Research Article Integrative Analysis of miRNA-mRNA and...

Research ArticleIntegrative Analysis of miRNA-mRNA andmiRNA-miRNA Interactions

Li Guo Yang Zhao Sheng Yang Hui Zhang and Feng Chen

Department of Epidemiology and Biostatistics School of Public Health Nanjing Medical University Nanjing 211166 China

Correspondence should be addressed to Feng Chen fengchennjmueducn

Received 5 October 2013 Revised 24 November 2013 Accepted 16 December 2013 Published 12 February 2014

Academic Editor Yufei Huang

Copyright copy 2014 Li Guo et al This is an open access article distributed under the Creative Commons Attribution License whichpermits unrestricted use distribution and reproduction in any medium provided the original work is properly cited

MicroRNAs (miRNAs) are small noncoding regulatory molecules They are involved in many essential biological processesand act by suppressing gene expression The present work reports an integrative analysis of miRNA-mRNA and miRNA-miRNA interactions and their regulatory patterns using high-throughput miRNA and mRNA datasets Aberrantly expressedmiRNA and mRNA profiles were obtained based on fold change analysis and qRT-PCR was used for further validation ofderegulated miRNAs miRNAs and target mRNAs were found to show various expression patterns miRNA-miRNA interactionsand clusteredhomologous miRNAs were also found to contribute to the flexible and selective regulatory network InteractingmiRNAs (eg miRNA-103a and miR-103b) showed more pronounced differences in expression which suggests the potentialldquorestricted interactionrdquo in themiRNAworld miRNAs from the same gene clusters (eg miR-23b gene cluster) or gene families (egmiR-10 gene family) always showed the same types of deregulation patterns although they sometimes differed in expression levelsThese clustered and homologous miRNAs may have close functional relationships which may indicate collaborative interactionsbetween miRNAs The integrative analysis of miRNA-mRNA based on biological characteristics of miRNA will further enrichmiRNA study

1 Introduction

MicroRNAs (miRNAs) are small (sim22 nts) endogenous non-coding RNAs (ncRNAs)They havemany biological roles andact by negatively regulating mRNA expression at the post-transcriptional level [1ndash3] They suppress gene expression viainteraction with their target messenger RNAs (mRNAs) andeither block the translation process or initiate cleavageThesesmall regulators have important roles in multiple essentialbiological processes including cell differentiation and apop-tosis [4] They are also involved in pathological processesand contribute to occurrence and the development of somecancers [5ndash7] Abnormal expression of the small ncRNAsmaylead to cell death or abnormal cell phenotypes via miRNA-mRNA interactions [8] Specifically abnormally expressedmiRNAs have been shown to be crucial contributors andmayserve as biomarkers in many human diseases

Bioinformatics analysis indicates that a specific miRNAcan regulate expression of up to thousand mRNAs through

miRNA-mRNA association and a specific mRNA can beregulated by multiple miRNAs miRNAs may also be regu-lated as potential targets in vivo [9] The flexible regulatorypattern should exist between the two coding (mRNA) andnoncoding (miRNA) RNA molecules Numerous reportshave shown that miRNA-mRNA interaction is more complexthan we had believed and a series of studies have beenperformed to predict miRNA-mRNA modules and correla-tion networks using miRNA and mRNA expression profiles[10ndash13] miRNA-miRNA interactions can also be observedbetween natural sense and antisense miRNAs [14ndash18] ThesemiRNAs alwaysmore pronounced divergences in expressionbecause they may complementarily bind and restrict eachother ManymiRNAs are not randomly distributed but ratherclustered on chromosomes and cotranscribed as a singlepolycistronic transcript [19 20] Some of these clusteredmiRNAs can be considered homologous miRNAs (membersof the same miRNA gene family) Their sequences are moresimilar to each other than those of other miRNAs This isespecially true of miRNAs with conserved seed sequences

Hindawi Publishing CorporationBioMed Research InternationalVolume 2014 Article ID 907420 8 pageshttpdxdoiorg1011552014907420

2 BioMed Research International

(nucleotides 2ndash8) [21] These miRNA gene clusters and genefamilies always have close functional relationships and coreg-ulate or coordinately regulate multiple biological processes[22ndash24]

Integrative analyses that are based on miRNA-mRNAinteractions always aim to develop algorithms or tools [2526] Few studies have addressed the biological characteristicsof miRNA in miRNA-mRNA interactions For examplemiRNAs are prone to cluster on the chromosomes somemiRNAs show more sequence similarity than others anda single miRNA locus can yield a cluster of isomiRs withvarious 51015840 and 31015840 ends and length distributions [23 27 28] Inthe present study an integrative analysis of miRNA-mRNAwas performed based on miRNA and mRNA expressionprofiles in human HepG2 and L02 cells by applying high-throughput techniques HepG2 cells are human hepatomacell lines and they are a suitablemodel to study occurrence ofdevelopment of humanhepatocellular carcinoma in vitro L02is the normal human liver cell line which is always used ascontrol cell lines ofHepG2 cellsThepurpose of this studywasto improve understanding of miRNA-mRNA interactions inregulatory networks The patterns of expression of potentialmiRNA-miRNA pairs were also analyzed comprehensivelyand the patterns of expression of miRNAs with potentialfunctional relationships including members of the samemiRNA gene clusters and gene families were surveyed

2 Materials and Methods

21 miRNA andmRNAProfiling Using High-Throughput Tech-niques HepG2 and L02 cells were obtained from theAmerican Type Tissue Collection miRNA expressionprofiles were generated from Illumina Genome Analyzer IIxand then analyzed using Novoalign software (httpwwwnovocraftcom v207011) based on the latest knownhuman pre-miRNAs in the miRBase database (Release 190httpwwwmirbaseorg) [29] To further understandexpression patterns of target mRNAs of miRNAs mRNAexpression profiles were assessed using microarray hybri-dization Hybridization was performed in Agilentrsquos SureHybHybridization Chambers (Human LncRNA Array v208 times 60K Arraystar)

