Correlation between miRNA-targeting-specific promotermethylation and miRNA regulation of target genes

Y-h Taguchi

doi:10.12688/f1000research.2-21.v1

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Research Article

Correlation between miRNA-targeting-specific promotermethylation and miRNA regulation of target genes

[version 1; peer review: 1 approved, 1 approved with reservations]

Y-h Taguchi

PUBLISHED 23 Jan 2013

Author details Author details

Department of Physics, Chuo University, Tokyo, Japan

OPEN PEER REVIEW

REVIEWER STATUS

Abstract

Background miRNA regulation of target genes and promoter methylation were known to be the primary mechanisms underlying the epigenetic regulation of gene expression. However, how these two processes cooperatively regulate gene expression has not been extensively studied.
Methods Gene expression and promoter methylation profiles of 271 distinct human cell lines were obtained from gene expression omnibus. P-values that describe both miRNA-targeting-specific promoter methylation and miRNA regulation of target genes were computed with the MiRaGE method proposed recently by the author.
Results We found that promoter methylation was miRNA-targeting-specific. In other words, changes in promoter methylation were associated with miRNA binding at target genes. It was also found that miRNA-targeting-specific promoter hypomethylation was related to miRNA regulation; the genes with miRNA-targeting-specific promoter hypomethylation were downregulated during cell senescence and upregulated during cellulardierentiation. Promoter hypomethylation was especially enhanced for genes targeted by miR-548 miRNAs, which are non-conserved, and primate-specific miRNAs that are typically expressed at lower levels than the frequently investigated conserved miRNAs.
Conclusions It was found that promoter methylation was affected by miRNA targeting. Furthermore, miRNA-targeting-specific promoter hypomethylation was suggested to facilitate gene regulation by miRNAs that are not strongly expressed (e.g., miR-548 miRNAs).

Keywords

cell lines, methylation

Corresponding author: Y-h Taguchi

Competing interests: No competing interests were disclosed.

Grant information: The author(s) declared that no grants were involved in supporting this work.

Copyright: © 2013 Taguchi Yh. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Data associated with the article are available under the terms of the Creative Commons Zero "No rights reserved" data waiver (CC0 1.0 Public domain dedication).

How to cite: Taguchi Yh. Correlation between miRNA-targeting-specific promotermethylation and miRNA regulation of target genes [version 1; peer review: 1 approved, 1 approved with reservations]. F1000Research 2013, 2:21 (https://doi.org/10.12688/f1000research.2-21.v1) First published: 23 Jan 2013, 2:21 (https://doi.org/10.12688/f1000research.2-21.v1) Latest published: 11 Sep 2013, 2:21 (https://doi.org/10.12688/f1000research.2-21.v3)

Introduction

The epigenetic regulation of gene expression¹ has recently attracted the interests of many researchers. The mechanism is known to regulate gene expression without modifying DNA sequences. Some examples of epigenetic regulation include promoter methylation², histone modification³, the binding of transcription factors to gene promoter regions⁴, and miRNA regulation of target genes⁵.

Among them, the promoter methylation and the miRNA regulation of target genes are particularly important in the epigenetic regulation of gene expression. Promoter methylation is relatively stable, long term, and in some cases, heritable. It is generally believed that genes with hypermethylated promoters are repressed. In addition, there is mounting evidence that DNA methylation is involved in the development and progression of certain disease states. For example, aberrant methylation in cancer is frequently observed⁶ and the distinct patterns of promoter methylation between monozygotic (MZ) twin pairs have also been found to result in different health conditions⁷. In contrast to DNA methylation, miRNA regulation of target genes is more flexible and rapidly changing. miRNA expression can change even during cellular differentiation⁸. miRNA expression is often tissue-specific, and similar to DNA methylation, miRNA expression has been linked to human disease⁹. Thus, although miRNA-directed gene regulation is thought to result in subtle changes, it is generally believed that miRNAs are involved in many important biological processes ranging from cell division to aging.

Although DNA methylation of miRNA promoters has been studied extensively (e.g., in accordance with tumor formation¹⁰), the relationship between promoter methylation and miRNA regulation of target genes has not been thoroughly investigated. One likely reason for this is that the regulation of gene expression by promoter methylation is a form of pre-transcriptional control, whereas miRNA regulation of target genes is a form of post-transcriptional control, with the former taking place inside the nucleus and the latter outside the nucleus (cytoplasm). Thus, these two mechanisms are separated by both time and space, and as a result, to date, there have not been plausible biological reasons to suspect that promoter methylation and miRNA-mediated gene regulation operate in concert.

However, Su et al.¹¹ recently found that miRNAs have a tendency to target genes with hypomethylated promoters. To my knowledge, their study was the first report suggesting cooperative regulation of gene expression by promoter methylation and miRNAs. In addition, Sinha et al. reported that genes promoters with high CpG content were more often targeted by miRNAs¹². Saito and Sætrom also dicussed the relationship between miRNA-mediated gene regulation and various features of target genes, but they did not consider the methylation status of target genes promoters¹³. Although the study of Su et al represents the first evidence of a direct link between promoter methylation and miRNA regulation, the biological significance of their findings is not clear. In this study, I report that promoter methylation is miRNA-targeting-specific; that is, the amount of methylation observed at a given gene promoter is dependent on whether that gene is also a target of miRNA regulation. Furthermore, the miRNA-targeting-specific promoter methylation is also related to how miRNAs regulate target gene expression. Especially, I find that miR-548 miRNAs target genes with highly hypomethylated promoters.

Methods

Promoter methylation profiles

In this study, I used publically available promoter methylation profiles from various resources, obtained from GEO ID: GSE30653¹⁴. This included 283 human promoter methylation profiles for distinct cell lines, ranging from hESC to various somatic samples, measured by HumanMethylation27 BeadChip (Illumina), which provides an efficient approach for surveying genome-wide DNA methylation profiles. The HumanMethylation27 panel targets CpG sites located within the proximal promoter regions of transcription start sites (TSS). Thus, it was suitable for our purpose. Promoter methylation profiles (GEO ID: GSE30653) also included data from both IMR90 and MRC5 cell lines, which were used to investigate relationships between promoter methylation and previously reported miRNA regulation and miRNA expression profile data^15,16. Promoter methylation profiles in both BG02 and BG03 were also included in this study, and were compared to miRNA regulation and miRNA expression profile data (see below).

Additional promoter methylation profiles in IMR90 cell lines were obtained from GEO ID: GSM868008¹⁴, GEO ID: GSM739940¹⁷, and GEO ID: GSM375442¹⁸. They were compared to IMR90 promoter methylation profiles, GEO ID: GSM760387 within GEO ID: GSE30653. I also used promoter methylation profiles for IMR90 from GEO ID: GSE31848, which includes GSM868008; these data were generated using the Illumina HumanMethylation450 BeadChip. This BeadChip allowed us to interrogate > 485000 methylation sites per sample at single-nucleotide resolution. In addition, because this array also includes CpG sites outside of promoter regions, I restricted probes to a subset labeled as either TSS200 or TSS1500. Data from GSM739940 includes IMR90 promoter methylation profiles measured by the Illumina HumanMethylation27 BeadChip. However, because these data were generated by a different research group than that of GSE30653, I tested profiles from this dataset to confirm that obtained results were not research group dependent. Finally, I also used methylation profile data from GSM375442, which was generated using next generation sequencing (NGS). CpG methylation profiles from promoter regions were extracted using Bismark Software (Ver. 0.7.4)¹⁹ (see below); promoter regions were defined as nucleotide positions between -200 and +1200 basepairs (bp) from the transcription start site (TSS).

mRNA and miRNA expression profiles

In order to compare miRNA-targeting-specific promoter methylation with target gene miRNA regulation and miRNA expression profile data from BG02 and BG03 cell lines, both miRNA and mRNA profiles were obtained from GEO ID: GSE14473²⁰. Gene (mRNA) expression profiles of undifferentiated and differentiated BG02 and BG03 cell lines were obtained from the GEO IDs GSM551204 and GSM551206, and GSM551216 and GSM551218, respectively. Corresponding miRNA expression profiles of these two cell lines were obtained from the GEO IDs GSM361147 and GSM361271 (BG02) and GSM361288 and GSM361289 (BG03). Raw data files were downloaded from the site for further analysis and were normalized so as to have a mean of 0 and a variance of 1.

Investigation of miRNA-targeting-specific promoter methylation

In order to infer miRNA-targeting-specific promoter methylation, I employed the MiRaGE method²¹ (see below). The MiRaGE method, which was implemented on a public domain MiRaGE server and Bioconductor MiRaGE package, was first used to infer miRNA regulation of target genes. This software was designed to infer the differential expression of miRNA target genes between two experimental conditions based on the expression profiles of the target genes in question; however, in this study, I used this method to infer miRNA-targeting-specific promoter methylation by substituting gene expression profiles with the promoter methylation profiles of each gene.

