978-1-4673-0878-6/12/$31.00 ©2012 IEEEApril 19-22, 2012Cappadocia, Turkey88

BUILDING DICER1 REGULATION NETWORK OF MOUSE LIVER HEPATOCYTES

Şeyma Ünsal1*, Mahmut Beyge1*, and Yeşim Aydın Son2 1Biotechnology Graduate Program, Middle East Technical University, 06800, Ankara,Turkey

2Health Informatics Department, Middle East Technical University, 06800, Ankara,Turkey * these authors contributed equally to the study

email: e147697@metu.edu.tr, e142314@metu.edu.tr, yesim@ii.metu.edu.tr web: http://hibit2012.ii.metu.edu.tr

Abstract RNA induced gene silencing complex (RISC) has a role

in many cellular processes which includes regulation of gene expression, immune response, cell differentiation and embryonic development. Dicer protein is a key regulator of these processes. Dicer1 specifically has central role in maturation of RISC substrate RNA and RISC assembly in mouse. Here the results of a microarray study that investigates the molecular level changes in dicer1 knockout mouse liver hepatocytes is re-analysed and the differentially regulated genes are hierarchically clustered based on molecular function and co-expression to construct the suggested regulation network of Dicer1 in mouse hepatocytes.

1. INTRODUCTION

RNA induced gene silencing is a comprehensive tool for both translational and transcriptional regulation. RNA induced gene silencing has role on immune response (Plasterk, 2002), regulation of gene expression (Kevin V. Morris, 2004), cell differentiation and embryonic development (Chryssa Kanellopoulou, 2005). Proposed mechanism for RNA induced translational silencing is synthesis of pri-miRNA, cleavage of pri-miRNA by DROSHA which is an RNAse III enzyme that leads to the formation of pre-miRNA. Obtained pre-miRNA is then cleaved by a second RNAse III enzyme that leads to the formation of miRNA. Then, mature miRNA is loaded to RNA induced gene silencing complex (RISC) (Bartel, 2004) (Witold Filipowicz, 2005). Proposed RISC action by possible different mechanisms can be found elsewhere (Ana Eulalio, 2008) . RISC may either cleave targeted mRNAs or may silence translation by a mechanism other than cleavage of targeted mRNAs (Ana Eulalio, 2008). In an experiment globally knocking out RISC causes lethal effect on organism (Bernstein, 2003). However, local knock out of any RISC component may not have lethal effect, such with Dicer1 knock-out in mouse hepatocytes (Nicholas J. Hand, 2009). We have analysed the gene expression data from microarray analysis of Dicer1 knocked out mouse liver to reveal biological pathways affected and to build the interaction network for regulated genes. A possible role of the regulated genes in the suggested network in RISC mechanism and hepatocyte metabolism is discussed.

2. METHODS

2a. Microarray Study Design and Data

Mutant mice were obtained by deletion of Dicer1 from hepatoblast-derived cells by means of crossing the AlfpCre transgenic line to the Dicer1flox conditional strain to obtain AlfpCre; Dicer1flox/flox animals. Control mice were AlfpCre- in genotype. After obtaining animals liver cell isolations were performed by collagenase treatment and Parcoll gradient purification as described (Zhang L, 2005) by the investigators of experiments. CELL file, raw data and normalized data can be obtained from NCBI GEO with accession number: GDS3685. Dicer1 level and level of liver specific miRNAs were obtained by the investigators for further verification of the results, and the results are in good agreement with expectation. 2b. Microarray Analysis Normalization was performed by GC-RMA method which uses shrunken mismatch intensity based on correlation between perfect match and mismatch intensities (Zhijin WU, 2004). Gene set selection was performed by SAM method which uses relative differences in gene expression between experimental and reference samples according to mean values of each genes in reference and experimental samples and collective standard deviation for each gene together with a correction or weighting constant (Virginia Goss Tusher, 2001). After obtaining scatter-plot SAM analysis was performed with p≤0.01, the false discovery rate was set to 0.1. After obtaining the results a second filter according to the truth value was performed and false results were excluded. 2c. Hierarchical Clustering Pathway analysis for gene set obtained from SAM analysis did not give value information. So hierarchical clustering based on cause-effect relationship between functions of proteins is used. Gene’s ontology terms were obtained from UniProtKwowledgeBase database. The final clustering performed according to the functions of the genes, manually.

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3. RESULTS

In analysis firstly, the quality of the data is assessed by using online analysis tool of arraymining.org (E. Glaab, 2009). According to the results obtained the quality of the data was in acceptable range. And the relative log intensities were centered at zero and nearly the same (Figure-1).

