circHIPK3 nucleates IGF2BP2 and functions as a competing endogenous RNA

Reviewer #1 (Public Review):

Short Assessment

In this work the authors propose a new regulatory role for one the most abundant circRNAs, circHIPK3. They demonstrate that circHIPK3 interacts with an RNA binding protein (IGF2BP2), sequestering it away from its target mRNAs. This interaction is shown to regulates the expression of hundreds of genes that share a specific sequence motif (11-mer motif) in their untranslated regions (3'-UTR), identical to one present in circHIPK3 where IGF2BP2 binds. The study further focuses on the specific case of STAT3 gene, whose mRNA product is found to be downregulated upon circHIPK3 depletion. This suggests that circHIPK3 sequesters IGF2BP2, preventing it from binding to and destabilizing STAT3 mRNA. The study presents evidence supporting this mechanism and discusses its potential role in tumor cell progression. These findings contribute to the growing complexity of understanding cancer regulation and highlight the intricate interplay between circRNAs and protein-coding genes in tumorigenesis.

Strengths:
The authors show mechanistic insight into a proposed novel "sponging" function of circHIPK3 which is not mediated by sequestering miRNAs but rather a specific RNA binding protein (IGF2BP2). They address the stoichiometry of the molecules involved in the interaction, which is a critical aspect that is frequently overlooked in this type of studies. They provide both genome-wide analysis and a specific case (STAT3) which is relevant for cancer progression. Overall, the authors have significantly improved their manuscript in their revised version.

Weaknesses:
While the authors have performed northern blots to measure circRNA levels, an estimation of the circRNA overexpression efficiency, namely the circular-to-linear expression ratio, would be desired. The seemingly contradictory effects of circHIPK3 and STAT3 depletion in cancer progression, are now addressed by the authors in their revised manuscript, incorporating potential reasons that might explain such complexity.

Major points about revised manuscript

(1) In Supplementary Figure S5H, the membrane may have been trimmed too closely to the circRNA band, potentially resulting in the absence of the linear RNA band. Could the authors provide a full image of the membrane that includes the loading points? Having access to the complete image would allow for a more comprehensive evaluation of the results, including the presence or absence of expected linear and circular RNA bands.

Reviewer #2 (Public Review):

Summary:

The authors have diligently addressed most of the points raised during the review process (except the important point of "additional in vitro experiments [...] needed to investigate the implication of circHIPK3 in bladder cancer cell phenotype" for which no additional experiments were performed), resulting in an improvement in the study. The data are now described with clarity and conciseness, enhancing the overall quality of the manuscript.

Strengths:

New, well-defined molecular mechanism of circRNAs involvement in bladder cancer.

Weaknesses:

Lack of solid translational significance data.

Reviewer #3 (Public Review):

In Okholm et al., the authors evaluate the functional impact of circHIPK3 in bladder cancer cells. By knocking down circHIPK3 and performing an RNA-seq analysis, the authors found thousands of deregulated genes which look unaffected by miRNAs sponging function and that are, instead, enriched for a 11-mer motif. Further investigations showed that the 11-mer motif is shared with the circHIPK3 and able to bind the IGF2BP2 protein. The authors validated the binding of IGF2BP2 and demonstrated that IGF2BP2 KD antagonizes the effect of circHIPK3 KD and leads to the upregulation of genes containing the 11-mer. Among the genes affected by circHIPK3 KD and IGF2BP2 KD, resulting in downregulation and upregulation respectively, the authors found the STAT3 gene, which also consistently has concomitant upregulation of one of its targets TP53. The authors propose a mechanism of competition between circHIPK3 and IGF2BP2 triggered by IGF2BP2 nucleation, potentially via phase separation.

Strengths:

Although the number of circRNAs continues to grow, this field lacks many instances of detailed molecular investigations. The presented work critically addresses some of the major pitfalls in the field of circRNAs, and there has been a careful analysis of aspects frequently poorly investigated. Experiments involving use of time-point knockdown followed by RNA-seq, investigation of miRNA-sponge function of circHIPK3, identification of 11-mer motif, identification and validation of IGF2BP2, and the analysis of copy number ratio between circHIPK3 and IGF2BP2 in assessing the potential ceRNA mode of action are thorough and convincing.

