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MicroRNA-mediated regulation of target genes in several brain regions is correlated to both microRNA-targeting-specific promoter methylation and differential microRNA expression

BioData Mining20136:11

DOI: 10.1186/1756-0381-6-11

Received: 17 January 2013

Accepted: 23 May 2013

Published: 31 May 2013

Abstract

Background

Public domain databases nowadays provide multiple layers of genome-wide data e.g., promoter methylation, mRNA expression, and miRNA expression and should enable integrative modeling of the mechanisms of regulation of gene expression. However, researches along this line were not frequently executed.

Results

Here, the public domain dataset of mRNA expression, microRNA (miRNA) expression and promoter methylation patterns in four regions, the frontal cortex, temporal cortex, pons and cerebellum, of human brain were sourced from the National Center for Biotechnology Informations gene expression omnibus, and reanalyzed computationally. A large number of miRNA-mediated regulation of target genes and miRNA-targeting-specific promoter methylation were identified in the six pairwise comparisons among the four brain regions. The miRNA-mediated regulation of target genes was found to be highly correlated with one or both of miRNA-targeting-specific promoter methylation and differential miRNA expression. Genes enriched for Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways that were related to brain function and/or development were found among the target genes of miRNAs whose differential expression patterns were highly correlated with the miRNA-mediated regulation of their target genes.

Conclusions

The combinatorial analysis of miRNA-mediated regulation of target genes, miRNA-targeting-specific promoter methylation and differential miRNA expression can help reveal the brain region-specific contributions of miRNAs to brain function and development.

Keywords

MicroRNA Target gene regulation Brain regions Promoter methylation Pathway analysis

Background

miRNAs are short non-coding RNAs that are believed to suppress target gene expression through the binding of miRNA “seed” regions to complementary sequences of 3’ untranslated regions (UTR) of target genes [1]. miRNAs are generally assumed to regulate cellular processes related to animal development [2] and cellular differentiation, and have been implicated in several diseases, including cancer. Thus, miRNAs have been put forth as candidates for tumor suppression [3] and cancer biomarkers [4]. miRNAs are also known to be involved in reprogramming [5]. As such, miRNAs are considered to play critical roles in a wide range of biological processes.

Recently, miRNA expression in the brain has attracted the interest of many researchers [69]. Although there are extensive researches about miRNA regulation of target genes [6, 7], it is generally believed that the expression of many genes is regulated by miRNAs indirectly [10]. In this sense, in order to understand miRNA regulation of gene expression in brain regions, it is also important to understand the mechanisms by which such regulation occurs.

Together with miRNAs, transcription factors (TFs) bind to promoter regions and cooperatively regulate miRNA target genes [1115]. TFs form a protein complex that binds to gene promoters during the initiation of transcription. Since there are many TFs known to regulate biological processes in regions of the brain [1618], it is natural to investigate the combinatorial effects of TFs and miRNA gene regulation in the brain [19, 20]. In contrast to what is known about cooperative regulation by miRNA and TFs, investigations of gene coregulation mediated by both miRNA and promoter methylation are limited; however, siRNA-induced promoter methylation in CpG islands has been reported [2124]. Promoter methylation is generally thought to suppress gene expression [25]. Suppression of gene expression by promoter methylation is often important. For example, aberrant promoter methylation is often related to cancers [26, 27]. Promoter methylation also plays critical roles in reprogramming [28].

Despite the known importance of promoter methylation, the relationship between promoter methylation and miRNA-mediated gene regulation has received little attention. However, it was recently shown that promoters of genes not targeted by miRNAs have higher levels of methylation [29]. We recently found that miRNA-targeting-specific promoter methylation takes place in many cell lines [30, 31]. miRNA-targeting-specific promoter methylation refers to the association between 3 BUTR miRNA targetting and promoter methylation levels for a given gene.

In this paper, we report that miRNA-targeting-specific promoter methylaion also exists between distinct brain-regions in a brain-region specific manner. Considered brain regions are frontal cortex, temporal cortex, pons, and cerebellum [32]. The frontal cortex is located at the front of the head in human. It is considered to be the hub of most higher functions and understanding, and is believed to govern most behavioral traits, motor skills, and problem solving tactics [33]. The temporal cortex is located in the lower right and left regions of the brain, and is involved in hearing, understanding languages, face recognition, and certain memory functions [34]. The cerebellum is located in the lower region at the back of the brain, and is central to motion control [35]. Finally, the pons is located in the center of these three regions and mediates information transfer between several other brain regions, including the cortex and cerebellum [32]. Given the diverse functions of these brain regions, I hypothesized that miRNA-targeting-specific promoter methylation would occur in a region-specific manner. Not only did I determine that patterns of miRNA regulation were indeed brain-region specific, I also revealed that some miRNA regulation of target genes turned out to be controlled by not only differential miRNA expression itself but also miRNA-targeting-specific promoter methylaion. In addition, target genes of miRNAs whose regulation was significantly correlated to differential miRNA expression were also found to be enriched for brain-region-specific functions and related KEGG pathways.

