Add how you tagged each tree. E.g., did you tag in the centre of each crown, at the base of the trunk, within a set distance from the trunk?

Note: This response was posted by the corresponding author to Review Commons. The content has not been altered except for formatting.

Learn more at Review Commons

Reply to the reviewers

Manuscript number: RC -2025-03175

Corresponding author(s): Gernot Längst

[Please use this template only if the submitted manuscript should be considered by the affiliate journal as a full revision in response to the points raised by the reviewers.

• *

If you wish to submit a preliminary revision with a revision plan, please use our "Revision Plan" template. It is important to use the appropriate template to clearly inform the editors of your intentions.]

1. General Statements [optional]

This section is optional. Insert here any general statements you wish to make about the goal of the study or about the reviews.

2. Point-by-point description of the revisions

This section is mandatory. *Please insert a point-by-point reply describing the revisions that were already carried out and included in the transferred manuscript. *

We thank the reviewers for their efforts and detailed evaluation of our manuscript. We think that the comments of the reviewers allowed us to significantly improve the manuscript.

With best regards

The authors of the manuscript

Reviewer #1 (Evidence, reproducibility and clarity (Required)):

Summary: Holzinger et al. present a new automated pipeline, nucDetective, designed to provide accurate nucleosome positioning, fuzziness, and regularity from MNase-seq data. The pipeline is structured around two main workflows-Profiler and Inspector-and can also be applied to time-series datasets. To demonstrate its utility, the authors re-analyzed a Plasmodium falciparum MNase-seq time-series dataset (Kensche et al., 2016), aiming to show that nucDetective can reliably characterize nucleosomes in challenging AT-rich genomes. By integrating additional datasets (ATAC-seq, RNA-seq, ChIP-seq), they argue that the nucleosome positioning results from their pipeline have biological relevance.

Major Comments:

Despite being a useful pipeline, the authors draw conclusions directly from the pipeline's output without integrating necessary quality controls. Some claims either contradict existing literature or rely on misinterpretation or insufficient statistical support. In some instances, the pipeline output does not align with known aspects of Plasmodium biology. I outline below the key concerns and suggested improvements to strengthen the manuscript and validate the pipeline:

Clarification of +1 Nucleosome Positioning in P. falciparum The authors should acknowledge that +1 nucleosomes have been previously reported in P. falciparum. For example, Kensche et al. (2016) used MNase-seq to map ~2,278 TSSs (based on enriched 5′-end RNA data) and found that the +1 nucleosome is positioned directly over the TSS in most genes:

"Analysis of 2278 start sites uncovered positioning of a +1 nucleosome right over the TSS in almost all analysed regions" (Figure 3A).

They also described a nucleosome-depleted region (NDR) upstream of the TSS, which varies in size, while the +1 nucleosome frequently overlaps the TSS. The authors should nuance their claims accordingly. Nevertheless, I do agree that the +1 positioning in P. falciparum may be fuzzier as compared to yeast or mammals. Moreover, the correlation between +1 nucleosome occupancy and gene expression is often weak and that several genes show similar nucleosome profiles regardless of expression level. This raises my question: did the authors observe any of these patterns in their new data?

We appreciate the reviewer’s insightful comment and agree that +1 nucleosomes and nucleosome depleted promoter regions have been previously reported in P. falciparum, notably by the Bartfai and Le Roch groups, including Kensche et al. (PMID: 26578577). Our study advances this understanding by providing, for the first time, a comprehensive view of the entirety of a canonical eukaryotic promoter architecture in P. falciparum—encompassing the NDR, the well-positioned +1 nucleosome, and the downstream phased nucleosome array. This downstream nucleosome array structure has not been characterized before, as prior studies noted a “lack of downstream nucleosomal arrays” (PMID: 26578577) or “relatively random” nucleosome organization within gene bodies (PMID: 24885191). We have revised the manuscript to more clearly acknowledge previous work and highlight our contributions. The changes we applied in the manuscript are highlighted in yellow and shown as well below.

In the Abstract L26-L230: Contrary to the current view of irregular chromatin, we demonstrate for the first time regular phased nucleosome arrays downstream of TSSs, which, together with the established +1 nucleosome and upstream nucleosome-depleted region, reveal a complete canonical eukaryotic promoter architecture in Pf.

Introduction L156-L159: For example, we identify a phased nucleosome array downstream of the TSS. Together with a well-positioned +1 nucleosome and an upstream nucleosome-free region. These findings support a promoter architecture in Pf that resembles classical eukaryotic promoters (Bunnik et al. 2014, Kensche et al. 2016).

Results L181-L183: These new Pf nucleosome maps reveal a nucleosome organisation at transcription start sites (TSS) reminiscent of the general eukaryotic chromatin structure, featuring a reported well-positioned +1 nucleosome , an upstream nucleosome-free region (NFR, Bunnik et al. 2014, Kensche et al. 2016), and shown for the first time in Pf, a phased nucleosome array downstream of the TSS.

Discussion L414-L419: Previous analyses of Pf chromatin have identified +1 nucleosomes and NFRs (Bunnik et al 2014, Kensche et al. 2016). Here we extend this understanding by demonstrating phased nucleosome array structures throughout the genome. This finding provides evidence for a spatial regulation of nucleosome positioning in Pf, challenging the notion that nucleosome positioning is relatively random in gene bodies (Bunnik et al. 2014, Kensche et al. 2016). Consequently our results contribute to the understanding that Pf exhibits a typical eukaryotic chromatin structure, including well-defined nucleosome positioning at the TSS and regularly spaced nucleosome arrays (Schones et al. 2008; Yuan et al. 2005).

Regarding the reviewer’s question on +1 nucleosome dynamics. Our data agrees with the reviewer and other studies (e.g. PMID: 31694866), that the +1 nucleosome position is robust and does not correlate with gene expression strength. In the manuscript we show that dynamic nucleosomes are preferentially detected at the –1 nucleosome position (Figure 2C). In line with that we show that the +1 nucleosome position does not markedly change during transcription initiation of a subset of late transcribed genes (Figure 5A). However, we observe an opening of the NDR and within the gene body increased fuzziness and decreased nucleosome array regularity (Figure S4A). To illustrate the relationship between the +1 nucleosome positioning and expression strength, we have included a heatmap showing nucleosome occupancy at the TSS, ordered according to expression strength (NEW Figure S4C):

We included a sentence describing the relationship of +1 nucleosome position with gene expression in L257-L258: Furthermore, the +1 nucleosome positioning is unaffected by the strength of gene expression (Figure S2C).

__ Lack of Quality Control in the Pipeline __

The authors claim (lines 152-153) that QC is performed at every stage, but this is not supported by the implementation. On the GitHub page (GitHub - uschwartz/nucDetective), QC steps are only marked at the Profiler stage using standard tools (FastQC, MultiQC). The Inspector stage, which is crucial for validating nucleosome detection, lacks QC entirely. The authors should implement additional steps to assess the quality of nucleosome calls. For example, how are false positives managed? ROC curves should be used to evaluate true positive vs. false positive rates when defining dynamic nucleosomes. How sequencing biases are adressed?

The workflow overview chart on GitHub was not properly color coded. Therefore, we changed the graphics and highlighted the QC steps in the overview charts accordingly:

Based on our long standing expertise of analysing MNase-seq data (PMID: 38959309, PMID: 37641864, PMID: 30496478, PMID: 25608606), the best quality metrics to assess the performance of the challenging MNase experiment are the fragment size distributions revealing the typical nucleosomal DNA lengths and the TSS plots showing a positioned +1 nucleosome and regularly phased nucleosome arrays downstream of the +1 nucleosome. Additionally, visual inspection of the nucleosome profiles in a genome browser is advisable. We make those quality metrics easily available in the nucDetective Profiler workflow (Insertsize Histogram, TSS plot and provide nucleosome profile bigwig files). Furthermore, the PC and correlation analysis based on the nucleosome occupancy in the inspector workflow allows to evaluate replicate reproducibility or integrity of time series data, as shown for data evaluated in this manuscript.

The inspector workflow uses the well-established DANPOS toolkit to call nucleosome positions. Based on our experience, this step is particularly robust and well-established in the DANPOS toolkit (PMID: 23193179), so there is no need to reinvent it. Nevertheless, appropriate pre-processing of the data as done in the nucDetective pipeline is crucial to obtain highly resolved nucleosome positions. Using the final nucleosome profiles (bigwig) and the nucleosome reference positions (bed) as output of the Inspector workflow allows visual inspection of the called nucleosomes in a genome viewer. Furthermore, to avoid using false positive nucleosome positions for dynamic nucleosome analysis, we take only the 20% best positioned nucleosomes of each sample, as determined by the fuzziness score.

We understand the value of a gold standard of dynamic nucleosomes to test performance using ROC curves. However, we are not aware that such a gold standard exists in the nucleosome analysis field, especially not when using multi-sample settings, such as time series data. One alternative would be to use simulated data; however, this has several limitations:

• __Lack of biological complexity: __simulated data often fails to capture the full complexity of biological systems including the heterogeneity, variability, and subtle dependencies present in real-world data. Simplifications and omissions in simulation models can result in test datasets that are more tractable but less realistic, causing software to appear robust or accurate under idealized conditions, while underperforming on actual experimental data.
• __Risks of Overfitting: __Software may be tuned to perform well on simulated datasets leading to overfitting and falsely inflated performance metrics. This undermines the predictive or diagnostic value of the results for real biological data

• Poor Model Fidelity and Hidden Assumptions: The authenticity of simulated data is bounded by the fidelity of the underlying models. If those models are inaccurate or make untested assumptions, the generated data may not reflect real experimental or clinical scenarios. This can mask software shortcomings or bias validation toward specific, perhaps irrelevant, scenarios. Therefore, we decided to validate the performance of the pipeline in the biological context of the analyzed data:

• PCA analysis of the individual nucleosome features shows a cyclic structure as expected for the IDC (Fig. 1D-G).

• Nucleosome occupancy changes anti-correlate with chromatin accessibility (Fig. 3B) as expected.
• Dynamic nucleosome features correlate with expression changes (Fig. 5C) We are aware that MNase-seq experiments might have sequence bias caused by the enzyme's endonuclease sequence preference (PMID: 30496478). However, the main aim of the nucDetective pipeline is to identify dynamic nucleosome features genome wide. Therefore, we are comparing the nucleosome features across multiple samples to find the positions in the genome with the highest variability. Comparisons are performed between the same nucleosome positions at the same genomic sites across multiple conditions, so the sequence context is constant and does not confound the analysis. This is like the differential expression analysis of RNA-seq data, where the gene counts are not normalized by gene length. Introducing a sequence normalization step might distort and bias the results of dynamic nucleosomes.

We included a paragraph describing the limitations to the discussion (L447-457):

Depending on the degree of MNase digestion, preferentially nucleosomes from GC rich regions are revealed in MNase-seq experiments (Schwartz et al. 2019). However, no sequence or gDNA normalisation step was included in the nucDetective pipeline. To identify dynamic nucleosomes, comparisons are performed between the same nucleosome positions at the same genomic sites across multiple samples. Hence, the sequence context is constant and does not confound the analysis. Introducing a sequence normalization step might even distort and bias the results. Nevertheless, it is highly advisable to use low MNase concentrations in chromatin digestions to reduce the sequence bias in nucleosome extractions. This turned out to be a crucial condition to obtain a homogenous nucleosome distribution in the AT-rich intergenic regions of eukaryotic genomes and especially in the AT-rich genome of Pf (Schwartz et al. 2019, Kensche et al. 2016).

__ Use of Mono-nucleosomes Only __

The authors re-analyze the Kensche et al. (2016) dataset using only mono-nucleosomes and claim improved nucleosome profiles, including identification of tandem arrays previously unreported in P. falciparum. Two key issues arise: 1. Is the apparent improvement due simply to focusing on mono-nucleosomes (as implied in lines 342-346)?

The default setting in nucDetective is to use fragment sizes of 140 – 200 bp, which corresponds to the main mono-nucleosome fraction in standard MNase-seq experiments. However, the correct selection of fragment sizes may vary depending on the organism and the variations in MNase-seq protocols. Therefore, the pipeline offers the option of changing the cutoff parameter (--minLen; --maxLen), accordingly. Kensche et al thoroughly tested and established the best parameters for the data set. We agree with their selected parameters and used the same cutoffs (75-175 bp) in this manuscript. For this particular data set, the fragment size selection is not the reason why we obtain a better resolution. MNase-seq analysis is a multistep process which is optimized in the nucDetective pipeline. Differences in the analysis to Kensche et al are at the pre-processing stage and alignment step:

Kensche et al. : “Paired-end reads were clipped to 72 bp and all data was mapped with BWA sample (Version 0.6.2-r126)”

nucDetective:

• Trimming using TrimGalore --paired -q 10 --stringency 2
• Mapping using bowtie2 --very-sensitive –dovetail --no-discordant
• MAPQ >= 20 filtering of aligned read-pairs (samtools). The manuscript text L379 was changed to

This is achieved using MNase-seq optimized alignment settings, and proper selection of the fragment sizes corresponding to mono-nucleosomal DNA to obtain high resolution nucleosome profiles.

How does the pipeline perform with di- or tri-nucleosomes, which are also biologically relevant (Kensche et al., 2016 and others)? Furthermore, the limitation to mono-nucleosomes is only mentioned in the methods, not in the results or discussion, which could mislead readers.

The pipeline is optimized for mono-nucleosome analysis. However, the cutoffs for fragment size selection can be adjusted to analyse other fragment populations in MNase-seq data (--minLen; --maxLen). For example we know from previous studies that the settings in the pipeline could be used for sub-nucleosome analysis as well (PMID: 38959309). Di- or Tri-nucleosome analysis we have not explicitly tested. However, in a previous study (PMID: 30496478) we observed that the inherited MNase sequence bias is more pronounced in di-nucleosomes, which are preferentially isolated from GC-rich regions. This is in line with the depletion of di-nucleosomes in AT-rich intergenic regions in Pf, as was already described by Kensche et al.

Changes to the manuscript text: We included a paragraph describing the limitations to the discussion (L428-434):

The nucDetective pipeline has been optimized for the analysis of mono-nucleosomes. However, the selection of fragment sizes can be adjusted manually, enabling the pipeline to be used for other nucleosome categories. The pipeline is suitable to map and annotate sub-nucleosomal particles (

__ Reference Nucleosome Numbers __

The authors identify 49,999 reference nucleosome positions. How does this compare to previous analyses of similar datasets? This should be explicitly addressed.

We thank the reviewer for this suggestion. In order to put our results in perspective, it is important to distinguish between reference nucleosome positions (what we reported in the manuscript) and all detectable nucleosomes. The reference positions are our attempt to build a set of nucleosome positions with strong evidence, allowing confident further analysis across timepoints. The selection of a well positioned subset of nucleosomes for downstream analysis has been done previously (PMID: 26578577) and the merging algorithm we used across timepoints is also used by DANPOS to decide if a MNase-Seq peak is a new nucleosome position or belongs to an existing position (PMID: 23193179).

To be able to address the reviewer suggestion we prepared and added a table to the supplementary data, including the total number of all nucleosomes detected by our pipeline at each timepoint. We adjusted the results to the following (L223-226):

“The pipeline identified a total of 127370 ± 1151 (mean ± SD) nucleosomes at each timepoint (Supplementary Data X). To exclude false positive positions in our analysis, we conservatively selected 49,999 reference nucleosome positions, representing sites with a well-positioned nucleosome at least at one time point (see Methods). Among these 1192 nucleosomes exhibited […]”

Several groups reported nucleosome positioning data for P. falciparum (PMID: 20015349, PMID: 20054063, PMID: 24885191, PMID: 26578577), however only Ponts et al (2010) reported resolved numbers (~45000-90000 nucleosomes depending in development stage) and Bunnik et al reported ~ 75000 nucleosomes in a graph. Although we do not know the reason of why the other studies did not include specific numbers, we speculate that the data quality did not allow them to confidently report a number. In fact, nucleosomal reads are severely depleted in AT-rich intergenic regions in the Ponts and Bunnik datasets. In contrast, Kensche et al (and our analysis) shows that nucleosomes can be identified throughout the genome of Pf. Therefore, the nucleosome numbers reported by Ponts et al and Bunnik et al are very likely underestimated.

We included the following text in the discussion, addressing previously published datasets (L404 – 405):

“For example, our pipeline was able to identify a total of ~127,000 nucleosomes per timepoint (=5.4 per kb) in range with observed nucleosome densities in other eukaryotes (typically 5 to 6 per kb). From these, we extracted 49,999 reference nucleosome positions with strong positioning evidence across all timepoints, which we used to characterize nucleosome dynamics of Pf longitudinally. Previous studies of P. falciparum chromatin organization, did not report a total number of nucleosomes (Westenberger et al. 2009, Kensche et al. 2016), or estimated approximately ~45000-90000 nucleosomes across the genome at different developmental stages (Bunnik et al. 2014, Ponts et al. 2010). However, this value likely represents an underestimation due to the depletion of nucleosomal reads in AT-rich intergenic regions observed in their datasets.”

__ Figure 1B and Nucleosome Spacing __

The authors claim that Figure 1B shows developmental stage-specific variation in nucleosome spacing. However, only T35 shows a visible upstream change at position 0. In A4, A6, and A8 (Figure S4), no major change is apparent. Statistical tests are needed to validate whether the observed differences are significant and should be described in the figure legends and main text.

We would like to thank the reviewer for bringing this issue to our attention. We apologize for an error we made, wrongly labelling the figure numbers. The differences in nucleosome spacing across time are visible in Figure 1C. Figure 1B shows the precise array structure of the Pf nucleosomes, when centered on the +1 nucleosome, and is mentioned before. The mistake is now corrected.

In Figure 1C the mean NRL and 95% confidence interval are depicted, allowing a visual assessment of data significance (non-overlapping 95% CI-Intervals correspond to p Taken together we corrected this mistake and edited the text as follows (L194 – 199):

“With this +1 nucleosome annotation, regularly spaced nucleosome arrays downstream of the TSS were detected, revealing a precise nucleosome organization in Pf (Figure 1B). Due to the high resolution maps of nucleosomes we can now observe significantvariations in nucleosome spacing depending on the developmental stage (Figure 1C, ANOVA on bootstrapped values (3 per timepoint) F₇,₇₂ = 35.10, p

__ Genome-wide Occupancy Claims __

The claim that nucleosomes are "evenly distributed throughout the genome" (Figure S2A) is questionable. Chromosomes 3 and 11 show strong peaks mid-chromosome, and chromosome 14 shows little to no signal at the ends. This should be discussed. Subtelomeric regions, such as those containing var genes, are known to have unique chromatin features. For instance, Lopez-Rubio et al. (2009) show that subtelomeric regions are enriched for H3K9me3 and HP1, correlating with gene silencing. Should these regions not display different nucleosome distributions? Do you expect the Plasmodium genome (or any genome) to have uniform nucleosome distribution?

On global scale (> 10 kb) we would expect a homogenous distribution of nucleosomes genome wide, regardless of euchromatin or heterochromatin. We have shown this in a previous study for human cells (PMID: 30496478), which was later confirmed for drosophila melongaster (PMID: 31519205,PMID: 30496478) and yeast (PMID: 39587299).

However, Figure S2A shows the distribution of the dynamic nucleosome features during the IDC, called with our pipeline. We agree with the reviewer, that there are a few exceptions of the uniform distribution, which we address now in the manuscript.

Furthermore, we agree with the reviewer that the H3K9me3 / HP1 subtelomeric regions are special. Those regions are depleted of dynamic nucleosomes in the IDC as shown in Fig. 2D and now mentioned in L280 - L282.

We included an additional genome browser snapshot in Supplemental Figure S2B and changed the text accordingly (L245-249):

We observed a few exceptions to the even distribution of the nucleosomes in the center of chromosome 3, 11 and 12, where nucleosome occupancy changes accumulated at centromeric regions (Figure S2B). Furthermore, the ends of the chromosomes are rather depleted of dynamic nucleosome features.

Genome browser snapshot illustrating accumulation of nucleosome occupancy changes at a centromeric site. Centered nucleosome coverage tracks (T5-T40 colored coverage tracks), nucleosomes occupancy changes (yellow bar) and annotated centromers (grey bar) taken from (Hoeijmakers et al., 2012)

Dependence on DANPOS

The authors criticize the DANPOS pipeline for its limitations but use it extensively within nucDetective. This contradiction confuses the reader. Is nucDetective an original pipeline, or a wrapper built on existing tools?

One unique feature of the nucDetective pipeline is to identify dynamic nucleosomes (occupancy, fuzziness, regularity, shifts) in complex experimental designs, such as time series data (Inspector workflow). To our knowledge, there is no other tool for MNase-seq data which allows multi-condition/time-series comparisons (PMID: 35061087). For example, DANPOS allows only pair-wise comparisons, which cannot be used for time-series data. For the analysis of dynamic nucleosome features we require nucleosome profiles and positions at high resolution. For this purpose, several tools do already exist (PMID: 35061087). However, researchers without experience in MNase-seq analysis often find the plethora of available tools overwhelming, which makes it challenging to select the most appropriate ones. Here we share our experience and provide the user an automated workflow (Profiler), which builds on existing tools.

In summary the Profiler workflow is a wrapper built on existing tools and the Inspector workflow is partly a wrapper (uses DANPOS to normalize nucleosome profiles and call nucleosome positions) and implements our original algorithm to detect dynamic nucleosome features in multiple conditions / time-series data.

__ Control Data Usage __

The authors should clarify whether gDNA controls were used throughout the analysis, as done in Kensche et al. (2016). Currently, this is mentioned only in the figure legend for Figure 5, not in the methods or results.

We used the gDNA normalisation to optimize the visualization of the nucleosome depleted region upstream of the TSS in Fig 5A. Otherwise, we did not normalize the data by the gDNA control. The reason is the same as we did not include sequence normalization in the pipeline (see comment above)

We included a paragraph describing the limitations to the discussion (L447-457):

Depending on the degree of MNase digestion, preferentially nucleosomes from GC rich regions are revealed in MNase-seq experiments (Schwartz et al. 2019). However, no sequence or gDNA normalisation step was included in the nucDetective pipeline. To identify dynamic nucleosomes, comparisons are performed between the same nucleosome positions at the same genomic sites across multiple samples. Hence, the sequence context is constant and does not confound the analysis. Introducing a sequence normalization step might even distort and bias the results. Nevertheless, it is highly advisable to use low MNase concentrations in chromatin digestions to reduce the sequence bias in nucleosome extractions. This turned out to be a crucial condition to obtain a homogenous nucleosome distribution in the AT-rich intergenic regions of eukaryotic genomes and especially in the AT-rich genome of Pf (Schwartz et al. 2019, Kensche et al. 2016).

We added following statement to the methods part: Additionally, the TSS profile shown in Figure 5A was normalized by the gDNA control for better NDR visualization.

__ Lack of Statistical Power for Time-Series Analyses __

Although the pipeline is presented as suitable for time-series data, it lacks statistical tools to determine whether differences in nucleosome positioning or fuzziness are significant across conditions. Visual interpretation alone is insufficient. Statistical support is essential for any differential analysis.

We understand the value of statistical support in such an analysis. However, in biology we often face the limitations in terms of the appropriate sample sizes needed to accurately estimate the variance parameters required for statistical modeling. As MNase-seq experiments require a large amount of input material and high sequencing depth, the number of samples in most experiments is low, often with only two replicates (PMID: 23193179). Therefore, we decided that the nucDetective pipeline should be rather handled as a screening method to identify nucleosome features with high variance across all conditions. This prevents misuse of p-values. A common misinterpretation we observed is the use of non-significant p-values to conclude that no biological change exists, despite inadequate statistical power to detect such changes. We included a paragraph in the limitations section discussing the limitations of statistical analysis of MNase-Seq data.

Changes to the manuscript text: We included a paragraph describing the limitations to the discussion (L435-446).

As MNase-seq experiments require a large amount of input material and high sequencing depths, most published MNase-seq experiments do not provide the appropriate sample sizes required to accurately estimate the variance parameters necessary for statistical modelling (Chen et al. 2013). Therefore, dynamic nucleosomes are not identified through statistical testing but rather by ranking nucleosome features according to their variance across all samples and applying a variance threshold to distinguish them. This concept is well established to identify super-enhancers (Whyte et al. 2013). In this study we set the variance cutoff to a slope of 3, resulting in a high data confidence. However, other data sets might require further adjustment of the variance cutoff, depending on data quality or sequencing depth. The nucDetective identification of dynamic nucleosomes can be seen as a screening approach to provide a holistic overview of nucleosome dynamics in the system, which provides a basis for further research.

Reproducibility of Methods

The Methods section is not sufficient to reproduce the results. The GitHub repository lacks the necessary code to generate the paper's figures and focuses on an exemplary yeast dataset. The authors should either: o Update the repository with relevant scripts and examples, o Clearly state the repository's purpose, or o Remove the link entirely. Readers must understand that nucDetective is dedicated to assessing nucleosome fuzziness, occupancy, shift, and regularity dynamics-not downstream analyses presented in the paper.

We thank the reviewer for this helpful comment. In addition to the main nucDetective repository, a second GitHub link is provided in the Data Availability section, which contains the scripts used to generate the figures presented in the paper. This separation was intentional to distinguish the general-purpose nucDetective tool from the project-specific analyses performed for this study. We acknowledge that this may not have been sufficiently clear.

To have all resources available at a single citable permanent location we included a link to the corresponding Zenodo repository (https://doi.org/10.5281/zenodo.16779899) in the Data and materials availability statement.

The Zenodo repository contains:

Code (scripts.zip) and annotation of Plasmodium falciparum (Annotation.zip) to reproduce the nucDetective v1.1 (nucDetective-1.1.zip) analysis as done in the research manuscript entitled "Deciphering chromatin architecture and dynamics in Plasmodium falciparum using the nucDetective pipeline".

The folder "output_nucDetective" conains the complete output of the nucDetective analysis pipeline as generated by the "01_nucDetective_profiler.sh" and "02_nucDetective_inspector.sh" scripts.

Nucleosome coverage tracks, annotation of nucleosome positions and dynamic nucleosomes are deposited additonally in the folder "Pf_nucleosome_annotation_of_nucDetective".

To make this clearer we added following text to Material and Methods in ”The nucDetective pipeline” section:

Changes in the manuscript text (L518-519):

The code, software and annotations used to run the nucDetective pipeline along with the output have been deposited on Zenodo (https://doi.org/10.5281/zenodo.16779899).

__ Supplementary Tables __

Including supplementary tables showing pipeline outputs (e.g., nucleosome scores, heatmaps, TSS extraction) would help readers understand the input-output structure and support figure interpretations.

See comments above.

We included a link to the corresponding Zenodo repository (https://doi.org/10.5281/zenodo.16779899) in the Data and materials availability statement.

The repository contains:

Code (scripts.zip) and annotation of Plasmodium falciparum (Annotation.zip) to reproduce the nucDetective v1.1 (nucDetective-1.1.zip) analysis as done in the research manuscript entitled "Deciphering chromatin architecture and dynamics in Plasmodium falciparum using the nucDetective pipeline".

The folder "output_nucDetective" conains the complete output of the nucDetective analysis pipeline as generated by the "01_nucDetective_profiler.sh" and "02_nucDetective_inspector.sh" scripts.

Minor Comments:

The authors should moderate claims such as "no studies have reported a well-positioned +1 nucleosome" in P. falciparum, as this contradicts existing literature. Similarly, avoid statements like "poorly understood chromatin architecture of Pf," which undervalue extensive prior work (e.g., discovery of histone lactylation in Plasmodium, Merrick et al., 2023).

We would like to clarify that we neither wrote that ““no studies have reported a well-positioned +1 nucleosome”” in P. falciparum nor did we intend to imply such thing. However, we acknowledge that our original wording may have been unclear. To address this, we have revised the manuscript to explicitly acknowledge prior studies on chromatin organization and highlight our contribution.

In the Abstract L26-L30: Contrary to the current view of irregular chromatin, we demonstrate for the first time regular phased nucleosome arrays downstream of TSSs, which, together with the established +1 nucleosome and upstream nucleosome-depleted region, reveal a complete canonical eukaryotic promoter architecture in Pf.

Introduction L156-L159: For example, we identify a phased nucleosome array downstream of the TSS. Together with a well-positioned +1 nucleosome and an upstream nucleosome-free region. These findings support a promoter architecture in Pf that resembles classical eukaryotic promoters (Bunnik et al. 2014, Kensche et al. 2016).

Results L180-L183: These new Pf nucleosome maps reveal a nucleosome organisation at transcription start sites (TSS) reminiscent of the general eukaryotic chromatin structure, featuring a reported well-positioned +1 nucleosome , an upstream nucleosome-free region (NFR, Bunnik et al. 2014, Kensche et al. 2016), and shown for the first time in Pf, a phased nucleosome array downstream of the TSS.

Discussion L412-L421: Previous analyses of Pf chromatin have identified +1 nucleosomes and NFRs (Bunnik et al 2014, Kensche et al. 2016). Here we extend this understanding by demonstrating phased nucleosome array structures throughout the genome. This finding provides evidence for a spatial regulation of nucleosome positioning in Pf, challenging the notion that nucleosome positioning is relatively random in gene bodies (Bunnik et al. 2014, Kensche et al. 2016). Consequently our results contribute to the understanding that Pf exhibits a typical eukaryotic chromatin structure, including well-defined nucleosome positioning at the TSS and regularly spaced nucleosome arrays (Schones et al. 2008; Yuan et al. 2005).

The phrase “poorly understood chromatin architecture” has been modified to “underexplored chromatin architecture” in order to more accurately reflect the potential for further analyses and contributions to the field, while avoiding any potential misinterpretation of an attempt to undervalue previous work.

Track labels in figures (e.g., Figure 5B) are too small to be legible.

We made the labels bigger.

Several figures (e.g., Figure 5B, S4B) lack statistical significance tests. Are the differences marked with stars statistically significant or just visually different?

We added statistics to S4B.

Differences in 5B were identified by visual inspection. To clarify this, we exchanged the asterisks to arrows in Fig.5B and changed the text in the legend:

Arrows mark descriptive visual differences in nucleosome occupancy.

Figure S3 includes a small black line on top of the table. Is this an accidental crop?

We checked the figure carefully; however, the black line does not appear in our PDF viewer or on the printed paper

The authors should state the weaknesses and limitations of this pipeline.

We added a limitation section in discussion, see comments above

Reviewer #1 (Significance (Required)):

The proposed pipeline is useful and timely. It can benefit research groups willing to analyse MNase-Seq data of complex genomes such as P. falciparum. The tool requires users to have extensive experience in coding as the authors didn't include any clear and explicit codes on how to start processing the data from raw files. Nevertheless, there are multiple tool that can detect nucleosome occupancy and that are not cited by the authors not mention. I have included for the authors a link where a large list of tools for analysis of nucleosome positioning experiments tools/pipelines were developed for (Software to analyse nucleosome positioning experiments - Gene Regulation - Teif Lab). I think it would be useful for the authors to direct the reference this.

We appreciate the reviewer’s valuable suggestion. We included a citation to the comprehensive database of nucleosome analysis tools curated by the Teif lab (Shtumpf et al., 2022). We chose to reference only selected tools in addition to this resource rather than listing all individual tools to maintain clarity and avoid overloading the manuscript with numerous citations.

Despite valid, I still believe that controlling their pipeline by filtering out false positives and including more QC steps at the Inspector stage is strongly needed. That would boost the significance of this pipeline.

We thank the reviewer for the assessment of our study and for recognizing that our MNase-seq analysis pipeline nucDetective can be a useful tool for the chromatin community utilizing MNase-Seq in complex settings.

Reviewer #2 (Evidence, reproducibility and clarity (Required)):

In this manuscript, Holzinger and colleagues have developed a new pipeline to assess chromatin organization in linear space and time. They used this pipeline to reevaluate nucleosome organization in the malaria parasite, P. falciparum. Their analysis revealed typical arrangement of nucleosomes around the transcriptional start site. Furthermore, it further strengthened and refined the connection between specific nucleosome dynamics and epigenetic marks, transcription factor binding sites or transcriptional activity.

Major comments

• I am wondering what is the main selling point of this manuscript is. If it is the development of the nucDetective pipeline, perhaps it would be best to first benchmark it and directly compare it to existing tools on a dataset where nucleosome fussiness, shifting and regularity has been analyzed before. If on the other hand, new insights into Plasmodium chromatin biology is the primary target validation of some of the novel findings would be advantageous (e.g. refinement of TSS positions, relevance of novel motifs, etc).

NucDetective presents a novel pipeline to identify dynamic nucleosome properties within different datasets, like time series or developmental stages, as analysed for the erythrocytic cycle in this manuscript. As such kind of a pipeline, allowing direct comparisons, does not exist for MNase-Seq data, we used the existing analysis and high quality dataset of Kensche et al., to visualize the strong improvements of this kind of analysis. Accordingly, we combined the pipeline development and the reasearch of chromatin structure analysis, being able to showcase the utility of this new pipeline.

• The authors identify a strong positioning of +1 nucleosome by searching for a positioned nucleosomes in the vicinity of the assigned TSS. Given the ill-defined nature of TSSs, this approach sounds logic at first glance. However, given the rather broad search space from -100 till +300bp, I am wondering whether it is a sort of "self-fulfilling prophecy". Conversely, it would be good to validate that this approach indeed helps to refine TSS positions.

We thank the reviewer for raising this important point. We would like to clarify that we do not claim to redefine or precisely determine TSS positions in our study. Instead, we use annotated TSS coordinates as a reference to identify nucleosomes that correspond to the +1 nucleosome, based on their proximity to the TSS.

We selected the search window from -100 to +300 bp to account for known variability in Pf TSS annotation. For example, dominant transcription start sites identified by 5'UTR-seq tag clusters can differ by several hundred base pairs within a single time point (Chappell et al., 2020). The broad window thus allows us to capture the principal nucleosome positions near a TSS, even when the TSS itself is imprecise or heterogeneous. Based on the TSS centered plots (Figure 2C and Figure S1B), we reasoned that a window of -100 to +300 is sufficient to capture the majority of the +1 nucleosomes, which would have been missed by using smaller window sizes. This strategy aligns with well-established conventions in yeast chromatin biology, where the +1 nucleosome is defined relative to the TSS (Jiang and Pugh, 2009; Zhang et al. 2011) and commonly used as an anchor point to visualize downstream phased nucleosome arrays and upstream nucleosome-depleted regions (Rossi et al., 2021; Oberbeckmann et al., 2019; Krietenstein et al., 2016 and many more). Accordingly, our approach leverages these accepted standards to interpret nucleosome positioning without re-defining TSS annotations.

• Figure 1C: I am wondering how should the reader interpret the changes in nucleosomal repeat length changes throughout the cycle. Is linker DNA on average 10 nucleotides shorter at T30 compared to T5 timepoint? If so how could such "dramatic reorganization" be achieved at the molecular level in absence of a known linker DNA-binding protein. More importantly is this observation supported by additional evidence (e.g. dinucleosomal fragment length) or could it be due to slightly different digestion of the chromatin at the different stages or other technical variables?

We thank the reviewer for this insightful question regarding the interpretation of NRL changes across the cell cycle. The reviewer is right in her or his interpretation – linker DNA is on average ~10 bp shorter at T30 than at T5.

To address concerns about additional evidence and potential MNase digestion variability, we now analyzed MNase-seq fragment sizes by shifting mononucleosome peaks of each time point to the canonical 147 bp length, to correct for MNase digestion differences. After this normalisation, dinucleosome fragment length distributions revealed the shortest linker lengths at T30 and T35, whereas T5 and T10 showed longer DNA linkers. These results confirm our previous NRL measurements based on mononucleosomal read distances while controlling for MNase digestion bias.

The molecular basis of this reorganization, is still unclear. While linker histone H1 is considered absent in Plasmodium falciparum, presence of an uncharacterized linker DNA–binding protein or alternative factors fulfilling a similar role can not be excluded (Gill et al. 2010). However, H1 absence across all developmental stages, fails to explain stage-specific chromatin changes. We hypothesize that Apicomplexans evolved specialized chromatin remodelers to compensate for the missing H1, which may also drive the dynamic NRL changes observed. The low NRL coincides with high transcriptional activity in Pf during trophozoite stage is consistent with previous reports linking elevated transcription to reduced NRL in other eukaryotes (Baldi et al. 2018). In addition, the schizont stage involves multiple rounds of DNA replication requiring large histone supplies being produced during that time. It may well be that a high level of histone synthesis and DNA amplification, results in a short time period with increased nucleosome density and shorter NRL, until the system reaches again equilibrium (Beshnova et al. 2014). Although speculative we suggest a model wherein increased transcription promotes elevated nucleosome turnover and re-assembly by specialized remodeling enzymes, combined with high abundance of histones, resulting in higher nucleosome density and decreased NRL. Unfortunately, absolute quantification of nucleosome levels from this MNase-seq dataset is not possible without spike-in controls, which makes it infeasible to test the hypothesis with the available data set (Chen et al. 2016).

Minor comments

• I am wondering whether fuzziness and occupancy changes are truly independent categories. I am asking as both could lead to reduction of the signal at the nucleosome dyad and because they show markedly similar distribution in relation to the TSS and associate with identical epigenetic features (Figure 2B-D). Figure 2A indicates minimal overlap between them, but this could be due to the fact that the criteria to define these subtypes is defined such to place nucleosomes to one or the other category, but at the end they represent two flavors of the same thing.

Indeed, changes in occupancy and fuzziness can appear related because both features may reduce signal intensity at the nucleosome dyad and both are connected to “poor nucleosome positioning”. However, their definitions and measurements are clearly distinct and technically independent. Occupancy reflects the peak height at the nucleosome dyad, while fuzziness quantifies the spread of reads around the peak, measured as the standard deviation of read positions within each nucleosome peak (Jiang and Pugh, 2009; Chen et al., 2013). Although a reduction in occupancy can contribute to increased fuzziness by diminishing the dyad axis signal, fuzziness primarily arises from increased variability in the flanking regions around the nucleosome position center. While this distinction is established in the field, it is also often confused by the concept of well (high occupancy, low fuzziness) and poorly (high fuzziness, low occupancy) positioned nucleosomes, where both of these features are considered.

• Do the authors detect spatial relationship between fuzzy and repositioned/evicted nucleosomes at the level of individual nucleosomes pairs. With other words, can fuzziness be the consequence of repositioning/eviction of the neighboring nucleosome?

In Figure 2A we analyse the spatial overlap of all features to each other. The analysis clearly shows that fuzziness, occupancy changes and position changes occur mostly at distinct spatial sites (overlaps between 3 and 10%, Fig. 2A). Therefore, we suggest that the features correspond to independent processes. Likewise, we do observe an overlap between occupancy and ATAC-seq peaks, but not nucleosome positioning shifts, clearly discriminating different processes.

• Figure 4: enrichment values and measure of statistical significance for the different motifs are missing. Also have there been any other motifs identified.

This information is present in Supplemental Figure S3. Here we show the top 3 hits in each cluster. In the figure legend of Figure 4 we reference to Fig. S3:

L1054 –1055:

“Additional enriched motifs along with the significance of motif enrichment and the fraction of motifs at the respective nucleosome positions are shown in Figure S3”

• The M&M would benefit from some more details, e.g. settings in the piepline, or which fragment sizes were used to map the MNase-seq data?

We included a link to the corresponding Zenodo repository (https://doi.org/10.5281/zenodo.16779899) in the Data and materials availability statement.

The repository contains:

Code (scripts.zip) and annotation of Plasmodium falciparum (Annotation.zip) to reproduce the nucDetective v1.1 (nucDetective-1.1.zip) analysis as done in the research manuscript entitled "Deciphering chromatin architecture and dynamics in Plasmodium falciparum using the nucDetective pipeline".

The folder "output_nucDetective" conains the complete output of the nucDetective analysis pipeline as generated by the "01_nucDetective_profiler.sh" and "02_nucDetective_inspector.sh" scripts.

Nucleosome coverage tracks, annotation of nucleosome positions and dynamic nucleosomes are deposited additonally in the folder "Pf_nucleosome_annotation_of_nucDetective".

To make this clearer we added following text to Material and Methods in ”The nucDetective pipeline” section:

Changes in the manuscript (L518-519):

The code, software and annotations used to run the nucDetective pipeline along with the output have been deposited on Zenodo (https://doi.org/10.5281/zenodo.16779899).

which fragment sizes were used to map the MNase-seq data?

The default setting in nucDetective is to use fragment sizes of 140 – 200 bp, which corresponds to the main mono-nucleosome fraction in standard MNase-seq experiments. However, the correct selection of fragment sizes may vary depending on the organism and the variations in MNase-seq protocols. Therefore, the pipeline offers the option of changing the cutoff parameter (--minLen; --maxLen), accordingly. Kensche et al thoroughly tested the best selection of the fragment sizes for the data set, which is used in this manuscript. We agree with their selection and used the same cutoffs (75-175 bp).

This is stated in line 535-536:

The fragments are further filtered to mono-nucleosome sized fragments (here we used 75 – 175 bp)

We changed the text:

The fragments are further filtered to mono-nucleosome sized fragments (default setting 140-200 bp; changed in this study to 75 – 175 bp)

We highlighted other parameters used in this study in the material and methods part.

Reviewer #2 (Significance (Required)):

Overall, the manuscript is well written and findings are clearly and elegantly presented. The manuscript describes a new pipeline to map and analyze MNase-seq data across different stages or conditions, though the broader applicability of the pipeline and advancements over existing tools could be better demonstrated. Importantly, the manuscript make use of this pipeline to provide a refined and likely more accurate view on (the dynamics of) nucleosome positioning over the AT-rich genome of P. falciparum. While these observations make sense they remain rather descriptive/associative and lack further experimental validation. Overall, this manuscript could be interest to both researchers working on chromatin biology and Plasmodium gene-regulation.

We thank the reviewer for the assessment of our study and for recognizing that the results of our MNase-seq analysis pipeline nucDetective contribute to a better understanding of Pf chromatin biology.

Reviewer #3 (Evidence, reproducibility and clarity (Required)):

The manuscript "Deciphering chromatin architecture and dynamics in Plasmodium 2 falciparum using the nucDetective pipeline" describes computational analysis of previously published data of P falciparum chromatin. This work corrects the prevailing view that this parasitic organism has an unusually disorganized chromatin organization, which had been attributed to its high genomic AT content, lack of histone H1, and ancient derivation. The authors show that instead P falciparum has a very typical chromatin organization. Part of the refinement is due to aligning data on +1 nucleosome positions instead of TSSs, which have been poorly mapped. The computational tools corral some useful features, for querying epigenomic structure that make visualization straightforward, especially for fuzzy nucleosomes.

Reviewer #3 (Significance (Required)):

As a computational package this is a nice presentation of fairly central questions. The assessment and display of fuzzy nucleosomes is a nice feature.

We thank the reviewer for the assessment of our study and are pleased that the reviewer acknowledges the value and usability of our pipeline.

Remove the h tag

Take the pressure off and grow your business faster with scalable logistics and fulfilment services

Note: This response was posted by the corresponding author to Review Commons. The content has not been altered except for formatting.

Learn more at Review Commons

Reply to the reviewers

Reviewer #1 (Evidence, reproducibility and clarity (Required)):

These authors have developed a method to induce MI or MII arrest. While this was previously possible in MI, the advantage of the method presented here is it works for MII, and chemically inducible because it is based on a system that is sensitive to the addition of ABA. Depending on when the ABA is added, they achieve a MI or MII delay. The ABA promotes dimerizing fragments of Mps1 and Spc105 that can't bind their chromosomal sites. The evidence that the MI arrest is weaker than the MII arrest is convincing and consistent with published data and indicating the SAC in MI is less robust than MII or mitosis. The authors use this system to find evidence that the weak MI arrest is associated with PP1 binding to Spc105. This is a nice use of the system.

The remainder of the paper uses the SynSAC system to isolate populations enriched for MI or MII stages and conduct proteomics. This shows a powerful use of the system but more work is needed to validate these results, particularly in normal cells.

Overall the most significant aspect of this paper is the technical achievement, which is validated by the other experiments. They have developed a system and generated some proteomics data that maybe useful to others when analyzing kinetochore composition at each division. Overall, I have only a few minor suggestions.

We appreciate the reviewers’ support of our study.

1) In wild-type - Pds1 levels are high during M1 and A1, but low in MII. Can the authors comment on this? In line 217, what is meant by "slightly attenuated? Can the authors comment on how anaphase occurs in presence of high Pds1? There is even a low but significant level in MII.

The higher levels of Pds1 in meiosis I compared to meiosis II has been observed previously using immunofluorescence and live imaging1–3. Although the reasons are not completely clear, we speculate that there is insufficient time between the two divisions to re-accumulate Pds1 prior to separase re-activation.

We agree “slightly attenuated” was confusing and we have re-worded this sentence to read “Addition ABA at the time of prophase release resulted in Pds1securin stabilisation throughout the time course, consistent with delays in both metaphase I and II”.

We do not believe that either anaphase I or II occur in the presence of high Pds1. Western blotting represents the amount of Pds1 in the population of cells at a given time point. The time between meiosis I and II is very short even when treated with ABA. For example, in Figure 2B, spindle morphology counts show that the anaphase I peak is around 40% at its maxima (105 min) and around 40% of cells are in either metaphase I or metaphase II, and will be Pds1 positive. In contrast, due to the better efficiency of meiosis II, anaphase II hardly occurs at all in these conditions, since anaphase II spindles (and the second nuclear division) are observed at very low frequency (maximum 10%) from 165 minutes onwards. Instead, metaphase II spindles partially or fully breakdown, without undergoing anaphase extension. Taking Pds1 levels from the western blot and the spindle data together leads to the conclusion that at the end of the time-course, these cells are biochemically in metaphase II, but unable to maintain a robust spindle. Spindle collapse is also observed in other situations where meiotic exit fails, and potentially reflects an uncoupling of the cell cycle from the programme governing gamete differentiation3–5. We will explain this point in a revised version while referring to representative images that from evidence for this, as also requested by the reviewer below.

2) The figures with data characterizing the system are mostly graphs showing time course of MI and MII. There is no cytology, which is a little surprising since the stage is determined by spindle morphology. It would help to see sample sizes (ie. In the Figure legends) and also representative images. It would also be nice to see images comparing the same stage in the SynSAC cells versus normal cells. Are there any differences in the morphology of the spindles or chromosomes when in the SynSAC system?

This is an excellent suggestion and will also help clarify the point above. We will provide images of cells at the different stages. For each timepoint, 100 cells were scored. We have already included this information in the figure legends

3) A possible criticism of this system could be that the SAC signal promoting arrest is not coming from the kinetochore. Are there any possible consequences of this? In vertebrate cells, the RZZ complex streams off the kinetochore. Yeast don't have RZZ but this is an example of something that is SAC dependent and happens at the kinetochore. Can the authors discuss possible limitations such as this? Does the inhibition of the APC effect the native kinetochores? This could be good or bad. A bad possibility is that the cell is behaving as if it is in MII, but the kinetochores have made their microtubule attachments and behave as if in anaphase.

In our view, the fact that SynSAC does not come from kinetochores is a major advantage as this allows the study of the kinetochore in an unperturbed state. It is also important to note that the canonical checkpoint components are all still present in the SynSAC strains, and perturbations in kinetochore-microtubule interactions would be expected to mount a kinetochore-driven checkpoint response as normal. Indeed, it would be interesting in future work to understand how disrupting kinetochore-microtubule attachments alters kinetochore composition (presumably checkpoint proteins will be recruited) and phosphorylation but this is beyond the scope of this work. In terms of the state at which we are arresting cells – this is a true metaphase because cohesion has not been lost but kinetochore-microtubule attachments have been established. This is evident from the enrichment of microtubule regulators but not checkpoint proteins in the kinetochore purifications from metaphase I and II. While this state is expected to occur only transiently in yeast, since the establishment of proper kinetochore-microtubule attachments triggers anaphase onset, the ability to capture this properly bioriented state will be extremely informative for future studies. We appreciate the reviewers’ insight in highlighting these interesting discussion points which we will include in a revised version.

Reviewer #1 (Significance (Required)):

These authors have developed a method to induce MI or MII arrest. While this was previously possible in MI, the advantage of the method presented here is it works for MII, and chemically inducible because it is based on a system that is sensitive to the addition of ABA. Depending on when the ABA is added, they achieve a MI or MII delay. The ABA promotes dimerizing fragments of Mps1 and Spc105 that can't bind their chromosomal sites. The evidence that the MI arrest is weaker than the MII arrest is convincing and consistent with published data and indicating the SAC in MI is less robust than MII or mitosis. The authors use this system to find evidence that the weak MI arrest is associated with PP1 binding to Spc105. This is a nice use of the system.

The remainder of the paper uses the SynSAC system to isolate populations enriched for MI or MII stages and conduct proteomics. This shows a powerful use of the system but more work is needed to validate these results, particularly in normal cells.

Overall the most significant aspect of this paper is the technical achievement, which is validated by the other experiments. They have developed a system and generated some proteomics data that maybe useful to others when analyzing kinetochore composition at each division.

We appreciate the reviewer’s enthusiasm for our work.

Reviewer #2 (Evidence, reproducibility and clarity (Required)):

The manuscript submitted by Koch et al. describes a novel approach to collect budding yeast cells in metaphase I or metaphase II by synthetically activating the spinde checkpoint (SAC). The arrest is transient and reversible. This synchronization strategy will be extremely useful for studying meiosis I and meiosis II, and compare the two divisions. The authors characterized this so-named syncSACapproach and could confirm previous observations that the SAC arrest is less efficient in meiosis I than in meiosis II. They found that downregulation of the SAC response through PP1 phosphatase is stronger in meiosis I than in meiosis II. The authors then went on to purify kinetochore-associated proteins from metaphase I and II extracts for proteome and phosphoproteome analysis. Their data will be of significant interest to the cell cycle community (they compared their datasets also to kinetochores purified from cells arrested in prophase I and -with SynSAC in mitosis).

I have only a couple of minor comments:

1) I would add the Suppl Figure 1A to main Figure 1A. What is really exciting here is the arrest in metaphase II, so I don't understand why the authors characterize metaphase I in the main figure, but not metaphase II. But this is only a suggestion.

This is a good suggestion, we will do this in our full revision.

2) Line 197, the authors state: ...SyncSACinduced a more pronounced delay in metaphase II than in metaphase I. However, line 229 and 240 the auhtors talk about a "longer delay in metaphase Thank you for pointing this out, this is indeed a typo and we have corrected it.

3) The authors describe striking differences for both protein abundance and phosphorylation for key kinetochore associated proteins. I found one very interesting protein that seems to be very abundant and phosphorylated in metaphase I but not metaphase II, namely Sgo1. Do the authors think that Sgo1 is not required in metaphase II anymore? (Top hit in suppl Fig 8D).

This is indeed an interesting observation, which we plan to investigate as part of another study in the future. Indeed, data from mouse indicates that shugoshin-dependent cohesin deprotection is already absent in meiosis II in mouse oocytes6, though whether this is also true in yeast is not known. Furthermore, this does not rule out other functions of Sgo1 in meiosis II (for example promoting biorientation). We will include this point in the discussion.

Reviewer #2 (Significance (Required)):

The technique described here will be of great interest to the cell cycle community. Furthermore, the authors provide data sets on purified kinetochores of different meiotic stages and compare them to mitosis. This paper will thus be highly cited, for the technique, and also for the application of the technique.

Reviewer #3 (Evidence, reproducibility and clarity (Required)):

In their manuscript, Koch et al. describe a novel strategy to synchronize cells of the budding yeast Saccharomyces cerevisiae in metaphase I and metaphase II, thereby facilitating comparative analyses between these meiotic stages. This approach, termed SynSAC, adapts a method previously developed in fission yeast and human cells that enables the ectopic induction of a synthetic spindle assembly checkpoint (SAC) arrest by conditionally forcing the heterodimerization of two SAC components upon addition of the plant hormone abscisic acid (ABA). This is a valuable tool, which has the advantage that induces SAC-dependent inhibition of the anaphase promoting complex without perturbing kinetochores. Furthermore, since the same strategy and yeast strain can be also used to induce a metaphase arrest during mitosis, the methodology developed by Koch et al. enables comparative analyses between mitotic and meiotic cell divisions. To validate their strategy, the authors purified kinetochores from meiotic metaphase I and metaphase II, as well as from mitotic metaphase, and compared their protein composition and phosphorylation profiles. The results are presented clearly and in an organized manner.

We are grateful to the reviewer for their support.

Despite the relevance of both the methodology and the comparative analyses, several main issues should be addressed: 1.- In contrast to the strong metaphase arrest induced by ABA addition in mitosis (Supp. Fig. 2), the SynSAC strategy only promotes a delay in metaphase I and metaphase II as cells progress through meiosis. This delay extends the duration of both meiotic stages, but does not markedly increase the percentage of metaphase I or II cells in the population at a given timepoint of the meiotic time course (Fig. 1C). Therefore, although SynSAC broadens the time window for sample collection, it does not substantially improve differential analyses between stages compared with a standard NDT80 prophase block synchronization experiment. Could a higher ABA concentration or repeated hormone addition improve the tightness of the meiotic metaphase arrest?

For many purposes the enrichment and extended time for sample collection is sufficient, as we demonstrate here. However, as pointed out by the reviewer below, the system can be improved by use of the 4A-RASA mutations to provide a stronger arrest (see our response below). We did not experiment with higher ABA concentrations or repeated addition since the very robust arrest achieved with the 4A-RASA mutant deemed this unnecessary.

2.- Unlike the standard SynSAC strategy, introducing mutations that prevent PP1 binding to the SynSAC construct considerably extended the duration of the meiotic metaphase arrests. In particular, mutating PP1 binding sites in both the RVxF (RASA) and the SILK (4A) motifs of the Spc105(1-455)-PYL construct caused a strong metaphase I arrest that persisted until the end of the meiotic time course (Fig. 3A). This stronger and more prolonged 4A-RASA SynSAC arrest would directly address the issue raised above. It is unclear why the authors did not emphasize more this improved system. Indeed, the 4A-RASA SynSAC approach could be presented as the optimal strategy to induce a conditional metaphase arrest in budding yeast meiosis, since it not only adapts but also improves the original methods designed for fission yeast and human cells. Along the same lines, it is surprising that the authors did not exploit the stronger arrest achieved with the 4A-RASA mutant to compare kinetochore composition at meiotic metaphase I and II.

We agree that the 4A-RASA mutant is the best tool to use for the arrest and going forward this will be our approach. We collected the proteomics data and the data on the SynSAC mutant variants concurrently, so we did not know about the improved arrest at the time the proteomics experiment was done. Because very good arrest was already achieved with the unmutated SynSAC construct, we could not justify repeating the proteomics experiment which is a large amount of work using significant resources. However, we will highlight the potential of the 4A-RASA mutant more prominently in our full revision.

3.- The results shown in Supp. Fig. 4C are intriguing and merit further discussion. Mitotic growth in ABA suggest that the RASA mutation silences the SynSAC effect, yet this was not observed for the 4A or the double 4A-RASA mutants. Notably, in contrast to mitosis, the SynSAC 4A-RASA mutation leads to a more pronounced metaphase I meiotic delay (Fig. 3A). It is also noteworthy that the RVAF mutation partially restores mitotic growth in ABA. This observation supports, as previously demonstrated in human cells, that Aurora B-mediated phosphorylation of S77 within the RVSF motif is important to prevent PP1 binding to Spc105 in budding yeast as well.

We agree these are intriguing findings that highlight key differences as to the wiring of the spindle checkpoint in meiosis and mitosis and potential for future studies, however, currently we can only speculate as to the underlying cause. The effect of the RASA mutation in mitosis is unexpected and unexplained. However, the fact that the 4A-RASA mutation causes a stronger delay in meiosis I compared to mitosis can be explained by a greater prominence of PP1 phosphatase in meiosis. Indeed, our data (Figure 4A) show that the PP1 phosphatase Glc7 and its regulatory subunit Fin1 are highly enriched on kinetochores at all meiotic stages compared to mitosis.

We agree that the improved growth of the RVAF mutant is intriguing and points to a role of Aurora B-mediated phosphorylation, though previous work has not supported such a role 7.

We will include a discussion of these important points in a revised version.

4.- To demonstrate the applicability of the SynSAC approach, the authors immunoprecipitated the kinetochore protein Dsn1 from cells arrested at different meiotic or mitotic stages, and compared kinetochore composition using data independent acquisition (DIA) mass spectrometry. Quantification and comparative analyses of total and kinetochore protein levels were conducted in parallel for cells expressing either FLAG-tagged or untagged Dsn1 (Supp. Fig. 7A-B). To better detect potential changes, protein abundances were next scaled to Dsn1 levels in each sample (Supp. Fig. 7C-D). However, it is not clear why the authors did not normalize protein abundance in the immunoprecipitations from tagged samples at each stage to the corresponding untagged control, instead of performing a separate analysis. This would be particularly relevant given the high sensitivity of DIA mass spectrometry, which enabled quantification of thousands of proteins. Furthermore, the authors compared protein abundances in tagged-samples from mitotic metaphase and meiotic prophase, metaphase I and metaphase II (Supp. Fig. 7E-F). If protein amounts in each case were not normalized to the untagged controls, as inferred from the text (lines 333 to 338), the observed differences could simply reflect global changes in protein expression at different stages rather than specific differences in protein association to kinetochores.

While we agree with the reviewer that at first glance, normalising to no tag makes the most sense, in practice there is very low background signal in the no tag sample which means that any random fluctuations have a big impact on the final fold change. This approach therefore introduces artefacts into the data rather than improving normalisation.

To provide reassurance that our kinetochore immunoprecipitations are specific, and that the background (no tag) signal is indeed very low, we will provide a new supplemental figure showing the volcanos comparing kinetochore purifications at each stage with their corresponding no tag control. These volcano plots show very clearly that the major enriched proteins are kinetochore proteins and associated factors, in all cases.

It is also important to note that our experiment looks at relative changes of the same protein over time, which we expect to be relatively small in the whole cell lysate. We previously documented proteins that change in abundance in whole cell lysates throughout meiosis8. In this study, we found that relatively few proteins significantly change in abundance, supporting this view.

Our aim in the current study was to understand how the relative composition of the kinetochore changes and for this, we believe that a direct comparison to Dsn1, a central kinetochore protein which we immunoprecipitated is the most appropriate normalisation.

5.- Despite the large amount of potentially valuable data generated, the manuscript focuses mainly on results that reinforce previously established observations (e.g., premature SAC silencing in meiosis I by PP1, changes in kinetochore composition, etc.). The discussion would benefit from a deeper analysis of novel findings that underscore the broader significance of this study.

We strongly agree with this point and we will re-frame the discussion to focus on the novel findings, as also raised by the other reviewers.

Finally, minor concerns are: 1.- Meiotic progression in SynSAC strains lacking Mad1, Mad2 or Mad3 is severely affected (Fig. 1D and Supp. Fig. 1), making it difficult to assess whether, as the authors state, the metaphase delays depend on the canonical SAC cascade. In addition, as a general note, graphs displaying meiotic time courses could be improved for clarity (e.g., thinner data lines, addition of axis gridlines and external tick marks, etc.).

We will generate the data to include a checkpoint mutant +/- ABA for direct comparison. We will take steps to improve the clarity of presentation of the meiotic timecourse graphs, though our experience is that uncluttered graphs make it easier to compare trends.

2.- Spore viability following SynSAC induction in meiosis was used as an indicator that this experimental approach does not disrupt kinetochore function and chromosome segregation. However, this is an indirect measure. Direct monitoring of genome distribution using GFP-tagged chromosomes would have provided more robust evidence. Notably, the SynSAC mad3Δ mutant shows a slight viability defect, which might reflect chromosome segregation defects that are more pronounced in the absence of a functional SAC.

Spore viability is a much more sensitive way of analysing segregation defects that GFP-labelled chromosomes. This is because GFP labelling allows only a single chromosome to be followed. On the other hand, if any of the 16 chromosomes mis-segregate in a given meiosis this would result in one or more aneuploid spores in the tetrad, which are typically inviable. The fact that spore viability is not significantly different from wild type in this analysis indicates that there are no major chromosome segregation defects in these strains, and we therefore do not plan to do this experiment.

3.- It is surprising that, although SAC activity is proposed to be weaker in metaphase I, the levels of CPC/SAC proteins seem to be higher at this stage of meiosis than in metaphase II or mitotic metaphase (Fig. 4A-B).

We agree, this is surprising and we will point this out in the revised discussion. We speculate that the challenge in biorienting homologs which are held together by chiasmata, rather than back-to-back kinetochores results in a greater requirement for error correction in meiosis I. Interestingly, the data with the RASA mutant also point to increased PP1 activity in meiosis I, and we additionally observed increased levels of PP1 (Glc7 and Fin1) on meiotic kinetochores, consistent with the idea that cycles of error correction and silencing are elevated in meiosis I.

4.- Although a more detailed exploration of kinetochore composition or phosphorylation changes is beyond the scope of the manuscript, some key observations could have been validated experimentally (e.g., enrichment of proteins at kinetochores, phosphorylation events that were identified as specific or enriched at a certain meiotic stage, etc.).

We agree that this is beyond the scope of the current study but will form the start of future projects from our group, and hopefully others.

5.- Several typographical errors should be corrected (e.g., "Knetochores" in Fig. 4 legend, "250uM ABA" in Supp. Fig. 1 legend, etc.)

Thank you for pointing these out, they have been corrected.

Reviewer #3 (Significance (Required)):

Koch et al. describe a novel methodology, SynSAC, to synchronize budding yeast cells in metaphase I or metaphase II during meiosis, as well and in mitotic metaphase, thereby enabling differential analyses among these cell division stages. Their approach builds on prior strategies originally developed in fission yeast and human cells models to induce a synthetic spindle assembly checkpoint (SAC) arrest by conditionally forcing the heterodimerization of two SAC proteins upon addition of abscisic acid (ABA). The results from this manuscript are of special relevance for researchers studying meiosis and using Saccharomyces cerevisiae as a model. Moreover, the differential analysis of the composition and phosphorylation of kinetochores from meiotic metaphase I and metaphase II adds interest for the broader meiosis research community. Finally, regarding my expertise, I am a researcher specialized in the regulation of cell division.

Note: This response was posted by the corresponding author to Review Commons. The content has not been altered except for formatting.

Learn more at Review Commons

Reply to the reviewers

Reviewer #1

Evidence, reproducibility and clarity

This paper addresses a very interesting problem of non-centrosomal microtubule organization in developing Drosophila oocytes. Using genetics and imaging experiments, the authors reveal an interplay between the activity of kinesin-1, together with its essential cofactor Ensconsin, and microtubule organization at the cell cortex by the spectraplakin Shot, minus-end binding protein Patronin and Ninein, a protein implicated in microtubule minus end anchoring. The authors demonstrate that the loss of Ensconsin affects the cortical accumulation non-centrosomal microtubule organizing center (ncMTOC) proteins, microtubule length and vesicle motility in the oocyte, and show that this phenotype can be rescued by constitutively active kinesin-1 mutant, but not by Ensconsin mutants deficient in microtubule or kinesin binding. The functional connection between Ensconsin, kinesin-1 and ncMTOCs is further supported by a rescue experiment with Shot overexpression. Genetics and imaging experiments further implicate Ninein in the same pathway. These data are a clear strength of the paper; they represent a very interesting and useful addition to the field.

The weaknesses of the study are two-fold. First, the paper seems to lack a clear molecular model, uniting the observed phenomenology with the molecular functions of the studied proteins. Most importantly, it is not clear how kinesin-based plus-end directed transport contributes to cortical localization of ncMTOCs and regulation of microtubule length.

Second, not all conclusions and interpretations in the paper are supported by the presented data.

We thank the reviewer for recognizing the impact of this work. In response to the insightful suggestions, we performed extensive new experiments that establish a well-supported cellular and molecular model (Figure 7). The discussion has been restructured to directly link each conclusion to its corresponding experimental evidence, significantly strengthening the manuscript.

Below is a list of specific comments, outlining the concerns, in the order of appearance in the paper/figures.

Figure 1. The statement: "Ens loading on MTs in NCs and their subsequent transport by Dynein toward ring canals promotes the spatial enrichment of the Khc activator Ens in the oocyte" is not supported by data. The authors do not demonstrate that Ens is actually transported from the nurse cells to the oocyte while being attached to microtubules. They do show that the intensity of Ensconsin correlates with the intensity of microtubules, that the distribution of Ensconsin depends on its affinity to microtubules and that an Ensconsin pool locally photoactivated in a nurse cell can redistribute to the oocyte (and throughout the nurse cell) by what seems to be diffusion. The provided images suggest that Ensconsin passively diffuses into the oocyte and accumulates there because of higher microtubule density, which depends on dynein. To prove that Ensconsin is indeed transported by dynein in the microtubule-bound form, one would need to measure the residence time of Ensconsin on microtubules and demonstrate that it is longer than the time needed to transport microtubules by dynein into the oocyte; ideally, one would like to see movement of individual microtubules labelled with photoconverted Ensconsin from a nurse cell into the oocyte. Since microtubules are not enriched in the oocyte of the dynein mutant, analysis of Ensconsin intensity in this mutant is not informative and does not reveal the mechanism of Ensconsin accumulation.

As noted by Reviewer 3, the directional movement of microtubules traveling at ~140 nm/s from nurse cells toward the oocyte through Ring Canals was previously reported using a tagged Ens-MT binding domain reporter line by Lu et al. (2022). We have therefore added the citation of this crucial work in the novel version of the manuscript (lane 155-157) and removed the photo-conversion panel.

Critically, however, our study provides mechanistic insight that was missing from this earlier work: this mechanism is also crucial to enrich MAPs in the oocyte. The fact that Dynein mutants fail to enrich Ensconsin is a crucial piece of evidence: it supports a model of Ensconsin-loaded MT transport (Figure 1D-1F).

Figure 2. According to the abstract, this figure shows that Ensconsin is "maintained at the oocyte cortex by Ninein". However, the figure doesn't seem to prove it - it shows that oocyte enrichment of Ensonsin is partially dependent on Ninein, but this applies to the whole cell and not just to the cell cortex. Furthermore, it is not clear whether Ninein mutation affects microtubule density, which in turn would affect Ensconsin enrichment, and therefore, it is not clear whether the effect of Ninein loss on Ensconsin distribution is direct or indirect.

Ninein plays a critical role in Ensconsin enrichment and microtubule organization in the oocyte (new Figure 2, Figure 3, Figure S3). Quantification of total Tubulin signal shows no difference between control and Nin mutant oocytes (new Figure S3 panels A, B). We found decreased Ens enrichment in the oocyte, and Ens localization on MTs and to the cell cortex (Figure 2E, 2F, and Figure S3C and S3D).

Novel quantitative analyses of microtubule orientation at the anterior cortex, where MTs are normally preferentially oriented toward the posterior pole (Parton et al. 2011), demonstrate that Nin mutants exhibit randomized MT orientation compared to wild-type oocytes (new Figure 3C-3E).These findings establish that Ninein (although not essential) favors Ensconsin localization on MTs, Ens enrichment in the oocyte, ncMTOC cortical localization, and more robust MT orientation toward the posterior cortex. It also suggests that Ens levels in the oocyte acts as a rheostat to control Khc activation.

The observation that the aggregates formed by overexpressed Ninein accumulate other proteins, including Ensconsin, supports, though does not prove their interactions. Furthermore, there is absolutely no proof that Ninein aggregates are "ncMTOCs". Unless the authors demonstrate that these aggregates nucleate or anchor microtubules (for example, by detailed imaging of microtubules and EB1 comets), the text and labels in the figure would need to be altered.

We have modified the manuscript, we now refer to an accumulation of these components in large puncta, rather than aggregates, consistent with previous observations (Rosen et al., 2000). We acknowledge in the revised version that these puncta recruit Shot, Patronin and Ens without mentioning direct interaction (lane 218).

Importantly, we conducted a more detailed characterization of these Ninein/Shot/Patronin/Ens-containing puncta in a novel Figure S4. To rigorously assess their nucleation capacity, we analyzed Eb1-GFP-labeled MT comets, a robust readout of MT nucleation (Parton et al., 2011, Nashchekin et al., 2016). While few Eb1-positive comets occasionally emanate from these structures, confirming their identity as putative ncMTOCs, these puncta function as surprisingly weak nucleation centers (new Figure S4 E, Video S1) and, their presence does not alter overall MT architecture (new Figure S4 F). Moreover, these puncta disappear over time, are barely visible at stage 10B, they do not impair oocyte development or fertility (Figure S4 G and Table 1).

Minor comment: Note that a "ratio" (Figure 2C) is just a ratio, and should not be expressed in arbitrary units.

We have amended this point in all the figures.

Figure 3B: immunoprecipitation results cannot be interpreted because the immunoprecipitated proteins (GFP, Ens-GFP, Shot-YFP) are not shown. It is also not clear that this biochemical experiment is useful. If the authors would like to suggest that Ensconsin directly binds to Patronin, the interaction would need to be properly mapped at the protein domain level.

This is a good point: the GFP and Ens-GFP immunoprecipitated proteins are now much clearly identified on the blots and in the figure legend (new Figure 4G). Shot-YFP IP, was used as a positive control but is difficult to be detected by Western blot due to its large size (>106 Da) using conventional acrylamide gels (Nashchekin et al., 2016).

We now explicitly state that immunoprecipitations were performed at 4°C, where microtubules are fully depolymerized, thereby excluding undirect microtubule-mediated interactions. We agree with this reviewer: we cannot formally rule out interactions through bridging by other protein components. This is stated in the revised manuscript (lane 238-239).

One of the major phenotypes observed by the authors in Ens mutant is the loss of long microtubules. The authors make strong conclusions about the independence of this phenotype from the parameters of microtubule plus-end growth, but in fact, the quality of their data does not allow to make such a conclusion, because they only measured the number of EB1 comets and their growth rate but not the catastrophe, rescue or pausing frequency."Note that kinesin-1 has been implicated in promoting microtubule damage and rescue (doi: 10.1016/j.devcel.2021).In the absence of such measurements, one cannot conclude whether short microtubules arise through defects in the minus-end, plus-end or microtubule shaft regulation pathways.

We thank the reviewer for raising this important point. Our data demonstrate that microtubule (MT) nucleation and polymerization rates remain unaffected under Khc RNAi and ens mutant conditions, indicating that MT dynamics alterations must arise through alternative mechanisms.

As the reviewer suggested, recent studies on Kinesin activity and MT network regulation are indeed highly relevant. Two key studies from the Verhey and Aumeier laboratories examined Kinesin-1 gain-of-function conditions and revealed that constitutively active Kinesin-1 induces MT lattice damage (Budaitis et al., 2022). While damaged MTs can undergo self-repair, Aumeier and colleagues demonstrated that GTP-tubulin incorporation generates "rescue shafts" that promote MT rescue events (Andreu-Carbo et al., 2022). Extrapolating from these findings, loss of Kinesin-1 activity could plausibly reduce rescue shaft formation, thereby decreasing MT rescue frequency and stability. Although this hypothesis is challenging to test directly in our system, it provides a mechanistic framework for the observed reduction in MT number and stability.

Additionally, the reviewer highlighted the role of Khc in transporting the dynactin complex, an anti-catastrophe factor, to MT plus ends (Nieuwburg et al., 2017), which could further contribute to MT stabilization. This crucial reference is now incorporated into the revised Discussion.

Importantly, our work also demonstrates the contribution of Ens/Khc to ncMTOC targeting to the cell cortex. Our new quantitative analyses of MT organization (new Figure 5 B) reveal a defective anteroposterior orientation of cortical MTs in mutant conditions, pointing to a critical role for cortical ncMTOCs in organizing the MT network.

Taken together, we propose that the observed MT reduction and disorganization result from multiple interconnected mechanisms: (1) reduced rescue shaft formation affecting MT stability; (2) impaired transport of anti-catastrophe factors to MT plus ends; and (3) loss of cortical ncMTOCs, which are essential for minus-end MT stabilization and network organization. The Discussion has been revised to reflect this integrated model in a dedicated paragraph (“A possible regulation of MT dynamics in the oocyte at both plus end minus MT ends by Ens and Khc” lane 415-432).

It is important to note in that a spectraplakin, like Shot, can potentially affect different pathways, particularly when overexpressed.

We agree that Shot harbors multiple functional domains and acts as a key organizer of both actin and microtubule cytoskeletons. Overexpression of such a cytoskeletal cross-linker could indeed perturb both networks, making interpretation of Ens phenotype rescue challenging due to potential indirect effects.

To address this concern, we selected an appropriate Shot isoform for our rescue experiments that displayed similar localization to “endogenous” Shot-YFP (a genomic construct harboring shot regulatory sequences) and importantly that was not overexpressed.

Elevated expression of the Shot.L(A) isoform (see Western Blot Figure S8 A), considered as the wild-type form with two CH1 and CH2 actin-binding motifs (Lee and Kolodziej, 2002), showed abnormal localization such as strong binding to the microtubules in nurse cells and oocyte confirming the risk of gain-of-function artifacts and inappropriate conclusions (Figure S8 B, arrows).

By contrast, our rescue experiments using the Shot.L(C) isoform (that only harbors the CH2 motif) provide strong evidence against such artifacts for three reasons. First, Shot-L(C) is expressed at slightly lower levels than a Shot-YFP genomic construct (not overexpressed), and at much lower levels than Shot-L(A), despite using the same driver (Figure S8 A). Second, Shot-L(C) localization in the oocyte is similar to that of endogenous Shot-YFP, concentrating at the cell cortex (Figure S8 B, compare lower and top panels). Taken together, these controls rather suggest our rescue with the Shot-L(C) is specific.

Note that this Shot-L(C) isoform is sufficient to complement the absence of the shot gene in other cell contexts (Lee and Kolodziej, 2002).

Unjustified conclusions should be removed: the authors do not provide sufficient data to conclude that "ens and Khc oocytes MT organizational defects are caused by decreased ncMTOC cortical anchoring", because the actual cortical microtubule anchoring was not measured.

This is a valid point. We acknowledge that we did not directly measure microtubule anchoring in this study. In response, we have revised the discussion to more accurately reflect our observations. Throughout the manuscript, we now refer to "cortical microtubule organization" rather than "cortical microtubule anchoring," which better aligns with the data presented.

Minor comment: Microtubule growth velocity must be expressed in units of length per time, to enable evaluating the quality of the data, and not as a normalized value.

This is now amended in the revised version (modified Figure S7).

A significant part of the Discussion is dedicated to the potential role of Ensconsin in cortical microtubule anchoring and potential transport of ncMTOCs by kinesin. It is obviously fine that the authors discuss different theories, but it would be very helpful if the authors would first state what has been directly measured and established by their data, and what are the putative, currently speculative explanations of these data.

We have carefully considered the reviewer's constructive comments and are confident that this revised version fully addresses their concerns.

First, we have substantially strengthened the connection between the Results and Discussion sections, ensuring that our interpretations are more directly anchored in the experimental data. This restructuring significantly improves the overall clarity and logical flow of the manuscript.

Second, we have added a new comprehensive figure presenting a molecular-scale model of Kinesin-1 activation upon release of autoinhibition by Ensconsin (new Figure 7D). Critically, this figure also illustrates our proposed positive feedback loop mechanism: Khc-dependent cytoplasmic advection promotes cortical recruitment of additional ncMTOCs, which generates new cortical microtubules and further accelerates cytoplasmic transport (Figure 7 A-C). This self-amplifying cycle provides a mechanistic framework consistent with emerging evidence that cytoplasmic flows are essential for efficient intracellular transport in both insect and mammalian oocytes.

Minor comment: The writing and particularly the grammar need to be significantly improved throughout, which should be very easy with current language tools. Examples: "ncMTOCs recruitment" should be "ncMTOC recruitment"; "Vesicles speed" should be "Vesicle speed", "Nin oocytes harbored a WT growth,"- unclear what this means, etc. Many paragraphs are very long and difficult to read. Making shorter paragraphs would make the authors' line of thought more accessible to the reader.

We have amended and shortened the manuscript according to this reviewer feed-back. We have specifically built more focused paragraphs to facilitates the reading.

Significance

This paper represents significant advance in understanding non-centrosomal microtubule organization in general and in developing Drosophila oocytes in particular by connecting the microtubule minus-end regulation pathway to the Kinesin-1 and Ensconsin/MAP7-dependent transport. The genetics and imaging data are of good quality, are appropriately presented and quantified. These are clear strengths of the study which will make it interesting to researchers studying the cytoskeleton, microtubule-associated proteins and motors, and fly development.

The weaknesses of this study are due to the lack of clarity of the overall molecular model, which would limit the impact of the study on the field. Some interpretations are not sufficiently supported by data, but this can be solved by more precise and careful writing, without extensive additional experimentation.

We thank the reviewer for raising these important concerns regarding clarity and data interpretation. We have thoroughly revised the manuscript to address these issues on multiple fronts. First, we have substantially rewritten key sections to ensure that our conclusions are clearly articulated and directly supported by the data. Second, we have performed several new experiments that now allow us to propose a robust mechanistic model, presented in new figures. These additions significantly strengthen the manuscript and directly address the reviewer's concerns.

My expertise is cell biology and biochemistry of the microtubule cytoskeleton, including both microtubule-associated proteins and microtubule motors.

Reviewer #2

Evidence, reproducibility and clarity

In this manuscript, Berisha et al. investigate how microtubule (MT) organization is spatially regulated during Drosophila oogenesis. The authors identify a mechanism in which the Kinesin-1 activator Ensconsin/MAP7 is transported by dynein and anchored at the oocyte cortex via Ninein, enabling localized activation of Kinesin-1. Disruption of this pathway impairs ncMTOC recruitment and MT anchoring at the cortex. The authors combine genetic manipulation with high-resolution microscopy and use three key readouts to assess MT organization during mid-to-late oogenesis: cortical MT formation, localization of posterior determinants, and ooplasmic streaming. Notably, Kinesin-1, in concert with its activator Ens/MAP7, contributes to organizing the microtubule network it travels along. Overall, the study presents interesting findings, though we have several concerns we would like the authors to address. Ensconsin enrichment in the oocyte 1. Enrichment in the oocyte • Ensconsin is a MAP that binds MTs. Given that microtubule density in the oocyte significantly exceeds that in the nurse cells, its enrichment may passively reflect this difference. To assess whether the enrichment is specific, could the authors express a non-Drosophila MAP (e.g., mammalian MAP1B) to determine whether it also preferentially localizes to the oocyte?

To address this point, we performed a new series of experiments analyzing the enrichment of other Drosophila and non-Drosophila MAPs, including Jupiter-GFP, Eb1-GFP, and bovine Tau-GFP, all widely used markers of the microtubule cytoskeleton in flies (see new Figure S2). Our results reveal that Jupiter-GFP, Eb1-GFP, and bovine Tau-GFP all exhibit significantly weaker enrichment in the oocyte compared to Ens-GFP. Khc-GFP also shows lower enrichment. These findings indicate that MAP enrichment in the oocyte is MAP-dependent, rather than solely reflecting microtubule density or organization. Of note, we cannot exclude that microtubule post-translational modifications contribute to differential MAP binding between nurse cells and the oocyte, but this remains a question for future investigation.

The ability of ens-wt and ens-LowMT to induce tubulin polymerization according to the light scattering data (Fig. S1J) is minimal and does not reflect dramatic differences in localization. The authors should verify that, in all cases, the polymerization product in their in vitro assays is microtubules rather than other light-scattering aggregates. What is the control in these experiments? If it is just purified tubulin, it should not form polymers at physiological concentrations.

The critical concentration Cr for microtubule self-assembly in classical BRB80 buffer found by us and others is around 20 µM (see Fig. 2c in Weiss et al., 2010). Here, microtubules were assembled at 40 µM tubulin concentration, i.e., largely above the Cr. As stated in the materials and methods section, we systematically induced cooling at 4°C after assembly to assess the presence of aggregates, since those do not fall apart upon cooling. The decrease in optical density upon cooling is a direct control that the initial increase in DO is due to the formation of microtubules. Finally, aggregation and polymerization curves are widely different, the former displaying an exponential shape and the latter a sigmoid assembly phase (see Fig. 3A and 3B in Weiss et al., 2010).

Photoconversion caveatsMAPs are known to dynamically associate and dissociate from microtubules. Therefore, interpretation of the Ens photoconversion data should be made with caution. The expanding red signal from the nurse cells to the oocyte may reflect a any combination of dynein-mediated MT transport and passive diffusion of unbound Ensconsin. Notably, photoconversion of a soluble protein in the nurse cells would also result in a gradual increase in red signal in the oocyte, independent of active transport. We encourage the authors to more thoroughly discuss these caveats. It may also help to present the green and red channels side by side rather than as merged images, to allow readers to assess signal movement and spatial patterns better.

This is a valid point that mirrors the comment of Reviewers 1 and 3. The directional movement of microtubules traveling at ~140 nm/s from nurse cells toward the oocyte via the ring canals was previously reported by Lu et al. (2022) with excellent spatial resolution. Notably, this MT transport was measured using a fusion protein containing the Ens MT-binding domain. We now cite this relevant study in our revised manuscript and have removed this redundant panel in Figure 1.

Reduction of Shot at the anterior cortex• Shot is known to bind strongly to F-actin, and in the Drosophila ovary, its localization typically correlates more closely with F-actin structures than with microtubules, despite being an MT-actin crosslinker. Therefore, the observed reduction of cortical Shot in ens, nin mutants, and Khc-RNAi oocytes is unexpected. It would be important to determine whether cortical F-actin is also disrupted in these conditions, which should be straightforward to assess via phalloidin staining.

As requested by the reviewer, we performed actin staining experiments, which are now presented in a new Figure S5. These data demonstrate that the cortical actin network remains intact in all mutant backgrounds analyzed, ruling out any indirect effect of actin cytoskeleton disruption on the observed phenotypes.

MTs are barely visible in Fig. 3A, which is meant to demonstrate Ens-GFP colocalization with tubulin. Higher-quality images are needed.

The revised version now provides significantly improved images to show the different components examined. Our data show that Ens and Ninein localize at the cell cortex where they co-localize with Shot and Patronin (Figure 2 A-C). In addition, novel images show that Ens extends along microtubules (new Figure 4 A).

MT gradient in stage 9 oocytesIn ens-/-, nin-/-, and Khc-RNAi oocytes, is there any global defect in the stage 9 microtubule gradient? This information would help clarify the extent to which cortical localization defects reflect broader disruptions in microtubule polarity.

We now provide quantitative analysis of microtubule (MT) array organization in novel figures (Figure 3D and Figure 5B). Our data reveal that both Khc RNAi and ens mutant oocytes exhibit severe disruption of MT orientation toward the posterior (new Figure 5B). Importantly, this defect is significantly less pronounced in Nin-/- oocytes, which retain residual ncMTOCs at the cortex (new Figure 3D). This differential phenotype supports our model that cortical ncMTOCs are critical for maintaining proper MT orientation toward the posterior side of the oocyte.

Role of Ninein in cortical anchoringThe requirement for Ninein in cortical anchorage is the least convincing aspect of the manuscript and somewhat disrupts the narrative flow. First, it is unclear whether Ninein exhibits the same oocyte-enriched localization pattern as Ensconsin. Is Ninein detectable in nurse cells? Second, the Ninein antibody signal appears concentrated in a small area of the anterior-lateral oocyte cortex (Fig. 2A), yet Ninein loss leads to reduced Shot signal along a much larger portion of the anterior cortex (Fig. 2F)-a spatial mismatch that weakens the proposed functional relationship. Third, Ninein overexpression results in cortical aggregates that co-localize with Shot, Patronin, and Ensconsin. Are these aggregates functional ncMTOCs? Do microtubules emanate from these foci?

We now provide a more comprehensive analysis of Ninein localization. Similar to Ensconsin (Ens), endogenous Ninein is enriched in the oocyte during the early stages of oocyte development but is also detected in NCs (see modified Figure 2 A and Lasko et al., 2016). Improved imaging of Ninein further shows that the protein partially co-localizes with Ens, and ncMTOCs at the anterior cortex and with Ens-bound MTs (Figure 2B, 2C).

Importantly, loss of Ninein (Nin) only partially reduces the enrichment of Ens in the oocyte (Figure 2E). Both Ens and Kinesin heavy chain (Khc) remain partially functional and continue to target non-centrosomal microtubule-organizing centers (ncMTOCs) to the cortex (Figure 3A). In Nin-/- mutants, a subset of long cortical microtubules (MTs) is present, thereby generating cytoplasmic streaming, although less efficiently than under wild-type (WT) conditions (Figure 3F and 3G). As a non-essential gene, we envisage Ninein as a facilitator of MT organization during oocyte development.

Finally, our new analyses demonstrate that large puncta containing Ninein, Shot, Patronin, and despite their size, appear to be relatively weak nucleation centers (revised Figure S4 E and Video 1). In addition, their presence does not bias overall MT architecture (Figure S4 F) nor impair oocyte development and fertility (Figure S4 G and Table 1).

Inconsistency of Khc^MutEns rescueThe Khc^MutEns variant partially rescues cortical MT formation and restores a slow but measurable cytoplasmic flow yet it fails to rescue Staufen localization (Fig. 5). This raises questions about the consistency and completeness of the rescue. Could the authors clarify this discrepancy or propose a mechanistic rationale?

This is a good point. The cytoplasmic flows (the consequence of cargo transport by Khc on MTs) generated by a constitutively active KhcMutEns in an ens mutant condition, are less efficient than those driven by Khc activated by Ens in a control condition (Figure 6C). The rescued flow is probably not efficient enough to completely rescue the Staufen localization at stage 10.

Additionally, this KhcMutEns variant rescues the viability of embryos from Khc27 mutant germline clones oocytes but not from ens mutants (Table1). One hypothesis is that Ens harbors additional functions beyond Khc activation.

This incomplete rescue of Ens by an active Khc variant could also be the consequence of the “paradox of co-dependence”: Kinesin-1 also transport the antagonizing motor Dynein that promotes cargo transport in opposite directions (Hancock et al., 2016). The phenotype of a gain of function variant is therefore complex to interpret. Consistent with this, both KhcMutEns-GFP and KhcDhinge2 two active Khc only rescues partially centrosome transport in ens mutant Neural Stem Cells (Figure S10).

Minor points: 1. The pUbi-attB-Khc-GFP vector was used to generate the Khc^MutEns transgenic line, presumably under control of the ubiquitous ubi promoter. Could the authors specify which attP landing site was used? Additionally, are the transgenic flies viable and fertile, given that Kinesin-1 is hyperactive in this construct?

All transgenic constructs were integrated at defined genomic landing sites to ensure controlled expression levels. Specifically, both GFP-tagged KhcWT and KhcMutEns were inserted at the VK05 (attP9A) site using PhiC31-mediated integration. Full details of the landing sites are provided in the Materials and Methods section. Both transgenic flies are homozygous lethal and the transgenes are maintained over TM6B balancers.

On page 11 (Discussion, section titled "A dual Ensconsin oocyte enrichment mechanism achieves spatial relief of Khc inhibition"), the statement "many mutations in Kif5A are causal of human diseases" would benefit from a brief clarification. Since not all readers may be familiar with kinesin gene nomenclature, please indicate that KIF5A is one of the three human homologs of Kinesin heavy chain.

We clarified this point in the revised version (lane 465-466).

On page 16 (Materials and Methods, "Immunofluorescence in fly ovaries"), the sentence "Ovaries were mounted on a slide with ProlonGold medium with DAPI (Invitrogen)" should be corrected to "ProLong Gold."

This is corrected.

Significance

This study shows that enrichment of MAP7/ensconsin in the oocyte is the mechanism of kinesin-1 activation there and is important for cytoplasmic streaming and localization non-centrosomal microtubule-organizing centers to the oocyte cortex

We thank the reviewers for the accurate review of our manuscript and their positive feed-back.

Reviewer #3

Evidence, reproducibility and clarity

The manuscript of Berisha et al., investigates the role of Ensconsin (Ens), Kinesin-1 and Ninein in organisation of microtubules (MT) in Drosophila oocyte. At stage 9 oocytes Kinesin-1 transports oskar mRNA, a posterior determinant, along MT that are organised by ncMTOCs. At stage 10b, Kinesin-1 induces cytoplasmic advection to mix the contents of the oocyte. Ensconsin/Map7 is a MT associated protein (MAP) that uses its MT-binding domain (MBD) and kinesin binding domain (KBD) to recruit Kinesin-1 to the microtubules and to stimulate the motility of MT-bound Kinesin-1. Using various new Ens transgenes, the authors demonstrate the requirement of Ens MBD and Ninein in Ens localisation to the oocyte where Ens activates Kinesin-1 using its KBD. The authors also claim that Ens, Kinesin-1 and Ninein are required for the accumulation of ncMTOCs at the oocyte cortex and argue that the detachment of the ncMTOCs from the cortex accounts for the reduced localisation of oskar mRNA at stage 9 and the lack of cytoplasmic streaming at stage 10b. Although the manuscript contains several interesting observations, the authors' conclusions are not sufficiently supported by their data. The structure function analysis of Ensconsin (Ens) is potentially publishable, but the conclusions on ncMTOC anchoring and cytoplasmic streaming not convincing.

We are grateful that the regulation of Khc activity by MAP7 was well received by all reviewers. While our study focuses on Drosophila oogenesis, we believe this mechanism may have broader implications for understanding kinesin regulation across biological systems.

For the novel function of the MAP7/Khc complex in organizing its own microtubule networks through ncMTOC recruitment, we have carefully considered the reviewers' constructive recommendations. We now provide additional experimental evidence supporting a model of flux self-amplification in which ncMTOC recruitment plays a key role. It is well established that cytoplasmic flows are essential for posterior localization of cell fate determinants at stage 10B. Slow flows have also been described at earlier oogenesis stages by the groups of Saxton and St Johnston. Building on these early publications and our new experiments, we propose that these flows are essential to promote a positive feedback loop that reinforces ncMTOC recruitment and MT organization (Figure 7).

1) The main conclusion of the manuscript is that "MT advection failure in Khc and ens in late oogenesis stems from defective cortical ncMTOCs recruitment". This completely overlooks the abundant evidence that Kinesin-1 directly drives cytoplasmic streaming by transporting vesicles and microtubules along microtubules, which then move the cytoplasm by advection (Palacios et al., 2002; Serbus et al, 2005; Lu et al, 2016). Since Kinesin-1 generates the flows, one cannot conclude that the effect of khc and ens mutants on cortical ncMTOC positioning has any direct effect on these flows, which do not occur in these mutants.

We regret the lack of clarity of the first version of the manuscript and some missing references. We propose a model in which the Kinesin-1- dependent slow flows (described by Serbus/Saxton and Palacios/StJohnston) play a central role in amplifying ncMTOC anchoring and cortical MT network formation (see model in the new Figure 7).

2) The authors claim that streaming phenotypes of ens and khs mutants are due to a decrease in microtubule length caused by the defective localisation of ncMTOCs. In addition to the problem raised above, However, I am not convinced that they can make accurate measurements of microtubule length from confocal images like those shown in Figure 4. Firstly, they are measuring the length of bundles of microtubules and cannot resolve individual microtubules. This problem is compounded by the fact that the microtubules do not align into parallel bundles in the mutants. This will make the "microtubules" appear shorter in the mutants. In addition, the alignment of the microtubules in wild-type allows one to choose images in which the microtubule lie in the imaging plane, whereas the more disorganized arrangement of the microtubules in the mutants means that most microtubules will cross the imaging plane, which precludes accurate measurements of their length.

As mentioned by Reviewer 4, we have been transparent with the methodology, and the limitations that were fully described in the material and methods section.

Cortical microtubules in oocytes are highly dynamic and move rapidly, making it technically impossible to capture their entire length using standard Z-stack acquisitions. We therefore adopted a compromise approach: measuring microtubules within a single focal plane positioned just below the oocyte cortex. This strategy is consistent with established methods in the field, such as those used by Parton et al. (2011) to track microtubule plus-end directionality. To avoid overinterpretation, we explicitly refer to these measurements as "minimum detectable MT length," acknowledging that microtubules may extend beyond the focal plane, particularly at stage 10, where long, tortuous bundles frequently exit the plane of focus. These methodological considerations and potential biases are clearly described in the Materials and Methods section and the text now mentions the possible disorganization of the MT network in the mutant conditions (lane 272-273).

In this revised version, we now provide complementary analyses of MT network organization.Beyond length measurements (and the mentioned limitations), we also quantified microtubule network orientation at stage 9, assessing whether cortical microtubules are preferentially oriented toward the posterior axis as observed in controls (revised Figure 3D and Figure 5B). While this analysis is also subject to the same technical limitations, it reveals a clear biological difference: microtubules exhibit posterior-biased orientation in control oocytes similar to a previous study (Parton et al., 2011) but adopt a randomized orientation in Nin-/-, ens, and Khc RNAi-depleted oocytes (revised Figure 3D and Figure 5B).

Taken together, these complementary approaches, despite their technical constraints, provide convergent evidence for the role of the Khc/Ens complex in organizing cortical microtubule networks during oogenesis.

3) "To investigate whether the presence of these short microtubules in ens and Khc RNAi oocytes is due to defects in microtubule anchoring or is also associated with a decrease in microtubule polymerization at their plus ends, we quantified the velocity and number of EB1comets, which label growing microtubule plus ends (Figure S3)." I do not understand how the anchoring or not of microtubule minus ends to the cortex determines how far their plus ends grow, and these measurements fall short of showing that plus end growth is unaffected. It has already been shown that the Kinesin-1-dependent transport of Dynactin to growing microtubule plus ends increases the length of microtubules in the oocyte because Dynactin acts as an anti-catastrophe factor at the plus ends. Thus, khc mutants should have shorter microtubules independently of any effects on ncMTOC anchoring. The measurements of EB1 comet speed and frequency in FigS2 will not detect this change and are not relevant for their claims about microtubule length. Furthermore, the authors measured EB1 comets at stage 9 (where they did not observe short MT) rather than at stage 10b. The authors' argument would be better supported if they performed the measurements at stage 10b.

We thank the reviewer for raising this important point. The short microtubule (MT) length observed at stage 10B could indeed result from limited plus-end growth. Unfortunately, we were unable to test this hypothesis directly: strong endogenous yolk autofluorescence at this stage prevented reliable detection of Eb1-GFP comets, precluding velocity measurements.

At least during stage 9, our data demonstrate that MT nucleation and polymerization rates are not reduced in both KhcRNAi and ens mutant conditions, indicating that the observed MT alterations must arise through alternative mechanisms.

In the discussion, we propose the following interconnected explanations, supported by recent literature and the reviewers’ suggestions:

1- Reduced MT rescue events. Two seminal studies from the Verhey and Aumeier laboratories have shown that constitutively active Kinesin-1 induces MT lattice damage (Budaitis et al., 2022), which can be repaired through GTP-tubulin incorporation into "rescue shafts" that promote MT rescue (Andreu-Carbo et al., 2022). Extrapolating from these findings, loss of Kinesin-1 activity could plausibly reduce rescue shaft formation, thereby decreasing MT stability. While challenging to test directly in our system, this mechanism provides a plausible framework for the observed phenotype.

2- Impaired transport of stabilizing factors. As that reviewer astutely points out, Khc transports the dynactin complex, an anti-catastrophe factor, to MT plus ends (Nieuwburg et al., 2017). Loss of this transport could further compromise MT plus end stability. We now discuss this important mechanism in the revised manuscript.

3- Loss of cortical ncMTOCs. Critically, our new quantitative analyses (revised Figure 3 and Figure 5) also reveal defective anteroposterior orientation of cortical MTs in mutant conditions. These experiments suggest that Ens/Khc-mediated localization of ncMTOCs to the cortex is essential for proper MT network organization, and possibly minus-end stabilization as suggested in several studies (Feng et al., 2019, Goodwin and Vale, 2011, Nashchekin et al., 2016).

Altogether, we now propose an integrated model in which MT reduction and disorganization may result from multiple complementary mechanisms operating downstream of Kinesin-1/Ensconsin loss. While some aspects remain difficult to test directly in our in vivo system, the convergence of our data with recent mechanistic studies provides an interesting conceptual framework. The Discussion has been revised to reflect this comprehensive view in a dedicated paragraph (“A possible regulation of MT dynamics in the oocyte at both plus end minus MT ends by Ens and Khc” lane 415-432).

4) The Shot overexpression experiments presented in Fig.3 E-F, Fig.4D and TableS1 are very confusing. Originally , the authors used Shot-GFP overexpression at stage 9 to show that there is a decrease of ncMTOCs at the cortex in ens mutants (Fig.3 E-F) and speculated that this caused the defects in MT length and cytoplasmic advection at stage 10B. However the authors later state on page 8 that : "Shot overexpression (Shot OE) was sufficient to rescue the presence of long cortical MTs and ooplasmic advection in most ens oocytes (9/14), resembling the patterns observed in controls (Figures 4B right panel and 4D). Moreover, while ens females were fully sterile, overexpression of Shot was sufficient to restore that loss of fertility (Table S1)". Is this the same UAS Shot-GFP and VP16 Gal4 used in both experiments? If so, this contradictions puts the authors conclusions in question.

This is an important point that requires clarification regarding our experimental design.

The Shot-YFP construct is a genomic insertion on chromosome 3. The ens mutation is also located on chromosome 3 and we were unable to recombine this transgene with the ens mutant for live quantification of cortical Shot. To circumvent this technical limitation, we used a UAS-Shot.L(C)-GFP transgenic construct driven by a maternal driver, expressed in both wild-type (control) and ens mutant oocytes. We validated that the expression level and subcellular localization of UAS-Shot.L(C)-GFP were comparable to those of the genomic Shot-YFP (new Figure S8 A and B).

From these experiments, we drew two key conclusions. First, cortical Shot.L(C)-GFP is less abundant in ens mutant oocytes compared to wild-type (the quantification has been removed from this version). Second, despite this reduced cortical accumulation, Shot.L(C)-GFP expression partially rescues ooplasmic flows and microtubule streaming in stage 10B ens mutant oocytes, and restores fertility to ens mutant females.

5) The authors based they conclusions about the involvement of Ens, Kinesin-1 and Ninein in ncMTOC anchoring on the decrease in cortical fluorescence intensity of Shot-YFP and Patronin-YFP in the corresponding mutant backgrounds. However, there is a large variation in average Shot-YFP intensity between control oocytes in different experiments. In Fig. 2F-G the average level of Shot-YFP in the control sis 130 AU while in Fig.3 G-H it is only 55 AU. This makes me worry about reliability of such measurements and the conclusions drawn from them.

To clarify this point, we have harmonized the method used to quantify the Shot-YFP signals in Figure 4E with the methodology used in Figure 3B, based on the original images. The levels are not strictly identical (Control Figure 2 B: 132.7+/-36.2 versus Control Figure 4 E: 164.0+/- 37.7). These differences are usual when experiments are performed at several-month intervals and by different users.

6) The decrease in the intensity of Shot-YFP and Patronin-YFP cortical fluorescence in ens mutant oocytes could be because of problems with ncMTOC anchoring or with ncMTOCs formation. The authors should find a way to distinguish between these two possibilities. The authors could express Ens-Mut (described in Sung et al 2008), which localises at the oocyte posterior and test whether it recruits Shot/Patronin ncMTOCs to the posterior.

We tried to obtain the fly stocks described in the 2008 paper by contacting former members of Pernille Rørth's laboratory. Unfortunately, we learned that the lab no longer exists and that all reagents, including the requested stocks, were either discarded or lost over time. To our knowledge, these materials are no longer available from any source. We regret that this limitation prevented us from performing the straightforward experiments suggested by the reviewer using these specific tools.

7) According to the Materials and Methods, the Shot-GFP used in Fig.3 E-F and Fig.4 was the BDSC line 29042. This is Shot L(C), a full-length version of Shot missing the CH1 actin-binding domain that is crucial for Shot anchoring to the cortex. If the authors indeed used this version of Shot-GFP, the interpretation of the above experiments is very difficult.

The Shot.L(C) isoform lacks the CH1 domain but retains the CH2 actin-binding motif. Truncated proteins with this domain and fused to GST retains a weak ability to bind actin in vitro. Importantly, the function of this isoform is context-dependent: it cannot rescue shot loss-of-function in neuron morphogenesis but fully restores Shot-dependent tracheal cell remodeling (Lee and Kolodziej, 2002).

In our experiments, when the Shot.L(C) isoform was expressed under the control of a maternal driver, its localization to the oocyte cortex was comparable to that of the genomic Shot-YFP construct (new Figure S8). This demonstrates unambiguously that the CH1 domain is dispensable for Shot cortical localization in oocytes, and that CH2-mediated actin binding is sufficient for this localization. Of note, a recent study showed that actin network are not equivalent highlighting the need for specific Shot isoforms harboring specialized actin-binding domain (Nashchekin et al., 2024).

We note that the expression level of Shot.L(C)-GFP in the oocyte appeared slightly lower than that of Shot-YFP (expressed under endogenous Shot regulatory sequences), as assessed by Western blot (Figure S8 A).

Critically, Shot.L(C)-GFP expression was substantially lower than that of Shot.L(A)-GFP (that harbored both the CH1 and CH2 domain). Shot.L(A)-GFP was overexpressed (Figure 8 A) and ectopically localized on MTs in both nurse cells and the ooplasm (Figure S8 B middle panel and arrow). These observations are in agreement that the Shot.L(C)-GFP rescue experiment was performed at near-physiological expression levels, strengthening the validity of our conclusions.

8) Page 6 "converted in NCs, in a region adjacent to the ring canals, Dendra-Ens-labeled MTs were found in the oocyte compartment indicating they are able to travel from NC toward the oocyte through ring canals". I have difficulty seeing the translocation of MT through the ring canals. Perhaps it would be more obvious with a movie/picture showing only one channel. Considering that f Dendra-Ens appears in the oocyte much faster than MT transport through ring canals (140nm/s, Lu et al 2022), the authors are most probably observing the translocation of free Ens rather than Ens bound to MT. The authors should also mention that Ens movement from the NC to the oocyte has been shown before with Ens MBD in Lu et al 2022 with better resolution.

We fully agree on the caveat mentioned by this reviewer: we may observe the translocation of free Dendra-Ensconsin. The experiment, was removed and replaced by referring to the work of the Gelfand lab. The movement of MTs that travel at ~140 nm/s between nurse cells toward the oocyte through the Ring Canals was reported before by Lu et al. (2022) with a very good resolution. Notably, this directional directed movement of MTs was measured using a fusion protein encompassing Ens MT-binding domain. We decided to remove this inclusive experiment and rather refer to this relevant study.

9) Page 6: The co-localization of Ninein with Ens and Shot at the oocyte cortex (Figure 2A). I have difficulty seeing this co-localisation. Perhaps it would be more obvious in merged images of only two channels and with higher resolution images

10) "a pool of the Ens-GFP co-localized with Ch-Patronin at cortical ncMTOCs at the anterior cortex (Figure 3A)". I also have difficulty seeing this.

We have performed new high-resolution acquisitions that provide clearer and more convincing evidence for the localization cortical distribution of these proteins (revised Figure 2A-2C and Figure 4A). These improved images demonstrate that Ens, Ninein, Shot, and Patronin partially colocalize at cortical ncMTOCs, as initially proposed. Importantly, the new data also reveal a spatial distinction: while Ens localizes along microtubules extending from these cortical sites, Ninein appears confined to small cytoplasmic puncta adjacent but also present on cortical microtubules.

11) "Ninein co-localizes with Ens at the oocyte cortex and partially along cortical microtubules, contributing to the maintenance of high Ens protein levels in the oocyte and its proper cortical targeting". I could not find any data showing the involvement of Ninein in the cortical targeting of Ens.

We found decreased Ens localization to MTs and to the cell cortex region (new Figure S3 A-B).

12) "our MT network analyses reveal the presence of numerous short MTs cytoplasmic clustered in an anterior pattern." "This low cortical recruitment of ncMTOCs is consistent with poor MT anchoring and their cytoplasmic accumulation." I could not find any data showing that short cortical MT observed at stage 10b in ens mutant and Khc RNAi were cytoplasmic and poorly anchored.

The sentence was removed from the revised manuscript.

13) "The egg chamber consists of interconnected cells where Dynein and Khc activities are spatially separated. Dynein facilitates transport from NCs to the oocyte, while Khc mediates both transport and advection within the oocyte." Dynein is involved in various activities in the oocyte. It anchors the oocyte nucleus and transports bcd and grk mRNA to mention a few.

The text was amended to reflect Dynein involvement in transport activities in the oocyte, with the appropriate references (lane 105-107).

14) The cartoons in Fig.2H and 3I exaggerate the effect of Ninein and Ens on cortical ncMTOCs. According to the corresponding graphs, there is a 20 and 50% decrease in each case.

New cartoons (now revised Figure 3E and 4F), are amended to reflect the ncMTOC values but also MT orientation (Figure 3E).

Significance

Given the important concerns raised, the significance of the findings is difficult to assess at this stage.

We sincerely thank the reviewer for their thorough evaluation of our manuscript. We have carefully addressed their concerns through substantial new experiments and analyses. We hope that the revised manuscript, in its current form, now provides the clarifications and additional evidence requested, and that our responses demonstrate the significance of our findings.

Reviewer #4 (Evidence, reproducibility and clarity (Required)):

Summary: This manuscript presents an investigation into the molecular mechanisms governing spatial activation of Kinesin-1 motor protein during Drosophila oogenesis, revealing a regulatory network that controls microtubule organization and cytoplasmic transport. The authors demonstrate that Ensconsin, a MAP7 family protein and Kinesin-1 activator, is spatially enriched in the oocyte through a dual mechanism involving Dynein-mediated transport from nurse cells and cortical maintenance by Ninein. This spatial enrichment of Ens is crucial for locally relieving Kinesin-1 auto-inhibition. The Ens/Khc complex promotes cortical recruitment of non-centrosomal microtubule organizing centers (ncMTOCs), which are essential for anchoring microtubules at the cortex, enabling the formation of long, parallel microtubule streams or "twisters" that drive cytoplasmic advection during late oogenesis. This work establishes a paradigm where motor protein activation is spatially controlled through targeted localization of regulatory cofactors, with the activated motor then participating in building its own transport infrastructure through ncMTOC recruitment and microtubule network organization.

