Inference of Target Gene Regulationby miRNA via MiRaGE Server

Y-h. TaguchiDepartment of PhysicsChuo University, Japan

1 Introduction

microRNA (miRNA) is a short non-coding but functional RNA which is believed to suppress hundreds oftarget mRNAs by degradation or translation termination (Bartel, 2009). It has several remarkable features.

• There are manymiRNAs for each species. For example, human has more than a thousand of miRNAs,most of which are common with other species.

• Each miRNA often targets more than a hundred mRNAs by seed match between the 5’end of miRNAand the 3’UTR (UnTanslated Region) of mRNA.

• The expression of target mRNAs is generally weakly suppressed.

• Some miRNAs are highly conserved over several species, but there are also more species-specificmiRNAs, too.

• miRNAs are believed to contribute to wide range of biological processes, most of which are relatedto cell type changes: e.g., differentiation, cancer formation and several diseases.

Figure 1: Schematic of explanation of miRNA biogenesis

Figure 1 illustrates miRNA biogenesis. miRNAs are firstly transcribed into primary miRNAs (pri-miRNAs). The pri-miRNA is cleaved by the microprocessor complex, consisting of DGCR8 and Droshaproteins and undergoes double-strand cleavage to form the hairpin precursor miRNA (pre-miRNA). The re-sulting pre-miRNA is transported to the cytoplasm via a process that involves Exportin-5. The pre-miRNAis further cleaved by Dicer to generate a short, partially double-stranded (ds) RNA in which one strand isthe mature miRNA. The mature miRNA is taken up together with Argonaute (Ago2) proteins by a proteincomplex that is similar, if not identical, to the RNA Induced Silencing Complex (RISC). Via RISC, miR-NAs usually act as translational repressors by binding partially complementary binding sites of their targetmRNAs, or cleave them. The latter can be basically detected indirectly by the measurement of target genemRNA expression changes.

Figure 2: Seed match types in animals

Figure 2 illustrates how mature miRNA recognizes target sites of mRNAs in animals. Generally,5’ end of miRNA has 8 mer seed region. The most important criterion is that target mRNA must havecomplementary 6 mer to 5’ end 6mer of miRNA. Then, 8 mer or either type of 7 mer seed match is usuallyrecognized to be target sites. At the moment, there are no known criterion that decides which kind of seedmatch is dominant in which occasions.

Major drawbacks of miRNA research are the lack of effective experimental procedures for directlymeasuring the miRNAs regulation of target mRNAs (Thomson et al., 2011). Instead of that, target geneswere mainly predicted computationally (Barbato et al., 2009). Target gene predictions by computationalmethods were often successful. Some predictions were accepted as de facto standard and were even em-ployed for further researches.

Some more recent examples of miRNA target gene prediction methods include CoMeTa(Gennarino et al., 2012), mirBridge (Tsang et al., 2010), HOCTAR database (Gennarino et al., 2011),GenMiR++ (Huang et al., 2007), single-cell measurement (Mukherji et al., 2011), miRvestigator(Plaisier et al., 2011), MultiMiTar (Mitra & Bandyopadhyay, 2011), miRTrail (Laczny et al., 2012), andTarBase 6.0 (Vergoulis et al., 2012), although there are uncountable number of target gene predictions andmore predictions are continuously proposed.

However, all target genes are not always regulated by miRNAs. miRNAs regulation of target genes ishighly context-dependent (Bossel Ben-Moshe et al., 2012). Thus, the above listed rather “static” target geneprediction methods may miss some important context-dependent features. In this context, it is important toestimate “active” miRNAs which really regulate target gene expression in each situation. Although “active”miRNAs are usually selected based only on miRNA expression itself, the amount of miRNA expressiondoes not always decide if each miRNA regulates target genes (Mullokandov et al., 2012). Thus, thereshould be alternative and effective methods to infer miRNAs regulation of target genes not from miRNAexpression but from target gene expression. Compared with the “static” approaches, there are not so manytrials following this line (Liang et al., 2011; Bartonicek & Enright, 2010; Volinia et al., 2010).

In this chapter, we would like to introduce recently proposed method, MiRaGE, (Yoshizawa et al.,2011) MiRNA Ranking by Gene Expression, for the inference of target gene regulation based on target geneexpression.

2 Algorithm

2.1 Basic Algorithm

The basic algorithm of MiRaGE method is as follows:

1. Define the set of considered miRNAs, m0.

2. Define the set of target genes, G0, targeted by at least one of miRNAs which belong to m0.

3. Specify one of miRNAs, m ∈m0.

4. Define the set of genes, Gm, which are targeted by the miRNA m.

5. Define the set of genes, Gm ≡ G0 ∖Gm, which are not targeted by the miRNA m but targeted by anyother miRNAs, m′ ∈m0∖m

6. Compute P-value which rejects null hypothesis that “gene expression does not differ between Gm andGm” in favor of the alternative hypothesis either “genes in Gm are more expressive than genes in Gm”or “genes in Gm are less expressive (repressive) than genes in Gm”.

Target gene specification employed in MiRaGE method is based on the seed match using the 7mer-M8 seed (Bartel, 2009), i.e., from 2nd to 8th sites from 5’-end of each miRNA (see Figure 2), in 3’UTR oftarget mRNA. No any other conditions are considered.

Onemaywonder why the negative set,Gm, excludedmRNAswhich are not targeted by anymiRNAs.However, because of competition for Ago protein, the increased amount of some miRNA inevitably sup-presses the function of other miRNAs (Khan et al., 2009), which affects the target gene expression. Thus,mRNAs not targeted by any miRNAs should be distinct from mRNAs targeted by at least one miRNA.

Any statistical tests can be employed for P-value computations, but for the present implementations(see §3), t-test, Wilcoxon rank sum test, and Kolmogorov-Smirnov test are employed.

The measure of differential expression is not unique, either. For the present implementations weemployed either logarithmic ratio

log(xtreatmentxcontrol

) (1)

or differencextreatment−xcontrol. (2)

xtreatment and xcontrol are gene expression under the treatment condition and that under the controlcondition, respectively. The former (eq. (1)) is employed if gene expression is positive definite, e.g., counts.Otherwise the latter (eq. (2)) is employed (see §3).