Figure 1: Relative Log Expression levels across 10 arrays after normalization. After GC-RMA analysis, density histogram was generated, clustering and enrichment analysis were performed. According to the result obtained density histogram shows similar results between the arrays (Figure-2). Clustering results revealed that Dicer1 deficient sample 10, shows similarity with control samples (sample 1, 2, 3, 4, 5) (Figure-3). The reason of this may be because of contamination of non-parenchymal cells in the step of hepatocyte purification. Same contamination was observed in qRT-PCR for liver specific miRNAs results compared to purified hepatocyte miRNAs results (Nicholas J. Hand, 2009). However in analysis step sample 10 was included for normalization purposes as it is in applicable quality range. Rest of the analysis was performed by using BRB Array Tools (Simon, 2006). When a threshold of 1.5 fold-change is considered, nearly equal numbers of up-regulated and down-regulated genes were identified (Figure-4).

Figure 2: Density histogram of the arrays after GC-RMA normalization process.

Figure 3: Clustering of the arrays after GC-RMA normalization. Array1, Array2, Array3, Array4, Array5 were reference arrays. Array6, Array7, Array8, Array9, Array10 were experimental arrays.

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Figure 4: Scatter plot of the log intenshigher than 1,5 is considered to be differen In total 168 genes either up- or dowobtained from SAM gene set selection. Alist of genes identified to be differentiallyannotated and then KEGG pathway analTop enriched pathways include apoptsignalling pathways, glutathione metabmetabolism. Some of pathways that are parare secretion, steroid hormone biometabolism. As Dicer1 induces degradation of mRNAsto see significant increase or enrichment levels in dicer1 knockout mice. So, the gemRNA expression level that are known tspecific to mouse liver cells are selected fof Dicer1 regulation network. In order to first the regulated gene list is cross-cTargetScanMouse online tool that provimiRNAs targets. Then, the resultant miRacross 342 miRNAs that are present in obtained from miRBase miRNA databaER0000000252). The analysis of miRNshowed that both up- and down-regupotentially targeted with miRNAs (Table-1

sities. Fold-change ntially regulated.

wn-regulated were After obtaining the y regulated are first lysis is performed. tosis, cell cycle, bolism and lipid rtially up-regulated synthesis, purine

s, we would expect in certain mRNAs

enes with increased targets of miRNAs for further analysis select these genes

checked with the ides prediction of RNAs are scanned liver (they can be

ase, accession no: NA targets clearly ulated genes are ).

Table 1: Up-regulated and doobtained from SAM analysis. Rclustering step because some doand some are not well annotated

Table 2: Up-regulated and doware potentially targeted by miR100% complementarity with cer In Dicer1 knock-out hepatocyincrease in expression levels ofmiRNA; however, some of regulated, and some were higsome of the down regulated targets of liver miRNAs (TablemRNAs that has 100% cosequence; however, from the resof the mRNA that are found topossess fully complementary lwill argue effect of miRNAs onof the result section. Up to thatwere ignored. Hierarchical clustering based owas performed for the differenteffect principle is used for the able to cover 82, 5 % of the difthe suggested network. As disclevel of miRNAs were ignored bobserved profile of expression. the entire gene set obtained canprinciple. The cells were affectespecific transcription reguldevelopment and maintenancedevelopment factors, cell growtfactors.

own-regulated transcripts list Red ones are not included in o not possess protein products d.

wn-regulated transcripts that RNAs or not. Red ones have rtain liver specific miRNAs.

ytes, one would expected an f mRNAs that are targeted by miRNA targets were down

ghly expressed. Additionally, mRNAs were not potential

e-2). In general RISC cleaves omplementarity to miRNA sults it is clear that nearly half o be highly expressed do not liver miRNAs (Table-2). We n expression profile at the end t point the effect of miRNAs

on the function of the genes tially expressed gene. Cause-analysis (Figure-5). We were fferentially regulated genes in cussed above, effects of low because of inconsistency with The results show that nearly

n be explained by cause-effect ed from different types of cell lators including neuronal e factors, B-cell growth and th factors, and also embryonic

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Figure 5: Hierarchical clustering of expressed genes. Because of the space prwere in reverse direction. They are stated b The key players for the observed expressiin Figure-5 are: Csf2rb, Prlr, Gdf15, Igf2,Grb10, Cdkn1c, Myc, Ntrk2, Gnpda1, NBicc1. They were all up-regulated, exceGrb10, and Bicc1 . Also they are all miR100% complementarity, means they are ponormal cells (Table-B). The down regulatedhad 100% complementarity with liver were: Dicer1 which was knocked-out, NoxRgs16, Gas2, Tmie, Hddc3, and Chac1. down-regulation of these transcripts can secondary effect of Dicer1 deficiency on and the genes under their control (Table-Bthat degrade key regulators cannot be prknockout hepatocytes, that might resultnegative feedback and down-regulate the gDicer1 did not silence Bicc1 which is resregulation at embryonic development stagactive and this also shows the role ofsilencing. Cell division is promoted by cytokine receptors (Csf2rb, Prlr), insulin like growtits receptor (Igf2r) and apoptosis is inhibitegrowth apoptosis regulator protein Gadd4affected from growth inhibitors also acgrowth factor receptors signalling pa(Grb10, Gpc3) and they were up-regulabetween the growth and maintenance of cefate of cells (Figure-5).