22 Data Analysis Aberrantly expressed miRNAs andmRNAs in HepG2 cells were surveyed and identified via foldchange analysis To filter out rare species with lower levelsof relative expression fold change values were estimatedby adding additional units (10 units) A detailed flow chartshowing the integrative analysis of miRNA-mRNA is given inFigure 1 The main steps were as follows (1) Abnormal miR-NAs and mRNAs were first surveyed through bioinformaticsanalysis miRNA expression analysis was also performed atthe isomiR level including the different selections of isomiRs(the most abundant isomiR sum of all isomiRs and thecanonical miRNA sequence) [28] (2) Several deregulatedmiRNAs were further experimentally validated using qRT-PCR (3) The potential expression and functional relation-ships among miRNAs were evaluated through analysis of thepatterns of expression of clustered and homologous miRNAs

based on miRNA gene clusters and families miRNA-miRNApairs with potential interactions were also screened andanalyzed (4) GOpathway terms were enriched based onderegulated mRNAs and the target mRNAs of miRNAs andmiRNA-mRNA regulatory patterns were predicted based onexpression profiles

The experimentally validated target mRNAs of thoseabnormal miRNAs were obtained from the miRTarBaseand Tarbase databases [30] Common target mRNAs weresubjected to functional enrichment analysis using Capi-talBio Molecule Annotation System V40 and comparedto abnormally expressed mRNA profiles from microarraydatasets (MAS httpbioinfocapitalbiocommas3) GOandpathway analyses were used to determine the biologicalroles of deregulated miRNA and mRNA species Poten-tial miRNA-mRNA and miRNA-miRNA interactions andmiRNAmRNA expression profiles were used to constructfunctional interaction networks using Cytoscape v282 Plat-form [31]

23 qRT-PCR Validation Abnormal miRNAs were furthervalidated using quantitative real-time reverse transcriptionPCR (qRT-PCR) using SYBR premix Ex Taq (Takara Japan)Samples were amplified using the Mastercycler ep realplex2system (Eppendorf Hamburg Germany) qPCR was per-formed using specifically designed primers and used todetect hsa-miR-15b103a106b (Bulge-Loop miRNA qRT-PCRPrimer Set RiboBio Guangzhou China) andU6 servedas an internal control The relative amount of each miRNAwas measured using the 2(minusΔΔCT) method [32] All qRT-PCR reactions were carried out in triplicate and data werepresented as the mean plusmn standard deviation The two-tailedStudentrsquos 119905 test was used to compare the expression differencebetween tumor and normal cells

3 Results

31 Overview of miRNAmRNA Expression Profiles and Fur-ther Experimental Validation Upregulated and downregu-lated miRNAsmRNAs were identified using the fold changevalues (log 2) based on the control sample Many miRNAsand mRNAs were found to be differentially expressed (seeFigure S1 in the Supplementary Material available onlineat httpdxdoiorg1011552014907420) miRNA expressionpatterns were further analyzed at the isomiR level Foldchange values were found to diverge based on the dif-ferent selections of isomiRs (the most abundant isomiRsum of all isomiRs and the canonical miRNA sequence)(Figure 2(a)) Differences in fold change values rarely affectedthe selection of deregulated miRNA species The canonicalor annotated miRNA sequences were not always the mostdominant species in the miRNA locus They had even lowerlevels of expression The qRT-PCR primers used here weredesigned according to the canonical miRNA sequences in themiRBase database (Release 190 httpwwwmirbaseorg)[29] For this reason in order to further validate deregulatedmiRNAs using qRT-PCR technique we randomly selectedseveral abnormally expressed miRNAs (miR-15b miR-103a

BioMed Research International 3

miRNAmRNA profiling using high-throughput techniques in HepG2 and L02 cells

miRNA expression profiles mRNA expression profiles

Abnormal miRNAmRNA expression profiles in HepG2 cells

Further RT-PCR validation

miRNA gene cluster and

family miRNA-miRNA

interaction GOpathway analysis of

targetsmRNAs

Regulatory pattern of

miRNA-mRNA

Complex and flexible coding-noncoding RNA regulatory network and regulatoryexpression patterns in tumorigenesis

isomiRs

Figure 1 The miRNA-mRNA integrative analysis

Consistent Inconsistent

0

2

4

6

8

103a 106b 194 425 let-7f 15b 24 10a

The-mostAll-isomiRsThe-canonical

minus2

minus4

minus6

minus8 miRNA

Log 2

(fold

chan

ge)

Consistent

Inconsistent

(a)

miRNA

0

05

1

15

2

25

3

mir-15b mir-103a mir-106b

L02HepG2

lowast

lowast

lowast

miR

NA

leve

ls (n

orm

aliz

ed)

(b)

Figure 2 (a) miRNA expression analysis and (b) and further qRT-PCR validation (a) The fold change values (log 2) differ in the varietyof miRNA sequences involved ldquoThe-mostrdquo indicates the most abundant and dominant isomiR sequence ldquoAll-isomiRsrdquo indicates sum ofall isomiRs ldquoThe-canonicalrdquo indicates the reference miRNA sequence in the miRBase database The canonical miRNA sequence may beconsistent or inconsistent with the most abundant isomiR sequence Different methods of estimation may produce different fold changevalues (log 2) but they always show consistent deregulation patterns (b) Further RT-PCR validation is performed for miR-15b miR-103aand miR-106b and the experimental results show consistent deregulation patterns ldquolowastrdquo indicates that the 119875 value is less than 005

and miR-106b their canonical miRNA sequences were themost abundant isomiRs) for further experimental validation(Figure 2) Bioinformatic analysis showed that miR-103a andmiR-106b were upregulated in tumor cells while miR-15bwas identified as downregulated species (Figure 2(a)) Asexpected qRT-PCR experimental validation showed consis-tent results (Figure 2(b))

32 Expression Patterns of miRNA-miRNA Pairs and miRNAGene Clusters and Families The expression patterns ofmiRNA-miRNA pairs that can form miRNA-miRNAduplexes were also analyzed [18] Eight miRNA-miRNApairs were found to be abundantly expressed in HepG2 orL02 cells Expression analysis showed one member of eachnatural miRNA-miRNA pair to be abundantly expressed


Table 1 Differences in expression between natural sense and antisense miRNAs

miRNAmiRNA The most abundant isomiR Sum of all isomiRs The canonical miRNAHepG2 L02 HepG2 L02 HepG2 L02