Weaknesses:

It is unclear why the authors used certain bladder cancer cells versus non-bladder cells in some experiments. The efficacy of certain experiments (specifically rescue experiments) and some control conditions is still questionable. Overall, the presented study adds some further knowledge in describing circHIPK3 function, its capability to regulate some downstream genes, and its interaction and competition for IGF2BP2.

Author Response

The following is the authors’ response to the original reviews.

eLife assessment

In this valuable study the authors propose a new regulatory role for one the most abundant circRNAs, circHIPK3, mediated by the RNA binding protein IGF2BP2. While the study presents interesting and largely solid evidence, part of the work is incomplete, requiring additional controls to more robustly support the major claims. The work would also benefit from further discussion addressing the apparently contradictory effects of circHIPK3 and STAT3 depletion in cancer progression.

Public Reviews:

Reviewer #1 (Public Review):

In this work the authors propose a new regulatory role for one the most abundant circRNAs, circHIPK3, by showing that it interacts with an RNA binding protein (IGF2BP2) and, by sequestering it, it regulates the expression of hundreds of genes containing a sequence (11-mer motif) in their untranslated regions (3'-UTR). This sequence is also present in circHIPK3, precisely where IGF2BP2 binds. The study further focuses on one specific case, the STAT3 gene, whose mRNA product is downregulated upon circHIPK3 depletion apparently through sequestering IGF2BP2, which otherwise binds to and stabilizes STAT3 mRNA. The study presents mechanistic insight into the interactions, sequence motifs, and stoichiometries of the molecules involved in this new mode of regulation. Altogether, this new mechanism seems to underlie the effects of circHIPK3 in cancer progression.

Strengths:

The authors show mechanistic insight into a proposed novel "sponging" function of circHIPK3 which is not mediated by sequestering miRNAs but rather by a specific RNA binding protein (IGF2BP2). They address the stoichiometry of the molecules involved in the interaction, which is a critical aspect that is frequently overlooked in this type of study. They provide both genome-wide analysis and a specific case (STAT3) that is relevant for cancer progression.

Weaknesses:

One of the central conclusions of the manuscript, namely that circHIPK3 sequesters IGF2BP2 and thereby regulates target mRNAs, lacks more direct experimental evidence such as rescue experiments where both species are simultaneously knocked down. CircRNA overexpression lacks a demonstration of circularization efficiencies. There seem to be contradictory effects of circHIPK3 and STAT3 depletion in cancer progression, namely that while circHIPK3 is frequently downregulated in cancer, circHIPK3 downregulation in this study leads to downregulation of STAT3. This does not seem to fit the fact that STAT3 is normally activated in a wide diversity of cancers and is positively associated with cell proliferation. The result is neither consistent with the fact that circHIPK3 expression positively correlates with good clinical outcomes. Overall, the authors have achieved some of their aims but additional controls would be advisable to fully support their conclusions.

We thank the reviewer for the important and constructive criticism. All the raised points have now been addressed as described below.

Rescue experiment:

We have now performed the suggested rescue experiment, exploring the potential normalization of target expression upon double knockdown (both circHIPK3 and IGF2BP2). Expression of targets STAT3, NEU and TRAPPC9 were assessed, and all target mRNAs became normalized upon double knockdown, supporting our suggested IGF2BP2 sponging mechanism for circHIPK3. These results have been included in Supplementary Figure 5F.

Circularization efficiency of ectopically expressed circRNAs:

For efficient expression of circRNAs in human cells, we have used a state-of-the-art plasmid construct (Laccase2-circRNA; Kramer et al., 2015, Genes Dev. 2015 Oct 15;29(20):2168-82. doi: 10.1101/gad.270421.115), which has proved superior to many alternatives presented in the literature. To ensure proper circularization efficiency of circHIPK3, we have now subjected purified RNA from transfected HEK293 cells (and from HEK293 Flp-In T-Rex cells with stable integration of cassette) to northern blotting (Supplementary Figure S5H). This demonstrates the production of a single RNase R resistant band of correct size, for both circHIPK3 expression constructs. Due to relatively weak signal to noise ratio (rRNA background), we are unable to calculate an accurate linear-to-circ ratio. Nevertheless, the results suggest efficient production of WT and mutant circHIPK3 using the Laccase2 vector system.

circHIPK3 and STAT3 expression in cancer:

