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JNK-dependent gene regulatory circuitry governs mesenchymal fate

The epithelial to mesenchymal transition (EMT) is a biological process in which cells lose cell–cell contacts and become motile. EMT is used during development, for example, in triggering neural crest migration, and in cancer metastasis. Despite progress, the dynamics of JNK signaling, its role in genomewide transcriptional reprogramming, and involved downstream effectors during EMT remain largely unknown. Here, we show that JNK is not required for initiation, but progression of phenotypic changes associated with EMT. Such dependency resulted from JNK-driven transcriptional reprogramming of critical EMT genes and involved changes in their chromatin state. Furthermore, we identified eight novel JNK-induced transcription factors that were required for proper EMT. Three of these factors were also highly expressed in invasive cancer cells where they function in gene regulation to maintain mesenchymal identity. These factors were also induced during neuronal development and function in neuronal migration in vivo. These comprehensive findings uncovered a kinetically distinct role for the JNK pathway in defining the transcriptome that underlies mesenchymal identity and revealed novel transcription factors that mediate these responses during development and disease.

Thank you very much for submitting your rather comprehensive dataset on new TF's operating downstream of JNK in TGFbeta-induced EMT for consideration to The EMBO Journal editorial office.
I received comments from two expert scientists that assessed conclusiveness and thus general suitability of your findings for the more general readership of The EMBO Journal.
As you will recognize from the enclosed comments, there is some interest in principal. However, the truly constructive comments from ref#2 in particular reveal the still rather preliminary nature of your very comprehensive dataset. This is of certain concern, as significant functional and (patho-) physiological validations would be needed to propel that impressive dataset to the level of relevant biological insight requested at the level of our more general title. Well aware that these are very demanding and time-consuming further experimentations, that would also require establishing collaborations to gain access to clinical samples, I would prefer to leave the final decision up to your discretion as to either invest the necessary time, resources and efforts to attempt formal revisions for The EMBO Journal, OR rather seek possibly more immediate publication of your valuable datasets at a less stringent and selective publication venue.
Please consider your options at this point very carefully, also to avoid irritations/disappointments much later in the process.
As I do find the referee comments truly constructive, I would be willing to offer you the possibility and engage for further consideration at The EMBO Journal. If required, we would also be in a position to extend the usually limited period of three month revision upon your formal request.
I am sorry that I cannot be more encouraging at this stage. Given the nature of the study and the current preliminary conclusions that can be drawn from it, the project needs to be developed significantly further.
Please do not hesitate to get in touch with regard to feasibility and anticipated timeline for the necessary revisions (due to time constrains preferably via e-mail).
- In this manuscript by Sahu et al, (EMBOJ-2014-90693) the authors discovered that the JNK signaling is kinetically distinct from Smad signaling during TGFβ-induced EMT progression. Furthermore, they identify novel JNK-regulated transcription factors that are demonstrated to be essential for EMT. While many studies focus on the primary inducers of EMT, this study increases our knowledge of a signaling pathway required for the maintenance of EMT, and also uncovers a repertoire of novel transcription factors that facilitate the downstream effects of JNK signaling. Overall, these novel findings demonstrate that JNK signaling is critical in gene regulation to maintain the mesenchymal phenotype following TGFβ stimulation, and has a significant impact on our understanding of EMT.
-It has been known for some time that TGFβ induces JNK signaling but how this occurs is still unknown. Given that TGFβ has been shown to induce EMT through both Smad-dependent and Smad-independent mechanisms (i.e. PI3K/Akt signaling), and that the authors demonstrate that JNK signaling is dependent on TGFβ stimulation (Fig. S8), this study would benefit from an analysis to determine if TGFβ-induced JNK activation is itself Smad-dependent, or through non-canonical TGFβ-signaling. For instance, what effect does depletion of Smad4 have on JNK activation? Does pre-treatment of cells with a PI3K inhibitor prevent JNK activation? Experiments focusing on this aspect should be demonstrated.
-In addition, this also raises the question of how JNK signaling is modulated in breast cancer lines?
The data suggest that, in NMuMG cells, JNK signaling is maintained by downstream effectors of TGFβ signaling. Does this paradigm still hold true in mesenchymal breast cancer lines, which often secrete their own TGFβ ligands? Experiments should be performed to not only modulate JNK in these cells but to modulate TGFβ signaling and show that it effects JNK activation.
-To further establish the clinical relevance of their findings, the authors may want to consider screening patient tumor samples by IHC to correlate JNK activation and expression of the newly identified transcription factors with pathological grade.
Referee #2: In this manuscript, Sahu et al aim to provide a comprehensive analysis of the timely distinct role of JNK activity in defining the epithelial to mesenchymal transition (EMT). Additionally, they seek to causally validate their findings in transformed cellular systems and in development. Understanding the mechanisms and the genetic programs that govern EMT are of interest given its central role in development and in tumor progression and metastasis.
Conceptually, the manuscript builds on the previously well-established process of TGFb-mediated EMT and JNK contribution in NuMUG cells to analyze in detail the gene expression changes occurring in a timely fashion manner. This analysis provides interesting observations that are worth to be reported as they may provide information that may be of general interest. However, the current manuscript is full of inconsistencies, not streamlined, lacks important experiments to build on solid ground.
General Comments: The authors study TGF-beta mediated EMT, thus any interpretation (including the title) beyond this setting is out of context.
The reader's impression is that the text goes far beyond the data, which becomes a serious concern.
Third, the authors's take advantage of the new technologies available (RNA-seq) to identify changes in gene expression. Whereas this is a sound approach for an initial fishing expedition, it is out of place in validation analysis where the authors build their claims in one sample analysis per condition, one experiment and lack of statistics. This is a general caveat in various figures of the manuscript. A proper validation approach with bona fide, reproducible techniques is a must. Additionally, the axis must be labeled properly (not just "RNA") and, unless statistical analysis provided, the authors must refrain from using the word "significant" or "significant differences".
No validation of the JNKinhibitor function is provided (Substrate Phosphorylation Status of any well-known target) for any of the described experiments. This is a must. Additionally, validation of the downstream JNKi effectors with additional inhibitors and siJNK is needed to generalize the findings beyond the currently initial proposed experiments.
Major comments: 1. The authors develop an analysis of JNK activity dependent gene responses in TGFbeta mediated EMT. To this end, they initially analyze genes upregulated and downregulated upon TGFb-mediated EMT in NuMuG cells (Figuree 1A and B) yet it is unclear what parameters are used to define them. Details and specifics are needed beyond reporting than more than 20% of the genes in the genome change upon TGFbeta treatment for 7 days.
2. The authors claim that the patterns described in cell systems are also observed in human cancers of different invasiveness. Unfortunately, the authros do not show any convincing data in this regard. If various patient samples are analyzed, they should be quantified for the expression of the markers tested and an inverse correlation between the expressions of the markers draw. Additionally, statistical significance (p-value) of such correlation provided. Otherwise, the data is purely speculative and lacks of any clinical significance. Off note, the appropriate IHC controls using PFE cell pellets or tissue sections originated from cells lacking the indicated antigens are needed.
. The authors suggest in figure 1B that comparison between d0 and d7 reports over 1000 up regulated genes upon EMT. However, the latter cells, besides the EMT process, have been 7 days in culture contrary to those from d0. This methodological differences may cause important gene expression changes. Thus, the subtraction of such genes from the analysis in Figure 1B is needed to provide bona fide TGFbeta mediated EMT changes. figure H, the data is very difficult to read. First, if these RNA expression values are normalized to a housekeeping gene, please indicate it in the axis. Second, if expression differences vary so dramatically from gene to gene, please use fold changes or different plots with different axis. As it is depicted this plot is extremely misleading (This applies to various plots thorough the manuscript). Third, a carefully look into the values suggest that only FN1 levels are decrease/increase more than 2-fold upon treatment with the JNK inhibitor, implying that reversion of EMT following JNK inhibition is not accompanied by changes in the expression levels of classical EMT markers contrary to what is suggested in page 7 last paragraph. The text should reflect the data.

In
5. In figure 2, the authors aim to clarify the role of JNK function in late TGFb mediated EMT. To this end they treat the cells with a JNKi and performed RNA-seq to identify potential gene changes. Subsequently, they aim to validate what they consider are the most relevant targets based on qRT-PCR. Disappointingly, the presence of JNKi rescues some but not all of the genes and poses an important question regarding the significance of the variations detected in RNA-seq analysis. For example, ACE2 and FGFBP1 genes expression fold change upon treatment with inhibitor is insignificant and 10 times less than basal levels. This is misleading and confuses any interpretation.