103a103b 9232mdash 1833mdash 10639mdash 2525mdash 9232mdash 1833mdash1223591 163mdash 938mdash 837mdash 3550mdash 10mdash 17mdash2033545 mdashmdash 216mdash mdashmdash 705mdash mdashmdash 12mdash243074 7872 14998mdash 66183 29094mdash 3592mdash 1597mdash423-5p3184-3p 1208mdash 3159mdash 1882mdash 5790mdash 1208mdash 3159mdash423-3p3184-5p 981mdash 50361 1934mdash 82901 981mdash 5036mdash7-5p3529-3p 1132mdash 939mdash 1931mdash 2397mdash 238mdash 386mdash374b-5p374c-3p 137mdash 67mdash 318mdash 203mdash 137mdash 67mdashBased on the different methods of estimation the most abundant isomiR sum of all isomiRs and the canonical miRNA relative expression levels of these pairsof miRNA pairs were determined They are presented here using normalized data One member of each pair was always far more abundantly expressed thanthe other ldquomdashrdquo indicates an undetectable miRNA

0

1

2

3

miRNA gene cluster

106b 2593 23b 27b 24 7a 7f 7d99a le-7ct

106b cluster

23b cluster7a-1 cluster

minus1

minus2

minus3

minus4

minus5

Log 2

(fold

chan

ge)

99a cluster

(a)

minus2

minus4

minus6

minus8

0

2

4

6

miRNA gene family

103a 107103b

243037

106a20b18b1718a 20a 93 106b

10a 99a 10010b 99b 125a

125b

103 family17 family24 family

10 family

Log 2

(fold

chan

ge)

(b)

Figure 3 Examples of (a) deregulated miRNA gene clusters and (b) gene families (a) Clustered and (b) homologous miRNAs are alwaysconsistently upregulated or downregulated in tumor cells although they can differ in fold change values (log 2) and relative expression levelsmiRNAs shown here to have zero change (such asmiR-25) are not detected or did not show significant differences between tumor and normalcells

and the other to be quite rare (Table 1) For example themiR-103amiR-103b pairs showed a pronounced differencein the degree of expression miR-103a was abundantlyexpressed (normalized sequence count was more than 9232in tumor cells) and miR-103b was not detected Pronounceddifferences in degree of expression were quite commonbetween these two members of each miRNA-miRNA pair(Table 1)

The expression patterns of miRNAs that might havepotential functional relationships were also analyzed Clus-tered and homologous miRNAs always showed consistentpatterns of deregulation (Figure 3) although they could differin relative level of expression sometimes showing largedifferences These differences in expression may have led tothe various fold change values observed between these relatedmiRNAmembers (Figure 3) For example the miRNA in themiR-23b gene cluster were downregulated showing similarfold change values and those of the miR-106b gene clustershowed highly different fold change values (Figure 3(a))

33 Expression and Regulatory Patterns of miRNAsmRNAsand Functional Enrichment Analysis Although each aber-rantly expressed miRNA can negatively regulate targetmRNAs via miRNA-mRNA association their potential tar-gets always show dramatically different expression pat-terns (Figure 4) Common target mRNAs might be detectedbetween different deregulated miRNAs even between upreg-ulated and downregulated miRNAs (according to validatedmiRNA-mRNA interaction E2F3 can be negatively regu-lated by upregulatedmiR-106b and downregulatedmiR-125bFigure 4) Targets of miRNAs of the same gene clusters andfamilies also showed complex expression patterns althoughthese related miRNAs were downregulated in tumor cells(Figure 4(b))These homologous and clusteredmiRNAswerealways simultaneously upregulated or downregulated Theymight negatively target the same mRNAs (Figure 4(b))

Functional enrichment analysis based on the deregulatedtargetmRNAs suggestedmultiple biological roles (Figures S2S3 and S4)Theywere found to contribute tomany biological


DICER1CDK2

GPD1

CREB1

CCNE1

BCL2

EIF4A1

miR-15b

VEGFA

CCND1

CCND2

RECK MAPK9 ITCH

KAT2B

miR-106b

miR-103a

APP MMP13

ATM

ID1

RB1

E2F3

AKT1

KLF13 ERBB2

LACTB

PLK1

BBC3 EGR2

miR-100miR-125b

miR-194

RAC1 EP300

IGF1R CDH2

(a)

miR-23a

miR-23b

miR-27b

miR-24

miR-27a

27 family

23 family

23bcluster

CDK4

EIF4A1

DND1

AURKB

CCNA2

DHFR

BRCA1

IL6R

CeIF1

MET

RB1

EDNRA

ADORA2B

EY4A

PHB

ZBTB10

FOXO1

APP

HMGA2

PRDM1

DICER1

let-7a

let-7d

let-7f

let-7a-1 clusterlet-7 family

E2F2

CCND2

SLC11A2

PDGFA

KLK10

KLK6

(b)

Figure 4 Examples of flexible and selective regulatory network between miRNAs and mRNAs (a) Selected overexpressed (miR-103amiR-106b and miR-194) and underexpressed (miR-15b miR-100 and miR-125b) miRNAs are used to reconstruct the regulatory networkTheir experimentally validated target mRNAs show various expression patterns some are stably expressed and others are upregulated ordownregulated Overexpressed miRNAs and mRNAs are here highlighted in red octagons and ellipse respectively and underexpressedmiRNAs and mRNAs are highlighted in green octagon and ellipse respectively Grey ellipses indicate stably expressed mRNAs and mRNAsare not detected in the present studyThe targets common to differentmiRNAs are highlighted in blue rectangles (b) Selected underexpressedmiRNA gene clusters (miR-23b and let-7a-1) and gene families (miR-23 andmiR-27) also show complex regulatory networksThese clusteredand homologous members are consistently downregulated in tumor cells and their validated targets show various expression patternsmiRNAs in the let-7a-1 gene cluster are also members of the let-7 gene familyThe targets common to these miRNAs have shown upregulateddownregulated and stable patterns of expression

processes such as the cell cycle calcium signaling pathwayp53 signaling pathway and T cell receptor signaling pathwayThese aberrantly expressed mRNA species are also involvedin some human diseases including pancreatic cancer renalcell carcinoma prostate cancer and colorectal cancer

