Author response:

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

Reviewer #1 (Public review):

Summary:

This work investigated the role of CXXC-finger protein 1 (CXXC1) in regulatory T cells. CXXC1-bound genomic regions largely overlap with Foxp3-bound regions and regions with H3K4me3 histone modifications in Treg cells. CXXC1 and Foxp3 interact with each other, as shown by co-immunoprecipitation. Mice with Treg-specific CXXC1 knockout (KO) succumb to lymphoproliferative diseases between 3 to 4 weeks of age, similar to Foxp3 KO mice. Although the immune suppression function of CXXC1 KO Treg is comparable to WT Treg in an in vitro assay, these KO Tregs failed to suppress autoimmune diseases such as EAE and colitis in Treg transfer models in vivo. This is partly due to the diminished survival of the KO Tregs after transfer. CXXC1 KO Tregs do not have an altered DNA methylation pattern; instead, they display weakened H3K4me3 modifications within the broad H3K4me3 domains, which contain a set of Treg signature genes. These results suggest that CXXC1 and Foxp3 collaborate to regulate Treg homeostasis and function by promoting Treg signature gene expression through maintaining H3K4me3 modification.

Strengths:

Epigenetic regulation of Treg cells has been a constantly evolving area of research. The current study revealed CXXC1 as a previously unidentified epigenetic regulator of Tregs. The strong phenotype of the knockout mouse supports the critical role CXXC1 plays in Treg cells. Mechanistically, the link between CXXC1 and the maintenance of broad H3K4me3 domains is also a novel finding.

Weaknesses:

(1) It is not clear why the authors chose to compare H3K4me3 and H3K27me3 enriched genomic regions. There are other histone modifications associated with transcription activation or repression. Please provide justification.

Thank you for highlighting this important point. We chose to focus on H3K4me3 and H3K27me3 enriched genomic regions because these histone modifications are well-characterized markers of transcriptional activation and repression, respectively. H3K4me3 is predominantly associated with active promoters, while H3K27me3 marks repressed chromatin states, particularly in the context of gene regulation at promoters. This duality provides a robust framework for investigating the balance between transcriptional activation and repression in Treg cells. While histone acetylation, such as H3K27ac, is linked to enhancer activity and transcriptional elongation, our focus was on promoter-level regulation, where H3K4me3 and H3K27me3 are most relevant. Although other histone modifications could provide additional insights, we chose to focus on these two to maintain clarity and feasibility in our analysis. We have revised the text accordingly; please refer to Page 18, lines 353-356.

(2) It is not clear what separates Clusters 1 and 3 in Figure 1C. It seems they share the same features.

We apologize for not clarifying these clusters clearly. Cluster 1 and 3 are both H3K4me3 only group, with H3K4me3 enrichment and gene expression levels being higher in Cluster 1. At first, we divided the promoters into four categories because we wanted to try to classify them into four categories: H3K4me3 only, H3K27me3 only, H3K4me3-H3K27me3 co-occupied, and None. However, in actual classification, we could not distinguish H3K4me3-H3K27me3 co-occupied group. Instead, we had two categories of H3K4me3 only, with cluster 1 having a higher enrichment level for H3K4me3 and gene expression levels.

(3) The claim, "These observations support the hypothesis that FOXP3 primarily functions as an activator by promoting H3K4me3 deposition in Treg cells." (line 344), seems to be a bit of an overstatement. Foxp3 certainly can promote transcription in ways other than promoting H3K3me3 deposition, and it also can repress gene transcription without affecting H3K27me3 deposition. Therefore, it is not justified to claim that promoting H3K4me3 deposition is Foxp3's primary function.

Thank you for your insightful feedback. We agree that the statement in line 344 may have overstated the role of FOXP3 in promoting H3K4me3 deposition as its primary function. As you pointed out, FOXP3 is indeed a multifaceted transcription factor that regulates gene expression through various mechanisms. It can promote transcription independent of H3K4me3 deposition, as well as repress transcription without directly influencing H3K27me3 levels.

To more accurately reflect the broader regulatory functions of FOXP3, we have revised the manuscript. The updated text (Page 19, lines 385-388) now reads:

"These findings collectively support the conclusion that FOXP3 contributes to transcriptional activation in Treg cells by promoting H3K4me3 deposition at target loci, while also regulating gene expression directly or indirectly through other epigenetic modifications.

(4) For the in vitro suppression assay in Figure S4C, and the Treg transfer EAE and colitis experiments in Figure 4, the Tregs should be isolated from Cxxc1 fl/fl x Foxp3 cre/wt female heterozygous mice instead of Cxxc1 fl/fl x Foxp3 cre/cre (or cre/Y) mice. Tregs from the homozygous KO mice are already activated by the lymphoproliferative environment and could have vastly different gene expression patterns and homeostatic features compared to resting Tregs. Therefore, it's not a fair comparison between these activated KO Tregs and resting WT Tregs.

Thank you for raising this insightful point regarding the potential activation status of Treg cells in homozygous knockout mice. To address this concern, we performed additional experiments using Treg cells isolated from Foxp3^Cre/+Cxxc1^fl/fl (hereafter referred to as “het-KO”) female mice and their littermate controls, Foxp3^Cre/+Cxxc1^fl/+ (referred to as “het-WT”) mice.

The results of these new experiments are now included in the manuscript (Page25, lines 507–509, Figure 6E and Figure S6A-E):

(1) In the in vitro suppression assay, Treg cells from het-KO mice exhibited reduced suppressive function compared to het-WT Treg cells. This finding underscores the intrinsic defect in Treg cells suppressive capacity attributable to the loss of one Cxxc1 allele.

(2) In the experimental autoimmune encephalomyelitis (EAE) model, Treg cells isolated from het-KO mice also demonstrated impaired suppressive function.

(5) The manuscript didn't provide a potential mechanism for how CXXC1 strengthens broad H3K4me3-modified genomic regions. The authors should perform Foxp3 ChIP-seq or Cut-n-Taq with WT and Cxxc1 cKO Tregs to determine whether CXXC1 deletion changes Foxp3's binding pattern in Treg cells.

Thank you for raising this important point. To address your suggestion, we performed CUT&Tag experiments and found that Cxxc1 deletion does not alter FOXP3 binding patterns in Treg cells. Most FOXP3-bound regions in WT Treg cells were similarly enriched in KO Treg cells, indicating that Cxxc1 deficiency does not impair FOXP3’s DNA-binding ability. These results have been added to the revised manuscript (Page 28, lines 567-575, Figure S8A-B) and are further discussed in the Discussion (Pages 28-29, lines 581-587).

Reviewer #2 (Public review):

FOXP3 has been known to form diverse complexes with different transcription factors and enzymes responsible for epigenetic modifications, but how extracellular signals timely regulate FOXP3 complex dynamics remains to be fully understood. Histone H3K4 tri-methylation (H3K4me3) and CXXC finger protein 1 (CXXC1), which is required to regulate H3K4me3, also remain to be fully investigated in Treg cells. Here, Meng et al. performed a comprehensive analysis of H3K4me3 CUT&Tag assay on Treg cells and a comparison of the dataset with the FOXP3 ChIP-seq dataset revealed that FOXP3 could facilitate the regulation of target genes by promoting H3K4me3 deposition.

Moreover, CXXC1-FOXP3 interaction is required for this regulation. They found that specific knockdown of Cxxc1 in Treg leads to spontaneous severe multi-organ inflammation in mice and that Cxxc1-deficient Treg exhibits enhanced activation and impaired suppression activity. In addition, they have also found that CXXC1 shares several binding sites with FOXP3 especially on Treg signature gene loci, which are necessary for maintaining homeostasis and identity of Treg cells.

The findings of the current study are pretty intriguing, and it would be great if the authors could fully address the following comments to support these interesting findings.

Major points:

(1) There is insufficient evidence in the first part of the Results to support the conclusion that "FOXP3 functions as an activator by promoting H3K4Me3 deposition in Treg cells". The authors should compare the results for H3K4Me3 in FOXP3-negative conventional T cells to demonstrate that at these promoter loci, FOXP3 promotes H3K4Me3 deposition.

Thank you for this insightful comment. We have already performed additional experiments comparing H3K4Me3 levels between FOXP3-positive Treg cells and FOXP3-negative conventional T cells (Tconv). Please refer to Pages 18, lines 361-368, and Figure 1C and Figure S1C for the results. Our results show that H3K4Me3 abundance is higher at many Treg-specific gene loci in Treg cells compared to Tconv cells. This supports our conclusion that FOXP3 promotes H3K4Me3 deposition at these loci.

(2) In Figure 3 F&G, the activation status and IFNγ production should be analyzed in Treg cells and Tconv cells separately rather than in total CD4+ T cells. Moreover, are there changes in autoantibodies and IgG and IgE levels in the serum of cKO mice?

Thank you for your valuable suggestions. In response to your comment, we reanalyzed the data in Figures 3F and 3G to assess the activation status and IFN-γ production in Tconv cells. The updated analysis revealed that Cxxc1 deletion in Treg cells leads to increased activation and IFN-γ production in Tconv cells. Additionally, we corrected the analysis of IL-17A and IL-4 expression, which were upregulated in Tconv cells. These updated results are now included in the revised manuscript (Page 21, lines 429-431, Figure 3I and Figure S3E-F).

Additionally, we examined autoantibodies and immunoglobulin levels in the serum of Cxxc1 cKO mice. Our data show a significant increase in serum IgG levels, accompanied by elevated IgG autoantibodies, indicating heightened autoimmune responses. In contrast, serum IgE levels remained largely unchanged. The results are detailed in the revised manuscript (Page 21, lines 421-423, Figure 3E and Figure S3B).

(3) Why did Cxxc1-deficient Treg cells not show impaired suppression than WT Treg during in vitro suppression assay, despite the reduced expression of Treg cell suppression assay -associated markers at the transcriptional level demonstrated in both scRNA-seq and bulk RNA-seq?

Thank you for your thoughtful comment. The absence of impaired suppression in Cxxc1-deficient Treg cells from homozygous knockout (KO) mice during the in vitro suppression assay, despite the reduced expression of Treg-associated markers at the transcriptional level (as demonstrated by scRNA-seq), can likely be explained by the activated state of these Treg cells. In homozygous KO mice, Treg cells are already activated due to the lymphoproliferative environment, resulting in gene expression patterns that differ from those of resting Treg cells. This pre-activation may obscure the effect of Cxxc1 deletion on their suppressive function in vitro.

To address this limitation, we used heterozygous Foxp3^Cre/+Cxxc1^fl/fl (het-KO) female mice, along with their littermate controls, Foxp3^Cre/+Cxxc1^fl/+ (het-WT) mice. In these heterozygous mice, we observed an impairment in Treg cell suppressive function in vitro, which was accompanied by the downregulation of several key Treg-associated genes, as confirmed by RNA-Seq analysis.

These updated findings, based on the use of het-KO mice, are now incorporated into the revised manuscript (Page 25, lines 507–509, Figure 6E).

(4) Is there a disease in which Cxxc1 is expressed at low levels or absent in Treg cells? Is the same immunodeficiency phenotype present in patients as in mice?

This is indeed a very meaningful and intriguing question, and we are equally interested in understanding whether low or absent Cxxc1 expression in Treg cells is associated with any human diseases. However, despite an extensive review of the literature and available data, we found no reports linking Cxxc1 deficiency in Treg cells to immunodeficiency phenotypes in patients comparable to those observed in mice.

Reviewer #3 (Public review):

In the report entitled "CXXC-finger protein 1 associates with FOXP3 to stabilize homeostasis and suppressive functions of regulatory T cells", the authors demonstrated that Cxxc1-deletion in Treg cells leads to the development of severe inflammatory disease with impaired suppressive function. Mechanistically, CXXC1 interacts with Foxp3 and regulates the expression of key Treg signature genes by modulating H3K4me3 deposition. Their findings are interesting and significant. However, there are several concerns regarding their analysis and conclusions.

Major concerns:

(1) Despite cKO mice showing an increase in Treg cells in the lymph nodes and Cxxc1-deficient Treg cells having normal suppressive function, the majority of cKO mice died within a month. What causes cKO mice to die from severe inflammation?

Considering the results of Figures 4 and 5, a decrease in the Treg cell population due to their reduced proliferative capacity may be one of the causes. It would be informative to analyze the population of tissue Treg cells.

Thank you for your insightful observation regarding the mortality of cKO mice despite increased Treg cells in lymph nodes and the normal suppressive function of Cxxc1-deficient Treg cells.

As suggested, we hypothesized that the reduction of tissue-resident Treg cells could be a key factor. Additional experiments revealed a significant decrease in Treg cell populations in the small intestine lamina propria (LPL), liver, and lung of cKO mice. These findings highlight the critical role of tissue-resident Treg cells in preventing systemic inflammation.

This reduction aligns with Figures 4 and 5, which demonstrate impaired proliferation and survival of Cxxc1-deficient Treg cells. Together, these defects lead to insufficient Treg populations in peripheral tissues, escalating localized inflammation into systemic immune dysregulation and early mortality.

These additional results have been incorporated into the revised manuscript (Page21, lines 424-427, Figure 3G and Figure S3C).

(2) In Figure 5B, scRNA-seq analysis indicated that the Mki67+ Treg subset is comparable between WT and Cxxc1-deficient Treg cells. On the other hand, FACS analysis demonstrated that Cxxc1-deficient Treg shows less Ki-67 expression compared to WT in Figure 5I. The authors should explain this discrepancy.

Thank you for pointing out the apparent discrepancy between the scRNA-seq and FACS analyses regarding Ki-67 expression in Cxxc1-deficient Treg cells.

In Figure 5B, the scRNA-seq analysis identified the Mki67+ Treg subset as comparable between WT and Cxxc1-deficient Treg cells. This finding reflects the overall proportion of cells expressing Mki67 transcripts within the Treg population. In contrast, the FACS analysis in Figure 5I specifically measures Ki-67 protein levels, revealing reduced expression in Cxxc1-deficient Treg cells compared to WT.

To resolve this discrepancy, we performed additional analyses of the scRNA-seq data to directly compare the expression levels of Mki67 mRNA between WT and Cxxc1-deficient Treg cells. The results revealed a consistent reduction in Mki67 transcript levels in Cxxc1-deficient Treg cells, aligning with the reduced Ki-67 protein levels observed by FACS.

These new analyses have been included in the revised manuscript (Author response image 1) to clarify this point and demonstrate consistency between the scRNA-seq and FACS data.

Author response image 1.

Violin plots displaying the expression levels of Mki67 in T_reg cells from Foxp3^cre and Foxp3^creCxxc1^fl/fl mice.

In addition, the authors concluded on line 441 that CXXC1 plays a crucial role in maintaining Treg cell stability. However, there appears to be no data on Treg stability. Which data represent the Treg stability?

Thank you for your valuable comment. We agree that our wording in line 441 may have been too conclusive. Our data focus on the impact of Cxxc1 deficiency on Treg cell homeostasis and transcriptional regulation, rather than directly measuring Treg cell stability. Specifically, the downregulation of Treg-specific suppressive genes and upregulation of pro-inflammatory markers suggest a shift in Treg cell function, which points to disrupted homeostasis rather than stability.

We have revised the manuscript to clarify that CXXC1 plays a crucial role in maintaining Treg cell function and homeostasis, rather than stability (Page 24, lines 489-491).

(3) The authors found that Cxxc1-deficient Treg cells exhibit weaker H3K4me3 signals compared to WT in Figure 7. This result suggests that Cxxc1 regulates H3K4me3 modification via H3K4 methyltransferases in Treg cells. The authors should clarify which H3K4 methyltransferases contribute to the modulation of H3K4me3 deposition by Cxxc1 in Treg cells.

We appreciate the reviewer’s insightful comment regarding the role of H3K4 methyltransferases in regulating H3K4me3 deposition by CXXC1 in Treg cells.

CXXC1 has been reported to function as a non-catalytic component of the Set1/COMPASS complex, which includes the H3K4 methyltransferases SETD1A and SETD1B—key enzymes responsible for H3K4 trimethylation(1-4). Based on these findings, we propose that CXXC1 modulates H3K4me3 levels in Treg cells by interacting with and stabilizing the activity of the Set1/COMPASS complex.

These revisions are further discussed in the Discussion (Page 30-31, lines 624-632).

Furthermore, it would be important to investigate whether Cxxc1-deletion alters Foxp3 binding to target genes.

Thank you for raising this important point. To address your suggestion, we performed CUT&Tag experiments and found that Cxxc1 deletion does not alter FOXP3 binding patterns in Treg cells. Most FOXP3-bound regions in WT Treg cells were similarly enriched in KO Treg cells, indicating that Cxxc1 deficiency does not impair FOXP3’s DNA-binding ability. These results have been added to the revised manuscript (Page 28, lines 567-575, Figure S8A-B) and are further discussed in the Discussion (Pages 28-29, lines 581-587).

(4) In Figure 7, the authors concluded that CXXC1 promotes Treg cell homeostasis and function by preserving the H3K4me3 modification since Cxxc1-deficient Treg cells show lower H3K4me3 densities at the key Treg signature genes. Are these Cxxc1-deficient Treg cells derived from mosaic mice? If Cxxc1-deficient Treg cells are derived from cKO mice, the gene expression and H3K4me3 modification status are inconsistent because scRNA-seq analysis indicated that expression of these Treg signature genes was increased in Cxxc1-deficient Treg cells compared to WT (Figure 5F and G).

Thank you for your insightful comment. To clarify, the Cxxc1-deficient Treg cells analyzed for H3K4me3 modifications in Figure 7 were derived from Cxxc1 conditional knockout (cKO) mice, not mosaic mice.

Regarding the apparent inconsistency between reduced H3K4me3 levels and the increased expression of Treg signature genes observed in scRNA-seq analysis (Figure 5F and G), we believe this discrepancy can be attributed to distinct mechanisms regulating gene expression. H3K4me3 is an epigenetic mark that facilitates chromatin accessibility and transcriptional regulation, reflecting upstream chromatin dynamics. However, gene expression levels are influenced by a combination of factors, including transcriptional activators, downstream compensatory mechanisms, and the inflammatory environment in cKO mice.

The upregulation of Treg signature genes in scRNA-seq data likely reflects an activated or pro-inflammatory state of Cxxc1-deficient Treg cells in response to systemic inflammation, as previously described in the manuscript. This contrasts with the intrinsic reduction in H3K4me3 levels at these loci, indicating a loss of epigenetic regulation by CXXC1.

To further support this interpretation, RNA-seq analysis of Treg cells from Foxp3^Cre/+ Cxxc1^fl/fl (“het-KO”) and their littermate Foxp3^Cre/+ Cxxc1^fl/+ (“het-WT”) female mice (Figure S6C) revealed a significant reduction in key Treg signature genes such as Icos, Ctla4, Tnfrsf18, and Nt5e in het-KO Treg cells. These results align with the diminished H3K4me3 modifications observed in cKO Treg cells, further underscoring the role of CXXC1 as an epigenetic regulator.

In summary, while the gene expression changes observed in scRNA-seq may reflect adaptive responses to inflammation, the reduced H3K4me3 modifications directly highlight the critical role of CXXC1 in maintaining the epigenetic landscape essential for Treg cell homeostasis and function.

Recommendations for the authors:

Reviewer #1 (Recommendations for the authors):

In Figure 7E, the y-axis scale for H3K4me3 peaks at the Ctla4 locus should be consistent between WT and cKO samples.

We thank the reviewer for pointing out the inconsistency in the y-axis scale for the H3K4me3 peaks at the Ctla4 locus in Figure 7E. We have carefully revised the figure to ensure that the y-axis scale is now consistent between the WT and cKO samples.

We appreciate the reviewer’s attention to this detail, as it enhances the rigor of the data presentation. Please find the updated Figure 7E in the revised manuscript.

Reviewer #2 (Recommendations for the authors):

In lines 455 and 466, the name of Treg signature markers validated by flow cytometry should be written as protein name and capitalized.

Thank you for pointing this out. We have carefully reviewed lines 455 and 466 and have revised the text to ensure that the Treg signature markers validated by flow cytometry are referred to using their protein names, with proper capitalization.

Reviewer #3 (Recommendations for the authors):

(1) On line 431, "Cxxc1-deficient cells" should be Cxxc1-deficient Treg cells".

We thank the reviewer for highlighting this oversight. On line 431, we have revised "Cxxc1-deficient cells" to "Cxxc1-deficient Treg cells" to provide a more accurate and specific description. We appreciate the reviewer's attention to detail, as this correction improves the precision of our manuscript.

(2) In Figure 4H, negative values should be removed from the y-axis.

Thank you for your observation. We have revised Figure 4H to remove the negative values from the y-axis, as requested. This adjustment ensures a more accurate and meaningful representation of the data.

(3) It is better to provide the lists of overlapping genes in Figure 7C.

Thank you for your suggestion. We agree that providing the lists of overlapping genes in Figure 7C would enhance the clarity and reproducibility of the results. We have now included the gene lists as supplementary information (Supplementary Table 3) accompanying Figure 7C.

(1) Lee, J. H. & Skalnik, D. G. CpG-binding protein (CXXC finger protein 1) is a component of the mammalian set1 histone H3-Lys4 methyltransferase complex, the analogue of the yeast Set1/COMPASS complex. Journal of Biological Chemistry 280, 41725-41731, doi:10.1074/jbc.M508312200 (2005).

(2) Thomson, J. P., Skene, P. J., Selfridge, J., Clouaire, T., Guy, J., Webb, S., Kerr, A. R. W., Deaton, A., Andrews, R., James, K. D., Turner, D. J., Illingworth, R. & Bird, A. CpG islands influence chromatin structure via the CpG-binding protein Cfp1. Nature 464, 1082-U1162, doi:10.1038/nature08924 (2010).

(3) Shilatifard, A. in Annual Review of Biochemistry, Vol 81 Vol. 81 Annual Review of Biochemistry (ed R. D. Kornberg)  65-95 (2012).

(4) Brown, D. A., Di Cerbo, V., Feldmann, A., Ahn, J., Ito, S., Blackledge, N. P., Nakayama, M., McClellan, M., Dimitrova, E., Turberfield, A. H., Long, H. K., King, H. W., Kriaucionis, S., Schermelleh, L., Kutateladze, T. G., Koseki, H. & Klose, R. J. The SET1 Complex Selects Actively Transcribed Target Genes via Multivalent Interaction with CpG Island Chromatin. Cell Reports 20, 2313-2327, doi:10.1016/j.celrep.2017.08.030 (2017).

Author response:

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

Reviewer 1 (Public Comments):

(1) The central concern for this manuscript is the apparent lack of reproducibility. The way the authors discuss the issue (lines 523-554) it sounds as though they are unable to reproduce their initial results (which are reported in the main text), even when previous versions of AlphaFold2 are used. If this is the case, it does not seem that AlphaFold can be a reliable tool for predicting antibody-peptide interactions.

The driving point behind the multiple sequence alignment (MSA) discussion was indeed to point out that AlphaFold2 (AF2) performance when predicting scFv:peptide complexes is highly dependent upon the MSA, but that is a function of MSA generation algorithm (MMseqs2, HHbiltz, jackhmmer, hhsearch, kalign, etc) and sequence databases, and less an intrinsic function of AF2. It is important to report MSA-dependent performance precisely because this results in changing capabilities with respect to peptide prediction.

Performance also significantly varies with the target peptide and scFv framework changes. By reporting the varying success rates (as a function of MSA, peptide target, and framework changes) we aim to help future researchers craft modified algorithms that can achieve increased reliability at protein-peptide binding predictions. Ultimately, tracking down how MSA generation details vary results (especially when the MSA’s are hundreds long) is significantly outside the scope of this paper. Our goal for this paper was to show a general method for identification of linear antibody epitopes using only sequence information, and future work by us or others should focus on optimization of the process. 

(2) Aside from the fundamental issue of reproducibility, the number of validating tests is insufficient to assess the ability of AlphaFold to predict antibody-peptide interactions. Given the authors' use of AlphaFold to identify antibody binding to a linear epitope within a whole protein (in the mBG17:SARS-Cov-2 nucleocapsid protein interaction), they should expand their test set well beyond Myc- and HA-tags using antibody-antigen interactions from existing large structural databases.

Performing the calculations at the scale that the reviewer is requesting is not feasible at this time. We showed in this manuscript that we were able to predict 3 of 3 epitopes, including one antigen and antibody pair that have not been deposited into the PDB with no homologs. While we feel that an N=3 is acceptable to introduce this method to the scientific community, we will consider adding more examples of success and failure in the future to optimize and refine the method as computational resources become available. Notably, future efforts that attempt high-throughput predictions of this class using existing databases should take particular care to avoid contamination.

(3) As discussed in lines 358-361, the authors are unsure if their primary control tests (antibody binding to Myc-tag and HA-tag) are included in the training data. Lines 324-330 suggest that even if the peptides are not included in the AlphaFold training data because they contain fewer than 10 amino acids, the antibody structures may very well be included, with an obvious "void" that would be best filled by a peptide. The authors must confirm that their tests are not included in the AlphaFold training data, or re-run the analysis with these templates removed.

First, we address the simpler question of templates.

The reruns of AF2 with the local 2022 rebuild, the most reproducible method used with results most on par with the MMSEQS server in the Fall of 2022, were run without templates. This is because the MSA was generated locally; no templates were matched and generated locally. The only information passed then was the locally generated MSA, and the fasta sequence of the unchanging scFv and the dynamic epitope sequence. Because of how well this performed despite the absence of templates, we can confidently say the inclusion of the template flag is not significant with respect to how universally accurately PAbFold can identify the correct epitope. 

Second, we can partially address the question of whether the AlphaFold models had access to models suitable, in theory, for “memorization” of pertinent structural details. 

With respect to tracking the exact role and inclusion of specific PDB entries, the AF2 paper provides the following:

“Structures from the PDB were used for training and as templates (https://www.wwpdb.org/ftp/pdb-ftp-sites; for the associated sequence data and 40% sequence clustering see also https://ftp.wwpdb.org/pub/pdb/derived_data/ and https://cdn.rcsb.org/resources/sequence/clusters/bc-40.out). Training used a version of the PDB downloaded 28 August 2019, while the CASP14 template search used a version downloaded 14 May 2020. The template search also used the PDB70 database, downloaded 13 May 2020 (https://wwwuser.gwdg.de/~compbiol/data/hhsuite/databases/hhsuite_dbs/).”

Three of these links are dead. As such, it is difficult to definitively assess the role of any particular PDB entry with respect to AF2 training/testing, nor what impact homologous training structures given the very large number of immunoglobin structures in the training set. That said, we can summarize information for the potentially relevant PDB entries (l 2or9, which is shown in Fig. 1 and 1frg), and believe it is most conservative to assume that each such entry was within the training set.

PDB entry 2or9 (released 2008): the anti-c-myc antibody 9E10 Fab fragment in complex with an 11-amino acid synthetic epitope: EQKLISEEDLN. This crystal structure is also noteworthy for featuring a binding mode where the peptide is pinned between two Fab. The apo structure (2orb) is also in the database but lacks the peptide and a resolved structure for CDR H3.

PDB entry 1a93 (released 1998): a c-Myc-Max leucine zipper structure, where the c-Myc epitope (in a 34-amino acid protein) adopts an alpha helical conformation completely different from the epitope captured in entry 2or9.

PDB entries 5xcs and 5xcu (released 2017): engineered Fv-clasps (scFv alternatives) in complex with the 9-amino acid synthetic HA epitope: YPYDVPDYA.

PDB entry 1frg (released 1994): anti-HA peptide Fab in complex with HA epitope subset Ace-DVPDYASL-NH2.

Since the 2or9 entry has our target epitope (10 aa) embedded within an 11aa sequence, we have revised this line in the manuscript:

The AlphaFold2 training set was reported to exclude chains of less than 10, which would eliminate the myc and HA epitope peptides. => The AlphaFold2 training set was reported to exclude chains of less than 10, which would eliminate the HA epitope peptide from potential training PDB entries such as 5xcs or 5xcu”

It is important to note that we obtained the best prediction performance for the scFv:peptide pair that had no pertinent PDB entries (mBG17). Specifically, doing a Protein Blast against the PDB using the mBG17 scFv revealed diverse homologs, but a maximum sequence identity of 89.8% for the heavy chain (to an unrelated antibody) and 93.8% for the light chain (to an unrelated antibody). Additionally, while it is possible that the AF2 models might have learned from the complex in pdb entry 2or9, Supplemental Figure 3 shows how often the peptide is “misplaced”, and the performance does not exceed the performance for mBG17.

(4) The ability of AlphaFold to refine the linear epitope of antibody mBG17 is quite impressive and robust to the reproducibility issues the authors have run into. However, Figure 4 seems to suggest that the target epitope adopts an alpha-helical structure. This may be why the score is so high and the prediction is so robust. It would be very useful to see along with the pLDDT by residue plots a structure prediction by residue plot. This would help to see if the high confidence pLDDT is coming more from confidence in the docking of the peptide or confidence in the structure of the peptide.

The reviewer is correct that target mBG17 epitope adopts an alpha helical conformation, and we concur that this likely contributes to the more reliable structure prediction performance.  When we predict the structure of the epitope alone without the mBG17 scFv, AF2 confidently predicts an alpha helix with an average pLDDT of 88.2 (ranging from 74.6 to 94.4). 

Author response image 1.

The AF2 prediction for the mBG17 epitope by itself.

However, as one interesting point of comparison, a 10 a.a. poly-alanine peptide is also consistently folded into an alpha-helical coil by AF2. The A₁₀ peptide is also predicted to bind among the traditional scFv CDR loops, but the pLDDT scores are very poor (Supplemental Figure 5J). We also observed the opposite case; when a peptide has a very unstructured region in the binding domain but is nonetheless still be placed confidently, as seen in Supplemental Figure 3 C&D. Therefore, while we suspect peptides with strong alpha helical propensity are more likely to be accurately predicted, the data suggests that that alpha helix adoption is neither necessary nor sufficient to reach a confident prediction.

(5) Related to the above comment, pLDDT is insufficient as a metric for assessing antibody antigen interactions. There is a chance (as is nicely shown in Figure S3C) that AlphaFold can be confident and wrong. Here we see two orange-yellow dots (fairly high confidence) that place the peptide COM far from the true binding region. While running the recommended larger validation above, the authors should also include a peptide RMSD or COM distance metric, to show that the peptide identity is confident, and the peptide placement is roughly correct. These predictions are not nearly as valuable if AlphaFold is getting the right answer for the wrong reasons (i.e. high pLDDT but peptide binding to a nonCDR loop region). Eventual users of the software will likely want to make point mutations or perturb the binding regions identified by the structural predictions (as the authors do in Figure 4).

We agree with the reviewer that pLDDT is not a perfect metric, and we are following with great interest the evolving community discussion as to what metrics are most predictive of binding affinity (e.g. pAE, or pITM as a decent predictor for binding, but not affinity ranking). To our knowledge, there is not yet a consensus for the most predictive metrics for protein:protein binding nor protein:peptide binding. Intriguingly, since the antigen peptides are so small in our case, the pLDDT of the peptide residues should be mostly reporting on the confidence of the distances to neighboring protein residues.

As to the suggestion for a RMSD or COM distance metric, we agree that these are useful -with the caveat that these require a reference structure. The goal of our method is to quickly narrow down candidate linear epitopes and thereby guide experimentalists to more efficiently determine the actual binding sequence of an antibody-antigen sequence. Presumably this would not be necessary if a reference structure were known. 

It may also be possible to invent a method to filter unlikely binding modes that is specific to antibodies and peptide epitopes that does not require a known reference structure, but this would be an interesting problem for subsequent study.

Reviewer 1 (Recommendations for the Authors):

(1) "Linear epitope" should be more precisely defined in the text. It isn't clear whether the authors hope that they can use AlphaFold to predict where on a given protein antigen an antibody will bind, or which antigenic peptide the antibody will bind to. The authors discuss both problems, and there is an important distinction between the two. If the authors are only concerned with isolated antigenic peptides, rather than linear epitopes in their full length structural contexts, they should be more precise in the introduction and discussion.

We thank the reviewer for the prompt towards higher precision. We are using the short contiguous antigen definition of “linear epitope” that depends on secondary rather than tertiary structure. The linear epitopes this paper considers are short “peptides” that form secondary structure independent of their structure in the complete folded antigen protein. We have clarified our definition of “linear epitope” in the text (lines 64-66). 

(2) Line 101: "Not all portions of the antibody are critical". First, this is not consistent with the literature, particularly where computational biology is concerned.

See https://pubs.acs.org/doi/10.1021/acs.jctc.7b00080 . Second, while I largely agree with what I think the authors are trying to say (that we can largely reduce the problem to the CDR loops), this is inconsistent with what the authors later find, which is that inexplicably the VH/VL scaffold used alters results strongly.

We have adopted verbiage that should be less provocative: “Fortunately, with respect to epitope specificity, antibody constant domains are less critical than the CDR loops and the remainder of the variable domain framework regions.”

(3) Related to the above comment, do the authors have any idea why epitope prediction performance improved for the chimeric scFvs? Is this due to some stochasticity in AlphaFold? Or is there something systematic? Expanding the test dataset would again help answer this question.

We agree that future study with a larger test set could help address this intriguing result, for which we currently lack a conclusive explanation. Part of our motivation for this publication was to bring to light this unexpected result. Notably, these framework differences are not only implicated as a factor in driving AF2 performance, but also changing experimental intracellular performance as reported by our group (DOI: 10.1038/s41467-019-10846-1 ). We can generate a variety of hypotheses for this phenomenon. Just as MSA sub-sampling has been a popular approach to drive AF2 to sample alternative conformations, sequence recombination may be a generically effective way to generate usefully different binding predictions. However, it is difficult to discriminate between recombination inducing subtle structural tweaks that increase protein intracellular fitness and binding, from recombination causing changes to the MSA that affect the likelihood of sampling a good epitope binding conformation. It is also possible that the chimeras are more deftly predicted by AF2 due to differences in sequence representation during the training of the AF2 models (e.g. more exposure to models containing 15F11 or 2E2 structures). We attempted to deconvolute MSA differences by using single-sequence mode (Supplementary Figure 13) but this ablated performance.

(4) Figure 2: The reported consensus pLDDT scores are actually quite low here, suggesting low confidence in the result. This is in strong contrast to the reported consensus scores for mBG17. Again, a larger test dataset would help set a quantitative cutoff for where to draw the line for "trustworthy" AlphaFold predictions in antibody-peptide binding applications.

We agree that a larger dataset will be useful to begin to establish metrics and thresholds and will contribute to the aforementioned community discussion about reliable predictors of binding. Our current focus is not structure prediction per se. In the current work we are more focused on relative binding likelihood and increasing the efficiency of experimental epitope verification by flagging the most likely linear epitopes. Thus, while the pLDDT scores are low for Myc in Figure 2, it is remarkable (and worth reporting) that there is still useful signal in the relative variation in pLDDT. The utility of the signal variation is evident in the ability to short-list correct lead peptides via the two methods we demonstrate (consensus and per-residue max).

(5) Figure 4: if the authors are going to draw conclusions from the actual structure predictions of AlphaFold (not just the pLDDT scores), the side-chain accuracy placement should be assessed in the test dataset (RMSD or COM distance).

We agree with the reviewer that side-chain placement accuracy is important when evaluating the accuracy of AF2 structure predictions. However, here our focus was relative binding likelihood rather than structure prediction. The one case where we attempted to draw conclusions from the structure prediction was in the context of mBG17, where there is not yet an experimental reference structure. Absolutely, if we were to obtain a crystal structure for that complex, we would assess side-chain placement accuracy. 

(6) Lines 493-508: I am not sure that this assessment for why AlphaFold has difficulty with antibody-antigen interactions is correct. If the authors' interpretation is correct (larger complicated structures are more challenging to move) then AlphaFold-Multimer (https://www.biorxiv.org/content/10.1101/2021.10.04.463034v2.full) wouldn't perform as well as it does. Instead, the issue is likely due to the incredibly high diversity in antibody CDR loops, which reduces the ability of the AlphaFold MSA step (which the authors show is quite critical to predictions: Figure S13) to inform structure prediction. This, coupled with the importance of side chain placement in antibody and TCR interactions, which is notoriously difficult (https://elifesciences.org/articles/90681), are likely the largest source of uncertainty in antibody-antigen interaction prediction.

We agree with the reviewer that CDR loop diversity (and associated side chain placement challenges) are a major barrier to successfully predict antibody-antigen complexes. Presumably this is true for both peptide antigens and protein antigens. Indeed, the authors of AlphaFold-multimer admit that the updated model struggles with antibody-antigen complexes, saying “As a limitation, we observe anecdotally that AlphaFold-Multimer is generally not able to predict binding of antibodies and this remains an area for future work.” The point about how loop diversity could reduce MSA quality is well taken. We have included the following thanks to the guidance of the reviewer when discussing MSA sensitivity is discussed later on in lines 570-572.: 

“These challenges are presumably compounded by the incredible diversity of the CDR loops in antibodies which could decrease the useful signal from the MSA as well as drive inconsistent MSA-dependent performance”.

With respect to lines 493-508, we have also rephrased a key sentence to try to better explain that we are comparing the often-good recognition performance for short epitopes to the never-good performance when those epitopes are embedded within larger sequences. Instead of saying, “In contrast, a larger and complicated structure may be more challenging to move during the AlphaFold2 structure prediction or recycle steps.” we now say in lines 520-522 , “In contrast, embedding the epitope within a larger and more complicated structure appears to degrade the ability of AlphaFold2 to sample a comparable bound structure within the allotted recycle steps.”

(7) Related to major comment 1: Are AlphaFold predictions deterministic? That is, if you run the same peptide through the PAbFold pipeline 20 times, will you get the same pLDDT score 20 times? The lack of reproducibility may be in part due to stochasticity in AlphaFold, which the authors could actually leverage to provide more consistent results.

This is a good question that we addressed while dissecting the variable performance. When the random seed is fixed, AF2 returns the same prediction every time. After running this 10 times with a fixed seed, the mBG17 epitope was predicted with an average pLDDT of 88.94, with a standard deviation of 1.4 x 10^-14. In contrast, when no seed is specified, AF2 did not return an *identical* result. However, the results were still remarkably consistent. Running the mBG17 epitope prediction 10 times with a different seed gave an average pLDDT of 89.24, with a standard deviation of 0.49. 

(8) Related to major comment 2: The authors could use, for example, this previous survey of 1833 antibody-antigen interactions (https://www.sciencedirect.com/science/article/pii/S2001037023004725) the authors could likely pull out multiple linear epitopes to test AlphaFold's performance on antibody peptide interactions. A large number of tests are necessary for validation.

We thank the reviewer for this report of antibody-antigen interactions and will use it as a source of complexes in a future expanded study. Given the quantity and complexity of the data that we are already providing, as well as logistical challenges for compute and personnel the reviewer is asking for, we must defer this expansion to future work.

(9) Related to major comment 3: Apologies if this is too informal for a review, but this Issue on the AlphaFold GitHub may be useful: https://github.com/googledeepmind/alphafold/issues/416 .

We thank the reviewer for the suggestion – per our response above we have indeed run predictions with no templates. Since we are using local AlphaFold2 calculations with localcolabfold, the use or non-use of templates is fairly simple: including a “—templates” flag or not.

(10) Related to major comment 4: I am not sure if AlphaFold outputs by-residue secondary structure prediction by default, but I know that Phyre2 does http://www.sbg.bio.ic.ac.uk/~phyre2/html/page.cgi?id=index .

To our knowledge, AF2 does not predict secondary structure independent of the predicted tertiary structure. When we need to analyze the secondary structure we typically use the program DSSP from the tertiary structure. 

(11) The documentation for this software is incomplete. The GitHub ReadMe should include complete guidelines for users with details of expected outputs, along with a thorough step-by-step walkthrough for use.

We thank the reviewer for pointing this out, but we feel that the level of detail we provide in the GitHub is sufficient for users to utilize the method described.

Stylistic comments:

(1) I do not think that the heatmaps (as in 1C, top) add much information for the reader. They are largely uniform across the y-axis (to my eyes), and the information is better conveyed by the bar and line graphs (as in 1C, middle and bottom panels).

We thank the reviewer for this feedback but elect to leave it in on the premise of more data presented is (usually) better. Including the y-axis reveals common patterns such as the lower confidence of the peptide termini, as well as the lack of some patterns that might have occurred. For example, if a subset of five contiguous residues was necessary and sufficient for local high confidence this could be visually apparent as a “staircase” in the heat map.

(2) A discussion of some of the shortcomings of other prediction-based software (lines 7177) might be useful. Why are these tools less well-equipped than AlphaFold for this problem? And if they have tried to predict antibody-antigen interactions, why have they failed?

We agree with the reviewer that a broader review of multiple methods would be interesting and useful. One challenge is that the suite of available methods is evolving rapidly, though only a subset work for multimeric systems. Some detail on deficiencies of other approaches was provided in lines 71-77 originally, although we did not go into exhaustive detail since we wanted to focus on AF2. We view using AF2 in this manner is novel and that providing additional options predict antibody epitopes will be of interest to the scientific community. We also chose AF2 because we have ample experience with it and is a software that many in the scientific community are already using and comfortable with. Additionally, AF2 provided us with a quantification parameter (pLDDT) to assess the peptides’ binding abilities. We think a future study that compares the ability of multiple emerging tools for scFv:peptide prediction will be quite interesting. 

(3) Similar to the above comment, more discussion focused on why AlphaFold2 fails for antibodies (lines 126-128) might be useful for readers.  

We thank the reviewer for the suggestion. The following line has been added shortly after lines 135-137:

“Another reason for selecting AF2 is to attempt to quantify its abilities the compare simple linear epitopes, since the team behind AF-multimer reported that conformational antibody complexes were difficult to predict accurately (14).”

Per earlier responses, we also added text that flags one particular possible reason for the general difficulty of predicting antibody-antigen complexes (the diversity of the CDR loops and associated MSA challenges).

(4) The first two paragraphs of the results section (lines 226-254) could likely be moved to the Methods. Additionally, details of how the scores are calculated, not just how the commands are run in python, would be useful.

Per the reviewer suggestion, we moved this section to the end of the Methods section. Also, to aid in the reader’s digestion of the analysis, the following text has been added to the Results section (lines 256-264):

“Both the ‘Simple Max’ and ‘Consensus’ methods were calculated first by parsing every pLDDT score received by every residue in the antigen sequence sliding window output structures. From the resulting data structure, the Simple Max method simply finds the maximum pLDDT value ever seen for a single residue (across all sliding windows and AF2 models). For the Consensus method, per-residue pLDDT was first averaged across the 5 AF2 models. These averages are reported in the heatmap view, and further averaged per sliding window for the bar chart below.

In principle, the strategy behind the Consensus method is to take into account agreement across the 5 AF2 models and provide insight into the confidence of entire epitopes (whole sliding windows of n=10 default) instead of disconnected, per-residue pLDDT maxima.” 

(5) Figure 1 would be more useful if you could differentiate specifically how the Consensus and Simple Max scoring is different. Providing examples for how and why the top 5 peptide hits can change (quite significantly) using both methods would greatly help readers understand what is going on.

Per the reviewer suggestion, we have added text to discuss the variable hit selection that results from the two scoring metrics. The new text (lines 264-271) adds onto the added text block immediately above:

“Having two scoring metrics is useful because the selection of predicted hits can differ. As shown in Figure 2, part of the Myc epitope makes it into the top 5 peptides when selection is based on summing per-residue maximum pLDDT (despite there being no requirement that these values originate in the same physical prediction). In contrast, a Consensus method score more directly reports on a specific sliding window, and the strength of the highest confidence peptides is more directly revealed with superior signal to noise as shown in Figure 3. Variability in the ranking of top hits between the two methods arises from the fundamental difference in strategy (peptide-centric or residue-centric scoring) as well as close competition between the raw AF2 confidence in the known peptide and competing decoy sequences.”

(6) Hopefully the reproducibility issue is alleviated, but if not the discussion of it (lines 523554) should be moved to the supplement or an appendix.

The ability of the original AF2 model to predict protein-protein complexes was an emergent behavior, and then an explicit training goal for AF2.multimer. In this vein, the ability to predict scFv:peptide complexes is also an emergent capability of these models. It is our hope that by highlighting this capacity, as well as the high level of sensitivity, that this capability will be enhanced and not degraded in future models/algorithms (both general and specialized). In this regard, with an eye towards progress, we think it is actually important to put this issue in the scientific foreground rather than the background. When it comes to improving machine learning methods negative results are also exceedingly important.

Reviewer 2 (Recommendations for the Author):

- Line 113, page 3 - the structures of the novel scFv chimeras can be rapidly and confidently be predicted by AlphaFold2 to the structures of the novel scFv chimeras can be rapidly and confidently predicted by AlphaFold2.

The superfluous “be” was removed from the text.

- Line 276 and 278 page 9 - peptide sequences QKLSEEDLL and EQKLSEEDL in the text are different from the sequences reported in Figures 1 and 2 (QKLISEEDLL and EQKLISEEDL). Please check throughout the manuscript and also in the Figure caption (as in Figure 2).

These changes were made throughout the text. 

- I would include how you calculate the pLDDT score for both Simple Max approach and Consensus analysis.

Good suggestion, this should be covered via the additions noted above.

Author response:

The following is the authors’ response to the original reviews

We thank the reviewers for the constructive comments, which have improved the manuscript. In response to these comments, we have made the following major changes to the main text and reviewer response:

(1) Added experimental and computational evidence to support the use of Cut&Tag to determine speckle location.

(2) Performed new Transmission Electron Microscopy (TEM) experiments to visualize interchromatin granule clusters +/- speckle degradation.

(3) Altered the text of the manuscript to remove qualitative statements and clarify effect sizes.

(4) Performed new analyses of published whole genome bisulfite data from LIMe-Hi-C following DNMT1 inhibition to demonstrate that CpG methylation is lost at DNMT1i-specific gained CTCF sites.

(5) Included citations for relevant literature throughout the text.

These revisions in addition to others are described in the point-by-point response below.

Reviewer #1 (Public review):

Summary

Roseman et al. use a new inhibitor of the maintenance DNA methyltransferase DNMT1 to probe the role of methylation on binding of the CTCF protein, which is known to be involved chromatin loop formation. As previous reported, and as expected based on our knowledge that CTCF binding is methylation-sensitive, the authors find that loss of methylation leads to additional CTCF binding sites and increased loop formation. By comparing novel loops with the binding of the pre-mRNA splicing factor SON, which localizes to the nuclear speckle compartment, they propose that these reactivated loops localize to near speckles. This behavior is dependent on CTCF whereas degradation of two speckle proteins does not affect CTCF binding or loop formation. The authors propose a model in which DNA methylation controls the association of genome regions with speckles via CTCF-mediated insulation.

Strengths

The strengths of the study are 1) the use of a new, specific DNMT1 inhibitor and 2) the observation that genes whose expression is sensitive to DNMT1 inhibition and dependent on CTCF (cluster 2) show higher association with SON than genes which are sensitive to DNMT1 inhibition but are CTCF insensitive, is in line with the authors' general model.

Weaknesses

There are a number of significant weaknesses that as a whole undermine many of the key conclusions, including the overall mechanistic model of a direct regulatory role of DNA methylation on CTCF-mediated speckle association of chromatin loops.

We appreciate the reviewer’s constructive comments and address them point-by-point below.

(1) The authors frequently make quasi-quantitative statements but do not actually provide the quantitative data, which they actually all have in hand. To give a few examples: "reactivated CTCF sites were largely methylated (p. 4/5), "many CTCF binding motifs enriched..." (p.5), "a large subset of reactivated peaks..."(p.5), "increase in strength upon DNMT1 inhibition" (p.5); "a greater total number....." (p.7). These statements are all made based on actual numbers and the authors should mention the numbers in the text to give an impression of the extent of these changes (see below) and to clarify what the qualitative terms like "largely", "many", "large", and "increase" mean. This is an issue throughout the manuscript and not limited to the above examples.

Related to this issue, many of the comparisons which the authors interpret to show differences in behavior seem quite minor. For example, visual inspection suggests that the difference in loop strength shown in figure 1E is something like from 0 to 0.1 for K562 cells and a little less for KCT116 cells. What is a positive control here to give a sense of whether these minor changes are relevant. Another example is on p. 7, where the authors claim that CTCF partners of reactivated peaks tend to engage in a "greater number" of looping partners, but inspection of Figure 2A shows a very minor difference from maybe 7 to 7.5 partners. While a Mann-Whitney test may call this difference significant and give a significant P value, likely due to high sample number, it is questionable that this is a biologically relevant difference.

We have amended the text to include actual values, instead of just qualitative statements. We have also moderated our claims in the text to note where effect sizes are more modest.

The following literature examples can serve as positive controls for the effect sizes that we might expect when perturbing CTCF. Our observed effect sizes are largely in line with these expected magnitudes.

https://pmc.ncbi.nlm.nih.gov/articles/PMC8386078/ Fig. 2E

https://www.cell.com/cell-reports/pdf/S2211-1247(23)01674-1.pdf Fig. 3J,K

https://academic.oup.com/nar/article/52/18/10934/7740592 Fig. S5D (CTCF binding only).

(2) The data to support the central claim of localization of reactivated loops to speckles is not overly convincing. The overlap with SON Cut&Tag (figure 2F) is partial at best and although it is better with the publicly available TSA-seq data, the latter is less sensitive than Cut&Tag and more difficult to interpret. It would be helpful to validate these data with FISH experiments to directly demonstrate and measure the association of loops with speckles (see below).

A recent publication we co-authored validated the use of speckle (SON) Cut&Run using FISH (Yu et al, NSMB 2025, doi: 10.1038/s41594-024-01465-6). This paper also supports a role of CTCF in positioning DNA near speckles. Unfortunately, the resolution of these FISH probes is in the realm of hundreds of kilobases. This was not an issue for Yu et. al., as they were looking at large-scale effects of CTCF degradation on positioning near speckles. However, FISH does not provide the resolution we need to look at more localized changes over methylation-specific peak sites.

Instead, we use Cut&Tag to look at these high-resolution changes. In Figure 3C, we show that SON localizes to DNMT1i-specific peaks only upon DNMT1 inhibition. We further demonstrate that this interaction is dependent on CTCF. In response to reviewer comments, we have now also performed spike-in normalized Cut&Tag upon acute (6 hr) SON degradation to validate that our signal is also directly dependent on SON and not merely due to a bias toward open chromatin.

Author response image 1.

TSA-seq has been validated with FISH (Chen et. al., doi: 10.1083/jcb.201807108), Alexander et. Al 10.1016/j.molcel.2021.03.006) Fig 6. We include TSA-seq data where possible in our manuscript to support our claims.

We also note that Fig 2F shows all CTCF peaks and loops, not just methylation-sensitive peaks and loops, to give a sense of the data. We apologize for any confusion and have clarified this in the figure legend.

(3) It is not clear that the authors have indeed disrupted speckles from cells by degrading SON and SRRM2. Speckles contain a large number of proteins and considering their phase separated nature stronger evidence for their complete removal is needed. Note that the data published in ref 58 suffers from the same caveat.

Based upon the reviewers’ feedback, we generated Tranmission electron microscopy (TEM) data to visualize nuclear speckles +/- degradation of SON and SRRM2 (DMSO and dTAG). We were able to detect Interchromatin Granules Clusters (ICGs) that are representative of nuclear speckles in the DMSO condition. However, even at baseline, we observed a large degree of cell-to-cell variability in these structures. In addition, we also observe potential structural changes in the distribution of heterochromatin upon speckle degradation. Consequently, we hesitate to make quantitative conclusions regarding loss of these nuclear bodies. In the interest of transparency, we have included representative raw images from both conditions for the reviewers’ consideration.

We also note that in Ref 58 (Ilik et. Al., https://doi.org/10.7554/eLife.60579), the authors show diffusion of speckle client proteins RBM25, SRRM1, and PNN upon SON and SRRM2 depletion, further supporting speckle dissociation in these conditions.

Author response image 2.

Author response image 3.

(4) The authors ascribe a direct regulatory role to DNA methylation in controlling the association of some CTCF-mediated loops to speckles (p. 20). However, an active regulatory role of speckle association has not been demonstrated and the observed data are equally explainable by a more parsimonious model in which DNA methylation regulates gene expression via looping and that the association with speckles is merely an indirect bystander effect of the activated genes because we know that active genes are generally associated with speckles. The proposed mechanism of a regulatory role of DNA methylation in controlling speckle association is not convincingly demonstrated by the data. As a consequence, the title of the paper is also misleading.

While it is difficult to completely rule out indirect effects, we do not believe that the relationship between methylation-sensitive CTCF sites and speckles relies only on gene activity.

We can partially decouple SON Cut&Tag signal from gene activation if we break down Figure 4D to look only at methylation-sensitive CTCF peaks on genes whose expression is unchanged upon DNMT1 inhibition (using thresholds from manuscript, P-adj > 0.05 and/or |log2(fold-change)| < 0.5). This analysis shows that many methylation-sensitive CTCF peaks on genes with unchanged expression still change speckle association upon DNMT1 inhibition. This result refutes the necessity of transcriptional activation to recruit speckles to CTCF.

Author response image 4.

We note the comparator upregulated gene set here is small (~20 genes with our stringent threshold for methylation-sensitive CTCF after 1 day DNMT1i treatment).

However, we acknowledge that these effects cannot be completely disentangled. We previously included the statement “other features enriched near speckles, such as open chromatin, high GC content, and active gene expression, could instead contribute to increased CTCF binding and looping near speckles” in the discussion. In response to the reviewer’s comment, we have further tempered our statements on page 20/21 and also added a statement noting that DNA demethylation and gene activation cannot be fully disentangled. While we are also open to a title change, we are unsure which part of the title is problematic. 

(5) As a minor point, the authors imply on p. 15 that ablation of speckles leads to misregulation of genes by altering transcription. This is not shown as the authors only measure RNA abundance, which may be affected by depletion of constitutive splicing factors, but not transcription. The authors would need to show direct effects on transcription.

We agree, and we have changed this wording to say RNA abundance.

Reviewer #2 (Public review):

Summary:

CTCF is one of the most well-characterized regulators of chromatin architecture in mammals. Given that CTCF is an essential protein, understanding how its binding is regulated is a very active area of research. It has been known for decades that CTCF is sensitive to 5-cystosine DNA methylation (5meC) in certain contexts. Moreover, at genomic imprints and in certain oncogenes, 5meC-mediated CTCF antagonism has very important gene regulatory implications. A number of labs (eg, Schubeler and Stamatoyannopoulos) have assessed the impact of DNA methylation on CTCF binding, but it is important to also interrogate the effect on chromatin organization (ie, looping). Here, Roseman and colleagues used a DNMT1 inhibitor in two established human cancer lines (HCT116 [colon] and K562 [leukemia]), and performed CTCF ChIPseq and HiChIP. They showed that "reactivated" CTCF sites-that is, bound in the absence of 5meC-are enriched in gene bodies, participate in many looping events, and intriguingly, appear associated with nuclear speckles. This last aspect suggests that these reactivated loops might play an important role in increased gene transcription. They showed a number of genes that are upregulated in the DNA hypomethylated state actually require CTCF binding, which is an important result.

Strengths:

Overall, I found the paper to be succinctly written and the data presented clearly. The relationship between CTCF binding in gene bodies and association with nuclear speckles is an interesting result. Another strong point of the paper was combining DNMT1 inhibition with CTCF degradation.

Weaknesses:

The most problematic aspect of this paper in my view is the insufficient evidence for the association of "reactivated" CTCF binding sites with nuclear speckles needs to be more diligently demonstrated (see Major Comment). One unfortunate aspect was that this paper neglected to discuss findings from our recent paper, wherein we also performed CTCF HiChIP in a DNA methylation mutant (Monteagudo-Sanchez et al., 2024 PMID: 39180406). It is true, this is a relatively recent publication, although the BioRxiv version has been available since fall 2023. I do not wish to accuse the authors of actively disregarding our study, but I do insist that they refer to it in a revised version. Moreover, there are a number of differences between the studies such that I find them more complementary rather than overlapping. To wit, the species (mouse vs human), the cell type (pluripotent vs human cancer), the use of a CTCF degron, and the conclusions of the paper (we did not make a link with nuclear speckles). Furthermore, we used a constitutive DNMT knockout which is not viable in most cell types (HCT116 cells being an exception), and in the discussion mentioned the advantage of using degron technology:

"With high-resolution techniques, such as HiChIP or Micro-C (119-121), a degron system can be coupled with an assessment of the cis-regulatory interactome (118). Such techniques could be adapted for DNA methylation degrons (eg, DNMT1) in differentiated cell types in order to gauge the impact of 5meC on the 3D genome."

The authors here used a DNMT1 inhibitor, which for intents and purposes, is akin to a DNMT1 degron, thus I was happy to see a study employ such a technique. A comparison between the findings from the two studies would strengthen the current manuscript, in addition to being more ethically responsible.

We thank the reviewer for the helpful comments, which we address in the point-by-point response below. We sincerely apologize for this oversight in our references. We have included references to your paper in our revised manuscript. It is exciting to see these complementary results! We now include discussion of this work to contextualize the importance of methylation-sensitive CTCF sites and motivate our study.

Recommendations for the authors:

Reviewer #1 (Recommendations for the authors):

To address the above points, the authors should:

(1) Provide quantitative information in the text on all comparisons and justify that the small differences observed, albeit statistically significant, are biologically relevant. Inclusion of positive controls to give an indication of what types of changes can be expected would be helpful.

We have added quantitative information to the text, as discussed in the response to public comments above.  We also provide literature evidence of expected effect sizes in that response.

(2) Provide FISH data to a) validate the analysis of comparing looping patterns with SON Cut&Tag data as an indicator of physical association of loops with speckles and b) demonstrate by FISH increased association of some of the CTCF-dependent loops/genes (cluster 2) with speckles upon DNMT1 inhibition.

Please see response to Reviewer 1 comment #2 above. Unfortunately, FISH will not provide the resolution we need for point a). We have confidence in our use of TSA-seq and Cut&Tag to study SON association with CTCF sites on a genome-wide scale, which would not be possible with individual FISH probes. Specifically, since the submission of our manuscript several other researchers (Yu et al, Nat. Struct. and Mol. Biol. 2025, Gholamalamdari et al eLife 2025) have leveraged CUT&RUN/CUT&TAG and TSA-seq to map speckle associated chromatin and have validated these methods with orthogonal imaging based approaches.

(3) Demonstrate loss of speckles upon SON or SRRM2 by probing for other speckle components and ideally analysis by electron microscopy which should show loss of interchromatin granules.  

We have performed TEM in K562 cells +/- SON/SRRM2 degradation. Please see response to Reviewer 1 comment #3. Specifically, interchromatin granule clusters are visible in the TEM images of the DMSO sample (see highlighted example above), however, given the heterogeneity of these structures and potential global alterations in heterochromatin that may be occurring following speckle loss, we refrained from making quantitative conclusions from this data. We instead include the raw images above.

(4) The authors should either perform experiments to clearly show whether loop association is transcription dependent or whether association is merely a consequence of gene activation. Alternatively, they should tone down their model ascribing a direct regulatory role of methylation in control of loop association with speckles and also discuss other models. Unless the model is more clearly demonstrated, the title of the paper should be changed to reflect the uncertainty of the central conclusion.

Please see response to Reviewer 1 comment #4 above.

(5) The authors should either probe directly for the effect of speckle ablation on transcription or change their wording.

We have changed our wording to RNA abundance.

Reviewer #2 (Recommendations for the authors):

Major:

⁃ There was no DNA methylation analysis after inhibitor treatment. Ideally, genome bisulfite sequencing should be performed to show that the DNMT1i-specific CTCF binding sites are indeed unmethylated. But at the very least, a quantitative method should be employed to show the extent to which 5meC levels decrease in the presence of the DNMT1 inhibitor

Response: We have now included analysis of genome wide bisulfite information from LIMe-Hi-C (bisulfite Hi-C) in K562 following DNMT1i inhibition. Specifically, we leverage the CpG methylation readout and find that DNTM1i-specific CTCF sites are more methylated than non-responsive CTCF peaks at baseline. In addition, these sites show the greatest decrease in CpG methylation upon 3 days of DNMT1 inhibition. We include a figure detailing these analyses in the supplement (Fig S1E). In addition, we have added CpG methylation genome browser tracks to (Fig S1D). In terms of global change, we have found that 3 days of DNMT1 inhibitor treatment leads to a reduction in methylation to about ~1/4 the level at baseline.

I am not convinced that CUT&Tag is the proper technique to assess SON binding. CUT&Tag only works under stringent conditions (high salt), and can be a problematic assay for non-histone proteins, which bind less well to chromatin. In our experience, even strong binders such as CTCF exhibit a depleted binding profile when compared to ChIP seq data. I would need to be strongly convinced that the analysis presented in figures 2F-J and S2 D-I simply do not represent ATAC signal (ie, default Tn5 activity). For example, SON ChIP Seq, CUT&Tag in the SON degron and/or ATAC seq could be performed. What worries me is that increased chromatin accessibility would also be associated with increased looping, so they have generated artifactual results that are consistent with their model.

As the reviewer suggested, we have now performed spike-in normalized SON Cut&Tag with DNMT1 inhibition and 6 hours of SON/SRRM2 degradation in our speckle dTAG knockin cell line. These experiments confirm that the SON Cut&Tag signal we see is SON-dependent. If the signal was truly due to artifactual binding, gained peaks would be open irrespective of speckle binding, however we see a clear speckle dependence as this signal is much lower if SON is degraded.

Author response image 5.

Moreover, in our original Cut&Tag experiments, we did not enrich detectable DNA without using the SON antibody (see last 4 samples-IgG controls). This further suggests that our signal is SON-dependent.

Author response image 6.

Finally, we see good agreement between Cut&Tag and TSA-seq (Spearman R=0.82).  The agreement is particularly strong in the top quadrant, which is most relevant since this is where the non-zero signal is.

Author response image 7.

Minor points

⁃ Why are HCT116 cells more responsive to treatment than K562 cells? This is something that could be addressed with DNA methylation analysis, for example

K562 is a broadly hypomethylated cell line (Siegenfeld et.al, 2022 https://doi.org/10.1038/s41467-022-31857-5 Fig S2A-C). Thus, there may be less dynamic range to lose methylation compared to HCT116.

Our results are also consistent with previous results comparing DKO HCT116 and aza-treated K562 cells (Maurano 2015, http://dx.doi.org/10.1016/j.celrep.2015.07.024). They state “In K562 cells, 5-aza-CdR treatment resulted in weaker reactivation than in DKO cells…”  In addition, cell-type-specific responsiveness to DNA methyltransferase KO depending upon global CpG methylation levels, has also been observed in ES and EpiLC cells (Monteagudo-Sanchez et al., 2024), which we now comment on in the manuscript.

⁃ How many significant CTCF loops in DNMTi, compared to DMSO? It was unclear what the difference in raw totals is.

We now include a supplemental table with the HiChIP loop information. We call similar numbers of raw loops comparing DNMT1i and DMSO, as only a small subset of loops is changing.

⁃ For the architectural stripes, it would be nice to see a representative example in the form of a contact plot. Is that possible to do with the hiChIP data?

As described in our methods, we called architectural stripes using Stripenn (Yoon et al 2022) from LIMe-Hi-C data under DNMT1i conditions (Siegenfeld et al, 2022). Shown below is a representative example of a stripe in the form of a Hi-C contact map.

Author response image 8.

⁃ Here 4-10x more DNMT1i-specific CTCF binding sites were observed than we saw in our study. What are thresholds? Could the thresholds for DNMT1i-specific peaks be defined more clearly? For what it's worth, we defined our DNMT KO-specific peaks as fold-change {greater than or equal to} 2, adjusted P< 0.05. The scatterplots (1B) indicate a lot of "small" peaks being called "reactivated."

We called DNMT1i-specific peaks using HOMER getDifferentialPeaksReplicates function. We used foldchange >2 and padj <0.05. We further restricted these peaks to those that were not called in the DMSO condition. 

⁃ On this note, is "reactivated" the proper term? Reactivated with regards to what? A prior cell state? I think DNMT1i-specific is a safer descriptor.

We chose this term based on prior literature (Maurano 2015 http://dx.doi.org/10.1016/j.celrep.2015.07.024, Spracklin 2023 https://doi.org/10.1038/s41594-022-00892-7) . However, we agree it is not very clear, so we’ve altered the text to say “DNMT1i-specific”. We thank the reviewer for suggesting this improved terminology.

⁃ It appears there is a relatively small enrichment for CTCF peaks (of any class) in intergenic regions. How were intergenic regions defined? For us, it is virtually half of the genome. We did some enrichment of DNMT KO-specific peaks in gene bodies (our Supplemental Figure 1C), but a substantial proportion were still intergenic.

We defined intergenic peaks using HOMER’s annotatepeaks function, with the -gtf option using Ensembl gene annotations (v104). We used the standard annotatepeaks priority order, which is TSS > TTS> CDS Exons > 5’UTR exons >3’ UTR exons > Introns > Intergenic.

Maurano et. al. 2015 (http://dx.doi.org/10.1016/j.celrep.2015.07.024) also found reduced representation of intergenic sites among demethylation-reactivated CTCF sites in their Fig S5A. We note this is not a perfect comparison because their data is displayed as a fraction of all intergenic peaks.

⁃ We also recently published a review on this subject: The impact of DNA methylation on CTCF-mediated 3D genome organization NSMB 2024 (PMID: 38499830) which could be cited if the authors choose.

We have cited this relevant review.

Author response:

The following is the authors’ response to the original reviews

Public Reviews:

Reviewer #1 (Public review):

Summary:

This study puts forth the model that under IFN-B stimulation, liquid-phase WTAP coordinates with the transcription factor STAT1 to recruit MTC to the promoter region of interferon-stimulated genes (ISGs), mediating the installation of m⁶A on newly synthesized ISG mRNAs. This model is supported by strong evidence that the phosphorylation state of WTAP, regulated by PPP4, is regulated by IFN-B stimulation, and that this results in interactions between WTAP, the m⁶A methyltransferase complex, and STAT1, a transcription factor that mediates activation of ISGs. This was demonstrated via a combination of microscopy, immunoprecipitations, m⁶A sequencing, and ChIP. These experiments converge on a set of experiments that nicely demonstrate that IFN-B stimulation increases the interaction between WTAP, METTL3, and STAT1, that this interaction is lost with the knockdown of WTAP (even in the presence of IFN-B), and that this IFN-B stimulation also induces METTL3-ISG interactions.

Strengths:

The evidence for the IFN-B stimulated interaction between METTL3 and STAT1, mediated by WTAP, is quite strong. Removal of WTAP in this system seems to be sufficient to reduce these interactions and the concomitant m⁶A methylation of ISGs. The conclusion that the phosphorylation state of WTAP is important in this process is also quite well supported.

Weaknesses:

The evidence that the above mechanism is fundamentally driven by different phase-separated pools of WTAP (regulated by its phosphorylation state) is weaker. These experiments rely relatively heavily on the treatment of cells with 1,6-hexanediol, which has been shown to have some off-target effects on phosphatases and kinases (PMID 33814344).

Given that the model invoked in this study depends on the phosphorylation (or lack thereof) of WTAP, this is a particularly relevant concern.

We are grateful for the reviewer’s positive comment and constructive feedback. 1,6-hexanediol (hex) was considered an inhibitor of hydrophobic interaction, thereby capable of dissolving protein phase separation condensates. Hex (5%-10% w/v) was still widely used to explore the phase separation characteristic and function on various protein, including some phosphorylated proteins such as pHSF1, or kinases such as NEMO1-3. Since hydrophobic interactions involved in various types of protein-protein interaction, the off-target effects of hex were inevitable. It has been reported that hex impaired RNA polymerase II CTD-specific phosphatase and kinase activity at 5% concentration4, which led to the reviewer’s concern. In response to this concern, we investigated the phosphorylation level of WTAP under hex concentration gradient treatment. Surprisingly, we found that both 2% and 5% hex maintained the PPP4c-mediated dephosphorylation level of WTAP but still led to the dissolution of WTAP LLPS condensates (Figure 5-figure supplement 1H; Author response image 1), indicating that hex dispersed WTAP phase separation in a phosphorylation-independent manner. Consistent with our findings, Ge et al. used 10% hex to dissolve WTAP phase separation condensates5. Additionally, we found the phosphorylation level of STAT1 was not affected by both 2% and 5% hex, revealing the off-target and impairment function of hex on kinases or phosphatases might not be universal (Figure 5-figure supplement 1H). Collectively, since the 5% hex we used did not influence the de-phosphorylation event of WTAP, function of WTAP LLPS mediating interaction between methylation complex and STAT1 revealed by our results was independent from its phosphorylation status.

Author response image 1.

mCherry-WTAP-rescued HeLa cells were treated with 10 ng/mL IFN-β together with or without 2% or 5% hex and 20 μg/mL digitonin for 1 hour or left untreated. Phase separation of mCherry-WTAP was observed through confocal microscopy. The number of WTAP condensates that diameter over 0.4 μm of n = 20 cells were counted through ImageJ and shown. Scale bars indicated 10 μm. All error bars, mean values ± SD, P-values were determined by unpaired two-tailed Student’s t-test of n = 20 cells in (B). For (A), similar results were obtained for three independent biological experiments.

Related to this point, it is also interesting (and potentially concerning for the proposed model) that the initial region of WTAP that was predicted to be disordered is in fact not the region that the authors demonstrate is important for the different phase-separated states.

A considerable number of proteins undergo phase separation via interactions between intrinsically disordered regions (IDRs). IDR contains more charged and polar amino acids to present multiple weakly interacting elements, while lacking hydrophobic amino acids to show flexible conformations6. In our study, we used PLAAC websites (http://plaac.wi.mit.edu/) to predict IDR domain of WTAP, and a fragment (234-249 amino acids) was predicted as prion-like domain (PLD). However, deletion of this fragment failed to abolish the phase separation properties of WTAP, which might be the main confusion to reviewers. To explain this issue, we checked the WTAP structure (within part of MTC complex) from protein data bank (https://www.rcsb.org/structure/7VF2) and found that the prediction of IDR has been renewed due to the update of different algorithm. IDR of WTAP expanded to 245-396 amino acids, encompassing the entire CTD region. Our results demonstrate that the CTD was critical for WTAP LLPS, as CTD deficiency significantly inhibited the formation of liquid condensates both in vitro and in cells. Also, phosphorylation sites on CTD were important for the phase transition of WTAP. These observations highlight the phosphorylation status on CTD region as a key driving force of phase separation, consistent with the established role of IDR in regulating LLPS. We have revised our description on WTAP IDR in our article following the reviewers’ suggestion.

Taking all the data together, it is also not clear to me that one has to invoke phase separation in the proposed mechanism.

In this article, we observed that WTAP underwent phase transition during virus infection and IFN-β treatment, and proposed a novel mechanism whereby post translational modification (PTM)-driven WTAP LLPS was required for the m⁶A modification of ISG mRNAs. To verify the hypothesis, we first demonstrated the relationship between PTM and phase separation of WTAP. We constructed WTAP 5ST-D and 5ST-A mutant to mimic WTAP phosphorylation and dephosphorylation status respectively, and figured out that dephosphorylated WTAP underwent LLPS. We also found that PPP4 was the main phosphatase to regulate WTAP dephosphorylation. To further investigation, we introduced the potent PPP4 inhibitor, fostriecin. Consistent with our findings in PPP4 deficient model, fostriecin treatment significantly inhibited the IFN-β-induced dephosphorylation of WTAP and disrupted its LLPS condensates, indicating that PPP4 was the key phosphatase recruited by IFN-β to regulate WTAP, confirming that PTM governs WTAP LLPS dynamics (Figure 2-figure supplement 1C and H). Furthermore, fostriecin-induced impairment of WTAP phosphorylation and phase separation correlated with reduced m⁶A modification level and elevated ISGs expression level (Figure 4C and Figure 4-figure supplement 1E). These findings together emphasized that dephosphorylation is required for WTAP LLPS.

As for the function of WTAP LLPS, we assumed that WTAP might undergo LLPS to sequester STAT1 together with m⁶A methyltransferase complex (MTC) for mediating m⁶A deposition on ISG mRNAs co-transcriptionally under IFN-β stimulation. Given that hex dissolved WTAP LLPS condensates without affecting dephosphorylation status (Figure 5-figure supplement 1H; Author response image 1), various experiments we performed previously actually confirmed the critical role of WTAP LLPS during m⁶A modification (Figure 4A), as well as the mechanism that WTAP phase separation enhanced the interaction between MTC and STAT1 (Figure 5E-F). Subsequent to reviewer’s comments, more experiments were conducted for further validation. We found the WTAP liquid condensates formed by wild type (WT) WTAP or WTAP 5ST-A mutant (which mimics dephosphorylated-WTAP) could be dissembled by hex, which also led to the impairment of the interaction between STAT1, METTL3 and WTAP (Figure 5-figure supplement 1E). In addition, in vitro experiments demonstrated that hex treatment significantly disrupted the interaction between recombinant GFP-STAT1 and mCherry-WTAP which expressed in the E. coli system (Figure 5F and Figure 5-figure supplement 1G). Notably, this prokaryotic expression system lacks endogenous phosphorylation machinery, resulting in non-phosphorylated mCherry-WTAP. For further validation, we performed the interaction of WTAP WT or 5ST-A with the promoter regions of ISG as well as the m⁶A modification of ISG mRNAs were attenuated by WTAP LLPS dissolution (Figure 4E and Figure 6E). These findings together revealed that WTAP LLPS were the critical mediators of the STAT1-MTC interactions, ISG promoter regions binding and the co-transcription m⁶A modification.

Collectively, our results demonstrated that IFN-β treatment recruited PPP4c to dephosphorylate WTAP, thereby driving the formation of WTAP liquid condensates which were essential for facilitating STAT1-MTC interaction and m⁶A deposition, subsequently mediated ISG expression. Since we revealed a strong association between PTM-regulated WTAP phase transition and its m⁶A modification function, WTAP LLPS was a novel and functionally distinct mechanism that must be reckoned with in this study.

Author response image 2.

Wild type (WT) WTAP or 5ST-A mutant-rescued WTAP^sgRNA THP-1-derived macrophages are stimulated with or without with 10 ng/mL IFN-β together followed with 2% or 5% 1,6-hexanediol (hex) and 20 μg/mL digitonin for 1 hour or left untreated. antibody and imaged using confocal microscope. Scale bars indicated 10 μm.

Reviewer #2 (Public review):

In this study, Cai and colleagues investigate how one component of the m⁶A methyltransferase complex, the WTAP protein, responds to IFNb stimulation. They find that viral infection or IFNb stimulation induces the transition of WTAP from aggregates to liquid droplets through dephosphorylation by PPP4. This process affects the m⁶A modification levels of ISG mRNAs and modulates their stability. In addition, the WTAP droplets interact with the transcription factor STAT1 to recruit the methyltransferase complex to ISG promoters and enhance m⁶A modification during transcription. The investigation dives into a previously unexplored area of how viral infection or IFNb stimulation affects m⁶A modification on ISGs. The observation that WTAP undergoes a phase transition is significant in our understanding of the mechanisms underlying m⁶A's function in immunity. However, there are still key gaps that should be addressed to fully accept the model presented.

Major points:

(1) More detailed analyses on the effects of WTAP sgRNA on the m⁶A modification of ISGs:

a. A comprehensive summary of the ISGs, including the percentage of ISGs that are m⁶A-modified. merip-isg percentage

b. The distribution of m⁶A modification across the ISGs. Topology

c. A comparison of the m⁶A modification distribution in ISGs with non-ISGs. Topology

In addition, since the authors propose a novel mechanism where the interaction between phosphorylated STAT1 and WTAP directs the MTC to the promoter regions of ISGs to facilitate co-transcriptional m⁶A modification, it is critical to analyze whether the m⁶A modification distribution holds true in the data.

We appreciate the reviewer’s summary of our manuscript and the constructive assessment. We have conducted the related analysis accordingly to present the m⁶A modification in ISGs in our model as reviewers suggested. Our results showed that about 64.29% of core ISGs were m⁶A modified under IFN-β stimulation (Figure 3-figure supplement 1B; Figure 3G), which was consistent with the similar percentage in previous studies[7,8]. The m⁶A distribution of the ISGs transcripts including IFIT1, IFIT2, OAS1 and OAS2 was validated (Figure 3-figure supplement 1H).

Generally, m⁶A deposition preferentially located in the vicinity of stop codon, while m⁶A modification was highly dynamic under different cellular condition. However, we compared the topology of m⁶A deposition of ISGs with non-ISGs, and found that m⁶A modification of ISG mRNAs showed higher preference of coding sequences (CDS) localization compared to global m⁶A deposition (Figure 3H). Similarly, various researches uncovered the m⁶A deposition regulated by co-transcriptionally m⁶A modification preferred to locate in the CDS [9-11]. Since our results of m⁶A modification distribution of ISGs was consistent with the co-transcriptional m⁶A modification characteristics, we believed that our hypothesis and results were correlated and consistent.

(2) Since a key part of the model includes the cytosol-localized STAT1 protein undergoing phosphorylation to translocate to the nucleus to mediate gene expression, the authors should focus on the interaction between phosphorylated STAT1 and WTAP in Figure 4, rather than the unphosphorylated STAT1. Only phosphorylated STAT1 localizes to the nucleus, so the presence of pSTAT1 in the immunoprecipitate is critical for establishing a functional link between STAT1 activation and its interaction with WTAP.

Thank you for the constructive comments. As we showed in Figure 4, we found the enhanced interaction between phase-separated WTAP and the nuclear-translocated STAT1 which were phosphorylated under IFN-β stimulation, indicating the importance of phosphorylation of STAT1. We repeated the immunoprecipitation experiments to clarify the function of pSTAT1 in WTAP interaction. Our results showed that IFN-β stimulation induced the interaction of WTAP with both pSTAT1 and STAT1 (Figure 5-figure supplement 1C), indicating that most of the WTAP-associated STAT1 was phosphorylated. Taken together, our data proved that LLPS WTAP bound with nuclear-translocated pSTAT1 under IFN-β stimulation.

(3) The authors should include pSTAT1 ChIP-seq and WTAP ChIP-seq on IFNb-treated samples in Figure 5 to allow for a comprehensive and unbiased genomic analysis for comparing the overlaps of peaks from both ChIP-seq datasets. These results should further support their hypothesis that WTAP interacts with pSTAT1 to enhance m⁶A modifications on ISGs.

Thank you for raising this thoughtful comment. In this study, MeRIP-seq and RNA-seq along with immunoprecipitation and immunofluorescence experiments supported that phase transition of WTAP enhanced its interaction to pSTAT1, thus mediating ISGs m⁶A modification and expression by enhancing its interaction with nuclear-translocated pSTAT1 during virus infection and IFN-β stimulation. However, how WTAP-mediated m⁶A modification was related to STAT1-mediated transcription remained unclear. Several researches have revealed the recruitment of m⁶A methyltransferase complex (MTC) to transcription start sites (TSS) of coding genes and R-loop structure by interacting with transcriptional factors STAT5B, SMAD2/3, DNA helicase DDX21, indicating the engagement of MTC mediated m⁶A modification on nascent transcripts at the very beginning of transcription [11-13]. These researches inspired us that phase-separated WTAP could be recruited to the ISG promoter regions via interacting with nuclear-translocated pSTAT1. To validate this mechanism, we have conducted ChIP-qPCR experiments targeting STAT1 and WTAP, revealed that IFN-β induced the comparable recruitment dynamics of both STAT1 and WTAP to ISG promoter regions (Figure 6A-B). Notably, STAT1 deficiency significantly abolished the bindings between WTAP and ISG promoter regions (Figure 6C). These findings established nuclear-translocated STAT1-dependent recruitment of phase separated WTAP and ISG promoter region, substantiated our hypothesis within the current study. We totally agree that ChIP-seq data will be more supportive to explore the mechanism in depth. We will continuously focus on the whole genome chromatin distribution of WTAP and explore more functional effect of transcriptional factor-dependent WTAP-promoter regions interaction in the future.

Minor points:

(1) Since IFNb is primarily known for modulating biological processes through gene transcription, it would be informative if the authors discussed the mechanism of how IFNb would induce the interaction between WTAP and PPP4.

Protein phosphatase 4 (PPP4) is a multi-subunit serine/threonine phosphatase complex that participates in diverse biologic process, including DDR, cell cycle progression, and apoptosis[14]. Several signaling pathway such as NF-κB and mTOR signaling, can be regulated by PPP4. Previous research showed that deficiency of PPP4 enhanced IFN-β downstream signaling and ISGs expression, which was consistent with our findings that knockdown of PPP4C impaired WTAP-mediated m⁶A modification, enhanced the ISGs expression[15,16]. Since there was no significant enhancement in PPP4 expression level during 0-3 hours of IFN-β stimulation in our results, we explored the PTM that may influence the protein-protein interaction, such as ubiquitination. Intriguingly, we found the ubiquitination level of PPP4 was enhanced after IFN-β stimulation, which may affect the interaction between PPP4 and WTAP (Author response image 3). Further investigation between PPP4 and WTAP will be conducted in our future study.

Author response image 3.

HEK 293T transfected with HA-ubiquitin (HA-Ub) and Flag-PPP4 were treated with 10 ng/mL IFN-β or left untreated. Whole cell lysate (WCL) was collected and immunoprecipitation (IP) experiment using anti-Flag antibody was performed, followed with immunoblot. Similar results were obtained for three independent biological experiments.

(2) The authors should include mCherry alone controls in Figure 1D to demonstrate that mCherry does not contribute to the phase separation of WTAP. Does mCherry have or lack a PLD?

According to the crystal structure of mCherry protein in protein structure database RCSB-PDB, mCherry protein presents as a β-barrel protein, with no IDRs or multivalent interaction domains including PLD, indicating that mCherry protein has no capability to undergo phase separation. This characteristic makes it a suitable protein to tag and trace the localization or expression levels of proteins, and a negative control for protein phase separation studies. As the reviewer suggested, we include mCherry alone controls in the revised version (Figure 1D).

(3) The authors should clarify the immunoprecipitation assays in the methods. For example, the labeling in Figure 2A suggests that antibodies against WTAP and pan-p were used for two immunoprecipitations. Is that accurate?

Due to the lack of phosphorylated-WTAP antibody, the detection of phosphorylation of WTAP was conducted by WTAP-antibody-mediated immunoprecipitation. We conducted immunoprecipitation to pull-down WTAP protein by WTAP antibody, then used antibody against phosphoserine/threonine/tyrosine (pan-p) to detect the phosphorylation level of WTAP. To avoid the misunderstanding, we have modified the description from pan-p to pWTAP (pan-p) in figures and revised the figure legends.

(4) The authors should include overall m⁶A modification levels quantified of GFP^sgRNA and WTAP^sgRNA cells, either by mass spectrometry (preferably) or dot blot.

We thank reviewer for raising these useful suggestions. As we showed in Figure 3F and J-K, the m⁶A modification event and degradation of ISG mRNAs were significantly depleted in WTAP^sgRNA cell lines, indicating that function of WTAP was deficient. Thus, we used the WTAP^sgRNA #2 cell line to perform most of our experiment. Furthermore, we also found 46.4% of global m⁶A modification was decreased in WTAP^sgRNA THP-1 cells than that of control cells with or without IFN-β stimulation (Author response image 4), further validating that level of m⁶A modification was significantly affected in WTAP^sgRNA cells. Taken together, our data confirmed that m⁶A methyltransferase activity was efficiently inhibited in our WTAP^sgRNA cells.

Author response image 4.

Control (GFP^sgRNA) and WTAP^sgRNA #2 THP-1-derived macrophages were treated with 10 ng/mL IFN-β for 4 hours. Global m⁶A level was detected and quantified through ELISA assays. All error bars, mean values ± SEM, P-values were determined by two-way ANOVA test independent biological experiments.

Reviewer #3 (Public review):

Summary:

This study presents a valuable finding on the mechanism used by WTAP to modulate the IFN-β stimulation. It describes the phase transition of WTAP driven by IFN-β-induced dephosphorylation. The evidence supporting the claims of the authors is solid, although major analysis and controls would strengthen the impact of the findings. Additionally, more attention to the figure design and to the text would help the reader to understand the major findings.

Strength:

The key finding is the revelation that WTAP undergoes phase separation during virus infection or IFN-β treatment. The authors conducted a series of precise experiments to uncover the mechanism behind WTAP phase separation and identified the regulatory role of 5 phosphorylation sites. They also succeeded in pinpointing the phosphatase involved.

Weaknesses:

However, as the authors acknowledge, it is already widely known in the field that IFN and viral infection regulate m⁶A mRNAs and ISGs. Therefore, a more detailed discussion could help the reader interpret the obtained findings in light of previous research.

We are grateful for the positive comments and the unbiased advice by the reviewer. To interpret our findings in previous research, we have revised the manuscript carefully and added more detailed discussion on ISG mRNAs m⁶A modification during virus infection or IFN stimulation.

It is well-known that protein concentration drives phase separation events. Similarly, previous studies and some of the figures presented by the authors show an increase in WTAP expression upon IFN treatment. The authors do not discuss the contribution of WTAP expression levels to the phase separation event observed upon IFN treatment. Similarly, METTL3 and METTL14, as well as other proteins of the MTC are upregulated upon IFN treatment. How does the MTC protein concentration contribute to the observed phase separation event?

We thank reviewer for pointing out the importance of the concentration dependent phase transition. Previously, Ge et al. discovered that expression level of WTAP was up-regulated during LPS stimulation, thereby promoting WTAP phase separation ability and m⁶A modification, indicating that WTAP concentration indeed affects the phase separation event. In our article, we have generated the phase diagram with different concentration of recombinant mCherry-WTAP in vitro (Figure 1-figure supplement 1C). For in cells experiments, we constructed the TRE-mCherry-WTAP HeLa cells in which the expression of mCherry-WTAP was induced by doxycycline (Dox) in a dose-dependent manner (Author response image 5A). We detected the LLPS of mCherry-WTAP at different concentrations by increasing the doses of Dox, and found that WTAP automatically underwent LLPS only in an artificially high expression level (Author response image 5B). However, the cells we used to detect the WTAP phase separation in our article was mCherry-WTAP-rescued HeLa cells, in which mCherry-WTAP was introduced in WTAP^sgRNA HeLa cells to stably express mCherry-WTAP. We had adjusted and verified the mCherry-WTAP expression level precisely to make the protein abundance similar to endogenous WTAP in wild type (WT) HeLa cells (Author response image 5C). We also repeated the IFN-β stimulation experiments and found no significant increase of WTAP protein level (Figure 5-figure supplement 1A). These findings indicated the phase separation of WTAP in our article was not artificially induced due to the extremely high protein expression level.

MTC protein expression level was crucial for m⁶A modification during virus infection event. Rubio et al. and Winkler et al. revealed that WTAP, METTL3 and METTL14 were up-regulated after 24 hours of HCMV infection[8,17]. Recently, Ge et al. proposed that WTAP protein was degraded after 12 hours of VSV and HSV stimulation5,18. However, these studies only focused on the virus infection event, how the MTC protein expression change after direct IFN-β stimulation was still unclear. Our study investigated the transition change of WTAP under IFNβ stimulation in a short time, we detected the expression level of MTC proteins within 6 hours of IFN-β treatment, and found no significant enhancement of WTAP, METTL3 or METTL14 protein level and mRNA level (Figure 5-figure supplement 1B and Figure 5-figure supplement 1A;). Our in vitro experiments showed that introducing CFP-METTL3 protein have no significant influence on WTAP phase separation (Figure 4H). Additionally, we performed in cells experiments and found that increased expression of METTL3 had no effect on WTAP phase separation event (Author response image 5D). Taken together, WTAP phase separation can be promoted by dramatically increased concentration of WTAP both in vitro and in cells, but the phase separation of WTAP under IFN-β stimulation in our study was not related with the expression level of MTC proteins.

Author response image 5.

(A) Immunoblot analysis of the expression of mCherry-WTAP in TRE-mCherry-WTAP HeLa cells treated with different doses of doxycycline (Dox). Protein level of mCherry-WTAP was quantified and normalized to β-actin of n=3 independent biological experiments. Results were obtained for three independent biological experiments. (B) Phase separation diagram of mCherry-WTAP in TRE-mCherry-WTAP HeLa cells treated with different doses of Dox were observed through confocal microscopy. Representative images for three independent biological experiments were shown in b while number of WTAP condensates that diameter over 0.4 μm of n=80 cells were counted and shown as medium with interquartile range. Dotted white lines indicated the location of nucleus. Scale bars indicated 10 μm. (C) Immunoblot analysis of the expression of endogenous WTAP in wildtype (WT) HeLa cells and mCherry-WTAP-rescued WTAP^sgRNA HeLa cells. (D) mCherry-WTAP-rescued HeLa cells were transfected with 0, 200 or 400 ng of Flag-METTL3, followed with 10 ng/mL IFN-β for 1 hour or left untreated (UT). Phase separation of mCherry-WTAP was observed through confocal microscopy. The number of WTAP condensates that diameter over 0.4 μm of n = 20 cells were counted through ImageJ and shown. Representative images of n=20 cells were shown. All error bars, mean values ± SD were determined by unpaired two-tailed Student’s t-test of n = 3 independent biological experiments in (A). For (A, C), similar results were obtained for three independent biological experiments.

How is PP4 related to the IFN signaling cascade?

Both reviewer #2 and reviewer #3 raised a similar point on the relationship between PPP4 and IFN signaling. In short, protein phosphatase 4 (PPP4) participates in diverse biologic process, including DDR, cell cycle progression and apoptosis14 and several signaling pathway. Previous research showed that deficiency of PPP4 enhanced IFN-β downstream signaling and ISGs expression, which was consistent with our findings that knockdown of PPP4C impaired WTAP-mediated m⁶A modification, and enhanced the ISGs expression[15,16]. Since there was no significant enhancement in PPP4C expression level during 0-3 hours of IFN-β stimulation in our results, we tried to explore the post-translation modification which may influence the protein-protein interaction, such as ubiquitination. Intriguingly, we found the ubiquitination level of PPP4 was enhanced after IFN-β stimulation, which may affect the interaction between PPP4 and WTAP (Author response image 4). Investigation between PPP4 and WTAP will be conducted in our further study (also see minor points 1 of reviewer#2).

In general, it is very confusing to talk about WTAP KO as WTAPgRNA.

As we describe above, all WTAP-deficient THP-1 cells were generated using the CRISPR-Cas9 system with WTAP-specific sgRNA, and used bulk cells instead of the monoclonal knockout cell for further experiments. Since monoclonal knockout cells were not obtained, we refer it as WTAP^sgRNA THP-1 cells rather than WTAP-KO THP-1 cells. We confirmed that WTAP expression was efficiently knocked down in WTAP^sgRNA THP-1 cells, and the m⁶A modification level was significantly impaired (Figure 3I, Figure 3-figure supplement 1A and Author response image 4), which was suitable for mechanism investigation.

Recommendations for the authors:

Reviewer #1 (Recommendations for the authors):

Related to the points raised in 'weaknesses' above, if the authors want to claim that this mechanism is reliant on WTAP phase-separated states, additional controls should be done to demonstrate this. Based on the available data it seems that it is just as likely that the phosphorylation state of WTAP is mediating interactions with other factors and/or altering its subcellular localization, which may or may not be related to phase separation.

We are grateful for the constructive suggestions. As we showed in , Figure 5-figure supplement 1H; Author response image 1 along with the explanation above, 5% hex dispersed the phase separation condensates of WTAP without affecting its phosphorylation status, proving the interaction between STAT1 and methylation complex impaired by hex was depended on WTAP LLPS but not its phosphorylation status (Figure 5E-H). To further confirmed the recruitment of WTAP LLPS on ISG promoter region, we performed the immunoprecipitation and ChIP-qPCR experiments of wild type (WT) WTAP, 5ST-D and 5ST-A rescued THP-1 cells. Our results uncovered the interaction between de-phosphorylated-mimic WTAP mutant and STAT1, and its binding ability with ISG promoter regions were depleted by hex without affecting its phosphorylation status (Author response image 2, Figure 5-figure supplement 1 F, Figure 6E). Taken together, we identified the de-phosphorylation event that regulated phase transition of WTAP during IFN-β stimulation, and proposed that LLPS of WTAP mediated by dephosphorylation was the key mechanism to bind with STAT1 and mediate the m⁶A modification on ISG mRNAs.

Reviewer #2 (Recommendations for the authors):

The author order is different for the main text and the supplementary file.

Thank you for pointing out this mistake. We have corrected it in our revised version.

Reviewer #3 (Recommendations for the authors):

Signaling molecules? Do you mean RNA or protein post-translational modification?

Thank you for pointing out this problem. In this sentence, we mean the post-translational modification of signaling proteins. We have corrected this mistake in our revised version.

Line 145: Do you mean Figure 1C?

We have corrected it in our revised version.

Line 214: Are the cells KO for WTAP? Do you mean CRISPR KO? In general, it is easier to present WTAPgRNA as WTAPKO.

Thank you for the constructive suggestion. As we explained above, in this project, all WTAP-deficient THP-1 cells were generated using the CRISPR-Cas9 system with WTAP-specific sgRNA, and used bulk cells instead of the monoclonal knockout cells. We confirmed that WTAP expression was efficiently knocked down in WTAP^sgRNA THP-1 cells, and the m⁶A modification level was significantly impaired (Figure 3I, Figure3-figure supplement 1A and Author response image 4). Since monoclonal knockout cells were not obtained, we refer it as WTAP^sgRNA THP-1 cells rather than WTAP-KO THP-1 cells.

Line 221: WTAP KO has no effect on the expression level of transcription factors?

Thank you for pointing out the uncritical expression. We have corrected this in our revised version.

Figure 3C: I would suggest removing the tracks and showing the expression levels in TPMs.

According to the reviewer’s suggestion, we have analyzed the results and showed the ISGs expression levels in fold change of TPMs (Figure 3D).

Figure 4C: It seems that the IP efficiency from METTL3 is lower in WTAP KO cells. That may impact the author's conclusions.

We have repeated the immunoprecipitation experiments of METTL3 and confirmed the immunoprecipitation (IP) efficiency from METTL3 had no difference between WTAP^sgRNA cells and the control cells (Figure 5C).

I would suggest performing an IP of WTAP with the 5StoA mutation to confirm the missing interaction with WTAP.

According to the reviewer’s suggestion, we investigated the interaction between STAT1 and WTAP in WT cells and WTAP 5ST-A-rescued THP-1 cells. Our results showed that interaction between STAT1, METTL3 and WTAP were enhanced with WTAP 5ST-A mutation, which was depleted by hex treatment (Figure 5-figure supplement 1E). Thus, the interaction of WTAP WT or 5ST-A with the promoter regions of ISG were attenuated by WTAP LLPS dissolution (Figure 6E). Taken together, the interaction between STAT1 and MTC were relied on LLPS of WTAP.

In the graphical abstract, it is confusing to represent WTAP as a line. All other proteins are presented as circles. It is easy to confuse WTAP protein with an RNA. Additionally, m⁶A is too big in size. It should be smaller than a protein.

We thank the reviewer for raising this suggestion. We have modified the graphical abstract to avoid the confusion in our revised version (Figure 6F).

References

(1) Wegmann, S., Eftekharzadeh, B., Tepper, K., Zoltowska, K.M., Bennett, R.E., Dujardin, S., Laskowski, P.R., MacKenzie, D., Kamath, T., Commins, C., et al. (2018). Tau protein liquid-liquid phase separation can initiate tau aggregation. The EMBO journal 37. 10.15252/embj.201798049.

(2) Lu, Y., Wu, T., Gutman, O., Lu, H., Zhou, Q., Henis, Y.I., and Luo, K. (2020). Phase separation of TAZ compartmentalizes the transcription machinery to promote gene expression. Nat Cell Biol 22, 453-464. 10.1038/s41556-020-0485-0.

(3) Zhang, H., Shao, S., Zeng, Y., Wang, X., Qin, Y., Ren, Q., Xiang, S., Wang, Y., Xiao, J., and Sun, Y. (2022). Reversible phase separation of HSF1 is required for an acute transcriptional response during heat shock. Nat Cell Biol 24, 340-352. 10.1038/s41556-022-00846-7.

(4) Duster, R., Kaltheuner, I.H., Schmitz, M., and Geyer, M. (2021). 1,6-Hexanediol, commonly used to dissolve liquid-liquid phase separated condensates, directly impairs kinase and phosphatase activities. J Biol Chem 296, 100260. 10.1016/j.jbc.2021.100260.

(5) Ge, Y., Chen, R., Ling, T., Liu, B., Huang, J., Cheng, Y., Lin, Y., Chen, H., Xie, X., Xia, G., et al. (2024). Elevated WTAP promotes hyperinflammation by increasing m⁶A modification in inflammatory disease models. J Clin Invest 134. 10.1172/JCI177932.

(6) Hou, S., Hu, J., Yu, Z., Li, D., Liu, C., and Zhang, Y. (2024). Machine learning predictor PSPire screens for phase-separating proteins lacking intrinsically disordered regions. Nat Commun 15, 2147. 10.1038/s41467-024-46445-y.

(7) McFadden, M.J., McIntyre, A.B.R., Mourelatos, H., Abell, N.S., Gokhale, N.S., Ipas, H., Xhemalce, B., Mason, C.E., and Horner, S.M. (2021). Post-transcriptional regulation of antiviral gene expression by N6-methyladenosine. Cell Rep 34, 108798. 10.1016/j.celrep.2021.108798.

(8) Winkler, R., Gillis, E., Lasman, L., Safra, M., Geula, S., Soyris, C., Nachshon, A., Tai-Schmiedel, J., Friedman, N., Le-Trilling, V.T.K., et al. (2019). m(6)A modification controls the innate immune response to infection by targeting type I interferons. Nat Immunol 20, 173-182. 10.1038/s41590-018-0275-z.

(9) Li, Y., Xia, L., Tan, K., Ye, X., Zuo, Z., Li, M., Xiao, R., Wang, Z., Liu, X., Deng, M., et al. (2020). N(6)-Methyladenosine co-transcriptionally directs the demethylation of histone H3K9me2. Nat Genet 52, 870-877. 10.1038/s41588-020-0677-3.

(10) Huang, H., Weng, H., Zhou, K., Wu, T., Zhao, B.S., Sun, M., Chen, Z., Deng, X., Xiao, G., Auer, F., et al. (2019). Histone H3 trimethylation at lysine 36 guides m(6)A RNA modification co-transcriptionally. Nature 567, 414-419. 10.1038/s41586-019-1016-7.

(11) Barbieri, I., Tzelepis, K., Pandolfini, L., Shi, J., Millan-Zambrano, G., Robson, S.C., Aspris, D., Migliori, V., Bannister, A.J., Han, N., et al. (2017). Promoter-bound METTL3 maintains myeloid leukaemia by m(6)A-dependent translation control. Nature 552, 126-131. 10.1038/nature24678.

(12) Hao, J.D., Liu, Q.L., Liu, M.X., Yang, X., Wang, L.M., Su, S.Y., Xiao, W., Zhang, M.Q., Zhang, Y.C., Zhang, L., et al. (2024). DDX21 mediates co-transcriptional RNA m(6)A modification to promote transcription termination and genome stability. Mol Cell 84, 1711-1726 e1711. 10.1016/j.molcel.2024.03.006.

(13) Bhattarai, P.Y., Kim, G., Lim, S.C., and Choi, H.S. (2024). METTL3-STAT5B interaction facilitates the co-transcriptional m(6)A modification of mRNA to promote breast tumorigenesis. Cancer Lett 603, 217215. 10.1016/j.canlet.2024.217215.

(14) Dong, M.Z., Ouyang, Y.C., Gao, S.C., Ma, X.S., Hou, Y., Schatten, H., Wang, Z.B., and Sun, Q.Y. (2022). PPP4C facilitates homologous recombination DNA repair by dephosphorylating PLK1 during early embryo development. Development 149. 10.1242/dev.200351.

(15) Zhan, Z., Cao, H., Xie, X., Yang, L., Zhang, P., Chen, Y., Fan, H., Liu, Z., and Liu, X. (2015). Phosphatase PP4 Negatively Regulates Type I IFN Production and Antiviral Innate Immunity by Dephosphorylating and Deactivating TBK1. J Immunol 195, 3849-3857. 10.4049/jimmunol.1403083.

(16) Raja, R., Wu, C., Bassoy, E.Y., Rubino, T.E., Jr., Utagawa, E.C., Magtibay, P.M., Butler, K.A., and Curtis, M. (2022). PP4 inhibition sensitizes ovarian cancer to NK cell-mediated cytotoxicity via STAT1 activation and inflammatory signaling. J Immunother Cancer 10. 10.1136/jitc-2022-005026.

(17) Rubio, R.M., Depledge, D.P., Bianco, C., Thompson, L., and Mohr, I. (2018). RNA m(6) A modification enzymes shape innate responses to DNA by regulating interferon beta. Genes Dev 32, 1472-1484. 10.1101/gad.319475.118.

(18) Ge, Y., Ling, T., Wang, Y., Jia, X., Xie, X., Chen, R., Chen, S., Yuan, S., and Xu, A. (2021). Degradation of WTAP blocks antiviral responses by reducing the m(6) A levels of IRF3 and IFNAR1 mRNA. EMBO Rep 22, e52101. 10.15252/embr.202052101.

Author response:

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

Public Reviews:

Reviewer #1 (Public Review):

The model of phosphotransfer from Y169 IKK to S32 IkBa is compelling and an important new contribution to the field. In fact, this model will not be without controversy, and publishing the work will catalyze follow-up studies for this kinase and others as well. As such, I am supportive of this paper, though I do also suggest some shortening and modification.

We appreciate the reviewers candid response on the difficulty of this study and the requirement of follow-up studies to confirm a direct transfer of the phosphate. We also have edited the manuscript to make it shorter.

Generally, the paper is well written, but several figures should be quantified, and experimental reproducibility is not always clear. The first 4 figures are slow-going and could be condensed to show the key points, so that the reader gets to Figures 6 and 7 which contain the "meat" of the paper.

We have indicated the experimental reproducibility in the methodology section against each assay. We have shortened the manuscript corresponding to sections describing figures 1-4. However, when we talked to some of our colleagues whose expertise do not align with kinases and IKK, we realized that some description were necessary to introduce them to the next figures. Additionally, we added Fig. S6 indicating that the radiolabelled phospho-IKK2 Y169F is unable to transfer its own phosphate group(s) to the substrate IkBa.

Reviewer #2 (Public Review):

Phosphorylation of IκBα is observed after ATP removal, although there are ambiguous requirements for ADP.

We agree with the reviewer that this observation is puzzling. We hypothesize that ADP is simultaneously regulating the transfer process likely through binding to the active site.

It seems that the analysis hinges on the fidelity of pan-specific phosphotyrosine antibodies.

We agree with the reviewer. To bolster our conclusion, we used antibodies from two different sources. These were Monoclonal mouse anti-Phospho-Tyrosine (catalogue number: 610000) was from BD Biosciences or from EMD Millipore (catalogue no. 05-321X).

The analysis often returns to the notion that tyrosine phosphorylation(s) (and critical active site Lys44) dictate IKK2 substrate specificity, but evidence for this seems diffuse and indirect. This is an especially difficult claim to make with in vitro assays, omitting the context of other cellular specificity determinants (e.g., localization, scaffolding, phosphatases).

We agree with the concerns that the specificity could be dependent on other cellular specificity determinants and toned down our claims where necessary. However, we would like to point out that the specificity of IKK2 towards S32 and S36 of IkBa in cells in response to specific stimuli is well-established. It is also well-established that its non-catalytic scaffolding partner NEMO is critical in selectively bringing IkBa to IKK from a large pool of proteins. The exact mechanism of how IKK2 choose the two serines amongst many others in the substrate is not clear.