I first prepared a control dataset (pseudo) in which the expression level of all genes assigned a value of 1. Then, the amount of methylation at each gene was used as values of treatment data set. Although, usually, the ratio of number of methylated sites to the total number of methylated and non-methylated sites is used to describe promoter methylation levels, I employed a method in which total methylation values were used. I did this because I found that P-values computed when using methylation data were more strongly correlated to the P-values calculated from target gene miRNA regulation data (see below), which is likely due to the fact that the frequency of CpGs is also related to miRNA targeting¹²; i.e., genes with promoters that contain more CpGs were more often targeted by miRNAs as mentioned above. Using this procedure, I attributed two P-values to each miRNA, one expressing the degree of promoter hypermethylation, and the other expressing the degree of promoter hypomethylation. For this purpose, depending on the methodology and/or type of deposited data set, I computed P-values that describe promoter methylation as follows:

GSM868008

In this case, the promoter methylation profiles used to replace "gene expression" values within the MiRaGE method were

x_{control, i} = 1, (pseudo) control set

x_{treat, i} = exp (M_{0 i}), treatment set

where M_0i represents the scaled values of signal_B (intensity estimated of methylated DNA), which are expressed as the amount of promoter methylation of ith gene,

M_{0 i} \equiv \frac{M_{i} - 〈 M_{i} 〉}{σ M_{i}}

〈 M_{i} 〉 \equiv \frac{1}{N} \sum_{i} M_{i}

σ M_{i} \equiv \sqrt{\frac{1}{N} \sum_{i} {(M_{i} - 〈 M_{i} 〉)}^{2}},

where N is the total number of genes considered and M_i is the raw value of signal_B. This signifies that the amount of promoter methylation is scaled so as to have a mean 〈M_0i〉 of zero and standard deviation σ_{M_0i} of 1. exp is applied in this instance because I want to consider the amount of methylation rather than the ratio of the amount of promoter methylation. Because P-values are computed after the pair of input values are transformed to a logarithmic ratio, by substituting 1 in the control dataset and an exponential value in the treatment dataset based on raw values results in the usage of raw values as differential expression/promoter methylation (see below).

GSE30653

In this case, the promoter methylation profiles used to replace "gene expression" values within the MiRaGE method were

x_{control, i} = 1, (pseudo) control set

x_{treat, i} = M_{i} + C_{i}, treatment set

where C_i takes 1 only when M_i = 0; otherwise, it takes on 0, so as to avoid infinite values after transformation to the logarithmic ratio.

GSM739940

In this case, the promoter methylation profiles used to replace "gene expression" values within the MiRaGE method were

x_{control, i} = 1, (pseudo) control set

x_{treat, i} = exp (β_{i}), treatment set

where β_i is the ratio of methylated sites to unmethylated sites,

β_{i} \equiv \frac{M_{i} + C}{M_{i} + U_{i} + C},

where U_i is the signal from unmethylated sites (signal_A) and C is the regulation constant, which typically takes on the value of 100. Since only β values were deposited in the public datasets used, we could not avoid using them; however, the correlation with target gene miRNA regulation was substantially decreased. An explanation for this is noted above.

GSM375442

In this case, the promoter methylation profiles used to replace "gene expression" values within the MiRaGE method were

x_{control, i} = 1, (pseudo) control set

x_{treat, i} = exp (\frac{M_{i}}{max (M_{i})}), treatment set

where max(M_i) is the maximum value of M_i and M_i is computed in this case as follows:

M_{i} \equiv \sum_{j \in promoter of gene i} y_{i},

where y_j, 0 ≤ y_j ≤ 100 is the percentage of methylation at site j, which was computed by the Bismark Software¹⁹ (see below). The summation was taken over the length of the promoter region as defined above (i.e., between -200 bp and +1200 bp from the TSS).

Methylation computation by Bismark Software

Here are the command line inputs used for generating the methylation values of CpG sites within the Bismark Software package¹⁹.

% bismark_genome_preparation \

–path_to_bowtie bowtie_dir \

–verbose ./hg19/ &

% R

>x <- scan("GSM375442_CpgMIP-IMR90.seq.txt",

sep="\n",what=character(0))

>write.table(file="sequence.fa",

paste(paste(">p",1:length(x),sep=""),

x,sep="\n"),sep="\n",row.names=F,

quote=F,col.names=F)

>q()

% bismark ,/hg19/ \

–path_to_bowtie bowtie_dir \

–bowtie2 -f sequence.fa

% methylation_extractor -s –comprehensive \

sequence.fa_bt2_bismark.sam

% genome_methylation_bismark2bedGraph_v3.pl \

CpG_content_sequence.fa_bt2_bismark.sam.txt \

> sequence.fa_bt2_bismark.sam.bed

where bowtie_dir is the directory where bowtie2 is installed. We also assumed that R²² was installed but the part executed by R can be perfomed by any other alternative script languages. GSM375442_CpgMIP-IMR90.seq.txt is a file downloaded from GEO and sequence.fa_bt2_bismark.sam.bed includes methylation percentages of each CpG sites, y_j, which was explained above.

Inference of miRNA-targeting-specific promoter methylation/miRNA regulation of target genes by MiRaGE method

The inference of miRNA-targeting-specific promoter methylation/miRNA regulation of target genes was carried out using the MiRaGE method, which has been described previously²¹.

Although the MiRaGE method is typically used for datasets with two experimental conditions, each of which contains more than one replicate, I used this method for instances in which each condition consisted of only a single replicate. In each case, I based our analysis on the premise that for a given gene i, there were a pair of gene expression datasets or promoter methylation profile datasets, which were measured under a control condition x_control,i and a treatment condition x_treat,i. From this, I computed the logarithmic ratio

Δ x_{i} \equiv log (\frac{x_{treat, i}}{x_{control, i}})

In such an example, when a difference between raw values is favorable, exponential values exp(x_i) can be used instead of x_i, in which case I get

Δ x_{i} \equiv log {\frac{exp (x_{treat, i})}{exp (x_{control, i})}} = x_{treat, i} - x_{control, i}

When computing P-values that reject the null hypothesis by using the alternative hypothesis that Δx_is of the target genes of the miRNA m are less (greater) than those that are off-target but form a target of any other miRNAs, I compute

P_{m}^{< (>)} \equiv P [{Δ x_{i} | i \in G_{m}} < (>) {Δ x_{i} | i \in G_{m}^{'}}]

where P[A < (>)B] is P-values computed by statistical tests when two set A and B are compared. The tests implemented within the MiRaGE Server/package are the t-test, Wilcoxon rank sum test, and Kolmogorov-Smironov test.

Thus, judging A < (>)B depends upon the selected statistical test used. G_m represents the set of genes targeted by miRNA m and G’_m is the intersection of the set of off-target genes of miRNA m and the set of target genes of all other miRNAs. It should be noted that genes that were not the targets of any miRNAs were totally excluded from the analysis; however, all miRNAs were considered and no exclusions based on conservation were applied. When inferring promoter methylation, x_control,i = 1 and x_treat,i were used to represent the amount of promoter methylation. When inferring miRNA regulation of target genes during cell senescence in IMR90 and MRC5 cell lines, x_control,i was used to represent gene expression of young cell line and x_treat,i was used to represent gene expression in senescent cell lines. When inferring regulation of target genes during differentiation in BG02 and BG03 cell lines, x_control,i was used to represent gene expression in undifferentiated cell lines and x_treat,i for gene expression in differentiated cell lines.

Correlation between miRNA-targeting-specific promoter methylation and miRNA regulation of target genes

I have used two types of P-values, P^<(>)_m:methy, which corresponded to the miRNA-targeting-specific promoter methylation, and P^<(>)_m:regul which corresponded to the miRNA regulation of target genes of miRNA m. When P^<(>)_{m:methy[regul]} is small enough, the target genes of miRNA m are significantly hypermethylated or hypomethylated and thus downregulated or upregulated. In order to see if these two types of P-values were correlated, I computed various correlation coefficients:

ρ^{Pearson} \equiv ρ [P_{m : methy}, P_{m : regul}]

ρ_{log}^{Pearson} \equiv ρ [log (P_{m : methy}), log (P_{m : regul})]

ρ^{Spearman} \equiv ρ [rank (P_{m : methy}), rank (P_{m : regul})]

and accompanied P-values to reject null hypothesis that ρ = 0 by using the alternative hypothesis that ρ ≠ 0. ρ[a, b] is the Pearson’s correlation coefficients between a and b and rank(x_m) is the rank order of x_m among {x_m}. P_m:z where z ∈ {methyl, regul} is either P^<_m, P^>_m, 1 – P^<_m or 1 – P^>_m. Thus, there are 4 × 4 = 16 possible combinations of P_m:methy and P_m:regul. ρs of Kolmogorov-Smirnov test and ρ^Pearson_logs for all tests can change when P^<(>) is replaced with 1 – P^>(<) because P^<(>) ≠ 1 – P^>(<) for Kolmogorov-Smirnov test and log(P^<(>)) = log(1 – P^>(<)) for all tests. Thus, optimal combinations with the maximum absolute correlation coefficients were employed.

The reciprocal relationship between miRNA regulation of target genes and miRNA expression during differentiation in BG02 and BG03

In contrast to the cell senescence study¹⁵ in which miRNA expression was investigated by NGS, only microarray measurements were available for differentiation in BG02 and BG03 cell lines. Due to issues related to the accuracy and quality of microarray data and the relatively small amount of miRNA expression, very few miRNAs were found to be differentially expressed between undifferentiated and differentiated cell lines. Thus, miRNAs differentially expressed between undifferentiated and differentiated cell lines were selected based on two criteria before being subjected to further analyses;

Absolute differential expression |x_diff,i – x_undiff,i|> Δx_c, where x_diff,i and x_undiff,i are the normalized expression of gene i of differentiated and undifferentiated cell lines, respectively. Δx_c is the threshold value that can be used to select genes associated with significant differential expression during differentiation.
For this method, it was required that adjusted P-values based on the BH criterion²³ were less than 0.05 for significant upregulation or downregulation. Here, P-values were computed using a t test between two sets of probe values attributed to gene i.