Methods

Patterns of miRNA and mRNA expression and promoter methylation

Datasets used in this study were downloaded from Gene Expression Omnibus (GEO) under GEO ID GSE15745. These included miRNA and mRNA expression, and promoter methylation data from four distinct brain regions (frontal cortex, temporal cortex, pons and cerebellum) in 150 human subjects [36], which had been analyzed in detail in connection with genomic variants, such as single nucleotide polymorphisms and copy number variants; however, miRNA expression had not been analyzed previously [36]. Thus, in total, 600 tissue samples were included. Processed signals were used without any further normalization. For more details about data processing and analysis, see the Supplementary Document (see Additional file 1).

Results and discussion

In this section, I will discuss the mutual relationships between miRNA-related features and their biological meaning.

Mutual relationships between miRNA-mediated regulation of genes, miRNA-targeting-specific promoter methylation, and differential miRNA expression

I investigated miRNA-mediated gene regulation and miRNA-targeting-specific promoter methylation in the frontal cortex, temporal cortex, pons, and cerebellum of the human brain, based on the P-values, P mj , < ℓℓ s or P mj , > s, which were used to estimate miRNA-mediated gene regulation and miRNA-targeting-specific promoter methylation. Figure 1 illustrates the results of this analysis. It is clear that target genes of a substantial number of miRNAs are up/downregulated between these four brain regions. It is also evident that some miRNA target genes are differentially methylated between these four brain regions. This strongly suggests that both miRNA-mediated gene regulation and miRNA-targeting-specific promoter methylation play critical roles in the development and function of these four brain regions. For example, from the miRNA-centric point of view (Figure 1), compared to the other three brain regions investigated, the pons has more genes with hypermethylated promoters and lower expression levels, although these characteristics are not always associated. This observation is consistent with the general belief that the hypermethylation of promoters is associated with reduced expression. This also signifies that mRNA expression in the pons is distinct from the other three brain regions.
Figure 1

Schematic illustration of the relationship between miRNA-mediated gene regulation and miRNA-targeted-specific promoter methylation. Arrows/segments indicate up/downregulation of miRNA target genes (a) and miRNA-targeted-specific promoter methylation (b). Black (red) numbers next to inequality signs are the averaged number of miRNAs whose target genes are significantly up/downregulated (whose target genes promoters are hyper/hypomethylated). TCTX refers to the temporal cortex, FCTX to the frontal cortes, CRBLM to the cerebellum, and PONS to the pons. For example, there are 280 (183) miRNAs whose target gene promoters are significantly hyper(hypo)methylated in FCFX compared to TCTX. Similarly, there are 889 (173) miRNAs whose target genes are significantly up(down)regulated in FCFX compared to TCTX. Since 280 is larger than 183, gene promoters in FCFX are considered to be more hypermethylated than those in TCTX based on the miRNA-centric-view, thus the red arrow directs the reader from TCTX to FCTX. Likewise, because 889 is larger than 173, genes in FCFX are considered to be upregulated when compared to TCTX, thus the black arrow directs the reader from TCTX to FCTX. The numbers in the rectangle indicate Spearman correlation coefficients between miRNA-mediated gene regulation and miRNA-targeted-specific promoter methylation, ρ mRNA , Methyl. . Standard deviations of Spearman correlation coefficients, Δ ρ mRNA , Methyl. are shown in parentheses.

Mutual relationships between miRNA-mediated regulation of target genes and miRNA-targeting-specific promoter methylation

In order to understand the mutual relationship between miRNA-mediated gene regulation and miRNA-targeting-specific promoter methylation, I computed the correlation coefficient of the mean rank of P-values, ρ mRNA , Methyl. , for six pairwise comparisons between the frontal cortex, temporal cortex, pons, and cerebellum (see Figure 1). Here, the means were taken over all samples in each brain region. Excluding a single pairwise comparison between the cerebellum and pons, correlation coefficients for the remaining five comparisons varied between 0.25 and 0.51. These values were considered to be sufficiently large taking into account the fact that the number of P-values in a given brain region is as large as M, the number of miRNAs comsidered. The P-values of each correlation coefficient are less than 2.2×10−16. This means, the correlation between miRNA-mediated gene regulation and miRNA-targeting-specific promoter methylation is highly significant independent of pairs of brain regions. The smallest correlation coefficients were observed in the cerebellum and pons. Although the correlation coefficient was large in aggregate (0.09), individual P-value was as small as 4×10−5, which is highly significant.