Multiple phosphorylated tyrosines in IKK2 were apparently identified by mass spectrometric analyses, but the data and methods are not described. It is common to find non-physiological post-translational modifications in over-expressed proteins from recombinant sources. Are these IKK2 phosphotyrosines evident by MS in IKK2 immunoprecipitated from TNFa-stimulated cells? Identifying IKK2 phosphotyrosine sites from cells would be especially helpful in supporting the proposed model.

Mass spectrometric data for identification of phosphotyrosines from purified IKK2 is now incorporated (Figure S3A). Although we have not analyzed IKK2 from TNF-a treated cells in this study, a different study of phospho-status of cellular IKK2 indicated tyrosine phosphorylation (Meyer et al 2013).

Reviewer #3 (Public Review):

The identity and purity of the used proteins is not clear. Since the findings are so unexpected and potentially of wide-reaching interest - this is a weakness. Similar specific detection of phospho-Ser/Thr vs phospho-Tyr relies largely on antibodies which can have varying degrees of specificity.

We followed a stringent purification protocol of several steps (optimized for the successful crystallization of the IKK2) that removed most impurities (PMID: 23776406, PMID: 39227404). The samples analysed with ESI MS did not show any significant contaminating kinase from the Sf9 cells.

Sequence specific phospho-antibodies used in this study are very well characterized and have been used in the field for years (Basak et al 2007, PMID: 17254973). We agree on the reviewer’s concerns on the pan-specific phospho-antibodies. Since phospho-tyrosine detection is the crucial aspect of this study, we minimized such bias by using pan-specific phosphotyrosine antibodies from two independent sources.

Reviewer #1 (Recommendations For The Authors):

I understand that Figure 3 shows that K44M abolishes both S32/26 phosphorylation and tyrosine phosphorylation, but not PEST region phosphorylation. This suggests that autophosphorylation is reflective of its known specific biological role in signal transduction. But I do not understand why "these results strongly suggest that IKK2-autophosphorylation is critical for its substrate specificity". That statement would be supported by a mutant that no longer autophosphorylates, and as a result shows a loss of substrate specificity, i.e. phosphorylates non-specific residues more strongly. Is that the case? Maybe Darwech et al 2010 or Meyer et al 2013 showed this.

Later figures seem to address this point, so maybe this conclusion should be stated later in the paper.

We have now clarified this in the manuscript and moved the comment to the next section. We have consolidated the results in Figure 3 and 4 in the previous version into a single figure in Figure. The text has also been modified accordingly.

Page 10: mentions DFG+1 without a proper introduction. The Chen et al 2014 paper appears to inform the author's interest in Y169 phosphorylation, or is it just an additional interesting finding? Does this publication belong in the Introduction or the Discussion?

The position of Y169 at the DFG+1 was intriguing and the 2014 article Chen et al further bolstered our interest in this residue to be investigated. We think this publication is important in both sections. 

To understand the significance of Figure 4D, we need a WT IKK2 control: or is there prior literature to cite? This is relevant to the conclusion that Y169 phosphorylation is particularly important for S32 phosphorylation.

We have now added a new supplementary figure where activities of WT and Y169F IKK2 towards WT and S32/S36 mutants are compared (Figure S3F). At a similar concentration, the activity of WT-IKK2 is many fold higher than that of YtoF mutants (Fig. 4C). The experiments were performed simultaneously, although samples were loaded on different gels but otherwise processed in a similar way. The corresponding data is now included in the manuscript as Figure S3F.

The cold ATP quenching experiment is nice for testing the model that Y169 functions as a phospho sink that allows for a transfer reaction. However, there is only a single timepoint and condition, which does not allow for a quantitative analysis. Furthermore, a positive control would make this experiment more compelling, and Y169F mutant should show that cold ATP quenching reduces the phosphorylation of IkBa.

We thank the reviewer for appreciating our experimental design, and pointing out the concerns. We kept the ATP-time point as the maximum of the non-competition experiment. Also, we took 50mM ATP to compare its competition with highest concentration of ADP used. The idea behind using the maximum time and ATP (comparable to ADP) was to capture the effect of competitive-effect of ATP, if any, that would be maximal in the given assay condition in comparison with the phospho-transfer set up in absence of cold ATP. We agree that finer ranges of ATP concentration and time points would have enabled more quantitative analyses. We have now included data where different time intervals are tested (Figure S5D).

Why is the EE mutant recognized by anti-phospho-serine antibodies? In Figure 2F.

We anticipate Serine residues besides those in the activation loop to be phosphorylated when IKK2 is overexpressed and purified from the Sf9 cells. Since Glu (E) mimics phospho-Ser, the said antibody cross reacts with the IKK2-EE that mimics IKK2 phosphorylated at Ser177 and 181.

Figure 7B is clear, but 7C does not add much.

We have now removed the Fig. 7C in the current version. Figure 7 is now renumbered as Figure 6 that does not contain the said cartoon.  

Reviewer #2 (Recommendations For The Authors):

Regarding the specificity arguments (see above in public review), the authors note that NEMO is very important in IKK specificity, and - if I'm understanding correctly - most of these assays were performed without NEMO. Would the IKK2-NEMO complex change these conclusions?

NEMO is a scaffolding protein whose action goes beyond the activation of the IKK-complex. In cells, NEMO brings IkBa from a pool of thousands of proteins to its bonafide kinase when the cells encounter specific signals. In other words, NEMO channels IKK-activity towards its bonafide substrate IkBa at that moment. Though direct proof is lacking, it is likely that NEMO present IkBa in the correct pose to IKK such that the S32/S36 region of IkBa is poised for phosphorylation. The proposed mechanism in the current study further ensures the specificity and fidelity of that phosphorylation event. We believe this mechanism will be preserved in the IKK-NEMO complex unless proven otherwise. As shown below, IKK2 undergoes tyrosine autophosphorylation in presence of NEMO.

Author response image 1.

The work primarily focuses on Y169 as a candidate target for IKK autophosphorylation. This seems reasonable given the proximity to the ATP gamma phosphate. However, Y188F more potently disrupted IκBα phosphorylation. The authors note that this could be due to folding perturbations, but this caveat would also apply to Y169F. A test for global fold perturbations for both Tyr mutants would be helpful.

Y188 is conserved in S/T kinases and that in PKA (Y204) has been studied extensively using structural, biochemical and biophysical tools. It was found in case of PKA that Y204 participates in packing of the hydrophobic core of the large lobe. Disruption of this core structure by mutation allosterically affect the activity of the kinase. We also observed similar engagement of Y188 in IKK2’s large lobe, and speculated folding perturbations in analogy with the experimental evidence observed in PKA. What we meant was mutation of Y188 would allosterically affect the kinase activity. Y169 on the other hand is unique at that position, an no experimental evidence on the effect of phospho-ablative mutation of this residue exist in the literature. Hence, we refrained from speculating its effect on the folding or conformational allostery, however, such a possibility cannot be ruled out. 

I struggled to follow the rationalization of the results of Figure 4D, the series of phosphorylation tests of Y169F against IκBα with combinations of phosphoablative or phosphomimetic variants at Ser32 and Ser36. This experiment is hard to interpret without a direct comparison to WT IKK2.

We agree with the reviewer’s concerns. Through this experiment we wanted to inform about the importance of Tyr-phosphorylation of IKK2 in phosphorylating S32 of IκBα which is of vital importance in NF-kB signaling. We have now provided a comparison with WT-IKK2 in the supplementary Figure S3F. We hope this will help bring more clarity to the issue.

MD simulations were performed to compare structures of unphosphorylated vs. Ser-phosphorylated (p-IKK2) vs. Ser+Tyr-phosphorylated (P-IKK2) forms of IKK2. These simulations were performed without ATP bound, and then a representative pose was subject to ADP or ATP docking. The authors note distortions in the simulated P-IKK2 kinase fold and clashes with ATP docking. Given the high cellular concentration of ATP, it seems more logical to approach the MD with the assumption of nucleotide availability. Most kinase domains are highly dynamic in the absence of substrate. Is it possible that the P-IKK2 poses are a result of simulation in a non-physiological absence of bound ATP? Ultimately, this MD observation is linked to the proposed model where ADP-binding is required for efficient phospho-relay to IκBα. Therefore, this observation warrants scrutiny. Perhaps the authors could follow up with binding experiments to directly test whether P-IKK2 binds ADP and fails to bind ATP.

We thank that reviewer for bringing up this issue. This is an important issue and we must agree that we don’t fully understand it yet. We took more rigorous approach this time where we used three docking programs: ATP and ADP were docked to the kinase structures using LeDock and GOLD followed by rescoring with AutoDock Vina. We found that ATP is highly unfavourable to P-IKK2 compared to ADP. To further address these issues, we performed detailed MM-PBSA (Molecular Mechanics Poisson-Boltzmann Surface Area) analyses after MD-simulation to estimate binding free energies and affinities of ADP and ATP for each of the three differently phosphorylated states of IKK2. These analyses (Figure S4 E and F) clearly indicate that phosphorylated IKK2 have much higher preference for ADP over ATP. However, it does not negate ATP-binding by P-IKK2 in a different pose that may not support kinase activity.

We could not perform any binding experiment because of the following reason. We incubated FL IKK2 WT with or without cold ATP for 30mins, and then incubated these samples with ³²P-ATP and analysed the samples by autoradiography after resolving them on a 10% SDS-PAGE. We found that even after pre-incubation of the kinase with excess cold ATP it still underwent autophosphorylation when radioactive ATP was added as shown below. This prevented us from doing direct binding experiment with ATP as it would not represent true binding event. We also noticed that after removal of bulk ATP post autophosphorylation, phosphorylated IKK2 is capable of further autophosphorylation when freshly incubated with ATP. We have not been able to come up with a condition that would only account for binding of ATP and not hydrolysis. 

Author response image 2.

The authors could comment on whether robust phosphorylation of NEMO was expected (Figure 1D). On a related note, why is NEMO a single band in the 1D left panel and double bands on the right?

No, we did not expect robust phosphorylation of NEMO. However, robust phosphorylation of NEMO is observed only in the absence of IκBα. In presence of IκBα, phosphorylation of NEMO goes down drastically. These were two different preparations of NEMO. When TEV-digestion to remove His-tag is incomplete it gives two bands as the tagged and untagged versions cannot be separated in size exclusion chromatography which is the final step.

Page 14, line 360. "...observed phosphorylation of tyrosine residue(s) only upon fresh ATP-treatment..." I'm not sure I understand the wording here (or the relevance of the citation). Is this a comment on unreported data demonstrating the rapid hydrolysis of the putative phosphotyrosine(s)? If so, that would be helpful to clarify and report in the supporting information.

In our X-ray crystallographic studies with phosphorylated IKK2 we failed to observe any density of phosphate moiety. Furthermore, this IKK2 showed further autophosphorylation when incubated with fresh ATP. These two observations lead us to believe that some of the autophosphorylation are transient in nature. However, quantitative kinetic analyses of this dephosphorylation have not been performed.

Figure S3 middle panel: The PKA substrate overlaid on the IKK2 seems sterically implausible for protein substrate docking. Is that just a consequence of the viewing angle? On a related note, Figure S3 may be mislabeled as S4 in the main text).

It is a consequence of the viewing angle. Also, we apologize for this inadvertent mislabelling. It has been corrected in the current version.

Reviewer #3 (Recommendations For The Authors):

The detection of phosphorylated amino acids relies largely on antibodies which can have a varying degree of specificity. An alternative detection mode of the phospho-amino acids for example by MS would strengthen the evidence.

We agree with the concern of specificity bias of antibodies. We tried to minimize such bias by using two different p-Tyr antibodies as noted previously and also in the methodology section. We were also able to detect phospho-tyrosine residues by MS/MS analyses, representative spectra are now added (Figure S3A).

IKK2 purity - protocol states "desired purity". What was the actual purity and how was it checked? MS would be useful to check for the presence of other kinases.

Purity of the recombinantly purified IKK2s are routinely checked by silver staining. A representative silver stained SDS-PAGE is shown (Figure S1C). It may be noted that, there’s a direct correlation of expression level and solubility, and hence purification yield and quality with the activity of the kinase. Active IKK2s express at much higher level and yields cleaner prep. In our experience, inactive IKKs like K44M give rise to poor yield and purity. We analysed K44M by LC MS/MS to identify other proteins present in the sample. We did not find any significant contaminant kinase the sample (Figure S1D). The MS/MS result is attached.

Figure 1C&D: where are the Mw markers? What is the size of the band? What is the MS evidence for tyrosine phosphorylation?

We have now indicated MW marker positions on these figures.

MS/MS scan data for the two peptides containing pTyr169 and pTyr188 are shown separately (Figure S3A).

Figure 2F: Why is fresh ATP necessary? Why was Tyr not already phosphorylated? The kinetics of this process appear to be unusual when the reaction runs to completion within 5 minutes ?

As stated earlier, we believe some of the autophosphorylation are transient in nature. We think the Tyr-phosphorylation are lost due to the action of cellular phosphatases. We agree with the concern of the reviewer that, the reaction appears to reach completion within 5 minutes in Fig 2F. We believe it is probably due to the fact that the amount of kinase used in this study exceeds the linear portion of the dynamic range of the antibody used. Lower concentration of the kinase do show that reaction does not reach completion until 60mins as shown in Fig. 2A.

Figure 3: Can the authors exclude contamination with a Tyr kinase in the IKK2-K44M prep? The LC/MS/MS data should be included.

We have reanalysed the sample on orbitrap to check if there’s any Tyr-kinase or any other kinase contamination. We used Spodoptera frugiperda proteome available on the Uniprot website for this analysis. These analyses confirmed that there’s no significant kinase contaminant present in the fraction (Figure S1D).

What is the specificity of IKK-2 Inhibitor VII? Could it inhibit a contaminant kinase?

This inhibitor is highly potent against IKK2 and the IKK-complex, and to a lesser extent to IKK1. No literature is available on its activity on other kinases. In an unrelated study, this compound was used alongside MAPK inhibitor SB202190 wherein they observed completely different outcomes of these two inhibitors (Matou-Nasri S, Najdi M, AlSaud NA, Alhaidan Y, Al-Eidi H, Alatar G, et al. (2022) Blockade of p38 MAPK overcomes AML stem cell line KG1a resistance to 5-Fluorouridine and the impact on miRNA profiling. PLoS ONE 17(5):e0267855. https://doi.org/10.1371/journal.pone.0267855). This study indirectly proves that IKK inhibitor VII does not fiddle with the MAPK pathways. We have not found any literature on the non-specific activity of this inhibitor.

Figure 6B: the band corresponding to "p-IkBa" appears to be similar in the presence of ADP (lanes 4-7) or in the absence of ADP but the presence of ATP (lane 8).

Radioactive p-IκBα level is more when ADP is added than in absence of ADP. In presence of cold ATP, radioactive p-IκBα level remains unchanged. This result strongly indicate that the addition of phosphate group to IκBα happens directly from the radioactively labelled kinase that is not competed out by the cold ATP.

Author response:

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

Public Reviews:

Reviewer #1 (Public Review):

Summary:

In this manuscript, Eaton et al. examine the regulation of transcription directionality using a powerful genomic approach (more about the methodology below). Their data challenge the notion that the polyadenylation signal-reading Cleavage and Polyadenylation (CPA) complex is responsible for controlling promoter directionality by terminating antisense transcription. Namely, depletion of the required CPA factor RBBP6 has little effect on antisense transcription measured by POINT. They find instead that initiation is intrinsically preferential in the sense direction and additionally maintained by the activities of an alternative processing complex called Integrator, together with the kinase CDK9. In the presence of CDK9 activity, depletion of Integrator endoribonuclease INTS11 leads to globally increased transcription in the antisense direction, and minor effects in the sense direction. However, CDK9 inhibition reveals that sense transcription is also sensitive to INS11 depletion. The authors suggest that CDK9 activity is stronger in the sense direction, preventing INTS11-mediated premature termination of sense transcrpts.

Strengths:

The combination of acute depletion of the studied factors using degron approaches (important to limit possible secondary effects), together with novel and very sensitive nascent transcriptomics methods POINT and sPOINT is very powerful. The applied spike-in normalization means the analysis is more rigorous than most. Using this methodology allowed the authors to revisit the interesting question of how promoter/transcription directionality is determined.

The data quality appears very good and the fact that both global analysis as well as numerous gene-specific examples are shown makes it convincing.

The manuscript is well written and hence a pleasure to read.

We appreciate this positive assessment.

Weaknesses:

I am slightly worried about the reproducibility of the data - it is unclear to me from the manuscript if and which experiments were performed in replicate (lack of table with genomic experiments and GEO access, mentioned in more detail in below recommendations to authors), and the methods could be more detailed.

All sequencing data was deposited with GEO. Multiple biological replicates were performed for each sequencing experiment.  Bigwig files are presented as a table in the GEO submissions. This data has now been made public.

A separate discussion section would be useful, particularly since the data provided challenge some concepts in the field. How do the authors interpret U1 data from the Dreyfuss lab in light of their results? How about the known PAS-density directionality bias (more PAS present in antisense direction than in sense) - could the differential PAS density be still relevant to transcription directionality?

As suggested, we have expanded our discussion to relate our findings to existing data. We think the results from the Dreyfuss lab are very important and highlight the role of U1 snRNA in enforcing transcriptional elongation.  It does this in part by shielding PAS sequences.  Recent work from our lab also shows that U1 snRNA opposes the Restrictor complex and PNUTS, which otherwise suppress transcription (Estell et al., Mol Cell 2023).  Most recently, the Adelman lab has demonstrated that U1 snRNA generally enhances transcription elongation (Mimoso and Adelman., Mol Cell 2023).  Our work does not challenge and is not inconsistent with these studies.

The role of U1 in opposing PAS-dependent termination inspired the idea that antisense transcriptional termination may utilise PASs.  This was because such regions are rich in AAUAAA and comparatively poor in U1 binding sites. However, our RBBP6 depletion and POINT-seq data suggest that PAS-dependent termination is uncommon in the antisense direction. As such, other mechanisms suppress antisense transcription and influence promoter directionality. In our paper, we propose a major role for the Integrator complex.

We do not completely rule out antisense PAS activity and discuss the prior work that identified polyadenylated antisense transcripts. Nevertheless, this was detected by oligo-dT primed RT-PCR/Northern blotting, which cannot determine the fraction of non-polyadenylated RNA that could result from PAS-independent termination (e.g. by Integrator).  To do that requires an analysis of total nascent transcription as achieved by our POINT-seq.  Based on these experiments, Integrator depletion has a greater impact on antisense transcription than RBBP6 depletion. 

I find that the provided evidence for promoter directionality to be for the most part due to preferential initiation in the sense direction should be stressed more. This is in my eyes the strongest effect and is somehow brushed under the rug.

We agree that this is an important finding and incorporated it into the title and abstract.  As the reviewer recommends, we now highlight it further in the new discussion.

References 12-17 report an effect of Integrator on 5' of protein-coding genes, while data in Figure 2 appears contradictory. Then, experiments in Figure 4 show a global effect of INST11 depletion on promoter-proximal sense transcription. In my opinion, data from the 2.5h time-point of depletion should be shown alongside 1.5h in Figure 2 so that it is clear that the authors found an effect similar to the above references. I find the current presentation somehow misleading.

We are grateful for this suggestion and present new analyses demonstrating that our experiment in Figure 2 concurs with previous findings (Supplemental Figures 2A and B). Our original heatmap (Figure 2E) shows a very strong and general antisense effect of INTS11 loss. On the same scale, the effects in the sense direction are not as apparent, which is also the case using metaplots.  New supplemental figure 2A now shows sense transcription from this experiment in isolation and on a lower scale, demonstrating that a subset of genes shows promoter-proximal increases in transcription following INTS11 depletion.  This is smaller and less general than the antisense effect but consistent with previous findings.  Indeed, our new analysis in supplemental figure 2B shows that affected protein-coding genes are lowly expressed, in line with Hu et al., Mol Cell 2023. This explains why a sense effect is not as apparent by metaplot, for which highly expressed genes contribute the most signal.

As a result of our analyses, we are confident that the apparently larger effect at the 2.5hr timepoint (Figure 4) that we initially reported is due to experimental variability and not greater effects of extended INTS11 depletion. Overlaying the 1.5h and 2.5h datasets (Supplemental Figure 4B) revealed a similar number of affected protein-coding genes with a strong (83%) overlap between the affected genes.  To support this, we performed qPCR on four affected protein-coding transcripts which revealed no significant difference in the level of INTS11 effect after 2.5h vs 1.5h (Supplemental Figure 4C).

We now present data for merged replicates in Figures 2 and 4 which reveal very similar average profiles for -INTS11 vs +INTS11 at both timepoints. Overall, we believe that we have resolved this discrepancy by showing that it amounts to experimental variability and because the most acutely affected protein-coding genes are lowly expressed. As detailed above, we show this in multiple ways (and validate by qPCR) We have revised the text accordingly and removed our original speculation that differences reflected the timeframe of INTS11 loss.

Conclusion/assessment:

This important work substantially advances our understanding of the mechanisms governing the directionality of human promoters. The evidence supporting the claims of the authors is compelling, with among others the use of advanced nascent transcriptomics including spike-in normalization controls and acute protein depletion using degron approaches.

In my opinion, the authors' conclusions are in general well supported.

Not only the manuscript but also the data generated will be useful to the wide community of researchers studying transcriptional regulation. Also, the POINT-derived novel sPOINT method described here is very valuable and can positively impact work in the field.

We are grateful for the reviewers' positive assessment of our study.

Reviewer #2 (Public Review):

Summary:

Eaton and colleagues use targeted protein degradation coupled with nascent transcription mapping to highlight a role for the integrator component INST11 in terminating antisense transcription. They find that upon inhibition of CDK9, INST11 can terminate both antisense and sense transcription - leading to a model whereby INST11 can terminate antisense transcription and the activity of CDK9 protects sense transcription from INST11-mediated termination. They further develop a new method called sPOINT which selectively amplifies nascent 5' capped RNAs and find that transcription initiation is more efficient in the sense direction than in the antisense direction. This is an excellent paper that uses elegant experimental design and innovative technologies to uncover a novel regulatory step in the control of transcriptional directionality.

Strengths:

One of the major strengths of this work is that the authors endogenously tag two of their proteins of interest - RBBP6 and INST11. This tag allows them to rapidly degrade these proteins - increasing the likelihood that any effects they see are primary effects of protein depletion rather than secondary effects. Another strength of this work is that the authors immunoprecipitate RNAPII and sequence extracted full-length RNA (POINT-seq) allowing them to map nascent transcription. A technical advance from this work is the development of sPOINT which allows the selective amplification of 5' capped RNAs < 150 nucleotides, allowing the direction of transcription initiation to be resolved.

We appreciate this positive assessment.

Weaknesses:

While the authors provide strong evidence that INST11 and CDK9 play important roles in determining promoter directionality, their data suggests that when INST11 is degraded and CDK9 is inhibited there remains a bias in favour of sense transcription (Figures 4B and C). This suggests that there are other unknown factors that promote sense transcription over antisense transcription and future work could look to identify these.

We agree that other (so far, unknown) factors promote sense transcription over antisense, which was demonstrated by our short POINT.  We have provided an expanded discussion on this in the revision. In our opinion, demonstrating that sense transcription is driven by preferential initiation in that direction is a key finding and we agree that the identification of the underlying mechanism constitutes an interesting avenue for future study.

Reviewer #3 (Public Review):

Summary:

Using a protein degradation approach, Eaton et al show that INST11 can terminate the sense and anti-sense transcription but higher activity of CDK9 in the sense direction protects it from INS11-dependent termination. They developed sPOINT-seq that detects nascent 5'-capped RNA. The technique allowed them to reveal robust transcription initiation of sense-RNA as compared to anti-sense.

Strengths:

The strength of the paper is the acute degradation of proteins, eliminating the off-target effects. Further, the paper uses elegant approaches such as POINT and sPOINT-seq to measure nascent RNA and 5'-capped short RNA. Together, the combination of these three allowed the authors to make clean interpretations of data.

We appreciate this positive assessment.

Weaknesses:

While the manuscript is well written, the details on the panel are not sufficient. The methods could be elaborated to aid understanding. Additional discussion on how the authors' findings contradict the existing model of anti-sense transcription termination should be added.

We have added more detail to the figure panels, which we hope will help readers to navigate the paper more easily. Specifically, the assay employed for each experiment is indicated in each figure panel. As requested, we provide a new and separate discussion section in the revision.

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

Congratulations on this important piece of work!

Some specific suggestions.

MAJOR

-The data are not available (Accession "GSE243266" is currently private and is scheduled to be released on Sep 01, 2026.) This should be corrected and as a minimum, the raw sequencing files as well as the spike-in scaled bigwig files should be provided in GEO.

We have made the data public. Raw and bigwig files are provided as part of the GEO upload.

MINOR

- It would be useful for readers if you could include catalog numbers of the reagents used in the study.

We have included this information in our revision.

- A table in experimental procedures summarizing the genomic experiments performed in this study as well as published ones reanalyzed here would be helpful.

This is now provided as part of the resources table.

- It would be easier for reviewers to evaluate the manuscript if the figure legends were included together with the figures on one page. This is now allowed by most journals.

We have used this formatting in the revision.

- Providing some captions for the results sections would be helpful.

We have included subheadings as suggested.

Reviewer #2 (Recommendations For The Authors):

Generally, I would suggest writing the experiment-type above panels where it is not immediately obvious what they are so a reader can appreciate the figures without referencing the legend. E.g. write POINT-seq on Figure 1B just to make it obvious to someone looking at the figures what methodology they are looking at. Likewise, you could write RNAPII ChIP-seq for Supplementary Figures 3D and 3E.

We have carried out this recommendation.

Can a y-axis be indicated on POINT-seq genome browser tracks? This could make them easier to interpret.

Y-axis scales are provided as RPKM as stated in the figure legends.

The authors could address/speculate in the text why there is less POINT-seq signal for the antisense transcript in the treatment condition in Figure 1B? Or could consider including a different example locus where this is not the case for clarity.

Acute depletion of poly(A) factors (like RBBP6) results in a strong read-through beyond the poly(A) signal of protein-coding genes as Figure 1 shows.  However, it also causes a reduction in transcription levels, which can be seen in the figure and is correctly noted by the reviewer in this comment.  We see this with other poly(A) factor depletions (e.g. CPSF73 and CPSF30 – Eaton et al., 2020 and Estell et al., 2021) and other labs have observed this too (e.g for CPSF73-dTAG depletion (Cugusi et al., Mol Cell 2022)).  Plausible reasons include a limited pool of free RNAPII due to impaired transcriptional termination or limited nucleotide availability due to their incorporation within long read-through transcripts. For these reasons, we have retained the example in Figure 1B as a typical representation of the effect. Moreover, the heatmap in Figure 1D fairly represents the spectrum of effects following RBBP6 loss – highlighting the strong read-through beyond poly(A) signals and the marginal antisense effects.

"The established effect of INTS11 at snRNAs was detected in our POINT-seq data and demonstrates the efficacy of this approach (Figure 2B)." The authors could explain this point more clearly in the text and describe the data - e.g. As expected, depletion of INTS11 leads to increased POINT-seq signal at the 3' end of snRNAs, consistent with defects in transcriptional termination. This is highlighted by the RNU5A-1 and RNU5B-1 loci (Figure 2B).

We agree and have added more context to clarify this.

I would suggest adjusting the scale of the heatmap in Figure 2E - I think it would be easier to interpret if the value of 0 was white - with >0 a gradient of orange and <0 a gradient of blue (as is done in Figure 1C). I think making this change would make the point as written in the text clearer i.e. "heatmap analysis demonstrates the dominant impact of INTS11 on antisense versus sense transcription at most promoters (Figure 2E)." I'm assuming most of the sense transcription would be white (more clearly unchanging) when the scale is adjusted.

We agree and have done this. The reviewer is correct that most sense transcription is unchanged by INTS11 loss.  However, as we alluded to in the original submission, a subset of transcripts shows a promoter-proximal increase after INTS11 depletion. We have expanded the analyses of this effect (see responses to other comments) but stress that it is neither as general nor as large as the antisense effect.

The authors make the point that there is mildly increased transcription over the 5' end of some genes upon INST11 depletion and show a track (Supplementary Fig 2A). It is not immediately obvious from the presentation of the meta-analysis in Figure 2D how generalisable this statement is. Perhaps the size of the panel or thickness of the lines in Figure 2D could be adjusted so that the peak of the control (in blue) could be seen. Perhaps an arrow indicating the peak could be added? I'm assuming the peak at the TSS is slightly lower in the control compared to INST11 depletion based on the authors' statement.

We have provided multiple new analyses of this data to highlight where there are promoter-proximal effects of INTS11 loss in the sense direction.  Please see our response to the public review of reviewer 1 and new supplemental figures 2A, 2B, 4A and 4B which highlight the sense transcription increased in the absence of INTS11.

The authors label Figure 4 "Promoters lose their directionality when CDK9 is inhibited" - but in INST11 depleted cells treated with CDK9i they find that there still is a bias towards sense transcription. Suggested edit "Some promoter directionality is lost when CDK9 is inhibited" or similar.

We agree and have made this change.

The authors conclude that INTS11-mediated effects are the result of perturbation of the catalytic activities of Integrator, the authors should perform rescue experiments with the catalytically dead E203Q-INTS11 mutant.

This is a very good suggestion and something we had intended to pursue.  However, as we will describe below (and shown in Supplemental Figure 4G), there were confounding issues with this experiment.

The E203Q mutant of INTS11 is widely used in the literature to test for catalytic functions of INTS11.  However, we have found that this mutation impairs the ability of INTS11 to bind other Integrator modules in cells. Based on co-immunoprecipitation of flag-tagged WT and E203Q derivatives, INTS1 (backbone module), 10 (tail module), and 8 (phosphatase module) all show reduced binding to E203Q vs. WT. Because E203Q INTS11 is defective in forming Integrator complexes, rescue experiments might not fully distinguish the effects of INTS11 activity from those caused by defects in complex assembly. While this may at first seem unexpected, in the analogous 3’ end processing complex, catalytic mutants of CPSF73 (which is highly related to INTS11) negatively affect its interaction with other complex members (Kolev and Steitz, EMBO Reports 2005).

We hypothesise that INTS11 activity is most likely involved in attenuating promoter-proximal transcription, but we cannot formally rule out other explanations and discuss this in our revision. Regardless of how INTS11 attenuates transcription, our main conclusion is on its requirement to terminate antisense transcription whether this involves its cleavage activity or not.

The authors suggest that CDK9 modulates INTS11 activity/assembly and suggest this may be related to SPT5. Is there an effect of CDK9 inhibition on the snRNA's highlighted in Figure 2B?

We believe that snRNAs are different from protein-coding genes concerning CDK9 function. Shona Murphy’s lab previously showed that, unlike protein-coding genes, snRNA transcription is insensitive to CDK9 inhibition, and that snRNA processing is impaired by CDK9 inhibition (Medlin et al., EMBO 2003 and EMBO 2005).  We reproduce these findings by metaanalysis of 15 highly expressed and well-separated snRNAs and by qRT-PCR of unprocessed RNU1-1, RNU5A-1 and RNU7-1 snRNA following CDK9 inhibition. We observe snRNA read-through by POINT-seq following INTS11 loss whether CDK9 is inhibited or not (left panel, below). Note the higher TES proximal signal in CDK9i conditions, which likely reflects the accumulation of unprocessed snRNA as validated by qPCR for three example snRNAs (right panel, below).

Author response image 1.

For Figure 4, would similar results be observed using inhibitors targeting other transcriptional CDKs such as CDK7,12/13?

In response to this suggestion, we analysed four selected protein-coding transcripts (the same 4 that we used to validate the CDK9i results) by qRT-PCR in a background of CDK7 inhibition using the THZ2 compound (new Supplemental Figure 4E).  THZ2 suppresses transcription from these genes as expected.  Interestingly, expression is restored by co-depleting Integrator, recapitulating our findings with CDK9 inhibition.  As CDK7 is the CDK-activating kinase for CDK9, its inhibition will also inhibit CDK9 so THZ2 may simply hit this pathway upstream of where CDK9 inhibitors.  Second, CDK7 may independently shield transcription from INTS11.  We allude to both interesting possibilities.

What happens to the phosphorylation state of anti-sense engaged RNAPII when INTS11 is acutely depleted and/or CDK9 is inhibited? This could be measured by including Ser5 and Ser2 antibodies in the sPOINT-seq assay and complemented with Western Blot analysis.

We have performed the western blot for Ser5 and Ser2 phosphorylation as suggested.  Both signals are mildly enhanced by INTS11 loss, which is consistent with generally increased transcription.  Ser2p is strongly reduced by CDK9 inhibition, which is consistent with the loss of nascent transcription in this condition.  Interestingly, both modifications are partly recovered when INTS11 is depleted in conjunction with CDK9 inhibition. This is consistent with the effects that we see on POINT-seq and shows that the recovered transcription is associated with some phosphorylation of RNAPII CTD.  This presumably reflects the action(s) of kinases that can act redundantly with CDK9.

We have not performed POINT-seq with Ser5p and Ser2p antibodies under these various conditions.  Our rationale is that our existing data uses an antibody that captures all RNAPII (regardless of its phosphorylation status), which we feel most comprehensively assays transcription in either direction. Moreover, the lab of Fei Chen (Hu et al., Mol Cell 2023) recently published Ser5p and Ser2p ChIP-seq following INTS11 loss. By ChIP-seq, they observe a bigger increase in antisense RNAPII occupancy vs. sense providing independent and orthogonal support for our POINT-seq data.  Interestingly, this antisense increase is not paralleled by proportional increases in Ser5p or Ser2p signals.  This suggests that the unattenuated antisense transcription resulting from INTS11 loss does not have high Ser5p or Ser2p.  Since CDK7 and 9 are major Ser5 and 2 kinases, this supports our model that their activity is less prevalent for antisense transcription.  We now discuss these data in our revision.   

The HIV reporter RNA experiments should be performed with the CDK9 inhibitor added to the experimental conditions. Presumably CDK9 inhibition would result in no upregulation of the reporter upon addition of TAT and/or dTAG. Perhaps the amount of TAT should be reduced to still have a dynamic window in which changes can be detected. It is possible that reporter activation is simply at a maximum. Can anti-sense transcription be measured from the reporter?

We have performed the requested CDK9 inhibitor experiment to confirm that TAT-activated transcription from the HIV promoter is CDK9-dependent (new supplemental figure 4F).  Consistent with previous literature on HIV transcription, CDK9 inhibition attenuates TAT-activated transcription.  Importantly, and in line with our other experiments, depletion of INTS11 results in significant restoration of transcription from the HIV promoter when CDK9 is inhibited. Thus, TAT-activated transcription is CDK9-dependent and, as for endogenous genes, CDK9 prevents attenuation by INTS11.

While TAT-activated transcription is high, we do not think that the plasmid is saturated. When considering this question, we revisited previous experiments using this system to study RNA processing (Dye et al., Mol Cell 1999, Cell 2001, Mol Cell 2006). In these cases, mutations in splice sites or polyadenylation sites have a strong effect on RNA processing and transcription around HIV reporter plasmids. Effects on transcription and RNA processing are; therefore, apparent in the appropriate context. In contrast, we find that the complete elimination of INTS11 has no impact on RNA output from the HIV reporter. Our original experiment assessing the impact of INTS11 loss in +TAT conditions used total RNA.  One possibility is that this allows non-nascent RNA to accumulate which might confound our interpretation of INTS11 effects on ongoing transcription.  However, the new experiment described in the paragraph above was performed on chromatin-associated (nascent) RNA to rule this out.  This again shows no impact of INTS11 loss on HIV promoter-derived transcription in the presence of TAT.

To our knowledge, antisense transcription is not routinely assayed from plasmids. They generally employ very strong promoters (e.g. CMV, HIV) to drive sense transcription.  Crucially, their circular nature means that RNAPII going around the plasmid could interfere with antisense transcription coming the other way which does not happen in a linear genomic context. This is why we restricted our use of plasmids to looking at the effects of stimulated CDK9 recruitment (via TAT) on transcription rather than promoter directionality.   

The authors should clearly state how many replicates were performed for the genomics experiments. Ideally, a signal should be quantified and compared statistically rather than relying on average profiles only.

We have stated the replicate numbers for sequencing experiments in the relevant figure legends. All sequencing experiments were performed in at least two biological replicates, but often three. In addition, we validated their key conclusions by qPCR or with orthogonal sequencing approaches.

Reviewer #3 (Recommendations For The Authors):

The authors provide strong evidence in support of their claims.

ChIP-seq of pol2S5 and S2 upon INST11 and CDK9 inhibition will strengthen the observation that transcription in the sense direction is more efficient.

We view the analysis of total RNAPII as the most unbiased way of establishing how much RNAPII is going one way or the other. Importantly, ChIP-seq was very recently performed for Ser2p and Ser5p RNAPII derivatives in the lab of Fei Chen (Hu et al., Mol Cell 2023). Their data shows that loss of INTS11 increases the occupancy of total RNAPII in the antisense direction more than in the sense direction, which is consistent with our finding. Interestingly, the increased antisense RNAPII was not paralleled with an increase in Ser2p or Ser5p. This suggests that, following INTS11 loss, the unattenuated antisense transcription is not associated with full/normal Ser2p or Ser5p. These modifications are normally established by CDK7 and 9; therefore, this published ChIP-seq suggests that they are not fully active on antisense transcription when INTS11 is lost. This supports our overall model that CDK9 (and potentially CDK7 as suggested for a small number of genes in new Supplemental Figure 4E) is more active in the sense direction to prevent INTS11-dependent attenuation. We now discuss these data in our revision.

In Supplementary Figure 2, the eRNA expression increases upon INST11 degradation, I wonder if the effects of this will be appreciated on cognate promoters? Can the authors test some enhancer:promoter pairs?

We noticed that some genes (e.g. MYC) that are regulated by enhancers show reduced transcription in the absence of INTS11. Whilst this could suggest a correlation, the transcription of other genes (e.g. ACTB and GAPDH) is also reduced by INTS11 loss although they are not regulated by enhancers.  A detailed and extensive analysis would be required to establish any link between INTS11-regulated enhancer transcription and the transcription of genes from their cognate promoters.  We agree that this would be interesting, but it seems beyond the scope of our short report on promoter directionality.

Line 111, meta plot was done of 1316 genes. Details on this number should be provided. Overall, the details of methods and analysis need improvement. The layout of panels and labelling on graphs can be improved.

We have now explained the 1316 gene set.  In essence, these are the genes separated from an expressed neighbour by at least 10kb.  This distance was selected because depletion of RBBP6 induces extensive read-through transcription beyond the polyadenylation site of protein-coding genes.  To avoid including genes affected by transcriptional read-through from nearby transcription units we selected those with a 10kb gap between them. This was the only selection criteria so is unlikely to induce any unintended biases. Finally, we have added more information to the figure panels and their legends, which we hope will make our manuscript more accessible.

Author response:

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

Public Reviews:

Reviewer #1 (Public Review):

The manuscript by Li et al. investigates the metabolism-independent role of nuclear IDH1 in chromatin state reprogramming during erythropoiesis. The authors describe accumulation and redistribution of histone H3K79me3, and downregulation of SIRT1, as a cause for dyserythropoiesis observed due to IDH1 deficiency. The authors studied the consequences of IDH1 knockdown, and targeted knockout of nuclear IDH1, in normal human erythroid cells derived from hematopoietic stem and progenitor cells and HUDEP2 cells respectively. They further correlate some of the observations such as nuclear localization of IDH1 and aberrant localization of histone modifications in MDS and AML patient samples harboring IDH1 mutations. These observations are intriguing from a mechanistic perspective and they hold therapeutic significance, however there are major concerns that make the inferences presented in the manuscript less convincing.

(1) The authors show the presence of nuclear IDH1 both by cell fractionation and IF, and employ an efficient strategy to knock out nuclear IDH1 (knockout IDH1/ Sg-IDH1 and rescue with the NES tagged IDH1/ Sg-NES-IDH1 that does not enter the nucleus) in HUDEP2 cells. However, some important controls are missing.

A) In Figure 3C, for IDH1 staining, Sg-IDH1 knockout control is missing.

Thanks for the reviewer’s suggestion. We have complemented the staining of Sg-IDH1 knockout cells, and made corresponding revision in Figure 3C in the revised manuscript.

B) Wild-type IDH1 rescue control (ie., IDH1 without NES tag) is missing to gauge the maximum rescue that is possible with this system.

Thanks for the reviewer’s suggestion. We have overexpressed wild-type IDH1 in the IDH1-deficient HUDEP2 cell line and detected the phenotype. The results are presented in Supplementary Figure 9 in the revised manuscript. As shown in Supplementary Figure 9A, IDH1 deficiency resulted in reduced cell number in HUDEP2 cells, a phenotype that was rescued by overexpression of wild-type IDH1 but not by NES-IDH1. Given IDH1's well-established role in redox homeostasis through catalyzing isocitrate to α-KG conversion, we hypothesized that both wild-type IDH1 and NES-IDH1 overexpression would significantly restore α-KG levels compared to the IDH1-deficient group. Supplementary Figure 9B demonstrates that IDH1 depletion resulted in a dramatic decrease in α-KG levels, whereas overexpression of either wild-type IDH1 or NES-IDH1 almost completely restored α-KG levels, as anticipated. These results suggest that wild-type IDH1 overexpression can restore metabolic regulatory functions as effectively as NES-IDH1 overexpression. To investigate whether apoptosis contributes to the impaired cell expansion caused by IDH1 deficiency, we performed Annexin V/PI staining to quantify apoptotic cells. As shown in Supplementary Figure 9C and D, flow cytometry analysis revealed no significant changes in apoptosis rates following either IDH1 depletion or ectopic expression of wild-type IDH1 or NES-IDH1 in IDH1 deficient HUDEP2 cells.

Flow cytometric analysis demonstrated that IDH1 deficiency triggered S-phase accumulation at day 8, indicative of cell cycle arrest. Whereas ectopic expression of wild-type IDH1 significantly rescued this cell cycle defect, overexpression of NES-IDH1 failed to ameliorate the S-phase accumulation phenotype induced by IDH1 depletion, as presented in Supplementary Figure 9E and F. Although NES-IDH1 overexpression rescued metabolic regulatory function defect, it failed to alleviate the cell cycle arrest induced by IDH1 deficiency. In contrast, wild-type IDH1 overexpression fully restored normal cell cycle progression. This functional dichotomy demonstrates that nuclear-localized IDH1 executes critical roles distinct from its cytoplasmic counterpart, and overexpression of wild-type IDH1 could efficient restore the functional impairment induced by depletion of nuclear localized IDH1.

(2) Considering the nuclear knockout of IDH1 (Sg-NES-IDH1 referenced in the previous point) is a key experimental system that the authors have employed to delineate non-metabolic functions of IDH1 in human erythropoiesis, some critical experiments are lacking to make convincing inferences.

A) The authors rely on IF to show the nuclear deletion of Sg-NES-IDH1 HUDEP2 cells. As mentioned earlier since a knockout control is missing in IF experiments, a cellular fractionation experiment (similar to what is shown in Figure 2F) is required to convincingly show the nuclear deletion in these cells.

We sincerely thank the reviewer for raising this critical point. As suggested, we have performed additional IF experiments and cellular fractionation experiments to comprehensively address the subcellular localization of IDH1.

The results of IF staining were shown in Figure 3C of the revised manuscript. In Control HUDEP2 cells, endogenous IDH1 was detected in both the cytoplasm and nucleus. This dual localization may reflect its dynamic roles in cytoplasmic metabolic processes and potential nuclear functions under specific conditions. In Sg-IDH1 cells (IDH1 knockout), IDH1 signal was undetectable, confirming efficient depletion of the protein. In Sg-NES-IDH1 cells (overexpressing NES-IDH1 in IDH1 deficient cells), IDH1 predominantly accumulated in the cytoplasm, consistent with the disruption of its nuclear export signal. The dual localization of IDH1 that was determined by IF staining experiment were then further confirmed by cellular fractionation assays, as shown in Figure 3D.

B) Since the authors attribute nuclear localization to a lack of metabolic/enzymatic functions, it is important to show the status of ROS and alpha-KG in the Sg-NES-IDH1 in comparison to control, wild type rescue, and knockout HUDEP2 cells. The authors observe an increase of ROS and a decrease of alpha-KG upon IDH1 knockdown. If nuclear IDH1 is not involved in metabolic functions, is there only a minimal or no impact of the nuclear knockout of IDH1 on ROS and alpha-KG, in comparison to complete knockout? These studies are lacking.

We appreciate the insightful suggestions of the reviewers and agree that the detection of ROS and alpha-KG is useful for the demonstration of the non-canonical function of IDH1. We examined alpha-KG concentrations in control, IDH1 knockout and nuclear IDH1 knockout HUDEP2 cell lines. The results showed a significant decrease in alpha-KG content after complete knockout of IDH1, whereas there was no significant change in nuclear knockout IDH1 (Supplementary Figure 9B). As to the detection of ROS level, the commercial ROS assay kits that we can get are detected using PE (Excitation: 565nm; Emission: 575nm) and FITC (Excitation: 488nm; Emission: 518nm) channels in flow cytometry. We constructed HUDEP2 cell lines of Sg-IDH1 and Sg-NES-IDH1 to express green fluorescent protein (Excitation: 488nm; Emission: 507nm) and Kusabira Orange fluorescent protein (Excitation: 500nm; Emission: 561nm) by themselves. Unfortunately, due to the spectral overlap of the fluorescence channels, we were unable to detect the changes in ROS levels in these HUDEP2 cell lines using the available commercial kit.

(3) The authors report abnormal nuclear phenotype in IDH1 deficient erythroid cells. It is not clear what parameters are used here to define and quantify abnormal nuclei. Based on the cytospins (eg., Figure 1A, 3D) many multinucleated cells are seen in both shIDH1 and Sg-NES-IDH1 erythroid cells, compared to control cells. Importantly, this phenotype and enucleation defects are not rescued by the administration of alpha-KG (Figures 1E, F). The authors study these nuclei with electron microscopy and report increased euchromatin in Figure 4B. However, there is no discussion or quantification of polyploidy/multinucleation in the IDH1 deficient cells, despite their increased presence in the cytospins.

A) PI staining followed by cell cycle FACS will be helpful in gauging the extent of polyploidy in IDH1 deficient cells and could add to the discussions of the defects related to abnormal nuclei.

We appreciate the reviewer’s insightful suggestion. Since PI dye is detected using the PE channel (Excitation: 565nm; Emission: 575nm) of the flow cytometer and the HUDEP2 cell line expresses Kusabira orange fluorescent protein (Excitation: 500nm; Emission: 561nm), we were unable to use PI staining to detect the cell cycle. Edu staining is another commonly used method to determine cell cycle progression, and we performed Edu staining followed by flow cytometry analysis on Control, Sg-IDH1 and Sg-NES-IDH1 HUDEP2 cells, respectively. The results showed that complete knockdown of IDH1 resulted in S-phase block and increased polyploidy in HUDEP2 cells on day 8 of erythroid differentiation, and overexpression of IDH1-NES did not reverse this phenotype (Supplemental Figure 9E-F). Moreover, we have added a discussion of abnormal nuclei being associated with impaired erythropoiesis.

B) For electron microscopy quantification in Figures 4B and C, how the quantification was done and the labelling of the y-axis (% of euchromatin and heterochromatin) in Figure 4 C is not clear and is confusingly presented. The details on how the quantification was done and a clear label (y-axis in Figure 4C) for the quantification are needed.

Thanks for the reviewer's suggestion. In this study, we calculated the area of nuclear, heterochromatin and euchromatin by using Image J software. We addressed the quantification strategy in the section of Supplementary methods of the revised Supplementary file. In addition, the y-axis label in Figure 4C was changed to “the area percentage of euchromatin and heterochromatin’’.

C) As mentioned earlier, what parameters were used to define and quantify abnormal nuclei (e.g. Figure 1A) needs to be discussed clearly. The red arrows in Figure 1A all point to bi/multinucleated cells. If this is the case, this needs to be made clear.

We thank the reviewer for their suggestion. In our present study, nuclear malformations were defined as cells exhibiting binucleation or multinucleation based on cytospin analysis. A minimum of 300 cells per group were evaluated, and the proportion of aberrant nuclei was calculated as (number of abnormal cells / total counted cells) × 100%.

(4) The authors mention that their previous study (reference #22) showed that ROS scavengers did not rescue dyseythropoiesis in shIDH1 cells. However, in this referenced study they did report that vitamin C, a ROS scavenger, partially rescued enucleation in IDH1 deficient cells and completely suppressed abnormal nuclei in both control and IDH1 deficient cells, in addition to restoring redox homeostasis by scavenging reactive oxygen species in shIDH1 erythroid cells. In the current study, the authors used ROS scavengers GSH and NAC in shIDH1 erythroid cells and showed that they do not rescue abnormal nuclei phenotype and enucleation defects. The differences between the results in their previous study with vitamin C vs GSH and NAC in the context of IDH1 deficiency need to be discussed.

We appreciate the reviewer’s insightful observation. The apparent discrepancy between the effects of vitamin C (VC) in our previous study and glutathione (GSH)/N-acetylcysteine (NAC) in the current work can be attributed to divergent molecular mechanisms beyond ROS scavenging. A growing body of evidence has identified vitamin C as a multifunctional regulator. In addition to acting as an antioxidant maintaining redox homeostasis, VC also acts as a critical epigenetic modulator. VC have been identified as a cofactor for α-ketoglutarate (α-KG)-dependent dioxygenases, including TET2, which catalyzes 5-methylcytosine (5mC) oxidation to 5-hydroxymethylcytosine (5hmC) [1,2]. Structural studies confirm its direct interaction with TET2’s catalytic domain to enhance enzymatic activity in vitro [3]. The biological significance of the epigenetic modulation induced by vitamin C is illustrated by its ability to improve the generation of induced pluripotent stem cells and to induce a blastocyst-like state in mouse embryonic stem cells by promoting demethylation of H3K9 and 5mC, respectively [4,5]. In contrast, GSH and NAC are canonical ROS scavengers lacking intrinsic epigenetic-modifying activity. While they effectively neutralize oxidative stress (as validated by reduced ROS levels in our current data, Supplemental Figure 7), their inability to rescue nuclear abnormalities or enucleation defects in IDH1 deficient cells suggests that IDH1 deficiency-driven dyserythropoiesis is not solely ROS-dependent.

References:

(1) Blaschke K, Ebata KT, Karimi MM, Zepeda-Martínez JA, Goyal P, et al. Vitamin C induces Tet-dependent DNA demethylation and a blastocyst-like state in ES cells. Nature. 20138;500(7461): 222-226.

(2) Minor EA, Court BL, Young JI, Wang G. Ascorbate induces ten-eleven translocation (Tet) methylcytosine dioxygenase-mediated generation of 5-hydroxymethylcytosine. J Biol Chem. 2013;288(19): 13669-13674.

(3) Yin R, Mao S, Zhao B, Chong Z, Yang Y, et al. Ascorbic acid enhances Tet-mediated 5-methylcytosine oxidation and promotes DNA demethylation in mammals. J Am Chem Soc. 2013;135(28):10396-10403.

(4) Esteban MA, Wang T, Qin B, Yang J, Qin D, et al. Vitamin C enhances the generation of mouse and human induced pluripotent stem cells. Cell Stem Cell. 2010;6(1):71-79.

(5) Chung T, Brena RM, Kolle G, Grimmond SM, Berman BP, et al. Vitamin C promotes widespread yet specific DNA demethylation of the epigenome in human embryonic stem cells. Stem Cells. 2010;28(10):1848-1855.

(5) The authors describe an increase in euchromatin as the consequential abnormal nuclei phenotype in shIDH1 erythroid cells. However, in their RNA-seq, they observe an almost equal number of genes that are up and down-regulated in shIDH1 cells compared to control cells. If possible, an RNA-Seq in nuclear knockout Sg-NES-IDH1 erythroid cells in comparison with knockout and wild-type cells will be helpful to tease out whether a specific absence of IDH1 in the nucleus (ie., lack of metabolic functions of IDH) impacts gene expression differently.

Thanks for the reviewer's suggestion. ATAC-seq showed an increase in chromatin accessibility after IDH1 deletion, but the number of up-regulated genes was slightly larger than that of down-regulated genes, which may be caused by the metabolic changes affected by IDH1 deletion. In order to explore the effect of chromatin accessibility changes on gene expression after IDH1 deletion, we analyzed the changes in differential gene expression at the differential ATAC peak region (as shown in Author response image 1), and the results showed that the gene expression at the ATAC peak region with increased chromatin accessibility was significantly up-regulated. This may explain the regulation of chromatin accessibility on gene expression.

Author response image 1.

Box plots of gene expression differences of differential ATAC peaks located in promoter for the signal increasing and decreasing groups.

(6) In Figure 8, the authors show data related to SIRT1's role in mediating non-metabolic, chromatin-associated functions of IDH1.

A) The authors show that SIRT1 inhibition leads to a rescue of enucleation and abnormal nuclei. However, whether this rescues the progression through the late stages of terminal differentiation and the euchromatin/heterochromatin ratio is not clear.

Thanks for the reviewer's suggestion. As shown in Supplementary Figure 14 and 15 in the revised Supplementary Data, our data showed that both the treatment of SRT1720 on normal erythroid cells and treatment of IDH1-deficient erythroid cells with SIRT1 inhibitor both have no effect on the terminal differentiation.

(7) In Figure 4 and Supplemental Figure 8, the authors show the accumulation and altered cellular localization of H3K79me3, H3K9me3, and H3K27me2, and the lack of accumulation of other three histone modifications they tested (H3K4me3, H3K35me4, and H3K36me2) in shIDH1 cells. They also show the accumulation and altered localization of the specific histone marks in Sg-NES-IDH1 HUDEP2 cells.

A) To aid better comparison of these histone modifications, it will be helpful to show the cell fractionation data of the three histone modifications that did not accumulate (H3K4me3, H3K35me4, and H3K36me2), similar to what was shown in Figure 4E for H3K79me3, H3K9me3, and H3K27me2).

We appreciate the reviewer’s insightful suggestion. We collected erythroblasts on day 15 of differentiation from cord blood-derived CD34⁺ hematopoietic stem cells to erythroid lineage and performed ChIP assay. As shown in Author response image 2, the results showed that the concentration of bound DNA of H3K9me3, H3K27me2 and H3K79me3 was too low to meet the sequencing quality requirement, which was consistent with that of WB. In addition, we tried to perform ChIP-seq for H3K79me3, and the results showed that there was no marked peak signal.

Author response image 2.

ChIP-seq analysis show that there was no marked peak signal of H3K79me3 on D15. (A) Quality control of ChIP assay for H3K9me3, H3K27me2, and H3K79me3. (B) Representative peaks chart image showed normalized ChIP signal of H3K79me3 at gene body regions. (C) Heatmaps displayed normalized ChIP signal of H3K79me3 at gene body regions. The window represents ±1.5 kb regions from the gene body. TES, transcriptional end site; TSS, transcriptional start site.

C) Among the three histone marks that are dysregulated in IDH1 deficient cells (H3K79me3, H3K9me3, and H3K27me2), the authors show via ChIP-seq (Fig5) that H3K79me3 is the critical factor. However, the ChIP-seq data shown here lacks many details and this makes it hard to interpret the data. For example, in Figure 5A, they do not mention which samples the data shown correspond to (are these differential peaks in shIDH1 compared to shLuc cells?). There is also no mention of how many replicates were used for the ChIP seq studies.

We thank the reviewer for pointing this out. We apologize for not clearly describing the ChIP-seq data for H3K9me3, H3K27me2 and H3K79me3 and we have revised them in the corresponding paragraphs. Since H3 proteins gradually translocate from the nucleus to the cytoplasm starting at day 11 (late Baso-E/Ploy-E) of erythroid lineage differentiation, we performed ChIP-seq for H3K9me3, H3K27me2 and H3K79me3 only for the shIDH1 group, and set up three independent biological replicates for each of them.

Reviewer #2 (Public Review):

Li and colleagues investigate the enzymatic activity-independent function of IDH1 in regulating erythropoiesis. This manuscript reveals that IDH1 deficiency in the nucleus leads to the redistribution of histone marks (especially H3K79me3) and chromatin state reprogramming. Their findings suggest a non-typical localization and function of the metabolic enzyme, providing new insights for further studies into the non-metabolic roles of metabolic enzymes. However, there are still some issues that need addressing:

(1) Could the authors show the RNA and protein expression levels (without fractionation) of IDH1 on different days throughout the human CD34+ erythroid differentiation?

We sincerely appreciate the reviewer’s constructive feedback. To address this point, we have now systematically quantified IDH1 expression dynamics across erythropoiesis stages using qRT-PCR and Western blot analyses. As quantified in Supplementary fige 1, IDH1 expression exhibited a progressive upregulation during early erythropoiesis and subsequently stabilized throughout terminal differentiation.

(2) Even though the human CD34+ erythroid differentiation protocol was published and cited in the manuscript, it would be helpful to specify which erythroid stages correspond to cells on days 7, 9, 11, 13, and 15.

We sincerely thank the reviewer for raising this important methodological consideration. Our research group has previously established a robust platform for staged human erythropoiesis characterization using cord blood-derived CD34⁺ hematopoietic stem cells (HSCs) [6-9]. This standardized protocol enables high-purity isolation and functional analysis of erythroblasts at defined differentiation stages.

Thanks for the reviewer’s suggestion. Our previous work (Jingping Hu et.al, Blood 2013. Xu Han et.al, Blood 2017.Yaomei Wang et.al, Blood 2021.) have isolation and functional characterization of human erythroblasts at distinct stages by using Cord blood. These works illustrated that using cord blood-derived hematopoietic stem cells and purification each stage of human erythrocytes can facilitate a comprehensive cellular and molecular characterization.

Following isolation from cord blood, CD34⁺ cells were cultured in a serum-free medium and induced to undergo erythroid differentiation using our standardized protocol. The process of erythropoiesis was comprised of 2 phases. During the early phase (day 0 to day 6), hematopoietic stem progenitor cells expanded and differentiated into erythroid progenitors, including BFU-E and CFU-E cells.

During terminal erythroid maturation (day 7 to day 15), CFU-E cells progressively transitioned through defined erythroblast stages, as validated by daily cytospin morphology and expression of band 3/α4 integrin. The stage-specific composition was quantitatively characterized as follows:

Author response table 1.

The composition of erythroblast during terminal stage erythropoiesis.

This differentiation progression from proerythroblasts (Pro-E) through basophilic (Baso-E), polychromatic (Poly-E), to orthochromatic erythroblasts (Ortho-E) recapitulates physiological human erythropoiesis, confirming the validity of our differentiation system for mechanistic studies.

Reference:

(6) Li J, Hale J, Bhagia P, Xue F, Chen L, et al. Isolation and transcriptome analyses of human erythroid progenitors: BFU-E and CFU-E. Blood. 2014;124(24):3636-3645.

(7) Hu J, Liu J, Xue F, Halverson G, Reid M, et al. Isolation and functional characterization of human erythroblasts at distinct stages: implications for understanding of normal and disordered erythropoiesis in vivo. Blood. 2013;121(16):3246-3253.

(8) Wang Y, Li W, Schulz VP, Zhao H, Qu X, et al. Impairment of human terminal erythroid differentiation by histone deacetylase 5 deficiency. Blood. 2021;138(17):1615-1627.

(9) Li M, Liu D, Xue F, Zhang H, Yang Q, et al. Stage-specific dual function: EZH2 regulates human erythropoiesis by eliciting histone and non-histone methylation. Haematologica. 2023;108(9):2487-2502.

(3) It is important to mention on which day the lentiviral knockdown of IDH1 was performed. Will the phenotype differ if the knockdown is performed in early vs. late erythropoiesis? In Figures 1C and 1D, on which day do the authors begin the knockdown of IDH1 and administer NAC and GSH treatments?

We sincerely appreciate the reviewer’s inquiry regarding the experimental timeline. The day of getting CD34⁺ cells was recorded as day 0. Lentivirus of IDH1-shRNA and Luciferase -shRNA was transduced in human CD34⁺ at day 2. Puromycin selection was initiated 24h post-transduction to eliminate non-transduced cells. IDH1-KD cells were then selected for 3 days. The knock down deficiency of IDH1 was determined on day 7. NAC or GSH was added to culture medium and replenished every 2 days.

(4) While the cell phenotype of IDH1 deficiency is quite dramatic, yielding cells with larger nuclei and multi-nuclei, the authors only attribute this phenotype to defects in chromatin condensation. Is it possible that IDH1-knockdown cells also exhibit primary defects in mitosis/cytokinesis (not just secondary to the nuclear condensation defect)?), given the function of H3K79Me in cell cycle regulation?

We appreciate the reviewer’s insightful suggestion. We performed Edu based cell cycle analysis on Control, Sg-IDH1 and Sg-NES-IDH1 HUDEP2 cells, respectively. The results showed that IDH1 deficiency resulted in S-phase block and increased polyploidy in HUDEP2 cells on day 8 of erythroid differentiation. NES-IDH1 overexpression failed to rescue these defects, indicating nuclear IDH1 depletion as the primary driving factor (Figure 3E,F). Recent studies have established a clear link between cell cycle arrest and nuclear malformation. Chromosome mis-segregation, nuclear lamina disruption, mechanical stress on the nuclear envelope, and nucleolar dysfunction all contribute to nuclear abnormalities that trigger cell cycle checkpoints [10,11]. Based on all these findings, it reasonable for us to speculate that the cell cycle defect in IDH1 deficient cells might caused by the nuclear malfunction.

Reference:

(10) Hong T, Hogger AC, Wang D, Pan Q, Gansel J, et al. Cell Death Discov. CDK4/6 inhibition initiates cell cycle arrest by nuclear translocation of RB and induces a multistep molecular response. 2024;10(1):453.

(11) Hervé S, Scelfo A, Marchisio GB, Grison M, Vaidžiulytė K, et al. Chromosome mis-segregation triggers cell cycle arrest through a mechanosensitive nuclear envelope checkpoint. Nat Cell Biol. 2025;27(1):73-86. 

(5) Why are there two bands of Histone H3 in Figure 4A?

We sincerely appreciate the reviewer's insightful observation regarding the dual bands of Histone H3 in our original Figure 4A. Upon rigorous investigation, we identified that the observed doublet pattern likely originated from the inter-batch variability of the original commercial antibody. To conclusively resolve this technical artifact, we have procured a new lot of Histone H3 antibody and repeated the western blot experimental under optimized conditions, and the results demonstrates a single band of H3.

(6) Displaying a heatmap and profile plots in Figure 5A between control and IDH1-deficient cells will help illustrate changes in H3K79me3 density in the nucleus after IDH1 knockdown.

Thank you for your suggestion. As presented in Author response image 2, we performed ChIP assays on erythroblasts collected at day 15. However, the concentration of H3K79me3-bound DNA was insufficient to meet the quality threshold required for reliable sequencing. Consequently, we are unable to generate the requested heatmap and profile plots due to limitations in data integrity from this experimental condition.

Reviewer #3 (Public Review):

Li, Zhang, Wu, and colleagues describe a new role for nuclear IDH1 in erythroid differentiation independent from its enzymatic function. IDH1 depletion results in a terminal erythroid differentiation defect with polychromatic and orthochromatic erythroblasts showing abnormal nuclei, nuclear condensation defects, and an increased proportion of euchromatin, as well as enucleation defects. Using ChIP-seq for the histone modifications H3K79me3, H3K27me2, and H3K9me3, as well as ATAC-seq and RNA-seq in primary CD34-derived erythroblasts, the authors elucidate SIRT1 as a key dysregulated gene that is upregulated upon IDH1 knockdown. They furthermore show that chemical inhibition of SIRT1 partially rescues the abnormal nuclear morphology and enucleation defect during IDH1-deficient erythroid differentiation. The phenotype of delayed erythroid maturation and enucleation upon IDH1 shRNA-mediated knockdown was described in the group's previous co-authored study (PMID: 33535038). The authors' new hypothesis of an enzyme- and metabolism-independent role of IDH1 in this process is currently not supported by conclusive experimental evidence as discussed in more detail further below. On the other hand, while the dependency of IDH1 mutant cells on NAD+, as well as cell survival benefit upon SIRT1 inhibition, has already been shown (see, e.g, PMID: 26678339, PMID: 32710757), previous studies focused on cancer cell lines and did not look at a developmental differentiation process, which makes this study interesting.

(1) The central hypothesis that IDH1 has a role independent of its enzymatic function is interesting but not supported by the experiments. One of the author's supporting arguments for their claim is that alpha-ketoglutarate (aKG) does not rescue the IDH1 phenotype of reduced enucleation. However, in the group's previous co-authored study (PMID: 33535038), they show that when IDH1 is knocked down, the addition of aKG even exacerbates the reduced enucleation phenotype, which could indicate that aKG catalysis by cytoplasmic IDH1 enzyme is important during terminal erythroid differentiation. A definitive experiment to test the requirement of IDH1's enzymatic function in erythropoiesis would be to knock down/out IDH1 and re-express an IDH1 catalytic site mutant. The authors perform an interesting genetic manipulation in HUDEP-2 cells to address a nucleus-specific role of IDH1 through CRISPR/Cas-mediated IDH1 knockout followed by overexpression of an IDH1 construct containing a nuclear export signal. However, this system is only used to show nuclear abnormalities and (not quantified) accumulation of H3K79me3 upon nuclear exclusion of IDH1. Otherwise, a global IDH1 shRNA knockdown approach is employed, which will affect both forms of IDH1, cytoplasmic and nuclear. In this system and even the NES-IDH1 system, an enzymatic role of IDH1 cannot be excluded because (1) shRNA selection takes several days, prohibiting the assessment of direct effects of IDH1 loss of function (only a degron approach could address this if IDH1's half-life is short), and (2) metabolic activity of this part of the TCA cycle in the nucleus has recently been demonstrated (PMID: 36044572), and thus even a nuclear role of IDH1 could be linked to its enzymatic function, which makes it a challenging task to separate two functions if they exist.

We appreciate the reviewer’s emphasis on rigorously distinguishing between enzymatic and enzymatic independent roles of IDH1. In our revised manuscript, we have removed all assertions of a "metabolism-independent" mechanism. Instead, we focus on demonstrating that nuclear-localized IDH1 contributes to chromatin state regulation during terminal erythropoiesis (e.g., H3K79me3 accumulation). While we acknowledge that nuclear IDH1’s enzymatic activity may still play a role [12], our data emphasize its spatial association with chromatin remodeling. We now explicitly state that nuclear IDH1’s function may involve both enzymatic and structural roles, and further studies are required to dissect these mechanisms.

Reference:

(12) Kafkia E, Andres-Pons A, Ganter K, Seiler M, Smith TS, et al.Operation of a TCA cycle subnetwork in the mammalian nucleus. Sci Adv. 2022;8(35):eabq5206.

(2) It is not clear how the enrichment of H3K9me3, a prominent marker of heterochromatin, upon IDH1 knockdown in the primary erythroid culture (Figure 4), goes along with a 2-3-fold increase in euchromatin. Furthermore, in the immunofluorescence (IF) experiments presented in Figure 4Db, it seems that H3K9me3 levels decrease in intensity (the signal seems more diffuse), which seems to contrast the ChIP-seq data. It would be interesting to test for localization of other heterochromatin marks such as HP1gamma. As a related point, it is not clear at what stage of erythroid differentiation the ATAC-seq was performed upon luciferase- and IDH1-shRNA-mediated knockdown shown in Figure 6. If it was done at a similar stage (Day 15) as the electron microscopy in Figure 4B, then the authors should explain the discrepancy between the vast increase in euchromatin and the rather small increase in ATAC-seq signal upon IDH1 knockdown.

Thank you for raising this important point. We agree that while H3K9me3 and H3K27me2 modifications are detectable in the nucleus, their functional association with chromatin in this context remains unclear. Our ChIP-seq data did not reveal distinct enrichment peaks for H3K9me3 or H3K27me2 (unlike the well-defined H3K79me3 peaks), suggesting that these marks may not be stably bound to specific chromatin regions under the experimental conditions tested. However, we acknowledge that the absence of clear peaks in our dataset does not definitively rule out chromatin interactions, as technical limitations or transient binding dynamics could influence these results. To avoid over-interpretation, we have removed speculative statements about the chromatin-unbound status of H3K9me3 and H3K27me2 from the revised manuscript. This revision aligns with our broader effort to present conclusions strictly supported by the current data while highlighting open questions for future investigation.

(3)The subcellular localization of IDH1, in particular its presence on chromatin, is not convincing in light of histone H3 being enriched in the cytoplasm on the same Western blot. H3 would be expected to be mostly localized to the chromatin fraction (see, e.g., PMID: 31408165 that the authors cite). The same issue is seen in Figure 4A.

We sincerely appreciate the reviewer's insightful comment regarding the subcellular distribution of histone H3 in our study. We agree that histone H3 is classically associated with chromatin-bound fractions, and its cytoplasmic enrichment in our Western blot analyses appears counterintuitive at first glance. However, this observation is fully consistent with the unique biology of terminal erythroid differentiation, which involves drastic nuclear remodeling and histone release - a hallmark of terminal stage erythropoiesis. Terminal erythroid differentiation is characterized by progressive nuclear condensation, chromatin compaction, and eventual enucleation. During this phase, global chromatin reorganization leads to the active eviction of histones from the condensed nucleus into the cytoplasm. This process has been extensively documented in erythroid cells, with studies demonstrating cytoplasmic accumulation of histones H3 and H4 as a direct consequence of nuclear envelope breakdown and chromatin decondensation preceding enucleation [13-16]. Our experiments specifically analyzed terminal-stage polychromatic and orthochromatic erythroblasts. At this stage, histone releasing into the cytoplasm is a dominant biological event, explaining the pronounced cytoplasmic H3 signal in our subcellular fractionation assays.

In summary, the cytoplasmic enrichment of histone H3 in our data aligns with established principles of erythroid biology and reinforces the physiological relevance of our findings. We thank the reviewer for raising this critical point, which allowed us to better articulate the unique aspects of our experimental system.

Reference:

(13) Hattangadi SM, Martinez-Morilla S, Patterson HC, Shi J, Burke K, et al. Histones to the cytosol: exportin 7 is essential for normal terminal erythroid nuclear maturation. Blood. 2014;124(12):1931-1940.

(14) Zhao B, Mei Y, Schipma MJ, Roth EW, Bleher R, et al. Nuclear Condensation during Mouse Erythropoiesis Requires Caspase-3-Mediated Nuclear Opening. Dev Cell. 2016;36(5): 498-510.

(15) Zhao B, Liu H, Mei Y, Liu Y, Han X, et al. Disruption of erythroid nuclear opening and histone release in myelodysplastic syndromes. Cancer Med. 2019;8(3):1169-1174. 

(16) Zhen R, Moo C, Zhao Z, Chen M, Feng H, et al.  Wdr26 regulates nuclear condensation in developing erythroblasts. Blood. 2020;135(3):208-219.

(4) This manuscript will highly benefit from more precise and complete explanations of the experiments performed, the material and methods used, and the results presented. At times, the wording is confusing. As an example, one of the "Key points" is described as "Dyserythropoiesis is caused by downregulation of SIRT1 induced by H3K79me3 accumulation." It should probably read "upregulation of SIRT1".

We sincerely thank the reviewer for highlighting the need for improved clarity in our experimental descriptions and textual precision. We fully agree that rigorous wording is essential to accurately convey scientific findings. Specific modifications have been made and are highlighted in Track Changes mode in the resubmitted manuscript.

The reviewer correctly identified an inconsistency in the original phrasing of one key finding. The sentence in question ("Dyserythropoiesis is caused by downregulation of SIRT1 induced by H3K79me3 accumulation") has been revised to:"Dyserythropoiesis is caused by the upregulation of SIRT1 mediated through H3K79me3 accumulation." This correction aligns with our experimental data showing that H3K79me3 elevation promotes SIRT1 transcriptional activation. We apologize for this oversight and have verified the consistency of all regulatory claims in the text.

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

(1) It will be helpful to mention/introduce the cells used for the study at the beginning of the results section. For example, for Figure 1A neither the figure legend nor the results text includes information on the cells used.

Thanks for the reviewer’s suggestion. The detail information of the cells that were used in our study have been provided in the revised manuscript.

(2) Important details for many figures are lacking. For example, in Figure 5, there is no mention of the replicates for ChIP-Seq studies. Also, the criteria used for quantifications of abnormal nuclei, % euchromatin vs heterochromatin, the numbers of biological replicates, and how many fields/cells were used for these quantifications are missing.

We thank the reviewer for emphasizing the importance of methodological transparency. It has been revised accordingly. The ChIP-Seq data in Figure 5 was generated from three independent biological replicates to ensure reproducibility. In this study, Image J software was used to calculate the area of nuclear, heterochromatin/euchromatin and to quantify the percentage of euchromatin and heterochromatin. A minimum of 300 cells per group were evaluated, and the proportion of aberrant nuclei was calculated as (number of abnormal cells / total counted cells) × 100%.

(3) It will be helpful if supplemental data are ordered according to how they are discussed in the text. Currently, the order of the supplemental data is hard to keep track of eg., the results section starts describing supplemental Figure 1, then the text jumps to supplemental Figure 5 followed by Supplemental Figure 3 (and so on).

Thanks for the reviewer’s suggestion. It has been revised accordingly.

(4) Overall, there are many incomplete sentences and typos throughout the manuscript including some of the figures e.g. on page 10 the sentence "Since the generation of erythroid with abnormal nucleus and reduction of mature red blood cells caused by IDH1 absence are notable characteristics of MDS and AML." is incomplete. On page 11, it reads "Histone post-modifications". This needs to be either histone modifications or histone post-translational modifications. In Figure 4C, the y-axis title is hard to understand "% of euchromatin and heterochromatin". Overall, the document needs to be proofread and revised carefully.

Thanks for the reviewer’s suggestion. We have made revision accordingly in the revised manuscript. The sentence "Since the generation of erythroid with abnormal nucleus and reduction of mature red blood cells caused by IDH1 absence are notable characteristics of MDS and AML." has been revised to “The production of erythrocytes with abnormal nuclei and the reduction of mature erythrocytes due to IDH1 deletion are prominent features of MDS and AML.”  “% of euchromatin and heterochromatin” has been modified to “Area ratio of euchromatin to heterochromatin”.

Reviewer #3 (Recommendations For The Authors):

The following critique points aim to help the authors to improve their manuscript:

(1) The authors reason (p. 10) that because mutant IDH1 has been shown to result in altered chromatin organization, this could be the case in their system, too. However, mutant IDH1 has an ascribed metabolic consequence, the generation of 2-HG, which further weakens the author's argument for an enzymatically independent role of IDH1 in their system. The same is true for the author's observation in Supplementary Figure 9B that in IDH1-mutant AML/MDS samples, H3K79me3 colocalized with the IDH1 mutants in the nucleus. Again, this speaks in favor of IDH1's role being linked to metabolism. The authors could re-write this manuscript, not so much emphasizing the separation of function between different subcellular forms of IDH1 but rather focusing on the chromatin changes and how they could be linked to the actual phenotype, the nuclear condensation and enucleation defect - if so, addressing the surprising finding of enrichment of both active and repressive chromatin marks will be important.

Thanks for the reviewer’s suggestion. We agree with the reviewers and editors all the data we present in the current are not robust enough to rigorously distinguish between enzymatic and enzymatic-independent roles of IDH1. In our revised manuscript, we have removed all assertions of a "metabolism-independent" mechanism. Instead, we focus on demonstrating that nuclear-localized IDH1 contributes to chromatin state regulation during terminal erythropoiesis (e.g., H3K79me3 accumulation).

(2) How come so many genes were downregulated by RNA-seq (about an equal number as upregulated genes) but not more open by ATAC-seq? The authors should discuss this result.

Thanks for the reviewer's suggestion. ATAC-seq showed an increase in chromatin accessibility after IDH1 deletion, but the number of up-regulated genes was slightly larger than that of down-regulated genes, which may be caused by the metabolic changes affected by IDH1 deletion. In order to explore the effect of chromatin accessibility changes on gene expression after IDH1 deletion, we analyzed the changes in differential gene expression at the differential ATAC peak region (as shown in the figure below), and the results showed that the gene expression at the ATAC peak region with increased chromatin accessibility was significantly up-regulated. This may explain the regulation of chromatin accessibility on gene expression.

(3) For the ChIP-seq analyses of H3K79me3, H3K27me2, and H3K9me3, the authors should not just show genome-wide data but also several example gene tracks to demonstrate the differential abundance of peaks in control versus IDH1 knockdown. Furthermore, the heatmap shown in Figure 5A should include broader regions spanning the gene bodies, to visualize the intergenic H3K27me2 and H3K9me3 peaks. Expression could very well be regulated from these intergenic regions as they could bear enhancer regions. ChIP-seq for H3K27Ac in the same setting would be very useful to identify those enhancers.

Thanks for the reviewer’s suggestion. It has been revised accordingly. We reanalyzed the ChIP-seq peak signal of H3K79me3, H3K27me2 and H3K9me3 in a wider region (±5Kb) at gene body, and the results showed that the H3K27me2 and H3K9me3 peak signals did not change significantly. Since H3K79me3 showed a higher peak signal and was mainly enriched in the promoter region, our subsequent analysis focusing on the impact of H3K79me3 accumulation on chromatin accessibility and gene expression might be more valuable.

Author response image 3.

ChIP-seq analysis show that the peak signal of H3K79me3,H3K27me2 and H3K9me3. (A) Heatmaps displayed normalized ChIP signal of H3K9me3, H3K27me2, and H3K79me3 at gene body regions. The window represents ±5 kb regions from the gene body. TES, transcriptional end site; TSS, transcriptional start site. (B) Representative peaks chart image showed normalized ChIP signal of H3K9me3, H3K27me2, and H3K79me3 at gene body regions.

(4) The absent or very mild delay (also no significance visible in the quantification plots) in the generation of orthochromatic erythroblasts on Day 13 upon IDH1 shRNA knockdown as per a4-integrin/Band3 flow cytometry does not correspond to the already quite prominent number of multinucleated cells at that stage seen by cytospin/Giemsa staining. Why do the authors think this is the case? Cytospin/Giemsa staining might be the better method to quantify this phenotype and the authors should quantify the cells at different stages in at least 100 cells from non-overlapping cytospin images.

Thanks for the reviewer’s suggestion. We have supplemented the cytpspin assay and the results were presented in Supplemental Figure 4.

(5) The pull-down assay in Figure 7E does not show a specific binding of H3K79me3 to the SIRT1 promoter. Rather, there is just more H3K79me3 in the nucleus, thus leading to generally increased binding. The authors should show that H3K79me3 does not bind more just everywhere but to specific loci. The ChIP-seq data mention only categories but don't show any gene lists that could hint at the specificity of H3K79me3 binding at genes that would promote nuclear abnormalities and enucleation defects.

We thank the reviewer for pointing this out. The GSEA results of H3K79me3 peak showed enrichment of chromatin related biological processes, and the list of associated genes is shown Figure 7B. In addition, we also displayed the changes in H3K79me3 peak signals, ATAC peak signals, and gene expression at gene loci of three chromatin-associated genes (SIRT1, KMT5A and NUCKS1).

(6) P. 12: "Representatively, gene expression levels and ATAC peak signals at SIRT1 locus were elevated in IDH1-shRNA group and were accompanied by enrichment of H3K9me3 (Figure 7F)." Figure 7F does not show an enrichment of H3K9me3, but if the authors found such, they should explain how this modification correlates with the activation of gene expression.

Thank you for bringing this issue to our attention. We sincerely apologize for the mistake in the description of Figure 7F on page 12. We have already corrected this error in the revised manuscript.

(7) Related to the mild phenotype by flow cytometry on Day 13, are the "3 independent biological replicates" from culturing and differentiating CD34 cells from 3 different donors? If all are from the same donor, experiments from at least a second donor should be performed to generalize the results.

In our current study, CD34⁺ cells were derived from different donors. 

(8) If the images in Supplementary Figure 4 are only the indicated cell type, then it is not clear how the data were quantified since only some cells in each image are pointed at and others do not seem to have as large nuclei. There is also no explanation in the legend what the colors mean (nuclei were presumably stained with DAPI, not clear what the cytoplasm stain is - GPA?).

We thank the reviewer for pointing this out. We have revised the manuscript accordingly. Specifically, the nuclei was stained with DAPI and the color was blue. The cell membrane was stained with GPA and the color was red. This staining method allows for clear visualization of the cell structure and helps to better understand the localization of the proteins of interest.

(9) It is not clear to this reviewer whether Figure 4F is a quantification of the Western Blot or of the IF data.

Figure 4F is a quantification of the Western Blot experiment.

(10) The authors sometimes do not describe experiments well, e.g., "treatment of IDH1-deficient erythroid cells with IDH1-EX527" (p. 13). EX-527 is a SIRT1 inhibitor, which the authors only explicitly mention later in that paragraph. It is unclear to this reviewer, why the authors call it IDH1-EX527.

Thank you for pointing out the unclear description in our manuscript. We apologize for the confusion caused by the unclear statement. We have revised the manuscript accordingly. The compound EX-527 is a SIRT1 inhibitor, and we have corrected the description to simply "EX-527" in the revised manuscript.

(11) The end of the introduction needs revising to be more concise; the last paragraph on p. 4 ("Recently, the decreased expression of IDH1...") partially should be integrated with the previous paragraph, and partially is repeated in the last paragraph (top paragraph on p. 5). The last sentence on p. 4, "These findings strongly suggest that aberrant expression of IDH1 is also an important factor in the pathogenesis of AML and MDS.", should rather read "increased expression of IDH1", to distinguish it from mutant IDH1 (mutant IDH1 is also aberrantly expressed IDH1).

We appreciated the reviewer for the helpful suggestion. Considering that the inclusion of this paragraph did not provide a valuable contribution to the formulation of the scientific question, we have removed it after careful consideration, and the revised manuscript is generally more logically smooth.

(12) Abstract and last sentence of the introduction: "innovative perspective" should be re-worded, as the authors present data, not a perspective. Maybe could use "evidence".

Thanks for the reviewer’s suggestion. It has been revised accordingly.

(13) "IDH1-mut AML/MDS" on p. 11. The authors should provide more information about these AML/MDS samples. The legend contains no information about them/their mutational status. How many samples did the authors look at? Do these cells contain mutations other than IDH1?

Thanks for the reviewer’s suggestion. The detail information of these AML/MDS samples are provide in supplemental table 1. In our current study, we collected ten AML/MDS samples and the majority of the samples only contain IDH1 mutations at different sites.

(14) The statement, "Taken together, these results indicated that IDH1 deficiency reshaped chromatin states and subsequently altered gene expression pattern, especially for genes regulated by H3K79me3, which was the mechanism underlying roles of IDH1 in modulation of terminal erythropoiesis." (p. 10), is not correct at that point in the manuscript as the authors have not yet introduced the RNA-seq data.

Thanks for the reviewer’s suggestion. The statement has been revised to “Taken together, these results indicated that IDH1 deficiency reshaped chromatin states by altering the abundance and distribution of H3K79me3, which was the mechanism underlying roles of IDH1 in modulation of terminal erythropoiesis”.

(15) For easier readability, the authors should present the data in order. For example, the supplemental data for IDH shRNA and siRNA should be presented together and not in Supplementary Figures 1 and 5. Supplementary Figure 3 is mentioned after Supplementary Figure 1, but before Supplementary Figure 2 - again, all data need to be presented in subsequent figures to be viewed together.

Thank you for your suggestion regarding the order of data presentation. We have reorganized the figures in the manuscript to improve readability. We apologize for any confusion caused by the previous arrangement and hope that the revised version meets your expectations.

Author Response

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

Reviewer #1 (Public Review):

Several concerns are raised from the current study.

1) Previous studies showed that iTregs generated in vitro from culturing naïve T cells with TGF-b are intrinsically unstable and prone to losing Foxp3 expression due to lack of DNA demethylation in the enhancer region of the Foxp3 locus (Polansky JK et al, Eur J Immunol., 2008, PMID: 18493985). It is known that removing TGF-b from the culture media leads to rapid loss of Foxp3 expression. In the current study, TGF-b was not added to the media during iTreg restimulation, therefore, the primary cause for iTreg instability should be the lack of the positive signal provided by TGF-b. NFAT signal is secondary at best in this culturing condition.

In restimulation, void of TGFb is necessary to cause iTreg instability. Otherwise, the setup is similar to the iTreg-inducing environment (Author response image 1). On the other hand, the ultimate goal of this study is to provide a scenario that bears some resemblance of clinical treatment, where TGFb may not be available. The reviewer is correct in stating that TGFb is essential for iTreg stability, we are studying the role played by NFAT in iTreg instability in vitro, and possibly in potential clinical use of iTreg .

Author response image 1.

Restimulation with TGFb will persist iTreg inducing environment, resulting in less pronounced instability. Sorted Foxp3-GFP+ iTregs were rested for 1d, and then rested or restimulated in the presence of TGF-β for 2 d. Percentages of Foxp3+ cells were analyzed by intracellular staining of Foxp3 after 2 d.

2) It is not clear whether the NFAT pathway is unique in accelerating the loss of Foxp3 expression upon iTreg restimulation. It is also possible that enhancing T cell activation in general could promote iTreg instability. The authors could explore blocking T cell activation by inhibiting other critical pathways, such as NF-kb and c-Jun/c-Fos, to see if a similar effect could be achieved compared to CsA treatment.

We thank the reviewer for this suggestion. We performed this experiment according to see extent of the role that NFAT plays, or whether other major pathways are involved. As Author response image 2 shows, solely inhibiting NFAT effectively rescued the instability of iTreg. The inhibition of NFkB (BAY 11-7082), c-Jun (SP600125), or a c-Jun/c-Fos complex (T5224) had no discernable effect, or in one case, possibly further reduction in stability. These results may indicate that NFAT plays a crucial and special role in TCR activation, which leads to iTreg instability. Other pathways, as far as how this experiment is designed, do not appear to be significantly involved.

Author response image 2.

Comparing effects of NFAT, NF-kB and c-Jun/c-Fos inhibitors on iTreg instability. Sorted Foxp3-GFP+ iTregs were rested for 1d, then restimulated by anti-CD3 and CD28 in the presence of listed inhibitors. Percentages of Foxp3+ cells were analyzed by intracellular staining after 2d restimulation.

3) The authors linked chromatin accessibility and increased expression of T helper cell genes to the loss of Foxp3 expression and iTreg instability. However, it is not clear how the former can lead to the latter. It is also not clear whether NFAT binds directly to the Foxp3 locus in the restimulated iTregs and inhibits Foxp3 expression.

T helper gene activation is likely to cause instability in iTregs by secreting more inflammatory cytokines, as shown in Figure Q9, for example, IL-21 secretion. Further investigation is needed to understand how these genes contribute to Foxp3 gene instability exactly. With our limited insight, there may be two possibilities. 1. IL-21 directly affects Foxp3 through its impact on certain inflammation-related transcription factors (TFs). 2. There could be an indirect relationship where NFAT has a greater tendency to bind to those inflammatory TFs when iTreg instability appears, promoting the upregulation of these Th genes like in activated T cells, while being less likely to bind to SMAD and Foxp3, representing a competitive behavior. We at the moment cannot comprehend the intricacies that lead to the differential effects on T helper genes and Treg related genes.

With that said, we have previously attempted to explore the direct effect of NFAT on Foxp3 gene locus. Foxp3 transcription in iTregs primarily relies on histone modifications such as H3K4me3 (Tone et al., 2008; Lu et al., 2011) rather than DNA demethylation (Ohkura et al., 2012; Hilbrands et al., 2016). Previous studies have reported that NFAT and SMAD3 can together promote the histone acetylation of Foxp3 genes (Tone et al., 2008). In our previous set of experiments, we simultaneously obtained information of NFAT binding sites and H3K4me3. In Foxp3 locus, we observed a decreasing trend in NFAT binding to the CNS3 region of Foxp3 in restimulated iTregs compared to resting iTregs (Author response image 3). Additionally, the H3K4me3 modification in the CNS3 region of Foxp3 decreased upon iTreg restimulation, but inhibiting NFAT nuclear translocation with CsA could maintain this modification at its original level (Author response image 3).

Author response image 3.

The NFAT binding and histone modification on Foxp3 gene locus. Genome track visualization of NFAT binding profiles and H3K4me3 profiles in Foxp3 CNS3 locus in two batches of dataset.

Based on these preliminary explorations, it is concluded that NFAT can directly bind to the Foxp3 locus, and it appears that NFAT decreases upon restimulation, resulting in a decrease in H3K4me3, ultimately leading to the close association of NFAT and Foxp3 instability. However, due to limited sample replicates, these data need to be verified for more solid conclusions. We speculate that during the induction of iTregs, NFAT may recruit histone-modifying enzymes to open the Foxp3 CNS3 region, and this effect is synergistic with SMAD. When instability occurs upon restimulation, NFAT binding to Foxp3 weakens due to the absence of SMAD's assistance, subsequently reducing the recruitment of histone modifications enzyme and ultimately inhibiting Foxp3 transcription.

Reviewer #2 (Public Review):

(1) Some concerns about data processing and statistic analysis.

The authors did not provide sufficient information on statistical data analysis; e.g. lack of detailed descriptions about

-the precise numbers of technical/biological replicates of each experiment

-the method of how the authors analyze data of multiple comparisons... Student t-test alone is generally insufficient to compare multiple groups; e.g. figure 1.

These inappropriate data handlings are ruining the evidence level of the precious findings.

We thank the reviewer for pointing out this important aspect. In the figure legend, numbers of independently-performed experiment repeats are shown as N, biological replicates of each experiment as n. Student’s t test was used for comparing statistical significance between two groups. In this manuscript, all calculations of significant differences were based on comparisons between two groups. There were no multiple conditions compared simultaneously within a single group, and thus, no other calculation methods were used.

(2) Untransparent data production; e.g. the method of Motif enrichment analysis was not provided. Thus, we should wait for the author's correction to fully evaluate the significance and reliability of the present study.

Per this reviewer’s request, we have provided detailed descriptions of the data analysis for Fig 5, including both the method section and the Figure legend, as presented below:

“The peaks annotations were performed with the “annotatePeak” function in the R package ChIPseeker (Yu et al, 2015).

The plot of Cut&Tag signals over a set of genomic regions were calculated by using “computeMatrix” function in deepTools and plotted by using “plotHeatmap” and “plotProfile” functions in deepTools. The motif enrichment analysis was performed by using the "findMotifsGenome.pl" command in HOMER with default parameters.

The motif occurrences in each peak were identified by using FIMO (MEME suite v5.0.4) with the following settings: a first-order Markov background model, a P value cutoff of 10-4, and PWMs from the mouse HOCOMOCO motif database (v11).”

Additionally, we have also supplemented the method section with further details on the analysis of RNA-seq and ATAC-seq data.

(3) Lack of evidence in human cells. I wonder whether human PBMC-derived iTreg cells are similarly regulated.

This is a rather complicated issue, human T cells express FoxP3 upon TCR stimulation (PNAS, 103(17): 6659–6664), whose function is likely to protect T cells from activation induced cell death, and does not offer Treg like properties. In contrast in mice, FoxP3 can be used as an indicator of Treg. Currently, this is not a definitive marker for Treg in human, our FoxP3 based readouts do not apply. Nevertheless, we have now investigated whether inhibiting calcium signaling or NFAT could enhance the stability of human iTreg. As shown in Author response image 4, we found that the proportion of Foxp3-expressing cells did not show significant changes across the different conditions, while the MFI analysis revealed that CsA-treated iTreg exhibited higher Foxp3 expression levels compared to both restimulated iTreg and rest iTreg. However, CM4620 had no significant effect on Foxp3 stability, consistent with the observation of its limited efficacy in suppressing human iTreg long term activation. In summary, our results suggest that inhibiting NFAT signaling through CsA treatment can help maintain higher levels of Foxp3 expression in human iTreg.

Author response image 4.

Effect of inhibiting NFAT and calcium on human iTreg stability. Human naïve CD4 cells from PBMC were subjected to a two-week induction process to generate human iTreg. Subsequently, human iTreg were restimulated for 2 days with dynabeads followed by 2 days of rest in the prescence of CsA and CM-4620. Four days later, percentages of Foxp3+ cells and Foxp3 mean fluorescence intensity (MFI) were analyzed by intracellular staining.

(4) NFAT regulation did not explain all of the differences between iTregs and nTregs, as the authors mentioned as a limitation. Also, it is still an open question whether NFAT can directly modulate the chromatin configuration on the effector-type gene loci, or whether NFAT exploits pre-existing open chromatin due to the incomplete conversion of Treg-type chromatin landscape in iTreg cells. The authors did not fully demonstrate that the distinct pattern of chromatin regional accessibility found in iTreg cells is the direct cause of an effector-type gene expression.

To our surprise, the inhibition of NFkB (BAY 11-7082), c-Jun (SP600125), and the c-Jun/c-Fos complex (T5224) resulted in minimal alterations, as shown in Fig Q1. This seems to argue that NFAT may play a more special role in events leading iTreg instability.

We hypothesize that NFAT takes advantage of pre-existing open chromatin state due to the incomplete conversion of chromatin landscape in iTreg cells. Because iTreg cells, after induction, already exhibit inherent chromatin instability, with highly-open inflammatory genes. Furthermore, when iTreg cells were restimulated, the subsequent change in chromatin accessibility was relatively limited and not rescued by NFAT inhibitor treatment (Author response image 5). Therefore, in the case of iTreg cells, we propose that NFAT exploits the easy access of those inflammatory genes, leading to rapid destabilization of iTreg cells in the short term.

In contrast, tTreg cells possess a relatively stable chromatin structure in the beginning, it would be interesting to investigate whether NFAT or calcium signaling could disrupt chromatin accessibility during the activation or expansion of tTreg cells. It is possible that NFAT might cause the loss of the originally established demethylation map and open up inflammatory loci, thereby inducing a shift in gene transcriptional profiles, equally leading to instability.

Author response image 5.

Chromatin accessibility of Rest, Retimulated, CsA/ORAIinh treated restimulated iTreg. PCA visualization of chromatin accessibility profiles of different cell types. Color indicates cell type.

To establish a direct relationship between gene locus accessibility and its overexpression, a controlled experimental approach can be employed. One such method involves precise manipulation of the accessibility of a specific genomic locus using CRISPR-mediated epigenetic modifications at targeted loci. Subsequently, the impact of this manipulation on the expression level of the target gene can be precisely examined. By conducting these experiments, it will be possible to determine whether the augmented gene accessibility directly causes the observed gene overexpression.

Reviewer #1 (Recommendations For The Authors):

1) It might be helpful to add TGF-b to the iTreg restimulation culture to remove the influence of the lack of TGF-b from the equation, and measure the influence of SOCE/NFAT on iTreg instability.

Please refer to Author response image 1.

2) Alternatively, authors can also culture iTreg cells with TGF-b for 2 weeks when they undergo epigenetic changes and become more stabilized (Polansky JK et al, Eur J Immunol., 2008, PMID: 18493985). At this point, the stabilized iTregs can be used to measure the influence of SOCE/NFAT on iTreg instability.

In the study conducted by Polansky, it was observed in Figure 1 that prolonged exposure to TGF-β fails to induce stable Foxp3 expression and demethylation of the Treg-specific demethylated region (TSDR). Based on this finding, we could consider exploring alternative approaches to obtain a more stabilized iTreg population. One such approach could be isolating Foxp3+helios-Nrp1- iTreg cells directly from the peripheral in vivo, which are also known as pTregs. Generally, pTreg cells generated in vivo tend to be more stable compared to iTreg cells induced in vitro, and they already exhibit partial demethylation of the Treg signature, as shown in Fig 6C (Polansky JK et al, Eur J Immunol., 2008, PMID: 18493985). Investigating the role of NFAT and calcium signaling in pTreg cells would provide further insights into the additional roles of NFAT in Treg phenotypical transitions, particularly its role in chromatin accessibility.

3) In Figure 3, NFAT binding to the inflammatory genes in iTreg cells was even stronger than in activated T conventional cells. This is possibly due to Tconv cells being stimulated only once while iTregs were restimulated. A fair comparison should be conducted with restimulated activated conventional T cells.

Figure 3 demonstrates the accessibility of inflammatory gene loci, rather than NFAT binding. Comparing restimulated Tconvs with restimulated iTreg cells is indeed a valuable suggestion, as their activation state and polarization in iTreg directions could lead to distinct chromatin accessibility. Although one is activated long term regularly and the other is activated long term under iTreg polarization, it is highly likely that the chromatin state of both activated Tconvs and iTreg cells is highly open, especially in terms of the accessibility of inflammatory genes. This may provide us with a new perspective to understand iTreg cells, but will unlikely affect our central conclusion.

4) In the in vivo experiment in Figure 6, a control condition without OVA immunization should be included as a baseline.

We have performed this experiment in the absence of OVA, as depicted in Author response image 6. In the absence of OVA immunization, both WT-ORAI and DN-ORAI iTreg exhibited substantial stability, although DN-ORAI demonstrated a slightly less stable trend. Upon activation with 40ug and 100ug of OVA, DN-ORAI iTreg demonstrated enhanced stability than WT-ORAI iTreg, maintaining a higher proportion of Foxp3 expression.

Author response image 6.

Stability of DN-ORAI iTreg in vivo with or without OVA immunization. WT-ORAI/DN-ORAI-GFP+-transfected CD45.2+ Foxp3-RFP+ OT-II iTregs were transferred i.v. into CD45.1 mice. Recipients were left or immunized with OVA323-339 in Alum adjuvant. On day 5, mLN were harvested and analyzed for Foxp3 expression by intracellular staining.

Reviewer #2 (Recommendations For The Authors):

Major

Some concerns about the data processing and statistic analysis, as mentioned in the public review. In the figure legend, what does it mean e.g. n=3, N=3? Technical triplicate experiments? Three mice? Independently-performed three experiments? The authors should define it at least in the "Statistical analysis" in the method section otherwise the readers cannot determine the reason why they mainly use SEM for the data description.

Moreover, in some cases, the number of experiments was not sure; e.g., Fig.1B, Fig. 5.

How did the authors analyze data including multiple comparisons? Student t-test alone is generally insufficient to compare multiple groups; e.g. figure 1.

We thank the reviewer for pointing out this omission. Now, in the figure legend, numbers of independently-performed experiment repeats are shown as N, biological replicates of each experiment as n. For Fig. 1B, N=2, and for Fig 5, we have acquired NFAT Cut&Tag data for 2 times, N=2. Student’s t test was used for comparing statistical significance between two groups. In this manuscript, all calculations of significant differences were based on comparisons between two groups. There were no multiple conditions compared simultaneously within a single group, and thus, no other calculation methods were involved apart from the Student's t-test.

In Figure 1A, the difference in suppressiveness seemed subtle. Data collection of multiple doses of Tconv:Treg ratio will enhance the reliability of such kind of analysis.

We have now attempted the suppression assay with varying Treg:Tconv ratios and observed that the suppressive effect of iTreg was more obvious than that of tTreg when co-cultured at a 1:1 ratio with Tconv cells. However, as the cell number of tTreg and iTreg decreased, the inhibitory effects converged.

Author response image 7.

Compare multiple dose of Tconv:Treg ratio in suppression function CFSE-labelled OT-II T cells were stimulated with OVA-pulsed DC, then different number of Foxp3-GFP+ iTregs and tTregs were added to the culture to suppress the OT-II proliferation. After 4 days, CFSE dilution were analyzed. Left, Representative histograms of CFSE in divided Tconvs. Right, graph for the percentage of divided Tconvs.

In Figure 3F, to which group did the shaded peaks belong? In this context, the authors should focus on "Activation Region" peaks (open chromatin signature in both TcAct & iTreg defined in Fig. 4D) but I did not find the peak in the focusing DNA regions in TcAct (e.g. the shaded regions in IL-4 loci). The clear attribution of the peaks to the heatmap will enhance the visibility and understanding of readers.

We have selected some typical peaks that belong to Fig 3D. These genes encompass some T-cell activation-associated transcription factors, such as Irf4, Atf3, as well as multiple members of the Tnf family including Lta, Tnfsf4, Tnfsf8, and Tnfsf14. Additionally, genes related to inflammation such as Il12rb2, Il9, and Gzmc are included. These genes show elevated accessibility upon T-cell activation, partially open in activated nTreg cells, referred to as the "Activation Region." They collectively exhibit high accessibility in iTreg cells, which may contribute to their instability.

Author response image 8.

Chromatin accessibility of some “Activation Region”. Genomic track showing chromatin accessibility of Irf4, Atf3, Lta, Tnfsf8, Tnfsf4, Tnsfsf14, Il12rb2, Il9, Gzmc in activated Tconv and iTreg.

In Figure 4A/S4A, the information on cell death will help the understanding of readers because the sustained SOCE is associated with cell survival as shown in Fig. S2. The authors can discuss the relationships between cell death and Foxp3 retention, which potentially leads to a further interesting question; e.g. the selective/resistance to activation-induced cell death as the identity of Treg cells.

As shown in Author response image 9, activated iTreg cells indeed exhibit a certain degree of cell death compared to resting iTreg cells. The inhibition of NFAT by CsA enhances the survival rate of iTreg cells, but the inhibition of ORAI by CM-4620 leads to more severe cell death. The cell death induced by CsA and CM-4620 is not consistent, indicating that there may not be a direct proportional relationship between cell death and the expression of Foxp3 and Treg identity.

Author response image 9.

Relationship of cell death and Foxp3 stability in restimulated iTregs. Sorted Foxp3-GFP+ iTregs were rested for 1d, then restimulated by anti-CD3 and CD28 in the presence of CsA or CM-4620. After 2d restimulation, live cell percentage were analyzed by staining of Live/Dead fixable Aqua, and percentages of Foxp3+ cells were analyzed by intracellular staining of Foxp3. Upper, live cell percentage of iTregs. Lower, percentages of Foxp3 in iTregs.

In Figure 5, the information for the data interpretation was insufficient.

We have provided detailed descriptions of the data analysis for Fig 5, including both the method section and the Figure legend, as presented below:

“The peaks annotations were performed with the “annotatePeak” function in the R package ChIPseeker (Yu et al, 2015). The plot of Cut&Tag signals over a set of genomic regions were calculated by using “computeMatrix” function in deepTools and plotted by using “plotHeatmap” and “plotProfile” functions in deepTools. The motif enrichment analysis was performed by using the "findMotifsGenome.pl" command in HOMER with default parameters. The motif occurrences in each peak were identified by using FIMO (MEME suite v5.0.4) with the following settings: a first-order Markov background model, a P value cutoff of 10-4, and PWMs from the mouse HOCOMOCO motif database (v11).”

Additionally, we have also supplemented the method section with further details on the analysis of RNA-seq and ATAC-seq data.

The correlation between the open chromatin status of the gene loci described in Fig.5E and the expression at mRNA level? e.g.; Do iTreg-Act cells produce a higher level of IL-21 than nTreg-act? The analysis in Fig.5F-G should be performed in parallel with nTreg cells to emphasize the distinct NFAT-chromatin regulation in iTreg cells.