the differentially roblems some lines by arrows.

ion profile mapped , Igf2r, Myc, Gpc3,

Nr6a1, Klf6, Ucp2 ept Csf2rb, Gdf15, RNAs targets with ossibly degraded in d transcripts which specific miRNAs

x4, Ellovl3, Bri3bp, Except for Bicc1, be explained as a the key regulators

B). As the miRNAs roduced in Dicer1 t in a continuous genes listed above. sponsible for gene

ge because it is not f Dicer1 in gene

(Gdf15), cytokine th factor (Igf2) and ed by the action of

45b. However cells ct on insulin like athway negatively ated. The balance

ell would define the

Klf6 product, which is up-reactivator that binds to GCmitochondrial uncoupling protemotif in promoter region. Cellsof the mitochondrial uncouuncouples proton gradient fortogether with inhibition odehydrogenase by Pdk4 protAdropin protein both of whichshifts cells toward adipose tissuAdropin protein is affected frolevel of insulin-like growth fachowever Igf2 and Igf2r down-stblocked by Gpc3 and Grb10 prIgf2r and Igf2 was leading toprotein. Decreased level of Adradipose like behaviour (K. increase in expression of Afp ctissue like behaviour of thinactivation. Also Afp does notliver which have 100% complwere not coupled to ATP prowould leak from mitochondriformation of superoxides and pwould lead to increased pH of would lead to increased exprproteins for removing toxic peroxides. And also increased expression of Ca7 protein whiwater and carbon dioxide. SupeDNA damage which leads udependent apoptotic DNA However, p53 protein was stabiand Smyd2 proteins. Hic2 in

gulated, is a transcriptional C-box motifs can activate ein Ucp2 which has GC-box produce less energy because

upling protein (Ucp2) that r production of ATP. Ucp2 of mitochondrial pyruvate tein and decrease level of h affect glucose homeostasis ue like behaviour. The level of om insulin activity. Increased ctor would increase its level; tream signalling pathway was oteins. Blocking signalling of

o decreased level of Adropin ropin is related to obesity and Ganesh Kumar, 2008). So can be explained by adipose

he cells rather than RISC t targeted by any miRNAs in lementarity. Also, as protons oduction excessive electrons ial ETS and would lead to eroxides. Proton leakage also the cells. These toxic effects ression of Gpx7 and Gstt3 effects of superoxides and pH was stabilized by higher

ich converts carbonic acid to er oxides and peroxides cause up-regulation of Hic2, p53 damage response protein.

ilized by the action of Bri3bp neffectiveness together with

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growth promotion was leading to down-regulation of apoptotic Dnases (Figure-5). Myc protein which is activated by growth signals has also role on embryonic central nervous system development. Myc together with Ntrk2 protein which also has role on neural development and maintenance were leading to up-regulation of certain ion channels (Scn4b, Slc13a3), neurotropic factors (P311, Kiaa0319l, Nek3, Csnk1g1) and G-proteins and G-protein related genes (Adrb2, Pde6c, Pde7a, Gipc2, Ragd, Rab30, Arhgef7) which have central role in nervous system. Neurotropic factors in turn, lead modulation of cell skeleton proteins. As several receptors were activated in the cell and at P28 cell growth was induced, phosphatases were inhibited by Ppp1r3g and Ppm1m (Figure-5). Some of embryonic factors that has to be silenced were not silenced in cells such as Bicc1 protein, which has role on embryonic gene expression by RNA binding ability, and also Gnpda1 protein, which has role on egg Ca+2 oscillation in mammals. And also Nr6a1 protein which has role on gametogenesis in germ cells was active. Nr6a1 in turn activated spermatogenesis related genes and some polypeptides of P450 family proteins (Cyp2b13, Cyp17a1, Cyp2g1) which increase sex-related steroid hormones. P450 family proteins are conserved and co-regulated, co-targeted by miRNAs in cells. P450 is a large family of protein, however only three of them were up-regulated in the Dicer1 knock-out hepatocytes. This can only be explained by involvement of gametogenesis which specifically increases expression of certain polypeptides of P450 that is related to spermatogenesis, most certainly in the synthesis of testosterones. This also explains low level of expression of Hsd3b5 protein that only catalyses the conversion of dihydrotestosterone to 5 alpha-androstanediol (Figure-5). Few essential proteins like Ntrk2 and Klf6 and other DNA zinc finger binding proteins were also found to be up-regulated., along with Slc39a5, S100a, which are zinc carriers and binders. Along with up-regulation of general transcription apparatus protein Taf7, observed gene expression profile shows that Dicer1 knock-out hepatocytes were able to continue their active state. Additionally, up-regulation of Tncr6a, a RISC component was observed, which might be in response to lack of Dicer1 in RISC complex. As shown in Figure-5 cells were suggested to be under the effect of several gene expression regulators. In order to verify the suggested relations the experimentally validated co-expression profiles within the regulated genes are mapped with the online tool GeneMANIA (Figure-6) (David Warde-Farley, 2010). The GeneMANIA results fit well with gene list selected for hierarchical clustering, presenting a consistent gene expression profile. GeneMANIA analysis revealed additional twenty experimentally validated proteins interacting with the up-

