Impact of miR-7 over-expression on the proteome of Chinese hamster ovary cells

Journal of Biotechnology 160 (2012) 251–262

Contents lists available at SciVerse ScienceDirect

Journal of Biotechnology journal homepage: www.elsevier.com/locate/jbiotec

Impact of miR-7 over-expression on the proteome of Chinese hamster ovary cells Paula Meleady ∗,1 , Mark Gallagher 1 , Colin Clarke, Michael Henry, Noelia Sanchez, Niall Barron, Martin Clynes National Institute for Cellular Biotechnology, Dublin City University, Glasnevin, Dublin 9, Ireland

a r t i c l e

i n f o

Article history: Received 21 December 2011 Received in revised form 5 March 2012 Accepted 7 March 2012 Available online 15 March 2012 Keywords: microRNA miR-7 Label-free LC–MS proteomics Chinese hamster ovary Cell growth

a b s t r a c t MicroRNAs play critical roles in the regulation of biological processes such as growth, apoptosis, productivity and secretion thus representing a potential route toward enhancing desirable characteristics of mammalian cells for biopharmaceutical production. We have previously found that miR-7 overexpression significantly inhibits the growth of CHO-SEAP cells without impacting cellular viability, with an associated increase in normalised productivity. Understanding the biological basis of this effect might open the way to new strategies for bioprocess-relevant growth regulation. In this study we have carried out a quantitative label-free LC–MS profiling study of proteins exhibiting altered levels following over-expression of miR-7 to gain insights into potential mechanisms involved in the observed phenotype. From the analysis we found 93 proteins showing decreased levels and 74 proteins with increased levels following over-expression of miR-7. Pathway analysis suggests that proteins involved in protein translation (e.g. ribosomal proteins), RNA and DNA processing (including histones) are enriched in the list of proteins showing decreased expression. Proteins involved in protein folding and secretion were found to be up-regulated following miR-7 over-expression. In silico bioinformatic analysis using miRWalk, which combined the output from 6 selected miRNA target prediction algorithms, was used to evaluate if any of the down-regulated proteins were potential direct targets of miR-7. Two genes, stathmin and catalase, which both have known roles in the regulation of cellular growth, were found to overlap a number of the predictive target database searches in both mouse and rat, and are likely to be possible direct targets of miR-7 in CHO cells. This is the first report investigating the impact of a miRNA on the proteome of CHO cells. © 2012 Elsevier B.V. All rights reserved.

1. Introduction MicroRNAs (miRNAs) are small non-coding RNAs (∼22 nucleotides in length) that can post-transcriptionally regulate gene expression through inhibition of protein translation or degradation of target mRNAs (Bartel, 2009). miRNAs are known to have regulatory roles in many biological processes that are important to the biopharmaceutical industry including apoptosis, cell growth and proliferation, protein expression and secretion. As a result miRNAs are of interest for cell engineering approaches as they have the ability to impact larger groups of genes and proteins thus potentially targeting critical pathways involved in growth or productivity

Abbreviations: CHO, Chinese hamster ovary; SEAP, secreted alkaline phosphatase; LC–MS, liquid chromatography–mass spectrometry; RT, retention time; mgf, MASCOT generic file. ∗ Corresponding author. Tel.: +353 1 7005700/5910; fax: +353 1 7005484. E-mail address: [email protected] (P. Meleady). 1 Both authors contributed equally to this manuscript. 0168-1656/$ – see front matter © 2012 Elsevier B.V. All rights reserved. doi:10.1016/j.jbiotec.2012.03.002

(Barron et al., 2011b; Muller et al., 2008). They also have an advantage in that when they are exogenously over-expressed in cells they do not compete for the translational machinery of the host cell (Muller et al., 2008). Chinese hamster ovary cells are the cell line of choice for manufacture of recombinant proteins; however there have been relatively few studies to date exploring the potential of miRNAs as cell line engineering tools for bioprocess applications (Barron et al., 2011a; Bort et al., 2011; Druz et al., 2011; Gammell et al., 2007; Lin et al., 2011). It is only recently that microRNAs have been extensively sequenced and analysed in CHO cells (Hackl et al., 2011; Johnson et al., 2011), but nothing is known to date about miRNA targets in CHO cells. Label-free LC–MS proteomics has become increasingly popular as a method for analysing quantitative changes in protein expression between biological samples (Becker and Bern, 2010; Neilson et al., 2011; Schulze and Usadel, 2010), though studies using this approach in CHO cells are very limited to date. It is not restricted by many of the limitations associated with 2D gels such as difficulties in separating hydrophobic proteins (e.g. membrane proteins) or proteins with low or high molecular weights (Panchaud et al.,

252

P. Meleady et al. / Journal of Biotechnology 160 (2012) 251–262

2008). Label-free LC–MS allows for the quantification of peptides based on two main approaches; the first involves measuring area under the curve (AUC) by directly comparing the ion intensities between peptides at specific retention times, while the second approach known as spectral counting involves counting the number of times a peptide has been selected for fragmentation in data-dependent acquisition mode with higher abundant protein generating more MS/MS spectra (Lundgren et al., 2010; Neilson et al., 2011). Spectral counting has recently been used to quantitate protein expression changes in higher producing CHO cells (Carlage et al., 2009). We have previously found that exogenous over-expression of miR-7 in CHO cells results in reduced cell proliferation over a 96 h culture period with an associated increase in normalised productivity of SEAP (Barron et al., 2011a). This effect that we have observed with miR-7 in CHO cells may be analogous to the effect of reducing culture temperature from 37 ◦ C to a lower temperature (28–33 ◦ C) which results in a reduced cell growth rate while specific productivity is increased (Al-Fageeh et al., 2006). Understanding the nature of molecular targets through which miR-7 mediates this effect may suggest methods for the fine-tuning of proliferation rates of CHO cells during the fermentation cycle, as an alternative to temperature shift, a process that is both challenging and time consuming in larger fermenters. By increasing miR-7 expression in CHO cells specific genes may be selectively transcribed similar to what is observed during low temperature culture of mammalian cells (Fox et al., 2005; Roobol et al., 2009). In this study we have used a quantitative label-free LC–MS proteomic approach to identify proteins and pathways that are altered following transient over-expression of miR-7 in recombinant CHO cells. 2. Materials and methods 2.1. Cell lines and transfection CHO K1 cells stably transfected with an expression vector encoding human secreted alkaline (SEAP) were previously established in our laboratory (Barron et al., 2011a). The cells were cultured in serum free CHO-S-SFM-II medium (Invitrogen) at 37 ◦ C in a Kuhner Climo-Shaker ISF1-X orbital shaker at 170 rpm. Cells were routinely passaged every 72 h and viability was assessed using the trypan blue exclusion method. Transient transfections were carried out in 50 mL filter-topped tubes (Sartorius) using NeoFX transfection reagent (Applied Biosystems) as previously described (Barron et al., 2011a). The hsa-mir-7 mimic (PM-7), the miRNA non-specific (negative) control (PMNeg), and the VCP-specific siRNA (growth control) were purchased from NBS Biologics. Cellular growth and viability were assessed after 48 and 96 h using a Guava Benchtop Cytometer after staining with ViacountTM (Millipore). Cells were collected at 48 and 96 h for label-free LC–MS and western blot analysis and stored at −80 ◦ C. qRT-PCR was carried out as previously described (Barron et al., 2011a). Each transfection experiment was performed three independent times. 2.2. Sample preparation for label-free LC–MS analysis Cell pellets were lysed with lysis buffer consisting of 6 M Urea, 2 M Thiourea, 10 mM Tris, 1× DNase (GE Healthcare), and 1× protease inhibitor (HaltTM , Pierce) and samples were cleaned up using the Ready Prep 2-D clean up kit (Biorad). Protein concentration was determined using the Quick Start Bradford assay (Biorad). Protein assay results between PM-Neg and PM-7 cells showed little variation in protein concentration per equal cell number lysed and assayed (not shown), thus equal protein concentrations were used