We have now compared the secretion levels of IL-21 in tTreg and iTreg upon activation and treated with CsA by ELISA. As shown in Author response image 10, tTreg did not secrete IL-21 regardless of activation status (undetectable), while iTreg did not secrete IL-21 at resting state but exhibited IL-21 secretion after 48 h of activation. Moreover, the secretion of IL-21 was inhibited by CsA and CM-4620 treatment. This observation aligns with our earlier findings where we observed nuclear binding of NFAT to gene loci of these cytokines, enhancing their expression and pushing iTreg unstable under inflammatory conditions. These findings further underscore the likelihood that the inhibition of calcium and NFAT signaling might contribute to the stabilization of iTreg by suppressing the secretion of inflammatory cytokines.

Author response image 10.

IL-21 secretion in tTreg and iTreg upon activation. iTregs and tTregs were sorted and restimulated with anti-CD3 and anti-CD28 antibodies, in the presence of CsA and CM-4620. Cell culture supernatant were harvested after 2 d restimulation and IL-21 secretion was analyzed by ELISA.

Performing a parallel comparison of NFAT activity between tTreg and iTreg cells was initially part of our experimental plan. However, it proved challenging in practice, as we encountered difficulties in efficiently infecting tTreg cells with NFAT-flag. Consequently, we could not obtain a sufficient number of tTreg cells for conducting Cut&Tag experiments.

Based on our observations, we speculate that there might be substantial differences in the accessibility of genes in tTreg cells, leading to considerable variations in the repertoire of genes available for NFAT to regulate. As a result, we expect significant differences in the nuclear localization and activity of NFAT between iTreg and tTreg cells.

In Figure 6C, what does the FCM plot between Foxp3-CFSE look like?

The authors can discuss the mechanism of ORAI-DN-mediated through such analysis; e.g. the possibility that selective proliferation defect by ORAI-DN in Foxp3- cells led to an increased percentage of Foxp3, not only just unstable transcription of Foxp3.

This is an in vitro experiment to assess the suppressive effect of iTreg on Tconv proliferation. Therefore, CFSE is used to stain Tconv cells, but not iTreg cells, so we did not detect proliferation feature of iTreg.

Minor

Confusing terminology of "tTreg" at line 47, etc. "natural Treg" contains both thymic-derived Treg and periphery-derived Treg cells. (A Abbas et al. Nat Immunol. 2013)

We have now changed the designation to tTreg at line 47. tTreg refers to thymus-derived regulatory T cells, while nTreg includes both tTreg and pTreg. However, it is important to note that the Treg cells used in our study were isolated from the spleen of 2-4-month-old Foxp3-GFP or Foxp3-RFP mice. The CD4+ T cells were first enriched using the CD4 Isolation kit, and the FACSAriaII was utilized to collect CD4+ Foxp3-GFP/RFP+ Treg cells. Subsequently, Helios and Nrp-1 staining revealed that the majority of these cells were nTreg, with only approximately 6% being pTreg. Overall, we consider the cells we used as tTreg.

In all FCM analyses, the authors should clarify how to detect Foxp3 expression; Foxp3-GFP/Foxp3-RFP/Intracellular staining like Figure S5A (but not specified in the other FCM plots)

All Foxp3 expressions in the article were assessed using intracellular staining, as described in the methods section, and we have added specific descriptions to each figure legend. The reason for employing intracellular staining is that we used Foxp3-IRES-GFP mice, where GFP and Foxp3 are not fused into a single protein, existing as separate proteins after expression. Therefore, during induction, the appearance of GFP protein might potentially represent the presence of Foxp3. However, in cases of Foxp3 instability, the degradation of GFP protein may not be entirely synchronized with that of Foxp3 protein, making GFP an unreliable indicator of Foxp3 expression levels. As a result, for the purification of pure iTreg cells, we used Foxp3-GFP/RFP fluorescence, while for observing instability, we employed intranuclear staining of Foxp3.

In Figure 6B, the captions were lacking in the two graphs on the right side

The two restimulation conditions, 0.125+0.25 and 0.25+0.5, have been added into Fig 6B right side.

In Figure S2, the annotation of the x-y axis was missing.

Added.

Lack of reference at line 292.

Reference 42-46 were added.

In the method section, the authors should note the further product information of antibodies and reagents to enhance reproducibility and transparency. Making a list that clarifies the suppliers, Ab clone, product IDs, etc. is encouraged. The authors did not specify the supplier of recombinant proteins and which type of TGF-beta (TGF-beta 1, 2, or 3?).

A detailed description of the mice, antibodies, Peptide recombinant protein, commercial kit, and software has been provided and incorporated into the methods section.

In the method section, the authors should clarify which Foxp3-reporter strain. There are many strains of Foxp3-reporter mice in the world. In line 373, is the "FoxP3-IRES-GFP transgenic mice" true? Knock-in strain or BAC-transgene?

This mouse is a gift from Hai Qi Lab in Tsinghua University. They acquired this mouse strain from Jackson Laboratory, and the strain name is B6.Cg-Foxp3tm2Tch/J, Strain #:006772. An IRES-EGFP-SV40 poly A sequence was inserted immediately downstream of the endogenous Foxp3 translational stop codon, but upstream of the endogenous polyA signal, generating a bicistronic locus encoding both Foxp3 and EGFP.

The age of mice used in the experiments should be specified, and confusing words such as "young" should not be used in any method descriptions; e.g. line 405.

The detailed mouse age has been added in the methods section. “To prepare Tconv, tTreg and iTreg for experiments, spleen was isolated from 2-4-month-old Foxp3-GFP mice for Tconv and tTreg sorting, and 6-week-old mice for iTreg induction.”

The method of how the original ATAC-seq/Cut & Tag data were generated was not described in the method section.

Added in method section.

The reference section was incomplete, and the style was not unified. e.g.; ref 7, 24, 25, 26 ... I gave up checking all.

The style of ref 7, 22, 24, 26, 28, 31, 33, 35 were modified.

Changes in manuscript:

Author Name: “Huiyun Lv” to “Huiyun Lyu”.

Fig 1A was updated according to Reviwer 2’s suggestion.

Fig S3E and associated description was added according to Reviwer 2’s suggestion.

Fig S4C and associated description was added according to Reviwer 1’s suggestion.

Fig 5H and associated description was added according to Reviwer 2’s suggestion.

Fig 6D were updated according to Reviwer 1’s suggestion.

Fig 2D was corrected, the labels for gapdh and actin in the iTreg panel were inadvertently switched. The mistake has been rectified, and the original gel image will be provided.

Fig 2A and Fig 4A was updated.

The style of Fig 6B and Fig S2A was modified.

Method:

Mice: FoxP3-IRES-GFP with more description.

Flow Cytometry sorting and FACS: the detailed mouse age has been added. RNA-seq analysis, ATAC-sequencing, ATAC-seq analysis, Cut&Tag assay, Cut&Tag data analysis: more description was added.

Statistical analysis: “Numbers of independently-performed experiment repeats are shown as N, biological replicates of each experiment as n.” were added.

Reference: Ref 42-46 and 49-52 were added. The style of ref 7, 22, 24, 26, 28, 31, 33, 35 were corrected.

A detailed description of the mice, antibodies, Peptide recombinant protein, commercial kit, and software has been provided.

Author response:

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

Reviewer #1 (Public review):

(1) The mechanism by which STAMBPL1 mediates GRHL3 transcription through its interaction with FOXO1 is not sufficiently discussed, especially in relation to how STAMBPL1 regulates FOXO1. Some reported effects are modest.

We appreciate the reviewer’s comments. In response, we have added a discussion on the potential mechanisms by which STAMPBL1 regulates FOXO1 transcriptional activity in Discussion, highlighted in red on page 18, lines 342 to 352. The specific reply content is as follows: “The transcriptional activity of FOXO1 is primarily regulated by its nucleocytoplasmic shuttling process (Van Der Heide, Hoekman et al. 2004). The PI3K/AKT pathway promotes the phosphorylation of FOXO1, resulting in the formation of a complex with members of the 14-3-3 family (including 14-3-3σ, 14-3-3ε, and 14-3-3ζ), which facilitates its export from the nucleus and inhibits its transcriptional activity (Huang and Tindall 2007, Tzivion, Dobson et al. 2011). It’s reported that TDAG51 prevents the binding of 14-3-3ζ to FOXO1 in the nucleus by interacting with FOXO1, thereby enhancing its transcriptional activity through increased accumulation within the nucleus (Park, Jeon et al. 2023). Our results indicate that the overexpression of STAMBPL1 and STAMBPL1-E292A did not affect the protein levels of FOXO1 (Fig.7E and Fig.S5E), but STAMBPL1 co-localizes with FOXO1 in the nucleus (Fig.7M) and interacts with it (Fig.7N and Fig.S5I-J). This suggests that STAMBPL1 enhances the transcriptional activity of FOXO1 on GRHL3 by interacting with nuclear FOXO1.” The result was added to Supplementary Figure 5 as Fig.S5E.

Reviewer #2 (Public review):

(1) A potential limitation of the study is the reliance on specific cellular and animal models, which may constrain the extrapolation of these findings to the broader spectrum of human TNBC biology. Furthermore, while the study provides evidence for a novel regulatory axis involving STAMBPL1, FOXO1, and GRHL3, the multifaceted nature of angiogenesis may implicate additional regulatory factors not exhaustively addressed in this research.

We appreciate the valuable suggestions provided by the reviewer. In Discussion, we have added an in-depth discussion of the limitations of the study, as well as an analysis of the regulatory factors related to tumor angiogenesis, which highlighted in red on pages 20 to 21, lines 396 to 412. The relevant content added is as follows: “In this study, we utilized two triple-negative breast cancer cell lines, HCC1806 and HCC1937, along with human primary umbilical vein endothelial cells (HUVECs) and a nude mouse breast orthotopic transplantation tumor model to investigate the regulatory mechanism by which STAMBPL1 activates the GRHL3/HIF1α/VEGFA signaling pathway through its interaction with FOXO1, thereby promoting angiogenesis in TNBC. The results of this study have certain limitations regarding their applicability to human TNBC biology. Furthermore, in addition to the HIF1α/VEGFA signaling pathway emphasized in this study, tumor cells can continuously release or upregulate various pro-angiogenic factors, such as Angiopoietin and FGF, which activate endothelial cells, pericytes (PCs), cancer-associated fibroblasts (CAFs), endothelial progenitor cells (EPCs), and immune cells (ICs). This leads to capillary dilation, basement membrane disruption, extracellular matrix remodeling, pericyte detachment, and endothelial cell differentiation, thereby sustaining a highly active state of angiogenesis (Liu, Chen et al. 2023). It is important to collect clinical TNBC tissue samples in the future to analyze the expression of the STAMBPL1/FOXO1/GRHL3/HIF1α/VEGFA signaling axis. Furthermore, patient-derived organoid and xenograft models are useful to elucidate the regulatory relationship of this axis in TNBC angiogenesis”

Reviewer #3 (Public review):

The main weaknesses of this work are that the relevance of this molecular axis to the pathogenesis of TNBC is not clear, and it is not clearly established whether this is a regulatory pathway that occurs in hypoxic conditions or independently of oxygen levels.

(1) With respect to the first point, both FOXO1 and GRHL3 have been previously described as tumor suppressors, with reports of FOXO1 inhibiting tumor angiogenesis. Therefore, this works describes an apparently contradictory function of these proteins in TNBC. While it is not surprising that the same genes perform divergent functions in different tumor contexts, a stronger evidence in support of the oncogenic function of these two genes should be provided to make the data more convincing. As an example, the data in support of high STAMBPL1, FOXO and GRHL3 gene expression in TNBC TCGA specimens provided in Figure 8 is not very strong and it is not clear what the non-TNBC specimens are (whether other breast cancers or other tumors, perhaps those tumors whether these genes perform tumor suppressive functions). To strengthen the notion that STAMBPL1, FOXO and GRHL3 are overexpressed in TNCB, the authors could provide a comparison with normal tissue, as well as the analysis of other publicly available datasets (like the NCI Clinical Proteomic Tumor Analysis Consortium as an example). Finally, is it not clear what are the basal protein expression levels of STAMBPL1 in the cell lines used in this study, as based on the data presented in Figures 2D and F it appears that the protein is not expressed if not exogenously overexpressed. It would be helpful if the authors addressed this issue and provided further evidence of STAMBPL1 expression in TNBC cell lines.

We appreciate the suggestions. In this study, we utilized the BCIP online tool to analyze the Metabric database, incorporating adjacent normal tissues as controls. Although the expression levels of STAMBPL1, FOXO1, and GRHL3 in breast cancer tissues are not uniformly higher than those in adjacent tissues, their expression levels in triple-negative breast cancer (TNBC) are significantly elevated compared to non-TNBC. The results of this re-analysis have been added in Supplementary Figure 6 as Fig.S6A-C.

About the question of the basal protein expression levels of STAMBPL1 in the cell lines used in this study, our response is that Fig. 2A showed the endogenous level of STAMBPL1 in HCC1806 and HCC1937. For Fig. 2D and 2F, the overexpressed STAMBPL1 was fused with a 3xFlag tag, resulting in a higher molecular weight compared to the endogenous STAMBPL1. In the revised Figure 2, we have indicated the positions of the endogenous (Endo.) and exogenous (OE.) STAMBPL1 bands with arrows.

(2) Linked to these considerations is the second major criticism, namely that it is not made clear if this new regulatory axis is proposed to act in normoxic or hypoxic conditions. The experiments presented in this paper are performed in both conditions but a clear explanation as to why cells are exposed to hypoxia is not given and would be necessary being that HIF-1a transcription and not protein stability is being analyzed. Also, different hypoxic conditions are sometimes used, resulting in different mRNA levels of HIF-1a and its downstream targets and quite significant fluctuations within the same cell line from one experimental setting to the next. The authors should provide an explanation as to why experimental conditions are changed and, more importantly, the experiments presented in Figure 2 should be performed also in normoxia.

Thanks for the comments. Under normoxic conditions, HIF1α is recognized by pVHL due to hydroxylation and is rapidly degraded via the proteasomal pathway. In contrast, under hypoxic conditions, HIF1α protein is accumulated. To investigate the effect of STAMBPL1 knockdown on HIF1A gene transcription levels, we conducted experiments under hypoxic conditions to avoid interference from the rapid degradation of HIF1α at the protein level, as shown in Figures 2B-C. Furthermore, under normoxic conditions, the overexpression of STAMBPL1 had been demonstrated to significantly enhance the protein levels of HIF1α and upregulate the transcription of VEGFA through HIF1α. To avoid the potential impact of excessive accumulation of HIF1α protein under hypoxic conditions on its protein level detection and the transcription of downstream VEGFA, the related experiments shown in Figure 2D-G were performed under normoxic conditions. We have explained the corresponding experimental conditions in the “Result” and “Figure legends” according to the reviewer's comments, highlighted in red.

(3) Another critical point is that necessary experimental controls are sometimes missing, and this is reducing the strength of some of the conclusions enunciated by the authors. As examples, experiments where overexpression of STAMBPL1 is coupled to silencing of FOXO1 to demonstrate dependency lack FOXO1 silencing the absence of STAMBPL1 overexpression. Because diminishing FOXO1 expression affects HIF-1a/VEGF transcription even in the absence of STAMBPL1 (shown in Figure 7C, D), it is not clear if the data presented in Figure 7G are significant. The difference between HIF-1a expression upon FOXO1 silencing should be compared in the presence or absence of STAMBPL1 overexpression to understand if FOXO1 impacts HIF-1a transcription dependently or independently of STAMBPL1.

Thank you for this comment. For Fig.7G-H, our experimental objective was to determine whether the activation of HIF1A/VEGFA transcription by STAMBPL1 via FOXO1. Therefore, under STAMBPL1 overexpression, we knocked down FOXO1 to investigate whether FOXO1 silencing could reverse the upregulation of HIF1A/VEGFA transcription induced by STAMBPL1 overexpression.

(4) In addition, some minor comments to improve the quality of this manuscript are provided.

(4.1) As a general statement, the manuscript is extremely synthetic. While this is not necessarily a negative feature, sometimes results are discussed in the figure legends and not in the main text (as an example, western blots showing HIF-1a expression) and this makes it hard to read thought the data in an easy and enjoyable manner.

Thank you for this suggestion. We have revised the figure legends to make them clearer and more concise, highlighted in red.

(4.2) The effect of STAMBPL1 overexpression on HIF-1a transcription is minor (Figure 2) The authors should explain why they think this is the case and whether hypoxia may provide a molecular environment that is more permissive to this type of regulation.

Thank you for the comment. Under normoxic conditions, we conducted WB to examine the protein expression of HIF1α after the overexpression of STAMBPL1 and the knockdown of HIF1α. To visually illustrate the impact of STAMBPL1 overexpression on HIF1A protein levels, as well as the effectiveness of HIF1α knockdown, we annotated the grayscale analysis results of the bands in Figures 2D and 2F. As the reviewer pointed out, under normoxic conditions, HIF1α is rapidly degraded, which may explain why the upregulation of HIF1α protein levels by STAMBPL1 overexpression is not very pronounced.

(4.3) HIF-1a does not appear upregulated at the protein level protein by STAMBPL1 or GRLH3 overexpression, even though this is stated in the legends of Figures 2 and 6. The authors should show unsaturated western blots images and provide quantitative data of independent experiments to make this point.

Thank you for this comment. We have added the unsaturated image of HIF1α into Fig.2D, and performed a grayscale analysis of the HIF1α bands in Fig.2D and Fig.6A to indicate the relative protein level of HIF1α.

Reviewer #1 (Recommendations for the authors):

(1) The authors previously reported that STAMBPL1 stabilizes MKP1 in TNBC. However, in this study, they focus on HIF1a. Given that STAMBPL1 affects HIF1a expression, it would be valuable to examine the levels of ROS in TNBC cells with or without STAMBPL1, as ROS is known to influence HIF1a stability.

Thank you for your comments. It’s known that STAMBPL1 functions as a deubiquitinating enzyme. However, our study reveals that the upregulation of HIF1α by STAMBPL1 is independent of its deubiquitinating activity. This conclusion is supported by the observation that overexpression of the deubiquitinase active site mutant, STAMBPL1-E292A, also upregulated HIF1α expression (Figure 1F). Moreover, STAMBPL1 overexpression enhanced HIF1α transcription (Figures 4E and S3E), while STAMBPL1 knockdown was able to inhibit the transcription of HIF1α (Figures 2B-C). These results indicate that STAMBPL1 mediates the transcription of HIF1α but does not affect the stability of HIF1α. For these reasons, we think that it is unnecessary to examine the ROS levels.

(2) Figure 1A: The regulation of HIF1a mRNA by STAMBPL1, but not its protein levels, could be better addressed by using MG132 to rule out the impact of protein degradation.

Thanks for this comment. Under normoxic conditions, the oxygen-sensitive prolyl hydroxylases PHD1-3 act on HIF1α, specifically inducing hydroxylation at the proline 402 and 564 residues. These hydroxylated residues are recognized by the pVHL/E3 ubiquitin ligase complex, leading to ubiquitination and subsequent degradation via the proteasome pathway. Conversely, under hypoxic conditions, PHD1-3 are inactivated, and non-hydroxylated HIF1α is not recognized by the pVHL/E3 ubiquitin ligase complex, thereby avoiding ubiquitination and proteasomal degradation (DOI: 10.1073/pnas.95.14.7987, DOI: 10.1515/BC.2004.016, and DOI: 10.1042/BJ20040620). The mechanism of HIF1α accumulation under hypoxia is analogous to the action of the proteasome inhibitor MG132. When we treated cells with hypoxia, the ubiquitination and proteasomal degradation pathway of HIF1α was blocked. At this time, STAMBPL1 knockdown could downregulate the expression of HIF1α (Fig.1A). Meanwhile, since the knockdown of STAMBPL1 significantly downregulated the mRNA level of HIF1α under hypoxia (Fig.2B-C), we concluded that STAMBPL1 affects the expression of HIF1α by mediating its transcription. In addition, MG132 will block all proteasomal substrate degradation and may affect HIF1α mRNA levels indirectly.

(3) Figure 2D and 2F: The effect of STAMBPL1 in promoting HIF1a expression is quite mild, and the effect of HIF1a knockdown is also modest. Given the high levels of STAMBPL1 in TNBC cell lines (Figure 2A), it would be better to repeat these experiments in a STAMBPL1-knockdown setting for clearer insights.

We appreciate this insightful suggestion. Considering that the regulation of HIF1α expression by STAMBPL1 occurs at the transcriptional level, and to prevent excessive accumulation of HIF1a during hypoxia that could confound the effect of STAMBPL1 overexpression on HIF1α regulation, we opted to overexpress STAMBPL1 under normoxic conditions and subsequently knock down HIF1α, as shown in Fig.2D and Fig.2F. This approach allowed us to observe that STAMBPL1 overexpression can upregulate HIF1a expression to some extent. Additionally, in response to the reviewer's suggestion to knock down STAMBPL1, we have conducted the corresponding experiments, with results presented in Fig.1A-E and Fig.2B-C.

(4) Figure 4A: Why does the RNA-seq pattern differ significantly between the two siRNAs? Additionally, the authors should clarify why they focus primarily on transcription factors, as other mechanisms, such as mRNA stability and RNA modification, could also influence gene transcription.

Thank you for this comment. Two siRNAs for STAMBPL1 were designed and synthesized by a biotechnology company. Although both siRNAs target STAMBPL1, they target different sequences. While both siRNAs effectively knocked down STAMBPL1 (Fig. 1A and Fig. 2A), the possibility of off-target effects cannot be completely ruled out. Therefore, we needed to use two siRNAs simultaneously for RNA-seq, ensuring that the gene expression changes observed are due to the knockdown of STAMBPL1 by focusing on genes downregulated by both two siRNAs. Additionally, among the 27 genes downregulated by both two siRNAs, only 18 genes were annotated. Of these 18 genes, except for GRHL3, which is a transcription factor reported to be involved in gene transcription regulation, the remaining 17 genes have no documented association with RNA transcription, stability, or modification. Therefore, we focused on the GRHL3 gene.

(5) Figure 5G: To investigate whether STAMBPL1 and GRHL3 function epistatically in the pathway, a double knockdown of STAMBPL1 and GRHL3 should be examined. Additionally, a double knockdown of STAMBPL1 and FOXO1 should be assessed.

Thank you for your comment. In Figure 5G, we aimed to assess the knockdown efficiency of GRHL3 using siRNAs. To determine whether STAMBPL1 upregulates the HIF1a/VEGFA axis via GRHL3, we overexpressed STAMBPL1 and subsequently knocked down GRHL3. Our findings indicated that STAMBPL1 overexpression indeed enhanced the HIF1a/VEGFA axis, which was rescued by the knockdown of GRHL3, as shown in Figures 4E-F and S3E-F. Similarly, upon overexpressing STAMBPL1 and knocking down FOXO1, we observed that STAMBPL1 overexpression increased the GRHL3/HIF1a/VEGFA axis, which could also be rescued by knocking down FOXO1, as shown in Figures 7F-H. These results suggest that STAMBPL1 upregulates the GRHL3/HIF1a/VEGFA axis through FOXO1. We do not think it is a right way to double knock down STAMBPL1 and FOXO1 or GRHL3.

(6) Figure 7: It remains unclear how STAMBPL1 regulates FOXO1. The authors show that STAMBPL1 increases the transcriptional activation of FOXO1 at the GRHL3 promoter, but it is not clear if STAMBPL1 is required for FOXO1 binding to the GRHL3 promoter. To address this, STAMBPL1-knockdown should be included to examine its effect on FOXO1 binding to the GRHL3 promoter. Furthermore, it would be important to determine whether the STAMBPL1-FOXO1 interaction is essential for GRHL3 transcription. Since the interaction sites of STAMBPL1-FOXO1 have been mapped, a mutant disrupting the interaction would provide better insight into how STAMBPL1 promotes GRHL3 transcription by interacting with FOXO1.

Thank you for this comment. It has been reported that FOXO1 promotes the transcription of the GRHL3 gene by interacting with its promoter (DOI: 10.1093/nar/gkw1276). We also verified through ChIP assay that FOXO1 can bind to the promoter of GRHL3 gene (Fig.7I) and mediate its transcription. Specifically, knocking down FOXO1 significantly down-regulated the mRNA level of GRHL3 (Fig.7B), and the GRHL3 promoter lacking FOXO1 binding site almost completely lost transcriptional activity (Fig.7J), indicating that FOXO1 is crucial for the transcriptional activity of the GRHL3 promoter. Overexpression of STAMBPL1 enhances the activating effect of FOXO1 on the transcriptional activity of the GRHL3 promoter (Fig.7K). However, the up-regulation of GRHL3 transcription by overexpression of STAMBPL1 is completely blocked by FOXO1 knockdown (Fig.7F), and the knockdown of FOXO1 essentially blocks the binding of STAMBPL1 to the GRHL3 promoter (Fig.7L), suggesting that STAMBPL1 affects the transcriptional expression of GRHL3 based on FOXO1. As we added in Discussion, the transcription factor activity of FOXO1 is mainly regulated by its nucleoplasm shuttling process, and the accumulation of FOXO1 in nucleus can enhance its transcription factor activity (DOI: 10.1042/BJ20040167; DOI: 10.15252/embj.2022111867). In our research, neither STAMBPL1 nor its mutant of deubiquitinating enzyme site affected the expression of FOXO1 (Fig.S5E), but STAMBPL1 and FOXO1 co-located in the nucleus (Fig.7M), and they interacted with each other (Fig.7N, Fig.S5I-J). Therefore, we speculate that STAMBPL1 interacts with FOXO1 in the nucleus, obstructs the binding of FOXO1 with the members of 14-3-3 family, inhibits the export of FOXO1, thereby enhancing its transcriptional activity. This interaction between STAMBPL1 and FOXO1 does not necessarily affect the binding of FOXO1 with DNA, including the GRHL3 promoter.

(7) Figure 8 A-C: What is the correlation among the expressions of STAMBPL1, FOXO1, and GRHL3 in TNBC tumors compared to non-TNBC tumors?

Thank you for your comment. In Figure 8A-C, we analyzed the expression levels of STAMBPL1, FOXO1, and GRHL3 in both TNBC and non-TNBC samples using the BCIP. The results indicate that the expression levels of these three genes are significantly higher in TNBC compared to non-TNBC samples. To investigate the correlation among the expressions of STAMBPL1, FOXO1, and GRHL3 in TNBC versus non-TNBC, we further utilized the Metabric data. Besides the positive correlation trend between STAMBPL1 and GRHL3 expression in TNBC clinical samples (Pearson R = 0.27), no significant correlation was observed in the expression levels of STAMBPL1, FOXO1, and GRHL3 in TNBC and non-TNBC clinical samples (as shown in Author response image 1 below). Since STAMBPL1 and FOXO1 are involved as protein molecules in the transcriptional regulation of GRHL3 gene, and the data obtained from the Metabric database are the transcriptional levels of these three genes, this might be the reason why the correlation between their expressions was not observed.

Author response image 1.

Reviewer #2 (Recommendations for the authors):

The authors have thoroughly elucidated the role of STAMBPL1 in TNBC. However, it would be beneficial to discuss the potential clinical implications of these findings, such as how targeting STAMBPL1 or FOXO1 might impact current treatment strategies for TNBC. However, several issues need to be addressed.

Major:

(1) While the study provides an exhaustive analysis of the molecular mechanisms, a comparison with other subtypes of breast cancer could enhance our understanding of the specificity of the STAMBPL1/FOXO1/GRHL3/HIF1α/VEGFA axis in TNBC.

Thank you for your comment. According to report, STAMBPL1 is significantly associated with the mesenchymal characteristics of breast cancer (DOI: 10.1038/s41416-020-0972-x). We utilized cBioPortal (http://www.cbioportal.org/) to analyze the expression of STAMBPL1 across various clinical subtypes of breast cancer. The results indicated that STAMBPL1 is highly expressed in invasive breast cancer, which has been added to Supplementary Figure 6 as Fig.S6D. Given that TNBC is an aggressive type of invasive breast cancer, we further examined the expression of STAMBPL1 in TNBC compared to non-TNBC using BCIP (http://omicsnet.org/bcancer/database). Our findings revealed that the expression level of STAMBPL1 in TNBC was elevated relative to its levels in non-TNBC (Fig.8A). Additionally, since tumor angiogenesis is a critical factor influencing the metastasis of cancer cells, our study focused specifically on the pro-angiogenic effects of STAMBPL1 in TNBC.

(2) The authors might consider discussing any potential off-target effects of the siRNA and shRNA used in the study to bolster the conclusions drawn from the knockdown experiments.

We appreciate the reviewer's suggestion. It is well-known that siRNA or shRNA have off-target effects. To address this concern, we employed two siRNAs for each gene knockdown in our study. Specifically, we knocked down genes such as STAMBPL1, FOXO1, GRHL3, and HIF1A in two TNBC cell lines, HCC1806 and HCC1937, using two siRNAs. Except for siRNA#1 targeting HIF1A, which did not show a significant knockdown effect in HCC1806 cells (Fig.2D and Fig.6A), the knockdown effects of other siRNAs on their respective genes were effective, and the resulting phenotypes were consistent. As shown in Fig.2F and Fig.S4H, siRNA#1 targeting HIF1A had a significant knockdown effect in HCC1937 cells. The lower knockdown efficiency of this siRNA in HCC1806 cell line might be attributed to cell-specific factors.

(3) It would be advantageous if the authors could provide further details on the patient demographics and tumor characteristics in the TCGA database analysis to better comprehend the clinical relevance of their findings.

Thanks for the reviewer's suggestions. We have now indicated the number of clinical samples in each group in the legend of Fig.8A-C. Since we utilized the BCIP online database to analyze and compare the expression levels of the three genes STAMBPL1, FOXO1, and GRHL3 in TNBC and non-TNBC, we are unable to obtain more specific information regarding the tumor characteristics of each sample. However, our analysis clearly shows that the expression levels of these three genes are significantly higher in TNBC compared to non-TNBC.

(4) The authors should consider discussing any limitations regarding the generalizability of their findings, such as potential variations among different TNBC subtypes or the specificity of their observations to certain stages of the disease.

We appreciate the reviewer's comment. Accordingly, we have added a discussion on the limitation of this study in Discussion, highlighted in red font on pages 20 to 21, lines 396 to 412. In addition, we utilized the bc-GenExMiner online database to conduct a comparative analysis of STAMBPL1 expression in different subtypes of non-TNBC and TNBC. The result indicates that STAMBPL1 is highly expressed in mesenchymal-like and basal-like TNBC, which has been added into Supplementary Figure 6 as Fig.S6E. Since these two subtypes of TNBC are highly invasive and metastatic, it suggests that targeting the signaling pathway of STAMBPL1/FOXO1/GRHL3/HIF1α/VEGFA may offer clinical benefits for patients with invasive TNBC.

Minor:

The paper is generally well-written, but it's crucial to maintain vigilance for subject-verb agreement, proper use of tense, and consistent terminology.

Thank you for this suggestion. We have thoroughly revised the article for issues such as grammar, including tense, subject-verb agreement, and terminology.

Author response:

The following is the authors’ response to the original reviews

Reviewer #1:

While very positive towards our manuscript, this reviewer also points out three suggestions for improvement.

Overall, there are not many weaknesses. The main one I noticed is with the lipidomic analysis shown in Figs 3C, 7C, S1 and S3. While these data are an essential part of the analysis and provide strong evidence for the conclusions of the study, it is unfortunate that the methods used did not enable the distinction between two 18:1 isomers. These two isomers of 18:1 are important in C. elegans biology, because one is a substrate for FAT-2 (18:1n-9, oleic acid) and the other is not (18:1n-7, cis vaccenic acid). Although rarer in mammals, cisvaccenic acid is the most abundant fatty acid in C. elegans and is likely the most important structural MUFA. The measurement of these two isomers is not essential for the conclusions of the study, but the manuscript should include a comment about the abundance of oleic vs vaccenic acid in C. elegans (authors can find this information, even in the fat-2 mutant, in other publications of C. elegans fatty acid composition). Otherwise, readers who are not familiar with C. elegans might assume the 18:1 that is reported is likely to be mainly oleic acid, as is common in mammals.

Excellent point. As suggested by the reviewer, we now include a clarification of this in the text: "Consistent with previous publications [10], the levels of 18:1 fatty acids were greatly increased in the fat-2(wa17) mutant. It is important to note that the majority of these 18:1 fatty acids is likely 18:1n7 (vaccenic acid) and not 18:1n9 (OA) [10,23], which is the substrate of FAT-2; the lipid analysis methods used here are not able to distinguish between the two 18:1 species."

The title could be less specific; it might be confusing to readers to include the allele name in the title.

We thank the reviewer for the suggestion, and we have now modified the title:

"Forward Genetics In C. elegans Reveals Genetic Adaptations To Polyunsaturated Fatty Acid Deficiency"

There are two errors in the pathway depicted in Figure 1A. The16:0-16:1 desaturation can be performed by FAT-5, FAT-6, and FAT-7. The 18:0-18:1 desaturation can only be performed by FAT-6 and FAT-7.

We thank the reviewer for pointing out this mistake. The pathway in Fig. 1A has been corrected.

Reviewer #2:

This reviewer was also very positive towards our manuscript but also pointed out several suggestions for additional experiments or changes to the manuscript.

Major recommendations

(1) To conclude that membrane rigidification is not the major cause of defects associated with fat-2 mutations, the authors need to show that fluidity is rescued by their treatments (oleic acid or NP-40). I honestly doubt that it is the case, as oleic acid is already abundant in fat-2 mutants. It is possible that the treatments, which are effective in rescuing fluidity in paqr-2 mutants, do not have the same effects in fat-2 mutants.

The reviewer raises an important point. In an effort to address this, we have now performed a FRAP study on fat-2(wa17) mutants with/without NP40 as a fluidizing agent (with wild-type and paqr-2 mutants as controls). The new data, now included as Fig. 2J, shows that NP40 did improve the fluidity of the intestinal cell membrane in the fat-2(wa17) mutant, though not to the same degree as in the paqr-2 mutant. This is now cited in the text as follows:

"However, cultivating the fat-2(wa17) mutant in the presence of the non-ionic detergent NP40, which improves the growth of the paqr-2(tm3410) mutant [17], did not suppress the poor growth phenotype of the fat-2(wa17) mutant even though it did improve membrane fluidity as measured using FRAP (Fig. 2I-J). Similarly, supplementing the fat-2(wa17) mutant with the MUFA oleic acid (OA, 18:1), which also suppresses paqr-2(tm3410) phenotypes [17], did not suppress the poor growth phenotype of the fat-2(wa17) mutant (Fig 2K)."

(2) It is not validated experimentally that the mutations converge into FTN-2 repression. This can be verified by analyzing mRNA or protein expression of FTN-2 in the egl-9 and hif-1 mutants obtained in the screening.

Our manuscript does lean on several publications that previously established the HIF-1 pathway in C. elegans. Additionally, we now added a qPCR experiment showing that the newly isolated hif-1(et69) allele indeed suppresses the expression of ftn-2. This was an especially valuable experiment since the hif-1(et69) is proposed to act as a gain-of-function allele that would constitutively suppress ftn-2 expression. This new result is included as Fig. 6C and mentioned in the text:

"Inhibition of egl-9 promotes HIF-1 activity [41], which we here verified for the egl-9(et60) allele using western blots (Fig 6A). Additionally, we found by qPCR that ftn-2 mRNA levels are as expected reduced by the proposed gain-of-function hif-1(et69) allele (Fig 6C). We conclude that the egl-9 and hif-1 suppressor mutations likely converge on inhibiting ftn-2 and thus act similarly to the ftn-2 loss-of-function alleles."

(3) In the hif-1(et69) and ftn-2(et68) mutants, the rescues in lipid composition seem to be minor, with eicosapentaenoic acid (EPA) levels remaining low. The ftn-2 mutant data is especially concerning, as it suggests that egl-9 mutants rescue lipid composition via distinct mechanisms not including ftn-2 suppression. I suggest that the authors test the minimal doses of linoleic acid or EPA required to rescue fat-2 mutants and perform lipidomics to test which is the degree of EPA restoration that is needed. If a low level of restoration is sufficient, the hif-1 and ftn-2 mutants might indeed rescue phenotypes via a restoration of EPA levels. Otherwise, other mechanisms have to be considered.

In line with the above issue, the low level or EPA restoration in hif-1 and ftn-2 mutants raise the possibility that the mutations rescue fat-2 mutants downstream of lipid changes. The reduction in HIF-1 levels in fat-2 mutants also suggest that lipid changes affect HIF-1 expression. Thus, the "impossibility to genetically compensate PUFA deficiency" might be wrong. The above experiment would answer to this point too.

The reviewer is entirely correct to consider alternative explanations. In the lipidomics in Fig 3, we see that fat-2(wa17) worms on NGM have only ~1.5-2%mol EPA in phosphatidylcholines. When treated with 2 mM LA, the levels of EPA rise to ~10%mol, still below the ~ 25% observed in N2 but perhaps this is sufficient cause for restoring fat-2(wa17) health. Similarly, the hif-1(et69) and ftn-2(et68) mutant alleles elevate EPA levels to 5- 7% in fat-2(wa17). Thus, we have a correlation where a significant increase in EPA, obtained either through LA supplementation or through suppressor mutations (e.g. egl-9 (et60), hif-1(et69) or ftn-2(et68)), is associated with improved growth and health of the fat-2(wa17) mutant. However, correlation is of course not proof. The suggested experiment to titrate EPA to its lowest fat-2(wa17) rescuing levels and then perform lipidomics analysis was not possible in a reasonable time frame during this revision. However, preliminary experiments showed that even 25 μM LA (most of which will be converted to EPA by the worms) is enough to rescue the fat-2(wa17) or null mutant (Author response image 1), suggesting that even tiny amounts (much below the >250 μM used in our article) bring great benefits.

Author response image 1.

Nevertheless, we now acknowledge in the discussion that alternative explanations exist:

"Other mechanisms are also possible. For example, mutations in the HIF-1 pathway could somehow reduce EPA turnover rates in the fat-2(wa17) mutant and allow its levels to rise above an essential threshold. This hypothesis is consistent with the observation that the suppressors can rescue both the fat-2(wa17) mutant and fat-2 RNAi-treated worms but not the fat-2 null mutant. It is even possible, though deemed unlikely, that the fat-2(wa17) suppressors act by compensating for the PUFA shortage via some undefined separate process downstream of the lipid changes and that they only indirectly result in elevated EPA levels."

Additionally, another possible mechanism of action of the fat-2(wa17) suppressors could have been that they all cause upregulation of the FAT-2 protein. We have now explored this possibility using Western blots and found that this is an unlikely mechanism. This is presented in Fig. 6D-E and S3C-D, mentioned in the text as follows:

"We also used Western blots to evaluate the abundance of the FAT-2 protein expressed from endogenous wild-type or mutant loci but to which a HA tag was fused using CRISPR/Cas9. We found that the FAT-2::HA levels are severely reduced when the locus contains the S101F substitution present in the wa17 allele, but restored close to wild-type levels by the fat2(et65) suppressor mutation (Fig 6D-E, S3C-D Fig). The levels of FAT-2 in the HIF-1 pathway suppressors varied between experiments, with the suppressors sometimes restoring FAT-2 levels and sometimes not even when the worms were growing well (Fig 6D-E, S3C-D Fig). The fat-2(wa17) suppressors, except for the intragenic fat-2 alleles, likely do not act by increasing FAT-2 protein levels."

(4) It should be tested how Fe2+ levels are changed in the mutants, and how effective the ferric ammonium citrate treatment is. The authors might use a ftn-1::GFP reporter for this purpose.

We did obtain a strain carrying the ftn-1::GFP reporter but could not generate conclusive data with it. In particular, we saw no increase in fluorescence in fat-2(wa17) worms carrying suppressor mutations. However, we also found that even FAC treatment that rescue the fat2(wa17) mutant did not result in a measurable increased GFP levels suggesting that the reporter is not sensitive enough.

Minor comments

(1) I think that putting Figure 6A in Figure 5 would be helpful for the readers, so that they understand that the mutations converge in the same pathway.

This is now done.

(2) Page 3: While it is clear that paqr-2 regulates lipid composition, I believe that it remains unclear if it "promote the production and incorporation of PUFAs into phospholipids to restore membrane homeostasis".

A reference was missing to support that statement. Ruiz et al. (2023) is now cited for this (ref. 7).

(3) C. elegans is extremely rich in EPA (see for example DOI: 10.3390/jcm5020019), but the lipidomics data in this study rather suggest that oleic acid is predominant. I recommend to check why this discrepancy occurs.

OA (18:1n9) makes up only ~2%, but vaccenic acid (18:1n7) is ~21% in WT worms, EPA is slightly less at ~19% (Watts et al. 2002). These match with our lipidomics results although we cannot distinguish between 18:1n9 and n7. See also answer to Reviewer #1, comment 1.

(4) Abstract: The authors write that mammals do not synthesize PUFAs, which is almost correct, but they still produce the PUFA mead acid. Thus, the statement is not completely right.

Didn't know that! From literature, it is our understanding that mammals synthesize mead acid during FA deficiency but not in normal conditions, so they are not regularly producing mead acid. We have now updated the introduction:

"An exception to this exists during severe essential fatty acid deficiency when mammals can synthesize mead acid (20:3n9), though this is not a common occurrence [11]"

(5) Page 10: Eicosanoids are C20 lipid mediators, thus those produced from docosahexaenoic acid are not eicosanoids. Correct the statement.

We thank the reviewer for pointing this out. We now write:

" EPA and DHA, being long chain PUFAs should have similar fluidizing effects on membrane properties (though in vitro experiments challenge this view [78]), and both can serve as precursors of eicosanoids or docosanoids, particularly inflammatory ones [79]."

(6) Page 7: "hif-1(et69) is similarly able to suppress fat-2(wa17) when ftn-2 is knocked out" I am not sure that the data agrees with this statement, and it is unclear what we can conclude from such observation.

Fig. 2D shows that ftn-2(et68) suppresses fat-2(wa17) even in the presence of a hif-1(ok2654) null allele, showing that no HIF-1 function is required once ftn-2 is mutated. Conversely, Fig S2E shows that combining both the hif-1(et69) and the ftn-2(ok404) null allele also suppresses fat-2(wa17) (the worms do not fully reach N2 length, but they are significantly longer and were fertile adults); this is merely the expected outcome if the pathway converges on loss of ftn-2 function, though other interpretations could be possible from this experiment alone.

(7) S3 Fig: in panel B, is the last column ftn-2;egl-9 mutant? I would imagine that it is ftn2;fat-2.

We thank the reviewer for pointing this out. This has been corrected.

(8) Fig 6B, how many times has been this experiment done?

With these exact conditions (6h and 20h hypoxia) and order of strains the blot was done once, but the blot overall was done 5 times. We now added another replicate in Fig. S3A.

Note also that a few minor modifications have been made throughout the text, which can be seen in the Word file with tracked changes.

Author Response

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

eLife assessment:

This important study represents a comprehensive computational analysis of Plasmodium falciparum gene expression, with a focus on var gene expression, in parasites isolated from patients; it assesses changes that occur as the parasites adapt to short-term in vitro culture conditions. The work provides technical advances to update a previously developed computational pipeline. Although the findings of the shifts in the expression of particular var genes have theoretical or practical implications beyond a single subfield, the results are incomplete and the main claims are only partially supported.

The authors would like to thank the reviewers and editors for their insightful and constructive assessment. We particularly appreciate the statement that our work provides a technical advance of our computational pipeline given that this was one of our main aims. To address the editorial criticisms, we have rephrased and restructured the manuscript to ensure clarity of results and to support our main claims. For the same reason, we removed the var transcript differential expression analysis, as this led to confusion.

Public Reviews:

Reviewer #1:

The authors took advantage of a large dataset of transcriptomic information obtained from parasites recovered from 35 patients. In addition, parasites from 13 of these patients were reared for 1 generation in vivo, 10 for 2 generations, and 1 for a third generation. This provided the authors with a remarkable resource for monitoring how parasites initially adapt to the environmental change of being grown in culture. They focused initially on var gene expression due to the importance of this gene family for parasite virulence, then subsequently assessed changes in the entire transcriptome. Their goal was to develop a more accurate and informative computational pipeline for assessing var gene expression and secondly, to document the adaptation process at the whole transcriptome level.

Overall, the authors were largely successful in their aims. They provide convincing evidence that their new computational pipeline is better able to assemble var transcripts and assess the structure of the encoded PfEMP1s. They can also assess var gene switching as a tool for examining antigenic variation. They also documented potentially important changes in the overall transcriptome that will be important for researchers who employ ex vivo samples for assessing things like drug sensitivity profiles or metabolic states. These are likely to be important tools and insights for researchers working on field samples.

One concern is that the abstract highlights "Unpredictable var gene switching..." and states that "Our results cast doubt on the validity of the common practice of using short-term cultured parasites...". This seems somewhat overly pessimistic with regard to var gene expression profiling and does not reflect the data described in the paper. In contrast, the main text of the paper repeatedly refers to "modest changes in var gene expression repertoire upon culture" or "relatively small changes in var expression from ex vivo to culture", and many additional similar assessments. On balance, it seems that transition to culture conditions causes relatively minor changes in var gene expression, at least in the initial generations. The authors do highlight that a few individuals in their analysis showed more pronounced and unpredictable changes, which certainly warrants caution for future studies but should not obscure the interesting observation that var gene expression remained relatively stable during transition to culture.

Thank you for this comment. We were happy to modify the wording in the abstract to have consistency with the results presented by highlighting that modest but unpredictable var gene switching was observed while substantial changes were found in the core transcriptome. Moreover, any differences observed in core transcriptome between ex vivo samples from naïve and pre-exposed patients are diminished after one cycle of cultivation making inferences about parasite biology in vivo impossible.

Therefore, – to our opinion – the statement in the last sentence is well supported by the data presented.

Line 43–47: “Modest but unpredictable var gene switching and convergence towards var2csa were observed in culture, along with differential expression of 19% of the core transcriptome between paired ex vivo and generation 1 samples. Our results cast doubt on the validity of the common practice of using short-term cultured parasites to make inferences about in vivo phenotype and behaviour.” Nevertheless, we would like to note that this study was in a unique position to assess changes at the individual patient level as we had successive parasite generations. This comparison is not done in most cross-sectional studies and therefore these small, unpredictable changes in the var transcriptome are missed.

Reviewer #2:

In this study, the authors describe a pipeline to sequence expressed var genes from RNA sequencing that improves on a previous one that they had developed. Importantly, they use this approach to determine how var gene expression changes with short-term culture. Their finding of shifts in the expression of particular var genes is compelling and casts some doubt on the comparability of gene expression in short-term culture versus var expression at the time of participant sampling. The authors appear to overstate the novelty of their pipeline, which should be better situated within the context of existing pipelines described in the literature.

Other studies have relied on short-term culture to understand var gene expression in clinical malaria studies. This study indicates the need for caution in over-interpreting findings from these studies.

The novel method of var gene assembly described by the authors needs to be appropriately situated within the context of previous studies. They neglect to mention several recent studies that present transcript-level novel assembly of var genes from clinical samples. It is important for them to situate their work within this context and compare and contrast it accordingly. A table comparing all existing methods in terms of pros and cons would be helpful to evaluate their method.

We are grateful for this suggestion and agree that a table comparing the pros and cons of all existing methods would be helpful for the general reader and also highlight the key advantages of our new approach. A table comparing previous methods for var gene and transcript characterisation has been added to the manuscript and is referenced in the introduction (line 107).

Author response table 1.

Comparison of previous var assembly approaches based on DNA- and RNA-sequencing.

Reviewer #3:

This work focuses on the important problem of how to access the highly polymorphic var gene family using short-read sequence data. The approach that was most successful, and utilized for all subsequent analyses, employed a different assembler from their prior pipeline, and impressively, more than doubles the N50 metric.

The authors then endeavor to utilize these improved assemblies to assess differential RNA expression of ex vivo and short-term cultured samples, and conclude that their results "cast doubt on the validity" of using short-term cultured parasites to infer in vivo characteristics. Readers should be aware that the various approaches to assess differential expression lack statistical clarity and appear to be contradictory. Unfortunately, there is no attempt to describe the rationale for the different approaches and how they might inform one another.

It is unclear whether adjusting for life-cycle stage as reported is appropriate for the var-only expression models. The methods do not appear to describe what type of correction variable (continuous/categorical) was used in each model, and there is no discussion of the impact on var vs. core transcriptome results.

We agree with the reviewer that the different methods and results of the var transcriptome analysis can be difficult to reconcile. To address this, we have included a summary table with a brief description of the rationale and results of each approach in our analysis pipeline.

Author response table 2.

Summary of the different levels of analysis performed to assess the effect of short-term parasite culturing on var and core gene expression, their rational, method, results, and interpretation.

Additionally, the var transcript differential expression analysis was removed from the manuscript, because this study was in a unique position to perform a more focused analysis of var transcriptional changes across paired samples, meaning the per-patient approach was more suitable. This allowed for changes in the var transcriptome to be identified that would have gone unnoticed in the traditional differential expression analysis.

We thank the reviewer for his highly important comment about adjusting for life cycle stage. Var gene expression is highly stage-dependent, so any quantitative comparison between samples does need adjustment for developmental stage. All life cycle stage adjustments were done using the mixture model proportions to be consistent with the original paper, described in the results and methods sections:

• Line 219–221: “Due to the potential confounding effect of differences in stage distribution on gene expression, we adjusted for developmental stage determined by the mixture model in all subsequent analyses.”

• Line 722–725: “Var gene expression is highly stage dependent, so any quantitative comparison between samples needs adjustment for developmental stage. The life cycle stage proportions determined from the mixture model approach were used for adjustment.“

The rank-expression analysis did not have adjustment for life cycle stage as the values were determined as a percentage contribution to the total var transcriptome. The var group level and the global var gene expression analyses were adjusted for life cycle stages, by including them as an independent variable, as described in the results and methods sections.

Var group expression:

• Line 321–326: “Due to these results, the expression of group A var genes vs. group B and C var genes was investigated using a paired analysis on all the DBLα (DBLα1 vs DBLα0 and DBLα2) and NTS (NTSA vs NTSB) sequences assembled from ex vivo samples and across multiple generations in culture. A linear model was created with group A expression as the response variable, the generation and life cycle stage as independent variables and the patient information included as a random effect. The same was performed using group B and C expression levels.“

• Line 784–787: “DESeq2 normalisation was performed, with patient identity and life cycle stage proportions included as covariates and differences in the amounts of var transcripts of group A compared with groups B and C assessed (Love et al., 2014). A similar approach was repeated for NTS domains.”

Gobal var gene expression:

• Line 342–347: “A linear model was created (using only paired samples from ex vivo and generation 1) (Supplementary file 1) with proportion of total gene expression dedicated to var gene expression as the response variable, the generation and life cycle stage as independent variables and the patient information included as a random effect. This model showed no significant differences between generations, suggesting that differences observed in the raw data may be a consequence of small changes in developmental stage distribution in culture.”

• Line 804–806: “Significant differences in total var gene expression were tested by constructing a linear model with the proportion of gene expression dedicated to var gene expression as the response variable, the generation and life cycle stage as an independent variables and the patient identity included as a random effect.“

The analysis of the conserved var gene expression was adjusted for life cycle stage:

• Line 766–768: “For each conserved gene, Salmon normalised read counts (adjusted for life cycle stage) were summed and expression compared across the generations using a pairwise Wilcoxon rank test.”

And life cycle stage estimates were included as covariates in the design matrix for the domain differential expression analysis:

• Line 771–773: “DESeq2 was used to test for differential domain expression, with five expected read counts in at least three patient isolates required, with life cycle stage and patient identity used as covariates.”

Reviewer #1:

1. In the legend to Figure 1, the authors cite "Deitsch and Hviid, 2004" for the classification of different var gene types. This is not the best reference for this work. Better citations would be Kraemer and Smith, Mol Micro, 2003 and Lavstsen et al, Malaria J, 2003.

We agree and have updated the legend in Figure 1 with these references, consistent with the references cited in the introduction.

1. In Figures 2 and 3, each of the boxes in the flow charts are largely filled with empty space while the text is nearly too small to read. Adjusting the size of the text would improve legibility.

We have increased the size of the text in these figures.

1. My understanding of the computational method for assessing global var gene expression indicates an initial step of identifying reads containing the amino acid sequence LARSFADIG. It is worth noting that VAR2CSA does not contain this motif. Will the pipeline therefore miss expression of this gene, and if so, how does this affect the assessment of global var gene assessment? This seems relevant given that the authors detect increased expression of var2csa during adaptation to culture.

To address this question, we have added an explanation in the methods section to better explain our analysis. Var2csa was not captured in the global var gene expression analysis, but was analyzed separately because of its unique properties (conservation, proposed role in regulating var gene switching, slightly divergent timing of expression, translational repression).

• Line 802/3: “Var2csa does not contain the LARSFADIG motif, hence this quantitative analysis of global var gene expression excluded var2csa (which was analysed separately).”

1. In Figures 4 and 7, panels a and b display virtually identical PCA plots, with the exception that panel A displays more generations. Why are both panels included? There doesn't appear to be any additional information provided by panel B.

We agree and have removed Figure 7b for the core transcriptome PCA as it did not provide any new information. The var transcript differential analysis (displayed in Figure 4) has been removed from the manuscript.

1. On line 560-567, the authors state "However, the impact of short-term culture was the most apparent at the var transcript level and became less clear at higher levels." What are the high levels being referred to here?

We have replaced this sentence to make it clearer what the different levels are (global var gene expression, var domain and var type).

• Line 526/7: “However, the impact of short-term culture was the most apparent at the var transcript level and became less clear at the var domain, var type and global var gene expression level.”

Reviewer #2:

The authors make no mention or assessment of previously published var gene assembly methods from clinical samples that focus on genomic or transcriptomic approaches. These include:

https://pubmed.ncbi.nlm.nih.gov/28351419/

https://pubmed.ncbi.nlm.nih.gov/34846163/

These methods should be compared to the method for var gene assembly outlined by the co-authors, especially as the authors say that their method "overcomes previous limitations and outperforms current methods" (128-129). The second reference above appears to be a method to measure var expression in clinical samples and so should be particularly compared to the approach outlined by the authors.

Thank you for pointing this out. We have included the second reference in the introduction of our revised manuscript, where we refer to var assembly and quantification from RNA-sequencing data. We abstained from including the first paper in this paragraph (Dara et al., 2017) as it describes a var gene assembly pipeline and not a var transcript assembly pipeline.

• Line 101–105: “While approaches for var assembly and quantification based on RNA-sequencing have recently been proposed (Wichers et al., 2021; Stucke et al., 2021; Andrade et al., 2020; TonkinHill et al., 2018, Duffy et al., 2016), these still produce inadequate assembly of the biologically important N-terminal domain region, have a relatively high number of misassemblies and do not provide an adequate solution for handling the conserved var variants (Table S1).”

Additionally, we have updated the manuscript with a table (Table S1) comparing these two methods plus other previously used var transcript/gene assembly approaches (see comment to the public reviews).

But to address this particular comment in more detail, the first paper (Dara et al., 2017) is a var gene assembly pipeline and not a var transcript assembly pipeline. It is based on assembling var exon 1 from unfished whole genome assemblies of clinical samples and requires a prior step for filtering out human DNA. The authors used two different assemblers, Celera for short reads (which is no longer maintained) and Sprai for long reads (>2000bp), but found that Celera performed worse than Sprai, and subsequently used Sprai assemblies. Therefore, this method does not appear to be suitable for assembling short reads from RNA-seq.

The second paper (Stucke et al. 2021) focusses more on enriching for parasite RNA, which precedes assembly. The capture method they describe would complement downstream analysis of var transcript assembly with our pipeline. Their assembly pipeline is similar to our pipeline as they also performed de novo assembly on all P. falciparum mapping and non-human mapping reads and used the same assembler (but with different parameters). They clustered sequences using the same approach but at 90% sequence identity as opposed to 99% sequence identity using our approach. Then, Stucke et al. use 500nt as a cut-off as opposed to the more stringent filtering approach used in our approach. They annotated their de novo assembled transcripts with the known amino acid sequences used in their design of the capture array; our approach does not assume prior information on the var transcripts. Finally, their approach was validated only for its ability to recover the most highly expressed var transcript in 6 uncomplicated malaria samples, and they did not assess mis-assemblies in their approach.

For the methods (619–621), were erythrocytes isolated by Ficoll gradient centrifugation at the time of collection or later?

We have updated the methods section to clarify this.

• Line 586–588: “Blood was drawn and either immediately processed (#1, #2, #3, #4, #11, #12, #14, #17, #21, #23, #28, #29, #30, #31, #32) or stored overnight at 4oC until processing (#5, #6, #7, #9, #10, #13, #15, #16, #18, #19, #20, #22, #24, #25, #26, #27, #33).”

Was the current pipeline and assembly method assessed for var chimeras? This should be described.

Yes, this was quantified in the Pf 3D7 dataset and also assessed in the German traveler dataset. For the 3D7 dataset it is described in the result section and Figure S1.

• Line 168–174: “However, we found high accuracies (> 0.95) across all approaches, meaning the sequences we assembled were correct (Figure 2 – Figure supplement 1b). The whole transcript approach also performed the best when assembling the lower expressed var genes (Figure 2 – Figure supplement 1e) and produced the fewest var chimeras compared to the original approach on P. falciparum 3D7. Fourteen misassemblies were observed with the whole transcript approach compared to 19 with the original approach (Table S2). This reduction in misassemblies was particularly apparent in the ring-stage samples.” - Figure S1:

Author response image 1.

Performance of novel computational pipelines for var assembly on Plasmodium falciparum 3D7: The three approaches (whole transcript: blue, domain approach: orange, original approach: green) were applied to a public RNA-seq dataset (ENA: PRJEB31535) of the intra-erythrocytic life cycle stages of 3 biological replicates of cultured P. falciparum 3D7, sampled at 8-hour intervals up until 40hrs post infection (bpi) and then at 4-hour intervals up until 48 (Wichers al., 2019). Boxplots show the data from the 3 biological replicates for each time point in the intra-erythrocytic life cycle: a) alignment scores for the dominantly expressed var gene (PF3D7_07126m), b) accuracy scores for the dominantly var gene (PF3D7_0712600), c) number of contigs to assemble the dominant var gene (PF3D7_0712600), d) alignment scores for a middle ranking expressed vargene (PF3D7_0937800), e) alignment scores for the lowest expressed var gene (PF3D7_0200100). The first best blast hit (significance threshold = le-10) was chosen for each contig. The alignment score was used to evaluate the each method. The alignment score represents √accuracy* recovery. The accuracy is the proportion of bases that are correct in the assembled transcript and the recovery reflects what proportion of the true transcript was assembled. Assembly completeness of the dominant vargene (PF3D7 071200, length = 6648nt) for the three approaches was assessed for each biological f) biological replicate 1, g) biological replicate 2, h) biological replicate 3. Dotted lines represent the start and end of the contigs required to assemble the vargene. Red bars represent assembled sequences relative to the dominantly whole vargene sequence, where we know the true sequence (termed “reference transcript”).

For the ex vivo samples, this has been discussed in the result section and now we also added this information to Table 1.

• Line 182/3: “Remarkably, with the new whole transcript method, we observed a significant decrease (2 vs 336) in clearly misassembled transcripts with, for example, an N-terminal domain at an internal position.”

• Table 1:

Author response table 3.

Statistics for the different approaches used to assemble the var transcripts. Var assembly approaches were applied to malaria patient ex vivo samples (n=32) from (Wichers et al., 2021) and statistics determined. Given are the total number of assembled var transcripts longer than 500 nt containing at least one significantly annotated var domain, the maximum length of the longest assembled var transcript in nucleotides and the N50 value, respectively. The N50 is defined as the sequence length of the shortest var contig, with all var contigs greater than or equal to this length together accounting for 50% of the total length of concatenated var transcript assemblies. Misassemblies represents the number of misassemblies for each approach. **Number of misassemblies were not determined for the domain approach due to its poor performance in other metrics.

Line 432: "the core gene transcriptome underwent a greater change relative to the var transcriptome upon transition to culture." Can this be shown statistically? It's unclear whether the difference in the sizes of the respective pools of the core genome and the var genes may account for this observation.

We found 19% of the core transcriptome to be differentially expressed. The per patient var transcript analysis revealed individually highly variable but generally rather subtle changes in the var transcriptome. The different methods for assessing this make it difficult to statistically compare these two different results.

The feasibility of this approach for field samples should be discussed in the Discussion.

In the original manuscript we reflected on this already several times in the discussion (e.g., line 465/6; line 471–475; line 555–568). We now have added another two sentences at the end of the paragraph starting in line 449 to address this point. It reads now:

• Line 442–451: “Our new approach used the most geographically diverse reference of var gene sequences to date, which improved the identification of reads derived from var transcripts. This is crucial when analysing patient samples with low parasitaemia where var transcripts are hard to assemble due to their low abundancy (Guillochon et al., 2022). Our approach has wide utility due to stable performance on both laboratory-adapted and clinical samples. Concordance in the different var expression profiling approaches (RNA-sequencing and DBLα-tag) on ex vivo samples increased using the new approach by 13%, when compared to the original approach (96% in the whole transcript approach compared to 83% in Wichers et al., 2021. This suggests the new approach provides a more accurate method for characterising var genes, especially in samples collected directly from patients. Ultimately, this will allow a deeper understanding of relationships between var gene expression and clinical manifestations of malaria.”

MINOR

The plural form of PfEMP1 (PfEMP1s) is inconsistently used throughout the text.

Corrected.

404-405: statistical test for significance?

Thank you for this suggestion. We have done two comparisons between the original analysis from Wichers et al., 2021 and our new whole transcript approach to test concordance of the RNAseq approaches with the DBLα-tag approach using paired Wilcoxon tests. These comparisons suggest that our new approach has significantly increased concordance with DBLα-tag data and might be better at capturing all expressed DBLα domains than the original analysis (and the DBLα-approach), although not statistically significant. We describe this now in the result section.

• Line 352–361: “Overall, we found a high agreement between the detected DBLα-tag sequences and the de novo assembled var transcripts. A median of 96% (IQR: 93–100%) of all unique DBLα-tag sequences detected with >10 reads were found in the RNA-sequencing approach. This is a significant improvement on the original approach (p= 0.0077, paired Wilcoxon test), in which a median of 83% (IQR: 79–96%) was found (Wichers et al., 2021). To allow for a fair comparison of the >10 reads threshold used in the DBLα-tag approach, the upper 75th percentile of the RNA-sequencingassembled DBLα domains were analysed. A median of 77.4% (IQR: 61–88%) of the upper 75th percentile of the assembled DBLα domains were found in the DBLα-tag approach. This is a lower median percentage than the median of 81.3% (IQR: 73–98%) found in the original analysis (p= 0.28, paired Wilcoxon test) and suggests the new assembly approach is better at capturing all expressed DBLα domains.”

Figure 4: The letters for the figure panels need to be added.

The figure has been removed from the manuscript.

Reviewer #3:

It is difficult from Table S2 to determine how many unique var transcripts would have enough coverage to be potentially assembled from each sample. It seems unlikely that 455 distinct vars (~14 per sample) would be expressed at a detectable level for assembly. Why not DNA-sequence these samples to get the full repertoire for comparison to RNA? Why would so many distinct transcripts be yielded from fairly synchronous samples?

We know from controlled human malaria infections of malaria-naive volunteers, that most var genes present in the genomic repertoire of the parasite strain are expressed at the onset of the human blood phase (heterogenous var gene expression) (Wang et al., 2009; Bachmann et al, 2016; Wichers-Misterek et al., 2023). This pattern shifts to a more restricted, homogeneous var expression pattern in semi-immune individuals (expression of few variants) depending on the degree of immunity (Bachmann et al., 2019).

Author response image 2.

In this cohort, 15 first-time infections are included, which should also possess a more heterogenous var gene expression in comparison to the pre-exposed individuals, and indeed such a trend is already seen in the number of different DBLa-tag clusters found in both patient groups (see figure panel from Wichers et al. 2021: blue-first-time infections; grey–pre-exposed). Moreover, Warimwe et al. 2013 have shown that asymptomatic infections have a more homogeneous var expression in comparison to symptomatic infections. Therefore, we expect that parasites from symptomatic infections have a heterogenous var expression pattern with multiple var gene variants expressed, which we could assemble due to our high read depth and our improved var assembly pipeline for even low expressed variants.

Moreover, the distinct transcripts found in the RNA-seq approach were confirmed with the DBLα tag data. To our opinion, previous approaches may have underestimated the complexity of the var transcriptome in less immune individuals.

Mapping reads to these 455 putative transcripts and using this count matrix for differential expression analysis seems very unlikely to produce reliable results. As acknowledged on line 327, many reads will be mis-mapped, and perhaps most challenging is that most vars will not be represented in most samples. In other words, even if mapping were somehow perfect, one would expect a sparse matrix that would not be suitable for statistical comparisons between groups. This is likely why the per-patient transcript analysis doesn't appear to be consistent. I would recommend the authors remove the DE sections utilizing this approach, or add convincing evidence that the count matrix is useable.

We agree that this is a general issue of var differential expression analysis. Therefore, we have removed the var differential expression analysis from this manuscript as the per patient approach was more appropriate for the paired samples. We validated different mapping strategies (new Figure S6) and included a paragraph discussing the problem in the result section:

• Line 237–255: “In the original approach of Wichers et al., 2021, the non-core reads of each sample used for var assembly were mapped against a pooled reference of assembled var transcripts from all samples, as a preliminary step towards differential var transcript expression analysis. This approach returned a small number of var transcripts which were expressed across multiple patient samples (Figure 3 – Figure supplement 2a). As genome sequencing was not available, it was not possible to know whether there was truly overlap in var genomic repertoires of the different patient samples, but substantial overlap was not expected. Stricter mapping approaches (for example, excluding transcripts shorter than 1500nt) changed the resulting var expression profiles and produced more realistic scenarios where similar var expression profiles were generated across paired samples, whilst there was decreasing overlap across different patient samples (Figure 3 – Figure supplement 2b,c). Given this limitation, we used the paired samples to analyse var gene expression at an individual subject level, where we confirmed the MSP1 genotypes and alleles were still present after short-term in vitro cultivation. The per patient approach showed consistent expression of var transcripts within samples from each patient but no overlap of var expression profiles across different patients (Figure 3 – Figure supplement 2d). Taken together, the per patient approach was better suited for assessing var transcriptional changes in longitudinal samples. It has been hypothesised that more conserved var genes in field isolates increase parasite fitness during chronic infections, necessitating the need to correctly identify them (Dimonte et al., 2020, Otto et al., 2019). Accordingly, further work is needed to optimise the pooled sample approach to identify truly conserved var transcripts across different parasite isolates in cross-sectional studies.” - Figure S6:

Author response image 3.

Var expression profiles across different mapping. Different mapping approaches Were used to quantify the Var expression profiles of each sample (ex Vivo (n=13), generation I (n=13), generation 2 (n=10) and generation 3 (n=l). The pooled sample approach in Which all significantly assembled van transcripts (1500nt and containing3 significantly annotated var domains) across samples were combined into a reference and redundancy was removed using cd-hit (at sequence identity = 99%) (a—c). The non-core reads of each sample were mapped to this pooled reference using a) Salmon, b) bowtie2 filtering for uniquely mapping paired reads with MAPQ and c) bowtie2 filtering for uniquely mapping paired reads with a MAPQ > 20. d) The per patient approach was applied. For each patient, the paired ex vivo and in vitro samples were analysed. The assembled var transcripts (at least 1500nt and containing3 significantly annotated var domains) across all the generations for a patient were combined into a reference, redundancy was removed using cd-hit (at sequence identity: 99%), and expression was quantified using Salmon. Pie charts show the var expression profile With the relative size of each slice representing the relative percentage of total var gene expression of each var transcript. Different colours represent different assembled var transcripts with the same colour code used across a-d.

For future cross-sectional studies a per patient analysis that attempts to group per patient assemblies on some unifying structure (e.g., domain, homology blocks, domain cassettes etc) should be performed.

Line 304. I don't understand the rationale for comparing naïve vs. prior-exposed individuals at ex-vivo and gen 1 timepoints to provide insights into how reliable cultured parasites are as a surrogate for var expression in vivo. Further, the next section (per patient) appears to confirm the significant limitation of the 'all sample analysis' approach. The conclusion on line 319 is not supported by the results reported in figures S9a and S9b, nor is the bold conclusion in the abstract about "casting doubt" on experiments utilizing culture adapted

We have removed this comparison from the manuscript due to the inconsistencies with the var per patient approach. However, the conclusion in the abstract has been rephrased to reflect the fact we observed 19% of the core transcript differentially expressed within one cycle of cultivation.

Line 372/391 (and for the other LMM descriptions). I believe you mean to say response variable, rather than explanatory variable. Explanatory variables are on the right hand side of the equation.

Thank you for spotting this inaccuracy, we changed it to “response variable” (line 324, line 343, line 805).

Line 467. Similar to line 304, why would comparisons of naïve vs. prior-exposed be informative about surrogates for in vivo studies? Without a gold-standard for what should be differentially expressed between naïve and prior-exposed in vivo, it doesn't seem prudent to interpret a drop in the number of DE genes for this comparison in generation 1 as evidence that biological signal for this comparison is lost. What if the generation 1 result is actually more reflective of the true difference in vivo, but the ex vivo samples are just noisy? How do we know? Why not just compare ex vivo vs generation 1/2 directly (as done in the first DE analysis), and then you can comment on the large number of changes as samples are less and less proximal to in vivo?

In the original paper (Wichers et al., 2021), there were differences between the core transcriptome of naïve vs previously exposed patients. However, these differences appeared to diminish in vitro, suggesting the in vivo core transcriptome is not fully maintained in vitro.

We have added a sentence explaining the reasoning behind this analysis in the results section:

• Lines 414–423: “In the original analysis of ex vivo samples, hundreds of core genes were identified as significantly differentially expressed between pre-exposed and naïve malaria patients. We investigated whether these differences persisted after in vitro cultivation. We performed differential expression analysis comparing parasite isolates from naïve (n=6) vs pre-exposed (n=7) patients, first between their ex vivo samples, and then between the corresponding generation 1 samples. Interestingly, when using the ex vivo samples, we observed 206 core genes significantly upregulated in naïve patients compared to pre-exposed patients (Figure 7 – Figure supplement 3a). Conversely, we observed no differentially expressed genes in the naïve vs pre-exposed analysis of the paired generation 1 samples (Figure 7 – Figure supplement 3b). Taken together with the preceding findings, this suggests one cycle of cultivation shifts the core transcriptomes of parasites to be more alike each other, diminishing inferences about parasite biology in vivo.”

Overall, I found the many DE approaches very frustrating to interpret coherently. If not dropped in revision, the reader would benefit from a substantial effort to clarify the rationale for each approach, and how each result fits together with the other approaches and builds to a concise conclusion.

We agree that the manuscript contains many different complex layers of analysis and that it is therefore important to explain the rationale for each approach. Therefore, we now included the summary Table 3 (see comment to public review). Additionally, we have removed the var transcript differential expression due to its limitations, which we hope has already streamlined our manuscript.

Author response:

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

Public Reviews:

Reviewer #1 (Public Review):

This article by Navratna et al. reports the first structure of human HGSNAT in an acetyl-CoA-bound state. Through careful structural analysis, the authors propose potential reasons why certain human mutations lead to lysosomal storage disorders and outline a catalytic mechanism. The structural data are of good quality, and the manuscript is clearly written. This study represents an important step toward understanding the mechanism of HGSNAT and is valuable to the field. I have the following suggestions:

(1) The authors should characterize whether the purified protein is active. Otherwise, how does one know if the detergent used maintains the protein in a biologically relevant state? The authors should at least attempt to do so. If these prove to be challenging, at the very least, the authors should try a cell-based assay to demonstrate that the GFP tag does not interfere with the function.

We have addressed these concerns in the revised version and mentioned these efforts in our previous response letter. We’re briefly mentioning them here again. We attempted measuring HGSNAT catalyzed reaction by monitoring the decrease in acetyl-CoA in the presence of D-glucosamine (acetyl group acceptor) using a coupled enzyme acetyl-CoA assay kit from SIGMA (MAK039) that converts acetyl-CoA to a fluorescent product measurable at Ex/Em of 535/587 nm. We noticed a decrease in the level of acetyl-CoA (gray) upon the addition of HGSNAT (red) (Rebuttal figure 1).

Author response image 1.

Acetyl-CoA levels in absence and presence of HGSNAT purified in digitonin. Decrease in the levels of 10 M acetyl-CoA was measured in presence of 10 M D-glucosamine and 30 nM HGSNAT at pH 7.5.

While optimizing the assay, Xu et al. (2024, Nat Struct Mol Biol) published structural and biochemical characterization of HGSNAT, showing that detergent-purified HGSNAT is active. In addition, we have shown by cryo-EM that GFP-tagged HGSNAT that we purified in detergent was already bound to the endogenous substrate ACO, an observation that has been observed by Xu et al., as well. Finally, we performed LC-MS on GFP-tagged HGSNAT purified in detergent to detect bound ACO, which could be further removed by dialysis. These results have been included in Figure S9. The endogenous binding of ACO to HGSNAT in detergent suggests that neither the tag nor detergent are detrimental to the function.

(2) In Figure 5, the authors present a detailed schematic of the catalytic cycle, which I find to be too speculative. There is no evidence to suggest that this enzyme undergoes isomerization, similar to a transporter, between open-to-lumen and open-to-cytosol states. Could it not simply involve some movements of side chains to complete the acetyl transfer?

We have already changed this figure in our latest submission. Perhaps the changes made were not obvious while reviewing. We agreed with this reviewer that the enzyme could likely achieve catalysis by simple side chain movements without undergoing extensive isomerization steps, as depicted in Figure 5. In the absence of data supporting large movements during the acetyl transfer reaction, old Figure 5 appeared speculative. Hence, we have edited Figure 5 in the revised version of the manuscript based on the observations we made in this study, and different states shown in the figure do not show any conformational changes and only depict acetyl transfer.

Reviewer #2 (Public Review):

Summary:

This work describes the structure of Heparan-alpha-glucosaminide N-acetyltransferase (HGSNAT), a lysosomal membrane protein that catalyzes the acetylation reaction of the terminal alpha-D-glucosamine group required for degradation of heparan sulfate (HS). HS degradation takes place during the degradation of the extracellular matrix, a process required for restructuring tissue architecture, regulation of cellular function and differentiation. During this process, HS is degraded into monosaccharides and free sulfate in lysosomes.

HGSNAT catalyzes the transfer of the acetyl group from acetyl-CoA to the terminal non-reducing amino group of alpha-D-glucosamine. The molecular mechanism by which this process occur has not been described so far. One of the main reasons to study the mechanism of HGSNAT is that multiple mutations spanning the entire sequence of the protein, such as, nonsense mutations, splice-site variants, and missense mutations lead to dysfunction that causes abnormal accumulation of HS within the lysosomes. This accumulation is a cause of mucopolysaccharidosis IIIC (MPS IIIC), an autosomal recessive neurodegenerative lysosomal storage disorder, for which there are no approved drugs or treatment strategies.

This paper provides a 3.26A structure of HGSNAT, determined by single-particle cryo-EM. The structure reveals that HGSNAT is a dimer in detergent micelles, and a density assigned to acetyl-CoA. The authors speculate about the molecular mechanism of the acetylation reaction, map the mutations known to cause MPS IIIC on the structure and speculate about the nature of the HGSNAT disfunction caused by such mutations.

Strengths:

The paper describes a structure of HGSNAT a member of the transmembrane acyl transferase (TmAT) superfamily. The high-resolution of a HGSNAT bound to acetyl-CoA is important for our understanding of HGSNAT mechanism. The density map is of high-quality, except for the luminal domain. The location of the acetyl-CoA allows speculation about the mechanistic role of multiple residues surrounding this molecule. The authors thoroughly describe the architecture of HGSNAT and map the mutations leading to MPS IIIC.

Reviewer #3 (Public Review):

Summary:

Navratna et al. have solved the first structure of a transmembrane N-acetyltransferase (TNAT), resolving the architecture of human heparan-alpha-glucosaminide N-acetyltransferase (HGSNAT) in the acetyl-CoA bound state using single particle cryo-electron microscopy (cryoEM). They show that the protein is a dimer, and define the architecture of the alpha- and beta-GSNAT fragments, as well as convincingly characterizing the binding site of acetyl-CoA.

Strengths:

This is the first structure of any member of the transmembrane acyl transferase superfamily, and as such it provides important insights into the architecture and acetyl-CoA binding site of this class of enzymes.

The structural data is of a high quality, with an isotropic cryoEM density map at 3.3Å facilitating building of a high-confidence atomic model. Importantly, the density for the acetyl-CoA ligand is particularly well-defined, as are the contacting residues within the transmembrane domain.

The structure of HSGNAT presented here will undoubtedly lay the groundwork for future structural and functional characterization of the reaction cycle of this class of enzymes.

Weaknesses:

While the structural data for the state presented in this work is very convincing, and clearly defines the binding site of acetyl-CoA, to get a complete picture of the enzymatic mechanism of this family, additional structures of other states will be required.

A weakness of the study is the lack of functional validation. The enzymatic activity of the enzyme characterized was not measured, and the enzyme lacks native proteolytic processing, so it is a little unclear whether the structure represents an active enzyme.

Recommendations for the authors:

Reviewer #3 (Recommendations For The Authors):

In the response to reviewers, the authors mention revised coordinates, but the revised coordinates provided to this reviewer do not reflect the stated changes (I assume a technical error somewhere)

Perhaps, the old coordinates in the deposition system were resubmitted with the revised draft. Nevertheless, we have made the changes suggested by this reviewer to structure in the previous round and have released the new coordinates (PDB ID: 8TU9).

Is there any evidence for the interprotomer disulfide except for the map? e.g. if it is a disulfide-linked dimer, one should see a shift in mobility on non-reducing vs reducing SDS-PAGE. Without this, the evidence from the map is not conclusive - while the symmetry-related cysteines are nearby to one another, based on the map I could argue that they could just as well be modeled with the cys sidechains reduced and pointing away from one another.

In addition to building the density based on cryo-EM maps, we have performed FSEC-based thermal melt analysis of the Ala mutation of C334 that is involved in disulfide at the dimer interface. C334A is still expressed as a dimer, suggesting that C334A is not the only residue stabilizing the dimer. Upon heating the detergent-solubilized protein, we noticed that the FSEC peak for C334A shows a monomeric HGSNAT (Figure 4-Figure supplement 1 in main manuscript). We hypothesize that in the absence of C334 disulfide, the extensive hydrophobic side-chain interaction network displayed in Figure 2C is responsible for maintaining the integrity of the dimer. Heating disturbs these non-disulfide interactions, thereby rendering the protein monomer. We have also performed PAGE analysis as suggested by this reviewer and noticed that reducing conditions result in a monomeric protein band (Rebuttal figure 2). While we were revising this manuscript, two other groups published structures of HGSNAT (Xu et al., 2024, Nat. Struct Mol Biol, and Zhao et al., 2024, Nat. Comm). These groups have also identified this disulfide at the dimer interface in their HGSNAT structures. Zhao et al. showed that this disulfide is not crucial for dimerization and also suggested that it can break depending on the conformation of HGSNAT. Our FSEC results agree with this observation.

Author response image 2.

Comparison of purified HGSNAT on native and reducing SDS-PAGE. The arrows on both the gels indicate N-GFP-HGSNAT. The two bands on the SDS PAGE are, perhaps, two differentially glycosylated forms of HGSNAT.

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

(1) The authors should characterize whether the purified protein is active. Otherwise, how does one know if the detergent used maintains the protein in a biologically relevant state? The authors should at least attempt to do so. If these prove to be challenging, at the very least, the authors should try a cell-based assay to demonstrate that the GFP tag does not interfere with the function. The authors would need to establish an in vitro assay using purified protein and assess the level of Acetyl-CoA in the reaction (there are commercial kits and a long list of literature showing how to measure this). They could also follow the HS acetylation reaction by e.g. HPLC-MS or NMR (among other methods).

The cryo-EM sample was prepared without the exogenous addition of ligand, as noted in the manuscript. However, we see that acetyl-CoA was intrinsically bound to the protein, indicating the ability of GFP-tagged HGSNAT protein to bind the ligand. Upon dialysis, we see release of acetyl-CoA from the protein, which we have confirmed by LC-MS analysis (Fig S9). We purified the protein at a pH optimal for acetyl-CoA binding, as suggested by Bame, K. J. and Rome, L. H. (1985) and Meikle, P. J. et al., (1995). Because we see acetyl-CoA in a structure obtained using a GFP fusion, we argue that GFP does not interfere with protein stability and ability to bind to the co-substrate. As demonstrated by existing literature HGSNAT catalyzed reaction is compartmentalized spatially and conditionally. The binding of acetyl-CoA happens towards the cytosol and is optimal at pH 7-0.8.0, while the transfer of the acetyl group to heparan sulfate occurs towards the luminal side and is optimal at pH 5.0-6.0. We attempted measuring HGSNAT catalyzed reaction by monitoring decrease in acetyl-CoA in presence of D-glucosamine (acetyl group acceptor) using a coupled enzyme acetyl-CoA assay kit from SIGMA (MAK039) that converts acetyl-CoA to a fluorescent product measurable at Ex/Em of 535/587 nm. We noticed a decrease in the level of acetyl-CoA in the presence of HGSNAT-ACO complex (blue) and apo HGSNAT (red); the difference compared to the ACO standard (gray) was not significant. While optimizing the assay, Xu et al. (2024, Nat Struct Mol Biol) published structural and biochemical characterization of HGSNAT, showing that detergent-purified HGSNAT is active.

Author response image 3.

Acetyl-CoA levels in absence and presence of HGSNAT purified in digitonin. Decrease in the levels of 10 mM acetyl-CoA was measured in presence of 10 mM D-glucosamine and 30 nM HGSNAT at pH 7.5.

(2) In Figure 5, the authors present a detailed schematic of the catalytic cycle, which I find to be too speculative. There is no evidence to suggest that this enzyme undergoes isomerization, similar to a transporter, between open-to-lumen and open-to-cytosol states. Could it not simply involve some movements of side chains to complete the acetyl transfer? The speculative nature of this assumption needs to be clearly acknowledged throughout the manuscript and discussed in more detail. The authors could use HDX-MS or introduce cysteine residues in the hypothetical inward- and outward-facing cavities and test accessibility by incubating the purified protein with maleimides or other agents reacting with free cysteine.

We thank the reviewers for this insightful critique. Yes, the enzyme could likely achieve catalysis by simple side chain movements without undergoing extensive isomerization steps, as depicted in Figure 5. We also agree with the reviewer that HDX-MS could be the best way to monitor the substrate-induced conformational dynamics within HGSNAT experimentally. In the absence of data supporting large movements during the acetyl transfer reaction, figure 5 is speculative. We have now edited Figure 5 in the revised version of the manuscript based on the observations we made in this study.

(3) The acetyl-CoA-bound state is described as the open-to-lumen state. Indeed, from Figure 1C, the lumen opening appears much larger than the cytosol opening. Is there any small tunnel that connects the substrate site to the cytosol? In other words, is this state accessible to both the lumen and the cytosol, albeit with a larger opening toward the lumen? This question arises because, in Figure S5, the tunnel calculated by MOLE seems to also connect to the cytosol.

Yes, it is likely that the ACOS is accessible via lumen and cytosol to varying degrees, as evidenced by MOLE prediction. However, binding of the bulky nucleoside head group of acetyl-CoA at ACOS blocks the cytosolic entrance in the confirmation discussed in this manuscript. MOLE prediction was performed on a structure devoid of acetyl-CoA, and it is possible that the protein doesn’t essentially undergo isomerization between open-to-lumen and open-to-cytosol confirmations during acetyl transfer. Likely, ACOS is always accessible from both the lumen and cytosol, but depending on the substrates or products bound, the accessibility could be limited to either the lysosomal lumen or cytosol. We have rewritten all the statements mentioning an open-to-lumen confirmation to reflect this argument.

(4) The authors state, "Interestingly, in most of the detergent conditions we tested, HGSNAT was predominantly dimeric (Fig S1C-H)," and also mention, "In all the detergents we tested, HGSNAT eluted as a dimer, a testament to the extensive side-chain interaction network." The dimerization is said to be mediated by a disulfide bond. I would be surprised if the detergents the authors tested could break a disulfide bond. Therefore, can this observation truly serve as a testament to an "extensive" side-chain interaction network?

We agree with the reviewer that detergents are unlikely to break a disulfide bond. To address this comment, we generated a C334A mutant of HGSNAT and extracted it from cells in 1% digitonin. It is still expressed as a dimer (Fig S8E). However, upon heating the detergent solubilized protein, we noticed that the FSEC peak for C334A shows a monomeric HGSNAT (Fig S8I and S8K). We hypothesize that in the absence of C334 disulfide, the extensive hydrophobic side-chain interaction network displayed in Figure 2C is responsible for maintaining the integrity of the dimer. Heating disturbs these non-disulfide interactions, thereby rendering the protein monomer.

(5) Apart from the cryo-EM structure, the article does not provide any other experimental evidence to support or explain a molecular mechanism. Due to the complete absence of functional assays, mutagenesis analysis, or other structures such as a ternary complex or an acetylated enzyme intermediate, the mechanistic model depicted in Figure 5 should be taken with caution. This uncertainty needs to be clearly described in the manuscript text. Performing additional mutagenesis experiments to test key hypotheses, or further discussing relevant data from the literature, would strengthen the manuscript.

We agree with the reviewer on the lack of supporting evidence for the mechanistic models proposed in Fig 5. They were made based on previously reported biochemical characterization of HGSNAT by Rome & Crain (1981), Rome et al. (1983), Miekle et al. (1995), and Fan et al. (2011). However, we agree with the reviewer that this schematic is not experimentally proven and is speculative at best. We have edited Figure 5 in the revised version of the manuscript. In addition, we have also performed mutagenesis analysis to study the stability of mutants (Fig S8) and performed LC-MS analysis to identify endogenously bound acetyl-CoA (Fig S9) to strengthen parts of the manuscript. We have discussed our findings in the results and modified the discussion according to these suggestions.

(6) It is discussed that H269 is an essential residue that participates in the acetylation reaction, possibly becoming acetylated during the process. However, there is no solid experimental evidence, e.g. mutagenesis analysis or structural analysis, in this or previous articles, that demonstrates this to be the case. Providing more information, ideally involving additional experimental work, would strengthen this aspect of the mechanism that is proposed. This would require establishing an in vitro assay, as described in 1).

H269, as a crucial catalytic residue, was suggested by monitoring the effect of chemical modifications of amino acids on acetylation of HGSNAT membranes by Bame, K. J. and Rome, L. H. (1986). We generated N258I and H269A mutants of HGSNAT and analyzed their stability. We noticed a greater destabilization in N258I compared to H269A (Fig S8). We believe this is because of the loss of ability to bind acetyl-CoA, as the TMs around a catalytic core of the protein in our cryo-EM structure were stabilized by interactions with acetyl-CoA. Recently, Xu et al. (2024, Nat Struct Mol Biol) suggested that they do not observe acetylated histidine in their structure. However, our structure and that reported by Xu et al. (2024) are obtained at cytosolic pH. Perhaps, acetylation of H269 occurs at acidic lysosomal pH. Extensive structural and catalytic investigation of HGSNAT at low pH is required to rule out H269 acetylation as a step in the HGSNAT catalyzed reaction.

(7) In the discussion part, the authors mention previous studies in which it was postulated that the catalytic reaction can be described by a random order mechanistic model or a Ping Pong Bi Bi model. However, the authors leave open the question of which of these mechanisms best describes the acetylation reaction. The structure presented here does not provide evidence that could support one mechanism or the other. The authors could explore if an in vitro experimental measurement of protein activity would provide any information in this regard.

We agree with the reviewer that a more detailed kinetic analysis is necessary to define the bisubstrate reaction mechanism of HGSNAT. All the existing structural data on two isoforms of HGSNAT is obtained at basic pH. As a result, the existing structures do not unambiguously demonstrate the bisusbtrate mechanism of HGSNAT. We believe low pH structural characterization and a detailed kinetic and structural characterization of HGSNAT in membrane mimetics like nanodiscs could provide more insights into the mechanism. However, these studies are a future undertaking and are not a part of this manuscript.

(8) Although the authors map the mutations leading to MPS IIIC on the structure and use FoldX software to predict the impact of these mutations on folding and fold stability, there is no experimental evidence to support FoldX's predictions. It would be ideal if an additional test for these predictions were included in the manuscript. The authors could follow the unfolding of purified mutants by SEC, FSEC, or changes in intrinsic fluorescence to assess protein stability.

As suggested here, we prepared HGSNAT MPSIIIC variants and tested their expression and stability (please see Fig S8). These results have been included in the revised version of the manuscript.

(9) Some sidechains that have quite strong sidechain density are missing atoms. I would be particularly careful with omitting sidechains that pack in the hydrophobic core, as this can tend to artificially reduce the clash score. Check F81, L62, P91 and V87, for example.

We have revisited the modeling of these regions and deposited new coordinates.

(10) W316 seems to have the wrong rotamer.

This has been corrected in the new coordinate file that has been released.

(11) N134 and N433 seem to have extra density. Are these known glycosylation sites?

As per Hrebicek M. et al., 2006 and Feldhammer M. et al., 2009, there are five predicted glycosylation sites: N66, N114, N134, N433, and N602. However, we see evidence for NAG density at N114, N134, and N433. These have now been modeled in the structure.

(12) At the C-terminal residue (Ile-635), the very C-terminal carboxylate is modeled pointing to a hydrophobic environment. It seems more likely to me that the Ile sidechain is packing here, with the C-terminal carboxylate facing the solvent.

Thank you for pointing this out. We have edited the orientation of the Ile sidechain accordingly.

Presentation and wording of results/methods:

- Figure S3 legend "At places with missing density, the side chains were trimmed to C- alpha" - this is incorrect, I think the authors mean C-beta.

We have corrected this error in the revised version of the manuscript.

- Figure S3 legend - the authors refer to a gray mesh, where a transparent surface is displayed.

Thanks for pointing this error out. We have corrected this in the revised version.

- Some colloquial/vague wording in the main text (a lot of sentences starting with "Interestingly, ...". Making the wording more specific would help the reader I think.

We have edited out ‘interestingly’ from the document and have re-written parts of the manuscript, per reviewers’ suggestion, for brevity.

- Figure S2 legend, "throughout the processing workflow the resolution of luminal domain was used as a guidepost" - it is not entirely clear to me what this means in this context, perhaps revise the wording?

We have rephrased this line in the revised draft of the manuscript.

- Figure S2 and methods, Local refinements of LD and TMD are mentioned, but not indicated on the processing workflow.

We have included a new Fig S2 & edited the legend, including these changes, per the reviewers’ suggestions.

Author response:

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

Reviewer #1:

I will summarize my comments and suggestions below.

(1) Abstract:

"Non-catalytic (pseudo)kinase signaling mechanisms have been described in metazoans, but information is scarce for plants." To the best of my understanding EFR is an active protein kinase in vitro and in vivo and cannot be considered a pseudokinase. Consider rephrasing.

We rephrased to: “Non-catalytic signaling mechanisms of protein kinase domains have been described in metazoans, but information is scarce for plants.”

(2) Page 4: It should be noted, that while membrane associated Rap-RiD systems have been used in planta to activate receptor kinase intracellular domains by promoting interaction with a co-receptor kinase domain, this system does not resemble the actual activation mechanism in the plasma membrane. This would be worth discussing when introducing the system. For example, the first substrates of the RK signaling complex may also be membrane associated and not freely diffuse in solution, which may be important for enzyme-substrate interaction.

We inserted on page 4: “The RiD system was previously applied in planta, maintaining membrane-association by N-terminal myristoylation (Kim et al., 2021). For the in vitro experiments, the myristoylation sites were excluded to facilitate the production of recombinant protein.”

(3) Page 4 and Fig 1: The catalytic Asp in BRI1 is D1027 and not D1009 (https://pubmed.ncbi.nlm.nih.gov/21289069/). Please check and prepare the correct mutant protein if needed.

We clarified this in the text by stating that we mutated the HRD-aspartate to asparagine in all our catalytic-dead mutants: “Kinase-dead variants with the catalytic residue (HRD-aspartate) replaced by asparagine (EFRD849N and BRI1D1009N), had distinct effects […]”. D1027 in BRI1 is the DFG-Asp, which was not mutated in our study.

(4) Page 4 and Fig 1: Is BIK1 a known component of the BR signaling pathway and a direct BRI1 substrate? Or in other words how specific is the trans-phosphorylation assay? In my opinion, a more suitable substrate for BRI1/BAK1 would be BSK1 or BSK3 (for example https://pubmed.ncbi.nlm.nih.gov/30615605/).

Kinase-dead BIK1 is a reported substrate of BRI1. We clarified this in the results section by inserting: “BIK1 was chosen as it is reported substrate of both, EFR/BAK1 and BRI1/BAK1 complexes (Lin et al., 2013).”

(5) Fig. 1B Why is BIK1 D202N partially phosphorylated in the absence of Rap? I would suggest to add control lanes showing BRI1, EFR, FLS2, BAK1 and BIK1 in isolation. Given that a nice in vitro activation system with purified components is available, why not compare the different enzyme kinetics rather than band intensities at only 1 enzyme : substrate ratio?

BIK1 D202N is partially phosphorylated due to the presence of active BAK1 that is capable of transphosphorylating BIK1 D202N as it has been reported in a previous study: (DOI: 10.1038/s41586-018-0471-x).

(6) Page 4 and Fig 1: Is the kinase dead variant of EFR indeed kinase dead? I could still see a decent autorad signal for this mutant when expressed in E. coli (Fig 1 A in Bender et al., 2021; https://pubmed.ncbi.nlm.nih.gov/34531323/)? If this mutant is not completely inactive, could this change the interpretation of the experiments performed with the mutant protein in vitro and in planta in the current manuscript? In my opinion, it could be possible that a partially active EFR mutant can be further activated by BAK1, and in turn can phosphorylate BIK1 D202N. The differences in autorad signal for BRI1D1009?N and EFRD849N is very small, and the entire mechanism hinges on this difference.

We would like to emphasize that the mechanism hinges on the difference between non-dimerized and dimerized kinase domains in the in vitro kinase assay. BRI1 D1009N fails to enhance BIK1 D202N trans-phosphorylation compared to the non-dimerized sample, while EFR D849N is still capable of enhancing BIK1 transphosphorylation upon dimerization as indicated by quantification of autorads (Figure 1B/C). We have also addressed this point in a section on the limitations of our study.

(7) Fig 1B. "Our findings therefore support the hypothesis that EFR increases BIK1 phosphorylation by allosterically activating the BAK1 kinase domain." To the best of my understanding presence of wild-type EFR in the EFR-BAK1 signaling complex leads to much better phosphorylation of BIK1D202N when compared to the EFRD849N mutant. How does that support the allosteric mechanism? By assuming that the D849N mutant is in an inactive conformation and fully catalytically inactive (see above)? Again, I think the data could also be interpreted in such a way that the small difference in autorad signal for BIK1 between BRI1 inactive (but see above) and ERF inactive are due to EFR not being completely kinase dead (see above), rather than EFR being an allosteric regulator. To clarify this point I would suggest to a) perform quantitative auto- and trans-(generic substrate) phosphorylation assays with wt and D849N EFR to derive enzyme kinetic parameters, to (2) include the EFRD849 mutant in the HDX analysis and (3) to generate transgenic lines for EFRD489N/F761H/Y836F // EFRD489N/F761H/SSAA and compare them to the existing lines in Fig. 3.

Mutations of proteins, especially those that require conformational plasticity for their function can have pleiotropic effects as the mutation may affect the conformational plasticity and consequently catalytic and non-catalytic functions that depend on the conformational plasticity. In such cases, it is difficult to fully untangle catalytic and non-catalytic functions. Coming back to EFR D849N, the D849N mutation may also impact the non-catalytic function by altering the conformational plasticity, explaining the difference observed in EFR vs EFR D849N. As you rightly suggested, HDX would be a way to address this but would still not clarify whether catalytic activity contributes to activation. We instead attempted to produce analog sensitive EFR variants for in vivo characterization of EFR-targeted catalytic inhibition. Unfortunately, we failed in producing an analog-sensitive variant for which we could show ATP-analog binding. To address your concern, we inserted a section on limitations of the study.

(8) Fig. 2B,C, supplement 3 C,D. Has it been assessed if the different EFR versions were expressed to similar protein levels and still localized to the PM?

Localization of the mutant receptors has not been explicitly evaluated by confocal microscopy. However, the selected mutation EFRF761H is shown to accumulate in stable Arabidopsis lines (Figure 3 – Supplement 1C) and BAK1 could be coIPed by all EFR variants upon elf18-treatment (Figure 3 B), indicating plasma membrane localization.

(9) How the active-like conformation of EFR is in turn activating BAK1 is poorly characterized, but appears to be the main step in the activation of the receptor complex. Extending the HDX analyses to resting and Rap-activated receptor complexes could be a first step to address this question. I tried to come up with an experimental plan to test if indeed the kinase activity of BAK1 and not of EFR is essential for signal propagation, but this is a complex issue. You would need to be able to mimic an activated form of EFR (which you can), to make sure its inactive (possibly, see above) and likewise to engineer a catalytically inactive form of BAK1 in an active-like state (difficult). As such a decisive experiment is difficult to implement, I would suggest to discuss different possible interpretations of the existing data and alternative scenarios in the discussion section of the manuscript.

We addressed your concern whether BAK1 kinase activity is essential for signaling propagation by pairing EFRF761H and BAK1D416N (Figure 4 Supplement 2 C) which fails to induce signaling. In this case, EFRF761H is in its activated conformation but cannot activate downstream signaling. We also attempted to address your concern by an in vitro kinase assay by pairing EFR and BAK1D416N and using a range of concentrations of the substrate BIK1D202N. We observed that catalytic activity of BAK1 but not EFR was essential for BIK1 phosphorylation. However, this experiment does not address whether activated EFR can efficiently propagate signaling in the absence of BAK1 catalytic activity. In the limitations of the study section, we now discuss the catalytic importance of EFR for signaling activation.

Author response image 1.

BIK1 trans-phosphorylation depends on BAK1 catalytic activity. Increasing concentrations of BIK1 D202N were used as substrate for Rap-induced dimers of EFR-BAK1, EFR D849N-BAK1, and EFR-BAK1 D416N respectively. BIK1 trans-phosphorylation depended on the catalytic activity of BAK1. Proteins were purified from E. coli λPP cells. Three experiments yielded similar results of which a representative is shown here.

Reviewer #2:

All of my suggestions are minor.

Figure 1B, I think it would be more useful to readers to explain the amino acid in the D-N change, rather than just call it D-to-N? Also, please label the bands on the stained gel; the shift on FKBP-BRI1 and FKBP-EFR are noticeable on the Coomassie stain.

We implemented your suggestions.

Figure 1-Supplement 1. There is still a signal in pS612 BAK1 (it states 'also failed to induce BAK1 S612 phosphorylation' in the text, which is not quite correct). Also, could mention the gel shift seen in BAK1, which appears absent in Y836F.

We corrected the text which now states: “To test whether the requirement for Y836 phosphorylation is similar, we immunoprecipitated EFR-GFP and EFRY836F-GFP from mock- or elf18-treated seedlings and probed co-immunoprecipitated BAK1 for S612 phosphorylation. EFRY836F also obstructed the induction of BAK1 S612 phosphorylation (Figure 1 – Supplement 1), indicating that EFRY836F and EFRSSAA impair receptor complex activation.” The gel shift of BAK1 you pointed out was not observed in replications and thus we prefer not to comment on it.

Figure 2 and 3 are full of a, b, c,d's, which I don't understand. Sorry

We used uppercase letters to indicate subpanels and lowercase letters to indicate the results of the statistical testing. In the figure caption, we have clarified that the lowercase letters refer to statistical comparisons.

Figure 2 A. If each point on the x-axis is one amino acid, I think it would again be useful to name the amino acids that the gold or purple or blue colored lines extend through.

Each point stands for a peptide which are sorted by position of their starting amino acid from N-terminus to C-terminus. We now added plots of HDX for individual peptides that correspond to the highlighted region in subpanel A.

Figure Supplement 1 is very small for what it is trying to show, even on the printed page. If this residue were to be phosphorylated, what would happen to the H-bond?

We suppose that VIa-Tyr phosphorylation would break the H-bond and causes displacement of the aC-b4 loop. Recent studies, published after our submission, highlight the importance of this loop for substrate coordination and ATP binding. Thus, phosphorylation of VIa-Tyr and displacing this loop may render the kinase rather unproductive. We have expanded the discussion to include this point.

Figure 2B: Tyr 836 is not present in any of the alignments in Figure 2A. This should be rectified, because the text talks about the similarity to Tyr 156 in PKA.

We have adjusted the alignments such that they now contain the VIa-Tyr residues of EFR and PKA.

Figure 4D. Is there any particular reason that these Blots are so hard to compare or FKBP and BAK1?

We assume it is referred to Figure 4 – Supplement 2 D. FKBP-EFR and FRB-BAK1 both are approximately the size of RubisCo, the most abundant protein in plant protein samples and which overlay the FKBP- and FRB-tagged kinase. Thus, it is difficult to detect these proteins.

Reviewer #3:

(1) The paper reporting the allosteric activation mechanism of EGFR should be cited.

Will be included.

(2)The authors showed that "Rap addition increased BIK1 D202N phosphorylation when the BRI1 or EFR kinase domains were dimerized with BAK1, but no such effect was observed with FLS2". Please explain why FLS2 failed to enhance BIK1 transphosphorylation by Rap treatment?

Even though BIK1 is a reported downstream signaling component of FLS2/BAK1, it might be not the most relevant downstream signaling component and rather related RLCKs, like PBL1, might be better substrates for dimerized FLS2/BAK1. We haven’t tested this, however. Alternatively, the purified FLS2 kinase domain might be labile and quickly unfolds even though it was kept on ice until the start of the assay, or the N-terminal FKBP-tag may disrupt function. As the reason for our observation is not clear, we have removed FLS2 in vitro dimerization experiments from the manuscript.

(3) Based solely on the data presented in Figure 1, it can be concluded that EFR's kinase activity is not required to facilitate BIK1 transphosphorylation. Therefore, the title of Figure 1, "EFR Allosterically Activates BAK1," may be inappropriate.

We have changed the figure title to: “EFR facilitates BIK1 trans-phosphorylation by BAK1 non-catalytically.”

(4) In Figure 1- Supplement 1, I could not find any bands in anti-GFP and anti-BAK1 pS612 of input. Please redo it.

Indeed, we could not detect protein in the input samples of this experiment. BAK1 S612 phosphorylation is an activation mark and not necessarily expected to be abundant enough for detection in input samples. EFR-GFP, however, is usually detected in input samples and is reported in Macho et al. 2014 from which manuscript these lines come. Why EFR-GFP is not detected in this set of experiments is unclear but, in our opinion, does not detract from the conclusions drawn since similar amounts of EFR-GFP are pulled-down across all samples.

(5) For Figure 2A, please mark the structure represented by each color directly in the figure.

We have made the suggested change.

(6) Please modify "EFRF761/Y836F and EFRF761H/SSAA restore BIK1 trans-phosphorylation" to "EFRF761H/Y836F and EFRF761H/SSAA restore BIK1 trans-phosphorylation".

Thank you for spotting this. We changed it.

(7) The HDX-MS analysis demonstrated that the EFR (Y836F) mutation inhibits the formation of the active-like conformation. Conversely, the EFR (F761H) mutation serves as a potent intragenic suppressor, significantly stabilizing the active-like conformation. Confirming through HDX-MS conformational testing that the EFR (Y836F F761H) double mutation does not hinder the formation of the active-like EFR kinase conformation would greatly strengthen the conclusions of the article.

