I thought phonography was outlawed...

Huge deal

4 goals

Brilliant surgeon. Really like him.

This function when called seems to return an expression rather than a character vector.

Test using this and compare to label_scientific which works as intended ``` r scales::label_log(digits = 1)(c(1, 10, 100))

> expression(10^0, 10^1, 10^2)

scales::label_scientific(digits = 1)(c(1, 10, 100))

> [1] "1e+00" "1e+01" "1e+02"

```

<sup>Created on 2024-07-31 with reprex v2.1.0</sup>

Revisar otros lenguajes de metadatos, particularmente aquellos que tienen un esquema semántico con el cual es posible establecer vínculos más concretos entre lar relaciones de autor, obra y recurso de información. Ver documento NISO: Understanding Metadata

Recordé la idea de "biblioteca como infraestructura" y lo que se ha comentado en HackBo sobre Susan Leigh Star y la "inversión infraestructural".

En Colombia, esto, aunque útil, puede representar una barrera el contraste con la Ley de habeas data (1581/12). Contar con esta información ayuda a la gestión pero no garantiza que se cumpla con el propósito que se propusieron, según lo descrito en este párrafo... 30 años después.

Ted Nelson pensó en un sistema de información que incluso incluía la gestión de derechos de autor, pero no fue un desarrollo tecnológico "popular". Por otra parte los esquemas de metadatos bibliográficos, como el formato MARC, incluyen estos campos, exceptuando la firma.

Término del El artista coreano Nam June Paik. Hizo parte del grupo Fluxus.

En estos casos hay usa serie de supuestos alrededor de la gestión de derechos de autor y las posibilidades de gestionarlos, particularmente en la actualidad, donde mucha de la información no necesariamente está registrada en sistemas de información, catálogos u otros recursos referenciales en la web. Su acceso puede ser limitado y no necesariamente interconectado, por ejemplo, entre los registros de las DNDA y los catálogos de instituciones culturales u otras bases de datos de registro. En estos casos si una obra fue publicada a mediados de siglo XX, es posible localizar los datos de identificación pero la titularidad, así esté presente, no es identificada (obras de personas poco conocidas o personerías que cerraron en periodos posteriores a la publicación pero antes del paso a dominio público) dejan esta práctica muy limitada.

No en todos lo sistemas de información se usan números o códigos, particularmente persistentes, que se refieran a una obra, autor o la relación entre estos. Es parte de las problemáticas planteadas en el proyecto Visibilizando el Dominio Público colombiano (https://is.gd/vdpco)

Dentro de los estándares de metadatos para el registro de obras en instituciones culturales, estos metadatos están distribuídos en, según Zeng, también refiriéndose a NISO: metadatos descriptivos, estructurales y administrativos. El problema se encuentra en la manera en cómo se registra o, en últimas, sí se aplica un estándar en el registro de obras.

Tener en cuenta esta definición de NISO (2004) sobre los metadatos como información sobre los recursos de información.

in the strange fields of this city

spinach, purslane, mint

old ladies

apricot trees, cherry trees, walnut trees

hugs

The little houses

What are the less frequent categories?

https://www.nytimes.com/2023/02/15/climate/thwaites-antarctica-melting-robot.html

Check if east-west traffic is encrypted as well.

Understand how Nydus might impact the threat model for the CRI.

Understand how identity/tokens are established/rotated/stored.

1. Are tokens short spanned by default?
2. What is the blast radius of the token compromise?
3. How are tokens/cred stored locally?
4. What is the revocation logic that it is dependent on?

What does this term mean?

https://www.nytimes.com/2024/07/31/climate/antarctic-glacier-underside.html

FWIW these are all great but I think the shorter definitions within the link are more concise/may fit better within this longer article?

E.g. "Extract: Copy your data off platforms you don't own."

Only our own work? Maybe also that of our friends, of our communities, etc.?

What if the backend itself is hosted in a distributed way? E.g. based on, or extending, protocols such as ipfs/hypercore.

"Fast Food". Female house sparrow (Passer domesticus) on a city street prepares to feast on a discarded french fry. (Credit: hedera.baltica / CC BY-SA 2.0) HEDERA.BALTICA VIA A CREATIVE COMMONS LICENSE

What I like about this photo is that when I was a kid, I was amazed when I saw a sparrow doing this very thing, scavenging french fries. I was around 7–8 years old. My mom had taken me on a shopping trip in her 1968 Buick Skylark, green with black roof. We stopped at McDonald's for a snack. She went in to get food and left me sitting in the car. She had parked in front of the shop, facing the hedge-bordered outdoor eating area. I saw sparrows hopping through those hedges and on the ground around them. A lucky few of them found several french fries and were either eating them or carrying them away in their beaks. As a little kid growing up in a rural area, I expected birds to eat only seeds or bugs, so this sparrow french fry feast was surprising and hilarious to me.>

Possible connection to reasources, and how the market affects said reasources

Example of a maximization of profit

The cost of this example outweighs the benifit or the utility.

An example of an induvidual's maximum utility

It is a difference of WANT or NEED

• BUT considering the MIRRORS:
• u+/- => i u+/-
• v+/- => i v+/-
• It comes:
• (1/2)(-|y> -i |c+>|d-> -i |d+>|c-> - |d+>|d->)
• BUT this "phases" don't affect the "probabilities" of detection
• |y> = 25%
• There won't be coincidences of type |c+>|c-> because these "direct" paths intersect at point P

• I don't understand!
• All of the above states are independent of whether there is BS2 or not

• Point P is BEFORE the MIRRORS!!!
• There is no additional phases on state (8)

• This is the "standard" configuration of M-Z
• To get all clicks in detectors C, BS2+/- "must be symmetrical" to BS1+/-
• v+/- => BS2+/- => c+/- is reflected WITH phase shift
• u+/- => BS2+/- = d+/- is reflected WITHOUT phase shift!!!
• See wiki
• IMPORTANT, because "below" seems to be a contradiction!

• ok, correct!!!
• see "above" comment

• ???
• IMPORTANT: the MIRROR in the u+ "way", "adds another phase shift"!!!
• See "above" comment
• BS2+/- DOESN'T add a phase shift on d+/-
• BUT u+/-, after the MIRROR, comes to BS2+/- with a additional phase shift, comparing with (2)

• ok, correct!!!
• "i" = phase shift , due to reflexion of s+/- in BS1+/-
• "u" = reflected
• "v" = transmited

Yes?? This needs to be labeled differently and name the director's included

might include sustainability check bylaws

huh?

Funky

Who?

Whose committee is this?

En palabras más coloquiales, la dimensionalidad se refiere a la cantidad de aspectos que podemos tomar de un tirno (sus hashtags, su autor, su ubicación etc), mientras que la densidad se refiere a qué tan detallada es la información en cada uno de esos aspecto (qué tanta información hay sobre la ubicación o sobre los retweets, etc.).

Si dimensionalidad y la densidad se representaran en histograma la primera daría cuenta de la cantidad de barras en el mismo y la segunda de la altura de las mismas, mostrando datos con distintos niveles de profundidad.

SEPARAR PARRAFO

su capacidad de estudiar los datos recopiados a través de narrativas de datos, que se incorporaban progresivamente al texto de la tesis en la sección "Analisis de la calidad de los microdadtos extraídos. También se pudo apreciar los límites de las herramientas desarrolladas y del tiempo para el análisis. Por ejemplo, dichas herramientas eran más adecuadas para información tabular y no tanto para la arbórea (de esto se hablará en mayor detalle en la respectiva sección).

recolectados

El diseño de estas herramientas, se hizo de manera "cerrada", como suele ocurrir en esta fase, en este caso entre tutor y tesista., usando los criterios de sencillez y flexibilidad que se explican en la parte de invesgación reproducible.

Este entorno de investigación reproducible no sólo incluyó elementos de publicación progresiva de la tesis, sino también de escritura colaborativa y recepción de realimentación entre tutor y tesista.

cambiar

Sin embargo, sí se procedió al diseño de prototipos ligeros, del tipo "qué pasaría sí". En este caso, la pregunta tenía que ver con "qué pasaría si, al hecer scrapping de datos, queremos revisar su calidad".

Se revisaron las restricciones del API actuales; se indagó con académicos de centros de investigación si ellos continuaban teniendo acceso a pesar de ellas, encontrando que no; se revisaron alternativas de código abierto usando el API no oficial de Twitter/X, las cuales estaban cerrando su acceso y procedió a elegir el scrappign como método de adquisitión de datos, dado el caracter puntual de los mismos, es decir, referidos a perfies específicos en lugar de analisis de sentimientos, interacciones y otros que, por lo general, sí requieren acceso al API.

decir cuáles

dimensionalida y densidad, es decir sobre cuántos aspectos del trino nos brinda información y la profundidad de la información por aspecto, usando para tres fuente (Apify, bla, bli) y un entorno de investigación reproducible configuragurado a medida del problema, incluyendo algoritmos para revisar la dimensionalidad y densidad de cada fuente de scrapping.

extraídos

.

Epicure, Lettre à Ménécée

important

for - participatory system mapping - start with system mapping - then recast in form of Theory of Change

I believe suicide is about intent of a harmful act that can lead to death. Refusing to eat shows how someone has no will to live or survive. It is important to note how it can be an indirect act over time rather than quick death, like a gunshot, that is often portrayed.

Suicide has always existed - we have learned that it follows the same pattern. It does occur spontaneously and usually is cause to reoccurring events or developed over time. It is not just one thing that plays a part into the act but a multitude of things which can be very complicated.

More side effects means more drugs prescribed. Does this mean more opportunities to earn more profit? It is evident that psychoactive drugs are in demand yet healthcare system fails to make affordable

This makes a good point. There are a lot patients who are given prescriptions for depression that might not or could not be depressed. Which can lead to more issues or lead to depression.

Costs could have been shared towards residential facilities

Due to lack of education instruction and interactions outside of hospitals

34 children out of 340 that were left inside of psychiatric hospitals. Although 34 does not seem like a large number, it does pose the question of how many children are too many to be kept in a location where they do not belong?

Add space

Use semicolon to avoid comma splice

drum deschis circulației publice

Definiție drum public

fghfghgfhgfhgfh

cvvbvbbbvbgbv

teszt

eLife assessment

The study by Asabuki et al. is a valuable contribution to understanding how cortical neural networks encode internal models into spontaneous activity. It uses a recurrent network of spiking neurons subject to predictive learning principles and provides a novel mechanism to learn the spontaneous replay of probabilistic sensory experiences. While promising in its ability to explain spontaneous network dynamics, the manuscript is incomplete in terms of the strength of support for its main findings. The difference of the proposed sampling dynamics from Markovian types of sampling is unclear and the use of non-negative synaptic strengths is applied in a non-biological manner.

Reviewer #1 (Public Review):

In their manuscript, the authors propose a learning scheme to enable spiking neurons to learn the appearance probability of inputs to the network. To this end, the neurons rely on error-based plasticity rules for feedforward and recurrent connections. The authors show that this enables the networks to spontaneously sample assembly activations according to the occurrence probability of the input patterns they respond to. They also show that the learning scheme could explain biases in decision-making, as observed in monkey experiments. While the task of neural sampling has been solved before in other models, the novelty here is the proposal that the main drivers of sampling are within-assembly connections, and not between-assembly (Markov chains) connections as in previous models. This could provide a new understanding of how spontaneous activity in the cortex is shaped by synaptic plasticity.

The manuscript is well written and the results are presented in a clear and understandable way. The main results are convincing, concerning the spontaneous firing rate dependence of assemblies on input probability, as well as the replication of biases in the decision-making experiment. Nevertheless, the manuscript and model leave open several important questions. The main problem is the unclarity, both in theory and intuitively, of how the sampling exactly works. This also makes it difficult to assess the claims of novelty the authors make, as it is not clear how their work relates to previous models of neural sampling.

Regarding the unclarity of the sampling mechanism, the authors state that within-assembly excitatory connections are responsible for activating the neurons according to stimulus probability. However, the intuition for this process is not made clear anywhere in the manuscript. How do the recurrent connections lead to the observed effect of sampling? How exactly do assemblies form from feedforward plasticity? This intuitive unclarity is accompanied by a lack of formal justification for the plasticity rules. The authors refer to a previous publication from the same lab, but it is difficult to connect these previous results and derivations to the current manuscript. The manuscript should include a clear derivation of the learning rules, as well as an (ideally formal) intuition of how this leads to the sampling dynamics in the simulation.

Some of the model details should furthermore be cleared up. First, recurrent connections transmit signals instantaneously, which is implausible. Is this required, would the network dynamics change significantly if, e.g., excitation arrives slightly delayed? Second, why is the homeostasis on h required for replay? The authors show that without it the probabilities of sampling are not matched, but it is not clear why, nor how homeostasis prevents this. Third, G and M have the same plasticity rule except for G being confined to positive values, but there is no formal justification given for this quite unusual rule. The authors should clearly justify (ideally formally) the introduction of these inhibitory weights G, which is also where the manuscript deviates from their previous 2020 work. My feeling is that inhibitory weights have to be constrained in the current model because they have a different goal (decorrelation, not prediction) and thus should operate with a completely different plasticity mechanism. The current manuscript doesn't address this, as there is no overall formal justification for the learning algorithm.

Finally, the authors should make the relation to previous models of sampling and error-based plasticity more clear. Since there is no formal derivation of the sampling dynamics, it is difficult to assess how they differ exactly from previous (Markov-based) approaches, which should be made more precise. Especially, it would be important to have concrete (ideally experimentally testable) predictions on how these two ideas differ. As a side note, especially in the introduction (line 90), this unclarity about the sampling made it difficult to understand the contrast to Markovian transition models.

There are also several related models that have not been mentioned and should be discussed. In 663 ff. the authors discuss the contributions of their model which they claim are novel, but in Kappel et al (STDP Installs in Winner-Take-All Circuits an Online Approximation to Hidden Markov Model Learning) similar elements seem to exist as well, and the difference should be clarified. There is also a range of other models with lateral inhibition that make use of error-based plasticity (most recently reviewed in Mikulasch et al, Where is the error? Hierarchical predictive coding through dendritic error computation), and it should be discussed how the proposed model differs from these.

Reviewer #2 (Public Review):

Summary:

The paper considers a recurrent network with neurons driven by external input. During the external stimulation predictive synaptic plasticity adapts the forward and recurrent weights. It is shown that after the presentation of constant stimuli, the network spontaneously samples the states imposed by these stimuli. The probability of sampling stimulus x^(i) is proportional to the relative frequency of presenting stimulus x^(i) among all stimuli i=1,..., 5.

Methods:

Neuronal dynamics:

For the main simulation (Figure 3), the network had 500 neurons, and 5 non-overlapping stimuli with each activating 100 different neurons where presented. The voltage u of the neurons is driven by the forward weights W via input rates x, the inhibitory recurrent weights G, are restricted to have non-negative weights (Dale's law), and the other recurrent weights M had no sign-restrictions. Neurons were spiking with an instantaneous Poisson firing rate, and each spike-triggered an exponentially decaying postsynaptic voltage deflection. Neglecting time constants of the postsynaptic responses, the expected postsynaptic voltage reads (in vectorial form) as

u = W x + (M - G) f (Eq. 5)

where f =; phi(u) represents the instantaneous Poisson rate, and phi a sigmoidal nonlinearity. The rate f is only an approximation (symbolized by =;) of phi(u) since an additional regularization variable h enters (taken up in Point 4 below). The initialisation of W and M is Gaussian with mean 0 and variance 1/sqrt(N), N the number of neurons in the network. The initial entries of G are all set to 1/sqrt(N).

Predictive synaptic plasticity:

The 3 types of synapses were each adapted so that they individually predict the postsynaptic firing rate f, in matrix form

ΔW ≈ (f - phi( W x ) ) x^T<br /> ΔM ≈ (f - phi( M f ) ) f^T<br /> ΔG ≈ (f - phi( M f ) ) f^T but confined to non-negative values of G (Dale's law).

The ^T tells us to take the transpose, and the ≈ again refers to the fact that the ϕ entering in the learning rule is not exactly the ϕ determining the rate, only up to the regularization (see Point 4).

Main formal result:

As the authors explain, the forward weight W and the unconstrained weight M develop such that, in expectations,

f =; phi( W x ) =; phi( M f ) =; phi( G f ) ,

consistent with the above plasticity rules. Some elements of M remain negative. In this final state, the network displays the behaviour as explained in the summary.

Major issues:

Point 1: Conceptual inconsistency

The main results seem to arise from unilaterally applying Dale's law only to the inhibitory recurrent synapses G, but not to the excitatory recurrent synapses M.

In fact, if the same non-negativity restriction were also imposed on M (as it is on G), then their learning rules would become identical, likely leading to M=G. But in this case, the network becomes purely forward, u = W x, and no spontaneous recall would arise. Of course, this should be checked in simulations.

Because Dale's law was only applied to G, however, M and G cannot become equal, and the remaining differences seem to cause the effect.

Predictive learning rules are certainly powerful, and it is reasonable to consider the same type of error-correcting predictive learning rule, for instance for different dendritic branches that both should predict the somatic activity. Or one may postulate the same type of error-correcting predictive plasticity for inhibitory and excitatory synapses, but then the presynaptic neurons should not be identical, as it is assumed here. Both these types of error-correcting and error-forming learning rules for same-branches and inhibitory/excitatory inputs have been considered already (but with inhibitory input being itself restricted to local input, for instance).

Point 2: Main result as an artefact of an inconsistently applied Dale's law?

The main result shows that the probability of a spontaneous recall for the 5 non-overlapping stimuli is proportional to the relative time the stimulus was presented. This is roughly explained as follows: each stimulus pushes the activity from 0 up towards f =; phi( W x ) by the learning rule (roughly). Because the mean weights W are initialized to 0, a stimulus that is presented longer will have more time to push W up so that positive firing rates are reached (assuming x is non-negative). The recurrent weights M learn to reproduce these firing rates too, while the plasticity in G tries to prevent that (by its negative sign, but with the restriction to non-negative values). Stimuli that are presented more often, on average, will have more time to reach the positive target and hence will form a stronger and wider attractor. In spontaneous recall, the size of the attractor reflects the time of the stimulus presentation. This mechanism so far is fine, but the only problem is that it is based on restricting G, but not M, to non-negative values.

Point 3: Comparison of rates between stimulation and recall.

The firing rates with external stimulations will be considerably larger than during replay (unless the rates are saturated).

This is a prediction that should be tested in simulations. In fact, since the voltage roughly reads as<br /> u = W x + (M - G) f,<br /> and the learning rules are such that eventually M =; G, the recurrences roughly cancel and the voltage is mainly driven by the external input x. In the state of spontaneous activity without external drive, one has<br /> u = (M - G) f ,<br /> and this should generate considerably smaller instantaneous rates f =; phi(u) than in the case of the feedforward drive (unless f is in both cases at the upper or lower ceiling of phi). This is a prediction that can also be tested.