It is correct that STAT3 expression is oden positively correlated with disease progression in many patients suffering from different cancers, and that the observed expression pattern with downregulation of circHIPK3 and STAT3 in BC cells can be perceived as counterintuitive. We note that the STAT3 profile in our time-course knockdown experiments is somewhat dynamic. While downregulation of STAT3 is most pronounced After 24 hrs of circHIPK3 knockdown, the expression tends to be more normalized After 48 and 72 hrs, which could be due to initiating compensatory mechanisms elicited by the cells. Indeed, comparing long-term development of tumors in patients, with numerous primary and accumulating secondary effects, to transient (0-72 hrs) geneexpression analyses has limitations. In addition, despite the oncogenic role of STAT3 having been widely demonstrated, evidence suggest that STAT3 functions are multifaced and not always trivial to classify. Recent evidence has shown that STAT3 can have opposite functions in cancer and act as both a potent tumor promoter and a tumor suppressor (reviewed in Tolomeo and Cascio, 2021, Int J Mol Sci. 2021 Jan; 22(2): 603. doi: 10.3390/ijms22020603). We have now discussed this in more detail (in the discussion section) and stated some of the limitations of our study in terms of the regulation of the STAT3/p53 axis.

Reviewer #2 (Public Review):

The manuscript by Okholm and colleagues identified an interesting new instance of ceRNA involving a circular RNA. The data are clearly presented and support the conclusions. Quantification of the copy number of circRNA and quantification of the protein were performed, and this is important to support the ceRNA mechanism.

We thank the reviewer for the positive feedback.

Reviewer #3 (Public Review):

In Okholm et al., the authors evaluate the functional impact of circHIPK3 in bladder cancer cells. By knocking it down and performing an RNA-seq analysis, the authors found thousands of deregulated genes that look unaffected by miRNAs sponging function and that are, instead, enriched for an 11mer motif. Further investigations showed that the 11-mer motif is shared with the circHIPK3 and able to bind the IGF2BP2 protein. The authors validated the binding of IGF2BP2 and demonstrated that IGF2BP2 KD antagonizes the effect of circHIPK3 KD and leads to the upregulation of genes containing the 11-mer. Among the genes affected by circHIPK3 KD and IGF2BP2 KD (resulting in downregulation and upregulation, respectively) the authors found the STAT3 gene. This was accompanied by consistent concomitant upregulation of one of its targets, TP53. The authors propose a mechanism of competition between circHIPK3 and IGF2BP2 triggered by IGF2BP2 nucleation, potentially via phase separation.

Strengths:

The number of circRNAs continues to drastically grow; however, the field lacks detailed molecular investigations. The presented work critically addresses some of the major pi‘alls in the field of circRNAs and there has been a careful analysis of aspects frequently poorly investigated. The timepoint KD followed by RNA-seq, investigation of the miRNAs-sponge function of circHIPK3, identification of 11-mer motif, identification, and validation of IGF2BP2, and the analysis of copy number ratio between circHIPK3 and IGF2BP2 in assessing the potential ceRNA mode of action have been extensively explored and, comprehensively are convincing.

Weaknesses:

In some parts, the manuscript lacks appropriate internal controls (eg: comparison with normal bladder cells, linear transcript measurements upon the KD, RIP internal controls/ WB analysis, etc), statistical analysis and significance (in some qPCRs), exhaustive description in the methods of microscopy and image analysis, western blot, and a separate section of cell lines used. The use of certain cell lines bladder cancer cells vs non-bladder cells in some experiments for the purpose of the study is also unclear.

Overall, the presented study adds new knowledge in describing circHIPK3 function, its capability to regulate some downstream genes and its interaction and competition for IGF2BP2. However, whereas the experimental part appears technically logical, it remains unclear the overall goal of this study and the final conclusions. The mechanism of condensation proposed, although interesting and encouraging, would need further experimental support and information, especially in the context of cancer.

In summary, this study is a promising step forward in the comprehension of the functional role of circHIPK3. These data could possibly help to better understand the circHIPK3 role in cancer.

We thank the reviewer for the important and constructive criticism. All the raised points have now been addressed as described below.

Internal controls/description of methods:

We have now included suggested internal controls and provided statistical significance measures where needed. We have also described in more detail the usage of different cell lines for different experiments and a comprehensive description of microscopy, image, and western analyses.
The condensation mechanism of circHIPK3 and IGF2BP2 that we propose has been toned down slightly in the discussion, as we agree that these observations are not unequivocal and could potentially be explained by alternative and yet undefined events as discussed in further detail.