Response: We agree that this is beneficial, and we attempted to do it but failed to produce enough protein for HDX-MS analysis. We stated this now in an extra section of the paper (“Limitations of the study”).

Author response:

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

Reviewer #1 (Public Review):

The authors in this paper investigate the nature of the activity in the rodent EPN during a simple freely moving cue-reward association task. Given that primate literature suggests movement coding whereas other primate and rodent studies suggest mainly reward outcome coding in the EPNs, it is important to try to tease apart the two views. Through careful analysis of behavior kinematics, position, and neural activity in the EPNs, the authors reveal an interesting and complex relationship between the EPN and mouse behavior.

Strengths:

(1) The authors use a novel freely moving task to study EPN activity, which displays rich movement trajectories and kinematics. Given that previous studies have mostly looked at reward coding during head-fixed behavior, this study adds a valuable dataset to the literature. (2) The neural analysis is rich and thorough. Both single neuron level and population level (i.e. PCA) analysis are employed to reveal what EPN encodes.

Thank you very much for this appreciation.

Weaknesses:

(1) One major weakness in this paper is the way the authors define the EPN neurons. Without a clear method of delineating EPN vs other surrounding regions, it is not convincing enough to call these neurons EPNs solely from looking at the electrode cannula track from Figure 2B. Indeed, EPN is a very small nucleus and previous studies like Stephenson-Jones et al (2016) have used opto-tagging of Vglut2 neurons to precisely label EPN single neurons. Wallace et al (2017) have also shown the existence of SOM and PV-positive neurons in the EPN. By not using transgenic lines and cell-type specific approaches to label these EPN neurons, the authors miss the opportunity to claim that the neurons recorded in this study do indeed come from EPN. The authors should at least consider showing an analysis of neurons slightly above or below EPN and show that these neurons display different waveforms or firing patterns.

We thank the reviewer for their comment, and we thank the opportunity to expand on the inclusion criteria of studied units after providing an explanation. 

As part of another study, we performed experiments recording in EPN with optrodes and photoidentification in PV-Cre animals. We found optoidentified units in both: animals with correct placement (within the EPN) and on those with off-target placement (within the thalamus or medial to the EPN). Thus, despite the use of Cre animals, we relied on histology to ensure correct EPN recording. We believe that the optotagging based purely on neural makers such as PV, SOM, VGLUT, VGAT would not provide a better anatomical delineation of the EPN since adjacent structures are rich in those same markers. The thalamic reticular nucleus is just dorsal to the EPN and it has been shown to express both SOM and PV (Martinez-Garcia et al., 2020). 

On the other hand, the lateral hypothalamus (just medial to the EPN) also expresses vGlut2 and SOM. Stephenson-Jones (2016), Extended Data Figure 1, panel g, shows vGluT2 and somatostatin labeling of neurons, with important expression of neurons dorsal, ventral and medial to the EPN. Thus, we believe that viral strategies relying on single neuronal markers still depend on careful histological analysis of recording sites.

A combination of neural markers or more complex viral strategies might be more suitable to delineate the EPN. As an example, for anatomical tracing Stephenson-Jones et al. 2016 performed a rabies-virus based approach involving retrogradely transported virus making use of projection sites through two injections. Two step viral approaches were also performed in Wallace, M. et al. 2017. We attempted to perform a two-step viral approach, using an anterogradely transported Cre-expressing virus (AAV1.hSyn.Cre.WPRE.hGH) injected into the striatum and a second Cre dependent ChR2 into the EPN. However, our preliminary experiments showed that this double viral approach had a stark effect decreasing the performance of animals during the task (we attempted re-training 2-3 weeks after viral infections and animals failed to turn to the contralateral side of the injections). We believe that this approach might have had a toxic effect (Zingg et al., 2017). 

To this point, a recent paper (Lazaridis et al., 2019) repeated an optogenetic experiment performed in the Stephenson-Jones et al. study, using a set of different viral approaches and concluded that increasing the activity of GPi-LHb is not aversive, as it had been previously reported. Thus, future studies attempting to increase anatomical specificity are a must, but they will require using viral approaches amenable to the behavioral paradigm.

We attempted to find properties regarding waveforms, firing rate, and firing patterns from units above or below, however, we did not find a marker that could generate a clear demarcation. We show here a figure that includes the included units in this study as well as excluded ones to show that there is a clear overlap.

Author response image 1.

Finally, we completely agree with the reviewer in that there is still room for improvement. We have further expanded the Methods section to explain better our efforts to include units recorded within the EPN. Further, we have added a paragraph within the Discussion section to point out this limitation (lines 871-876).

Methods (lines 116-131):

“Recordings. Movable microwire bundles (16 microwires, 32 micrometers in diameter, held inside a cannula, Innovative Neurophysiology, Durham, NC)] were stereotaxtically implanted just above the entopeduncular nucleus (-0.8 AP, 1.7 ML, 3.9 DV). Post surgical care included antibiotic, analgesic and antiinflammatory pharmacological treatment. After 5 days of recovery, animals were retrained for 1-2 weeks. Unitary activity was recorded for 2-6 days at each dorsoventral electrode position and the session with the best electrophysiological (signal to noise ratio (>2), stability across time) and behavioral [performance, number of trials (>220)] quality was selected. Microwire electrodes were advanced in 50 micrometer dorsoventral steps for 500 micrometers in total. After experiment completion, animals were perfused with a 4% paraformaldehyde solution. Brains were extracted, dehydrated with a 30% sucrose solution and sectioned in a cryostat into 30micron thick slices. Slices were mounted and photographed using a light microscope. Microwire tracks of the 16-microwire bundle were analyzed (Fig. 2A-B) and only animals with tracks traversing the EPN were selected (6 out of 10). Finally, we located the final position of microwire tips and inferred the dorsoventral recording position of each of the recording sessions. Only units recorded within the EPN were included.” 

Discussion (lines 871-876):

“A weakness of the current study is the lack of characterization of neuronal subtypes. An area of opportunity for future research could be to perform photo-identification of neuronal subtypes within the EPN which could contribute to the overall description of the information representation. Further, detailed anatomical viral vector strategies could aid to improve anatomical localization of recordings, reduce reliance on histological examination, and solve some current controversies (Lazaridis et al., 2019).” 

(2) The authors fail to replicate the main finding about EPN neurons which is that they encode outcome in a negative manner. Both Stephenson-Jones et al (2016) and Hong and Hikosaka (2008) show a reward response during the outcome period where firing goes down during reward and up during neutral or aversive outcome. However, Figure 2 G top panel shows that the mean population is higher during correct trials and lower during incorrect trials. This could be interesting given that the authors might try recording from another part of EPN that has not been studied before. However, without convincing evidence that the neurons recorded are from EPN in the first place (point 1), it is hard to interpret these results and reconcile them with previous studies.

We really thank the reviewer for pointing out that we need to better explain how EPN units encode outcome. We now provide an additional panel in Figure 4, its corresponding text in the results section (lines 544-562) and a new paragraph in the discussion related to this comment.

We believe that we do indeed recapitulate findings of both of Stephenson-Jones et al (2016) and Hong and Hikosaka (2008). Both studies focus on a specific subpopulation of GPi/EPN neurons that project to the lateral habenula (LHb). Stephenson-Jones et al (2016) posit that GPi-LHb neurons (which they opto-tag as vGluT2) exhibit a decreased firing rate during rewarding outcomes. Hong and Hikosaka (2008) antidromically identified LHb projecting neurons through within the GPi and found reward positive and reward negative neurons, which were respectively modulated either by increasing or decreasing their firing rate with a rewarding outcome (red and green dots on the x-axis of Figure 5A in their paper).

As the reviewer pointed out the zScore may be misleading. Therefore, in our study we also decomposed population activity on reward axis through dPCA. When marginalizing for reward in Figure 3F, we find that the weights of individual units on this axis are centered around zero, with positive and negative values (Figure 3F, right panel). Thus, units can code a rewarding outcome as either an increase or a decrease of activity. We show example units of such modulation in Figure 3-1g and h.

We had segregated our analysis of spatio-temporal and kinematic coding upon the reward coding of units in Figure 4L-M. Yet, following this comment and in an effort of further clarifying this segregation, we introduced panels with the mean zScore of units during outcome evaluation in Figure 4L.

We amended the main text to better explain these findings (lines 544-562).

“Previous reports suggest that EPN units that project to the lateral habenula encode reward as a decrease in firing rate. Thus, we wished to ask whether reward encoding units can code kinematic and spatio-temporal variables as well.

To this end, we first segregated units upon their reward coding properties: reward positive (which increased activity with reward) and reward negative units (which decreased activity with reward). We performed auROC on the 250ms after head entry comparing rewarded trials and incorrect trails (p<0.001, permutation test). Mean activity of reward insensitive, positive and negative units is shown in Fig. 4L. Next, we performed a dimensionality reduction on the coefficients of the model that best explained both contexts (kinematic + spatio-temporal model on pooled data) using UMAP (McInnes et al., 2018). We observe a continuum rather than discrete clusters (Fig. 4L). Note that individual units are color coded according to their responsivity to reward. We did not find a clear clustering either.”  

Paragraph added in the discussion (lines 749-755):

“In this study, we found that rewarding outcomes can be represented by EPN units through either an increase or a decrease in firing rate (Fig. 3F, 3-1g-h, 4L). While Stephenson-Jones et al., 2016 found that lateral habenula (LHb)-projecting neurons within the EPN of mice primarily encoded rewarding outcomes by a decrease in firing rate, Hong and Hikosaka, 2008 observed that in primates, LHb-projecting units could encode reward through either a decrease or an increase in firing rate. Thus, our results align more closely with the latter study, which also employed an operant conditioning task.”

(3) The authors say that: 'reward and kinematic doing are not mutually exclusive, challenging the notion of distinct pathways and movement processing'. However, it is not clear whether the data presented in this work supports this statement. First, the authors have not attempted to record from the entire EPN. Thus it is possible that the coding might be more segregated in other parts of EPN. Second, EPNs have previously been shown to display positive firing for negative outcomes and vice versa, something which the authors do not find here. It is possible that those neurons might not encode kinematic and movement variables. Thus, the authors should point out in the main text the possibility that the EPN activity recorded might be missing some parts of the whole EPN.

We thank the reviewer for the opportunity to expand on this topic. We believe it is certainly possible that other not-recorded regions of the EPN might exhibit greater segregation of reward and kinematics. However, we considered it worthwhile pointing out that from the dataset collected in this study reward-sensitive units encode kinematics in a similar fashion to reward-insensitive ones (Fig. 4L,M). Moreover, we asked specifically whether reward-negative units (that decrease firing rate with rewarding outcomes, as previously reported) could encode kinematics and spatio-temporal variables with different strength than reward-insensitive ones and could not find significant differences (Fig. 4M).

We did indeed find units that displayed decreased firing rate upon rewarding outcomes, as has been previously reported. We have addressed this fact more thoroughly in point (2). 

Finally, we agree with the reviewer that the dataset collected in this study is by no means exhaustive of the entire EPN and have thus included a sentence pointing this out in the Discussion section (lines 805-806):

“Given that we did not record from the entire EPN, it is still possible that another region of the nucleus might exhibit more segregation.”

(4) The authors use an IR beam system to record licks and make a strong claim about the nature of lick encoding in the EPN. However, the authors should note that IR beam system is not the most accurate way of detecting licks given that any object blocking the path (paw or jaw-dropping) will be detected as lick events. Capacitance based, closed-loop detection, or video capturing is better suited to detect individual licks. Given that the authors are interested in kinematics of licking, this is important. The authors should either point this out in the main text or verify in the system if the IR beam is correctly detecting licks using a combination of those methods.

We thank the reviewer for the opportunity of clarifying the lick event acquisition. We have experience using electrical alternatives to lickometers; however, we believe they were not best suited to this application. Closed-loop lickometers generally use a metallic grid upon which animals stand so that the loop can be closed; however, we wanted to have a transparent floor. We have found capacitance based lickometers to be useful in head-fixed conditions but have noticed that they are very dependent on animal position and proximity of other bodyparts such as limbs. Given the freely moving aspect of the task this was difficult to control. Finally, both electric alternatives for lickometers are more prone to noise and may introduce electrical artifacts that might contaminate the spiking signal. This is why we opted to use a slit in combination with an IR beam that would only fit the tongue and that forced enough protrusion such that individual licks could be monitored. Further, the slit could not fit other body-parts like the paw or jaw. We have now included a video (Supp. Video 2) showing a closeup of this behavior that better conveys how the jaw and paw do not fit inside the slit. The following text has been added in the corresponding methods section (lines 97-98):

“The lickometer slit was just wide enough to fit the tongue and deep enough to evoke a clear tongue protrusion.”

Reviewer #1 (Recommendations For The Authors):

(1)The authors should verify using opto-tagging of either Vglut2, SOM, or PV neurons whether they can see the same firing pattern. If not, the authors should address this weakness in the paper.

We thank the reviewer for this important point, we have provided a more detailed reply above.

(2)The way dPCA or PCA is applied to the data is not stated at all in the main text. Are all units from different mice combined? Or applied separately for each mouse? How does that affect the interpretation of the data? At least a brief text should be included in the main text to guide the readers.

We thank the reviewer for pointing out this important omission. We have included an explanation in the Methods section and in the Main text.

Methods (lines 182-184):

“For all population level analyses individual units recorded from all sessions and all animals were pooled to construct pseudo-simultaneous population response of combined data mostly recorded separately.”

Main text (lines 397-399):

“For population level analyses throughout the study, we pooled recorded units from all animals to construct a pseudo-simultaneous population.”

Discussion (lines 729-730):

“…(from pooled units from all animals to construct a pseudo-simultaneous population, which assumes homogeneity across subjects)”

(3) The authors argue that they do not find 'value coding' in this study. However, the authors never manipulate reward size or probability, but only the uncertainty or difficulty of the task. This might be better termed 'difficulty', and it is difficult to say whether this correlates with value in this task. For instance, mice might be very confident about the choice, even for an intermediate frequency sweep, if the mouse had waited long enough to hear the full sweep. In that case, the difficulty would not correlate with value, given that the mouse will think the value of the port it is going to is high. Thus, authors should avoid using the term value.

We agree with the reviewer. We have modified the text to specify that difficulty was the variable being studied and added the following sentence in the Discussion (lines 747-748):

“It is still possible that by modifying reward contingencies such as droplet size value coding could be evidenced.”

(4) How have the authors obtained Figure 7D bottom panel? It is unclear at all what this correlation represents. Are the authors looking at a correlation between instantaneous firing rate and lick rate during a lick bout?

We thank the reviewer for pointing out that omission. It is indeed correlation coefficient between the instantaneous firing rate and the instantaneous lick rate for a lick bout. We have included labeling in Figure 7D and pointed this out in the main text [lines 680-681]:

“Fig.7D, lower panel shows the correlation coefficient between the instantaneous firing rate and the instantaneous lick rate within a lick bout for all units.”

Reviewer #2 (Public Review):

This paper examined how the activity of neurons in the entopeduncular nucleus (EPN) of mice relates to kinematics, value, and reward. The authors recorded neural activity during an auditory-cued two-alternative choice task, allowing them to examine how neuronal firing relates to specific movements like licking or paw movements, as well as how contextual factors like task stage or proximity to a goal influence the coding of kinematic and spatiotemporal features. The data shows that the firing of individual neurons is linked to kinematic features such as lick or step cycles. However, the majority of neurons exhibited activity related to both movement types, suggesting that EPN neuronal activity does not merely reflect muscle-level representations. This contradicts what would be expected from traditional action selection or action specification models of the basal ganglia.

The authors also show that spatiotemporal variables account for more variability compared to kinematic features alone. Using demixed Principal Component Analysis, they reveal that at the population level, the three principal components explaining the most variance were related to specific temporal or spatial features of the task, such as ramping activity as mice approached reward ports, rather than trial outcome or specific actions. Notably, this activity was present in neurons whose firing was also modulated by kinematic features, demonstrating that individual EPN neurons integrate multiple features. A weakness is that what the spatiotemporal activity reflects is not well specified. The authors suggest some may relate to action value due to greater modulation when approaching a reward port, but acknowledge action value is not well parametrized or separated from variables like reward expectation.

We thank the reviewer for the comment. We indeed believe that further exploring these spatiotemporal signals is important and will be the subject of future studies.

A key goal was to determine whether activity related to expected value and reward delivery arose from a distinct population of EPN neurons or was also present in neurons modulated by kinematic and spatiotemporal features. In contrast to previous studies (Hong & Hikosaka 2008 and Stephenson-Jones et al., 2016), the current data reveals that individual neurons can exhibit modulation by both reward and kinematic parameters. Two potential differences may explain this discrepancy: First, the previous studies used head-fixed recordings, where it may have been easier to isolate movement versus reward-related responses. Second, those studies observed prominent phasic responses to the delivery or omission of expected rewards - responses largely absent in the current paper. This absence suggests a possibility that neurons exhibiting such phasic "reward" responses were not sampled, which is plausible since in both primates and rodents, these neurons tend to be located in restricted topographic regions. Alternatively, in the head-fixed recordings, kinematic/spatial coding may have gone undetected due to the forced immobility.

Thank you for raising this point. Nevertheless, there is some phasic activity associated with reward responses, which can be seen in the new panel in Figure 4L.

Overall, this paper offers needed insight into how the basal ganglia output encodes behavior. The EPN recordings from freely moving mice clearly demonstrate that individual neurons integrate reward, kinematic, and spatiotemporal features, challenging traditional models. However, the specific relationship between spatiotemporal activity and factors like action value remains unclear.

We really appreciate this reviewer for their valuable comments.

Reviewer #2 (Recommendations For The Authors):

One small suggestion is to make sure that all the panels in the figures are well annotated. I struggled in places to know what certain alignments or groupings meant because they were not labelled. An example would be what do the lines correspond to in the lower panels of Figure 2D and E. I could figure it out from other panels but it would have helped if each panel had better labelling.

Thanks for pointing this out, we have improved labelling across the figures and corrected the specific example you have pointed out.

The paper is very nice though. Congratulations!

Thank you very much.

Editor's note:

Should you choose to revise your manuscript, please include full statistical reporting including exact p-values wherever possible alongside the summary statistics (test statistic and df) and 95% confidence intervals. These should be reported for all key questions and not only when the p-value is less than 0.05 in the main manuscript.

We thank the editor for the comment. A statistics table has been added.

References:

Lazaridis, I., Tzortzi, O., Weglage, M., Märtin, A., Xuan, Y., Parent, M., Johansson, Y., Fuzik, J., Fürth, D., Fenno, L. E., Ramakrishnan, C., Silberberg, G., Deisseroth, K., Carlén, M., & Meletis, K. (2019). A hypothalamus-habenula circuit controls aversion. Molecular Psychiatry, 24(9), 1351–1368. https://doi.org/10.1038/s41380-019-0369-5

Martinez-Garcia, R. I., Voelcker, B., Zaltsman, J. B., Patrick, S. L., Stevens, T. R., Connors, B. W., & Cruikshank, S. J. (2020). Two dynamically distinct circuits drive inhibition in the sensory thalamus. Nature, 583(7818), 813–818. https://doi.org/10.1038/s41586-0202512-5

McInnes, L., Healy, J., Saul, N., & Großberger, L. (2018). UMAP: Uniform Manifold Approximation and Projection. Journal of Open Source Software, 3(29), 861. https://doi.org/10.21105/joss.00861

Zingg, B., Chou, X. lin, Zhang, Z. gang, Mesik, L., Liang, F., Tao, H. W., & Zhang, L. I. (2017). AAV-Mediated Anterograde Transsynaptic Tagging: Mapping Corticocollicular Input-Defined Neural Pathways for Defense Behaviors. Neuron, 93(1), 33–47. https://doi.org/10.1016/j.neuron.2016.11.045

Author response:

The following is the authors’ response to the original reviews

Reviewer #1 (Recommendations For The Authors):

Comment 1: The authors need to do more to cite the prior work of others. CCL2 allelic expression imbalance tied to the rs13900 alleles was first reported by Johnson et al. (Pharmacogenet Genomics. 2008 Sep; 18(9): 781-791) and should be cited in the Introduction on line 128 next to the Pham 2012 reference. Also, in the Results section, line 142, please provide references for the statement "We and others have previously reported a perfect linkage disequilibrium between rs1024611 in the CCL2 cis-regulatory region and rs13900 in its 3′ UTR" since the linkage disequilibrium for these 2 SNPs is not reported in the ENSEMBL server for the 1000 genomes dataset. #

We thank the reviewer for pointing out the omission regarding the citation of prior work. We acknowledge that Johnson et al. (2008) reported the association between rs13900 and CCL2 allelic expression imbalance based on Snapshot methodology while examining _cis-_acting variants of 42 candidate genes. To acknowledge these prior studies, we have cited the previous works of Johnson et al. (Johnson et al., 2008) along with Pham et al. (Pham et al., 2012) that linked rs13900 to CCL2 allelic expression imbalance. The text in the introduction section (Lines 128-130) has been updated to reflect the above-mentioned changes.

“We and others have demonstrated AEI in CCL2 using rs13900 as a marker with the T allele showing a higher expression level relative to C allele (Johnson et al., 2008; Pham et al., 2012).”

We have cited some previous studies that suggested strong linkage disequilibrium between rs1024611 and rs13900 within CCL2 gene, with D’=1 and R²=0.96 (Hubal et al., 2010; Intemann et al., 2011; Kasztelewicz et al., 2017; Pham et al., 2012) on Line 144. To address the concern regarding unreported linkage disequilibrium between rs1024611 and rs13900, we reviewed the pairwise linkage disequilibrium data by population in the ENSEMBL server for 1000 Genome dataset and confirm that the linkage disequilibrium (LD) between rs1024611 and rs13900 has been observed, with D’=1 and R²=0.92 to 1.0 in specific populations. We have included a table (Author response table 1) depicting pairwise LD between rs13900 and rs1024611 as reported in the ENSEMBL server for the 1000 genome dataset, a URL reference to the ENSEMBL server data.

Author response table 1.

Pairwise linkage disequilibrium data between rs13900 and rs1024611 by population reported in the ENSEMBL server for the 1000 genome dataset

F. Variant, Focus Variant; R², correlation between the pair loci; D’, difference between the observed and expected frequency of a given haplotype.

URL: https://www.ensembl.org/Homo_sapiens/Variation/HighLD?db=core;r=17:34252269-34253269;v=rs1024611;vdb=variation;vf=959559590;second_variant_name=rs13900

Comment 2: Certain details of the experimental protocols need to be further elaborated or clarified to contextualize the significance of the findings. For example, in the results line 184 the authors state "Using nascent RNA allows accurate determination of mRNA decay by eliminating the effects of preexisting mRNA." How does measuring nascent RNA enable the accurate determination of mRNA decay? Doesn't it measure allele-specific mRNA synthesis? Please elaborate, as this is a key result of the study. Can the authors provide a reference supporting this statement?

It is worthwhile to mention that mRNA decay can be precisely measured by eliminating the effect of any preexisting mRNA. Metabolic labeling with 4-thiouridine allows exclusive capture of newly synthesized RNA which will allow quantification of RNA decay eliminating any interference from preexisting RNA. We agree that nascent RNA measurement primarily reflects synthesis rate rather than degradation. However, in conjugation with actinomycin-D based inhibition studies it can be exploited for accurate mRNA decay determination of the newly synthesized RNA (Russo et al., 2017). Therefore, our aim was to use the nascent RNA to study decay kinetics. The imbalance in the CCL2 allele expression does occur at the transcriptional level as seen in non-actinomycin-D treatment group (Figure 2C) although the impact of post-transcriptional mechanisms that alter transcripts stability cannot be ruled out. Therefore, we employed a novel approach that could assess both the synthesis and the degradation by combining actinomycin-D inhibition and nascent RNA capture in the same experimental setup. In the presence of actinomycin-D, we could detect much greater allelic difference in the expression levels of the rs13900T and C allele four-hour post-treatment, suggesting a role for post-transcriptional mechanisms in CCL2 AEI.

“We have expanded the method section in the revised draft to include experimental details on capture of nascent RNA and subsequent downstream analysis” (Lines 553-563).

Newly synthesized RNA was isolated using the Click-It Nascent RNA Capture Kit (Invitrogen, Cat No: C10365) following the manufacturer’s protocol. Peripheral blood mononuclear cells (PBMCs) or monocyte-derived macrophages (MDMs) obtained from heterozygous individuals were stimulated with lipopolysaccharide (LPS) for 3 hours in presence of 0.2 mM 5-ethynyl uridine (EU) (Jao and Salic, 2008; Paulsen et al., 2013). After the pulse, the culture medium was replaced with fresh growth medium devoid of EU. To assess RNA stability, actinomycin-D (5 µg/mL) was added, and samples were collected at 0, 1, 2, and 4 h post-treatment. The EU RNA was subjected to a click reaction that adds a biotin handle which was then captured by streptavidin beads. The captured RNA was used for cDNA synthesis (Superscript Vilo kit, Cat No: 11754250), PCR amplification, and allelic quantification.”

Comment 3: Also, they next state that the assay was carried out using cells treated with actinomycin D (line 186). Doesn't actinomycin D block transcription? The original study by Jia et al 2008 in PNAS reported that low concentration of ActD (100 nM) blocked RNA pol I and higher concentration (2 uM) blocked RNA pol II. This or the study on which the InVitrogen kit is based should be cited. The concentration of actinomycin D used to treat the cells should be given. They report that the T allele transcript was more abundant than the C allele transcript in nascent RNA. Why doesn't that argue for a transcriptional mechanism rather than an RNA-stability mechanism? This result should be discussed in the Discussion.

In our study, we used a concentration of 5 µg/mL (3.98 µM), which as noted by the reviewer can effectively inhibit RNA polymerase II (Pl II) activity. We have updated our manuscript to include details and cited the original work of (Jao and Salic, 2008; Paulsen et al., 2013), which thoroughly investigate the effect of various concentrations of ActD on RNA polymerase I and II (Line no 557). A discussion of the RNA stability mechanism is provided in the Result section (Lines 196-198).

Comment 4: In their bioinformatics analysis of the allele-specific CCL2 mRNAs, they reported that the analysis obtained a score of 1e (line 214). What does that mean? Is it significant?

We acknowledge that the notation “a score of 1e” was unclear and thank the reviewer for pointing it out. We have clarified its significance in the revised manuscript. The following text has been included in the result section (Line no 223)

“The score of 1e was obtained using RBP-Var, a bioinformatics tool that scores variants involved in posttranscriptional interaction and regulation (Mao et al., 2016). Here, the annotation system rates the functional confidence of variants from category 1 to 6. While Category 1 is the most significant category and includes variants that are known to be expression quantitative trait loci (eQTLs), likely affecting RBP binding site, RNA secondary structure and expression, category 6 is assigned to minimal possibility to affect RBP binding. Additionally, subcategories provide further annotation ranging from the most informational variants (a) to the least informational variant (e). Reported 1e denotes that the variant has a motif for RBP binding. Although the employed scoring system is hierarchical from 1a to 1e, with decreasing confidence in the variant’s function. However, all the variants in category 1 are considered potentially functional to some degree.”

Comment 5: In Figure 3A, why is the rare SNP rs181021073 shown? This SNP does not comeup anywhere else in the paper. For clarity, it should be removed from Figure 3A.

We thank the reviewer for pointing out the error in Figure 3A and apologize for the oversight. We agree that the SNP rs1810210732 is not mentioned anywhere in the manuscript and its inclusion in Figure 3A may have caused confusion. We have removed this SNP from the revised figure.

Comment 6: For the RNA EMSA results presented in Fig. 4C with recombinant ELAVL1 (HuR), there is clearly a loss of unbound T allele probe with increasing concentrations of the recombinant protein (without a concomitant increase in shifted complex). This suggests that the T allele probe is degraded or loses its fluorescent tag in the presence of recombinant HuR, whereas the C allele probe does not. The quantitation of the shifted complex presented in Fig. 4D as a percentage of bound and unbound probe is therefore artificially elevated for the T allele compared to the C allele. In fact, there seems to be little difference between the shifted complexes with the T and C allele probes. The authors should explain this difference in free probe levels.

We appreciate the constructive critique of the reviewer regarding the RNA EMSA results in Fig. 4C. To address this, we repeated the experiments to analyze the differential binding of rs13900T/C allele bearing probes with increasing concentration of the recombinant HuR. No degradation/ loss of fluorescence tag for T allele was noted in presence of recombinant HuR in three independent experiments (Author response image 1). This indicates that both the probes with C or T allele show comparable stability and are not affected by increasing concentration of recombinant HuR. The apparent reduction in the unbound T allele probe in Figure 4C may be due to saturation at higher HuR concentration rather than degradation.

Author response image 1.

Differential binding and stability of oligoribonucleotide probes containing rs13900C or T alleles with recombinant HuR. (A) REMSA with labeled oligoribonucleotides containing either rs13900C or rs13900T and recombinant HuR at indicated concentrations. (B&C) Representative quantitative densitometric analysis of HuR binding to the oligoribonucleotides bearing rs13900 T or C. The signal in the bound fractions were normalized with the free probe. The figure represents data from three independent experiments (mean ± SEM).

Comment 7: In the Methods section, concentrations and source of reagents should be given. For example, what was the bacterial origin of LPS and concentration? What concentration of actinomycin D? What was the source? Was it provided with the nascent RNA kit? In describing the riboprobes used for REMSA, please underline the allele in the sequences (lines 549 and 550).

Thank you for your detailed feedback and suggestions regarding the Materials and Methods Section. We regret the oversight in providing detailed information on reagent concentrations and sources in the method section. We have now rectified this omission and have provided the necessary details and a summary of material/reagents used is presented as a supplementary table (Supplementary Table 4) to enable others to replicate our experiments accurately. Regarding the description of riboprobes for RNA Electrophoretic Mobility Shift Assay, we underlined and bold the allele in the sequences as suggested (Lines 603-604).

Comment 8: For polysome profiling on line 603, please provide a protocol for the differentiation of primary macrophages from monocytes (please cite an original protocol, not a prior paper that does not give a detailed protocol).

We agree with the reviewer’s comment and have included the following text for primary macrophage differentiation from monocytes in the method section cited the original protocol (Line 668).

“Human monocytes were isolated from fresh blood as described earlier (Gavrilin et al., 2009) with slight modification. Briefly, peripheral blood mononuclear cells were isolated by density gradient centrifugation using Histopaque, followed by immunomagnetic negative selection using EasySep Human Monocyte isolation kit. A high purity level for CD14+ cells was consistently achieved (≥90%) through this procedure, as confirmed by flowcytometry. The purified monocytes were immediately used for macrophage differentiation by treating them with 50 ng/mL M-CSF (PeproTech) for 72 h and flow cytometric measurement of surface markers CD64+,

CD206+, CD44 was used to confirm the differentiation”. This data is now shown in the new Supplementary Figure S6.

Comment 9: In the legend of Figure 2, please replace "5 ug of actinomycin D" with the actual concentration used.

We appreciate your attention to detail and thank you for pointing out the error in the legend of Figure 2. We regret the oversight and have made the suggested change (Line 739).

Comment 10: In the Discussion, the authors cite the study of CCL2 mRNA stabilization by HuR in mice by Sasaki et al (lines 407-9). Is regulation of CCL2 mRNA by HuR in the mouse relevant to human studies?

How conserved is the 3'UTR of mouse and human CCL2? Is the rs13900 variant located in a conserved region? How many putative HuR sites are found in the 3'UTR of human and mouse CCL2 3'UTR? Does HuR dimerize (see Pabis et al 2019, NAR)? This information could be added to the Discussion.

Thank you for your valuable comment. We appreciate your suggestion to include information on the dimerization of HuR in our discussion. While reporting the overall structure and domain arrangement of HuR, Pabis et al. (2019) deciphered dimerization involving Trp261 in RRM3 as key requirement for functional activity of HuR in vitro. This finding provides additional context for understanding HuR’s role in regulating CCL2 expression. We have added the following few lines in the discussion (Lines 421-428) acknowledging HuR’s ability to dimerize and cite the relevant references.

“HuR consists of three RNA recognition motifs (RRMs) that are highly conserved and canonical in nature (Ripin et al., 2019). In absence of RNA the three RRMs are flexibly linked but upon RNA binding they transition to a more compact arrangement. Mutational analysis revealed that HuR function is inseparably linked to RRM3 dimerization and RNA binding. Dimerization enables recognition of tandem AREs by dimeric HuR (Pabis et al., 2019) and explains how this protein family can regulate numerous targets found in pre-mRNAs, mature mRNAs, miRNAs and long noncoding RNAs.”

We aligned the CCL2 3’UTR from five different mammalian species and found that the region flanking rs13900/ HuR binding site is relatively conserved (Author response image 2). Based on PAR-CLIP datasets there are four HuR binding regions in human CCL2 3’ UTR (Lebedeva et al., 2011). However, the region overlapping rs13900 seems to be predominantly involved in the CCL2 regulation (Fan et al., 2011). This information has been included in the discussion.

Author response image 2.

Cross-species alignment of the CCL2 3’UTR region flanking the rs13900 using homologous regions from 5 different mammals. (Hu, Human; CH, Chimps; MO, Mouse; RA, Rat; DO, Dog, rs13900 is shown within the brackets Y, pyrimidine)

Reviewer #2 (Recommendations For The Authors):

Comment 1: The supplemental figures need appropriate figure legends.

We regret the oversight and thank the reviewer for bringing it to our attention. We have now included the figure legend for the supplemental figures in the revised manuscript.

Comment 2: The data on LPS-induced CCL2 expression in PBMCs should be represented as a scatter plot with statistical significance to enhance clarity and interpretability.

We thank the reviewer for this constructive suggestion. In the revised Figure 2A the induction of CCL2 expression by LPS in PBMCs obtained from 6 volunteers is represented as a scatter plot. We have also included individual data points in the updated figure and statistical significance to improve clarity and interpretability.

Comment 3: The stability of CCL2 mRNA in control cells needs comparison with treated cells for context. The stability of a housekeeping gene (such as GAPDH or ACTB) should always be included as a control in actinomycin D experiments. Clarify the differential stability of rs13900C vs. rs13900T alleles.

We used 18S to normalize data for the mRNA stability studies, as it is abundant and has been recommended for such studies, as it is relatively unaltered when compared to other housekeeping genes following Act D treatment in well-controlled studies (Barta et al., 2023). We also compared Ct values between the Act D-treated samples and the Act D-untreated samples in this study and found them to be comparable (Author response image 3).

Author response image 3.

Ct values of 18s rRNA in ACT-D and control samples in Fig 2.

Comment 4: In the main text and the methods, the authors state that nascent RNA was obtained in the presence of actinomycin D and EU. However, actinomycin D blocks the transcription of nascent RNAs, therefore the findings in Figure 2C do not reflect nascent RNA

Please see our response to Reviewer 1 Comment 2. We would like to emphasize that to assess the differential role of the rs13900 in nascent RNA decay we integrated nascent RNA labeling and transcriptional inhibition. Briefly, PBMC from a heterozygous individual were either unstimulated or stimulated with LPS and pulsed with 5-ethynyl uridine (0.2 mM) for 3 h and the media was replaced with EU free growth medium. RNA was obtained at 0,1, 2 and 4 h following actinomycin-D treatment (5 µg/mL) to assess the stability of nascent RNA.

Comment 5: Figure 4A is not clearly described or labeled. What are lanes 2 and 6?

Figure 4 has now been updated to clearly describe all the lanes. Lanes 2 and 6 represent the mobility shift seen following the incubation by whole cell extracts and oligonucleotide bearing rs13900C and rs13900T probes respectively.

Comment 6: Figure 4C and Figure 4D: the charts in Figure 4D do not seem to reflect the changes in Figure 4C. How was the mean variant calculated? How do the authors explain the different quantities in unbound/free RNA in rs13900C compared to rs13900T?

We appreciate the constructive critique of the reviewer regarding the RNA EMSA results in Fig. 4C. To address this, we repeated the experiments to analyze the differential binding of rs13900T/C probes with increasing concentration of the recombinant HuR. No degradation/ loss of fluorescence tag in presence of HuR was noted in case of T allele (Author response image 1). This indicates that both the C and T allele probes exhibit comparable stability and are not affected by increasing the concentration of recombinant HuR. The apparent reduction in the unbound T allele probe in Figure 4C may be due to saturation due to higher HuR concentration rather than degradation. Also please note under limiting HuR concentration (50µM) there is more binding of purified HuR by the T bearing oligoribonucleotide (compare lanes 2 & 6 in Author response image 1).

Comment 7: Figure 5A does not look like an IP. The authors should show the heavy and light chains and clarify why there is co-precipitation of beta-actin with IgG and HuR. Also, they should include input samples. Figure 5B: given that in a traditional RIP the mRNA is not cross-linked and fragmented, any region of CCL2 mRNA would be amplified, not just the 3'UTR. In other words, Figure 5B can be valuable to show the enrichment of CCL2 mRNA in general, but not the enrichment of a specific region.

We understand the reviewer’s concern on Figure 5A and 5B. Due to sample limitations we are unable to confirm these results using heavy and light chains antibodies. However, it is important to note that co-precipitation of β-actin with IgG and HuR can be due to its non-specific binding with protein G. In a recent study non-specific precipitation by protein G or A was reported for proteins such as p53, p65 and β-actin (Zeng et al., 2022). We are including a figure provided by MBL Life Sciences as the quality check document for their RIP Assay Kit (RN 1001) that was used in our study. It is evident from Author response image 4 that even pre-clearing the lysate may not remove the ubiquitously expressed proteins such as β-actin or GAPDH and they will persist as contaminants in pull-down samples. Hence the presence of β-actin in the IgG and HuR IP fractions may be due to non-specific interactions with the agarose beads.

Author response image 4.

MBL RIP-Assay Kit’s Quality Check. Quality check of immunoprecipitated endogenous PTBP1 expressed in Jurkat cells. Lane 1: Jurkat (WB positive cells), Lane 2: Jurkat + normal Rabbit IgG, Lane 3: Jurkat+ anti-PTBP1.

We agree with the reviewer’s comments that traditional RIP without cross-linking and fragmentation allows amplification of any region of CCL2 mRNA. However, the upregulation of CCL2 gene expression in α-HuR immunoprecipitated samples indirectly reflects the enrichment of CCL2 mRNA associated with HuR. Moreover, 3’-UTR targeting primers were used for amplification to examine HuR binding at this region. We believe this approach ensures that the above enrichment specifically reflects HuR association with the 3’-UTR rather than other parts of the transcript.

Comment 8: Construct Validation in Luciferase Assays (Figure 6): The authors need to confirm equal transfection amounts of constructs and show changes in luciferase mRNA levels. It would be better to use a dual luciferase construct for internal normalization.

We would like to thank the reviewer for his concern and comments related to the luciferase reporter assay. As mentioned in the Methods equal transfection amount (0.5 µg) were used in our study (Line 658). We chose to normalize the reporter activity using total protein concentration instead of using a dual-reporter system to avoid crosstalk with co-transfected control plasmids. This is now included in the Materials and Method section (Lines 662-664). The optimized design of the LightSwitch Assay system used in our study allows a single assay design when a highly efficient transfection system is used (as recommended by the manufacturer). We verified the presence of the correct insert in the CCL2 Light Switch 3’UTR reporter constructs (Author response image 5). We also sequenced the vector backbone of both constructs to rule out any inadvertently added mutations.

Author response image 5.

Schematic of the Lightswitch 3’UTR vector. (A) Vector information. The vector contains a multiple cloning site (MCS) upstream of the Renilla Luciferase gene (RenSP). Human 3’UTR CCL2 is cloned into MCS downstream of the reporter gene and it becomes a part of a hybrid transcript that contains the luciferase coding sequence used to the UTR sequence of CCL2. Constructs containing rs13900C or rs13900T allele were generated using site-specific mutagenesis on CCL2 LightSwitch 3’UTR reporter. The constructs were validated by Sanger sequencing. (B&C) Sequence chromatograph of the constructs containing CCL2-3’UTR insert showing rs13900C and rs13900T respectively. The result confirms the fidelity of the constructs used in the reporter assay.

Comment 9: Polysome Data Presentation: The authors should present the distribution of luciferase mRNA (rs13900T and rs13900C) in all fractions separately and include data on the translation of a control like ACTB or GAPDH.

Since our assessment of CCL2 allele-specific enrichment in the polysome fractions from MDMs of heterozygous donors did not yield a consistent pattern for differential loading (Supplementary Table3), we used a 3’UTR reporter-based assays that estimated the impact of rs13900 T and C alleles on overall translational output (translatability). The translatability was calculated as luciferase activity normalized by luciferase mRNA levels after adjusting for protein and 18S rRNA using a previously reported method (Zhang et al., 2017). As the measurement of relative allele enrichment in polysome fractions was not included in our invitro reporter assays, it is not possible to present the distribution of luciferase mRNA in various fractions separately. Author response image 6 shows the proportion of CCL2 mRNA in different fractions corresponding to cytosolic, monosome and polysome fractions obtained from MDM lysates from heterozygous donors along with 18S rRNA quantification.

Author response image 6.

Determination of rs13900C/T allelic enrichment in polysome fractions and its effect on polysome loading. Polysome profile obtained by sucrose gradient centrifugation of macrophages before and after stimulation with LPS (1 µg/mL) for 3 h. (A&B) The CCL2 mRNA shifts from monosome-associated fractions to heavier polysomes following LPS stimulation, indicating increased translation efficiency. (C&D) In contrast, the distribution of 18S shows no significant shift due to LPS treatment. (mean ± SEM, n=4). The percentage of mRNA loading on polysome was calculated using ΔCT method (mean ± SEM, n=4). (E&F) CCL2 AEI measurement in polysomes of macrophages from heterozygous donors (n=2). Genomic and cDNA were subjected to Sanger sequencing and the peak height of both the alleles were used to determine the relative abundance of each allele.

Comment 10: Please explain in detail how primary monocytes were transfected with siRNAs for more than 72 hours. Typically, primary monocytes are very hard to transfect, have a very limited lifespan in culture (around 48 hours), and show a high level of cell death upon transfection. If monocytes were differentiated from macrophages, explain in detail how it was done and provide supporting citations from the literature.

We agree with the challenges associated with transfecting primary monocytes, including their limited lifespan in culture and susceptibility to cell death following transfection and apologize for not elaborating the method section on lentiviral transduction of primary macrophages. To overcome these limitations, we utilized monocytes undergoing differentiation into macrophages rather than fully differentiated macrophages for our experiments. Cells were transfected by slightly modifying the method described by Plaisance-Bonstaff et.al 2019 (Plaisance-Bonstaff et al., 2019). Briefly, monocytes were purified from PBMCs obtained from homozygous donors for rs13900 C or rs13900T by negative selection. Upon purification cells were resuspended in 24 well plates at a seeding density of 0.5 x10⁶ cells per well and were further cultured in the medium supplemented with 50 ng/mL M-CSF (Fig S7 and Fig. S6). After 24 h, ready to use GFP-tagged pCMV6-HuR or CMV-null lentiviral particles (Amsbio, Cambridge, M.A) were transduced into 0.5 x10⁶ cells in presence of polybrene (60 µg/mL) at a MOI of 1. The cells were processed for HuR and CCL2 expression 72 h after transduction after stimulation with LPS for 3 h. This data is now shown in new Supplementary Figure S7.

Comment 11: The authors should prove the binding of HuR to the 3'UTR of CCL2 not only in vitro but also in cells. For this aim, a CLIP including RNA fragmentation followed by RT-PCR or sequencing would be more informative than a RIP. It would be helpful also to demonstrate the different binding to the 3'UTR variants (rs13900C vs. rs13900T).

We thank the reviewer for his valuable suggestion on validating binding of HuR to the 3’UTR in cells. It is important to highlight that several independent datasets including CLIP have already demonstrated that HuR binds to the 3’UTR of CCL2 including the region spanning the rs13900 locus. We have summarized the relevant studies in a tabular form (Supplementary Table-2). We are unable to confirm these results in new experiments due to sample limitation. The already existing data and experimental evidence provided in this manuscript strongly suggest that HuR binds within the 3’UTR. Also, a previously published study (Fan et al, 2011) showed that only the first 125 bp of the CCL2 3’UTR that flanks rs13900 showed strong binding to HuR but not the CCL2 coding region or other regions of 3’UTR. This further suggests that the HuR binding to the CCL2 is localized to the 3’UTR that flanks rs13900. Please note that the primers used for amplification of the RIP material were 3’-UTR specific.

Comment 12: To quantify nascent RNA, Figure 2C should be replaced by new experiments. To label nascent RNA, authors can perform a run on/run-off experiments only with EU, without actinomycin D. As aforementioned, ActD blocks the transcription of new RNA, therefore is not useful for studying nascent RNA.

We thank the reviewer for the suggestion and would like to emphasize that while measuring the rs13900C/T allelic ratio in nascent RNA, the experimental setup included evaluating the AEI both in presence and absence of the transcriptional inhibitor actinomycin D. The data presented in Figure 2C shows that the AEI in presence of actinomycin D is amplified in comparison to non-actinomycin D treatment. This provides definitive evidence to our hypothesis that rs13900T confers greater stability to the CCL2 message. We apologize for the oversight of not mentioning non-ACT D treatment in the methods. Necessary changes have been made to the revised manuscript (Lines 553-63).

Comment 13: The authors should also investigate the role of TIA1 as a potential RBP and explore the possibility that TIA1 may interact more with the C allele to suppress translation.

Based on the existing studies, we highlighted the importance of RNA-binding proteins such as TIA1 and U2AF56 that may interact with CCL2 transcript (Lines 408-09). However, exploring TIA1 binding and its functional consequences are beyond the scope of the current study. We thank the reviewer for this comment and this aspect will be pursued in future studies.

Comment 14: It would be informative if the authors included study limitations and potential clinical implications of these findings, particularly regarding therapeutic approaches targeting CCL2.

We would like to inform the reviewer that the submitted manuscript included the limitations of our study. They were discussed at appropriate places and were not included as a separate section. For instance, Line 398 emphasizes the need for in-depth studies for association of rs13900 and canonical CCL2 transcript. The need for additional studies regarding SNP-induced structural changes in RNA and its implication for RBP accessibility was highlighted at Lines 417-419. The inconclusive results of differential loading of polysomes and the need to conduct further research on the impact of rs13900 on CCL2 translatability in primary cells (Lines 457-459). We noted at Lines 484-485 about our further studies exploring the differential binding of HuR to the other regions of CCL2 3’UTR.

Multiple studies have indicated that functional interference of HuR as a novel therapeutic strategy, particularly in the context of cancer, inflammation, neurodegeneration, and autoimmune disorders. These approaches include inhibitors such as MS-444, KH-3, and CMLD-2 that disrupt the interaction between HuR and ARE elements or mRNAs of target genes involved in disease pathology (Chaudhary et al., 2023; Fattahi et al., 2022; Lang et al., 2017; Liu et al., 2020; Wang et al., 2019; Wei et al., 2024), offering a potential new avenue for disease treatment. Findings from our studies provide unique insights on regulation of CCL2 expression by both rs13900 and HuR. We strongly believe that the SNP rs13900 and HuR represent a new druggable target for M/M-mediated disorders such as inflammatory diseases, cancer, and cardiovascular diseases. The potential clinical implications have been discussed in the revised manuscript (Lines 487-494)

References

Barta, N., Ordog, N., Pantazi, V., Berzsenyi, I., Borsos, B.N., Majoros, H., Pahi, Z.G., Ujfaludi, Z., Pankotai, T., 2023. Identifying Suitable Reference Gene Candidates for Quantification of DNA Damage-Induced Cellular Responses in Human U2OS Cell Culture System. Biomolecules 13.

Chaudhary, S., Appadurai, M.I., Maurya, S.K., Nallasamy, P., Marimuthu, S., Shah, A., Atri, P., Ramakanth, C.V., Lele, S.M., Seshacharyulu, P., Ponnusamy, M.P., Nasser, M.W., Ganti, A.K., Batra, S.K., Lakshmanan, I., 2023. MUC16 promotes triple-negative breast cancer lung metastasis by modulating RNA-binding protein ELAVL1/HUR. Breast Cancer Res 25, 25.

Fan, J., Ishmael, F.T., Fang, X., Myers, A., Cheadle, C., Huang, S.K., Atasoy, U., Gorospe, M., Stellato, C., 2011. Chemokine transcripts as targets of the RNA-binding protein HuR in human airway epithelium. J Immunol 186, 2482-2494.

Fattahi, F., Ellis, J.S., Sylvester, M., Bahleda, K., Hietanen, S., Correa, L., Lugogo, N.L., Atasoy, U., 2022. HuR-Targeted Inhibition Impairs Th2 Proinflammatory Responses in Asthmatic CD4(+) T Cells. J Immunol 208, 38-48.

Hubal, M.J., Devaney, J.M., Hoffman, E.P., Zambraski, E.J., Gordish-Dressman, H., Kearns, A.K., Larkin, J.S., Adham, K., Patel, R.R., Clarkson, P.M., 2010. CCL2 and CCR2 polymorphisms are associated with markers of exercise-induced skeletal muscle damage. J Appl Physiol (1985) 108, 1651-1658.

Intemann, C.D., Thye, T., Forster, B., Owusu-Dabo, E., Gyapong, J., Horstmann, R.D., Meyer, C.G., 2011. MCP1 haplotypes associated with protection from pulmonary tuberculosis. BMC Genet 12, 34.

Jao, C.Y., Salic, A., 2008. Exploring RNA transcription and turnover in vivo by using click chemistry. Proc Natl Acad Sci U S A 105, 15779-15784.

Johnson, A.D., Zhang, Y., Papp, A.C., Pinsonneault, J.K., Lim, J.E., Saffen, D., Dai, Z., Wang, D., Sadee, W., 2008. Polymorphisms affecting gene transcription and mRNA processing in pharmacogenetic candidate genes: detection through allelic expression imbalance in human target tissues. Pharmacogenet Genomics 18, 781791.

Kasztelewicz, B., Czech-Kowalska, J., Lipka, B., Milewska-Bobula, B., Borszewska-Kornacka, M.K., Romanska, J., Dzierzanowska-Fangrat, K., 2017. Cytokine gene polymorphism associations with congenital cytomegalovirus infection and sensorineural hearing loss. Eur J Clin Microbiol Infect Dis 36, 1811-1818. Lang, M., Berry, D., Passecker, K., Mesteri, I., Bhuju, S., Ebner, F., Sedlyarov, V., Evstatiev, R., Dammann, K., Loy, A., Kuzyk, O., Kovarik, P., Khare, V., Beibel, M., Roma, G., Meisner-Kober, N., Gasche, C., 2017. HuR Small-Molecule Inhibitor Elicits Differential Effects in Adenomatosis Polyposis and Colorectal Carcinogenesis. Cancer Res 77, 2424-2438.

Lebedeva, S., Jens, M., Theil, K., Schwanhausser, B., Selbach, M., Landthaler, M., Rajewsky, N., 2011. Transcriptome-wide analysis of regulatory interactions of the RNA-binding protein HuR. Mol Cell 43, 340-352.

Liu, S., Huang, Z., Tang, A., Wu, X., Aube, J., Xu, L., Xing, C., Huang, Y., 2020. Inhibition of RNA-binding protein HuR reduces glomerulosclerosis in experimental nephritis. Clin Sci (Lond) 134, 1433-1448.

Mao, F., Xiao, L., Li, X., Liang, J., Teng, H., Cai, W., Sun, Z.S., 2016. RBP-Var: a database of functional variants involved in regulation mediated by RNA-binding proteins. Nucleic Acids Res 44, D154-163.

Pabis, M., Popowicz, G.M., Stehle, R., Fernandez-Ramos, D., Asami, S., Warner, L., Garcia-Maurino, S.M., Schlundt, A., Martinez-Chantar, M.L., Diaz-Moreno, I., Sattler, M., 2019. HuR biological function involves RRM3-mediated dimerization and RNA binding by all three RRMs. Nucleic Acids Res 47, 1011-1029.

Paulsen, M.T., Veloso, A., Prasad, J., Bedi, K., Ljungman, E.A., Tsan, Y.C., Chang, C.W., Tarrier, B., Washburn, J.G., Lyons, R., Robinson, D.R., Kumar-Sinha, C., Wilson, T.E., Ljungman, M., 2013. Coordinated regulation of synthesis and stability of RNA during the acute TNF-induced proinflammatory response. Proc Natl Acad Sci U S A 110, 2240-2245.

Pham, M.H., Bonello, G.B., Castiblanco, J., Le, T., Sigala, J., He, W., Mummidi, S., 2012. The rs1024611 regulatory region polymorphism is associated with CCL2 allelic expression imbalance. PLoS One 7, e49498.

Plaisance-Bonstaff, K., Faia, C., Wyczechowska, D., Jeansonne, D., Vittori, C., Peruzzi, F., 2019. Isolation, Transfection, and Culture of Primary Human Monocytes. J Vis Exp.

Ripin, N., Boudet, J., Duszczyk, M.M., Hinniger, A., Faller, M., Krepl, M., Gadi, A., Schneider, R.J., Sponer, J., Meisner-Kober, N.C., Allain, F.H., 2019. Molecular basis for AU-rich element recognition and dimerization by the HuR C-terminal RRM. Proc Natl Acad Sci U S A 116, 2935-2944.

Russo, J., Heck, A.M., Wilusz, J., Wilusz, C.J., 2017. Metabolic labeling and recovery of nascent RNA to accurately quantify mRNA stability. Methods 120, 39-48.

Wang, J., Hjelmeland, A.B., Nabors, L.B., King, P.H., 2019. Anti-cancer effects of the HuR inhibitor, MS-444, in malignant glioma cells. Cancer Biol Ther 20, 979-988.

Wei, L., Kim, S.H., Armaly, A.M., Aube, J., Xu, L., Wu, X., 2024. RNA-binding protein HuR inhibition induces multiple programmed cell death in breast and prostate cancer. Cell Commun Signal 22, 580.

Zeng, X., Zeng, W.H., Zhou, J., Liu, X.M., Huang, G., Zhu, H., Xiao, S., Zeng, Y., Cao, D., 2022. Removal of nonspecific binding proteins is required in co-immunoprecipitation with nuclear proteins. Biotechniques 73, 289-296.

Zhang, X., Chen, X., Liu, Q., Zhang, S., Hu, W., 2017. Translation repression via modulation of the cytoplasmic poly(A)-binding protein in the inflammatory response. Elife 6.

Author Response

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

Public Comments

Reviewer 1

(1) Despite the well-established role of Netrin-1 and UNC5C axon guidance during embryonic commissural axons, it remains unclear which cell type(s) express Netrin-1 or UNC5C in the dopaminergic axons and their targets. For instance, the data in Figure 1F-G and Figure 2 are quite confusing. Does Netrin-1 or UNC5C express in all cell types or only dopamine-positive neurons in these two mouse models? It will also be important to provide quantitative assessments of UNC5C expression in dopaminergic axons at different ages.

Netrin-1 is a secreted protein and in this manuscript we did not examine what cell types express Netrin-1. This question is not the focus of the study and we consider it irrelevant to the main issue we are addressing, which is where in the forebrain regions we examined Netrin-1+ cells are present. As per the reviewer’s request we include below images showing Netrin-1 protein and Netrin-1 mRNA expression in the forebrain. In Figure 1 below, we show a high magnification immunofluorescent image of a coronal forebrain section showing Netrin-1 protein expression.

Author response image 1.

This confocal microscope image shows immunofluorescent staining for Netrin-1 (green) localized around cell nuclei (stained by DAPI in blue). This image was taken from a coronal section of the lateral septum of an adult male mouse. Scale bar = 20µm

In Figures 2 and 3 below we show low and high magnification images from an RNAscope experiment confirming that cells in the forebrain regions examined express Netrin-1 mRNA.

Author response image 2.

This confocal microscope image of a coronal brain section of the medial prefrontal cortex of an adult male mouse shows Netrin-1 mRNA expression (green) and cell nuclei (DAPI, blue). Brain regions are as follows: Cg1: Anterior cingulate cortex 1, DP: dorsopeduncular cortex, fmi: forceps minor of the corpus callosum, IL: Infralimbic Cortex, PrL: Prelimbic Cortex

Author response image 3.

A higher resolution image from the same sample as in Figure 2 shows Netrin-1 mRNA (green) and cell nuclei (DAPI; blue). DP = dorsopeduncular cortex

Regarding UNC5c, this receptor homologue is expressed by dopamine neurons in the rodent ventral tegmental area (Daubaras et al., 2014; Manitt et al., 2010; Phillips et al., 2022). This does not preclude UNC5c expression in other cell types. UNC5c receptors are ubiquitously expressed in the brain throughout development, performing many different developmental functions (Kim and Ackerman, 2011; Murcia-Belmonte et al., 2019; Srivatsa et al., 2014). In this study we are interested in UNC5c expression by dopamine neurons, and particularly by their axons projecting to the nucleus accumbens. We therefore used immunofluorescent staining in the nucleus accumbens, showing UNC5 expression in TH+ axons. This work adds to the study by Manitt et al., 2010, which examined UNC5 expression in the VTA. Manitt et al. used Western blotting to demonstrate that UNC5 expression in VTA dopamine neurons increases during adolescence, as can be seen in the following figure:

References:

Daubaras M, Bo GD, Flores C. 2014. Target-dependent expression of the netrin-1 receptor, UNC5C, in projection neurons of the ventral tegmental area. Neuroscience 260:36–46. doi:10.1016/j.neuroscience.2013.12.007

Kim D, Ackerman SL. 2011. The UNC5C Netrin Receptor Regulates Dorsal Guidance of Mouse Hindbrain Axons. J Neurosci 31:2167–2179. doi:10.1523/jneurosci.5254-10.20110.2011

Manitt C, Labelle-Dumais C, Eng C, Grant A, Mimee A, Stroh T, Flores C. 2010. Peri-Pubertal Emergence of UNC-5 Homologue Expression by Dopamine Neurons in Rodents. PLoS ONE 5:e11463-14. doi:10.1371/journal.pone.0011463

Murcia-Belmonte V, Coca Y, Vegar C, Negueruela S, Romero C de J, Valiño AJ, Sala S, DaSilva R, Kania A, Borrell V, Martinez LM, Erskine L, Herrera E. 2019. A Retino-retinal Projection Guided by Unc5c Emerged in Species with Retinal Waves. Current Biology 29:1149-1160.e4. doi:10.1016/j.cub.2019.02.052

Phillips RA, Tuscher JJ, Black SL, Andraka E, Fitzgerald ND, Ianov L, Day JJ. 2022. An atlas of transcriptionally defined cell populations in the rat ventral tegmental area. Cell Reports 39:110616. doi:10.1016/j.celrep.2022.110616

Srivatsa S, Parthasarathy S, Britanova O, Bormuth I, Donahoo A-L, Ackerman SL, Richards LJ, Tarabykin V. 2014. Unc5C and DCC act downstream of Ctip2 and Satb2 and contribute to corpus callosum formation. Nat Commun 5:3708. doi:10.1038/ncomms4708

(2) Figure 1 used shRNA to knockdown Netrin-1 in the Septum and these mice were subjected to behavioral testing. These results, again, are not supported by any valid data that the knockdown approach actually worked in dopaminergic axons. It is also unclear whether knocking down Netrin-1 in the septum will re-route dopaminergic axons or lead to cell death in the dopaminergic neurons in the substantia nigra pars compacta?

First we want to clarify and emphasize, that our knockdown approach was not designed to knock down Netrin-1 in dopamine neurons or their axons. Our goal was to knock down Netrin-1 expression in cells expressing this guidance cue gene in the dorsal peduncular cortex.

We have previously established the efficacy of the shRNA Netrin-1 knockdown virus used in this experiment for reducing the expression of Netrin-1 (Cuesta et al., 2020). The shRNA reduces Netrin-1 levels in vitro and in vivo.

We agree that our experiments do not address the fate of the dopamine axons that are misrouted away from the medial prefrontal cortex. This research is ongoing, and we have now added a note regarding this to our manuscript.

Our current hypothesis, based on experiments being conducted as part of another line of research in the lab, is that these axons are rerouted to a different brain region which they then ectopically innervate. In these experiments we are finding that male mice exposed to tetrahydrocannabinol in adolescence show reduced dopamine innervation in the medial prefrontal cortex in adulthood but increased dopamine input in the orbitofrontal cortex. In addition, these mice show increased action impulsivity in the Go/No-Go task in adulthood (Capolicchio et al., Society for Neuroscience 2023 Abstracts)

References:

Capolicchio T., Hernandez, G., Dube, E., Estrada, K., Giroux, M., Flores, C. (2023) Divergent outcomes of delta 9 - tetrahydrocannabinol in adolescence on dopamine and cognitive development in male and female mice. Society for Neuroscience, Washington, DC, United States [abstract].

Cuesta S, Nouel D, Reynolds LM, Morgunova A, Torres-Berrío A, White A, Hernandez G, Cooper HM, Flores C. 2020. Dopamine Axon Targeting in the Nucleus Accumbens in Adolescence Requires Netrin-1. Frontiers Cell Dev Biology 8:487. doi:10.3389/fcell.2020.00487

(3) Another issue with Figure1J. It is unclear whether the viruses were injected into a WT mouse model or into a Cre-mouse model driven by a promoter specifically expresses in dorsal peduncular cortex? The authors should provide evidence that Netrin-1 mRNA and proteins are indeed significantly reduced. The authors should address the anatomic results of the area of virus diffusion to confirm the virus specifically infected the cells in dorsal peduncular cortex.

All the virus knockdown experiments were conducted in wild type mice, we added this information to Figure 1k.

The efficacy of the shRNA in knocking down Netrin-1 was demonstrated by Cuesta et al. (2020) both in vitro and in vivo, as we show in our response to the reviewer’s previous comment above.

We also now provide anatomical images demonstrating the localization of the injection and area of virus diffusion in the mouse forebrain. In Author response image 4 below the area of virus diffusion is visible as green fluorescent signal.

Author response image 4.

Fluorescent microscopy image of a mouse forebrain demonstrating the localization of the injection of a virus to knock down Netrin-1. The location of the virus is in green, while cell nuclei are in blue (DAPI). Abbreviations: DP: dorsopeduncular cortex IL: infralimbic cortex

References:

Cuesta S, Nouel D, Reynolds LM, Morgunova A, Torres-Berrío A, White A, Hernandez G, Cooper HM, Flores C. 2020. Dopamine Axon Targeting in the Nucleus Accumbens in Adolescence Requires Netrin-1. Frontiers Cell Dev Biology 8:487. doi:10.3389/fcell.2020.00487

(4) The authors need to provide information regarding the efficiency and duration of knocking down. For instance, in Figure 1K, the mice were tested after 53 days post injection, can the virus activity in the brain last for such a long time?

In our study we are interested in the role of Netrin-1 expression in the guidance of dopamine axons from the nucleus accumbens to the medial prefrontal cortex. The critical window for these axons leaving the nucleus accumbens and growing to the cortex is early adolescence (Reynolds et al., 2018b). This is why we injected the virus at the onset of adolescence, at postnatal day 21. As dopamine axons grow from the nucleus accumbens to the prefrontal cortex, they pass through the dorsal peduncular cortex. We disrupted Netrin-1 expression at this point along their route to determine whether it is the Netrin-1 present along their route that guides these axons to the prefrontal cortex. We hypothesized that the shRNA Netrin-1 virus would disrupt the growth of the dopamine axons, reducing the number of axons that reach the prefrontal cortex and therefore the number of axons that innervate this region in adulthood.

We conducted our behavioural tests during adulthood, after the critical window during which dopamine axon growth occurs, so as to observe the enduring behavioral consequences of this misrouting. This experimental approach is designed for the shRNa Netrin-1 virus to be expressed in cells in the dorsopeduncular cortex when the dopamine axons are growing, during adolescence.

References:

Capolicchio T., Hernandez, G., Dube, E., Estrada, K., Giroux, M., Flores, C. (2023) Divergent outcomes of delta 9 - tetrahydrocannabinol in adolescence on dopamine and cognitive development in male and female mice. Society for Neuroscience, Washington, DC, United States [abstract].

Reynolds LM, Yetnikoff L, Pokinko M, Wodzinski M, Epelbaum JG, Lambert LC, Cossette M-P, Arvanitogiannis A, Flores C. 2018b. Early Adolescence is a Critical Period for the Maturation of Inhibitory Behavior. Cerebral cortex 29:3676–3686. doi:10.1093/cercor/bhy247

(5) In Figure 1N-Q, silencing Netrin-1 results in less DA axons targeting to infralimbic cortex, but why the Netrin-1 knocking down mice revealed the improved behavior?

This is indeed an intriguing finding, and we have now added a mention of it to our manuscript. We have demonstrated that misrouting dopamine axons away from the medial prefrontal cortex during adolescence alters behaviour, but why this improves their action impulsivity ability is something currently unknown to us. One potential answer is that the dopamine axons are misrouted to a different brain region that is also involved in controlling impulsive behaviour, perhaps the dorsal striatum (Kim and Im, 2019) or the orbital prefrontal cortex (Jonker et al., 2015).

We would also like to note that we are finding that other manipulations that appear to reroute dopamine axons to unintended targets can lead to reduced action impulsivity as measured using the Go No Go task. As we mentioned above, current experiments in the lab, which are part of a different line of research, are showing that male mice exposed to tetrahydrocannabinol in adolescence show reduced dopamine innervation in the medial prefrontal cortex in adulthood, but increased dopamine input in the orbitofrontal cortex. In addition, these mice show increased action impulsivity in the Go/No-Go task in adulthood (Capolicchio et al., Society for Neuroscience 2023 Abstracts)

References

Capolicchio T., Hernandez, G., Dube, E., Estrada, K., Giroux, M., Flores, C. (2023) Divergent outcomes of delta 9 - tetrahydrocannabinol in adolescence on dopamine and cognitive development in male and female mice. Society for Neuroscience, Washington, DC, United States [abstract].

Jonker FA, Jonker C, Scheltens P, Scherder EJA. 2015. The role of the orbitofrontal cortex in cognition and behavior. Rev Neurosci 26:1–11. doi:10.1515/revneuro2014-0043 Kim B, Im H. 2019. The role of the dorsal striatum in choice impulsivity. Ann N York Acad Sci 1451:92–111. doi:10.1111/nyas.13961

(6) What is the effect of knocking down UNC5C on dopamine axons guidance to the cortex?

We have found that mice that are heterozygous for a nonsense Unc5c mutation, and as a result have reduced levels of UNC5c protein, show reduced amphetamine-induced locomotion and stereotypy (Auger et al., 2013). In the same manuscript we show that this effect only emerges during adolescence, in concert with the growth of dopamine axons to the prefrontal cortex. This is indirect but strong evidence that UNC5c receptors are necessary for correct adolescent dopamine axon development.

References

Auger ML, Schmidt ERE, Manitt C, Dal-Bo G, Pasterkamp RJ, Flores C. 2013. unc5c haploinsufficient phenotype: striking similarities with the dcc haploinsufficiency model. European Journal of Neuroscience 38:2853–2863. doi:10.1111/ejn.12270

(7) In Figures 2-4, the authors only showed the amount of DA axons and UNC5C in NAcc. However, it remains unclear whether these experiments also impact the projections of dopaminergic axons to other brain regions, critical for the behavioral phenotypes. What about other brain regions such as prefrontal cortex? Do the projection of DA axons and UNC5c level in cortex have similar pattern to those in NAcc?

UNC5c receptors are expressed throughout development and are involved in many developmental processes (Kim and Ackerman, 2011; Murcia-Belmonte et al., 2019; Srivatsa et al., 2014). We cannot say whether the pattern we observe here is unique to the nucleus accumbens, but it is certainly not universal throughout the brain.

The brain region we focus on in our manuscript, in addition to the nucleus accumbens, is the medial prefrontal cortex. Close and thorough examination of the prefrontal cortices of adult mice revealed practically no UNC5c expression by dopamine axons. However, we did observe very rare cases of dopamine axons expressing UNC5c. It is not clear whether these rare cases are present before or during adolescence.

Below is a representative set of images of this observation, which is now also included as Supplementary Figure 4:

Author response image 5.

Expression of UNC5c protein in the medial prefrontal cortex of an adult male mouse. Low (A) and high (B) magnification images demonstrate that there is little UNC5c expression in dopamine axons in the medial prefrontal cortex. Here we identify dopamine axons by immunofluorescent staining for tyrosine hydroxylase (TH, see our response to comment #9 regarding the specificity of the TH antibody for dopamine axons in the prefrontal cortex). This figure is also included as Supplementary Figure 4 in the manuscript. Abbreviations: fmi: forceps minor of the corpus callosum, mPFC: medial prefrontal cortex.

References:

Kim D, Ackerman SL. 2011. The UNC5C Netrin Receptor Regulates Dorsal Guidance of Mouse Hindbrain Axons. J Neurosci 31:2167–2179. doi:10.1523/jneurosci.5254- 10.20110.2011

Murcia-Belmonte V, Coca Y, Vegar C, Negueruela S, Romero C de J, Valiño AJ, Sala S, DaSilva R, Kania A, Borrell V, Martinez LM, Erskine L, Herrera E. 2019. A Retino-retinal Projection Guided by Unc5c Emerged in Species with Retinal Waves. Current Biology 29:1149-1160.e4. doi:10.1016/j.cub.2019.02.052

Srivatsa S, Parthasarathy S, Britanova O, Bormuth I, Donahoo A-L, Ackerman SL, Richards LJ, Tarabykin V. 2014. Unc5C and DCC act downstream of Ctip2 and Satb2 and contribute to corpus callosum formation. Nat Commun 5:3708. doi:10.1038/ncomms4708

(8) Can overexpression of UNC5c or Netrin-1 in male winter hamsters mimic the observations in summer hamsters? Or overexpression of UNC5c in female summer hamsters to mimic the winter hamster? This would be helpful to confirm the causal role of UNC5C in guiding DA axons during adolescence.

This is an excellent question. We are very interested in both increasing and decreasing UNC5c expression in hamster dopamine axons to see if we can directly manipulate summer hamsters into winter hamsters and vice versa. We are currently exploring virus-based approaches to design these experiments and are excited for results in this area.

(9) The entire study relied on using tyrosine hydroxylase (TH) as a marker for dopaminergic axons. However, the expression of TH (either by IHC or IF) can be influenced by other environmental factors, that could alter the expression of TH at the cellular level.

This is an excellent point that we now carefully address in our methods by adding the following:

In this study we pay great attention to the morphology and localization of the fibres from which we quantify varicosities to avoid counting any fibres stained with TH antibodies that are not dopamine fibres. The fibres that we examine and that are labelled by the TH antibody show features indistinguishable from the classic features of cortical dopamine axons in rodents (Berger et al., 1974; 1983; Van Eden et al., 1987; Manitt et al., 2011), namely they are thin fibres with irregularly-spaced varicosities, are densely packed in the nucleus accumbens, sparsely present only in the deep layers of the prefrontal cortex, and are not regularly oriented in relation to the pial surface. This is in contrast to rodent norepinephrine fibres, which are smooth or beaded in appearance, relatively thick with regularly spaced varicosities, increase in density towards the shallow cortical layers, and are in large part oriented either parallel or perpendicular to the pial surface (Berger et al., 1974; Levitt and Moore, 1979; Berger et al., 1983; Miner et al., 2003). Furthermore, previous studies in rodents have noted that only norepinephrine cell bodies are detectable using immunofluorescence for TH, not norepinephrine processes (Pickel et al., 1975; Verney et al., 1982; Miner et al., 2003), and we did not observe any norepinephrine-like fibres.

Furthermore, we are not aware of any other processes in the forebrain that are known to be immunopositive for TH under any environmental conditions.

To reduce confusion, we have replaced the abbreviation for dopamine – DA – with TH in the relevant panels in Figures 1, 2, 3, and 4 to clarify exactly what is represented in these images. As can be seen in these images, fluorescent green labelling is present only in axons, which is to be expected of dopamine labelling in these forebrain regions.

References:

Berger B, Tassin JP, Blanc G, Moyne MA, Thierry AM (1974) Histochemical confirmation for dopaminergic innervation of the rat cerebral cortex after destruction of the noradrenergic ascending pathways. Brain Res 81:332–337.

Berger B, Verney C, Gay M, Vigny A (1983) Immunocytochemical Characterization of the Dopaminergic and Noradrenergic Innervation of the Rat Neocortex During Early Ontogeny. In: Proceedings of the 9th Meeting of the International Neurobiology Society, pp 263–267 Progress in Brain Research. Elsevier.

Levitt P, Moore RY (1979) Development of the noradrenergic innervation of neocortex. Brain Res 162:243–259.

Manitt C, Mimee A, Eng C, Pokinko M, Stroh T, Cooper HM, Kolb B, Flores C (2011) The Netrin Receptor DCC Is Required in the Pubertal Organization of Mesocortical Dopamine Circuitry. J Neurosci 31:8381–8394.

Miner LH, Schroeter S, Blakely RD, Sesack SR (2003) Ultrastructural localization of the norepinephrine transporter in superficial and deep layers of the rat prelimbic prefrontal cortex and its spatial relationship to probable dopamine terminals. J Comp Neurol 466:478–494.

Pickel VM, Joh TH, Field PM, Becker CG, Reis DJ (1975) Cellular localization of tyrosine hydroxylase by immunohistochemistry. J Histochem Cytochem 23:1–12.

Van Eden CG, Hoorneman EM, Buijs RM, Matthijssen MA, Geffard M, Uylings HBM (1987) Immunocytochemical localization of dopamine in the prefrontal cortex of the rat at the light and electron microscopical level. Neurosci 22:849–862.

Verney C, Berger B, Adrien J, Vigny A, Gay M (1982) Development of the dopaminergic innervation of the rat cerebral cortex. A light microscopic immunocytochemical study using anti-tyrosine hydroxylase antibodies. Dev Brain Res 5:41–52.

(10) Are Netrin-1/UNC5C the only signal guiding dopamine axon during adolescence? Are there other neuronal circuits involved in this process?

Our intention for this study was to examine the role of Netrin-1 and its receptor UNC5C specifically, but we do not suggest that they are the only molecules to play a role. The process of guiding growing dopamine axons during adolescence is likely complex and we expect other guidance mechanisms to also be involved. From our previous work we know that the Netrin-1 receptor DCC is critical in this process (Hoops and Flores, 2017; Reynolds et al., 2023). Several other molecules have been identified in Netrin-1/DCC signaling processes that control corpus callosum development and there is every possibility that the same or similar molecules may be important in guiding dopamine axons (Schlienger et al., 2023).

References:

Hoops D, Flores C. 2017. Making Dopamine Connections in Adolescence. Trends in Neurosciences 1–11. doi:10.1016/j.tins.2017.09.004

Reynolds LM, Hernandez G, MacGowan D, Popescu C, Nouel D, Cuesta S, Burke S, Savell KE, Zhao J, Restrepo-Lozano JM, Giroux M, Israel S, Orsini T, He S, Wodzinski M, Avramescu RG, Pokinko M, Epelbaum JG, Niu Z, Pantoja-Urbán AH, Trudeau L-É, Kolb B, Day JJ, Flores C. 2023. Amphetamine disrupts dopamine axon growth in adolescence by a sex-specific mechanism in mice. Nat Commun 14:4035. doi:10.1038/s41467-023-39665-1

Schlienger S, Yam PT, Balekoglu N, Ducuing H, Michaud J-F, Makihara S, Kramer DK, Chen B, Fasano A, Berardelli A, Hamdan FF, Rouleau GA, Srour M, Charron F. 2023. Genetics of mirror movements identifies a multifunctional complex required for Netrin-1 guidance and lateralization of motor control. Sci Adv 9:eadd5501. doi:10.1126/sciadv.add5501

(11) Finally, despite the authors' claim that the dopaminergic axon project is sensitive to the duration of daylight in the hamster, they never provided definitive evidence to support this hypothesis.

By “definitive evidence” we think that the reviewer is requesting a single statistical model including measures from both the summer and winter groups. Such a model would provide a probability estimate of whether dopamine axon growth is sensitive to daylight duration. Therefore, we ran these models, one for male hamsters and one for female hamsters.

In both sexes we find a significant effect of daylength on dopamine innervation, interacting with age. Male age by daylength interaction: F = 6.383, p = 0.00242. Female age by daylength interaction: F = 21.872, p = 1.97 x 10-9. The full statistical analysis is available as a supplement to this letter (Response_Letter_Stats_Details.docx).

Reviewer 3

(1) Fig 1 A and B don't appear to be the same section level.

The reviewer is correct that Fig 1B is anterior to Fig 1A. We have changed Figure 1A to match the section level of Figure 1B.

(2) Fig 1C. It is not clear that these axons are crossing from the shell of the NAC.

We have added a dashed line to Figure 1C to highlight the boundary of the nucleus accumbens, which hopefully emphasizes that there are fibres crossing the boundary. We also include here an enlarged image of this panel:

Author response image 6.

An enlarged image of Figure1c in the manuscript. The nucleus accumbens (left of the dotted line) is densely packed with TH+ axons (in green). Some of these TH+ axons can be observed extending from the nucleus accumbens medially towards a region containing dorsally oriented TH+ fibres (white arrows).

(3) Fig 1. Measuring width of the bundle is an odd way to measure DA axon numbers. First the width could be changing during adult for various reasons including change in brain size. Second, I wouldn't consider these axons in a traditional bundle. Third, could DA axon counts be provided, rather than these proxy measures.

With regards to potential changes in brain size, we agree that this could have potentially explained the increased width of the dopamine axon pathway. That is why it was important for us to use stereology to measure the density of dopamine axons within the pathway. If the width increased but no new axons grew along the pathway, we would have seen a decrease in axon density from adolescence to adulthood. Instead, our results show that the density of axons remained constant.

We agree with the reviewer that the dopamine axons do not form a traditional “bundle”. Therefore, throughout the manuscript we now avoid using the term bundle.

Although we cannot count every single axon, an accurate estimate of this number can be obtained using stereology, an unbiassed method for efficiently quantifying large, irregularly distributed objects. We used stereology to count TH+ axons in an unbiased subset of the total area occupied by these axons. Unbiased stereology is the gold-standard technique for estimating populations of anatomical objects, such as axons, that are so numerous that it would be impractical or impossible to measure every single one. Here and elsewhere we generally provide results as densities and areas of occupancy (Reynolds et al., 2022). To avoid confusion, we now clarify that we are counting the width of the area that dopamine axons occupy (rather than the dopamine axon “bundle”).

References:

Reynolds LM, Pantoja-Urbán AH, MacGowan D, Manitt C, Nouel D, Flores C. 2022. Dopaminergic System Function and Dysfunction: Experimental Approaches. Neuromethods 31–63. doi:10.1007/978-1-0716-2799-0_2

(4) TH in the cortex could also be of noradrenergic origin. This needs to be ruled out to score DA axons

This is the same comment as Reviewer 1 #9. Please see our response below, which we have also added to our methods:

In this study we pay great attention to the morphology and localization of the fibres from which we quantify varicosities to avoid counting any fibres stained with TH antibodies that are not dopamine fibres. The fibres that we examine and that are labelled by the TH antibody show features indistinguishable from the classic features of cortical dopamine axons in rodents (Berger et al., 1974; 1983; Van Eden et al., 1987; Manitt et al., 2011), namely they are thin fibres with irregularly-spaced varicosities, are densely packed in the nucleus accumbens, sparsely present only in the deep layers of the prefrontal cortex, and are not regularly oriented in relation to the pial surface. This is in contrast to rodent norepinephrine fibres, which are smooth or beaded in appearance, relatively thick with regularly spaced varicosities, increase in density towards the shallow cortical layers, and are in large part oriented either parallel or perpendicular to the pial surface (Berger et al., 1974; Levitt and Moore, 1979; Berger et al., 1983; Miner et al., 2003). Furthermore, previous studies in rodents have noted that only norepinephrine cell bodies are detectable using immunofluorescence for TH, not norepinephrine processes (Pickel et al., 1975; Verney et al., 1982; Miner et al., 2003), and we did not observe any norepinephrine-like fibres.

References:

Berger B, Tassin JP, Blanc G, Moyne MA, Thierry AM (1974) Histochemical confirmation for dopaminergic innervation of the rat cerebral cortex after destruction of the noradrenergic ascending pathways. Brain Res 81:332–337.

Berger B, Verney C, Gay M, Vigny A (1983) Immunocytochemical Characterization of the Dopaminergic and Noradrenergic Innervation of the Rat Neocortex During Early Ontogeny. In: Proceedings of the 9th Meeting of the International Neurobiology Society, pp 263–267 Progress in Brain Research. Elsevier.

Levitt P, Moore RY (1979) Development of the noradrenergic innervation of neocortex. Brain Res 162:243–259.

Manitt C, Mimee A, Eng C, Pokinko M, Stroh T, Cooper HM, Kolb B, Flores C (2011) The Netrin Receptor DCC Is Required in the Pubertal Organization of Mesocortical Dopamine Circuitry. J Neurosci 31:8381–8394.

Miner LH, Schroeter S, Blakely RD, Sesack SR (2003) Ultrastructural localization of the norepinephrine transporter in superficial and deep layers of the rat prelimbic prefrontal cortex and its spatial relationship to probable dopamine terminals. J Comp Neurol 466:478–494.

Pickel VM, Joh TH, Field PM, Becker CG, Reis DJ (1975) Cellular localization of tyrosine hydroxylase by immunohistochemistry. J Histochem Cytochem 23:1–12.

Van Eden CG, Hoorneman EM, Buijs RM, Matthijssen MA, Geffard M, Uylings HBM (1987) Immunocytochemical localization of dopamine in the prefrontal cortex of the rat at the light and electron microscopical level. Neurosci 22:849–862.

Verney C, Berger B, Adrien J, Vigny A, Gay M (1982) Development of the dopaminergic innervation of the rat cerebral cortex. A light microscopic immunocytochemical study using anti-tyrosine hydroxylase antibodies. Dev Brain Res 5:41–52.

(5) Netrin staining should be provided with NeuN + DAPI; its not clear these are all cell bodies. An in situ of Netrin would help as well.

A similar comment was raised by Reviewer 1 in point #1. Please see below the immunofluorescent and RNA scope images showing expression of Netrin-1 protein and mRNA in the forebrain.

Author response image 7.

This confocal microscope image shows immunofluorescent staining for Netrin-1 (green) localized around cell nuclei (stained by DAPI in blue). This image was taken from a coronal section of the lateral septum of an adult male mouse. Scale bar = 20µm

Author response image 8.

This confocal microscope image of a coronal brain section of the medial prefrontal cortex of an adult male mouse shows Netrin-1 mRNA expression (green) and cell nuclei (DAPI, blue). RNAscope was used to generate this image. Brain regions are as follows: Cg1: Anterior cingulate cortex 1, DP: dorsopeduncular cortex, IL: Infralimbic Cortex, PrL: Prelimbic Cortex, fmi: forceps minor of the corpus callosum

Author response image 9.

A higher resolution image from the same sample as in Figure 2 shows Netrin-1 mRNA (green) and cell nuclei (DAPI; blue). DP = dorsopeduncular cortex

(6) The Netrin knockdown needs validation. How strong was the knockdown etc?

This comment was also raised by Reviewer 1 #1.

We have previously established the efficacy of the shRNA Netrin-1 knockdown virus used in this experiment for reducing the expression of Netrin-1 (Cuesta et al., 2020). The shRNA reduces Netrin-1 levels in vitro and in vivo.

References:

Cuesta S, Nouel D, Reynolds LM, Morgunova A, Torres-Berrío A, White A, Hernandez G, Cooper HM, Flores C. 2020. Dopamine Axon Targeting in the Nucleus Accumbens in Adolescence Requires Netrin-1. Frontiers Cell Dev Biology 8:487. doi:10.3389/fcell.2020.00487

(7) If the conclusion that knocking down Netrin in cortex decreases DA innervation of the IL, how can that be reconciled with Netrin-Unc repulsion.

This is an intriguing question and one that we are in the planning stages of addressing with new experiments.

Although we do not have a mechanistic answered for how a repulsive receptor helps guide these axons, we would like to note that previous indirect evidence from a study by our group also suggests that reducing UNC5c signaling in dopamine axons in adolescence increases dopamine innervation to the prefrontal cortex (Auger et al, 2013).

References

Auger ML, Schmidt ERE, Manitt C, Dal-Bo G, Pasterkamp RJ, Flores C. 2013. unc5c haploinsufficient phenotype: striking similarities with the dcc haploinsufficiency model. European Journal of Neuroscience 38:2853–2863. doi:10.1111/ejn.12270

(8) The behavioral phenotype in Fig 1 is interesting, but its not clear if its related to DA axons/signaling. IN general, no evidence in this paper is provided for the role of DA in the adolescent behaviors described.

We agree with the reviewer that the behaviours we describe in adult mice are complex and are likely to involve several neurotransmitter systems. However, there is ample evidence for the role of dopamine signaling in cognitive control behaviours (Bari and Robbins, 2013; Eagle et al., 2008; Ott et al., 2023) and our published work has shown that alterations in the growth of dopamine axons to the prefrontal cortex leads to changes in impulse control as measured via the Go/No-Go task in adulthood (Reynolds et al., 2023, 2018a; Vassilev et al., 2021).

The other adolescent behaviour we examined was risk-like taking behaviour in male and female hamsters (Figures 4 and 5), as a means of characterizing maturation in this behavior over time. We decided not to use the Go/No-Go task because as far as we know, this has never been employed in Siberian Hamsters and it will be difficult to implement. Instead, we chose the light/dark box paradigm, which requires no training and is ideal for charting behavioural changes over short time periods. Indeed, risk-like taking behavior in rodents and in humans changes from adolescence to adulthood paralleling changes in prefrontal cortex development, including the gradual input of dopamine axons to this region.

References:

Bari A, Robbins TW. 2013. Inhibition and impulsivity: Behavioral and neural basis of response control. Progress in neurobiology 108:44–79. doi:10.1016/j.pneurobio.2013.06.005

Eagle DM, Bari A, Robbins TW. 2008. The neuropsychopharmacology of action inhibition: cross-species translation of the stop-signal and go/no-go tasks. Psychopharmacology 199:439–456. doi:10.1007/s00213-008-1127-6

Ott T, Stein AM, Nieder A. 2023. Dopamine receptor activation regulates reward expectancy signals during cognitive control in primate prefrontal neurons. Nat Commun 14:7537. doi:10.1038/s41467-023-43271-6

Reynolds LM, Hernandez G, MacGowan D, Popescu C, Nouel D, Cuesta S, Burke S, Savell KE, Zhao J, Restrepo-Lozano JM, Giroux M, Israel S, Orsini T, He S, Wodzinski M, Avramescu RG, Pokinko M, Epelbaum JG, Niu Z, Pantoja-Urbán AH, Trudeau L-É, Kolb B, Day JJ, Flores C. 2023. Amphetamine disrupts dopamine axon growth in adolescence by a sex-specific mechanism in mice. Nat Commun 14:4035. doi:10.1038/s41467-023-39665-1

Reynolds LM, Pokinko M, Torres-Berrío A, Cuesta S, Lambert LC, Pellitero EDC, Wodzinski M, Manitt C, Krimpenfort P, Kolb B, Flores C. 2018a. DCC Receptors Drive Prefrontal Cortex Maturation by Determining Dopamine Axon Targeting in Adolescence. Biological psychiatry 83:181–192. doi:10.1016/j.biopsych.2017.06.009

Vassilev P, Pantoja-Urban AH, Giroux M, Nouel D, Hernandez G, Orsini T, Flores C. 2021. Unique effects of social defeat stress in adolescent male mice on the Netrin-1/DCC pathway, prefrontal cortex dopamine and cognition (Social stress in adolescent vs. adult male mice). Eneuro ENEURO.0045-21.2021. doi:10.1523/eneuro.0045-21.2021

(9) Fig2 - boxes should be drawn on the NAc diagram to indicate sampled regions. Some quantification of Unc5c would be useful. Also, some validation of the Unc5c antibody would be nice.

The images presented were taken medial to the anterior commissure and we have edited Figure 2 to show this. However, we did not notice any intra-accumbens variation, including between the core and the shell. Therefore, the images are representative of what was observed throughout the entire nucleus accumbens.

To quantify UNC5c in the accumbens we conducted a Western blot experiment in male mice at different ages. A one-way ANOVA analyzing band intensity (relative to the 15-day-old average band intensity) as the response variable and age as the predictor variable showed a significant effect of age (F=5.615, p=0.01). Posthoc analysis revealed that 15-day-old mice have less UNC5c in the nucleus accumbens compared to 21- and 35-day-old mice.

Author response image 10.

The graph depicts the results of a Western blot experiment of UNC5c protein levels in the nucleus accumbens of male mice at postnatal days 15, 21 or 35 and reveals a significant increase in protein levels at the onset adolescence.

Our methods for this Western blot were as follows: Samples were prepared as previously (Torres-Berrío et al., 2017). Briefly, mice were sacrificed by live decapitation and brains were flash frozen in heptane on dry ice for 10 seconds. Frozen brains were mounted in a cryomicrotome and two 500um sections were collected for the nucleus accumbens, corresponding to plates 14 and 18 of the Paxinos mouse brain atlas. Two tissue core samples were collected per section, one for each side of the brain, using a 15-gauge tissue corer (Fine surgical tools Cat no. NC9128328) and ejected in a microtube on dry ice. The tissue samples were homogenized in 100ul of standard radioimmunoprecipitation assay buffer using a handheld electric tissue homogenizer. The samples were clarified by centrifugation at 4C at a speed of 15000g for 30 minutes. Protein concentration was quantified using a bicinchoninic acid assay kit (Pierce BCA protein assay kit, Cat no.PI23225) and denatured with standard Laemmli buffer for 5 minutes at 70C. 10ug of protein per sample was loaded and run by SDS-PAGE gel electrophoresis in a Mini-PROTEAN system (Bio-Rad) on an 8% acrylamide gel by stacking for 30 minutes at 60V and resolving for 1.5 hours at 130V. The proteins were transferred to a nitrocellulose membrane for 1 hour at 100V in standard transfer buffer on ice. The membranes were blocked using 5% bovine serum albumin dissolved in tris-buffered saline with Tween 20 and probed with primary (UNC5c, Abcam Cat. no ab302924) and HRP-conjugated secondary antibodies for 1 hour. a-tubulin was probed and used as loading control. The probed membranes were resolved using SuperSignal West Pico PLUS chemiluminescent substrate (ThermoFisher Cat no.34579) in a ChemiDoc MP Imaging system (Bio-Rad). Band intensity was quantified using the ChemiDoc software and all ages were normalized to the P15 age group average.

Validation of the UNC5c antibody was performed in the lab of Dr. Liu, from whom it was kindly provided. Briefly, in the validation study the authors showed that the anti-UNC5C antibody can detect endogenous UNC5C expression and the level of UNC5C is dramatically reduced after UNC5C knockdown. The antibody can also detect the tagged-UNC5C protein in several cell lines, which was confirmed by a tag antibody (Purohit et al., 2012; Shao et al., 2017).

References:

Purohit AA, Li W, Qu C, Dwyer T, Shao Q, Guan K-L, Liu G. 2012. Down Syndrome Cell Adhesion Molecule (DSCAM) Associates with Uncoordinated-5C (UNC5C) in Netrin-1mediated Growth Cone Collapse. The Journal of biological chemistry 287:27126–27138. doi:10.1074/jbc.m112.340174

Shao Q, Yang T, Huang H, Alarmanazi F, Liu G. 2017. Uncoupling of UNC5C with Polymerized TUBB3 in Microtubules Mediates Netrin-1 Repulsion. J Neurosci 37:5620–5633. doi:10.1523/jneurosci.2617-16.2017

(10) "In adolescence, dopamine neurons begin to express the repulsive Netrin-1 receptor UNC5C, and reduction in UNC5C expression appears to cause growth of mesolimbic dopamine axons to the prefrontal cortex".....This is confusing. Figure 2 shows a developmental increase in UNc5c not a decrease. So when is the "reduction in Unc5c expression" occurring?

We apologize for the mistake in this sentence. We have corrected the relevant passage in our manuscript as follows:

In adolescence, dopamine neurons begin to express the repulsive Netrin-1 receptor UNC5C, particularly when mesolimbic and mesocortical dopamine projections segregate in the nucleus accumbens (Manitt et al., 2010; Reynolds et al., 2018a). In contrast, dopamine axons in the prefrontal cortex do not express UNC5c except in very rare cases (Supplementary Figure 4). In adult male mice with Unc5c haploinsufficiency, there appears to be ectopic growth of mesolimbic dopamine axons to the prefrontal cortex (Auger et al., 2013). This miswiring is associated with alterations in prefrontal cortex-dependent behaviours (Auger et al., 2013).

References:

Auger ML, Schmidt ERE, Manitt C, Dal-Bo G, Pasterkamp RJ, Flores C. 2013. unc5c haploinsufficient phenotype: striking similarities with the dcc haploinsufficiency model. European Journal of Neuroscience 38:2853–2863. doi:10.1111/ejn.12270

Manitt C, Labelle-Dumais C, Eng C, Grant A, Mimee A, Stroh T, Flores C. 2010. Peri-Pubertal Emergence of UNC-5 Homologue Expression by Dopamine Neurons in Rodents. PLoS ONE 5:e11463-14. doi:10.1371/journal.pone.0011463

Reynolds LM, Pokinko M, Torres-Berrío A, Cuesta S, Lambert LC, Pellitero EDC, Wodzinski M, Manitt C, Krimpenfort P, Kolb B, Flores C. 2018a. DCC Receptors Drive Prefrontal Cortex Maturation by Determining Dopamine Axon Targeting in Adolescence. Biological psychiatry 83:181–192. doi:10.1016/j.biopsych.2017.06.009

(11) In Fig 3, a statistical comparison should be made between summer male and winter male, to justify the conclusions that the winter males have delayed DA innervation.

This analysis was also suggested by Reviewer 1, #11. Here is our response:

We analyzed the summer and winter data together in ANOVAs separately for males and females. In both sexes we find a significant effect of daylength on dopamine innervation, interacting with age. Male age by daylength interaction: F = 6.383, p = 0.00242. Female age by daylength interaction: F = 21.872, p = 1.97 x 10-9. The full statistical analysis is available as a supplement to this letter (Response_Letter_Stats_Details.docx).

(12) Should axon length also be measured here (Fig 3)? It is not clear why the authors have switched to varicosity density. Also, a box should be drawn in the NAC cartoon to indicate the region that was sampled.

It is untenable to quantify axon length in the prefrontal cortex as we cannot distinguish independent axons. Rather, they are “tangled”; they twist and turn in a multitude of directions as they make contact with various dendrites. Furthermore, they branch extensively. It would therefore be impossible to accurately quantify the number of axons. Using unbiased stereology to quantify varicosities is a valid, well-characterized and straightforward alternative (Reynolds et al., 2022).

References:

Reynolds LM, Pantoja-Urbán AH, MacGowan D, Manitt C, Nouel D, Flores C. 2022. Dopaminergic System Function and Dysfunction: Experimental Approaches. Neuromethods 31–63. doi:10.1007/978-1-0716-2799-0_2

(13) In Fig 3, Unc5c should be quantified to bolster the interesting finding that Unc5c expression dynamics are different between summer and winter hamsters. Unc5c mRNA experiments would also be important to see if similar changes are observed at the transcript level.

We agree that it would be very interesting to see how UNC5c mRNA and protein levels change over time in summer and winter hamsters, both in males, as the reviewer suggests here, and in females. We are working on conducting these experiments in hamsters as part of a broader expansion of our research in this area. These experiments will require a lengthy amount of time and at this point we feel that they are beyond the scope of this manuscript.

(14) Fig 4. The peak in exploratory behavior in winter females is counterintuitive and needs to be better discussed. IN general, the light dark behavior seems quite variable.

This is indeed a very interesting finding, which we have expanded upon in our manuscript as follows:

When raised under a winter-mimicking daylength, hamsters of either sex show a protracted peak in risk taking. In males, it is delayed beyond 80 days old, but the delay is substantially less in females. This is a counterintuitive finding considering that dopamine development in winter females appears to be accelerated. Our interpretation of this finding is that the timing of the risk-taking peak in females may reflect a balance between different adolescent developmental processes. The fact that dopamine axon growth is accelerated does not imply that all adolescent maturational processes are accelerated. Some may be delayed, for example those that induce axon pruning in the cortex. The timing of the risk-taking peak in winter female hamsters may therefore reflect the amalgamation of developmental processes that are advanced with those that are delayed – producing a behavioural effect that is timed somewhere in the middle. Disentangling the effects of different developmental processes on behaviour will require further experiments in hamsters, including the direct manipulation of dopamine activity in the nucleus accumbens and prefrontal cortex.

Full Reference List

Auger ML, Schmidt ERE, Manitt C, Dal-Bo G, Pasterkamp RJ, Flores C. 2013. unc5c haploinsufficient phenotype: striking similarities with the dcc haploinsufficiency model. European Journal of Neuroscience 38:2853–2863. doi:10.1111/ejn.12270

Bari A, Robbins TW. 2013. Inhibition and impulsivity: Behavioral and neural basis of response control. Progress in neurobiology 108:44–79. doi:10.1016/j.pneurobio.2013.06.005

Cuesta S, Nouel D, Reynolds LM, Morgunova A, Torres-Berrío A, White A, Hernandez G, Cooper HM, Flores C. 2020. Dopamine Axon Targeting in the Nucleus Accumbens in Adolescence Requires Netrin-1. Frontiers Cell Dev Biology 8:487. doi:10.3389/fcell.2020.00487

Daubaras M, Bo GD, Flores C. 2014. Target-dependent expression of the netrin-1 receptor, UNC5C, in projection neurons of the ventral tegmental area. Neuroscience 260:36–46. doi:10.1016/j.neuroscience.2013.12.007

Eagle DM, Bari A, Robbins TW. 2008. The neuropsychopharmacology of action inhibition: crossspecies translation of the stop-signal and go/no-go tasks. Psychopharmacology 199:439– 456. doi:10.1007/s00213-008-1127-6

Hoops D, Flores C. 2017. Making Dopamine Connections in Adolescence. Trends in Neurosciences 1–11. doi:10.1016/j.tins.2017.09.004

Jonker FA, Jonker C, Scheltens P, Scherder EJA. 2015. The role of the orbitofrontal cortex in cognition and behavior. Rev Neurosci 26:1–11. doi:10.1515/revneuro-2014-0043

Kim B, Im H. 2019. The role of the dorsal striatum in choice impulsivity. Ann N York Acad Sci 1451:92–111. doi:10.1111/nyas.13961

Kim D, Ackerman SL. 2011. The UNC5C Netrin Receptor Regulates Dorsal Guidance of Mouse Hindbrain Axons. J Neurosci 31:2167–2179. doi:10.1523/jneurosci.5254-10.2011

Manitt C, Labelle-Dumais C, Eng C, Grant A, Mimee A, Stroh T, Flores C. 2010. Peri-Pubertal Emergence of UNC-5 Homologue Expression by Dopamine Neurons in Rodents. PLoS ONE 5:e11463-14. doi:10.1371/journal.pone.0011463

Murcia-Belmonte V, Coca Y, Vegar C, Negueruela S, Romero C de J, Valiño AJ, Sala S, DaSilva R, Kania A, Borrell V, Martinez LM, Erskine L, Herrera E. 2019. A Retino-retinal Projection Guided by Unc5c Emerged in Species with Retinal Waves. Current Biology 29:1149-1160.e4. doi:10.1016/j.cub.2019.02.052

Ott T, Stein AM, Nieder A. 2023. Dopamine receptor activation regulates reward expectancy signals during cognitive control in primate prefrontal neurons. Nat Commun 14:7537. doi:10.1038/s41467-023-43271-6

Phillips RA, Tuscher JJ, Black SL, Andraka E, Fitzgerald ND, Ianov L, Day JJ. 2022. An atlas of transcriptionally defined cell populations in the rat ventral tegmental area. Cell Reports 39:110616. doi:10.1016/j.celrep.2022.110616

Purohit AA, Li W, Qu C, Dwyer T, Shao Q, Guan K-L, Liu G. 2012. Down Syndrome Cell Adhesion Molecule (DSCAM) Associates with Uncoordinated-5C (UNC5C) in Netrin-1-mediated Growth Cone Collapse. The Journal of biological chemistry 287:27126–27138. doi:10.1074/jbc.m112.340174

Reynolds LM, Hernandez G, MacGowan D, Popescu C, Nouel D, Cuesta S, Burke S, Savell KE, Zhao J, Restrepo-Lozano JM, Giroux M, Israel S, Orsini T, He S, Wodzinski M, Avramescu RG, Pokinko M, Epelbaum JG, Niu Z, Pantoja-Urbán AH, Trudeau L-É, Kolb B, Day JJ, Flores C. 2023. Amphetamine disrupts dopamine axon growth in adolescence by a sex-specific mechanism in mice. Nat Commun 14:4035. doi:10.1038/s41467-023-39665-1

Reynolds LM, Pantoja-Urbán AH, MacGowan D, Manitt C, Nouel D, Flores C. 2022. Dopaminergic System Function and Dysfunction: Experimental Approaches. Neuromethods 31–63. doi:10.1007/978-1-0716-2799-0_2

Reynolds LM, Pokinko M, Torres-Berrío A, Cuesta S, Lambert LC, Pellitero EDC, Wodzinski M, Manitt C, Krimpenfort P, Kolb B, Flores C. 2018a. DCC Receptors Drive Prefrontal Cortex Maturation by Determining Dopamine Axon Targeting in Adolescence. Biological psychiatry 83:181–192. doi:10.1016/j.biopsych.2017.06.009

Reynolds LM, Yetnikoff L, Pokinko M, Wodzinski M, Epelbaum JG, Lambert LC, Cossette M-P, Arvanitogiannis A, Flores C. 2018b. Early Adolescence is a Critical Period for the Maturation of Inhibitory Behavior. Cerebral cortex 29:3676–3686. doi:10.1093/cercor/bhy247

Schlienger S, Yam PT, Balekoglu N, Ducuing H, Michaud J-F, Makihara S, Kramer DK, Chen B, Fasano A, Berardelli A, Hamdan FF, Rouleau GA, Srour M, Charron F. 2023. Genetics of mirror movements identifies a multifunctional complex required for Netrin-1 guidance and lateralization of motor control. Sci Adv 9:eadd5501. doi:10.1126/sciadv.add5501

Shao Q, Yang T, Huang H, Alarmanazi F, Liu G. 2017. Uncoupling of UNC5C with Polymerized TUBB3 in Microtubules Mediates Netrin-1 Repulsion. J Neurosci 37:5620–5633. doi:10.1523/jneurosci.2617-16.2017

Srivatsa S, Parthasarathy S, Britanova O, Bormuth I, Donahoo A-L, Ackerman SL, Richards LJ, Tarabykin V. 2014. Unc5C and DCC act downstream of Ctip2 and Satb2 and contribute to corpus callosum formation. Nat Commun 5:3708. doi:10.1038/ncomms4708

Torres-Berrío A, Lopez JP, Bagot RC, Nouel D, Dal-Bo G, Cuesta S, Zhu L, Manitt C, Eng C, Cooper HM, Storch K-F, Turecki G, Nestler EJ, Flores C. 2017. DCC Confers Susceptibility to Depression-like Behaviors in Humans and Mice and Is Regulated by miR-218. Biological psychiatry 81:306–315. doi:10.1016/j.biopsych.2016.08.017

Vassilev P, Pantoja-Urban AH, Giroux M, Nouel D, Hernandez G, Orsini T, Flores C. 2021. Unique effects of social defeat stress in adolescent male mice on the Netrin-1/DCC pathway, prefrontal cortex dopamine and cognition (Social stress in adolescent vs. adult male mice). Eneuro ENEURO.0045-21.2021. doi:10.1523/eneuro.0045-21.2021

Private Comments

Reviewer #1

(12) The language should be improved. Some expression is confusing (line178-179). Also some spelling errors (eg. Figure 1M).

