Global microrna level regulation of EGFR-driven cell cycle protein network in breast cancer

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1 Molecular Systems Biology Peer Review Process File Global microrna level regulation of EGFR-driven cell cycle protein network in breast cancer Stefan Uhlmann, Heiko Mannsperger, Jitao David Zhang, Emőke-Ágnes Horvát, Christian Schmidt, Moritz Küblbeck, Frauke Henjes, Aoife Ward, Ulrich Tschulena, Katharina Zweig, Ulrike Korf, Stefan Wiemann, Özgür Sahin Corresponding author: Özgür Sahin, German Cancer Research Center DKFZ Review timeline: Submission date: 07 July 2011 Editorial Decision: 07 September 2011 Revision received: 28 October 2011 Editorial Decision: 22 November 2011 Revision received: 09 December 2011 Accepted: 12 December 2011 Transaction Report: (Note: With the exception of the correction of typographical or spelling errors that could be a source of ambiguity, letters and reports are not edited. The original formatting of letters and referee reports may not be reflected in this compilation.) 1st Editorial Decision 07 September 2011 Thank you again for submitting your work to Molecular Systems Biology. We have now heard back from the three referees who agreed to evaluate your manuscript. As you will see from the reports below, the referees find the topic of your study of potential interest. They raise, however, substantial concerns on your work, which, I am afraid to say, preclude its publication in its present form. The first two reviewers recognized the novelty of the approach applied in this work, and were broadly supportive. Nonetheless, two reviewers had clear concerns regarding the generality of these findings, and the first reviewer clearly indicated that some of these results must be tested in another cell line to demonstrate that they are not specific only to MDA-MB-231 cells. On a somewhat related topic the last reviewer wonders how many of these micrornas are actually expressed in MDA-MB-231 cells, and to what degree expression anti-correlation between microrna and targets across cell lines would have suggested these same regulatory relationships. In addition, the reviewers had clear concerns regarding the clarity of presentation, particularly in the figures, and made a series of suggestions for improvements. In addition, the editor found it somewhat hard to determine whether the bootstrap-based p-values for the inferred microrna-protein relationships are multiple-test corrected, which seems important when trying to assess the statistical significance of the bipartite graph as a whole. This should be clarified in any revised manuscript, and, if possible, an estimate of the FDR for the graph as a whole should be reported. If you feel you can satisfactorily deal with these points and those listed by the referees, you may wish to submit a revised version of your manuscript. Please attach a covering letter giving details of European Molecular Biology Organization 1

2 Molecular Systems Biology Peer Review Process File the way in which you have handled each of the points raised by the referees. A revised manuscript will be once again subject to review and you probably understand that we can give you no guarantee at this stage that the eventual outcome will be favorable. *PLEASE NOTE* As part of the EMBO Publications transparent editorial process initiative (see Molecular Systems Biology now publishes online a Review Process File with each accepted manuscript. Please be aware that in the event of acceptance, your cover letter/point-by-point document will be included as part of this file, which will be available to the scientific community. Authors may opt out of the transparent process at any stage prior to publication (contact us at msb@embo.org). More information about this initiative is available in our Instructions to Authors. Sincerely, Editor Molecular Systems Biology msb@embo.org Referee reports: Reviewer #1 (Remarks to the Author): Uhlmann et al., used a systems biology approach to identify mirnas controlling the EGFR pathway. The results show interesting networking aspects of this regulation. Moreover, it led to the identification of novel tumor suppressor mirnas. To the best of my knowledge, this is the first study of this kind and therefore, this manuscript can be considered of high relevance. The text is organized and well written. It certainly has a lot of potential to be accepted. However, it is important that the authors address the points listed below: 1)The entire study was done with only one cell line, MDA-MB-231. The authors do not present a rationale to use this particular line. To increase the relevance of their results, it is important that the most crucial results be recapitulated in at least another cell line. The use of a cell line from another tumor type is particularly encouraged. EGFR is important in glioblastoma, for instance. 2)It would be good if the authors include in the main body of the paper a table or a figure showing all the genes that were screened. 3)Figure 1 does not match the legend. This needs to be fixed. 4)Figure 2 has major problems. Parts A, B and C are too small and show poor resolution. I suggest authors to increase size and show these three parts as an independent figure. 5)Figure 4. To avoid confusion, use 1, 2,3... instead of (a), (b), (c). 6)The authors failed to discuss Boutz et al., 2011 (JBC 286(20): ). This is another example of mirna analysis using proteomics and demonstrates the networking aspects of mir-122 regulation. Therefore, this manuscript is related very closely to the content of this article. 7)To polish the low stringency network, the authors chose to filter the data by selecting only genes derived from predictions or already validated. These parameters might be too stringent. I wonder if the authors tried to perform a less stringent analysis, by including genes with mapped seed sequences. 8)The results shown in Figure 6 (luciferase and RNA analysis) do not entirely recapitulate the ones shown in figure 5. Do the authors have any comments on that? There are commercially available 3'UTR reporter collections (Switchgear Genomics, for instance). The authors might get better results using an optimized reporter. Reviewer #2 (Remarks to the Author): The paper by Uhlman et al present a comprehensive paper of a microrna screen based approach in order to identify the effect of micrornas on a set of human proteins with focus on teh EGFR protein network. This is the first time that a systematic approach is presented using all micrornas on a set of protein targets. The authors should be commended for undertaking a technically difficult proteomic approach utilising reverse phase arrays. The data analysis involved the development of network analysis tools that will be of interest for microrna based and other systems biology approaches. European Molecular Biology Organization 2