After Δx_c was suitably selected, the correlation coefficient between differential expression x_diff,i – x_undiff,i and log (P^<_m:regul) were computed. Positive values were taken to indicate a reciprocal relationship between miRNA expression and miRNA regulation of target genes; smaller P^<_m:regul were assumed to signify that target genes were upregulated (and vice versa); thus, a reciprocal relationship required that miRNA should be downregulated, i.e., x_diff,i – x_undiff,i < (>)0, which should result in a positive correlation between x_diff,i – x_undiff,i and log (P^<_m:regul). Since miRNA names in GSE14473 were old (miRBase, release 9.1), miRNA names were converted to miRNA names used in the present version of the MiRaGE software package (miRBase release 18) and by the miRConverter implemented in miRSystem²⁴.

Ranking of miRNAs having target genes with miRNA-targeting-hypomethylated promoters

miRNAs were ranked based on P^>_m in each of 283 samples in GSE30653, after excluding 12 control samples. Each miRNA was ranked based within each sample, and then, the cumulative rank was determined for 271 samples.

Ranking of miRNA regulation of target genes

miRNAs were ranked based on P-values for either downregulation during cell senescence (IMR90 and MRC5) or upregulation during differentiation (BG02 and BG03) for each statistical test. Then, each miRNA was ranked based on order summed up over three statistical tests.

Results and discussion

Based on the inference from the data produced by the MiRaGE method²¹ (see Methods), the promoters of genes that are targets of 70–90% of human miRNAs were significantly hypomethylated, dependent on the statistical tests used, and the definition of promoter methylation levels: the β-value or the amount of methylation (see Table 1 and Additional files). This finding was consistent with conclusions made by Su et al.¹¹, who stated that miRNAs had a tendency to target genes with hypomethylated promoters. Given that promoter methylation patterns do not change drastically between certain cell types, the amount of miRNA-targeting-specific promoter methylation is also highly cell-type-independent. Mean correlation coefficients of P^>_m range between 0.8 and 0.9, again depending on the statistical test used and the definition of promoter methylation levels (Table 2).

Table 1. miRNA-targeting-specific promoter hypomethylation.

Amount of methylation
Statistical tests	Number of miRNAs
Statistical tests	Not significant	Significant
t-test	136054	384537	74%
Wilcoxon rank sum test	144572	376019	72%
Kolmogorov-Smirnov test	129998	390593	75%
β-values
Statistical tests	Number of genes
Statistical tests	Not significant	Significant
t-test	39390	481201	92%
Wilcoxon rank sum test	45188	475403	91%
Kolmogorov-Smirnov test	41952	478639	92%

The summation of the number of miRNAs that target genes with significantly hypomethylated promoters in each cell line. The total numbers of miRNAs and cell lines were 1921 and 271, respectively (the number of cell lines was out of 283 in GSE30653, excluding 12 control samples). Thus, the total number of combinations of an miRNA and a cell line is 1921 × 271 = 520591. Promoter methylation levels were defined as either the amount of methylation or the β-values. Observed P-values based on the BH criterion²³, P^>_m of < 0.05 were used to signify the promoters of the genes targeted by miRNAs that significantly hypomethylated. P-values were adjusted using the p.adjust function in R²². A complete list of raw (not adjusted) P-values is available in additional file 1. Cell line names can be also identified as column names of the additional file 1.

Table 2. Mean correlation coefficients between P-values of miRNA-targeting-specific promoter hypomethylation of samples.

Mean Pearson’s correlation coefficients of P^>_m between pairs of cell lines.

Statistical test	Averaged correlation coefficients
Amount of methylation
t-test	0.9112043
Wilcoxon rank sum test	0.8981256
Kolmogorov-Smirnov test	0.8263153
β-values
t-test	0.8919359
Wilcoxon rank sum test	0.8832241
Kolmogorov-Smirnov test	0.8257556

In order to see if miRNA-targeting-specific promoter methylation was genuinely related to miRNA regulation of target genes, I compared P-values of miRNA-targeting-specific promoter methylation to P-values of miRNA regulation of target genes during the senescence of IMR90 and MRC5 cell lines^15,16 and during the differentiation of BG02 and BG03 cell lines²⁰. It was clear that promoter methylation and miRNA regulation of target genes were significantly correlated during both cell senescence and differentiation (Table 3), despite the fact that correlation coefficients exhibited opposite directionalities in cell senescence and differentiation. This means that genes with miRNA-targeting-specific promoter hypomethylation are downregulated during cell senescence, but upregulated during differentiation.

Table 3. Correlation coefficients between P-values of miRNA-targeting-specific promoter hypomethylation and miRNA regulation of target genes.

Statistical test	ρ^Pearson	ρ^Pearson_log	ρ^Spearman
Cell senescence	P_{m: methy} = P^>_m vs. P_m:regul = P^>_m
IMR90
t test	0.33	0.40	0.41
P-values	*	*	*
Wilcoxon rank sum test	0.28	0.35	0.33
P-values	*	*	*
Kolmogorov-Smirnov test	0.31	0.72	0.61
P-values	*	*	*
MRC5
t test	0.22	0.23	0.25
P-values	*	*	*
Wilcoxon rank sum test	0.53	0.76	0.73
P-values	*	*	*
Kolmogorov-Smirnov test	0.38	0.82	0.67
P-values	*	*	*
Differentiation	P_{m: methy} = P^>_m vs. P_m:regul = 1 – P^<_m
BG02
t test	-0.25	-0.68	-0.67
P-values	*	*	*
Wilcoxon rank sum test	-0.13	-0.52	-0.53
P-values	2.4 × 10^-9	*	*
Kolmogorov-Smirnov test	-0.16	-0.70	-0.69
P-values	2.8 × 10^-12	*	*
BG03
t test	-0.31	-0.57	-0.53
P-values	*	*	*
Wilcoxon rank sum test	-0.14	-0.23	-0.23
P-values	8.9 × 10^-11	*	*
Kolmogorov-Smirnov test	-0.14	-0.21	-0.19
P-values	1.3 × 10^-10	*	*

Various correlation coefficients between P-values of promoter methylation and regulation of target genes in IMR90 and MRC5 during cell senescence and in BG02 and BG03 during differentiation; P-values associated with the correlation coefficients are indicated. P^>_ms were employed for cell senescence while P^>_ms of promoter methylation and 1 – P^>_m of regulation of target genes were employed for differentiation as shown, as these combinations exhibited the most significant correlations. Genes with miRNA-targeting-specific promoter hypomethylation were recognized to be downregulated during cell senescence based on positive correlations and upregulated during differentiation based on negative correlations (for details, see Methods).

Table 3 : Note

* Indicates < 2.2 × 10^-16.

To confirm that the significant correlation noted above between miRNA-targeting-specific promoter methylation and miRNA regulation of target genes was not a false-positive finding, I also tested for this association in IMR90 cell lines by using a different microarray and, NGS approach, as described by another group of researchers¹⁸ (Table 4). Again, I observed a significant correlation between promoter methylation status and miRNA regulation, suggesting that the relationship found between these two mechanisms was independent of experimental conditions.

Table 4. Evaluation of the correlation between miRNA-targeting-specific promoter methylation and miRNA regulation of target genes.

Statistical test	ρ^Pearson	ρ^Pearson_log	ρ^Spearman
GSM868008	P_m:methy = P^>_m vs. P_m:regul = P^>_m
t test	0.28	0.37	0.37
P-values	*	*	*
Wilcoxon rank sum test	0.18	0.32	0.28
P-values	4.4 × 10^-16	*	*
Kolmogorov-Smirnov test	0.16	0.70	0.54
P-values	1.9 × 10^-12	*	*
GSM739940	P_m:methy = P^>_m vs. P_m:regul = P^>_m
t test	0.11	0.40	0.34
P-values	3.0 × 10^-6	*	*
Wilcoxon rank sum test	0.07	0.33	0.27
P-values	2.5 × 10^-3	*	*
Kolmogorov-Smirnov test	0.07	0.79	0.51
P-values	2.7 × 10^-3	*	*
GSM375442	P_m:methy = P^>_m vs. P_m:regul = P^>_m
t test	0.19	0.17	0.26
P-values	3.0 × 10^-6	1.5 × 10^-14	*
Wilcoxon rank sum test	0.19	0.27	0.24
P-values	2.2 × 10^-16	*	*
Kolmogorov-Smirnov test	0.28	0.57	0.50
P-values	*	*	*

Correlation coefficients between miRNA-targeting-specific promoter methylation and miRNA regulation of target genes during cell senescence shown in Table 3 were evaluated by a comparison with the results produced by different array design (GSM868008), distinct research group, alternative measures of promoter methylation (GSM739940), and NGS (GSM375442). Independent of these factors, miRNA-targeting-specific promoter methylation was found to be always positively correlated to miRNA regulation of target genes (i.e., genes with miRNA-targeting-specific promoter hypomethylation were downregulated during cell senescence).

In addition to these analyses, I also confirmed a significant reciprocal relationship between miRNA expression and target gene expression. Because it is usually believed that miRNAs downregulate target gene expression, downregulated genes should be targeted by upregulated miRNAs and vice versa. In fact, the reciprocal relationship during the senescence process of IMR90 cells has been reported^15,16. In this study, I observed a similar reciprocal relationship during the differentiation of BG02 and BG03 cells²⁰ (Table 5).