4 Discussion

In the study integrative analysis of miRNA-mRNA is per-formed using biological characteristic of miRNAs andmiRNA-miRNA interaction is simultaneously analyzed based


on the relationships between different miRNAs (Figure 1)Compared to other algorithms or tools of miRNA-mRNAanalysis [25 26] the approach aims to track miRNA-mRNAand miRNA-miRNA interactions based on characteristic ofmiRNAs Specifically (1) miRNAs are prone to detectedhomologous miRNAs with higher level of sequence simi-larity (2) miRNAs are prone to cluster together with closephysical distance (3) some miRNAs are located on senseand antisense strands of specific genomic regions and (4)miRNA locus can generate multiple isomiRs with varioussequences and expression levels and so forth Although thesespecific features of miRNAs have been widely concernedin miRNA study they are rarely mentioned or involvedin miRNA-mRNA analysis Indeed many miRNAs coordi-nately contribute to biological processes and one specificbiological pathway will involved in a series of mRNAsand regulatory miRNAs Therefore it is quite necessaryto study miRNA-mRNA interactions using characteristic ofmiRNAs especially homologous andor clustered miRNAsare prone to have functional relationships More importantlythe canonical or annotated miRNA sequence is only onespecific member of the multiple isomiRs and the study atthe isomiR level will enrich miRNA study IsomiR expressionpatterns contribute to tracking pre-miRNA processing andmiRNA maturation processes and understanding regulatorynetwork at the isomiR levels

According to the integrative analysismethod firstly aber-rantly expressed miRNA and mRNA profiles were collectedbased on fold change analysis To further validate thesederegulated miRNA species several deregulated miRNAsthat had been experimentally validated using qRT-PCRwere randomly selected As expected qRT-PCR experimentsshowed results consistent with those of bioinformatic analysis(Figure 2) As in other reports miR-103a and miR-106b wereoverexpressed in hepatocellular carcinoma (HCC) and servedas important negative regulators [33 34] However miR-15bwas found to be upregulated [35]The overexpression ofmiR-15b may restrict cell proliferation and increase the rate ofcellular apoptosis and abundant expression may indicate alow risk of HCC recurrence [36] The dynamic expression ofmiR-15b may play multiple biological roles in tumorigenesis

Many reports have shown that multiple isomiRs (miRNAvariants) can be detected at the same miRNA locus Thisis due to imprecise and alternative cleavage of Drosha andDicer [23 27 28] According to three different methods ofestimation methods the most abundant isomiR the sumof all isomiRs and the canonical miRNA the phenomenonof the multiple miRNA variants may influence the relativeexpression levels and lead to various fold change values(Figure 2(a)) [24 28] This is mainly because of differencesamong isomiR repertoires and expression patterns althoughthey are always well conserved across different tissues andanimal species [28 37 38] Differences in isomiR expressionprofiles may play a role in occurrence and development ofdisease [28] Generally consistent deregulatedmiRNAs couldbe identified using different methods of estimation methodseven if they have different fold change values (Figure 2(a))However if abnormal miRNA expression profiles are col-lected using the typical methods of analysis of canonical

miRNA or the sum of all isomiRs the difference in foldchange valuesmay affect the collection of deregulatedmiRNAspecies and may require further experimental validationAmong multiple isomiRs the canonical miRNAs are notalways the most abundant (Figure 2(a)) Some of them canbe very rare Other abundant isomiR species especiallyisomiRs with novel 51015840 ends and seed sequences (51015840 isomiRs)may also be regulatory molecules These 51015840 isomiRs mayhave novel potential target mRNAs and may contribute tothe regulation of previously unknown biological processesCollectively it may be best to observe deregulated miRNAsthrough bioinformatic analysis at the miRNA level using themost abundant and dominant isomiR sequence and isomiRprofiles through bioinformatic analysis at the isomiR levelbased on variations in sequence and expression levels

miRNAs negatively regulate mRNA expression and con-tribute to many biological processes through complementarybinding to their target mRNAs Some miRNAs can interactwith the 31015840-untranslated region (UTR) of target mRNAand reduce the level of mRNA expression [39] An attemptwas here made to reconstruct the coding-noncoding RNAregulatory network according to negative regulation andthe deregulation of miRNAs and target mRNAs AlthoughmiRNAs can be either downregulated or upregulated intumor cells their experimentally validated and predictedtargets may show consistent or inconsistent deregulation pat-terns (Figure 4) Abnormal miRNA and mRNA expressionprofiles complicate the regulatory network although theyshowed close functional relationships by forming miRNA-mRNA duplexes A single miRNA can regulate multipletarget mRNAs and vice versa The fact that a single miRNAcan engage inmany possible miRNA-mRNA interactions canrender regulatory networks highly complex Flexible regu-latory patterns indicate that a specific miRNA may regulateselected specific targets and so contribute to specific stages ofdevelopmentmiRNA-mRNAmay affect the spatial-temporalexpression patterns of miRNAs but these interactions canalso be more strictly regulated during specific stages ofdevelopment The selection of regulated target mRNAs mayhave been driven by functional pressure in cellular envi-ronments through complex regulatory mechanisms In thisway overexpressed underexpressed and stably expressedtarget mRNAs can be identified for specific upregulatedand downregulated miRNAs (Figure 4) A single mRNA canbe negatively regulated by selected specific miRNAs Thecoding-noncoding RNA regulatory network is more com-plexity than previously thought especially for complicatedand selective multiple interactions of miRNAs and mRNAs(Figure 4)

