Feminist epistemology values storytelling, oral traditions, and embodied knowledge. This line points to the need to replace degrading colonial accounts with narratives rooted in lived experience, dignity, and self-representation.

Western knowledge systems privilege written, rational accounts, often reducing lives to statistics or silence. The line reflects how feminist scholars must fashion stories to bridge past and present, because official records fail to capture marginalized realities.

Knowledge comes from lived experience, trauma, and survival. This line echoes that by insisting that the present is always entangled with embodied histories of violence and survival.

This ties into rage because it exposes injustice, this is why rage is so productive because it is a sign of growth, transforming into power. The injustice are from stories that only exist as records of domination, and feminist rage seeks to reclaim those erased voices.

Western epistemologies privilege written records and rational accounts, while oral traditions, embodied knowledge, and silence are dismissed. This line shows how enslaved women’s voices were excluded because they did not or could not fit into the dominant modes of recording knowledge.

I think this line refers to the archival grain from lecture on Sept. 22, institutions such as government records, colonial documents, such patriarchal institutions exclude or distort marginalized people. Reading against the grain questions what may be missing from official records. How did no one remember her name?

Atmosphere is very good at absorbing long wave infrared radiation due to carbon dioxide & water vapur Because the atmosphere is mostly transpoarent to short wave radiation, but it does absorb long wave, it heats from the group up. Water vapour, espeically at the earths surface, absorbs 60% of radiation emited by earth and is the gas mainly respoinble for warmer temps inteh lower troposphere Becase the atmosphere gets most of its energy from the surface and not directly the sun it is critical for driving weather processes.

The visible and radio regions are important parts of the pectrum where the atmosphere is pretty transparent Radio & visible regions are known as windows because these wavelenghts can pass through the air without being absrobs Because the atmosphere is mostly tarnsparent to visible radiation most of it reaches the surface and doesn't warm the atmopshere.

Water vapour, oxygen and ozone are important to absorbing solar radiation Nitrogen is a poor absorber of SR Water vapour is the dominate gas which absorbs & scatters radiation across many regions of the specturm. Oxygen & Ozon are v. effective absorbing sWL, high energy radiation, less that 0.3mm reaches the earths surfaces

The image is curved but is actually lots of little lines

Peaks in gas absorption spectrum correspond to specific vibrational and rotational transitions of its molecules, with each occuring at a characteristic energy and specific WL of EMR

Gases in the atmosphere are selective, 20% of radiation that arrives at the top is abosrobed, occuring at differen regions & wavelenghts due to the properties of the gases present

White absorbs all colours and darker clours don't

Chlorophyll absorbs more blue and red so it appears green

the sky is blue because gas scxatter blue and violet more effectively than the longerwave orange lights

Albedo of earth is 30%, largely determined by clouds

Scattering - larger number of weak rays travelling in different directions as the light is bent through angles by partciles/rough surfaces mainly molecules, dust and aerosols, smoke and mist are scattering Also known as diffuse or indirect radiation Because its light travelling in different directions, areas not in a direct line from the sun can recieve light Scattering more than 90degrees is called back-scattering

Reflectin - light striking the surface bounces back at the same angle & intensity it arrived

Albedo is the proportion of radiation which is reflected

WL and size/mature of object determine SR's fate

When radiation ecounters matter, three things can happy Absorbed - molecules then vibrate faster and increase in temp Scattered - redirected or bounce off (how we see things) Transmission - when its passed through like air and water

for - anthropocene - activating shared social struggle to turn it around

• SRG comment - anthropocene struggle - TPF

for - wellbeing - claim - has regressed since hunter-gatherers -we've maximized brutality, economic power

• SRG comment
  • weigh against standard progress argument

for - planetary boundaries - postgrowth - provisioning energy - saturation - past a certain point, more energy does not create more wellbeing - decline - energy for high wellbeing has been dropping significantly over time - inefficiency - growth in prmary energy can only account for 25% of improvements in life expectancy - degrowth is technically possible - at less than half the current final energy used

for - anthropocene - provisioning - between planetary boundaries and wellbeing

for - green growth - alternative - postgrowth demand reduction

for - green growth - illusion - too little

for - growth economy - important economic decisions - people have no say

for - growth economy - anti-democratic - free-market rhetoric prevents - democratic - social - environmental - oversight

for - anthropocene - novel entities - nano particles - micro plastic - crossing blood-brain barrier - unkjnown effects

for - anthropocene - many interpretations -SRG comment - interpretant fingerprint

for - neuroscience - obedience - alternative - empathy to fellow perpetrators - brotherhood effect

for - stats - genocide obedience - perpetrators vs rescuers - Rawanda 94.66% perpetrators - Nazi 87.15 %

for - comparison - neuroscience - pain center activation - following orders - activations reduced compared to free decision case

for comparison - neuroscience - guilt center activation - following orders - activations reduced compared to the free decision case

for - genocide - preceded by 10 preliminary stages - 1. classification - 2. symbolization - 3. discrmination - 4. dehumanization - 5. organization - 6. polarization - 7. preparation - 8. persecution - 9. extermination - 10. denial

for - genocide - why? just following orders - Rawanda and Cambodia interviewees reported the same results - just following orders

for genocide - Israeli soldiers - give same reason for committing atrocities - just following orders

for - genocide - stem from obedience

for - neuroscience - of obedience - Emily Casper - book - the neuroscience of (dis)obedience

for - trivia - Trinity test 80th anniversary - Enrico Fermi took bets on Edward Teller's calculation

for - correlation - status competition - anti-least effort (grand monuments)

for - stats - psychopathy - 1% of male population

for - stats - very few people willing to do violence for status

for - holocene - social shift - from - egalitarianism - to - power hierarchy

for - anthropocene - evolution of

for - quote - phasing out is a challenge - maybe collapse is needed

for - definition - transition risk - definition - transition pain

for - definition - anthropocene - rejection of the term - it was rejected on dogmatic grounds, not on the evidence provided

for - Bill gates climate article - is AND, not EITHER OR

for - the network may look different to different people - new trailmark - SRG comment - distinguishes when I'm making a comment from comments from the source - SRG comment - perspectival knowing

for - example - wicked problem graph visualization - World Economic Forum

for - question - anthropocene - era instead of epoch? - professor Alasdair Skelton, Stockholm University - great presentation comparing anthropocene vs other eras in the past 66 million years

for - quote - holocene - should have at least 50 during the last ice age

for - comparison - anthropocene - past similiar epochs - miocene and possibly eocene - no humans alive at that time - unknown impacts of living in such an environment

for - definition - anthropocene - synchronized signals of great acceleration at all 12 sites, not just Crawford Lake - Francine McCarthy, Brock University

for - definition - anthropocene - post rejection definition - future work - even though it's been rejected as a geological epoch, due to so many uses of it, it still needs a proper definition

This addresses a broader issue than just Hannah. I feel like this happens a lot in a concert band setting of course not as extreme as Hannah. But when you have one concert band in the entire high school how do you program pieces that will be playable for the freshman but challenge the seniors.

Business owners then hiring wage laborers at predetermined rates for their work, while the owners get the excess business profits makes my blood boil. Relating this back to my experiences, working for target was my worst nightmare. It was the biggest, busiest, target in Seattle. We were being pushed to extreme workloads by higher ups like crazy. For such little pay. It was infuriating. But of course Target was making millions a day within this target.

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Reply to the reviewers

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

Summary The manuscript by Aarts et al. explores the role of GRHL2 as a regulator of the progesterone receptor (PR) in breast cancer cells. The authors show that GRHL2 and PR interact in a hormone-independent manner and based on genomic analyses, propose that they co-regulate target genes via chromatin looping. To support this model, the study integrates both newly generated and previously published datasets, including ChIP-seq, CUT&RUN, RNA-seq, and chromatin interaction assays, in breast cancer cell models (T47DS and T47D).

Major comments: R1.1 Novelty of GRHL2 in steroid receptor biology The role of GRHL2 as a co-regulator of steroid hormone receptors has previously been described for ER (J Endocr Soc. 2021;5(Suppl 1):A819) and AR (Cancer Res. 2017;77:3417-3430). In the ER study, the authors also employed a GRHL2 ΔTAD T47D cell model. Therefore, while this manuscript extends GRHL2 involvement to PR, the contribution appears incremental rather than conceptual.

We are fully aware of the previously described role of GRHL2 as a co-regulator of steroid hormone receptors, particularly ER and AR. As acknowledged in our introduction (lines 104-108), we explicitly state: "Grainyhead-like 2 (GRHL2) has recently emerged as a potential pioneer factor in hormone receptor-positive cancers, including breast cancer21. However, nearly all studies to date have focused on GRHL2 in the context of ER and estrogen signaling, leaving its role in PR- and progesterone-mediated regulation unexplored22-26".

As for the specific publications that the reviewer refers to: The first refers to an abstract from an annual meeting of the Endocrine Society. As we have been unable to assess the original data underpinning the abstract - including the mentioned GRHL2 DTAD model - we prefer not to cite this particular reference. We do cite other work by the same authors (Reese et al. 2022, our ref. 25). We also cite the AR study mentioned by the reviewer (our ref. 55) in our discussion. As such, we think we do give credit to prior work done in this area.

By characterizing GRHL2 as a co-regulator of the progesterone receptor (PR), we expand on the current understanding of GRHL2 as a common transcriptional regulator within the broader context of steroid hormone receptor biology. Given that ER and PR are frequently co-expressed and active within the same breast cancer cells, our findings raise the important possibility that GRHL2 may actively coordinate or modulate the balance between ER- and PR-driven transcriptional programs, as postulated in the discussion paragraph.

Importantly, we also functionally link PR/GRHL2-bound enhancers to their target genes (Fig5), providing novel insights into the downstream regulatory networks influenced by this interaction. These results not only offer a deeper mechanistic understanding of PR signaling in breast cancer but also lay the groundwork for future comparative analyses between GRHL2's role in ER-, AR-, and PR-mediated gene regulation.

As such, we respectfully suggest that our work offers more than an incremental advance in our knowledge and understanding of GRHL2 and steroid hormone receptor biology.

R1.2 Mechanistic depth The study provides limited mechanistic insight into how GRHL2 functions as a PR co-regulator. Key mechanistic questions remain unaddressed, such as whether GRHL2 modulates PR activation, the sequential recruitment of co-activators/co-repressors, engages chromatin remodelers, or alters PR DNA-binding dynamics. Incorporating these analyses would considerably strengthen the mechanistic conclusions.

Although our RNA-seq data demonstrate that GRHL2 modulates the expression of PR target genes, and our CUT&RUN experiments show that GRHL2 chromatin binding is reshaped upon R5020 exposure, we acknowledge that we have not further dissected the molecular mechanisms by which GRHL2 functions as a PR co-regulator.

We did consider several follow-up experiments to address this, including PR CUT&RUN in GRHL2 knockdown cells, CUT&RUN for known co-activators such as KMT2C/D and P300, as well as functional studies involving GRHL2 TAD and DBD mutants. However, due to technical and logistical challenges, we were unable to carry out these experiments within the timeframe of this study.

That said, we fully recognize that such approaches would provide deeper mechanistic insight into the interplay between PR and GRHL2. We have therefore explicitly acknowledged this limitation in our limitations of the study section (line 502-507) and mention this as an important avenue for future investigation.

R1.3 Definition of GRHL2-PR regulatory regions (Figure 2) The 6,335 loci defined as GRHL2-PR co-regulatory regions are derived from a PR ChIP-seq performed in the presence of hormone and a GRHL2 ChIP-seq performed in its absence. This approach raises doubts about whether GRHL2 and PR actually co-occupy these regions under ligand stimulation. GRHL2 ChIP-seq experiments in both hormone-treated and untreated conditions are necessary to provide stronger support for this conclusion.

Although bulk ChIP-seq cannot definitively demonstrate simultaneous binding of PR and GRHL2 at the same genomic regions, we agree that the ChIP-seq experiments we present do not provide a definitive answer on if GRHL2 and PR co-occupy these regions under ligand stimulation. As a first step to address this, we performed CUT&RUN experiments for both GRHL2 and PR under untreated and R5020-treated conditions. These experiments revealed a subset of overlapping PR and GRHL2 binding sites (approximately {plus minus}5% of the identified PR peaks under ligand stimulation).

We specifically chose CUT&RUN to minimize artifacts from crosslinking and sonication, thereby reducing background and enabling the mapping of high-confidence direct DNA-binding events: Given that a fraction of GRHL2 physically interacts with PR (Fig1D), it is possible that ChIP-seq detects indirect binding of GRHL2 at PR-bound sites and vice versa. CUT&RUN, by contrast, allows us to identify direct binding sites with higher confidence.

Nonetheless, although outside the scope of the current manuscript, we agree that a dedicated GRHL2 ChIP with and without ligand stimulation would provide additional insight, and we have accordingly added this suggestion to the discussion (line 502-507).

R1.4 Cell model considerations The manuscript relies heavily on the T47DS subclone, which expresses markedly higher PR levels than parental T47D cells (Aarts et al., J Mammary Gland Biol Neoplasia 2023; Kalkhoven et al., Int J Cancer 1995). This raises concerns about physiological relevance. Key findings, including co-IP and qPCR-ChIP experiments, should be validated in additional breast cancer models such as parental T47D, BT474, and MCF-7 cells to generalize the conclusions. Furthermore, data obtained from T47D (PR ChIP-seq, HiChIP, CTCF and Rad21 ChIP-seq) and T47DS (RNA-seq, CUT&RUN) are combined along the manuscript. Given the substantial differences in PR expression between these cell lines, this approach is problematic and should be reconsidered.

We agree that physiological relevance is important to consider. Here, all existing model systems have some limitations. In our experience, it is technically challenging to robustly measure gene expression changes in parental T47D cells (or MCF7 cells, for that matter) in response to progesterone stimulation (Aarts et al., J Mammary Gland Biol Neoplasia 2023). As we set out to integrate PR and GRHL2 binding to downstream target gene induction, we therefore opted for the most progesterone responsive model system (T47DS cells). We agree that observations made in T47D and T47DS cells should not be overinterpreted and require further validation. We have now explicitly acknowledged this and added it to the discussion (line 507-509).

As for the reviewer's suggestion to use MCF7 cells: apart from its suboptimal PR-responsiveness, this cell line is also known to harbor GRHL2 amplification, resulting in elevated GRHL2 levels (Reese et al., Endocrinology2019). By that line of reasoning, the use of MCF7 cells would also introduce concerns about physiological relevance. That being said, and as noted in the discussion (line 390-391), the study by Mohammed et al. which identified GRHL2 as a PR interactor using RIME, was performed in both MCF7 and T47D cells. This further supports the notion that the PR-GRHL2 interaction is not limited to a single cell line.

R1.5 CUT&RUN vs ChIP-seq data The CUT&RUN experiments identify fewer than 10% of the PR binding sites reported in the ChIP-seq datasets. This discrepancy likely results from methodological differences (e.g., absence of crosslinking, potential loss of weaker binding events). The overlap of only 158 sites between PR and GRHL2 under hormone treatment (Figure 3B) provides limited support for the proposed model and should be interpreted with greater caution.

We acknowledge the discrepancy between the number of binding sites between ChIP-seq and CUT&RUN. Indeed, methodological differences likely contribute to the differences in PR binding sites reported between the ChIP-seq and CUT&RUN datasets. As the reviewer correctly notes, the absence of crosslinking and sonication in CUT&RUN reduces detection of weaker binding events. However, it also reduces the detection of indirect binding events which could increase the reported number of peaks in ChIPseq data (e.g. the common presence of "shadow peaks").

As also discussed in our response to R1.3, we deliberately chose the CUT&RUN approach to enable the identification of high-confidence direct DNA-binding events. Since GRHL2 physically interacts with PR, ChIP-seq could potentially capture indirect binding of GRHL2 at PR-bound sites, and vice versa. By contrast, CUT&RUN primarily captures direct DNA-protein interactions, offering a more specific binding profile. Thus, while the number of CUT&RUN binding sites is much smaller than previously reported by ChIP-seq, we are confident that they represent true, direct binding events.

We would also like to emphasize that the model presented in figure 6 does not represent a generic or random gene, but rather a specific gene that is co-regulated by both GRHL2 and PR. In this specific case, regulation is proposed to occur via looping interactions from either individual TF-bound sites (e.g., PR-only or GRHL2-only) or shared GRHL2/PR sites. We do not propose that only shared sites are functionally relevant, nor do we assume that GRHL2 and PR must both be directly bound to DNA at these shared sites. Therefore, overlapping sites identified by ChIP-seq-potentially reflecting indirect binding events-could indeed be missed by CUT&RUN, yet still contribute to gene regulation. To clarify this, we have revised the main text (line 331-334) and the legend of Figure 6 to explicitly state that the model refers to a gene with established co-regulation by both GRHL2 and PR.

R1.6 Gene expression analyses (Figure 4) The RNA-seq analysis after 24 hours of hormone treatment likely captures indirect or secondary effects rather than the direct PR-GRHL2 regulatory program. Including earlier time points (e.g., 4-hour induction) in the analysis would better capture primary transcriptional responses. The criteria used to define PR-GRHL2 co-regulated genes are not convincing and may not reflect the regulatory interactions proposed in the model. Strong basal expression changes in GRHL2-depleted cells suggest that much of the transcriptional response is PR-independent, conflicting with the model (Figure 6). A more straightforward approach would be to define hormone-regulated genes in shControl cells and then examine their response in GRHL2-depleted cells. Finally, integrating chromatin accessibility and histone modification datasets (e.g., ATAC-seq, H3K27ac ChIP-seq) would help establish whether PR-GRHL2-bound regions correspond to active enhancers, providing stronger functional support for the proposed regulatory model.

We thank the reviewer for pointing this out. We now recognize that our criteria for selecting PR/GRHL2 co-regulated genes were not clearly described. To address this, we have revised our approach as per the reviewer's suggestion: we first identified early and sustained PR target genes based on their response at 4 and 24 hours of induction and subsequently overlaid this list with the gene expression changes observed in GRHL2-depleted cells. This revised approach reduced the amount of PR-responsive, GRHL2 regulated target genes from 549 to 298 (46% reduction). We consequently updated all following analyses, resulting in revised figures 4 and 5 and supplementary figures 2,3 and 4. As a result of this revised approach, the number of genes that are transcriptionally regulated by GRHL2 and PR (RNAseq data) that also harbor a PR loop anchor at or near their TSS after 30 minutes of progesterone stimulation (PR HiChIP data) dropped from 114 to 79 (30% reduction). We thank the reviewer for suggesting this more straightforward approach and want to emphasize that our overall conclusions remain unaltered.

As above in our response to R1.3, we want to emphasize that the model presented in figure 6 does not depict a generic or randomly chosen gene, but a gene that is specifically co-regulated by both GRHL2 and PR. We also want to emphasize that the majority of GRHL2's transcriptional activity is PR-independent. This is consistent with the limited fraction of GRHL2 that co-immunoprecipitated with PR (Figure 1D), and with the well-established roles of GRHL2 beyond steroid receptor signaling. In fact, the overall importance of GRHL2 for cell viability in T47D(S) cells is underscored by our inability to generate a full knockout (multiple failed attempts of CRISPR/Cas mediated GRHL2 deletion in T47D(S) and MCF7 cells), and by the strong selection we observed against high-level GRHL2 knockdown using shRNA.

As for the reviewer's suggestion to assess whether GRHL2/PR co-bound regions correspond to active enhancers by integrating H3K27ac and ATAC-seq data: We have re-analyzed publicly available H3K27ac and ATAC-seq datasets from T47D cells (references 42 and 43). These analyses are now added to figure 2 (F and G). The H3K27Ac profile suggests that GRHL2-PR overlapping sites indeed correspond to more active enhancers (Figure 2F), with a proposed role for GRHL2 since siGRHL2 affects the accessibility of these sites (Figure 2G).

Minor comments Page 19: The statement that "PR and GRHL2 trigger extensive chromatin reorganization" is not experimentally supported. ATAC-seq would be an appropriate method to test this directly.

We agree with the reviewer and have removed this sentence, as it does not contribute meaningfully to the flow of the manuscript.

Prior literature on GRHL2 as a steroid receptor co-regulator should be discussed more thoroughly.

We now added additional literature on GRHL2 as a steroid hormone receptor co-regulator in the discussion (line 397-401) and we cite the papers suggested by R1 in R1.1 (references 25 and 54).

Reviewer #1 (Significance (Required)):

The identification of novel PR co-regulators is an important objective, as the mechanistic basis of PR signaling in breast cancer remains incompletely understood. The main strength of this study lies in highlighting GRHL2 as a factor influencing PR genomic binding and transcriptional regulation, thereby expanding the repertoire of regulators implicated in PR biology.

That said, the novelty is limited, given the established roles of GRHL2 in ER and AR regulation. Mechanistic insight is underdeveloped, and the reliance on an engineered T47DS model with supra-physiological PR levels reduces the general impact. Without validation in physiologically relevant breast cancer models and clearer separation of direct versus indirect effects, the overall advance remains modest.

The manuscript will be of interest to a specialized audience in the fields of nuclear receptor signaling, breast cancer genomics, and transcriptional regulation. Broader appeal, including translational or clinical relevance, is limited in its current form.

We have addressed all of these points in our response above and agree that with our implemented changes, this study should reach (and appeal to) an audience interested in transcriptional regulation, chromatin biology, hormone receptor signaling and breast cancer.

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

The authors present a study investigating the role of GRHL2 in hormone receptor signaling. Previous research has primarily focused on GRHL2 interaction with estrogen receptor (ER) and androgen receptor (AR). In breast cancer, GRHL2 has been extensively studied in relation to ER, while its potential involvement with the progesterone receptor (PR) remains largely unexplored. This is the rationale of this study to investigate the relation between PR and GRHL2. The authors demonstrate an interaction between GRHL2 and PR and further explore this relationship at the level of genomic binding sites. They also perform GRHL2 knockdown experiments to identify target genes and link these transcriptional changes back to GRHL2-PR chromatin occupancy. However, several conceptual and technical aspects of the study require clarification to fully support the authors' conclusions.

R2.1 Given the high sequence similarity among GRHL family members, this raises questions about the specificity of the antibody used for GRHL2 RIME. The authors should address whether the antibody cross-reacts with GRHL1 or GRHL3. For example, GRHL1 shows a higher log fold change than GRHL2 in the RIME data.

Indeed, GRHL1, GRHL2, and GRHL3 are structurally related. They share a similar domain organization and are all {plus minus}70kDa in size. Sequence similarity is primarily confined to the DNA-binding domain, with GRHL2 and GRHL3 showing 81% similarity in this region, and GRHL1 showing 63% similarity to GRHL2/3 (Ming, Nucleic Acids Res 2018).

The antibody used, sourced from the Human Protein Atlas, is widely used in the field. It targets an epitope within the transactivation domain (TAD) of GRHL2-a region with relatively low sequence similarity to the corresponding domains in GRHL1 and GRHL3.

We assessed the specificity of the antibody using western blotting (Supplementary Figure 2A) in T47DS wild-type and GRHL2 knockdown cells. As expected, GRHL2 protein levels were reduced in the knockdown cells providing convincing evidence that the antibody recognizes GRHL2. The remaining signal in shGRHL2 knockdown cells could either be due to remaining GRHL2 protein or due to the antibody detecting GRHL1/3. Furthermore, the observed high log-fold enrichment of GRHL1 in our RIME may reflect known heterodimer formation between GRHL1 and GRHL2, rather dan antibody cross-reactivity. As such, we cannot formally rule out cross-reactivity and have mentioned this in the limitations section (line 497-501).

R2.2 In addition, in RIME experiments, one would typically expect the bait protein to be among the most highly enriched proteins compared to control samples. If this is not the case, it raises questions about the efficiency of the pulldown, antibody specificity, or potential technical issues. The authors should comment on the enrichment level of the bait protein in their data to reassure readers about the quality of the experiment.

We agree with the reviewer that this information is crucial for assessing the quality of the experiment. We have therefore added the enrichment levels (log₂ fold change of IgG control over pulldown) to the methods section (line 592).

As the reviewer notes, GRHL2 was not among the top enriched proteins in our dataset. This is due to unexpectedly high background binding of GRHL2 to the IgG control antibody/beads, for which we currently have no explanation. As a result, although we detected many unique GRHL2 peptides, observed high sequence coverage (>70%), and GRHL2 ranked among the highest in both iBAQ and LFQ values, its relative enrichment was reduced due to the elevated background. During our RIME optimization, Coomassie blue staining of input and IP samples revealed a band at the expected molecular weight of GRHL2 in the pull down samples that was absent in the IgG control (see figure 1 for the reviewer below, 4 right lanes), supporting the conclusion that GRHL2 is specifically enriched in our GRHL2 RIME samples. Combined with enrichment of some of the expected interacting proteins (e.g. KMT2C and KMT2D), we are convinced that the experiment of sufficient quality to support our conclusions.

Figure 1 for reviewer: Coomassie blue staining of input and IP GRHL2 and IgG RIME samples. NT = non-treated, T = treated.

R2.3 The authors report log2 fold changes calculated using iBAQ values for the bait versus IgG control pulldown. While iBAQ provides an estimate of protein abundance within samples, it is not specifically designed for quantitative comparison between samples without appropriate normalization. It would be helpful to clarify the normalization strategy applied and consider using LFQ intensities.

We understand the reviewer's concern. Due to the high background observed in the IgG control sample (see R2.2), the LFQ-based normalization did not accurately reflect the enrichment of GRHL2, which was clearly supported by other parameters such as the number of unique peptides (see rebuttal Table 1). After discussions with our Mass Spectrometry facility, we decided to consider the iBAQ values-which reflect the absolute protein abundance within each sample-as a valid and informative measure of enrichment. In the context of elevated background levels, iBAQ provides an alternative and reliable metric for assessing protein enrichment and was therefore used for our interactor analysis.

Unique peptides

IBAQ GRHL2

IBAQ IgG

LFQ GRHL2

LFQ IgG

GRHL2

52

1753400.00

155355.67

5948666.67

3085700.00

GRHL1

23

56988.33

199.03

334373.33

847.23

*Table 1. Unique peptide, IBAQ and LFQ values of the GRHL2 and IgG pulldowns for GRHL2 and GRHL1 *

R2.4 Other studies have reported PR RIME, which could be a valuable source to investigate whether GRHL proteins were detected.

We thank the reviewer for pointing this out. We are aware of the PR RIME, generated by Mohammed et al., which we refer to in the discussion (lines 390-391). This study indeed identified GRHL2 as a PR-interacting protein in MCF7 and T47D cells. Although they do not mention this interaction in the text, the interaction is clearly indicated in one of the figures from their paper, which supports our findings. To our knowledge, no other PR RIME datasets in MCF7 or T47D cells have been published to date.

R2.5 In line 137, the term "protein score" is mentioned. Could the authors please clarify what this means and how it was calculated.

We agree that this point was not clearly explained in the original text. The scores presented reflect the MaxQuant protein identification confidence, specifically the sum of peptide-level scores (from Andromeda), which indicates the relative confidence in protein detection. We have now added this clarification to line 137 and to the legend of Figure 1.

R2.6 In line 140-141. The fact that GRHL2 interacts with chromatin remodelers does not by itself prove that GRHL2 acts as a pioneer factor or chromatin modulator. Demonstrating pioneer function typically requires direct evidence of chromatin opening or binding to closed chromatin regions (e.g., ATAC-seq, nucleosome occupancy assays). I recommend revising this statement or providing supporting evidence.

We agree that the fact that GRHL2 interacts with chromatin remodelers does not by itself prove that GRHL2 acts as a pioneer factor or chromatin modulator. However, a previous study (Jacobs et al, Nature genetics, 2018) has shown directly that the GRHL family members (including GRHL2) have pioneering function and regulate the accessibility of enhancers. We adapted line 140-141 to state this more clearly. In addition, our newly added data in Figure 2G also support the fact that GRHL2 has a role in regulating chromatin accessibility in T47D cells.

R2.7 The pulldown Western blot lacks an IgG control in panel D.

This is correct. As the co-IP in Figure 1D served as a validation of the RIME and was specifically aimed at determining the effect of hormone treatment on the observed PR/GRHL2 interaction, we did not perform this control given the scale of the experiment. However, during RIME optimization, we performed GRHL2 staining of the IgG controls by western blot, shown in figure 2 for the reviewer below. As stated above, some background GRHL2 signal was observed in the IgG samples, but a clear enrichment is seen in the GRHL2 IP.

Taken together, we believe that the well-controlled RIME, combined with the co-IP presented, provides strong evidence that the observed signal reflects a genuine GRHL-PR interaction.

Figure 2 for reviewer: WB of input and IP GRHL2 and IgG RIME samples stained for GRHL2. NT = non-treated, T = treated

R2.8 Depending on the journal and target audience, it may be helpful to briefly explain what R5020 is at its first mention (line 146).

Thank you. We have adapted this accordingly.

R2.9 The authors state that three technical replicates were performed for each experimental condition. It would be helpful to clarify the expected level of overlap between biological replicates of RIME experiments. This clarification is necessary, especially given the focus on uniquely enriched proteins in untreated versus treated cells, and the observation that some identified proteins in specific conditions are not chromatin-associated. Replicates or validations would strengthen the findings.