3 Molecular Systems Biology Peer Review Process File The main conclusion of the paper is that three micrornas (mir-124, mir147 and mir-193-3p) have been identified that target the EGFR driven cell cycle network. Extensive validations using a number of techniques (expression constructs, Western blots) are supporting the findings. This represents a novel finding and a model study for future investigations. Some points on the paper: 1.The main figures 2&3 are not very informative in their present form. Figure 2 parts A-B are too small and complex to be of any meaningful value, at least in an A4 format. These should be revised in order to make part A bigger and part be to be retained as a graph (cum. edge number vs z-score) to explain the thresholds used. The networks can be presented as separate suppl. Figures in an enlarged format. In Figure 2. C the extra orange pinheads" representing other mirnas are not very informative. Either include which mirnas are important or leave out. Alternatively the networks in part B can be presented as a new figure 3 and figure 3 can be moved into supplementary. 2. In the introduction the authors mention that transcriptomic approaches to mirna target identification have a weakness in not being able to identify targets regulated at the protein level, while proteomic approaches are either not amenable to high through put or not sensitive enough to detect large numbers of proteins. In the transcriptomic approaches the authors should include RISC- IP followed by RNA-seq (or array hybridization) as another tool that can identify directly irrespective of whether the steady state levels of an mrna or protein change). 3. The authors should comment on the fact that their results are obtained using a model cell line system and the microrna-target relationships must be further validated in large breast cancer sample collections through expression profiling or immunohistochemistry based approaches Reviewer #3 (Remarks to the Author): The paper of Uhlmann et. al. aims to describe the mirna regulation level of EGFR-driven cell cycle. The authors farther aim the development of a novel network analysis methodology. I have the following concerns regarding the paper: 1) The authors choose to investigate a number of proteins based on a previous publication and after analysis of deep sequencing data of the specific cells. Then they investigate the expression of 810 mirnas in relation of these proteins, but is not mentioned if all these mirnas ( and in which levels - from deep sequencing data for example ) are normally expressed in the same cells. 2) The first computational analysis involves a mirnome-protein interaction network where mirna are linked to proteins ( up- & down regulation). This data is then farther analyzed including computational mirna target prediction. The authors are not explaining if the results of Fig. 2c could not directly be computed by using computational target prediction and the expression levels of mirna and proteins (mirna present, protein down regulated, computational mirna:protein target present). 3) In fig. 3 the authors analyze the relation of the seed match between mirna and protein sequence to the repression of the protein expression. Such analysis has been done several times and is not producing some new knowledge. 4) The authors select three mirnas and investigate their targets and their role. Could it be that by just investigating which mirnas have the most significant targets in the first set of collected proteins the investigation will lead in a much simpler way to the same outcome? 1st Revision - authors' response 28 October 2011 European Molecular Biology Organization 3

4 Manuscript Number: MSB Title: Global microrna level regulation of EGFR-driven cell cycle protein network in breast cancer Reviewer #1 (Remarks to the Author): Uhlmann et al., used a systems biology approach to identify mirnas controlling the EGFR pathway. The results show interesting networking aspects of this regulation. Moreover, it led to the identification of novel tumor suppressor mirnas. To the best of my knowledge, this is the first study of this kind and therefore, this manuscript can be considered of high relevance. The text is organized and well written. It certainly has a lot of potential to be accepted. However, it is important that the authors address the points listed below: 1)The entire study was done with only one cell line, MDA-MB-231. The authors do not present a rationale to use this particular line. To increase the relevance of their results, it is important that the most crucial results be recapitulated in at least another cell line. The use of a cell line from another tumor type is particularly encouraged. EGFR is important in glioblastoma, for instance. We fully accepted this criticism. In line with the reviewer s suggestion, we have now extended our analyses to another breast cancer cell line (MCF-7) as well as to a glioblastoma cell line (U87). Three mirnas (mir-124, mir-147 and mir-193a-3p), which we had identified as tumor suppressors in the MDA-MB-231 breast cancer cell line with the networkbased proteomics approach (current Figure 6 and 7), have been investigated in these two additional cell lines, with same assaying techniques including cell viability and cell cycle, that we had initially applied in the MDA-MB-231 cell line. In addition, we performed luciferase reporter assays to validate the direct targeting and qrt-pcr to verify mrna level reduction to be recapitulated also in the other cell lines investigated. Indeed, all the major results we had obtained with the MDA-MB-231 cell line about these three mirnas were supported by results from the other two cell lines: Similar to the results we had obtained in MDA-MB-231 cell line, all three mirnas (mir-124, mir-147 and mir- 193a-3p) led to the reduction of cell population at S-phase and reduced cell viability of the other cell lines (Figure 7E and F). Furthermore, all direct targeting regulations were recapitulated in these two cell lines (Supplementary Figure S9A). The only differences observed were different targeting mechanisms for AKT2 by mir-124 or mir-147. While targeting of AKT2 by mir-124 or mir-147 was via translation repression in MDA-MB-231 cell line, it was via mrna cleavage in MCF-7 (in case of mir-147) and in U87 (in case of both mir-124 and mir-147) (Supplementary Figure S9B). Considering the different origin and heterogeneity of the additional cell lines tested (ER+ adenocarcinoma MCF-7 and glioblastoma U87), we conclude that our observations are indeed not cell-line specific, but can be generalized also to other cell systems. Along these lines we have added new text and figures in the revised version of the manuscript in the results (presented as Figure 7E, F and Supplementary Figure S9) and discussion within the main text (Page 14, paragraph 1 and Page 18, paragraph 2), and also provided further below. 1

