A role for JAK2 in mediating cell surface GHR-PRLR interaction

Reviewer #3 (Public review):

Summary:

The authors are interested in the relative importance of PRL versus GH and their interactive signaling in breast cancer. After examining GHR-PRLR interactions in response to ligands, they suggest that a reduction in cell surface GHR in response to PRL may be a mechanism whereby PRL can sometimes be protective against breast cancer.

Strengths:

The strengths of the study include the interesting question being addressed and the application of multiple complementary techniques, including dSTORM, which is technically very challenging, especially when using double labeling. Thus, dSTORM is used to analyze co-clustering of GHR and PRLR, and, in response to PRL, rapid internalization of GHR and increased cell surface PRLR. Conclusions from Proximity ligation assays are that some GHR and PRLR are within 40 nm (≈ 4 plasma membranes) of each other and that upon ligand stimulation, they move apart. Intact receptor knockin and knockout approaches and receptor constructs without the Jak2 binding domain demonstrate a) a requirement for the PRLR for there to be PRL- driven internalization of GHR, and b) that Jak2-PRLR interactions are necessary for stability of the GHR-PRLR colocalizations.

Weaknesses:

Although improved over the first version, the manuscript still suffers from a lack of detail, which in places makes it difficult to evaluate the data and would make it very difficult for the results to be replicated by others.

Comments on revised version:

Points for improvement of the manuscript:

(1) There is still insufficient detail about the proximity ligation assay. For example, PLAs that use reagents from Sigma (as now reported) require primary antibodies from two different species and yet both the anti-PRLR and anti-GHR used for dSTORM were mouse monoclonals. On line 356 it says that the ECD antibodies were used for microscopy and the PLA is microscopy. Were instead the ICD antibodies used for the PLA? If so, how do we know that one or more of the proteins in the very strong "non-specific" bands seen on Figure 5A are not what is being localized? Could you do a Western blot of just cell membrane proteins? There needs to be further clarity/explanation.

(2) Although the manuscript now shows a Western blot using the antibodies against intracellular regions of the receptor, a full Western blot is not provided for the antibodies against the S2 extracellular domain used for the dSTORM. While I haven't checked the papers showing characterization of the anti-GHR, I did re-check reference 70, which the authors say shows full characterization of the PRLR antibody, and this does not show a full Western (only portions of gels). How do we know that this antibody is not recognizing some other cell surface molecule, the surface expression of which increases upon stimulation of the cells with PRL? Is there only one band when blotting whole cell extracts with either the GHR or PRLR ECD antibodies so we can be sure of specificity? Figure S2 helps some, but these are different cells and the relative expression of the PRLR versus some other potential cell surface protein in these engineered cells may well be completely different.

Author response:

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

Reviewer #1 (Public Review):

(1) The questions after reading this manuscript are what novel insights have been gained that significantly go beyond what was already known about the interaction of these receptors and, more importantly, what are the physiological implications of these findings? The proposed significance of the results in the last paragraph of the Discussion section is speculative since none of the receptor interactions have been investigated in TNBC cell lines. Moreover, no physiological experiments were conducted using the PRLR and GH knockout T47D cells to provide biological relevance for the receptor heteromers. The proposed role of JAK2 in the cell surface distribution and association of both receptors as stated in the title was only derived from the analysis of box 1 domain receptor mutants. A knockout of JAK2 was not conducted to assess heteromers formation.

We thank the reviewer for these comments. The novel insight is that two different cytokine receptors can interact in an asymmetric, ligand-dependent manner, such that one receptor regulates the other receptor’s surface availability, mediated by JAK2. To our knowledge this has not been reported before. Beyond our observations, there is the question if this could be a much more common regulatory mechanism and if it has therapeutic relevance. However, answering these questions is beyond the scope of this work.

Along the same line, the question regarding the biological relevance of our receptor heteromers and JAK2’s role in cell surface distribution is undoubtfully very important. Studying GHR-PRLR cell surface distributions in JAK2 knockout cells and certain TNBC cell lines as proposed by the reviewer could perhaps be insightful. However, most TNBCs down-regulate PRLR [1], so we would first have to identify TNBC cell lines that actually express PRLR at sufficiently high levels. Moreover, knocking out JAK2 is known to significantly reduce GHR surface availability [2,3], such that the proposed experiment would probably provide only limited insights.