Recommendations for the authors:

Major points

(1) In Figure 1B the authors show neither error bars nor statistical analysis. Did they sequence each cell line in single replicates? A clarification on this point would be of help.

All timepoints for J82 and UMUC3 were sequenced in biological triplicates (Figure 1C-G). The data shown in Figure 1B represents prior single RNA-seq runs of all specific cell lines sequenced for selection of appropriate BC cell lines used for further study.

(2) In Figure 1C the quantification of the cognate linear Hipk3 RNA would be desired in order to rule out changes in this species levels that could account for the observed effects upon circHIPK3 KD.

We do not observe a non-specific downregulation of the HIPK3 mRNA upon circHIPK3 knockdown, rather we observe a moderate upregulation at later timepoints. However, western blotting shows that this upregulation is not translated into significantly increased protein levels. This data is now available in Supplementary Figure S1A and S1B.

(3) In Supplementary Figure S1B the authors show the number of differentially expressed genes between time points and baseline upon circHIPK3 KD or scr siRNA transfection. However, in this referee's opinion, the relevant comparison would be the differentially expressed genes between circHIPK3 KD and scr siRNA at different time points. Otherwise, they would be focusing on both circHIPK3-specific and non-specific effects.

The requested comparison is part of the main figures (Figure 1F). The plotted data in Supplementary Figure 1B (Supplementary Figure S1D in the revised version) was included to allow the reviewer to better assess the variability in the data. We therefore believe it provides relevant information and that it should be kept in the final version.

(4) Figure 1E. How many hours of KD do these measurements correspond to? Even if they correspond to 72 h, there seems to be a discrepancy between Fig 1E and 1F in terms of the total number of differentially expressed (DE) genes. Why are there more DE genes in 1E?

The number of differentially expressed genes in Figure 1E represents the total number at all timepoints, while Figure 1F represent single timepoints. We have modified the figure legend to clarify this issue.

(5) In Figure 3B, in order to verify pulldown efficiency, RT-qPCR should be performed instead of endpoint RT-PCR. Otherwise, no robust claim can be made regarding interaction affinities.

We agree that these RIP-PCR results in Figure 3B are only semi-quantitative and therefore do not unequivocally assess binding strength. However, since IGF2BP2 is the RNA binding protein in focus throughout the rest of the study, where additional quantitative RIP-RT-qPCR experiments have been performed, we find this issue negligible. In addition, the semi-quantitative nature of the endpoint PCR experiment has now been mentioned in the main text and figure legend.

(6) The authors claim that IGF2BP2 KD counteracts the effect of circHIPK3 KD on target mRNAs. However, in order to support this claim the authors should perform a rescue experiment where they simultaneously knock down both circHIPK3 and IGF2BP2. Otherwise, the conclusion remains largely supported by a correlation.

Indeed, such an experiment is important. A rescue experiment with double knockdown has now been performed and demonstrates that levels of tested targets; STAT3, NEU and TRAPPC9 become normalized under these conditions, supporting our IGF2BP2/circHIPK3 sponging model. The data is available in Supplementary Figure S5F.

(7) The authors claim that circHIPK3 interacts strongly with IGF2BP2 in bladder cancer cells but not with GRWD1. This is shown in Figure 4A where neither standard errors nor statistical analysis is shown. The authors need to show replicates of this experiment and perform statistics in order to support their claims.

These experiments have been redone with even higher stringency in biological triplicates and fully supports our claims. The data is available in a modified Figure 4A – now including error bars and indications of significance. In addition, we have included western blots demonstrating Input (IN), Flowthrough (FT) and Immunoprecipitation (IP) of correctly sized proteins in Supplementary Figure S4A.

(8) The authors claim that the STAT3 gene, which contains the 11-mer motif in its 3'UTR, becomes downregulated upon circHIPK3 KD in UMUC3 and J82 cells, while it is upregulated upon IGF2BP2 depletion in both cell lines. It is unclear why they show the effect of circHIPK3 KD on STAT3 within a time course while the effect of IGF2BP2 KD in a fixed time point (Figures 5A/S5A and 5B/S5B respectively), and it would be convenient to clarify this point.

