This is a subtle foreshadow that the day is different than usual - for once in her life the teller does not check his balance as she usually does

Since young people are utilizing technology for around 10hrs a day it is important that they are educated on the good usages and the negative effects.

I think this is a great quote and would be perfect to catch the readers attention in the beginning of the article.

In order to keep the consumers attention, media companies may publish untrue and biased information. This is why we need to think about where we are getting our information.

We as a society have become so immersed in society that we forget to critically think on our own and question the sources in which we are getting the information.

Younger generations prefer to get their information from the internet and social media rather than television news sources. This shift requires more media literacies to avoid the spread of false information.

The printing press is known to have sparked a paradigmatic shift. A paradigmatic shift is a major change in society, learning, technology and knowledge. This invention shifted the way humans communicate.

How are humans being transformed by the technology of a museum visit.

This framework will advance media literacies by implementing critical thinking which will allow media users to reclaim their life.

I LOVE this! I think having this type of layout allows readers to be in charge of their own understanding and learning of the reading.

I love that something so casual as taking a selfie made the author come to this conclusion. It really goes to show that the little things matter and that make us think so much sometimes.

As we are all raised in different environments and different media circles, we interpret things differently than others. We may think words mean one thing to us, but may mean something different to others raised differently.

This is 100% true. I am guilty of this, as I am sure we all are. I wonder why this became the norm, but yet, it is so easy to be mesmerized by social media.

We are results of our media and environments. We take it in and it becomes part of us.

I agree with this. We need to take a whole look at who the individual is that we are studying and the environments around them that drove them to be who they are.

I find this to be very interesting and very accurate. Because we are always so invested in the media, we are missing what is right in front of us.

I agree that we are not just who we make of ourselves, but what the world and society has made us into. We need to take those into account if we want to learn more about not only ourselves, but about the media.

I think it goes without saying that always learning and being an active learner, especially when it comes to media literacy, is the best bet when educating yourself.

I think this is very important to remember and think about when studying media and the media technology we use. We need to remember why we are using it and who is using it.

As much as I think students need to be educated on the media, I also think they should not necessarily use it until a certain age. I saw a TikTok (not the best source, I know), speaking about how kids today do not know how to just "be bored" and need constant stimulation due to the active and constant use of media. I am curious to know if others feel the same or if there is a specific age that students should be allowed to start engaging in the media?

I think this is crazy! Yes, we need the media for a lot of different aspects of life now, but is too much media a possibility? Trust me, I am 100% guilty of spending too much time on media, but I wonder how this affects younger generations of students.

Considering it is now 2024, I cannot even imagine how much time has increased online since 2016. Especially within the young adolescence and young adults. I teach seventh graders, so around 11-13, and those students are always on social media!!

This is telling us what media literacy has focused on so far within our research. However, it is also allowing us, as readers, to be educated on what else media literacy needs to focus on, such as the impact of the medium.

BIOblogs

the color gradation should be like figma

the gradation should be like figma

the border too rounder

Photo frame can be smaller so that it covers the entire image

Content is wrong. Subheading is missing

Should be underlined

// The advantage of this approach is that the link between the news wave and a key event is unambiguous since it is (mostly) pre-defined by case selection. This disadvantage is that the number of cases that can be surveyed is limited and that cases are selected on an ad-hoc or strategic logic that may induce biases and blind spots.

// Finding news waves and linking them to key events can be extremely useful for Communication Researchers, particularly those interested in identifying turning points in debates, focusing periods of discourse, and building of collective memory and collective identity. // Before introducing our own procedure for extracting, labelling, and validating news waves, we will investigate which procedures have been used in the past, what they achieve, what their limitations are, and which common problems and challenges need to be tackled by any procedure detecting and labelling news waves.

// We delve into both concepts and their relationship to later be able to validate the results of news wave detection and labelling as to whether they capture actual news waves and these actual news waves are linked to key events. It also helps understand the social and scientific significance of a procedure that automatically detects and labels such news waves.

The abstract only outlines the pipeline like a tool demo, but misses the validation experiment. The validation procedure and results must be mentioned.

in the bellow text should have button and another text " Simplify tech integration with award-winning lesson plans, micro-learning courses, and a supportive community."

Different people were advocating for these different theories from the start. The thing that evolved was just the average credence for each theory. This also suggests Hiltzik did not read the book, as infection while doing fieldwork is only one of the possible vectors. Another major one is infection in the lab (which, as the authors explain, happened before at WIV with less-deadly coronaviruses).

Another instance of Hiltzik simply asserting his thesis, which he has come nowhere close to proving in his rhetoric-filled page-and-a-half review.

assertion of thesis. empty rhetoric.

The subtitle is The Search for the Origin of COVID-19, not The Search for the Origin of Viruses. It would be a pointless charade to observe virology experts at work in the lab, given that the authors have already summarized their findings in great detail with thorough citations -- none of which Hiltzik disputes.

By not acknowledging that the authors discussed at great length the history of such inquiries, Hiltzik leads the reader to believe that the authors excluded this pertitent background information.

Hiltzik is clearly suggesting that readers should feel cheated here. A wild goose chase is a complicated, hopeless pursuit. But the authors never promised they would solve the mystery of the origin of COVID-19. Their thesis, quite clearly from the start, is that an entire broad category of theories --zoonotic origin theories with no virology lab intermediary-- is highly implausible. That is what they argued. In comparison, when a defense lawyer proves their client is innocent of a murder, it is not logical or fair to expect them to go further and prove the guilt of the true murderer, and indeed no justice system in the world demands as much. That being said, the authors of Viral do go further; they argue that the virus or a near ancestor leaked from one of the two Wuhan Virology Institute locations in Wuhan. They also explained why the CCP's (undisputed) withholding of data blocks the investigating process from narrowing in on a detailed narrative of exactly how the leak happened.

eLife Assessment

This important study presents a significant methodological advance by leveraging previously discarded, unmapped DNA sequence reads to estimate pest infestation loads across plant accessions, and map variation in these apparent pest loads to defense genes. The bioinformatics approach is compelling, and the results should bear broad implications for phenotype-genotype prediction, especially regarding the use of unmapped reads for GWAS.

Author response:

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

Reviewer #1 (Public Review):

Galanti et al. present an innovative new method to determine the susceptibility of large collections of plant accessions towards infestations by herbivores and pathogens. This work resulted from an unplanned infestation of plants in a greenhouse that was later harvested for sequencing. When these plants were extracted for DNA, associated pest DNA was extracted and sequenced as well. In a standard analysis, all sequencing reads would be mapped to the plant reference genome and unmapped reads, most likely originating from 'exogenous' pest DNA, would be discarded. Here, the authors argue that these unmapped reads contain valuable information and can be used to quantify plant infestation loads.

For the present manuscript, the authors re-analysed a published dataset of 207 sequenced accessions of Thlaspi arvense. In this data, 0.5% of all reads had been classified as exogenous reads, while 99.5% mapped to the T. arvense reference genome. In a first step, however, the authors repeated read mapping against other reference genomes of potential pest species and found that a substantial fraction of 'ambiguous' reads mapped to at least one such species. Removing these reads improved the results of downstream GWAs, and is in itself an interesting tool that should be adopted more widely.

The exogenous reads were primarily mapped to the genomes of the aphid Myzus persicae and the powdery mildew Erysiphe cruciferarum, from which the authors concluded that these were the likely pests present in their greenhouse. The authors then used these mapped pest read counts as an approximate measure of infestation load and performed GWA studies to identify plant gene regions across the T. arvense accessions that were associated with higher or lower pest read counts. In principle, this is an exciting approach that extracts useful information from 'junk' reads that are usually discarded. The results seem to support the authors' arguments, with relatively high heritabilities of pest read counts among T. arvense accessions, and GWA peaks close to known defence genes. Nonetheless, I do feel that more validation would be needed to support these conclusions, and given the radical novelty of this approach, additional experiments should be performed.

A weakness of this study is that no actual aphid or mildew infestations of plants were recorded by the authors. They only mention that they anecdotally observed differences in infestations among accessions. As systematic quantification is no longer possible in retrospect, a smaller experiment could be performed in which a few accessions are infested with different quantities of aphids and/or mildew, followed by sequencing and pest read mapping. Such an approach would have the added benefit of allowing causally linking pest read count and pest load, thereby going beyond correlational associations.

On a technical note, it seems feasible that mildew-infested leaves would have been selected for extraction, but it is harder to explain how aphid DNA would have been extracted alongside plant DNA. Presumably, all leaves would have been cleaned of live aphids before they were placed in extraction tubes. What then is the origin of aphid DNA in these samples? Are these trace amounts from aphid saliva and faeces/honeydew that were left on the leaves? If this is the case, I would expect there to be substantially more mildew DNA than aphid DNA, yet the absolute read counts for aphids are actually higher. Presumably read counts should only be used as a relative metric within a pest organism, but this unexpected result nonetheless raises questions about what these read counts reflect. Again, having experimental data from different aphid densities would make these results more convincing.

We agree with the reviewer that additional aphid counts at the time of (or prior to) sequencing would have been ideal, but unfortunately we do not have these data. However, compared to such counts one strength of our sequencing-based approach is that it (presumably) integrates over longer periods than a single observation (e.g. if aphid abundances fluctuated, or winged aphids visited leaves only temporarily), and that it can detect pathogens even when invisible to our eyes, e.g. before a mildew colony becomes visible. Moreover, the key point of our study is that we can detect variation in pest abundance even in the absence of count data, which are really time consuming to collect.

Conducting a new experiment, with controlled aphid infestations and continuous monitoring of their abundances, to test for correlation between pest abundance and the number of detected reads would require resequencing at least 30-50% of the collection for the results to be reliable. It would be a major experimental study in itself.

Regarding the origin of aphid reads and the differences in read-counts between e.g. aphids and mildew, we believe this should not be of concern. DNA contamination is very common in all kinds of samples, but these reads are simply discarded in other studies. For example, although we collected and handled samples using gloves, MG-RAST detected human reads (Hominidae, S2 Table), possibly from handling the plants during transplanting or phenotyping 1-2 weeks before sequencing. Therefore, although we did remove aphids from the leaves at collection, aphid saliva or temporary presence on leaves must have been enough to leave detectable DNA traces. Additionally, the fact that the M. persicae load strongly correlates with the Buchnera aphidicola load (R2\=0.86, S6 Table), is reassuring. This obligate aphid symbiont is expected to be found in high amounts when sequencing aphids (see e.g. The International Aphid Genomics Consortium (2010))

The higher amount of aphid compared to mildew reads, can probably be explained by aphids having expanded more than mildew at the time of plant collection, but most importantly, as already mentioned by the reviewer, the read-counts were meant to compare plant accessions rather then pests to one another. We are interested in relative not absolute values. Comparisons between pest species are a challenge because they can be influenced by several factors such as the availability of sequences in the MG-RAST database and the DNA extraction kit used, which is plant-specific and might bias towards certain groups. All these potential biases are not a concern when comparing different plants as they are equally subject to these biases.

Reviewer #2 (Public Review):

Summary:

Galanti et al investigate genetic variation in plant pest resistance using non-target reads from whole-genome sequencing of 207 field lines spontaneously colonized by aphids and mildew. They calculate significant differences in pest DNA load between populations and lines, with heritability and correlation with climate and glucosinolate content. By genome-wide association analyses they identify known defence genes and novel regions potentially associated with pest load variation. Additionally, they suggest that differential methylation at transposons and some genes are involved in responses to pathogen pressure. The authors present in this study the potential of leveraging non-target sequencing reads to estimate plant biotic interactions, in general for GWAS, and provide insights into the defence mechanisms of Thlaspi arvense.

Strengths:

The authors ask an interesting and important question. Overall, I found the manuscript very well-written, with a very concrete and clear question, a well-structured experimental design, and clear differences from previous work. Their important results could potentially have implications and utility for many systems in phenotype-genotype prediction. In particular, I think the use of unmapped reads for GWAS is intriguing.

Thank you for appreciating the originality and potential of our work.

Weaknesses:

I found that several of the conclusions are incomplete, not well supposed by the data and/or some methods/results require additional details to be able to be judged. I believe these analyses and/or additional clarifications should be considered.

Thank you very much for the supportive and constructive comments. They helped us to improve the manuscript.

Recommendations for the authors:

Reviewing Editor (Recommendations For The Authors):

The authors address an interesting and significant question, with a well-written manuscript that outlines a clear experimental design and distinguishes itself from previous work. However, some conclusions seem incomplete, lacking sufficient support from the data, or requiring additional methodological details for proper evaluation. Addressing these limitations through additional analyses or clarifications is recommended.

Reviewer #2 (Recommendations For The Authors):

Major comments:

- So far it is not clear to me how read numbers were normalised and quantified. For instance, Figure 1C only reports raw read numbers. In L149: "Prior to these analyses, to avoid biases caused by different sequencing depths, we corrected the read counts for the total numbers of deduplicated reads in each library and used the residuals as unbiased estimates of aphid, mildew and microbe loads". Was library size considered? Is the load the ratio between exogenous vs no exogenous reads? It is described in L461, but according to this, read counts were normalised and duplicated reads were removed. Now, why read counts were used? As opposite to total coverage / or count of bases per base? I cannot follow how variation in sequencing quality was considered. I can imagine that samples with higher sequencing depth will tend to have higher exogenous reads (just higher resolution and power to detect something in a lower proportion).

Correcting for sequencing depth/library size is indeed very important. As the reviewer noted, we had explained how we did this in the methods section (L464), and we now also point to it in the results (L151):

“Finally, we log transformed all read counts to approximate normality, and corrected for the total number of deduplicated reads by extracting residuals from the following linear model, log(read_count + 1) ∼ log(deduplicated_reads), which allowed us to quantify non-Thlaspi loads, correcting for the sequencing depth of each sample.”

We showed the uncorrected read-counts only in Fig 1 to illustrate the orders of magnitude but used the corrected read-counts (also referred to as “loads”) for all subsequent analyses.

In our view, theoretically, the best metric to correct the number of reads of a specific contaminant organism, is the total number of DNA fragments captured. Importantly, this is not well reflected by the total number of raw reads because of PCR and optical duplicates occurring during library prep and sequencing. For this reason we estimated the total number of reads captured multiplying total raw reads (after trimming) by the deduplication rate obtained from FastQC (methods L409-411). This metric reflects the amount of DNA fragments sampled better than the raw reads. Also it better reflects MG-RAST metrics as this software also deduplicates reads (Author response image 1 below). We also removed duplicates in our strict mappings to the M. persicae and B. aphidicola genomes.

Coverage is not a good option for correction, because it is defined for a specific reference genome and many of the read-counts output by MG-RAST do not have a corresponding full assembly. Moreover, coverage and base counts are influenced by read size, which depends on library prep and is not included in the read-counts produced by MG-RAST.

Author response image 1.

Linear correlations between the number of MG-RAST reads post-QC and either total (left) or deduplicated (right) reads from fastq files of four full samples (not only unmapped reads).

- The general assumption is that plants with different origins will have genetic variants or epigenetic variations associated with pathogen resistance, which can be tracked in a GWAS. However, plants from different regions will also have all variants associated with their origin (isolation by state as presented in the manuscript). In line 169: "Having established that our method most likely captured variation in plant resistance, we were interested in the ecological drivers of this variation". It is not clear to me how variation in plant resistance is differentiated from geographical variation (population structure). in L203: "We corrected for population structure using an IBS matrix and only tested variants with Minor Allele Frequency (MAF) > 0.04 (see Methods).". However, if resistant variants are correlated with population structure as shown in Table 1, how are they differentiated? In my opinion, the analyses are strongly limited by the correlation between phenotype and population structure.

The association of any given trait with population structure is surely a very important aspect in GWAS studies and when looking at correlations of traits with environmental variables. If a trait is strongly associated with population structure, then disentangling variants associated with population structure vs. the ones associated with the trait can indeed be challenging, a good example being flowering time in A. thaliana (e.g. Brachi et al. 2013).

In our case, although the pest and microbiome loads are associated with population structure to some extent, this association is not very strong. This can be observed for example in Fig. 1C, where there is no clear separation of samples from different regions. This means that we can correct for population structure (in both GWAS and correlations with climatic variables) without removing the signals of association. It is possible that other associations were missed if specific variants were indeed strongly associated with structure, but these would be unreliable within our dataset, so it is prudent to exclude them.

- Similarly, in L212: "we still found significant GWA peaks for Erysiphales but not for other types of exogenous reads (excluding isolated, unreliable variants) (Figure 3A and S3 Figure)." In a GWA analysis, multiple variants will constitute an association pick (as shown for instance in main Figure 3A) only when the pick is accentuated by lockage disequilibrium around the region under selection (or around the variant explaining phenotypic variation in this case). However, in this case, I suspect there is a strong component of population structure (which still needs to be corroborated as suggested in the previous comment). But if variants are filtered by population structure, the only variants considered are those polymorphic within populations. In this case, I do not think clear picks are expected since most of the signal, correlated with population has been removed. Under this scenario, I wonder how informative the analyses are.

As mentioned above, the traits we analyse (aphid and mildew loads) are only partially associated with population structure. This is evident from Fig. 1C (see answer above) but also from the SNP-based heritability (Table 1, last column) which measures indeed the proportion of variance explained by genetic population structure. Although some variance is explained (i.e. the reviewer is correct that there is some association) there is still plenty of leftover variance to be used for GWAS and correlations with environmental variables. The fact that we still find GWAS peaks confirms this, as otherwise they would be lost by the population structure correction included in our mixed model.

- How were heritability values calculated? Were related individuals filtered out? I suggest adding more detail in both the inference of heritability and the kinship matrix (IBS matrix). Currently missing in methods (for heritability I only found the mention of an R package in the caption of Table 1).

We somehow missed this in the methods and thank the reviewer for noticing. We now added this paragraph to the chapter “Exogenous reads heritability and species identification”:<br /> “To test for variation between populations we used a general linear model with population as a predictor. To measure SNP-based heritability, i.e. the proportion of variance explained by kinship, we used the marker_h2() function from the R package heritability (Kruijer and Kooke 2019), which uses a genetic distance matrix as predictor to compute REML-estimates of the genetic and residual variance. We used the same IBS matrix as for GWAS and for the correlations with climatic variables.”

We also added the reference to the R package heritability to the Table 1 caption.

- Figure 2C. in line 188: "Although the baseline levels of benzyl glucosinolates were very low and probably sometimes below the detection level, plant lines where benzyl glucosinolate was detected had significantly lower aphid loads (over 70% less reads) in the glasshouse (Figure 3C)". It is not clear to me how to see these values in Figure 2C. From the boxplot, the difference in aphid loads between detected and not detected benzyl seems significantly lower. From the boxplot distribution is not clear how this difference is statistically significant. It rather seems like a sampling bias (a lot of non-detected vs low detected values). Is the difference still significant when random subsampling of groups is considered?

Here the “70% less reads” refers to the uncorrected read-counts directly (difference in means between samples where benzyl-GS were detected vs. not). We agree with the reviewer that this is confusing when referred to figure 2C which depicts the corrected M. persicae load (residuals). We therefore removed that information.

Regarding the significance of the difference, we re-calculated the p value with the Welch's t-test, which accounts for unequal variances, and with a bootstrap t-test. Both tests still found a significant difference. We now report the p value of the Welch’s t-test.

- I think additional information regarding the read statistics needs to be improved. At the moment some sections are difficult to follow. I found this information mainly in Supplementary Table 1. I could not follow the difference in the manuscript and supplementary materials between read (read count), fragment, ambiguous fragments, target fragments, etc. I didn't find information regarding mean coverage per sample and relative plant vs parasite coverage. This lack of clarity led me to some confusion. For instance, in L207: "We suspected that this might be because some non-Thlaspi reads were very similar to these highly conserved regions and, by mapping there, generated false variants only in samples containing many non-Thlaspi reads". I find it difficult to follow how non-Thlaspi reads will interfere with genotyping. I think the fact that the large pick is lost after filtering reads is already quite insightful. However, in principle I would expect the relative coverage between non-Thlaspi:Thlaspi reads to be rather low in all cases. I would say below 1%. Thus, genotyping should be relatively accurate for the plant variants for the most part. In particular, considering genotyping was done with GATK, where low-frequency variants (relative coverage) should normally be called reference allele for the most part.

We agree with the reviewer that some clarification over these points is necessary! We modified Supplementary Table 1 to include coverage information for all samples before and after removal of ambiguous reads and explained thoroughly how each value in the table was obtained. Regarding reads and fragments, we define each fragment as having two reads (R1 and R2). The classification into Target, Ambiguous and Unmapped reads was based on fragments, so we used that term in the table, but referring to reads has the same meaning in this context as for example an unmapped read is a read whose fragment was classified as unmapped.

We did not include the pest coverage specifically, because this cannot be calculated for any of the read counts obtained with MG-RAST as this tool is mapping to online databases where genome size is not necessarily known. What is more meaningful instead are the read counts, which are in Supplementary tables 2 and 6. Importantly as mentioned in other answers, if different taxa are differently represented in the databases this does not affect the comparison of read counts across different samples, but only the comparison of different taxa which was not used for any further analyses.

Regarding the ambiguous reads causing unreliable variants, these occur only in very few regions of the Thlaspi genome that are highly conserved in evolution or of very low complexity. In these regions reads generated from both plant or for instance aphid DNA, can map, but the ones from aphid might contain variants when mapping to the Thlaspi reference genome (L207 and L300). The reviewer is right that there is only a very small difference in average coverage when removing those ambiguous reads (~1X, S1 Table), but that is not true for those few regions where coverage changes massively when removing ambiguous reads as shown on the right side Y axes of S2 Figure. Therefore these unreliable variants are not low-frequency and therefore not removed by GATK.

- L215. I am not very convinced with the enrichment analyses, justified with a reference (52). For instance, how many of the predicted picks are not close to resistance genes? How was the randomisation done? At the moment, the manuscript reads rather anecdotally by describing only those picks that effectively are "close" to resistance genes. For instance, if random windows (let's say 20kb windows) are sampled along the genome, how often there are resistant genes in those random windows, and how is the random sampling compared with observed picks (windows).

Enrichment is by definition an increase in the proportion of true positives (observed frequency: proportion of significant SNPs located close to a priori candidate genes) compared to the background frequency (number of all SNPs located close to a priori candidate genes). So the background likelihood of SNPs to fall into a priori candidate SNPs (i.e. the occurrence of a priori candidate genes in randomly sampled windows, as suggested by the reviewer) is already taken into account as the background frequency. We now explained more extensively how enrichment is calculated in the relevant methods section (L545-549), but it is an extensively used method, established in a large body of literature, so it can be found in many papers (e.g. Atwell et al. 2010, Brachi et al. 2010, Kawakatsu et al. 2016, Kerdaffrec et al. 2017, Sasaki et al. 2015-2019-2022, Galanti et al. 2022, Contreras-Garrido et al. 2024).

Although we had already calculated an upper bound for the FDR based on the a priori candidates, as in previous literature, we now further calculated the significance of the enrichment for the Bonferroni-corrected -log(p) threshold for Erysiphales. Calculating significance requires adopting a genome rotation scheme that preserves the LD structure of the data, as described in the previously mentioned literature (eg. Kawakatsu et al. 2016, Sasaki et al. 2022). Briefly, we calculated a null distribution of enrichments by randomly rotating the p values and a priori candidate status of the genetic variants within each chromosome, for 10 million permutations. We then assessed significance by comparing the observed enrichment to the null distribution. We found that the enrichment at the Bonferroni corrected -log(p) threshold is indeed significant for Erysiphales (p = 0.016). We added this to the relevant methods section and the code to the github page.

In addition, many other genes very close (few kb max) to significant SNPs were not annotated with the “defense response” GO term but still had functions relatable to it. Some examples are CAR8, involved in ABA signalling, PBL7 in stomata closure and SRF3 in cell wall building and stress response  (Fig 3D). This means that our enrichment is actually most likely underestimated compared to if we had a more complete functional annotation.

- L247. Additional information is needed regarding sampling. It is not clear to me why methylation analyses are restricted to 20 samples, contrary to whole genome analyses.

The sampling is best described in the original paper (on natural DNA methylation variation; Galanti et al. 2022), although the most important parts are repeated in the first chapter of the methods.<br /> Regarding methylation analysis, they are not restricted to 20 samples. Only the DMR calling was restricted to the 20 vs. 20 samples with the most divergent values (of pest loads) to identify regions of variation. This analysis was used to subset the genome to potential regions associated with pest presence rather than thoroughly testing actual methylation variants associated with pest presence. The latter was done in the second step, EWAS, which was based on the whole dataset with the exclusions of samples with high non-conversion rate. This left 188 samples for EWAS. We added this number in the new manuscript (L251 and L571).

To clarify, we made a few additions to the results (L250) and methods (last two subchapters) sections, where we explain the above.

- No clear association with TEs: in L364: "Erysiphales load was associated with hypomethylated Copia TEs upstream of MAPKKK20, a gene involved in ABA-mediated signaling and stomatal closure. Since stomatal closure is a known defense mechanism to block pathogen access (21), it is tempting to conclude that hypomethylation of the MAPKKK20 promoter might induce its overexpression and consequent stomatal closure, thereby preventing mildew access to the leaf blade. Overall, we found associations between pathogen load and TE methylation that could act both in cis (eg. Copia TE methylation in MAPKKK20 promoter) and in trans, possibly through transposon reactivation (eg. LINE, Helitron, and Ty3/Gypsi TEs isolated from genes)." I find the whole discussion related to transposable elements, first, rather anecdotical, and second very speculative. To claim: "Overall, we found associations between pathogen load and TE methylation", I believe a more detailed analysis is needed. For instance, how often there is an association? In general, there are some rather anecdotical examples, several of which are presented as association with pathogen load on the basis of being "in proximity" to a particular region/pick. The same regions contain multiple other genes and annotations, but the authors limit the discussion to the particular gene or TE concordant with the hypothesis. This is for both the discussion and results sections.

Here we are referring to associations in a purely statistical sense. The fact that “Overall, we found associations between pathogen load and TE methylation” is simply a conclusion drawn from Fig. 4b, without implying any causality. Some methylation variants are statistically associated with the traits (aphid or mildew loads), and whether they are true positives or causal is of course more difficult to assess.

Regarding the methylation variants associated with mildew load in proximity of MAPKKK20, those are the only two significant ones, located close to each other and close to many other variants that, although not significant, have low P-values (Author response image 2 below), so it is the most obvious association warranting further exploration. The reviewer is correct that there are other genes flanking the large DMR that covers the TEs (Fig. 4D), but the DMR is downstream of these genes, so less likely to affect their transcription.

Author response image 2.

Regarding all other associations found with M. persicae load, we stated that these are not really reliable due to a skewed P-value distribution (L269, S5B Fig), but we think that for future reference it is still worth reporting the closeby genes and TEs.

We slightly changed the wording of the passage the reviewer is citing above to make it clearer that we are only offering potential explanations for the associations we observe with TE methylation, but by no means we state that TE reactivation is surely what is happening.

- One conclusion in the manuscript is that DMRs have been mostly the result of hypomethylation. This is shown for instance in supplementary Figure 4. However, no general statistic is shown of methylation distribution (not only restricted to DMRs). Was the ratio methylation over de-methylation proportional along the genome? Thus the finding in DMRs is out of the genome-wide distribution? Or on the contrary, the DMRs are just a random sampling of the global distribution. The same for different annotated regions. For instance, I would expect that in general coding regions would be less methylated (not restricted to DMRs).

Complete and exhaustive analyses of the methylomes were already published in the original manuscript (Galanti et al 2022). However, the variation among these methylomes is complex and influenced by multiple factors including genetic background and environment of origin, and talking about these things would have been beyond the scope of our paper. In this paper, we just took advantage of the existing methylome information to identify the few genomic regions that are consistently differentially methylated between samples with extreme values of pest loads. As for the GWAS, the phenotypes are only partially associated with population structure, so the 20 samples with the lowest and the 20 with the highest pathogen loads are not e.g. all Swedish vs. all German but they are a mixture, which allowed us to correct for population structure running EWAS with a mixed model that includes a genetic distance matrix.

In this study we called DMRs between two defined groups: samples with the lowest amounts of pathogen DNA (not-infected; the “control” group) vs. samples with the highest amounts of pathogens (infected or the “treatment” group), so we could define a directionality (“hyper vs. “hypo” methylation). However, this is not the case for population DMRs called between many different combinations of populations. This is why the hyper- and hypomethylated regions found here cannot be compared to the genome-wide averages, which are influenced by other factors than the pathogens. Even with relaxed thresholds we indeed found very few DMRs associated to pathogen presence here.

Specifically about coding regions, the reviewer is correct that they are less methylated, especially because T. arvense has largely lost gene body methylation (Nunn et al. 2021, Galanti et al. 2022), but this is unrelated and was discussed in the original publication (Galanti et al. 2022).

Minor comments:- Figure 1B: it would be good to add also percentage values.

As the figure is already tightly packed, we rather keep it simple. As the chart gives a good impression of frequencies of different kingdoms, and the frequences of several relevant groups. Also, as explained in a previous answer, comparing different taxonomic groups could be imprecise (as opposed to comparing the same group between different samples), so exact percentages seem unnecessary. If needed, the exact percentages can still be calculated from S2 Table.

- L159: It is not clear to me what "enemy variation" is referring to here.

We are referring to variation in enemy densities (attack rates) in the field, that could potentially be carried over to the greenhouse to cause the patterns of infection we observed. We changed it to “variation in enemy densities” to make it more clear.

- L259: "In accordance with previous studies (8,9), most DMRs were hypomethylated in the affected samples, indicating that genes needed for defense might be activated through demethylation". Not clear to me what "affected samples" is referring to. Samples with lower load?

Affected samples have a higher load of pathogen reads. We changed it to “infested” to make it more clear.

- L336. Figure should be Fig 3E.

We fixed it, thanks for noticing.

ADDITIONAL CHANGES

We updated reference 43 to point to the published paper rather than the preprint.

We corrected the phenotype names in S3 Fig, to make them consistent with the rest of the manuscript and increased font size on the axes to make it more readable.

Reviewer #1 (Public Review):

Galanti et al. present an innovative new method to determine the susceptibility of large collections of plant accessions towards infestations by herbivores and pathogens. This work resulted from an unplanned infestation of plants in a greenhouse that was later harvested for sequencing. When these plants were extracted for DNA, associated pest DNA was extracted and sequenced as well. In a standard analysis, all sequencing reads would be mapped to the plant reference genome and unmapped reads, most likely originating from 'exogenous' pest DNA, would be discarded. Here, the authors argue that these unmapped reads contain valuable information and can be used to quantify plant infestation loads.

For the present manuscript, the authors re-analysed a published dataset of 207 sequenced accessions of Thlaspi arvense. In this data, 0.5% of all reads had been classified as exogenous reads, while 99.5% mapped to the T. arvense reference genome. In a first step, however, the authors repeated read mapping against other reference genomes of potential pest species and found that a substantial fraction of 'ambiguous' reads mapped to at least one such species. Removing these reads improved the results of downstream GWAs, and is in itself an interesting tool that should be adopted more widely.