We use the term technical rather than biological replicates because for cell lines, defining true biological replicates is challenging, as most variability arises from experimental rather than biological differences. To introduce some variation, we split our T47DS cells into three parallel dishes 5 days prior to starting the treatment. We purposely did this, to minimize to minimize the likelihood that proteins identified as uniquely enriched are artifacts. Each of the three technical replicates comes from one of these three parallel splits (so equal passage numbers but propagated in parallel dishes for 5 days before the start of the experiment).

To generate the three technical replicates for our RIME, we plated cells from the parallel grown splits. Treatments for the three replicates were performed per replicate. Samples were crosslinked, harvested and lysed for subsequent RIME analysis, the three replicates were processed in parallel, for technical and logistical reasons. To clarify the experimental setup, we have updated the methods section accordingly (lines 566-568).

As for the detection of non-chromatin-associated proteins: We cannot rule out that these are artifacts, as they may arise from residual cytosolic lysate during nuclear extraction. Alternatively, they could reflect a more dynamic subcellular localization of these proteins than currently annotated or appreciated.

R2.10 The volcano plot for the RIME experiment appears to show three distinct clusters of proteins on the right, which is unusual for this type of analysis. The presence of these apparent groupings may suggest an artifact from the data processing, such as imputation. Can the authors clarify the origin of these groupings? If it is due to imputation or missing values, I recommend applying a stricter threshold, such as requiring detection in all three replicates (3/3) to improve the robustness of the enrichment analysis and increase confidence in the identified interactors.

We thank the reviewer for pointing this out. As suggested, we re-evaluated the imputation and applied a stricter threshold, requiring detection in all three replicates. Indeed, the separate clusters were due to missing values, therefore we now revised the imputation method by imputing values based on the normal distribution. Using this revised analysis, we identify 2352 GRHL2 interactors instead of 1140, but the number of interacting proteins annotated as transcription factors or chromatin-associated/modifying proteins was still 103. Figure 1B, 1E, and Supplementary Figure 4A have been updated accordingly. We also revised the methods section to reflect this change. We think this suggestion has improved our analysis of the data and we thank the reviewer for pointing this out.

R2.11 The statement that "PR and GRHL2 frequently overlap" may be overstated given that only ~700 overlapping sites are reported (cut&run).

We have replaced "frequently overlap" by "can overlap" (line 229-230).

R2.12 The model in Figure 6 suggests limited chromatin occupancy of PR and GRHL2 in hormone-depleted conditions, consistent with the known requirement of ligand for stable PR-DNA binding. However, Figure 1 shows no major difference in GRHL2-PR interaction between untreated and hormone-treated cells. This raises questions about where and how this interaction occurs in the absence of hormone. Since PR binding to chromatin is typically minimal without ligand, can the authors clarify this given that RIME data reflect chromatin-bound interactions.

Indeed, the model in figure 6 suggests limited chromatin occupancy of PR and GRHL2 under hormone-depleted conditions. It is, however, important to note that the locus shown represents a gene regulated by both PR and GRHL2 - and not just any gene. We recognize that this was not sufficiently clear in the original version, and we have now clarified this in both the main text (line 331-334) and the figure legend.

We propose that PR and GRHL2 bind or become enriched at enhancer sites associated with their target genes upon ligand stimulation. This is consistent with the known requirement of ligand for stable PR-DNA binding and with our observation that PR/GRHL2 overlapping peaks are detected only in the ligand-treated condition of the CUT&RUN experiment. Given the broader role of GRHL2, it also binds chromatin independently of progesterone and the progesterone receptor, which is why we included-but did not focus on-GRHL2-only binding events in our model.

We would also like to clarify that, although RIME includes a nuclear enrichment step that enriches for chromatin-associated proteins, the pulldown is performed on nuclear lysates. Therefore, it captures both chromatin-bound protein complexes and freely soluble nuclear complexes, which unfortunately cannot be distinguished. GRHL2 is well established as a nuclear protein (Zeng et al., Cancers 2024; Riethdorf et al., International Journal of Cancer 2015), and although PR is classically described as translocating to the nucleus upon hormone stimulation, several studies-including our own-have shown that PR is continuously present in the nucleus (Aarts et al., J Mammary Gland Biol Neoplasia 2023; Frigo et al., Essays Biochem. 2021).

We therefore propose that PR and GRHL2 may already interact in the nucleus without directly binding to chromatin. Given our observation that GRHL2 binding sites on the chromatin are redistributed upon R5020 mediated signaling activation, we hypothesize that such pre-formed PR-GRHL2 nuclear complexes may assist the rapid recruitment of GRHL2 to progesterone-responsive chromatin regions.

We have expanded the discussion to include a dedicated section addressing this point (line 376-388).

R2.13 It would be of interest to assess the overlap between the proteins identified in the RIME experiment and the motif analysis results.

In the discussion section of our original manuscript, we highlighted some overlapping proteins in the RIME and motif analysis, including STAT6 and FOXA1. However, we had not yet systematically analyzed overlap in both analyses. To address this, we now compared all enriched motifs (so not only the top 5 as displayed in our figures) under GRHL2, PR, and GRHL2/PR shared sites from both the CUT&RUN and ChIP-seq datasets with the proteins identified as GRHL2 interactors in our RIME. Although we identified numerous GRHL2-associated proteins, relatively few of them were transcription factors whose binding motifs were also enriched under GRHL2 peaks.

In our revised manuscript we have added a section in the discussion highlighting our systematic overlap of the results of our RIME experiment and the motif enrichment of the ChIP-seq and CUT&RUN analysis (line 415-436).

R2.14 The authors chose CUT&RUN to assess chromatin binding of PR and GRHL2. Given that RIME is also based on chromatin immunoprecipitation - ChIP protocol, it would be helpful to clarify why CUT&RUN was selected over ChIP-seq for the DNA-binding assays. What is the overlap with published data?

As also mentioned in our response to R1.3 and R1.5, we deliberately chose the CUT&RUN approach to minimize artifacts introduced by crosslinking and sonication, thereby reducing background and allowing the identification of high-confidence, direct DNA-binding events. Since GRHL2 physically interacts with PR, ChIP-seq could potentially capture indirect binding of GRHL2 at PR-bound sites (and vice versa). In contrast, CUT&RUN primarily detects direct DNA-protein interactions, providing a more specific and accurate binding profile. Additionally, CUT&RUN serves as an independent validation method for data obtained using ChIP-like protocols.

Since CUT&RUN, similar to ChIP, can show limited reproducibility (Nordin et al., Nucleic Acids Research, 2024), and to our knowledge few PR CUT&RUN and no GRHL2 CUT&RUN datasets are currently available, it is challenging to directly compare our data with published datasets. Nevertheless, studies performing PR or ER CUT&RUN (Gillis et al., Cancer Research, 2024; Reese et al., Molecular and Cellular Biology, 2022) report a comparable number of peaks-in the same range of thousands-as observed in our data. This suggests that a single CUT&RUN experiment in general may detect fewer events than a single ChIP-seq experiment, but that the peaks that are found are likely to reflect direct binding events.

Reviewer #2 (Significance (Required)):

General Assessment: This study investigates the role of the transcription factor GRHL2 in modulating PR function, using RIME and CUT&RUN to explore protein-protein and protein-chromatin interactions. GRHL2 have been implicated in epithelial biology and transcriptional regulation and interaction with steroid hormone receptors has been reported. This study extends the field by showing a functional link between GRHL2 and PR, which has implications for understanding hormone-dependent gene regulation.

The research will primarily interest a specialized audience in transcriptional regulation, chromatin biology, and hormone receptor signaling.

Key words for this reviewer: chromatin biology, transcription factor function, epigenomics, and proteomics.

We agree that with our implemented changes, this study should reach (and appeal to) an audience interested in transcriptional regulation, chromatin biology, hormone receptor signaling and breast cancer.

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

This study explores the important transcriptional coordination role of Grainyhead-like 2 (GRHL2) on the transcriptional regulatory function of progesterone receptor (PR). In this paper, the authors start with their recruitment characteristics, take into account their regulatory effects on downstream genes and their effects on the occurrence and development of breast cancer, and further clarify the coordination between them in three-dimensional space. The interaction between GRHL2 and PR, and the subsequent important influence on the co-regulated genes by GRHL2 and PR are analyzed. The overall framework of this study is mainly by RNA seq and CUT-TAG analysis, the molecular mechanism underlying the association between GRHL2 and PR and regulation function of two proteins in breast cancer is not clearly clarified. Some details need to be further improved：

Major comments: R3.1 For Fig.1D, the molecular weight of each protein should be marked in the diagram, and the expression of GRHL2 in the input group should be supplemented.

We apologize for not including molecular weights in our initial submission. We are not entirely clear what the reviewer means with their statement that "the expression of GRHL2 in the input group should be supplemented". The blot depicted in Figure 1D shows both the input signal and the IP. For the reviewer's information, the full Western blot is depicted below.

Figure 3 for reviewer: Full WBs of input and IP GRHL2 samples stained for GRHL2 or PR. NT = non-treated, T = treated

R3.2 In Fig.2B and Fig 5C, it should be describe well whether GRHL2 recruitment is in the absence or presence of R5020? How about the co-occupancy of PR and GRHL2 region, Promoter or enhancer region? It would be better to show histone marks such as H3K27ac and H3K4me1 to annotate the enhancer region.

As also stated in our response to R1.3, we acknowledge that the ChIP-seq experiments cannot definitively determine whether GRHL2 and PR co-occupy genomic regions under ligand-stimulated conditions, since the GRHL2 dataset was generated in the absence of progesterone stimulation (as indicated in lines 167-169). To clarify this, we have now specified this detail in the legend of figure 2 by noting "untreated GRHL2 ChIP." To directly assess GRHL2 chromatin binding under progesterone-stimulated conditions, we performed CUT&RUN experiments for both GRHL2 and PR under untreated and R5020-treated conditions. These experiments revealed a subset of overlapping PR and GRHL2 binding sites (approximately 5% of all identified PR peaks.

In our original manuscript, we performed genomic annotation of the GRHL2, PR, and GRHL2/PR overlapping peaks (Figure 2E) and found that most of these sites were located in intergenic regions, where enhancers are typically found, with ~20% located in promoter regions. We appreciate the reviewer's suggestion to further overlap the ChIP-seq peaks with histone marks such as H3K27ac and H3K4me1. We have now incorporated publicly available ATAC-seq and H3K27ac ChIP datasets in our revised manuscript (as also suggested by Reviewer 1) and find that shared GRHL2/PR sites are indeed located in active enhancer regions marked by H3K27ac (see Figure 2F). Additionally, as expected, we find that GRHL2/PR overlapping sites are enriched at open chromatin (Figure 2G).

R3.3 What is the biological function analysis by KEGG or GO analysis for the overlapping genes from VN plots of RNA-seq with CUT-TAG peaks. The genes co-regulated by GRH2L and PR are further determined.

For us, it is not entirely clear what reviewer 3 is asking here, but we can explain the following: as it is challenging to integrate HiChIP with CUT&RUN, due to the fundamentally different nature of the two techniques, we chose not to directly assign genes to CUT&RUN peaks. However, we did carefully link the GRHL2, PR, and GRHL2/PR ChIP-seq peaks to their target genes by integrating chromatin looping data from a PR HiChIP analysis. The result from this analysis is depicted in Figure 4B.

As suggested by this reviewer, we also performed a GO-term analysis on the 79 genes that are regulated by both GRHL2 and PR (we now have 79 genes after the re-analysis as suggested in R1.6). The corresponding results are provided for the reviewer in figure 3 of this rebuttal (below). As this additional analysis does not provide further biological insight beyond what is already presented in Figure 4C, we decided to not include this figure in the manuscript.

Figure 4 for reviewer: GO-term analysis on the 79 GRHL2-PR co-regulated genes that are transcriptionally regulated by GRHL2 and PR and that also harbor a PR HiChIP loop anchor at or near their TSS

R3.4 Western blotting should be performed to determine the protein levels of downstream genes co-regulated genes by GRH2L and PR in the absence or presence of R5020.

We agree that determining the response of co-regulated is important. Therefore, in Figure 4D, we present three representative examples of genes that are directly co-regulated by GRHL2 and PR-specifically, genes that are differentially expressed after 4 hours of R5020 exposure. Although protein levels of the targets are of functional importance, GRHL2 and PR are of transcription factors whose immediate effects are primarily exerted at the level of gene transcription. Therefore, in our opinion, changes in mRNA abundance provide the most direct and mechanistically relevant readout of their regulatory activity.

R3.5 The author mentioned that this study positions that GRHL2 acts as a crucial modulator of steroid hormone receptor function, while the authors do not provide the evidences that how does GRHL2 regulate PR-mediated transactivation, and how about these two proteins subcellular distribution in breast cancer cells.

We agree that while our RNA-seq data demonstrate that GRHL2 modulates the expression of PR target genes, and our CUT&RUN experiments show that GRHL2 chromatin binding is reshaped upon R5020 exposure, we have not yet further dissected the molecular mechanism by which GRHL2 functions as a PR co-regulator.

As also mentioned in our response to R1.2, we did consider several follow-up experiments to address this, including PR CUT&RUN in GRHL2 knockdown cells, CUT&RUN for known co-activators such as KMT2C/D and P300, as well as functional studies involving GRHL2 TAD and DBD mutants. However, due to technical and logistical challenges, we were unable to carry out these experiments within the timeframe of this study.

That said, we fully recognize that such approaches would provide deeper mechanistic insight into the interplay between PR and GRHL2. We have therefore explicitly acknowledged this limitation in our limitations of the study section (lines 502-507) and consider it an important avenue for future investigation.

Regarding the subcellular distribution in breast cancer cells: As also mentioned in our response to R2.12, GRHL2 is well established as a nuclear protein (Zeng et al., Cancers 2024; Riethdorf et al., International Journal of Cancer 2015), and although PR is classically described as translocating to the nucleus upon hormone stimulation, several studies-including our own-have shown that PR is continuously present in the nucleus (Aarts et al., J Mammary Gland Biol Neoplasia 2023; Frigo et al., Essays Biochem. 2021). Thus, both proteins mostly reside in the nucleus in breast (cancer) cells both in the absence and presence of hormone stimulation, but dynamic subcellular shuttling is likely to occur.

Minor comments: Please describe in more detail the relationship between PR and GRHL2 binding independent of the hormone in the discussion section.

As also mentioned in our response to R2.12, we have expanded the discussion to include a dedicated section addressing this point (lines 376-388).

Reviewer #3 (Significance (Required)):

Advance: Compare the study to existing published knowledge, it fills a gap. The authors provide RNA seq and CUT-TAG sequence analysis to show the recruitment of GRHL2 and PR and the co-regulated genes in the absence or presence of progesterone.

Audience: breast surgery will be interested, and the audiences will cover clinical and basic research.

My expertise is focused on the epigenetic modulation of steroid hormone receptors in the related cancers, such as breast cancer, prostate cancer, and endometrial carcinoma.

We agree that with our implemented changes, this study should reach (and appeal to) an audience interested in transcriptional regulation, chromatin biology, hormone receptor signaling and breast cancer.

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

This study explores the important transcriptional coordination role of Grainyhead-like 2 (GRHL2) on the transcriptional regulatory function of progesterone receptor (PR). In this paper, the authors start with their recruitment characteristics, take into account their regulatory effects on downstream genes and their effects on the occurrence and development of breast cancer, and further clarify the coordination between them in three-dimensional space. The interaction between GRHL2 and PR, and the subsequent important influence on the co-regulated genes by GRHL2 and PR are analyzed. The overall framework of this study is mainly by RNA seq and CUT-TAG analysis, the molecular mechanism underlying the association between GRHL2 and PR and regulation function of two proteins in breast cancer is not clearly clarified. Some details need to be further improved：

Major comments:

1. For Fig.1D, the molecular weight of each protein should be marked in the diagram, and the expression of GRHL2 in the input group should be supplemented.
2. In Fig.2B and Fig 5C, it should be describe well whether GRHL2 recruitment is in the absence or presence of R5020? How about the co-occupancy of PR and GRHL2 region, Promoter or enhancer region? It would be better to show histone marks such as H3K27ac and H3K4me1 to annotate the enhancer region.
3. What is the biological function analysis by KEGG or GO analysis for the overlapping genes from VN plots of RNA-seq with CUT-TAG peaks. The genes co-regulated by GRH2L and PR are further determined.
4. Western blotting should be performed to determine the protein levels of downstream genes co-regulated genes by GRH2L and PR in the absence or presence of R5020.
5. The author mentioned that this study positions that GRHL2 acts as a crucial modulator of steroid hormone receptor function, while the authors do not provide the evidences that how does GRHL2 regulate PR-mediated transactivation, and how about these two proteins subcellular distribution in breast cancer cells.

Minor comments:

Please describe in more detail the relationship between PR and GRHL2 binding independent of the hormone in the discussion section.

Significance

Advance: Compare the study to existing published knowledge, it fills a gap. The authors provide RNA seq and CUT-TAG sequence analysis to show the recruitment of GRHL2 and PR and the co-regulated genes in the absence or presence of progesterone.

Audience: breast surgery will be interested, and the audiences will cover clinical and basic research.

My expertise is focused on the epigenetic modulation of steroid hormone receptors in the related cancers, such as breast cancer, prostate cancer, and endometrial carcinoma.

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

The authors present a study investigating the role of GRHL2 in hormone receptor signaling. Previous research has primarily focused on GRHL2 interaction with estrogen receptor (ER) and androgen receptor (AR). In breast cancer, GRHL2 has been extensively studied in relation to ER, while its potential involvement with the progesterone receptor (PR) remains largely unexplored. This is the rational of this study to investigate the relation between PR and GRHL2. The authors demonstrate an interaction between GRHL2 and PR and further explore this relationship at the level of genomic binding sites. They also perform GRHL2 knockdown experiments to identify target genes and link these transcriptional changes back to GRHL2-PR chromatin occupancy. However, several conceptual and technical aspects of the study require clarification to fully support the authors' conclusions.

1. Given the high sequence similarity among GRHL family members, this raises questions about the specificity of the antibody used for GRHL2 RIME. The authors should address whether the antibody cross-reacts with GRHL1 or GRHL3. For example, GRHL1 shows a higher log fold change than GRHL2 in the RIME data.
2. In addition, in RIME experiments, one would typically expect the bait protein to be among the most highly enriched proteins compared to control samples. If this is not the case, it raises questions about the efficiency of the pulldown, antibody specificity, or potential technical issues. The authors should comment on the enrichment level of the bait protein in their data to reassure readers about the quality of the experiment.
3. The authors report log2 fold changes calculated using iBAQ values for the bait versus IgG control pulldown. While iBAQ provides an estimate of protein abundance within samples, it is not specifically designed for quantitative comparison between samples without appropriate normalization. It would be helpful to clarify the normalization strategy applied and consider using LFQ intensities.
4. Other studies have reported PR RIME, which could be a valuable source to investigate whether GRHL proteins were detected.
5. In line 137, the term "protein score" is mentioned. Could the authors please clarify what this means and how it was calculated.
6. In line 140-141. The fact that GRHL2 interacts with chromatin remodelers does not by itself prove that GRHL2 acts as a pioneer factor or chromatin modulator. Demonstrating pioneer function typically requires direct evidence of chromatin opening or binding to closed chromatin regions (e.g., ATAC-seq, nucleosome occupancy assays). I recommend revising this statement or providing supporting evidence.
7. The pulldown Western blot lacks an IgG control in panel D.
8. Depending on the journal and target audience, it may be helpful to briefly explain what R5020 is at its first mention (line 146).
9. The authors state that three technical replicates were performed for each experimental condition. It would be helpful to clarify the expected level of overlap between biological replicates of RIME experiments. This clarification is necessary, especially given the focus on uniquely enriched proteins in untreated versus treated cells, and the observation that some identified proteins in specific conditions are not chromatin-associated. Replicates or validations would strengthen the findings.
10. The volcano plot for the RIME experiment appears to show three distinct clusters of proteins on the right, which is unusual for this type of analysis. The presence of these apparent groupings may suggest an artifact from the data processing, such as imputation. Can the authors clarify the origin of these groupings? If it is due to imputation or missing values, I recommend applying a stricter threshold, such as requiring detection in all three replicates (3/3) to improve the robustness of the enrichment analysis and increase confidence in the identified interactors.
11. The statement that "PR and GRHL2 frequently overlap" may be overstated given that only ~700 overlapping sites are reported (cut&run).
12. The model in Figure 6 suggests limited chromatin occupancy of PR and GRHL2 in hormone-depleted conditions, consistent with the known requirement of ligand for stable PR-DNA binding. However, Figure 1 shows no major difference in GRHL2-PR interaction between untreated and hormone-treated cells. This raises questions about where and how this interaction occurs in the absence of hormone. Since PR binding to chromatin is typically minimal without ligand, can the authors clarify this given that RIME data reflect chromatin-bound interactions.
13. It would be of interest to assess the overlap between the proteins identified in the RIME experiment and the motif analysis results.
14. The authors chose CUT&RUN to assess chromatin binding of PR and GRHL2. Given that RIME is also based on chromatin immunoprecipitation - ChIP protocol, it would be helpful to clarify why CUT&RUN was selected over ChIP-seq for the DNA-binding assays. What is the overlap with published data?

Significance

General Assessment:

This study investigates the role of the transcription factor GRHL2 in modulating PR function, using RIME and CUT&RUN to explore protein-protein and protein-chromatin interactions. GRHL2 have been implicated in epithelial biology and transcriptional regulation and interaction with steroid hormone receptors has been reported. This study extends the field by showing a functional link between GRHL2 and PR, which has implications for understanding hormone-dependent gene regulation.

The research will primarily interest a specialized audience in transcriptional regulation, chromatin biology, and hormone receptor signaling.

Key words for this reviewer: chromatin biology, transcription factor function, epigenomics, and proteomics.

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 #1

Evidence, reproducibility and clarity

Summary

The manuscript by Aarts et al. explores the role of GRHL2 as a regulator of the progesterone receptor (PR) in breast cancer cells. The authors show that GRHL2 and PR interact in a hormone-independent manner and, based on genomic analyses, propose that they co-regulate target genes via chromatin looping. To support this model, the study integrates both newly generated and previously published datasets, including ChIP-seq, CUT&RUN, RNA-seq, and chromatin interaction assays, in breast cancer cell models (T47DS and T47D).

Major comments:

1. Novelty of GRHL2 in steroid receptor biology The role of GRHL2 as a co-regulator of steroid hormone receptors has previously been described for ER (J Endocr Soc. 2021;5(Suppl 1):A819) and AR (Cancer Res. 2017;77:3417-3430). In the ER study, the authors also employed a GRHL2 ΔTAD T47D cell model. Therefore, while this manuscript extends GRHL2 involvement to PR, the contribution appears incremental rather than conceptual.
2. Mechanistic depth The study provides limited mechanistic insight into how GRHL2 functions as a PR co-regulator. Key mechanistic questions remain unaddressed, such as whether GRHL2 modulates PR activation, the sequential recruitment of co-activators/co-repressors, engages chromatin remodelers, or alters PR DNA-binding dynamics. Incorporating these analyses would considerably strengthen the mechanistic conclusions.
3. Definition of GRHL2-PR regulatory regions (Figure 2) The 6,335 loci defined as GRHL2-PR co-regulatory regions are derived from a PR ChIP-seq performed in the presence of hormone and a GRHL2 ChIP-seq performed in its absence. This approach raises doubts about whether GRHL2 and PR actually co-occupy these regions under ligand stimulation. GRHL2 ChIP-seq experiments in both hormone-treated and untreated conditions are necessary to provide stronger support for this conclusion.
4. Cell model considerations The manuscript relies heavily on the T47DS subclone, which expresses markedly higher PR levels than parental T47D cells (Aarts et al., J Mammary Gland Biol Neoplasia 2023; Kalkhoven et al., Int J Cancer 1995). This raises concerns about physiological relevance. Key findings, including co-IP and qPCR-ChIP experiments, should be validated in additional breast cancer models such as parental T47D, BT474, and MCF-7 cells to generalize the conclusions. Furthermore, data obtained from T47D (PR ChIP-seq, HiChIP, CTCF and Rad21 ChIP-seq) and T47DS (RNA-seq, CUT&RUN) are combined along the manuscript. Given the substantial differences in PR expression between these cell lines, this approach is problematic and should be reconsidered.
5. CUT&RUN vs ChIP-seq data The CUT&RUN experiments identify fewer than 10% of the PR binding sites reported in the ChIP-seq datasets. This discrepancy likely results from methodological differences (e.g., absence of crosslinking, potential loss of weaker binding events). The overlap of only 158 sites between PR and GRHL2 under hormone treatment (Figure 3B) provides limited support for the proposed model and should be interpreted with greater caution.
6. Gene expression analyses (Figure 4) The RNA-seq analysis after 24 hours of hormone treatment likely captures indirect or secondary effects rather than the direct PR-GRHL2 regulatory program. Including earlier time points (e.g., 4-hour induction) in the analysis would better capture primary transcriptional responses. The criteria used to define PR-GRHL2 co-regulated genes are not convincing and may not reflect the regulatory interactions proposed in the model. Strong basal expression changes in GRHL2-depleted cells suggest that much of the transcriptional response is PR-independent, conflicting with the model (Figure 6). A more straightforward approach would be to define hormone-regulated genes in shControl cells and then examine their response in GRHL2-depleted cells. Finally, integrating chromatin accessibility and histone modification datasets (e.g., ATAC-seq, H3K27ac ChIP-seq) would help establish whether PR-GRHL2-bound regions correspond to active enhancers, providing stronger functional support for the proposed regulatory model.

Minor comments

Page 19: The statement that "PR and GRHL2 trigger extensive chromatin reorganization" is not experimentally supported. ATAC-seq would be an appropriate method to test this directly.

Prior literature on GRHL2 as a steroid receptor co-regulator should be discussed more thoroughly.

Significance

The identification of novel PR co-regulators is an important objective, as the mechanistic basis of PR signaling in breast cancer remains incompletely understood. The main strength of this study lies in highlighting GRHL2 as a factor influencing PR genomic binding and transcriptional regulation, thereby expanding the repertoire of regulators implicated in PR biology. That said, the novelty is limited, given the established roles of GRHL2 in ER and AR regulation. Mechanistic insight is underdeveloped, and the reliance on an engineered T47DS model with supra-physiological PR levels reduces the general impact. Without validation in physiologically relevant breast cancer models and clearer separation of direct versus indirect effects, the overall advance remains modest.

The manuscript will be of interest to a specialized audience in the fields of nuclear receptor signaling, breast cancer genomics, and transcriptional regulation. Broader appeal, including translational or clinical relevance, is limited in its current form.

3.2.2 The Matcher

Given what we know about "Venus," as Hartman describes as girls who were deprived of their voice and presence in historical records and could experience abuse/exploitation, how can we trust this description of "consent"? Could women on slave ships, with an obvious power dynamic of master/slave consent?

Hartman raises a central ethical problem in feminist and Black studies: how to represent historical violence without reproducing it. This reminds me of discussions that are made in regards to horror movies or other media that depict sensitive subject matter, which is "what draws the line between accurate portrayal and the glamourization/aestheticization of abuse and suffering?" If the victims, like Emmett Till who was mentioned in the passage, cannot decide that how can historians decide what's appropriate?

Seem like an empty reference.

The main example of this Part

In (and wouldn't a domain expert be needed in the development of any search strategy?)

If artificial intelligence is more effective at a given job than humans, even if it's not in all aspects, wouldn't companies choose artificial intelligence due to their overall proficiency, availability, and not needing to compensate them. Also, in some industries, such as art and entertainment, I find it hard to think of jobs that would be created if AI is fully used to generate the content.

locating the cause vs solving the issue is very different. I am skeptical of this claim

An interesting thing to think about is if there is a vaccine for everything, then what will people who resort to biological weapons do. I assume if there are people who choose to continue to make biological weapons, we could come across things that vaccines cannot fix

If artificial intelligence was used to enhance the rate at which biological discoveries are made, I do not think that this will improve biological progress by that much because of limitations on funding and research into new discoveries.

I think this might be his radical thinking, since being able to improve upon the experimentation models is not certain at all.

I think this greatly highlights his point, if things that are discovered a lot earlier are analyzed thoroughly for insights, then we can develop these technologies much more faster.

I wonder if he is making this claim because he believes there aren't solvable solutions to these problems or because the method of increasing intelligence doesn't allow for the solution to these problems

Its interesting how despite the progress towards robots currently, he doesn't define a strong AI as needing physical componenets

I did not think about this limitation regarding complex concepts like particle acceleration, but it makes sense with the copious amounts of data needed to train large language models and other forms of artificial intelligence.

It's especially important to consider this alongside the use of AI for art/music. For creative tasks, I think that should solely be reserved for humans, as it's a genuine form of expression. Taking away this expression I think would take away meaning from human life.

This is the hinge of the ending: the author reframes AI not as a disruptor but as an accelerator of moral destiny. It’s an elegant story, but it rests on the assumption that human values won’t fracture under the pressure of rapid transformation.

Transparency behind AI decisions would significantly increase user trust in it - allowing for further understanding of how the AI made a decision and whether or not there were any biases involved.