2.2 Preprocessing

Often several types of preprocessing help MiRaGE method to achieve better performances. For example,there are many cases where more than two experimental conditions. Typical examples of this type are timecourse data and subclasses of tumors. For these cases, MiRaGE method cannot be applied to them directly.

Our recommendation for such cases is the application of principal component analysis (PCA). Sup-pose xgs is gene expression of the gth gene in the sth sample. PCA gives us principal component (PC)scores

xgi =S

∑s=1

cisxgs, i = 1, ...,S.

where S is the total number of samples. c1s s is decided so as to maximize the variance

⟨(xgi− ⟨xgi⟩g)2⟩g ≡1

N(G0)∑(xgi− ⟨xgi⟩g)2 (3)

where N(G0) is the number of genes in the set G0 and ⟨xgi⟩g is the average,

⟨xgi⟩g ≡1

N(G0)∑

gxgi,

under constraint that matrix {cis} is unitary.

cis for i > 1 is decided so as to maximize the variance eq. (3) in the space perpendicular to all of

proceeding c js , j < i. Empirically, there often exists xgi which characterizes the overall structure of multi-

ple treatments. For example, for the time course data, xgi can often correspond to the component whichis a monotonically decreasing/increasing function of time. Alternatively, for tumor subclasses, ggi can ex-press the component which is monotonically decreasing/increasing function of progress of diseases. Whenone can find such a suitable component xsi, we can replace xgi with the previously proposed differentialexpressions eqs. (1) and (2).

Simple application of PCA sometimes fails and no suitable PC scores are found. In that case, weneed more sophisticated approach: the combined usage of liner discriminant analysis (LDA) with PCA.LDA can give us the discriminant function, Ls for each sample as

Lks ≡∑

jbk

jx js,k = 1, ..,K

such that Lks discriminates multiple treatments, where K is the total number of treatments. x js is again PC

score, but this time reciprocal PCA is applied and

x js ≡∑g

a jgxgs

where a js is decided so as to maximize the other variance defined differently from eq. (3) as follows,

⟨(x js− ⟨x js⟩s)2⟩s ≡1S∑(x js− ⟨x js⟩s)2

where ⟨x js⟩s is the average,

⟨x js⟩s ≡1S∑

sx js.

Then we employxk

g ≡∑j

a jgbk

j

instead of the differential expression eqs. (1) and (2). Typically, x1g is employed.

3 Implementations

In order for readers to be able to apply MiRaGE method to their data easily, there are two publicly availableimplementations of MiRaGE method, MiRaGE Server and MiRaGE package. MiRaGE is at the momentbased on mature miRNA sequences taken frommiRBase release 181 and 3’UTR sequences taken from hg19human genome2 and mm9 mouse genome3. For more details, see manual and vignette in MiRaGE package(§3.2) page in Bioconductor.

3.1 MiRaGE Serever

The first implementation of MiRaGE method is MiRaGE Server4. It is publicly available and is usable freeof charge (Figure 3). At the moment, supported species are only human and mouse. But any other speciescan be supported if both their 3’ UTR sequences of genes and mature miRNA sequences are available.Please send the requirement to the author. In the followings, we introduce how to use MiRaGE Server stepby step.

3.1.1 Preparation of input file

First we have to prepare a file to be uploaded to MiRaGE Server. The format of input file5 is as follows.The input file must be tab limited csv file. There are no limitations for file extensions. The first columnincludes gene identifier. Acceptable gene identifier can be found in pull down menu on MiRaGE Server(RefSeqmRNA is a primary one). The first row of the the first columnmust be “gene”. From the second line,gene expression values must be placed. Each cell in the first row must identify each column in the followingformat: “group1.1”,“group1.2”, ...., “group1.N”,“group2.1”,“group2.2”,...,“group2.M”. Here “group1” and“group2” express two treatment conditions and can be any words. N and M are the number of replicates ineach treatment sample, respectively. The example is shown in Table 1

gene control.1 control.2 control.3 treatment.1 treatment.2 treatment.3NM 032291 3.00 3.12 3.33 2.33 0.44 0.55NM 052998 2.00 3.42 5.33 2.83 7.44 0.45

. . .

Table 1: An example of input file. Two treatments are called as “control” and “treatment”respectively. Each treatment has three replicates in the example.

The prepared input file can be uploaded at the page which appears when click “here” after selecting“How to Use” tab at the top page.

1ftp://mirbase.org/pub/mirbase/182http://hgdownload.soe.ucsc.edu/goldenPath/hg19/bigZips3http://hgdownload.soe.ucsc.edu/goldenPath/mm9/bigZips4http://www.granular.com/MiRaGE5An example file is available at http://granular.com/MiRaGE/example.csv

Figure 3: A screen shot of MiRaGE Server: http://www.granular.com/MiRaGE. In orderto start inference, select “How to Use” tab located at the second left on the yellow row.

3.1.2 Options

Prior to uploading file by pressing “upload” button, one has to specify several options by selecting suitableterms from pull down menus.

Select the species of your data [M. musculus]Select the conservation of miRNA [conserved]Select type of identifier [Refseq(Primary)]Select how to treat samples [mean]Select Statistical test [Kolmogorov-Smirnov](*) Gene expression profiles[text area](*) file uploadname of file: [select file][upload]

At the top of the menu, one can specify target species. The species which can be selected are “M.musculus” and “H. sapiens” at the moment. The next option is the amount of miRNA conservations: “con-served”, “weakly conserved”, or “all”. The first one corresponds to “Broadly conserved” and the secondone corresponds to “Conserved” in TargetScan6. At the moment, it is based on Release 6.0. The thirdoption specifies the type of gene identifier. RefSeq mRNA is the primary one. Any other gene IDs, e.g.,GenBank ID or several probe IDs for microarrays, are transformed from RefSeq using BioMart7. The forthoption chooses the method how to treat replicates. When one chooses “mean”, replicated are averagedbefore P-value computation. When “mixed” is selected, each replicates are used as it is for P-value com-putation. Finally, “one by one” means that all pairs of replicates are considered. This means, if there arethree replicated for each treatments, in total 3×3 = 9 P-values are attributed to each miRNA.