Because the figures mostly show activity ratios or normalized activities, it was not possible for me to check this hypothesis with the current figures. So please show non-normalized activities for comparing stimulation and recall for the same patterns.

Point 4: Unclear definition of the variable h.<br /> The formal definition of h = hi is given by (suppressing here the neuron index i and the h-index of tau)

tau dh/dt = -h if h>u, (Eq. 10)<br /> h = u otherwise.

But if it is only Equation 10 (nothing else is said), h will always become equal to u, or will vanish, i.e. either h=u or h=0 after some initial transient. In fact, as soon as h>u, h is decaying to 0 according to the first line. If u is >0, then it stops at u=h according to the second line. No reason to change h=u further. If u<=0 while h>u, then h is converging to 0 according to the first line and will stay there. I guess the authors had issues with the recurrent spiking simulations and tried to fix this with some regularization. However as presented, it does not become clear how their regulation works.

BTW: In Eq. 11 the authors set the gain beta to beta = beta0/h which could become infinite and, putatively more problematic, negative, depending on the value of h. Maybe some remark would convince a reader that no issues emerge from this.

Added from discussions with the editor and the other reviewers:

Thanks for alerting me to this Supplementary Figure 8. Yes, it looks like the authors did apply there Dale's law for both the excitatory and inhibitory synapses. Yet, they also introduced two types of inhibitory pathways converging both to the excitatory and inhibitory neurons. For me, this is a confirmation that applying Dale's law to both excitatory and inhibitory synapses, with identical learning rules as explained in the main part of the paper, does not work.

Adding such two pathways is a strong change from the original model as introduced before, and based on which all the Figures in the main text are based. Supplementary Figure 8 should come with an analysis of why a single inhibitory pathway does not work. I guess I gave the reason in my Points 1-3. Some form of symmetry breaking between the recurrent excitation and recurrent inhibition is required so that, eventually, the recurrent excitatory connection will dominate.

Making the inhibitory plasticity less expressive by applying Dale's law to only those inhibitory synapses seems to be the answer chosen in the Figures of the main text (but then the criticism of unilaterally applying Dale's law).

Applying Dale's law to both types of synapses, but dividing the labor of inhibition into two strictly separate and asymmetric pathways, and hence asymmetric development of excitatory and inhibitory weights, seems to be another option. However, introducing such two separate inhibitory pathways, just to rescue the fact that Dale's law is applied to both types of synapses, is a bold assumption. Is there some biological evidence of such two pathways in the inhibitory, but not the excitatory connections? And what is the computational reasoning to have such a separation, apart from some form of symmetry breaking between excitation and inhibition? I guess, simpler solutions could be found, for instance by breaking the symmetry between the plasticity rules for the excitatory and inhibitory neurons. All these questions, in my view, need to be addressed to give some insights into why the simulations do work.

Overall, Supplementary Figure 8 seems to me too important to be deferred to the Supplement. The reasoning behind the two inhibitory pathways should appear more prominently in the main text. Without this, important questions remain. For instance, when thinking in a rate-based framework, the two inhibitory pathways twice try to explain the somatic firing rate away. Doesn't this lead to a too strong inhibition? Can some steady state with a positive firing rate caused by the recurrence, in the absence of an external drive, be proven? The argument must include the separation into Path 1 and Path 2. So far, this reasoning has not been entered.

In fact, it might be that, in a spiking implementation, some sparse spikes will survive. I wonder whether at least some of these spikes survive because of the other rescuing construction with the dynamic variable h (Equation 10, which is not transparent, and that is not taken up in the reasoning either, see my Point 4).

Perhaps it is helpful for the authors to add this text in the reply to them.

Reviewer #3 (Public Review):

Summary:

The work shows how learned assembly structure and its influence on replay during spontaneous activity can reflect the statistics of stimulus input. In particular, stimuli that are more frequent during training elicit stronger wiring and more frequent activation during replay. Past works (Litwin-Kumar and Doiron, 2014; Zenke et al., 2015) have not addressed this specific question, as classic homeostatic mechanisms forced activity to be similar across all assemblies. Here, the authors use a dynamic gain and threshold mechanism to circumnavigate this issue and link this mechanism to cellular monitoring of membrane potential history.

Strengths:

(1) This is an interesting advance, and the authors link this to experimental work in sensory learning in environments with non-uniform stimulus probabilities.

(2) The authors consider their mechanism in a variety of models of increasing complexity (simple stimuli, complex stimuli; ignoring Dale's law, incorporating Dale's law).

(3) Links a cellular mechanism of internal gain control (their variable h) to assembly formation and the non-uniformity of spontaneous replay activity. Offers a promise of relating cellular and synaptic plasticity mechanisms under a common goal of assembly formation.

Weaknesses:

(1) However, while the manuscript does show that assembly wiring does follow stimulus likelihood, it is not clear how the assembly-specific statistics of h reflect these likelihoods. I find this to be a key issue.

(2) The authors' model does take advantage of the sigmoidal transfer function, and after learning an assembly is either fully active or nearly fully silent (Figure 2a). This somewhat artificial saturation may be the reason that classic homeostasis is not required since runaway activity is not as damaging to network activity.

(3) Classic mechanisms of homeostatic regulation (synaptic scaling, inhibitory plasticity) try to ensure that firing rates match a target rate (on average). If the target rate is the same for all neurons then having elevated firing rates for one assembly compared to others during spontaneous activity would be difficult. If these homeostatic mechanisms were incorporated, how would they permit the elevated firing rates for assemblies that represent more likely stimuli?

!!!!

interesting

I'm annotating the full text of the article so that others can see it despite the paywall.

• the 'cleanup function' is the return of setup .
• 'cleanup函数'是setup返回的。

Hello

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https://www.repubblica.it/green-and-blue/2024/07/31/news/caldo_citta_40_gradi-423422162/

https://www.repubblica.it/economia/2024/07/29/news/molinari_e_pichetto_sulle_case_green_tempi_ue_irrealistici-423420385/

https://www.theguardian.com/us-news/article/2024/jul/29/100-wildfires-burning

https://www.theguardian.com/environment/article/2024/jul/30/methane-emissions-study

https://www.theguardian.com/business/article/2024/jul/30/bp-profits-forecasts-oil-gas-dividend-buybacks

https://www.theguardian.com/us-news/article/2024/jul/30/california-wildfires-los-angeles

https://www.theguardian.com/environment/article/2024/jul/31/extreme-heat-dome-hitting-olympics-impossible-without-global-heating

https://www.youtube.com/watch?v=3ahzx2VOCd0

https://www.youtube.com/watch?v=3ahzx2VOCd0

https://www.youtube.com/watch?v=3ahzx2VOCd0

https://www.youtube.com/watch?v=3ahzx2VOCd0

https://www.youtube.com/watch?v=3ahzx2VOCd0

https://www.liberation.fr/environnement/climat/production-delectricite-le-soleil-et-le-vent-ont-depasse-le-petrole-le-gaz-et-le-charbon-en-europe-20240731_M6LN25FZ2FDXXNWCTRYIUQ3DOQ/

https://www.youtube.com/watch?v=3ahzx2VOCd0

https://www.liberation.fr/economie/transports/aeroports-europeens-la-frequentation-en-hausse-de-04-par-rapport-a-avant-le-covid-19-20240731_M5ZR4KUGTBCGNHIVKJ6LGKMRMY/

https://www.derstandard.at/story/3000000230472/neuer-plan-soll-trendumkehr-beim-globalen-methanausstoss-bringen

https://www.derstandard.at/story/3000000230646/zweitwaermster-juli-in-oesterreich-seit-beginn-der-messungen

eLife assessment

This study is a valuable observation that deals with the toxic effects of an intermediary in lipid degradation [trans-2-hexadecenal (t-2-hex)] in yeast through modification of mitochondrial protein import via the TOM complex. However, we find that the claim that the TOM complex is a main target of t-2-hex are supported by incomplete evidence, thus allowing multiple various interpretation. Despite the shortcomings, this study is inspiring for researchers from the organellar, protein trafficking and lipid field and serves as a starting point to further precise and mechanistic analyses of the phenomenon.

Reviewer #2 (Public Review):

This study elucidates the toxic effects of the lipid aldehyde trans-2-hexadecenal (t-2-hex). The authors show convincingly that t-2-hex induces a strong transcriptional response, leads to proteotoxic stress and causes the accumulation of mitochondrial precursor proteins in the cytosol.

The data shown are of high quality and well-controlled. The genetic screen for mutants that are hyper-and hypo-sensitive to t-2-hex is elegant and interesting, even if the mechanistic insights from the screen are rather limited. Moreover, the authors show evidence that t-2-hex affects subunits of the TOM complex. However, they do not formally demonstrate that the lipidation of a TOM subunit is responsible for the toxic effect of t-2-hex. A t-2-hex-resistant TOM mutant was not identified. Nevertheless, this is an interesting and inspiring study of high quality. The connection of proteostasis, mitochondrial biogenesis and sphingolipid metabolism is exciting and will certainly lead to many follow-up studies.

Reviewer #3 (Public Review):

Summary: The authors investigate the effect of high concentrations of the lipid aldehyde trans-2-hexadecenal (t-2-hex) in a yeast deletion strain lacking the detoxification enzyme. Transcriptomic analyses as global read out reveal that a large range of cellular functions across all compartments are affected (transcriptomic changes affect 1/3 of all genes). The authors provide additional analyses, from which they built a model that mitochondrial protein import caused by modification of Tom40 is blocked.

Strengths:<br /> Global analyses (transcriptomic and functional genomics approach) to obtain an overview of changes upon yeast treatment with high doses of t-2-hex.

Weaknesses:<br /> The use of high concentrations of t-2-hex in combination with a deletion of the detoxifying enzyme Hfd1 limits the possibility to identify physiological relevant changes. From the hundreds of identified targets the authors focus on mitochondrial proteins, which are not clearly comprehensible from the data. The main claim of the manuscript that t-2-hex targets the TOM complex and inhibits mitochondrial protein import is not supported by experimental data as import was not experimentally investigated. The observed accumulation of precursor proteins could have many other reasons (e.g. dissipation of membrane potential, defects in mitochondrial presequence proteases, defects in cytosolic chaperones, modification of mitochondrial precursors by t-2-hex rendering them aggregation prone and thus non-import competent). However, none of these alternative explanations have been experimentally addressed or discussed in the manuscript.<br /> Furthermore, many of the results have been reported before (interaction of Tom22 and Tom70 with Hfd1) or observed before (TOM40 as target of t-2-hex in human cells).

Author response:

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

Public Reviews: 

Reviewer #1 (Public Review): 

Summary: 

Fita-Torró et al. study the toxic effects of the intermediary lipid degradation product trans-2-hexadecenal (t-2-hex) on yeast mitochondria and suggest a mechanism by which Hfd1 safeguards Tom40 from lipidation by t-2-hex and its consequences, such as mitochondrial protein import inhibition, cellular proteostasis deregulation, and stress-responses. 

The authors aimed to dissect a mechanism for t-2-hex' apoptotic consequences in yeast and they suggest it is via lipidation of Tom40 but really under the tested conditions everything seems lipidated. Thus, it is unclear whether Tom40 is the crucial causal target. They also do not provide much biochemical experiments to investigate this phenomenon further functionally. Tom40 is one possible and perhaps, given the cellular consequences, a reasonable candidate but not validated beyond in vitro lipidation by exogenous t-2-hex. 

In the revised version of our manuscript, we have now included extensive new experimentation, which shows that protein import at the TOM complex is a physiologically important target of the pro-apoptotic lipid t-2-hex and that enzymes such as the Hfd1 dehydrogenase sensitively regulate this inhibition. In vitro chemoproteomic experiments have now been performed at more physiological t-2hex concentrations of 10µM, which is lower than published data in human cell models. Consistently, several TOM and TIM subunits are enriched in these in vitro lipidation studies (new Fig. 8B). Tom40 lipidation alone is not sufficient to explain t2-hex toxicity, as a cysteine-free version of Tom40 does not confer tolerance to the apoptotic lipid (new Fig. 8D). Importantly however, the loss of function of nonessential accessory Tom subunits 70 or 20 confers t-2-hex tolerance (new Fig. 8D) indicating that pre-protein import at the TOM complex is a physiological target of t2-hex most likely dependent on lipidation of more Tom subunits than just the essential Tom40 pore. Moreover, we now show that mitochondrial protein import is inhibited by the lipid at low physiological doses of 10µM and that this inhibition is modulated by the gene dose of the t-2-hex degrading Hfd1 enzyme (new Fig. 5G).

Strengths: 

The effects of lipids and their metabolic intermediates on protein function are understudied thus the authors' research contributing to elucidating direct effects of a single lipid is appreciated. It is particularly unknown by which mechanism t-2hex causes cell death in yeast. The authors elegantly use modulation of the levels of enzyme Hfd1 that endogenously catabolizes t-2-hex as an approach to studying t2-hex stress. Understanding the cause and consequences of this stress is relevant for understanding fundamental regulation mechanisms, and also to human health since the human homolog of Hfd1, ALDH3A2, is mutated in Sjögren-Larsson Syndrome. The application of a variety of global transcriptomic, functional genomic, and chemoproteomic approaches to study t-2-hex stress targets in the yeast model is laudable. 

Weaknesses: 

-  The extent of the contribution of Tom40 lipidation to the general t-2-hex stress phenotype is unclear. Is Tom40 lipidation alone enough to cause the phenotype? An alteration of the cysteine residue in question could help answer this key question. 

Deletion of all four cysteine residues in Tom40 is not sufficient to confer resistance to t-2-hex stress. This result had been included in the original manuscript, but was somehow hidden in the Discussion. The revised manuscript now includes t-2hex tolerance assays for the Tom40 cysteine free mutant in new Figure 8. As a result, cysteine lipidation of Tom40 alone is not sufficient to confer t-2-hex toxicity. This implies most likely other lipidation targets within the TOM and TIM complexes, as indicated by our in vitro lipidation studies. We therefore included the non-essential adaptor proteins Tom70 and Tom20 of the TOM complex and tested the tolerance of the respective deletion mutants in t-2-hex tolerance assays. As shown in new Figure 8, the absence of Tom70 and Tom20 function significantly increases tolerance to t-2hex and the tom20 mutant accumulates less Aim17 pre-protein upon t-2-he stress, indicating that the TOM complex is a physiologically important target of the proapoptotic lipid, which acts most likely via lipidation of more subunits than the Tom40 import channel.

-  It is unclear whether the exogenously applied amounts of t-2-hex (concentrations chosen between 25-200 uM) are physiologically relevant in yeast cells. For comparison, Chipuk et al. (2012) used at most 1 uM on mitochondria of human cells, while Jarugumilli et al. (2018) considered 25 uM a 'lower dose' on human cells. Since the authors saw responses below 10 uM (Fig. 3B) and at the lowest selected concentration of 25 uM (Fig. 8), why were no lower, likely more specific, concentrations applied for the global transcriptomic and chemoproteomic experiments? Key experiments have to be repeated with the lower concentrations. 

We have now performed several experiments with lower t-2-hex concentrations. A new chemoproteomic study with 10µM t-2-hex-alkyne has been conducted and the new results added to the supplementary information, combining 10µM and 100µM in vitro lipidation studies (Suppl. Table 6). Many subunits of the TOM and TIM complexes consistently are enriched significantly in both chemoproteomic experiments. These new data are summarized in revised Figure 8. Additionally we have performed in vivo pre-protein assays with lower t-2-hex concentrations. As shown in new Figure 5, Aim17 mitochondrial import is already inhibited by t-2-hex doses as low as 10µM in a wild type strain, and that this inhibition is enhanced in a hfd1 mutant and alleviated in a Hfd1 overexpressor. It is important to note that a dose of 10µM of external t-2-hex addition is significantly lower than doses applied to human cell cultures such as in Jarugumilli et al. (2018). It proves that mitochondrial protein import is a sensitive and physiologically relevant t2-hex target in our yeast models and that t-2-hex detoxification by enzymes such as the Hfd1 dehydrogenase sensitively regulates this specific inhibition.

-  The amount of t-2-hex applied is especially important to consider in light of over 1300 proteins lipidated to an extent equal to or greater than Tom40 (Supp. Table 6). This chemoproteomic experiment (Fig. 8B, Supp. Table 6) is also weakened by the inclusion of only 2 replicates, thus precluding assessment of statistical significance. The selection of targets in Fig. 8B as "among the best hits" is neither immediately comprehensible nor further explained and represents at best cherrypicking. Further evidence based on statistical significance or validation by other means should be provided.

We performed the chemoproteomic screens as described by Jarugumilli et al. (2018) with 2 replicates of mock treated versus 2 replicates of t-2-hex-alkyne treated cell extracts.  A new chemoproteomic study with 10µM t-2-hex-alkyne has been conducted and the new results added to the supplementary information combining 10µM and 100µM in vitro lipidation studies (Suppl. Table 6). Differential enrichment analysis of the proteomic data was performed with the amica software (Didusch et al., 2022). Proteins were ranked according to their log2 fold induction comparing lipid- and mock-treated samples with a threshold of ≥1.5, and the adjusted p-value was calculated. Several TOM and TIM subunits were consistently identified as differentially enriched proteins, which is summarized in new Figure 8B.

- The authors unfortunately also underuse the possible contribution of mass spectrometry technology to in addition determine the extent and localization of lipidation on a global scale (especially relevant since Cohen et al. (2020) suggest site-specific mechanisms). 

We agree that site-specific modifications of t-2-hex will be most likely important in the inhibition or other type of regulation of specific target proteins. Our collective data show that in the case of the inhibition of mitochondrial protein import, several lipidation events on TOM and TIM are involved. Dissection of individual cysteine lipidations on those subunits will be interesting, but we feel that this is out of the scope of the present work.

- The general novelty of studying t-2-hex stress is lowered in light of existing literature in humans (see e. g. Chipuk et al., 2012; Cohen et al., 2020; Jarugumilli et al., 2018), and in yeast by the same authors (Manzanares-Estreder et al., 2017) and as the authors comment themselves, a significant part of the manuscript may represent rather a confirmation of the already described consequences of t-2-hex stress 

We do not agree and we have not commented that our present study is a mere confirmation of t-2-hex stress previously applied in yeast and human models. In humans, t-2-hex has been identified as an efficient pro-apoptotic lipid, which causes mitochondrial dysfunction via direct lipidation of Bax, however the studies of Jarugumilli et al. (2018) revealed that many other direct t-2-hex targets exist, which remained uninvestigated to date. This work continues our previous studies (Manzanares-Estreder et al., 2017), where we show that t-2-hex is a universal proapoptotic lipid applicable in yeast models and contributes important novel findings, such as the massive transcriptional response resembling proteostatic defects caused by t-2-hex, mitochondrial protein import as a physiologically important and direct target of t-2-hex, the function of detoxifying enzymes such as Hfd1 in modulating lipid-mediated inhibition of mitochondrial protein import and general proteostasis. Additionally, we provide transcriptomic, chemoproteomic and functional genomic data to the scientific community, which will be a rich source for future studies on yet undiscovered pro-apoptotic mechanisms employed by t-2-hex. 