for proteomic comparison of PM-Neg and PM-7 cells. (Preliminary analysis of the Progenesis LC–MS data showed that the housekeeping protein GAPDH (SwissProt Accession no. P17244) was found to be unchanged following transfection when comparing PM-7 with PM-Neg samples at 48 h (1.06 fold change, p = 0.34) and 96 h (1.05 fold change, p = 0.72)) thus allowing us to compare equal protein concentrations per sample). Ten micrograms of protein sample were resuspended in 50 ␮L of 50 mM ammonium bicarbonate. Reduction was performed by adding 1 ␮L of 100 mM DTT at 60 ◦ C for 30 min, and allowed to cool to room temperature for approximately 20 min. Samples were alkylated by adding 5 ␮L of 0.3 mM Iodoacetamide and then incubated for 30 min in the dark at room temperature. Digestion with sequence grade Lys-C (Promega) was carried out at a ratio of 1:20 Lys-C:Protein at 37 ◦ C for 4 h, followed by a second digestion with sequence grade Trypsin (Promega) at a ratio of 1:25 Trypsin:Protein overnight at 37 ◦ C. Samples were then cleaned up using Pep-clean C18 spin columns (Thermo Fisher Scientific), dried under a vacuum and stored at −80 ◦ C. Prior to mass spectrometry analysis dried peptides were resuspended in 50 ␮L of 0.1% trifluoroacetic acid (TFA) in 2% acetonitrile (ACN), vortexed and sonicated to ensure an even suspension. 2.3. Mass spectrometry using LC–MS/MS Nano LC–MS/MS analysis was carried out using an Ultimate 3000 nanoLC system (Dionex) coupled to a hybrid linear ion trap/Orbitrap mass spectrometer (LTQ Orbitrap XL; Thermo Fisher Scientific). Five microlitres of digest were loaded onto a C18 trap column (C18 PepMap, 300 ␮m ID × 5 mm, 5 ␮m particle size, 100 A˚ pore size; Dionex) and desalted for 10 min using a flow rate of 25 ␮L/min in 0.1% TFA. The trap column was then switched online with the analytical column (PepMap C18, 75 ␮m ID × 250 mm, 3 ␮m particle and 100 A˚ pore size; (Dionex)) and peptides were eluted with the following binary gradients of solvent A and B: 0–25% solvent B in 120 min and 25–50% solvent B in a further 60 min, where solvent A consisted of 2% acetonitrile (ACN) and 0.1% formic acid in water and solvent B consisted of 80% ACN and 0.08% formic acid in water. Column flow rate was set to 350 nL/min. Data were acquired with Xcalibur software, version 2.0.7 (Thermo Fisher Scientific). The mass spectrometer was operated in data-dependent mode and externally calibrated. Survey MS scans were acquired in the Orbitrap in the 300–2000 m/z range with the resolution set to a value of 60,000 at m/z 400. Up to seven of the most intense ions (1+, 2+ and 3+) per scan were CID fragmented in the linear ion trap. A dynamic exclusion window was applied within 40 s. All tandem mass spectra were collected using a normalised collision energy of 35%, an isolation window of 3 m/z, and one microscan. 2.4. Label-free LC–MS quantitative profiling Label-free LC–MS analysis was carried out using Progenesis label-free LC–MS software version 3.1 (NonLinear Dynamics), essentially as recommended by the manufacturer (see www.nonlinear.com for further background to alignment, normalisation, calculation of peptide abundance, etc.). The software processed the raw data in two steps. Firstly each sample run was subjected to alignment which involved aligning the data based on the LC retention time of each sample; this allows for any drift in retention time giving an adjusted retention time for all runs in the analysis. The sample run that yielded most features (i.e. peptide ions) was used as the reference run, to which retention time of all of the other runs were aligned and peak intensities were normalised. The Progenesis peptide quantification algorithm calculates peptide abundance as the sum of the peak areas within its isotope

P. Meleady et al. / Journal of Biotechnology 160 (2012) 251–262

253

boundaries. Each abundance value is then transformed to a normalised abundance value by applying a global scaling factor. Protein abundance was calculated as the sum of the abundances of all peptide ions which have been identified as coming from the same protein. A number of criteria were used to filter the data before exporting the MS/MS output files to MASCOT (www.matrixscience.com) for protein identification; peptide features with ANOVA < 0.05 between experimental groups, mass peaks (features) with charge states from +1 to +3, and greater than 3 isotopes per peptide. All MS/MS spectra were exported from Progenesis software as a MASCOT generic file (mgf) and used for peptide identification with MASCOT (version 2.2) searched against the UniProtKB–SwissProt database (taxonomy, Mammalia). The search parameters used were as follows: peptide mass tolerance set to 20 ppm, MS/MS mass tolerance set at 0.5 Da; up to two missed cleavages were allowed, carbamidomethylation set as a fixed modification and methionine oxidation set as a variable modification. Only peptides with ion scores of 30 and above were considered and re-imported back into Progenesis LC–MS software for further analysis. A number of criteria were applied to assign a protein as identified; proteins with ≥2 peptides matched, a ≥1.25 fold difference in abundance, an ANOVA between experimental groups of ≤0.05 and a MASCOT score > 61. 2.5. Bioinformatic analysis of predicted miR-7 targets To prepare the down-regulated protein lists for miRWalk analysis Swissprot protein accession numbers were converted to Entrez gene IDs using DAVID (http://david.abcc.ncifcrf.gov/) (Huang et al., 2009). The resulting Entrez gene IDs were mapped to human, mouse and rat Entrez gene IDs using the HomoloGene database (Release 65) (http://www.ncbi.nlm.nih.gov/homologene) for compatibility with miRWalk (http://www.ma.uniheidelberg.de/apps/zmf/mirwalk/) (Dweep et al., 2011). (Note: where a match could not be found annotation was carried out manually). The resulting lists of Entrez gene IDs for each species were submitted to miRWalk individually. The algorithm utilised a seed length of 7 and searched all regions of the gene including 3 and 5 UTR, promoter (2000 bp upstream) and the coding sequence (CDS). Targets with a p-value < 0.05 were considered significant. 2.6. Biological process enrichment within differentially expressed protein lists To determine significant enrichment of Gene Ontology (GO) biological processes within the differentially expressed protein lists the DAVID system was used (http://david.abcc.ncifcrf.gov/). Enrichment was considered to be significant when the Bonferroni p-value adjustment was ≤0.05. The up and down-regulated protein lists at both 48 and 96 h were analysed individually. 2.7. Western blotting 10 ␮g of protein samples were prepared in SDS-PAGE sample buffer (Sigma), heated at 100 ◦ C for 5 min and cooled on ice prior to loading onto 4–12% NuPAGE Bis Tris precast gradient gels (Invitrogen). Electrophoretic transfer, blocking and development of western blots were carried out as described previously (Meleady et al., 2008). Blots were probed with the following primary antibodies (anti-Histone H3 and anti-Histone H4 (Cell Signalling Technologies), anti-HSPA5, anti-HSPA8, anti-14-3-3epsilon, anti-catalase and anti-stathmin (all from Abcam)) diluted in Tris-buffered saline containing 0.1% Tween 20 (TBST). An antirabbit GAPDH monoclonal antibody (Abcam) was used as an internal loading control in all experiments.

Fig. 1. (A) qRT-PCR verification of over-expression of miR-7 at 48 and 96 h following transient transfection of CHO-SEAP cells with miR-7 pre-mir (PM-7) and negative control (PM-Neg). A representative graph from one transfection experiment is shown. Growth (B) and viability (C) of SEAP-secreting CHO-K1 cells following exogenous up-regulation with miR-7 pre-mir (PM-7). Viable cells numbers were assessed at 48 and 96 h following transfection. NeoFX, transfection reagent only; VCP-siRNA, siRNA targeting VCP; PM-7, miR-7 pre-mir; PM-Neg, non-specific (negative) pre-mir control (n = 3).

3. Results 3.1. Proteomic analysis of the effect of over-expression of miR-7 on CHO cells Samples from three independently run experiments were collected at 48 and 96 h following transfection of miR-7 pre-mir (PM-7) into CHO-SEAP cells. Previous work in our laboratory has shown that miR-7 levels are increased following transfection of PM7 and that growth is significantly inhibited in these cells (Barron et al., 2011a). Fig. 1A confirms increased levels of miR-7 at 48 and

254

P. Meleady et al. / Journal of Biotechnology 160 (2012) 251–262

96 h post-transfection by qPCR. Fig. 1B shows that this causes a decrease in growth in CHO-SEAP cells compared to a non-specific (negative) pre-mir control (PM-Neg). Fig. 1C shows that though the growth of the cells is inhibited especially at 96 h following transfection, the viability of the cells remains comparable to control cells at greater than 95% viability. We also used an siRNA targeted against VCP as a growth control in the experiment (Doolan et al., 2010). For proteomics analysis each of the three biological replicate samples (collected at each 48 and 96 h time points) from the three independent transfection experiments was run by LC–MS using a 3 h reverse phase gradient. The resultant data was transferred to Progenesis LC–MS software to compare the PM-7 with PM-Neg transfected samples at both 48 and 96 h after transfection (i.e. two individual comparisons; PM-7 versus PM-Neg at 48 h, and PM-7 versus PM-Neg at 96 h). Following Progenesis LC–MS analysis peptide features with ANOVA < 0.05 and 1+, 2+ and 3+ charge states were subjected to MASCOT database searching. The resultant MASCOT mgf files were then resubmitted to the Progenesis software to yield a list of identified proteins. The lists were further interrogated to remove proteins with less than 2 peptides matched, a fold change <1.2 and not statistically significant. A relatively small fold change cut off of 1.2 was chosen as previous work in our laboratory analysing the proteome of human cancer cells following over-expression of miR-29a found that there was only a modest change in protein expression levels (ranging from 1.2 to 1.7 fold) following transfection (Muniyappa et al., 2009). Further work to remove peptide conflicts was carried out, i.e. where peptides from one protein were present in another protein from a different species or a related protein family member. The limit of at least two peptides matched per protein was chosen in order not to eliminate low molecular weight proteins from being statistically identified in the quantitative label-free analysis. A total of 93 proteins were found to be decreased and 74 proteins increased following transfection with PM-7 compared to PM-Neg transfected samples. From the list of 93 down-regulated proteins, 48 were found to be decreased in miR-7 cells at 48 h and 73 proteins decreased in miR-7 cells at 96 h and are listed in Table 1A. 28 proteins were found to overlap both these 48 and 96 h lists. From the list of 74 up-regulated proteins following over-expression of miR-7, 29 showed increased expression at 48 h and 67 at 96 h with 22 proteins common to both lists, see Table 1B. All of the proteins listed had peptides present from the differential analysis that were unique only to that protein and did not conflict with other species or related family members. Fig. 2 shows a representative output from Progenesis LC–MS software of catalase identified as showing decreased expression following over-expression of miR-7 in CHO-SEAP cells. Table 2 shows the MASCOT scores and sequence for each of the individual four peptides which comprise the confirmed catalase identification. 3.2. Validation of quantitative label-free proteomic approach by immunoblot A number of differentially expressed proteins were chosen for immunoblot validation of the label-free profiling results. All of the antibodies tested were confirmed to work in CHO cells. Fig. 3 shows western blot confirmation of altered protein expression following over-expression of miR-7 in CHO-SEAP cells. Histone H3 and histone H4 showed large decreases in protein expression at both 48 and 96 h following over-expression of miR-7 by label-free LC–MS profiling and this is reflected by western blot analysis. The western blot for heat shock cognate 71 kDa protein (HSPA8) which is moderately decreased at 48 h and no change detected at 96 h also reflected the label-free profiling results (i.e. 1.5 fold down at 48 h). HSPA5, 14-3-3 epsilon and PDIA6 were all confirmed to be upregulated following transfection with exogenous miR-7. GAPDH

Fig. 2. Representative output from Progenesis LC–MS label-free software showing decreased expression after 96 h of the four individual peptides identified from catalase (P24270) following over-expression of miR-7 in CHO-SEAP cells. The graph shows average normalised abundance volumes of the 4 peptides identified from catalase. The horizontal axis represents the three individual biological replicates from the negative control (PM-Neg) and from miR-7 pre-mir (PM-7) transfected cells. The vertical axis represents normalised abundance volumes (log). The associated MASCOT scores and sequence for each peptide are found in Table 2. The MS/MS spectra for each of these four peptides including the b and y ion fragment series are shown in Supplementary Fig. 1.

was used as an internal loading control as this protein was found to be unchanged following transfection from the label-free proteomic comparisons of PM-Neg and PM-7. These western blot results confirm that the label-free approach we have used has successfully selected differentially expressed proteins.