There's a lot to like about this paper! The data are generally lovely and nicely presented. The authors also use a combination of experimental approaches, combining genetics, live and fixed imaging, and protein biochemistry.

We thank the reviewer for this enthusiastic and supportive review, which helped us further strengthen the manuscript.

Concerns: Page 6: "to assay if elevation of Ninein levels was able to mis-regulate Ens localization, we overexpressed a tagged Ninein-RFP protein in the oocyte. At stage 9 the overexpressed Ninein accumulated at the anterior cortex of the oocyte and also generated large cortical aggregates able to recruit high levels of Ens (Figures 2D and 2H)... The examination of Ninein/Ens cortical aggregates obtained after Ninein overexpression showed that these aggregates were also able to recruit high levels of Patronin and Shot (Figures 2E and 2H)." Firstly, I'm not crazy about the use of "overexpressed" here, since there isn't normally any Ninein-RFP in the oocyte. In these experiments it has been therefore expressed, not overexpressed. Secondly, I don't understand what the reader is supposed to make of these data. Expression of a protein carrying a large fluorescent tag leads to large aggregates (they don't look cortical to me) that include multiple proteins - in fact, all the proteins examined. I don't understand this to be evidence of anything in particular, except that Ninein-RFP causes the accumulation of big multi-protein aggregates. While I can understand what the authors were trying to do here, I think that these data are inconclusive and should be de-emphasized.

We have revised the manuscript by replacing overexpressed with expressed (lanes 211 and 212). In addition, we now provide new localization data in both cortical (new Figure S4 A, top) and medial focal planes (new Figure S4 A, bottom), demonstrating that Ninein puncta (the word used in Rosen et al, 2019), rather than aggregates are located cortically. We also show that live IRP-labelled MTs do not colocalize with Ninein-RFP puncta. In light of the new experiments and the comments from the other reviewers, the corresponding text has been revised and de-emphasized accordingly.

Page 7: "Co-immunoprecipitations experiments revealed that Patronin was associated with Shot-YFP, as shown previously (Nashchekin et al., 2016), but also with EnsWT-GFP, indicating that Ens, Shot and Patronin are present in the same complex (Figure 3B)." I do not agree that association between Ens-GFP and Patronin indicates that Ens is in the same complex as Shot and Patronin. It is also very possible that there are two (or more) distinct protein complexes. This conclusion could therefore be softened. Instead of "indicating" I suggest "suggesting the possibility."

We have toned down this conclusion and indicated “suggesting the possibility” (lane 238-239).

Page 7: "During stage 9, the average subcortical MT length, taken at one focal plane in live oocytes (see methods)..." I appreciate that the authors have been careful to describe how they measured MT length, as this is a major point for interpretation. I think the reader would benefit from an explanation of why they decided to measure in only one focal plane and how that decision could impact the results.

We appreciate this helpful suggestion. Cortical microtubules are indeed highly dynamic and extend in multiple directions, including along the Z-axis. Moreover, their diameter is extremely small (approximately 25 nm), making it technically challenging to accurately measure their full length with high resolution using our Zeiss Airyscan confocal microscope (over several, microns): the acquisition of Z-stacks is relatively slow and therefore not well suited to capturing the rapid dynamics of these microtubules. Consequently, our length measurements represent a compromise and most likely underestimate the actual lengths of microtubules growing outside the focal plane. We note that other groups have encountered similar technical limitations (Parton et al., 2011).

Page 7: "... the MTs exhibited an orthogonal orientation relative to the anterior cortex (Figures 4A left panels, 4C and 4E)." This phenotype might not be obvious to readers. Can it be quantified?

We have now analyzed the orientation of microtubules (MTs) along the dorso-ventral axis. Our analysis shows that ens, Khc RNAi oocytes (new Figure 5B), and, to a lesser extent, Nin mutant oocytes (new Figure 3D), display a more random MT orientation compared to wild-type (WT) oocytes. In WT oocytes, MTs are predominantly oriented toward the posterior pole, consistent with previous findings (Parton et al., 2011).

Page 8: "Altogether, the analyses of Ens and Khc defective oocytes suggested that MT organization defects during late oogenesis (stage 10B) were caused by an initial failure of ncMTOCs to reach the cell cortex. Therefore, we hypothesized that overexpression of the ncMTOC component Shot could restore certain aspects of microtubule cortical organization in ens-deficient oocytes. Indeed, Shot overexpression (Shot OE) was sufficient to rescue the presence of long cortical MTs and ooplasmic advection in most ens oocytes (9/14)..." The data are clear, but the explanation is not. Can the authors please explain why adding in more of an ncMTOC component (Shot) rescues a defect of ncMTOC cortical localization?

We propose that cytoplasmic ncMTOCs can bind the cell cortex via the Shot subunit that is so far the only component that harbors actin-binding motifs. Therefore, we propose that elevating cytoplasmic Shot increase the possibility of Shot to encounter the cortex by diffusion when flows are absent. This is now explained lane 282-285.

I'm grateful to the authors for their inclusion of helpful diagrams, as in Figures 1G and 2H. I think the manuscript might benefit from one more of these at the end, illustrating the ultimate model.

We have carefully considered and followed the reviewer’s suggestions. In response, we have included a new figure illustrating our proposed model: the recruitment of ncMTOCs to the cell cortex through low Khc-mediated flows at stage 9 enhances cortical microtubule density, which in turn promotes self-amplifying flows (new Figure 7, panels A to C). Note that this Figure also depicts activation of Khc by loss of auto-inhibition (Figure 7, panel D).

I'm sorry to say that the language could use quite a bit of polishing. There are missing and extraneous commas. There is also regular confusion between the use of plural and singular nouns. Some early instances include:

1. Page 3: thought instead of "thoughted."
2. Page 5: "A previous studies have revealed"
3. Page 5: "A significantly loss"
4. Page 6: "troughs ring canals" should be "through ring canals"
5. Page 7: lives stage 9 oocytes
6. Page 7: As ens and Khc RNAi oocytes exhibits
7. Page 7: we examined in details
8. Page 7: This average MT length was similar in Khc RNAi and ens mutant oocyte..

We apologize for errors. We made the appropriate corrections of the manuscript.

Reviewer #4 (Significance (Required)):

This work makes a nice conceptual advance by showing that motor activation controls its own transport infrastructure, a paradigm that could extend to other systems requiring spatially regulated transport.

We thank the reviewers for their evaluation of the manuscript and helpful comments.

Note: This preprint has been reviewed by subject experts for Review Commons. Content has not been altered except for formatting.

Learn more at Review Commons

Referee #4

Evidence, reproducibility and clarity

Summary: This manuscript presents an investigation into the molecular mechanisms governing spatial activation of Kinesin-1 motor protein during Drosophila oogenesis, revealing a regulatory network that controls microtubule organization and cytoplasmic transport. The authors demonstrate that Ensconsin, a MAP7 family protein and Kinesin-1 activator, is spatially enriched in the oocyte through a dual mechanism involving Dynein-mediated transport from nurse cells and cortical maintenance by Ninein. This spatial enrichment of Ens is crucial for locally relieving Kinesin-1 auto-inhibition. The Ens/Khc complex promotes cortical recruitment of non-centrosomal microtubule organizing centers (ncMTOCs), which are essential for anchoring microtubules at the cortex, enabling the formation of long, parallel microtubule streams or "twisters" that drive cytoplasmic advection during late oogenesis. This work establishes a paradigm where motor protein activation is spatially controlled through targeted localization of regulatory cofactors, with the activated motor then participating in building its own transport infrastructure through ncMTOC recruitment and microtubule network organization.

There's a lot to like about this paper! The data are generally lovely and nicely presented. The authors also use a combination of experimental approaches, combining genetics, live and fixed imaging, and protein biochemistry.

Concerns:

Page 6: "to assay if elevation of Ninein levels was able to mis-regulate Ens localization, we overexpressed a tagged Ninein-RFP protein in the oocyte. At stage 9 the overexpressed Ninein accumulated at the anterior cortex of the oocyte and also generated large cortical aggregates able to recruit high levels of Ens (Figures 2D and 2H)... The examination of Ninein/Ens cortical aggregates obtained after Ninein overexpression showed that these aggregates were also able to recruit high levels of Patronin and Shot (Figures 2E and 2H)." Firstly, I'm not crazy about the use of "overexpressed" here, since there isn't normally any Ninein-RFP in the oocyte. In these experiments it has been therefore expressed, not overexpressed. Secondly, I don't understand what the reader is supposed to make of these data. Expression of a protein carrying a large fluorescent tag leads to large aggregates (they don't look cortical to me) that include multiple proteins - in fact, all the proteins examined. I don't understand this to be evidence of anything in particular, except that Ninein-RFP causes the accumulation of big multi-protein aggregates. While I can understand what the authors were trying to do here, I think that these data are inconclusive and should be de-emphasized.

Page 7: "Co-immunoprecipitations experiments revealed that Patronin was associated with Shot-YFP, as shown previously (Nashchekin et al., 2016), but also with EnsWT-GFP, indicating that Ens, Shot and Patronin are present in the same complex (Figure 3B)." I do not agree that association between Ens-GFP and Patronin indicates that Ens is in the same complex as Shot and Patronin. It is also very possible that there are two (or more) distinct protein complexes. This conclusion could therefore be softened. Instead of "indicating" I suggest "suggesting the possibility."

Page 7: "During stage 9, the average subcortical MT length, taken at one focal plane in live oocytes (see methods)..." I appreciate that the authors have been careful to describe how they measured MT length, as this is a major point for interpretation. I think the reader would benefit from an explanation of why they decided to measure in only one focal plane and how that decision could impact the results.

Page 7: "... the MTs exhibited an orthogonal orientation relative to the anterior cortex (Figures 4A left panels, 4C and 4E)." This phenotype might not be obvious to readers. Can it be quantified?

Page 8: "Altogether, the analyses of Ens and Khc defective oocytes suggested that MT organization defects during late oogenesis (stage 10B) were caused by an initial failure of ncMTOCs to reach the cell cortex. Therefore, we hypothesized that overexpression of the ncMTOC component Shot could restore certain aspects of microtubule cortical organization in ens-deficient oocytes. Indeed, Shot overexpression (Shot OE) was sufficient to rescue the presence of long cortical MTs and ooplasmic advection in most ens oocytes (9/14)..." The data are clear, but the explanation is not. Can the authors please explain why adding in more of an ncMTOC component (Shot) rescues a defect of ncMTOC cortical localization?

I'm grateful to the authors for their inclusion of helpful diagrams, as in Figures 1G and 2H. I think the manuscript might benefit from one more of these at the end, illustrating the ultimate model.

I'm sorry to say that the language could use quite a bit of polishing. There are missing and extraneous commas. There is also regular confusion between the use of plural and singular nouns. Some early instances include:

1. Page 3: thought instead of "thoughted."
2. Page 5: "A previous studies have revealed"
3. Page 5: "A significantly loss"
4. Page 6: "troughs ring canals" should be "through ring canals"
5. Page 7: lives stage 9 oocytes
6. Page 7: As ens and Khc RNAi oocytes exhibits
7. Page 7: we examined in details
8. Page 7: This average MT length was similar in Khc RNAi and ens mutant oocyte..

Significance

This work makes a nice conceptual advance by showing that motor activation controls its own transport infrastructure, a paradigm that could extend to other systems requiring spatially regulated transport.

Author response:

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

Reviewer #1 (Public Review): 

Strengths: 

The work uses a simple and straightforward approach to address the question at hand: is dynein a processive motor in cells? Using a combination of TIRF and spinning disc confocal microscopy, the authors provide a clear and unambiguous answer to this question. 

Thank you for the recognition of the strength of our work

Weaknesses: 

My only significant concern (which is quite minor) is that the authors focus their analysis on dynein movement in cells treated with docetaxol, which could potentially affect the observed behavior. However, this is likely necessary, as without it, motility would not have been observed due to the 'messiness' of dynein localization in a typical cell (e.g., plus end-tracking in addition to cargo transport).

You are exactly correct that this treatment was required to provided us a clear view of motile dynein and p50 puncta. One concern about the treatment that we had noted in our original submission was that the docetaxel derivative SiR tubulin could increase microtubule detyrosination, which has been implicated in affecting the initiation of dynein-dynactin motility but not motility rates (doi: 10.15252/embj.201593071). In response to a comment from reviewer 2 we investigated whether there was a significant increase in alpha-tubulin detyrosination in our treatment conditions and found that there was not. We have removed the discussion of this possibility from the revised version. Please also see response to comments raised by reviewer 2. 

Reviewer 1 (Recommendations for the authors):

Major points: 

(1) The authors measured kinesin-1-GFP intensities in a different cell line (drosophila S2 cells) than what was used for the DHC and p50 measurements (HeLa cells). It is unclear if this provides a fair comparison given the cells provide different environments for the GFP. Although the differences may in fact be trivial, without somehow showing this is indeed a fair comparison, it should at least be noted as a caveat when interpreting relative intensity differences. Alternatively, the authors could compare DHC and p50 intensities to those measured from HeLa cells treated with taxol. 

Thank you for this suggestion. We conducted new rounds of imaging with the DHCEGFP and p50-EGFP clones in conjunction with HeLa cells transiently expressing the human kinesin-1-EGFP and now present the datasets from the new experiments. Importantly, our new data was entirely consistent with the prior analyses as there was not a significant difference between the kinesin-1-EGFP dimer intensities and the DHC-EGFP puncta intensities and there was a statistically significant difference in the intensity of p50 puncta, which were approximately half the intensity of the kinesin-1 and DHC. We have moved the old data comparing the intensities in S2 cells expressing kinesin-1-EGFP to Figure 3 - figure supplement 2 A-D and the new HeLa cell data is now shown in Figure 3 D-G.

(2) Given the low number of observations (41-100 puncta), I think a scatter plot showing all data points would offer readers a more transparent means of viewing the single-molecule data presented in Figures 3A, B, C, and G. I also didn't see 'n' values for plots shown in Figure 3. 

The box and whisker plots have now been replaced with scatter plots showing all data points. The accompanying ‘n’ values have been included in the figure 3 legend as well as the histograms in figures 1 and 2 that are represented in the comparative scatter plots.  

(3) Given the authors have produced a body of work that challenges conclusions from another pre-print (Tirumala et al., 2022 bioRxiv) - specifically, that dynein is not processive in cells - I think it would be useful to include a short discussion about how their work challenges theirs. For example, one significant difference between the two experimental systems that may account for the different observations could simply be that the authors of the Tirumala study used a mouse DHC (in HeLa cells), which may not have the ability to assemble into active and processive dynein-dynactin-adaptor complexes. 

Thank you for pointing this out! At the time we submitted our manuscript we were conflicted about citing a pre-print that had not been peer reviewed simply to point out the discrepancy. If we had done so at that time we would have proposed the exact potential technical issue that you have proposed here. However, at the time we felt it would be better for these issues to be addressed through the review process. Needless to say, we agree with your interpretation and now that the work is published (Tirumala et al. JCB, 2024) it is entirely appropriate to add a discussion on Tirumala et al. where contradictory observations were reported. 

The following statement has been added to the manuscript: 

“In contrast, a separate study (Tirumala et al., 2024) reported that dynein is not highly processive, typically exhibiting runs of very short duration (~0.6 s) in HeLa cells. A notable technical difference that may account for this discrepancy is that our study visualizes endogenously tagged human DHC, whereas Tirumala et al. characterized over-expressed mouse DHC in HeLa cells. Over-expression of the DHC may result in an imbalance of the subunits that comprise the active motor complex, leading to inactive, or less active complexes. Similarly, mouse DHC may not have the ability to efficiently assemble into active and processive dynein-dynactin-adaptor complexes to the same extent as human DHC.”

Minor points: 

(1) "Specifically, the adaptor BICD2 recruited a single dynein to dynactin while BICDR1 and HOOK3 supported assembly of a "double dynein" complex." It would be more accurate to say that dynein-dynactin complexes assembled with Bicd2 "tend to favor single dynein, and the Bicdr1 and Hook3 tend to favor two dyneins" since even Bicd2 can support assembly of 2 dynein-1 dynactin complexes (see Urnavicius et al, Nature 2018). 

Thank you, the manuscript has been edited to reflect this point. 

(2) "Human HeLa cells were engineered using CRISPR/Cas9 to insert a cassette encoding FKBP and EGFP tags in the frame at the 3' end of the dynein heavy chain (DYNC1H1) gene (SF1)." It is unclear to what "SF1" is referring. 

SF1 is supplementary figure 1, which we have now clarified as being Figure 1 – figure supplement 1A.

(3) "The SiR-Tubulin-treated cells were subjected to two-color TIRFM to determine if the DHC puncta exhibited motility and; indeed, puncta were observed streaming along MTs..." This sentence is strangely punctuated (the ";" is likely a typo?). 

Thank you for pointing this out, the typo has been corrected and the sentence now reads:

“The SiR-Tubulin-treated cells were subjected to two-color TIRFM and DHC-EGFP puncta were clearly observed streaming on Sir-Tubulin labeled MTs, which was especially evident on MTs that were pinned between the nucleus and the plasma membrane (Video 3)”

(4) I am unfamiliar with the "MK" acronym shown above the molecular weight ladders in Figure 3H and I. Did the authors mean to use "MW" for molecular weight? 

We intended this to mean MW and the typo has been corrected.

(5) "This suggests that the cargos, which we presume motile dynein-dynactin puncta are bound to, any kinesins..." This sentence is confusing as written. Did the authors mean "and kinesins"? 

Agreed. We have changed this sentence to now read: 

“The velocity and low switching frequency of motile puncta suggest that any kinesin motors associated with cargos being transported by the dynein-dynactin visualized here are inactive and/or cannot effectively bind the MT lattice during dynein-dynactin-mediated transport in interphase HeLa cells.”

Reviewer 2 (Recommendations for the authors):

(1) I am confused as to why the authors introduced an FKBP tag to the DHC and no explanation is given. Is it possible this tag induces artificial dimerization of the DHC? 

FKBP was tagged to DHC for potential knock sideways experiments. Since the current cell line does not express the FKBP counterpart FRB, having FKBP alone in the cell line would not lead to artificial dimerization of DHC.

(2) The authors use a high concentration of SiR-tubulin (1uM) before washing it out. However, they observe strong effects on MT dynamics. The manufacturer states that concentrations below 100nM don't affect MT dynamics, so I am wondering why the authors are using such a high amount that leads to cellular phenotypes. 

We would like to note that in our hands even 100 nM SiR-tubulin impacted MT dynamics if it was incubated for enough time to get a bright signal for imaging, which makes sense since drugs like docetaxel and taxol become enriched in cells over time. Thus, it was a trade-off between the extent/brightness of labeling and the effects on MT dynamics. We opted for shorter incubation with a higher concentration of Sir-Tubulin to achieve rapid MT labeling and efficient suppression of plus-end MT polymerization. This approach proved useful for our needs since the loss of the tip-tacking pool of DHC provided a clearer view of the motile population of MT-associated DHC.

(3) The individual channels should be labeled in the supplemental movies. 

They have now been labelled.

(4) I would like to see example images and kymographs of the GFP-Kinesin-1 control used for fluorescent intensity analysis. Further, the authors use the mean of the intensity distribution, but I wonder why they don't fit the distribution to a Gaussian instead, as that seems more common in the field to me. Do the data fit well to a Gaussian distribution? 

Example images and kymographs of the kinesin-1-EGFP control HeLa cells used for the updated fluorescent intensity analysis have been now added to the manuscript in Figure 3 - figure supplement 1. The kinesin-1-EGFP transiently expressed in HeLa cells exhibited a slower mean velocity and run length than the endogenously tagged HeLa dynein-dynactin. Regarding the distribution, we applied 6 normality tests to the new datasets acquired with DHC and p50 in comparison to human kinesin-EGFP in HeLa cells. While we are confident concluding that the data for p50 was normally distributed (p > 0.05 in 6/6), it was more difficult to reach conclusions about the normality of the datasets for kinesin-1 (p > 0.05 in 4/6) and DHC (p > 0.5 in 1/6). We have decided to report the data as scatter plots (per the suggestion in major point 1 by reviewer 1) in the new Figure 3G since it could be misleading to fit a non-normal distribution with a single Gaussian. We note that the likely non-normal distribution of the DHC data (since it “passed” only 1/6 normality tests) could reflect the presence of other populations (e.g. 1 DHC-EGFP in a motile puncta), but we could also not confidently conclude this since attempting to fit the data with a double Gaussian did not pass statistical muster. Indeed, as stated in the text, on lines 197-198 we do not exclude that the range of DHC intensities measured here may include sub-populations of complexes containing a single dynein dimer with one DHC-EGFP molecule.   

Ultimately, we feel the safest conclusion is that there was not a statically significant difference between the DHC and kinesin-1 dimers (p = 0.32) but there was a statistically significant difference between both the DHC and kinesin-1 dimers compared to the p50 (p values < 0.001), which was ~50% the intensity of DHC and kinesin-1. Altogether this leads us to the fairly conservative conclusion that DHC puncta contain at least one dimer while the p50 puncta likely contain a single p50-EGFP molecule. 

(5) The authors suggest the microtubules in the cells treated with SiR-tubulin may be more detyrosinated due to the treatment. Why don't they measure this using well-characterized antibodies that distinguish tyrosinated/detyrosinated microtubules in cells treated or not with SiR-tubulin? 

At your suggestion, we carried out the experiment and found that under our labeling conditions there was not a notable difference in microtubule detyrosination between DMSO- and SiR-Tubulin-treated cells. Thus, we have removed this caveat from the revised manuscript.

(6) "While we were unable to assess the relative expression levels of tagged versus untagged DHC for technical reasons." Please describe the technical reasons for the inability to measure DHC expression levels for the reader.

We made several attempts to quantify the relative amounts of untagged and tagged protein by Western blotting. The high molecular weight of DHC (~500kDa) makes it difficult to resolve it on a conventional mini gel. We attempted running a gradient mini gel (4%-15%), and doing a western blot; however, we were still unable to detect DHC. To troubleshoot, the experiments were repeated with different dilutions of a commercially available antibody and varying concentrations of cell lysate; however, we were unable to obtain a satisfactory result. 

We hold the view that even if it had it worked it would have been difficult to detect a relatively small difference between the untagged (MW = 500kDa) and tagged DHC (MW = 527kDa) by western blot. We have added language to this effect in the revised manuscript. 

Reviewer #3 (Public Review):

(1). CRISPR-edited HeLa clones: 

(i) The authors indicate that both the DHC-EGFP and p50-EGFP lines are heterozygous and that the level of DHC-EGFP was not measured due to technical difficulties. However, quantification of the relative amounts of untagged and tagged DHC needs to be performed - either using Western blot, immunofluorescence or qPCR comparing the parent cell line and the cell lines used in this work. 

See response to reviewer 2 above. 

(ii) The localization of DHC predominantly at the plus tips (Fig. 1A) is at odds with other work where endogenous or close-to-endogenous levels of DHC were visualized in HeLa cells and other non-polarized cells like HEK293, A-431 and U-251MG (e.g.: OpenCell (https://opencell.czbiohub.org/target/CID001880), Human Protein Atlas  ), https://www.biorxiv.org/content/10.1101/2021.04.05.438428v3). The authors should perform immunofluorescence of DHC in the parental cells and DHC-EGFP cells to confirm there are no expression artifacts in the latter. Additionally, a comparison of the colocalization of DHC with EB1 in the parental and DHC-EGFP and p50-EGFP lines would be good to confirm MT plus-tip localisation of DHC in both lines. 

The microtubule (MT) plus-tip localization of DHC was already observed in the 1990s, as evidenced by publications such as (PMID:10212138) and (PMID:12119357), which were further confirmed by Kobayashi and Murayama  in 2009 (PMID:19915671). We hold the view that further investigation into this localization is not worthwhile since the tip-tracking behavior of DHC-dynactin has been long-established in the field.

(iii) It would also be useful to see entire fields of view of cells expressing DHC-EGFP and p50EGFP (e.g. in Spinning Disk microscopy) to understand if there is heterogeneity in expression. Similarly, it would be useful to report the relative levels of expression of EGFP (by measuring the total intensity of EGFP fluorescence per cell) in those cells employed for the analysis in the manuscript. 

Representative images of fields have been added as Figure 1 - figure supplement 1B and Figure 2 – figure supplement 1 in the revised manuscript. We did not see drastic cell-tocell variation of expression within the clonal cell lines.

(iv) Given that the authors suspect there is differential gene regulation in their CRISPR-edited lines, it cannot be concluded that the DHC-EGFP and p50-EGFP punctae tracked are functional and not piggybacking on untagged proteins. The authors could use the FKBP part of the FKBPEGFP tag to perform knock-sideways of the DHC and p50 to the plasma membrane and confirm abrogation of dynein activity by visualizing known dynein targets such as the Golgi (Golgi should disperse following recruitment of EGFP-tagged DHC-EGFP or p50-EGFP to the PM), or EGF (movement towards the cell center should cease). 

Despite trying different concentrations and extensive troubleshooting, we were not able to replicate the reported observations of Ciliobrevin D or Dynarrestin during mitosis. We would like to emphasize that the velocity (1.2 μm/s) of dynein-dynactin complexes that we measured in HeLa cells was comparable to those measured in iNeurons by Fellows et al. (PMID: 38407313) and for unopposed dynein under in vitro conditions. 

(2) TIFRM and analysis: 

(i) What was the rationale for using TIRFM given its limitation of visualization at/near the plasma membrane? Are the authors confident they are in TIRF mode and not HILO, which would fit with the representative images shown in the manuscript? 

To avoid overcrowding, it was important to image the MT tracks that that were pinned between the nucleus and the plasma membrane. It is unclear to us why the reviewer feels that true TIRFM could not be used to visualize the movement of dynein-dynactin on this population of MTs since the plasma membrane is ~ 3-5 nm and a MT is ~25-27 nm all of which would fall well within the 100-200 nm excitable range of the evanescent wave produced by TIRF. While we feel TIRF can effectively visualize dynein-dynactin motility in cells, we have mentioned the possibility that some imaging may be HILO microscopy in the materials and methods.

(ii) At what depth are the authors imaging DHC-EGFP and p50-EGFP? 

The imaging depth of traditional TIRFM is limited to around 100-200 nm. In adherent interphase HeLa cells the nucleus is in very close proximity (nanometer not micron scale) to the plasma membrane with some cytoskeletal filaments (actin) and microtubules positioned between the plasma membrane and the nuclear membrane. The fact that we were often visualizing MTs positioned between the nucleus and the membrane makes us confident that we were imaging at a depth (100 - 200nm) consistent with TIRFM. 

(iii) The authors rely on manual inspection of tracks before analyzing them in kymographs - this is not rigorous and is prone to bias. They should instead track the molecules using single particle tracking tools (eg. TrackMate/uTrack), and use these traces to then quantify the displacement, velocity, and run-time. 

Although automated single particle tracking tools offer several benefits, including reduced human effort, and scalability for large datasets, they often rely on specialized training datasets and do not generalize well to every dataset. The authors contend that under complex cellular environments human intervention is often necessary to achieve a reliable dataset. Considering the nature of our data we felt it was necessary to manually process the time-lapses. 

(iv) It is unclear how the tracks that were eventually used in the quantification were chosen. Are they representative of the kind of movements seen? Kymographs of dynein movement along an entire MT/cell needs to be shown and all punctae that appear on MTs need to be tracked, and their movement quantified. 

Considering the densely populated environment of a cell, it will be nearly impossible to quantity all the datasets. We selected tracks for quantification, focusing on areas where MTs were pinned between the nucleus and plasma membrane where we could track the movement of a single dynein molecule and where the surroundings were relatively less crowded. 

(v) What is the directionality of the moving punctae? 

In our experience, cells rarely organized their MTs in the textbook radial MT array meaning that one could not confidently conclude that “inward” movements were minus-end directed. Microtubule polarity was also not able to be determined for the MTs positioned between the plasma membrane and the nucleus on which many of the puncta we quantified were moving. It was clear that motile puncta moving on the same MT moved in the same direction with the exception of rare and brief directional switching events. What was more common than directional switching on the same MT were motile puncta exhibiting changes in direction at sharp (sometimes perpendicular) angles indicative of MT track switching, which is a well-characterized behavior of dynein-dynactin (See DOI: 10.1529/biophysj.107.120014).

(vi) Since all the quantification was performed on SiR tubulin-treated cells, it is unclear if the behavior of dynein observed here reflects the behavior of dynein in untreated cells. Analysis of untreated cells is required. 

It was important to quantify SiR tubulin-treated cells because SiR-Tubulin is a docetaxel derivative, and its addition suppressed plus-end MT polymerization resulting in a significant reduction in the DHC tip-tracking population and a clearer view of the motile population of MT-associated DHC puncta. Otherwise, it was challenging to reliably identify motile puncta given the abundance of DHC tip-tracking populations in untreated cells.  

(3) Estimation of stoichiometry of DHC and p50 

Given that the punctae of DHC-EGFP and p50 seemingly bleach on MT before the end of the movie, the authors should use photobleaching to estimate the number of molecules in their punctae, either by simple counting the number of bleaching steps or by measuring single-step sizes and estimating the number of molecules from the intensity of punctae in the first frame. 

Comparing the fluorescence intensity of a known molecule (in our case a kinesin-1EGFP dimer) to calculate the numbers of an unknown protein molecule (in our case Dynein or p50) is a widely accepted technique in the field. For example, refer to PMID: 29899040. To accurately estimate the stoichiometry of DHC and p50 and address the concerns raised by other reviewers, we expressed the human kinesin-EGFP in HeLa cells and analyzed the datasets from new experiments. We did not observe any significant differences between our old and new datasets.

(4) Discussion of prior literature 

Recent work visualizing the behavior of dyneins in HeLa cells (DOI:  10.1101/2021.04.05.438428), which shows results that do not align with observations in this manuscript, has not been discussed. These contradictory findings need to be discussed, and a more objective assessment of the literature in general needs to be undertaken.

Again, another good source for Arthur, this time gives context for the second source and the reburial of arthur

Author Response:

Reviewer #1 (Public Review):

The manuscript by Chakraborty focuses on methods to direct dsDNA to specific cell types within an intact multicellular organism, with the ultimate goal of targeting DNA-based nanodevices, often as biosensors within endosomes and lysosomes. Taking advantage of the endogenous SID-2 dsRNA receptor expressed in C. elegans intestinal cells, the authors show that dsDNA conjugated to dsRNA can be taken into the intestinal endosomal system via feeding and apical endocytosis, while dsDNA alone is not an efficient endocytic cargo from the gut lumen. Since most cells do not express a dsRNA receptor, the authors sought to develop a more generalizable approach. Via phage display screening they identified a novel camelid antibody 9E that recognizes a short specific DNA sequence that can be included at the 3' end of synthesized dsDNAs. The authors then showed that this antibody can direct binding, and in some cases endocytosis, of such DNAs when 9E was expressed as a fusion with transmembrane protein SNB-1. This approach was successful in targeting microinjected dsDNA pan-neuronally when expressed via the snb-1 promoter, and to specific neuronal subsets when expressed via other promoters. Endocytosed dsDNA appeared in puncta moving in neuronal processes, suggesting entry into endosomes. Plasma membrane targeting appeared feasible using 9E fusion to ODR-2.

The major strength of the paper is in the identification and testing of the 9E camelid antibody as part of a generalizable dsDNA targeting system. This aspect of the paper will likely be of wide interest and potentially high impact, since it could be applied in any intact animal system subject to transgene expression. A weakness of the paper is the choice of "nanodevice". It was not clear what utility was present in the DNAs used, such as D38, that made them "devices", aside from their fluorescent tag that allowed tracking their localization.

We used a DNA nanodevice, denoted pHlava-9E, that uses pHrodo as a pH-sensitive dye. pHlava-9E is designed to provide a digital output of compartmentalization i.e., its pH profile is such that even if it is internalized into a mildly acidic vesicle, the pH readout is as high as one would observe with a lysosome. This gives an unambiguous readout of surface-immobilized probe to endocytosed probe.

Another potential weakness is that the delivered DNA is limited to the cell surface or the lumen of endomembrane compartments without access to the cytoplasm or nucleus. In general the data appeared to be of high quality and was well controlled, supporting the authors conclusions.

We completely agree that we cannot target DNA nanodevices to sub-cellular locations such as the cytoplasm or the nucleus with this strategy. However, we do not see this as a “weakness”, but rather, as a limitation of the current capabilities of DNA nanotechnology. It must be mentioned that though fluorescent proteins were first described in 1962, it was 30 years before others targeted them to the endoplasmic reticulum (1992) or the nucleus (1993)(Brini et al., 1993; Kendall et al., 1992). Probe technologies undergo stage-wise improvements/expansions. We have therefore added a small section in the conclusions section outlining the future challenges in sub-cellular targeting of DNA-nanodevices.

Reviewer #2 (Public Review):

The authors demonstrate the tissue-specific and cell-specific targeting of double-stranded DNA (dsDNA) using C. elegans as a model host animal. The authors focused on two distinct tissues and delivery routes: feeding dsDNA to target a class of organelles within intestinal cells, and injecting dsDNA to target presynaptic endocytic structures in neurons. To achieve efficient intestinal targeting, the authors leveraged dsRNA uptake via endogenous intestinal SID-2 receptors by fusing dsRNA to a fluorophore-labeled dsDNA probe. In contrast, neuronal endosome/synaptic vesicle (SV) targeting was achieved by designing a nanobody that specifically binds a short dsDNA motif fused to the fluorophore-labeled dsDNA probe. Combining dsDNA probe injection with nanobody neuronal expression (fused to a neuronal vSNARE to achieve synaptic targeting), the authors demonstrated that the injected dsDNA could be taken up by a variety of distinct neuronal subtypes.

Strengths:

While nanodevices built on dsDNA platforms have been shown to be taken up by scavenger receptors in C. elegans (including previous work from several of these authors), this strategy will not work in many tissue types lacking these receptors. The authors successfully circumvented this limitation using distinct strategies for two cell types in the worm, thereby providing a more general approach for future efforts. The approaches are creative, and the nanobody development in particular allows for endocytic delivery in any cell type. The authors exploited quantitative imaging approaches to examine the subcellular targeting of dsDNA probes in living animals and manipulated endogenous receptors to demonstrate the mechanism of dsRNA-based dsDNA uptake in intestinal cells.

Weaknesses:

To validate successful delivery of a functional nanodevice, one would ideally demonstrate the function of a particular nanodevice in at least one of the examples provided in this work. The authors have successfully used a variety of custom-designed dsDNA probes in living worms in numerous past studies, so this would not be a technical hurdle. In the current study, the reader has no means of assessing whether the dsDNA is intact and functional within its intracellular compartment.

We now demonstrate the use of a functional nanodevice to detect pH profiles of a given microenvironment. This functional nanodevice contains two fluorescent reporter dyes, each attached to one of the strands of a DNA duplex. In order to obtain pH readouts, the device integrity is essential for ratiometric sensing.

Coelomocytes are cells known for their scavenging and degradative lysosomal machinery. Previous studies of the stability of variously structured DNA nanodevices in coelomocytes, have shown that DNA devices based on 38 bp DNA duplexes have a half life of >8 hours in actively scavenging cells such as coelomocytes (Chakraborty et al., 2017; Surana et al., 2013) Given that our sensing in the gut as well as in the neuron are performed in <1 hour post feeding or injection, pHlava-9E is >97% intact.

Another minor weakness is the lack of a quantitative assessment of colocalization in intestinal cells or neurons in an otherwise nicely quantitative study. Since characterization of the targeting described here is an essential part of evaluating the method, a stronger demonstration of colocalization would significantly buttress the authors' claims.

We have now quantified colocalization in each cellular system. Please see Figure R1 below (Figure 1 Supplementary figure 1 and Figure 4 Supplementary figure 2 of the revised manuscript).

Figure R1: a) Pearson’s correlation coefficient (PCC) calculated for the colocalization between R50D38 (red) and lysosomal markers LMP-1 or GLO-1 (green) in the indicated transgenic worms. b) & d) Representative images of nanodevice nD647 uptake (red) in transgenics expressing both prab-3::gfp::rab-3 (green) and psnb-1:snb-1::9E c - e) Normalized line intensity profiles across the indicated lines in b and d; f) Percentage colocalization of nD647 (red) with RAB3:GFP (green). Error bar represents the standard deviation between two data sets.

While somewhat incomplete, this study represents a step forward in the development of a general targeting approach amenable to nanodevice delivery in animal models.

Author Response

Summary: A major tenet of plant pathogen effector biology has been that effectors from very different pathogens converge on a small number of host targets with central roles in plant immunity. The current work reports that effectors from two very different pathogens, an insect and an oomycete, interact with the same plant protein, SIZ1, previously shown to have a role in plant immunity. Unfortunately, apart from some technical concerns regarding the strength of the data that the effectors and SIZ1 interact in plants, a major limitation of the work is that it is not demonstrated that the effectors alter SIZ1 activity in a meaningful way, nor that SIZ1 is specifically required for action of the effects.

We thank the editor and reviewers for their time to review our manuscript and their helpful and constructive comments. The reviews have helped us focus our attention on additional experiments to test the hypothesis that effectors Mp64 (from an aphid) and CRN83-152 (from an oomycete) indeed alter SIZ1 activity or function. We have revised our manuscript and added the following data:

1) Mp64, but not CRN83-152, stabilizes SIZ1 in planta. (Figure 1 in the revised manuscript).

2) AtSIZ1 ectopic expression in Nicotiana benthamiana triggers cell death from 3-4 days after agroinfiltration. Interestingly CRN83-152_6D10 (a mutant of CRN83-152 that has no cell death activity), but not Mp64, enhances the cell death triggered by AtSIZ1 (Figure 2 in the revised manuscript).

For 1) we have added the following panel to Figure 1 as well as three biological replicates of the stabilisation assays in the Supplementary data (Fig S3):

Figure 1 panel C. Stabilisation of SIZ1 by Mp64. Western blot analyses of protein extracts from agroinfiltrated leaves expressing combinations of GFP-GUS, GFP Mp64 and GFP-CRN83_152_6D10 with AtSIZ1-myc or NbSIZ1-myc. Protein size markers are indicated in kD, and equal protein amounts upon transfer is shown upon ponceau staining (PS) of membranes. Blot is representative of three biological replicates , which are all shown in supplementary Fig. S3. The selected panels shown here are cropped from Rep 1 in supplementary Fig. S3.

For 2) we have added the folllowing new figure (Fig. 2 in the revised manuscript):

Fig. 2. SIZ1-triggered cell death in N. benthamiana is enhanced by CRN83_152_6D10 but not Mp64. (A) Scoring overview of infiltration sites for SIZ1 triggered cell death. Infiltration site were scored for no symptoms (score 0), chlorosis with localized cell death (score 1), less than 50% of the site showing visible cell death (score 2), more than 50% of the site showing cell death (score 3). (B) Bar graph showing the proportions of infiltration sites showing different levels of cell death upon expression of AtSIZ1, NbSIZ1 (both with a C-terminal RFP tag) and an RFP control. Graph represents data from a combination of 3 biological replicates of 11-12 infiltration sites per experiment (n=35). (C) Bar graph showing the proportions of infiltration sites showing different levels of cell death upon expression of SIZ1 (with C-terminal RFP tag) either alone or in combination with aphid effector Mp64 or Phytophthora capsica effector CRN83_152_6D10 (both effectors with GFP tag), or a GFP control. Graph represent data from a combination of 3 biological replicates of 11-12 infiltration sites per experiment (n=35).

Our new data provide further evidence that SIZ1 function is affected by effectors Mp64 (aphid) and CRN83-152 (oomycete), and that SIZ1 likely is a vital virulence target. Our latest results also provide further support for distinct effector activities towards SIZ1 and its variants in other species. SIZ1 is a key immune regulator to biotic stresses (aphids, oomycetes, bacteria and nematodes), on which distinct virulence strategies seem to converge. The mechanism(s) underlying the stabilisation of SIZ1 by Mp64 is yet unclear. However, we hypothesize that increased stability of SIZ1, which functions as an E3 SUMO ligase, leads to increased SUMOylation activity towards its substrates. We surmise that SIZ1 complex formation with other key regulators of plant immunity may underpin these changes. Whether the cell death, triggered by AtSIZ1 upon transient expression in Nicotiana benthamiana, is linked to E3 SUMO ligase activity remains to be investigated. Expression of AtSIZ1 in a plant species other than Arabidopsis may lead to mistargeting of substrates, and subsequent activation of cell death. Dissecting the mechanistic basis of SIZ1 targeting by distinct pathogens and pests will be an important next step in addressing these hypotheses towards understanding plant immunity.

Reviewer #1:

In this manuscript, the authors suggest that SIZ1, an E3 SUMO ligase, is the target of both an aphid effector (Mp64 form M. persicae) and an oomycete effector (CRN83_152 from Phytophthora capsica), based on interaction between SIZ1 and the two effectors in yeast, co-IP from plant cells and colocalization in the nucleus of plant cells. To support their proposal, the authors investigate the effects of SIZ1 inactivation on resistance to aphids and oomycetes in Arabidopsis and N. benthamiana. Surprisingly, resistance is enhanced, which would suggest that the two effectors increase SIZ1 activity.

Unfortunately, not only do we not learn how the effectors might alter SIZ1 activity, there is also no formal demonstration that the effects of the effectors are mediated by SIZ1, such as investigating the effects of Mp64 overexpression in a siz1 mutant. We note, however, that even this experiment might not be entirely conclusive, since SIZ1 is known to regulate many processes, including immunity. Specifically, siz1 mutants present autoimmune phenotype, and general activation of immunity might be sufficient to attenuate the enhanced aphid susceptibility seen in Mp64 overexpressers.

To demonstrate unambiguously that SIZ1 is a bona fide target of Mp64 and CRN83_152 would require assays that demonstrate either enhanced SIZ1 accumulation or altered SIZ1 activity in the presence of Mp64 and CRN83_152.

The enhanced resistance upon knock-down/out of SIZ1 suggests pathogen and pest susceptibility requires SIZ1. We hypothesize that the effectors either enhance SIZ1 activity or that the effectors alter SIZ1 specificity towards substrates rather than enzyme activity itself. To investigate how effectors coopt SIZ1 function would require a comprehensive set of approaches and will be part of our future work. While we agree that this aspect requires further investigation, we think the proposed experiments go beyond the scope of this study.

After receiving reviewer comments, including on the quality of Figure 1, which shows western blots of co-immunoprecipitation experiments, we re-analyzed independent replicates of effector-SIZ1 coexpression/ co-immunoprecipitation experiments. The reviewer rightly pointed out that in the presence of Mp64, SIZ1 protein levels increase when compared to samples in which either the vector control or CRN83-152_6D10 are co-infiltrated. Through carefully designed experiments, we can now affirm that Mp64 co-expression leads to increased SIZ1 protein levels (Figure 1C and Supplementary Figure S3, revised manuscript). Our results offer both an explanation of different SIZ1 levels in the input samples (original submission, Figure 1A/B) as well as tantalizing new clues to the nature of distinct effector activities.

Besides, we were able to confirm a previous preliminary finding not included in the original submission that ectopic expression of AtSIZ1 in Nicotiana benthamiana triggers cell death (3/4 days after infiltration) and that CRN83-152_6D10 (which itself does not trigger cell death) enhances this phenotype.

We have considered overexpression of Mp64 in the siz1 mutant, but share the view that the outcome of such experiments will be far from conclusive.

In summary, we have added new data that further support that SIZ1 is a bonafide target of Mp64 and CRN83-152 (i.e. increased accumulation of SIZ1 in the presence of Mp64, and enhanced SIZ cell death activation in the presence of CRN83-152_6D10).

Reviewer #2:

The study provides evidence that an aphid effector Mp64 and a Phytophthora capsici effector CRN83_152 can both interact with the SIZ1 E3 SUMO-ligase. The authors further show that overexpression of Mp64 in Arabidopsis can enhance susceptibility to aphids and that a loss-of-function mutation in Arabidopsis SIZ1 or silencing of SIZ1 in N. benthamiana plants lead to increased resistance to aphids and P. capsici. On siz1 plants the aphids show altered feeding patterns on phloem, suggestive of increased phloem resistance. While the finding is potentially interesting, the experiments are preliminary and the main conclusions are not supported by the data.

Specific comments:

The suggestion that SIZ1 is a virulence target is an overstatement. Preferable would be knockouts of effector genes in the aphid or oomycete, but even with transgenic overexpression approaches, there are no direct data that the biological function of the effectors requires SIZ1. For example, is SIZ1 required for the enhanced susceptibility to aphid infestation seen when Mp64 is overexpressed? Or does overexpression of SIZ1 enhance Mp64-mediated susceptibility?

What do the effectors do to SIZ1? Do they alter SUMO-ligase activity? Or are perhaps the effectors SUMOylated by SIZ1, changing effector activity?

We agree that having effector gene knock-outs in aphids and oomycetes would be ideal for dissecting effector mediated targeting of SIZ1. Unfortunately, there is no gene knock-out system established in Myzus persicae (our aphid of interest), and CAS9 mediated knock-out of genes in Phytophthora capsici has not been successful in our lab as yet, despite published reports. Moreover, repeated attempts to silence Mp64, other effector and non-effector coding genes, in aphids (both in planta and in vitro) have not been successful thus far, in our hands. As detailed in our response to Reviewer 1, we considered the use of transgenic approaches not appropriate as data interpretation would become muddied by the strong immunity phenotype seen in the siz1-2 mutant.

As stated before, we hypothesize that the effectors either enhance SIZ1 activity or alter SIZ1 substrate specificity. Mp64-induced accumulation of SIZ1 could form the basis of an increase in overall SIZ1 activity. This hypothesis, however, requires testing. The same applies to the enhanced SIZ1 cell death activation in the presence of CRN83-152_6D10.

Whilst our new data support our hypothesis that effectors Mp64 and CRN83-152 affect SIZ1 function, how exactly these effectors trigger susceptibility, requires significant work. Given the substantial effort needed and the research questions involved, we argue that findings emanating from such experiments warrant standalone publication.

While stable transgenic Mp64 overexpressing lines in Arabidopsis showed increased susceptibility to aphids, transient overexpression of Mp64 in N. benthamiana plants did not affect P. capsici susceptibility. The authors conclude that while the aphid and P. capsici effectors both target SIZ1, their activities are distinct. However, not only is it difficult to compare transient expression experiments in N. benthamiana with stable transgenic Arabidopsis plants, but without knowing whether Mp64 has the same effects on SIZ1 in both systems, to claim a difference in activities remains speculative.

We agree that we cannot compare effector activities between different plant species. We carefully considered every statement regarding results obtained on SIZ1 in Arabidopsis and Nicotiana benthamiana. We can, however, compare activities of the two effectors when expressed side by side in the same plant species. In our original submission, we show that expression of CRN83 152 but not Mp64 in Nicotiana benthamiana enhances susceptibility to Phytophthora capsici. In our revised manuscript, we present new data showing distinct effector activities towards SIZ1 with regards to 1) enhanced SIZ1 stability and 2) enhanced SIZ1 triggered cell death. These findings raise questions as to how enhanced SIZ1 stability and cell death activation is relevant to immunity. We aim to address these critical questions by addressing the mechanistic basis of effector-SIZ1 interactions.

The authors emphasize that the increased resistance to aphids and P. capsici in siz1 mutants or SIZ1 silenced plants are independent of SA. This seems to contradict the evidence from the NahG experiments. In Fig. 5B, the effects of siz1 are suppressed by NahG, indicating that the resistance seen in siz1 plants is completely dependent on SA. In Fig 5A, the effects of siz1 are not completely suppressed by NahG, but greatly attenuated. It has been shown before that SIZ1 acts only partly through SNC1, and the results from the double mutant analyses might simply indicate redundancy, also for the combinations with eds1 and pad4 mutants.

We emphasized that siz1-2 increased resistance to aphids is independent of SA, which is supported by our data (Figure 5A). Still, we did not conclude that the same applies to increased resistance to Phytophthora capsici (Figure 5B). In contrast, the siz1-2 enhanced resistance to P. capsici appears entirely dependent on SA levels, with the level of infection on the siz1-2/NahG mutants even slightly higher than on the NahG line and Col-0 plants. We exercise caution in the interpretation of this data given the significant impact SA signalling appears to have on Phytophthora capsici infection.

The reviewer commented on the potential for functional redundancy in the siz1-2 double mutants. Unfortunately, we are unsure what redundancy s/he is referring to. SNC1, EDS1, and PAD4 all are components required for immunity, and their removal from the immune signalling network (using the mutations in the lines we used here) impairs immunity to various plant pathogens. The siz1-2 snc1-11, siz1-2 eds1-2, and siz1-2 pad4-1 double mutants have similar levels of susceptibility to the bacterial pathogen Pseudomonas syringae when compared to the corresponding snc1-11, eds1-2 and pad4-1 controls (at 22oC). These previous observations indicate that siz1 enhanced resistance is dependent on these signalling components (Hammoudi et al., 2018, Plos Genetics).

In contrast to this, we observed a strong siz1 enhanced resistance phenotype in the absence of snc1- 11, eds1 2 and pad4-1. Notably, the siz1-2 snc1-11 mutant does not appear immuno-compromised when compared to siz1-2 in fecundity assays, indicating that the siz1-2 phenotype is independent of SNC1. In our view, these data suggest that signalling components/pathways other than those mediated by SNC1, EDS1, and PAD4 are involved. We consider this to be an exciting finding as our data points to an as of yet unknown SIZ1-dependent signalling pathway that governs immunity to aphids.

How do NahG or Mp64 overexpression affect aphid phloem ingestion? Is it the opposite of the behavior on siz1 mutants?

We have not performed further EPG experiments on additional transgenic lines used in the aphid assay. These experiments are quite challenging and time consuming. Moreover, accommodating an experimental set-up that allows us to compare multiple lines at the same time is not straightforward. Considering that NahG did not affect aphid performance (Figure 5A), we do not expect to see an effect on phloem ingestion.

Author Response

Reviewer #1:

Hutchings et al. report an updated cryo-electron tomography study of the yeast COP-II coat assembled around model membranes. The improved overall resolution and additional compositional states enabled the authors to identify new domains and interfaces--including what the authors hypothesize is a previously overlooked structural role for the SEC31 C-Terminal Domain (CTD). By perturbing a subset of these new features with mutants, the authors uncover some functional consequences pertaining to the flexibility or stability of COP-II assemblies.

Overall, the structural and functional work appears reliable, but certain questions and comments should be addressed prior to publication. However, this reviewer failed to appreciate the conceptual advance that warrants publication in a general biology journal like eLIFE. Rather, this study provides a valuable refinement of our understanding of COP-II that I believe is better suited to a more specialized, structure-focused journal.

We agree that in our original submission our description of the experimental setup, indeed similar to previous work, did not fully capture the novel findings of this paper. Rather than being simply a higher resolution structure of the COPII coat, in fact we have discovered new interactions in the COPII assembly network, and we have probed their functional roles, significantly changing our understanding of the mechanisms of COPII-mediated membrane curvature. In the revised submission we have included additional genetic data that further illuminate this mechanism, and have rewritten the text to better communicate the novel aspects of our work.

Our combination of structural, functional and genetic analyses goes beyond refining our textbook understanding of the COPII coat as a simple ‘adaptor and cage’, but rather it provides a completely new picture of how dynamic regulation of assembly and disassembly of a complex network leads to membrane remodelling.

These new insights have important implications for how coat assembly provides structural force to bend a membrane but is still able to adapt to distinct morphologies. These questions are at the forefront of protein secretion, where there is debate about how different types of carriers might be generated that can accommodate cargoes of different size.

Major Comments: 1) The authors belabor what this reviewer thinks is an unimportant comparison between the yeast reconstruction of the outer coat vertex with prior work on the human outer coat vertex. Considering the modest resolution of both the yeast and human reconstructions, the transformative changes in cryo-EM camera technology since the publication of the human complex, and the differences in sample preparation (inclusion of the membrane, cylindrical versus spherical assemblies, presence of inner coat components), I did not find this comparison informative. The speculations about a changing interface over evolutionary time are unwarranted and would require a detailed comparison of co-evolutionary changes at this interface. The simpler explanation is that this is a flexible vertex, observed at low resolution in both studies, plus the samples are very different.

We do agree that our proposal that the vertex interface changes over evolutionary time is speculative and we have removed this discussion. We agree that a co-evolutionary analysis will be enlightening here, but is beyond the scope of the current work.

We respectfully disagree with the reviewer’s interpretation that the difference between the two vertices is due to low resolution. The interfaces are clearly different, and the resolutions of the reconstructions are sufficient to state this. The reviewer’s suggestion that the difference in vertex orientation might be simply attributable to differences in sample, such as inclusion of the membrane, cylindrical versus spherical morphology, or presence of inner coat components were ruled out in our original submission: we resolved yeast vertices on spherical vesicles (in addition to those on tubes) and on membrane-less cages. These analyses clearly showed that neither the presence of a membrane, nor the change in geometry (tubular vs. spherical) affect vertex interactions. These experiments are presented in Supplementary Fig 4 (Supplementary Fig. 3 in the original version). Similarly, we discount that differences might be due to the presence or absence of inner coat components, since membrane-less cages were previously solved in both conditions and are no different in terms of their vertex structure (Stagg et al. Nature 2006 and Cell 2008).

We believe it is important to report on the differences between the two vertex structures. Nevertheless, we have shifted our emphasis on the functional aspects of vertex formation and moved the comparison between the two vertices to the supplement.

2) As one of the major take home messages of the paper, the presentation and discussion of the modeling and assignment of the SEC31-CTD could be clarified. First, it isn't clear from the figures or the movies if the connectivity makes sense. Where is the C-terminal end of the alpha-solenoid compared to this new domain? Can the authors plausibly account for the connectivity in terms of primary sequence? Please also include a side-by-side comparison of the SRA1 structure and the CTD homology model, along with some explanation of the quality of the model as measured by Modeller. Finally, even if the new density is the CTD, it isn't clear from the structure how this sub-stoichiometric and apparently flexible interaction enhances stability. Hence, when the authors wrote "when the [CTD] truncated form was the sole copy of Sec31 in yeast, cells were not viable, indicating that the novel interaction we detect is essential for COPII coat function." Maybe, but could this statement be a leap to far? Is it the putative interaction essential, or is the CTD itself essential for reasons that remain to be fully determined?

The CTD is separated from the C-terminus of the alpha solenoid domain by an extended domain (~350 amino acids) that is predicted to be disordered, and contains the PPP motifs and catalytic fragment that contact the inner coat. This is depicted in cartoon form in Figures 3A and 7, and discussed at length in the text. This arrangement explains why no connectivity is seen, or expected. We could highlight the C-terminus of the alpha-solenoid domain to emphasize where the disordered region should emerge from the rod, but connectivity of the disordered domain to the CTD could arise from multiple positions, including from an adjacent rod.

The reviewer’s point about the essentiality of the CTD being independent of its interaction with the Sec31 rod, is an important one. The basis for our model that the CTD enhances stability or rigidity of the coat is the yeast phenotype of Sec31-deltaCTD, which resembles that of a sec13 null. Both mutants are lethal, but rescued by deletion of emp24, which leads to more easily deformable membranes (Čopič et al. Science 2012). We agree that even if this model is true, the interaction of the CTD with Sec31 that our new structure reveals is not proven to drive rigidity or essentiality. We have tempered this hypothesis and added alternative possibilities to the discussion.

We have included the SRA1 structure in Supplementary Fig 5, as requested, and the model z-score in the Methods. The Z-score, as calculated by the proSA-web server is -6.07 (see figure below, black dot), and falls in line with experimentally determined structures including that of the template (PDB 2mgx, z-score = -5.38).

3) Are extra rods discussed in Fig. 4 are a curiosity of unclear functional significance? This reviewer is concerned that these extra rods could be an in vitro stoichiometry problem, rather than a functional property of COP-II.

This is an important point, that, as we state in the paper, cannot be answered at the moment: the resolution is too low to identify the residues involved in the interaction. Therefore we are hampered in our ability to assess the physiological importance of this interaction. We still believe the ‘extra’ rods are an important observation, as they clearly show that another mode of outer coat interaction, different from what was reported before, is possible.

The concern that interactions visualised in vitro might not be physiologically relevant is broadly applicable to structural biology approaches. However, our experimental approach uses samples that result from active membrane remodelling under near-physiological conditions, and we therefore expect these to be less prone to artefacts than most in vitro reconstitution approaches, where proteins are used at high concentrations and in high salt buffer conditions.

4) The clashsccore for the PDB is quite high--and I am dubious about the reliability of refining sidechain positions with maps at this resolution. In addition to the Ramchandran stats, I would like to see the Ramachandran plot as well as, for any residue-level claims, the density surrounding the modeled side chain (e.g. S742).

The clashscore is 13.2, which, according to molprobity, is in the 57th percentile for all structures and in the 97th for structures of similar resolutions. We would argue therefore that the clashscore is rather low. In fact, the model was refined from crystal structures previously obtained by other groups, which had worse clashscore (17), despite being at higher resolution. Our refinement has therefore improved the clashscore. During refinement we have chosen restraint levels appropriate to the resolution of our map (Afonine et al., Acta Cryst D 2018)

The Ramachandran plot is copied here and could be included in a supplemental figure if required. We make only one residue-level claim (S742), the density for which is indeed not visible at our resolution. We claim that S742 is close to the Sec23-23 interface, and do not propose any specific interactions. Nevertheless we have removed reference to S742 from the manuscript. We included this specific information because of the potential importance of this residue as a site of phosphorylation, thereby putting this interface in broader context for the general eLife reader.

Minor Comments:

1) The authors wrote "To assess the relative positioning of the two coat layers, we analysed the localisation of inner coat subunits with respect to each outer coat vertex: for each aligned vertex particle, we superimposed the positions of all inner coat particles at close range, obtaining the average distribution of neighbouring inner coat subunits. From this 'neighbour plot' we did not detect any pattern, indicating random relative positions. This is consistent with a flexible linkage between the two layers that allows adaptation of the two lattices to different curvatures (Supplementary Fig 1E)." I do not understand this claim, since the pattern both looks far from random and the interactions depend on molecular interactions that are not random. Please clarify.

We apologize for the confusion: the pattern of each of the two coats are not random. Our sentence refers to the positions of inner and outer coats relative to each other. The two lattices have different parameters and the two layers are linked by flexible linkers (the 350 amino acids referred to above). We have now clarified the sentence.

2) Related to major point #1, the author wrote "We manually picked vertices and performed carefully controlled alignments." I do now know what it means to carefully control alignments, and fear this suggests human model bias.

We used different starting references for the alignments, with the precise aim to avoid model bias. For both vesicle and cage vertex datasets, we have aligned the subtomograms against either the vertex obtained from tubules, or the vertex from previously published membrane-less cages. In all cases, we retrieved a structure that resembles the one on tubules, suggesting that the vertex arrangement we observe isn’t simply the result of reference bias. This procedure is depicted in Supplementary Fig 4 (Supplementary Fig. 3 in the original manuscript), but we have now clarified it also in the methods section.

3) Why do some experiments use EDTA? I may be confused, but I was surprised to see the budding reaction employed 1mM GMPPNP, and 2.5mM EDTA (but no Magnesium?). Also, for the budding reaction, please replace or expand upon the "the 10% GUV (v/v)" with a mass or molar lipid-to-protein ratio.

We regret the confusion. As stated in the methods, all our budding reactions are performed in the presence of EDTA and Magnesium, which is present in the buffer (at 1.2 mM). The reason is to facilitate nucleotide exchange, as reported and validated in Bacia et al., Scientific Reports 2011.

Lipids in GUV preparations are difficult to quantify. We report the stock concentrations used, but in each preparation the amount of dry lipid that forms GUVs might be different, as is the concentration of GUVs after hydration. However since we analyse reactions where COPII proteins have bound and remodelled individual GUVs, we do not believe the protein/lipid ratio influences our structures.

4) Please cite the AnchorMap procedure.

We cite the SerialEM software, and are not aware of other citations specifically for the anchor map procedure.

5) Please edit for typos (focussing, functionl, others)

Done

Reviewer #2:

The manuscript describes new cryo-EM, biochemistry, and genetic data on the structure and function of the COPII coat. Several new discoveries are reported including the discovery of an extra density near the dimerization region of Sec13/31, and "extra rods" of Sec13/31 that also bind near the dimerization region. Additionally, they showed new interactions between the Sec31 C-terminal unstructured region and Sec23 that appear to bridge multiple Sec23 molecules. Finally, they increased the resolution of the Sec23/24 region of their structure compared to their previous studies and were able to resolve a previously unresolved L-loop in Sec23 that makes contact with Sar1. Most of their structural observations were nicely backed up with biochemical and genetic experiments which give confidence in their structural observations. Overall the paper is well-written and the conclusions justified.

However, this is the third iteration of structure determination of the COPII coat on membrane with essentially the same preparation and methods. Each time, there has been an incremental increase in resolution and new discoveries, but the impact of the present study is deemed to be modest. The science is good, but it may be more appropriate for a more specialized journal. Areas of specific concern are described below.

As described above, we respectfully disagree with this interpretation of the advance made by the current work. This work improves on previous work in many aspects. The resolution of the outer coat increases from over 40A to 10-12A, allowing visualisation of features that were not previously resolved, including a novel vertex arrangement, the Sec31 CTD, and the outer coat ‘extra rods’. An improved map of the inner coat also allows us to resolve the Sec23 ‘L-loop’. We would argue that these are not just extra details, but correspond to a suite of novel interactions that expand our understanding of the complex COPII assembly network. Moreover, we include biochemical and genetic experiments that not only back up our structural observations but bring new insights into COPII function. As pointed out in response to reviewer 1, we believe our work contributes a significant conceptual advance, and have modified the manuscript to convey this more effectively.

1) The abstract is vague and should be re-written with a better description of the work.

We have modified the abstract to specifically outline what we have done and the major new discoveries of this paper.

2) Line 166 - "Surprisingly, this mutant was capable of tubulating GUVs". This experiment gets to one of the fundamental unknown questions in COPII vesiculation. It is not clear what components are driving the membrane remodeling and at what stages during vesicle formation. Isn't it possible that the tubulation activity the authors observe in vitro is not being driven at all by Sec13/31 but rather Sec23/24-Sar1? Their Sec31ΔCTD data supports this idea because it lacks a clear ordered outer coat despite making tubules. An interesting experiment would be to see if tubules form in the absence of all of Sec13/31 except the disordered domain of Sec31 that the authors suggest crosslinks adjacent Sec23/24s.

This is an astute observation, and we agree with the reviewer that the source of membrane deformation is not fully understood. We favour the model that budding is driven significantly by the Sec23-24 array. To further support this, we have performed a new experiment, where we expressed Sec31ΔN in yeast cells lacking Emp24, which have more deformable membranes and are tolerant to the otherwise lethal deletion of Sec13. While Sec31ΔN in a wild type background did not support cell viability, this was rescued in a Δemp24 yeast strain, strongly supporting the hypothesis that a major contributor to membrane remodelling is the inner coat, with the outer coat becoming necessary to overcome membrane bending resistance that ensues from the presence of cargo. We now include these results in Figure 1.

However, we must also take into account the results presented in Fig. 6, where we show that weakening the Sec23-24 interface still leads to budding, but only if Sec13-31 is fully functional, and that in this case budding leads to connected pseudo-spherical vesicles rather than tubes. When Sec13-31 assembly is also impaired, tubes appear unstructured. We believe this strongly supports our conclusions that both inner and outer coat interactions are fundamental for membrane remodelling, and it is the interplay between the two that determines membrane morphology (i.e. tubes vs. spheres).

To dissect the roles of inner and outer coats even further, we have done the experiment that the reviewer suggests: we expressed Sec31768-1114, but the protein was not well-behaved and co-purified with chaperones. We believe the disordered domain aggregates when not scaffolded by the structured elements of the rod. Nonetheless, we used this fragment in a budding reaction, and could not see any budding. We did not include this experiment as it was inconclusive: the lack of functionality of the purified Sec31 fragment could be attributed to the inability of the disordered region to bind its inner coat partner in the absence of the scaffolding Sec13-31 rod. As an alternative approach, we have used a version of Sec31 that lacks the CTD, and harbours a His tag at the N-terminus (known from previous studies to partially disrupt vertex assembly). We think this construct is more likely to be near native, since both modifications on their own lead to functional protein. We could detect no tubulation with this construct by negative stain, while both control constructs (Sec31ΔCTD and Nhis-Sec31) gave tubulation. This suggests that the cross-linking function of Sec31 is not sufficient to tubulate GUV membranes, but some degree of functional outer coat organisation (either mediated by N- or C-terminal interactions) is needed. It is also possible that the lack of outer coat organisation might lead to less efficient recruitment to the inner coat and cross-linking activity. We have added this new observation to the manuscript.

3) Line 191 - "Inspecting cryo-tomograms of these tubules revealed no lozenge pattern for the outer 192 coat" - this phrasing is vague. The reviewer thinks that what they mean is that there is a lack of order for the Sec13/31 layer. Please clarify.

The reviewer is correct, we have changed the sentence.

4) Line 198 - "unambiguously confirming this density corresponds to 199 the CTD." This only confirms that it is the CTD if that were the only change and the Sec13/31 lattice still formed. Another possibility is that it is density from other Sec13/31 that only appears when the lattice is formed such as the "extra rods". One possibility is that the density is from the extra rods. The reviewer agrees that their interpretation is indeed the most likely, but it is not unambiguous. The authors should consider cross-linking mass spectrometry.

We have removed the word ‘unambiguously’, and changed to ‘confirming that this density most likely corresponds to the CTD’. Nonetheless, we believe that our interpretation is correct: the extra rods bind to a different position, and themselves also show the CTD appendage. In this experiment, the lack of the CTD was the only biochemical change.

5) In the Sec31ΔCTD section, the authors should comment on why ΔCTD is so deleterious to oligomer organization in yeast when cages form so abundantly in preparations of human Sec13/31 ΔC (Paraan et al 2018).

We have added a comment to address this. “Interestingly, human Sec31 proteins lacking the CTD assemble in cages, indicating that either the vertex is more stable for human proteins and sufficient for assembly, or that the CTD is important in the context of membrane budding but not for cage formation in high salt conditions.”

6) The data is good for the existence of the "extra rods", but significance and importance of them is not clear. How can these extra densities be distinguished from packing artifacts due to imperfections in the helical symmetry.

Please also see our response to point 3 from reviewer 1. Regarding the specific concern that artefacts might be a consequence of imperfection in the helical symmetry, we would argue such imperfections are indeed expected in physiological conditions, and to a much higher extent. For this reason interactions seen in the context of helical imperfections are likely to be relevant. In fact, in normal GTP hydrolysis conditions, we expect long tubes would not be able to form, and the outer coat to be present on a wide range of continuously changing membrane curvatures. We think that the ability of the coat to form many interactions when the symmetry is imperfect might be exactly what confers the coat its flexibility and adaptability.

7) Figure 5 is very hard to interpret and should be redone. Panels B and C are particularly hard to interpret.

We have made a new figure where we think clarity is improved.

8) The features present in Sec23/24 structure do not reflect the reported resolution of 4.7 Å. It seems that the resolution is overestimated.

We report an average resolution of 4.6 Å. In most of our map we can clearly distinguish beta strands, follow the twist of alpha helices and see bulky side chains. These features typically become visible at 4.5-5A resolution. We agree that some areas are worse than 4.6 Å, as typically expected for such a flexible assembly, but we believe that the average resolution value reported is accurate. We obtained the same resolution estimate using different software including relion, phenix and dynamo, so that is really the best value we can provide. To further convince ourselves that we have the resolution we claim, we sampled EM maps from the EMDB with the same stated resolution (we just took the 7 most recent ones which had an associated atomic model), and visualised their features at arbitrary positions. For both beta strands and alpha helices, we do not feel our map looks any worse than the others we have examined. We include a figure here.

9) Lines 315/316 - "We have combined cryo-tomography with biochemical and genetic assays to obtain a complete picture of the assembled COPII coat at unprecedented resolution (Fig. 7)"

10) Figure 7. is a schematic model/picture the authors should reference a different figure or rephrase the sentence.

We now refer to Fig 7 in a more appropriate place.

Reviewer #3:

The manuscript by Hutchings et al. describes several previously uncharacterised molecular interactions in the coats of COP-II vesicles by using a reconstituted coats of yeast COPI-II. They have improved the resolution of the inner coat to 4.7A by tomography and subtomogram averaging, revealing detailed interactions, including those made by the so-called L-loop not observed before. Analysis of the outer layer also led to new interesting discoveries. The sec 31 CTD was assigned in the map by comparing the WT and deletion mutant STA-generated density maps. It seems to stabilise the COP-II coats and further evidence from yeast deletion mutants and microsome budding reconstitution experiments suggests that this stabilisation is required in vitro. Furthermore, COP-II rods that cover the membrane tubules in right-handed manner revealed sometimes an extra rod, which is not part of the canonical lattice, bound to them. The binding mode of these extra rods (which I refer to here a Y-shape) is different from the canonical two-fold symmetric vertex (X-shape). When the same binding mode is utilized on both sides of the extra rod (Y-Y) the rod seems to simply insert in the canonical lattice. However, when the Y-binding mode is utilized on one side of the rod and the X-binding mode on the other side, this leads to bridging different lattices together. This potentially contributes to increased flexibility in the outer coat, which maybe be required to adopt different membrane curvatures and shapes with different cargos. These observations build a picture where stabilising elements in both COP-II layers contribute to functional cargo transport. The paper makes significant novel findings that are described well. Technically the paper is excellent and the figures nicely support the text. I have only minor suggestions that I think would improve the text and figure.

We thank the reviewer for helpful suggestions which we agree improve the manuscript.

Minor Comments:

L 108: "We collected .... tomograms". While the meaning is clear to a specialist, this may sound somewhat odd to a generic reader. Perhaps you could say "We acquired cryo-EM data of COP-II induced tubules as tilt series that were subsequently used to reconstruct 3D tomograms of the tubules."

We have changed this as suggested

L 114: "we developed an unbiased, localisation-based approach". What is the part that was developed here? It seems that the inner layer particle coordinates where simply shifted to get starting points in the outer layer. Developing an approach sounds more substantial than this. Also, it's unclear what is unbiased about this approach. The whole point is that it's biased to certain regions (which is a good thing as it incorporates prior knowledge on the location of the structures).

We have modified the sentence to “To target the sparser outer coat lattice for STA, we used the refined coordinates of the inner coat to locate the outer coat tetrameric vertices”, and explain the approach in detail in the methods.

L 124: "The outer coat vertex was refined to a resolution of approximately ~12 A, revealing unprecedented detail of the molecular interactions between Sec31 molecules (Supplementary Fig 2A)". The map alone does not reveal molecular interactions; the main understanding comes from fitting of X-ray structures to the low-resolution map. Also "unprecedented detail" itself is somewhat problematic as the map of Noble et al (2013) of the Sec31 vertex is also at nominal resolution of 12 A. Furthermore, Supplementary Fig 2A does not reveal this "unprecedented detail", it shows the resolution estimation by FSC. To clarify, these points you could say: "Fitting of the Sec31 atomic model to our reconstruction vertex at 12-A resolution (Supplementary Fig 2A) revealed the molecular interactions between different copies of Sec31 in the membrane-assembled coat.

We have changed the sentence as suggested.

L 150: Can the authors exclude the possibility that the difference is due to differences in data processing? E.g. how the maps amplitudes have been adjusted?

Yes, we can exclude this scenario by measuring distances between vertices in the right and left handed direction. These measurements are only compatible with our vertex arrangement, and cannot be explained by the big deviation from 4-fold symmetry seen in the membrane-less cage vertices.

L 172: "that wrap tubules either in a left- or right-handed manner". Don't they do always both on each tubule? Now this sentence could be interpreted to mean that some tubules have a left-handed coat and some a right-handed coat.

We have changed this sentence to clarify. “Outer coat vertices are connected by Sec13-31 rods that wrap tubules both in a left- and right-handed manner.”

L276: "The difference map" hasn't been introduced earlier but is referred to here as if it has been.

We now introduce the difference map.

L299: Can "Secondary structure predictions" denote a protein region "highly prone to protein binding"?

Yes, this is done through DISOPRED3, a feature include in the PSIPRED server we used for our predictions. The reference is: Jones D.T., Cozzetto D. DISOPRED3: precise disordered region predictions with annotated protein-binding activity Bioinformatics. 2015; 31:857–863. We have now added this reference to the manuscript.

L316: It's true that the detail in the map of the inner coat is unprecedented and the model presented in Figure 7 is partially based on that. But here "unprecedented resolution" sounds strange as this sentence refers to a schematic model and not a map.

We have changed this by moving the reference to Fig 7 to a more appropriate place

L325: "have 'compacted' during evolution" -> remove. It's enough to say it's more compact in humans and less compact in yeast as there could have been different adaptations in different organisms at this interface.

We have changed as requested. See also our response to reviewer 1, point 1.

L327: What's exactly meant by "sequence diversity or variability at this density".

We have now clarified: “Since multiple charge clusters in yeast Sec31 may contribute to this interaction interface (Stancheva et al., 2020), the low resolution could be explained by the fact that the density is an average of different sequences.”

L606-607: The description of this custom data processing approach is difficult to follow. Why is in-plane flip needed and how is it used here?

Initially particles are picked ignoring tube directionality (as this cannot be assessed easily from the tomograms due to the pseudo-twofold symmetry of the Sec23/24/Sar1 trimer). So the in plane rotation of inner coat subunit could be near 0 or 180°. For each tube, both angles are sampled (in-plane flip). Most tubes result in the majority of particles being assigned one of the two orientations (which is then assumed as the tube directionality). Particles that do not conform are removed, and rare tubes where directionality cannot be determined are also removed. We have re-written the description to clarify these points: “Initial alignments were conducted on a tube-by-tube basis using the Dynamo in-plane flip setting to search in-plane rotation angles 180° apart. This allowed to assign directionality to each tube, and particles that were not conforming to it were discarded by using the Dynamo dtgrep_direction command in custom MATLAB scripts”

L627: "Z" here refers to the coordinate system of aligned particles not that of the original tomogram. Perhaps just say "shifted 8 pixels further away from the membrane".

Changed as requested.

L642-643: How can the "left-handed" and "right-handed" rods be separated here? These terms refer to the long-range organisation of the rods in the lattice it's not clear how they were separated in the early alignments.

They are separated by picking only one subset using the dynamo sub-boxing feature. This extracts boxes from the tomogram which are in set positions and orientation relative to the average of previously aligned subtomograms. From the average vertex structure, we sub-box rods at 4 different positions that correspond to the centre of the rods, and the 2-fold symmetric pairs are combined into the same dataset. We have clarified this in the text: “The refined positions of vertices were used to extract two distinct datasets of left and right-handed rods respectively using the dynamo sub-boxing feature.”

Figure 2B. It's difficult to see the difference between dark and light pink colours.

We have changed colours to enhance the difference.

Figure 3C. These panels report the relative frequency of neighbouring vertices at each position; "intensity" does not seem to be the right measure for this. You could say that the colour bar indicates the "relative frequency of neighbouring vertices at each position" and add detail how the values were scaled between 0 and 1. The same applies to SFigure 1E.

Changed as requested.

Figure 4. The COP-II rods themselves are relatively straight, and they are not left-handed or right-handed. Here, more accurate would be "architecture of COPII rods organised in a left-handed manner". (In the text the authors may of course define and then use this shorter expression if they so wish.) Panel 4B top panel could have the title "left-handed" and the lower panel should have the title "right-handed" (for consistency and clarity).

We have now defined left- and right-handed rods in the text, and have changed the figure and panel titles as requested.

Reviewer #2 (Public review):

The authors addressed the question how mitochondrial proteins that are dually localized or only to a minor fraction localized to mitochondria can be visualized. For this they used an established and previously published method called BiG split-GFP, in which GFP strands 1-10 are encoded in the mitochondrial DNA and fused the GFP11 strand C-terminally to the yeast ORFs using the C-SWAT library. The generated library was imaged under different growth and stress conditions and yielded positive mitochondrial localization for approximately 400 proteins. The strength of this method is the detection of proteins that are dually localized with only a minor fraction within mitochondria, which was so far has hampered due to strong fluorescent signals from other cellular localizations. The weakness of this method is that due to the localization of the GFP1-10 in the mitochondrial matrix, only matrix proteins and IM protein with their C-termini facing the matrix can be detected. In addition, The C-terminal GFP11 might impact on assembly of proteins into multimeric complexes or interfere with biogenesis trapping the tagged protein in an unproductive transport intermediate. Taken these limitations into consideration, the authors provide a new library that can help in identification of eclipsed protein distribution within mitochondria, thus further increasing our knowledge on the complete mitochondrial proteome. The approach of global tagging of the yeast genome is the logical consequence after the successful establishment of the BiG split-GFP for mitochondria. The authors also propose that their approach can be applied to investigate the topology of inner membrane proteins, however, for this the inherent issue remains that even the small GFP11 tag can impact on protein biogenesis and topology. Thus, the approach will not overcome the need to assess protein topology via biochemical approaches detecting endogenous untagged proteins.

Comments on revisions:

The first sentence of the abstract should be changed as the statement that "The majority of the mitochondrial proteins (...) often lack clear targeting signals" is in particular for the here analysed IM and matrix protein not correct: Several N-proteomics analysis have defined N-terminal cleavable targeting signals in great detail.

Also the statement in the title that the assay illuminates protein targeting routes should be reconsidered as experimental evidence for this statement is still scarce.

Reviewer #3 (Public review):

Summary:

Here, Bykov et al move the bi-genomic split-GFP system they previously established to the genome-wide level in order to obtain a more comprehensive list of mitochondrial matrix and inner membrane proteins. In this very elegant split-GFP system, the longer GFP fragment, GFP1-10, is encoded in the mitochondrial genome and the shorter one, GFP11, is C-terminally attached to every protein encoded in the genome of yeast Saccharomyces cerevisiae. GFP fluorescence can therefore only be reconstituted if the C-terminus of the protein is present in the mitochondrial matrix, either as part of a soluble protein, a peripheral membrane protein or an integral inner membrane protein. The system, combined with high-throughput fluorescence microscopy of yeast cells grown under six different conditions, enabled the authors to visualize ca. 400 mitochondrial proteins, 50 of which were not visualised before and 8 of which were not shown to be mitochondrial before. The system appears to be particularly well suited for analysis of dually localized proteins and could potentially be used to study sorting pathways of mitochondrial inner membrane proteins.

Strengths:

Many fluorescence-based genome-wide screen were previously performed in yeast and were central to revealing the subcellular location of a large fraction of yeast proteome. Nonetheless, these screens also showed that tagging with full-length fluorescent proteins (FP) can affect both the function and targeting of proteins. The strength of the system used in the current manuscript is that the shorter tag is beneficial for detection of a number of proteins whose targeting and/or function is affected by tagging with full length FPs.

Furthermore, the system used here can nicely detect mitochondrial pools of dually localized proteins. It is especially useful when these pools are minor and their signals are therefore easily masked by the strong signals coming from the major, nonmitochondrial pools of the proteins.

Weaknesses:

My only concern is that the biological significance of the screen performed appears limited. The dataset obtained is largely in agreement with several previous proteomic screens but it is, unfortunately, not more comprehensive than them, rather the opposite. For proteins that were identified inside mitochondria for the first time here or were identified in an unexpected location within the organelle, it remains unclear whether these localizations represent some minor, missorted pools of proteins or are indeed functionally important fractions and/or productive translocation intermediates. The authors also allude to several potential applications of the system but do little to explore any of these directions.

Comments on revisions:

The revised version of the manuscript submitted by Bykov et al addresses the comments and concerns raised by the Reviewers. It is a pity that the verification of the newly obtained data and its further biological exploration is apparently more challenging than perhaps anticipated.

Author response:

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

Reviewer #1 (Public Review):

Summary:

The study conducted by the Schuldiner's group advances the understanding of mitochondrial biology through the utilization of their bi-genomic (BiG) split-GFP assay, which they had previously developed and reported. This research endeavors to consolidate the catalog of matrix and inner membrane mitochondrial proteins. In their approach, a genetic framework was employed wherein a GFP fragment (GFP1-10) is encoded within the mitochondrial genome. Subsequently, a collection of strains was created, with each strain expressing a distinct protein tagged with the GFP11 fragment. The reconstitution of GFP fluorescence occurs upon the import of the protein under examination into the mitochondria.

We are grateful for the positive evaluation. We would like to clarify that the bi-genomic (BiG) split-GFP assay was developed by the labs of H. Becker and Roza Kucharzyk by highly laborious construction of the strain with mtDNA-encoded GFP_1-10 (Bader et al, 2020). 

Strengths:

Notably, this assay was executed under six distinct conditions, facilitating the visualization of approximately 400 mitochondrial proteins. Remarkably, 50 proteins were conclusively assigned to mitochondria for the first time through this methodology. The strains developed and the extensive dataset generated in this study serve as a valuable resource for the comprehensive study of mitochondrial biology. Specifically, it provides a list of 50 "eclipsed" proteins whose role in mitochondria remains to be characterized.

Weaknesses:

The work could include some functional studies of at least one of the newly identified 50 proteins.

In response to this we have expanded the characterization of phenotypic effects resulting from changing the targeting signal and expression levels of the dually localized Gpp1 protein and expanded the data in Fig. 3, panels H and I.

Reviewer #2 (Public Review):

The authors addressed the question of how mitochondrial proteins that are dually localized or only to a minor fraction localized to mitochondria can be visualized on the whole genome scale. For this, they used an established and previously published method called BiG split-GFP, in which GFP strands 1-10 are encoded in the mitochondrial DNA and fused the GFP11 strand C-terminally to the yeast ORFs using the C-SWAT library. The generated library was imaged under different growth and stress conditions and yielded positive mitochondrial localization for approximately 400 proteins. The strength of this method is the detection of proteins that are dually localized with only a minor fraction within mitochondria, which so far has hampered their visualization due to strong fluorescent signals from other cellular localizations. The weakness of this method is that due to the localization of the GFP1-10 in the mitochondrial matrix, only matrix proteins and IM proteins with their C-termini facing the matrix can be detected. Also, proteins that are assembled into multimeric complexes (which will be the case for probably a high number of matrix and inner membrane-localized proteins) resulting in the C-terminal GFP11 being buried are likely not detected as positive hits in this approach. Taking these limitations into consideration, the authors provide a new library that can help in the identification of eclipsed protein distribution within mitochondria, thus further increasing our knowledge of the complete mitochondrial proteome. The approach of global tagging of the yeast genome is the logical consequence after the successful establishment of the BiG split-GFP for mitochondria. The authors also propose that their approach can be applied to investigate the topology of inner membrane proteins, however, for this, the inherent issue remains that it cannot be excluded that even the small GFP11 tag can impact on protein biogenesis and topology. Thus, the approach will not overcome the need to assess protein topology analysis via biochemical approaches on endogenous untagged proteins.

Reviewer #3 (Public Review):

Summary:

Here, Bykov et al move the bi-genomic split-GFP system they previously established to the genomewide level in order to obtain a more comprehensive list of mitochondrial matrix and inner membrane proteins. In this very elegant split-GFP system, the longer GFP fragment, GFP1-10, is encoded in the mitochondrial genome and the shorter one, GFP11, is C-terminally attached to every protein encoded in the genome of yeast Saccharomyces cerevisiae. GFP fluorescence can therefore only be reconstituted if the C-terminus of the protein is present in the mitochondrial matrix, either as part of a soluble protein, a peripheral membrane protein, or an integral inner membrane protein. The system, combined with high-throughput fluorescence microscopy of yeast cells grown under six different conditions, enabled the authors to visualize ca. 400 mitochondrial proteins, 50 of which were not visualised before and 8 of which were not shown to be mitochondrial before. The system appears to be particularly well suited for analysis of dually localized proteins and could potentially be used to study sorting pathways of mitochondrial inner membrane proteins.

Strengths:

Many fluorescence-based genome-wide screens were previously performed in yeast and were central to revealing the subcellular location of a large fraction of yeast proteome. Nonetheless, these screens also showed that tagging with full-length fluorescent proteins (FP) can affect both the function and targeting of proteins. The strength of the system used in the current manuscript is that the shorter tag is beneficial for the detection of a number of proteins whose targeting and/or function is affected by tagging with full-length FPs.

Furthermore, the system used here can nicely detect mitochondrial pools of dually localized proteins. It is especially useful when these pools are minor and their signals are therefore easily masked by the strong signals coming from the major, nonmitochondrial pools of the proteins.

Weaknesses:

My only concern is that the biological significance of the screen performed appears limited. The dataset obtained is largely in agreement with several previous proteomic screens but it is, unfortunately, not more comprehensive than them, rather the opposite. For proteins that were identified inside mitochondria for the first time here or were identified in an unexpected location within the organelle, it remains unclear whether these localizations represent some minor, missorted pools of proteins or are indeed functionally important fractions and/or productive translocation intermediates. The authors also allude to several potential applications of the system but do little to explore any of these directions.

We agree with the reviewer that a single method may not be used for the construction of the complete protein inventory of an organelle or its sub-compartment. We suggest that the value of our assay is in providing a complementary view to the existing data and approaches. For example, we confirm the matrix localization of several proteins that were only found in the two proteomic data and never verified before (Vögtle et al, 2017; Morgenstern et al, 2017). Given that proteomics is a very sensitive technique and false positives are hard to completely exclude, our complementary verification is valuable.

Reviewer #1 (Recommendations for the authors):

In my opinion, the manuscript can be published as it is, and I would expect that future work will advance the functional properties of the newly found mitochondrial proteins.

We thank the reviewer for their positive evaluation

Reviewer #2 (Recommendations for the authors)

(1) Due to the localization of the GFP1-10 in the matrix, only matrix and IM proteins with C-termini facing the matrix can be detected, this should be added e.g. in the heading of the first results part and discussed earlier in the manuscript. In addition, the limitation that assembly into protein complexes will likely preclude detection of matrix and IM proteins needs to be discussed.

To address the first point, we edited the title of the first section to only mention the visualization of the matrix-facing proteome and remove the words “inner membrane”. We also clarified early in the Results section that we only consider the matrix-facing C-termini by extending the sentence early in the results section “To compare our findings with published data, we created a unified list of 395 proteins that are observed with high confidence using our assay indicating that their C-terminus is positioned in the matrix (Fig. 2 – figure supplement 1B-D, Table S1).” (P. 6 Lines 1-3). Concluding the comparison with the earlier proteomic studies we also added the sentence “Many proteins are missing because their C-termini are facing the IMS” (P.8 Line 2). 

To address the second point concerning the possible interference of the complex assembly and protein detection by our assay, we conducted an additional analysis. The analysis takes advantage of the protein complexes with known structures where we could estimate if the C-terminus with the GFP₁₁ tag would be available for GFP1-10 binding. We added the additional figure (Figure 3 – figure supplement 2) and following text in the Results section (P.7 Lines 22-34): 

“To examine the influence of protein complex assembly on the performance of the BiG Mito-Split assay we analyzed the published structures of the mitoribosome and ATP synthase (Desai et al, 2017; Srivastava et al, 2018; Guo et al, 2017) and classified all proteins as either having C-termini in, or out of,  the complex. There was no difference between the “in” and “out” groups in the percentage observed in the BiG Mito-Split collection (Fig. 3 – figure supplement 2A) suggesting that the majority of the GFP11tagged proteins have a chance to interact with GFP1-10 before (or instead of) assembling into the complex. PCR and western blot verification of eight strains with the tagged complex subunits for which we observed no signal showed that mitoribosomal proteins were incorrectly tagged or not expressed, and the ATP synthase subunits Atp7, Atp19, and Atp20 were expressed (Fig. 3 – Supplement 2B). Atp19 and Atp20 have their C-termini most likely oriented towards the IMS (Guo et al, 2017) while Atp7 is completely in the matrix and may be the one example of a subunit whose assembly into a complex prevents its detection by the BiG Mito-Split assay.”

We also consider related points on the interference of the tag and the influence of protein essentiality in the replies to points 3) and 12) of these reviews.

(2) The imaging data is of high quality, but the manuscript would greatly benefit from additional analysis to support the claims or hypothesis brought forward by the authors. The idea that the nonmitochondrial proteins are imported due to their high sequence similarity to MTS could be easily addressed at least for some of these proteins via import studies, as also suggested by the authors.

The idea that non-mitochondrial proteins may be imported into mitochondria due to occasional sequence similarity was recently demonstrated experimentally by (Oborská-Oplová et al, 2025). We incorporate this information in the Discussion section as follows (P. 14 Lines 10-16):

“It was also recently shown that the r-protein uS5 (encoded by RPS2 in yeast) has a latent MTS that is masked by a special mitochondrial avoidance segment (MAS) preceding it (Oborská-Oplová et al, 2025). The removal of the MAS leads to import of uS5 into mitochondria killing the cells. The case of uS5 is an example of occasional similarity between an r-protein and an MTS caused by similar requirements of positive charges for rRNA binding and mitochondrial import. It remains unclear if other r-proteins have a MAS and if there are other mechanisms that protect mitochondria from translocation of cytosolic proteins.”

We also conducted additional analysis to substantiate the claim that ribosomal (r)-proteins are similar in their physico-chemical properties to MTS-containing mitochondrial proteins. For this we chose not to use prediction algorithms like TartgetP and MitoFates that were already trained on the same dataset of yeast proteins to discriminate cytosolic and mitochondrial localization. Instead, we extended the analysis earlier made by (Woellhaf et al, 2014) and calculated several different properties such as charge, hydrophobicity, hydrophobic moment and amino acid content for mitochondrial MTS-containing proteins, cytosolic non-ribosomal proteins, and r-proteins. The analysis showed striking similarity of r-proteins and mitochondrial proteins. We incorporate a new Figure 3 – figure supplement 3 and the following text in the Results section (P. 8 Lines14-22): 

“Five out of eight proteins are components of the cytosolic ribosome (r-proteins). In agreement with previous reports (Woellhaf et al, 2014) we find that their unique properties, such as charge, hydrophobicity and amino acid content, are indeed more similar to mitochondrial proteins than to cytosolic ones (Fig. 3 – figure supplement 3). Additional experiments with heterologous protein expression and in vitro import will be required to confirm the mitochondrial import and targeting mechanisms of these eight non-mitochondrial proteins. The data highlights that out of hundreds of very abundant proteins with high prediction scores only few are actually imported and highlights the importance of the mechanisms that help to avoid translocation of wrong proteins (Oborská-Oplová et al, 2025).”

To further prove the possibility of r-protein import into mitochondria we aimed to clone the r-proteins identified in this work for cell-free expression and import into purified mitochondria. Despite the large effort, we have succeeded in cloning and efficiently expressing only Rpl23a (Author response image 1 A). Rpl23a indeed forms proteinase-protected fractions in a membrane potential-dependent manner when incubated with mitochondria. The inverse import dynamics of Rpl23a could be either indicative of quick degradation inside mitochondria or of background signal during the import experiments (Author response image 1.A). To address the r-protein degradation possibility, we measured how does GFP signal change in the BiG Mito-Split diploid collection strains after blocking cytosolic translation with cycloheximide (CHX). For this we selected Mrpl12a, that had one of the highest signals. We did not detect any drop in fluorescence signal for Rpl12a and the control protein Mrpl6 (Author response image 1 B). This might indicate the lack of degradation, or the degradation of the whole protein except GFP₁₁ that remains connected to GFP_1-10. Due to time constrains we could not perform all experiments for the whole set of potentially imported r-proteins. Since more experiments are required to clearly show the mechanisms of mitochondrial r-protein import, degradation, and toxicity, or possible moonlighting functions (such as import into mitochondria derived from pim1∆ strain, degradation assays, fractionations, and analyses with antibodies for native proteins) we decided not to include this new data into the manuscript itself.

Author response image 1.

The import of r-proteins into mitochondria and their stability. (A) Rpl23 was synthesized in vitro (Input), radiolabeled, and imported into mitochondria isolated from BY4741 strain as described before (Peleh et al, 2015); the import was performed for 5,10, or 15 minutes and mitochondria were treated with proteinase K (PK) to degrade nonimported proteins; some reactions were treated with the mix of valinomycin, antimycin, and oligomycin (VAO) to dissipate mitochondrial membrane potential; the proteins were visualized by SDS-PAGE and autoradiography (B) The strains from the diploid BiG Mito-Split collection were grown in YPD to mid-logarithmic growth phase, then CHX was added to block translation and cell aliquots were taken from the culture and analyzed by fluorescence microscopy at the indicated time points. Scale bar is 5 µm.

(3) The claim that the approach can be used to assess the topology of inner membrane proteins is problematic as the C-terminal tag can alter the biogenesis pathway of the protein or impact on the translocation dynamics (in particular as the imaging method applied here does not allow for analysis of dynamics). The hypothesis that the biogenesis route can be monitored is therefore far-reaching. To strengthen the hypothesis the authors should assess if the C-terminal GFP11 influences protein solubility by assessing protein aggregation of e.g. Rip1.

We agree with the reviewer that the tag and assembly of GFP_1-10/11 can further complicate the assessment of topology of the IM proteins that already have complex biogenesis routes (lateral transfer, conservative, and a Rip1-specific Bcs1 pathway). To emphasize that the assessment of the steady state topology needs to be backed up by additional biochemical approaches, we edited the beginning of the corresponding Results sections as follows (P. 11 Lines 2-6): 

“Studying membrane protein biogenesis requires an accurate way to determine topology in vivo. The mitochondrial IM is one of the most protein-rich membranes in the cell supporting a wide variety of TMD topologies with complex biogenesis pathways. We aimed to find out if our BiG Mito-Split collection can accurately visualize the steady-state localization of membrane protein C-termini protruding into the matrix or trap protein transport intermediates” (inserted text is underlined).

The collection that we studied by microscopy is diploid and contains one WT copy of each 3xGFP₁₁tagged gene. To assess the influence of the tag on the protein function we performed growth assays with haploid strains which have one 3xGFP₁₁-tagged gene copy and no GFP_1-10. We find that Rip13xGFP₁₁ displays slower growth on glycerol at 30˚C and even slower at 37˚C while tagged Qcr8, Qcr9, and Qcr10 grow normally (Author response image 2 A). Based on the growth assays and microscopy it is not possible to conclude whether the “Qcr” proteins’ biogenesis is affected by the tag. It may be that laterally sorted proteins are functional with the tag and constitute the majority while only a small portion is translocated into the matrix, trapped and visualized with GFP_1-10. In case of Rip1 it was shown that C-terminal tag can affect its interaction with the chaperone Mzm1 and promote Rip1 aggregation (Cui et al, 2012). The extent of Rip1 function disruption can be different and depends on the tag. We hypothesize that our split-assay may trap the pre-translocation intermediate of Rip1 and can be helpful to study its interactors. To test this, we performed anti-GFP immune-precipitation (IP) using GFP-Trap beads (Author response image 2 B).

Author response image 2.

The influence of 3x-GFP11 on the function and processing of the inner membrane proteins. (A) Drop dilution assays with haploid strains from C-SWAT 3xGFP<Sub>11</sub> library on fermentative (YPD) and respiratory (YPGlycerol) media at different temperatures. (B) Immuno-precipitation with GFP-Trap agarose was performed on haploid strain that has only Rip1-3xGFP₁₁ and on the diploid strain derived from this haploid mated with BiG Mito-Split strain containing mtGFP_1-10 and WT untagged Rip1 using the lysis (1% TX-100) and washing protocols provided by the manufacturer; the total (T) and eluted with the Laemmli buffer (IP) samples were analyzed by immunoblotting with polyclonal rabbit antibodies against GFP (only visualizes GFP<Sub>11</sub> in these samples) and Rip1 (visualizes both tagged and WT Rip1). Polyclonal home-made rabbit antisera for GFP and Rip1 were kindly provided by Johannes Herrmann (Kaiserslautern) and Thomas Becker (Bonn); the antisera were diluted 1:500 for decorating the membranes.

We find that the haploid strain with Rip1-3xGFP₁₁ contains not only mature (m) and intermediate (i) forms but also an additional higher Mw band that we interpreted as precursor that was not cleaved by MPP. WT Rip1 in the diploid added two more lower Mw bands: (m) and (i) forms of the untagged Rip1. IP successfully enriched GFP_1-10 fragment as visualized by anti-GFP staining. Interestingly only the highest Mw Rip1-3xGFP₁₁ band was also enriched when anti-Rip1 antibodies were used to analyze the samples. This suggests that Rip1 precursor gets completely imported and interacts with GFP_1-10 and can be pulled down. It is however not processed. Processed Rip1 is not interacting with GFP_1-10. Based on the literature we expect all Rip1 in the matrix to be cleaved by MPP including the one interacting with GFP. Due to this discrepancy, we did not include this data in the manuscript. This is however clear that the assay may be useful to analyze biogenesis intermediates of the IM and matrix proteins. To emphasize this, we added information on the C-terminal tagging of Rip1 in the Results section (P. 11 Lines 18-20):

“It was shown that a C-terminal tag on Rip1 can prevent its interaction with the chaperone Mzm1 and promote aggregation in the matrix (Cui et al, 2012). It is also possible that our assay visualizes this trapped biogenesis intermediate.”

We also added a note on biogenesis intermediates in the Discussion (P. 14 Line 36 onwards): 

“It is possible that the proteins with C-termini that are translocated into the IMS from the matrix side can be trapped by the interaction with GFP_1-10. In that case, our assay can be a useful tool to study these pre-translocation intermediates.”

(4) The hypothesis that the method can reveal new substrates for Bcs1 is interesting, and it would strongly increase the relevance for the scientific community if this would be directly tested, e.g. by deleting BCS1 and testing if more IM proteins are then detected by interaction with the matrix GFP110.

we attempted to move the BiG Mito-Split assay into haploid strains where BCS1 and other factors can be deleted, however, this was not successful. Since this was a big effort (We cloned 10 potential substrate proteins but none of them were expressed) we decided not to pursue this further.

(5) The screening of six different growth conditions reflects the strength of the high-throughput imaging readout. However, the interpretation of the data and additional follow-up on this is rather short and would be a nice addition to the present manuscript. In addition, one wonders, what was the rationale behind these six conditions (e.g. DTT treatment)? The direct metabolic shift from fermentation to respiration to boost mitochondrial biogenesis would be a highly interesting condition and the authors should consider adding this in the present manuscript.

we agree with the reviewer that the analysis of different conditions is a strength of this work. However, we did not reveal any clear protein groups with strong conditional import and thus it was hard to select a follow-up candidate. The selection of conditions was partially driven by the technical possibilities: the media change is challenging on the robotic system; heat shock conditions make microscope autofocus unstable; library strain growth on synthetic respiratory media is very slow and the media cannot be substituted with rich media due to its autofluorescence. However, the usage of the spinning disc confocal microscope allowed us to screen directly in synthetic oleate media which has a lot of background on widefield systems due to oil micelles. We extended the explanation of condition choice as follows (P. 4 Line 34 onwards): 

“The diploid BiG Mito-Split collection was imaged in six conditions representing various carbon sources and a diversity of stressors the cells can adapt to: logarithmic growth on glucose as a control carbon source and oleic acid as a poorly studied carbon source; post-diauxic (stationary) phase after growth on glucose where mitochondria, are more active and inorganic phosphate (Pi) depletion that was recently described to enhance mitochondrial membrane potential (Ouyang et al, 2024); as stress conditions we chose growth on glucose in the presence of 1 mM dithiothreitol (DTT) that might interfere with the disulfide relay system in the IMS, and nitrogen starvation as a condition that may boost biosynthetic functions of mitochondria. DTT and nitrogen starvation were earlier used for a screen with the regular C’-GFP collection (Breker et al, 2013). Another important consideration for selecting the conditions was the technical feasibility to implement them on automated screening setups.”

Reviewer #3 (Recommendations for the authors )

(6) This is a very elegant and clearly written study. As mentioned above, my only concern is that the biological significance of the obtained data, at this stage, is rather limited. It would have been nice if the authors explored one of the potential applications of the system they propose. For example, it should be relatively easy to analyze whether Cox26, Qcr8, Qcr9, or Qcr10 are new substrates of Bsc1, as the authors speculate.

we thank the reviewer for their positive feedback. We addressed the biological application of the screen by including new data on metabolite concentrations in the strains where Gpp1 N-terminus was mutated leading to loss of the mitochondrial form. We added panels H and I to Figure 4, the new Supplementary Table S2 and appended the description of these results at the end of the third Results subsection (P. 10 Lines 19-35). Our data now show a role for the mitochondrial fraction of Gpp1 which adds mechanistic insight into this dually localized protein.

We also were interested in the applications of our system to the study of mitochondrial import. However, the study of Cox26, Qcr8, Qcr9, and Qcr10 was not successful (also related to point 4, Reviewer #2). We thus decided to investigate the import mechanisms of the poorly studied dually localized proteins Arc1, Fol3, and Hom6 (related to Figure 4 of the original manuscript). To this end, we expressed these proteins in vitro, radiolabeled, and performed import assays with purified mitochondria. Arc1 was not imported, Fol3 and Hom6 gave inconclusive results (Author response image 3). Since it is known that even some genuine fully or dually localized mitochondrial proteins such as Fum1 cannot be imported in vitro post-translationally (Knox et al, 1998), we cannot draw conclusions from these experiments and left them out of the revised manuscript. Additional investigation is required to clarify if there exist special cytosolic mechanisms for the import of these proteins that were not reconstituted in vitro such as co-translational import.

Author response image 3.

In vitro import of poorly studies dually localized proteins. Arc1, Fol3, and Hom6 were cloned into pGEM4 plasmid, synthesized in vitro (Input), radiolabeled, and imported into mitochondria isolated from BY4741 strain as described before (Peleh et al, 2015); the import was performed for 5,10, or 15 minutes and mitochondria were treated with proteinase K (PK) to degrade non-imported proteins; some reactions were treated with the mix of valinomycin, antimycin, and oligomycin (VAO) to dissipate mitochondrial membrane potential. The proteins were separated by SDS-PAGE and visualized by autoradiography.

Minor comments:

(7) It is unclear why the authors used the six growth conditions they used, and why for example a nonfermentable medium was not included at all.

we address this shortcoming in the reply to the previous point 5 (Reviewer #2).

(8) Page 2, line 17 - "Its" should be corrected to "its".

Changed

(9) Page 2, line 25 to the end of the paragraph - the authors refer to the TIM complex when actually the TIM23 complex is probably meant. Also, it would be clearer if the TIM22 complex was introduced as well, especially in the context of the sentence stating that "the IM is a major protein delivery destination in mitochondria".

This was corrected.

(10) Page 5, line 35 - "who´s" should be corrected to "whose".

This was corrected.

(11) Page 9, line 5 - "," after Gpp1 should probably be "and".

This was corrected.

(12) Page 11 - the authors discuss in several places the possible effects of tags and how they may interfere with "expression, stability and targeting of proteins". Protein function may also be dramatically affected by tags - a quick look into the dataset shows that several mitochondrial matrix and inner membrane proteins that are essential for cell viability were not identified in the screen, likely because their function is impaired.

we agree with the reviewer that the influence of tags needs to be carefully evaluated. This is not always possible in the context of whole genomic screens. Sometimes, yeast collections (and proteomic datasets) can miss well-known mitochondrial residents without a clear reason. To address this important point we conducted an additional analysis to look specifically at the essential proteins. We indeed found that several of the mitochondrial proteins that are essential for viability were absent from the collection at the start, but for those present, their essentiality did not impact the likelihood to be detected in our assay. To describe the analysis we added the following text and a Fig. 3 – figure supplement 2. Results now read (P.7 Lines 8-21): 

“Next, we checked the two categories of proteins likely to give biased results in high-throughput screens of tagged collections: proteins essential for viability, and molecular complex subunits. To look at the first category we split the proteomic dataset of soluble matrix proteins (Vögtle et al. 2017) into essential and non-essential ones according to the annotations in the Saccharomyces Genome Database (SGD) (Wong et al, 2023). We found that there was no significant difference in the proportion of detected proteins in both groups (17 and 20 % accordingly), despite essential proteins being less represented in the initial library (Fig. 3 – figure supplement 2A). From the three essential proteins of the (Vögtle et al. 2017) dataset for which the strains present in our library but showed no signal, two were nucleoporins Nup57 and Nup116, and one was a genuine mitochondrial protein Ssc1. Polymerase chain reaction (PCR) and western blot verification showed that the Ssc1 strain was incorrect (Fig. 3 – figure supplement 2B). We conclude that essential proteins are more likely to be absent or improperly tagged in the original C’-SWAT collection, but the essentiality does not affect the results of the BiG Mito-Split assay.” 

Discussion (P. 13 Lines 23-26): 

“We did not find that protein complex components or essential proteins are more likely to be falsenegatives. However, some essential proteins were absent from the collection to start with (Fig. 3 – figure supplement 2A). Thus, a small tag allows visualization of even complex proteins.” 

From our data it is difficult to estimate the effect of tagging on protein function. We also addressed the effect of tagging Rip1 as well as performed growth assays on the tagged small “Qcr proteins” in the reply to point 3 (Reviewer #2). It is also difficult to estimate the effect of GFP_1-10 and ₁₁ complex assembly on protein function since the presence of functional, unassembled GFP₁₁ tagged pool cannot be ruled out in our assay. 

Other changes

Figure and table numbers changed after new data additions.

A sentence added in the abstract to highlight the additional experiments on Gpp1 function: “We use structure-function analysis to characterize the dually localized protein Gpp1, revealing an upstream start codon that generates a mitochondrial targeting signal and explore its unique function.”

The reference to the PCR verification (Fig. 3 – Supplement 2B) of correct tagging of Ycr102c was added to the Results section (P.8 Line 6), western blot verification added on.