The initial time course knockdown experiment for circHIPK3 was conducted to provide a comprehensive dataset for circHIPK3-mediated events and clarify any temporal effects. After identification of IGF2BP2 as an interaction partner of circHIPK3, we chose to harvest cells After knockdown at 48 hrs as knockdown efficiency was prominent at this point. The temporal knockdown efficiency of RNAs (circHIPK3) and proteins (IGF2BP2) differ considerably due to increased stability of proteins compared to target RNA. This is the main reason why only a single timepoint has been assessed.

(9) In Figure 5F the authors show that upon overexpression of wildtype or 11-mer motif-mutant circHIPK3, the binding of IGF2BP2 was reduced while the binding of STAT3 mRNA to IGF2BP2 was increased. In order to rule out differences in circularization efficiencies, it would be convenient to show a northern blot comparing the efficiency of circHIPK3 overexpression relative to its linear cognate RNA for both constructs.

Indeed, circRNA expression constructs may differ considerably in circularization efficiencies. We are using the Laccase2 system developed by the Jeremy Wilusz lab (Kramer et al., 2015), which, at least in our hands, efficiently produces circRNAs from almost any inserted sequence. To address whether the WT and mutant circHIPK3 express similar amounts of circRNA with high efficiency, we performed the suggested northern blot, which displays very similar RNase R resistant circHIPK3 levels. The data is now available in Supplementary Figure S5H. Due to background signal from 18S rRNA in non-RNase R treated samples, we cannot accurately calculate a linear/circular RNA ratio, since no distinct linear RNA species above background is visible on the blot. However, the important part that mutant and WT (RNase R resistant) circRNA are expressed at similar levels, makes us confident about our conclusion that WT circHIPK3 expression interferes with IGF2BP2 binding to STAT3 mRNA.

(10) Figure 1G, several genes were selected as up and downregulated for J82 and UMUc3 cell lines. Were these consistently involved in specific biological processes?

Genes were classified as down or upregulated based on significant (FDR<0.1) fold changes. The most significant genes in both directions were named, disregarding of involvement in any specific biological processes. Initially, we performed a GO-term analysis on these genes and received many hits, but we did not observe a very specific pattern or cluster of genes, suggesting that we are looking at both primary and secondary effects of knocking down circHIPK3. We believe our GSEA of the 50 hallmarks of cancer genes sets, presented in Figure 4D, 4E and Supplementary Figure S4E and S4F is addressing this point in a satisfactory manner.

(11) For differential expression analysis, which data sets were used to group outcomes at different time points. Also, there is an increased number of genes affected after KD - please describe in more detail how you reached that gene number.

As also discussed above (point 3), at each timepoint (Figure 1F) “Scr” was compared to “circHIPK3” knockdown. It makes sense that more and more genes are DE over the course of time as both primary and secondary effects of knockdown will build up over time. We have now clarified which datasets have been used in the figure legend and rewritten the Methods’ section on differential expression analysis.

(12) What happens with the expression of circHIPK3 if STAT3 is KD? What biological processes are modulated by silencing circHIPK3?

(13) What happens in bladder cancer cells if STAT3 and circHIPK3 are KD?

The main goal of our work is to clarify how circRNAs (here circHIPK3) affect gene-expression and cancer pathways. While it would be interesting to explore the consequences of STAT3 knockdown and in combination with circHIPK3, such experiments would require comprehensive additional analyses (RNA-seq), which we believe is beyond the scope of this study at this point.

(14) The rationale of the study and conclusions are unclear. Quote "we extensively evaluate the functional impact of circHIPK3 in bladder cancer cells". As previously published by the authors, as well as mentioned in the manuscript, circHIPK3 is downregulated in cancers and possesses tumor suppressor functions in bladder cancers. Could the authors clarify how the results of the presented study based on the depletion of circHIPK3 fit with the previous discoveries? If the circHIPK3 is generally downregulated compared to normal cells (although higher compared to the linear transcript) why do the authors use a KD approach? Are the bladder cancer cells simply a cell model to study circRNA vs linear? How the condensation model reconciles with circHIPK3 tumor suppressor function based on these results?