In order to confirm the correlation between miRNA-mediated gene regulation and miRNA-targeting-specific promoter methylation, the root mean squared averages of the correlation coefficients in each sample, Δ ρ mRNA , Methyl. , were also computed. Excluding pairwise comparisons for the frontal cortex and pons for which the absolute value of ρ mRNA , Methyl. was the maximum, Δ ρ mRNA , Methyl. was larger than the absolute value of ρ mRNA , Methyl. . This signifies that the correlation coefficients within each sample were not small, but that when averaged over all samples, the value was seemingly small because of the occurrence of both positive and negative correlations with equal probabilities. Thus, I conclude that miRNA-mediated regulation and miRNA-targeting-specific promoter methylation are significantly correlated. Worth noting is that the signs of correlation coefficients, ρ mRNA , Methyl. , are neither definitively positive nor negative. One may think that they should be positive, as both promoter methylation and miRNA targeting should suppress gene expression. However, because genes targeted by miRNAs are expected to be downregulated (upregulated) only when miRNA itself is upregulated (downregulated), there is no reason to expect that the correlation coefficients between miRNA-mediated gene regulation and miRNA-targeting-specific promoter methylation should always take positive or negative values.

Relationships between miRNA-mediated regulation of target genes, miRNA-targeting-specific promoter methylation and differential miRNA expression

In order to determine the relationship between miRNA-mediated gene regulation, P mj , < or P mj , > , and differential expression of miRNA, log x mjℓ x mj , the correlation coefficients were computed. However, these correlation coefficients were too small to be significant (not shown here). This seemingly contradicts the observed correlation between miRNA-mediated gene regulation and miRNA-targeted-specific promoter methylation.

Thus, in order to resolve this apparent discrepancy, I employed multivariate regression models between miRNA-mediated gene regulation, miRNA-targeting-specific promoter methylation, and differential miRNA expression, also considering both sample gender and age (see Methods). In contrast to the above discrepancy, depending upon the miRNA considered, I identified significant correlations between only selected variables that were included in the regression model. In other words, I found that all of the variables were not always correlated, but were instead selectively correlated. In order to quantize these correlations, for each miRNA, I picked out the combinations of variables that were significantly correlated (see Methods). Table 1 lists the miRNAs selected for each pair of brain regions based on the criterion described in the subsection, “The selection of miRNAs that significantly regulate target genes based on multiple regression” in Supplementary Document (see Additional file 1), i.e., miRNAs whose differential expression is significantly correlated to miRNA-mediated gene regulation. To our knowledge, this is the first time that miRNA gene regulation has been shown to be mediated by both differential miRNA expression and miRNA-targeting-specific promoter methylation.
Table 1

miRNAs that significantly regulate target genes

CRBLM vs FCTX

CRBLM vs PONS

CRBLM vs TCTX

Reciprocal

Nonreciprocal

Reciprocal

Nonreciprocal

Reciprocal

Nonreciprocal

hsa-miR-181c-5p

hsa-miR-200a-5p

hsa-miR-20a-5p

hsa-let-7b-5p

hsa-miR-210

hsa-miR-99a-5p

hsa-miR-135a-5p

hsa-miR-381

hsa-miR-23a-3p

hsa-let-7e-5p

 

hsa-miR-191-5p

hsa-miR-137 *

hsa-miR-202-3p *

hsa-miR-148a-3p

hsa-miR-197-3p

 

hsa-miR-99b-5p *

hsa-miR-363-3p

hsa-miR-561-3p

hsa-miR-10a-5p

hsa-miR-181b-5p

 

hsa-miR-617

hsa-miR-369-3p

hsa-miR-568

hsa-miR-221–3p

hsa-let-7i-5p

  

hsa-miR-487a *

hsa-miR-618

hsa-miR-223–3p

hsa-miR-9-5p

FCFX vs PONS

hsa-miR-514a-3p

hsa-miR-630 *

hsa-miR-1

hsa-miR-126-3p

hsa-miR-365a-3p

hsa-miR-302d-3p

hsa-miR-553

 

hsa-miR-133a

hsa-miR-134

hsa-miR-378a-5p

hsa-miR-432-5p

hsa-miR-554

 

hsa-miR-137 *

hsa-miR-154-3p

 

hsa-miR-595

hsa-miR-655

 

hsa-miR-146a-5p

hsa-miR-299-5p

  

hsa-miR-421

 

hsa-miR-452-5p

hsa-miR-99b-5p *

FCTX vs TCTX

  

hsa-miR-484

hsa-miR-377-3p

has-miR-373-3p

hsa-miR-24-3p

  

hsa-miR-511

hsa-miR-383

 

hsa-miR-485-5p

  

hsa-miR-515-5p

hsa-miR-431-5p

 

hsa-miR-766-3p

  

hsa-miR-571

hsa-miR-329

  
  