3.3. Analysis of predicted miR-7 targets using miRWalk To investigate translational inhibition upon miR-7 transfection we focussed on proteins that were shown to be significantly down-regulated using label-free LC–MS analysis. We compared those proteins with lower abundance following miR-7 transfection to in silico predicted targets using the miRWalk system (http://www.ma.uni-heidelberg.de/apps/zmf/mirwalk/). miRWalk amalgamates the output of a number of distinct miRNA target prediction algorithms; we used RNA22 (Miranda et al., 2006), miRanda (Enright et al., 2003), miRDB (Wang, 2008), TargetScan (Lewis et al., 2005) and RNAhybrid (Rehmsmeier et al., 2004). The combination of multiple prediction algorithms is the currently accepted best-practice method for prioritising potential miRNA target-interactions (Li et al., 2010). Table 3 shows the top hits from the predicted target analyses of miR-7 in human, mouse and rat using the five different miRNA predicted target databases listed above as well as miRWALK. A number of genes were found to overlap the lists particularly from mouse and rat including catalase (CAT) and stathmin (STMN1), and could be possible direct targets of miR-7. These two proteins were selected for further laboratory confirmation by western blotting. Fig. 4 shows the large decrease in expression of catalase at both 48 and 96 h in cells transfected with PM-7 which reflects the differential expression observed in the label-free analysis (i.e. down by 4 fold at 48 h and down by 3 fold at 96 h). Fig. 4 also shows the large decrease in expression of stathmin at 96 h in PM-7 transfected cells, again validating the results from the label-free profiling analysis which showed a large ∼7 fold decrease at 96 h in cells transfected with PM-7 compared to PM-Neg cells. These results suggest that catalase and stathmin are likely to be direct targets of miR-7 in CHO cells, though further work would need to be carried out to confirm this. Follow-on work will include investigation of direct binding of target 3 UTRs by miR-7.

P. Meleady et al. / Journal of Biotechnology 160 (2012) 251–262

255

Table 1A List of proteins with decreased expression following over-expression of miR-7 in CHO cells. Accessiona

Q3T0F4 Q3T0V4 P25398 Q9WVH0 P62265 Q5R938 Q3T0X6 P63274 Q3T0R1 Q5R8M9 P25444 Q3ZBH8 Q3T199 Q6Q311 Q0Z8U2 P49242 P47961 P38982 P05388 Q5R931 Q3T087 Q6QMZ7 Q5EAD6 P35980 Q3T0W9 Q4R5I3 P21531 Q6QMZ4 Q2TBQ5 Q4R596 Q8SQH5 Q5R874 Q9CWJ9 P24270 Q5R6X7 P62629 Q9D8N0 Q3SZC0 Q3SZ54 Q99PF5 Q14444 Q3T054 Q4R7Y4 Q9N1U2 P19378 P46633 Q4R4T5 Q9Z2X1 Q00839 P02253 P0C0S8 Q96QV6 P0C169 P0C0S4 Q2M2T1 Q32L48 Q64400 P62802 P00494 Q14974 P12269 Q5R1W9 Q09666 Q63525 Q28618 P08199 P06748 P28656 Q8NC51 Q5E9A3 Q9EPH8 Q2NL22 P57761

Gene nameb

RPS10 RPS11 RPS12 RPS13 RPS14 RPS15A RPS16 RPS17 RPS18 RPS19 RPS2 RPS20 RPS23 RPS25 RPS3 RPS3A RPS4 RPSA RPLP0 RPL10 RPL11 RPL12 RPL15 RPL18 RPL19 RPL22 RPL3 RPL6 RPL7A AHCY SLC25A5 DHX9 ATIC CAT CBX3 EEF1A1 EEF1G ERH EIF4A1 KHSRP CAPRIN1 RAN GNB2L1 HSPA6 HSPA8 HSP90AA1 HSP90AB1 HNRNPF HNRNPU

HIST1H2AA H2AFZ HIST1H2BK HIST1H2BN

HPRT1 KPNB1 IMPDH2 LDHA AHNAK NUDC YBX1 NCL NPM1 NAP1L1 SERBP1 PCBP1 PABPC1 EIF4A3 PCNA

Protein description

40S ribosomal protein S10 40S ribosomal protein S11 40S ribosomal protein S12 40S ribosomal protein S13 40S ribosomal protein S14 40S ribosomal protein S15a 40S ribosomal protein S16 40S ribosomal protein S17 40S ribosomal protein S18 40S ribosomal protein S19 40S ribosomal protein S2 40S ribosomal protein S20 40S ribosomal protein S23 40S ribosomal protein S25 40S ribosomal protein S3 40S ribosomal protein S3a 40S ribosomal protein S4 40S ribosomal protein SA 60S acidic ribosomal protein P0 60S ribosomal protein L10 60S ribosomal protein L11 60S ribosomal protein L12 60S ribosomal protein L15 60S ribosomal protein L18 60S ribosomal protein L19 60S ribosomal protein L22 60S ribosomal protein L3 60S ribosomal protein L6 60S ribosomal protein L7a Adenosylhomocysteinase ADP/ATP translocase 2 ATP-dependent RNA helicase A Bifunctional purine biosynthesis protein PURH Catalase Chromobox protein homolog 3 Elongation factor 1-alpha 1 Elongation factor 1-gamma Enhancer of rudimentary homolog Eukaryotic initiation factor 4A-I Far upstream element-binding protein 2 Caprin-1 GTP-binding nuclear protein Ran Guanine nucleotide-binding protein subunit beta 2-like 1 Heat shock 70 kDa protein 6 Heat shock cognate 71 kDa protein Heat shock protein HSP 90-alpha Heat shock protein HSP 90-beta Heterogeneous nuclear ribonucleoprotein F Heterogeneous nuclear ribonucleoprotein U Histone H1.1 Histone H2A type 1 Histone H2A type 1-A Histone H2A type 1-C Histone H2A.Z Histone H2B type 1-K Histone H2B type 1-N Histone H3.2 Histone H4 Hypoxanthine-guanine phosphoribosyltransferase Importin beta-1 subunit Inosine-5 -monophosphate dehydrogenase 2 L-lactate dehydrogenase A chain Neuroblast differentiation-associated protein AHNAK Nuclear migration protein nudC Nuclease sensitive element-binding protein 1 Nucleolin Nucleophosmin Nucleosome assembly protein 1-like 1 Plasminogen activator inhibitor 1 RNA-binding protein Poly(rC)-binding protein 1 Polyadenylate-binding protein 1 Eukaryotic initiation factor 4A-III Proliferating cell nuclear antigen

PM-7 v PM-Neg 48 h

PM-7 v PM-Neg 96 h

Foldc

Foldc

ANOVA

1.32

0.03

1.68

0.04

5.09

7.10 × 10−3

2.97 1.54

5.12 × 10−3 0.02

1.7

0.02

1.74

0.01

2.89 2

3.53 × 10−3 0.04

2.55 4.07 2.05 1.43 1.37

4.52 × 10−3 4.34 × 10−3 0.04 0.04 0.03

4.87 2.41

5.07 × 10−3 4.45 × 10−3

1.57

0.03

1.47 1.32 1.64 1.48 1.73

4.32 × 10−3 0.024 7.11 × 10−3 6.21 × 10−3 0.01

2.09 3.83 3.62

0.04 0.05 0.05

2.01 2.96 1.59 1.36 1.76 1.82 1.92

0.05 8.07 × 10−3 4.33 × 10−3 7.84 × 10−3 6.51 × 10−3 5.65 × 10−3 0.05

1.45

0.03

1.88

3.04 × 10−3

ANOVA

1.25 3.8

0.05 1.94 × 10−3

17.21 1.71 13.21 5.43 2.67 5.69 1.27 5.81 1.83 3.21 4.22 1.49 2.25 4.12 1.4 1.46 2.93 1.39 1.69 2.55 5.25 6.36 1.73 1.79 11.97 3.47

3.16 × 10−3 0.01 0.01 1.04 × 10−3 0.05 2.03 × 10−3 4.13 × 10−4 0.03 6.09 × 10−4 6.50 × 10−3 0.04 0.03 0.04 5.74 × 10−3 0.05 0.05 0.05 0.02 0.02 0.04 0.02 0.02 2.74 × 10−3 0.04 0.01 6.46 × 10−3

