Extensive changes in mRNA profiles induced by ectopic miR-155 expression

In order to identify changes in the miRNome and targetome upon ectopic miR-155 expression, RIP-Seq experiments using HEK-miR-155 and HEK-vector cells were performed. In addition, mRNA-Seq of the total lysates of both cell lines was performed to dissect changes on the cellular gene expression profile, which were directly and indirectly caused by the enforced expression of miR-155. The specificity of AGO2 immunoprecipitation was confirmed by western blot analyses (Fig. S2) and mass spectrometry. For quality control, we tested the enrichment of miR-155 in the AGO2 IPs by qPCR. As expected, miR-155 was almost absent in the IP fractions of HEK-vector cells, but specifically enriched in the AGO2 IP of HEK-miR-155 cells with a more than 7-fold enrichment compared with the control IP with isotype antibodies (Fig. S3).

The mRNA content of the total lysate (TL) and immuneprecipitate (IP) fractions of three independently performed RIP experiments was analyzed by high-throughput sequencing. The obtained reads were mapped against the human reference genome version 19 using the TopHat algorithm.¹⁵ For the IP and TL fractions on average 35M and 68M reads were generated, respectively. Comparing the mRNA profiles in the TL fractions of HEK-vector and HEK-miR-155 cells revealed that 339 mRNAs were downregulated and 121 mRNAs were upregulated in HEK293T cells upon ectopic miR-155 expression (Fig. 2A, Table 1; Table S1). Notably, among the top 20 upregulated genes, we identified CCND1 and all three members of the ETS transcription factor subfamily PEA3, which are ETV1, ETV4 and ETV5 (Table S1). Upregulation of these genes was confirmed by quantitative real-time PCR (Fig. S4). The IP fractions of both cell lines were compared, to identify transcripts enriched in the RISC. Here, 213 mRNAs with higher abundance and 409 mRNAs with lower abundance in the IP fraction of HEK-miR-155 cells were identified (Fig. 2B, Table 1; Table S2). Taken together, these data suggest that the enforced expression of miR-155 led to wide-spread secondary changes within the gene expression profile. Furthermore, these changes might subsequently have an impact on the IP profiles. For instance, CCND1 was—besides being one of the most highly upregulated transcripts in HEK-miR-155—among the mRNAs, which were highly enriched in the IP of these cells. Computational miRNA target prediction for the CCND1 3′UTR indicated a potential miR-155 binding site and the presence of several miRNA target sites for miR-17, miR-19a, miR-15, miR-16-1, miR-20a and miR-106a, which were shown to be functionally active.^16,17 The miR-155 target gene CEBPB¹⁸ was described to repress the expression of CCND1 in macrophages.¹⁹ Furthermore, screening of the CCND1 promotor region for protein interaction sites using the TFSEARCH algorithm revealed several putative CEBPB binding sites. We therefore performed knockdown experiments using CEBPB-specific siRNAs. Quantitative RT-PCR analysis revealed a knockdown of the CEBPB transcript of approximately 40% 24 h and 48 h after transfection. Associated with that, a slight increase of up to 1.8-fold of CCND1 mRNA was observed (Fig. S5), suggesting that the increased expression of CCND1 in HEK-miR-155 cells might be in part caused by miR-155-mediated downregulation of CEBPB. Therefore, its enrichment in the IP fraction might not be due to a direct interaction with miR-155, but rather reflects the higher abundance of this mRNA in the lysate of HEK-miR-155 cells and its subsequent interaction with endogenous miRNAs like miR-17 or miR-106a. Thus, in order to reliably identify miR-155 targets, mRNA levels detected in the IP fractions have to be normalized to their expression levels measured in the total lysates of the respective sample.