Table 5. The reciprocal relationship between target gene regulation by miRNA and miRNA expression.

Cell lines	Δx_c	Correlation coefficients	P-value
BG02	0.00	0.18	1.9 × 10^-4
BG03	0.04	0.51	0.04

The reciprocal relationship between target gene regulation by miRNA and miRNA expression is evaluated in BG02 and BG03 cell lines during differentiation. Given that only a limited number of miRNAs are differentially expressed during differentiation, miRNAs were screened as described in the Methods section before computing correlation. Then the correlation coefficients between differential expression of miRNA and logarithmic value of P-values of miRNA regulation of target genes were computed A positive correlation implied the reciprocal relationship, that is, if the target genes are expressive, then, the miRNA itself should be suppressive. (for details, see Methods).

Thus, I conclude that miRNA-targeting-specific promoter methylation is not an artifact, but a genuine biological process. One may doubt this conclusion given the finding that miRNA-targeting-specific promoter methylation is cell-line-independent and miRNAs are believed to regulate target gene expression in a cell-line-dependent manner. Although this implies an apparent discrepancy, the correlation coefficients obtained, which were at most 0.7 to 0.8, indicate that miRNA-targeting-specific promoter methylation governs at most 50% to 60% of miRNA regulation of target genes. Thus, cell line specific miRNA regulation of target genes can act within the remaining 50% to 40%. In actuality, as can be seen in Table 3, the correlations between miRNA-targeting-specific promoter methylation and miRNA regulation of target genes have opposite signs dependent on the biological processes being considered. Therefore, the fact that cell-line-independent and miRNA-targeting-specific promoter methylation partially governs cell line dependent miRNA regulation of target genes is not a discrepancy.

In order to better understand the biological significance of our findings regarding miRNA-targeting-specific promoter methylation, I sought to identify the specific miRNAs that target genes with hypomethylated promoters. I found that most gene promoters targeted by miR-548 miRNAs were highly hypomethylated (Table 6; here, I define miRNAs called "miR-548" as miR-548 miRNAs). At present, little is known about miR-548 miRNAs, which likely results from the fact that miR-548 miRNAs are primate-specific miRNAs (ps-miRNAs). Because of this, ps-miRNAs were not expected to play critical roles in fundamental biological processes like cell differentiation and development, and it has so far been widely assumed that such basic biological processes are conserved among mammals. This assumption has resulted in the exclusion of ps-miRNAs from consideration as candidates for basic and important biological processes. Thus, it is thought that the roles of these ps-miRNAs may be limited to primate-specific biological processes. Another reason that miR-548 miRNAs have not been extensively investigated is that they are generally expressed at low levels; miRNAs that play critical roles are thought to be highly expressed. For example, although miRBase stores the amount of short reads detected by next generation sequencing (NGS) for various miRNAs, miR-548 miRNAs have at most 100 short reads; this is in comparison to more abundant miRNAs like those from the let-7 family, which are typically represented by several millions reads. This makes it difficult to detect miR-548 miRNAs using microarray technology or sequencing. Actually, although there have been a few reports noting that miR-548 family members, including miR-548 miRNAs, play critical roles, these data come exclusively from quantitative PCR (qPCR) experiments^25,26.

Table 6. Evaluation of miR-548 miRNAs targeting genes with miRNA-targeting-specific promoter hypomethylation.

Statistical Test	Number of miR-548 miRNAs		Mean rank
Statistical Test	Within top 100 ranked	Total number	Mean rank
t test	43	68	216.2
Wilcoxon rank sum test	433	68	214.0
Kolmogorov-Smirnov test	433	68	215.0

68 miR-548 miRNAs were ranked as described in the Methods (promoter methylation was defined as the amount of methylation). Independent of the statistical methods used for computation of P^>_m, 43 out of 68 miR-548 miRNAs were ranked within the top-ranked 100 miRNAs. The mean rank of all miR-548 miRNAs is ~200 independent of the statistical method used. Given that the total number of miRNAs considered was 1921, these values are highly significant, P = 2 × 10^-35.

In this study, here I found that promoters of genes targeted by miR-548 miRNAs were hypomethylated. Because it is generally assumed that genes with hypomethylated promoters are expressed, the fact that promoters of target genes of miR-548 miRNAs are hypomethylated may mean that, compared to genes that are targets of other miRNAs, target genes of miR-548 miRNAs are more sensitive to changes in miRNA expression. This could explain why promoters of target genes of non-expressed miR-548 mIRNAs are specifically hypomethylated.

miR-548 miRNAs represent a large set of miRNAs, with 68 family members; thus, although these miRNAs exhibit low levels of expression, the fact that there are many family members may imply that they have important biological roles. Indeed, it has been reported that potential target genes of miR-548 miRNAs play critical roles in various biological processes^27,28. Since it is also suggested that they originate from transposable elements (TEs), miR-548 miRNAs exhibit high sequence similarities while maintaining significant sequence diversity²⁷.

During senescence processes of IMR90 and MRC5 cell lines, the target genes of miR-548 miRNAs were significantly downregulated (Table 7), whereas during differentiation of the BG02 cell line, the target genes of miR-548 miRNAs were significantly upregulated (Table 7). The fact that I observed significant changes in expression of target genes without significant changes in expression of miRNAs that target these genes implicates a role miRNA-targeting-specific promoter hypomethylation.

Table 7. Ranking of miRNA regulation of target genes.

Cell lines	Target genes	Number of miR-548 miRNAs		Mean rank	P-values
Cell lines	Target genes	Within top 100 ranked	Total number	Mean rank	P-values
IMR90	downregulated	24	68	438.2	9 × 10^-14
MRC5	downregulated	16	68	382.6	5 × 10^-7
BG02	upregulated	24	68	427.8	9 × 10^-14
BG03	upregulated	3	68	762.4	7 × 10^-1

miRNAs were ranked as described in the Methods. Target genes of miR-548 miRNAs were significantly downregulated during cell senescence (IMR90 and MRC5 cell lines) and upregulrated during differentiation (BG02). Insignificant upregulation of target genes of miR-548 miRNAs in the BG03 cell line was weakly correlated to miRNA expression and miRNA regulation of target genes (Table 5). Given that the total number of miRNAs considered was 1921, the values for IMR90, MRC5, and BG02 cell lines are highly significant.

Conclusions

Figure 1 schematically summarises the principal findings of this study. To the best of my knowledge, this is the first time that miRNA-targeting-specific promoter methylation in many types of cell lines have been observed (Table 1 and Table 2). miRNA-targeting-specific hypomethylation was correlated (Table 3 and Table 4) with regulation of miRNA target genes, which had a reciprocal relationship with miRNA expression (Table 5); genes with miRNA-targeting-specific hypomethylated promoters were downregulated during cell senescence and upregulated during differentiation. I also found that genes with miRNA-targeting-specific hypomethylated promoters were specific targets of miR-548 miRNAs (Table 6 and Table 7). Regulation of target genes by miR-548 miRNAs, which were typically expressed at low levels are likely the result of miRNA-targeting-specific promoter hypomethylation.

Figure 1. Schematic representing the principal findings of the present study together as well as those reported previously.

Promoters are methylated in a miRNA-targeting-specific manner (Table 1 and Table 2). Genes with miRNA-targeting-specific hypomethylated promoters are downregulated during cell senescence and upregulated during cellular differentiation (Table 3 and Table 4), whereas miRNAs that target genes with hypomethylated promoters were upregulated during cell senescence^17,18 and downregulated during differentiation (Table 5). miR-548 miRNAs, which are expressed at low levels, were suggested to regulate target genes with the assistance of miRNA-targeting-specific promoter hypomethylation (Table 6 and Table 7).

List of abbreviations

TSS: transcription start sites;

GEO: gene expression omnibus;

bp: basepair;

miRNA: microRNA;

ps-miRNA: primate-specific miRNAs;

TE: transposable elements;

NGS: next generation sequencing.

Competing interests

No relevant competing interests disclosed.

Author’s contributions

Conceived and designed the experiments: YHT. Analyzed the data: YHT. Wrote the paper: YHT.

Acknowledgements

The author thanks the anonymous reviewer¹⁶ who suggested the investigation of the promoter methylation of miRNA target genes. This study was supported by KAKENHI (23300357).

Additional files

Additional file 1 – Full list of P-values from the analysis of miRNA-targeting-specific promoter hypomethylation: t test

t test.xlsx: List of P-values from analysis of promoter hypomethylation of miRNA target genes calculated using the t test based on the amount of methylation.

Additional file 2 – Full list of P-values from the analysis of miRNA-targeting-specific promoter hypomethylation: Wilcoxon rank sum test

Wilcoxon rank sum test.xlsx: List of P-values from the analysis of promoter hypomethylation of miRNA target genes calculated using the Wilcoxon rank sum test based on the amount of methylation.

Additional file 3 – Full list of P-values from the analysis of miRNA-targeting-specific promoter hypomethylation: Kolmogorov-Smirnov test

Kolmogorov Smirnov test.xlsx: List of P-values from the analysis of promoter hypomethylation of miRNA target genes calculated using the Kolmogorov-Smirnov test based on the amount of methylation.

Additional file 4 – Full list of P-values from the analysis of miRNA-targeting-specific promoter hypomethylation: t test

beta t test.xlsx: List of P-values from the analysis of promoter hypomethylation of miRNA target genes calculated using the t test based on β-values.