After choosing all options, one can upload input file either by cut & paste to the text window or byspecifying a file using “file selection” button. Then, press “upload”. Computation will start.

3.1.3 Results retrieval

The pages which indicate progress of computation will be automatically presented.

gene ID:refseqSpecies :MMTest :ks.testTraetment of samples :mean===============================Progressing ....156/181

Finally, the link to the results page appears, “Finished. Click here”.

gene ID:refseqSpecies :MMTest :ks.testTraetment of samples :mean===============================Finished. Click here

By pressing this link, one is guided to the page including links to the results. There are a pair of linksat the two bottom lines of the page.

Species : MMTraetment of samples : meanTest : ks .testResults for target gene expression is : group1 < group2Results for target gene expression is : group1 > group2

The former (upper) is for miRNAs whose target genes are significantly suppressive in group1 thanin group2 and the latter (lower) is vice varsa. The results are presented as a html formated page (Table 2).If one would like to keep it, one can copy and paste the results to some spread sheet software, e.g., Excel orLibreOffice Calc.

6http://www.targetscan.org7http://asia.ensembl.org/biomart/martview

Refseq P-values1 hsa-miR-33a-5p 7.26e-062 hsa-miR-190a 4.13e-053 hsa-miR-218-5p 1.05e-044 hsa-miR-99a-5p 9.50e-045 hsa-miR-100-5p 9.50e-046 hsa-miR-99b-5p 9.50e-047 hsa-miR-205-5p 1.63e-038 hsa-miR-219-5p 3.71e-039 hsa-miR-574-5p 3.72e-0310 hsa-miR-365a-5p 4.04e-0311 hsa-miR-375 7.11e-0... ... ...

Table 2: An example of results page.

3.1.4 Heatmap

When more than one replicates are given for each treatment, one can draw heatmap based on the results. Inorder to draw heatmap, one should go back to “How to Use” tab. Then press click “here” below “Heatmap”.Then one is forwarded to the page where how many top ranked significant (i.e., smaller P-values) miRNAsare used. Then press “send”. One can see heatmap as a PDF file.

3.2 MiRaGE Package in Bioconductor

The second implementation is a registered package in Bioconductor (Gentleman et al., 2004)8. Biocon-ductor is a R (R Core Team, 2012)-based open source project which is dedicated to tools for the analysisand comprehension of high-throughput genomic data. In the present release 2.10, it includes 554 softwarepackages. The most popular one, Biobase, was downloaded more than one hundred and thirty thousandstimes over the last 12 months. Although detailed manual and vignette can be obtained in MiRaGE page inBioconductor, the brief explanation of how to use MiRaGE in Bioconductor will be shown in the following.

MiRaGE was accepted to be included into the previous release 2.11 as a part of Bioconductor9.

3.2.1 Installation

Suppose R (R Core Team, 2012) has already been installed. Then, one can do simply

> source("http://bioconductor.org/biocLite.R")> biocLite("MiRaGE")> require(MiRaGE)

Now, you are prepared to start to use MiRaGE.

8http://www.bioconductor.org9http://bioconductor.org/packages/2.11/bioc/html/MiRaGE.html

3.2.2 Example

In order to see how it works, just execute followings.

> data(gene_exp)> result <- MiRaGE(gene_exp,location="web",species="HS")

where gene_exp is the ExpressionSet object which stores target gene expression5. You have to be con-nected to internet to execute the above because MiRaGE tries to download data set from MiRaGE Server4.result consists of two dataframe. P0 includes P-values which reject the null hypothesis in favor of the al-ternative hypothesis of “target genes in group1 is less expressive than group2”. The example of result$P0is

> result$P0[1:9,]Refseq mixed

1 hsa-let-7a-5p 0.996935122 hsa-let-7b-5p 0.996935123 hsa-let-7c 0.996935124 hsa-let-7d-5p 0.996935125 hsa-let-7e-5p 0.996935126 hsa-let-7f-5p 0.996935127 hsa-miR-15a-5p 0.961182768 hsa-miR-16-5p 0.961182769 hsa-miR-17-5p 0.17071767

where the first column includes miRNA names and the second column includes P-vales attributed to miR-NAs. P1 includes P-values which reject the null hypothesis in favor of the alternative hypothesis of “targetgenes in group1 is more expressive than group2”. Basically, MiRaGE package can do everything possiblein MiRaGE Server excluding heatmap drawing which can be easily drawn by standard functions imple-mented in R. Thus, in order to avoid duplicated explanation, we omit the detailed description on how to useMiRaGE package in order to infer miRNAs regulation of target genes. For more details, see manual andvignette in MiRaGE package page in Bioconductor.

4 Applications to some biological examples

In this section, we will discuss about several applications of MiRaGE method to biological examples.

4.1 miRNA transfection to human lung cancer cell lines

Although it has already been reported (Taguchi &Yasuda, 2012; Taguchi &Yasuda, 2010), we reanalyze thisexample here by new (updated) version of MiRaGE method. In their research, Takahashi et al. transfectedthree miRNAs, let-7a, miR-107 and miR-185 to human lung cancer cell line (Takahashi et al., 2009). Theauthors also confirmed that transfected miRNAs cause cell cycle arrest. The problem is if MiRaGE methodcorrectly infers transfected miRNA form the target gene expression profile.

Table 3 shows frequency that target genes of transfected miRNA are significantly suppressive thannegative control. P-values are computed byMiRaGE package implemented in Bioconductor with specifying“one by one” option. Since there are two biological replicates for both transfected sample and negative

control, in total four combinations are possible. Logarithmic ratios eq. (1) are employed for the differentialexpression. miRNA conservations are set to be “conserved” excluding miR-185 which is not conserved.Then, “weak conserve” option is set for miR-185. Used statistical tests are Kolmogorov-Smirnov test, t-test,and Wilcoxon rank sum test.