This suggests moral progress is directional. That’s a big philosophical stance. Critics would say moral trajectories aren’t laws of nature and could easily reverse, even with AI.

I think if this were to happen, AI would need to be rid of all biases in order to allow for true justice. But also, the same way that judges have a human aspect in their decisions, should there be the same allowance for AI?

This passage banks heavily on a best-case alignment scenario without acknowledging that even small misalignments or geopolitical conflict could slow things dramatically. It functions more as inspirational rhetoric than probabilistic forecast.

An educated population is one of the biggest defenses against a tyrannical government. It's important that a population knows its rights, and if AI is the best way to distribute this knowledge, then it can definitely be helpful, so long as the knowledge is accurate.

I agree, I think that the overreliance of AI in daily life may lead to easier implementation of ethically bad use cases by people in power, and so because of this, it is important to consider how AI is being used in daily life and whether or not it is truly necessary.

I think that the effect of AI on the climate needs to be considered as well. Even in the U.S., there are so many lower-income communities that are being destroyed environmentally because huge datacenters are being built and stealing all of the resources available to the members of these communities, especially water. This impact is skewed towards those of lower socioeconomic status, which already mimics other technological advancements and the exploitation of less-developed countries.

If this "AI coach" use case truly comes to fruition, there would need to be some safeguards and frameworks in place because even now, there's people who are unfortunately dependent on AI chatbots to relplace a lack of human connection in their lives. I feel like an "AI coach" would have to encourage real human interaction in one's life instead of being the first thing that a person turns to.

While I can certainly appreciate Amodei's passion, I feel like this application is a bit far-fetched and kind of goes back into the rhetoric about these sci-fi claims that people have about AI. Also, with how gen AI has been causing so many environmental issues, I wonder what the environment would look like when human lifespan reaches 150.

This reminds me of our discussion about the movie Megan and how we would have to implement safeguards in AI in order to preserve the interests of the human race

I understand the excitement and push toward powerful artificial intelligence to solve complex problems and automate processes, but I don't understand the desire to use it to fully generate art, literature, and entertainment.

The emphasis on "to learn" is important because with people just using AI without truly understanding it (or even understanding its output) might create a population where the level of intelligence that AI can produce is maxed out as it can just generally be accepted. (There's no desire for more intelligent AI)

I appreciate Amodei's humbleness in this statement because I feel like a lot of CEOs try to present themselves as "all-knowning" (or at the very least knowledgeable in the aspects that their technologies hope to be applicable in). However, I think that Amodei's approach is very human, which creates a sense of trust/connection with the reader.

I agree with this - I feel like there's already a stereotype of tech CEOs of being disconnected from the general population's goals (even just thinking about Musk and Zuckerberg), and language like this doesn't help clear this stereotype up.

I agree that artificial intelligence can be used to improve the quality of human life on many fronts, but I also believe that it can detriment our lives and be used maliciously in a plethora of ways. For example, two of the categories that he is most excited for AI to be applied to are peace and governance, and work and meaning. Applications such as lethal autonomous weapons and job displacement due to automation make me rather cynical about AI's usage in these areas.

It's typically easier to identify the benefits of new technologies because they are created with specific purposes in mind. Innovators get caught up in how they will change an area for the better and are unable to recognize significant harms with those technologies. For example, with artificial intelligence, it was mainly created to automate tasks and solve complex problems, but overreliance on AI can lead to cognitive atrophy and diminished critical thinking skills.

This shows the scale of what he thinks AI will be capable of doing. It reframes AI not as a single tool, but as millions of expert-level workers operating at once, which explains why he believes the impact will change the world.

He calls out inequality as a real risk even in the "good" future. It shows that for him, success is not just technological, it's about whether the benefits reach everyone.

I don't see why this wouldn't be a capability based on my current understanding of AI. If the formation of the AI system and the data that it's built on lack bias, there is seemingly no reason for the system to later develop any new biases and either way that's something the system can be monitored for as a precaution.

For most cases in this area there is no outside factors that are evolving rapidly. One of the big risks of AI is bias which medically since it is based almost solely on biology there shouldn't have any impact on the deployment.

If it ends up developing this far I think the success rate of what could be discovered is endless but I think the driving factor of that will be the parallelism especially since as mentioned experiments take so long that in the time one is being done there are so many other things that can be ran and tested as well but we don't have the people or resources to do so right now.

Colleagues, I invite you to use this space to collaboratively annotate the module introduction and summary. Please highlight areas that are clear or engaging, note any points that may need clarification or stronger alignment with learning outcomes, and add suggestions for improvement. You are welcome to tag your comments (e.g., clarity, engagement, alignment, accessibility) and build on each other’s observations. Your insights and feedback will contribute to improving instructional clarity, learner support, and the overall quality of the module. Thank you for engaging in this shared learning process.

An indigenous group of the Pacific Northwest Coast, in southwestern Canada.

Subtitle

We could include here my suggestion of documenting what functions generated by AI were "touch" and/or alter by the user and which are as suggested by AI. Just to make sure which functions the authors have more knowledge over because they modify them.

I think before testing we need to create a section for efficiency check (the issue that Zander mentioned in the meeting). We could either create a protocol to ask AI to check if the objective can be done more efficient, or review it on our own and find places where it seems there is not needed code. I think the second option is better because it gives us the possibility to check if the author really review the code create by the AI (at least skimmed).

I still fee this is a bit to vague. Maybe per function the testthat and the validation checklist per task?

This might be a bit vague. We could decide if doing it per function or maybe per task. Also, it would be great that if it is per task, we ask to create a diagram of the new functions and how do they interact with old functions.

I think one of the most important part of creating the prompts is what is the context the AI is using. These are the documents/files we attach when creating the prompts. So I think this could also go in the summary. Record the context used to produce the responses. (Also, maybe even what AI is answering, GTP vs Claude)

This is a very strong list in my opinion! Two suggestions: - slightly differentiate “step-by-step explanation of the code” from “plain-language explanation,” since they can overlap (do we mean technical explanation vs high level rationale behind the code?) - maybe add a point on error handling expectations? for example, how the code responds to invalid or missing data

Here is where I disagree somehow with the approach. I find it safer to ask ** Copilot how would it solve it first, show me the steps and the plan. Then modified its plan according to what you think is right. Then ask the agent ** to modify the code. I think testing the logic before the modifications makes it easier.

We might need to be more clear on what is the level of specification of a Task. Would/Could we have many tasks summaries per PR? If we are, are we going to keep all summaries for all tasks? Or are we doing clean up of these summaries at some point? A suggestion: maybe one summary of the task summaries per PR.

One improvement here could be to ask Copilot to evaluate the code incrementally and at execution level (at runtime), that is to evaluate the code based on how it actually runs, not just how it looks or reads. For example, verifying assumptions about inputs/outputs and testing components in isolation to prevent that individual errors/failures trigger cascading error that are very difficult to debug

Maybe create the document right away and save it, in case the computer gets rebooted.

Colleagues, I invite you to use this space to collaboratively annotate the module introduction and summary. Please highlight areas that are clear or engaging, note any points that may need clarification or stronger alignment with learning outcomes, and add suggestions for improvement. You are welcome to tag your comments (e.g., clarity, engagement, alignment, accessibility) and build on each other’s observations. Your insights and feedback will contribute to improving instructional clarity, learner support, and the overall quality of the module. Thank you for engaging in this shared learning process.

Researching an Ams-Paris route for 2027. Through Gent in B, not Brussels. Keolis is French which should help in getting the space on F rail network.

GoVolta no longer a rail provider itself , Keolis is the transporter.

I feel that this statement may cause confusion when discussing fevers. A temperature of 102.5 from exercise or exposure to a warm environment would be a disruption of homeostasis. A fever of 102.5 due to illness does not constitute a disruption of homeostasis, rather a resetting of the set point. In this case, the body will not try to restore a temperature of 98.6F, but will maintain the higher temperature. More info on this discussion here:

https://jamanetwork.com/journals/jamainternalmedicine/fullarticle/209609

I would maybe add the phrase "due to external sources" or some other qualifier so that students don't later become confused about the relationship between homeostasis and fever.

Another Qwen model, without the focus on visual inputs. Alibaba. Listed in ollama

Qwen models are by Alibaba. The VL versions are for visual inputs to generating code. Listed in ollama

The AI toolkit adds LLM based options to VSCode. Installed it, as it allows me to access different models via ollama locally.

eLife Assessment

This manuscript uses adaptive-bandit simulations to describe the dynamics of the Pseudomonas-derived chephalosporinase PDC-3 β-lactamase and its mutants to better understand antibiotic resistance. The finding, that clinically observed mutations alter the flexibility of the Ω- and R2-loops, reshaping the cavity of the active site, is valuable to the field. The evidence is considered incomplete, however, with the need for analysis to demonstrate equilibrium weighting of adaptive trajectories and related measures of statistical significance.

Reviewer #2 (Public review):

Summary:

In the manuscript entitled "Ω-Loop mutations control dynamics 2 of the active site by modulating the 3 hydrogen-bonding network in PDC-3 4 β-lactamase", Chen and coworkers provide a computational investigation of the dynamics of the enzyme Pseudomonas-derived chephalosporinase 3 (PDC3) and some mutants associated with increased antibiotic resistance. After an initial analysis of the enzyme dynamics provided by RMSD/RMSF, the author conclude that the mutations alter the local dynamics within the omega loop and the R2 loop. The authors show that the network of hydrogen bonds in disrupted in the mutants. Constant pH calculations showed that the mutations also change the pKa of the catalytic lysine 67 and pocket volume calculations showed that the mutations expand the catalytic pocket. Finally, time-independent componente analysis (tiCA) showed different profiles for the mutant enzyme as compared to the wild type.

Strengths:

The scope of the manuscript is definitely relevant. Antibiotic resistance is an important problem and, in particular, Pseudomonas aeruginosa resistance is associated with an increasing number of deaths. The choice of the computational methods is also something to highlight here. Although I am not familiar with Adaptive Bandit Molecular Dynamics (ABMD), the description provided in the manuscript that this simulation strategy is well suited for the problem under evaluation.

Weaknesses:

In the revised version, the authors addressed my concerns regarding their use of the MSM, and in my view, their conclusions are now much more robust and well-supported by the data. While it would be very interesting to see a quantitative correlation between the effects of the mutations observed in the MD data and relevant experimental findings, I understand that this may be beyond the scope of the manuscript.

Reviewer #3 (Public review):

Summary:

This manuscript aims to explore how mutations in the PDC-3 3 β-lactamase alter its ability to bind and catalyse reactions of antibiotic compounds. The topic is interesting and the study uses MD simulations and to provide hypotheses about how the size of the binding site is altered by mutations that change the conformation and flexibility of two loops that line the binding pocket. Some greater consideration of the uncertainties and how the method choice affect the ability to compare equilibrium properties would strengthen the quantitative conclusions. While many results appear significant by eye, quantifying this and ensuring convergence would strengthen the conclusions.

Strengths:

The significance of the problem is clearly described the relationship to prior literature is discussed extensively.

Comments on revised version:

I am concerned that the authors state in the response to reviews that it is not possible to get error bars on values due to the use of the AB-MD protocol that guides the simulations to unexplored basins. Yet the authors want to compare these values between the WT and mutants. This relates to RMSD, RMSF, % H-bond and volume calculations. I don't accept that you cannot calculate an uncertainty on a time averaged property calculated across the entire simulation. In these cases you can either run repeat simulations to get multiple values on which to do statistical analysis, or you can break the simulation into blocks and check both convergence and calculate uncertainties.

I note that the authors do provide error bars on the volumes, but the statistics given for these need closer scrutiny (I cant test this without the raw data). For example the authors have p<0.0001 for the following pair of volumes 1072 {plus minus} 158 and 1115 {plus minus} 242, or for SASA p<0.0001 is given for 2 identical numbers 155+/- 3.

I also remain concerned about comparisons between simulations run with the AB-MD scheme. While each simulation is an equilibrium simulation run without biasing forces, new simulations are seeded to expand the conformational sampling of the system. This means that by definition the ensemble of simulations does not represent and equilibrium ensemble. For example, the frequency at which conformations are sampled would not be the same as in a single much longer equilibrium simulation. While you may be able to see trends in the differences between conditions run in this way, I still don't understand how you can compare quantitative information without some method of reweighing the ensemble. It is not clear that such a rewieghting exists for this methods, in which case I advise some more caution in the wording of the comparisons made from this data.

At this stage I don't feel the revision has directly addressed the main comments I raised in the earlier review, although there is a stronger response to the comments of Reviewer #2.

Author response:

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

Reviewer #1 (Public review):

Summary:

This manuscript uses adaptive sampling simulations to understand the impact of mutations on the specificity of the enzyme PDC-3 β-lactamase. The authors argue that mutations in the Ω-loop can expand the active site to accommodate larger substrates.

Strengths:

The authors simulate an array of variants and perform numerous analyses to support their conclusions. The use of constant pH simulations to connect structural differences with likely functional outcomes is a strength.

Weaknesses:

I would like to have seen more error bars on quantities reported (e.g., % populations reported in the text and Table 1).

We appreciate this point. Here, the population we analyze is intended to showcase conformational differences across variants rather than to estimate equilibrium occupancies. Although each system includes 100 trajectories, they were generated using an adaptive-bandit protocol. The protocol deliberately guides towards underexplored basins, therefore conformational heterogeneity betweentrajectories is expected by design. For example, in E219K the MSM decomposition shows that in states 1, 6, and 7 the K67(NZ)–S64(OG) distance is almost entirely > 6 Å, whereas in states 2 and 3 it is almost entirely < 3.5 Å (Figure 5—figure supplement 12). These distances suggest that the hydrogen bond fraction is approximately zero in states 1, 6, and 7, and close to one in states 2 and 3. In addition, the mean first passage time of the Markov state models suggests that the formation and disruption of this hydrogen bond occur on the microsecond timescale, which is far longer than the length of each individual trajectory (300 ns). Consequently, across the 100 replicas, some trajectories exhibit very low fractions, while others display the opposite trend. Under such bimodal, protocol-induced heterogeneity, computing an error bar across trajectories mainly visualizes the protocol’s dispersion and risks being misread as thermodynamic uncertainty, which is not central to our aim of comparing conformational differences between wild-type PDC-3 and variants. We therefore do not include the error bars. 

Reviewer #2 (Public review):

Summary:

In the manuscript entitled "Ω-Loop mutations control dynamics of the active site by modulating the 3 hydrogen-bonding network in PDC-3 4 β-lactamase", Chen and coworkers provide a computational investigation of the dynamics of the enzyme Pseudomonas-derived cephalosporinase 3 (PDC3) and some mutants associated with increased antibiotic resistance. After an initial analysis of the enzyme dynamics provided by RMSD/RMSF, the author concludes that the mutations alter the local dynamics within the omega loop and the R2 loop. The authors show that the network of hydrogen bonds is disrupted in the mutants. Constant pH calculations showed that the mutations also change the pKa of the catalytic lysine 67, and pocket volume calculations showed that the mutations expand the catalytic pocket. Finally, time-independent component analysis (tiCA) showed different profiles for the mutant enzyme as compared to the wild type.

Strengths:

The scope of the manuscript is definitely relevant. Antibiotic resistance is an important problem, and, in particular, Pseudomonas aeruginosa resistance is associated with an increasing number of deaths. The choice of the computational methods is also something to highlight here. Although I am not familiar with Adaptive Bandit Molecular Dynamics (ABMD), the description provided in the manuscript suggests that this simulation strategy is well-suited for the problem under evaluation.

Weaknesses:

In the description of many of their results, the authors do not provide enough information for a deep understanding of the biochemistry/biophysics involved. Without these issues addressed, the strength of the evidence is of concern.

We thank the reviewer for pointing out the need for deeper discussion of the biochemical and biophysical implications of our results. In our manuscript, we begin by examining basic structural metrics (e.g., RMSD and RMSF) which clearly indicate that the major conformational changes occur in the Ω-loop and the R2 loop. We have now added a paragraph to describe the importance of the Ωloop and highlighted it in the revised manuscript on lines 142-166 of page 6. This observation guided our subsequent focus on these regions, as well as on the catalytic site. Our analysis revealed notable alterations in the hydrogen bonding network—especially in interactions involving the K67-S64, K67N152, K67-G220, Y150-A292, and N287-N314 pairs. These observations led us to conclude that:

(1) Mutations E219K and Y221A facilitate the proton transfer of catalytic residues. This is consistent with prior experimental data showing that these substitutions produce the most pronounced increase in sensitivity to cephalosporin antibiotics (lines 210-212 in page 8 of the revised manuscript). 

(2) Substitutions enlarge the active-site pocket to accommodate bulkier R1 and R2 groups of β-lactams.This is in line with MIC measurements reported by Barnes et al. (2018), which showed that mutants with larger active-site pockets exhibit markedly greater sensitivity to cephalosporins with bulky side chains than others (lines 249-259 in pages 10).

Furthermore, we applied Markov state models (MSMs) to explore the timescales of the transitions between these different conformational states. We believe that these methodological steps support our conclusions.

Reviewer #3 (Public review):

Summary:

This manuscript aims to explore how mutations in the PDC-3 3 β-lactamase alter its ability to bind and catalyse reactions of antibiotic compounds. The topic is interesting, and the study uses MD simulations to provide hypotheses about how the size of the binding site is altered by mutations that change the conformation and flexibility of two loops that line the binding pocket. However, the study doesn't clearly describe the way the data is generated. While many results appear significant by eye, quantifying this and ensuring convergence would strengthen the conclusions.

Strengths:

The significance of the problem is clearly described, and the relationship to prior literature is discussed extensively.

Weaknesses:

The methods used to gain the results are not explained clearly, meaning it was hard to determine exactly how some data was obtained. The convergence and uncertainties in the data were not adequately quantified. The text is also a little long, which obscures the main findings.

We thank the reviewer for the suggestion. We respectfully ask the reviewer to specify which aspects of the data-generation methods are unclear so that we can include the necessary details in the next revision. Moreover, all statistics that are reported in the manuscript are obtained from extensive analyses of 300,000 simulation frames. The Markov state models have been validated by the ITS plots and Chapman-Kolmogorov (CK) test. The two-sample t-tests were also carried out for the volume and SASA.

Reviewer #2 (Recommendations for the authors):

(1) Figure 1D focus on the PDC3 catalytic site. However, the authors mentioned before that the enzyme has two domains, an alpha domain and an alpha/beta domain. The reader would benefit from a more detailed description of the enzyme, its active site, AND the location of the mutants under investigation in the figure.

We have updated Figure 1D and marked the positions of all mutations (V211A/G, G214A/R, E219A/G/K and Y221A/H), which have now been highlighted as spheres.

(2) Since in the journal format, the results come before the methods. It would be interesting to add a brief description of where the results came from. For example, in the first section of the results, the authors describe the flexibility of the omega loop and the R2 loop. However, the reader won't know what kind of simulation was used and for how long, for example. A sentence would add the required context for a deeper understanding here.

At the beginning of the Results and Discussion section we now state: “To investigate how the mutations in the Ω-loop affect PDC-3 dynamics, adaptive-bandit molecular dynamics (AB-MD) simulations were carried out for each system. 100 trajectories of 300 ns each (totaling 30 μs per system) were run.”

(3) Still in the same section, the authors don't define what change in RMSF is considered significant. For example, I can't see a relevant change in the RMSF for the omega loop between the et enzyme and the E219 mutants in Figure 2D. A more objective definition would be of benefit here.

Our analysis reveals that while the wild-type PDC-3 and the G214A, G214R, E214G, and Y221A variants exhibit an average per-residue RMSF of around 4 Å in the Ω-loop, the V211A and V211G variants show markedly lower values (around 1.5 Å), and the E219K and Y221H variants exhibit intermediate values between 2 and 2.5 Å. In addition, the fluctuations around the binding site should be seen collectively along with the fluctuations in the R2-loop. Importantly, we urge the reviewer to focus on the MDLovofit analysis in Figure 2C, where the dynamic differences between the core and the fluctuating loops is clearly evident.  

(4) In line 138, the authors state that "Therefore, the flexibility of these proteins is mainly caused by the fluctuations in the Ω-loops and R2-loop". This is quite a bold statement to be drawn at this point. First of all, there is no mention of it in the manuscript, but is there any domain movement? Figure 2C clearly shows that there is some mobility in omega and R2 loops. But there is no evidence shown in the manuscript that shows that "the flexibility of these proteins is mainly caused by the fluctuations in the" loops. Please consider rephrasing this sentence or adding more data, if available.

We have revised the wording to take the reviewer’s concern into account. The sentence now states: “Therefore, flexibility of PDC-3 is predominantly localized to the Ω- and R2-loops, whereas the remainder of the structure is comparatively rigid.” To further explain to the reviewer, the β lactamase enzymes are fairly rigid structures, where no large-scale domain motions occur. Instead, the enzyme communicates structurally via cross correlation of loop dynamics ( https://doi.org/10.7554/eLife.66567 ).  

(5) I guess, the most relevant question for the scope of the paper is not answered in this section. The authors show that the mobility of the omega- and R2-loops is altered by some mutations. Why is that? I wish I could see a figure showing where the mutations are and where the loops are. This question will come back in other sections.

We have updated Figure 1D to mark the positions of all mutations (V211A/G, G214A/R, E219A/G/K and Y221A/H) as spheres. The Ω- and R2-loops are also highlighted. All mutations map to the Ω-loop, indicating that these substitutions directly perturb this region. Notably, K67 forms a hydrogen bond with the backbone of G220 within the Ω-loop and another with the phenolic hydroxyl of Y150. Y150, in turn, hydrogen-bonds with A292 in the R2 loop. Together, the residue interaction network (G220– K67–Y150–A292) suggest a pathway by which Ω-loop mutations propagate their effects to the R2 loop.

(6) The authors then analyze the network of polar residues in the active site and the hydrogen bonds observed there. For the K67-N152 hydrogen bond, for example, there is a reduction in the occupancy from ~70% in the wild-type enzyme to ~30% and 40% in the mutants E219K and Y221, respectively. This finding is interesting. The question that remains is "why is that"? From the structural point of view, how does the replacement of E219 with a Lysine alter the hydrogen bond formation between K67 and N152? Is it due to direct competition? Solvent rearrangement? The reader is left without a clue in this section. Also, Figure 3B won't help the reader, since the mutated residues are not shown there. Please consider adding some information about why the authors believe that the mutations are disrupting the active site hydrogen bond network and showing it in Figure 3B.

We appreciate the comment and have updated Figures 1D and 3B to highlight the mutation sites. The change from ~70% in the wild type to ~30–40% in the E219K and Y221T variants reported in Table 1 refers to the S64–K67 hydrogen bond. In the wild type, K67 forms an additional hydrogen bond with G220 on the Ω-loop, which helps anchor the K67 side chain in a geometry that favors the S64–K67 interaction. In the variants, the mutations reshape the Ω-loop and frequently disrupt the K67–G220 contact. The loss of this local anchor increases the conformational dispersion of K67, which is consistent with the observed reduction of the S64–K67 occupancy. Furthermore, our observation that the mutations are disrupting the active-site hydrogen-bond network is a data-driven conclusion rather than a subjective inference. Across ten systems, our AB-MD simulations provided 30 µs of sampling per system. Saving one frame every nanosecond yielded 30,000 conformations per system and 300,000 in total. All hydrogen-bond and salt-bridge statistics were computed over this full ensemble. Thus, the conclusion that the mutations disrupt the active-site hydrogen-bond network follows directly from these ensemble statistics. 

(7) The pKa calculations and the pocket volume calculations show that the mutations expand the volume of the catalytic site and alter the microenvironment. Is there any change in the solvation associated with these changes? If the volume expands and the environment becomes more acidic, are there more water molecules in the mutants as compared to the wt enzyme? If so, can changes in solvation be associated with the changes in the hydrogen bond network? Would a simulation in the presence of a substrate be meaningful here? ( I guess it would!).

Regarding solvation, we observe a modest increase in transient water occupancy associated with the increase in volume of the pocket. The conserved deacylation water molecule is the most important and is always present throughout the simulation. Additional waters enter and leave the pocket but do not form persistent interactions that measurably perturb the hydrogen-bond network of the Ω- and R2-loops. We agree that simulations with a bound substrate would be informative. However, our study focuses on how Ω-loop mutations modulate the active site of apo PDC-3 and its variants. Within this scope, we find: (i) Amino acid substitutions change the flexibility of Ω-loops and R2-loops; (ii) E219K and Y221A mutations facilitate the proton transfer; (iii) Substitutions enlarge the active-site pocket to accommodate bulkier R1 and R2 groups of β-lactams.

(8) I have some concerns regarding the Markov State Modeling as shown here. After a time-independent component analysis, the authors show the projections on the components, which is different between wild wild-type enzyme and the mutants, and draw some conclusions from these changes. For example, the authors state that "From the metastable state results, we observe that E219K adopts a highly stable conformation in which all the tridentate hydrogen-bonding interactions (K67(NZ)-S64(OG), K67(NZ)N152(OD1) and K67(NZ)-G220(O) mentioned above are broken". This is conclusion is very difficult to draw from Figure 5 alone. Unless the macrostates observed in the MSM can be shown (their structures) and could confirm the broken interactions, I really don't believe that the reader can come to the same conclusion as drawn by the authors here. I would recommend the authors to map the macrostates back to the coordinates and show them (what structure corresponds to what macrostate). After showing that, it makes sense to discuss what macrostate is being favored by what mutation. Taking conclusions from tiCA projections only is not recommended. I very strongly suggest that the authors revisit this entire section, adding more context so that the reader can draw conclusions from the data that is shown.

We appreciate the reviewer’s concern. In the Markov state modeling section, our objective is to quantify the timescales (via mean first passage times) associated with the formation and disruption of the critical hydrogen bonds (K67(NZ)-S64(OG), K67(NZ)-N152(OD1), K67(NZ)-G220(O), Y150(N)A292(O), N287(ND2)-N314(OD1)) mentioned above. Representative structures illustrating these interactions are shown in Figures 3B and 4A. We agree that the main Figure 5 alone does not convey structural information. Accordingly, we provide Figure 5—figure supplements 12–16. Together, Figure 5B and Figure 5—figure supplements 12–16 map structures to metastable states, whereas Figures 3B and 4A supply atomistic detail of the interactions. Author response image 1 presents selected subplots from Figure 5— figure supplements 12–14. Together with the free-energy landscape in Figure 5A, these data indicate that E219K adopts a highly stable conformation in which all three K67-centered hydrogen bonds (K67(NZ)–S64(OG), K67(NZ)–N152(OD1), and K67(NZ)–G220(O)) are broken.

Author response image 1.

TICA plot illustrates the distribution of E219K with the colour indicating the K67(NZ)-S64(OG), K67(NZ)-N152(OD1) and K67(NZ)-G220(O) distance.

(9) As a very minor issue, there are a few typos in the manuscript text. The authors might want to take some time to revisit their entire text. Examples in lines 70, 197, etc.

Thank you for your comment. We have corrected these typos.

Reviewer #3 (Recommendations for the authors):

This manuscript aims to explore how mutations in the PDC-3 3 β-lactamase alter its ability to bind and catalyse reactions of antibiotic compounds. The topic is interesting, and the study uses MD simulations to provide hypotheses about how the size of the binding site is altered by mutations that change the conformation and flexibility of two loops that line the binding pocket.

However, the study doesn't clearly describe the way the data is generated and potentially lacks statistical rigour, which makes it uncertain if the key results are significant. As such, it is difficult to judge if the conclusions made are supported by data.

All necessary data-acquisition methods are described in the Methods section. The Markov state models have been validated by the ITS plot and the Chapman-Kolmogorov (CK) test (Figure 5—figure supplement 2–11) . The two-sample t-tests were also carried out for the volume and SASA (Table 2).

The results section jumps straight to reporting RMSD and RMSF values; however, it is not clear what simulations are used to generate this information. Indeed, the main text does not mention the simulations themselves at all. The methods section mentions that 10 independent MD simulations were set up for each system, but no information is given as to how long these were run or the equilibration protocol used. Then it says that AB-MD simulations were run, but it is not clear what starting coordinates were used for this or how the 10 replicates were fed into these simulations. Most importantly, are the RMSD and RMSF calculations and later distance distribution information derived from the equilibrium MD runs or from the AB-MD simulations?

Thank you for pointing this out. We have added “To investigate how the mutations in the Ω-loop affect PDC-3 dynamics, adaptive-bandit molecular dynamics (AB-MD) simulations were carried out for each system. 100 trajectories of 300 ns each (totaling 30 μs per system) were run.” to the Results and Discussion section. We didn’t run 10 independent MD simulations per system. We regret the typo in the Methods section that confused the reviewer. The sentence should have read – ‘All-atom MD simulations of wild-type PDC-3 and its variants were performed.’ Each system was equilibrated for 5 ns at 1 atmospheric pressure using Berendsen barostat. AB-MD simulations were initiated from these equilibrated structures. All analyses, apart from CpHMD, are based on the AB-MD trajectories.