Additional file 5 – Full list of P-values from the analysis of miRNA-targeting-specific promoter hypomethylation: Wilcoxon rank sum test

beta Wilcoxon rank sum test.xlsx: List of P-values from the analysis of promoter hypomethylation of miRNA target genes calculated using the Wilcoxon rank sum test based on β-values.

Additional file 6 – Full list of P-values from the analysis of miRNA-targeting-specific promoter hypomethylation: Kolmogorov-Smirnov test

beta Kolmgorov Smirnov test.xlsx: List of P-values from the analysis of promoter hypomethylation of miRNA target genes calculated using the Kolmogorov-Smirnov test based on β-values.

References

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8. Berardi E, Pues M, Thorrez L, et al:microRNAs in ES Cell Differentiation. Am J Physiol Heart Circ Physiol. 2012303(8): H931–9 in press.
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13. Saito T, Sffitrom P: Target gene expression levels and competition between transfected and endogenous microRNAs are strong confounding factors in microRNA high-throughput experiments. Silence 2012, 3: 3.
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19. Krueger F, Andrews SR: Bismark: a flexible aligner and methylation caller for Bisulfite-Seq applications. Bioinformatics 2011, 27(11): 1571–1572.
20. Stadler B, Ivanovska I, Mehta K, et al:Characterization of microRNAs involved in embryonic stem cell states. Stem Cells Dev. 2010, 19(7): 935–950.
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Department of Physics, Chuo University, Tokyo, Japan

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© 2013 Taguchi Yh. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Data associated with the article are available under the terms of the Creative Commons Zero "No rights reserved" data waiver (CC0 1.0 Public domain dedication).

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Taguchi Yh. Correlation between miRNA-targeting-specific promotermethylation and miRNA regulation of target genes [version 1; peer review: 1 approved, 1 approved with reservations]. F1000Research 2013, 2:21 (https://doi.org/10.12688/f1000research.2-21.v1)

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Reviewer Report 06 Feb 2013

Denis Dupuy, Institut Europeen de Chimie et Biologie, Pessac, France

Approved with Reservations

https://doi.org/10.5256/f1000research.1183.r751

I find that the often-used term of ‘miRNA-targeting-specific promoter methylation’ (sic) should be replaced by a less confusing expression, e.g. ‘miRNA-targeted-gene promoter methylation’, as ‘targeting’ and ‘specific’ are redundant and confuse the point. Specifically, the grammatical construction would seem to indicate that there are hypomethylated promoter specific targeting miRNAs (title of a subsection) which doesn't seem to be what the author means. Otherwise, the term ‘specific’ could also lead to believe that there is a dedicated mechanism for demethylation of these subset of (miRNA targeted) promoters that would be distinct from other methylation pathways. As there is no way to know if it is the case or whether there is a unique activity biased toward genes submitted to post transcriptional regulation.

Moreover, the use of both: ‘miRNA-targeting-specific promoter hypomethylation’ and ‘miRNA-targeting-specific promoter methylation’ adds to the confusion as these should indicate opposite trends or mechanisms.

The author states that; ‘[MiRaGE] was designed to infer the differential expression of miRNA target genes between two experimental conditions based on the expression profiles of the target genes in question’, this is not exactly right: as stated on the MiRaGE page, ‘MiRaGE method is the method used to infer gene expression regulation via miRNA based upon target gene expression.’ Thus, it doesn't infer the expression of target genes! It infers the contribution of miRNA to the measured expression levels. So by replacing the expression levels by the methylation status of the promoter the author is trying to uncover a link between miRNA expression and the methylation of their target genes.

I feel that a much needed control would be required to see how the correlation between methylation status and miRNA target gene expression differs from the correlation between non-targeted genes with similar expression levels.

Another potentially damning issue is raised in Table 3: ‘It was clear that promoter methylation and miRNA regulation of target genes were significantly correlated during both cell senescence and differentiation’. Another way to interpret this would be that the expression level of those genes is correlated with the methylation level of their promoter (which is well reported in the literature), this could be considered an alternative explanation to the modification of expression levels observed in those genes (i.e. not through miRNA regulation but through chromatin remodeling). This should warrant the use of a control set of non-miRNA targeted genes to check if the promoter methylation is truly ‘specific’ of miRNA targeting.

More troubling is this statement: ‘correlation coefficients exhibited opposite directionalities in cell senescence and differentiation. This means that genes with miRNA-targeting-specific promoter hypomethylation are downregulated during cell senescence, but upregulated during differentiation’. It seems that there should be a more in depth discrimination between genes that are up- or downregulated in different conditions. Is the point of this paper to indicate that miRNA downregulation is associated with hypomethylation of the promoter? If so, how do you interpret an inverted correlation in different conditions in this framework? It would seem that the expression of the ‘miRNA target genes’ is more correlated to the status of their promoter than to the presence of the miRNA.

The author states that this inverse correlation ‘is not a discrepancy’ but it is actually highlighting the discrepancy that has to be explained rather than brushed off.

Similarly the following statement by the author doesn't seem to be so obvious to me:

‘0.7 to 0.8, indicate that miRNA-targeting-specific promoter methylation governs at most 50% to 60% of miRNA regulation of target genes. Thus, cell line specific miRNA regulation of target genes can act within the remaining 50% to 40%.’ An alternate explanation could be that the methylation doesn't govern the expression status of the genes but reflects it partially (i.e. a degraded messenger could trigger an unknown pathway that would lead the modification of the promoter methylation and stabilize the downregulation at the genomic level.

A similar logical leap is made further down in the text: ‘The fact that I observed significant changes in expression of target genes without significant changes in expression of miRNAs that target these genes implicates a role miRNA-targeting-specific promoter hypomethylation.’ It could also mean that these genes are regulated transcriptionally without the intervention of miR-548 miRNAs, there are probably other gene groups that are differently expressed in these cell lines.

Competing Interests: No competing interests were disclosed.

I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above.

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Reviewer Report 04 Feb 2013

Wenqian Hu, Center for RNA Molecular Biology, Whitehead Institute for Biomedical Research, Cambridge, MA, USA

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https://doi.org/10.5256/f1000research.1183.r748

This paper reports interesting correlations between the regulation of genes by miRNAs and the methylation status of the miRNA target genes. Although the two processes occur in two different compartments of the cell, these correlations suggest that there may be ... Continue reading

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Wenqian Hu, Whitehead Institute for Biomedical Research, Cambridge, MA, USA
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Pål Sætrom, Norwegian University of Science and Technology, Trondheim, Norway

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Pål Sætrom, Department of Cancer Research and Molecular Medicine, Norwegian University of Science and Technology, Trondheim, Norway

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Although this article’s topic is interesting, I found that its contents were confusing and inaccessible. I also have some reservations regarding the methods.

The abstract is misleading.

The author states in the abstract: “It was found that promoter methylation was affected by miRNA targeting.“ I find this statement very misleading, as the author has not shown that miRNA targeting has an effect on promoter methylation. What the authors’ results do suggest is that the promoters of genes that are predicted to be miRNA targets tend to be hypomethylated compared to promoters of genes predicted not to be miRNA targets. Although this correlation is an interesting observation in itself (also previously made by Su et al., 2011), the author provides no compelling evidence for miRNA targeting and regulation being the cause of (or even influence) the genes’ reduced promoter methylation. Specifically, the author neither demonstrates that loss (or increase) of miRNA regulation in general nor that loss (or increase) of regulation by specific miRNAs affects promoter methylation status. Instead, the author’s results indicate that predicted miRNA regulation apparently can have both a positive and negative correlation with promoter methylation status (Table 5 and Table 7). Consequently, the author’s current results only support that predicted miRNA targets tend to have hypomethylated promoters in the specific cell lines used in the author’s study.

There are no figures to illustrate analyses, show data trends, and visualize correlations.

The author consistently presents his results as correlation coefficients and p-values. These values are important, but also hide important aspects of the data, such as the magnitude of a significant difference and the trend of or the number of observations contributing to a significant correlation. To make his results more accessible, the author should at least create a figure that compares the distributions of promoter methylation for miRNA target and non-target genes (Table 3). Moreover, some of the author’s analyses, such as the correlations of p-values in Tables 5-6, are fairly complex. A figure that shows the underlying data and illustrates the different steps in the analyses would make the results more accessible.

The analyses apparently ignore miRNA expression.