First, we adjusted obtained P-values by BH criterion (Benjamini & Hochberg, 1995). If adjusted P-values, i.e., q-values, are less than 0.05, we regard target genes are downregulated by miRNA transfection.The maximum frequency that target genes of each miRNA is downregulated is four. One can see that thetarget genes of the transfected miRNAs are always most frequently downregulated significantly regardlessused statistical test (bold numbers in Table 3). Although there are many false positives since there aremore than one miRNAs whose target genes are significantly downregulated as frequently as those of thetransfected miRNA, detailed views reduce this impression. For example, in Table 4 where P-values arecomputed by Kolmogorov-Smirnov test, miR-107 and 103 are miRNAs whose target genes are significantlyand most frequently downregulated when miR-107 is transfected. However, miR-107 and 103 share thesame target tables. Thus, essentially, there are no false positives. For let-7a transfection, there are elevenmiRNAs downregulated most frequently and significantly. However, the only two miRNAs miR-196a and196b, which share the same target gene table, have distinct target gene table from that of let-7a. Thus, thenumber of false positives is only one. For miR-185 transfection there are more false positives, but it is clearthat miR-185 has extremely small q-value compared with other false positive miRNAs. Thus, in principal,inference of transfected miRNA by MiRaGE method is very well. The achievements by other two statistictests (Tables 5 and 6) are better or at least competitive.

Since there are relatively small number of “active” methods to infer miRNAs regulation of targetgenes, there are not so many methods whose performances can be compared with the above results. Amongthem, we employ mirAct (Liang et al., 2011) in order to validate our performance. Since there are too manyoptions in mirAct, we have basically used the default settings. Only one option without the default value istype of miRNA Target Prediction that was set to be TargetScan. The option to determine miRNA activitywas also changed from “sample scores” (default value) to “transformed expression levels of miRNA targets”because default setting could give us no significant P-values because of insufficient number of replicates.We have uploaded four replicates for each miRNA transfection experiment as a file. For the miR-107transfection experiment, mirAct reported that significant P-vales, i.e., q < 0.05, were attributes to 6 seedfamilies out of 143 seed families that share same seed sequence and have two to five members. Among theselected 6 seed families, the one including miR-107 had the smallest P-value. For the let-7a transfectionexperiment, mirAct reported that 14 seed families have significant P-values. The one including let-7a hasthe smallest P-value. For the miR-185 transfection experiment, there were 77 seed families that significantP-values were attributed to. The seed family including miR-185 was ranked 62th among the selected 77seed families. Thus, both from the point of estimation of the correct transfected miRNAs and from the pointof the number of false positives, MiRaGE Server can outperform mirAct, at least, for this example.

In conclusion, performance of MiRaGE method is very well.

4.2 Differentiation of cell lines

Differentiation is one of biological processes where miRNAs are believed to play critical roles (Schmittgen,2008). Although we previously investigated miRNAs regulation of target genes during differentiation fromES cell to neuronal cell (Yoshizawa et al., 2011), miRNA expression profiles were not available. In this

transfected miRNA Kolmogorov-Smirnov t Wilcoxon rank sumferq. of q < 0.05 0 1 2 3 4 0 1 2 3 4 0 1 2 3 4

miR-107 119 54 6 2 0 123 50 0 8 0 135 38 0 8 0miR-185 62 120 40 26 13 67 112 48 28 6 70 124 31 29 7let-7a 77 89 4 0 11 86 77 7 0 11 114 55 1 0 11

Table 3: Frequency (maximum is four) of P-values which reject the null hypothesis in favor ofthe alternative hypothesis “target genes one day after miRNAs transfection are suppressive thanthose in negative control” corrected by BH criterion (Benjamini & Hochberg, 1995), q-values,is less than 0.05 for each miRNA. Bold numbers include transfected miRNA.

miRNA neg.1.vs.day1.1 neg.1.vs.day1.2 neg.2.vs.day1.1 neg.2.vs.day1.2miR-107 transfection

1 hsa-miR-103a-3p,107 1.55e-05 7.51e-03 4.14e-07 3.30e-09

miR-185 transfection1 hsa-miR-185-5p 9.04e-25 2.26e-26 4.24e-16 1.48e-342 hsa-miR-330-5p,326 1.46e-10 1.53e-10 2.75e-03 3.57e-154 hsa-miR-2861 1.39e-10 1.99e-09 7.43e-04 2.08e-135 hsa-miR-516b-5p 4.18e-06 5.56e-05 2.74e-04 1.53e-166 hsa-miR-4270 5.37e-09 1.78e-04 1.82e-05 1.95e-077 hsa-miR-2467-5p 8.02e-04 6.86e-07 2.88e-02 1.22e-088 hsa-miR-370 2.01e-05 8.01e-04 8.45e-04 6.06e-079 hsa-miR-138-5p 2.85e-03 4.47e-05 2.64e-02 2.35e-0710 hsa-miR-615-5p 2.44e-04 2.12e-03 8.60e-03 2.84e-0711 hsa-miR-214-3p 7.28e-04 1.30e-04 1.14e-03 2.28e-0412 hsa-miR-371b-3p 9.97e-04 8.23e-05 2.95e-02 5.14e-0413 hsa-miR-4437 4.99e-04 3.28e-03 1.62e-02 5.95e-05

let-7a transfection1 hsa-let-7a,b,d,e,f,g,i-5p,7c,miR-98 2.26e-22 1.27e-13 6.79e-22 1.43e-3010 hsa-miR-196a,b-5p 1.06e-13 1.91e-03 5.11e-10 1.24e-07

Table 4: q-values computed by Kolmogorov-Smirnov test. miRNAs in bold phase correspondto tranfected miRNA.

subsection, we investigate the case where both mRNA and miRNA expression are available, for mice neuralstem/progenitor cells (NSPCs) and mice brain endothelial cells (ECs) (Roitbak et al., 2011). Although thepurpose of their work was not the comparison between two cell lines, we can make use of miRNA andmRNA expression profiles provided by them.

EC and NSPC are believed to interact with each other. The interaction between EC and NSPC isbelieved to be important during cell differentiation. If how differently miRNAs regulate target genes inboth cell types is known, it is very useful to understand how miRNAs take places during cell differentiation.