Functional miRNA groups also contribute to the com-plexity of regulatory networks miRNAs that have completelyor partially complementary structures can form miRNA-miRNA duplexes through reverse complementary bindingevents They can also form miRNAmiRNAlowast or miRNA--5pmRNA--3p duplexes [14 16ndash18] miRNAmiRNA inter-actions are specific phenomenon They are especially com-mon between natural or endogenous sense and anti-sense miRNAs Possibly because of restricted interactions


these miRNA-miRNA pairs show greater differences in thelevel expression than other miRNAs do one member typ-ically has a far higher level of enrichment than the otherwhich can be quite rare (Table 1)This indicates that restrictedinteractions may be a regulatory pattern in the miRNAworld Another very different type of interaction betweenmiRNAs termed coordinated interaction also contributes tothe pronounced efficiency of the regulatory process SomemiRNAs such as clustered and homologous miRNA speciesmay coregulate or coordinately regulate biological processes[19 40] They may be located close to another (clustered inthe same genomic region miRNA gene cluster) or may sharesequence similarity (homologous miRNAs miRNA genefamily) Some clustered miRNAs share sequence similarityand are identified as both members of the same cluster and ofthe same familyThese phenomena are not randombut ratherderived from functional and evolutionary pressures Theserelated miRNAs always show similar or consistent patternsof deregulation (Figure 3) although they may have differentlevels of enrichment because of maturation and degradationmechanisms Deregulation patterns may cause functionalrelationships This indicates that collaborative interactionsmay take place within the coding-noncoding RNA regulatorynetwork Therefore related miRNAs further complicate theregulatory patterns especially when they share specific targetmRNAs In summary coordinated interactions and restrictedinteractions both exist in the world of small noncodingRNA Although they can be thought of as indirect and directinteractions respectively these interactions represent theversatility and complexity of the functional and evolution-ary relationships among different miRNAs miRNA-miRNAinteractions enrich and complicate the coding-noncodingRNA regulatory network and contribute to the robustness ofthe regulatory network in organism

Conflict of Interests

The authors declare no potential conflict of interests withrespect to the authorship andor publication of this paper

Acknowledgments

This work was supported by the National Natural ScienceFoundation of China (nos 61301251 81072389 and 81373102)the Research Fund for the Doctoral Program of HigherEducation of China (no 211323411002 and 20133234120009)the China Postdoctoral Science Foundation funded project(no 2012M521100) the key Grant of the Natural ScienceFoundation of the Jiangsu Higher Education Institutionsof China (no 10KJA33034) the National Natural ScienceFoundation of Jiangsu (no BK20130885) the Natural ScienceFoundation of the Jiangsu Higher Education Institutions(nos 12KJB310003 and 13KJB330003) the Jiangsu PlannedProjects for Postdoctoral Research Funds (no 1201022B) theScience and Technology Development Fund Key Project ofNanjing Medical University (no 2012NJMU001) and thePriority Academic Program Development of Jiangsu HigherEducation Institutions (PAPD)

References

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[2] H Guo N T Ingolia J S Weissman and D P BartelldquoMammalian microRNAs predominantly act to decrease targetmRNA levelsrdquo Nature vol 466 no 7308 pp 835ndash840 2010

[3] E Huntzinger and E Izaurralde ldquoGene silencing by microR-NAs contributions of translational repression and mRNAdecayrdquo Nature Reviews Genetics vol 12 no 2 pp 99ndash110 2011

[4] L B Frankel N R Christoffersen A Jacobsen M Lindow AKrogh and A H Lund ldquoProgrammed cell death 4 (PDCD4)is an important functional target of the microRNA miR-21 inbreast cancer cellsrdquoThe Journal of Biological Chemistry vol 283no 2 pp 1026ndash1033 2008

[5] P M Voorhoeve C le Sage M Schrier et al ldquoA geneticscreen implicates miRNA-372 and miRNA-373 as oncogenes intesticular germ cell tumorsrdquo Cell vol 124 no 6 pp 1169ndash11812006

[6] W C S Cho ldquoOncomiRs the discovery and progress ofmicroRNAs in cancersrdquo Molecular Cancer vol 6 article 602007

[7] S M Hammond ldquoMicroRNAs as tumor suppressorsrdquo NatureGenetics vol 39 no 5 pp 582ndash583 2007

[8] B J Reinhart F J Slack M Basson et al ldquoThe 21-nucleotidelet-7 RNA regulates developmental timing in Caenorhabditiselegansrdquo Nature vol 403 no 6772 pp 901ndash906 2000

[9] J Wang M Lu C Qiu and Q Cui ldquoTransmiR a transcriptionfactor microRNA regulation databaserdquo Nucleic Acids Researchvol 38 supplement 1 pp D119ndashD122 2010

[10] X Peng Y Li K-A Walters et al ldquoComputational iden-tification of hepatitis C virus associated microRNA-mRNAregulatory modules in human liversrdquo BMC Genomics vol 10article 373 2009

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[12] C Girardi C de Pitta S Casara et al ldquoAnalysis of miRNAandmRNA expression profiles highlights alterations in ionizingradiation response of human lymphocytes under modeledmicrogravityrdquo PLoS ONE vol 7 no 2 Article ID e31293 2012

[13] W Zhang A Edwards W Fan E K Flemington andK Zhang ldquomiRNA-mRNA correlation-network modules inhuman prostate cancer and the differences between primaryand metastatic tumor subtypesrdquo PLoS One vol 7 Article IDe40130 2012

[14] E C Lai C Wiel and G M Rubin ldquoComplementary miRNApairs suggest a regulatory role for miRNAmiRNA duplexesrdquoRNA vol 10 no 2 pp 171ndash175 2004

[15] C F Hongay P L Grisafi T Galitski and G R Fink ldquoAntisensetranscription controls cell fate in Saccharomyces cerevisiaerdquoCell vol 127 no 4 pp 735ndash745 2006

[16] A Stark N Bushati C H Jan et al ldquoA single Hox locusin Drosophila produces functional microRNAs from oppositeDNA strandsrdquo Genes amp Development vol 22 no 1 pp 8ndash132008

[17] L Guo T Liang W Gu Y Xu Y Bai and Z Lu ldquoCross-mapping events in miRNAs reveal potential miRNA-Mimicsand evolutionary implicationsrdquo PLoS ONE vol 6 no 5 ArticleID e20517 2011


[18] L Guo B Sun Q Wu S Yang and F Chen ldquomiRNA-miRNAinteraction implicates for potential mutual regulatory patternrdquoGene vol 511 pp 187ndash194 2012

[19] L P Lim M E Glasner S Yekta C B Burge and D P BartelldquoVertebrate microRNA genesrdquo Science vol 299 no 5612 p1540 2003

[20] V N Kim and J-W Nam ldquoGenomics of microRNArdquo Trends inGenetics vol 22 no 3 pp 165ndash173 2006