Unfortunately, our team is currently not in the position to perform any experiments (due to lack of funding and shortage of personnel). However, to address the reviewer’s comment as much as possible, we have revised the respective paragraph of the discussion section to emphasize the speculative nature of our statement and have added another paragraph discussing shortcoming and future experiments (see revised manuscript, pages 23-24).

(1) López-Ozuna, V., Hachim, I., Hachim, M. et al. Prolactin Pro-Differentiation Pathway in Triple Negative Breast Cancer: Impact on Prognosis and Potential Therapy. Sci Rep 6, 30934 (2016). https://www.nature.com/articles/srep30934

(2) He, K., Wang, X., Jiang, J., Guan, R., Bernstein, K.E., Sayeski, P.P., Frank, S.J. Janus kinase 2 determinants for growth hormone receptor association, surface assembly, and signaling. Mol Endocrinol. 2003;17(11):2211-27. doi: 10.1210/me.2003-0256. PMID: 12920237.

(3) He, K., Loesch, K., Cowan, J.W., Li, X., Deng, L., Wang, X., Jiang, J., Frank, S.J. Janus Kinase 2 Enhances the Stability of the Mature Growth Hormone Receptor, Endocrinology, Volume 146, Issue 11, 2005, Pages 4755–4765,https://doi.org/10.1210/en.2005-0514

(2) Except for some investigation of γ2A-JAK2 cells, most of the experiments in this study were conducted on a single breast cancer cell line. In terms of rigor and reproducibility, this is somewhat borderline. The CRISPR/Cas9 mutant T47D cells were not used for rescue experiments with the corresponding full-length receptors and the box1 mutants. A missed opportunity is the lack of an investigation correlating the number of receptors with physiological changes upon ligand stimulation (e.g., cellular clustering, proliferation, downstream signaling strength).

We appreciate the reviewer’s comments. While we are confident in the reproducibility of our findings, including those obtained in the T47D cell line, we acknowledge that testing in additional cell lines would have strengthened the generalizability of our results. We also recognize that performing a rescue experiment using our T47D hPRLR or hGHR KO cells would have been valuable. Furthermore, examining physiological changes, such as proliferation rates and downstream signaling responses, would have provided additional insights. Unfortunately, these experiments were not conducted at the time, and we currently lack the resources to carry them out.

(3) An obvious shortcoming of the study that was not discussed seems to be that the main methodology used in this study (super-resolution microscopy) does not distinguish the presence of various isoforms of the PRLR on the cell surface. Is it possible that the ligand stimulation changes the ratio between different isoforms? Which isoforms besides the long form may be involved in heteromers formation, presumably all that can bind JAK2?

This is a very good point. We fully agree with the reviewer that a discussion of the results in the light of different PRLR isoforms is appropriate. We have added information on PRLR isoforms to the Introduction (see revised manuscript, page 2) and Discussion sections (see revised manuscript, pages 23-24).

(4) Changes in the ligand-inducible activation of JAK2 and STAT5 were not investigated in the T47D knockout models for the PRL and GHR. It is also a missed opportunity to use super-resolution microscopy as a validation tool for the knockouts on the single cell level and how it might affect the distribution of the corresponding other receptor that is still expressed.

We thank the reviewer for his comment. We fully agree that such additional experiments could be very valuable. We are sorry but, as already mentioned above, this is not something we are able to address at this stage due to lack of personnel and funding. However, we do hope to address these and other proposed experiments in the future.

(5) Why does the binding of PRL not cause a similar decrease (internalization and downregulation) of the PRLR, and instead, an increase in cell surface localization? This seems to be contrary to previous observations in MCF-7 cells (J Biol Chem. 2005 October 7; 280(40): 33909-33916).

It has been recently reported for GHR that not only JAK2 but also LYN binds to the box1-box2 region, creating competition that results in divergent signaling cascades and affects GHR nanoclustering [1]. So, it is reasonable to assume that similar mechanisms may be at work that regulate PRLR cell surface availability. Differences in cells’ expression of such kinases could perhaps play a role in the perceived inconsistency. Also, Lu et al. [2] studied the downregulation of the long PRLR isoform in response to PRL. All other PRLR isoforms were not detectable in MCF-7 cells. So, differences between MCF-7 and T47D may lead to this perceived contradiction.