From Gene Expression Omnibus (GEO), mRNA and miRNA expression profiles with GEO IDGSE29759 were downloaded. CEL files were loaded by ReadAffy function in affy package in Biocon-ductor

miRNA neg.1.vs.day1.1 neg.1.vs.day1.2 neg.2.vs.day1.1 neg.2.vs.day1.2miR-107 transfection1 hsa-miR-15a,b,16,195,424,497-5p 2.17e-03 9.99e-01 3.21e-13 1.01e-077 hsa-miR-103a-3p,107 1.25e-04 2.25e-01 2.85e-08 1.17e-10

miR-185 transfection1 hsa-miR-185-5p 6.42e-26 1.80e-20 1.09e-09 7.73e-352 hsa-miR-330-5p,326 9.93e-10 2.51e-06 2.77e-04 3.01e-084 hsa-miR-2861 1.03e-09 2.64e-05 7.65e-05 2.74e-085 hsa-miR-516b-5p 3.37e-06 5.14e-06 3.10e-03 2.39e-116 hsa-miR-122-5p 4.59e-04 1.08e-03 8.05e-03 1.95e-07

let-7a transfection1 hsa-let-7a,b,d,e,f,g,h-5p,7c,miR-98 7.28e-27 2.63e-14 2.54e-19 2.22e-3010 hsa-miR-196a,b-5p 5.70e-14 1.50e-04 6.94e-09 5.34e-09

Table 5: q-values computed by t-test. Other notations are the same as Table 4

miRNA neg.1.vs.day1.1 neg.1.vs.day1.2 neg.2.vs.day1.1 neg.2.vs.day1.2miR-107 transfection1 hsa-miR-103a-3p,107 4.29e-05 7.44e-02 4.32e-08 9.32e-103 hsa-miR-15a,b,16.195.424,497-5p 1.15e-03 9.99e-01 8.76e-10 1.84e-08

miR-185 transfection1 hsa-miR-185-5p 1.70e-22 3.39e-23 1.41e-10 3.95e-382 hsa-miR-2861 2.72e-11 4.76e-07 1.15e-03 1.44e-113 hsa-miR-516b-5p 1.77e-06 2.15e-06 1.83e-04 1.41e-154 hsa-miR-330-5p,326 2.41e-10 1.48e-08 2.69e-03 1.02e-106 hsa-miR-138-5p 1.45e-03 1.10e-03 1.91e-02 1.34e-067 hsa-miR-122-5p 9.76e-04 7.44e-04 8.60e-03 1.54e-05

let-7a transfection1 hsa-let-7a,b,d,e,f,g,i-5p,7c,miR-98 1.46e-28 7.14e-15 1.94e-23 1.03e-3710 hsa-miR-196a,b-5p 3.13e-14 1.40e-04 6.31e-11 1.01e-09

Table 6: q-values computed by Wilcoxon rank sum test. Other notations are the same as Table4

(Gentleman et al., 2004). Then rma functionwas used for normalization. Three biological replicates for eachcell lines are used for further analysis, i.e., mRNA for EC: GSM737672-4, mRNA for NSPC: GSM737678-80, miRNA for EC: GSM737620-2, and miRNA for NSPC: GSM737626-8.

First, we inferred miRNAs regulation of target genes by miRNAs using MiRaGE package. Optionswere as follows. Species is mouse, only conserved miRNAs (in total 187) are considered, gene identifier isAffy mouse430 2 (affy mouse430 2), and method was one by one. Since there are three replicates for eachcell lines, in total 9 P-values were attributed to each miRNA.

Among 187 miRNAs considered, 177, 178 and 172 miRNAs’ target genes are significantly upreg-ulated in NSPC for all nine combinations (q-values < 0.05 after the correction by BH criterion) when

Kolmogorov-Smirnov test, t test, and Wilcoxon rank sum test were used, respectively (Table 7). Thus,regardless employed statistical tests, target genes were mostly upragulated in NSPC. One may think that itis strange because some miRNAs’ target genes should be upregulated in EC, too. However, since there aremany other factors which can regulate gene expression, target gene expression may be affected by theseother factors, too. For example, promoter methylation is correlated to miRNAs regulation of target genes(see §4.4). Thus, it is possible that most of target genes are upregulated only in NSPC if compared withEC. Since the performances of three statistical tests are similar, we consider 178 miRNAs whose targetgenes are judged to be always upregulated in NSPC by t test, which gave us maximum number of miRNAsupregulated consistently in NSPC.

ferq. of q < 0.05 0 1 2 4 6 7 8 9Kolmogorov-Smirnov test 4 5 1 177t test 1 1 3 3 1 178Wilcoxon rank sum test 5 1 1 5 3 172

Table 7: Distribution of frequency (at most, nine) that target genes are significantly upregulatedin NSPC compared with EC.

Figure 4: Schematic that illustrates the reciprocal relationship between miRNAs and targetgenes (mRNAs). Denser gradation indicates more expressive miRNA/mRNA. The miRNAssuppressive in NSPC (i.e., expressive in EC) have tendency to target mRNAs expressive inNSPC(i.e., suppressive in EC). Rank is the ascending-order of P-values which reject the null hypoth-esis in favor of the alternative hypothesis “target genes are more expressive in NSPC than EC”and is used in Table 8.

Second, we would like to compare miRNAs regulation of target genes with miRNAs expressiondifference (2) between EC and NSPC (Figure 4). Generally, since miRNAs are believed to suppress targetgene expression, miRNAs whose target genes are upregulated in NSPC should be downregulated in NSPC.In other words, miRNAs with small P-values which reject the null hypothesis in favor of the alternativehypothesis “target genes are more expressive in NSPC than EC” should be more suppressive in NSPC thanthe miRNAs with larger P-values.