5 Figure 7 (E) The effects of mirnas on cell cycle progression of MCF-7 and U87 cell lines. After overexpressing the mirnas in MCF-7 or U87 cells, total DNA content (7-AAD) and S-phase population (BrdU) were determined by flow cytometry after 48 hours. The red rectangles show the gating of proliferating cells in S- phase (percentages given in the rectangles). Results are shown as representative of three biological and three technical replicates. (F) The effects of mirnas on the viability of MCF-7 and U87 cell lines. After overexpressing the mirnas in MCF-7 or U87 cells, viable cells were counted by a luciferase-based viability assay after 72 hours. The results are shown as average of 2 biological and 5 technical replicates. Two asterisks (**) denote the p-value less than 0.01, three asterisks (***) denote the p-value less than Supplementary Figure S9. Validation of direct targeting and quantification of mrnas upon transfection with mir-124, mir-147 and mir-193a-3p in MCF-7 and U87 cell lines. (A) Validation of direct targeting of genes by mirnas. A luciferase reporter assay was performed after co-transfecting MCF-7 or U87 cells with the luciferase vector harbouring the 3 -UTR of the gene of interest and mirna mimics. Luciferase activities were measured after 48 hours of transfection, and results are presented as average of 4 biological and 3 technical replicates. P1, P2 and P3 denote the parts of 3 -UTRs analysed for the genes with very long or difficult to clone 3 -UTRs. (B) Quantification of effects of mir-124, mir-147 and mir-193a-3p on the mrna level by qrt-pcr in MCF-7 and U87 cell lines. The results are presented as average of 2 biological and 3 technical replicates. Two asterisks (**) denote the p-value less than 0.01, three asterisks (***) denote the p-value less than

6 2)It would be good if the authors include in the main body of the paper a table or a figure showing all the genes that were screened. In the revised version of the manuscript, the proteins which were screened are now provided as Table 1 in the main body of the paper. This table includes Official HGNC Gene Symbol, Protein Symbol, UniprotKB accession numbers and Protein Names. For simplicity we used merely protein names throughout the revised version of the manuscript. 3)Figure 1 does not match the legend. This needs to be fixed. This has been fixed in the revised version of the manuscript. 4)Figure 2 has major problems. Parts A, B and C are too small and show poor resolution. I suggest authors to increase size and show these three parts as an independent figure. We agree with this criticism which was also raised by the other reviewer (Reviewer 2). Accordingly, we have modified Figure 2 where the networks in Figure 2B are now new Figure 2C and 2D with higher resolution. Furthermore, the mirna-protein network in Figure 2C is now the current Figure 3 where all the names of mirnas regulating the proteins were given in Supplementary Table S7. 5)Figure 4. To avoid confusion, use 1, 2,3... instead of (a), (b), (c). In the revised version of the manuscript, the old Figure 4 is now Figure 5. Subfigures are presented as (1), (2), (3), (4) and (5) instead of (a), (b), (c), (d) and (e). 6)The authors failed to discuss Boutz et al., 2011 (JBC 286(20): ). This is another example of mirna analysis using proteomics and demonstrates the networking aspects of mir-122 regulation. Therefore, this manuscript is related very closely to the content of this article. We thank the reviewer for drawing our attention to this very relevant paper. In the revised version of the manuscript we cited and discussed this study in the Introduction section (Page 4, paragraph 1). 7)To polish the low stringency network, the authors chose to filter the data by selecting only genes derived from predictions or already validated. These parameters might be too stringent. I wonder if the authors tried to perform a less stringent analysis, by including genes with mapped seed sequences. In order to provide the reviewer as well as readers with a network based on less-stringent statistical significance (p<0.05) and less-stringent sequence-matching requirement (seed sequence mapping), we used pre-compiled microrna-mrna sequence mappings provided by the microrna.org database ( Release August 2010). In this dataset, all target site predictions which have a 6-mer or better were listed. 3