At this stage, we can only speculate about the actual reasons for these seemingly contradictory results. However, for full transparency, we are now mentioning this apparent contradiction in the Discussion section (see page 23) and have added the references below.

(1) Chhabra, Y., Seiffert, P., Gormal, R.S., et al. Tyrosine kinases compete for growth hormone receptor binding and regulate receptor mobility and degradation. Cell Rep. 2023;42(5):112490. doi: 10.1016/j.celrep.2023.112490. PMID: 37163374.

https://www.cell.com/cell-reports/pdf/S2211-1247(23)00501-6.pdf

(2) Lu, J.C., Piazza, T.M., Schuler, L.A. Proteasomes mediate prolactin-induced receptor down-regulation and fragment generation in breast cancer cells. J Biol Chem. 2005 Oct 7;280(40):33909-16. doi: 10.1074/jbc.M508118200. PMID: 16103113; PMCID: PMC1976473.

(6) Some figures and illustrations are of poor quality and were put together without paying attention to detail. For example, in Fig 5A, the GHR was cut off, possibly to omit other nonspecific bands, the WB images look 'washed out'. 5B, 5D: the labels are not in one line over the bars, and what is the point of showing all individual data points when the bar graphs with all annotations and SD lines are disappearing? As done for the y2A cells, the illustrations in 5B-5E should indicate what cell lines were used. No loading controls in Fig 5F, is there any protein in the first lane? No loading controls in Fig 6B and 6H.

We thank the reviewer for pointing this out. We have amended Fig. 5A to now show larger crops of the two GHR and PRLR Western Blot images and thus a greater range of proteins present in the extracts. Please note that the bands in the WBs other than what is identified as GHR and PRLR are non-specific and reflect roughly equivalent loading of protein in each lane.

We also made some changes to Figures 5B-5E.

(7) The proximity ligation method was not described in the M&M section of the manuscript.

We thank the reviewer for pointing this out. We have added a description of the PL method to the Methods section.

Reviewer #1 (Recommendations for the Authors):

A final suggestion for future investigations: Instead of focusing on the heteromer formation of the GHR/PRLR which both signal all through the same downstream effectors (JAK2, STAT5), it would have been more cancer-relevant, and perhaps even more interesting, to look for heteromers between the PRLR and receptors of the IL-6 family since it had been shown that PRL can stimulate STAT3, which is a unique feature of cancer cells. If that is the case, this would require a different modality of the interaction between different JAK kinases.

We highly appreciate the reviewer’s recommendation and hope to follow up on it in the near future.

Reviewer #2 (Public Review):

(1) I could not fully evaluate some of the data, mainly because several details on acquisition and analysis are lacking. It would be useful to know what the background signal was in dSTORM and how the authors distinguished the specific signal from unspecific background fluorescence, which can be quite prominent in these experiments. Typically, one would evaluate the signal coming from antibodies randomly bound to a substrate around the cells to determine the switching properties of the dyes in their buffer and the average number of localisations representing one antibody. This would help evaluate if GHR or PRLR appeared as monomers or multimers in the plasma membrane before stimulation, which is currently a matter of debate. It would also provide better support for the model proposed in Figure 8.

We are grateful for the reviewer’s comment. In our experience, the background signal is more relevant in dSTORM when imaging proteins that are located at deeper depths (> 3 μm) above the coverslip surface. In our experiments, cells are attached to the coverslip surface and the proteins being imaged are on the cell membrane. In addition, we employed dSTORM’s TIRF (total internal reflection fluorescence) microscopy mode to image membrane receptor proteins. TIRFM exploits the unique properties of an induced evanescent field in a limited specimen region immediately adjacent to the interface between two media having different refractive indices. It thereby dramatically reduces background by rejecting fluorescence from out-of-focus areas in the detection path and illuminating only the area right near the surface.