upregulated in ECmiRNAs release 18 rank

1 mmu-miR-134 — none2 mmu-miR-143 140-3p none3 mmu-miR-145 145-3p 844 mmu-miR-182 182-5p 195 mmu-miR-193* 193-5p none6 mmu-miR-199a-3p — none7 mmu-miR-199a-5p — 718 mmu-miR-199b — none9 mmu-miR-214* — none10 mmu-miR-214 214-3p 7711 mmu-miR-222 222-3p 6112 mmu-miR-299 — none13 mmu-miR-300 — none14 mmu-miR-31* 31-5p 6915 mmu-miR-31 31-3p none16 mmu-miR-34b-3p — none17 mmu-miR-34c* 34c-3p none18 mmu-miR-34c 34c-5p 12219 mmu-miR-379 — none20 mmu-miR-409-3p — none21 mmu-miR-574-3p — none22 mmu-miR-590-5p — none

upregulated in NSPCmiRNAs release 18 rank

1 mmu-miR-106a 106a-5p 302 mmu-miR-124 — none3 mmu-miR-128 128 514 mmu-miR-129-5p — 705 mmu-miR-138* 138-3p none6 mmu-miR-138 138-5p 1407 mmu-miR-15a 15a-5p 228 mmu-miR-187 187-3p 1759 mmu-miR-192 192-5p 17210 mmu-miR-194 194-5p 7211 mmu-miR-19a 19a-3p 4712 mmu-miR-20b — none13 mmu-miR-212 212-3p 14214 mmu-miR-26b 26b-5p 4515 mmu-miR-301a 301a-3p 13616 mmu-miR-301b — none17 mmu-miR-30a* 30a-3p none18 mmu-miR-30b 30b-5p 419 mmu-miR-325 — none20 mmu-miR-338-5p — none21 mmu-miR-342-5p — none22 mmu-miR-344 — none23 mmu-miR-346 — none24 mmu-miR-350 — none25 mmu-miR-362-5p — none26 mmu-miR-383 383-5p 13427 mmu-miR-421 — none28 mmu-miR-672 672-5p 12629 mmu-miR-674 — none30 mmu-miR-676 — none31 mmu-miR-802 — none32 mmu-miR-9 — none33 mmu-miR-92b — none

Table 8: Rank order of target gene upregulated in NSPC of miRNAs significantly upregultatedin NSPC and EC.

In order to confirm this, we first selected miRNAs expressed differently between NSPC and EC.Since there are only three replicates for NSPC and EC, we could not have many miRNAs up/downregulatedsignificantly between NSPC and EC. The significance was judged by fold changes and q-values, which areP-values adjusted by BH criterion (Benjamini & Hochberg, 1995), where P-values were computed by t testusing differential expression eq. (2). Table 8 shows the list of miRNAs which are significantly upregulatedin either EC or NSPC. Here “significantly upregulated” miRNAs are associated with q < 0.05 and more thantwo fold changes between NSPC and EC. In total, there are 22 miRNAs upregulated in EC and 33 miRNAsupregulated in NSPC.

Thenwe try to check the reciprocal relationship betweenmiRNAs expression andmiRNAs regulationof target genes (Figure 4). In order to do this, we arrange selected 178 miRNAs (Table 7) in ascending-orderof P-values which reject the null hypothesis in favor of the alternative hypothesis “target genes are moreexpressive in NSPC than EC”. Thus, if miRNAs suppress target genes, miRNAs with smaller rank shouldbe expressive in EC. As can be seen in Table 8, seven miRNAs are included in 178 miRNAs whose targetgenes are judged to be always upregulated in NSPC by t test. Most of seven miRNAs have ranks less thanhalf of 178. This means that miRNAs whose target genes are relatively expressive in NSPC are expressivein EC, i.e., it is probable that miRNAs suppress target genes.

In order to quantitatively confirm this conclusion “miRNAs whose target genes are relatively expres-sive in NSPC are expressive in EC”, we apply four statistical tests to compare the seven miRNAs regulationof target genes with the other miRNAs regulation of target genes (Table 9). The first one is binomial test.We count miRNAs whose rank order (Figure 4) is less than half of total 178 miRNAs and compute P-valueof occurrence by chance. The result is P = 0.008. The second test is t-test using P-values which rejectthe null hypothesis in favor of the alternative hypothesis “target genes are more expressive in NSPC thanEC”. We could not detect any significant difference using t test which compares P-values between sevenmiRNAs and others. Thus we employed logPs instead of raw P-values. Then we get P = 0.03 which re-ject null hypothesis in favor of the alternative hypothesis “seven miRNAs have smaller logP than othermiRNAs”. We also apply Wilcoxon rank sum test and Kolmogorov-Smirnov test using raw P-values andobtained P = 0.006 and 2×10−5, respectively. Thus, above conclusion that “miRNAs whose target genesare relatively expressive in NSPC are expressive in EC” is supported quantitatively.

miRNAs mRNA miRNAs mRNAExpressive in EC NSPC NSPC ECBinomial test 0.008 not significantt test 0.03 not significantWilcoxon rank sum test 0.006 not significantKolmogorov-Smirnov test 2×10−5 0.03

Table 9: Significant tests of reciprocal relationship betweenmRNAs (target genes) and miRNAs

We also checked if miRNAs whose target genes are expressive in EC are expressive in NSPC (Table9). The results were less clear. Only Kormogorv-Smirnov test can detect the significance and obtainedP-value is larger than that for miRNAs which are expressive in EC and whose target genes are expressivein NSPC. However, at least, the results does not disagree with the prospect that target genes of expressivemiRNAs should be suppressive.

Although reciprocal relationship between miRNAs and target genes must not always be fulfilled, atleast MiRaGE method turned out to have enough sensitivity to detect the reciprocal relationship if it exists.

Before closing this section, we would like to discuss biological significance of selected miRNAs.As can be seen in Table 8 there are seven miRNAs which are expressive in EC and whose target genesare relatively expressive in NSPC (mmu-miR-145-3p, 182-5p, 199a-5p, 214-3p, 222-3p, 31-5p, 34c-5p)and 15 miRNAs which are expressive in NSPC and whose target genes are relatively expressive in EC (mmu-miR-106a-5p, 128, 129-5p, 138-5p, 15a-5p, 187-3p, 192-5p, 194-5p, 19a-3p, 212-3p, 26b, 301a-3p,30b-5p, 383-5p, 672-5p). It is supposed that these miRNAs were previously reported to play critical rolesin brain functions/development.