Added the Key Resources Table at the beginning of the Methods section.

Small grammar edits, see tracked changes.

References:

Bader G, Enkler L, Araiso Y, Hemmerle M, Binko K, Baranowska E, De Craene J-O, Ruer-Laventie J, Pieters J, Tribouillard-Tanvier D, et al (2020) Assigning mitochondrial localization of dual localized proteins using a yeast Bi-Genomic Mitochondrial-Split-GFP. eLife 9: e56649

Cui T-Z, Smith PM, Fox JL, Khalimonchuk O & Winge DR (2012) Late-Stage Maturation of the Rieske Fe/S Protein: Mzm1 Stabilizes Rip1 but Does Not Facilitate Its Translocation by the AAA ATPase Bcs1. Mol Cell Biol 32: 4400–4409

Desai N, Brown A, Amunts A & Ramakrishnan V (2017) The structure of the yeast mitochondrial ribosome. Science 355: 528–531

Guo H, Bueler SA & Rubinstein JL (2017) Atomic model for the dimeric FO region of mitochondrial ATP synthase. Science 358: 936–940

Knox C, Sass E, Neupert W & Pines O (1998) Import into Mitochondria, Folding and Retrograde Movement of Fumarase in Yeast. J Biol Chem 273: 25587–25593

Morgenstern M, Stiller SB, Lübbert P, Peikert CD, Dannenmaier S, Drepper F, Weill U, Höß P, Feuerstein R, Gebert M, et al (2017) Definition of a High-Confidence Mitochondrial Proteome at Quantitative Scale. Cell Rep 19: 2836–2852

Oborská-Oplová M, Geiger AG, Michel E, Klingauf-Nerurkar P, Dennerlein S, Bykov YS, Amodeo S, Schneider A, Schuldiner M, Rehling P, et al (2025) An avoidance segment resolves a lethal nuclear–mitochondrial targeting conflict during ribosome assembly. Nat Cell Biol 27: 336–346

Peleh V, Ramesh A & Herrmann JM (2015) Import of Proteins into Isolated Yeast Mitochondria. In Membrane Trafficking: Second Edition, Tang BL (ed) pp 37–50. New York, NY: Springer

Srivastava AP, Luo M, Zhou W, Symersky J, Bai D, Chambers MG, Faraldo-Gómez JD, Liao M & Mueller DM (2018) High-resolution cryo-EM analysis of the yeast ATP synthase in a lipid membrane. Science 360: eaas9699

Vögtle F-N, Burkhart JM, Gonczarowska-Jorge H, Kücükköse C, Taskin AA, Kopczynski D, Ahrends R, Mossmann D, Sickmann A, Zahedi RP, et al (2017) Landscape of submitochondrial protein distribution. Nat Commun 8: 290

Woellhaf MW, Hansen KG, Garth C & Herrmann JM (2014) Import of ribosomal proteins into yeast mitochondria. Biochem Cell Biol 92: 489–498

Author Response:

Reviewer #1 (Public Review):

In this manuscript, the authors challenge the long-standing conclusion that Orco and IR-dependent olfactory receptor neurons are segregated into subtypes such that Orco and IR expression do not overlap. First, the authors generate new knock-in lines to tag the endogenous loci with an expression reporter system, QF/QUAS. They then compare the observed expression of these knock-ins with the widely used system of enhancer transgenes of the same receptors, namely Orco, IR8a, IR25a, and IR76b. Surprisingly, they observe an expansion of the expression of the individual knock-in reporters as compared to the transgenic reporters in more chemosensory neurons targeting more glomeruli per receptor type than previously reported. They verify the expression of the knock-in reporters with antibody staining, in situ hybridization and by mining RNA sequencing data.

Finally, they address the question of physiological relevance of such co-expression of receptor systems by combining optogenetic activation with single sensillum recordings and mutant analysis. Their data suggests that IR25a activation can modulate Orco-dependent signaling and activation of olfactory sensory neurons.

The paper is well written and easy to follow. The data are well presented and very convincing due in part to the combination of complementary methods used to test the same point. Thus, the finding that co-receptors are more broadly and overlappingly expressed than previously thought is very convincing and invites speculation of how this might be relevant for the animal and chemosensory processing in general. In addition, the new method to make knock-ins and the generated knock-ins themselves will be of interest to the fly community.

We thank the reviewer for their enthusiasm and support of our work!

The last part of the manuscript, although perhaps the most interesting, is the least developed compared to the other parts. In particular, the following points could be addressed:

• It would be good to see a few more traces and not just the quantifications. For instance, the trace of ethyl acetate in Fig. 6C, and penthyl acetate for 6G.

Thank you for the suggestion. We have added a new figure supplement (Figure 6-Figure Supplement 3) with additional example traces for all odorants from Figure 6 for which we found a statistically significant difference between the two genotypes (Ir25a versus wildtype).

• In Fig. 4D, the authors show the non-retinal fed control, which is great. An additional genetic control fed with retinal would have been nice.

For these experiments, we followed a standard practice in Drosophila optogenetics to test the same experimental genotype in the presence or absence of the essential cofactor all-trans-retinal. This controls for potential effects from the genetic background. It is possible our description of these experiments was unclear (as also suggested by comments from Reviewer 2). As such, we have clarified our experimental design for the optogenetic experiments in the revised manuscript:

Modified text: “No light-induced responses were found in control flies, which had the same genotype as experimental flies but were not fed all-trans retinal (-ATR), a necessary co-factor for channelrhodopsin function (see Methods).” and “Bottom trace is control animal, which has the same genotype as the experimental animal but was not fed the required all-trans retinal cofactor (-ATR).”

Figure 4-Figure Supplement 1 legend: “In all optogenetic experiments, control animals have the same genotypes as the corresponding experimental animals but have not been fed all-trans retinal.”

Methods: “For all optogenetic experiments, the control flies were of the same genotype as experimental flies but had not been fed all-trans retinal.”

• It appears that mostly IR25a is strongly co-expressed with other co-receptors. The provided experiments suggest a possible modulation between IR25a and Orco-dependent neuronal activity. However, what does this mean? How could this be relevant? And moreover, is this a feature of Drosophila melanogaster after many generations in laboratories?

We share this reviewer’s excitement regarding the numerous questions our work now raises. While testing additional functional ramifications of chemosensory co-receptor expression is beyond the scope of this work (but will undoubtedly be the focus of future studies), we did expand on what this might mean in the revised Discussion section of the revised manuscript. Previously, we had raised the hypothesis that chemoreceptor co-expression could be an evolutionary relic of Ir25a expression in all chemoreceptor neurons , or a biological mechanism to broaden the response profile of an olfactory neuron without sacrificing its ability to respond to specific odors. We now extend our discussion to raise additional possible ramifications. For example, we suggest that modulating Ir25a coexpression could alter the electrical properties of a neuron, making it more (or possibly less) sensitive to Orco-dependent responses. We also suggest that Ir25a coexpression might be an evolutionary mechanism to allow olfactory neurons to adjust their response activities. That is, that most Orco-positive olfactory neurons are already primed to be able to express a functional Ir receptor if one were to be expressed. Such co-expression in some olfactory neurons might present an evolutionary advantage by ensuring olfactory responses to a complex but crucial biologically relevant odor, like human odors to some mosquitoes.

Reviewer #2 (Public Review):

In the present study, the authors: 1) generated knock-in lines for Orco, Ir8a, Ir25a, and IR7ba, and examined their expression, with a main focus on the adult olfactory organs. 2) confirmed the expression of these receptors using antibody staining. 3) examined the innervation patterns of these knock-in lines in the nervous system. 4) identified a glomerulus, VM6, that is divided into three subdivisions. 5) examined olfactory responses of neurons co-expressing Orco and Ir25a

The results of the first four sets of experiments are well presented and support the conclusions, but the results of the last set of experiments (the electrophysiology part) need some details. Please find my detailed comments below.

We thank the reviewer for their support of our work and appreciating the importance of our findings. In the revised manuscript, we now provide the additional experimental details for the electrophysiology work as requested.

Major points

Line 167-171: I wonder if the authors also compared the Orco-T2A-QF2 knock-in with antibody staining of the antenna.

We did perform whole-mount anti-Orco antibody staining on Orco-T2A-QF2 > GFP antennae (example image below). We saw broad overlap between Orco+ and GFP+ cells, similar to the palps. However, we did not include these results since quantification of these tissues is challenging for the following reasons:

1. There are ~1,200 olfactory neurons in each antenna, many of which are Orco+.
2. The thickness of the tissue makes determinations of co-localization difficult in wholemount staining.
3. Co-localization is further complicated by the sub-cellular localization of the signals: Orco antibodies preferentially label dendrites and weakly label cell bodies, while our GFP reporter is cytoplasmic and preferentially labels cell bodies. For these reasons, we focused on the numerically simpler palps for quantification. For the Ir8aT2A-QF2 and Ir76b-T2A-QF2 lines, palp quantification was not an option as neither knock-in drove expression in the palps (and the available antibodies did not work with the whole-mount staining protocol). This is why we performed antennal cryosections to validate these lines. Below is an example image of the antennal whole-mount staining in the Orco-T2A-QF2 knock-in line, illustrating the quantification challenges enumerated above.

*Co-staining of anti-Orco and GFP in Orco-T2A-QF2 > 10xQUAS-6xGFP antenna *

Lines 316-319 (Figure 4D): It would be better if the authors compare the responses of Ir25a>CsChrimson to those of Orco>CsChrimson.

The goal of the optogenetic experiments was to provide experimental support for Ir25a expression in Orco+ neurons in an approach independent to previous methods. Our main question was whether we could activate what was previously considered Orco-only olfactory neurons using the Ir25a knock-in. These experiments were not designed to determine if this optogenetic activation recapitulated the normal activity of these neurons. For these reasons, we did not attempt the optogenetic experiments with Orco>CsChrimson flies.

Line 324-326: Why the authors tested control flies not fed all-trans retinal? They should test Ir25a-T2A-QF2>QUAS-CsChrimson not fed all-trans retinal as a control.

We apologize for the confusion. The “control” flies we used were indeed Ir25a-T2AQF2>QUAS-CsChrimson flies not fed all-trans retinal as suggested by the reviewer. This detail was in the methods, yet likely was not clear. We have amended the main text in multiple locations to state the full genotype of the control fly more clearly:

Modified text: “No light-induced responses were found in control flies, which had the same genotype as experimental flies but were not fed all-trans retinal (-ATR), a necessary co-factor for channelrhodopsin function (see Methods).” and “Bottom trace is control animal, which has the same genotype as the experimental animal but was not fed the required all-trans retinal cofactor (-ATR).”

Figure 4-Figure Supplement 1 legend: “In all optogenetic experiments, control animals have the same genotypes as the corresponding experimental animals but have not been fed all-trans retinal.”

Methods: “For all optogenetic experiments, the control flies were of the same genotype as experimental flies but had not been fed all-trans retinal.”

Line 478-500: I wonder if the observed differences between the wildtype and Ir25a2 mutant lines are due to differences in the genetic background between both lines. Did the authors backcross Ir25a2 mutant line with the used wildtype for at least five generations?

Yes, the mutants are outcrossed into the same genetic background as the wildtypes for at least five generations. Please see Methods, revised manuscript: “Ir25a2 and Orco2 mutant fly lines were outcrossed into the w1118 wildtype genetic background for at least 5 generations.”

Line 1602-1603: Does the identification of ab3 sensilla using fluorescent-guided SSR apply for ab3 sensilla in Orco mutant flies. How does this ab3 fluorescent-guided SSR work?

In fluorescence guided SSR (fgSSR; Lin and Potter, PloS One, 2015), the ab3 sensilla is GFPlabelled (genotype: Or22a-Gal4>UAS-mCD8:GFP), which allows this sensilla to be specifically identified under a microscope and targeted for SSR recordings. We generated fly stocks for fgSSR identification of ab3 in all three genetic backgrounds (wildtype, Orco mutant, Ir25a mutant).

These three genotypes are described in the methods:

“Full genotypes for ab3 fgSSR were:

Pin/CyO; Or22a-Gal4,15XUAS-IVS-mcd8GFP/TM6B (wildtype),

Ir25a2; Or22a-Gal4,15XUAS-IVS-mcd8GFP/TM6B (Ir25a2 mutant),

Or22a-Gal4/10XUAS-IVS-mcd8GFP (attp40); Orco2 (Orco2 mutant).”

Line 1602-1604: There is no mention of how the authors identified ab9 sensilla.

Information on the identification of ab9 sensilla is under the optogenetics section of the methods: “Identification of ab9 sensilla was assisted by fluorescence-guided Single Sensillum Recording (fgSSR) (Lin and Potter, 2015) using Or67b-Gal4 (BDSC #9995) recombined with 15XUAS-IVS-mCD8::GFP (BDSC #32193).”

Line 1648: what are the set of odorants that were used to identify the different coeloconic sensilla?

We have added the specific odorants used for sensillar identification for coeloconic SSR in the Methods. The protocol and odorants used were:

*2,3-butanedione (BUT), 1,4-diaminobutane (DIA), Ammonia (AM), hexanol (HEX), phenethylamine (PHEN), and propanal (PROP) to distinguish coeloconic sensilla:

o Wildtype flies: Strong DIA and BUT responses identify ac2 and rule out ac4. Absence of strong AM response rules out ac1, absence of HEX response rules out ac3, absence of PHEN response further rules out ac4.

o Ir25a mutant flies (amine responses lost, so cannot use PHEN and DIA as diagnostics): Strong BUT response and moderate PROP response identify ac2 and rule out ac4. Absence of strong AM response rules out ac1, absence of HEX response rules out ac3. Ac4 is further ruled out anatomically based on sensillar location compared to ac2.

Revised text: “Different classes of coeloconic sensilla were identified by their known location on the antenna and confirmed with their responses to a small panel of diagnostic odorants: in wildtype flies, ac2 sensilla were identified by their strong responses to 1,4-diaminobutane and 2,3-butanedione. The absence of a strong response to ammonia was used to rule out ac1 sensilla, the absence of a hexanol response was used to rule out ac3 sensilla, and the absence of a phenethylamine response was used to rule out ac4 sensilla. In Ir25a mutant flies in which amine responses were largely abolished, ac2 and ac4 sensilla were distinguished based on anatomical location, as well as the strong response of ac2 to 2,3-butanedione and the moderate response to propanal (both absent in ac4). Ac1 and ac3 sensilla were excluded similarly in the mutant and wildtype flies. No more than 4 sensilla per fly were recorded. Each sensillum was tested with multiple odorants, with a rest time of at least 10s between applications.

• Add the above script tag to the top-level document
• Do not add the script tag to the iframed documents themselves, the client will do this itself.
• Opt iframes into annotation by adding the "enable-annotation" attribute:

I've definitely taken part in crowdsourcing without even realizing it - like helping tag videos, report spam, or add info to online maps. I think social media companies should take some responsibility for what their users do, especially when harmful content spreads because of their design. Platforms could do more to guide people toward helpful actions, like fact-checking or supporting good causes, instead of just chasing clicks and treads.

Author response:

Reviewer #1 (Public Review):

In this paper, Tompary & Davachi present work looking at how memories become integrated over time in the brain, and relating those mechanisms to responses on a priming task as a behavioral measure of memory linkage. They find that remotely but not recently formed memories are behaviorally linked and that this is associated with a change in the neural representation in mPFC. They also find that the same behavioral outcomes are associated with the increased coupling of the posterior hippocampus with category-sensitive parts of the neocortex (LOC) during a post-learning rest period-again only for remotely learned information. There was also correspondence in rest connectivity (posterior hippocampus-LOC) and representational change (mPFC) such that for remote memories specifically, the initial post-learning connectivity enhancement during rest related to longer-term mPFC representational change.

This work has many strengths. The topic of this paper is very interesting, and the data provide a really nice package in terms of providing a mechanistic account of how memories become integrated over a delay. The paper is also exceptionally well-written and a pleasure to read. There are two studies, including one large behavioral study, and the findings replicate in the smaller fMRI sample. I do however have two fairly substantive concerns about the analytic approach, where more data will be required before we can know whether the interpretations are an appropriate reflection of the findings. These and other concerns are described below.

Thank you for the positive comments! We are proud of this work, and we feel that the paper is greatly strengthened by the revisions we made in response to your feedback. Please see below for specific changes that we’ve made.

1) One major concern relates to the lack of a pre-encoding baseline scan prior to recent learning.

a) First, I think it would be helpful if the authors could clarify why there was no pre-learning rest scan dedicated to the recent condition. Was this simply a feasibility consideration, or were there theoretical reasons why this would be less "clean"? Including this information in the paper would be helpful for context. Apologies if I missed this detail in the paper.

This is a great point and something that we struggled with when developing this experiment. We considered several factors when deciding whether to include a pre-learning baseline on day two. First, the day 2 scan session was longer than that of day 1 because it included the recognition priming and explicit memory tasks, and the addition of a baseline scan would have made the length of the session longer than a typical scan session – about 2 hours in the scanner in total – and we were concerned that participant engagement would be difficult to sustain across a longer session. Second, we anticipated that the pre-learning scan would not have been a ‘clean’ measure of baseline processing, but rather would include signal related to post-learning processing of the day 1 sequences, as multi-variate reactivation of learned stimuli have been observed in rest scans collected 24-hours after learning (Schlichting & Preston, 2014). We have added these considerations to the Discussion (page 39, lines 1047-1070).

b) Second, I was hoping the authors could speak to what they think is reflected in the post-encoding "recent" scan. Is it possible that these data could also reflect the processing of the remote memories? I think, though am not positive, that the authors may be alluding to this in the penultimate paragraph of the discussion (p. 33) when noting the LOC-mPFC connectivity findings. Could there be the reinstatement of the old memories due to being back in the same experimental context and so forth? I wonder the extent to which the authors think the data from this scan can be reflected as strictly reflecting recent memories, particularly given it is relative to the pre-encoding baseline from before the remote memories, as well (and therefore in theory could reflect both the remote + recent). (I should also acknowledge that, if it is the case that the authors think there might be some remote memory processing during the recent learning session in general, a pre-learning rest scan might not have been "clean" either, in that it could have reflected some processing of the remote memories-i.e., perhaps a clean pre-learning scan for the recent learning session related to point 1a is simply not possible.)

We propose that theoretically, the post-learning recent scan could indeed reflect mixture of remote and recent sequences. This is one of the drawbacks of splitting encoding into two sessions rather than combining encoding into one session and splitting retrieval into an immediate and delayed session; any rest scans that are collected on Day 2 may have signal that relates to processing of the Day 1 remote sequences, which is why we decided against the pre-learning baseline for Day 2, as you had noted.

You are correct that we alluded to in our original submission when discussing the LOC-mPFC coupling result, and we have taken steps to discuss this more explicitly. In Brief, we find greater LOC-mPFC connectivity only after recent learning relative to the pre-learning baseline, and cortical-cortical connectivity could be indicative of processing memories that already have undergone some consolidation (Takashima et al., 2009; Smith et al., 2010). From another vantage point, the mPFC representation of Day 1 learning may have led to increased connectivity with LOC on Day 2 due to Day 1 learning beginning to resemble consolidated prior knowledge (van Kesteren et al., 2010). While this effect is consistent with prior literature and theory, it's unclear why we would find evidence of processing of the remote memories and not the recent memories. Furthermore, the change in LOC-mPFC connectivity in this scan did not correlate with memory behaviors from either learning session, which could be because signal from this scan reflects a mix of processing of the two different learning sessions. With these ideas in mind, we have fleshed out the discussion of the post-encoding ‘recent’ scan in the Discussion (page 38-39, lines 1039-1044).

c) Third, I am thinking about how both of the above issues might relate to the authors' findings, and would love to see more added to the paper to address this point. Specifically, I assume there are fluctuations in baseline connectivity profile across days within a person, such that the pre-learning connectivity on day 1 might be different from on day 2. Given that, and the lack of a pre-learning connectivity measure on day 2, it would logically follow that the measure of connectivity change from pre- to post-learning is going to be cleaner for the remote memories. In other words, could the lack of connectivity change observed for the recent scan simply be due to the lack of a within-day baseline? Given that otherwise, the post-learning rest should be the same in that it is an immediate reflection of how connectivity changes as a function of learning (depending on whether the authors think that the "recent" scan is actually reflecting "recent + remote"), it seems odd that they both don't show the same corresponding increase in connectivity-which makes me think it may be a baseline difference. I am not sure if this is what the authors are implying when they talk about how day 1 is most similar to prior investigation on p. 20, but if so it might be helpful to state that directly.

We agree that it is puzzling that we don’t see that hippocampal-LOC connectivity does not also increase after recent learning, equivalently to what we see after remote learning. However, the fact that there is an increase from baseline rest to post-recent rest in mPFC – LOC connectivity suggests that it’s not an issue with baseline, but rather that the post-recent learning scan is reflecting processing of the remote memories (although as a caveat, there is no relationship with priming).

On what is now page 23, we were referring to the notion that the Day 1 procedure (baseline rest, learning, post-learning rest) is the most straightforward replication of past work that finds a relationship between hippocampal-cortical coupling and later memory. In contrast, the Day 2 learning and rest scan are less ‘clean’ of a replication in that they are taking place in the shadow of Day 1 learning. We have clarified this in the Results (page 23, lines 597-598).

d) Fourth and very related to my point 1c, I wonder if the lack of correlations for the recent scan with behavior is interpretable, or if it might just be that this is a noisy measure due to imperfect baseline correction. Do the authors have any data or logic they might be able to provide that could speak to these points? One thing that comes to mind is seeing whether the raw post-learning connectivity values (separately for both recent and remote) show the same pattern as the different scores. However, the authors may come up with other clever ways to address this point. If not, it might be worth acknowledging this interpretive challenge in the Discussion.

We thought of three different approaches that could help us to understand whether the lack of correlations in between coupling and behavior in the recent scan was due to noise. First, we correlated recognition priming with raw hippocampal-LOC coupling separately for pre- and post-learning scans, as in Author response image 1:

Author response image 1.

Note that the post-learning chart depicts the relationship between post-remote coupling and remote priming and between post-recent coupling and recent priming (middle). Essentially, post-recent learning coupling did not relate to priming of recently learned sequences (middle; green) while there remains a trend for a relationship between post-remote coupling and priming for remotely learned sequences (middle; blue). However, the significant relationship between coupling and priming that we reported in the paper (right, blue) is driven both by the initial negative relationship that is observed in the pre-learning scan and the positive relationship in the post-remote learning scan. This highlights the importance of using a change score, as there may be spurious initial relationships between connectivity profiles and to-be-learned information that would then mask any learning- and consolidation-related changes.

We also reasoned that if comparisons between the post-recent learning scan and the baseline scan are noisier than between the post-remote learning and baseline scan, there may be differences in the variance of the change scores across participants, such that changes in coupling from baseline to post-recent rest may be more variable than coupling from baseline to post-remote rest. We conducted F-tests to compare the variance of the change in these two hippocampal-LO correlations and found no reliable difference (ratio of difference: F(22, 22) = 0.811, p = .63).

Finally, we explored whether hippocampal-LOC coupling is more stable across participants if compared across two rest scans within the same imaging session (baseline and post-remote) versus across two scans across two separate sessions (baseline and post-recent). Interestingly, coupling was not reliably correlated across scans in either case (baseline/post-remote: r = 0.03, p = 0.89 Baseline/post-recent: r = 0.07, p = .74).

Finally, we evaluated whether hippocampal-LOC coupling was correlated across different rest scans (see Author response image 2). We reasoned that if such coupling was more correlated across baseline and post-remote scans relative to baseline and post-recent scans, that would indicate a within-session stability of participants’ connectivity profiles. At the same time, less correlation of coupling across baseline and post-recent scans would be an indication of a noisier change measure as the measure would additionally include a change in individuals’ connectivity profile over time. We found that there was no difference in the correlation of hipp-LO coupling is across sessions, and the correlation was not reliably significant for either session (baseline/post-remote: r = 0.03, p = 0.89; baseline/post-recent: r = 0.07, p = .74; difference: Steiger’s t = 0.12, p = 0.9).

Author response image 2.

We have included the raw correlations with priming (page 25, lines 654-661, Supplemental Figure 6) as well as text describing the comparison of variances (page 25, lines 642-653). We did not add the comparison of hippocampal-LOC coupling across scans to the current manuscript, as an evaluation of stability of such coupling in the context of learning and reactivation seems out of scope of the current focus of the experiment, but we find this result to be worthy of follow-up in future work.

In summary, further analysis of our data did not reveal any indication that a comparison of rest connectivity across scan sessions inserted noise into the change score between baseline and post-recent learning scans. However, these analyses cannot fully rule that possibility out, and the current analyses do not provide concrete evidence that the post-recent learning scan comprises signals that are a mixture of processing of recent and remote sequences. We discuss these drawbacks in the Discussion (page 39, lines 1047-1070).

2) My second major concern is how the authors have operationalized integration and differentiation. The pattern similarity analysis uses an overall correspondence between the neural similarity and a predicted model as the main metric. In the predicted model, C items that are indirectly associated are more similar to one another than they are C items that are entirely unrelated. The authors are then looking at a change in correspondence (correlation) between the neural data and that prediction model from pre- to post-learning. However, a change in the degree of correspondence with the predicted matrix could be driven by either the unrelated items becoming less similar or the related ones becoming more similar (or both!). Since the interpretation in the paper focuses on change to indirectly related C items, it would be important to report those values directly. For instance, as evidence of differentiation, it would be important to show that there is a greater decrease in similarity for indirectly associated C items than it is for unrelated C items (or even a smaller increase) from pre to post, or that C items that are indirectly related are less similar than are unrelated C items post but not pre-learning. Performing this analysis would confirm that the pattern of results matches the authors' interpretation. This would also impact the interpretation of the subsequent analyses that involve the neural integration measures (e.g., correlation analyses like those on p. 16, which may or may not be driven by increased similarity among overlapping C pairs). I should add that given the specificity to the remote learning in mPFC versus recent in LOC and anterior hippocampus, it is clearly the case that something interesting is going on. However, I think we need more data to understand fully what that "something" is.

We recognize the importance of understanding whether model fits (and changes to them) are driven by similarity of overlapping pairs or non-overlapping pairs. We have modified all figures that visualize model fits to the neural integration model to separately show fits for pre- and post-learning (Figure 3 for mPFC, Supp. Figure 5 for LOC, Supp. Figure 9 for AB similarity in anterior hippocampus & LOC). We have additionally added supplemental figures to show the complete breakdown of similarity each region in a 2 (pre/post) x 2 (overlapping/non-overlapping sequence) x 2 (recent/remote) chart. We decided against including only these latter charts rather than the model fits since the model fits strike a good balance between information and readability. We have also modified text in various sections to focus on these new results.

In brief, the decrease in model fit for mPFC for the remote sequences was driven primarily by a decrease in similarity for the overlapping C items and not the non-overlapping ones (Supplementary Figure 3, page 18, lines 468-472).

Interestingly, in LOC, all C items grew more similar after learning, regardless of their overlap or learning session, but the increase in model fit for C items in the recent condition was driven by a larger increase in similarity for overlapping pairs relative to non-overlapping ones (Supp. Figure 5, page 21, lines 533-536).

We also visualized AB similarity in the anterior hippocampus and LOC in a similar fashion (Supplementary Figure 9).

We have also edited the Methods sections with updated details of these analyses (page 52, lines 1392-1397). We think that including these results considerably strengthen our claims and we are pleased to have them included.

3) The priming task occurred before the post-learning exposure phase and could have impacted the representations. More consideration of this in the paper would be useful. Most critically, since the priming task involves seeing the related C items back-to-back, it would be important to consider whether this experience could have conceivably impacted the neural integration indices. I believe it never would have been the case that unrelated C items were presented sequentially during the priming task, i.e., that related C items always appeared together in this task. I think again the specificity of the remote condition is key and perhaps the authors can leverage this to support their interpretation. Can the authors consider this possibility in the Discussion?

It's true that only C items from the same sequence were presented back-to-back during the priming task, and that this presentation may interfere with observations from the post-learning exposure scan that followed it. We agree that it is worth considering this caveat and have added language in the Discussion (page 40, lines 1071-1086). When designing the study, we reasoned that it was more important for the behavioral priming task to come before the exposure scans, as all items were shown only once in that task, whereas they were shown 4-5 times in a random order in the post-learning exposure phase. Because of this difference in presentation times, and because behavioral priming findings tend to be very sensitive, we concluded that it was more important to protect the priming task from the exposure scan instead of the reverse.

We reasoned, however, that the additional presentation of the C items in the recognition priming task would not substantially override the sequence learning, as C items were each presented 16 times in their sequence (ABC1 and ABC2 16 times each). Furthermore, as this reviewer suggests, the order of C items during recognition was the same for recent and remote conditions, so the fact that we find a selective change in neural representation for the remote condition and don’t also see that change for the recent condition is additional assurance that the recognition priming order did not substantially impact the representations.

4) For the priming task, based on the Figure 2A caption it seems as though every sequence contributes to both the control and primed conditions, but (I believe) this means that the control transition always happens first (and they are always back-to-back). Is this a concern? If RTs are changing over time (getting faster), it would be helpful to know whether the priming effects hold after controlling for trial numbers. I do not think this is a big issue because if it were, you would not expect to see the specificity of the remotely learned information. However, it would be helpful to know given the order of these conditions has to be fixed in their design.

This is a correct understanding of the trial orders in the recognition priming task. We chose to involve the baseline items in the control condition to boost power – this way, priming of each sequence could be tested, while only presenting each item once in this task, as repetition in the recognition phase would have further facilitated response times and potentially masked any priming effects. We agree that accounting for trial order would be useful here, so we ran a mixed-effects linear model to examine responses times both as a function of trial number and of priming condition (primed/control). While there is indeed a large effect of trial number such that participants got faster over time, the priming effect originally observed in the remote condition still holds at the same time. We now report this analysis in the Results section (page 14, lines 337-349 for Expt 1 and pages 14-15, lines 360-362 for Expt 2).

5) The authors should be cautious about the general conclusion that memories with overlapping temporal regularities become neurally integrated - given their findings in MPFC are more consistent with overall differentiation (though as noted above, I think we need more data on this to know for sure what is going on).

We realize this conclusion was overly simplistic and, in several places, have revised the general conclusions to be more specific about the nuanced similarity findings.

6) It would be worth stating a few more details and perhaps providing additional logic or justification in the main text about the pre- and post-exposure phases were set up and why. How many times each object was presented pre and post, and how the sequencing was determined (were any constraints put in place e.g., such that C1 and C2 did not appear close in time?). What was the cover task (I think this is important to the interpretation & so belongs in the main paper)? Were there considerations involving the fact that this is a different sequence of the same objects the participants would later be learning - e.g., interference, etc.?

These details can be found in the Methods section (pages 50-51, lines 1337-1353) and we’ve added a new summary of that section in the Results (page 17, lines 424- 425 and 432-435). In brief, a visual hash tag appeared on a small subset of images and participants pressed a button when this occurred, and C1 and C2 objects were presented in separate scans (as were A and B objects) to minimize inflated neural similarity due to temporal proximity.

Reviewer #2 (Public Review):

The manuscript by Tompary & Davachi presents results from two experiments, one behavior only and one fMRI plus behavior. They examine the important question of how to separate object memories (C1 and C2) that are never experienced together in time and become linked by shared predictive cues in a sequence (A followed by B followed by one of the C items). The authors developed an implicit priming task that provides a novel behavioral metric for such integration. They find significant C1-C2 priming for sequences that were learned 24h prior to the test, but not for recently learned sequences, suggesting that associative links between the two originally separate memories emerge over an extended period of consolidation. The fMRI study relates this behavioral integration effect to two neural metrics: pattern similarity changes in the medial prefrontal cortex (mPFC) as a measure of neural integration, and changes in hippocampal-LOC connectivity as a measure of post-learning consolidation. While fMRI patterns in mPFC overall show differentiation rather than integration (i.e., C1-C2 representational distances become larger), the authors find a robust correlation such that increasing pattern similarity in mPFC relates to stronger integration in the priming test, and this relationship is again specific to remote memories. Moreover, connectivity between the posterior hippocampus and LOC during post-learning rest is positively related to the behavioral integration effect as well as the mPFC neural similarity index, again specifically for remote memories. Overall, this is a coherent set of findings with interesting theoretical implications for consolidation theories, which will be of broad interest to the memory, learning, and predictive coding communities.

Strengths:

1) The implicit associative priming task designed for this study provides a promising new tool for assessing the formation of mnemonic links that influence behavior without explicit retrieval demands. The authors find an interesting dissociation between this implicit measure of memory integration and more commonly used explicit inference measures: a priming effect on the implicit task only evolved after a 24h consolidation period, while the ability to explicitly link the two critical object memories is present immediately after learning. While speculative at this point, these two measures thus appear to tap into neocortical and hippocampal learning processes, respectively, and this potential dissociation will be of interest to future studies investigating time-dependent integration processes in memory.

2) The experimental task is well designed for isolating pre- vs post-learning changes in neural similarity and connectivity, including important controls of baseline neural similarity and connectivity.

3) The main claim of a consolidation-dependent effect is supported by a coherent set of findings that relate behavioral integration to neural changes. The specificity of the effects on remote memories makes the results particularly interesting and compelling.

4) The authors are transparent about unexpected results, for example, the finding that overall similarity in mPFC is consistent with a differentiation rather than an integration model.

Thank you for the positive comments!

Weaknesses:

1) The sequence learning and recognition priming tasks are cleverly designed to isolate the effects of interest while controlling for potential order effects. However, due to the complex nature of the task, it is difficult for the reader to infer all the transition probabilities between item types and how they may influence the behavioral priming results. For example, baseline items (BL) are interspersed between repeated sequences during learning, and thus presumably can only occur before an A item or after a C item. This seems to create non-random predictive relationships such that C is often followed by BL, and BL by A items. If this relationship is reversed during the recognition priming task, where the sequence is always BL-C1-C2, this violation of expectations might slow down reaction times and deflate the baseline measure. It would be helpful if the manuscript explicitly reported transition probabilities for each relevant item type in the priming task relative to the sequence learning task and discussed how a match vs mismatch may influence the observed priming effects.

We have added a table of transition probabilities across the learning, recognition priming, and exposure scans (now Table 1, page 48). We have also included some additional description of the change in transition probabilities across different tasks in the Methods section. Specifically, if participants are indeed learning item types and rules about their order, then both the control and the primed conditions would violate that order. Since C1 and C2 items never appeared together, viewing C1 would give rise to an expectation of seeing a BL item, which would also be violated. This suggests that our priming effects are driven by sequence-specific relationships rather than learning of the probabilities of different item types. We’ve added this consideration to the Methods section (page 45, lines 1212-1221).

Another critical point to consider (and that the transition probabilities do not reflect) is that during learning, while C is followed either by A or BL, they are followed by different A or BL items. In contrast, a given A is always followed by the same B object, which is always followed by one of two C objects. While the order of item types is semi-predictable, the order of objects (specific items) themselves are not. This can be seen in the response times during learning, such that response times for A and BL items are always slower than for B and C items. We have explained this nuance in the figure text for Table 1.

2) The choice of what regions of interest to include in the different sets of analyses could be better motivated. For example, even though briefly discussed in the intro, it remains unclear why the posterior but not the anterior hippocampus is of interest for the connectivity analyses, and why the main target is LOC, not mPFC, given past results including from this group (Tompary & Davachi, 2017). Moreover, for readers not familiar with this literature, it would help if references were provided to suggest that a predictable > unpredictable contrast is well suited for functionally defining mPFC, as done in the present study.

We have clarified our reasoning for each of these choices throughout the manuscript and believe that our logic is now much more transparent. For an expanded reasoning of why we were motivated to look at posterior and not anterior hippocampus, see pages 6-7, lines 135-159, and our response to R2. In brief, past research focusing on post-encoding connectivity with the hippocampus suggests that posterior aspect is more likely to couple with category-selective cortex after learning neutral, non-rewarded objects much like the stimuli used in the present study.

We also clarify our reasoning for LOC over mPFC. While theoretically, mPFC is thought to be a candidate region for coupling with the hippocampus during consolidation, the bulk of empirical work to date has revealed post-encoding connectivity between the hippocampus and category-selective cortex in the ventral and occipital lobes (page 6, lines 123-134).

As for the use of the predictable > unpredictable contrast for functionally defining cortical regions, we reasoned that cortical regions that were sensitive to the temporal regularities generated by the sequences may be further involved in their offline consolidation and long-term storage (Danker & Anderson, 2010; Davachi & Danker, 2013; McClelland et al., 1995). We have added this justification to the Methods section (page 18, lines 454-460).

3) Relatedly, multiple comparison corrections should be applied in the fMRI integration and connectivity analyses whenever the same contrast is performed on multiple regions in an exploratory manner.

We now correct for multiple comparisons using Bonferroni correction, and this correction depends on the number of regions in which each analysis is conducted. Please see page 55, lines 1483-1490, in the Methods section for details of each analysis.

Reviewer #3 (Public Review):

The authors of this manuscript sought to illuminate a link between a behavioral measure of integration and neural markers of cortical integration associated with systems consolidation (post-encoding connectivity, change in representational neural overlap). To that aim, participants incidentally encoded sequences of objects in the fMRI scanner. Unbeknownst to participants, the first two objects of the presented ABC triplet sequences overlapped for a given pair of sequences. This allowed the authors to probe the integration of unique C objects that were never directly presented in the same sequence, but which shared the same preceding A and B objects. They encoded one set of objects on Day 1 (remote condition), another set of objects 24 hours later (recent condition) and tested implicit and explicit memory for the learned sequences on Day 2. They additionally collected baseline and post-encoding resting-state scans. As their measure of behavioral integration, the authors examined reaction time during an Old/New judgement task for C objects depending on if they were preceded by a C object from an overlapping sequence (primed condition) versus a baseline object. They found faster reaction times for the primed objects compared to the control condition for remote but not recently learned objects, suggesting that the C objects from overlapping sequences became integrated over time. They then examined pattern similarity in a priori ROIs as a measure of neural integration and found that participants showing evidence of integration of C objects from overlapping sequences in the medial prefrontal cortex for remotely learned objects also showed a stronger implicit priming effect between those C objects over time. When they examined the change in connectivity between their ROIs after encoding, they also found that connectivity between the posterior hippocampus and lateral occipital cortex correlated with larger priming effects for remotely learned objects, and that lateral occipital connectivity with the medial prefrontal cortex was related to neural integration of remote objects from overlapping sequences.

The authors aim to provide evidence of a relationship between behavioral and neural measures of integration with consolidation is interesting, important, and difficult to achieve given the longitudinal nature of studies required to answer this question. Strengths of this study include a creative behavioral task, and solid modelling approaches for fMRI data with careful control for several known confounds such as bold activation on pattern analysis results, motion, and physiological noise. The authors replicate their behavioral observations across two separate experiments, one of which included a large sample size, and found similar results that speak to the reliability of the observed behavioral phenomenon. In addition, they document several correlations between neural measures and task performance, lending functional significance to their neural findings.

Thank you for this positive assessment of our study!

However, this study is not without notable weaknesses that limit the strength of the manuscript. The authors report a behavioral priming effect suggestive of integration of remote but not recent memories, leading to the interpretation that the priming effect emerges with consolidation. However, they did not observe a reliable interaction between the priming condition and learning session (recent/remote) on reaction times, meaning that the priming effect for remote memories was not reliably greater than that observed for recent. In addition, the emergence of a priming effect for remote memories does not appear to be due to faster reaction times for primed targets over time (the condition of interest), but rather, slower reaction times for control items in the remote condition compared to recent. These issues limit the strength of the claim that the priming effect observed is due to C items of interest being integrated in a consolidation-dependent manner.

We acknowledge that the lack of a day by condition interaction in the behavioral priming effect should discussed and now discuss this data in a more nuanced manner. While it’s true that the priming effect emerges due to a slowing of the control items over time, this slowing is consistent with classic time-dependent effects demonstrating slower response times for more delayed memories. The fact that the response times in the primed condition does not show this slowing can be interpreted as a protection against this slowing that would otherwise occur. Please see page 29, lines 758-766, for this added discussion.

Similarly, the interactions between neural variables of interest and learning session needed to strongly show a significant consolidation-related effect in the brain were sometimes tenuous. There was no reliable difference in neural representational pattern analysis fit to a model of neural integration between the short and long delays in the medial prefrontal cortex or lateral occipital cortex, nor was the posterior hippocampus-lateral occipital cortex post-encoding connectivity correlation with subsequent priming significantly different for recent and remote memories. While the relationship between integration model fit in the medial prefrontal cortex and subsequent priming (which was significantly different from that occurring for recent memories) was one of the stronger findings of the paper in favor of a consolidation-related effect on behavior, is it possible that lack of a behavioral priming effect for recent memories due to possible issues with the control condition could mask a correlation between neural and behavioral integration in the recent memory condition?

While we acknowledge that lack of a statistically reliable interaction between neural measures and behavioral priming in many cases, we are heartened by the reliable difference in the relationship between mPFC similarity and priming over time, which was our main planned prediction. In addition to adding caveats in the discussion about the neural measures and behavioral findings in the recent condition (see our response to R1.1 and R1.4 for more details), we have added language throughout the manuscript noting the need to interpret these data with caution.

These limitations are especially notable when one considers that priming does not classically require a period of prolonged consolidation to occur, and prominent models of systems consolidation rather pertain to explicit memory. While the authors have provided evidence that neural integration in the medial prefrontal cortex, as well as post-encoding coupling between the lateral occipital cortex and posterior hippocampus, are related to faster reaction times for primed objects of overlapping sequences compared to their control condition, more work is needed to verify that the observed findings indeed reflect consolidation dependent integration as proposed.

We agree that more work is needed to provide converging evidence for these novel findings. However, we wish to counter the notion that systems consolidation models are relevant only for explicit memories. Although models of systems consolidation often mention transformations from episodic to semantic memory, the critical mechanisms that define the models involve changes in the neural ensembles of a memory that is initially laid down in the hippocampus and is taught to cortex over time. This transformation of neural traces is not specific to explicit/declarative forms of memory. For example, implicit statistical learning initially depends on intact hippocampal function (Schapiro et al., 2014) and improves over consolidation (Durrant et al., 2011, 2013; Kóbor et al., 2017).

Second, while there are many classical findings of priming during or immediately after learning, there are several instances of priming used to measure consolidation-related changes to newly learned information. For instance, priming has been used as a measure of lexical integration, demonstrating that new word learning benefits from a night of sleep (Wang et al., 2017; Gaskell et al., 2019) or a 1-week delay (Tamminen & Gaskell, 2013). The issue is not whether priming can occur immediately, it is whether priming increases with a delay.

Finally, it is helpful to think about models of memory systems that divide memory representations not by their explicit/implicit nature, but along other important dimensions such as their neural bases, their flexibility vs rigidity, and their capacity for rapid vs slow learning (Henke, 2010). Considering this evidence, we suggest that systems consolidation models are most useful when considering how transformations in the underlying neural memory representation affects its behavioral expression, rather than focusing on the extent that the memory representation is explicit or implicit.

With all this said, we have added text to the discussion reminding the reader that there was no statistically significant difference in priming as a function of the delay (page 29, lines 764 - 766). However, we are encouraged by the fact that the relationship between priming and mPFC neural similarity was significantly stronger for remotely learned objects relative to recently learned ones, as this is directly in line with systems consolidation theories.

References

Abolghasem, Z., Teng, T. H.-T., Nexha, E., Zhu, C., Jean, C. S., Castrillon, M., Che, E., Di Nallo, E. V., & Schlichting, M. L. (2023). Learning strategy differentially impacts memory connections in children and adults. Developmental Science, 26(4), e13371. https://doi.org/10.1111/desc.13371

Dobbins, I. G., Schnyer, D. M., Verfaellie, M., & Schacter, D. L. (2004). Cortical activity reductions during repetition priming can result from rapid response learning. Nature, 428(6980), 316–319. https://doi.org/10.1038/nature02400

Durrant, S. J., Cairney, S. A., & Lewis, P. A. (2013). Overnight consolidation aids the transfer of statistical knowledge from the medial temporal lobe to the striatum. Cerebral Cortex, 23(10), 2467–2478. https://doi.org/10.1093/cercor/bhs244

Durrant, S. J., Taylor, C., Cairney, S., & Lewis, P. A. (2011). Sleep-dependent consolidation of statistical learning. Neuropsychologia, 49(5), 1322–1331. https://doi.org/10.1016/j.neuropsychologia.2011.02.015

Gaskell, M. G., Cairney, S. A., & Rodd, J. M. (2019). Contextual priming of word meanings is stabilized over sleep. Cognition, 182, 109–126. https://doi.org/10.1016/j.cognition.2018.09.007

Henke, K. (2010). A model for memory systems based on processing modes rather than consciousness. Nature Reviews Neuroscience, 11(7), 523–532. https://doi.org/10.1038/nrn2850

Kóbor, A., Janacsek, K., Takács, Á., & Nemeth, D. (2017). Statistical learning leads to persistent memory: Evidence for one-year consolidation. Scientific Reports, 7(1), 760. https://doi.org/10.1038/s41598-017-00807-3

Kuhl, B. A., & Chun, M. M. (2014). Successful remembering elicits event-specific activity patterns in lateral parietal cortex. The Journal of Neuroscience, 34(23), 8051–8060. https://doi.org/10.1523/JNEUROSCI.4328-13.2014

Richter, F. R., Chanales, A. J. H., & Kuhl, B. A. (2016). Predicting the integration of overlapping memories by decoding mnemonic processing states during learning. NeuroImage, 124, Part A, 323–335. https://doi.org/10.1016/j.neuroimage.2015.08.051

Schapiro, A. C., Gregory, E., Landau, B., McCloskey, M., & Turk-Browne, N. B. (2014). The necessity of the medial-temporal lobe for statistical learning. Journal of Cognitive Neuroscience, 1–12. https://doi.org/10.1162/jocn_a_00578

Schlichting, M. L., & Preston, A. R. (2014). Memory reactivation during rest supports upcoming learning of related content. Proceedings of the National Academy of Sciences, 111(44), 15845–15850. https://doi.org/10.1073/pnas.1404396111

Smith, J. F., Alexander, G. E., Chen, K., Husain, F. T., Kim, J., Pajor, N., & Horwitz, B. (2010). Imaging systems level consolidation of novel associate memories: A longitudinal neuroimaging study. NeuroImage, 50(2), 826–836. https://doi.org/10.1016/j.neuroimage.2009.11.053

Takashima, A., Nieuwenhuis, I. L. C., Jensen, O., Talamini, L. M., Rijpkema, M., & Fernández, G. (2009). Shift from hippocampal to neocortical centered retrieval network with consolidation. The Journal of Neuroscience, 29(32), 10087–10093. https://doi.org/10.1523/JNEUROSCI.0799-09.2009

Tamminen, J., & Gaskell, M. G. (2013). Novel word integration in the mental lexicon: Evidence from unmasked and masked semantic priming. The Quarterly Journal of Experimental Psychology, 66(5), 1001–1025. https://doi.org/10.1080/17470218.2012.724694

van Kesteren, M. T. R. van, Fernández, G., Norris, D. G., & Hermans, E. J. (2010). Persistent schema-dependent hippocampal-neocortical connectivity during memory encoding and postencoding rest in humans. Proceedings of the National Academy of Sciences, 107(16), 7550–7555. https://doi.org/10.1073/pnas.0914892107

Wang, H.-C., Savage, G., Gaskell, M. G., Paulin, T., Robidoux, S., & Castles, A. (2017). Bedding down new words: Sleep promotes the emergence of lexical competition in visual word recognition. Psychonomic Bulletin & Review, 24(4), 1186–1193. https://doi.org/10.3758/s13423-016-1182-7

Author Response:

Reviewer #3:

The authors modified a previously reported hybrid cytochrome bcc-aa3 supercomplex, consisting of bcc from M. tuberculosis and aa3 from M. smegmatis, (Kim et al 2015) by appending an affinity tag facilitating purification. The cryo-EM experiments are based on the authors' earlier work (Gong et al. 2018) on the structure of the bcc-aa3 supercomplex from M. smegmatis. The authors then determine the structure of the bcc part alone and in complex with Q203 and TB47.

The manuscript is well written and the obtained results are presented in a concise, clear-cut manner. In general, the data support the conclusions drawn.

We thank the reviewer for this evaluation.

To this reviewer, the following points are unclear:

1. The purified enzyme elutes from the gel filtration column as one peak, but there seems to be no information given on the subunit composition and the enzymatic activity of the purified hybrid cytochrome bcc-aa3 supercomplex.

See answers to Question 1 from the major Essential Revisions and Question 1 from the minor Essential Revisions.

"We have now shown that the purified chimeric supercomplex is a functional assembly with a (mean ± s.d., n = 4), in agreement with the previous study that shows M. tuberculosis CIII can functionally complement native M. smegmatis CIII and maintain the growth of M. smegmatis (Kim et al., 2015). The in vitro inhibitions of this enzyme by Q203 and TB47 was determined by means of an DMNQH2/oxygen oxidoreductase activity assay. In the assay, 500 nM Q203 or TB47 was chosen, which is close to the median inhibitory concentration (IC50) obtained from the menadiol-induced oxygen consumption in our previous study (Gong et al., 2018). After addition of Q203 and TB47, the values of turnover number of the hybrid supercomplex are reduced to 5.8 +/- 2.4 e-s-1 (Figure 4-figure supplement 4) and 5.1 +/- 2.9 e-s-1 (Figure 5-figure supplement 4) respectively, from 23.3 +/- 2.4 e-s-1. We have incorporated this new data into the text (lines 90-93, 187-189, 206-209)."

"The subunit composition of the purified enzyme has now been provided in Figure 2-figure supplement 1."

1. It is unclear what is the conclusion of the structure comparison (Fig 6) is regarding the affinity of Q203 for M. smegmatis.

The structural comparison indicates that Q203 should have a similar binding mechanism and a similar effect on the activity of cytochrome bcc from M. smegmatis and M. tuberculosis. This is in good agreement with previous antimycobacterial activity data and inhibition data for the bcc complexes from M. smegmatis and M. tuberculosis (Gong et al., 2018; Lu et al., 2018a). These have now been incorporated into the revised manuscript (line 223-227).

Author Response

Reviewer #1 (Public Review):

The manuscript by Royall et al. builds on previous work in the mouse that indicates that neural progenitor cells (NPCs) undergo asymmetric inheritance of centrosomes and provides evidence that a similar process occurs in human NPCs, which was previously unknown.

The authors use hESC-derived forebrain organoids and develop a novel recombination tag-induced genetic tool to birthdate and track the segregation of centrosomes in NPCs over multiple divisions. The thoughtful experiments yield data that are concise and well-controlled, and the data support the asymmetric segregation of centrosomes in NPCs. These data indicate that at least apical NPCs in humans undergo asymmetric centrosome inheritance. The authors attempt to disrupt the process and present some data that there may be differences in cell fate, but this conclusion would be better supported by a better assessment of the fate of these different NPCs (e.g. NPCs versus new neurons) and would support the conclusion that younger centriole is inherited by new neurons.

We thank the reviewer for their supportive comments (“…thoughtful experiments yield data that are concise and well-controlled…”).

Reviewer #2 (Public Review):

Royall et al. examine the asymmetric inheritance of centrosomes during human brain development. In agreement with previous studies in mice, their data suggest that the older centrosome is inherited by the self-renewing daughter cell, whereas the younger centrosome is inherited by the differentiating daughter cell. The key importance of this study is to show that this phenomenon takes place during human brain development, which the authors achieved by utilizing forebrain organoids as a model system and applying the recombination-induced tag exchange (RITE) technology to birthdate and track the centrosomes.

Overall, the study is well executed and brings new insights of general interest for cell and developmental biology with particular relevance to developmental neurobiology. The Discussion is excellent, it brings this study into the context of previous work and proposes very appealing suggestions on the evolutionary relevance and underlying mechanisms of the asymmetric inheritance of centrosomes. The main weakness of the study is that it tackles asymmetric inheritance only using fixed organoid samples. Although the authors developed a reasonable mode to assign the clonal relationships in their images, this study would be much stronger if the authors could apply time-lapse microscopy to show the asymmetric inheritance of centrosomes.

We thank the reviewer for their constructive and supportive comments (“…the study is well executed and brings new insights of general interest for cell and developmental biology with particular relevance to developmental neurobiology….”). We understand the request for clonal data or dynamic analyses in organoids (e.g., using time-lapse microscopy). We also agree that such data would certainly strengthen our findings. However, as outlined above (please refer to point #1 of the editorial summary), this is unfortunately currently not feasible. However, we have explicitly discussed this shortcoming in our revised manuscript and why future experiments (with advanced methodology) will have to do these experiments.

Reviewer #3 (Public Review):

In this manuscript, the authors report that human cortical radial glia asymmetrically segregates newly produced or old centrosomes after mitosis, depending on the fate of the daughter cell, similar to what was previously demonstrated for mouse neocortical radial glia (Wang et al. 2009). To do this, the authors develop a novel centrosome labelling strategy in human ESCs that allows recombination-dependent switching of tagged fluorescent reporters from old to newly produced centrosome protein, centriolin. The authors then generate human cortical organoids from these hESCs to show that radial glia in the ventricular zone retains older centrosomes whereas differentiated cells, i.e. neurons, inherit the newly produced centrosome after mitosis. The authors then knock down a critical regulator of asymmetric centrosome inheritance called Ninein, which leads to a randomization of this process, similar to what was observed in mouse cortical radial glia.

A major strength of the study is the combined use of the centrosome labelling strategy with human cortical organoids to address an important biological question in human tissue. This study is similarly presented as the one performed in mice (Wang et al. 2009) and the existence of the asymmetric inheritance mechanism of centrosomes in another species grants strength to the main claim proposed by the authors. It is a well-written, concise article, and the experiments are well-designed. The authors achieve the aims they set out in the beginning, and this is one of the perfect examples of the right use of human cortical organoids to study an important phenomenon. However, there are some key controls that would elevate the main conclusions considerably.

We thank the reviewer for their overall support of our findings (“..authors achieve the aims they set out in the beginning, and this is one of the perfect examples of the right use of human cortical organoids to study an important phenomenon…”). We also understand the reviewer’s request for additional experiments/controls that “…would elevate the main conclusions considerably.”

1) The lack of clonal resolution or timelapse imaging makes it hard to assess whether the inheritance of centrosomes occurs as the authors claim. The authors show that there is an increase in newly made non-ventricular centrosomes at a population level but without labelling clones and demonstrating that a new or old centrosome is inherited asymmetrically in a dividing radial glia would grant additional credence to the central conclusion of the paper. These experiments will put away any doubt about the existence of this mechanism in human radial glia, especially if it is demonstrated using timelapse imaging. Additionally, knowing the proportions of symmetric vs asymmetrically dividing cells generating old/new centrosomes will provide important insights pertinent to the conclusions of the paper. Alternatively, the authors could soften their conclusions, especially for Fig 2.

We understand the reviewer’s request. As outlined above (please refer to point #1 of the editorial summary), we had tried previously to add data using single cell timelapse imaging. However, due to the size and therefore weakness of the fluorescent signal we had failed despite extensive efforts. According to the reviewer’s suggestion we have now explicitly discussed this shortcoming and softened our conclusions.

2) Some critical controls are missing. In Fig. 1B, there is a green dot that does not colocalize with Pericentrin. This is worrying and providing rigorous quantifications of the number of green and tdTom dots with Pericentrin would be very helpful to validate the labelling strategy. Quantifications would put these doubts to rest. Additionally, an example pericentrin staining with the GFP/TdTom signal in figure 4 would also give confidence to the reader. For figure 4, having a control for the retroviral infection is important. Although the authors show a convincing phenotype, the effect might be underestimated due to the incomplete infection of all the analyzed cells.

We have included more rigorous quantifications in our revised manuscript.

For Figure 1: There are indeed some green speckles that might be misinterpreted as a green centrosome. However, the speckles are usually smaller and by applying a strict size requirement we exclude speckles. To check whether the classifier might interpret any speckles as centrosomes, we manually checked 60 green “dots” that were annotated as centrosome. From these images all green spots detected as centrosome co-localized with Pericentrin signal (Images shown in Author response image 1).

For Figure 4: as we are comparing cells that were either infected with a retrovirus expressing scrambled or Ninein-targeting shRNA we compare cells that experienced a similar treatment. Besides that, only cells infected with the virus express Cre-ERT2 whereby only the centrosomes of targeted cells were analyzed. Accordingly, we only compare cells expressing scrambled or Ninein-targeting shRNA, all surrounding “wt” cells are not considered.

Author response image 1.

Pictures used to test the classifier. Each of the green “dots” recognized by the classifier as a Centriolin-NeonGreen-containing centrosome (green) co-localized with Pericentrin signal (white).

3) It would be helpful if the authors expand on the presence of old centrosomes in apical radial glia vs outer radial glia. Currently, in figure 3, the authors only focus on Sox2+ cells but this could be complemented with the inclusion of markers for outer radial glia and whether older centrosomes are also inherited by oRGCs. This would have important implications on whether symmetric/asymmetric division influences the segregation of new/old centrosomes.

That is an interesting question and we do agree that additional analyses, stratified by ventricular vs. oRGCs would be interesting. However, at the time points analysed there are only very few oRGCs present (if any) in human ESC-derived organoids (Qian et al., Cell, 2016). However, we have now added this point for future experiments to our discussion.

Author Response:

Reviewer #1 (Public Review):

The physical principles underlying oligomerization of GPCRs are not well understood. Here, authors focused on oligomerization of A2AR. They found that oligomerization of A2AR is mediated by the intrinsically disordered, extramembraneous C-terminal tail. Using experiment and MD simulation, they mapped the regions that are responsible for oligomerization and dissected the driving forces in oligomerization.

This is a nice piece of work that applies fundamental physical principles to the understanding of an important biological problem. It is a significant finding that oligomerization of A2AR is mediated by multiple weak interactions that are "tunable" by environmental factors. It is also interesting that solute-induced, solvent-mediated "depletion interactions" can be a key driving force in membrane protein-protein interactions.

Although this work is potentially a significant contribution to the fields of GPCRs and molecular biophysics of membrane proteins in general, there are several concerns that would need to be implemented to strengthen the conclusions.

1) How reasonably would the results obtained in the micellar environment be translated into the phenomenon in the cell membranes?

1a) Here authors measured oligomerization of A2AR in detergent micelles, not in the bilayer or cellular context. Although the cell membranes would provide another layer of complexity, the hydrophobic properties and electrostatics of the negatively charged membrane surface may cooperate or compete with the interactions mediated by the C-terminal tail, especially if the oligomerization is mediated by multiple weak interactions.

The translatability of properties of membrane proteins in detergent micelles to the cellular context is a valid concern. However, this shortcoming applies to all biophysical studies of membrane proteins in non-native environments. Even for membrane proteins reconstituted in liposomes, the question arises whether the artificial lipid composition that differs from that in the human plasma membrane would alter protein properties, especially as surface charges and cholesterol content can impact membrane protein dynamics, association, and stability. In that sense, this question cannot be answered satisfyingly, especially for GPCRs that are notoriously difficult to isolate. However, we can offer some perspectives. The propensity for membrane proteins to associate and oligomerize, if anything, is greater in lipid bilayers compared to that in detergent micelles, while detergent micelles can effectively solubilize membrane protein monomers (Popot and Engelman, Biochem 1990, 29 (17), 4031–4037). Hence, the findings that A2AR readily oligomerizes in detergent micelles and that the degree of oligomerization can be systematically tuned by the C-terminal length of A2AR in the same micellar system suggest that inter-A2AR interactions are modulating receptor oligomerization; we speculate that A2AR oligomers will be present or be enhanced in the lipid bilayer environment. In fact, in the cellular context, it has been shown that A2AR assembles into homodimers at the cell surface in transfected HEK293 cells (Canals et al, J Neurochem 2004, 88, 726–734) and into higher- order oligomers at the plasma membrane in Cath.A differentiated neuronal cells (Vidi et al, FEBS Lett 2008, 582, 3985–3990). Furthermore, C-terminally truncated A2AR has been demonstrated to show no protein aggregation or clustering on the cell surface, a process otherwise observed in the WT form (Burgueno et al, J Biol Chem 2003, 278 (39), 37545–37552). These results provide the research community with a valid starting point to discover factors that control oligomerization of A2AR in the cellular context.

1b) Related to the point above (1a), I wonder if MD simulation could provide an insight into the role of the lipid bilayer in the inter- or intra-molecular interactions involving the tail. Although the neutral POPC bilayer was employed in the simulation, the tail-membrane interaction may affect oligomerization since the tail is intrinsically disordered and possess a significant portion of nonpolar residues (Fig. S4).

The reviewer brings up a valid point about the ability for MD simulations to provide insights into the role of membrane-protein interactions. In response to the reviewer, we performed additional analysis focusing on the interactions of the C-terminus with the lipid bilayer. Overall, as the C-terminus is extended, there is a decrease in its interaction with the cytoplasmic leaflet of the membrane (left figure below). More specifically, we find that the C-terminal segment associated with helix 8 (residues 291 to 314) interacts tightly with the membrane, while the rest of the C-terminus (an intrinsically disordered segment) more weakly interacts with the membrane, regardless of truncation (right figure below). As the C-terminus is extended, the inherent conformational flexibility leads to a decrease in the interactions between the protein and the bilayer. We also observe that shorter stretches of the disordered segment do have the ability to interact more closely with the membrane. While these portions include charged residues that can participate in formation of the dimer interface, no general trends are observed. We therefore cannot draw any conclusions regarding the role of C-terminal-membrane interactions on the dimerization of A2AR. What we do know is that the MD simulations presented here should be considered a model study that reveals that the charged and disordered C-terminus of A2AR can account for oligomerization via multiple and weak inter-protomer contacts.

MD simulations showing (Left) average distance of all C-terminal residues and (right) average per-residue distance from the cytoplasmic membrane of the lipid bilayer.

2) Ensuring that the oligomer distributions are thermodynamic products.

Since authors interpret the SEC results on the basis of thermodynamic concepts (driving forces, depletion interactions, etc.), it would be important to verify that the distribution of different oligomeric states is the outcome of the thermodynamic control. There is a possibility that the distribution is the outcome of the "kinetic trapping" during detergent solubilization.

This is an important question. As we have shown in the manuscript, the A2AR dimer level was found to be reduced in the presence of TCEP (Figure 2B), suggesting that disulfide linkages have a role in facilitating A2AR oligomerization. However, disulfide cross-linking reaction cannot be the sole driving force of A2AR oligomerization because (1) a significant population of A2AR dimer remained resistant to TCEP (Figure 2B), (2) A2AR oligomer levels decreased progressively with the shortening of the C-terminus (Figure 3), and (3) A2AR oligomerization is driven by depletion interactions enhanced with increasing ionic strength (Figure 5).

To answer whether A2AR oligomer is a thermodynamic or kinetic product, we tested the stability and reversibility of the A2AR monomer and dimer/oligomer population. We used SEC to separate these populations of both the A2AR-WT and A2AR-Q372ΔC variants, then performed a second round of SEC to observe their repopulation, if any. The results are summarized in the figure below, which we will include in the revised manuscript as Figure 5-figure supplement 1.

We find that the SEC-separated monomers repopulate measurably into dimer/oligomer, with the total oligomer level after redistribution comparable with that of the initial samples for both A2AR WT (initial: 2.87; redistributed: 1.60) and A2AR-Q372ΔC (initial: 1.49; redistributed: 1.40) (Figure 5-figure supplement 1A). This observation indicates that A2AR oligomer is a thermodynamic product with a lower free energy compared with that of the monomer. This is consistent with the results we have shown in the manuscript that the oligomer levels of A2AR-WT are consistent (1.34–2.87; Table S1) and that A2AR oligomerization can be modulated with ionic strengths via depletion interactions (Figure 5).

Figure S5. The dimer/oligomerization of A2AR is a thermodynamic process where the dimer and HMW oligomer once formed are kinetically trapped. (A) SEC chromatograms of the consecutive rounds of SEC performed on A2AR-WT and Q372ΔC. The first rounds of SEC are to separate the dimer/oligomer population and the monomer population, while the second rounds of SEC are performed on these SEC-separated populations to assess their stability and reversibility. The total oligomer level is expressed relative to the monomeric population in arbitrary units. (B) Energy diagram depicting A2AR oligomerization progress. The monomer needs to overcome an activation barrier (EA), driven by depletion interactions, to form the dimer/oligomer. Once formed, the dimer/oligomer populations are kinetically trapped by disulfide linkages.

Interestingly, the SEC-separated dimer/oligomer populations do not repopulate to form monomers (Figure 5-figure supplement 1). This observation is, again, consistent with a published study of ours on A2AR dimers (Schonenbach et al, FEBS Lett 2016, 590, 3295–3306). This observation furthermore indicates that once the oligomers are formed, some are kinetically trapped and thus cannot redistribute into monomers.

We believe that disulfide linkages are likely candidates that kinetically stabilize A2AR oligomers, as demonstrated by their redistribution into monomers only in the presence of a reducing agent (Figure 2B). Taken together, we suggest that A2AR oligomerization is a thermodynamic process (Figure 5-figure supplement 1B), with the monomer overcoming the activation energy (EA) by depletion interactions to repopulate into dimer/oligomer with a slightly lower free energy (given that we see a distribution between the two). Once formed, the redistributed dimer/oligomer populations can be kinetically stabilized by disulfide linkages.

3) The claim that the C-terminal tail is engaged in "cooperative" interactions is too qualitative (p. 11 line 274, p.12 line 279 and p.18 line 426).

This claim seems derived from Fig. 3b and Figs. 4b-c. However, the gradual decrease in the dimer level and the number of interactions may indicate that different parts in the C-terminal tail contribute to dimerization additively rather than cooperatively. The large decrease in the number of interactions may stem from the large decrease in the length (395 to 354). Probably, a more quantitative measure would be the number of interactions (H-bonds/salt bridges) normalized to the tail length upon successive truncation. Even in that case, the polar/charged residues would not be uniformly distributed along the primary sequence, making the quantitative argument of cooperativity challenging.

The request to clarify our basis to refer to a cooperative interaction is well taken. Figure 4B and 4C show that the truncation of one part of the C-terminus (segment 335–394) leads to a reduction in contacts of a different part (segment 291–334) of A2AR. Therefore, we conclude that the binding interactions that occur in segment 291–334 are altered by the interactions exerted by the segment 335–394. This characteristic is consistent with allosteric interactions. We believe that characterizing these interactions as “cooperative” is possible but is not fully justified in this work. We also agree with the comment that quantifying the role and segments involved in contacts would be challenging. The manuscript has been amended to use the term “allosteric” in place of “cooperative”.

4) On the compactness and conformation of the C-terminal tail:

Although the C-terminal tail is known as "intrinsically disordered", the results seem to indicate that its conformation is rather compact (or collapsed) with a number of intra- and intermolecular polar interactions (Fig. 4) and buried nonpolar residues (Fig. 6), which are subject to depletion interactions (Fig. 5). This raises a question if the tail indeed "intrinsically disordered" as is known. Recent folding studies on IDPs (Riback et al. Science 2017, 358, 238-; Best, Curr Opin Struct Biol 2020, 60, 27-) suggest that IDPs are partially expanded or expanded rather than collapsed.

We agree that our results seem to suggest that the conformation of the C-terminus could be partially compact. However, by stating that the C-terminus on average is an intrinsically disordered region (IDR), we do not exclude the possibility of partially structured regions, or greater compactness than that of an excluded volume polymer. IDR or IDP should refer to all proteins or protein regions that do not adopt a unique structure. By that standard, we know that the C-terminus of A2AR falls into that category according to our experiments and MD simulation, as well as the literature. In isolation, the majority the C-terminus is indeed an IDR, as has been demonstrated not only by simulations but also by experimental data. In fact, the C-terminus exhibits partial alpha-helical structure, and transiently populates beta-sheet conformations, depending on its state and buffer conditions (Piirainen et al, Biophys J 2015, 108 (4), 903–917). The literature studies suggest that A2AR’s C-terminus may adopt a greater level of compactness when interactions are formed between the C-terminus and the rest of the A2AR oligomer.

Reviewer #2 (Public Review):

The authors expressed A2A receptor as wild type and modified with truncations/mutations at the C-terminus. The receptor was solubilized in detergent solution, purified via a C-terminal deca-His tag and the fraction of ligand binding-competent receptor separated by an affinity column. Receptor oligomerization was studied by size exclusion chromatography on the purified receptor solubilized in a DDM/CHAPS/CHS detergent solution. It was observed that truncation greatly reduces the tendency of A2A to form dimers and oligomers. Mechanistic insights into interactions that facilitate oligomerization were obtained by molecular simulations and the study of aggregation behavior of peptide sequences representing the C-terminus of A2A. It is concluded that a multitude of interactions including disulfide linkages, hydrogen bonds electrostatic- and depletion interactions contribute to aggregation of the receptor.

The general conclusions appear to be correct and the paper is well written. This is a study of protein association in detergent solution. It is conceivable that observations are relevant for A2A receptors in cell membranes as well. However, extrapolation of mechanisms observed on receptor in detergent micelles to receptor in membranes should proceed with caution. In particular, the spatial arrangement of oligomerized receptor molecules in micelles may differ from arrangement in lipid bilayers. The lipid matrix may have a profound influence on oligomerization.

The ultimate question to answer is how oligomerization alters receptor function. This will have to be addressed in a future study.

We could not agree more. We address the concern regarding the translatability of properties of membrane proteins in detergent micelles to the cellular context in our response to Reviewer 1. In short, we believe the general propensity for A2AR to form dimers/oligomers and the role of the C-terminus will hold in the cellular context. However, even if it does not, given that biophysical structure-function studies of GPCRs are conducted in detergent micelles and other artificial environments, it is critical to understand the role of the C-terminus in the oligomerization of reconstituted A2AR in detergent micelles. How oligomerization alters receptor function is a question that is always on our mind and should be the the focus of future studies. Indeed, it has been demonstrated that truncation of the A2AR C-terminus significantly reduces receptor association with Gαs and cAMP production in cellular assays (Koretz et al, Biophys J 2021, https://doi.org/10.1016/j.bpj.2021.02.032). The results presented in this manuscript, which have demonstrated the impact of C-terminal truncation on A2AR oligomerization, will offer critical understanding for such study of the functional consequences of A2AR oligomerization.

Reviewer #3 (Public Review):

The work of Nguyen et al. demonstrates the relevant role of the C-terminus of A2AR for its homo-oligomerization. A previous work (Schonenbach et al. 2016) found that a point mutation of C394 in the C-terminus (C394S) reduces homo-oligomerization. Following this direction, more mutants were generated, the C-terminus was also truncated at different levels, and, using size-exclusion chromatography (SEC), the oligomerization levels of A2AR variants were assessed. Overall, these experiments support the role of the C-terminus in the oligomerization process. MD studies were performed and the non-covalent interactions were monitored. To 'identify the types of non-covalent interaction(s)', A2AR variants were also analysed modulating the ionic strength from 0.15 to 0.95 M. The C-terminus peptides were investigated to assess their interaction in absence of the TM domain.

The SEC results on the A2AR variants strongly support the main conclusion of the paper, but some passages and methodologies are less convincing. The different results obtained for dimerization and oligomerization are low discussed. The MD simulations are performed on models that are not accurately described - structural information currently available may compromise the quality of the model and the validity of the results (i.e., applying MD simulations to low-resolution models may not be appropriate for the goal of this analysis, moreover the formation of disulfide bonds cannot be simulated but this can affect the conformation and consequently the interactions to be monitored). Although the C-terminus is suggested as 'a driving factor for the oligomerization', the TM domain is indeed involved in the process and if and how it will be affected by modulating the solvent ionic strength should be discussed.

We thank the reviewer for the overall positive assessment and critical input. We will respond to the comments as followed.

The qualitative trend for dimerization is consistent with that for oligomerization, as demonstrated in Figs. 2A, 3B, and 5. For example, a reduction in both dimerization and oligomerization was observed upon C394X mutations (Figure 2A), as well as upon systematic truncations (Figure 3B), while very similar trends were seen for the change in the dimer and oligomer levels of all four constructs upon variation of ionic strength (Figure 5).

We agree that the experimental observation and MD simulation only incompletely describe the state of the A2AR dimer/oligomer. For example, we discover the impact of ERR:AAA mutations of the C-terminus (Figure 3C) on oligomer formation, but do not know whether this segment interacts with the TM domain or C-terminus of the neighboring A2AR. MD simulations suggest that the inter-protomer interface certainly involves inter-C-termini contact. We also mention that the A2AR oligomeric interfaces could be asymmetric, suggesting that the C-terminus can interact with other parts of the receptor, including the TM domain. However, we do not have evidence that the TM domain directly interact with each other to stabilize A2AR oligomers, and thus cannot discuss the effect of the solvent ionic strength on how the TM domain contributes to A2AR oligomerization. We minimize such discussion in our manuscript because we have incomplete insights. What we can say is that multiple and weak inter-protomer interactions that contribute to the dimer and oligomer interface formation prominently involve the C-terminus. Ultimately, the structure of the A2AR dimer/oligomer needs to be solved to answer the reviewer’s question fully.

With respect to the validity of our model, we restricted ourselves to using the best-available X-ray crystal structure for A2AR. Since this structure (PDB 5G53) does not include the entire C-terminus, we resorted to using homology modeling software (i.e., MODELLER) to predict the structures of the C-terminus. In our model, the first segment of the C-terminus consisting of residues 291 to 314 were modeled as a helical segment parallel to the cytoplasmic membrane surface while the rest of the C-terminus was modeled as intrinsically disordered. MODELLER is much more accurate in structural predictions for segments less than 20 residues. This limitation necessitated that we run an equilibrium MD simulation for 2 µs to obtain a well-equilibrated structure that possesses a more viable starting conformation. We have included this detailed description of our model in lines 641–650. To validate our models of all potential variants of A2AR, we calculated the RMSD and RMSF for each truncated variant. Our results clearly show that the transmembrane helical bundle is very stable, as expected, and that the C-terminus is more flexible (see figure below). This flexibility is somewhat consistent for lengths up to 359 residues, with a more noticeable increase in flexibility for the 394-residue variant of A2AR.

Root mean square fluctuation (RMSF) from sample trajectories of truncated variants modeled from the crystal structure of the adenosine A2AR bound to an engineered G protein (PDB ID 5G53), and the root mean square deviation (RMSD) of the C-terminus of each variant starting from residue 291.

Author Response

Reviewer #3 (Public Review):

1) Validation of reagents: The authors generated a pY1230 Afadin antibody claiming that (page 6) "this new antibody is specific to tyrosine phosphorylated Afadin, and that pY1230 is targeted for dephosphorylation by PTPRK, in a D2-domain dependent manner". The WB in Fig 1B shows a lot of background, two main bands are visible which both diminish in intensity in ICT WT pervanadate-treated MCF10A cell lysates. The claim that the developed peptide antibody is selective for pY1230 in Afadin would need to be substantiated, for instance by pull down studies analysed by pY-MS to substantiate a claim of antibody specificity for this site. However, for the current study it would be sufficient to demonstrate that pY1230 is indeed the dephosphorylated site. I suggest therefore including a site directed mutant (Y1230F) that would confirm dephosphorylation at this site and the ability of the antibody recognizing the phosphorylation state at this position.

We would like this antibody to be a useful and freely accessible tool in the field and have taken on board the request for additional validation. To this end we have significantly expanded Supplementary Figure 2 (now Figure 1 - figure supplement 2) and included a dedicated section of the results as follows: 1. We have now included information about all of the Afadin antibodies used in this study, since Afadin(BD) appears to be sensitive to phosphorylation (Figure 1 - figure supplement 2A). 2. We have demonstrated that the Afadin pY1230 antibody detects an upregulated band in PTPRK KO MCF10A cells, consistent with our previous tyrosine phosphoproteomics (Figure 1 - figure supplement 2B). This indicates that the antibody can be used to detect endogenous Afadin phosphorylation. 3. We have included two new knock down experiments demonstrating the recognition of Afadin by our antibody (Figure 1 - figure supplement 2C). There appear to be two Afadin isoforms recognised in HEK293T cells by both the BD and pY1230 antibody, consistent with previous reports (Umeda et al. MBoC, 2015). We have highlighted these in the figure. 4. We have performed mutagenesis to demonstrate the specificity of the antibody. We tagged Afadin with a fluorescent protein tag, reasoning that it would cause a shift in molecular weight that could be resolved by SDS PAGE, as is the case. We noted that the phosphopeptide used spans an additional tyrosine, Y1226, which has been detected as phosphorylated (although to a much lower extent than Y1230) on Phosphosite plus. The data clearly show that Afadin cannot be phosphorylated when Y1230 is mutated to a phenylalanine (compared to CIP control), indicating that this is the predominant site recognised by the antibody. In addition, the endogenous pervanadate-stimulated signal is completely abolished by CIP treatment (Figure 1 - figure supplement 2D). 5. We have included densitometric quantification of the dephosphorylation assay shown in Figure 1B, which was part of a time course and shows preferential dephosphorylation by the PTPRK ICD compared to the PTPRK D1. The signal stops declining with time, which could indicate antibody background, or an inaccessible pool of Afadin-pY1230 (Figure 1 - figure supplement 2E). 6. To further demonstrate that this site is modulated by PTPRK in post-confluent cells, we have used doxycycline (dox)-inducible cell lines generated in Fearnley et al, 2019. Upon treatment with 500 ng/ml Dox for 48 hours PTPRK is induced to lower levels than wildtype, however, normalized quantification of the Afadin pY1230 against the Afadin (CST) signal clearly indicates downregulation by PTPRK WT, but not the catalytically inactive mutant (Figure 1 - figure supplement 2F and 2G). Together these data strengthen our assertion that this antibody recognises endogenously phosphorylated Afadin at site Y1230, which is modulated in vitro and in cells by PTPRK phosphatase activity. For clarity, we have highlighted and annotated the relevant bands in figures. We have also included identifiers for each Afadin total antibody was used in particular experiments.

2) The authors claim that a short, 63-residue predicted coiled coil (CC) region, is both necessary and sufficient for binding to the PTPRK-ICD. The region is predicted to have alpha-helical structure and as a consequence, a helical structure has been used in the docking model. Considering that the authors recombinantly expressed this region in bacteria, it would be experimentally simple confirming the alpha-helical structure of the segment by CD or NMR spectroscopy.

To clarify, the helical structure in the docking model was independently predicted by several sequence and structural analysis programmes including AlphaFold2, RobettaFold, NetSurfP and as annotated in Uniprot (as a coiled coil). We did not stipulate prior to the AF2 prediction that it was helical. Isolated short peptides frequently adopt helical structure, therefore prediction of a helix within the context of the full Afadin sequence is, in our opinion, stronger evidence than CD of an isolated fragment.

3) Only two mutants have been introduced into PTPRK-ICD to map the Afadin interaction site. One of the mutations changes a possibly structurally important residues (glycine) into a histidine. Even though this residue is present in PTPRM, it does not exclude that the D2 domain no longer functionally folds. Also the second mutation represents a large change in chemical properties and the other 2 predicted residues have not been investigated.

The residues that were selected for mutation are all localised to the protein surface and therefore are unlikely to be involved in stable folding of PTPRK. In support of the correct folding of the mutated PTPRK, we include in Figure 1 below SEC elution traces for wild-type and mutant D2 showing that they elute as single symmetric peaks at the same elution volume as the WT protein. This is consistent with them having a similar shape and size, and not being aggregated or unfolded.

Figure 1. PTPRK-D2 wild-type and mutant preparative SEC elution profiles. A280nm has been normalised to help illustrate that the different proteins elute at the same volume. The main peak from these samples was used for binding assays in the main paper.

Furthermore, the yield for the double mutant was very high (4 mg of pure protein from a 2 L culture, see A280 value in graph below), whereas poorly folded proteins tend to have significantly reduced yields. This protein was also very stable over time whereas unfolded proteins tend to degrade during or following purification.

Figure 2. Analytical SEC elution profile for the PTPRK-D2 DM construct showing the very high yield consistent with a well-folded, stable protein.

Finally, we have carried out thermal melt curves of the WT and mutant PTPRK D2 domains showing that they all possess melting temperatures between 39.3°C and 41.7°C, supporting that they are all equivalently folded. We include these data as an additional Supplementary Figure (Figure 4 - figure supplement 3) in the paper.

4) The interface on the Afadin substrate has not been investigated apart from deleting the entire CC or a central charge cluster. Based on the docking model the authors must have identified key positions of this interaction that could be mutated to confirm the proposed interaction site.

We have now made and tested several additional mutations within both the Afadin-CC and PTPRK-D2 domains to further validate the AF2 predicted model of the complex.

For Afadin-CC we introduced several single and double mutations along the helix including residues predicted to be in the interface and residues distal from the interface. These mutations and the pulldown with PTPRK are described in the text and are included as additional panels to a modified Figure 3. All mutations have the expected effect on the interaction based on the predicted complex structure. To help illustrate the positions of these mutations we have also included a figure of the interface with the residues highlighted.

For the PTPRK-D2 we have also introduced two new mutations, one buried in the interface (F1225A) and one on the edge of the interface encompassing a loop that is different in PTPRM (labelled the M-loop). GST-Afadin WT protein was bound to GSH beads and tested for their ability to pulldown WT and mutated PTPRK. These new mutations (illustrated in the new Figure 4 – figure supplement 2) further support the model prediction. F1225A almost completely abolishes binding as predicted, while the M-loop retains binding. These mutations and their effects are now described in the main text and the pull-down data, including controls and retesting of the original DM mutant, are included as panel H in a newly modified Figure 4 focussed solely on the PTPRK interface.

5) A minor point is that ITC experiments have not been run long enough to determine the baseline of interaction heats. In addition, as large and polar proteins were used in this experiment, a blank titration would be required to rule out that dilution heats effect the determined affinities.

All control experiments including buffer into buffer, Afadin into buffer and buffer into PTPRK were carried out at the same time as the main binding experiment and are shown below overlaid with the binding curve. These demonstrate the very small dilution heats consistent with excellent buffer matching of the samples.

We were able to obtain excellent fits to the titration curves by fitting 1:1 binding with a calculated linear baseline (see Figure 2B,D). Very similar results were obtained by fitting to the sum (‘composite’) of fitted linear baselines obtained for the three control experiments for each titration.

Author Response

Reviewer #1 (Public Review):

The authors start the study with an interesting clinical observation, found in a small subset of prostate cancers: FOXP2-CPED1 fusion. They describe how this fusion results in enhanced FOXP2 protein levels, and further describe how FOXP2 increases anchorageindependent growth in vitro, and results in pre-malignant lesions in vivo. Intrinsically, this is an interesting observation. However, the mechanistic insights are relatively limited as it stands, and the main issues are described below.

Main issues:

1) While the study starts off with the FOXP2 fusion, the vast majority of the paper is actually about enhanced FOXP2 expression in tumorigenesis. Wouldn't it be more logical to remove the FOXP2 fusion data? These data seem quite interesting and novel but they are underdeveloped within the current manuscript design, which is a shame for such an exciting novel finding. Along the same lines, for a study that centres on the prostate lineage, it's not clear why the oncogenic potential of FOXP2 in mouse 3T3 fibroblasts was tested.

We thank the reviewer very much for the comment. We followed the suggestion and added a set of data regarding the newly identified FOXP2 fusion in Figure 1 to make our manuscript more informative. We tested the oncogenic potential of FOXP2 in NIH3T3 fibroblasts because NIH3T3 cells are a widely used model to demonstrate the presence of transformed oncogenes2,3. In our study, we observed that when NIH3T3 cells acquired the exogenous FOXP2 gene, the cells lost the characteristic contact inhibition response, continued to proliferate and eventually formed clonal colonies. Please refer to "Answer to Essential Revisions #1 from the Editors” for details.

2) While the FOXP2 data are compelling and convincing, it is not clear yet whether this effect is specific, or if FOXP2 is e.g. universally relevant for cell viability. Targeting FOXP2 by siRNA/shRNA in a non-transformed cell line would address this issue.

We appreciate these helpful comments. Please refer to the "Answer to Essential Revisions #1 from the Editors” for details.

3) Unfortunately, not a single chemical inhibitor is truly 100% specific. Therefore, the Foretinib and MK2206 experiments should be confirmed using shRNAs/KOs targeting MEK and AKT. With the inclusion of such data, the authors would make a very compelling argument that indeed MEK/AKT signalling is driving the phenotype.

We thank the reviewer for highlighting this point and we agree with the reviewer’s point that no chemical inhibitor is 100% specific. In this study, we used chemical inhibitors to provide further supportive data indicating that FOXP2 confers oncogenic effects by activating MET signaling. We characterized a FOXP2-binding fragment located in MET and HGF in LNCaP prostate cancer cells by utilizing the CUT&Tag method. We also found that MET restoration partially reversed oncogenic phenotypes in FOXP2-KD prostate cancer cells. All these data consistently supported that FOXP2 activates MET signaling in prostate cancer. Please refer to the "Answer to Essential Revisions #2 from the Editors” and to the "Answer to Essential Revisions #7 from the Editors” for details.

4) With the FOXP2-CPED1 fusion being more stable as compared to wild-type transcripts, wouldn't one expect the fusion to have a more severe phenotype? This is a very exciting aspect of the start of the study, but it is not explored further in the manuscript. The authors would ideally elaborate on why the effects of the FOXP2-CPED1 fusion seem comparable to the FOXP2 wildtype, in their studies.

We thank the reviewer very much for the comment. We had quantified the number of colonies of FOXP2- and FOXP2-CPED1-overexpressing cells, and we found that both wildtype FOXP2 and FOXP2-CPED1 had a comparable putative functional influence on the transformation of human prostate epithelial cells RWPE-1 and mouse primary fibroblasts NIH3T3 (P = 0.69, by Fisher’s exact test for RWPE-1; P = 0.23, by Fisher’s exact test for NIH3T3). We added the corresponding description to the Results section in Line 487 on Page 22 in the tracked changes version of the revised manuscript. Please refer to the "Answer to Essential Revisions #5 from the Editors” for details.

5) The authors claim that FOXP2 functions as an oncogene, but the most-severe phenotype that is observed in vivo, is PIN lesions, not tumors. While this is an exciting observation, it is not the full story of an oncogene. Can the authors justifiably claim that FOXP2 is an oncogene, based on these results?

We appreciate the comment, and we made the corresponding revision in the revised manuscript. Please refer to the "Answer to Essential Revisions #3 from the Editors” for details.

6) The clinical and phenotypic observations are exciting and relevant. The mechanistic insights of the study are quite limited in the current stage. How does FOXP2 give its phenotype, and result in increased MET phosphorylation? The association is there, but it is unclear how this happens.

We appreciate this valuable suggestion. In the current study, we used the CUT&Tag method to explore how FOXP2 activated MET signaling in LNCaP prostate cancer cells, and we identified potential FOXP2-binding fragments in MET and HGF. Therefore, we proposed that FOXP2 activates MET signaling in prostate cancer through its binding to MET and METassociated gene. Please refer to the "Answer to Essential Revisions #2 from the Editors” for details.

Reviewer #2 (Public Review):

1) The manuscript entitled "FOXP2 confers oncogenic effects in prostate cancer through activating MET signalling" by Zhu et al describes the identification of a novel FOXP2CPED1 gene fusion in 2 out of 100 primary prostate cancers. A byproduct of this gene fusion is the increased expression of FOXP2, which has been shown to be increased in prostate cancer relative to benign tissue. These data nominated FOXP2 as a potential oncogene. Accordingly, overexpression of FOXP2 in nontransformed mouse fibroblast NIH-3T3 and human prostate RWPE-1 cells induced transforming capabilities in both cell models. Mechanistically, convincing data were provided that indicate that FOXP2 promotes the expression and/or activity of the receptor tyrosine kinase MET, which has previously been shown to have oncogenic functions in prostate cancer. Notably, the authors create a new genetically engineered mouse model in which FOXP2 is overexpressed in the prostatic luminal epithelial cells. Overexpression of FOXP2 was sufficient to promote the development of prostatic intraepithelial neoplasia (PIN) a suspected precursor to prostate adenocarcinoma and activate MET signaling.

Strengths:

This study makes a convincing case for FOXP2 as 1) a promoter of prostate cancer initiation and 2) an upstream regulator of pro-cancer MET signaling. This was done using both overexpression and knockdown models in cell lines and corroborated in new genetically engineered mouse models (GEMMs) of FOXP2 or FOXP2-CPED1 overexpression in prostate luminal epithelial cells as well as publicly available clinical cohort data.

Major strengths of the study are the demonstration that FOXP2 or FOXP2-CPED1 overexpression transforms RWPE-1 cells to now grow in soft agar (hallmark of malignant transformation) and the creation of new genetically engineered mouse models (GEMMs) of FOXP2 or FOXP2-CPED1 overexpression in prostate luminal epithelial cells. In both mouse models, FOXP2 overexpression increased the incidence of PIN lesions, which are thought to be a precursor to prostate cancer. While FOXP2 alone was not sufficient to cause prostate cancer in mice, it is acknowledged that single gene alterations causing prostate cancer in mice are rare. Future studies will undoubtedly want to cross these GEMMs with established, relatively benign models of prostate cancer such as Hi-Myc or Pb-Pten mice to see if FOXP2 accelerates cancer progression (beyond the scope of this study).

We appreciate these positive comments from the reviewer. We agree with the suggestion from the reviewer that it is worth exploring whether FOXP2 is able to cooperate with a known disease driver to accelerate the progression of prostate cancer. Therefore, we are going to cross Pb-FOXP2 transgenic mice with Pb-Pten KO mice to assess if FOXP2 is able to accelerate malignant progression.

2) Weaknesses: It is unclear why the authors decided to use mouse fibroblast NIH3T3 cells for their transformation studies. In this regard, it appears likely that FOXP2 could function as an oncogene across diverse cell types. Given the focus on prostate cancer, it would have been preferable to corroborate the RWPE-1 data with another prostate cell model and test FOXP2's transforming ability in RWPE-1 xenograft models. To that end, there is no direct evidence that FOXP2 can cause cancer in vivo. The GEMM data, while compelling, only shows that FOXP2 can promote PIN in mice and the lone xenograft model chosen was for fibroblast NIH-3T3 cells.

To determine the oncogenic activity of FOXP2 and the FOXP2-CPDE1 fusion, we initially used mouse primary fibroblast NIH3T3 for transformation experiments, because NIH3T3 cells are a widely used cell model to discover novel oncogenes2,3,10,11. Subsequently, we observed that overexpression of FOXP2 and its fusion variant drove RWPE-1 cells to lose the characteristic contact inhibition response, led to their anchorage-independent growth in vitro, and promoted PIN in the transgenic mice. During preparation of the revised manuscript, we tested the transformation ability of FOXP2 and FOXP2-CPED1 in RWPE1 xenograft models. We subcutaneously injected 2 × 106 RWPE-1 cells into the flanks of NOD-SCID mice. The NODSCID mice were divided into five groups (n = 5 mice in each group): control, FOXP2overexpressing (two stable cell lines) and FOXP2-CPED1- overexpressing (two cell lines) groups. The experiment lasted for 4 months. We observed that no RWPE-1 cell-injected mice developed tumor masses. We propose that FOXP2 and its fusion alone are not sufficient to generate the microenvironment suitable for RWPE-1-xenograft growth. Collectively, our data suggest that FOXP2 has oncogenic potential in prostate cancer, but is not sufficient to act alone as an oncogene.

3) There is a limited mechanism of action. While the authors provide correlative data suggesting that FOXP2 could increase the expression of MET signaling components, it is not clear how FOXP2 controls MET levels. It would be of interest to search for and validate the importance of potential FOXP2 binding sites in or around MET and the genes of METassociated proteins. At a minimum, it should be confirmed whether MET is a primary or secondary target of FOXP2. The authors should also report on what happened to the 4-gene MET signature in the FOXP2 knockdown cell models. It would be equally significant to test if overexpression of MET can rescue the anti-growth effects of FOXP2 knockdown in prostate cancer cells (positive or negative results would be informative).

We appreciate all the valuable comments. As suggested, we performed corresponding experiments, please refer to the " Answers to Essential Revisions #2 from the Editors”, to the "Answer to Essential Revisions #6 from the Editors”, and to the "Answer to Essential Revisions #7 from the Editors” for details.

Reviewer #3 (Public Review):

1) In this manuscript, the authors present data supporting FOXP2 as an oncogene in PCa. They show that FOXP2 is overexpressed in PCa patient tissue and is necessary and sufficient for PCa transformation/tumorigenesis depending on the model system. Overexpression and knock-down of FOXP2 lead to an increase/decrease in MET/PI3K/AKT transcripts and signaling and sensitizes cells to PI3K/AKT inhibition.

Key strengths of the paper include multiple endpoints and model systems, an over-expression and knock-down approach to address sufficiency and necessity, a new mouse knock-in model, analysis of primary PCa patient tumors, and benchmarking finding against publicly available data. The central discovery that FOXP2 is an oncogene in PCa will be of interest to the field. However, there are several critically unanswered questions.

1) No data are presented for how FOXP2 regulates MET signaling. ChIP would easily address if it is direct regulation of MET and analysis of FOXP2 ChIP-seq could provide insights.

2) Beyond the 2 fusions in the 100 PCa patient cohort it is unclear how FOXP2 is overexpressed in PCa. In the discussion and in FS5 some data are presented indicating amplification and CNAs, however, these are not directly linked to FOXP2 expression.

3) There are some hints that full-length FOXP2 and the FOXP2-CPED1 function differently. In SF2E the size/number of colonies between full-length FOXP2 and fusion are different. If the assay was run for the same length of time, then it indicates different biologies of the overexpressed FOXP2 and FOXP2-CPED1 fusion. Additionally, in F3E the sensitization is different depending on the transgene.

We appreciate these valuable comments and constructive remarks. As suggested, we performed the CUT&Tag experiments to detect the binding of FOXP2 to MET, and to examine the association of CNAs of FOXP2 with its expression. Please refer to the " Answer to Essential Revisions #2 from the Editors" and the " Answer to Essential Revisions #4 from the Editors" for details. We also added detailed information to show the resemblance observed between FOXP2 fusion- and wild-type FOXP2-overexpressing cells. We added the corresponding description to the Results section in Line 487 on Page 22 in the tracked changes version of the revised manuscript. Please refer to the “Answer to Essential Revisions #5 from the Editors” for details.

2) The relationship between FOXP2 and AR is not explored, which is important given 1) the critical role of the AR in PCa; and 2) the existing relationship between the AR and FOXP2 and other FOX gene members.

We thank the reviewer very much for highlighting this point. We agree that it is important to examine the relationship between FOXP2 and AR. We therefore analyzed the expression dataset of 255 primary prostate tumors from TCGA and observed that the expression of FOXP2 was significantly correlated with the expression of AR (Spearman's ρ = 0.48, P < 0.001) (Figure 1. a). Next, we observed that both FOXP2- and FOXP2-CPED1overexpressing 293T cells had a higher AR protein abundance than control cells (Figure 1. b). In addition, shRNA-mediated FOXP2 knockdown in LNCaP cells resulted in a decreased AR protein level compared to that in control cells (Figure 1. c). However, we analyzed our CUT&Tag data and observed no binding of FOXP2 to AR (Figure 1. d). Our data suggest that FOXP2 might be associated with AR expression.

Figure 1. a. AR expression in a human prostate cancer dataset (TCGA, Prostate Adenocarcinoma, Provisional; n = 493) classified by FOXP2 expression level (bottom 25%, low expression, n = 120; top 25%, high expression, n = 120; negative expression, n = 15). P values were calculated by the MannWhitney U test. The correlation between FOXP2 and AR expression was evaluated by determining the Spearman's rank correlation coefficient. b. Immunoblot analysis of the expression levels of AR in 293T cells with overexpression of FOXP2 or FOXP2-CPED1. c. Immunoblot analysis of the expression levels of AR in LNCaP cells with stable expression of the scrambled vector or FOXP2 shRNA. d. CUT&Tag analysis of FOXP2 association with the promoter of AR. Representative track of FOXP2 at the AR gene locus is shown.

Reference

1. Mayr C, Bartel DP. Widespread shortening of 3'UTRs by alternative cleavage and polyadenylation activates oncogenes in cancer cells. Cell. 2009 Aug 21;138(4):673-84.
2. Gara SK, Jia L, Merino MJ, Agarwal SK, Zhang L, Cam M et al., Germline HABP2 Mutation Causing Familial Nonmedullary Thyroid Cancer. N Engl J Med. 2015 Jul 30;373(5):448-55.
3. Kohno T, Ichikawa H, Totoki Y, Yasuda K, Hiramoto M, Nammo T et al., KIF5B-RET fusions in lung adenocarcinoma. Nat Med. 2012 Feb 12;18(3):375-7.
4. Chen F, Byrd AL, Liu J, Flight RM, DuCote TJ, Naughton KJ et al., Polycomb deficiency drives a FOXP2-high aggressive state targetable by epigenetic inhibitors. Nat Commun. 2023 Jan 20;14(1):336.
5. Kaya-Okur HS, Wu SJ, Codomo CA, Pledger ES, Bryson TD, Henikoff JG et al., CUT&Tag for efficient epigenomic profiling of small samples and single cells. Nat Commun. 2019 Apr 29;10(1):1930.
6. Spiteri E, Konopka G, Coppola G, Bomar J, Oldham M, Ou J et al., Identification of the transcriptional targets of FOXP2, a gene linked to speech and language, in developing human brain. Am J Hum Genet. 2007 Dec;81(6):1144-57.
7. Lai CS, Fisher SE, Hurst JA, Vargha-Khadem F, Monaco AP. A forkhead-domain gene is mutated in a severe speech and language disorder. Nature. 2001 Oct 4;413(6855):519-23.
8. Hannenhalli S, Kaestner KH. The evolution of Fox genes and their role in development and disease. Nat Rev Genet. 2009 Apr;10(4):233-40.
9. Shu W, Yang H, Zhang L, Lu MM, Morrisey EE. Characterization of a new subfamily of winged-helix/forkhead (Fox) genes that are expressed in the lung and act as transcriptional repressors. J Biol Chem. 2001 Jul 20;276(29):27488-97.
10. Wang C, Liu H, Qiu Q, Zhang Z, Gu Y, He Z. TCRP1 promotes NIH/3T3 cell transformation by over-activating PDK1 and AKT1. Oncogenesis. 2017 Apr 24;6(4):e323.
11. Suh YA, Arnold RS, Lassegue B, Shi J, Xu X, Sorescu D et al., Cell transformation by the superoxide-generating oxidase Mox1. Nature. 1999 Sep 2;401(6748):79-82.

Author Response

Reviewer #1 (Public Review):

In one of the most creative eDNA studies I have had the pleasure to review, the authors have taken advantage of an existing program several decades old to address whether insect declines are indeed occurring - an active area of discussion and debate within ecology. Here, they extracted arthropod environmental DNA (eDNA) from pulverized leaf samples collected from different tree species across different habitats. Their aim was to assess the arthropod community composition within the canopies of these trees during the time of collection to assess whether arthropod richness, diversity, and biomass were declining. By utilizing these leaf samples, the greatest shortcoming of assessing arthropod declines - the lack of historical data to compare to - was overcome, and strong timeseries evidence can now be used to inform the discussion. Through their use of eDNA metabarcoding, they were able to determine that richness was not declining, but there was evidence of beta diversity loss due to biotic homogenization occurring across different habitats. Furthermore, their application of qPCR to assess changes in eDNA copy number temporally and associate those changes with changes to arthropod biomass provided support to the argument that arthropod biomass is indeed declining. Taken together, these data add substantial weight to the current discussion regarding how arthropods are being affected in the Anthropocene.

Thank you very much for the positive assessment of our work.

I find the conclusions of the paper to be sound and mostly defensible, though there are some issues to take note of that may undermine these findings.

Firstly, I saw no explanation of the requisite controls for such an experiment. An experiment of this scale should have detailed explanations of the field/equipment controls, extraction controls, and PCR controls to ensure there are no contamination issues that would otherwise undermine the entirety of the study. At one point in the manuscript the presence of controls is mentioned just once, so I surmise they must exist. Trusting such results needs to be taken with caution until such evidence is clearly outlined. Furthermore, the plate layout which includes these controls would help assess the extent of tag-jumping, should the plate plan proposed in Taberlet et al., 2018 be adopted.

Second, without the presence of adequate controls, filtering schemes would be unable to determine whether there were contaminants and also be unable to remove them. This would also prevent samples from being filtered out should there be excessive levels of contamination present. Without such information, it makes it difficult to fully trust the data as presented.

Finally, there is insufficient detail regarding the decontamination procedures of equipment used to prepare the samples (e.g., the cryomil). Without clear explanations of the steps the authors took to ensure samples were handled and prepared correctly, there is yet more concern that there may be unseen problems with the dataset.

We are well aware of the potential issues and consequences of contamination in our work. However, we are also confident that our field and laboratory procedures adequately rule out these issues. We agree with the reviewer that we should expand more on our reasoning. Hence, we have now significantly expanded the Methods section outlining controls and sample purity, particularly under “Tree samples of the German Environmental Specimen Bank – Standardized time series samples stored at ultra-low temperatures” (lines 303-304), “Test for DNA carryover in the cryomill” (lines 448-464) and “Statistical analysis” (lines 570-575).

We ran negative control extractions as well as negative control PCRs with all samples. These controls were sequenced along with all samples and used to explore the effect of experimental contamination. With the exception of a few reads of abundant taxa, these controls were mostly clean. We report this in more detail now in the Methods under “Sequence analysis” (lines 570-575). This suggests that our data are free of experimental contamination or tag jumping issues.

We have also expanded on the avoidance of contamination in our field sampling protocols. The ESB has been set up for monitoring even the tiniest trace amounts of chemicals. Carryover between samples would render the samples useless. Hence, highly clean and standardized protocols are implemented. All samples are only collected with sterilized equipment under sterile conditions. Each piece of equipment is thoroughly decontaminated before sampling.

The cryomill is another potential source of cross-contamination. The mill is disassembled after each sample and thoroughly cleaned. Milled samples have already been tested for chemical carryover, and none was found. We have now added an additional analysis to rule out DNA carryover. We received the milling schedule of samples for the past years. Assuming samples get contaminated by carryover between milling runs, two consecutive samples should show signatures of this carryover. We tested this for singletaxon carryover as well as community-wide beta diversity, but did not find any signal of contamination. This gives us confidence that our samples are very pure. The results of this test are now reported in the manuscript (Suppl. Fig 12 & Suppl. Table 3).

Reviewer #2 (Public Review):

Krehenwinkel et al. investigated the long-term temporal dynamics of arthropod communities using environmental DNA (eDNA) remained in archived leave samples. The authors first developed a method to recover arthropod eDNA from archived leave samples and carefully tested whether the developed method could reasonably reveal the dynamics of arthropod communities where the leave samples originated. Then, using the eDNA method, the authors analyzed 30-year-long well-archived tree leaf samples in Germany and reconstructed the long-term temporal dynamics of arthropod communities associated with the tree species. The reconstructed time series includes several thousand arthropod species belonging to 23 orders, and the authors found interesting patterns in the time series. Contrary to some previous studies, the authors did not find widespread temporal α-diversity (OTU richness and haplotype diversity) declines. Instead, β-diversity among study sites gradually decreased, suggesting that the arthropod communities are more spatially homogenized in recent years. Overall, the authors suggested that the temporal dynamics of arthropod communities may be complex and involve changes in α- and β-diversity and demonstrated the usefulness of their unique eDNA-based approach.

Strengths:

The authors' idea that using eDNA remained in archived leave samples is unique and potentially applicable to other systems. For example, different types of specimens archived in museums may be utilized for reconstructing long-term community dynamics of other organisms, which would be beneficial for understanding and predicting ecosystem dynamics.

A great strength of this work is that the authors very carefully tested their method. For example, the authors tested the effects of powdered leaves input weights, sampling methods, storing methods, PCR primers, and days from last precipitation to sampling on the eDNA metabarcoding results. The results showed that the tested variables did not significantly impact the eDNA metabarcoding results, which convinced me that the proposed method reasonably recovers arthropod eDNA from the archived leaf samples. Furthermore, the authors developed a method that can separately quantify 18S DNA copy numbers of arthropods and plants, which enables the estimations of relative arthropod eDNA copy numbers. While most eDNA studies provide relative abundance only, the DNA copy numbers measured in this study provide valuable information on arthropod community dynamics.

Overall, the authors' idea is excellent, and I believe that the developed eDNA methodology reasonably reconstructed the long-term temporal dynamics of the target organisms, which are major strengths of this study.

Thank you very much for the positive assessment of our work.

Weaknesses:

Although this work has major strengths in the eDNA experimental part, there are concerns in DNA sequence processing and statistical analyses.

Statistical methods to analyze the temporal trend are too simplistic. The methods used in the study did not consider possible autocorrelation and other structures that the eDNA time series might have. It is well known that the applications of simple linear models to time series with autocorrelation structure incorrectly detect a "significant" temporal trend. For example, a linear model can often detect a significant trend even in a random walk time series.

We have now reanalyzed our data controlling for autocorrelation and for non-linear changes of abundance and recover no change to our results. We have added this information to the manuscript under “Statistical analysis” (lines 629-644).

Also, there are some issues regarding the DNA sequence analysis and the subsequent use of the results. For example, read abundance was used in the statistical model, but the read abundance cannot be a proxy for species abundance/biomass. Because the total 18S DNA copy numbers of arthropods were quantified in the study, multiplying the sequence-based relative abundance by the total 18S DNA copy numbers may produce a better proxy of the abundance of arthropods, and the use of such a better proxy would be more appropriate here. In addition, a coverage-based rarefaction enables a more rigorous comparison of diversity (OTU diversity or haplotype diversity) than the readbased rarefaction does.