hsa-miR-549

hsa-miR-485-3p

PONS VS TCTX

   

hsa-miR-487a *

hsa-miR-9-3p

hsa-miR-222-3p

   

hsa-miR-202-3p *

hsa-miR-302a-3p

hsa-miR-125b-5p

   

hsa-miR-432-3p

hsa-miR-410

hsa-miR-328

   

hsa-miR-495

hsa-miR-487b

hsa-miR-581

   

hsa-miR-504

hsa-miR-630 *

hsa-miR-661

   

hsa-miR-505-3p

  
   

hsa-miR-563

  
   

hsa-miR-578

  
   

hsa-miR-630 *

  
   

hsa-miR-668

  

miRNAs predicted to regulate target genes based on six pairwise comparisons among four brain regions: the frontal cortex (FCTX), temporal cortex (TCTX), pons (PONS), and cerebellum (CRBLM). The labels “Reciprocal” and “nonreciprocal” indicate whether the observed relationship between miRNA expression and target gene mRNA was either reciprocal or nonreciprocal. Asterisked miRNAs appear more than once. Bold faced miRNAs were previously reported to be related to brain development/diseases [3739]. See subsection “The selection of miRNAs that significantly regulate target genes based on multiple regression model” in Supplementary Document (see Additional file 1) for the detailed criterion of miRNAs selection.

Biological meanings of findings

As can be seen in Table 1, miRNAs selected for each pair of brain regions are not unique, but rather divergent. Some of the listed miRNAs were previously reported to be important in specific brain regions. For example, Yao et al recently investigated miRNA expression in the rat cerebral cortex during brain development [37]. Many of the top 20 most highly expressed miRNAs identified by Yao et al at each of eight different developmental stages, ranging from early developmental stages to late post natal stages, were also significant in our dataset (rno-let-7b, 7e, 7i, rno-miR-181b, 99a/b, 9, 125b-5p, and 191). Yao et al also emphasized the importance of miR-137, the ortholog of the human miRNA, hsa-miR-137; this miRNA was found to be significant twice in our analysis, compared to the most of other miRNAs which were only identified as significant once. In addition, many of the miRNAs listed in Table 1 have also been previously implicated in brain diseases, including Alzheimers disease (AD), Parkinsons disease (PD), Huntingtons disease (HD), and various other neurodegenerative disorders [38, 39]. This overlap lends support to the utility of our method for identifying miRNAs with potential functional relevance in the brain. Although there have been other investigations of brain miRNA expression, to our knowledge, I am the first to interrogate miRNA expression data across multiple brain regions.

In order to better understand the biological functions of the miRNA targets identified in our analysis, I employed pathway analysis (Table 2), which has been shown previously to be effective for miRNA target genes (e.g., [40, 41]). For this purpose, I used DIANA-mirPath [42], which is a web tool developed for KEGG pathway enrichment analysis of miRNA target genes.
Table 2

miRNA target genes KEGG pathway enrichment

  

CRBLM

CRBLM

CRBLM

FCTX

FCTX

PONS

  

vs

vs

vs

vs

vs

vs

  

FCTX

PONS

TCTX

PONS

TCTX

TCTX

 

KEGG pathways

R

N

R

N

R

N

R

N

R

N

R

N

   1

TGF- β signaling pathway

   

  

 

   2

Glioma

  

    

   3

MAPK signaling pathway

     

  

   4

Axon guidance

        

   5

Phosphatidylinositol signaling system

 

        

   6

mTOR signaling pathway

         

   7

Adipocytokine signaling pathway

 

 

      

   8

Pancreatic cancer

 

   

    

   9

Endocytosis

      

 

10

Focal adhesion

      

 

11

Insulin signaling pathway

        

12

Neurotrophin signaling pathway

        

13

Colorectal cancer

        

14

Arrhythmogenic right ventricular cardiomyopathy (ARVC)

     

  

15

Wnt signaling pathway

 

   

  

16

Non-small cell lung cancer

          

17

Adherens junction

      

 

18

ErbB signaling pathway

        

19

Pathways in cancer

   

  

 

20

Glycosaminoglycan biosynthesis - heparan sulfate

   

   

21

Type II diabetes mellitus

          

22

Melanoma

 

   

    

23

Renal cell carcinoma

        

24

Inositol phosphate metabolism

          

25

Chronic myeloid leukemia

   

    

26

T cell receptor signaling pathway

          

27

Small cell lung cancer

         

28

Fc gamma R-mediated phagocytosis

 

        

29

Prostate cancer

         

30

Salivary secretion

          

31

Osteoclast differentiation

          

32

Regulation of actin cytoskeleton

        

33

Endocrine and other factor-regulated calcium reabsorption

          

34

Lysine degradation

  

 

      

35

Circadian rhythm - mammal

  

   

 

  