Having said that, a few other sources such as auto-fluorescence, scattering, and non-bleached fluorescent molecules close to and distant from the focal plane can contribute to the background signal. We tried to reduce auto-fluorescence by ensuring that cells are grown in phenol-red-free media, imaging is performed in STORM buffer which reduces autofluorescence, and our immunostaining protocol includes a quenching step aside from using blocking buffer with different serum, in addition to BSA. Moreover, we employed extensive washing steps following antibody incubations to eliminate non-specifically bound antibodies. Ensuring that the TIRF illumination field is uniform helps reduce scatter. Additionally, an extended bleach step prior to the acquisition of frames to determine localizations helped further reduce the probability of non-bleached fluorescent molecules.

In short, due to the experimental design we do not expect much background. However, in the future, we will address this concern and estimate background in a subtype dependent manner. To this end we will distinguish two types of background noise: (A) background with a small change between subsequent frames, which mainly consists of auto-fluorescence and non-bleached out-of-focus fluorescent molecules; and (B) background that changes every imaging frame, which is mainly from non-bleached fluorescent molecules near the focal plane. For type (A) background, temporal filters must be used for background estimation [1]; for type (B) background, low-pass filters (e.g., wavelet transform) should be used for background estimation [2].

(1) Hoogendoorn, Crosby, Leyton-Puig, Breedijk, Jalink, Gadella, and Postma (2014). The fidelity of stochastic single-molecule super-resolution reconstructions critically depends upon robust background estimation. Scientific reports, 4, 3854. https://doi.org/10.1038/srep03854

(2) Patel, Williamson, Owen, and Cohen (2021). Blinking statistics and molecular counting in direct stochastic reconstruction microscopy (dSTORM). Bioinformatics, Volume 37, Issue 17, September 2021, Pages 2730–2737, https://doi.org/10.1093/bioinformatics/btab136

(2) Since many of the findings in this work come from the evaluation of localisation clusters, an image showing actual localisations would help support the main conclusions. I believe that the dSTORM images in Figures 1 and 2 are density maps, although this was not explicitly stated. Alexa 568 and Alexa 647 typically give a very different number of localisations, and this is also dependent on the concentration of BME. Did the authors take that into account when interpreting the results and creating the model in Figures 2 and 8?

I believe that including this information is important as findings in this paper heavily rely on the number of localisations detected under different conditions.

Including information on proximity labelling and CRISPR/Cas9 in the methods section would help with the reproducibility of these findings by other groups.

Figures 1 and 2 show Gaussian interpolations of actual localizations, not density maps. Imaging captured the fluorophores’ blinking events and localizations were counted as true localizations, when at least 5 consecutive blinking events had been observed. Nikon software was used for Gaussian fitting. In other words, we show reconstructed images based on identifying true localizations using gaussian fitting and some strict parameters to identify true fluorophore blinking. This allowed us to identify true localizations with high confidence and generate a high-resolution image for membrane receptors.

Indeed, Alexa 568 and 647 give different numbers of localization. This is dependent on the intrinsic photo-physics of the fluorophores. Specifically, each fluorophore has a different duty cycle, switching cycle, and survival fraction. However, we note that we focused on capturing the relative changes in receptor numbers over time, before and after stimulation by ligands, not the absolute numbers of surface GHR and PRLR. We are not comparing the absolute numbers of localizations or drawing comparisons for localization numbers between 568 and 647. For all these different conditions/times, the photo-physics for a particular fluorophore remains the same. This allows us to make relative comparisons.

As far as the effect of BME is concerned, the concentration of mercaptoethanol needs to be carefully optimized, as too high a concentration can potentially quench the fluorescence or affect the overall stability of the sample. However, we are using an optimized concentration which has been previously validated across multiple STORM experiments. This makes the concerns relating to the concentration of BME irrelevant to the current experimental design. Besides, the concentration of BME is maintained across all experimental conditions.

We have added information regarding PL and CRISPR/Cas9 for generating hGHR KO and hPRLR KO cells in two new subsections to the Methods section.

Reviewer #2 (Recommendations for the authors):

In the methods please include:
(1) A section with details on proximity ligation assays.

We have added a description of the PL method to the Methods section.

(2) A section on CRISPR/Cas9 technology.