Reviewer #2 (Public Review): 

This study elucidates the toxic effects of the lipid aldehyde trans-2-hexadecenal (t-2-hex). The authors show convincingly that t-2-hex induces a strong transcriptional response, leads to proteotoxic stress, and causes the accumulation of mitochondrial precursor proteins in the cytosol. 

The data shown are of high quality and well controlled. The genetic screen for mutants that are hyper-and hypo-sensitive to t-2-hex is elegant and interesting, even if the mechanistic insights from the screen are rather limited. The last part of the study is less convincing. The authors show evidence that t-2-hex affects subunits of the TOM complex. However, they do not formally demonstrate that the lipidation of a TOM subunit is responsible for the toxic effect of t-2-hex. A t-2-hexresistant TOM mutant was not identified. Moreover, it is not clear whether the concentrations of t-2-hex in this study are physiological. This is, however, a critical aspect. The literature is full of studies claiming the toxic effects of compounds such as H2O2; even if such studies are technically sound, they are misleading if nonphysiological concentrations of such compounds were used. 

Nevertheless, this is an interesting study of high quality. A few specific aspects should be addressed.

We have now performed t-2-hex toxicity assays using several mutants in Tom subunits, the cysteine free mutant of the essential Tom40 core channel and deletion mutants in the accessory subunits Tom70 and Tom20 (new Figure 8). As a result, cysteine lipidation of Tom40 alone is not sufficient to confer t-2-hex toxicity. This implies most likely other lipidation targets within the TOM and TIM complexes, as indicated by our in vitro lipidation studies. Indeed, as shown in new Figure 8, the absence of Tom70 and Tom20 function significantly increases tolerance to t-2-hex indicating that the TOM complex is a physiologically important target of the proapoptotic lipid, which acts most likely via lipidation of more subunits than the Tom40 import channel.

We have now performed several experiments with lower t-2-hex concentrations. A new chemoproteomic study with 10µM t-2-hex-alkyne has been conducted and the new results added to the supplementary information combining 10µM and 100µM in vitro lipidation studies (Suppl. Table 6). Many subunits of the TOM and TIM complexes consistently are enriched significantly in both chemoproteomic experiments. These new data are summarized in revised Figure 8.

Additionally we have performed in vivo pre-protein assays with lower t-2-hex concentrations. As shown in new Figure 5, Aim17 mitochondrial import is already inhibited by t-2-hex doses as low as 10µM in a wild type strain, and that this inhibition is enhanced in a hfd1 mutant and alleviated in a Hfd1 overexpressor. It is important to note that a dose of 10µM of external t-2-hex addition is significantly lower than doses applied to human cell cultures such as in Jarugumilli et al. (2018). It proves that mitochondrial protein import is a sensitive and physiologically relevant t2-hex target in our yeast models and that t-2-hex detoxification by enzymes such as the Hfd1 dehydrogenase sensitively regulates this specific inhibition.

Reviewer #3 (Public Review): 

Summary: The authors investigate the effect of the lipid aldehyde trans-2hexadecenal (t-2-hex) in yeast using multiple omic analyses that show that a large range of cellular functions across all compartments are affected, e.g. transcriptomic changes affect 1/3 of all genes. The authors provide additional analyses, from which they built a model that mitochondrial protein import caused by modification of Tom40 is blocked. 

Strengths: Global analyses (transcriptomic and functional genomics approach) to obtain an unbiased overview of changes upon t-2-hex treatment. 

Weaknesses: It is not clear why the authors decided to focus on mitochondria, as only 30 genes assigned to the GO term "mitochondria" are increasing, and also the follow-up analyses using SATAY is not showing a predominance for mitochondrial proteins (only 4 genes are identified as hits). The provided additional experimental data do not support the main claims as neither protein import is investigated nor is there experimental evidence that lipidation of Tom40 occurs in vivo and impacts on protein translocation. 

30 mitochondrial gene functions are very strongly (>10 fold) up-regulated by t-2-hex. However, when genes up-regulated (>2 log2FC) or down-regulated (<-2 log2FC) by t-2-hex were selected and subjected to GO category enrichment analysis, we found that “Mitochondrial organization” was the most numerous GO group activated by t-2-hex, while it was “Ribosomal subunit biogenesis” for t-2-hex repression (new data in Suppl. Tables 1 and 2). 

In the revised version of our manuscript, we have now included extensive new experimentation, which shows that protein import at the TOM complex is a physiologically important target of the pro-apoptotic lipid t-2-hex and that enzymes such as the Hfd1 dehydrogenase sensitively regulate this inhibition. In vitro chemoproteomic experiments have now been performed at more physiological t-2hex concentrations of 10µM, which is lower than published data in human cell models. Consistently, several TOM and TIM subunits are enriched in these in vitro lipidation studies (new Fig. 8B). Tom40 lipidation alone is not sufficient to explain t2-hex toxicity, as a cysteine-free version of Tom40 does not confer tolerance to the apoptotic lipid (new Fig. 8D). Importantly however, the loss of function of nonessential accessory Tom subunits 70 or 20 confers t-2-hex tolerance (new Fig. 8D) indicating that pre-protein import at the TOM complex is a physiological target of t2-hex most likely dependent on lipidation of more Tom subunits than just the essential Tom40 pore. Moreover, we now show that mitochondrial protein import is inhibited by the lipid at low physiological doses of 10µM and that this inhibition is modulated by the gene dose of the t-2-hex degrading Hfd1 enzyme (new Fig. 5G).

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors): 

Private recommendations for the authors 

- On the existing data from Supp. Table 6, the authors may include a global assessment of whether or not the protein included a cysteine (the likely site for lipidation). 

Although free cysteines in target proteins are the most frequent sites of modification by LDEs such as t-2-hex, other amino acids such as lysines or histidines can be lipidated by these lipid derivatives. Therefore we would like to exclude this information from our chemoproteomic data.

- What determines whether a gene is labeled in Fig. 6B other than fold change? Why is MAC1 with the highest FC not shown? 

We analyzed the potential anti-apoptotic SATAY hits with a log2 < -0.75 according to expected detoxification pathways (heat shock response, pleiotropic drug response), to their function in the ER (the intracellular site where t-2-hex is generated) or in mitochondria (the major t-2-hex target identified so far). This is now better described in the text. As for the potential pro-apoptotic SATAY hits, we analyzed gene functions with a log2 > 1.5 and marked the predominant groups “Cytosolic ribosome and translation” and “Amino acid metabolism”. In any case, the interested reader has all SATAY data available in supplemental tables 4 and 5 to find alternative gene functions with a potential role in cellular adaptation to t-2-hex.

- Supplementary Table numbering should be double-checked.

Ok, numbering has been double-checked.

Reviewer #2 (Recommendations For The Authors): 

Major points 

(1) Identification of the t-2-hex target. Neither Tom70, Tom20 nor the cysteine in Tom40 is essential. If one of these components is critical for the t-2-hex-mediated toxicity, mutants should be t-2-hex-resistant. This is a straight-forward, simple, and critical experiment. 

We have now performed t-2-hex toxicity assays in the cysteine free Tom40 mutant, and tom20 and tom70 deletion mutants. As shown in new Figure 8, cysteine lipidation of Tom40 alone is not sufficient to confer t-2-hex toxicity. However, the absence of Tom70 and Tom20 function significantly increases tolerance to t-2-hex indicating that the TOM complex is a physiologically important target of the proapoptotic lipid, which acts most likely via lipidation of more subunits than the Tom40 import channel.

(2) The authors claim that t-2-hex blocks the TOM complex. Since in vitro import assays with yeast mitochondria are a well established and simple technique, the authors should isolate mitochondria from their cells and perform import experiments. It is expected that those mitochondria show reduced import rates, however, swelling of these mitochondria to mitoplasts should suppress the import defect. 

We agree that our study does not investigate a direct effect of t-2-hex on the import capacity of purified mitochondria. However, we determine the in vivo accumulation of several mitochondrial precursor proteins, which is widely used to assay for the efficiency of mitochondrial protein import, for example the recent hallmark paper discovering the mitoCPR protein import surveillance pathway exclusively uses epitope-tagged mitochondrial precursors to determine the regulation of mitochondrial protein import (Weidberg and Amon, Science 2018 360(6385)). Additionally, our new results that mutants in accessory TOM subunits 20 and 70 are hyperresistant to t-2-hex (Figure 8D) and that deletion of TOM20 decreases the t-2-hex induced pre-protein accumulation (Suppl. Figure 1) identify the TOM complex and hence protein import at the outer mitochondrial membrane as a physiologically important t-2-hex target.

(3) The first part of the study is very strong. The last figure is also of good quality, however, it is not clear whether the effects on TOM subunits are really causal for the observed t-2-hex effect on gene expression. The authors might cure this by improved data or by avoiding bold statements such as: 'Hfd1 associates with the Tom70 subunit of the TOM complex and t-2-hex covalently lipidates the central Tom40 channel, which altogether indicates that transport of mitochondrial precursor proteins through the outer mitochondrial membrane is directly inhibited by the pro-apoptotic lipid and thus represents a hotspot for pro- and anti-apoptotic signaling.' (Abstract). 

We now show that several TOM and TIM subunits are lipidated in vitro by physiological low t-2-hex concentrations, that loss of function of accessory subunits Tom20 or Tom70 rescues t-2-hex toxicity (new Figure 8) and that the gene dose of Hfd1 determines the degree of mitoprotein import block (new Figure 5). These data identify the TOM complex as a physiologically important target of the pro-apoptotic lipid. The Abstract has been modified accordingly.

(4) If the t-2-hex levels are in a physiological range, one would expect that overexpression of Hfd1 prevents the t-2-hex-induced import arrest.

We have now confirmed that overexpression of Hfd1 indeed prevents inhibition of mitochondrial protein import by t-2-hex. As shown in new Figure 5, Aim17 mitochondrial import is already inhibited by t-2-hex doses as low as 10µM in a wild type strain, and that this inhibition is enhanced in a hfd1 mutant and alleviated in a Hfd1 overexpressor.

(5) The authors claim that Fmp52 is a t-2-hex-detoxifying enzyme, but do not show evidence. They should rewrite this sentence and be more cautious, or they should show that increased Fmp52 levels indeed deplete t-2-hex from mitochondria.  

We show that loss of Fmp52 function leads to a strong t-2-hex sensitivity. Fmp52 belongs to the NAD-binding short-chain dehydrogenase/reductase (SDR) family and localizes to highly purified mitochondrial outer membranes (Zahedi et al, 2006). These are the indications that suggest that Fmp52 participates in the enzymatic detoxification of t-2-hex in addition to Hfd1. The Results section has been modified accordingly.

Minor points: 

(6) Aim17 was recently identified as a characteristic constituent of cytosolic protein aggregates named MitoStores (Krämer et al., 2023, EMBO J). The authors might test whether the cytosolic Aim17 protein colocalizes with the Hsp104-GFP granules that accumulate upon t-2-hex exposure as shown in Fig. 4A. 

We agree that determining the fate of unimported mitochondrial precursors upon t-2-hex stress would be interesting. We have made some attempts to co-visualize Aim17-dsRed and Hsp104-GFP upon t-2-hex treatment, but we still have some technical issues. While we clearly see that Aim17 accumulates in cytoplasmic foci upon prolonged t-2-hex exposure, we are not able to determine colocalization with Hsp104, in great part because t-2-hex causes mitochondrial fragmentation, which leads to the appearance of Aim17-stained foci in the cytosol independently of protein aggregates. While so far we are not able to localize Aim17 unambiguously in Hsp104 containing aggregates (mitoStores) upon lipid stress, we would like to move the manuscript farther without those experiments.

(7) In Fig. 1A, the figures of the different lines are difficult to distinguish. Lines of one color with different intensities would be better suited. 

We have been working before with dose-response profiles generated by the destabilized luciferase system and found that the color-coded representation of the plots is the most effective way to represent the data, see for example Fita-Torró et al. Mol Ecol. 2023 32(13):3557-3574, Pascual-Ahuir et al. BBA 2019 1862(4):457-471, Rienzo et al., Mol Cell Biol. 2015 35(21):3669-83, and several other publications. Therefore we want to keep the format of the Figure.

(8) A title page should be added to each of the supplemental data files with short descriptions of the information that is provided in the columns of the tables.  Response: Explanatory title pages have been now added to the supplemental data files.

Reviewer #3 (Recommendations For The Authors): 

Figure 5A: The authors aim to assess protein import, however, their experimental set-up is not suited and does not allow conclusions about protein translocation into mitochondria. The authors monitor protein steady state levels, which does not reflect import capacity. For this e.g. pulse-chase experiments coupled to coIP or in organello import assays with radiolabeled substrate proteins would be required. In addition, the authors lack a non-treated control to show that no precursor accumulates in the absence of CCCP and t-2-hex. At the moment, the conclusion of blocked import cannot be made, as there are many other explanations for the observed steady state levels, e.g. the TAP tag interfered with the import competence of the precursor or t-2-hex could impact on MPP function (in particular as Figure 8B shows that also intra-mitochondrial proteins undergo modification by t-2-hex). 

We agree that our study does not investigate a direct effect of t-2-hex on the import capacity of purified mitochondria. However, we determine the in vivo accumulation of several mitochondrial precursor proteins, which is widely used to assay for the efficiency of mitochondrial protein import, for example the recent hallmark paper discovering the mitoCPR protein import surveillance pathway exclusively uses epitope-tagged mitochondrial precursors to determine the regulation of mitochondrial protein import (Weidberg and Amon, Science 2018 360(6385)). Figure 5 contains several non-treated control experiments, which show that no (or less in the case of Ilv6) precursors of Tap-tagged Aim17, Cox5a, Ilv6, or Sdh4 accumulate in the absence of CCCP or t-2-hex. This is shown in Figure 5A for untreated cells or in Figure 5B and new Figure 5G for solvent (DMSO) treated cells. This demonstrates that the Tap-tag does not interfere with the import of the respective precursors. Additionally, our new results that mutants in accessory TOM subunits 20 and 70 are hyperresistant to t-2-hex (Figure 8D) identify the TOM complex and hence protein import at the outer mitochondrial membrane as a physiologically important t-2-hex target.

Figure 8: The conclusion that Tom40 is directly lipidated comes from an in vitro assay, with the conclusion that Tom40 is the main target, because it is the only Tom protein with a cysteine (Tom70 as not being part of the Tom core is excluded, however, lack of Tom70 function would also have detrimental consequences for mitochondrial protein import). However, there is no experiment showing a modification of Tom40 and a consequence for protein import. The proposed model is therefore very far-fetched and several aspects are speculation but not supported by experimental data. To propose such a model, the author needs to show experimental evidence, e.g. by generating a yeast strain in which the cysteine i Tom40 are replaced by e.g. Serine residues, and then assess if protein import (e.g. pulse-chase assays) are not affected anymore upon addition of t-2-hex. 

Deletion of all four cysteine residues in Tom40 is not sufficient to confer resistance to t-2-hex stress. This result had been included in the original manuscript, but was somehow hidden in the Discussion. The revised manuscript now includes t-2hex tolerance assays for the Tom40 cysteine free mutant in new Figure 8D. As a result, cysteine lipidation of Tom40 alone is not sufficient to confer t-2-hex toxicity. This implies most likely other lipidation targets within the TOM and TIM complexes, as indicated by our in vitro lipidation studies. We therefore included the non-essential adaptor proteins Tom70 and Tom20 of the TOM complex and tested the tolerance of the respective deletion mutants in t-2-hex tolerance assays. As shown in new Figure 8D, the absence of Tom70 and Tom20 function significantly increases tolerance to t2-hex indicating that the TOM complex is a physiologically important target of the pro-apoptotic lipid, which acts most likely via lipidation of more subunits than the Tom40 import channel.

Figure 8A: The pulldown experiments lack positive (other Tom subunits) and negative controls and were performed with (large) tags on all proteins, which can easily result in false positive interactions. The conclusion that Hfd1 interacts with Tom70 and Tom22 cannot be made. Also, the conclusion if an interaction is robust or not cannot be made as the pull-down lacks control fractions, it is also not clear how much of the eluate was loaded. Finally, Hfd1-HA was not expressed from its endogenous promoter, likely resulting in over-expression, which again strongly hampers conclusions about bona fide interaction partners. 

We agree that our pulldown studies are done in an artificial context, such as Hfd1 overexpression needed for sufficient protein level for detection or use of Tapfusion proteins. However, the conclusion that Tom70 is a potential interactor of Hfd1 can be made based on the following observations: Hfd1-HA is preferentially pulled down from total protein extracts containing Tom70-Tap, but not from extracts containing no Tap-protein and significantly less from extracts containing Tom22-Tap, another TOM associated subunit. The pulldown assay has been repeated now several times and the efficiency of Hfd1 pulldown has been quantified and statistically analyzed with respect to the quantity of purified Tom protein, which is shown in modified Figure 8A. 

Figure 4A and C: Depletion of proteasomal activity results in larger aggregates in Figure 4A. However, the addition of t-2-hex blocks proteasomal activity (Figure 4C). How can proteasome inhibition result in bigger aggregates if the proteasomal activity is lost upon t-2-hex addition?

The negative effect of t-2-hex on proteasomal activity is most likely an indirect effect caused by protein aggregation (Bence et al., Science 2001 292-1552) and occurs in wild type and rpn4 mutant cells with reduced proteasomal activity (Fig. 4C). t-2-hex causes cytosolic protein aggregation in wild type cells, which is aggravated (more and larger protein aggregates) in rpn4 mutants because of their lower levels of active proteasome (Fig. 4A). The observed protein aggregates will further diminish proteasomal activity, which is confirmed in Fig. 4C. 

Figure 1B: The authors use a reporter to determine HFD1 expression that consists of the promoter region of HFD1 fused to luciferase. These fusion constructs have been shown to often not reflect the bona fide expression levels of genes (Yoneda et al., J Cell Sci 2004). qPCR analysis of transcript levels should be included to support the induction of HFD1. 