7 With this dataset, we built a network of less stringency than current Figure 3 (Figure 2C in the first submission), namely keeping all edges where p<0.05 and a target site (6-mer or better) is predicted. The network is visualized as below (which is also provided in the Supplementary Figure S5). Supplementary Figure S5: Intermediate network with relaxed seed-matching filter. Each yellow dot indicates one microrna, and proteins are shown in blue boxes. There are 241 micrornas and 25 genes/proteins (PIK3CB did not have microrna mapped to), and 355 edges connecting them. Note that edges in this network have been filtered by (1) screening z-score z<1.96 (corresponding to two-sided p<0.05) and (2) micrornas have been mapped to gene 3 -UTR by at least one six-mer match or better. As one could suspect, many more edges are visible in this network compared to the one shown in Figure 3. The complexity of this intermediate network (total edge number E=355) lies between the one in the current Figure 2C (E=1783), where no sequence-matching filter is set, and the network in the current Figure 3 (E=120), where a more stringent sequencematching filter based on evolutionary conservations is active. We prefer the network in the current Figure 3 to the intermediate network in the main text for the sake of visualization clarity, while providing the latter one as supplementary information (Supplementary Figure S5 and Supplementary Table S6). This part is now implemented into the main text (Page 7, paragraph 3 and page 8, paragraph 1). 8)The results shown in Figure 6 (luciferase and RNA analysis) do not entirely recapitulate the ones shown in figure 5. Do the authors have any comments on that? There are commercially 4

8 available 3'UTR reporter collections (Switchgear Genomics, for instance). The authors might get better results using an optimized reporter. For the initial submission, in Figure 6C and 6D we used gene symbols while we used protein symbols in Figure 5B and 5C. This obviously led to confusion. In order to improve consistency between these figures, we have changed all gene symbols in Figure 6 (new Figure 7) to protein symbols. Furthermore, we have prepared a table (Table 1) in the main body of the manuscript with both gene and protein symbols. We used protein symbols throughout the manuscript in the revised version of the manuscript. From the experimental point of view and as we explain in the revised version of the manuscript, we had tested the effects of each mirna (mir-124, mir-147 and mir-193a-3p) on the down-regulated proteins (old Figure 5B and 5C, new Figure 6B and 6C) in luciferase assays (old Figure 6C, new Figure 7C) to test direct targeting. Furthermore, to differentiate between targeting at mrna and protein levels, we had tested the effects of all three mirnas also at the mrna level (old Figure 6D, new Figure 7D). We had been aware of the commercially available 3 -UTR luciferase reporter construct collection that is suggested by the reviewer. We had tested constructs from this collection and compared the results with those obtained with the vectors that are described in the manuscript and that we have cloned using psicheck2. The results obtained with the psicheck2 system were more robust, likely because the Renilla luciferase and the Firefly normaliser are present in the same vector backbone. Introduction of Firefly luciferase in the psicheck2 (Promega) vector allows normalization of Renilla luciferase expression, achieving robust and reproducible results whereas a second vector is required for normalization in case of the alternative system which, however, could lead to different expression levels as well as to more stress in the cells. We compared the two reporter systems (one from Switchgear and one from psicheck2) for the 3 -UTR of the EGFR gene which is a known direct target of mir-7 (Webster et al, J Biol Chem, 2009) as an example. As the results below indicate the luciferase reporter assay results seem to be similar for the two systems, where the psicheck2 appears to be slightly more robust. These results are not part of manuscript, but are provided in the Figure below. 5

9 Figure I: Luciferase reporter assay results comparing the targeting of 3 -UTR of EGFR by mir-7 using both psicheck2 (vectors we used for validation of direct targeting) and plightswitch (vector commercially available from SwitchGear). Reviewer #2 (Remarks to the Author): The paper by Uhlman et al present a comprehensive paper of a microrna screen based approach in order to identify the effect of micrornas on a set of human proteins with focus on teh EGFR protein network. This is the first time that a systematic approach is presented using all micrornas on a set of protein targets. The authors should be commended for undertaking a technically difficult proteomic approach utilising reverse phase arrays. The data analysis involved the development of network analysis tools that will be of interest for microrna based and other systems biology approaches. The main conclusion of the paper is that three micrornas (mir-124, mir147 and mir-193-3p) have been identified that target the EGFR driven cell cycle network. Extensive validations using a number of techniques (expression constructs, Western blots) are supporting the findings. This represents a novel finding and a model study for future investigations. Some points on the paper: 1.The main figures 2&3 are not very informative in their present form. Figure 2 parts A-B are too small and complex to be of any meaningful value, at least in an A4 format. These should be revised in order to make part A bigger and part be to be retained as a graph (cum. edge number vs z-score) to explain the thresholds used. The networks can be presented as separate suppl. Figures in an enlarged format. In Figure 2. C the extra orange pinheads" representing other mirnas are not very informative. Either include which mirnas are important or leave out. Alternatively the networks in part B can be presented as a new figure 3 and figure 3 can be moved into supplementary. We thank the reviewer for his/her suggestions which were also raised by reviewer 1. In the revised version of the manuscript, as suggested by this reviewer, we kept old Figure 2A with better resolution and only the middle panel of Figure 2B (cumulative edge number vs z-score) as new Figure 2B. The networks (dense and sparse, left and right panels in old Figure2B) are now represented as new Figures 2C and 2D with increased size. This has indeed led to a better resolution of both figures. Furthermore, old Figure 2C is now presented as a separate figure (Figure 3). In order to improve the representation of its content, we initially removed the orange pinheads from the figure and wrote the names of the mirnas instead. However, this compromised the clarity and resulted in very complicated figure. Therefore, we prefer the network as shown in Figure 3 for the sake of visualization clarity. Instead of complicating the figure, we now provide the names of all mirnas that are shown as orange pinheads in Figure 3 in the supplementary information (Supplementary Table S7). The old Figure 3 is presented as Figure 4 in the revised version of the manuscript. 2. In the introduction the authors mention that transcriptomic approaches to mirna target identification have a weakness in not being able to identify targets regulated at the protein level, while proteomic approaches are either not amenable to high through put or not sensitive enough to detect large numbers of proteins. In the transcriptomic approaches the authors should include RISC-IP followed by RNA-seq (or array hybridization) as another tool 6