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Figure 2. Volcano and starburst plots displaying significantly altered mRNAs within IP and TL fractions. Co-immunoprecipitated mRNAs of TL and IP fractions of three RIP experiments using HEK-vector and HEK-miR-155 cells were identified and quantified by next generation sequencing. (A) TL fractions of HEK-vector and HEK-miR-155 cells were compared. In total, 460 mRNAs showed differential expression between both cell lines, with 339 significantly downregulated and 121 significantly upregulated transcripts upon ectopic miR-155 expression (p < 0.05, black). (B) Comparing the IP fractions of both cell lines revealed 409 mRNAs with significantly lower levels and 213 mRNAs with significantly higher levels in HEK-miR-155 IPs (p < 0.05, black). (C) Transcripts with enhanced enrichment in the HEK-miR-155 IPs after compensation for secondary effects, like TCF4, JARID2, PHC2 CEBPB and PHC2, are displayed in black (FDR 10%, SAM).

Identification of potential miR-155 target genes by RIP-Seq

The mRNA levels detected in the IP fractions were normalized to their respective TL fractions in order to compensate for changes in mRNA expression as described above. Therefore, log2-ratios of IP vs. TL values were calculated for each transcript and statistically significant differences between HEK-miR-155 and HEK-vector cells were obtained by performing a Significance Analysis of Microarrays (SAM). As a result, 100 transcripts were identified with significant enrichment in the IPs of HEK-miR-155 cells in comparison to the control cell line (false discovery rate 10%, SAM) (Fig. 2C, black, Table 1; Table S3). Notably, no significant enrichment of transcripts was observed in HEK-vector IPs (Fig. 2C). Prediction algorithms were used in order to identify putative miR-155 targets within the lists of co-precipitated mRNAs for the isogenic cell lines. For this purpose, five target prediction tools TargetScan, MiRANDA, DianaMicro-T, MiRWALK and PITA were used.^20-25 Transcripts predicted by at least three of these five algorithms with a seed match of seven or eight nucleotides and a p value lower than 0.05 were considered as putative miRNA targets predicting regulation of 67 (of 100) enriched mRNAs in HEK-miR-155 IPs (Table 2). At least 21 of these are experimentally validated miR-155 targets (Table 2; indicated with *).

To confirm an enrichment of target genes, qPCR was performed for the known miR-155 target PHC2 in one technical and two biological replicates. Since GAPDH mRNA was not enriched in our RIP-Seq experiments, it was used as a negative control. The obtained results verified a consistent enrichment of PHC2 mRNA in HEK-miR-155 IPs relative to HEK-vector IPs in all three experiments (data not shown). In accordance with the RNA-Seq data, GAPDH mRNA showed no consistent enrichment in HEK-miR-155 IPs, indicating specific precipitation of miR-155 targets. To identify functionally relevant transcripts regulated by miR-155, we searched the Ingenuity database for genes related to proliferation. Of the 100 genes enriched in HEK-miR-155 IPs, 25 were associated to proliferation and 17 of these were predicted as direct miR-155 targets (Table 2; bold letters). Notably, at least seven of these genes, among them CEBPB, PHC2 and TCF4, were experimentally verified miR-155 targets.^18,26,27 By screening RIP-Seq data of two B-cell lines, MEC-1 and JEKO-1 (unpublished data), we observed an enrichment of 36 and 19 of the 67 putative HEK miR-155 targets in the respective IPs. We further tested ZFP36, JARID2 and BACH1 regulation after 24 h of anti-miR-155 treatment in MEC-1 cells by qRT-PCR. Accordingly, we observed a tendency of de-repression for all candidates, which was significant for ZFP36, a so far unknown miR-155 target (Fig. S6).