It is unlikely that genes express 10 times less than in basal conditions are relevant but rather behave as bystander markers of the treatment. Given these genes represent 40% of those analyzed by qRT PCR, it is worrisome and questions the results interpretation. Fold change should be plotted in these figures rather then actual expression.
The lack of validation is expected when a careful look into details of the analysis is made. Among the genes reported in supplementary figure 4A and 4C to be considered relevant in the RNA-seq analysis, some are questionable and many are not potential JNK dependent targets including (i.e. Figure S4A BRCA1, CKAP2, CCND1, CCNF, CNG1 etc...). Please revise thoroughly as this questions the results interpretation. Bioinformatics do not account for physiology and the compromise between both is to make the earlier biologically meaningful.
6. In figure 2E and F, occupancy of potential target genes promoter by PcG complex is analyzed based on H3K27me3 and Faire marks. This is an interesting analysis. However, disappointing results are observed with the FAIRE assay since many of the promoters do not depict differences upon JNKi, thus expression is restored without a more open chromatin state (i.e. ACE2 or S100a9). Please explain.
7. In figure 3A and B, the main central discovery is provided. However, I must confess I got lost at this stage: the authors claimed that the selected TFs (15 in total, based on RNA-seq data) are TGFbeta mediated EMT responsive genes whose expression depends upon JNK activity. However, treatment with JNK does not restore to basal levels neither the expression of SPFI1, MEOX2, CDX2, LMO7 ( see column plot in figure 3A) or PCPB4, FCF12 ZEB1, PRKRIR and TSC22D1 (column plot in figure 3B), implying that according to the definition used (JNK-dependent TGFbetamediated EMT regulated genes) the only potential targets to be further explore should be HNF4, HOXB9, NR1H4, SRF and ETV6. If the focus is placed on Figures S5C and D and qRT-PCR validation of the same experiment, the data changes dramatically. In the latter, the only validated TFs following the authors' hypothesis are HNF4, SFPI1, LMO7, HOXB9 and NR1H4. Collectively only 3 factors accomplished the proposed hypothesis driven training-validation schema. Surprisingly, figure 3C focuses only in 1 TF that fits with the authors' hypothesis in the abovementioned analysis. The other four studied, while relevant are not dependent on JNK activity for expression and may be the focus of a different study. Thus, the logic of the study derails at this point and becomes largely confusing. Further, it is impressive how the five transcription factors tested in 3C are necessary to sustain the same phenotype to the same extend. One would have expected a synergistic or additive effect between them, given JNK is proposed to be an overarching activity above all of them.
8. At this point, the central question is whether the restoration of NR1H4, for example, is sufficient to prevent JNK inhibition in TGFbeta mediated EMT. This is a must since any NR1H4-depletion phenotype does not imply that the factor function is downstream of JNK activity in TGFbeta mediated EMT, which is the question aimed to addressed herein.
9. In figure 4, suddenly NR1H4 is no longer part of the novel transcription factors as it is not expressed in the breast cancer cells and the study focuses on those TFs that are not bona fide JNKi targets in Figure 3. How come? As a consequence, figure 4 focuses on a different topic, which is the role of 4 TFs in sustaining MDA-231 mesenchymal state. Additionally, TSC22D1 is equally expressed in MDA-361 and MDA-231 ( Figure 4b). Why is then subsequently studied as a mesenchymal TF? A central question is whether JNKi causes epithelial differentiation in these cells and whether any of the proposed factors (loss or gain of function) prevents this effect. Again, lack of p-values is relevant in panel 4I and J.
10. In page 12, last paragraph and subsequently in several occasions (i.e. page 14)the authors attribute metastatic traits to the cells and the analysis proposed herein. Yet, they do not show any metastatic assay in the figures. Migration or a more mesenchymal state may provide one or various of the multiple functions required for metastasis (a complex process that can only be tested in vivo) and it is fair to speculate in discussion section that cells more prone to migrate or more mesenchymal may be better fit to perform metastasis. However, by no means such assumption can be done in the result sections without data sustaining it. Please modify the text accordingly.
11. Next, the question is how JNK activity controls the expression of these genes, which unfortunately is not unraveled herein. This is particularly relevant as the mechanism is indirect and may depend on one or several mediators to execute the function. In this regard, there is a missing gap to be addressed. Studies such as those in Figure 2· and F are needed herein as well as the identification of all the elements in the TGFbeta-Mediated JNK-dependent EMT axis.
12. In page 15, the authors claim that the newly identified factors are significantly increased in neuronal differentiation ( Figure 5A). This is not true, no significant p-value is provided in the analysis. Additionally, NR1H4 (the only factor fitting the hypothesis) is not tested, thus this part of the study focuses on a different topic from that originally proposed. As expected, none of those genes is JNK dependent and their expression is not reduced more than 20% upon JNKi treatment (see figure 5D). In panel E, the authors focused on the genes control by TSC22D1, the only factor whose expression is not linked to the mesenchymal state of MDA-231 compared to the epithelial phenotype of MDA-361. How come? What is the rational to focus on a non EMT specific factor?
13. In figure 51, loss of TSC22D1 only lead to down regulation of FMNL1. Please stick to the data when interpreting the results. A general re-evaluation of the data and the results sections is a must.
14. The discussion section is long and reads as a repetition of the results in some parts. Please address.
Minor points 15. Pictures displayed in Figure 1F are out of standards.
16. The proposed model in figure 6 is more confusing than helpful. Is it necessary? 17.
In the abstract what is the "kinetically distinct mode of activation of JNK signaling" discovered herein?

RESPONSES TO REVIEWERS
Reviewer 1 (Remarks to the Author): We thank the reviewer for many positive remarks and for the opinion that, "In this manuscript by Sahu et al, (EMBOJ-2014-90693)  Authors' response: We agree with the reviewer that it will be interesting to assess whether TGFβ-induced JNK activation is itself Smad-dependent or through noncanonical TGFβ-signaling. To address this, we have performed experiments as suggested by the reviewer: 1) Using siRNAs, we pre-depleted Smad4 in mammary epithelial cells and then induced EMT by adding TGFβ and measured activity of JNK pathway 24 hours later. This analysis revealed a significant reduction in p-JNK levels upon depletion of Smad4 (new figure: Fig S2D).
2) Following pretreatment with the PI3-kinase inhibitor (Wortmannin) or Akt inhibitor (Akt Inhibitor IV), we induced EMT with TGFβ. Analysis of phospho-JNK levels 24 hours later showed a significant increase in its levels under both inhibitor conditions (new figure: Fig S2E).
These results suggest that Smad signaling promotes JNK activation. However given the delayed activation patterns of JNK pathway, it seems likely that such Smaddependent induction of JNK pathway is not direct. These new findings also show that PI3K/Akt signaling is inhibitory to JNK signaling during early stages of TGFβ-induced EMT. However, since PI3K/Akt pathway is also known to promote EMT (Yao et al. 2007, LoPiccolo et al. 2008, it is also likely that TGFβ signaling induces both PI3K/Akt and JNK pathways that function in parallel in EMT progression and when one is inhibited, the other cascade attempts to compensate by an increased activity. We are highly thankful to the reviewer for motivating us for these additional experiments that have clearly added new, relevant findings to this study. Authors' response: We thank the reviewer for placing this exciting and highly relevant question i.e. how JNK signaling is maintained in breast cancer cell lines. Our previous analysis has shown that Smad signaling is very low in mesenchymal breast cancer cell lines as compared to epithelial cancer cell lines (Fig. S2A). Following reviewer's suggestion, we blocked the TGF-β receptor with SB-431542 in mesenchymal breast cancer line MDA MB 231 and investigated whether the observed residual TGFβ signaling has any influence on JNK signaling by analyzing p-JNK levels. Interestingly, we observed that p-JNK levels were further increased upon such blockage of TGF-β receptor (new figure: Supplementary Fig S10B). Furthermore, this also accompanied a slight increase in the wound healing potential of these cells (new figure: Supplementary Fig S10A-B). Thus, even though canonical TGFβ signaling is already very lowly active in these cells, a further blockage by chemical inhibition results in an increase in JNK activity. These results also imply that during tumor progression mesenchymal breast cancer cells evolve TGF-βindependent mechanisms to sustain high JNK activity that allows maintenance of the mesenchymal fate independent of the original signal (TGF-β).  Table  S1). To address the second part of the reviewers concern, we searched IHC compatible antibodies for all three novel factors (TSC22D1, PRKRIR, and PCBP4) but unfortunately none of them are commercially available. Furthermore, raising these antibodies and validating them for the desired application would have taken time much beyond the allowed time for the revision. Despite these limitations, we still attempted to validate their expression in non-invasive and invasive tumor samples using quantitative RT-PCRs. Towards this we isolated RNA from a large number of well-characterized non-Invasive (n=20) and invasive tumors (n=20) and assessed the expression of the three novel transcription factors PRKRIR, PCBP4, and TSC22D1. We have also tested ZEB1 as an established aggressive mesenchymal marker. These results show a significantly higher expression of all three novel transcription factors in invasive tumors as compared to the non-invasive tumors (new figure: Fig 6H). These observations clearly establish the clinical relevance of our findings and supported our existing observations that these factors are critical for metastatic progression of breast cancer cells. We thank the reviewer for suggesting these experiments as this has clearly increased the translational impact of our findings.