We did not use read abundance as a proxy for abundance, but used our qPCR approach to measure relative copy number of arthropods. While there are biases to this (see our explanations above), the assay proved very reliable and robust. We thus believe it should indeed provide a rough estimate of biomass. As biomass is very commonly discussed in insect decline (in fact the first study on insect decline entirely relies on biomass; Hallmann et al. 2017), we feel it is important go include a proxy for this as well. However, we also discuss the alternative option that a turnover of diversity is affecting the measured biomass. A pattern of abundance loss for common species has been described in other works on insect decline.

We liked the reviewer’s suggestion to use copy number information to perform abundance-informed rarefaction. We have done this now and added an additional analysis rarefying by copy number/biomass. A parallel analysis using this newly rarefied table was done for the total diversity as well as single species abundance change. Details can be found in the Methods and Results section of the manuscript. However, the result essentially remains the same. Even abundance-informed rarefaction does not lead to a pattern of loss of species richness over time (see “Statistical analysis”).

The overall results are supporting a scenario of no overall loss of species richness over time, but a loss of abundance for common species. And we indeed see the pattern of declining abundance for once-common species in our data, for example the loss of the Green Silver-Line moth, once a very common species in beech canopy (Suppl. Fig. 10). We have added details on this to the Discussion (lines 254-260).

These points may significantly impact the conclusions of this work.

Reviewer #3 (Public Review):

The aim of Weber and colleagues' study was to generate arthropod environmental DNA extracted from a unique 30-year time series of deep-frozen leaf material sampled at 24 German sites, that represent four different land use types. Using this dataset, they explore how the arthropod community has changed through time in these sites, using both conventional metabarcoding to reconstruct the OTUs present, and a new qPCR assay developed to estimate the overall arthropod diversity on the collected material. Overall their results show that while no clear changes in alpha diversity are found, the βdiversity dropped significantly over time in many sites, most notable in the beech forests. Overall I believe their data supports these findings, and thus their conclusion that diversity is becoming homogenized through time is valid.

Thank you for the positive assessment.

While overall I do not doubt the general findings, I have a number of comments. Firstly while I agree this is a very nice study on a unique dataset - other temporal datasets of insects that were used for eDNA studies do exist, and perhaps it would be relevant to put the findings into context (or even the study design) of other work that has been done on such datasets. One example that jumps to my mind is Thomsen et al. 2015 https://besjournals.onlinelibrary.wiley.com/doi/full/10.1111/1365-2656.12452 but I am sure there are others.

We have expanded the introduction and discussion on this citing this among other studies now (lines 71-72, 276-278).

From a technical point of view, the conclusions of course rely on several assumptions, including (1) that the biomass assay is effective and (2) that the reconstructed levels of OTU diversity are accurate,

With regards to biomass although it is stated in the manuscript that "Relative eDNA copy number should be a predictor for relative biomass ", this is in fact only true if one assumes a number of things, e.g. there is a similar copy number of 18s rDNA per species, similar numbers of mtDNA per cell, a similar number of cells per individual species etc. In this regard, on the positive side, it is gratifying to see that the authors perform a validation assay on 7 mock controls, and these seem to indicate the assay works well. Given how critical this is, I recommend discussing the details of this a bit more, and why the authors are convinced the assay is effective in the main text so that the reader is able to fully decide if they are in agreement. However perhaps on the negative side, I am concerned about the strategy taken to perform the qPCR may have not been ideal. Specifically, the assay is based on nested PCR, where the authors first perform a 15cycle amplification, this product is purified, then put into a subsequent qPCR. Given how both PCR is notorious for introducing amplification biases in general (especially when performed on low levels of DNA), and the fact that nested PCRs are notoriously contamination prone - this approach seems to be asking for trouble. This raises the question - why not just do the qPCR directly on the extracts (one can still dilute the plant DNA 100x prior to qPCR if needed). Further, given the qPCRs were run in triplicate I think the full data (Ct values) for this should be released (as opposed to just stating in the paper that the average values were used). In this way, the readers will be able to judge how replicable the assay was - something I think is critical given how noisy the patterns in Fig S10 seem to be.

We agree with this point, and this is why we do not want to overstate the decline in copy number. This is an additional source of data next to genetic and species diversity. We have added to our discussion of turnover as another potential driver of copy number change (lines 257-260). We have also added text addressing the robustness of the mock community assay (lines 138-141).

However, we are confident of the reliability and robustness of our qPCR assay for the detection of relative arthropod copy number. We performed several validations and optimizations before using the assay. We have added additional details to the manuscript on this (see “Detection of relative arthropod DNA copy number using quantitative PCR”, lines 548-556). We got the idea for the nested qPCR from a study (Tran et al.) showing its high accuracy and reproducibility. We show that our assay has a very high replicability using triplicates of each qPCR, which we will now include in the supplementary data on Dryad. The SD of Ct values is very low (~ 0.1 on average). NTC were run with all qPCRs to rule out contamination as an issue in the experiments. We also find a very high efficiency of the assay. At dilutions far outside the observed copy number in our actual leaf data, we still find the assay to be accurate. We found very comparable abundance changes across our highly taxonomically diverse mock communities. This also suggests that abundance changes are a more likely explanation than simple turnover for the observed drop in copy number. A biomass loss for common species is well in line with recent reports on insect decline. We can also rely on several other mock community studies (Krehenwinkel et al. 2017 & 2019) where we used read abundance of 18S and found it to be a relatively good predictor of relative biomass.

The pattern in Fig. S10 is not really noisy. It just reflects typical population fluctuations for arthropods. Most arthropod taxa undergo very pronounced temporal abundance fluctuations between years.

Next, with regards to the observation that the results reveal an overall decrease in arthropod biomass over time: The authors suggest one alternate to their theory, that the dropping DNA copy number may reflect taxonomic turnover of species with different eDNA shedding rates. Could there be another potential explanation - simply be that leaves are getting denser/larger? Can this be ruled out in some way, e.g. via data on leaf mass through time for these trees? (From this dataset or indeed any other place).

This is a very good point. However, we can rule out this hypothesis, as the ESB performs intensive biometric data analysis. The average leaf weight and water content have not significantly changed in our sites. We have addressed this in the Methods section (see ”Tree samples of the German Environmental Specimen Bank – Standardized time series samples stored at ultra-low temperatures”, lines 308-311).

With regards to estimates of OTU/zOTU diversity. The authors state in the manuscript that zOTUs represent individual haplotypes, thus genetic variation within species. This is only true if they do not represent PCR and/or sequencing errors. Perhaps therefore they would be able to elaborate (for the non-computational/eDNA specialist reader) on why their sequence processing methods rule out this possibility? One very good bit of evidence would be that identical haplotypes for the individual species are found in the replicate PCRs. Or even between different extractions at single locations/timepoints.

We have repeated the analysis of genetic variation with much more stringent filtering criteria (see “Statistical analysis”, lines 611-615). Among other filtering steps, this also includes the use of only those zOTUs that occur in both technical replicates, as suggested by the reviewer. Another reason to make us believe we are dealing with true haplotypic variation here is that haplotypes show geographic variation. E.g., some haplotypes are more abundant in some sites than in others. NUMTS would consistently show a simple correlation in their abundance with the most abundant true haplotype.

With regards to the bigger picture, one thing I found very interesting from a technical point of view is that the authors explored how modifying the mass of plant material used in the extraction affects the overall results, and basically find that using more than 200mg provides no real advantage. In this regard, I draw the authors and readers attention to an excellent paper by Mata et al. (https://onlinelibrary.wiley.com/doi/full/10.1111/mec.14779) - where these authors compare the effect of increasing the amount of bat faeces used in a bat diet metabarcoding study, on the OTUs generated. Essentially Mata and colleagues report that as the amount of faeces increases, the rare taxa (e.g. those found at a low level in a single faeces) get lost - they are simply diluted out by the common taxa (e.g those in all faeces). In contrast, increasing biological replicates (in their case more individual faecal samples) increased diversity. I think these results are relevant in the context of the experiment described in this new manuscript, as they seem to show similar results - there is no benefit of considerably increasing the amount of leaf tissue used. And if so, this seems to point to a general principal of relevance to the design of metabarcoding studies, thus of likely wide interest.

Thank you for this interesting study, which we were not aware of before. The cryomilling is an extremely efficient approach to equally disperse even traces of chemicals in a sample. This has been established for trace chemicals early during the operation of the ESB, but also seems to hold true for eDNA in the samples. We have recently done more replication experiments from different ESB samples (different terrestrial and marine samples for different taxonomic groups) and find that replication of extraction does not provide much more benefit than replication of PCR. Even after 2 replicates, diversity approaches saturation. This can be seen in the plot below, which shows recovered eDNA diversity for different ESB samples and different taxonomic groups from 1-4 replicates. A single extract of a small volume contains DNA from nearly all taxa in the community. Rare taxa can be enriched with more PCR replicates.

Author Response

Reviewer #2 (Public Review):

The manuscript by Ma et al, "Two RNA-binding proteins mediate the sorting of miR223 from mitochondria into exosomes" examines the contribution of two RNA-binding proteins on the exosomal loading of miR223. The authors conclude that YBX1 and YBAP1 work in tandem to traffic and load miR223 into the exosome. The manuscript is interesting and potentially impactful. It proposes the following scenario regarding the exosomal loading of miR223: (1) YBAP1 sequesters miR223 in the mitochondria, (2) YBAP1 then transfers miR223 to YBX1, and (3) YBX1 then delivers miR223 into the early endosome for eventual secretion within an exosome. While the authors propose plausible explanations for this phenomenon, they do not specifically test them and no mechanism by which miR223 is shuttled between YBAP1 and YBX1, and the exosome is shown. Thus, the paper is missing critical mechanistic experiments that could have readily tested the speculative conclusions that it makes.

Comments:

1) The major limitation of this paper is that it fails to explore the mechanism of any of the major changes it describes. For example, the authors propose that miR223 shuttles from mitochondrially localized YBAP1 to P-body-associated YBX1 to the exosome. This needs to be tested directly and could be easily addressed by showing a transfer of miR223 from YBAP1 to YBX1 to the exosome.

Testing this idea using fluorescently labeled miR223 would indeed be an ideal experiment. However, miRNA imaging presents challenges. As reviewer 1 pointed out, and we have now confirmed, the atto-647 dye itself localizes to mitochondria. We will continue our efforts to identify a suitable fluorescent label for miR223in order to be in a position to evaluate the temporal relationship between mitochondrial and endosomal miR223.

2) If YBAP1 retains miR223 in mitochondria, what is the trigger for YBAP1 to release it and pass it off to YBX1? The authors speculate in their discussion that sequestration of mito-miR223 plays a "role in some structural or regulatory process, perhaps essential for mitochondrial homeostasis, controlled by the selective extraction of unwanted miRNA into RNA granules and further by secretion in exosomes...". This is readily testable by altering mitochondria dynamics and/or integrity.

A previous study has reported that YBAP1 can be released from mitochondria to the cytosol during HSV-1 infection (Song et al., 2021). However, due to restrictions, we are unable to conduct experiments using HSV to verify this condition. We attempted to induce mitochondrial stress by using different concentrations of CCCP, but we did not observe the release of YBAP1 from mitochondria after CCCP treatment. We speculate that not all mitochondrial stress conditions can trigger YBAP1 release. Investigating the mechanism of mito-miR223 release from mitochondria is one of our interests that we aim to explore in future studies.

3) Much of the miRNA RT-PCR analysis is presented as a ratio of exosomal/cellular. This particular analysis assumes that cellular miRNA is unaffected by treatments. For example, Figure 1a shows that the presence of exosomal miR223 is significantly reduced when YBX1 is knocked out. This analysis does not consider the possibility that YBX1-KO alters (up or down-regulates) intracellular miR223 levels. Should that be the case, the ratiometric analysis is greatly skewed by intracellular miRNA changes. It would be better to not only show the intracellular levels of the miRs but also normalize the miRNA levels to the total amount of RNA isolated or an irrelevant/unchanged miRNA.

Our previous publications demonstrated that miR223 levels are increased in YBX1-KO cells and decreased in exosomes derived from YBX1 KO cells. However, no significant changes were observed in miR190 levels (Liu et al., 2021; Shurtleff et al., 2016). The repeated data has been included in Figure 1a.

For the analysis of other miRNAs by RT-PCR, we assessed changes in intracellular and exosomal miRNA levels in the corresponding figures. In the qPCR analysis, miRNA levels were normalized to the total amount of RNA.

4) In figure 1, the authors show that in YBX1-KO cells, miR223 levels are decreased in the exosome. They further suggest this is because YBX1 binds with high affinity to miR223. This binding is compared to miR190 which the authors state is not enriched in the exosome. However, no data showing that miR190 is not present in the exosome is shown. A figure showing the amount of cellular and exosomal miR223 and 190 should be shown together on the same graph.

In previous publications we demonstrated that miR190 is not localized in exosomes and not significantly changed in YBX1 knockout (KO) cells and exosomes derived from YBX1 KO cells (Liu et al., 2021; Shurtleff et al., 2016). The repeated data has been included in Figure 1a.

5) Figure 2 Supplement 1 - As to determine the nucleotides responsible for interacting with YBX1, the authors made several mutations within the miR223 sequence. However, no explanation is given regarding the mutant sequences used or what the ratios mean. Mutant sequences need to be included. How do the authors conclude that UCAGU is important when the locations of the mutations are unclear? Also, the interpretation of this data would benefit from a binding affinity curve as shown in Fig 2C.

The ratio is of labeled miR223/unlabeled miR223 (wt and mutant). All mutant sequences of miR223 have been included in Figure 2 supplement 1.

6) While the binding of miR223mut to YBX1 is reduced, there is still significant binding. Does this mean that the 5nt binding motif is not exact? Do the authors know if there are multiple nucleotide possibilities at these positions that could facilitate binding? Perhaps confirming binding "in vivo" via RIP assay would further solidify the UCAGU motif as critical for binding to YBX1.

The binding affinity of miR223mut with YBX1 is reduced approximately 27-fold compared to miR223. We speculate that the secondary structure of miR223 may contribute to the interaction with YBX1.

Our EMSA data, in vitro packaging data, and exosome analysis reinforce the conclusion that UCAGU is critical for YBX1 binding. These findings suggest that the presence of the UCAGU motif in miR223 is crucial for its interaction with YBX1 and subsequent sorting into exosomes.

7) Figures 2g, h - It would be nice to show that miR190mut also packages in the cell-free system. This would confirm that the sequence is responsible. Also, to confirm that the sorting of miR223 is YBX1-dependent, a cell-free reaction using cytosol and membranes from YBX1 KO cells is needed.

Although we have not performed the suggested experiment, we purified exosomes from cells overexpressing miR190sort and observed an increase in the enrichment of miR190sort in exosomes compared to miR190. This finding confirmed that the UCAGU motif facilitates miRNA sorting into exosomes.

Regarding the in vitro packaging assay, our previously published paper demonstrated that cytosol from YBX1 knockout (KO) cells significantly reduces the protection of miR223 from RNase digestion. We concluded that the sorting of miR223 into exosomes is dependent on YBX1 (Shurtleff et al., 2016).

8) In Figure 3a, the authors show that miR223 is mitochondrially localized. Does the sequence of miR223 (WT or Mut) matter for localization? Does it matter for shuttling between YBAP1 and YBX1?

The localization of miR223mut has not been tested in our current study. We plan to conduct these experiments in the future.

9) Supplement 3c - Is it strange that miR190 is not localized to any particular compartment? Is miR190 present ubiquitously and equally among all intracellular compartments?

Most mature miRNAs are predominantly localized in the cytoplasm. Although there is no specific subcellular localization reported for miR190 in the literature, our experimental findings indicate a relatively high expression of miR190 in 293T cells. It is likely that most of miR190 is localized in the cytosol. However, it is also possible that a small fraction of miR190 may associate with a membrane, which could explain its distribution in various subcellular structures. Importantly, we did not observe enrichment of miR190 in the mitochondria or exosomes.

10) Figure 3h - Why would the miR223 levels increase if you remove mitochondria? Does CCCP also cause miR223 upregulation? I would have thought miR223 would just be mis-localized to the cytosol.

We report that the levels of cytoplasmic miR223 increase following the removal of mitochondria using CCCP treatment. While we cannot rule out the possibility that upregulation of miR223 is directly caused by CCCP treatment, we suggest that miR223 becomes mis-localized to the cytosol upon mitochondrial removal. Our data suggests that mitochondria contribute to the secretion of miR223 into exosomes. When mitochondria are removed by mitophagy, cytosolic miR223 is not efficiently secreted, which provides an alternative explanation for the observed increase in miR223 level after mitochondrial removal.

11) Figure 3i - What is the meaning of "Urd" in the figure label? This isn't mentioned anywhere.

“Urd” represents Uridine. Uridine is now spelled out in figure 3i. The absence of mitochondria can impact the function of the mitochondrial enzyme dihydroorotate dehydrogenase, which plays a role in pyrimidine synthesis. To address this issue, one approach is to supplement the cell culture medium with Urd. A previous study demonstrated that primary fibroblasts showed positive responses when Urd was added to the cell culture medium, resulting in improved cell viability for extended periods of time (Correia-Melo et al., 2017).

12) Figure 3j - The data is presented as a ratio of EV/cell. Again, this inaccurately represents the amount of miR223 in the EV. This issue is apparent when looking at Figures 3h and 3j. In 3h, CCCP causes an upregulation of intracellular miR223. As such, the presumed decrease in EV miR233 after CCCP (3j) could be an artifact due to increased levels of intracellular miR223. Both intracellular and EV levels of miRs need to be shown.

Both the intracellular and exosomal levels of miR223 have been included in Figure 3j.

13) In Figure 4, the authors show that when overexpressed, YBX1 will pulldown YBAP1. Can the authors comment as to why none of the earlier purifications show this finding (Figure 1 for example)? Even more curious is that when YBAP1 is purified, YBX1 does not co-purify (Figure 4 supplement 1a, b).

In Figure 4a-b, human YBX1 fused with a Strep II tag was purified from 293T cells using Strep-Tactin® Sepharose® resin in a one-step purification process. Our data has shown that YBAP1 is expressed in 293T cells.

In Figure 1 and Figure 4 Supplement 1a, human YBX1 or YBAP1 fused with His and MBP tags were purified from insect cells using a three-step purification process involving Ni-NTA His-Pur resin, amylose resin, and Superdex-200 gel filtration chromatography.

One possibility is that human YBX1 or YBAP1 may not interact well with insect YBAP1 or YBX1, which could result in separate tagged forms of YBX1 or YBAP1 isolated from insect cells.

Another possibility is that the expression levels of insect YBAP1 and YBX1 may be too low. Consequently, tagged forms YBX1 or YBAP1 expressed in insect cells may copurify with partners not readily detected by Coomassie blue stain. However, in Figure 4 Supplement 1b, human YBX1 fused with His and MBP tags was co-expressed with non-tagged human YBAP1, and both bands of YBX1 and YBAP1 were visible on the Coomassie blue gel after purification using Ni-NTA His-Pur resin, amylose resin, and Superdex-200 gel filtration chromatography.

14) Figure 4f, g - The text associated with these figures is very confusing, as is the labeling for the input. Also, what is "miR223 Fold change" in this regard? Seeing as your IgG should not have IP'd anything, normalizing to IgG can amplify noise. As such, RIP assays are typically presented as % input or fold enrichment.

The RIP assay results have been calculated and presented as a % input in Figure 4g.

15) Figure 4h - The authors show binding between miR223 and YBAP1 however it is not clear how significant this binding is. There is more than a 30-fold difference in binding affinity between miR223 and YBX1 than between miR223 and YBAP1. Even more, when comparing the EMSAs and fraction bound from figures 1 and 2 to those of Figure 4h, the binding between miR223 and YBAP1 more closely resembles that of miR190 and YBX1, which the authors state is a non-binder of YBX1. The authors will need to reconcile these discrepancies.

We agree that the binding of YBAP and YBX1 differ quite significantly in the affinity of their interaction with miR223. It is difficult to draw conclusions from a comparison of the affinities of YBX1 for miR190 and YBAP1 for miR223. Nonetheless, a quantitative difference in the interaction of YBAP1 with miR223 and miR190 is apparent (Fig. 4 h, I, j) and we observed no enrichment miR190 in isolated mitochondria (Fig. 3 supplement 1a) whereas YBAP1 selectively IP’d miR223 from isolated mitochondria (Fig. 4 f and g).

16) Can the authors present the Kd values for EMSA data?

The Kd values for the EMSA data have been added to the respective figures.

17) Figure 5 - Does YBAP1-KO affect mitochondrial protein integrity or numbers?

We generated stable cell lines expressing 3xHA-GFP-OMP25 in both 293T WT and YBAP1-KO cells, but we did not observe any alterations in mitochondrial morphology (Author response image 1).

Author response image 1.

Additionally, we performed a comparison of different mitochondrial markers using immunoblot in 293T WT cells and YBAP1-KO cells and did not observe any changes in these markers (data has been included in Figure 5b.).

18) Figure 6a - Are the authors using YBAP1 as their mitochondrial marker? Please include TOM20 and/or 22.

In Figure 4c and 4e, our data clearly demonstrate that the majority of YBAP1 is localized in the mitochondria.

To further validate this localization, we performed immunofluorescence staining using antibodies against endogenous Tom20 and YBX1. The immunofluorescence images document YBX1 associated with mitochondria (Author response image 2 and new Fig 6a.).

Author response image 2.

19) Figure 6b - Rab5 is an early endosome marker and may not fully represent the organelles that become MVBs. Co-localization at this point does not suggest that associating proteins will be present in the exosome, and it is possible that the authors are looking at the precursor of a recycling endosome. Even more, exosome loading does not occur at the early endosome, but instead at the MVB. Perhaps looking at markers of the late endosome such as Rab7 or ideally markers of the MVB such as M6P or CD63 would help draw an association between YBX1, YBAP1, and the exosome. Also, If the authors want to make the claim that interactions at the early endosome leads to secretion as an exosome, the authors should show that isolated EVs from Rab5Q79L-expressing cells contain miR223.

We have previously used overexpressed Rab5(Q79L) to monitor the localization of exosomal content, specifically CD63 and YBX1, in enlarged endosomes (Liu et al. 2021, Fig. 4A, B). These endosomes exhibit a mixture of early and late endocytic markers, including CD63. (Wegner et al., 2010). Hence, the presence of Rab5(Q79L)-positive enlarged endosomes does not solely indicate early endosomes.

20) The mentioning of P-bodies is interesting but at no time is an association addressed. This is therefore an overly speculative conclusion. Either show an association or leave this out of the manuscript.

In a previous paper we demonstrated that YBX1 puncta colocalize with P-body markers EDC4, Dcp1 and DDX6 (Liu et al., 2021).

21) In lines 55-58, the authors make the comment "However, many of these studies used sedimentation at ~100,000 g to collect EVs, which may also collect RNP particles not enclosed within membranes which complicates the interpretation of these data." Do RNPs not dissolve when secreted? Can the authors give a reference for this statement?

In a previous paper, we demonstrated that the RNP Ago2 does not dissolve in the conditioned medium and is not in vesicles but sediments to the bottom of a density gradient (Temoche-Diaz et al., 2019).

Author Response

Reviewer #1 (Public Review):

High resolution mechanistic studies would be instrumental in driving the development of Cas7-11 based biotechnology applications. This work is unfortunately overshadowed by a recent Cell publication (PMID: 35643083) describing the same Cas7-11 RNA-protein complex. However, given the tremendous interest in these systems, it is my opinion that this independent study will still be well cited, if presented well. The authors obviously have been trying to establish a unique angle for their story, by probing deeper into the mechanism of crRNA processing and target RNA cleavage. The study is carried out rigorously. The current version of the manuscript appears to have been rushed out. It would benefit from clarification and text polishing.

We thank the reviewer for the positive and helpful comments that have made the manuscript more impactful.

To summarize the revisions, we have resolved the metal-dependence issue, updated the maps in both main and supplementary figures that support the model, re-organized the labels for clarity, and added the comparison between our and Kato et al.’ structures.

In addition, we describe a new result with an isolated C7L.1 fragment that retains the processing and crRNA binding activities.

Reviewer #2 (Public Review):

In this manuscript, Gowswami et al. solved a cryo-EM structure of Desulfonema ishimotonii Cas7-11 (DiCas7-11) bound to a guiding CRISPR RNA (crRNA) and target RNA. Cas7-11 is of interest due to its unusual architecture as a single polypeptide, in contrast to other type III CRISPR-Cas effectors that are composed of several different protein subunits. The authors have obtained a high-quality cryo-EM map at 2.82 angstrom resolution, allowing them to build a structural model for the protein, crRNA and target RNA. The authors used the structure to clearly identify a catalytic histidine residue in the Cas7-11 Cas7.1 domain that is important for crRNA processing activity. The authors also investigated the effects of metal ions and crRNAtarget base pairing on target RNA cleavage. Finally, the authors used their structure to guide engineering of a compact version of Cas7-11 in which an insertion domain that is disordered in the cryo-EM map was removed. This compact Cas7-11 appears to have comparable cleavage activity to the full-length protein.

The cryo-EM map presented in this manuscript is generally of high quality and the manuscript is very well illustrated. However, some of the map interpretation requires clarification (outlined below). This structure will be valuable as there is significant interest in DiCas7-11 for biotechnology. Indeed, the authors have begun to engineer the protein based on observations from the structure. Although characterization of this engineered Cas7-11 is limited in this study and similar engineering was also performed in a recently published paper (PMID 35643083), this proof-of-principle experiment demonstrates the importance of having such structural information.

The biochemistry experiments presented in the study identify an important residue for crRNA processing, and suggest that target RNA cleavage is not fully metal-ion dependent. Most of these conclusions are based on straightforward structure-function experiments. However, some results related to target RNA cleavage are difficult to interpret as presented. Overall, while the cryo-EM data presented in this work is of high quality, both the structural model and the biochemical results require further clarification as outlined below.

We thank the reviewer for the positive and helpful comments that have made the manuscript more impactful.

To summarize the revisions, we have resolved the metal-dependence issue, updated the maps in both main and supplementary figures that support the model, re-organized the labels for clarity, and added the comparison between our and Kato et al.’ structures.

In addition, we describe a new result with an isolated C7L.1 fragment that retains the processing and crRNA binding activities.

1. The DiCas7-11 structure bound to target RNA was also recently reported by Kato et al. (PMID 35643083). The authors have not cited this work or compared the two structures. While the structures are likely quite similar, it is notable that the structure reported in the current paper is for the wild-type protein and the sample was prepared under reactive conditions, resulting in a partially cleaved target. Kato et al. used a catalytically dead version of Cas7-11 in which the target RNA should remain fully intact. Are there differences in the Cas7-11 structure observed in the presence of a partially cleaved target RNA in comparison to the Kato et al. structure? Such a comparison is appropriate given the similarities between the two reports. A figure comparing the two structures could be included in the manuscript.

We have added a paragraph on page 12 that describe the differences in preparation of the two complexes and their structures. We observed minor differences in the overall protein structure (r.m.s.d. 0.918 Å for 8114 atoms) but did observe quite different interactions between the protein and the first 5’-tag nucleotide (U(-15) vs. G(-15)) due to the different constructs in pre-crRNA, which suggests an importance of U(-15) in forming the processing-competent active site. We added Figure 2-figure supplementary 3 that illustrates the similarities and the differences.

2.The cryo-EM density map is of high quality, but some of the structural model is not fully supported by the experimental data (e.g. protein loops from the alphafold model were not removed despite lack of cryo-EM density). Most importantly, there is little density for the target RNA beyond the site 1 cleavage site, suggesting that the RNA was cleaved and the product was released. However, this region of the RNA was included in the structural model. It is unclear what density this region of the target RNA model was based on. Further discussion of the interpretation of the partially cleaved target RNA is necessary. Were 3D classes observed in various states of RNA cleavage and with varied density for the product RNAs?

We should have made it clear in the Method that multiple maps were used in building the structure but only submitted the post-processed map to reviewers. When using the Relion 4.0’s local resolution estimation-generated map, we observed sufficient density for some of the regions the reviewer is referring to. For instance, the site 1 cleavage density does support the model for the two nucleotides beyond site 1 cleavage site (see the revised Figure 1 & Figure 1- figure supplement 3).

However, there are protein loops that remain lack of convincing density. These include 134141 and 1316-1329 that are now removed from the final coordinate.

The “partially cleaved target RNA” phrase is a result of weak density for nucleotides downstream of site 1 (+2 and +3) but clear density flanking site 2. This feature indicates that cleavage likely had taken place at site 1 but not site 2 in most of the particles went into the reconstruction. To further clarify this phrase, we added “The PFS region plus the first base paired nucleotide (+1*) are not observed.” on page 4 and better indicate which nucleotides are or are not built in our model in Figure 1.

1. The authors argue that site 1 cleavage of target RNA is independent of metal ions. This is a potentially interesting result, but it is difficult to determine whether it is supported by the evidence provided in the manuscript. The Methods section only describes a buffer containing 10 mM MgCl2, but does not describe conditions containing EDTA. How much EDTA was added and was MgCl2 omitted from these samples? In addition, it is unclear whether the site 1 product is visible in Figures 2d and 3d. To my eye, the products that are present in the EDTA conditions on these gels migrated slightly slower than the typical site 1 product. This may suggest an alternate cleavage site or chemistry (e.g. cyclic phosphate is maintained following cleavage). Further experimental details and potentially additional experiments are required to fully support the conclusion that site 1 cleavage may be metal independent.

As we pointed out in response to Reviewer 1’s #8 comment, this conclusion may have been a result of using an older batch of DiCas7-11 that contains degraded fragments.

As shown in the attached figure below, “batch Y” was an older prep from our in-house clone and “batch X” is a newer prep from the Addgene purchased clone (gel on right), and they consistently produce metal-independent (batch Y) or metal-dependent (batch X) cleavage (gel on left). It is possible that the degraded fragments in batch Y carry a metal-independent cleavage activity that is absent in the more pure batch X.

We further performed mass spectrometry analysis of two of the degraded fragments from batch Y (indicated by arrows below) and discovered that these are indeed part of DiCas7-11. We, however, cannot rationalize, without more experimental evidence, why these fragments might have generated metal-independent cleavage at site 1. Therefore, we simply updated all our cleavage results from the new and cleaner prep (batch X) (For instance, Figure 3c). As a result, all references to “metal-independence” were removed.

With regard to the nature of cleaved products, we found both sites could be inhibited by specific 2’-deoxy modifications, consistent with the previous observation that Type III systems generate a 2’, 3’-cyclic product in spite of the metal dependence (for instance, see Hale, C. R., Zhao, P., Olson, S., Duff, M. O., Graveley, B. R., Wells, L., ... & Terns, M. P. (2009). RNA-guided RNA cleavage by a CRISPR RNA-Cas protein complex. Cell, 139(5), 945-956.)

We added this rationale based on the new results and believe that these characterizations are now thorough and conclusive

1. The authors performed an experiment investigating the importance of crRNA-target base pairing on cleavage activity (Figure 3e). However, negative controls for the RNA targets in the absence of crRNA and Cas7-11 were not included in this experiment, making it impossible to determine which bands on the gel correspond to substrates and which correspond to products. This result is therefore not interpretable by the reader and does not support the conclusions drawn by the authors.

Our original gel image (below) does contain these controls but we did not include them for the figure due to space considerations (we should have included it as a supplementary figure). We have now completely updated Figure 3e with much better quality and controls. Both the older and the updated experiments show the same results.

Original gel for Figure 3e containing controls.

Author Response

Reviewer #1 (Public Review):

The work by Yijun Zhang and Zhimin He at al. analyzes the role of HDAC3 within DC subsets. Using an inducible ERT2-cre mouse model they observe the dependency of pDCs but not cDCs on HDAC3. The requirement of this histone modifier appears to be early during development around the CLP stage. Tamoxifen treated mice lack almost all pDCs besides lymphoid progenitors. Through bulk RNA seq experiment the authors identify multiple DC specific target gens within the remaining pDCs and further using Cut and Tag technology they validate some of the identified targets of HDAC3. Collectively the study is well executed and shows the requirement of HDAC3 on pDCs but not cDCs, in line with the recent findings of a lymphoid origin of pDC.

1) While the authors provide extensive data on the requirement of HDAC3 within progenitors, the high expression of HDAC3 in mature pDCs may underly a functional requirement. Have you tested INF production in CD11c cre pDCs? Are there transcriptional differences between pDCs from HDAC CD11c cre and WT mice?

We greatly appreciate the reviewer’s point. We have confirmed that Hdac3 can be efficiently deleted in pDCs of Hdac3fl/fl-CD11c Cre mice (Figure 5-figure supplement 1 in revised manuscript). Furthermore, in those Hdac3fl/fl-CD11c Cre mice, we have observed significantly decreased expression of key cytokines (Ifna, Ifnb, and Ifnl) by pDCs upon activation by CpG ODN (shown in Author response image 1). Therefore, HDAC3 is also required for proper pDC function. However, we have yet to conduct RNA-seq analysis comparing pDCs from HDAC CD11c cre and WT mice.

Author response image 1.

Cytokine expression in Hdac3 deficient pDCs upon activation

2) A more detailed characterization of the progenitor compartment that is compromised following depletion would be important, as also suggested in the specific points.

We thank the reviewer for this constructive suggestion. We have performed thorough analysis of the phenotype of hematopoietic stem cells and progenitor cells at various developmental stages in the bone marrow of Hdac3 deficient mice, based on the gating strategy from the recommended reference. Briefly, we analyzed the subpopulations of progenitors based on the description in the published report by "Pietras et al. 2015", namely MPP2, MPP3 and MPP4, using the same gating strategy for hematopoietic stem/progenitor cells. As shown in Author response image 2 and Author response image 3, we found that the number of LSK cells was increased in Hdac3 deficient mice, especially the subpopulations of MPP2 and MPP3, whereas no significant changes in MPP4. In contrast, the numbers of LT-HSC, ST-HSC and CLP were all dramatically decreased. This result has been optimized and added as Figure 3A in revised manuscript. The relevant description has been added and underlined in the revised manuscript Page 6 Line 164-168.

Author response image 2.

Gating strategy for hematopoietic stem/progenitor cells in bone marrow.

Author response image 3.

Hematopoietic stem/progenitor cells in Hdac3 deficient mice

Reviewer #2 (Public Review):

In this article Zhang et al. report that the Histone Deacetylase-3 (HDAC3) is highly expressed in mouse pDC and that pDC development is severely affected both in vivo and in vitro when using mice harbouring conditional deletion of HDAC3. However, pDC numbers are not affected in Hdac3fl/fl Itgax-Cre mice, indicating that HDCA3 is dispensable in CD11c+ late stages of pDC differentiation. Indeed, the authors provide wide experimental evidence for a role of HDAC3 in early precursors of pDC development, by combining adoptive transfer, gene expression profiling and in vitro differentiation experiments. Mechanistically, the authors have demonstrated that HDAC3 activity represses the expression of several transcription factors promoting cDC1 development, thus allowing the expression of genes involved in pDC development. In conclusion, these findings reveals HDAC3 as a key epigenetic regulator of the expression of the transcription factors required for pDC vs cDC1 developmental fate.

These results are novel and very promising. However, supplementary information and eventual further investigations are required to improve the clarity and the robustness of this article.

Major points

1) The gating strategy adopted to identify pDC in the BM and in the spleen should be entirely described and shown, at least as a Supplementary Figure. For the BM the authors indicate in the M & M section that they negatively selected cells for CD8a and B220, but both markers are actually expressed by differentiated pDC. However, in the Figures 1 and 2 pDC has been shown to be gated on CD19- CD11b- CD11c+. What is the precise protocol followed for pDC gating in the different organs and experiments?

We apologize for not clearly describing the protocols used in this study. Please see the detailed gating strategy for pDC in bone marrow, and for pDC and cDC in spleen (Figure 4 and Figure 5). These information are now added to Figure1−figure supplement 3, The relevant description has been underlined in Page 5 Line 113-116, in revised manuscript.

We would like to clarify that in our study, we used two different panels of antibody cocktails, one for bone marrow Lin- cells, including mAbs to CD2/CD3/TER-119/Ly6G/B220/CD11b/CD8/CD19; the other for DC enrichment, including mAbs to CD3/CD90/TER-119/Ly6G/CD19. We included B220 in the Lineage cocktails to deplete B cells and pDCs, in order to enrich for the progenitor cells from bone marrow. However, when enriching for the pDC and cDC, B220 or CD8a were not included in the cocktail to avoid depletion of pDC and cDC1 subsets . For the flow cytometry analysis of pDCs, we gated pDCs as the CD19−CD11b−CD11c+B220+SiglecH+ population in both bone marrow and spleen. The relevant description has been underlined in the revised manuscript Page 16 Line 431-434.

2) pDC identified in the BM as SiglecH+ B220+ can actually contain DC precursors, that can express these markers, too. This could explain why the impact of HDAC3 deletion appears stronger in the spleen than in the BM (Figures 1A and 2A). Along the same line, I think that it would important to show the phenotype of pDC in control vs HDAC3-deleted mice for the different pDC markers used (SiglecH, B220, Bst2) and I would suggest to include also Ly6D, taking also in account the results obtained in Figures 4 and 7. Finally, as HDCA3 deletion induces downregulation of CD8a in cDC1 and pDC express CD8a, it would important to analyse the expression of this marker on control vs HDAC3-deleted pDC.

We agree with the reviewer’s points. In the revised manuscript, we incorporated major surface markers, including Siglec H, B220, Ly6D, and PDCA-1, all of which consistently demonstrated a substantial decrease in the pDC population in Hdac3 deficient mice. Moreover, we did notice that Ly6D+ pDCs showed higher degree of decrease in Hdac3 deficient mice. Additionally, percentage and number of both CD8+ pDC and CD8- pDC were decreased in Hdac3 deficient mice (Author response image 4). These results are shown in Figure1−figure supplement 4 of the revised manuscript. The relevant description has been added and underlined in the revised manuscript Page 5 Line 121-125.

Author response image 4.

Bone marrow pDCs in Hdac3 deficient mice revealed by multiple surface markers

3) How do the authors explain that in the absence of HDAC3 cDC2 development increased in vivo in chimeric mice, but reduced in vitro (Figures 2B and 2E)?

As shown in the response to the Minor point 5 of Reviewer#1. Briefly, we suggested that the variabilities maybe explained by the timing of anaysis after HDAC3 deletion. In Figure 2C, we analyzed cells from the recipients one week after the final tamoxifen treatment and observed no significant change in the percentage of cDC2 when further pooled all the experiment data. In Figure 2E, where tamoxifen was administered at Day 0 in Flt3L-mediated DC differentiation in vitro, the DC subsets generated were then analyzed at different time points. We observed no significant changes in cDCs and cDC2 at Day 5, but decreases in the percentage of cDC2 were observed at Day 7 and Day 9. This suggested that the cDC subsets at Day 5 might have originated from progenitors at a later stage, while those at Day 7 and Day 9 might originate form the earlier progenitors. Therefore, based on these in vitro and in vivo experiments, we believe that the variation in the cDC2 phenotype might be attributed to the progenitors at different stages that generated these cDCs.

4) More generally, as reported also by authors (line 207), the reconstitution with HDAC3-deleted cells is poorly efficient. Although cDC seem not to be impacted, are other lymphoid or myeloid cells affected? This should be expected as HDAC3 regulates T and B development, as well as macrophage function. This should be important to know, although this does not call into question the results shown, as obtained in a competitive context.

In this study, we found no significant influence on T cells, mature B cells or NK cells, but immature B cells were significantly decreased, in Hdac3-ERT2-Cre mice after tamoxifen treatment (Figure 6). However, in the bone marrow chimera experiments, the numbers of major lymphoid cells were decreased due to the impaired reconstitution capacity of Hdac3 deficient progenitors. Consistent with our finding, it has been reported that HDAC3 was required for T cell and B cell generation, in HDAC3-VavCre mice (Summers et al., 2013), and was necessary for T cell maturation (Hsu et al., 2015). Moreover, HDAC3 is also required for the expression of inflammatory genes in macrophages upon activation (Chen et al., 2012; Nguyen et al., 2020).

5) What are the precise gating strategies used to identify the different hematopoietic precursors in the Figure 4 ? In particular, is there any lineage exclusion performed?

We apologize for not describing the experimental procedures clearly. In this study we enriched the lineage negative (Lin−) cells from the bone marrow using a Lineage-depleting antibody cocktail including mAbs to CD2/CD3/TER-119/Ly6G/B220/CD11b/CD8/CD19. We also provide the gating strategy implemented for sorting LSK and CDP populations from the Lin− cells in the bone marrow (Author response image 5), shown in the Figure 3A and Figure4−figure supplement 1 of revised manuscript.

Author response image 5.

Gating strategy for LSK, CD115+ CDP and CD115− CDP in bone marrow

6) Moreover, what is the SiglecH+ CD11c- population appearing in the spleen of mice reconstituted with HDAC3-deleted CDP, in Fig 4D?

We also noticed the appearance of a SiglecH+CD11c− cell population in the spleen of recipient mice reconstituted with HDAC3-deficient CD115−CDPs, while the presence of this population was not as significant in the HDAC3-Ctrl group, as shown in Figure 4D. We speculate that this SiglecH+CD11c− cell population might represent some cells at a differentiation stage earlier than pre-DCs. Alternatively, the relatively increased percentage of this population derived from HDAC3-deficient CD115−CDP might be due to the substantially decreased total numbers of DCs. This could be clarified by further analysis using additional cell surface markers.

7) Finally, in Fig 4H, how do the authors explain that Hdac3fl/fl express Il7r, while they are supposed to be sorted CD127- cells?

This is indeed an interesting question. In this study, we confirmed that CD115−CDPs were isolated from the surface CD127− cell population for RNA-seq analysis, and the purity of the sorted cells were checked (Author response image 6), as shown in Figure4−figure supplement 1 in revised manuscript.

The possible explanation for the expression of Il7r mRNA in some HDAC3fl/fl CD115−CDPs, as revealed in Figure 4H by RNA-seq analysis, could be due to a very low level of cell surface expression of CD127, these cells therefore could not be efficiently excluded by sorting for surface CD127- cells.

Author response image 6.

CD115−CDPs sorting from Hdac3-Ctrl and Hdac3-KO mice

8) What is known about the expression of HDAC3 in the different hematopoietic precursors analysed in this study? This information is available only for a few of them in Supplementary Figure 1. If not yet studied, they should be addressed.

We conducted additional analysis to address the expression of Hdac3 in various hematopoietic progenitor cells at different stages, based on the RNA-seq analyis. The data revealed a relatively consistent level of Hdac3 expression in progenitor populations, including HSC, MMP4, CLP, CDP and BM pDCs (Author response image 7). That suggests that HDAC3 may play an important role in the regulation of hematopoiesis at multiple stages. This information is now added in Figure1−figure supplement 1B of revised manuscript.

Author response image 7.

Hdac3 expression in hematopoietic progenitor cells

9) It would be highly informative to extend CUT and Tag studies to Irf8 and Tcf4, if this is technically feasible.

We totally agree with the reviewer. We have indeed attempted using CUT and Tag study to compare the binding sites of IRF8 and TCF4 in wild-type and Hdac3-deficient pDCs. However, it proved that this is technically unfeasible to get reliable results due to the limited number of cells we could obtain from the HDAC3 deficient mice. We are committed to explore alternative approaches or technologies in future studies to address this issue.

Author Response

Reviewer #1 (Public Review):

The manuscript by Lujan and colleagues describes a series of cellular phenotypes associated with the depletion of TANGO2, a poorly characterized gene product but relevant to neurological and muscular disorders. The authors report that TANGO2 associates with membrane-bound organelles, mainly mitochondria, impacting in lipid metabolism and the accumulation of reactive-oxygen species. Based on these observations the authors speculate that TANGO2 function in Acyl-CoA metabolism.

The observations are generally convincing and most of the conclusions appear logical. While the function of TANGO2 remains unclear, the finding that it interferes with lipid metabolism is novel and important. This observation was not developed to a great extent and based on the data presented, the link between TANGO2 and acyl-CoA, as proposed by the authors, appears rather speculative.

We thank you for your advice and now include additional data that lends support to the role of TANGO2 in lipid metabolism. We have changed the title accordingly.

1) The data with overexpressed TANGO2 looks convincing but I wonder if the authors analyzed the localization of endogenous TANGO2 by immunofluorescence using the antibody described in Figure S2. The idea that TANGO2 localizes to membrane contact sites between mitochondria and the ER and LDs would also be strengthened by experiments including multiple organelle markers.

We agree that most of the data on TANGO2 localization are based on the overexpression of the protein. As suggested by the reviewer and despite the lack of commercial antibodies for immunofluorescence-based evaluation, see the following chart, we tested the commercial antibody described in Figure 2 on HepG2 and U2OS cells. Moreover, we used Förster resonance energy transfer (FRET) technology to analyze the proximity of TANGO2 and Tom20, a specific outer mitochondrial membrane protein. In addition, we visualized cells expressing tagged TANGO2 and tagged VAP-B, an integral ER protein in the mitochondria-associated membranes (doi:10.1093/hmg/ddr559) or tagged TANGO2 and tagged GPAT4-Hairpin, an integral LD protein (doi:10.1016/j.devcel.2013.01.013). These data strengthen our proposal and are presented in the revised manuscript.

As suggested by the reviewer, we have also visualized two additional cell lines (HepG2 and U2OS) with the anti-TANGO2( from Novus Biologicals) that have been used for western blot (see chart above). As shown in the following figure, the commercial antibody shows a lot of staining in addition to mitochondria, especially in U2OS cells, where it also appears to label the nucleus.

2) The changes in LD size in TANGO2-depleted cells are very interesting and consistent with the role of TANGO2 in lipid metabolism. From the lipidomics analysis, it seems that the relative levels of the main neutral lipids in TANGO2-depleted cells remain unaltered (TAG) or even decrease (CE). Therefore, it would be interesting to explore further the increase in LD size for example analyze/display the absolute levels of neutral lipids in the various conditions.

We agree with the reviewer and now present the absolute levels of lipids of interest in the various conditions of the lipidomics analyses (Figure S 3).

3) Most of the lipidomics changes in TANGO2-depleted cells are observed in lipid species present in very low amounts while the relative abundance of major phospholipids (PC, PE PI) remains mostly unchanged. It would be good to also display the absolute levels of the various lipids analyzed. This is an important point to clarify as it would be unlikely that these major phospholipids are unaffected by an overall defect in Acyl-CoA metabolism, as proposed by the authors.

As stated above, we have now included the absolute levels of lipids of interest in the various conditions of the lipidomics analyses (Figure S 3).

Author Response

Reviewer #3: (Public Review):

In this ms Li et al. examine the molecular interaction of Rabphilin 3A with the SNARE complex protein SNAP25 and its potential impact in SNARE complex assembly and dense core vesicle fusion.

Overall the literature of rabphilin as a major rab3/27effector on synaptic function has been quite enigmatic. After its cloning and initial biochemical analysis, rather little new has been found about rabphilin, in particular since loss of function analysis has shown rather little synaptic phenotypes (Schluter 1999, Deak 2006), arguing against that rabphilin plays a crucial role in synaptic function.

While the interaction of rabphilin to SNAP25 via its bottom part of the C2 domain has been already described biochemically and structurally in the Deak et al. 2006, and others, the authors make significant efforts to further map the interactions between SNAP25 and rabphilin and indeed identified additional binding motifs in the first 10 amino acids of SNAP25 that appear critical for the rabphilin interaction.

Using KD-rescue experiments for SNAP25, in TIRF based imaging analysis of labeled dense core vesicles showed that the N-terminus of SN25 is absolutely essential for SV membrane proximity and release. Similar, somewhat weaker phenotypes were observed when binding deficient rabphilin mutants were overexpressed in PC12 cells coexpressing WT rabphilin. The loss of function phenotypes in the SN25 and rabphilin interaction mutants made the authors to claim that rabphilin-SN25 interactions are critical for docking and exocytosis. The role of these interaction sites were subsequently tested in SNARE assembly assays, which were largely supportive of rabphilin accelerating SNARE assembly in a SN25 -terminal dependent way.

Regarding the impact of this work, the transition of synaptic vesicles to form fusion competent trans-SNARE complex is very critical in our understanding of regulated vesicle exocytosis, and the authors put forward an attractive model forward in which rabphilin aids in catalyzing the SNARE complex assembly by controlling SNAP25 a-helicalicity of the SNARE motif. This would provide here a similar regulatory mechanism as put forward for the other two SNARE proteins via their interactions with Munc18 and intersection, respectively.

We thank the reviewer #3 for the summary of the paper and for the praise of our work. The point-to-point replies are as follow:

While discovery of the novel interaction site of rabphilin with the N-Terminus of SNAP25 is interesting, I have issues with the functional experiments. The key reliance of the paper is whether it provides convincing data on the functional role of the interactions, given the history of loss of function phenotypes for Rabphilin. First, the authors use PC12 cells and dense core vesicle docking and fusion assays. Primary neurons, where rabphilin function has been tested before, has unfortunately not been utilized, reducing the impact of docking and fusion phenotype.

We have discussed these questions as mentioned in our response to Essential Revisions 3 and added this corresponding passage to the Discussion section (pp.18-19, lines 407-427).

In particular the loss of function phenotype in figure 3 of the n-terminally deleted SNAP25 in docking and fusion is profound, and at a similar level than the complete loss of the SNARE protein itself. This is of concern as this is in stark contrast to the phenotype of rabphilin loss in mammalian neurons where the phenotype of SNAP25 loss is very severe while rabphilin loss has almost no effect on secretion. This would argue that the N-terminal of SNAPP25 has other critical functions besides interacting with rabphilin. In addition, it could argue that the n-Terminal SNAP25 deletion mutant may be made in the cell (as indicated from the western blot) but may not be properly trafficked to the site of release

To test whether the N-peptide deletion mutant of SN25 can properly target to the plasma membrane, we overexpressed the SN25 FL or SN25 (11–206) with C-terminal EGFP-tag in PC12 cells and monitored the localization of SN25 FL-EGFP and SN25 (11–206)-EGFP near the plasma membrane by TIRF microscopy. We observed that the average fluorescence intensity of SN25 (11–206)-EGFP showed no significant difference with SN25 FL-EGFP as below, suggesting that the N-peptide deletion mutant may not influence the trafficking of SN25 to plasma membrane.

(A) TIRF imaging assay to monitor the localization of SN25-EGFP near the plasma membrane. Overexpression of SN25 FL-EGFP (left) and SN25 (11–206)-EGFP (right) using pEGFP-N3 vector in PC12 cells. Scale bars, 10 μm. (B) Quantification of the average fluorescence intensity of SN25-EGFP near the plasma membrane in (A). Data are presented as mean ± SEM (n ≥ 10 cells in each). Statistical significance and P values were determined by Student’s t-test. ns, not significant.

Author Response

Reviewer #1 (Public Review):

In this manuscript, Abdellatef et al. describe the reconstitution of axonemal bending using polymerized microtubules (MTs), purified outer-arm dyneins, and synthesized DNA origami. Specifically, the authors purified axonemal dyneins from Chlamydomonas flagella and combined the purified motors with MTs polymerized from purified brain tubulin. Using electron microscopy, the authors demonstrate that patches of dynein motors of the same orientation at both MT ends (i.e., with their tails bound to the same MT) result in pairs of MTs of parallel alignment, while groups of dynein motors of opposite orientation at both MT ends (i.e., with the tails of the dynein motors of both groups bound to different MTs) result in pairs of MTs with anti-parallel alignment. The authors then show that the dynein motors can slide MTs apart following photolysis of caged ATP, and using optical tweezers, demonstrate active force generation of up to ~30 pN. Finally, the authors show that pairs of anti-parallel MTs exhibit bidirectional motion on the scale of ~50-100 nm when both MTs are cross-linked using DNA origami. The findings should be of interest for the cytoskeletal cell and biophysics communities.

We thank the reviewer for these comments.

We might be misunderstanding this reviewer’s comment, but the complexes with both parallel and anti-parallel MTs had dynein molecules with their tails bound to two different MTs in most cases, as illustrated in Fig.2 – suppl.1. The two groups of dyneins produce opposing forces in a complex with parallel MTs, and majority of our complexes had parallel arrangement of the MTs. To clarify the point, we have modified the Abstract:

“Electron microscopy (EM) showed pairs of parallel MTs crossbridged by patches of regularly arranged dynein molecules bound in two different orientations depending on which of the MTs their tails bind to. The oppositely oriented dyneins are expected to produce opposing forces when the pair of MTs have the same polarity.”

Reviewer #2 (Public Review):

Motile cilia generate rhythmic beating or rotational motion to drive cells or produce extracellular fluid flow. Cilia is made of nine microtubule doublets forming a spoke-like structure and it is known that dynein motor proteins, which connects adjacent microtubule doublet, are the driving force of ciliary motion. However the molecular mechanism to generate motion is still unclear. The authors proved that a pair of microtubules stably linked by DNA-origami and driven by outer dynein arms (ODA) causes beating motion. They employed in vitro motility assay and negative stain TEM to characterize this complex. They demonstrated stable linking of microtubules and ODAs anchored on the both microtubules are essential for oscillatory motion and bending of the microtubules.

Strength

This is an interesting work, addressing an important question in the motile cilia community: what is the minimum system to generate a beating motion? It is an established fact that dynein power stroke on the microtubule doublet is the driving force of the beating motion. It was also known that the radial spoke and the central pair are essential for ciliary motion under the physiological condition, but cilia without radial spokes and the central pair can beat under some special conditions (Yagi and Kamiya, 2000). Therefore in the mechanistic point of view, they are not prerequisite. It is generally thought that fixed connection between adjacent microtubules by nexin converts sliding motion of dyneins to bending, but it was never experimentally investigated. Here the authors successfully enabled a simple system of nexin-like inter-microtubule linkage using DNA origami technique to generate oscillatory and beating motions. This enables an interesting system where ODAs form groups, anchored on two microtubules, orienting oppositely and therefore cause tag-of-war type force generation. The authors demonstrated this system under constraints by DNA origami generates oscillatory and beating motions.

The authors carefully coordinated the experiments to demonstrate oscillations using optical tweezers and sophisticated data analysis (Fourier analysis and a step-finding algorithm). They also proved, using negative stain EM, that this system contains two groups of ODAs forming arrays with opposite polarity on the parallel microtubules. The manuscript is carefully organized with impressive movies. Geometrical and motility analyses of individual ODAs used for statistics are provided in the supplementary source files. They appropriately cited similar past works from Kamiya and Shingyoji groups (they employed systems closer to the physiological axoneme to reproduce beating) and clarify the differences from this study.

We thank the reviewer for these comments.

Weakness

The authors claim this system mimics two pairs of doublets at the opposite sites from 9+2 cilia structure by having two groups of ODAs between two microtubules facing opposite directions within the pair. It is not exactly the case. In the real axoneme, ODA makes continuous array along the entire length of doublets, which means at any point there are ODAs facing opposite directions. In their system, opposite ODAs cannot exist at the same point (therefore the scheme of Dynein-MT complex of Fig.1B is slightly misleading).

Actually, opposite ODAs can exist at the same point in our system as well, and previous work using much higher concentration of dyneins (e.g, Oda et al., J. Cell biol., 2007) showed two continuous arrays of dynein molecules between a pair of microtubules. To observe the structures of individual dynein molecules we used low concentrations of dynein and searched for the areas where dynein could be observed without superposition, but there were some areas where opposite dyneins existed at the same point.

We realize that we did not clearly explain this issue, so we have revised the text accordingly.

In the 1st paragraph of Results: “In the dynein-MT complexes prepared with high concentrations of dynein, a pair of MTs in bundles are crossbridged by two continuous arrays of dynein, so that superposition of two rows of dynein molecules is observed in EM images (Haimo et al., 1979; Oda et al., 2007). On the other hand, when a low concentration of the dynein preparation (6.25–12.5 µg/ml (corresponding to ~3-6 nM outer-arm dynein)) was mixed with 20-25 µg/ml MTs (200-250 nM tubulin dimers), the MTs were only partially decorated with dynein, so that we were able to observe single layers of crossbridges without superposition in many regions.” Legend of Fig. 1(C): “Note that the geometry of dyneins in the dynein-MT complex shown in (B) mimics that of a combination of the dyneins on two opposite sides of the axoneme (cyan boxes), although the dynein arrays in (B) are not continuous.”

If they want to project their result to the ciliary beating model, more insight/explanation would be necessary. For example, arrays of dyneins at certain positions within the long array along one doublet are activated and generate force, while dyneins at different positions are activated on another doublet at the opposite site of the axoneme. This makes the distribution of dyneins and their orientations similar to the system described in this work. Such a localized activation, shown in physiological cilia by Ishikawa and Nicastro groups, may require other regulatory proteins.

We agree that the distributions of activated dyneins in 3D are extremely important in understanding ciliary beating, and that other regulatory proteins would be required to coordinate activation in different places in an axoneme. However, the main goal of this manuscript is to show the minimal components for oscillatory movements, and we feel that discussing the distributions of activated dyneins along the length of the MTs would be too complicated and beyond the scope of this study.

They attempted to reveal conformational change of ODAs induced by power stroke using negative stain EM images, which is less convincing compared to the past cryo-ET works (Ishikawa, Nicastro, Pigino groups) and negative stain EM of sea urchin outer dyneins (Hirose group), where the tail and head parts were clearly defined from the 3D map or 2D averages of two-dynein ODAs. Probably three heavy chains and associated proteins hinder detailed visualization of the tail structure. Because of this, Fig.2C is not clear enough to prove conformational change of ODA. This reviewer imagines refined subaverage (probably with larger datasets) is necessary.

As the reviewer suggests, one of the reasons for less clear averaged images compared to the past images of sea urchin ODA is the three-headed structure of Chlamydomonas ODA. Another and perhaps the bigger reason is the difficulty of obtaining clear images of dynein molecules bound between 2 MTs by negative stain EM: the stain accumulates between MTs that are ~25 nm in diameter and obscures the features of smaller structures. We used cryo-EM with uranyl acetate staining instead of negative staining for the images of sea urchin ODA-MT complexes we previously published (Ueno et al., 2008) in order to visualize dynein stalks. We agree with the reviewer that future work with larger datasets and by cryo-ET is necessary for revealing structural differences.

That having been said, we did not mean to prove structural changes, but rather intended to show that our observation suggests structural changes and thus this system is useful for analyzing structural changes in future. In the revised manuscript, we have extensively modified the parts of the paper discussing structural changes (Please see our response to the next comment).

It is not clear, from the inset of Fig.2 supplement3, how to define the end of the tail for the length measurement, which is the basis for the authors to claim conformational change (Line263-265). The appearance of the tail would be altered, seen from even slightly different view angles. Comparison with 2D projection from apo- and nucleotide-bound 3-headed ODA structures from EM databank will help.

We agree with the reviewer that difference in the viewing angle affects the apparent length of a dynein molecule, although the 2 MTs crossbridged by dyneins lie on the carbon membrane and thus the variation in the viewing angle is expected to be relatively small. To examine how much the apparent length is affected by the view angle, we calculated 2D-projected images of the cryo-ET structures of Chlamydomonas axoneme (emd_1696 and emd_1697; Movassagh et al., 2010) with different view angles, and measured the apparent length of the dynein molecule using the same method we used for our negative-stain images (Author response image 1). As shown in the plot, the effect of view angles on the apparent lengths is smaller than the difference between the two nucleotide states in the range of 40 degrees measured here. Thus, we think that the length difference shown in Fig.2-suppl.4 reflects a real structural difference between no-ATP and ATP states. In addition, it would be reasonable to think that distributions of the view angles in the negative stain images are similar for both absence and presence of ATP, again supporting the conclusion.

Nevertheless, since we agree with the reviewer that we cannot measure the precise length of the molecule using these 2D images, we have revised the corresponding parts of the manuscript, adding description about the effect of view angles on the measured length in the manuscript.

Author response image 1. Effects of viewing angles on apparent length. (A) and (B) 2D-projected images of cryo-electron tomograms of Chlamydomonas outer arm dynein in an axoneme (Movassagh et al., 2010) viewed from different angles. (C) apparent length of the dynein molecule measured in 2D-projected images.

In this manuscript, we discuss two structural changes: 1) a difference in the dynein length between no-nucleotide and +ATP states (Fig.2-suppl.4), and 2) possible structural differences in the arrangement of the dynein heads (Fig.2-suppl.3). Although we realize that extensive analysis using cryo-ET is necessary for revealing the second structural change, we attempted to compare the structures of oppositely oriented dyneins, hoping that it would lead to future research. In the revised manuscript, we have added 2D projection images of emd_1696 and emd_1697 in Fig.2-suppl.3, so that the readers can compare them with our negative stain images. We had an impression that some of our 2D images in the presence of ATP resembled the cryo-ET structure with ADP.Vi, whereas some others appeared to be closer to the no-nucleotide cryo-ET structure. We have also attempted to calculate cross-correlations, but difficulties in removing the effect of MTs sometimes overlapped with a part of dynein, adjusting the magnifications and contrast of different images prevented us from obtaining reliable results.

To address this and the previous comments, we have extensively modified the section titled ‘Structures of dynein in the dynein-MT-DNA-origami complex’.

In Fig.5B (where the oscillation occurs), the microtubule was once driven >150nm unidirectionally and went back to the original position, before oscillation starts. Is it always the case that relatively long unidirectional motion and return precede oscillation? In Fig.7B, where the authors claim no oscillation happened, only one unidirectional motion was shown. Did oscillation not happen after MT returned to the original position?

Long unidirectional movement of ~150 nm was sometimes observed, but not necessarily before the start of oscillation. For example, in Figure 5 – figure supplement 1A, oscillation started soon after the UV flash, and then unidirectional movement occurred.

With the dynein-MT complex in which dyneins are unidirectionally aligned (Fig.7B, Fig.7-suppl.2), the MTs kept moving and escaped from the trap or just stopped moving probably due to depletion of ATP, so we did not see a MT returning to the original position.

Line284-290: More characterization of bending motion will be necessary (and should be possible). How high frequency is it? Do they confirm that other systems (either without DNA-origami or without ODAs arraying oppositely) cannot generate repetitive beating?

The frequencies of the bending motions measured from the movies in Fig.8 and Fig.8-suppl.1 were 0.6 – 1 Hz, and the motions were rather irregular. Even if there were complexes bending at high frequencies, it would not have been possible to detect them due to the low time resolution of these fluorescence microscopy experiments (~0.1 s). Future studies at a higher time resolution will be necessary for further characterization of bending motions.

To observe bending motions, the dynein-MT complex should be fixed to the glass or a bead at one part of the complex while the other end is free in solution. With the dynein-MT-DNA-origami complexes, we looked for such complexes and found some showing bending motions as in Fig. 8. To answer the reviewer’s question asking if we saw repetitive bending in other systems, we checked the movies of the complexes without DNA-origami or without ODAs arraying oppositely but did not notice any repetitive bending motions. However, future studies using the system with a higher temporal resolution and perhaps with an improved method for attaching the complex would be necessary in these cases as well.

Author Response

eLife assessment:

This study addresses whether the composition of the microbiota influences the intestinal colonization of encapsulated vs unencapsulated Bacteroides thetaiotaomicron, a resident micro-organism of the colon. This is an important question because factors determining the colonization of gut bacteria remain a critical barrier in translating microbiome research into new bacterial cell-based therapies. To answer the question, the authors develop an innovative method to quantify B. theta population bottlenecks during intestinal colonization in the setting of different microbiota. Their main finding that the colonization defect of an acapsular mutant is dependent on the composition of the microbiota is valuable and this observation suggests that interactions between gut bacteria explains why the mutant has a colonization defect. The evidence supporting this claim is currently insufficient. Additionally, some of the analyses and claims are compromised because the authors do not fully explain their data and the number of animals is sometimes very small.

Thank you for this frank evaluation. Based on the Reviewers’ comments, the points raised have been addressed by improving the writing (apologies for insufficient clarity), and by the addition of data that to a large extent already existed or could be rapidly generated. In particularly the following data has been added:

1. Increase to n>=7 for all fecal time-course experiments

2. Microbiota composition analysis for all mouse lines used

3. Data elucidating mechanisms of SPF microbiome/ host immune mechanisms restriction of acapsular B. theta

4. Short- versus long-term recolonization of germ-free mice with a complete SPF microbiota and assessment of the effect on B. theta colonization probability.

5. Challenge of B. theta monocolonized mice with avirulent Salmonella to disentangle effects of the host inflammatory response from other potential explanations of the observations.

6. Details of all inocula used

7. Resequencing of all barcoded strains

Additionally, we have improved the clarity of the text, particularly the methods section describing mathematical modeling in the main text. Major changes in the text and particularly those replying to reviewers comment have been highlighted here and in the manuscript.

Reviewer #1 (Public Review):

The study addresses an important question - how the composition of the microbiota influences the intestinal colonization of encapsulated vs unencapsulated B. theta, an important commensal organism. To answer the question, the authors develop a refurbished WITS with extended mathematical modeling to quantify B. theta population bottlenecks during intestinal colonization in the setting of different microbiota. Interestingly, they show that the colonization defect of an acapsular mutant is dependent on the composition of the microbiota, suggesting (but not proving) that interactions between gut bacteria, rather than with host immune mechanisms, explains why the mutant has a colonization defect. However, it is fairly difficult to evaluate some of the claims because experimental details are not easy to find and the number of animals is very small. Furthermore, some of the analyses and claims are compromised because the authors do not fully explain their data; for example, leaving out the zero values in Fig. 3 and not integrating the effect of bottlenecks into the resulting model, undermines the claim that the acapsular mutant has a longer in vivo lag phase.

We thank the reviewer for taking time to give this details critique of our work, and apologies that the experimental details were insufficiently explained. This criticism is well taken. Exact inoculum details for experiment are now present in each figure (or as a supplement when multiple inocula are included). Exact microbiome composition analysis for OligoMM12, LCM and SPF microbiota is now included in Figure 2 – Figure supplement 1.

Of course, the models could be expanded to include more factors, but I think this comment is rather based on the data being insufficiently clearly explained by us. There are no “zero values missing” from Fig. 3 – this is visible in the submitted raw data table (excel file Source Data 1), but the points are fully overlapped in the graph shown and therefore not easily discernable from one another. Time-points where no CFU were recovered were plotted at a detection limit of CFU (50 CFU/g) and are included in the curve-fitting. However, on re-examination we noticed that the curve fit was carried out on the raw-data and not the log-normalized data which resulted in over-weighting of the higher values. Re-fitting this data does not change the conclusions but provides a better fit. These experiments have now been repeated such that we now have >=7 animals in each group. This new data is presented in Fig. 3C and D and Fig. 3 Supplement 2.

Limitations:

1) The experiments do not allow clear separation of effects derived from the microbiota composition and those that occur secondary to host development without a microbiota or with a different microbiota. Furthermore, the measured bottlenecks are very similar in LCM and Oligo mice, even though these microbiotas differ in complexity. Oligo-MM12 was originally developed and described to confer resistance to Salmonella colonization, suggesting that it should tighten the bottleneck. Overall, an add-back experiment demonstrating that conventionalizing germ-free mice imparts a similar bottleneck to SPF would strengthen the conclusions.

These are excellent suggestions and have been followed. Additional data is now presented in Figure 2 – figure supplement 8 showing short, versus long-term recolonization of germ-free mice with an SPF microbiota and recovering very similar values of beta, to our standard SPF mouse colony. These data demonstrate a larger total niche size for B. theta at 2 days post-colonization which normalizes by 2 weeks post-colonization. Independent of this, the colonization probability, is already equivalent to that observed in our SPF colony at day 2 post-colonization. Therefore, the mechanisms causing early clonal loss are very rapidly established on colonization of a germ-free mouse with an SPF microbiota. We have additionally demonstrated that SPF mice do not have detectable intestinal antibody titers specific for acapsular B. theta. (Figure 2 – figure supplement 7), such that this is unlikely to be part of the reason why acapsular B. theta struggles to colonize at all in the context of an SPF microbiota. Experiments were also carried to detect bacteriophage capable of inducing lysis of B. theta and acapsular B. theta from SPF mouse cecal content (Figure 2 – figure supplement 7). No lytic phage plaques were observed. However, plaque assays are not sensitive for detection of weakly lytic phage, or phage that may require expression of surface structures that are not induced in vitro. We can therefore conclude that the restrictive activity of the SPF microbiota is a) reconstituted very fast in germ-free mice, b) is very likely not related to the activity of intestinal IgA and c) cannot be attributed to a high abundance of strongly lytic bacteriophage. The simplest explanation is that a large fraction of the restriction is due to metabolic competition with a complex microbiota, but we cannot formally exclude other factors such as antimicrobial peptides or changes in intestinal physiology.

2) It is often difficult to evaluate results because important parameters are not always given. Dose is a critical variable in bottleneck experiments, but it is not clear if total dose changes in Figure 2 or just the WITS dose? Total dose as well as n0 should be depicted in all figures.

We apologized for the lack of clarity in the figures. Have added panels depicting the exact inoculum for each figure legend (or a supplementary figure where many inocula were used). Additionally, the methods section describing how barcoded CFU were calculated has been rewritten and is hopefully now clearer.

3) This is in part a methods paper but the method is not described clearly in the results, with important bits only found in a very difficult supplement. Is there a difference between colonization probability (beta) and inoculum size at which tags start to disappear? Can there be some culture-based validation of "colonization probability" as explained in the mathematics? Can the authors contrast the advantages/disadvantages of this system with other methods (e.g. sequencing-based approaches)? It seems like the numerator in the colonization probability equation has a very limited range (from 0.18-1.8), potentially limiting the sensitivity of this approach.

We apologized for the lack of clarity in the methods. This criticism is well taken, and we have re-written large sections of the methods in the main text to include all relevant detail currently buried in the extensive supplement.

On the question of the colonization probability and the inoculum size, we kept the inoculum size at 107 CFU/ mouse in all experiments (except those in Fig.4, where this is explicitly stated); only changing the fraction of spiked barcoded strains. We verified the accuracy of our barcode recovery rate by serial dilution over 5 logs (new figure added: Figure 1 – figure supplement 1). “The CFU of barcoded strains in the inoculum at which tags start to disappear” is by definition closely related to the colonization probability, as this value (n0) appears in the calculation. Note that this is not the total inoculum size – this is (unless otherwise stated in Fig. 4) kept constant at 107 CFU by diluting the barcoded B. theta with untagged B. theta. Again, this is now better explained in all figure legends and the main text.

We have added an experiment using peak-to-trough ratios in metagenomic sequencing to estimate the B. theta growth rate. This could be usefully employed for wildtype B. theta at a relatively early timepoint post-colonization where growth was rapid. However, this is a metagenomics-based technique that requires the examined strain to be present at an abundance of over 0.1-1% for accurate quantification such that we could not analyze the acapsular B. theta strain in cecum content at the same timepoint. These data have been added (Figure 3 – figure supplement 3). Note that the information gleaned from these techniques is different. PTR reveals relative growth rates at a specific time (if your strain is abundant enough), whereas neutral tagging reveals average population values over quite large time-windows. We believe that both approaches are valuable. A few sentences comparing the approaches have been added to the discussion.

The actual numerator is the fraction of lost tags, which is obtained from the total number of tags used across the experiment (number of mice times the number of tags lost) over the total number of tags (number of mice times the number of tags used). Very low tag recovery (less than one per mouse) starts to stray into very noisy data, while close to zero loss is also associated with a low-information-to-noise ratio. Therefore, the size of this numerator is necessarily constrained by us setting up the experiments to have close to optimal information recovery from the WITS abundance. Robustness of these analyses is provided by the high “n” of between 10 and 17 mice per group.

4) Figure 3 and the associated model is confusing and does not support the idea that a longer lag-phase contributes to the fitness defect of acapsular B.theta in competitive colonization. Figure 3B clearly indicates that in competition acapsular B. theta experiences a restrictive bottleneck, i.e., in competition, less of the initial B. theta population is contributed by the acapsular inoculum. There is no need to appeal to lag-phase defects to explain the role of the capsule in vivo. The model in Figure 3D should depict the acapsular population with less cells after the bottleneck. In fact, the data in Figure 3E-F can be explained by the tighter bottleneck experienced by the acapsular mutant resulting in a smaller acapsular founding population. This idea can be seen in the data: the acapsular mutant shedding actually dips in the first 12-hours. This cannot be discerned in Figure 3E because mice with zero shedding were excluded from the analysis, leaving the data (and conclusion) of this experiment to be extrapolated from a single mouse.

We of course completely agree that this would be a correct conclusion if only the competitive colonization data is taken into account. However, we are also trying to understand the mechanisms at play generating this bottleneck and have investigated a range of hypotheses to explain the results, taking into account all of our data.

Hypothesis 1) Competition is due to increased killing prior to reaching the cecum and commencing growth: Note that the probability of colonization for single B. theta clones is very similar for OligoMM12 mouse single-colonization by the wildtype and acapsular strains. For this hypothesis to be the reason for outcompetition of the acapsular strain, it would be necessary that the presence of wildtype would increase the killing of acapsular B. theta in the stomach or small intestine. The bacteria are at low density at this stage and stomach acid/small intestinal secretions should be similar in all animals. Therefore, this explanation seems highly unlikely

Hypothesis 2) Competition between wildtype and acapsular B. theta occurs at the point of niche competition before commencing growth in the cecum (similar to the proposal of the reviewer). It is possible that the wildtype strain has a competitive advantage in colonizing physical niches (for example proximity to bacteria producing colicins). On the basis of the data, we cannot exclude this hypothesis completely and it is challenging to measure directly. However, from our in vivo growth-curve data we observe a similar delay in CFU arrival in the feces for acapsular B. theta on single colonization as in competition, suggesting that the presence of wildtype (i.e., initial niche competition) is not the cause of this delay. Rather it is an intrinsic property of the acapsular strain in vivo,

Hypothesis 3) Competition between wildtype and acapsular B. theta is mainly attributable to differences in growth kinetics in the gut lumen. To investigate growth kinetics, we carried our time-courses of fecal collection from OligoMM12 mice single-colonized with wildtype or acapsular B. theta, i.e., in a situation where we observe identical colonization probabilities for the two strains. These date, shown now in Figure 3 C and D and Figure 3 – figure supplement 2, show that also without competition, the CFU of acapsular B. theta appear later and with a lower net growth rate than the wildtype. As these single-colonizations do not show a measurable difference between the colonization probability for the two strains, it is not likely that the delayed appearance of acapsular B. theta in feces is due to increased killing (this would be clearly visible in the barcode loss for the single-colonizations). Rather the simplest explanation for this observation is a bona fide lag phase before growth commences in the cecum. Interestingly, using only the lower net growth rate (assumed to be a similar growth rate but increased clearance rate) produces a good fit for our data on both competitive index and colonization probability in competition (Figure 3, figure supplement 5). This is slightly improved by adding in the observed lag-phase (Figure 3). It is very difficult to experimentally manipulate the lag phase in order to directly test how much of an effect this has on our hypothesis and the contribution is therefore carefully described in the new text.

Please note that all data was plotted and used in fitting in Fig 3E, but “zero-shedding” is plotted at a detection limit and overlayed, making it look like only one point was present when in fact several were used. This was clear in the submitted raw data tables. To sure-up these observations we have repeated all time-courses and now have n>=7 mice per group.

5) The conclusions from Figure 4 rely on assumptions not well-supported by the data. In the high fat diet experiment, a lower dose of WITS is required to conclude that the diet has no effect. Furthermore, the authors conclude that Salmonella restricts the B. theta population by causing inflammation, but do not demonstrate inflammation at their timepoint or disprove that the Salmonella population could cause the same effect in the absence of inflammation (through non-inflammatory direct or indirect interactions).

We of course agree that we would expect to see some loss of B. theta in HFD. However, for these experiments the inoculum was ~109 CFUs/100μL dose of untagged strain spiked with approximately 30 CFU of each tagged strain. Decreasing the number of each WITS below 30 CFU leads to very high variation in the starting inocula from mouse-to-mouse which massively complicates the analysis. To clarify this point, we have added in a detection-limit calculation showing that the neutral tagging technique is not very sensitive to population contractions of less than 10-fold, which is likely in line with what would be expected for a high-fat diet feeding in monocolonized mice for a short time-span.

This is a very good observation regarding our Salmonella infection data. We have now added the fecal lipocalin 2 values, as well as a group infected with a ssaV/invG double mutant of S. Typhimurium that does not cause clinical grade inflammation (“avirulent”). This shows 1) that the attenuated S. Typhimurium is causing intestinal inflammation in B. theta colonized mice and 2) that a major fraction of the population bottleneck can be attributed to inflammation. Interestingly, we do observe a slight bottleneck in the group infected with avirulent Salmonella which could be attributable either to direct toxicity/competition of Salmonella with B. theta or to mildly increased intestinal inflammation caused by this strain. As we cannot distinguish these effects, this is carefully discussed in the manuscript.

6) Several of the experiments rely on very few mice/groups.

We have increased the n to over 5 per group in all experiments (most critically those shown in Fig 3, Supplement 5). See figure legends for specific number of mice per experiment.

Reviewer #2 (Public Review):

The goal of this study was to understand population bottlenecks during colonization in the context of different microbial communities. Capsular polysaccharide mutants, diet, and enteric infection were also used paired to short-term monitoring of overall colonization and the levels of specific strains. The major strength of this study is the innovative approach and the significance of the overall research area.

The first major limitation is the lack of clear and novel insight into the biology of B. theta or other gut bacterial species. The title is provocative, but the experiments as is do not definitively show that the microbiota controls the relative fitness of acapsular and wild-type strains or provide any mechanistic insights into why that would be the case. The data on diet and infection seem preliminary. Furthermore, many of the experiments conflict with prior literature (i.e., lack of fitness difference between acapsular and wild-type strain and lack of impact of diet) but satisfying explanations are not provided for the lack of reproducibility.

In line with suggestions from Reviewer 1, the paper has undergone quite extensive re-writing to better explain the data presented and its consequences. Additionally, we now explicitly comment on apparent discrepancies between our reported data and the literature – for example the colonization defect of acapsular B. theta is only published for competitive colonizations, where we also observe a fitness defect so there is no actual conflict. Additionally, we have calculated detection limits for the effect of high-fat diet and demonstrate that a 10-fold reduction in the effective population size would not be robustly detected with the neutral tagging technique such that we are probably just underpowered to detect small effects, and we believe it is important to point out the numerical limits of the technique we present here. Additionally for the Figure 4 experiments, we have added data on colonization/competition with an avirulent Salmonella challenge giving some mechanistic data on the role of inflammation in the B. theta bottleneck.

Another major limitation is the lack of data on the various background gut microbiotas used. eLife is a journal for a broad readership. As such, describing what microbes are in LCM, OligoMM, or SPF groups is important. The authors seem to assume that the gut microbiota will reflect prior studies without measuring it themselves.

All gnotobiotic lines are bred as gnotobiotic colonies in our isolator facility. This is now better explained in the methods section. Additionally, 16S sequencing of all microbiotas used in the paper has been added as Figure 2 – figure supplement 1.

I also did not follow the logic of concluding that any differences between SPF and the two other groups are due to microbial diversity, which is presumably just one of many differences. For example, the authors acknowledge that host immunity may be distinct. It is essential to profile the gut microbiota by 16S rRNA amplicon sequencing in all these experiments and to design experiments that more explicitly test the diversity hypotheses vs. alternatives like differences in the membership of each community or other host phenotypes.

This is an important point. We have carried out a number of experiments to potentially address some issues here.

1) We carried out B. theta colonization experiments in germ-free mice that had been colonized by gavage of SPF feces either 1 day prior to colonization of 2 weeks prior to colonization. While the shorter pre-colonization allowed B. theta to colonize to a higher population density in the cecum, the colonization probability was already reduced to levels observed in our SPF colony in the short pre-colonization. Therefore, the factors limiting B. theta establishment in the cecum are already established 1-2 days post-colonization with an SPF microbiota (Figure 2 - figure supplement 8). 2) We checked for the presence of secretory IgA capable of binding to the surface of live B. theta, compared to a positive control of a mouse orally vaccinated against B. theta. (Fig. 2, Supplement 7) and could find no evidence of specific IgA targeting B. theta in the intestinal lavages of our SPF mouse colony. 3) We isolated bacteriophage from the intestine of SPF mice and used this to infect lawns of B. theta wildtype and acapsular in vitro. We could not detect and plaque-forming phage coming from the intestine of SPF mice (Figure 2 – figure supplement 7).

We can therefore exclude strongly lytic phage and host IgA as dominant driving mechanisms restricting B. theta colonization. It remains possible that rapidly upregulated host factors such as antimicrobial peptide secretion could play a role, but metabolic competition from the microbiota is also a very strong candidate hypothesis. The text regarding these experiments has been slightly rewritten to point out that colonization probability inversely correlates with microbiota complexity, and the mechanisms involved may involve both direct microbe-microbe interactions as well as host factors.

Given the prior work on the importance of capsule for phage, I was surprised that no efforts are taken to monitor phage levels in these experiments. Could B. theta phage be present in SPF mice, explaining the results? Alternatively, is the mucus layer distinct? Both could be readily monitored using established molecular/imaging methods.

See above: no plaque-forming phage could be recovered from the SPF mouse cecum content. The main replicative site that we have studied here, in mice, is the cecum which does not have true mucus layers in the same way as the distal colon and is upstream of the colon so is unlikely to be affected by colon geography. Rather mucus is well mixed with the cecum content and may behave as a dispersed nutrient source. There is for sure a higher availability of mucus in the gnotobiotic mice due to less competition for mucus degradation by other strains. However, this would be challenging to directly link to the B. theta colonization phenotype as Muc2-deficient mice develop intestinal inflammation.

The conclusion that the acapsular strain loses out due to a difference of lag phase seems highly speculative. More work would be needed to ensure that there is no difference in the initial bottleneck; for example, by monitoring the level of this strain in the proximal gut immediately after oral gavage.

This is an excellent suggestion and has been carried out. At 8h post-colonization with a high inoculum (allowing easy detection) there were identical low levels of B. theta in the upper and lower small intestine, but more B. theta wildtype than B. theta acapsular in the cecum and colon, consistent with commencement of growth for B. theta wildtype but not the acapsular strain at this timepoint. We have additionally repeated the single-colonization time-courses using our standard inoculum and can clearly see the delayed detection of acapsular B. theta in feces even in the single-colonization state when no increased bottleneck is observed. This can only be reasonably explained by a bona fide lag-phase extension for acapsular B. theta in vivo. These data also reveal and decreased net growth rate of acapsular B. theta. Interestingly, our model can be quite well-fitted to the data obtained both for competitive index and for colonization probability using only the difference in net growth rate. Adding the (clearly observed) extended lag-phase generates a model that is still consistent with our observations.

Another major limitation of this paper is the reliance on short timepoints (2-3 days post colonization). Data for B. theta levels over 2 weeks or longer is essential to put these values in context. For example, I was surprised that B. theta could invade the gut microbiota of SPF mice at all and wonder if the early time points reflect transient colonization.

It should be noted that “SPF” defines microbiota only on missing pathogens and not on absolute composition. Therefore, the rather efficient B. theta colonization in our SPF colony is likely due to a permissive composition and this is likely to be not at all reproducible between different SPF colonies (a major confounder in reproducibility of mouse experiments between institutions. In contrast the gnotobiotic colonies are highly reproducible). We do consistently see colonization of our SPF colony by wildtype B. theta out to at least 10 days post-inoculation (latest time-point tested) at similar loads to the ones observed in this work, indicating that this is not just transient “flow-through” colonization. Data included below:

For this paper we were very specifically quantifying the early stages of colonization, also because the longer we run the experiments for, the more confounding features of our “neutrality” assumptions appear (e.g., host immunity selecting for evolved/phase-varied clones, within-host evolution of individual clones etc.). For this reason, we have used timepoints of a maximum of 2-3 days.

Finally, the number of mice/group is very low, especially given the novelty of these types of studies and uncertainty about reproducibility. Key experiments should be replicated at least once, ideally with more than n=3/group.

For all barcode quantification experiments we have between 10 and 17 mice per group. Experiments for the in vivo time-courses of colonization have been expanded to an “n” of at least 7 per group.

Author Response:

Reviewer #1:

Zappia et al investigate the function of E2F transcriptional activity in the development of Drosophila, with the aim of understanding which targets the E2F/Dp transcription factors control to facilitate development. They follow up two of their previous papers (PMID 29233476, 26823289) that showed that the critical functions of Dp for viability during development reside in the muscle and the fat body. They use Dp mutants, and tissue-targetted RNAi against Dp to deplete both activating and repressive E2F functions, focussing primarily on functions in larval muscle and fat body. They characterize changes in gene expression by proteomic profiling, bypassing the typical RNAseq experiments, and characterize Dp loss phenotypes in muscle, fat body, and the whole body. Their analysis revealed a consistent, striking effect on carbohydrate metabolism gene products. Using metabolite profiling, they found that these effects extended to carbohydrate metabolism itself. Considering that most of the literature on E2F/Dp targets is focused on the cell cycle, this paper conveys a new discovery of considerable interest. The analysis is very good, and the data provided supports the authors' conclusions quite definitively. One interesting phenotype they show is low levels of glycolytic intermediates and circulating trehalose, which is traced to loss of Dp in the fat body. Strikingly, this phenotype and the resulting lethality during the pupal stage (metamorphosis) could be rescued by increasing dietary sugar. Overall the paper is quite interesting. It's main limitation in my opinion is a lack of mechanistic insight at the gene regulation level. This is due to the authors' choice to profile protein, rather than mRNA effects, and their omission of any DNA binding (chromatin profiling) experiments that could define direct E2F1/ or E2F2/Dp targets.

We appreciate the reviewer’s comment. Based on previously published chromatin profiling data for E2F/Dp and Rbf in thoracic muscles (Zappia et al 2019, Cell Reports 26, 702–719) we discovered that both Dp and Rbf are enriched upstream the transcription start site of both cell cycle genes and metabolic genes (Figure 5 in Zappia et al 2019, Cell Reports 26, 702–719). Thus, our data is consistent with the idea that the E2F/Rbf is binding to the canonical target genes in addition to a new set of target genes encoding proteins involved in carbohydrate metabolism. We think that E2F takes on a new role, and rather than being re-targeted away from cell cycle genes. We agree that the mechanistic insight would be relevant to further explore.

Reviewer #2:

The study sets out to answer what are the tissue specific mechanisms in fat and muscle regulated by the transcription factor E2F are central to organismal function. The study also tries to address which of these roles of E2F are cell intrinsic and which of these mechanisms are systemic. The authors look into the mechanisms of E2F/Dp through knockdown experiments in both the fat body* (see weakness) and muscle of drosophila. They identify that muscle E2F contributes to fat body development but fat body KD of E2F does not affect muscle function. To then dissect the cause of adult lethality in flies, the authors proteomic and metabolomic profiling of fat and muscle to gain insights. While in the muscle, the cause seems to be an as of yet undetermined systemic change , the authors do conclude that adult lethality in fat body specific Dp knockdown is the result of decrease trehalose in the hemolymph and defects in lipid production in these flies. The authors then test this model by presenting fat body specific Dp knockdown flies with high sugar diet and showing adult survival is rescued. This study concurs with and adds to the emerging idea from human studies that E2F/Dp is critical for more than just its role in the cell-cycle and functions as a metabolic regulator in a tissue-specific manner. This study will be of interest to scientists studying inter-organ communication between muscle and fat.

The conclusions of this paper are partially supported by data. The weaknesses can be mitigated by specific experiments and will likely bolster conclusions.

1) This study relies heavily on the tissue specificity of the Gal4 drivers to study fat-muscle communication by E2F. The authors have convincingly confirmed that the cg-Gal4 driver is never turned on in the muscle and vice versa for Dmef2-Gal4. However, the cg-Gal4 driver itself is capable of turning on expression in the fat body cells and is also highly expressed in hemocytes (macrophage-like cells in flies). In fact, cg-Gal4 is used in numerous studies e.g.:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4125153/ to study the hemocytes and fat in combination. Hence, it is difficult to assess what contribution hemocytes provide to the conclusions for fat-muscle communication. To mitigate this, the authors could test whether Lpp-Gal4>Dp-RNAi (Lpp-Gal4 drives expression exclusively in fat body in all stages) or use ppl-Gal4 (which is expressed in the fat, gut, and brain) but is a weaker driver than cg. It would be good if they could replicate their findings in a subset of experiments performed in Figure 1-4.

This is indeed an important point. We apologize for previously not including this information. Reference is now on page 7.

Another fat body driver, specifically expressed in fat body and not in hemocytes, as cg-GAL4, was tested in previous work (Guarner et al Dev Cell 2017). The driver FB-GAL4 (FBti0013267), and more specifically the stock yw; P{w[+mW.hs]=GawB}FB P{w[+m*] UAS-GFP 1010T2}#2; P{w[+mC]=tubP-GAL80[ts]}2, was used to induce the loss of Dp in fat body in a time-controlled manner using tubGAL80ts. The phenotype induced in larval fat body of FB>DpRNAi,gal80TS recapitulates findings related to DNA damage response characterized in both Dp -/- and CG>Dp- RNAi (see Figure 5A-B, Guarner et al Dev Cell 2017). The activation of DNA damage response upon the loss of Dp was thoroughly studied in Guarner et al Dev Cell 2017. The appearance of binucleates in cg>DpRNAi is presumably the result of the abnormal transcription of multiple G2/M regulators in cells that have been able to repair DNA damage and to resume S-phase (see discussion in Guarner et al Dev Cell 2017). More details regarding the fully characterized DNA damage response phenotype were added on page 6 & 7 of manuscript.

Additionally, r4-GAL4 was also used to drive Dp-RNAi specifically to fat body. But since this driver is weaker than cg-GAL4, the occurrence of binucleated cells in r4>DpRNAi fat body was mild (see Figure R1 below).

As suggested by the reviewer, Lpp-GAL4 was used to knock down the expression of Dp specifically in fat body. All animals Lpp>DpRNAi died at pupa stage. New viability data were included in Figure 1-figure supplement 1. Also, larval fat body were dissected and stained with phalloidin and DAPI to visualize overall tissue structure. Binucleated cells were present in Lpp>DpRNAi fat body but not in the control Lpp>mCherry-RNAi (Figure 2-figure supplement 1B). These results were added to manuscript on page 7.

Furthermore, Dp expression was knockdowned using a hemocyte-specific driver, hml-GAL4. No defects were detected in animal viability (data not shown).

Thus, overall, we conclude that hemocytes do not seem to contribute to the formation of binucleated-cells in cg>Dp-RNAi fat body.

Finally, since no major phenotype was found in muscles when E2F was inactivated in fat body (please see point 3 for more details), we consider that the inactivation E2F in both fat body and hemocytes did not alter the overall muscle morphology. Thus, exploring the contribution of cg>Dp-RNAi hemocytes in muscles would not be very informative.

2) The authors perform a proteomics analysis on both fat body and muscle of control or the respective tissue specific knockdown of Dp. However, the authors denote technical limitations to procuring enough third instar larval muscle to perform proteomics and instead use thoracic muscles of the pharate pupa. While the technical limitations are understandable, this does raise a concern of comparing fat body and muscle proteomics at two distinct stages of fly development and likely contributes to differences seen in the proteomics data. This may impact the conclusions of this paper. It would be important to note this caveat of not being able to compare across these different developmental stage datasets.

We appreciate the suggestion of the reviewer. This caveat was noted and included in the manuscript. Please see page 11.

3) The authors show that the E2F signaling in the muscle controls whether binucleate fat body nuclei appear. In other words, is the endocycling process in fat body affected if muscle E2F function is impaired. However, they conclude that imparing E2F function in fat does not affect muscle. While muscle organization seems fine, it does appear that nuclear levels of Dp are higher in muscles during fat specific knock-down of Dp (Figure 1A, column 2 row 3, for cg>Dp-RNAi). Also there is an increase in muscle area when fat body E2F function is impaired. This change is also reflected in the quantification of DLM area in Figure 1B. But the authors don't say much about elevated Dp levels in muscle or increased DLM area of Fat specific Dp KD. Would the authors not expect Dp staining in muscle to be normal and similar to mCherry-RNAi control in Cg>dpRNAi? The authors could consider discussing and contextualizing this as opposed to making a broad statement regarding muscle function all being normal. Perhaps muscle function may be different, perhaps better when E2F function in fat is impaired.

The overall muscle structure was examined in animals staged at third instar larva (Figure 1A-B). No defects were detected in muscle size between cg>Dp-RNAi animals and controls. In addition, the expression of Dp was not altered in cg>Dp-RNAi muscles compared to control muscles. The best developmental stage to compare the muscle structure between Mef2>Dp-RNAi and cg>Dp-RNAi animals is actually third instar larva, prior to their lethality at pupa stage (Figure 1- figure supplement 1).

Based on the reviewer’s comment, we set up a new experiment to further analyze the phenotype at pharate stage. However, when we repeated this experiment, we did not recover cg>Dp-RNAi pharate, even though 2/3 of Mef2>Dp-RNAi animals survived up to late pupal stage. We think that this is likely due to the change in fly food provider. Since most cg>DpRNAi animals die at early pupal stage (>75% animals, Figure 1-figure supplement 1), pharate is not a good representative developmental stage to examine phenotypes. Therefore, panels were removed.

Text was revised accordingly (page 6).

4) In lines 376-380, the authors make the argument that muscle-specific knockdown can impair the ability of the fat body to regulate storage, but evidence for this is not robust. While the authors refer to a decrease in lipid droplet size in figure S4E this is not a statistically significant decrease. In order to make this case, the authors would want to consider performing a triglyceride (TAG) assay, which is routinely performed in flies.

Our conclusions were revised and adjusted to match our data. The paragraph was reworded to highlight the outcome of the triglyceride assay, which was previously done. We realized the reference to Figure 6H that shows the triglyceride (TAG) assay was missing on page 17. Please see page 17 and page 21 of discussion.

Author Response

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

eLife assessment

This important paper exploits new cryo-EM tomography tools to examine the state of chromatin in situ. The experimental work is meticulously performed and convincing, with a vast amount of data collected. The main findings are interpreted by the authors to suggest that the majority of yeast nucleosomes lack a stable octameric conformation. Despite the possibly controversial nature of this report, it is our hope that such work will spark thought-provoking debate, and further the development of exciting new tools that can interrogate native chromatin shape and associated function in vivo.

We thank the Editors and Reviewers for their thoughtful and helpful comments. We also appreciate the extraordinary amount of effort needed to assess both the lengthy manuscript and the previous reviews. Below, we provide our point-by-point response in bold blue font. Nearly all comments have been addressed in the revised manuscript. For a subset of comments that would require us to speculate, we have taken a conservative approach because we either lack key information or technical expertise: Instead of adding the speculative replies to the main text, we think it is better to leave them in the rebuttal for posterity. Readers will thereby have access to our speculation and know that we did not feel confident enough to include these thoughts in the Version of Record.

Reviewer #1 (Public Review):

This manuscript by Tan et al is using cryo-electron tomography to investigate the structure of yeast nucleosomes both ex vivo (nuclear lysates) and in situ (lamellae and cryosections). The sheer number of experiments and results are astounding and comparable with an entire PhD thesis. However, as is always the case, it is hard to prove that something is not there. In this case, canonical nucleosomes. In their path to find the nucleosomes, the authors also stumble over new insights into nucleosome arrangement that indicates that the positions of the histones is more flexible than previously believed.

Please note that canonical nucleosomes are there in wild-type cells in situ, albeit rarer than what’s expected based on our HeLa cell analysis and especially the total number of yeast nucleosomes (canonical plus non-canonical). The negative result (absence of any canonical nucleosome classes in situ) was found in the histone-GFP mutants.

Major strengths and weaknesses:

Personally, I am not ready to agree with their conclusion that heterogenous non-canonical nucleosomes predominate in yeast cells, but this reviewer is not an expert in the field of nucleosomes and can't judge how well these results fit into previous results in the field. As a technological expert though, I think the authors have done everything possible to test that hypothesis with today's available methods. One can debate whether it is necessary to have 35 supplementary figures, but after working through them all, I see that the nature of the argument needs all that support, precisely because it is so hard to show what is not there. The massive amount of work that has gone into this manuscript and the state-of-the art nature of the technology should be warmly commended. I also think the authors have done a really great job with including all their results to the benefit of the scientific community. Yet, I am left with some questions and comments:

Could the nucleosomes change into other shapes that were predetermined in situ? Could the authors expand on if there was a structure or two that was more common than the others of the classes they found? Or would this not have been found because of the template matching and later reference particle used?

Our best guess (speculation) is that one of the class averages that is smaller than the canonical nucleosome contains one or more non-canonical nucleosome classes. However, we do not feel confident enough to single out any of these classes precisely because we do not yet know if they arise from one non-canonical nucleosome structure or from multiple – and therefore mis-classified – non-canonical nucleosome structures (potentially with other non-nucleosome complexes mixed in). We feel it is better to leave this discussion out of the manuscript, or risk sending the community on wild goose chases.

Our template-matching workflow uses a low-enough cross-correlation threshold that any nucleosome-sized particle (plus minus a few nanometers) would be picked, which is why the number of hits is so large. So unless the noncanonical nucleosomes quadrupled in size or lost most of their histones, they should be grouped with one or more of the other 99 class averages (WT cells) or any of the 100 class averages (cells with GFP-tagged histones). As to whether the later reference particle could have prevented us from detecting one of the non-canonical nucleosome structures, we are unable to tell because we’d really have to know what an in situ non-canonical nucleosome looks like first.

Could it simply be that the yeast nucleoplasm is differently structured than that of HeLa cells and it was harder to find nucleosomes by template matching in these cells? The authors argue against crowding in the discussion, but maybe it is just a nucleoplasm texture that side-tracks the programs?

Presumably, the nucleoplasmic “side-tracking” texture would come from some molecules in the yeast nucleus. These molecules would be too small to visualize as discrete particles in the tomographic slices, but they would contribute textures that can be “seen” by the programs – in particular RELION, which does the discrimination between structural states. We are not sure what types of density textures would side-track RELION’s classification routines.

The title of the paper is not well reflected in the main figures. The title of Figure 2 says "Canonical nucleosomes are rare in wild-type cells", but that is not shown/quantified in that figure. Rare is comparison to what? I suggest adding a comparative view from the HeLa cells, like the text does in lines 195-199. A measure of nucleosomes detected per volume nucleoplasm would also facilitate a comparison.

Figure 2’s title is indeed unclear and does not align with the paper’s title and key conclusion. The rarity here is relative to the expected number of nucleosomes (canonical plus non-canonical). We have changed the title to:

“Canonical nucleosomes are a minority of the expected total in wild-type cells”.

We would prefer to leave the reference to HeLa cells to the main text instead of as a figure panel because the comparison is not straightforward for a graphical presentation. Instead, we now report the total number of nucleosomes estimated for this particular yeast tomogram (~7,600) versus the number of canonical nucleosomes classified (297; 594 if we assume we missed half of them). This information is in the revised figure legend:

“In this tomogram, we estimate there are ~7,600 nucleosomes (see Methods on how the calculation is done), of which 297 are canonical structures. Accounting for the missing disc views, we estimate there are ~594 canonical nucleosomes in this cryolamella (< 8% the expected number of nucleosomes).”

If the cell contains mostly non-canonical nucleosomes, are they really non-canonical? Maybe a change of language is required once this is somewhat sure (say, after line 303).

This is an interesting semantic and philosophical point. From the yeast cell’s “perspective”, the canonical nucleosome structure would be the form that is in the majority. That being said, we do not know if there is one structure that is the majority. From the chromatin field’s point of view, the canonical nucleosome is the form that is most commonly seen in all the historical – and most contemporary – literature, namely something that resembles the crystal structure of Luger et al, 1997. Given these two lines of thinking, we added the following clarification as lines 312 – 316:

“At present, we do not know what the non-canonical nucleosome structures are, meaning that we cannot even determine if one non-canonical structure is the majority. Until we know the non-canonical nucleosomes’ structures, we will use the term non-canonical to describe all the nucleosomes that do not have the canonical (crystal) structure.”

The authors could explain more why they sometimes use conventional the 2D followed by 3D classification approach and sometimes "direct 3-D classification". Why, for example, do they do 2D followed by 3D in Figure S5A? This Figure could be considered a regular figure since it shows the main message of the paper.

Since the classification of subtomograms in situ is still a work in progress, we felt it would be better to show one instance of 2-D classification for lysates and one for lamellae. While it is true that we could have presented direct 3-D classification for the entire paper, we anticipate that readers will be interested to see what the in situ 2-D class averages look like.

The main message is that there are canonical nucleosomes in situ (at least in wild-type cells), but they are a minority. Therefore, the conventional classification for Figure S5A should not be a main figure because it does not show any canonical nucleosome class averages in situ.

Figure 1: Why is there a gap in the middle of the nucleosome in panel B? The authors write that this is a higher resolution structure (18Å), but in the even higher resolution crystallography structure (3Å resolution), there is no gap in the middle.

There is a lower concentration of amino acids at the middle in the disc view; unfortunately, the space-filling model in Figure 1A hides this feature. The gap exists in experimental cryo-EM density maps. See Author response image 1 for an example (pubmed.ncbi.nlm.nih.gov/29626188). The size of the gap depends on the contour level and probably the contrast mechanism, as the gap is less visible in the VPP subtomogram averages. To clarify this confusing phenomenon, we added the following lines to the figure legend:

“The gap in the disc view of the nuclear-lysate-based average is due to the lower concentration of amino acids there, which is not visible in panel A due to space-filling rendering. This gap’s visibility may also depend on the contrast mechanism because it is not visible in the VPP averages.”

Author response image 1.

Reviewer #2 (Public Review):

Nucleosome structures inside cells remain unclear. Tan et al. tackled this problem using cryo-ET and 3-D classification analysis of yeast cells. The authors found that the fraction of canonical nucleosomes in the cell could be less than 10% of total nucleosomes. The finding is consistent with the unstable property of yeast nucleosomes and the high proportion of the actively transcribed yeast genome. The authors made an important point in understanding chromatin structure in situ. Overall, the paper is well-written and informative to the chromatin/chromosome field.

We thank Reviewer 2 for their positive assessment.

Reviewer #3 (Public Review):

Several labs in the 1970s published fundamental work revealing that almost all eukaryotes organize their DNA into repeating units called nucleosomes, which form the chromatin fiber. Decades of elegant biochemical and structural work indicated a primarily octameric organization of the nucleosome with 2 copies of each histone H2A, H2B, H3 and H4, wrapping 147bp of DNA in a left handed toroid, to which linker histone would bind.

This was true for most species studied (except, yeast lack linker histone) and was recapitulated in stunning detail by in vitro reconstitutions by salt dialysis or chaperone-mediated assembly of nucleosomes. Thus, these landmark studies set the stage for an exploding number of papers on the topic of chromatin in the past 45 years.

An emerging counterpoint to the prevailing idea of static particles is that nucleosomes are much more dynamic and can undergo spontaneous transformation. Such dynamics could arise from intrinsic instability due to DNA structural deformation, specific histone variants or their mutations, post-translational histone modifications which weaken the main contacts, protein partners, and predominantly, from active processes like ATP-dependent chromatin remodeling, transcription, repair and replication.

This paper is important because it tests this idea whole-scale, applying novel cryo-EM tomography tools to examine the state of chromatin in yeast lysates or cryo-sections. The experimental work is meticulously performed, with vast amount of data collected. The main findings are interpreted by the authors to suggest that majority of yeast nucleosomes lack a stable octameric conformation. The findings are not surprising in that alternative conformations of nucleosomes might exist in vivo, but rather in the sheer scale of such particles reported, relative to the traditional form expected from decades of biochemical, biophysical and structural data. Thus, it is likely that this work will be perceived as controversial. Nonetheless, we believe these kinds of tools represent an important advance for in situ analysis of chromatin. We also think the field should have the opportunity to carefully evaluate the data and assess whether the claims are supported, or consider what additional experiments could be done to further test the conceptual claims made. It is our hope that such work will spark thought-provoking debate in a collegial fashion, and lead to the development of exciting new tools which can interrogate native chromatin shape in vivo. Most importantly, it will be critical to assess biological implications associated with more dynamic - or static forms- of nucleosomes, the associated chromatin fiber, and its three-dimensional organization, for nuclear or mitotic function.

Thank you for putting our work in the context of the field’s trajectory. We hope our EMPIAR entry, which includes all the raw data used in this paper, will be useful for the community. As more labs (hopefully) upload their raw data and as image-processing continues to advance, the field will be able to revisit the question of non-canonical nucleosomes in budding yeast and other organisms. 

Reviewer #1 (Recommendations For The Authors):

The manuscript sometimes reads like a part of a series rather than a stand-alone paper. Be sure to spell out what needs to be known from previous work to read this article. The introduction is very EM-technique focused but could do with more nucleosome information.

We have added a new paragraph that discusses the sources of structural variability to better prepare readers, as lines 50 – 59:

“In the context of chromatin, nucleosomes are not discrete particles because sequential nucleosomes are connected by short stretches of linker DNA. Variation in linker DNA structure is a source of chromatin conformational heterogeneity (Collepardo-Guevara and Schlick, 2014). Recent cryo-EM studies show that nucleosomes can deviate from the canonical form in vitro, primarily in the structure of DNA near the entry/exit site (Bilokapic et al., 2018; Fukushima et al., 2022; Sato et al., 2021; Zhou et al., 2021). In addition to DNA structural variability, nucleosomes in vitro have small changes in histone conformations (Bilokapic et al., 2018). Larger-scale variations of DNA and histone structure are not compatible with high-resolution analysis and may have been missed in single-particle cryo-EM studies.”

Line 165-6 "did not reveal a nucleosome class average in..". Add "canonical", since it otherwise suggests there were no nucleosomes.

Thank you for catching this error. Corrected.

Lines 177-182: Why are the disc views missed by the classification analysis? They should be there in the sample, as you say.