We have added two new sections on “Generating hGHR knockout and hPRLR knockout T47D cells” and “Design of sgRNAs for hGHR or hPRLR knockout” to the Methods section.

(3) List the precise composition of the buffer or cite the paper that you followed.

We used the buffer recipe described in this protocol [1] and have added the components with concentrations as well as the following reference to the manuscript.

(1) Beggs, R.R., Dean, W.F., Mattheyses, A.L. (2020). dSTORM Imaging and Analysis of Desmosome Architecture. In: Turksen, K. (eds) Permeability Barrier. Methods in Molecular Biology, vol 2367. Humana, New York, NY. https://doi.org/10.1007/7651_2020_325

(4) Exposure time used for image acquisition to put 40 000 frames in the context of total imaging time and clarify why you decided to take 40 000 images per channel.

Our Nikon Ti2 N-STORM microscope is equipped with an iXon DU-897 Ultra EMCCD camera from Andor (Oxford Instruments). According to the camera’s manufacturer, this camera platform uses a back-illuminated 512 x 512 frame transfer sensor and overclocks readout to 17 MHz, pushing speed performance to 56 fps (in full frame mode). We note that we always tried to acquire STORM images at the maximal frame rate. As for the exposure time, according to the manufacturer it can be as short as 17.8 ms. We would like to emphasize that we did not specify/alter the exposure time.

See also: https://andor.oxinst.com/assets/uploads/products/andor/documents/andor-ixon-ultra-emccd-specifications.pdf

The decision to take 40,000 images per frame was based on our intention to identify the true population of the molecules of interest that are localized and accurately represented in the final reconstruction image. The total number of frames depends on the sample complexity, density of sample labeling and desired resolution. We tested a range of frames between 20,000 and 60,000 and found for our experimental design and output requirements that 40,000 frames provided the best balance between achieving maximal resolution and desired localizations to make consistent and accurate localization estimates across different stimulation conditions compared to basal controls.

(5) The lasers used to switch Alexa 568 and Alexa 647. Were you alternating between the lasers for switching and imaging of dyes? Intermittent and continuous illumination will produce very different unspecific background fluorescence.

Yes, we used an alternating approach for the lasers exciting Alexa 647 and Alexa 568, for both switching and imaging of the dyes.

(6) A paragraph with a detailed description of methods used to differentiate the background fluorescence from the signal.

We have addressed the background fluorescence under Point 1 (Public Review). We have added a paragraph in the Methods section on this issue.

(7) Minor corrections to the text:

It appears as though there is a large difference in the expression level of GHR and PRLR in basal conditions in Figure 1. This can be due to the switching properties of the dyes, which is related to the amount of BME in the buffer, or it can be because there is indeed more PRL. Would the authors be able to comment on this?

We thank the reviewer for this suggestions. According to expression data available online there is indeed more PRLR than GHR in T47D cells. According to CellMiner [1], T47D cells have an RNA-Seq gene expression level log2(FPKM + 1) of 6.814 for PRLR, and 3.587 for GHR, strongly suggesting that there is more PRLR than GHR in basal conditions, matching the reviewer’s interpretation of our images in Fig. 1 (basal). However, we would advise against using STORM images for direct comparisons of receptor expression. First, with TIRF images, we are only looking at the membrane fraction (~150 nm close to the coverslip membrane interface) that is attached to the coverslip. Secondly, as discussed above, our data represent relative cell surface receptor levels that allow for comparison of different conditions (basal vs. stimulation) and does not represent absolute quantifications. Everything is relative and in comparison to controls.

Also, BME is not going to change the level of expression. The differences in growth factor expression as estimated by relative comparison can be attributed to the actual changes in growth factors and is not an artifact of the amount of BME in the buffer or the properties of dyes. These factors are maintained across all experimental conditions and do not influence the final outcome.

(1) https://discover.nci.nih.gov/cellminer/

(8) I would encourage the authors to use unspecific binding to characterize the signal coming from single antibodies bound to the substrate. This would provide a mean number of localizations that a single antibody generates. With this information, one can evaluate how many receptors there are per cluster, which would strengthen the findings and potentially provide additional support for the model presented in Figure 8. It would also explain why the distributions of localisations per cluster in Fig. 3B look very different for hGHR and hPRLR. As the authors point out in the discussion, the results on predimerization of these receptors in basal conditions are conflicting and therefore it is important to shed more light on this topic.