We have removed the word “Already” to make the sentence in lines 178-179 clearer, however we cannot find a spelling error in Figure 1M or its caption. We have further edited the manuscript for clarity and flow.

Reviewer #2

(1) The authors claim to have revealed how the 'timing of adolescence is programmed in the brain'. While their findings certainly shed light on molecular, circuit and behavioral processes that are unique to adolescence, their claim may be an overstatement. I suggest they refine this statement to discuss more specifically the processes they observed in the brain and animal behavior, rather than adolescence itself.

We agree with the reviewer and have revised the manuscript to specify that we are referring to the timing of specific developmental processes that occur in the adolescent brain, not adolescence overall.

(2) Along the same lines, the authors should also include a more substantiative discussion of how they selected their ages for investigation (for both mice and hamsters), For mice, their definition of adolescence (P21) is earlier than some (e.g. Spear L.P., Neurosci. and Beh. Reviews, 2000).

There are certainly differences of opinion between researchers as to the precise definition of adolescence and the period it encompasses. Spear, 2000, provides one excellent discussion of the challenges related to identifying adolescence across species. This work gives specific ages only for rats, not mice (as we use here), and characterizes post-natal days 28-42 as being the conservative age range of “peak” adolescence (page 419, paragraph 1). Immediately thereafter the review states that the full adolescent period is longer than this, and it could encompass post-natal days 20-55 (page 419, paragraph 2).

We have added the following statement to our methods:

There is no universally accepted way to define the precise onset of adolescence. Therefore, there is no clear-cut boundary to define adolescent onset in rodents (Spear, 2000). Puberty can be more sharply defined, and puberty and adolescence overlap in time, but the terms are not interchangeable. Puberty is the onset of sexual maturation, while adolescence is a more diffuse period marked by the gradual transition from a juvenile state to independence. We, and others, suggest that adolescence in rodents spans from weaning (postnatal day 21) until adulthood, which we take to start on postnatal day 60 (Reynolds and Flores, 2021). We refer to “early adolescence” as the first two weeks postweaning (postnatal days 21-34). These ranges encompass discrete DA developmental periods (Kalsbeek et al., 1988; Manitt et al., 2011; Reynolds et al., 2018a), vulnerability to drug effects on DA circuitry (Hammerslag and Gulley, 2014; Reynolds et al., 2018a), and distinct behavioral characteristics (Adriani and Laviola, 2004; Makinodan et al., 2012; Schneider, 2013; Wheeler et al., 2013).

References:

Adriani W, Laviola G. 2004. Windows of vulnerability to psychopathology and therapeutic strategy in the adolescent rodent model. Behav Pharmacol 15:341–352. doi:10.1097/00008877-200409000-00005

Hammerslag LR, Gulley JM. 2014. Age and sex differences in reward behavior in adolescent and adult rats. Dev Psychobiol 56:611–621. doi:10.1002/dev.21127

Hoops D, Flores C. 2017. Making Dopamine Connections in Adolescence. Trends in Neurosciences 1–11. doi:10.1016/j.tins.2017.09.004

Kalsbeek A, Voorn P, Buijs RM, Pool CW, Uylings HBM. 1988. Development of the Dopaminergic Innervation in the Prefrontal Cortex of the Rat. The Journal of Comparative Neurology 269:58–72. doi:10.1002/cne.902690105

Makinodan M, Rosen KM, Ito S, Corfas G. 2012. A critical period for social experiencedependent oligodendrocyte maturation and myelination. Science 337:1357–1360. doi:10.1126/science.1220845

Manitt C, Mimee A, Eng C, Pokinko M, Stroh T, Cooper HM, Kolb B, Flores C. 2011. The Netrin Receptor DCC Is Required in the Pubertal Organization of Mesocortical Dopamine Circuitry. J Neurosci 31:8381–8394. doi:10.1523/jneurosci.0606-11.2011

Reynolds LM, Flores C. 2021. Mesocorticolimbic Dopamine Pathways Across Adolescence: Diversity in Development. Front Neural Circuit 15:735625. doi:10.3389/fncir.2021.735625

Reynolds LM, Yetnikoff L, Pokinko M, Wodzinski M, Epelbaum JG, Lambert LC, Cossette MP, Arvanitogiannis A, Flores C. 2018. Early Adolescence is a Critical Period for the Maturation of Inhibitory Behavior. Cerebral cortex 29:3676–3686. doi:10.1093/cercor/bhy247

Schneider M. 2013. Adolescence as a vulnerable period to alter rodent behavior. Cell and tissue research 354:99–106. Doi:10.1007/s00441-013-1581-2

Spear LP. 2000. Neurobehavioral Changes in Adolescence. Current directions in psychological science 9:111–114. doi:10.1111/1467-8721.00072

Wheeler AL, Lerch JP, Chakravarty MM, Friedel M, Sled JG, Fletcher PJ, Josselyn SA, Frankland PW. 2013. Adolescent Cocaine Exposure Causes Enduring Macroscale Changes in Mouse Brain Structure. J Neurosci 33:1797–1803. doi:10.1523/jneurosci.3830-12.2013

(3) Figure 1 - the conclusions hinge on the Netrin-1 staining, as shown in panel G, but the cells are difficult to see. It would be helpful to provide clearer, more zoomed images so readers can better assess the staining. Since Netrin-1 expression reduces dramatically after P4 and they had to use antigen retrieval to see signal, it would be helpful to show some images from additional brain regions and ages to see if expression levels follow predicted patterns. For instance, based on the allen brain atlas, it seems that around P21, there should be high levels of Netrin-1 in the cerebellum, but low levels in the cortex. These would be nice controls to demonstrate the specificity and sensitivity of the antibody in older tissue.

We do not study the cerebellum and have never stained this region; doing so now would require generating additional tissue and we’re not sure it would add enough to the information provided to be worthwhile. Note that we have stained the forebrain for Netrin-1 previously, providing broad staining of many brain regions (Manitt et al., 2011)

References:

Manitt C, Mimee A, Eng C, Pokinko M, Stroh T, Cooper HM, Kolb B, Flores C. 2011. The Netrin Receptor DCC Is Required in the Pubertal Organization of Mesocortical Dopamine Circuitry. J Neurosci 31:8381–8394. doi:10.1523/jneurosci.0606-11.2011

(4) Figure 3 - Because mice tend to avoid brightly-lit spaces, the light/dark box is more commonly used as a measure of anxiety-like behavior than purely exploratory behavior (including in the paper they cited). It is important to address this possibility in their discussion of their findings. To bolster their conclusions about the coincidence of circuit and behavioral changes in adolescent hamsters, it would be useful to add an additional measure of exploratory behaviors (e.g. hole board).

Regarding the light/dark box test, this is an excellent point. We prefer the term “risk taking” to “anxiety-like” and now use the former term in our manuscript. Furthermore, our interest in the behaviour is purely to chart the development of adolescent behaviour across our treatment groups, not to study a particular emotional state. Regardless of the specific emotion or emotions governing the light/dark box behaviour, it is an ideal test for charting adolescent shifts in behaviour as it is well-characterized in this respect, as we discuss in our manuscript.

(5) Supplementary Figure 4,5 The authors defined puberty onset using uterine and testes weights in hamsters. While the weights appear to be different for summer and winter hamsters, there were no statistical comparison. Please add statistical analyses to bolster claims about puberty start times. Also, as many studies use vaginal opening to define puberty onset, it would be helpful to discuss how these measurements typically align and cite relevant literature that described use of uterine weights. Also, Supplementary Figures 4 and 5 were mis-cited as Supp. Fig. 2 in the text (e.g. line 317 and others).

These are great suggestions. We have added statistical analyses to Supplementary Figures 5 and 6 and provided Vaginal Opening data as Supplementary Figure 7. The statistical analyses confirm that all three characters are delayed in winter hamsters compared to summer hamsters.

We have also added the following references to the manuscript:

Darrow JM, Davis FC, Elliott JA, Stetson MH, Turek FW, Menaker M. 1980. Influence of Photoperiod on Reproductive Development in the Golden Hamster. Biol Reprod 22:443–450. doi:10.1095/biolreprod22.3.443

Ebling FJP. 1994. Photoperiodic Differences during Development in the Dwarf Hamsters Phodopus sungorus and Phodopus campbelli. Gen Comp Endocrinol 95:475–482. doi:10.1006/gcen.1994.1147

Timonin ME, Place NJ, Wanderi E, Wynne-Edwards KE. 2006. Phodopus campbelli detect reduced photoperiod during development but, unlike Phodopus sungorus, retain functional reproductive physiology. Reproduction 132:661–670. doi:10.1530/rep.1.00019

(6) The font in many figure panels is small and hard to read (e.g. 1A,D,E,H,I,L...). Please increase the size for legibility.

We have increased the font size of our figure text throughout the manuscript.

Reviewer #3

(15) Fig 1 C,D. Clarify the units of the y axis

We have now fixed this.

Full Reference List

Adriani W, Laviola G. 2004. Windows of vulnerability to psychopathology and therapeutic strategy in the adolescent rodent model. Behav Pharmacol 15:341–352. doi:10.1097/00008877-200409000-00005

Hammerslag LR, Gulley JM. 2014. Age and sex differences in reward behavior in adolescent and adult rats. Dev Psychobiol 56:611–621. doi:10.1002/dev.21127

Hoops D, Flores C. 2017. Making Dopamine Connections in Adolescence. Trends in Neurosciences 1–11. doi:10.1016/j.tins.2017.09.004

Kalsbeek A, Voorn P, Buijs RM, Pool CW, Uylings HBM. 1988. Development of the Dopaminergic Innervation in the Prefrontal Cortex of the Rat. The Journal of Comparative Neurology 269:58–72. doi:10.1002/cne.902690105

Makinodan M, Rosen KM, Ito S, Corfas G. 2012. A critical period for social experiencedependent oligodendrocyte maturation and myelination. Science 337:1357–1360. doi:10.1126/science.1220845

Manitt C, Mimee A, Eng C, Pokinko M, Stroh T, Cooper HM, Kolb B, Flores C. 2011. The Netrin Receptor DCC Is Required in the Pubertal Organization of Mesocortical Dopamine Circuitry. J Neurosci 31:8381–8394. doi:10.1523/jneurosci.0606-11.2011

Reynolds LM, Flores C. 2021. Mesocorticolimbic Dopamine Pathways Across Adolescence: Diversity in Development. Front Neural Circuit 15:735625. doi:10.3389/fncir.2021.735625 Reynolds LM, Yetnikoff L, Pokinko M, Wodzinski M, Epelbaum JG, Lambert LC, Cossette M-P, Arvanitogiannis A, Flores C. 2018. Early Adolescence is a Critical Period for the Maturation of Inhibitory Behavior. Cerebral cortex 29:3676–3686. doi:10.1093/cercor/bhy247

Schneider M. 2013. Adolescence as a vulnerable period to alter rodent behavior. Cell and tissue research 354:99–106. doi:10.1007/s00441-013-1581-2

Spear LP. 2000. Neurobehavioral Changes in Adolescence. Current directions in psychological science 9:111–114. doi:10.1111/1467-8721.00072

Wheeler AL, Lerch JP, Chakravarty MM, Friedel M, Sled JG, Fletcher PJ, Josselyn SA, Frankland PW. 2013. Adolescent Cocaine Exposure Causes Enduring Macroscale Changes in Mouse Brain Structure. J Neurosci 33:1797–1803. doi:10.1523/jneurosci.3830-12.2013

Author response:

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

Public Reviews:  

Reviewer #1 (Public Review): 

Summary: 

In their manuscript entitled 'The domesticated transposon protein L1TD1 associates with its ancestor L1 ORF1p to promote LINE-1 retrotransposition', Kavaklıoğlu and colleagues delve into the role of L1TD1, an RNA binding protein (RBP) derived from a LINE1 transposon. L1TD1 proves crucial for maintaining pluripotency in embryonic stem cells and is linked to cancer progression in germ cell tumors, yet its precise molecular function remains elusive. Here, the authors uncover an intriguing interaction between L1TD1 and its ancestral LINE-1 retrotransposon. 

The authors delete the DNA methyltransferase DNMT1 in a haploid human cell line (HAP1), inducing widespread DNA hypo-methylation. This hypomethylation prompts abnormal expression of L1TD1. To scrutinize L1TD1's function in a DNMT1 knock-out setting, the authors create DNMT1/L1TD1 double knock-out cell lines (DKO). Curiously, while the loss of global DNA methylation doesn't impede proliferation, additional depletion of L1TD1 leads to DNA damage and apoptosis.  

To unravel the molecular mechanism underpinning L1TD1's protective role in the absence of DNA methylation, the authors dissect L1TD1 complexes in terms of protein and RNA composition. They unveil an association with the LINE-1 transposon protein L1-ORF1 and LINE-1 transcripts, among others.  

Surprisingly, the authors note fewer LINE-1 retro-transposition events in DKO cells than in DNMT1 KO alone.  

Strengths: 

The authors present compelling data suggesting the interplay of a transposon-derived human RNA binding protein with its ancestral transposable element. Their findings spur interesting questions for cancer types, where LINE1 and L1TD1 are aberrantly expressed.  

Weaknesses: 

Suggestions for refinement:  

The initial experiment, inducing global hypo-methylation by eliminating DNMT1 in HAP1 cells, is intriguing and warrants a more detailed description. How many genes experience misregulation or aberrant expression? What phenotypic changes occur in these cells? 

This is an excellent suggestion. We have gene expression data on WT versus DNMT1 KO HAP1 cells and have included them now as Suppl. Figure S1. The  transcriptome analysis of DNMT1 KO cells showed hundreds of deregulated genes upon DNMT1 ablation. As expected, the majority were up-regulated and gene ontology analysis revealed that among the strongest up-regulated genes were gene clusters with functions in “regulation of transcription from RNA polymerase II promoter” and “cell differentiation” and genes encoding proteins with KRAB domains. In addition, the de novo methyltransferases DNMT3A and DNMT3B were up-regulated in DNMT1 KO cells suggesting the set-up of compensatory mechanisms in these cells. 

Why did the authors focus on L1TD1? Providing some of this data would be helpful to understand the rationale behind the thorough analysis of L1TD1. 

We have previously discovered that conditional deletion of the maintenance DNA methyltransferase DNMT1 in the murine epidermis results not only in the up-regulation of mobile elements, such as IAPs but also the induced expression of L1TD1 ([1], Suppl. Table 1 and Author response image 1). Similary, L1TD1 expression was induced by treatment of primary human keratinocytes or squamous cell carcinoma cells with the DNMT inhibitor azadeoxycytidine (Author response images 2 and 3). These findings are in accordance with the observation  that inhibition of DNA methyltransferase activity by aza-deoxycytidine in human non-small cell lung cancer cells (NSCLCs) results in up-regulation of L1TD1 [2]. Our interest in L1TD1 was further fueled by reports on a potential function of L1TD1 as prognostic tumor marker. We have included this information in the last paragraph of the Introduction in the revised manuscript.

Author response image 1. RT-qPCR of L1TD1 expression in cultured murine control and Dnmt1 Δ/Δker keratinocytes. mRNA levels of L1td1 were analyzed in keratinocytes isolated at P5 from conditional Dnmt1 knockout mice [1]. Hprt expression was used for normalization of mRNA levels and wildtype control was set to 1. Data represent means ±s.d. with n=4. **P < 0.01 (paired t-test). 

Author response image 2. RT-qPCR analysis of L1TD1 expression in primary human keratinocytes. Cells were treated with 5-aza-2-deoxycidine for 24 hours or 48 hours, with PBS for 48 hours or were left untreated. 18S rRNA expression was used for normalization of mRNA levels and PBS control was set to 1. Data represent means ±s.d. with n=3. **P < 0.01 (paired t-test).

Author response image 3. Induced L1TD1 expression upon DNMT inhibition in squamous cell carcinoma cell lines SCC9 and SCCO12. Cells were treated with 5-aza-2-deoxycidine for 24 hours, 48 hours or 6 days. (A) Western blot analysis of L1TD1 protein levels using beta-actin as loading control. (B) Indirect immunofluorescence microscopy analysis of L1TD1 expression in SCC9 cells. Nuclear DNA was stained with DAPI. Scale bar: 10 µm. (C)  RT-qPCR analysis of L1TD1 expression in primary human keratinocytes. Cells were treated with 5-aza-2deoxycidine for 24 hours or 48 hours, with PBS for 48 hours or were left untreated. 18S rRNA expression was used for normalization of mRNA levels and PBS control was set to 1. Data represent means ±s.d. with n=3. *P < 0.05, **P < 0.01 (paired t-test).

The finding that L1TD1/DNMT1 DKO cells exhibit increased apoptosis and DNA damage but decreased L1 retro-transposition is unexpected. Considering the DNA damage associated with retro-transposition and the DNA damage and apoptosis observed in L1TD1/DNMT1 DKO cells, one would anticipate the opposite outcome. Could it be that the observation of fewer transposition-positive colonies stems from the demise of the most transposition-positive colonies? Further exploration of this phenomenon would be intriguing. 

This is an important point and we were aware of this potential problem. Therefore, we calibrated the retrotransposition assay by transfection with a blasticidin resistance gene vector to take into account potential differences in cell viability and blasticidin sensitivity. Thus, the observed reduction in L1 retrotransposition efficiency is not an indirect effect of reduced cell viability. We have added a corresponding clarification in the Results section on page 8, last paragraph. 

Based on previous studies with hESCs and germ cell tumors [3], it is likely that, in addition to its role in retrotransposition, L1TD1 has further functions in the regulation of cell proliferation and differentiation. L1TD1 might therefore attenuate the effect of DNMT1 loss in KO cells generating an intermediate phenotype (as pointed out by Reviewer 2) and simultaneous loss of both L1TD1 and DNMT1 results in more pronounced effects on cell viability. This is in agreement with the observation that a subset of L1TD1 associated transcripts encode proteins involved in the control of cell division and cell cycle. It is possible that subtle changes in the expression of these protein that were not detected in our mass spectrometry approach contribute to the antiproliferative effect of L1TD1 depletion as discussed in the Discussion section of the revised manuscript. 

Reviewer #2 (Public Review):           

In this study, Kavaklıoğlu et al. investigated and presented evidence for the role of domesticated transposon protein L1TD1 in enabling its ancestral relative, L1 ORF1p, to retrotranspose in HAP1 human tumor cells. The authors provided insight into the molecular function of L1TD1 and shed some clarifying light on previous studies that showed somewhat contradictory outcomes surrounding L1TD1 expression. Here, L1TD1 expression was correlated with L1 activation in a hypomethylation-dependent manner, due to DNMT1 deletion in the HAP1 cell line. The authors then identified L1TD1-associated RNAs using RIP-Seq, which displays a disconnect between transcript and protein abundance (via Tandem Mass Tag multiplex mass spectrometry analysis). The one exception was for L1TD1 itself, which is consistent with a model in which the RNA transcripts associated with L1TD1 are not directly regulated at the translation level. Instead, the authors found the L1TD1 protein associated with L1-RNPs, and this interaction is associated with increased L1 retrotransposition, at least in the contexts of HAP1 cells. Overall, these results support a model in which L1TD1 is restrained by DNA methylation, but in the absence of this repressive mark, L1TD1 is expressed and collaborates with L1 ORF1p (either directly or through interaction with L1 RNA, which remains unclear based on current results), leads to enhances L1 retrotransposition. These results establish the feasibility of this relationship existing in vivo in either development, disease, or both.   

Recommendations for the authors:

Reviewer #2 (Recommendations For The Authors):        

Major 

(1) The study only used one knockout (KO) cell line generated by CRISPR/Cas9. Considering the possibility of an off-target effect, I suggest the authors attempt one or both of these suggestions. 

A) Generate or acquire a similar DMNT1 deletion that uses distinct sgRNAs, so that the likelihood of off-targets is negligible. A few simple experiments such as qRT-PCR would be sufficient to suggest the same phenotype.  

B) Confirm the DNMT1 depletion also by siRNA/ASO KD to phenocopy the KO effect.  (2) In addition to the strategies to demonstrate reproducibility, a rescue experiment restoring DNMT1 to the KO or KD cells would be more convincing. (Partial rescue would suffice in this case, as exact endogenous expression levels may be hard to replicate). 

We have undertook several approaches to study the effect of DNMT1 loss or inactivation: As described above, we have generated a conditional KO mouse with ablation of DNMT1 in the epidermis. DNMT1-deficient keratinocytes isolated from these mice show a significant increase in L1TD1 expression.  In addition, treatment of primary human keratinocytes and two squamous cell carcinoma cell lines with the DNMT inhibitor aza-deoxycytidine led to upregulation of L1TD1 expression. Thus, the derepression of L1TD1 upon loss of DNMT1 expression or activity is not a clonal effect. Also, the spectrum of RNAs identified in RIP experiments as L1TD1-associated transcripts in HAP1 DNMT1 KO cells showed a strong overlap with the RNAs isolated by a related yet different method in human embryonic stem cells. When it comes to the effect of L1TD1 on L1-1 retrotranspostion, a recent study has reported a similar effect of L1TD1 upon overexpression in HeLa cells [4].  

All of these points together help to convince us that our findings with HAP1 DNMT KO are in agreement with results obtained in various other cell systems and are therefore not due to off-target effects. With that in mind, we would pursue the suggestion of Reviewer 1 to analyze the effects of DNA hypomethylation upon DNMT1 ablation.

(3) As stated in the introduction, L1TD1 and ORF1p share "sequence resemblance" (Martin 2006). Is the L1TD1 antibody specific or do we see L1 ORF1p if Fig 1C were uncropped?  (6) Is it possible the L1TD1 antibody binds L1 ORF1p? This could make Figure 2D somewhat difficult to interpret. Some validation of the specificity of the L1TD1 antibody would remove this concern (see minor concern below).  

This is a relevant question. We are convinced that the L1TD1 antibody does not crossreact with L1 ORF1p for the following reasons: Firstly, the antibody does not recognize L1 ORF1p (40 kDa) in the  uncropped Western blot for Figure 1C (Author response image 4A). Secondly, the L1TD1 antibody gives only background signals in DKO cells in the  indirect immunofluorescence experiment shown in Figure 1E of the manuscript. 

Thirdly, the immunogene sequence of L1TD1 that determines the specificity of the antibody was checked in the antibody data sheet from Sigma Aldrich. The corresponding epitope is not present in the L1 ORF1p sequence. Finally, we have shown that the ORF1p antibody does not cross-react with L1TD1 (Author response image 4B).

Author response image 4. (A) Uncropped L1TD1 Western blot shown in Figure 1C. An unspecific band is indicated by an asterisk. (B) Westernblot analysis of WT, KO and DKO cells with L1 ORF1p antibody.

(4) In abstract (P2), the authors mentioned that L1TD1 works as an RNA chaperone, but in the result section (P13), they showed that L1TD1 associates with L1 ORF1p in an RNAindependent manner. Those conclusions appear contradictory. Clarification or revision is required. 

Our findings that both proteins bind L1 RNA, and that L1TD1 interacts with ORF1p are compatible with a scenario where L1TD1/ORF1p heteromultimers bind to L1 RNA. The additional presence of L1TD1 might thereby enhance the RNA chaperone function of ORF1p. This model is visualized now in Suppl. Figure S7C. 

(5) Figure 2C fold enrichment for L1TD1 and ARMC1 is a bit difficult to fully appreciate. A 100 to 200-fold enrichment does not seem physiological. This appears to be a "divide by zero" type of result, as the CT for these genes was likely near 40 or undetectable. Another qRT-PCRbased approach (absolute quantification) would be a more revealing experiment. 

This is the validation of the RIP experiments and the presentation mode is specifically developed for quantification of RIP assays (Sigma Aldrich RIP-qRT-PCR: Data Analysis Calculation Shell). The unspecific binding of the transcript in the absence of L1TD1 in DNMT1/L1TD1 DKO cells is set to 1 and the value in KO cells represents the specific binding relative the unspecific binding. The calculation also corrects for potential differences in the abundance of the respective transcript in the two cell lines. This is not a physiological value but the quantification of specific binding of transcripts to L1TD1. GAPDH as negative control shows no enrichment, whereas specifically associated transcripts show strong enrichement. We have explained the details of RIPqRT-PCR evaluation in Materials and Methods (page 14) and the legend of Figure 2C in the revised manuscript.       

(6) Is it possible the L1TD1 antibody binds L1 ORF1p? This could make Figure 2D somewhat difficult to interpret. Some validation of the specificity of the L1TD1 antibody would remove this concern (see minor concern below).            

See response to (3).  

(7) Figure S4A and S4B: There appear to be a few unusual aspects of these figures that should be pointed out and addressed. First, there doesn't seem to be any ORF1p in the Input (if there is, the exposure is too low). Second, there might be some L1TD1 in the DKO (lane 2) and lane 3. This could be non-specific, but the size is concerning. Overexposure would help see this.

The ORF1p IP gives rise to strong ORF1p signals in the immunoprecipitated complexes even after short exposure. Under these contions ORF1p is hardly detectable in the input. Regarding the faint band in DKO HAP1 cells, this might be due to a technical problem during Western blot loading. Therefore, the input samples were loaded again on a Western blot and analyzed for the presence of ORF1p, L1TD1 and beta-actin (as loading control) and shown as separate panel in Suppl. Figure S4A. 

(8) Figure S4C: This is related to our previous concerns involving antibody cross-reactivity. Figure 3E partially addresses this, where it looks like the L1TD1 "speckles" outnumber the ORF1p puncta, but overlap with all of them. This might be consistent with the antibody crossreacting. The western blot (Figure 3C) suggests an upregulation of ORF1p by at least 2-3x in the DKO, but the IF image in 3E is hard to tell if this is the case (slightly more signal, but fewer foci). Can you return to the images and confirm the contrast are comparable? Can you massively overexpose the red channel in 3E to see if there is residual overlap? 

In Figure 3E the L1TD1 antibody gives no signal in DNMT1/L1TD1 DKO cells confirming that it does not recognize ORF1p. In agreement with the Western blot in Figure 3C the L1 ORF1p signal in Figure 3E is stronger in DKO cells. In DNMT1 KO cells the L1 ORF1p antibody does not recognize all L1TD1 speckles. This result is in agreement with the Western blot shown above in Figure R4B and indicates that the L1 ORF1p antibody does not recognize the L1TD1 protein. The contrast is comparable and after overexposure there are still L1TD1 specific speckles. This might be due to differences in abundance of the two proteins.

(9) The choice of ARMC1 and YY2 is unclear. What are the criteria for the selection?

ARMC1 was one of the top hits in a pilot RIP-seq experiment (IP versus input and IP versus  IgG IP). In the actual RIP-seq experiment with DKO HAP1 cells instead of IgG IP as a negative control, we found ARMC1 as an enriched hit, although it was not among the top 5 hits. The results from the 2nd RIP-seq further confirmed the validity of ARMC1 as an L1TD1-interacting transcript. YY2 was of potential biological relevance as an L1TD1 target due to the fact that it is a processed pseudogene originating from YY1 mRNA as a result of retrotransposition. This is mentioned on page 6 of the revised manuscript.

(10) (P16) L1 is the only protein-coding transposon that is active in humans. This is perhaps too generalized of a statement as written. Other examples are readily found in the literature. Please clarify.  

We will tone down this statement in the revised manuscript. 

(11) In both the abstract and last sentence in the discussion section (P17), embryogenesis is mentioned, but this is not addressed at all in the manuscript. Please refrain from implying normal biological functions based on the results of this study unless appropriate samples are used to support them.

Much of the published data on L1TD1 function are related to embryonic stem cells [3-7]. Therefore, it is important to discuss our findings in the context of previous reports.

(12) Figure 3E: The format of Figures 1A and 3E are internally inconsistent. Please present similar data/images in a cohesive way throughout the manuscript.  

We show now consistent IF Figures in the revised manuscript.

Minor: 

(1) Intro:           

- Is L1Td1 in mice and Humans? How "conserved" is it and does this suggest function?  

Murine and human L1TD1 proteins share 44% identity on the amino acid level and it was suggested that the corresponding genes were under positive selection during evolution with functions in transposon control and maintenance of pluripotency [8].  

- Why HAP1? (Haploid?) The importance of this cell line is not clear.          

HAP1 is a nearly haploid human cancer cell line derived from the KBM-7 chronic myelogenous leukemia (CML) cell line [9, 10]. Due to its haploidy is perfectly suited and widely used for loss-of-function screens and gene editing. After gene editing  cells can be used in the nearly haploid or in the diploid state. We usually perform all experiments with diploid HAP1 cell lines.  Importantly, in contrast to other human tumor cell lines, this cell line tolerates ablation of DNMT1. We have included a corresponding explanation in the revised manuscript on page 5, first paragraph.

- Global methylation status in DNMT1 KO? (Methylations near L1 insertions, for example?) 

The HAP1 DNMT1 KO cell line with a 20 bp deletion in exon 4 used in our study was validated in the study by Smits et al. [11]. The authors report a significant reduction in overall DNA methylation. However, we are not aware of a DNA methylome study on this cell line. We show now data on the methylation of L1 elements in HAP1 cells and upon DNMT1 deletion in the revised manuscript in Suppl. Figure S1B.

(2) Figure 1:  

- Figure 1C. Why is LMNB used instead of Actin (Fig1D)?  

We show now beta-actin as loading control in the revised manuscript.  

- Figure 1G shows increased Caspase 3 in KO, while the matching sentence in the result section skips over this. It might be more accurate to mention this and suggest that the single KO has perhaps an intermediate phenotype (Figure 1F shows a slight but not significant trend). 

We fully agree with the reviewer and have changed the sentence on page 6, 2nd paragraph accordingly.  

- Would 96 hrs trend closer to significance? An interpretation is that L1TD1 loss could speed up this negative consequence. 

We thank the reviewer for the suggestion. We have performed a time course experiment with 6 biological replicas for each time point up to 96 hours and found significant changes in the viability upon loss of DNMT1 and again significant reduction in viability upon additional loss of L1TD1 (shown in Figure 1F). These data suggest that as expexted loss of DNMT1 leads to significant reduction viability and that additional ablation of L1TD1 further enhances this effect.

- What are the "stringent conditions" used to remove non-specific binders and artifacts (negative control subtraction?) 

Yes, we considered only hits from both analyses, L1TD1 IP in KO versus input and L1TD1 IP in KO versus L1TD1 IP in DKO. This is now explained in more detail in the revised manuscript on page 6, 3rd paragraph.  

(3) Figure 2:  

- Figure 2A is a bit too small to read when printed. 

We have changed this in the revised manuscript.

- Since WT and DKO lack detectable L1TD1, would you expect any difference in RIP-Seq results between these two?

Due to the lack of DNMT1 and the resulting DNA hypomethylation, DKO cells are more similar to KO cells than WT cells with respect to the expressed transcripts.

- Legend says selected dots are in green (it appears blue to me). 

We have changed this in the revised manuscript.           

- Would you recover L1 ORF1p and its binding partners in the KO? (Is the antibody specific in the absence of L1TD1 or can it recognize L1?) I noticed an increase in ORF1p in the KO in Figure 3C.  

Thank you for the suggestion. Yes, L1 ORF1p shows slightly increased expression in the proteome analysis and we have marked the corresponding dot in the Volcano plot (Figure 3A).

- Should the figure panel reference near the (Rosspopoff & Trono) reference instead be Sup S1C as well? Otherwise, I don't think S1C is mentioned at all. 

- What are the red vs. green dots in 2D? Can you highlight ERV and ALU with different colors? 

We added the reference to Suppl. Figure S1C (now S3C) in the revised manuscript. In Figure 2D L1 elements are highlighted in green, ERV elements in yellow, and other associated transposon transcripts in red.     

- Which L1 subfamily from Figure 2D is represented in the qRT-PCR in 2E "LINE-1"? Do the primers match a specific L1 subfamily? If so, which? 

We used primers specific for the human L1.2 subfamily. 

- Pulling down SINE element transcripts makes some sense, as many insertions "borrow" L1 sequences for non-autonomous retro transposition, but can you speculate as to why ERVs are recovered? There should be essentially no overlap in sequence. 

In the L1TD1 evolution paper [8], a potential link between L1TD1 and ERV elements was discussed: 

"Alternatively, L1TD1 in sigmodonts could play a role in genome defense against another element active in these genomes. Indeed, the sigmodontine rodents have a highly active family of ERVs, the mysTR elements [46]. Expansion of this family preceded the death of L1s, but these elements are very active, with 3500 to 7000 species-specific insertions in the L1-extinct species examined [47]. This recent ERV amplification in Sigmodontinae contrasts with the megabats (where L1TD1 has been lost in many species); there are apparently no highly active DNA or RNA elements in megabats [48]. If L1TD1 can suppress retroelements other than L1s, this could explain why the gene is retained in sigmodontine rodents but not in megabats." 

Furthermore, Jin et al. report the binding of L1TD1 to repetitive sequences in transcripts [12]. It is possible that some of these sequences are also present in ERV RNAs.

- Is S2B a screenshot? (the red underline). 

No, it is a Powerpoint figure, and we have removed the red underline.

(4) Figure 3: 

- Text refers to Figure 3B as a western blot. Figure 3B shows a volcano plot. This is likely 3C but would still be out of order (3A>3C>3B referencing). I think this error is repeated in the last result section. 

- Figure and legends fail to mention what gene was used for ddCT method (actin, gapdh, etc.). 

- In general, the supplemental legends feel underwritten and could benefit from additional explanations. (Main figures are appropriate but please double-check that all statistical tests have been mentioned correctly).

Thank you for pointing this out. We have corrected these errors in the revised manuscript.

(5) Discussion: 

-Aluy connection is interesting. Is there an "Alu retrotransposition reporter assay" to test whether L1TD1 enhances this as well? 

Thank you for the suggestion. There is indeed an Alu retrotransposition reporter assay reported be Dewannieux et al. [13]. The assay is based on a Neo selection marker. We have previously tested a Neo selection-based L1 retrotransposition reporter assay, but this system failed to properly work in HAP1 cells, therefore we switched to a blasticidinbased L1 retrotransposition reporter assay. A corresponding blasticidin-based Alu retrotransposition reporter assay might be interesting for future studies (mentioned in the Discussion, page 11 paragraph 4 of the revised manuscript.

(6) Material and Methods       : 

- The number of typos in the materials and methods is too numerous to list. Instead, please refer to the next section that broadly describes the issues seen throughout the manuscript. 

Writing style  

(1) Keep a consistent style throughout the manuscript: for example, L1 or LINE-1 (also L1 ORF1p or LINE-1 ORF1p); per or "/"; knockout or knock-out; min or minute; 3 times or three times; media or medium. Additionally, as TE naming conventions are not uniform, it is important to maintain internal consistency so as to not accidentally establish an imprecise version. 

(2) There's a period between "et al" and the comma, and "et al." should be italic. 

(3) The authors should explain what the key jargon is when it is first used in the manuscript, such as "retrotransposon" and "retrotransposition".    

(4) The authors should show the full spelling of some acronyms when they use it for the first time, such as RNA Immunoprecipitation (RIP).  

(5) Use a space between numbers and alphabets, such as 5 µg.  

(6) 2.0 × 105 cells, that's not an "x".  

(7) Numbers in the reference section are lacking (hard to parse).  

(8) In general, there are a significant number of typos in this draft which at times becomes distracting. For example, (P3) Introduction: Yet, co-option of TEs thorough (not thorough, it should be through) evolution has created so-called domesticated genes beneficial to the gene network in a wide range of organisms. Please carefully revise the entire manuscript for these minor issues that collectively erode the quality of this submission.  

Thank you for pointing out these mistakes. We have corrected them in the revised manuscript. A native speaker from our research group has carefully checked the paper. In summary, we have added Supplementary Figure S7C and have changed Figures 1C, 1E, 1F, 2A, 2D, 3A, 4B, S3A-D, S4B and S6A based on these comments. 

REFERENCES

(1) Beck, M.A., et al., DNA hypomethylation leads to cGAS-induced autoinflammation in the epidermis. EMBO J, 2021. 40(22): p. e108234.

(2) Altenberger, C., et al., SPAG6 and L1TD1 are transcriptionally regulated by DNA methylation in non-small cell lung cancers. Mol Cancer, 2017. 16(1): p. 1.

(3) Narva, E., et al., RNA-binding protein L1TD1 interacts with LIN28 via RNA and is required for human embryonic stem cell self-renewal and cancer cell proliferation. Stem Cells, 2012. 30(3): p. 452-60.

(4) Jin, S.W., et al., Dissolution of ribonucleoprotein condensates by the embryonic stem cell protein L1TD1. Nucleic Acids Res, 2024. 52(6): p. 3310-3326.

(5) Emani, M.R., et al., The L1TD1 protein interactome reveals the importance of posttranscriptional regulation in human pluripotency. Stem Cell Reports, 2015. 4(3): p. 519-28.

(6) Santos, M.C., et al., Embryonic Stem Cell-Related Protein L1TD1 Is Required for Cell Viability, Neurosphere Formation, and Chemoresistance in Medulloblastoma. Stem Cells Dev, 2015. 24(22): p. 2700-8.

(7) Wong, R.C., et al., L1TD1 is a marker for undifferentiated human embryonic stem cells. PLoS One, 2011. 6(4): p. e19355.

(8) McLaughlin, R.N., Jr., et al., Positive selection and multiple losses of the LINE-1-derived L1TD1 gene in mammals suggest a dual role in genome defense and pluripotency. PLoS Genet, 2014. 10(9): p. e1004531.

(9) Andersson, B.S., et al., Ph-positive chronic myeloid leukemia with near-haploid conversion in vivo and establishment of a continuously growing cell line with similar cytogenetic pattern. Cancer Genet Cytogenet, 1987. 24(2): p. 335-43.

(10) Carette, J.E., et al., Ebola virus entry requires the cholesterol transporter Niemann-Pick C1. Nature, 2011. 477(7364): p. 340-3.

(11) Smits, A.H., et al., Biological plasticity rescues target activity in CRISPR knock outs. Nat Methods, 2019. 16(11): p. 1087-1093.

(12) Jin, S.W., et al., Dissolution of ribonucleoprotein condensates by the embryonic stem cell protein L1TD1. Nucleic Acids Res, 2024.

(13) Dewannieux, M., C. Esnault, and T. Heidmann, LINE-mediated retrotransposition of marked Alu sequences. Nat Genet, 2003. 35(1): p. 41-8.

Author response:

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

Reviewer #1 (Public review):

Summary:

Chen and Phillips describe the dynamic appearance of cytoplasmic granules during embryogenesis analogous to SIMR germ granules, and distinct from CSR-1-containing granules, in the C. elegans germline. They show that the nuclear Argonaute NRDE-3, when mutated to abrogate small RNA binding, or in specific genetic mutants, partially colocalizes to these granules along with other RNAi factors, such as SIMR-1, ENRI-2, RDE-3, and RRF-1. Furthermore, NRDE-3 RIP-seq analysis in early vs. late embryos is used to conclude that NRDE-3 binds CSR-1-dependent 22G RNAs in early embryos and ERGO-1dependent 22G RNAs in late embryos. These data lead to their model that NRDE-3 undergoes small RNA substrate "switching" that occurs in these embryonic SIMR granules and functions to silence two distinct sets of target transcripts - maternal, CSR-1 targeted mRNAs in early embryos and duplicated genes and repeat elements in late embryos.

Strengths:

The identification and function of small RNA-related granules during embryogenesis is a poorly understood area and this study will provide the impetus for future studies on the identification and potential functional compartmentalization of small RNA pathways and machinery during embryogenesis.

Weaknesses:

(1) While the authors acknowledge the following issue, their finding that loss of SIMR granules has no apparent impact on NRDE-3 small RNA loading puts the functional relevance of these structures into question. As they note in their Discussion, it is entirely possible that these embryonic granules may be "incidental condensates." It would be very welcomed if the authors could include some evidence that these SIMR granules have some function; for example, does the loss of these SIMR granules have an effect on CSR-1 targets in early embryos and ERGO-1-dependent targets in late embryos?

We appreciate reviewer 1’s concern that we do not provide enough evidence for the function of the SIMR granules. As suggested, we examined the NRDE-3 bound small RNAs more deeply, and we do observe a slight but significant increased CSR-class 22G-RNAs binding to NRDE-3 in late embryos of simr-1 and enri-2 mutants (see below, right). We hypothesize that this result could be due to a slower switch from CSR to ERGO 22G-RNAs in the absence of SIMR granules. We added these data to Figure 6G.

(2) The analysis of small RNA class "switching" requires some clarification. The authors re-define ERGO1-dependent targets in this study to arrive at a very limited set of genes and their justification for doing this is not convincing. What happens if the published set of ERGO-1 targets is used? 

As we mentioned in the manuscript, we initially attempted to use the previously defined ERGO targets. However, the major concern is fewer than half the genes classified as ERGO targets by Manage et al. and Fischer et al. overlap with one another (Figure 6—figure supplement 1D and below). We reason this might because the gene sets were defined as genes that lose small RNAs in various ERGO pathway mutants and because different criteria were used to define the lists as discussed in the manuscript (lines 471-476). As a result, some of the previously defined ERGO target genes may actually be indirect targets of the pathway. Here we focus on genes targeted by small RNAs enriched in an ERGO pathway Argonaute IP, which should be more specific.

In this manuscript, we are interested specifically in the ERGO targets bound by NRDE-3, thus we utilized the IP-small RNA sequencing data from young adult animals (Seroussi et al, 2023), to define a new ERGO list. We are confident about this list because 1) Most of our new ERGO genes overlap with the overlap between ERGO-Manage and ERGO-Fischer list (see Figure 6—figure supplement 1D in our manuscript and below). 2) We observed the most significant decrease of small RNA levels and increase of mRNA levels in the nrde-3 mutants using our newly defined list (see Figure 6—figure supplement 1E-F in our manuscript).

To further address reviewer 1’s concern about whether the data would look significantly different when using the ERGO-Manage and ERGO-Fischer lists, we made new scatter plots shown in Author response image 1 panels A-C below (ERGO-Manage – purple, ERGO-Fischer- yellow, and the overlap - yellow with purple ring). We found that the small switching pattern of NRDE-3 is consistent with our newly defined list, particularly if we look at the overlap of ERGO-Manage and ERGO-Fischer list (Author response image 1 panels D-F below, red).

Author response image 1.

Further, the NRDE-3 RIP-seq data is used to conclude that NRDE-3 predominantly binds CSR-1 class 22G RNAs in early embryos, while ERGO-1-dependent 22G RNAs are enriched in late embryos. a) The relative ratios of each class of small RNAs are given in terms of unique targets. What is the total abundance of sequenced reads of each class in the NRDE-3 IPs? 

To address the reviewer’s question about the total abundance of sequenced reads of each class in the NRDE-3 IPs: Author response image 2 panel A-B below show the total RPM of CSR and ERGO class sRNAs in inputs and IPs at different stages. Focusing on late embryos, the total abundance of ERGO-dependent sRNAs is similar to CSR-class sRNAs in input, while much higher in IP, indicating an enrichment of ERGO-dependent 22G-RNAs in NRDE-3 consistent with our log2FC (IP vs input) in Figure 6B. This data supports our conclusion that NRDE-3 preferentially binds to ERGO targets in late embryos.

Author response image 2.

b) The "switching" model is problematic given that even in late embryos, the majority of 22G RNAs bound by NRDE-3 is the CSR-1 class (Figure 5D). 

It is important to keep in mind the difference in the total number of CSR target genes (3834) and ERGO target genes (119).  The pie charts shown in Figure 6D are looking at the total proportion of the genes enriched in the NRDE-3 IP that are CSR or ERGO targets. For the NRDE-3 IP in late embryos, that would be 70/119 (58.8%) of ERGO targets are enriched, while 172/3834 (4.5%) of CSR targets are enriched. These data are also supported by the RPM graphs shown in Author response image 2 panels A-B above, which show that the majority of the small RNA bound by NRDE-3 in late embryos are ERGO targets. Nonetheless, NRDE-3 still binds to some CSR targets shown as Figure 6D and panel B, which may be because the amount of CSR-class 22G-RNAs is reduced gradually across embryonic development as the maternally-deposited NRDE-3 loaded with CSR-class 22G-RNAs is diluted by newly transcribed NRDE-3 loaded with ERGOdependent 22G-RNAs (lines 857-862). 

c) A major difference between NRDE-3 small RNA binding in eri-1 and simr-1 mutants appears to be that NRDE-3 robustly binds CSR-1 22G RNAs in eri-1 but not in simr-1 in late embryos. This result should be better discussed.

In the eri-1 mutant, we hypothesize that NRDE-3 robustly binds CSR-class 22G-RNAs because ERGOclass 22G-RNAs are not synthesized during mid-embryogenesis, so either NRDE-3 is unloaded (in granule at 100-cell stage in Figure 2A) or mis-loaded with CSR-class 22G-RNAs (in the nucleus at 100cell stage in Figure 2A). We don’t have a robust method to address the proportion of loaded vs. unloaded NRDE-3 so it is difficult to address the degree to which NRDE-3 is misloaded in the eri-1 mutant. In the simr-1 mutant, both classes of small RNAs are present and NRDE-3 is still preferentially loaded with ERGO-dependent 22G-RNAs, though we do see a subtle increase in association with CSR-class 22GRNAs. These data could suggest a less efficient loading of NRDE-3 with ERGO-dependent 22G-RNAs, but we would need more precise methods to address the loading dynamics in the simr-1 mutant.

(3) Ultimately, if the switching is functionally important, then its impact should be observed in the expression of their targets. RNA-seq or RT-qPCR of select CSR-1 and ERGO-1 targets should be assessed in nrde-3 mutants during early vs late embryogenesis.

The function of NRDE-3 at ERGO targets has been well studied (Guang et al, 2008) and is also assessed in our H3K9me3 ChIP-seq analysis in Figure 7E where, in mixed staged embryos, H3K9me3 level on ERGO targets (labeled as ‘NRDE-3 targets in young adults’) is reduced significantly in the nrde-3 mutant.

To understand the function of NRDE-3 binding on CSR targets in early embryos, we attempted to do RTqPCR, smFISH, and anti-H3K9me3 CUT&Tag-seq on early embryos, and we either failed to obtain enough signal or failed to detect any significant difference (data not shown). We additionally tested the possibility that NRDE-3 functions with CSR-class 22G-RNAs in oocytes. We present new data showing that NRDE-3 represses RNA Pol II in oocytes to promote global transcriptional repression at the oocyteto-embryo transition, we now included these data in Figure 8. 

Reviewer #2 (Public review):

Summary:

NRDE-3 is a nuclear WAGO-clade Argonaute that, in somatic cells, binds small RNAs amplified in response to the ERGO-class 26G RNAs that target repetitive sequences. This manuscript reports that, in the germline and early embryos, NRDE-3 interacts with a different set of small RNAs that target mRNAs. This class of small RNAs was previously shown to bind to a different WAGO-clade Argonaute called CSR1, which is cytoplasmic, unlike nuclear NRDE-3. The switch in NRDE-3 specificity parallels recent findings in Ascaris where the Ascaris NRDE homolog was shown to switch from sRNAs that target repetitive sequences to CSR-class sRNAs that target mRNAs.

The manuscript also correlates the change in NRDE-3 specificity with the appearance in embryos of cytoplasmic condensates that accumulate SIMR-1, a scaffolding protein that the authors previously implicated in sRNA loading for a different nuclear Argonaute HRDE-1. By analogy, and through a set of corelative evidence, the authors argue that SIMR foci arise in embryogenesis to facilitate the change in NRDE-3 small RNA repertoire. The paper presents lots of data that beautifully documents the appearance and composition of the embryonic SIMR-1 foci, including evidence that a mutated NRDE-3 that cannot bind sRNAs accumulates in SIMR-1 foci in a SIMR-1-dependent fashion.

Weaknesses:

The genetic evidence, however, does not support a requirement for SIMR-1 foci: the authors detected no defect in NRDE-3 sRNA loading in simr-1 mutants. Although the authors acknowledge this negative result in the discussion, they still argue for a model (Figure 7) that is not supported by genetic data. My main suggestion is that the authors give equal consideration to other models - see below for specifics.

We appreciate reviewer 2’s comments on the genetic evidence for the function of SIMR foci.  A similar concern was also brought up by reviewer 1. By re-examining our sequencing data, we found that there is a modest but significant increase in NRDE-3 association with CSR-class sRNAs in simr-1 and enri-2 mutants in late embryos. We believe that this data supports our model that SIMR-1 and ENRI-2 are required for an efficient switch of NRDE-3 bound small RNAs. Please refer our response to the reviewer 1 - point (1), and Figure 6G in the updated manuscript. 

Reviewer #3 (Public review):

Summary:

Chen and Phillips present intriguing work that extends our view on the C. elegans small RNA network significantly. While the precise findings are rather C. elegans specific there are also messages for the broader field, most notably the switching of small RNA populations bound to an argonaute, and RNA granules behavior depending on developmental stage. The work also starts to shed more light on the still poorly understood role of the CSR-1 argonaute protein and supports its role in the decay of maternal transcripts. Overall, the work is of excellent quality, and the messages have a significant impact.

Strengths:

Compelling evidence for major shift in activities of an argonaute protein during development, and implications for how small RNAs affect early development. Very balanced and thoughtful discussion.

Weaknesses:

Claims on col-localization of specific 'granules' are not well supported by quantitative data

We have now included zoomed images of individual granules to better show the colocalization in Figure 4 and Figure 4—figure supplement 1, and performed Pearson’s colocalization analysis between different sets of proteins in Figure 4B. 

Reviewer #2 (Recommendations for the authors):

- The manuscript is very dense and the gene names are not helpful. For example, the authors mention ERGO-1 without clarifying the type of protein, etc. I suggest the authors include a figure to go with the introduction that describes the different classes of primary and secondary sRNAs, associated Argonautes, and other accessory proteins. Also include a table listing relevant gene names, protein classes, main localizations, and proposed functions for easy reference by the readers.

We agree that the genes names in different small RNA pathways are easily confused. We added a diagram and table in Figure 1—figure supplement 1 depicting the ERGO/NRDE and CSR pathways and added clarification about the ERGO/NRDE-3 pathway in the text from line 126-128.  

- Line 424 - the wording here and elsewhere seems to imply that SIMR-1 and ENRI-2, although not essential, contribute to NRDE-3 sRNA loading. The sequencing data, however, do not support this - the authors should be clearer on this. If the authors believe there are subtle but significant differences, they should show them perhaps by adding a panel in Figure 5 that directly compares the NRDE-3 IPs in wildtype versus simr-1 mutants. Figure 5H however does not support such a requirement.

As brought up by reviewer 1, we do not see difference in binding of ERGO-dependent sRNA in simr-1 mutant in late embryos. We do, however, see a modest, but significant, increase of CSR-sRNAs bound by NRDE-3 in simr-1 and enri-2 mutants, which we hypothesize could be due to a less efficient loading of ERGO-dependent 22G-RNAs by NRDE-3. The updated data are now in Figure 6G. We have also edited the text and model figure to soften these conclusions.

- Condensates of PGL proteins appear at a similar time and place (somatic cells of early embryos) as the embryonic SIMR-1 foci. The PGL foci correspond to autophagy bodies that degrade PGL proteins. Is it possible that SIMR-1 foci also correspond to degradative structures? The possibility that SIMR-1 foci are targeted for autophagy and not functional would fit with the finding that simr-1 mutants do not affect NRDE-3 loading in embryos.

We appreciate reviewer 2’s comments on possibility of SIMR granules acting as sites for degradation of SIMR-1 and NRDE-3. We think this is not the case for the following reasons: 1) if SIMR granules are sites of autophagic degradation, then we would expect that embryonic SIMR granules in somatic cells, like PGL granules, should only be observed in autophagy mutants; however we see them in wild-type embryos 2) we would not expect a functional Tudor domain to be required for granule localization; however in Figure 1—figure supplement 2B, we show that a point mutation in the Tudor domain of SIMR-1 abrogates SIMR granule formation, and 3) if NRDE-3(HK-AA) is recruited to SIMR granules for degradation while wild-type NRDE-3 is cytoplasmic, then NRDE-3(HK-AA) should shows a significantly reduced protein level comparing to wild-type NRDE-3. In the western blot in Figure 2—figure supplement 1B, NRDE-3 and NRDE-3(HK-AA) protein levels are similar, indicating that NRDE-3(HK-AA) is not degraded despite being unloaded. This is in contrast to what we have observed previously for HRDE-1, which is degraded in its unloaded state. If SIMR-1 played a role directly in promoting degradation of NRDE-3(HK-AA), we would similarly expect to see a change in NRDE-3 or NRDE-3(HK-AA) expression in a simr-1 mutant. We performed western blot and did not observe a significant change in protein expression for NRDE-3 (Figure 3—figure supplement 1A). 

Although under wild-type conditions, SIMR granules do not appear to be sites of autophagic degradation, upon treatment with lgg-1 (an autophagy protein) RNAi, we found that SIMR-1, as well as many other germ granule and embryonic granule-localized proteins, increase in abundance in late embryos.  This data demonstrates that ZNFX-1, CSR-1, SIMR-1, MUT-2/RDE-3, RRF-1, and unloaded NRDE-3 are removed by autophagic degradation similar to what have been shown previously for PGL-1 proteins (Zhang et al, 2009, Cell). We added these data to Figure 5. It is important to emphasize, however, that the timing of degradation differs for each granule assayed (Lines 447-450), indicating that there must be multiple waves of autophagy to selectively degrade subsets of proteins when they are no longer needed by the embryo.

- The observation that an NRDE-3 mutant that cannot load sRNAs localizes to SIMR-1 foci does not necessarily imply that wild-type unloaded NRDE-3 would also localize there. Unless the authors have additional data to support this idea, the authors should acknowledge that this hypothesis is speculative. In fact, why does cytoplasmic NRDE-3 not localize to granules in the rde-3;ego-1degron strain shown in Figure 6B?? Is it possible that the NRDE-3 mutant accumulates in SIMR-1 foci because it is unfolded and needs to be degraded?

We believe that wild-type NRDE-3 also localize to SIMR foci when unloaded. This is supported by the localization of wild-type NRDE-3 in eri-1 and rde-3 mutants, where a subset of small RNAs are depleted. Wild-type NRDE-3 localizes to both somatic SIMR-1 granules and the nucleus, depending on embryo stage (Figure 2A, Figure 2—figure supplement 1C). The granule numbers in eri-1 and rde-3 mutants are less than the nrde-3(HK-AA) mutant, consistent with the imaging data that NRDE-3 only partially localize to somatic granule (Figure 2A – 100-cell stage).

In the rde-3; ego-1 double mutant, the embryos have severe developmental defect: they cannot divide properly after 4-8 cell stage and exhibit morphology defects after that stage. In wild-type, SIMR foci does not appear until around 8-28-cell stage (shown in Figure 1C), so we believe that cytoplasmic NRDE-3 does not localize to foci in the double mutant is because of the timing.

- The authors propose that NRDE-3 functions in nuclei to target mRNAs also targeted in the cytoplasm by CSR-1. If so, how do they propose that NRDE-3 might do this since little transcription occurs in oocytes/early embryos?? Are the authors suggesting that NRDE-3 targets germline genes for silencing specifically at the times that zygotic transcription comes back on, or already in maturing oocytes? Is the transcription of most CSR-1 targets silenced in early embryos??

We appreciate the suggestions to check the function of NRDE-3 in oocytes. We tested this possibility and found it to be correct. NRDE-3 functions in oocytes for transcriptional repression by inhibiting RNA Pol II elongation. We added these data to Figure 8. We also attempted to do RT-qPCR, smFISH, and antiH3K9me3 Cut&Tag-seq on early embryos to further test the hypothesis that NRDE-3 acts with CSR-class 22G-RNAs in early embryos, but we either failed to obtain enough signal or failed to detect any significant difference (data not shown). Therefore, we think that the primary role for NRDE-3 bound to CSR-class 22G-RNAs may be for global transcriptional repression of oocytes prior to fertilization.

- Line 684-686: "In summary, this work investigating the role of SIMR granules in embryos, together with our previous study of SIMR foci in the germline (Chen and Phillips 2024), has identified a new mechanism for small RNA loading of nuclear Argonaute proteins in C. elegans". This statement appears overstated/incorrect since there is no evidence that SIMR-1 foci are required for sRNA loading of NRDE3. The authors should emphasize other models, as suggested above.

We have revised the text on line 869-871 to emphasize that SIMR granule regulate the localization of nuclear Argonaute proteins, rather than suggesting a direct role on controlling small RNA loading. We also edit the title, text, and legend for our model in Figure 9. 

Reviewer #3 (Recommendations for the authors):

Issues to be addressed:

- The authors show a switch in 22G RNA binding by NRDE-3 during embryogenesis. While the data is convincing, it would be great if it could be tested if the preferred NRDE-3 replacement model is indeed correct. This could be done relatively easily by giving NRDE-3 a Dendra tag, allowing one to colour-switch the maternal WAGO-3 pool before the zygotic pool comes up. Such data would significantly enhance the manuscript, as this would allow the authors to follow the fate of maternal NRDE-3 more precisely, perhaps identifying a period of sharp decline of maternal NRDE-3.

We think the NRDE-3 Dendra tag experiment suggested by the reviewer is a clever approach and we will consider generating this strain in the future. However, we feel that optimization of the color-switching tag between the maternal germline and the developing embryos is beyond the scope of this manuscript. To partially address the question about NRDE-3 fate during embryogenesis, we examined the single-cell sequencing data of C. elegans embryos from 1-cell to 16-cell stage (Tintori et al, 2016, Dev Cell; Visualization tool from John I Murray lab), as shown in Author response image 3 Panel A below, NRDE-3 transcript level increases as embryo develops, indicating that zygotic NRDE-3 is being actively expressed starting very early in development. We hypothesize that maternal NRDE-3 will either be diluted as the embryo develops or actively degraded during early embryogenesis. 

Author response image 3.

- Figure 3A: * should mark PGCs, but this seems incorrect. At the 8-cell stage there still is only one PGC (P4), not two, and at 100 cells there are only two, not three germ cells. Also, the identification of PGCs with a maker (PGL for instance) would be much more convincing.

We apologize for the confusion in Figure 3A. We changed the figure legend to clarify that the * indicate nuclear NRDE-3 localization in somatic cells for 8- and 100-cell stage embryos rather than the germ cells.  

- Overall, the authors should address colocalization more robustly. In the current manuscript, just one image is provided, and often rather zoomed-out. How robust are the claims on colocalization, or lack thereof? With the current data, this cannot be assessed. Pearson correlation, combined with line-scans through a multitude of granules in different embryos will be required to make strong claims on colocalization. This applies to all figures (main and supplement) where claims on different granules are derived from.

We thank reviewer 3 for this important suggestion. To better address the colocalization, we included insets of individual granules in Figure 2D and Figure 4. We also performed colocalization analysis by calculating the Pearson’s R value between different groups of proteins in Figure 4B, to highlight that SIMR-1 colocalizes with ENRI-2, NRDE-3(HK-AA), RDE-3, and RRF-1, while CSR-1 colocalizes with EGO-1.

For the proteins that lack colocalization in Figure 4—figure supplement 1, we also added insets of individual granules. Additionally, we included a new set of panels showing SIMR-1 localization compared to tubulin::GFP (Figure 4—figure supplement 1I) in response to a recent preprint (Jin et al, 2024, BioRxiv), which finds NRDE-3 (expressed under a mex-5 promoter) associating with pericentrosomal foci and the spindle in early embryos. We do not see SIMR-1 (or NRDE-3, data not shown) at centrosomes or spindles in wild-type conditions but made a similar observation for SIMR-1 in a mut-16 mutant (Figure 4E). All of the localization patterns were examined on at least 5 individual 100-cell staged embryos with same localization pattern.

- Figure 7: Its title is: Function of cytoplasmic granules. This is a much stronger statement than provided in the nicely balanced discussion. The role of the granules remains unclear, and they may well be just a reflection of activity, not a driver. While this is nicely discussed in the text, figure 7 misses this nuance. For instance, the title suggests function, and also the legend uses phrases like 'recruited to granule X'. If granules are the results of activity, 'recruitment' is really not the right way to express the findings. The nuance that is so nicely worded in the discussion should come out fully in this figure and its legend as well.

We have changed the title of Figure 7 (now Figure 9) to “Model for temporally- and developmentallyregulated NRDE-3 function” to deemphasize the role of the granules and to highlight the different functions of NRDE-3. Similarly, we have rephrased the text in the figure and legend and add a some details about our new results.

Minor:

Typo: line 663 Acaris

We corrected the typo.

Author response:

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

Reviewer #1 (Recommendations For The Authors):

Major concerns:

(1) It is not clear about the biological significance of the inhibitory effects of human Abeta42 on gammasecretase activity. As the authors mentioned in the Discussion, it is plausible that Abeta42 may concentrate up to microM level in endosomes. However, subsets of FAD mutations in APP and presenilin 1 and 2 increase Abeta42/Abeta40 ratio and lead to Abeta42 deposition in brain. APP knock-in mice NLF and NLGF also develop Abeta42 deposition in age-dependent manner, although they produce more human Abeta42 than human Abeta40. 

If the production of Abeta42 is attenuated, which results in less Abeta42 deposition in brain. So, it is unlikely that human Abeta42 interferes gamma-secretase activity in physiological conditions. This reviewer has an impression that inhibition of gamma-secretase by human Abeta42 is an interesting artifact in high Abeta42 concentration. If the authors disagree with this reviewer's comment, this manuscript needs more discussion in this point of view. 

We thank the Reviewer for raising this key conceptual point, we acknowledge that it was insufficiently discussed in the original manuscript. In response to this point, we introduced the following paragraph in the discussion section of the revised manuscript:

“From a mechanistic standpoint, the competitive nature of the Aβ42-mediated inhibition implies

that it is partial, reversible, and regulated by the relative concentrations of the Aβ42 peptide (inhibitor) and the endogenous substrates (Figure 10C and 10D). The model that we put forward is that cellular uptake, as well as endosomal production of Aβ, result in increased intracellular concentration of Aβ42, facilitating γ-secretase inhibition and leading to the buildup of APP-CTFs (and γ-secretase substrates in general). As Aβ42 levels fall, the augmented concentration of substrates shifts the equilibrium towards their processing and subsequent Aβ production. As Aβ42 levels rise again, the equilibrium is shifted back towards inhibition. This cyclic inhibitory mechanism will translate into pulses of (partial) γsecretase inhibition, which will alter γ-secretase mediated-signaling (arising from increased CTF levels at the membrane or decreased release of soluble intracellular domains from substrates). These alterations may affect the dynamics of systems oscillating in the brain, such as NOTCH signaling, implicated in memory formation, and potentially others (related to e.g. cadherins, p75 or neuregulins). It is worth noting that oscillations in γ-secretase activity induced by treatment with a γ-secretase inhibitor semagacestat have been proposed to have contributed to the cognitive alterations observed in semagacestat treated patients in the failed Phase-3 IDENTITY clinical trial (7) and that semagacestat, like Aβ42, acts as a high affinity competitor of substrates (85).

The convergence of Aβ42 and tau at the synapse has been proposed to underlie synaptic dysfunction in AD (86-89), and recent assessment of APP-CTF levels in synaptosome-enriched fractions from healthy control, SAD and FAD brains (temporal cortices) has shown that APP fragments concentrate at higher levels in the synapse in AD-affected than in control individuals (90).  Our analysis adds that endogenous Aβ42 concentrates in synaptosomes derived from end-stage AD brains to reach ~10 nM, a concentration that in CM from human neurons inhibits γ-secretase in PC12 cells (Figure 7). Furthermore, the restricted localization of Aβ in endolysosomal vesicles, within synaptosomes, likely increases the local peptide concentration to the levels that inhibit γ-secretase-mediated processing of substrates in this compartment. In addition, we argue that the deposition of Aβ42 in plaques may be preceded a critical increase in the levels of Aβ present in endosomes and the cyclical inhibition of γsecretase activity that we propose. Under this view, reductions in γ-secretase activity may be a (transient) downstream consequence of increases in Aβ due to failed clearance, as represented by plaque deposition, contributing to AD pathogenesis.“

We have also added figures 10C and 10D, presented here for convenience.

Author response image 1.

(2) It is not clear whether the FRET-based assay in living cells really reflects gamma-secretase activity.

This reviewer thinks that the authors need at least biochemical data, such as levels of Abeta. 

We have established a novel, HiBiT tag based assay reporting on the global γ-secretase activity in cells, using as a proxy the total levels of secreted HiBiT-tagged Aβ peptides. The assay and findings are presented in the revised manuscript as follows:

In the result section, in the “Aβ42 treatment leads to the accumulation of APP C-terminal fragments in neuronal cell lines and human neuron” subsection:

“The increments in the APP-CTF/FL ratio suggested that Aβ42 (partially) inhibits the global γ-

secretase activity. To further investigate this, we measured the direct products of the γ-secretase mediated proteolysis of APP. Since the detection of the endogenous Aβ products via standard ELISA methods was precluded by the presence of exogenous human Aβ42 (treatment), we used an N-terminally tagged version of APPC99 and quantified the amount of total secreted Aβ, which is a proxy for the global γsecretase activity. Briefly, we overexpressed human APPC99 N-terminally tagged with a short 11 amino acid long HiBiT tag in human embryonic kidney (HEK) cells, treated these cultures with human Aβ42 or p3 17-42 peptides at 1 μM or DAPT (GSI) at 10 µM, and determined total HiBiT-Aβ levels in conditioned media (CM). DAPT was considered to result in full γ-secretase inhibition, and hence the values recorded in DAPT treated conditions were used for the background subtraction. We found a ~50% reduction in luminescence signal, directly linked to HiBiT-Aβ levels, in CM of cells treated with human Aβ42 and no effect of p3 peptide treatment, relative to the DMSO control (Figure 3D). The observed reduction in the total Aβ products is consistent with the partial inhibition of γ -secretase by Aβ42.”

In Methods:

“Analysis of γ-secretase substrate proteolysis in cultured cells using secreted HiBiT-Aβ or -Aβ-like peptide levels as a proxy for the global γ-secretase endopeptidase activity

HEK293 stably expressing APP-CTF (C99) or a NOTCH1-based substrate (similar in size as

APP- C99) both N-terminally tagged with the HiBiT tag were plated at the density of 10000 cells per 96-well, and 24h after plating treated with Aβ or p3 peptides diluted in OPTIMEM (Thermo Fisher Scientific) supplemented with 5% FBS (Gibco). Conditioned media was collected and subjected to analysis using Nano-Glo® HiBiT Extracellular Detection System (Promega). Briefly, 50 µl of the medium was mixed with 50 µl of the reaction mixture containing LgBiT Protein (1:100) and Nano-Glo HiBiT Extracellular Substrate (1:50) in Nano-Glo HiBiT Extracellular Buffer, and the reaction was incubated for 10 minutes at room temperature. Luminescence signal corresponding to the amount of the extracellular HiBiT-Aβ or -Aβ-like peptides was measured using victor plate reader with default luminescence measurement settings.”

As the direct substrate of γ -secretase was used in this analysis, the observed reduction (~50%) in the levels of N-terminally-tagged (HiBiT) Aβ peptides in the presence of 1 µM Aβ42, relative to control conditions, demonstrates a selective inhibition of γ-secretase by Aβ42 (not by the p3). These data complement the FRET-based findings presented in Figure 5.

(3) Processing of APP-CTF in living cells is not only the cleavage by gamma-secretase. This reviewer thinks that the authors need at least biochemical data, such as levels of Abeta in Figures 4, 5 and 7.

We tried to measure the levels of Aβ peptides secreted by cells into the culture medium directly by ELISA (using different protocols) or MS (using established methods, as reported in Koch et al, 2023), but exogenous Aβ42 (treatment) present at relatively high levels interfered with the readout and rendered the analysis inconclusive. 

However, we were successful in the determination of total secreted (HiBiT-tagged) Aβ peptides from the HiBiT tagged APP-C99 substrate, as indicated in the previous point. The quantification of the levels of these peptides showed that Aβ42 treatment resulted in ~50% reduction in the γ -secretase mediated processing of the tagged substrate.    

In addition, we would like to highlight that our analysis of the contribution of other APP-CTF degradation pathways, using cycloheximide-based assays in the constant presence of γ-secretase inhibitor, failed to reveal significant differences between Aβ42 treated cells and controls (Figure 6B & C). The lack of a significant impact of Aβ42 on the half-life of APP-CTFs under the conditions of γsecretase inhibition maintained by inhibitor treatment is consistent with the proposed Aβ42-mediated inhibitory mechanism.

(4) Similar to comment #3. Processing of Pancad-CTF and p75 in living cells may be not only the cleavage by gamma-secretase. This reviewer thinks that the authors need at least biochemical data, such as levels of ICDs in Figures 6C and E. 

To address this comment we have now performed additional experiments where we measured Nterminal Aβ-like peptides derived from NOTCH1-based substrate using the HiBiT-based assay. These experiments showed a reduction in the aforementioned peptides in the cells treated with Aβ42 relative to the vehicle control, and hence further confirmed the inhibitory action of Aβ42. These new data have been included as Figure 8D in the revised manuscript and described as follow:

Finally, we measured the direct N-terminal products generated by γ-secretase proteolysis from a HiBiT-tagged NOTCH1-based substrate, an estimate of the global γ-secretase activity. We quantified the Aβ-like peptides secreted by HEK 293 cells stably expressing this HiBiT-tagged substrate upon treatment with 1 µM Aβ1-42,  p3 17-42 peptide or  DAPT (GSI) (Figure 8D). DAPT treatment was considered to result in a complete γ-secretase inhibition, and hence the values recorded in the DAPT condition were used for background subtraction. A ~20% significant reduction in the amount of secreted

N-terminal HiBiT-tagged peptides derived from the NOTCH1-based substrates in cells treated with Aβ1-

42 supports the inhibitory action of Aβ1-42 on γ-secretase mediated proteolysis.

Minor concerns:

(1) Murine Abeta42 may be converted to murine Abeta38 easily, compared to human Abeta42. This may be a reason why murine Abeta42 exhibits no inhibitory effect on gamma-secretase activity. 

In order to address this question, we performed additional experiments where we assessed the processing of murine Aβ42 into Aβ38. Analogous to human Aβ42, the murine Aβ42 peptide was not processed to Aβ38 in the assay conditions. These new data have been integrated in the manuscript and added as a Supplementary figure 1B.

(2) It is curious to know the levels of C99 and C83 in cells in supplementary figure 3.  

The conditions used in these assays were analogous to the conditions used in the figure 3 (i.e. treatment with Aβ peptides at 1 µM concentrations). Such conditions were associated with profound and consistent APP-CTF accumulation in this model system.

Reviewer #2 (Recommendations For The Authors):

In the current study, the authors show that Aβs with low affinity for γ-secretase, but when present at relatively high concentrations, can compete with the longer, higher affinity APPC99 substrate for binding and processing. They also performed kinetic analyses and demonstrate that human Aβ1-42 inhibits γ-secretase-mediated processing of APP C99 and other substrates. Interestingly, neither murine Aβ1-42 nor human p3 (17-42 amino acids in Aβ) peptides exerted inhibition under similar conditions. The authors also show that human Aβ1-42-mediated inhibition of γ-secretase activity results in the accumulation of unprocessed, which leads to p75-dependent activation of caspase 3 in basal forebrain cholinergic neurons (BFCNs) and PC12 cells. 

These analyses demonstrate that, as seen for γ-secretase inhibitors, Aβ1-42 potentiates this marker of apoptosis. However, these are no any in vivo data to support the physiological significance of the current finding. The author should show in APP KO mice whether gamma-secretase enzymatic activity is elevated or not, and putting back Aβ42 peptide will abolish these in vivo effects. 

The findings presented in this manuscript form the basis for further in vitro and in vivo research to investigate the mechanisms of inhibition and its contribution to brain pathophysiology. Here, we used well-controlled model systems to investigate a novel mechanism of Aβ42 toxicity. Multiple mechanisms regulate the local concentration of Aβ42 in vivo, making the dissection of the biochemical mechanisms of the inhibition more complex. Nevertheless, beyond the scope of this report, we consider these very reasonable comments as a motivation for further research activities. 

The experimental concentrations for Aβ42 peptide in the assay are too high, which are far beyond the physiological concentrations or pathological levels. The artificial observations are not supported by any in vivo experimental evidence.

It is correct that in the majority of the experiments we used low μM concentrations of Aβ42. However, we would like to note that we have also performed experiments where conditioned medium collected from human APP.Swe expressing neurons was used as a source of Aβ. In these experiments total Aβ concentration was in low nM range (0.5-1 nM) (Figure 7). Treatment with this conditioned medium  led to the increase APP-CTF levels, supporting  that low nM concentrations of Aβ are sufficient for partial inhibition of  γ-secretase. 

In addition, we highlight that analyses of the brains of the AD affected individuals have shown that APPCTFs accumulate in both sporadic and genetic forms of the disease (Pera et al. 2013, Vaillant-Beuchot et al. 2021); and recently, Ferrer-Raventós et al. 2023 have revealed a correlation between APP-CTFs and Aβ levels at the synapse (Ferrer-Raventós et al. 2023). We therefore assessed the concentration of Aβ42 in synaptosomes derived from frontal cortices of post-mortem AD and age-matched non-demented (ND) control individuals. Our findings and conclusions are included in the revised version as follows: 

In the results section:

“We next investigated the levels of Aβ42 in synaptosomes derived from frontal cortices of post-mortem AD and age-matched non-demented (ND) control individuals (Figure 10B). Towards this, we prepared synaptosomes from frozen brain tissues using Percoll gradient procedure (62, 63). Intact synaptosomes were spun to obtain a pellet which was resuspended in minimum amount of PBS, allowing us to estimate the volume containing the resuspended synaptosome sample. This is likely an overestimate of the actual synaptosome volume. Finally, synaptosomes were lysed in RIPA buffer and Aβ peptide concentrations measured using ELISA (MSD). We observed that the concentration of Aβ42 in the synaptosomes from (end-stage) AD tissues was significantly higher (10.7 nM)  than those isolated from non-demented tissues (0.7 nM), p<0.0005***. These data provide evidence for accumulation at nM concentrations of endogenous Aβ42 in synaptosomes in end-stage AD brains. Given that we measured Aβ42 concentration in synaptosomes, we speculate that even higher concentrations of this peptide may be present in the endolysosome vesicle system, and therein inhibit the endogenous processing of APP-CTF at the synapse. Of note treatment of PC12 cells with conditioned medium containing even lower amounts of Aβ (low nanomolar range (0.5-1 nM)) resulted in the accumulation of APP-CTFs.” 

In the discussion: 

“The convergence of Aβ42 and tau at the synapse has been proposed to underlie synaptic dysfunction in AD (86-89), and recent assessment of APP-CTF levels in synaptosome-enriched fractions from healthy control, SAD and FAD brains (temporal cortices) has shown that APP fragments concentrate at higher levels in the synapse in AD-affected than in control individuals (90).  Our analysis adds that endogenous Aβ42 concentrates in synaptosomes derived from end-stage AD brains to reach ~10 nM, a concentration that in CM from human neurons inhibits γ-secretase in PC12 cells (Figure 7). Furthermore, the restricted localization of Aβ in endolysosomal vesicles, within synaptosomes, likely increases the local peptide concentration to the levels that inhibit γ-secretase-mediated processing of substrates in this compartment. In addition, we argue that the deposition of Aβ42 in plaques may be preceded by a critical increase in the levels of Aβ present in endosomes and the cyclical inhibition of γ-secretase activity that we propose. Under this view, reductions in γ-secretase activity may be a (transient) downstream consequence of increases in Aβ due to failed clearance, as represented by plaque deposition, contributing to AD pathogenesis. ”

Author response:

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

Summary of revisions

Title

We have changed the title of the manuscript to “Chromatin endogenous cleavage provides a global view of yeast RNA polymerase II transcription kinetics”.

Text

Additional discussion of the patterns for elongation factors added (detailed below).

Small text changes throughout, as mentioned in the detailed response below.

Figures

Updated legend-image in Figure 2F to reflect correct colors

Added Figure 2 – supplement 1F – RNAPII enrichment with shorter promoter dwell times

Added Figure 2 - supplement 2 with ChIP-seq outcomes (and text legend)

Removed gene numbers in Figure 5C and put them in the legend.

Substituted Med1 and Med8 ChEC over Rap1 sites in Figure 5F.

Moved kin28-is growth inhibition to Figure 5 – Supplement 1.

Substituted a new panel overlaying the RNAPII enrichment over UASs or promoters for all three strains in Figure 7D.

Improved the labeling and legend of Figure 7E

Methods

Added ChIP-seq performed to confirm that the MNase fusion proteins are able to produce the expected pattern for ChIP.

Point-by-point response to reviewers’ comments

Reviewer 1:

(1) Extending this work to elongation factors Ctk1 and Spt5 unexpectedly give strong signals near the PIC location and little signals over the coding region. This, and mapping CTD S2 and S5 phosphorylation by ChEC suggests to me that, for some reason, ChEC isn't optimal for detecting components of the elongation complex over coding regions. 

(3) mapping the elongation factors Spt5 and Ctk1 by ChEC gives unexpected results as the signals over the coding sequences appear weak but unexpectedly strong at promoters and terminators. It would be helpful if the authors could comment on reasons why ChEC may not work well with elongation factors. For example, could this be something to do with the speed of Pol elongation and/or the chromatin structure of coding sequences such that coding sequence DNA is less accessible to MNase cleavage? 

(7) The mintbodys are an interesting attempt to measure Pol II CTD modifications during elongation but give unexpected results as the signals in the coding region are lower than at promoters and terminators. It seems like ChIP is still a much better option for elongation factors unless I'm missing something. 

We agree with the reviewer that this is a point that could confuse the reader.  Therefore, we have devoted two additional paragraphs to possible interpretations of our data in the Discussion:

ChEC with factors involved in elongation (Ctk1, Spt5, Ser2p-RNAPII), when normalized to total RNAPII, showed greater enrichment over the CDS (Figure 3G), as expected. However, it is surprising that we also observed clear enrichment of these factors at promoters (e.g. Figure 3A, E & F). The association of elongation factors with the promoter seems to be biologically relevant. Changes in transcription correlate with changes in ChEC enrichment for these factors and modifications (Figure 4C). Blocking initiation by inhibiting TFIIH kinase led to a reduction of Ser5p RNAPII and Ser2p RNAPII over both the promoter and the transcribed region (Figure 5G). This suggests either that the true signal of these factors over transcribed regions is less evident by ChEC than by ChIP or that ChEC can reveal interactions of elongation factors at early stages of transcription that are missed by ChIP. The expectations for enrichment of elongation factors and phosphorylated CTD are largely based on ChIP data. Because ChIP fails to capture RNAPII enrichment at UASs and promoters, it is possible that ChIP also fails to capture promoter interaction of factors involved in elongation as well.

Factors important for elongation can also function at the promoter. For example, Ctk1 is required for the dissociation of basal transcription factors from RNAPII at the promoter (Ahn et al., 2009). Transcriptional induction leads to increases in Ctk1 ChEC enrichment both over the promoter and over the 3’ end of the transcribed region (Figure 4C). Dynamics of Spt4/5 association with RNAPII from in vitro imaging (Rosen et al., 2020) indicate that the majority of Spt4/5 binding to RNAPII does not lead to elongation; Spt4/5 frequently dissociates from DNA-bound RNAPII. Association of Spt4/5 with RNAPII may represent a slow, inefficient step in the transition to productive elongation. If so, then ChEC-seq2 may capture transient Spt4/5 interactions that occur prior to productive elongation, producing enrichment of Spt5 at the promoter.

(2) Finally, the role of nuclear pore binding by Gcn4 is explored, although the results do not seem convincing (10) In Figure 7, it's not convincing to me that ChEC is revealing the reason for the transcriptional defect in the Gcn4 PD mutant. The plots in panel D look nearly the same and I don't follow the authors' description of the differences stated in the text. In panel A, replotting the data in some other way might make the transcriptional differences between WT and Gcn4 PD mutants more obvious. 

The phenotype of the gcn4-pd mutant is a quantitative decrease in transcription and this leads to a quantitative decrease, rather than qualitative loss, of RNA polymerase II over the promoter, without impacting the association of RNA polymerase II over the UAS region. This effect is small but statistically significant (p = 4e5). We have changed the title of this section of the manuscript to “ChEC-seq2 suggests a role for the NPC in stabilizing promoter association of RNAPII”. Also, to make comparison clearer, we have plotted the data together in the revised figure (Figure 7D).

The magnitude of the decrease is not large, but we would highlight that is almost as large as that produced by inhibiting the Kin28 kinase (Figure 5H). Because the promoter-bound RNAPII is poorly captured by ChIP, this effect might be difficult to observe by techniques other than ChEC. Obviously, more mechanistic studies will need to be performed to fully understand this phenotype, but this result supports a role for the interaction with the nuclear pore complex in either enhancing the transfer of RNA polymerase II from the enhancer to the promoter or in preventing its dissociation from the promoter.

I think that the related methods cut&run/cut&tag have been used to map elongating pol II. The authors should summarize what is known from this approach in the introduction and/or discussion. 

CUT&RUN has been used to map RNAPII in mammals, but we are not aware of reports in S. cerevisiae.  Work from the Henikoff Lab in yeast mapped transcription factors and histone modifications (PMIDs 28079019 and 31232687).  A report using CUT&RUN in a human cell line reported a promoter-5’ bias of RNAPII that appeared to be dependent on fragment length (PMID 33070289). Regardless, the report highlights a key distinction between yeast and other eukaryotes: paused RNAPII. Indeed, paused RNAPII dominates ChIP-seq tracks in metazoans, and so we are hesitant to speculate between CUT&RUN in other species vs. ChEC-seq2 in S. cerevisiae. 

Are the Rpb1, Rpb3, TFIIA, and TFIIE cleavage patterns expected based on the known structure of the PIC (Figures 2C, E)? 

Rpb1 and 3 show peaks at approximately -17 and +34 with respect to TATA. TFIIA (Toa2) shows peaks at -12 and + 12.  And TFIIE (Tfa1) shows a peak around +34 (Figure 2C & E):

As shown in the supplementary movie (based on the cMed-PIC structure; PDB #5OQM; Schilbach et al., 2017), upon binding to TBP/TFIID, TFIIA would be expected to cleave slightly upstream and downstream of the protected TATA (-12 and +12), while TFIIE binds downstream after the +12 site is protected and would be closest to the +34 unprotected site (to the right in the image below). RNAPII, which binds the fully assembled PIC, should be able to access either the upstream site (-12) or the downstream site (+34). Rpb1’s unstructured carboxy terminal domain, to which MNase is fused, would give it maximum flexibility, which likely explains why Rpb1 cleaves both at -12 and +34, with a preference for -12. Rpb3 also cleaves both sites, but without an obvious preference. 

Author response image 1.

Author response image 2.

cleavage at -12, +12 and +34

Author response image 3.

Highlighted sites corresponding to the peaks in TFIIA assembled with TBP:

Author response image 4.

The complete PIC, protecting the +12 site, but leaving the +34 site exposed: 

(6) Figure 2 S1: Pol II ChIP in the coding region gives a better correlation with transcription vs ChEC in promoters. Also, Pol II ChIP at terminators is almost as good as ChEC at promoters for estimating transcription. This latter point seems at odds with the text. The authors should comment on this and modify the text as needed. 

Thank you for this comment.  We have clarified the text.

In Figures 4 and 5, it's hard to tell how well changes in transcription correlate with changes in Pol II ChEC signals. It might be helpful to have a scatterplot or some other type of plot so that this relationship can be better evaluated. 

While we find corresponding increase/decrease in ChEC-seq2 signal in genes identified as up/downregulated by SLAM-seq, the magnitude in change is not well correlated between the two techniques.  This was not surprising, because neither ChIP nor ChEC correlate especially well with SLAM-seq (Figure 2 – supplement 1E).

In Figure 5, it's unclear why Pol association with Rap1 is being measured. Buratowski/Gelles showed that Pol associates with strong acidic activators - presumably through Mediator. Rap1 supposedly does not bind Mediator - so how is Pol associating here? Perhaps it would be better to measure Pol binding at STM genes that show Mediator-UAS binding. 

Thank you; this is a good point.  We chose Rap1 because we had generated high-confidence binding sites in our strains under these conditions by ChEC-seq2. The results suggest that RNAPII is recruited well to these sites and that this recruitment does not require TFIIB. However, in disagreement with the notion that Mediator does not interact with Rap1, ChEC with Mediator subunits Med1 and Med8 also show peaks at these sites (new Figure 5F; the old Figure 5F is now Figure 5 – Supplement 1).  Therefore, either these sites are co-occupied by other transcription factors that mind Mediator, or Mediator is recruited by Rap1.  In either case, this correlates with binding of RNAPII. 

Reviewer 2:

(1) The term "nascent transcription" is all too often used interchangeably for NET-seq, PRO-seq, 4sUseq, and other assays that often provide different types of information. The authors should make it clear their use of the term refers to SLAM-seq data. 

We have clarified throughout the manuscript that nascent transcription measured by SLAM-seq.

The authors should explicitly state that experiments were performed in S. cerevisiae in the Results section. 

We have made it clear in the title and the text that these experiments were performed in S. cerevisiae.

Lines 216-218 state that "None of the 24 predicted the strong signal over the transcribed region with promoter depletion characteristic of ChIP-seq". I understand the authors' point, but there are parameter combinations that produce a flat profile with slightly less signal over the promoter (e.g., 5 sec dwell times and 3000 bp/ min elongation rate). If flanking windows were included, this profile would look something like ChIP-seq. I'd encourage the authors to be more precise with their language. 

Thank you for highlighting this over-statement.

We have now clarified the text and added another supplementary panel as follows:

“While some combinations predicted a relatively flat distribution across the gene with lower levels in the promoter, none of the 24 predicted the strong signal over the transcribed region with promoter depletion characteristic of ChIP-seq. Only very short promoter dwell times (i.e., < 1s), produced the low promoter occupancy seen in ChIP-seq (Figure 2 – supplement 1F).”

Author Response:

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

We were pleased with the overall enthusiastic comments of the reviewers:

• Reviewer #1: “This manuscript by Mahlandt, et al. presents a significant advance in the manipulation of endothelial barriers with spatiotemporal precision”

• Reviewer #2: “The immediate and repeatable responses of barrier integrity changes upon light-on and light-off switches are fascinating and impressive.”

• Reviewer #3: “, these molecular tools will be of broad interest to cell biologists interested in this family of GTPases.”

We thank the reviewers for their fair and constructive comments that helped us to improve the manuscript.

Reviewer #1 (Recommendations For The Authors):

1) This paper is likely to attract a diverse audience. However, the order of data presented in this manuscript can be confusing or challenging to follow for the naive reader. This is because the tool characterization is split into two parts: before the barrier strength assay (selection of optogenetic platform and tool expression) and after (characterization of cell morphology with global and local optogenetic stimulation). Reorganizing the results such that the barrier strength results follows from an understanding of individual cell responses to stimulation may improve the ability of this readership to understand the factors at play in the changes in barrier strength observed when opto-RhoGEFs are activated.

We appreciate this idea, and we initially structured the paper in the proposed order and then decided, that we wanted to put more focus on the barrier strength results by already presenting them in the second figure. Therefore, we prefer to keep this order of figures.

2) While the description of the selection of iLID as the study's optogenetic platform is clear, a better job could be done motivating the need for engineering new optogenetic tools for the control of GEF recruitment. Given that iLID-based tools for GEFs of RhoA, Rac1, and Cdc42 already exist, some of which are cited in the introduction, more information on why these tools were not used would be helpful-were these tools tested in endothelial cells and found lacking.

The original system has the domain structure DHPH-tagRFP-SspB. But we wanted to work with a SspB-FP-GEF construct, which would allow easy exchange of the FP and the DHPH domain. This modular approach allowed us to generate and compare the mCherry, iRFP647 and HaloTag version. We don’t want to claim that we engineered an entirely new optogenetic tool but rather optimized an existing one with different tags. To make this more clear we added : ‘The membrane tag of the original iLID was changed to an optimized anchor. In addition, we modified the sequence of the domains to SspB, tag, GEF to simplify the exchange of GEF and genetically encoded tag. A set of plasmids with different fluorescent tags was created for more flexibility in co-imaging.’

3) Comment on the reason behind using DHPH vs. DH domains for each GEF is needed.

We have previously found (and this is supported by biochemical analysis of GEF activity) that the selected domains provide the best activity. We will add reference and the following to the text: ‘Their catalytic active DHPH domains were used for ITSN1 and TIAM1 (Reinhard et al., 2019).  In case of p63 the DH domain only was used, because the PH domain of p63 inhibits the GEF activity (Van Unen et al., 2015) (Fig. 1E).’

4) Since multiple Rho GTPases (e.g., RhoA, RhoB, RhoC) exist and Rho is used as the name of the GTPase family, please use RhoA where applicable for clarity.

Since the RhoGEFp63 will activate RhoA/B/C we would rather not refer to RhoA only. We will clarify this in the text: ‘Three GEFs were selected, ITSN1, TIAM1 and RhoGEFp63, which are known to specifically activate respectively Cdc42, Rac and Rho and their isoforms.’

5) A brief comment on the use of HeLa cells for protein engineering and characterization (versus the endothelial cells motivated in the introduction) may be helpful.

We added the following to the text: ‘HeLa cells were used for the tool optimization because of easier handling and  higher transfection rate in comparison to endothelial cells.’

Minor suggestions:

In figure 1C, line sections showing intensity profiles before and after protein dimerization might further emphasize the change in biosensor localization.

We are not a fan of intensity profiles as the profile depends strongly on the position of the line and it basically turns a 2D image in 1D data, for a single image. So, we prefer to stick to the quantification as shown in panel 1B (which shows data from multiple cells).

Reviewer #2 (Recommendations For The Authors):

1)The study has analyzed the effects of light-induced activation of the three optogenetic constructs in endothelial cells on their barrier function (electrical resistance) at high cell density and correlated the findings with the cellular overlap-producing effects on endothelial cells cultured at sparse cell density. It should be tried to show these effects at a cell density where these light-induced effects increase electrical resistance. Lifeact with different chromophores in adjacent cells might be useful.

We had attempted to measure the overlap in a monolayer by taking advantage of the Halotag and the variety of dyes available by staining one pool of cells red with JF 552 nm and the other far red with the JF 635 nm dye. However, the cells need at least 24 h to form a monolayer and by then they had exchanged the dye and red and far red pool could not be distinguished any longer.

Therefore, we used the Lck-mTq2-iLID construct, which already marks the plasma membrane of the cells. We created a mosaic monolayer of cells expressing mScarlet-CaaX and cells expressing Lck-mTq2-iLID + SspB-HaloTag-TIAM(DHPH). We observed and increase in the overlap between cells under this condition. The results have been added to figure 4 - figure supplement 2I&J. To the text we added:

'Additionally, cell-cell membrane overlap increased about 20 %, up on photo-activation of OptoTIAM, in a mosaic expression monolayer (figure 4 - figure supplement 2I,J, Animation 22)‘

2) The authors correctly state that some reports have shown that S1P can increase endothelial barrier function in VE-cadherin independent ways and these are related to Rac and Cdc42. This was also shown for Tie-2 in vitro and even in vitro in the absence of VE-cadherin and should also be mentioned.

We added the following to the text: ‘Not only S1P promotes endothelial barrier independent from VE-cadherin, also Tie2 can increase barrier resistance in the absence of VE-cadherin (Frye et al. 2015).’

Since a blocking antibody against VE-cadherin was used, a negative control antibody should be tested which also binds to endothelial cells.

To visualize the cell-cell junctions in the experiment shown in Supplemental Fig 3.1, we added a non-blocking VE-cadherin antibody that is directly labeled with ALEXA 647 and shows normal junction morphology. These experiments already give an indication that the live labeling antibody of VE-cadherin does not disturb the junction morphology. However, when we added the blocking antibody against VE-cadherin, known to interfere with the trans-interactions of VE-cadherin, a rapid disruption of the junctions is observed.

Additionally, previous work has shown, that VE-cadherin labeling antibody does not interfere with junction dynamics and function (see Figure 2.A, Kroon et al. 2014 ‘Real-time imaging of endothelial cell-cell junctions during neutrophil transmigration under physiological flow’, jove.). We have added the figures below, showing that addition of the control IgG and VE-cadherin 55-7H1 Abs at the timepoint where the dotted line is, did not interfere with the resistance whereas the blocking Ab drastically reduced resistance. We have added this reference to the results. ‘Previous work has shown the specific blocking effect of this antibody in comparison to the VE-cadherin (55-7H1) labeling antibody (Kroon et al., 2014).’

Author response image 1.

Reviewer #3 (Recommendations For The Authors):

Additional comments for the authors:

1) The introduction is very long and would benefit from a more concise emphasis on the information required to put the work and results in context and understand their importance.

Comment: we appreciate the comment of the reviewer. However, we wish to introduce the topic and the tools thoroughly and therefore we chose to keep the introduction as it is.

2) The N-terminal membrane-binding domain does not homogeneously translocate to the plasma membrane, since lck is a raft-associated kinase. Please comment on this.

In our hands, the Lck is among the most selective and efficient tags for plasma membrane localization (https://doi.org/10.1101/160374). We do observe homogeneous translocation, but our resolution is limited to ~200 nm and so we cannot exclude that the Lck concentrates in structures smaller than 200 nm. Given the robust performance of the lck-based iLID anchor in the optogenetics experiments, we think that the Lck anchor is a good choice.

3) Figure 1D is not very clear. What does 25 or 36% change mean? If iLID tg is conjugated to these sequences, its cytosolic localization should be reduced versus iLID alone. Is this what the graph wants to express? If so, please, label properly the ordinate axis in the graph (% of non-tagged iLID values?)

The graph is representing the recruitment efficiency of SspB to the plasma membrane for the two different membrane tags, targeting iLID to the plasma membrane. The recruitment efficiency was measured by the depletion of SspB-mScarlet intensity in the cytosol, up on light activation, and represented as a change in percentage.

We added the following to the title of the graph_: SspB recruitment efficiency for Plasma Membrane tagged iLID._

4) Supplemental figures in the main text. Fig S1D in the text refers to data in Fig S1E and Fig S1E is supposed to be Fig S1F? (page 11).

That is correct. The mistakes have been corrected (and this is now renamed to figure 1 - figure supplement 1E and 1F).

5) Figure 3. Contribution of VE-cadherin. Other junctional complexes, such as tight junctions may also intervene. However, these results would also suggest that cell-substrate adhesion rather than cell-cell junctions may modulate the barrier properties, as it has been previously demonstrated for example by imatinib-mediated activation of Rac1 (Aman et al. Circulation 2012). The ECIS system used to measure TEER in the quantitative barrier function assays can modulate these measurements and discriminate between paracellular permeability (Rb) and cell-substrate adhesion (alpha). Please, provide whether the optogenetic modulation of these GTPases does indeed regulate Rb or alpha.

The measured impedance is made up of two components: capacitance and resistance. At relatively high AC frequencies (> 32,000 Hz) more current capacitively couples directly through the plasma membranes. At relatively low frequencies (≤ 4000 Hz), the current flows in the solution channels under and between adjacent endothelial cells’ (https://www.biophysics.com/whatIsECIS.php).

Therefore, the high frequency impedance is representing cell-substrate adhesion whereas the low frequency responds more strongly to changes in cell-cell junction connections.

We only measured at 4000 Hz, representing the paracellular permeability. We chose a single frequency to maximize time resolution.

We have added this extra comment to the legend of the figure: ‘(B) Resistance of a monolayer of BOECs stably expressing Lck-mTurquoise2-iLID, solely as a control (grey), and either SspB-HaloTag-TIAM1(DHPH)(purple)/ ITSN1(DHPH) (blue) or p63RhoGEF(DH) (green) measured with ECIS at 4000 Hz, representing paracellular permeability, every 10 s.’

Author response:

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

Reviewer #1 (Recommendations for the authors):

Minor Points:

• HEK293T cells are not typically Type 1 IFN-producing cells; it is recommended to use other immune cell lines to validate results obtained with ORMDL3 overexpression in 293T cells. The same applies to A549 alveolar basal epithelial cells.

Thanks for the reviewer’s insightful comment. In Figure 1C, we overexpressed ORMDL3 in mouse primary BMDM cell and stimulated it with poly(I:C) or poly(dG:dC), which suggests that ORMDL3 inhibits IFN expression in primary cell BMDM.

• Clarify whether TLR3 is expressed in the cell lines used in Figure 1 and whether TLR3 is present in mouse BMDMs.

Thanks for your suggestions. We identified whether TLR3 is expressed in HEK293T, A549 and BMDM. We designed primers of human TLR3 and murine Tlr3, and the results showed that Tlr3 is expressed in BMDM but not in HEK293T and A549. As it shown in Author response image 1.

Author response image 1.

PCR amplification of human TLR3 was conducted on cDNA derived from HEK293T and A549 cells (lanes 1 and 2, respectively), and PCR amplification of murine Tlr3 was performed on cDNA from BMDM (lane 3). Human spleen cDNA (lane 4, TAKARA Human MTCTM Panel I, Cat# 636742) served as a positive control, and 18s rRNA was used as an internal control.

primer sequences:

human TLR3: forward TTGCCTTGTATCTACTTTTGGGG   reverse TCAACACTGTTATGTTTGTGGGT

murine Tlr3: forward GTGAGATACAACGTAGCTGACTG   reverse TCCTGCATCCAAGATAGCAAGT

18s (human/mice): forward GTAACCCGTTGAACCCCATT   reverse CCATCCAATCGGTAGTAGCG

• Specify the type of luciferase reporter assay used in Figure 1E.

Thanks for the reviewer’s insightful comment. The Dual-Luciferase® Reporter (DLR™) Assay System efficiently measures two luciferase signals. In brief, the IFN-reporter luciferase is derived from firefly (Photinus pyralis), while the internal control luciferase is from Renilla (Renilla reniformis or sea pansy). These dual luciferases are measured sequentially from a single sample. In Figure 1E, we measured the luciferase activity of IFN (firefly) and internal control gene TK (Renilla), and their ratio is shown in Figure 1E.

• Clarify what was knocked down in the A549 stable KD cell line and whether HSV-1 infects and replicates in A549 cells.

We sincerely appreciate the reviewer’s concern and apologize for any ambiguous descriptions. In Figure 1H, we knocked down ORMDL3 and infected the cell with HSV-1, which shows that ORMDL3 does not affect the infection and replication of HSV-1 in A549.

• In Figure 2E, provide the rationale for using the same tag (Flag) in overexpression experiments with different molecules such as Flag-ORDML3 and Flag-RIG-I.

We thank the reviewer’s concern. We tried to co-express different tags of ORMDL3 and innate immunity proteins, and we got the same results as before. ORMDL3-Myc overexpression can only promote the degradation of Flag-RIG-I-N, as shown in current Figure 2E.

• Address the low knockdown efficiency shown in Figure 2D and consider whether it is sufficient for drawing conclusions.

Thanks for the reviewer’s concern. Because ORMDL3 antibody (Abcam 107639) can recognize all ORMDL family members (ORMDL1, 2 and 3), this may explain why the knockdown efficiency of ORMDL3 is not apparent in Figure2D. We also detect the knockdown efficiency of ORMDL3 at mRNA level, which showed that ORMDL3 was silenced efficiently and specifically (Figure S2C).

• Replace the Tubulin/β-Actin WB control with a more distinguishable band.

Thanks for the suggestion. Owing to different gel concentration, sometimes the protein bands appear fused, but it is distinguishable that the internal controls are consistent.

• In Figures 3D/E, the expression level of the Lysine mutant of RIG-I-N is too low. Please provide an explanation or repeat the experiment to achieve comparable expression levels and update the figure accordingly.

Thanks for the question. The expression of lysine mutant of RIG-I-N is low, we have increased the amount of plasmid in transfection, but this still hasn't increased its expression level. Though its abundance is low, we provided evidence to show that it would not be degraded by ORMDL3. In some literatures (for example: RNF122 suppresses antiviral type I interferon production by targeting RIG-I CARDs to mediate RIG-I degradation. Proc Natl Acad Sci U S A. 2016 Aug 23;113(34):9581-6; TRIM4 modulates type I interferon induction and cellular antiviral response by targeting RIG-I for K63-linked ubiquitination. J Mol Cell Biol. 2014 Apr;6(2):154-63.), it has also been reported that lysine mutant can affect RIG-I stability. In addition, we speculate that the 4KR mutant (K146R, K154R, K164R, K172R) may change RIG-I conformation, so its expression is lower.

• Explain why there is no difference in MAVS expression levels despite binding with MAVS.

Thanks for the question. In our experiment, ORMDL3 has no effect on MAVS expression. Our results showed that ORMDL3 interacts with MAVS and promotes the degradation of RIG-I, so only RIG-I level has a significant difference.

• Verify if Flag-tagged ORMDL3 is present in the IP sample in Figure 3G.

Thanks for the comment. We reloaded the samples and blot flag, and we found that ORMDL3 cannot be pulled down by RIG-I. We have added the results in Figure 3G.

• Reload the samples in Figure 4C to clearly identify the correct band for GFP-tagged ORMDL3.

Thanks for the question. As ORMDL3 is small molecular protein, we fused it and its fragments to GFP to increase its molecular weight. In our GFP vector, for some unknown reason, the 26kDa band always exists. This is actually a technical difficulty. Although the GFP-fused protein and GFP band are very close, they can still be distinguished as two bands.

• Rerun the Western blot for Actin IB in Figure 4E, as the ORMDL3-GFP (1-153) full-length appears abnormal.

Thanks for the question. As we first blot GFP and then blot actin on the same membrane, so it appears abnormal. We reloaded the previous sample and blotted the actin again.

• Clarify in which figure RIG-I ubiquitination is shown and whether ORMDL3 has E3 ubiquitin ligase activity. Explain how ORMDL3 facilitates USP10 transfer to RIG-I despite no direct interaction.

Thank you for your question. In Figure 3B we showed the ubiquitination of RIG-I and ORMDL3 does not have an E3 ubiquitin ligase activity. Our results showed that although ORMDL3 does not directly interacted with RIG-I, it forms complex with USP10 (Figure 5B, 5C) and disrupt USP10 induced RIG-I stabilization by decreasing the interaction between USP10 and RIG-I (Figure 6A). The detailed mechanism needs further investigation.

• Provide quantification for Figure 5D. Explain why the bands are not degraded by RIG-I and USP10.

Thanks for the concern. We quantified the bands and found that overexpression of USP10 increased RIG-I protein abundance. The quantitative gray values are added into the image. USP10 functions to stabilize RIG-I rather than promoting its degradation.

• Explain the decrease in RIG-I levels in Figure 5E when USP10 levels decrease.

Thanks for the concern. As shown in the working model (Supplementary Figure 8), USP10 is a deubiquitinase that stabilizes RIG-I by decreasing its K48-linked ubiquitination. So, in Figure 5E, we knocked down USP10 and found a decrease in RIG-I levels, which is consistent with Figure 5D.

• Clarify whether K48 ubiquitination on RIG-I has decreased in Figure 5F, as this is not clear from the image.

Thanks for the question. In Figure 5F it is shown that the K48 ubiquitination level of RIG-I significantly decreased (please see the density of the bands in the IP samples).

• Address whether ORMDL3 reduces RIG-I-N degradation in Figure 5H, as the results do not clearly support this claim.