[21] A A Aravin M Lagos-Quintana A Yalcin et al ldquoThe smallRNA profile during Drosophila melanogaster developmentrdquoDevelopmental Cell vol 5 no 2 pp 337ndash350 2003

[22] J Yu F Wang G-H Yang et al ldquoHuman microRNA clustersgenomic organization and expression profile in leukemia celllinesrdquo Biochemical and Biophysical Research Communicationsvol 349 no 1 pp 59ndash68 2006

[23] P Landgraf M Rusu R Sheridan et al ldquoA mammalianmicroRNAexpression atlas based on small RNA library sequen-cingrdquo Cell vol 129 no 7 pp 1401ndash1414 2007

[24] L Guo and Z Lu ldquoGlobal expression analysis of miRNA genecluster and family based on isomiRs from deep sequencingdatardquo Computational Biology and Chemistry vol 34 no 3 pp165ndash171 2010

[25] N Hecker C Stephan H J Mollenkopf K Jung R Preissnerand H A Meyer ldquoA new algorithm for integrated analysis ofmiRNA-mRNA interactions based on individual classificationreveals insights into bladder cancerrdquo PLoS One vol 8 ArticleID e64543 2013

[26] M Khorshid J Hausser M Zavolan and E van Nimwegen ldquoAbiophysical miRNA-mRNA interaction model infers canonicaland noncanonical targetsrdquoNatureMethods vol 10 pp 253ndash2552013

[27] R D Morin M D OrsquoConnor M Griffith et al ldquoApplicationof massively parallel sequencing to microRNA profiling anddiscovery in human embryonic stem cellsrdquo Genome Researchvol 18 no 5 pp 610ndash621 2008

[28] L Guo Q Yang J Lu et al ldquoA comprehensive survey of miRNArepertoire and 31015840 addition events in the placentas of patientswith pre-eclampsia from high-throughput sequencingrdquo PLoSONE vol 6 no 6 Article ID e21072 2011

[29] A Kozomara and S Griffiths-Jones ldquoMiRBase integratingmicroRNA annotation and deep-sequencing datardquo NucleicAcids Research vol 39 no 1 pp D152ndashD157 2011

[30] T Vergoulis I S Vlachos P Alexiou et al ldquoTarBase 60 captur-ing the exponential growth ofmiRNA targets with experimentalsupportrdquo Nucleic Acids Research vol 40 pp D222ndashD229 2012

[31] M E Smoot K Ono J Ruscheinski P-L Wang and T IdekerldquoCytoscape 28 new features for data integration and networkvisualizationrdquo Bioinformatics vol 27 no 3 pp 431ndash432 2011

[32] K J Livak and T D Schmittgen ldquoAnalysis of relative geneexpression data using real-time quantitative PCR and the 2-ΔΔCT methodrdquoMethods vol 25 no 4 pp 402ndash408 2001

[33] R RWei G L Huang M Y Zhang et al ldquoClinical significanceand prognostic value of microRNA expression signatures inhepatocellular carcinomardquo Clinical Cancer Research vol 19 no17 pp 4780ndash4791 2013

[34] W L Yau C S Lam L Ng et al ldquoOver-expression of miR-106bpromotes cell migration and metastasis in hepatocellular carci-noma by activating epithelial-mesenchymal transition processrdquoPLoS One vol 8 Article ID e57882 2013

[35] F An B Gong H Wang et al ldquomiR-15b and miR-16 regulateTNF mediated hepatocyte apoptosis via BCL2 in acute liverfailurerdquo Apoptosis vol 17 pp 702ndash716 2012

[36] G E Chung J-H Yoon S J Myung et al ldquoHigh expression ofmicroRNA-15b predicts a low risk of tumor recurrence follow-ing curative resection of hepatocellular carcinomardquo OncologyReports vol 23 no 1 pp 113ndash119 2010

[37] A M Burroughs Y Ando M J L de Hoon et al ldquoAcomprehensive survey of 31015840 animal miRNAmodification eventsand a possible role for 31015840 adenylation in modulating miRNAtargeting effectivenessrdquo Genome Research vol 20 no 10 pp1398ndash1410 2010

[38] S L Fernandez-Valverde R J Taft and J S Mattick ldquoDynamicisomiR regulation in Drosophila developmentrdquo RNA vol 16no 10 pp 1881ndash1888 2010

[39] J Soh J Iqbal J Queiroz C Fernandez-Hernando and M MHussain ldquoMicroRNA-30c reduces hyperlipidemia and athero-sclerosis in mice by decreasing lipid synthesis and lipoproteinsecretionrdquo Nature Medicine vol 19 pp 892ndash900 2013

[40] J Z Xu and C W Wong ldquoA computational screen for mousesignaling pathways targeted by microRNA clustersrdquo RNA vol14 no 7 pp 1276ndash1283 2008

Submit your manuscripts athttpwwwhindawicom

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Anatomy Research International

PeptidesInternational Journal of


Hindawi Publishing Corporation httpwwwhindawicom

International Journal of

Volume 2014

Zoology


Molecular Biology International

GenomicsInternational Journal of


The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014


BioinformaticsAdvances in

Marine BiologyJournal of



Signal TransductionJournal of


BioMed Research International

Evolutionary BiologyInternational Journal of



Biochemistry Research International

ArchaeaHindawi Publishing Corporationhttpwwwhindawicom Volume 2014


Genetics Research International


Advances in

Virolog y

Hindawi Publishing Corporationhttpwwwhindawicom

Nucleic AcidsJournal of

Volume 2014

Stem CellsInternational



Enzyme Research



Microbiology


(nucleotides 2ndash8) [21] These miRNA gene clusters and genefamilies always have close functional relationships and coreg-ulate or coordinately regulate multiple biological processes[22ndash24]