If these are taken from the equilibrium simulations, then it is critical that the reproducibility and statistical significance of the simulations is established. This can be done by calculating the RMSD and RMSF values independently for each replicate and determining the error bars. From this, the significance of differences between WT and mutant simulations can be determined. Without this, I have no data to judge if the main conclusions are supported or not. If these are derived from the AB-MD simulations, then I want to know how the independent simulations were combined and reweighted to generate overall RMSD, RMSF, and distance distributions. Unless I misunderstand the approach, the individual simulations no longer sample all regions of conformational space the same relative amount you would see in a standard MD simulation - specific conformational regions are intentionally run more to enhance sampling, then the overall conformational distributions cannot be obtained from these simulations without some form of reweighting scheme. But no such scheme is described. In addition, convergence of the data is required to ensure that the RMSD, RMSF, and distances have reached stable values. It is possible that I am misunderstanding the approach here. But in that case, I hope the authors can clarify the method and provide a means of ensuring that the data presented is converged. Many of the differences are clear by eye, but it is important to know they are not random differences between simulations and rather reflect differences between them.

Thank you for raising this important point. In our AB-MD workflow, the adaptive bandit is used only for starting-structure selection (adaptive seeding). After each epoch, it chooses new starting snapshots from previously sampled conformations and launches the next runs. Each trajectory itself is standard, unbiased MD with no biasing potentials and no modification of the Hamiltonian. In other words, AB decides where we start, but does not alter the physics or sampling dynamics within an individual trajectory. In addition, our goal in this work is to compare variants under the same adaptive-bandit (AB) protocol, rather than to estimate equilibrium (Boltzmann) populations. Hence, we did not apply equilibrium reweighting to RMSD, RMSF, or distance distributions. However, MSM section provides reweighted reference results based on the MSM stationary distribution.

In the response to reviews, the authors state that the "RMSF is a statistical quantity derived from averaging the time series of atomic displacements, resulting in a fixed value without an inherent error bar." But normally we would run multiple replicates and get an error bar from the different values in each. To dismiss the request for uncertainties and error bars seems to miss the point. I strongly agree with the prior reviewer that comparisons between RMSF or other values should be accompanied by uncertainties and estimates of statistical significance.

Regarding the reviewers’ suggestion to present the data as a bar graph with error bars, we would like to note that RMSF is calculated as the time average of the fluctuations of each residue’s Cα atom over the entire simulation. As such, RMSF is a statistical quantity derived from averaging the time series of atomic displacements, resulting in a fixed value without an inherent error bar. We believe that our current presentation clearly and accurately reflects the local flexibility differences among the variants. Nearly all published studies report RMSF in this way, as indicated by the following examples:

Figure 3a in DOI: https://doi.org/10.1021/jacsau.2c00077

Figure 2 in DOI: https://doi.org/10.1021/acs.jcim.4c00089

Supplementary Fig. 1, 2, 5, 9, 12, 20, 22, 24, and 26 in DOI: https://doi.org/10.1038/s41467-022-293313

However, in response to the reviewers’ strong request, we present RMSF plots with error bars in our response letter. 

Author response image 2.

The root-mean-square fluctuation (RMSF) profiles of wild-type PDC-3 and its variants. Blue lines show the mean RMSF across 100 independent MD trajectories for each system; red translucent bands denote the standard deviation across trajectories. The Ω-loop (residues G183 to S226) is highlighted in yellow, and the R2-loop (residues L280 to Q310) is highlighted in blue.

It was good to see that convergence of the constant-pH simulations was shown. While it can be challenging to get absolute pH values from the implicit solvent-based simulations, the differences between the systems are large and the trends appear significant. I was not clear how the starting coordinates were chosen for these simulations. Is the end point of the classical simulations, or is a representative snapshot chosen somehow?

To ensure comparison, all systems used the X-ray crystal structure (PDB ID: 4HEF) with T79A substitution as the initial structure. The E219K and Y221A mutants were generated in silico using the ICM mutagenesis module. We have added the clarification in Methods section: “The starting structures were identical to those used for AB-MD.”

Significant figures: Throughout the text and tables, the authors present data with more figures than are significant. 1071.81+-157.55 should be reported as 1100 +/ 160 or 1070 =- 160 . See the eLife guidelines for advice on this.

Thank you for your suggestion. We have amended these now. 

The manuscript is very long for the results presented, and I feel that a clearer story would come across if the authors shortened the text so that the main conclusions and results were not lost.

We appreciate the suggestion. We examined the twenty most recent research articles published in eLife and found that they are either longer than or comparable in length to our manuscript.

huh?

culture of impossible. myth of complexity

eLife Assessment

This study makes a valuable contribution by elucidating the genetic determinants of growth and fitness across multiple clinical strains of Mycobacterium intracellulare, an understudied non-tuberculous mycobacterium. Using transposon sequencing (Tn-seq), the authors identify a core set of 131 genes essential for bacterial adaptation to hypoxia, providing a convincing foundation for anti-mycobacterial drug discovery.

Reviewer #1 (Public review):

Summary:

In this descriptive study, Tateishi et al. report a Tn-seq based analysis of genetic requirements for growth and fitness in 8 clinical strains of Mycobacterium intracellulare Mi), and compare the findings with a type strain ATCC13950. The study finds a core set of 131 genes that are essential in all nine strains, and therefore are reasonably argued as potential drug targets. Multiple other genes required for fitness in clinical isolates have been found to be important for hypoxic growth in the type strain.

Strengths:

The study has generated a large volume of Tn-seq datasets of multiple clinical strains of Mi from multiple growth conditions, including from mouse lungs. The dataset can serve as an important resource for future studies on Mi, which despite being clinically significant, remains a relatively understudied species of mycobacteria.

Weaknesses:

The primary claim of the study that the clinical strains are better adapted for hypoxic growth is yet to be comprehensively investigated. However, this reviewer thinks such an investigation would require a complex experimental design and perhaps form an independent study.

Comments on revisions:

The revised paper has satisfactorily addressed my previous concerns, and I have no further issues with this paper.

Author response:

The following is the authors’ response to the previous reviews

Reviewer #1 (Public review) :

Comments on revisions:

The revised manuscript has responded to the previous concerns of the reviewers, albeit modestly. The overemphasis on hypoxic adaptation of the clinical isolates persist as a key concern in the paper. The authors have compared the growth-curve of each of the clinical and ATCC strains under normal and hypoxic conditions (Fig. 8), but don't show how mutations in some of the genes identified in Tn-seq would impact the growth phenotype under hypoxia. They largely base their arguments on previously published results.

As I mentioned previously, the paper will be better without over-interpreting the TnSeq data in the context of hypoxia.

Thank you for the comment on the issue of not determining the impact of individual gene mutations identified in TnSeq on the growth phenotypes under hypoxia.

We agree that the lack of validation of TnSeq results is a limitation of this study. Without evidence of growth pattern of each gene-deletion mutant under hypoxia there might be a risk of over-interpretating the data, even though the data are carefully interpreted based on previous reports. We consider that it is necessary to confirm the phenomenon by using knockout mutants.

We have just recently succeeded in constructing the vector plasmids for making knockout mutants of M intracellulare (Tateishi. Microbiol Immunol. 2024). We will proceed to the validation experiment of TnSeq-hit genes by constructing knockout mutants. We already mentioned this point as a limitation of this study in the Discussion (pages 35-36 lines 630-640 in the revised manuscript).

Reference.

Tateishi, Y., Nishiyama, A., Ozeki, Y. & Matsumoto, S. Construction of knockout mutants in Mycobacterium intracellulare ATCC13950 strain using a thermosensitive plasmid containing negative selection marker rpsL+. Microbiol Immunol 68, 339-347 (2024).

Other points:

The y-axis legends of plots in Fig.8c are illegible.

Following the comment, we have corrected Figure 8c and checked the uploaded PDF

The statements in lines 376-389 are convoluted and need some explanation. If the clinical strains enter the log phase sooner than ATCC strain under hypoxia, then how come their growth rates (fig. 8c) are lower? Aren't they expected to grow faster?

Thank you for the comment on the interpretation of the difference in bacterial growth under hypoxia between MAC-PD strains and the ATCC type strain. The growth curve consists of the onset of logarithmic growth and its growth speed. In this study, we evaluated the former as timing of midpoint and the latter as growth rate at midpoint. Timing of midpoint and growth rate at midpoint are individual parameters. The early entry to log-phase does not mean the fast growth rate at midpoint.

Our results demonstrated that 5 (M.i.198, M.i.27, M003, M019 and M021) out of 8 clinical MAC-PD strains entered log-phase early and continued to grow logarithmically long time (slow growth). This data suggests the capacity for MAC-PD to continue replication long time under hypoxic conditions. By contrast, the ATCC type strain showed delayed onset of logarithmic growth caused by long-term lag phase. The duration of logarithmic growth was short even once after it started. The log phase soon transited to the stationary phase. This data suggests the lower capacity for the ATCC strain to continue replication under hypoxic conditions.

Following the comment, we have added the interpretation of the growth curve pattern as follows (page 22 lines 379-392 in the revised manuscript): “The growth rate at midpoint under hypoxic conditions was significantly lower in these 5 clinical MAC-PD strains than in ATCC13950. The early entry to log phase followed by long-term logarithmic growth (slow growth rate at midpoint) suggests the capacity for these 5 clinical MAC-PD strains to continue replication long time under hypoxic conditions. On the other hand, the rest 3 clinical MAC-PD strains (M018, M001 and MOTT64) did not show significant change in the growth rate between aerobic and hypoxic conditions, suggesting that there are different levels of capacity in maintaining long-term replication under hypoxia among clinical MAC-PD strains. In ATCC13950, the entry to log phase was significantly delayed under 5% oxygen compared to aerobic conditions, and the growth rate at midpoint was significantly increased under hypoxic conditions compared to aerobic conditions in ATCC13950. Such long-term lag phase followed by short-term log phase suggests lower capacity for ATCC13950 to continue replication under hypoxic conditions compared to clinical MAC-PD strains.”

Reviewer #4 (Public review):

Comments on revisions:

The revised version has satisfactorily addressed my initial comments in the discussion section.

The authors thank the Reviewer for understanding our reply.

Reviewer #5 (Public review):

Comments on revisions:

There is quite a lot of data and this could have been a really impactful study if the authors had channelized the Tn mutagenesis by focusing on one pathway or network. It looks scattered. However, from the previous version, the authors have made significant improvements to the manuscript and have provided comments that fairly address my questions.

The authors thank the Reviewer for understanding our reply. And the authors thank the Reviewer for the comments suggesting the future studies of TnSeq that focus on one pathway or network.

eLife Assessment

This is an important study that utilized in vivo optical measurements of the cortical metabolic rate of O2 and blood flow, as well as measurements in isolated mitochondria to assess the uncoupling of the oxidative phosphorylation due to hypoxia-ischemia injury of the neonatal brain, and effects of the hypothermia treatment. The combination of state-of-the-art optical measurements, mitochondrial assays, and the use of various control experiments provides convincing evidence for the derived conclusions. This work will be of interest to those in the mitochrondrial metabolomics, brain injury and hypoxia fields.

Reviewer #1 (Public review):

Summary:

This manuscript addresses the important problem of the uncoupling of oxidative phosphorylation due to hypoxia-ischemia injury in the neonatal brain and provides insight into the neuroprotective mechanisms of hypothermia treatment.

Strengths:

The authors used a combination of in vivo imaging of awake P10 mice and experiments on isolated mitochondria to assess various key parameters of brain metabolism during hypoxia-ischemia with and without hypothermia treatment. This unique approach resulted in a comprehensive data set that provides solid evidence to support the derived conclusions.

Weaknesses:

Several potential weaknesses were identified in the original submission, which the authors subsequently addressed in the revised manuscript. Here is the brief list of the questions:

(1) Is it possible that the observed relatively low baseline OEF and trends of increased OEF and CBF over several hours after the imaging start were partially due to slow recovery from anesthesia?

(2) What was the pain management, and is there a possibility that some of the observations were influenced by the pain-reducing drugs or their absence?

(3) Were P10 mice significantly stressed during imaging in the awake state because they didn't have head-restraint habituation training?

(4) Considering high metabolism and blood flow in the cortex, it could be potentially challenging to predict cortical temperature based on the skull temperature, particularly in the deeper part of the cortex.

(5) The map of estimated CMRO2 looks quite heterogeneous across the brain surface. Could this be partially resulting from the measurement artefact?

(6) It would be beneficial to provide more detailed justification for using P10 mice in the experiments.

Reviewer #3 (Public review):

Sun et al. present a comprehensive study using a novel photoacoustic microscopy setup and mitochondrial analysis to investigate the impact of hypoxia-ischemia (HI) on brain metabolism and the protective role of therapeutic hypothermia. The authors elegantly demonstrate three connected findings: (1) HI initially suppresses brain metabolism, (2) subsequently triggers a metabolic surge linked to oxidative phosphorylation uncoupling and brain damage, and (3) therapeutic hypothermia mitigates HI-induced damage by blocking this surge and reducing mitochondrial stress.

The study's design and execution are great, with a clear presentation of results and methods. Data is nicely presented, and methodological details are thorough.

However, a minor concern is the extensive use of abbreviations, which can hinder readability. As all the abbreviations are introduced in the text, their overuse may render the text hard to read to non-specialist audiences. Additionally, sharing the custom Matlab and other software scripts online, particularly those used for blood vessel segmentation, would be a valuable resource for the scientific community. In addition, while the study focuses on the short-term effects of HI, exploring the long-term consequences and definitively elucidating HI's impact on mitochondria would further strengthen the manuscript's impact.

Despite these minor points, this manuscript is very interesting.

Comments on revisions:

All addressed.

Author response:

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

Reviewer #1 (Public review)

(1) This manuscript addresses an important problem of the uncoupling of oxidative phosphorylation due to hypoxia-ischemia injury of the neonatal brain and provides insight into the neuroprotective mechanisms of hypothermia treatment.

The authors used a combination of in vivo imaging of awake P10 mice and experiments on isolated mitochondria to assess various key parameters of the brain metabolism during hypoxia-ischemia with and without hypothermia treatment. This unique approach resulted in a comprehensive data set that provides solid evidence for the derived conclusions

We thank the reviewer for the positive feedback.

(2) The experiments were performed acutely on the same day when the surgery was performed. There is a possibility that the physiology of mice at the time of imaging was still affected by the previously applied anesthesia. This is particularly of concern since the duration of anesthesia was relatively long. Is it possible that the observed relatively low baseline OEF (~20%) and trends of increased OEF and CBF over several hours after the imaging start were partially due to slow recovery from prolonged anesthesia? The potential effects of long exposure to anesthesia before imaging experiments were not discussed.

We thank the reviewer for this important comment and for pointing out the potential influence of anesthesia on the physiological state of the animals. We apologize for any confusion. To clarify, all PAM imaging experiments were conducted in awake animals. Isoflurane anesthesia was used only during two brief surgical procedures: (1) the installation of the head-restraint plastic head plate and (2) the right common carotid artery (CCA) ligation. Each anesthesia session lasted less than 20 minutes.

We have revised the Methods section to provide additional details:

For the subsection Procedures for PAM Imaging on page 17, we clarified the sequence of procedures during the head plate installation, as well as the corresponding anesthesia duration:

“After the applied glue was solidified (~20 min), the animal was first returned to its cage for full recovery from anesthesia, and then carefully moved to the treadmill and secured to the metal arm-piece with two #4–40 screws for awake PAM imaging. The total duration of anesthesia, including preparation and glue solidification, was approximately 20 minutes.”

For the subsection Neonatal Cerebral HI and Hypothermia Treatment on page 19, we also clarified the CCA ligation procedure:

“Briefly, P10 mice of both sexes anesthetized with 2% isoflurane were subjected to the right CCA-ligation. To manage pain, 0.25% Bupivacaine was administered locally prior to the surgical procedures, which took less than 10 minutes. After a recovery period for one hour, awake mice were exposed to 10% O₂ for 40 minutes in a hypoxic chamber at 37 °C.”

Regarding the reviewer’s concern about the observed trends in OEF and CBF, we agree that residual effects of anesthesia could, in principle, influence physiological parameters. However, we believe this is unlikely in this study for the following reasons. First, all imaging was conducted in awake animals after a clearly defined recovery period. Second, the trend of increasing OEF and CBF over time was consistent across animals and aligned with expected physiological responses following hypoxic-ischemic injury. In particular, the relatively low baseline OEF (0.21 at 37°C) is consistent with our previous study (0.25; (Cao et al., 2018)). The gradual increase in CBF and OEF reflects metabolic compensation and reperfusion following hypoxia-ischemia, as previously described (Lin and Powers, 2018). Therefore, we believe the observed changes are of physiological origin rather than anesthesia-related artifacts.

(3) The Methods Section does not provide information about drugs administered to reduce the pain. If pain was not managed, mice could be experiencing significant pain during experiments in the awake state after the surgery. Since the imaging sessions were long (my impression based on information from the manuscript is that imaging sessions were ~4 hours long or even longer), the level of pain was also likely to change during the experiments. It was not discussed how significant and potentially evolving pain during imaging sessions could have affected the measurements (e.g., blood flow and CMRO₂). If mice received pain management during experiments, then it was not discussed if there are known effects of used drugs on CBF, CMRO₂, and lesion size after 24 hr.

We thank the reviewer for this valuable comment regarding pain management. We confirm that local analgesia was administered to all animals prior to surgical procedures. Specifically, 0.25% Bupivacaine was applied locally before both the head-restraint plate installation and the CCA ligation. These details have now been clarified in the Methods section:

For the subsection Procedures for PAM Imaging on page 16, we added:

“To manage pain, 0.25% Bupivacaine was administered locally prior to the surgical procedures.”

For the subsection Neonatal Cerebral HI and Hypothermia Treatment on page 18, we added:

“To manage pain, 0.25% Bupivacaine was administered locally prior to the surgical procedures, which took less than 10 minutes.”

To our knowledge, Bupivacaine has minimal systemic effects at the dose used and is unlikely to significantly alter CBF, CMRO₂, or lesion development (Greenberg et al., 1998). No other analgesics (e.g., NSAIDs or opioids) were administered unless distress symptoms were observed—which did not occur in this study.

Additionally, although imaging sessions were extended (up to 2 hours), animals remained calm and showed no signs of pain or distress during or after the procedures. Throughout the experimental period (up to 24 hours post-surgery), animals were monitored for signs of discomfort (e.g., abnormal activity, breathing, or weight gain), but no additional analgesia was required. The neonatal HI procedures are considered minimally invasive, and based on our protocol and prior experience, local Bupivacaine provides effective analgesia during and after the brief surgeries. We have added a corresponding note in the Discussion section (newly added subsection: Limitations in this study, the last paragraph) on page 15:

“We observed no signs of distress or pain and did not use stress- or pain-reducing drugs during imaging. However, potential effects of stress or residual pain on CBF and CMRO₂ cannot be fully ruled out. Future studies could incorporate more detailed pain assessment and stress-mitigation strategies to further enhance physiological reliability.”

(4) Animals were imaged in the awake state, but they were not previously trained for the imaging procedure with head restraint. Did animals receive any drugs to reduce stress? Our experience with well-trained young-adult as well as old mice is that they can typically endure 2 and sometimes up to 3 hours of head-restrained awake imaging with intermittent breaks for receiving the rewards before showing signs of anxiety. We do not have experience with imaging P10 mice in the awake state. Is it possible that P10 mice were significantly stressed during imaging and that their stress level changed during the imaging session? This concern about the potential effects of stress on the various measured parameters was not discussed.

We thank the reviewer for this important comment regarding the potential effects of stress during awake imaging. The neonatal mice used in our study were P10, a stage at which animals are still physiologically immature and relatively inactive. Due to their small size and limited mobility, these animals did not struggle or show signs of distress during the imaging sessions. All animals remained calm and stable throughout the procedure, and no stress-reducing drugs were administered.

We agree that, unlike older animals, P10 mice are not amenable to prior behavioral training. However, their underdeveloped motor activity and natural docility at this stage allowed for stable head-restrained imaging without inducing overt stress responses. Although no behavioral signs of stress were observed, we acknowledge that subtle physiological effects cannot be entirely excluded. We have added a brief discussion in the Discussion section (newly added subsection: Limitations in this study, the last paragraph) on page 15:

“Lastly, for awake imaging, the small size of neonatal mice at P10 aids stability during awake PAM imaging, though it limits the feasibility of prior training, which is typically possible in older animals.”

(5) The temperature of the skull was measured during the hypothermia experiment by lowering the water temperature in the water bath above the animal's head. Considering high metabolism and blood flow in the cortex, it could be challenging to predict cortical temperature based on the skull temperature, particularly in the deeper part of the cortex.

We thank the reviewer for this helpful comment and for highlighting an important technical consideration. We acknowledge that we did not directly measure intracortical tissue temperature during the hypothermia experiments. While we recognize that relying on skull temperature may have limitations—particularly in reflecting temperature changes in deeper cortical regions—this approach is consistent with clinical practice, where intracortical temperature is typically not measured. Moreover, prior studies have shown that skull or brain surface temperature generally reflects cortical thermal dynamics to a reasonable extent under controlled conditions (Kiyatkin, 2007). We have added the following note in the Discussion section (newly added subsection: Limitations in this study, the 2^nd paragraph) on page 14:

“A technical limitation is the absence of direct intracortical temperature measurements during hypothermia; we relied on skull temperature, which may not fully capture temperature dynamics in deeper cortical layers. However, this approach aligns with clinical practice, where intracortical temperature is not typically measured. Future studies could benefit from more precise intracortical assessments.”

(6) The map of estimated CMRO₂ (Fig. 4B) looks very heterogeneous across the brain surface. Is it a coincidence that the highest CMRO₂ is observed within the central part of the field of view? Is there previous evidence that CMRO₂ in these parts of the mouse cortex could vary a few folds over a 1-2 mm distance?

We appreciate the reviewer’s insightful observation regarding the spatial heterogeneity observed in the estimated CMRO₂ map (Fig. 4B). This heterogeneity is not a result of scanning bias, as uniform contour scanning was performed across the entire field of view. The higher CMRO₂ values observed in the central region are unlikely to be artifacts and more likely reflect underlying physiological variability.

Our CMRO₂ estimation is based on an algorithm we previously developed and validated in other tissues. Specifically, we have successfully applied this algorithm to assess oxygen metabolism in the mouse kidney (Sun et al., 2021) and to monitor vascular adaptation and tissue oxygen metabolism during cutaneous wound healing (Sun et al., 2022). These studies demonstrated the algorithm's capability to capture spatial variations in oxygen metabolism. Although the current application to the brain is novel, the algorithm has been validated in controlled experimental settings and shown to produce consistent results. We acknowledge that the observed range of CMRO₂ appears relatively broad across a 1–2 mm distance; however, such heterogeneity may arise from local differences in vascular density, metabolic demand, or tissue oxygenation — all of which can vary across cortical regions, even within small spatial scales. We have added a brief note in the Discussion (Subsection: Optical CMRO₂ detection in neonatal care) on page 13 to acknowledge this point:

“Additionally, the spatial heterogeneity in estimated CMRO₂ observed in our data may reflect underlying physiological variability, including differences in vascular structure or metabolic demand across cortical regions. Future studies will aim to further validate and interpret these spatial patterns.”

(7) The justification for using P10 mice in the experiments has not been well presented in the manuscript.

We thank the reviewer for pointing out the need to clarify our choice of developmental stage. We chose P10 mice for our hypoxia-ischemia injury model because this stage is widely recognized as developmentally comparable to human term infants in terms of brain maturation. This approach has been validated by several previous studies (Clancy et al., 2007; Mallard and Vexler, 2015; Sheldon et al., 2018). We have added the following clarification to the Methods section (Subsection: Neonatal Cerebral HI and Hypothermia Treatment) on page 18:

“P10 mice were chosen for our experiments as they are widely used to model near-term infants in humans. At this developmental stage, the brain maturation in mice closely parallels that of near-term infants, making them an appropriate model for studying neonatal brain injury and therapeutic interventions (Clancy et al., 2007; Mallard and Vexler, 2015; Sheldon et al., 2018).”

(8) It was not discussed how the observations made in this manuscript could be affected by the potential discrepancy between the developmental stages of P10 mice and human babies regarding cellular metabolism and neurovascular coupling.

We thank the reviewer for raising this important point regarding developmental differences between P10 mice and human infants. We have discussed this issue by adding the following statement to the Discussion section (newly added subsection: Limitations in this study, the 1^st paragraph) on page 15, where we summarize the overall study design and model selection:

“While P10 mice are widely used to model near-term human infants, developmental differences in cellular metabolism and neurovascular coupling may affect the observed outcomes and limit direct clinical translation (Clancy et al., 2007; Mallard and Vexler, 2015; Sheldon et al., 2018). Nevertheless, the P10 model remains a valuable and widely accepted tool for studying neonatal hypoxia-ischemia mechanisms and evaluating therapeutic interventions.”

(9) Regarding the brain temperature measurements, the authors should use a new cohort of mice, implant the miniature thermocouples 1 mm, 0.5 mm, and immediately below the skull in different mice, and verify the temperature in the brain cortex under conditions applied in the experiments. The same approach could be applied to a few mice undergoing 4-hr-long hypothermia treatment in a chamber, which will provide information about the brain temperature that resulted in observed protection from the injury.

We thank the reviewer for this helpful recommendation. We fully agree that direct intracortical temperature measurement would provide more accurate insight into thermal dynamics during hypothermia treatment. However, the primary aim of this study was not to characterize the precise intracortical temperature response under hypothermic conditions, but rather to examine the effects of hypothermia on CMRO₂ and mitochondrial function. Due to the substantial time and resources required to perform direct intracortical temperature monitoring—and considering the technical focus of the current work—we respectfully suggest reserving such investigations for a future study specifically focused on thermal dynamics in hypoxia-ischemia models.

We have acknowledged this limitation in the subsection Limitations in this study of the Discussion on page 15, noting that skull temperature was used as an approximation of brain temperature and that this approach is consistent with clinical practice, where intracortical temperature is typically not measured. We also note that future studies may benefit from more precise assessments using intracortical probes.

(10) The mean values presented in Fig. 4G are much lower than the peak values in the 2D panels and potentially were calculated as the average values over the entire field of view. Please provide more details on how CMRO₂ was estimated and if the validity of the measurements is expected across the entire field of view. If there are parts of the field of view where the estimation of CMRO₂ is more reliable for technical reasons, maybe one way to compute the mean values is to restrict the usable data to the more centralized part of the field of view.

We thank the reviewer for this thoughtful comment. We confirm that CMRO₂ values shown in Figure 4G were calculated as spatial averages over the entire field of view (FOV; ~5 × 3 mm²) encompassing both hemicortices, as shown in Figure 1C. Regarding the observed CMRO₂ values, The apparent difference likely reflects a comparison between two different post-HI time points. Specifically, the ~0.5 value shown for the 37°C ipsilateral group in Figure 4G reflects the average CMRO₂ measured 24 hours after HI, while the ~1.5 value in Figure 2D (red line) corresponds to CMRO₂ during the early 0–2 hour post-HI period. The temporal difference accounts for the apparent discrepancy in magnitude. We understand the importance of consistency across the field of view and have clarified this point in the subsection Procedures for PAM Imaging in the Methods on page 17 “For the imaging field covering both hemicortices between the Bregma and Lambda of the neonatal mouse (5 × 3 mm² as shown in Figure 1C, with each hemicortex measuring 2.5 × 3 mm²)”, as well as in the Figure 4 legend on page 34 “Correlation of CMRO₂ and post-HI brain infarction in mouse neonates at 24 hours”.

In our model and setup, CMRO₂ estimation is spatially robust across the FOV under standard imaging conditions. We recognize, however, that certain peripheral regions may be more prone to signal attenuation. Future refinement of region selection could further improve spatial averaging strategies. For the current study, full-FOV averaging was used consistently across all groups to maintain comparability.

(11) Minor: Results presented in Supplementary Tables have too many significant digits.

Thank you for the helpful suggestion. We have revised Supplementary Tables S1 and S2 to reduce the number of significant digits and improve clarity.

Reviewer #2 (Public review)

(1) In this study, authors have hypothesized that mitochondrial injury in HIE is caused by OXPHOS-uncoupling, which is the cause of secondary energy failure in HI. In addition, therapeutic hypothermia rescues secondary energy failure. The methodologies used are state-of-the art and include PAM technique in live animal, bioenergetic studies in the isolated mitochondria, and others.

The study is comprehensive and impressive. The article is well written and statistical analyses are appropriate.

We thank the reviewer for the positive feedback.

(2) The manuscript does not discuss the limitation of this animal model study in view of the clinical scenario of neonatal hypoxia-ischemia.

We thank the reviewer for this valuable feedback. In response, we have added a dedicated “Limitations in this study” subsection in the Discussion, where we address the potential limitations of this animal model in the context of the clinical scenario of neonatal hypoxia-ischemia in the first paragraph on page 14, including the developmental differences between P10 mice and human infants.

(3) I see many studies on Pubmed on bioenergetics and HI. Hence, it is unclear what is novel and what is known.

We thank the reviewer for this important comment regarding the novelty of our study in the context of existing research on bioenergetics and hypoxia-ischemia (HI). To better clarify the novel aspects of our work, we have highlighted the relevant content in the Abstract (page 4) and Introduction (page 5). Specifically, while many studies have explored HI-related bioenergetic dysfunction, the mechanisms by which therapeutic hypothermia modulates CMRO₂ and mitochondrial function post-HI remain poorly understood.