Presumably, a miRNA has to be expressed to have a role in gene regulation – whether that role is post-transcriptional or otherwise. It is unclear, however, whether the author takes miRNA expression into account in his analyses. For example, in Table 1, the author has apparently included all 1921 mature miRNAs from miRBase 18.0 in his analyses, but I doubt that all of these are expressed in all the 271 cell lines. One reason for including all miRNAs may be that the author has no data on which miRNAs are expressed in the different cell lines. Nevertheless, as many of the miRNAs will likely not be expressed in a given cell line, a large fraction of the 520,591 combinations tested appear irrelevant. Similarly, for the analyses presented in Table 5, where the author does have data on the miRNAs that are expressed, the author provides no information on the miRNAs actually used in the analysis. Finally, in the last result section, the author specifically studies the miR-548 family, which has very low expression levels in the cells where it has been detected. The author seems to suggest that the predicted targets of the miR-548 family are especially likely to be hypo-methylated and concludes that “Regulation of target genes by miR-548 miRNAs, which were typically expressed at low levels are likely the result of miRNA-targeted-gene promoter hypomethylation.” The section also contains several confusing statements such as “Because […] genes with hypomethylated promoters are expressed, the fact that promoters of target genes of miR-548 miRNAs are hypomethylated may mean that, compared to genes that are targets of other miRNAs, target genes of miR-548 miRNAs are more sensitive to changes in miRNA expression. This could explain why promoters of target genes of non-expressed miR-548 mIRNAs are specifically hypomethylated.“ If, for arguments sake, miRNAs really affect promoter methylation, I still fail to understand why a lowly expressed miRNA such as miR-548 should have a stronger effect than other more highly expressed miRNAs, as the author seem to suggest. Instead, I believe that the miR-548 family appear to be significantly associated with hypomethylated promoters compared with other miRNAs because of a bias in the author’s analysis. Specifically, many of the members of the miR-548 family have identical seed sequences and will therefore be predicted to target the same genes (the number of unique 7mer seeds in the miR-548 family in miRBase 18.0 is 33), but the author does not seem to correct for this identical targeting in his ranking analyses (Table 9 and 10). Overall, the author should take into account both miRNA expression and similar or identical miRNA targeting in his analyses.

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No competing interests were disclosed.

I confirm that I have read this submission and believe that I have an appropriate level of expertise to state that I do not consider it to be of an acceptable scientific standard, for reasons outlined above.

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09 Sep 2013

Y-h. Taguchi, Department of Physics, Chuo University, Tokyo, Japan

Thank you very much for your efforts to review my article. In the following, I would like to reply to your valuable comments point by point.

- Although this article’s topic is interesting, I found that its contents were confusing and inaccessible. I also have some reservations regarding the methods. The abstract is misleading. The author states in the abstract: “It was found that promoter methylation was affected by miRNA targeting.“ I find this statement very misleading, as the author has not shown that miRNA targeting has an effect on promoter methylation.

I have modified the abstract to emphasize that what I found was not causalities but solely correlations.

- What the authors’ results do suggest is that the promoters of genes that are predicted to be miRNA targets tend to be hypomethylated compared to promoters of genes predicted not to be miRNA targets. Although this correlation is an interesting observation in itself (also previously made by Su et al., 2011), the author provides no compelling evidence for miRNA targeting and regulation being the cause of (or even influence) the genes’ reduced promoter methylation. Specifically, the author neither demonstrates that loss (or increase) of miRNA regulation in general nor that loss (or increase) of regulation by specific miRNAs affects promoter methylation status. Instead, the author’s results indicate that predicted miRNA regulation apparently can have both a positive and negative correlation with promoter methylation status (Table 5 and Table 7). Consequently, the author’s current results only support that predicted miRNA targets tend to have hypomethylated promoters in the specific cell lines used in the author’s study.

I have recently published a paper that demonstrates miRNA-targeted-promoter methylation can be found also in human brain [PMID: 23725297]. Thus, I believe that miRNA-targeted-promoter methylation is observed not only in the specific cell lines, but also in other biological situations.

- There are no figures to illustrate analyses, show data trends, and visualize correlations.

I have added additional file 8 that shows the scatter plots corresponding to Tables 5 and 6.

- The author consistently presents his results as correlation coefficients and p-values. These values are important, but also hide important aspects of the data, such as the magnitude of a significant difference and the trend of or the number of observations contributing to a significant correlation. To make his results more accessible, the author should at least create a figure that compares the distributions of promoter methylation for miRNA target and non-target genes (Table 3).

I have added additional file 8 in order to fulfill the reviewer's requirements.

- Moreover, some of the author’s analyses, such as the correlations of p-values in Tables 5-6, are fairly complex. A figure that shows the underlying data and illustrates the different steps in the analyses would make the results more accessible.

I have added Fig. 1 in order to fulfill the reviewer's requirements.

- The analyses apparently ignore miRNA expression. Presumably, a miRNA has to be expressed to have a role in gene regulation – whether that role is post-transcriptional or otherwise. It is unclear, however, whether the author takes miRNA expression into account in his analyses. For example, in Table 1, the author has apparently included all 1921 mature miRNAs from miRBase 18.0 in his analyses, but I doubt that all of these are expressed in all the 271 cell lines. One reason for including all miRNAs may be that the author has no data on which miRNAs are expressed in the different cell lines. Nevertheless, as many of the miRNAs will likely not be expressed in a given cell line, a large fraction of the 520,591 combinations tested appear irrelevant. Similarly, for the analyses presented in Table 5, where the author does have data on the miRNAs that are expressed, the author provides no information on the miRNAs actually used in the analysis.

In Tables 1 and 5, I have considered all miRNAs in miRBase whether they are expressed or not. I think that the amount of miRNA expression is not always a primary factor with regards to the control of miRNA regulation for the target genes. References 36 and 37 in the article support this point. In addition to this, instead of the miRNA expression, I demonstrated that miRNA-targeted-gene promoter methylation is related to seed region features (see Fig. 2, additional file 9, and section “miRNA-targeted-genes promoter methylation is correlated to miRNA seed region features”). Although I do not intend to insist that miRNA seed region features can control miRNA-targeted-gene promoter methylation, I think that the amount of miRNA expression does not always have to be the only factor which controls the miRNA-regulation of target genes.

- Finally, in the last result section, the author specifically studies the miR-548 family, which has very low expression levels in the cells where it has been detected. The author seems to suggest that the predicted targets of the miR-548 family are especially likely to be hypo-methylated and concludes that “Regulation of target genes by miR-548 miRNAs, which were typically expressed at low levels are likely the result of miRNA-targeted-gene promoter hypomethylation.” The section also contains several confusing statements such as “Because […] genes with hypomethylated promoters are expressed, the fact that promoters of target genes of miR-548 miRNAs are hypomethylated may mean that, compared to genes that are targets of other miRNAs, target genes of miR-548 miRNAs are more sensitive to changes in miRNA expression. This could explain why promoters of target genes of non-expressed miR-548 mIRNAs are specifically hypomethylated.“ If, for arguments sake, miRNAs really affect promoter methylation, I still fail to understand why a lowly expressed miRNA such as miR-548 should have a stronger effect than other more highly expressed miRNAs, as the author seem to suggest. Instead, I believe that the miR-548 family appear to be significantly associated with hypomethylated promoters compared with other miRNAs because of a bias in the author’s analysis.

As mentioned above, I do not think that the amount of miRNA expression is always the only factor which controls miRNA-regulation of target genes. Apparently, miR-548 target genes have more distinct seed region features than other miRNAs. I think that this may be the reason why low amounts of miR-548 expression can be related to miRNA-targeted-gene promoter methylation (see Fig. 3, Table 11, and section “miRNA-targeted-genes promoter methylation is correlated to miRNA seed region features”).

- Specifically, many of the members of the miR-548 family have identical seed sequences and will therefore be predicted to target the same genes (the number of unique 7mer seeds in the miR-548 family in miRBase 18.0 is 33), but the author does not seem to correct for this identical targeting in his ranking analyses (Table 9 and 10). Overall, the author should take into account both miRNA expression and similar or identical miRNA targeting in his analyses.

I do not think that merging seed-sharing-miRNAs into one is always the only correct way to predict miRNA regulation of target genes. Please see the newly added section “Treatment of shared miRNA seed sequences” and Appendix. In addition to this, my MiRaGE method - which does not merge seed-sharing-miRNAs into one - was applied to many biological processes, and I found that it works reasonably well (see References 15, 16, and 21 from the article).

I would like to thank the reviewer, whose comments have greatly improved the quality of this paper. I would be glad if the present version is approved by the reviewer.

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Reviewer Report

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14 May 2013 | for Version 2

Denis Dupuy, Institut Europeen de Chimie et Biologie, Pessac, France

53 Views Cite this report Responses(1)

Not Approved

I had initially understood that the title of this study could be: "Correlation between promoter methylation and miRNA regulation of target genes." Which in simpler terms could mean that miRNA targeted genes tend to have more hypomethylated promoters (or hypermethylated maybe) than expected by chance.

In the current form it is still unclear to me how the data actually support this conclusion.

The lack of illustration makes the article hard to follow, the author present the results of statistical tests without providing a clear description of what effect he is actually evaluating. And on further reading it seems that the objective of this study is largely incomprehensible to me.

The first question comes in the datset compilation: how was it done and what were the criteria used to accept or reject data used in the study?

A figure representing the process of dataset generation would be welcome.

The author states: “I tested profiles from this dataset to confirm that obtained results were not research group dependent.” How was this done? It is well known that biological experiments on a genome scale are inherently noisy so results even within the same group will not be the exact replicate of one another. What was considered acceptable to create a dataset compendium? Was any normalization applied? Was there an a posteriori check that the independent sets were behaving in the same way as the compendium? If not, would the same conclusion have been obtained by analyzing them separately? Is only part of the data driving the conclusion and can it be explained by the different cell lines?

Elsewhere, the author states “It should be noted that genes that were not the targets of any miRNAs were totally excluded from the analysis” which makes me think that I totally missed the initial point of this work as I thought the objective was to test the hypothesis that promoter methylation status was related to miRNA targeting of the produced messenger. In this, genes that are not targets of miRNA would be useful as a control group.

Even if the author is trying to demonstrate a relationship between the amount of miRNA regulation and methylation, non-target genes would be an essential control.