We thank the reviewer for this suggestions. While we are unable to perform this experiment at this stage, we will keep it in mind for future experiments.

(9) Minor corrections to the figures:

Figure 1:

In the legend, please say what representation was used. Are these density maps or another representation? Please provide examples of actual localisations (either as dots or crosses representing the peaks of the Gaussians). Most findings of this work rely on the characterisation of the clusters of localisations and therefore it is of essence to show what the clusters look like. This could potentially go to the supplemental info to minimise additional work. It's very hard to see the puncta in this figure.

If the authors created zoomed regions in each of the images (as in Figure 3), it would be much easier to evaluate the expression level and the extent of colocalisation. Halfway through GHR 3 min green pixels become grey, but this may be the issue with the document that was created. Please check. Either increase the font on the scale bars in this figure or delete it.

As described above, Figure 1 does not show density maps. Imaging captured the fluorophores’ blinking events and localizations were counted as true localizations, when at least 5 consecutive blinking events had been observed. Nikon software was used for Gaussian fitting and smoothing.

We have generated zoomed regions. In our files (original as well as pdf) we do not see pixels become grey. We increased the font size above one of the scale bars and removed all others.

Figure 3:

In A, the GHR clusters are colour coded but PRLR are not. Are both DBSCN images? Explain the meaning of colour coding or show it as black and white. Was brightness also increased in the PRLR image? The font on the scale bars is too small. In B, right panels, the font on the axes is too small. In the figure legend explain the meaning of 33.3 and 16.7

In our document, both GHR and PRLR are color coded but the hGHR clusters are certainly bigger and therefore appear brighter than the hPRLR clusters. Both are DBSCAN images. The color coding allows to distinguish different clusters (there is no other meaning). We have kept the color-coding but have added a sentence to the caption addressing this. Brightness was increased in both images of Panel B equally. 33.3 and 16.7 are the median cluster sizes. We have added a sentence to the caption explaining this. We have increased the font on the axes in B (right panels).

Figure 4:

I struggled to see any colocalization in the 2nd and the 3rd image. Please show zoomed-in sections. In the panels B and C, the data are presented as fractions. Is this per cell? My interpretation is that ~80% of PRL clusters also contain GHR.

Is this in agreement with Figures 1 and 2? In Figure 1, PRL 3 min, Merge, colocalization seems much smaller. Could the authors give the total numbers of GHR and PRLR from which the fractions were calculated at least in basal conditions?

We have provided zoom-in views. As for panels B and C, fractions are number of clusters containing both receptors divided by the total number of clusters. We used the same strategy that we had used for calculating the localization changes: We randomly selected 4 ROIs (regions of interest) per cell to calculate fractions and then calculated the average of three different cells from independently repeated experiments. We did not calculate total numbers of GHR/PRLR. The numbers are fractions of cluster numbers.

Moreover, the reviewer interprets results in panels B and C that ~80% of PRLR clusters also contain GHR. We assume the reviewer refers to Basal state. Now, the reviewer’s interpretation is not correct for the following reason: ~80% of clusters have both receptors. How many of the remaining (~20%) clusters have only PRLR or only GHR is not revealed in the panels. Only if 100% of clusters have PRLR, we can conclude that 80% of PRLR clusters also contain GHR.

Also, while Figures 1 and 2 show localization based on dSTORM images, Figure 3 indicates and quantifies co-localization based on proximity ligation assays following DBSCAN analysis using Clus-DoC. We do not think that the results are directly comparable.