Abstract on page 4: “However, it is unclear how post-HI hypothermia helps to restore the balance, as cooling reduces CMRO₂. Also, how transient HI leads to secondary energy failure (SEF) in neonatal brains remains elusive. Using photoacoustic microscopy, we examined the effects of HI on CMRO₂ in awake 10-day-old mice, supplemented by bioenergetic analysis of purified cortical mitochondria.”

Introduction on page 5: “The use of awake mouse neonates avoided the confounding effects of anesthesia on CBF and CMRO₂ (Cao et al., 2017; Gao et al., 2017; Sciortino et al., 2021; Slupe and Kirsch, 2018). In addition, we measured the oxygen consumption rate (OCR), reactive oxygen species (ROS), and the membrane potential of mitochondria that were immediately purified from the same cortical area imaged by PAM. This dual-modal analysis enabled a direct comparison of cerebral oxygen metabolism and cortical mitochondrial respiration in the same animal. Moreover, we compared the effects of therapeutic hypothermia on oxygen metabolism and mitochondrial respiration, and correlated the extent of CMRO₂-reduction with the severity of infarction at 24 hours after HI. Our results suggest that blocking HI-induced OXPHOS-uncoupling is an acute effect of hypothermia and that optical detection of CMRO₂ may have clinical applications in HIE.”

In this study, we propose that uncoupled oxidative phosphorylation (OXPHOS) underlies the secondary energy failure observed after HI, and we demonstrate that hypothermia suppresses this pathological CMRO₂ surge, thereby protecting mitochondrial integrity and preventing injury. Additionally, our use of photoacoustic microscopy (PAM) in awake neonatal mice represents a novel, non-invasive approach to track cerebral oxygen metabolism, with potential clinical relevance for guiding hypothermia therapy.

(4) What are the limitations of ex-vivo mitochondrial studies?

We thank the reviewer for this insightful comment. We acknowledge that ex-vivo mitochondrial assays do not fully replicate in vivo physiological conditions, as they lack systemic factors such as blood flow, cellular interactions, and intact tissue architecture. However, these assays are well-established and widely accepted in the field for evaluating mitochondrial function under controlled conditions (Caspersen et al., 2008; Niatsetskaya et al., 2012). Despite their limitations, they enable direct comparisons of mitochondrial activity across experimental groups and provide valuable mechanistic insights that complement in vivo observations.

(5) PAM technique limits the resolution of the image beyond 500-750 micron depth. Assessing basal ganglia may not be possible with this approach?

We thank the reviewer for this important comment. We agree that the imaging depth of PAM is limited and may not allow assessment of deeper brain structures such as the basal ganglia. However, in our neonatal HI model—as in many clinical cases of HIE—cortical injury is typically more severe and represents a major focus for mechanistic and therapeutic investigations. The cortical regions assessed with PAM are thus highly relevant to the pathophysiology of neonatal HI. We have now acknowledged this depth limitation in the third paragraph of the newly added Limitations in this study subsection of the Discussion on page 15:

“Another limitation of this study is the restricted imaging depth of the PAM technique, which is typically less than 1 mm and therefore does not allow assessment of deeper brain structures such as the basal ganglia. However, in both our neonatal HI model and most clinical cases of neonatal hypoxia-ischemia, cortical injury tends to be more prominent and functionally significant. As such, our cortical measurements remain highly relevant for investigating the mechanisms of injury and evaluating therapeutic interventions.”

(6) Hypothermia in present study reduces the brain temperature from 37 to 29-32 degree centigrade. In clinical set up, head temp is reduced to 33-34.5 in neonatal hypoxia ischemia. Hence a drop in temperature to 29 degrees is much lower relative to the clinical practice. How the present study with greater drop in head temperature can be interpreted for understanding the pathophysiology of therapeutic hypothermia in neonatal HIE. Moreover, in HIE model using higher temperature of 37 and dropping to 29 seems to be much different than the clinical scenario. Please discuss.

We thank the reviewer for raising this important point regarding temperature ranges in our study. In Figure 1, we used a broader temperature range (down to 29°C) to explore the general relationship between temperature and CMRO₂ in uninjured neonatal mice. This experiment was not intended to model therapeutic hypothermia directly, but rather to characterize the baseline physiological responses.

For all experiments involving hypothermia as a therapeutic intervention following HI, we consistently maintained a brain temperature of 32°C, which falls within the clinically accepted mild hypothermia range for neonatal HIE (typically 33–34.5°C). We believe this temperature closely mimics clinical practice and supports the translational relevance of our findings.

(7) NMR was assessed ex-vivo. How does it relate to in vivo assessment. Infants admitted in Neonatal intensive Care Unit, frequently get MRI with spectroscopy. How do the MRS findings in human newborns with HIE correlate with the ex-vivo evaluation of metabolites.

We thank the reviewer for this insightful question. While our study assessed brain metabolites ex vivo, similar metabolic changes have been observed in vivo using proton magnetic resonance spectroscopy (¹H-MRS) in infants with HIE. Specifically, reductions in N-acetylaspartate (NAA) — a marker of neuronal integrity — have been reported in neonates with severe brain injury, aligning with our ex vivo findings. This correlation between in vivo and ex vivo assessments supports the translational relevance of our model for studying metabolic disruption in neonatal HIE. We have added this point to the subsection Using Optically Measured CMRO₂ to Detect Neonatal HI Brain Injury of the Results on page 8, along with a supporting clinical reference (Lally et al., 2019):

“In addition, in vivo proton MRS in infants with HIE has also shown a reduction in NAA, particularly in cases of severe injury (Lally et al., 2019). This reduction in NAA, observed in neonatal intensive care settings, reflects neuronal and axonal loss or dysfunction and serves as a biomarker for injury severity. The alignment between our ex vivo observations and in vivo MRS findings in clinical studies reinforces the translational relevance of our model for investigating metabolic disturbances in neonatal HIE.”

Reviewer #3 (Public review)

(1) In Sun et al. present a comprehensive study using a novel photoacoustic microscopy setup and mitochondrial analysis to investigate the impact of hypoxia-ischemia (HI) on brain metabolism and the protective role of therapeutic hypothermia. The authors elegantly demonstrate three connected findings: (1) HI initially suppresses brain metabolism, (2) subsequently triggers a metabolic surge linked to oxidative phosphorylation uncoupling and brain damage, and (3) therapeutic hypothermia mitigates HI-induced damage by blocking this surge and reducing mitochondrial stress.

The study's design and execution are great, with a clear presentation of results and methods. Data is nicely presented, and methodological details are thorough.

We thank the reviewer for the positive feedback.

(2) However, a minor concern is the extensive use of abbreviations, which can hinder readability. As all the abbreviations are introduced in the text, their overuse may render the text hard to read to non-specialist audiences. Additionally, sharing the custom Matlab and other software scripts online, particularly those used for blood vessel segmentation, would be a valuable resource for the scientific community. In addition, while the study focuses on the short-term effects of HI, exploring the long-term consequences and definitively elucidating HI's impact on mitochondria would further strengthen the manuscript's impact.

We thank the reviewer for these valuable suggestions. Please find our point-by-point responses below:

Abbreviations: To improve readability, we have added a List of Abbreviations on page 3 to help readers, especially non-specialists, navigate the terminology more easily.

MATLAB Code Availability: The methodology for blood vessel segmentation was described in detail in our previous publication (Sun et al., 2020). We have now updated the subsection Quantification of Cerebral Hemodynamics and Oxygen Metabolism by PAM of the Methods on page 18 to provide additional details and have indicated that the MATLAB scripts are available upon request.

“Briefly, this process involves generating a vascular map using signal amplitude from the Hilbert transformation, selecting a region slightly larger than the vessel of interest, and applying Otsu’s thresholding method to remove background pixels. Isolated or spurious boundary fragments are then removed to improve boundary smoothness. The customized MATLAB code used for vessel segmentation is available upon request.”

Long-Term Effects of Hypothermia: We agree that exploring long-term outcomes would enhance the broader impact of this research. While our study focuses on the acute phase following HI, prior studies have shown long-term neuroprotective benefits of therapeutic hypothermia, such as enhanced white matter development (Koo et al., 2017). We have added this point to the fourth paragraph in the subsection Limitations in this study of the Discussion on page 15:

“While our study focuses on the acute effects of hypothermia, previous research has shown long-term neuroprotective benefits, including improved white matter development post-injury (Koo et al., 2017). These findings highlight hypothermia's potential for both immediate and extended recovery, warranting further study of long-term outcomes.”

(3) Extensive use of abbreviations.

Thank you for the helpful suggestion. To improve readability for a broader audience, we have added a List of Abbreviations on page 3 of the manuscript to assist readers in navigating terminology used throughout the text. This has been included as Response #2 to Reviewer #3.

(4) Share code used to conduct the study.

Thank you for the suggestion. The methodology for vessel segmentation was previously published (Sun et al., 2020), and we have noted in the subsection Quantification of Cerebral Hemodynamics and Oxygen Metabolism by PAM of the Methods on page 18 that the MATLAB code is available upon request. This has also been included as Response #2 to Reviewer #3.

Reference:

Cao R, Li J, Kharel Y, Zhang C, Morris E, Santos WL, Lynch KR, Zuo Z, Hu S. 2018. Photoacoustic microscopy reveals the hemodynamic basis of sphingosine 1-phosphate-induced neuroprotection against ischemic stroke. Theranostics 8:6111–6120. doi:10.7150/thno.29435

Caspersen CS, Sosunov A, Utkina-Sosunova I, Ratner VI, Starkov AA, Ten VS. 2008. An Isolation Method for Assessment of Brain Mitochondria Function in Neonatal Mice with Hypoxic-Ischemic Brain Injury. Developmental Neuroscience 30:319–324. doi:10.1159/000121416

Clancy B, Kersh B, Hyde J, Darlington RB, Anand KJS, Finlay BL. 2007. Web-based method for translating neurodevelopment from laboratory species to humans. Neuroinformatics 5:79–94. doi:10.1385/ni:5:1:79

Greenberg RS, Zahurak M, Belden C, Tunkel DE. 1998. Assessment of oropharyngeal distance in children using magnetic resonance imaging. Anesth Analg 87:1048–1051. doi:10.1097/00000539-199811000-00014

Kiyatkin EA. 2007. Brain temperature fluctuations during physiological and pathological conditions. Eur J Appl Physiol 101:3–17. doi:10.1007/s00421-007-0450-7

Koo E, Sheldon RA, Lee BS, Vexler ZS, Ferriero DM. 2017. Effects of therapeutic hypothermia on white matter injury from murine neonatal hypoxia-ischemia. Pediatr Res 82:518–526. doi:10.1038/pr.2017.75

Lally PJ, Montaldo P, Oliveira V, Soe A, Swamy R, Bassett P, Mendoza J, Atreja G, Kariholu U, Pattnayak S, Sashikumar P, Harizaj H, Mitchell M, Ganesh V, Harigopal S, Dixon J, English P, Clarke P, Muthukumar P, Satodia P, Wayte S, Abernethy LJ, Yajamanyam K, Bainbridge A, Price D, Huertas A, Sharp DJ, Kalra V, Chawla S, Shankaran S, Thayyil S, MARBLE consortium. 2019. Magnetic resonance spectroscopy assessment of brain injury after moderate hypothermia in neonatal encephalopathy: a prospective multicentre cohort study. Lancet Neurol 18:35–45. doi:10.1016/S1474-4422(18)30325-9

Lin W, Powers WJ. 2018. Oxygen metabolism in acute ischemic stroke. J Cereb Blood Flow Metab 38:1481–1499. doi:10.1177/0271678X17722095

Mallard C, Vexler Z. 2015. Modeling ischemia in the immature brain: how translational are animal models? Stroke 46:3006–3011. doi:10.1161/STROKEAHA.115.007776

Niatsetskaya ZV, Sosunov SA, Matsiukevich D, Utkina-Sosunova IV, Ratner VI, Starkov AA, Ten VS. 2012. The Oxygen Free Radicals Originating from Mitochondrial Complex I Contribute to Oxidative Brain Injury Following Hypoxia–Ischemia in Neonatal Mice. J Neurosci 32:3235–3244. doi:10.1523/JNEUROSCI.6303-11.2012

Sheldon RA, Windsor C, Ferriero DM. 2018. Strain-Related Differences in Mouse Neonatal Hypoxia-Ischemia. Dev Neurosci 40:490–496. doi:10.1159/000495880

Sun N, Bruce AC, Ning B, Cao R, Wang Y, Zhong F, Peirce SM, Hu S. 2022. Photoacoustic microscopy of vascular adaptation and tissue oxygen metabolism during cutaneous wound healing. Biomed Opt Express, BOE 13:2695–2706. doi:10.1364/BOE.456198

Sun N, Ning B, Bruce AC, Cao R, Seaman SA, Wang T, Fritsche-Danielson R, Carlsson LG, Peirce SM, Hu S. 2020. In vivo imaging of hemodynamic redistribution and arteriogenesis across microvascular network. Microcirculation 27:e12598. doi:10.1111/micc.12598

Sun N, Zheng S, Rosin DL, Poudel N, Yao J, Perry HM, Cao R, Okusa MD, Hu S. 2021. Development of a photoacoustic microscopy technique to assess peritubular capillary function and oxygen metabolism in the mouse kidney. Kidney International 100:613–620. doi:10.1016/j.kint.2021.06.018

perverse incentives...

eLife Assessment

This valuable study presents a well-designed set of experiments demonstrating how a planthopper salivary carbonic anhydrase can promote rice stripe virus infection by modulating callose deposition in the host plant. The authors provide solid data for the proposed protein-protein interactions, including strengthened evidence for the LssaCA-NP-OsTLP complex and clarified dynamics of LssaCA presence in planta. Overall, the work reveals a mechanistic link whereby a vector salivary protein enhances a plant β-1,3-glucanase to suppress callose-based defense, thereby facilitating early viral establishment.

Reviewer #2 (Public Review):

There is increasing evidence that viruses manipulate vectors and hosts to facilitate transmission. For arthropods, saliva plays an essential role for successful feeding on a host and consequently for arthropod-borne viruses that are transmitted during arthropod feeding on new hosts. This is so because saliva constitutes the interaction interface between arthropod and host and contains many enzymes and effectors that allow feeding on a compatible host by neutralizing host defenses. Therefore, it is not surprising that viruses change saliva composition or use saliva proteins to provoke altered vector-host interactions that are favorable for virus transmission. However, detailed mechanistic analyses are scarce. Here, Zhao and coworkers study transmission of rice stripe virus (RSV) by the planthopper Laodelphax striatellus. RSV infects plants as well as the vector, accumulates in salivary glands and is injected together with saliva into a new host during vector feeding.

The authors present evidence that a saliva-contained enzyme - carbonic anhydrase (CA) - might facilitate virus infection of rice by interfering with callose deposition, a plant defense response. In vitro pull-down experiments, yeast two hybrid assay and binding affinity assays show convincingly interaction between CA and a plant thaumatin-like protein (TLP) that degrades callose. Similar experiments show that CA and TLP interact with the RSV nuclear capsid protein NT to form a complex. Formation of the CA-TLP complex increases TLP activity by roughly 30% and integration of NT increases TLP activity further. This correlates with lower callose content in RSV-infected plants and higher virus titer. Further, silencing CA in vectors decreases virus titers in infected plants. Interestingly, aphid CA was found to play a role in plant infection with two non-persistent non-circulative viruses, turnip mosaic virus and cucumber mosaic virus (Guo et al. 2023 doi.org/10.1073/pnas.2222040120), but the proposed mode of action is entirely different.

Editors' note: this version was assessed by the editors, without further input from the reviewers.

Author response:

The following is the authors’ response to the previous reviews

Reviewer #1 (Public review):

In this study, the authors identified an insect salivary protein LssaCA participating viral initial infection in plant host. LssaCA directly bond to RSV nucleocapsid protein and then interacted with a rice OsTLP that possessed endo-β-1,3-glucanase activity to enhance OsTLP enzymatic activity and degrade callose caused by insects feeding. The manuscript suffers from fundamental logical issues, making its central narrative highly unconvincing.

(1) These results suggested that LssaCA promoted RSV infection through a mechanism occurring not in insects or during early stages of viral entry in plants, but in planta after viral inoculation. As we all know that callose deposition affects the feeding of piercing-sucking insects and viral entry, this is contradictory to the results in Fig. S4 and Fig. 2. It is difficult to understand callose functioned in virus reproduction in 3 days post virus inoculation. And authors also avoided to explain this mechanism.

We appreciate your insightful comment and acknowledge that our initial description may not have been sufficiently clear.

(1) Based on the EPG results, we found that LssaCA deficiency did not significantly affect total feeding time, time to first non-phloem phase, or time to first phloem feeding (Fig. S8A-D in the revised manuscript). However, the continuity of sap ingestion was disturbed—the N4 waveform of dsLssaCA SBPHs was occasionally interrupted for brief periods (newly added Fig. S8E in the revised manuscript), likely due to phloem blockage. In the revised manuscript, we have added this analysis to the Result section (Lines 285-291 and 578-587) and provided the EPG procedure in Material and Methods section (Lines 670-680).

(2) We assessed RSV titers immediately post-feeding to confirm the inoculation viral loads (Fig. 2G) and at 3 dpf (Fig. 2H-I) to assess the in-planta effects following viral inoculation. This did not mean that callose functions in virus reproduction at 3 days post viral inoculation. Rather, callose deposition typically occurs immediately in response to insect feeding and virus inoculation. When measuring callose deposition, we allowed insects to feed for 24 h and quantified the callose levels immediately post feeding. The EPG results showed that sap ingestion continuity was disrupted—the N4 waveform of dsLssaCA-treated SBPHs was occasionally interrupted for brief periods (newly added Fig. S8E in the revised manuscript), likely due to phloem blockage. We have reorganized the description to avoid confusion. Please see Lines 139-144 and Fig. S8E for detail.

(1) Missing significant data. For example, the phenotypes of the transgenic plants, the RSV titers in the transgenic plants (OsTLP OE, ostlp). The staining of callose deposition were also hard to convince. The evidence about RSV NP-LssaCA-OsTLP tripartite interaction to enhance OsTLP enzymatic activity is not enough.

We thank the reviewer for this insightful comment.

(1) We constructed OsTLP overexpression and mutant transgenic plants (OsTLP OE and ostlp) and assessed their phenotypes regarding RSV infection levels. Compared with wild-type plants, OsTLP OE plants exhibited accelerated growth, while ostlp plants showed growth inhibition. Following feeding by viruliferous L. striatellus, OsTLP OE plants had significantly higher RSV titers compared with wild-type plants, whereas ostlp mutant plants exhibited significantly lower RSV titers (Lines 221-228 and new Fig. 3I). These results indicate that OsTLP facilitates RSV infection in planta.

(2) The images showing callose deposition staining are representative of 15 images from 3 independent insect treatments. In addition to the staining images, we quantified fluorescence intensity and measured callose concentration by ELISA.

(2)  Figure 4a, there was the LssaCA signal in the fourth lane of pull-down data. Did MBP also bind LsssCA? The characterization of pull-down methods was rough a little bit. The method of GST pull-down and MBP pull-down should be characterized more in more detail.

We thank the reviewer for this helpful comment. MBP did not bind LssaCA. We have repeated the pull-down experiment and provide clearer figure with improved results. We have also revised and provided more detailed descriptions of the GST pull-down and MBP pull-down methods. Please refer to Lines 744-774 and Figure 4A for details.

doh, what else??

yes, "postwar" is the key here

after wars - there's always less crime

Less money goes towards solving crimes. There are no strong incentives to reduce it.

If someone asks - how come and why - it's difficult to see what are the models that are being used by the politicians to decide whether something is harmful and to what extent for their ideology or chances for being elected / re-elected. That's because systems are multi-variant - it's the effect of something in the mix rather than stand alone.

you either base it on evidence or you don't. It's not a single variant system.

eLife Assessment

The medicinal leech preparation is an amenable system in which to understand the neural basis of locomotion. Here a previously identified non-spiking neuron was studied in leech and found to alter the mean firing frequency of a crawl-related motoneuron, which fires during the contraction phase of crawling. The findings are valuable and the experiments were diligently done and considered solid. The results lay a foundation for additional studies in this system.

Reviewer #1 (Public review):

The medicinal leech preparation is an amenable system in which to understand how the underlying cellular networks for locomotion function. A previously identified non-spiking neuron (NS) was studied and found to alter the mean firing frequency of a crawl-related motoneuron (DE-3), which fires during the contraction phase of crawling. The data are solid. Identifying upstream neurons responsible for crawl motor patterning is essential for understanding how rhythmic behavior is controlled.

Reviewer #2 (Public review):

This study by Radice et al., takes advantage of the very well-established leach preparation to investigate questions related to motor control, more precisely the question of how the activity of motoneurons taking part in leach crawling behavior are finely tuned.

The paper is overall well written. The findings are clearly presented, and the data seems solid overall.

Author response:

The following is the authors’ response to the previous reviews

Reviewer #1 (Public review): 

The medicinal leech preparation is an amenable system in which to understand how the underlying cellular networks for locomotion function. A previously identified non-spiking neuron (NS) was studied and found to alter the mean firing frequency of a crawl-related motoneuron (DE-3), which fires during the contraction phase of crawling. The data are mostly solid. Identifying upstream neurons responsible for crawl motor patterning is essential for understanding how rhythmic behavior is controlled.

Review of Revision: 

On a positive note, the rationale for the study is clearer to me now after reading the authors' responses to both reviewers, but that information, as described in the authors' responses, is minimally incorporated into the current revised paper. Incorporating a discussion of previous work on the NS cell has, indeed, improved the paper. 

I suggested earlier that the paper be edited for clarity but not much text has been changed since the first draft. I will provide an example of the types of sentences that are confusing. The title of the paper is: "Phase-specific premotor inhibition modulates leech rhythmic motor output". Are the authors referring to the inhibition created by premotor neurons (e.g., on to the motoneurons) or the inhibition that the premotor neurons receive? 

In this case, this is an interesting ambiguity: NS is inhibited and that inhibition is directly transmitted to the motoneurons because both cells are electrically coupled.  We believe that the title does not disguise the findings conveyed by the manuscript.

I also find the paper still confusing with regard to the suggested "functional homology" with the vertebrate Renshaw cells. When the authors set up this expectation of homology (should be analogy) in the introduction and other sections of the paper, one would assume that the NS cell would be directly receiving excitation from a motoneuron (like DE-3) and, in turn, the motoneuron would then receive some sort of inhibitory input to regulate its firing frequency. Essentially, I have always viewed the Renshaw cells as nature's clever way to monitor the ongoing activity of a motoneuron while also providing recurrent feedback or "recurrent inhibition" to modify that cell's excitatory state. The authors present their initial idea below on line 62. Authors write: "These neurons are present as bilateral pairs in each segmental ganglion and are functional homologs of the mammalian Renshaw cells (Szczupak, 2014). These spinal cord cells receive excitatory inputs from motoneurons and, in turn, transmit inhibitory signals to the motoneurons (Alvarez and Fyffe, 2007)." 

We agree with Reviewer #2: the correct term is "analogous," not "homologous." Thanks for pointing this out. We changed the term throughout the text.

The Reviewer is also right in the appreciation of the role of Renshaw cells. NS plays exactly the role that the Reviewer expresses. The ONLY difference is that NS is inhibited by the motoneurons, and in turn transmits this inhibition to the motoneurons via the rectifying electrical junctions. Attending the confusion that our description caused in the Reviewer, we have modified the cited sentence accordingly now in lines 65-67.

Minor note:

I suggest re-writing this last sentence as "these" is confusing. Change to: 'In the spinal cord, Renshaw interneurons receive excitatory inputs from motoneurons and, in turn, transmit inhibitory signals to them (Alvarez and Fyffe, 2007).'] 

Please, see the changes mentioned above.

Furthermore, the authors note that (line 69 on): "In the context of this circuit the activity of excitatory motoneurons evokes chemically mediated inhibitory synaptic potentials in NS. Additionally, the NS neurons are electrically coupled......In physiological conditions this coupling favors the transmission of inhibitory signals from NS to motoneurons." Based on what is being conveyed here, I see a disconnect with the "functional homology" being presented earlier. I may be missing something, but the Renshaw analogy seems to be quite different compared to what looks like reciprocal inhibition in the leech. If the authors want to make the analogy to Renshaw cells clearer, then they should make a simple ball and stick diagram of the leech system and visually compare it to the Renshaw/motoneuron circuit with regard to functionality. This simple addition would help many readers. 

We have simplified the description regarding the Renshaw cell (lines 65-67) to avoid the “details” of the connectivity between the two circuits.

This report focuses on NS neurons and their role in crawling; we mention the analogy with Renshaw cells to widen the interest of the results. We do not think that making a special diagram to compare how the two neurons play a similar role via different connections among the players is useful in the context of this manuscript.

The Abstract, Authors write (line 19), "Specifically, we analyzed how electrophysiological manipulation of a premotor nonspiking (NS) neuron, that forms a recurrent inhibitory circuit (homologous to vertebrate Renshaw cells)...."

First, a circuit would not be homologous to a cell, and the term homology implies a strict developmental/evolutionary commonality. At best, I would use the term functionally analogous but even then I am still not sure that they are functionally that similar (see comments above). 

Reviewer #2 is right. We changed the sentence in line 20.

Line 22: "The study included a quantitative analysis of motor units active throughout the fictive crawling cycle that shows that the rhythmic motor output in isolated ganglia mirrors the phase relationships observed in vivo." This sentence must be revised to indicate that not all of the extracellular units were demonstrated to be motor units. Revise to: "The study included a quantitative analysis of identified and putative motor units active throughout the fictive crawling cycle that shows.....' 

Line 187 regarding identifying units as motoneurons: Authors write, "While multiple extracellular recordings have been performed previously (Eisenhart et al., 2000), these results (Figure 4) present the first quantitative analysis of motor units activated throughout the crawling cycle in this type of recordings." The authors cannot assume that the units in the recorded nerves belong only to motoneurons. Based on their first rebuttal, the authors seem to be reluctant to accept the idea that the extracellularly recorded units might represent a different class of neurons. They admit that some sensory neurons (with somata located centrally) do, indeed, travel out the same nerves recorded, but go on to explain why they would not be active. 

The leech has a variety of sensory organs that are located in the periphery, and some of these sensory neurons do show rhythmic activity correlated with locomotor activity (see Blackshaw's early work). The numerous stretch receptors, in fact, have very large axons that pass through all the nerves recorded in the current paper. 

In Fig. 4, it is interesting that the waveforms of all the units recorded in the PP nerve exhibit a reversal in waveform as compared to those in the DP nerve, which might indicate (based on bipolar differential recording) that the units in the PP nerve are being propagated in the opposite direction (i.e., are perhaps afferent). Rhythmic presynaptic inhibition and excitation is commonly seen for stretch receptors within the CNS (see the work of Burrows) and many such cells are under modulatory control. 

Most likely, the majority of the units are from motoneurons, but we do not really know at this point. The authors should reframe their statements throughout the paper as: 'While multiple extracellular recordings have been performed previously (Eisenhart et al., 2000), these results (Figure 4) present the first quantitative analysis of multiple extracellular units, using spike sorting methods, which are activated throughout the crawling cycle.' In cases where the identity of the unit is known, then it is fine to state that, but when the identity of the unit is not known, then there should be some qualification and stated as 'putative motor units' 

We understand the concern of Reviewer #2 regarding the type of neurons active during dopamine-induced crawling in isolated ganglia. However, we believe there is sufficient evidence to support that the recorded spikes originate from motoneurons. As readers may share the same concern, we have added a paragraph explaining why spikes from somatic sensory neurons such as P or T cells, or from stretch receptors, are unlikely to contribute (lines 206-214). We included the term putative in the abstract.

The Methods section:

Needs to include the full parameters that were used to assess whether bursting activity was qualified in ways to be considered crawling activity or not. Typically, crawl-like burst periods of no more than 25 seconds have been the limit for their qualification as crawling activity. In Fig 2F, for example, the inter-burst period is over 35 seconds; that coupled with an average 5 second burst duration would bring the burst period to 40 seconds, which is substantially out of range for there to be bursting relevant to crawl activity. Simply put, long DE-3 burst periods are often observed but may not be indicative of a crawl state as the CV motoneurons are no longer out of phase with DE-3. A number of papers have adopted this criterion. 

We now indicate in the methods the range of period values measured in our experiments.  For the reviewer informatio we show here histograms depicting the variability of period and duty cycle values recorded in our experiments (control conditions). The Reviewer can see that the bursting activity of DE-3 fall within what has been published.

Author response image 1.

Crawling in isolated ganglia. A. Histogram of periods end-to-end during crawling in isolated ganglia. The dotted line indicates the mean obtained from the averages of all experiments. The solid black line represents the mean of all cycles across all experiments. B. As in A, for the duty cycle calculated using end-to-end periods.  (n = 210 cycles from 45 ganglia obtained from 32 leeches in all cases).

Reviewer #1 (Recommendations for the authors): 

Minor comments-

Line 100: "In the frame of the recurrent inhibitory circuit, NS is the target of inhibitory signals". Suggestion: 'Within the framework of the recurrent inhibitory circuit, NS is the target of inhibitory signals.' 