Thanks for the concern. We quantified the bands and the results showed that ORMDL3 promotes the degradation of RIG-I-N. The quantitative gray values are added into the image.

• Reload Flag-ORMDL3 in Figure 6C to determine whether RIG-I-N is restored in the MG132-treated samples.

Thank you for your question. We quantified the bands and the results showed that RIG-I-N is restored in the MG132-treated samples. The quantitative gray values are added into the image.

• Correct numerous typos and errors, especially in the Discussion section, to improve readability

Thanks for the suggestion. We have revised the manuscript carefully to correct these errors.

Reviewer #2 (Recommendations for the authors):

(1) In Figure 1G and H, The number of virus-infected cells was observed using a fluorescence microscope. In addition, can the author use other techniques to detect the impact of ORMDL3 on virus replication?

Thanks for the question. Except for using a fluorescence microscope, we also used RT-PCR to quantify the amount of viral mRNA, and results were added in Figure 1G and H.

(2) In Figure 3C, ORMDL3 overexpression promotes the degradation of RIG-I-N. ORMDL3 is one of three ORMDL proteins with similar amino acid sequences, does ORMDL1/2 also have this function?

Thanks for the suggestion. We compared the function between ORMDLs and found that only ORMDL3 overexpression facilitated RIG-I-N degradation. The results were shown in Figure S2D.

(3) In Figure 5A, USP10 is not the top protein in the Mass spec assay. Does the author verified the interaction between ORMDL3 and other protein (for example CAND1)?

Thanks for your suggestion. We verified that ORMDL3 has no interaction with CAND1 and UFL1 but only interacts with USP10, as Figure S5 shows.

(4) A scale bar to be added to the images in Figure 1 G, H and Figure 7K.

Thanks for the suggestion. We have added the scale bars.

(5) The annotations in Figure 4B, C and E should be aligned.

Thanks for the suggestion. We have aligned the annotations.

(6) Provide Statistical methods

Thanks for the suggestion. We have provided the statistical methods in the materials and methods part.

Author response:

(1) General Statements

As you will see in our attached rebuttal to the reviewers, we have added several new experiments and revised manuscript to fully address their concerns.

(2) Point-by-point description of the revisions

Reviewer #1:

Evidence, reproducibility and clarity

Summary:

The manuscript by Yang et al. describes a new CME accessory protein. CCDC32 has been previously suggested to interact with AP2 and in the present work the authors confirm this interaction and show that it is a bona fide CME regulator. In agreement with its interaction with AP2, CCDC32 recruitment to CCPs mirrors the accumulation of clathrin. Knockdown of CCDC32 reduces the amount of productive CCPs, suggestive of a stabilisation role in early clathrin assemblies. Immunoprecipitation experiments mapped the interaction of CCDC42 to the α-appendage of the AP2 complex α-subunit. Finally, the authors show that the CCDC32 nonsense mutations found in patients with cardio-facial-neuro-developmental syndrome disrupt the interaction of this protein to the AP2 complex. The manuscript is well written and the conclusions regarding the role of CCDC32 in CME are supported by good quality data. As detailed below, a few improvements/clarifications are needed to reinforce some of the conclusions, especially the ones regarding CFNDS.

We thank the referee for their positive comments. In light of a recently published paper describing CCDC32 as a co-chaperone required for AP2 assembly (Wan et al., PNAS, 2024, see reviewer 2), we have added several additional experiments to address all concerns and consequently gained further insight into CCDC32-AP2 interactions and the important dual role of CCDC32 in regulating CME. 

Major comments:

(1) Why did the protein could just be visualized at CCPs after knockdown of the endogenous protein? This is highly unusual, especially on stable cell lines. Could this be that the tag is interfering with the expressed protein function rendering it incapable of outcompeting the endogenous? Does this points to a regulated recruitment?

The reviewer is correct, this would be unusual; however, it is not the case. We misspoke in the text (although the figure legend was correct) these experiments were performed without siRNA knockdown and we can indeed detect eGFP-CCDC32 being recruited to CCPs in the presence of endogenous protein. Nonetheless, we repeated the experiment to be certain (see Author response image 1).  

Author response image 1.

Cohort-averaged fluorescence intensity traces of CCPs (marked with mRuby-CLCa) and CCP-enriched eGFPCCDC32(FL).

(2) The disease mutation used in the paper does not correspond to the truncation found in patients. The authors use an 1-54 truncation, but the patients described in Harel et al. have frame shifts at the positions 19 (Thr19Tyrfs*12) and 64 (Glu64Glyfs*12), while the patient described in Abdalla et al. have the deletion of two introns, leading to a frameshift around amino acid 90. Moreover, to be precisely test the function of these disease mutations, one would need to add the extra amino acids generated by the frame shift. For example, as denoted in the mutation description in Harel et al., the frameshift at position 19 changes the Threonine 19 to a Tyrosine and ads a run of 12 extra amino acids (Thr19Tyrfs*12).

The label of the disease mutant p.(Thr19Tyrfs12) and p.(Glu64Glyfs12) is based on a 194aa polypeptide version of CCDC32 initiated at a nonconventional start site that contains a 9 aa peptide (VRGSCLRFQ) upstream of the N-terminus we show. Thus, we are indeed using the appropriate mutation site (see: https://www.uniprot.org/uniprotkb/Q9BV29/entry). The reviewer is correct that we have not included the extra 12 aa in our construct; however as these residues are not present in the other CFNDS mutants, we think it unlikely that they contribute to the disease phenotype.  Rather, as neither of the clinically observed mutations contain the 78-98 aa sequence required for AP2 binding and CME function, we are confident that this defect contributed to the disease. Thus, we are including the data on the CCDC32(1-54) mutant, as we believe these results provide a valuable physiological context to our studies. 

(3) The frameshift caused by the CFNDS mutations (especially the one studied) will likely lead to nonsense mediated RNA decay (NMD). The frameshift is well within the rules where NMD generally kicks in. Therefore, I am unsure about the functional insights of expressing a diseaserelated protein which is likely not present in patients.

We thank the reviewer for bringing up this concern. However, as shown in new Figure S1, the mutant protein is expressed at comparable levels as the WT, suggesting that NMD is not occurring.

(4) Coiled coils generally form stable dimers. The typically hydrophobic core of these structures is not suitable for transient interactions. This complicates the interpretation of the results regarding the role of this region as the place where the interaction to AP2 occurs. If the coiled coil holds a stable CCDC32 dimer, disrupting this dimer could reduce the affinity to AP2 (by reduced avidity) to the actual binding site. A construct with an orthogonal dimeriser or a pulldown of the delta78-98 protein with of the GST AP2a-AD could be a good way to sort this issue.

We were unable to model a stable dimer (or other oligomer) of this protein with high confidence using Alphafold 3.0. Moreover, we were unable to detect endogenous CCDC32 coimmunoprecipitating with eGFP-CCDC32 (Fig. S6C). Thus, we believe that the moniker, based solely on the alpha-helical content of the protein is a misnomer.  We have explained this in the main text.

Minor comments:

(1) The authors interchangeably use the term "flat CCPs" and "flat clathrin lattices". While these are indeed related, flat clathrin lattices have been also used to refer to "clathrin plaques". To avoid confusion, I suggest sticking to the term "flat CCPs" to refer to the CCPs which are in their early stages of maturation.

Agreed. Thank you for the suggestion. We have renamed these structures flat clathrin assemblies, as they do not acquire the curvature needed to classify them as pits, and do not grow to the size that would classify then as plaques. 

Significance

General assessment:

CME drives the internalisation of hundreds of receptors and surface proteins in practically all tissues, making it an essential process for various physiological processes. This versatility comes at the cost of a large number of molecular players and regulators. To understand this complexity, unravelling all the components of this process is vital. The manuscript by Yang et al. gives an important contribution to this effort as it describes a new CME regulator, CCDC32, which acts directly at the main CME adaptor AP2. The link to disease is interesting, but the authors need to refine their experiments. The requirement for endogenous knockdown for recruitment of the tagged CCDC32 is unusual and requires further exploration.

Advance:

The increased frequency of abortive events presented by CCDC32 knockdown cells is very interesting, as it hints to an active mechanism that regulates the stabilisation and growth of clathrin coated pits. The exact way clathrin coated pits are stabilised is still an open question in the field.

Audience:

This is a basic research manuscript. However, given the essential role of CME in physiology and the growing number of CME players involved in disease, this manuscript can reach broader audiences.

We thank the referee for recognizing the ‘interesting’ advances our studies have made and for considering these studies as ‘an important contribution’ to ‘an essential process for various physiological processes’ and able ‘to reach broader audiences’. We have addressed and reconciled the reviewer’s concerns in our revised manuscript. 

Field of expertise of the reviewer:

Clathrin mediated endocytosis, cell biology, microscopy, biochemistry.

Reviewer #2:

Evidence, reproducibility and clarity

In this manuscript, the authors demonstrate that CCDC32 regulates clathrin-mediated endocytosis (CME). Some of the findings are consistent with a recent report by Wan et al. (2024 PNAS), such as the observation that CCDC32 depletion reduces transferrin uptake and diminishes the formation of clathrin-coated pits. The primary function of CCDC32 is to regulate AP2 assembly, and its depletion leads to AP2 degradation. However, this study did not examine AP2 expression levels. CCDC32 may bind to the appendage domain of AP2 alpha, but it also binds to the core domain of AP2 alpha.

We thank the reviewer for drawing our attention to the Wan et al. paper, that appeared while this work was under review.  However, our in vivo data are not fully consistent with the report from Wan et al. The discrepancies reveal a dual function of CCDC32 in CME that was masked by complete knockout vs siRNA knockdown of the protein, and also likely affected by the position of the GFP-tag (C- vs N-terminal) on this small protein. Thus:

-  Contrary to Wan et al., we do not detect any loss of AP2 expression (see new Figure S3A-B) upon siRNA knockdown. Most likely the ~40% residual CCDC32 present after siRNA knockdown is sufficient to fulfill its catalytic chaperone function but not its structural role in regulating CME beyond the AP2 assembly step.  

- Contrary to Wan et al., we have shown that CCDC32 indeed interacts with intact AP2 complex (Figure S3C and 6B,C) showing that all 4 subunits of the AP2 complex co-IP with full length eGFP-CCDC32. Interestingly, whereas the full length CCDC32 pulls down the intact AP2 complex, co-IP of the ∆78-98 mutant retains its ability to pull down the β2-µ2 hemicomplex, its interactions with α:σ2 are severely reduced.  While this result is consistent with the report of Wan et al that CCDC32 binds to the α:σ2 hemi-complex, it also suggests that the interactions between CCDC32 and AP2 are more complex and will require further studies.

- Contrary to Wan et al., we provide strong evidence that CCDC32 is recruited to CCPs. Interestingly, modeling with AlphaFold 3.0 identifies a highly probably interaction between alpha helices encoded by residues 66-91 on CCDC32 and residues 418-438 on α. The latter are masked by µ2-C in the closed confirmation of the AP2 core, but exposed in the open confirmation triggered by cargo binding, suggesting that CCDC32 might only bind to membrane-bound AP2.

Thus, our findings are indeed novel and indicate striking multifunctional roles for CCDC32 in CME, making the protein well worth further study. 

(1) Besides its role in AP2 assembly, CCDC32 may potentially have another function on the membrane. However, there is no direct evidence showing that CCDC32 associates with the plasma membrane.

We disagree, our data clearly shows that CCDC32 is recruited to CCPs (Fig. 1B) and that CCPs that fail to recruit CCDC32 are short-lived and likely abortive (Fig. 1C). Wan et al. did not observe any colocalization of C-terminally tagged CCDC32 to CCPs, whereas we detect recruitment of our N-terminally tagged construct, which we also show is functional (Fig. 6F).  Further, we have demonstrated the importance of the C-terminal region of CCDC32 in membrane association (see new Fig. S7).  Thus, we speculate that a C-terminally tagged CCDC32 might not be fully functional. Indeed, SIM images of the C-terminally-tagged CCDC32 in Wan et al., show large (~100 nm) structures in the cytosol, which may reflect aggregation. 

(2) CCDC32 binds to multiple regions on AP2, including the core domain. It is important to distinguish the functional roles of these different binding sites.

We have localized the AP2-ear binding region to residues 78-99 and shown these to be critical for the functions we have identified. As described above we now include data that are complementary to those of Wan et al. However, our data also clearly points to additional binding modalities. We agree that it will be important and map these additional interactions and identify their functional roles, but this is beyond the scope of this paper.  

(3) AP2 expression levels should be examined in CCDC32 depleted cells. If AP2 is gone, it is not surprising that clathrin-coated pits are defective.

Agreed and we have confirmed this by western blotting (Figure S3A-B) and detect no reduction in levels of any of the AP2 subunits in CCDC32 siRNA knockdown cells. As stated above this could be due to residual CCDC32 present in the siRNA KD vs the CRISPR-mediated gene KO.

(4) If the authors aim to establish a secondary function for CCDC32, they need to thoroughly discuss the known chaperone function of CCDC32 and consider whether and how CCDC32 regulates a downstream step in CME.

Agreed. We have described the Wan et al paper, which came out while our manuscript was in review, in our Introduction.  As described above, there are areas of agreement and of discrepancies, which are thoroughly documented and discussed throughout the revised manuscript.  

(5) The quality of Figure 1A is very low, making it difficult to assess the localization and quantify the data.

The low signal:noise in Fig. 1A the reviewer is concerned about is due to a diffuse distribution of CCDC32 on the inner surface of the plasma membrane. We now, more explicitly describe this binding, which we believe reflects a specific interaction mediated by the C-terminus of CCDC32; thus the degree of diffuse membrane binding we observe follows: eGFP-CCDC32(FL)> eGFPCCDC32(∆78-98)>eGFP-CCDC32(1-54)~eGFP/background (see new Fig. S7). Importantly, the colocalization of CCDC32 at CCPs is confirmed by the dynamic imaging of CCPs (Fig 1B).

(6) In Figure 6, why aren't AP2 mu and sigma subunits shown?

Agreed. Not being aware of CCDC32’s possible dual role as a chaperone, we had assumed that the AP2 complex was intact.  We have now added this data in Figure 6 B,C and Fig. S3C, as discussed above. 

Page 5, top, this sentence is confusing: "their surface area (~17 x 10 nm²) remains significantly less than that required for the average 100 nm diameter CCV (~3.2 x 103 nm²)."

Thank you for the criticism. We have clarified the sentence and corrected a typo, which would definitely be confusing.  The section now reads,  “While the flat CCSs we detected in CCDC32 knockdown cells were significantly larger than in control cells (Fig. 4D, mean diameter of 147 nm vs. 127 nm, respectively), they are much smaller than typical long-lived flat clathrin lattices (d≥300 nm)(Grove et al., 2014). Indeed, the surface area of the flat CCSs that accumulate in CCDC32 KD cells (mean ~1.69 x 10⁴ nm²) remains significantly less than the surface area of an average 100 nm diameter CCV (~3.14 x 10⁴ nm²). Thus, we refer to these structures as ‘flat clathrin assemblies’ because they are neither curved ‘pits’ nor large ‘lattices’. Rather, the flat clathrin assemblies represent early, likely defective, intermediates in CCP formation.” 

Significance

Overall, while this work presents some interesting ideas, it remains unclear whether CCDC32 regulates AP2 beyond the assembly step.

Our responses above argue that we have indeed established that CCDC32 regulates AP2 beyond the assembly step. We have also identified several discrepancies between our findings and those reported by Wan et al., most notably binding between CCDC32 and mature AP2 complexes and the AP2-dependent recruitment of CCDC32 to CCPs.  It is possible that these discrepancies may be due to the position of the GFP tag (ours is N-terminal, theirs is C-terminal; we show that the N-terminal tagged CCDC32 rescues the knockdown phenotype, while Wan et al., do not provide evidence for functionality of the C-terminal construct). 

Reviewer #3: 

Evidence, reproducibility and clarity (Required): 

In this manuscript, Yang et al. characterize the endocytic accessory protein CCDC32, which has implications in cardio-facio-neuro-developmental syndrome (CFNDS). The authors clearly demonstrate that the protein CCDC32 has a role in the early stages of endocytosis, mainly through the interaction with the major endocytic adaptor protein AP2, and they identify regions taking part in this recognition. Through live cell fluorescence imaging and electron microscopy of endocytic pits, the authors characterize the lifetimes of endocytic sites, the formation rate of endocytic sites and pits and the invagination depth, in addition to transferrin receptor (TfnR) uptake experiments. Binding between CCDC32 and CCDC32 mutants to the AP2 alpha appendage domain is assessed by pull down experiments. Together, these experiments allow deriving a phenotype of CCDC32 knock-down and CCDC32 mutants within endocytosis, which is a very robust system, in which defects are not so easily detected. A mutation of CCDC32, known to play a role in CFNDS, is also addressed in this study and shown to have endocytic defects.

We thank the reviewer for their positive remarks regarding the quality of our data and the strength of our conclusions.  

In summary, the authors present a strong combination of techniques, assessing the impact of CCDC32 in clathrin mediated endocytosis and its binding to AP2, whereby the following major and minor points remain to be addressed: 

- The authors show that CCDC32 depletion leads to the formation of brighter and static clathrin coated structures (Figure 2), but that these were only prevalent to 7.8% and masked the 'normal' dynamic CCPs. At the same time, the authors show that the absence of CCDC32 induces pits with shorter life times (Figure 1 and Figure 2), the 'majority' of the pits.

Clarification is needed as to how the authors arrive at these conclusions and these numbers. The authors should also provide (and visualize) the corresponding statistics. The same statement is made again later on in the manuscript, where the authors explain their electron microscopy data. Was the number derived from there? 

These points are critical to understanding CCDC32's role in endocytosis and is key to understanding the model presented in Figure 8. The numbers of how many pits accumulate in flat lattices versus normal endocytosis progression and the actual time scales could be included in this model and would make the figure much stronger. 

Thank you for these comments.  We understand the paradox between the visual impression and the reality of our dynamic measurements. We have been visually misled by this in previous work (Chen et al., 2020), which emphasizes the importance of unbiased image analysis afforded to us through the well-documented cmeAnalysis pipeline, developed by us (Aguet et al., 2013) and now used by many others (e.g. (He et al., 2020)). 

The % of static structures was not derived from electron microscopy data, but quantified using cmeAnalysis, which automatedly provides the lifetime distribution of CCPs. We have now clarified this in the manuscript and added a histogram (Fig. S4) quantifying the fraction of CCPs in lifetime cohorts  <20s, 21-60s, 61-100s, 101-150s and >150s (static). 

- In relation to the above point, the statistics of Figure 2E-G and the analysis leading there should also be explained in more detail: For example, what are the individual points in the plot (also in Figures 6G and 7G)? The authors should also use a few phrases to explain software they use, for example DASC, in the main text. 

Each point in these bar graphs represents a movie, where n≥12. These details have been added to the respective figure legend. We have also added a brief description of DASC analysis in the text. 

-  There are several questions related to the knock-down experiments that need to be addressed:

Firstly, knock-down of CCDC32 does not seem to be very strong (Figure S2B). Can the level of knock-down be quantified? 

We have now quantified the KD efficiency. It is ~60%. This turns out to be fortuitous (see responses to reviewer 2), as a recent publication, which came out after we completed our study, has shown by CRISPR-mediated knockout, that CCD32 also plays an essential chaperone function required for AP2 assembly.  We do not see any reduction in AP2 levels or its complex formation under our conditions (see new Supplemental Figure S3), which suggests that the effects of CCDC32 on CCP dynamics are more sensitive to CCDC32 concentration than its roles as a chaperone. Our phenotypes would have been masked by more efficient depletion of CCDC32.  

In page 6 it is indicated that the eGFP-CCDC32(1-54) and eGFP-CCDC32(∆78-98) constructs are siRNA-resistant. However in Fig S2B, these proteins do not show any signal in the western blot, so it is not clear if they are expressed or simply not detected by the antibody. The presence of these proteins after silencing endogenous CCDC32 needs to be confirmed to support Figures 6 and Figures 7, which critically rely on the presence of the CCDC32 mutants. 

Unfortunately, the C-terminally truncated CCDC32 proteins are not detected because they lack the antibody epitope, indeed even the ∆78-98 deletion is poorly detected (compare the GFP blot in new S1A with the anti-CCDC32 blot in S1B).  However, these constructs contain the same siRNA-resistance mutation as the full length protein. That they are expressed and siRNA resistant can be seen in Fig. S2A (now Fig. S1A) blotting for GFP.

In Figures 6 and 7, siRNA knock-down of CCDC32 is only indicated for sub-figures F to G. Is this really the case? If not, the authors should clarify. The siRNA knock-down in Figure 1 is also only mentioned in the text, not in the figure legend. The authors should pay attention to make their figure legends easy to understand and unambiguous. 

No, it is not the case.  Thank you for pointing out the uncertainty. We have added these details to the Figure legends and checked all Figure legends to ensure that they clearly describe the data shown.  

- It is not exactly clear how the curves in Figure 3C (lower panel) on the invagination depth were obtained. Can the authors clarify this a bit more? For example, what are kT and kE in Figure 3A? What is I0? And how did the authors derive the logarithmic function used to quantify the invagination depth? In the main text, the authors say that the traces were 'logarithmically transformed'. This is not a technical term. The authors should refer to the actual equation used in the figure. 

This analysis was developed by the Kirchhausen lab (Saffarian and Kirchhausen, 2008). We have added these details and reference them in the Figure legend and in the text. We also now use the more accurate descriptor ‘log-transformed’.

- In the discussion, the claim 'The resulting dysregulation of AP2 inhibits CME, which further results in the development of CFNDS.' is maybe a bit too strong of a statement. Firstly, because the authors show themselves that CME is perturbed, but by no means inhibited. Secondly, the molecular link to CFNDS remains unclear. Even though CCDC32 mutants seem to be responsible for CFNDS and one of the mutant has been shown in this study to have a defect in endocytosis and AP2 binding, a direct link between CCDC32's function in endocytosis and CFNDS remains elusive. The authors should thus provide a more balanced discussion on this topic. 

We have modified and softened our conclusions, which now read that the phenotypes we see likely “contribute to” rather than “cause” the disease.

- In Figure S1, the authors annotate the presence of a coiled-coil domain, which they also use later on in the manuscript to generate mutations. Could the authors specify (and cite) where and how this coiled-coil domain has been identified? Is this predicted helix indeed a coiled-coil domain, or just a helix, as indicated by the authors in the discussion?

See response to Reviewer 1, point 4.  We have changed this wording to alpha-helix. The ‘coiled-coil’ reference is historical and unlikely a true reflection of CCDC32 structure. AlphaFold 3.0 predictions were unable to identify with certainly any coiled-coil structures, even if we modelled potential dimers or trimers; and we find no evidence of dimerization of CCDC32 in vivo. We have clarified this in the text.

Minor comments

- In general, a more detailed explanation of the microscopy techniques used and the information they report would be beneficial to provide access to the article also to non-expert readers in the field. This concerns particularly the analysis methods used, for example: 

How were the cohort-averaged fluorescence intensity and lifetime traces obtained? 

How do the tools cmeAnalysis and DASC work? A brief explanation would be helpful. 

We have expanded Methods to add these details, and also described them in the main text. 

- The axis label of Figure 2B is not quite clear. What does 'TfnR uptake % of surface bound' mean? Maybe the authors could explain this in more detail in the figure legend? Is the drop in uptake efficiency also accessible by visual inspection of the images? It would be interesting to see that. 

This is a standard measure of CME efficiency. 'TfnR uptake % of surface bound' = Internalized TfnR/Surface bound TfnR. Again, images may be misleading as defects in CME lead to increased levels of TfnR on the cell surface, which in turn would result in more Tfn uptake even if the rate of CME is decreased.

- Figure 4: How is the occupancy of CCPs in the plasma membrane measured? What are the criteria used to divide CCSs into Flat, Dome or Sphere categories? 

We have expanded Methods to add these details. Based on the degree of invagination, the shapes of CCSs were classified as either: flat CCSs with no obvious invagination; dome-shaped CCSs that had a hemispherical or less invaginated shape with visible edges of the clathrin lattice; and spherical CCSs that had a round shape with the invisible edges of clathrin lattice in 2D projection images. In most cases, the shapes were obvious in 2D PREM images. In uncertain cases, the degree of CCS invagination was determined using images tilted at ±10–20 degrees. The area of CCSs were measured using ImageJ and used for the calculation of the CCS occupancy on the plasma membrane.

- Figure 5B: Can the authors explain, where exactly the GFP was engineered into AP2 alpha? This construct does not seem to be explained in the methods section. 

We have added this information. The construct, which corresponds to an insertion of GFP into the flexible hinge region of AP2, at aa649, was first described by (Mino et al., 2020) and shown to be fully functional.  This information has been added to the Methods section.

- Figure S1B: The authors should indicate the colour code used for the structural model.

We have expanded our structural modeling using AlphaFold 3.0 in light of the recent publication suggesting the CCDC32 interacts with the µ2 subunit and does not bind full length AP2. These results are described in the text. The color coding now reflects certainty values given by AlphaFold 3.0 (Fig. S6B, D). 

- The list of primers referred to in the materials and methods section does not exist. There is a Table S1, but this contains different data. The actual Table S1 is not referenced in the main text. This should be done. 

We apologize for this error. We have now added this information in Table S2.

Significance (Required):

In this study, the authors analyse a so-far poorly understood endocytic accessory protein, CCDC32, and its implication for endocytosis. The experimental tool set used, allowing to quantify CCP dynamics and invagination is clearly a strength of the article that allows assessing the impact of an accessory protein towards the endocytic uptake mechanism, which is normally very robust towards mutations. Only through this detailed analysis of endocytosis progression could the authors detect clear differences in the presence and absence of CCDC32 and its mutants. If the above points are successfully addressed, the study will provide very interesting and highly relevant work allowing a better understanding of the early phases in CME with implication for disease. 

The study is thus of potential interest to an audience interested in CME, in disease and its molecular reasons, as well as for readers interested in intrinsically disordered proteins to a certain extent, claiming thus a relatively broad audience. The presented results may initiate further studies of the so-far poorly understood and less well known accessory protein CCDC32.

We thank the reviewer for their positive comments on the significance of our findings and the importance of our detailed phenotypic analysis made possible by quantitative live cell microscopy. We also believe that our new structural modeling of CCDC32 and our findings of complex and extensive interactions with AP2 make the reviewers point regarding intrinsically disordered proteins even more interesting and relevant to a broad audience.  We trust that our revisions indeed address the reviewer’s concerns. 

The field of expertise of the reviewer is structural biology, biochemistry and clathrin mediated endocytosis. Expertise in cell biology is rather superficial.

References:

Aguet, F., Costin N. Antonescu, M. Mettlen, Sandra L. Schmid, and G. Danuser. 2013. Advances in Analysis of Low Signal-to-Noise Images Link Dynamin and AP2 to the Functions of an Endocytic Checkpoint. Developmental Cell. 26:279-291.

Chen, Z., R.E. Mino, M. Mettlen, P. Michaely, M. Bhave, D.K. Reed, and S.L. Schmid. 2020. Wbox2: A clathrin terminal domain–derived peptide inhibitor of clathrin-mediated endocytosis. Journal of Cell Biology. 219.

Grove, J., D.J. Metcalf, A.E. Knight, S.T. Wavre-Shapton, T. Sun, E.D. Protonotarios, L.D. Griffin, J. Lippincott-Schwartz, and M. Marsh. 2014. Flat clathrin lattices: stable features of the plasma membrane. Mol Biol Cell. 25:3581-3594.

He, K., E. Song, S. Upadhyayula, S. Dang, R. Gaudin, W. Skillern, K. Bu, B.R. Capraro, I. Rapoport, I. Kusters, M. Ma, and T. Kirchhausen. 2020. Dynamics of Auxilin 1 and GAK in clathrinmediated traffic. J Cell Biol. 219.

Mino, R.E., Z. Chen, M. Mettlen, and S.L. Schmid. 2020. An internally eGFP-tagged α-adaptin is a fully functional and improved fiduciary marker for clathrin-coated pit dynamics. Traffic. 21:603-616.

Saffarian, S., and T. Kirchhausen. 2008. Differential evanescence nanometry: live-cell fluorescence measurements with 10-nm axial resolution on the plasma membrane. Biophys J. 94:23332342.

Author response:

Reviewer #1:

(1) After Figure 1, a single saturated (palmitic acid; PA) and a single unsaturated (linoleic acid; LA) fatty acid are used for the remaining studies, bringing into question whether effects are in fact the result of a difference in saturation vs. other potential differences.

PA, SA, OA and LA are the most common FA species in humans (Figure 1A in manuscript). Among them, PA predominantly represents saturated FAs while LA is the main unsaturated FAs, respectively. Of note, although both SA and OA were included in our studies, their effects were comparable to those of PA and LA, respectively. Due to space constraints, the data of SA and OA are not presented in the figures.  

(2) While primary macrophages are used in several mechanistic studies, tumor-associated macrophages (TAMs) are not used. Rather, correlative evidence is provided to connect mechanistic studies in macrophage cell lines and primary macrophages to TAMs.

The roe of FABP4 in TAMs has been demonstrated in our previous studies using in vivo animal models1. Therefore, we did not include TAM-specific data in the current study.

(3) CEBPA and FABP4 clearly regulate LA-induced changes in gene expression. However, whether these two key proteins act in parallel or as a pathway is not resolved by presented data.

Multiple lines of evidence in our studies suggest that FABP4 and CEBPA act as a pathway in LA-induced changes: 1) FABP4-negative macrophages exhibit reduced expression of CEBPA in single cell sequencing data; 2) FABP4 KO macrophages exhibited reduced CEBPA expression; 3) LA-induced CEBPA expression in macrophages was compromised when FABP4 was absent.

(4) It is very interesting that FABP4 regulates both lipid droplet formation and lipolysis, yet is unclear if the regulation of lipolysis is direct or if the accumulation of lipid droplets - likely plus some other signal(s) - induces upregulation of lipolysis genes.

Yes, it is likely that tumor cells induce lipolysis signals. Multiple studies have shown that various tumor types stimulate lipolysis to support their growth and progression2-4.  In this process, lipid-loaded macrophages have emerged as a promising therapeutic target in cancer5, 6. Consistent with findings that lipolysis is essential for tumor-promoting M2 alternative macrophage activation7, our data using FABP4 WT and KO macrophages demonstrate that FABP4 plays a critical role in LA-induced lipid accumulation and lipolysis for tumor metastasis. 

(5) In several places increased expression of genes coding for enzymes with known functions in lipid biology is conflated with an increase in the lipid biology process the enzymes mediate. Additional evidence would be needed to show these processes are in fact increased in a manner dependent on increased enzyme expression.

We fully agree with the reviewer that increased gene expression does not necessarily equate to increased activity. The key finding of this study is that FABP4 plays a pivotal role in linoleic acid (LA)-mediated lipid accumulation and lipolysis in macrophages that promote tumor metastasis. Numerous lipid metabolism-related genes, including FABP4, CEBPA, GPATs, DGATs, and HSL, are involved in this process. While it was not feasible to verify the activity of all these genes, we confirmed the functional roles of key genes like FABP4 and CEBPA through various functional assays, such as gene silencing, knockout cell lines, lipid droplet formation, and tumor migration assays. Supported by established lipid metabolism pathways, our data provide compelling evidence that FABP4 functions as a crucial lipid messenger, facilitating unsaturated fatty acid-driven lipid accumulation and lipolysis in tumor-associated macrophages (TAMs), thus promoting breast cancer metastasis.   

Reviewer #2:

Overall, there is solid evidence for the importance of FABP4 expression in TAMs on metastatic breast cancer as well as lipid accumulation by LA in the ER of macrophages. A stronger rationale for the exclusive contribution of unsaturated fatty acids to the utilization of TAMs in breast cancer and a more detailed description and statistical analysis of data will strengthen the findings and resulting claims.

We greatly appreciated the positive comments from Reviewer #2. In our study, we evaluated the effects of both saturated and unsaturated fatty acids (FA) on lipid metabolism in macrophages.  Our results showed that unsaturated FAs exhibited a preference for lipid accumulation in macrophages compared to saturated FAs. Further analysis revealed that unsaturated LA, but not saturated PA, induced FABP4 nuclear translocation and CEBPA activation, driving the TAG synthesis pathway. For in vitro experiments, statistical analyses were performed using a two-tailed, unpaired student t-test, two-way ANOVA followed by Bonferroni’s multiple comparison test, with GraphPad Prism 9. For experiments analyzing associations of FABP4, TAMs and other factors in breast cancer patients, the Kruskal-Wallis test was applied to compare differences across levels of categorical predictor variable. Additionally, multiple linear regression models were used to examine the association between the predictor variables and outcomes, with log transformation and Box Cox transformation applied to meet the normality assumptions of the model. It is worth noting that in some experiments, only significant differences were observed in groups treated with unsaturated fatty acids. Non-significant results from groups treated with saturated fatty acids were not included in the figures.

Reviewer #3

(1) While the authors speculate that UFA-activated FABP4 translocates to the nucleus to activate PPARgamma, which is known to induce C/EBPalpha expression, they do not directly test involvement of PPARgamma in this axis.

Yes, LA induced FABP4 nuclear translocation and activation of PPARgamma in macrophages (see Figure below). Since these findings have been reported in multiple other studies 8, 9, we did not include the data in the current manuscript.

Author response image 1.

LA induced PPARg expression in macrophages. Bone-marrow derived macrophages were treated with 400μM saturated FA (SFA), unsaturated FA (UFA) or BSA control for 6 hours. PPARg expression was measured by qPCR (***p<0.001).

(2) While there is clear in vitro evidence that co-cultured murine macrophages genetically deficient in FABP4 (or their conditioned media) do not enhance breast cancer cell motility and invasion, these macrophages are not bonafide TAM - which may have different biology. Use of actual TAM in these experiments would be more compelling. Perhaps more importantly, there is no in vivo data in tumor bearing mice that macrophage-deficiency of FABP4 affects tumor growth or metastasis.

In our previous studies, we have shown that macrophage-deficiency of FABP4 reduced tumor growth and metastasis in vivo in mouse models1.

(3) Related to this, the authors find FABP4 in the media and propose that macrophage secreted FABP4 is mediating the tumor migration - but don't do antibody neutralizing experiments to directly demonstrate this.

Yes, we have recently published a paper of developing anti-FABP4 antibody for treatment of breast cancer in moue models10.

(4) No data is presented that the mechanisms/biology that are elegantly demonstrated in the murine macrophages also occurs in human macrophages - which would be foundational to translating these findings into human breast cancer.

Thanks for the excellent suggestions. Since this manuscript primarily focuses on mechanistic studies using mouse models, we plan to apply these findings in our future human studies. 

(5) While the data from the human breast cancer specimens is very intriguing, it is difficult to ascertain how accurate IHC is in determining that the CD163+ cells (TAM) are in fact the same cells expressing FABP4 - which is central premise of these studies. Demonstration that IHC has the resolution to do this would be important. Additionally, the in vitro characterization of FABP4 expression in human macrophages would also add strength to these findings.

The expression of FABP4 in CD163+ TAM observed through IHC is consistent with our previous findings, where we confirmed FABP4 expression in CD163+ TAMs using confocal microscopy. Emerging evidence further supports the pro-tumor role of FABP4 expression in human macrophages across various types of obesity-associated cancers11-13. 

References

(1) Hao J, Yan F, Zhang Y, Triplett A, Zhang Y, Schultz DA, Sun Y, Zeng J, Silverstein KAT, Zheng Q, Bernlohr DA, Cleary MP, Egilmez NK, Sauter E, Liu S, Suttles J, Li B. Expression of Adipocyte/Macrophage Fatty Acid-Binding Protein in Tumor-Associated Macrophages Promotes Breast Cancer Progression. Cancer Res. 2018;78(9):2343-55. Epub 2018/02/14. doi: 10.1158/0008-5472.CAN-17-2465. PubMed PMID: 29437708; PMCID: PMC5932212.

(2) Nieman KM, Kenny HA, Penicka CV, Ladanyi A, Buell-Gutbrod R, Zillhardt MR, Romero IL, Carey MS, Mills GB, Hotamisligil GS, Yamada SD, Peter ME, Gwin K, Lengyel E. Adipocytes promote ovarian cancer metastasis and provide energy for rapid tumor growth. Nat Med. 2011;17(11):1498-503. Epub 20111030. doi: 10.1038/nm.2492. PubMed PMID: 22037646; PMCID: PMC4157349.

(3) Wang YY, Attane C, Milhas D, Dirat B, Dauvillier S, Guerard A, Gilhodes J, Lazar I, Alet N, Laurent V, Le Gonidec S, Biard D, Herve C, Bost F, Ren GS, Bono F, Escourrou G, Prentki M, Nieto L, Valet P, Muller C. Mammary adipocytes stimulate breast cancer invasion through metabolic remodeling of tumor cells. JCI Insight. 2017;2(4):e87489. Epub 20170223. doi: 10.1172/jci.insight.87489. PubMed PMID: 28239646; PMCID: PMC5313068.

(4) Balaban S, Shearer RF, Lee LS, van Geldermalsen M, Schreuder M, Shtein HC, Cairns R, Thomas KC, Fazakerley DJ, Grewal T, Holst J, Saunders DN, Hoy AJ. Adipocyte lipolysis links obesity to breast cancer growth: adipocyte-derived fatty acids drive breast cancer cell proliferation and migration. Cancer Metab. 2017;5:1. Epub 20170113. doi: 10.1186/s40170-016-0163-7. PubMed PMID: 28101337; PMCID: PMC5237166.

(5) Masetti M, Carriero R, Portale F, Marelli G, Morina N, Pandini M, Iovino M, Partini B, Erreni M, Ponzetta A, Magrini E, Colombo P, Elefante G, Colombo FS, den Haan JMM, Peano C, Cibella J, Termanini A, Kunderfranco P, Brummelman J, Chung MWH, Lazzeri M, Hurle R, Casale P, Lugli E, DePinho RA, Mukhopadhyay S, Gordon S, Di Mitri D. Lipid-loaded tumor-associated macrophages sustain tumor growth and invasiveness in prostate cancer. J Exp Med. 2022;219(2). Epub 20211217. doi: 10.1084/jem.20210564. PubMed PMID: 34919143; PMCID: PMC8932635.

(6) Marelli G, Morina N, Portale F, Pandini M, Iovino M, Di Conza G, Ho PC, Di Mitri D. Lipid-loaded macrophages as new therapeutic target in cancer. J Immunother Cancer. 2022;10(7). doi: 10.1136/jitc-2022-004584. PubMed PMID: 35798535; PMCID: PMC9263925.

(7) Huang SC, Everts B, Ivanova Y, O'Sullivan D, Nascimento M, Smith AM, Beatty W, Love-Gregory L, Lam WY, O'Neill CM, Yan C, Du H, Abumrad NA, Urban JF, Jr., Artyomov MN, Pearce EL, Pearce EJ. Cell-intrinsic lysosomal lipolysis is essential for alternative activation of macrophages. Nat Immunol. 2014;15(9):846-55. Epub 2014/08/05. doi: 10.1038/ni.2956. PubMed PMID: 25086775; PMCID: PMC4139419.

(8) Gillilan RE, Ayers SD, Noy N. Structural basis for activation of fatty acid-binding protein 4. J Mol Biol. 2007;372(5):1246-60. Epub 2007/09/01. doi: 10.1016/j.jmb.2007.07.040. PubMed PMID: 17761196; PMCID: PMC2032018.

(9) Bassaganya-Riera J, Reynolds K, Martino-Catt S, Cui Y, Hennighausen L, Gonzalez F, Rohrer J, Benninghoff AU, Hontecillas R. Activation of PPAR gamma and delta by conjugated linoleic acid mediates protection from experimental inflammatory bowel disease. Gastroenterology. 2004;127(3):777-91. doi: 10.1053/j.gastro.2004.06.049. PubMed PMID: 15362034.

(10) Hao J, Jin R, Yi Y, Jiang X, Yu J, Xu Z, Schnicker NJ, Chimenti MS, Sugg SL, Li B. Development of a humanized anti-FABP4 monoclonal antibody for potential treatment of breast cancer. Breast Cancer Res. 2024;26(1):119. Epub 20240725. doi: 10.1186/s13058-024-01873-y. PubMed PMID: 39054536; PMCID: PMC11270797.

(11) Liu S, Wu D, Fan Z, Yang J, Li Y, Meng Y, Gao C, Zhan H. FABP4 in obesity-associated carcinogenesis: Novel insights into mechanisms and therapeutic implications. Front Mol Biosci. 2022;9:973955. Epub 20220819. doi: 10.3389/fmolb.2022.973955. PubMed PMID: 36060264; PMCID: PMC9438896.

(12) Miao L, Zhuo Z, Tang J, Huang X, Liu J, Wang HY, Xia H, He J. FABP4 deactivates NF-kappaB-IL1alpha pathway by ubiquitinating ATPB in tumor-associated macrophages and promotes neuroblastoma progression. Clin Transl Med. 2021;11(4):e395. doi: 10.1002/ctm2.395. PubMed PMID: 33931964; PMCID: PMC8087928.

(13) Yang J, Liu S, Li Y, Fan Z, Meng Y, Zhou B, Zhang G, Zhan H. FABP4 in macrophages facilitates obesity-associated pancreatic cancer progression via the NLRP3/IL-1beta axis. Cancer Lett. 2023;575:216403. Epub 20230921. doi: 10.1016/j.canlet.2023.216403. PubMed PMID: 37741433.

Author response:

Reviewer #1 (Evidence, reproducibility and clarity):

Authors has provided a mechanism by which how presence of truncated P53 can inactivate function of full length P53 protein. Authors proposed this happens by sequestration of full length P53 by truncated P53.

In the study, performed experiments are well described.

My area of expertise is molecular biology/gene expression, and I have tried to provide suggestions on my area of expertise. The study has been done mainly with overexpression system and I have included few comments which I can think can be helpful to understand effect of truncated P53 on endogenous wild type full length protein. Performing experiments on these lines will add value to the observation according to this reviewer.

Major comments:

(1) What happens to endogenous wild type full length P53 in the context of mutant/truncated isoforms, that is not clear. Using a P53 antibody which can detect endogenous wild type P53, can authors check if endogenous full length P53 protein is also aggregated as well? It is hard to differentiate if aggregation of full length P53 happens only in overexpression scenario, where lot more both of such proteins are expressed. In normal physiological condition P53 expression is usually low, tightly controlled and its expression get induced in altered cellular condition such as during DNA damage. So, it is important to understand the physiological relevance of such aggregation, which could be possible if authors could investigate effect on endogenous full length P53 following overexpression of mutant isoforms.

Thank you very much for your insightful comments.

(1) To address “what happens to endogenous wild-type full-length P53 in the context of mutant/truncated isoforms," we employed a human A549 cell line expressing endogenous wild-type p53 under DNA damage conditions such as an etoposide treatment(1). We choose the A549 cell line since similar to H1299, it is a lung cancer cell line (www.atcc.org). For comparison, we also transfected the cells with 2 μg of V5-tagged plasmids encoding FLp53 and its isoforms Δ133p53 and Δ160p53. As shown in Author response image 1A, lanes 1 and 2, endogenous p53 expression, remained undetectable in A549 cells despite etoposide treatment, which limits our ability to assess the effects of the isoforms on the endogenous wild-type FLp53. We could, however, detect the V5-tagged FLp53 expressed from the plasmid using anti-V5 (rabbit) as well as with antiDO-1 (mouse) antibody (Author response image 1). The latter detects both endogenous wildtype p53 and the V5-tagged FLp53 since the antibody epitope is within the Nterminus (aa 20-25). This result supports the reviewer’s comment regarding the low level of expression of endogenous p53 that is insufficient for detection in our experiments.   

In summary, in line with the reviewer’s comment that ‘under normal physiological conditions p53 expression is usually low,’ we could not detect p53 with an anti-DO-1 antibody. Thus, we proceeded with V5/FLAG-tagged p53 for detection of the effects of the isoforms on p53 stability and function. We also found that protein expression in H1299 cells was more easily detectable than in A549 cells (Compare Author response image 1A and B). Thus, we decided to continue with the H1299 cells (p53-null), which would serve as a more suitable model system for this study.  

(2) We agree with the reviewer that ‘It is hard to differentiate if aggregation of full-length p53 happens only in overexpression scenario’. However, it is not impossible to imagine that such aggregation of FLp53 happens under conditions when p53 and its isoforms are over-expressed in the cell. Although the exact physiological context is not known and beyond the scope of the current work, our results indicate that at higher expression, p53 isoforms drive aggregation of FLp53. Given the challenges of detecting endogenous FLp53, we had to rely on the results obtained with plasmid mediated expression of p53 and its isoforms in p53-null cells.

Author response image 1.

Comparative analysis of protein expression in A549 and H1299 cells. (A) A549 cells (p53 wild-type) were treated with etoposide to induce endogenous wild-type p53 expression. To assess the effects of FLp53 and its isoforms Δ133p53 and Δ160p53 on endogenous wild-type p53 aggregation, A549 cells were transfected with 2 μg of V5-tagged p53 expression plasmids, with or without etoposide (20μM for 8h) treatment. Western blot analysis was done with the anti-V5 (rabbit) to detect V5-tagged proteins and anti-DO-1 (mouse), the latter detects both endogenous wild-type p53 and V5-tagged FLp53. The merged image corresponds to the overlay between the V5 and DO1 antibody signals. (B) H1299 cells (p53-null) were transfected with 2 μg V5tagged p53 expression plasmids or the empty vector control pcDNA3.1. Western blot analysis was done with the anti-V5 (mouse) antibody. 

(2) Can presence of mutant P53 isoforms can cause functional impairment of wild type full length endogenous P53? That could be tested as well using similar ChIP assay authors has performed, but instead of antibody against the Tagged protein if the authors could check endogenous P53 enrichment in the gene promoter such as P21 following overexpression of mutant isoforms. May be introducing a condition such as DNA damage in such experiment might help where endogenous P53 is induced and more prone to bind to P53 target such as P21.

Thank you very much for your valuable comments and suggestions. To investigate the potential functional impairment of endogenous wild-type p53 by p53 isoforms, we initially utilized A549 cells (p53 wild-type), aiming to monitor endogenous wild-type p53 expression following DNA damage. However, as mentioned and demonstrated in Author response image 1, endogenous p53 expression was too low to be detected under these conditions, making the ChIP assay for analyzing endogenous p53 activity unfeasible. Thus, we decided to utilize plasmid-based expression of FLp53 and focus on the potential functional impairment induced by the isoforms.

(3) On similar lines, authors described:

"To test this hypothesis, we escalated the ratio of FLp53 to isoforms to 1:10. As expected, the activity of all four promoters decreased significantly at this ratio (Figure 4A-D). Notably, Δ160p53 showed a more potent inhibitory effect than Δ133p53 at the 1:5 ratio on all promoters except for the p21 promoter, where their impacts were similar (Figure 4E-H). However, at the 1:10 ratio, Δ133p53 and Δ160p53 had similar effects on all transactivation except for the MDM2 promoter (Figure 4E-H)."

Again, in such assay authors used ratio 1:5 to 1:10 full length vs mutant. How authors justify this result in context (which is more relevant context) where one allele is Wild type (functional P53) and another allele is mutated (truncated, can induce aggregation). In this case one would except 1:1 ratio of full-length vs mutant protein, unless other regulation is going which induces expression of mutant isoforms more than wild type full length protein. Probably discussing on these lines might provide more physiological relevance to the observed data.

Thank you for raising this point regarding the physiological relevance of the ratios used in our study.

(1) In the revised manuscript (lines 193-195), we added in this direction that “The elevated Δ133p53 protein modulates p53 target genes such as miR‑34a and p21, facilitating cancer development(2, 3). To mimic conditions where isoforms are upregulated relative to FLp53, we increased the ratios to 1:5 and 1:10.” This approach aims to simulate scenarios where isoforms accumulate at higher levels than FLp53, which may be relevant in specific contexts, as also elaborated above.

(2) Regarding the issue of protein expression, where one allele is wild-type and the other is isoform, this assumption is not valid in most contexts. First, human cells have two copies of TPp53 gene (one from each parent). Second, the TP53 gene has two distinct promoters: the proximal promoter (P1) primarily regulates FLp53 and ∆40p53, whereas the second promoter (P2) regulates ∆133p53 and ∆160p53(4, 5). Additionally, ∆133TP53 is a p53 target gene(6, 7) and the expression of Δ133p53 and FLp53 is dynamic in response to various stimuli. Third, the expression of p53 isoforms is regulated at multiple levels, including transcriptional, post-transcriptional, translational, and post-translational processing(8). Moreover, different degradation mechanisms modify the protein level of p53 isoforms and FLp53(8). These differential regulation mechanisms are regulated by various stimuli, and therefore, the 1:1 ratio of FLp53 to ∆133p53 or ∆160p53 may be valid only under certain physiological conditions. In line with this, varied expression levels of FLp53 and its isoforms, including ∆133p53 and ∆160p53, have been reported in several studies(3, 4, 9, 10). 

(3) In our study, using the pcDNA 3.1 vector under the human cytomegalovirus (CMV) promoter, we observed moderately higher expression levels of ∆133p53 and ∆160p53 relative to FLp53 (Author response image 1B). This overexpression scenario provides a model for studying conditions where isoform accumulation might surpass physiological levels, impacting FLp53 function. By employing elevated ratios of these isoforms to FLp53, we aim to investigate the potential effects of isoform accumulation on FLp53.

(4) Finally does this altered function of full length P53 (preferably endogenous one) in presence of truncated P53 has any phenotypic consequence on the cells (if authors choose a cell type which is having wild type functional P53). Doing assay such as apoptosis/cell cycle could help us to get this visualization.

Thank you for your insightful comments. In the experiment with A549 cells (p53 wild-type), endogenous p53 levels were too low to be detected, even after DNA damage induction. The evaluation of the function of endogenous p53 in the presence of isoforms is hindered, as mentioned above. In the revised manuscript, we utilized H1299 cells with overexpressed proteins for apoptosis studies using the Caspase-Glo® 3/7 assay (Figure 7). This has been shown in the Results section (lines 254-269). “The Δ133p53 and Δ160p53 proteins block pro-apoptotic function of FLp53.

One of the physiological read-outs of FLp53 is its ability to induce apoptotic cell death(11). To investigate the effects of p53 isoforms Δ133p53 and Δ160p53 on FLp53-induced apoptosis, we measured caspase-3 and -7 activities in H1299 cells expressing different p53 isoforms (Figure 7). Caspase activation is a key biochemical event in apoptosis, with the activation of effector caspases (caspase-3 and -7) ultimately leading to apoptosis(12). The caspase-3 and -7 activities induced by FLp53 expression was approximately 2.5 times higher than that of the control vector (Figure 7). Co-expression of FLp53 and the isoforms Δ133p53 or Δ160p53 at a ratio of 1: 5 significantly diminished the apoptotic activity of FLp53 (Figure 7). This result aligns well with our reporter gene assay, which demonstrated that elevated expression of Δ133p53 and Δ160p53 impaired the expression of apoptosis-inducing genes BAX and PUMA (Figure 4G and H). Moreover, a reduction in the apoptotic activity of FLp53 was observed irrespective of whether Δ133p53 or Δ160p53 protein was expressed with or without a FLAG tag (Figure 7). This result, therefore, also suggests that the FLAG tag does not affect the apoptotic activity or other physiological functions of FLp53 and its isoforms. Overall, the overexpression of p53 isoforms Δ133p53 and Δ160p53 significantly attenuates FLp53-induced apoptosis, independent of the protein tagging with the FLAG antibody epitope.”

Referees cross-commenting

I think the comments from the other reviewers are very much reasonable and logical.

Especially all 3 reviewers have indicated, a better way to visualize the aggregation of full-length wild type P53 by truncated P53 (such as looking at endogenous P53# by reviewer 1, having fluorescent tag #by reviewer 2 and reviewer 3 raised concern on the FLAG tag) would add more value to the observation.

Thank you for these comments. The endogenous p53 protein was undetectable in A549 cells induced by etoposide (Figure R1A). Therefore, we conducted experiments using FLAG/V5-tagged FLp53.  To avoid any potential side effects of the FLAG tag on p53 aggregation, we introduced untagged p53 isoforms in the H1299 cells and performed subcellular fractionation. Our revised results, consistent with previous FLAG-tagged p53 isoforms findings, demonstrate that co-expression of untagged isoforms with FLAG-tagged FLp53 significantly induced the aggregation of FLAG-FLp53, while no aggregation was observed when FLAG-tagged FLp53 was expressed alone (Supplementary Figure 6). These results clearly indicate that the FLAG tag itself does not contribute to protein aggregation. 

Additionally, we utilized the A11 antibody to detect protein aggregation, providing additional validation (Figure 8 from Jean-Christophe Bourdon et al. Genes Dev. 2005;19:2122-2137). Given that the fluorescent proteins (~30 kDa) are substantially bigger than the tags used here (~1 kDa) and may influence oligomerization (especially GFP), stability, localization, and function of p53 and its isoforms, we avoided conducting these vital experiments with such artificial large fusions. 

Reviewer #1 (Significance):

The work in significant, since it points out more mechanistic insight how wild type full length P53 could be inactivated in the presence of truncated isoforms, this might offer new opportunity to recover P53 function as treatment strategies against cancer.

Thank you for your insightful comments. We appreciate your recognition of the significance of our work in providing mechanistic insights into how wild-type FLp53 can be inactivated by truncated isoforms. We agree that these findings have potential for exploring new strategies to restore p53 function as a therapeutic approach against cancer. 

Reviewer #2 (Evidence, reproducibility and clarity):

The manuscript by Zhao and colleagues presents a novel and compelling study on the p53 isoforms, Δ133p53 and Δ160p53, which are associated with aggressive cancer types. The main objective of the study was to understand how these isoforms exert a dominant negative effect on full-length p53 (FLp53). The authors discovered that the Δ133p53 and Δ160p53 proteins exhibit impaired binding to p53-regulated promoters. The data suggest that the predominant mechanism driving the dominant-negative effect is the coaggregation of FLp53 with Δ133p53 and Δ160p53.

This study is innovative, well-executed, and supported by thorough data analysis. However, the authors should address the following points:

(1) Introduction on Aggregation and Co-aggregation: Given that the focus of the study is on the aggregation and co-aggregation of the isoforms, the introduction should include a dedicated paragraph discussing this issue. There are several original research articles and reviews that could be cited to provide context.

Thank you very much for the valuable comments. We have added the following paragraph in the revised manuscript (lines 74-82): “Protein aggregation has become a central focus of modern biology research and has documented implications in various diseases, including cancer(13, 14, 15). Protein aggregates can be of different types ranging from amorphous aggregates to highly structured amyloid or fibrillar aggregates, each with different physiological implications. In the case of p53, whether protein aggregation, and in particular, co-aggregation with large N-terminal deletion isoforms, plays a mechanistic role in its inactivation is yet underexplored. Interestingly, the Δ133p53β isoform has been shown to aggregate in several human cancer cell lines(16). Additionally, the Δ40p53α isoform exhibits a high aggregation tendency in endometrial cancer cells(17). Although no direct evidence exists for Δ160p53 yet, these findings imply that p53 isoform aggregation may play a major role in their mechanisms of actions.”

(2) Antibody Use for Aggregation: To strengthen the evidence for aggregation, the authors should consider using antibodies that specifically bind to aggregates.

Thank you for your insightful suggestion. We addressed protein aggregation using the A11 antibody which specifically recognizes amyloid-like protein aggregates. We analyzed insoluble nuclear pellet samples prepared under identical conditions as described in Figure 6B. To confirm the presence of p53 proteins, we employed the anti-p53 M19 antibody (Santa Cruz, Cat No. sc-1312) to detect bands corresponding to FLp53 and its isoforms Δ133p53 and Δ160p53. The monomer FLp53 was not detected (Figure 8, lower panel, Jean-Christophe Bourdon et al. Genes Dev. 2005;19:2122-2137), which may be attributed to the lower binding affinity of the anti-p53 M19 antibody to it. These samples were also immunoprecipitated using the A11 antibody (Thermo Fischer Scientific, Cat No. AHB0052) to detect aggregated proteins. Interestingly, FLp53 and its isoforms, Δ133p53 and Δ160p53, were clearly visible with Anti-A11 antibody when co-expressed at a 1:5 ratio suggesting that they underwent co-aggregation. However, no FLp53 aggregates were observed when it was expressed alone (Author response image 2). These results support the conclusion in our manuscript that Δ133p53 and Δ160p53 drive FLp53 aggregation. 

Author response image 2.

Induction of FLp53 Aggregation by p53 Isoforms Δ133p53 and Δ160p53. H1299 cells transfected with the FLAG-tagged FLp53 and V5-tagged Δ133p53 or Δ160p53 at a 1:5 ratio. The cells were subjected to subcellular fractionation, and the resulting insoluble nuclear pellet was resuspended in RIPA buffer. The samples were heated at 95°C until the pellet was completely dissolved, and then analyzed by Western blotting. Immunoprecipitation was performed using the A11 antibody, which specifically recognizes amyloid protein aggregates, and the anti-p53 M19 antibody, which detects FLp53 as well as its isoforms Δ133p53 and Δ160p53. 

(3) Fluorescence Microscopy: Live-cell fluorescence microscopy could be employed to enhance visualization by labeling FLp53 and the isoforms with different fluorescent markers (e.g., EGFP and mCherry tags).

We appreciate the suggestion to use live-cell fluorescence microscopy with EGFP and mCherry tags for the visualization FLp53 and its isoforms. While we understand the advantages of live-cell imaging with EGFP / mCherry tags, we restrained us from doing such fusions as the GFP or corresponding protein tags are very big (~30 kDa) with respect to the p53 isoform variants (~30 kDa).  Other studies have shown that EGFP and mCherry fusions can alter protein oligomerization, solubility and aggregation(18, 19) Moreover, most fluorescence proteins are prone to dimerization (i.e. EGFP) or form obligate tetramers (DsRed)(20, 21, 22), potentially interfering with the oligomerization and aggregation properties of p53 isoforms, particularly Δ133p53 and Δ160p53.

Instead, we utilized FLAG- or V5-tag-based immunofluorescence microscopy, a well-established and widely accepted method for visualizing p53 proteins. This method provided precise localization and reliable quantitative data, which we believe meet the needs of the current study. We believe our chosen method is both appropriate and sufficient for addressing the research question.

Reviewer #2 (Significance):

The manuscript by Zhao and colleagues presents a novel and compelling study on the p53 isoforms, Δ133p53 and Δ160p53, which are associated with aggressive cancer types. The main objective of the study was to understand how these isoforms exert a dominant negative effect on full-length p53 (FLp53). The authors discovered that the Δ133p53 and Δ160p53 proteins exhibit impaired binding to p53-regulated promoters. The data suggest that the predominant mechanism driving the dominant-negative effect is the coaggregation of FLp53 with Δ133p53 and Δ160p53.

We sincerely thank the reviewer for the thoughtful and positive comments on our manuscript and for highlighting the significance of our findings on the p53 isoforms, Δ133p53 and Δ160p53. 

Reviewer #3 (Evidence, reproducibility and clarity):

In this manuscript entitled "Δ133p53 and Δ160p53 isoforms of the tumor suppressor protein p53 exert dominant-negative effect primarily by coaggregation", the authors suggest that the Δ133p53 and Δ160p53 isoforms have high aggregation propensity and that by co-aggregating with canonical p53 (FLp53), they sequestrate it away from DNA thus exerting a dominantnegative effect over it.

First, the authors should make it clear throughout the manuscript, including the title, that they are investigating Δ133p53α and Δ160p53α since there are 3 Δ133p53 isoforms (α, β, γ), and 3 Δ160p53 isoforms (α, β, γ).

Thank you for your suggestion. We understand the importance of clearly specifying the isoforms under study. Following your suggestion, we have added α in the title, abstract, and introduction and added the following statement in the Introduction (lines 57-59): “For convenience and simplicity, we have written Δ133p53 and Δ160p53 to represent the α isoforms (Δ133p53α and Δ160p53α) throughout this manuscript.” 

One concern is that the authors only consider and explore Δ133p53α and Δ160p53α isoforms as exclusively oncogenic and FLp53 dominant-negative while not discussing evidences of different activities. Indeed, other manuscripts have also shown that Δ133p53α is non-oncogenic and non-mutagenic, do not antagonize every single FLp53 functions and are sometimes associated with good prognosis. To cite a few examples:

(1) Hofstetter G. et al. D133p53 is an independent prognostic marker in p53 mutant advanced serous ovarian cancer. Br. J. Cancer 2011, 105, 15931599.

(2) Bischof, K. et al. Influence of p53 Isoform Expression on Survival in HighGrade Serous Ovarian Cancers. Sci. Rep. 2019, 9,5244.

(3) Knezovi´c F. et al. The role of p53 isoforms' expression and p53 mutation status in renal cell cancer prognosis. Urol. Oncol. 2019, 37, 578.e1578.e10.

(4) Gong, L. et al. p53 isoform D113p53/D133p53 promotes DNA doublestrand break repair to protect cell from death and senescence in response to DNA damage. Cell Res. 2015, 25, 351-369.

(5) Gong, L. et al. p53 isoform D133p53 promotes efficiency of induced pluripotent stem cells and ensures genomic integrity during reprogramming. Sci. Rep. 2016, 6, 37281.

(6) Horikawa, I. et al. D133p53 represses p53-inducible senescence genes and enhances the generation of human induced pluripotent stem cells. Cell Death Differ. 2017, 24, 1017-1028.

(7) Gong, L. p53 coordinates with D133p53 isoform to promote cell survival under low-level oxidative stress. J. Mol. Cell Biol. 2016, 8, 88-90.

Thank you very much for your comment and for highlighting these important studies. 

We agree that Δ133p53 isoforms exhibit complex biological functions, with both oncogenic and non-oncogenic potentials. However, our mission here was primarily to reveal the molecular mechanism for the dominant-negative effects exerted by the Δ133p53α and Δ160p53α isoforms on FLp53 for which the Δ133p53α and Δ160p53α isoforms are suitable model systems. Exploring the oncogenic potential of the isoforms is beyond the scope of the current study and we have not claimed anywhere that we are reporting that. We have carefully revised the manuscript and replaced the respective terms e.g. ‘prooncogenic activity’ with ‘dominant-negative effect’ in relevant places (e.g. line 90). We have now also added a paragraph with suitable references that introduces the oncogenic and non-oncogenic roles of the p53 isoforms.

After reviewing the papers you cited, we are not sure that they reflect on oncogenic /non-oncogenic role of the Δ133p53α isoform in different cancer cases.  Although our study is not about the oncogenic potential of the isoforms, we have summarized the key findings below:

(1) Hofstetter et al., 2011: Demonstrated that Δ133p53α expression improved recurrence-free and overall survival (in a p53 mutant induced advanced serous ovarian cancer, suggesting a potential protective role in this context.

(2) Bischof et al., 2019: Found that Δ133p53 mRNA can improve overall survival in high-grade serous ovarian cancers. However, out of 31 patients, only 5 belong to the TP53 wild-type group, while the others carry TP53 mutations.

(3) Knezović et al., 2019: Reported downregulation of Δ133p53 in renal cell carcinoma tissues with wild-type p53 compared to normal adjacent tissue, indicating a potential non-oncogenic role, but not conclusively demonstrating it.

(4) Gong et al., 2015: Showed that Δ133p53 antagonizes p53-mediated apoptosis and promotes DNA double-strand break repair by upregulating RAD51, LIG4, and RAD52 independently of FLp53.

(5) Gong et al., 2016: Demonstrated that overexpression of Δ133p53 promotes efficiency of cell reprogramming by its anti-apoptotic function and promoting DNA DSB repair. The authors hypotheses that this mechanism is involved in increasing RAD51 foci formation and decrease γH2AX foci formation and chromosome aberrations in induced pluripotent stem (iPS) cells, independent of FL p53.

(6) Horikawa et al., 2017: Indicated that induced pluripotent stem cells derived from fibroblasts that overexpress Δ133p53 formed noncancerous tumors in mice compared to induced pluripotent stem cells derived from fibroblasts with complete p53 inhibition. Thus, Δ133p53 overexpression is "non- or less oncogenic and mutagenic" compared to complete p53 inhibition, but it still compromises certain p53-mediated tumor-suppressing pathways. “Overexpressed Δ133p53 prevented FL-p53 from binding to the regulatory regions of p21WAF1 and miR-34a promoters, providing a mechanistic basis for its dominant-negative

inhibition of a subset of p53 target genes.”

(7) Gong, 2016: Suggested that Δ133p53 promotes cell survival under lowlevel oxidative stress, but its role under different stress conditions remains uncertain.

We have revised the Introduction to provide a more balanced discussion of Δ133p53’s dule role (lines 62-73):

“The Δ133p53 isoform exhibit complex biological functions, with both oncogenic and non-oncogenic potentials. Recent studies demonstrate the non-oncogenic yet context-dependent role of the Δ133p53 isoform in cancer development. Δ133p53 expression has been reported to correlate with improved survival in patients with TP53 mutations(23, 24), where it promotes cell survival in a nononcogenic manner(25, 26), especially under low oxidative stress(27). Alternatively, other recent evidences emphasize the notable oncogenic functions of Δ133p53 as it can inhibit p53-dependent apoptosis by directly interacting with the FLp53 (4, 6). The oncogenic function of the newly identified Δ160p53 isoform is less known, although it is associated with p53 mutation-driven tumorigenesis(28) and in melanoma cells’ aggressiveness(10). Whether or not the Δ160p53 isoform also impedes FLp53 function in a similar way as Δ133p53 is an open question. However, these p53 isoforms can certainly compromise p53-mediated tumor suppression by interfering with FLp53 binding to target genes such as p21 and miR-34a(2, 29) by dominant-negative effect, the exact mechanism is not known.” On the figures presented in this manuscript, I have three major concerns:

(1) Most results in the manuscript rely on the overexpression of the FLAGtagged or V5-tagged isoforms. The validation of these construct entirely depends on Supplementary figure 3 which the authors claim "rules out the possibility that the FLAG epitope might contribute to this aggregation. However, I am not entirely convinced by that conclusion. Indeed, the ratio between the "regular" isoform and the aggregates is much higher in the FLAG-tagged constructs than in the V5-tagged constructs. We can visualize the aggregates easily in the FLAG-tagged experiment, but the imaging clearly had to be overexposed (given the white coloring demonstrating saturation of the main bands) to visualize them in the V5-tagged experiments. Therefore, I am not convinced that an effect of the FLAG-tag can be ruled out and more convincing data should be added. 

Thank you for raising this important concern. We have carefully considered your comments and have made several revisions to clarify and strengthen our conclusions.

First, to address the potential influence of the FLAG and V5 tags on p53 isoform aggregation, we have revised Figure 2 and removed the previous Supplementary Figure 3, where non-specific antibody bindings and higher molecular weight aggregates were not clearly interpretable. In the revised Figure 2, we have removed these potential aggregates, improving the clarity and accuracy of the data.

To further rule out any tag-related artifacts, we conducted a coimmunoprecipitation assay with FLAG-tagged FLp53 and untagged Δ133p53 and Δ160p53 isoforms. The results (now shown in the new Supplementary Figure 3) completely agree with our previous result with FLAG-tagged and V5tagged Δ133p53 and Δ160p53 isoforms and show interaction between the partners. This indicates that the FLAG / V5-tags do not influence / interfere with the interaction between FLp53 and the isoforms. We have still used FLAGtagged FLp53 as the endogenous p53 was undetectable and the FLAG-tagged FLp53 did not aggregate alone. 

In the revised paper, we added the following sentences (Lines 146-152): “To rule out the possibility that the observed interactions between FLp53 and its isoforms Δ133p53 and Δ160p53 were artifacts caused by the FLAG and V5 antibody epitope tags, we co-expressed FLAG-tagged FLp53 with untagged Δ133p53 and Δ160p53. Immunoprecipitation assays demonstrated that FLAGtagged FLp53 could indeed interact with the untagged Δ133p53 and Δ160p53 isoforms (Supplementary Figure 3, lanes 3 and 4), confirming formation of hetero-oligomers between FLp53 and its isoforms. These findings demonstrate that Δ133p53 and Δ160p53 can oligomerize with FLp53 and with each other.”

Additionally, we performed subcellular fractionation experiments to compare the aggregation and localization of FLAG-tagged FLp53 when co-expressed either with V5-tagged or untagged Δ133p53/Δ160p53. In these experiments, the untagged isoforms also induced FLp53 aggregation, mirroring our previous results with the tagged isoforms (Supplementary Figure 5). We’ve added this result in the revised manuscript (lines 236-245): “To exclude the possibility that FLAG or V5 tags contribute to protein aggregation, we also conducted subcellular fractionation of H1299 cells expressing FLAG-tagged FLp53 along with untagged Δ133p53 or Δ160p53 at a 1:5 ratio. The results showed (Supplementary Figure 6) a similar distribution of FLp53 across cytoplasmic, nuclear, and insoluble nuclear fractions as in the case of tagged Δ133p53 or Δ160p53 (Figure 6A to D). Notably, the aggregation of untagged Δ133p53 or Δ160p53 markedly promoted the aggregation of FLAG-tagged FLp53 (Supplementary Figure 6B and D), demonstrating that the antibody epitope tags themselves do not contribute to protein aggregation.” 

We’ve also discussed this in the Discussion section (lines 349-356): “In our study, we primarily utilized an overexpression strategy involving FLAG/V5tagged proteins to investigate the effects of p53 isoforms Δ133p53 and Δ160p53 on the function of FLp53. To address concerns regarding potential overexpression artifacts, we performed the co-immunoprecipitation (Supplementary Figure 6) and caspase-3 and -7 activity (Figure 7) experiments with untagged Δ133p53 and Δ160p53. In both experimental systems, the untagged proteins behaved very similarly to the FLAG/V5 antibody epitopecontaining proteins (Figures 6 and 7 and Supplementary Figure 6). Hence, the C-terminal tagging of FLp53 or its isoforms does not alter the biochemical and physiological functions of these proteins.”

In summary, the revised data set and newly added experiments provide strong evidence that neither the FLAG nor the V5 tag contributes to the observed p53 isoform aggregation.

(2) The authors demonstrate that to visualize the dominant-negative effect, Δ133p53α and Δ160p53α must be "present in a higher proportion than FLp53 in the tetramer" and the need at least a transfection ratio 1:5 since the 1:1 ration shows no effect. However, in almost every single cell type, FLp53 is far more expressed than the isoforms which make it very unlikely to reach such stoichiometry in physiological conditions and make me wonder if this mechanism naturally occurs at endogenous level. This limitation should be at least discussed.

Thank you for your insightful comment. However, evidence suggests that the expression levels of these isoforms such as Δ133p53, can be significantly elevated relative to FLp53 in certain physiological conditions(3, 4, 9). For example, in some breast tumors, with Δ133p53 mRNA is expressed at a much levels than FLp53, suggesting a distinct expression profile of p53 isoforms compared to normal breast tissue(4). Similarly, in non-small cell lung cancer and the A549 lung cancer cell line, the expression level of Δ133p53 transcript is significantly elevated compared to non-cancerous cells(3). Moreover, in specific cholangiocarcinoma cell lines, the Δ133p53 /TAp53 expression ratio has been reported to increase to as high as 3:1(9). These observations indicate that the dominant-negative effect of isoform Δ133p53 on FLp53 can occur under certain pathological conditions where the relative amounts of the FLp53 and the isoforms would largely vary. Since data on the Δ160p53 isoform are scarce, we infer that the long N-terminal truncated isoforms may share a similar mechanism.

(3) Figure 5C: I am concerned by the subcellular location of the Δ133p53α and Δ160p53α as they are commonly considered nuclear and not cytoplasmic as shown here, particularly since they retain the 3 nuclear localization sequences like the FLp53 (Bourdon JC et al. 2005; Mondal A et al. 2018; Horikawa I et al, 2017; Joruiz S. et al, 2024). However, Δ133p53α can form cytoplasmic speckles (Horikawa I et al, 2017) when it colocalizes with autophagy markers for its degradation.

The authors should discuss this issue. Could this discrepancy be due to the high overexpression level of these isoforms? A co-staining with autophagy markers (p62, LC3B) would rule out (or confirm) activation of autophagy due to the overwhelming expression of the isoform.

Thank you for your thoughtful comments. We have thoroughly reviewed all the papers you recommended (Bourdon JC et al., 2005; Mondal A et al., 2018; Horikawa I et al., 2017; Joruiz S. et al., 2024)(4, 29, 30, 31). Among these, only the study by Bourdon JC et al. (2005) provided data regarding the localization of Δ133p53(4). Interestingly, their findings align with our observations, indicating that the protein does not exhibit predominantly nuclear localization in the Figure 8 from Jean-Christophe Bourdon et al. Genes Dev. 2005;19:2122-2137. The discrepancy may be caused by a potentially confusing statement in that paper(4).

The localization of p53 is governed by multiple factors, including its nuclear import and export(32). The isoforms Δ133p53 and Δ160p53 contain three nuclear localization sequences (NLS)(4). However, the isoforms Δ133p53 and Δ160p53 were potentially trapped in the cytoplasm by aggregation and masking the NLS. This mechanism would prevent nuclear import. 

Further, we acknowledge that Δ133p53 co-aggregates with autophagy substrate p62/SQSTM1 and autophagosome component LC3B in cytoplasm by autophagic degradation during replicative senescence(33). We agree that high overexpression of these aggregation-prone proteins may induce endoplasmic reticulum (ER) stress and activates autophagy(34). This could explain the cytoplasmic localization in our experiments. However, it is also critical to consider that we observed aggregates in both the cytoplasm and the nucleus (Figures 6B and E and Supplementary Figure 6B). While cytoplasmic localization may involve autophagy-related mechanisms, the nuclear aggregates likely arise from intrinsic isoform properties, such as altered protein folding, independent of autophagy. These dual localizations reflect the complex behavior of Δ133p53 and Δ160p53 isoforms under our experimental conditions.

In the revised manuscript, we discussed this in Discussion (lines 328-335): “Moreover, the observed cytoplasmic isoform aggregates may reflect autophagy-related degradation, as suggested by the co-localization of Δ133p53 with autophagy substrate p62/SQSTM1 and autophagosome component LC3B(33). High overexpression of these aggregation-prone proteins could induce endoplasmic reticulum stress and activate autophagy(34). Interestingly, we also observed nuclear aggregation of these isoforms (Figure 6B and E and Supplementary Figure 6B), suggesting that distinct mechanisms, such as intrinsic properties of the isoforms, may govern their localization and behavior within the nucleus. This dual localization underscores the complexity of Δ133p53 and Δ160p53 behavior in cellular systems.”

Minor concerns:

-  Figure 1A: the initiation of the "Δ140p53" is shown instead of "Δ40p53"

Thank you! The revised Figure 1A has been created in the revised paper.

-  Figure 2A: I would like to see the images cropped a bit higher, so the cut does not happen just above the aggregate bands

Thank you for this suggestion. We’ve changed the image and the new Figure 2 has been shown in the revised paper.

-  Figure 3C: what ratio of FLp53/Delta isoform was used?

We have added the ratio in the figure legend of Figure 3C (lines 845-846) “Relative DNA-binding of the FLp53-FLAG protein to the p53-target gene promoters in the presence of the V5-tagged protein Δ133p53 or Δ160p53 at a 1: 1 ratio.”

-  Figure 3C suggests that the "dominant-negative" effect is mostly senescencespecific as it does not affect apoptosis target genes, which is consistent with Horikawa et al, 2017 and Gong et al, 2016 cited above. Furthermore, since these two references and the others from Gong et al. show that Δ133p53α increases DNA repair genes, it would be interesting to look at RAD51, RAD52 or Lig4, and maybe also induce stress.

Thank you for your thoughtful comments and suggestions. In Figure 3C, the presence of Δ133p53 or Δ160p53 only significantly reduced the binding of FLp53 to the p21 promoter. However, isoforms Δ133p53 and Δ160p53 demonstrated a significant loss of DNA-binding activity at all four promoters: p21, MDM2, and apoptosis target genes BAX and PUMA (Figure 3B). This result suggests that Δ133p53 and Δ160p53 have the potential to influence FLp53 function due to their ability to form hetero-oligomers with FLp53 or their intrinsic tendency to aggregate. To further investigate this, we increased the isoform to FLp53 ratio in Figure 4, which demonstrate that the isoforms Δ133p53 and Δ160p53 exert dominant-negative effects on the function of FLp53. 

These results demonstrate that the isoforms can compromise p53-mediated pathways, consistent with Horikawa et al. (2017), which showed that Δ133p53α overexpression is "non- or less oncogenic and mutagenic" compared to complete p53 inhibition, but still affects specific tumor-suppressing pathways. Furthermore, as noted by Gong et al. (2016), Δ133p53’s anti-apoptotic function under certain conditions is independent of FLp53 and unrelated to its dominantnegative effects.

We appreciate your suggestion to investigate DNA repair genes such as RAD51, RAD52, or Lig4, especially under stress conditions. While these targets are intriguing and relevant, we believe that our current investigation of p53 targets in this manuscript sufficiently supports our conclusions regarding the dominant-negative effect. Further exploration of additional p53 target genes, including those involved in DNA repair, will be an important focus of our future studies.

- Figure 5A and B: directly comparing the level of FLp53 expressed in cytoplasm or nucleus to the level of Δ133p53α and Δ160p53α expressed in cytoplasm or nucleus does not mean much since these are overexpressed proteins and therefore depend on the level of expression. The authors should rather compare the ratio of cytoplasmic/nuclear FLp53 to the ratio of cytoplasmic/nuclear Δ133p53α and Δ160p53α.

Thank you very much for this valuable suggestion. In the revised paper, Figure 5B has been recreated.  Changes have been made in lines 214215: “The cytoplasm-to-nucleus ratio of Δ133p53 and Δ160p53 was approximately 1.5-fold higher than that of FLp53 (Figure 5B).” 

Referees cross-commenting

I agree that the system needs to be improved to be more physiological.

Just to precise, the D133 and D160 isoforms are not truncated mutants, they are naturally occurring isoforms expressed in almost every normal human cell type from an internal promoter within the TP53 gene.

Using overexpression always raises concerns, but in this case, I am even more careful because the isoforms are almost always less expressed than the FLp53, and here they have to push it 5 to 10 times more expressed than the FLp53 to see the effect which make me fear an artifact effect due to the overwhelming overexpression (which even seems to change the normal localization of the protein).

To visualize the endogenous proteins, they will have to change cell line as the H1299 they used are p53 null.

Thank you for these comments. We’ve addressed the motivation of overexpression in the above responses. We needed to use the plasmid constructs in the p53-null cells to detect the proteins but the expression level was certainly not ‘overwhelmingly high’. 

First, we tried the A549 cells (p53 wild-type) under DNA damage conditions, but the endogenous p53 protein was undetectable. Second, several studies reported increased Δ133p53 level compared to wild-type p53 and that it has implications in tumor development(2, 3, 4, 9). Third, the apoptosis activity of H1299 cells overexpressing p53 proteins was analyzed in the revised manuscript (Figure 7). The apoptotic activity induced by FLp53 expression was approximately 2.5 times higher than that of the control vector under identical plasmid DNA transfection conditions (Figure 7). These results rule out the possibility that the plasmid-based expression of p53 and its isoforms introduced artifacts in the results. We’ve discussed this in the Results section (lines 254269).

Reviewer #3 (Significance):

Overall, the paper is interesting particularly considering the range of techniques used which is the main strength.

The main limitation to me is the lack of contradictory discussion as all argumentation presents Δ133p53α and Δ160p53α exclusively as oncogenic and strictly FLp53 dominant-negative when, particularly for Δ133p53α, a quite extensive literature suggests a not so clear-cut activity.

The aggregation mechanism is reported for the first time for Δ133p53α and Δ160p53α, although it was already published for Δ40p53α, Δ133p53β or in mutant p53.

This manuscript would be a good basic research addition to the p53 field to provide insight in the mechanism for some activities of some p53 isoforms.

My field of expertise is the p53 isoforms which I have been working on for 11 years in cancer and neuro-degenerative diseases

Thank you very much for your positive and critical comments. We’ve included a fair discussion on the oncogenic and non-oncogenic function of Δ133p53 in the Introduction following your suggestion (lines 62-73). 

References

(1) Pitolli C, Wang Y, Candi E, Shi Y, Melino G, Amelio I. p53-Mediated Tumor Suppression: DNA-Damage Response and Alternative Mechanisms. Cancers 11,  (2019).

(2) Fujita K, et al. p53 isoforms Delta133p53 and p53beta are endogenous regulators of replicative cellular senescence. Nature cell biology 11, 1135-1142 (2009).

(3) Fragou A, et al. Increased Δ133p53 mRNA in lung carcinoma corresponds with reduction of p21 expression. Molecular medicine reports 15, 1455-1460 (2017).

(4) Bourdon JC, et al. p53 isoforms can regulate p53 transcriptional activity. Genes & development 19, 2122-2137 (2005).

(5) Ghosh A, Stewart D, Matlashewski G. Regulation of human p53 activity and cell localization by alternative splicing. Molecular and cellular biology 24, 7987-7997 (2004).

(6) Aoubala M, et al. p53 directly transactivates Δ133p53α, regulating cell fate outcome in response to DNA damage. Cell death and differentiation 18, 248-258 (2011).

(7) Marcel V, et al. p53 regulates the transcription of its Delta133p53 isoform through specific response elements contained within the TP53 P2 internal promoter. Oncogene 29, 2691-2700 (2010).

(8) Zhao L, Sanyal S. p53 Isoforms as Cancer Biomarkers and Therapeutic Targets. Cancers 14,  (2022).

(9) Nutthasirikul N, Limpaiboon T, Leelayuwat C, Patrakitkomjorn S, Jearanaikoon P. Ratio disruption of the ∆133p53 and TAp53 isoform equilibrium correlates with poor clinical outcome in intrahepatic cholangiocarcinoma. International journal of oncology 42, 1181-1188 (2013).

(10) Tadijan A, et al. Altered Expression of Shorter p53 Family Isoforms Can Impact Melanoma Aggressiveness. Cancers 13,  (2021).

(11) Aubrey BJ, Kelly GL, Janic A, Herold MJ, Strasser A. How does p53 induce apoptosis and how does this relate to p53-mediated tumour suppression? Cell death and differentiation 25, 104-113 (2018).

(12) Ghorbani N, Yaghubi R, Davoodi J, Pahlavan S. How does caspases regulation play role in cell decisions? apoptosis and beyond. Molecular and cellular biochemistry 479, 1599-1613 (2024).

(13) Petronilho EC, et al. Oncogenic p53 triggers amyloid aggregation of p63 and p73 liquid droplets. Communications chemistry 7, 207 (2024).

(14) Forget KJ, Tremblay G, Roucou X. p53 Aggregates penetrate cells and induce the coaggregation of intracellular p53. PloS one 8, e69242 (2013).

(15) Farmer KM, Ghag G, Puangmalai N, Montalbano M, Bhatt N, Kayed R. P53 aggregation, interactions with tau, and impaired DNA damage response in Alzheimer's disease. Acta neuropathologica communications 8, 132 (2020).

(16) Arsic N, et al. Δ133p53β isoform pro-invasive activity is regulated through an aggregation-dependent mechanism in cancer cells. Nature communications 12, 5463 (2021).

(17) Melo Dos Santos N, et al. Loss of the p53 transactivation domain results in high amyloid aggregation of the Δ40p53 isoform in endometrial carcinoma cells. The Journal of biological chemistry 294, 9430-9439 (2019).

(18) Mestrom L, et al. Artificial Fusion of mCherry Enhances Trehalose Transferase Solubility and Stability. Applied and environmental microbiology 85,  (2019).

(19) Kaba SA, Nene V, Musoke AJ, Vlak JM, van Oers MM. Fusion to green fluorescent protein improves expression levels of Theileria parva sporozoite surface antigen p67 in insect cells. Parasitology 125, 497-505 (2002).

(20) Snapp EL, et al. Formation of stacked ER cisternae by low affinity protein interactions. The Journal of cell biology 163, 257-269 (2003).

(21) Jain RK, Joyce PB, Molinete M, Halban PA, Gorr SU. Oligomerization of green fluorescent protein in the secretory pathway of endocrine cells. The Biochemical journal 360, 645-649 (2001).

(22) Campbell RE, et al. A monomeric red fluorescent protein. Proceedings of the National Academy of Sciences of the United States of America 99, 7877-7882 (2002).

(23) Hofstetter G, et al. Δ133p53 is an independent prognostic marker in p53 mutant advanced serous ovarian cancer. British journal of cancer 105, 1593-1599 (2011).

(24) Bischof K, et al. Influence of p53 Isoform Expression on Survival in High-Grade Serous Ovarian Cancers. Scientific reports 9, 5244 (2019).

(25) Gong L, et al. p53 isoform Δ113p53/Δ133p53 promotes DNA double-strand break repair to protect cell from death and senescence in response to DNA damage. Cell research 25, 351-369 (2015).

(26) Gong L, et al. p53 isoform Δ133p53 promotes efficiency of induced pluripotent stem cells and ensures genomic integrity during reprogramming. Scientific reports 6, 37281 (2016).

(27) Gong L, Pan X, Yuan ZM, Peng J, Chen J. p53 coordinates with Δ133p53 isoform to promote cell survival under low-level oxidative stress. Journal of molecular cell biology 8, 88-90 (2016).

(28) Candeias MM, Hagiwara M, Matsuda M. Cancer-specific mutations in p53 induce the translation of Δ160p53 promoting tumorigenesis. EMBO reports 17, 1542-1551 (2016).

(29) Horikawa I, et al. Δ133p53 represses p53-inducible senescence genes and enhances the generation of human induced pluripotent stem cells. Cell death and differentiation 24, 1017-1028 (2017).

(30) Mondal AM, et al. Δ133p53α, a natural p53 isoform, contributes to conditional reprogramming and long-term proliferation of primary epithelial cells. Cell death & disease 9, 750 (2018).

(31) Joruiz SM, Von Muhlinen N, Horikawa I, Gilbert MR, Harris CC. Distinct functions of wild-type and R273H mutant Δ133p53α differentially regulate glioblastoma aggressiveness and therapy-induced senescence. Cell death & disease 15, 454 (2024).

(32) O'Brate A, Giannakakou P. The importance of p53 location: nuclear or cytoplasmic zip code? Drug resistance updates : reviews and commentaries in antimicrobial and anticancer chemotherapy 6, 313-322 (2003).

(33) Horikawa I, et al. Autophagic degradation of the inhibitory p53 isoform Δ133p53α as a regulatory mechanism for p53-mediated senescence. Nature communications 5, 4706 (2014).

(34) Lee H, et al. IRE1 plays an essential role in ER stress-mediated aggregation of mutant huntingtin via the inhibition of autophagy flux. Human molecular genetics 21, 101-114 (2012).

Author response:

Public Reviews:

Reviewer #1 (Public Review):

Summary:

In their manuscript entitled 'The domesticated transposon protein L1TD1 associates with its ancestor L1 ORF1p to promote LINE-1 retrotransposition', Kavaklıoğlu and colleagues delve into the role of L1TD1, an RNA binding protein (RBP) derived from a LINE1 transposon. L1TD1 proves crucial for maintaining pluripotency in embryonic stem cells and is linked to cancer progression in germ cell tumors, yet its precise molecular function remains elusive. Here, the authors uncover an intriguing interaction between L1TD1 and its ancestral LINE-1 retrotransposon.

The authors delete the DNA methyltransferase DNMT1 in a haploid human cell line (HAP1), inducing widespread DNA hypo-methylation. This hypomethylation prompts abnormal expression of L1TD1. To scrutinize L1TD1's function in a DNMT1 knock-out setting, the authors create DNMT1/L1TD1 double knock-out cell lines (DKO). Curiously, while the loss of global DNA methylation doesn't impede proliferation, additional depletion of L1TD1 leads to DNA damage and apoptosis.

To unravel the molecular mechanism underpinning L1TD1's protective role in the absence of DNA methylation, the authors dissect L1TD1 complexes in terms of protein and RNA composition. They unveil an association with the LINE-1 transposon protein L1-ORF1 and LINE-1 transcripts, among others.

Surprisingly, the authors note fewer LINE-1 retro-transposition events in DKO cells than in DNMT1 KO alone.

Strengths:

The authors present compelling data suggesting the interplay of a transposon-derived human RNA binding protein with its ancestral transposable element. Their findings spur interesting questions for cancer types, where LINE1 and L1TD1 are aberrantly expressed.

Weaknesses:

Suggestions for refinement:

The initial experiment, inducing global hypo-methylation by eliminating DNMT1 in HAP1 cells, is intriguing and warrants a more detailed description. How many genes experience misregulation or aberrant expression? What phenotypic changes occur in these cells?

The transcriptome analysis of DNMT1 KO cells showed hundreds of deregulated genes upon DNMT1 ablation. As expected, the majority were up-regulated and gene ontology analysis revealed that among the strongest up-regulated genes were gene clusters with functions in “regulation of transcription from RNA polymerase II promoter” and “cell differentiation” and genes encoding proteins with KRAB domains. In addition, the de novo methyltransferases DNMT3A and DNMT3B were up-regulated in DNMT1 KO cells suggesting the set-up of compensatory mechanisms in these cells. We will include this data set in the revised version of the manuscript.

Why did the authors focus on L1TD1? Providing some of this data would be helpful to understand the rationale behind the thorough analysis of L1TD1.

We have previously discovered that conditional deletion of the maintenance DNA methyltransferase DNMT1 in the murine epidermis results not only in the up-regulation of mobile elements, such as IAPs but also the induced expression of L1TD1 ((Beck et al, 2021), Suppl. Table 1 and Author response image 1). Similary, L1TD1 expression was induced by treatment of primary human keratinocytes or squamous cell carcinoma cells with the DNMT inhibitor aza-deoxycytidine (Author response image 2 and 3). These finding are in accordance with the observation that inhibition of DNA methyltransferase activity by azadeoxycytidine in human non-small cell lung cancer cells (NSCLCs) results in upregulation of L1TD1 (Altenberger et al, 2017). Our interest in L1TD1 was further fueled by reports on a potential function of L1TD1 as prognostic tumor marker. We will include this information in the revised manuscript.

Author response image 1.

RT-qPCR of L1TD1 expression in cultured murine control and Dnmt1 Δ/Δker keratinocytes. mRNA levels of L1td1 were analyzed in keratinocytes isolated at P5 from conditional Dnmt1 knockout mice (Beck et al., 2021). Hprt expression was used for normalization of mRNA levels and wildtype control was set to 1. Data represent means ±s.d. with n=4. **P < 0.01 (paired t-test).

Author response image 2.

RT-qPCR analysis of L1TD1 expression in primary human keratinocytes. Cells were treated with 5-aza-2-deoxycidine for 24 hours or 48 hours, with PBS for 48 hours or were left untreated. 18S rRNA expression was used for normalization of mRNA levels and PBS control was set to 1. Data represent means ±s.d. with n=3. **P < 0.01 (paired t-test).

Author response image 3.

Induced L1TD1 expression upon DNMT inhibition in squamous cell carcinoma cell lines SCC9 and SCCO12. Cells were treated with 5-aza-2-deoxycidine for 24 hours, 48 hours or 6 days. (A) Western blot analysis of L1TD1 protein levels using beta-actin as loading control. (B) Indirect immunofluorescence microscopy analysis of L1TD1 expression in SCC9 cells. Nuclear DNA was stained with DAPI. Scale bar: 10 µm. (C) RT-qPCR analysis of L1TD1 expression in primary human keratinocytes. Cells were treated with 5-aza-2deoxycidine for 24 hours or 48 hours, with PBS for 48 hours or were left untreated. 18S rRNA expression was used for normalization of mRNA levels and PBS control was set to 1. Data represent means ±s.d. with n=3. P < 0.05, *P < 0.01 (paired t-test).

The finding that L1TD1/DNMT1 DKO cells exhibit increased apoptosis and DNA damage but decreased L1 retro-transposition is unexpected. Considering the DNA damage associated with retro-transposition and the DNA damage and apoptosis observed in L1TD1/DNMT1 DKO cells, one would anticipate the opposite outcome. Could it be that the observation of fewer transposition-positive colonies stems from the demise of the most transposition-positive colonies? Further exploration of this phenomenon would be intriguing.

This is an important point and we were aware of this potential problem. Therefore, we calibrated the retrotransposition assay by transfection with a blasticidin resistance gene vector to take into account potential differences in cell viability and blasticidin sensitivity. Thus, the observed reduction in L1 retrotransposition efficiency is not an indirect effect of reduced cell viability.

Based on previous studies with hESCs, it is likely that, in addition to its role in retrotransposition, L1TD1 has additional functions in the regulation of cell proliferation and differentiation. L1TD1 might therefore attenuate the effect of DNMT1 loss in KO cells generating an intermediate phenotype (as pointed out by Reviewer 2) and simultaneous loss of both L1TD1 and DNMT1 results in more pronounced effects on cell viability.

Reviewer #2 (Public Review):

In this study, Kavaklıoğlu et al. investigated and presented evidence for the role of domesticated transposon protein L1TD1 in enabling its ancestral relative, L1 ORF1p, to retrotranspose in HAP1 human tumor cells. The authors provided insight into the molecular function of L1TD1 and shed some clarifying light on previous studies that showed somewhat contradictory outcomes surrounding L1TD1 expression. Here, L1TD1 expression was correlated with L1 activation in a hypomethylation-dependent manner, due to DNMT1 deletion in the HAP1 cell line. The authors then identified L1TD1-associated RNAs using RIP-Seq, which displays a disconnect between transcript and protein abundance (via Tandem Mass Tag multiplex mass spectrometry analysis). The one exception was for L1TD1 itself, which is consistent with a model in which the RNA transcripts associated with L1TD1 are not directly regulated at the translation level. Instead, the authors found the L1TD1 protein associated with L1-RNPs, and this interaction is associated with increased L1 retrotransposition, at least in the contexts of HAP1 cells. Overall, these results support a model in which L1TD1 is restrained by DNA methylation, but in the absence of this repressive mark, L1TD1 is expressed and collaborates with L1 ORF1p (either directly or through interaction with L1 RNA, which remains unclear based on current results), leads to enhances L1 retrotransposition. These results establish the feasibility of this relationship existing in vivo in either development, disease, or both.

Author Response:

Assessment note: “Whereas the results and interpretations are generally solid, the mechanistic aspect of the work and conclusions put forth rely heavily on in vitro studies performed in cultured L6 myocytes, which are highly glycolytic and generally not viewed as a good model for studying muscle metabolism and insulin action.”

While we acknowledge that in vitro models may not fully recapitulate the complexity of in vivo systems, we believe that our use of L6 myotubes is appropriate for studying the mechanisms underlying muscle metabolism and insulin action. As mentioned below (reviewer 2, point 1), L6 myotubes possess many important characteristics relevant to our research, including high insulin sensitivity and a similar mitochondrial respiration sensitivity to primary muscle fibres. Furthermore, several studies have demonstrated the utility of L6 myotubes as a model for studying insulin sensitivity and metabolism, including our own previous work (PMID: 19805130, 31693893, 19915010).

In addition, we have provided evidence of the similarities between L6 cells overexpressing SMPD5 and human muscle biopsies at protein levels and the reproducibility of the negative correlation between ceramide and Coenzyme Q observed in L6 cells in vivo, specifically in the skeletal muscle of mice in chow diet. These findings support the relevance of our in vitro results to in vivo muscle metabolism.

Finally, we will supplement our findings by demonstrating a comparable relationship between ceramide and Coenzyme Q in mice exposed to a high-fat diet, to be shown in Supplementary Figure 4 H-I. Further animal experiments will be performed to validate our cell-line based conclusions. We hope that these additional results address the concerns raised by the reviewer and further support the relevance of our in vitro findings to in vivo muscle metabolism and insulin action.

Points from reviewer 1:

1. Although the authors' results suggest that higher mitochondrial ceramide levels suppress cellular insulin sensitivity, they rely solely on a partial inhibition (i.e., 30%) of insulin-stimulated GLUT4-HA translocation in L6 myocytes. It would be critical to examine how much the increased mitochondrial ceramide would inhibit insulin-induced glucose uptake in myocytes using radiolabel deoxy-glucose.

Response: The primary impact of insulin is to facilitate the translocation of glucose transporter type 4 (GLUT4) to the cell surface, which effectively enhances the maximum rate of glucose uptake into cells. Therefore, assessing the quantity of GLUT4 present at the cell surface in non-permeabilized cells is widely regarded as the most reliable measure of insulin sensitivity (PMID: 36283703, 35594055, 34285405). Additionally, plasma membrane GLUT4 and glucose uptake are highly correlated. Whilst we have routinely measured glucose uptake with radiolabelled glucose in the past, we do not believe that evaluating glucose uptake provides a better assessment of insulin sensitivity than GLUT4.

We will clarify the use of GLUT4 translocation in the Results section:

“...For this reason, several in vitro models have been employed involving incubation of insulin sensitive cell types with lipids such as palmitate to mimic lipotoxicity in vivo. In this study we will use cell surface GLUT4-HA abundance as the main readout of insulin response...”

1. Another important question to be addressed is whether glycogen synthesis is affected in myocytes under these experimental conditions. Results demonstrating reductions in insulin-stimulated glucose transport and glycogen synthesis in myocytes with dysfunctional mitochondria due to ceramide accumulation would further support the authors' claim.

Response: We have carried out supplementary experiments to investigate glycogen synthesis in our insulin-resistant models. Our approach involved L6-myotubes overexpressing the mitochondrial-targeted construct ASAH1 (as described in Fig. 3). We then challenged them with palmitate and measured glycogen synthesis using 14C radiolabeled glucose. Our observations indicated that palmitate suppressed insulin-induced glycogen synthesis, which was effectively prevented by the overexpression of ASAH1 (N = 5, * p<0.05). These results provide additional evidence highlighting the role of dysfunctional mitochondria in muscle cell glucose metabolism.

These data will be added to Supplementary Figure 4K and the results modified as follows:

“Notably, mtASAH1 overexpression protected cells from palmitate-induced insulin resistance without affecting basal insulin sensitivity (Fig. 3E). Similar results were observed using insulin-induced glycogen synthesis as an ortholog technique for Glut4 translocation. These results provide additional evidence highlighting the role of dysfunctional mitochondria in muscle cell glucose metabolism (Sup. Fig. 5K). Importantly, mtASAH1 overexpression did not rescue insulin sensitivity in cells depleted…”

We will add to the method section:

“L6 myotubes overexpressing ASAH were grown and differentiated in 12-well plates, as described in the Cell lines section, and stimulated for 16 h with palmitate-BSA or EtOH-BSA, as detailed in the Induction of insulin resistance section.

On day seven of differentiation, myotubes were serum starved in plain DMEM for 3 and a half hours. After incubation for 1 hour at 37C with 2 µCi/ml D-[U-14C]-glucose in the presence or absence of 100 nM insulin, glycogen synthesis assay was performed, as previously described (Zarini S. et al., J Lipid Res, 63(10): 100270, 2022).”

1. In addition, it would be critical to assess whether the increased mitochondrial ceramide and consequent lowering of energy levels affect all exocytic pathways in L6 myoblasts or just the GLUT4 trafficking. Is the secretory pathway also disrupted under these conditions?

Response: As the secretory pathway primarily involves the synthesis and transportation of soluble proteins that are secreted into the extracellular space, and given that the majority of cellular transmembrane proteins (excluding those of the mitochondria) use this pathway to arrive at their ultimate destination, we believe that the question posed by the reviewer is highly challenging and beyond the scope of our research. We will add this to the discussion:

“...the abundance of mPTP associated proteins suggesting a role of this pore in ceramide induced insulin resistance (Sup. Fig. 6E). In addition, it is yet to be determined whether the trafficking defect is specific to Glut4 or if it affects the exocytic-secretory pathway more broadly…”

Points from reviewer 2:

1. The mechanistic aspect of the work and conclusions put forth rely heavily on studies performed in cultured myocytes, which are highly glycolytic and generally viewed as a poor model for studying muscle metabolism and insulin action. Nonetheless, the findings provide a strong rationale for moving this line of investigation into mouse gain/loss of function models.

Response: The relative contribution of the anaerobic (glycolysis) and aerobic (mitochondria) contribution to the muscle metabolism can change in L6 depending on differentiation stage. For instance, Serrage et al (PMID30701682) demonstrated that L6-myotubes have a higher mitochondrial abundance and aerobic metabolism than L6-myoblasts. Others have used elegant transcriptomic analysis and metabolic characterisation comparing different skeletal muscle models for studying insulin sensitivity. For instance, Abdelmoez et al in 2020 (PMID31825657) reported that L6 myotubes exhibit greater insulin-stimulated glucose uptake and oxidative capacity compared with C2C12 and Human Mesenchymal Stem Cells (HMSC). Overall, L6 cells exhibit higher metabolic rates and primarily rely on aerobic metabolism, while C2C12 and HSMC cells rely on anaerobic glycolysis. It is worth noting that L6 myotubes are the cell line most closely related to adult human muscle when compared with other muscle cell lines (PMID31825657). Our presented results in Figure 6 H and I provide evidence for the similarities between L6 cells overexpressing SMPD5 and human muscle biopsies. Additionally, in Figure 3J-K, we demonstrate the reproducibility of the negative correlation between ceramide and Coenzyme Q observed in L6 cells in vivo, specifically in the skeletal muscle of mice in chow diet. Furthermore, we have supplemented these findings by demonstrating a comparable relationship in mice exposed to a high-fat diet, as shown in Supplementary Figure 4 H-I (refer to point 4). We will clarify these points in the Discussion:

“In this study, we mainly utilised L6-myotubes, which share many important characteristics with primary muscle fibres relevant to our research. Both types of cells exhibit high sensitivity to insulin and respond similarly to maximal doses of insulin, with Glut4 translocation stimulated between 2 to 4 times over basal levels in response to 100 nM insulin (as shown in Fig. 1-4 and (46,47)). Additionally, mitochondrial respiration in L6-myotubes have a similar sensitivity to mitochondrial poisons, as observed in primary muscle fibres (as shown in Fig. 5 (48)). Finally, inhibiting ceramide production increases CoQ levels in both L6-myotubes and adult muscle tissue (as shown in Fig. 2-3). Therefore, L6-myotubes possess the necessary metabolic features to investigate the role of mitochondria in insulin resistance, and this relationship is likely applicable to primary muscle fibres”.

We will also add additional data - in point 2 - from differentiated human myocytes that are consistent with our observations from the L6 models. Additional experiments are in progress to further extend these findings.

1. One caveat of the approach taken is that exposure of cells to palmitate alone is not reflective of in vivo physiology. It would be interesting to know if similar effects on CoQ are observed when cells are exposed to a more physiological mixture of fatty acids that includes a high ratio of palmitate, but better mimics in vivo nutrition.

Response: Palmitate is widely recognized as a trigger for insulin resistance and ceramide accumulation, which mimics the insulin resistance induced by a diet in rodents and humans. Previous studies have compared the effects of a lipid mixture versus palmitate on inducing insulin resistance in skeletal muscle, and have found that the strong disruption in insulin sensitivity caused by palmitate exposure was lessened with physiologic mixtures of fatty acids, even with a high proportion of saturated fatty acids. This was associated, in part, to the selective partitioning of fatty acids into neutral lipids (such as TAG) when muscle cells are exposed to physiologic lipid mixtures (Newsom et al PMID25793412). Hence, we think that using palmitate is a better strategy to study lipid-induced insulin resistance in vitro. We will add to results:

“In vitro, palmitate conjugated with BSA is the preferred strategy for inducing insulin resistance, as lipid mixtures tend to partition into triacylglycerides (33)”.