4.83 2.32 1.84 3.14

1.92 × 10−3 0.01 1.55 × 10−3 8.43 × 10−3

1.26 1.26 3.5 1.37

3.81 × 10−3 0.01 0.02 8.01 × 10−3

1.3 1.48 1.2

0.01 0.01 0.01

1.66 4.48 2.78 2.85 2.78 3.06 2.76 2.81 9.13 8.07 1.6 1.44 5.2 2.06

1.94 × 10−3 3.90 × 10−3 1.27 × 10−3 1.53 × 10−3 1.48 × 10−3 6.79 × 10−4 4.34 × 10−4 4.59 × 10−4 8.33 × 10−6 4.51 × 10−5 0.04 0.04 4.76 × 10−5 7.69 × 10−3

1.43 1.25 1.3

0.01 0.04 3.74 × 10−3

1.44 2.31 1.34 1.36 1.41

4.36 × 10−3 4.92 × 10−3 0.01 5.85 × 10−4 1.4 × 10−3

256

P. Meleady et al. / Journal of Biotechnology 160 (2012) 251–262

Table 1A (Continued) Accessiona

Gene nameb

Protein description

PM-7 v PM-Neg 48 h c

Q9UQ80 Q9JIF0 Q9EQU5 P26350 P14618 P43487 Q2HJ58 Q5RE47 Q3YLA6 Q3MHR5 Q8VIJ6 Q3T0C7 P80318 Q86V81 P37802 Q2XVP4 Q3MHM5 Q3ZBU7 Q922F4 P69893

PA2G4 PRMT1 SET PTMA PKM2 RANBP1 PRPS1 BAT1 SRSF1 SRSF2 SFPQ STMN1 CCT3 THOC4 TAGLN2 TUBA1B TUBB2 C TUBB4 TUBB6 TUBB5

Proliferation-associated protein 2G4 Protein arginine N-methyltransferase 1 Protein SET (Phosphatase 2A inhibitor I2PP2A) Prothymosin alpha Pyruvate kinase isozymes M1/M2 Ran-specific GTPase-activating protein Ribose-phosphate pyrophosphokinase I Spliceosome RNA helicase BAT1 Splicing factor, arginine/serine-rich 1 Splicing factor, arginine/serine-rich 2 Splicing factor, proline- and glutamine-rich Stathmin T-complex protein 1 subunit gamma THO complex subunit 4 Transgelin-2 Tubulin alpha-1B chain Tubulin beta-2 C chain Tubulin beta-4 chain Tubulin beta-6 chain Tubulin beta-5 chain

Fold

ANOVA

1.8 1.39 1.3

0.02 0.02 7.21 × 10−3

1.4 1.68

0.01 1.58 × 10−3

3.29

0.03

1.63 2.03 1.35 1.67 1.91 1.85 1.68 1.96

3.25 × 10−3 0.02 0.02 7.24 × 10−3 7.95 × 10−3 0.02 0.04 4.91 × 10−3

PM-7 v PM-Neg 96 h Foldc

ANOVA

1.5

3.80 × 10−3

36.14

6.96 × 10−3

2.57 1.37 2.01 2.89

0.03 0.05 5.45 × 10−3 4.67 × 10−4

6.88

0.04

1.61

0.01

1.3 1.21 1.29

0.02 0.05 7.41 × 10−3

1.27

0.03

Further information on MASCOT scores, numbers of peptides matched and molecular weight for each protein can be found in Supplementary Table 1A. a Uniprot accession number from MASCOT search of UniProtKB-SwissProt, taxonomy Mammalia. b Official recommended gene name taken from UniProtKB-SwissProt (in some cases there is no official gene name available so it has not been included). c Fold change showing decreased protein expression in PM-7 transfected cells compared to PM-Neg transfected cells at 48 and at 96 h.

3.4. Pathway analysis of proteins up- or down-regulated by miR-7 We examined biological process enrichment among the differentially expressed proteins in PM-7 treated cells at both 48 and 96 h following transfection using DAVID and Gene Ontology analysis (GO). Significant enrichment of biological processes related to protein translation and RNA processing in the down-regulated lists at 48 and 96 h was observed (Tables 4 and 5). GO analysis of the up-regulated protein lists showed a significant enrichment of biological processes related to apoptosis and cell death at 96 h post-transfection (see Table 5).

Fig. 3. Western blot analysis of selected proteins, Histone H3, Histone H4, HSPA8, 14-3-3 epsilon, HSPA5 and PDIA6, showing altered expression following overexpression of miR-7 in CHO-SEAP cells. Samples were collected at 48 and 96 h following transfection of miR-7 pre-mir (PM-7) and negative control (PM-Neg) into CHO-SEAP cells. GAPDH was used as an internal loading control.

4. Discussion Identification of miRNA target genes is essential for the understanding of the molecular mechanisms of how miRNAs are involved in bioprocess-relevant phenotypes such as growth, apoptosis, productivity and secretion. This is vitally important if they are to be exploited for cell line engineering for process improvements. Little work has been carried out to date in CHO cells concerning the potential for miRNAs to improve phenotype. The first studies are now beginning to emerge where we showed that over-expression of miR-7 altered growth of CHO cells (Barron et al., 2011a) and miR446h appears to have a pro-apoptotic role in CHO cells (Druz et al., 2011). MiR-7 is known to affect growth in other cell systems (Chou et al., 2010; Jiang et al., 2010; Reddy et al., 2008; Saydam et al., 2011). A relatively small number of direct targets of miR-7 have been identified to date including EGFR (Kefas et al., 2008), Raf1 (Webster et al., 2009), insulin receptor substrate 1 (IRS1) (Kefas et al., 2008), Ets transcriptional repressor ERF (Chou et al., 2010),

Fig. 4. Western blot analysis showing decreased expression of catalase and stathmin in CHO-SEAP cells transfected with PM-7; these proteins are possible direct targets of miR-7 from bioinformatic predicted target software analyses. Samples were collected at 48 and 96 h following transfection of miR-7 pre-mir (PM-7) and negative control (PM-Neg) into CHO-SEAP cells. GAPDH was used as an internal loading control.

P. Meleady et al. / Journal of Biotechnology 160 (2012) 251–262

257

Table 1B List of proteins with increased expression following over-expression of miR-7 in CHO cells. Accessiona

Gene nameb

Protein description

PM-7 v PM-Neg 48 h Fold changec

Q4R572 P62258 Q5RC20 Q3SZI4 Q5R651 P17980 P07823 P60712 Q4R4I6 P14550 O60218 P16116 O08782 Q9XSJ4 P12763 P07150 P07356 Q4R4H7 Q03265 P56480 Q5RAD2 Q8K3H7 Q5R957 Q68FD5 Q9D1A2 P00639 P08113 P15311 P29389 O46638 Q923D2 P05064 P48538 P30116 P08263 P30115 P04905 P08010 O35660 P46424 Q00285 P08009 P46413 P15991 Q9Z2K8 P48678 P49129 P24452 Q2HJ49 Q9JKY1 Q9BGI2 Q5RFB8 P11598 Q5R6T1 P04785 P05964 P11980 P50399 Q5R9L3 Q8BH97 P02787 P19324 Q9BYN0 P08228 Q62465 Q2IBA3 P10639 Q9JMH6

YWHAB YWHAE YWHAG YWHAQ YWHAZ PSMC3 HSPA5 ACTB CAP1 AKR1A1 AKR1B10 AKR1B1 AKR1B8 ENO1 AHSG ANXA1 ANXA2 ANXA5 ATP5A1 ATP5B CALM CALR CLIC4 CLTC CNDP2 DNASE1 HSP90B1 EZR FTH1 FKBP3 BLVRB ALDOA LGALS1 GSTA1 GSTA3 GSTM1 GSTM2 GSTM6 GSTP1 GSTM3 GSS HSPB1 IDH1 LMNA LAMP1 CAPG MSN PRDX1 PRDX4 PGAM1 PDIA3 PDIA6 P4HB S100A6 PKM2 GDI2 G3BP2 RCN3 TF SERPINH1 SRXN1 SOD1 VAT1 TES TXN TXNRD1

14-3-3 protein beta/alpha 14-3-3 protein epsilon (14-3-3E) 14-3-3 protein gamma 14-3-3 protein theta 14-3-3 protein zeta/delta 26S protease regulatory subunit 6A 78 kDa glucose-regulated protein Actin, cytoplasmic 1 Adenylyl cyclase-associated protein 1 Alcohol dehydrogenase [NADP+] Aldo-keto reductase family 1 member B10 Aldose reductase Aldose reductase-related protein 2 Alpha enolase Alpha-2-HS-glycoprotein Annexin A1 Annexin A2 Annexin A5 ATP synthase subunit alpha, mitochondrial ATP synthase subunit beta, mitochondrial Calmodulin Calreticulin Chloride intracellular channel protein 4 Clathrin heavy chain 1 Cytosolic nonspecific dipeptidase Deoxyribonuclease-1 Endoplasmin Ezrin Ferritin heavy chain Peptidyl-prolyl cis-trans isomerase FKBP3 Flavin reductase Fructose-bisphosphate aldolase A Galectin-1 Glutathione S-transferase Glutathione S-transferase A1 Glutathione S-transferase A3 Glutathione S-transferase Mu 1 Glutathione S-transferase Mu 2 Glutathione S-transferase Mu 6 Glutathione S-transferase P Glutathione S-transferase Y1 Glutathione S-transferase Yb-3 Glutathione synthetase Heat-shock protein beta-1 Isocitrate dehydrogenase [NADP] cytoplasmic Prelamin-A/C Lysosome-associated membrane glycoprotein 1 Macrophage capping protein Moesin Peroxiredoxin-1 Peroxiredoxin-4 Phosphoglycerate mutase 1 Protein disulfide-isomerase A3 Protein disulfide-isomerase A6 Protein disulfide-isomerase Protein S100-A6 Pyruvate kinase isozymes M1/M2 Rab GDP dissociation inhibitor beta Ras GTPase-activating protein-binding protein 2 Reticulocalbin-3 Serotransferrin Serpin H1 Sulfiredoxin-1 Superoxide dismutase [Cu-Zn] Synaptic vesicle membrane protein VAT-1 homolog Testin Thioredoxin Thioredoxin reductase 1, cytoplasmic