Changed as suggested (line 107).

Line 163: "This series of experiments proves that, as predicted based on the known circuit (Figure 164 1C), inhibitory signals onto NS premotor neurons were transmitted to DE-3 motoneurons and counteracted their excitatory drive during crawling, limiting their firing frequency". I think this sentence is too strong plus needs some editing. Suggestion: 'As predicted based on the known circuit (Figure 164 1C), this series of experiments indicates that inhibitory signals onto NS premotor neurons are transmitted to DE-3 motoneurons, thus limiting their firing frequency and counteracting their excitatory drive during crawling."

Changed as suggested.

Lines 86, 292 and 304 and Fig 4 legend: "Different from DE-3, In-Phase units showed a marked decrease in the maximum bFF along time." Suggestion: Replace the word "along" with 'across' time. Also replace those words in the Fig 4 legend and Line 80...."along" (replace with 'across') the different stages of crawling. 

Changed as suggested.

Line 311: "bursts and a concurrent inhibitory input via NS (Figure 7). Coherent with this interpretation, the activity level of the Anti- Phase units was not influenced by these inhibitory signals". Suggestion: Replace the word "coherent" with 'consistent'. 

Changed as suggested.

Line 332: "...offer the particular advantage of allowing electrical manipulation of individual neurons in wildtype adults," I am unsure what the authors are attempting to convey. Not sure what they mean by "wildtype" in this context and why that would matter. 

“wildtype” was eliminated

We thank Reviewer #2 for the suggested edits to the text.

Working smarter is more important.

This is included in the camp fee

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With its remarkable architecture and outstanding sporting resources, Winchester College offers an inspiring environment for players on the Performance Camp to take their game to the next level.

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(delete - Foundation)

computation is about functions functions are encoded and code is data so computation is about data and how

data moves how it transfers in the network what properties it has how fault-tolerant that system is has vast

implications into what our computation can do what our software does what our applications do and therefore what we as

humans are capable of doing

eLife Assessment

This important Research Advance builds on the authors' previous work delineating the roles of the rodent perirhinal cortex and the basolateral amygdala in first- and second-order learning. The convincing results show that serial exposure of non-motivationally relevant stimuli influences how those stimuli are encoded within the perirhinal cortex and basolateral amygdala when paired with a shock. This manuscript will be interesting for researchers in cognitive and behavioral neuroscience.

Reviewer #1 (Public review):

Summary:

This study advances the lab's growing body of evidence exploring higher-order learning and its neural mechanisms. They recently found that NMDA receptor activity in the perirhinal cortex was necessary for integrating stimulus-stimulus associations with stimulus-shock associations (mediated learning) to produce preconditioned fear, but it was not necessary for forming stimulus-shock associations. On the other hand, basolateral amygdala NMDA receptor activity is required for forming stimulus-shock memories. Based on these facts, the authors assessed: 1. why the perirhinal cortex is necessary for mediated learning but not direct fear learning and 2. the determinants of perirhinal cortex versus basolateral amygdala necessity for forming direct versus indirect fear memories. The authors used standard sensory preconditioning and variants designed to manipulate the novelty and temporal relationship between stimuli and shock and, therefore, the attentional state under which associative information might be processed. Under experimental conditions where information would presumably be processed primarily in the periphery of attention (temporal distance between stimulus/shock or stimulus pre-exposure), perirhinal cortex NMDA receptor activation was required for learning indirect associations. On the other hand, when information would likely be processed in focal attention (novel stimulus contiguous with shock), basolateral amygdala NMDA activity was required for learning direct associations. Together, the findings indicate that the perirhinal cortex and basolateral amygdala subserve peripheral and focal attention, respectively. The authors provide support for their conclusions using careful, hypothesis-driven experimental design, rigorous methods, and integrating their findings with the relevant literature on learning theory, information processing, and neurobiology. Therefore, this work will be highly interesting to several fields.

Strengths:

(1) The experiments were carefully constructed and designed to test hypotheses that were rooted in the lab's previous work, in addition to established learning theory and information processing background literature.

(2) There are clear predictions and alternative outcomes. The provided table does an excellent job of condensing and enhancing the readability of a large amount of data.

(3) In a broad sense, attention states are a component of nearly every behavioral experiment. Therefore, identifying their engagement by dissociable brain areas and under different learning conditions is an important area of research.

(4) The authors clearly note where they replicated their own findings, report full statistical measures, effect sizes, and confidence intervals, indicating the level of scientific rigor.

(5) The findings raise questions for future experiments that will further test the authors' hypotheses; this is well discussed.

Reviewer #2 (Public review):

This paper continues the authors' research on the roles of the basolateral amygdala (BLA) and the perirhinal cortex (PRh) in sensory preconditioning (SPC) and second order conditioning (SOC). In this manuscript, the authors explore how prior exposure to stimuli may influence which regions are necessary for conditioning to the second-order cue (S2). The authors perform a series of experiments which first confirm prior results shown by the author - that NMDA receptors in the PRh are necessary in SPC during conditioning of the first-order cue (S1) with shock to allow for freezing to S2 at test; and that NMDA receptors in the BLA are necessary for S1 conditioning during the S1-shock pairings. The authors then set out to test the hypothesis that the PRh encodes associations in a peripheral state of attention whereas the BLA encodes associations in a focal state of attention, similar to the A1 and A2 states in Wagner's theory of SOP. To do this, they show that BLA is necessary for conditioning to S2 when the S2 is first exposed during a serial compound procedure - S2-S1-shock. To determine whether pre-exposure of S2 will shift S2 to a peripheral focal state, the authors run a design in which S2-S1 presentations are given prior to the serial compound phase. The authors show that this restores NMDA receptor activity within the PRh as necessary for fear response to S2 at test. They then test whether the presence of S1 during the serial compound conditioning allows the PRh to support the fear responses to S2 by introducing a delay conditioning paradigm in which S1 is no longer present. The authors find that PRh is no longer required and suggest that this is due to S2 remaining in the primary focal state.

Strengths:

As with their earlier work, the authors have performed a rigorous series of experiments to better understand the roles of the BLA and PRh in the learning of first- and second-order stimuli. The experiments are well-designed and clearly presented, and the results show definitive differences in functionality between the PRh and BLA. The first experiment confirms earlier findings from the lab (and others), and the authors then build on their previous work to more deeply reveal how these regions differ in how they encode associations between stimuli. The authors have done a commendable job on pursuing these questions.

Table 1 is an excellent way to highlight the results and provide the reader with a quick look-up table of the findings.

Reviewer #3 (Public review):

Summary:

This manuscript presents a series of experiments that further investigate the roles of the BLA and PRH in sensory preconditioning, with a particular focus on understanding their differential involvement in the association of S1 and S2 with shock.

Strengths:

The motivation for the study is clearly articulated, and the experimental designs are thoughtfully constructed. I especially appreciate the inclusion of Table 1, which makes the designs easy to follow. The results are clearly presented, and the statistical analyses are rigorous.

During the revision, the authors have adequately addressed my minor suggestions from the original version.

Author response:

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

Reviewer #1 (Public review):

Summary:

This study advances the lab's growing body of evidence exploring higher-order learning and its neural mechanisms. They recently found that NMDA receptor activity in the perirhinal cortex was necessary for integrating stimulus-stimulus associations with stimulus-shock associations (mediated learning) to produce preconditioned fear, but it was not necessary for forming stimulus-shock associations. On the other hand, basolateral amygdala NMDA receptor activity is required for forming stimulus-shock memories. Based on these facts, the authors assessed: (1) why the perirhinal cortex is necessary for mediated learning but not direct fear learning, and (2) the determinants of perirhinal cortex versus basolateral amygdala necessity for forming direct versus indirect fear memories. The authors used standard sensory preconditioning and variants designed to manipulate the novelty and temporal relationship between stimuli and shock and, therefore, the attentional state under which associative information might be processed. Under experimental conditions where information would presumably be processed primarily in the periphery of attention (temporal distance between stimulus/shock or stimulus pre-exposure), perirhinal cortex NMDA receptor activation was required for learning indirect associations. On the other hand, when information would likely be processed in focal attention (novel stimulus contiguous with shock), basolateral amygdala NMDA activity was required for learning direct associations. Together, the findings indicate that the perirhinal cortex and basolateral amygdala subserve peripheral and focal attention, respectively. The authors provide support for their conclusions using careful, hypothesis-driven experimental design, rigorous methods, and integrating their findings with the relevant literature on learning theory, information processing, and neurobiology. Therefore, this work will be highly interesting to several fields.

Strengths:

(1) The experiments were carefully constructed and designed to test hypotheses that were rooted in the lab's previous work, in addition to established learning theory and information processing background literature.

(2) There are clear predictions and alternative outcomes. The provided table does an excellent job of condensing and enhancing the readability of a large amount of data.

(3) In a broad sense, attention states are a component of nearly every behavioral experiment. Therefore, identifying their engagement by dissociable brain areas and under different learning conditions is an important area of research.

(4) The authors clearly note where they replicated their own findings, report full statistical measures, effect sizes, and confidence intervals, indicating the level of scientific rigor.

(5) The findings raise questions for future experiments that will further test the authors' hypotheses; this is well discussed.

Weaknesses:

As a reader, it is difficult to interpret how first-order fear could be impaired while preconditioned fear is intact; it requires a bit of "reading between the lines".

We appreciate the Reviewer’s point and have attempted to address on lines 55-63 of the revised paper: “In a recent pair of studies, we extended these findings in two ways. First, we showed that S1 does not just form an association with shock in stage 2; it also mediates an association between S2 and the shock. Thus, S2 enters testing in stage 3 already conditioned, able to elicit fear responses (Wong et al., 2019). Second, we showed that this mediated S2-shock association requires NMDAR-activation in the PRh, as well as communication between the PRh and BLA (Wong et al., 2025). These findings raise two critical questions: 1) why is the PRh engaged for mediated conditioning of S2 but not for direct conditioning of S1; and 2) more generally, what determines whether the BLA and/or PRh is engaged for conditioning of the S1 and/or S2?”

Reviewer #2 (Public review):

Summary:

This paper continues the authors' research on the roles of the basolateral amygdala (BLA) and the perirhinal cortex (PRh) in sensory preconditioning (SPC) and second-order conditioning (SOC). In this manuscript, the authors explore how prior exposure to stimuli may influence which regions are necessary for conditioning to the second-order cue (S2). The authors perform a series of experiments which first confirm prior results shown by the author - that NMDA receptors in the PRh are necessary in SPC during conditioning of the first-order cue (S1) with shock to allow for freezing to S2 at test; and that NMDA receptors in the BLA are necessary for S1 conditioning during the S1-shock pairings. The authors then set out to test the hypothesis that the PRh encodes associations in a peripheral state of attention, whereas the BLA encodes associations in a focal state of attention, similar to the A1 and A2 states in Wagner's theory of SOP. To do this, they show that BLA is necessary for conditioning to S2 when the S2 is first exposed during a serial compound procedure - S2-S1-shock. To determine whether pre-exposure of S2 will shift S2 to a peripheral focal state, the authors run a design in which S2-S1 presentations are given prior to the serial compound phase. The authors show that this restores NMDA receptor activity within the PRh as necessary for the fear response to S2 at test. They then test whether the presence of S1 during the serial compound conditioning allows the PRh to support the fear responses to S2 by introducing a delay conditioning paradigm in which S1 is no longer present. The authors find that PRh is no longer required and suggest that this is due to S2 remaining in the primary focal state.

Strengths:

As with their earlier work, the authors have performed a rigorous series of experiments to better understand the roles of the BLA and PRh in the learning of first- and second-order stimuli. The experiments are well-designed and clearly presented, and the results show definitive differences in functionality between the PRh and BLA. The first experiment confirms earlier findings from the lab (and others), and the authors then build on their previous work to more deeply reveal how these regions differ in how they encode associations between stimuli. The authors have done a commendable job of pursuing these questions.

Table 1 is an excellent way to highlight the results and provide the reader with a quick look-up table of the findings.

Weaknesses:

The authors have attempted to resolve the question of the roles of the PRh and BLA in SPC and SOC, which the authors have explored in previous papers. Laudably, the authors have produced substantial results indicating how these two regions function in the learning of first- and second-order cues, providing an opportunity to narrow in on possible theories for their functionality. Yet the authors have framed this experiment in terms of an attentional framework and have argued that the results support this particular framework and hypothesis - that the PRh encodes peripheral and the BLA encodes focal states of learning. This certainly seems like a viable and exciting hypothesis, yet I don't see why the results have been completely framed and interpreted this way. It seems to me that there are still some alternative interpretations that are plausible and should be included in the paper.

We appreciate the Reviewer’s point and have attempted to address it on lines 566-594 of the Discussion: “An additional point to consider in relation to Experiments 3A, 3B, 4A and 4B is the level of surprise that rats experienced following presentations of the familiar S2 in stage 2. Specifically, in Experiments 3A and 3B, S2 was followed by the expected S1 (low surprise) and its conditioning required activation of NMDA receptors in the PRh and not the BLA. By contrast, in Experiments 4A and 4B, S2 was followed by omission of the expected S1 (high surprise) and its conditioning required activation of NMDA receptors in the BLA and not the PRh. This raises the possibility that surprise, or prediction error, also influences the way that S2 is processed in focal and peripheral states of attention. When prediction error is low, S2 is processed in the peripheral state of attention: hence, learning under these circumstances requires NMDA receptor activation in the PRh and not the BLA. By contrast, when prediction error is high, S2 is preserved in the focal state of attention: hence, learning under these circumstances requires NMDA receptor activation in the BLA and not the PRh. The impact of prediction error on the processing of S2 could be assessed using two types of designs. In the first design, rats are pre-exposed to S2-S1 pairings in stage 1 and this is followed by S2-S3-shock pairings in stage 2. The important feature of this design is that, in stage 2, the S2 is followed by surprise in omission of S1 and presentation of S3. Thus, if a large prediction error maintains processing of the familiar S2 in the BLA, we might expect that its conditioning in this design would require NMDA receptor activation in the BLA (in contrast to the results of Experiment 3B) and no longer require NMDA receptor activation in the PRh (in contrast to the results of Experiment 3A). In the second design, rats are pre-exposed to S2 alone in stage 1 and this is followed by S2-[trace]-shock pairings in stage 2. The important feature of this design is that, in stage 2, the S2 is not followed by the surprising omission of any stimulus. Thus, if a small prediction error shifts processing of the familiar S2 to the PRh, we might expect that its conditioning in this design would no longer require NMDA receptor activation in the BLA (in contrast to the results of Experiment 4B) but, instead, require NMDA receptor activation in the PRh (in contrast to the results of Experiment 4A). Future studies will use both designs to determine whether prediction error influences the processing of S2 in the focus versus periphery of attention and, thereby, whether learning about this stimulus requires NMDA receptor activation in the BLA or PRh.”

Reviewer #3 (Public review):

Summary:

This manuscript presents a series of experiments that further investigate the roles of the BLA and PRH in sensory preconditioning, with a particular focus on understanding their differential involvement in the association of S1 and S2 with shock.

Strengths:

The motivation for the study is clearly articulated, and the experimental designs are thoughtfully constructed. I especially appreciate the inclusion of Table 1, which makes the designs easy to follow. The results are clearly presented, and the statistical analyses are rigorous. My comments below mainly concern areas where the writing could be improved to help readers more easily grasp the logic behind the experiments.

Weaknesses:

(1) Lines 56-58: The two previous findings should be more clearly summarized. Specifically, it's unclear whether the "mediated S2-shock" association occurred during Stage 2 or Stage 3. I assume the authors mean Stage 2, but Stage 2 alone would not yet involve "fear of S2," making this expression a bit confusing.

We apologise for the confusion and have revised the summary of our previous findings on lines 55-63. The revised text now states: “In a recent pair of studies, we extended these findings in two ways. First, we showed that S1 does not just form an association with shock in stage 2; it also mediates an association between S2 and the shock. Thus, S2 enters testing in stage 3 already conditioned, able to elicit fear responses (Wong et al., 2019). Second, we showed that this mediated S2-shock association requires NMDAR-activation in the PRh, as well as communication between the PRh and BLA (Wong et al., 2025). These findings raise two critical questions: 1) why is the PRh engaged for mediated conditioning of S2 but not for direct conditioning of S1; and 2) more generally, what determines whether the BLA and/or PRh is engaged for conditioning of the S1 and/or S2?”

(2) Line 61: The phrase "Pavlovian fear conditioning" is ambiguous in this context. I assume it refers to S1-shock or S2-shock conditioning. If so, it would be clearer to state this explicitly.

Apologies for the ambiguity - we have omitted the term “Pavlovian” which may have been the source of confusion: The revised text on lines 60-63 now states: “These findings raise two critical questions: 1) why is the PRh engaged for mediated conditioning of S2 but not for direct conditioning of S1; and 2) more generally, what determines whether the BLA and/or PRh is engaged for conditioning of the S1 and/or S2?”

(3) Regarding the distinction between having or not having Stage 1 S2-S1 pairings, is "novel vs. familiar" the most accurate way to frame this? This terminology could be misleading, especially since one might wonder why S2 couldn't just be presented alone on Stage 1 if novelty is the critical factor. Would "outcome relevance" or "predictability" be more appropriate descriptors? If the authors choose to retain the "novel vs. familiar" framing, I suggest providing a clear explanation of this rationale before introducing the predictions around Line 118.

We have incorporated the suggestion regarding “predictability” while also retaining “novelty” as follows. 

L76-85: “For example, different types of arrangements may influence the substrates of conditioning to S2 by influencing its novelty and/or its predictive value at the time of the shock, on the supposition that familiar stimuli are processed in the periphery of attention and, thereby, the PRh (Bogacz & Brown, 2003; Brown & Banks, 2015; Brown & Bashir, 2002; Martin et al., 2013; McClelland et al., 2014; Morillas et al., 2017; Murray & Wise, 2012; Robinson et al., 2010; Suzuki & Naya, 2014; Voss et al., 2009; Yang et al., 2023) whereas novel stimuli are processed in the focus of attention and, thereby, the amygdala (Holmes et al., 2018; Qureshi et al., 2023; Roozendaal et al., 2006; Rutishauser et al., 2006; Schomaker & Meeter, 2015; Wright et al., 2003).”

L116-120: “Subsequent experiments then used variations of this protocol to examine whether the engagement of NMDAR in the PRh or BLA for Pavlovian fear conditioning is influenced by the novelty/predictive value of the stimuli at the time of the shock (second implication of theory) as well as their distance or separation from the shock (third implication of theory; Table 1).”

(4) Line 121: This statement should refer to S1, not S2.

(5) Line 124: This one should refer to S2, not S1.

We have checked the text on these lines for errors and confirmed that the statements are correct. The lines encompassing this text (L121-130) are reproduced here for convenience:

(1) When rats are exposed to novel S2-S1-shock sequences, conditioning of S2 and S1 will be disrupted by a DAP5 infusion into the BLA but not into the PRh (Experiments 2A and 2B);

(2) When rats are exposed to S2-S1 pairings and then to S2-S1-shock sequences, conditioning of S2 will be disrupted by a DAP5 infusion into the PRh but not the BLA whereas conditioning of S1 will be disrupted by a DAP5 infusion into the BLA not the PRh (Experiments 3A and 3B);

(3) When rats are exposed to S2-S1 pairings and then to S2 (trace)-shock pairings, conditioning of S2 will be disrupted by a DAP5 into the BLA not the PRh (Experiments 4A and 4B).

(6) Additionally, the rationale for Experiment 4 is not introduced before the Results section. While it is understandable that Experiment 4 functions as a follow-up to Experiment 3, it would be helpful to briefly explain the reasoning behind its inclusion.

Experiment 4 follows from the results obtained in Experiment 3; and, as noted, the reasoning for its inclusion is provided locally in its introduction. We attempted to flag this experiment earlier in the general introduction to the paper; but this came at the cost of clarity to the overall story. As such, our revised paper retains the local introduction to this experiment. It is reproduced here for convenience:

“In Experiments 3A and 3B, conditioning of the pre-exposed S1 required NMDAR-activation in the BLA and not the PRh; whereas conditioning of the pre-exposed S2 required NMDAR-activation in the PRh and not the BLA. We attributed these findings to the fact that the pre-exposed S2 was separated from the shock by S1 during conditioning of the S2-S1-shock sequences in stage 2: hence, at the time of the shock, S2 was no longer processed in the focal state of attention supported by the BLA; instead, it was processed in the peripheral state of attention supported by the PRh.

“Experiments 4A and 4B employed a modification of the protocol used in Experiments 3A and 3B to examine whether a pre-exposed S1 influences the processing of a pre-exposed S2 across conditioning with S2-S1-shock sequences. The design of these experiments is shown in Figure 4A. Briefly, in each experiment, two groups of rats were exposed to a session of S2-S1 pairings in stage 1 and, 24 hours later, a session of S2-[trace]-shock pairings in stage 2, where the duration of the trace interval was equivalent to that of S1 in the preceding experiments. Immediately prior to the trace conditioning session in stage 2, one group in each experiment received an infusion of DAP5 or vehicle only into either the PRh (Experiment 4A) or BLA (Experiment 4B). Finally, all rats were tested with presentations of the S2 alone in stage 3. If the substrates of conditioning to S2 are determined only by the amount of time between presentations of this stimulus and foot shock in stage 2, the results obtained in Experiments 4A and 4B should be the same as those obtained in Experiments 3A and 3B: acquisition of freezing to S2 will require activation of NMDARs in the PRh and not the BLA. If, however, the presence of S1 in the preceding experiments (Experiments 3A and 3B) accelerated the rate at which processing of S2 transitioned from the focus of attention to its periphery, the results obtained in Experiments 4A and 4B will differ from those obtained in Experiments 3A and 3B. That is, in contrast to the preceding experiments where acquisition of freezing to S2 required NMDAR-activation in the PRh and not the BLA, here acquisition of freezing to S2 should require NMDAR-activation in the BLA but not the PRh.”

Reviewer #1 (Recommendations for the authors):

I greatly enjoyed reading and reviewing this manuscript, and so I only have boilerplate recommendations.

(1) I might add a couple of sentences discussing how/why preconditioned fear could be intact while first-order fear is impaired. Of course, if I am interpreting the provided interpretation correctly, the reason is that peripheral processing is still intact even when BLA NMDA receptors are blocked, and so mediated conditioning still occurs. Does this mean that mediated conditioning does not require learning the first-order relationship, and that they occur in parallel? Perhaps I just missed this, but I cannot help but wonder whether/how the psychological processes at play might change when first-order learning is impaired, so this would be greatly appreciated.

As noted above, we have revised the general introduction (around lines 55-59) to clarify that the direct S1-shock and mediated S2-shock associations form in parallel. Hence, manipulations that disrupt first-order fear to the S1 (such as a BLA infusion of the NMDA receptor antagonist, DAP5) do not automatically disrupt the expression of sensory preconditioned fear to the S2.

(2) Adding to the above - does the SOP or another theory predict serial vs parallel information flow from focal state to peripheral, or perhaps it is both to some extent?

SOP predicts both serial and parallel processing of information in its focal and peripheral states. That is, some proportion of the elements that comprise a stimulus may decay from the focal state of attention to the periphery (serial processing); hence, at any given moment, the elements that comprise a stimulus can be represented in both focal and peripheral states (parallel processing).

Given the nature of the designs and tools used in the present study (between-subject assessment of a DAP5 effect in the BLA or PRh), we selected parameters that would maximize the processing of the S2 and S1 stimuli in one or the other state of activation; hence the results of the present study. We are currently examining the joint processing of stimulus elements across focal and peripheral states using simultaneous recordings of activity in the BLA and PRh. These recordings are collected from rats trained in the different stages of a within-subject sensory preconditioning protocol. The present study created the basis for this work, which will be published separately in due course.

(3) The organization of PRh vs BLA is nice and consistent across each figure, but I would suggest adding any kind of additional demarcation beyond the colors and text, maybe just more space between AB / CD. The figure text indicating PRh/BLA is a bit small.

Thank you for the suggestion – we have added more space between the top and bottom panels of the figure.

(4) Line 496 typo ..."in the BLA but not the BLA".

Apologies for the type - this has been corrected.

Reviewer #2 (Recommendations for the authors):

I found the experiments to be extremely well-designed and the results convincing and exciting. The hypothesis of the focal and peripheral states of attention being encoded by BLA and PRh respectively, is enticing, yet as indicated in the public review, this does not seem to be the only possible interpretation. This is my only serious comment for the authors.

(1) I think it would be worth reframing the article slightly to give credence to alternative hypotheses. Not to say that the authors' intriguing hypothesis shouldn't be an integral part of the introduction, but no alternatives are mentioned. In experiment 2, could the fact that S2 is already being a predictor of S1, not block new learning to S2? In the framework of stimulus-stimulus associations, there would be no surprise in the serial-compound stage of conditioning at the onset of S1. This may prevent direct learning of the S2-shock association within the BLA. This type of association may as well (S2 predicts S1, but it's omitted), which could support learning by S2. fall under the peripheral/focal theory, but I don't think it's necessary to frame this possibility in terms of a peripheral/focal theory. To build on this alternative interpretation, the absence of S1 in experiment 4 may induce a prediction error. The peripheral and focal states appear to correspond to A2 and A1 in SOP extremely well, and I think it would potentially add interest and support. If the authors do intend to make the paper a strong argument for their hypothesis, perhaps a few additional experiments may be introduced. If the novelty of S2 is critical for S2 not to be processed in a focal state during the serial compound stage, could pre-exposure of S2 alone allow for dependence of S2-shock on the PRh? Assuming this is what the authors would predict, this might disentangle the S-S theory mentioned above from the peripheral/focal theory. Or perhaps run an experiment S2-X in stage 1 and S2-S1-shock in stage 2? This said, I think the experiments are more than sufficient for an exciting paper as is, and I don't think running additional experiments is necessary. I would only argue for this if the authors make a hard claim about the peripheral/focal theory, as is the case for the way the paper is currently written.

We appreciate the reviewer’s excellent point and suggestions. We have included an additional paragraph in the Discussion on page 24 (lines 566-594).  “An additional point to consider in relation to Experiments 3A, 3B, 4A and 4B is the level of surprise that rats experienced following presentations of the familiar S2 in stage 2. Specifically, in Experiments 3A and 3B, S2 was followed by the expected S1 (low surprise) and its conditioning required activation of NMDA receptors in the PRh and not the BLA. By contrast, in Experiments 4A and 4B, S2 was followed by omission of the expected S1 (high surprise) and its conditioning required activation of NMDA receptors in the BLA and not the PRh. This raises the possibility that surprise, or prediction error, also influences the way that S2 is processed in focal and peripheral states of attention. When prediction error is low, S2 is processed in the peripheral state of attention: hence, learning under these circumstances requires NMDA receptor activation in the PRh and not the BLA. By contrast, when prediction error is high, S2 is preserved in the focal state of attention: hence, learning under these circumstances requires NMDA receptor activation in the BLA and not the PRh. The impact of prediction error on the processing of S2 could be assessed using two types of designs. In the first design, rats are pre-exposed to S2-S1 pairings in stage 1 and this is followed by S2-S3-shock pairings in stage 2. The important feature of this design is that, in stage 2, the S2 is followed by surprise in omission of S1 and presentation of S3. Thus, if a large prediction error maintains processing of the familiar S2 in the BLA, we might expect that its conditioning in this design would require NMDA receptor activation in the BLA (in contrast to the results of Experiment 3B) and no longer require NMDA receptor activation in the PRh (in contrast to the results of Experiment 3A). In the second design, rats are pre-exposed to S2 alone in stage 1 and this is followed by S2-[trace]-shock pairings in stage 2. The important feature of this design is that, in stage 2, the S2 is not followed by the surprising omission of any stimulus. Thus, if a small prediction error shifts processing of the familiar S2 to the PRh, we might expect that its conditioning in this design would no longer require NMDA receptor activation in the BLA (in contrast to the results of Experiment 4B) but, instead, require NMDA receptor activation in the PRh (in contrast to the results of Experiment 4A). Future studies will use both designs to determine whether prediction error influences the processing of S2 in the focus versus periphery of attention and, thereby, whether learning about this stimulus requires NMDA receptor activation in the BLA or PRh.”

(3) I was surprised the authors didn't frame their hypothesis more in terms of Wagner's SOP model. It was minimally mentioned in the introduction or the authors' theory if it were included more in the introduction. I was wondering whether the authors may have avoided this framing to avoid an expectation for modeling SOP in their design. If this were the case, I think the paper stands on its own without modeling, and at least for myself, a comparison to SOP would not require modeling of SOP. If this was the authors' concern for avoiding it, I would suggest to the authors that they need not be concerned about it.

We appreciate the endorsement of Wagner’s SOP theory as a nice way of framing our results. We are currently working on a paper in which we use simulations to show how Wagner’s theory can accommodate the present findings as well as others in the literature on sensory preconditioning. For this reason, we have not changed the current paper in relation to this point.

eLife Assessment

This study presents an important new approach to quantifying parsimony preferences in human inference. The work provides convincing evidence that humans are sensitive to specific formalizations of parsimony, such as the dimensionality of perceptual shapes. The work is considered timely, well-written, and technically sophisticated, effectively bridging concepts from statistical inference and human decision-making.