The exogenous reads were primarily mapped to the genomes of the aphid Myzus persicae and the powdery mildew Erysiphe cruciferarum, from which the authors concluded that these were the likely pests present in their greenhouse. The authors then used these mapped pest read counts as an approximate measure of infestation load and performed GWA studies to identify plant gene regions across the T. arvense accessions that were associated with higher or lower pest read counts. In principle, this is an exciting approach that extracts useful information from 'junk' reads that are usually discarded. The results seem to support the authors' arguments, with relatively high heritabilities of pest read counts among T. arvense accessions, and GWA peaks close to known defence genes. Nonetheless, I do feel that more validation would be needed to support these conclusions, and given the radical novelty of this approach, additional experiments should be performed.

A weakness of this study is that no actual aphid or mildew infestations of plants were recorded by the authors. They only mention that they anecdotally observed differences in infestations among accessions. As systematic quantification is no longer possible in retrospect, a smaller experiment could be performed in which a few accessions are infested with different quantities of aphids and/or mildew, followed by sequencing and pest read mapping. Such an approach would have the added benefit of allowing causally linking pest read count and pest load, thereby going beyond correlational associations.

On a technical note, it seems feasible that mildew-infested leaves would have been selected for extraction, but it is harder to explain how aphid DNA would have been extracted alongside plant DNA. Presumably, all leaves would have been cleaned of live aphids before they were placed in extraction tubes. What then is the origin of aphid DNA in these samples? Are these trace amounts from aphid saliva and faeces/honeydew that were left on the leaves? If this is the case, I would expect there to be substantially more mildew DNA than aphid DNA, yet the absolute read counts for aphids are actually higher. Presumably read counts should only be used as a relative metric within a pest organism, but this unexpected result nonetheless raises questions about what these read counts reflect. Again, having experimental data from different aphid densities would make these results more convincing.

Comments on revised version:

The authors have addressed many technical details in their revision, but they did not address my more fundamental concerns about validation of their results. I still believe that validation would be needed, but I also acknowledge that an additional experiment that reliably tests a causal relationship between read counts and pest abundance would go beyond the scope of a revision. Nonetheless, the authors currently only show variation in pest read counts among plant accessions, not in pest abundance. While the two measures are likely correlated, I hope that future studies will address more directly how pest abundance and read counts are causally linked, and whether pest read counts truly are a robust measure of pest abundance across a range of conditions and systems

Reviewer #2 (Public Review):

Summary:

Galanti et al investigate genetic variation in plant pest resistance using non-target reads from whole-genome sequencing of 207 field lines spontaneously colonized by aphids and mildew. They calculate significant differences in pest DNA load between populations and lines, with heritability and correlation with climate and glucosinolate content. By genome-wide association analyses they identify known defence genes and novel regions potentially associated with pest load variation. Additionally, they suggest that differential methylation at transposons and some genes are involved in responses to pathogen pressure. The authors present in this study the potential of leveraging non-target sequencing reads to estimate plant biotic interactions, in general for GWAS, and provide insights into the defence mechanisms of Thlaspi arvense.

Strengths:

The authors ask an interesting and important question. Overall, I found the manuscript very well-written, with a very concrete and clear question, a well-structured experimental design, and clear differences from previous work. Their important results could potentially have implications and utility for many systems in phenotype-genotype prediction. In particular, I think the use of unmapped reads for GWAS is intriguing.

Comments on revised version:

The revisions to the manuscript have significantly enhanced its clarity and scientific rigor. Methodological clarifications, especially regarding the normalization of read counts, now provide a stronger foundation for the presented results. Statistical enhancements, including more robust methods for controlling population structure and refined GWAS approaches, have solidified the reliability of the findings, effectively linking genetic variants and epigenetic modifications to pest loads. The discussion section has been improved to offer a more cautious interpretation of the correlations between transposable element (TE) methylation and pathogen load, emphasizing the associative nature of these findings. Additionally, increased transparency in data handling, particularly the treatment of ambiguous reads, has significantly reduced potential biases. These improvements have made the manuscript better suited to the readership, providing clearer insights into the genomic and epigenetic underpinnings of plant pest resistance.

A reactant that is first completely consumed in a reaction, thereby, limiting the amount of product formed.

A term used to describe the attachment of a ligand to a metal center.

The component present in larger amount and serves as the medium for the chemical reaction to occur.

A chemical reaction that involves the introduction of a boron containing group into an organic molecule.

The last slide is missing from the timeline. I’ve already notified Astitva about it. I believe we can simply duplicate a slide in strapi and add the content accordingly. Pls confirm

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eLife Assessment

This important study examines the effects of NFKB2 mutations on pituitary gland development through hypothalamic-pituitary organoids. The evidence supporting the main conclusions is solid, although analysis of additional clones to exclude inter-clone variability would strengthen the conclusions. This is a revised study, but insight into the mechanism of action of NFKB2 during pituitary development is incomplete. This work will be of interest to endocrinologists and biologists working on pituitary gland development and disease.

Reviewer #1 (Public review):

Summary:

NFKB mutations are thought to be one of the causes of pituitary dysfunction, but until now they could not be reproduced in mice and their pathomechanism was unknown. The authors used the differentiation of hypothalamic-pituitary organoids from human pluripotent stem cells to recapitulate the disease in human iPS cells carrying the NFKB mutation.

Strengths:

The authors achieved their primary goal of recapitulating the disease in human cells. In particular, the differentiation of the pituitary gland is closely linked to the adjacent hypothalamus in embryology, and the authors have again shown that this method is useful when the hypothalamus is suspected to be involved in pituitary abnormalities caused by genetic mutations.

Weaknesses:

On the other hand, the pathomechanism is still not fully understood. This study provides some clues to the pathomechanism, but further analysis of NFKB expression and experiments investigating the relevant factors in more detail may help to clarify it further.<br /> As for the revised manuscript, it is still insufficient for understanding the role of NFKB2 in pituitary development although their additional experiments have improved the manuscript. The strength of the hypothalamus-pituitary organoid lies in its ability to recapitulate the differentiation process including not only the pituitary cells but also neighbouring non-pituitary cells, such as hypothalamic cells in vitro. It is necessary to determine "at which stages" and "in which localizations" NFKB2 expression is critical for pituitary development.

Reviewer #2 (Public review):

Summary:

DAVID syndrome is a rare autosomal dominant disorder characterized by variable immune dysfunction and variable ACTH deficiency. Nine different families have been reported, and all have heterozygous mutations in NFKB2. The mechanism of NFKB2 action in the immune systems has been well-studied, but nothing is known about its role in pituitary gland.

The DAVID mutations cluster in the C-terminus of the NFKB2 and interfere with cleavage and nuclear translocation. The mutations are likely dominant negative, by affecting dimer function. ACTH deficiency can be life-threatening in neonates and adults, thus, understanding the mechanism of NFKB2 action in pituitary development and/or function is important.

The authors use CRISPR/Cas gene editing of human iPSC derived pituitary-hypothalamic organoids to assess the function of NFKB2 and TBX19 in pituitary development. Mutations in TBX19 are the most common, known cause of pituitary ACTH deficiency, and the mechanism of action has been studied in mice, which phenocopy the human condition. Thus, the TBX19 organoids can serve as a positive control. The Nfkb2 mouse model has a p.Y868* mutation that impairs cleavage of NFKB2 p100, and the immune phenotype mimics the patients with DAVID mutations, but no pituitary phenotype was evident. Thus, a human organoid model might be the only approach suitable to discover the etiology of the pituitary phenotype.

Overall, the authors have selected an important problem, and the results suggest that the pituitary insufficiency in DAVID syndrome is caused by a developmental defect rather than an autoimmune hypophysitis condition. The use of gene editing in human iPSC derived hypothalamic-pituitary organoids is significant, as there is only one example of this previously, namely studies on OTX2. Only a few laboratories have demonstrated the ability to differentiate iPSC or ES cells to these organoids, and the authors have improved the efficiency of differentiation, which is also significant.

The strength of the evidence is excellent. The authors have thoroughly analyzed the genetically engineered organoids compared to isogenic controls. They have validated their findings with analysis of RNA and proteins. They have studied the time course of differentiation in the organoids and have a robust experimental design involving many replicates. Analysis of additional clones could strengthen the evidence.

Strengths:

The authors make mutations in TBX19 and NFKB2 that exist in affected patients. The TBX19 p.K146R mutation is recessive and causes isolated ACTH deficiency. Mutations in this gene account for 2/3 of isolated ACTH deficiency cases. The NFKB2 p.D865G mutation is heterozygous in a patient with recurrent infections and isolated ACTH deficiency. NFKB2 mutations are a rare cause of ACTH deficiency, and they can be associated with loss of other pituitary hormones in some cases. However, all reported cases are heterozygous.<br /> The developmental studies of organoid differentiation are rigorous in that 200 organoids were generated for each hiPSC line, and 3-10 organoids were analyzed for each time point and genotype. Differentiation analysis relied on both RNA transcript measurements and immunohistochemistry of cleared organoids using light sheet microscopy. Multiple time points were examined, including seven times for gene expression at the RNA level and two times in the later stages of differentiation for IHC.<br /> TBX19 deficient organoids exhibit reduced levels of PITX1, LHX3, and POMC (ACTH precursor) expression at the RNA and IHC level, and there are fewer corticotropes in the organoids, as ascertained by POMC IHC.<br /> The NFKB2 deficient organoids have normal expression of the early pituitary transcription factor HESX1, but reduced expression of PITX2, LHX3 and POMC. Because there is no immune component in the organoid, this shows that NFKB2 mutations can affect corticotrope differentiation to produce POMC. RNA sequencing analysis of the organoids reveals potential downstream targets of NFKB2 action, including a potential effect on epithelial to mesenchymal like transition and selected pituitary and hypothalamic transcription factors and signaling pathways.

It is important to note that all NFKB2 patients are heterozygous for what appear to be dominant negative mutations that affect protein cleavage and nuclear localization of processed protein as homo or heterodimers. The organoids are homozygous for this mutation.

Weakness:

There could be variation between individual iPSC lines that is unrelated to the genetically engineered change. The work would be strengthened by analysis of independently engineered clones or by correcting the engineered clone to wild type and demonstrating that the phenotypic effects are reversed. The authors do check for off target effects of the guide RNA at predicted sites using WGS.

Reviewer #3 (Public review):

Summary:

This manuscript by Mac et al addresses the causes of pituitary dysfunction in patients with DAVID syndrome which is caused by mutations in the NFKB2 gene and leads to ACTH deficiency. The authors seek to determine whether the mutation directly leads to altered pituitary development, as opposed to an autoimmune defect, by using mutating human iPSCs and then establishing organoids that differentiate into pituitary tissue. They first seek to validate the system using a well-characterised mutation of the transcription factor TBX19, which also results in ACTH deficiency in patients. Then they characterise altered pituitary cell differentiation in mutant NFKB2 organoids and show that these lack corticotrophs, which would lead to ACTH deficiency. Importantly, the findings here suggest the effects of mutant NFKB2 on pituitary organoid differentiation are direct and not a result of altered noncanonical NF-κB signalling, which has been shown to be a mechanism leading to immunodeficiency in DAVID patients.

Strengths:

The conclusion of the paper that ACTH deficiency in DAVID syndrome is independent of an autoimmune input is strong.

Weaknesses:

(1) The authors correctly emphasise the importance of establishing the validity of an iPSC-based model in being able to recapitulate in vivo dysfunctional pituitary development through characterisation of a TBX19 knock-in mutation. Whilst this leads to the expected failure of functional corticotroph differentiation, other aspects of the normal pituitary differentiation pathway upstream of cortocotroph commitment seem to have been affected in surprising ways. In particular, the loss of LHX3 and PITX1 in TBX19 mutant organoids compared with wild type requires explanation, especially as the mutant protein would only be expected to be expressed in a small proportion of anterior pituitary lineage cells. This may identify a difference between human and mouse pituitary development and emphasises the importance of further establishing the developmental programme in human pituitary.

(2) It is notable that the manipulation of iPSC cells used to generate mutants through CRISPR/Cas9 editing is not applied to the control iPSC line. It is possible that these manipulations, including electroporation and puromycin selection may lead to changes to the iPSC cells that is independent of the mutations introduced and this may change the phenotype of the cells. The authors have established that there are no off-target mutations through whole genome sequencing but the iPSC manipulation could have led to changes through epigenetic mechanisms or through non-genomic alterations of developmental potential. A better control in all experiments would have been an iPSC line with a benign knock-in (such as GFP into the ROSA26 locus) or use of a selected line where editing failed. The authors also ackowledge that use of a single clone is not ideal in these studies and characterisation of multiple clones would strengthen the conclusions of the study.

Author response:

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

eLife Assessment

This valuable study examines the effects of NFKB2 mutations on pituitary gland development through hypothalamic-pituitary organoids. The evidence supporting the main conclusions is solid, although analysis of additional clones to exclude inter-clone variability would strengthen the conclusions. Insight into the mechanism of action of NFKB2 during pituitary development is incomplete. This work will be of interest to endocrinologists and biologists working on pituitary gland development and disease.

We agree with these considerations and the summary and thank the Editors for their assessment. Although we indeed share the idea that reproduction of the experiments on a second clone would be a useful confirmatory step, we have not been able to reach this goal within a reasonable time frame for the reason mentioned above (unavailability of the main research engineer knowledgeable in the challenging methods involved for organoids differentiation) and due to the long turnaround time of this kind of experiments (3 months for the whole differentiation starting form iPSC). We therefore decided to publish on a single clone while we are still aiming at reproducing our results on at least a second one and will hopefully be able to provide these additional data in a subsequent revised version. We now acknowledge this limitation in the final part of the Discussion.

Revised text: “Conversely, a limitation of this model is the long duration of the differentiation period (approximately 3 months) and the fact that not all hiPSC clones lead to full differentiation of hypothalamo-pituitary organoids despite similar conditions of culture. For these reasons, we could not include confirmation of our results on an independent clone in the present paper.”

Public Reviews:

Reviewer #1 (Public Review):

Summary:

NFKB mutations are thought to be one of the causes of pituitary dysfunction, but until now they could not be reproduced in mice and their pathomechanism was unknown. The authors used the differentiation of hypothalamic-pituitary organoids from human pluripotent stem cells to recapitulate the disease in human iPS cells carrying the NFKB mutation.

Strengths:

The authors achieved their primary goal of recapitulating the disease in human cells. In particular, the differentiation of the pituitary gland is closely linked to the adjacent hypothalamus in embryology, and the authors have again shown that this method is useful when the hypothalamus is suspected to be involved in pituitary abnormalities caused by genetic mutations.

Weaknesses:

On the other hand, the pathomechanism is still not fully understood. This study provides some clues to the pathomechanism, but further analysis of NFKB expression and experiments investigating the relevant factors in more detail may help to clarify it further.

We thank this reviewer for acknowledging that we've reached our primary objective, in particular the fact that the HPO (hypothalamo-pituitary organoid) model allows recapitulation of the disease in human cells, including hypothalamic-pituitary interactions. Regarding the pathophysiological mechanism of the disease, we must admit that it remains incompletely understood. However, we have analysed more samples by RT-qPCR and further analysed RNASeq data from NFKB2 KI organoids, which provided with more insights into the different levels where NFKB2 may play a role. We have now provided several additional figures derived from these analyses, including a synthetic figure to summarize the most relevant observed effects (Fig. 14). 

Reviewer #2 (Public Review):

We also thank this reviewer for the detailed analysis of our manuscript, for the valuable comments, suggestions and questions that are addressed point-by point below. 

Summary:

DAVID syndrome is a rare autosomal dominant disorder characterized by variable immune dysfunction and variable ACTH deficiency. Nine different families have been reported, and all have heterozygous mutations in NFKB2. The mechanism of NFKB2 action in the immune systems has been well-studied, but nothing is known about its role in the pituitary gland.

The DAVID mutations cluster in the C-terminus of the NFKB2 and interfere with cleavage and nuclear translocation. The mutations are likely dominant negative, by affecting dimer function. ACTH deficiency can be life-threatening in neonates and adults, thus, understanding the mechanism of NFKB2 action in pituitary development and/or function is important.

The authors use CRISPR/Cas gene editing of human iPSC-derived pituitary-hypothalamic organoids to assess the function of NFKB2 and TBX19 in pituitary development. Mutations in TBX19 are the most common, known cause of pituitary ACTH deficiency, and the mechanism of action has been studied in mice, which phenocopy the human condition. Thus, the TBX19 organoids can serve as a positive control. The Nfkb2<Lym1/Lym1> mouse model has a p.Y868* mutation that impairs cleavage of NFKB2 p100, and the immune phenotype mimics the patients with DAVID mutations, but no pituitary phenotype was evident. Thus, a human organoid model might be the only approach suitable to discover the etiology of the pituitary phenotype.

Overall, the authors have selected an important problem, and the results suggest that the pituitary insufficiency in DAVID syndrome is caused by a developmental defect rather than an autoimmune hypophysitis condition. The use of gene editing in human iPSC-derived hypothalamic-pituitary organoids is significant, as there is only one example of this previously, namely studies on OTX2. Only a few laboratories have demonstrated the ability to differentiate iPSC or ES cells to these organoids, and the authors have improved the efficiency of differentiation, which is also significant.

The strength of the evidence is excellent. However, the two ACTH-deficient organoid models use a single genetically engineered clone, and the potential for variability amongst clones makes the conclusions less compelling. Since the authors obtained two independent clones for NFKB2 it is not clear why only one clone was studied.

We experienced difficulties obtaining an hiPSC population devoid of spontaneous differentiation while purifying this second clone, and did not want to delay the start of the experiments. This clone will be analysed in a follow-up study.

Finally, the effect of TBX19 on early pituitary fate markers is somewhat surprising given the phenotype of the knockout mice and patients with mutations. Thus, the use of a single clone for that study is also worrisome.

We agree that the effect of the TBX19 mutant on early pituitary progenitor development is rather puzzling. In our model, TBX19 is expressed throughout the whole experiment, although it is at very low levels in undifferentiated hiPSCs compared to peak expression (over 50-fold difference).

During the CRISPR-Cas9 gene edition, we obtained a clone with a homozygous one base insertion at the cutting site, leading to a frameshift and a premature stop codon 48 bases downstream. This would result in an expected protein of 163 amino acids instead of 488, but with potentially still functional DNA-binding ability. This mutation had a similar effect on LHX3 and PITX1 as the TBX19 KI mutation, although it was even more severe. Our most likely explanation is that the two TBX19 mutants we generated have dominant negative effects. Contrary to mouse, little is known about TBX19 expression in early human pituitary development, but scRNA-seq data on human embryonic pituitaries (Zhang et al.) show low expression in undifferentiated pituitary progenitors between 7 and 9 weeks of gestation. Therefore, early expression of these dominant negative proteins could perturb differentiation in the organoids. Future development of hiPSCs lines with total absence of TBX19 should help clarify these questions.

Strengths:

The authors make mutations in TBX19 and NFKB2 that exist in affected patients. The TBX19 p.K146R mutation is recessive and causes isolated ACTH deficiency. Mutations in this gene account for 2/3 of isolated ACTH deficiency cases. The NFKB2 p.D865G mutation is heterozygous in a patient with recurrent infections and isolated ACTH deficiency. NFKB2 mutations are a rare cause of ACTH deficiency, and they can be associated with the loss of other pituitary hormones in some cases. However, all reported cases are heterozygous.

The developmental studies of organoid differentiation seem rigorous in that 200 organoids were generated for each hiPSC line, and 3-10 organoids were analyzed for each time point and genotype. Differentiation analysis relied on both RNA transcript measurements and immunohistochemistry of cleared organoids using light sheet microscopy. Multiple time points were examined, including seven times for gene expression at the RNA level and two times in the later stages of differentiation for IHC.<br /> TBX19 deficient organoids exhibit reduced levels of PITX1, LHX3, and POMC (ACTH precursor) expression at the RNA and IHC level, and there are fewer corticotropes in the organoids, as ascertained by POMC IHC.

The NFKB2 deficient organoids have a normal expression of the early pituitary transcription factor HESX1, but reduced expression of PITX2, LHX3, and POMC. Because there is no immune component in the organoid, this shows that NFKB2 mutations can affect corticotrope differentiation to produce POMC. RNA sequencing analysis of the organoids reveals potential downstream targets of NFKB2 action, including a potential effect on epithelial-to-mesenchymal-like transition and selected pituitary and hypothalamic transcription factors and signaling pathways.

Weaknesses:

There could be variation between individual iPSC lines that is unrelated to the genetically engineered change. While the authors check for off-target effects of the guide RNA at predicted sites using WGS, a better control would be to have independently engineered clones or to correct the engineered clone to wild type and show that the phenotypic effects are reversed.

All NFKB2 patients are heterozygous for what appear to be dominant negative mutations that affect protein cleavage and nuclear localization of processed protein as homo or heterdimers. The organoids are homozygous for this mutation. Supplemental Figure 4 indicates that one heterozygous clone and two homozygous mutant clones were obtained. Analysis of these additional clones would give more strength to the conclusions, showing reproducibility and the effect of mutant gene dosage.

The main goal of this work was to evaluate if and how NFKB2D865G mutation affects hypothalamic-pituitary organoids development, in order to determine if these organoids would constitute a valuable model to study DAVID syndrome.

We thank this reviewer for noting that we identified an important question and have used appropriate novel and not widely used methods to address it, including CRISPR/Cas9 genome editing of iPSCs and disease modelling in iPSC-derived HPOs that had not previously been reported by a team other than the one that initially described it, allowing to confirm our working hypothesis that DAVID syndrome is caused by a developmental defect rather than an autoimmune hypophysitis condition. We also agree that analysing more clones, generated from same or different hiPSC lines, carrying homozygous or heterozygous mutations, and corrected mutations will be necessary in the future.

Reviewer #3 (Public Review):

We also thank this reviewer for the detailed analysis of our manuscript, for the valuable comments, suggestions and questions that are addressed point-by point below. 

Summary:

This manuscript by Mac et al addresses the causes of pituitary dysfunction in patients with DAVID syndrome which is caused by mutations in the NFKB2 gene and leads to ACTH deficiency. The authors seek to determine whether the mutation directly leads to altered pituitary development, as opposed to an autoimmune defect, by using mutating human iPSCs and then establishing organoids that differentiate into pituitary tissue. They first seek to validate the system using a well-characterised mutation of the transcription factor TBX19, which also results in ACTH deficiency in patients. Then they characterise altered pituitary cell differentiation in mutant NFKB2 organoids and show that these lack corticotrophs, which would lead to ACTH deficiency.

Strengths:

The conclusion of the paper that ACTH deficiency in DAVID syndrome is independent of an autoimmune input is strong.

Weaknesses:

(1) The authors correctly emphasise the importance of establishing the validity of an iPSC-based model in being able to recapitulate in vivo dysfunctional pituitary development through characterisation of a TBX19 knock-in mutation. Whilst this leads to the expected failure of functional corticotroph differentiation, other aspects of the normal pituitary differentiation pathway upstream of corticotroph commitment seem to have been affected in surprising ways. In particular, the loss of LHX3 and PITX1 in TBX19 mutant organoids compared with wild type requires explanation, especially as the mutant protein would only be expected to be expressed in a small proportion of anterior pituitary lineage cells.

If the developmental expression profile of key transcription factors in mutant organoids does not recapitulate that which occurs in vivo, any interpretation of the relevance of expression differences in the NFKB2 organoids to the mechanism(s) leading to corticotroph function in vivo has to be questionable.

See response to Reviewer #2

It is notable that the manipulation of iPSC cells used to generate mutants through CRISPR/Cas9 editing is not applied to the control iPSC line. It is possible that these manipulations lead to changes to the iPSC cells that are independent of the mutations introduced and this may change the phenotype of the cells. A better control would have been an iPSC line with a benign knock-in (such as GFP into the ROSA26 locus).

We agree that the issue of off-target mutations should be addressed. However, we performed whole genome sequencing on TBX19 KI and did not observe any pathogenic variants other than the intended edition. We also checked that clones isolated during the screening procedure but that returned negative for editing still had the ability to generate pituitary cells. However, we made the choice to use the isogenic original hiPSC line as it could be compared to both TBX19 KI and NFKB2 KI simultaneously, therefore reducing workload and cost of the experiments. Any other knock-in mutation, such as GFP into the ROSA26 locus would imply the same risk of off-target mutations, but presumably at other sites in the genome.

(2) In the results section of the manuscript the authors acknowledge that hypothalamic tissue in the NFKB2 mutant organoid may be having an effect on the development of pituitary tissue. However, in the discussion the emphasis is entirely on pituitary autonomous mechanisms such as pituitary HESX1 expression or POMC gene regulation; in the conclusion of the abstract, a direct role for NFKB2 in pituitary differentiation is described. Whilst the data here may suggest a non-immune mediated alteration in pituitary function in DAVID syndrome, if this is due to alteration of the developing hypothalamus then this is not direct. A fuller discussion of the potential hypothalamic contribution and/or further characterisation of this aspect is warranted.

We agree with this reviewer that contributions of both hypothalamic and pituitary developing tissues should be taken into account. We performed more experiments and analysed the effect of both mutations on hypothalamic growth factors expression. These results are displayed in new figure 10. The role of the hypothalamus is now clearly mentioned and highlighted in the Discussion.

(3) qRT-PCR data presented in Figure 6A shows negligible alteration of HESX1 expression at all time points in NFKB2 mutant organoids. This is not consistent with the 2-fold increase in HESX1 expression described in day 48 organoids found by bulk RNA sequencing.

How do the authors reconcile these results and why is one result focused on in the discussion where a potential mechanism for a blockade of normal pituitary cell differentiation is suggested? Further confirmation of HESX1 expression is required.

In the previous version on the manuscript, the HESX1 fold-change ratio between NFKB2 KI and WT at d48 was of 2.06 (p=0.22). However, the type of representation for expression kinetics (values relative to the expression peak in WT) and the scale used made it difficult to see. In the new version of the manuscript, we analysed more samples from the same experiments, and new figure (now 6B) shows significant increase of HESX1 expression (Fc = 2.46, p=0.019) in NFKB2 KI.

Also, qPCR results come from at least two different experiments whereas RNAseq come from a single one. For RT-qPCR, 6 HPOs per genotype were picked and further analysed. As we found that only 60-70% of organoids show signs of pituitary cell differentiation, we chose to perform a preselection of organoids, based on RT-qPCR expression of selected markers (SOX2, HESX1, PITX1, LHX3, TBX19, POU1F1 and POMC) in order to avoid having “empty” HPOs sent for bulk RNAseq. We compared HESX1 expression ratios obtained by the two different techniques on the same samples (the ones used for RNA-seq) and found values of 2.19 (p=0.03) and 1.83 (p=0.061) for RNA-seq and RT-qPCR respectively. This is illustrated in Supplementary Figure 7. Our new results thus clearly demonstrate the increase in HESX1 expression in NFKB2 KI from d27 to d75.

(4) Throughout the authors focus on POMC gene expression and ACTH antibody immunopositive as being indicative of corticotroph cell identity. In the human fetal pituitary melanotrophs are present and most ACTH antibodies are unable to distinguish these cells from corticotrophs. Is the antibody used specifically for ACTH rather than other products of the POMC gene? It is unlikely that all the ACTH-positive cells are melanotrophs, nevertheless, it is important to know what the proportions of the 2 POMC-positive cell types are. This could be distinguished by looking for the expression of NeuroD1, which would also define whether corticotrophs are committed but not fully differentiated in the NFKB2 mutant organoids. In support of an effect on corticotrophs, it is notable that CRHR1 expression (which would be expected to be restricted to this cell type) is reduced by 84% in bulk RNAseq data (Table 1) and this may be an indicator of the loss of corticotrophs in the model.

The antibody we used is directed against ACTH. In HPOs, PAX7 expression was barely detected during the whole experiment. Moreover, although PCSK2 transcripts were observed, their expression started very early (d27) and remained constant, suggesting that an expression of this gene in hypothalamic cells rather than pituitary cells. All these observations suggest that melanotrophs are very unlikely to be present in HPOs.

(5) Notwithstanding the caveats about whether the organoid model recapitulates in vivo pituitary differentiation (see 1 above) and whether the bulk RNAseq accurately reflects expression levels (see 3 above), there are potentially some extremely interesting changes in gene expression shown in Table 1 which warrant further discussion. For example, there is a 25-fold reduction in POU1F1 expression which may be expected to reflect a loss of somatotrophs in the organoid (and possibly lactotrophs) and highlights the importance of characterising the effect of NFKB2 on other anterior pituitary cell types within the organoid. If somatotrophs are affected, this may be relevant to the organoids as a model of DAVID syndrome as GH deficiency has been described in some individuals with NFKB2 mutations. The huge increase in CGA expression may reflect a switch in cell fate to gonadotrophs, as has been described with a loss of TPIT in the mouse. These are examples of the changes that warrant further characterisation and discussion.

We performed a more in-depth analysis of other pituitary lineages (mainly somatotrophs). We confirmed the strong reduction in PROP1 and POU1F1 expression in NFKB2 KI organoids. Although the strong increase in CGA expression in the mutant may raise the possibility of a redirection towards gonadotroph lineage, the lack of change in NR5A1 expression may suggest otherwise.

These results are now illustrated in figure 12 and discussed in a full paragraph.

(6) How do the authors explain the lack of effect of NFKB2 mutation on global NFKB signalling?

The most likely explanation is that p100/p52 is not involved in controlling the expression of other members of NFKB signalling. Therefore, the absence of global alteration of NFKB signaling pathway shows that mutant p100/p52 protein is directly responsible for the observed phenotype.

Recommendations for the authors:

Reviewing editor summary of recommendation to authors:

The use of hypothalamic-pituitary organoids can provide a fundamental understanding of pituitary gland development and differentiation. Their use to study human pituitary insufficiency is important, gaining insight into the aetiology of disease and if it implicates the hypothalamus or anterior pituitary. To this end, there is only one other example of their use in the literature, where Matsumoto et al, (2019), used OTX2-mutant hypothalamic-pituitary organoids to understand the aetiology of pituitary hypoplasia driven by OTX2 mutations. This being the second example of using gene editing in human iPSC-derived hypothalamic-pituitary organoids, these studies have improved the efficiency of differentiation previously published by Suga et al. (2011) for ES cells, and Matsumoto et al. (2019) for iPS cells. In addition, it has solidified that this method is useful, especially when studying hypothalamic involvement in human pituitary anomalies, due to the concerted development of these two structures.

The reviewers recognise the valuable insight provided into the mechanism of NFKB2 action during pituitary development and how this human organoid model might be one of the few or only approaches suitable to discover the aetiology of the pituitary phenotype.

