcript, we compute a nominal abundance that can be converted to absolute by dividing by a scale factor determined in separate experiments: the yield coefficient of the transcript relative to that of a reference spike-in measured with the same protocol. The method is derived by maximum likelihood theory in the context of a complete statistical model for sequencing counts contributed by cellular RNA and spike-ins. The counts are based on a sample from a fixed number of cells to which a fixed population of spike-in molecules has been added. We illustrate and evaluate the method with applications to two global expression data sets, one from the model eukaryote Saccharomyces cerevisiae, proliferating at different growth rates, and differentiating cardiopharyngeal cell lineages in the chordate Ciona robusta. We tested the method in a technical replicate dilution study, and in a k-fold validation study

Steady-state and dynamic gene expression programs in Saccharomyces cerevisiae in response to variation in environmental nitrogen

Cell growth rate is regulated in response to the abundance and molecular form of essential nutrients. In Saccharomyces cerevisiae (budding yeast), the molecular form of environmental nitrogen is a major determinant of cell growth rate, supporting growth rates that vary at least threefold. Transcriptional control of nitrogen use is mediated in large part by nitrogen catabolite repression (NCR), which results in the repression of specific transcripts in the presence of a preferred nitrogen source that supports a fast growth rate, such as glutamine, that are otherwise expressed in the presence of a nonpreferred nitrogen source, such as proline, which supports a slower growth rate. Differential expression of the NCR regulon and additional nitrogen-responsive genes results in >500 transcripts that are differentially expressed in cells growing in the presence of different nitrogen sources in batch cultures. Here we find that in growth rate–controlled cultures using nitrogen-limited chemostats, gene expression programs are strikingly similar regardless of nitrogen source. NCR expression is derepressed in all nitrogen-limiting chemostat conditions regardless of nitrogen source, and in these conditions, only 34 transcripts exhibit nitrogen source–specific differential gene expression. Addition of either the preferred nitrogen source, glutamine, or the nonpreferred nitrogen source, proline, to cells growing in nitrogen-limited chemostats results in rapid, dose-dependent repression of the NCR regulon. Using a novel means of computational normalization to compare global gene expression programs in steady-state and dynamic conditions, we find evidence that the addition of nitrogen to nitrogen-limited cells results in the transient overproduction of transcripts required for protein translation. Simultaneously, we find that that accelerated mRNA degradation underlies the rapid clearing of a subset of transcripts, which is most pronounced for the highly expressed NCR-regulated permease genes GAP1, MEP2, DAL5, PUT4, and DIP5. Our results reveal novel aspects of nitrogen-regulated gene expression and highlight the need for a quantitative approach to study how the cell coordinates protein translation and nitrogen assimilation to optimize cell growth in different environments

Predicting childhood obesity using electronic health records and publicly available data.

BackgroundBecause of the strong link between childhood obesity and adulthood obesity comorbidities, and the difficulty in decreasing body mass index (BMI) later in life, effective strategies are needed to address this condition in early childhood. The ability to predict obesity before age five could be a useful tool, allowing prevention strategies to focus on high risk children. The few existing prediction models for obesity in childhood have primarily employed data from longitudinal cohort studies, relying on difficult to collect data that are not readily available to all practitioners. Instead, we utilized real-world unaugmented electronic health record (EHR) data from the first two years of life to predict obesity status at age five, an approach not yet taken in pediatric obesity research.Methods and findingsWe trained a variety of machine learning algorithms to perform both binary classification and regression. Following previous studies demonstrating different obesity determinants for boys and girls, we similarly developed separate models for both groups. In each of the separate models for boys and girls we found that weight for length z-score, BMI between 19 and 24 months, and the last BMI measure recorded before age two were the most important features for prediction. The best performing models were able to predict obesity with an Area Under the Receiver Operator Characteristic Curve (AUC) of 81.7% for girls and 76.1% for boys.ConclusionsWe were able to predict obesity at age five using EHR data with an AUC comparable to cohort-based studies, reducing the need for investment in additional data collection. Our results suggest that machine learning approaches for predicting future childhood obesity using EHR data could improve the ability of clinicians and researchers to drive future policy, intervention design, and the decision-making process in a clinical setting

Correction: Predicting childhood obesity using electronic health records and publicly available data.

[This corrects the article DOI: 10.1371/journal.pone.0215571.]