10 that can identify directly irrespective of whether the steady state levels of an mrna or protein change). We thank the reviewer for drawing our attention to this very relevant method used to identify targets of mirnas at transcriptomic level. This method relies on immunoprecipitation of the RISC complex and the associated transcripts which can then be identified and quantified using either array-based or sequence-based platforms. We now mention this methodology and implemented the references (Karginov FV, 2007, PNAS; Hanina et al, 2010, PLoS Genetics; Grey et al, 2010, PLoS Pathogens; Thomson et al, 2011, NAR) and discussed its potential advantages (e.g. identification of 5 -UTR targeting) and disadvantages (e.g. the necessity of stable interactions between mirna, mrnas and Ago proteins to survive immunoprecipitation process) in the Introduction section (Pages 3, paragraph 2). 3. The authors should comment on the fact that their results are obtained using a model cell line system and the microrna-target relationships must be further validated in large breast cancer sample collections through expression profiling or immunohistochemistry based approaches We are well aware of this fact which was also raised by another reviewer (Reviewer 1). In this line, first we have tested our results also in another breast cancer cell line as well as in a glioblastoma cell line (Figure 7E, F and Supplementary Figure S9). The findings we had made in the MDA-MB-231 cell line were recapitulated also in these other cell lines validating our conclusions and proving that the induced effects were not cell-line specific. Furthermore, in the revised version of the manuscript, we discussed that mirna-target relationships should further be validated in large cancer sample sets at mrna and/or protein level using expression profiling and/or IHC-based or protein array-based methods. This is now provided in the Discussion section (Pages 18, paragraph 2). Reviewer #3 (Remarks to the Author): The paper of Uhlmann et. al. aims to describe the mirna regulation level of EGFR-driven cell cycle. The authors farther aim the development of a novel network analysis methodology. I have the following concerns regarding the paper: 1) The authors choose to investigate a number of proteins based on a previous publication and after analysis of deep sequencing data of the specific cells. Then they investigate the expression of 810 mirnas in relation of these proteins, but is not mentioned if all these mirnas ( and in which levels - from deep sequencing data for example ) are normally expressed in the same cells. We examined the expression of mirbase registered mirnas in the cell system MDA-MB- 231 by using both in-house microarray profiling data and published next-generation sequencing (NGS) data (Sun et al, PLoS ONE, 2011; 16: e17490) (see Methods below). Both methods identified a large body of known human micrornas that were expressed in the MDA-MB-231 cell system (N=429 detected with microarray with p<0.01, and N=598 by 7

11 next-generation sequencing with maximum 2 mismatches to mature microrna sequences). Out of them 362 micrornas were identified to be expressed by both methods (the count of overlapping micrornas was highly significant, p<2.7e-18, hypergeometric test). Based on these data, we report approximately 52% (429) of the 810 micrornas tested to be above the detection limit of microrna microarray, and 74% (598) with next-generation sequencing. Thus, expression of a large proportion (45%, 362) of mirnome in the MDA-MB-231 cell system was supported by two independent technologies (please see figure below). Supplementary Figure S1: Expression levels of human micrornas in the MDA-MB-231 cell system using both array-based and sequence-based platforms. (A) Lines are density estimations of expression levels. In the microarray dataset, blue dots indicate the threshold of detection score (p<0.01, or equivalently, expression value x>6.22) N indicates the number of expressed micrornas reported by each technology. (B) The Venn diagram shows the overlap of the two lists. We reasoned that this information should not necessarily be part of the manuscript as in a gain-of-function screen (such as the mirna mimic library screen in our study) endogenous expression of the respective mirnas is not of equal importance as in a loss-of-function screens (e.g. using antagomirs - or sirnas). In the latter context lack of expression of target mirnas could have led to false-positive phenotypes. Still, in the revised manuscript, we provide the information on how many of the mirnas were indeed found to be expressed in the MDA-MB-231 cell line (Supplementary Figure S1 and Page 5, paragraph 2) and details on the expression analysis as Supplementary Information. 8