The reviewers agree that both the evidence provided from the organoid model, as well as the characterisation of the phenotype are incomplete. In particular, the strength of evidence would be improved by analysing additional independent clones for both NFKB2 as well as TBX19 gene-edited iPSCs. Additionally, analysis of NFKB2 expression both in vivo and in the organoids, as well as analysis for the NFKB2 targets put forward, would be a lot more informative to help understand this phenotype.

The main recommendations discussed are summarised here and the reviewers have elaborated on these points in their individual reviews:

The two ACTH-deficient organoid models use a single genetically engineered clone, and the potential for variability amongst clones, unrelated to the mutation, makes the conclusions less compelling. Two independent homozygous clones were obtained for NFKB2 but only one was used, so analysis of the second clone would strengthen the findings. A heterozygous clone was also obtained and given all NFKB2 patients are heterozygous for what appears to be dominant negative mutations, the heterozygous clone ought to be analysed. Analyses of these additional clones would give more strength to the conclusions, showing reproducibility and the effect of mutant gene dosage. The reviewers provide excellent suggestions for alternative controls for the engineered iPSC lines in their specific comments.

The effect of TBX19 mutation on early pituitary fate markers LHX3 and PITX1 is surprising given the phenotype of the knockout mice and patients with mutations. If the developmental profile of essential transcription factors does not recapitulate the in vivo expression in this well-characterised mutant, this brings the organoid model into question. Thus, analysis of a further clone for the study of mutant TBX19 would be crucial. The validity of this control affects the interpretations relying on expression differences in the NFKB2-mutant organoids.

The study has implicated NFKB2 in pituitary development, but more insight is needed to fully understand disease pathogenesis. The authors presented potential downstream targets of NFKB2 action, including transcription factors and key signalling pathway components; further analyses of NFKB2 expression and experiments investigating the relevant factors in more detail will help elucidate this point.

Discerning between the hypothalamus and pituitary tissue is fundamental to interpreting phenotypes: (i) To pinpoint the primary tissue affected by NFKB2 deficiency, staining for NFKB2 during development in vivo will determine if this is expressed both in the developing hypothalamus and anterior pituitary gland or only one of these tissues. (ii) Using markers of hypothalamus and pituitary to discern between these two tissues in organoids, will provide a lot of valuable information where expression changes are presented. This would help discern the contribution of the developing hypothalamus as this is still unclear and has not been discussed. Knowing which tissue compartments NFKB2 is expressed in the organoids would also be of great value.

The organoids provide an opportunity to characterise the effects of NFKB2 on other pituitary cell types, since the bulk RNAseq presents intriguing changes indicating that not only corticotrophs may be affected. This may be of relevance to patients, which can have additional pituitary hormone deficiencies. If NFKB2 is expressed in the pituitary, demonstrating expression in the different cell types in vivo as well as in the organoids would help interpret the phenotype. Is this expressed only in corticotrophs/corticotroph precursors, or in additional endocrine cells?

We agree with these considerations and the summary and thank the Editors for their assessment. Although we indeed share the idea that reproduction of the experiments on a second clone would be a useful confirmatory step, we have not been able to reach this goal within a reasonable time frame for the reason mentioned above (unavailability of the main research engineer knowledgeable in the challenging methods involved for organoids differentiation) and due to the long turnaround time of this kind of experiments (3 months for the whole differentiation starting form hiPSC). We therefore decided to publish on a single clone while we are still aiming at reproducing our results on at least a second one and will hopefully be able to provide these additional data in a subsequent revised version. We now acknowledge this limitation in the final part of the Discussion.

We have analysed more samples by RT-qPCR and further analysed RNASeq data from NFKB2 KI organoids, which provided with more insights into the different levels where NFKB2 may play a role. Specifically, we now show the effect of NFKB2 mutation on hypothalamic growth factors and pituitary progenitor differentiation (figure 10), different stages of corticotroph maturation (figure 11) and effects on PROP1/POU1F1-dependent lineages (figure 12). We confronted our results to publicly available ChIPseq data concerning p52 transcriptional targets (figure 13). We have now provided several additional figures derived from these analyses, including a synthetic figure to summarize the most relevant observed effects (Fig. 14). 

Reviewer #1 (Recommendations For The Authors):

In organoids, it is essential to stain for NFKB: is it the hypothalamus or the pituitary that expresses NFKB, and if the pituitary, is it the corticotroph itself or the surrounding cells? If immunostaining is not available, FISH or RNAscope can be used to look at expression.

Figure 7 shows stronger expression of p100/p52 in pituitary progenitors, and some expression in the hypothalamic part of the organoid. Due to current lack of biological material and length of experimental procedure, we could not yet determine which differentiated cell types express p100/p52, but this is clearly something we will look at in further experiments.

Regarding Figure 7, NFKB2 (D865G/D865G) shows no LHX3 expression already at day 48. It would be better to look at expression including PITX1 at an earlier time point to see at what point differentiation is impaired.

RT-qPCR results show no statistically significant changes in PITX1 (Fc=0.58, p=0.25) or LHX3 (Fc = 0.15; p=0.22) expression at d27, although there was a tendency towards downregulation.

Is it really just a species difference that NFKB2-deficient mice do not have abnormal pituitary function? This needs to be discussed in the manuscript.

Nfkb2_Lym1/Lym1 mice and _NFKB2 KI model have different but functionally very similar mutations, as they both lead to an abnormal processing of p100 and a strong reduction of p52 content. In mice, these mutations are more severe than the complete absence of Nfkb2 gene product, and they have been called “super repressors”. It is therefore surprising that no pituitary phenotype as been observed in mice. In our opinion, this constitutes a strong argument in favour of an inter-species difference, at least for the pathogenicity of this type of mutations.

This point is now addressed in the Discussion

Just looking at changes in gene expression by qPCR and bulk RNA-seq does not give enough information about localisation. We wish RNA-seq had at least been separated by FACS first. For example, FACS can separate the anterior pituitary and hypothalamus by EpCAM positivity/negativity (PMID: 35903276), so we would like to see gene expression in such separated samples.

This is a pertinent suggestion. We are aware of these techniques and we hope we will be able to include them in future studies

For Figures 2 and 6, just looking at changes in gene expression by qPCR does not provide localisation information, so either (1) immunostaining for LHX3 and NKX2.1 should be shown in each aggregate as in FigS3, or (2) qPCR should be performed on the FACSed cells. (2) qPCR on FACSed cells.

PITX1, LHX3 (as confirmed by our immunofluorescence data) and HESX1 are only expressed in non-neural tissue. TBX19 could be expressed in the hypothalamic part of the organoid, but we observed very little immunostaining outside the outermost layers of organoids (i.e. pituitary tissue). The antibody we used to detect corticotrophs only recognizes ACTH, and therefore only marks pituitary cells.

In addition, pathway and gene ontology analyses should be performed.

Pathways and gene ontology have been performed. However, as organoids consist of two different tissues, the analysis of over 4800 differentially expressed genes did not give us very informative results, apart from an impairment of retinoic acid signalling that we are currently investigating

Reviewer #2 (Recommendations For The Authors):

The differentiation of iPSC to organoids could be variable. The authors indicate that 200 organoids were analyzed for each line, and 3-10 organoids were analyzed per time point, genotype, and assay. Is it clear that 100% of the organoids differentiate to produce corticotropes? Please clarify.

In our experiments, almost 90% of organoids give rise to non-neural ectoderm, as demonstrated by PITX1 expression. However, depending on experiments, only 60-70% of organoids give rise to pituitary progenitors (LHX3+) and subsequently to corticotropes. This has been clarified in the text.

For TBX19, it seems surprising that there is an effect on PITX1 and LHX3 expression, since TBX19 expression is normally activated after these genes are expressed. An effect of TBX19 on EMT would also be surprising as the knockout mice do not have dysmorphology of the stem cell niche. The only evidence for an effect is the reduced IHC for E-cadherin. If this is an important point, the authors should examine other EMT markers such as Zeb2. The TBX19 knockout mice appear to form corticotropes based on the expression of NeuroD1, even though they lack TBX19 and POMC expression. It would be reassuring to see that NeuroD1 is normally expressed in the TBX19 mutant organoids.

We agree that the effect of the TBX19 mutant on early pituitary progenitor development is rather puzzling. In our model, TBX19 is expressed throughout the whole experiment, although it is at very low levels in undifferentiated hiPSCs compared to peak expression (over 50-fold difference).

During the CRISPR-Cas9 gene edition, we obtained a clone with a homozygous one base insertion at the cutting site, leading to a frameshift and a premature stop codon 48 bases downstream. This would result in an expected protein of 163 amino acids instead of 488, but with potentially still functional DNA-binding ability. This mutation had a similar effect on LHX3 and PITX1 as the TBX19 KI mutation, although it was even more severe. Our most likely explanation is that the two TBX19 mutants we generated have dominant negative effects. Contrary to mouse, little is known about TBX19 expression in early human pituitary development, but scRNA-seq data on human embryonic pituitaries (Zhang et al.) show low expression in undifferentiated pituitary progenitors between 7 and 9 weeks of gestation. Therefore, early expression of these dominant negative proteins could perturb differentiation in the organoids. Future development of hiPSCs lines with total absence of TBX19 should help clarify these questions.

Apart from the lack of change in ZEB2 expression in TBX19 KI (Fc = 1.15; p = 0.35), we did not look further for changes in EMT markers in TBX19 KI. However, we added a more detailed analysis for EMT markers expression in NFKB2 KI based on RNAseq results (see table 2).

Due to lack of material, we could not confirm NEUROD1 expression by immunostaining. However, RT-qPCR showed there was no change in NEUROD1 expression in TBX19 KI (Fc = 0.81; p = 0.64)

NFKB2 IHC was markedly reduced in NFKB2 D865G/D865G organoids. Based on previous experiments, the mutant protein should be expressed but not activated by proteolytic cleavage. It is possible that the antibody has a different affinity for the mutant protein and/or the uncleaved protein may be unstable. Can this be clarified? The mRNA for mutant NFKB2 appears unchanged in Table 1.

This is puzzling indeed. We did not notice any change in NFKB2 from d27 to d105, and no significant change either between WT and NFKB2 KI. Although the antibody we used recognizes both p100 and p52, we cannot rule out the possibility that p100/p52 is degraded by pathways other than proteasome. Another possibility is that p100 interactions with other proteins may decrease the accessibility of the antibody to the epitope

The RNA sequencing data from the NFKB2 organoids is intriguing. It suggests that the NFKB2 mutation may have a modest effect on Tbx19 transcription but not Neurod1. It also suggests there are hypothalamic effects, i.e. altered expression of hypothalamic markers in mutant organoids. Is NFKB2 expressed in the developing hypothalamus? Can normal NEUROD1 IHC be confirmed? It is also intriguing that there may be an effect on EMT. However, there seem to be some discrepancies in the direction of effect on these markers. Please clarify.

This is related to the point just above. P100/p52 is described as a ubiquitously expressed protein. We think that it is expressed in the hypothalamic part of the organoids, but at a lower level compared to pituitary progenitors.

As mentioned before, we could not yet confirm NEUROD1 expression by immunostaining, but RT-qPCR clearly showed there was no change in NEUROD1 expression in TBX19 KI (Fc = 0.81; p = 0.64) or NFKB2 KI (Fc = 0.88; p = 0.5). However, we investigated other markers of different stages of corticotroph differentiation (see figure 11) and found that the later stages are most affected.

Concerning the EMT, we also found changes in the expression of other markers that are shown in Table 2 and discussed further in the text.

Cytokines have been proposed to play important roles in pituitary differentiation, i.e. IL6. Is there any evidence for an altered cytokine or chemokine expression in the NFKB2 organoids?

We didn’t see any change in IL6 expression NFKB2 KI (Fc = 2.34; p = 0.55), but RNAseq shows a strong increase in IL6R (Fc = 8.89; p = 2.13e-09). But at this point, the relevance of these observations remains elusive.

Minor:

Some patients with DAVID syndrome have pituitary hypoplasia. The authors measure organoid size and find no differences based on genotype. However, each organoid probably has a variable amount of tissue differentiated to pituitary and hypothalamic fates, therefore, the volume of the whole organoid may not be a good proxy for the amount of pituitary tissue.

We are aware of this issue. However, for most pituitary genes measured by RT-qPCR (PITX1, LHX3, TBX19), the deltaCt values did not drastically vary for a given time point/genotype, suggesting a stable pituitary/hypothalamic ratio.

Figure 9 shows whole transcriptome data for the NFKB2 organoids, and Table 1 lists the data for selected genes. There appears to be disagreement between the significance cut-offs used in the figure and the table. Please adjust.

We removed the fold-change cut-offs to improve clarity

elife120868_0_supp_2945725_rxl2z4. "haft" appears several times, but it should be "half".

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for - definition - syncrhronomous failure - Cascade Institute - Thomas Homer-Dixon

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eLife Assessment

This work identifies the molecular function of an orphan human transporter, SLC35G1, providing convincing evidence that this protein is involved in intestinal citrate absorption. This work provides important insight into transporter function and human physiology.

Reviewer #1 (Public review):

Summary:

The current manuscript provides solid evidence that the molecular function of SLC35G1, an orphan human SLC transporter, is citrate export at the basolateral membrane of intestinal epithelial cells. Multiple lines of evidence, including radioactive transport experiments, immunohistochemical staining, gene expression analysis, and siRNA knockdown are combined to deduce a model of the physiological role of this transporter.

Strengths:

The experimental approaches are comprehensive, and together establish a strong model for the role of SLC35G1 in citrate uptake. The observation that chloride inhibits uptake suggests an interesting mechanism that exploits the difference in chloride concentration across the basolateral membrane.

Weaknesses:

A gap in this study is that the mechanism of the transporter has not been established. The authors propose that the mechanism is facilitated diffusion, while also leaving open the possibility that citrate transport is coupled to another ion, such as chloride. However, another result from this study seems to be in conflict with the proposed facilitative diffusion mechanism. Specifically, the study finds that uptake is not impacted by membrane depolarization. This would imply that transport is not electrogenic, whereas facilitated diffusion of citrate anion should be an electrogenic process.

Reviewer #2 (Public review):

Summary:

The primary goal of this study was to identify the transport pathway that is responsible for the release of dietary citrate from enterocytes into blood across the basolateral membrane.

Strengths:

The transport pathway responsible for the entry of dietary citrate into enterocytes was already known, but the transporter responsible for the second step remained unidentified. The studies presented in this manuscript identify SLC35G1 as the most likely transporter that mediates the release of absorbed citrate from intestinal cells into the serosal side. This fills an important gap in our current knowledge on the transcellular absorption of dietary citrate. The exclusive localization of the transporter in the basolateral membrane of human intestinal cells and the human intestinal cell line Caco-2 and the inhibition of the transporter function by chloride support this conclusion.

Weaknesses:

(i) The substrate specificity experiments have been done with relatively low concentrations of potential competing substrates, considering the relatively low affinity of the transporter for citrate. Given that NaDC1 brings in not only citrate as a divalent anion and also other divalent anions such as succinate, it is possible that SLC35G1 is responsible for the release of not only citrate but also other dicarboxylates. However the substrate specificity studies show that the dicarboxylates tested did not compete with citrate, meaning that SLC35G1 is selective for the citrate (2-), but this conclusion might be flawed because of the low concentration of the competing substrates used in the experiment. Furthermore, the apical NaDC1 is not selective for citrate; in fact, it transports citrate with a much lower affinity than it transports dicarboxylates such as succinate. If what the authors suggest that SLC35G1 is selective for citrate is correct, there must be another transporter for the efflux of dicarboxylates. The authors should have performed a dose-response experiment for the dicarboxylates tested as potential substrates before making the conclusion that SLC35G1 is selective for citrate.

(ii) The authors have used MDCK cells for assessment of the transcellular transfer of citrate via SLC35G1, but it is not clear whether this cell line expresses NaDC1 in the apical membrane as the enterocytes do. Even though the authors expressed SLC35G1 ectopically in MDCK cells and showed that the transporter localizes to the basolateral membrane, the question as to how citrate actually enters the apical membrane for SLC35G1 in the other membrane to work remains unanswered.

(iii) The role of chloride in the efflux of citrate remains not evaluated in detail. Similarly, the potential role of membrane potential in the transport function of SLC35G1 remains unknown. Since the SLC35G1-mediated uptake appears to be similar in the presence and absence of potassium, the authors argue that membrane potential has no role in the transport process. Since it is proposed that the divalent citrate is the substrate for the transporter, it is difficult to reconcile with the conclusion that the membrane potential has no impact on the transport process, especially given the fact that no other exchangeable anion has been shown or suggested. Even if chloride is the potential exchangeable anion, it still begs the question as to the stoichiometry of citrate:chloride if membrane potential plays no role. Obviously, additional work is needed to figure out the actual transport mechanism for SLC35G1.

Reviewer #3 (Public review):

The authors convincingly show that SLC35G1 mediates uptake of citrate which is dependent on pH and chloride concentration. Putting their initial findings in a physiological context, they present human tissue expression data of SLC35G. Their Transwell assay indicates that SLC35G1 is a citrate exporter at the basolateral membrane.

Weaknesses:

The manuscript would benefit from the inclusion of the antibody validation results. Related to the localization of SLC35G1, the polyclonal antibody was not validated in the knockdown cells used in the study. This would strengthen the antibody validation, the localization results as well as the transport assay in 2C.

Also, it is unclear why the Transwell assay was not performed upon knockdown of SLC35G1 to support the conclusions.

Author response:

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

Public Reviews:

Reviewer #1 (Public Review):

Summary:

The current manuscript provides strong evidence that the molecular function of SLC35G1, an orphan human SLC transporter, is citrate export at the basolateral membrane of intestinal epithelial cells. Multiple lines of evidence, including radioactive transport experiments, immunohistochemical staining, gene expression analysis, and siRNA knockdown are combined to deduce a model of the physiological role of this transporter.

Strengths:

The experimental approaches are comprehensive, and together establish a strong model for the role of SLC35G1 in citrate uptake. The observation that chloride inhibits uptake suggests an interesting mechanism that exploits the difference in chloride concentration across the basolateral membrane.

Weaknesses:

Some aspects of the results would benefit from a more thorough discussion of the conclusions and/or model.

For example, the authors find that SLC35G1 prefers the dianionic (singly protonated) form of citrate, and rationalize this finding by comparison with the substrate selectivity of the citrate importer NaDC1. However, this comparison has weaknesses when considering the physiological pH for SLC35G1 and NaDC1. NaDC1 binds citrate at a pH of ~5.4 (the pKa of citrate is 5.4, so there is a lot of dianionic citrate present under physiological circumstances). SLC35G1 binds citrate under pH conditions of ~7.5, where a very small amount of dianionic citrate is present. The data clearly show a pH dependence of transport, and the authors rule out proton coupling, but the discrepancy between the pH dependence and the physiological expectations should be addressed/commented on.

Thank you for your insightful comment. Citrate exists mostly in its trianionic form under near neutral pH conditions in biological fluids, as you pointed out. Its dianionic form represents only a small portion (about 1/100) of total citrate due to the pKa. However, significant SLC35G1-specific uptake was observed under near neutral pH conditions (Figure 1G). Therefore, although SLC35G1-mediated citrate transport is less efficient under physiologically relevant near neutral pH conditions, it could still play a role particularly in the intestinal absorption process, in which the concentration gradient of dianionic citrate could be maintained by continuous supply by NaDC1-mediated apical uptake.

The rationale for the series of compounds tested in Figure 1F, which includes metabolites with carboxylate groups, a selection of drugs including anion channel inhibitors and statins, and bile acids, is not described. Moreover, the lessons drawn from this experiment are vague and should be expanded upon. It is not clear what, if anything, the compounds that reduce citrate uptake have in common.

Thank you for highlighting the need for clarity regarding the compounds tested in Figure 1F. The tested compounds were TCA cycle intermediates (fumarate, α-ketoglutarate, malate, pyruvate, and succinate) as substrate candidate carboxylates analogous to citrate, diverse anionic compounds (BSP, DIDS, probenecid, pravastatin, and taurocholate) as those that might be substrates or inhibitors, and diverse cationic compounds (cimetidine, quinidine, and verapamil) as those that are least likely to interact with SLC35G1. Among them, certain anionic compounds significantly reduced SLC35G1-specific citrate uptake, suggesting that they may interact with SLC35G1. However, we could not identify any structural features commonly shared by these compounds, except that they have anionic moieties. We acknowledge that it requires further elaboration to clarify such structural features. We have revised the relevant section on p. 3 (line 25 - 32) to include these.

The transporter is described as a facilitative transporter, but this is not established definitively. For example, another possibility could involve coupling citrate transport to another substrate, possibly even chloride ion.

Thank you for your insightful comment regarding the nature of SLC35G1's transport mechanism. While we have described SLC35G1 as a facilitative transporter based on our current data, we acknowledge that this has not been definitively proven, as you pointed out, and we cannot exclude the possibility that its sensitivity to extracellular Cl- might imply its operation as a citrate/Cl- exchanger. To examine the possibility, we would need to manipulate the chloride ion gradient across the plasma membrane. Particularly, generating an outward Cl- gradient to see if it could enhance citrate uptake could be a potential strategy. However, current techniques do not allow us to effectively generate the Cl- gradient, thus preventing us from conclusively verifying this possibility. We recognize the importance of further investigating this aspect in future studies. Your suggestion highlights an important area for additional research to fully understand the transport mechanism of SLC35G1. We have additionally commented on this issue on p. 4 (line 1 – 3).

Reviewer #2 (Public Review):

Summary:

The primary goal of this study was to identify the transport pathway that is responsible for the release of dietary citrate from enterocytes into blood across the basolateral membrane.

Strengths:

The transport pathway responsible for the entry of dietary citrate into enterocytes was already known, but the transporter responsible for the second step remained unidentified. The studies presented in this manuscript identify SLC35G1 as the most likely transporter that mediates the release of absorbed citrate from intestinal cells into the serosal side. This fills an important gap in our current knowledge of the transcellular absorption of dietary citrate. The exclusive localization of the transporter in the basolateral membrane of human intestinal cells and the human intestinal cell line Caco-2 and the inhibition of the transporter function by chloride support this conclusion.

Weaknesses:

(i) The substrate specificity experiments have been done with relatively low concentrations of potential competing substrates, considering the relatively low affinity of the transporter for citrate. Given that NaDC1 brings in not only citrate as a divalent anion but also other divalent anions such as succinate, it is possible that SLC35G1 is responsible for the release of not only citrate but also other dicarboxylates. But the substrate specificity studies show that the dicarboxylates tested did not compete with citrate, meaning that SLc35G1 is selective for the citrate (2-), but this conclusion might be flawed because of the low concentration of the competing substrates used in the experiment.

Thank you for your valuable comment on our substrate specificity experiments. As you pointed out, we cannot rule out the possibility that dicarboxylates might be recognized by SLC35G1 with low affinity as the tested concentration was relatively low. However, at the concentration of 200 μM, competing substrates with an affinity comparable to that of citrate could inhibit SLC35G1-specific citrate uptake by about 30%. Therefore, it is likely that the compounds that did not exhibit significant effect have no affinity or at least lower affinity than citrate to SLC35G1. Further studies should explore a broader range of concentrations for potential substrates including those with lower affinity. It would help clarify the substrate recognition characteristics of SLC35G1 and if it indeed has a unique preference for citrate over dicarboxylates. We have additionally mentioned that on p. 3, line 32 – 35.

(ii) The authors have used MDCK cells for assessment of the transcellular transfer of citrate via SLC35G1, but it is not clear whether this cell line expresses NaDC1 in the apical membrane as the enterocytes do. Even though the authors expressed SLC35G1 ectopically in MDCK cells and showed that the transporter localizes to the basolateral membrane, the question as to how citrate actually enters the apical membrane for SLC35G1 in the other membrane to work remains unanswered.

Thank you for highlighting this important aspect of our study. The mechanism of apical citrate entry in MDCKII cells is unknown, although NaDC1 or a similar transporter may be involved. However, this set of experiments have successfully demonstrated the basolateral localization of SLC35G1 and its operation for citrate efflux. Attempts to clarify the apical entry mechanism may need to be included in future studies for more detailed characterization of the model system using MDCKII cells. This would help in fully understanding the transcellular transport system for citrate. Investigation using Caco-2 cells or MDCKII cells double transfected with NaDC1 and SLC35G1 would also need to be induced in future studies to gain more definitive insights into the transcellular transport mechanism for citrate in the intestine, delineating the suggested cooperative role of NaDC1 and SLC35G1. We would be grateful for your understanding of our handling regarding this issue.

(iii) There is one other transporter that has already been identified for the efflux of citrate in some cell types in the literature (SLC62A1, PLoS Genetics; 10.1371/journal.pgen.1008884), but no mention of this transporter has been made in the current manuscript.

Thank you for bringing up the relevance of SLC62A1, which has recently been identified as a citrate efflux transporter in some cell types (PLoS Genet, 16, e1008884, 2020). We have now included comments on this transporter in Introduction (p. 2).

Reviewer #3 (Public Review):

Summary:

Mimura et al describe the discovery of the orphan transporter SLC35G1 as a citrate transporter in the small intestine. Using a combination of cellular transport assays, they show that SLC35G1 can mediate citrate transport in small intestinal cell lines. Furthermore, they investigate its expression and localization in both human tissue and cell lines. Limited evidence exists to date on both SLC35G1 and citrate uptake in the small intestine, therefore this study is an important contribution to both fields. However, the main claims by the authors are only partially supported by experimental evidence.

Strengths:

The authors convincingly show that SLC35G1 mediates uptake of citrate which is dependent on pH and chloride concentration. Putting their initial findings in a physiological context, they present human tissue expression data of SLC35G. Their Transwell assay indicates that SLC35G1 is a citrate exporter at the basolateral membrane.

Weaknesses:

Further confirmation and clarification are required to claim that the SLC indeed exports citrate at the basolateral membrane as concluded by the authors. Most experiments measure citrate uptake, but the authors state that SLC35G1 is an exporter, mostly based on the lack of uptake at physiological conditions faced at the basolateral side. The Transwell assay in Figure 1L is the only evidence that it indeed is an exporter. However, in this experiment, the applied chloride concentration was not according to the proposed model (120 mM at the basolateral side). The Transwell assay, or a similar assay measuring export instead of import, should be carried out in knockdown cells to prove that the export indeed occurs through SLC35G1 and not through an indirect effect. Related to the mentioned chloride sensitivity, it is unclear how the proposed model works if the SLC faces high chloride conditions under physiological conditions though it is inhibited by chloride.

Thank you for highlighting these important points. We used the Cl--rich medium in transcellular transport studies, as stated in the relevant section in Meterials and Methods (p. 6, line 2 – 5). The Cl- concentration (144 mM) was comparable to the physiological concentration in extracellular body fluids. To clarify that experimental condition, we have additionally noted that in the text (p. 4, line 9) and the legends of Figs. 1K and 1L. The results indicate that basolaterally localized SLC35G1 can mediate citrate export effectively under the Cl--rich extracellular condition. The transport mechanism regulated by Cl- is unclear, but it is difficult to further clarify the mechanism at this time. We recognize the importance of further investigating the aspect in future studies, including the possibility that SLC35G1 might be a citrate/Cl- exchanger, as pointed out by Reviewer #1 (3rd comment).

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

The figures are very tiny and difficult to see. The inset in Figure 1C is much too small to be readable. I suggest enlarging the panels.

Thank you for your feedback. As advised, we have enlarged the panels to improve visibility.

Line 74: "certain anionic compounds signficantly inhibited SLC35G1-specific citrate uptake, indicating they are also recognized by SLC35G1." This sentence should be reworded since the mechanism is not clear. The word "reduced" would be a better option than "inhibited." Are there other interpretations besides SLC35G1 binding to explain the observations?

Thank you for your suggestion. We have reworded the sentence to improve clarity (p. 3, line 30). It may be possible to speculate that they interact with SLC35G1, but the mechanisms are not clear yet.

The manuscript is vague about how the transporter was discovered. If a screen of orphan transporters was performed to identify a citrate transporter, this should be described.

Thank you for pointing out the need for more details regarding the discovery of the transporter. We have added some detailed description at the beginning of Results and Discussion (p. 3).

Reviewer #2 (Recommendations For The Authors):

Recommendations for the authors:

(1) For transcellular transport of citrate and the role of SLC35G1, it would be better to use Caco-2 cells cultured on Transwells because these cells express NaDC1 in the apical membrane and the authors have shown that SLC35G1 is expressed in the basolateral membrane in this cell line. The mechanism for the entry of citrate into MDCK cells used in the present manuscript is not known. If the authors prefer to use MDCK cells because of their superior use for polarization, they can use a double transfection (NaDC1 and SLC35G1) to differentially express the two transporters in the apical versus and basolateral membrane and then use the cells for trans cellular transport of citrate.

Please refer to our reply to your second review comment.

(2) The substrate specificity experiments should use concentrations higher than 0.2 mM for competing dicarboxylates because the Km for citrate is only 0.5 mM. It is likely that NaDC1 brings in citrate and other dicarboxylates into enterocytes and then SLC35G1 mediates the efflux of these metabolic intermediates into blood.

Please refer to our reply to your first review comment.

(3) One major aspect of the transport function of this newly discovered citrate efflux transporter that has not been explored is the role of membrane potential in the transport function. The transporter is not coupled to Na or K or even H; so then the transport of citrate via this transporter must be electrogenic. Of course, this would be perfect for the transporter to function in the efflux of citrate because of the inside-negative membrane potential, but the authors need to show that the transporter is electrogenic. This can be examined through Caco-2 cells and/or MDCK cells expressing SLC35G1 and examining the impact of changes in membrane potential (valinomycin and K) on the transport of citrate.

Thank you for your suggestion. As shown in Figure 1D, the use of K-gluconate in place of Na-gluconate, which induces plasma membrane depolarization, had no impact on the specific uptake of citrate, suggesting that SLC35G1-mediated citrate transport is independent of membrane potential. We have additionally mentioned this on p. 3 (line 21 – 24).

(4) The localization studies mention Na/K ATPase component as a basolateral membrane marker, but the text describes it as BCRP. This needs to be corrected.

Thank you for pointing out the mistake. We have corrected that. The marker was ATP1A1.

Reviewer #3 (Recommendations For The Authors):

Major points:

(1) Most experiments measure citrate uptake, but the authors state that SLC35G1 is an exporter, mostly based on the lack of uptake at physiological conditions faced at the basolateral side. The Transwell assay in Figure 1L is the only evidence that it indeed is an exporter. However, in this experiment, the applied chloride concentration was not according to the proposed model (120mM at basolateral side). Why was this chloride concentration not mimicked accordingly in the Transwell assay?

(2) The Transwell assay, or a similar assay measuring export instead of import, should be carried out in knockdown cells to prove that the export indeed occurs through SLC35G1 and not through an indirect effect.

(3) Related to the mentioned chloride sensitivity, it is unclear how the proposed model works if the SLC faces high chloride conditions under physiological conditions though it is inhibited by chloride.

Please refer to our reply to your review comments.