Reviewer #3 (Public Review):

(1) The manuscript suffers from a lack of detail, which in places makes it difficult to evaluate the data and would make it very difficult for the results to be replicated by others. In addition, the manuscript would very much benefit from a full discussion of the limitations of the study. For example, the manuscript is written as if there is only one form of the PRLR while the anti-PRLR antibody used for dSTORM would also recognize the intermediate form and short forms 1a and 1b on the T47D cells. Given the very different roles of these other PRLR forms in breast cancer (Dufau, Vonderhaar, Clevenger, Walker and other labs), this limitation should at the very least be discussed. Similarly, the manuscript is written as if Jak2 essentially only signals through STAT5 but Jak2 is involved in multiple other signaling pathways from the multiple PRLRs, including the long form. Also, while there are papers suggesting that PRL can be protective in breast cancer, the majority of publications in this area find that PRL promotes breast cancer. How then would the authors interpret the effect of PRL on GHR in light of all those non-protective results? [Check papers by Hallgeir Rui]

We thank the reviewer for such thoughtful comments. We have added a paragraph in the Discussion section on the limitations of our study, including sole focus on T47D and γ2A-JAK2 cells and lack of PRLR isoform-specific data. Also, we are now mentioning that these isoforms play different roles in breast cancer, citing papers by Dufau, Vonderhaar, Clevenger, and Walker labs.

We did not mean to imply that JAK2 signals only via STAT5 or by only binding the long form. We have made this point clear in the Introduction as well as in our revised Discussion section. Moreover, we have added information and references on JAK2 signaling and PRLR isoform specific signaling.

In our Discussions section we are also mentioning the findings that PRL is promoting breast cancer. We would like to point out that it is well perceivable that PRL is protective in BC by reducing surface hGHR availability but that this effect may depend on JAK2 levels as well as on expression levels of other kinases that competitively bind Box1 and/or Box2 [1]. Besides, could it not be that PRL’s effect is BC stage dependent? In any case, we have emphasized the speculative nature of our statement.

(1) Chhabra, Y., Seiffert, P., Gormal, R.S., et al. Tyrosine kinases compete for growth hormone receptor binding and regulate receptor mobility and degradation. Cell Rep. 2023;42(5):112490. doi: 10.1016/j.celrep.2023.112490. PMID: 37163374.

Reviewer #3 (Recommendations for the authors):

Points for improvement of the manuscript:

(1) Method details -

a) "we utilized CRISPR/Cas9 to generate hPRLR knockout T47D cells ......" Exactly how? Nothing is said under methods. Can we be sure that you knocked out the whole gene?

We have addressed this point by adding two new sections on “Generating hGHR knockout and hPRLR knockout T47D cells” and “Design of sgRNAs for hGHR or hPRLR knockout” to the Methods section.

b) Some of the Western blots are missing mol wt markers. How specific are the various antibodies used for Westerns? For example, the previous publications are quoted as providing characterization of the antibodies also seem to use just band cutouts and do not show the full molecular weight range of whole cell extracts blotted. Anti-PRLR antibodies are notoriously bad and so this is important.

There is an antibody referred to in Figure 5 that is not listed under "antibodies" in the methods.

We have modified Figure 5a, showing the entire gel as well as molecular weight markers. As for specificity of our antibodies, we used monoclonal antibodies Anti-GHR-ext-mAB 74.3 and Anti-PRLR-ext-mAB 1.48, which have been previously tested and used. In addition, we did our own control experiments to ensure specificity. We have added some of our many control results as Supplementary Figures S2 and S3.

We thank the reviewer for noticing the missing antibody in the Methods section. We have now added information about this antibody.

c) There is no description of the proximity ligation assay.

We have addressed this by adding a paragraph on PLA in the Methods section.

d) What is the level of expression of GHR, PRLR, and Jak2 in the gamma2A-JAK2 cells compared to the T47D cells? Artifacts of overexpression are always a worry.

γ2A-JAK2 cell series are over-expressing the receptors. That’s the reason we did not only rely on the observation in γ2A-JAK2 cell lines but also did the experiment in T47D cell lines.

e) There are no concentrations given for components of the dSTORM imaging buffer. On line 380, I think the authors mean alternating lasers not alternatively.

Thank you. Indeed, we meant alternating lasers. We are referring to [1] (the protocol we followed) for information on the imaging buffer.

(1) Beggs, R.R., Dean, W.F., Mattheyses, A.L. (2020). dSTORM Imaging and Analysis of Desmosome Architecture. In: Turksen, K. (eds) Permeability Barrier. Methods in Molecular Biology, vol 2367. Humana, New York, NY. https://doi.org/10.1007/7651_2020_325

f) In general, a read-through to determine whether there is enough detail for others to replicate is required. 4% PFA in what? Do you mean PBS or should it be Dulbecco's PBS etc., etc.?