36

Glycosaminoglycan biosynthesis - chondroitin sulfate

  

    

   

37

N-Glycan biosynthesis

  

        

38

Long-term depression

   

   

    

39

Prion diseases

   

      

 

40

ECM-receptor interaction

  

     

   

41

Tight junction

   

     

  

42

Hypertrophic cardiomyopathy (HCM)

   

     

  

43

Cell adhesion molecules (CAMs)

     

     

44

Dilated cardiomyopathy

   

     

  

45

Fatty acid biosynthesis

           

46

Long-term potentation

  

         

47

Ubiquitin mediated proteolysis

  

         

48

Thyroid cancer

  

         

49

Notch signaling pathway

  

         

50

Mismatch repair

  

         

51

Acute myeloid leukemia

  

         

52

Glycosphingolipid biosynthesis - lacto and neolacto series

  

         

53

Glycosaminoglycan biosynthesis - keratan sulfate

   

        

54

Biotin metabolism

   

        

55

Gap junction

   

        

56

Gastric acid secretion

   

        

57

Taurine and hypotaurine metabolism

   

        

58

Aldosterone-regulated sodium reabsorption

   

        

59

GnRH signaling pathway

   

        

60

Metabolism of xenobiotics by cytochrome P450

     

      

61

Mucin type O-Glycan biosynthesis

       

    

62

Biosynthesis of unsaturated fatty acids

        

   

63

Viral myocarditis

         

  

64

Cytokine-cytokine receptor interaction

          

 

65

Hematopoietic cell lineage

          

 

66

Valine, leucine and isoleucine biosynthesis

           

KEGG pathways marked with are enriched by target genes of miRNAs selected in Table 1. “R” and “N” indicate whether the relationship between miRNA expression and target gene mRNA is reciprocal (nonreciprocal). Pathways asterisked and bold faced are directly related to brain and neurons, respectively, and discussed in detail in the Supplementary Document (see Additional file 1).

Compared to the variation observed in the miRNAs listed in Table 1, KEGG pathways for miRNA targets (Table 2) are highly universal and biologically meaningful as shiwn in the followings. For example, Paul et al[43] measured and analyzed transcritpomes in the mouse cerebellum. Cells were classified into Purkinje cells (PCs) at postnatal days 3, 7, 14, 21, 28, 35, and 56 (P3, P7, P14, P21, P28, P35, and P56), and the mixture of Stellate/Basket cells (StCs/BKCs) at P14, P21, P28, P35, and P56. They conducted pathway enrichment analysis using KEGG pathways based on developmental gene expression of PCs and S/BCs. From this, they found that many pathways were enriched at several different time points. In their data, upregulated genes identified between P3-PCs and P7-PCs were enriched for pathways such as “axon guidance”, “regulation of actin cytoskeleton”, “gap junction”, and “tight junctions”, implicating roles for these genes in the early stages of circuit integration by PCs. These changes are accompanied by an upregulation of other pathways such as insulin, TGF- β, Hedgehog, and Wnt signaling, which are important for axon guidance. The upregulation of GnRH signaling, which is known to have a modulatory effect on cerebellar neurons and P53 signaling, and is important for PC survival was also observed during this time.

In P14-PCs, Paul et al also reported that pathways related to “long-term potentiation”, “long-term depression”, “JAK/STAT”, “VEGF”, and “mTOR signaling” were elevated, which correlate to the development of parallel fiber synapses. Between P28 and P56, the upregulation of pathways related to “CAMs”, “chondroitin sulfate biosynthesis”, “focal adhesion”, “cytokine receptor interaction”, and “extracellular matrix receptor interaction” (ECM-interaction) correlate with the maturation and stabilization of PC connectivity. In S/BCs a number of similar pathways are also activated. “axon guidance”, “tight junction”, “adherens junction”, “insulin signaling”, “ErbB”, and “spliceosome” pathways were upregulated in P14S/BCs, reflecting delayed axogenesis of BskC and StC after they enter the ML during the second postnatal week. However, between P28 and P35, similar to PC cells, pathways of “ECM-receptor interaction”, “CAMs”, “cytokine receptor interaction”, “neuroactive ligand receptor interactions” and “regulation of cytoskeleton” were activated. These listed pathways largely overlap those listed in Table 2. Although Paul et al mainly investigated the cerebellum, I studied the cerebellum, as well as the pons, and frontal and temporal cortex; thus, I investigated previous studies related to individual pathways listed in Table 2 one by one.

Pathways directly related to brain/nervous system

Some pathways listed in Table 2 are obviously related to the brain and/or nervous system. For example, “axon guidance” is definitely included in brain development. “Glioma” is a brain tumor and the “neurotrophin signaling pathway” is related to neural systems. Enrichments in these three pathways further supports the notion that the genes I have identified are indeed relevant to brain function and development. For additional discussion of other selected pathways, see the Supplementary Document (see Additional file 1).