We are also performing additional in vivo experiments to add to the physiological relevance of the findings.

1. While the utility of targeting SMPD5 to the mitochondria is appreciated, the results in Figure 5 suggest that this manoeuvre caused a rather severe form of mitochondrial dysfunction. This could be more representative of toxicity rather than pathophysiology. It would be helpful to know if these same effects are observed with other manipulations that lower CoQ to a similar degree. If not, the discrepancies should be discussed.

Response: We conducted a staining procedure using the mitochondrial marker mitoDsRED to observe the effect of SMPD5 overexpression on cell toxicity. The resulting images, displayed in the figure below (Author response image 1), demonstrate that the overexpression of SMPD5 did not result in any significant changes in cell morphology or impact the differentiation potential of our myoblasts into myotubes.

Author response image 1.

In addition, we evaluated cell viability in HeLa cells following exposure to SACLAC (2 uM) to induce CoQ depletion (left panel). Specifically, we measured cell death by monitoring the uptake of Propidium iodide (PI) as shown in the right panel. Our results demonstrated that Saclac-induced CoQ depletion did not lead to cell death at the doses used for CoQ depletion (Author response image 2).

Author response image 2.

Therefore, we deemed it improbable that the observed effect is caused by cellular toxicity, but rather represents a pathological condition induced by elevated levels of ceramides. We will add to discussion:

“...downregulation of the respirasome induced by ceramides may lead to CoQ depletion. Despite the significant impact of ceramide on mitochondrial respiration, we did not observe any indications of cell damage in any of the treatments, suggesting that our models are not explained by toxic/cell death events.”

1. The conclusions could be strengthened by more extensive studies in mice to assess the interplay between mitochondrial ceramides, CoQ depletion and ETC/mitochondrial dysfunction in the context of a standard diet versus HF diet-induced insulin resistance. Does P053 affect mitochondrial ceramide, ETC protein abundance, mitochondrial function, and muscle insulin sensitivity in the predicted directions?

Response: We would like to note that the metabolic characterization and assessment of ETC/mitochondrial function in these mice (both fed a high-fat (HF) and chow diet, with or without P053) were previously published (Turner N, PMID30131496). In addition to this, we have conducted targeted metabolomic and lipidomic analyses to investigate the impact of P053 on ceramide and CoQ levels in HF-fed mice. As illustrated in the figures below (Author response image 3), the administration of P053 led to a reduction in ceramide levels (left panel) and an increase in CoQ levels (right panel) in HF-fed mice, which is consistent with our in vitro findings.

Author response image 3.

We will add to results:

“…similar effect was observed in mice exposed to a high fat diet for 5 wks (Supp. Fig. 4H-I further phenotypic and metabolic characterization of these animals can be found in (41))”

We will further perform more in-vivo studies to corroborate these findings.

Author response:

Reviewer #1 (Public Review):  

Weaknesses:  

The weakness of this study lies in the fact that many of the genomic datasets originated from novel methods that were not validated with orthogonal approaches, such as DNA-FISH. Therefore, the detailed correlations described in this work are based on methodologies whose efficacy is not clearly established. Specifically, the authors utilized two modified protocols of TSA-seq for the detection of NADs (MKI67IP TSA-seq) and LADs (LMNB1-TSA-seq). Although these methods have been described in a bioRxiv manuscript by Kumar et al., they have not yet been published. Moreover, and surprisingly, Kumar et al., work is not cited in the current manuscript, despite its use of all TSA-seq data for NADs and LADs across the four cell lines. Moreover, Kumar et al. did not provide any DNA-FISH validation for their methods. Therefore, the interesting correlations described in this work are not based on robust technologies.    

An attempt to validate the data was made for SON-TSA-seq of human foreskin fibroblasts (HFF) using multiplexed FISH data from IMR90 fibroblasts (from the lung) by the Zhuang lab (Su et al., 2020). However, the comparability of these datasets is questionable. It might have been more reasonable for the authors to conduct their analyses in IMR90 cells, thereby allowing them to utilize MERFISH data for validating the TSA-seq method and also for mapping NADs and LADs. 

We disagree with the statement that the TSA-seq approach and data has not been validated by orthogonal approaches and with the conclusion that the TSA-seq approach is not robust as summarized here and detailed below in “Specific Comments”.  TSA-seq is robust because it is based only on the original immunostaining specificity provided by the primary and secondary antibodies plus the diffusion properties of the tyramide-free radical. TSA-seq has been extensively validated by microscopy and by the orthogonal genomic measurements provided by LMNB1 DamID and NAD-seq.  This includes: a) the initial validation by FISH of both nuclear speckle (to an accuracy of ~50 nm) and nuclear lamina TSA-seq  and the cross-validation of nuclear lamina TSA-seq with lamin B1 DamID in a first publication (Chen et al, JCB 2018, doi: 10.1083/jcb.201807108); b) the further validation of SON TSA-seq by FISH in a second publication ((Zhang et al, Genome Research 2021, doi:10.1101/gr.266239.120); c) the cross-validation of nucleolar TSA-seq using NAD-seq and the validation by light microscopy of the predictions of differences in the relative distributions of centromeres, nuclear speckles, and nucleoli made from nuclear speckle, nucleolar, and pericentric heterochromatin TSA-seq in the Kumar et al, bioRxiv preprint (which is in a last revision stage involving additional formatting for the journal requirements) doi:https://doi.org/10.1101/2023.10.29.564613; d) the extensive validation of nuclear speckle, LMNB1, and nucleolar TSA-seq generated in HFF human fibroblasts using published light microscopy distance measurements of hundreds of probes generated by multiplexed immuno-FISH MERFISH data (Su et al, Cell 2020, https://doi.org/10.1016/j.cell.2020.07.032), as we described for nucleolar TSA-seq in the Kumar et al, bioRxiv preprint and to some extent for LMNB1 and SON TSA-seq in the current manuscript version (see Specific Comments with attached Author response image 2).

Reviewer 1 raised concerns regarding this FISH validation given that the HFF TSA-seq and DamID data was compared to IMR90 MERFISH measurements.  The Su et al, Cell 2020 MERFISH paper came out well after the 4D Nucleome Consortium settled on HFF as one of the two main “Tier 1” cell lines.  We reasoned that the nuclear genome organization in a second fibroblast cell line would be sufficiently similar to justify using IMR90 FISH data as a proxy for our analysis of our HFF data. Indeed, there is a high correlation between the HFF TSA-seq and distances measured by MERFISH to nuclear lamina, nucleoli, and nuclear speckles (Author response image 1).  Comparing HFF SON-TSA-seq data with published IMR90 SON TSA-seq data (Alexander et al, Mol Cell 2021, doi.org/10.1016/j.molcel.2021.03.006), the HFF SON TSA-seq versus MERFISH scatterplot is very similar to the IMR90 SON TSA-seq versus MERFISH scatterplot.  We acknowledge the validation provided by the IMR90 MERFISH is limited by the degree to which genome organization relative to nuclear locales is similar in IMR90 and HFF fibroblasts. However, the correlation between measured microscopic distances from nuclear lamina, nucleoli, and nuclear speckles and TSA-seq scores is already quite high. We anticipate the conclusions drawn from such comparisons are solid and will only become that much stronger with future comparisons within the same cell line.

Author response image 1.

Scatterplots showing the correlation between TSA-seq and MERFISH microscopic distances. Top: IMR90 SON TSA-seq (from Alexander et al, Mol Cell 2021) (left) and HFF SON TSA-seq (right) (x-axis) versus distance to nuclear speckles (y-axis). Bottom: HFF Lamin B1 TSA-seq (x-axis) versus distance to nuclear lamina (y-axis) (left) and HFF MKI67IP (nucleolar) TSA-seq (x-axis) versus distance to nucleolus (y-axis) (right).

In our revision, we will add justification of the use of IMR90 fibroblasts as a proxy for HFF fibroblasts through comparison of available data sets. 

Reviewer #2 (Public Review):  

Weaknesses:  

The experiments are largely descriptive, and it is difficult to draw many cause-and-effect relationships. Similarly, the paper would be very much strengthened if the authors provided additional summary statements and interpretation of their results (especially for those not as familiar with 3D genome organization). The study would benefit from a clear and specific hypothesis.

We acknowledge that this study was hypothesis-generating rather than hypothesis-testing in its goal. This research was funded through the NIH 4D-Nucleome Consortium, which had as its initial goal the development, benchmarking, and validation of new genomic technologies.  Our Center focused on the mapping of the genome relative to different nuclear locales and the correlation of this intranuclear positioning of the genome with functions- specifically gene expression and DNA replication timing. By its very nature, this project has taken a discovery-driven versus hypothesis-driven scientific approach.  Our question fundamentally was whether we could gain new insights into nuclear genome organization through the integration of genomic and microscopic measurements of chromosome positioning relative to multiple different nuclear compartments/bodies and their correlation with functional assays such as RNA-seq and Repli-seq.

Indeed, as described in this manuscript, this study resulted in multiple new insights into nuclear genome organization as summarized in our last main figure.  We believe our work and conclusions will be of general interest to scientists working in the fields of 3D genome organization and nuclear cell biology.  We anticipate that each of these new insights will prompt future hypothesis-driven science focused on specific questions and the testing of cause-and-effect relationships. 

Given the extensive scope of this manuscript, we were limited in the extent that we could describe and summarize the background, data, analysis, and significance for every new insight. In our editing to reach the eLife recommended word count, we removed some of the explanations and summaries that we had originally included. 

As suggested by Reviewer 2, in our revision we will add back additional summary and interpretation statements to help readers unfamiliar with 3D genome organization.

Specific Comments in response to Reviewer 1:

(1)  We disagree with the comment that TSA-seq has not been cross-validated by other orthogonal genomic methods.  In the first TSA-seq paper (Chen et al, JCB 2018, doi: 10.1083/jcb.201807108), we showed a good correlation between the identification of iLADs and LADs by nuclear lamin and nuclear speckle TSA-seq and the orthogonal genomic method of lamin B1 DamID, which is reproduced using our new TSA-seq 2.0 protocol in this manuscript.  Similarly, in the Kumar et al, bioRxiv preprint (doi:https://doi.org/10.1101/2023.10.29.564613), we showed a general agreement between the identification of NADs by nucleolar TSA-seq and the orthogonal genomic method of NAD-seq.  (We expect this preprint to be in press soon; it is now undergoing a last revision involving only reformatting for journal requirements.) Additionally, we also showed a high correlation between Hi-C compartments and subcompartments and TSA-seq in the Chen et al, JCB 2018 paper. Specifically, there is an excellent correlation between the A1 Hi-C subcompartment and Speckle Associated Domains as detected by nuclear speckle TSA-seq.  Additionally, the A2 Hi-C subcompartment correlated well with iLAD regions with intermediate nuclear speckle TSA-seq scores, and the B2 and B3 Hi-C subcompartments with LADs detected by both LMNB TSA-seq and LMNB1 DamID.  More generally, Hi-C A and B compartment identity correlated well with predictions of iLADs versus LADs from nuclear speckle and nuclear lamina TSA-seq.

(2)  In the Chen et al, JCB 2018 paper we also qualitatively and quantitatively validated TSA-seq using FISH.  Qualitatively, we showed that both nuclear speckle and nuclear lamin TSA-seq correlated well with distances to nuclear speckles versus the nuclear lamina, respectively, measured by immuno-FISH.

Quantitatively, we showed that SON TSA-seq could be used to estimate the microscopic mean distance to nuclear speckles with mean and median residuals of ~50 nm.  First, we used light microscopy to show that the spreading of tyramide-biotin signal from a point-source of TSA staining fits well with the exponential decay predicted theoretically by reaction-diffusion equations assuming a steady rate of tyramide-biotin free radical generation by the HRP enzyme and a constant probability throughout the nucleus of free-radical quenching (through reaction with protein tyrosine residues and nucleic acids).  Second, we used the exponential decay constant measured by light microscopy together with FISH measurements of mean speckle distance for several genomic regions to fit an exponential function and to predict distance to nuclear speckles genome-wide directly from SON TSA-seq sequencing reads.  Third, we used this approach to test the predictions against a new set of FISH measurements, demonstrating an accuracy of these predictions of ~50 nm.

(3)  The importance of the quantitative validation by immuno-FISH of using TSA-seq to estimate mean distance to nuclear speckles is that it demonstrates the robustness of the TSA-seq approach.  Specifically, it shows how the TSA-seq signal is predicted to depend only on the specificity of the primary and secondary antibody staining and the diffusion properties of the tyramide-biotin free radicals produced by the HRP peroxidase.  This is fundamentally different from the significant dependence on antibodies and choice of marker proteins for molecular proximity assays such as DamID, ChIP-seq, and Cut and Run/Tag which depend on molecular proximity for labeling and/or pulldown of DNA.

This robustness leads to specific predictions.  First, it predicts similar TSA-seq signals will be produced using antibodies against different marker proteins against the same nuclear compartment.  This is because the exponential decay constant (distance at which the signal drops by one half) for the spreading of the TSA is in the range of several hundred nm, as measured by light microscopy for several TSA staining conditions.  Indeed, we showed in the Chen et al, JCB 2018 paper that antibodies against two different nuclear speckle proteins produced very similar TSA-seq signals while antibodies against LMNB versus LMNA also produced very similar TSA-seq signals.  Similarly, we showed in the Kumar et al preprint that antibodies against four different nucleolar proteins showed similar TSA-seq signals, with the highest correlation coefficients for the TSA-seq signals produced by the antibodies against two GC nucleolar marker proteins and the TSA-seq signals produced by the antibodies against two FC/DFC nucleolar marker proteins.

Author response image 2.

Comparison of TSA-seq data from different cell lines versus IMR90 MERFISH.  The observed correlation between SON (nuclear speckle) TSA-seq versus MERFISH is nearly as high for TSA-seq data from HFF as it is for TSA-seq data from the IMR90 cell line (Alexander et al, Mol Cell 2021) in which the MERFISH was performed. The correlations for SON, LMNB1 (nuclear lamina) and MKI67IP (nucleolus) versus MERFISH are highest for HFF TSA-seq data as compared to TSA-seq data from other cell lines (H1, K562, HCT116).  Comparison of measured distances to nuclear locale (y-axis) versus TSA-seq scores (x-axis) from different cell lines labeled in red. Left to right: SON, LMNB1, and MKI67IP.  Top to bottom: SON TSA-seq versus MERFISH for two TSA-seq replicates; TSA-seq from HFF, H1, K562, and HCT116 versus MERFISH.

Second, it predicts that the quantitative relationship between TSA-seq signal and mean distance from a nuclear compartment will depend on the convolution of the predicted exponential decay of spreading of the TSA signal produced by a point source with the more complicated staining distribution of nuclear compartments such as the nuclear lamina or nucleoli.  We successfully used this concept to explain the differences emerging between LMNB1 DamID and TSA-seq signals for flat nuclei and to recognize the polarized distribution of different LADs over the nuclear periphery.

(4)  After our genomic data production and during our data analysis, a valuable resource from the Zhuang lab was published, using MERFISH to visualize hundreds of genomic loci in IMR90 cells. We acknowledge that the much more extensive validation of TSA-seq by the multiplexed immuno-FISH MERFISH data is dependent on the degree to which the nuclear genome organization is similar between IMR90 and HFF fibroblasts.  However, the correlation between distances to nuclear speckles, nucleoli, and the nuclear lamina measured in IMR90 fibroblasts and the nuclear speckle, nucleolar, and nuclear lamina TSA-seq measured in HFF fibroblasts is already striking (See Author response image 1).  With regard to SON TSA-seq, the MERFISH versus HFF TSA-seq correlation is close to what we observe using published IMR90 SON TSA-seq data (correlation coefficients of 0.89 (IMR90 TSA-seq) versus 0.86 (HFF TSA-seq).  Moreover, this correlation is highest using TSA-seq data from HFF cells as compared to the three other cell lines. (see Author response image 2).  We believe these correlations can be considered a lower bound on the actual correlations between the FISH distances and TSA-seq that we would have observed if we had performed both assays on the same cell line. 

(5)  Currently, we still require tens of millions of cells to perform each TSA-seq assay.  This requires significant expansion of cells and a resulting increase in passage numbers of the IMR90 cells before we can perform the TSA-seq. During this expansion we observe a noticeable slowing of the IMR90 cell growth as expected for secondary cell lines as we approach the Hayflick limit.  We still do not know to what degree nuclear organization relative to nuclear locales may change as a function of cell cycle composition (ie percentage of cycling versus quiescent cells) and cell age.  Thus, even if we performed TSA-seq on IMR90 cells we would be comparing MERFISH from lower passages with a higher percentage of actively proliferating cells with TSA-seq from higher passages with a higher percentage of quiescent cells. 

We are currently working on a new TSA-seq protocol that will work with thousands of cells.  We believe it is better investment of time and resources to wait until this new protocol is optimized before we repeat TSA-seq in IMR90 cells for a better comparison with multiplexed FISH data. 

Specific Comments in response to Reviewer 2:

(1)  As we acknowledge in our Response summary, we were limited in the degree to which we could actually follow-up our findings with experiments designed to test specific hypotheses generated by our data.  However, we do want to point out that our comparison of wild-type K562 cells with the LMNA/LBR double knockout was designed to test the long-standing model that nuclear lamina association of genomic loci contributes to gene silencing.  This experiment was motivated by our surprising result that gene expression differences between cell lines correlated strongly with differences in positioning relative to nuclear speckles rather than the nuclear lamina.  Despite documenting in these double knockout cells a decreased nuclear lamina association of most LADs, and an increased nuclear lamina association of the “p-w-v” fiLADs identified in this manuscript, we saw no significant change in gene expression in any of these regions as compared to wild-type K562 cells.  Meanwhile, distances to nuclear speckles as measured by TSA-seq remained nearly constant.

We would argue that this represents a specific example in which new insights generated by our genomics comparison of cell lines led to a clear and specific hypothesis and the experimental testing of this hypothesis.

In response to Reviewer 2, we are modifying the text to make this clearer and to explicitly describe how we were testing the hypothesis that distance to nuclear lamina is correlated with but not causally linked to gene expression and how to test this hypothesis we used a DKO of LMNA and LBR to change distances relative to the nuclear lamina and to test the effect on gene expression.

Author response:

Reviewer #1 (Public review):

The study examines how pyruvate, a key product of glycolysis that influences TCA metabolism and gluconeogenesis, impacts cellular metabolism and cell size. It primarily utilizes the Drosophila liver-like fat body, which is composed of large post-mitotic cells that are metabolically very active. The study focuses on the key observations that over-expression of the pyruvate importer MPC complex (which imports pyruvate from the cytoplasm into mitochondria) can reduce cell size in a cell-autonomous manner. They find this is by metabolic rewiring that shunts pyruvate away from TCA metabolism and into gluconeogenesis. Surprisingly, mTORC and Myc pathways are also hyper-active in this background, despite the decreased cell size, suggesting a non-canonical cell size regulation signaling pathway. They also show a similar cell size reduction in HepG2 organoids. Metabolic analysis reveals that enhanced gluconeogenesis suppresses protein synthesis. Their working model is that elevated pyruvate mitochondrial import drives oxaloacetate production and fuels gluconeogenesis during late larval development, thus reducing amino acid production and thus reducing protein synthesis.

Strengths:

The study is significant because stem cells and many cancers exhibit metabolic rewiring of pyruvate metabolism. It provides new insights into how the fate of pyruvate can be tuned to influence Drosophila biomass accrual, and how pyruvate pools can influence the balance between carbohydrate and protein biosynthesis. Strengths include its rigorous dissection of metabolic rewiring and use of Drosophila and mammalian cell systems to dissect carbohydrate:protein crosstalk.

Weaknesses:

However, questions on how these two pathways crosstalk, and how this interfaces with canonical Myc and mTORC machinery remain. There are also questions related to how this protein:carbohydrate crosstalk interfaces with lipid biosynthesis. Addressing these will increase the overall impact of the study.

We thank the reviewer for recognizing the significance of our work and for providing constructive feedback. Our findings indicate that elevated pyruvate transport into mitochondria acts independently of canonical pathways, such as mTORC1 or Myc signaling, to regulate cell size. To investigate these pathways, we utilized immunofluorescence with well-validated surrogate measures (p-S6 and p-4EBP1) in clonal analyses of MPC expression, as well as RNA-seq analyses in whole fat body tissues expressing MPC. These methods revealed hyperactivation of mTORC1 and Myc signaling in fat body cells expressing MPC in Drosophila, which are dramatically smaller than control cells. One explanation of these seemingly contradictory observations could be an excess of nutrients that activate mTORC1 or Myc pathways. However, our data is inconsistent with a nutrient surplus that could explain this hyperactivation. Instead, we observed reduced amino acid abundance upon MPC expression, which is very surprising given the observed hyperactivation of mTORC1. This led us to hypothesize the existence of a feedback mechanism that senses inappropriate reductions in cell size and activates signaling pathways to promote cell growth. The best characterized “sizer” pathway for mammalian cells is the CycD/CDK4 complex which has been well studied in the context of cell size regulation of the cell cycle (PMID 10970848, 34022133). However, the mechanisms that sense cell size in post-mitotic cells, such as fat body cells and hepatocytes, remain poorly understood. Investigating the hypothesized size-sensing mechanisms at play here is a fascinating direction for future research.

For the current study, we conducted epistatic analyses with mTOR pathway members by overexpressing PI3K and knocking down the TORC1 inhibitor Tuberous Sclerosis Complex 1 (Tsc1). These manipulations increased the size of control fat body cells but not those over-expressing the MPC (Supplementary Fig. 3c, 3d). Regarding Myc, its overexpression increased the size of both control and MPC+ clones (Supplementary Fig. 3e), but Myc knockdown had no additional effect on cell size in MPC+ clones (Supplementary Fig. 3f). These results suggest that neither mTORC1, PI3K, nor Myc are epistatic to the cell size effects of MPC expression. Consequently, we shifted our focus to metabolic mechanisms regulating biomass production and cell size.

When analyzing cellular biomolecules contributing to biomass, we observed a significant impact on protein levels in Drosophila fat body cells and mammalian MPC-expressing HepG2 spheroids. TAG abundance in MPC-expressing HepG2 spheroids and whole fat body cells showed a statistically insignificant decrease compared to controls. Furthermore, lipid droplets in fat body cells were comparable in MPC-expressing clones when normalized to cell size.

Interestingly, RNA-seq analysis revealed increased expression of fatty acid and cholesterol biosynthesis pathways in MPC-expressing fat body cells. Upregulated genes included major SREBP targets, such as ATPCL (2.08-fold), FASN1 (1.15-fold), FASN2 (1.07-fold), and ACC (1.26-fold). Since mTOR promotes SREBP activation and MPC-expressing cells showed elevated mTOR activity and upregulation of SREBP targets, we hypothesize that SREBP is activated in these cells. Nonetheless, our data on amino acid abundance and its impact on protein synthesis activity suggest that protein abundance, rather than lipids, is likely to play a larger causal role in regulating cell size in response to increased pyruvate transport into mitochondria.

Reviewer #2 (Public review):

In this manuscript, the authors leverage multiple cellular models including the drosophila fat body and cultured hepatocytes to investigate the metabolic programs governing cell size. By profiling gene programs in the larval fat body during the third instar stage - in which cells cease proliferation and initiate a period of cell growth - the authors uncover a coordinated downregulation of genes involved in mitochondrial pyruvate import and metabolism. Enforced expression of the mitochondrial pyruvate carrier restrains cell size, despite active signaling of mTORC1 and other pathways viewed as traditional determinants of cell size. Mechanistically, the authors find that mitochondrial pyruvate import restrains cell size by fueling gluconeogenesis through the combined action of pyruvate carboxylase and phosphoenolpyruvate carboxykinase. Pyruvate conversion to oxaloacetate and use as a gluconeogenic substrate restrains cell growth by siphoning oxaloacetate away from aspartate and other amino acid biosynthesis, revealing a tradeoff between gluconeogenesis and provision of amino acids required to sustain protein biosynthesis. Overall, this manuscript is extremely rigorous, with each point interrogated through a variety of genetic and pharmacologic assays. The major conceptual advance is uncovering the regulation of cell size as a consequence of compartmentalized metabolism, which is dominant even over traditional signaling inputs. The work has implications for understanding cell size control in cell types that engage in gluconeogenesis but more broadly raise the possibility that metabolic tradeoffs determine cell size control in a variety of contexts.

We thank the reviewer for their thoughtful recognition of our efforts, and we are honored by the enthusiasm the reviewer expressed for the findings and the significance of our research. We share the reviewer’s opinion that our work might help to unravel metabolic mechanisms that regulate biomass gain independent of the well-known signaling pathways.

Reviewer #3 (Public review):

Summary:

In this article, Toshniwal et al. investigate the role of pyruvate metabolism in controlling cell growth. They find that elevated expression of the mitochondrial pyruvate carrier (MPC) leads to decreased cell size in the Drosophila fat body, a transformed human hepatocyte cell line (HepG2), and primary rat hepatocytes. Using genetic approaches and metabolic assays, the authors find that elevated pyruvate import into cells with forced expression of MPC increases the cellular NADH/NAD+ ratio, which drives the production of oxaloacetate via pyruvate carboxylase. Genetic, pharmacological, and metabolic approaches suggest that oxaloacetate is used to support gluconeogenesis rather than amino acid synthesis in cells over-expressing MPC. The reduction in cellular amino acids impairs protein synthesis, leading to impaired cell growth.

Strengths:

This study shows that the metabolic program of a cell, and especially its NADH/NAD+ ratio, can play a dominant role in regulating cell growth.

The combination of complementary approaches, ranging from Drosophila genetics to metabolic flux measurements in mammalian cells, strengthens the findings of the paper and shows a conservation of MPC effects across evolution.

Weaknesses:

In general, the strengths of this paper outweigh its weaknesses. However, some areas of inconsistency and rigor deserve further attention.

Thank you for reviewing our manuscript and offering constructive feedback. We appreciate your recognition of the significance of our work and your acknowledgment of the compelling evidence we have presented. We will carefully revise the manuscript in line with the reviewers' recommendations.

The authors comment that MPC overrides hormonal controls on gluconeogenesis and cell size (Discussion, paragraph 3). Such a claim cannot be made for mammalian experiments that are conducted with immortalized cell lines or primary hepatocytes.

We appreciate the reviewer’s insightful comment. Pyruvate is a primary substrate for gluconeogenesis, and our findings suggest that increased pyruvate transport into mitochondria increases the NADH-to-NAD+ ratio, and thereby elevates gluconeogenesis. Notably, we did not observe any changes in the expression of key glucagon targets, such as PC, PEPCK2, and G6PC, suggesting that the glucagon response is not activated upon MPC expression. By the statement referenced by the reviewer, we intended to highlight that excess pyruvate import into mitochondria drives gluconeogenesis independently of hormonal and physiological regulation.

It seems the reviewer might also have been expressing the sentiment that our in vitro models may not fully reflect the in vivo situation, and we completely agree.  Moving forward, we plan to perform similar analyses in mammalian models to test the in vivo relevance of this mechanism. For now, we will refine the language in the manuscript to clarify this point.

Nuclear size looks to be decreased in fat body cells with elevated MPC levels, consistent with reduced endoreplication, a process that drives growth in these cells. However, acute, ex vivo EdU labeling and measures of tissue DNA content are equivalent in wild-type and MPC+ fat body cells. This is surprising - how do the authors interpret these apparently contradictory phenotypes?

We thank the reviewer for raising this important issue. The size of the nucleus is regulated by DNA content and various factors, including the physical properties of DNA, chromatin condensation, the nuclear lamina, and other structural components (PMID 32997613). Additionally, cytoplasmic and cellular volume also impacts nuclear size, as extensively documented during development (PMID 17998401, PMID 32473090).

In MPC-expressing cells, it is plausible that the reduced cellular volume impacts chromatin condensation or the nuclear lamina in a way that slightly decreases nuclear size without altering DNA content. Specifically, in our whole fat body experiments using CG-Gal4 (as shown in Supplementary Figure 2a-c), we noted that after 12 hours of MPC expression, cell size was significantly reduced (Supplementary Figure 2c and Author response image 1A). However, the reduction in nuclear size became significant only after 36 hours of MPC expression (Author response image 1B), suggesting that the reduction in cell size is a more acute response to MPC expression, followed only later by effects on nuclear size.

In clonal analyses, this relationship was further clarified. MPC-expressing cells with a size greater than 1000 µm² displayed nuclear sizes comparable to control cells, whereas those with a drastic reduction in cell size (less than 1000 µm²) exhibited smaller nuclei (Author response image 1C and D). These observations collectively suggest that changes in nuclear size are more likely to be downstream rather than upstream of cell size reduction. Given that DNA content remains unaffected, we focused on investigating the rate of protein synthesis. Our findings suggest that protein synthesis might play a causal role in regulating cell size, thereby reinforcing the connection between cellular and nuclear size in this context.

Author response image 1.

Cell Size vs. Nuclear Size in MPC-Expressing Fat Body Cells. A. Cell size comparison between control (blue, ay-GFP) and MPC+ (red, ay-MPC) fat body cells over time, measured in hours after MPC expression induction. B. Nuclear area measurements from the same fat body cells in ay-GFP and ay-MPC groups. C. Scatter plot of nuclear area vs. cell area for control (ay-GFP) cells, including the corresponding R^² value. D. Scatter plot of nuclear area vs. cell area for MPC-expressing (ay-MPC) cells, with the respective R^² value.

This image highlights the relationship between nuclear and cell size in MPC-expressing fat body cells, emphasizing the distinct cellular responses observed following MPC induction.

In Figure 4d, oxygen consumption rates are measured in control cells and those over-expressing MPC. Values are normalized to protein levels, but protein is reduced in MPC+ cells. Is oxygen consumption changed by MPC expression on a per-cell basis?

As described in the manuscript, MPC-expressing cells are smaller in size. In this context, we felt that it was most appropriate to normalize oxygen consumption rates (OCR) to cellular mass to enable an accurate interpretation of metabolic activity. Therefore, we normalized OCR with protein content to account for variations in cellular size and (probably) mitochondrial mass.

Trehalose is the main circulating sugar in Drosophila and should be measured in addition to hemolymph glucose. Additionally, the units in Figure 4h should be related to hemolymph volume - it is not clear that they are.

We appreciate this valuable suggestion. In the revised manuscript, we will quantify trehalose abundance in circulation and within fat bodies. As described in the Methods section, following the approach outlined in Ugrankar-Banerjee et al., 2023, we bled 10 larvae (either control or MPC-expressing) using forceps onto parafilm. From this, 2 microliters of hemolymph were collected for glucose measurement. We will apply this methodology to include the trehalose measurements as part of our updated analysis.

Measurements of NADH/NAD ratios in conditions where these are manipulated genetically and pharmacologically (Figure 5) would strengthen the findings of the paper. Along the same lines, expression of manipulated genes - whether by RT-qPCR or Western blotting - would be helpful to assess the degree of knockdown/knockout in a cell population (for example, Got2 manipulations in Figures 6 and S8).

We appreciate this suggestion, which will provide additional rigor to our study. We have already quantified NADH/NAD+ ratios in HepG2 cells under UK5099, NMN, and Asp supplementation, as presented in Figure 6k. As suggested, we will quantify the expression of Got2 manipulations mentioned in Figure 6j using RT-qPCR and validate the corresponding data in Supplementary Figure 8f through western blot analysis.

Additionally, we will assess the efficiency of pcb, pdha, dlat, pepck2, and Got2 manipulations used to modulate the expression of these genes. These validations will ensure the robustness of our findings and strengthen the conclusions of our study.

Author Response

eLife assessment

This study presents potentially valuable results on glutamine-rich motifs in relation to protein expression and alternative genetic codes. The author's interpretation of the results is so far only supported by incomplete evidence, due to a lack of acknowledgment of alternative explanations, missing controls and statistical analysis and writing unclear to non experts in the field. These shortcomings could be at least partially overcome by additional experiments, thorough rewriting, or both.

We thank both the Reviewing Editor and Senior Editor for handling this manuscript and will submit our revised manuscript after the reviewed preprint is published by eLife.  

Reviewer #1 (Public Review):

Summary

This work contains 3 sections. The first section describes how protein domains with SQ motifs can increase the abundance of a lacZ reporter in yeast. The authors call this phenomenon autonomous protein expression-enhancing activity, and this finding is well supported. The authors show evidence that this increase in protein abundance and enzymatic activity is not due to changes in plasmid copy number or mRNA abundance, and that this phenomenon is not affected by mutants in translational quality control. It was not completely clear whether the increased protein abundance is due to increased translation or to increased protein stability.

In section 2, the authors performed mutagenesis of three N-terminal domains to study how protein sequence changes protein stability and enzymatic activity of the fusions. These data are very interesting, but this section needs more interpretation. It is not clear if the effect is due to the number of S/T/Q/N amino acids or due to the number of phosphorylation sites.

In section 3, the authors undertake an extensive computational analysis of amino acid runs in 27 species. Many aspects of this section are fascinating to an expert reader. They identify regions with poly-X tracks. These data were not normalized correctly: I think that a null expectation for how often poly-X track occur should be built for each species based on the underlying prevalence of amino acids in that species. As a result, I believe that the claim is not well supported by the data.

Strengths

This work is about an interesting topic and contains stimulating bioinformatics analysis. The first two sections, where the authors investigate how S/T/Q/N abundance modulates protein expression level, is well supported by the data. The bioinformatics analysis of Q abundance in ciliate proteomes is fascinating. There are some ciliates that have repurposed stop codons to code for Q. The authors find that in these proteomes, Q-runs are greatly expanded. They offer interesting speculations on how this expansion might impact protein function.

Weakness

At this time, the manuscript is disorganized and difficult to read. An expert in the field, who will not be distracted by the disorganization, will find some very interesting results included. In particular, the order of the introduction does not match the rest of the paper.

In the first and second sections, where the authors investigate how S/T/Q/N abundance modulates protein expression levels, it is unclear if the effect is due to the number of phosphorylation sites or the number of S/T/Q/N residues.

There are three reasons why the number of phosphorylation sites in the Q-rich motifs is not relevant to their autonomous protein expression-enhancing (PEE) activities:

First, we have reported previously that phosphorylation-defective Rad51-NTD (Rad51-3SA) and wild-type Rad51-NTD exhibit similar autonomous PEE activity. Mec1/Tel1-dependent phosphorylation of Rad51-NTD antagonizes the proteasomal degradation pathway, increasing the half-life of Rad51 from ∼30 min to ≥180 min (Ref 27; Woo, T. T. et al. 2020).

1. T. T. Woo, C. N. Chuang, M. Higashide, A. Shinohara, T. F. Wang, Dual roles of yeast Rad51 N-terminal domain in repairing DNA double-strand breaks. Nucleic Acids Res 48, 8474-8489 (2020).

Second, in our preprint manuscript, we have also shown that phosphorylation-defective Rad53-SCD1 (Rad51-SCD1-5STA) also exhibits autonomous PEE activity similar to that of wild-type Rad53-SCD (Figure 2D, Figure 4A and Figure 4C).

Third, as revealed by the results of our preprint manuscript (Figure 4), it is the percentages, and not the numbers, of S/T/Q/N residues that are correlated with the PEE activities of Q-rich motifs.

The authors also do not discuss if the N-end rule for protein stability applies to the lacZ reporter or the fusion proteins.

The autonomous PEE function of S/T/Q-rich NTDs is unlikely to be relevant to the N-end rule. The N-end rule links the in vivo half-life of a protein to the identity of its N-terminal residues. In S. cerevisiae, the N-end rule operates as part of the ubiquitin system and comprises two pathways. First, the Arg/N-end rule pathway, involving a single N-terminal amidohydrolase Nta1, mediates deamidation of N-terminal asparagine (N) and glutamine (Q) into aspartate (D) and glutamate (E), which in turn are arginylated by a single Ate1 R-transferase, generating the Arg/N degron. N-terminal R and other primary degrons are recognized by a single N-recognin Ubr1 in concert with ubiquitin-conjugating Ubc2/Rad6. Ubr1 can also recognize several other N-terminal residues, including lysine (K), histidine (H), phenylalanine (F), tryptophan (W), leucine (L) and isoleucine (I) (Bachmair, A. et al. 1986; Tasaki, T. et al. 2012; Varshavshy, A. et al. 2019). Second, the Ac/N-end rule pathway targets proteins containing N-terminally acetylated (Ac) residues. Prior to acetylation, the first amino acid methionine (M) is catalytically removed by Met-aminopeptides, unless a residue at position 2 is non-permissive (too large) for MetAPs. If a retained N-terminal M or otherwise a valine (V), cysteine (C), alanine (A), serine (S) or threonine (T) residue is followed by residues that allow N-terminal acetylation, the proteins containing these AcN degrons are targeted for ubiquitylation and proteasome-mediated degradation by the Doa10 E3 ligase (Hwang, C. S., 2019).

A. Bachmair, D. Finley, A. Varshavsky, In vivo half-life of a protein is a function of its amino-terminal residue. Science 234, 179-186 (1986).

T. Tasaki, S. M. Sriram, K. S. Park, Y. T. Kwon, The N-end rule pathway. Annu Rev Biochem 81, 261-289 (2012).

A. Varshavsky, N-degron and C-degron pathways of protein degradation. Proc Natl Acad Sci 116, 358-366 (2019).

C. S. Hwang, A. Shemorry, D. Auerbach, A. Varshavsky, The N-end rule pathway is mediated by a complex of the RING-type Ubr1 and HECT-type Ufd4 ubiquitin ligases. Nat Cell Biol 12, 1177-1185 (2010).

The PEE activities of these S/T/Q-rich domains are unlikely to arise from counteracting the N-end rule for two reasons. First, the first two amino acid residues of Rad51-NTD, Hop1-SCD, Rad53-SCD1, Sup35-PND, Rad51-ΔN, and LacZ-NVH are MS, ME, ME, MS, ME, and MI, respectively, where M is methionine, S is serine, E is glutamic acid and I is isoleucine. Second, Sml1-NTD behaves similarly to these N-terminal fusion tags, despite its methionine and glutamine (MQ) amino acid signature at the N-terminus.

The most interesting part of the paper is an exploration of S/T/Q/N-rich regions and other repetitive AA runs in 27 proteomes, particularly ciliates. However, this analysis is missing a critical control that makes it nearly impossible to evaluate the importance of the findings. The authors find the abundance of different amino acid runs in various proteomes. They also report the background abundance of each amino acid. They do not use this background abundance to normalize the runs of amino acids to create a null expectation from each proteome. For example, it has been clear for some time (Ruff, 2017; Ruff et al., 2016) that Drosophila contains a very high background of Q's in the proteome and it is necessary to control for this background abundance when finding runs of Q's.

We apologize for not explaining sufficiently well the topic eliciting this reviewer’s concern in our preprint manuscript. In the second paragraph of page 14, we cite six references to highlight that SCDs are overrepresented in yeast and human proteins involved in several biological processes (32, 74), and that polyX prevalence differs among species (43, 75-77).

1. Cheung HC, San Lucas FA, Hicks S, Chang K, Bertuch AA, Ribes-Zamora A. An S/T-Q cluster domain census unveils new putative targets under Tel1/Mec1 control. BMC Genomics. 2012;13:664.

2. Mier P, Elena-Real C, Urbanek A, Bernado P, Andrade-Navarro MA. The importance of definitions in the study of polyQ regions: A tale of thresholds, impurities and sequence context. Comput Struct Biotechnol J. 2020;18:306-13.

3. Cara L, Baitemirova M, Follis J, Larios-Sanz M, Ribes-Zamora A. The ATM- and ATR-related SCD domain is over-represented in proteins involved in nervous system development. Sci Rep. 2016;6:19050.

4. Kuspa A, Loomis WF. The genome of Dictyostelium discoideum. Methods Mol Biol. 2006;346:15-30.

5. Davies HM, Nofal SD, McLaughlin EJ, Osborne AR. Repetitive sequences in malaria parasite proteins. FEMS Microbiol Rev. 2017;41(6):923-40.

6. Mier P, Alanis-Lobato G, Andrade-Navarro MA. Context characterization of amino acid homorepeats using evolution, position, and order. Proteins. 2017;85(4):709-19.

We will cite the two references by Kiersten M. Ruff in our revised manuscript.

K. M. Ruff and R. V. Pappu, (2015) Multiscale simulation provides mechanistic insights into the effects of sequence contexts of early-stage polyglutamine-mediated aggregation. Biophysical Journal 108, 495a.

K. M. Ruff, J. B. Warner, A. Posey and P. S. Tan (2017) Polyglutamine length dependent structural properties and phase behavior of huntingtin exon1. Biophysical Journal 112, 511a.

The authors could easily address this problem with the data and analysis they have already collected. However, at this time, without this normalization, I am hesitant to trust the lists of proteins with long runs of amino acid and the ensuing GO enrichment analysis.

Ruff KM. 2017. Washington University in St.

Ruff KM, Holehouse AS, Richardson MGO, Pappu RV. 2016. Proteomic and Biophysical Analysis of Polar Tracts. Biophys J 110:556a.

We thank Reviewer #1 for this helpful suggestion and now address this issue by means of a different approach described below.

Based on a previous study (43; Palo Mier et al. 2020), we applied seven different thresholds to seek both short and long, as well as pure and impure, polyX strings in 20 different representative near-complete proteomes, including 4X (4/4), 5X (4/5-5/5), 6X (4/6-6/6), 7X (4/7-7/7), 8-10X (≥50%X), 11-10X (≥50%X) and ≥21X (≥50%X).

To normalize the runs of amino acids and create a null expectation from each proteome, we determined the ratios of the overall number of X residues for each of the seven polyX motifs relative to those in the entire proteome of each species, respectively. The results of four different polyX motifs are shown below, i.e., polyQ (Author response image 1), polyN (Author response image 2), polyS (Author response image 3) and polyT (Author response image 4).

Author response image 1.

Q contents in 7 different types of polyQ motifs in 20 near-complete proteomes. The five ciliates with reassigned stops codon (TAAQ and TAGQ) are indicated in red. Stentor coeruleus, a ciliate with standard stop codons, is indicated in green.  

Author response image 2.

N contents in 7 different types of polyN motifs in 20 near-complete proteomes. The five ciliates with reassigned stops codon (TAAQ and TAGQ) are indicated in red. Stentor coeruleus, a ciliate with standard stop codons, is indicated in green.

Author response image 3.

S contents in 7 different types of polyS motifs in 20 near-complete proteomes. The five ciliates with reassigned stops codon (TAAQ and TAGQ) are indicated in red. Stentor coeruleus, a ciliate with standard stop codons, is indicated in green.  

Author response image 4.

T contents in 7 different types of polyT motifs in 20 near-complete proteomes. The five ciliates with reassigned stops codon (TAAQ and TAGQ) are indicated in red. Stentor coeruleus, a ciliate with standard stop codons, is indicated in green.

The results summarized in these four new figures support that polyX prevalence differs among species and that the overall X contents of polyX motifs often but not always correlate with the X usage frequency in entire proteomes (43; Palo Mier et al. 2020).

Most importantly, our results reveal that, compared to Stentor coeruleus or several non-ciliate eukaryotic organisms (e.g., Plasmodium falciparum, Caenorhabditis elegans, Danio rerio, Mus musculus and Homo sapiens), the five ciliates with reassigned TAAQ and TAGQ codons not only have higher Q usage frequencies, but also more polyQ motifs in their proteomes (Figure 1). In contrast, polyQ motifs prevail in Candida albicans, Candida tropicalis, Dictyostelium discoideum, Chlamydomonas reinhardtii, Drosophila melanogaster and Aedes aegypti, though the Q usage frequencies in their entire proteomes are not significantly higher than those of other eukaryotes (Figure 1). Due to their higher N usage frequencies, Dictyostelium discoideum, Plasmodium falciparum and Pseudocohnilembus persalinus have more polyN motifs than the other 23 eukaryotes we examined here (Figure 2). Generally speaking, all 26 eukaryotes we assessed have similar S usage frequencies and percentages of S contents in polyS motifs (Figure 3). Among these 26 eukaryotes, Dictyostelium discoideum possesses many more polyT motifs, though its T usage frequency is similar to that of the other 25 eukaryotes (Figure 4).

In conclusion, these new normalized results confirm that the reassignment of stop codons to Q indeed results in both higher Q usage frequencies and more polyQ motifs in ciliates.  

Reviewer #2 (Public Review):

Summary:

This study seeks to understand the connection between protein sequence and function in disordered regions enriched in polar amino acids (specifically Q, N, S and T). While the authors suggest that specific motifs facilitate protein-enhancing activities, their findings are correlative, and the evidence is incomplete. Similarly, the authors propose that the re-assignment of stop codons to glutamine-encoding codons underlies the greater user of glutamine in a subset of ciliates, but again, the conclusions here are, at best, correlative. The authors perform extensive bioinformatic analysis, with detailed (albeit somewhat ad hoc) discussion on a number of proteins. Overall, the results presented here are interesting, but are unable to exclude competing hypotheses.

Strengths:

Following up on previous work, the authors wish to uncover a mechanism associated with poly-Q and SCD motifs explaining proposed protein expression-enhancing activities. They note that these motifs often occur IDRs and hypothesize that structural plasticity could be capitalized upon as a mechanism of diversification in evolution. To investigate this further, they employ bioinformatics to investigate the sequence features of proteomes of 27 eukaryotes. They deepen their sequence space exploration uncovering sub-phylum-specific features associated with species in which a stop-codon substitution has occurred. The authors propose this stop-codon substitution underlies an expansion of ploy-Q repeats and increased glutamine distribution.

Weaknesses:

The preprint provides extensive, detailed, and entirely unnecessary background information throughout, hampering reading and making it difficult to understand the ideas being proposed. The introduction provides a large amount of detailed background that appears entirely irrelevant for the paper. Many places detailed discussions on specific proteins that are likely of interest to the authors occur, yet without context, this does not enhance the paper for the reader.

The paper uses many unnecessary, new, or redefined acronyms which makes reading difficult. As examples:

(1) Prion forming domains (PFDs). Do the authors mean prion-like domains (PLDs), an established term with an empirical definition from the PLAAC algorithm? If yes, they should say this. If not, they must define what a prion-forming domain is formally.

The N-terminal domain (1-123 amino acids) of S. cerevisiae Sup35 was already referred to as a “prion forming domain (PFD)” in 2006 (Tuite, M. F. 2006). Since then, PFD has also been employed as an acronym in other yeast prion papers (Cox, B.S. et al. 2007; Toombs, T. et al. 2011).

M. F., Tuite, Yeast prions and their prion forming domain. Cell 27, 397-407 (2005).

B. S. Cox, L. Byrne, M. F., Tuite, Protein Stability. Prion 1, 170-178 (2007).

J. A. Toombs, N. M. Liss, K. R. Cobble, Z. Ben-Musa, E. D. Ross, [PSI+] maintenance is dependent on the composition, not primary sequence, of the oligopeptide repeat domain. PLoS One 6, e21953 (2011).

(2) SCD is already an acronym in the IDP field (meaning sequence charge decoration) - the authors should avoid this as their chosen acronym for Serine(S) / threonine (T)-glutamine (Q) cluster domains. Moreover, do we really need another acronym here (we do not).

SCD was first used in 2005 as an acronym for the Serine (S)/threonine (T)-glutamine (Q) cluster domain in the DNA damage checkpoint field (Traven, A. and Heierhorst, J. 2005). Almost a decade later, SCD became an acronym for “sequence charge decoration” (Sawle, L. et al. 2015; Firman, T. et al. 2018).

A. Traven and J, Heierhorst, SQ/TQ cluster domains: concentrated ATM/ATR kinase phosphorylation site regions in DNA-damage-response proteins. Bioessays. 27, 397-407 (2005).

L. Sawle and K, Ghosh, A theoretical method to compute sequence dependent configurational properties in charged polymers and proteins. J. Chem Phys. 143, 085101(2015).

T. Firman and Ghosh, K. Sequence charge decoration dictates coil-globule transition in intrinsically disordered proteins. J. Chem Phys. 148, 123305 (2018).

(3) Protein expression-enhancing (PEE) - just say expression-enhancing, there is no need for an acronym here.

Thank you. Since we have shown that addition of Q-rich motifs to LacZ affects protein expression rather than transcription, we think it is better to use the “PEE” acronym.

The results suggest autonomous protein expression-enhancing activities of regions of multiple proteins containing Q-rich and SCD motifs. Their definition of expression-enhancing activities is vague and the evidence they provide to support the claim is weak. While their previous work may support their claim with more evidence, it should be explained in more detail. The assay they choose is a fusion reporter measuring beta-galactosidase activity and tracking expression levels. Given the presented data they have shown that they can drive the expression of their reporters and that beta gal remains active, in addition to the increase in expression of fusion reporter during the stress response. They have not detailed what their control and mock treatment is, which makes complete understanding of their experimental approach difficult. Furthermore, their nuclear localization signal on the tag could be influencing the degradation kinetics or sequestering the reporter, leading to its accumulation and the appearance of enhanced expression. Their evidence refuting ubiquitin-mediated degradation does not have a convincing control.

Based on the experimental results, the authors then go on to perform bioinformatic analysis of SCD proteins and polyX proteins. Unfortunately, there is no clear hypothesis for what is being tested; there is a vague sense of investigating polyX/SCD regions, but I did not find the connection between the first and section compelling (especially given polar-rich regions have been shown to engage in many different functions). As such, this bioinformatic analysis largely presents as many lists of percentages without any meaningful interpretation. The bioinformatics analysis lacks any kind of rigorous statistical tests, making it difficult to evaluate the conclusions drawn. The methods section is severely lacking. Specifically, many of the methods require the reader to read many other papers. While referencing prior work is of course, important, the authors should ensure the methods in this paper provide the details needed to allow a reader to evaluate the work being presented. As it stands, this is not the case.

Thank you. As described in detail below, we have now performed rigorous statistical testing using the GofuncR package.

Overall, my major concern with this work is that the authors make two central claims in this paper (as per the Discussion). The authors claim that Q-rich motifs enhance protein expression. The implication here is that Q-rich motif IDRs are special, but this is not tested. As such, they cannot exclude the competing hypothesis ("N-terminal disordered regions enhance expression").

In fact, “N-terminal disordered regions enhance expression” exactly summarizes our hypothesis.

On pages 12-13 and Figure 4 of our preprint manuscript, we explained our hypothesis in the paragraph entitled “The relationship between PEE function, amino acid contents, and structural flexibility”.

The authors also do not explore the possibility that this effect is in part/entirely driven by mRNA-level effects (see Verma Na Comms 2019).

As pointed out by the first reviewer, we show evidence that the increase in protein abundance and enzymatic activity is not due to changes in plasmid copy number or mRNA abundance (Figure 2), and that this phenomenon is not affected by translational quality control mutants (Figure 3).

As such, while these observations are interesting, they feel preliminary and, in my opinion, cannot be used to draw hard conclusions on how N-terminal IDR sequence features influence protein expression. This does not mean the authors are necessarily wrong, but from the data presented here, I do not believe strong conclusions can be drawn. That re-assignment of stop codons to Q increases proteome-wide Q usage. I was unable to understand what result led the authors to this conclusion.

My reading of the results is that a subset of ciliates has re-assigned UAA and UAG from the stop codon to Q. Those ciliates have more polyQ-containing proteins. However, they also have more polyN-containing proteins and proteins enriched in S/T-Q clusters. Surely if this were a stop-codon-dependent effect, we'd ONLY see an enhancement in Q-richness, not a corresponding enhancement in all polar-rich IDR frequencies? It seems the better working hypothesis is that free-floating climate proteomes are enriched in polar amino acids compared to sessile ciliates.

Thank you. These comments are not supported by the results in Figure 1.

Regardless, the absence of any kind of statistical analysis makes it hard to draw strong conclusions here.

We apologize for not explaining more clearly the results of Tables 5-7 in our preprint manuscript.

To address the concerns about our GO enrichment analysis by both reviewers, we have now performed rigorous statistical testing for SCD and polyQ protein overrepresentation using the GOfuncR package (https://bioconductor.org/packages/release/bioc/html/GOfuncR.html). GOfuncR is an R package program that conducts standard candidate vs. background enrichment analysis by means of the hypergeometric test. We then adjusted the raw p-values according to the Family-wise error rate (FWER). The same method had been applied to GO enrichment analysis of human genomes (Huttenhower, C., et al. 2009).

Curtis Huttenhower, C., Haley, E. M., Hibbs, M., A., Dumeaux, V., Barrett, D. R., Hilary A. Coller, H. A., and Olga G. Troyanskaya, O., G. Exploring the human genome with functional maps, Genome Research 19, 1093-1106 (2009).

The results presented in Author response image 5 and Author response image 6 support our hypothesis that Q-rich motifs prevail in proteins involved in specialized biological processes, including Saccharomyces cerevisiae RNA-mediated transposition, Candida albicans filamentous growth, peptidyl-glutamic acid modification in ciliates with reassigned stop codons (TAAQ and TAGQ), Tetrahymena thermophila xylan catabolism, Dictyostelium discoideum sexual reproduction, Plasmodium falciparum infection, as well as the nervous systems of Drosophila melanogaster, Mus musculus, and Homo sapiens (74). In contrast, peptidyl-glutamic acid modification and microtubule-based movement are not overrepresented with Q-rich proteins in Stentor coeruleus, a ciliate with standard stop codons.

1. Cara L, Baitemirova M, Follis J, Larios-Sanz M, Ribes-Zamora A. The ATM- and ATR-related SCD domain is over-represented in proteins involved in nervous system development. Sci Rep. 2016;6:19050.

Author response image 5.

Selection of biological processes with overrepresented SCD-containing proteins in different eukaryotes. The percentages and number of SCD-containing proteins in our search that belong to each indicated Gene Ontology (GO) group are shown. GOfuncR (Huttenhower, C., et al. 2009) was applied for GO enrichment and statistical analysis. The p values adjusted according to the Family-wise error rate (FWER) are shown. The five ciliates with reassigned stop codons (TAAQ and TAGQ) are indicated in red. Stentor coeruleus, a ciliate with standard stop codons, is indicated in green.

Author response image 6.

Selection of biological processes with overrepresented polyQ-containing proteins in different eukaryotes. The percentages and numbers of polyQ-containing proteins in our search that belong to each indicated Gene Ontology (GO) group are shown. GOfuncR (Huttenhower, C., et al. 2009) was applied for GO enrichment and statistical analysis. The p values adjusted according to the Family-wise error rate (FWER) are shown. The five ciliates with reassigned stops codons (TAAQ and TAGQ) are indicated in red. Stentor coeruleus, a ciliate with standard stop codons, is indicated in green.

Author response:

eLife Assessment 

This valuable study investigates how the neural representation of individual finger movements changes during the early period of sequence learning. By combining a new method for extracting features from human magnetoencephalography data and decoding analyses, the authors provide incomplete evidence of an early, swift change in the brain regions correlated with sequence learning, including a set of previously unreported frontal cortical regions. The addition of more control analyses to rule out that head movement artefacts influence the findings, and to further explain the proposal of offline contextualization during short rest periods as the basis for improvement performance would strengthen the manuscript. 

We appreciate the Editorial assessment on our paper’s strengths and novelty.  We have implemented additional control analyses to show that neither task-related eye movements nor increasing overlap of finger movements during learning account for our findings, which are that contextualized neural representations in a network of bilateral frontoparietal brain regions actively contribute to skill learning.  Importantly, we carried out additional analyses showing that contextualization develops predominantly during rest intervals.

Public Reviews:

We thank the Reviewers for their comments and suggestions, prompting new analyses and additions that strengthened our report.

Reviewer #1 (Public review): 

Summary: 

This study addresses the issue of rapid skill learning and whether individual sequence elements (here: finger presses) are differentially represented in human MEG data. The authors use a decoding approach to classify individual finger elements and accomplish an accuracy of around 94%. A relevant finding is that the neural representations of individual finger elements dynamically change over the course of learning. This would be highly relevant for any attempts to develop better brain machine interfaces - one now can decode individual elements within a sequence with high precision, but these representations are not static but develop over the course of learning. 

Strengths: The work follows a large body of work from the same group on the behavioural and neural foundations of sequence learning. The behavioural task is well established and neatly designed to allow for tracking learning and how individual sequence elements contribute. The inclusion of short offline rest periods between learning epochs has been influential because it has revealed that a lot, if not most of the gains in behaviour (ie speed of finger movements) occur in these so-called micro-offline rest periods. The authors use a range of new decoding techniques, and exhaustively interrogate their data in different ways, using different decoding approaches. Regardless of the approach, impressively high decoding accuracies are observed, but when using a hybrid approach that combines the MEG data in different ways, the authors observe decoding accuracies of individual sequence elements from the MEG data of up to 94%. 

We have previously showed that neural replay of MEG activity representing the practiced skill correlated with micro-offline gains during rest intervals of early learning, 1 consistent with the recent report that hippocampal ripples during these offline periods predict human motor sequence learning2.  However, decoding accuracy in our earlier work1 needed improvement.  Here, we reported a strategy to improve decoding accuracy that could benefit future studies of neural replay or BCI using MEG.

Weaknesses: 

There are a few concerns which the authors may well be able to resolve. These are not weaknesses as such, but factors that would be helpful to address as these concern potential contributions to the results that one would like to rule out. Regarding the decoding results shown in Figure 2 etc, a concern is that within individual frequency bands, the highest accuracy seems to be within frequencies that match the rate of keypresses. This is a general concern when relating movement to brain activity, so is not specific to decoding as done here. As far as reported, there was no specific restraint to the arm or shoulder, and even then it is conceivable that small head movements would correlate highly with the vigor of individual finger movements. This concern is supported by the highest contribution in decoding accuracy being in middle frontal regions - midline structures that would be specifically sensitive to movement artefacts and don't seem to come to mind as key structures for very simple sequential keypress tasks such as this - and the overall pattern is remarkably symmetrical (despite being a unimanual finger task) and spatially broad. This issue may well be matching the time course of learning, as the vigor and speed of finger presses will also influence the degree to which the arm/shoulder and head move. This is not to say that useful information is contained within either of the frequencies or broadband data. But it raises the question of whether a lot is dominated by movement "artefacts" and one may get a more specific answer if removing any such contributions. 

Reviewer #1 expresses concern that the combination of the low-frequency narrow-band decoder results, and the bilateral middle frontal regions displaying the highest average intra-parcel decoding performance across subjects is suggestive that the decoding results could be driven by head movement or other artefacts.

Head movement artefacts are highly unlikely to contribute meaningfully to our results for the following reasons. First, in addition to ICA denoising, all “recordings were visually inspected and marked to denoise segments containing other large amplitude artifacts due to movements” (see Methods). Second, the response pad was positioned in a manner that minimized wrist, arm or more proximal body movements during the task. Third, while head position was not monitored online for this study, the head was restrained using an inflatable air bladder, and head position was assessed at the beginning and at the end of each recording. Head movement did not exceed 5mm between the beginning and end of each scan for all participants included in the study. Fourth, we agree that despite the steps taken above, it is possible that minor head movements could still contribute to some remaining variance in the MEG data in our study. The Reviewer states a concern that “it is conceivable that small head movements would correlate highly with the vigor of individual finger movements”. However, in order for any such correlations to meaningfully impact decoding performance, such head movements would need to: (A) be consistent and pervasive throughout the recording (which might not be the case if the head movements were related to movement vigor and vigor changed over time); and (B) systematically vary between different finger movements, and also between the same finger movement performed at different sequence locations (see 5-class decoding performance in Figure 4B). The possibility of any head movement artefacts meeting all these conditions is extremely unlikely.

Given the task design, a much more likely confound in our estimation would be the contribution of eye movement artefacts to the decoder performance (an issue appropriately raised by Reviewer #3 in the comments below). Remember from Figure 1A in the manuscript that an asterisk marks the current position in the sequence and is updated at each keypress. Since participants make very few performance errors, the position of the asterisk on the display is highly correlated with the keypress being made in the sequence. Thus, it is possible that if participants are attending to the visual feedback provided on the display, they may move their eyes in a way that is systematically related to the task.  Since we did record eye movements simultaneously with the MEG recordings (EyeLink 1000 Plus; Fs = 600 Hz), we were able to perform a control analysis to address this question. For each keypress event during trials in which no errors occurred (which is the same time-point that the asterisk position is updated), we extracted three features related to eye movements: 1) the gaze position at the time of asterisk position update (or keyDown event), 2) the gaze position 150ms later, and 3) the peak velocity of the eye movement between the two positions. We then constructed a classifier from these features with the aim of predicting the location of the asterisk (ordinal positions 1-5) on the display. As shown in the confusion matrix below (Author response image 1), the classifier failed to perform above chance levels (Overall cross-validated accuracy = 0.21817):

Author response image 1.

Confusion matrix showing that three eye movement features fail to predict asterisk position on the task display above chance levels (Fold 1 test accuracy = 0.21718; Fold 2 test accuracy = 0.22023; Fold 3 test accuracy = 0.21859; Fold 4 test accuracy = 0.22113; Fold 5 test accuracy = 0.21373; Overall cross-validated accuracy = 0.2181). Since the ordinal position of the asterisk on the display is highly correlated with the ordinal position of individual keypresses in the sequence, this analysis provides strong evidence that keypress decoding performance from MEG features is not explained by systematic relationships between finger movement behavior and eye movements (i.e. – behavioral artefacts).

In fact, inspection of the eye position data revealed that a majority of participants on most trials displayed random walk gaze patterns around a center fixation point, indicating that participants did not attend to the asterisk position on the display. This is consistent with intrinsic generation of the action sequence, and congruent with the fact that the display does not provide explicit feedback related to performance. A similar real-world example would be manually inputting a long password into a secure online application. In this case, one intrinsically generates the sequence from memory and receives similar feedback about the password sequence position (also provided as asterisks), which is typically ignored by the user. The minimal participant engagement with the visual task display observed in this study highlights another important point – that the behavior in explicit sequence learning motor tasks is highly generative in nature rather than reactive to stimulus cues as in the serial reaction time task (SRTT).  This is a crucial difference that must be carefully considered when designing investigations and comparing findings across studies.

We observed that initial keypress decoding accuracy was predominantly driven by contralateral primary sensorimotor cortex in the initial practice trials before transitioning to bilateral frontoparietal regions by trials 11 or 12 as performance gains plateaued.  The contribution of contralateral primary sensorimotor areas to early skill learning has been extensively reported in humans and non-human animals. 1,3-5  Similarly, the increased involvement of bilateral frontal and parietal regions to decoding during early skill learning in the non-dominant hand is well known.  Enhanced bilateral activation in both frontal and parietal cortex during skill learning has been extensively reported6-11, and appears to be even more prominent during early fine motor skill learning in the non-dominant hand12,13.  The frontal regions identified in these studies are known to play crucial roles in executive control14, motor planning15, and working memory6,8,16-18 processes, while the same parietal regions are known to integrate multimodal sensory feedback and support visuomotor transformations6,8,16-18, in addition to working memory19. Thus, it is not surprising that these regions increasingly contribute to decoding as subjects internalize the sequential task.  We now include a statement reflecting these considerations in the revised Discussion.

A somewhat related point is this: when combining voxel and parcel space, a concern is whether a degree of circularity may have contributed to the improved accuracy of the combined data, because it seems to use the same MEG signals twice - the voxels most contributing are also those contributing most to a parcel being identified as relevant, as parcels reflect the average of voxels within a boundary. In this context, I struggled to understand the explanation given, ie that the improved accuracy of the hybrid model may be due to "lower spatially resolved whole-brain and higher spatially resolved regional activity patterns".

We strongly disagree with the Reviewer’s assertion that the construction of the hybrid-space decoder is circular. To clarify, the base feature set for the hybrid-space decoder constructed for all participants includes whole-brain spatial patterns of MEG source activity averaged within parcels. As stated in the manuscript, these 148 inter-parcel features reflect “lower spatially resolved whole-brain activity patterns” or global brain dynamics. We then independently test how well spatial patterns of MEG source activity for all voxels distributed within individual parcels can decode keypress actions. Again, the testing of these intra-parcel spatial patterns, intended to capture “higher spatially resolved regional brain activity patterns”, is completely independent from one another and independent from the weighting of individual inter-parcel features. These intra-parcel features could, for example, provide additional information about muscle activation patterns or the task environment. These approximately 1150 intra-parcel voxels (on average, within the total number varying between subjects) are then combined with the 148 inter-parcel features to construct the final hybrid-space decoder. In fact, this varied spatial filter approach shares some similarities to the construction of convolutional neural networks (CNNs) used to perform object recognition in image classification applications. One could also view this hybrid-space decoding approach as a spatial analogue to common time-frequency based analyses such as theta-gamma phase amplitude coupling (PAC), which combine information from two or more narrow-band spectral features derived from the same time-series data.

We directly tested this hypothesis – that spatially overlapping intra- and inter-parcel features portray different information – by constructing an alternative hybrid-space decoder (HybridAlt) that excluded average inter-parcel features which spatially overlapped with intra-parcel voxel features, and comparing the performance to the decoder used in the manuscript (HybridOrig). The prediction was that if the overlapping parcel contained similar information to the more spatially resolved voxel patterns, then removing the parcel features (n=8) from the decoding analysis should not impact performance. In fact, despite making up less than 1% of the overall input feature space, removing those parcels resulted in a significant drop in overall performance greater than 2% (78.15% ± SD 7.03% for HybridOrig vs. 75.49% ± SD 7.17% for HybridAlt; Wilcoxon signed rank test, z = 3.7410, p = 1.8326e-04) (Author response image 2).

Author response image 2.

Comparison of decoding performances with two different hybrid approaches. HybridAlt: Intra-parcel voxel-space features of top ranked parcels and inter-parcel features of remaining parcels. HybridOrig:  Voxel-space features of top ranked parcels and whole-brain parcel-space features (i.e. – the version used in the manuscript). Dots represent decoding accuracy for individual subjects. Dashed lines indicate the trend in performance change across participants. Note, that HybridOrig (the approach used in our manuscript) significantly outperforms the HybridAlt approach, indicating that the excluded parcel features provide unique information compared to the spatially overlapping intra-parcel voxel patterns.

Firstly, there will be a relatively high degree of spatial contiguity among voxels because of the nature of the signal measured, i.e. nearby individual voxels are unlikely to be independent. Secondly, the voxel data gives a somewhat misleading sense of precision; the inversion can be set up to give an estimate for each voxel, but there will not just be dependence among adjacent voxels, but also substantial variation in the sensitivity and confidence with which activity can be projected to different parts of the brain. Midline and deeper structures come to mind, where the inversion will be more problematic than for regions along the dorsal convexity of the brain, and a concern is that in those midline structures, the highest decoding accuracy is seen. 

We definitely agree with the Reviewer that some inter-parcel features representing neighboring (or spatially contiguous) voxels are likely to be correlated. This has been well documented in the MEG literature20,21 and is a particularly important confound to address in functional or effective connectivity analyses (not performed in the present study). In the present analysis, any correlation between adjacent voxels presents a multi-collinearity problem, which effectively reduces the dimensionality of the input feature space. However, as long as there are multiple groups of correlated voxels within each parcel (i.e. - the effective dimensionality is still greater than 1), the intra-parcel spatial patterns could still meaningfully contribute to the decoder performance. Two specific results support this assertion.

First, we obtained higher decoding accuracy with voxel-space features [74.51% (± SD 7.34%)] compared to parcel space features [68.77% (± SD 7.6%)] (Figure 3B), indicating individual voxels carry more information in decoding the keypresses than the averaged voxel-space features or parcel-space features.  Second, Individual voxels within a parcel showed varying feature importance scores in decoding keypresses (Author response image 3). This finding supports the Reviewer’s assertion that neighboring voxels express similar information, but also shows that the correlated voxels form mini subclusters that are much smaller spatially than the parcel they reside in.

Author response image 3.

Feature importance score of individual voxels in decoding keypresses: MRMR was used to rank the individual voxel space features in decoding keypresses and the min-max normalized MRMR score was mapped to a structural brain surface. Note that individual voxels within a parcel showed different contribution to decoding.

Some of these concerns could be addressed by recording head movement (with enough precision) to regress out these contributions. The authors state that head movement was monitored with 3 fiducials, and their time courses ought to provide a way to deal with this issue. The ICA procedure may not have sufficiently dealt with removing movement-related problems, but one could eg relate individual components that were identified to the keypresses as another means for checking. An alternative could be to focus on frequency ranges above the movement frequencies. The accuracy for those still seems impressive and may provide a slightly more biologically plausible assessment. 

We have already addressed the issue of movement related artefacts in the first response above. With respect to a focus on frequency ranges above movement frequencies, the Reviewer states the “accuracy for those still seems impressive and may provide a slightly more biologically plausible assessment”. First, it is important to note that cortical delta-band oscillations measured with local field potentials (LFPs) in macaques is known to contain important information related to end-effector kinematics22,23 muscle activation patterns24 and temporal sequencing25 during skilled reaching and grasping actions. Thus, there is a substantial body of evidence that low-frequency neural oscillatory activity in this range contains important information about the skill learning behavior investigated in the present study. Second, our own data shows (which the Reviewer also points out) that significant information related to the skill learning behavior is also present in higher frequency bands (see Figure 2A and Figure 3—figure supplement 1). As we pointed out in our earlier response to questions about the hybrid space decoder architecture (see above), it is likely that different, yet complimentary, information is encoded across different temporal frequencies (just as it is encoded across different spatial frequencies). Again, this interpretation is supported by our data as the highest performing classifiers in all cases (when holding all parameters constant) were always constructed from broadband input MEG data (Figure 2A and Figure 3—figure supplement 1).  

One question concerns the interpretation of the results shown in Figure 4. They imply that during the course of learning, entirely different brain networks underpin the behaviour. Not only that, but they also include regions that would seem rather unexpected to be key nodes for learning and expressing relatively simple finger sequences, such as here. What then is the biological plausibility of these results? The authors seem to circumnavigate this issue by moving into a distance metric that captures the (neural network) changes over the course of learning, but the discussion seems detached from which regions are actually involved; or they offer a rather broad discussion of the anatomical regions identified here, eg in the context of LFOs, where they merely refer to "frontoparietal regions". 

The Reviewer notes the shift in brain networks driving keypress decoding performance between trials 1, 11 and 36 as shown in Figure 4A. The Reviewer questions whether these substantial shifts in brain network states underpinning the skill are biologically plausible, as well as the likelihood that bilateral superior and middle frontal and parietal cortex are important nodes within these networks.

First, previous fMRI work in humans performing a similar sequence learning task showed that flexibility in brain network composition (i.e. – changes in brain region members displaying coordinated activity) is up-regulated in novel learning environments and explains differences in learning rates across individuals26.  This work supports our interpretation of the present study data, that brain networks engaged in sequential motor skills rapidly reconfigure during early learning.

Second, frontoparietal network activity is known to support motor memory encoding during early learning27,28. For example, reactivation events in the posterior parietal29 and medial prefrontal30,31 cortex (MPFC) have been temporally linked to hippocampal replay, and are posited to support memory consolidation across several memory domains32, including motor sequence learning1,33,34.  Further, synchronized interactions between MPFC and hippocampus are more prominent during early learning as opposed to later stages27,35,36, perhaps reflecting “redistribution of hippocampal memories to MPFC” 27.  MPFC contributes to very early memory formation by learning association between contexts, locations, events and adaptive responses during rapid learning37. Consistently, coupling between hippocampus and MPFC has been shown during, and importantly immediately following (rest) initial memory encoding38,39.  Importantly, MPFC activity during initial memory encoding predicts subsequent recall40. Thus, the spatial map required to encode a motor sequence memory may be “built under the supervision of the prefrontal cortex” 28, also engaged in the development of an abstract representation of the sequence41.  In more abstract terms, the prefrontal, premotor and parietal cortices support novice performance “by deploying attentional and control processes” 42-44 required during early learning42-44. The dorsolateral prefrontal cortex DLPFC specifically is thought to engage in goal selection and sequence monitoring during early skill practice45, all consistent with the schema model of declarative memory in which prefrontal cortices play an important role in encoding46,47.  Thus, several prefrontal and frontoparietal regions contributing to long term learning 48 are also engaged in early stages of encoding. Altogether, there is strong biological support for the involvement of bilateral prefrontal and frontoparietal regions to decoding during early skill learning.  We now address this issue in the revised manuscript.

If I understand correctly, the offline neural representation analysis is in essence the comparison of the last keypress vs the first keypress of the next sequence. In that sense, the activity during offline rest periods is actually not considered. This makes the nomenclature somewhat confusing. While it matches the behavioural analysis, having only key presses one can't do it in any other way, but here the authors actually do have recordings of brain activity during offline rest. So at the very least calling it offline neural representation is misleading to this reviewer because what is compared is activity during the last and during the next keypress, not activity during offline periods. But it also seems a missed opportunity - the authors argue that most of the relevant learning occurs during offline rest periods, yet there is no attempt to actually test whether activity during this period can be useful for the questions at hand here. 

We agree with the Reviewer that our previous “offline neural representation” nomenclature could be misinterpreted. In the revised manuscript we refer to this difference as the “offline neural representational change”. Please, note that our previous work did link offline neural activity (i.e. – 16-22 Hz beta power and neural replay density during inter-practice rest periods) to observed micro-offline gains49.

Reviewer #2 (Public review): 

Summary 

Dash et al. asked whether and how the neural representation of individual finger movements is "contextualized" within a trained sequence during the very early period of sequential skill learning by using decoding of MEG signal. Specifically, they assessed whether/how the same finger presses (pressing index finger) embedded in the different ordinal positions of a practiced sequence (4-1-3-2-4; here, the numbers 1 through 4 correspond to the little through the index fingers of the non-dominant left hand) change their representation (MEG feature). They did this by computing either the decoding accuracy of the index finger at the ordinal positions 1 vs. 5 (index_OP1 vs index_OP5) or pattern distance between index_OP1 vs. index_OP5 at each training trial and found that both the decoding accuracy and the pattern distance progressively increase over the course of learning trials. More interestingly, they also computed the pattern distance for index_OP5 for the last execution of a practice trial vs. index_OP1 for the first execution in the next practice trial (i.e., across the rest period). This "off-line" distance was significantly larger than the "on-line" distance, which was computed within practice trials and predicted micro-offline skill gain. Based on these results, the authors conclude that the differentiation of representation for the identical movement embedded in different positions of a sequential skill ("contextualization") primarily occurs during early skill learning, especially during rest, consistent with the recent theory of the "micro-offline learning" proposed by the authors' group. I think this is an important and timely topic for the field of motor learning and beyond. <br /> Strengths 

The specific strengths of the current work are as follows. First, the use of temporally rich neural information (MEG signal) has a large advantage over previous studies testing sequential representations using fMRI. This allowed the authors to examine the earliest period (= the first few minutes of training) of skill learning with finer temporal resolution. Second, through the optimization of MEG feature extraction, the current study achieved extremely high decoding accuracy (approx. 94%) compared to previous works. As claimed by the authors, this is one of the strengths of the paper (but see my comments). Third, although some potential refinement might be needed, comparing "online" and "offline" pattern distance is a neat idea. 

Weaknesses 

Along with the strengths I raised above, the paper has some weaknesses. First, the pursuit of high decoding accuracy, especially the choice of time points and window length (i.e., 200 msec window starting from 0 msec from key press onset), casts a shadow on the interpretation of the main result. Currently, it is unclear whether the decoding results simply reflect behavioral change or true underlying neural change. As shown in the behavioral data, the key press speed reached 3~4 presses per second already at around the end of the early learning period (11th trial), which means inter-press intervals become as short as 250-330 msec. Thus, in almost more than 60% of training period data, the time window for MEG feature extraction (200 msec) spans around 60% of the inter-press intervals. Considering that the preparation/cueing of subsequent presses starts ahead of the actual press (e.g., Kornysheva et al., 2019) and/or potential online planning (e.g., Ariani and Diedrichsen, 2019), the decoder likely has captured these future press information as well as the signal related to the current key press, independent of the formation of genuine sequential representation (e.g., "contextualization" of individual press). This may also explain the gradual increase in decoding accuracy or pattern distance between index_OP1 vs. index_OP5 (Figure 4C and 5A), which co-occurred with performance improvement, as shorter inter-press intervals are more favorable for the dissociating the two index finger presses followed by different finger presses. The compromised decoding accuracies for the control sequences can be explained in similar logic. Therefore, more careful consideration and elaborated discussion seem necessary when trying to both achieve high-performance decoding and assess early skill learning, as it can impact all the subsequent analyses.

The Reviewer raises the possibility that (given the windowing parameters used in the present study) an increase in “contextualization” with learning could simply reflect faster typing speeds as opposed to an actual change in the underlying neural representation. The issue can essentially be framed as a mixing problem. As correct sequences are generated at higher and higher speeds over training, MEG activity patterns related to the planning, execution, evaluation and memory of individual keypresses overlap more in time. Thus, increased overlap between the “4” and “1” keypresses (at the start of the sequence) and “2” and “4” keypresses (at the end of the sequence) could artefactually increase contextualization distances even if the underlying neural representations for the individual keypresses remain unchanged (assuming this mixing of representations is used by the classifier to differentially tag each index finger press). If this were the case, it follows that such mixing effects reflecting the ordinal sequence structure would also be observable in the distribution of decoder misclassifications. For example, “4” keypresses would be more likely to be misclassified as “1” or “2” keypresses (or vice versa) than as “3” keypresses. The confusion matrices presented in Figures 3C and 4B and Figure 3—figure supplement 3A in the previously submitted manuscript do not show this trend in the distribution of misclassifications across the four fingers.

Moreover, if the representation distance is largely driven by this mixing effect, it’s also possible that the increased overlap between consecutive index finger keypresses during the 4-4 transition marking the end of one sequence and the beginning of the next one could actually mask contextualization-related changes to the underlying neural representations and make them harder to detect. In this case, a decoder tasked with separating individual index finger keypresses into two distinct classes based upon sequence position might show decreased performance with learning as adjacent keypresses overlapped in time with each other to an increasing extent. However, Figure 4C in our previously submitted manuscript does not support this possibility, as the 2-class hybrid classifier displays improved classification performance over early practice trials despite greater temporal overlap.

We also conducted a new multivariate regression analysis to directly assess whether the neural representation distance score could be predicted by the 4-1, 2-4 and 4-4 keypress transition times observed for each complete correct sequence (both predictor and response variables were z-score normalized within-subject). The results of this analysis affirmed that the possible alternative explanation put forward by the Reviewer is not supported by our data (Adjusted R2 = 0.00431; F = 5.62). We now include this new negative control analysis result in the revised manuscript.

Overall, we do strongly agree with the Reviewer that the naturalistic, self-paced, generative task employed in the present study results in overlapping brain processes related to planning, execution, evaluation and memory of the action sequence. We also agree that there are several tradeoffs to consider in the construction of the classifiers depending on the study aim. Given our aim of optimizing keypress decoder accuracy in the present study, the set of trade-offs resulted in representations reflecting more the latter three processes, and less so the planning component. Whether separate decoders can be constructed to tease apart the representations or networks supporting these overlapping processes is an important future direction of research in this area. For example, work presently underway in our lab constrains the selection of windowing parameters in a manner that allows individual classifiers to be temporally linked to specific planning, execution, evaluation or memory-related processes to discern which brain networks are involved and how they adaptively reorganize with learning. Results from the present study (Figure 4—figure supplement 2) showing hybrid-space decoder prediction accuracies exceeding 74% for temporal windows spanning as little as 25ms and located up to 100ms prior to the keyDown event strongly support the feasibility of such an approach.

Related to the above point, testing only one particular sequence (4-1-3-2-4), aside from the control ones, limits the generalizability of the finding. This also may have contributed to the extremely high decoding accuracy reported in the current study. 

The Reviewer raises a question about the generalizability of the decoder accuracy reported in our study. Fortunately, a comparison between decoder performances on Day 1 and Day 2 datasets does provide some insight into this issue. As the Reviewer points out, the classifiers in this study were trained and tested on keypresses performed while practicing a specific sequence (4-1-3-2-4). The study was designed this way as to avoid the impact of interference effects on learning dynamics. The cross-validated performance of classifiers on MEG data collected within the same session was 90.47% overall accuracy (4-class; Figure 3C). We then tested classifier performance on data collected during a separate MEG session conducted approximately 24 hours later (Day 2; see Figure 3—supplement 3). We observed a reduction in overall accuracy rate to 87.11% when tested on MEG data recorded while participants performed the same learned sequence, and 79.44% when they performed several previously unpracticed sequences. Both changes in accuracy are important with regards to the generalizability of our findings. First, 87.11% performance accuracy for the trained sequence data on Day 2 (a reduction of only 3.36%) indicates that the hybrid-space decoder performance is robust over multiple MEG sessions, and thus, robust to variations in SNR across the MEG sensor array caused by small differences in head position between scans.  This indicates a substantial advantage over sensor-space decoding approaches. Furthermore, when tested on data from unpracticed sequences, overall performance dropped an additional 7.67%. This difference reflects the performance bias of the classifier for the trained sequence, possibly caused by high-order sequence structure being incorporated into the feature weights. In the future, it will be important to understand in more detail how random or repeated keypress sequence training data impacts overall decoder performance and generalization. We strongly agree with the Reviewer that the issue of generalizability is extremely important and have added a new paragraph to the Discussion in the revised manuscript highlighting the strengths and weaknesses of our study with respect to this issue.

In terms of clinical BCI, one of the potential relevance of the study, as claimed by the authors, it is not clear that the specific time window chosen in the current study (up to 200 msec since key press onset) is really useful. In most cases, clinical BCI would target neural signals with no overt movement execution due to patients' inability to move (e.g., Hochberg et al., 2012). Given the time window, the surprisingly high performance of the current decoder may result from sensory feedback and/or planning of subsequent movement, which may not always be available in the clinical BCI context. Of course, the decoding accuracy is still much higher than chance even when using signal before the key press (as shown in Figure 4 Supplement 2), but it is not immediately clear to me that the authors relate their high decoding accuracy based on post-movement signal to clinical BCI settings.

The Reviewer questions the relevance of the specific window parameters used in the present study for clinical BCI applications, particularly for paretic patients who are unable to produce finger movements or for whom afferent sensory feedback is no longer intact. We strongly agree with the Reviewer that any intended clinical application must carefully consider these specific input feature constraints dictated by the clinical cohort, and in turn impose appropriate and complimentary constraints on classifier parameters that may differ from the ones used in the present study.  We now highlight this issue in the Discussion of the revised manuscript and relate our present findings to published clinical BCI work within this context.

One of the important and fascinating claims of the current study is that the "contextualization" of individual finger movements in a trained sequence specifically occurs during short rest periods in very early skill learning, echoing the recent theory of micro-offline learning proposed by the authors' group. Here, I think two points need to be clarified. First, the concept of "contextualization" is kept somewhat blurry throughout the text. It is only at the later part of the Discussion (around line #330 on page 13) that some potential mechanism for the "contextualization" is provided as "what-and-where" binding. Still, it is unclear what "contextualization" actually is in the current data, as the MEG signal analyzed is extracted from 0-200 msec after the keypress. If one thinks something is contextualizing an action, that contextualization should come earlier than the action itself. 

The Reviewer requests that we: 1) more clearly define our use of the term “contextualization” and 2) provide the rationale for assessing it over a 200ms window aligned to the keyDown event. This choice of window parameters means that the MEG activity used in our analysis was coincident with, rather than preceding, the actual keypresses.  We define contextualization as the differentiation of representation for the identical movement embedded in different positions of a sequential skill. That is, representations of individual action elements progressively incorporate information about their relationship to the overall sequence structure as the skill is learned. We agree with the Reviewer that this can be appropriately interpreted as “what-and-where” binding. We now incorporate this definition in the Introduction of the revised manuscript as requested.

The window parameters for optimizing accurate decoding individual finger movements were determined using a grid search of the parameter space (a sliding window of variable width between 25-350 ms with 25 ms increments variably aligned from 0 to +100ms with 10ms increments relative to the keyDown event). This approach generated 140 different temporal windows for each keypress for each participant, with the final parameter selection determined through comparison of the resulting performance between each decoder.  Importantly, the decision to optimize for decoding accuracy placed an emphasis on keypress representations characterized by the most consistent and robust features shared across subjects, which in turn maximize statistical power in detecting common learning-related changes. In this case, the optimal window encompassed a 200ms epoch aligned to the keyDown event (t0 = 0 ms).  We then asked if the representations (i.e. – spatial patterns of combined parcel- and voxel-space activity) of the same digit at two different sequence positions changed with practice within this optimal decoding window.  Of course, our findings do not rule out the possibility that contextualization can also be found before or even after this time window, as we did not directly address this issue in the present study.  Ongoing work in our lab, as pointed out above, is investigating contextualization within different time windows tailored specifically for assessing sequence skill action planning, execution, evaluation and memory processes.

The second point is that the result provided by the authors is not yet convincing enough to support the claim that "contextualization" occurs during rest. In the original analysis, the authors presented the statistical significance regarding the correlation between the "offline" pattern differentiation and micro-offline skill gain (Figure 5. Supplement 1), as well as the larger "offline" distance than "online" distance (Figure 5B). However, this analysis looks like regressing two variables (monotonically) increasing as a function of the trial. Although some information in this analysis, such as what the independent/dependent variables were or how individual subjects were treated, was missing in the Methods, getting a statistically significant slope seems unsurprising in such a situation. Also, curiously, the same quantitative evidence was not provided for its "online" counterpart, and the authors only briefly mentioned in the text that there was no significant correlation between them. It may be true looking at the data in Figure 5A as the online representation distance looks less monotonically changing, but the classification accuracy presented in Figure 4C, which should reflect similar representational distance, shows a more monotonic increase up to the 11th trial. Further, the ways the "online" and "offline" representation distance was estimated seem to make them not directly comparable. While the "online" distance was computed using all the correct press data within each 10 sec of execution, the "offline" distance is basically computed by only two presses (i.e., the last index_OP5 vs. the first index_OP1 separated by 10 sec of rest). Theoretically, the distance between the neural activity patterns for temporally closer events tends to be closer than that between the patterns for temporally far-apart events. It would be fairer to use the distance between the first index_OP1 vs. the last index_OP5 within an execution period for "online" distance, as well. 

The Reviewer suggests that the current data is not convincing enough to show that contextualization occurs during rest and raises two important concerns: 1) the relationship between online contextualization and micro-online gains is not shown, and 2) the online distance was calculated differently from its offline counterpart (i.e. - instead of calculating the distance between last IndexOP5 and first IndexOP1 from a single trial, the distance was calculated for each sequence within a trial and then averaged).

We addressed the first concern by performing individual subject correlations between 1) contextualization changes during rest intervals and micro-offline gains; 2) contextualization changes during practice trials and micro-online gains, and 3) contextualization changes during practice trials and micro-offline gains (Author response image 4). We then statistically compared the resulting correlation coefficient distributions and found that within-subject correlations for contextualization changes during rest intervals and micro-offline gains were significantly higher than online contextualization and micro-online gains (t = 3.2827, p = 0.0015) and online contextualization and micro-offline gains (t = 3.7021, p = 5.3013e-04). These results are consistent with our interpretation that micro-offline gains are supported by contextualization changes during the inter-practice rest period.

Author response image 4.

Distribution of individual subject correlation coefficients between contextualization changes occurring during practice or rest with  micro-online and micro-offline performance gains. Note that, the correlation distributions were significantly higher for the relationship between contextualization changes during rest and micro-offline gains than for contextualization changes during practice and either micro-online or offline gain.

With respect to the second concern highlighted above, we agree with the Reviewer that one limitation of the analysis comparing online versus offline changes in contextualization as presented in the reviewed manuscript, is that it does not eliminate the possibility that any differences could simply be explained by the passage of time (which is smaller for the online analysis compared to the offline analysis). The Reviewer suggests an approach that addresses this issue, which we have now carried out.   When quantifying online changes in contextualization from the first IndexOP1 the last IndexOP5 keypress in the same trial we observed no learning-related trend (Author response image 5, right panel). Importantly, offline distances were significantly larger than online distances regardless of the measurement approach and neither predicted online learning (Author response image 6).

Author response image 5.

Trial by trial trend of offline (left panel) and online (middle and right panels) changes in contextualization. Offline changes in contextualization were assessed by calculating the distance between neural representations for the last IndexOP5 keypress in the previous trial and the first IndexOP1 keypress in the present trial. Two different approaches were used to characterize online contextualization changes. The analysis included in the reviewed manuscript (middle panel) calculated the distance between IndexOP1 and IndexOP5 for each correct sequence, which was then averaged across the trial. This approach is limited by the lack of control for the passage of time when making online versus offline comparisons. Thus, the second approach controlled for the passage of time by calculating distance between the representations associated with the first IndexOP1 keypress and the last IndexOP5 keypress within the same trial. Note that while the first approach showed an increase online contextualization trend with practice, the second approach did not.

Author response image 6.

Relationship between online contextualization and online learning is shown for both within-sequence (left; note that this is the online contextualization measure used in the reviewd manuscript) and across-sequence (right) distance calculation. There was no significant relationship between online learning and online contextualization regardless of the measurement approach.

A related concern regarding the control analysis, where individual values for max speed and the degree of online contextualization were compared (Figure 5 Supplement 3), is whether the individual difference is meaningful. If I understood correctly, the optimization of the decoding process (temporal window, feature inclusion/reduction, decoder, etc.) was performed for individual participants, and the same feature extraction was also employed for the analysis of representation distance (i.e., contextualization). If this is the case, the distances are individually differently calculated and they may need to be normalized relative to some stable reference (e.g., 1 vs. 4 or average distance within the control sequence presses) before comparison across the individuals. 

The Reviewer makes a good point here. We have now implemented the suggested normalization procedure in the analysis provided in the revised manuscript.

Reviewer #3 (Public review): 

Summary: 

One goal of this paper is to introduce a new approach for highly accurate decoding of finger movements from human magnetoencephalography data via dimension reduction of a "multi-scale, hybrid" feature space. Following this decoding approach, the authors aim to show that early skill learning involves "contextualization" of the neural coding of individual movements, relative to their position in a sequence of consecutive movements. Furthermore, they aim to show that this "contextualization" develops primarily during short rest periods interspersed with skill training and correlates with a performance metric which the authors interpret as an indicator of offline learning. <br /> Strengths: 

A clear strength of the paper is the innovative decoding approach, which achieves impressive decoding accuracies via dimension reduction of a "multi-scale, hybrid space". This hybrid-space approach follows the neurobiologically plausible idea of the concurrent distribution of neural coding across local circuits as well as large-scale networks. A further strength of the study is the large number of tested dimension reduction techniques and classifiers (though the manuscript reveals little about the comparison of the latter). 

We appreciate the Reviewer’s comments regarding the paper’s strengths.

A simple control analysis based on shuffled class labels could lend further support to this complex decoding approach. As a control analysis that completely rules out any source of overfitting, the authors could test the decoder after shuffling class labels. Following such shuffling, decoding accuracies should drop to chance level for all decoding approaches, including the optimized decoder. This would also provide an estimate of actual chance-level performance (which is informative over and beyond the theoretical chance level). Furthermore, currently, the manuscript does not explain the huge drop in decoding accuracies for the voxel-space decoding (Figure 3B). Finally, the authors' approach to cortical parcellation raises questions regarding the information carried by varying dipole orientations within a parcel (which currently seems to be ignored?) and the implementation of the mean-flipping method (given that there are two dimensions - space and time - what do the authors refer to when they talk about the sign of the "average source", line 477?). 

The Reviewer recommends that we: 1) conduct an additional control analysis on classifier performance using shuffled class labels, 2) provide a more detailed explanation regarding the drop in decoding accuracies for the voxel-space decoding following LDA dimensionality reduction (see Fig 3B), and 3) provide additional details on how problems related to dipole solution orientations were addressed in the present study.  

In relation to the first point, we have now implemented a random shuffling approach as a control for the classification analyses. The results of this analysis indicated that the chance level accuracy was 22.12% (± SD 9.1%) for individual keypress decoding (4-class classification), and 18.41% (± SD 7.4%) for individual sequence item decoding (5-class classification), irrespective of the input feature set or the type of decoder used. Thus, the decoding accuracy observed with the final model was substantially higher than these chance levels.  

Second, please note that the dimensionality of the voxel-space feature set is very high (i.e. – 15684). LDA attempts to map the input features onto a much smaller dimensional space (number of classes-1; e.g. –  3 dimensions, for 4-class keypress decoding). Given the very high dimension of the voxel-space input features in this case, the resulting mapping exhibits reduced accuracy. Despite this general consideration, please refer to Figure 3—figure supplement 3, where we observe improvement in voxel-space decoder performance when utilizing alternative dimensionality reduction techniques.

The decoders constructed in the present study assess the average spatial patterns across time (as defined by the windowing procedure) in the input feature space.  We now provide additional details in the Methods of the revised manuscript pertaining to the parcellation procedure and how the sign ambiguity problem was addressed in our analysis.

Weaknesses: 

A clear weakness of the paper lies in the authors' conclusions regarding "contextualization". Several potential confounds, described below, question the neurobiological implications proposed by the authors and provide a simpler explanation of the results. Furthermore, the paper follows the assumption that short breaks result in offline skill learning, while recent evidence, described below, casts doubt on this assumption. 

We thank the Reviewer for giving us the opportunity to address these issues in detail (see below).

The authors interpret the ordinal position information captured by their decoding approach as a reflection of neural coding dedicated to the local context of a movement (Figure 4). One way to dissociate ordinal position information from information about the moving effectors is to train a classifier on one sequence and test the classifier on other sequences that require the same movements, but in different positions50. In the present study, however, participants trained to repeat a single sequence (4-1-3-2-4). As a result, ordinal position information is potentially confounded by the fixed finger transitions around each of the two critical positions (first and fifth press). Across consecutive correct sequences, the first keypress in a given sequence was always preceded by a movement of the index finger (=last movement of the preceding sequence), and followed by a little finger movement. The last keypress, on the other hand, was always preceded by a ring finger movement, and followed by an index finger movement (=first movement of the next sequence). Figure 4 - Supplement 2 shows that finger identity can be decoded with high accuracy (>70%) across a large time window around the time of the key press, up to at least +/-100 ms (and likely beyond, given that decoding accuracy is still high at the boundaries of the window depicted in that figure). This time window approaches the keypress transition times in this study. Given that distinct finger transitions characterized the first and fifth keypress, the classifier could thus rely on persistent (or "lingering") information from the preceding finger movement, and/or "preparatory" information about the subsequent finger movement, in order to dissociate the first and fifth keypress. Currently, the manuscript provides no evidence that the context information captured by the decoding approach is more than a by-product of temporally extended, and therefore overlapping, but independent neural representations of consecutive keypresses that are executed in close temporal proximity - rather than a neural representation dedicated to context. 

Such temporal overlap of consecutive, independent finger representations may also account for the dynamics of "ordinal coding"/"contextualization", i.e., the increase in 2-class decoding accuracy, across Day 1 (Figure 4C). As learning progresses, both tapping speed and the consistency of keypress transition times increase (Figure 1), i.e., consecutive keypresses are closer in time, and more consistently so. As a result, information related to a given keypress is increasingly overlapping in time with information related to the preceding and subsequent keypresses. The authors seem to argue that their regression analysis in Figure 5 - Figure Supplement 3 speaks against any influence of tapping speed on "ordinal coding" (even though that argument is not made explicitly in the manuscript). However, Figure 5 - Figure Supplement 3 shows inter-individual differences in a between-subject analysis (across trials, as in panel A, or separately for each trial, as in panel B), and, therefore, says little about the within-subject dynamics of "ordinal coding" across the experiment. A regression of trial-by-trial "ordinal coding" on trial-by-trial tapping speed (either within-subject or at a group-level, after averaging across subjects) could address this issue. Given the highly similar dynamics of "ordinal coding" on the one hand (Figure 4C), and tapping speed on the other hand (Figure 1B), I would expect a strong relationship between the two in the suggested within-subject (or group-level) regression. Furthermore, learning should increase the number of (consecutively) correct sequences, and, thus, the consistency of finger transitions. Therefore, the increase in 2-class decoding accuracy may simply reflect an increasing overlap in time of increasingly consistent information from consecutive keypresses, which allows the classifier to dissociate the first and fifth keypress more reliably as learning progresses, simply based on the characteristic finger transitions associated with each. In other words, given that the physical context of a given keypress changes as learning progresses - keypresses move closer together in time and are more consistently correct - it seems problematic to conclude that the mental representation of that context changes. To draw that conclusion, the physical context should remain stable (or any changes to the physical context should be controlled for). 

The issues raised by Reviewer #3 here are similar to two issues raised by Reviewer #2 above and agree they must both be carefully considered in any evaluation of our findings.

As both Reviewers pointed out, the classifiers in this study were trained and tested on keypresses performed while practicing a specific sequence (4-1-3-2-4). The study was designed this way as to avoid the impact of interference effects on learning dynamics. The cross-validated performance of classifiers on MEG data collected within the same session was 90.47% overall accuracy (4-class; Figure 3C). We then tested classifier performance on data collected during a separate MEG session conducted approximately 24 hours later (Day 2; see Figure 3—supplement 3). We observed a reduction in overall accuracy rate to 87.11% when tested on MEG data recorded while participants performed the same learned sequence, and 79.44% when they performed several previously unpracticed sequences. This classification performance difference of 7.67% when tested on the Day 2 data could reflect the performance bias of the classifier for the trained sequence, possibly caused by mixed information from temporally close keypresses being incorporated into the feature weights.

Along these same lines, both Reviewers also raise the possibility that an increase in “ordinal coding/contextualization” with learning could simply reflect an increase in this mixing effect caused by faster typing speeds as opposed to an actual change in the underlying neural representation. The basic idea is that as correct sequences are generated at higher and higher speeds over training, MEG activity patterns related to the planning, execution, evaluation and memory of individual keypresses overlap more in time. Thus, increased overlap between the “4” and “1” keypresses (at the start of the sequence) and “2” and “4” keypresses (at the end of the sequence) could artefactually increase contextualization distances even if the underlying neural representations for the individual keypresses remain unchanged (assuming this mixing of representations is used by the classifier to differentially tag each index finger press). If this were the case, it follows that such mixing effects reflecting the ordinal sequence structure would also be observable in the distribution of decoder misclassifications. For example, “4” keypresses would be more likely to be misclassified as “1” or “2” keypresses (or vice versa) than as “3” keypresses. The confusion matrices presented in Figures 3C and 4B and Figure 3—figure supplement 3A in the previously submitted manuscript do not show this trend in the distribution of misclassifications across the four fingers.

Following this logic, it’s also possible that if the ordinal coding is largely driven by this mixing effect, the increased overlap between consecutive index finger keypresses during the 4-4 transition marking the end of one sequence and the beginning of the next one could actually mask contextualization-related changes to the underlying neural representations and make them harder to detect. In this case, a decoder tasked with separating individual index finger keypresses into two distinct classes based upon sequence position might show decreased performance with learning as adjacent keypresses overlapped in time with each other to an increasing extent. However, Figure 4C in our previously submitted manuscript does not support this possibility, as the 2-class hybrid classifier displays improved classification performance over early practice trials despite greater temporal overlap.

As noted in the above replay to Reviewer #2, we also conducted a new multivariate regression analysis to directly assess whether the neural representation distance score could be predicted by the 4-1, 2-4 and 4-4 keypress transition times observed for each complete correct sequence (both predictor and response variables were z-score normalized within-subject). The results of this analysis affirmed that the possible alternative explanation put forward by the Reviewer is not supported by our data (Adjusted R2 = 0.00431; F = 5.62). We now include this new negative control analysis result in the revised manuscript.

Finally, the Reviewer hints that one way to address this issue would be to compare MEG responses before and after learning for sequences typed at a fixed speed. However, given that the speed-accuracy trade-off should improve with learning, a comparison between unlearned and learned skill states would dictate that the skill be evaluated at a very low fixed speed. Essentially, such a design presents the problem that the post-training test is evaluating the representation in the unlearned behavioral state that is not representative of the acquired skill. Thus, this approach would not address our experimental question: “do neural representations of the same action performed at different locations within a skill sequence contextually differentiate or remain stable as learning evolves”.

A similar difference in physical context may explain why neural representation distances ("differentiation") differ between rest and practice (Figure 5). The authors define "offline differentiation" by comparing the hybrid space features of the last index finger movement of a trial (ordinal position 5) and the first index finger movement of the next trial (ordinal position 1). However, the latter is not only the first movement in the sequence but also the very first movement in that trial (at least in trials that started with a correct sequence), i.e., not preceded by any recent movement. In contrast, the last index finger of the last correct sequence in the preceding trial includes the characteristic finger transition from the fourth to the fifth movement. Thus, there is more overlapping information arising from the consistent, neighbouring keypresses for the last index finger movement, compared to the first index finger movement of the next trial. A strong difference (larger neural representation distance) between these two movements is, therefore, not surprising, given the task design, and this difference is also expected to increase with learning, given the increase in tapping speed, and the consequent stronger overlap in representations for consecutive keypresses. Furthermore, initiating a new sequence involves pre-planning, while ongoing practice relies on online planning (Ariani et al., eNeuro 2021), i.e., two mental operations that are dissociable at the level of neural representation (Ariani et al., bioRxiv 2023). 

The Reviewer argues that the comparison of last finger movement of a trial and the first in the next trial are performed in different circumstances and contexts. This is an important point and one we tend to agree with. For this task, the first sequence in a practice trial (which is pre-planned offline) is performed in a somewhat different context from the sequence iterations that follow, which involve temporally overlapping planning, execution and evaluation processes.  The Reviewer is particularly concerned about a difference in the temporal mixing effect issue raised above between the first and last keypresses performed in a trial. However, in contrast to the Reviewers stated argument above, findings from Korneysheva et. al (2019) showed that neural representations of individual actions are competitively queued during the pre-planning period in a manner that reflects the ordinal structure of the learned sequence.  Thus, mixing effects are likely still present for the first keypress in a trial. Also note that we now present new control analyses in multiple responses above confirming that hypothetical mixing effects between adjacent keypresses do not explain our reported contextualization finding. A statement addressing these possibilities raised by the Reviewer has been added to the Discussion in the revised manuscript.

In relation to pre-planning, ongoing MEG work in our lab is investigating contextualization within different time windows tailored specifically for assessing how sequence skill action planning evolves with learning.

Given these differences in the physical context and associated mental processes, it is not surprising that "offline differentiation", as defined here, is more pronounced than "online differentiation". For the latter, the authors compared movements that were better matched regarding the presence of consistent preceding and subsequent keypresses (online differentiation was defined as the mean difference between all first vs. last index finger movements during practice).  It is unclear why the authors did not follow a similar definition for "online differentiation" as for "micro-online gains" (and, indeed, a definition that is more consistent with their definition of "offline differentiation"), i.e., the difference between the first index finger movement of the first correct sequence during practice, and the last index finger of the last correct sequence. While these two movements are, again, not matched for the presence of neighbouring keypresses (see the argument above), this mismatch would at least be the same across "offline differentiation" and "online differentiation", so they would be more comparable. 

This is the same point made earlier by Reviewer #2, and we agree with this assessment. As stated in the response to Reviewer #2 above, we have now carried out quantification of online contextualization using this approach and included it in the revised manuscript. We thank the Reviewer for this suggestion.