Reviewer 2 (Remarks to the Author):

"In this manuscript, Sahu et al aim to provide a comprehensive analysis of the timely distinct role of JNK activity in defining the epithelial to mesenchymal transition (EMT). Additionally, they seek to causally validate their findings in transformed cellular systems and in development. Understanding the mechanisms and the genetic programs that govern EMT are of interest given its central role in development and in tumor progression and metastasis."
The reviewer further comments "Conceptually, the manuscript builds on the previously well-established process of TGFb-mediated EMT and JNK contribution in NMuMG cells to analyze in detail the gene expression changes occurring in a timely fashion manner. This analysis provides interesting observations that are worth to be reported as they may provide information that may be of general interest". We thank the reviewer for these encouraging comments on our study. In addition the reviewer raised concerns as he/she says "the current manuscript is full of inconsistencies, not streamlined, lacks important experiments to build on solid ground" and raised a number of concerns. We have carefully visited these concerns and attempted to address as follows:

Comment 1) The authors study TGF-beta mediated EMT, thus any interpretation (including the title) beyond this setting is out of context.
Authors' response: We agree with the reviewer that in the first part of the manuscript, we only studied TGF-β mediated EMT. However, a significant part of this study also deals with the function of JNK signaling in maintenance of the mesenchymal state independent of the canonical TGF-β pathway. For example, our study reveals that invasive breast cancer cells (also validated in primary tumor material from patients as well as breast cancer cell lines) maintain high levels of JNK signaling even in the absence of active TGF-β pathway. In support of this, we find that blocking TGF-β receptors in mesenchymal breast cancer cell lines does not inhibit JNK signaling (new figure: Supplementary Fig S10B). These results also imply that during tumor progression mesenchymal breast cancer cells evolve TGF-βindependent mechanisms to sustain high JNK activity that allows maintenance of the mesenchymal fate independent of the original signal (TGF-β). Furthermore, our study also shows that JNK-induced transcription factors have a functional role in the progression of TGF-β induced EMT, in maintenance of mesenchymal state in breast cancer cell lines as well as in neuronal EMT. Hope reviewer agrees why we wished to place a title that justifies our conclusions in a broader context, but we remain open to his/her suggestions.
Comment 2) The reader's impression is that the text goes far beyond the data, which becomes a serious concern.
Authors' response: We apologize if reviewer felt that we have occasionally speculated beyond the actual data. We have now severely edited the manuscript throughout and cut down on any speculation for which felt no solid grounds exist. We hope the reviewer finds the revised manuscript improved.

Comment 3) Third, the authors's take advantage of the new technologies available (RNA-seq) to identify changes in gene expression. Whereas this is a sound approach for an initial fishing expedition, it is out of place in validation analysis where the authors build their claims in one sample analysis per condition, one experiment and lack of statistics. This is a general caveat in various figures of the manuscript. A proper validation approach with bona fide, reproducible techniques is a must.
Additionally, the axis must be labeled properly (not just "RNA") and, unless statistical analysis provided, the authors must refrain from using the word "significant" or "significant differences".
Authors' response: We apologize to not have conferred enough information on the next generation sequencing datasets in this study, which would have easily solved this concern. It is very important to emphasize that none of the RNA-Seq data used in this study are derived using a single experiment. In fact if this would be the case, we would not be able to run DeSeq analysis that requires replicates for calculating statistical significance for differentially expressed genes (Anders & Huber, 2010). Therefore, it is important to understand that all gene expression changes displayed using RNA-Seq analyzed data are statistically significant, given the nature of the analysis (as also explained in the section materials and methods under header "RNA-Seq"). In fact the graphs where error bars and p values were missing from RNA plots contain normalized tag counts derived after averaging the RNA-Seq replicates and it is because it has generally been a practice in the field to not put error bars on averages derived from RNA-Seq samples. However, based on the reviewer's feedback, to facilitate visualizing existence of biological replicates in RNASeq experiment and the significance of changes we have now implemented error bars and p values as well as explained further in the legend. Furthermore, we have also labeled the axis as advised.
Importantly further, from the origin of the study we have been careful in the experimental design and execution as we were very keen to have these number of relevant, high quality, large datasets offered as a valuable resource to the entire scientific community. For example, we provide a high resolution, high coverage deepsequencing transcriptome data for the stepwise progression of epithelial to mesenchymal transition process (covering early, intermediate and late stages, in biological replicates; total 12 RNA-seq datasets). These data have identified several novel coding and noncoding RNAs that are transcriptionally modulated during this phenotypic transition and seek further functional validation for their role in EMT. In addition, we also provide genomewide transcriptome profiling of cells treated with JNK inhibitor during EMT progression (covering several early, intermediate and late stages, in biological replicates; total 22 RNA-seq datasets) that has revealed a number of genes that are under transcriptional control of this important signaling pathway. Furthermore, we have also generated transcriptome profiles following siRNA mediated depletion of the newly discovered transcription factors in metastatic cell lines (in biological replicates; total 10 RNA-seq datasets). Moreover, we provide details on a number of novel noncoding and coding genes including transcription factors that may have potential role in EMT in development as well as disease that warrants further mechanistic investigation. All these data (total 44 next generation sequencing samples) have already been submitted to GEO and will be publically available under accession number GSE54133. The reviewer is very welcome to explore this GEO to assess how each of these 44 sequencing experiments carried biological replicates, despite that it was heavy-cost genomics for our budget.

Comment 4) No validation of the JNK inhibitor function is provided (Substrate
Phosphorylation Status of any well-known target) for any of the described experiments. This is a must. Additionally, validation of the downstream JNKi effectors with additional inhibitors and siJNK is needed to generalize the findings beyond the currently initial proposed experiments.
Authors' response: We fully agree that validation of JNK inhibition with a downstream substrate, use of independent JNK inhibitor as well as siJNK are very critical to establish credibility on findings throughout the manuscript. Having recognized this during earlier stages of our study, we had already performed each of these experiments and had included in the previously submitted version of the manuscript. We apologize for not having emphasized enough on this in our text. We had chosen SP600125 for inhibiting JNK pathway in most of our experiments because it has been the most widely used JNK inhibitor in the literature and is considered relatively specific compared to other existing inhibitors. However, we had also performed two levels of validation for our findings by: Using another established chemical inhibitor of JNK, JNK-IN-8, and Employing siRNA mediated depletion of JNK1 and JNK2 (siJNK1/2) Under both of these conditions, cellular and molecular changes (e.g. expression and/or localization of EMT markers) acquired during TGF-β induced EMT were perturbed in a fashion very similar to those observed with SP600125 ( Supplementary  Fig S3C-E). Furthermore, the expression of newly identified factors was also similarly reduced in both siJNK1/2 as well as JNK-IN-8 treated cells (Supplementary Fig S8CD). These experiments establish that the observed effects of SP600125 on TGF-β induced EMT did not result from any off-target effects and further support the observed critical requirement of JNK signaling in driving mesenchymal progression.