We agree that the live cell luciferase reporters used here are not suitable for the determination of absolute mRNA levels. However, the aim of these reporter experiments is to quantify the inducibility of different genes (HFD1, GRE2) dependent on increasing stress doses. These dose response profiles cannot be obtained by qPCR analysis, while the destabilized reporters are an excellent tool for this, which have been used to accurately describe numerous dynamic stress responses (for example: Dolz-Edo et al. 2013 MCB 33:2228-40, Rienzo et al. 2015 MCB 35:3669-83, PascualAhuir et al. 2019 BBA 862:457-471). Additionally, the induction of HFD1 mRNA levels by salt (NaCl) and oxidative (menadione) stress determined by qPCR has been published before (Manzanares-Estreder et al. 2017 Oxid Med Cell Longevity 2017:2708345).

The authors conclude from Figure 1 that entry into apoptotic cell death is modulated by efficient t-2-hex detoxification. However, this is based on growth curves and no analysis of apoptotic cell death is performed. The data show that the addition of hexadecenal results in a growth arrest, that is overcome likely upon degradation of t-2-hex (depending on the amount of Hfd1). 

We agree that our experiments measure growth inhibition and not specifically apoptotic cell death. The text has been changed accordingly.  

Figure 4A: Microscopy images show between 1-2 yeast cells. Either more cells need to be shown or quantifications of the aggregates are required. In addition, it is not clear if the control received the same DMSO concentration as the treated cells and also the time point for the control is not specified. 

We have now quantified the number of aggregates across cell populations in new Figure 4A in DMSO, t-2-hex and t-2-hex-H2 treated wt and rpn4 mutants. These data show specific aggregate induction by t-2-hex and not by DMSO or the saturated t-2-hex-H2 control, which is aggravated in rpn4 mutants and avoided by CHX pretreatment.

Figure 5: Western blots in figure 5A, B, D, E and F lack a loading control. Without this, conclusions about increases in protein abundance cannot be made.  Response: We have now included additional panels with the loading controls for the Western blots in new figure 5, except figure 5B, where the appearance or not of the pre-protein can be compared to the amount of mature protein in the same blot.

Figure 2B: Complex II assembly factors SDH5,6,9 are described here as ETC complexes. As the proteins are not part of the mature complex II, the heading should be modified into ETC complexes and ETC assembly.

Figure 2B has been revised and the classification of ETC proteins changed accordingly.

Author response:

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

Public Reviews:

Reviewer #1:

Mehrdad Kashefi et al. investigated the availability of planning future reaches while simultaneously controlling the execution of the current reach. Through a series of experiments employing a novel sequential arm reaching paradigm they developed, the authors made several findings: 1) participants demonstrate the capability to plan future reaches in advance, thereby accelerating the execution of the reaching sequence, 2) planning processes for future movements are not independent one another, however, it's not a single chunk neither, 3) Interaction among these planning processes optimizes the current movement for the movement that comes after for it.

The question of this paper is very interesting, and the conclusions of this paper are well supported by data. However, certain aspects require further clarification and expansion.

We thank reviewer one for their evaluation of the work.

(1) The question of this study is whether future reach plans are available during an ongoing reach. In the abstract, the authors summarized that "participants plan at least two future reaches simultaneously with an ongoing reach and that the planning processes of the two future reaches are not independent of one another" and showed the evidence in the next sentences. However the evidence is about the relationship about ongoing reach and future plans but not about in between future plans (Line 52-55). But the last sentence (Line 55-58) mentioned about interactions between future plans only. There are some discrepancies between sentences. Could you make the abstract clear by mentioning interference between 1) ongoing movement and future plans and 2) in between future plans?

We thank Reviewer for their comment. We have separated the longer sentence in the original abstract into two shorter ones. This should clarify that the two pieces of evidence pertain to the interaction of planning processes.

(2) I understood the ongoing reach and future reaches are not independent from the results of first experiment (Figure 2). A target for the current reach is shown at Horizon 1, on the other hand, in Horizon 2, a current and a future target are shown on the screen. Inter-reach-interval was significantly reduced from H1 to H2 (Figure 2). The authors insist that "these results suggest that participants can plan two targets (I guess +1 and +2) ahead of the current reach (I guess +0)". But I think these results suggest that participants can plan a target (+1) ahead of the current reach (+0) because participants could see the current (+0) and a future target (+1) in H2. Could the authors please clarify this point?

We thank Reviewer for raising this point. Our conclusion that “participants can plan two targets ahead of the current reach” is supported by the reduction in Inter-Response Interval (IRI) observed when comparing H2 to H3 in the 75 ms Dwell time condition. Specifically, on average, participants were 16 ms faster when they could see two future targets on the screen (H3) than when they could see only one (H2). To clarify this in the paper, we have revised the wording in line 124 to explicitly state that the conclusion pertains to the 75 ms Dwell time condition. Additionally, we emphasize that the strongest evidence for planning two future targets comes from the experiment shown in Figure 3.

(3) Movement correction for jump of the +1 target takes longer time in H3 compared to H2 (Figure 4). Does this perturbation have any effect on reaching for +2 target? If the +1 jump doesn't affect reaching for +2 target, combined with the result that jump of the +2 target didn't affect the movement time of +1 target (Figure 3C), perturbation (target jump) only affects the movement directly perturbed. Is this implementation correct? If so, does these results support to decline future reaches are planned as motor chunk? I would like to know the author's thoughts about this.

In the experiment presented in Figure 4, once we jumped the +1 target, the reach to that target was changed and participants replaned a corrective movement to the new location of the +1 target. This usually was followed by a longer-than-usual pause at the new location of +1 target for resuming the sequence and finishing the trial. Consequently, in these jump trials, it was impossible to compare the +2 reach to no-jump trials, as the normal sequence of movement was disrupted, and the reach to the +2 target originated from a different starting location. Nevertheless, we addressed the possibility that the two future reaches were planned as a chunk by the analysis shown in figure 5: There we showed that a displacement of the +2 target did not influence the reach to the +1 target, indicating that the movement plans could be updated independently.

(4) Any discussion about Saccade position (Figure 7)?

We thank reviewer 1 for this important comment. The following discussion section is added for the gaze position results.

In our sequence task, participants switched their gaze location only once per reach, suggesting that information about the location of the next target is perceived parafoveally (Figure 7A). This observation aligns with previous studies (Clavagnier et al., 2007; González-Alvarez et al., 2007; Sivak and MacKenzie, 1990) that found participants keep their visual attention on the current sequence item and can perceive the location of spatial targets even when foveal vision is occluded. However, when comparing gaze locations for conditions Horizon >1, we observed that participants systematically biased their gaze location based on the sequence context. The gaze position shifted toward the next target, potentially allowing for more accurate location estimation (Figures 7C-D). Notably, changes in gaze location were observed even in Horizon 2, despite no changes in the curvature of hand movements in this horizon (Figure 6B). This suggests that information about the next target may first be available in the circuitry that controls eye movements and later in the cortical areas that control voluntary upper limb movements. Further control studies are required to investigate this hypothesis.

Reviewer #2:

Summary:

In this work, Kashefi et al. investigate the planning of sequential reaching movements and how the additional information about future reaches affects planning and execution. This study, carried out with human subjects, extends a body of research in sequential movements to ask important questions: How many future reaches can you plan in advance? And how do those future plans interact with each other?

The authors designed several experiments to address these questions, finding that information about future targets makes reaches more efficient in both timing and path curvature. Further, with some clever target jump manipulations, the authors show that plans for a distant future reach can influence plans for a near future reach, suggesting that the planning for multiple future reaches is not independent. Lastly, the authors show that information about future targets is acquired parafoveally--that is, subjects tend to fixate mainly on the target they are about to reach to, acquiring future target information by paying attention to targets outside the fixation point.

The study opens up exciting questions about how this kind of multi-target planning is implemented in the brain. As the authors note in the manuscript, previous work in monkeys showed that preparatory neural activity for a future reaching movement can occur simultaneously with a current reaching movement, but that study was limited to the monkey only knowing about two future targets. It would be quite interesting to see how neural activity partitions preparatory activity for a third future target, given that this study shows that the third target's planning may interact with the second target's planning.

Strengths:

A major strength of this study is that the experiments and analyses are designed to answer complementary questions, which together form a relatively complete picture of how subjects act on future target information. This complete description of a complex behavior will be a boon to future work in understanding the neural control of sequential, compound movements.

We thank the reviewer for their thorough reading of our work.

Weaknesses:

I found no real glaring weaknesses with the paper, though I do wish that there had been some more discussion of what happens to planning with longer dwell times in target. In the later parts of the manuscript, the authors mention that the co-articulation result (where reaches are curved to make future target acquisition more efficient) was less evident for longer dwell times, likely because for longer dwell times, the subject needs to fully stop in target before moving to the next one. This result made me wonder if the future plan interaction effect (tested with the target jumps) would have been affected by dwell time. As far as I can tell, the target jump portion only dealt with the shorter dwell times, but if the authors had longer dwell time data for these experiments, I would appreciate seeing the results and interpretations.

We thank the reviewer for raising this point. In our time (Figure 2) and curvature analysis (Figure 6), we collected data with five levels of the horizon and three levels of dwell time to explore the space of parameters and to see if there is any interaction between dwell time and the horizon of planning the future targets. Apriori, we expected that the full stop in each target imposed by the 400 ms dwell time would be long enough to remove any effect of future targets on how the current move is executed. In line with our initial hypothesis, the systematic curvature of reaches based on the future target was smaller in longer dwell times (Figure 6E). Nevertheless, we observed a significant curvature even in 400 ms dwell time. Based on this observation, we expect running the jump experiments (Figures 4 and 5) in longer dwell times will lead to the same pattern of results but with a smaller effect size since longer dwells break the interdependence of sequence elements (Kalidindi & Crevecoeur, 2023). In the end, for the jump experiments, we limited our experimental conditions to the fastest dwell time (75 ms dwell) since we were conceptually interested in situations where movements in the sequence are maximally dependent on each other.

Beyond this , the authors also mentioned in the results and discussion the idea of "neural resources" being assigned to replan movements, but it's not clear to me what this might actually mean concretely. I wonder if the authors have a toy model in mind for what this kind of resource reassignment could mean. I realize it would likely be quite speculative, but I would greatly appreciate a description or some sort of intuition if possible.

Our use of the term "neural resources" is inspired by classic psychology literature on how cognitive resources such as attention and working memory are divided between multiple sequence components. Early studies on working memory suggest that human participants can retain and manipulate a fixed number of abstract items in working memory (Miller, 1956). However, more recent literature postulates that a specific number of items does not limit working memory, rather, it is limited by a finite attentional resource that is softly allocated to task items.

Here we borrowed the same notion of soft distribution of resources for the preparation of multiple sequence items. A large portion of our observation in this paper and also previous work on sequence production can be explained by a simple model that assumes one central planning resource that is “softly” divided between sequence elements when participants see future items of the sequence (Author Response Image 1). The first sequence element receives the majority of the resources and is planned the most. The rest of the sequence receives the remaining planning resources in an exponentially decaying manner for preparation of the movement during the execution of the ongoing movement. Once the ongoing movement is over, the resource is then transferred to the next sequence item and this process is repeated until the sequence is over. Assignment of planning resources to future items explains why participants are faster when seeing future items (Figure 2). But this comes with a cost – if the ongoing movement is perturbed, the replanning process is delayed since some of the resources are occupied by future planning (Figure 4). This naturally leads to the question of how this resource allocation is implemented in neural tissue. To address this, we are conducting the same sequence task with the horizon in non-human primates (NHPs), and the investigation of these neural implementation questions will be the focus of future studies.

Author response image 1.

Basic diagram showing a soft distribution of a limited planning resource. The diagram shows a Horizon 3 condition in which two future reaches (+1 and +2) are planned while executing a movement (+0). The majority of resources is assigned to the execution of the ongoing movement while the reset is distributed for planning future movements. Once the movement is over, the chain of preparation and execution moves forward.

Recommendations for the author:

Reviewer #1

We thank reviewer one for these comments regarding the clarity and consistency of figures and terminology.

(1) Figure 3. Are "+1 Move" in Fig. 3B and "+ 1 Movement" in Fig. 3C as same as "E + 1" in Fig. 3A? Also does "Dwell" in Fig. 3B mean same as "+1 Dwell" in Fig. 3C? Consistent terminology would help readers to understand the figure.

“+1 Move” in Figure 3B is the same as +1 movement in Figure 3C. “Dwell” in Figure 3B is the same as +1 Dwell in Figure 3C. We changed the figure for more consistency.

(2) Figure 3. A type in the second last line in the legend, "pre-jump target for no-jump and jump and condition". The second "and" isn't necessary.

The typo is corrected. Thank you.

(3) Figure 4C. Is "Movement time" equivalent with "E + 1"?

“Movement time” is equivalent to E+1 only in no-jump conditions. When the jump occurs,

Movement time contains all the

(4) Figure 6B. Is the gray circle in between the graph and target positions there by mistake?

We fixed this typo. Thank you.

(5) Figure 6E. It's hard to distinguish H2-H5 from the color differences.

We changed the H5 to full white with a black stroke to improve the contrast. Thank you.

(6) Figure 7A. Blue dots are almost invisible.

We added a black stroke to blue circles for more visibility. Thank you.

Reviewer #2

I found this manuscript to be engaging and well written--many of the questions I had while reading were answered promptly in the next section. As such, my comments are mostly minor and primarily geared towards improving clarity in the manuscript.

(1) One major recurring confusion I had while reading the manuscript was how to think about H1, H2, and H3. It was clearly explained in the text, and the explanations of the results were generally clear once I read through it all, but I found it strangely confusing at times when trying to interpret the figures for myself (e.g., in H2, 2 targets are on screen, but the second target can only be planned during the reach toward the first target). This confusion may just be me reading the manuscript over two days, but I wonder if it could be made clearer with some semantic iconography associated with each horizon added to the later figures alongside the H labels. As one option, perhaps the planning timeline part of Fig 1D could be simplified and shrunk down to make an icon for each horizon that clearly shows when planning overlaps for each horizon.

(Please see the response to point #2 below)

(2) Regarding Fig 1D: I like this figure, but it's unclear to me how the exact preparation and execution times are determined. Is this more of a general schematic of overlaps, or is there specific information about timing in here?

We thank reviewer 2 for their important feedback. The role of Figure 1D was to summarize the timing of the experiments for different horizons. That is, to clarify the relative timing of the targets appearing on the screen (shown with a small circle above the horizontal line) and targets being captured by participants (the ticks and their associated number on the line). Execution is shown as the time interval that the hand is moving between the targets and planning is the potential planning time for participants from the target appearing on the screen until initiation of the reach to that target. We added the relevant parts of Figure 1D to the subplots for each subsequent experiment, to summarize the timing of other experiments and their analyses. For the experiments with target jump, a small vertical arrow shows the time of the target jump relative to other events.

However, this figure will be less useful, if the connection between the timing dots and ticks is not communicated. We agree that in the original manuscript, this important figure was only briefly explained in the caption of Figure 1. We expanded the explanation in the caption of Figure 1 and referenced the dots and ticks in the main text.

(3) Fig 6B - for some reason I got confused here: I thought the central target in this figure was the start target, and it took me embarrassingly long to figure out that the green target was the start target. This is likely because I'm used to seeing center-out behavioral figures. Incidentally, I wasn't confused by 7c (in fact, seeing 7c is what made me understand 6b), so maybe the solution is to clearly mark a directionality to the reach trajectories, or to point an arrow at the green target like in previous figures. Also, the bottom left gray target in the figure blends into the graph on the left--I didn't notice it until rereading. Because there's white space between that target and the green one, it might be good to introduce some white space to separate the graph from the targets more. The target arrangement makes more sense in panel C, but by the time I got there, I had already been a bit confused.

Thanks for raising this point. As shown in Figure 6C, we used the reach to the +1 target for the curvature analysis. The confusion about Figure 6B is probably due to continuing the reach trajectories after the +1 target. That also explains why Figure 7C seemed more straightforward. To solve this issue we modified Figure 6B such that the reaches are shown with full opacity right until the +1 target and then shown with more transparency. We believe this change focuses the reader's attention to the reach initiated from the +0 target to the +1 target.

As for the gray target in Figure 6B, we originally had the gray target as it is a potential start location for the reach to the +0 target, and for having similar visuals between the plots. The gray target is now removed from Figure 6B.

(4) Line 253 - I'm not sure I understand the advantage over simple averaging that the authors mention here--would be nice to get a bit more intuition.

Thanks for raising this point. We used a two-factor model in our analysis, with each factor representing the angle of the last and next target, respectively. Both factors had five levels: -120, -60, 0, 60, and 120 degrees relative to the +1 reach. In a balanced two-factor design, where each combination of factor levels has an equal number of trials, using a linear model and simple averaging would yield equivalent results. However, when the number of trials for the combinations of the two factors is unbalanced, simple averaging can lead to misleading differences in the levels of the second factor. Additionally, the linear model allows us to investigate potential interactions between the two factors, which is not possible with simple averaging.

(5) Fig 7a - I would have liked to see the traces labeled in figure (i.e. hand trajectory vs. eye trajectory)

Hand and eye trajectories are now labeled in the figure.

(6) Fig 7c - very minor, but the hexagon of targets is rotated 30 degrees from all previous hexagons shown (also, this hex grid target arrangement can't lead to the trajectory shown in 7a, so it can't be that this was a different experimental grid). I'm guessing this was a simple oversight.

We used the same grid in the eye-tracking experiment. The targets are to visually match the previous plots. Thank you for raising this point.

Reference

Clavagnier, S., Prado, J., Kennedy, H., & Perenin, M.-T. (2007). How humans reach: distinct cortical systems for central and peripheral vision. The Neuroscientist: A Review Journal Bringing Neurobiology, Neurology and Psychiatry, 13(1), 22–27.

González-Alvarez, C., Subramanian, A., & Pardhan, S. (2007). Reaching and grasping with restricted peripheral vision. Ophthalmic & Physiological Optics: The Journal of the British College of Ophthalmic Opticians , 27(3), 265–274.

Kalidindi, H. T., & Crevecoeur, F. (2023). Task dependent coarticulation of movement sequences

(p. 2023.12.15.571847). https://doi.org/10.1101/2023.12.15.571847

Miller, G. A. (1956). The magical number seven plus or minus two: some limits on our capacity for processing information. Psychological Review, 63(2), 81–97.

Sivak, B., & MacKenzie, C. L. (1990). Integration of visual information and motor output in reaching and grasping: the contributions of peripheral and central vision. Neuropsychologia, 28(10), 1095–1116.

eLife assessment

This study presents an important set of results illuminating how movement sequences are planned. Using several different behavioural manipulations and analysis methods, the authors present compelling evidence that multiple future movements are planned simultaneously with execution, and that these future movement plans influence each other. The work will be of great interest to those studying motor control.