1.35 1.58 1.42

PM-7 v PM-Neg 96 h ANOVA

0.04 0.02 0.05

1.49 1.73 1.34

6.39 × 10−3 0.05 0.02

1.66

0.03

2.01

6.95 × 10−3

1.48

5.99 × 10−5

1.26 1.94

0.05 6.75 × 10−3

3.14

0.02

2.02 2.16

6.85 × 10−7 0.03

1.53

0.01

2.25 2.28

8.14 × 10−3 9.77 × 10−5

1.89 2.15 1.46

1.98 × 10−3 1.16 × 10−3 4.48 × 10−3

44.11

Fold changec

ANOVA

1.3 1.44 1.41 1.32 1.32

0.03 7.37 × 10-4 0.02 0.02 0.04

2.06

2.48 × 10−4

2.2 1.72 2.55 1.93 2.3

0.02 0.05 2.74 × 10−5 7.93 × 10−4 2.02 × 10−3

7.01 1.49 1.72 3.41 1.43 1.37 2.2 1.74 5.46 1.77 2.19 8.15 1.49 1.77 2.51 1.42 2.06 1.45 1.64 1.54 5.12 4.94 1.57 1.79 1.56 2.93 1.71 1.62 3.16 1.50 2.63 1.91 3.23 1.62 1.92 2.21 2.02 1.29 2.4 2.88 2.19 1.4 1.28 1.51

1.12 × 10−3 8.26 × 10−3 2.89 × 10−3 8.44 × 10−4 2.56 × 10−3 0.02 0.04 3.18 × 10−3 2.56 × 10−3 8.83 × 10−3 7.61 × 10−3 1.83 × 10−3 8.15 × 10−3 1.95 × 10−3 4.5 × 10−3 0.01 1.64 × 10−3 6.33 × 10−3 2.28 × 10−3 7.62 × 10−3 1.29 × 10−3 1.12 × 10−3 0.03 7.00 × 10−3 0.02 3.59 × 10−4 9.47 × 10−3 0.02 0.03 1.93 × 10−3 0.04 5.87 × 10−4 6.45 × 10−4 0.02 4.04 × 10−5 7.43 × 10−4 7.34 × 10−3 4.38 × 10−3 1.75 × 10−4 6.37 × 10−3 4.03 × 10−6 0.01 0.02 0.01

1.7 4.99

0.02 1.56 × 10−3

21.9 2.78 3.96 3.61

1.47 × 10−4 2.91 × 10−3 1.19 × 10−3 8.29 × 10−4

2.84

2.80 × 10−3

0.01

1.64 5.28

0.02 9.02 × 10−4

1.78 2.43

4.54 × 10−4 0.01

258

P. Meleady et al. / Journal of Biotechnology 160 (2012) 251–262

Table 1B (Continued) Accessiona

Gene nameb

Protein description

PM-7 v PM-Neg 48 h c

Fold change P37802 Q01853 P40142 P48500 P02544 Q2KJH4

TAGLN2 VCP TKT TPI1 VIM WDR1

Transgelin-2 Transitional endoplasmic reticulum ATPase Transketolase Triosephosphate isomerase Vimentin WD repeat protein 1

PM-7 v PM-Neg 96 h Fold changec

ANOVA

2.01 1.57 1.26

4.86 × 10−4 0.03 0.04

3.81

0.02

ANOVA 2.47 × 10−4 1.75 × 10−3 6.93 × 10−4 3.43 × 10−3 0.04

1.43 2.08 2.16 1.65 1.32

Further information on MASCOT scores, numbers of peptides matched and molecular weight for each protein can be found in Supplementary Table IB. a Uniprot accession number from MASCOT search of UniProtKB-SwissProt, taxonomy Mammalia. b Official recommended gene name taken from UniProtKB-SwissProt (in some cases there is no official gene name available so it has not been included). c Fold change showing increased protein expression in PM-7 transfected cells compared to PM-Neg transfected cells at 48 and at 96 h. Table 2 List of the four peptide sequences identified from catalase (P24270) as significantly decreased in miR-7 pre-mir transfected cells (PM-7) compared to negative control cells (PM-Neg) at 96 h following transfection. The overall MASCOT score for the protein was 169.56. The MS/MS spectra for each of these four peptides including the b and y ion fragment series are shown in Supplementary Fig. 1. Peptide no.

MASCOT score

Fold change

ANOVA

Charge

3530 4899 5403 6293

42.35 41.13 32.52 53.56

2.7 2.4 3.3 6.3

0.005 0.048 0.006 0.044

2 2 2 2

Average normalised abundance PM-Neg

PM-7

2.39E+04 3.60E+04 2.94E+04 3.00E+04

8936.304 1.52E+04 8971.183 4781.143

Peptide sequence

FNSANEDNVTQVR EAETFPFNPFDLTK DAILFPSFIHSQK GAGAFGYFEVTHDITR

Table 3 Bioinformatic analysis of the list of proteins showing decreased expression following over-expression of miR-7 to determine if any are potential predicted direct targets of miR-7. Six different algorithms were used (miRanda, miRDB, miRWALK, RNA22, RNAhybrid and TargetScan) and the table shows the positive hits from the search where two or more of the genes overlapped the various search algorithms from mouse, rat and human (䊉 = predicted target from database). Gene name

MicroRNA

StemLoop ID

miRanda

miRDB

miRWalk

Mouse Cat Cat Stmn1 Serbp1 Sfrs1 Caprin1 Cct3 Sfrs1 Pa2g4 Caprin1 Stmn1 Impdh2

mmu-miR-7b mmu-miR-7a mmu-miR-7a mmu-miR-7b mmu-miR-7a mmu-miR-7a mmu-miR-7b mmu-miR-7b mmu-miR-7b mmu-miR-7b mmu-miR-7b mmu-miR-7b

mmu-mir-7b mmu-mir-7a-2 mmu-mir-7a-2 mmu-mir-7b mmu-mir-7a-2 mmu-mir-7a-2 mmu-mir-7b mmu-mir-7b mmu-mir-7b mmu-mir-7b mmu-mir-7b mmu-mir-7b

䊉 䊉 䊉 䊉

䊉 䊉

䊉 䊉 䊉 䊉 䊉 䊉

Rat Cat Stmn1 Cat Stmn1 Tubb5 Ran Nap1l1 Tagln2 Eef1a1 Hspa90aa1 Cct3 Kpnb1 Tubb5 Ran Nap1l1 Tagln2 Eef1a1 Hspa90aa1 Cct3 Kpnb1

rno-miR-7a rno-miR-7a rno-miR-7b rno-miR-7b rno-miR-7a rno-miR-7a rno-miR-7a rno-miR-7a rno-miR-7a rno-miR-7a rno-miR-7a rno-miR-7a rno-miR-7b rno-miR-7b rno-miR-7b rno-miR-7b rno-miR-7b rno-miR-7b rno-miR-7b rno-miR-7b

rno-mir-7a-2 rno-mir-7a-2 rno-mir-7b rno-mir-7b rno-mir-7a-2 rno-mir-7a-2 rno-mir-7a-2 rno-mir-7a-2 rno-mir-7a-2 rno-mir-7a-2 rno-mir-7a-2 rno-mir-7a-2 rno-mir-7b rno-mir-7b rno-mir-7b rno-mir-7b rno-mir-7b rno-mir-7b rno-mir-7b rno-mir-7b

Human PA2G4 RPL15 SFRS1 AHNAK CAPRIN1

hsa-miR-7 hsa-miR-7 hsa-miR-7 hsa-miR-7 hsa-miR-7

hsa-mir-7-3 hsa-mir-7-3 hsa-mir-7-3 hsa-mir-7-3 hsa-mir-7-3

䊉 䊉

RNA22

RNAhybrid

TargetScan

Overlap

䊉

䊉 䊉 䊉

5 4 3 3 2 2 2 2 2 2 2 2

䊉 䊉 䊉

䊉 䊉 䊉

䊉 䊉

䊉 䊉 䊉 䊉

䊉 䊉

䊉 䊉

䊉 䊉 䊉 䊉 䊉 䊉

䊉 䊉 䊉 䊉 䊉 䊉 䊉 䊉 䊉 䊉 䊉 䊉 䊉 䊉 䊉 䊉 䊉 䊉 䊉 䊉

䊉 䊉 䊉 䊉 䊉 䊉

䊉 䊉 䊉 䊉 䊉

䊉 䊉 䊉

䊉 䊉

䊉 䊉

䊉 䊉 䊉 䊉

䊉 䊉

䊉 䊉 䊉 䊉

䊉

4 4 4 3 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 3 2 2 2 2

P. Meleady et al. / Journal of Biotechnology 160 (2012) 251–262

259

Table 4 GO biological process enrichment for differentially expressed proteins at 48 h following transfection. Enrichment was considered significant upon observation of a pvalue ≤ 0.05 and a Bonferroni adjusted p-value ≤ 0.05. Count corresponds to the overlap between proteins on the list and a particular GO category. Biological process Up-regulated GO:0045454 ∼ cell redox homeostasis Down-regulated GO:0006414 ∼ translational elongation GO:0034622 ∼ cellular macromolecular complex assembly GO:0034621 ∼ cellular macromolecular complex subunit organisation GO:0006333 ∼ chromatin assembly or disassembly GO:0065003 ∼ macromolecular complex assembly GO:0042274 ∼ ribosomal small subunit biogenesis GO:0043933 ∼ macromolecular complex subunit organisation GO:0006334 ∼ nucleosome assembly GO:0006412 ∼ translation GO:0031497 ∼ chromatin assembly GO:0065004 ∼ protein–DNA complex assembly GO:0034728 ∼ nucleosome organisation GO:0006323 ∼ DNA packaging