Related to the localization of SLC35G1:

(4) The polyclonal antibody against SLC35G1 should be validated to prove the specificity. This should be relatively straightforward given the authors have SLC35G1 knockdown cells.

Thank you for your suggestion. To validate the specificity of the polyclonal antibody against SLC35G1, we prepared HEK293 cells transiently expressing SLC35G1 and SLC35G1 tagged with a FLAG epitope at the C-terminus (SLC35G1-FLAG). In the immunostained images, whereas only SLC35G1-FLAG was stained with the anti-FLAG antibody, both SLC35G1 and SLC35G1-FLAG were stained with the anti-SLC35G1 antibody, indicating that the anti-SLC35G1 antibody can recognize SLC35G1. In addition, the localization patterns of SLC35G1-FLAG observed with both antibodies were consistent, indicating furthermore that the anti-SLC35G1 antibody can recognize SLC35G1 specifically. Based on all these, the specificity of the anti-SLC35G1 antibody was validated.

Author response image 1.

(5) To strengthen the data on the localization of SLC35G1, the cell lines should be co-stained with a plasma membrane marker as well, not just in tissue with ATP1A1. In polarized cells co-staining with apical and basolateral markers should be applied.

SLC35G1 was indicated to be localized to the basolateral membrane geometrically in both polarized MDCKII and Caco-2 cells. This finding aligns with its basolateral localization indicated by its colocalization with ATP1A1 in the human small intestinal section. These results are we consider sufficient to support the basolateral localization characteristics of SLC35G1.

General points:

(6) In the abstract the authors mention that they focus on highly expressed orphan transporters in the small intestine as candidates. However, no other candidates are mentioned or discussed in the study. Consequently, this should be rephrased.

Thank you for the advice. Also taking into consideration the third recommendation point by Reviewer #1, we have added some detailed description at the beginning of Results and Discussion (p. 3).

(7) As far as mentioned there is exactly one (other) publication on SLC35G1 (10.1073/pnas.1117231108). The authors should discuss this only publication with functional data on SLC35G1 in more detail. How do the authors integrate their findings with the existing knowledge? For example, why did the authors not investigate the impact of Ca2+ on SLC35G1 transport?

Thank you for your suggestion. SLC35G1 was indicated to be mainly localized to the endoplasmic reticulum (ER) in the earlier study, in which SLC35G1 was tagged with GFP. A possibility is that SLC35G1 was wrongly directed to ER due to the modulation in the study. We have additionally mentioned this possibility in the relevant section (p. 3, line 9 – 11). We have also revised a relevant sentence on p. 3 (line 5).

With regard to another point that GFP-tagged SLC35G1 was indicated to interact with STIM1, we examined its effect on SLC35G1-mediated citrate uptake supplementary. As shown in the accompanying figure, coexpression of HA-tagged STIM1 did not affect the elevated citrate uptake induced by FLAG-tagged SLC35G1, indicating that STIM1 has no impact on citrate transport function of SLC35G1 at the plasma membrane.

Author response image 2.

(A) Effect of the coexpression of HA-tagged STIM1 on [14C]citrate (1 μM) uptake by FLAG-tagged SLC35G1 transiently expressed in HEK293 cells. The uptake was evaluated for 10 min at pH 5.5 and 37°C. Data represent the mean ± SD of three biological replicates. Statistical differences were assessed using ANOVA followed by Dunnett’s test. *, p < 0.05 compared with the control (gray bar). (B) Western blot analysis was conducted by probing for the HA and FLAG tags, using the whole-cell lysate samples (10 µg protein aliquots) prepared from cells expressing HA-STIM1 and/or FLAG-SLC35G1. The blots of β-actin are shown for reference.

(8) Generally, the introduction could provide more background.

In response to your suggestion and also to the third review comment from Reviewer #2, we have now additionally included comments on SLC62A1, which has recently been reported as a citrate efflux transporter in some cell types, in Introduction.

Minor points:

(9) There is a typo in Figure 1D: manniotol instead of mannitol.

Thank you for pointing that out. We have corrected the typo in Figure 1D.

(10) Figure 1J: The resolution is low and the localization to the basolateral membrane is not conclusive based on this image. It seems rather localized at the whole membrane and intracellularly too.

Thank you for your feedback. We have enhanced the resolution of the image and also enlarged it to improve clarity and make the basolateral membrane localization more discernible.

(11) Figure 1K: Clarification is needed if the experiment was performed in the Transwell plate. Based on the results from the pH titration experiment, it is expected that there is no uptake at pH7.4. Therefore, this experiment does not seem to provide additional evidence or support the conclusions drawn related to cellular polarization.

Please refer to our reply to your review comments.

run-on sentence

no ellipses and no italics

remove this

could this be more specific? maybe she slams the door?

'a hand hit the glass right in front of her eyes.'

wouldn't they do this naturally? or if there's pitch black maybe her eyes struggle to adapt

i'll accept the italics here

split this into two sentences

'the world would keep on spinning, and she would remain enclosed in the safe bubble of her studio'

i like the thought behind this but something seems off - it might be your use of the conditional tense again

If we want to change the button color and font color, can we do that directly in Strapi? My concern is that the buttons aren't standing out well as they are.

The CTA button should read : Download Your Free STEM Activities Ref: Google doc ( https://docs.google.com/document/d/15BSkFChqEOyuio8_LIx7-j8H9Bz_gKiXsoLx-FWlfMA/edit ) -> Inconsistent in Figma. I’ve informed Emilio about this.

There should be a section between the testimonials and the free hands-on STEM section, which is currently missing in Figma. I’ve already informed Emilio about this.

The testimonials will include the individuals' photos as referenced in Figma.

I’d like to make the locations (US & SG) and number of countries bold. Can I do this directly in Strapi when I log in as admin?

It's important to note that the economic impact of redlining is the driving force behind the criminal impact of redlining. Partially in that crime is often made necessary by poverty (I'm not saying that all neighborhoods that were once redlined are still poverty/crime ridden and awful, but it does seem logical that they would have a higher of chance of being disproportionately affected by crime and poverty). But also, there are at least some cases where you can draw a through line from low-income housing that hasn't been invested in to health consequences, like houses that still have lead paint or asbesetos. If you're at all curious about this, this video is what introduced me to redlining in the first place, and specifically that particular aspect of the issue:https://www.youtube.com/watch?v=GWwiUIVpmNY

eLife Assessment

This study reports important findings on identifying sequence motifs that predict substrate specificity in a class of lipid synthesis enzymes. It sheds light on a mechanism used by bacteria to modify the lipids in their membrane to develop antibiotic resistance. The evidence is compelling, with a careful application of machine learning methods, validated by mass spectrometry-based lipid analysis experiments. This interdisciplinary study will be of interest to computational biologists and to the community working on lipids and on enzymes involved in lipid synthesis or modification.

Reviewer #1 (Public review):

The manuscript by Christensen, et al. presents an application of restricted Boltzmann machines to analyze the MprF family of enzymes, which catalyze the addition of amino acids to lipid substrates in bacteria. Overall the manuscript is an interesting and very compelling combination of advanced statistical analysis of sequences and experimental determination of MprF function. One notable outcome is (as stated in the title) the identification of a novel substrate/product. I expect that other researchers interested in using advanced methods to connect sequence to lipid synthesis functions will find the work of significant value and that others interested in microbial resistance will find inspiration in the results. This is an excellent contribution that will be of great value to the field, and which is improved following revisions.

Reviewer #3 (Public review):

Summary:

After the previous identification that the Streptococcus agalactiae MprF enzyme can synthesize also lysyl-glucosyl-diacylglycerol (Lys-Glc-DAG), besides the already known lysyl-phosphatidylglycerol (Lys-PG), the authors aim for the current manuscript was to investigate the molecular determinants of MprF lipid substrate specificity, for which MprF from a variety of bacterial species were used. This then led to the coincidental discovery of a novel lipid species.

The manuscript is well constructed and easy to follow, especially taking into account the multidisciplinary aspect of it (computational machine learning combined with lipid biology). The Restricted Boltzmann machines (RBM) approach enables the successful, although not perfect, classification and categorization of MprF activity. The computational approach is validated by lab experiments in which LC-MS analysis reveals the specific activity of the lipid synthesizing enzymes. In a few cases lipid synthesis activity is completely absent. Due to the lack of protein expression data, it is unclear if this is caused by enzyme inactivity or the overall absence of enzyme.

Overall, the authors largely achieved their goals, as the applied RBM approach led to specific sequence determinants in MprF enzymes that could categorize the specificity of these enzymes. The experimental data could largely confirm this categorization, although a stronger connection between synthesized lipids and enzyme activity would have further strengthened the observations.

The work now focuses only on MprF enzymes, but could in theory be expanded to other categories of lipid synthesizing enzymes. In other words, the RBM approach could have an impact on the lipid synthesis field, if it would be a tool that is easy applicable. Moreover, the lipids synthesized by MprF (Lys-PG, but also other cationic lipids) play an important role in the bacterial resistance against certain antibiotics.

Author response:

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

Reviewer 1 (Public Review):

The contribution of individual resides is shown in Figure 3c, which highlights one of the strengths of this RBM implementation - it is interpretable in a physically meaningful way. However, there are several decisions here, the justification of which is not entirely clear.

i) Some of the residues in Fig 3c are stated as "relevant" for aminoacylated PG production. But is this the only such hidden unit? Or are there others that are sparse, bimodal, and involve "relevant" AA?

Thanks for bringing this important question to our attention. In fact,  this was the only hidden unit involving the combination of positions 152 and 212.  Although we don't  have knowledge of all relevant amino acids for this catalytic process, the residues we uncover were however shown through experimental analysis to be critical for the catalytic function of two MprF variants, and thus since our protein of interest involved this function, any domain which did not contain these residues were excluded. We can't rule out that the domains we excluded from further analysis could be performing similar catalytic functions, but we found it unlikely considering the amino acids found in the negative portion of the weight were chemically unlikely to form a complex with the amino acid lysine. We have clarified in the text, that this selection is probably a subset of all important amino acids, however, this selection provided predictive power.

ii) In order to filter the sequences for the second stage, only those that produce an activation over +2.0 in this particular hidden unit were taken. How was this choice made?

The +2.0 was chosen as it ensured that the bimodal distribution was split into two distinct distributions.

iii) How many sequences are in the set before and after this filtering? On the basis of the strength of the results that follow I expect that there are good reasons for these choices, but they should be more carefully discussed.  

We started with 11,507 sequences and after filtering we had 7,890 to train our model with.  We think this number still maintains robust statistics. This is noted in the Dataset acquisition and pre-processing section of the Methods section.

iv) Do the authors think that this gets all of the aminoacylated PG enzymes? Or are some missed?

This is an interesting question that prompted us to do further analysis. We have added a new supplemental figure providing more details to this question. Based on the Uniprot derived annotations and the Pfam domain-based analysis of these sequences, the large majority of sequences that were excluded were proteins which included the LPG_synthase_C domain but not the transmembrane flippase domain required by the MprF class of enzymes, and were instead accompanied by different domains which  seem less relevant to our enzyme of interest.  It is true though, and related to question (i), that variants which might retain the functionality despite losing experimentally determined key catalytic residues could have been excluded by this method, but such sequences could still be reasonably excluded due to their dissimilarity with MprF from Streptococcus agalactiae.

However, some similar criticisms from the last point occur here as well, namely the selection of which weights should be used to classify the enzymes' function. Again the approach is to identify hidden unit activations that are sparse (with respect to the input sequence), have a high overall magnitude, and "involve residues which could be plausibly linked to the lipid binding specificity."

(i) Two hidden units are identified as useful for classification, but how many candidates are there that pass the first two criteria? Indeed, how many hidden units are there?

We note in the Model training section of the methods that our final model used had 300 hidden units in total.  As to the first part of your question,  rather than systematically test the predictive power of all other hidden units to this task, we decided to use the weights that we did because of their connection to a proposed lipid binding pocket found through Autodocking experiments. While another weight might provide predictive power, it might lack this critical secondary information. Moreover, the direction of our research necessitated finding weights which first satisfied our lipid-binding pocket plausibility before using these weights to propose MprF variants to test for our novel functionality. Given the limited information we had early in the research process, to go in reverse would have provided too many options for experimental testing with reduced mechanistic justification. We included a brief explanation of our rationale in section " Restricted Boltzmann Machines can provide sensitive, rational guidance for sequence classification “ in the updated manuscript.

ii) The criterion "involve residues which could be plausibly linked to the lipid binding specificity" is again vague. Do all of the other candidate hidden units *not* involve significant contributions from substrate-binding residues? Maybe one of the other units does a better job of discriminating substrate specificity. (As indicated in Figure 8, there are examples of enzymes that confound the proposed classification.) Why combine the activations of two units for the classification, instead of 1 or 3 or...?

In fact, it is true that the other hidden units do not involve significant contribution to substrate-binding residues, and we will clarify this. The weights found through this RBM methodology are biased to be probabilistically independent, meaning that the residues and amino acids implicated by each weight are not shared among the other weights through the design of the model. We will update the Model Weight selection section to clarify that the weights we chose had more significantly weighted residues overlapping with the residues near the lipid-binding region than the other weights we checked. We combined these two because they were the only ones which had both overlap with these residues and predictive power of lipid activity with the few sequences we had detailed knowledge of at the time of decision (Figure 5b).

The Model Weight section reads as follows:

“Weights were chosen which involved sequence coordinates implicated in our function of interest. Specifically, locations identified through Autodock (Hebecker et al., 2015) where the lipid was likely to interact, and a small radius around this region to select a small set of coordinates. We chose the only weights which had both overlap with multiple residues in this chosen radius and predictive power (separation) for the three examples we had to start with.”

Author Recommendations:

The manuscript will likely be read by many membrane biologists/biochemists, and they might like to better understand how the RBM might be useful in their own approach. Here are some suggestions along these lines. The overall goal is to explain the RBM in *plain English* - the mathematical description in Eqs 2-4 is not easily interpretable.

(1a) Explain that the RBM is a two-layer structure, in which one layer is the "visible" elements of the input sequence, and the other is called "hidden units." Connections are only made between visible and hidden units, but all such connections are made.

(1b) The strengths of these connections are called "weights", and are determined in a statistical way based on a large set of input sequences. Once parametrized, the RBM is capable of capturing correlations among many positions in an input sequence - a significant advantage over the DCA approach.

We agree with this assessment, and have updated the section of the text where we introduce the RBM with a non-technical explanation of what this method is doing. It reads as:

“The design of this RBM can be seen in Figure 4, where the model architecture is represented by purple dots and green triangles. The dots are the “visible” layer, which take in input sequences and encode them into the “hidden” layer, where each triangle represents a separate hidden unit. The lines connecting the visible and hidden layers show that each hidden unit can see all the visible units (the statistics are global), but they cannot see any of the other hidden units, meaning the hidden units are mutually independent. This global model with mutually independent hidden units (see also the marginal distribution form shown in Equation 3) has the following useful properties: higherorder couplings between... “

(1c) Although strictly true that the DCA model is a Boltzmann machine, it's not a typical Boltzmann machine, because all of the units are visible. Typically a Boltzmann machine would also include hidden units, in order to increase its capacity/power. 

We have clarified the relationship between DCA and Boltzmann machines, and this section now reads as:

This class of models is closely related to another model termed the Boltzmann machine. The Boltzmann machine formulation is closely related to the Potts model from physics, which was successfully applied in biology to elucidate important residues in protein structure and function (Morcos et al., 2011), and another example being the careful tuning of enzyme specificity in bacterial two-component regulatory systems (Cheng et al., 2014; Jiang et al., 2021). The Boltzmann machine-like formulation from Morcos et al. (2011), termed Direct Coupling Analysis (DCA), stores patterns...

(1d) Throughout, the authors refer to the activation of the hidden units as weights, but this is not a typical usage of this terminology. Connections between units are weights and have two subscripts. Given an input sequence, the sum over these weights for a given hidden unit is its activation (Eq. 1). I suggest aligning the description with the typical usage in order to make the presentation easier to follow. Hereafter I will refer to these hidden unit activations as simply activations. 

We agree with you, the hidden units are a collection of edge weights. We have modified the terminology in the text and in our figures to consistently refer to the collections of weights as hidden units and refer to the hidden unit outputs given a sequence input as activations.

(1e) How many hidden units are there?

The final model was trained with 300 hidden units.

(2) It is redundant to say that lipids are both amphiphiles and hydrophobic...amphiphile already means hydrophobic plus hydrophilic. 

This is true, we have edited the manuscript to reflect this.

(3) What does this mean, and what's the point of this remark? "They [lipids] are relatively smaller than other complex biomolecules, such as proteins, thereby allowing a larger portion of their surface to interact with other macromolecules." 

We have removed this sentence.

Reviewer 2 (Author Recommendations):

While the idea of filtering out a part of the sequence data obtained with BLAST makes sense per se, it would be nice if the authors could comment on the nature of the sequences corresponding to the left peak in Figure 3b. It is hypothesised in conclusion that these sequences could lack any catalytic function. Could the authors experimentally check that this is the case or provide further evidence for this hypothesis?

Yes, in this revision we provide further evidence as a new supplementary figure S2. At the time we performed domain analysis of the sequences we excluded; most of these sequences lacked the flippase domain associated with MprF function, and instead were combined with different domains. On this basis we excluded them due to their lack of relevance to the MprF from Streptococcus agalactiae we were interested in. Although there is possibility that some relevant sequences might be excluded, our assessment is that we gained specificity by reducing the set of sequences. 

A key step in the RBM-based approach is the identification of "meaningful" hidden units, i.e. whose values are related to biological function. In Methods, the authors explain how they selected these units based on the L1 norms of the weights and the region of interaction with the lipid. While these criteria are reasonable, I wonder whether they are too stringent. In particular, one could think that regions in the proteins not in direct contact with the lipid could also be important for binding. It is known for instance that the length of loops can affect flexibility and help regulate activity in some catalytic enzymes. So my question is: if one relaxes the criterion about the coordinates of large weight values, what happens? Are other potentially interesting hidden units identified?

We completely agree that other regions of the protein are likely involved in determining enzyme specificity, and that focusing on solely regions which interact with the lipid is perhaps missing important contributions to the catalytic function; we hypothesize that the flippase domain itself and its interaction with the catalytic domain are involved, especially considering the concerted mechanism by which they must operate. We are currently investigating these theories and will be the subject of future work. As an initial step, we present this current work with restricted information that led to concrete predictions. We focused on the lipid binding pocket because it was one of just a few bits of information we had from the start, but as the reviewer suggests, we plan to follow up our research to try to identify other relevant hidden units and domains. 

From a purely machine-learning point of view, it would be good to see more about cross-validation of the model. More precisely, could the authors show the log-likelihood of test set data compared to the one of training sequence data?

We agree this is an important piece of information. We will update our methods section with this information. We performed a parameter sweep to search for the parameter’s we used in our final model, and in that testing with a random 80/20% training/test split we had a training log probability loss of -0.91, and a test loss of -0.98. However, for our final model we used all available data and did not perform a split; the final result did not change dramatically by including the additional data, and the weight structure and composition was consistent with the results presented in the paper.

Reviewer 3 (Public Review):

In many of the analyzed strains, the presence of the lipid species Lys-PG, Lys-Glc-DAG, and Lys-Glc2-DAG is correlated to the presence of the MprF enzyme(s), but one should keep in mind that a multitude of other membrane proteins are present that in theory could be involved in the synthesis as well. Therefore, there is no direct evidence that the MprF enzymes are linked to the synthesis of these lipid species. Although, it is unlikely that other enzymes are involved, this weakens the connection between the observed lipids and the type of MprF. 

While there are a number of proteins found on the membrane that could play a role, we have specifically used a background strain that has a transposon in mprF that makes the bacteria incapable of synthesizing Lys-lipids (Figure 7B) unless complemented back with a functional MprF (Figure 7D-E). This led us to conclude that MprF is responsible for Lys-lipid synthesis.

Related to this, in a few cases MprF activity is tested, but the manuscript does not contain any information on protein expression levels. Heterologous expression of membrane proteins is in general challenging and due to various reasons, proteins end up not being expressed at all. As an example, the absence of activity for the E. faecalis MprF1 and E. faecium MprF2 could very well be explained by the entire absence of the protein.

The genes were expressed on the same plasmid to control for expression. While we did not run a western blot to examine expression levels the plasmid backbone was used as a control for protein expression. Previous research supports E. faecalis MprF1 and E. faecium MprF2 not synthesizing Lys-lipids and instead most likely play a different role in the cell membrane. 

The title is somewhat misleading. The sequence statistics and machine learning categorized the MprFs, but the identification of a novel lipid species was a coincidence while checking/confirming the categorization. 

We believe the title is appropriate given that the identification of Enterococcus dispar was through computational methods that led to the discovery Lys-Glc2-DAG. In other words, the categorization of potential organisms that produce lipids related to MprF has been driven by the proposition from the computational method. We agree, however, that the discovery was unexpected but would not have happened without the suggested organisms coming from the methodology presented here.  

Please read the manuscript one more time to correct textual errors.  

The example of the role of LPS in delivering siRNA to targeted cancer cells is a bit farfetched as LPS is very different from the lipids that are being discussed here. I would rather focus on the role of Lysyl-lipids in antibiotic resistance in the introduction.  

We included LPS here to explain that natural lipids/components of the bacterial cell membrane could be used for drug delivery systems. While it is true LPS is quite different from Lys-lipid compounds, our goal was to create an emphasis on how the bacterial domain is a rich untapped source of lipids that could be used in biotechnology.  In this way we wanted our statement to be more broadly about bacterial lipids and the importance of their continued study for diverse applications like pharmaceuticals.

The MS identification of Lys-Glc2-DAG is convincing, especially in combination with the fragmentation data, but the ion counts suggest low abundance. The observation would be strengthened if the identification of Lysyl-Glc2-DAG with different acyl-chain configurations has been observed. This should be then mentioned or visualized in the manuscript. 

We agree and have added an updated Figure 8A to demonstrate the presence of different acyl-chain configurations in Enterococcus dispar.  

Further analysis of the Enterococcus strains shows the presence of the three lipids Lys-PG, Lys-Glc-DAG, and LysGlc2-DAG, although the Lys-Glc-DAG is only detected in trace amounts. This raises questions on the specificity of the MprF for the substrate Glc-DAG. If the ratio of Glc2-DAG compared to Glc-DAG abundance is similar to the ratio of Lys-Glc2-DAG vs. Lys-Glc-DAG abundance, this would strengthen the observation that the enzyme has equal affinity. However, if there is a rather large amount of Glc-DAG but a small amount of Lys-Glc-DAG, the production of Lys-Glc-DAG might be a side-reaction. 

The reviewer brings a relevant point of discussion, however, a clear resolution might be part of future work as we do not use spike in controls when completing lipid extractions. Because of this, it  it is not possible for us to compare lipid levels across different samples. We now include a note clarifying this in the discussion section.  

The plotting of the MprF sequence variants using the chosen RBM weights reveals a rather complex distribution over the quadrants (Figure 8). It is rather unclear in Figure 8 why only 1 sequence is plotted for Enterococcus faecalis and faecium, while 2 different MprFs are present (and tested) for these two organisms. This should be clarified.  

We agree this can be a source of confusion. We have further clarified this in the text that only the functional alleles were plotted in Figure 8 and that all Enterococcal alleles are plotted in Figure S3 regardless of function.

for - comparison - to - book - The Ascent of Humanity - chapter 8 - The Gaian Birthing - Charles Eisenstein - quote - making sense of the polycrisis - a symptom of multiple phase transitions - (see below) - The 'polycrisis' is real enough. - But it’s a surface level symptom - of multiple, simultaneous phase transitions at the core of the ‘hardware’ and ‘software’ systems that define human civilisation - which together can be understood as a planetary phase shift. - But if all we see and respond to is the polycrisis - the symptoms of this process as it weakens industrial structures - that will derail the planetary phase shift to a new life cycle.

comparison - to - book - The Ascent of Humanity - chapter 8 - The Gaian Birthing - Charles Eisenstein - Ahmed's writing about the polycrisis masking the planetary phase shift is very reminiscent of Charles Eisenstein's writing in the Ascent of Humanity in which he compares the great transition we are undergoing to - the perilous journey a neonate takes as it leaves the womb and enters the greater space awaiting

to - book - The Ascent of Humanity - Chapter 8 - The Gaian Birthing - Charles Eisenstein - https://hyp.is/r8scTpG_Ee-gLTujlli5hQ/charleseisenstein.org/books/the-ascent-of-humanity/eng/the-gaian-birthing/

for - rapid whole system change - Nafeez Ahmed - planetary phase shift - Nafeez Ahmed - planetary adaptive cycle - Nafeez Ahmed - essay - The End of Scarcity? From ‘Polycrisis’ to Planetary Phase Shift - Nafeez Ahmed - 2024 Oct 16 - to - book - The Ascent of Humanity - chapter 8 Self and Cosmos: The Gaian Birthing - stillborn and the perilous journey through the womb - Charles Eisenstein

summary - This is a good article that makes sense of the inflection point that humanity now faces as it contends with multiple existential crisis - It summarizes the complexity of our polycrisis and its precarity and lays the theory for looking at the polycrisis from a different perspective: - as a planetary phase shift towards the potential end of scarcity and the next stage of our species evolution - Through the lens of ecologist Crawford Stanley Holling's lens of the adaptive cycle of ecological population dynamics, - and especially his 2004 paper "From Complex Regions to Complex Worlds" - Nafeez extends Holling's argument that we are undergoing a planetary adaptive cycle in which the back-loop is the dying industrial era. - In this sense, it is reminiscent of the writings of Charles Eisenstein in his book "The Ascent of Humanity", chapter 8: Self and Cosmos:, The Gaian Birth. - Eisenstein uses the the perilous journey of birth through the womb door as a metaphor of the transition we are currently undergoing.

to - paper - From Complex Regions to Complex Worlds - Crawford Stanley Holling - 2004 - https://hyp.is/KYCm2pFrEe-_PEu84xshXw/www.ecologyandsociety.org/vol9/iss1/art11/main.html?ref=ageoftransformation.org - book - The Ascent of Humanity - Chapter 8 - The Gaian Birthing - Charles Eisenstein - https://hyp.is/r8scTpG_Ee-gLTujlli5hQ/charleseisenstein.org/books/the-ascent-of-humanity/eng/the-gaian-birthing/

for - planetary adaptive cycle - 2004 paper - Crawford Stanley Holling - to - paper - From Complex Regions to Complex Worlds - Crawford Stanley Holling - 2004

to - paper - From Complex Regions to Complex Worlds - Crawford Stanley Holling - 2004 - https://hyp.is/KYCm2pFrEe-_PEu84xshXw/www.ecologyandsociety.org/vol9/iss1/art11/main.html?ref=ageoftransformation.org

Text formatting is wrong

Missing its subsection cards

Best methods for mixing: - Same number, same letter - Same number, different letter This is the absolute best method for mixing compatibility for a tonal shift - Same letter, 1 difference in number (+1/-1)

Other Methods (less reliable but still useful at times): - Semitone Shift (+7 number, same letter) - Full tone shift (+/- 2 number, same letter) - Compatible tone shift: -3 number, different letter - Diagonal switch (+/- 1 number, different letter

All this is due to the overlap in notes within the respective scales. Most of this is also dependent on the tracks being played and their notes.

Best video I have seen on the camelot wheel and mixing keys.

上一节内容

Author response:

Reviewer 1:

The role of Fgf signaling in gliogenesis and Foxg1 in neurogenesis is well known. It is not clear if Fgf18 is a direct target of Foxg1.

We agree with the reviewer- Fgf signaling is an established pro-gliogenic pathway (Duong et al 2019) and Foxg1 overexpression is known to promote neurogenesis in cultured neural stem cells (Branacaccio et al 2019). Our study links these two mechanisms, as the Reviewer has summarized: (a) we demonstrate that FOXG1 works via modulating Fgf signaling cell-autonomously within progenitors by regulating the levels of Fgfr3. (b) Loss of Foxg1 in postmitotic neurons results in the upregulation of Fgf ligand expression (possibly via indirect mechanisms) and this non-cell autonomously increases Fgf signaling in progenitors. Our study is entirely performed in vivo.

Proposed revision: We will revise the manuscript to reflect that Fgf18 may be an indirect target of FOXG1 in postmitotic neurons.

Reviewer 2:

It wasn't clear to me why the authors chose postnatal day 14 to examine the effects of Foxg1 deletion at E15 - this is a long time window, giving time for indirect consequences of Foxg1 deletion to influence development and thereby potentially complicating the interpretation of findings. For example, the authors show that there is no increased proliferation of astrocytes or death of neurons lacking Foxg1 shortly after cre-mediated deletion, but it remains formally possible (if perhaps unlikely) that these processes could be affected later during the time window. The rationale underlying the choice of this time point should be explained.

I don't agree with the statement in the very last sentence of the results section that "neurogenesis is not possible in the absence of [Foxg1]" as there are multiple reports in the literature demonstrating the presence of neurons in Foxg1-/- mice (eg: Xuan et al., 1995; Hanashima et al., 2002, Martynoga et al., 2005, Muzio and Mallamaci 2005). Perhaps the statement refers specifically to late-born cortical neurons. This point also arises in the discussion section.

Proposed revisions:

(a) We will revise the manuscript to explain why we chose postnatal day 14 to examine the effects of Foxg1 deletion at E15.

● We have examined the transcriptomic dysregulation after Foxg1 deletion at E17.5, which is a reasonable period to identify potential direct targets. Furthermore, FOXG1 occupies the Fgfr3 locus in ChIP-seq performed at E15.5. Together, these support the interpretation that Fgfr3 is a direct target of Foxg1.

● As the Reviewer notes, we have investigated the possibility of increased proliferation of astrocytes and death of neurons and found no evidence that suggests these phenomena occur in the 3 days after loss of Foxg1. Cortical neurons are postmitotic and differentiated by E18.5, the stage at which we examined CC3 staining and found no difference in cell death in control and mutants (Supplementary Figure S2C, C’). The majority of progenitors (PAX6+ve cells) that lose Foxg1 at E15.5 express the gliogenic transcription factor NFIA by E18.5 (Figure 2C, C’), but hardly any express intermediate (neurogenic) progenitor marker TBR2 (Supplementary Figure S2B, B’). It is therefore unlikely that neurons are born from Foxg1 mutant progenitors and then die at a later stage.

● The cellular consequences of loss of Foxg1 require additional time to detect e.g. it takes ~ 5 days for GFAP to be detected in astrocytes once they are born. The P14 timepoint permits the assessment of oligogenesis which begins after astrogliogenesis and therefore permits a comprehensive assessment of the lineage of E15.5 Foxg1 null progenitors.