In further support the role of JNK signaling in EMT progression, we also observed similar cellular and molecular changes reflecting EMT reversal irrespective of whether the inhibitor was added at the very beginning or after the onset and progression of EMT ( Supplementary Fig S4G-I).
To validate the loss of JNK activity following inhibitor treatment, we have performed a number of analysis using antibodies against phosphorylated forms of known JNK substrates during EMT in mouse (NMuMG) and human (HMEC) mammary epithelial cells following JNK inhibitor treatment. Immunofluorescence analysis showed a drastic reduction in the phosphorylation of c-Jun in cells treated with either of the two JNK inhibitors (SP600125 and JNK-IN-8) ( Supplementary Fig S3A and F). Such loss of p-c-Jun levels following JNK inhibition was further confirmed by western blot analysis ( Supplementary Fig S3G-H). Similarly, we also find severe reduction in the phosphorylation of another JNK target, ATF-4, during EMT in human mammary epithelial cells (HMEC) upon treatment with the JNK inhibitor SP600125 (Supplementary Fig S3B). In line with these findings, another known JNK target, ATF2, showed a strong reduction in its nuclear localization following JNK inhibition (new figure: Supplementary Fig S8B). These observations establish that the JNK inhibitors used in this study are indeed highly effective in blocking JNK activity.
We very much hope that these experiments and results are in line with reviewers suggestions. Figure  1A and B) yet it is unclear what parameters are used to define them. Details and specifics are needed beyond reporting than more than 20% of the genes in the genome change upon TGFbeta treatment for 7 days.

Comment 5) The authors develop an analysis of JNK activity dependent gene responses in TGFbeta mediated EMT. To this end, they initially analyze genes upregulated and downregulated upon TGFb-mediated EMT in NuMuG cells (
Authors' response: As also discussed in response to comment 3, we had described details of the computational methods in the section materials & methods under header "RNA-Seq" that were employed to identify significantly differentially expressed genes upon TGF-β-induced EMT. In brief, we collected RNA samples from three independent biological replicates of the TGFβ-induced EMT time course in NMuMG cells. These RNA were depleted from rRNA prior to library preparation (using the Ribo-Zero kit, Epicentre) as per manufacturer's guidelines. Subsequently, these RNA samples were subjected to RNA-Seq library preparation using TruSeq kit from Illumina. In all cases, 50bp reads and single-end sequencing was derived using Illumina HiSeq2000 platform. Reads were aligned to mouse genome (with version mm9) using TopHat (Trapnell et al, 2009) (version 2.0.9) with default parameters using RefSeq transcript file for coding genes from UCSC. In case of lincRNA expression analysis, reads were aligned to lincRNA coordinates collated from different databases. The aligned reads were then provided as an input to HTSeq_count utility from HTSeq package. The raw read count files obtained from HTSeq-count were then processed for differential expression using DESeq package and normalized read count for coding and non-coding RNA were generated after library size normalization (Anders & Huber, 2010). Thus, all genes reported to be differentially expressed in this study are significantly differentially expressed based on the analysis from three independent biological replicates for each time point during EMT as compared to the three independent biological replicates of the untreated control, irrespective of their fold change in expression. Furthermore, in line with reviewer's suggestion, we have now provided both number as well as percentage of genes. Authors' response: We had previously shown in a limited set of samples that invasive tumors are marked by high p-JNK signaling ( Fig 1F). We have now expanded IHC staining for p-JNK and p-Smad in a larger set of clinical samples to include 14 non-invasive tumors and 35 invasive tumor samples (the maximum we could obtain in the period allowed for revision, with the help of our clinical collaborator). These tumor samples consist of all grades and various possible cohorts based on ER, PR and HER expression (new table: Supplementary Table  S1). The findings from these IHC analyses are in line with our previous observations and clearly show that a significant fraction of invasive tumors exhibit high p-JNK and low p-Smad levels whereas no such distinction was observed in non-invasive tumors  Table  S1). Here we had also tested positive and negative control samples to confirm the antigen specificity of the two antibodies. To address the second part of the reviewers concern, we searched IHC compatible antibodies for all three novel factors (TSC22D1, PRKRIR, and PCBP4) but unfortunately none of them are commercially available. Furthermore, raising these antibodies and validating them for the desired application would have taken time much beyond the allowed time for the revision. Despite these limitations, we still attempted to validate their expression in noninvasive and invasive tumors using quantitative RT-PCRs. Towards this we isolated RNA from a large number of well-characterized non-Invasive (n=20) and invasive tumors (n=20) and assessed the expression of the three novel transcription factors PRKRIR, PCBP4, and TSC22D1. We have also tested ZEB1 as an established aggressive mesenchymal marker. These results show a significantly higher expression of all three novel transcription factors in invasive tumors as compared to the non-invasive tumors (new figure: Fig 6H). These observations clearly establish the clinical relevance of our findings and supported our existing observations that these factors are critical for metastatic progression of breast cancer cells. We thank the reviewer for providing these suggestions that have clearly increased the translational impact of our observations.

Comment 7)
The authors suggest in figure 1B that comparison between d0 and d7 reports over 1000 up regulated genes upon EMT. However, the latter cells, besides the EMT process, have been 7 days in culture contrary to those from d0. This methodological differences may cause important gene expression changes. Thus, the subtraction of such genes from the analysis in Figure 1B is needed to provide bona fide TGF-β mediated EMT changes.
Authors' response: We decipher reviewer's concerns that some genes could be differentially expressed due to culturing of NMuMG cells for 7 days that are not result of the TGFβ-induced EMT. However, these experiments have been performed in independent replicates using epithelial cells that have been grown at different occasions and the statistical analysis would discard all non-significantly (or non8 reproducibly) changing genes (as also explained in response to comment 5). Furthermore, as reviewer himself kindly mentions that "the manuscript builds on the previously well-established process of TGF-β mediated EMT and JNK contribution in NMuMG cells", these cells have been shared by a number of leading EMT researchers around the globe, have been passaged and induced with TGFβ to undergo EMT and yet the results drawn have been comparable. Nevertheless, motivated by reviewer's comment, we have still attempted to test that culturing these cells does not introduce major alterations that could contribute to EMT gene expression program. Towards this we have taken several genes identified as differentially expressed in the RNA-Seq analysis of TGFβ-induced EMT in NMuMG cells (including from a range of expression categories) and performed RT-qPCRs in NMuMG cultured under similar conditions but without exposure to TGFβ. The results show no changes in these genes when no TGFβ was added, suggesting gene expression responses were driven by TGFβ and not by the culture conditions (new figure: Supplementary Fig S1K). Authors' response: Indeed the expression values were normalized to a housekeeping gene and we had already explained this in the figure legend previously as "mRNA levels for classical EMT markers in cells treated as in (F-current image D) were measured by qRT-PCRs relative to Ctcf and plotted on the y-axis. Mean and SEM is plotted from three independent biological replicates." As suggested by the reviewer, we have now also labeled the y-axis accordingly (modified figure: 1I and all other plots).
As discussed in detail later in response to comment 9, in general kinase inhibitors are more specific at low doses and it is suggested that a higher dose application could potentially target other non-specific substrates. For this reason, we had chosen to employ the least possible dose of the JNK inhibitor SP600125 (5nM) that has also been frequently used in the literature. On top of this low dose we applied, it is also known that the kinase inhibitors are hardly absolutely effective and therefore, it will be difficult to expect a full reversal in the gene expression response after JNK inhibitor treatment in our experiments.
Despite this, the changes induced by the JNK inhibitor SP600125 on the EMT markers are significant as indicated by the p values, suggesting that inhibition of JNK pathway is able to significantly change the expression of classical EMT markers ( Fig  1I). This was further supported by immunofluorescence assay that showed a drastic downregulation of crucial mesenchymal markers such as fibronectin and severe reduction in remodeling of the cytoskeleton from cortical actin to stress fibers as well as focal adhesion formation, indicating a reversal towards the epithelial state ( Fig  1H). Moreover, these effects were also reflected in cellular properties such as a significant reduction in their migration capacity ( Fig 1J). Altogether, inhibition of JNK pathway by SP600125 was able to significantly revert EMT, both at the molecular and cellular level.