We suspect that RELION 3 is misclassifying the disc-view canonical nucleosomes into the other classes. The RELION developers suspect that view-dependent misclassification arises from RELION 3’s 3-D CTF model. RELION 4 is reported to be less biased by the particles’ views. We have started testing RELION 4 but do not have anything concrete to report yet.

Line 222: a GFP tag.

Fixed.

Line 382: "Note that the percentage .." I can't follow this sentence. Why would you need to know how many chromosome's worth of nucleosomes you are looking at to say the percentage of non-canonical nucleosomes?

Thank you for noticing this confusing wording. The sentence has been both simplified and clarified as follows in lines 396 – 398:

“Note that the percentage of canonical nucleosomes in lysates cannot be accurately estimated because we cannot determine how many nucleosomes in total are in each field of view.”

Line 397: "We're not implying that..." Please add a sentence clearly stating what you DO mean with mobility for H2A/H2B.

We have added the following clarifying sentence in lines 412 – 413:

“We mean that H2A-H2B is attached to the rest of the nucleosome and can have small differences in orientation.”

Line 428: repeated message from line 424. "in this figure, the blurring implies.."

Redundant phrase removed.

Line 439: "on a HeLa cell" - a single cell in the whole study?

Yes, that study was done on a single cell.

A general comment is that the authors could help the reader more by developing the figures and making them more pedagogical, a list of suggestions can be found below.

Thank you for the suggestions. We have applied all of them to the specific figure callouts and to the other figures that could use similar clarification.

Figure 2: Help the reader by avoiding abbreviations in the figure legend. VPP tomographic slice - spell out "Volta Phase Plate". Same with the term "remapped" (panel B) what does that mean?

We spelled out Volta phase plate in full and explained “remapped” the additional figure legend text:

“the class averages were oriented and positioned in the locations of their contributing subtomograms”.

Supplementary figures:

Figure S3: It is unclear what you mean with "two types of BY4741 nucleosomes". You then say that the canonical nucleosomes are shaded blue. So what color is then the non-canonical? All the greys? Some of them look just like random stuff, not nucleosomes.

“Two types” is a typo and has been removed and “nucleosomes” has been replaced with “candidate nucleosome template-matching hits” to accurately reflect the particles used in classification.

Figure S6: Top left says "3 tomograms (defocus)". I wonder if you meant to add the defocus range here. I have understood it like this is the same data as shown in Figure S5, which makes me wonder if this top cartoon should not be on top of that figure too (or exclusively there).

To make Figures S6 (and S5) clearer, we have copied the top cartoon from Figure S6 to S5.

Note that we corrected a typo for these figures (and the Table S7): the number of template-matched candidate nucleosomes should be 93,204, not 62,428.

The description in the parentheses (defocus) is shorthand for defocus phase contrast and was not intended to also display a defocus range. All of the revised figure legends now report the meaning of both this shorthand and of the Volta phase plate (VPP).

To help readers see the relationship between these two figures, we added the following clarifying text to the Figure S5 and S6 legends, respectively:

“This workflow uses the same template-matched candidate nucleosomes as in Figure S6; see below.”

“This workflow uses the same template-matched candidate nucleosomes as in Figure S5.”

Figure S7: In the first panel, it is unclear why the featureless cylinder is shown as it is not used as a reference here. Rather, it could be put throughout where it was used and then put the simulated EM-map alone here. If left in, it should be stated in the legend that it was not used here.

It would indeed be much clearer to show the featureless cylinder in all the other figures and leave the simulated nucleosome in this control figure. All figures are now updated. The figure legend was also updated as follows:

“(A) A simulated EM map from a crystal structure of the nucleosome was used as the template-matching and 3-D classification reference.”

Figure S18: Why are there classes where the GFP density is missing? Mention something about this in the figure legend.

We have appended the following speculations to explain the “missing” GFP densities:

“Some of the class averages are “missing” one or both expected GFP densities. The possible explanations include mobility of a subpopulation of GFPs or H2A-GFPs, incorrectly folded GFPs, or substitution of H2A for the variant histone H2A.Z.”

Reviewer #2 (Recommendations For The Authors):

My specific (rather minor) comments are the following:

1) Abstract:

yeast -> budding yeast.

All three instances in the abstract have been replaced with “budding yeast”.

It would be better to clarify what ex vivo means here.

We have appended “(in nuclear lysates)” to explain the meaning of ex vivo.

2) Some subtitles are unclear.

e.g., "in wild-type lysates" -> "wild-type yeast lysates"

Thank you for this suggestion. All unclear instances of subtitles and sample descriptions throughout the text have been corrected.

3) Page 6, Line 113. "...which detects more canonical nucleosomes." A similar thing was already mentioned in the same paragraph and seems redundant.

Thank you for noticing this redundant statement, which is now deleted.

4) Page 25, Line 525. "However, crowding is an unlikely explanation..." Please note that many macromolecules (proteins, RNAs, polysaccharides, etc.) were lost during the nuclei isolation process.

This is a good point. We have rewritten this paragraph to separate the discussion on technical versus biological effects of crowding, in lines 538 – 546:

“Another hypothesis for the low numbers of detected canonical nucleosomes is that the nucleoplasm is too crowded, making the image processing infeasible. However, crowding is an unlikely technical limitation because we were able to detect canonical nucleosome class averages in our most-crowded nuclear lysates, which are so crowded that most nucleosomes are butted against others (Figures S15 and S16). Crowding may instead have biological contributions to the different subtomogram-analysis outcomes in cell nuclei and nuclear lysates. For example, the crowding from other nuclear constituents (proteins, RNAs, polysaccharides, etc.) may contribute to in situ nucleosome structure, but is lost during nucleus isolation.”

5) Page 7, Line 126. "The subtomogram average..." Is there any explanation for this?

Presumably, the longer linker DNA length corresponds to the ordered portion of the ~22 bp linker between consecutive nucleosomes, given the ~168 bp nucleosome repeat length. We have appended the following explanation as the concluding sentence, lines 137 – 140:

“Because the nucleosome-repeat length of budding yeast chromatin is ~168 bp (Brogaard et al., 2012), this extra length of DNA may come from an ordered portion of the ~22 bp linker between adjacent nucleosomes.”

6) "Histone GFP-tagging strategy" subsection:

Since this subsection is a bit off the mainstream of the paper, it can be shortened and merged into the next one.

We have merged the “Histone GFP-tagging strategy” and “GFP is detectable on nucleosome subtomogram averages ex vivo” subsections and shortened the text as much as possible. The new subsection is entitled “Histone GFP-tagging and visualization ex vivo”

7) Page 16, Line 329. "Because all attempts to make H3- or H4-GFP "sole source" strains failed..." Is there a possible explanation here? Cytotoxic effect because of steric hindrance of nucleosomes?

Yes, it is possible that the GFP tag is interfering with the nucleosomes interactions with its numerous partners. It is also possible that the histone-GFP fusions do not import and/or assemble efficiently enough to support a bare-minimum number of functional nucleosomes. Given that the phenotypic consequences of fusion tags is an underexplored topic and that we don’t have any data on the (dead) transformants, we would prefer to leave out the speculation about the cause of death in the attempted creation of “sole source” strains.

that's a game change

Note: This preprint has been reviewed by subject experts for Review Commons. Content has not been altered except for formatting.

Learn more at Review Commons

Referee #3

Evidence, reproducibility and clarity

Summary:

The manuscript by Dufour et al. is a follow-up on the groups' previous publication that introduced the photo-inducible Cre recombinase, LiCre. In the present work, the authors further characterize the properties and kinetics of their optogenetic switch. Initially, the authors show that light affects only LiCre-mediated recombination itself and not DNA binding. Following these observations, they measure and mathematically model LiCre kinetics demonstrating high efficiency in vivo and a surprising temperature sensitivity. Finally, Dufour et al. evaluate several mutations that affect the LOV photo-cycle and provide recommendation for LiCre applications. The study thoroughly investigates various aspects of the function of LiCre, confirming some previously known characteristics (i.e. temperature-dependence of Cre activity and functionality of LOV-based optogenetic tools in yeast without co-factor supplementation), while providing new LiCre-specific insights (kinetics, light-independent DNA binding). Please note that the reviewer is no expert in mathematical modeling and cannot fully judge the methodological details of the models. While I have some concerns as listed below, I believe study should be well-suited for publication after a revision.

Major comments:

1. After completing the initial experiment, the authors discovered that their plasmids carry different numbers of V5 epitopes. I am wondering whether this was due to a recombination event happening during the experiment or whether the constructs were not sequence verified prior to use? In any case, an additional ChIP experiment using Cre and LiCre constructs with the identical number of tag-repeats will be necessary. The result, i.e. the strong reduction of DNA-binding of LiCre (which is close to the negative control), is quite remarkable given that LiCre is still considerably active and high DNA affinities were observed in SPR experiments. In light of these counterindications, identical experiment conditions for test and reference group become even more important.
2. The conclusion that DNA-binding of LiCre is completely light-independent is not entirely convincing to me. The differences between the light and dark conditions in Fig. 2d are indeed small, but the values for LiCre are almost on par with the vector control and therefore hard to interpret. Based on this experiment alone, one could even be inclined to argue that LiCre does not bind DNA at all (which is of course falsified by the later experiments), showing that the resolution of the corresponding dataset is too low to draw final conclusions. Light-independent DNA binding should either be confirmed by a more sensitive method or the conclusion statements on this matter should be revised accordingly.
3. If I understand the explanations correctly, replicates and plotted data points refer to multiple samples (different colonies), that were handled in a single experiment, i.e. by one researcher at the same time/same day. As already mentioned by the authors in the main text, this workflow explains the considerable differences between some of the results in the present manuscript and an identical experiment in a previous publication by the same authors. Providing truly independent experiments (performed on different days) that are therefore independent towards variables such as the fluctuation in incubation temperature (which was the issue in the described experiments) will be crucial, at least for the key datasets.

Minor comments:

1. At the end of the Introduction, the authors mention that the interaction of the Cre heptamers was weakened via point mutations in LiCre. A short sentence about the engineering rationale behind this weakened interaction would help readers, who are not familiar with the author's prior work.
2. Fig. 2a-b depicts images relating to the purification procedure. These could be moved to the supplements as they don't provide any insight apart from the fact that the proteins were successfully purified.
3. The kinetic characterization was only performed for LiCre. Especially for scientists, who have worked with wildtype Cre before, a side-by-side comparison with wt Cre would be valuable to judge the loss in reaction speed that has to be expected when switching from Cre to LiCre.
4. The difference between the ChIP results and the SPR results is striking but not mentioned in the discussion section. Also, the statement: "Finally, our results have practical implications on experimental protocols employing LiCre. First, given its high affinity for loxP (Fig. 5b), over-expressing LiCre at high levels will probably not increase its efficiency." (line 502) refers only to the affinity but seems to ignore the low DNA-occupancy of LiCre observed in Fig. 2d. Adapting the discussion section accordingly would improve the manuscript.

Significance

General assessment and advance:

The present study provides a large set of experiments and analyses characterizing the optogenetic LiCre recombinase. In general, the study is well conceived and executed. Although some of my concerns listed above affect key aspects of the study, they should be straightforward to address. The manuscript is a follow-up study providing a more detailed characterization of an optogenetic tool previously developed by the same authors. Its novelty is therefore somewhat limited. While the study provides a rich body of additional data, many of the findings merely confirmed aspects that were to be expected based on the two proteins LiCre is built of (temperature-dependent activity of Cre, optogenetics in yeast w/o the need of co-factor supplementation, weaker DNA-affinity of the Cre fusion protein as compared to wildtype Cre). New insights are provided by the facts that (i) light only controls recombination but not DNA binding and (ii) light activation of only some protomers within the LiCre heptamer is likely to be sufficient to activate recombination. The former aspect is, however, not entirely evident from the results as described above.

Audience:

The study will be of interest for researchers focusing on inducible DNA recombination and especially relevant to those who plan to work with LiCre and can now rely on a more detailed and extended characterization compared to the original LiCre publication.

Reviewer #2 (Public review):

Summary:

The role of PRC2 in post neural crest induction was not well understood. This work developed an elegant mouse genetic system to conditionally deplete EED upon SOX10 activation. Substantial developmental defects were identified for craniofacial and bone development. The authors also performed extensive single-cell RNA sequencing to analyze differentiation gene expression changes upon conditional EED disruption.

Strengths:

(1) Elegant genetic system to ablate EED post neural crest induction.

(2) Single-cell RNA-seq analysis is extremely suitable for studying the cell type specific gene expression changes in developmental systems.

Original Weaknesses:

(1) Although this study is well designed and contains state-of-art single cell RNA-seq analysis, it lacks the mechanistic depth in the EED/PRC2-mediated epigenetic repression. This is largely because no epigenomic data was shown.

(2) The mouse model of conditional loss of EZH2 in neural crest has been previously reported, as the authors pointed out in the discussion. What is novelty in this study to disrupt EED? Perhaps a more detailed comparison of the two mouse models would be beneficial.

(3) The presentation of the single-cell RNA-seq data may need improvement. The complexity of the many cell types blurs the importance of which cell types are affected the most by EED disruption.

(4) While it's easy to identify PRC2/EED target genes using published epigenomic data, it would be nice to tease out the direct versus indirect effects in the gene expression changes (e.g Fig. 4e)

Comments on latest version:

The authors have addressed weaknesses 2 and 3 of my previous comment very well. For weaknesses 1 and 4, the authors have added a main Fig 5 and its associated supplemental materials, which definitely strengthen the mechanistic depth of the story. However, I think the audience would appreciate if the following questions/points could be further addressed regarding the Cut&Tag data (mostly related to main Figure 5):

(1) The authors described that Sox10-Cre would be expressed at E8.75, and in theory, EED-FL would be ablated soon after that. Why would E16.5 exhibit a much smaller loss in H3K27me3 compared to E12.5? Shouldn't a prolong loss of EED lead to even worse consequence?

(2) The gene expression change at E12.5 upon loss of EED (shown in Fig. 4h) seems to be massive, including many PRC2-target genes. However, the H3K27me3 alteration seems to be mild even at E12.5. Does this infer a PRC2 or H3K27 methylation - independent role of EED? To address this, I suggest the authors re-consider addressing my previously commented weakness #4 regarding the RNA-seq versus Cut&Tag change correlation. For example, a gene scatter plot with X-axis of RNA-seq changes versus Y-axis of H3K27me3 level changes.

(3) The CUT&Tag experiments seem to contain replicates according to the figure legend, but no statistical analysis was presented including the new supplemental tables. Also, for Fig. 5c-d, instead of showing the MRR in individual conditions, I think the audience would really want to know the differential MRR between Fl/WT and Fl/Fl. In other words, how many genes/ MRR have statistically lower H3K27me3 level upon EED loss.

Author response:

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

Reviewer #1 (Public review):

Epigenetic regulation complex (PRC2) is essential for neural crest specification, and its misregulation has been shown to cause severe craniofacial defects. This study shows that Eed, a core PRC2 component, is critical for craniofacial osteoblast differentiation and mesenchymal proliferation after neural crest induction. Using mouse genetics and single-cell RNA sequencing, the researcher found that conditional knockout of Eed leads to significant craniofacial hypoplasia, impaired osteogenesis, and reduced proliferation of mesenchymal cells in post-migratory neural crest populations.

Overall, the study is superficial and descriptive. No in-depth mechanism was analyzed and the phenotype analysis is not comprehensive.

We thank the reviewer for sharing their expertise and for taking the time to provide helpful suggestions to improve our study. We are gratified that the striking phenotypes we report from Eed loss in post-migratory neural crest craniofacial tissues were appreciated. The breadth and depth of our phenotyping techniques, including skeletal staining, micro-CT, echocardiogram, immunofluorescence, histology, and primary craniofacial cell culture provide comprehensive data in support our hypothesis that PRC2 is required for epigenetic control of craniofacial osteoblast differentiation. To provide mechanistic data in support of this hypothesis, we have now performed CUT&Tag H3K27me3 chromatin profiling on nuclei harvested from E12.5 or E16.5 Sox10-Cre Eed^Fl/WT and Sox10-Cre Eed^Fl/Fl craniofacial tissue. These new data, which are presented in Fig. 5, Supplementary Fig. 9, and Supplementary Tables 7-10 of our revised manuscript, validate our hypothesis that epigenetic regulation of chromatin architecture downstream of PRC2 activity underlies craniofacial osteoblast differentiation. In particular, we now show that Eed-dependent H3K27me3 methylation is associated with correct temporal expression of transcription factors that are necessary for craniofacial differentiation and patterning, such as including Msx1, Pitx1, Pax7, which were initially nominated by single-cell RNA sequencing of E12.5 Sox10-Cre Eed^Fl/WT and Sox10-Cre Eed^Fl/Fl craniofacial tissues in Fig. 4, Supplementary Fig. 5-7, and Supplementary Tables 1-6.

Reviewer #2 (Public review):

Summary:

The role of PRC2 in post-neural crest induction was not well understood. This work developed an elegant mouse genetic system to conditionally deplete EED upon SOX10 activation. Substantial developmental defects were identified for craniofacial and bone development. The authors also performed extensive single-cell RNA sequencing to analyze differentiation gene expression changes upon conditional EED disruption.

Strengths:

(1) Elegant genetic system to ablate EED post neural crest induction.

(2) Single-cell RNA-seq analysis is extremely suitable for studying the cell type-specific gene expression changes in developmental systems.

We thank the reviewer for their generous and helpful comments on our study. We are happy that our mouse genetic and single-cell RNA sequencing approaches were appropriate in pairing the craniofacial phenotypes we report with distinct gene expression changes in post-migratory neural crest tissues upon Eed deletion.

Weaknesses:

(1) Although this study is well designed and contains state-of-the-art single-cell RNA-seq analysis, it lacks the mechanistic depth in the EED/PRC2-mediated epigenetic repression. This is largely because no epigenomic data was shown.

Thank you for this suggestion. As described in response to Reviewer #1, we have now performed CUT&Tag H3K27me3 chromatin profiling on nuclei harvested from E12.5 or E16.5 Sox10-Cre Eed^Fl/WT and Sox10-Cre Eed^Fl/Fl craniofacial tissues to provide mechanistic epigenomic data in support of our hypothesis that hat PRC2 is required for craniofacial osteoblast differentiation. These new data, which are presented in Fig. 5, Supplementary Fig. 9, and Supplementary Tables 7-10 of our revised manuscript, integrate genome-wide and targeted metaplot visualizations across genotypes with in-depth analyses of methylation rich regions and genes associated with methylation rich loci. Broadly, these new data reveal that changes in H3K27me3 occupancy correlate with gene expression changes from single-cell RNA sequencing of E12.5 Sox10-Cre Eed^Fl/WT and Sox10-Cre Eed^Fl/Fl craniofacial tissues in Fig. 4, Supplementary Fig. 5-7, and Supplementary Tables 1-6.

(2) The mouse model of conditional loss of EZH2 in neural crest has been previously reported, as the authors pointed out in the discussion. What is novel in this study to disrupt EED? Perhaps a more detailed comparison of the two mouse models would be beneficial.

We acknowledge and cite the study the reviewer has indicated (Schwarz et al. Development 2014) in our initial and revised manuscripts. This elegant investigation uses Wnt1-Cre to delete Ezh2 and reports a phenotype similar to the one we observed with Sox10-Cre deletion of Eed, but our study adds depth to the understanding of PRC2’s vital role in neural crest development by ablating Eed, which has a unique function in the PRC2 complex by binding to H3K27me3 and allosterically activating Ezh2. In this sense, our study sheds light on whether phenotypes arising from deletion of Eed, the PRC2 “reader”, differ from phenotypes arising from deletion of Ezh2, the PRC2 “writer”, in neural crest derived tissues. Moreover, we provide the first single-cell RNA sequencing and epigenomic investigations of craniofacial phenotypes arising from PRC2 activity in the developing neural crest. Due to limitations associated with the Wnt1-Cre transgene (Lewis et al. Developmental Biology 2013), which targets pre-migratory neural crest cells, our investigations used Sox10Cre, which targets the migratory neural crest and is completely recombined by E10.5. We have included a detailed comparison of these mouse models in the Discussion section of our revised manuscript, and we thank the reviewer for this thoughtful suggestion. 

(3) The presentation of the single-cell RNA-seq data may need improvement. The complexity of the many cell types blurs the importance of which cell types are affected the most by EED disruption.

We thank the reviewer for the opportunity to improve the presentation of our single-cell RNA sequencing data. In response, we have added Supplementary Fig. 8 to our revised manuscript, which shows the cell clusters most affected by EED disruption in UMAP space across genotypes. Because we wanted to capture the fill diversity of cell types underlying the phenotypes we report, we did not sort Sox10+ cells (via FACS, for example) from craniofacial tissues before single-cell RNA sequencing. Our resulting single-cell RNA sequencing data are therefore inclusive of a diversity of cell types in UMAP space, and the prevalence of many of these cell types was unaffected by epigenetic disruption of neural crest derived tissues. The prevalence of the cell clusters that are most affected across genotypes and which are most relevant to our analyses of the developing neural crest are shown in Fig. 4c (and now also in Supplementary Fig. 8), including C0 (differentiating osteoblasts), C4 (mesenchymal stem cells), C5 (mesenchymal stem cells), and C7 (proliferating mesenchymal stem cells). Marker genes and pseudobulked differential expression analyses across these clusters are shown in Fig. 4d and Fig. 4e-h, respectively. 

(4) While it's easy to identify PRC2/EED target genes using published epigenomic data, it would be nice to tease out the direct versus indirect effects in the gene expression changes (e.g Figure 4e).

We agree with the reviewer that the single-cell RNA sequencing data in our initial submission do not provide insight into direct versus indirect changes in gene expression downstream of PRC2. In contrast, the CUT&Tag chromatin profiling data that we have generated for this revision provides mechanistic insight into H3K27me3 occupancy and direct effects on gene expression resulting from PRC2 inactivation in our mouse models.

REVIEWING EDITOR COMMENTS

The following are recommended as essential revisions

(1) The study is overall superficial and primarily descriptive, lacking in-depth mechanistic analysis and comprehensive phenotype evaluation.

Please see responses to Reviewer #1 and Reviewer #2 (weaknesses 1 and 4) above. 

(2) The authors did not investigate the temporal and spatial expression of Eed during cranial neural crest development, which is crucial for explaining the observed phenotypes.

The temporal and spatial expression of Eed during embryogenesis is well studied. Eed is ubiquitously expressed starting at E5.5, peaks at E9.5, and is downregulated but maintained at a high basal expression level through E18.5 (Schumacher et al. Nature 1996). Although comprehensive analysis of Eed expression in neural crest tissues has not been reported (to our knowledge), Eed physically and functionally interacts with Ezh2 (Sewalt et al. Mol Cell Biol 1998), which is enriched at a diversity of timepoints throughout all developing craniofacial tissues (Schwarz et al. Development 2014). In our study, we confirmed enrichment of Eed expression in craniofacial tissues throughout development using QPCR, and have provided a more detailed description of these published and new findings in the Discussion section of our revised manuscript. 

(3) There is no apoptosis analysis provided for any of the samples.

We evaluated the presence of apoptotic cells in E12.5 craniofacial sections using immunofluorescence for Cleaved Caspase 3 in Supplementary Fig. 3d. Although we found a modest increase in the labeling index of apoptotic cells, there was insufficient evidence to conclude that apoptosis is a substantial factor in craniofacial hypoplasia resulting from Eed loss in post-migratory neural crest craniofacial tissues. We have clarified these findings in the Results and Discussion sections of our revised manuscript. 

(4) As Eed is a core component of the PRC2 complex, were any other components altered in the Eed cKO mutant? How does Eed regulation influence osteogenic differentiation and proliferation through known pathways?

We thank the editors for this thoughtful inquiry. Although we did not specifically investigate expression or stability of other PRC2 components in Eed conditional mutants, and little is known about how Eed regulates osteogenic differentiation or proliferation through any pathway, our single-cell RNA sequencing data presented in Fig. 4, Supplementary Fig. 5-7, and Supplementary Tables 1-6 provide a significant conceptual advance with mechanistic implications for understanding bone development downstream of Eed and do not reveal any alterations in the expression of other PRC2 components across genotypes. We have clarified these important details in the Discussion section of our revised manuscript. 

(5) The authors may compare the Eed cKO phenotype with that of the previous EZH2 cKO mouse model since both Eed and EZH2 are essential subunits of PRC2.

Please see responses to editorial comment 2 above and the last paragraph of the Discussion section of our revised manuscript for comparisons between Eed and Ezh2 knockout phenotypes.

Note: This response was posted by the corresponding author to Review Commons. The content has not been altered except for formatting.

Learn more at Review Commons

Reply to the reviewers

Response to referee comments: ____RC-2025-03008

Reviewer #1 (Evidence, reproducibility and clarity (Required)):

Summary In this article, the authors used the synthetic TALE DNA binding proteins, tagged with YFP, which were designed to target five specific repeat elements in Trypanosoma brucei genome, including centromere and telomeres-associated repeats and those of a transposon element. This is in order to detect and identified, using YFP-pulldown, specific proteins that bind to these repetitive sequences in T. brucei chromatin. Validation of the approach was done using a TALE protein designed to target the telomere repeat (TelR-TALE) that detected many of the proteins that were previously implicated with telomeric functions. A TALE protein designed to target the 70 bp repeats that reside adjacent to the VSG genes (70R-TALE) detected proteins that function in DNA repair and the protein designed to target the 177 bp repeat arrays (177R-TALE) identified kinetochore proteins associated T. brucei mega base chromosomes, as well as in intermediate and mini-chromosomes, which imply that kinetochore assembly and segregation mechanisms are similar in all T. brucei chromosome.

Major comments: Are the key conclusions convincing? The authors reported that they have successfully used TALE-based affinity selection of protein-associated with repetitive sequences in the T. brucei genome. They claimed that this study has provided new information regarding the relevance of the repetitive region in the genome to chromosome integrity, telomere biology, chromosomal segregation and immune evasion strategies. These conclusions are based on high-quality research, and it is, basically, merits publication, provided that some major concerns, raised below, will be addressed before acceptance for publication. 1. The authors used TALE-YFP approach to examine the proteome associated with five different repetitive regions of the T. brucei genome and confirmed the binding of TALE-YFP with Chip-seq analyses. Ultimately, they got the list of proteins that bound to synthetic proteins, by affinity purification and LS-MS analysis and concluded that these proteins bind to different repetitive regions of the genome. There are two control proteins, one is TRF-YFP and the other KKT2-YFP, used to confirm the interactions. However, there are no experiment that confirms that the analysis gives some insight into the role of any putative or new protein in telomere biology, VSG gene regulation or chromosomal segregation. The proteins, which have already been reported by other studies, are mentioned. Although the author discovered many proteins in these repetitive regions, their role is yet unknown. It is recommended to take one or more of the new putative proteins from the repetitive elements and show whether or not they (1) bind directly to the specific repetitive sequence (e.g., by EMSA); (2) it is recommended that the authors will knockdown of one or a small sample of the new discovered proteins, which may shed light on their function at the repetitive region, as a proof of concept.

Response

The main request from Referee 1 is for individual evaluation of protein-DNA interaction for a few candidates identified in our TALE-YFP affinity purifications, particularly using EMSA to identify binding to the DNA repeats used for the TALE selection. In our opinion, such an approach would not actually provide the validation anticipated by the reviewer. The power of TALE-YFP affinity selection is that it enriches for protein complexes that associate with the chromatin that coats the target DNA repetitive elements rather than only identifying individual proteins or components of a complex that directly bind to DNA assembled in chromatin.

The referee suggests we express recombinant proteins and perform EMSA for selected candidates, but many of the identified proteins are unlikely to directly bind to DNA - they are more likely to associate with a combination of features present in DNA and/or chromatin (e.g. specific histone variants or histone post-translational modifications). Of course, a positive result would provide some validation but only IF the tested protein can bind DNA in isolation - thus, a negative result would be uninformative.

In fact, our finding that KKT proteins are enriched using the 177R-TALE (minichromosome repeat sequence) identifies components of the trypanosome kinetochore known (KKT2) or predicted (KKT3) to directly bind DNA (Marciano et al., 2021; PMID: 34081090), and likewise the TelR-TALE identifies the TRF component that is known to directly associate with telomeric (TTAGGG)n repeats (Reis et al 2018; PMID: 29385523). This provides reassurance on the specificity of the selection, as does the lack of cross selectivity between different TALEs used (see later point 3 below). The enrichment of the respective DNA repeats quantitated in Figure 2B (originally Figure S1) also provides strong evidence for TALE selectivity.

It is very likely that most of the components enriched on the repetitive elements targeted by our TALE-YFP proteins do not bind repetitive DNA directly. The TRF telomere binding protein is an exception - but it is the only obvious DNA binding protein amongst the many proteins identified as being enriched in our TelR-TALE-YFP and TRF-YFP affinity selections.

The referee also suggests that follow up experiments using knockdown of the identified proteins found to be enriched on repetitive DNA elements would be informative. In our opinion, this manuscript presents the development of a new methodology previously not applied to trypanosomes, and referee 2 highlights the value of this methodological development which will be relevant for a large community of kinetoplastid researchers. In-depth follow-up analyses would be beyond the scope of this current study but of course will be pursued in future. To be meaningful such knockdown analyses would need to be comprehensive in terms of their phenotypic characterisation (e.g. quantitative effects on chromosome biology and cell cycle progression, rates and mechanism of recombination underlying antigenic variation, etc) - simple RNAi knockdowns would provide information on fitness but little more. This information is already publicly available from genome-wide RNAi screens (www.tritrypDB.org), with further information on protein location available from the genome-wide protein localisation resource (Tryptag.org). Hence basic information is available on all targets selected by the TALEs after RNAi knock down but in-depth follow-up functional analysis of several proteins would require specific targeted assays beyond the scope of this study.

NonR-TALE-YFP does not have a binding site in the genome, but YFP protein should still be expressed by T. brucei clones with NLS. The authors have to explain why there is no signal detected in the nucleus, while a prominent signal was detected near kDNA (see Fig.2). Why is the expression of YFP in NonR-TALE almost not shown compared to other TALE clones?

Response

The NonR-TALE-YFP immunolocalisation signal indeed is apparently located close to the kDNA and away from the nucleus. We are not sure why this is so, but the construct is sequence validated and correct. However, we note that artefactual localisation of proteins fused to a globular eGFP tag, compared to a short linear epitope V5 tag, near to the kinetoplast has been previously reported (Pyrih et al, 2023; PMID: 37669165),

The expression of NonR-TALE-YFP is shown in Supplementary Fig. S2 in comparison to other TALE proteins. Although it is evident that NonR-TALE-YFP is expressed at lower levels than other TALEs (the different TALEs have different expression levels), it is likely that in each case the TALE proteins would be in relative excess.

It is possible that the absence of a target sequence for the NonR-TALE-YFP in the nucleus affects its stability and cellular location. Understanding these differences is tangential to the aim of this study.

However, importantly, NonR-TALE-YFP is not the only control for used for specificity in our affinity purifications. Instead, the lack of cross-selection of the same proteins by different TALEs (e.g. TelR-TALE-YFP, 177R-TALE-YFP) and the lack of enrichment of any proteins of interest by the well expressed ingiR-TALE-YFP or 147R-TALE-YFP proteins each provide strong evidence for the specificity of the selection using TALEs, as does the enrichment of similar protein sets following affinity purification of the TelR-TALE-YFP and TRF-YFP proteins which both bind telomeric (TTAGGG)n repeats. Moreover, control affinity purifications to assess background were performed using cells that completely lack an expressed YFP protein which further support specificity (Figure 6).

We have added text to highlight these important points in the revised manuscript:

Page 8:

"However, the expression level of NonR-TALE-YFP was lower than other TALE-YFP proteins; this may relate to the lack of DNA binding sites for NonR-TALE-YFP in the nucleus."

Page 8:

"NonR-TALE-YFP displayed a diffuse nuclear and cytoplasmic signal; unexpectedly the cytoplasmic signal appeared to be in the vicinity the kDNA of the kinetoplast (mitochrondria). We note that artefactual localisation of some proteins fused to an eGFP tag has previously been observed in T. brucei (Pyrih et al, 2023)."

Page 10:

Moreover, a similar set of enriched proteins was identified in TelR-TALE-YFP affinity purifications whether compared with cells expressing no YFP fusion protein (No-YFP), the NonR-TALE-YFP or the ingiR-TALE-YFP as controls (Fig. S7B, S8A; Tables S3, S4). Thus, the most enriched proteins are specific to TelR-TALE-YFP-associated chromatin rather than to the TALE-YFP synthetic protein module or other chromatin.

As a proof of concept, the author showed that the TALE method determined the same interacting partners enrichment in TelR-TALE as compared to TRF-YFP. And they show the same interacting partners for other TALE proteins, whether compared with WT cells or with the NonR-TALE parasites. It may be because NonR-TALE parasites have almost no (or very little) YFP expression (see Fig. S3) as compared to other TALE clones and the TRF-YFP clone. To address this concern, there should be a control included, with proper YFP expression.

Response

See response to point 2, but we reiterate that the ingi-TALE -YFP and 147R-TALE-YFP proteins are well expressed (western original Fig. S3 now Fig. S2) but few proteins are detected as being enriched or correspond to those enriched in TelR-TALE-YFP or TRF-YFP affinity purifications (see Fig. S9). Therefore, the ingi-TALE -YFP and 147R-TALE-YFP proteins provide good additional negative controls for specificity as requested. To further reassure the referee we have also included additional volcano plots which compare TelR-TALE-YFP, 70R-TALE-YFP or 177R-TALE-YFP to the ingiR-TALE-YFP affinity selection (new Figure S8). As with No-YFP or NonR-TALE-YFP controls, the use of ingiR-TALE-YFP as a negative control demonstrates that known telomere associated proteins are enriched in TelR-TALE-YFP affinity purification, RPA subunits enriched with 70R-TALE-YFP and Kinetochore KKT poroteins enriched with 177R-TALE-YFP. These analyses demonstrate specificity in the proteins enriched following affinity purification of our different TALE-YFPs and provide support to strengthen our original findings.

We now refer to use of No-YFP, NonR-TALE-YFP, and ingiR-TALE -YFP as controls for comparison to TelR-TALE-YFP, 70R-TALE-YFP or 177R-TALE-YFP in several places:

Page10:

"Moreover, a similar set of enriched proteins was identified in TelR-TALE-YFP affinity purifications whether compared with cells expressing no YFP fusion protein (No-YFP), the NonR-TALE-YFP or the ingiR-TALE-YFP as controls (Fig. S7B, S8A; Tables S3, S4)."

Page 11:

"Thus, the nuclear ingiR-TALE-YFP provides an additional chromatin-associated negative control for affinity purifications with the TelR-TALE-YFP, 70R-TALE-YFP and 177R-TALE-YFP proteins (Fig. S8)."

"Proteins identified as being enriched with 70R-TALE-YFP (Figure 6D) were similar in comparisons with either the No-YFP, NonR-TALE-YFP or ingiR-TALE-YFP as negative controls."

Top Page 12:

"The same kinetochore proteins were enriched regardless of whether the 177R-TALE proteomics data was compared with No-YFP, NonR-TALE or ingiR-TALE-YFP controls."

Discussion Page 13:

"Regardless, the 147R-TALE and ingiR-TALE proteins were well expressed in T. brucei cells, but their affinity selection did not significantly enrich for any relevant proteins. Thus, 147R-TALE and ingiR-TALE provide reassurance for the overall specificity for proteins enriched TelR-TALE, 70R-TALE and 177R-TALE affinity purifications."

After the artificial expression of repetitive sequence binding five-TALE proteins, the question is if there is any competition for the TALE proteins with the corresponding endogenous proteins? Is there any effect on parasite survival or health, compared to the control after the expression of these five TALEs YFP protein? It is recommended to add parasite growth curves, for all the TALE-proteins expressing cultures.

Response

Growth curves for cells expressing TelR-TALE-YFP, 177R-TALE-YFP and ingiR-TALE-YFP are now included (New Fig S3A). No deficit in growth was evident while passaging 70R-TALE-YFP, 147R-TALE-YFP, NonR-TALE-YFP cell lines (indeed they grew slightly better than controls).

The following text has been added page 8:

"Cell lines expressing representative TALE-YFP proteins displayed no fitness deficit (Fig. S3A)."

Since the experiments were performed using whole-cell extracts without prior nuclear fractionation, the authors should consider the possibility that some identified proteins may have originated from compartments other than the nucleus. Specifically, the detection of certain binding proteins might reflect sequence homology (or partial homology) between mitochondrial DNA (maxicircles and minicircles) and repetitive regions in the nuclear genome. Additionally, the lack of subcellular separation raises the concern that cytoplasmic proteins could have been co-purified due to whole cell lysis, making it challenging to discern whether the observed proteome truly represents the nuclear interactome.

Response

In our experimental design, we confirmed bioinformatically that the repeat sequences targeted were not represented elsewhere in the nuclear or mitochondrial genome (kDNA). The absence of subcellular fractionation could result in some cytoplasmic protein selection, but this is unlikely since each TALE targets a specific DNA sequence but is otherwise identical such that cross-selection of the same contaminating protein set would be anticipated if there was significant non-specific binding. We have previously successfully affinity selected 15 chromatin modifiers and identified associated proteins without major issues concerning cytoplasmic protein contamination (Staneva et al 2021 and 2022; PMID: 34407985 and 36169304). Of course, the possibility that some proteins are contaminants will need to be borne in mind in any future follow-up analysis of proteins of interest that we identified as being enriched on specific types of repetitive element in T. brucei. Proteins that are also detected in negative control, or negative affinity selections such as No-YFP, NoR-YFP, IngiR-TALE or 147R-TALE must be disregarded.

'6'. Should the authors qualify some of their claims as preliminary or speculative, or remove them altogether? As mentioned earlier, the author claimed that this study has provided new information concerning telomere biology, chromosomal segregation mechanisms, and immune evasion strategies. But there are no experiments that provides a role for any unknown or known protein in these processes. Thus, it is suggested to select one or two proteins of choice from the list and validate their direct binding to repetitive region(s), and their role in that region of interaction.

Response

As highlighted in response to point 1 the suggested validation and follow up experiments may well not be informative and are beyond the scope of the methodological development presented in this manuscript. Referee 2 describes the study in its current form as "a significant conceptual and technical advancement" and "This approach enhances our understanding of chromatin organization in these regions and provides a foundation for investigating the functional roles of associated proteins in parasite biology."

The Referee's phrase 'validate their direct binding to repetitive region(s)' here may also mean to test if any of the additional proteins that we identified as being enriched with a specific TALE protein actually display enrichment over the repeat regions when examined by an orthogonal method. A key unexpected finding was that kinetochore proteins including KKT2 are enriched in our affinity purifications of the 177R-TALE-YFP that targets 177bp repeats (Figure 6F). By conducting ChIP-seq for the kinetochore specific protein KKT2 using YFP-KKT2 we confirmed that KKT2 is indeed enriched on 177bp repeat DNA but not flanking DNA (Figure 7). Moreover, several known telomere-associated proteins are detected in our affinity selections of TelR-TALE-YFP (Figure 6B, FigS6; see also Reis et al, 2018 Nuc. Acids Res. PMID: 29385523; Weisert et al, 2024 Sci. Reports PMID: 39681615).

Would additional experiments be essential to support the claims of the paper? Request additional experiments only where necessary for the paper as it is, and do not ask authors to open new lines of experimentation. The answer for this question depends on what the authors want to present as the achievements of the present study. If the achievement of the paper was is the creation of a new tool for discovering new proteins, associated with the repeat regions, I recommend that they add a proof for direct interactions between a sample the newly discovered proteins and the relevant repeats, as a proof of concept discussed above, However, if the authors like to claim that the study achieved new functional insights for these interactions they will have to expand the study, as mentioned above, to support the proof of concept.

Response

See our response to point 1 and the point we labelled '6' above.

Are the suggested experiments realistic in terms of time and resources? It would help if you could add an estimated cost and time investment for substantial experiments. I think that they are realistic. If the authors decided to check the capacity of a small sample of proteins (which was unknown before as a repetitive region binding proteins) to interacts directly with the repeated sequence, it will substantially add of the study (e.g., by EMSA; estimated time: 1 months). If the authors will decide to check the also the function of one of at least one such a newly detected proteins (e.g., by KD), I estimate the will take 3-6 months.

Response

As highlighted previously the proposed EMSA experiment may well be uninformative for protein complex components identified in our study or for isolated proteins that directly bind DNA in the context of a complex and chromatin. RNAi knockdown data and cell location data (as well as developmental expression and orthology data) is already available through tritrypDB.org and trtyptag.org

Are the data and the methods presented in such a way that they can be reproduced? Yes

Are the experiments adequately replicated, and statistical analysis adequate? The authors did not mention replicates. There is no statistical analysis mentioned.

Response

The figure legends indicate that all volcano plots of TALE affinity selections were derived from three biological replicates. Cutoffs used for significance: PFor ChiP-seq two biological replicates were analysed for each cell line expressing the specific YFP tagged protein of interest (TALE or KKT2). This is now stated in the relevant figure legends - apologies for this oversight. The resulting data are available for scrutiny at GEO: GSE295698.

Minor comments: -Specific experimental issues that are easily addressable. The following suggestions can be incorporated: 1. Page 18, in the material method section author mentioned four drugs: Blasticidine, Phleomycin and G418, and hygromycin. It is recommended to mention the purpose of using these selective drugs for the parasite. If clonal selection has been done, then it should also be mentioned.

Response

We erroneously added information on several drugs used for selection in our labaoratory. In fact all TALE-YFP construct carry the Bleomycin resistance genes which we select for using Phleomycin. Also, clones were derived by limiting dilution immediately after transfection.

We have amended the text accordingly:

Page 17/18:

"Cell cultures were maintained below 3 x 106 cells/ml. Pleomycin 2.5 mg/ml was used to select transformants containing the TALE construct BleoR gene."

"Electroporated bloodstream cells were added to 30 ml HMI-9 medium and two 10-fold serial dilutions were performed in order to isolate clonal Pleomycin resistant populations from the transfection. 1 ml of transfected cells were plated per well on 24-well plates (1 plate per serial dilution) and incubated at 37{degree sign}C and 5% CO2 for a minimum of 6 h before adding 1 ml media containing 2X concentration Pleomycin (5 mg/ml) per well."

In the method section the authors mentioned that there is only one site for binding of NonR-TALE in the parasite genome. But in Fig. 1C, the authors showed zero binding site. So, there is one binding site for NonR-TALE-YFP in the genome or zero?

Response

We thank the reviewer for pointing out this discrepancy. We have checked the latest Tb427v12 genome assembly for predicted NonR-TALE binding sites and there are no exact matches. We have corrected the text accordingly.

Page 7:

"A control NonR-TALE protein was also designed which was predicted to have no target sequence in the T. bruceigenome."

Page 17:

"A control NonR-TALE predicted to have no recognised target in the T. brucei geneome was designed as follows: BLAST searches were used to identify exact matches in the TREU927 reference genome. Candidate sequences with one or more match were discarded."

The authors used two different anti-GFP antibodies, one from Roche and the other from Thermo Fisher. Why were two different antibodies used for the same protein?

Response

We have found that only some anti-GFP antibodies are effective for affinity selection of associated proteins, whereas others are better suited for immunolocalisation. The respective suppliers' antibodies were optimised for each application.

Page 6: in the introduction, the authors give the number of total VSG genes as 2,634. Is it known how many of them are pseudogenes?

Response

This value corresponds to the number reported by Consentino et al. 2021 (PMID: 34541528) for subtelomeric VSGs, which is similar to the value reported by Muller et al 2018 (PMID: 30333624) (2486), both in the same strain of trypanosomes as used by us. Based on the earlier analysis by Cross et al (PMID: 24992042), 80% of the identified VSGs in their study (2584) are pseudogenes. This approximates to the estimation by Consentino of 346/2634 (13%) being fully functional VSG genes at subtelomeres, or 17% when considering VSGs at all genomic locations (433/2872).

I found several typos throughout the manuscript.

Response

Thank you for raising this, we have read through the manuscipt several times and hopefully corrected all outstanding typos.

Fig. 1C: Table: below TOTAL 2nd line: the number should be 1838 (rather than 1828)

Corrected- thank you.

• Are prior studies referenced appropriately? Yes

• Are the text and figures clear and accurate? Yes

• Do you have suggestions that would help the authors improve the presentation of their data and conclusions? Suggested above

Reviewer #1 (Significance (Required)):

Describe the nature and significance of the advance (e.g., conceptual, technical, clinical) for the field: This study represents a significant conceptual and technical advancement by employing a synthetic TALE DNA-binding protein tagged with YFP to selectively identify proteins associated with five distinct repetitive regions of T. brucei chromatin. To the best of my knowledge, it is the first report to utilize TALE-YFP for affinity-based isolation of protein complexes bound to repetitive genomic sequences in T. brucei. This approach enhances our understanding of chromatin organization in these regions and provides a foundation for investigating the functional roles of associated proteins in parasite biology. Importantly, any essential or unique interacting partners identified could serve as potential targets for therapeutic intervention.

• Place the work in the context of the existing literature (provide references, where appropriate). I agree with the information that has already described in the submitted manuscript, regarding its potential addition of the data resulted and the technology established to the study of VSGs expression, kinetochore mechanism and telomere biology.

• State what audience might be interested in and influenced by the reported findings. These findings will be of particular interest to researchers studying the molecular biology of kinetoplastid parasites and other unicellular organisms, as well as scientists investigating chromatin structure and the functional roles of repetitive genomic elements in higher eukaryotes.

• 1Define your field of expertise with a few keywords to help the authors contextualize your point of view. 2Indicate if there are any parts of the paper that you do not have sufficient expertise to evaluate. (1) Protein-DNA interactions/ chromatin/ DNA replication/ Trypanosomes (2) None

Reviewer #2 (Evidence, reproducibility and clarity (Required)):

Summary

Carloni et al. comprehensively analyze which proteins bind repetitive genomic elements in Trypanosoma brucei. For this, they perform mass spectrometry on custom-designed, tagged programmable DNA-binding proteins. After extensively verifying their programmable DNA-binding proteins (using bioinformatic analysis to infer target sites, microscopy to measure localization, ChIP-seq to identify binding sites), they present, among others, two major findings: 1) 14 of the 25 known T. brucei kinetochore proteins are enriched at 177bp repeats. As T. brucei's 177bp repeat-containing intermediate-sized and mini-chromosomes lack centromere repeats but are stable over mitosis, Carloni et al. use their data to hypothesize that a 'rudimentary' kinetochore assembles at the 177bp repeats of these chromosomes to segregate them. 2) 70bp repeats are enriched with the Replication Protein A complex, which, notably, is required for homologous recombination. Homologous recombination is the pathway used for recombination-based antigenic variation of the 70bp-repeat-adjacent variant surface glycoproteins.

Major Comments

None. The experiments are well-controlled, claims well-supported, and methods clearly described. Conclusions are convincing.

Response Thank you for these positive comments.

Minor Comments

1) Fig. 2 - I couldn't find an uncropped version showing multiple cells. If it exists, it should be linked in the legend or main text; Otherwise, this should be added to the supplement.

Response

The images presented represent reproducible analyses, and independently verified by two of the authors. Although wider field of view images do not provide the resolution to be informative on cell location, as requested we have provided uncropped images in new Fig. S4 for all the cell lines shown in Figure 2A.

In addition, we have included as supplementary images (Fig. S3B) additional images of TelR-TALE-YFP, 177R-TALE-YFP and ingiR-TALE YFP localisation to provide additional support their observed locations presented in Figure 1. The set of cells and images presented in Figure 2A and in Fig S3B were prepared and obtained by a different authors, independently and reproducibly validating the location of the tagged protein.

2) I think Suppl. Fig. 1 is very valuable, as it is a quantification and summary of the ChIP-seq data. I think the authors could consider making this a panel of a main figure. For the main figure, I think the plot could be trimmed down to only show the background and the relevant repeat for each TALE protein, leaving out the non-target repeats. (This relates to minor comment 6.) Also, I believe, it was not explained how background enrichment was calculated.

Response

We are grateful for the reviewer's positive view of original Fig. S1 and appreciate the suggestion. We have now moved these analysis to part B of main Figure 2 in the revised manuscript - now Figure 2B. We have also provided additional details in the Methods section on the approaches used to assess background enrichment.

Page 19:

Background enrichment calculation

The genome was divided into 50 bp sliding windows, and each window was annotated based on overlapping genomic features, including CIR147, 177 bp repeats, 70 bp repeats, and telomeric (TTAGGG)n repeats. Windows that did not overlap with any of these annotated repeat elements were defined as "background" regions and used to establish the baseline ChIP-seq signal. Enrichment for each window was calculated using bamCompare, as log₂(IP/Input). To adjust for background signal amongst all samples, enrichment values for each sample were further normalized against the corresponding No-YFP ChIP-seq dataset.

Note: While revising the manuscript we also noticed that the script had a nomalization error. We have therefore included a corrected version of these analyses as Figure 2B (old Fig. S1)

3) Generally, I would plot enrichment on a log2 axis. This concerns several figures with ChIP-seq data.

Response

Our ChIP-seq enrichment is calculated by bamCompare. The resulting enrichment values are indeed log2 (IP/Input). We have made this clear in the updated figures/legends.

4) Fig. 4C - The violin plots are very hard to interpret, as the plots are very narrow compared to the line thickness, making it hard to judge the actual volume. For example, in Centromere 5, YFP-KKT2 is less enriched than 147R-TALE over most of the centromere with some peaks of much higher enrichment (as visible in panel B), however, in panel C, it is very hard to see this same information. I'm sure there is some way to present this better, either using a different type of plot or by improving the spacing of the existing plot.

Response

We thank the reviewer for this suggestion; we have elected to provide a Split-Violin plot instead. This improves the presentation of the data for each centromere. The original violin plot in Figure 4C has been replaced with this Split-Violin plot (still Figure 4C).

5) Fig. 6 - The panels are missing an x-axis label (although it is obvious from the plot what is displayed). Maybe the "WT NO-YFP vs" part that is repeated in all the plot titles could be removed from the title and only be part of the x-axis label?

Response

In fact, to save space the X axis was labelled inside each volcano plot but we neglected to indicate that values are a log2 scale indicating enrichment. This has been rectified - see Figure 6, and Fig. S7, S8 and S9.

6) Fig. 7 - I would like to have a quantification for the examples shown here. In fact, such a quantification already exists in Suppl. Figure 1. I think the relevant plots of that quantification (YFP-KKT2 over 177bp-repeats and centromere-repeats) with some control could be included in Fig. 7 as panel C. This opportunity could be used to show enrichment separated out for intermediate-sized, mini-, and megabase-chromosomes. (relates to minor comment 2 & 8)

Response

The CIR147 sequence is found exclusively on megabase-sized chromosomes, while the 177 bp repeats are located on intermediate- and mini-sized chromosomes. Due to limitations in the current genome assembly, it is not possible to reliably classify all chromosomes into intermediate- or mini- sized categories based on their length. Therefore, original Supplementary Fig. S1 presented the YFP-KKT2 enrichment over CIR147 and 177 bp repeats as a representative comparison between megabase chromosomes and the remaining chromosomes (corrected version now presented as main Figure 2B). Additionally, to allow direct comparison of YFP-KKT2 enrichment on CIR147 and 177 bp repeats we have included a new plot in Figure 7C which shows the relative enrichment of YFP-KKT2 on these two repeat types.

We have added the following text , page 12:

"Taking into account the relative to the number of CIR147 and 177 bp repeats in the current T.brucei genome (Cosentino et al., 2021; Rabuffo et al., 2024), comparative analyses demonstrated that YFP-KKT2 is enriched on both CIR147 and 177 bp repeats (Figure 7C)."

7) Suppl. Fig. 8 A - I believe there is a mistake here: KKT5 occurs twice in the plot, the one in the overlap region should be KKT1-4 instead, correct?

Response

Thanks for spotting this. It has been corrected

8) The way that the authors mapped ChIP-seq data is potentially problematic when analyzing the same repeat type in different regions of the genome. The authors assigned reads that had multiple equally good mapping positions to one of these mapping positions, randomly. This is perfectly fine when analysing repeats by their type, independent of their position on the genome, which is what the authors did for the main conclusions of the work. However, several figures show the same type of repeat at different positions in the genome. Here, the authors risk that enrichment in one region of the genome 'spills' over to all other regions with the same sequence. Particularly, where they show YFP-KKT2 enrichment over intermediate- and mini-chromosomes (Fig. 7) due to the spillover, one cannot be sure to have found KKT2 in both regions. Instead, the authors could analyze only uniquely mapping reads / read-pairs where at least one mate is uniquely mapping. I realize that with this strict filtering, data will be much more sparse. Hence, I would suggest keeping the original plots and adding one more quantification where the enrichment over the whole region (e.g., all 177bp repeats on intermediate-/mini-chromosomes) is plotted using the unique reads (this could even be supplementary). This also applies to Fig. 4 B & C.

Response

We thank the reviewer for their thoughtful comments. Repetitive sequences are indeed challenging to analyze accurately, particularly in the context of short read ChIP-seq data. In our study, we aimed to address YFP-KKT2 enrichment not only over CIR147 repeats but also on 177 bp repeats, using both ChIP-seq and proteomics using synthetic TALE proteins targeted to the different repeat types. We appreciate the referees suggestion to consider uniquely mapped reads, however, in the updated genome assembly, the 177 bp repeats are frequently immediately followed by long stretches of 70 bp repeats which can span several kilobases. The size and repetitive nature of these regions exceeds the resolution limits of ChIP-seq. It is therefore difficult to precisely quantify enrichment across all chromosomes.

Additionally, the repeat sequences are highly similar, and relying solely on uniquely mapped reads would result in the exclusion of most reads originating from these regions, significantly underestimating the relative signals. To address this, we used Bowtie2 with settings that allow multi-mapping, assigning reads randomly among equivalent mapping positions, but ensuring each read is counted only once. This approach is designed to evenly distribute signal across all repetitive regions and preserve a meaningful average.

Single molecule methods such as DiMeLo (Altemose et al. 2022; PMID: 35396487) will need to be developed for T. brucei to allow more accurate and chromosome specific mapping of kinetochore or telomere protein occupancy at repeat-unique sequence boundaries on individual chromosomes.

Reviewer #2 (Significance (Required)):

This work is of high significance for chromosome/centromere biology, parasitology, and the study of antigenic variation. For chromosome/centromere biology, the conceptual advancement of different types of kinetochores for different chromosomes is a novelty, as far as I know. It would certainly be interesting to apply this study as a technical blueprint for other organisms with mini-chromosomes or chromosomes without known centromeric repeats. I can imagine a broad range of labs studying other organisms with comparable chromosomes to take note of and build on this study. For parasitology and the study of antigenic variation, it is crucial to know how intermediate- and mini-chromosomes are stable through cell division, as these chromosomes harbor a large portion of the antigenic repertoire. Moreover, this study also found a novel link between the homologous repair pathway and variant surface glycoproteins, via the 70bp repeats. How and at which stages during the process, 70bp repeats are involved in antigenic variation is an unresolved, and very actively studied, question in the field. Of course, apart from the basic biological research audience, insights into antigenic variation always have the potential for clinical implications, as T. brucei causes sleeping sickness in humans and nagana in cattle. Due to antigenic variation, T. brucei infections can be chronic.

Response

Thank you for supporting the novelty and broad interest of our manuscript

My field of expertise / Point of view:

I'm a computer scientist by training and am now a postdoctoral bioinformatician in a molecular parasitology laboratory. The laboratory is working on antigenic variation in T. brucei. The focus of my work is on analyzing sequencing data (such as ChIP-seq data) and algorithmically improving bioinformatic tools.

iter(tag=None) が正しいような気がします。

https://docs.python.org/ja/3/library/xml.etree.elementtree.html#xml.etree.ElementTree.Element.iter

Note: This response was posted by the corresponding author to Review Commons. The content has not been altered except for formatting.

Learn more at Review Commons

Reply to the reviewers

• *

__Reviewer #1 __

Major comments

1. The manuscript posits that the loss of function of MASh components (Ogc1 and Aralar) decreases adrenergic-stimulated lipolysis by altering the cytosolic NAD⁺/NADH ratio, with AMPK/ACC mentioned as possible mediators. However, this remains speculative. Please provide mechanistic data directly linking MASh-dependent NAD⁺/NADH changes to the regulation of lipolysis in brown adipocytes during adrenergic stimulation. Answer 1) The reviewer raises an important point regarding the direct assessment of cytosolic NAD⁺/NADH redox changes as a mechanistic link for altered lipolysis in brown adipocytes lacking MASh components. To address this point, we added new data to the revised manuscript showing lactate/pyruvate ratio as measured by metabolomics. This is a well-established surrogate marker to monitor changes in redox balance. Notably, under basal (non-stimulated) conditions, the lactate/pyruvate ratio did not display any significant differences between Aralar 1 KD and control cells, suggesting preservation of cytosolic NAD⁺/NADH levels in the absence of functional MASh under these conditions. This finding is consistent with reports showing the robustness of NAD⁺ regeneration via multiple shuttles and the possibility of metabolic compensation when one shuttle is compromised (PMID: 40540398; PMID: 37647199).

The results have been added as new supplementary Figure 1 as following:

Our new metabolomics data also revealed substantial reductions in the aspartate/glutamate ratio in Aralar 1 knockdown cells, serving as a metabolomic signature of impaired MASh function and reduced exchange of these amino acids between the cytosol and mitochondria. Given that the MASh is a major mechanism for exporting cytosolic reducing equivalents into the mitochondria under high metabolic demand, its loss would be expected to impact redox homeostasis, particularly under adrenergic stimulation when glycolytic flux and lipolytic activity are elevated (PMID: 40540398).

Importantly, although our redox surrogate marker did not detect alterations, this may be explained by activation of compensatory pathways, most notably the glycerol phosphate shuttle (GPSh), which is highly expressed and active in brown adipocytes. Indirect support for this compensation comes from data shown in figure 4I showing reduced glycerol release in Aralar 1 KD cells upon norepinephrine stimulation and blocked lipolysis. This suggests a redirection of glycolytically derived G3P away from release and toward enhanced cycling within the GPSh, supporting cytosolic NAD⁺ regeneration via mitochondrial FAD-dependent G3PDH and cytosolic NAD⁺-dependent G3PDH activity. This is consistent with studies documenting that the combined action of MASh and GPSh maintains NAD redox homeostasis in brown adipocytes especially during non-thermogenic conditions (PMID: 168075; PMID: 40540398; PMID: 37647199). We have included a discussion about this possibility at page 9, third paragraph as follows:

*“Previous studies have shown that BAT exhibits high activity of mitochondrial FAD-dependent glycerol-3-phosphate dehydrogenase (mG3PDH), which functions as an electron sink to sustain low cytosolic NADH levels essential for continuous glycolytic flux [11]. Accordingly, suppression of the MASh, either genetically or pharmacologically, is likely to induce a compensatory upregulation of the GPSh. This adaptation would enhance G3P turnover, contributing to the maintenance of cytosolic NAD redox balance. Moreover, the increased flux through the GPSh could favor fatty acid esterification and triglyceride synthesis or re-esterification, consistent with our findings in Ogc and/or Aralar 1 KD cells, where (i) triglyceride content rises (Fig. 3), (ii) overall respiratory rates remain largely unaltered (Figs. 2D–G), and (iii) glycerol release declines significantly (Fig. 4I). Notably, the decrease in glycerol release persists even when lipolysis is blocked by ATGlistatin, suggesting that the available G3P pool is rerouted from dephosphorylation and extracellular release toward oxidation to DHAP by mG3PDH to regenerate cytosolic NAD+ under MASh-deficient conditions. We propose that interference with the MASh does not directly impact lipolysis but instead alters the cellular balance between DHAP and G3P owing to enhanced activity of the GPSh. This metabolic shift would favor the esterification of G3P with free fatty acids, thereby promoting triglyceride synthesis. These results support the notion that, even during adrenergic stimulation—when long-chain unsaturated fatty acids and their CoA esters strongly inhibit mG3PDH activity [11]—the residual flux through the glycerophosphate shuttle remains critical for sustaining cytosolic NAD redox equilibrium [11,19,32].” *

• *

At the mechanistic level, adrenergic stimulation in brown adipocytes activates robust lipolysis and thermogenic gene programs, generating high NADH that must be efficiently reoxidized to sustain flux through glycolysis and lipolysis-linked pathways. Our findings are consistent with a model in which the loss of MASh does not prevent cytosolic NAD⁺ regeneration or lipolytic flux during acute adrenergic stimulation, due to compensatory upregulation of the GPSh, as suggested by the glycerol release changes. Thus, while MASh normally acts as a conduit for NADH export and aspartate/glutamate exchange, in its absence, the GPSh maintains cytosolic redox balance, thereby sustaining glycolytic and lipolytic capacity.

We agree that future studies should employ direct measurements of cytosolic NAD⁺/NADH ratios (e.g., genetically-encoded redox sensors) during adrenergic stimulation and specific pharmacological inhibition of both shuttles to dissect these relationships in greater detail. We sincerely appreciate the reviewer's input, which has prompted us to clarify the indirect but robust evidence supporting a role for compensatory redox shuttle activity in preserving brown adipocyte lipolysis in the setting of MASh impairment.

We have further added a new paragraph in the discussion section (page 10)::

*“Mechanistically, the connection between the MASh and lipolysis appears to involve regulation of the cytosolic NAD⁺/NADH redox balance. MASh activity facilitates the regeneration of NAD⁺ from NADH in the cytosol, primarily through the reduction of oxaloacetate to malate by cytosolic malate dehydrogenase (Fig. 1G-H). Despite the theoretical expectation that reductions in MASh activity would disturb redox homeostasis, our metabolomic data show that the lactate/pyruvate ratio remains unchanged under conditions of MASh impairment, indicating that the overall cytosolic NAD⁺/NADH ratio is maintained (Figure S1A-C). While direct measurements of cytosolic NAD⁺/NADH were not performed, the preserved lactate/pyruvate ratio in Aralar 1 KD cells under basal conditions strongly suggests redox stability, likely due to compensatory activity by alternative mitochondrial shuttles or metabolic adaptations that maintain NAD redox homeostasis despite MASh impairment [18,33]. *

Previous evidence indicates that BAT exhibits high activity of mitochondrial FAD-dependent glycerol-3-phosphate dehydrogenase (G3PDH), which acts as an electron sink to sustain low cytosolic NADH levels critical for glycolysis [34]. In this sense, it is conceivable that genetic or pharmacological suppression of MASh triggers compensatory enhancement of the G3P shuttle, increasing G3P availability and facilitating the maintenance of cytosolic NAD redox balance. This adaptation could also promote fatty acid esterification and triglyceride synthesis or re-esterification, aligning with our observations that in Ogc and/or Aralar 1 KD cells: (i) triglyceride levels increase (Fig. 3); (ii) overall respiratory rates are preserved (Figs. 2D–G); and (iii) glycerol release is significantly reduced (Fig 4I).”

• *

__ The absence of in vivo analysis of lipid-droplet size in MASh loss-of-function models is a major concern. In vitro results could be confounded by differences in differentiation stage between groups. Please document equivalent adipogenesis across groups (e.g., Pparg/Cebpa/Plin1/Fabp4 expression).__

Answer 2) We thank the reviewer for the thoughtful and constructive comment regarding potential confounding by differences in differentiation stage, and for highlighting the importance of documenting equivalence between experimental groups. We appreciate the opportunity to clarify and provide additional assurance on this point.

As detailed in our manuscript, we have performed qPCR analysis of multiple well-established markers of brown adipocyte differentiation, including Ucp1, Elovl3, Prdm16, Pparg, Cebpa, Plin1, and Fabp4, in both scramble, aralar1 KD, and Ogc KD cells (see Fig. S1A and accompanying text). Our results show no apparent effect of these genetic interventions on overall differentiation, as the expression levels of these key markers were consistently unaltered across groups. Furthermore, adenoviral-mediated knockdown of Ogc achieved an approximate 80% reduction in Ogc mRNA (see Fig. S1B), yet most differentiation markers remained unaffected. We did observe significant increases in Atgl, Pgc1α, and Tfam mRNA levels, which may indicate a degree of pathway reprogramming without affecting the general differentiation profile. We propose that interference with the MASh does not directly impact lipolysis but instead alters the cellular balance between DHAP and G3P owing to enhanced activity of the GPSh. This metabolic shift would favor the esterification of G3P with free fatty acids, thereby promoting triglyceride synthesis.

Additional experimental support for equivalent differentiation can be drawn from our respirometry data presented in Figures 2E and 2G. These figures demonstrate that respiratory rates upon norepinephrine stimulation, which is a sensitive indicator of brown adipocyte thermogenic capacity, were essentially identical in scramble, aralar1 KD, and Ogc KD cells. Since norepinephrine-stimulated respiration requires both functional mitochondria and the full differentiation of brown adipocytes, these results strongly support the conclusion that silencing either MASh component does not impair the fundamental ability of cells to undergo brown adipocyte differentiation or achieve functional thermogenic competence.

This is consistent with published findings showing that norepinephrine triggers robust respiration and thermogenic activation only in fully differentiated and functional brown adipocytes, making such measurements a widely accepted proxy for differentiation status and mitochondrial integrity. Thus, the equivalent respiratory responses observed in all groups further validate that differentiation was not compromised by the genetic interventions.

We hope this clarifies that equivalent adipogenesis was carefully documented and that any observed phenotypes are unlikely to be attributable to differences in differentiation stages. Thank you again for your rigorous assessment and for helping to ensure the robustness of our study.

__ Please include rescue experiments (add-back OGC1 and Aralar) to rule out siRNA/shRNA off-target effects and verify that the phenotype stems from MASh loss of function.__

Answer 3) We thank the reviewer for this important suggestion regarding the inclusion of rescue experiments with add-back of Ogc and Aralar to definitively exclude off-target effects of the siRNA/shRNA-mediated knockdowns.

We would like to kindly point out that although we did not perform add-back rescue experiments directly, the consistency of phenotypes observed across two independent genetic interventions—aralar 1 KD and Ogc KD—strongly argues against off-target effects being responsible for the observed metabolic and functional alterations. Specifically, both knockdowns yielded remarkably similar phenotypes in multiple assays, including respirometry analyses, mitochondrial morphology, lipid droplet homeostasis, and lipid metabolism, supporting the conclusion that these effects stem from MASh loss of function rather than nonspecific silencing.

Furthermore, our new supplementary data (new Supplementary Figure 1A-F) reveals a significant reduction in the aspartate/glutamate ratio in Aralar 1 KD cells, a compelling functional readout for MASh impairment. This molecular evidence corroborates that our genetic interventions effectively disrupted MASh activity as intended.

We sincerely appreciate the reviewer’s thorough evaluation and understand the importance of rescue experiments. While recognizing their value, we believe the convergent genetic, metabolic, and functional evidence presented across two different MASh components provides strong and consistent support that the phenotypes observed are due to specific loss of MASh function.

__ Please expand on physiological significance: What is the importance of MASh regulation of BAT lipolysis in long-term adaptive thermogenesis?__

Answer 4) This is a very interesting aspect, and we have included a new paragraph in the discussion section (page 14) to address it as follows:

“Our results, supported by recent literature, strongly indicate that the malate–aspartate shuttle (MASh) plays a key role in facilitating fatty acid–dependent thermogenesis in brown adipocytes. Specifically, BAT-targeted overexpression of GOT1 has been shown to enhance β-oxidation and support acute cold-induced thermogenesis (PMID: 40540398). Interestingly, genetic ablation of GOT1—and thus MASh inhibition—preserves cold-induced thermogenesis by promoting a metabolic shift from fatty acid to glucose oxidation. Our findings corroborate and extend these observations by demonstrating that MASh impairment sustains overall respiratory activity in norepinephrine-stimulated brown adipocytes (Figures 2D–2G), while concurrently impairing lipolysis and resulting in an accumulation of small lipid droplets (Figures 3 and 4). Collectively, these data suggest that MASh not only modulates substrate preference towards fatty acid oxidation but also facilitates lipolysis, an essential upstream step that enables lipid oxidation and supports thermogenic heat production.”

Minor comments

1. __ Fig. 4 legend/title contains a typo ("lypolysis" → lipolysis).__ Answer 1) Corrected

__ In Fig. 2 legend line: "Adevirus-mediated" → Adenovirus-mediated; "OCAR" → OCR.__

Answer 2) Corrected

__ For lipolysis imaging, you already show Forskolin/Atglistatin/Etomoxir controls; add a vehicle-only time course overlay in the main figure (currently in text/legend) to aid visual comparison.__

Answer 3) We thank the reviewer for pointing this out. To improve clarity, we have updated the labeling in Figures 3 and 4: “basal” now clearly refers to the unstimulated/untreated condition, and the previously labeled “UT” condition has been clarified as “untransduced.” These changes make the figure legends and data presentation more consistent and easier to interpret.

__ Ensure consistent gene symbols (Atgl/Pnpla2), and protein capitalization.__

Answer 4) Corrected.

__Reviewer #2 __

Major points:

1. __ In the current manuscript, mitochondrial morphology (area, aspect ratio, and roundness) was analyzed in OGC1 KD cells using TMRE, whereas MitoTracker Deep Red (MTDR) was used in Aralar1 KD cells. Notably, TMRE is a ΔΨm-dependent probe. The signal intensity can change, or the distribution may reflect alterations in membrane potential rather than true morphological changes. Therefore, the observed differences in OGC1 KD cells based on TMRE staining may be confounded by the dye's functional dependence, potentially biasing the conclusions. It is recommended to evaluate mitochondrial morphology with consistent trackers across conditions. In addition, in the subsequent OCR analysis, mitochondrial area was used for normalization. Please clarify which staining method was employed, and provide justification for its suitability.__ Answer 1) We thank the reviewer for this insightful comment. Indeed, TMRE is a membrane potential-sensitive dye and could therefore potentially affect measurements of mitochondria.

We would like to point out that mitochondrial morphology was quantified based on mitochondrial area rather than fluorescence intensity. To create an accurate binary map of mitochondria, we used a low threshold, which allowed us to include even weakly stained mitochondria and thereby detect them independently of their membrane potential. In all imaged cells, TMRE signal was sufficient to reliably identify mitochondrial pixels. Moreover, these images were acquired using a confocal microscope, where the risk of pixel expansion due to higher fluorescence intensity is minimized. Lastly, given that overall mitochondrial oxygen consumption in these cells remains largely intact, we do not expect a substantial loss of membrane potential, although minor effects cannot be entirely excluded.

We opted to use TMRE for imaging Ogc KD cells because the scramble control for these shRNA viruses carries an mKate fluorescent tag, which overlaps with the MTDR signal. Since accurate assessment of transduction efficiency relied on detecting mKate, MTDR could not be used in these experiments. Importantly, we only compare mitochondrial morphology within the same staining condition and do not draw conclusions across cells stained with different dyes.

To ensure transparency, we have added a new section at the discussion (page 17, 2nd paragraph) highlighting the potential influence of ΔΨm-dependent dyes on morphological measurements as follows:

“It is also important to note that mitochondrial morphology was quantified using MTDR in Aralar 1 KD cells and TMRE in Ogc KD cells due to experimental constraints (see Methods). TMRE is a membrane potential–dependent dye, which could potentially influence morphology measurements. To minimize this risk, we used confocal microscopy, which reduces the likelihood of pixel expansion due to higher fluorescence intensity, and set thresholds to detect even weakly stained mitochondria. Nonetheless, we cannot fully exclude the possibility that the differences in morphology observed between Aralar 1 and Ogc KD are influenced by the use of different dyes; however, statistical comparisons were never performed across samples stained with different dyes.”

Also, we have expanded the Methods section (page 22, 2nd paragraph) to include a rationale for using these dyes and describe the analysis protocol as following:

“TMRE was used for Ogc KD cells because the scramble control for the shRNA viruses carries an mKate fluorescent tag, which overlaps with MTDR fluorescence, preventing its use. MTDR was used for Aralar KD cells. Image Analysis was performed in FIJI (ImageJ, NIH). For the quantification of mitochondrial morphology and area, images stained with TMRE or MTDR were analyzed. Thresholds were adjusted to ensure that even weakly stained mitochondria were detected and included in the analysis. Only the mitochondrial area was evaluated, independent of fluorescence intensity.”

Minor points:

1. __ In the introduction, the authors state that "LDH activity increases in the context of BAT activation". This point is important for the logic of the manuscript, reference [10] cited here is not sufficient to support this claim. It is recommended to provide appropriate references to support this statement.__ Answer 1) We have substantially changed this paragraph in the revised manuscript to better explain why LDH would not act as a major player in contributing to NAD redox balance in the context of BAT thermogenesis, as follows:

“In mammalian cells, cytosolic NAD⁺ is regenerated through lactate dehydrogenase (LDH), the glycerol-3-phosphate shuttle (GPSh), or the malate-aspartate shuttle (MASh). In BAT, however, lactate production rises only slightly with adrenergic activation and most lactate is oxidized via the TCA cycle, suggesting that LDH primarily consumes NAD⁺ rather than regenerating it [PMID: 30456392; PMID: 37337122; PMID: 30456392; PMID: 37802078; PMID: 40982723]. Consequently, mitochondrial redox shuttles become critical for sustaining cytosolic NAD⁺ supply”.

We have also provided additional references to support this new section at the introduction.

__ In Fig. 1A and B-D, there are inconsistencies and duplications in the abbreviation labels. Please check and revise accordingly. __

Answer 2) We thank the reviewer for this comment. We would like to clarify that Figure 1A is a schematic overview of the system, while Figures 1B–D show protein expression in specific contexts: whole BAT (B), whole liver (C), and BAT mitochondria (D). In Figures 1B and 1C, all components are shown because both cytosolic (MDH1 and GOT1) and mitochondrial proteins (MDH2, GOT2, Aralar 1 and 2 and OGC) are present. In contrast, Figure 1D shows only mitochondrial components (OGC, Aralar1, MDH2, and GOT2). Although Aralar2 is a mitochondrial protein, it was not detected in this study (Forner et al., 2009). Similarly, cytosolic components such as MDH1 and GOT1 are not shown in Figure 1D because they are absent in the mitochondrial fraction. We have revised the figure legend to make these distinctions clearer.

__ In Fig. S1, the number of n indicated does not match the number of data points shown. Please clarify whether these represent technical replicates or biological replicates, and provide a detailed description of the statistical methods used throughout the manuscript.__

Answer 3) We thank the reviewer for catching this and allowing us to correct our mistakes. In the revised version, we have corrected the figure legend of Supplementary Figure 1 so that the number of n matches the data points shown.

__ Please provide details on the normalization strategy used in the BODIPY-C12/BODIPY-493 staining analysis, such as whether fluorescence intensity was quantified as mean or integrated values, and whether the analysis was normalized to lipid droplet area, cell number, or baseline. Since lipolytic stimulation can reduce droplet size and increase droplet number, these factors may bias the results. __

Answer 4) We thank the reviewer for this important comment and apologize for the lack of detail regarding this analysis. The analysis of BODIPY-C12 and BODIPY-493 was performed by quantifying the mean fluorescence intensity of BODIPY-C12 detected within a mask generated from the BODIPY-493 signal. This approach allowed us to define all lipid droplets and measure the release of previously esterified C12. To account for variability across samples, the data were normalized to each sample’s individual baseline at time point 0 and expressed as fold change relative to this baseline. In the revised manuscript we have included this description in the Methods section (page 18, last paragraph) for clarity and reproducibility, as following:

“Lipid Droplet area was defined based on Bodipy 493/503 signal, which was used to generate a mask identifying all lipid droplets. Within this mask, the mean fluorescence intensity of BODIPY C12 was quantified over time to monitor the release of previously esterified C12. To account for variability between samples, data were normalized to each sample’s individual baseline at time point 0 and expressed as fold change relative to this baseline.”

__ The manuscript notes that the unexpected result in Fig. 3K-M in parallel with increased Atgl mRNA expression might be because it does not reflect protein levels or enzymatic activity. To strengthen this point, it is recommended to include data on ATGL and phosphorylation ATGL. __

Answer 5) We thank the reviewer for this constructive comment. We have clarified these aspects in the revised Results and Discussion sections to reflect this interpretation more accurately as follows:

“Notably, Atgl mRNA measurement in our study was primarily used as a marker of brown adipocyte differentiation, rather than as a direct indicator of ATGL protein abundance or enzymatic activity. We detected increased Atgl expression only in Ogc KD cells (Fig. S1H), but not in Aralar 1 KD cells (Fig. S1G). This likely does not reflect a major difference in differentiation status, as other brown adipocyte markers and norepinephrine-stimulated respiration were comparable between scramble and knockdown cells (Fig. 2D-G and 2N-O and S1G-H). Although lipolysis was not evaluated in Ogc KD cells, in Aralar 1 KD cells basal lipolysis remained unchanged (Fig. 4D-E and 4G-I), whereas norepinephrine-stimulated lipolysis was delayed or partially inhibited. Notably, the enhanced fatty acid esterification observed in Ogc KD cells despite elevated Atgl expression is not contradictory, since in brown adipocytes lipolysis and re-esterification occur concurrently to sustain high lipid turnover [34].

__ Red-on-black is not a great color code for IMFs, how about black-and-white? __

Answer 6) We have changed color text for white on figures 2H and K as suggested.

__Reviewer #3 __

Major points;

1. __ Although in the manuscript Veliova and coworkers demonstrated that MAS is functional in brown adipocytes showing kinetic parameters equivalent to that previously described in other tissues, surprisingly, when its components are downregulated, no effect, or very little, on mitochondrial respiration is found (figure 2). This is an intriguing result since MAS disruption has been widely reported to impair respiration in different cell types and tissues. However, since no direct evidence of MAS dysfunction is provided, it is possible that MAS may still remain partially or fully functional under the conditions used by the authors, and therefore this point needs to be clarified to validate these results.__ Answer 1) We thank the reviewer for the insightful comment and the opportunity to clarify these important points regarding MASh dysfunction validation in our study. We acknowledge the reviewer’s observation that mitochondrial respiration was largely unaffected by MASh component knockdown, which is indeed intriguing. Importantly, as already indicated in our responses to Reviewer 1, we have provided new data showing direct molecular evidence of MASh impairment through substantial reductions in the aspartate/glutamate ratio in Aralar 1 KD cells (new Supplementary Figure S1F). This ratio is a well-established functional readout reflecting MASh activity and amino acid exchange between cytosol and mitochondria, as demonstrated in original experimental studies of MASh function in multiple tissues including brown adipocytes (PMID: 4436323). The reduction in the aspartate/glutamate ratio directly confirms loss of MASh functionality even though respiratory rates remained unchanged, likely due to metabolic compensation by robust glycerol phosphate shuttle (GPSh) activity, as further supported by our data showing reduced glycerol release upon norepinephrine stimulation in Aralar 1 KD cells cells (Figure 4I). This metabolic rerouting maintains cytosolic NAD⁺ regeneration and partially preserves respiration and energy metabolism under these experimental conditions (PMID: 168075; PMID: 40540398; PMID: 37647199). Thus, the combination of metabolomic, respirometry, and functional lipid data strongly indicates that MASh activity was disrupted specifically and effectively by our genetic interventions. This molecular evidence was already signposted in our original manuscript and responses, underscoring that MASh loss of function—and not residual or compensatory MASh activity—is responsible for the phenotypes reported. We greatly appreciate the reviewer’s insightful attention to this critical mechanistic issue and hope this provides clear reassurance that MASh impairment was indeed achieved and functionally validated within our study framework.

Furthermore, strategies used to downregulate MAS components produce only a partial reduction in mRNA levels, about 70 %, but its outcome on protein levels has not been determined. and the remaining protein level could be sufficient to maintain shuttle activity. Therefore, the effect of silencing at protein level should be analyzed, because as authors also point out on page 16; "mRNA levels may not reflect actual protein levels or activity".

Answer 2) We thank the reviewer for this important point. Our knockdowns resulted in ~70–80% reduction in mRNA levels. While not complete, this represents a substantial decrease and is sufficient to produce strong functional effects. At the time the experiments were performed, we did not have access to suitable antibodies, and the available antibodies did not provide reliable signals in our samples, which is why we used qPCR to estimate knockdown efficiency. Importantly, we observed clear phenotypic changes in both knockdowns (Aralar and OGC), and both showed very similar phenotypes. This suggests that the level of knockdown was sufficient to significantly impair MAS activity. In the revised version we added new data which further validated the functional impact of Aralar KD (given that this protein has an alternative isoform, as pointed out by the reviewer). We performed metabolomics experiments measuring aspartate and glutamate levels. Our new data shows that the aspartate-to-glutamate ratio is significantly reduced in Aralar KD cells. This ratio serves as a proxy for glutamate catabolism, and the observed decrease suggests reduced glutamate catabolism, likely due to impaired MAS activity. Therefore, the reduced whole-cell aspartate/glutamate ratio serves as a metabolic signature of MAS impairment, consistent with Aralar KD. These data indicate that Aralar is sufficiently downregulated to produce a functional effect, supporting our conclusion that MAS activity is impaired. The results have been added as new supplementary Figure 1 as follows:

__ In the case of aspartate/glutamate carriers (AGCs) the role of citrin/slc25a13, the second AGC paralog, should also be analyzed. This AGC isoform is discarded based on proteomic data from brown adipose tissue, but, as it is shown in figure 1B, its levels are similar those of Aralar/slc25a12, the only AGC silenced. Besides, primary brown adipocytes differentiated for 7 days are used here, and it is possible that factors such as culture conditions or differentiation itself could alter AGC levels. Therefore, it is necessary to determine the protein levels of citrin/AGC2, and, if necessary, downregulate it together with the Aralar/AGC1 isoform. citrin/AGC2 activity may be responsible for the observed difference between the OGC and Aralar/AGC1 KD adipocytes.__

Answer 3) We thank the reviewer for this important point. We chose Aralar1 because it is the isoform predominantly expressed in brown adipose tissue (PMID: 23436904). We acknowledge, however, that compensatory increases in Citrin/AGC2 upon Aralar1 knockdown are possible. To address this, we have included new metabolomics data in the revised manuscript (added as Supplementary Figure 1), which provides additional support that downregulation of Aralar1, even if not complete, is sufficient to cause a metabolic change reflected by a reduced aspartate/glutamate ratio in these cells. This functional change supports that the knockdown of Aralar1 alone is sufficient to study its role in brown adipocytes, although minor compensation by Citrin/AGC2 cannot be entirely excluded.

To address this explicitly, we have added a paragraph to the discussion (page 13, 2nd paragraph) highlighting the potential for partial compensation by Citrin/AGC2 and explaining why the observed metabolic effects are still attributable to Aralar 1 knockdown, as follows:

“Phenotypes observed in Aralar 1 KD cells closely resemble those in Ogc KD cells, particularly in terms of lipid metabolism alterations and energy expenditure. The main difference lies in mitochondrial morphology, which is altered in Ogc KD cells but remains unchanged in Aralar 1-silenced cells (Fig. 2J,M). Unlike Ogc, which lacks an alternative isoform, Aralar 1 has a paralog Aralar 2 (Citrin, or SLC25A13) that may partially compensate for its loss. This potential compensation might explain the preservation of mitochondrial morphology in Aralar 1 KD cells. Nonetheless, our metabolomics data demonstrate that downregulation of Aralar 1 alone significantly reduces the aspartate/glutamate ratio (Fig. S1D-F). Since this ratio reflects glutamate catabolism, its decrease indicates impaired malate-aspartate shuttle activity and reduced glutamate catabolism. Therefore, although compensation by Aralar 2 cannot be entirely excluded, Aralar 1 KD alone suffices to cause substantial impairment of malate-aspartate shuttle function”.

• *

__ OGC and Aralar/AGC1 silencing is associated with the accumulation of smaller lipid droplets and impaired norepinephrine-induced lipolysis, but no mechanistical evidence is provided. The authors discuss a role for AMPK signaling associated with the redox unbalance generated by MAS disfunction but neither of them is proven.__

Answer 4) We thank the reviewer for this insightful question, which was also raised by Reviewer 1 (see Reviewer 1, Question 1 above). Here, we aim to clarify the mechanistic basis by which MASh may regulate lipolysis in BAT in a complementary and refined manner.

Our new data directly addresses this issue by examining cytosolic redox status through the lactate/pyruvate ratio, a well-established indicator of NAD⁺/NADH balance. Under basal conditions, Aralar 1 KD cells showed no change in this ratio compared to controls, indicating preserved cytosolic NAD⁺ regeneration despite reduced MASh activity. This observation is consistent with previous studies demonstrating the resilience of cellular redox homeostasis through overlapping NAD⁺-regenerating systems (PMID: 40540398; PMID: 37647199). The new results are shown in Supplementary Figure 1.

At the same time, we detected a marked decrease in the aspartate/glutamate ratio in Aralar 1 KD cells, confirming impaired MASh function and reduced amino acid exchange between cytosol and mitochondria. The lack of redox imbalance likely reflects compensatory mechanisms, most notably the GPSh, which is highly active in brown adipocytes. Supporting this view, Aralar 1 KD cells displayed significantly reduced glycerol release upon norepinephrine stimulation (Fig. 4I), suggesting enhanced metabolic cycling of G3P through mitochondrial and cytosolic G3PDH, thereby sustaining NAD⁺ regeneration and redox equilibrium.

We therefore propose that, although MASh normally facilitates NADH export and aspartate/glutamate exchange, its loss activates GPSh-mediated compensation that preserves cytosolic NAD⁺/NADH balance and maintains lipolytic flux during adrenergic stimulation. These findings refine our mechanistic understanding of how redox shuttle interplay supports glycolytic and lipolytic processes in BAT. Future studies employing NAD⁺/NADH sensors and simultaneous blockade of both shuttles will be essential to dissect these compensatory mechanisms in greater detail.

Minor points;

1. __ Is pyruvate present in respiration medium? If so, no effect on respiration is expected as pyruvate reverses the respiratory defects caused by MAS inactivation. __ Answer 1) Thanks for this important insight. In fact, as indicated in the methods section (page 17, last paragraph) all respirometry experiments were carried out in the absence of pyruvate in the media. Therefore, preserved overall respiratory rates in Aralar 1 and Ogc KD cannot be explained by compensatory pyruvate oxidation present in the media.

__ In figure 4, only data from Aralar KD cells in relation to norepinephrine-stimulated lipolysis are shown. What happens when OGC is silenced? __

Answer 2) This is a very interesting and relevant question. We did not perform the norepinephrine-stimulated lipolysis experiments in Ogc-silenced cells, since in most of the other experiments presented in the manuscript Ogc and Aralar 1 silencing converged to very similar, if not identical, phenotypes. Based on these consistent overlaps, we anticipate that Ogc KD would likely lead to comparable effects on lipolysis as observed in Aralar 1 KD cells. Nonetheless, we fully agree that direct assessment of lipolysis upon Ogc KD would strengthen this conclusion, and we consider this an important aspect for future studies.

__ Nomenclature used for mitochondrial carriers is confusing. Please do not use OGC1 as there is only one isoform. Furthermore, different names for OGC are used in the manuscript; oxoglutarate carrier, malate-ketoglutarate carrier or OGC1/SLC25A11. In the case of citrin/AGC2, Aralar2 is used and is a uncommon designation.__

Answer 3) We corrected all OGC naming in the revised manuscript. We also changed “aralar 2” for “citrin” since this was more commonly used in the literature.

__ Some panels of figures 3 and 4 should be improved. Panels 3J, 3L and 4G are difficult to see. In panel 3J please clarify UT line from untreated/NE, are they not transduced? No equivalents conditions are assayed in Aralar KD and OGC KO cells.__

Answer 4) We thank the reviewer for giving us the opportunity to improve this figure and apologize for the confusing labeling. In the revised version, we have clarified the labels in panels 3J, 3L, and 4G to improve visibility, and we have added descriptions of all abbreviations to the figure legends, accordingly.

Author response:

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

Reviewer #1 (Public review): 

The manuscript by Bru et al. focuses on the role of vacuoles as a phosphate buffering system for yeast cells. The authors describe here the crosstalk between the vacuole and the cytosol using a combination of in vitro analyses of vacuoles and in vivo assays. They show that the luminal polyphosphatases of the vacuole can hydrolyse polyphosphates to generate inorganic phosphate, yet they are inhibited by high

concentrations. This balances the synthesis of polyphosphates against the inorganic phosphate pool. Their data further show that the Pho91 transporter provides a valve for the cytosol as it gets activated by a decline in inositol pyrophosphate levels. The authors thus demonstrate how the vacuole functions as a phosphate buffering system to maintain a constant cytosolic inorganic phosphate pool. 

This is a very consistent and well-written manuscript with a number of convincing experiments, where the authors use isolated vacuoles and cellular read-out systems to demonstrate the interplay of polyphosphate synthesis, hydrolysis, and release. The beauty of this system the authors present is the clear correlation between product inhibition and the role of Pho91 as a valve to release Pi to the cytosol to replenish the cytosolic pool. I find the paper overall an excellent fit and only have a few issues, including: 

(1) Figure 3: The authors use in their assays 1 mM ZnCl2 or 1mM MgCl2. Is this concentration in the range of the vacuolar luminal ion concentration? Did they also test the effect of Ca2+, as this ion is also highly concentrated in the lumen? 

The concentrations inside vacuoles reach those values. However, given that polyP can chelate divalent metal ions, what would matter are the concentrations of free Zn²⁺ or Mg²⁺ inside the organelle. These are not known. This is not critical since we use those two conditions only as a convenient tool to differentiate Ppn1 and Ppn2 activity in vitro. In our initial characterisation of Ppn2 (10.1242/jcs.201061), we had also tested Mn, Co, Ca, Ni, Cu. Only Zn and Co supported activity. Ca did not. Andreeva et al. (10.1016/j.biochi.2019.06.001) reached similar conclusions and extended our results.

(2) Regarding the concentration of 30 mM K-PI, did the authors also use higher and lower concentrations? I agree that there is inhibition by 30 mM, but they cannot derive conclusions on the luminal concentration if they use just one in their assay. A titration is necessary here. 

The concentration of 30 mM was not chosen arbitrarily. It is the luminal Pⁱ concentration that the vacuoles reached through polyP synthesis and hydrolysis when they entered a plateau of luminal Pⁱ. We consider this as an upper limit because polyP kept increasing which luminal Pⁱ did not. Thus, there is no physiological motivation for trying higher values. We have nevertheless added a titration to the revised version (new Fig. 3A).

(3) What are the consequences on vacuole morphology if the cells lack Pho91? 

We had not observed significant abnormalities during a screen of the genome-wide deletion collection of yeast (10.1371/journal.pone.0054160), nor in other experiments with pho91 mutants, which we have not included in this manuscript due to a lack of effect.

(4) Discussion: The authors do not refer to the effect of calcium, even though I would expect that the levels of the counterion should affect the phosphate metabolism. I would appreciate it if they would extend their discussion accordingly. 

The situation is much more complex because Ca2+ is not the only counterion. Major pools of counterions (up to hundreds of mM) are constituted by vacuolar lysine, arginine, polyamines, Mg, Zn etc. Their interplay with polyP is probably complex and worth to be treated in a dedicated project. If we wanted to limit the discussion of this complexity not to the simple statement that it is not understood, which is not very useful, we would have to engage in a lot of speculation. We feel that this would make the discussion lose focus and not contribute concrete insights.

(5) I would appreciate a brief discussion on how phosphate sensing and control are done in human cells. Do they use a similar lysosomal buffer system? 

Mammalian cells have their Pi exporter XPR1 mainly on a lysosome-like compartment (10.1016/j.celrep.2024.114316). Whether and how it functions there for Pi export from the cytosol is not entirely clear. We have addressed this situation in the revised discussion section.

Reviewer #2 (Public review): 

Summary: 

This manuscript presents a well-conceived and concise study that significantly advances our understanding of polyphosphate (polyP) metabolism and its role in cytosolic phosphate (Pi) homeostasis in a model unicellular eukaryote. The authors provide evidence that yeast vacuoles function as dynamic regulatory buffers for Pi homeostasis, integrating polyP synthesis, storage, and hydrolysis in response to cellular metabolic demands. The work is methodologically sound and offers valuable insights into the conserved mechanisms of phosphate regulation across eukaryotes. 

Strengths: 

The results demonstrate that the vacuolar transporter chaperone (VTC) complex, in conjunction with luminal polyphosphatases (Ppn1/Ppn2) and the Pi exporter Pho91, establishes a finely tuned feedback system that balances cytosolic Pi levels. Under Pi-replete conditions, inositol pyrophosphates (InsPPs) promote polyP synthesis and storage while inhibiting polyP hydrolysis, leading to vacuolar Pi accumulation. 

Conversely, Pi scarcity triggers InsPP depletion, activating Pho91-mediated Pi export and polyP mobilization to sustain cytosolic phosphate levels. This regulatory circuit ensures metabolic flexibility, particularly during critical processes such as glycolysis, nucleotide synthesis, and cell cycle progression, where phosphate demand fluctuates dramatically. 

From my viewpoint, one of the most important findings is the demonstration that vacuoles act as a rapidly accessible Pi reservoir, capable of switching between storage (as polyP) and release (as free Pi) in response to metabolic cues. The energetic cost of polyP synthesis-driven by ATP and the vacuolar proton gradient-highlights the evolutionary importance of this buffering system. The study also draws parallels between yeast vacuoles and acidocalcisomes in other eukaryotes, such as Trypanosoma and Chlamydomonas, suggesting a conserved role for these organelles in phosphate homeostasis. 

Weaknesses: 

While the manuscript is highly insightful, referring to yeast vacuoles as "acidocalcisome-like" may warrant further discussion. Canonical acidocalcisomes are structurally and chemically distinct (e.g., electrondense, in most cases spherical, and not routinely subjected to morphological changes, and enriched with specific ions), whereas yeast vacuoles have well-established roles beyond phosphate storage. A comment on this terminology could strengthen the comparative analysis and avoid potential confusion in the field.  

Yeast vacuoles show all major chemical features of acidocalcisomes. They are acidified, contain high concentrations of Ca, polyP (which make them electron-dense, too), other divalent ions, such as Mg, Zn, Mn etc, and high concentrations of basic amino acids. Thus, they clearly have an acidocalcisome-like character. In addition, they have hydrolytic, lysosomelike functions and, depending on the strain background, they can be larger than acidocalcisomes described e.g. in protists. We have elaborated on this point in the introduction of the revised version.

Reviewer #3 (Public review): 

Bru et al. investigated how inorganic phosphate (Pi) is buffered in cells using S. cerevisiae as a model. Pi is stored in cells in the form of polyphosphates in acidocalcisomes. In S. cerevisiae, the vacuole, which is the yeast lysosome, also fulfills the function of Pi storage organelle. Therefore, yeast is an ideal system to study Pi storage and mobilization. 

They can recapitulate in their previously established system, using isolated yeast vacuoles, findings from their own and other groups. They integrate the available data and propose a working model of feedback loops to control the level of Pi on the cellular level. 

This is a solid study, in which the biological significance of their findings is not entirely clear. The data analysis and statistical significance need to be improved and included, respectively. The manuscript would have benefited from rigorously testing the model, which would also have increased the impact of the study. 

It is not clear to us what the reviewer would see as a more rigorous test of the model.  

Reviewer #1 (Recommendations for the authors): 

(1) Figure 2: Why do the authors label the blue curve in A and B as BY and in C and D as WT? Is this a different genetic background they used here? This should be specified in the legend. 

No, it is the same background. The figures had been reshuffled before submission and we overlooked to replace "BY" by "WT". This has been corrected. Now we consistently use WT in all figures

(2) Figure 4 has different scaling for the two panels, which should be labeled as A and B. I am aware that the authors do this for comparison, but it is rather confusing at first glance. I recommend having them at the same scale. 

We chose this representation on two separate scales because this figure shall primarily illustrate that the shift between pho91 and WT curves vanishes in the presence of IP7. We now highlight in the figure legend that the scales are different to avoid confusion.  

(3) Figure 8: I would appreciate a model with normal and low Pi concentrations in comparison, as this is what the authors worked out. 

We have modified the figure. It now compares Pi-rich and Pi-limited scenarios.

(4) Minor issue: Wouldn't it make more sense to show the molar concentration in the Figures rather than the nmol of Pi/ug of protein? I am aware that this would require information on the vacuole volume rather than the reaction volume, and the authors do this calculation later on. 

It depends. We often chose this representation because it illustrates the price to pay (metabolic input in terms of protein that must be dedicated to this task) to sequester a certain quantity of Pⁱ. But, as we provide the corresponding Pⁱ concentration in the text, this information is accessible to the reader, too.

Reviewer #2 (Recommendations for the authors): 

As stated above in the weaknesses section, while functional parallels exist, canonical acidocalcisomes are structurally and chemically distinct, typically smaller, electron-dense, and enriched with cations. Whereas yeast vacuoles are larger, multifunctional organelles with well-established roles beyond phosphate storage. Explicitly addressing these differences would strengthen the comparative framework and prevent potential confusion in interpreting the evolutionary relationships between these organelles. 

We agree to some degree, which is the reason why we refer to vacuoles as acidocalcisome-like organelles. In fact, vacuoles share virtually all defining chemical traits of acidocalcisomes. They just have a second functional domain as hydrolytic, lysosome-like organelles. Given the plasticity of endo-lysosomal compartments, and acidocalcisomes belong to this group because of their biogenesis through the AP3 pathway, this is not shocking to us. But the reviewer's comment made us realize that it is better to explicitly address this point. We have added a section to the introduction to do this.

Reviewer #3 (Recommendations for the authors): 

(1) Page 8: It is unclear why the authors only estimated the Pi concentration in wild-type vacuoles. This should also be done for vacuoles from other strains. 

This information is inherent in Figure 2. PolyP hyperaccumulating strains show the same plateau as the wildtype, meaning that they also reach around 30 mM luminal Pi concentration, whereas vtc4 vacuoles reach only around 1/10th of that increase, indicating that they remain at 3 mM. We mention this now in the text.

(2) The attempts of the localization of Pho91 through tagging are not satisfactory. The author described different localizations for Pho91 depending on whether it was tagged on the N- or C-terminus or when Nterminally tagged and overexpressed using two strong promoters. While it is not uncommon that proteins show different localization patterns, depending on where the tag is inserted, it is possible that one of the tags would reflect the localization of the endogenous protein. There is an easy way to test this, in particular when Pho91 is endogenously tagged. pho91∆ has reported phenotypes such as abnormal vacuolar morphology or increased autophagy. They could also measure PI content in vacuoles. The authors could compare the phenotypes of the endogenously tagged strains with WT and a pho91∆ strain. 

Indeed, the attempts to localise the protein through fluorescent tags are unsatisfactory, in our hands as in the hands of others. We would not have created a series of many different tagged versions (we present only a selection of these in the manuscript) if the creation of a faithful reporter for Pho91 localisation were so straightforward. Expression from the endogenous promoter yields quite low signals (which is why others have overexpressed their GFP fusion from strong promotors). But overexpression brings at least a significant part of the protein to the cell surface, where it can then function as Pi importer and suffice to restore much of the maximal Pi uptake capacity that genuine plasma membrane transporters provide and support normal growth of the cells (Wykoff & O’Shea, 2001). But the localisation pattern of Pho91-GFP, likewise overexpressed from a strong promotor, does not reflect this plasma membrane localisation (see the references that the reviewer mentioned under (3)). The published overexpressed GFP-fusions localise only to the vacuole, suggesting that even in this case the GFP tag may create an artefact. Therefore, we went through a large variety of Pho91 gene fusions, which led us to the conclusion that the protein is very sensitive to tags at both ends and that fusion proteins hence are unlikely to reliably report the correct location of the protein. Given this, we resorted to quantitative proteomics to clarify the issue. This quantitative experiment goes beyond previously published proteomics analyses that the reviewer mentions under (3), which found the protein in the vacuolar fraction but did not calculate the enrichment factors, which is crucial. 

A strong phenotype of abnormal vacuolar morphology is not apparent in our cultures. 

(3) Moreover, Pho91 has been identified as a component enriched in vacuolar-mitochondria contact sites (vCLAMP), and this localization was confirmed with GFP-Pho91 (PMID: 25026036). Likewise, PMID: 35175277 also detected Pho91 by mass spectrometry as a vacuolar protein and showed endogenously tagged GFP-Pho91 on the vacuole (co-staining with Vph1). The authors may request the strains from the authors of these papers and use them for their experiments. PMID: 17804816, the oldest of the three reports (from 2007) reports a GFP-Pho91 under either TEF or ADH promoter that localizes to the vacuole. They also showed that the fusion protein is functional. These and other experiments led them to conclude that Pho91 exports phosphate from the vacuolar lumen to the cytoplasma. 

We have now included these references. As argued above, we have analysed also the strains from PMID17804816. The observed clear localisation of the fusion protein to vacuoles is only visible upon overexpression, not upon expression from the endogenous locus. Apparently also this construct is unlikely to report Pho91 localisation reliably (though, by chance, overexpression leads it to the correct location). Thus, we maintain our conclusion that C- or N-terminally GFP-tagged versions of Pho91 are unreliable tools for localising the protein.

(4) The impact of pho91∆ on Pho4-GFP nuclear localization is modest at best (increase from 5% of cells showing Pho4-GFP in the nucleus in WT vs 10% in pho91∆), and only somewhat stronger in ppn1∆/ppn2∆. This means 90% of pho91∆ cells do not respond, and Pho4-GFP stays cytoplasmic. It is unclear how the author can derive a meaningful conclusion from these data. Moreover, are these data really supporting the model, or do these data rather indicate that there are additional factors/pathways needed? What is the biological significance of the marginal increase from 5% to 10% of cells that would respond? What happens to the cells that cannot respond? Will they die or at least have a growth disadvantage? It would be useful to provide some functional studies. 

We should have explained the nature of the assay better. The experiment exploits the fact that dividing yeast cells transiently fall into a state of Pi scarcity during S-phase. Since S-phase is less than a quarter of the cell cycle, only a small fraction of the cells transiently activates the PHO pathway. These cannot be well characterised by ensemble assays, but microscopy circumvents this background of the whole population and picks them up very clearly, allowing to quantify them. We have adapted the respective chapter in the results section to improve the description of this experiment.

(5) The quantification of the data is suboptimal, as in most assays the mean and standard error of the mean (SEM) are given. SEM is not really appropriate in these cases because it gives only the error of the mean and not of the entire data. Therefore, the standard deviation (SD) is needed, which reports on the variability of the data, and which is usually much larger than the SEM. Using the SD, would also allow the authors to do proper statistical analysis, which is missing entirely in this manuscript. 

SEM also comprises the variability of the data. It is linked with the SD (SEM=SD/SQRT(n)), but SEM also considers the number of the experiments n. The main goal is to compare the means, and SEM is an appropriate and frequently used tool for this because it illustrates how well the arithmetic mean may estimate the true mean of the population. Therefore, we kept the SEM but have added tests of significance for the differences shown.

(6) Statistical testing in Figure 7 is essential as the effects are very small. Again, are these changes big enough for a biologically meaningful response? The authors should at least discuss this. 

Our previous time course analyses of InsPP dynamics, performed under comparable conditions as in this study, showed that InsP8 decreases by around 50% in the first 30 min after transfer to Pi starvation (DOI: https://doi.org/10.7554/eLife.87956) and that this decline is already sufficient to trigger the PHO starvation program, as assessed by Pho4-GFP translocation into the nucleus. Thus, a 50% decrease, which is observed in ppn1 ppn2 mutants, is functionally significant. We have now also evaluated statistical significance in Fig. 7, which is given for the 50% reduction of InsP8 and 1-InsP7 in ppn1 ppn2. 

Minor points: 

(1) There are a number of smaller edits (use of italic or better the absence thereof, lacking information in the reference list, and some typos). 

Thank you. We have corrected those.

(2) The exact n should be given in the Figure legend. 

Corrected.

(3) Page 8, line 8: it would be nice to have a picture of the wild-type vacuoles and what you measured. 

We now present a sample image in the new Suppl. Fig. 1.

(4) PMID: 11779791 showed already that Pho91 cannot rescue the absence of the plasma membrane Pi transporters. This study should be at least cited. 

This is not quite correct. The study that the reviewer mentions showed that Pho91 supports slower growth and the authors concluded that "A synthetic lethal phenotype was observed when (all) five phosphate transporters were inactivated...". We had cited the same group and the same first author, just using their later study (Wykoff et al., 2007) that had recapitulated the results from PMID11779791 and showed in addition quite good growth of the PHO91 expressing strain on YPD (Suppl. Fig. 2). We had obtained the strains from this group. In reproducing their experiments, we noticed that the growth of Pho91 that these authors had observed is due to incomplete repression of Pho84. They had overexpressed Pho84 from a galactose inducible promotor to generate a background with a regulatable Pi transporter. This trick allowed them to conveniently manipulate the strain and reduce (but not abolish) Pho84 expression by transferring the cells from galactose to glucose for their experiments. Therefore, we chose a more rigorous plasmid shuffling strategy to test the individual P_i transporter, which allows an assessment without the leaky background expression of Pho84 on glucose. In contrast to O'Shea and colleagues, we observed zero growth of a strain expressing only PHO91. We have revised the results section to make this discrepancy more evident and provide a better motivation for our experiment.

(5) It would be nice to see the actual data in Figure 6; not only a quantification. 

We illustrate the phenotype of nuclear Pho4-GFP in panel A. Showing all the images necessary to appreciate the differences between the strains would require including many dozens of images into the figure, which would not be useful.

Saving indy0pad created html with hypothesys embed.js script preloaded

Just download the html file

add the script tag embed.js

``` html

```

http://bafybeigcrsxbwkpxjgdjyhjsl675hejqzn5swdg7xktlw5w47fa6jkkc74.ipfs.dweb.link/?filename=hyperpost%3D%F0%9F%97%BA%EF%B8%8FMEMEplEX.Map.html

Note: This response was posted by the corresponding author to Review Commons. The content has not been altered except for formatting.

Learn more at Review Commons

Reply to the reviewers

Overall Response.