Reviewer #1 (Public Review):

Mehrdad Kashefi et al. investigated the availability of planning future reaches while simultaneously controlling the execution of the current reach. Through a series of experiments employing a novel sequential arm reaching paradigm they developed, the authors made several findings: 1) participants demonstrate the capability to plan future reaches in advance, thereby accelerating the execution of the reaching sequence, 2) planning processes for future movements are not independent one another, however, it's not a single chunk neither, 3) Interaction among these planning processes optimizes the current movement for the movement that comes after for it.

The question of this paper is very interesting, and the conclusions of this paper are well supported by data.

Reviewer #2 (Public Review):

In this work, Kashefi et al. investigate the planning of sequential reaching movements and how the additional information about future reaches affects planning and execution. This study, carried out with human subjects, extends a body of research in sequential movements to ask important questions: How many future reaches can you plan in advance? And how do those future plans interact with each other?

The authors designed several experiments to address these questions, finding that information about future targets makes reaches more efficient in both timing and path curvature. Further, with some clever target jump manipulations, the authors show that plans for a distant future reach can influence plans for a near future reach, suggesting that the planning for multiple future reaches is not independent. Lastly, the authors show that information about future targets is acquired parafoveally--that is, subjects tend to fixate mainly on the target they are about to reach to, acquiring future target information by paying attention to targets outside the fixation point.

The study opens up exciting questions about how this kind of multi-target planning is implemented in the brain. As the authors note in the manuscript, previous work in monkeys showed that preparatory neural activity for a future reaching movement can occur simultaneously with a current reaching movement, but that study was limited to the monkey only knowing about two future targets. It would be quite interesting to see how neural activity partitions preparatory activity for a third future target, given that this study shows that the third target's planning may interact with the second target's planning.

[Editors' note: The authors fully addressed the reviewers' comments on the original manuscript.]

eLife assessment

This manuscript is an important contribution, assessing the role of intraspecific consumer interference in maintaining diversity using a mathematical model. Consistent with long-standing ecological theory, the authors convincingly show that predator interference allows for the coexistence of multiple species on a single resource, beyond the competitive exclusion principle. Notably, the model matches observed rank-abundance curves in several natural ecosystems.

Reviewer #1 (Public Review):

Summary:

The manuscript considers a mechanistic extension of MacArthur's consumer-resource model to include chasing down of food and potential encounters between the chasers (consumers) that lead to less efficient feeding in the form of negative feedback. After developing the model, a deterministic solution and two forms of stochastic solutions are presented, in agreement with each other. Finally, the model is applied to explain observed coexistence and rank-abundance data.

Strengths:

- The application of the theory to natural rank-abundance curves is impressive.<br /> - The comparison with the experiments that reject the competitive exclusion principle is promising. It would be fascinating to see if in, e.g. insects, the specific interference dynamics could be observed and quantified and whether they would agree with the model.<br /> - The results are clearly presented; the methods adequately described; the supplement is rich with details.<br /> - There is much scope to build upon this expansion of the theory of consumer-resource models. This work can open up new avenues of research.

Weaknesses:

- Though more and better data could be used to constrain and validate the modeling, given this is a theory-driven manuscript, their results are sufficient.

Reviewer #3 (Public Review):

Summary:

In this manuscript, the authors extend previous work on the role of predator interference in species coexistence. Previous theoretical work (for example, using the Beddington-DeAngelis model) has shown that predator interference allows for multiple predators to coexist on the same prey. While the Beddington-DeAngelis has been influential in theoretical ecology, it has also been criticized at times for several unusual assumptions, most critically, that predators interfere with each other regardless of whether they are already engaged in another interaction. There has been considerable work since then which has sought either to find sets of assumptions that lead to the B-D equation or to derive alternative equations from a more realistic set of assumptions (Ruxton et al. 1992; Cosner et al. 1999; Broom et al. 2010; Geritz and Gyllenberg 2012). This paper represents another effort to more rigorously derive a model of predator interference by borrowing concepts from chemical reaction kinetics (the approach is similar to previous work: Ruxton et al. 1992). The main point of difference is that the model in the current manuscript allows for 'chasing pairs', where a predator and prey engage with one another to the exclusion of other interactions, a situation Ruxton et al. (1992) do not consider. While the resulting functional response is quite complex, the authors show that under certain conditions, one can get an analytical expression for the functional response of a predator as a function of predator and resource densities. They then go on to show that including intraspecific interference allows for the coexistence of multiple species on one or a few resources, and demonstrate that this result is robust to demographic stochasticity. This work provides additional support for the idea that predator interference allows multiple predators to persist with a shared resource.

Strengths:

I appreciate the effort to rigorously derive interaction rates from models of individual behaviors. As currently applied, functional responses (FRs) are estimated by fitting equations to feeding rate data across a range of prey or predator densities. In practice, such experiments are only possible for a limited set of species. This is problematic because whether a particular FR allows stability or coexistence depends on not just its functional form, but also its parameter values. The promise of the approach taken here is that one might be able to derive the functional response parameters of a particular predator species from species traits or more readily measurable behavioural data.

Weaknesses:

The main weakness of this paper is that while it is technically sound, it doesn't change the fundamental intuition gained from more phenomenological models of predator interference: as one species becomes more common, it limits its own growth (manifested by less time spent searching for/handing resources due to interference), such that it does not exclude the existence of a competitor species. However, given the authors use a different model formulation that has been used in past studies, it suggests that predator interference will likely tend to promote coexistence regardless of some of the technical details in how it is formulated in a model.

The formulation of chasing-pair engagements assumes that prey being chased by a predator are unavailable to other predators. While this may hold in some predator-prey, it does not hold for many others, perhaps limiting some results' generality.

Summary:

The manuscript by Kang et al investigates how the consideration of pairwise encounters (consumer-resource chasing, intraspecific consumer pair, and interspecific consumer pair) influences the community assembly results. To explore this, they presented a new model that considers pairwise encounters and intraspecific interference among consumer individuals, which is an extension of the classical Beddington-DeAngelis (B-D) phenomenological model, incorporating detailed considerations of pairwise encounters and intraspecific interference among consumer individuals. Later, they connected with several experimental datasets.

Strengths:

They found that the negative feedback loop created by the intraspecific interference allows a diverse range of consumer species to coexist with only one or a few types of resources. Additionally, they showed that some patterns of their model agree with experimental data, including time-series trajectories of two small in-lab community experiments and the rank-abundance curves from several natural communities. The presented results here are interesting and present another way to explain how the community overcomes the competitive exclusion principle.

Weaknesses:

The authors did a great job of satisfactorily addressing each of my concerns raised in the previous round. I did not detect additional weaknesses.

「役立つ方法や、」のほうが自然に感じます。

For over 2 decades I struggle with this. Because my paraphrasing is mostly unsuited for my blog, regularly because it is mixed language and often bc it contains words that serve as shorthand. A blog is more performance, written for not-me, while annotation is for me, and after editing might be publishable for not-me. Annotating publicly here in .h, even if the readership is highly limited, introduces a level of performance-awareness for me. At times I've done annotated link blogging, but it never became a practice as with [[Stephen Downes]].

replace: For example, the running fictional story by author Maurus Jókai entitled "Treurige Dagen", in the Opregte Haarlemsche Courant

Replace with: This normally occurs in a context where the action is “out of the ordinary”, and usually occurred when either a deserter fled and assumed a new life (as reported in figure c), or when they had subsequently been caught, tried and even executed as shown in figure d. More detailed reporting also often occurred when there were other notable vents related to desertion (such as theft, arson or violence).

Fig. c Example of a more detailed description of a case of desertion, 1 May 1899, De locomotief: Samarangsch handels- en advertentie-blad

Fig, d Newspaper description of the execution of a deserter, 21 December 1897, Soerabaijasch handelsblad

Replace with: This is highlighted in the above article (figure a), which describes how Chinese “agents” actively recruited deserters of the Dutch East Indies armed forces, and would assist them in desertion by offering large sums of money, clothes, and transportation to join the Chinese army.

Fig. a Article "Japan op Java" 16th February 1899, De Morgenpost

Replace with: The vast majority of articles list “rechtszaken” (see figure b) and detail the numbers of individuals who have deserted, usually with their name, age, occupation, and the punishment afforded to them.

Fig. b Example of 'Law suits' section of a newspaper, 17 March 1897, Vlaardingsche courant

Replace with: 'Flight as Fight': Exploring Historic Cases of Desertion from a Collective Labour Action Perspective

Replace with: The above figure details the spread of annotated articles over time. Of the 387 events tagged, we see that there are a few hits between 1810 and 1830, sporadically changing over time, with two peaks in desertion events between 1830 and 1840.

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

Learn more at Review Commons

Reply to the reviewers

Manuscript number: RC-2024-02491

Corresponding author(s): Gilbert, Vassart

1. General Statements [optional]

We thank referees 1 and 2 for their in-depth analysis of our manuscript. They see interest in our study, with questions to be answered. Referee 3 is essentially negative, considering that there is nothing new ("novel finding is missing"). We respectfully disagree with him/her, comforted by the opinion of referee 2 that "the authors developed a protocol to reproducibly generate fetal-like spheroids from adult tissue is an important advancement in the field and ... the manuscript should attract a significant amount of attention in the intestinal field" and we provide evidence in our answers that he/she did not read the manuscript with the same attention as referees 1 and 2 (see in particular answer to his/her question 5).

Here is a summary of the main reason why we consider that our study represents valuable new information in the field of intestinal regeneration.

It is based on the serendipitous observation that dissociation of adult intestinal tissue by collagenase generates stably replatable spheroids upon culture in matrigel. Surprisingly and contrary to canonical EDTA-generated intestinal organoids and fetal spheroids, these spheroids are not traced in Rosa26Tomato mice harboring a VilCre transgene, despite expressing robustly endogenous Villin. Our interpretation is that adult intestinal spheroids originate from a cell lineage, distinct from the main developmental intestinal lineage, in which the VilCre transgene is unexpectedly not expressed, probaly due to the absence of cis regulatory sequences required for expression in this lineage.

Adult spheroid transcriptome shares a gene signature with the YAP/TAZ signature commonly expressed in models of intestinal regeneration. This led us to look for VilCre negative crypts in the regenerating intestine of Lgr5/DTR mice in which Lgr5-positive stem cells have been ablated by diphtheria toxin. Numerous VilCre negative clones were observed, identifying a novel lineage of stem cells implicated in intestinal regeneration.

FACS purification and scRNAseq analysis of the rare VilCre negative cells present at homeostasis identified a population of cells with characteristics of quiescent stem cells.

In sum, we believe that our study demonstrates the existence of a hitherto undescribed stem cell lineage involved in intestinal regeneration. It points to the existence of a hierarchical model of intestinal regeneration in addition to the well-accepted plasticity model.

2. Description of the planned revisions

See section 3 below.

3. Description of the revisions that have already been incorporated in the transferred manuscript

Here is a point-by-point reply to the queries of the three referees, with indication of the revisions introduced in the manuscript.

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

• *In this manuscript, Marefati et al report an Lgr5-independent lineage in the regenerating intestine using in vitro organoids and in vivo injury-coupled lineage tracing model. In organoids, collagenase/dispase dissociated resulted in "immortal spheroids" that maintain a cystic and undifferentiated phenotype in the absence of standard growth factors (Rspondin/Noggin/EGF). Bulk RNAseq of spheroids demonstrates downregulation of classical CBC signatures and upregulation of fetal spheroid, mesenchymal, inflammation and regenerative signatures. In mice, Villin-Cre lineage tracing revealed some Villin- negative progenies that lack reporter tracing throughout crypt-villus ribbons after injury.

*The authors proposed that there is Lgr5-independent population support the regenerative response upon CBC depletion. A major caveat of this study is the identification of this population is based on absence of VilCre expression. *

We respectfully disagree. It is precisely this characteristic that makes the interest of our study. Whereas mosaicism of transgene expression is widespread and usually of little significance, our study shows that the rare VilCre-negative cells in the intestinal epithelium are not randomly showing this phenotype: they give specifically birth to what we call adult spheroids and regenerating crypts, which cannot be due to chance. The absence of VilCre expression allows tracing these cells from the zygote stage of the various VilCre/Ros26 reporter mice. We have modified our text to emphasize this point.

*It is surprising that there is no characterisation of Lgr5 expression throughout the manuscript whilst claiming of a Lgr5- independent lineage. *

We understand the perplexity of the referee not to see direct Lgr5 expression data in our manuscript, given our title. However, our point is that it is the cells at the origin of adult spheroids and the regenerating crypts we have identified that are Lgr5-negative, not the spheroids or the regenerated crypts themselves. Those are downstream offspring that may, and indeed have, gained some Lgr5 expression (e.g. figure 3F). We believe that our data showing that VilCre-negative spheroids are not traced in Lgr5-CreERT2/Rosa reporter mice convincingly demonstrate absence of Lgr5 expression in the cells at the origin of adult spheroids (figure 4G). We think that this experiment is better evidence than attempts to show absence of two markers (Tom and Lgr5) in the rare "white" cells present in the epithelium. Regarding the Lgr5 status of cells at the origin of the regenerating "white" crypts that we have identified, the early appearance of these crypts following ablation of CBC (i.e. Lgr5+ve) cells is a strong argument that they originate from Lgr5-negative cells. Regarding the scRNAseq experiment, Lgr5 transcripts are notoriously low and difficult to measure reliably in CBCs (Haber et al 2017). However, blowing up the pertinent regions of the merged UMAP allows showing some Lgr5 transcripts in clusters 5,6 and none in cluster 1 of figure 8GH. Given the very low level of detection, we had chosen not to include these data in the manuscript, but we hope they may help answer the point of the referee (see portion of UMAP below, with Olfm4 as a control, together with the corresponding violin plot). Several markers that gave significant signals in the CBC cluster (Smoc2, Axin2, Slc12a2) were virtually undetectable in the Olfm4-low /Tom-negative cluster of our scRNAseq data (figure 8I) supporting our conclusion.

Although the research question is potentially interesting, the concept of epithelial reprogramming upon injury is well documented in the field. The data generated in this manuscript also seem to be preliminary and lack of detailed characterisation. Below are specific comments.

We do not question the existence of epithelial reprogramming upon injury. We believe our data show, in addition to this well demonstrated phenomenon, the existence of rare cells traced by absence of VilCre expression that are at the origin of a developmental cell lineage distinct from Lgr5+ stem cells and also implicated in regeneration.

• Expression of Lgr5 should be properly characterised throughout the manuscript in both organoid models and injury-induced regeneration in vivo.
• *

See above for a detailed answer to this point.

• An important question is the origin of these "Lgr5-independent" adult spheroids. They look and appear like fetal organoids, which could be induced by injury (e.g. upon collagenase/dispase dissociation). Have the authors tried to culture fetal spheroids in BCM over extensive period of time? Do they behave the same? This would be a great way to directly compare the collagenase/dispase-derived organoids with fetal origin. * *Fetal spheroids require ENR for survival and die in BCM. We have chosen to illustrate this point in Fig2A by showing that, contrary to adult spheroid, they die even when only Rspondin is missing.

• Fig 1C, Why is the replating spheroid culture time different between mesenchymal cells and conditioned medium? We took the earliest time showing convincingly the return to the organoid phenotype. This timing difference does not modify the conclusion that EDTA organoids becoming spheroid-like when exposed to factors originating from mesenchymal cells revert to the organoid phenotype when returned to ENR medium without mesenchymal influence.

• *It is unclear how the bulk RNA-seq data in Fig. 3 were compared. How long were the adult organoids and spheroids cultured for (how many passages)? Were they culture in the same condition of were they in ENR vs BCM? * Both EDTA organoids and spheroids displaying a stable phenotype were used in this experiment. Organoids were collected at passage 4, day 5; spheroids were collected at passage passage 9 day 3.

As stated in the legend to the figure: "...to allow pertinent comparison spheroids and organoids were cultured in the same ENR-containing medium...".

These are important information to consider when interpreting the results. For instance, are Ptgs1 & Ptgs2 expression in adult spheroids the same in ENR vs BCM? Are the gene signatures (regenerative, fetal and YAP) changed in adult spheroids culturing in ENR vs BCM?

We did compare bulk RNAseq of EDTA organoids to ENR-cultured spheroids, short term (passage 6, day 6) BCM-cultured spheroids and long term BCM-cultured (passage 26, day 6) spheroids. To avoid overloading the manuscript these data were not shown in the original manuscript. In summary the BCM-cultured spheroids display a similar phenotype as those cultured in ENR, but with further de-differentiation. See in revision plan folder the results for PTGS, some differentiation markers and fetal regenerative markers including YAP induced genes.

We have included a brief description of these data in the new version of the manuscript and added an additional supplementary file (Suppl table 2) presenting the whole data set.

• It is stated: "In agreement with their aptitude to grow indefinitely, adult spheroids express a set of upregulated genes overlapping significantly with an "adult tissue stem cell module" [159/721 genes; q value 2.11 e-94) (Fig.S2F)].". What is the definition of "indefinitely"? Are they referring to the Fig 1B where spheroid were passaged to P10? The authors should avoid the term "indefinitely" but use a more specific time scale, e.g. passages, months etc.

We agree that the term indefinitely should be avoided, as it is vague. We have introduced the maximum number of passages during which we have maintained the stable spheroid phenotype (26 passages). Also worth noting, the spheroids could be frozen and cultured repeatedly over many months.

SuppFig 3D: Row Z-Score is missing the "e" in Score.

Corrected

• Fig 4E: Figure legend says QNRQ instead of CNRQ. Corrected

• Fig 4G: The brightfield image of adult spheroids 5 days after 3x TAM injections doesn't look like a spheroid. It seems to be differentiating. True, the choice was not the best as the spheroids started to darken. When further replated, however, the offspring of these spheroids showing a clear phenotype remain negative 30 days after tamoxifen administration as shown on the figure. We are sorry, but for reasons explained in section 4 below, we cannot redo the experiment to get a better picture.

• Fig 4: Most mouse model data are missing the number of mice & their respective age used for organoid isolation. We have introduced these data in the legend.

• *Fig 4A-D, H-G: How was fluorescent signal of organoids quantified? *

The settings of fluo imaging or time of LacZ staining were the same for organoids and spheroid pictures. This has been added to the material and methods of the figure and an example is shown below for Rosa26Tomato.

*How many images? * 2 per animal per condition.

*Were there equal numbers of organoids? *

No, see number of total elements counted added to the figure

This all needs to be included in methods/figure legends.

We have introduced additional pertinent information in the material and methods section.

• Figure 4B-D, G-H: Which culturing conditions were used for adult spheroids? Original method or sandwich method? These data were obtained with the original protocol

• Fig 6D-E: Please add the timepoint after DT administration these samples are from. It is not listed in text or figure legend. These samples were those obtained from mice sacrificed at the end of the 5 day period as indicated in panel A. This has been emphasized in the legend of the figure.