Other notable observations

For further discussion of the divergence of miRNAs selected vs. the uniformity of pathways selected, positive vs. negative correlations between miRNA expression and target gene expression, and possible biological explanations underlying coregulation by both miRNA and promoter methylation, see the Supplementary Document (see Additional file 1).

Conclusion

In this paper, I demonstrated possible miRNA coreguation of target genes in brain regions by analyzing both differential miRNA expression and miRNA-targeting-specific promoter methylation. Selected miRNAs were enriched in brain-related KEGG pathways. Because this was simply descriptive and no mechanisms responsible for the cooperative regulation described above were presented, experiment-based follow up studies will be necessary to validate our findings.

Declarations

Acknowledgements

This research was supported by KAKENHI 23300357 and Japan Science and Technology Agency (JST), A-step feasibility study program (#AS242Z00112Q).

Authors’ Affiliations

(1)
Department of Physics, Chuo University

References

  1. Cai Y, Yu X, Hu S, Yu J: A Brief Review on the Mechanisms of miRNA Regulation. Genomics, Proteomics & Bioinformatics. 2009, 7 (4): 147-154. 10.1016/S1672-0229(08)60044-3. [http://www.sciencedirect.com/science/article/pii/S1672022908600443]View ArticleGoogle Scholar
  2. Wienholds E, Kloosterman WP, Miska E, Alvarez-Saavedra E, Berezikov E, de Bruijn E, Horvitz HR, Kauppinen S, Plasterk RH: MicroRNA expression in zebrafish embryonic development. Science. 2005, 309 (5732): 310-311. 10.1126/science.1114519.View ArticlePubMedGoogle Scholar
  3. Wang D, Qiu C, Zhang H, Wang J, Cui Q, Yin Y: Human microRNA oncogenes and tumor suppressors show significantly different biological patterns: from functions to targets. PLoS ONE. 2010, 5 (9): e13067-10.1371/journal.pone.0013067. [http://dx.doi.org/10.1371]View ArticlePubMedPubMed CentralGoogle Scholar
  4. Wittmann J, Jack HM: Serum microRNAs as powerful cancer biomarkers. Biochim Biophys Acta. 2010, 1806 (2): 200-207.PubMedGoogle Scholar
  5. Anokye-Danso F, Trivedi CM, Juhr D, Gupta M, Cui Z, Tian Y, Zhang Y, Yang W, Gruber PJ, Epstein JA, Morrisey EE: Highly efficient miRNA-mediated reprogramming of mouse and human somatic cells to pluripotency. Cell Stem Cell. 2011, 8 (4): 376-388. 10.1016/j.stem.2011.03.001.View ArticlePubMedPubMed CentralGoogle Scholar
  6. Krichevsky AM, King KS, Donahue CP, Khrapko K, Kosik KS: A microRNA array reveals extensive regulation of microRNAs during brain development. RNA. 2003, 9 (10): 1274-1281. 10.1261/rna.5980303.View ArticlePubMedPubMed CentralGoogle Scholar
  7. Schratt GM, Tuebing F, Nigh EA, Kane CG, Sabatini ME, Kiebler M, Greenberg ME: A brain-specific microRNA regulates dendritic spine development. Nature. 2006, 439 (7074): 283-289. 10.1038/nature04367.View ArticlePubMedGoogle Scholar
  8. Miska E, Alvarez-Saavedra E, Townsend M, Yoshii A, Šestan N, Rakic P, Constantine-Paton M, Horvitz H: Microarray analysis of microRNA expression in the developing mammalian brain. Genome Biol. 2004, 5 (9): R68-10.1186/gb-2004-5-9-r68.View ArticlePubMedPubMed CentralGoogle Scholar
  9. Shao NY, Hu HY, Yan Z, Xu Y, Hu H, Menzel C, Li N, Chen W, Khaitovich P: Comprehensive survey of human brain micro RNA by deep sequencing. BMC Genomics. 2010, 11: 409-10.1186/1471-2164-11-409.View ArticlePubMedPubMed CentralGoogle Scholar
  10. Shahab S, Matyunina L, Hill C, Wang L, Mezencev R, Walker L, McDonald J: The effects of MicroRNA transfections on global patterns of gene expression in ovarian cancer cells are functionally coordinated. BMC Medical Genomics. 2012, 5: 33-10.1186/1755-8794-5-33. [http://www.biomedcentral.com/1755-8794/5/33]View ArticlePubMedPubMed CentralGoogle Scholar