We believe that it remains unclear whether circHIPK3 is a direct tumor suppressor, although this is possible judged from the clinical patient data, since STAT3, which has been shown to become activated in many cancers, is also downregulated upon circHIPK3 knockdown. However, differences in immediate effects on gene-expression of circHIPK3 knockdown (0-72 hrs) and long-term development of tumors within patients, may be difficult to compare directly. If STAT3 downregulation contributes to cancer phenotypes in bladder cancer as suggested for several other cancer types (Glioblastoma, prostate cancer, lung cancer etc.) circHIPK3 may indeed still be classified as a tumor suppressor in bladder cancer. It is worth noting that circHIPK3 has been shown to be upregulated and have oncogenic phenotypes in many other cancers, which makes direct correlations between cancers complex and difficult to reconcile. We have revised the discussion to reflect these issues in a more comprehensive fashion. To fully delve into STAT3 regulation in terms of bladder cancer development, progression, cell invasiveness, and survival, we believe are more suitable for future experiments.

At this point, we have identified a novel mechanism of a circRNA deregulated in cancer being able to sponge/regulate the function of an oncogenic RNA binding protein, even though it is severely outnumbered in cells. Importantly, circHIPK3 likely does not function as a miRNA sponge as previously proposed in several previous studies based on circRNA overexpression, reporter constructs and miRNA mimics. We therefore believe that these findings provide new important insights into circHIPK3 function and that the current understanding of circRNAs functioning primarily as miRNA sponges, likely should be revised.

(15) Related to the previous point, if the purpose is to study the role of circHIPK3 in bladder cancer, there is a bit of a lack of consistency and it is sometimes confusing to understand the use of certain cell lines for specific experiments. The initial circHIPK3 KD experiments have been conducted in 2 (out of 11 not malignant/ metastatic) bladder cancer cell lines (J82 and UMUC3). Why this specific selection of exclusively metastatic bladder cell lines? For comparison are the normal bladder cell lines characterized by the same circRNA vs linear ratio?

The selection of bladder cancer cell lines (J82, UMUC3 and FL3) is based on several criteria including expression levels of circHIPK3, cell maintenance characteristics and knockdown/transfection efficiencies. Initially, we included HT1197 cells as well, but batch effects precluded the use of these data.

Furthermore, the subsequent miRNA analysis was conducted exclusively in one bladder cell line (J82 but not in UMUC3), the initial identification of motif again in bladder cells but the initial RBP identification and experimental interaction is conducted in non-bladder cells HepG2 and k562 (reported as main figure 3B) and only subsequently in bladder cell (4A), again in a different cell line (only FL3, but not in J82 and UMUC3). The validation of the interaction of STAT3 by RIP is performed exclusively in FL3. All this also makes someone wonder how specific this mechanism/binding is in bladder cancer cells. There is an attempt to explain this by comparing cell cycle progression analysis upon circHIPK3 KD and IGF2BP2 KD later on but the final conclusions of this analysis remain unclear. The authors should provide more explanation and information in this part of the manuscript.

It is correct that the different bladder cancer cell lines (FL3, J82 and UMUC3) have been used more or less interchangeably between experiments. This is due to the observed common phenotypes, e.g. sharing up to 92% DE genes, and highly significant enrichment of the IGF2BP2 11-mer-motif in downregulated mRNAs upon circHIK3 knockdown in all three cell lines. The ENCODE cell lines HepG2 and K562 were used since the accessible RBP-CLIP data originates from the ENCODE project, where these cells have been used exclusively. Hence, we validated the binding of candidate RBPs (semi-quantitatively) in HepG2 and K562 prior to assessing their RNA binding in the BC cell line FL3. We have used FL3 for RIP and validation of IGF2BP2 binding mainly due to better transfection efficiency and higher expression levels, allowing detection all interrogated components. The fact that we have included three BC cell lines in many experiments instead of only one, and obtained consistent results, solidifies the conclusions that our phenotypes and regulatory mechanisms are likely common for most, if not all, bladder cancer cell lines. We have included a paragraph in the materials and methods section to further clarify the usage of cell lines in the different experiments.

(16) STAT3 gene is used as an example. Where is this gene coming from? How has this gene been selected? Is there any complete list of RNA-seq data of up/down-regulated genes upon circHIPK3 KD? The raw data and gene list should be publicly available to the reviewers.

STAT3 is a major regulator of cancer pathways and therefore an interesting candidate for further analysis as it is differentially expressed between control and circHIPK3 knockdown in all cell lines. We have now included the complete list of DE genes from the time-resolved RNA-seq analyses (DESeq2 output files) in the supplementary material. This data is now available in Supplementary Tables S6 and S7.