12 Methods of data analysis: 1. Microarray profiling of micrornas was performed with Illumina Human MicroRNA Expression Profiling Array (version 2). Background signal was calculated and subtracted with the default method of the commercial software shipped with the microarray. Raw data was normalized by the variance-stabilization normalization (VSN) algorithm with the vsn package in Bioconductor. We calculated the detection score (DS), a score ranging from 0 to 1, for each microrna present on the microarray: DS equals the probability that the microrna is not expressed: therefore DS=0 suggests microrna might be expressed, while DS=1 suggests the microrna s expression is under the detection limit of the microarray. The calculation of DS use expression signals of scramble control probes: if a probe matching the mature sequence of a potentially expressing microrna has similar signal intensities like background controls, its DS will be towards 1 and hence one may assume that its expression is under the detection limit. By setting the threshold of DS as DS<0.01, equivalent to the (vsn-normalized) expression values larger than 6.22, the microarray experiment reported 429 mirbase micrornas whose expression was in the dynamic range of detection of the microrna microarray (p<0.01). 2. The small RNA next-generation sequencing (NGS) data was re-analyzed from a publicly available study (Sun et al, PLoS ONE, 2011; 16: e17490). The reads were first trimmed off adaptor sequences with Flicker, an add-on to the Illumina Genome Analyzer Pipeline software. Then they were aligned with the ELAND software against mirbase (maximum mis-match per read: 2 bases). Altogether 16.7 million reads were used for trimming and aligning. We only retain 5.1 million (30%) reads, which were aligned to both human genomic DNA and mirbase mature microrna sequences. These reads consist of distinct sequence tags, which were mapped to 598 mature microrna major and minor product sequences. 2) The first computational analysis involves a mirnome-protein interaction network where mirna are linked to proteins ( up- & down regulation). This data is then farther analyzed including computational mirna target prediction. The authors are not explaining if the results of Fig. 2c could not directly be computed by using computational target prediction and the expression levels of mirna and proteins (mirna present, protein down regulated, computational mirna:protein target present). We thank the reviewer for this interesting suggestion. To test this, we performed computational target prediction based on expression correlation analyses with two publicly available datasets (NCI60 cancer cell line panel, covering 60 cancer cell lines of 9 different tumor types). In-silico prediction was able to imply the potential regulatory relationships in a small proportion of microrna-mrna and microrna/protein pairs that we had identified with the proteomics screening (Supplementary Table S9). However, the method misses a large part of the microrna-protein network identified in the screening (Figure 2C, New Figure 3). In addition, we note that the prediction method based on correlation provides a very long list of candidate microrna-gene pairs, even after multiple testing corrections, which impede one-by-one experimental validations. In the revised manuscript, we implemented the details of the expression correlation analysis in the Supplementary Information and discussed its limitation and relationships to our screening 9

13 approach in the main text (Page 16, paragraph 2). We reason, however, that the correlation analysis, parallel to predictions based on sequence matching between micrornas and mrnas, could indeed be used as filters to prioritize microrna-gene pairs for experimental validation. However, due to the high false-positive and false-negative rates, these computational methods cannot replace wet-lab experiments like the proteomics screening approach we have followed. This non-exchangeability has been further supported, from another aspect, by a recent study where Shabab et al determined the levels of mirnas and their targeted mrnas by laser captured micro-dissected (LCM) in ovarian cancer epithelial cells (CEPI) and compared with levels present in ovarian surface epithelial cells (OSE). They found that inverse correlations between changes in levels of mirnas and those of their mrna targets were identified in only 11% of the computational predictions. Furthermore, positively correlated targets were detected at a similar rate as negatively correlated ones (Shabab et al, 2011, PLoS ONE ; 6(7):e22508). Methods of in silico target prediction with expression correlation: We took advantages of two large-scale public expression datasets produced by different technologies, and performed computational target predictions. The two datasets are: 1. Paired mrna and microrna expression profiling with Agilent microarray platforms (Liu et al, Molecular Cancer Therapeutics, 2010; 9: ) (NCI-60-mm hereafter, mm stands for microrna-mrna) 2. MicroRNA expression profiling used in the NCI-60-mm study, paired with the protein profile data set of NCI-60 cell lines based on reverse-phase protein lysate arrays (Shankavaram et al, Molecular Cancer Therapeutics, 2007; 6: ) (NCI-60-mp hereafter, mp stands for microrna-protein) Both datasets take use of the well-studied NCI-60 tumor cell line panel representing nine different tumor types, and altogether they construct a three-tier network of microrna, mrna and proteins. Expression correlation analyses here are based on the simple assumption that their expression should reflect in the form of high (anti-)correlation provided there were true regulatory relationships between micrornas and genes (mrna or protein). Since in most known cases micrornas negatively regulate gene expression, we anticipate negative correlations to exist between the expression of micrornas and their targets. This assumption, however, is very naïve considering that (1) gene expression can be modulated by many factors, therefore, a linear correlation might not be expected between regulating pairs and (2) correlation does not imply causality, i.e., even a significant negative correlation coefficient was determined, no conclusion about targeting could be made. Correlation analysis with the NCI-60-mm dataset The mrna and microrna expression profiling data of NCI-60 cell lines (Liu et al, Molecular Cancer Therapeutics, 2010; 9: ) was downloaded from the CellMiner database ( After normalizations as described in the publication, both mrna and microrna probes were filtered by the "gisgenedetected" flag in the raw data file. Only those mrna/microrna probes that were expressed in at least one cell line were used: mrna probes and 422 microrna probes passed this filter. 10