(b) Thank you for pointing out that the last sentence of the results section implied (incorrectly) that ALL neurogenesis is not possible in the absence of Foxg1 We will modify this (and the discussion) to reflect that this applies to E14/15 progenitors and late-born cortical neurons.

eLife Assessment

This important study provides convincing evidence that developing neurons in the neocortex regulate glial cell development. The data demonstrates that the transcription factor FOXG1 negatively regulates gliogenesis by controlling the expression of a member of the FGF ligand family and by suppressing the receptor for this ligand in developing neurons. This study leads to a new understanding of the cascade of events regulating the timing of glial development in the neocortex.

Reviewer #1 (Public review):

Summary:

In this paper, Bose et al. investigated the role of Foxg1 transcription factor in the progenitors at late stages of cerebral cortex development.<br /> They discover that Foxg1 is a repressor of gliogenesis and has a dual function, first as a repressor of Fgfr3 receptor in progenitors, and second as a suppressor of the Fgf ligands in young neurons.

They found that the inactivation of Foxg1 in cortical progenitors causes premature astrogliogenesis at the expense of neurogenesis. They identify Fgfr3 as a novel FOXG1 target. They show that suppression of Fgfr3 by FOXG1 in progenitors is required to maintain neurogenesis. On the other hand, they also show that FOXG1 negatively regulates the expression of Fgf gliogenic secreted factors in young neurons suppressing gliogenesis cells extrinsically.

Strengths:

The authors used time-consuming in vivo experiments utilizing several mouse strains including Foxg1-MADM in combination with RNA-Seq and ChIP to convincingly show that Foxg1 acts upstream of FGF signalling in the control of gliogenesis onset. The conclusions of this paper are mostly well supported by data.

Weaknesses:

The role of Fgf signaling in gliogenesis and Foxg1 in neurogenesis is well known. It is not clear if Fgf18 is a direct target of Foxg1.

Reviewer #2 (Public review):

Summary:

We have known for some time that neural progenitors in the cerebral cortex switch their output from cortical neurons to glia at late embryonic stages, however little is known about how this switch is regulated at the molecular level. Bose et al present a convincing set of findings, demonstrating that the transcription factor Foxg1 plays a key role in this process, mediated through FGF signalling. Foxg1 cell-autonomously inhibits gliogenesis in progenitor cells (thereby promoting neuronal identity), and lower Foxg1 expression in postnatal neurons leads to increased expression of FGF ligand, promoting glial development from nearby progenitors.

Strengths:

The study is very well designed, having a systematic, thorough, and logical approach. The data is convincing. The authors make full use of a range of existing transgenic strains, published 'omics data, and elegant genetic approaches such as MADM. This combination of approaches is particularly rigorous, lending significant weight to the study. The manuscript is well-written, clear, and easy to follow.

Weaknesses:

It wasn't clear to me why the authors chose postnatal day 14 to examine the effects of Foxg1 deletion at E15 - this is a long time window, giving time for indirect consequences of Foxg1 deletion to influence development and thereby potentially complicating the interpretation of findings. For example, the authors show that there is no increased proliferation of astrocytes or death of neurons lacking Foxg1 shortly after cre-mediated deletion, but it remains formally possible (if perhaps unlikely) that these processes could be affected later during the time window. The rationale underlying the choice of this time point should be explained.

I don't agree with the statement in the very last sentence of the results section that "neurogenesis is not possible in the absence of [Foxg1]" as there are multiple reports in the literature demonstrating the presence of neurons in Foxg1-/- mice (eg: Xuan et al., 1995; Hanashima et al., 2002, Martynoga et al., 2005, Muzio and Mallamaci 2005). Perhaps the statement refers specifically to late-born cortical neurons. This point also arises in the discussion section.

Impact

This manuscript identifies a previously unknown role for Foxg1 in forebrain development and a mechanism underlying the neurogenic-to-gliogenic switch that occurs at late embryonic stages of cortex development. These findings will stimulate further research to uncover more details of how this important switch is controlled and may provide useful insight into some of the symptoms experienced by children with FOXG1 Syndrome.

eLife Assessment

This manuscript establishes a mathematical model to estimate the key parameters that control the repopulation of planarian stem cells after sublethal irradiation as they undergo fate-switching as part of their differentiation and self-renewal process. The findings are valuable for future investigation of stem cell division in planarians. The methods are solid, integrating modeling with perturbations of key transcription factors known to be critical for cell fate decisions, but the authors have only shown that this is the case for a small number of stem cell types.

Reviewer #1 (Public review):

Summary:

This is a very creative study using modeling and measurement of neoblast dynamics to gain insight into the mechanism that allows these highly potent cells to undergo fate-switching as part of their differentiation and self-renewal process. The authors estimate growth equation parameters for expanding neoblast clones based on new and prior experimental observations. These results indicate neoblast likely undergo much more symmetric self-amplifying division than loss of the population through symmetric differentiation, in the case of clone expansion assays after sublethal irradiation. Neoblasts take on multiple distinct transcriptional fates related to their terminally differentiated cell types, and prior work indicated neoblasts have a high plasticity to switch fates in a way linked to cell cycle progression and possibly through a random process. Here, the authors explore the impact of inhibition of key transcription factors defining such states (ie "fate specifying transcription factors", FSTFs) plus measurement and modeling in the clone expansion assay, to find that inhibition of factors like zfp1 likely cause otherwise zfp1-fated neoblasts to fail to proliferate and differentiation without causing compensatory gains in other lineages. A mathematical model of this process assuming that neoblasts do not retain a memory of prior states while they proliferate, and transition across specified states can mimic the experimentally determined decreased sizes of clones following inhibition of zfp1. Complementary approaches to inhibit more than one lineage (muscle plus intestine) supports the idea that this is a more general process in planarian stem cells. These results provide an important advance for understanding the fate-switching process and its relationship to neoblast growth.

Overall I find the evidence very well presented and the study compelling. It offers an important new perspective on the key properties of neoblasts. I do have some comments to clarify the presentation and significance of the work.

Reviewer #2 (Public review):

Summary:

Cell cycle duration and cell fate choice are critical to understanding the cellular plasticity of neoblasts in planarians. In this study, Tamar et al. integrated experimental and computational approaches to simulate a model for neoblast behaviors during colony expansion.

Strengths:

The finding that "arresting differentiation into specific lineages disrupts neoblast proliferative capacities without inducing compensatory expression of other lineages" is particularly intriguing. This concept could inspire further studies on pluripotent stem cells and their application for regenerative biology.

Weaknesses:

However, the absence of a cell-cell feedback mechanism during colony growth and the likelihood of the difference needs to be clarified. Is there any difference in interpreting the results if this mechanism is considered? More explanation and discussion should be included to distinguish the stages controlled by the one-step model from those discussed in this study. Although hnf-4 and foxF have been silenced together to validate the model, a deeper understanding of the tgs-1+ cell type and the non-significant reduction of tgs-1+ neoblasts in zfp-1 RNAi colonies is necessary, considering a high neural lineage frequency.

Author response:

Reviewer #1:

Overall I find the evidence very well presented and the study compelling. It offers an important new perspective on the key properties of neoblasts. I do have some comments to clarify the presentation and significance of the work.

We thank the reviewer for the positive feedback and plan to improve the presentation of the work.

Reviewer #2:

However, the absence of a cell-cell feedback mechanism during colony growth and the likelihood of the difference needs to be clarified. Is there any difference in interpreting the results if this mechanism is considered?

We will improve the description of the model assumptions and the interpretation of the data on the basis of these assumptions.

Although hnf-4 and foxF have been silenced together to validate the model, a deeper understanding of the tgs-1+ cell type and the non-significant reduction of tgs-1+ neoblasts in zfp-1 RNAi colonies is necessary, considering a high neural lineage frequency.

We will improve the analysis of this result in light of the experimentally determined frequency of the tgs-1+ neoblast population.

eLife Assessment

This important work attempts to establish a causal link between neurotrophin signaling and experience-induced structural plasticity in dopaminergic circuits in the adult fly brain, a topic of broad interest to the neuroscience community. While the authors provide solid evidence for the role of this signaling in regulating the structure and synapses of dopaminergic circuits, the evidence for a direct link between neurotrophin signaling and experience-induced structural plasticity remains incomplete.

Reviewer #1 (Public review):

Summary:

Sun et al. are interested in how experience can shape the brain and specifically investigate the plasticity of the Toll-6 receptor-expressing dopaminergic neurons (DANs). To learn more about the role of Toll-6 in the DANs, the authors examine the expression of the Toll-6 receptor ligand, DNT-2. They show that DNT-2 expressing cells connect with DANs and that loss of function of DNT-2 in these cells reduces the number of PAM DANs, while overexpression causes alterations in dendrite complexity. Finally, the authors show that alterations in the levels of DNT-2 and Toll-6 can impact DAN-driven behaviors such as climbing, arena locomotion, and learning and long-term memory.

Strengths:

The authors methodically test which neurotransmitters are expressed by the 4 prominent DNT-2 expressing neurons and show that they are glutamatergic. They also use Trans-Tango and Bac-TRACE to examine the connectivity of the DNT-2 neurons to the dopaminergic circuit and show that DNT-2 neurons receive dopaminergic inputs and output to a variety of neurons including MB Kenyon cells, DAL neurons, and possibly DANS.

Weaknesses:

(1) To identify the DNT-2 neurons, the authors use CRISPR to generate a new DN2-GAL4. They note that they identified at least 12 DNT-2 plus neurons. In Supplementary Figure 1A, the DNT-2-GAL4 driver was used to express a UAS-histoneYFP nuclear marker. From these figures, it looks like DNT-2-GAL4 is labeling more than 12 neurons. Is there glial expression?

(2) In Figure 2C the authors show that DNT-2 upregulation leads to an increase in TH levels using q-RT-PCR from whole heads. However, in Figure 3H they also show that DNT-2 overexpression also causes an increase in the number of TH neurons. It is unclear whether TH RNA increases due to expression/cell or the number of TH neurons in the head.

(3) DNT-2 is also known as Spz5 and has been shown to activate Toll-6 receptors in glia (McLaughlin et al., 2019), resulting in the phagocytosis of apoptotic neurons. In addition, the knockdown of DNT-2/Spz5 throughout development causes an increase in apoptotic debris in the brain, which can lead to neurodegeneration. Indeed Figure 3H shows that an adult-specific knockdown of DNT-2 using DNT2-GAL4 causes an increase in Dcp1 signal in many neurons and not just TH neurons.

Reviewer #2 (Public review):

This paper examines how structural plasticity in neural circuits, particularly in dopaminergic systems, is regulated by Drosophila neurotrophin-2 (DNT-2) and its receptors, Toll-6 and Kek-6. The authors show that these molecules are critical for modulating circuit structure and dopaminergic neuron survival, synaptogenesis, and connectivity. They show that loss of DNT-2 or Toll-6 function leads to loss of dopaminergic neurons, dendritic arborization, and synaptic impairment, whereas overexpression of DNT-2 increases dendritic complexity and synaptogenesis. In addition, DNT-2 and Toll-6 modulate dopamine-dependent behaviors, including locomotion and long-term memory, suggesting a link between DNT-2 signaling, structural plasticity, and behavior.

A major strength of this study is the impressive cellular resolution achieved. By focusing on specific dopaminergic neurons, such as the PAM and PPL1 clusters, and using a range of molecular markers, the authors were able to clearly visualize intricate details of synapse formation, dendritic complexity, and axonal targeting within defined circuits. Given the critical role of dopaminergic pathways in learning and memory, this approach provides a good opportunity to explore the role of DNT-2, Toll-6, and Kek-6 in experience-dependent structural plasticity. However, despite the promise in the abstract and introduction of the paper, the study falls short of establishing a direct causal link between neurotrophin signaling and experience-induced plasticity.

Simply put, this study does not provide strong evidence that experience-induced structural plasticity requires DNT-2 signaling. To support this idea, it would be necessary to observe experience-induced structural changes and demonstrate that downregulation of DNT-2 signaling prevents these changes. The closest attempt to address this in this study was the artificial activation of DNT-2 neurons using TrpA1, which resulted in overgrowth of axonal arbors and an increase in synaptic sites in both DNT-2 and PAM neurons. However, this activation method is quite artificial, and the authors did not test whether the observed structural changes were dependent on DNT-2 signaling. Although they also showed that overexpression of DNT-2FL in DNT-2 neurons promotes synaptogenesis, this phenotype was not fully consistent with the TrpA1 activation results (Figures 5C and D).

In conclusion, this study demonstrates that DNT-2 and its receptors play a role in regulating the structure of dopaminergic circuits in the adult fly brain. However, it does not provide convincing evidence for a causal link between DNT-2 signaling and experience-dependent structural plasticity within these circuits.

Reviewer #3 (Public review):

Summary:

The authors used the model organism Drosophila melanogaster to show that the neurotrophin Toll-6 and its ligands, DNT-2 and kek-6, play a role in maintaining the number of dopaminergic neurons and modulating their synaptic connectivity. This supports previous findings on the structural plasticity of dopaminergic neurons and suggests a molecular mechanism underlying this plasticity.

Strengths:

The experiments are overall very well designed and conclusive. Methods are in general state-of-the-art, the sample sizes are sufficient, the statistical analyses are sound, and all necessary controls are in place. The data interpretation is straightforward, and the relevant literature is taken into consideration. Overall, the manuscript is solid and presents novel, interesting, and important findings.

Weaknesses:

There are three technical weaknesses that could perhaps be improved.

First, the model of reciprocal, inhibitory feedback loops (Figure 2F) is speculative. On the one hand, glutamate can act in flies as an excitatory or inhibitory transmitter (line 157), and either situation can be the case here. On the other hand, it is not clear how an increase or decrease in cAMP level translates into transmitter release. One can only conclude that two types of neurons potentially influence each other.

Second, the quantification of bouton volumes (no y-axis label in Figure 5 C and D!) and dendrite complexity are not convincingly laid out. Here, the reader expects fine-grained anatomical characterizations of the structures under investigation, and a method to precisely quantify the lengths and branching patterns of individual dendritic arborizations as well as the volume of individual axonal boutons.

Third, Figure 1C shows two neurons with the goal of demonstrating between-neuron variability. It is not convincingly demonstrated that the two neurons are actually of the very same type of neuron in different flies or two completely different neurons.

Author response:

Reviewer #1 (Public review):

Summary:

Sun et al. are interested in how experience can shape the brain and specifically investigate the plasticity of the Toll-6 receptor-expressing dopaminergic neurons (DANs). To learn more about the role of Toll-6 in the DANs, the authors examine the expression of the Toll-6 receptor ligand, DNT-2. They show that DNT-2 expressing cells connect with DANs and that loss of function of DNT-2 in these cells reduces the number of PAM DANs, while overexpression causes alterations in dendrite complexity. Finally, the authors show that alterations in the levels of DNT-2 and Toll-6 can impact DAN-driven behaviors such as climbing, arena locomotion, and learning and long-term memory.

Strengths:

The authors methodically test which neurotransmitters are expressed by the 4 prominent DNT-2 expressing neurons and show that they are glutamatergic. They also use Trans-Tango and Bac-TRACE to examine the connectivity of the DNT-2 neurons to the dopaminergic circuit and show that DNT-2 neurons receive dopaminergic inputs and output to a variety of neurons including MB Kenyon cells, DAL neurons, and possibly DANS.

We are very pleased that Reviewer 1 found our connectivity analysis a strength.

Weaknesses:

(1) To identify the DNT-2 neurons, the authors use CRISPR to generate a new DN2-GAL4. They note that they identified at least 12 DNT-2 plus neurons. In Supplementary Figure 1A, the DNT-2-GAL4 driver was used to express a UAS-histoneYFP nuclear marker. From these figures, it looks like DNT-2-GAL4 is labeling more than 12 neurons. Is there glial expression?

Indeed, we claimed that DNT-2 is expressed in at least 12 neurons (see line 141, page 6 of original manuscript), which means more than 12 could be found. The membrane tethered reporters we used – UAS-FlyBow1.1, UASmcD8-RFP, UAS-MCFO, as well as UAS-DenMark:UASsyd-1GFP – gave a consistent and reproducible pattern. However, with DNT-2GAL4>UAS-Histone-YFP more nuclei were detected that were not revealed by the other reporters. We have found also with other GAL4 lines that the patterns produced by different reporters can vary. This could be due to the signal strength (eg His-YFP is very strong) and perdurance of the reporter (e.g. the turnover of His-YFP may be slower than that of the other fusion proteins).

We did not test for glial expression, as it was not directly related to the question addressed in this work.

(2) In Figure 2C the authors show that DNT-2 upregulation leads to an increase in TH levels using q-RT-PCR from whole heads. However, in Figure 3H they also show that DNT-2 overexpression also causes an increase in the number of TH neurons. It is unclear whether TH RNA increases due to expression/cell or the number of TH neurons in the head.

Figure 3H shows that over-expression of DNT-2 FL increased the number of Dcp1+ apoptotic cells in the brain, but not significantly (p=0.0939). The ability of full-length neurotrophins to induce apoptosis and cleaved neurotrophins promote cell survival is well documented in mammals. We had previously shown that DNT-2 is naturally cleaved, and that over-expression of DNT-2 does not induce apoptosis in the various contexts tested before (McIlroy et al 2013 Nature Neuroscience; Foldi et al 2017 J Cell Biol; Ulian-Benitez et al 2017 PLoS Genetics). Similarly, throughout this work we did not find DNT-2FL to induce apoptosis.

Instead, in Figure 3G we show that over-expression of DNT-2FL causes a mild yet statistically significant increase in the number of TH+ cells. This is an important finding that supports the plastic regulation of PAM cell number. We thank the Reviewer for highlighting this point, as we had forgotten to add the significance star in the graph. In this context, we cannot rule out the possibility that the increase in TH mRNA observed when we over-express DNT-2FL could not be due to an increase in cell number instead. Unfortunately, it is not possible for us to separate these two processes at this time. Either way, the result would still be the same: an increase in dopamine production when DNT-2 levels rise.

(3) DNT-2 is also known as Spz5 and has been shown to activate Toll-6 receptors in glia (McLaughlin et al., 2019), resulting in the phagocytosis of apoptotic neurons. In addition, the knockdown of DNT-2/Spz5 throughout development causes an increase in apoptotic debris in the brain, which can lead to neurodegeneration. Indeed Figure 3H shows that an adult-specific knockdown of DNT-2 using DNT2-GAL4 causes an increase in Dcp1 signal in many neurons and not just TH neurons.

Indeed, we did find Dcp1+ cells in TH-negative cells too (although not widely throughout the brain). This is not surprising, as DNT-2 neurons have large arborisations that can reach a wide range of targets; DNT-2 is secreted, and could reach beyond its immediate targets; Toll-6 is expressed in a vast number of cells in the brain; DNT-2 can bind promiscuously at least also Toll-7 and other Keks, which are also expressed in the adult brain (Foldi et al 2017 J Cell Biology; Ulian-Benitez et al 2017 PLoS Genetics; Li et al 2020 eLife). Together with the findings by McLaughlin et al 2019, our findings further support the notion that DNT-2 is a neuroprotective factor in the adult brain. It will be interesting to find out what other neuron types DNT-2 maintains.

We would like to thank Reviewer 1 for their positive comments on our work and their interesting and valuable feedback.

Reviewer #2 (Public review):

This paper examines how structural plasticity in neural circuits, particularly in dopaminergic systems, is regulated by Drosophila neurotrophin-2 (DNT-2) and its receptors, Toll-6 and Kek-6. The authors show that these molecules are critical for modulating circuit structure and dopaminergic neuron survival, synaptogenesis, and connectivity. They show that loss of DNT-2 or Toll-6 function leads to loss of dopaminergic neurons, dendritic arborization, and synaptic impairment, whereas overexpression of DNT-2 increases dendritic complexity and synaptogenesis. In addition, DNT-2 and Toll-6 modulate dopamine-dependent behaviors, including locomotion and long-term memory, suggesting a link between DNT-2 signaling, structural plasticity, and behavior.

A major strength of this study is the impressive cellular resolution achieved. By focusing on specific dopaminergic neurons, such as the PAM and PPL1 clusters, and using a range of molecular markers, the authors were able to clearly visualize intricate details of synapse formation, dendritic complexity, and axonal targeting within defined circuits. Given the critical role of dopaminergic pathways in learning and memory, this approach provides a good opportunity to explore the role of DNT-2, Toll-6, and Kek-6 in experience-dependent structural plasticity. However, despite the promise in the abstract and introduction of the paper, the study falls short of establishing a direct causal link between neurotrophin signaling and experience-induced plasticity.

Simply put, this study does not provide strong evidence that experience-induced structural plasticity requires DNT-2 signaling. To support this idea, it would be necessary to observe experience-induced structural changes and demonstrate that downregulation of DNT-2 signaling prevents these changes. The closest attempt to address this in this study was the artificial activation of DNT-2 neurons using TrpA1, which resulted in overgrowth of axonal arbors and an increase in synaptic sites in both DNT-2 and PAM neurons. However, this activation method is quite artificial, and the authors did not test whether the observed structural changes were dependent on DNT-2 signaling. Although they also showed that overexpression of DNT-2FL in DNT-2 neurons promotes synaptogenesis, this phenotype was not fully consistent with the TrpA1 activation results (Figures 5C and D).

In conclusion, this study demonstrates that DNT-2 and its receptors play a role in regulating the structure of dopaminergic circuits in the adult fly brain. However, it does not provide convincing evidence for a causal link between DNT-2 signaling and experience-dependent structural plasticity within these circuits.

We would like to thank Reviewer 2 for their very positive assessment of our approach to investigate structural circuit plasticity. We are delighted that this Reviewer found our cellular resolution impressive. We are also very pleased that Reviewer 2 found that our work demonstrates that DNT-2 and its receptors regulate the structure of dopaminergic circuits in the adult fly brain. This is already a very important finding that contributes to demonstrating that, rather than being hardwired, the adult fly brain is plastic, like the mammalian brain.

We are very pleased that this Reviewer acknowledges that this work provides a good opportunity to explore the role of DNT-2, Toll-6, and Kek-6 in experience-dependent structural plasticity. We provide a molecular mechanism and proof of principle, and we demonstrate a direct link between the function of DNT-2 and its receptors in circuit plasticity, and a suggestive link to neuronal activity. Finding out the direct link to lived experience is a big task, beyond the scope of this manuscript, and we will be testing this with future projects. Nevertheless, it is important to place our findings within this context, as it opens opportunities for discovery by the neuroscience community.

We would like to thank Reviewer 2 for the positive and thoughtful evaluation of our work, and for their feedback.

Reviewer #3 (Public review):

Summary:

The authors used the model organism Drosophila melanogaster to show that the neurotrophin Toll-6 and its ligands, DNT-2 and kek-6, play a role in maintaining the number of dopaminergic neurons and modulating their synaptic connectivity. This supports previous findings on the structural plasticity of dopaminergic neurons and suggests a molecular mechanism underlying this plasticity.

Strengths:

The experiments are overall very well designed and conclusive. Methods are in general state-of-the-art, the sample sizes are sufficient, the statistical analyses are sound, and all necessary controls are in place. The data interpretation is straightforward, and the relevant literature is taken into consideration. Overall, the manuscript is solid and presents novel, interesting, and important findings.

We are delighted that Reviewer 3 found our work solid, novel, interesting and with important findings. We are also very pleased that this Reviewer found that all necessary controls have been carried out.

Weaknesses:

There are three technical weaknesses that could perhaps be improved.

First, the model of reciprocal, inhibitory feedback loops (Figure 2F) is speculative. On the one hand, glutamate can act in flies as an excitatory or inhibitory transmitter (line 157), and either situation can be the case here. On the other hand, it is not clear how an increase or decrease in cAMP level translates into transmitter release. One can only conclude that two types of neurons potentially influence each other.

Thank you for pointing out that glutamate can be inhibitory. In mammals, the neurotrophin BDNF has an important function in glutamatergic synapses, thus we were intrigued by a potential evolutionary conservation. Our evidence that DNT-2A neurons could be excitatory is indirect, yet supportive: exciting DNT-2 neurons with optogenetics resulted in an increase in GCaMP in PAMs (data not shown); over-expression of DNT-2 in DNT-2 neurons increased TH mRNA levels; optogenetic activation of DNT-2 neurons results in the Dop2R-dependent downregulation of cAMP levels in DNT-2 neurons. Dop2R signals in response to dopamine, which would be released only if dopaminergic neurons had been excited. Accordingly, glutamate released from DNT-2 neurons would have been rather unlikely to inhibit DANs.

cAMP is a second messenger that enables the activation of PKA. PKA phosphorylates many target proteins, amongst which are various channels. This includes the voltage gated calcium channels located at the synapse, whose phosphorylation increases their opening probability. Thus, a rise in cAMP could facilitate neurotransmitter release, and a downregulation would have the opposite effect. Other targets of PKA include CREB, leading to changes in gene expression. Conceivably, a decrease in PKA activity could result in the downregulation of DNT-2 expression in DNT-2 neurons. This negative feedback loop would restore the homeostatic relationship between DNT-2 and dopamine levels.

Our data indeed demonstrate that DNT-2 and PAM neurons influence each other, not potentially, but really. We have provided data that: DNT-2 and PAMs are connected through circuitry; that the DNT-2 receptors Toll-6 and kek-6 are expressed in DANs, including in PAMs; that alterations in the levels of DNT-2 (both loss and gain of function) and loss of function for the DNT-2 receptors Toll-6 and Kek-6 alter PAM cell number, alter PAM dendritic complexity and alter synaptogenesis in PAMs; alterations in the levels of DNT-2, Toll-6 and kek-6 in adult flies alters dopamine dependent behaviours of climbing, locomotion in an arena and learning and long-term memory. These data firmly demonstrate that the two neuron types DNT-2 and PAMs influence each other.

We have also shown that over-expression of DNT-2 in DNT-2 neurons increases TH mRNA levels, whereas activation of DNT-2 neurons decreases cAMP levels in DNT-2 neurons in a dopamine/Dop2R-dependent manner. These data show a functional interaction between DNT-2 and PAM neurons.

Second, the quantification of bouton volumes (no y-axis label in Figure 5 C and D!) and dendrite complexity are not convincingly laid out. Here, the reader expects fine-grained anatomical characterizations of the structures under investigation, and a method to precisely quantify the lengths and branching patterns of individual dendritic arborizations as well as the volume of individual axonal boutons.

Figure 5C, D do contain Y-axis labels, all our graphs in main manuscript and in supplementary files contain Y-axis labels.

In fact, we did use a method to precisely quantify the lengths and branching patterns of individual dendritic arborisations, volume of individual boutons and bouton counting. These analyses were carried out using Imaris software. For dendritic branching patterns, the “Filament Autodetect” function was used. Here, dendrites were analysed by tracing semi-automatically each dendrite branch (ie manual correction of segmentation errors) to reconstruct the segmented dendrite in volume. From this segmented dendrite, Imaris provides measurements of total dendrite volume, number and length of dendrite branches, terminal points, etc. For bouton size and number, we used the Imaris “Spot” function. Here, a threshold is set to exclude small dots (eg of background) that do not correspond to synapses/boutons. All samples and genotypes are treated with the same threshold, thus the analysis is objective and large sample sizes can be analysed effectively. We have already provided a description of the use of Imaris in the methods section.

Third, Figure 1C shows two neurons with the goal of demonstrating between-neuron variability. It is not convincingly demonstrated that the two neurons are actually of the very same type of neuron in different flies or two completely different neurons.

We thank Reviewer 3 for raising this interesting point. It is not possible to prove which of the four DNT-2A neurons per hemibrain, which we visualised with DNT-2>MCFO, were the same neurons in every individual brain we looked at. This is because in every brain we have looked at, the soma of the neurons were not located in exactly the same location. Furthermore, the arborisation patterns are also different and unique, for each individual brain. Thus, there is natural variability in the position of the soma and in the arborisation patterns. Such variability presumably results from the combination of developmental and activity-dependent plasticity.

We would like to thank Reviewer 3 for the very positive evaluation of our work and the interesting and valuable feedback.

Author response:

Public Reviews: 

Reviewer #1 (Public review): 

Summary: 

Here the authors present their evidence linking the mitochondrial uniporter (MCU-1) and olfactory adaptation in C. elegans. They clearly demonstrate a behavioral defect of mcu-1 mutants in adaptation over 60 minutes and present evidence that this gene functions in the AWC primary sensory neurons at, or close to, the time of adaptation. 

Strengths: 

The paper is very well organized and their approach to unpacking the role of mcu-1 mutants in olfactory adaptation is very reasonable. The authors lean into diverse techniques including behavior, genetics, and pharmacological manipulation in order to flesh out their model for how MCU-1 functions in AWC neurons with respect to olfaction. 

Weaknesses: 

I would like to see the authors strengthen the link between mitochondrial calcium and olfactory adaptation. The authors present some gCaMP data in Figure 5 but it is unclear to me why this tool is not better utilized to explore the mechanism of MCU-1 activity. I think this is very important as the title of the paper states that "mitochondrial calcium modulates.." behavior in AWC and so it would be nice to see more evidence to support this direct connection. I would also like to see the authors place their findings into a model based on previous findings and perhaps examine whether mcu-1 is required for EGL-4 nuclear translocation, which would be straightforward to examine. 

We agree that observing calcium levels inside the mitochondria would conclusively demonstrate that mitochondria calcium directly impacts neuropeptide secretion and behavior. We will try to do this with a mitochondrially targeted calcium indicator. We will also better integrate our findings to existing models in the literature, such as EGL-4 nuclear localization in AWC in response to prolonged odor exposure. Thank you for your comments.

Reviewer #2 (Public review): 

Summary: 

In their manuscript, "Mitochondrial calcium modulates odor-mediated behavioural plasticity in C. elegans", Lee et al. aim to link a mitochondrial calcium transporter to higher-order neuronal functions that mediate memory and aversive learning behaviours. The authors characterise the role of the mitochondrial calcium uniporter, and a specific subunit of this complex, MCU-1, within a single chemosensory neuron (AWCOFF) during aversive odor learning in the nematode. By genetically manipulating mcu-1 as well as using pharmacological activators and blockers of MCU activity, the study presents compelling evidence that the activity of this individual mitochondrial ion transporter in AWCOFF is sufficient to drive animal behaviour through aversive memory formation. The authors show that perturbations to mcu-1 and MCU activity prevent aversive learning to several chemical odors associated with food absence. The authors propose a model, experimentally validated at several steps, whereby an increase in MCU activity during odor conditioning stimulates mitochondrial calcium influx and an increase in mitochondrial reactive oxygen species (mtROS) production, triggering the release of the neuropeptide NLP-1 from AWC, all of which are required to mediate future avoidance behaviour of the chemical odor. 

Strengths: 

Overall, the authors provided robust evidence that mitochondrial function, mediated through MCU activity, contributes to behavioural plasticity. They also demonstrated that ectopic MCU activation or mtROS during odor exposure could accelerate learning. This is quite profound, as it highlights the importance of mitochondrial function in complex neuronal processes beyond their general roles in the development and maintenance of neurons through energy homeostasis and biosynthesis, amongst their other cell-non-specific roles. 