In addition, given all the caveats of using the kinase inhibitors discussed above, we had validated our critical observations made with the JNK inhibitor SP600125 using an independent JNK inhibitor, IN-8, as well as siRNA mediated knockdown of JNK ( Supplementary Fig S3C-E).  Fig S4A and 4C and draws conclusion that the presence of JNKi does not rescue all the genes. This is due to his/her observation that JNKi does not bring expression level close to day 0 (epithelial state). Since this issue has constituted most of the later comments and is a central issue to later discussions, we would like to discuss this in detail.
We respectfully differ from the reviewer's opinion that JNKi does not rescue the genes in Fig 2E and 2F and Fig. S4A and S4C. We do agree that these genes do not completely revert back the epithelial like state but all show an effect, which is also statistically validated both in RNA-Seq analysis and RT-qPCR results. In fact, these genes were chosen to represent the wide range of gene expression responses that were observed. Furthermore, using independent biological replicates, we observed 611 genes that were reproducibly and significantly differentially expressed (statistically validated) (as also explained in detail in response to comment 3). A reversal of EMT on a global transcriptome level is also very visible in our principle Component Analysis (PCA) (Fig 2D) and in comparison of normal EMT transcriptome with JNK inhibitor-induced changes (Fig 3A-B and Supplementary Fig S4A and C). When we compared JNK inhibitor affected genes (Fig 2B and C) with the transcriptome changes acquired during normal EMT (Fig 1B and C), we found that 44% of genes that are upregulated in response to JNK inhibition were repressed during normal EMT (Fig 3A). These genes show enrichment for GO terms associated with an epithelial cell-fate such as cell-cycle (Supplementary Fig S5A). Furthermore, overlap of genes downregulated upon JNK inhibition with genes that are induced during EMT revealed 57% of genes in common (Fig 3B). These genes included hallmark mesenchymal genes (e.g. Fn1, Cdh2, Mcam, Zeb1 and Tnc) and showed GO term enrichments such as actin cytoskeletal reorganization, cell junction and extracellular matrix organization (Supplementary Fig S5B).
It is further important to emphasize that the existing literature suggests that many of the critical EMT genes are possibly subject to regulation by a variety of pathways and thus, while JNK inhibition during EMT is able to revert their gene expression, other pathways may still attempt to progress the normal path. This counter-action may also contribute to why many genes don't revert massively upon JNKi. In addition, as also discussed in response to comment 8, kinase inhibitors are generally more specific at low concentrations and it is known that their specificity gets compromised at higher doses. Keeping this in mind, we chose to employ a low concentration of the JNK inhibitor SP600125 (5nM) in all our experiments. Furthermore, given that these kinase inhibitors are not highly potent in blocking target signaling cascades to an absolute level, researchers have often observed an incomplete reversal at the gene expression level following such kinase inhibition. Given these reasons, we had complemented our important observations derived with the JNK inhibitor SP600125 using an independent JNK inhibitor, IN-8, as well as siRNA mediated knockdown of JNK1/2 (siJNK1/2) (as explained previously in comment 4) -all of which support the observed transcriptional and cellular changes. Furthermore, we had also provided addition support to link JNK signaling with the target gene regulation during MET (mesenchymal to epithelial transition) and during neuronal EMT. All together, these five levels of experimental validations (SP600125, IN-8 and siJNK1/2 during EMT of mammary epithelial cells, MET as well as neuronal EMT) clearly establish that the identified target genes are regulated by the JNK pathway.
Importantly, the transcription factors that were identified to be significantly regulated by JNK using the same criteria functionally proved to be crucial regulators of progression and maintenance of mesenchymal identity in both disease and developmental context. These observations support that while it may not be possible in many cases to see a full transcriptional reversal following JNK inhibition, the observed changes are physiologically relevant. These observations were further supported by our findings that overexpression of these JNK-induced factors was able to rescue the changes observed in JNK inhibitor treated cells (new figure: Fig 5F).
Furthermore, as suggested by the reviewer, we have now plotted fold changes by placing untreated population as 1 wherever required. In addition, with respect to the question on gene expression changes shown in Supplementary Fig S4A and C, we have now performed statistical analysis and with the exception of one gene, all ten genes are significantly reverted upon JNKi.
We very much hope that these experiments and explanations satisfy reviewers concerns.
Comment 10) In figure 2E and F, occupancy of potential target genes promoter by PcG complex is analyzed based on H3K27me3 and Faire marks. This is an interesting analysis. However, disappointing results are observed with the FAIRE assay since many of the promoters do not depict differences upon JNKi, thus expression is restored without a more open chromatin state (i.e. ACE2 or S100a9). Please explain.
Authors' response: We thank the reviewer for findings the observations on chromatin changes interesting. The reviewer asks for why out of the examples shown, two promoters, ACE2 and S100a9, do not completely revert in their accessibility as measured by FAIRE while they revert well in H3K27me3 and transcription. In fact, by showing these examples, we wanted to show genes that exhibit distinct patterns in epigenomic changes in response to inhibition of JNK pathway and these results imply that not all promoters would undergo epigenetic reprogramming in exact same way in a JNK-dependent manner. Furthermore it is also known in the literature that in response to external stimuli, different gene promoters behave differently with respect to the accessibility of their gene promoters Comment 11) In figure 3A  Authors' response: This concern is largely related to the comment 9 from the reviewer where the reviewer suggests that the presence of JNKi does not rescue all the genes based on observations that JNKi at day 7 (mesenchymal state) does not bring expression level of genes close to day 0 (epithelial state) (reviewer: "the treatment with JNKi does not restore to basal levels neither the expression of SPFI1, MEOX2, CDX2, LMO7 (see column plot in figure 3A) or PCPB4, FCF12 ZEB1, PRKRIR and TSC22D1 (column plot in figure 3B),"). We have already explained in detail in response to comment 9 where we justify that these changes are statistically significant and also explained why we consider them to be relevant, at the same time provided multiple experimental validations to support our observations (please refer to the details in the authors response to comment 9). We believe that these detailed explanations should help reviewer decipher our logics of considering the chosen genes for further investigation.
Out of five transcription factors (Sfpi1, Meox2, Cdx2, Hnf4a, and Lmo7) that are downregulated during EMT but transcriptionally induced following inhibition of JNK signaling, all were known to inhibit EMT and tumor progression as well as promote epithelial properties (Ohshima et al, 2009;Rosenbauer et al, 2006;Santangelo et al, 2011;Spath & Weiss, 1998;Tanaka-Okamoto et al, 2009;Valcourt et al, 2007;Walter et al, 2005). While these data uncovered JNK signaling as a novel upstream regulator functioning in suppression of these critical epithelial determinant genes during EMT, it did not provide us a new factor that we could investigate further. Similarly, among the ten transcription factors that are upregulated during EMT but become downregulated upon JNK inhibition we identified Zeb1 and Srf that have an established role in tumor progression (Choi et al, 2009;Kalluri & Weinberg, 2009;Psichari et al, 2002;Thiery & Sleeman, 2006). Importantly however, remaining eight factors have been either only partly (Tcf12, Etv6, Hoxb9) or never (Nr1h4. Pcbp4, Prkrir, Tsc22d1, Nfil3) investigated in the context of EMT. We, therefore, decided to explore the functional role of these eight factors in EMT progression, with a deeper focus on the completely novel transcription factors i.e. Nr1h4. Pcbp4, Prkrir, Tsc22d1, Nfil3. Since Nr1h4 expression was relatively low as compared to other transcription factors in mammary epithelial cells and was not at all expressed in breast cancer cells while Nfil3 has been partly indicated in neuronal migration, we decided to whole-heartedly follow only Pcbp4, Prkrir, and Tsc22d1 in mammary epithelial cell EMT, metastasis as well as neuronal EMT.
In addition, we have now also provided calculation of statistical significance of observed changes for these factors in these RNA-seq data as well as RT-PCR validations (Fig 3A-B, S5C-D). Furthermore, the same genes show significant reduction in their expression as tested by RT-qPCR in additional validations performed by an independent JNK inhibitor (IN-8) as well as siRNA mediated depletion of JNK1/2 (Fig S8C-D). Moreover, these genes also significantly revert back in their expression closer to epithelial cells upon MET that parallels loss of JNK signaling (Fig S8E-G). Overall, since all three of these factors were statistically significantly reduced following these conditions of JNK inhibition (via two independent means: SP600125 and JNK-IN-8), siRNA mediated knockdown of JNK1/2, and MET and these were completely novel factors in mammary epithelial cell EMT, metastasis as well as neuronal EMT-we were very excited to investigate their function in these contexts.