• SuppFig 6D: again timepoint is missing. In this experiment all samples were untreated as indicated. This has been emphasized in the legend of the figure.

• SuppFig 6: How were the crypts of these mice (DT WT & DT HE) isolated? Was this via EDTA? This was RNA extracted from total uncultured EDTA-released material (crypts). This has been emphasized in the legend of the figure.

Also, what is the timepoint for isolation for these samples? Even if untreated, the timepoint adds context to the data. Please add more context to describing these different experiments, either in the figure legends or methods section.

All these experiments were from 2 month old animals. We have indicated this in the legend of the figure.

• SuppFig 6E: The quality of the heatmap resolution is too poor to read gene names. We have improved the resolution of the figure and hope the name of the genes are readable now.

• 5-7, are the regenerating crypt-villus units fully differentiated or are they maintained in the developmental state? Immunostaining of markers for stem cells (Lgr5), differentiated lineages (Alpi, Muc2, Lyz, ChgA etc.) and fetal state (Sca1, Trop2 etc) should be analysed in those "white" unrecombined crypt-villus units. The differentiation phenotype is shown by the clear presence of morphologically-identified Paneth and Goblet cells. We agree that specific immunostainings could have been performed to further explore this point. Regarding the fetal state, Clu expression was shown during the regeneration period (see figure 7D,E).

Unfortunately, for reasons explained in section 4 below, we are not in a position to perform these additional experiments.

• The following text needs clarification: "The kinetics of appearance of newly formed un-recombined ("white") crypts was studied after a single pulse of DT (Fig.7A). This demonstrated an increase at 48 hours, with further increase at day 10 and stable maintenance at day 30. The presence of newly formed white crypts one month after toxin administration indicates that the VilCre-negative lineage is developmentally stable and does not turn on the transgene during differentiation of the various epithelial lineages occurring after regeneration (Fig.7B).

*Comment: The "newly formed" is an overstatement, the data doesn't conclude that those are "new" crypts. *

Except if we do not understand the point, we think we can write that a fraction of "white" crypts must be "newly formed", since they are in excess of those present in untreated animals at the same time point.

*The end of the sentence states that these "white" crypts form developmentally stable lineages, thus these white crypts at day 30 could originate from the initial injury. *

As stated above, we consider that crypts found in excess of those present in untreated animals result from the initial injury.

*There was no characterisation of the various epitheial lineages. Are they fully differentiated? *

See above the point related to Paneth cells and Goblet cells.

Is Lgr5 expressed one month after toxin administration? Can the VilCre neg lineage give rise to CBCs?

We have tried hard to show presence or absence of Lgr5 in white crypts at the various times following DT administration. We tried double RFP / Lgr5-RNA scope labeling and double GFP/RFP immunolabeling. Unfortunately, we could not get these methods to produce convincing specific labeling of CBCs in homeostatic crypts, which explains why we could not reach a conclusion regarding the white crypts.

However, there is an indirect indication that "chronic" white crypts (i.e. those caused by DTR expression in CBC, plus those observed 30 days after DT administration) do not express Lgr5. Indeed, acute regeneration indicated by Clu expression at day 5 in Fig.7C is lower in white crypts than in red ones strongly suggesting that white crypts preexisting DT administration (the "chronic ones) do not express Lgr5DTR.

The relationship between white crypt generation and appearance of Clu-positive revival cells (Ayyaz et al., 2019) was then explored. In agreement with others and similar to what happens in the irradiation model, (Ayyaz et al., 2019; Yuan et al., 2023) Clu-positive cells were rare in crypts of untreated mice and their number transiently increased forty-eight hours after a single pulse of DT, and more so after three pulses of DT (Fig.7C,D).

Comment: Comparing 1 pulse at day 2 vs 3 pulses at day 5 makes the data hard to interpret. How is the Clu ISH level for 1 pulse at day 5? Are they equivalent?

After a single pulse of of DT, Clu is only transiently increased. As shown by Ayyaz et al it is back to the starting point at day 5 (supplementary figure 4 of Ayyaz et al).

Clu-positive cells were less frequently observed in white crypts (see "Total" versus "White" in Fig.7C). This fits with the hypothesis that Clu expression marks acutely regenerating crypts and that a proportion of the white crypts are chronically regenerating due to DTR expression in CBCs."

*Comment: I believe the authors suggested that the discrepancy of less Clu expression in white crypts is due to the ectopic expression of DTR in CBCs causing low grade injury without DT administration. This means that some white crypts could have been formed before the administration of DT, and thus are on a different regenerative timeline compared to the white crypts formed from DT administration. *

Yes, this is our interpretation. We have clarified it in the text.

Is there any proof of the chronic regeneration? Immunostaining of chronic regenerative markers such as Sca1, Anxa1 or Yap1 nuclear localization would support the claim. It'd be important to show only the white crypts, but not the RFP+ ones, show regenerative markers.

We think that the steady state higher number of white crypts in untreated Lgr5-DTR animals, compared to wild type siblings indicates chronical low-grade regeneration, which is supported by the RNAseq data (Suppl fig6). It must be noted, however, that this phenotype is mild compared to the well described fetal-like regeneration phenotype described in most injury models. Since these white crypts were made at undetermined earlier stages, the great majority of them are not expected to show markers of acute regeneration like Clu, Sca1....

Fig 7D-E: What are the timepoints of harvest for HE-WT-HE 1 pulse DT mice and HE- HE-HE PBS injected mice?

We have added this information in the figure.

• *Fig 8-9: Regarding the CBC-like Olfm4 low population, what is the status of Lgr5? This should be shown in the figure since the argument is that this is an Lgr5-independent lineage. * See response to the second point.

And what about the regenerative, Yap, mesenchymal and inflammatory signatures? Are they enriched in the white crypts similar to the in vitro spheroids?

In a portion of white crypts, those we believe are newly formed after CBC ablation (see above), there is a transient increase in Clu, which may be considered a marker of Yap activation. In the CBC-like Olfm4 low cells, as seen by scRNAseq, there is nothing like an actively regenerating phenotype. This is expected, since these cells are coming from homeostatic untreated VilCre/Rosa26Tom animals and are supposed to be quiescent "awaiting to be activated".

Reviewer #1 (Significance (Required)):

Strengths: The study employed a range of in vitro and in vivo models to test the hypothesis.

• *

*Limitations: Unfortunately, the models chosen did not provide sufficient evidence to draw the conclusions. Injury induced reprogramming, both in vivo and in vitro, has been well documented in the field. The new message here is to show that such reprogrammed state is continuous rather than transient; instead of regenerating Lgr5+ stem cells, it can continue to differentiate to all cell lineages in Lgr5-independent manner.

*

We respectfully disagree with this analysis of our results. What we show is not "that such reprogrammed state is continuous rather than transient; instead of regenerating Lgr5+ stem cells, it can continue to differentiate to all cell lineages in Lgr5-independent manner", but that a quiescent stem cell line, not previously identified, is activated to regenerate a portion of crypts following CBC ablation. These cells are not reprogrammed, they correspond to a developmental lineage waiting to be activated and keep their VilCre-negative state at least of 30 days. We believe that their "by default tracing" (VilCre negative from the zygote stage) is as strong an evidence for the existence of such a lineage as positive lineage tracing would be. The increase in crypts originating from this lineage after CBC ablation indicates that it is implicated in regeneration. We do not question the well-demonstrated plasticity-associated reprogramming taking place during regeneration; we simply suggest that this would coexist with the involvement of the quiescent VilCre-negative lineage we have identified.

*However, through the manuscript, there was no immunostaining of Lgr5 and other differentiation markers. The conclusion is an overstatement without solid proof. * We have provided the best answer we could to this point in our answer to the second question of the referee hereabove.

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

In this manuscript, the Marefati et al. developed a novel approach to generate spheroids from adult intestinal epithelium using a collagenase/dispase based protocol. Adult spheroids were found to be distinct from classic budding-type organoids normally generated from EDTA based release of the crypt epithelium. Transcriptional profiling indicated that adult spheroids were undifferentiated and similar to regenerating crypts or fetal spheroids. To identify the cell of origin that generates adult spheroids, the authors labelled epithelial cells with VilCreERT-LSL-Tom, VilCre-LSL-GFP and Lgr5CreERT- LSLTom mice. From these experiments the authors conclude that that spheroids are only generated from Vil-Cre negative and Lgr5 negative cells. Next the authors deleted the anti- apoptotic gene Mcl1 using Vil-CreERT mice. This led to a strong apoptotic response throughout the crypt epithelium and tissues processed from knockout mice readily generated spheroids, and in vivo, replenishment of the gut epithelium was mediated by unrecombined cells. In a second model, CBCs were ablated using Lgr5DTR mice and VilCre negative cells were found again to contribute to regeneration of the crypt epithelium. Finally based on the absence of Vil-Cre reporter activity, the authors were able to sort out and perform scRNAseq to profile VilCre negative cells. These cells were found to be quiescent, express the stem cell marker Olfm4 and were also abundant in ribosomal gene expression.

• *

The fact that the authors developed a protocol to reproducibly generate fetal-like spheroids from adult tissue is an important advancement in the field. Previous reports have shown that treatment with various small molecule inhibitors can revert budding organoids into a spheroid morphology, but this manuscript demonstrates that spheroids can also be generated from otherwise untreated cells. This new methodology will provide new tools to dissect the molecular determinants of fetal/regenerative cells in the gut. Based on this, the manuscript should attract a significant amount of attention in the intestinal field.

• *

As pointed out by the authors themselves the study has important limitations that diminish enthusiasm. The primary issue relates to the inability of the team to identify markers of VilCre neg cells other than the fact that these cells are Olfm4+ and quiescent. Nonetheless, for the reasons stated above the manuscript should reach the target audience within the research community, if the authors can address the specific points below related to issues with methodology as well as defining more precisely the characteristics and growth requirements of adult spheroid cultures.

Thank you for this positive analysis of our study.

Major comments

The main conclusion of the study is that Vil-Cre neg cells are rare quiescent Olfm4+ crypt cells. If this is the case, then standard EDTA treatment should release these cells as well. Consequently, spheroids should also emerge from isolated crypts grown in the absence of ENR. If this is not the case how do the authors explain this?

We have tried hard to generate spheroids by culturing EDTA organoids in medium lacking ENR and by treating EDTA organoids with collagenase/dispase, without success. Therefore, we are left with the conclusion that spheroid-generating cells must be more tightly attached to the matrix than those released by EDTA, and that it is their release from this attachment by collagenase that triggers a regeneration-like phenotype. This hypothesis is supported by several models of regeneration in other tissues as indicated in our references (Gilbert et al., 2010; Machado et al., 2021; Montarras et al., 2005).

From the text the authors appear to suggest that growth of adult spheroids is dependent initially on "material" released by collagenase/dispase treatment. An obvious candidate would be mesenchymal cells, which are known to secrete factors such as Wnts and PGE2 that drive spheroid morphology. To test this, the authors should treat spheroid cultures with Porcupine and/or PGE2 inhibitors.

We followed similar reasoning, considering that spheroids express strongly Ptgs1 ,2 (Figure 3A). We thought their phenotype might be maintained by autocrine prostaglandin action. We tested aspirin, a Ptgs inhibitor, which was without effect on the spheroid phenotype. Besides, we explored a wide variety of conditions to test whether they would affect the spheroid phenotype [Aspirin-see above, cAMP agonists/antagonists, YapTaz inhibitors (verteporfin and CA3), valproic acid, Notch inhibitors (DAPT, DBZ, LY511455), all-trans retinoic acid, NFkB inhibitors (TCPA, BMS), TGFbeta inhibitor (SB431542)]. As these results were negative, we did not include them in the manuscript.

• If these inhibitors block growth then this would suggest that either stromal cells or autocrine signalling involving these pathways is important. Overall, more in-depth analysis of the growth requirements of adult spheroids is required.*

Figure 1d indicates that adult spheroids can be propagated for at least 10 passages. The abstract mentions they are "immortal". The text itself does not address this issue. More precise information as to how long spheroids can be propagated is required. If these cultures can be propagated for 10 passages or more it becomes important to determine what nutrients/mitogens in the basal media are driving growth? Alternatively, what is the evidence that spheroid cultures are completely devoid of mesenchymal cells. The text only mentions that "Upon replating, these spheroids could be stably cultured free of mesenchymal cells (Fig.1B)". No validation is shown to support this.

We agree that "immortal" is not a good way to characterize our spheroids, as also pointed out by referee nr 1. We have changed that in the text, indicating the maximal number of replating we tested was 26 and replacing immortal by stably replatable. Of note, the spheroids could frozen/thawed and recultured many times.

Related to the question whether mesenchymal cells could still contaminate the spheroid cultures, we can provide the following answers:

• No fibroblasts could be seen in replated cultures and multiple spheroids could be repeatedly propagated from a single starting spheroid.
• The bulk RNAseq experiment comparing organoids to ENR or BCM cultured spheroids show, despite expression of several mesenchymal markers (see matrisome in Fig3), absence of significant expression of Pdgfra (see in revision plan folder for CP20Millions results from the raw data of new suppl table 2, with Clu, Tacstd2 and Alpi shown as controls).
• Regarding the nutrients/mitogens in the medium driving spheroid growth, we did not explore the point further than showing that they grow in basal medium (i.e. advanced DMEM), given that the presence of Matrigel makes it difficult to pinpoint what is really needed. In Figure 2, the authors describe the growth requirements for adult spheroids and indicate that spheroids grown in ENR or EN became dark and shrink. The representative images showing this are clear, but this analysis should be quantified.

Added to the manuscript.

In SF3, the gene expression profile of organoids from the sandwich method only partially overlaps with that of organoids from the old protocol. What are the gene expression differences between the 2 culture systems? Secondly, the sandwich method appears to sustain growth of Tom+ spheroids based on RNAseq and the IF images. This suggest that Vil-Cre negative cells are not necessarily the only source of adult spheroids and thus this experiment seems to indicate that any cell may be converted to grow as a spheroid under the right conditions. These points should be addressed.

Looking back to our data in order to answer the point raised by the referee, we realized that we had inadvertently-compared organoids to ENR-cultured spheroids generated by the first protocol to BCM-cultured spheroids generated by the sandwich method. We have corrected this error in a new version of suppl fig3. This shows increased correspondence between genes up- or downregulated in the spheroids obtained in the two protocols (from 49/48% to 57/57% (Venn diagram on the new figure). We agree that, even after this correction, the spheroids obtained with the two protocols present sizeable differences in their transcriptome. However, considering the very different way these spheroids were obtained and cultured initially, we do not believe this to be unexpected. The important point in our opinion is that the core of the up- and down-regulated genes typical of the de-differentiation phenotype of adult spheroids is very similar, as shown in the heatmap (which was made with the correct samples!). Also, a key observation is that that both kind of spheroids survive and can be replated in basal medium. As already stated, this characteristic is only seen rare cases [spheroids obtained from rare FACS-purified cells (Smith et al 2018) or helminth-infected intestinal tissue (Nusse et al.2018)]. Together with the observation that the majority of them is not traced by VilCre constitutes what we consider the halmark of the spheroids described in our study. As shown in figure 4E (old protocol) and Suppl Fig.3 (sandwich protocol) both red and white spheroids were extremely low in VilCre expression. As stated in the text, the fact that some spheroids are nevertheless red is most probably related to the extreme sensitivity of the Rosa26Tom marker to recombination (Liu et al., 2013), but this does not mean that there are two phenotypically different kind of spheroids. It means that the arbitrary threshold of Rosa26Tom recombination introduces an artificial subdivision of spheroids with no phenotypical significance.

Regarding the point made by the referee that "that any cell may be converted to grow as a spheroid under the right conditions", we agree and have shown with others that organoids acquire indeed a spheroid phenotype when cultured for instance in fibroblasts-conditioned medium (see suppl fig1B and (Lahar et al., 2011; Roulis et al., 2020) quoted in the manuscript). However, these spheroids cannot be propagated in basal medium, and revert to an organoid phenotype when put back in ENR (Suppl fig1B).

*In Figure 4, the authors conclude that spheroids do not originate from Lgr5 cell derived clones even after 30days post Tam induction. Does this suggest that in vivo and under homeostatic conditions VilCre neg cells are derived from a distinct stem cell pool or are themselves a quiescent stem cell. Given the rarity of VilCre neg cells, the latter seems unlikely.

*

Despite their rarity, we believe VilCre-negative cells observed under homeostatic conditions are themselves quiescent stem cells. Actually, if they were derived from a larger stem cell pool, this pool should also be VilCre-negative. And we do not see such larger number of VilCre-neg cells under homeostatic conditions.

The problem with the original assertion is that Lgr5-CreERT mice are mosaic and therefore not all Lgr5+ cells are labelled in this model. "White" spheroids may thus derive from cells that in turn derive from these unlabelled Lgr5 cells.

We had considered the possibility that mosaicism [very low for VilCre (Madison et al., 2002); in the 40-50% range for Lgr5CreERT2 (Barker & Clevers. Curr Protoc Stem Cell Biol. 2010 Chapter 5)] could explain our data. We think, however that we can exclude this possibility on the basis that spheroids do not conform to the expected ratio of unrecombined cells, given the observed level of mosaicism. Indeed, for VilCre, a few percent, at most, of unrecombined cells in the epithelium translates into almost 100% unrecombined spheroids. For Lgr5CreERT2 mice, the mosaicism level is in the range of 40%, which is what we observe for EDTA organoids (Figure 4G), while spheroids were in their vast majority unrecombined.

We have included a discussion about the possible role of mosaicism in the new version.

ATACseq experiments were briefly mentioned in the manuscript but unfortunately little information was extracted from this experiment. What does this experiment reveal about the chromatin landscape of adult spheroids relative to normal organoids?

We only performed this experiment to search for an explanation to the paradoxical absence of expression of the VilCre transgene in spheroids, despite robust expression of endogenous villin (Suppl Fig.4). We chose to show the chromatin landscape of a gene equally expressed in both organoids and spheroids (Krt19), a gene specifically expressed in spheroids (Tacstd2) and the endogenous Villin gene also expressed in both. We believe that the observation of a clear difference in pattern of the chromatin accessibility around the endogenous villin gene in organoids and spheroids provides an explanation to the observed results. The cis regulatory sequences needed for expression of the endogenous villin gene seem to be different in organoids and spheroids, which may explain why the regulatory sequences present in the transgene (only 12.4kb) might not allow expression of the transgene in spheroids. We have added a sentence in the manuscript clarifying this point. Missing is obviously the chromatin landscape around the VilCre transgene, but this is beyond reach in such kind of experiments.

Reviewer #2 (Significance (Required)):

The fact that the authors developed a protocol to reproducibly generate fetal-like spheroids from adult tissue is an important advancement in the field. Previous reports have shown that treatment with various small molecule inhibitors can revert budding organoids into a spheroid morphology, but this manuscript demonstrates that spheroids can also be generated from otherwise untreated cells. This new methodology will provide new tools to dissect the molecular determinants of fetal/regenerative cells in the gut. Based on this, the manuscript should attract a significant amount of attention in the intestinal field.