Actually, there are many reports about the relationship between these miRNAs and brain functions.For example Li et al. investigated microRNA expression profiles in human colorectal cancers with brainmetastases (Li et al., 2012). They found overexpression of miR-145, 199a-5p, and downregulation of miR-31. Campanini et al. observed underexpression of miR-145 in craniopharyngioma (Campanini et al., 2010). Weeraratne et al found that pleiotropic effects of miR-183˜96˜182 converge to regulate cell survival, pro-liferation and migration in medulloblastoma (Weeraratne et al., 2012). Juhila et al. found overexpressionof miR-182∣miR-96∣miR-183 cluster in frontal cortex and miR-34 family overexpressed in hippocampus(Juhila et al., 2011). Chen et al. found that miR-214 are specifically expressed during neuroblastoma dif-ferentiation, cortical development and embryonic stem cells differentiation, and control neurite outgrowthin vitro (Chen et al., 2010). Quintavalle et al. found that miR-222 overexpression in human glioblas-toma increases invasiveness by targeting the protein phosphate PTPµ (Quintavalle et al., 2012). Yang etal. found that miR-106a inhibits glioma cell growth by targeting E2F1 independent of p53 status (Yanget al., 2011). Papagiannakopoulos et al. found that pro-neural miR-128 is a glioma tumor suppressor thattargets mitogenic kinases (Papagiannakopoulos et al., 2012). Kisliouk et al. found that miR-138 inhibitsEZH2 methyltransferase expression and methylation of histone H3 at lysine 27, and affects thermotoler-ance acquisition (Kisliouk et al., 2011). Buechner et al. found that miR-19a is one of miRNAs that cansuppress endogenous N-myc protein in a MYCN-amplified neuroblastoma cell line (Buechner et al., 2011).miR-212, together with miR-132, is famous to be important in neuron function (Wanet et al., 2012). Itwas also reported that brain derived neurotrophic factor (BDNF) expression is regulated by miR-26b allele-specific binding (Caputo et al., 2011). Amplification and overexpression of hsa-miR-30b at 8q24.22-q24.23in medulloblastoma was observed (Lu et al., 2009). Downregulation of miR-383 in ependymoma was re-ported (Costa et al., 2011). miR-672 was reported to be underexprssed in neuroblastoma cells (Liu et al.,2012).

In conclusion, many of selected miRNAs were reported to be related to brain functions. Thus, Mi-RaGE method turned out to have ability to depict biologically critical miRNAs from target gene expression.

4.3 Cell senescence

Compared with achievement in miRNA transfection experiment (§4.1), the achievements in the previoussubsection (§4.2) was limited. This is possibly because EC is substantially different from NSPC. In contrastto the comparison between EC and NSPC, the target genes’ differential expression caused by miNRA trans-fection is specific and limited. There are many other factors which regulate gene expression. If two statesare very different from each other, it is not easy to distinguish between gene expression difference caused bymiRNA and that by others. This means, MiRaGE works better when gene expression is compared between

slightly different two states.In order to confirm this postulate, in this subsection we consider cell senescence (Taguchi, 2012a;

Taguchi, 2012b). Cell senescence is the phenomenon by which normal diploid cells lose the ability todivide, normally after about 50 cell divisions in vitro. It is believed to be analogous to aging processes.Recently, miRNA are believed to play critical roles during cell senescence (Bonifacio & Jarstfer, 2010).Since the detailed were already reported (Taguchi, 2012a; Taguchi, 2012b), we would like to simply outlinethe obtained results in the below.

• We employed miRNA expression, measured by next generation sequencing (NGS) (Dhahbi et al.,2011).

• Cell lines used for sequencing were young and senescent IMR90 cell lines.

• Target gene expression was measured by microarray for both IMR90 (GEO ID: GSE19018) andMRC5 (GEO ID: GSE15919) cell lines.

• P-values of miRNAs regulation of target genes during cell senescence were computed by MiRaGEServer.

• Coincidence between IMR90 and MRC5 cell lines of target gene regulation during cell senescencewas very high (P < 10−10).

• Reciprocal relationship between miRNA and mRNA(target gene) in IMR90 cell line was significant(P < 10−2).

• Although more than a hundred miRNAs were reported significantly up/downregulated during cellsenescence by NGS (Dhahbi et al., 2011), we could reduce the number of critical miRNAs to 10downregulated miRNAs and 32 upregulated miRNAs by taking into account the significance of targetgene regulation together.

The results are more feasible than the results for the comparison between EC and NSPC. Thus, ourpostulate that comparison between closer states enables us to get better performance seems to be true.

4.4 miRNAs regulation of target genes vs promoter methylation

The miRNAs regulation of Target genes is usually classified to post transcriptional gene expression reg-ulation. Thus, it can cooperate with or compete with pre-transcriptional gene expression regulation, i.e.,epigenetics. For example, transciption factor (TF) regulates gene expression in eukaryotes by binding topromoter region. Gene expression regulation by TF binding to promoter often functions with the miRNAsregulation of target genes (Zacher et al., 2012). There are many other factors which cooperate with orcompete with miRNAs regulation of target genes.

Recently, promoter methylation was reported to complement miRNA targeting to gene (Su et al.,2011). However, it was not yet discussed if the miRNAs regulation of target genes is really related topromoter methylation. In this subsection, we would like to present preliminary results about this issue.

For this purpose, we consider cell senescence process of IMR90 cell line. The primary reason of thischoice is because miRNAs regulation of target genes during cell senescence of IMR90 turns out to correlatewithmiRNA expression (§4.3), thus it is more plausible. Second, The previous and pioneering research dealt

with this cell line (Su et al., 2011). Third, there are several independent reports on promoter methylationof IMR90 cell lines (Nazor et al., 2012; Ohm et al., 2010; Deng et al., 2009). Thus, it is relatively easy toconfirm the obtained results by comparing between various samples measured by different groups/methods.

Although MiRaGE packages were developed for inferring miRNAs regulation of target genes, ifwe used the amount of promoter methylation as input instead of target gene differential expression, wecan investigate how promoter methylation is affected by being targeted by miRNAs. In other words, ifpromoter of target genes of the considered miRNA is significantly methylated/demethylated, we can obtainsignificant P-values which reject the null hypothesis in favor of “promoters of target genes of a miRNA ismore methylated than those of other genes” attributed to miRNAs by MiRaGE method.

Figure 5: Schematic that illustrates the reciprocal relationship between promoter methylationand miRNAs regulation of target genes (mRNAs). Denser gradation indicates more expressivegene or more methylated promoters. miRNAs whose target genes are more expressive duringcell senescence have more target genes whose promotors are methylated.