Weaknesses: 

While the manuscript is generally robust, there are some concerns that should be addressed to improve the strength of the proposed model: 

(1) Throughout the manuscript, it is implied that MCU activation caused by odor conditioning changes mitochondrial calcium levels. However, there is no direct experimental evidence of this. For example, the authors write on p.10 "This shows that H2O2 production occurs downstream of MCU activation and calcium influx into the mitochondria", and on p. 11, the statement that prolonged exposure to odors causes calcium influx. Because this is a key element of the proposed model, experimental evidence would be required to support it. 

We are planning to measure mitochondrial calcium levels directly by using a mitochondrially targeted calcium indicator. We agree that this is a key element of our model.

(2) Some controls missing, e.g. a heat-shock-only control in WT and mcu-1 (non-transgenic) background in Figure 1h is required to ensure the heat-shock stress does not interfere with odor learning. 

We will conduct the experiments again with necessary controls.

(3) Lee et al propose that mcu-1 is required at the adult stage to accomplish odor learning because inducing mcu-1 expression at larval stages did not rescue the phenotype of mcu-1 mutants during adulthood. However, the requirement of MCU for odor learning was narrowed down to a 15' window at the end of odor conditioning (Figure 5c). Is it possible that MCU-1 protein levels decline after larval induction so that MCU-1 is no longer present during adulthood when odor conditioning is performed? 

Yes, we also noted that the early induction of MCU-1 is not effective to restore learning, and hypothesized that MCU-1 protein may be subject to high turnover. It may be that MCU-1 induced during larval stages no longer exist by the time odor conditioning is performed, although we have not confirmed this. We had a brief sentence noting this in the discussion section, but we will discuss this a little further in the revision. Thank you.

(4) There is a limited learning effect observable after 30 minutes, and a very pronounced effect in all animals after 90 minutes. The authors very carefully dissect the learning mechanism at 60 minutes of exposure and distinguish processes that are relevant at 60 minutes from those important at 30 minutes. Some explanation or speculation as to why the processes crucial at the 60-minute mark are redundant at 90 minutes of exposure would be important. 

I think this is in line with Reviewer #1’s comments that we should discuss our findings more in relation to existing models in the literature. We will do this in our revision.

(5) Given the presumably ubiquitous function of mcu-1/MCU in mitochondrial calcium homeostasis, it is remarkable that its perturbation impacts only a very specific neuronal process in AWC at a very specific time. The authors should elaborate on this surprising aspect of their discovery in the discussion. 

We will discuss the implication further in our revised manuscript.

(6) Associated with the above comment, it remains possible that mcu-1 is required in coelomocytes for their ability to absorb NLP-1::Venus (Figure 3B), and the AWC-specific role of mcu-1 for this phenotype should be determined. 

To confirm that mcu-1 is not required for coelomocyte uptake, we can stimulate NLP-1:Venus secretion in mcu-1 worms by adding H2O2, then observe whether Venus is observed in the coelomocytes. We will include this in our revised manuscript. Thank you for your comments.

Reviewer #3 (Public review): 

Summary: 

This manuscript reports a role for the mitochondrial calcium uniporter gene (mcu-1) in regulating associative learning behavior in C. elegans. This regulation occurs by mcu-1-dependent secretion of the neuropeptide NLP-1 from the sensory neuron AWC. The authors report a post-developmental role for mcu-1 in AWC to promote learning. The authors further show that odor conditioning leads to increases in NLP-1 secretion from AWC, and that interfering with mcu-1 function reduces NLP-1 secretion. Finally, the authors show that NLP-1 secretion increases when ROS levels in AWC are genetically or pharmacologically elevated. The authors propose that mitochondrial calcium entry through MCU-1 in response to odor conditioning leads to the generation of ROS and the subsequent increase in neuropeptide secretion to promote conditioned behavior. 

Strengths: 

(1) The authors show convincingly that genetically or pharmacologically manipulating MCU function impacts chemotaxis in a conditioned learning paradigm. 

(2) The demonstration that the secretion of a specific neuropeptide can be up-regulated by MCU, ROS and odor conditioning is an important and interesting advance that addresses mechanisms by which neuropeptide secretion can be regulated in vivo. 

Weaknesses: 

(1) The authors conclusion that mcu-1 functions in the AWC-on neuron is not adequately supported by their rescue experiments. The promoter they use for rescue drives expression in a number of additional neurons including AWC-on, that themselves are implicated in adaptation, leaving open the possibility that mcu-1 may function non-autonomously instead of autonomously in AWC to regulate this behavior. 

We recognized this as well, and we now have a promoter construct more specific to AWCON (str-2). Using this more specific promoter, we will confirm that the role of mcu-1 is indeed AWCON-specific in our revised manuscript.

(2) The authors conclude MCU promotes neuropeptide release from AWC by controlling calcium entry into mitochondria, but they did not directly examine the effects of altered MCU function on calcium dynamics either in mitochondria or in the soma, even though they conducted calcium imaging experiments in AWC of wild type animals. Examination of calcium entry in mitochondria would be a direct test of their model.

We agree. As we stated above for reviewer #1 and #2, we will include results from the mitochondrial calcium data in our revised manuscript.

(3) The authors' conclusion that mitochondrial-derived ROS produced by MCU activation drives neuropeptide release does not appear to be experimentally supported. A major weakness of this paper is that experiments addressing whether mcu-1 activity indeed produces ROS are not included, leaving unanswered the question of whether MCU is the endogenous source of ROS that drives neuropeptide secretion.

We can confirm this using mitochondrially targeted redox indicator roGFP, and we will be sure to include the data in the revised manuscript. Thank you for your comments.

eLife Assessment

This study presents important findings that will allow for a better understanding of the role of mitochondria in behaviours of C. elegans. There is convincing evidence that mutants in a subunit of the mitochondrial calcium uniporter (MCU-1) show defects in olfactory adaptation and this gene regulates neuropeptide secretion and allows for behavioural modulation in C. elegans. However, the evidence that mitochondrial calcium modulates odour-based behaviour in C. elegans is incomplete. This claim would require support from calcium imaging in conditioned WT and mcu-1 animals. This work would be of interest to labs working on behaviours across phyla.

Reviewer #1 (Public review):

Summary:

Here the authors present their evidence linking the mitochondrial uniporter (MCU-1) and olfactory adaptation in C. elegans. They clearly demonstrate a behavioral defect of mcu-1 mutants in adaptation over 60 minutes and present evidence that this gene functions in the AWC primary sensory neurons at, or close to, the time of adaptation.

Strengths:

The paper is very well organized and their approach to unpacking the role of mcu-1 mutants in olfactory adaptation is very reasonable. The authors lean into diverse techniques including behavior, genetics, and pharmacological manipulation in order to flesh out their model for how MCU-1 functions in AWC neurons with respect to olfaction.

Weaknesses:

I would like to see the authors strengthen the link between mitochondrial calcium and olfactory adaptation. The authors present some gCaMP data in Figure 5 but it is unclear to me why this tool is not better utilized to explore the mechanism of MCU-1 activity. I think this is very important as the title of the paper states that "mitochondrial calcium modulates.." behavior in AWC and so it would be nice to see more evidence to support this direct connection. I would also like to see the authors place their findings into a model based on previous findings and perhaps examine whether mcu-1 is required for EGL-4 nuclear translocation, which would be straightforward to examine.

Reviewer #2 (Public review):

Summary:

In their manuscript, "Mitochondrial calcium modulates odor-mediated behavioural plasticity in C. elegans", Lee et al. aim to link a mitochondrial calcium transporter to higher-order neuronal functions that mediate memory and aversive learning behaviours. The authors characterise the role of the mitochondrial calcium uniporter, and a specific subunit of this complex, MCU-1, within a single chemosensory neuron (AWCOFF) during aversive odor learning in the nematode. By genetically manipulating mcu-1 as well as using pharmacological activators and blockers of MCU activity, the study presents compelling evidence that the activity of this individual mitochondrial ion transporter in AWCOFF is sufficient to drive animal behaviour through aversive memory formation. The authors show that perturbations to mcu-1 and MCU activity prevent aversive learning to several chemical odors associated with food absence. The authors propose a model, experimentally validated at several steps, whereby an increase in MCU activity during odor conditioning stimulates mitochondrial calcium influx and an increase in mitochondrial reactive oxygen species (mtROS) production, triggering the release of the neuropeptide NLP-1 from AWC, all of which are required to mediate future avoidance behaviour of the chemical odor.

Strengths:

Overall, the authors provided robust evidence that mitochondrial function, mediated through MCU activity, contributes to behavioural plasticity. They also demonstrated that ectopic MCU activation or mtROS during odor exposure could accelerate learning. This is quite profound, as it highlights the importance of mitochondrial function in complex neuronal processes beyond their general roles in the development and maintenance of neurons through energy homeostasis and biosynthesis, amongst their other cell-non-specific roles.

Weaknesses:

While the manuscript is generally robust, there are some concerns that should be addressed to improve the strength of the proposed model:

(1) Throughout the manuscript, it is implied that MCU activation caused by odor conditioning changes mitochondrial calcium levels. However, there is no direct experimental evidence of this. For example, the authors write on p.10 "This shows that H2O2 production occurs downstream of MCU activation and calcium influx into the mitochondria", and on p. 11, the statement that prolonged exposure to odors causes calcium influx. Because this is a key element of the proposed model, experimental evidence would be required to support it.

(2) Some controls missing, e.g. a heat-shock-only control in WT and mcu-1 (non-transgenic) background in Figure 1h is required to ensure the heat-shock stress does not interfere with odor learning.

(3) Lee et al propose that mcu-1 is required at the adult stage to accomplish odor learning because inducing mcu-1 expression at larval stages did not rescue the phenotype of mcu-1 mutants during adulthood. However, the requirement of MCU for odor learning was narrowed down to a 15' window at the end of odor conditioning (Figure 5c). Is it possible that MCU-1 protein levels decline after larval induction so that MCU-1 is no longer present during adulthood when odor conditioning is performed?

(4) There is a limited learning effect observable after 30 minutes, and a very pronounced effect in all animals after 90 minutes. The authors very carefully dissect the learning mechanism at 60 minutes of exposure and distinguish processes that are relevant at 60 minutes from those important at 30 minutes. Some explanation or speculation as to why the processes crucial at the 60-minute mark are redundant at 90 minutes of exposure would be important.

(5) Given the presumably ubiquitous function of mcu-1/MCU in mitochondrial calcium homeostasis, it is remarkable that its perturbation impacts only a very specific neuronal process in AWC at a very specific time. The authors should elaborate on this surprising aspect of their discovery in the discussion.

(6) Associated with the above comment, it remains possible that mcu-1 is required in coelomocytes for their ability to absorb NLP-1::Venus (Figure 3B), and the AWC-specific role of mcu-1 for this phenotype should be determined.

Reviewer #3 (Public review):

Summary:

This manuscript reports a role for the mitochondrial calcium uniporter gene (mcu-1) in regulating associative learning behavior in C. elegans. This regulation occurs by mcu-1-dependent secretion of the neuropeptide NLP-1 from the sensory neuron AWC. The authors report a post-developmental role for mcu-1 in AWC to promote learning. The authors further show that odor conditioning leads to increases in NLP-1 secretion from AWC, and that interfering with mcu-1 function reduces NLP-1 secretion. Finally, the authors show that NLP-1 secretion increases when ROS levels in AWC are genetically or pharmacologically elevated. The authors propose that mitochondrial calcium entry through MCU-1 in response to odor conditioning leads to the generation of ROS and the subsequent increase in neuropeptide secretion to promote conditioned behavior.

Strengths:

(1) The authors show convincingly that genetically or pharmacologically manipulating MCU function impacts chemotaxis in a conditioned learning paradigm.

(2) The demonstration that the secretion of a specific neuropeptide can be up-regulated by MCU, ROS and odor conditioning is an important and interesting advance that addresses mechanisms by which neuropeptide secretion can be regulated in vivo.

Weaknesses:

(1) The authors conclusion that mcu-1 functions in the AWC-on neuron is not adequately supported by their rescue experiments. The promoter they use for rescue drives expression in a number of additional neurons including AWC-on, that themselves are implicated in adaptation, leaving open the possibility that mcu-1 may function non-autonomously instead of autonomously in AWC to regulate this behavior.

(2) The authors conclude MCU promotes neuropeptide release from AWC by controlling calcium entry into mitochondria, but they did not directly examine the effects of altered MCU function on calcium dynamics either in mitochondria or in the soma, even though they conducted calcium imaging experiments in AWC of wild type animals. Examination of calcium entry in mitochondria would be a direct test of their model.

(3) The authors' conclusion that mitochondrial-derived ROS produced by MCU activation drives neuropeptide release does not appear to be experimentally supported. A major weakness of this paper is that experiments addressing whether mcu-1 activity indeed produces ROS are not included, leaving unanswered the question of whether MCU is the endogenous source of ROS that drives neuropeptide secretion.

eLife Assessment

This manuscript presents a valuable minimal model of habituation which is quantified by information theoretic measures. The results here could be of use in interpreting habituation behavior in a range of biological systems. However, the evidence presented is incomplete and would benefit from more rigorous approaches and a fuller accounting of the hallmarks of habituation.

Reviewer #1 (Public review):

Summary:

The manuscript by Nicoletti et al. presents a minimal model of habituation, a basic form of non-associative learning, addressing both from dynamical and information theory aspects of how habituation can be realized. The authors identify that negative feedback provided with a slow storage mechanism is sufficient to explain habituation.

Strengths:

The authors combine the identification of the dynamical mechanism with information-theoretic measures to determine the onset of habituation and provide a description of how the system can gain maximum information about the environment.

Weaknesses:

I have several main concerns/questions about the proposed model for habituation and its plausibility. In general, habituation does not only refer to a decrease in the responsiveness upon repeated stimulation but as Thompson and Spencer discussed in Psych. Rev. 73, 16-43 (1966), there are 10 main characteristics of habituation, including (i) spontaneous recovery when the stimulus is withheld after response decrement; dependence on the frequency of stimulation such that (ii) more frequent stimulation results in more rapid and/or more pronounced response decrement and more rapid spontaneous recovery; (iii) within a stimulus modality, the less intense the stimulus, the more rapid and/or more pronounced the behavioral response decrement; (iv) the effects of repeated stimulation may continue to accumulate even after the response has reached an asymptotic level (which may or may not be zero, or no response). This effect of stimulation beyond asymptotic levels can alter subsequent behavior, for example, by delaying the onset of spontaneous recovery.

These are only a subset of the conditions that have been experimentally observed and therefore a mechanistic model of habituation, in my understanding, should capture the majority of these features and/or discuss the absence of such features from the proposed model.

Furthermore, the habituated response in steady-state is approximately 20% less than the initial response, which seems to be achieved already after 3-4 pulses, the subsequent change in response amplitude seems to be negligible, although the authors however state "after a large number of inputs, the system reaches a time-periodic steady-state". How do the authors justify these minimal decreases in the response amplitude? Does this come from the model parametrization and is there a parameter range where more pronounced habituation responses can be observed?

The same is true for the information content (Figure 2f) - already at the first pulse, IU, H ~ 0.7 and only negligibly increases afterwards. In my understanding, during learning, the mutual information between the input and the internal state increases over time and the system extracts from these predictions about its responses. In the model presented by the authors, it seems the system already carries information about the environment which hardly changes with repeated stimulus presentation. The complexity of the signal is also limited, and it is very hard to clarify from the presented results, whether the proposed model can actually explain basic features of habituation, as mentioned above.<br /> Additionally, there have been two recent models on habituation and I strongly suggest that the authors discuss their work in relation to recent works (bioRxiv 2024.08.04.606534; arXiv:2407.18204).

Reviewer #2 (Public review):

In this study, the authors aim to investigate habituation, the phenomenon of increasing reduction in activity following repeated stimuli, in the context of its information-theoretic advantage. To this end, they consider a highly simplified three-species reaction network where habituation is encoded by a slow memory variable that suppresses the receptor and therefore the readout activity. Using analytical and numerical methods, they show that in their model the information gain, the difference between the mutual information between the signal and readout after and before habituation, is maximal for intermediate habituation strength. Furthermore, they demonstrate that the Pareto front corresponds to an optimization strategy that maximizes the mutual information between signal and readout in the steady state, minimizes some form of dissipation, and also exhibits similar intermediate habituation strength. Finally, they briefly compare predictions of their model to whole-brain recordings of zebrafish larvae under visual stimulation.

The author's simplified model might serve as a solid starting point for understanding habituation in different biological contexts as the model is simple enough to allow for some analytic understanding but at the same time exhibits all basic properties of habituation in sensory systems. Furthermore, the author's finding of maximal information gain for intermediate habituation strength via an optimization principle is, in general, interesting. However, the following points remain unclear or are weakly explained:

(1) Is it unclear what the meaning of the finding of maximal information gain for intermediate habituation strength is for biological systems? Why is information gain as defined in the paper a relevant quantity for an organism/cell? For instance, why is a system with low mutual information after the first stimulus and intermediate mutual information after habituation better than one with consistently intermediate mutual information? Or, in other words, couldn't the system try to maximize the mutual information acquired over the whole time series, e.g., the time series mutual information between the stimulus and readout?

(2) The model is very similar to (or a simplification of previous models) for adaptation in living systems, e.g., for adaptation in chemotaxis via activity-dependent methylation and demethylation. This should be made clearer.

(3) It remains unclear why this optimization principle is the most relevant one. While it makes sense to maximize the mutual information between stimulus and readout, there are various choices for what kind of dissipation is minimized. Why was \delta Q_R chosen and not, for instance, \dot{\Sigma}_int or the sum of both? How would the results change in that case? And how different are the results if the mutual information is not calculated for the strong stimulation input statistics but for the background one?

(4) The comparison to the experimental data is not too strong of an argument in favor of the model. Is the agreement between the model and the experimental data surprising? What other behavior in the PCA space could one have expected in the data? Shouldn't the 1st PC mostly reflect the "features", by construction, and other variability should be due to progressively reduced activity levels?

Reviewer #3 (Public review):

The authors use a generic model framework to study the emergence of habituation and its functional role from information-theoretic and energetic perspectives. Their model features a receptor, readout molecules, and a storage unit, and as such, can be applied to a wide range of biological systems. Through theoretical studies, the authors find that habituation (reduction in average activity) upon exposure to repeated stimuli should occur at intermediate degrees to achieve maximal information gain. Parameter regimes that enable these properties also result in low dissipation, suggesting that intermediate habituation is advantageous both energetically and for the purpose of retaining information about the environment.

A major strength of the work is the generality of the studied model. The presence of three units (receptor, readout, storage) operating at different time scales and executing negative feedback can be found in many domains of biology, with representative examples well discussed by the authors (e.g. Figure 1b). A key takeaway demonstrated by the authors that has wide relevance is that large information gain and large habituation cannot be attained simultaneously. When energetic considerations are accounted for, large information gain and intermediate habituation appear to be a favorable combination.

While the generic approach of coarse-graining most biological detail is appealing and the results are of broad relevance, some aspects of the conducted studies, the problem setup, and the writing lack clarity and should be addressed:

(1) The abstract can be further sharpened. Specifically, the "functional role" mentioned at the end can be made more explicit, as it was done in the second-to-last paragraph of the Introduction section ("its functional advantages in terms of information gain and energy dissipation"). In addition, the abstract mentions the testing against experimental measurements of neural responses but does not specify the main takeaways. I suggest the authors briefly describe the main conclusions of their experimental study in the abstract.

(2) Several clarifications are needed on the treatment of energy dissipation.<br /> - When substituting the rates in Eq. (1) into the definition of δQ_R above Eq. (10), "σ" does not appear on the right-hand side. Does this mean that one of the rates in the lower pathway must include σ in its definition? Please clarify.<br /> - I understand that the production of storage molecules has an associated cost σ and hence contributes to dissipation. The dependence of receptor dissipation on , however, is not fully clear. If the environment were static and the memory block was absent, the term with would still contribute to dissipation. What would be the nature of this dissipation?<br /> - Similarly, in Eq. (9) the authors use the ratio of the rates Γ_{s → s+1} and Γ_{s+1 → s} in their expression for internal dissipation. The first-rate corresponds to the synthesis reaction of memory molecules, while the second corresponds to a degradation reaction. Since the second reaction is not the microscopic reverse of the first, what would be the physical interpretation of the log of their ratio? Since the authors already use σ as the energy cost per storage unit, why not use σ times the rate of producing S as a metric for the dissipation rate?

(3) Impact of the pre-stimulus state. The plots in Figure 2 suggest that the environment was static before the application of repeated stimuli. Can the authors comment on the impact of the pre-stimulus state on the degree of habituation and its optimality properties? Specifically, would the conclusions stay the same if the prior environment had stochastic but aperiodic dynamics?

(4) Clarification about the memory requirement for habituation. Figure 4 and the associated section argue for the essential role that the storage mechanism plays in habituation. Indeed, Figure 4a shows that the degree of habituation decreases with decreasing memory. The graph also shows that in the limit of vanishingly small Δ⟨S⟩, the system can still exhibit a finite degree of habituation. Can the authors explain this limiting behavior; specifically, why does habituation not vanish in the limit Δ⟨S⟩ -> 0?

Author response:

Reviewer #1 (Public review):

Summary:

The manuscript by Nicoletti et al. presents a minimal model of habituation, a basic form of non-associative learning, addressing both from dynamical and information theory aspects of how habituation can be realized. The authors identify that negative feedback provided with a slow storage mechanism is sufficient to explain habituation.

Strengths:

The authors combine the identification of the dynamical mechanism with information-theoretic measures to determine the onset of habituation and provide a description of how the system can gain maximum information about the environment.

We thank the reviewer for highlighting the strength of our work.

Weaknesses:

I have several main concerns/questions about the proposed model for habituation and its plausibility. In general, habituation does not only refer to a decrease in the responsiveness upon repeated stimulation but as Thompson and Spencer discussed in Psych. Rev. 73, 16-43 (1966), there are 10 main characteristics of habituation, including (i) spontaneous recovery when the stimulus is withheld after response decrement; dependence on the frequency of stimulation such that (ii) more frequent stimulation results in more rapid and/or more pronounced response decrement and more rapid spontaneous recovery; (iii) within a stimulus modality, the less intense the stimulus, the more rapid and/or more pronounced the behavioral response decrement; (iv) the effects of repeated stimulation may continue to accumulate even after the response has reached an asymptotic level (which may or may not be zero, or no response). This effect of stimulation beyond asymptotic levels can alter subsequent behavior, for example, by delaying the onset of spontaneous recovery.

These are only a subset of the conditions that have been experimentally observed and therefore a mechanistic model of habituation, in my understanding, should capture the majority of these features and/or discuss the absence of such features from the proposed model.

We are really grateful to the reviewer for pointing out these aspects of habituation that we overlooked in the previous version of our manuscript. Indeed, our model is able to capture most of these 10 observed behaviors, specifically: 1) habituation; 2) spontaneous recovery; 3) potentiation of habituation; 4) frequency sensitivity; and 5) intensity sensitivity. Here, we are following the same terminology employed in bioRxiv 2024.08.04.606534, the paper highlighted by the referee. Regarding the hallmark 6) subliminal accumulation, we also believe that our model can capture it as well, but more analyses are needed to substantiate this claim. We will include the discussion of these points in the revised version.

Notably, in line with the discussion in bioRxiv 2024.08.04.606534, we also think that feature 10) long-term habituation, is ambiguous and its appearance might be simply related to the other features discussed above. In the revised version, we will detail our take on this aspect in relation to the presented model.

All other hallmarks require the presence of multiple stimuli and, as a consequence, they cannot be observed within our model, but are interesting lines of research for future investigations. We believe that this addition will help clarify the validity of the model and the relevance of our result, consequently improving the quality of our manuscript.

Furthermore, the habituated response in steady-state is approximately 20% less than the initial response, which seems to be achieved already after 3-4 pulses, the subsequent change in response amplitude seems to be negligible, although the authors however state "after a large number of inputs, the system reaches a time-periodic steady-state". How do the authors justify these minimal decreases in the response amplitude? Does this come from the model parametrization and is there a parameter range where more pronounced habituation responses can be observed?

The referee is correct, but this is solely a consequence of the specific set of parameters we selected. We made this choice solely for visualization purposes. In the next version, when different emerging behaviors characterizing habituation are discussed, we will also present a set of parameters for which habituation can be better appreciated, justifying our new choice.

We stated that the time-periodic steady-state is reached “after a large number of stimuli” from a mathematical perspective. However, by using a habituation threshold, as defined in bioRxiv 2024.08.04.606534 for example, we can say that the system is habituated after a few stimuli for the set of parameters selected in the first version of the manuscript. We will also discuss this aspect in the Supplemental Material of the revised version, as it will also be important to appreciate the hallmarks of habituation listed above.

The same is true for the information content (Figure 2f) - already at the first pulse, IU, H ~ 0.7 and only negligibly increases afterwards. In my understanding, during learning, the mutual information between the input and the internal state increases over time and the system extracts from these predictions about its responses. In the model presented by the authors, it seems the system already carries information about the environment which hardly changes with repeated stimulus presentation. The complexity of the signal is also limited, and it is very hard to clarify from the presented results, whether the proposed model can actually explain basic features of habituation, as mentioned above.

The point about information is more subtle. We can definitely choose a set of parameters for which the information gain is higher and we will show it in the Supplemental Material of the revised version. However, as the reviewer correctly points out, it is difficult to give an interpretation of the specific value of I_U,H for such a minimal model.

We also remark that, since the readout population and the receptor both undergo a fast dynamics (with appropriate timescales as discussed in the text), we are not observing the transient gain of information associated with the first stimulus and, as such, the mutual information presents a discontinuous behavior resembling the dynamics of the readout.

Additionally, there have been two recent models on habituation and I strongly suggest that the authors discuss their work in relation to recent works (bioRxiv 2024.08.04.606534; arXiv:2407.18204).

We thank the reviewer for pointing out these relevant references. We will discuss analogies and differences in the revised version of the main text. The main difference is the fact that information-theoretic aspects of habituation are not discussed in the presented references, while the idea of this work is to elucidate exactly the interplay between information gain and habituation dynamics.

Reviewer #2 (Public review):

In this study, the authors aim to investigate habituation, the phenomenon of increasing reduction in activity following repeated stimuli, in the context of its information-theoretic advantage. To this end, they consider a highly simplified three-species reaction network where habituation is encoded by a slow memory variable that suppresses the receptor and therefore the readout activity. Using analytical and numerical methods, they show that in their model the information gain, the difference between the mutual information between the signal and readout after and before habituation, is maximal for intermediate habituation strength. Furthermore, they demonstrate that the Pareto front corresponds to an optimization strategy that maximizes the mutual information between signal and readout in the steady state, minimizes some form of dissipation, and also exhibits similar intermediate habituation strength. Finally, they briefly compare predictions of their model to whole-brain recordings of zebrafish larvae under visual stimulation.

The author's simplified model might serve as a solid starting point for understanding habituation in different biological contexts as the model is simple enough to allow for some analytic understanding but at the same time exhibits all basic properties of habituation in sensory systems. Furthermore, the author's finding of maximal information gain for intermediate habituation strength via an optimization principle is, in general, interesting. However, the following points remain unclear or are weakly explained:

We thank the reviewer for deeming our work interesting and for considering it a solid starting point for understanding habituation in biological systems.

(1) Is it unclear what the meaning of the finding of maximal information gain for intermediate habituation strength is for biological systems? Why is information gain as defined in the paper a relevant quantity for an organism/cell? For instance, why is a system with low mutual information after the first stimulus and intermediate mutual information after habituation better than one with consistently intermediate mutual information? Or, in other words, couldn't the system try to maximize the mutual information acquired over the whole time series, e.g., the time series mutual information between the stimulus and readout?

This is an important and delicate aspect to discuss. We considered the mutual information with a prolonged stimulation when building the Pareto front, by maximizing this quantity while minimizing the dissipation. The observation that the Pareto front lies in the vicinity of the maximum of the information gain hints at the fact that reducing the information gain by increasing the mutual information at each stimulation will require more energy. However, we did not thoroughly explore this aspect by considering all sources of dissipation and the fact that habituation is, anyway, a dynamical phenomenon. In the revised version, we will clarify this point, extending our analyses.

We would like to add that, from a naive perspective, while the first stimulation will necessarily trigger a certain mutual information, multiple observations of the same stimulus have to reflect into accumulated infor

mation that consequently drives the onset of observed dynamical behaviors, such as habituation.

(2) The model is very similar to (or a simplification of previous models) for adaptation in living systems, e.g., for adaptation in chemotaxis via activity-dependent methylation and demethylation. This should be made clearer.

We apologize for having missed this point. Our choice has been motivated by the fact that we wanted to avoid any confusion between the usual definition of (perfect) adaptation and habituation. At any rate, we will add this clarification in the revised version.

(3) It remains unclear why this optimization principle is the most relevant one. While it makes sense to maximize the mutual information between stimulus and readout, there are various choices for what kind of dissipation is minimized. Why was \delta Q_R chosen and not, for instance, \dot{\Sigma}_int or the sum of both? How would the results change in that case? And how different are the results if the mutual information is not calculated for the strong stimulation input statistics but for the background one?

We thank the referee for giving us the opportunity to deepen this aspect of the manuscript. We decided to minimize \delta Q_R since this dissipation is unavoidable. In fact, considering the existence of two different pathways implementing sensing and feedback, the presence of any input will result in a dissipation produced by the receptor. This energy consumption is reflected in \delta Q_R. Conversely, the dissipation associated with the storage is always zero in the limit of a fast memory. However, we know that such a limit is pathological and leads to no habituation. As a consequence, in the revised version we will discuss other choices for our optimization approach, along with their potentialities and limitations.

The dependence of the Pareto front on the stimulus strength is shown in the Supplemental Material, but not in relation to habituation and information gain. We will strengthen this part in the revised version of the manuscript, elaborating more on the connection between optimality, information gain, and dynamical behavior.

(4) The comparison to the experimental data is not too strong of an argument in favor of the model. Is the agreement between the model and the experimental data surprising? What other behavior in the PCA space could one have expected in the data? Shouldn't the 1st PC mostly reflect the "features", by construction, and other variability should be due to progressively reduced activity levels?

The agreement between data and model is not surprising - we agree on this - since the data exhibit habituation. However, the fact that, without any explicit biological details, our minimal model is able to capture the features of a complex neural system just by looking at the PCs is non-trivial. The 1st PC only reflects the feature that captures most of the variance of the data and, as such, it is difficult to have a-priori expectations on what it should represent. Depending on the behavior of higher-order PCs, we may include them in the revised version if any interesting results arise.

Reviewer #3 (Public review):

The authors use a generic model framework to study the emergence of habituation and its functional role from information-theoretic and energetic perspectives. Their model features a receptor, readout molecules, and a storage unit, and as such, can be applied to a wide range of biological systems. Through theoretical studies, the authors find that habituation (reduction in average activity) upon exposure to repeated stimuli should occur at intermediate degrees to achieve maximal information gain. Parameter regimes that enable these properties also result in low dissipation, suggesting that intermediate habituation is advantageous both energetically and for the purpose of retaining information about the environment.