regulated genes in Dicer1 knockout hepatocytes as shown with white circles in Figure 6. Half of these experimentally validated co-expressed proteins belong to the P450 family of polypeptides that have oxidoreductase activity and involve in cholesterol metabolism. All the proteins in P450 polypeptide co-expression network was also co-targeted by liver specific miRNAs. Most of the up-regulated P450 polypeptides are known to share a common domain as shown with the green edges in Figure 6. We suggest that Dicer1 might have a regulatory control on the common domain P450 which in return affects the expression levels of wide range of P450 polypeptides sharing the same domain. Also it is known that silencing of telomers were achieved by Dicer1 mediated mechanisms, chromosomal instability in Dicer1 deficient cells must be seen (Chryssa Kanellopoulou, 2005). As DNA repair mechanisms are also silenced by high growth promotion, this might lead to the apoptosis of the cells. As these observations haven’t been verified previously, further studies are required for experimental validation.

1. DISCUSSION

Here, we have presented the construction of a Dicer1 regulation network for mouse liver hepatocytes by hierarchical clustering based on expression data microarray analysis of Dicer1 knockout mice, molecular function and co-expression networks. Our results supported the role of Dicer1 in both gene silencing and transcriptional silencing. Additionally, we have demonstrated first identifying the primary interactions of Dicer1 and then interpreting the data according to absence of RISC help building a much accurate network where interactions can be verified through literature. In embryonic development Dicer1 has a big role on silencing certain differentiation genes for proper differentiation of cells and a global role on embryonic development as suggested by Kanellopoulou et. al. in the original study. They have noted that the liver cells were functional, but as these hepatocytes were under effects of neuronal development, B-cell and embryonic development agents. These agents are silenced according to cell type in embryonic development and direct cells for differentiation. As there were more than one cell-type specific growth and transcriptional regulators present in Dicer1 deficient hepatocytes, the Dicer1 knock-out hepatocytes weren’t accurately representing the molecular profile of mature hepatocytes. We predict that lack or decreased level of Dicer1 in mature hepatocytes should give rise to a different expression profile, as the effect of embryonic gene silencing will be diminished. Our results supported Kanellopoulou et. al, suggesting Dicer1 is essential for proper cell differentiation and stability, specifically at embryonic development stages, but this may or may not be through RISC coupling or miRNAs regulation; Even Dicer1 is essential for differentiation and development,

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Figure-6: Grey spots define the genes which are uploaded manually, and white spots define the genes which are co-expressed, automatically found by the program. Green lines

show proteins that have shared protein domains. Brown lines show predicted interactions between proteins. Purple lines show co-localization of proteins. we suggest that Dicer1 deficiency is a dispensable event in mature hepatocytes. Because after certain developmental

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stages (for example after P28) fully functional hepatocytes would arise in which certain growth factors will be silenced. Additionally the microarray analysis did not provide the pre-miRNA profile of the Dicer1 knocked out hepatocytes. This profile would give the miRNA profile of the cells that are imprinted by Dicer1 related pathways. So the question on whether miRNA imprinted by miRNA induced mechanisms and other RNAs, or miRNAs imprinted according to their concentration in embryonic cells, cannot be addressed in this study. In original study only certain miRNAs were analysed by using qRT-PCR. There may be certain possible Dicer1 independent pathway for maturation of miRNAs and RISC action (Daniel Cifuentes, 2010). Rather than analysing miRNA profile by microarray experiment, a second control experiment, in which Ago2 is knocked-out must be carried for observing whether obtained results were similar or not. The reason why experimentally obtained CYp family protein results did not match to GeneMANIA results would be because of Dicer1 independent pathways. In conclusion, we suggest that the effect of transcription factors and regulatory proteins is more pronounced than deficiency of miRNAs for gene regulation. miRNAs in somatic cells are not sole controllers of the gene level regulation, instead mainly responsible for fine-tuning of mRNAs levels according to the developmental state and environmental factors. Transcriptional regulatory proteins are key-players in the global control of gene expression. REFERENCES [1] Ana Eulalio, E. H. (2008). Getting to the Root of

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