The second question arises from the choice of the values used for methylation. The author states:
“I employed a method in which total methylation values were used(..) because I found that P-values computed when using methylation data were more strongly correlated to the P-values calculated from target gene miRNA regulation data, which is likely due to the fact that the frequency of CpGs is also related to miRNA targeting”

and later:

“βi was the ratio of methylated sites to unmethylated sites, (...) were deposited in the public datasets used, their use could not be avoided; however, as a result, the correlation with target gene miRNA regulation was substantially decreased. An explanation for this is noted above.”

“Obtaining a better P-value”, is not an acceptable rationale for changing the way the methylation of a promoter is “typically represented”.

The reason why the association between CpG island associated genes and miRNA targeting explains the difference in P-values between using total methylation values and proportion of methylated sites, is not obvious. It hints at a different behavior between CpG and non CpG promoters, would it not then make sense to process them separately?

It seems that the two ways of representing the data mean very different things and that decision could greatly affect the conclusions that can be extracted from the results. If I understand correctly, with the absolute number method a promoter with 2/3 methylated sites would be counted the same as a 2/10 yet in one case one could consider the first to be 66% methylated and the other 20%. The implication of this choice on the results of the study should be discussed more thoroughly.

In table 1, if the top part is indeed calculated with ratio and bottom with absolute, what were the thresholds to be counted as hypomethylated in each case?

What does the “number of miRNA” actually represent? The author indicates that he multiplied the number of miRNA (1971) by the number of cell lines included (271). What does this product represent in the end? Why were the datapoints not aggregated by promoter and/or miRNA in the context of this study?

Table 3 for example is symptomatic of how the lack of illustration makes the article hard to follow: “P-values that represent the difference of promoter methylation between miRNA target genes and miRNA non-target genes.” The P-value doesn't represent a “difference in promoter methylation”, it evaluates the likelihood of the observation happening by chance given the dataset distribution. But what is the observation here? Are promoters of genes targeted by miRNA 60% methylated vs 58% for the non targeted? (is it 10 vs 87?)

Are we considering a very slight effect with a very strong statistical significance or a very obvious strong effect for which the statistical test is not even needed to be convinced of the solidity of the result?

Moreover, just below that table the author explains why he doesn't discriminate between hypo and hypermethylation of promoters. That makes table 3 very puzzling; is this P-value evaluating the likelihood of miRNA targeted gene promoters to be significantly BOTH over and/or under methylated relative to non-targeted genes?

Table 4 has been added as a response to my previous comment however it seems to bring further question as to what “miRNA-targeted-gene promoter methylation” really means. My first comment is that this control should not constitute a separate table but an additional entry in the original table. Secondly, there are many ways to generate random shuffling of the data that will destroy the “signal”, however for a random shuffling to be a valid control of the biological significance of the observation, the randomization should be performed in a way that preserves at least part of the actual data structure. For example, if one works on a scale free distribution and discovers biological associations within this distribution, one would not generate a shuffled set as control that would have a gaussian distribution but instead would have to preserve the scale-free distribution. In this case, no details are provided regarding what steps have been taken to ensure that the randomization preserved any of the original data structure.

It is hard to believe what part of the data structure is conserved in this randomization when in the actual data between 72% to 92% of the >500,000 items studied are found to be significant (Table 1) and only 3 to 6 /500,000 have that characteristic in Table 4.

Similarly, in Table 5 “Correlation coefficients between P-values(..)” it is unclear what the numbers actually represent. I am familiar with measuring the P-value of a correlation coefficient through multiple randomly shuffled distributions to insure the significance of the observed correlation. But it is unclear to me what a correlation between P-values actually represents since they could well be associated with opposite effects.

Finally, Figure 1 that should bear the take home message doesn't provide a better idea of what the conclusions of the paper are: the top part shows two genes targeted by different numbers of miRNA having different levels of methylation. From this it seems that the correlation found is actually between the amount of regulatory miRNA and hypomethylation, but how this was derived from table 1 and 2 is totally unclear. What happens in the boxed part of the figure is even more confusing as it shows a young cell, with 3 (expressed?) genes, becoming a senescent cell, expressing only one with methylation unchanged even though the amount of mir-548 targeting is represented higher.

Meanwhile, the opposite is the case for the differentiation scheme. All the represented promoters (?) in the box seem to be hypomethylated regardless of the number of miRNA targeting the corresponding mRNA in total opposition to what the study seems to claim.

I think overall the methodology and lack of illustrations obfuscate the point for readers. Non-specialists and specialists alike will find it hard to follow the author’s reasoning for want of clarity in the methods used. A scientific paper should strive to present the data in a way that can convince experts of the field and also explain to non specialists how the observed results lead to the presented conclusions. I feel that on both accounts this manuscript is not reaching these goals.

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Author Response

16 May 2013

Y-h. Taguchi, Department of Physics, Chuo University, Tokyo, Japan

Dear Denis,

Thank you very much for your efforts to review my paper. Here I respond to you point by point. If these responses resolve disagreements between us and then if you can approve my manuscript, I will be happy. Alternatively, you may suggest how to revise my manuscript such that you can approve it.

>I had initially understood that the title of this study could be: "Correlation between promoter methylation and miRNA regulation of target genes." Which in simpler terms could mean that miRNA targeted genes tend to have more hypomethylated promoters (or hypermethylated maybe) than expected by chance. >In the current form it is still unclear to me how the data actually support this conclusion.

Unfortunately, I did not intend to conclude that “miRNA targeted genes tend to have more hypomethylated promoters (or hypermethylated maybe) than expected by chance”, but tried to conclude that “a set of genes targeted by a miRNA A is differently hypo/hypermethylated than a set of genes not targeted by the miRNA A but targeted by any other miRNAs”. These two are very different.

>The lack of illustration makes the article hard to follow, the author present the results of statistical tests without providing a clear description of what effect he is actually evaluating. And on further reading it seems that the objective of this study is largely incomprehensible to me.

What I have evaluated is, as mentioned in the main text;
1.) “a set of genes targeted by a miRNA A is differently hypo/hypermethylated than a set of genes not targeted by the miRNA A but targeted by any other miRNAs”
2.) “the correlation of P-values between miRNA-targeting-promoter methylation (in the sense denoted in 1.) and miRNA-mediated regulation of target genes”. I also defined these two using equations, more exactly. I would be glad if you can mention in which point you are not sure.

>The first question comes in the dataset compilation: how was it done and what were the criteria used to accept or reject data used in the study? >A figure representing the process of dataset generation would be welcome.

As I have mentioned in the Methods section (mRNA and miRNA expression profiles), what I have done is to just make mean and variance, zero and one, respectively. I have never done any other treatments. Thus, I assumed that no detailed explanations were required. If I was not correct, please advise me about how to to prepare suitable figures.

>The author states: “I tested profiles from this dataset to confirm that obtained results were not research group dependent.” How was this done? It is well known that biological experiments on a genome scale are inherently noisy so results even within the same group will not be the exact replicate of one another. What was considered acceptable to create a dataset compendium? Was any normalization applied? Was there an a posteriori check that the independent sets were behaving in the same way as the compendium? If not, would the same conclusion have been obtained by analyzing them separately? Is only part of the data driving the conclusion and can it be explained by the different cell lines?

I have compared one experiment with another experiment, only using the correlation coefficients of P-values. Almost all analyses were performed in P-value based. As you pointed out, raw values like gene expression and promoter methylation are hard to compare between different experiments. All success in this study is achieved by the employment of P-value based approaches.

>Elsewhere, the author states “It should be noted that genes that were not the targets of any miRNAs were totally excluded from the analysis” which makes me think that I totally missed the initial point of this work as I thought the objective was to test the hypothesis that promoter methylation status was related to miRNA targeting of the produced messenger. In this, genes that are not targets of miRNA would be useful as a control group.
>Even if the author is trying to demonstrate a relationship between the amount of miRNA regulation and methylation, non-target genes would be an essential control.

As I have mentioned above, the purpose of this study was to compare promoter methylation between genes targeted by different miRNAs. Thus, non-target genes cannot be any controls in this context. However, since you require me to clearly point this out, I show this in Table 3.

>The second question arises from the choice of the values used for methylation. The author states: “I employed a method in which total methylation values were used(..) because I found that P-values computed when using methylation data were more strongly correlated to the P-values calculated from target gene miRNA regulation data, which is likely due to the fact that the frequency of CpGs is also related to miRNA targeting”
>and later:
>“βi was the ratio of methylated sites to unmethylated sites, (...) were deposited in the public datasets used, their use could not be avoided; however, as a result, the correlation with target gene miRNA regulation was substantially decreased. An explanation for this is noted above.”
>“Obtaining a better P-value”, is not an acceptable rationale for changing the way the methylation of a promoter is “typically represented”.

I did not insist that “Obtaining a better P-value” is the criterion to choose which definition of methylation should be employed. I wrote that better correlation of P-values between miRNA-targeting-specific promoter methylation and miRNA regulation of target genes is the criterion to choose which definition of methylation should be employed.

>The reason why the association between CpG island associated genes and miRNA targeting explains the difference in P-values between using total methylation values and proportion of methylated sites, is not obvious. It hints at a different behavior between CpG and non CpG promoters, would it not then make sense to process them separately?

As I have mentioned in the manuscript, there are some differences. However, the correlation between P-values for miRNA-targeting-specific promoter methylation and P-values for miRNA-mediated regulation of target genes is significant. In fact, for GSM739940, I had to employ β-values, but the correlation is still significant (see Table 6). Thus, I think that selection of values that represent promoter methylation to be investigated is not essential for the purpose of this manuscript. If you think that you need more evaluations, please advise me about what kind of evaluation is useful for you to be satisfied.