We prepared a 4% PFA in PBS solution. We mean Dulbecco's PBS.

(2) There are no controls shown or described for the dSTORM. For example, non-specific primary antibody and second antibodies alone for non-specific sticking. Do the second antibodies cross-react with the other primary antibody? Is there only one band when blotting whole cell extracts with the GHR antibody so we can be sure of specificity?

We used monoclonal antibodies Anti-GHR-ext-mAB 74.3 and Anti-PRLR-ext-mAB 1.48 (but also tested several other antibodies). While these antibodies have been previously tested and used, we performed additional control experiments to ensure specificity of our primary antibodies and absence of non-specific binding of our secondary antibodies. We have added some of our many control results as Supplementary Figures S2 and S3.

(3) Writing/figures-

a) As discussed in the public review regarding different forms of the PRLR and the presence of other Jak2-dependent signaling

We have added paragraphs on PRLR isoforms and other JAK2-dependent signaling pathways to the Introduction. Also, we have added a paragraph on PRLR isoforms (in the context of our findings) to the Discussion section.

b) What are the units for figure 3c and d?

The figures show numbers of localizations (obtained from fluorophore blinking events). In the figure caption to 3C and 3D, we have specified the unit (i.e. counts).

c) The wheat germ agglutinin stains more than the plasma membrane and so this sentence needs some adjustment.

We thank the reviewer for this comment. We have rephrased this sentence (see caption to Fig. 4).

d) It might be better not to use the term "downregulation" since this is usually associated with expression and not internalization.

While we understand the reviewer’s discomfort with the use of the word “downregulation”, we still think that it best describes the observed effect. Moreover, we would like to note that in the field of receptorology “downregulation” is a specific term for trafficking of cell surface receptors in response to ligands. That said, to address the reviewer’s comment, we are now using the terms “cell surface downregulation” or “downregulation of cell surface [..] receptor” throughout the manuscript in order to explicitly distinguish it from gene downregulation.

e) Line 420 talks about "previous work", a term that usually indicates work from the same lab. My apologies if I am wrong, but the reference doesn't seem to be associated with the authors.

At the end of the sentence containing the phrase “previous work”, we are referring to reference [57], which has Dr. Stuart Frank as senior and corresponding author. Dr. Frank is also a co-corresponding author on this manuscript. While in our opinion, “previous work” does not imply some sort of ownership, we are happy to confirm that one of us was responsible for the work we are referencing.

Reviewing Editor's recommendations:

The reviewers have all provided a very constructive assessment of the work and offered many useful suggestions to improve the manuscript. I'd advise thinking carefully about how many of these can be reasonably addressed. Most will not require further experiments. I consider it essential to improve the methods to ensure others could repeat the work. This includes adding methods for the PLA and including detail about the controls for the dSTORM. The reviewers have offered suggestions about types of controls to include if these have not already been done.

We thank the editor for their recommendations. We have revised the methods section, which now includes a paragraph on PLA as well as on CRISPR/Cas9-based generation of mutant cell lines. We have also added information on the dSTORM buffer to the manuscript. Data of controls indicating antibody specificity (using confocal microscopy) have been added to the manuscript’s supplementary material (see Fig. S2 and S3).

I agree with the reviewers that the different isoforms of the prolactin receptor need to be considered. I think this could be done as an acknowledgment and point of discussion.

We have revised the discussions section and have added a paragraph on the different PRLR isoforms, among others.

For Figure 2E, make it clear in the figure (or at least in legend) that the middle line is the basal condition.

We thank the editor for their comment. We have made changes to Fig 2E and have added a sentence to the legend making it clear that the middle depicts the basal condition.

My biggest concern overall was the fact that this is all largely conducted in a single cell line. This was echoed by at least one of the reviewers. I wonder if you have replicated this in other breast cancer cell lines or mammary epithelial cells? I don't think this is necessary for the current manuscript but would increase confidence if available.

We thank the editor for their comment and fully agree with their assessment. Unfortunately, we have not replicated these experiments in other BC cell lines nor mammary epithelial cells but would certainly want to do so in the near future.