Integrative analyses that are based on miRNA-mRNAinteractions always aim to develop algorithms or tools [2526] Few studies have addressed the biological characteristicsof miRNA in miRNA-mRNA interactions For examplemiRNAs are prone to cluster on the chromosomes somemiRNAs show more sequence similarity than others anda single miRNA locus can yield a cluster of isomiRs withvarious 51015840 and 31015840 ends and length distributions [23 27 28] Inthe present study an integrative analysis of miRNA-mRNAwas performed based on miRNA and mRNA expressionprofiles in human HepG2 and L02 cells by applying high-throughput techniques HepG2 cells are human hepatomacell lines and they are a suitablemodel to study occurrence ofdevelopment of humanhepatocellular carcinoma in vitro L02is the normal human liver cell line which is always used ascontrol cell lines ofHepG2 cellsThepurpose of this studywasto improve understanding of miRNA-mRNA interactions inregulatory networks The patterns of expression of potentialmiRNA-miRNA pairs were also analyzed comprehensivelyand the patterns of expression of miRNAs with potentialfunctional relationships including members of the samemiRNA gene clusters and gene families were surveyed

2 Materials and Methods

21 miRNA andmRNAProfiling Using High-Throughput Tech-niques HepG2 and L02 cells were obtained from theAmerican Type Tissue Collection miRNA expressionprofiles were generated from Illumina Genome Analyzer IIxand then analyzed using Novoalign software (httpwwwnovocraftcom v207011) based on the latest knownhuman pre-miRNAs in the miRBase database (Release 190httpwwwmirbaseorg) [29] To further understandexpression patterns of target mRNAs of miRNAs mRNAexpression profiles were assessed using microarray hybri-dization Hybridization was performed in Agilentrsquos SureHybHybridization Chambers (Human LncRNA Array v208 times 60K Arraystar)

22 Data Analysis Aberrantly expressed miRNAs andmRNAs in HepG2 cells were surveyed and identified via foldchange analysis To filter out rare species with lower levelsof relative expression fold change values were estimatedby adding additional units (10 units) A detailed flow chartshowing the integrative analysis of miRNA-mRNA is given inFigure 1 The main steps were as follows (1) Abnormal miR-NAs and mRNAs were first surveyed through bioinformaticsanalysis miRNA expression analysis was also performed atthe isomiR level including the different selections of isomiRs(the most abundant isomiR sum of all isomiRs and thecanonical miRNA sequence) [28] (2) Several deregulatedmiRNAs were further experimentally validated using qRT-PCR (3) The potential expression and functional relation-ships among miRNAs were evaluated through analysis of thepatterns of expression of clustered and homologous miRNAs

based on miRNA gene clusters and families miRNA-miRNApairs with potential interactions were also screened andanalyzed (4) GOpathway terms were enriched based onderegulated mRNAs and the target mRNAs of miRNAs andmiRNA-mRNA regulatory patterns were predicted based onexpression profiles

The experimentally validated target mRNAs of thoseabnormal miRNAs were obtained from the miRTarBaseand Tarbase databases [30] Common target mRNAs weresubjected to functional enrichment analysis using Capi-talBio Molecule Annotation System V40 and comparedto abnormally expressed mRNA profiles from microarraydatasets (MAS httpbioinfocapitalbiocommas3) GOandpathway analyses were used to determine the biologicalroles of deregulated miRNA and mRNA species Poten-tial miRNA-mRNA and miRNA-miRNA interactions andmiRNAmRNA expression profiles were used to constructfunctional interaction networks using Cytoscape v282 Plat-form [31]

23 qRT-PCR Validation Abnormal miRNAs were furthervalidated using quantitative real-time reverse transcriptionPCR (qRT-PCR) using SYBR premix Ex Taq (Takara Japan)Samples were amplified using the Mastercycler ep realplex2system (Eppendorf Hamburg Germany) qPCR was per-formed using specifically designed primers and used todetect hsa-miR-15b103a106b (Bulge-Loop miRNA qRT-PCRPrimer Set RiboBio Guangzhou China) andU6 servedas an internal control The relative amount of each miRNAwas measured using the 2(minusΔΔCT) method [32] All qRT-PCR reactions were carried out in triplicate and data werepresented as the mean plusmn standard deviation The two-tailedStudentrsquos 119905 test was used to compare the expression differencebetween tumor and normal cells

3 Results

31 Overview of miRNAmRNA Expression Profiles and Fur-ther Experimental Validation Upregulated and downregu-lated miRNAsmRNAs were identified using the fold changevalues (log 2) based on the control sample Many miRNAsand mRNAs were found to be differentially expressed (seeFigure S1 in the Supplementary Material available onlineat httpdxdoiorg1011552014907420) miRNA expressionpatterns were further analyzed at the isomiR level Foldchange values were found to diverge based on the dif-ferent selections of isomiRs (the most abundant isomiRsum of all isomiRs and the canonical miRNA sequence)(Figure 2(a)) Differences in fold change values rarely affectedthe selection of deregulated miRNA species The canonicalor annotated miRNA sequences were not always the mostdominant species in the miRNA locus They had even lowerlevels of expression The qRT-PCR primers used here weredesigned according to the canonical miRNA sequences in themiRBase database (Release 190 httpwwwmirbaseorg)[29] For this reason in order to further validate deregulatedmiRNAs using qRT-PCR technique we randomly selectedseveral abnormally expressed miRNAs (miR-15b miR-103a







family miRNA-miRNA


targetsmRNAs


miRNA-mRNA


isomiRs



0

2

4

6

8

103a 106b 194 425 let-7f 15b 24 10a


minus2

minus4

minus6

minus8 miRNA

Log 2

(fold

chan

ge)

Consistent

Inconsistent

(a)

miRNA

0

05

1

15

2

25

3


L02HepG2

lowast

lowast

lowast

miR

NA

leve

ls (n

orm

aliz

ed)

(b)








0

1

2

3

miRNA gene cluster

106b 2593 23b 27b 24 7a 7f 7d99a le-7ct

106b cluster


minus1

minus2

minus3

minus4

minus5

Log 2

(fold

chan

ge)

99a cluster

(a)

minus2

minus4

minus6

minus8

0

2

4

6

miRNA gene family

103a 107103b

243037

106a20b18b1718a 20a 93 106b

10a 99a 10010b 99b 125a

125b


10 family

Log 2

(fold

chan

ge)

(b)