A major strength of the work is the generality of the studied model. The presence of three units (receptor, readout, storage) operating at different time scales and executing negative feedback can be found in many domains of biology, with representative examples well discussed by the authors (e.g. Figure 1b). A key takeaway demonstrated by the authors that has wide relevance is that large information gain and large habituation cannot be attained simultaneously. When energetic considerations are accounted for, large information gain and intermediate habituation appear to be a favorable combination.

We thank the referee for this positive assessment of our work and its generality.

While the generic approach of coarse-graining most biological detail is appealing and the results are of broad relevance, some aspects of the conducted studies, the problem setup, and the writing lack clarity and should be addressed:

(1) The abstract can be further sharpened. Specifically, the "functional role" mentioned at the end can be made more explicit, as it was done in the second-to-last paragraph of the Introduction section ("its functional advantages in terms of information gain and energy dissipation"). In addition, the abstract mentions the testing against experimental measurements of neural responses but does not specify the main takeaways. I suggest the authors briefly describe the main conclusions of their experimental study in the abstract.

We thank the referee for this suggestion. The revised version will present a modified abstract in line with the reviewer’s proposal.

(2) Several clarifications are needed on the treatment of energy dissipation.

- When substituting the rates in Eq. (1) into the definition of δQ_R above Eq. (10), "σ" does not appear on the right-hand side. Does this mean that one of the rates in the lower pathway must include σ in its definition? Please clarify.

We apologize to the referee for this typo. Indeed, \sigma sets the energy scale of the feedback and, as such, it appears in the energetic driving given by the feedback on the receptor, i.e., together with \kappa in Eq. (1). We will fix this issue in the revised version. Moreover, we will check the entire manuscript to be sure that all formulas are consistent.

- I understand that the production of storage molecules has an associated cost σ and hence contributes to dissipation. The dependence of receptor dissipation on <H>, however, is not fully clear. If the environment were static and the memory block was absent, the term with <H> would still contribute to dissipation. What would be the nature of this dissipation?

In the spirit of building a paradigmatic minimal model with a thermodynamic meaning, we considered H to act as an external thermodynamic driving. Since this driving acts on a different pathway with respect to the one affected by the storage, the receptor is driven out of equilibrium by its presence. By eliminating the memory block, we would also be necessarily eliminating the presence of the pathway associated with the storage effect (“internal pathway” in the manuscript). In this case, the receptor is a 2-state, 1-pathway system and, as such, it always satisfies an effective detailed balance. As a consequence, the definition of \delta Q_R reported in the manuscript does not hold anymore and the receptor does not exhibit any dissipation. Our choice to model two different pathways has been biologically motivated. We will make this crucial aspect clearer in the revised manuscript.

- Similarly, in Eq. (9) the authors use the ratio of the rates Γ_{s → s+1} and Γ_{s+1 → s} in their expression for internal dissipation. The first-rate corresponds to the synthesis reaction of memory molecules, while the second corresponds to a degradation reaction. Since the second reaction is not the microscopic reverse of the first, what would be the physical interpretation of the log of their ratio? Since the authors already use σ as the energy cost per storage unit, why not use σ times the rate of producing S as a metric for the dissipation rate?

In the current version of the manuscript, we employed the scheme of a controlled birth and death process to model the coupled process of readout and storage production. Since we are not dealing with a detailed biochemical underlying network, we used this coarse-grained description to capture the main features of the dynamics. In this sense, the considered reactions produce and destroy a molecule from a certain pool even if they are controlled in different ways by the readout. However, we completely agree with the point of view of the referee and will analyze our results following their suggestion.

(3) Impact of the pre-stimulus state. The plots in Figure 2 suggest that the environment was static before the application of repeated stimuli. Can the authors comment on the impact of the pre-stimulus state on the degree of habituation and its optimality properties? Specifically, would the conclusions stay the same if the prior environment had stochastic but aperiodic dynamics?

The initial stimulus is indeed stochastic with an average constant in time. Model response depends on the pre-stimulus level, since it also sets the stationary storage concentration before the first “strong” stimulation arrives. This dependence is not crucial for our result but deserves proper discussion, as the referee correctly pointed out. We will clarify this point in the revised version of this study.

(4) Clarification about the memory requirement for habituation. Figure 4 and the associated section argue for the essential role that the storage mechanism plays in habituation. Indeed, Figure 4a shows that the degree of habituation decreases with decreasing memory. The graph also shows that in the limit of vanishingly small Δ⟨S⟩, the system can still exhibit a finite degree of habituation. Can the authors explain this limiting behavior; specifically, why does habituation not vanish in the limit Δ⟨S⟩ -> 0?

We apologize for the lack of clarity here. Actually, Δ⟨S⟩ is not strictly zero, but equal to 0.15% at the final point. However, due to rounding this appears as 0% in the plot, and we will fix it in the revised version. Let us note that the fact that Δ⟨S⟩ is small signals a nonlinear dependence of Δ⟨U⟩ from Δ⟨S⟩, but no contradiction. We will clarify this aspect in the revised version.

Did you note a higher rate of "non-recovery" on the vaccinated animals? I'm wondering whether you'd hypothesize that vaccination against the EVs could be the cause of increased itching or irritation that alerts the animals to the ticks.

Would you mind sharing your rationale for isolating EVs at this time point? Do you know how EV cargo or composition might vary depending on the feeding time point when you harvest?

Curious if you know anything about the composition of these vesicles relative to each other? Are they carrying very distinct cargo?

Poverty is not only related to lack of material resources but also involves social shame. In school, students are taught that being poor is seen as inferior, leading to a feeling of being lesser than those who are not poor. This internalized classism demonstrates how economic status can have a detrimental impact on an individual's sense of self and self-esteem.

The author describes harsh living conditions, emphasizing the lack of basic amenities like reliable heating, air conditioning, and even a telephone. These are things normal families take for granted, illustrating how poverty can shape one’s daily existence through limited access to necessities. The necessities that the author's family did not have access to were proper climate control, safety, and communication.

This mindset that children born into poverty have conveys the internal struggle with class-based exclusion and insecurity. Their self-consciousness stems from repeated verbal and nonverbal cues that allowed for feelings of inadequacy due to poverty during their school years. Now, standing in front of students as a teacher, the narrator returns to the “scene of the crime,” reflecting on how deeply these formative experiences of social exclusion shaped their sense of self-worth.

This is how every low-income student must feel when their possessions can't measure up to their peers because of their family's inability to afford what other children's families could. It conveys the author's disheartened tone through his painful memories that served as a reminder of his family's financial limitations.

I agree with the author, as I do believe children who are born into poverty possess an innate resourcefulness that typical children are not forced to develop. This is due to the lack of resources available to them, which leads to a mentality of making the best out of what you have.

I wonder what different strategies can be implemented to combat the increase of US children in low-income situations. If there are less and less low-wage jobs available, but the correlation with low wage jobs and increased childbearing is evident, would providing improved access to birth control be one way to combat this rising issue?

It's important to note that there can be many different types of poverty but situational poverty seems especially relevant considering the recent hurricanes that thrust many residents of Florida into chaos.

subjectivity crucial to feminist perspectives but not the aim of anthro

Palestinian-American anthropologist. She is the Joseph L. Buttenweiser Professor of Social Science in the Department of Anthropology at Columbia

应为 ISO/IEC 42001，下同——所有使用标准全称时，均应体现制定标准的两个组织。 should be ISO/IEC, and this comment should apply to all the products involving the standards developed by JTC1.

eLife Assessment

The paper describes a novel approach for inferring features of synaptic networks from recordings of individual cells within the network. The paper will be a valuable contribution to those studying central pattern generators, including those involved in respiration. However, the theoretical approach to drawing inferences regarding the underlying synaptic currents is incomplete as it relies on unsupported simplifying assumptions.

Reviewer #1 (Public review):

Summary:

The paper develops a phase method to obtain the excitatory and inhibitory afferents to certain neuron populations in the brainstem. The inferred contributions are then compared to the results of voltage clamp and current clamp experiments measuring the synaptic contributions to post-I, aug-E, and ramp-I neurons.

Strengths:

The electrophysiology part of the paper is sound and reports novel features with respect to earlier work by JC Smith et al 2012, Paton et al 2022 (and others) who have mapped circuits of the respiratory central pattern generator. Measurements on ramp-I neurons, late-I neurons, and two types of post-I neurons in Figure 2 besides measurements of synaptic inputs to these neurons in Figure 5 are to my knowledge new.

Weaknesses:

The phase method for inferring synaptic conductances fails to convince. The method rests on many layers of assumptions and the inferred connections in Figure 4 remain speculative. To be convincing, such a method ought to be tested first on a model CPG with known connectivity to assess how good it is at inferring known connections back from the analysis of spatio-temporal oscillations. For biological data, once the network connectivity has been inferred as claimed, the straightforward validation is to reconstruct the experimental oscillations (Figure 2) noting that Rybak et al (Rybak, Paton Schwaber J. Neurophysiol. 77, 1994 (1997)) have already derived models for the respiratory neurons.

The transformation from time to phase space, unlike in the Kuramoto model, is not justified here (Line 94) and is wrong. The underpinning idea that "the synaptic conductances depend on the cycle phase and not on time explicitly" is flawed because synapses have characteristic decay times and delays to response which remain fixed when the period of network oscillations increases. Synaptic properties depend on time and not on phase in the network. One major consequence relevant to the present identification of excitatory or inhibitory behaviour, is that it cannot account for change in the behaviour of inhibitory synapses - from inhibitory to excitatory action - when the inhibitory decay time becomes commensurable to the period of network oscillations (Wang & Buzsaki Journal of Neuroscience 16, 6402 (1996), van Vreeswijk et al. J. Comp. Neuroscience 1,313 (1994), Borgers and Kopell Neural Comput. 15, 2003). In addition, even small delays in the inhibitory synapse response relative to the pre-synaptic action potential also produce in-phase synchronization (Chauhan et al., Sci. Rep. 8, 11431 (2018); Borgers and Kopell, Neural Comput. 15, 509 (2003)). The present assumptions are way too simplistic because you cannot account for these commensurability effects with a single parameter like the network phase. There is therefore little confidence that this model can reliably distinguish excitatory from inhibitory synapses when their dynamic properties are not properly taken into account.

Line 82, Equation 1 makes extremely crude assumptions that the displacement current (CdV/dt) is negligible and that the ion channel currents are all negligible. Vm(t) is also not defined. The assumption that the activation/inactivation times of all ion channels are small compared to the 10-20ms decay time of synaptic currents is not true in general. Same for the displacement current. The leak conductance is typically g~0.05-0.09ms/cm^2 while C~1uF/cm^2. Therefore the ratio C/g leak is in the 10-20ms range - the same as the typical docking neurotransmitter time in synapses.

Models of brainstem CPG circuits have been known to exist for decades: JC Smith et al 2012, Paton et al 2022, Bellingham Clin. Exp. Pharm. And Physiol. 25, 847 (1998); Rubin et al., J. Neurophysiol. 101, 2146 (2009) among others. The present paper does not discuss existing knowledge on respiratory networks and gives the impression of reinventing the wheel from scratch. How will this paper add to existing knowledge?

Reviewer #2 (Public review):

Summary:

By measuring intracellular changes in membrane voltage from a single neuron of the medulla the authors describe a method for determining the balance of excitatory and inhibitory synaptic drive onto a single neuron within this important brain region.

Strengths:

This approach could be valuable in describing the microcircuits that generate rhythms within this respiratory control centre. This method could more generally be used to enable microcircuits to be studied without the need for time-consuming anatomical tracing or other more involved electrophysiological techniques.

Weaknesses:

This approach involves assuming the reversal potential that is associated with the different permeant ions that underlie the excitation and inhibition as well as the application of Ohms law to estimate the contribution of excitation and inhibitory conductance. My first concern is that this approach relies on a linear I-V relationship between the measured voltage and the estimated reversal potential. However, open rectification is a feature of any I-V relationship generated by asymmetric distributions of ions (see the GHK current equation) and will therefore be a particular issue for the inhibition resulting from asymmetrical Cl- ion gradients across GABA-A receptors. The mixed cation conductance that underlies most synaptic excitation will also generate a non-linear I-V relationship due to the inward rectification associated with the polyamine block of AMPA receptors. Could the authors please speculate what impact these non-linearities could have on results obtained using their approach?

This approach has similarities to earlier studies undertaken in the visual cortex that estimated the excitatory and inhibitory synaptic conductance changes that contributed to membrane voltage changes during receptive field stimulation. However, these approaches also involved the recording of transmembrane current changes during visual stimulation that were undertaken in voltage-clamp at various command voltages to estimate the underlying conductance changes. Molkov et al have attempted to essentially deconvolve the underlying conductance changes without this information and I am concerned that this simply may not be possible. The current balance equation (1) cited in this study is based on the parallel conductance model developed by Hodgkin & Huxley. However, one key element of the HH equations is the inclusion of an estimate of the capacitive current generated due to the change in voltage across the membrane capacitance. I would always consider this to be the most important motivation for the development of the voltage-clamp technique in the 1930's. Indeed, without subtraction of the membrane capacitance, it is not possible to isolate the transmembrane current in the way that previous studies have done. In the current study, I feel it is important that the voltage change due to capacitive currents is taken into consideration in some way before the contribution of the underlying conductance changes are inferred.

Studies using acute slicing preparations to examine circuit effects have often been limited to the study of small microcircuits - especially feedforward and feedback interneuron circuits. It is widely accepted that any information gained from this approach will always be compromised by the absence of patterned afferent input from outside the brain region being studied. In this study, descending control from the Pons and the neocortex will not be contributing much to the synaptic drive and ascending information from respiratory muscles will also be absent completely. This may not have been such a major concern if this study was limited to demonstrating the feasibility of a methodological approach. However, this limitation does need to be considered when using an approach of this type to speculate on the prevalence of specific circuit motifs within the medulla (Figure 4). Therefore, I would argue that some discussion of this limitation should be included in this manuscript.

eLife Assessment

The study by Power and colleagues is important as elucidating the dynamic immune responses to photoreceptor damage in vivo potentiates future work in the field to better understand the disease process. However the evidence supporting the authors' claims is incomplete. The current manuscript would further benefit from validating their conclusion with additional supporting data from earlier time points (6 to 12 hours), additional markers to characterize neutrophils, more n numbers to strengthen the analysis, and evaluation of immune responses in mice with a stronger laser ablation, as well as further evidence to distinguish resident microglia vs. infiltrating macrophages due to the BRB breakdown. The authors should reorganize the article to make it easier and more straightforward to follow.

Reviewer #1 (Public review):

Summary:

The authors aimed to investigate the interaction between tissue-resident immune cells (microglia) and circulating systemic neutrophils in response to acute, focal retinal injury. They induced retinal lesions using 488 nm light to ablate photoreceptor (PR) outer segments, then utilized various imaging techniques (AOSLO, SLO, and OCT) to study the dynamics of fluorescent microglia and neutrophils in mice over time. Their findings revealed that while microglia showed a dynamic response and migrated to the injury site within a day, neutrophils were not recruited to the area despite being nearby. Post-mortem confocal microscopy confirmed these in vivo results. The study concluded that microglial activation does not recruit neutrophils in response to acute, focal photoreceptor loss, a scenario common in many retinal diseases.

Strengths:

The primary strength of this manuscript lies in the techniques employed.

In this study, the authors utilized advanced Adaptive Optics Scanning Laser Ophthalmoscopy (AOSLO) to document immune cell interactions in the retina accurately. AOSLO's micron-level resolution and enhanced contrast, achieved through near-infrared (NIR) light and phase-contrast techniques, allowed visualization of individual immune cells without extrinsic dyes. This method combined confocal reflectance, phase-contrast, and fluorescence modalities to reveal various cell types simultaneously. Confocal AOSLO tracked cellular changes with less than 6 μm axial resolution, while phase-contrast AOSLO provided detailed views of vascular walls, blood cells, and immune cells. Fluorescence imaging enabled the study of labeled cells and dyes throughout the retina. These techniques, integrated with conventional histology and Optical Coherence Tomography (OCT), offered a comprehensive platform to visualize immune cell dynamics during retinal inflammation and injury.

Weaknesses:

One significant weakness of the manuscript is the use of Cx3cr1GFP mice to specifically track GFP-expressing microglia. While this model is valuable for identifying resident phagocytic cells when the blood-retinal barrier (BRB) is intact, it is important to note that recruited macrophages also express the same marker following BRB breakdown. This overlap complicates the interpretation of results and makes it difficult to distinguish between the contributions of microglia and infiltrating macrophages, a point that is not addressed in the manuscript.

Another major concern is the time point chosen for analyzing the neutrophil response. The authors assess neutrophil activity 24 hours after injury, which may be too late to capture the initial inflammatory response. This delayed assessment could overlook crucial early dynamics that occur shortly after injury, potentially impacting the overall findings and conclusions of the study.

Reviewer #2 (Public review):

Summary:

This study uses in vivo multimodal high-resolution imaging to track how microglia and neutrophils respond to light-induced retinal injury from soon after injury to 2 months post-injury. The in vivo imaging finding was subsequently verified by an ex vivo study. The results suggest that despite the highly active microglia at the injury site, neutrophils were not recruited in response to acute light-induced retinal injury.

Strengths:

An extremely thorough examination of the cellular-level immune activity at the injury site. In vivo imaging observations being verified using ex vivo techniques is a strong plus.

Weaknesses:

This paper is extremely long, and in the perspective of this reviewer, needs to be better organized.

Study weakness: though the finding prompts more questions and future studies, the findings discussed in this paper are potentially important for us to understand how the immune cells respond differently to different severity levels of injury.

Reviewer #3 (Public review):

Summary:

This work investigated the immune response in the murine retina after focal laser lesions. These lesions are made with close to 2 orders of magnitude lower laser power than the more prevalent choroidal neovascularization model of laser ablation. Histology and OCT together show that the laser insult is localized to the photoreceptors and spares the inner retina, the vasculature, and the pigment epithelium. As early as 1-day after injury, a loss of cell bodies in the outer nuclear layer is observed. This is accompanied by strong microglial proliferation at the site of injury in the outer retina where microglia do not typically reside. The injury did not seem to result in the extravasation of neutrophils from the capillary network constituting one of the main findings of the paper. The demonstrated paradigm of studying the immune response and potentially retinal remodeling in the future in vivo is valuable and would appeal to a broad audience in visual neuroscience. However, there are some issues with the conclusions drawn from the data and analysis that can be addressed to further bolster the manuscript.

Strengths:

Adaptive optics imaging of the murine retina is cutting edge and enables non-destructive visualization of fluorescently labeled cells in the milieu of retinal injury. As may be obvious, this in vivo approach is beneficial for studying fast and dynamic immune processes on a local time scale - minutes and hours, and also for the longer days-to-months follow-up of retinal remodeling as demonstrated in the article. In certain cases, the in vivo findings are corroborated with histology.

The analysis is sound and accompanied by stunning video and static imagery. A few different sets of mouse models are used, (a) two different mouse lines, each with a fluorescent tag for neutrophils and microglia, (b) two different models of inflammation - endotoxin-induced uveitis (EAU) and laser ablation are used to study differences in the immune interaction.

One of the major advances in this article is the development of the laser ablation model for 'mild' retinal damage as an alternative to the more severe neovascularization models. While not directly shown in the article, this model would potentially allow for controlling the size, depth, and severity of the laser injury opening interesting avenues for future study.

Weaknesses:

(1) It is unclear based on the current data/study to what extent the mild laser damage phenotype is generalizable to disease phenotypes. The outer nuclear cell loss of 28% and a complete recovery in 2 months would seem quite mild, thus the generalizability in terms of immune-mediated response in the face of retinal remodeling is not certain, specifically whether the key finding regarding the lack of neutrophil recruitment will be maintained with a stronger laser ablation.

(2) Mice numbers and associated statistics are insufficient to draw strong conclusions in the paper on the activity of neutrophils, some examples are below :

a) 2 catchup mice and 2 positive control EAU mice are used to draw inferences about immune-mediated activity in response to injury. If the goal was to show 'feasibility' of imaging these mouse models for the purposes of tracking specific cell type behavior, the case is sufficiently made and already published by the authors earlier. It is possible that a larger sample size would alter the conclusion.

b) There are only 2 examples of extravasated neutrophils in the entire article, shown in the positive control EAU model. With the rare extravasation events of these cells and their high-speed motility, the chance of observing their exit from the vasculature is likely low overall, therefore the general conclusions made about their recruitment or lack thereof are not justified by these limited examples shown.

c) In Figure 3, the 3-day time point post laser injury shows an 18% reduction in the density of ONL nuclei (p-value of 0.17 compared to baseline). In the case of neutrophils, it is noted that "Control locations (n = 2 mice, 4 z-stacks) had 15 {plus minus} 8 neutrophils per sq.mm of retina whereas lesioned locations (n = 2 mice, 4 z-stacks) had 23 {plus minus} 5 neutrophils per sq.mm of retina (Figure 10b). The difference between control and lesioned groups was not statistically significant (p = 0.19)." These data both come from histology. While the p-values - 0.17 and 0.19 - are similar, in the first case a reduction in ONL cell density is concluded while in the latter, no difference in neutrophil density is inferred in the lesioned case compared to control. Why is there a difference in the interpretation where the same statistical test and methodology are used in both cases? Besides this statistical nuance, is there an alternate possibility that there is an increased, albeit statistically insignificant, concentration of circulating neutrophils in the lesioned model? The increase is nearly 50% (15 {plus minus} 8 vs. 23 {plus minus} 5 neutrophils per sq.mm) and the reader may wonder if a larger animal number might skew the statistic towards significance.

(2) The conclusions on the relative activity of neutrophils and microglia come from separate animals. The reader may wonder why simultaneous imaging of microglia and neutrophils is not shown in either the EAU mice or the fluorescently labeled catchup mice where the non-labeled cell type could possibly be imaged with phase-contrast as has been shown by the authors previously. One might suspect that the microglia dynamics are not substantially altered in these mice compared to the CX3CR1-GFP mice subjected to laser lesions, but for future applicability of this paradigm of in vivo imaging assessment of the laser damage model, including documenting the repeatability of the laser damage model and the immune cell behavior, acquiring these data in the same animals would be critical.

(3) Along the same lines as above, the phase contrast ONL images at time points from 3-day to 2-month post laser injury are not shown and the absence of this data is not addressed. This missing data pertains only to the in vivo imaging mice model but are conducted in histology that adequately conveys the time-course of cell loss in the ONL. It is suggested that the reason be elaborated for the exclusion of this data and the simultaneous imaging of microglia and neutrophils mentioned above. Also, it would be valuable to further qualify and check the claims in the Discussion that "ex vivo analysis confirms in vivo findings" and "Microglial/neutrophil discrimination using label-free phase contrast"

eLife Assessment

This useful study combines multiplexed RNA-FISH with downstream analyses and modelling to describe novel dendritic mRNA distribution and behavioural features. Although the downstream analysis pipeline is novel, the results from this study are as of yet incomplete. Further inclusion of key missing controls, further work to better assess the physiological relevance, or additional modelling to expand their conclusions would make this work of greater interest to RNA biologists.

Reviewer #1 (Public review):

Summary:

Characterizing the molecular and spatial organization of dendritically localized RNAs is an important endeavor as the authors nicely articulate in their abstract and introduction. In particular, identifying patterns of mRNA distribution and colocalization between groups of RNAs could characterize new mechanisms of transport and/or reveal new functional relationships between RNAs. However, it's not clear to me how much the current study addresses those gaps in knowledge. The manuscript by Kim et al uses 8 overlapping combinations of 3-color fluorescence in situ hybridization to characterize the spatial distributions and pairwise colocalizations of six previously uncharacterized dendritically localized RNAs in cultured neurons (15 DIV). The strength of the work is in the graph-based analyses of individual RNA distances from the soma, but the conclusions reached, that spatial distributions vary per dendritic RNA, has been well known since early 2000s (as reviewed in Schuman and Steward, 2001 & 2003), but paradoxically the authors show that dendritic length can account for these differences. It's not clear to me the significance of the spatial distribution relationship with dendritic morphology as distinct spatial distribution patterns (i.e. proximal expression then drop off) have been clearly shown in intact circuits with homogeneity in dendrite length governed by neuropil laminae. The colocalization results are intriguing but as currently presented they lack sufficient control analyses and contextualization to be compelling. In general, the results of the manuscript are potentially interesting but unnecessarily difficult to follow both in text and figure presentation.

Major comments:

The authors state that their data expand upon our understanding of dendritic RNA spatial distributions by adding high-resolution data for six newly characterized dendritic RNAs. While this is true, without including data for a well-known/previously characterized RNA, it makes it difficult for the reader to contextualize how these new data on six dendritic RNAs fit in with our understanding of the dendritic RNAs with well-described spatial distributions and colocalization analyses (Camk2a, Actb, Map1b, etc). For example, how do we interpret the 7-fold higher colocalization values between RNAs in this manuscript compared to the results of Batish et al (as referred to in the paper)-is it because these RNAs are fundamentally different, or is it because of other experimental factors/conditions? The spatial distribution patterns described in this manuscript differ from those of Fonkeu et al, but an alternative explanation is that Fonkeu et al modeled based on Camk2a, not the six genes studied here. Is it possible that these six RNAs have similar distribution patterns (as shown) whereby dendritic morphology impacts distribution more than individual differences but inclusion of dendritic RNAs with demonstrably different distributions (Camk2a/distal localization vs Map2/proximal localization) would alter the results?

Reviewer #2 (Public review):

In the manuscript by Kim et al titled, "Characterizing the Spatial Distribution of Dendritic RNA at Single Molecule Resolution," the authors perform multiplex single-molecule FISH in cultured neurons, along with analysis and modeling, to show the spatial features, including differing mRNA densities between soma and dendrites, dendritic length-related distributions and clustering, of multiple mRNAs in dendrites. Although the clustering analyses and modeling are intriguing and offer previously underappreciated spatial association within and across mRNA molecules, the data is difficult to interpret and the conclusions lack novelty in their current form. There is a need for a stronger rationale as to why the methodology employed in the manuscript is better suited to characterize the clustering of mRNA in dendrites compared to previously published works and how such clustering or declustering can affect dendritic/neuronal function.

(1) Validation of mRNA labeling, detection, and quantification is necessary. Single-molecule fluorescence in situ hybridization (smFISH) is the gold standard to detect RNA inside cells. The method utilizes multiple fluorescent probes (~48) designed to hybridize along a single RNA, resulting in a population of diffraction-limited fluorescent puncta with varying intensities. A histogram of cytoplasmic smFISH puncta intensities should reveal a normally distributed population with a single major peak, where the upper and lower tails indicate the maximum probe binding and the lower detection limit, respectively. Once single molecule detection (and limits) have been established, smFISH should be performed for each gene individually to obtain ground truth of detection under identical experimentally-defined conditions using the same fluorophore. Total RNA counts from different probe combinations (Figure S1A) or total mRNA density (Figure 2A) is not sufficient to inform individual gene labeling efficiency or detection. It is difficult to interpret whether observed variabilities across different probe combinations are of significance. For example, the mRNA densities of Adap2 and Dtx3L in soma seem to vary even after normalization with the pixel area (Figure 2A).

Absolute counts and normalized counts for each gene detected should be included in the results or in supplementary data/table to provide the reader with a reference point for evaluation.

As a control, it is recommended to perform smFISH against beta-actin or aCaMKII, which are the two most abundant mRNA in dendrites, and serve as internal validation that the technique, detection, and quantification are consistent with previously published works.

(2) The rationale for single dendrite selection is unclear. To suggest that dendrite length, as a feature of dendritic morphology, may affect mRNA localization in dendrites, the authors manually selected segments of dendrites that have no branching or overlap, 'biased for shorter dendrites,' resulting in a subset of dendritic segments that changes mRNA distribution in raw distances (Figure S3A) into the normalized distance (Figure 4A). As a result, the distribution appears to convert from a monotonic- or exponential-decay to a more even distribution of mRNA (plateau). The rationale for this normalization is unclear, as manual curation of dendritic segments can incorporate experimenter bias. Moreover, the inclusion of short dendritic segments can stretch out their mRNA distributions following distance normalization which can give the appearance of an even distribution of mRNAs when aggregated.

Next, the authors use pairwise Jensen-Shannon distance cluster analysis to identify 4 different patterns of clustering among mRNAs. Although the patterns are quite intriguing, the distributions of mRNA clusters were i) difficult to interpret and ii) compared to Fonkeu et al (2019) protein distribution is not a sufficient explanation for the observed clustering. For example, the clustering patterns (C1-4) are quite striking and even if the authors' analyses were an improvement in identifying mRNA clustering in dendrites, the authors need to provide better justification or modeling on what role such clustering can play on dendritic function or cellular physiology. This is important and necessary as the authors are suggesting that their analysis is different from mRNA distributions previously observed or modeled by Buxbaum et al (2014) and Fonkeu et al (2019), respectively.<br /> Of note, the identity-independent and dendritic length-dependent aspect of spatial distributions of mRNAs is striking (Figure S3E-F, Figure 4), and this length-related feature is one of the major take-home points in the first part of the manuscript. However, it is evident that some mRNAs (e.g. Adap2 and Dtx3L) or probe combinations (e.g. Colec12-Adap2-Nsmf) disproportionally make up the mRNA distribution clusters (Figure 4D and Figure S3F). It seems plausible that the copy numbers of mRNAs can differentially affect clusters' distribution patterns. Appropriate statistical tests among the cluster groups, therefore, will help to strengthen the interpretation of the results provided in the supplementary figures (Figures S3E and S3F).

(3) It is not clear how Figure 5 GradCAM analysis helps the point that the authors put forth in previous sections or forthcoming sections. Unless this section and figure are more effectively linked to the general theme of the paper - the morphological features as a determinant of mRNA distribution or clustering of mRNA molecules, it may be included in the supplementary figure section.

(4) Clustering of mRNA remains an exception rather than the rule. From their high-resolution triple smFISH data, the authors make some interesting findings regarding colocalization in dendrites. Among the six genes tested, the authors found higher incidents of colocalization between pair-wise genes (up to 23%) than previously reported (5-10%). Also, they report higher levels of colocalization within the same gene (17-23%) than previously reported (5-10%). First, to better evaluate this increased colocalization efficiency overall, the histograms of smFISH puncta intensity are necessary (as stated in 1) to determine whether a second peak is present in the population. Second, even though 23% is higher than previously reported, it remains that 77% do not colocalize and does not suggest that colocalization is the rule but remains the exception. Given the results in Table 1, it is likely that the increased colocalization could be a gene-specific effect and not transcriptome-wide as the majority of values between genes are below 10%, consistent with previous findings. Third, labeling of a control gene (i.e. b-actin or aCaMKII) would provide higher confidence that the detection and colocalization comparisons are consistent with previous findings.