We would like to thank the reviewers for their analysis of the manuscript. From their comments it is clear that our manuscript was not. We completely rewrote the manuscript to focus on the central core question which was how does Adam13 regulates gene expression in general and TFap2a in particular leading to the expression of Calpain8 a protein required for CNC migration.

The following model will be the central line of our story. It will address all of the proteins involved and mechanistical evidences that link Adam13 to one of its proven effector target Calpain8.

• *

*Reviewer #1 (Evidence, reproducibility and clarity (Required)): **

In this manuscript, Pandey et al. show that the ADAM13 protein modulates histone modifications in cranical neural crest and that the Arid3a protein binds the Tfap2a promoter in an Adam13-dependent manner and has promoter-specific effects on transcription. Furthermore, they show that the Adam13 and human ADAM9 proteins associated with histone modifiers as well as proteins involved in RNA splicing. Although the manuscript is mostly clearly written and the figures well assembled, it reads like a couple of separate and unfinished stories.*

I believe that our story line was not clear and that the overarching questions was not well stated. We have made every effort to change this in the revised manuscript. I would like to include a figure that explains the story.

In short:

1 We knew that Adam13 could regulate gene expression in CNC via its cytoplasmic domain.

2 We also knew that this required Adam13 interaction with Arid3a and that a direct target with the transcription factor TFAP2a which in turn regulates functional targets that we had identified including the protocadherin PCNS and the protease Calpain8.

Our goal was to understand the mechanism allowing Adam13 to regulate gene expression.

3 This first part of this manuscript shows how Adam13 modulates Histone modification in vivo in the CNC globally as well as specifically on the Tfap2a promoter. This results I an Open chromatin.

4 Using Chip we show that Adam13 and Arid3a both bind to the Tfap2a promoter and that Arid3a binding to the first ATG depends on Adam13.

5 Using Luciferase reporter we show that both Adam13 and Arid3a can induce expression at the first ATG.

*They show using immunocytochemistry and qPCR that ADAM13 knockouts in CNCs afffects histone modifications. Here ChIP-seq or Cut-n-Run experiments would be more appropriate and would result in a more comprehensive understanding of the changes mediated. *

I agree but we did not have the fund and now I have nobody working in the lab to do this experiment. These are also likely to overlap with the RNAseq data that we have and would simply add more open leads. We selected to go after the only direct target that we know which is TFAP2a and focus on this gene to understand the mechanism.

We believe that the Chip PCR experiment are sufficient for this story.

*The immunohistochemistry assays should at least be verified further using western blotting or other more quantiative methods. *

Immunofluorescence and statistical analysis is a valid quantification method. Western blot of CNC explants is not trivial and requires a large amount of material. Given the small overall change we also would not expect to be able to detect the change over the noise of western blot. The Chip PCR confirms our finding in a completely independent manner.

*The authors then show that ADAM13 interacts with a number of histone modifiers such as KDM3B, KDM4B and KMT2A but strangely they do not follow up this interesting observation to map the interactions further (apart from a co-ip with KMT2A), the domains involved, the functional role of the interactions or how they mediate the changes in chromatin modifications. *

We selected KMT2a because it is expressed in the Hek293T cells. KMT2D has been shown to regulate CNC development in Xenopus and is responsible for the Kabuki syndrome in human. We used aphafold to predict interaction and found that Adam13 interact with the Set domain. In addition we see multiple Set- containing domain protein in our mass spec data. The mass spec is done on Human hek293T cells that express a subset of KMT proteins. We now include evidence that Adam13 interact with the KMT2D SET domain (new figure 5D)

The authors then show that ADAM13 affects expression of the TFAP2a gene in a promoter specific manner - affecting expression from S1 but not S2.

It is the S1 but not S3. Adam13 has no effect on S2.

• They further show that ADAM13 affects the binding of the Arid3 transcription fator to the S1-promoter but not to the S3 promoter. However, ADAM13 was present at both promoters. Absence of ADAM13 resulted in increased H3K9me2/3 and decreased H3K4me3 at the S1 promoter whereas only H3K4me3 was changed at the S2 promoter*

S3 not S2*. Unfortunately, they do not show how this is mediated or through which binding elements this takes place. Why is ADAM13 present at both promoters but only affects Arid3 binding at S1? *

We agree this is a very interesting question that could be the subject of an entire publication. Promoter deletion and mutation to identify which site are bound by and modulated by Adam13/Arid3a is not trivial.

*The authors claim that transfecting Arid3a and Adam13 together further increases expression from a reporter (Fig 4E) but this is not true as no statistical comparison is done between the singly transfected and double transfected cells. *

This is correct, there is a small increase that is not significant with both. The fact that both proteins can induce the promoter suggest but does not prove that they can have additive roles. The loss of function experiment shows that the human Arid3a expressed in Hek293T cells is important for Adam13 increases of S1. It is possible that the dose of the endogenous Arid3a is sufficient to get full activity of Adam13.* Then the authors surprisingly start investigating association of proteins with the two isoforms of TFAP2a which in the mind of this reviewer is a different question entirely. *

We agree and have removed this part of the manuscript.

*They find a number of proteins involved in splicing. And the observation that ADAM13 also interacts with splicing factors is really irrelevant in terms of the story that they are trying to tell. Transcription regulation and splicing are different processes and although both affect the final outcome, mRNA, they need to be investigated separately. The link is at least not very clear from the manuscript. Again, the effects on splicing are not further investigated through functional analysis and as presented the data presented is too open-ended and lacking in clarity. *

We agree that beside the different activities of the TFap2a isoform, the rest of the splicing regulation could be a separate study. We were interested to understand how these two isoforms could activate Calpain8 so differently this is why we looked at LC/MS/MS. We have removed this part of the story from the manuscript.*

Additional points: 1. In the abstract they propose that the ADAMs may act as extracellular sensors. This is not substantiated by the results. *

As an extracellular protein translocating into the nucleus it is a possibility that we propose, but I agree this is not investigated in this manuscript. We will modify the text.* 2. Page 5, line 16: what is referred to by 6 samples 897 proteins? Were 6 samples analyzed for each condition? The number of repeats for the mass spec analysis is not clear from the text nor are the statistical parameters used to analyse the data. This is also true for the mass spec presented in the part on TFAP2aL-S1 and Adam13 regulate splicing. Statistics and repeats are not presented. *

In general we provide biological triplicate and use the statistical function of Scaffold to identify proteins that are significantly enriched or absent in each samples.

When we specify 6 samples it means 6 independent proteins samples were analyzed and used for our statistic. We use Scafold T-test with a p value less than 0.05. Peptides were identified with 95% confidence and proteins with 99% confidence.* 3. Page 6, line 19: set domain should be SET domain. *

Yes* 4. The number of repeats in the RNA sequencing of the CNCs is not clear from the text. *

Three biological replicates (Different batch of embryos from different females).* 5. The explanation of Figure C is a bit lacking. There are two forms of TFAP2a, L and S, but only one is presented in the figure. Do both forms have the extra S1-3 exons? Also, at the top of the figure it is not clear that the boxes are part of a continuous DNA sequence. Also, it is not clear which codon is not coding. *

Xenopus laevis are pseudo tetraploid giving in most cases L and S genes in addition to the 2 alleles from being diploid. The TFAP2a gene structure is conserved between both aloalleles and is similar to the human gene. For promoter analysis and Chip PCR we chose one of the alloallele (L), given that the RNAseq data showed that both genes and variant behave the same in response to Adam13. This only becomes important in loss of function experiment in which both L and S version need to be knock down or Knock out.

* In the sashimi plot there are green and pink shaded areas. What do they denote? What exactly is lacking in the MO13 mutant - seems that a particular exon is missing suggesting skipping?*

MO13 is a morpholino that bocks the translation of Adam13 (Already characterized with >90% of the protein absent) but does not affect Adam13 mRNA expression.* 7. Page 11, line 9: „with either MbC or MbC and MO13" needs to be rephrased. *

Will do *8. Page 11, line 19: „the c-terminus of....and S3) and" should be „the C-terminus of...and S3 and". ** 9. Page 15, line 10: substrateS 10. Page 16, line 23: the sentence „increases H3K9 to the promoter of the most upstream" needs revision. 11. Page 26, line 12: Here the authors say: „for two samples two-tail unpaired". What does this mean? Statistics should not be performed on fewer than three samples. In legnd to Figure 6 it indicates that T-test was performed on two samples. 12. The discussion should be shortened and simplified. 13. Figure 1 legend. How many images were quantitated for each condition? *

At least 3 images per condition. For 3 independent experiments. (9 images per condition).* 14. Figure 2 has a strange order of panels where G is below B. 15. Figure 6 legend, line 12. „proteins that were significantly enriched in either of the 2 samples" is not very clear. What exactly does this mean?

Reviewer #1 (Significance (Required)):

If the authors follow up on either the transcription-part of the story, or the splicing part of the story, they are likely to have important results to present. However, in the present format the paper is lacking in focus as both issues are mixed together without a clear end-result. *

We have entirely changed the paper according to these comments.

*

• *

*Reviewer #2 (Evidence, reproducibility and clarity (Required)): **

Panday et al seeks to determine the function of ADAM13 in regulating histone modifications, gene expression and splicing during cranial neural crest development. Specifically, the authors tested how Adam13, a metalloprotease, could modify chromatin by interaction with Arid3a and Tfap2a and RNA splicing and gene expression. They then utilize knockouts in Xenopus and HEK293T cells followed by immunofluorescence, IPs, BioID, luciferase assays, Mass spec and RNA assays. Although there is some strong data in the BioID and luciferase experiments, the manuscript tells multiple stories, linking together too many things to make a compelling story. The result is a paper that is very difficult to read and understand the take home message. In addition, some of the conclusions are not supported by the data. This unfortunately means it is not ready for publication. However, I have added below some suggestions that would strengthen the manuscript. My comments are below: *

Clarity is clearly an issue here. The new version is entirely re-written.

Here is the take home message:

We knew that Adam13 could regulate gene expression via its cytoplasmic domain. One of the key targets was identified as Calpain8, a protein critical for CNC migration. We subsequently showed that Adam13 and Arid3a regulated Tfap2a expression which in turn regulated Calpain8.

In this manuscript we investigated 1) how Adam13 regulates TFAP2a and 2) how Tfap2a controls Calpain8 expression.

The take home message is that Adam13 bind to Histone methyl transferase and changes the histone methylation code overall in the CNC and in particular at the TFAP2a promoter. This results in more open chromatin. We further find that Adam13 binds to the Tfap2a promoter in vivo and is important for Arid3a binding to the first start. Tfap2a that include this N-terminus sequence regulates Capn8 expression.*

Major comments: 1. I think it would be better to split out the chromatin modification function from the splicing in two separate papers. While there is a connection, having it all together makes the story difficult to follow. *

Agree but I believe that the S1 vs S3 story of Tfap2a is important for the overall story. The new paper does not emphasize splicing.* 2. The immunofluorescence of H3K9me2/3, in Figure 1, 2, 3 following Adam13 knockdown is not convincing. There seems to be a strong edge effect especially in Figure 2 and 3. *

The statistical analysis shows that the results, while modest, are significant (Three independent experiments using 3 different females and 3 explants for each condition were analyzed). The edge effect observed is eliminated by the mask that we use that normalize the expression to either DAPI or Snai2. The edge effect is seen in both control and KD as well. These are further confirmed by the Chip PCR on one direct target.

Similarly the Arid3a expression in Supp Figure 1 if anything seems increased.

We have previously shown that Arid3a expression is not affected by Adam13 KD (Khedgikar et al). Our point here is simply that the difference in Tfap2a cannot be explained by a decrease in Arid3a expression. It is not a critical figure and was eliminated in the new manuscript.

*It would be better to quantify by western blot and not by fluorescent intensity since it is difficult to determine what a small change in fluorescent intensity means in vivo. *

Not all antibodies used here work by western blot and the quantity of material required for western blot is much larger than IF. Given the small overall changes and the variability observed in Western blot it is not a viable alternative.

IF is a quantitative method that has been used widely to assert increase or decrease of protein level or post translational modification. The fact that the same post translational modification that we see in cranial neural crest explants can also be seen by ChipPCR on the Tfap2a promoter confirm this observation.

*Also, it does not say in the text or the figure legend what these are, Xenopus explants of CNC? *

These are CNC explants. It is now clearly stated in the figure legend.* 3. The rationale for isolating KMT2A from the other chromatin modifiers in the dataset is not clear. *

The new manuscript is clarifying that point. Because we are using Hek293T cells in this assay, which are human embryonic kidney derived instead of Xenopus Cranial neural crest cells, we are not interested in a specific protein but rather a family of protein that can modify histones (KMT and KDM). Our rational is if Adam13 can bind to KMT2 via the SET domain, it is likely to interact with KTM2 that are expressed in the CNC. KMT2A and D are expressed in the CNC. This is why we selected KMT2a here (Hek293T). We now include 1 co-IP with the Set domain of Xenopus KMT2D (new figure 5D)

From the RNA-seq in Supp Figure 2 it is not changed as much as likely some of the others.

The new manuscript addresses this point. We did not show or expect that the loss of Adam13 would affect mRNA expression of Kmt2.

*Also, the arrow seems to indicate that it is right above the cutoff. What about other proteins with ATPase activity? That is the top hit in the Dot plot nuclear function. Would be helpful to write out Adam13 cytoplasm/nucleus here. *

We have used another set of proteomics data that does not include the cytoplasmic/nuclear extract to simplify the results. We hope that the changes make it more obvious.

Given that we are looking at Chromatin remodeling enzyme here we did not chose to investigate further in this report the ATPase. This is such a wide category that it could lead us away from the main story here.* 4. The splicing information, while interesting would be better as a different manuscript. The sashimi plot requires more explanation as written. *

We agree and think that a simple representation of the fold change of the different isoform is more obvious. It is now a minor part of figure 1 and the legend has been improved to describe the method here.

How do you tell if the interactions are changed from this?

I do not understand this question. The sashimi plot indicate the read through from the mRNA that goes from one exon to the next quantifying the specific exon usage. It can therefore be quantified and compared between different conditions.

• The authors argue there is a reduction of Tfap2a in Figure 3H but half the explant is not expressing sox9 in the Adam13 knockdown. How is this kind of experiment controlled when measure areas that don't have any fluorescence because of the nature of the explants? *

We have removed this figure as we had already shown previously by western blot that Tfap2a protein decreased in MO13 embryos. As noted on the histogram, the fluorescence is only measured in Sox9 positive cells in each explant. Three independent experiments with 3 explants for each. We also have seen a decrease by Western blot and mRNA expression (Both RNAseq and realtime PCR). In most of our explants, the vast majority of the cells are positive for Snai2 and Sox9, while those that are negative are positive for Sox3 (data not shown here). There is always less signal in the center of the explant possibly due to the penetration of antibody or interference with the signal by the cells pigment or yolk autofluorescence. Our control explants have the same effect so our quantification is valid.* 5. The use of a germ line Xenopus mutant for Adam13 is great but how were these knockouts validated? *

All of the KO were validated by sequencing, RNAseq and protein expression. These are now included in the supplemental figure 1.

*More information is required here. The Chip-qPCR has a lot of variability between the samples, especially in the H3K9me2/3. *

All ChipPCR were performed on Xenopus embryos. The variability is tested by statistical analysis and is either significant or not.

Because these are in cell lines, this should be more consistent.

They are not in cell lines but in Xenopus embryos.

• In addition, it is difficult to understand what this means for cranial neural crest cells when assaying in HEK293T cells with the luciferase assay. *

We use Luciferase assay in Hek293T cells to test if Xenopus protein can induce a specific reporter (Gain of function). We also use luciferase reporter in Xenopus to test if they can perceive the loss of a specific protein (For example Adam13).

Our result show that Adam13 or Arid3a expression in Hek293T cells can induce the TFAP2S1 reporter. * 6. The migration assay shows only an example of what it looks like to have defective migration. But it would be better to show control embryos, embryos with Adam13 knockdown and what the rescues look like so the reader can make their own conclusion.*

We can certainly include this but have published this assay in multiple publication before. The picture is a single example, the histogram shows that statistical validation.

• The argument from the section above suggests the S1 isoform is the primary one but S3 in this assay also rescues, please explain what this result means since it seems to suggest that even though these isoforms have different activity the function is similar in terms of the ability to rescue defective migration. *

The result in Hek293T cells shows that only TFAP2aS1 can induce Calpain8, while both S1 and S3 can partially rescue CNC migration in embryos lacking Adam13. The issue here is the dose of mRNA injected for each variant might be too high. Adam13 proteolytic activity is also critical, so we do not expect a complete rescue. The fact that S1 is significantly better at rescuing than S3 is relevant here. It is possible that if we were to decrease the dose of each mRNA we would find one in which S3 no longer rescues but S1 does.

* The next section again talks about yet another protein Calpain-8. Here the authors use MO13 for luciferase assays instead of HEK293 cells. The authors do not explain why they decided to switch from cells to MO.*

Calpain8 is one of the validate target of Adam13 that can rescue CNC migration (Cousin et al Dev Cell). We use the luciferase reporter corresponding to the Xenopus Capn8 reporter to show 1 in vivo that loss of Adam13 reduce its expression (Similar to the Capn8 gene). We then went in vitro using Hek293T cells for gain of function experiment that shows that only the Tfaps2S1 variant can induce it while S3 does not.

We hope that the graphical summary and the new manuscript make this clear.* 8. The experiment to IP RNA supports only the correlation that Adam9 and Adam13 bind RNA and RNA binding proteins to regulate splicing. This conclusion presented is not supported by the data presented here. While there is a sentence about why Adam9 was chosen here, it would be preferred to focus on Adam13 as the rest of the manuscript is focused on Adam13. The conclusions are generalized to all ADAMs, but ADAM13 and ADAM9 are the only ADAMs investigated in the manuscript *

This figure is no longer included. For each of the protein classes that we identify by Masspec we try to find a validation. RNA-IP is simply a validation that Adam13 and Adam9 can bind to complexes that include RNA in a cytoplasmic domain dependent fashion. The conclusion that Adam13 and possibly ADAM9 might be involved in regulating splicing is 1) that the protein associated with Adam13 are include multiple splicing factors, 2) that the RNAseq analysis shows abnormal splicing in CNC missing Adam13 and 3) that the form of TFAP2a induced by Adam13 (S1) associate significantly more with splicing factor than the S3 isoform.

We agree that the generalization to other ADAM is not demonstrated here but only suggested. We selected ADAM9 and ADAM19 because we have shown that they can each rescue Adam13 function in the CNC. Unfortunately there are no ADAM19 antibody that work by IP on the market. We have tested multiple company and multiple cell lines.

We believe that the ADAM9 experiment is critical to show that the protein associated with Adam13 are not simply the result of overexpressing a different species protein sin ADAM9 is the endogenous protein.*

Minor comments 1. The manuscript using a lot of abbreviations (PCNS, NI, MO, SH3) and lingo that are unclear to a general reader. Please define acronyms when first used, as well as be clear on which model is being used in each experiment. *

We have corrected this* 2. Similarly, the figures are not labeled such that a reader would be able to understand ie MO13 should be Adam13 knockdown etc. *

We have corrected this in the legend

• Please identify the genes on the heatmap and some highlighted genes from volcano plot from the RNA-seq.*

The volcano plot is from MS/MS not RNAseq. We have list of all of the genes and/or proteins corresponding to each figure in tables

We now have a figure from the RNAseq and a subset of genes of interest are show. *4. Why use the flag tag in Figure 5? *

We used Flag-tagged construct to only immunoprecipitated the variants and not the endogenous TFPA2a in these experiments. Also we used RFP-Flag to eliminate any protein that bound to the tag or the antibody.

This figure is no longer in the manuscript.* 5. Is the data in figure 4A-D the same as Supp. Figure 4A-D? *

These are independent biological replicates of the same experiment.* 6. Please italicize gene symbols - e.g. "key transcription factors that exemplify CNC, such as the SOX9, FOXD3, SNAI1, SNAI2, and TFAP2 family". *

We clearly have missed some, we are using italicized for gene, and regular for proteins. It might not be clear in the text when we are referring to genes and proteins. We will correct this in the rewrite. 7. Please review the manuscript for grammatical and typographical errors. * We have used all available software including Word and Grammarly. We will try to improve on the next version. **Cross-commenting**

I think the two reviewers on one the same page on this manuscript.

Reviewer #2 (Significance (Required)):

If more solid, would be a conceptual advance in role of Adam13 in mediating chromatin modification and transcription factors, adds to exiting work from this lab, good for a specialize audience, my expertise is in in neural crest development, non-mammalian modes, epigenetic regulators.*

• *

Note: This preprint has been reviewed by subject experts for Review Commons. Content has not been altered except for formatting.

Learn more at Review Commons

Referee #2

Evidence, reproducibility and clarity

Panday et al seeks to determine the function of ADAM13 in regulating histone modifications, gene expression and splicing during cranial neural crest development. Specifically, the authors tested how Adam13, a metalloprotease, could modify chromatin by interaction with Arid3a and Tfap2a and RNA splicing and gene expression. They then utilize knockouts in Xenopus and HEK293T cells followed by immunofluorescence, IPs, BioID, luciferase assays, Mass spec and RNA assays. Although there is some strong data in the BioID and luciferase experiments, the manuscript tells multiple stories, linking together too many things to make a compelling story. The result is a paper that is very difficult to read and understand the take home message. In addition, some of the conclusions are not supported by the data. This unfortunately means it is not ready for publication. However, I have added below some suggestions that would strengthen the manuscript. My comments are below:

Major comments:

1. I think it would be better to split out the chromatin modification function from the splicing in two separate papers. While there is a connection, having it all together makes the story difficult to follow.
2. The immunofluorescence of H3K9me2/3, in Figure 1, 2, 3 following Adam13 knockdown is not convincing. There seems to be a strong edge effect especially in Figure 2 and 3. Similarly the Arid3a expression in Supp Figure 1 if anything seems increased. It would be better to quantify by western blot and not by fluorescent intensity since it is difficult to determine what a small change in fluorescent intensity means in vivo. Also, it does not say in the text or the figure legend what these are, Xenopus explants of CNC?
3. The rationale for isolating KMT2A from the other chromatin modifiers in the dataset is not clear. From the RNA-seq in Supp Figure 2 it is not changed as much as likely some of the others. Also, the arrow seems to indicate that it is right above the cutoff. What about other proteins with ATPase activity? That is the top hit in the Dot plot nuclear function. Would be helpful to write out Adam13 cytoplasm/nucleus here.
4. The splicing information, while interesting would be better as a different manuscript. The sashimi plot requires more explanation as written. How do you tell if the interactions are changed from this? The authors argue there is a reduction of Tfap2a in Figure 3H but half the explant is not expressing sox9 in the Adam13 knockdown. How is this kind of experiment controlled when measure areas that don't have any fluorescence because of the nature of the explants?
5. The use of a germ line Xenopus mutant for Adam13 is great but how were these knockouts validated? More information is required here. The Chip-qPCR has a lot of variability between the samples, especially in the H3K9me2/3. Because these are in cell lines, this should be more consistent. In addition, it is difficult to understand what this means for cranial neural crest cells when assaying in HEK293T cells with the luciferase assay.
6. The migration assay shows only an example of what it looks like to have defective migration. But it would be better to show control embryos, embryos with Adam13 knockdown and what the rescues look like so the reader can make their own conclusion. The argument from the section above suggests the S1 isoform is the primary one but S3 in this assay also rescues, please explain what this result means since it seems to suggest that even though these isoforms have different activity the function is similar in terms of the ability to rescue defective migration.
7. The next section again talks about yet another protein Calpain-8. Here the authors use MO13 for luciferase assays instead of HEK293 cells. The authors do not explain why they decided to switch from cells to MO.
8. The experiment to IP RNA supports only the correlation that Adam9 and Adam13 bind RNA and RNA binding proteins to regulate splicing. This conclusion presented is not supported by the data presented here. While there is a sentence about why Adam9 was chosen here, it would be preferred to focus on Adam13 as the rest of the manuscript is focused on Adam13. The conclusions are generalized to all ADAMs, but ADAM13 and ADAM9 are the only ADAMs investigated in the manuscript

Minor comments

1. The manuscript using a lot of abbreviations (PCNS, NI, MO, SH3) and lingo that are unclear to a general reader. Please define acronyms when first used, as well as be clear on which model is being used in each experiment.
2. Similarly, the figures are not labeled such that a reader would be able to understand ie MO13 should be Adam13 knockdown etc.
3. Please identify the genes on the heatmap and some highlighted genes from volcano plot from the RNA-seq.
4. Why use the flag tag in Figure 5?
5. Is the data in figure 4A-D the same as Supp. Figure 4A-D?
6. Please italicize gene symbols - e.g. "key transcription factors that exemplify CNC, such as the SOX9, FOXD3, SNAI1, SNAI2, and TFAP2 family".
7. Please review the manuscript for grammatical and typographical errors.

Cross-commenting

I think the two reviewers on one the same page on this manuscript.

Significance

If more solid, would be a conceptual advance in role of Adam13 in mediating chromatin modification and transcription factors, adds to exiting work from this lab, good for a specialize audience, my expertise is in in neural crest development, non-mammalian modes, epigenetic regulators

nitsですが、エラーの最後も提示したほうがよいように思いました。

Traceback (most recent call last): File "<stdin>", line 1, in <module> yaml.load('none: !!python/none null', Loader=yaml.SafeLoader) ~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ （エラー情報省略） yaml.constructor.ConstructorError: could not determine a constructor for the tag 'tag:yaml.org,2002:python/none' in "<unicode string>", line 1, column 7: none: !!python/none null

Author response:

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

Reviewer #1 (Public review): 

Overall, the conclusions of the paper are mostly supported by the data but may be overstated in some cases, and some details are also missing or not easily recognizable within the figures. The provision of additional information and analyses would be valuable to the reader and may even benefit the authors' interpretation of the data. 

We thank the reviewer for the thoughtful and constructive feedback. We are pleased that the reviewer found the overall conclusions of our paper to be well supported by the data, and we appreciate the suggestions for improving figure clarity and interpretive accuracy. Below, we address each point with corresponding revisions.

The conclusion that DREADD expression gradually decreases after 1.5-2 years is only based on a select few of the subjects assessed; in Figure 2, it appears that only 3 hM4Di cases and 2 hM3Dq cases are assessed after the 2-year timepoint. The observed decline appears consistent within the hM4Di cases, but not for the hM3Dq cases (see Figure 2C: the AAV2.1-hSyn-hM3Dq-IRES-AcGFP line is increasing after 2 years.) 

We agree that our interpretation should be stated more cautiously, given the limited number of cases assessed beyond the two-year timepoint. In the revised manuscript, we have clarified in the Results that the observed decline is based on a subset of animals. We have also included a text stating that while a consistent decline was observed in hM4Di-expressing monkeys, the trajectory for hM3Dq expression was more variable with at least one case showing an increased signal beyond two years.

Revised Results section:

Lines 140, “hM4Di expression levels remained stable at peak levels for approximately 1.5 years, followed by a gradual decline observed in one case after 2.5 years, and after approximately 3 years in the other two cases (Figure 2B, a and e/d, respectively). Compared with hM4Di expression, hM3Dq expression exhibited greater post-peak fluctuations. Nevertheless, it remained at ~70% of peak levels after about 1 year. This post-peak fluctuation was not significantly associated with the cumulative number of DREADD agonist injections (repeated-measures two-way ANOVA, main effect of activation times, F_(1,6) = 5.745, P = 0.054). Beyond 2 years post-injection, expression declined to ~50% in one case, whereas another case showed an apparent increase (Figure 2C, c and m, respectively).”

Given that individual differences may affect expression levels, it would be helpful to see additional labels on the graphs (or in the legends) indicating which subject and which region are being represented for each line and/or data point in Figure 1C, 2B, 2C, 5A, and 5B. Alternatively, for Figures 5A and B, an accompanying table listing this information would be sufficient. 

We thank the reviewer for these helpful suggestions. In response, we have revised the relevant figures (Fig. 1C, 2B, 2C, and 5) as noted in the “Recommendations for the authors”, including simplifying visual encodings and improving labeling. We have also updated Table 2 to explicitly indicate the animal ID and brain regions associated with each data point shown in the figures.

While the authors comment on several factors that may influence peak expression levels, including serotype, promoter, titer, tag, and DREADD type, they do not comment on the volume of injection. The range in volume used per region in this study is between 2 and 54 microliters, with larger volumes typically (but not always) being used for cortical regions like the OFC and dlPFC, and smaller volumes for subcortical regions like the amygdala and putamen. This may weaken the claim that there is no significant relationship between peak expression level and brain region, as volume may be considered a confounding variable. Additionally, because of the possibility that larger volumes of viral vectors may be more likely to induce an immune response, which the authors suggest as a potential influence on transgene expression, not including volume as a factor of interest seems to be an oversight. 

We thank the reviewer for raising this important issue. We agree that injection volume could act as a confounding variable, particularly since larger volumes were used in only handheld cortical injections. This overlap makes it difficult to disentangle the effect of volume from those of brain region or injection method. Moreover, data points associated with these larger volumes also deviated when volume was included in the model.

To address this, we performed a separate analysis restricted to injections delivered via microinjector, where a comparable volume range was used across cases. In this subset, we included injection volume as additional factor in the model and found that volume did not significantly impact peak expression levels. Instead, the presence of co-expressed protein tags remained a significant predictor, while viral titer no longer showed a significant effect. These updated results have replaced the originals in the revised Results section and in the new Figure 5. We have also revised the Discussion to reflect these updated findings.

The authors conclude that vectors encoding co-expressed protein tags (such as HA) led to reduced peak expression levels, relative to vectors with an IRES-GFP sequence or with no such element at all. While interesting, this finding does not necessarily seem relevant for the efficacy of long-term expression and function, given that the authors show in Figures 1 and 2 that peak expression (as indicated by a change in binding potential relative to non-displaced radioligand, or ΔBPND) appears to taper off in all or most of the constructs assessed. The authors should take care to point out that the decline in peak expression should not be confused with the decline in longitudinal expression, as this is not clear in the discussion; i.e. the subheading, "Factors influencing DREADD expression," might be better written as, "Factors influencing peak DREADD expression," and subsequent wording in this section should specify that these particular data concern peak expression only. 

We appreciate this important clarification. In response, we have revised the title to "Protein tags reduce peak DREADD expression levels" in the Results section and “Factors influencing peak DREADD expression levels” in the Discussion section. Additionally, we specified that our analysis focused on peak ΔBP_ND values around 60 days post-injection. We have also explicitly distinguished these findings from the later-stage changes in expression seen in the longitudinal PET data in both the Results and Discussion sections.

Reviewer #1 (Recommendations for the authors):

(1) Will any of these datasets be made available to other researchers upon request?

All data used to generate the figures have been made publicly available via our GitHub repository (https://github.com/minamimoto-lab/2024-Nagai-LongitudinalPET.git). This has been stated in the "Data availability" section in the revised manuscript.

(2) Suggested modifications to figures:

a) In Figures 2B and C, the inclusion of "serotype" as a separate legend with individual shapes seems superfluous, as the serotype is also listed as part of the colour-coded vector

We agree that the serotype legend was redundant since this information is already included in the color-coded vector labels. In response, we have removed the serotype shape indicators and now represent the data using only vector-construct-based color coding for clarity in Figure 2B and C.

b) In Figures 3A and B, it would be nice to see tics (representing agonist administration) for all subjects, not just the two that are exemplified in panels C-D and F-H. Perhaps grey tics for the non-exemplified subjects could be used.

In response, we have included black and white ticks to indicate all agonist administration across all subjects in Figure 3A and B, with the type of agonist clearly specified.　

c) In Figure 4C, a Nissl- stained section is said to demonstrate the absence of neuronal loss at the vector injection sites. However, if the neuronal loss is subtle or widespread, this might not be easily visualized by Nissl. I would suggest including an additional image from the same section, in a non-injected cortical area, to show there is no significant difference between the injected and non-injected region.

To better demonstrate the absence of neuronal loss at the injection site, we have included an image from the contralateral, non-injected region of the same section for comparison (Fig. 4C).

d) In Figure 5A: is it possible that the hM3Dq construct with a titer of 5×10^13 gc/ml is an outlier, relative to the other hM3Dq constructs used?

We thank the reviewer for raising this important observation. To evaluate whether the high-titer constructs represented a statistical outlier that might artifactually influence the observed trends, we performed a permutation-based outlier analysis. This assessment identified this point in question, as well as one additional case (titer 4.6 x 10e13 gc/ml, #255, L_Put), as significant outlier relative to the distribution of the dataset.

Accordingly, we excluded these two data points from the analysis. Importantly, this exclusion did not meaningfully alter the overall trend or the statistical conclusions—specifically, the significant effect of co-expressed protein tags on peak expression levels remain robust. We have updated the Methods section to describe this outlier handling and added a corresponding note in the figure legend.

Reviewer #2 (Public review): 

Weaknesses 

This study is a meta-analysis of several experiments performed in one lab. The good side is that it combined a large amount of data that might not have been published individually; the downside is that all things were not planned and equated, creating a lot of unexplained variances in the data. This was yet judiciously used by the authors, but one might think that planned and organized multicentric experiments would provide more information and help test more parameters, including some related to inter-individual variability, and particular genetic constructs. 

We thank the reviewer for bringing this important point to our attention. We fully acknowledge that the retrospective nature of our dataset—compiled from multiple studies conducted within a single laboratory—introduces variability related to differences in injection parameters and scanning timelines. While this reflects the practical realities and constraints of long-term NHP research, we agree that more standardized and prospectively designed studies would better control such source of variances. To address this, we have added the following statement to the "Technical consideration" section in Discussion:

Lines 297, "This study included a retrospective analysis of datasets pooled from multiple studies conducted within a single laboratory, which inherently introduced variability across injection parameters and scan intervals. While such an approach reflects real-world practices in long-term NHP research, future studies, including multicenter efforts using harmonized protocols, will be valuable for systematically assessing inter-individual differences and optimizing key experimental parameters."

Reviewer #2 (Recommendations for the authors):

I just have a few minor points that might help improve the paper:

(1) Figure 1C y-axis label: should add deltaBPnd in parentheses for clarity.

We have added “ΔBP_ND” to the y-axis label for clarity.

The choice of a sigmoid curve is the simplest clear fit, but it doesn't really consider the presence of the peak described in the paper. Would there be a way to fit the dynamic including fitting the peak?

We agree that using a simple sigmoid curve for modeling expression dynamics is a limitation. In response to this and a similar comment from Reviewer #3, we tested a double logistic function (as suggested) to see if it better represented the rise and decline pattern. However, as described below, the original simple sigmoid curve was a better fit for the data. We have included a discussion regarding this limitation of this analysis. See Reviewer #3 recommendations (2) for details.

The colour scheme in Figure 1C should be changed to make things clearer, and maybe use another dimension (like dotted lines) to separate hM4Di from hM3Dq.

We have improved the visual clarity of Figure 1C by modifying the color scheme to represent vector construct and using distinct line types (dashed for hM4Di and solid for hM3Dq data) to separate DREADD type.

(2) Figure 2

I don't understand how the referencing to 100 was made: was it by selecting the overall peak value or the peak value observed between 40 and 80 days? If the former then I can't see how some values are higher than the peak. If the second then it means some peak values occurred after 80 days and data are not completely re-aligned.

We thank the reviewer for the opportunity to clarify this point. The normalization was based on the peak value observed between 40–80 days post-injection, as this window typically captured the peak expression phase in our dataset (see Figure 1). However, in some long-term cases where PET scans were limited during this period—e.g., with one scan performing at day 40—it is possible that the actual peak occurred later. Therefore, instances where ΔBP_ND values slightly exceeded the reference peak at later time points likely reflect this sampling limitation. We have clarified this methodological detail in the revised Results section to improve transparency.

The methods section mentions the use of CNO but this is not in the main paper which seems to state that only DCZ was used: the authors should clarify this

Although DCZ was the primary agonist used, CNO and C21 were also used in a few animals (e.g., monkeys #153, #221, and #207) for behavioral assessments. We have clarified this in the Results section and revised Figure 3 to indicate the specific agonist used for each subject. Additionally, we have updated the Methods section to clearly specify the use and dosage of DCZ, CNO, and C21, to avoid any confusion regarding the experimental design.

Reviewer #3 (Public review): 

Minor weaknesses are related to a few instances of suboptimal phrasing, and some room for improvement in time course visualization and quantification. These would be easily addressed in a revision. <br /> These findings will undoubtedly have a very significant impact on the rapidly growing but still highly challenging field of primate chemogenetic manipulations. As such, the work represents an invaluable resource for the community.

We thank the reviewer for the positive assessment of our manuscript and for the constructive suggestions. We address each comment in the following point-by-point responses and have revised the manuscript accordingly.

Reviewer #3 (Recommendations for the authors):

(1) Please clarify the reasoning was, behind restricting the analysis in Figure 1 only to 7 monkeys with subcortical AAV injection?

We focused the analysis shown in Figure 1 on 7 monkeys with subcortical AAV injections who received comparative injection volumes. These data were primary part of vector test studies, allowing for repeated PET scans within 150 days post-injection. In contrast, monkeys with cortical injections—including larger volumes—were allocated to behavioral studies and therefore were not scanned as frequently during the early phase. We will clarify this rationale in the Results section.

(2) Figure 1: Not sure if a simple sigmoid is the best model for these, mostly peaking and then descending somewhat, curves. I suggest testing a more complex model, for instance, double logistic function of a type f(t) = a + b/(1+exp(-c*(t-d))) - e/(1+exp(-g*(t-h))), with the first logistic term modeling the rise to peak, and the second term for partial decline and stabilization

We appreciate the reviewer’s thoughtful suggestion to use a double logistic function to better model both the rising and declining phases of the expression curve. In response to this and similar comments from Reviewer #1, we tested the proposed model and found that, while it could capture the peak and subsequent decline, the resulting fit appeared less biologically plausible (See below). Moreover, model comparison using BIC favored the original simple sigmoid model (BIC = 61.1 vs. 62.9 for the simple and double logistic model, respectively). This information has been included in the revised figure legend for clarity.

Given these results, we retained the original simple sigmoid function in the revised manuscript, as it provides a sufficient and interpretable approximation of the early expression trajectory—particularly the peak expression-time estimation, which was the main purpose of this analysis. We have updated the Methods section to clarify our modeling and rationale as follows:

Lines 530, "To model the time course of DREADD expression, we used a single sigmoid function, referencing past in vivo fluorescent measurements (Diester et al., 2011). Curve fitting was performed using least squares minimization. For comparison, a double logistic function was also tested and evaluated using the Bayesian Information Criterion (BIC) to assess model fit."

We also acknowledge that a more detailed understanding of post-peak expression changes will require additional PET measurements, particularly between 60- and 120-days post-injection, across a larger number of animals. We have included this point in the revised Discussion to highlight the need for future work focused on finer-grained modeling of expression decline:

Lines 317, “Although we modeled the time course of DREADD expression using a single sigmoid function, PET data from several monkeys showed a modest decline following the peak. While the sigmoid model captured the early-phase dynamics and offered a reliable estimate of peak timing, additional PET scans—particularly between 60- and 120-days post-injection—will be essential to fully characterize the biological basis of the post-peak expression trajectories.”

Author response image 1.<br />

(3) Figure 2: It seems that the individual curves are for different monkeys, I counted 7 in B and 8 in C, why "across 11 monkeys"? Were there several monkeys both with hM4Diand hM3Dq? Does not look like that from Table 1. Generally, I would suggest associating specific animals from Tables 1 and 2 to the panels in Figures 1 and 2.

Some animals received multiple vector types, leading to more curves than individual subjects. We have revised the figure legends and updated Table 2 to explicitly relate each curve with the specific animal and brain region.

(4) I also propose plotting the average of (interpolated) curves across animals, to convey the main message of the figure more effectively.

We agree that plotting the mean of the interpolated expression curves would help convey the group trend. We added averaged curves to Figure 2BC.

(5) Similarly, in line 155 "We assessed data from 17 monkeys to evaluate ... Monkeys expressing hM4Di were assessed through behavioral testing (N = 11) and alterations in neuronal activity using electrophysiology (N = 2)..." - please explain how 17 is derived from 11, 2, 5 and 1. It is possible to glean from Table 1 that it is the calculation is 11 (including 2 with ephys) + 5 + 1 = 17, but it might appear as a mistake if one does not go deep into Table 1.

We have clarified in both the text and Table 1 that some monkeys (e.g., #201 and #207) underwent both behavioral and electrophysiological assessments, resulting in the overlapping counts. Specifically, the dataset includes 11 monkeys for hM4Di-related behavior testing (two of which underwent electrophysiology testing), 5 monkeys assessed for hM3Dq with FDG-PET, and 1 monkey assessed for hM3Dq with electrophysiology, totaling 19 assessments across 17 monkeys. We have revised the Results section to make this distinction more explicit to avoid confusion, as follows:

Lines 164, "Monkeys expressing hM4Di (N = 11) were assessed through behavioral testing, two of which also underwent electrophysiological assessment. Monkeys expressing hM3Dq (N = 6) were assessed for changes in glucose metabolism via [¹⁸F]FDG-PET (N = 5) or alterations in neuronal activity using electrophysiology (N = 1).”

(6) Line 473: "These stock solutions were then diluted in saline to a final volume of 0.1 ml (2.5% DMSO in saline), achieving a dose of 0.1 ml/kg and 3 mg/kg for DCZ and CNO, respectively." Please clarify: the injection volume was always 0.1 ml? then it is not clear how the dose can be 0.1 ml/kg (for a several kg monkey), and why DCZ and CNO doses are described in ml/kg vs mg/kg?

We thank the reviewer for pointing out this ambiguity. We apologize for the oversight and also acknowledge that we omitted mention of C21, which was used in a small number of cases. To address this, we have revised the “Administration of DREADD agonist” section of the Methods to clearly describe the preparation, the volume, and dosage for each agonist (DCZ, CNO, and C21) as follows:

Lines 493, “Deschloroclozapine (DCZ; HY-42110, MedChemExpress) was the primary agonist used. DCZ was first dissolved in dimethyl sulfoxide (DMSO; FUJIFILM Wako Pure Chemical Corp.) and then diluted in saline to a final volume of 1 mL, with the final DMSO concentration adjusted to 2.5% or less. DCZ was administered intramuscularly at a dose of 0.1 mg/kg for hM4Di activation, and at 1–3 µg/kg for hM3Dq activation. For behavioral testing, DCZ was injected approximately 15 min before the start of the experiment unless otherwise noted. Fresh DCZ solutions were prepared daily.

In a limited number of cases, clozapine-N-oxide (CNO; Toronto Research Chemicals) or Compound 21 (C21; Tocris) was used as an alternative DREADD agonist for some hM4Di experiments. Both compounds were dissolved in DMSO and then diluted in saline to a final volume of 2–3 mL, also maintaining DMSO concentrations below 2.5%. CNO and C21 were administered intravenously at doses of 3 mg/kg and 0.3 mg/kg, respectively.”

(7) Figure 5A: What do regression lines represent? Do they show a simple linear regression (then please report statistics such as R-squared and p-values), or is it related to the linear model described in Table 3 (but then I am not sure how separate DREADDs can be plotted if they are one of the factors)?

We thank the reviewer for the insightful question. In the original version of Figure 5A, the regression lines represented simple linear fits used to illustrate the relationship between viral titer and peak expression levels, based on our initial analysis in which titer appeared to have a significant effect without any notable interaction with other factors (such as DREADD type).

However, after conducting a more detailed analysis that incorporated injection volume as an additional factor and excluded cortical injections and statistical outliers (as suggested by Reviewer #1), viral titer was no longer found to significantly predict peak expression levels. Consequently, we revised the figure to focus on the effect of reporter tag, which remained the most consistent and robust predictor in our model.

In the updated Figure 5, we have removed the relationship between viral titer and expression level with regression lines.

Author response:

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

Reviewer #1 (Recommendations for the authors):

Because many conclusions are drawn from overexpression studies and from a single cell line (HEK293), it is unclear how general these effects are. In particular, one of the main claims put forth in this manuscript is that of specificity, namely, that FZD5/8, and none of the other FZDs, are uniquely involved in this internalization and degradation. While there are examples of similar specificities, many of these examples can be attributed to a particular cellular context. Without demonstrating that this FZD5/8 specificity is observed in multiple cell lines and contexts, this point remains unconvincing and questionable. One way to address this point of criticism is to omit the word "specifically" in the title and soften the language concerning this idea throughout the manuscript.

We appreciate your valuable comments and suggestions. We have removed the word “specifically” from the title and softened the language concerning this idea throughout the manuscript. Moreover, we performed new experiments to show that Wnt3a/5a induces FZD5/8 endocytosis and degradation and that IWP-2 treatment increases the cell surface levels of FZD5/8 in cell lines other than 293A (Figure 1-Figure supplement 1 and Figure 2-Figure supplement 1). These results indicate that Wnt-induced FZD5/8 endocytosis and degradation are not cell specific.

The starting point for these studies is a survey of all 10 FZDs, V5-tagged and overexpressed in HEK293 cells. Here, the authors observed a decline in cell surface levels of only FZD5 and 8 in response to Wnt3a and Wnt5a. As illustrated in the immunoblot (Fig 1B), several FZDs were poorly expressed, including FZD1, 3, 6 and 9, which calls into question that only FZD5 and 8 were affected. Furthermore, total levels of FZD8 don't diminish appreciably, as claimed by the authors, and only FZD5 shows a subtle decline upon WNT treatment. All of these experiments are performed with overexpressed V5-tagged FZD proteins or with endogenously V5-tagged (KI) proteins, and it is possible that overexpression or tagging lead to potentially artifactual observations. Examining the effects of WNTs on FZD protein localization and levels need to be done with endogenously expressed, non-tagged FZDs. In this context, it is somewhat puzzling that the authors don't show such an experiment using the pan- and FZD5/8-specific antibodies, which they use in multiple experiments throughout the manuscript. With these available tools it should be possible to examine FZD levels at the cell surface in response to Wnt3a and Wnt5a, ideally in multiple cell lines.

We appreciate your valuable comments and suggestions. Figure 1B shows the results of the follow-up study shown in Figure 1A. As shown in Figure 1A, we used flow cytometry analysis to detect the cell surface levels of stably expressed FZDs and found that Wnt3a/5a specifically reduced the levels of FZD5/8 on the cell surface, suggesting that Wnt3a/5a induces FZD5/8 endocytosis. As shown in Figure 1B and C, we performed immunoblotting to examine whether Wnt3a/5a-induced FZD5/8 internalization resulted in FZD5/8 degradation. Notably, most FZDs exhibit two bands on immunoblots, as also suggested by other published studies, and the upper bands represent the mature form that is fully glycosylated and presented to the cell surface (see also new Figure 2L), whereas the lower bands represent the immature form. Our results clearly indicated that Wnt3a/5a treatment reduced the levels of the mature forms of both FZD5 and FZD8, although the immunoblotting signals of the mature form of FZD8 (upper bands) were relatively weak. The immunoblotting signals of the other FZDs varied, and some of them (including FZD1, -3, -6 and -9) were relatively weak; however, according to the results in Figure 1A, all of the FZDs were expressed and present on the cell surface.

Commercially available FZD5/8 antibodies, including those used in published studies, cannot detect endogenous FZD5/8 or can only recognize immature FZD5 in our hands, which is why we have to use the CRISPR-CAS9-based KI technique to introduce a V5 tag to FZD5 and FZD7. Notably, in the overexpression experiments, the V5 tag is on the amino terminus, and in the KI experiments, the V5 tag is on the carboxyl terminus of FZDs, which may minimize the potential artificial effects of the V5 tag on the immunoblotting assays.

The monoclonal antibodies used in this study, such as anti-pan-FZD, anti-FZD5/8, and anti-FZD4 antibodies, are neutralizing antibodies that can compete with Wnt ligands to bind to the FZD CRD. These antibodies have been successfully used to detect the surface levels of FZDs via flow cytometry assays. However, as the binding affinity of the Wnt-FZD CRD is comparable to the binding affinity of the antibody-FZD, we were cautious in using these antibodies to detect the cell surface levels of FZDs when the cells were treated with Wnt3a/5a CM, which contains relatively high concentrations of Wnt3a/5a. As shown in Author response image 1, Wnt3a or Wnt5a treatment dramatically reduced the endogenous cell surface level of FZD5/8, as detected by flow cytometry using the anti-FZD5/8 antibody. However, in another experiment, HEK293A cells were first incubated with cold Wnt3a or Wnt5a CM at 4°C to minimize endocytosis and then analyzed via flow cytometry using the anti-FZD5/8 antibody. The results showed that Wnt3a/5a incubation reduced the floe cytometry signals, suggesting that Wnt3a/5a binding to FZD5/8 might interfere with antibody-FZD5/8 binding, although we cannot exclude the possibility that Wnt3a/5a may induce FZD5/8 endocytosis at 4°C (Author response image 1).

Author response image 1.

(A) HEK293A cells were treated with control, Wnt3a or Wnt5a CM for 2 hours at 37°C in a humidified incubator and were analyzed via flow cytometry using the anti-FZD5/8 antibody.

(B) HEK293A cells were incubated with control, Wnt3a or Wnt5a CM for 1 h at 4°C and analyzed by flow cytometry using the anti-FZD5/8 antibody.

Several experiments rely on gene-edited clonal cell lines, including knockouts of FZD5/8, RNF43/ZNRF3, and DVL. Gene knockouts were confirmed by genomic DNA sequencing and, for DVL and FZD5/8, by loss of protein expression. While these KO lines are powerful tools to study gene function, there is a concern for clonal variability. Each cell line may have acquired additional changes as a result of gene editing. In addition, there may be compensatory changes in gene expression as a consequence of the loss of certain genes. For example, expression of other FZDs may increase in FZD5/8 DKO cells. To address this critique, the authors should show that re-expression of the knocked-out genes rescues the observed effect. This is done in some instances (Fig 5E, G, H) but not in other instances, such as with the DVL TKO (Fig. 3). Since the authors assert that DVL is important for FZD internalization in the absence of WNT, but not for FZD internalization in the presence of WNT, this particular rescue experiment is important. This is a potentially important finding and it should be confirmed by re-expression of DVL in the TKO line. As an alternative, conditional knockdown using Tet-inducible shRNA expression could address concerns for clonal variability.

We appreciate your valuable comments and suggestions. We re-expressed DVL2 in DVLTKO cells stably expressing V5-linker-FZD5 or V5-linker-FZD7. As shown in Figure 3G-K, re-expression of DVL2 rescued the decreased Wnt-independent endocytosis of FZD5 and FZD7 caused by DVL1/2/3 knockout.

Given the significant differences in signaling activity by Wnt3a and Wnt5a, it is somewhat surprising that all experiments shown in this manuscript do not identify distinguishing features between Wnt3a and Wnt5a. In addition, it is unclear why the authors switch between Wnt3a and Wnt5a. For example, Figures 1C, 3G-J, 4C-D only use Wnt5a. In contrast, Figures 6E and H use Wnt3a, most likely because b-catenin stabilization is examined, an effect generally not observed with Wnt5a. The choice of which Wnt is examined/used appears to be somewhat arbitrary and the authors never provide any explanations for these choices. In the end, this type of inconsistency becomes puzzling when the authors present, quite convincingly, in Figure 7, that both Wnt3a and 5a promote an interaction between FZD5/8 and RNF43 through proximity biotin labeling.

Although Wnt3a and Wnt5a are significantly different in triggering intracellular signaling pathways, both bind FZD5/8 and induce FZD5/8 endocytosis and degradation similarly. When FZD5 is stably overexpressed, Wnt5a has slightly stronger effects on inducing FZD5 endocytosis and degradation, possibly because the Wnt5a concentration may be higher than the Wnt3a concentration in our CM, which is why we used Wnt5a CM in some experiments when V5-FZD5 was overexpressed. In the revised manuscript, we used both Wnt3a and Wnt5a CM in the experiments as you suggested, as shown in Figure 1C, 3G-K and Figure 4-Figure supplement 1.

Minor Points:

Figure 3G and I: it is curious that individual cells are shown in the "0 h" samples, while the "Con 1 h" and "Wnt5a 1 h" show multiple cells with several making direct contact with each other. This is notable because the V5 staining at sites of cell-cell contact are quite distinct and variable between control and Wnt5a-treated and WT versus DVL TKO cells. Also, sub-cellular localization of FZD5 (V5 tag) puncta is quite distinct between Con and Wnt5a: puncta in Wnt5a-treated cells appear to be more plasma membrane proximal than in Con cells. These points may be easy to address by showing images of cells that are more similar with respect to cell number and density for each condition.

Thank you for your suggestions. We repeated these experiments and added Wnt3a treatment and adjusted the cell density. Images including an individual cell were selected for presentation.

Figure 5E: the following statement is confusing/misleading: "Furthermore, reintroducing ZNRF3 or RNF43 into ZRDKO cells efficiently restored the increase in cytosolic β-catenin levels, whereas the expression of RNF130 or RNF150, two structurally similar transmembrane E3 ubiquitin ligases, did not (Fig. 5E)." First, reintroduction of ZNRF3 or RNF43 restores cytosolic b-catenin levels; it does not restore the increase in b-catenin. Second, the claim that RNF130 fails to have this effect is not substantiated since it is barely expressed.

Thank you for your suggestions and comments. We reorganized the language to make the statement clearer. Notably, the expression level of RNF130 was relatively low compared with that of other E3 ligases, but RNF130 was expressed (Figure 5E darker exposure) and could reduce the cell surface levels of FZDs, as shown in Figure 5G.

Reviewer #2 (Recommendations for the authors):

(1) Given their results the authors conclude that upregulation of Frizzled on the plasma membrane is not sufficient to explain the stabilization of beta-catenin seen in the ZNRF3/RNF43 mutant cells. This interpretation is sound, and they suggest in the discussion that ZNRF3/RNF43-mediated ubiquitination could serve as a sorting signal to sort endocytosed FZD to lysosomes for degradation and that absence or inhibition of this process would promote FZD recycling. This should be relatively easy to test using surface biotinylation experiments and would considerably strengthen the manuscript.

Thank you for your valuable suggestions and comments. We performed cell surface biotinylation experiments in HEK293A FZD5KI cells, as shown in Figure 2L. The results indicated that Wnt3a or Wnt5a treatment induced the degradation of FZD5 on the cell surface, which was antagonized by cotreatment with RSPO1. We did not perform a more detailed endocytosis/recycling biotinylation experiment that requires complex reversible biotinylation and multiple washing steps because HEK293A cells are fragile in culture and not easy to handle. Furthermore, the results shown in Figure 4 indicate that knockout of ZNRF3/RNF43 or RSPO1 significantly blocked the degradation of internalized FZD5 and reduced the colocalization of internalized FZD5 with lysosomal markers, suggesting that Wnt3a/5a induced lysosomal degradation of FZD5 in the presence of ZNRF3/RNF43 and that the internalized FZD5 was most likely recycled back to the cell surface when ZNRF3/RNF43 was knocked out or inhibited by RSPO1.

(2) The authors show that the FZD5 CRD domain is required for endocytosis since a mutant FZD5 protein in which the CRD is removed does not undergo endocytosis. This is perhaps not surprising since this is the site of Wnt binding, but the authors show that a chimeric FZD5CRD-FZD4 receptor can confer Wnt-dependent endocytosis to an otherwise endocytosis incompetent FZD4 protein. Since the linker region between the CRD and the first TM differs between FZD5 and FZD4, it would be interesting to understand whether the CRD specifically or the overall arrangement (such as the spacing) is the most important determinant.

Our results in Figure 1D-H clearly show that the CRD of FZD5 specifically is both necessary and sufficient for Wnt3a/5a-induced FZD5 endocytosis, as replacing the CRD alone in FZD5 with the CRD from either FZD4 or FZD7 completely abolished Wnt-induced endocytosis, whereas replacing the CRD alone in FZD4 or FZD7 with the FZD5 CRD alone could confer Wnt-induced endocytosis.

(3) I find it surprising that only FZD5 and FZD8 appear to undergo endocytosis or be stabilized at the cell surface upon ZNRF3/RNF43 knockout. Is this consistent with previous literature? Is that a cell-specific feature? These findings should be tested in a different cell line, with possibly different relative levels of ZNRF3 and RNF43 expression.

Thank you for your comments and suggestions. Our finding that ZNRF3/RNF43 specifically regulates FZD5/8 degradation is consistent with recent published studies in which FZD5 is required for the survival of RNF43-mutant PDAC or colorectal cancer cells (Nature Medicine, 2017, PMID: 27869803) and FZD5 is required for the maintenance of intestinal stem cells (Developmental Cell, 2024, PMID: 39579768 and 39579769), and in both cases, FZDs other than FZD5/8 are also expressed but not sufficient to compensate for the function of FZD5. The mechanism by which Wnt3a/5a specifically induces FZD5/8 endocytosis and degradation is currently unknown and needs to be explored in the future. We speculate that Wnt binding to FZD5/8 may recruit another protein on the cell surface to specifically facilitate FZD5/8 endocytosis. On the other hand, we cannot exclude the possibility that Wnts other than Wnt3a/5a may induce the endocytosis and degradation of FZDs other than FZD5/8 since there are 19 Wnts and 10 FZDs in humans. Notably, several previous studies have suggested that ZNRF3/RNF43 may regulate the endocytosis and degradation of all FZDs without selectivity (such as Nature, 2012, PMID: 22575959; Nature, 2012, PMID: 22895187; Mol Cell, 2015, PMID: 25891077). However, their conclusions were drawn mostly on the basis of overexpression studies. According to the results shown in Figure 5E-H, overexpressing a membrane-tethered E3 ligase (such as ZNRF3, RNF43, RNF130, or RNF150) may nonspecifically degrade FZD proteins on the cell surface.

Furthermore, in the revised manuscript, we showed that Wnt3a/5a induced FZD5/8 endocytosis and degradation in multiple cell lines, including Huh7, U2OS, MCF7, and 769P cells (Figure 1-Figure supplement 1 and Figure 2-Figure supplement 1), suggesting that these phenomena are not specific to 293A cells.

(4) If FZD7 is not a substrate of ZNRF3/RNF43 and therefore is not ubiquitinated and degraded, how do the authors reconcile that its overexpression does not lead to elevated cytosolic beta-catenin levels in Figure 5B?

We are currently not sure of the mechanism underlying this result. Considering that most FZDs are expressed in 293A cells, we do not know how much of the mature form of overexpressed FZD7 was presented to the plasma membrane.

(5) For Figure 5B, it would be interesting if the authors could evaluate whether overexpression of FZD5 in the ZNRF3/RNF43 double knockout lines would synergize and lead to further increase in cytosolic beta-catenin levels. As control if the substrate selectivity is clear FZD7 overexpression in that line should not do anything.

Thank you for your suggestion. We performed these experiments as suggested, and the results indicated that overexpressing FZD5 further increased cytosolic beta-catenin levels in ZRDKO cells, whereas FZD7 had no effect (Figure 6D).

(6) In Figure 6G, the authors need to show cytosolic levels of beta-catenin in the absence of Wnt in all cases.

We did not add Wnt CM in this experiment. RSPO1 activity, which relies on endogenous Wnt, has been well documented in previous studies.

(7) Since the authors show that DVL is not involved in the Wnt and ZRNF3-dependent endocytosis they should repeat the proximity biotinylation experiment in figure 7 in the DVL triple KO cells. This is an important experiment since previous studies showed that DVL was required for the ZRNF3/RNF43-mediated ubiqtuonation of FZD.

Thank you for your valuable suggestions. As you suggested, we performed a proximity biotinylation experiment in DVL TKO cells, and the results showed that Wnt3a/5a could still induce the interaction of FZD5 and RNF43 in DVLTKO cells (Figure 7-figure supplement 1), suggesting that the Wnt-induced FZD5‒RNF43 interaction is DVL independent.

Author response:

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

Reviewer #1 (Public Review): 

Summary: 

The study by Klug et al. investigated the pathway specificity of corticostriatal projections, focusing on two cortical regions. Using a G-deleted rabies system in D1-Cre and A2a-Cre mice to retrogradely deliver channelrhodopsin to cortical inputs, the authors found that M1 and MCC inputs to direct and indirect pathway spiny projection neurons (SPNs) are both partially segregated and asymmetrically overlapping. In general, corticostriatal inputs that target indirect pathway SPNs are likely to also target direct pathway SPNs, while inputs targeting direct pathway SPNs are less likely to also target indirect pathway SPNs. Such asymmetric overlap of corticostriatal inputs has important implications for how the cortex itself may determine striatal output. Indeed, the authors provide behavioral evidence that optogenetic activation of M1 or MCC cortical neurons that send axons to either direct or indirect pathway SPNs can have opposite effects on locomotion and different effects on action sequence execution. The conclusions of this study add to our understanding of how cortical activity may influence striatal output and offer important new clues about basal ganglia function. 

The conceptual conclusions of the manuscript are supported by the data, but the details of the magnitude of afferent overlap and causal role of asymmetric corticostriatal inputs on behavioral outcomes were not yet fully resolved. 

We appreciate the reviewer’s thoughtful understanding and acknowledgment that the conceptual conclusion of asymmetric projections from the cortex to the striatum is well supported by our data. We also recognize the importance of further elucidating the extent of afferent overlap and the causal contributions of asymmetric corticostriatal inputs to behavioral outcomes. However, we respectfully note that current technical limitations pose significant challenges to addressing these questions with high precision.

In response to the reviewer’s comments, we have now clarified the sample size, added proper analysis and elaborated on the experimental design to ensure that our conclusions are presented more transparently and are more accessible to the reader.

After virally labeling either direct pathway (D1) or indirect pathway (D2) SPNs to optogenetically tag pathway-specific cortical inputs, the authors report that a much larger number of "non-starter" D2-SPNs from D2-SPN labeled mice responded to optogenetic stimulation in slices than "non-starter" D1 SPNs from D1-SPN labeled mice did. Without knowing the relative number of D1 or D2 SPN starters used to label cortical inputs, it is difficult to interpret the exact meaning of the lower number of responsive D2-SPNs in D1 labeled mice (where only ~63% of D1-SPNs themselves respond) compared to the relatively higher number of responsive D1-SPNs (and D2-SPNs) in D2 labeled mice. While relative differences in connectivity certainly suggest that some amount of asymmetric overlap of inputs exists, differences in infection efficiency and ensuing differences in detection sensitivity in slice experiments make determining the degree of asymmetry problematic. 

Thank you for highlighting this point. As it lies at the core of our manuscript, we agree that it is essential to present it clearly and convincingly. As shown by the statistics (Fig. 2B-F), non-starter D1- and D2-SPNs appear to receive fewer projections from D1-projecting cortical neurons (Input D1-record D1, 0.63; Input D1-record D2, 0.40) compared to D2-projecting cortical neurons (Input D2 - record D1, 0.73; Input D2 -record D2, 0.79).

While it is not technically feasible to quantify the number of infected cells in brain slices following electrophysiological recordings, we addressed this limitation by collecting data from multiple animals and restricting recordings to cells located within the injection sites. In Figure 2D, we used 7 mice in the D1-projecting to D1 EGFP(+) group, 8 mice in the D1-projecting to D2 EGFP(-) group, 10 mice in the D2-projecting to D2 EGFP(+) group, and 8 mice in the D2-projecting to D1 EGFP(-) group. In Figure 2G, the group sizes were as follows: 8 mice in the D1-projecting to D2 EGFP(+) group, 7 mice in the D1-projecting to D1 EGFP(-) group, 8 mice in the D2-projecting to D1 EGFP(+) group, and 10 mice in the D2-projecting to D2 EGFP(-) group. In both panels, connection ratios were compared using Fisher’s exact test. Comparisons were then made across experimental groups. Furthermore, as detailed in our Methods section (page 20, line 399-401), we assessed cortical expression levels prior to performing whole-cell recordings. Taken together, these precautions help ensure that the calculated connection ratios are unlikely to be confounded by differences in infection efficiency.

It is also unclear if retrograde labeling of D1-SPN- vs D2-SPN- targeting afferents labels the same densities of cortical neurons. This gets to the point of specificity in the behavioral experiments. If the target-based labeling strategies used to introduce channelrhodopsin into specific SPN afferents label significantly different numbers of cortical neurons, might the difference in the relative numbers of optogenetically activated cortical neurons itself lead to behavioral differences? 

Thank you for bringing this concern to our attention. While optogenetic manipulation has become a widely adopted tool in functional studies of neural circuits, it remains subject to several technical limitations due to the nature of its implementation. Factors such as opsin expression efficiency, optic fiber placement, light intensity, stimulation spread, and other variables can all influence the specificity and extent of neuronal activation or inhibition. As such, rigorous experimental controls are essential when interpreting the outcomes of optogenetic experiments.

In our study, we verified both the expression of channelrhodopsin in D1- or D2-projecting cortical neurons and the placement of the optic fiber following the completion of behavioral testing. To account for variability, we compared the behavioral effects of optogenetic stimulation within the same animals, stimulated versus non-stimulated conditions, as shown in Figures 3 and 4. Moreover, Figure S3 includes important controls that rule out the possibility that the behavioral effects observed were due to direct activation of D1- or D2-SPNs in striatum or to light alone in the cortex.

An additional point worth emphasizing is that the behavioral effects observed in the open field and ICSS tests cannot be attributed to differences in the number of neurons activated. Specifically, activation of D1-projecting cortical neurons promoted locomotion in the open field, whereas activation of D2-projecting cortical neurons did not. However, in the ICSS test, activation of both D1- and D2-projecting cortical neurons reinforced lever pressing. Given that only D1-SPN activation, but not D2-SPN activation, supports ICSS behavior, these effects are unlikely to result merely from differences in the number of neurons recruited.

This rationale underlies our use of multiple behavioral paradigms to examine the functions of D1- and D2-projecting cortical neurons. By assessing behavior across distinct tasks, we aimed to approach the question from multiple angles and reduce the likelihood of spurious or confounding effects influencing our interpretation.

In general, the manuscript would also benefit from more clarity about the statistical comparisons that were made and sample sizes used to reach their conclusions.

We thank the reviewer for the valuable suggestion to improve the manuscript. In response, we have made the following changes and provided additional clarification:

(1) In Figure 2D, we used 7 mice in the D1-projecting to D1 EGFP(+) group, 8 mice in the D1-projecting to D2 EGFP(-) group, 10 mice in the D2-projecting to D2 EGFP(+) group, and 8 mice in the D2-projecting to D1 EGFP(-) group. In Figure 2G, the group sizes were as follows: 8 mice in the D1-projecting to D2 EGFP(+) group, 7 mice in the D1-projecting to D1 EGFP(-) group, 8 mice in the D2-projecting to D1 EGFP(+) group, and 10 mice in the D2-projecting to D2 EGFP(-) group. In both panels, connection ratios were compared using Fisher’s exact test.

(2) In Figure 3, we reanalyzed the data in panels O, P, R, and S using permutation tests to assess whether each individual group exhibited a significant ICSS learning effect. The figure legend has been revised accordingly as follows:

(O-P) D1-SPN (red) but not D2-SPN stimulation (black) drives ICSS behavior in both the DMS (O: D1, n = 6, permutation test, slope = 1.5060, P = 0.0378; D2, n = 5, permutation test, slope = -0.2214, P = 0.1021; one-tailed Mann Whitney test, Day 7 D1 vs. D2, P = 0.0130) and the DLS (P: D1, n = 6, permutation test, slope = 28.1429, P = 0.0082; D2, n = 5, permutation test, slope = -0.3429, P = 0.0463; one-tailed Mann Whitney test, Day 7 D1 vs. D2, P = 0.0390). *, P < 0.05. (Q) Timeline of helper virus injections, rabies-ChR2 injections and optogenetic stimulation for ICSS behavior. (R-S) Optogenetic stimulation of the cortical neurons projecting to either D1- or D2-SPNs induces ICSS behavior in both the MCC (R: MCC-D1, n = 5, permutation test, Day1-Day7, slope = 2.5857, P = 0.0034; MCC-D2, n = 5, Day2-Day7, permutation test, slope = 1.4229, P = 0.0344; no significant effect on Day7, MCC-D1 vs. MCC-D2,  two-tailed Mann Whitney test, P = 0.9999) and the M1 (S: M1-D1, n = 5, permutation test, Day1-Day7, slope = 1.8214, P = 0.0259; M1-D2, n = 5, Day1-Day7, permutation test, slope = 1.8214, P = 0.0025; no significant effect on Day7, M1-D1 vs. M1-D2, two-tailed Mann Whitney test, P = 0.3810). n.s., not statistically significant.

(3) In Figure 4, we have added a comparison against a theoretical percentage change of zero to better evaluate the net effect of each manipulation. The results showed that in Figure 4D, optogenetic stimulation of D1-projecting MCC neurons significantly increased the pressing rate, whereas stimulation of D2-projecting MCC neurons did not (MCC-D1: n = 8, one-sample two-tailed t-test, t = 2.814, P = 0.0131; MCC-D2: n = 7, t = 0.8481, P = 0.4117). In contrast, in Figure 4H, optogenetic stimulation of both D1- and D2-projecting M1 neurons significantly increased the sequence press rate (M1-D1: n = 6, one-sample two-tailed Wilcoxon signed-rank test, P = 0.0046; M1-D2: n = 7, P = 0.0479).

Reviewer #2 (Public Review):

Summary: 

Klug et al. use monosynaptic rabies tracing of inputs to D1- vs D2-SPNs in the striatum to study how separate populations of cortical neurons project to D1- and D2-SPNs. They use rabies to express ChR2, then patch D1-or D2-SPNs to measure synaptic input. They report that cortical neurons labeled as D1-SPN-projecting preferentially project to D1-SPNs over D2-SPNs. In contrast, cortical neurons labeled as D2-SPN-projecting project equally to D1- and D2-SPNs. They go on to conduct pathway-specific behavioral stimulation experiments. They compare direct optogenetic stimulation of D1- or D2-SPNs to stimulation of MCC inputs to DMS and M1 inputs to DLS. In three different behavioral assays (open field, intra-cranial self-stimulation, and a fixed ratio 8 task), they show that stimulating MCC or M1 cortical inputs to D1-SPNs is similar to D1-SPN stimulation, but that stimulating MCC or M1 cortical inputs to D2-SPNs does not recapitulate the effects of D2-SPN stimulation (presumably because both D1- and D2-SPNs are being activated by these cortical inputs). 

Strengths: 

Showing these same effects in three distinct behaviors is strong. Overall, the functional verification of the consequences of the anatomy is very nice to see. It is a good choice to patch only from mCherry-negative non-starter cells in the striatum.

Thank you for your profound understanding and appreciation of our manuscript’s design and the methodologies employed. In the realm of neuroscience, quantifying synaptic connections is a formidable challenge. While the roles of the direct and indirect pathways in motor control have long been explored, the mechanism by which upstream cortical inputs govern these pathways remains shrouded in mystery at the circuitry level.

In the ‘Go/No-Go’ model, the direct and indirect pathways operate antagonistically; in contrast, the ‘Co-activation’ model suggests that they work cooperatively to orchestrate movement. These distinct theories raise a compelling question: Do these two pathways receive inputs from the same upstream cortical neurons, or are they modulated by distinct subpopulations? Answering this question could provide vital clues as to whether these pathways collaborate or operate independently.

Previous studies have revealed both differences and similarities in the cortical inputs to direct and indirect pathways at population level. However, our investigation delves deeper to understand how a singular cortical input simultaneously drives these pathways, or might it regulate one pathway through distinct subpopulations? To address this, we employed rabies virus–mediated retrograde tracing from D1- or D2-SPNs and recorded non-starter SPNs to determine if they receive the same inputs as the starter SPNs. This approach allowed us to calculate the connection ratio and estimate the probable connection properties.

Weaknesses: 

One limitation is that all inputs to SPNs are expressing ChR2, so they cannot distinguish between different cortical subregions during patching experiments. Their results could arise because the same innervation patterns are repeated in many cortical subregions or because some subregions have preferential D1-SPN input while others do not.

Thank you for raising this thoughtful concern. It is indeed not feasible to restrict ChR2 expression to a specific cortical region using the first-generation rabies-ChR2 system alone. A more refined approach would involve injecting Cre-dependent TVA and RG into the striatum of D1- or A2A-Cre mice, followed by rabies-Flp infection. Subsequently, a Flp-dependent ChR2 virus could be injected into the MCC or M1 to selectively label D1- or D2-projecting cortical neurons. This strategy would allow for more precise targeting and address many of the current limitations.

However, a significant challenge lies in the cytotoxicity associated with rabies virus infection. Neuronal health begins to deteriorate substantially around 10 days post-infection, which provides an insufficient window for robust Flp-dependent ChR2 expression. We have tested several new rabies virus variants with extended survival times (Chatterjee et al., 2018; Jin et al., 2024), but unfortunately, they did not perform effectively or suitably in the corticostriatal systems we examined.

In our experimental design, the aim is to delineate the connectivity probabilities to D1 or D2-SPNs from cortical neurons. Our hypothesis considered includes the possibility that similar innervation patterns could occur across multiple cortical subregions, or that some subregions might show preferential input to D1-SPNs while others do not, or a combination of both scenarios. This leads us to perform a series behavior test that using optogenetic activation of the D1- or D2-projecting cortical populations to see which could be the case.

In the cortical areas we examined, MCC and M1, during behavioral testing, there is consistency with our electrophysiological results. Specifically, when we stimulated the D1-projecting cortical neurons either in MCC or in M1, mice exhibited facilitated local motion in open field test, which is the same to the activation of D1 SPNs in the striatum along (MCC: Fig 3C & D vs. I; M1: Fig 3F & G vs. L). Conversely, stimulation of D2-projecting MCC or M1 cortical neurons resulted in behavioral effects that appeared to combine characteristics of both D1- and D2-SPNs activation in the striatum (MCC: Fig 3C & D vs. J; M1: Fig 3F & G vs. M). The similar results were observed in the ICSS test. Our interpretation of these results is that the activation of D1-projecting neurons in the cortex induces behavior changes akin to D1 neuron activation, while activation of D2-projecting neurons in the cortex leads to a combined effect of both D1 and D2 neuron activation. This suggests that at least some cortical regions, the ones we tested, follow the hypothesis we proposed.

There are also some caveats with respect to the efficacy of rabies tracing. Although they only patch non-starter cells in the striatum, only 63% of D1-SPNs receive input from D1-SPN-projecting cortical neurons. It's hard to say whether this is "high" or "low," but one question is how far from the starter cell region they are patching. Without this spatial indication of where the cells that are being patched are relative to the starter population, it is difficult to interpret if the cells being patched are receiving cortical inputs from the same neurons that are projecting to the starter population. Convergence of cortical inputs onto SPNs may vary with distance from the starter cell region quite dramatically, as other mapping studies of corticostriatal inputs have shown specialized local input regions can be defined based on cortical input patterns (Hintiryan et al., Nat Neurosci, 2016, Hunnicutt et al., eLife 2016, Peters et al., Nature, 2021).

This is a valid concern regarding anatomical studies. Investigating cortico-striatal connectivity at the single-cell level remains technically challenging due to current methodological limitations. At present, we rely on rabies virus-mediated trans-synaptic retrograde tracing to identify D1- or D2-projecting cortical populations. This anatomical approach is coupled with ex vivo slice electrophysiology to assess the functional connectivity between these projection-defined cortical neurons and striatal SPNs. This enables us to quantify connection ratios, for example, the proportion of D1-projecting cortical neurons that functionally synapse onto non-starter D1-SPNs.

To ensure the robustness of our conclusions, it is essential that both the starter cells and the recorded non-starter SPNs receive comparable topographical input from the cortex and other brain regions. Therefore, we carefully designed our experiments so that all recorded cells were located within the injection site, were mCherry-negative (i.e., non-starter cells), and were surrounded by ChR2-mCherry-positive neurons. This configuration ensured that the distance between recorded and starter cells did not exceed 100 µm, maintaining close anatomical proximity and thereby preserving the likelihood of shared cortical innervation within the examined circuitry.

These methodological details are also described in the section on ex vivo brain slice electrophysiology, specifically in the Methods section, lines 396–399:

“D1-SPNs (eGFP-positive in D1-eGFP mice, or eGFP-negative in D2-eGFP mice) or D2-SPNs (eGFP-positive in D2-eGFP mice, or eGFP-negative in D1-eGFP mice) that were ChR2-mCherry-negative, but in the injection site and surrounded by cells expressing ChR2-mCherry were targeted for recording.”

This experimental strategy was implemented to control for potential spatial biases and to enhance the interpretability of our connectivity measurements.

A caveat for the optogenetic behavioral experiments is that these optogenetic experiments did not include fluorophore-only controls.

Thank you for bringing this to our attention. A fluorophore-only control is indeed a valuable negative control, commonly used to rule out effects caused by light exposure independent of optogenetic manipulation. In this study, however, comparisons were made between light-on and light-off conditions within the same animal. This within-subject design, as employed in recent studies (Geddes et al., 2018; Zhu et al., 2025), is considered sufficient to isolate the effects of optogenetic manipulation.

Furthermore, as shown in Figure S3, we conducted an additional control experiment in which optogenetic stimulation was applied to M1, while ensuring that ChR2 expression was restricted to the striatum via targeted viral infection. This approach serves as a functional equivalent to the control you suggested. Importantly, we observed no effects that could be attributed solely to light exposure, further supporting the conclusion that the observed outcomes in our main experiments are due to targeted optogenetic manipulation, rather than confounding effects of illumination.

Lastly, by employing an in-animal comparison, measuring changes between stimulated and non-stimulated trials, we account for subject-specific variability and strengthen the interpretability of our findings.

Another point of confusion is that other studies (Cui et al, J Neurosci, 2021) have reported that stimulation of D1-SPNs in DLS inhibits rather than promotes movement.

Thank you for bringing the study by Cui and colleagues to our attention. While that study has generated some controversy, other independent investigations have demonstrated that activation of D1-SPNs in DLS facilitates local motion and lever-press behaviors (Dong et al., 2025; Geddes et al., 2018; Kravitz et al., 2010).

It is still worth to clarify. The differences in behavioral outcomes observed between our study and that of Cui et al. may be attributable to several methodological factors, including differences in both the stereotaxic targeting coordinates and the optical fiber specifications used for stimulation.

Specifically, in our experiments, the dorsomedial striatum (DMS) was targeted at coordinates AP +0.5 mm, ML ±1.5 mm, DV –2.2 mm, and the DLS at AP +0.5 mm, ML ±2.5 mm, DV –2.2 mm. In contrast, Cui et al. targeted the DMS at AP +0.9 mm, ML ±1.4 mm, DV –3.0 mm and the DLS at AP +0.7 mm, ML ±2.3 mm, DV –3.0 mm. These coordinates correspond to sites that are slightly more rostral and ventral compared to our own. Even subtle differences in anatomical targeting can result in activation of distinct neuronal subpopulations, which may account for the differing behavioral effects observed during optogenetic stimulation.

In addition, the optical fibers used in the two studies varied considerably. We employed fibers with a 200 µm core diameter and a numerical aperture (NA) of 0.37, whereas Cui et al. used fibers with a 250 µm core diameter and a higher NA of 0.66. The combination of a larger core and higher NA in their setup implies a broader spatial spread and deeper tissue penetration of light, likely resulting in activation of a larger neural volume. This expanded volume of stimulation may have engaged additional neural circuits not recruited in our experiments, further contributing to the divergent behavioral outcomes. Taken together, these differences in targeting and photostimulation parameters are likely key contributors to the distinct effects reported between the two studies.

Reviewer #3 (Public Review): 

In the manuscript by Klug and colleagues, the investigators use a rabies virus-based methodology to explore potential differences in connectivity from cortical inputs to the dorsal striatum. They report that the connectivity from cortical inputs onto D1 and D2 MSNs differs in terms of their projections onto the opposing cell type, and use these data to infer that there are differences in cross-talk between cortical cells that project to D1 vs. D2 MSNs. Overall, this manuscript adds to the overall body of work indicating that there are differential functions of different striatal pathways which likely arise at least in part by differences in connectivity that have been difficult to resolve due to difficulty in isolating pathways within striatal connectivity and several interesting and provocative observations were reported. Several different methodologies are used, with partially convergent results, to support their main points.

However, I have significant technical concerns about the manuscript as presented that make it difficult for me to interpret the results of the experiments. My comments are below.

Major:

There is generally a large caveat to the rabies studies performed here, which is that both TVA and the ChR2-expressing rabies virus have the same fluorophore. It is thus essentially impossible to determine how many starter cells there are, what the efficiency of tracing is, and which part of the striatum is being sampled in any given experiment. This is a major caveat given the spatial topography of the cortico-striatal projections. Furthermore, the authors make a point in the introduction about previous studies not having explored absolute numbers of inputs, yet this is not at all controlled in this study. It could be that their rabies virus simply replicates better in D1-MSNs than D2-MSNs. No quantifications are done, and these possibilities do not appear to have been considered. Without a greater standardization of the rabies experiments across conditions, it is difficult to interpret the results.

We thank the reviewer for raising these questions, which merit further discussion.

Firstly, the primary aim of our study is to investigate the connectivity of the corticostriatal pathway. Given the current technical limitations, it is not feasible to trace all the striatal SPNs connected to a single cortical neuron. Therefore, we approached this from the opposite direction, starting from D1- or D2-SPNs to retrogradely label upstream cortical neurons, and then identifying their connected SPNs via functional synaptic recordings. To achieve this, we employed the only available transsynaptic retrograde method: rabies virus-mediated tracing. Because we crossed D1- or D2-GFP mice with D1- or A2A-Cre mice to identify SPN subtypes during electrophysiological recordings, the conventional rabies-GFP system could not be used to distinguish starter cells without conflicting with the GFP labeling of SPNs. To overcome this, we tagged ChR2 expression with mCherry. In this setup, we recorded from mCherry-negative D1- or D2-SPNs within the injection site and surrounded by mCherry-positive neurons. This ensures that the recorded neurons are topographically matched to the starter cell population and receive input from the same cortical regions. We acknowledge that TVA-only and ChR2-expressing cells are both mCherry-positive and therefore indistinguishable in our system. As such, mCherry-positive cells likely comprise a mixture of starter cells and TVA-only cells, representing a somewhat broader population than starter cells alone. Nevertheless, by restricting recordings to mCherry-negative SPNs within the injection site, it is ensured that our conclusions about functional connectivity remain valid and aligned with the primary objective of this study.

Secondly, if rabies virus replication were significantly more efficient in D1-SPNs than in D2-SPNs, this would likely result in a higher observed connection probability in the D1-projecting group. However, we used consistent genetic strategies across all groups: D1-SPNs were defined as GFP-positive in D1-GFP mice and GFP-negative in D2-GFP mice, with D2-SPNs defined analogously. Recordings from both D1- and D2-SPNs were performed using the same methodology and under the same injection conditions within the same animals. This internal control helps mitigate the possibility that differential rabies infection efficiency biased our results.

With these experimental safeguards in place, we found that 40% of D2-SPNs received input from D1-SPN-projecting cortical neurons, while 73% of D1-SPNs received input from D2-SPN-projecting cortical neurons. Although the ideal scenario would involve an even larger sample size to refine these estimates, the technical demands of post-rabies-infection electrophysiological recordings inherently limit throughput. Nonetheless, our approach represents the most feasible and accurate method currently available, and provides a significant advance in characterizing the functional connectivity within corticostriatal circuits.

The authors claim using a few current clamp optical stimulation experiments that the cortical cells are healthy, but this result was far from comprehensive. For example, membrane resistance, capacitance, general excitability curves, etc are not reported. In Figure S2, some of the conditions look quite different (e.g., S2B, input D2-record D2, the method used yields quite different results that the authors write off as not different). Furthermore, these experiments do not consider the likely sickness and death that occurs in starter cells, as has been reported elsewhere. The health of cells in the circuit is overall a substantial concern that alone could invalidate a large portion, if not all, of the behavioral results. This is a major confound given those neurons are thought to play critical roles in the behaviors being studied. This is a major reason why first-generation rabies viruses have not been used in combination with behavior, but this significant caveat does not appear to have been considered, and controls e.g., uninfected animals, infected with AAV helpers, etc, were not included.

We understand and appreciate the reviewer’s concern regarding the potential cytotoxicity of rabies virus infection. Indeed, this is a critical consideration when interpreting functional connectivity data. We have tested several newer rabies virus variants reported to support extended survival times (Chatterjee et al., 2018; Jin et al., 2024), but unfortunately, these variants did not perform reliably in the corticostriatal circuits we examined.

Given these limitations, we relied on the rabies virus approach originally developed by Osakada et al. (Osakada et al., 2011), which demonstrated that neurons infected with rabies virus expressing ChR2 remain both viable and functional up to at least 10 days post-infection (Fig. 3, cited below). In our own experiments, we further validated the health and viability of cortical neurons, the presynaptic partners of SPNs, particularly around day 7 post-infection.

To minimize the risk of viral toxicity, we performed ex vivo slice recordings within a conservative time window, between 4 and 8 days after infection, when the health of labeled neurons is well maintained. Moreover, the recorded SPNs were consistently mCherry-negative, indicating they were not directly infected by rabies virus, thus further reducing the likelihood of recording from compromised cells.

Taken together, these steps help ensure that our synaptic recordings reflect genuine functional connectivity, rather than artifacts of viral toxicity. We hope this clarifies the rationale behind our experimental design.

For the behavioral tests, including a naïve uninfected group and an AAV helper virus-only group as negative controls could be beneficial to isolate the specific impact of rabies virus infection. However, our primary focus is on the activation of selected presynaptic inputs to D1- or D2-SPNs by optogenetic method. Therefore, comparing stimulated versus non-stimulated trials within the same animal offers more direct and relevant results for our study objectives.

It is also important to note that the ICSS test is particularly susceptible to the potential cytotoxic effects of rabies virus, as it spans a relatively extended period, from Day 4 to Day 12 post-infection. To mitigate this issue, we focused our analysis on the first 7 days of ICSS testing, thereby keeping the behavioral observations within 10 days post-rabies injection. This approach minimizes potential confounds from rabies-induced neurotoxicity while still capturing the relevant behavioral dynamics. Accordingly, we have revised Figure 3 and updated the statistical analyses to reflect this adjustment.

The overall purity (e.g., EnvA-pseudotyping efficiency) of the RABV prep is not shown. If there was a virus that was not well EnvA-pseudotyped and thus could directly infect cortical (or other) inputs, it would degrade specificity.

We agree that anatomical specificity is crucial for accurately labeling inputs to defined SPN populations in our study. The rabies virus strain employed here has been rigorously validated for its specificity in numerous previous studies from our group and others (Aoki et al., 2019; Klug et al., 2018; Osakada et al., 2011; Smith et al., 2016; Wall et al., 2013; Wickersham et al., 2007). For example, in a recent study by Aoki et al. (Aoki et al., 2019), we tested the same rabies virus strain by co-injecting the glycoprotein-deleted rabies virus and the TVA-expressing helper virus, without glycoprotein expressing AAV, into the SNr. As shown in Figure S1 (related to Figure 2), GFP expression was restricted to starter cells within the SNr, with no evidence of transsynaptic labeling in upstream regions such as the striatum, EPN, GPe, or STN (see panels F–H). These findings provide strong evidence that the rabies virus used in our experiments is properly pseudotyped and exhibits high specificity for starter cell labeling without off-target spread.

We appreciate the reviewer’s emphasis on specificity, and we hope this clarification further supports the reliability of our anatomical tracing approach.

While most of the study focuses on the cortical inputs, in slice recordings, inputs from the thalamus are not considered, yet likely contribute to the observed results. Related to this, in in vivo optogenetic experiments, technically, if the thalamic or other inputs to the dorsal striatum project to the cortex, their method will not only target cortical neurons but also terminals of other excitatory inputs. If this cannot be ruled it, stating that the authors are able to selectively activate the cortical inputs to one or the other population should be toned down.

We agree with the reviewer that the thalamus is also a significant source of excitatory input to the striatum. However, current techniques do not allow for precise and exclusive labeling of upstream neurons in a given brain region, such as the cortex or thalamus. This technical limitation indeed makes it difficult to definitively determine whether inputs from these regions follow the same projection rules. Despite this, our findings show that stimulation of defined cortical populations, specifically, D1- or D2-projecting neurons in MCC and M1, elicits behavioral outcomes that closely mirror those observed in our ex vivo slice recordings, providing strong support for the cortical origin of the effects we observed.

In our in vivo optogenetic experiments, we acknowledge that stimulating a specific cortical region may also activate axonal terminals from rabies-infected cortical or thalamic neurons. While somatic stimulation is generally more effective than terminal stimulation, we recognize the possibility that terminals on non-rabies-traced cortical neurons could be activated through presynaptic connections. To address this, we considered the finding of a previous study (Cruikshank et al., 2010), which demonstrated that while brief optogenetic stimulation (0.05 ms) of thalamo-cortical terminals can elicit few action potentials in postsynaptic cortical neurons, sustained terminal stimulation (500 ms) also results in only transient postsynaptic firing rather than prolonged activation (Fig. 3C, cited below). This suggests that cortical neurons exhibit only short-lived responses to continuous presynaptic stimulation of thalamic origin.

In comparison, our behavioral paradigms employed prolonged optogenetic stimulation protocols- 20 Hz, 10 ms pulses for 15 s (open-field test), 1 s (ICSS), and 8 s (FR4/8)—which more closely resemble sustained stimulation conditions. Given these parameters, and the robust behavioral responses observed, it means that the effects are primarily mediated by activation of rabies-labeled, ChR2-expressing D1- or D2-projecting cortical neurons rather than indirect activation through thalamic input.

We appreciate the reviewer’s valuable comment, and we have now incorporated this point into the revised manuscript (page 13, line 265 to 275) to more clearly address the potential contribution of thalamic inputs in our experimental design.

The statements about specificity of connectivity are not well-founded. It may be that in the specific case where they are assessing outside of the area of injections, their conclusions may hold (e.g., excitatory inputs onto D2s have more inputs onto D1s than vice versa). However, how this relates to the actual site of injection is not clear. At face value, if such a connectivity exists, it would suggest that D1-MSNs receive substantially more overall excitatory inputs than D2s. It is thus possible that this observation would not hold over other spatial intervals. This was not explored and thus the conclusions are over-generalized. e.g., the distance from the area of red cells in the striatum to recordings was not quantified, what constituted a high level of cortical labeling was not quantified, etc. Without more rigorous quantification of what was being done, it is difficult to interpret the results. 

We sincerely thank the reviewer for the thoughtful comments and critical insights into our interpretation of connectivity data. These concerns are valid and provide an important opportunity to clarify and reinforce our experimental design and conclusions.

Firstly, as described in our previous response, all patched neurons were carefully selected to be within the injection site and in close proximity to ChR2-mCherry-positive cells. Specifically, the estimated distance from each recorded neuron to the nearest starter cells did not exceed 100 µm. This design choice was made to minimize variability associated with spatial distance or heterogeneity in viral expression, thereby allowing for a more consistent sampling of putatively connected neurons.

Secondly, quantifying both the number of starter and input neurons would, in principle, provide a more comprehensive picture of connectivity. However, given the technical limitations of the current approach particularly when combining rabies tracing with functional recordings it is not feasible to obtain such precise cell counts. Instead, we focused on connection ratios derived from targeted electrophysiological recordings, which offer a reliable and practical means of estimating connectivity within these defined circuits.

Thirdly, regarding the potential influence of rabies-labeled neurons beyond the immediate recording site: while we acknowledge that rabies tracing labels a broad set of upstream neurons, our analysis was confined to a well-defined and localized area. The analogy we find helpful here is that of a spotlight - our recordings were restricted to the illuminated region directly under the beam, where the projection pattern is fixed and interpretable, regardless of what lies outside that area. Although we cannot fully account for all possible upstream connections, our methodology was designed to minimize variability and maintain consistency in the region of interest, which we believe supports the robustness of our conclusions in the ex vivo slice recording experiment.

We hope this additional explanation addresses the reviewer’s concerns and helps clarify the rationale of our experimental strategy.

The results in figure 3 are not well controlled. The authors show contrasting effects of optogenetic stimulation of D1-MSNs and D2-MSNs in the DMS and DLS, results which are largely consistent with the canon of basal ganglia function. However, when stimulating cortical inputs, stimulating the inputs from D1-MSNs gives the expected results (increased locomotion) while stimulating putative inputs to D2-MSNs had no effect. This is not the same as showing a decrease in locomotion - showing no effect here is not possible to interpret.

We apologize for any confusion and appreciate the opportunity to clarify this point. Our electrophysiological recordings demonstrated that D1-projecting cortical neurons preferentially innervate D1-SPNs in the striatum, whereas D2-projecting cortical neurons provide input to both D1- and D2-SPNs, without a clear preference. These synaptic connectivity patterns are further supported by our behavioral experiments: optogenetic stimulation of D1-projecting neurons in cortical areas such as MCC and M1 led to behavioral effects consistent with direct D1-SPN activation. In contrast, stimulation of D2-projecting cortical neurons produced behavioral outcomes that appeared to reflect a mixture of both D1- and D2-SPN activation.

We acknowledge that interpreting negative behavioral findings poses inherent challenges, as it is difficult to distinguish between a true lack of effect and insufficient experimental manipulation. To mitigate this, we ensured that all animals included in the analysis exhibited appropriate viral expression and correctly placed optic fibers in the targeted regions. These controls help to confirm that the observed behavioral effects - or lack thereof - are indeed due to the activation of the intended neuronal populations rather than technical artifacts such as weak expression or fiber misplacement.

As shown in Author response image 1 below, our verification of virus expression and fiber positioning confirms effective targeting in MCC and M1 of A2A-Cre mice. Therefore, we interpret the negative behavioral outcomes as meaningful consequences of specific neural circuit activation.

Author response image 1.

Confocal image from A2A-Cre mouse showing targeted optogenetic stimulation of D2-projecting cortical neurons in MCC or M1. ChR2-mCherry expression highlights D2-projecting neurons, selectively labeled via rabies-mediated tracing. Optic fiber placement is confirmed above the cortical region of interest. Image illustrates robust expression and anatomical specificity necessary for pathway-selective stimulation in behavioral assays.

In light of their circuit model, the result showing that inputs to D2-MSNs drive ICSS is confusing. How can the authors account for the fact that these cells are not locomotor-activating, stimulation of their putative downstream cells (D2-MSNs) does not drive ICSS, yet the cortical inputs drive ICSS? Is the idea that these inputs somehow also drive D1s? If this is the case, how do D2s get activated, if all of the cortical inputs tested net activate D1s and not D2s? Same with the results in figure 4 - the inputs and putative downstream cells do not have the same effects. Given the potential caveats of differences in viral efficiency, spatial location of injections, and cellular toxicity, I cannot interpret these experiments.

We apologize for any confusion in our previous explanation. In our behavioral experiments, the primary objective was to determine whether activation of D1- or D2-projecting cortical neurons would produce behavioral outcomes distinct from those observed with pure D1 or D2 activation.

Our findings show that stimulation of D1-projecting cortical neurons produced behavioral effects closely resembling those of selective D1 activation in both open field and ICSS tests. This is consistent with our slice recording data, which revealed that D1-projecting cortical neurons exhibit a higher connection probability with D1-SPNs than with D2-SPNs.

In contrast, interpreting the effects of D2-projecting cortical neuron stimulation is inherently more nuanced. In the open field test, activation of these neurons did not significantly modulate local motion. This could reflect a balanced influence of D1 activation, which facilitates movement, and D2 activation, which suppresses it - resulting in a net neutral behavioral outcome. In the ICSS test, the absence of a strong reinforcement effect typically associated with D2 activation, combined with partial reinforcement likely due to concurrent D1 activation, suggests that stimulation of D2-projecting neurons produces a mixed behavioral signal. This outcome supports the interpretation that these neurons synapse onto both D1- and D2-SPNs, leading to a blended behavioral response that differs from selective D1 or D2 activation alone.

Together, these two behavioral assays offer complementary perspectives, providing a more complete view of how projection-specific cortical inputs influence striatal output and behavior.

In Figure 4 of the current manuscript (as cited below), we show that optogenetic activation of MCC neurons projecting to D1-SPNs facilitates sequence lever pressing, whereas activation of MCC neurons projecting to D2-SPNs does not induce significant behavioral changes. Conversely, activation of M1 neurons projecting to either D1- or D2-SPNs enhances lever pressing sequences. These observations align with our prior findings (Geddes et al., 2018; Jin et al., 2014), where we demonstrated that in the striatum, D1-SPN activation facilitates ongoing lever pressing, whereas D2-SPN activation is more involved in suppressing ongoing actions and promoting transitions between sub-sequences, shown in Fig. 4 from (Geddes et al., 2018; Jin et al., 2014) and Fig. 5K from (Jin et al., 2014) . Taken together, the facilitation of lever pressing by D1-projecting MCC and M1 neurons is consistent with their preferential connectivity to D1-SPNs and their established behavioral role.

What is particularly intriguing, though admittedly more complex, is the behavioral divergence observed upon activation of D2-SPN-projecting cortical neurons. Activation of D2-projecting MCC neurons does not alter lever pressing, possibly reflecting a counterbalancing effect from concurrent D1- and D2-SPN activation. In contrast, stimulation of D2-projecting M1 neurons facilitates lever pressing, albeit less robustly than their D1-projecting counterparts. This discrepancy may reflect regional differences in striatal targets, DMS for MCC versus DLS for M1, as also supported by our open field test results. Furthermore, our recent findings (Zhang et al., 2025) show that synaptic strength from Cg to D2-SPNs is stronger than to D1-SPNs, whereas the M1 pathway exhibits the opposite pattern. These data suggest that beyond projection ratios, synaptic strength also shapes cortico-striatal functional output. Thus, stronger D2-SPN synapses in the DMS may offset D1-SPN activation during MCC-D2 stimulation, dampening lever pressing increase. Conversely, weaker D2 synapses in the DLS may permit M1-D2 projections to facilitate behavior more readily.

In summary, the behavioral outcomes of our optogenetic manipulations support the proposed asymmetric cortico-striatal connectivity model. While the effects of D2-projecting neurons are not uniform, they reflect varying balances of D1 and D2-SPN influence, which further underscores the asymmetrical connections of cortical inputs to the striatum.

Recommendations For The Authors:

Reviewer #1 (Recommendations For The Authors): 

(1) What are the sample sizes for Fig S2? Some trends that are listed as nonsignificant look like they may just be underpowered. Related to this point, S2C indicates that PPR is statistically similar in all conditions. The traces shown in Figure 2 suggest that PPR is quite different in "Input D1"- vs "Input D2" projections. If there is indeed no difference, the exemplar traces should be replaced with more representative ones to avoid confusion. 

Thank you for your suggestion. The sample size reported in Figure S2 corresponds to the neurons identified as connected in Figure 2. The representative traces shown in Figure 2 were selected based on their close alignment with the amplitude statistics and are intended to reflect typical responses. Given this, it is appropriate to retain the current examples as they accurately illustrate the underlying data.

(2) Previous studies have described that SPN-SPN collateral inhibition is also asymmetric, with D2->D1 SPN connectivity stronger than the other direction. While cortical inputs to D2-SPNs may also strongly innervate D1-SPNs, it would be helpful to speculate on how collateral inhibition may further shape the biases (or lack thereof) reported here. 

This would indeed be an interesting topic to explore. SPN-SPN mutual inhibition and/or interneuron inhibition may also play a role in the functional organization and output of the striatum. In the present study, we focused on the primary layer of cortico-striatal connectivity to examine how cortical neurons selectively connect to the striatal direct and indirect pathways, as these pathways have been shown to have distinct yet cooperative functions. To achieve this, we applied a GABAA receptor inhibitor to isolate only excitatory synaptic currents in SPNs, yielding the relevant results.

To investigate additional circuit organization involving SPN-SPN mutual inhibition, the current available technique would involve single-cell initiated rabies tracing. This approach would help identify the starter SPN and the upstream SPNs that provide input to the starter cell, thereby offering a clearer understanding of the local circuit.

(3) In Fig 3N-S there are no stats confirming that optogenetic stimulation does indeed increase lever pressing in each group (though it obviously looks like it does). It would be helpful to add statistics for this comparison, in addition to the between-group comparisons that are shown. 

We thank the reviewer for this thoughtful suggestion. To assess whether optogenetic stimulation increases lever pressing in each group shown in Figures 3O, 3P, 3R, and 3S, we employed a permutation test (10,000 permutations). This non-parametric statistical method does not rely on assumptions about the underlying data distribution and is particularly appropriate for our analysis given the relatively small sample sizes.

Additionally, in response to Reviewer 3’s concern regarding the potential cytotoxicity of rabies virus affecting behavioral outcomes during in vivo optogenetic stimulation experiments, we focused our analysis on Days 1 through 7 of the ICSS test. This time window remains within 10 days post-rabies infection, a period during which previous studies have reported minimal cytopathic effects (Osakada et al., 2011).

Accordingly, we have updated Figure 3N-S and revised the associated statistical analyses in the figure legend as follows:

(O-P) D1-SPN (red) but not D2-SPN stimulation (black) drives ICSS behavior in both the DMS (O: D1, n = 6, permutation test, slope = 1.5060, P = 0.0378; D2, n = 5, permutation test, slope = -0.2214, P = 0.1021; one-tailed Mann Whitney test, Day 7 D1 vs. D2, P = 0.0130) and the DLS (P: D1, n = 6, permutation test, slope = 28.1429, P = 0.0082; D2, n = 5, permutation test, slope = -0.3429, P = 0.0463; one-tailed Mann Whitney test, Day 7 D1 vs. D2, P = 0.0390). *, P < 0.05. (Q) Timeline of helper virus injections, rabies-ChR2 injections and optogenetic stimulation for ICSS behavior. (R-S) Optogenetic stimulation of the cortical neurons projecting to either D1- or D2-SPNs induces ICSS behavior in both the MCC (R: MCC-D1, n = 5, permutation test, Day1-Day7, slope = 2.5857, P = 0.0034; MCC-D2, n = 5, Day2-Day7, permutation test, slope = 1.4229, P = 0.0344; no significant effect on Day7, MCC-D1 vs. MCC-D2,  two-tailed Mann Whitney test, P = 0.9999) and the M1 (S: M1-D1, n = 5, permutation test, Day1-Day7, slope = 1.8214, P = 0.0259; M1-D2, n = 5, Day1-Day7, permutation test, slope = 1.8214, P = 0.0025; no significant effect on Day7, M1-D1 vs. M1-D2, two-tailed Mann Whitney test, P = 0.3810). n.s., not statistically significant.

We believe this updated analysis and additional context further strengthen the validity of our conclusions regarding the reinforcement effects.

(4) Line 206: mice were trained for "a few more days" is not a very rigorous description. It would be helpful to state the range of additional days of training. 

We thank the reviewer for the suggestion. In accordance with the Methods section, we have now specified the number of days, which is 4 days, in the main text (line 207).

(5) In Fig 4D,H, the statistical comparison is relative modulation (% change) by stimulation of D1- vs D2- projecting inputs. Please show statistics comparing the effect of stimulation on lever presses for each individual condition. For example, is the effect of MCC-D2 stimulation in panel D negative or not significant? 

Thank you for your suggestion. Below are the statistical results, which we have also incorporated into the figure legend for clarity. To assess the net effects of each manipulation, we compared the observed percentage changes with a theoretical value of zero.

In Figure 4D, optogenetic stimulation of D1-projecting MCC neurons significantly increased the pressing rate (MCC-D1, n = 8, one-sample two-tailed t-test, t = 2.814, P = 0.0131), whereas stimulation of D2-projecting MCC neurons did not produce a significant effect (MCC-D2, n = 7, one-sample two-tailed t-test, t = 0.8481, P = 0.4117).

In contrast, Figure 4H shows that optogenetic stimulation of both D1- and D2-projecting M1 neurons significantly increased the sequence press rate (M1-D1, n = 6, one-sample two-tailed Wilcoxon signed-rank test, P = 0.0046; M1-D2, n = 7, one-sample two-tailed Wilcoxon signed-rank test, P = 0.0479).

These analyses help clarify the distinct behavioral effects of manipulating different corticostriatal projections.

(6) Are data in Fig 1G-H from a D1- or A2a- cre mouse? 

The data in Fig 1G-H are from a D1-Cre mouse.

(7) In Fig S3 it looks like there may actually be an effect of 20Hz simulation of D2-SPNs. Though it probably doesn't affect the interpretation. 

As indicated by the statistics, there is a slight, but not statistically significant, decrease in local motion when 20 Hz stimulation is delivered to the motor cortex with ChR2 expression in D2-SPNs in the striatum.

Reviewer #2 (Recommendations For The Authors): 

The rabies tracing is referred to on several occasions as "new" but the reference papers are from 2011, 2013, and 2018. It is unclear what is new about the system used in the paper and what new feature is relevant to the experiments that were performed. Either clarify or remove "new" terminology. 

Thank you for bringing this to our attention. We have revised the relevant text accordingly at line 20 in the Abstract, line 31 in the In Brief, line 69 in the Introduction, line 83 in the Results, and line 226 in the Discussion to improve clarity and accuracy.

In Figure 2 D and G, D1 eGFP (+) and D2 eGFP(-) are plotted separately. These are the same cell type; therefore it may work best to combine that data. This could also be done for 'input to D2- Record D2' in panel D as well as 'input D1-Record D2' and 'input D2-Record D1' in panel G. Combining the information in panel D and G and comparing all 4 conditions to each other would give a better understanding of the comparison of functional connectivity between cortical neurons and D1 and D2 SPNs. 

We thank the reviewer for the thoughtful suggestion. While presenting single bars for each condition (e.g., ‘input D1 - record D1’) might improve visual simplicity, it would obscure an important aspect of our experimental design. Specifically, we aimed to highlight that the comparisons between D1- and D2-projecting neurons to D1 and D2 SPNs were counterbalanced within the same animals - not just across different groups. By showing both D1-eGFP(+) and D2-eGFP(-), or vice versa, within each group and at similar proportions, we provide a more complete picture of the internal control built into our design. This format helps ensure the audience that our conclusions are not biased by group-level differences, but are supported by within-subject comparisons. Therefore, that the current presentation better could serve to communicate the rigor and balance of our experimental approach.