DICER1CDK2

GPD1

CREB1

CCNE1

BCL2

EIF4A1

miR-15b

VEGFA

CCND1

CCND2

RECK MAPK9 ITCH

KAT2B

miR-106b

miR-103a

APP MMP13

ATM

ID1

RB1

E2F3

AKT1

KLF13 ERBB2

LACTB

PLK1

BBC3 EGR2

miR-100miR-125b

miR-194

RAC1 EP300

IGF1R CDH2

(a)

miR-23a

miR-23b

miR-27b

miR-24

miR-27a

27 family

23 family

23bcluster

CDK4

EIF4A1

DND1

AURKB

CCNA2

DHFR

BRCA1

IL6R

CeIF1

MET

RB1

EDNRA

ADORA2B

EY4A

PHB

ZBTB10

FOXO1

APP

HMGA2

PRDM1

DICER1

let-7a

let-7d

let-7f


E2F2

CCND2

SLC11A2

PDGFA

KLK10

KLK6

(b)



4 Discussion













Acknowledgments


References

















































Volume 2014

Zoology






















Advances in

Virolog y



Volume 2014




Enzyme Research



Microbiology







family miRNA-miRNA


targetsmRNAs


miRNA-mRNA


isomiRs



0

2

4

6

8

103a 106b 194 425 let-7f 15b 24 10a


minus2

minus4

minus6

minus8 miRNA

Log 2

(fold

chan

ge)

Consistent

Inconsistent

(a)

miRNA

0

05

1

15

2

25

3


L02HepG2

lowast

lowast

lowast

miR

NA

leve

ls (n

orm

aliz

ed)

(b)








0

1

2

3

miRNA gene cluster

106b 2593 23b 27b 24 7a 7f 7d99a le-7ct

106b cluster


minus1

minus2

minus3

minus4

minus5

Log 2

(fold

chan

ge)

99a cluster

(a)

minus2

minus4

minus6

minus8

0

2

4

6

miRNA gene family

103a 107103b

243037

106a20b18b1718a 20a 93 106b

10a 99a 10010b 99b 125a

125b


10 family

Log 2

(fold

chan

ge)

(b)







DICER1CDK2

GPD1

CREB1

CCNE1

BCL2

EIF4A1

miR-15b

VEGFA

CCND1

CCND2

RECK MAPK9 ITCH

KAT2B

miR-106b

miR-103a

APP MMP13

ATM

ID1

RB1

E2F3

AKT1

KLF13 ERBB2

LACTB

PLK1

BBC3 EGR2

miR-100miR-125b

miR-194

RAC1 EP300

IGF1R CDH2

(a)

miR-23a

miR-23b

miR-27b

miR-24

miR-27a

27 family

23 family

23bcluster

CDK4

EIF4A1

DND1

AURKB

CCNA2

DHFR

BRCA1

IL6R

CeIF1

MET

RB1

EDNRA

ADORA2B

EY4A

PHB

ZBTB10

FOXO1

APP

HMGA2

PRDM1

DICER1

let-7a

let-7d

let-7f


E2F2

CCND2

SLC11A2

PDGFA

KLK10

KLK6

(b)



4 Discussion













Acknowledgments


References

















































Volume 2014

Zoology






















Advances in

Virolog y



Volume 2014




Enzyme Research



Microbiology





0

1

2

3

miRNA gene cluster

106b 2593 23b 27b 24 7a 7f 7d99a le-7ct

106b cluster


minus1

minus2

minus3

minus4

minus5

Log 2

(fold

chan

ge)

99a cluster

(a)

minus2

minus4

minus6

minus8

0

2

4

6

miRNA gene family

103a 107103b

243037

106a20b18b1718a 20a 93 106b

10a 99a 10010b 99b 125a

125b


10 family

Log 2

(fold

chan

ge)

(b)







DICER1CDK2

GPD1

CREB1

CCNE1

BCL2

EIF4A1

miR-15b

VEGFA

CCND1

CCND2

RECK MAPK9 ITCH

KAT2B

miR-106b

miR-103a

APP MMP13

ATM

ID1

RB1

E2F3

AKT1

KLF13 ERBB2

LACTB

PLK1

BBC3 EGR2

miR-100miR-125b

miR-194

RAC1 EP300

IGF1R CDH2

(a)

miR-23a

miR-23b

miR-27b

miR-24

miR-27a

27 family

23 family

23bcluster

CDK4

EIF4A1

DND1

AURKB

CCNA2

DHFR

BRCA1

IL6R

CeIF1

MET

RB1

EDNRA

ADORA2B

EY4A

PHB

ZBTB10

FOXO1

APP

HMGA2

PRDM1

DICER1

let-7a

let-7d

let-7f


E2F2

CCND2

SLC11A2

PDGFA

KLK10

KLK6

(b)



4 Discussion













Acknowledgments


References

















































Volume 2014

Zoology






















Advances in

Virolog y



Volume 2014




Enzyme Research



Microbiology


DICER1CDK2

GPD1

CREB1

CCNE1

BCL2

EIF4A1

miR-15b

VEGFA

CCND1

CCND2

RECK MAPK9 ITCH

KAT2B

miR-106b

miR-103a

APP MMP13

ATM

ID1

RB1

E2F3

AKT1

KLF13 ERBB2

LACTB

PLK1

BBC3 EGR2

miR-100miR-125b

miR-194

RAC1 EP300

IGF1R CDH2

(a)

miR-23a

miR-23b

miR-27b

miR-24

miR-27a

27 family

23 family

23bcluster

CDK4

EIF4A1

DND1

AURKB

CCNA2

DHFR

BRCA1

IL6R

CeIF1

MET

RB1

EDNRA

ADORA2B

EY4A

PHB

ZBTB10

FOXO1

APP

HMGA2

PRDM1

DICER1

let-7a

let-7d

let-7f


E2F2

CCND2

SLC11A2

PDGFA

KLK10

KLK6

(b)



4 Discussion













Acknowledgments


References

















































Volume 2014

Zoology






















Advances in

Virolog y



Volume 2014




Enzyme Research



Microbiology












Acknowledgments


References

















































Volume 2014

Zoology






















Advances in

Virolog y



Volume 2014




Enzyme Research



Microbiology





Acknowledgments


References

















































Volume 2014

Zoology






















Advances in

Virolog y



Volume 2014




Enzyme Research



Microbiology
































Volume 2014

Zoology






















Advances in

Virolog y



Volume 2014




Enzyme Research



Microbiology








Volume 2014

Zoology






















Advances in

Virolog y



Volume 2014




Enzyme Research



Microbiology

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