BackgroundDNA methylation at promoters is largely correlated with inhibition of gene expression. However, the role of DNA methylation at enhancers is not fully understood, although a crosstalk with chromatin marks is expected. Actually, there exist contradictory reports about positive and negative correlations between DNA methylation and H3K4me1, a chromatin hallmark of enhancers.ResultsWe investigated the relationship between DNA methylation and active chromatin marks through genome-wide correlations, and found anti-correlation between H3K4me1 and H3K4me3 enrichment at low and intermediate DNA methylation loci. We hypothesized “seesaw” dynamics between H3K4me1 and H3K4me3 in the low and intermediate DNA methylation range, in which DNA methylation discriminates between enhancers and promoters, marked by H3K4me1 and H3K4me3, respectively. Low methylated regions are H3K4me3 enriched, while those with intermediate DNA methylation levels are progressively H3K4me1 enriched. Additionally, the enrichment of H3K27ac, distinguishing active from primed enhancers, follows a plateau in the lower range of the intermediate DNA methylation level, corresponding to active enhancers, and decreases linearly in the higher range of the intermediate DNA methylation. Thus, the decrease of the DNA methylation switches smoothly the state of the enhancers from a primed to an active state. We summarize these observations into a rule of thumb of one-out-of-three methylation marks: “In each genomic region only one out of these three methylation marks {DNA methylation, H3K4me1, H3K4me3} is high. If it is the DNA methylation, the region is inactive. If it is H3K4me1, the region is an enhancer, and if it is H3K4me3, the region is a promoter”. To test our model, we used available genome-wide datasets of H3K4 methyltransferases knockouts. Our analysis suggests that CXXC proteins, as readers of non-methylated CpGs would regulate the “seesaw” mechanism that focuses H3K4me3 to unmethylated sites, while being repulsed from H3K4me1 decorated enhancers and CpG island shores.ConclusionsOur results show that DNA methylation discriminates promoters from enhancers through H3K4me1-H3K4me3 seesaw mechanism, and suggest its possible function in the inheritance of chromatin marks after cell division. Our analyses suggest aberrant formation of promoter-like regions and ectopic transcription of hypomethylated regions of DNA. Such mechanism process can have important implications in biological process in where it has been reported abnormal DNA methylation status such as cancer and aging.Electronic supplementary materialThe online version of this article (10.1186/s12864-017-4353-7) contains supplementary material, which is available to authorized users.
The determination of the optimal sample size of a clinical trial is considered when the number of subsequent users of a new treatment is a function of both the statistical signi®cance of the difference and of the magnitude of the apparent difference between the performance of the new treatment and that of the treatment in current use. An objective function is proposed consisting of the total bene®t from the resulting change in the number of patients using the new treatment minus the cost of the trial. From this the optimal sample size may be calculated. The model has features which allow for the following contingencies: a cost difference between the two treatments; a pay-off function de®ned either from the public health or from a drug company standpoint; the performance of the existing treatment is either known or unknown; varying degrees of severity of the condition to be treated; a set-up cost for conducting the trial.
The aim of this paper is to review some key techniques of Bayesian methods of sample size determination. The approach is to cover a small number of simple problems, such as estimating the mean of a normal distribution. The methods considered are in two groups: inferential and decision theoretic. In the inferential Bayesian methods of sample size determination, we are solely concerned with the inference about the parameter(s) of interest. The fully Bayesian or decision theoretic approach treats the problem as a decision problem and employs a loss or utility function.
In this paper we introduce a fully Bayesian approach to sample size determination in clinical trials. In contrast to the usual Bayesian decision theoretic methodology, which assumes a single decision maker; our approach recognizes the existence of three decision makers, namely: the pharmaceutical company conducting the trial, which decides on its size; the regulator; whose approval is necessary for the drug to be licensed for sale; and the public at large, who determine ultimate usage. Moreover; we model the subsequent usage by plausible assumptions for actual behavior; rather than assuming that it represents decisions which are in some sense optimal.The results, not surprisingly, show that the optimal sample size depends strongly on the expected benefit from a conclusively favorable outcome, and on the strength of the evidence required by the regulator:
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