The findings in Figure 2 are stated as D1 projecting excitatory inputs have a higher probability of targeting D1 SPNs while D2 projecting excitatory inputs target both D1 SPNs and D2 SPNs. It may be more clear to say that some cortical neurons project specifically to D1 SPNs while other cortical neurons project to both D1 and D2 SPNs equally. A better summary diagram could also help with clarity. 

Thank you for bringing this up. The data we present reflect the connection probabilities of D1- or D2-projecting cortical neurons to D1 or D2 SPNs. One possible interpretation is like the reviewer said that a subset of cortical neurons preferentially target D1 SPNs, while others exhibit more balanced projections to both D1 and D2 SPNs. However, we cannot rule out alternative explanations - for example, that some D2-projecting neurons preferentially target D2 SPNs, or that the observed differences arise from the overall proportions of D1- and D2-projecting cortical neurons connecting to each striatal subtype.

There are multiple possible patterns of connectivity that could give rise to the observed differences in connection ratios. Based on our current data, we can confidently conclude the existence of asymmetric cortico-striatal projections to the direct and indirect pathways, but the precise nature of this asymmetry will require further investigation.

Figure 4 introduces the FR8 task, but there are similar takeaways to the findings from Figure 3. Is there another justification for the FR8 task or interesting way of interpreting that data that could add richness to the manuscript?

The FR8 task is a self-initiated operant sequence task that relies on motor learning mechanisms, whereas the open field test solely assesses spontaneous locomotion. Furthermore, the sequence task enables us to dissect the functional role of specific neuronal populations in the initiation, maintenance, and termination of sequential movements through closed-loop optogenetic manipulations integrated into the task design. These methodological advantages underscore the rationale for including Figure 4 in the manuscript, as it highlights the unique insights afforded by this experimental paradigm.

I am somewhat surprised to see that D1-SPN stimulation in DLS gave the results in Figure 3 F and P, as mentioned in the public review. These contrast with some previous results (Cui et al, J Neurosci, 2021). Any explanation? Would be useful to speculate or compare parameters as this could have important implications for DLS function.

Thank you for raising this point. While Cui’s study has generated some debate, several independent investigations have consistently demonstrated that stimulation of D1-SPNs in the dorsolateral striatum (DLS) facilitates local motion and lever-press behaviors (Dong et al., 2025; Geddes et al., 2018; Kravitz et al., 2010). These findings support the functional role of D1-SPNs in promoting movement and motivated actions.

The differences in behavioral outcomes observed between our study and that of Cui et al. may stem from several methodological factors, particularly related to anatomical targeting and optical stimulation parameters.

Specifically, our experiments targeted the DMS at AP +0.5 mm, ML ±1.5 mm, DV –2.2 mm, and the DLS at AP +0.5 mm, ML ±2.5 mm, DV –2.2 mm. In contrast, Cui’s study targeted the DMS at AP +0.9 mm, ML ±1.4 mm, DV –3.0 mm, and the DLS at AP +0.7 mm, ML ±2.3 mm, DV –3.0 mm. These differences indicate that their targeting was slightly more rostral and more ventral than ours, which could have led to stimulation of distinct neuronal populations within the striatum, potentially accounting for variations in behavioral effects observed during optogenetic activation.

In addition, the optical fibers used in the two studies differed markedly. We employed optical fibers with a 200 µm core diameter and a numerical aperture (NA) of 0.37. Cui’s study used fibers with a larger core diameter (250 µm) and a higher NA (0.66), which would produce a broader spread and deeper penetration of light. This increased photostimulation volume may have recruited a more extensive network of neurons, possibly including off-target circuits, thus influencing the behavioral outcomes in a manner not seen in our more spatially constrained stimulation paradigm.

Taken together, these methodological differences, both in anatomical targeting and optical stimulation parameters, likely contribute to the discrepancies in behavioral results observed between the two studies. Our findings, consistent with other independent reports, support the role of D1-SPNs in facilitating movement and reinforcement behaviors under more controlled and localized stimulation conditions.

Reviewer #3 (Recommendations For The Authors): 

Minor: 

The authors repeatedly state that they are using a new rabies virus system, but the system has been in widespread use for 16 years, including in the exact circuits the authors are studying, for over a decade. I would not consider this new. 

Thank you for bringing this to our attention. We have revised the relevant text accordingly at line 20 in the Abstract, line 31 in the In Brief, line 69 in the Introduction, line 83 in the Results, and line 226 in the Discussion to improve clarity and accuracy.

Figure 2G, how many mice were used for recordings?

In Fig. 2G, we used 8 mice in the D1-projecting to D2 EGFP(+) group, 7 mice in the D1-projecting to D1 EGFP(-) group, 8 mice in the D2-projecting to D1 EGFP(+) group, and 10 mice in the D2-projecting to D2 EGFP(-) group.

The amplitude of inputs was not reported in figure 2. This is important, as the strength of the connection matters. This is reported in Figure S2, but how exactly this relates to the presence or absence of connections should be made clearer.

The amplitude data presented in Figure S2 summarize all recorded currents from confirmed connections, as detailed in the Methods section. A connection is defined by the presence of a detectable and reliable postsynaptic current with an onset latency of less than 10 ms following laser stimulation.

Reference in the reply-to-review comments:

Aoki, S., Smith, J.B., Li, H., Yen, X.Y., Igarashi, M., Coulon, P., Wickens, J.R., Ruigrok, T.J.H., and Jin, X. (2019). An open cortico-basal ganglia loop allows limbic control over motor output via the nigrothalamic pathway. Elife 8, e49995.

Chatterjee, S., Sullivan, H.A., MacLennan, B.J., Xu, R., Hou, Y.Y., Lavin, T.K., Lea, N.E., Michalski, J.E., Babcock, K.R., Dietrich, S., et al. (2018). Nontoxic, double-deletion-mutant rabies viral vectors for retrograde targeting of projection neurons. Nat Neurosci 21, 638-646.

Cruikshank, S.J., Urabe, H., Nurmikko, A.V., and Connors, B.W. (2010). Pathway-Specific Feedforward Circuits between Thalamus and Neocortex Revealed by Selective Optical Stimulation of Axons. Neuron 65, 230-245.

Dong, J., Wang, L.P., Sullivan, B.T., Sun, L.X., Smith, V.M.M., Chang, L.S., Ding, J.H., Le, W.D., Gerfen, C.R., and Cai, H.B. (2025). Molecularly distinct striatonigral neuron subtypes differentially regulate locomotion. Nat Commun 16, 2710.

Geddes, C.E., Li, H., and Jin, X. (2018). Optogenetic Editing Reveals the Hierarchical Organization of Learned Action Sequences. Cell 174, 32-43.

Jin, L., Sullivan, H.A., Zhu, M., Lavin, T.K., Matsuyama, M., Fu, X., Lea, N.E., Xu, R., Hou, Y.Y., Rutigliani, L., et al. (2024). Long-term labeling and imaging of synaptically connected neuronal networks in vivo using double-deletion-mutant rabies viruses. Nat Neurosci 27, 373-383.

Jin, X., Tecuapetla, F., and Costa, R.M. (2014). Basal ganglia subcircuits distinctively encode the parsing and concatenation of action sequences. Nat Neurosci 17, 423-430.

Klug, J.R., Engelhardt, M.D., Cadman, C.N., Li, H., Smith, J.B., Ayala, S., Williams, E.W., Hoffman, H., and Jin, X. (2018). Differential inputs to striatal cholinergic and parvalbumin interneurons imply functional distinctions. Elife 7, e35657.

Kravitz, A.V., Freeze, B.S., Parker, P.R.L., Kay, K., Thwin, M.T., Deisseroth, K., and Kreitzer, A.C. (2010). Regulation of parkinsonian motor behaviours by optogenetic control of basal ganglia circuitry. Nature 466, 622-626.

Osakada, F., Mori, T., Cetin, A.H., Marshel, J.H., Virgen, B., and Callaway, E.M. (2011). New Rabies Virus Variants for Monitoring and Manipulating Activity and Gene Expression in Defined Neural Circuits. Neuron 71, 617-631.

Smith, J.B., Klug, J.R., Ross, D.L., Howard, C.D., Hollon, N.G., Ko, V.I., Hoffman, H., Callaway, E.M., Gerfen, C.R., and Jin, X. (2016). Genetic-Based Dissection Unveils the Inputs and Outputs of Striatal Patch and Matrix Compartments. Neuron 91, 1069-1084.

Wall, N.R., De La Parra, M., Callaway, E.M., and Kreitzer, A.C. (2013). Differential Innervation of Direct- and Indirect-Pathway Striatal Projection Neurons. Neuron 79, 347-360.

Wickersham, I.R., Lyon, D.C., Barnard, R.J.O., Mori, T., Finke, S., Conzelmann, K.K., Young, J.A.T., and Callaway, E.M. (2007). Monosynaptic restriction of transsynaptic tracing from single, genetically targeted neurons. Neuron 53, 639-647.

Zhang, B.B., Geddes, C.E., and Jin, X. (2025) Complementary corticostriatal circuits orchestrate action repetition and switching. Sci Adv, in press.

Zhu, Z.G., Gong, R., Rodriguez, V., Quach, K.T., Chen, X.Y., and Sternson, S.M. (2025). Hedonic eating is controlled by dopamine neurons that oppose GLP-1R satiety. Science 387, eadt0773.

Author response:

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

Reviewer #1 (Public review):

Summary:

In this study, Ana Lapao et al. investigated the roles of Rab27 effector SYTL5 in cellular membrane trafficking pathways. The authors found that SYTL5 localizes to mitochondria in a Rab27A-dependent manner. They demonstrated that SYTL5-Rab27A positive vesicles containing mitochondrial material are formed under hypoxic conditions, thus they speculate that SYTL5 and Rab27A play roles in mitophagy. They also found that both SYTL5 and Rab27A are important for normal mitochondrial respiration. Cells lacking SYTL5 undergo a shift from mitochondrial oxygen consumption to glycolysis which is a common process known as the Warburg effect in cancer cells. Based on the cancer patient database, the author noticed that low SYTL5 expression is related to reduced survival for adrenocortical carcinoma patients, indicating SYTL5 could be a negative regulator of the Warburg effect and potentially tumorigenesis.

Strengths:

The authors take advantage of multiple techniques and novel methods to perform the experiments.

(1) Live-cell imaging revealed that stably inducible expression of SYTL5 co-localized with filamentous structures positive for mitochondria. This result was further confirmed by using correlative light and EM (CLEM) analysis and western blotting from purified mitochondrial fraction.

(2) In order to investigate whether SYTL5 and Rab27A are required for mitophagy in hypoxic conditions, two established mitophagy reporter U2OS cell lines were used to analyze the autophagic flux.

Weaknesses:

This study revealed a potential function of SYTL5 in mitophagy and mitochondrial metabolism. However, the mechanistic evidence that establishes the relationship between SYTL5/Rab27A and mitophagy is insufficient. The involvement of SYTL5 in ACC needs more investigation. Furthermore, images and results supporting the major conclusions need to be improved.

We thank the reviewer for their constructive comments. We agree that a complete understanding of the mechanism by which SYTL5 and Rab27A are recruited to the mitochondria and subsequently involved in mitophagy requires further investigation. Here, we have shown that SYTL5 recruitment to the mitochondria requires both its lipid-binding C2 domains and the Rab27A-binding SHD domain (Figure 1G-H). This implies a coincidence detection mechanism for mitochondrial localisation of SYTL5.  Additionally, we find that mitochondrial recruitment of SYTL5 is dependent on the GTPase activity and mitochondrial localisation of Rab27A (Figure 2D-E). We also identified proteins linked to the cellular response to oxidative stress, reactive oxygen species metabolic process, regulation of mitochondrion organisation and protein insertion into mitochondrial membrane to be enriched in the SYTL5 interactome (Figure 3A and C).

However, less details regarding the mitochondrial localisation of Rab27A are understood. To investigate this, we have now performed a mass spectrometry analysis to identify the interactome of Rab27A (see Author response table 1 below,). U2OS cells with stable expression of mScarlet-Rab27A or mScarlet only, were subjected to immunoprecipitation, followed by MS analysis.  Of the 32 significant Rab27A-interacting hits (compared to control), two of the hits are located in the inner mitochondrial membrane (IMM); ATP synthase F(1) complex subunit alpha (P25705), and mitochondrial very long-chain specific acyl-CoA dehydrogenase (VLCAD)(P49748). However, as these IMM proteins are not likely involved in mitochondrial recruitment of Rab27A, observed under basal conditions, we choose not to include these data in the manuscript. 

It is known that other RAB proteins are recruited to the mitochondria. During parkin-mediated mitophagy, RABGEF1 (a guanine nucleotide exchange factor) is recruited through its ubiquitin-binding domain and directs mitochondrial localisation of RAB5, which subsequently leads to recruitment of RAB7 by the MON1/CCZ1 complex[1]. As already mentioned in the discussion (p. 12), ubiquitination of the Rab27A GTPase activating protein alpha (TBC1D10A) is reduced in the brain of Parkin KO mouse compared to controls[35], suggesting a possible connection of Rab27A with regulatory mechanisms that are linked with mitochondrial damage and dysfunction. While this an interesting avenue to explore, in this paper we will not follow up further on the mechanism of mitochondrial recruitment of Rab27A. 

Author response table 1.

Rab27A interactome. Proteins co-immunoprecipitated with mScarlet-Rab27A vs mScarlet expressing control. The data show average of three replicates. 

To investigate the role of SYTL5 in the context of ACC, we acquired the NCI-H295R cell line isolated from the adrenal gland of an adrenal cancer patient. The cells were cultured as recommended from ATCC using DMEM/F-12 supplemented with NuSerum and ITS +premix. It is important to note that the H295R cells were adapted to grow as an adherent monolayer from the H295 cell line which grows in suspension. However, there can still be many viable H295R cells in the media. 

We attempted to conduct OCR and ECAR measurements using the Seahorse XF upon knockdown of SYTL5 and/or Rab27A in H295R cells. For these assays, it is essential that the cells be seeded in a monolayer at 70-90% confluency with no cell clusters[4]. Poor adhesion of the cells can cause inaccurate measurements by the analyser. Unfortunately, the results between the five replicates we carried out were highly inconsistent, the same knockdown produced trends in opposite directions in different replicates. This is likely due to problems with seeding the cells. Despite our best efforts to optimise seeding number, and pre-coating the plate with poly-D-lysine[5] we observed poor attachment of cells and inability to form a monolayer. 

To study the localisation of SYTL5 and Rab27A in an ACC model, we transduced the H295R cells with lentiviral particles to overexpress pLVX-SV40-mScarlet-I-Rab27A and pLVX-CMV-SYTL5-EGFP-3xFLAG. Again, this proved unsuccessful after numerous attempts at optimising transduction. 

These issues limited our investigation into the role of SYTL5 in ACC to the cortisol assay (Supplementary Figure 6). For this the H295R cells were an appropriate model as they are able to produce an array of adrenal cortex steroids[6] including cortisol[7]. In this assay, measurements are taken from cell culture supernatants, so the confluency of the cells does not prevent consistent results as the cortisol concentration was normalised to total protein per sample. With this assay we were able to rule out a role for SYTL5 and Rab27A in the secretion of cortisol.  

Another consideration when investigating the involvement of SYTL5 in ACC, is that in general ACC cells should have a low expression of SYTL5 as is seen from the patient expression data (Figure 6B).

The reviewer also writes “Furthermore, images and results supporting the major conclusions need to be improved.”. We have tried several times, without success, to generate U2OS cells with CRISPR/Cas9-mediated C-terminal tagging of endogenous SYTL5 with mNeonGreen, using an approach that has been successfully implemented in the lab for other genes. This is likely due to a lack of suitable sgRNAs targeting the C-terminal region of SYTL5, which have a low predicted efficiency score and a large number of predicted off-target sites in the human genome including several other gene exons and introns (see Author response image 2). 

We have also included new data (Supplementary Figure 4B) showing that some of the hypoxia-induced SYTL5-Rab27A-positive vesicles stain positive for the autophagy markers p62 and LC3B when inhibiting lysosomal degradation, further strengthening our data that SYTL5 and Rab27A function as positive regulators of mitophagy.  

Reviewer #2 (Public review): 

Summary:

The authors provide convincing evidence that Rab27 and STYL5 work together to regulate mitochondrial activity and homeostasis.

Strengths:

The development of models that allow the function to be dissected, and the rigorous approach and testing of mitochondrial activity.

Weaknesses:

There may be unknown redundancies in both pathways in which Rab27 and SYTL5 are working which could confound the interpretation of the results.

Suggestions for revision:

Given that Rab27A and SYTL5 are members of protein families it would be important to exclude any possible functional redundancies coming from Rab27B expression or one of the other SYTL family members. For Rab27 this would be straightforward to test in the assays shown in Figure 4 and Supplementary Figure 5. For SYTL5 it might be sufficient to include some discussion about this possibility.

We thank the reviewer for pointing out the potential redundancy issue for Rab27A and SYTL5. There are multiple studies demonstrating the redundancy between Rab27A and Rab27B. For example, in a study of the disease Griscelli syndrome, caused by Rab27A loss of function, expression of either Rab27A or Rab27B rescues the healthy phenotype indicating redundancy[8]. This redundancy however applies to certain function and cell types. In fact, in a study regarding hair growth, knockdown of Rab27B had the opposite effect to knockdown of Rab27A[9].

In this paper, we conducted all assays in U2OS cells, in which the expression of Rab27B is very low. Human Protein Atlas reports expression of 0.5nTPM for Rab27B, compared to 18.4nTPM for Rab27A. We also observed this low level of expression of Rab27B compared to Rab27A by qPCR in U2OS cells. Therefore, there would be very little endogenous Rab27B expression in cells depleted of Rab27A (with siRNA or KO). In line with this, Rab27B peptides were not detected in our SYTL5 interactome MS data (Table 1 in paper). Moreover, as Rab27A depletion inhibits mitochondrial recruitment of SYTL5 and mitophagy, it is not likely that Rab27B provides a functional redundancy. It is possible that Rab27B overexpression could rescue mitochondrial localisation of SYTL5 in Rab27A KO cells, but this was not tested as we do not have any evidence for a role of Rab27B in these cells. Taken together, we believe our data imply that Rab27B is very unlikely to provide any functional redundancy to Rab27A in our experiments. 

For the SYTL family, all five members are Rab27 effectors, binding to Rab27 through their SHD domain. Together with Rab27, all SYTL’s have been implicated in exocytosis in different cell types. For example, SYTL1 in exocytosis of azurophilic granules from neutrophils[10], SYTL2 in secretion of glucagon granules from pancreatic α cells[11], SYTL3 in secretion of lytic granules from cytotoxic T lymphocytes[12], SYTL4 in exocytosis of dense hormone containing granules from endocrine cells[13] and SYTL5 in secretion of the RANKL cytokine from osteoblasts[14]. This indicates a potential for redundancy through their binding to Rab27 and function in vesicle secretion/trafficking. However, one study found that different Rab27 effectors have distinct functions at different stages of exocytosis[15].

Very little known about redundancy or hierarchy between these proteins. Differences in function may be due to the variation in gene expression profile across tissues for the different SYTL’s (see Author response image 1 below). SYTL5 is enriched in the brain unlike the others, suggesting possible tissue specific functions. There are also differences in the binding affinities and calcium sensitivities of the C2iA and C2B domains between the SYTL proteins[16].

Author response image 1.

GTEx Multi Gene Query for SYTL1-5

All five SYTL’s are expressed in the U2OS cell line with nTPMs according to Human Protein Atlas of SYTL1: 7.5, SYTL2: 13.4, SYTL3:14.2, SYTL4: 8.7, SYTL5: 4.8. In line with this, in the Rab27A interactome, when comparing cells overexpressing mScarlet-Rab27A with control cells, we detected all five SYTL’s as specific Rab27A-interacting proteins (see Author response table 1 above). Whereas, in the SYTL5 interactome we did not detect any other SYTL protein (table 1 in paper), confirming that they do not form a complex with SYTL5. 

We have included the following text in the discussion (p. 12): “SYTL5 and Rab27A are both members of protein families, suggesting possible functional redundancies from Rab27B or one of the other SYTL isoforms. While Rab27B has a very low expression in U2OS cells, all five SYTL’s are expressed. However, when knocking out or knocking down SYTL5 and Rab27A we observe significant effects that we presume would be negated if their isoforms were providing functional redundancies. Moreover, we did not detect any other SYTL protein or Rab27B in the SYTL5 interactome, confirming that they do not form a complex with SYTL5.”

Suggestions for Discussion: 

Both Rab27A and STYL5 localize to other membranes, including the endolysosomal compartments. How do the authors envisage the mechanism or cellular modifications that allow these proteins, either individually or in complex to function also to regulate mitochondrial funcYon? It would be interesYng to have some views.

We agree that it would be interesting to better understand the mechanism involved in modulation of the localisation and function of SYTL5 and Rab27A at different cellular compartments, including the mitochondria. Here, we have shown that SYTL5 recruitment to the mitochondria involves coincidence detection, as both its lipid-binding C2 domains and the Rab27A-binding SHD domain are required (Figure 1G-H). Both these domains also seem required for localisation of SYTL5 to vesicles, and we can only speculate that binding to different lipids (Figure 1F) may regulate SYTL5 localisation. Additionally, we find that mitochondrial recruitment of SYTL5 is dependent on the GTPase activity and mitochondrial localisation of Rab27A (Figure 2D-E). However, this seems also the case for vesicular recruitment of SYTL5, although a few SYTL5-Rab27A (T23N) positive vesicles were seen (Figure 2E). 

To characterise the mechanisms involved in mitochondrial localisation of Rab27A, we have performed mass spectrometry analysis to identify the interactome of Rab27A (see Author response table 1 above). U2OS cells with stable expression of mScarlet-Rab27A or mScarlet only were subjected to immunoprecipitation, followed by MS analysis.  Of the 32 significant Rab27A-interacting hits (compared to control), two of the hits localise in the inner mitochondrial membrane (IMM); ATP synthase F(1) complex subunit alpha (P25705), and mitochondrial very long-chain specific acyl-CoA dehydrogenase (VLCAD)(P49748). However, as these IMM proteins are not likely involved in mitochondrial recruitment of Rab27A, observed under basal conditions, we chose not to include these data in the manuscript. 

It is known that other RAB proteins are recruited to the mitochondria by regulation of their GTPase activity. During parkin-mediated mitophagy, RABGEF1 (a guanine nucleotide exchange factor) is recruited through its ubiquitin-binding domain and directs mitochondrial localisation of RAB5, which subsequently leads to recruitment of RAB7 by the MON1/CCZ1 GEF complex[1]. As already mentioned in the discussion (p.12), ubiquitination of the Rab27A GTPase activating protein alpha (TBC1D10A) is reduced in the brain of Parkin KO mouse compared to controls[35], suggesting a possible connection of Rab27A with regulatory mechanisms that are linked with mitochondrial damage and dysfunction. While this an interesting avenue to explore, it is beyond the scope of this paper. 

Our data suggest that SYTL5 functions as a negative regulator of the Warburg effect, the switch from OXPHOS to glycolysis. While both SYTL5 and Rab27A seem required for mitophagy of selective mitochondrial components, and their depletion leading to reduced mitochondrial respiration and ATP production, only depletion of SYTL5 caused a switch to glycolysis. The mechanisms involved are unclear, but we found several proteins linked to the cellular response to oxidative stress, reactive oxygen species metabolic process, regulation of mitochondrion organisation and protein insertion into mitochondrial membrane to be enriched in the SYTL5 interactome (Figure 3A and C).

We have addressed this comment in the discussion on p.12 

Reviewer #3 (Public review):

Summary:

In the manuscript by Lapao et al., the authors uncover a role for the Rab27A effector protein SYTL5 in regulating mitochondrial function and turnover. The authors find that SYTL5 localizes to mitochondria in a Rab27A-dependent way and that loss of SYTL5 (or Rab27A) impairs lysosomal turnover of an inner mitochondrial membrane mitophagy reporter but not a matrix-based one. As the authors see no co-localization of GFP/mScarlet tagged versions of SYTL5 or Rab27A with LC3 or p62, they propose that lysosomal turnover is independent of the conventional autophagy machinery. Finally, the authors go on to show that loss of SYTL5 impacts mitochondrial respiration and ECAR and as such may influence the Warburg effect and tumorigenesis. Of relevance here, the authors go on to show that SYTL5 expression is reduced in adrenocortical carcinomas and this correlates with reduced survival rates.

Strengths:

There are clearly interesting and new findings here that will be relevant to those following mitochondrial function, the endocytic pathway, and cancer metabolism.

Weaknesses:

The data feel somewhat preliminary in that the conclusions rely on exogenously expressed proteins and reporters, which do not always align.

As the authors note there are no commercially available antibodies that recognize endogenous SYTL5, hence they have had to stably express GFP-tagged versions. However, it appears that the level of expression dictates co-localization from the examples the authors give (though it is hard to tell as there is a lack of any kind of quantitation for all the fluorescent figures). Therefore, the authors may wish to generate an antibody themselves or tag the endogenous protein using CRISPR.

We agree that the level of SYTL5 expression is likely to affect its localisation. As suggested by the reviewer, we have tried hard, without success, to generated U2OS cells with CRISPR knock-in of a mNeonGreen tag at the C-terminus of endogenous SYTL5, using an approach that has been successfully implemented in the lab for other genes. This is likely due to a lack of suitable sgRNAs targeting the C-terminal region of SYTL5, which have a low predicted efficiency score and a large number of predicted off-target sites in the human genome including several other gene exons and introns (see Author response image 2). 

Author response image 2.

Overview of sgRNAs targeting the C-terminal region of SYTL5 

Although the SYTL5 expression level might affect its cellular localization, we also found the mitochondrial localisation of SYTL5-EGFP to be strongly increased in cells co-expressing mScarletRab27A, supporting our findings of Rab27A-mediated mitochondrial recruitment of SYTL5. We have also included new data (Supplementary Figure 4B) showing that some of the hypoxia-induced SYTL5Rab27A-positive vesicles stain positive for the autophagy markers p62 and LC3B when inhibiting lysosomal degradation, further strengthening our data that SYTL5 and Rab27A function as positive regulators of mitophagy.  

In relation to quantitation, the authors found that SYTL5 localizes to multiple compartments or potentially a few compartments that are positive for multiple markers. Some quantitation here would be very useful as it might inform on function. 

We find that SYTL5-EGFP localizes to mitochondria, lysosomes and the plasma membrane in U2OS cells with stable expression of SYTL5-EGFP and in SYTL5/Rab27A double knock-out cells rescued with SYTL5EGFP and mScralet-Rab27A. We also see colocalization of SYTL5-EGFP with endogenous p62, LC3 and LAMP1 upon induction of mitophagy. However, as these cell lines comprise a heterogenous pool with high variability we do not believe that quantification of the overexpressing cell lines would provide beneficial information in this scenario. As described above, we have tried several times to generate SYTL5 knock-in cells without success.  

The authors find that upon hypoxia/hypoxia-like conditions that punctate structures of SYTL5 and Rab27A form that are positive for Mitotracker, and that a very specific mitophagy assay based on pSu9-Halo system is impaired by siRNA of SYTL5/Rab27A, but another, distinct mitophagy assay (Matrix EGFP-mCherry) shows no change. I think this work would strongly benefit from some measurements with endogenous mitochondrial proteins, both via immunofluorescence and western blot-based flux assays. 

In addition to the western blotting for different endogenous ETC proteins showing significantly increased levels of MTCO1 in cells depleted of SYTL5 and/or Rab27A (Figure 5E-F), we have now blotted for the endogenous mitochondrial proteins, COXIV and BNIP3L, in DFP and DMOG conditions upon knockdown of SYTL5 and/or Rab27A (Figure 5G and Supplementary Figure 5A). Although there was a trend towards increased levels, we did not see any significant changes in total COXIV or BNIP3L levels when SYTL5, Rab27A or both are knocked down compared to siControl. Blotting for endogenous mitochondrial proteins is however not the optimum readout for mitophagy. A change in mitochondrial protein level does not necessarily result from mitophagy, as other factors such as mitochondrial biogenesis and changes in translation can also have an effect. Mitophagy is a dynamic process, which is why we utilise assays such as the HaloTag and mCherry-EGFP double tag as these indicate flux in the pathway. Additionally, as mitochondrial proteins have different half-lives, with many long-lived mitochondrial proteins[17], differences in turnover rates of endogenous proteins make the results more difficult to interpret. 

A really interesting aspect is the apparent independence of this mitophagy pathway on the conventional autophagy machinery. However, this is only based on a lack of co-localization between p62or LC3 with LAMP1 and GFP/mScarlet tagged SYTL5/Rab27A. However, I would not expect them to greatly colocalize in lysosomes as both the p62 and LC3 will become rapidly degraded, while the eGFP and mScarlet tags are relatively resistant to lysosomal hydrolysis. -/+ a lysosome inhibitor might help here and ideally, the functional mitophagy assays should be repeated in autophagy KOs. 

We thank the reviewer for this suggestion. We have now repeated the colocalisation studies in cells treated with DFP with the addition of bafilomycin A1 (BafA1) to inhibit the lysosomal V-ATPase. Indeed, we find that a few of the SYTL5/Rab27A/MitoTracker positive structures also stain positive for p62 and LC3 (Supplementary Figure 4B). As expected, the occurrence of these structures was rare, as BafA1 was only added for the last 4 hrs of the 24 hr DFP treatment. However, we cannot exclude the possibility that there are two different populations of these vesicles.

The link to tumorigenesis and cancer survival is very interesYng but it is not clear if this is due to the mitochondrially-related aspects of SYTL5 and Rab27A. For example, increased ECAR is seen in the SYTL5 KO cells but not in the Rab27A KO cells (Fig.5D), implying that mitochondrial localization of SYTL5 is not required for the ECAR effect. More work to strengthen the link between the two sections in the paper would help with future direcYons and impact with respect to future cancer treatment avenues to explore. 

We agree that the role of SYTL5 in ACC requires future investigation. While we observe reduced OXPHOS levels in both SYTL5 and Rab27A KO cells (Figure 5B), glycolysis was only increased in SYTL5 KO cells (Figure 5D). We believe this indicates that Rab27A is being negatively regulated by SYTL5, as ECAR was unchanged in both the Rab27A KO and Rab27A/SYTL5 dKO cells. This suggests that Rab27A is required for the increase in ECAR when SYTL5 is depleted, therefore SYTL5 negatively regulates Rab27A. The mechanism involved is unclear, but we found several proteins linked to the cellular response to oxidative stress, reactive oxygen species metabolic process, regulation of mitochondrion organisation and protein insertion into mitochondrial membrane to be enriched in the SYTL5 interactome (Figure 3A and C).

To investigate the link to cancer further, we tested the effect of knockdown of SYTL5 and/or Rab27A on the levels of mitochondrial ROS. ROS levels were measured by flow cytometry using the MitoSOX Red dye, together with the MitoTracker Green dye to normalise ROS levels to the total mitochondria. Cells were treated with the antioxidant N-acetylcysteine (NAC)[18] as a negative control and menadione as a positive control, as menadione induces ROS production via redox cycling[19]. We must consider that there is also a lot of autofluorescence from cells that makes it impossible to get a level of ‘zero ROS’ in this experiment. We did not see a change in ROS with knockdown of SYTL5 and/or Rab27A compared to the NAC treated or siControl samples (see Author response image 3 below). The menadione samples confirm the success of the experiment as ROS accumulated in these cells. Thus, based on this, we do not believe that low SYTL5 expression would affect ROS levels in ACC tumours.

Author response image 3.

Mitochondrial ROS production normalised to total mitochondria

As discussed in our response to Reviewer #1, we tried hard to characterise the role of SYTL5 in the context of ACC using the NCI-H295R cell line isolated from the adrenal gland of an adrenal cancer patient. We attempted to conduct OCR and ECAR measurements using the Seahorse XF upon knockdown of SYTL5 and/or Rab27A in H295R cells without success, due to poor attachment of the cells and inability to form a monolayer. We also transduced the H295R cells with lentiviral particles to overexpress pLVX-SV40-mScarlet-I-Rab27A and pLVX-CMV-SYTL5-EGFP-3xFLAG to study the localisation of SYTL5 and Rab27A in an ACC model. Again, this proved unsuccessful after numerous attempts at optimising the transduction. These issues limited our investigation into the role of SYTL5 in ACC to the cortisol assay (Supplementary Figure 6). For this the H295R cells were an appropriate model as they are able to produce an array of adrenal cortex steroids[6] including cortisol[7] In this assay, measurements are taken from cell culture supernatants, so the confluency of the cells does not prevent consistent results as the cortisol concentration was normalised to total protein per sample. With this assay we were able to rule out a role for SYTL5 and Rab27A in the secretion of cortisol.  

Another consideration when investigating the involvement of SYTL5 in ACC, is that in general ACC cells should have a low expression of SYTL5 as is seen from the patient expression data (Figure 6B).

Further studies into the link between SYTL5/Rab27A and cancer are beyond the scope of this paper as we are limited to the tools and expertise available in the lab.

References

(1) Yamano, K. et al. Endosomal Rab cycles regulate Parkin-mediated mitophagy. eLife 7 (2018). https://doi.org:10.7554/eLife.31326

(2) Carré, M. et al. Tubulin is an inherent component of mitochondrial membranes that interacts with the voltage-dependent anion channel. The Journal of biological chemistry 277, 33664-33669 (2002). https://doi.org:10.1074/jbc.M203834200

(3) Hoogerheide, D. P. et al. Structural features and lipid binding domain of tubulin on biomimetic mitochondrial membranes. Proceedings of the National Academy of Sciences 114, E3622-E3631 (2017). https://doi.org:10.1073/pnas.1619806114

(4) Plitzko, B. & Loesgen, S. Measurement of Oxygen Consumption Rate (OCR) and Extracellular Acidification Rate (ECAR) in Culture Cells for Assessment of the Energy Metabolism. Bio Protoc 8, e2850 (2018). https://doi.org:10.21769/BioProtoc2850

(5) Yavin, E. & Yavin, Z. Attachment and culture of dissociated cells from rat embryo cerebral hemispheres on polylysine-coated surface. The Journal of cell biology 62, 540-546 (1974). https://doi.org:10.1083/jcb.62.2.540

(6) Wang, T. & Rainey, W. E. Human adrenocortical carcinoma cell lines. Mol Cell Endocrinol 351, 5865 (2012). https://doi.org:10.1016/j.mce.2011.08.041

(7) Rainey, W. E. et al. Regulation of human adrenal carcinoma cell (NCI-H295) production of C19 steroids. J Clin Endocrinol Metab 77, 731-737 (1993). https://doi.org:10.1210/jcem.77.3.8396576

(8) Barral, D. C. et al. Functional redundancy of Rab27 proteins and the pathogenesis of Griscelli syndrome. J. Clin. Invest. 110, 247-257 (2002). https://doi.org:10.1172/jci15058

(9) Ku, K. E., Choi, N. & Sung, J. H. Inhibition of Rab27a and Rab27b Has Opposite Effects on the Regulation of Hair Cycle and Hair Growth. Int. J. Mol. Sci. 21 (2020). https://doi.org:10.3390/ijms21165672

(10) Johnson, J. L., Monfregola, J., Napolitano, G., Kiosses, W. B. & Catz, S. D. Vesicular trafficking through cortical actin during exocytosis is regulated by the Rab27a effector JFC1/Slp1 and the RhoA-GTPase–activating protein Gem-interacting protein. Mol. Biol. Cell 23, 1902-1916 (2012). https://doi.org:10.1091/mbc.e11-12-1001

(11) Yu, M. et al. Exophilin4/Slp2-a targets glucagon granules to the plasma membrane through unique Ca2+-inhibitory phospholipid-binding activity of the C2A domain. Mol. Biol. Cell 18, 688696 (2007). https://doi.org:10.1091/mbc.e06-10-0914

(12) Kurowska, M. et al. Terminal transport of lyXc granules to the immune synapse is mediated by the kinesin-1/Slp3/Rab27a complex. Blood 119, 3879-3889 (2012). https://doi.org:10.1182/blood-2011-09-382556

(13) Zhao, S., Torii, S., Yokota-Hashimoto, H., Takeuchi, T. & Izumi, T. Involvement of Rab27b in the regulated secretion of pituitary hormones. Endocrinology 143, 1817-1824 (2002). https://doi.org:10.1210/endo.143.5.8823

(14) Kariya, Y. et al. Rab27a and Rab27b are involved in stimulation-dependent RANKL release from secretory lysosomes in osteoblastic cells. J Bone Miner Res 26, 689-703 (2011). https://doi.org:10.1002/jbmr.268

(15) Zhao, K. et al. Functional hierarchy among different Rab27 effectors involved in secretory granule exocytosis. Elife 12 (2023). https://doi.org:10.7554/eLife.82821

(16) Izumi, T. Physiological roles of Rab27 effectors in regulated exocytosis. Endocr J 54, 649-657 (2007). https://doi.org:10.1507/endocrj.kr-78

(17) Bomba-Warczak, E. & Savas, J. N. Long-lived mitochondrial proteins and why they exist. Trends in cell biology 32, 646-654 (2022). https://doi.org:10.1016/j.tcb.2022.02.001

(18) Curtin, J. F., Donovan, M. & Cotter, T. G. Regulation and measurement of oxidative stress in apoptosis. Journal of Immunological Methods 265, 49-72 (2002). https://doi.org:https://doi.org/10.1016/S0022-1759(02)00070-4

(19) Criddle, D. N. et al. Menadione-induced Reative Oxygen Species Generation via Redox Cycling Promotes Apoptosis of Murine Pancreatic Acinar Cells. Journal of Biological Chemistry 281, 40485-40492 (2006). https://doi.org:https://doi.org/10.1074/jbc.M607704200

Author response:

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

Reviewer #1 (Public Review): 

Summary: 

LRRK2 protein is familially linked to Parkinson's disease by the presence of several gene variants that all confer a gain-of-function effect on LRRK2 kinase activity. 

The authors examine the effects of BDNF stimulation in immortalized neuron-like cells, cultured mouse primary neurons, hIPSC-derived neurons, and synaptosome preparations from the brain. They examine an LRRK2 regulatory phosphorylation residue, LRRK2 binding relationships, and measures of synaptic structure and function. 

Strengths: 

The study addresses an important research question: how does a PD-linked protein interact with other proteins, and contribute to responses to a well-characterized neuronal signalling pathway involved in the regulation of synaptic function and cell health? 

They employ a range of good models and techniques to fairly convincingly demonstrate that BDNF stimulation alters LRRK2 phosphorylation and binding to many proteins. Some effects of BDNF stimulation appear impaired in (some of the) LRRK2 knock-out scenarios (but not all). A phosphoproteomic analysis of PD mutant Knock-in mouse brain synaptosomes is included. 

We thank this Reviewer for pointing out the strengths of our work. 

Weaknesses: 

The data sets are disjointed, conclusions are sweeping, and not always in line with what the data is showing. Validation of 'omics' data is very light. Some inconsistencies with the major conclusions are ignored. Several of the assays employed (western blotting especially) are likely underpowered, findings key to their interpretation are addressed in only one or other of the several models employed, and supporting observations are lacking. 

We appreciate the Reviewer’s overall evaluaVon. In this revised version, we have provided several novel results that strengthen the omics data and the mechanisVc experiments and make the conclusions in line with the data.

As examples to aid reader interpretation: (a) pS935 LRRK2 seems to go up at 5 minutes but goes down below pre-stimulation levels after (at times when BDNF-induced phosphorylation of other known targets remains very high). This is ignored in favour of discussion/investigation of initial increases, and the fact that BDNF does many things (which might indirectly contribute to initial but unsustained changes to pLRRK2) is not addressed.  

We thank the Reviewer for raising this important point, which we agree deserves additional investigation. Although phosphorylation does decrease below pre-stimulation levels, a reduction is also observed for ERK/AKT upon sustained exposure to BDNF in our experimental paradigm (figure 1F-G). This phenomenon is well known in response to a number of extracellular stimuli and can be explained by mechanisms related to cellular negative feedback regulation, receptor desensitization (e.g. phosphorylation or internalization), or cellular adaptation. The effect on pSer935, however, is peculiar as phosphorylation goes below the unstimulated level, as pointed by the reviewer. In contrast to ERK and AKT whose phosphorylation is almost absent under unstimulated conditions (Figure 1F-G), the stoichiometry of Ser935 phosphorylation under unstimulated conditions is high. This observation is consistent with MS determination of relative abundance of pSer935 (e.g. in whole brain LRRK2 is nearly 100% phosphorylated at Ser935, see Nirujogi et al., Biochem J 2021).  Thus we hypothesized that the modest increase in phosphorylation driven by BDNF likely reflects a saturation or ceiling effect, indicating that the phosphorylation level is already near its maximum under resting conditions. Prolonged BDNF stimulation would bring phosphorylation down below pre-stimulation levels, through negative feedback mechanisms (e.g. phosphatase activity) explained above. To test this hypothesis, we conducted an experiment in conditions where LRRK2 is pretreated for 90 minutes with MLi-2 inhibitor, to reduce basal phosphorylation of S935. After MLi-2 washout, we stimulated with BDNF at different time points. We used GFP-LRRK2 stable lines for this experiment, since the ceiling effect was particularly evident (Figure S1A) and this model has been used for the interactomic study. As shown below (and incorporated in Fig. S1B in the manuscript), LRRK2 responds robustly to BDNF stimulation both in terms of pSer935 and pRABs. Phosphorylation peaks at 5-15 mins, while it decreases to unstimulated levels at 60 and 180 minutes. Notably, while the peak of pSer935 at 5-15 mins is similar to the untreated condition (supporting that Ser935 is nearly saturated in unstimulated conditions), the phosphorylation of RABs during this time period exceeds unstimulated levels. These findings support the notion that, under basal conditions, RAB phosphorylation is far from saturation. The antibodies used to detect RAB phosphorylation are the following: RAB10 Abcam # ab230261 e RAB8 (pan RABs) Abcam # ab230260.

Given the robust response of RAB10 phosphorylation upon BDNF stimulation, we further investigated RAB10 phosphorylation during BDNF stimulation in naïve SH-SY5Y cells. We confirmed that the increase in pSer935 is coupled to increase in pT73-RAB10. Also in this case, RAB10 phosphorylation does not go below the unstimulated level, which aligns with the  low pRAB10 stoichiometry in brain (Nirujogi et al., Biochem J 2021). This experiment adds the novel and exciting finding that BDNF stimulation increases LRRK2 kinase activity (RAB phosphorylation) in neuronal cells. 

Note that new supplemental figure 1 now includes: A) a comparison of LRRK2 pS935 and total protein levels before and after RA differentiation; B) differentiated GFP-LRRK2 SH-SY5Y (unstimulated, BDNF, MLi-2, BDNF+MLi-2); C) the kinetic of BDNF response in differentiated GFP-LRRK2 SH-SY5Y.

(b) Drebrin coIP itself looks like a very strong result, as does the increase after BDNF, but this was only demonstrated with a GFP over-expression construct despite several mouse and neuron models being employed elsewhere and available for copIP of endogenous LRRK2. Also, the coIP is only demonstrated in one direction. Similarly, the decrease in drebrin levels in mice is not assessed in the other model systems, coIP wasn't done, and mRNA transcripts are not quantified (even though others were). Drebrin phosphorylation state is not examined.  

We appreciate the Reviewer suggestions and provided additional experimental evidence supporting the functional relevance of LRRK2-drebrin interaction.

(1) As suggested, we performed qPCR and observed that 1 month-old KO midbrain and cortex express lower levels of Dbn1 as compared to WT brains (Figure 5G). This result is in agreement with the western blot data (Figure 5H). 

(2)To further validate the physiological relevance of LRRK2-drebrin interaction we performed two experiments:

i) Western blots looking at pSer935 and pRab8 (pan Rab) in Dbn1 WT and knockout brains. As reported and quantified in Figure 2I, we observed a significant decrease in pSer935 and a trend decrease in pRab8 in Dbn1 KO brains. This finding supports the notion that Drebrin forms a complex with LRRK2 that is important for its activity, e.g. upon BDNF stimulation. 

ii) Reverse co-immunoprecipitation of YFP-drebrin full-length, N-terminal domain (1-256 aa) and C-terminal domain (256-649 aa) (plasmids kindly received from Professor Phillip R. Gordon-Weeks, Worth et al., J Cell Biol, 2013) with Flag-LRRK2 co-expressed in HEK293T cells. As shown in supplementary Fig. S2C, we confirm that YFP-drebrin binds LRRK2, with the Nterminal region of drebrin appearing to be the major contributor to this interaction. This result is important as the N-terminal region contains the ADF-H (actin-depolymerising factor homology) domain and a coil-coil region known to directly bind actin (Shirao et al., J Neurochem 2017; Koganezawa et al., Mol Cell Neurosci. 2017). Interestingly, both full-length Drebrin and its truncated C-terminal construct cause the same morphological changes in Factin, indicating that Drebrin-induced morphological changes in F-actin are mediated by its N-terminal domains rather than its intrinsically disordered C-terminal region (Shirao et al., J Neurochem, 2017; Koganezawa et al., Mol Cell Neurosci. 2017). Given the role of LRRK2 in actin-cytoskeletal dynamics and its binding with multiple actin-related protein binding (Fig. 2 and Meixner et al., Mol Cell Proteomics. 2011; Parisiadou and Cai, Commun Integr Biol 2010), these results suggest the possibility that LRRK2 controls actin dynamics by competing with drebrin binding to actin and open new avenues for futures studies.

(3) To address the request for examining drebrin phosphorylation state, we decided to perform another phophoproteomic experiment, leveraging a parallel analysis incorporated in our latest manuscript (Chen et al., Mol Theraphy 2025). In this experiment, we isolated total striatal proteins from WT and G2019S KI mice and enriched the phospho-peptides. Unlike the experiment presented in Fig. 7, phosphopeptides were enriched from total striatal lysates rather than synaptosomal fractions, and phosphorylation levels were normalized to the corresponding total protein abundance. This approach was intended to avoid bias toward synaptic proteins, allowing for the analysis of a broader pool of proteins derived from a heterogeneous ensemble of cell types (neurons, glia, endothelial cells, pericytes etc.). We were pleased to find that this new experiment confirmed drebrin S339 as a differentially phosphorylated site, with a 3.7 fold higher abundance in G2019S Lrrk2 KI mice. The fact that this experiment evidenced an increased phosphorylation stoichiometry in G2019S mice rather than a decreased is likely due to the normalization of each peptide by its corresponding total protein. Gene ontology analysis of differentially phosphorylated proteins using stringent term size (<200 genes) showed post-synaptic spines and presynaptic active zones as enriched categories (Fig. 3F). A SynGO analysis confirms both pre and postsynaptic categories, with high significance for terms related to postsynaptic cytoskeleton (Fig. 3G). As pointed, this is particularly interesting as the starting material was whole striatal tissue – not synaptosomes as previously – indicating that most significant phosphorylation differences occur in synaptic compartments. This once again reinforces our hypothesis that LRRK2 has a prominent role in the synapse. Overall, we confirmed with an independent phosphoproteomic analysis that LRRK2 kinase activity influences the phosphorylation state of proteins related to synaptic function, particularly postsynaptic cytoskeleton. For clarity in data presentation, as mentioned by the Reviewers, we removed Figure 7 and incorporated this new analysis in figure 3, alongside the synaptic cluster analysis. 

Altogether, three independent OMICs approaches – (i) experimental LRRK2 interactomics in neuronal cells, (ii) a literature-based LRRK2 synaptic/cytoskeletal interactor cluster, and (iii) a phospho-proteomic analysis of striatal proteins from G2019S KI mice (to model LRRK2 hyperactivity) – converge to synaptic actin-cytoskeleton as a key hub of LRRK2 neuronal function.

(c) The large differences in the CRISPR KO cells in terms of BDNF responses are not seen in the primary neurons of KO mice, suggesting that other differences between the two might be responsible, rather than the lack of LRRK2 protein. 

Considering that some variability is expected for these type of cultures and across different species, any difference in response magnitude and kinetics could be attributed to the levels of TrKB  and downstream components expressed by the two cell types. 

We are confident that differentiated SH-SY5Y cells provide a reliable model for our study as we could translate the results obtained in SH-SY5Y cells in other models. However, to rule out the possibility that the more pronounced effect observed in SH-SY5Y KO cells as respect to Lrrk2 KO primary neurons was due to CRISPR off-target effect, we performed an off-target analysis. Specifically, we selected the first 8 putative off targets exhibiting a CDF (Cutting Frequency Determination) off-target-score >0.2. 

As shown in supplemental file 1, sequence disruption was observed only in the LRRK2 ontarget site in LRRK2 KO SH-SY5Y cells, while the 8 off-target regions remained unchanged across the genotypes and relative to the reference sequence. 

(d) No validation of hits in the G2019S mutant phosphoproteomics, and no other assays related to the rest of the paper/conclusions. Drebrin phosphorylation is different but unvalidated, or related to previous data sets beyond some discussion. The fact that LRRK2 binding occurs, and increases with BDNF stimulation, should be compared to its phosphorylation status and the effects of the G2019S mutation. 

As illustrated in the response to point (b), we performed a new phosphoproteomics investigation – with total striatal lysates instead of striatal synaptosomes and normalization phospho-peptides over total proteins – and found that S339 phosphorylation increases when LRRK2 kinase activity increases (G2019S). To address the request of validating drebrin phosphorylation, the main limitation is that there are no available antibodies against Ser339. While we tried phos-Tag gels in striatal lysates, we could not detect any reliable and specific signal with the same drebrin antibody used for western blot (Thermo Fisher Scientific: MA120377) due to technical limitations of the phosTag method. We are confident that phosphorylation at S339 has a physiological relevance, as it was identified 67 times across multiple proteomic discovery studies and they are placed among the most frequently phosphorylated sites in drebrin (https://www.phosphosite.org/proteinAction.action?id=2675&showAllSites=true).

To infer a possible role of this phosphorylation, we looked at the predicted pathogenicity of using AlphaMissense (Cheng et al., Science 2023). included as supplementary figure (Fig. S3), aminoacid substitutions within this site are predicted not to be pathogenic, also due to the low confidence of the AlphaFold structure. 

Ser339 in human drebrin is located just before the proline-rich region (PP domain) of the protein. This region is situated between the actin-binding domains and the C-terminal Homerbinding sequences and plays a role in protein-protein interactions and cytoskeletal regulation (Worth et al., J Cell Biol, 2013). Of interest, this region was previously shown to be the interaction site of adafin (ADFN), a protein involved in multiple cytoskeletal-related processes, including synapse formation and function by regulating puncta adherentia junctions, presynaptic differentiation, and cadherin complex assembly, which are essential for hippocampal excitatory synapses, spine formation, and learning and memory processes (Beaudoin, G. M., 3rd et al., J Neurosci, 2013). Of note, adafin is in the list of LRRK2 interacting proteins (https://www.ebi.ac.uk/intact/home), supporting a possible functional relevance of LRRK2-mediated drebrin phosphorylation in adafin-drebrin complex formation. This has been discussed in the discussion section.

The aim of this MS analysis in G2019S KI mice – now included in figure 3 – was to further validate the crucial role of LRRK2 kinase activity in the context of synaptic regulation, rather than to discover and characterize novel substrates. Consequently, Figure 7 has been eliminated. 

Reviewer #2 (Public Review):  

Taken as a whole, the data in the manuscript show that BDNF can regulate PD-associated kinase LRRK2 and that LRRK2 modifies the BDNF response. The chief strength is that the data provide a potential focal point for multiple observations across many labs. Since LRRK2 has emerged as a protein that is likely to be part of the pathology in both sporadic and LRRK2 PD, the findings will be of broad interest. At the same time, the data used to imply a causal throughline from BDNF to LRRK2 to synaptic function and actin cytoskeleton (as in the title) are mostly correlative and the presentation often extends beyond the data. This introduces unnecessary confusion. There are also many methodological details that are lacking or difficult to find. These issues can be addressed. 

We appreciate the Reviewer’s positive feedback on our study. We also value the suggestion to present the data in a more streamlined and coherent way. In response, we have updated the title to better reflect our overall findings: “LRRK2 Regulates Synaptic Function through Modulation of Actin Cytoskeletal Dynamics.” Additionally, we have included several experiments that we believe enhance and unify the study.

(1) The writing/interpretation gets ahead of the data in places and this was confusing. For example, the abstract highlights prior work showing that Ser935 LRRK2 phosphorylation changes LRRK2 localization, and Figure 1 shows that BDNF rapidly increases LRRK2 phosphorylation at this site. Subsequent figures highlight effects at synapses or with synaptic proteins. So is the assumption that LRRK2 is recruited to (or away from) synapses in response to BDNF? Figure 2H shows that LRRK2-drebrin interactions are enhanced in response to BDNF in retinoic acid-treated SH-SY5Y cells, but are synapses generated in these preps? How similar are these preps to the mouse and human cortical or mouse striatal neurons discussed in other parts of the paper (would it be anticipated that BDNF act similarly?) and how valid are SHSY5Y cells as a model for identifying synaptic proteins? Is drebrin localization to synapses (or its presence in synaptosomes) modified by BDNF treatment +/- LRRK2? Or do LRRK2 levels in synaptosomes change in response to BDNF? The presentation requires re-writing to stay within the constraints of the data or additional data should be added to more completely back up the logic. 

We thank the Reviewer for the thorough suggestions and comments. We have extensively revised the text to accurately reflect our findings without overinterpreting. In particular, we agree with the Reviewer that differentiated SH-SY5Y cells are not  identical to primary mouse or human neurons; however both neuronal models respond to BDNF. Supporting our observations, it is known that SH-SY5Y cells respond to BDNF.  In fact, a common protocol for differentiating SH-SY5Y cells involve BDNF in combination with retinoic acid (Martin et al., Front Pharmacol, 2022; Kovalevich et al., Methods in mol bio, 2013). Additionally, it has been reported that SH-SY5Y cells can form functional synapses (Martin et al., Front Pharmacol, 2022). While we are aware that BDNF, drebrin or LRRK2 can also affect non-synaptic pathways, we focused on synapses when moved to mouse models since: (i) MS and phosphoMS identified several cytoskeletal proteins enriched at the synapse, (ii) we and others have previously reported a role for LRRK2 in governing synaptic and cytoskeletal related processes; (iii) the synapse is a critical site that becomes dysfunctional in the early  stages of PD. We have now clarified and adjusted the text as needed. We have also performed additional experiments to address the Reviewer’s concern:

(1) “Is the assumption that LRRK2 is recruited to (or away from) synapses in response to BDNF”? This is a very important point. There is consensus in the field that detecting endogenous LRRK2 in brain slices or in primary neurons via immunofluorescence is very challenging with the commercially available  antibodies (Fernandez et al., J Parkinsons Dis, 2022). We established a method in our previous studies to detect LRRK2 biochemically in synaptosomes (Cirnaru et al., Front Mol Neurosci, 2014; Belluzzi et al., Mol Neurodegener., 2016). While these data indicate LRRK2 is present in the synaptic compartments, it would be quite challenging to apply this method to the present study. In fact, applying acute BDNF stimulation in vivo and then isolate synaptosomes is a complex experiment beyond the timeframe of the revision due to the need of mouse ethical approvals. However, this is definitely an intriguing angle to explore in the future.

(2)“Is drebrin localization to synapses (or its presence in synaptosomes) modified by BDNF treatment +/- LRRK2?” To try and address this question, we adapted a previously published assay to measure drebrin exodus from dendritic spines. During calcium entry and LTP, drebrin exits dendritic spines and accumulates in the dendritic shafts and cell body (Koganezawa et al., 2017). This facilitates the reorganization of the actin cytoskeleton (Shirao et al., 2017). Given the known role of drebrin and its interaction with LRRK2, we hypothesized that LRRK2 loss might affect drebrin relocalization during spine maturation.

To test this, we treated DIV14 primary cortical neurons from Lrrk2 WT and KO mice with BDNF for 5, 15, and 24 hours, then performed confocal imaging of drebrin localization (Author response image 1). Neurons were transfected at DIV4 with GFP (cell filler) and PSD95 (dendritic spines) for visualization, and endogenous drebrin was stained with an anti-drebrin antibody. We then measured drebrin's overlap with PSD95-positive puncta to track its localization at the spine.

In Lrrk2 WT neurons, drebrin relocalized from spines after BDNF stimulation, peaking at 15 minutes and showing higher co-localization with PSD95 at 24 hours, indicating the spine remodeling occurred. In contrast, Lrrk2 KO neurons showed no drebrin exodus. These findings support the notion that LRRK2's interaction with drebrin is important for spine remodeling via BDNF. However, additional experiments with larger sample sizes are needed, which were not feasible within the revision timeframe (here n=2 experiments with independent neuronal preparations, n=4-7 neurons analyzed per experiment). Thus, we included the relevant figure as Author response image 1 but chose not to add it in the manuscript (figure 3).

Author response image 1.

Lrrk2 affects drebrin exodus from dendritic spines. After the exposure to BDNF for different times (5 minutes, 15 minutes and 24 hours), primary neurons from Lrrk2 WT and KO mice have been transfected with GFP and PSD95 and stained for endogenous drebrin at DIV4. The amount of drebrin localizing in dentritic spines outlined by PSD95 has been assessed at DIV14. The graph shows a pronounced decrease in drebrin content in WT neurons during short time treatments and an increase after 24 hours. KO neurons present no evident variations in drebrin localization upon BDNF stimulation. Scale bar: 4 μm.<br />

(2) The experiments make use of multiple different kinds of preps. This makes it difficult at times to follow and interpret some of the experiments, and it would be of great benefit to more assertively insert "mouse" or "human" and cell type (cortical, glutamatergic, striatal, gabaergic) etc. 

We thank the Reviewer for pointing this out. We have now more clearly specified the cell type and species identity throughout the text to improve clarity and interpretation.

(3) Although BDNF induces quantitatively lower levels of ERK or Akt phosphorylation in LRRK2KO preps based on the graphs (Figure 4B, D), the western blot data in Figure 4C make clear that BDNF does not need LRRK2 to mediate either ERK or Akt activation in mouse cortical neurons and in 4A, ERK in SH-SY5Y cells. The presentation of the data in the results (and echoed in the discussion) writes of a "remarkably weaker response". The data in the blots demand more nuance. It seems that LRRK2 may potentiate a response to BDNF that in neurons is independent of LRRK2 kinase activity (as noted). This is more of a point of interpretation, but the words do not match the images.  

We thank the Reviewer for pointing this out. We have rephrased our data  presentation to better convey  our findings. We were not surprised to find that loss of LRRK2 causes only a reduction of ERK and AKT activation upon BDNF rather than a complete loss. This is because these pathways are complex and redundant and are activated by a number of cellular effectors. The fact that LRRK2 is one among many players whose function can be compensated by other signaling molecules is also supported by the phenotype of Lrrk2 KO mice that is measurable at 1 month but disappears with adulthood (4 and 18 months) (figure 5).

Moreover, we removed the sentence “Of note, 90 mins of Lrrk2 inhibition (MLi-2) prior to BDNF stimulation did not prevent phosphorylation of Akt and Erk1/2, suggesting that LRRK2 participates in BDNF-induced phosphorylation of Akt and Erk1/2 independently from its kinase activity but dependently from its ability to be phosphorylated at Ser935 (Fig. 4C-D and Fig. 1B-C)” since the MLi-2 treatment prior to BDNF stimulation was not quantified and our new data point to an involvement of LRRK2 kinase activity upon BDNF stimulation.

(4) Figure 4F/G shows an increase in PSD95 puncta per unit length in response to BDNF in mouse cortical neurons. The data do not show spine induction/dendritic spine density/or spine morphogenesis as suggested in the accompanying text (page 8). Since the neurons are filled/express gfp, spine density could be added or spines having PSD95 puncta. However, the data as reported would be expected to reflect spine and shaft PSDs and could also include some nonsynaptic sites. 

The Reviewer is right. We have rephrased the text to reflect an increase in postsynaptic density (PSD) sites, which may include both spine and shaft PSDs, as well as potential nonsynaptic sites.

(5) Experimental details are missing that are needed to fully interpret the data. There are no electron microscopy methods outside of the figure legend. And for this and most other microscopy-based data, there are few to no descriptions of what cells/sites were sampled, how many sites were sampled, and how regions/cells were chosen. For some experiments (like Figure 5D), some detail is provided in the legend (20 segments from each mouse), but it is not clear how many neurons this represents, where in the striatum these neurons reside, etc. For confocal z-stacks, how thick are the optical sections and how thick is the stack? The methods suggest that data were analyzed as collapsed projections, but they cite Imaris, which usually uses volumes, so this is confusing. The guide (sgRNA) sequences that were used should be included. There is no mention of sex as a biological variable. 

We thank the Reviewer for pointing out this missing information. We have now included:

(1) EM methods (page 24)

(2) Methods for ICC and confocal microscopy now incorporates the Z-stack thickness (0.5 μm x 6 = 3 μm) on page 23.

(3) Methods for Golgi-Cox staining now incorporates the Z-stack thickness and number of neurons and segments per neuron analyzed. 

(4) The sex of mice is mentioned in the material and methods (page 17): “Approximately equal numbers of males and females were used for every experiment”.

(6) For Figures 1F, G, and E, how many experimental replicates are represented by blots that are shown? Graphs/statistics could be added to the supplement. For 1C and 1I, the ANOVA p-value should be added in the legend (in addition to the post hoc value provided). 

The blots relative to figure 1F,G and E are representative of several blots (at least n=5). The same redouts are part of figure 4 where quantifications are provided. We added the ANOVA p-value in the legend for figure 1C, 1I and 1K.

(7) Why choose 15 minutes of BDNF exposure for the mass spec experiments when the kinetics in Figure 1 show a peak at 5 mins?  

This is an important point. We repeated the experiment in GFP-LRRK2 SH-SY5Y cells (figure S1C) and included the 15 min time point. In addition to confirming that pSer935 increases similarly at 5 and 15 minutes, we also observed an increase in RAB phosphorylation at these time points. As mentioned in our response to Reviewer’s 1, we pretreated with MLi-2 for 90 minutes in this experiment to reduce the high basal phosphorylation stoichiometry of pSer935. 

(8) The schematic in Figure 6A suggests that iPSCs were plated, differentiated, and cultured until about day 70 when they were used for recordings. But the methods suggest they were differentiated and then cryopreserved at day 30, and then replated and cultured for 40 more days. Please clarify if day 70 reflects time after re-plating (30+70) or total time in culture (70). If the latter, please add some notes about re-differentiation, etc. 

We thank the reviewer for providing further clarity on the iPSC methodology. In the submitted manuscript 70DIV represents the total time in vitro and the process involved a cryostorage event at 30DIV, with a thaw of the cells and a further 40 days of maturation before measurement.  We have adjusted the methods in both the text and figure (new schematic) to clarify this.  The cryopreservation step has been used in other iPSC methods to great effect (Drummond et al., Front Cell Dev Biol, 2020). Due to the complexity and length of the iPSC neuronal differentiation process, cryopreservation represents a useful method with which to shorten and enhance the ability to repeat experiments and reduce considerable variation between differentiations. User defined differences in culture conditions for each batch of neurons thawed can usefully be treated as a new and separate N compared to the next batch of neurons.

(9) When Figures 6B and 6C are compared it appears that mEPSC frequency may increase earlier in the LRRK2KO preps than in the WT preps since the values appear to be similar to WT + BDNF. In this light, BDNF treatment may have reached a ceiling in the LRRK2KO neurons.

We thank the reviewer for his/her comment and observations about the ceiling effects. It is indeed possible that the loss of LRRK2 and the application of BDNF could cause the same elevation in synaptic neurotransmission. In such a situation, the increased activity as a result of BDNF treatment would be masked by the increased activity  observed as a result of LRRK2 KO. To better visualize the difference between WT and KO cultures and the possible ceiling effect, we merged the data in one single graph.  

(10) Schematic data in Figures 5A and C and Figures 5B and E are too small to read/see the data. 

We thank the Reviewer for this suggestion. We have now enlarged figure 5A and moved the graph of figure 5D in supplemental figure S5, since this analysis of spine morphology is secondary to the one shown in figure 5C.

Reviewer #1 (Recommendations For The Authors): 

Please forgive any redundancy in the comments, I wanted to provide the authors with as much information as I had to explain my opinion. 

Primary mouse cortical neurons at div14, 20% transient increase in S935 pLRRK2 5min after BDNF, which then declines by 30 minutes (below pre-stim levels, and maybe LRRK2 protein levels do also). 

In differentiated SHSY5Y cells there is a large expected increase in pERK and pAKT that is sustained way above pre-stim for 60 minutes. There is a 50% initial increase in pLRRK2 (but the blot is not very clear and no double band in these cells), which then looks like reduced well below pre-stim by 30 & 60 minutes. 

We thank the Reviewer for bring up this important point. We have extensively addressed this issue in the public review rebuttal. In essence, the phosphorylation of Ser935 is near saturation under unstimulated conditions, as evidenced by its high basal stoichiometry, whereas Rab phosphorylation is far from saturation, showing an increase upon BDNF stimulation before returning to baseline levels. This distinction highlights that while pSer935 exhibits a ceiling effect due to its near-maximal phosphorylation at rest, pRab responds dynamically to BDNF, indicating low basal phosphorylation and a significant capacity for increase. Figure 1 in the rebuttal summarizes the new data collected. 

GFP-fused overexpressed LRRK2 coIPs with drebrin, and this is double following 15 min BDNF. Strong result.

We thank the Reviewer.

BDNF-induced pAKT signaling is greatly impaired, and pERK is somewhat impaired, in CRISPR LKO SHSY5Y cells. In mouse primaries, both AKT and Erk phosph is robustly increased and sustained over 60 minutes in WT and LKO. This might be initially less in LKO for Akt (hard to argue on a WB n of 3 with huge WT variability), regardless they are all roughly the same by 60 minutes and even look higher in LKO at 60. This seems like a big disconnect and suggests the impairment in the SHSy5Y cells might have more to do with the CRISPR process than the LRRK2. Were the cells sequenced for off-target CRISPR-induced modifications?  

Following the Reviewer suggestion – and as discussed in the public review section - we performed an off-target analysis. Specifically, we selected the first 8 putative off targets exhibiting a CDF (Cutting Frequency Determination) off-target-score >0.2. As shown in supplemental file 1, sequence disruption was observed only in the LRRK2 on-target site in LRRK2 KO SH-SY5Y cells, while the 8 off-target regions remained unchanged across the genotypes and relative to the reference sequence.  

No difference in the density of large PSD-95 puncta in dendrites of LKO primary relative to WT, and the small (10%) increase seen in WT after BDNF might be absent in LKO (it is not clear to me that this is absent in every culture rep, and the data is not highly convincing). This is also referred to as spinogenesis, which has not been quantified. Why not is confusing as they did use a GFP fill... 

The Reviewer is right that spinogenesis is not the appropriate term for the process analyzed. We replaced “spinogenesis” with “morphological alternation of dendritic protrusions” or “synapse maturation” which is correlated with the number of PSD95 positive puncta (ElHusseini et al., Science, 2000) . 

There is a difference in the percentage of dendritic protrusions classified as filopodia to more being classified as thin spines in LKO striatal neurons at 1 month, which is not seen at any other age, The WT filopodia seems to drop and thin spine percent rise to be similar to LKO at 4 months. This is taken as evidence for delayed maturation in LKO, but the data suggest the opposite. These authors previously published decreased spine and increased filopodia density at P15 in LKO. Now they show that filopodia density is decreased and thin spine density increased at one month. How is that shift from increased to decreased filopodia density in LKO (faster than WT from a larger initial point) evidence of impaired maturation? Again this seems accelerated? 

We agree with the Reviewer that the initial interpretation was indeed confusing. To adhere closely to our data and avoid overinterpretation – as also suggested by Reviewer 2 – we revised  the text and moved figure 5D to supplementary materials. In essence, our data point out to alterations in the structural properties of dendritic protrusions in young KO mice, specifically a reduction in  their size (head width and neck height) and a decrease in postsynaptic density (PSD) length, as observed with TEM. These findings suggest that LRRK2 is involved in morphological processes during spine development. 

Shank3 and PSD95 mRNA transcript levels were reduced in the LKO midbrain, only shank3 was reduced in the striatum and only PSD was reduced in the cortex. No changes to mRNA of BDNF-related transcripts. None of these mRNA changes protein-validated. Drebrin protein (where is drebrin mRNA?) levels are reduced in LKO at 1&4 but not clearly at 18 months (seems the most robust result but doesn't correlate with other measures, which here is basically a transient increase (1m) in thin striatal spines).  

As illustrated before, we performed qPCR for Dbn1 and found that its expression is significantly reduced in the cortex and midbrain and non-significantly reduced in the striatum (1 months old mice, a different cohort as those used for the other analysis in figure 5).  

24h BDNF increases the frequency of mEPSCs on hIPSC-derived cortical-like neurons, but not LKO, which is already high. There are no details of synapse number or anything for these cultures and compares 24h treatment. BDNF increases mEPSC frequency within minutes PMC3397209, and acute application while recording on cells may be much more informative (effects of BDNF directly, and no issues with cell-cell / culture variability). Calling mEPSC "spontaneous electrical activity" is not standard.  

We thank the reviewer for this point. We provided information about synapse number (Bassoon/Homer colocalization) in supplementary figure S7. The lack of response of LRRK2 KO cultures in terms of mEPSC is likely due to increase release probability as the number of synapses does not change between the two genotypes. 

The pattern of LRRK2 activation is very disconnected from that of BDNF signalling onto other kinases. Regarding pLRRK2, s935 is a non-autophosph site said to be required for LRRK2 enzymatic activity, that is mostly used in the field as a readout of successful LRRK2 inhibition, with some evidence that this site regulates LRRK2 subcellular localization (which might be more to do with whether or not it is p at 935 and therefor able to act as a kinase). 

The authors imply BDNF is activating LRRK2, but really should have looked at other sites, such as the autophospho site 1292 and 'known' LRRK2 substrates like T73 pRab10 (or other e.g., pRab12) as evidence of LRRK2 activation. One can easily argue that the initial increase in pLRRK2 at this site is less consequential than the observation that BDNF silences LRRK2 activity based on p935 being sustained to being reduced after 5 minutes, and well below the prestim levels... not that BDNF activates LRRK2. 

As described above, we have collected new data showing that BDNF stimulation increases LRRK2 kinase activity toward its physiological substrates Rab10 and Rab8 (using a panphospho-Rab antibody) (Figure 1 and Figure S1). Additionally, we have also extensively commented the ceiling effect of pS935.

BDNF does a LOT. What happens to network activity in the neural cultures with BDNF application? Should go up immediately. Would increasing neural activity (i.e., through depolarization, forskolin, disinhibition, or something else without BDNF) give a similar 20% increase in pS935 LRRK2? Can this be additive, or occluded? This would have major implications for the conclusions that BDNF and pLRRK2 are tightly linked (as the title suggests).  

These are very valuable observations; however, they fall outside the scope and timeframe of this study. We agree that future research should focus on gaining a deeper mechanistic understanding of how LRRK2 regulates synaptic activity, including vesicle release probability and postsynaptic spine maturation, independently of BDNF.

Figures 1A & H "Western blot analysis revealed a rapid (5 mins) and transient increase of Ser935 phosphorylation after BDNF treatment (Fig. 1B and 1C). Of interest, BDNF failed to stimulate Ser935 phosphorylation when neurons were pretreated with the LRRK2 inhibitor MLi-2" . The first thing that stands out is that the pLRRK2 in WB is not very clear at all (although we appreciate it is 'a pig' to work with, I'd hope some replicates are clearer); besides that, the 20% increase only at 5min post-BDNF stimulation seems like a much less profound change than the reduction from base at 60 and more at 180 minutes (where total LRRK2 protein is also going down?). That the blot at 60 minutes in H is representative of a 30% reduction seems off... makes me wonder about the background subtraction in quantification (for this there is much less pLRRK2 and more total LRRK2 than at 0 or 5). LRRK2 (especially) and pLRRK2 seem very sketchy in H. Also, total LRRK2 appears to increase in the SHSY5Y cell not the neurons, and this seems even clearer in 2 H. 

To better visualize the dynamics of pS935 variation relative to time=0, we presented the data as the difference between t=0 and t=x. It clearly shows that pSe935 goes below prestimulation levels, whereas pRab10 does not. The large difference in the initial stoichiometry of these two phosphorylation is extensively discussed above.

That MLi2 eliminates pLRRK2 (and seems to reduce LRRK2 protein?) isn't surprising, but a 90min pretreatment with MLi-2 should be compared to MLi-2's vehicle alone (MLi-2 is notoriously insoluble and the majority of diluents have bioactive effects like changing activity)... especially if concluding increased pLRRK2 in response to BDNF is a crucial point (when comparing against effects on other protein modifications such as pAKT). This highlights a second point... the changes to pERK and pAKT are huge following BDNF (nothing to massive quantities), whereas pLRRK2 increases are 20-50% at best. This suggests a very modest effect of BDNF on LRRK in neurons, compared to the other kinases. I worry this might be less consequential than claimed. Change in S1 is also unlikely to be significant... 

These comments have been thoroughly addressed in the previous responses. Regarding fig. S1, we added an additional experiment (Figure S1C) in GFP-LRRK2 cells showing robust activation of LRRK2 (pS935, pRabs) at the timepoint of MS (15 min).

"As the yields of endogenous LRRK2 purification were insufficient for AP-MS/MS analysis, we generated polyclonal SH-SY5Y cells stably expressing GFP-LRRK2 wild-type or GFP control (Supplementary Fig. 1)" . I am concerned that much is being assumed regarding 'synaptic function' from SHSY5Y cells... also overexpressing GFP-LRRK2 and looking at its binding after BDNF isn't synaptic function.  

We appreciate the reviewer’s comment. We would like to clarify that the interactors enriched upon BDNF stimulation predominantly fall into semantic categories related to the synapse and actin cytoskeleton. While this does not imply that these interactors are exclusively synaptic, it suggests that this tightly interconnected network likely plays a role in synaptic function. This interpretation is supported by several lines of evidence: (1) previous studies have demonstrated the relevance of this compartment to LRRK2 function; (2) our new phosphoproteomics data from striatal lysate highlight enrichment of synaptic categories; and (3) analysis of the latest GWAS gene list (134 genes) also indicates significant enrichment of synapse-related categories. Taken together, these findings justify further investigation into the role of LRRK2 in synaptic biology, as discussed extensively in the manuscript’s discussion section.

Figure 2A isn't alluded to in text and supplemental table 1 isn't about LRRK2 binding, but mEPSCs. 

We have added Figure 2A and added supplementary .xls table 1, which refers to the excel list of genes with modulated interaction upon BDNF (uploaded in the supplemental material).

We added the extension .xls also for supplementary table 2 and 3. 

Figure 2A is useless without some hits being named, and the donut plots in B add nothing beyond a statement that "35% of 'genes' (shouldn't this be proteins?) among the total 207 LRRK2 interactors were SynGO annotated" might as well [just] be the sentence in the text. 

We have now included the names of the most significant hits, including cytoskeletal and translation-related proteins, as well as known LRRK2 interactors. We decided to retain the donut plots, as we believe they simplify data interpretation for the reader, reducing the need to jump back and forth between the figures and the text.

Validation of drebrin binding in 2H is great... although only one of 8 named hits; could be increased to include some of the others. A concern alludes to my previous point... there is no appreciable LRRK2 in these cells until GFP-LRRK2 is overexpressed; is this addressed in the MS? Conclusions would be much stronger if bidirectional coIP of these binding candidates were shown with endogenous (GFP-ve) LRRK2 (primaries or hIPSCs, brain tissue?) 

To address the Reviewer’s concerns to the best of our abilities, we have added a blot in Supplemental figure S1A showing how the expression levels of LRRK2 increase after RA differentiation. Moreover, we have included several new data further strengthening the functional link between LRRK2 and drebrin, including qPCR of Dbn1 in one-month old Lrrk2 KO brains, western blots of Lrrk2 and Rab in Dbn1 KO brains, and co-IP with drebrin N- and Cterm domains. 

Figures 3 A-C are not informative beyond the text and D could be useful if proteins were annotated. 

To avoid overcrowding, proteins were annotated in A and the same network structure reported for synaptic and actin-related interactors. 

Figure 4. Is this now endogenous LRRK2 in the SHSY5Y cells? Again not much LRRK2 though, and no pLRRK shown. 

We confirm that these are naïve SH-SY5Y cells differentiated with RA and LRRK2 is endogenous. We did not assess pS935 in this experiment, as the primary goal was to evaluate pAKT and pERK1/2 levels. To avoid signal saturation, we loaded less total protein (30 µg instead of the 80 µg typically required to detect pS935). pS935 levels were extensively assessed in Figure 1. This experimental detail has now been added in the material and methods section (page 18).

In C (primary neurons) There is very little increase in pLRRK2 / LRRK2 at 5 mins, and any is much less profound a change than the reduction at 30 & 60 mins. I think this is interesting and may be a more substantial consequence of BDNF treatment than the small early increase. Any 5 min increase is gone by 30 and pLRRK2 is reduced after. This is a disconnect from the timing of all the other pProteins in this assay, yet pLRRK2 is supposed to be regulating the 'synaptic effects'? 

The first part of the question has already been extensively addressed. Regarding the timing, one possibility is that LRRK2 is activated upstream of AKT and ERK1/2, a hypothesis supported by the reduced activation of AKT and ERK1/2 observed in LRRK2 KO cells, as discussed in the manuscript, and in MLi-2 treated cells (Author response image 2). Concerning the synaptic effects, it is well established that synaptic structural and functional plasticity occurs downstream of receptor activation and kinase signaling cascades. These changes can be mediated by both rapid mechanisms (e.g., mobilization of receptor-containing endosomes via the actin cytoskeleton) and slower processes involving gene transcription of immediate early genes (IEGs). Since structural and functional changes at the synapse generally manifest several hours after stimulation, we typically assessed synaptic activity and structure 24 hours post-stimulation.

Akt Erk1&2 both go up rapidly after BDNF in WT, although Akt seems to come down with pLRRK2. If they aren't all the same Akt is probably the most different between LKO and WT but I am very concerned about an n=3 for wb, wb is semi-quantitative at best, and many more than three replicates should be assessed, especially if the argument is that the increases are quantitively different between WT v KO (huge variability in WT makes me think if this were done 10x it would all look same). Moreover, this isn't similar to the LKO primaries  "pulled pups" pooled presumably. 

Despite some variability in the magnitude of the pAKT/pERK response in naïve SH-SY5Y cells, all three independent replicates consistently showed a reduced response in LRRK2 KO cells, yielding a highly significant result in the two-way ANOVA test. In contrast, the difference in response magnitude between WT and LRRK2 KO primary cultures was less pronounced, which justified repeating the experiments with n=9 replicates. We hope the Reviewer acknowledges the inherent variability often observed in western blot experiments, particularly when performed in a fully independent manner (different cultures and stimulations, independent blots).

To further strengthen the conclusion that this effect is reproducible and dependent on LRRK2 kinase activity upstream of AKT and ERK, we probed the membranes in figure 1H with pAKT/total AKT and pERK/total ERK. All things considered and consistent with our hypothesis, MLi-2 significantly reduced BDNF-mediated AKT and ERK1/2 phosphorylation levels (Author response image 2). 

Author response image 2.

Western blot (same experiments as in figure 1) was performed using antibodies against phospho-Thr202/185 ERK1/2, total ERK1/2 and phospho-Ser473 AKT, total AKT protein levels Retinoic acid-differentiated SH-SY5Y cells stimulated with 100 ng/mL BDNF for 0, 5, 30, 60 mins. MLi-2 was used at 500 nM for 90 mins to inhibit LRRK2 kinase activity.

G lack of KO effect seems to be skewed from one culture in the plot (grey). The scatter makes it hard to read, perhaps display the culture mean +/- BDNF with paired bars. The fact that one replicate may be changing things is suggested by the weirdly significant treatment effect and no genotype effect. Also, these are GFP-filled cells, the dendritic masks should be shown/explained, and I'm very surprised no one counted the number (or type?) of protrusions, especially as the text describes this assay (incorrectly) as spinogenesis... 

As suggested by the Reviewer we have replotted the results as bar graphs. Regarding the number of protrusions, we initially counted the number of GFP+ puncta in the WT and did not find any difference (Author response image 3). Due to our imaging setup (confocal microscopy rather than super-resolution imaging and Imaris 3D reconstruction), we were unable to perform a fine morphometric analysis. However, this was not entirely unexpected, as BDNF is known to promote both the formation and maturation of dendritic spines. Therefore, we focused on quantifying PSD95+ puncta as a readout of mature postsynaptic compartments. While we acknowledge that we cannot definitively conclude that each PSD95+ punctum is synaptically connected to a presynaptic terminal, the data do indicate an increase in the number of PSD95+ structures following BDNF stimulation.

Author response image 3.

GFP+ puncta per unit of neurite length (µm) in DIV14 WT primary neurons untreated or upon 24 hour of BDNF treatment (100 ng/ml). No significant difference were observed (n=3).

Figure 5. "Dendritic spine maturation is delayed in Lrrk2 knockout mice". The only significant change is at 1 month in KO which shows fewer filopodia and increased thin spines (50% vs wt). At 4 months the % of thin spines is increased to 60% in both... Filopodia also look like 4m in KO at 1m... How is that evidence for delayed maturation? If anything it suggests the KO spines are maturing faster. "the average neck height was 15% shorter and the average head width was 27% smaller, meaning that spines are smaller in Lrrk2 KO brains" - it seems odd to say this before saying that actually there are just MORE thin spines, the number of mature "mushroom' is same throughout, and the different percentage of thin comes from fewer filopodia. This central argument that maturation is delayed is not supported and could be backwards, at least according to this data. Similarly, the average PSD length is likely impacted by a preponderance of thin spines in KO... which if mature were fewer would make sense to say delayed KO maturation, but this isn't the case, it is the fewer filopodia (with no PSD) that change the numbers. See previous comments of the preceding manuscript. 

We agree that thin spines, while often considered more immature, represent an intermediate stage in spine development. The data showing an increase in thin spines at 1 month in the KO mice, along with fewer filopodia, could suggest a faster stabilization of these spines, which might indeed be indicative of premature maturation rather than delayed maturation. This change in spine morphology may indicate that the dynamics of synaptic plasticity are affected. Regarding the PSD length, as the Reviewer pointed out, the increased presence of thin spines in KO might account for the observed changes in PSD measurements, as thin spines typically have smaller PSDs. This further reinforces the idea that the overall maturation process may be altered in the KO, but not necessarily delayed. 

We rephrase the interpretation of these data, and moved figure 5D as supplemental figure S4.

"To establish whether loss of Lrrk2 in young mice causes a reduction in dendritic spines size by influencing BDNF-TrkB expression" - there is no evidence of this.  

We agree and reorganized the text, removing this sentence.  

Shank and PSD95 mRNA changes being shown without protein adds very little. Why is drebrin RNA not shown? Also should be several housekeeping RNAs, not one (RPL27)? 

We measured Dbn1 mRNA, which shows a significant reduction in midbrain and cortex. Moreover we have now normalized the transcript levels against the geometrical means of three housekeeping genes (RPL27, actin, and GAPDH) relative abundance.

Drebrin levels being lower in KO seems to be the strongest result of the paper so far (shame no pLRRK2 or coIP of drebrin to back up the argument). DrebrinA KO mice have normal spines, what about haploinsufficient drebrin mice (LKO seem to have half derbrin, but only as youngsters?)  

As extensively explained in the public review, we used Dbn1 KO mouse brains and were able to show reduced Lrrk2 activity.

Figure 6. hIPSC-derived cortical neurons. The WT 'cortical' neurons have a very low mEPSC frequency at 0.2Hz relative to KO. Is this because they are more or less mature? What is the EPSC frequency of these cells at 30 and 90 days for comparison? Also, it is very very hard to infer anything about mEPSC frequency in the absence of estimates of cell number and more importantly synapse number. Furthermore, where are the details of cell measures such as capacitance, resistance, and quality control e.g., Ra? Table s1 seems redundant here, besides suggesting that the amplitude is higher in KO at base. 

We agree that the developmental trajectory of iPSC-derived neurons is critical to accurately interpreting synaptic function and plasticity. In response, we have included additional data now presented in the supplementary figure S7 and summarize key findings below:

At DIV50, both WT and LRRK2 KO neurons exhibit low basal mEPSC activity (~0.5 Hz) and no response to 24 h BDNF stimulation (50 ng/mL).

At DIV70 WT neurons show very low basal activity (~0.2 Hz), which increases ~7.5-fold upon BDNF treatment (1.5 Hz; p < 0.001), and no change in synapse number. KO neurons display elevated basal activity (~1 Hz) similar to BDNF-treated WT neurons, with no further increase upon BDNF exposure (~1.3 Hz) and no change in synapse number.

At DIV90, no significant effect of BDNF in both WT and KO, indicating a possible saturation of plastic responses. The lack of BDNF response at DIV90 may be due to endogenous BDNF production or culture-based saturation effects. While these factors warrant further investigation (e.g., ELISA, co-culture systems), they do not confound the key conclusions regarding the role of LRRK2 in synaptic development and plasticity:

LRRK2 Enables BDNF-Responsive Synaptic Plasticity. In WT neurons, BDNF induces a significant increase in neurotransmitter release (mEPSC frequency) with no reduction in synapse number. This dissociation suggests BDNF promotes presynaptic functional potentiation. KO neurons fail to show changes in either synaptic function or structure in response to BDNF, indicating that LRRK2 is required for activity-dependent remodeling.

LRRK2 Loss Accelerates Synaptic Maturation. At DIV70, KO neurons already exhibit high spontaneous synaptic activity equivalent to BDNF-stimulated WT neurons. This suggests that LRRK2 may act to suppress premature maturation and temporally gate BDNF responsiveness, aligning with the differences in maturation dynamics observed in KO mice (Figure 5).  

As suggested by the reviewer we reported the measurement of resistance and capacitance for all DIV (Table 1, supplemental material). A reduction in capacitance was observed in WT neurons at DIV90, which may reflect changes in membrane complexity. However, this did not correlate with differences in synapse number and is unlikely to account for the observed differences in mEPSC frequency. To control for cell number between groups, cell count prior to plating was performed (80k/cm2; see also methods) on the non-dividing cells to keep cell number consistent.

The presence of BDNF in WT seems to make them look like LKO, in the rest of the paper the suggestion is that the LKO lack a response to BDNF. Here it looks like it could be that BDNF signalling is saturated in LKO, or they are just very different at base and lack a response.

Knowing which is important to the conclusions, and acute application (recording and BDNF wash-in) would be much more convincing.

We agree with the Reviewer’s point that saturation of BDNF could influence the interpretation of the data if it were to occur. However, it is important to note that no BDNF exists in the media in base control and KO neuronal culture conditions. This is  different from other culture conditions and allows us to investigate the effects of  BDNF treatment. Thus, the increased mEPSC frequency observed in KO neurons compared to WT neurons is defined only by the deletion of the gene and not by other extrinsic factors which were kept consistent between the groups. The lack of response or change in mEPSC frequency in KO is proposed to be a compensatory mechanism due to the loss of LRRK2. Of Note, LRRK2 as a “synaptic break” has already been described (Beccano-Kelly et al., Hum Mol Gen, 2015). However, a comprehensive analysis of the underlying molecular mechanisms will  require future studies beyond  with the scope of this paper.

"The LRRK2 kinase substrates Rabs are not present in the list of significant phosphopeptides, likely due to the low stoichiometry and/or abundance" Likely due to the fact mass spec does not get anywhere near everything. 

We removed this sentence in light of the new phosphoproteomic analysis.

Figure 7 is pretty stand-alone, and not validated in any way, hard to justify its inclusion?  

As extensively explained we removed figure 7 and included the new phospho-MS as part of figure. 3

Writing throughout shows a very selective and shallow use of the literature.  

We extensively reviewed the citations.

"while Lrrk1 transcript in this region is relatively stable during development" The authors reference a very old paper that barely shows any LRRK1 mRNA, and no protein. Others have shown that LRRK1 is essentially not present postnatally PMC2233633. This isn't even an argument the authors need to make. 

We thank the reviewer and included this more appropriate citation. 

Reviewer #2 (Recommendations For The Authors): 

Cyfip1 (Fig 3A) is part of the WAVE complex (page 13). 

We thank the reviewer and specified it.

The discussion could be more focused. 

We extensively revised the discussion to keep it more focused.

Note that we updated the GO ontology analyses to reflect the updated information present in g:Profiler.

References.

Nirujogi, R. S., Tonelli, F., Taylor, M., Lis, P., Zimprich, A., Sammler, E., & Alessi, D. R. (2021). Development of a multiplexed targeted mass spectrometry assay for LRRK2phosphorylated Rabs and Ser910/Ser935 biomarker sites. The Biochemical journal, 478(2), 299–326. https://doi.org/10.1042/BCJ20200930

Worth, D. C., Daly, C. N., Geraldo, S., Oozeer, F., & Gordon-Weeks, P. R. (2013). Drebrin contains a cryptic F-actin-bundling activity regulated by Cdk5 phosphorylation. The Journal of cell biology, 202(5), 793–806. https://doi.org/10.1083/jcb.201303005

Shirao, T., Hanamura, K., Koganezawa, N., Ishizuka, Y., Yamazaki, H., & Sekino, Y. (2017). The role of drebrin in neurons. Journal of neurochemistry, 141(6), 819–834. https://doi.org/10.1111/jnc.13988

Koganezawa, N., Hanamura, K., Sekino, Y., & Shirao, T. (2017). The role of drebrin in dendritic spines. Molecular and cellular neurosciences, 84, 85–92. https://doi.org/10.1016/j.mcn.2017.01.004

Meixner, A., Boldt, K., Van Troys, M., Askenazi, M., Gloeckner, C. J., Bauer, M., Marto, J. A., Ampe, C., Kinkl, N., & Ueffing, M. (2011). A QUICK screen for Lrrk2 interaction partners--leucine-rich repeat kinase 2 is involved in actin cytoskeleton dynamics. Molecular & cellular proteomics: MCP, 10(1), M110.001172. https://doi.org/10.1074/mcp.M110.001172

Parisiadou, L., & Cai, H. (2010). LRRK2 function on actin and microtubule dynamics in Parkinson disease. Communicative & integrative biology, 3(5), 396–400. https://doi.org/10.4161/cib.3.5.12286

Chen, C., Masotti, M., Shepard, N., Promes, V., Tombesi, G., Arango, D., Manzoni, C., Greggio, E., Hilfiker, S., Kozorovitskiy, Y., & Parisiadou, L. (2024). LRRK2 mediates haloperidol-induced changes in indirect pathway striatal projection neurons. bioRxiv : the preprint server for biology, 2024.06.06.597594. https://doi.org/10.1101/2024.06.06.597594

Cheng, J., Novati, G., Pan, J., Bycroft, C., Žemgulytė, A., Applebaum, T., Pritzel, A.,Wong, L. H., Zielinski, M., Sargeant, T., Schneider, R. G., Senior, A. W., Jumper, J., Hassabis, D., Kohli, P., & Avsec, Ž. (2023). Accurate proteome-wide missense variant effect prediction with AlphaMissense. Science (New York, N.Y.), 381(6664), eadg7492. https://doi.org/10.1126/science.adg7492

Beaudoin, G. M., 3rd, Schofield, C. M., Nuwal, T., Zang, K., Ullian, E. M., Huang, B., & Reichardt, L. F. (2012). Afadin, a Ras/Rap effector that controls cadherin function, promotes spine and excitatory synapse density in the hippocampus. The Journal of neuroscience : the official journal of the Society for Neuroscience, 32(1), 99–110. https://doi.org/10.1523/JNEUROSCI.4565-11.2012

Fernández, B., Chittoor-Vinod, V. G., Kluss, J. H., Kelly, K., Bryant, N., Nguyen, A. P. T., Bukhari, S. A., Smith, N., Lara Ordóñez, A. J., Fdez, E., Chartier-Harlin, M. C., Montine, T. J., Wilson, M. A., Moore, D. J., West, A. B., Cookson, M. R., Nichols, R. J., & Hilfiker, S. (2022). Evaluation of Current Methods to Detect Cellular Leucine-Rich Repeat Kinase 2 (LRRK2) Kinase Activity. Journal of Parkinson's disease, 12(5), 1423–1447. https://doi.org/10.3233/JPD-213128

Cirnaru, M. D., Marte, A., Belluzzi, E., Russo, I., Gabrielli, M., Longo, F., Arcuri, L., Murru, L., Bubacco, L., Matteoli, M., Fedele, E., Sala, C., Passafaro, M., Morari, M., Greggio, E., Onofri, F., & Piccoli, G. (2014). LRRK2 kinase activity regulates synaptic vesicle trafficking and neurotransmitter release through modulation of LRRK2 macromolecular complex. Frontiers in molecular neuroscience, 7, 49. https://doi.org/10.3389/fnmol.2014.00049

Belluzzi, E., Gonnelli, A., Cirnaru, M. D., Marte, A., Plotegher, N., Russo, I., Civiero, L., Cogo, S., Carrion, M. P., Franchin, C., Arrigoni, G., Beltramini, M., Bubacco, L., Onofri, F., Piccoli, G., & Greggio, E. (2016). LRRK2 phosphorylates pre-synaptic Nethylmaleimide sensitive fusion (NSF) protein enhancing its ATPase activity and SNARE complex disassembling rate. Molecular neurodegeneration, 11, 1. https://doi.org/10.1186/s13024-015-0066-z

Martin, E. R., Gandawijaya, J., & Oguro-Ando, A. (2022). A novel method for generating glutamatergic SH-SY5Y neuron-like cells utilizing B-27 supplement. Frontiers in pharmacology, 13, 943627. https://doi.org/10.3389/fphar.2022.943627

Kovalevich, J., & Langford, D. (2013). Considerations for the use of SH-SY5Y neuroblastoma cells in neurobiology. Methods in molecular biology (Clifton, N.J.), 1078, 9–21. https://doi.org/10.1007/978-1-62703-640-5_2

Drummond, N. J., Singh Dolt, K., Canham, M. A., Kilbride, P., Morris, G. J., & Kunath, T. (2020). Cryopreservation of Human Midbrain Dopaminergic Neural Progenitor Cells Poised for Neuronal Differentiation. Frontiers in cell and developmental biology, 8, 578907. https://doi.org/10.3389/fcell.2020.578907

Tao, X., Finkbeiner, S., Arnold, D. B., Shaywitz, A. J., & Greenberg, M. E. (1998). Ca2+ influx regulates BDNF transcription by a CREB family transcription factor-dependent mechanism. Neuron, 20(4), 709–726. https://doi.org/10.1016/s0896-6273(00)810107

El-Husseini, A. E., Schnell, E., Chetkovich, D. M., Nicoll, R. A., & Bredt, D. S. (2000). PSD95 involvement in maturation of excitatory synapses. Science (New York, N.Y.), 290(5495), 1364–1368.

Glebov OO, Cox S, Humphreys L, Burrone J. Neuronal activity controls transsynaptic geometry. Sci Rep. 2016 Mar 8;6:22703. doi: 10.1038/srep22703. Erratum in: Sci Rep. 2016 May 31;6:26422. doi: 10.1038/srep26422. PMID: 26951792; PMCID: PMC4782104.

Beccano-Kelly DA, Volta M, Munsie LN, Paschall SA, Tatarnikov I, Co K, Chou P, Cao LP, Bergeron S, Mitchell E, Han H, Melrose HL, Tapia L, Raymond LA, Farrer MJ, Milnerwood AJ. LRRK2 overexpression alters glutamatergic presynaptic plasticity, striatal dopamine tone, postsynaptic signal transduction, motor activity and memory. Hum Mol Genet. 2015 Mar 1;24(5):1336-49. doi: 10.1093/hmg/ddu543. Epub 2014 Oct 24. PMID: 25343991.

Author response:

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

Reviewer #1 (Public Review):

Major concerns:

(1) Is the direct binding of MCAK to the microtubule cap important for its in vivo function?

a.The authors claim that their "study provides mechanistic insights into understanding the end-binding mechanism of MCAK". I respectfully disagree. My concern is that the paper offers limited insights into the physiological significance of direct end-binding for MCAK activity, even in vitro. The authors estimate that in the absence of other proteins in vitro, ~95% of MCAK molecules arrive at the tip by direct binding in the presence of ~ physiological ATP concentration (1 mM). In cells, however, the major end-binding pathway may be mediated by EB, with the direct binding pathway contributing little to none. This is a reasonable concern because the apparent dissociation constant measured by the authors shows that MCAK binding to microtubules in the presence of ATP is very weak (69 uM). This concern should be addressed by 1) calculating relative contributions of direct and EB-dependent pathways based on the affinities measured in this and other published papers and estimated intracellular concentrations. Although there are many unknowns about these interactions in cells, a modeling-based analysis may be revealing. 2) the recapitulation of these pathways using purifying proteins in vitro is also feasible. Ideally, some direct evidence should be provided, e.g. based on MCAK function-separating mutants (GDP-Pi tubulin binding vs. catalytic activity at the curled protofilaments) that contribution from the direct binding of MCAK to microtubule cap in EB presence is significant.

We thank the reviewer for the thoughtful comments.

(1) We think that the end-binding affinity of MCAK makes a significant contribution for its cellular functions. To elucidate this concept, we now use a simple model shown in Supplementary Appendix-2 (see pages 49-51, lines 1246-1316). In this model, we simplified MCAK and EB1 binding to microtubule ends by considering only these two proteins while neglecting other factors (e.g. XMAP215). Specifically, we considered two scenarios: one in which both proteins freely diffuse in the cytoplasm and another where MCAK is localized to specific cellular structures, such as the centrosome or centromere. Based on the modeling results, we argue that MCAK's functional impact at microtubule ends derives both from its intrinsic end-binding capacity and its ability to strengthen the EB1-mediated end association pathway.

(2) We agree with the reviewer that MCAK exhibiting a lower end-binding affinity (69 µM) is indeed intriguing, as one might intuitively expect a stronger affinity, e.g. in the nanomolar range. Several factors may contribute to this observation. First, this could be partly due to the in vitro system employed, which may not perfectly replicate in vivo conditions, especially when considering cellular processes quantitatively. Variations in medium composition can significantly influence the binding state. For example, reducing salt concentration leads to a marked increase in MCAK’s binding affinity (Helenius et al., 2006; Maurer et al., 2011; McHugh et al., 2019). Additionally, while numerous binding events with short durations were detected, we excluded transient interactions from our analysis to facilitate quantification. This likely leads to an underestimation of the on-rate and, consequently, the binding affinity. Moreover, to minimize the interference of purification tags (His-tag), we ensured their complete removal during protein sample preparation. Previous studies reported that retaining the His-tag of MAPs affects the binding affinity to microtubules (Maurer et al., 2011; Zhu et al., 2009). Finally, a low affinity is not necessarily unexpected. Considering the microtubule end as a receptor with multiple binding sites for MCAK, the overall binding affinity is in the nanomolar range (260 nM). This does not necessarily contradict MCAK being a microtubule dynamics regulator as only a few MCAK molecules may suffice to induce microtubule catastrophe (as discussed on page 13, lines 408-441).

(3) Ideally, we would search for mutants that specifically interfere with the binding of GDP-Pi-tubulin or the curled protofilaments. However, the mutant we tested significantly impacts the overall affinity of MCAK to microtubules (both end and lattice), making it challenging to isolate and discuss the function of MCAK with respect to the binding to GDP-Pi-tubulin alone. Additionally, we also think that the GDP-Pi-tubulin in the EB cap and the tubulin in the curved protofilaments may share structural similarities. For instance, the tubulin dimers in both states may be less compact compared to those in the lattice, which could explain why MCAK recognizes both simultaneously (Manka and Moores, 2018). However, this remains a conjecture, as there is currently no direct evidence to support it.

b. As mentioned in the Discussion, preferential MCAK binding to tubulins near the MT tip may enhance MCAK targeting of terminal tubulins AFTER the MCAK has been "delivered" to the distal cap via the EB-dependent mechanism. This is a different targeting mechanism than the direct MCAK-binding. However, the measured binding affinity between MCAK and GMPCPP tubulins is so weak (69 uM), that this effect is also unlikely to have any impact because the binding events between MCAK and microtubule should be extremely rare. Without hard evidence, the arguments for this enhancement are very speculative.

Please see our response to the comment No. 1. Additionally, we have revised our discussion to discuss the end-binding affinity of MCAK as well as its physiological relevance (please see page 13, lines 408-441; and see Supplementary Appendix-2 in pages 49-51, lines 1246-1316).

(2) The authors do not provide sufficient justification and explanation for their investigation of the effects of different nucleotides in MCAK binding affinity. A clear summary of the nucleotide-dependent function of MCAK (introduction with references to prior affinity measurements and corresponding MCAK affinities), the justifications for this investigation, and what has been learned from using different nucleotides (discussion) should be provided. My take on these results is that by far the strongest effect on microtubule wall and tip binding is achieved by adding any adenosine, whereas differences between different nucleotides are relatively minor. Was this expected? What can be learned from the apparent similarity between ATP and AMPPNP effects in some assays (Fig 1E, 4C, etc) but not others (Fig 1D,F, etc)?

We thank the reviewer for this suggestion. We have revised the manuscript accordingly, and below are the main points of our response

(1) The experiment investigating the effects of different nucleotides on MCAK binding affinity was inspired by the previous studies demonstrating that kinesin-13 interactions with microtubules are highly dependent on their adenosine-bound states. For example, kinesin-13s tightly bind microtubules and prefer to form protofilament curls or rings with tubulin in the AMPPNP state, whereas kinesin-13s are considered to move along the microtubule lattice via one-dimensional diffusion in the ADP·Pi state (Asenjo et al., 2013; Benoit et al., 2018; Friel and Howard, 2011; Helenius et al., 2006). Based on these observations, we wondered whether MCAK's adenosine-bound states might similarly affect its binding preference for growing microtubule ends. We have made the motivation clear in the revised manuscript (please see page 7, lines 199-209).

(2) Our main finding regarding the effects of nucleotides is that MCAK shows differential end-binding affinity and preference based on its nucleotide state. First, MCAK shows the greatest preference for growing microtubule ends in the ATP state, supporting the idea that diffusive MCAK (MCAK·ATP) can directly bind to growing microtubule ends. Second, MCAK·ATP also demonstrates a binding preference for GTPγS microtubules and the ends of GMPCPP microtubules. The similar trends in binding preference suggest that the affinity for GDP·Pi-tubulin and GTP-tubulin likely underpins MCAK’s preference for growing microtubule ends. To clarify these points, we have added further discussions in the manuscript (please see page 8, lines 230-233; page9, lines 258-270 and pages 13-14, lines 443-458).

(3) It is not clear why the authors decided to use these specific mutant MCAK proteins to advance their arguments about the importance of direct tip binding. Both mutants are enzymatically inactive. Both show roughly similar tip interactions, with some (minor) differences. Without a clear understanding of what these mutants represent, the provided interpretations of the corresponding results are not convincing.

We thank the reviewer for this comment. In the revised manuscript, we no longer draw conclusions about the importance of end-binding based on the mutant data. Instead, we think that the mutant data provide insights into the structural basis of the end-binding preference. Therefore, we have rewritten the results in this section to more accurately reflect these findings (please see page 10, lines 295-327).

(4) GMPCPP microtubules are used in the current study to represent normal dynamic microtubule ends, based on some published studies. However, there is no consensus in the field regarding the structure of growing vs. GMPCPP-stabilized microtubule ends, which additionally may be sensitive to specific experimental conditions (buffers, temperature, age of microtubules, etc). To strengthen the authors' argument, Taxol-stabilized microtubules should be used as a control to test if the effects are specific. Additionally, the authors should consider the possibility that stronger MCAK binding to the ends of different types of microtubules may reflect MCAK-dependent depolymerization events on a very small scale (several tubulin rows). These nano-scale changes to tubulins and the microtubule end may lead to the accumulation of small tubulin-MCAK aggregates, as is seen with other MAPs and slowly depolymerizing microtubules. These effects for MCAK may also depend on specific nucleotides, further complicating the interpretation. This possibility should be addressed because it provides a different interpretation than presented in the manuscript.

Regarding the two points raised here, our thoughts are as following

(1) The end of GMPCPP-stabilized microtubules differs from that of growing microtubules, with the most obvious known difference being the absence of the region enriched in GDP-Pi-tubulin. We consider the end of GMPCPP microtubules as an analogue of the distal tip of growing microtubules, based on two key features: (1) curled protofilaments and (2) GMPCPP-tubulin, a close analogue of GTP-tubulin. Notably, both features are present at the ends of both GMPCPP-stabilized and growing microtubules. Moreover, we agree with the suggestion to use taxol-stabilized microtubules as a control. This would eliminate the second feature (absence of GTP-tubulin), allowing us to isolate the effect of the first feature. Therefore, we conducted this experiment, and our data showed that MCAK exhibits only a mild binding preference for the ends of taxol-stabilized microtubules, which is much less pronounced than for the ends of GMPCPP microtubules. This observation supports the idea that GMPCPP-stabilized ends closely resemble the growing ends of microtubules.

(2) The reviewer suggested that stronger MCAK binding to the ends of different types of microtubules might reflect MCAK-dependent depolymerization events on a very small scale. This is an insightful possibility, which we had overlooked in the original manuscript. Fortunately, we performed the experiments at the single-molecule concentrations. Upon reviewing the raw data, we found that under ATP conditions, the binding events of MCAK were not cumulative (see Fig. X1 below) and showed no evidence of local accumulation of MCAK-tubulin aggregates.

Author response image 1.