Reviewer #1 (Public review):

I have to preface my evaluation with a disclosure that I lack the mathematical expertise to fully assess what seems to be the authors' main theoretical contribution. I am providing this assessment to the best of my ability, but I cannot substitute for a reviewer with more advanced mathematical/physical training.

Summary:

This paper describes a new theoretical framework for measuring parsimony preferences in human judgments. The authors derive four metrics that they associate with parsimony (dimensionality, boundary, volume, and robustness) and measure whether human adults are sensitive to these metrics. In two tasks, adults had to choose one of two flower beds which a statistical sample was generated from, with or without explicit instruction to choose the flower bed perceptually closest to the sample. The authors conduct extensive statistical analyses showing that humans are sensitive to most of the derived quantities, even when the instructions encouraged participants to choose only based on perceptual distance. The authors complement their study with a computational neural network model that learns to make judgments about the same stimuli with feedback. They show that the computational model is sensitive to the tasks communicated by feedback and only uses the parsimony-associated metrics when feedback trains it to do so.

Strengths:

(1) The paper derives and applies new mathematical quantities associated with parsimony. The mathematical rigor is very impressive and is much more extensive than in most other work in the field, where studies often adopt only one metric (such as the number of causes or parameters). These formal metrics can be very useful for the field.

(2) The studies are preregistered, and the statistical analyses are strong.

(3) The computational model complements the behavioral findings, showing that the derived quantities are not simply equivalent to maximum-likelihood inference in the task.

(4) The speculations in the discussion section (e.g., the idea that human sensitivity is driven by the computational demands each metric requires) are intriguing and could usefully guide future work.

Weaknesses:

(1) The paper is very hard to understand. Many of the key details of the derived metrics are in the appendix, with very little accessible explanation in the main text. The figures helped me understand the metrics somewhat, although I am still not sure how some of them (such as boundary or robustness as measured here) are linked to parsimony. I understand that this is addressed by the derivations in the appendix, but as a computational cognitive scientist, I would have benefited from more accessible explanations. Important aspects of the human studies are also missing from the main text, such as the sample size for Experiment 2.

(2) It is not fully clear whether the sensitivity of human participants to some of the quantities convincingly reported here actually means that participants preferred shapes according to the corresponding aspect of parsimony. The title and framing suggest that parsimony "guides" human decision-making, which may lead readers to conclude that humans prefer more parsimonious shapes. I am not sure the sensitivity findings alone support this framing, but it might just be my misunderstanding of the analyses.

(3) The stimulus set included only four combinations of shapes, each designed to diagnostically target one of the theoretical quantities. It is unclear whether the results are robust or specific to these particular 4 stimuli.

(4) The study is framed as measuring "decision-making," but the task resembles statistical inference (e.g., which shape generated the data) or perceptual judgment. This is a minor point since "decision-making" is not well defined in the literature, yet the current framing in the title gave me the initial impression that humans would be making preference choices and learning about them over time with feedback.

Reviewer #2 (Public review):

This manuscript presents a sophisticated investigation into the computational mechanisms underlying human decision-making, and it presents evidence for a preference for simpler explanations (Occam's razor). The authors dissect the simplicity bias into four different components, and they design experiments to target each of them by presenting choices whose underlying models differ only in one of these components. In the learning tasks, participants must infer a "law" (a logical rule) from observed data in a way that operationalizes the process of scientific reasoning in a controlled laboratory setting. The tasks are complex enough to be engaging but simple enough to allow for precise computational modeling.

As a further novel feature, authors derive a further term in the expansion of the log-evidence, which arises from boundary terms. This is combined with a choice model, which is the one that is tested in experiments. Experiments are run, but with humans and with artificial intelligence agents, showing that humans have an enhanced preference for simplicity as compared to artificial neural networks.

Overall, the work is well written, interesting, and timely, bridging concepts in statistical inference and human decision making. Although technical details are rather elaborate, my understanding is that they represent the state of the art.

I have only one main comment that I think deserves more comments. Computing the complexity penalty of models may be hard. It is unlikely that humans can perform such a calculation on the fly. As authors discuss in the final section, while the dimensionality term may be easier to compute, others (e.g., the volume term, which requires an integral) may be considerably harder to compute (it is true that they should be computed once and for all for each task, but still...). I wonder whether the sensitivity of human decision making with reference to the different terms is so different, and in particular whether it aligns with computational simplicity, or with the possibility of approximating each term by simple heuristics. Indeed, the sensitivity to the volume term is significantly and systematically lower than that of other terms. I wonder whether this relation could be made more quantitative using neural networks, using as a proxy of computational hardness the number of samples needed to reach a given error level in learning each of these terms.

Reviewer #3 (Public review):

Summary:

This is a very interesting paper that documents how humans use a variety of factors that penalize model complexity and integrate over a possible set of parameters within each model. By comparison, trained neural networks also use these biases, but only on tasks where model selection was part of the reward structure. In the situation where training emphasizes maximum-likelihood decisions, only neural networks, but not humans, were able to adapt their decision-making. Humans continue to use model integration simplicity biases.

Strengths:

This study used a pre-registered plan for analyzing human data, which exceeds the standards compared to other current studies.

The results are technically correct.

Weaknesses:

The presentation of the results could be improved.

Author response:

Reviewer #1 (Public review)

I have to preface my evaluation with a disclosure that I lack the mathematical expertise to fully assess what seems to be the authors' main theoretical contribution. I am providing this assessment to the best of my ability, but I cannot substitute for a reviewer with more advanced mathematical/physical training.

Summary:

This paper describes a new theoretical framework for measuring parsimony preferences in human judgments. The authors derive four metrics that they associate with parsimony (dimensionality, boundary, volume, and robustness) and measure whether human adults are sensitive to these metrics. In two tasks, adults had to choose one of two flower beds which a statistical sample was generated from, with or without explicit instruction to choose the flower bed perceptually closest to the sample. The authors conduct extensive statistical analyses showing that humans are sensitive to most of the derived quantities, even when the instructions encouraged participants to choose only based on perceptual distance. The authors complement their study with a computational neural network model that learns to make judgments about the same stimuli with feedback. They show that the computational model is sensitive to the tasks communicated by feedback and only uses the parsimony-associated metrics when feedback trains it to do so.

Strengths:

(1)  The paper derives and applies new mathematical quantities associated with parsimony. The mathematical rigor is very impressive and is much more extensive than in most other work in the field, where studies often adopt only one metric (such as the number of causes or parameters). These formal metrics can be very useful for the field.

(2)  The studies are preregistered, and the statistical analyses are strong.

(3)  The computational model complements the behavioral findings, showing that the derived quantities are not simply equivalent to maximum-likelihood inference in the task.

(4)  The speculations in the discussion section (e.g., the idea that human sensitivity is driven by the computational demands each metric requires) are intriguing and could usefully guide future work.

Weaknesses:

(1) The paper is very hard to understand. Many of the key details of the derived metrics are in the appendix, with very little accessible explanation in the main text. The figures helped me understand the metrics somewhat, although I am still not sure how some of them (such as boundary or robustness as measured here) are linked to parsimony. I understand that this is addressed by the derivations in the appendix, but as a computational cognitive scientist, I would have benefited from more accessible explanations. Important aspects of the human studies are also missing from the main text, such as the sample size for Experiment 2.

(2) It is not fully clear whether the sensitivity of human participants to some of the quantities convincingly reported here actually means that participants preferred shapes according to the corresponding aspect of parsimony. The title and framing suggest that parsimony "guides" human decision-making, which may lead readers to conclude that humans prefer more parsimonious shapes. I am not sure the sensitivity findings alone support this framing, but it might just be my misunderstanding of the analyses.

(3) The stimulus set included only four combinations of shapes, each designed to diagnostically target one of the theoretical quantities. It is unclear whether the results are robust or specific to these particular 4 stimuli.

(4) The study is framed as measuring "decision-making," but the task resembles statistical inference (e.g., which shape generated the data) or perceptual judgment. This is a minor point since "decision-making" is not well defined in the literature, yet the current framing in the title gave me the initial impression that humans would be making preference choices and learning about them over time with feedback.

We are grateful for the supportive comments highlighting the rigor of our experimental design and data analysis. The Reviewer lists four points under “weaknesses”, to which we reply below. 

(1)  The paper is very hard to understand

In the revised version of the paper, we will expand the main text to include a more detailed and intuitive description of the terms of the Fisher Information Approximation, in particular clarifying the interpretation of robustness and boundary as parsimony. We also will include more details that are now given only in Methods, such as the sample size for the second experiment. 

(2) Sensitivity of human participants 

We do argue, and believe, that our data show that people tend to prefer simpler shapes. However, giving a well-posed definition of "preference" in this context turns out to be nontrivial.

At the very least, any statement such as "people prefer shape A over B" should be qualified with something like “when the distance of the data from both shapes is the same.” In other words, one should control for goodness-of-fit. Even before making any reference to our behavioral model, this phenomenon (a preference for the simpler model when goodness of fit is matched between models) is visible in Figure 3a, where the effective decision boundary used by human participants is closer to the more complex model than the cyan line representing the locus of points with equal goodness of fit under the two models (or equivalently, with the same Euclidean distance from the two shapes). The goal of our theory and our behavioral model is precisely to systematize this sort of control, extending it beyond just goodness-of-fit and allowing us to control simultaneously for multiple features of model complexity that may affect human behavior in different ways. In other words, it allows us not only to ask whether people prefer shape A over B after controlling for the distance of the data to the shapes, but also to understand to what extent this preference is driven by important geometrical features such as dimensionality, volume, curvature, and boundaries of the shapes. More specifically, and importantly, our theory makes it possible to measure the strength of the preference, rather than merely asserting its existence. In our modeling framework, the existence of a preference for simpler shapes is captured by the fact that the estimated sensitivities to the complexity penalties are positive (and although they differ in magnitude, all are statistically reliable).

(3) Generalization to different shapes  

Thank you for bringing up this important topic. First, note that while dimensionality and volume are global properties of models and only take two possible values in our human tasks, the boundary and robustness penalties depend on the model and on the data and therefore assume a continuum of values through the tasks (note also that the boundary penalty is relevant for all task types, not just the one designed specifically to study it, because all models except the zero-dimensional dot have boundaries). Therefore, our experimental setting is less restrictive of what it may seem, because it explores a range of possible values for two of the four model features. However, we agree that it would be interesting to repeat our experiment with a broader range of models, perhaps allowing their dimensionality and volume to vary more. In the same spirit, it would be interesting to study the dependence of human behavior on the amount of available data. We believe that these are all excellent ideas for further study that exceed the scope of the present paper. We will include these important points in a revised Discussion. 

(4) Usage of “decision making” vs “perceptual judgment”

Thank you. We will clarify better in the text that our usage of “decision making” overlaps with the idea of a perceptual judgment and that our experiments do not tackle sequential aspects of repeated decisions. 

Reviewer #2 (Public review):

This manuscript presents a sophisticated investigation into the computational mechanisms underlying human decision-making, and it presents evidence for a preference for simpler explanations (Occam's razor). The authors dissect the simplicity bias into four different components, and they design experiments to target each of them by presenting choices whose underlying models differ only in one of these components. In the learning tasks, participants must infer a "law" (a logical rule) from observed data in a way that operationalizes the process of scientific reasoning in a controlled laboratory setting. The tasks are complex enough to be engaging but simple enough to allow for precise computational modeling.

As a further novel feature, authors derive a further term in the expansion of the logevidence, which arises from boundary terms. This is combined with a choice model, which is the one that is tested in experiments. Experiments are run, but with humans and with artificial intelligence agents, showing that humans have an enhanced preference for simplicity as compared to artificial neural networks.

Overall, the work is well written, interesting, and timely, bridging concepts in statistical inference and human decision making. Although technical details are rather elaborate, my understanding is that they represent the state of the art.

I have only one main comment that I think deserves more comments. Computing the complexity penalty of models may be hard. It is unlikely that humans can perform such a calculation on the fly. As authors discuss in the final section, while the dimensionality term may be easier to compute, others (e.g., the volume term, which requires an integral) may be considerably harder to compute (it is true that they should be computed once and for all for each task, but still...). I wonder whether the sensitivity of human decision making with reference to the different terms is so different, and in particular whether it aligns with computational simplicity, or with the possibility of approximating each term by simple heuristics. Indeed, the sensitivity to the volume term is significantly and systematically lower than that of other terms. I wonder whether this relation could be made more quantitative using neural networks, using as a proxy of computational hardness the number of samples needed to reach a given error level in learning each of these terms.

Thank you. The computational complexity associated with calculating the different terms and its potential connection to human sensitivity to the terms is an intriguing topic. As we hinted at in the discussion, we agree with the reviewer that this is a natural candidate for further research, which likely deserves its own study and exceeds the scope of the present paper. 

As a minor aside, at least for the present task the volume term may not be that hard to compute, because it can be expressed with the number of distinguishable probability distributions in the model (Balasubramanian 1996). Given the nature of our task, where noise is Gaussian, isotropic and with known variance, the geometry of the model is actually the Euclidean geometry of the plane, and the volume is simply the (log of the) length of the line that represents the one-dimensional models, measured in units of the standard deviation of the noise.

Reviewer #3 (Public review):

Summary:

This is a very interesting paper that documents how humans use a variety of factors that penalize model complexity and integrate over a possible set of parameters within each model. By comparison, trained neural networks also use these biases, but only on tasks where model selection was part of the reward structure. In the situation where training emphasizes maximum-likelihood decisions, only neural networks, but not humans, were able to adapt their decision-making. Humans continue to use model integration simplicity biases.

Strengths:

This study used a pre-registered plan for analyzing human data, which exceeds the standards compared to other current studies.

The results are technically correct.

Weaknesses:

The presentation of the results could be improved.

We thank the reviewer for their appreciation of our experimental design and methodology, and for pointing out (in the separate "recommendations to authors") a few passages of the paper where the presentation could be improved. We will clarify these passages in the revision.

CVV Less Flow is not yet present in i18n

CVV Less Flow is not yet present in i18n

eLife Assessment

This valuable study successfully decoded visual representations of facial expressions and stereoscopic depth information from electroencephalogram (EEG) signals recorded in an immersive virtual reality (VR) environment. The evidence is solid in demonstrating the technical feasibility of integrating state-of-the-art EEG decoding and VR with eye tracking. This work will interest neuroscience researchers, as well as engineers developing brain-machine interfaces and/or virtual reality displays.

Reviewer #1 (Public review):

Summary:

The study by Klotzsche et al. examines whether emotional facial expressions can be decoded from EEG while participants view 3D faces in immersive VR and whether stereoscopic depth cues affect these neural representations. Participants viewed computer-generated faces (three identities, four emotions) rendered either stereoscopically or monoscopically, while performing an emotion recognition task. Time-resolved multivariate decoding revealed above-chance decodability of facial expressions from EEG. Importantly, decoding accuracy did not differ between monoscopic and stereoscopic viewing. This indicates that the neural representation of expressions is robust against stereoscopic disparity for the relevant features. However, a separate classifier could distinguish the depth condition (mono vs. stereo) from EEG, i.e., the pattern of neuronal activity differs between conditions, but not in ways relevant for the decoding of emotions. It had an early peak and a temporal profile similar to identity decoding, suggesting that early, task-irrelevant visual differences are captured neurally. Cross-decoding further demonstrated that expression decoders trained in one depth condition could generalize to the other, supporting the idea of representational invariance. Eye-tracking analyses showed that expressions and identities could be decoded from gaze patterns, but not the depth condition, and EEG- and gaze-based decoding performances were not correlated across participants. Overall, this work shows that EEG decoding in VR is feasible and sensitive, and suggests that stereoscopic cues are represented in the brain but do not influence the neural processing of facial expressions. This study addresses a relevant question with state-of-the-art experimental and data analysis techniques.

Strengths:

(1) It combines EEG, virtual reality stereoscoptic and monoscopic presentation of visual stimuli, and advanced data analysis methods to address a timely question.

(2) The figures are of very high quality.

(3) The reference list is appropriate and up to date.

Weaknesses:

(1) The introduction-results-discussion-methods order makes it hard to follow the Results without repeatedly consulting the Methods. Please introduce minimal, critical methodological context at the start of each Results subsection; reserve technical details for Methods/Supplement.

(2) Many Results subsections begin with a crisp question and present rich analyses, but end without a short synthesis. Please add 1-2 sentences that explicitly answer the opening question and state what the analyses demonstrate.

(3) The Results compellingly show that (a) expressions are decodable from EEG and (b) mono vs stereo trials are decodable from EEG; yet expression decoding is comparable across mono and stereo. It would help if you articulate why depth is neurally distinguishable while leaving expression representations unchanged. Maybe improve the discussion of the results of source localization and give a more detailed connection to what we already know about the processing of disparity.

Reviewer #2 (Public review):

Summary:

The authors' main aim was to determine the extent to which the emotional expression of face images could be inferred from electrophysiological data under the viewing conditions imposed by immersive virtual reality displays. Further, given that stereoscopic depth cues can be easily manipulated in such displays, the authors wished to investigate whether successful emotion decoding was affected by the presence or absence of these depth cues, and also if the presence/absence of depth cues was itself a property of the viewing experience that could be decoded from neural data.

Overall, the authors use fairly standard approaches to decoding neural data to demonstrate that above-chance results (slightly above the 0.5 chance threshold for their measure of choice) are in general achievable for emotion decoding, decoding the identity of faces from neural data, and decoding the presence/absence of depth cues in an immersive virtual reality display. They further examine the contribution of specific components of the response to visual stimuli with similar outcomes.

Strengths:

The main contribution of the manuscript is methodological. Rather than shedding particular light on the neural mechanisms supporting depth processing or face perception, what is on offer is primarily a straightforward examination of an applied question. With regard to the goal of answering that applied question, I think the paper succeeds. The overall experimental design is not novel, but in this case, that is a good thing. The authors have used relatively unadorned tasks and previous approaches to applying decoding tools to EEG data to see what they can get out of the neural data collected under these viewing conditions. While I would say that there is not a great deal that is especially surprising about these results, the authors do meet the goal they set for themselves.

Weaknesses:

Some of the key weaknesses I see are points that the authors raise themselves in their discussion, particularly with regard to the generalizability of their results. In particular, the 3D faces they have employed here perhaps exhibit a somewhat limited repertoire of emotional expression and do not necessarily cover a representative gamut of emotional face appearances, such as one would encounter in naturalistic settings. Then again, part of the goal of the paper was to examine the decodability of emotional expression in a specific, non-natural viewing environment - a viewing environment in which one could reasonably expect to encounter artificial faces like these. Still, the limitations of the stimuli potentially limit the scope of the conclusions one should draw from the data. I also think that there is a great deal of room for low-level image properties to drive the decoding results for faces, which could have been addressed in a number of ways (matching power spectra, for example, or using an inverted-image control condition). The absence of such control comparisons means that it is difficult to know if this is really a result that reflects face processing or much lower-level image differences that are diagnostic of emotion or identity in this subset of images. Again, to some extent, this is potentially acceptable - if one is mostly interested in whether this result is achievable at all (by hook or by crook), then it is not so important how the goal is met. Then again, one would perhaps like to know if what has been measured here is more a reflection of spatial vision vs. face processing mechanisms.

Reviewer #3 (Public review):

Summary:

This study investigates two main questions:

(1) whether brain activity recorded during immersive virtual reality can differentiate facial expressions and stereoscopic depth, and

(2) whether depth cues modulate facial information processing.

The results show that both expression and depth information can be decoded from multivariate EEG recorded in a head-mounted VR setup. However, the results show that the decoding performance of facial expressions does not benefit from depth information.

Strengths:

The study is technically strong and well executed. EEG data are of high quality despite the challenges of recording inside a head-mounted VR system. The work effectively combines stereoscopic stimulus presentation, eye-tracking to monitor gaze behavior, and time-resolved multivariate decoding techniques. Together, these elements provide an exemplary demonstration of how to collect and analyze high-quality EEG data in immersive VR environments.

Weaknesses:

The major limitation concerns the theoretical question about how stereoscopic depth modulates facial expression processing. While previous work has suggested that stereoscopic depth cues can shape natural face perception and emphasize the importance of binocular information in recognizing facial expressions (lines 95-97), the present study reports a null effect of depth. However, the stimulus configuration they used likely constrained the ability to detect any depth-related effects. All facial stimuli were static, frontal, and presented at a fixed distance. This design leads to near-ceiling behavioral performance and no behavioral effect of depth on expression recognition. It makes the null modulation of depth on expression processing unsurprising and limits the theoretical reach of the study. Adding more subtle or naturalistic features (such as various viewing angles and dynamic expressions) to the stimulus set if the authors aim to advance a strong theoretical claim about the role of binocular disparity. Or reframing the work as a technical validation of EEG decoding in this context.

Another issue relates to the claim that eye movements cannot explain the EEG decoding results. It is a real challenge to remove eye-movement-related artifacts and confounds, as the VR setup tends to encourage viewers to explore the environment freely. However, nearly half of the eye-tracking datasets were lost (usable in only 17 of 33 participants), which substantially weakens the evidence for EEG-gaze dissociation. Moreover, it would be almost impossible to decode facial information from only two-dimensional gaze direction, given that with 60 EEG channels, the decoding accuracy was modest (AUC ≈ 0.60). These two factors together limited the strength of the reported null correlation between neural and eye-data decoding.

The decoding analysis appears to use all 60 EEG channels as input features. I wonder why the authors did not examine using more spatially specific channel subsets. Facial expression and depth cues are known to preferentially engage occipito-temporal regions (e.g., N170-related sites), yet the current approach treats all sensors equally. Including all the channels may add noise and irrelevant signals to facial information decoding. Besides, using a subset of spatial-specific channels would align more directly with the subsequent source reconstruction.

Author response:

We thank the reviewers for their thoughtful and constructive comments. We are pleased that they found the study technically strong and the integration of EEG decoding, immersive VR, and eye tracking valuable.

Across all three reviews, several points of clarification emerged. In our revision, we will focus on:

(1) Improving clarity and structure of the manuscript (Reviewer #1).

We will strengthen the flow between the Methods and Results subsections and include explicit concluding statements for the single results.

(2) Emphasize methodological scope and limitations in terms of stimulus set and generalizability (Reviewers #2 and #3).

We will further emphasize that a key objective was to establish, for the first time, the methodological feasibility of decoding facial features (especially emotional expressions) under VR conditions, and that our stimulus set (consisting of facial expressions that were easy to distinguish) limits (a) the task-relevance (and thus possibly the neural integration) of depth information and (b) the generalizability to less easily distinguishable settings. We appreciate the suggestion of an inverted-face control to further investigate the extent to which the decoding results were based on low-level features; however, we do not plan a follow-up experiment at this stage; instead, we will discuss this limitation more explicitly.

We believe these revisions will substantially strengthen the manuscript and further highlight its methodological focus.

eLife Assessment

This important study reveals that mitotic release of an ER-microtubule tether is critical for normal mitotic progression. Manipulating CLIMP63 phosphorylation, the authors provide convincing evidence that persistent microtubule-ER contacts activate the spindle assembly checkpoint and, if mitosis is forced to proceed, drive severe micronucleation. While the study provides new mechanistic insights, some evidence is indirect, and additional experiments would further refine the model.

Reviewer #1 (Public review):

Summary:

In the present manuscript, de Bos and Kutay investigate the functional implications of persistent microtubule-ER contacts as cells go through mitosis. To do so, they resorted to investigating phosphorylation mutants of the ER-Microtubule crosslinker Climp63. They found that phosphodeficient Climp63 mutants induce a severe SAC-dependent mitotic delay after normal chromosome alignment, with an impressive mitotic index of approximately 75%. Strikingly, this was often associated with massive nuclear fragmentation into up to 30 micronuclei that are able to recruit both core and non-core nuclear envelope components. One particular residue (S17) that is phosphorylated by Cdk1 seems to account for most, if not all, these phenotypes. Furthermore, the authors use the impact on mitosis as an indirect way to map the microtubule binding domain of Climp63, which has remained controversial, and found that it is mostly restricted to the N-terminal 28 residues of Climp63. Of note, despite the strong impact on mitosis, persistent microtubule-ER contacts did not affect the distribution of other organelles during mitosis, such as mitochondria or lysosomes.

Strengths:

Overall, this work provides important mechanistic insight into the functional implications of ER-microtubule network remodelling during mitosis and should be of great interest to a vast readership of cell biologists.

Weaknesses:

Some of the key findings appear somewhat preliminary and would be worth exploring further to substantiate some of the claims and clarify the respective impact on mitosis and nuclear envelope reassembly on the resulting micronuclei.

The following suggestions would significantly clarify some key points:

(1) The striking increase in mitotic index in cells expressing the Climp63 phosphodefective mutant, together with their live cell imaging data indicating extensive mitotic delays that can be relieved by SAC inhibition, suggests that SAC silencing is significantly delayed or even impossible to achieve. The fact that most chromosomes align in 12 min, irrespective of the expression of the Climp63 phosphodefective mutant, suggests that initial microtubule-kinetochore interactions are not compromised, but maybe cannot be stably maintained. Alternatively, the stripping of SAC proteins from kinetochores by dynein along attached microtubules might be compromised, despite normal microtubule-kinetochore attachments. The authors allude to both these possibilities, but unfortunately, they never really test them. This could easily be done by immunofluorescence with a Mad1 or c-Mad2 antibody to inspect which fraction of kinetochores (co-stained with a constitutive kinetochore marker, such as CENP-A or CENP-C) are positive for these SAC proteins. If just a small fraction, then the stability of some attachments is likely the cause. If most/all kinetochores retain Mad1/c-Mad2, then it is probably an issue of silencing the SAC.

(2) The authors use the increase in mitotic index (H3 S10 phosphorylation levels) as a readout for the MT binding efficiency of Climp63 and respective mutants. Although suggestive, this is fairly indirect and requires additional confirmation. For example, the authors could perform basic immunofluorescence in fixed cells to inspect co-localization of Climp63 (and its mutants) with microtubules.

(3) The authors refer in the discussion that the striking nuclear fragmentation seen upon mitotic exit of cells expressing Climp63 phosphodefective mutant has not been reported before, and yet it is strikingly similar to what has been previously observed in cells treated with taxol (they cite Samwer et al. 2017, but they might elect to cite also Mitchison et al., Open Biol, 2017 and most relevantly Jordan et al., Cancer Res, 1996). This striking similarity and given the extensive mitotic delay observed in the Climp63 phosphodefective mutant, it is tempting to speculate that these cells are undergoing mitotic slippage (i.e., cells exit mitosis without ever satisfying the SAC) because they are unable to silence/satisfy the SAC. Indeed, the scattered micronuclei morphology has also been observed in cells undergoing mitotic slippage (e.g., Brito and Rieder, Curr Biol., 2006). The experiment suggested in point #1 should also shed light on this problem. The authors might want to consider discussing this possible explanation to interpret the observed phenotypes.

(4) One of the most significant implications of the findings reported in this paper is that microtubule proximity does not seem to impact the assembly of either core or non-core nuclear envelope proteins on micronuclei (that possibly form due to mitotic slippage, rather than normal anaphase). These results challenge some models explaining nuclear envelope defects in micronuclei derived from lagging chromosomes due to the proximity of microtubules, and, as the authors point out at the very end, other reasons might underlie these defects. Along this line, the authors might elect to cite Afonso et al. Science, 2014, and Orr et al., Cell Reports, 2022, who provide evidence that a spindle midzone-based Aurora B gradient, rather than microtubules per se, underlie the nuclear envelope defects commonly seen in micronuclei derived from lagging chromosomes during anaphase.

Reviewer #2 (Public review):

Mitotic phosphorylation of the ER-microtubule linker CLIMP63 was discovered decades ago and was shown to release CLIMP63 from microtubules. Here, the authors describe for the first time the significance of CLIMP63 phosphorylation for mitotic division in cells. Expression of non-phosphorylatable CLIMP63 led to a massive re-localization of ER into the area of the mitotic spindle. This was not unexpected, as another ER-microtubule linker, STIM1, is phosphorylated during mitosis to release it from microtubules, and unphosphorylatable STIM1 also leads to an invasion of the ER into the spindle. The authors map CLIMP63's microtubule-binding domain and define S17 as the critical residue that needs to be phosphorylated for release from microtubules and as a target of Cdk1, albeit with an indirect assay that is based on the ability of overexpressed mutants to disrupt mitosis. The authors further demonstrate that aberrant, microtubule-tethered membranes in the spindle disrupt spindle function. This is in line with the group's prior findings that chromosome-tethered membranes lead to severe chromosome segregation defects. Cells overexpressing phospho-deficient CLIMP63 arrested in prometaphase with an active checkpoint. When these cells were forced to exit mitosis, a large number of micronuclei formed. Interestingly, these micronuclei had different compositions and properties from previously described ones, suggesting that there are diverse paths for a cell to become multinucleated. Lastly, the authors asked whether mitochondria and lysosomes depend on ER for their distribution in mitotic cells. However, the position of these other organelles was unchanged in cells in which ER was re-localized due to the overexpression of phospho-deficient CLIMP63. This is an interesting observation in the context of how the interior organisation of mitotic cells is achieved.