Degradation vs. translational repression of miR-155 targets

The modes of miRNA activity are still under debate. Recent data suggest that translational repression precedes mRNA deadenylation and decay.^3,28,29 Here, we investigated miRNA targets at a steady-state level to determine to which extent the identified miR-155 targets were degraded or translationally repressed. We therefore evaluated the expression levels of the 67 putative HEK miR-155 targets to identify transcripts with differential expression between HEK-vector and HEK-miR-155 cells. Out of these, nine targets (e.g., BACH1 and JARID2) showed significantly lower levels of mRNA in the TL fraction, which might be explained by miR-155-mediated decay of the transcripts, but only a minor enrichment in the IP of HEK-miR-155 cells (p < 0.05). Furthermore, 20 targets showed a significant enrichment in the IP fraction (p < 0.05) but no significant change in the TL (e.g., PHC2 and DUSP14), suggesting that these transcripts were primarily regulated by translational repression. One single target gene, ZFP36, was identified by a significant enrichment in the IP and a robust decay in the TL of HEK-miR-155 cells. To validate these findings, the expression levels of eight putative miR-155 targets were quantified by qRT-PCR using the TL fractions of HEK-vector and HEK-miR-155 cells. Thereby, we observed reduced mRNA levels for ZFP36, ZNF652 and BACH1 in HEK-miR-155 cells, whereas the mRNA levels of PHC2, FBXO9, AGTRAP and DUSP14 were similar in both isogenic cell lines (Fig. 3A), confirming the RNA sequencing data.

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Figure 3. Validation of miR-155 targets on transcript and protein level. (A) Quantification of transcripts from 4 transcriptionally degraded and four translationally repressed miR-155 targets by qRT-PCR in HEK-vector and HEK-mir-155 cells. Ct-values were normalized to the mean of two housekeeping genes GAPDH and DCTN2 (HK). Relative transcript levels of HEK-vector and HEK-miR-155 cells are depicted. (B) Luciferase sensor assays of miR-155 targets identified by RIP-Seq. The control experiments without miR-155 expression were set to 1 (black line). Statistical significance was calculated by student’s t-test (* = p < 0.05). (C) Western blot analysis of ZFP36 and AGO2 protein expression in HEK cells with and without enforced miR-155 expression. Equal loading of protein lysates was confirmed by β-Actin detection. (D) Correlation of seed length and number of putative miR-155 binding sites per transcript to the mode of miRNA-mediated target regulation as predicted by the PITA algorithm.

To test for the regulation of identified miR-155 target genes on protein level, 3′UTR luciferase sensor assays were performed for CEBBP, PHC2, DUSP14 and MAP3K14. The sensor constructs were co-transfected either with miR-155 or backbone vectors. The obtained results revealed a significant reduction of the luciferase signals upon miR-155 co-transfection for CEBBP, PHC2 and MAP3K14 (Fig. 3B), which suggests that these genes are direct targets of miR-155, and a non-significant trend of reduction for DUSP14. We further quantified ZFP36 and DUSP14 protein levels upon transient miR-155 expression in HEK293T cells by western blot analyses. Thereby, we observed a significant reduction of ZFP36 protein by miR-155 of 0.3-fold and a less distinct effect of 0.5-fold and 0.8-fold for DUSP14 (Fig. 3C; Fig. S7), which confirms the results of the luciferase assays. Taken together, these data suggest that despite several putative miR-155 target transcripts were barely degraded, their protein levels were reduced, indicating translational repression as an important mode of miRNA-mediated regulation for a subset of targets.

We further analyzed whether the mode of miRNA activity correlates with seed sequence length or the amount of miR-155 binding sites by using the PITA prediction algorithm. For seed length determination we counted the longest seed interaction site, in case there were more than one predicted binding sites per transcript. Interestingly, we observed that the group of degraded miR-155 targets showed an enrichment for a seed length of 8 nucleotides (50% of targets) compared with the group of translationally inhibited targets (21%; Fig. 3D). Furthermore, the group of translationally repressed transcripts showed an enrichment for targets with just one putative miR-155 binding site (74% compared with 30% in the group of degraded targets). Whereas degraded transcripts contained more transcripts with four putative miR-155 binding sites (30% compared with 5% in the other group; Fig. 3D). Whether these observations can be explained by a dependency of the mode of miRNA activity on seed length and number of binding sites has to be clarified by more detailed analyses.