14 Subsequently, probes with very low expression variations among cell lines were removed. Inter-quantile ranges (IQR) were used as filter, and only probes with IQR no less than 0.75 were further used for the correlation analysis (see Figure below). After applying this filter, mrna probes and 345 mirna probes remained for correlation analysis. Supplementary Figure S10: Distributions of interquantile ranges (IQR) in both mrna and microrna datasets of NCI-60 panel of cancer cell lines. Red dot line indicates the threshold, under which the probe will be removed. The top-right legend (A/B) shows the number of remaining probes (A) and that of all probes after the expression filter was applied (B). Pearson correlation coefficients were computed pairwise between each gene and microrna, giving a 24697x345 matrix. P-values were calculated based on Pearson correlation coefficients subsequently (with F-statistic, degree of freedom (1,58)). To alleviate the multiple testing problem, all p-values were corrected by the Benjamini-Hochberg (BH) method (less stringent, estimating FDR), as well as the Bonferroni method (very stringent) (102300) microrna-mrna pairs had BH-adjusted p<0.05 (0.01), and 4700 (3093) pairs had Bonferroni-adjusted p<0.05 (0.01). If we only focus on microrna-mrna pairs with significant negative correlations, pairs have BH-adjusted p<0.05 and 1590 pairs had Bonferroni-adjusted p<0.05. Correlation analysis with the NCI-60-mp dataset The dataset was also downloaded from the CellMiner database. Data pre-processing was done similarly as in the microrna/mrna correlation analysis above. Relative abundances of 89 proteins were available for the correlation analysis. The only difference was that no variancefiltering was performed on the protein data, since almost all proteins had moderate to large variances among cell lines. Pairwise Pearson correlation coefficients were also converted into p-values. After multiple testing corrections, 1311 (475) microrna-protein pairs had BH-adjusted p<0.05 (0.01); 77 (39) pairs had Bonferroni-adjusted p<0.05 (0.01). If we only focus on those micrornaprotein pairs with significant negative correlations, 594 pairs had BH-adjusted p<0.05 and 25 pairs had Bonferroni-adjusted p<

15 Co-mapping the results of correlation analyses and results of this study Out of 120 mirna-protein interactions shown in figure 2C (figure 3 in the revised manuscript), only 5 pairs could have been detected from significant negative correlations with the Benjamini-Hochberg multiple testing correction (BH p<0.05). Note that, none of them had Bonferroni adjusted p<0.05 (see below, Supplementary Table S9). mirna GeneSymbol z-score cor p-value p.bhadj p.bonadj hsa-mir-148a ERRFI e hsa-mir-194 MAPK e hsa-mir-200b PLCG e hsa-mir-429 PLCG e hsa-mir-93 CCND e Supplementary Table S9: Common mirna-target pairs obtained by both our screen approach and mirna/target correlation analyses. Among 89 proteins tested in the NCI-60-mp dataset, 4 proteins were also investigated in the current study (CDK4, GRB2, MAPK1 and PTPN11). In our work, we identified 29 TargetScan predicted micrornas to negatively regulate these 4 proteins (z<-1.96), forming 34 binary microrna-protein pairs (several micrornas regulated more than one of these four proteins). However, none of these pairs had significant negative correlations observed in the NCI-60-mp dataset (BH adjusted p-value p<0.05). One may criticize our method of multiple testing might be too strict, since we used all mirna-mrna (mirna-protein) data for the correction, instead of using only mrnas or proteins that were used in our study. In fact, even if we use uncorrected p value p<0.05 of the correlation coefficient as filtering threshold, only 12 microrna-mrna pairs could have been discovered from the NCI-60-mm study or merely one microrna-protein pair (mir- 153:GRB2) from the NCI-60-mp study. At such a significance level, however, there are microrna-mrna pairs and 5817 microrna-protein pairs to be tested. In summary, out of 120 mirna-protein interactions we have identified in this study, only very few (5~13 depending on the significance level) could have been computed from correlation analyses based on mrna-microrna-protein expression datasets of the NCI-60 panel. In conclusion, this approach misses most interactions. Some reasons for this discrepancy have been discussed above in the limitation of this method. Besides, inconsistencies between mrna and protein expression levels could further add to the low amount of information obtained only with the bioinformatic prediction. 3) In fig. 3 the authors analyze the relation of the seed match between mirna and protein sequence to the repression of the protein expression. Such analysis has been done several times and is not producing some new knowledge. We thank the reviewer for the suggestion. Having considered the volume of novel knowledge this figure delivers as well as the integrity of this work, however, we decided to keep this 12