Pak1 (Reddy et al., 2008), IGF-1R (Jiang et al., 2010), cdc42 kinase 1 (Saydam et al., 2011) and ␣-synuclein (Junn et al., 2009). Using our MS profiling approach, we did not detect the known growth regulatory proteins such as EGFR or Pak1 as differentially expressed following over-expression of miR-7 in CHO cells. This is not surprising as CHO cells are known not to express EGFR so this pathway is clearly not the route to growth suppression in CHO cells (Shi et al., 2000). Other known direct targets of miR-7 were not detected using this MS profiling approach, possibly because only a portion of proteins in a sample are detected at any one time compared to using microarray analysis. In our study we found that ribosomal proteins were the most significantly enriched group of proteins found to be down-regulated following over-expression of miR-7, with 29 ribosomal proteins showing decreased expression. Similarly a recent proteomic study

Count

p-value

Adjusted

5

4.2 × 10−06

1.4 × 10−03

9 11 11 7 12 4 12 6 9 6 6 6 6

4.6 × 10−10 2.0 × 10−08 6.0 × 10−08 1.8 × 10−06 2.3 × 10−06 3.8 × 10−06 4.4 × 10−06 4.5 × 10−06 4.5 × 10−06 5.4 × 10−06 6.7 × 10−06 7.5 × 10−06 2.2 × 10−05

1.8 × 10−07 8.1 × 10−06 2.4 × 10−05 7.1 × 10−04 9.3 × 10−04 1.5 × 10−03 1.7 × 10−03 1.8 × 10−03 1.8 × 10−03 2.1 × 10−03 2.6 × 10−03 2.9 × 10−03 9.1 × 10−03

has shown that ribosomal proteins were the most significant set of proteins down-regulated by miR-34a in neuroblastoma cells (Chen et al., 2011). A large number of ribosomal genes were also found to be down-regulated in a microarray profiling study on the effect of low temperature in CHO and MAK hybridoma cells (Yee et al., 2009). Reduced culture temperature is known to attenuate protein translation (Fujita, 1999; Roobol et al., 2009). Ribosomal proteins are involved in ribosomal biogenesis, and thus play an essential role in protein translation (Brodersen and Nissen, 2005; FerreiraCerca et al., 2005). Recently a number of extra-ribosomal functions of ribosomal proteins have begun to emerge including specific roles for individual ribosomal proteins in growth, apoptosis and development (reviewed (Lindstrom, 2009; Warner and McIntosh, 2009). Specifically RPS10, down-regulated in our study, has been shown to cause a decrease in cell proliferation following shRNA

Table 5 GO biological process enrichment for differentially expressed proteins at 96 h following transfection. Enrichment was considered significant upon observation of a pvalue ≤ 0.05 and a Bonferroni adjusted p-value ≤ 0.05. Count corresponds to the overlap between proteins on the list and a particular GO category. Biological process Up regulated GO:0019725 ∼ cellular homeostasis GO:0042981 ∼ regulation of apoptosis GO:0043067 ∼ regulation of programmed cell death GO:0010941 ∼ regulation of cell death GO:0045454 ∼ cell redox homeostasis GO:0051235 ∼ maintenance of location GO:0006916 ∼ anti-apoptosis Down-regulated GO:0006414 ∼ translational elongation GO:0006412 ∼ translation GO:0006334 ∼ nucleosome assembly GO:0031497 ∼ chromatin assembly GO:0065004 ∼ protein-DNA complex assembly GO:0034621 ∼ cellular macromolecular complex subunit organisation GO:0034728 ∼ nucleosome organisation GO:0034622 ∼ cellular macromolecular complex assembly GO:0006323 ∼ DNA packaging GO:0006333 ∼ chromatin assembly or disassembly GO:0043933 ∼ macromolecular complex subunit organisation GO:0006396 ∼ RNA processing GO:0042254 ∼ ribosome biogenesis GO:0022613 ∼ ribonucleoprotein complex biogenesis GO:0065003 ∼ macromolecular complex assembly GO:0042274 ∼ ribosomal small subunit biogenesis GO:0006364 ∼ rRNA processing GO:0000398 ∼ nuclear mRNA splicing, via spliceosome GO:0000375 ∼ RNA splicing, via transesterification reactions GO:0000377 ∼ RNA splicing, via transesterification reactions with bulged adenosine as nucleophile GO:0016072 ∼ rRNA metabolic process

Count

p-value

Adjusted

13 15 15 15 6 6 8

5.1 × 10−07 5.3 × 10−06 6.0 × 10−06 6.2 × 10−06 7.0 × 10−06 7.6 × 10−06 2.6 × 10−05

4.3 × 10−04 4.5 × 10−03 5.1 × 10−03 5.3 × 10−03 6.0 × 10−03 6.4 × 10−03 2.2 × 10−02

30 31 9 9 9 14 9 13 9 9 16 14 8 9 15 4 6 7 7 7 6

9.1 × 10−46 2.9 × 10−31 6.7 × 10−09 8.9 × 10−09 1.2 × 10−08 1.4 × 10−08 1.5 × 10−08 3.6 × 10−08 9.3 × 10−08 1.7 × 10−07 1.2 × 10−06 1.9 × 10−06 2.0 × 10−06 2.5 × 10−06 3.0 × 10−06 1.7 × 10−05 8.4 × 10−05 9.8 × 10−05 9.8 × 10−05 9.8 × 10−05 1.0 × 10−04

4.0 × 10−43 1.3 × 10−28 2.9 × 10−06 3.9 × 10−06 5.6 × 10−06 6.2 × 10−06 6.7 × 10−06 1.6 × 10−05 4.1 × 10−05 7.8 × 10−05 5.3 × 10−04 8.4 × 10−04 9.0 × 10−04 1.1 × 10−03 1.3 × 10−03 7.8 × 10−03 3.6 × 10−02 4.2 × 10−02 4.2 × 10−02 4.2 × 10−02 4.4 × 10−02

260

P. Meleady et al. / Journal of Biotechnology 160 (2012) 251–262

knockdown (Ren et al., 2010). Other ribosomal proteins decreased in our study, RPS13, RPL15 and RPL6, have been implicated in affecting the growth of gastric cancer cells (Gou et al., 2010; Guo et al., 2011; Wang et al., 2006). RPL15 could also be a potential direct target of miR-7 in CHO cells as it was found to overlap two of the predicted target database searches in human (see Table 3). We also found a number of histone proteins highly downregulated following transfection with miR-7. Histones are required for the formation of nucleosomes which are the primary building blocks of chromatin (Workman and Kingston, 1998). Regulation of gene expression occurs through post-transcriptional modification of histone tails including acetylation, methylation and phosphorylation. This complex epigenetic information (histone code) combined with DNA methylation determines if a gene is transcriptionally active. MiRNAs are known to be regulated by epigenetic mechanisms and they are also capable themselves of regulating the expression of components of the epigenetic machinery (Iorio et al., 2010; Sato et al., 2011). Recently HDAC-1 has been found to be a direct target of miR-449a in prostate cancer cells causing growth arrest following over-expression of miR-449a (Noonan et al., 2009). Further work to confirm if miR-7 directly targets aspects of the epigenetic machinery is required and may reveal an insight into how it regulates growth of CHO cells. One of the proteins with decreased expression following overexpression of miR-7 was Proliferation-associated protein 2G4 (PA2G4) also known as ErbB3 binding protein-1 (EBP-1). In silico analysis of miR-7 target genes showed that EBP-1 is possibly a direct target of miR-7 as it overlapped three of the predicted target database analyses in human and two in mouse. This protein is involved in growth regulation where its over-expression can inhibit the growth of fibroblasts (Squatrito et al., 2004), breast cancer (Lessor et al., 2000) and prostate cancer (Zhang et al., 2002) cells. On the other hand Ebp-1 deficient mice were 30% smaller than wildtype and growth was significantly retarded (Zhang et al., 2008). Interestingly EBP-1 has been shown to be localised to the nucleolus and is possibly involved in the regulation of intermediate and late steps of rRNA processing and ribosome assembly (Squatrito et al., 2004). Immunoprecipitation of EBP-1 in HeLa cells found that EBP-1 associated with a ribonucleoprotein complex that includes RPLP0, RPL12, RPL15, RPL18 and RPL7A (Squatrito et al., 2004), proteins which we also found decreased in miR-7 transfected CHO cells. In this complex, histone H1.1 and nucleolin were also identified (Squatrito et al., 2004). In a further study, EBP-1 was found to bind nucleophosmin (Okada et al., 2007). We also found that the expression of histone H1.1, nucleolin and nucleophosmin were decreased following transfection of miR-7 in CHO cells. From the bioinformatic analysis to predict possible direct targets of miR-7, two proteins, stathmin and catalase, were found to overlap multiple predicted target analyses in mouse and rat. Both catalase and stathmin were found to be highly down-regulated following over-expression of miR-7. Catalase is a well studied enzyme that plays a critical role in protecting cells against the toxic effects of hydrogen peroxide (Goyal and Basak, 2010). Catalase has also been found to have growth promoting activity in a range of cell types (Takeuchi et al., 1995). Evidence is beginning to emerge that miRNAs may play a role in the regulation of reactive oxygen species as miR-128a can induce senescence and increase steady state levels of ROS in medulloblastoma cells (Venkataraman et al., 2010). An imbalance between ROS generation and the capacity of specific enzymes and antioxidants to neutralise ROS can potentially lead to oxidative stress. In CHO cells perhaps over-expression of miR7 is capable of regulating ROS by directly targeting catalase with a resultant decrease in cellular growth. Stathmin is a phosphoprotein that plays a critical role in the regulation of mitosis by destabilizing microtubules (Rubin and Atweh, 2004). It is also over-expressed in a variety of cancers (Rana et al., 2008) and when down-regulated