  11. Shalgi R, Lieber D, Oren M, Pilpel Y: Global and local architecture of the mammalian microRNA-transcription factor regulatory network. PLoS Comput Biol. 2007, 3 (7): e131-10.1371/journal.pcbi.0030131.View ArticlePubMedPubMed CentralGoogle Scholar
  12. Hobert O: Gene regulation by transcription factors and microRNAs. Science. 2008, 319 (5871): 1785-1786. 10.1126/science.1151651.View ArticlePubMedGoogle Scholar
  13. Hobert O: Common logic of transcription factor and microRNA action. Trends Biochem Sci. 2004, 29 (9): 462-468. 10.1016/j.tibs.2004.07.001.View ArticlePubMedGoogle Scholar
  14. Chen K, Rajewsky N: The evolution of gene regulation by transcription factors and microRNAs. Nat Rev Genet. 2007, 8 (2): 93-103.View ArticlePubMedGoogle Scholar
  15. Zhou Y, Ferguson J, Chang JT, Kluger Y: Inter- and intra-combinatorial regulation by transcription factors and microRNAs. BMC Genomics. 2007, 8: 396-10.1186/1471-2164-8-396.View ArticlePubMedPubMed CentralGoogle Scholar
  16. Cholfin JA, Rubenstein JL: Frontal cortex subdivision patterning is coordinately regulated by Fgf8, Fgf17, and Emx2. J Comp Neurol. 2008, 509 (2): 144-155. 10.1002/cne.21709.View ArticlePubMedPubMed CentralGoogle Scholar
  17. Okuno H, Miyashita Y: Expression of the transcription factor Zif268 in the temporal cortex of monkeys during visual paired associate learning. Eur J Neurosci. 1996, 8 (10): 2118-2128. 10.1111/j.1460-9568.1996.tb00733.x.View ArticlePubMedGoogle Scholar
  18. Cheng LE, Zhang J, Reed RR: The transcription factor Zfp423/OAZ is required for cerebellar development and CNS midline patterning. Dev Biol. 2007, 307: 43-52. 10.1016/j.ydbio.2007.04.005.View ArticlePubMedPubMed CentralGoogle Scholar
  19. Sun J, Gong X, Purow B, Zhao Z: Uncovering MicroRNA and Transcription Factor Mediated Regulatory Networks in Glioblastoma. PLoS Comput Biol. 2012, 8 (7): e1002488-10.1371/journal.pcbi.1002488. [http://dx.doi.org/10.1371]View ArticlePubMedPubMed CentralGoogle Scholar
  20. Guo AY, Sun J, Jia P, Zhao Z: A novel microRNA and transcription factor mediated regulatory network in schizophrenia. BMC Syst Biol. 2010, 4: 10-10.1186/1752-0509-4-10.View ArticlePubMedPubMed CentralGoogle Scholar
  21. Taira K: Induction of DNA methylation and gene silencing by short interfering RNAs in human cells. Nature. 1176, 441 (7097):
  22. Mette MF, Aufsatz W, van der Winden J, Matzke MA, Matzke AJ: Transcriptional silencing and promoter methylation triggered by double-stranded RNA. EMBO J. 2000, 19 (19): 5194-5201. 10.1093/emboj/19.19.5194.View ArticlePubMedPubMed CentralGoogle Scholar
  23. Otagaki S, Kawai M, Masuta C, Kanazawa A: Size and positional effects of promoter RNA segments on virus-induced RNA-directed DNA methylation and transcriptional gene silencing. Epigenetics. 2011, 6 (6): 681-691. 10.4161/epi.6.6.16214.View ArticlePubMedPubMed CentralGoogle Scholar
  24. Halytskiy V: Hypothesis of initiation of DNA methylation de novo and allelic exclusion by small RNAs. Cell and Tissue Biol. 2008, 2: 97-106. 10.1134/S1990519X08020016. [http://dx.doi.org/10.1134/S1990519X08020016]View ArticleGoogle Scholar
  25. Suzuki MM, Bird A: DNA methylation landscapes: provocative insights from epigenomics. Nat Rev Genet. 2008, 9 (6): 465-476. 10.1038/nrg2341.View ArticlePubMedGoogle Scholar
  26. Palmisano WA, Divine KK, Saccomanno G, Gilliland FD, Baylin SB, Herman JG, Belinsky SA: Predicting lung cancer by detecting aberrant promoter methylation in sputum. Cancer Res. 2000, 60 (21): 5954-5958.PubMedGoogle Scholar
  27. Maruyama R, Toyooka S, Toyooka KO, Harada K, Virmani AK, Zochbauer-Muller S, Farinas AJ, Vakar-Lopez F, Minna JD, Sagalowsky A, Czerniak B, Gazdar AF: Aberrant promoter methylation profile of bladder cancer and its relationship to clinicopathological features. Cancer Res. 2001, 61 (24): 8659-8663.PubMedGoogle Scholar