(17) In performing the KD of circHIPK3 the authors use a unique siRNA on a splice junction. The authors claim that this is a way to not affect the linear transcript, however, have the authors also ensured experimentally that this doesn't affect in any way the linear RNA? This should be included as an initial internal control.

We do not observe a downregulation of the HIPK3 mRNA upon circHIPK3 downregulation, rather we observe a moderate upregulation at later timepoints. When assessing the HIPK3 protein levels, we observe no significant change After 48 hrs of knockdown. This data is now available in Supplementary Figure S1A and S1B.

(18) Additional controls should be provided for RIP, especially for Fig3B and 4A, Sfig4, 5C such as an internal positive control (es: AGAP2-AS1) of the correct pulldown of IGF2BP2 and/or WB should be shown (in the methods it is told that WB has been used for the analysis of RIP but I couldn't find any)

Indeed, IGF2BP2 likely binds to many mRNAs in the cell. We have now included b-actin mRNA as a low affinity control in the Figure 4A RIP data, showing that circHIPK3 represents a tight binding substrate for IGF2BP2. We have also included a western blot showing the IP of IGF2BP2, IGF2BP2, GRWD1 and GFP. This data is now available in Supplementary Figure S4A.

(19) Additional internal experimental controls should be included to assess the successful transfection and overexpression of circHIPK3 with the laccase-2 driven plasmid and mutated versions before the RIP in 4B and in the 5F. Supportive controls to show equal transfection would be required for Figure 6C-D. Further controls to show that the ASO specifically targets the 11-mer in circHIPK3 but not IGF2BP2 target genes should also be included. Please include this information in the supplementary materials.

We have now included a northern blot showing successful transfection and expression of RNase R resistant circHIPK3 from the Laccase2 vector (WT and mutant) in relation to RIP experiments. This data is now available in Supplementary Figure S5H (see also comments about this above). Equal transfections in cells shown in Figure 6C-D is assessed by comparable levels of GFP expression, which is included as an expression cassette in the modified Laccase2 construct. Pictures were acquired with same exposure time and scaling to ensure that they can be compared directly. The ASO targets circHIPK3 with full complementarity, while STAT3 mRNA has 2 mismatches, leaving the “lesser interaction” with STAT3 theoretical. This has now been clarified in the main text.

(20) Specifically, in 1C and 4A, Sfig4 there is no statistical analysis made and/or significance? This is only reported for the RIP experiment in Fig 5C.

Statistical analyses have now been performed and shown in Figure 4A and we have included binding of ACTB as a low affinity control. In Figure 1C, which displays knockdown efficiency (highly efficient) at the various timepoints, no statistical significance has been displayed, since this is normally not done for such knockdown experiments. In addition, it is also not clear which comparisons would be beneficial. Except for the J82 cell line at 12 hrs compared to 0 hrs, knockdown efficiency is high and statistically significant at all timepoints.

(21) In the assessment of copy number ensuring the same primer efficiency is fundamental, it can't be simply "assumed". Please clarify this point and possibly include this information in the supplementary materials.

It is correct that identical, or at least very similar, primer efficiencies are necessary to make the conclusion that the relationship between GAPDH mRNA and circHIPK3 levels in the cell reflects the quantitatively measured number of molecules. However, since this single comment is only to support the quantitatively measured circHIPK3 molecules by a ballpark estimate, and since we already assume that there are an estimated 10.000-20.000 copies of GAPDH mRNAs in most cells (which we also do not know precisely), we have chosen to remove this statement.

(22) The methodology section is not well organized and looks incomplete. For example, there are two separate sections for circHIPK3 expression conducted in different cell lines, this would be better explained in a single paragraph.

We have now rewritten this section to make it clearer.

The section reporting cell lines and growth conditions is incorporated in "circHIPK3 KD and overexpression" while it should be a separate paragraph and valid for all experiments where these cells have been used. There is no information regarding Western blots, including Antibodies used, and densitometry performed.

This information has now been included.

In "immunofluorescence microscopy" it is not clear what microscope has been used, how many acquisitions have been made, and how acquisition has been performed. Related to this, how the image analysis has been performed? Figures 5I-J "Finally, immunofluorescence staining showed that nuclear and overall STAT3 protein levels are significantly lower upon circHIPK3 KD, while nuclear p53 protein levels are higher" and 6C and D "we observed a significantly higher prevalence of large cytoplasmic condensates in cells expressing high levels of circHIPK3 compared to controls" how this quantification has been made? The conclusive part about the condensation role remains a bit too loose and mostly speculative, largely due to the lack of robust information provided on microscopy and image analysis

We have now included a better description of the acquisition and quantification methods.