With respect to the last question of reviewer where he/she suggests that one would expect a synergistic or additive effect between the five TFs analyzed. We thank the reviewer for pointing this out and apologize for not having discussed this enough in the manuscript. First of all, it is important to realize that siRNAs against these three transcription factors were extremely efficient (Fig S6A, D) and likely underlie the strong cellular and molecular changes observed under these conditions as compared to JNK inhibition that is not as effective given previously discussed reasons. With respect to reviewers question whether we observe a synergistic or additive effect between the five TFs analyzed, indeed a deeper look into the data suggests that the studied factors might contribute together to EMT. While cellular changes observed following depletion of these five factors during EMT look similar (Immunofluorescence assay) (Fig 3C, S6C), RT-qPCR analysis of EMT markers showed varying effects (Fig S6B, E). Importantly further, a comparison of transcriptome changes (RNA-Seq) following siRNA mediated depletion of three of these transcription factors showed that the target genes are largely non-overlapping (Fig 6D-E). Altogether, these observations indeed imply that these transcription factors largely regulate distinct set of genes and therefore collectively contribute to the JNK-driven mesenchymal progression.

Comment 12) At this point, the central question is whether the restoration of NR1H4, for example, is sufficient to prevent JNK inhibition in TGF-β mediated EMT. This is a must since any NR1H4-depletion phenotype does not imply that the factor function is downstream of JNK activity in TGF-β mediated EMT, which is the question aimed to addressed herein.
Authors' response: We agree with the reviewer that this will be an interesting experiment to inhibit JNK pathway during EMT and then transiently transfect the overexpression constructs of JNK-induced transcription factors to assess whether it can rescue the observed changes. However, despite over attempts, we failed to transfect plasmids in mesenchymal NMuMG cells with high efficiency. It is also known previously that mesenchymal cells become slightly resistant to transfection compared to epithelial cells.
We therefore attempted to address this question in mesenchymal breast cancer cell lines. Here we transfected either empty vector or the overexpression constructs of the three transcription factors (TSC22D1, PCBP4 and PRKRIR) in JNK inhibitor treated cells. Out of these three factors, PRKRIR construct mysteriously failed to overexpress due to unidentified technical reasons. Interestingly, while control breast cancer cells treated with the JNK inhibitor showed a gain of epithelial markers and loss of mesenchymal markers, overexpression of TSC22D1 and PCBP4 was able to rescue the cells from the effects of JNKi (new figure: Fig 5F). These observations indeed imply that the restoration of these downstream factors is able to counteract the JNK inhibition effects on the mesenchymal state. Figure 3. How come? As a consequence, figure 4 focuses on a different topic, which is the role of 4 TFs in sustaining MDA-231 mesenchymal state. Additionally, TSC22D1 is equally expressed in MDA-361 and MDA-231 (Figure 4b).

Why is then subsequently studied as a mesenchymal TF? A central question is whether JNKi causes epithelial differentiation in these cells and whether any of the proposed factors (loss or gain of function) prevents this effect. Again, lack of p-values is relevant in panel 4I and J.
Authors' response: First half of this comment is highly redundant with comment 9 and 11 and we have already explained in large detail why and how, based on a range of experimental validations, we have trusted these three transcription factors (Tsc22d1, Pcbp4 and Prkrir) to be JNK-induced novel transcription factors for further studies in the mesenchymal state.
With regard to the question on why we had chosen Tsc22d1 (in addition to Prkrir and Pcbp4) to study in MDA-MB-231 cells-while it seems that the levels of Tsc22d1 were almost as high in MDA-MB-361 cells (epithelial) as in MDA-MB-231(mesenchymal), a separate plotting and analysis of other breast cancer cell lines indicated that Tsc22d1 may be higher expressed in mesenchymal breast cancer cell lines ( Supplementary  Fig 10J). Importantly further, siRNA mediated depletion of Tsc22d1 in MDA-MB-361 cells (epithelial) had no effect on the cellular properties (data not shown) while it lead to major changes in cellular properties (including invasion) as well as gene expression profile in MDA-MB-231 cells (mesenchymal). Therefore, we speculated that the high levels of Tsc22d1 have a function in maintaining mesenchymal properties of MDA-MB-231 cells, but has no role in MDA-MB-361 cells despite being expressed. Importantly, compared to other factors, depletion of TSC22D1 in MDAMB-231 cells (mesenchymal) led to the most dramatic reduction in wound healing and migration potential as well as most severe changes in the transcriptome (Fig 5DE,  6A-B). Furthermore, our new observations derived using primary material from a large number of patients revealed that TSC22D1 is significantly higher expressed in metastatic as compared to non-metastatic tumors (new figure: Fig 6H).
To answer reviewers next question whether JNKi causes epithelial differentiation in these cells-we already had indication from the wound healing assay that showed a significant reduction in the wound healing capacity following JNK inhibition (Fig 5A). Motivated by reviewers suggestion, we have now investigated this further by analyzing a number of EMT markers by RT-qPCRs that revealed that the inhibition of JNK pathway in MDA-MB-231 cells results in the upregulation of epithelial markers and downregulation of mesenchymal markers (new figure: Fig 5B). Overall, these observations suggest that JNKi indeed results in the epithelial differentiation of these cells.
With respect to the comment whether any of the proposed factors prevents JNK inhibitor induced effects in MDA-MB-231 cells, we performed the rescue experiment already described in detail in response to comment 12. In brief, overexpression of TSC22D1 or PCBP4 (unable to test PRKRIR due to technical reasons) in MDA MB 231 cells was highly effective in significantly reverting back the effects induced by the JNK inhibitor (new figure: Fig 5F).
With respect to the last point about missing p values in Fig 4I and J, we apologize for not having provided this earlier. We have now calculated them and the differences are statistically significant. In the revised version of the manuscript, these figures have now become Fig 6F and Fig 6G. Comment 14) In page 12, last paragraph and subsequently in several occasions (i.e. page 14) the authors attribute metastatic traits to the cells and the analysis proposed herein. Yet, they do not show any metastatic assay in the figures. Migration or a more mesenchymal state may provide one or various of the multiple functions required for metastasis (a complex process that can only be tested in vivo) and it is fair to speculate in discussion section that cells more prone to migrate or more mesenchymal may be better fit to perform metastasis. However, by no means such assumption can be done in the result sections without data sustaining it. Please modify the text accordingly.
Authors' response: We fully agree with the reviewer that in vivo metastasis assay will be needed to establish the role of newly identified transcription factors in metastasis. We have therefore modified the text accordingly in the discussion to convey the message that these factors make cells more prone to migrate or more mesenchymal that would likely facilitate metastasis. We thank the reviewer for this suggestion.
Comment 15) Next, the question is how JNK activity controls the expression of these genes, which unfortunately is not unraveled herein. This is particularly relevant as the mechanism is indirect and may depend on one or several mediators to execute the function. In this regard, there is a missing gap to be addressed. Studies such as those in Figure 2• E and F are needed herein as well as the identification of all the elements in the TGFbeta-Mediated JNK-dependent EMT axis.
Authors' response: We fully agree with the reviewer that it would be very exciting to discover the intermediate components in the cascade through which it regulates the expression of the genes. However, untangling this completely would primarily require a phosphoproteome analysis in control vs JNK inhibitor treated mesenchymal cells to identify JNK substrates in this process and then further validation of most sensible candidates in functional experiments. However, as reviewer may recognize, this is a large scale study in itself and beyond the scope of this manuscript.
Nevertheless, we speculated that JNK may regulate expression of genes by chromatin changes and/or via post-translationally modifying downstream critical transcription factors that then target the promoters of these genes. We analyzed a number of histone modifications that define active and inactive transcriptional state, but did not find any significant results at the promoters of genes encoding the studied transcription factors. Therefore, we looked for transcription factors that have been previously shown in various contexts to be downstream effectors of JNK signaling and purchased antibodies against ATF4, ATF2, EGR1, c-Jun, NF-YA and Elk1. We next performed ChIP assay using each of these antibodies during TGFβ-induced EMT in the presence and absence of JNK inhibitor and analyzed the promoter of these genes by RT-qPCRs at day 7 (DMSO and JNKi). While we did not detect binding of most of these transcription factors, we noticed that ATF2 showed binding at the promoter of all three genes in mesenchymal cells and such binding was dramatically reduced following JNK inhibition (new figure: Fig S8A). Such loss of binding at the promoter of these genes was further accompanied by reduced nuclear localization of ATF2 following JNK inhibition (new figure: Fig S8B). Future investigations should aim to unravel how ATF2 contributes genomewide to the gene regulatory function of JNK during EMT.