Suggestions:

(1) The authors should confirm the mapping of the microtubule-binding domain by more direct assays, such as microtubule co-pelleting or proximity ligation assays.

(2) The authors should clarify why they performed phenotypic studies and live microscopy experiments (Figures 4 and 5) using the CLIMP63(3A) mutant, despite knowing that the relevant phosphorylation site was S17. Were the phenotypes different for S17A versus the triple mutant?

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use the same content as in Octa GO

Střední řada nůžkových stanů Zesílený profil stanové nohy o průměru 48 mm, prodloužená záruka a stále zachovaná stavba do 60 s! Stany Octa Optima lze označit za zlatou střední cestu. (next paragraph) Hodí se do náročnějších podmínek nebo tam, kde se dá očekávat zhoršené počasí. Stany v této řadě zvládnou zastřešit od 3x3 m až do největšího rozměru 6x6 m. + change the text in video for CZ

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eLife Assessment

This study provides useful insights into addressing the question of whether the prevalence of autoimmune disease could be driven by sex differences in the T cell receptor (TCR) repertoire, correlating with higher rates of autoimmune disease in females. The authors compare male and female TCR repertoires using bulk RNA sequencing, from sorted thymocyte subpopulations in pediatric and adult human thymuses; however, the results do not provide sufficient analytical rigor and incompletely support the central claims.

Reviewer #1 (Public review):

Summary:

The goal of this paper was to determine whether the T cell receptor (TCR) repertoire differs between a male and a female human. To address this, this group sequenced TCRs from double-positive and single-positive thymocytes in male and female humans of various ages. Such an analysis on sorted thymocyte subsets has not been performed in the past. The only comparable dataset is a pediatric thymocyte dataset where total thymocytes were sorted.

They report on participant ages and sexes, but not on ethnicity, race, nor provide information about HLA typing of individuals. Though the experiments themselves are heroic, they do represent a relatively small sampling of diverse humans. They observed no differences in TCRbeta or TCRalpha usage, combinational diversity, or differences in the length of the CDR3 region, or amino acid usage in the CD3aa region between males or females. Though they observed some TCRbeta CD3aa sequence motifs that differed between males and females, these findings could not be replicated using an external dataset and therefore were not generalizable to the human population.

They also compared TCRbeta sequences against those identified in the past using computational approaches to recognize cancer-, bacterial-, viral-, or autoimmune-antigens. They found very little overlap of their sequences with these annotated sequences (depending on the individual, ranging from 0.82-3.58% of sequences). Within the sequences that were in overlap, they found that certain sequences against autoimmune or bacterial antigens were significantly over-represented in female versus male CD8 SP cells. Since no other comparable dataset is available, they could not conclude whether this is a finding that is generalizable to the human population.

Strengths:

This is a novel dataset. Overall, the methodologies appear to be sound. There was an attempt to replicate their findings in cases where an appropriate dataset was available. I agree that there are no gross differences in TCR diversity between males and females.

Weaknesses:

Overall, the sample size is small given that it is an outbred population. The cleaner experiment would have been to study the impact of sex in a number of inbred MHC I/II identical mouse strains or in humans with HLA-identical backgrounds.

It is unclear whether there was consensus between the three databases they used regarding the antigens recognized by the TCR sequences. Given the very low overlap between the TCR sequences identified in these databases and their dataset, and the lack of replication, they should tone down their excitement about the CD8 T cell sequences recognizing autoimmune and bacterial antigens being over-represented in females.

The dataset could be valuable to the community.

Reviewer #2 (Public review):

Summary:

This study addresses the hypothesis that the strikingly higher prevalence of autoimmune diseases in women could be the result of biased thymic generation or selection of TCR repertoires. The biological question is important, and the hypothesis is valuable. Although the topic is conceptually interesting and the dataset is rich, the study has a number of major issues that require substantial improvement. In several instances, the authors conclude that there are no sex-associated differences for specific parameters, yet inspection of the data suggests visible trends that are not properly quantified. The authors should either apply more appropriate statistical approaches to test these trends or provide stronger evidence that the observed differences are not significant. In other analyses, the authors report the differences between sexes based on a pulled analysis of TCR sequences from all the donors, which could result in differences driven by one or two single donors (e.g., having particular HLA variants) rather than reflect sex-related differences.

Strengths:

The key strength of this work is the newly generated dataset of TCR repertoires from sorted thymocyte subsets (DP and SP populations). This approach enables the authors to distinguish between biases in TCR generation (DP) and thymic selection (SP). Bulk TCR sequencing allows deeper repertoire coverage than single-cell approaches, which is valuable here, although the absence of TRA-TRB pairing and HLA context limits the interpretability of antigen specificity analyses. Importantly, this dataset represents a valuable community resource and should be openly deposited rather than being "available upon request."

Weaknesses:

Major:

(1) The authors state that there is "no clear separation in PCA for both TRA and TRB across all subsets." However, Figure 2 shows a visible separation for DP thymocytes (especially TRA, and to a lesser degree TRB) and also for TRA of Tregs. This apparent structure should be acknowledged and discussed rather than dismissed.

(2) Supplementary Figures 2-5 involve many comparisons, yet no correction for multiple testing appears to be applied. After appropriate correction, all the reported differences would likely lose significance. These analyses must be re-evaluated with proper multiple-testing correction, and apparent differences should be tested for reproducibility in an external dataset (for example, the pediatric thymus and peripheral blood repertoires later used for motif validation).

(3) Supplementary Figure 6 suggests that women consistently show higher Rényi entropies across all subsets. Although individual p-values are borderline, the consistent direction of change is notable. The authors should apply an integrated statistical test across subsets (for example, a mixed-effects model) to determine whether there is an overall significant trend toward higher diversity in females.

(4) Figures 4B and S8 clearly indicate enrichment of hydrophobic residues in female CDR3s for both TRA and TRB (excluding alanine, which is not strongly hydrophobic). Because CDR3 hydrophobicity has been linked to increased cross-reactivity and self-reactivity (see, e.g., Stadinski et al., Nat Immunol 2016), this observation is biologically meaningful and consistent with higher autoimmune susceptibility in females.

(5) The majority of "hundreds of sex-specific motifs" are probably donor-specific motifs confounded by HLA restriction. This interpretation is supported by the failure to validate motifs in external datasets (pediatric thymus, peripheral blood). The authors should restrict analysis to public motifs (shared across multiple donors) and report the number of donors contributing to each motif.

(6) When comparing TCRs to VDJdb or other databases, it is critical to consider HLA restriction. Only database matches corresponding to epitopes that can be presented by the donor's HLA should be counted. The authors must either perform HLA typing or explicitly discuss this limitation and how it affects their conclusions.

(7) Although the age distributions of male and female donors are similar, the key question is whether HLA alleles are similarly distributed. If women in the cohort happen to carry autoimmune-associated alleles more often, this alone could explain observed repertoire differences. HLA typing and HLA comparison between sexes are therefore essential.

(8) In some analyses (e.g., Figures 8C-D) data are shown per donor, while others (e.g., Fig. 8A-B) pool all sequences. This inconsistency is concerning. The apparent enrichment of autoimmune or bacterial specificities in females could be driven by one or two donors with particular HLAs. All analyses should display donor-level values, not pooled data.

(9) The reported enrichment of matches to certain specificities relative to the database composition is conceptually problematic. Because the reference database has an arbitrary distribution of epitopes, enrichment relative to it lacks biological meaning. HLA distribution in the studied patients and HLA restrictions of antigens in the database could be completely different, which could alone explain enrichment and depletions for particular specificities. Moreover, differences in Pgen distributions across epitopes can produce apparent enrichment artifacts. Exact matches typically correspond to high-Pgen "public" sequences; thus, the enrichment analysis may simply reflect variation in Pgen of specific TCRs (i.e., fraction of high-Pgen TCRs) across epitopes rather than true selection. Consequently, statements such as "We observed a significant enrichment of unique TRB CDR3aa sequences specific to self-antigens" should be removed.

(10) The overrepresentation of self-specific TCRs in females is the manuscript's most interesting finding, yet it is not described in detail. The authors should list the corresponding self-antigens, indicate which autoimmune diseases they relate to, and show per-donor distributions of these matches.

(11) The concept of polyspecificity is controversial. The authors should clearly explain how polyspecific TCRs were defined in this study and highlight that the experimental evidence supporting true polyspecificity is very limited (e.g., just a single TCR from Figure 5 from Quiniou et al.).

Minor:

(1) Clarify why the Pgen model was used only for DP and CD8 subsets and not for others.

(2) The Methods section should define what a "high sequence reliability score" is and describe precisely how the "harmonized" database was constructed.

(3) The statement "we generated 20,000 permuted mixed-sex groups" is unclear. It is not evident how this permutation corrects for individual variation or sex bias. A more appropriate approach would be to train the Pgen model separately for each individual's nonproductive sequences (if the number of sequences is large enough).

eLife Assessment

The authors ask whether a simple whole-head spectral power analysis of human magnetoencephalography data recorded at rest in a large cohort of adults shows robust effects of age, and their results provide compelling evidence that it does. The relative simplicity of the analysis is a major strength of the paper, and the authors are careful to control for many different confounds - although perhaps highly correlated factors like brain anatomy still pose a slight issue. The paper provides a valuable power analysis framework that should inform researchers across the broader neuroimaging community

Reviewer #1 (Public review):

Summary:

This is a careful, well-powered treatment of age effects in resting-state MEG. Rather than extracting (say) complex connectivity measures, the authors look at the 'simplest possible thing': changes in the overall power spectrum across age.

Strengths:

They find significant age-related changes at different frequency bands: broadly, attenuation at low-frequency (alpha) and increased beta. These patterns are identified in a large dataset (CamCAN) and then verified in other public data.

Weaknesses:

Some secondary interpretations (what is "unique" to age vs global anatomy) may go beyond what the statistics strictly warrant in the current form, but these can be tightened with (I think, fairly quick) additions already foreshadowed by the authors' own analyses.

Aims:

The authors set out to replace piecemeal, band-by-band ageing claims with t-maps, and Cohen's f2 over sensors×frequency ("GLM-Spectrum").

On CamCAN, six spatio-spectral peaks survive relatively strict statistical controls. The larger effects are in low-frequency and upper-alpha/beta ranges (f2 approx 0.2-0.3), while lower-alpha and gamma reach significance but with small practical impact (f2 < 0.075). A nice finding is that the same qualitative profile appears in three additional independent datasets.

Two analyses are especially interesting. First, the authors show a difference between absolute and relative spectral magnitude (basically, within-subject normalization). Relative scaling sharpens the spectral specificity of the spatial maps, while absolute magnitude is dominated by a broad spatial mode that correlates positively across frequencies, likely reflecting head-position/field-spread factors. The replication of the main age profile is robust to preprocessing decisions (e.g., SSS movement compensation choices) - the bigger determinant of the effect is whether they apply sensor normalization (relative vs absolute).

Second, lots of brain-related things might be related to age, and the authors spend some time trying to back out confounds/covariates. This section is handled transparently (in general, I found the writing style very clear throughout) - they examine single covariates (sex, BP, GGMV, etc.) and compare simple vs partial age effects. For example, aging is correlated with reductions in global grey-matter volume (GGMV), but it would be nice to find a measure that is independent of this: controlling for GGMV (via a linear model) reduces age-related effect sizes heterogeneously across space/frequency but does not eliminate them, a nuance the authors treat carefully.

This is a nice paper, and I have only a few concrete suggestions:

(1) High-gamma:

There can be a lot of EMG / eye movement contamination (I know these were RS eyes closed data, but still..) above 30-40 Hz, and these effects are the weakest anyway. Could you add an analysis (e.g., ICA/label-based muscle component removal) and show the gamma band's sensitivity to that step? Or just note this point more clearly?

(2) GGMV confound control:

Controlling for GGMV reduces, but does not eliminate, age effects. I have a few questions about this: a) Could we see the residuals as a function of age? I wonder if there are non-linear effects or something else that the regression is not accounting for. Also, b) GGMV and age are highly colinear - is this an issue? Can regression really split them apart robustly? I think by some cunning orthogonalisation, you can compute the effect of age independent of GGVM. I don't think this is the same as the effect 'adjusted' for GGMV (which is what is shown here if I'm reading it correctly). Finally, of course, GGMV might actually be the thing you want to look at (because it might more accurately reflect clinical issues) - so strong correlations are not really a problem: I think really the focus might even be on using MEG to predict GGMV and controlling for age.

Reviewer #2 (Public review):

This paper describes the application of the "GLM-Spectrum" mass univariate approach to examine the effects of age on M/EEG power spectra. Its strengths include promotion of the unbiased approach, suitable for future meta/mega-analyses, and the provision of effect sizes for powering future studies. These are useful contributions to the literature. What is perhaps lacking is a discussion of the limitations of this approach, in comparison to other methods.

An analogy is the mass univariate approach to spatial localisation of effects in fMRI/PET images. This approach is unbiased by prior assumptions about the organisation of the brain, but potentially also less sensitive, by ignoring that prior knowledge. For example, a voxelwise univariate approach is less sensitive to detecting effects in functionally homogeneous brain regions, where SNR can be increased by averaging over voxels. In the context of power spectra, the authors' approach deliberately ignores knowledge about the dominant frequency bands/oscillations in human power spectra. This is in contrast to approaches like FOOOF and IRASA, which explicitly parametrise frequency components. I am not saying these methods are better; I just think that the authors should acknowledge that these approaches have advantages over their mass univariate approach (in sensitivity and interpretation; see below). I guess it is a type of bias-sensitivity trade-off: the authors want to avoid bias, but they should acknowledge the corresponding loss of sensitivity, as well as loss of interpretation compared to model-based approaches (i.e, models that parameterise frequency; I don't mean the statistical models for each frequency separately).

An example of the interpretational loss can be seen in the authors' observation of opposite-signed effects of age around the alpha peak. While the authors acknowledge that this pattern can arise from a reduction in alpha frequency with age, this is an indirect inference, and a direct (and likely much more sensitive) approach would be to parametrise and estimate the peak alpha frequency directly for each participant, as done with FOOOF for example (possibly with group priors, as in Medrano et al, 2025, EJN). The authors emphasise the nonlinear effects of age in Figure 2A, but their approach cannot test this directly (e.g., in terms of plotting effects of age on frequency, magnitude, and width for each participant), so for me, this figure illustrates a weakness of their approach, not a strength.

Then I think the section "Two dissociable and opposite effects in the alpha range" in the Discussion section is confusing, because if there is a single reduction in alpha peak frequency and magnitude with age, then there is only one "effect", not "two dissociable" ones. If the authors do want to claim that there are two dissociable age effects within the alpha range, then they need to do a statistical test, e.g., that the topographies of low and high alpha are significantly different. This then reveals another limitation of the mass univariate approach - that space (channel) is not parametrised either - so one cannot test for significant channel x effect interactions within this framework, as necessary to really claim a dissociation (e.g., in underlying neural generators).

While the authors show that normalisation of each person's power spectra by the sum across frequencies helps improve some statistics, they might want to say more about disadvantages of this approach, e.g., loss of sensitivity to any effects (eg of age) that are broadly distributed across majority of frequencies, loss of real SI units (absolute effect sizes) (as well as problems if normalisation were used for techniques like FOOOF, where the 1/f exponent would be affected).

The authors should give more information on how artifactual ICs were defined. This may be important for cardiac artefacts, since Schmidt et al (2004, eLife) have pointed out how "standard" ICA thresholds can fail to remove all cardiac effects. This is very important for the effects of age, given that age affects cardiac dynamics (even though the focus of Schmidt et al is the 1/f exponent, could residual cardiac effects cause artifactual age effects in current results, even above ~1Hz?).

The authors should clarify the precise maxfilter arguments, and explain what "reference" was used for the "trans" option - e.g., did the authors consider transforming the data to match a sphere at the centre of the helmet, which might not only remove some of the global power differences due to different head positions, but also be best for generalisation of the effect sizes they report to future studies (assuming the centre of the helmet is the most likely location on average)? And on that matter, did head positions actually differ by age at all?

eLife Assessment

This study explores how exogenous attention operates at the finest spatial scale of vision, within the foveola - a topic that has not been previously explored. The question is important for understanding how attention shapes perception, and how it differs between the periphery and the central regions of highest visual acuity. The evidence is compelling, as shown by carefully designed experiments with state-of-the-art eye tracking to monitor attended locations just a few tens of minutes of arc away from the fixation target, but additional clarification regarding analyses and implications for vision and oculomotor control would broaden the impact of the study.

Reviewer #1 (Public review):

Summary:

The manuscript investigates how exogenous attention modulates spatial frequency sensitivity within the foveola. Using high-precision eye-tracking and gaze-contingent stimulus control, the authors show that exogenous attention selectively improves contrast sensitivity for low- to mid-range spatial frequencies (4-8 cycles/degree), but not for higher frequencies (12-20 CPD). In contrast, improvements in asymptotic performance at the highest contrast levels occur across all spatial frequencies. These results suggest that, even within the foveola, exogenous attention operates through a mechanism similar to that observed in peripheral vision, preferentially enhancing lower spatial frequencies.

Strengths:

The study shows strong methodological rigor. Eye position was carefully controlled, and the stimulus generation and calibration were highly precise. The authors also situate their work well within the existing literature, providing a clear rationale for examining the fine-grained effects of exogenous attention within the foveola. The combination of high spatial precision, gaze-contingent presentation, and detailed modeling makes this a valuable technical contribution.

Weaknesses:

The manipulation of attention raises some interpretive concerns. Clarifying this issue, together with additional detail about statistics, participant profiles, other methodological elements, and further discussion in relation to oculomotor control in general, could broaden the impact of the findings.

Reviewer #2 (Public review):

Summary:

This study aims to test whether foveal and non-foveal vision share the same mechanisms for endogenous attention. Specifically, they aim to test whether they can replicate at the foveola previous results regarding the effects of exogenous attention for different spatial frequencies.

Strengths:

Monitoring the exact place where the gaze is located at this scale requires very precise eye-tracking methods and accurate and stable calibration. This study uses state-of-the-art methods to achieve this goal. The study builds on many other studies that show similarities between foveal vision and non-foveal vision, adding more data supporting this parallel.

Weaknesses:

The study lacks a discussion of the strength of the effect and how it relates to previous studies done away from the fovea. It would be valuable to know if not just the range of frequencies, but the size of the effect is also comparable.

Reviewer #3 (Public review):

Summary:

This paper explores how spatial attention affects foveal information processing across different spatial frequencies. The results indicate that exogenously directed attention enhances contrast sensitivity for low- to mid-range spatial frequencies (4-8 CPD), with no significant benefits for higher spatial frequencies (12-20 CPD). However, asymptotic performance increased as a result of spatial attention independently of spatial frequency.

Strengths:

The strengths of this article lie in its methodological approach, which combines a psychophysical experiment with precise control over the information presented in the foveola.

Weaknesses:

The authors acknowledge that they used the standard approach of analyzing observer-averaged data, but recognize that this method has limitations: it ignores the uncertainty associated with parameter estimates and the relationships between different parameters of the psychometric model. This may affect the interpretation of attentional effects. In the future, mixed-effects models at the trial level could overcome these limitations.

eLife Assessment

This valuable study provides solid evidence for deficits in aversive taste learning and taste coding in a mouse model of autism spectrum disorders. Specifically, the authors found that Shank3 knockout mice exhibit behavioral deficits in learning and extinction of conditioned taste aversion, and calcium imaging of the gustatory cortex identified impaired neuronal responses to taste stimuli. This paper will likely be of interest to researchers studying how learning and sensory processes are affected by genetic causes of autism spectrum disorders.

Reviewer #1 (Public review):

Summary:

The study from Wu and Turrigiano investigates how disruption of taste coding in a mouse model of autism spectrum disorders (ASDs) affects aversive learning in the context of a conditioned taste aversion (CTA) paradigm. The experiments combine 2-photon calcium imaging of neurons in the gustatory portion of the anterior insular cortex (i.e., gustatory cortex) with behavioral training and testing. The authors rely on Shank3 knockout mice as a model for ASDs. The authors found that Shank3 mice learn CTA more slowly and extinguish the memory more rapidly than control subjects. Calcium imaging identified impairments in taste-evoked activity associated with memory encoding and extinction. During memory encoding, the authors found less suppressed neuronal activity and increased correlated variability in Shank3 mice compared to controls. During extinction, they observed a faster loss of taste selectivity and degradation of taste discriminability in mutants compared to controls.

Strengths:

This is a well-written manuscript that presents interesting findings. The results on the learning and extinction deficits in Shank3 mice are of particular interest. Analyses of neural activity are well conducted and provide important information on the type of impaired cortical activity that may correlate with behavioral deficits.

Weaknesses:

(1) The experiments rely on three groups: CS-only WT, CTA WT, and CTA KO. Can the authors provide a rationale for not having a CS-only KO group?

(2) The authors design an effective behavioral paradigm comparing consumption of water and saccharin and tracking extinction (Figure 3). This paradigm shows differences in licking across distinct behavioral conditions. For instance, during T1, licking to water strongly differs from licking to saccharin for both WT and KO. During T2, licking to water strongly differs from licking to saccharin only for WT (much less for KO), and licking to saccharin in WT differs from that in KO. These differences in taste sampling across conditions could contribute to some of the effects on neural activity and discriminability reported in Figures 5 and 6. That is sucrose and water trials may be highly discriminable because in one case the mouse licks and in the other it does not (or licks much less). The author may want to address this issue.

(3) Are there any omission trials following CTA? If so, they should be quantified and reported. How are the omission trials treated with regard to the analyses?

(4) The authors describe the extinction paradigm as "alternative choice". In decision-making, alternative choice paradigms typically require 2 lateral spouts to report decisions following the sampling from a central spout. To avoid confusion, the authors may want to define their paradigm as alternative sampling.

(5) Figure 4 reports that CTA increases the proportion of neurons that consistently respond to saccharin and water across days. While the saccharin result could be an effect of aversive learning, it is less clear why the phenomenon would generalize to water as well. Can the authors provide an explanation?

(6) The recordings are performed in the part of the anterior insular cortex that is typically defined as "gustatory cortex" (GC). Given the functional heterogeneity of the anterior insular cortex (AIC) and given that the authors do not sample all of the anteroposterior extent of AIC, I would suggest being more explicit about their positioning in GC. Also, some citations (e.g., Gogolla et al, 2014) refer to the posterior insular cortex, which is considered more inherently multimodal than GC. GC multimodality is typically associative in nature, as only a few neurons respond to sound and light in naïve animals.

(7) It would be useful to add summary figures showing the extent of viral spread as well as GRIN lens placement.

(8) I encourage the authors to add Ns every time percentages are reported. How many neurons have been recorded in each condition? Can the authors provide the average number of neurons recorded per session and per animal?

(9) It looks like some animals learned more than others (Figure 1E or Figure 3C). Is it possible to compare neural activity across animals that showed different degrees of learning?

Reviewer #2 (Public review):

Wu and Turrigiano investigated how cortical taste coding during conditioned taste aversion (CTA) learning is affected in Shank3 knockout (KO) mice, a model of monogenic ASD. Using longitudinal two-photon calcium imaging of AIC neurons, the authors show that Shank3 KO mice exhibit reduced suppression of activity in a subset of neurons and a higher correlated variability in neural activity. This is accompanied by slower learning and faster extinction of aversive taste memories. These results suggest that Shank3 loss compromises the flexibility and stability of cortical representations underlying adaptive behaviour.

Major strengths:

(1) Conceptual significance: The study connects a molecular ASD risk gene (Shank3) to flexible sensory encoding, bridging genetics, systems neuroscience, and behaviour.

(2) Technical rigour: Longitudinal calcium imaging with cell-registration across learning and extinction sessions is technically demanding and well-executed.

(3) Behavioural paradigm: The use of both acquisition and extinction paradigms provides a more nuanced picture of learning dynamics.

(4) Analyses: Correlated variability, discriminability indices, and population decoding analyses are robust and appropriate for addressing behavioural and network-level coding changes.

Major weaknesses:

(1) Causality: The paper infers that increased correlated variability causes learning deficits, but no causal tests (e.g., optogenetic modulation of inhibition or interneuron rescue) are presented to confirm this.

(2) Behavioural scope: The study focuses exclusively on taste aversion; generalisation to other flexible learning paradigms (e.g., reversal or probabilistic tasks) is not addressed.

(3) Mechanistic insights: While providing interesting findings of altered sensory perception and extinction of learning-related signals in AIC, it offered nearly no mechanistic insights. This makes the interpretation, especially on how generalisable these findings are, difficult. Also, different reported findings are "potentially" connected, but the exact relation between increased correlated variability and faster loss of taste selectivity cannot be assessed.

Reviewer #3 (Public review):

In this study, Wu & Turrigiano investigate an ethologically relevant form of associative learning (conditioned taste aversion - CTA) and its extinction in the Shank3 KO mouse model of ASD. They also examine the underlying circuits in the anterior insular cortex (AIC) simultaneously, using two-photon calcium imaging through a GRIN lens. They report that Shank3 KO mice learn CTA slower and suggest that this is mediated by a reduction in tastant-stimulus activity suppression of AIC neurons and a reduced signal-to-noise ratio due to increased noise correlations in AIC neurons. Interestingly, once Shank3 KO mice acquire CTA, they extinguish the aversive memory more rapidly than wild-type mice. This accelerated extinction is accompanied by a faster loss of neuronal and population-level taste selectivity and coding in the AIC compared to WT mice.

This is an important study that uses in vivo methods to assess circuit dysfunction in a mouse model of ASD, related to sensory perception valence (in this case, taste). The study is well executed, the data are of high quality, and the analytical procedures are detailed. Furthermore, the behavioural paradigm is well thought out, particularly the approach for assessing extinction through repeated retrieval sessions (T1-T5), which effectively tests discrimination between saccharin and water rather than relying solely on lick counts or total consumption as a measure of extinction. Finally, the statistical tests used are appropriate and justified.

There is, however, a missing link between the behavioural findings and the underlying mechanisms. More specifically:

(1) The authors don't make a causal link between the behaviour and AIC neurophysiology, both the percentage of suppressed cells and the coactivity measurements. For the % of suppressed cells, it seems that both WT and KO cells are suppressed in the transition between CST1 and CST2 (Figure 1L), yet only the WT mice exhibit CTA (at least by CST2). For the taste-elicited coactivity measure, it seems that there is an increase in coactivity from CST1 to CST2 in WT (Figure 2C - blue, although not statistically tested?), but persistently higher coactivity in KO. Is this change of coactivity in WT important for the expression of CTA? Plotting behavioral performance (from Figure 1G) against coactivity (from Figure 2C) for each animal would be informative.

(2) Shank3 KO cells already show an increase in baseline coactivity (Figure 2- figure supplement 1), and the authors never examine CS-only responses in the KO group, therefore making it difficult to determine whether elevated coactivity and noise correlations reflect a generalized AIC abnormality in Shank3 KOs (perhaps through impaired PV-mediated inhibition in insular cortex - Gogolla et al, 2014) that is not directly responsible/related to CTA?

(3) How do the authors interpret the large range of lick ratios (Figure 1G) for WT (almost bi-modal distribution)? Is there a within-subject correlation with any of the neurophysiological measurements to suggest a relationship between AIC neurophysiology and behavioural expression of CTA?

(4) Indeed, CTA appears to be successfully achieved for Shank3 KO mice delayed by 1 day, as the level of saccharin aversion during the first retrieval session (T1) is comparable between Shank3 KO and WTs. In this context, not extending the first part of the paradigm to include CST3 seems to be a missed opportunity. Doing so would have allowed for within-cell and within-subject comparison of taste-elicited pairwise correlation across the learning and to investigate the neural mechanism of delayed extinction in KOs more effectively.

(5) How to interpret Figure 5F: Absolute discriminability is lower for T5 for CTA WT and CTA KO compared to CS-only? Why would AIC neurons have less information on taste identity by the end of extinction than during the unconditioned (CS-only) condition? And if that is the case, how is decoding accuracy in Figure 6C higher in T5 for CTA WT vs CS-only?

The legacy provider problem: The legacy system processes CIDs one at a time, requiring a separate DHT lookup (10-20 seconds each) to find the 20 closest peers for each CID. This sequential approach typically handles less than 10,000 CID over 22h (Provide.DHT.Interval). If your node has more CIDs than can be reprovided within Provide.DHT.Interval, provider records start expiring after amino.DefaultProvideValidity, making content undiscoverable.

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What’s new: Kubo v0.39 ships Provide Sweep as the default provider system.

"The day after he got Telepathy, he was playing [Civilization VI] for 9 hours straight" 3:36

The input file contains 1000 boxes. If I connect together 1000 (or as few as 999) pairs following the procedure described above, I end up with one circuit connecting all boxes.

I should actually count the connection within components towards the total of 1000.

Wull what are you waiting for?

Or the filters that people's surroundings are uniformly presented through. Map always being north. Mercator projection.

"I need more data..."--Dune Messiah

NB

This is existing yet also still developing tech. a third of 5G base stations uses FPGA chips. The point seems to be: are there any FPGA producers in Europe?

A potential path away from NVIDIA it seems, but not at the moment, the text suggests.