In order to execute this analysis, we have collected promoter methylation data of IMR90 from severalresources. First we have downloaded DNA methylation patterns of human cell line measured by Illuminainfinium 450K DNA methylation microarray [GSE31848 raw.txt from GEO ID: GSE31848 (Nazor et al.,2012)]. From them, gene expression profile of IMR90 cell line (GEO ID :GSM868008) was extracted. Thenannotation data file of the microarray was also downloaded (GPL13534 HumanMethylation450 15017482v.1.1.csv from GEO ID: GPL13534). The probes annotated as 5’UTR based on the annotation file areemployed for further analysis and RefSeq gene IDs were also attributed to the selected probes.

Then, MiRaGE package was used for all probes with statistical test of t test, Wilcoxon rank sum test,and Kolmogorov-Smirnov test. For a control sample, all of probes are supposed to have 1 and extractedamount of promoter methylation are used as a treatment sample. Thus, input file consists of a controlsample and a treatment sample. P-values that reject the null hypothesis in favor of the alternative hypothesis“promoter regions of target genes of the miRNA is significantly methylated than those of other miRNAs”were attributed to each miRNA. The obtained P-values were compared with P-values [Supp2 (Taguchi,2012a)] that reject the null hypothesis in favor of the alternative hypothesis “target genes in young cellis significantly suppressive than those in senescent cells”. The positive correlation between two types of

P-values means that target genes whose promoter are significantly methylated are expressive during cellsenescence. In other words, genes with methylated promoters are not downregulated (not targeted) duringcell senescence by miRNA, but genes with less methylated promoters are downregulated (targeted) duringcell senescence by miRNA (Figure 5). It is coincident with the previous report that miRNAs tended to targetthe genes with a low DNA methylation level in their promoter region (Su et al., 2011).

array/sequencing Pearson Spearmantest P 1−P logP log(1−P) P 1−P

t 0.36 0.37 0.32 0.39Illumina P-values * * * *

450K DNA Wilcoxon 0.29 0.25 0.25 0.31methylation P-values * * * *microarray KS — 0.29 — 0.54 — 0.50

P-values — * — * — *t 0.33 0.30 0.40 0.41

Illumina P-values * * * *27K DNA Wilcoxon 0.28 0.19 0.35 0.33methylation P-values * * * *microarray KS — 0.31 — 0.72 — 0.61

(GSM760387) P-values — * — * — *t 0.10 0.05 0.39 0.34

Illumina P-values 3.6×10−6 0.03 * *27K DNA Wilcoxon 0.07 0.02 0.33 0.27methylation P-values 0.002 0.3 * *microarray KS — 0.07 — 0.69 — 0.51

(GSM739940) P-values — 0.003 — * — *t 0.25 0.18 0.24 0.29

P-values * 6.6×10−16 * *Bisulfite Wilcoxon 0.21 0.21 0.23 0.24sequencing P-values * * * *

KS — 0.25 — 0.43 — 0.43P-values — * — * — *

Table 10: Correlations between P-vales of miRNAs regulation of target genes [Supp2 (Taguchi,2012a)] and target gene promoter methylation (see text). P-values are attributed to miRNAs.When correlations between 1−P differ from those between P, the former was explicitly shown.The correlations between P-values computed by Kolmogorv-Smirnov test are omitted becauseof possible misscomputations. * indicates that P-values are less than 2.2×10−16. “test” meansstatistical tests used for computation of P-values; t: t test, Wilcoxon: Wilcoxon rank sum test,and KS : Kolmogorov-Smirnov test.

Table 10 shows the results. All correlation coefficients listed in Table 10 is significant. In our knowl-edge, this is the first indication that promoter methylation and miRNAs regulation of target genes are recip-rocal.

Secondwe have obtained DNAmethylation pattern of the same samplemeasured by another microar-ray, Illumina infinium 27K DNAmethylation (GSE30653 signal intensities.txt from GEO ID: GSE30653).

The methylation patterns of IMR90 cell line was extracted (GEO ID: GSM760387). Following the sameprocedure as the above, we obtained P-values of promoter methylation. The results are shown in Table 10,too.

The third sample of IMR90 DNA methylome was also measured by Illumina infinium 27K DNAmethylation microarray, but by the different group [GSM739940.txt from GEO ID : GSM739940 (Ohmet al., 2010)]. Although the results are relatively poor (Table 10), most cases have significant P-values.

The fourth and the last sample is measured by bisulfite sequencing [GSM375442 CpgMIP-IMR90.seq.txt from GEO ID : GSM375442 (Deng et al., 2009)]. The downloaded fastq file was mapped to hg19genome by bowtie210 (Langmead et al., 2009). The obtained SAM file was transformed to BED file formatby sam2bed function of Rsubread package in Bioconductor (Gentleman et al., 2004). Then, BED file wastreated byBismark11 (Krueger&Andrews, 2011). Finally, overlap betweenmathylation sites and 5’UTR areextracted by findOverlaps function of GenomicRanges package in Bioconductor (Gentleman et al., 2004).Again, the correlations are significant.

In conclusion, MiRaGE method can be used to validate how promoter methylation and target generegulation by miRNA can compete with each other.

5 Conclusion

In this chapter, we introduced MiRaGE method. After introducing the algorithm and implementation, wediscussed the application of MiRaGE method to biological examples. First, MiRaGE method was appliedto miRNA transfection experiments and it tuned out that MiRAGE method has ability to detect tarnsfectedmiRNA with high accuracy. Second, MiRaGE was applied to comparison between NSPC and EC celllines. miRNAs selected by MiRaGE method have reciprocal relationship between miRNA expression andtarget gene expression. Also, selected miRNAs were extensively reported to be related to brain functions.Third, the previous application of MiRaGE method to cell senescence was briefly reported. In this appli-cation, more plausible reciprocal relationship between miRNA expression and target gene expression wasobserved. Finally, we have demonstrated the application which detects reciprocal relationship betweenpromoter methylation and target gene regulation by miRNA.

Recently, miRNA expression itself was reported to be not always related to target gene regulationby miRNA (Mullokandov et al., 2012). In this context, inference of target gene regulation by miRNAfrom target gene expression is very important and useful. MiRaGE method is expected to contribute to thisproblem in the future.

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