Comment 16) In page 15, the authors claim that the newly identified factors are significantly increased in neuronal differentiation ( Figure 5A). This is not true, no significant p-value is provided in the analysis. Additionally, NR1H4 (the only factor fitting the hypothesis) is not tested, thus this part of the study focuses on a different topic from that originally proposed. As expected, none of those genes is JNK dependent and their expression is not reduced more than 20% upon JNKi treatment (see figure 5D). In panel E, the authors focused on the genes control by TSC22D1, the only factor whose expression is not linked to the mesenchymal state of MDA-231 compared to the epithelial phenotype of MDA-361. How come? What is the rational to focus on a non EMT specific factor?
Authors' response: We apologize for not having provided p values-however, this was simply because these values were derived from RNA-Seq where it is generally not a practice to put error bars on tag counts derived from replicates. We have now analyzed expression of these three factors in RT-qPCRs derived from independent biological replicates of neuronal differentiation. These analyses showed that Tsc22d1 and Prkrir are significantly induced upon neurogenesis (as indicated by the p value), but Pcbp4 is not (new figure: Fig 7C).
With regard to the expression changes following JNK inhibition-these data are derived from a previously published study where neurons were treated for a very short time (6 hours) and a global gene expression profiling was performed in DMSO versus JNKi condition (Tiwari et al., Nature Genetics 2011). Having explained this experiment and given the half-life of mammalian RNAs, it will be difficult to expect massive changes within 6 hours of blocking JNK pathway and in this study this global analysis had shown that most genes are only moderately, but statistically significantly misregulated under these conditions. Nevertheless, such a short period of JNK inhibition during neurogenesis was still sufficient to result in a reduction in the expression of these genes that was statistically significant (indicated by p value) ( Fig  7D).
Our choice of Tsc22d1 as a mesenchymal factor has already been explained in great detail in response to comment 9, 11 and especially 13. Since neuronal differentiation in vitro as well as in vivo showed an increase in the expression of Tsc22d1, a process that shows hallmark of EMT, we had attempted to test a role for Tsc22d1 in this process. In the revised version of this study, we have gone ahead to do a very sophisticated, but highly informative experiment where we have performed knockdown of Tsc22d1 during mouse brain development and assessed for its effect on neuronal migration in vivo. For this functional test, we performed in utero electroporations as described previously (Saito et al. 2006) in the mouse cortex at E12.5 with plasmids containing either non-target or validated shRNAs against Tsc22d1 and sacrificed the animals at E16.5 for sectioning and analysis (new figure: Fig 7H). Interestingly, such knockdown of Tsc22d1 led to a very strong reduction in the number of electroporated cells (GFP positive) in the upper neuronal layer and a noticeable retention in the lower layer (new figures: Fig 7I-J). These observations establish that Tsc22d1 is indeed critical for neuronal migration during mouse brain development.
Comment 17) In figure 51, loss of TSC22D1 only lead to down regulation of FMNL1. Please stick to the data when interpreting the results. A general re-evaluation of the data and the results sections is a must.
gene regulatory circuitry and a large number of transcription factors were discussed-we found it important to attempt summarizing the findings through a model. In case reviewer disagrees, we would be happy to remove it from the manuscript.

Comment 21) In the abstract what is the "kinetically distinct mode of activation of JNK signaling" discovered herein?
Authors' response: We wished to emphasize that both Smad and JNK signaling pathways are activated at different time points during TGF-β-induced EMT. Furthermore, we find that while Smad seems crucial to initiate EMT, JNK pathway becomes active at a distinct time when Smad signaling starts to weaken and increasingly becomes more prominent in regulating EMT gene regulatory program. These observations made us postulate that JNK signaling has a kinetically distinct mode of activation and function during EMT.
2nd Editorial Decision 07 May 2015 Thank you for submitting the revised version of your manuscript for consideration by the EMBO Journal. It has now been seen by the two original referees whose comments are shown below.
As you will see from the reports, both referees find the study to be greatly improved relative to the previous submission and they now support publication in The EMBO Journal, following minor revision..
Given the referees' positive recommendations, I would thus like to invite you to submit a final revision of the manuscript, addressing the remaining minor comments raised by ref#1. I will leave it up to you to decide whether you want to make changes to the final model figure as suggested by ref#2.
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What is the effect of PI3K inhibition on JNK signaling in the absence of TGFβ? Furthermore, do other non-canonical TGFβ pathways, such as the MAPK pathway, have an effect on JNK signaling? Comment 2: The inhibitor SB-431542 not only blocks TGFβ signaling but also Activin and nodal, therefore, the effect of this inhibitor on JNK phosphorylation and wound healing in MDA-MB-231 cells may not be due to TGFβ signaling changes. Comment 3: A graph of IHC score data for tumor samples in Figure 1F needs to be included, this is described in the figure legend but not present in the figure itself.
In addition, the IHC images shown in Figure S2B are of poor quality and should be removed; as IHC micrographs are already presented in Fig 1F, this data is redundant. Finally, is there a statistically significant difference in pSmad2 or pJNK levels between invasive and non-invasive tumors? In addition, is JNK activation associated with clincopathological features of these tumors such as grade? Furthermore, is the inverse correlation between pSmad2 and pJNK statistically significant in invasive tumors and not in non-invasive tumors? The authors state that "a significant fraction of invasive tumors exhibit high p-JNK and low p-Smad levels whereas this pattern was not observed in non-invasive tumors" but no statistical analyses is provided. The data in Figure 1G and S2C should be presented as scatter plots of pSmad2 and pJNK score. Figure 1H: the figure legend for this panel state that all images are 63x magnifications but control (d0) images appear to be taken at a much lower magnification. Figure 4: Figure 4 did not display correctly in the uploaded pdf, most of the data was missing. Figure 7A: ES key for this graph is the wrong color.
Referee #2: The current revised manuscript represents a larger improvement compared to the earlier version. In particular the flow of the manuscript has been largely improved as well as the abstract. Similarly, the consistency, robustness and logical flow of the text suffered a major turn around. Of significance, all the text modifications introduced in page 11 and onwards. As currently read the manuscript is streamlined, focused and with a logical flow. In addition, key experiments to address central questions have been added. Similarly, statistical analysis as well as better display and delivery of the previous data has improved the manuscript. Finally, several of my previous concerns have been also addressed and sound explanations provided.
In detail, the authors have provided a context and largely edited the text to minimize data interpretation and unsupported conclusions in the results section. Subsequently, statistical analysis has been introduced in their analysis display. Thus, a proper sense of expression fold changes and variability as well as significant statements can be made. As per point 4, a thorough validation of the JNK inhibitor has been provided as well (Fig S3 and S4) and a larger wealth of clinical samples analysis included. The latter incorporates the appropriate statistical analysis and a well-defined EMT gene for benchmarking purposes (Fig 6H). New figure S1K is also suited and clarified the robustness of the experimental design. The new text and figure arrangement regarding figure 3-6, as well as several explanations brought light into the conflictive previous display of the results in these sections. Figure 5F data is of importance as well to provide causality to the findings regarding the function of the novel JNKi downstream transcription factors. Finally, the new data on ATF2 as a potential mediator of the JNK-driven EMT sustainment is very appealing.
I still contain that the model displayed in figure 8 is confusing, out of style (letter sizing etc). If the authors aim to maintain it a better schematic must be drawn.
2nd Revision -authors' response 25 May 2015 Reviewer 1 (Remarks to the Author): We are glad that the reviewer is satisfied with the revised work as he comments "The authors have addressed the majority of the concerns raised." In addition the reviewer had few suggestions that we have implemented as follows: Authors' response: Indeed we also find this observation very interesting. As suggested by the reviewer we have now also inhibited PI3K and Akt (with Wortmannin and Akt inhibitor IV respectively) and another established non-canonical TGFβ pathway, Erk1/2 (with U0126), and assessed phospho-JNK levels 24 hours later in the absence of TGFβ. Interestingly, while Erk1/2 and PI3K inhibition did not have an effect on JNK signaling, Akt inhibition led to an increase in phospho-JNK levels even in the absence of TGFβ (new figure: Fig S2H). It is possible that Akt pathway is somehow involved in inhibiting JNK signaling and blockage of Akt pathway relieves this repression resulting in JNK activation. We thank the reviewer for suggesting these additional experiments that have added new findings.