MicroRNA expression changes upon ectopic miR-155 expression

In order to delineate changes in the miRNA profile caused by ectopic expression of miR-155, TL and IP fractions of HEK-miR-155 and HEK-vector cells were analyzed by TaqMan arrays covering the 377 most frequently expressed miRNAs. In total, 248 and 193 miRNAs were detected in HEK-miR-155 IP and TL fractions, respectively. In HEK-vector cells, 249 miRNAs were detected in the IP fraction and 197 in the TL indicating an enrichment of miRNAs in AGO2 precipitates with an average difference of more than 3.5 Ct-values between IP and respective TL fraction. Comparison of the miRNA profiles of the TLs of the isogenic cell lines uncovered a differential expression of several miRNAs. For instance, miR-150 and miR-34a were expressed at higher levels in HEK-miR-155 cells with a Ct > 3. As expected, miR-155 was detected with a difference of more than six Ct-values, which confirmed our initial analysis using single plex miRNA assays (Fig. 1A). Furthermore, several miRNAs showed lower levels in HEK-miR-155 compared with HEK-vector cells. For instance miR-224, miR-135b and miR-98 levels were decreased with a difference of at least 2.5 Ct-values. Notably, no changes were observed for U6 snRNA controls measured as quadruplicates on the plates, indicating a robust detection with the TaqMan arrays. Furthermore, there were only minor variations in the levels of the housekeeping RNAs RNU44 (Ct < 0.15) and RNU48 (Ct < 0.08) between HEK-vector and HEK-miR-155 cells indicating equal amounts of starting material.

The comparison of miRNAs with the highest abundance in the IP fractions of both isogenic cell lines revealed a large overlap, with 28 of the top 30 miRNAs in HEK-miR-155 IPs shared with HEK-vector IPs (Fig. 4). We further detected four miRNAs (miR-17, -222, -106a and -20a) with apparently higher abundance in the IP fractions compared with the ectopically expressed miR-155. Thus, we assume that the transgenic expression of miR-155 was near a physiologically relevant level. Furthermore, the relative abundance of the top 30 miRNAs, except for miR-155, was similar in both IP fractions, indicating that the miRNA machinery, including AGO2, was not affected or saturated and the activity of the most highly expressed miRNAs was not disturbed by the ectopic miR-155 expression (Fig. 4).

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Figure 4. Top 30 miRNAs detected in AGO2 IPs of HEK-miR-155 cells. Normalized Ct-values (Ct) obtained by TaqMan arrays of the 30 most abundant miRNAs in the IP fraction of HEK-miR-155 cells were compared in HEK-miR-155 (black) and HEK-vector (gray) IP fractions.

Quantification of mRNA and miRNA

For mRNA quantification, total RNA was isolated using the miRNeasy kit (Qiagen) and reverse transcribed using Superscript II and random primer (Invitrogen) according to the manufacturer’s instructions. Amplification and quantification of cDNA was performed in 7900 HT Fast Real-Time PCR System (Applied Biosystems) in triplicates using SYBR Green (Thermo Scientific) according to the manufacturer’s protocol and as described before.⁴³ Relative quantities were calculated in relation to the housekeeping genes (GAPDH, DCTN2). The qPCR primer sequences are listed in Table S5.

Quantitative RT-PCR analysis of miRNAs was performed using RNA isolated by miRNeasy kit (Qiagen) and TaqMan MicroRNA Assays (Applied Biosystems). Relative amounts of miRNAs were calculated in relation to the house keeping small RNA RNU-6B.

MicroRNA expression profiles were generated using TaqMan Array System version 3 (Applied Biosystems) according to the manufacturer’s protocol. After reverse transcription of 20 ng RNA isolated by the RIP protocol, cDNA was pre-amplified, detected and quantified using TaqMan Array “A” cards. Ct-values higher than 35 were considered as not expressed. Ct-values were normalized to the median value of all expressed miRNAs in the respective sample (Ct).