it can inhibit cell proliferation in oesophageal cancer (Wang et al., 2011). Stathmin has recently been shown to be a direct target of miR-9 and miR-223 (Delaloy et al., 2010; Wong et al., 2008). Among the list of up-regulated proteins following transfection of miR-7 were a number of proteins known to play a role in protein folding and secretion including HSPA5, HSP90B1, HSPB1, P4HB, PDIA3, PDIA6 and Calreticulin. Previously we have found that over-expression of miR-7 in CHO-SEAP cells caused an increase in normalised (per cell) production of SEAP which may explain the increased expression of these proteins (Barron et al., 2011a). These results obtained in relation to productivity are also not likely to be SEAP-specific; results in relation to Qp would be expected to be different only if the product gene contained specific miRNA target sites. MiR-7 is also known to be expressed in specialised neurosecretory cells suggesting a possible involvement in the regulation of protein secretion (Correa-Medina et al., 2009; Joglekar et al., 2009). In conclusion we have identified a wide range of proteins to be differentially regulated following over-expression of miR-7 in CHO cells. The largest group of proteins with decreased expression (i.e. ribosomal and histone proteins) play a crucial role in growth and proliferation, and this may reflect the observed inhibitory effect of miR-7 on the growth of CHO-SEAP cells over 96 h. We also identified two other growth regulating proteins, catalase and stathmin, which from predicted target bioinfomatic analysis could be direct targets of miR-7 though further work needs to be carried out to confirm this. We have also previously found that over-expression of miR-7 results in decreased yield but an increased cell specific productivity of SEAP protein (Barron et al., 2011a). This may be analogous to the effect of temperature shift which results in a reduced cell growth rate while specific productivity is increased (Al-Fageeh et al., 2006), as from the proteomic profiling approach used here a large number of proteins involved in growth and proliferation showed decreased expression while other proteins which are known to play active roles in protein folding and secretion showed increased expression. Increasing miR-7 expression in CHO cells is therefore likely to result in selective translation of specific genes analogous to the response of mammalian cells to low temperature culture.

Acknowledgement This work was supported by funding from Science Foundation Ireland (SFI) grant number 07/IN.1/B1323.

Appendix A. Supplementary data Supplementary data associated with this article can be found, in the online version, at doi:10.1016/j.jbiotec.2012.03.002.

References Al-Fageeh, M.B., Marchant, R.J., Carden, M.J., Smales, C.M., 2006. The cold-shock response in cultured mammalian cells: harnessing the response for the improvement of recombinant protein production. Biotechnol. Bioeng. 93, 829–835. Barron, N., Kumar, N., Sanchez, N., Doolan, P., Clarke, C., Meleady, P., O’Sullivan, F., Clynes, M., 2011a. Engineering CHO cell growth and recombinant protein productivity by overexpression of miR-7. J. Biotechnol. 151, 204–211. Barron, N., Sanchez, N., Kelly, P., Clynes, M., 2011b. MicroRNAs: tiny targets for engineering CHO cell phenotypes? Biotechnol. Lett. 33, 11–21. Bartel, D.P., 2009. MicroRNAs: target recognition and regulatory functions. Cell 136, 215–233. Becker, C.H., Bern, M., 2010. Recent developments in quantitative proteomics. Mutat. Res. 722 (2), 171–182. Bort, J.A., Hackl, M., Hoflmayer, H., Jadhav, V., Harreither, E., Kumar, N., Ernst, W., Grillari, J., Borth, N., 2011. Dynamic mRNA and miRNA profiling of CHO-K1 suspension cell cultures. Biotechnol. J. June 21 (Epub ahead of print). Brodersen, D.E., Nissen, P., 2005. The social life of ribosomal proteins. FEBS J. 272, 2098–2108.

P. Meleady et al. / Journal of Biotechnology 160 (2012) 251–262 Carlage, T., Hincapie, M., Zang, L., Lyubarskaya, Y., Madden, H., Mhatre, R., Hancock, W.S., 2009. Proteomic profiling of a high-producing Chinese hamster ovary cell culture. Anal. Chem. 81, 7357–7362. Chen, Q.R., Yu, L.R., Tsang, P., Wei, J.S., Song, Y.K., Cheuk, A., Chung, J.Y., Hewitt, S.M., Veenstra, T.D., Khan, J., 2011. Systematic proteome analysis identifies transcription factor YY1 as a direct target of miR-34a. J. Proteome Res. 10, 479–487. Chou, Y.T., Lin, H.H., Lien, Y.C., Wang, Y.H., Hong, C.F., Kao, Y.R., Lin, S.C., Chang, Y.C., Lin, S.Y., Chen, S.J., Chen, H.C., Yeh, S.D., Wu, C.W., 2010. EGFR promotes lung tumorigenesis by activating miR-7 through a Ras/ERK/Myc pathway that targets the Ets2 transcriptional repressor ERF. Cancer Res. 70, 8822–8831. Correa-Medina, M., Bravo-Egana, V., Rosero, S., Ricordi, C., Edlund, H., Diez, J., Pastori, R.L., 2009. MicroRNA miR-7 is preferentially expressed in endocrine cells of the developing and adult human pancreas. Gene Expr. Patterns 9, 193–199. Delaloy, C., Liu, L., Lee, J.A., Su, H., Shen, F., Yang, G.Y., Young, W.L., Ivey, K.N., Gao, F.B., 2010. MicroRNA-9 coordinates proliferation and migration of human embryonic stem cell-derived neural progenitors. Cell Stem Cell 6, 323–335. Doolan, P., Meleady, P., Barron, N., Henry, M., Gallagher, R., Gammell, P., Melville, M., Sinacore, M., McCarthy, K., Leonard, M., Charlebois, T., Clynes, M., 2010. Microarray and proteomics expression profiling identifies several candidates, including the valosin-containing protein (VCP), involved in regulating high cellular growth rate in production CHO cell lines. Biotechnol. Bioeng. 106, 42–56. Druz, A., Chu, C., Majors, B., Santuary, R., Betenbaugh, M., Shiloach, J., 2011. A novel microRNA mmu-miR-466h affects apoptosis regulation in mammalian cells. Biotechnol. Bioeng. 108, 1651–1661. Dweep, H., Sticht, C., Pandey, P., Gretz, N., 2011. miRWalk–database: prediction of possible miRNA binding sites by walking the genes of three genomes. J. Biomed. Inform. 44, 839–847. Enright, A.J., John, B., Gaul, U., Tuschl, T., Sander, C., Marks, D.S., 2003. MicroRNA targets in drosophila. Genome Biol. 5 (1), R1. Ferreira-Cerca, S., Poll, G., Gleizes, P.E., Tschochner, H., Milkereit, P., 2005. Roles of eukaryotic ribosomal proteins in maturation and transport of pre-18S rRNA and ribosome function. Mol. Cell 20, 263–275. Fox, S.R., Tan, H.K., Tan, M.C., Wong, S.C., Yap, M.G., Wang, D.I., 2005. A detailed understanding of the enhanced hypothermic productivity of interferon-gamma by Chinese-hamster ovary cells. Biotechnol. Appl. Biochem. 41, 255–264. Fujita, J., 1999. Cold shock response in mammalian cells. J. Mol. Microbiol. Biotechnol. 1, 243–255. Gammell, P., Barron, N., Kumar, N., Clynes, M., 2007. Initial identification of low temperature and culture stage induction of miRNA expression in suspension CHO-K1 cells. J. Biotechnol. 130, 213–218. Gou, Y., Shi, Y., Zhang, Y., Nie, Y., Wang, J., Song, J., Jin, H., He, L., Gao, L., Qiao, L., Wu, K., Fan, D., 2010. Ribosomal protein L6 promotes growth and cell cycle progression through upregulating cyclin E in gastric cancer cells. Biochem. Biophys. Res. Commun. 393, 788–793. Goyal, M.M., Basak, A., 2010. Human catalase: looking for complete identity. Protein Cell 1, 888–897. Guo, X., Shi, Y., Gou, Y., Li, J., Han, S., Zhang, Y., Huo, J., Ning, X., Sun, L., Chen, Y., Sun, S., Fan, D., 2011. Human ribosomal protein S13 promotes gastric cancer growth through downregulating p27(Kip1). J. Cell. Mol. Med. 15 (2), 296–306. Hackl, M., Jakobi, T., Blom, J., Doppmeier, D., Brinkrolf, K., Szczepanowski, R., Bernhart, S.H., Siederdissen, C.H., Bort, J.A., Wieser, M., Kunert, R., Jeffs, S., Hofacker, I.L., Goesmann, A., Puhler, A., Borth, N., Grillari, J., 2011. Next-generation sequencing of the chinese hamster ovary microRNA transcriptome: identification annotation and profiling of microRNAs as targets for cellular engineering. J. Biotechnol. 153 (1-2), 62–75. Huang, D.W., Sherman, B.T., Lempicki, R.A., 2009. Systemic and integrative analysis of large gene lists using DAVID bioinformatic resources. Nat. Protoc. 4, 44–57. Iorio, M.V., Piovan, C., Croce, C.M., 2010. Interplay between microRNAs and the epigenetic machinery. An intricate network. Biochim. Biophys. Acta 1799, 694–701. Jiang, L., Liu, X., Chen, Z., Jin, Y., Heidbreder, C.E., Kolokythas, A., Wang, A., Dai, Y., Zhou, X., 2010. MicroRNA-7 targets IGF1R (insulin-like growth factor 1 receptor) in tongue squamous cell carcinoma cells. Biochem. J. 432, 199–205. Joglekar, M.V., Joglekar, V.M., Hardikar, A.A., 2009. Expression of islet-specific microRNAs during human pancreatic development. Gene Expr. Patterns 9, 109–113. Johnson, K.C., Jacob, N.M., Nissom, P.M., Hackl, M., Lee, L.H., Yap, M., Hu, W.S., 2011. Conserved microRNAs in Chinese hamster ovary cell lines. Biotechnol. Bioeng. 108, 475–480. Junn, E., Lee, K.W., Jeong, B.S., Chan, T.W., Im, J.Y., Mouradian, M.M., 2009. Repression of alpha-synuclein expression and toxicity by microRNA-7. Proc. Natl. Acad. Sci. U.S.A. 106, 13052–13057. Kefas, B., Godlewski, J., Comeau, L., Li, Y., Abounader, R., Hawkinson, M., Lee, J., Fine, H., Chiocca, E.A., Lawler, S., Purow, B., 2008. MicroRNA-7 inhibits the epidermal growth factor receptor and the akt pathway and is down-regulated in glioblastoma. Cancer Res. 68, 3566–3572. Lessor, T.J., Yoo, J.Y., Xia, X., Woodford, N., Hamburger, A.W., 2000. Ectopic expression of the ErbB-3 binding protein ebp1 inhibits growth and induces differentiation of human breast cancer cell lines. J. Cell. Physiol. 183, 321–329. Lewis, B.P., Burge, C.B., Bartel, D.P., 2005. Conserved seed pairing, often flanked by adenosines, indicates that thousands of human genes are microRNA targets. Cell 120, 15–20. Li, L., Xu, J., Yang, D., Tan, X., Wang, H., 2010. Computational approaches for microRNA studies: a review. Mamm. Genome 21, 1–12.