Minor

(1) The Van Nostrand et al 2018 citation should refer to the updated publication in Nature and not to the original preprint in Biorxiv.

This reference has now been updated.

(2) In Supplementary Figure S3B, the authors offer no explanation as to why genes that become upregulated upon circHIPK3 knockdown generally contain more circHIPK3-RBP binding sites other than for IGF2BP2. A clarification would be of help.

We do not have any evidence to explain this observation. One possibility is that other RBPs elicit mRNA stabilizing effects on average, whereas abundant IGF2BP2 (~ 120.000-200.000 copies per cell) now able to bind more target mRNAs and elicit destabilization. This remains highly speculative though.

(3) In Supplementary Figure S3D, the authors' claim that the 11-mer motif is found more bound to IGF2BP2 than for other circHIPK3-RBPs should be referred to the corresponding dataset/reference.

This information is stated in the figure legend (K562) and we have now included it in the main text as well: “We evaluated how oden binding sites of circHIPK3-RBPs overlap the 11-mer motif and found that this is more oden the case for IGF2BP2 binding sites than binding sites of the other circHIPK3-RBPs when scrutinizing K562 datasets (Supplementary Figure S3D)”.

(4) In Figure 4C the authors show that, according to previously performed experiments of their group, the 11-mer motif is enriched in upregulated genes compared to downregulated genes upon IGF2BP2 KD in UMUC3. This seems like a confirmation of the results presented in the preceding section (Figure 3H) and it would be clearer if it were presented in the same section.

The data in Figure 3H is based on ENCODE data from IGF2BP2 knockdowns in K562 cells, while in Figure 4C these are from IGF2BP2 knockdown followed by sequencing in UMUC3 cells. We believe the timing of the data is fitting as is, since they relate to non-BC cells and BC cells, respectively.

(5) More in vitro experiments are needed to investigate the implication of circHIPK3 in bladder cancer cell phenotype, and how different cancer hallmarks are modulated by this ceRNA network.

We agree that this study does not fully clarify how these complex molecular interactions relate to bladder cancer progression, including fluctuations of key cancer genes/proteins. Since our focus has been on the mechanisms of circRNA function in relation to bladder cancer, these issues will await further future experimentation.

(6) "apparent" competition (introduction - pag4)? Maybe rephrase more appropriately.

This has been rephrased and “apparent” excluded.´

(7) Fig1C. Relative quantification. Statistical analysis? Is this significant?

See also comment to point 20 above. In Figure 1C we show the knockdown efficiency at the different timepoints. At all timepoints knockdowns are highly significant compared to the control (Scr), which is not significantly changed over time. It seems somewhat redundant to include pvalues for such data. Also, which comparisons should be highlighted? Knockdown is highly efficient, which is what we want to show.

(8) Figure 5H. Western blot. Densitometry quantification performed, how?

This is now described in the Materials and Methods section.

(9) Please specify the concentration of circHIPK3-specific siRNA used.

20 nM. The information is included in the Materials and Methods section.

(10) The control sample refers to scrambled or untreated cells? Instead of using "control samples without siRNA transfection" or "No siRNA" use untreated cells - otherwise, it is a bit confusing.

This has now been modified.

(11) Figure 3 is starting with hepatocellular and leukemia cells; why not with bladder cells?

These experiments were performed based on CLIP-data and RBP knockdown data from the ENCODE project. The cells used are limited to HepG2 and K562.

(12) For Figure 4B, which is the time-point?

This is 24 hrs. Has now been stated.

(13) Figure 5I and J, the expression of STAT3 and circHIPK3 can be also investigated for cellular distribution.

The expression of STAT3 is investigated in Figure 5I. Localization of circRNA by standard RNA-FISH protocols using multiple (>20) probes is inherently difficult due to the cross reaction of probes with the linear mRNA. Certain amplification steps can be included if using a single backsplicing junction probe, but this is oden giving rise to highly ambiguous results as specificity is very limited due to the “one probe“ nature of the design.

(14) Some discussion of the limitations of the study would be of value.

We have included this in the discussion.