RNA-interacting protein IP (RIP)-Seq

Cells (2 × 10⁸ – 4 × 10⁸ per IP) were washed with ice-cold PBS and lysed in 400 µl polysome lysis buffer (PLB)¹³ for 5 min on ice and freezing at -80°C. Protein G sepharose (GE Healthcare Europe GmbH; 50 µl per tube) was coupled to a mixture of 5 µg each of two AGO2-specific antibodies (Abnova, clone 2E12-1C9; Sigma Aldrich, clone 11A9) or 10 µg IgG1 isotype control antibody (Jackson Immunoresearch laboratories) in 500 µl NT2 buffer¹³ in low binding tubes for 1 h at 4°C, followed by three washing steps with NT2 buffer. Thawed lysates were centrifuged at 13,000 rpm and 4°C for 10 min, added 1:10 to NT2-buffer containing 5 µl RNaseOUT (Invitrogen), 2 µl vanadyl ribonucleoside complex (New England Biolabs), 10 µl of 100 mM DTT (Invitrogen), 10 µl of 25 mM EDTA (Invitrogen) and 10 µl protease inhibitor cocktail (Roche) per 1 ml buffer. Of this mixture 100 µl were recovered as total lysate fraction (TL). The remaining lysate was portioned equally to four tubes containing AGO2-antibody coated beads and incubated for one hour at 4°C. Thereafter, the beads were collected by centrifugation, the supernatant (SN) was stored at -80°C for later use, and the sepharose beads were washed five times in 1 ml of NT2-buffer, prior to elution of bound protein by incubation in 50 µl of 1 M glycine (pH 2.3) at room temperature for 10 min, followed by neutralization using 5 µl of 1 M TRIS-HCl (pH 8). Proteins were then degraded by adding 3 µl of proteinase K (Qiagen), and RNA was isolated using 350 µl TRIZOL LS reagent (Invitrogen) followed by miRNeasy kit (Qiagen) purification.

Luciferase sensor assays

For luciferase sensor assays 3x10⁴ HEK293T cells were co-transfected with pMiRReport vector containing the 3′UTR of the target gene of interest, a TK renilla vector and either the miR-155 overexpressing or the corresponding control vector using TransIT-LT1 transfection reagent (Mirus Bio). The cells were lysed 24 h after transfection using 65 µl of passive lysis buffer (Promega). Luciferase signals were detected on a Mithras 96-well plate reader (Berthold technologies) using a custom made dual luciferase buffer system⁴⁴ and 20 µl of the protein lysate. All measurements were performed as triplactes. The firefly luciferase signals were normalized to the respective renilla signals. Control experiments without ectopic miR-155 expression were scaled to 1. All experiments were performed in three biological replicates.

Western blot analyses

HEK293T cells were lysed 48 h after transfection with miR-155 or control vector, using RIPA buffer, containing EDTA-free proteinase inhibitor complete mini (Roche) and Benzonase (Merck). Cellular debris was removed by spinning the lysates. Protein concentrations were tested by BCA assays (Pierce). Equal amounts of each lysate were loaded onto a denaturing poly-acryl-amide gel. The proteins were size-separated and transferred to a PVDF-membrane (Merck). DUSP14 protein was detected using a monoclonal antibody (clone 4B5-E6, Abnova coporation). ZFP36 was detected using a polyclonal antibody (ABE285, Merck Millipore). The AGO2 protein was detected by mouse monoclonal antibody (clone 2E12-1C9). A polyclonal antibody against β-Actin (clone sc-1616, Santa Cruz) was used as loading control. Both antibodies were detected using HRP-linked secondary antibodies against mouse or rabbit, respectively (Cell signaling technology) and ECL plus detection chemistry (GE Healthcare). The DUSP14 signal was removed by incubating the membrane for 30 min with 0.1% sodium-azide in PBS prior to β-Actin detection.

The authors thank Angela Schulz, Sibylle Ohl and Verena Kalter for experimental support. We thank Thorsten Kolb, Angela Schulz, Daniel Haag and Verena Thewes for helpful discussions and André Leischwitz, Berit Haldemann and Stephan Wolf for next-generation sequencing support. Furthermore, we thank Thore Kempf and Martina Schnölzer for the mass spec analyses. We thank Sven Diederichs for critically reading and discussing the manuscript.