16 figure in the revised manuscript. Our reasoning started with reviewing relevant approaches in literature: 1. Selbach et al., has done a similar analysis where they examined the effect of a single mirna (mir-155) on 3,299 proteins (Selbach et al, 2008, Nature). 2. Baek et al., did a similar analysis upon mir-124 expression and upon deletion of mir-223 in mouse neutrophils at both mrna and protein levels (Baek et al, 2008, Nature). 3. Boutz et al., also did a similar analysis by comparing downregulated proteins with predicted ones upon mir-122 expression (Boutz et al, 2011, JBC). However, none of these studies examined or compared the seed-3 -UTR match at genomewide mirna level on more than two dozens of proteins. Therefore, our analysis with high dimensional mirna number (810) vs moderate number of proteins (26), especially at the protein level, provides new knowledge to both mirna and protein research field. 4) The authors select three mirnas and investigate their targets and their role. Could it be that by just investigating which mirnas have the most significant targets in the first set of collected proteins the investigation will lead in a much simpler way to the same outcome? We have tested the approach suggested by the reviewer. Taking three different significance values of screening z-score (with two-sided p-values p<0.10, p<0.05 and p<0.01), the top 10 micrornas of having most significant protein targets are listed in the following table (Table I): p<0.10 p<0.05 p<0.01 hsa-mir-124 (17) hsa-mir-124 (16) hsa-mir-124 (7) hsa-mir-491 (13) hsa-mir-147 (10) hsa-mir-661 (7) hsa-mir-661 (13) hsa-mir-661 (10) hsa-mir-892b (7) hsa-mir-147 (12) hsa-mir-124* (9) hsa-mir-124* (5) hsa-mir-21 (12) hsa-mir-342 (9) hsa-mir-193a-3p (5) hsa-mir-892b (12) hsa-mir-17-3p (8) hsa-mir-26a (5) hsa-mir-124* (11) hsa-mir-193a-3p (8) hsa-mir-105 (4) hsa-mir-17-3p (11) hsa-mir-892b (8) hsa-mir-147 (4) hsa-mir-193a-3p (11) hsa-mir-21 (7) hsa-mir-21 (4) hsa-mir-26a (11) hsa-mir-491 (7) hsa-mir-491 (4) Table I: Top 10 micrornas having most significant protein targets. Each column shows the top 10 micrornas, ranked by the number of significantly regulated proteins at that given significance level. The numbers in parentheses indicate the respective number of protein targets. Our first observation was that despite slight variations in gene lists at different significance levels, many of these micrornas always had more targets than other micrornas: 7 micrornas (mir-124*, mir-124, mir-147, mir-193a-3p, mir-21, mir-491, mir-661 and mir-892b) were among the top 10 at all three levels. We take this as evidence supporting the robustness of our screening data: the relative importance of these micrornas remained stable, when measured by number of significantly regulated proteins, irrespective of the significance level that was set. 13

17 Indeed, all three micrornas selected by the network approach were among the abovementioned 7 micrornas. While mir-124 ranked the first at all three significance levels, mir- 147 and mir-193a-3p had more variations in the relative ranking: mir-147 ranked 3 rd /2 nd /3 rd (tied) at three levels, and mir-193a ranked 4 th /4 th /2 nd (tied). Although a simpler p-value approach might also bring us to the same conclusion (maybe at the cost of more experimental validations because of ties in the ranks), we believe that the network approach has the following advantages that the simple method of counting significant targets does not possess. By using the network approach, we do not only discern which micrornas regulate what proteins. Importantly, we could identify which proteins were co-regulated by micrornas, and whether these co-regulations are statistically significant. The ability of answering these questions is exclusively rendered by the bipartite network analysis. As visualized in new Figure 6C (old Figure 5C), by establishing the more-than-random regulation model, we could show that proteins controlling cell cycle in the MDA-MB-231 are significantly co-regulated by multiple micrornas, to which mir-124, mir-147 and mir-193a-3p belong to. We believe that the simplified method illustrated above might indeed have led us to conclude that cell cycle proteins would be co-regulated by these top micrornas, however, under these circumstances it would remain unclear whether or not this co-regulation was a random effect. We think it is important to address this question, because the aim of this study had not simply been to detect and analyze pair-wise regulatory relationships between micrornas and proteins: on top of that, we were out to discover patterns behind the scenes of microrna regulations. The network approach we have devised has given us the chance of having a glimpse of one such pattern: the more-than-random co-regulation of cell cycle proteins. Therefore, we devised and applied the network approach to build the consensus network of proteins that were non-randomly co-regulated by micrornas; and within this consensus network, the cell cycle module was used as an example to demonstrate the co-regulation induced by three micrornas. 14

18 Molecular Systems Biology Peer Review Process File 2nd Editorial Decision 22 November 2011 Thank you again for submitting your work to Molecular Systems Biology. We have now heard back from the two referees who agreed to evaluate this revised study. As you will see, the referees feel that the revisions made to this work have satisfied their concerns, and they are now supportive of publication. Before this work will be acceptable for publication at Molecular Systems Biology, we have some issues of an editorial nature that we would ask to address in a final revision of this work. 1. The editor feels that this work could use a careful and thorough writing revision to improve its readability. When revising this manuscript please keep the following general writing tips in mind: --Avoid overly-long paragraphs. Paragraph breaks should be clearly marked by an empty line or indentation of the first line. --Be concise. --Use active voice whenever possible. To help illustrate these points, I have attached an edited version of your manuscript with suggestions for changes in the abstract and introduction. Before resubmitting this work, we strongly encourage you to have the full document proof-read by a native English speaker. 2. The resolution of the supplied figure images remains somewhat low; the text in many cases is noticeably blocky/blurry when zooming in. You will get the best results if the figures are made directly in a professional quality vector graphics program like Illustrator or the free, opensource alternative Inkscape, and saved directly in EPS format (PDF may also be acceptable). Please feel free to contact us if you have any questions about these points. Thank you for submitting this paper to Molecular Systems Biology. Sincerely, Editor Molecular Systems Biology msb@embo.org Referee reports Reviewer #1 (Remarks to the Author): The authors managed to correct all the deficiencies and address the concerns raised by the reviewers. This a superb article that certainly will cause a great impact. It is definitely ready to be accepted. Reviewer #3 (Remarks to the Author): The authors have successfully answered my comments 2nd Revision - authors' response 09 December 2011 European Molecular Biology Organization 4