The representative kymograph showing GFP-MCAK binding at the ends and lattice of GMPCPP microtubules in the presence of 1 mM ATP (10 nM GFP-MCAK), which corresponded to Fig. 5A. The arrow: the end-binding of MCAK. Vertical bar: 1 s; horizontal bar: 2 mm.

(5) It would be helpful if the authors provided microtubule polymerization rates and catastrophe frequencies for assays with dynamic microtubules and MCAK in the presence of different nucleotides. The video recordings of microtubules under these conditions are already available to the authors, so it should not be difficult to provide these quantifications. They may reveal that microtubule ends are different (or not) under the examined conditions. It would also help to increase the overall credibility of this study by providing data that are easy to compare between different labs.

We thank the reviewer for this suggestion. In the revised manuscript, we have provided data on the growth rates, which are similar across the different nucleotide states (Fig. s1). However, due to the short duration of our recordings (usually 5 minutes, but with a high frame rate, 10 fps), we did not observe many catastrophe events, which prevented us from quantifying catastrophe frequency using the current dataset. Since we measured the binding kinetics of MCAK during the growing phase of microtubules, the similar growth rates and microtubule end morphologies suggest that the microtubule ends are comparable across the different conditions.

Reviewer #1 (Recommendations For The Authors):

a. Please provide more details about how the microtubule-bound molecules were selected for analysis (include a description of scripts, selection criteria, and filters, if any). Fig 1A arrows do not provide sufficient information.

We first measured the fluorescence intensity of each binding event. A probability distribution of these intensities was then constructed and fitted with a Gaussian function. A binding event was considered to correspond to a single molecule if its intensity fell within μ±2σ of the distribution. The details of the single-molecule screening process are now provided in the revised manuscript (see page17, lines 574-583).

b. Evidence that MCAK is dimeric in solution should be provided (gel filtration results, controls for Figs1A - bleaching, or comparison with single GFP fluorophore).

In the revised manuscript, we provide the gel filtration results of purified MCAK and other proteins used in this study. The elution volume of the peak for GFP-MCAK corresponded to a molecular weight range between 120 kDa (EB1-GFP dimer) and 260 kDa (XMAP215-GFP-his6), suggesting that GFP-MCAK exists as a dimer (~220 kDa) under experimental condition (please see Fig.s1 and page 5, lines 104-105). In addition, we also measured the fluorescence intensity of both MCAK^sN+M and MCAK. MCAK^sN+M is a monomeric mutant that contains the neck domain and motor domain (Wang et al., 2012). The average intensity of MCAK^sN+M is 196 A.U., about 65% of that of MCAK (300 A.U.). These two measurements suggest that the purified MCAK used in this study exists dimers (see Fig. s1).

c. Evidence that MCAK on microtubules represents single molecules should be provided (distribution of GFP brightness with controls - GFP imaged under identical conditions). Since assay buffers include detergent, which is not desirable, all controls should be done using the same assay conditions. The authors should rule out that their main results are detergent-sensitive.

(1) Regarding if MCAK on microtubules represent single molecules: please refer to our responses to the two points above.

(2) To rule out the effect of tween-20 (0.0001%, v/v), we performed additional control experiments. The results showed that it has no significant effect on microtubule-binding affinity of MCAK (see Figure below).

Author response image 2.

Tween-20 (0.0001%, v/v) has no significant effect on microtubule-binding affinity of MCAK. (A) The representative projection images of GFP-MCAK (5 nM) binding to taxol-stabled GDP microtubules in the presence of 1 mM AMPPNP with or without tween-20. The upper panel showed the results of the control experiments performed without MCAK. Scale bar: 5 mm. (B) Statistical quantification of the binding intensity of GFP-MCAK binding to GDP microtubules with or without tween-20 (53 microtubules from 3 assays and 70 microtubules from 3 assays, respectively). Data were presented as mean ± SEM. Statistical comparisons were performed using the two-tailed Mann-Whitney U test with Bonferroni correction, n.s., no significance.

d. How did the authors plot single-molecule intensity distributions? I am confused as to why the intensity distribution for single molecules in Fig 1D and 2A looks so perfectly smooth, non-pixelated, and broader than expected for GFP wavelength. Please provide unprocessed original distributions, pixel size, and more details about how the distributions were processed.

In the revised manuscript, we provided unprocessed original data in Fig. 1B and Fig. 2A. We thank the reviewer for pointing out this problem.

e. Many quantifications are based on a limited number of microtubules and the number of molecules is not provided, starting from Fig 1D and down. Please provide detailed statistics and explain what is plotted (mean with SEM?) on each graph.

We performed a thorough inspection of the manuscript and corrected the identified issues.

f. Plots with averaged data should be supplemented with error bars and N should be provided in the legend. E.g. Fig 1C - average position of MT and peak positions.

We agree with the reviewer. In the revised manuscript, we have made the changes accordingly (e.g. Fig. 2C).

g. Detailed information should be provided about protein constructs used in this work including all tags. The use of truncated proteins or charged/bulky tags can modify protein-microtubule interactions.

We agree with the reviewer. In the revised manuscript, we provide the information of all constructs (see Fig. s1 and the related descriptions in Methods, pages 15-16, lines 476-534).

h. Line 515: We estimated that the accuracy of microtubule end tracking was ~6 nm by measuring the standard error of the distribution of the estimated error in the microtubule end position. - evidence should be provided using the conditions of this study, not the reference to the prior work by others.

i. Line 520: We estimated that the accuracy of the measured position was ~2 nm by measuring the standard error of the fitting peak location". Please provide evidence.

Point h-i: we now provide detailed descriptions of how to estimate tracking and measurement accuracy and error in our work. Please see pages 18-19, lines 626-645.

j. Kymographs in Fig 5G are barely visible. Please provide single-channel greyscale images. What are the dim molecules diffusing on this microtubule?

We have incorporated the changes suggested by the reviewer. We think that some of the dim signals may result from stochastic background noise, while others likely represent transient bindings of MCAK. The exposure time in our experiments was approximately 0.05 seconds; if the binding duration were shorter than this, the signal would be lower (i.e. the “dim” signals). It is important to note that in this study, we selected binding events lasting at least 2 consecutive frames, meaning transient binding events were not included. This point has been clarified in the Methods section (see page17, lines 573-583).

k. Please provide a methods description for Fig 6. Did the buffer include 1 mM ATP? The presence of ATP would make these conditions more physiological. ATP concentration should be stated clearly in the main text or figure legend.

The buffer contains ATP. In the revised manuscript, we have provided the methods for the experiments of microtubule dynamics assay, as well as the analysis of microtubule lifetimes and catastrophe frequency (see page 17, lines 561-572 and page 20, lines 685-690).

l. Line 104: experiment was performed in BRB80 supplemented with 50 mM KCl and 1 mM ATP, providing a nearly physiological ion strength. Please provide a reference or add your calculations in Methods.

We have provided references on page 5, lines 101-104 of our manuscript.

m. What was the MCAK concentration in Figure 4? Did the microtubule shorten under any of these conditions?

In these experiments, we used a very low concentration of MCAK and taxol-stabilized microtubules, so there’s no microtubule shortening observed here. ATP: 10 nM GFP-MCAK; AMPPNP: 1 nM GFP-MCAK; ADP: 10 nM GFP-MCAK; APO state: 0.1 nM GFP-MCAK.

Other criticism:

Text improvements are recommended in the Discussion. For example, line 348: Fourth, the loss of the binding preference.. suggests that the binding preference .. is required for the optimal .. preference.

We thank the reviewer for pointing out this. In the revised manuscript, we conducted a thorough revision and review of the text.

Reviewer #2 (Public Review):

Summary:

In this manuscript, Chen et al. investigate the localization of microtubule kinesin-13 MCAK to the microtubule ends. MCAK is a prominent microtubule depolymerase whose molecular mechanisms of action have been extensively studied by a number of labs over the last ~twenty years. Here, the authors use single-molecule approaches to investigate the precise localization of MCAK on growing microtubules and conclude that MCAK preferentially binds to a GDP-Pi-tubulin portion of the microtubule end. The conclusions are speculative and not well substantiated by the data, making the impact of the study in its current form rather limited. Specifically, greater effort should be made to define the region of MCAK binding on microtubule ends, as well as its structural characteristics. Given that MCAK has been previously shown to effectively tip-track growing microtubule ends through an established interaction with EB proteins, the physiological relevance of the present study is unclear. Finally, the manuscript does not cite or properly discuss a number of relevant literature references, the results of which should be directly compared and contrasted to those presented here.

We thank the reviewer for the comments. As these suggestions are more thoroughly expressed in the following comments for authors, we will provide the responses in the corresponding sections, as shown below.

Reviewer #2 (Recommendations For The Authors):

Significant concerns:

(1) Establishing the precise localization of MCAK wrt microtubule end is highly non-trivial. More details should be provided, including substantial supplementary data. In particular, the authors claim ~6 nm accuracy in microtubule end positioning - this should be substantiated by data showing individual overlaid microtubule end intensity profiles as well as fits with standard deviations etc. Furthermore, to conclude that MCAK binds behind XMAP215, the authors should look at the localization of the two proteins simultaneously, on the same microtubule end. Notably, EB binding profiles are well known to exponentially decay along the microtubule lattice - this is not very apparent from the presented data. If MCAK's autonomous binding pattern matches that of EB, we should be seeing an exponentially-decaying localization for MCAK as well? However, averaged MCAK signals seem to only be fitted to Gaussian. Note that the EB binding region (i.e. position and size of the EB comet) can be substantially modulated by increasing the microtubule growth rate - this can be easily accomplished by increasing tubulin concentrations or the addition of XMAP215 (e.g. see Maurer et al. Cur Bio 2014). Thus to establish that MCAK on its own binds the same region as EB, experiments that directly modulate the size and the position of this region should be added.

(1) We thank the reviewer for this comment. Regarding the accuracy in microtubule end positioning, we now provide more details, and please see pages 18-19, lines 625-645 in the revised manuscript.

(2) Regarding the relative localization of XMAP215 and MCAK, we performed additional experiments to record their colocalizations simultaneously, on the same microtubule end. Our results showed that MCAK predominantly binds behind XMAP215, with 14.5% appearing within the XMAP215’s binding region. Please see Fig. 2.D-E and lines 184-197 in the revised manuscript.

(3) Regarding the exponential decay of the EB1 signal along microtubules, we observed that the position probability distribution measured in the present study follows a Gaussian distribution, and the expected exponential decay was not apparent. Since the exponential decay is thought to result from the time delay between tubulin polymerization and GTP hydrolysis, slower polymerization is expected to reduce this latency (Maurer et al., 2014). In our experiments, the growth rate was relatively low (~0.7 mm/min), much slower than the rate observed in cells, where the comet-shaped EB1 signal is most pronounced. The previous study has shown that the exponential decay of EB1 is more pronounced at growth rates exceeding 3 mm/min in vitro (Maurer et al., 2014). Therefore, we think that the relatively slow growth may account for the observed non-exponential decay distribution of the EB1 signals. The same reason may also explain the distribution of MCAK.

(4) We agree with the reviewer’s suggestion that altering microtubule growth rate is a valid and effective approach to regulate the EB cap length. However, the conclusion that MCAK binds to the EB region is supported by three lines of evidence: (1) the localization of MCAK at the ends of microtubules, (2) new experimental data showing that MCAK binds to the proximal end of the XMAP215 site, and (3) the tendency of MCAK to bind GTPγS microtubules, similar to EB1. Based on these findings, we did not pursue additional experiments to modify the length of the EB cap.

(2) Even if MCAK indeed binds behind XMAP215, there is no evidence that this region is defined by the GDP-Pi nucleotide state; it could still be curved protofilaments. GTPyS is an analogue of GTP - to what extent GTPyS microtubules exactly mimic the GDP-Pi-tubulin state remains controversial. Furthermore, nucleotide sensing for EB is thought to be achieved through its binding at the interface of four tubulin dimers. However MCAK's binding site is distinct, and it has been shown to recognize intradimer tubulin curvature. Thus it is not clear how MCAK would sense the nucleotide state. On the other hand, there is mounting evidence that the morphology of the growing microtubule end can be highly variable, and that curved protofilaments may be protruding off the growing ends for tens of nanometers or more, previously observed both by EM as well as by fluorescence (e.g. Mcintosh, Moores, Chretien, Odde, Gardner, Akhmanova, Hancock, Zanic labs). Thus, to establish that MCAK indeed localizes along the closed lattice, EM approaches should be used.

First, we conducted additional experiments that demonstrate MCAK indeed binds behind XMAP215, supporting the conclusion that MCAK interacts with the EB cap (please see Fig. 2 in the revised manuscript). Second, our argument that MCAK preferentially binds to GDP-Pi tubulin is based on two observations: (1) the binding regions of MCAK overlap with those of EB1, and (2) MCAK preferentially binds to GTPγS microtubules, which are considered a close analogue of GDP-Pi tubulin. Third, understanding the structural basis of how MCAK senses the nucleotide state of tubulin is beyond the scope of the present study. However, inspired by the reviewer’s suggestion, we looked into the structure of the MCAK-tubulin complex. The L2 loop of MCAK makes direct contact with the interdimer interface (Trofimova et al., 2018; Wang et al., 2017), which could provide a structural basis for recognizing the changes induced by GTP hydrolysis. While this remains a hypothesis, it is certainly a promising direction for future research. Forth, we agree with the reviewer that an EM approach would be ideal for establishing that MCAK localizes along the closed lattice. However, this is not the focus of the current study. Instead, we argue that MCAK binds to the EB cap, where at least some lateral interactions are likely to have formed.

(3) The physiological relevance of the study is rather questionable: MCAK has been previously established to be able to both diffuse along the microtubule lattice (e.g. Helenius et al.) as well as hitchhike on EBs (Gouveia et al.). Given the established localization of EBs to growing microtubule ends in cells, and apparently higher affinity of MCAK for EB vs. the microtubule end itself (although direct comparisons with the literature have not been reported here), the relevance of MCAK's autonomous binding to dynamic microtubule ends is dubious.

We thank the reviewer for raising the importance of physiological relevance. Please refer to our response to the comment No.1 of reviewer 1. Briefly, we think that the end-binding affinity of MCAK makes a significant contribution for its cellular functions. To elucidate this concept, we now use a simple model shown in Supplementary Appendix-2 (see pages 49-51, lines 1246-1316). In this model, we simplified MCAK and EB1 binding to microtubule ends by considering only these two proteins while neglecting other factors (e.g. XMAP215). Specifically, we considered two scenarios: one in which both proteins freely diffuse in the cytoplasm and another where MCAK is localized to specific cellular structures, such as the centrosome or centromere. Based on the modeling results, we argue that MCAK's functional impact at microtubule ends derives both from its intrinsic end-binding capacity and its ability to strengthen the EB1-mediated end association pathway.

(4) Finally, the study seriously lacks discussion of and comparison with the existing literature on this topic. There are major omissions in citing relevant literature, such as e.g. landmark study by Kinoshita et al. Science 2001. Several findings reported here directly contradict previous findings in the literature. Direct comparison with e.g. Gouveia et al findings, Helenius et al. findings, and others need to be included. For example, Gouveia et al reported that EB is necessary for MCAK plus-end-tracking in vitro (please see Figure 1 of their manuscript). The authors should discuss how they reconcile the differences in their findings when compared to this earlier study.

We thank the reviewer for this helpful suggestion. In the revised manuscript, we have updated the text description and included comparative discussions with other relevant studies in the Discussion section. Specifically, we added comparisons with the research on XMAP215 in page 14, lines 459-472 (Barr and Gergely, 2008; Kinoshita et al., 2001; Tournebize et al., 2000). Additionally, we have compared our findings with those of Gouveia et al. and Helenius et al. regarding MCAK's preference for binding microtubule ends in page 6, lines 145-157 and page 13, 408-441, respectively (Gouveia et al., 2010; Helenius et al., 2006).

Additional specific comments:

Figure 1

Gouveia et al. (Figure 1) reported that MCAK does not autonomously preferentially localize to growing tips. Specifically, Gouveia et al. found equal association rates of MCAK to both the lattice and the tip in the presence of EB3delT, an EB3 construct that does not directly interact with MCAK. How can these findings be reconciled with the results presented here?

We are uncertain why there was no observed difference in the on-rates to the lattice and the end in the study by Gouveia et al. Even when considering only the known affinity of MCAK for curved protofilaments at the distal tip of growing microtubules, we would still expect to observe an end-binding preference. After carefully comparing the experimental conditions, we nevertheless identified some differences. First, we used a 160 nm tip size to calculate the on-rate (k_on), whereas Gouveia et al. used a 450 nm tip. Using a longer tip size would naturally lead to a smaller(k_on) value. Note that we chose 160 nm for several reasons: (i) a previous cryo-electron tomography study has elucidated that the sheet structures of dynamic microtubule ends have an average length of around 180 nm (Guesdon et al., 2016); (ii) Analysis of fluorescence signals at dynamic microtubule ends has demonstrated that the taper length at the microtubule end is less than 180 nm (Maurer et al., 2014); (iii) in the present study, we estimated that the length of MCAK's end-binding region is approximately 160 nm. Second, in Gouveia et al., single-molecule binding events were recorded in the presence of 75 nM EB3ΔT, which could potentially create a crowded environment at the tip, reducing MCAK binding. Third, as mentioned in our response to Reviewer 1, we took great care to minimize the interference from purification tags (e.g., His-tag) by ensuring their complete removal during protein preparation. Previous studies reported that retaining the His-tag of MAPs led to a significant increase in binding for microtubules (Maurer et al., 2011; Zhu et al., 2009). We believe that some of the factors mentioned above, or their combined effects, may account for the differences in these two observations.

1C shows the decay of tubulin signal over several hundred nm - should show individual traces? How aligned? Doesn't this long decay suggest protruding protofilaments? (E.g. Odde/Gardner work).

(1) In the revised manuscript, we now show individual traces (e.g. in Fig. 1B and Fig. 2A). The average trace for tubulin signal with standard deviation was shown in Fig. 2C.

(2) The microtubule lattice was considered as a Gaussian wall and its end as a half-Gaussian in every frame. Use the peak position of the half-Gaussian of every frame to align and average microtubule end signals, during the dwell time. The average microtubule ends' half-Gaussion peak used as a reference to measure the intensity profile of individual single-molecule binding event in every frame (see page18, lines 607-624).

(3) We think that the decay of tubulin signal results from the convolution of the tapered end structure and the point spread function. In the revised manuscript, we have updated the Figures to provide unprocessed original data in Fig. 1B and Fig. 2A.

Please show absolute numbers of measurements in 1C (rather than normalized distribution only).

In the revised manuscript, we have included the raw data for both tubulin and MCAK signals as part of the methods description. In Fig. 1, using normalized values allows for the simultaneous representation of microtubule and protein signals on a unified graph.

How do the results in 1D-G compare with the previous literature? Particularly comparison of on-rates between this study and the Gouveia et al? Assuming 1 um = 1625 dimers, it appears that in the presence of EB3, the on-rate of MCAK to the tips reported in Gouveia et al. is an order of magnitude higher than reported here in the absence of EB3 (4.3 x 10E-4 vs. 2 x 10E-5). If so, and given the robust presence of EB proteins at growing microtubule ends in cells, this would invalidate the potential physiological relevance of the current study. Note that the dwell times measured in Gouveia et al. are also longer than those measured here.

Note that in Gouveia et al, the concentration of mCherry-EB3 was 75 nM, about 187.5 times higher than that of MCAK (0.4 nM). The relative concentrations of these two proteins are not always the case in cells. Regarding the physiological relevance of the end-binding affinity of MCAK itself, please refer to our response to the point No.1 of Reviewer 1.

Notably, Helenius et al reported a diffusion constant for MCAK of 0.38 um^2/s, which is more than an order of magnitude higher than reported here. The authors should comment on this!

In the revised manuscript, we have provided an explanation for the difference in diffusion coefficient. Please see page 6, line 142-157. In short, low salt condition facilitates rapid diffusion of MCAK.

Figure 2:

This figure is critical and really depends on the analysis of the tubulin signal. Note significant variability in tubulin signal between presented examples in 2A. Also, while 2C looks qualitatively similar, there appears to be significant variability over the several hundred nm from the tip along the lattice. This is the crucial region; statistical significance testing should be presented. More detailed info, including SDs etc. is necessary.

In the revised manuscript, we have provided raw data in Fig. 1B and Fig. 2A. Additionally, we have provided statistical analysis on the tubulin signals (Fig. 2C) and performed significance test. Please see page 5, lines 111-116 and page 7, lines 179-183 for detailed descriptions.

Insights into the morphology of microtubule ends based on TIRF imaging have been previously gained in the literature, with reports of extended tip structures/protruding protofilaments (see e.g. Coombes et al. Cur Bio 2013, based on the methods of Demchouk et al. 2011). Such analysis should be performed here as well, if we are to conclude that nucleotide state alone, as opposed to the end morphology, specifies MCAK's tip localization.

We appreciate the reviewer’s suggestion and agree that it provides a valid optical microscopy-based approach for estimating microtubule end morphology. However, this method did not establish a direct correlation between microtubule end morphology and tubulin nucleotide status. Therefore, we think that refining the measurement of microtubule end morphology will not necessarily provide more information to the understanding of tubulin nucleotide status at MCAK binding sites. Based on the available data in the present study, there are two main pieces of evidence supporting the idea that MCAK can sense tubulin nucleotide status: (1) the binding regions of MCAK and EB overlap significantly, and (2) MCAK shows a clear preference for binding to GTPγS microtubules, similar to EB1 (we provide a new control to support this, Fig. s4). Of course, we do not consider this to be a perfect set of evidence. As the reviewer has pointed out here and in other suggestions, future work should aim to further distinguish the nucleotide status of tubulin in the dynamic versus non-dynamic regions at the ends of microtubules, and to investigate the structural basis by which MCAK recognizes tubulin nucleotide status.

EB comet profile should be clearly reproduced. MCAK should follow the comet profile.

Please see our 3^rd response to the point 1 of this reviewer.

The conclusion that the MCAK binding region is larger than XMAP215 is not firm, based on the data presented. The authors state that 'the binding region of MCAK was longer than that of XMAP215'. What is the exact width of the region of the XMAP215 localization and how much longer is the MCAK end-binding region? Is this statistically significant?

We have revised this part in the revised manuscript (page 6, lines 167-172). The position probability distributions of MCAK and XMAP215 were significantly different (K-S test, p< 10^-5), and the binding region of MCAK (FWHM=185 nm) was significantly longer than that of XMAP215 (FWHM=123 nm).

MCAK localization with AMPPNP should also be performed here. Even low concentrations of MCAK have been shown to induce microtubule catastrophe/end depolymerization. This will dramatically affect microtubule end morphology, and thus apparent positioning of MCAK at the end.

In the end positioning experiment, we used a low concentration of MCAK (1 nM). Under this condition, microtubule dynamics remained unchanged, and the morphology of the microtubule ends was comparable across different conditions (with EB1, MCAK or XMAP215). Additionally, in the revised manuscript, we present a new experiment in which we recorded the localization of both MCAK and XMAP215 on the same microtubule. The results support the conclusion regarding their relative localization: most MCAK is found at the proximal end of the XMAP215 binding region, while approximately 15% of MCAK is located within the XMAP215 binding region. Please see Fig. 2D-E and page 7, lines 184-197 for the corresponding descriptions.

Figure 3:

For clearer presentation, projections showing two microtubule lattice types on the same image (in e.g. two different colors) should be shown first without MCAK, and then with MCAK.

We thank the reviewer for this suggestion. We have adjusted the figure accordingly. Please see Fig. 4 in the revised manuscript.

Please comment on absolute intensity values - scales seem to be incredibly variable.

The fluorescence value presented here is the result of multiple images being summed. Therefore, the difference in absolute values is influenced not only by the binding affinity of MCAK in different states to microtubules, but also by the number of images used. In this analysis, we are not comparing MCAK in different states, but rather evaluating the binding ability of MCAK in the same state on different types of microtubules.

Given that the authors conclude that MCAK binding mimics that of EB, EB intensity measurements and ratios on different lattice substrates should be performed as a positive control.

We performed additional experiments with EB1, in the revised manuscript, we provide the data as a positive control (please see Fig. s4).

Figure 4:

MCAK-nucleotide dependence of GMPCPP microtubule-end binding has been previously established (see e.g. Helenius et al, others?) - what is new here? Need to discuss the literature. This would be more appropriate as a supplemental figure?

In the present study, we reproduced the GMPCPP microtubule-end binding of MCAK in the AMPPNP state, as shown in several previous reports (Desai et al., 1999; Hertzer et al., 2006). Here, we also quantified the end to lattice binding preference, and our results showed that the nucleotide state-dependence shows the same trend as the binding preference of MCAK to the growing microtubule ends. Therefore, we prefer to keep this figure in the main text (Fig. 5).

Figure 5:

Please note that both MCAK mutants show an additional two orders of magnitude lower microtubule binding on-rates when compared to wt MCAK. This makes the analysis of preferential binding substrate for these mutants dubious.

We agreed with this point. We have rewritten this part. Please see page 10, lines 295-327, in the revised manuscript.

Figure 6:

Combined effects of XMAP215 and XKCM1 (MCAK) have been previously explored in the landmark study by Kinoshita et al. Science 2001, which should be cited and discussed. Also note that Moriwaki et al. JCB 2016 explored the combined effects of XMA215 and MCAK - which should be discussed here and compared to the current results.

We agree with the reviewer. We have revised the discussion on this part. Please see page 11, lines 329-342 and page 14, lines 459-472 in the revised manuscript.

Please report quantification for growth rate and lifetime.

In the revised manuscript, we provide all these data. Please see pages 11-12, lines 343-374.

To obtain any new quantitative information on the combined effects of the two proteins, at the very minimum, the authors should perform a titration in protein concentration.

We agree with the reviewer on this point. In our pilot experiments, we performed titration experiments to determine the appropriate concentrations of MCAK and XMAP215, respectively. We selected 50 nM for XMAP215, as it clearly enhances the growth rate and exhibits a mild promoting effect on catastrophe—two key effects of XMAP215 reported in previous studies (Brouhard et al., 2008; Farmer et al., 2021). Reducing the XMAP215 concentration eliminates the catastrophe-promoting effect, while increasing it would not much enhance the growth rate. For MCAK, we chose 20 nM, as it effectively promotes catastrophe; increasing the concentration beyond this point leads to no microtubule growth, at least in the MCAK-only condition. If there’s no microtubule growth, it would be difficult to quantify the parameters of microtubule dynamics, hindering a clear comparison of the combined versus individual effects. Therefore, we think that the concentrations used in this study are appropriate and representative. In the revised manuscript, we make this point clearer (see pages 11 and lines 329-342).

Finally, the writing could be improved for overall clarity.

We thank the reviewer for pointing out this. In the revised manuscript, we conducted a thorough revision and review of the text.

Reviewer #3 (Public Review):

The authors revisit an old question of how MCAK goes to microtubule ends, partially answered by many groups over the years. The authors seem to have omitted the literature on MCAK in the past 10-15 years. The novelty is limited due to what has previously been done on the question. Previous work showed MCAK targets to microtubule plus-ends in cells through association with EB proteins and Kif18b (work from Wordeman, Medema, Walczak, Welburn, Akhmanova) but none of their work is cited.

We thank the reviewer for the suggestion. Some of the referenced work has already been cited in our manuscript, such as studies on the interaction between MCAK and EB1. However, other relevant literature had not been properly cited. In the revised manuscript, we have added further discussion on this topic in the context of existing findings. Please refer to pages 3-4, lines 68-85, and pages 13, lines 425-441.

It is not obvious in the paper that these in vitro studies only reveal microtubule end targeting, rather than plus end targeting. MCAK diffuses on the lattice to both ends and its conformation and association with the lattice and ends has also been addressed by other groups-not cited here. I want to particularly highlight the work from Friel's lab where they identified a CDK phosphomimetic mutant close to helix4 which reduces the end preference of MCAK. This residue is very close to the one mutated in this study and is highly relevant because it is a site that is phosphorylated in vivo. This study and the mutant produced here suggest a charge-based recognition of the end of microtubules.

Here the authors analyze this MCAK recognition of the lattice and microtubule ends, with different nucleotide states of MCAK and in the presence of different nucleotide states for the microtubule lattice. The main conclusion is that MCAK affinity for microtubules varies in the presence of different nucleotides (ATP and analogs) which was partially known already. How different nucleotide states of the microtubule lattice influence MCAK binding is novel. This information will be interesting to researchers working on the mechanism of motors and microtubules. However, there are some issues with some experiments. In the paper, the authors say they measure MCAK residency of growing end microtubules, but in the kymographs, the microtubules don't appear dynamic - in addition, in Figure 1A, MCAK is at microtubule ends and does not cause depolymerization. I would have expected to see depolymerization of the microtubule after MCAK targeting. The MCAK mutants are not well characterized. Do they still have ATPase activity? Are they folded? Can the authors also highlight T537 and discuss this?

Finally, a few experiments are done with MCAK and XMAP215, after the authors say they have demonstrated the binding sites overlap. The data supporting this statement were not obvious and the conclusions that the effect of the two molecules are additive would argue against competing binding sites. Overall, while there are some interesting quantitative measurements of MCAK on microtubules - in particular in relation to the nucleotide state of the microtubule lattice - the insights into end-recognition are modest and do not address or discuss how it might happen in cells. Often the number of events is not recorded. Histograms with large SEM bars are presented, so it is hard to get a good idea of data distribution and robustness. Figures lack annotations. This compromises therefore their quantifications and conclusions. The discussion was hard to follow and needs streamlining, as well as putting their work in the context of what is known from other groups who produced work on this in the past few years.

We thank the reviewer for the comments. Regarding the physiological relevance of the end-binding of MCAK itself, please refer to our response to the point No.1 of reviewer 1. Moreover, as we feel that other suggestions are more thoroughly expressed in the following comments for authors, we will provide the responses in the corresponding sections, as shown below.

Reviewer #3 (Recommendations For The Authors):

Why, on dynamic microtubules, is MCAK at microtubule plus ends and does not cause a catastrophe?

At this concentration (10 nM MCAK with 16 mM tubulin in Fig. 1; 1 nM MCAK with 12 mM tubulin in Fig. 2), MCAK has little effect on microtubule dynamics in our experiments. Using TIRFM, we were able to observe individual MCAK binding events. Based on these observations, we think that in the current experimental condition, a single binding event of MCAK is insufficient to induce microtubule catastrophe; rather, it likely requires cumulative changes resulting from multiple binding events.

Do the MCAK mutants still have ATPase activity?

The ATPase activities of MCAK^K525A and MCAK^V298S are both reduced to about 1/3 of the wild-type (Fig. s6).

The intensities of GFP are not all the same on the microtubule lattice (eg 1A). See blue and white arrowheads. The authors could be looking at multiple molecules of GFP-MCAK instead of single dimers. How do they account for this possibility?

In the revised manuscript, we provide the gel filtration result of the purified MCAK, and the position of the peak corresponds to ~220 kDa, demonstrating that the purified MCAK in solution is dimeric (please see Fig.s1 and page 5, lines 101-103). We measured the fluorescence intensity of each binding event. A probability distribution of these intensities was then constructed and fitted with a Gaussian function. A binding event was considered to correspond to a single molecule if its intensity fell within μ±2σ of the distribution. The details of the single-molecule screening process are provided in the revised manuscript (see page 17, lines 574-583).

In addition, we also measured the fluorescence intensity of both MCAK^sN+M and MCAK. MCAK^sN+M is a monomeric mutant that contains the neck domain and motor domain (Wang et al., 2012). The average intensity of MCAK^sN+M is 196 A.U., about 65 % of that of MCAK (300 A.U.), suggesting that MCAK is a dimer (see Fig. s1). Moreover, we think that some of the dim signals may result from stochastic background noise, while others likely represent transient bindings of MCAK. The exposure time in our experiments was approximately 0.05 seconds; if the binding duration were shorter than this, the signal would be lower. It is important to note that in this study, we specifically selected binding events lasting at least 2 consecutive frames, meaning transient binding events were not included. This point has been clarified in the Methods section (see page 17, lines 568-569 and lines 574-583).

Could the authors provide a kymograph of an MT growing, in the presence of MCAK+AMPPNP? Can MCAK track the cap?

Under single-molecule conditions, we observed a single MCAK molecule briefly binding to the end of the microtubule. However, we did not record if MCAK at high concentrations could track microtubule ends under AMPPNP conditions.

In the experiments in Figure 6, the authors should also show the localization of MCAK and XMAP215 at microtubule plus ends in their kymographs to show the two molecules overlap.

Regarding the relative localization of XMAP215 and MCAK, we conducted additional experiments to record their colocalization simultaneously at the same microtubule end. Our results show that MCAK predominantly binds behind XMAP215, with 14.5% of MCAK binding within the XMAP215 binding region. Please see Fig. 2.D-E and page 7, lines 184-197 in the revised manuscript. However, we argue that the effects of XMAP215 and MCAK are additive, and their binding sites do not necessarily need to overlap for these effects to occur.

The authors do not report what statistical tests are done in their graphs, and one concern is over error propagation of their data. Instead of bar graphs, showing the data points would be helpful.

We have now shown all data points in the revised manuscript.

MCAK+AMPPNP accumulates at microtubule ends. Appropriate quotes from previous work should be provided.

We have made the revisions accordingly. Please see page 9, lines 273-276.

Controls are missing. An SEC profile for all purified proteins should be presented. Also, the authors need to explain if they report the dimeric or monomeric concentration of MCAK, XMAP215, etc...

We have provided the gel filtration result for all purified proteins in the revised manuscript (Fig.s1). Moreover, we now make it clear that the concentrations of MCAK and EB1 are monomeric concentration. Please see the legend for Fig. 1, line 893 in the revised manuscript.

Figure 1: the microtubules don't look dynamic at all. This is also why the authors can record MCAK at microtubule ends, because their structure is not changing.

The microtubules are dynamic, but they may appear non-dynamic due to the relatively slow growth rate and the high frame rate at which we are recording. We propose that individual binding events of MCAK induce structural changes at the nanoscopic or molecular scale, which are not detectable using TIRFM.

I recommend the authors measure the Kon and Koff for single GFP-MCAK mutant molecules and provide the information alongside their normalized and averaged binding intensities of GFP-MCAK in Fig 5. Showing data points instead of bar graphs would be better.

(1) We measured k_on and dwell time for mutants at growing microtubule end. However, we did not perform single-molecule tracking for MCAK’s binding on stabilized microtubules. This is mainly because the superimposed signal on the stable microtubule already indicates the changes in the mutant's binding affinity to different microtubule structures, and moreover, the binding of the mutants is highly transient, making accurate single-molecule tracking and calculations difficult.

(2) In the revised figure, we have included the data points in all plots.

When discussing how Kinesin-13 interacts with the lattice, the authors should quote the papers that report the organization of full-length Kinesin-13 on tubulin heterodimers: Trofimova et al, 2018; McHugh et al 2019; Benoit et al, 2018. It would reinforce their model and account for the full-length protein, rather than just the motor domain.

We thank the suggestion for the reviewer. In our manuscript, we have cited papers on full-length Kinesin-13 to discuss the interaction between MCAK and microtubule end-curved structure. Additionally, we have utilized the MCAK-tubulin crystal structure (PDB ID: 5MIO) in Fig. 6, as it depicts a human MCAK, which is consistent with the protein used in our study. This structure illustrates the interaction sites between MCAK and tubulin dimer, guiding our mutation studies on specific residues. Thus, we prefer to use the structure (PDB ID: 5MIO) in Fig.6.

Figure 5A. What type of model is this? A PDB code is mentioned. Is this from an X-ray structure? If so, mention it.

We have now included the structural information in the Figure legend (see page 37, lines 1045).

Figure 5B. It is not possible to distinguish the different microtubule lattices (GTPyS, GDP, and GMPCPP). The experiment needs to be better labelled.

We thank the reviewer for this comment. We have now rearranged the figure for better clarity (see Fig. 6).

"Figure 5D: what are the statistical tests? I don't understand " The statistical comparisons were made versus the corresponding value of 848 GFP-MCAK".

We have made this point clearer in the revised manuscript (see pages 38, line 1078-1080).

What is the "EB cap"? This needs explaining.

We provide this explanation for this, please see page 4, lines 87-89 in the revised manuscript.

Work from Friel and co-workers showed MCAK T537E did not have depolymerizing activity and a reduced affinity for microtubule ends. The work of the authors should be discussed with respect to this previously published work.

We thank the reviewer for this suggestion. In the revised manuscript, we have added discussions on this (see page 10, lines 303-307).

The concentration of protein used in the assays is not always described.

We have checked throughout the manuscript and made revisions accordingly.

"Having revealed the novel binding sites of MCAK in dynamic microtubule ends " should be on "we wondered how MCAK may work ..with EB1". This is not addressed so should be removed. Instead, they can quote the work from Akhmanova's lab. Realistically this section should be rephrased as there are other plus-end targeting molecules that compete with MCAK, not just XMAP215 and EB1.

We have rephrased this section as suggested by this reviewer to be more specific. Please see page 11, lines 329-342.

What is AMPCPP?

It should be “AMPPNP”

Typos in Figure 5.

Corrected

Author response:

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

Reviewer #1:

(1) Peptides were synthesized with fluorescein isothiocyanate (FITC) and Tat tag, and then PEGylated with methoxy PEG Succinimidyl Succinate.

I have two concerns about the peptide design. First, FTIC was intended "for monitoring" (line 129), but was never used in the manuscript. Second, PEGylation targets the two lysine sidechains on the Tat, which would alter its penetration property.

(1) We conducted an analysis of the cellular trafficking of FITC-tagged peptides following their permeabilization into cells.

Author response image 1.

However, we did not include it in the main text because it is a basic result.

(2) As can be seen in the figure above, after pegylation and permeabilization, the cells were stained with FITC. It appears that this does not affect the ability to penetrate into the cells.

(2) "Superdex 200 increase 10/300 GL column" (line 437) was used to isolate mono/di PEGylated PDZ and separate them from the residual PEG and PDZ peptide. "m-PEG-succinimidyl succinate with an average molecular weight of 5000 Da" (lines 133 and 134).

To my knowledge, the Superdex 200 increase 10/300 GL column is not suitable and is unlikely to produce traces shown in Figure 1B.

As Superdex 200 increase 10/300 GL featrues a fractionation range of 10,000 to 600,000 Da, we used it to fractionate PEGylated products including DiPEGylated PDZ (approx. 15 kDa) and MonoPEGylated PDZ (approx. 10 kDa) from residuals (PDZ and PEG), demonstrating successful isolation of PEGylated products (Figure 1C). Considering the molecular weights of PDZ and PEG are approximately 4.1 kDa and and 5.0 kDa, respectively, the late eluting peaks from SEC were likely to represent a mixed absorbance of PDZ and PEG at 215 nm.

However, as the reviewer pointed out, it could be unreasonable to annotate peaks representing PDZ and PEG, respectively, from mixed absorbance detected in a region (11-12 min) beyond the fractionation range.

In our revised manuscript, therefore, multiple peaks in the late eluting volume (11-12 min) were labeled as 'Residuals' all together. As a reference, the revised figure 1B includes a chromatogram of pure PDZ-WT under the same analytic condition.

Therefore, we changed Fig.1B to new results.

(3) "the in vivo survival effect of LPS and PDZ co-administration was examined in mice. The pretreatment with WT PDZ peptide significantly increased survival and rescued compared to LPS only; these effects were not observed with the mut PDZ peptide (Figure 2a)." (lines 159-160).

Fig 2a is the weight curve only. The data is missing in the manuscript.

We added the survived curve into Fig. 2A.

(4) Table 1, peptide treatment on ALT and AST appears minor.

In mice treated with LPS, levels of ALT and AGT in the blood are elevated, but these levels decrease upon treatment with WT PDZ. However, the use of mut PDZ does not result in significant changes. Figure 3A shows inflammatory cells within the central vein, yet no substantial hepatotoxicity is observed during the 5-day treatment with LPS. Normally, the ranges of ALT and AGT in C57BL6 mice are 16 ~ 200 U/L and 46 ~ 221 U/L, respectively, according to UCLA Diagnostic Labs. Therefore, the values in all experiments fall within these normal ranges. In summary, a 5-day treatment with LPS induces inflammation in the liver but is too short a duration to induce hepatotoxicity, resulting in lower values.

(5) MitoTraker Green FM shouldn't produce red images in Figure 6.

We changed new results (GREEN one) into Figs 6A and B.

(6) Figure 5. Comparison of mRNA expression in PDZ-treated BEAS-2B cells. Needs a clearer and more detailed description both in the main text and figure legend. The current version is very hard to read.

We changed Fig. 5A to new one to understand much easier and added more detailed results and figure legend.

Results Section in Figure 5:

we performed RNA sequencing analysis. The results of RNA-seq analysis showed the expression pattern of 24,424 genes according to each comparison combination, of which the results showed the similarity of 51 genes overlapping in 4 gene categories and the similarity between each comparison combination (Figure 5a). As a result, compared to the control group, it was confirmed that LPS alone, WT PDZ+LPS, and mut PDZ+LPS were all upregulated above the average value in each gene, and when LPS treatment alone was compared with WT PDZ+LPS, it was confirmed that they were averaged or downregulated. When comparing LPS treatment alone and mut PDZ+LPS, it was confirmed that about half of the genes were upregulated. Regarding the similarity between comparison combinations, the comparison combination with LPS…

Figure 5 Legend Section:

Figure 5. Comparison of mRNA expression in PDZ-treated BEAS-2B cells.

BEAS-2B cells were treated with wild-type PDZ or mutant PDZ peptide for 24 h and then incubated with LPS for 2 h, after which RNA sequencing analysis was performed. (a) The heat map shows the general regulation pattern of about 51 inflammation-related genes that are differentially expressed when WT PDZ and mut PDZ are treated with LPS, an inflammatory substance. All samples are RED = upregulated and BLUE = downregulated relative to the gene average. Each row represents a gene, and the columns represent the values of the control group treated only with LPS and the WT PDZ and mut PDZ groups with LPS. This was used by converting each log value into a fold change value. All genes were adjusted to have the same mean and standard deviation, the unit of change is the standard deviation from the mean, and the color value range of each row is the same. (b) Significant genes were selected using Gene category chat (Fold change value of 2.00 and normalized data (log2) value of 4.00). The above pie chart shows the distribution of four gene categories when comparing LPS versus control, WT PDZ+LPS/LPS, and mut PDZ+LPS/LPS. The bar graph below shows RED=upregulated, GREEN=downregulated for each gene category, and shows the number of upregulated and downregulated genes in each gene category. (c) The protein-protein interaction network constructed by the STRING database differentially displays commonly occurring genes by comparing WT PDZ+LPS/LPS, mut PDZ+LPS/LPS, and LPS. These nodes represent proteins associated with inflammation, and these connecting lines denote interactions between two proteins. Different line thicknesses indicate types of evidence used in predicting the associations.

Reviewer #2:

(1) In this paper, the authors demonstrated the anti-inflammatory effect of PDZ peptide by inhibition of NF-kB signaling. Are there any results on the PDZ peptide-binding proteins (directly or indirectly) that can regulate LPS-induced inflammatory signaling pathway? Elucidation of the PDZ peptide-its binding partner protein and regulatory mechanisms will strengthen the author's hypothesis about the anti-inflammatory effects of PDZ peptide.

As mentioned in the Discussion section, we believe it is crucial to identify proteins that directly interact with PDZ and regulate it. This direct interaction can modulate intracellular signaling pathways, so we plan to express GST-PDZ and induce binding with cellular lysates, then characterize it using the LC-Mass/Mass method. We intend to further research these findings and submit them for publication.

(2) The authors presented interesting insights into the therapeutic role of the PDZ motif peptide of ZO-1. PDZ domains are protein-protein interaction modules found in a variety of species. It has been thought that many cellular and biological functions, especially those involving signal transduction complexes, are affected by PDZ-mediated interactions. What is the rationale for selecting the core sequence that regulates inflammation among the PDZ motifs of ZO-1 shown in Figure 1A?

The rationale for selecting the core sequence that regulates inflammation among the PDZ motifs of ZO-1, as shown in Figure 1A, is grounded in the specific roles these motifs play in signal transduction pathways that are crucial for inflammatory processes. PDZ domains are recognized for their ability to function as scaffolding proteins that organize signal transduction complexes, crucial for modulating cellular and biological functions. The chosen core sequence is particularly important because it is conserved across ZO-1, ZO-2, and ZO-3, indicating a fundamental role in maintaining cellular integrity and signaling pathways. This conservation suggests that the sequence’s involvement in inflammatory regulation is not only significant in ZO-1 but also reflects a broader biological function across the ZO family.

(3) In Figure 3, the authors showed the representative images of IHC, please add the quantification analysis of Iba1 expression and PAS-positive cells using Image J or other software. To help understand the figure, an indication is needed to distinguish specifically stained cells (for example, a dotted line or an arrow).

We added the semi-quantitative results into Figs. 3d,e,f.

Result section: The specific physiological mechanism by which WT PDZ peptide decreases LPS-induced systemic inflammation in mice and the signal molecules involved remain unclear. These were confirmed by a semi-quantitative analysis of Iba-1 immunoreactivity and PAS staining in liver, kidney, and lung,respectively (Figures 4d, e, and f). To examine whether WT PDZ peptide can alter LPS-induced tissue damage in the kidney, cell toxicity assay was performed (Figure 3g). LPS induced cell damage in the kidney, however, WT PDZ peptide could significantly alleviate the toxicity, but mut PDZ peptide could not. Because cytotoxicity caused by LPS is frequently due to ROS production in the kidney (Su et al., 2023; Qiongyue et al., 2022), ROS production in the mitochondria was investigated in renal mitochondria cells harvested from kidney tissue (Figure 3h)......

Figure legend section: Indicated scale bars were 20 μm. (d,e,f) Semi-quantitative analysis of each are positive for Iba-1 in liver and kidney, and positive cells of PAS in lung, respectively. (g) After the kidneys were harvested, tissue lysates were used for MTT assay. (h) After.....

(4) In Figure 6G, H, the authors confirmed the change in expression of the M2 markers by PDZ peptide using the mouse monocyte cell line Raw264.7. It would be good to add an experiment on changes in M1 and M2 markers caused by PDZ peptides in human monocyte cells (for example, THP-1).

We thank you for your comments. To determine whether PDZ peptide regulates M1/M2 polarization in human monocytes, we examined changes in M1 and M2 gene expression in THP-1 cells. As a result, wild-type PDZ significantly suppressed the expression of M1 marker genes (hlL-1β, hIL-6, hIL-8, hTNF-ɑ), while increasing the expression of M2 marker genes (hlL-4, hIL-10, hMRC-1). However, mutant PDZ did not affect M1/M2 polarization. These results suggest that PDZ peptide can suppress inflammation by regulating M1/M2 polarization of human monocyte cells. These results are for the reviewer's reference only and will not be included in the main content.

Author response image 2.

Minor point:

The use of language is appropriate, with good writing skills. Nevertheless, a thorough proofread would eliminate small mistakes such as:

• line 254, " mut PDZ+LPS/LPS (45.75%) " → " mut PDZ+LPS/LPS (47.75%) "

• line 296, " Figure 6f " → " Figure 6h "

We changed these points into the manuscript.

Author Response

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

Public Reviews:

Reviewer #1 (Public Review):

Summary:

The authors develop a method to fluorescently tag peptides loaded onto dendritic cells using a two-step method with a tetracystein motif modified peptide and labelling step done on the surface of live DC using a dye with high affinity for the added motif. The results are convincing in demonstrating in vitro and in vivo T cell activation and efficient label transfer to specific T cells in vivo. The label transfer technique will be useful to identify T cells that have recognised a DC presenting a specific peptide antigen to allow the isolation of the T cell and cloning of its TCR subunits, for example. It may also be useful as a general assay for in vitro or in vivo T-DC communication that can allow the detection of genetic or chemical modulators.

Strengths:

The study includes both in vitro and in vivo analysis including flow cytometry and two-photon laser scanning microscopy. The results are convincing and the level of T cell labelling with the fluorescent pMHC is surprisingly robust and suggests that the approach is potentially revealing something about fundamental mechanisms beyond the state of the art.

Weaknesses:

The method is demonstrated only at high pMHC density and it is not clear if it can operate at at lower peptide doses where T cells normally operate. However, this doesn't limit the utility of the method for applications where the peptide of interest is known. It's not clear to me how it could be used to de-orphan known TCR and this should be explained if they want to claim this as an application. Previous methods based on biotin-streptavidin and phycoerythrin had single pMHC sensitivity, but there were limitations to the PE-based probe so the use of organic dyes could offer advantages.

We thank the reviewer for the valuable comments and suggestions. Indeed, we have shown and optimized this labeling technique for a commonly used peptide at rather high doses to provide a proof of principle for the possible use of tetracysteine tagged peptides for in vitro and in vivo studies. However, we completely agree that the studies that require different peptides and/or lower pMHC concentrations may require preliminary experiments if the use of biarsenical probes is attempted. We think it can help investigate the functional and biological properties of the peptides for TCRs deorphaned by techniques. Tetracysteine tagging of such peptides would provide a readily available antigen-specific reagent for the downstream assays and validation. Other possible uses for modified immunogenic peptides could be visualizing the dynamics of neoantigen vaccines or peptide delivery methods in vivo. For these additional uses, we recommend further optimization based on the needs of the prospective assay.

Reviewer #2 (Public Review):

Summary:

The authors here develop a novel Ovalbumin model peptide that can be labeled with a site-specific FlAsH dye to track agonist peptides both in vitro and in vivo. The utility of this tool could allow better tracking of activated polyclonal T cells particularly in novel systems. The authors have provided solid evidence that peptides are functional, capable of activating OTII T cells, and that these peptides can undergo trogocytosis by cognate T cells only.

Strengths:

-An array of in vitro and in vivo studies are used to assess peptide functionality.

-Nice use of cutting-edge intravital imaging.

-Internal controls such as non-cogate T cells to improve the robustness of the results (such as Fig 5A-D).

-One of the strengths is the direct labeling of the peptide and the potential utility in other systems.

Weaknesses:

1. What is the background signal from FlAsH? The baselines for Figure 1 flow plots are all quite different. Hard to follow. What does the background signal look like without FLASH (how much fluorescence shift is unlabeled cells to No antigen+FLASH?). How much of the FlAsH in cells is actually conjugated to the peptide? In Figure 2E, it doesn't look like it's very specific to pMHC complexes. Maybe you could double-stain with Ab for MHCII. Figure 4e suggests there is no background without MHCII but I'm not fully convinced. Potentially some MassSpec for FLASH-containing peptides.

We thank the reviewer for pointing out a possible area of confusion. In fact, we have done extensive characterization of the background and found that it has varied with the batch of FlAsH, TCEP, cytometer and also due to the oxidation prone nature of the reagents. Because Figure 1 subfigures have been derived from different experiments, a combination of the factors above have likely contributed to the inconsistent background. To display the background more objectively, we have now added the No antigen+Flash background to the revised Fig 1.

It is also worthwhile noting that nonspecific Flash incorporation can be toxic at increasing doses, and live cells that display high backgrounds may undergo early apoptotic changes in vitro. However, when these cells are adoptively transferred and tracked in vivo, the compromised cells with high background possibly undergo apoptosis and get cleared by macrophages in the lymph node. The lack of clearance in vitro further contributes to different backgrounds between in vitro and in vivo, which we think is also a possible cause for the inconsistent backgrounds throughout the manuscript. Altogether, comparison of absolute signal intensities from different experiments would be misleading and the relative differences within each experiment should be relied upon. We have added further discussion about this issue.

1. On the flip side, how much of the variant peptides are getting conjugated in cells? I'd like to see some quantification (HPLC or MassSpec). If it's ~10% of peptides that get labeled, this could explain the low shifts in fluorescence and the similar T cell activation to native peptides if FlasH has any deleterious effects on TCR recognition. But if it's a high rate of labeling, then it adds confidence to this system.

We agree that mass spectrometry or, more specifically tandem MS/MS, would be an excellent addition to support our claim about peptide labeling by FlAsH being reliable and non-disruptive. Therefore, we have recently undertaken a tandem MS/MS quantitation project with our collaborators. However, this would require significant time to determine the internal standard based calibration curves and to run both analytical and biological replicates. Hence, we have decided pursuing this as a follow up study and added further discussion on quantification of the FlAsH-peptide conjugates by tandem MS/MS.

1. Conceptually, what is the value of labeling peptides after loading with DCs? Why not preconjugate peptides with dye, before loading, so you have a cleaner, potentially higher fluorescence signal? If there is a potential utility, I do not see it being well exploited in this paper. There are some hints in the discussion of additional use cases, but it was not clear exactly how they would work. One mention was that the dye could be added in real-time in vivo to label complexes, but I believe this was not done here. Is that feasible to show?

We have already addressed preconjugation as a possible avenue for labeling peptides. In our hands, preconjugation resulted in low FlAsH intensity overall in both the control and tetracysteine labeled peptides (Author response image 1). While we don’t have a satisfactory answer as to why the signal was blunted due to preconjugation, it could be that the tetracysteine tagged peptides attract biarsenical compounds better intracellularly. It may be due to the redox potential of the intracellular environment that limits disulfide bond formation. (PMID: 18159092)

Author response image 1.

Preconjugation yields poor FlAsH signal. Splenic DCs were pulsed with peptide then treated with FlAsH or incubated with peptide-FlAsH preconjugates. Overlaid histograms show the FlAsH intensities on DCs following the two-step labeling (left) and preconjugation (right). Data are representative of two independent experiments, each performed with three biological replicates.

1. Figure 5D-F the imaging data isn't fully convincing. For example, in 5F and 2G, the speeds for T cells with no Ag should be much higher (10-15micron/min or 0.16-0.25micron/sec). The fact that yours are much lower speeds suggests technical or biological issues, that might need to be acknowledged or use other readouts like the flow cytometry.

We thank the reviewer for drawing attention to this technical point. We would like to point out that the imaging data in fig 5 d-f was obtained from agarose embedded live lymph node sections. Briefly, the lymph nodes were removed, suspended in 2% low melting temp agarose in DMEM and cut into 200 µm sections with a vibrating microtome. Prior to imaging, tissue sections were incubated in complete RPMI medium at 37 °C for 2 h to resume cell mobility. Thus, we think the cells resuming their typical speeds ex vivo may account for slightly reduced T cell speeds overall, for both control and antigen-specific T cells (PMID: 32427565, PMID: 25083865). We have added text to prevent the ambiguity about the technique for dynamic imaging. The speeds in Figure 2g come from live imaging of DC-T cell cocultures, in which the basal cell movement could be hampered by the cell density. Additionally, glass bottom dishes have been coated with Fibronectin to facilitate DC adhesion, which may be responsible for the lower average speeds of the T cells in vitro.

Reviewer #1 (Recommendations For The Authors):

Does the reaction of ReAsH with reactive sites on the surface of DC alter them functionally? Functions have been attributed to redox chemistry at the cell surface- could this alter this chemistry?

We thank the reviewer for the insight. It is possible that the nonspecific binding of biarsenical compounds to cysteine residues, which we refer to as background throughout the manuscript, contribute to some alterations. One possible way biarsenicals affect the redox events in DCs can be via reducing glutathione levels (PMID: 32802886). Glutathione depletion is known to impair DC maturation and antigen presentation (PMID: 20733204). To avoid toxicity, we have carried out a stringent titration to optimize ReAsH and FlAsH concentrations for labeling and conducted experiments using doses that did not cause overt toxicity or altered DC function.

Have the authors compared this to a straightforward approach where the peptide is just labelled with a similar dye and incubated with the cell to load pMHC using the MHC knockout to assess specificity? Why is this that involves exposing the DC to a high concentration of TCEP, better than just labelling the peptide? The Davis lab also arrived at a two-step method with biotinylated peptide and streptavidin-PE, but I still wonder if this was really necessary as the sensitivity will always come down to the ability to wash out the reagents that are not associated with the MHC.

We agree with the reviewer that small undisruptive fluorochrome labeled peptide alternatives would greatly improve the workflow and signal to noise ratio. In fact, we have been actively searching for such alternatives since we have started working on the tetracysteine containing peptides. So far, we have tried commercially available FITC and TAMRA conjugated OVA323-339 for loading the DCs, however failed to elicit any discernible signal. We also have an ongoing study where we have been producing and testing various in-house modified OVA323-339 that contain fluorogenic properties. Unfortunately, at this moment, the ones that provided us with a crisp, bright signal for loading revealed that they have also incorporated to DC membrane in a nonspecific fashion and have been taken up by non-cognate T cells from double antigen-loaded DCs. We are actively pursuing this area of investigation and developing better optimized peptides with low/non-significant membrane incorporation.

Lastly, we would like to point out that tetracysteine tags are visible by transmission electron microscopy without FlAsH treatment. Thus, this application could add a new dimension for addressing questions about the antigen/pMHCII loading compartments in future studies. We have now added more in-depth discussion about the setbacks and advantages of using tetracysteine labeled peptides in immune system studies.

The peptide dosing at 5 µM is high compared to the likely sensitivity of the T cells. It would be helpful to titrate the system down to the EC50 for the peptide, which may be nM, and determine if the specific fluorescence signal can still be detected in the optimal conditions. This will not likely be useful in vivo, but it will be helpful to see if the labelling procedure would impact T cell responses when antigen is limited, which will be more of a test. At 5 µM it's likely the system is at a plateau and even a 10-fold reduction in potency might not impact the T cell response, but it would shift the EC50.

We thank the reviewer for the comment and suggestion. We agree that it is possible to miss minimally disruptive effects at 5 µM and titrating the native peptide vs. modified peptide down to the nM doses would provide us a clearer view. This can certainly be addressed in future studies and also with other peptides with different affinity profiles. A reason why we have chosen a relatively high dose for this study was that lowering the peptide dose had costed us the specific FlAsH signal, thus we have proceeded with the lowest possible peptide concentration.

In Fig 3b the level of background in the dsRed channel is very high after DC transfer. What cells is this associated with and does this appear be to debris? Also, I wonder where the ReAsH signal is in the experiments in general. I believe this is a red dye and it would likely be quite bright given the reduction of the FlAsH signal. Will this signal overlap with signals like dsRed and PHK-26 if the DC is also treated with this to reduce the FlAsH background?

We have already shown that ReAsH signal with DsRed can be used for cell-tracking purposes as they don’t get transferred to other cells during antigen specific interactions (Author response image 2). In fact, combining their exceptionally bright fluorescence provided us a robust signal to track the adoptively transferred DCs in the recipient mice. On the other hand, the lipophilic membrane dye PKH-26 gets transferred by trogocytosis while the remaining signal contributes to the red fluorescence for tracking DCs. Therefore, the signal that we show to be transferred from DCs to T cells only come from the lipophilic dye. To address this, we have added a sentence to elaborate on this in the results section. Regarding the reviewer’s comment on DsRed background in Figure 3b., we agree that the cells outside the gate in recipient mice seems slightly higher that of the control mice. It may suggest that the macrophages clearing up debris from apoptotic/dying DCs might contribute to the background elicited from the recipient lymph node. Nevertheless, it does not contribute to any DsRed/ReAsH signal in the antigen-specific T cells.

Author response image 2.

ReAsH and DsRed are not picked up by T cells during immune synapse. DsRed+ DCs were labeled with ReAsH, pulsed with 5 μM OVACACA, labeled with FlAsH and adoptively transferred into CD45.1 congenic mice mice (1-2 × 106 cells) via footpad. Naïve e450-labeled OTII and e670-labeled polyclonal CD4+ T cells were mixed 1:1 (0.25-0.5 × 106/ T cell type) and injected i.v. Popliteal lymph nodes were removed at 42 h post-transfer and analyzed by flow cytometry. Overlaid histograms show the ReAsh/DsRed, MHCII and FlAsH intensities of the T cells. Data are representative of two independent experiments with n=2 mice per group.

In Fig 5b there is a missing condition. If they look at Ea-specific T cells for DC with without the Ova peptide do they see no transfer of PKH-26 to the OTII T cells? Also, the FMI of the FlAsH signal transferred to the T cells seems very high compared to other experiments. Can the author estimate the number of peptides transferred (this should be possible) and would each T cell need to be collecting antigens from multiple DC? Could the debris from dead DC also contribute to this if picked up by other DC or even directly by the T cells? Maybe this could be tested by transferring DC that are killed (perhaps by sonication) prior to inoculation?

To address the reviewer’s question on the PKH-26 acquisition by T cells, Ea-T cells pick up PKH-26 from Ea+OVA double pulsed DCs, but not from the unpulsed or single OVA pulsed DCs. OTII T cells acquire PKH-26 from OVA-pulsed DCs, whereas Ea T cells don’t (as expected) and serve as an internal negative control for that condition. Regarding the reviewer’s comment on the high FlAsH signal intensity of T cells in Figure 5b, a plausible explanation can be that the T cells accumulate pMHCII through serial engagements with APCs. In fact, a comparison of the T cell FlAsH intensities 18 h and 36-48 h post-transfer demonstrate an increase (Author response image 3) and thus hints at a cumulative signal. As DCs are known to be short-lived after adoptive transfer, the debris of dying DCs along with its peptide content may indeed be passed onto macrophages, neighboring DCs and eventually back to T cells again (or for the first time, depending on the T:DC ratio that may not allow all T cells to contact with the transferred DCs within the limited time frame). We agree that the number and the quality of such contacts can be gauged using fluorescent peptides. However, we think peptides chemically conjugated to fluorochromes with optimized signal to noise profiles and with less oxidation prone nature would be more suitable for quantification purposes.

Author response image 3.

FlAsH signal acquisition by antigen specific T cells becomes more prominent at 36-48 h post-transfer. DsRed+ splenic DCs were double-pulsed with 5 μM OVACACA and 5 μM OVA-biotin and adoptively transferred into CD45.1 recipients (2 × 106 cells) via footpad. Naïve e450-labeled OTII (1 × 106 cells) and e670-labeled polyclonal T cells (1 × 106 cells) were injected i.v. Popliteal lymph nodes were analyzed by flow cytometry at 18 h or 48 h post-transfer. Overlaid histograms show the T cell levels of OVACACA (FlAsH). Data are representative of three independent experiments with n=3 mice per time point

Reviewer #2 (Recommendations For The Authors):

As mentioned in weaknesses 1 & 2, more validation of how much of the FlAsH fluorescence is on agonist peptides and how much is non-specific would improve the interpretation of the data. Another option would be to preconjugate peptides but that might be a significant effort to repeat the work.

We agree that mass spectrometry would be the gold standard technique to measure the percentage of tetracysteine tagged peptide is conjugated to FlAsH in DCs. However, due to the scope of such endevour this can only be addressed as a separate follow up study. As for the preconjugation, we have tried and unfortunately failed to get it to work (Reviewer Figure 1). Therefore, we have shifted our focus to generating in-house peptide probes that are chemically conjugated to stable and bright fluorophore derivates. With that, we aim to circumvent the problems that the two-step FlAsH labeling poses.

Along those lines, do you have any way to quantify how many peptides you are detecting based on fluorescence? Being able to quantify the actual number of peptides would push the significance up.

We think two step procedure and background would pose challenges to such quantification in this study. although it would provide tremendous insight on the antigen-specific T cell- APC interactions in vivo, we think it should be performed using peptides chemically conjugated to fluorochromes with optimized signal to noise profiles.

In Figure 3D or 4 does the SA signal correlate with Flash signal on OT2 cells? Can you correlate Flash uptake with T cell activation, downstream of TCR, to validate peptide transfers?

To answer the reviewer’s question about FlAsH and SA correlation, we have revised the Figure 3d to show the correlation between OTII uptake of FlAsH, Streptavidin and MHCII. We also thank the reviewer for the suggestion on correlating FlAsH uptake with T cell activation and/or downstream of TCR activation. We have used proliferation and CD44 expressions as proxies of activation (Fig 2, 6). Nevertheless, we agree that the early events that correspond to the initiation of T-DC synapse and FlAsH uptake would be valuable to demonstrate the temporal relationship between peptide transfer and activation. Therefore, we have addressed this in the revised discussion.

Author response image 4.

FlAsH signal acquisition by antigen specific T cells is correlates with the OVA-biotin (SA) and MHCII uptake. DsRed+ splenic DCs were double-pulsed with 5 μM OVACACA and 5 μM OVA-biotin and adoptively transferred into CD45.1 recipients (2 × 106 cells) via footpad. Naïve e450-labeled OTII (1 × 106 cells) and e670-labeled polyclonal T cells (1 × 106 cells) were injected i.v. Popliteal lymph nodes were analyzed by flow cytometry. Overlaid histograms show the T cell levels of OVACACA (FlAsH) at 48 h post-transfer. Data are representative of three independent experiments with n=3 mice.

Minor:

Figure 3F, 5D, and videos: Can you color-code polyclonal T cells a different color than magenta (possibly white or yellow), as they have the same look as the overlay regions of OT2-DC interactions (Blue+red = magenta).

We apologize for the inconvenience about the color selection. We have had difficulty in assigning colors that are bright and distinct. Unfortunately, yellow and white have also been easily mixed up with the FlAsH signal inside red and blue cells respectively. We have now added yellow and white arrows to better point out the polyclonal vs. antigen specific cells in 3f and 5d.

Author response:

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

Reviewer #1 (Public Review):

Summary:

The authors used structural and biophysical methods to provide insight into Parkin regulation. The breadth of data supporting their findings was impressive and generally well-orchestrated. Still, the impact of their results builds on recent structural studies and the stated impact is based on these prior works.

Strengths:

(1) After reading through the paper, the major findings are:

- RING2 and pUbl compete for binding to RING0.

- Parkin can dimerize.

- ACT plays an important role in enzyme kinetics.

(2) The use of molecular scissors in their construct represents a creative approach to examining inter-domain interactions.

(3) From my assessment, the experiments are well-conceived and executed.

We thank the reviewer for their positive remark and extremely helpful suggestions.

Weaknesses:

The manuscript, as written, is NOT for a general audience. Admittedly, I am not an expert on Parkin structure and function, but I had to do a lot of homework to try to understand the underlying rationale and impact. This reflects, I think, that the work generally represents an incremental advance on recent structural findings.

To this point, it is hard to understand the impact of this work without more information highlighting the novelty. There are several structures of Parkin in various auto-inhibited states, and it was hard to delineate how this is different.

For the sake of the general audience, we have included all the details of Parkin structures and conformations seen (Extended Fig. 1). The structures in the present study are to validate the biophysical/biochemical experiments, highlighting key findings. For example, we solved the phospho-Parkin (complex with pUb) structure after treatment with 3C protease (Fig. 2C), which washes off the pUbl-linker, as shown in Fig 2B. The structure of the pUbl-linker depleted phospho-Parkin-pUb complex showed that RING2 returned to the closed state (Fig. 2C), which is confirmation of the SEC assay in Fig. 2B. Similarly, the structure of the pUbl-linker depleted phospho-Parkin R163D/K211N-pUb complex (Fig. 3C), was done to validate the SEC data showing displacement of pUbl-linker is independent of pUbl interaction with the basic patch on RING0 (Fig. 3B). In addition, the latter structure also revealed a new donor ubiquitin binding pocket in the linker (connecting REP and RING2) region of Parkin (Fig. 9). Similarly, trans-complex structure of phospho-Parkin (Fig. 4D) was done to validate the biophysical data (Fig. 4A-C, Fig. 5A-D) showing trans-complex between phospho-Parkin and native Parkin. The latter also confirmed that the trans-complex was mediated by interactions between pUbl and the basic patch on RING0 (Fig. 4D). Furthermore, we noticed that the ACT region was disordered in the trans-complex between phospho-Parkin (1-140 + 141-382 + pUb) (Fig. 8A) which had ACT from the trans molecule, indicating ACT might be present in the cis molecule. The latter was validated from the structure of trans-complex between phospho-Parkin with cis ACT (1-76 + 77-382 + pUb) (Fig. 8C), showing the ordered ACT region. The structural finding was further validated by biochemical assays (Fig. 8 D-F, Extended Data Fig. 9C-E).

The structure of TEV-treated R0RBR (TEV) (Extended Data Fig. 4C) was done to ensure that the inclusion of TEV and treatment with TEV protease did not perturb Parkin folding, an important control for our biophysical experiments.

As noted, I appreciated the use of protease sites in the fusion protein construct. It is unclear how the loop region might affect the protein structure and function. The authors worked to demonstrate that this did not introduce artifacts, but the biological context is missing.

We thank the reviewer for appreciating the use of protease sites in the fusion protein construct.  Protease sites were used to overcome the competing mode of binding that makes interactions very transient and beyond the detection limit of methods such as ITC or SEC. While these interactions are quite transient in nature, they could still be useful for the activation of various Parkin isoforms that lack either the Ubl domain or RING2 domain (Extended Data Fig. 6, Fig. 10). Also, our Parkin localization assays also suggest an important role of these interactions in the recruitment of Parkin molecules to the damaged mitochondria (Fig. 6).

While it is likely that the binding is competitive between the Ubl and RING2 domains, the data is not quantitative. Is it known whether the folding of the distinct domains is independent? Or are there interactions that alter folding? It seems plausible that conformational rearrangements may invoke an orientation of domains that would be incompatible. The biological context for the importance of this interaction was not clear to me.

This is a great point. In the revised manuscript, we have included quantitative data between phospho-Parkin and untethered ∆Ubl-Parkin (TEV) (Fig. 5B) showing similar interactions using phospho-Parkin K211N and untethered ∆Ubl-Parkin (TEV) (Fig. 4B). Folding of Ubl domain or various combinations of RING domains lacking Ubl seems okay. Also, folding of the RING2 domain on its own appears to be fine. However, human Parkin lacking the RING2 domain seems to have some folding issues, majorly due to exposure of hydrophobic pocket on RING0, also suggested by previous efforts (Gladkova et al.ref. 24, Sauve et al. ref. 29).  The latter could be overcome by co-expression of RING2 lacking Parkin construct with PINK1 (Sauve et al. ref. 29) as phospho-Ubl binds on the same hydrophobic pocket on RING0 where RING2 binds. A drastic reduction in the melting temperature of phospho-Parkin (Gladkova et al.ref. 24), very likely due to exposure of hydrophobic surface between RING0 and RING2, correlates with the folding issues of RING0 exposed human Parkin constructs.

From the biological context, the competing nature between phospho-Ubl and RING2 domains could block the non-specific interaction of phosphorylated-ubiquitin-like proteins (phospho-Ub or phospho-NEDD8) with RING0 (Lenka et al. ref. 33), during Parkin activation. 

(5) What is the rationale for mutating Lys211 to Asn? Were other mutations tried? Glu? Ala? Just missing the rationale. I think this may have been identified previously in the field, but not clear what this mutation represents biologically.

Lys211Asn is a Parkinson’s disease mutation; therefore, we decided to use the same mutation for biophysical studies.  

I was confused about how the phospho-proteins were generated. After looking through the methods, there appear to be phosphorylation experiments, but it is unclear what the efficiency was for each protein (i.e. what % gets modified). In the text, the authors refer to phospho-Parkin (T270R, C431A), but not clear how these mutations might influence this process. I gather that these are catalytically inactive, but it is unclear to me how this is catalyzing the ubiquitination in the assay.

This is an excellent question. Because different phosphorylation statuses would affect the analysis, we ensured complete phosphorylation status using Phos-Tag SDS-PAGE, as shown below.

Author response image 1.

Our biophysical experiments in Fig. 5C show that trans complex formation is mediated by interactions between the basic patch (comprising K161, R163, K211) on RING0 and phospho-Ubl domain in trans. These interactions result in the displacement of RING2 (Fig. 5C). Parkin activation is mediated by displacement of RING2 and exposure of catalytic C431 on RING2. While phospho-Parkin T270R/C431A is catalytically dead, the phospho-Ubl domain of phospho-Parkin T270R/C431would bind to the basic patch on RING0 of WT-Parkin resulting in activation of WT-Parkin as shown in Fig. 5E. A schematic figure is shown below to explain the same.

Author response image 2.

(7) The authors note that "ACT can be complemented in trans; however, it is more efficient in cis", but it is unclear whether both would be important or if the favored interaction is dominant in a biological context.

First, this is an excellent question about the biological context of ACT and needs further exploration. While due to the flexible nature of ACT, it can be complemented both in cis and trans, we can only speculate cis interactions between ACT and RING0 could be more relevant from the biological context as during protein synthesis and folding, ACT would be translated before RING2, and thus ACT would occupy the small hydrophobic patch on RING0 in cis. Unpublished data shows the replacement of the ACT region by Biogen compounds to activate Parkin (https://doi.org/10.21203/rs.3.rs-4119143/v1). The latter finding further suggests the flexibility in this region.        

(8) The authors repeatedly note that this study could aid in the development of small-molecule regulators against Parkin to treat PD, but this is a long way off. And it is not clear from their manuscript how this would be achieved. As stated, this is conjecture.

As suggested by this reviewer, we have removed this point in the revised manuscript.

Reviewer #2 (Public Review):

This manuscript uses biochemistry and X-ray crystallography to further probe the molecular mechanism of Parkin regulation and activation. Using a construct that incorporates cleavage sites between different Parkin domains to increase the local concentration of specific domains (i.e., molecular scissors), the authors suggest that competitive binding between the p-Ubl and RING2 domains for the RING0 domain regulates Parkin activity. Further, they demonstrate that this competition can occur in trans, with a p-Ubl domain of one Parkin molecule binding the RING0 domain of a second monomer, thus activating the catalytic RING1 domain. In addition, they suggest that the ACT domain can similarly bind and activate Parkin in trans, albeit at a lower efficiency than that observed for p-Ubl. The authors also suggest from crystal structure analysis and some biochemical experiments that the linker region between RING2 and repressor elements interacts with the donor ubiquitin to enhance Parkin activity.<br /> Ultimately this manuscript challenges previous work suggesting that the p-Ubl domain does not bind to the Parkin core in the mechanism of Parkin activation. The use of the 'molecular scissors' approach to probe these effects is an interesting approach to probe this type of competitive binding. However, there are issues with the experimental approach manuscript that detract from the overall quality and potential impact of the work.

We thank the reviewer for their positive remark and constructive suggestions.

The competitive binding between p-Ubl and RING2 domains for the Parkin core could have been better defined using biophysical and biochemical approaches that explicitly define the relative affinities that dictate these interactions. A better understanding of these affinities could provide more insight into the relative bindings of these domains, especially as it relates to the in trans interactions.

This is an excellent point regarding the relative affinities of pUbl and RING2 for the Parkin core (lacking Ubl and RING2). While we could purify p-Ubl, we failed to purify human Parkin (lacking RING2 and phospho-Ubl). The latter folding issues were likely due to the exposure of a highly hydrophobic surface on RING0 (as shown below) in the absence of pUbl and RING2 in the R0RB construct. Also, RING2 with an exposed hydrophobic surface would be prone to folding issues, which is not suitable for affinity measurements. A drastic reduction in the melting temperature of phospho-Parkin (Gladkova et al.ref. 24) also highlights the importance of a hydrophobic surface between RING0 and RING2 on Parkin folding/stability. A separate study would be required to try these Parkin constructs from different species and ensure proper folding before using them for affinity measurements.

Author response image 3.

I also have concerns about the results of using molecular scissors to 'increase local concentrations' and allow for binding to be observed. These experiments are done primarily using proteolytic cleavage of different domains followed by size exclusion chromatography. ITC experiments suggest that the binding constants for these interactions are in the µM range, although these experiments are problematic as the authors indicate in the text that protein precipitation was observed during these experiments. This type of binding could easily be measured in other assays. My issue relates to the ability of a protein complex (comprising the core and cleaved domains) with a Kd of 1 µM to be maintained in an SEC experiment. The off-rates for these complexes must be exceeding slow, which doesn't really correspond to the low µM binding constants discussed in the text. How do the authors explain this? What is driving the Koff to levels sufficiently slow to prevent dissociation by SEC? Considering that the authors are challenging previous work describing the lack of binding between the p-Ubl domain and the core, these issues should be better resolved in this current manuscript. Further, it's important to have a more detailed understanding of relative affinities when considering the functional implications of this competition in the context of full-length Parkin. Similar comments could be made about the ACT experiments described in the text.

This is a great point. In the revised manuscript, we repeated ITC measurements in a different buffer system, which gave nice ITC data. In the revised manuscript, we have also performed ITC measurements using native phospho-Parkin. Phospho-Parkin and untethered ∆Ubl-Parkin (TEV) (Fig. 5B) show similar affinities as seen between phospho-Parkin K211N and untethered ∆Ubl-Parkin (TEV) (Fig. 4B). However, Kd values were consistent in the range of 1.0 ± 0.4 µM which could not address the reviewer’s point regarding slow off-rate. The crystal structure of the trans-complex of phospho-Parkin shows several hydrophobic and ionic interactions between p-Ubl and Parkin core, suggesting a strong interaction and, thus, justifying the co-elution on SEC. Additionally, ITC measurements between E2-Ub and P-Parkin-pUb show similar affinity (Kd = 0.9 ± 0.2 µM) (Kumar et al., 2015, EMBO J.), and yet they co-elute on SEC (Kumar et al., 2015, EMBO J.).

Ultimately, this work does suggest additional insights into the mechanism of Parkin activation that could contribute to the field. There is a lot of information included in this manuscript, giving it breadth, albeit at the cost of depth for the study of specific interactions. Further, I felt that the authors oversold some of their data in the text, and I'd recommend being a bit more careful when claiming an experiment 'confirms' a specific model. In many cases, there are other models that could explain similar results. For example, in Figure 1C, the authors state that their crystal structure 'confirms' that "RING2 is transiently displaced from the RING0 domain and returns to its original position after washing off the p-Ubl linker". However, it isn't clear to me that RING2 ever dissociated when prepared this way. While there are issues with the work that I feel should be further addressed with additional experiments, there are interesting mechanistic details suggested by this work that could improve our understanding of Parkin activation. However, the full impact of this work won't be fully appreciated until there is a more thorough understanding of the regulation and competitive binding between p-Ubl and RIGN2 to RORB both in cis and in trans.

We thank the reviewer for their positive comment. In the revised manuscript, we have included the reviewer’s suggestion. The conformational changes in phospho-Parkin were established from the SEC assay (Fig. 2A and Fig. 2B), which show displacement/association of phospho-Ubl or RING2 after treatment of phospho-Parkin with 3C and TEV, respectively. For crystallization, we first phosphorylated Parkin, where RING2 is displaced due to phospho-Ubl (as shown in SEC), followed by treatment with 3C protease, which led to pUbl wash-off. The Parkin core separated from phospho-Ubl on SEC was used for crystallization and structure determination in Fig. 2C, where RING2 returned to the RING0 pocket, which confirms SEC data (Fig. 2B).

Reviewer #3 (Public Review):

Summary:

In their manuscript "Additional feedforward mechanism of Parkin activation via binding of phospho-UBL and RING0 in trans", Lenka et al present data that could suggest an "in trans" model of Parkin ubiquitination activity. Parkin is an intensely studied E3 ligase implicated in mitophagy, whereby missense mutations to the PARK2 gene are known to cause autosomal recessive juvenile parkinsonism. From a mechanistic point of view, Parkin is extremely complex. Its activity is tightly controlled by several modes of auto-inhibition that must be released by queues of mitochondrial damage. While the general overview of Parkin activation has been mapped out in recent years, several details have remained murky. In particular, whether Parkin dimerizes as part of its feed-forward signaling mechanism, and whether said dimerization can facilitate ligase activation, has remained unclear. Here, Lenka et al. use various truncation mutants of Parkin in an attempt to understand the likelihood of dimerization (in support of an "in trans" model for catalysis).

Strengths:

The results are bolstered by several distinct approaches including analytical SEC with cleavable Parkin constructs, ITC interaction studies, ubiquitination assays, protein crystallography, and cellular localization studies.

We thank the reviewer for their positive remark.

Weaknesses:

As presented, however, the storyline is very confusing to follow and several lines of experimentation felt like distractions from the primary message. Furthermore, many experiments could only indirectly support the author's conclusions, and therefore the final picture of what new features can be firmly added to the model of Parkin activation and function is unclear.

We thank the reviewer for their constructive criticism, which has helped us to improve the quality of this manuscript.

Major concerns:

(1) This manuscript solves numerous crystal structures of various Parkin components to help support their idea of in trans transfer. The way these structures are presented more resemble models and it is unclear from the figures that these are new complexes solved in this work, and what new insights can be gleaned from them.

The structures in the present study are to validate the biophysical/biochemical experiments highlighting key findings. For example, we solved the phospho-Parkin (complex with pUb) structure after treatment with 3C protease (Fig. 2C), which washes off the pUbl-linker, as shown in Fig. 2B. The structure of pUbl-linker depleted phospho-Parkin-pUb complex showed that RING2 returned to the closed state (Fig. 2C), which is confirmation of the SEC assay in Fig. 2B. Similarly, the structure of the pUbl-linker depleted phospho-Parkin R163D/K211N-pUb complex (Fig. 3C), was done to validate the SEC data showing displacement of pUbl-linker is independent of pUbl interaction with the basic patch on RING0 (Fig. 3B). In addition, the latter structure also revealed a new donor ubiquitin binding pocket in the linker (connecting REP and RING2) region of Parkin (Fig. 9). Similarly, trans-complex structure of phospho-Parkin (Fig. 4D) was done to validate the biophysical data (Fig. 4A-C, Fig. 5A-D) showing trans-complex between phospho-Parkin and native Parkin. The latter also confirmed that the trans-complex was mediated by interactions between pUbl and the basic patch on RING0 (Fig. 4D). Furthermore, we noticed that the ACT region was disordered in the trans-complex between phospho-Parkin (1-140 + 141-382 + pUb) (Fig. 8A) which had ACT from the trans molecule, indicating ACT might be present in the cis molecule. The latter was validated from the structure of trans-complex between phospho-Parkin with cis ACT (1-76 + 77-382 + pUb) (Fig. 8C), showing the ordered ACT region. The structural finding was further validated by biochemical assays (Fig. 8 D-F, Extended Data Fig. 9C-E).

The structure of TEV-treated R0RBR (TEV) (Extended Data Fig. 4C) was done to ensure that the inclusion of TEV and treatment with TEV protease did not perturb Parkin folding, an important control for our biophysical experiments.

(2) There are no experiments that definitively show the in trans activation of Parkin. The binding experiments and size exclusion chromatography are a good start, but the way these experiments are performed, they'd be better suited as support for a stronger experiment showing Parkin dimerization. In addition, the rationale for an in trans activation model is not convincingly explained until the concept of Parkin isoforms is introduced in the Discussion. The authors should consider expanding this concept into other parts of the manuscript.

We thank the reviewer for appreciating the Parkin dimerization. Our biophysical data in Fig. 5C shows that Parkin dimerization is mediated by interactions between phospho-Ubl and RING0 in trans, leading to the displacement of RING2. However, Parkin K211N (on RING0) mutation perturbs interaction with phospho-Parkin and leads to loss of Parkin dimerization and loss of RING2 displacement (Fig. 5C). The interaction between pUbl and K211 pocket on RING0 leads to the displacement of RING2 resulting in Parkin activation as catalytic residue C431 on RING2 is exposed for catalysis. The biophysical experiment is further confirmed by a biochemical experiment where the addition of catalytically in-active phospho-Parkin T270R/C431A activates autoinhibited WT-Parkin in trans using the mechanism as discussed (a schematic representation also shown in Author response image 2).

We thank this reviewer regarding Parkin isoforms. In the revised manuscript, we have included Parkin isoforms in the results section, too.

(2a) For the in trans activation experiment using wt Parkin and pParkin (T270R/C431A) (Figure 3D), there needs to be a large excess of pParkin to stimulate the catalytic activity of wt Parkin. This experiment has low cellular relevance as these point mutations are unlikely to occur together to create this nonfunctional pParkin protein. In the case of pParkin activating wt Parkin (regardless of artificial point mutations inserted to study specifically the in trans activation), if there needs to be much more pParkin around to fully activate wt Parkin, isn't it just more likely that the pParkin would activate in cis?

To test phospho-Parkin as an activator of Parkin in trans, we wanted to use the catalytically inactive version of phospho-Parkin to avoid the background activity of p-Parkin. While it is true that a large excess of pParkin (T270R/C431A) is required to activate WT-Parkin in the in vitro set-up, it is not very surprising as in WT-Parkin, the unphosphorylated Ubl domain would block the E2 binding site on RING1. Also, due to interactions between pParkin (T270R/C431A) molecules, the net concentration of pParkin (T270R/C431A) as an activator would be much lower. However, the Ubl blocking E2 binding site on RING1 won’t be an issue between phospho-Parkin molecules or between Parkin isoforms (lacking Ubl domain or RING2).

(2ai) Another underlying issue with this experiment is that the authors do not consider the possibility that the increased activity observed is a result of increased "substrate" for auto-ubiquitination, as opposed to any role in catalytic activation. Have the authors considered looking at Miro as a substrate in order to control for this?

This is quite an interesting point. However, this will be only possible if Parkin is ubiquitinated in trans, as auto-ubiquitination is possible with active Parkin and not with catalytically dead (phospho-Parkin T270R, C431A) or autoinhibited (WT-Parkin). Also, in the previous version of the manuscript, where we used only phospho-Ubl as an activator of Parkin in trans, we tested Miro1 ubiquitination and auto-ubiquitination, and the results were the same (Author response image 4).

Author response image 4.

(2b) The authors mention a "higher net concentration" of the "fused domains" with RING0, and use this to justify artificially cleaving the Ubl or RING2 domains from the Parkin core. This fact should be moot. In cells, it is expected there will only be a 1:1 ratio of the Parkin core with the Ubl or RING2 domains. To date, there is no evidence suggesting multiple pUbls or multiple RING2s can bind the RING0 binding site. In fact, the authors here even show that either the RING2 or pUbl needs to be displaced to permit the binding of the other domain. That being said, there would be no "higher net concentration" because there would always be the same molar equivalents of Ubl, RING2, and the Parkin core.

We apologize for the confusion. “Higher net concentration” is with respect to fused domains versus the domain provided in trans. Due to the competing nature of the interactions between pUbl/RING2 and RING0, the interactions are too transient and beyond the detection limit of the biophysical techniques. While the domains are fused (for example, RING0-RING2 in the same polypeptide) in a polypeptide, their effective concentrations are much higher than those (for example, pUbl) provided in trans; thus, biophysical methods fail to detect the interaction. Treatment with protease solves the above issue due to the higher net concentration of the fused domain, and trans interactions can be measured using biophysical techniques. However, the nature of these interactions and conformational changes is very transient, which is also suggested by the data. Therefore, Parkin molecules will never remain associated; rather, Parkin will transiently interact and activate Parkin molecules in trans.

(2c) A larger issue remaining in terms of Parkin activation is the lack of clarity surrounding the role of the linker (77-140); particularly whether its primary role is to tether the Ubl to the cis Parkin molecule versus a role in permitting distal interactions to a trans molecule. The way the authors have conducted the experiments presented in Figure 2 limits the possible interactions that the activated pUbl could have by (a) ablating the binding site in the cis molecule with the K211N mutation; (b) further blocking the binding site in the cis molecule by keeping the RING2 domain intact. These restrictions to the cis parkin molecule effectively force the pUbl to bind in trans. A competition experiment to demonstrate the likelihood of cis or trans activation in direct comparison with each other would provide stronger evidence for trans activation.

This is an excellent point. In the revised manuscript, we have performed experiments using native phospho-Parkin (Revised Figure 5), and the results are consistent with those in Figure 2 ( Revised Figure 4), where we used the K211N mutation.

(3) A major limitation of this study is that the authors interpret structural flexibility from experiments that do not report directly on flexibility. The analytical SEC experiments report on binding affinity and more specifically off-rates. By removing the interdomain linkages, the accompanying on-rate would be drastically impacted, and thus the observations are disconnected from a native scenario. Likewise, observations from protein crystallography can be consistent with flexibility, but certainly should not be directly interpreted in this manner. Rigorous determination of linker and/or domain flexibility would require alternative methods that measure this directly.

We also agree with the reviewer that these methods do not directly capture structural flexibility. Also, rigorous determination of linker flexibility would require alternative methods that measure this directly. However, due to the complex nature of interactions and technical limitations, breaking the interdomain linkages was the best possible way to capture interactions in trans. Interestingly, all previous methods that report cis interactions between pUbl and RING0 also used a similar approach (Gladkova et al.ref. 24, Sauve et al. ref. 29).  

(4) The analysis of the ACT element comes across as incomplete. The authors make a point of a competing interaction with Lys48 of the Ubl domain, but the significance of this is unclear. It is possible that this observation could be an overinterpretation of the crystal structures. Additionally, the rationale for why the ACT element should or shouldn't contribute to in trans activation of different Parkin constructs is not clear. Lastly, the conclusion that this work explains the evolutionary nature of this element in chordates is highly overstated.

We agree with the reviewer that the significance of Lys48 is unclear. We have presented this just as one of the observations from the crystal structure. As the reviewer suggested, we have removed the sentence about the evolutionary nature of this element from the revised manuscript.

(5) The analysis of the REP linker element also seems incomplete. The authors identify contacts to a neighboring pUb molecule in their crystal structure, but the connection between this interface (which could be a crystallization artifact) and their biochemical activity data is not straightforward. The analysis of flexibility within this region using crystallographic and AlphaFold modeling observations is very indirect. The authors also draw parallels with linker regions in other RBR ligases that are involved in recognizing the E2-loaded Ub. Firstly, it is not clear from the text or figures whether the "conserved" hydrophobic within the linker region is involved in these alternative Ub interfaces. And secondly, the authors appear to jump to the conclusion that the Parkin linker region also binds an E2-loaded Ub, even though their original observation from the crystal structure seems inconsistent with this. The entire analysis feels very preliminary and also comes across as tangential to the primary storyline of in trans Parkin activation.

We agree with the reviewer that crystal structure data and biochemical data are not directly linked. In the revised manuscript, we have also highlighted the conserved hydrophobic in the linker region at the ubiquitin interface (Fig. 9C and Extended Data Fig. 11A), which was somehow missed in the original manuscript. We want to add that a very similar analysis and supporting experiments identified donor ubiquitin-binding sites on the IBR and helix connecting RING1-IBR (Kumar et al., Nature Str. and Mol. Biol., 2017), which several other groups later confirmed. In the mentioned study, the Ubl domain of Parkin from the symmetry mate Parkin molecule was identified as a mimic of “donor ubiquitin” on IBR and helix connecting RING1-IBR.

In the present study, a neighboring pUb molecule in the crystal structure is identified as a donor ubiquitin mimic (Fig. 9C) by supporting biophysical/biochemical experiments. First, we show that mutation of I411A in the REP linker of Parkin perturbs Parkin interaction with E2~Ub (donor) (Fig. 9F). Another supporting experiment was performed using a Ubiquitin-VS probe assay, which is independent of E2. Assays using Ubiquitin-VS show that I411A mutation in the REP-RING2 linker perturbs Parkin charging with Ubiquitin-VS (Extended Data Fig. 11 B). Furthermore, the biophysical data showing loss of Parkin interaction with donor ubiquitin is further supported by ubiquitination assays. Mutations in the REP-RING2 linker perturb the Parkin activity (Fig. 9E), confirming biophysical data. This is further confirmed by mutations (L71A or L73A) on ubiquitin (Extended Data Fig. 11C), resulting in loss of Parkin activity. The above experiments nicely establish the role of the REP-RING2 linker in interaction with donor ubiquitin, which is consistent with other RBRs (Extended Data Fig. 11A).

While we agree with the reviewer that this appears tangential to the primary storyline in trans-Parkin activation, we decided to include this data because it could be of interest to the field.

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

(1) For clarity, a schematic of the domain architecture of Parkin would be helpful at the outset in the main figures. This will help with the introduction to better understand the protein organization. This is lost in the Extended Figure in my opinion.

We thank the reviewer for suggesting this, which we have included in Figure 1 of the revised manuscript.

(2) Related to the competition between the Ubl and RING2 domains, can competition be shown through another method? SPR, ITC, etc? ITC was used in other experiments, but only in the context of mutations (Lys211Asn)? Can this be done with WT sequence?

This is an excellent suggestion. In the revised Figure 5, we have performed ITC experiment using WT Parkin, and the results are consistent with what we observed using Lys211Asn Parkin.

(3) The authors also note that "the AlphaFold model shows a helical structure in the linker region of Parkin (Extended Data Figure 10C), further confirming the flexible nature of this region"... but the secondary structure would not be inherently flexible. This is confusing.

The flexibility is in terms of the conformation of this linker region observed under the open or closed state of Parkin. In the revised manuscript, we have explained this point more clearly.

(4) The manuscript needs extensive revision to improve its readability. Minor grammatical mistakes were prevalent throughout.

We thank the reviewer for pointing out this and we have corrected these in the revised manuscript.

(5) The confocal images are nice, but inset panels may help highlight the regions of interest (ROIs).

This is corrected in the revised manuscript.

(6) Trans is misspelled ("tans") towards the end of the second paragraph on page 16.

This is corrected in the revised manuscript.

(7) The schematics are helpful, but some of the lettering in Figure 2 is very small.

This is corrected in the revised manuscript.

Reviewer #3 (Recommendations For The Authors):

(1) A significant portion of the results section refers to the supplement, making the overall readability very difficult.

We accept this issue as a lot of relevant data could not be added to the main figures and thus ended up in the supplement.  In the revised manuscript, we have moved some of the supplementary figures to the main figures.

(2) Interpretation of the experiments utilizing many different Parkin constructs and cleavage scenarios (particularly the SEC and crystallography experiments) is extremely difficult. The work would benefit from a layout of the Parkin model system, highlighting cleavage sites, key domain terminology, and mutations used in the study, presented together and early on in the manuscript. Using this to identify a simpler system of referencing Parkin constructs would also be a large improvement.

This is a great suggestion. We have included these points in the revised manuscript, which has improved the readability.

(3) Lines 81-83; the authors say they "demonstrate the conformational changes in Parkin during the activation process", but fail to show any actual conformational changes. Further, much of what is demonstrated in this work (in terms of crystal structures) corroborates existing literature. The authors should use caution not to overstate their original conclusions in light of the large body of work in this area.

We thank the reviewer for pointing out this. We have corrected the above statement in the revised manuscript to indicate that we meant it in the context of trans conformational changes.

(4) Line 446 and 434; there is a discrepancy about which amino acid is present at residue 409. Is this a K408 typo? The authors also present mutational work on K416, but this residue is not shown in the structure panel.

We thank the reviewer for pointing out this. In the revised manuscript, we have corrected these typos.

Author response:

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

Reviewer 1:

(1) Peptides were synthesized with fluorescein isothiocyanate (FITC) and Tat tag, and then PEGylated with methoxy PEG Succinimidyl Succinate.

I have two concerns about the peptide design. First, FTIC was intended "for monitoring" (line 129), but was never used in the manuscript. Second, PEGylation targets the two lysine sidechains on the Tat, which would alter its penetration property.

We conducted an analysis of the cellular trafficking of FITC-tagged peptides following their permeabilization into cells.

Author response image 1.

However, we did not include it in the main text because it is a basic result.

(2) As can be seen in the figure above, after pegylation and permeabilization, the cells were stained with FITC. It appears that this does not affect the ability to penetrate into the cells.

(2) "Superdex 200 increase 10/300 GL column" (line 437) was used to isolate mono/di PEGylated PDZ and separate them from the residual PEG and PDZ peptide. "m-PEG-succinimidyl succinate with an average molecular weight of 5000 Da" (lines 133 and 134).

To my knowledge, the Superdex 200 increase 10/300 GL column is not suitable and is unlikely to produce traces shown in Figure 1B.

As Superdex 200 increase 10/300 GL featrues a fractionation range of 10,000 to 600,000 Da, we used it to fractionate PEGylated products including DiPEGylated PDZ (approx. 15 kDa) and MonoPEGylated PDZ (approx. 10 kDa) from residuals (PDZ and PEG), demonstrating successful isolation of PEGylated products (Figure 1C). Considering the molecular weights of PDZ and PEG are approximately 4.1 kDa and and 5.0 kDa, respectively, the late eluting peaks from SEC were likely to represent a mixed absorbance of PDZ and PEG at 215 nm.

However, as the reviewer pointed out, it could be unreasonable to annotate peaks representing PDZ and PEG, respectively, from mixed absorbance detected in a region (11-12 min) beyond the fractionation range.

In our revised manuscript, therefore, multiple peaks in the late eluting volume (11-12 min) were labeled as 'Residuals' all together. As a reference, the revised figure 1B includes a chromatogram of pure PDZ-WT under the same analytic condition.

Therefore, we changed Fig.1B to new results as followed:

(3) "the in vivo survival effect of LPS and PDZ co-administration was examined in mice. The pretreatment with WT PDZ peptide significantly increased survival and rescued compared to LPS only; these effects were not observed with the mut PDZ peptide (Figure 2a)." (lines 159-160).

Fig 2a is the weight curve only. The data is missing in the manuscript.

We added the survived curve into Fig. 2A as followed:

(4) Table 1, peptide treatment on ALT and AST appears minor.

In mice treated with LPS, levels of ALT and AGT in the blood are elevated, but these levels decrease upon treatment with WT PDZ. However, the use of mut PDZ does not result in significant changes. Figure 3A shows inflammatory cells within the central vein, yet no substantial hepatotoxicity is observed during the 5-day treatment with LPS. Normally, the ranges of ALT and AGT in C57BL6 mice are 16 ~ 200 U/L and 46 ~ 221 U/L, respectively, according to UCLA Diagnostic Labs. Therefore, the values in all experiments fall within these normal ranges. In summary, a 5-day treatment with LPS induces inflammation in the liver but is too short a duration to induce hepatotoxicity, resulting in lower values.

(5) MitoTraker Green FM shouldn't produce red images in Figure 6.

We changed new results (GREEN one) into Figs 6A and B as followed:

(6) Figure 5. Comparison of mRNA expression in PDZ-treated BEAS-2B cells. Needs a clearer and more detailed description both in the main text and figure legend. The current version is very hard to read.

We changed Fig. 5A to new one to understand much easier and added more detailed results and figure legend as followed:

Results Section in Figure 5:

“…we performed RNA sequencing analysis. The results of RNA-seq analysis showed the expression pattern of 24,424 genes according to each comparison combination, of which the results showed the similarity of 51 genes overlapping in 4 gene categories and the similarity between each comparison combination (Figure 5a). As a result, compared to the control group, it was confirmed that LPS alone, WT PDZ+LPS, and mut PDZ+LPS were all upregulated above the average value in each gene, and when LPS treatment alone was compared with WT PDZ+LPS, it was confirmed that they were averaged or downregulated. When comparing LPS treatment alone and mut PDZ+LPS, it was confirmed that about half of the genes were upregulated. Regarding the similarity between comparison combinations, the comparison combination with LPS…”

Figure 5 Legend Section:

“Figure 5. Comparison of mRNA expression in PDZ-treated BEAS-2B cells.

BEAS-2B cells were treated with wild-type PDZ or mutant PDZ peptide for 24 h and then incubated with LPS for 2 h, after which RNA sequencing analysis was performed. (a) The heat map shows the general regulation pattern of about 51 inflammation-related genes that are differentially expressed when WT PDZ and mut PDZ are treated with LPS, an inflammatory substance. All samples are RED = upregulated and BLUE = downregulated relative to the gene average. Each row represents a gene, and the columns represent the values of the control group treated only with LPS and the WT PDZ and mut PDZ groups with LPS. This was used by converting each log value into a fold change value. All genes were adjusted to have the same mean and standard deviation, the unit of change is the standard deviation from the mean, and the color value range of each row is the same. (b) Significant genes were selected using Gene category chat (Fold change value of 2.00 and normalized data (log2) value of 4.00). The above pie chart shows the distribution of four gene categories when comparing LPS versus control, WT PDZ+LPS/LPS, and mut PDZ+LPS/LPS. The bar graph below shows RED=upregulated, GREEN=downregulated for each gene category, and shows the number of upregulated and downregulated genes in each gene category. (c) The protein-protein interaction network constructed by the STRING database differentially displays commonly occurring genes by comparing WT PDZ+LPS/LPS, mut PDZ+LPS/LPS, and LPS. These nodes represent proteins associated with inflammation, and these connecting lines denote interactions between two proteins. Different line thicknesses indicate types of evidence used in predicting the associations.”

Reviewer 2:

(1) In this paper, the authors demonstrated the anti-inflammatory effect of PDZ peptide by inhibition of NF-kB signaling. Are there any results on the PDZ peptide-binding proteins (directly or indirectly) that can regulate LPS-induced inflammatory signaling pathway? Elucidation of the PDZ peptide-its binding partner protein and regulatory mechanisms will strengthen the author's hypothesis about the anti-inflammatory effects of PDZ peptide

As mentioned in the Discussion section, we believe it is crucial to identify proteins that directly interact with PDZ and regulate it. This direct interaction can modulate intracellular signaling pathways, so we plan to express GST-PDZ and induce binding with cellular lysates, then characterize it using the LC-Mass/Mass method. We intend to further research these findings and submit them for publication.

(2) The authors presented interesting insights into the therapeutic role of the PDZ motif peptide of ZO-1. PDZ domains are protein-protein interaction modules found in a variety of species. It has been thought that many cellular and biological functions, especially those involving signal transduction complexes, are affected by PDZ-mediated interactions. What is the rationale for selecting the core sequence that regulates inflammation among the PDZ motifs of ZO-1 shown in Figure 1A?

The rationale for selecting the core sequence that regulates inflammation among the PDZ motifs of ZO-1, as shown in Figure 1A, is grounded in the specific roles these motifs play in signal transduction pathways that are crucial for inflammatory processes. PDZ domains are recognized for their ability to function as scaffolding proteins that organize signal transduction complexes, crucial for modulating cellular and biological functions. The chosen core sequence is particularly important because it is conserved across ZO-1, ZO-2, and ZO-3, indicating a fundamental role in maintaining cellular integrity and signaling pathways. This conservation suggests that the sequence’s involvement in inflammatory regulation is not only significant in ZO-1 but also reflects a broader biological function across the ZO family.

(3) In Figure 3, the authors showed the representative images of IHC, please add the quantification analysis of Iba1 expression and PAS-positive cells using Image J or other software. To help understand the figure, an indication is needed to distinguish specifically stained cells (for example, a dotted line or an arrow).

We added the semi-quantitative results into Figs. 4d,e,f as followed:

Result section: “The specific physiological mechanism by which WT PDZ peptide decreases LPS-induced systemic inflammation in mice and the signal molecules involved remain unclear. These were confirmed by a semi-quantitative analysis of Iba-1 immunoreactivity and PAS staining in liver, kidney, and lung,respectively (Figures 4d, e, and f). To examine whether WT PDZ peptide can alter LPS-induced tissue damage in the kidney, cell toxicity assay was performed (Figure 3g). LPS induced cell damage in the kidney, however, WT PDZ peptide could significantly alleviate the toxicity, but mut PDZ peptide could not. Because cytotoxicity caused by LPS is frequently due to ROS production in the kidney (Su et al., 2023; Qiongyue et al., 2022), ROS production in the mitochondria was investigated in renal mitochondria cells harvested from kidney tissue (Figure 3h)....”

Figure legend section: “Indicated scale bars were 20 μm. (d,e,f) Semi-quantitative analysis of each are positive for Iba-1 in liver and kidney, and positive cells of PAS in lung, respectively. (g) After the kidneys were harvested, tissue lysates were used for MTT assay. (h) After...”

(4) In Figure 6G, H, the authors confirmed the change in expression of the M2 markers by PDZ peptide using the mouse monocyte cell line Raw264.7. It would be good to add an experiment on changes in M1 and M2 markers caused by PDZ peptides in human monocyte cells (for example, THP-1).

We thank you for your comments. To determine whether PDZ peptide regulates M1/M2 polarization in human monocytes, we examined changes in M1 and M2 gene expression in THP-1 cells. As a result, wild-type PDZ significantly suppressed the expression of M1 marker genes (hlL-1β, hIL-6, hIL-8, hTNF-ɑ), while increasing the expression of M2 marker genes (hlL-4, hIL-10, hMRC-1). However, mutant PDZ did not affect M1/M2 polarization. These results suggest that PDZ peptide can suppress inflammation by regulating M1/M2 polarization of human monocyte cells. These results are for the reviewer's reference only and will not be included in the main content.

Author response image 2.

Author response image 3.

Minor point:

The use of language is appropriate, with good writing skills. Nevertheless, a thorough proofread would eliminate small mistakes such as:

- line 254, " mut PDZ+LPS/LPS (45.75%) " → " mut PDZ+LPS/LPS (47.75%) "

- line 296, " Figure 6f " → " Figure 6h "

We changed these points into the manuscript.

The dataset i'm created is itself a "cognitive artefact". It represents the Iamata in a particular way, that means that I must show how I moved from inscription text to tags, so that readers can evaluate my choices, meaning that I should add a little "from text to tag" example for one inscription to demonstrate my tagging logic.

Reading this section made me think about how normalized copying has become online. Platforms like TikTok, Twitter, and even meme pages thrive on remixing and reposting, but most people never think about who originally made something. Personally, I’ve shared memes and gifs without even realizing they came from artists who might want credit. I think Confucius’s idea of “li”—doing what’s proper and respectful—applies here: giving credit isn’t just a rule, it’s a way of showing respect for the creator and the community.

At the same time, I agree with Michael Wesch’s point that remixing can be a form of cultural expression and creativity, not just theft. It’s tricky, though, when remixing turns into cultural appropriation—like when certain slang or imagery from Black culture is taken and used for jokes by people outside the culture. I think the line between cultural exchange and appropriation comes down to intent and respect. If you’re sharing something to appreciate and understand, that’s exchange. But if it’s just for clout or laughs, it’s exploitation.

This section really made me rethink how I use memes and social media. I’m going to start paying more attention to where things come from—and maybe even give credit when I can, even if it’s just a tag or mention.

It is super helpful that you included so many tag designs already in the construct. It really adds to the usability right off the bat.