A further complication in interpreting the results regarding "contextualization" stems from the visual feedback that participants received during the task. Each keypress generated an asterisk shown above the string on the screen, irrespective of whether the keypress was correct or incorrect. As a result, incorrect (e.g., additional, or missing) keypresses could shift the phase of the visual feedback string (of asterisks) relative to the ordinal position of the current movement in the sequence (e.g., the fifth movement in the sequence could coincide with the presentation of any asterisk in the string, from the first to the fifth). Given that more incorrect keypresses are expected at the start of the experiment, compared to later stages, the consistency in visual feedback position, relative to the ordinal position of the movement in the sequence, increased across the experiment. A better differentiation between the first and the fifth movement with learning could, therefore, simply reflect better decoding of the more consistent visual feedback, based either on the feedback-induced brain response, or feedback-induced eye movements (the study did not include eye tracking). It is not clear why the authors introduced this complicated visual feedback in their task, besides consistency with their previous studies.

We strongly agree with the Reviewer that eye movements related to task engagement are important to rule out as a potential driver of the decoding accuracy or contextualization effect. We address this issue above in response to a question raised by Reviewer #1 about the impact of movement related artefacts in general on our findings.

First, the assumption the Reviewer makes here about the distribution of errors in this task is incorrect. On average across subjects, 2.32% ± 1.48% (mean ± SD) of all keypresses performed were errors, which were evenly distributed across the four possible keypress responses. While errors increased progressively over practice trials, they did so in proportion to the increase in correct keypresses, so that the overall ratio of correct-to-incorrect keypresses remained stable over the training session. Thus, the Reviewer’s assumptions that there is a higher relative frequency of errors in early trials, and a resulting systematic trend phase shift differences between the visual display updates (i.e. – a change in asterisk position above the displayed sequence) and the keypress performed is not substantiated by the data. To the contrary, the asterisk position on the display and the keypress being executed remained highly correlated over the entire training session. We now include a statement about the frequency and distribution of errors in the revised manuscript.

Given this high correlation, we firmly agree with the Reviewer that the issue of eye movement-related artefacts is still an important one to address. Fortunately, we did collect eye movement data during the MEG recordings so were able to investigate this. As detailed in the response to Reviewer #1 above, we found that gaze positions and eye-movement velocity time-locked to visual display updates (i.e. – a change in asterisk position above the displayed sequence) did not reflect the asterisk location above chance levels (Overall cross-validated accuracy = 0.21817; see Author response image 1). Furthermore, an inspection of the eye position data revealed that a majority of participants on most trials displayed random walk gaze patterns around a center fixation point, indicating that participants did not attend to the asterisk position on the display. This is consistent with intrinsic generation of the action sequence, and congruent with the fact that the display does not provide explicit feedback related to performance. As pointed out above, a similar real-world example would be manually inputting a long password into a secure online application. In this case, one intrinsically generates the sequence from memory and receives similar feedback about the password sequence position (also provided as asterisks), which is typically ignored by the user. Notably, the minimal participant engagement with the visual task display observed in this study highlights an important difference between behavior observed during explicit sequence learning motor tasks (which is highly generative in nature) with reactive responses to stimulus cues in a serial reaction time task (SRTT).  This is a crucial difference that must be carefully considered when comparing findings across studies. All elements pertaining to this new control analysis are now included in the revised manuscript.

The authors report a significant correlation between "offline differentiation" and cumulative micro-offline gains. However, it would be more informative to correlate trial-by-trial changes in each of the two variables. This would address the question of whether there is a trial-by-trial relation between the degree of "contextualization" and the amount of micro-offline gains - are performance changes (micro-offline gains) less pronounced across rest periods for which the change in "contextualization" is relatively low? Furthermore, is the relationship between micro-offline gains and "offline differentiation" significantly stronger than the relationship between micro-offline gains and "online differentiation"? 

In response to a similar issue raised above by Reviewer #2, we now include new analyses comparing correlation magnitudes between (1) “online differention” vs micro-online gains, (2) “online differention” vs micro-offline gains and (3) “offline differentiation” and micro-offline gains (see Author response images 4, 5 and 6 above). These new analyses and results have been added to the revised manuscript. Once again, we thank both Reviewers for this suggestion.

The authors follow the assumption that micro-offline gains reflect offline learning.

This statement is incorrect. The original Bonstrup et al (2019) 49 paper clearly states that micro-offline gains must be carefully interpreted based upon the behavioral context within which they are observed, and lays out the conditions under which one can have confidence that micro-offline gains reflect offline learning.  In fact, the excellent meta-analysis of Pan & Rickard (2015) 51, which re-interprets the benefits of sleep in overnight skill consolidation from a “reactive inhibition” perspective, was a crucial resource in the experimental design of our initial study49, as well as in all our subsequent work. Pan & Rickard stated:

“Empirically, reactive inhibition refers to performance worsening that can accumulate during a period of continuous training (Hull, 1943). It tends to dissipate, at least in part, when brief breaks are inserted between blocks of training. If there are multiple performance-break cycles over a training session, as in the motor sequence literature, performance can exhibit a scalloped effect, worsening during each uninterrupted performance block but improving across blocks52,53. Rickard, Cai, Rieth, Jones, and Ard (2008) and Brawn, Fenn, Nusbaum, and Margoliash (2010) 52,53 demonstrated highly robust scalloped reactive inhibition effects using the commonly employed 30 s–30 s performance break cycle, as shown for Rickard et al.’s (2008) massed practice sleep group in Figure 2. The scalloped effect is evident for that group after the first few 30 s blocks of each session. The absence of the scalloped effect during the first few blocks of training in the massed group suggests that rapid learning during that period masks any reactive inhibition effect.”

Crucially, Pan & Rickard51 made several concrete recommendations for reducing the impact of the reactive inhibition confound on offline learning studies. One of these recommendations was to reduce practice times to 10s (most prior sequence learning studies up until that point had employed 30s long practice trials). They stated:

“The traditional design involving 30 s-30 s performance break cycles should be abandoned given the evidence that it results in a reactive inhibition confound, and alternative designs with reduced performance duration per block used instead 51. One promising possibility is to switch to 10 s performance durations for each performance-break cycle Instead 51. That design appears sufficient to eliminate at least the majority of the reactive inhibition effect 52,53.”

We mindfully incorporated recommendations from Pan and Rickard51  into our own study designs including 1) utilizing 10s practice trials and 2) constraining our analysis of micro-offline gains to early learning trials (where performance monotonically increases and 95% of overall performance gains occur), which are prior to the emergence of the “scalloped” performance dynamics that are strongly linked to reactive inhibition effects. 

However, there is no direct evidence in the literature that micro-offline gains really result from offline learning, i.e., an improvement in skill level.

We strongly disagree with the Reviewer’s assertion that “there is no direct evidence in the literature that micro-offline gains really result from offline learning, i.e., an improvement in skill level.”  The initial Bönstrup et al. (2019) 49 report was followed up by a large online crowd-sourcing study (Bönstrup et al., 2020) 54. This second (and much larger) study provided several additional important findings supporting our interpretation of micro-offline gains in cases where the important behavioral conditions clarified above were met (see Author response image 7 below for further details on these conditions).

Author response image 7.

Micro-offline gains observed in learning and non-learning contexts are attributed to different underlying causes. (A) Micro-offline and online changes relative to overall trial-by-trial learning. This figure is based on data from Bönstrup et al. (2019) 49. During early learning, micro-offline gains (red bars) closely track trial-by-trial performance gains (green line with open circle markers), with minimal contribution from micro-online gains (blue bars). The stated conclusion in Bönstrup et al. (2019) is that micro-offline gains only during this Early Learning stage reflect rapid memory consolidation (see also 54). After early learning, about practice trial 11, skill plateaus. This plateau skill period is characterized by a striking emergence of coupled (and relatively stable) micro-online drops and micro-offline increases. Bönstrup et al. (2019) as well as others in the literature 55-57, argue that micro-offline gains during the plateau period likely reflect recovery from inhibitory performance factors such as reactive inhibition or fatigue, and thus must be excluded from analyses relating micro-offline gains to skill learning.  The Non-repeating groups in Experiments 3 and 4 from Das et al. (2024) suffer from a lack of consideration of these known confounds.

Evidence documented in that paper54 showed that micro-offline gains during early skill learning were: 1) replicable and generalized to subjects learning the task in their daily living environment (n=389); 2) equivalent when significantly shortening practice period duration, thus confirming that they are not a result of recovery from performance fatigue (n=118);  3) reduced (along with learning rates) by retroactive interference applied immediately after each practice period relative to interference applied after passage of time (n=373), indicating stabilization of the motor memory at a microscale of several seconds consistent with rapid consolidation; and 4) not modified by random termination of the practice periods, ruling out a contribution of predictive motor slowing (N = 71) 54.  Altogether, our findings were strongly consistent with the interpretation that micro-offline gains reflect memory consolidation supporting early skill learning. This is precisely the portion of the learning curve Pan and Rickard51 refer to when they state “…rapid learning during that period masks any reactive inhibition effect”.

This interpretation is further supported by brain imaging evidence linking known memory-related networks and consolidation mechanisms to micro-offline gains. First, we reported that the density of fast hippocampo-neocortical skill memory replay events increases approximately three-fold during early learning inter-practice rest periods with the density explaining differences in the magnitude of micro-offline gains across subjects1. Second, Jacobacci et al. (2020) independently reproduced our original behavioral findings and reported BOLD fMRI changes in the hippocampus and precuneus (regions also identified in our MEG study1) linked to micro-offline gains during early skill learning. 33 These functional changes were coupled with rapid alterations in brain microstructure in the order of minutes, suggesting that the same network that operates during rest periods of early learning undergoes structural plasticity over several minutes following practice58. Third, even more recently, Chen et al. (2024) provided direct evidence from intracranial EEG in humans linking sharp-wave ripple events (which are known markers for neural replay59) in the hippocampus (80-120 Hz in humans) with micro-offline gains during early skill learning. The authors report that the strong increase in ripple rates tracked learning behavior, both across blocks and across participants. The authors conclude that hippocampal ripples during resting offline periods contribute to motor sequence learning. 2

Thus, there is actually now substantial evidence in the literature directly supporting the assertion “that micro-offline gains really result from offline learning”.  On the contrary, according to Gupta & Rickard (2024) “…the mechanism underlying RI [reactive inhibition] is not well established” after over 80 years of investigation60, possibly due to the fact that “reactive inhibition” is a categorical description of behavioral effects that likely result from several heterogenous processes with very different underlying mechanisms.

On the contrary, recent evidence questions this interpretation (Gupta & Rickard, npj Sci Learn 2022; Gupta & Rickard, Sci Rep 2024; Das et al., bioRxiv 2024). Instead, there is evidence that micro-offline gains are transient performance benefits that emerge when participants train with breaks, compared to participants who train without breaks, however, these benefits vanish within seconds after training if both groups of participants perform under comparable conditions (Das et al., bioRxiv 2024). 

It is important to point out that the recent work of Gupta & Rickard (2022,2024) 55 does not present any data that directly opposes our finding that early skill learning49 is expressed as micro-offline gains during rest breaks. These studies are essentially an extension of the Rickard et al (2008) paper that employed a massed (30s practice followed by 30s breaks) vs spaced (10s practice followed by 10s breaks) to assess if recovery from reactive inhibition effects could account for performance gains measured after several minutes or hours. Gupta & Rickard (2022) added two additional groups (30s practice/10s break and 10s practice/10s break as used in the work from our group). The primary aim of the study was to assess whether it was more likely that changes in performance when retested 5 minutes after skill training (consisting of 12 practice trials for the massed groups and 36 practice trials for the spaced groups) had ended reflected memory consolidation effects or recovery from reactive inhibition effects. The Gupta & Rickard (2024) follow-up paper employed a similar design with the primary difference being that participants performed a fixed number of sequences on each trial as opposed to trials lasting a fixed duration. This was done to facilitate the fitting of a quantitative statistical model to the data.  To reiterate, neither study included any analysis of micro-online or micro-offline gains and did not include any comparison focused on skill gains during early learning. Instead, Gupta & Rickard (2022), reported evidence for reactive inhibition effects for all groups over much longer training periods. Again, we reported the same finding for trials following the early learning period in our original Bönstrup et al. (2019) paper49 (Author response image 7). Also, please note that we reported in this paper that cumulative micro-offline gains over early learning did not correlate with overnight offline consolidation measured 24 hours later49 (see the Results section and further elaboration in the Discussion). Thus, while the composition of our data is supportive of a short-term memory consolidation process operating over several seconds during early learning, it likely differs from those involved over longer training times and offline periods, as assessed by Gupta & Rickard (2022).

In the recent preprint from Das et al (2024) 61,  the authors make the strong claim that “micro-offline gains during early learning do not reflect offline learning” which is not supported by their own data.   The authors hypothesize that if “micro-offline gains represent offline learning, participants should reach higher skill levels when training with breaks, compared to training without breaks”.  The study utilizes a spaced vs. massed practice group between-subjects design inspired by the reactive inhibition work from Rickard and others to test this hypothesis. Crucially, the design incorporates only a small fraction of the training used in other investigations to evaluate early skill learning1,33,49,54,57,58,62.  A direct comparison between the practice schedule designs for the spaced and massed groups in Das et al., and the training schedule all participants experienced in the original Bönstrup et al. (2019) paper highlights this issue as well as several others (Author response image 8):

Author response image 8.

(A) Comparison of Das et al. Spaced & Massed group training session designs, and the training session design from the original Bönstrup et al. (2019) 49 paper. Similar to the approach taken by Das et al., all practice is visualized as 10-second practice trials with a variable number (either 0, 1 or 30) of 10-second-long inter-practice rest intervals to allow for direct comparisons between designs. The two key takeaways from this comparison are that (1) the intervention differences (i.e. – practice schedules) between the Massed and Spaced groups from the Das et al. report are extremely small (less than 12% of the overall session schedule) and (2) the overall amount of practice is much less than compared to the design from the original Bönstrup report 49  (which has been utilized in several subsequent studies). (B) Group-level learning curve data from Bönstrup et al. (2019) 49 is used to estimate the performance range accounted for by the equivalent periods covering Test 1, Training 1 and Test 2 from Das et al (2024). Note that the intervention in the Das et al. study is limited to a period covering less than 50% of the overall learning range.

First, participants in the original Bönstrup et al. study 49 experienced 157.14% more practice time and 46.97% less inter-practice rest time than the Spaced group in the Das et al. study (Author response image 8).  Thus, the overall amount of practice and rest differ substantially between studies, with much more limited training occurring for participants in Das et al.  

Second, and perhaps most importantly, the actual intervention (i.e. – the difference in practice schedule between the Spaced and Massed groups) employed by Das et al. covers a very small fraction of the overall training session. Identical practice schedule segments for both the Spaced & Massed groups are indicated by the red shaded area in Author response image 8. Please note that these identical segments cover 94.84% of the Massed group training schedule and 88.01% of the Spaced group training schedule (since it has 60 seconds of additional rest). This means that the actual interventions cover less than 5% (for Massed) and 12% (for Spaced) of the total training session, which minimizes any chance of observing a difference between groups.

Also note that the very beginning of the practice schedule (during which Figure R9 shows substantial learning is known to occur) is labeled in the Das et al. study as Test 1.  Test 1 encompasses the first 20 seconds of practice (alternatively viewed as the first two 10-second-long practice trials with no inter-practice rest). This is immediately followed by the Training 1 intervention, which is composed of only three 10-second-long practice trials (with 10-second inter-practice rest for the Spaced group and no inter-practice rest for the Massed group). Author response image 8 also shows that since there is no inter-practice rest after the third Training practice trial for the Spaced group, this third trial (for both Training 1 and 2) is actually a part of an identical practice schedule segment shared by both groups (Massed and Spaced), reducing the magnitude of the intervention even further.

Moreover, we know from the original Bönstrup et al. (2019) paper49 that 46.57% of all overall group-level performance gains occurred between trials 2 and 5 for that study. Thus, Das et al. are limiting their designed intervention to a period covering less than half of the early learning range discussed in the literature, which again, minimizes any chance of observing an effect.

This issue is amplified even further at Training 2 since skill learning prior to the long 5-minute break is retained, further constraining the performance range over these three trials. A related issue pertains to the trials labeled as Test 1 (trials 1-2) and Test 2 (trials 6-7) by Das et al. Again, we know from the original Bönstrup et al. paper 49 that 18.06% and 14.43% (32.49% total) of all overall group-level performance gains occurred during trials corresponding to Das et al Test 1 and Test 2, respectively. In other words, Das et al averaged skill performance over 20 seconds of practice at two time-points where dramatic skill improvements occur. Pan & Rickard (1995) previously showed that such averaging is known to inject artefacts into analyses of performance gains.

Furthermore, the structure of the Test in Das et. al study appears to have an interference effect on the Spaced group performance after the training intervention.  This makes sense if you consider that the Spaced group is required to now perform the task in a Massed practice environment (i.e., two 10-second-long practice trials merged into one long trial), further blurring the true intervention effects. This effect is observable in Figure 1C,E of their pre-print. Specifically, while the Massed group continues to show an increase in performance during test relative to the last 10 seconds of practice during training, the Spaced group displays a marked decrease. This decrease is in stark contrast to the monotonic increases observed for both groups at all other time-points.

Interestingly, when statistical comparisons between the groups are made at the time-points when the intervention is present (as opposed to after it has been removed) then the stated hypothesis, “If micro-offline gains represent offline learning, participants should reach higher skill levels when training with breaks, compared to training without breaks”, is confirmed.

The data presented by Gupta and Rickard (2022, 2024) and Das et al. (2024) is in many ways more confirmatory of the constraints employed by our group and others with respect to experimental design, analysis and interpretation of study findings, rather than contradictory. Still, it does highlight a limitation of the current micro-online/offline framework, which was originally only intended to be applied to early skill learning over spaced practice schedules when reactive inhibition effects are minimized49. Extrapolation of this current framework to post-plateau performance periods, longer timespans, or non-learning situations (e.g. – the Non-repeating groups from Experiments 3 & 4 in Das et al. (2024)), when reactive inhibition plays a more substantive role, is not warranted. Ultimately, it will be important to develop new paradigms allowing one to independently estimate the different coincident or antagonistic features (e.g. - memory consolidation, planning, working memory and reactive inhibition) contributing to micro-online and micro-offline gains during and after early skill learning within a unifying framework.

References

(1) Buch, E. R., Claudino, L., Quentin, R., Bonstrup, M. & Cohen, L. G. Consolidation of human skill linked to waking hippocampo-neocortical replay. Cell Rep 35, 109193 (2021). https://doi.org:10.1016/j.celrep.2021.109193

(2) Chen, P.-C., Stritzelberger, J., Walther, K., Hamer, H. & Staresina, B. P. Hippocampal ripples during offline periods predict human motor sequence learning. bioRxiv, 2024.2010.2006.614680 (2024). https://doi.org:10.1101/2024.10.06.614680

(3) Classen, J., Liepert, J., Wise, S. P., Hallett, M. & Cohen, L. G. Rapid plasticity of human cortical movement representation induced by practice. J Neurophysiol 79, 1117-1123 (1998).

(4) Karni, A. et al. Functional MRI evidence for adult motor cortex plasticity during motor skill learning. Nature 377, 155-158 (1995). https://doi.org:10.1038/377155a0

(5) Kleim, J. A., Barbay, S. & Nudo, R. J. Functional reorganization of the rat motor cortex following motor skill learning. J Neurophysiol 80, 3321-3325 (1998).

(6) Shadmehr, R. & Holcomb, H. H. Neural correlates of motor memory consolidation. Science 277, 821-824 (1997).

(7) Doyon, J. et al. Experience-dependent changes in cerebellar contributions to motor sequence learning. Proc Natl Acad Sci U S A 99, 1017-1022 (2002).

(8) Toni, I., Ramnani, N., Josephs, O., Ashburner, J. & Passingham, R. E. Learning arbitrary visuomotor associations: temporal dynamic of brain activity. Neuroimage 14, 1048-1057 (2001).

(9) Grafton, S. T. et al. Functional anatomy of human procedural learning determined with regional cerebral blood flow and PET. J Neurosci 12, 2542-2548 (1992).

(10) Kennerley, S. W., Sakai, K. & Rushworth, M. F. Organization of action sequences and the role of the pre-SMA. J Neurophysiol 91, 978-993 (2004). https://doi.org:10.1152/jn.00651.2003 00651.2003 [pii]

(11) Hardwick, R. M., Rottschy, C., Miall, R. C. & Eickhoff, S. B. A quantitative meta-analysis and review of motor learning in the human brain. Neuroimage 67, 283-297 (2013). https://doi.org:10.1016/j.neuroimage.2012.11.020

(12) Sawamura, D. et al. Acquisition of chopstick-operation skills with the non-dominant hand and concomitant changes in brain activity. Sci Rep 9, 20397 (2019). https://doi.org:10.1038/s41598-019-56956-0

(13) Lee, S. H., Jin, S. H. & An, J. The difference in cortical activation pattern for complex motor skills: A functional near- infrared spectroscopy study. Sci Rep 9, 14066 (2019). https://doi.org:10.1038/s41598-019-50644-9

(14) Battaglia-Mayer, A. & Caminiti, R. Corticocortical Systems Underlying High-Order Motor Control. J Neurosci 39, 4404-4421 (2019). https://doi.org:10.1523/JNEUROSCI.2094-18.2019

(15) Toni, I., Thoenissen, D. & Zilles, K. Movement preparation and motor intention. Neuroimage 14, S110-117 (2001). https://doi.org:10.1006/nimg.2001.0841

(16) Wolpert, D. M., Goodbody, S. J. & Husain, M. Maintaining internal representations: the role of the human superior parietal lobe. Nat Neurosci 1, 529-533 (1998). https://doi.org:10.1038/2245

(17) Andersen, R. A. & Buneo, C. A. Intentional maps in posterior parietal cortex. Annu Rev Neurosci 25, 189-220 (2002). https://doi.org:10.1146/annurev.neuro.25.112701.142922 112701.142922 [pii]

(18) Buneo, C. A. & Andersen, R. A. The posterior parietal cortex: sensorimotor interface for the planning and online control of visually guided movements. Neuropsychologia 44, 2594-2606 (2006). https://doi.org:S0028-3932(05)00333-7 [pii] 10.1016/j.neuropsychologia.2005.10.011

(19) Grover, S., Wen, W., Viswanathan, V., Gill, C. T. & Reinhart, R. M. G. Long-lasting, dissociable improvements in working memory and long-term memory in older adults with repetitive neuromodulation. Nat Neurosci 25, 1237-1246 (2022). https://doi.org:10.1038/s41593-022-01132-3

(20) Colclough, G. L. et al. How reliable are MEG resting-state connectivity metrics? Neuroimage 138, 284-293 (2016). https://doi.org:10.1016/j.neuroimage.2016.05.070

(21) Colclough, G. L., Brookes, M. J., Smith, S. M. & Woolrich, M. W. A symmetric multivariate leakage correction for MEG connectomes. NeuroImage 117, 439-448 (2015). https://doi.org:10.1016/j.neuroimage.2015.03.071

(22) Mollazadeh, M. et al. Spatiotemporal variation of multiple neurophysiological signals in the primary motor cortex during dexterous reach-to-grasp movements. J Neurosci 31, 15531-15543 (2011). https://doi.org:10.1523/JNEUROSCI.2999-11.2011

(23) Bansal, A. K., Vargas-Irwin, C. E., Truccolo, W. & Donoghue, J. P. Relationships among low-frequency local field potentials, spiking activity, and three-dimensional reach and grasp kinematics in primary motor and ventral premotor cortices. J Neurophysiol 105, 1603-1619 (2011). https://doi.org:10.1152/jn.00532.2010

(24) Flint, R. D., Ethier, C., Oby, E. R., Miller, L. E. & Slutzky, M. W. Local field potentials allow accurate decoding of muscle activity. J Neurophysiol 108, 18-24 (2012). https://doi.org:10.1152/jn.00832.2011

(25) Churchland, M. M. et al. Neural population dynamics during reaching. Nature 487, 51-56 (2012). https://doi.org:10.1038/nature11129

(26) Bassett, D. S. et al. Dynamic reconfiguration of human brain networks during learning. Proc Natl Acad Sci U S A 108, 7641-7646 (2011). https://doi.org:10.1073/pnas.1018985108

(27) Albouy, G., King, B. R., Maquet, P. & Doyon, J. Hippocampus and striatum: dynamics and interaction during acquisition and sleep-related motor sequence memory consolidation. Hippocampus 23, 985-1004 (2013). https://doi.org:10.1002/hipo.22183

(28) Albouy, G. et al. Neural correlates of performance variability during motor sequence acquisition. Neuroimage 60, 324-331 (2012). https://doi.org:10.1016/j.neuroimage.2011.12.049

(29) Qin, Y. L., McNaughton, B. L., Skaggs, W. E. & Barnes, C. A. Memory reprocessing in corticocortical and hippocampocortical neuronal ensembles. Philos Trans R Soc Lond B Biol Sci 352, 1525-1533 (1997). https://doi.org:10.1098/rstb.1997.0139

(30) Euston, D. R., Tatsuno, M. & McNaughton, B. L. Fast-forward playback of recent memory sequences in prefrontal cortex during sleep. Science 318, 1147-1150 (2007). https://doi.org:10.1126/science.1148979

(31) Molle, M. & Born, J. Hippocampus whispering in deep sleep to prefrontal cortex--for good memories? Neuron 61, 496-498 (2009). https://doi.org:S0896-6273(09)00122-6 [pii] 10.1016/j.neuron.2009.02.002

(32) Frankland, P. W. & Bontempi, B. The organization of recent and remote memories. Nat Rev Neurosci 6, 119-130 (2005). https://doi.org:10.1038/nrn1607

(33) Jacobacci, F. et al. Rapid hippocampal plasticity supports motor sequence learning. Proc Natl Acad Sci U S A 117, 23898-23903 (2020). https://doi.org:10.1073/pnas.2009576117

(34) Albouy, G. et al. Maintaining vs. enhancing motor sequence memories: respective roles of striatal and hippocampal systems. Neuroimage 108, 423-434 (2015). https://doi.org:10.1016/j.neuroimage.2014.12.049

(35) Gais, S. et al. Sleep transforms the cerebral trace of declarative memories. Proc Natl Acad Sci U S A 104, 18778-18783 (2007). https://doi.org:0705454104 [pii] 10.1073/pnas.0705454104

(36) Sterpenich, V. et al. Sleep promotes the neural reorganization of remote emotional memory. J Neurosci 29, 5143-5152 (2009). https://doi.org:10.1523/JNEUROSCI.0561-09.2009

(37) Euston, D. R., Gruber, A. J. & McNaughton, B. L. The role of medial prefrontal cortex in memory and decision making. Neuron 76, 1057-1070 (2012). https://doi.org:10.1016/j.neuron.2012.12.002

(38) van Kesteren, M. T., Fernandez, G., Norris, D. G. & Hermans, E. J. Persistent schema-dependent hippocampal-neocortical connectivity during memory encoding and postencoding rest in humans. Proc Natl Acad Sci U S A 107, 7550-7555 (2010). https://doi.org:10.1073/pnas.0914892107

(39) van Kesteren, M. T., Ruiter, D. J., Fernandez, G. & Henson, R. N. How schema and novelty augment memory formation. Trends Neurosci 35, 211-219 (2012). https://doi.org:10.1016/j.tins.2012.02.001

(40) Wagner, A. D. et al. Building memories: remembering and forgetting of verbal experiences as predicted by brain activity. Science (New York, N.Y.) 281, 1188-1191 (1998).

(41) Ashe, J., Lungu, O. V., Basford, A. T. & Lu, X. Cortical control of motor sequences. Curr Opin Neurobiol 16, 213-221 (2006).

(42) Hikosaka, O., Nakamura, K., Sakai, K. & Nakahara, H. Central mechanisms of motor skill learning. Curr Opin Neurobiol 12, 217-222 (2002).

(43) Penhune, V. B. & Steele, C. J. Parallel contributions of cerebellar, striatal and M1 mechanisms to motor sequence learning. Behav. Brain Res. 226, 579-591 (2012). https://doi.org:10.1016/j.bbr.2011.09.044

(44) Doyon, J. et al. Contributions of the basal ganglia and functionally related brain structures to motor learning. Behavioural brain research 199, 61-75 (2009). https://doi.org:10.1016/j.bbr.2008.11.012

(45) Schendan, H. E., Searl, M. M., Melrose, R. J. & Stern, C. E. An FMRI study of the role of the medial temporal lobe in implicit and explicit sequence learning. Neuron 37, 1013-1025 (2003). https://doi.org:10.1016/s0896-6273(03)00123-5

(46) Morris, R. G. M. Elements of a neurobiological theory of hippocampal function: the role of synaptic plasticity, synaptic tagging and schemas. The European journal of neuroscience 23, 2829-2846 (2006). https://doi.org:10.1111/j.1460-9568.2006.04888.x

(47) Tse, D. et al. Schemas and memory consolidation. Science 316, 76-82 (2007). https://doi.org:10.1126/science.1135935

(48) Berlot, E., Popp, N. J. & Diedrichsen, J. A critical re-evaluation of fMRI signatures of motor sequence learning. Elife 9 (2020). https://doi.org:10.7554/eLife.55241

(49) Bonstrup, M. et al. A Rapid Form of Offline Consolidation in Skill Learning. Curr Biol 29, 1346-1351 e1344 (2019). https://doi.org:10.1016/j.cub.2019.02.049

(50) Kornysheva, K. et al. Neural Competitive Queuing of Ordinal Structure Underlies Skilled Sequential Action. Neuron 101, 1166-1180 e1163 (2019). https://doi.org:10.1016/j.neuron.2019.01.018

(51) Pan, S. C. & Rickard, T. C. Sleep and motor learning: Is there room for consolidation? Psychol Bull 141, 812-834 (2015). https://doi.org:10.1037/bul0000009

(52) Rickard, T. C., Cai, D. J., Rieth, C. A., Jones, J. & Ard, M. C. Sleep does not enhance motor sequence learning. J Exp Psychol Learn Mem Cogn 34, 834-842 (2008). https://doi.org:10.1037/0278-7393.34.4.834

53) Brawn, T. P., Fenn, K. M., Nusbaum, H. C. & Margoliash, D. Consolidating the effects of waking and sleep on motor-sequence learning. J Neurosci 30, 13977-13982 (2010). https://doi.org:10.1523/JNEUROSCI.3295-10.2010

(54) Bonstrup, M., Iturrate, I., Hebart, M. N., Censor, N. & Cohen, L. G. Mechanisms of offline motor learning at a microscale of seconds in large-scale crowdsourced data. NPJ Sci Learn 5, 7 (2020). https://doi.org:10.1038/s41539-020-0066-9

(55) Gupta, M. W. & Rickard, T. C. Dissipation of reactive inhibition is sufficient to explain post-rest improvements in motor sequence learning. NPJ Sci Learn 7, 25 (2022). https://doi.org:10.1038/s41539-022-00140-z

(56) Jacobacci, F. et al. Rapid hippocampal plasticity supports motor sequence learning. Proceedings of the National Academy of Sciences 117, 23898-23903 (2020).

(57) Brooks, E., Wallis, S., Hendrikse, J. & Coxon, J. Micro-consolidation occurs when learning an implicit motor sequence, but is not influenced by HIIT exercise. NPJ Sci Learn 9, 23 (2024). https://doi.org:10.1038/s41539-024-00238-6

(58) Deleglise, A. et al. Human motor sequence learning drives transient changes in network topology and hippocampal connectivity early during memory consolidation. Cereb Cortex 33, 6120-6131 (2023). https://doi.org:10.1093/cercor/bhac489

(59) Buzsaki, G. Hippocampal sharp wave-ripple: A cognitive biomarker for episodic memory and planning. Hippocampus 25, 1073-1188 (2015). https://doi.org:10.1002/hipo.22488

(60) Gupta, M. W. & Rickard, T. C. Comparison of online, offline, and hybrid hypotheses of motor sequence learning using a quantitative model that incorporate reactive inhibition. Sci Rep 14, 4661 (2024). https://doi.org:10.1038/s41598-024-52726-9

(61) Das, A., Karagiorgis, A., Diedrichsen, J., Stenner, M.-P. & Azanon, E. “Micro-offline gains” convey no benefit for motor skill learning. bioRxiv, 2024.2007.2011.602795 (2024). https://doi.org:10.1101/2024.07.11.602795

(62) Mylonas, D. et al. Maintenance of Procedural Motor Memory across Brief Rest Periods Requires the Hippocampus. J Neurosci 44 (2024). https://doi.org:10.1523/JNEUROSCI.1839-23.2024

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Reviewer #1 (Public review):

Summary:

In this study from Zhu and colleagues, a clear role for MED26 in mouse and human erythropoiesis is demonstrated that is also mapped to amino acids 88-480 of the human protein. The authors also show the unique expression of MED26 in later-stage erythropoiesis and propose transcriptional pausing and condensate formation mechanisms for MED26's role in promoting erythropoiesis. Despite the author's introductory claim that many questions regarding Pol II pausing in mammalian development remain unanswered, the importance of transcriptional pausing in erythropoiesis has actually already been demonstrated (Martell-Smart, et al. 2023, PMID: 37586368, which the authors notably did not cite in this manuscript). Here, the novelty and strength of this study is MED26 and its unique expression kinetics during erythroid development.

Strengths:

The widespread characterization of kinetics of mediator complex component expression throughout the erythropoietic timeline is excellent and shows the interesting divergence of MED26 expression pattern from many other mediator complex components. The genetic evidence in conditional knockout mice for erythropoiesis requiring MED26 is outstanding. These are completely new models from the investigators and are an impressive amount of work to have both EpoR-driven deletion and inducible deletion. The effect on red cell number is strong in both. The genetic over-expression experiments are also quite impressive, especially the investigators' structure-function mapping in primary cells. Overall the data is quite convincing regarding the genetic requirement for MED26. The authors should be commended for demonstrating this in multiple rigorous ways.

Thank you for your positive feedback.

Weaknesses:

(1) The authors state that MED26 was nominated for study based on RNA-seq analysis of a prior published dataset. They do not however display any of that RNA-seq analysis with regards to Mediator complex subunits. While they do a good job showing protein-level analysis during erythropoiesis for several subunits, the RNA-seq analysis would allow them to show the developmental expression dynamics of all subunit members.

Thank you for this helpful suggestion. While we did not originally nominate MED26 based on RNA-seq analysis, we have analyzed the transcript levels of Mediator complex subunits in our RNA-seq data across different stages of erythroid differentiation (Author response image 1). The results indicate that most Mediator subunits, including MED26, display decreased RNA expression over the course of differentiation, with the exception of MED25, as reported previously (Pope et al., Mol Cell Biol 2013. PMID: 23459945).

Notably, our study is based on initial observations at the protein level, where we found that, unlike most other Mediator subunits that are downregulated during erythropoiesis, MED26 remains relatively abundant. Protein expression levels more directly reflect the combined influences of transcription, translation and degradation processes within cells, and are likely more closely related to biological functions in this context. It is possible that post-transcriptional regulation (such as m6A-mediated improvement of translational efficiency) or post-translational modifications (like escape from ubiquitination) could contribute to the sustained levels of MED26 protein, and this will be an interesting direction for future investigation.

Author response image 1.

Relative RNA expression of Mediator complex subunits during erythropoiesis in human CD34+ erythroid cultures. Different differentiation stages from HSPCs to late erythroblasts were identified using CD71 and CD235a markers, progressing sequentially as CD71-CD235a-, CD71+CD235a-, CD71+CD235a+, and CD71-CD235a+. Expression levels were presented as TPM (transcripts per million).

(2) The authors use an EpoR Cre for red cell-specific MED26 deletion. However, other studies have now shown that the EpoR Cre can also lead to recombination in the macrophage lineage, which clouds some of the in vivo conclusions for erythroid specificity. That being said, the in vitro erythropoiesis experiments here are convincing that there is a major erythroid-intrinsic effect.

Thank you for this insightful comment. We recognize that EpoR-Cre can drive recombination in both erythroid and macrophage lineages (Zhang et al., Blood 2021, PMID: 34098576). However, EpoR-Cre remains the most widely used Cre for studying erythroid lineage effects in the hematopoietic community. Numerous studies have employed EpoR-Cre for erythroid-specific gene knockout models (Pang et al, Mol Cell Biol 2021, PMID: 22566683; Santana-Codina et al., Haematologica 2019, PMID: 30630985; Xu et al., Science 2013, PMID: 21998251.).

While a GYPA (CD235a)-Cre model with erythroid specificity has recently been developed (https://www.sciencedirect.com/science/article/pii/S0006497121029074), it has not yet been officially published. We look forward to utilizing the GYPA-Cre model for future studies. As you noted, our in vivo mouse model and primary human CD34+ erythroid differentiation system both demonstrate that MED26 is essential for erythropoiesis, suggesting that the regulatory effects of MED26 in our study are predominantly erythroid-intrinsic.

(3) Te donor chimerism assessment of mice transplanted with MED26 knockout cells is a bit troubling. First, there are no staining controls shown and the full gating strategy is not shown. Furthermore, the authors use the CD45.1/CD45.2 system to differentiate between donor and recipient cells in erythroblasts. However, CD45 is not expressed from the CD235a+ stage of erythropoiesis onwards, so it is unclear how the authors are detecting essentially zero CD45-negative cells in the erythroblast compartment. This is quite odd and raises questions about the results. That being said, the red cell indices in the mice are the much more convincing data.

Thank you for your careful and thorough feedback. We have now included negative staining controls (Author response image 2A, top). We agree that CD45 is typically not expressed in erythroid precursors in normal development. Prior studies have characterized BFU-E and CFU-E stages as c-Kit+CD45+Ter119−CD71low and c-Kit+CD45−Ter119−CD71high cells in fetal liver (Katiyar et al, Cells 2023, PMID: 37174702).

However, our observations indicate that erythroid surface markers differ during hematopoiesis reconstitution following bone marrow transplantation.  We found that nearly all nucleated erythroid progenitors/precursors (Ter119+Hoechst+) express CD45 after hematopoiesis reconstitution (Author response image 2A, bottom).

To validate our assay, we performed next-generation sequencing by first mixing mouse CD45.1 and CD45.2 total bone marrow cells at a 1:2 ratio. We then isolated nucleated erythroid progenitors/precursors (Ter119+Hoechst+) by FACS and sequenced the CD45 gene locus by targeted sequencing. The resulting CD45 allele distribution matched our initial mixing ratio, confirming the accuracy of our approach (Author response image 2B).

Moreover, a recent study supports that reconstituted erythroid progenitors can indeed be distinguished by CD45 expression following bone marrow transplantation (He et al., Nature Aging 2024, PMID: 38632351. Extended Data Fig. 8). 

In conclusion, our data indicate that newly formed erythroid progenitors/precursors post-transplant express CD45, enabling us to identify nucleated erythroid progenitors/precursors by Ter119+Hoechst+ and determine their origin using CD45.1 and CD45.2 markers.

Author response image 2.

Representative flow cytometry gating strategy of erythroid chimerism following mouse bone marrow transplantation. A. Gating strategy used in the erythroid chimerism assay. B. Targeted sequencing result of Ter119+Hoechst+ cells isolated by FACS. The cell sample was pre-mixed with 1/3 CD45.2 and 2/3 CD45.1 bone marrow cells. Ptprc is the gene locus for CD45.

(4) The authors make heavy use of defining "erythroid gene" sets and "non-erythroid gene" sets, but it is unclear what those lists of genes actually are. This makes it hard to assess any claims made about erythroid and non-erythroid genes.

Thank you for this helpful suggestion. We defined "erythroid genes" and "non-erythroid genes" based on RNA-seq data from Ludwig et al. (Cell Reports 2019. PMID: 31189107. Figure 2 and Table S1). Genes downregulated from stages k1 to k5 are classified as “non-erythroid genes,” while genes upregulated from stages k6 to k7 are classified as “erythroid genes.” We will add this description in the revised manuscript.

(5) Overall the data regarding condensate formation is difficult to interpret and is the weakest part of this paper. It is also unclear how studies of in vitro condensate formation or studies in 293T or K562 cells can truly relate to highly specialized erythroid biology. This does not detract from the major findings regarding genetic requirements of MED26 in erythropoiesis.

Thank you for the rigorous feedback. Assessing the condensate properties of MED26 protein in primary CD34+ erythroid cells or mouse models is indeed challenging. As is common in many condensate studies, we used in vitro assays and cellular assays in HEK293T and K562 cells to examine the biophysical properties (Figure S7), condensation formation capacity (Figure 5C and Figure S7C), key phase-separation regions of MED26 protein (Figure S6), and recruitment of pausing factors (Figure 6A-B) in live cells. We then conducted functional assays to demonstrate that the phase-separation region of MED26 can promote erythroid differentiation similarly to the full-length protein in the CD34+ system and K562 cells (Figure 5A). Specifically, overexpressing the MED26 phase-separation domain accelerates erythropoiesis in primary human erythroid culture, while deleting the Intrinsically Disordered Region (IDR) impairs MED26’s ability to form condensates and recruit PAF1 in K562 cells.

In summary, we used HEK293T cells to study the biochemical and biophysical properties of MED26, and the primary CD34+ differentiation system to examine its developmental roles. Our findings support the conclusion that MED26-associated condensate formation promotes erythropoiesis.

(6) For many figures, there are some panels where conclusions are drawn, but no statistical quantification of whether a difference is significant or not.

Thank you for your thorough feedback. We have checked all figures for statistical quantification and added the relevant statistical analysis methods to the corresponding figure legends (Figure 2L and Figure S4C) to clarify the significance of the observed differences. The updated information will be incorporated into the revised manuscript.

Reviewer #2 (Public review):

Summary:

The manuscript by Zhu et al describes a novel role for MED26, a subunit of the Mediator complex, in erythroid development. The authors have discovered that MED26 promotes transcriptional pausing of RNA Pol II, by recruiting pausing-related factors.

Strengths:

This is a well-executed study. The authors have employed a range of cutting-edge and appropriate techniques to generate their data, including: CUT&Tag to profile chromatin changes and mediator complex distribution; nuclear run-on sequencing (PRO-seq) to study Pol II dynamics; knockout mice to determine the phenotype of MED26 perturbation in vivo; an ex vivo erythroid differentiation system to perform additional, important, biochemical and perturbation experiments; immunoprecipitation mass spectrometry (IP-MS); and the "optoDroplet" assay to study phase-separation and molecular condensates.

This is a real highlight of the study. The authors have managed to generate a comprehensive picture by employing these multiple techniques. In doing so, they have also managed to provide greater molecular insight into the workings of the MEDIATOR complex, an important multi-protein complex that plays an important role in a range of biological contexts. The insights the authors have uncovered for different subunits in erythropoiesis will very likely have ramifications in many other settings, in both healthy biology and disease contexts.

Thank you for your thoughtful summary and encouraging feedback.

Weaknesses:

There are almost no discernible weaknesses in the techniques used, nor the interpretation of the data. The IP-MS data was generated in HEK293 cells when it could have been performed in the human CD34+ HSPC system that they employed to generate a number of the other data. This would have been a more natural setting and would have enabled a more like-for-like comparison with the other data.

Thank you for your positive feedback and insightful suggestions. We will perform validation of the immunoprecipitation results in CD34+ derived erythroid cells to further confirm our findings.

Reviewer #3 (Public review):

Summary:

The authors aim to explore whether other subunits besides MED1 exert specific functions during the process of terminal erythropoiesis with global gene repression, and finally they demonstrated that MED26-enriched condensates drive erythropoiesis through modulating transcription pausing.

Strengths:

Through both in vitro and in vivo models, the authors showed that while MED1 and MED26 co-occupy a plethora of genes important for cell survival and proliferation at the HSPC stage, MED26 preferentially marks erythroid genes and recruits pausing-related factors for cell fate specification. Gradually, MED26 becomes the dominant factor in shaping the composition of transcription condensates and transforms the chromatin towards a repressive yet permissive state, achieving global transcription repression in erythropoiesis.

Thank you for your positive summary and feedback.

Weaknesses:

In the in vitro model, the author only used CD34+ cell-derived erythropoiesis as the validation, which is relatively simple, and more in vitro erythropoiesis models need to be used to strengthen the conclusion.

Thank you for your thoughtful suggestions. We have shown that MED26 promotes erythropoiesis using the primary human CD34+ differentiation system (Figure 2 K-M and Figure S4) and have demonstrated its essential role in erythropoiesis through multiple mouse models (Figure 2A-G and Figure S1-3). Together, these in vitro and in vivo results support our conclusion that MED26 regulates erythropoiesis. However, we are open to further validating our findings with additional in vitro erythropoiesis models, such as iPSC or HUDEP erythroid differentiation systems.

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Reviewer #2 (Public review):

Summary:

The authors purified and solved by cryo-EM a structure of tri-heteromeric GluN1/GluN2A/GluN3A NMDA receptors, whose existence has long been contentious. Using patch-clamp electrophysiology on GluN1/GluN2/GluN3A NMDARs reconstituted into liposomes, they characterized the function of this NMDAR subtype. Finally, thanks to site-targeted crosslinking using unnatural amino acid incorporation, they show that the GluN2A subunit can crosslink with the GluN3A subunit in a cellular context, both in recombinant systems (HEK cells) and neuronal cultures and in vivo.

Strengths:

The NMDAR GluN3 subunit is a glycine-binding subunit that was long thought to assemble into GluN1/GluN2/GluN3 tri-heteromeric receptors during development, acting as a brake for synaptic development. However, several studies based on single subunit counting (Ulbrich et al., PNAS 2008) and ex vivo/in vivo electrophysiology have challenged the existence of these tri-heteromers (see Bossi, Pizzamiglio et al., Trends Neurosci. 2023). A large part of the controversy stems from the difficulty in isolating the tri-heteromeric population from their di-heteromeric counterparts, which led to a lack of knowledge on the biophysical and pharmacological properties of putative GluN1/GluN2/GluN3 receptors. To counteract this problem, the authors used a two-step purification method - first with a strep-tag attached to the GluN3 subunit, then with a His tag attached to the GluN2 subunit - to isolate GluN1/GluN2/GluN3 tri-heteromers from GluN1/GluN2A and GluN1/GluN3 di-heteromers, and they did observe these entities in Western blot and FSEC. They solved a cryo-EM structure of this NMDAR subtype using specific FAbs to identify the GluN1 and GluN2A subunits, showing an asymmetrical, splayed architecture. Then, they reconstituted the purified receptors in lipid vesicles to perform single-channel electrophysiological recordings. Finally, in order to validate the tri-heteromeric arrangement in a cellular system, they performed photocrosslinking experiments between the GluN2A and GluN3 subunits. For this purpose, a photoactivatable unnatural amino acid (AzF) was incorporated at the bottom of GluN2A NTD, a region embedded within the receptor complex that is predicted to be in close proximity to the GluN3 subunit. This is an elegant approach to validate the existence of GluN1/GluN2/GluN3 tri-hets, since at the chosen AzF incorporation position, crosslinking between GluN2A and GluN3 is more likely to reflect interaction of subunits within the same receptor complex than between two receptors. They show crosslinking between GluN2A and GluN3 in the presence of AzF and UV light, but not if UV light or AzF were not provided, suggesting that GluN2A and GluN3 can indeed be incorporated in the same complex. In a further attempt to demonstrate the physiological relevance of these tri-heteromers, they performed the same crosslinking experiments in cultured neurons and even native brain samples. While unnatural amino acid incorporation is now a well-established technique in vitro, such an approach is very difficult to implement in vivo. The technical effort put into the validation of the presence of these tri-heteromers in vivo should thus be commended.

Overall, all the strategies used by this paper to prove the existence of GluN1/GluN2/GluN3 tri-heteromers, and investigate their structure and function, are well-thought-out and very elegant. But the current data do not fully support the conclusions of the paper.

Weaknesses:

All the experiments aiming at proving the existence of GluN1/GluN2/GluN3 tri-heteromers rely on the purification of these receptors from whole cell extracts. There is therefore no proof that these receptors are expressed at the membrane and are functional. This is a limitation that has been overlooked and should be discussed in the manuscript. In addition, in the current manuscript state, each demonstration suffers from caveats that do not allow for a firm conclusion about the existence and the properties of this receptor subtype.

(1) In Cryo-EM images of GluN1/GluN2A/GluN3A receptors, the GluN3 subunit is identified as the subunit having no Fab bound to it. How can the authors be sure that this is indeed the GluN3A subunit and not a GluN2A subunit that has not bound the Fab? Does the GluN3A subunit carry features that would allow distinguishing it independently of Fab binding? In addition, it is surprising that the authors did not incubate the tri-heteromers with a Fab against GluN3A, since Extended Figure 3 shows that such a Fab is available.

(2) Whether the single-channel recordings reflect the activity of GluN1/GluN2/GluN3 tri-heteromers is not convincing. Indeed, currents from liposomes containing these tri-heteromers have two conductance levels that correspond to the conductances of the corresponding di-heteromers. There is therefore a need for additional proof that the measured currents do not reflect a mixture of currents from N1/2A di-heteromers on one side, and N1/3A di-heteromers on the other side. What is the purity of the N1/3A sample? Indeed, given the high open probability and high conductance of N1/2A tri-heteromers, even a small fraction of them could significantly contribute to the single-channel currents. Additionally, although the authors show no current induced by 3uM glycine alone on proteoliposomes with the N1/2A/3A prep (no stats provided, though), given the sharp dependence of N1/3A currents on glycine concentration, this control alone cannot rule out the presence of contaminant N1/3A dihets in the preparation.

Finally, pharmacological characterization of these tri-heteromers is lacking. In vivo, the presence of tri-heteromeric GluN1/GluN2/GluN3 tri-heteromers was inferred from recordings of NMDARs activated by glutamate but with low magnesium sensitivity. What is the effect of magnesium on N1/2A/3A currents? Does APV, the classical NMDAR antagonist acting at the glutamate site, inhibit the tri-heteromers? What is the effect of CGP-78608, which inhibits GluN1/GluN2 NMDARs but potentiates GluN1/GluN3 NMDARs? Such pharmacological characterization is critical to validate that the measured currents are indeed carried by a tri-heteromeric population, and would also be very important to identify such tri-heteromers in native tissues.

(3) Validation of GluN1/GluN2/GluN3 tri-heteromer expression by photocrosslinking: The mixture of constructions used (full-length or CTD-truncated constructs, with or without tags) is confusing, and it is difficult to track the correct molecular weight of the different constructs. In Figure 6, the band corresponding to a putative GluN3/GluN2A dimer is very weak. In addition, given the differences in molecular weights between the GluN2 subunits and GluN3, we would expect the band corresponding to a GluN2A/GluN2B to migrate differently from the GluN2A/GluN3 dimer, but all high molecular weight bands seem to be a the same level in the blot. Finally, in the source data, the blots display additional bands that were not dismissed by the authors without justification. In short, better clarification of the constructs and more careful interpretation of the blots are necessary to support the conclusions claimed by the authors.

The way I think this should be approached is credit to the original user. Acknowledging that the repost does help the popularity of the content. For example a simple tag or credit label to the original poster should be more than enough.

Reviewer #1 (Public review):

Summary:

Overall, this study is an excellent and systematic investigation of the expansion of repeat sequences in Arabidopsis thaliana, and the genetic mechanisms underlying these expansions. Many of the key findings here confirm smaller studies of both repeat sequence variation and the individual genes associated with the expansion of various repeat classes. The authors present a highly effective and practical approach that requires datasets that are far more readily available than the multiple reference genomes used to annotate repeat variation in recent works. Therefore, they provide an approach that shows significant promise in non-model systems in which far less is known of repeat variation and its underlying drivers.

Strengths:

This is a very methodologically sound study that extends the relatively well-studied Arabidopsis thaliana repeat landscape with more systematic sampling, highlights the loci associated with repeat expansions (many of which were previously identified in a piecemeal manner), and provides some evolutionary inference on these.

Weaknesses:

Regarding cis-QTLs: I foresee at least two causes of these associations: non-repetitive cis-acting sequences that promote or permit the expansion of local repeats, and variation in repeat sequences themselves that directly tag the expanding sequence itself. It's arguable whether these are truly two distinct classes, but an attempt to discriminate between them may provide some insight as to the local factors that allow for repeat expansion, beyond the mere presence of a repeat sequence. One way to discriminate these could be to map the ~1300 12-mer frequency profiles on the reference genome, and filter any SNPs with elevated 12-mer frequency from the GWAS (or to categorize them independently).

I also have a question regarding the choice of k=12 in kmer profile analyses. Did the authors perform any GWAS with other values of K? If so, how did the results change? I would expect that as K is increased, the associations would become more specific to individual repeat families, possibly to the point where only cis-acting loci are detected. The authors show convincing evidence that k=12 is appropriate; however, I would be interested to see if/how GWAS results vary among e.g. k=10, 12, 15, 18.

Author response:

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

Reviewer #1 (Public review): 

In recent years, our understanding of the nuclear steps of the HIV-1 life cycle has made significant advances. It has emerged that HIV-1 completes reverse transcription in the nucleus and that the host factor CPSF6 forms condensates around the viral capsid. The precise function of these CPSF6 condensates is under investigation, but it is clear that the HIV-1 capsid protein is required for their formation. This study by Tomasini et al. investigates the genesis of the CPSF6 condensates induced by HIV-1 capsid, what other co-factors may be required, and their relationship with nuclear speckels (NS). The authors show that disruption of the condensates by the drug PF74, added post-nuclear entry, blocks HIV-1 infection, which supports their functional role. They generated CPSF6 KO THP-1 cell lines, in which they expressed exogenous CPSF6 constructs to map by microscopy and pull down assays of the regions critical for the formation of condensates. This approach revealed that the LCR region of CPSF6 is required for capsid binding but not for condensates whereas the FG region is essential for both. Using SON and SRRM2 as markers of NS, the authors show that CPSF6 condensates precede their merging with NS but that depletion of SRRM2, or SRRM2 lacking the IDR domain, delays the genesis of condensates, which are also smaller. 

The study is interesting and well conducted and defines some characteristics of the CPSF6-HIV-1 condensates. Their results on the NS are valuable. The data presented are convincing. 

I have two main concerns. Firstly, the functional outcome of the various protein mutants and KOs is not evaluated. Although Figure 1 shows that disruption of the CPSF6 puncta by PF74 impairs HIV-1 infection, it is not clear if HIV-1 infection is at all affected by expression of the mutant CPSF6 forms (and SRRM2 mutants) or KO/KD of the various host factors. The cell lines are available, so it should be possible to measure HIV-1 infection and reverse transcription. Secondly, the authors have not assessed if the effects observed on the NS impact HIV-1 gene expression, which would be interesting to know given that NS are sites of highly active gene transcription. With the reagents at hand, it should be possible to investigate this too. 

We thank the reviewer for her/his valuable feedback on our manuscript. We are pleased to see her/his appreciation of our results, and we did our utmost to address the highlighted points to further improve our work.

To correctly perform the infectivity assay, we generated stable cell clones—a process that required considerable time, particularly during the selection of clones expressing protein levels comparable to wild-type (WT) cells. To accurately measure infectivity, it was essential to use stable clones expressing the most important deletion mutant, ∆FG CPSF6, at levels similar to those of CPSF6 in WT cells (new Fig.5 A-B). Importantly, we assessed the reproducibility of our experiments by freezing and thawing these clones.

Regarding SRRM2, in THP-1 cells we were only able to achieve a knockdown, which still retains residual SRRM2 protein, albeit at much lower levels. Due to the essential role of SRRM2 in cell survival, obtaining a complete knockout in this cell line is not feasible, making it difficult to draw definitive conclusions from these experiments.

In contrast, 293T cells carrying the endogenous SRRM2 deletion mutant (ΔIDR) cannot be infected with replication-competent HIV-1, as they lack expression of CD4 and either CCR4 or CCR5. These cells were instead used to monitor the dynamics of CPSF6 puncta assembly within nuclear speckles. However, they are not a suitable model for studying the impact of the depletion of SRRM2 in viral infection.

Thus, we performed infectivity assays in a more relevant cell line for HIV-1 infection, THP-1 macrophage-like cells, using both a single-round virus and a replication-competent virus. The new results, shown in Figure 5 C-D, indicate that complete depletion of CPSF6 reduces infectivity, as measured by luciferase expression in a single-round infection (KO: ~65%; ΔFG: ~74%; compared to WT: 100% on average). Notably, a more pronounced defect in viral particle production was observed when WT virus was used for infection (KO: ~21%; ΔFG: ~16%; compared to WT: 100% on average). These findings support the referee’s insightful suggestion that the absence of CPSF6 could also impair HIV-1 gene expression. 

Reviewer #2 (Public review): 

Summary: 

HIV-1 infection induces CPSF6 aggregates in the nucleus that contain the viral protein CA. The study of the functions and composition of these nuclear aggregates have raised considerable interest in the field, and they have emerged as sites in which reverse transcription is completed and in the proximity of which viral DNA becomes integrated. In this work, the authors have mutated several regions of the CPSF6 protein to identify the domains important for nuclear aggregation, in addition to the alreadyknown FG region; they have characterized the kinetics of fusion between CPSF6 aggregates and SC35 nuclear speckles and have determined the role of two nuclear speckle components in this process (SRRM2, SUN2). 

Strengths: 

The work examines systematically the domains of CPSF6 of importance for nuclear aggregate formation in an elegant manner in which these mutants complement an otherwise CPSF6-KO cell line. In addition, this work evidences a novel role for the protein SRRM2 in HIV-induced aggregate formation, overall advancing our comprehension of the components required for their formation and regulation. 

Weaknesses: 

Some of the results presented in this manuscript, in particular the kinetics of fusion between CPSF6aggregates and SC35 speckles have been published before (PMID: 32665593; 32997983). 

The observations of the different effects of CPSF6 mutants, as well as SRRM2/SUN2 silencing experiments are not complemented by infection data which would have linked morphological changes in nuclear aggregates to function during viral infection. More importantly, these functional data could have helped stratify otherwise similar morphological appearances in CPSF6 aggregates. 

Overall, the results could be presented in a more concise and ordered manner to help focus the attention of the reader on the most important issues. Most of the figures extend to 3-4 different pages and some information could be clearly either aggregated or moved to supplementary data. 

First, we thank the reviewer for her/his appreciation of our study and to give to us the opportunity to better explain our results and to improve our manuscript. We appreciate the reviewer’s positive feedback on our study, and we will do our best to address her/his concerns. In the meantime, we would like to clarify the focus of our study. Our research does not aim to demonstrate an association between CPSF6 condensates (we use the term "condensates" rather than "aggregates," as aggregates are generally non-dynamic (Alberti & Hyman, 2021; Banani et al., 2017; Scoca et al., JMCB 2022), and our work specifically examines the dynamic behavior of CPSF6 puncta formed during infection and nuclear speckles. The association between CPSF6 puncta and NS has already been established in previous studies, as noted in the manuscript (PMID: 32665593; 32997983). The previous studies (PMID: 32665593; 32997983) showed that CPSF6 puncta colocalize with SC35 upon HIV infection and in the submitted study we study their kinetics.

About the point highlighted by the reviewer: "Kinetics of fusion between CPSF6-aggregates and SC35 speckles have been published before."  

Our study differs from prior work PMID 32665593 because we utilize a full-length HIV genome, and we did not follow the integrase (IN) fluorescence in trans and its association with CPSF6 but we specifically assess if CPSF6 clusters form in the nucleus independently of NS factors and next to fuse with them. In the current study we evaluated the dynamics of formation of CPSF6/NS puncta, which it has not been explored before. Given this focus, we believe that our work offers a novel perspective on the molecular interactions that facilitate HIV / CPSF6-NS fusion.

We calculated that 27% of CPSF6 clusters were independent from NS at 6 h post-infection, compared to only 9% at 30 h. This likely reflects a reduction in individual clusters as more become fused with nuclear speckles over time. At the same time, these data suggest that the fusion process can begin even earlier. Indeed, it has been reported that in macrophages, the peak of viral nuclear import occurs before 6 h post-infection (doi: 10.1038/s41564-020-0735-8).

In addition, we have incorporated new experiments assessing viral infectivity in the absence of CPSF6, or in CPSF6-knockout cells expressing either a CPSF6 mutant lacking the FG peptide or the WT protein. As shown in our new Figure 5, these results demonstrate that the FG peptide is critical for viral replication in THP-1 cells.

For better clarity, we would like to specify that our study focuses on the role of SON, a scaffold factor of nuclear speckles, rather than SUN2 (SUN domain-containing protein 2), which is a component of the LINC (Linker of Nucleoskeleton and Cytoskeleton) complex.

As suggested by the reviewer, we have revised the text and combined figures to improve clarity and facilitate reader comprehension. We appreciate the constructive comment of the reviewer.

Reviewer #3 (Public review): 

In this study, the authors investigate the requirements for the formation of CPSF6 puncta induced by HIV-1 under a high multiplicity of infection conditions. Not surprisingly, they observe that mutation of the Phe-Gly (FG) repeat responsible for CPSF6 binding to the incoming HIV-1 capsid abrogates CPSF6 punctum formation. Perhaps more interestingly, they show that the removal of other domains of CPSF6, including the mixed-charge domain (MCD), does not affect the formation of HIV-1-induced CPSF6 puncta. The authors also present data suggesting that CPSF6 puncta form individual before fusing with nuclear speckles (NSs) and that the fusion of CPSF6 puncta to NSs requires the intrinsically disordered region (IDR) of the NS component SRRM2. While the study presents some interesting findings, there are some technical issues that need to be addressed and the amount of new information is somewhat limited. Also, the authors' finding that deletion of the CPSF6 MCD does not affect the formation of HIV-1-induced CPSF6 puncta contradicts recent findings of Jang et al. (doi.org/10.1093/nar/gkae769). 

We thank the reviewer for her/his thoughtful feedback and the opportunity to elaborate on why our findings provide a distinct perspective compared to those of Jang et al. (doi.org/10.1093/nar/gkae769).

One potential reason for the differences between our findings and those of Jang et al. could be the choice of experimental systems. Jang et al. conducted their study in HEK293T cells with CPSF6 knockouts, as described in Sowd et al., 2016 (doi.org/10.1073/pnas.1524213113). In contrast, our work focused on macrophage-like THP-1 cells, which share closer characteristics with HIV-1’s natural target cells. 

Our approach utilized a complete CPSF6 knockout in THP-1 cells, enabling us to reintroduce untagged versions of CPSF6, such as wild-type and deletion mutants, to avoid potential artifacts from tagging. Jang et al. employed HA-tagged CPSF6 constructs, which may lead to subtle differences in experimental outcomes due to the presence of the tag.

Finally, our investigation into the IDR of SRRM2 relied on CRISPR-PAINT to generate targeted deletions directly in the endogenous gene (Lester et al., 2021, DOI: 10.1016/j.neuron.2021.03.026). This approach provided a native context for studying SRRM2’s role.

We will incorporate these clarifications into the discussion section of the revised manuscript.  

Reviewer #1 (Recommendations for the authors): 

(1) Figure 2E: The statistical analysis should be extended to the comparison between the "+HIV" samples. 

We showed the statistics between only HIV+ cells now new Fig. 2D.  

(2) Figure 4A top panel is out of focus. 

We modified the figure now figure 6A.

Reviewer #2 (Recommendations for the authors): 

(1) Some of the sentences could be rewritten for the sake of simplicity, also taking care to avoid overstatement. 

We modified the sentences as best as we could.

(2) For instance: There is no evidence that "viral genomes in nuclear niches may be contributing to the formation of viral reservoirs" (lines 33-35). 

We changed the sentence as follows: “Despite antiretroviral treatment, viral genomes can persist in these nuclear niches and reactivate upon treatment interruption, raising the possibility that they could play a role in the establishment of viral reservoirs.”

(3) Line 53: unclear sentence. "The initial stages of the viral life cycle have been understood....." The authors certainly mean reverse transcription, but as formulated this is not clear. The authors should also bear in mind that reverse transcription starts already in budding/just released virions. 

We clarified the concept as follows: “the initial stages of the viral life cycle, such as the reverse transcription (the conversion of the viral RNA in DNA) and the uncoating (loss of the capsid), have been understood to mainly occur within the host cytoplasm.”

(4) Line 124: the results in Figure 1 are not at all explained in the text. PF74 does not act on CPSF6, it acts on CA and this in turn leads to CPS6 puncta disappearance. 

PF74 binds the same hydrophobic pocket of the viral core as CPSF6. However, when viral cores are located within CPSF6 puncta, treatment with a high dose of PF74 leads to a rapid disassembly of these puncta, while viral cores remain detectable up to 2 hours post-treatment (Ay et al., EMBO J. 2024). Here, we simply describe what we observed by confocal microscopy. Said that HIV-Induced CPSF6 Puncta include both CPSF6 proteins and viral cores as we have now specified.

(5) Line 130; 'hinges into two key ...' should be 'hinges on'. 

Thanks we modified it.

(6) Supplementary Figures are not cited sequentially in the text. 

We have now modified the numbers of the supplementary figures according to their appearance in the text.

(7) Line 44: define FG. 

We defined it.

Reviewer #3 (Recommendations for the authors): 

Specific comments that the authors should address are outlined below. 

(1) As mentioned in the summary above, the authors' findings seem to be in direct contradiction with recent work published by Alan Engelman's lab in NAR. The authors should address the possible reason(s) for this discrepancy. 

We mention the potential reasons for the differences in the results between our study and Engelman’s lab study in the discussion.

(2) The major finding here that deletion of the CFSF6 FG repeat prevents the formation of CFSP6 puncta is unsurprising, as the FG repeat is responsible for capsid binding. This has been reported previously and such mutants have been used as controls in other studies. 

Our study demonstrates that the FG domain is the sole region responsible for the formation of CPSF6 puncta, rather than the LCR or MCD domains. The unique role of the FG domain in CPSF6 that promotes the formation of CPSF6 puncta without the help of the other IDRs during viral infection is a finding particularly novel, as it has not yet been reported in the literature.

(3) Line 339, the authors state: "incoming viral RNA has been observed to be sequestered in nuclear niches in cells treated with the reversible reverse transcriptase inhibitor, NEV. When macrophage-like cells are infected in the presence of NEV, the incoming viral RNA is held within the nucleus (Rensen et al., 2021; Scoca et al., 2023). This scenario is comparable to what is observed in patients undergoing antiretroviral therapy". In what way is this comparable to what is observed in individuals on ART? I see no basis for this statement. Sequestration of viral RNA in the nucleus is not the basis for maintaining the viral reservoir in individuals on therapy. 

Thanks, we rephrased the sentence.

(4) General comment: analyzing single-cell-derived KO clones is very risky because of random clonal variability between individual cells in the population. If single-cell-derived clones are used, phenotypes could be confirmed with multiple, independent clones. 

We used a clone completely KO for CPSF6 mainly to investigate the role of a specific domain in condensate formation and it will be difficult that clone selection could have introduced artifacts in this context. Other available clones retain residual endogenous protein, which prevents us from accurately assessing CPSF6 cluster formation in the various deletion mutants. A complete CPSF6 knockout is essential for studying puncta formation, as it eliminates potential artifacts arising from protein tags that could alter the phase separation properties of the protein under investigation.

(5) Line 214. "It is predicted to form two short α helices and a ß strand, arranged as: α helix - FG - ß strand - α helix". What is this based on? No citation is provided and no data are shown. 

In fact, the statement "It is predicted to form two short α helices and a ß strand, arranged as: α helix - FG - ß strand - α helix" is based on the data shown in Figure 4E presenting data generated by PSIPRED. 

(6) Figure 1B. "Luciferase values were normalized by total proteins revealed with the Bradford kit". What does this mean? I couldn't find anything explaining how the viral inputs were normalized. 

The amount of the virus used is the same for all samples, we used MOI 10 as described in the legend of Figure 1. It is important to normalize the RLU (luciferase assay) with the total amount of proteins to be sure that we are comparing similar number of cells. Obviously, the cells were plated on the same amount on each well, the normalization in our case it is just an additional important control.

(7) I can't interpret what is being shown in the movies. 

We updated the movie 1B and rephrased the movie legends and we added a new suppl. Fig.4B.

(8) Figure 5B. The differences seen are very small and of questionable significance. The data suggest that by 6 hpi, around 75% of HIV-induced CPSF6 puncta are already fused with NSs. 

We calculated that 27% of CPSF6 clusters were independent from NS at 6 h post-infection, compared to only 9% at 30 h. This likely reflects a reduction in individual clusters as more become fused with nuclear speckles over time. At the same time, these data suggest that the fusion process can begin even earlier. Indeed, it has been reported that in macrophages, the peak of viral nuclear import occurs before 6 h post-infection (doi: 10.1038/s41564-020-0735-8).

(9) Figure 6. Immunofluorescence is not a good method for quantifying KD efficiency. The authors should perform western blotting to measure KD efficiency. This is an important point, because the effect sizes are small, quite likely due to incomplete KD. 

We performed WB and quantified the results, which correlated with the IF data and their imaging analysis. These new findings have been incorporated into Figure 8A. Of note, deletion of the IDR of SRRM2 does not affect the number of SON puncta (Fig.8C), but significantly reduces the number of CPSF6 puncta in infected cells compared to those expressing full-length SRRM2 (Fig.8D).

(10) There are a variety of issues with the text that should be corrected. 

The authors use "RT" to mean both the enzyme (reverse transcriptase) and the process (reverse transcription). This is incorrect and will confuse the reader. RT refers to the enzyme (noun, not verb). 

The commonly used abbreviation for nevirapine is NVP, not NEV. 

In line 60, it is stated that the capsid contains 250 hexamers. This number is variable, depending on the size and shape of the capsid. By contrast, the capsid has exactly 12 pentamers. 

Line 75. Typo: "nuclear niches containing, such as like". 

Line 82. Typo: "the mechanism behinds". 

Line 102. Typo: "we aim to elucidate how these HIV-induced CPSF6 form". 

Line 107. Type: "CPSF6 is responsible for tracking the viral core" ("trafficking the viral core"?). 

Thanks, we corrected all of them.

\peergos release

// now that hjyerpost.peergos.me web hosted page

can readily be annotated

it becomes possible to add comments, notes on he annotation margin s

most imortantly - introduce in line morphic notation - call it in0

and of course use trailmrks' in line notations on the margins

in0

about annotated elements on the page

introducing te hypothesy tag:

dev-meta-design-note

This concept definition really resonated with me and made me think about the reality of the old and new twitter, specifically the verified tag. People used to listen and authenticate people online based on their verification tags, such as celebrities. Nowadays, that aspect is taken away because the verification tag can be bought now so it removes that barrier from fake/real.

Author response:

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

Reviewer #1 (Public review):

Summary:

This study provides a comprehensive single-cell and multiomic characterization of trabecular meshwork (TM) cells in the mouse eye, a structure critical to intraocular pressure (IOP) regulation and glaucoma pathogenesis. Using scRNA-seq, snATAC-seq, immunofluorescence, and in situ hybridization, the authors identify three transcriptionally and spatially distinct TM cell subtypes. The study further demonstrates that mitochondrial dysfunction, specifically in one subtype (TM3), contributes to elevated IOP in a genetic mouse model of glaucoma carrying a mutation in the transcription factor Lmx1b. Importantly, treatment with nicotinamide (vitamin B3), known to support mitochondrial health, prevents IOP elevation in this model. The authors also link their findings to human datasets, suggesting the existence of analogous TM3-like cells with potential relevance to human glaucoma.

Strengths:

The study is methodologically rigorous, integrating single-cell transcriptomic and chromatin accessibility profiling with spatial validation and in vivo functional testing. The identification of TM subtypes is consistent across mouse strains and institutions, providing robust evidence of conserved TM cell heterogeneity. The use of a glaucoma model to show subtype-specific vulnerability, combined with a therapeutic intervention-gives the study strong mechanistic and translational significance. The inclusion of chromatin accessibility data adds further depth by implicating active transcription factors such as LMX1B, a gene known to be associated with glaucoma risk. The integration with human single-cell datasets enhances the potential relevance of the findings to human disease.

We thank the reviewers for their thorough reading of our manuscript and helpful comments.

Weaknesses:

(1) Although the LMX1B transcription factor is implicated as a key regulator in TM3 cells, its role in directly controlling mitochondrial gene expression is not fully explored. Additional analysis of motif accessibility or binding enrichment near relevant target genes could substantiate this mechanistic link. 

We show that the Lmx1b mutation induces mitochondrial dysfunction with mitochondrial gene expression changes but agree with the referee in that we do not show direct regulation of mitochondrial genes by LMX1B. Emerging data suggest that LMX1B regulates the expression of mitochondrial genes in other cell types [1, 2] making the direct link reasonable. Future work that is beyond the scope of the current paper will focus on sequencing cells at earlier timepoints to help distinguish gene expression changes associated with the V265D mutation from those secondary to ongoing disease and elevated IOP. Additional studies, including ATAC seq at more ages, ChIP-seq and/or Cut and Run/Tag (in TM cells) will be necessary to directly investigate LMX1B target genes.

As we studied adult mice, mitochondrial gene expression changes could be secondary to other disease induced stresses. Because we did not intend to say we have shown a direct link, we have now added a sentence to the discussion ensure clarity. 

Lines 932-934: “Although our studies show a clear effect of the Lmx1b mutation on mitochondria, future studies are needed to determine if LMX1B directly modulates mitochondrial genes in V265D mutant TM cells”

(2) The therapeutic effect of vitamin B3 is clearly demonstrated phenotypically, but the underlying cellular and molecular mechanisms remain somewhat underdeveloped - for instance, changes in mitochondrial function, oxidative stress markers, or NAD+ levels are not directly measured. 

We agree that further experiments towards a fuller mechanistic understanding of vitamin B3’s therapeutic effects are needed. Such experiments are planned but are beyond the scope of this paper, which is already very large (7 Figures and 16 Supplemental Figures).

(3) While the human relevance of TM3 cells is suggested through marker overlap, more quantitative approaches, such as cell identity mapping or gene signature scoring in human datasets, would strengthen the translational connection.

We appreciate the reviewer’s suggestion and agree that additional quantitative analyses will further strengthen the translational relevance of TM3 cells. It is not yet clear if humans have a direct TM3 counterpart or if TM cell roles are compartmentalized differently between human cell types. We are currently limited in our ability to perform these comparative analyses. Specifically, we were unable to obtain permission to use the underlying dataset from Patel et al., and our access to the Van Zyl et al. dataset was through the Single Cell Portal, which does not support more complex analyses (ex. cell identity mapping or gene signature scoring). Differences between human studies themselves also affect these comparisons. Future work aimed at resolving differences and standardizing human TM cell annotations, as well as cross species comparisons are needed (working groups exist and this ongoing effort supports 3 human TM cell subtypes as also reported by Van Zyl). This is beyond what we are currently able to do for this paper. We present a comprehensive assessment using readily available published resources.

Reviewer #2 (Public review):

Summary:

This elegant study by Tolman and colleagues provides fundamental findings that substantially advance our knowledge of the major cell types within the limbus of the mouse eye, focusing on the aqueous humor outflow pathway. The authors used single-cell and single-nuclei RNAseq to very clearly identify 3 subtypes of the trabecular meshwork (TM) cells in the mouse eye, with each subtype having unique markers and proposed functions. The U. Columbia results are strengthened by an independent replication in a different mouse strain at a separate laboratory (Duke). Bioinformatics analyses of these expression data were used to identify cellular compartments, molecular functions, and biological processes. Although there were some common pathways among the 3 subtypes of TM cells (e.g., ECM metabolism), there also were distinct functions. For example:

TM1 cell expression supports heavy engagement in ECM metabolism and structure, as well as TGFb2 signaling.

TM2 cells were enriched in laminin and pathways involved in phagocytosis, lysosomal function, and antigen expression, as well as End3/VEGF/angiopoietin signaling.

TM3 cells were enriched in actin binding and mitochondrial metabolism.

They used high-resolution immunostaining and in situ hybridization to show that these 3 TM subtypes express distinct markers and occupy distinct locations within the TM tissue. The authors compared their expression data with other published scRNAseq studies of the mouse as well as the human aqueous outflow pathway. They used ATAC-seq to map open chromatin regions in order to predict transcription factor binding sites. Their results were also evaluated in the context of human IOP and glaucoma risk alleles from published GWAS data, with interesting and meaningful correlations. Although not discussed in their manuscript, their expression data support other signaling pathways/ proteins/ genes that have been implicated in glaucoma, including: TGFb2, BMP signaling (including involvement of ID proteins), MYOC, actin cytoskeleton (CLANs), WNT signaling, etc.

In addition to these very impressive data, the authors used scRNAseq to examine changes in TM cell gene expression in the mouse glaucoma model of mutant Lmxb1-induced ocular hypertension. In man, LMX1B is associated with Nail-Patella syndrome, which can include the development of glaucoma, demonstrating the clinical relevance of this mouse model. Among the gene expression changes detected, TM3 cells had altered expression of genes associated with mitochondrial metabolism. The authors used their previous experience using nicotinamide to metabolically protect DBA2/J mice from glaucomatous damage, and they hypothesized that nicotinamide supplementation of mutant Lmx1b mice would help restore normal mitochondrial metabolism in the TM and prevent Lmx1b-mediated ocular hypertension. Adding nicotinamide to the drinking water significantly prevented Lmxb1 mutant mice from developing high intraocular pressure. This is a laudable example of dissecting the molecular pathogenic mechanisms responsible for a disease (glaucoma) and then discovering and testing a potential therapy that directly intervenes in the disease process and thereby protects from the disease.

Strengths:

There are numerous strengths in this comprehensive study including:

Deep scRNA sequencing that was confirmed by an independent dataset in another mouse strain at another university.

Identification and validation of molecular markers for each mouse TM cell subset along with localization of these subsets within the mouse aqueous outflow pathway.

Rigorous bioinformatics analysis of these data as well as comparison of the current data with previously published mouse and human scRNAseq data.

Correlating their current data with GWAS glaucoma and IOP "hits".

Discovering gene expression changes in the 3 TM subgroups in the mouse mutant Lmx1b model of glaucoma.

Further pursuing the indication of dysfunctional mitochondrial metabolism in TM3 cells from Lmx1b mutant mice to test the efficacy of dietary supplementation with nicotinamide. The authors nicely demonstrate the disease modifying efficacy of nicotinamide in preventing IOP elevation in these Lmx1b mutant mice, preventing the development of glaucoma. These results have clinical implications for new glaucoma therapies.

We thank the reviewer for these generous and thoughtful comments on the strengths of this study.

Weaknesses:

(1) Occasional over-interpretation of data. The authors have used changes in gene expression (RNAseq) to implicate functions and signaling pathways. For example: they have not directly measured "changes in metabolism", "mitochondrial dysfunction" or "activity of Lmx1b".

We thank the reviewer for this feedback. We did not intend to overstate and agree. Our gene expression changes support, but do not by themselves prove, metabolic disturbances. We had felt that this was obvious and did not want to clutter the text. We have revised the manuscript to clarify that our conclusions about metabolic changes and LMX1B activity are based on gene expression patterns rather than direct functional assays and have added EM data (see below under “Recommendations for the authors”).

We have also added the following to the results:

Lines 715-721: “Although the documented gene expression changes strongly suggest metabolic and mitochondrial dysfunction, they do not directly prove it. Using electron microscopy to directly evaluate mitochondria in the TM, we found a reduction in total mitochondria number per cell in mutants (P = 0.015, Figure 6G). In addition, mitochondria in mutants had increased area and reduced cristae (inner membrane folds) in mutants consistent with mitochondrial swelling and metabolic dysfunction (all P < 0.001 compared to WT, Figure 6G-H).”

More detailed EM and metabolic studies are underway but are beyond the scope of this paper.

(2) In their very thorough data set, there is enrichment of or changes in gene expression that support other pathways that have been previously reported to be associated with glaucoma (such as TGFb2, BMP signaling, actin cytoskeletal organization (CLANs), WNT signaling, ossification, etc. that appears to be a lost opportunity to further enhance the significance of this work.

We appreciate the reviewer’s suggestions for enhancing the relevance of our work, we had not initially discussed this due to length concerns. We have now incorporated some of this information into the manuscript (see below under “Recommendations for the authors”).

Reviewer #3 (Public review):

Summary: In this study, the authors perform multimodal single-cell transcriptomic and epigenomic profiling of 9,394 mouse TM cells, identifying three transcriptionally distinct TM subtypes with validated molecular signatures. TM1 cells are enriched for extracellular matrix genes, TM2 for secreted ligands supporting Schlemm's canal, and TM3 for contractile and mitochondrial/metabolic functions. The transcription factor LMX1B, previously linked to glaucoma, shows the highest expression in TM3 cells and appears to regulate mitochondrial pathways. In Lmx1bV265D mutant mice, TM3 cells exhibit transcriptional signs of mitochondrial dysfunction associated with elevated IOP. Notably, vitamin B3 treatment significantly mitigates IOP elevation, suggesting a potential therapeutic avenue.

This is an excellent and collaborative study involving investigators from two institutions, offering the most detailed single-cell transcriptomic and epigenetic profiling of the mouse limbal tissues-including both TM and Schlemm's canal (SC), from wild-type and Lmx1bV265D mutant mice. The study defines three TM subtypes and characterizes their distinct molecular signatures, associated pathways, and transcriptional regulators. The authors also compare their dataset with previously published murine and human studies, including those by Van Zyl et al., providing valuable crossspecies insights.

Strengths: 

(1) Comprehensive dataset with high single-cell resolution

(2) Use of multiple bioinformatic and cross-comparative approaches

(3) Integration of 3D imaging of TM and SC for anatomical context

(4) Convincing identification and validation of three TM subtypes using molecular markers.

We thank the reviewer for their comments on the strengths of this study.

Weaknesses:

(1) Insufficient evidence linking mitochondrial dysfunction to TM3 cells in Lmx1bV265D mice: While the identification of TM3 cells as metabolically specialized and Lmx1b-enriched is compelling, the proposed link between Lmx1b mutation and mitochondrial dysfunction remains underdeveloped. It is unclear whether mitochondrial defects are a primary consequence of Lmx1b-mediated transcriptional dysregulation or a secondary response to elevated IOP. Additional evidence is needed to clarify whether Lmx1b directly regulates mitochondrial genes (e.g., via ChIP-seq, motif analysis, or ATAC-seq), or whether mitochondrial changes are downstream effects.

We agree and refer the reviewer to our responses to the other referees including Reviewer 1, Comment 1 and Reviewer 2 comments 1 and 17. As noted there, these mechanistic questions are the focus of ongoing and future studies. We have revised the text where appropriate to ensure it accurately reflects the scope of our current data.

(2) Furthermore, the protective effects of nicotinamide (NAM) are interpreted as evidence of mitochondrial involvement, but no direct mitochondrial measurements (e.g., immunostaining, electron microscopy, OCR assays) are provided. It is essential to validate mitochondrial dysfunction in TM3 cells using in vivo functional assays to support the central conclusion of the paper. Without this, the claim that mitochondrial dysfunction drives IOP elevation in Lmx1bV265D mice remains speculative. Alternatively, authors should consider revising their claims that mitochondrial dysfunction in these mice is a central driver of TM dysfunction.

We again refer the reviewer to our other response including Reviewer 1, Comment 1 and Reviewer 2 comments 1 and 17.

(3) Mechanism of NAM-mediated protection is unclear: The manuscript states that NAM treatment prevents IOP elevation in Lmx1bV265D mice via metabolic support, yet no data are shown to confirm that NAM specifically rescues mitochondrial function. Do NAM-treated TM3 cells show improved mitochondrial integrity? Are reactive oxygen species (ROS) reduced? Does NAM also protect RGCs from glaucomatous damage? Addressing these points would clarify whether the therapeutic effects of NAM are indeed mitochondrial.

We refer the reviewer to our response to Reviewer 1, Comment 2.

(4) Lack of direct evidence that LMX1B regulates mitochondrial genes: While transcriptomic and motif accessibility analyses suggest that LMX1B is enriched in TM3 cells and may influence mitochondrial function, no mechanistic data are provided to demonstrate direct regulation of mitochondrial genes. Including ChIP-seq data, motif enrichment at mitochondrial gene loci, or perturbation studies (e.g., Lmx1b knockout or overexpression in TM3 cells) would greatly strengthen this central claim.

We refer the reviewer to our response to Reviewer 1, Comment 1.

(5) Focus on LMX1B in Fig. 5F lacks broader context: Figure 5F shows that several transcription factors (TFs)-including Tcf21, Foxs1, Arid3b, Myc, Gli2, Patz1, Plag1, Npas2, Nr1h4, and Nfatc2exhibit stronger positive correlations or motif accessibility changes than LMX1B. Yet the manuscript focuses almost exclusively on LMX1B. The rationale for this focus should be clarified, especially given LMX1B's relatively lower ranking in the correlation analysis. Were the functions of these other highly ranked TFs examined or considered in the context of TM biology or glaucoma? Discussing their potential roles would enhance the interpretation of the transcriptional regulatory landscape and demonstrate the broader relevance of the findings.

Our analysis (Figure 5F) indicates that Lmx1b is the transcription factor most strongly associated with its predicted target gene expression across all TM cells, as reflected by its highest value along the X-axis. While other transcription factors exhibit greater motif accessibility (Y-axis), this likely reflects their broader expression across TM subtypes. In contrast, Lmx1b is minimally expressed in TM1 and TM2 cells, which may account for its lower motif accessibility overall (motifs not accessible in cells where Lmx1b is not / minimally expressed).

Our emphasis on LMX1B is further supported by its direct genetic association with glaucoma. In contrast, the other transcription factors lack clear links to glaucoma and are supported primarily by indirect evidence. Nonetheless, we agree that the transcription factors highlighted in our analysis are promising candidates for future investigation. However, to maintain focus on the central narrative of this study, we have chosen not to include an extended discussion of these additional genes.

(6) In abstract, they say a number of 9,394 wild-type TM cell transcriptomes. The number of Lmx1bV265D/+ TM cell transcriptomes analyzed is not provided. This information is essential for evaluating the comparative analysis and should be clearly stated in the Abstract and again in the main text (e.g., lines 121-123). Including both wild-type and mutant cell counts will help readers assess the balance and robustness of the dataset.

We thank the reviewer for noticing this oversight and have added this value to the abstract and results section. 

Lines 41 and 696: 2,491 mutant TM cells.  

(7) Did the authors monitor mouse weight or other health parameters to assess potential systemic effects of treatment? It is known that the taste of compounds in drinking water can alter fluid or food intake, which may influence general health. Also, does Lmx1bV265D/+ have mice exhibit non-ocular phenotypes, and if so, does nicotinamide confer protection in those tissues as well? Additionally, starting the dose of the nicotinamide at postnatal day 2, how long the mice were treated with water containing nicotinamide, and after how many days or weeks IOP was reduced, and how long the decrease in the IOP was sustained.

Water intake was monitored in both treatment groups, and dosing was based on the average volume consumed by adult mice (lines 1017–1018, young pups do not drink water and so drug is largely delivered through mothers’ milk until weaning and so we do not know an accurate dose for young pups). Mouse health was assessed throughout the experiment through regular monitoring of body weight and general condition.

Depending on genetic context, Lmx1b mutations can cause kidney disease and impact other systems. Non-ocular phenotypes were not the focus of this study and were not characterized.

We added a comment to the method to clarify the NAM treatment timeline. NAM was administered continuously in the drinking water starting at P2 and maintained throughout the experiment. IOP was measured beginning at 2 months and then at monthly time points. NAM lessened IOP at 2 and 3 months. We terminated IOP assessment at 3 months.

Lines 1028-1029: “Treatment was started at postnatal day 2 and continued throughout the experiment.”

(8) While the IOP reduction observed in NAM-treated Lmx1bV265D/+ mice appears statistically significant, it is unclear whether this reflects meaningful biological protection. Several untreated mice exhibit very high IOP values, which may skew the analysis. The authors should report the mean values for IOP in both untreated and NAM-treated groups to clarify the magnitude and variability of the response.

We have added supplemental table 7 with the statistical information. Regarding the high IOP values observed in a subset of untreated V265D mutant mice, we consistently detect individual mutant eyes with IOPs exceeding 30 mmHg across independent cohorts and time points [3-5]. It is important to note that IOP is subject to fluctuation and in disease states such as glaucoma, circadian rhythms can be disrupted with stochastic and episodic IOP spikes throughout the day. This may be occurring in those untreated mice. This is also why we strive to use sample sizes of 40 or more. Additionally, we observe that some mutant eyes with IOPs measured within the normal range have anterior chamber deepening (ACD) - a persistent anatomical change associated with sustained or recurrent high IOP that stretches the cornea and may posteriorly displace the lens. This suggests mutant mice experience transient IOP elevations that are not always captured at a single time point due to the stochastic nature of these fluctuations. To account for this, we include ACD as an additional readout alongside IOP measurements. The reduction in ACD observed in NAM-treated mice provides independent evidence supporting the biological relevance of NAM-mediated IOP reduction.   

(9) Additionally, since NAM has been shown to protect RGCs in other glaucoma models directly, the authors should assess whether RGCs are preserved in NAM-treated Lmx1b V265D/+ mice. Demonstrating RGC protection would support a synergistic effect of NAM through both IOP reduction and direct neuroprotection, strengthening the translational relevance of the treatment.

We again thank the referee. We note the possibility of dual IOP protection and neuroprotection in the manuscript (lines 961–963). The goal of the present study, however, was to determine mechanisms underlying IOP elevation in patients with LMX1B variants. Therefore, we limited our focus to IOP elevation (LMX1B is expressed in the TM but not RGCs). Studies of the RGCs and optic nerve in V265D mutant mice treated with NAM take considerable effort but are underway. They will be reported in a subsequent manuscript. Initial data support protection, but that is a work in progress.  

Additionally, we recently reported a similar pattern of IOP protection to that reported here using pyruvate - in experiments where we analyzed the optic nerve as the focus of the study was assessment of pyruvate as a resilience factor against high genetic risk of glaucoma [4]. In that case, there was statistically significant protection from glaucomatous optic nerve damage, arguing for translational relevance again with a possible synergistic effect through both IOP reduction and direct neuroprotection.

(10) Can the authors add any other functional validation studies to explore to understand the pathways enriched in all the subtypes of TM1, TM2, and TM3 cells, in addition to the ICH/IF/RNAscope validation?

We agree with the reviewer on the importance of further functional validation of pathways active in TM cell subtypes that influence IOP. However, comprehensive investigation of the pathways active in subtypes need to be in future studies. It is beyond the scope of his already large paper.

(11) The authors should include a representative image of the limbal dissection. While Figure S1 provides a schematic, mouse eyes are very small, and dissecting unfixed limbal tissue is technically challenging. It is also difficult to reconcile the claim that the majority of cells in the limbal region are TM and endothelium. As shown in Figure S6, DAPI staining suggests a much higher abundance of scleral cells compared to TM cells within the limbal strip. Additional clarification or visual evidence would help validate the dissection strategy and cellular composition of the captured region.

We appreciate the reviewer’s suggestion and have added additional images to Figure S1 to show our limbal strip dissection. However, we clarify that we do not intend to suggest that TM and endothelial cells are the most abundant populations in these dissected strips.  When we say “are enriched for drainage tissues” we mean in comparison to dissecting the anterior segment as a whole. We have clarified this in the text. In fact, epithelial cells (primarily from the cornea) constituted the largest cluster in our dataset (Figure 1A). Additionally, to avoid misinterpretation, we generally refrain from drawing conclusions about the relative abundance of cell types based on sequencing data. Single-cell and single nucleus RNA sequencing results are sensitive to technical factors that alter cell proportions depending on exact methodological details. In our study, TM cells comprised 24.4% of the single-cell dataset and 11.8% of the single-nucleus dataset, illustrating the impact of methodological variability. 

Lines 163-164: “Individual eyes were dissected to isolate a strip of limbal tissue, which is enriched for TM cells in comparison to dissecting the anterior segment as a whole.”

Reviewer #1 (Recommendations for the authors):

To enhance the reproducibility and transparency of the findings presented in this study, we strongly recommend that the authors make all analysis scripts and computational tools publicly available.

We agree with the reviewer’s emphasis on transparency and are currently building a GitHub page to share our scripts. However, we did not develop any new tools for this study. All tools that we used are publicly available and provided in our methods section. All data will be available as raw data and through the Broad Institute’s Single Cell Portal.

Reviewer #2 (Recommendations for the authors):

The authors are to be commended for a well-written presentation of high-quality data, their comparisons of datasets (other mouse and human scRNAseq data), correlation with clinical glaucoma risk alleles, and curative therapy for the mouse model of Lmx1b glaucoma. There are several minor suggestions that the authors might consider to further improve their manuscript:

(1) Lines 42-43: Although their data strongly support the role of mitochondrial dysfunction in Lmx1b glaucoma, they might want to soften their conclusion "supports a primary role of mitochondrial dysfunction within TM3 cells initiating the IOP elevation that causes glaucoma".

With the inclusion of EM data supporting mitochondrial dysfunction in Lmx1b mutant TM cells, we have revised this sentence to more accurately reflect our findings.

Lines 42-44 (previously lines 42-43): “Mitochondria in TM cells of V265D/+ mice are swollen with a reduced cristae area, further supporting a role for mitochondrial dysfunction in the initiation of IOP elevation in these mice.”

(2) Figure 1: Why is the shape of the "TM containing" cluster in 1A so different than the cluster shown in 1B?

We isolated cells from the 'TM-containing' cluster and performed unbiased reclustering, which alters their positioning in UMAP space. The figure legend has been updated to clarify this point.

Lines 143-144 “A separate UMAP representation of the trabecular meshwork (TM) containing cluster following subclustering.”

(3) Line 160: change "data was" to "data were"

Corrected

(4) S4 Fig C: Please comment on why the Columbia and Duke heatmaps for TM3 are not as congruent as the heatmaps for TM1 and TM2.

We cannot definitively determine the reason for this. However, differences in tissue processing techniques between the Columbia and Duke preparations may contribute. Such variations have been shown to affect cellular transcriptomes in certain contexts. It is possible that TM3 cells are more susceptible to these effects than others. We have added a statement addressing this point to the figure legend.

Lines 238-240: “Because tissue processing techniques can alter gene expression [52], the heatmap variation between institutes likely reflects differences in processing techniques (Methods) and suggests that TM3 cells are more susceptible to these effects than other cell types.”

(5) S9 Fig: It is very difficult to see any staining for TM1 CHIL1 (2nd panel), TM2 End3 (2nd panel), and TM3 Lypd1 (both panels)

We apologize for the difficulty in visualizing these panels. To improve clarity, we have increased the brightness of all relevant marker signals, within standard bounds, to facilitate easier interpretation.

(6) Line 380: "are significantly higher"; since statistical analysis was not reported, please do not use "significantly"

Done

(7) The authors should consider discussing several of their findings that agree with published literature. For example:

Figure 3B: "Wnt protein binding" (PMID: 18274669), "TGFb "binding" (numerous references), "integrin binding" (work of Donna Peters), "actin binding"/"actin filament binding"/"actin filament bundle" (CLANs references)

S10 Fig c: "ossification" (work of Torretta Borres)

S11 Fig A: ID2/ID3 (PMID: 33938911); (B) BMP4 (PMID: 17325163)

S12 Fig A: MYOC in TM1 cells (numerous references)

We appreciate the reviewer’s diligent review and comments regarding these pathways. We have added a comment to the discussion regarding the agreement of these pathways.

Lines 855-858: In addition, the expression of genes that we document generally agrees with the literature. For example, the following genes and signaling molecules have been reported in TM cells, WNT signaling [78], TGF-β signaling [79-85], integrin binding [86-88], actin cytoskeletal networks [89], calcification genes [90, 91], and Myocilin [91-94].

(8) Line 541: was confocal microscopy used to measure the "3D shapes" of nuclei or was this done with a single image to determine sphericity?

This analysis was performed using confocal microscopy and 3D reconstructed models of the TM nuclei. We have added text to clarify this in the figure legend 

Lines 553-556: “To rigorously assess whether TM1 nuclei are more spherical, we analyzed their reconstructed 3D shapes from whole mounts images by confocal microscopy, comparing them to TM3 nuclei using the ‘Sphericity’ tool in Imaris.”

(9) Line 545: please add a close parentheses after "scoring 1"

Done

(10) S15 Fig: (A) There does not appear to be "good agreement" (line 653) between the datasets for TM1. (C) please provide a better explanation on how to interpret these "Confusion Matrix" results.

We understand the referee's concern, the patterns likely appear different to the referee due to limited sampling in snRNA-seq data. Based on our results, TM1 seems particularly susceptible, possibly because these cells do not tolerate the isolation process as well. Although we are confident that TM1 shows good agreement between the two techniques based on our experience, we have revised the language in the text to “generally” to reflect this nuance.

Lines 633-635 (previously line 653): The generated clusters and their marker genes generally agreed with our scRNA-seq analyses (Fig 5A-B, S15A Fig).

We have also added additional clarification for how to interpret the Confusion Matrix. 

Lines 669-672: “Colors indicate the fraction of cells identified in each ATAC cluster (row) which are also identified in each RNA cell type (columns), where darker colors represent stronger correspondence between RNA and ATAC clusters.”

(11) Line 676: The transition from discussing the sc/snRNAseq data to the work in Lmx1b mutant mice is quite abrupt and could use a better transition to introduce this metabolism work.

We have revised this transition for improved flow but prefer to keep all transitions brief due to the paper's length.

Lines 691-694 (previously line 676): To evaluate the utility of our new TM cell atlas, we used it to examine how Lmx1b mutations affect the TM cell transcriptome and to identify potential mechanisms underlying IOP elevation. We selected LMX1B because it causes IOP elevation and glaucoma in humans and was identified as a highly active transcription factor in our TM cell dataset.

(12) Lines 696-697: It appears counter-intuitive that upregulation of ubiquitin pathways would lead to proteostasis (proteosome protein degradation requires ubiquination).

We have clarified that the protein tagging pathway was significantly upregulated. However, polyubiquitin precursor itself was downregulated. In general, the statistical significance of the protein tagging pathway suggests perturbation of the system tagging proteins for degradation. We have clarified this in the text. 

Lines 711-714 (previously lines 696-697): “In addition, mutant TM3 cells showed an upregulation of protein tagging genes. However, there is a downregulation of the polyubiquitin precursor gene (Ubb, P = 4.5E-30), indicating a general dysregulation of pathways that tag proteins for degradation.”

(13) Line 715: Please justify why "perturbed metabolism" was chosen to pursue vs the other differentially expressed pathways

We chose to narrow our focus on TM3 cells because of the enrichment for Lmx1b expression.Most pathways identified in our analysis of TM3 cells implicate mitochondrial metabolism.Therefore, we chose to further explore this avenue. We clarified that perturbed metabolism was the strongest gene expression signature in the text. 

Lines 753-754 (previously line 715): “Our findings most strongly implicate perturbed metabolism within TM3 cells as responsible for IOP elevation in an Lmx1b glaucoma model.”

(14) Line 759: The authors clearly demonstrate that Lmx1b is most expressed in TM3 cells; however, they did not demonstrate that "Lmx1b was most active"

ATAC analysis showed that Lmx1b was most active in TM cells overall. We inferred its activity in TM3 because Lmx1b is most enriched in that subtype. This has been clarified in the text.

Lines 799-800 (previously line 759): “More specifically, we demonstrate that Lmx1b is the most active TM cell TF and is enriched in TM3 cells,…”

(15) Lines 830-835: Please include references documenting increased TGFβ2 concentrations in POAG aqueous humor and TM, effects of TGFβ2 on TM ECM deposition, and TGFβ2 induced ocular hypertension ex vivo and in vivo.

Done.

(16) Line 875: The authors provide no direct evidence for enhances "oxidative stress" in Lmx1b TM3 cells

The mitochondrial abnormalities and changed pathways support oxidative stress, but we have not directly tested this. Experiments are currently underway to evaluate its role, but these additional analyses are beyond the scope of this paper. We removed oxidative stress from the sentence.

Lines 920-922 (previously line 875): “Importantly, in heterozygous mutant V265D/+ mice, TM3 cells had pronounced gene expression changes that implicate mitochondrial dysfunction, but that were absent or much lower in other cells including TM1 and TM2.”

(17) Line 880: Similarly, the authors have not directly assessed effects on metabolism in TM3 cells; they only have shown changes in the expression of mitochondrial genes that may affect metabolism

We have no way to specifically isolating TM3 cells to test this. Future work is underway to test this more broadly in isolated TM cells but is beyond the scope of this is already large paper. Considering our gene expression data and the addition of supporting EM data, we have qualified the text.

Lines 930-931 (previously 880): “Our data extend these published findings by showing that inheritance of a single dominant mutation in Lmx1b similarly affects mitochondria in TM cells.”

(18) Line 892: What markers were used to detect "cell stress"?

We have revised the text. Although our RNA data show stress gene changes, characterization of these markers is beyond the scope of the current study and will be included in a subsequent paper.

Lines 945-948 (previously line 892): “However, these processes were not limited to TM3 cells or even to cell types that express detectable Lmx1b, suggesting that they are secondary damaging processes that are subsequent to the initiating, Lmx1b-induced perturbations in TM3 cells.”

Additional author driven change

While revising and reviewing our data, we identified a coding error that resulted in the WT and V265D mutant group labels being switched in Figure 6. Importantly, the significance of the differentially expressed genes (DEGs), the implicated biological pathways, and the interpretation of pathway directionality in the manuscript remain accurate. The only issue was the incorrect labeling in the figure. We have corrected the labels in Figure 6 to accurately reflect the data. As noted above, all data and code will be made available to ensure full reproducibility of our results.

References

(1) Doucet-Beaupre H, Gilbert C, Profes MS, Chabrat A, Pacelli C, Giguere N, et al. Lmx1a and Lmx1b regulate mitochondrial functions and survival of adult midbrain dopaminergic neurons. Proc Natl Acad Sci U S A. 2016;113(30):E4387-96. Epub 2016/07/14. doi: 10.1073/pnas.1520387113. PubMed PMID: 27407143; PubMed Central PMCID: PMCPMC4968767.

(2) Jimenez-Moreno N, Kollareddy M, Stathakos P, Moss JJ, Anton Z, Shoemark DK, et al. ATG8-dependent LMX1B-autophagy crosstalk shapes human midbrain dopaminergic neuronal resilience. J Cell Biol. 2023;222(5). Epub 2023/04/05. doi: 10.1083/jcb.201910133. PubMed PMID: 37014324; PubMed Central PMCID: PMCPMC10075225.

(3) Cross SH, Macalinao DG, McKie L, Rose L, Kearney AL, Rainger J, et al. A dominantnegative mutation of mouse Lmx1b causes glaucoma and is semi-lethal via LDB1mediated dimerization [corrected]. PLoS Genet. 2014;10(5):e1004359. Epub 2014/05/09. doi: 10.1371/journal.pgen.1004359. PubMed PMID: 24809698; PubMed Central PMCID: PMCPMC4014447.

(4) Li K, Tolman N, Segre AV, Stuart KV, Zeleznik OA, Vallabh NA, et al. Pyruvate and related energetic metabolites modulate resilience against high genetic risk for glaucoma. Elife. 2025;14. Epub 2025/04/24. doi: 10.7554/eLife.105576. PubMed PMID: 40272416; PubMed Central PMCID: PMCPMC12021409.

(5) Tolman NG, Balasubramanian R, Macalinao DG, Kearney AL, MacNicoll KH, Montgomery CL, et al. Genetic background modifies vulnerability to glaucoma-related phenotypes in Lmx1b mutant mice. Dis Model Mech. 2021;14(2). Epub 2021/01/20. doi: 10.1242/dmm.046953. PubMed PMID: 33462143; PubMed Central PMCID: PMCPMC7903917.

Cool that graffiti has kind of changed in a way where people will tag pretty much whatever just with their name when it used to be more informative. That informative part of street art I think it has been taken by flyers or posters that will have updates or messages. But more and more these days I am seeing explicitly politic graffiti around witch seems a bit closer to it's original use.