  28. Farthing CR, Ficz G, Ng RK, Chan CF, Andrews S, Dean W, Hemberger M, Reik W: Global mapping of DNA methylation in mouse promoters reveals epigenetic reprogramming of Pluripotency genes. PLoS Genet. 2008, 4 (6): e1000116-10.1371/journal.pgen.1000116. [http://dx.plos.org/10.1371]View ArticlePubMedPubMed CentralGoogle Scholar
  29. Su Z, Xia J, Zhao Z: Functional complementation between transcriptional methylation regulation and post-transcriptional microRNA regulation in the human genome. BMC Genomics. 2011, 12 (Suppl 5): S15-10.1186/1471-2164-12-S5-S15.View ArticlePubMedPubMed CentralGoogle Scholar
  30. Taguchi Y h: Competitive target gene regulation by promoter methylation and miRNA. IPSJ SIG Technical Reports. 2012, 2012: 1-6. [http://ci.nii.ac.jp/naid/110009459611/en/]Google Scholar
  31. Taguchi Y h: Inference of the target gene regulation by miRNA via MiRaGE Server. Introduction to Genetics Ű DNA Methylation and Gene Regulation. Edited by: Wan J. 2013, Hong Kong: iConcept Press, [in press. http://www.iconceptpress.com/download/paper/12050422170896.pdf]Google Scholar
  32. Carter R: The Brain Book: An Illustrated Guide to Its Structure, Function Disorders. 2009, London: Dorling KindersleyGoogle Scholar
  33. Stuss DT, Floden D: Frontal Cortex. 2006, West Sussex: John Wiley & Sons, Ltd, [http://dx.doi.org/10.1002/0470018860.s00312]View ArticleGoogle Scholar
  34. Murray EA: Temporal Cortex. 2006, West Sussex: John Wiley & Sons, Ltd, [http://dx.doi.org/10.1002/0470018860.s00313]View ArticleGoogle Scholar
  35. Middleton FA, Tillery SIH: Cerebellum. Encyclopedia of Cognitive Science. 2006, West Sussex: John Wiley & Sons, Ltd, [http://dx.doi.org/10.1002/0470018860.s00398]Google Scholar
  36. Gibbs JR, van der Brug MP, Hernandez DG, Traynor BJ, Nalls MA, Lai SL, Arepalli S, Dillman A, Rafferty IP, Troncoso J, Johnson R, Zielke HR, Ferrucci L, Longo DL, Cookson MR, Singleton AB: Abundant quantitative trait loci exist for DNA methylation and gene expression in human brain. PLoS Genet. 2010, 6 (5): e1000952-10.1371/journal.pgen.1000952.View ArticlePubMedPubMed CentralGoogle Scholar
  37. Yao MJ, Chen G, Zhao PP, Lu MH, Jian J, Liu MF, Yuan XB: Transcriptome analysis of microRNAs in developing cerebral cortex of rat. BMC Genomics. 2012, 13: 232-10.1186/1471-2164-13-232.View ArticlePubMedPubMed CentralGoogle Scholar
  38. Babenko O, Kovalchuk I, Metz GA: Epigenetic programming of neurodegenerative diseases by an adverse environment. Brain Res. 2012, 1444: 96-111.View ArticlePubMedGoogle Scholar
  39. Roshan R, Ghosh T, Scaria V, Pillai B: MicroRNAs: novel therapeutic targets in neurodegenerative diseases. Drug Discov Today. 2009, 14 (23-24): 1123-1129. 10.1016/j.drudis.2009.09.009.View ArticlePubMedGoogle Scholar
  40. Tabunoki H, Satoh J i: Comprehensive analysis of human microRNA target networks. BioData Mining. 2011, 4: 17-10.1186/1756-0381-4-17. [http://www.biodatamining.org/content/4/1/17]View ArticlePubMedPubMed CentralGoogle Scholar
  41. Satoh J i: Molecular network analysis of human microRNA targetome: from cancers to Alzheimer’s disease. BioData Mining. 2012, 5: 17-10.1186/1756-0381-5-17. [http://www.biodatamining.org/content/5/1/17]View ArticlePubMed CentralGoogle Scholar
  42. Vlachos IS, Kostoulas N, Vergoulis T, Georgakilas G, Reczko M, Maragkakis M, Paraskevopoulou MD, Prionidis K, Dalamagas T, Hatzigeorgiou AG: DIANA miRPath v.2.0: investigating the combinatorial effect of microRNAs in pathways. Nucleic Acids Res. 2012, 40 (Web Server issue): 498-504.View ArticleGoogle Scholar
  43. Paul A, Cai Y, Atwal GS, Huang ZJ: Developmental coordination of gene expression between synaptic partners during GABAergic circuit assembly in cerebellar cortex. Frontiers in Neural Circuits. 2012, 6 (37): [http://www.frontiersin.org/neural_circuits/10.3389/fncir.2012.00037/abstract]Google Scholar

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