261

Lin, N., Davis, A., Bahr, S., Borgschulte, T., Achtien, K., Kayser, K., 2011. Profiling highly conserved microrna expression in recombinant IgGproducing and parental chinese hamster ovary cells. Biotechnol. Prog. 27, 1163–1171. Lindstrom, M.S., 2009. Emerging functions of ribosomal proteins in genespecific transcription and translation. Biochem. Biophys. Res. Commun. 379, 167–170. Lundgren, D.H., Hwang, S.I., Wu, L., Han, D.K., 2010. Role of spectral counting in quantitative proteomics. Expert Rev. Proteomics 7, 39–53. Meleady, P., Henry, M., Gammell, P., Doolan, P., Sinacore, M., Melville, M., Francullo, L., Leonard, M., Charlebois, T., Clynes, M., 2008. Proteomic profiling of CHO cells with enhanced rhBMP-2 productivity following co-expression of PACEsol. Proteomics 8, 2611–2624. Miranda, K.C., Huynh, T., Tay, Y., Ang, Y.S., Tam, W.L., Thomson, A.M., Lim, B., Rigoutsos, I., 2006. A pattern-based method for the identification of MicroRNA binding sites and their corresponding heteroduplexes. Cell 126, 1203–1217. Muller, D., Katinger, H., Grillari, J., 2008. MicroRNAs as targets for engineering of CHO cell factories. Trends Biotechnol. 26, 359–365. Muniyappa, M.K., Dowling, P., Henry, M., Meleady, P., Doolan, P., Gammell, P., Clynes, M., Barron, N., 2009. MiRNA-29a regulates the expression of numerous proteins and reduces the invasiveness and proliferation of human carcinoma cell lines. Eur. J. Cancer 45, 3104–3118. Neilson, K.A., Ali, N.A., Muralidharan, S., Mirzaei, M., Mariani, M., Assadourian, G., Lee, A., van Sluyter, S.C., Haynes, P.A., 2011. Less label, more free: approaches in label-free quantitative mass spectrometry. Proteomics 11, 535–553. Noonan, E.J., Place, R.F., Pookot, D., Basak, S., Whitson, J.M., Hirata, H., Giardina, C., Dahiya, R., 2009. MiR-449a targets HDAC-1 and induces growth arrest in prostate cancer. Oncogene 28, 1714–1724. Okada, M., Jang, S.W., Ye, K., 2007. Ebp1 association with nucleophosmin/B23 is essential for regulating cell proliferation and suppressing apoptosis. J. Biol. Chem. 282, 36744–36754. Panchaud, A., Affolter, M., Moreillon, P., Kussmann, M., 2008. Experimental and computational approaches to quantitative proteomics: Status quo and outlook. J. Proteomics 71, 19–33. Rana, S., Maples, P.B., Senzer, N., Nemunaitis, J., 2008. Stathmin 1 A novel therapeutic target for anticancer activity. Expert Rev. Anticancer Ther. 8, 1461–1470. Reddy, S.D., Ohshiro, K., Rayala, S.K., Kumar, R., 2008. MicroRNA-7, a homeobox D10 target, inhibits p21-activated kinase 1 and regulates its functions. Cancer Res. 68, 8195–8200. Rehmsmeier, M., Steffen, P., Hochsmann, M., Giegerich, R., 2004. Fast and effective prediction of microRNA/target duplexes. RNA 10, 1507–1517. Ren, J., Wang, Y., Liang, Y., Zhang, Y., Bao, S., Xu, Z., 2010. Methylation of ribosomal protein S10 by protein-arginine methyltransferase 5 regulates ribosome biogenesis. J. Biol. Chem. 285, 12695–12705. Roobol, A., Carden, M.J., Newsam, R.J., Smales, C.M., 2009. Biochemical insights into the mechanisms central to the response of mammalian cells to cold stress and subsequent rewarming. FEBS J. 276, 286–302. Rubin, C.I., Atweh, G.F., 2004. The role of stathmin in the regulation of the cell cycle. J. Cell. Biochem. 93, 242–250. Sato, F., Tsuchiya, S., Meltzer, S.J., Shimizu, K., 2011. MicroRNAs and epigenetics. FEBS J. 278, 1598–1609. Saydam, O., Senol, O., Wurdinger, T., Mizrak, A., Ozdener, G.B., StemmerRachamimov, A.O., Yi, M., Stephens, R.M., Krichevsky, A.M., Saydam, N., Brenner, G.J., Breakefield, X.O., 2011. MiRNA-7 attenuation in schwannoma tumors stimulates growth by upregulating three oncogenic signaling pathways. Cancer Res. 71 (3), 852–861. Schulze, W.X., Usadel, B., 2010. Quantitation in mass-spectrometry-based proteomics. Ann. Rev. Plant Biol. 61, 491–516. Shi, W., Fan, H., Shum, L., Derynck, R., 2000. The tetraspanin CD9 associates with transmembrane TGF-alpha and regulates TGF-alpha-induced EGF receptor activation and cell proliferation. J. Cell Biol. 148, 591–602. Squatrito, M., Mancino, M., Donzelli, M., Areces, L.B., Draetta, G.F., 2004. EBP1 is a nucleolar growth-regulating protein that is part of pre-ribosomal ribonucleoprotein complexes. Oncogene 23, 4454–4465. Takeuchi, A., Miyamoto, T., Yamaji, K., Masuho, Y., Hayashi, M., Hayashi, H., Onozaki, K., 1995. A human erythrocyte-derived growth-promoting factor with a wide target cell spectrum: identification as catalase. Cancer Res. 55, 1586–1589. Venkataraman, S., Alimova, I., Fan, R., Harris, P., Foreman, N., Vibhakar, R., 2010. MicroRNA 128a increases intracellular ROS level by targeting bmi-1 and inhibits medulloblastoma cancer cell growth by promoting senescence. PloS One 5, e10748. Wang, F., Wang, L.X., Li, S.L., Li, K., He, W., Liu, H.T., Fan, Q.X., 2011. Downregulation of stathmin is involved in malignant phenotype reversion and cell apoptosis in esophageal squamous cell carcinoma. J. Surg. Oncol. 103, 704–715. Wang, H., Zhao, L.N., Li, K.Z., Ling, R., Li, X.J., Wang, L., 2006. Overexpression of ribosomal protein L15 is associated with cell proliferation in gastric cancer. BMC Cancer 6, 91. Wang, X., 2008. miRDB: A microRNA target prediction and functional annotation database with a wiki interface. RNA 14, 1012–1017. Warner, J.R., McIntosh, K.B., 2009. How common are extraribosomal functions of ribosomal proteins? Mol. Cell 34, 3–11.

262

P. Meleady et al. / Journal of Biotechnology 160 (2012) 251–262

Webster, R.J., Giles, K.M., Price, K.J., Zhang, P.M., Mattick, J.S., Leedman, P.J., 2009. Regulation of epidermal growth factor receptor signaling in human cancer cells by microRNA-7. J. Biol. Chem. 284, 5731–5741. Wong, Q.W., Lung, R.W., Law, P.T., Lai, P.B., Chan, K.Y., To, K.F., Wong, N., 2008. MicroRNA-223 is commonly repressed in hepatocellular carcinoma and potentiates expression of Stathmin1. Gastroenterology 135, 257–269. Workman, J.L., Kingston, R.E., 1998. Alteration of nucleosome structure as a mechanism of transcriptional regulation. Ann. Rev. Biochem. 67, 545–579.

Yee, J.C., Gerdtzen, Z.P., Hu, W.S., 2009. Comparative transcriptome analysis to unveil genes affecting recombinant protein productivity in mammalian cells. Biotechnol. Bioeng. 102, 246–263. Zhang, Y., Fondell, J.D., Wang, Q., Xia, X., Cheng, A., Lu, M.L., Hamburger, A.W., 2002. Repression of androgen receptor mediated transcription by the ErbB-3 binding protein, Ebp1. Oncogene 21, 5609–5618. Zhang, Y., Lu, Y., Zhou, H., Lee, M., Liu, Z., Hassel, B.A., Hamburger, A.W., 2008. Alterations in cell growth and signaling in ErbB3 binding protein-1 (Ebp1) deficient mice. BMC Cell Biol. 9, 69.