Comment 2) The inhibitor SB-431542 not only blocks TGFβ signaling but also Activin and nodal, therefore, the effect of this inhibitor on JNK phosphorylation and wound healing in MDA-MB-231 cells may not be due to TGFβ signaling changes. Authors' response: We had chosen SB-431542 to block TGFβ receptor given the plethora of existing literature employing this inhibitor for similar purposes. However, we agree with the reviewer that we cannot be absolutely sure that this effect is entirely due to the inhibition of TGFβ signaling. Therefore, to further substantiate our findings we checked the expression of TGFB1, activin and nodal using RNA-Seq data derived from MDA-MB-231 cells. This analysis revealed that while activin is basally expressed (INHBA= 163.71) and nodal is completely silenced (NODAL= 2.45), TGFB1 levels were extremely high (1016.72) in these cells. This may imply that TGFβ signaling is the dominant cascade operating via TGFβ receptor in these cells that we inhibit using SB-431542. We thank the reviewer for bringing this to our attention as this will help us during further follow-up studies. Figure 1F needs to be included, this is described in the figure legend but not present in the figure itself. In addition, the IHC images shown in Figure S2B are of poor quality and should be removed; as IHC micrographs are already presented in Fig 1F, this data is redundant. Finally, is there a statistically significant difference in pSmad2 or pJNK levels between invasive and non-invasive tumors? In addition, is JNK activation associated with clincopathological features of these tumors such as grade? Furthermore, is the inverse correlation between pSmad2 and pJNK statistically significant in invasive tumors and not in non-invasive tumors? The authors state that "a significant fraction of invasive tumors exhibit high p-JNK and low p-Smad levels whereas this pattern was not observed in non-invasive tumors" but no statistical analyses is provided. The data in Figure 1G and S2C should be presented as scatter plots of pSmad2 and pJNK score. Authors' response: We thank the reviewer for making these suggestions. With regard to Figure S2B, we had to severely reduce the figure size to facilitate uploading at the journal's website and this led to a compromise in the quality. However, we agree that this figure is redundant with Figure 1F and therefore we have completely removed it from this final version of our manuscript.

Comment 3) A graph of IHC score data for tumor samples in
With reference to the missing plot and in reference to the question about p-Smad and p-JNK level differences between the tumor types, we have compared the p-Smad2 and p-JNK levels between invasive and non-invasive tumors and we find that p-Smad2 levels are significantly lower in invasive tumors as compared to the non-invasive tumors (new figure: Fig S2D). Unlike p-Smad2, under same conditions, p-JNK levels stay similar in both invasive and non-invasive tumors. Since both non-invasive and invasive tumors represent malignant conditions, it is comprehendible why JNK signaling is equally active in these tumor types. It is required to investigate whether normal breast epithelial cells show no/low activity of JNK pathway (which was not feasible for us due to the difficulty in obtaining such normal samples for IHC analysis of p-JNK levels). Furthermore, these observations also imply that JNK signaling becomes active as soon as mesenchymal (malignant) state is set up, and during its progression, Smad signaling goes down while JNK activity is maintained once acquired. This would also be in line with our earlier observations during EMT in mouse mammary epithelial cells where Smad signaling gradually declines while JNK signaling is induced and maintained throughout EMT (Fig 1D-E).
We have also looked whether JNK activation associates with clinicopathological features of these tumors such as grade in invasive and non-invasive samples. We were already limited with the number of clinical samples that could be collected in the allowed revision time for the two categories (non-invasive vs. invasive). Therefore, a further sub-categorization of these tumor samples according to grade would result in a much smaller sample size, inhibiting us to make calls for statistical significance. Nevertheless, we have attempted to do this and found that there is tendency towards higher JNK activity in high grade invasive tumors, a finding that needs to be statistically validated in a larger cohort of samples (new figure: Fig S2E). With reference to the reviewer's comment on whether the inverse correlation between pSmad2 and pJNK is statistically significant in invasive tumors and not in non-invasive tumors, we discussed this with our clinical partners as well as bioinformatics colleagues and they could not suggest an optimal way to compare both pie charts as they represent percentage of tumor samples with various levels of p-JNK and p-Smad in invasive tumors ( Fig 1G) and non-invasive tumors ( Fig S2C). However, to still be able to address the reviewer's concerns, we have plotted the ratio of p-JNK vs p-Smad (p-JNK/p-Smad) in non-invasive versus invasive tumors which clearly shows a shift towards increased JNK signaling in invasive tumors (new figure: Fig S2C). Furthermore, this difference is coming close to statistical significance (p value= 0.06) which could certainly be improved by having much larger cohort of samples than the ones we could collect within this short period allowed for revision (n=49). With respect to the last point, as the clinical partners performed quantification of staining with a four tier system (0, 1, 2, 3), it is not possible to generate scatter plot for these datasets (as many samples would overlap at certain positions on the graph due to the nature of this scoring system)-however the bar graphs discussed earlier should also provide answer to this question (new figure: Fig S2C). Hope the reviewer finds these explanations satisfactory.
Comment 4) Figure 1H: the figure legend for this panel state that all images are 63x magnifications but control (d0) images appear to be taken at a much lower magnification. Authors' response: We apologized for not having explained enough on this issue. In fact, all the images were taken at the same magnification. The untreated epithelial cells are highly proliferative and appear small in size, round in shape and adhere to surrounding cells whereas day 7 TGF-βtreated (mesenchymal) cells are much bigger in size, elongated in shape and detached from the nearby cells. We have further clarified this in the figure legend.
Comment 5) Figure 4: Figure 4 did not display correctly in the uploaded pdf, most of the data was missing. Authors' response: We have checked but we did not find an issue with Figure 4 at our end. In this current upload of the final version of the manuscript we have further ensured that this figure is uploaded properly.
Comment 6) Figure 7A: ES key for this graph is the wrong color. Authors' response: We apologize for this mistake and thank the reviewer for pointing this out. We have now corrected the color for the ES key.
Reviewer 2 (Remarks to the Author): We are extremely excited that the reviewer finds the current version of the manuscript suitable for publication and is satisfied with all revisions to the manuscript including addition of many new datasets, better representation of figures and improved text. He comments "The current revised manuscript represents a larger improvement compared to the earlier version. In particular the flow of the manuscript has been largely improved as well as the abstract. Similarly, the consistency, robustness and logical flow of the text suffered a major turn around. Of significance, all the text modifications introduced in page 11 and onwards. As currently read the manuscript is streamlined, focused and with a logical flow. In addition, key experiments to address central questions have been added. Similarly, statistical analysis as well as better display and delivery of the previous data has improved the manuscript. Finally, several of my previous concerns have been also addressed and sound explanations provided. In detail, the authors have provided a context and largely edited the text to minimize data interpretation and unsupported conclusions in the results section. Subsequently, statistical analysis has been introduced in their analysis display. Thus, a proper sense of expression fold changes and variability as well as significant statements can be made. As per point 4, a thorough validation of the JNK inhibitor has been provided as well (Fig S3 and S4) and a larger wealth of clinical samples analysis included. The latter incorporates the appropriate statistical analysis and a well-defined EMT gene for benchmarking purposes (Fig 6H). New figure S1K is also suited and clarified the robustness of the experimental design. The new text and figure arrangement regarding figure 3-6, as well as several explanations brought light into the conflictive previous display of the results in these sections. Figure 5F data is of importance as well to provide causality to the findings regarding the function of the novel JNKi downstream transcription factors. Finally, the new data on ATF2 as a potential mediator of the JNK-driven EMT sustainment is very appealing." The reviewer had only one comment which we have addressed as follows: Comment 1) I still contain that the model displayed in figure 8 is confusing, out of style (letter sizing etc). If the authors aim to maintain it a better schematic must be drawn. Authors' response: We acknowledge reviewers concern and therefore we have now completely changed the simplistic model to a more comprehendible and detailed schematic diagram that depicts the essence of a majority of our findings. Hope the reviewer finds this version of the model satisfactory.