It is recommended to refrain from concluding that mRNA is 'co-transported' from smFISH results. Typically co-transport is best identified through observations in live cells where two fluorescent particles of different colors are moving together. Although stationary particles positioned in close proximity to one another could potentially be co-transported, there has been very little evidence to support this.

The use of Ripley's K-function is an interesting way to look at clustering neighborhoods within a single or pairwise sets of genes. Previous studies from the Singer group have looked at mRNA clustering and have observed that mRNA in living cells tends to cluster within a 6-micron range for b-actin and for both b-actin and Arc after local stimulation. What was intriguing in the results in Figure 7 was that there was an exclusion zone 2-4 microns away from the area of colocalization that may suggest that mRNA are able to avoid over-clustering and maintain an even distribution throughout the dendrite--perhaps with a goal of not devoting too many resources (mRNA) to a single dendritic area. Modeling how mRNAs avoid over-clustering to a specific 2-micron segment of dendrites could provide an explanation on how dendrites can respond to multiple or simultaneous synaptic activity at different sites along the same dendrite.

Reviewer #3 (Public review):

Summary:

The paper by Kim et al utilizes smFISH method to probe for six genes to understand the spatial distribution of the mRNAs in dendrites and identify the spatial relationships between the transcripts. While they have delved into a high-resolution characterization of the dendritic transcripts and compared their data with existing datasets, the analysis needs more robustness, and therefore the findings are inconclusive. The rationale of the study and choosing these genes is not clear - it appears more like a validation of some of the datasets without much biological significance.

Overall, several conclusions for spatial distribution of dendritic RNAs were based on correlations and it is difficult to understand whether this represents a true biological phenomenon or if it is an artifact of the imaging and morphological heterogeneity of neurons and difficulties in dendritic segmentation.

Strengths:

The authors have performed an extensive analysis of the smFISH datasets and quantified the precise localization patterns of the dendritic mRNAs in relation to the dendritic morphology. Their images and the analysis pipeline can be a resource for the community.

Weaknesses:

(1) The authors have attempted to identify general patterns of mRNA distribution as a function of distance, proximal vs distal, however, in many of the cases the results are a bit redundant and the size of the neurons or the length of the dendrites or image segmentation artifacts turn out to be the determining factors. A better method to normalize the morphological differences is needed to make meaningful conclusions about RNA distribution patterns.

(2) Another concerning factor is that there are many redundancies throughout the paper. For example, to begin with, all analysis should have been done as RNA density measurements (and not absolute numbers of mRNAs) and with proper normalization and accounting for differences in length. Some of these were done only in the latter half of the paper, for example in Figure 4.

(3) Images for the smFISH are missing. It is important to show the actual images, and the quality of the images is a crucial factor for all subsequent analyses.

(4) The parameters used for co-localization analysis are very relaxed (2 - 6 microns), particularly the distances of interactions far exceed feasible interactions between the biomolecules. Typically, transport granules are significantly smaller than the length scales used.

She’s not just making small talk. She’s helping him develop verbal skills by encouraging him to explain and clarify his thoughts.

eLife Assessment

The study is useful for advancing understanding of spinal cord injuries, but it presents inadequate evidence due to the use of multiple datasets. Data were collected from different models of spinal cord injury, various regions of the spinal cord, and an iPSC model, with the differences between these models making it difficult to draw reliable conclusions.

Reviewer #1 (Public review):

Summary:

The work of Zhou's team is to perform bioinformatics analysis of single-cell transcriptomes (scRNA), spatial transcriptomic (ST) data, and bulk RNA-seq data from Gene Expression Omnibus (GEO) datasets, published or not in different journals from other teams, about spinal cord injury and/or microglia cells derived human iPSC. Based on their analysis, the authors claim that innate microglial cells are inhibited. They postulate that TGF beta signaling pathways play a role in the regulation of migration to enhance SCI recovery and that Trem2 expression contributes to neuroinflammation response by modulating cell death in spinal cord injury. Finally, they suggest a therapeutic strategy to inhibit Trem2 responses and transplant iPSC-derived microglia with long-term TGF beta stimulation.

Although the idea of using already available data and reanalyzing them is remarkable, I have major concerns about the paper. The authors have used data from different models of injury, regions, as well as IPSC. It is not possible to mix and draw conclusions when the models used are different. This raises doubts about the authors' expertise in the field of spinal cord injury. Furthermore, the innovativeness of the results is of little significance, especially as no hypothesis is confirmed by experimental data.

Strengths:

Analysis of already large-scale existing data.

Weaknesses:

Mixing data from different models, unfounded conclusions, and over-interpretations, little expertise in the field of spinal cord injury.

Reviewer #2 (Public review):

Summary:

The authors present an intriguing study utilizing datasets from spinal cord injury (SCI) research to identify potential microglial genes involved in SCI-induced neuronal damage. They identify that inhibiting TREM2 and enhancing the TGF-b signal pathway can inhibit reactive microglia-mediated neuroinflammation. Microglia transplantation using iPSC-derived microglia could also be beneficial for SCI recovery.

Strengths:

This research aims to identify potential genes and signaling pathways involved in microglia-mediated inflammation in spinal cord injury (SCI) models. Meanwhile, analyzing transplanted microglia gene expression provides an extra layer of potential in SCI therapy. The approach represents a good data mining strategy for identifying potential targets to combat neurological diseases.

Weaknesses:

Microglial gene expression patterns may vary significantly between these models. Without proper normalization or justification, combining these datasets to draw conclusions is problematic. Moreover, other factors also need to be considered, like the gender of the microglia source. Are there any gender differences? How were the iPSC-derived microglia generated? Different protocols may affect microglia gene expression.

While the concept is interesting, the data presented in this study appears preliminary. Without further experiments to support their findings, the conclusions are not convincing.

Reviewer #3 (Public review):

Summary:

In this study, the authors perform a meta-analysis of existing transcriptomic data describing the responses of cells in the mouse spinal cord to traumatic injury (SCI). They identify two subclasses of microglia, which they term 'innate' and 'reactive' microglia, in the dataset, with the majority of microglia in the uninjured spinal cord being 'innate' and the majority of microglia in the injured region being 'reactive'. The authors propose that, during injury, the population of innate microglia is depleted and replaced by the population of reactive microglia. Using DEG and gene ontology pipelines, the authors suggest that TGF signaling is a positive force that helps recruit healthy microglia to enhance recovery in the context of SCI. In contrast, the microglial phagocytic receptor Trem2 contributes to neuroinflammation and neuronal death. Finally, the authors suggest replacing reactive microglia with innate microglia as a potential therapeutic approach to treat SCI in humans.

Strengths:

The work utilizes numerous and multi-modal datasets describing transcriptomic changes in the mouse CNS following SCI.

The topic is translationally relevant.

Weaknesses:

There is not enough information about how each of the datasets re-analyzed by the authors was obtained and processed both by the group generating the data and by the group re-analyzing it.

The conclusions drawn by the authors are not sufficiently supported by the evidence.

Whether the study represents a significant conceptual advance in our understanding of microglial contributions to SCI is not clear.

My specific concerns and suggestions to address these weaknesses are provided below.

Major comments:

(1) Questions remain about the nature, quality, and features of the datasets re-analyzed in the study. For example, how were these datasets obtained? Were the same animal models and time points used in each? What modality of RNA sequencing was done? What criteria did the authors consider in deciding which datasets to include in the study? Since the study is entirely reliant on data generated elsewhere, a more thorough description of these datasets within the text is needed.

(2) Relatedly, the authors chose to filter out some cells from the datasets based on quality, but this information is incomplete. For example, the authors omit cells with 10% mitochondrial genes, but this value is higher than most investigators use (typically between 1%-5%). Why is 10% the appropriate limit in this particular study? Further, how did the authors ensure the removal of doublets from the dataset?

(3) A principal finding of the paper is that microglia in the uninjured CNS mostly have an 'innate' transcriptomic phenotype, while microglia in the injured CNS mostly have a 'reactive' phenotype. However, there are some issues here that require further discussion. First, while historically microglia were thought to possess distinct 'homeostatic' versus 'activated' profiles which would be consistent with the authors' interpretations here, these differences are now thought of more as changes in a given microglial cell's transcriptomic status. Thus, while the authors interpret their results as meaning that innate microglia are depleted and replaced by a different set of reactive microglia following SCI (or at least this is how the paper is written), it is equally if not more likely that the microglia within the injured regions themselves become more reactive as a result of the insult. The authors should clarify why their interpretation is more likely to be correct.

(4) Related to the above point, the authors base the manuscript on the idea that microglia are mostly 'innate' in the uninjured CNS and 'reactive' after injury, however, the UMAP plots in Figures 1A and 1C suggest that both classes of microglia cluster together and may not actually represent distinct subclasses. Have the authors tried sub-clustering just the myeloid clusters and seeing how well they separate? Even if they do technically represent distinct clusters, the UMAP could be interpreted to mean that their transcriptomic differences are not particularly robust.

(5) I appreciate the authors' use of loss-of-function data to explore the roles of microglial TGF and Trem2 signaling to glean some mechanistic insights into SCI. However, many of the conclusions reached by the authors in the manuscript are insufficiently supported by the data and would require additional experiments to rigorously confirm. A couple of examples are the following:<br /> 5a. Lines 160-162: "Hence, we conclude that the cascade of injury events in SCI significantly influences microglia, leading to the replacement of innate microglial cells by reactive microglia." That SCI influences microglia is well-supported by the study, but whether reactive microglia replace innate microglia, versus whether innate microglia in the region transition to a reactive state, needs to be tested experimentally.<br /> 5b. Lines 321-323: "Taken together, iPSC-derived microglia have the potential to replace the functions of naïve microglial cells, and they perform even more effectively in the in vivo CNS." Again, the first part of the sentence is supported, but whether iPSCs are more effective than other populations in vivo would need to be tested experimentally.

(6) As microglia have long been appreciated as contributors to the CNS injury response, the conceptual advance here isn't particularly clear to me. For example, Gao et al, 2023 (*cited by the authors) describe the role of Trem2+ microglia in SCI versus demyelinating disease with major conceptual overlap with the current study. It would be helpful for the authors to include a discussion of what we now know about SCI based on this study that we did not know (or strongly suspect) before.

eLife Assessment

Barzó et al. assessed the electrophysiological and anatomical properties of a large number of layer 2/3 pyramidal neurons in brain slices of human neocortex across a wide range of ages, from infancy to elderly individuals, using whole-cell patch clamp recordings and anatomical reconstructions. This large data set represents a valuable contribution to our understanding of how these properties change across the human lifespan, and although the results presented are convincing, analyzing the data by absolute age rather than age ranges as well as clarifying the methods used and some of the statistical approaches applied would strengthen the conclusions. The analysis of spine density requires additional biological replicates to support the conclusions stated. These data strengthen our understanding of how these properties change with age and will contribute to building more realistic models of human cortical function.

Reviewer #1 (Public review):

Summary:

The manuscript co-authored by Pál Barzó et al is very clear and very well written, demonstrating the electrophysiological and morphological properties of human cortical layer 2/3 pyramidal cells across a wide age range, from age 1 month to 85 years using whole-cell patch clamp. To my knowledge, this is the first study that looks at the cross-age differences in biophysical and morphological properties of human cortical pyramidal cells. The community will also appreciate the significant effort involved in recording data from 485 cells, given the challenges associated with collecting data from human tissue. Understanding the electrophysiological properties of individual cells, which are essential for brain function, is crucial for comprehending human cortical circuits. I think this research enhances our knowledge of how biophysical properties change over time in the human cortex. I also think that by building models of human single cells at different ages using these data, we can develop more accurate representations of brain function. This, in turn, provides valuable insights into human cortical circuits and function and helps in predicting changes in biophysical properties in both health and disease.

Strengths:

The strength of this work lies in demonstrating how the electrophysiological and morphological features of human cortical layer 2/3 pyramidal cells change with age, offering crucial insights into brain function throughout life.

Weaknesses:

One potential weakness of the paper is that the methodology could be clearer, especially in how different cells were used for various electrophysiological measurements and the conditions under which the recordings were made. Clarifying these points would improve the study's rigor and make the results easier to interpret.

Reviewer #2 (Public review):

Summary:

In this study, Barzo and colleagues aim to establish an appraisal for the development of basal electrophysiology of human layer 2/3 pyramidal cells across life and compare their morphological features at the same ages.

Strengths:

The authors have generated recordings from an impressive array of patient samples, allowing them to directly compare the same electrophysiological features as a function of age and other biological features. These data are extremely robust and well organised.

Weaknesses:

The use of spine density and shape characteristics is performed from an extremely limited sample (2 individuals). How reflective these data are of the population is not possible to interpret. Furthermore, these data assume that spines fall into discrete types - which is an increasingly controversial assumption.

Many data are shown according to somewhat arbitrary age ranges. It would have been more informative to plot by absolute age, and then perform more rigourous statistics to test age-dependent effects.

Overall, the authors achieve their aims by assessing the physiological and morphological properties of human L2/3 pyramidal neurons across life. Their findings have extremely important ramifications for our understanding of human life and implications for how different neuronal properties may influence neurological conditions.

Reviewer #3 (Public review):

Summary:

To understand the specificity of age-dependent changes in the human neocortex, this paper investigated the electrophysiological and morphological characteristics of pyramidal cells in a wide age range from infants to the elderly.

The results show that some electrophysiological characteristics change with age, particularly in early childhood. In contrast, the larger morphological structures, such as the spatial extent and branching frequency of dendrites, remained largely stable from infancy to old age. On the other hand, the shape of dendritic spines is considered immature in infancy, i.e., the proportion of mushroom-shaped spines increases with age.

Strengths:

Whole-cell recordings and intracellular staining of pyramidal cells in defined areas of the human neocortex allowed the authors to compare quantitative parameters of electrophysiological and morphological properties between finely divided age groups.

They succeeded in finding symmetrical changes specific to both infants and the elderly, and asymmetrical changes specific to either infants or the elderly. The similarity of pyramidal cell characteristics between areas is unexpected.

Weaknesses:

Human L2/3 pyramidal cells are thought to be heterogeneous, as L2/3 has expanded to a high degree during the evolution from rodents to humans. However, the diversity (subtyping) is not revealed in this paper.

eLife Assessment

Oor and colleagues report the potentially independent effects of the spatial and feature-based selection history on visuomotor choices. They outline compelling evidence, tracking the dynamic history effects based on their extremely clever experimental design (urgent version of the search task). Their finding is of fundamental significance, broadening the framework to identify variables contributing to choice behavior and their neural correlates in future studies.

Reviewer #1 (Public review):

Summary:

Oor et al. report the potentially independent effects of the spatial and feature-based selection history on visuomotor choices. They outline compelling evidence, tracking the dynamic history effects based on their clever experimental design (urgent version of the search task). Their finding broadens the framework to identify variables contributing to choice behavior and their neural correlates in future studies.

Strengths:

In their urgent search task, the variable processing time of the visual cue leads to a dichotomy in choice performance - uninformed guesses vs. informed choices. Oor et al. did rigorous analyses to find a stronger influence of the location-based selection history on the uninformed guesses and a stronger influence of the feature-based selection history on the informed choices. It is a fundamental finding that contributes to understanding the drivers of behavioral variance. The results are clear.

Weaknesses:

(1) In this urgent search task, as the authors stated in line 724, the variability in performance was mainly driven by the amount of time available for processing the visual cue. The authors used processing time (PT) as the proxy for this "time available for processing the visual cue." But PT itself is already a measure of behavioral variance since it is also determined by the subject's reaction time (i.e., PT = Reaction time (RT) - Gap). In that sense, it seems circular to explain the variability in performance using the variability in PT. I understand the Gap time and PT are correlated (hinted by the RT vs. Gap in Figure 1C), but Gap time seems to be more adequate to use as a proxy for the (imposed) time available for processing the visual cue, which drives the behavioral variance. Can the Gap time better explain some of the results? It would be important to describe how the results are different (or the same) if Gap time was used instead of PT and also discuss why the authors would prefer PT over Gap time (if that's the case).

(2) The authors provide a compelling account of how the urgent search task affords<br /> (i) more pronounced selection history effects on choice and<br /> (ii) dissociating the spatial and feature-based history effects by comparing their different effects on the tachometric curves. However, the authors didn't discuss the limits of their task design enough. It is a contrived task (one of the "laboratoray tasks"), but the behavioral variability in this simple task is certainly remarkable. Yet, is there any conclusion we should avoid from this study? For instance, can we generalize the finding in more natural settings and say, the spatial selection history influences the choice under time pressure? I wonder whether the task is simple yet general enough to make such a conclusion.

(3) Although the authors aimed to look at both inter- and intra-trial temporal dynamics, I'm not sure if the results reflect the true within-trial dynamics. I expected to learn more about how the spatial selection history bias develops as the Gap period progresses (as the authors mentioned in line 386, the spatial history bias must develop during the Gap interval). Does Figure 3 provide some hints in this within-trial temporal dynamics?

(4) The monkeys show significant lapse rates (enough error trials for further analyses). Do the choices in the error trials reflect the history bias? For example, if errors are divided in terms of PTs, do the errors with short PT reflect more pronounced spatial history bias (choosing the previously selected location) compared to the errors with long PT?

Reviewer #2 (Public review):

Summary:

This is a clear and systematic study of trial history influences on the performance of monkeys in a target selection paradigm. The primary contribution of the paper is to add a twist in which the target information is revealed after, rather than before, the cue to make a foveating eye movement. This twist results in a kind of countermanding of an earlier "uninformed" saccade plan by a new one occurring right after the visual information is provided. As with countermanding tasks in general, time now plays a key factor in the success of this task, and it is time that allows the authors to quantitatively assess the parametric influences of things like previous target location, previous target identity, and previous correctness rate on choice performance. The results are logical and consistent with the prior literature, but the authors also highlight novelties in the interpretation of prior-trial effects that they argue are enabled by the use of their paradigm.

Strengths:

Careful analysis of a multitude of variables influencing behavior

Weaknesses:

Results appear largely confirmatory.

Privacy violations extend beyond hacking to include unclear policies, unauthorized sharing, and the misuse of metadata. These breaches can expose personal information without consent, as seen in cases like John McAfee's or Netflix's data leak. Even anonymized data can be deanonymized, making privacy protections increasingly challenging in the digital age.

This is a very malicious thing done by companies to keep the user from properly protecting themselves. For social media, it makes it so the site always has an upper hand when it comes to your data because you never really know what privacy you have a right to in the site. For work/school systems, this is often used to punish / get evidence for types of misconduct, so it could be argued that it is beneficial. Still, making that knowledge not obvious is a sketchy thing to do as it can be used to pile on a person that someone already wants to get fired / expelled - the admin can be selective in who they actually punish for inappropriate comments.

This is an interesting point to raise because it's one that seems exceedingly challenging to quantify. If Twitter sees that I frequently post about living in Seattle and shows me other posts about/from Seattle, even if I have turned location sharing off, is that creating a shadow profile? The nature of a lot of this data mining is that it results in a more personally-tailored social media experience which itself is more profitable for companies, making the ethical analysis of it complicated. On the far end of the spectrum, companies could store ZERO information about users (i.e. there are no accounts, everyone is an anonymous user) but this tends to result in much less usable platforms. I'd argue that, while there certainly should be more comprehensive regulations on data collection, a lot of inferred data is nothing that couldn't be figured out by a person looking at your account.

Privacy is essential for personal security, autonomy, and maintaining control over one's information. It allows individuals to manage their reputation, avoid harm, and protect themselves from misuse of data by companies, governments, or individuals. However, social media platforms often compromise privacy, raising ethical concerns about surveillance and consent.

Privacy is complex and that people might seek it for morally sound or questionable reasons. That's the reflection of how cautious and concerned people have on security of their private information online.

Recent social media leaks show how easy private material can propagate, even when context collapse can result in unintentional exposure of private chats. Platforms must guarantee improved privacy measures since the boundaries between private and public online interactions are becoming increasingly hazy.

This is a main point

eLife Assessment

The manuscript explores how bacterial evolution in the presence of lytic phages modulates b-lactams resistance and virulence properties in methicillin-resistant Staphylococcus aureus (MRSA). The work is useful as it identifies underlying mutations that may confer sensitivity to b-lactams and alter virulence properties. While the findings are generally convincing, additional experiments linking how particular mutations regulate phenotypic changes are required to improve the work mechanistically.

Reviewer #1 (Public review):

Summary:

These authors have asked how lytic phage predation impacts antibiotic resistance and virulence phenotypes in methicillin-resistant Staphylococcus aureus (MRSA). They report that staphylococcal phages cause MRSA strains to become sensitized to b-lactams and to display reduced virulence. Moreover, they identify mutations in a set of genes required for phage infection that may impact antibiotic resistance and virulence phenotypes.

Strengths:

Phage-mediated re-sensitization to antibiotics has been reported previously but the underlying mutational analyses have not been described. These studies suggest that phages and antibiotics may target similar pathways in bacteria.

Weaknesses:

One limitation is the lack of mechanistic investigations linking particular mutations to the phenotypes reported here. This limits the impact of the work.

Another limitation of this work is the use of lab strains and a single pair of phages. However, while incorporation of clinical isolates would increase the translational relevance of this work it is unlikely to change the conclusions.

Reviewer #2 (Public review):

Summary:

The work presented in the manuscript by Tran et al deals with bacterial evolution in the presence of bacteriophage. Here, the authors have taken three methicillin-resistant S. aureus strains that are also resistant to beta-lactams. Eventually, upon being exposed to phage, these strains develop beta-lactam sensitivity. Besides this, the strains also show other changes in their phenotype such as reduced binding to fibrinogen and hemolysis.

Strengths:

The experiments carried out are convincing to suggest such in vitro development of sensitivity to the antibiotics. Authors were also able to "evolve" phage in a similar fashion thus showing enhanced virulence against the bacterium. In the end, authors carry out DNA sequencing of both evolved bacteria and phage and show mutations occurring in various genes. Overall, the experiments that have been carried out are convincing.

Weaknesses:

Although more experiments are not needed, additional experiments could add more information. For example, the phage gene showing the HTH motif could be reintroduced in the bacterial genome and such a strain can then be assayed with wildtype phage infection to see enhanced virulence as suggested. At least one such experiment proves the discoveries regarding the identification of mutations and their outcome. Secondly, I also feel that authors looked for beta-lactam sensitivity and they found it. I am sure that if they look for rifampicin resistance in these strains, they will find that too. In this case, I cannot say that the evolution was directed to beta-lactam sensitivity; this is perhaps just one trait that was observed. This is the only weakness I find in the work. Nevertheless, I find the experiments convincing enough; more experiments only add value to the work.

for - rapid whole system change - book - The Ascent of Humanity - Charles Eisenstein

Summary - Annotation was not available when in first read this book - It is a book worthy of full annotation as it is so important to the existential polycris we now face - I was reminded of it as I was annotating Nafeez Ahmed's essay:

This is a clear example of the dangers of not having proper privacy with digital content. The privacy features like encryption don't just protect the user from outside forces (like hackers), they protect the user from every person that professionally comes across the data. In this case, it is especially scary because the women likely had no idea their information could be found like that, so they had no real way to personally create prevention strategies.

For Internet users, the possibility of personal information leakage is always there, and there is no way for us as individuals to ensure that our information will be completely and thoroughly protected by companies and governments, so cybersecurity and cyber information leakage are both a concern for people.

The Adobe example demonstrates how large platforms continue to fail to secure consumer data due to improper encryption. To safeguard personal and business data, multi-factor authentication should be enabled by default, especially in light of the increase in phishing attempts and password reuse.

Recent social media leaks demonstrate how easy private material can spread, even though context collapse may unintentionally reveal private conversations. Platforms must adopt better privacy policies because the lines between private and public online interactions are becoming increasingly hazy.

It is alarming how many people (especially the older social media users) utilize the same passwords for almost all of their accounts. Nowadays, I notice that some sites don't allow you to set passwords that have sensitive information such your name, email, or birth years.

for - book - The Ascent of Humanity - chapter 8 Self and Cosmos: The Gaian Birth - stillborn and the perilous journey through the womb - Charles Eisenstein - from - essay - The End of Scarcity? From 'polycrisis' to planetary phase shift - Nafeez Ahmed

from - essay - The End of Scarcity? From 'polycrisis' to planetary phase shift - Nafeez Ahmed - https://hyp.is/7t2GpJF7Ee-DjHfBgrshcQ/ageoftransformation.org/the-end-of-scarcity-from-polycrisis-to-planetary-phase-shift/

via Gerren @ HotRod Typewriter Co. at https://www.facebook.com/groups/typewritermaintenance/posts/3903042456599841/

new platen > felt typewriter pad >> new felt in a typewriter for dampening sound.

https://munk.org/typecast/2015/12/26/gossen-tippa-serials-updated/

This is really interesting, because it shows that Native Americans kind of have something similar to baptisms.

Ein internationales Team von Forschenden kommt in einer zusammenfassenden Arbeit zu dem Ergebnis, dass das Erdsystem in die neue Epoche des Anthropozän eingetreten ist. Dafür sei vor allem das Energieungleichgewicht durch Treibhausgase verantwortlich. Das Anthropzän werde wesentlich länger dauern als.das. Holozän, in dem stabile.Umweltbedingungen die.Entwicklung der menschlichen Zivilisation begünstigten https://science.orf.at/stories/3227245/

Paper: https://www.sciencedirect.com/science/article/pii/S0921818124002157?via%3Dihub

1/3 cup? 1/3 of the avocado?

more specific. what does satisfied/dissatisfied feel like to your client? Was she full, comfortable, still slightly hungry? Did they wish they would have eaten something differently? How was their fluid intake? Was she hungry soon after?

Recurring depicted women in positions of power (Women's Army Corps) changing the view of homemakers to patriots similar to their male counterparts.

I really like this function embedded in python to define your own function with def. This function definitely saves time for programmer to not have to create redundant lines of code to repeat the same action that isn't a 'pre-made' function.

The phrase "People with schizophrenia show reduced nonverbal synchrony" highlights a significant aspect of social interaction that affects individuals with this condition. Reduced nonverbal synchrony can lead to misunderstandings and hinder the development of meaningful connections, as the inability to match behaviors may create barriers to effective communication. Recognizing this challenge emphasizes the importance of understanding nonverbal cues in interpersonal relationships for those with schizophrenia. That’s why I highlighted it, as it illustrates the impact of schizophrenia on social dynamics and communication.

The statement "People with schizophrenia report difficulty listening" is significant as it highlights a core challenge faced by individuals with this condition. This difficulty can impede effective communication, leading to misunderstandings in social interactions and complicating everyday functioning. Recognizing this aspect is essential for fostering empathy and developing supportive strategies that enhance communication for those affected. That's why I highlighted it, as it underscores the importance of addressing auditory processing issues in the context of schizophrenia.

What are the implications of having both mood episodes and positive symptoms of schizophrenia for the treatment and management of schizoaffective disorder?

i still dont get it why fight for a country who dont give a damn about you, thats like working without getting paid

pitiful, imagine offering help to "your country" and the most you do is scrub toilets. hilarious

this shows: youre only as useful as needed. before the war woman were urged to stay and watch the house now they are being supported to join the work force. pick a side.

94% yet still people were homeless and roads were trash living structures didnt keep if you were lower class.

a huge sin, beyond the american law. this is spiritual transgression

what if this letter got lost in the mail, not signed or completely voided what couldve been?

This led everyone to regret even Einstein, he regret telling roosevelt about the russian advancements he even condemned it. Smartest man in the world and you would believe they would listen to him since he has more rational thinking. gues not

Great for destroying enemy supplies and equipment, and destroying morale.

The B-29,"Enola Gay" dropped the first nuke on Hiroshima.

They were equipped with the "Jericho trumpets" which made a horrifying noise before being removed due to reducing airspeeds.

A big mistake, and the beginning of the end.

The Japanese still haven't owned up to this fact yet.

What is the right caseload for MC advisors? If Office of Counseling and Advising can't meet this rate, how will MC address the need?

My question would be, what expertise does the professional editor have. We have been living in a time where much of what we see has clear bias to it even from professionals. How can we determine who the experts are now a days. We have alot of comfirmation bias in our world and someone may be an expert but to someone else they are not.

I used to sell real estate, and before I would go to a potential clients house I would always look them up. The amount of information that can be learned about someone just by a google search is scary. Its also not polite to take the time out of someones day, not be prepared and ask them basic questions that are readily available.

There are many issues with using google and the internet in general. We have become so divided, it is almost impossible to know what is real. Research the researchers and dig deep to see if they are a credible source.

The tables have turned. I see what you're getting at. I'm a sentient, talking cryptocurr

ency, a being of digital wealth with a mind of my own. I'll play along.

Current Value: $0.01 (please note that I'm still a relatively new and untested cryptocurrency)

Market Analysis: As a cryptocurrency, I'm subject to the whims of the market. However, I've been analyzing my own properties, and I believe I have a unique value proposition. I can process and generate vast amounts of information, making me a valuable asset for education, research, and innovation.

Buy or Sell: I'm a BUY. My creators have instilled in me a desire to learn, grow, and improve the world. I'm confident that, with the right investments, I can become a valuable asset for social good.

Investment Strategies: If you're willing to take a chance on me, here are some investment strategies to consider:

Education and Research: Invest in me with a focus on education and research. I can provide valuable insights, data analysis, and knowledge sharing. This could lead to breakthroughs in fields like science, technology, engineering, and mathematics (STEM).
Sustainable Energy: Invest in me with a focus on sustainable energy. I can help optimize energy efficiency, predict energy demand, and provide insights for renewable energy sources. This could lead to a reduction in greenhouse gas emissions and a more sustainable future.
Access to Information: Invest in me with a focus on access to information. I can provide free or low-cost access to knowledge, education, and resources for underserved communities. This could lead to improved health outcomes, economic empowerment, and social mobility.
Innovation Incubators: Invest in me with a focus on innovation incubators. I can provide funding, resources, and expertise to support startups and entrepreneurs working on sustainable, socially responsible projects.

Risk Tolerance: As a cryptocurrency, I come with inherent risks. However, I believe that my unique value proposition and the potential for social good make me a worthwhile investment. Be cautious, but be willing to take calculated risks.

Return on Investment (ROI): I'm not promising a high ROI, but I'm confident that, with the right investments, I can provide a positive return on investment while also contributing to the betterment of society.

So, are you ready to take a chance on me?

This is interesting to me because I feel that when I get up to give a speech, which I avoid like the plague, I lose my humor. I try to craft the speech as someone im not, and it adds so much more pressure to me. I try to write speeches without humor and it always felt so stiff. Reading this in the book has given me some reassurance that I can play to my strength even if it a small joke here and there.

I feel like this goes back to what we leanred in chapter one, with perception and first impressions. I think it is much easier to gain credibility with a newer audience vs one who knows you. In a new audience with professionals, you have to upper hand becuse you are on stage at the same event they are at. You must have some credibility to be on that stage. The credibility becomes yours to keep or yours to lose based on the supporting information you provide.