>It seems that the two ways of representing the data mean very different things and that decision could greatly affect the conclusions that can be extracted from the results. If I understand correctly, with the absolute number method a promoter with 2/3 methylated sites would be counted the same as a 2/10 yet in one case one could consider the first to be 66% methylated and the other 20%. The implication of this choice on the results of the study should be discussed more thoroughly.

As I have written in the above, since both ratio and amount of methylation work more or less well, I do not think that “decision could greatly affect the conclusions”. However, if you can suggest some kind of additional evaluations, I am happy to perform this so as to fulfill your requirements.

>In table 1, if the top part is indeed calculated with ratio and bottom with absolute, what were the thresholds to be counted as hypomethylated in each case?

As I have written in the text, I employed P-values to check if the promoters are hyper/hypomethylated. Threshold of P-values is, as denoted in the manuscript, P<0.05 after adjusted based on BH criterion.

>What does the “number of miRNA” actually represent? The author indicates that he multiplied the number of miRNA (1971) by the number of cell lines included (271). What does this product represent in the end? Why were the datapoints not aggregated by promoter and/or miRNA in the context of this study?

As I have written in the manuscript, P-values are attributed not to each gene but to each miRNA. Thus, in the framework of the present study, it is impossible to attribute P-values to each promoter. Also, P-values are attributed to miRNAs in each cell line, because each cell line has a different pattern of promoter methylation. Thus, in some cell lines a miRNA's target genes' promoters are significantly hyper/hypomethylated, while they may not be in other cell lines. Thus, to each miRNA, the number of P-values attributed is the number of cell line. Thus, I get the number of cell lines times number of miRNAs P-values in total.

>Table 3 for example is symptomatic of how the lack of illustration makes the article hard to follow: “P-values that represent the difference of promoter methylation between miRNA target genes and miRNA non-target genes.” The P-value doesn't represent a “difference in promoter methylation”, it evaluates the likelihood of the observation happening by chance given the dataset distribution. But what is the observation here? Are promoters of genes targeted by miRNA 60% methylated vs 58% for the non targeted? (is it 10 vs 87?)

It depends upon the statistical test employed, since I used three statistical tests. For the t-test, your expectation is more or less true, since the t-test compares mean values between two sets (although I used amount of methylation, not ratio, as denoted in the text) . For other tests, other statistical values are used in order to check significant difference.

>Are we considering a very slight effect with a very strong statistical significance or a very obvious strong effect for which the statistical test is not even needed to be convinced of the solidity of the result?

As I have written in the above, the essence of this study is to consider the correlation of P-values. This is the only useful variable to compare different experiments. Thus, I cannot answer your question. Using only P-values, I cannot distinguish between two cases:“a very slight effect with a very strong statistical significance” and “a very obvious strong effect for which the statistical test is not even needed to be convinced of the solidity of the result”, but considering other variables make the comparison between the different experiments impossible, as you suggested in the above. However, as I have mentioned in the above, employment of P-value based analyses is essential to derive conclusions in this paper.

>Moreover, just below that table the author explains why he doesn't discriminate between hypo and hypermethylation of promoters. That makes table 3 very puzzling; is this P-value evaluating the likelihood of miRNA targeted gene promoters to be significantly BOTH over and/or under methylated relative to non-targeted genes?

Yes, I did not distinguish between these two cases. However, if you wish, it is not difficult to investigate it if I used a one-sided test.

>Table 4 has been added as a response to my previous comment however it seems to bring further question as to what “miRNA-targeted-gene promoter methylation” really means. My first comment is that this control should not constitute a separate table but an additional entry in the original table.

I am not sure what kind of information you need. Please advise me what to do.

>Secondly, there are many ways to generate random shuffling of the data that will destroy the “signal”, however for a random shuffling to be a valid control of the biological significance of the observation, the randomization should be performed in a way that preserves at least part of the actual data structure. For example, if one works on a scale free distribution and discovers biological associations within this distribution, one would not generate a shuffled set as control that would have a gaussian distribution but instead would have to preserve the scale-free distribution. In this case, no details are provided regarding what steps have been taken to ensure that the randomization preserved any of the original data structure.

P-values are computed based upon the comparison of promoter methylation between genes targeted by a miRNA A and genes not targeted by the miRNA A but targeted by any other miRNAs. Permutation was performed simply between these two sets. In this occasion, what should be conserved? I did not find anything to be conserved on this occasion. I would be happy if you can suggest it.

>It is hard to believe what part of the data structure is conserved in this randomization when in the actual data between 72% to 92% of the >500,000 items studied are found to be significant (Table 1) and only 3 to 6 /500,000 have that characteristic in Table 4.

I do not think that permutation can conserve anything. I would be glad if you can suggest what to be conserved.

>Similarly, in Table 5 “Correlation coefficients between P-values(..)” it is unclear what the numbers actually represent. I am familiar with measuring the P-value of a correlation coefficient through multiple randomly shuffled distributions to insure the significance of the observed correlation. But it is unclear to me what a correlation between P-values actually represents since they could well be associated with opposite effects.

Since I used one-sided tests as denoted in the manuscript, P-values can distinguish between opposite effects.

>Finally, Figure 1 that should bear the take home message doesn't provide a better idea of what the conclusions of the paper are: the top part shows two genes targeted by different numbers of miRNA having different levels of methylation. From this it seems that the correlation found is actually between the amount of regulatory miRNA and hypomethylation, but how this was derived from table 1 and 2 is totally unclear. What happens in the boxed part of the figure is even more confusing as it shows a young cell, with 3 (expressed?) genes, becoming a senescent cell, expressing only one with methylation unchanged even though the amount of mir-548 targeting is represented higher.
>Meanwhile, the opposite is the case for the differentiation scheme. All the represented promoters (?) in the box seem to be hypomethylated regardless of the number of miRNA targeting the corresponding mRNA in total opposition to what the study seems to claim.

Figure 1 is just an illustration about what “can” happen. Since I investigated only P-values, I cannot strictly decide what has happened exactly. There are no ways to derive Figure 1 from Table 1 and 2 directly. I think that it helps readers to imagine what is the example of what happened in the cell. If you think that it is not suitable, I will remove it.

>I think overall the methodology and lack of illustrations obfuscate the point for readers. Non-specialists and specialists alike will find it hard to follow the author’s reasoning for want of clarity in the methods used. A scientific paper should strive to present the data in a way that can convince experts of the field and also explain to non specialists how the observed results lead to the presented conclusions. I feel that on both accounts this manuscript is not reaching these goals.

I hope that reading my responses, you can approve my manuscript as it is or suggest ways to revise my manuscript so as to fulfill your requirements. I believe that what I have found is worthwhile publishing, thus it is better to be indexed by your approval.

Yours, Tag.
Prof. Y-h. Taguchi, Department of Physics, Chuo University, Tokyo 112-8551, Japan

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06 Feb 2013 | for Version 1

Denis Dupuy, Institut Europeen de Chimie et Biologie, Pessac, France

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Approved With Reservations

The author states that this inverse correlation ‘is not a discrepancy’ but it is actually highlighting the discrepancy that has to be explained rather than brushed off.

Similarly the following statement by the author doesn't seem to be so obvious to me:

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04 Feb 2013 | for Version 1

Wenqian Hu, Center for RNA Molecular Biology, Whitehead Institute for Biomedical Research, Cambridge, MA, USA

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Approved

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I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard.

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Alongside their report, reviewers assign a status to the article:

Approved - the paper is scientifically sound in its current form and only minor, if any, improvements are suggested

Approved with reservations - A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit.

Not approved - fundamental flaws in the paper seriously undermine the findings and conclusions

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Correlation between miRNA-targeting-specific promotermethylation and miRNA regulation of target genes

Abstract

Keywords

Introduction

Methods

Promoter methylation profiles

mRNA and miRNA expression profiles

Investigation of miRNA-targeting-specific promoter methylation

Methylation computation by Bismark Software

Inference of miRNA-targeting-specific promoter methylation/miRNA regulation of target genes by MiRaGE method

Correlation between miRNA-targeting-specific promoter methylation and miRNA regulation of target genes

The reciprocal relationship between miRNA regulation of target genes and miRNA expression during differentiation in BG02 and BG03

Ranking of miRNAs having target genes with miRNA-targeting-hypomethylated promoters

Ranking of miRNA regulation of target genes

Results and discussion

Table 1. miRNA-targeting-specific promoter hypomethylation.

Table 2. Mean correlation coefficients between P-values of miRNA-targeting-specific promoter hypomethylation of samples.

Table 3. Correlation coefficients between P-values of miRNA-targeting-specific promoter hypomethylation and miRNA regulation of target genes.

Table 3 : Note

Table 4. Evaluation of the correlation between miRNA-targeting-specific promoter methylation and miRNA regulation of target genes.

Table 5. The reciprocal relationship between target gene regulation by miRNA and miRNA expression.

Table 6. Evaluation of miR-548 miRNAs targeting genes with miRNA-targeting-specific promoter hypomethylation.

Table 7. Ranking of miRNA regulation of target genes.

Conclusions

Figure 1. Schematic representing the principal findings of the present study together as well as those reported previously.

List of abbreviations

Competing interests

Author’s contributions

Acknowledgements

Additional files

References

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