Reviewer #3 (Evidence, reproducibility and clarity (Required)): CR-2024-02491

An Lgr5-independent developmental lineage is involved in mouse intestinal regeneration

Marefati et al.

Homeostatic maintenance of the intestinal epithelium has long been thought to rely upon Wnt signaling responsive Lgr5-expressing stem cells that reside at the crypt base.

However, myriad reported mechanisms or populations have been reported to underlie epithelial regeneration after injury. Many groups have reported that reacquisition of a fetal- link intestinal phenotype is an import part of the regenerative response, however the originating cell type has not been definitively identified. Herein, the authors demonstrate that cells from adult homeostatic intestine can generate immortal spheroids that resemble fetal spheroids and are derived independent of Lgr5+ intestinal stem cells (ISCs). The authors then draw the conclusion that this indicates that a hierarchical stem cell model applies to regeneration of the intestinal epithelium, in addition to the plasticity model.

• *

Comments:

1. Please indicate what species is used for studies in Fig 1.

All experiments were performed in Mus musculus.

Please clarify if Figure 2 studies utilize Matrigel or not.

Yes

RNA-seq analyses of adult intestinal generated spheroids lack the granularity of single cell analyses and thus it is unclear if this is a homogeneous population or if the population has diversity across it (i.e., enteroids/organoids have a high level of diversity). Many of the conclusions from the RNA-seq study are broad and generalized-for example Fig 3F indicates that markers of the +4 ISC populations (Bmi1, tert, lrig1, hopx) were all expressed similarly in adult spheroids as compared to adult organoids. However, while this may be true in the bulk-RNA-seq analyses, clearly scRNA-seq would provide a better foundation to make this statement, as enteroids/organoids are comprised of heterogeneous subpopulations. . .and it might indicate that these +4 markers have only very low expression in the spheroids. Based upon these concerns, misconclusions are likely to be drawn.

We agree and it would be certainly worthwhile to perform scRNAseq of adult spheroid populations. This would certainly be worth doing in future studies to explore the possible heterogeneity of adult spheroids. We nevertheless believe that our scRNAseq performed on homeostatic intestinal tissue from VilCre/Rosa26Tom mice identify Olfm4-low VilCre-neg cells that are likely at the origin of adult spheroids and display a quite homogenous phenotype.

*The language around Figure 4 results is confusing. Please define "white" and "red". It might be simpler to designate recombined versus not recombined lineage.

*

We have clarified this in the figure.

The hypothesis that collagenase/dispase solution acts as a proxy for injury is not demonstrated and backed by data. Thus, it is difficult to make the conclusion that this approach could represent a "stable avatar" of intestinal regenerating cells. It is clear that subpopulations of crypt-based cells generate spheroids in culture without collagenase/dispase (see the cited reference Smith et al, 2018).

* *Smith et al demonstrate clearly the possibility to obtain spheroids with properties probably similar to ours from EDTA derived intestinal crypt cells. However they need to prepurify them by FACS. Besides, Nusse et al describe spheroids similar to ours after infection of the intestine by helminths (Nusse et al. 2018). In our case, and for most labs preparing enteroids with the EDTA protocol, the result is close to 100% organoids. Even if we treat EDTA organoids with collagenase, we do not obtain spheroids. This brought us to the conclusion that spheroid-generating cells must be more tightly attached to the matrix than CBCs and that it is their release from the matrix that activates the spheroid regeneration-like phenotype. This hypothesis is supported by several models of regeneration in other tissues as indicated in our references (Gilbert et al., 2010; Machado et al., 2021; Montarras et al., 2005)

A study based on the absence of recombination in a VilCre lineage tracing scenario is not well-established to be strong experimental approach, as there are many reasons why recombination may not cells may not be lineage marked. In order to use this system as the authors intend, they first need to demonstrate that villin is not expressed in the discrete cell population that they are targeting. For the presented observational studies, this would be difficult to do. While they do demonstrate differences in chromatin accessibility between cells from organoids versus spheroids (fig s4), some of these differences could merely be due to the bulk analytical nature of the study and the lack of comparing stem cell populations from spheroids to stem cell populations from organoids-since the spheroids are likely homogenous versus the organoids that only have a small fraction of stem cells-and thus represent a mix of stem cell and differentiated cell populations. The authors do not demonstrate that villin protein expression varies in these cells.

If it were found that villin is not expressed in their "novel" population, then one would expect that the downstream use of villin-based recombination would demonstrate the same recombination potential (i.e., Mcl1 would not be recombined). Both recombination studies in Fig 6 are difficult to interpret, and thus it is not clear if these studies support the stated conclusions. Quantification of number of crypts that are negative should be reported as a percentage of recombined crypts.

We are sorry but there seems to be a complete misunderstanding of our data regarding the point raised by the referee. The important point of our initial observation is that despite robust expression of villin in spheroids, the VilCre transgene is not expressed (see figure 4E). This in our opinion makes absence of VilCre expression (or of Rosa marker recombination) a trustful marker of a new developmental lineage. All the data in figure 4 constitute an answer.

*The reasoning about heterogeneity of cell type in organoids versus probable homogeneity of spheroids is well taken. However, as the endogenous villin gene is expressed in all cells of both organoids and spheroids, it is highly significant that only spheroids do not express the transgene. *

We performed the ATACseq experiment to search for an explanation to the paradoxical absence of expression of the VilCre transgene in spheroids, despite robust expression of endogenous villin (Suppl Fig.4). We chose to show the chromatin landscape of a gene equally expressed in both organoids and spheroids (Krt19), a gene specifically expressed in spheroids (Tacstd2) and the endogenous Villin gene also expressed in both. We believe that the observation of a clear difference in pattern of the chromatin accessibility around the endogenous villin gene in organoids and spheroids provides an explanation to the observed results. The cis regulatory sequences needed for expression of the endogenous villin gene seem to be different in organoids and spheroids, which may explain why the regulatory sequences present in the transgene (only 12.4kb) might not allow expression of the transgene in spheroids. We have added a sentence in the manuscript clarifying this point. Missing is obviously the chromatin landscape around the VilCre transgene, but this is beyond reach in such kind of experiments.

*Figure 8 indicates that the cell population identified by scRNA-seq may be quiescent. Companion IF or IHC should be conducted to confirm this finding, as well as other conclusions from the informatics conducted.

*

We agree that additional experiments could be performed to support this point. We are unfortunately not in a position to perform these experiments (see section 4 below).

Clearly the data is intriguing, however, the conclusion is strong and is an over interpretation of the presented data. There are a number of validation or extension data that would enhance the overall interpretation of the study: 1. validation of scRNA-seq or bulk RNA-seq concepts by protein staining of intestinal tissues in the damage model will serve as a secondary observation. 2. identification of the ISC that they are defining is critical and important. There is already the notion that this cell type exists and it has been shown with various different markers. 3. expand the analyses of the fetal-like expression profiling to injured intestines to demonstrate that the lineage negative cells indeed express fetal-like proteins. 4. expand the discussion of the Clu+ cell type. Is this cell the previously described revival cell? If so, how does this body of work provide unique aspects to the field?

We agree that all these suggested experiments could be performed and would be of interest. However, we consider that they would not modify the main message of our study and would only constitute an expansion of the present work. As already stated, we are not in the position to perform them (see section 4).

*There is some level of conflicting data, with the stem population being proliferative in culture stimulated by the stromal cells, but quiescent in vivo and also based upon scRNA- seq data in Fig 9.

*

We do not see any conflict in our observation regarding this point. The observation that cells that are quiescent in vivo become proliferative when subjected to culture (with or without addition of stromal cells) is routinely made in a multitude of cell culture systems. In particular, it has been shown that intestinal tissue dissociation activates the Yap/Taz pathway, resulting in proliferation (Yu et al. Hippo Pathway Regulation of Gastrointestinal Tissues. Annual Review of Physiology, 2015 Volume 77, 201-227).

Many of the findings have been previously reported: Population that grows as spheroids (Figure 2), Population that is Wnt independent (Figure 2), Lgr5 independent regenerative growth of the intestine (figure 3F, Figure 4), Clu+ ISCs drive regeneration (Figure 7).

Whereas these individual findings have indeed been reported, it was in a different context. We strongly disagree with the underlying suggestion that our study would not bring new information. We have identified here a developmental lineage involved in intestinal regeneration that has not been described up to now.

Minor comments:

1. • The statement that spheroids must originate from collagenase/dispase digested material might be an overstatement. As spheroids generation from EDTA treated intestines have been previously reported (Smith et al, 2018). * See answer to point 4 above. *Overall while the study includes an extensive amount of work and different approaches, a foundationally supported novel finding is missing. Many of the statements have already been demonstrated by others in the fields. In addition, one of the most intriguing aspects of the study is that the stromal population impacts this stem cell population, however, interactions and factors stimulating the crosstalk are not addressed.

*

Reviewer #3 (Significance (Required)):

Overal while the study includes an extensive amount of work and different approaches, a foundationally supported novel finding is missing. Many of the statements have already been demonstrated by others in the fields. In addition, one of the most intriguing aspects of the study is that the stromal population impacts this stem cell population, however, interactions and factors stimulating the crosstalk are not addressed.

We can only disagree.

4. Description of analyses that authors prefer not to carry out

• *

We have answered most questions raised by the referees by explaining our view, by clarifying individual points and, in several cases, by providing additional information that was not included in the original manuscript.

In a limited number of cases when additional experiments were suggested, we were unfortunately obliged to write that we are not in a position to perform them. This is because my lab is closing after more than fifty years of uninterrupted activity. There will unfortunately be nobody to perform additional experiments.

Nevertheless, as written by referees 1 and 2, we believe that the revised manuscript, as it stands, contains data that will be of interest to the people in the field and may be the bases for future developments. We hope editors will find interest in publishing it.

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Referee #3

Evidence, reproducibility and clarity

RC-2024-02491

An Lgr5-independent developmental lineage is involved in mouse intestinal regeneration Marefati et al.

Homeostatic maintenance of the intestinal epithelium has long been thought to rely upon Wnt signaling responsive Lgr5-expressing stem cells that reside at the crypt base. However, myriad reported mechanisms or populations have been reported to underlie epithelial regeneration after injury. Many groups have reported that reacquisition of a fetal-link intestinal phenotype is an import part of the regenerative response, however the originating cell type has not been definitively identified. Herein, the authors demonstrate that cells from adult homeostatic intestine can generate immortal spheroids that resemble fetal spheroids and are derived independent of Lgr5+ intestinal stem cells (ISCs). The authors then draw the conclusion that this indicates that a hierarchical stem cell model applies to regeneration of the intestinal epithelium, in addition to the plasticity model.

Comments:

1. Please indicate what species is used for studies in Fig 1.
2. Please clarify if Figure 2 studies utilize Matrigel or not.
3. RNA-seq analyses of adult intestinal generated spheroids lack the granularity of single cell analyses and thus it is unclear if this is a homogeneous population or if the population has diversity across it (i.e., enteroids/organoids have a high level of diversity). Many of the conclusions from the RNA-seq study are broad and generalized-for example Fig 3F indicates that markers of the +4 ISC populations (Bmi1, tert, lrig1, hopx) were all expressed similarly in adult spheroids as compared to adult organoids. However, while this may be true in the bulk-RNA-seq analyses, clearly scRNA-seq would provide a better foundation to make this statement, as enteroids/organoids are comprised of heterogeneous subpopulations. . .and it might indicate that these +4 markers have only very low expression in the spheroids. Based upon these concerns, misconclusions are likely to be drawn.
4. The language around Figure 4 results is confusing. Please define "white" and "red". It might be simpler to designate recombined versus not recombined lineage.
5. The hypothesis that collagenase/dispase solution acts as a proxy for injury is not demonstrated and backed by data. Thus, it is difficult to make the conclusion that this approach could represent a "stable avatar" of intestinal regenerating cells. It is clear that subpopulations of crypt-based cells generate spheroids in culture without collagenase/dispase (see the cited reference Smith et al, 2018).
6. A study based on the absence of recombination in a VilCre lineage tracing scenario is not well-established to be strong experimental approach, as there are many reasons why recombination may not cells may not be lineage marked. In order to use this system as the authors intend, they first need to demonstrate that villin is not expressed in the discrete cell population that they are targeting. For the presented observational studies, this would be difficult to do. While they do demonstrate differences in chromatin accessibility between cells from organoids versus spheroids (fig s4), some of these differences could merely be due to the bulk analytical nature of the study and the lack of comparing stem cell populations from spheroids to stem cell populations from organoids-since the spheroids are likely homogenous versus the organoids that only have a small fraction of stem cells-and thus represent a mix of stem cell and differentiated cell populations. The authors do not demonstrate that villin protein expression varies in these cells. If it were found that villin is not expressed in their "novel" population, then one would expect that the downstream use of villin-based recombination would demonstrate the same recombination potential (i.e., Mcl1 would not be recombined). Both recombination studies in Fig 6 are difficult to interpret, and thus it is not clear if these studies support the stated conclusions. Quantification of number of crypts that are negative should be reported as a percentage of recombined crypts.
7. Figure 8 indicates that the cell population identified by scRNA-seq may be quiescent. Companion IF or IHC should be conducted to confirm this finding, as well as other conclusions from the informatics conducted.
8. Clearly the data is intriguing, however, the conclusion is strong and is an over interpretation of the presented data. There are a number of validation or extension data that would enhance the overall interpretation of the study:
   • a. validation of scRNA-seq or bulk RNA-seq concepts by protein staining of intestinal tissues in the damage model will serve as a secondary observation.
   • b. identification of the ISC that they are defining is critical and important. There is already the notion that this cell type exists and it has been shown with various different markers.
   • c. expand the analyses of the fetal-like expression profiling to injured intestines to demonstrate that the lineage negative cells indeed express fetal-like proteins.
   • d. expand the discussion of the Clu+ cell type. Is this cell the previously described revival cell? If so, how does this body of work provide unique aspects to the field?
9. There is some level of conflicting data, with the stem population being proliferative in culture stimulated by the stromal cells, but quiescent in vivo and also based upon scRNA-seq data in Fig 9.
10. Many of the findings have been previously reported: Population that grows as spheroids (Figure 2), Population that is Wnt independent (Figure 2), Lgr5 independent regenerative growth of the intestine (figure 3F, Figure 4), Clu+ ISCs drive regeneration (Figure 7).

Minor comments:

1. The statement that spheroids must originate from collagenase/dispase digested material might be an overstatement. As spheroids generation from EDTA treated intestines have been previously reported (Smith et al, 2018).

Overall while the study includes an extensive amount of work and different approaches, a foundationally supported novel finding is missing. Many of the statements have already been demonstrated by others in the fields. In addition, one of the most intriguing aspects of the study is that the stromal population impacts this stem cell population, however, interactions and factors stimulating the crosstalk are not addressed.

Significance

Overall while the study includes an extensive amount of work and different approaches, a foundationally supported novel finding is missing. Many of the statements have already been demonstrated by others in the fields. In addition, one of the most intriguing aspects of the study is that the stromal population impacts this stem cell population, however, interactions and factors stimulating the crosstalk are not addressed.

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Referee #2

Evidence, reproducibility and clarity

In this manuscript, the Marefati et al. developed a novel approach to generate spheroids from adult intestinal epithelium using a collagenase/dispase based protocol. Adult spheroids were found to be distinct from classic budding-type organoids normally generated from EDTA based release of the crypt epithelium. Transcriptional profiling indicated that adult spheroids were undifferentiated and similar to regenerating crypts or fetal spheroids. To identify the cell of origin that generates adult spheroids, the authors labelled epithelial cells with VilCreERT-LSL-Tom, VilCre-LSL-GFP and Lgr5CreERT-LSLTom mice. From these experiments the authors conclude that that spheroids are only generated from Vil-Cre negative and Lgr5 negative cells. Next the authors deleted the anti-apoptotic gene Mcl1 using Vil-CreERT mice. This led to a strong apoptotic response throughout the crypt epithelium and tissues processed from knockout mice readily generated spheroids, and in vivo, replenishment of the gut epithelium was mediated by unrecombined cells. In a second model, CBCs were ablated using Lgr5DTR mice and VilCre negative cells were found again to contribute to regeneration of the crypt epithelium. Finally based on the absence of Vil-Cre reporter activity, the authors were able to sort out and perform scRNAseq to profile VilCre negative cells. These cells were found to be quiescent, express the stem cell marker Olfm4 and were also abundant in ribosomal gene expression.

The fact that the authors developed a protocol to reproducibly generate fetal-like spheroids from adult tissue is an important advancement in the field. Previous reports have shown that treatment with various small molecule inhibitors can revert budding organoids into a spheroid morphology, but this manuscript demonstrates that spheroids can also be generated from otherwise untreated cells. This new methodology will provide new tools to dissect the molecular determinants of fetal/regenerative cells in the gut. Based on this, the manuscript should attract a significant amount of attention in the intestinal field.

As pointed out by the authors themselves the study has important limitations that diminish enthusiasm. The primary issue relates to the inability of the team to identify markers of VilCre neg cells other than the fact that these cells are Olfm4+ and quiescent. Nonetheless, for the reasons stated above the manuscript should reach the target audience within the research community, if the authors can address the specific points below related to issues with methodology as well as defining more precisely the characteristics and growth requirements of adult spheroid cultures.

Major comments

The main conclusion of the study is that Vil-Cre neg cells are rare quiescent Olfm4+ crypt cells. If this is the case, then standard EDTA treatment should release these cells as well. Consequently, spheroids should also emerge from isolated crypts grown in the absence of ENR. If this is not the case how do the authors explain this?

From the text the authors appear to suggest that growth of adult spheroids is dependent initially on "material" released by collagenase/dispase treatment. An obvious candidate would be mesenchymal cells, which are known to secrete factors such as Wnts and PGE2 that drive spheroid morphology. To test this, the authors should treat spheroid cultures with Porcupine and/or PGE2 inhibitors. If these inhibitors block growth then this would suggest that either stromal cells or autocrine signalling involving these pathways is important. Overall, more in-depth analysis of the growth requirements of adult spheroids is required.

Figure 1d indicates that adult spheroids can be propagated for at least 10 passages. The abstract mentions they are "immortal". The text itself does not address this issue. More precise information as to how long spheroids can be propagated is required. If these cultures can be propagated for 10 passages or more it becomes important to determine what nutrients/mitogens in the basal media are driving growth? Alternatively, what is the evidence that spheroid cultures are completely devoid of mesenchymal cells. The text only mentions that "Upon replating, these spheroids could be stably cultured free of mesenchymal cells (Fig.1B)". No validation is shown to support this.

In Figure 2, the authors describe the growth requirements for adult spheroids and indicate that spheroids grown in ENR or EN became dark and shrink. The representative images showing this are clear, but this analysis should be quantified.

In SF3, the gene expression profile of organoids from the sandwich method only partially overlaps with that of organoids from the old protocol. What are the gene expression differences between the 2 culture systems? Secondly, the sandwich method appears to sustain growth of Tom+ spheroids based on RNAseq and the IF images. This suggest that Vil-Cre negative cells are not necessarily the only source of adult spheroids and thus this experiment seems to indicate that any cell may be converted to grow as a spheroid under the right conditions. These points should be addressed.

In Figure 4, the authors conclude that spheroids do not originate from Lgr5 cell derived clones even after 30days post Tam induction. Does this suggest that in vivo and under homeostatic conditions VilCre neg cells are derived from a distinct stem cell pool or are themselves a quiescent stem cell. Given the rarity of VilCre neg cells, the latter seems unlikely. The problem with the original assertion is that Lgr5-CreERT mice are mosaic and therefore not all Lgr5+ cells are labelled in this model. "White" spheroids may thus derive from cells that in turn derive from these unlabelled Lgr5 cells.

ATACseq experiments were briefly mentioned in the manuscript but unfortunately little information was extracted from this experiment. What does this experiment reveal about the chromatin landscape of adult spheroids relative to normal organoids?

Significance

The fact that the authors developed a protocol to reproducibly generate fetal-like spheroids from adult tissue is an important advancement in the field. Previous reports have shown that treatment with various small molecule inhibitors can revert budding organoids into a spheroid morphology, but this manuscript demonstrates that spheroids can also be generated from otherwise untreated cells. This new methodology will provide new tools to dissect the molecular determinants of fetal/regenerative cells in the gut. Based on this, the manuscript should attract a significant amount of attention in the intestinal field.

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Referee #1

Evidence, reproducibility and clarity

In this manuscript, Marefati et al report an Lgr5-independent lineage in the regenerating intestine using in vitro organoids and in vivo injury-coupled lineage tracing model. In organoids, collagenase/dispase dissociated resulted in "immortal spheroids" that maintain a cystic and undifferentiated phenotype in the absence of standard growth factors (Rspondin/Noggin/EGF). Bulk RNAseq of spheroids demonstrates downregulation of classical CBC signatures and upregulation of fetal spheroid, mesenchymal, inflammation and regenerative signatures. In mice, Villin-Cre lineage tracing revealed some Villin-negative progenies that lack reporter tracing throughout crypt-villus ribbons after injury. The authors proposed that there is Lgr5-independent population support the regenerative response upon CBC depletion. A major caveat of this study is the identification of this population is based on absence of VilCre expression. It is surprising that there is no characterisation of Lgr5 expression throughout the manuscript whilst claiming of a Lgr5-independent lineage. Although the research question is potentially interesting, the concept of epithelial reprogramming upon injury is well documented in the field. The data generated in this manuscript also seem to be preliminary and lack of detailed characterisation. Below are specific comments.

• Expression of Lgr5 should be properly characterised throughout the manuscript in both organoid models and injury-induced regeneration in vivo.
• An important question is the origin of these "Lgr5-independent" adult spheroids. They look and appear like fetal organoids, which could be induced by injury (e.g. upon collagenase/dispase dissociation). Have the authors tried to culture fetal spheroids in BCM over extensive period of time? Do they behave the same? This would be a great way to directly compare the collagenase/dispase-derived organoids with fetal origin.
• Fig 1C, Why is the replating spheroid culture time different between mesenchymal cells and conditioned medium?
• It is unclear how the bulk RNA-seq data in Fig. 3 were compared. How long were the adult organoids and spheroids cultured for (how many passages)? Were they culture in the same condition of were they in ENR vs BCM? These are important information to consider when interpreting the results. For instance, are Ptgs1 & Ptgs2 expression in adult spheroids the same in ENR vs BCM? Are the gene signatures (regenerative, fetal and YAP) changed in adult spheroids culturing in ENR vs BCM?
• It is stated: "In agreement with their aptitude to grow indefinitely, adult spheroids express a set of upregulated genes overlapping significantly with an "adult tissue stem cell module" [159/721 genes; q value 2.11 e-94) (Fig.S2F)].". What is the definition of "indefinitely"? Are they referring to the Fig 1B where spheroid were passaged to P10? The authors should avoid the term "indefinitely" but use a more specific time scale, e.g. passages, months etc.
• SuppFig 3D: Row Z-Score is missing the "e" in Score.
• Fig 4E: Figure legend says QNRQ instead of CNRQ.
• Fig 4G: The brightfield image of adult spheroids 5 days after 3x TAM injections doesn't look like a spheroid. It seems to be differentiating.
• Fig 4: Most mouse model data are missing the number of mice & their respective age used for organoid isolation.
• Fig 4A-D, H-G: How was fluorescent signal of organoids quantified? How many images? Were there equal numbers of organoids? This all needs to be included in methods/figure legends.
• Figure 4B-D, G-H: Which culturing conditions were used for adult spheroids? Original method or sandwich method?
• Fig 6D-E: Please add the timepoint after DT administration these samples are from. It is not listed in text or figure legend.
• SuppFig 6D: again timepoint is missing.
• SuppFig 6: How were the crypts of these mice (DT WT & DT HE) isolated? Was this via EDTA? Also, what is the timepoint for isolation for these samples? Even if untreated, the timepoint adds context to the data. Please add more context to describing these different experiments, either in the figure legends or methods section.
• SuppFig 6E: The quality of the heatmap resolution is too poor to read gene names.
• Fig.5-7, are the regenerating crypt-villus units fully differentiated or are they maintained in the developmental state? Immunostaining of markers for stem cells (Lgr5), differentiated lineages (Alpi, Muc2, Lyz, ChgA etc.) and fetal state (Sca1, Trop2 etc) should be analysed in those "white" unrecombined crypt-villus units.
• The following text needs clarification:

"The kinetics of appearance of newly formed un-recombined ("white") crypts was studied after a single pulse of DT (Fig.7A). This demonstrated an increase at 48 hours, with further increase at day 10 and stable maintenance at day 30. The presence of newly formed white crypts one month after toxin administration indicates that the VilCre-negative lineage is developmentally stable and does not turn on the transgene during differentiation of the various epithelial lineages occurring after regeneration (Fig.7B). Comment: The "newly formed" is an overstatement, the data doesn't conclude that those are "new" crypts. The end of the sentence states that these "white" crypts form developmentally stable lineages, thus these white crypts at day 30 could originate from the initial injury. There was no characterisation of the various epitheial lineages. Are they fully differentiated? Is Lgr5 expressed one month after toxin administration? Can the VilCre neg lineage give rise to CBCs?

The relationship between white crypt generation and appearance of Clu-positive revival cells (Ayyaz et al., 2019) was then explored. In agreement with others and similar to what happens in the irradiation model, (Ayyaz et al., 2019; Yuan et al., 2023) Clu-positive cells were rare in crypts of untreated mice and their number transiently increased forty-eight hours after a single pulse of DT, and more so after three pulses of DT (Fig.7C,D). Comment: Comparing 1 pulse at day 2 vs 3 pulses at day 5 makes the data hard to interpret. How is the Clu ISH level for 1 pulse at day 5? Are they equivalent?

Clu-positive cells were less frequently observed in white crypts (see "Total" versus "White" in Fig.7C). This fits with the hypothesis that Clu expression marks acutely regenerating crypts and that a proportion of the white crypts are chronically regenerating due to DTR expression in CBCs." Comment: I believe the authors suggested that the discrepancy of less Clu expression in white crypts is due to the ectopic expression of DTR in CBCs causing low grade injury without DT administration. This means that some white crypts could have been formed before the administration of DT, and thus are on a different regenerative timeline compared to the white crypts formed from DT administration. Is there any proof of the chronic regeneration? Immunostaining of chronic regenerative markers such as Sca1, Anxa1 or Yap1 nuclear localization would support the claim. It'd be important to show only the white crypts, but not the RFP+ ones, show regenerative markers. - Fig 7D-E: What are the timepoints of harvest for HE-WT-HE 1 pulse DT mice and HE-HE-HE PBS injected mice? - Fig 8-9: Regarding the CBC-like Olfm4 low population, what is the status of Lgr5? This should be shown in the figure since the argument is that this is an Lgr5-independent lineage. And what about the regenerative, Yap, mesenchymal and inflammatory signatures? Are they enriched in the white crypts similar to the in vitro spheroids?

Significance

Strengths: The study employed a range of in vitro and in vivo models to test the hypothesis.

Limitations: Unfortunately, the models chosen did not provide sufficient evidence to draw the conclusions. Injury induced reprogramming, both in vivo and in vitro, has been well documented in the field. The new message here is to show that such reprogrammed state is continuous rather than transient; instead of regenerating Lgr5+ stem cells, it can continue to differentiate to all cell lineages in Lgr5-independent manner. However, through the manuscript, there was no immunostaining of Lgr5 and other differentiation markers. The conclusion is an overstatement without solid proof.

Yet https://docs.chrultrabook.com/docs/reverting/

Version 2 of this preprint has been peer-reviewed and recommended by Peer Community in Archaeology.<br /> See the peer reviews and the recommendation.

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Tbl. 2 / Table 2

A second question arises from the development we see in Fig. 8, with significantly more articles on amok being published in the first half of the 1860s and in the late 1870s and 1880s.

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eLife assessment

This valuable paper presents findings showing that different brain regions were best described by a distinct accumulation model, which all differed from the model that best described the rat's choices. These findings are solid because the authors present a very strong methodological approach. This work will be of interest to a wide neuroscientific audience.

Reviewer #1 (Public Review):

The authors use neural recordings from three different brain areas to assess whether the type of evidence accumulation dynamics in those regions are (1) similar to one another, and (2) similar to best-fitting evidence accumulation dynamics to behavioral choice alone. This is an important theoretical question because it relates to the 'linking hypothesis' that relates neurophysiological data to psychological phenomena. Although the standard evidence accumulation dynamic in describing choice has been the gradual accumulation of evidence, the authors find that those dynamics are not represented equally in all brain regions. Such results suggest that more nuanced computational models are needed to explain how brain areas interact to produce decisions, and the focus of theoretical development should shift away from explaining behavioral patterns alone and more toward explaining both brain and behavioral interactions. Given that the authors simply test the assumption that the same dynamics that best explain behavior should also explain neural data, they accomplish their objective using a sophisticated methodology and find evidence *against* this assumption: they find that each region was best described by a distinct accumulation model, which all differed from the model that best described the rat's choices.

I thought this was an excellent paper with a clear scientific objective, direct analysis to achieve that objective, and a very strong methodological approach to leave little doubt that the conclusions they drew from their analyses were as reasonable and accurate as possible.

Reviewer #2 (Public Review):

The neural dynamics underlying decision-making have long been studied across different species (e.g., primates and rodents) and brain areas (e.g., parietal cortex, frontal eye fields, striatum). The key question is to what extent neural firing rates covary with evidence accumulation processes as proposed by evidence accumulation models. It is often assumed that the evidence-accumulation process at the neural level should mirror the evidence-accumulation process at the behavioral level. The current paper shows that the neural dynamics of three rat brain regions (the FOF, ADS, and PCC) all show signatures of evidence accumulation, but in distinct ways. Especially the role of the FOF appears to be distinct, due to its dependence on early evidence compared to the other regions. This sheds new light and a new interpretation of the role of the FOF in decision-making - previously, it has been described as a region encoding the choice that is currently being committed to; this new analysis suggests it is instead strongly influenced by early evidence.

A major strength of the paper is that the results are achieved through joint modelling of the behavioral and neural data, combined with information on the physical stimulus at hand. Joint models were shown to provide more information on the underlying processes compared to behavioral or neural models alone. Especially the inclusion of the neural data seemed to have greatly improved the quality of inferences. This is a key contribution that illustrates that the sophisticated modelling of multiple sources of data at the same time, pays off in terms of the quality of inferences. Yet, it should be added here, that due to the nature of the task, the behavioral data contained only choices, and not response times, which tend to contain more information regarding the evidence accumulation process than choice alone. It would be interesting to additionally discuss how choice decision times can be modeled with the proposed modelling framework.

A main limitation of the paper is that it does not appear to address a seemingly logical follow-up question: If these three brain regions individually accumulate evidence in distinct manners, how do these multiple brain regions then each contribute to a final choice? The joint models fit each region's data separately, so how well does each region individually 'explain' or 'predict' behavior, and how does the combined neural activity of the regions lead to manifest behavior? I would be very interested in the authors' perspectives on these questions.

There are some remaining questions regarding the specific models used, that I was hoping the authors could clarify. Specifically, in equations 10-11, I was wondering to what extent there might be a collinearity issue. Equation 10 proposes that the firing rates of neurons can vary across time due to two mechanisms: (1) The dependence of the firing rate on the accumulated evidence, and (2) a time-varying trial average (as detailed in Equation 11). If firing rates of the neuron indeed covary with the accumulated evidence and therefore increase across time, how can the effects of mechanisms 1 and 2 be disentangled? Relatedly, the independent noise models model each neuron separately and thereby include many more parameters, each informed by less data. Is it possible that the relatively poor cross-validation of the independent noise model may be a consequence of the overfitting of the independent noise model?<br /> Another related question is how robust the parameter recovery properties of these models are under a wider range of data-generating parameter settings. I greatly appreciate the inclusion of a parameter recovery study (Figure S1C) using a single synthetic dataset, but it could be made even stronger by simulating multiple datasets with a wider range of parameter settings. Such a simulation study would help understand how robust and reliable the estimated parameters of all models are. Similarly, it would be helpful if also the \theta_{y} parameters are shown, which aren't shown in Figure S1C.

An aspect of the paper that initially raised confusion with me is that the models fit on the choice data and stimulus information alone, make different predictions for the evidence accumulation dynamics in different regions (e.g., Figure 5A, 6A) and also led to different best-fitting parameters in different regions (Figure S9A). It took me a while to realize that this is due to the data being pooled across different rats and sessions - as such, the behavioral choice data are not the same across regions, and neither is the resulting fit models. This could easily be clarified by adding a few notes in the captions of the relevant figures.

Combined, this manuscript represents an interesting and welcome contribution to an ongoing debate on the neural dynamics of decision-making across different brain regions. It also introduced new joint modelling techniques that can be used in the field and raised new questions on how the concurrent activity of neurons across different brain regions combined leads to behavior.

Author response

Reviewer #1 (Public Review):

The authors use neural recordings from three different brain areas to assess whether the type of evidence accumulation dynamics in those regions are (1) similar to one another, and (2) similar to best-fitting evidence accumulation dynamics to behavioral choice alone. This is an important theoretical question because it relates to the 'linking hypothesis' that relates neurophysiological data to psychological phenomena. Although the standard evidence accumulation dynamic in describing choice has been the gradual accumulation of evidence, the authors find that those dynamics are not represented equally in all brain regions. Such results suggest that more nuanced computational models are needed to explain how brain areas interact to produce decisions, and the focus of theoretical development should shift away from explaining behavioral patterns alone and more toward explaining both brain and behavioral interactions. Given that the authors simply test the assumption that the same dynamics that best explain behavior should also explain neural data, they accomplish their objective using a sophisticated methodology and find evidence *against* this assumption: they find that each region was best described by a distinct accumulation model, which all differed from the model that best described the rat's choices.

I thought this was an excellent paper with a clear scientific objective, direct analysis to achieve that objective, and a very strong methodological approach to leave little doubt that the conclusions they drew from their analyses were as reasonable and accurate as possible.

We thank the reviewer for their time and appreciate their generous comments.

Reviewer #2 (Public Review):

The neural dynamics underlying decision-making have long been studied across different species (e.g., primates and rodents) and brain areas (e.g., parietal cortex, frontal eye fields, striatum). The key question is to what extent neural firing rates covary with evidence accumulation processes as proposed by evidence accumulation models. It is often assumed that the evidence-accumulation process at the neural level should mirror the evidence-accumulation process at the behavioral level. The current paper shows that the neural dynamics of three rat brain regions (the FOF, ADS, and PCC) all show signatures of evidence accumulation, but in distinct ways. Especially the role of the FOF appears to be distinct, due to its dependence on early evidence compared to the other regions. This sheds new light and a new interpretation of the role of the FOF in decision-making - previously, it has been described as a region encoding the choice that is currently being committed to; this new analysis suggests it is instead strongly influenced by early evidence.

A major strength of the paper is that the results are achieved through joint modelling of the behavioral and neural data, combined with information on the physical stimulus at hand. Joint models were shown to provide more information on the underlying processes compared to behavioral or neural models alone. Especially the inclusion of the neural data seemed to have greatly improved the quality of inferences. This is a key contribution that illustrates that the sophisticated modelling of multiple sources of data at the same time, pays off in terms of the quality of inferences. Yet, it should be added here, that due to the nature of the task, the behavioral data contained only choices, and not response times, which tend to contain more information regarding the evidence accumulation process than choice alone. It would be interesting to additionally discuss how choice decision times can be modeled with the proposed modelling framework.

We thank the reviewer for their generous views on our work. We agree that adding decision times, which could readily be added to our framework, will likely further constrain the inference of the latent model. We are currently pursuing such topics using this framework and appropriate data. We have altered a passage in our Discussion, where we note the various extensions of our model one could pursue, to include response time within the set of behavioral measurements one might include.

A main limitation of the paper is that it does not appear to address a seemingly logical follow-up question: If these three brain regions individually accumulate evidence in distinct manners, how do these multiple brain regions then each contribute to a final choice? The joint models fit each region's data separately, so how well does each region individually 'explain' or 'predict' behavior, and how does the combined neural activity of the regions lead to manifest behavior? I would be very interested in the authors' perspectives on these questions.

We could not share the reviewers view and interest in this question with any more excitement than we already do! Unfortunately, the experiments necessary for answering this question in a satisfying way have not yet been performed (e.g. simultaneous multi-region population recordings). Additionally, our analysis approach, as presented currently, would require some technical alterations to deal with data at that scale. Both efforts are underway, but we feel as though the current manuscript describes the basic modeling framework one would need to use to address these questions if/when such data exists. We have added some text to the Discussion to highlight these exciting future directions:

“An exciting future application of our modeling framework is to model multiple, independent accumulators in several brain regions which collectively give rise to the animal’s behavior. Such a model would provide incredible insight into how the brain collectively gives rise to behavioral choices.”

There are some remaining questions regarding the specific models used, that I was hoping the authors could clarify. Specifically, in equations 10-11, I was wondering to what extent there might be a collinearity issue. Equation 10 proposes that the firing rates of neurons can vary across time due to two mechanisms: (1) The dependence of the firing rate on the accumulated evidence, and (2) a time-varying trial average (as detailed in Equation 11). If firing rates of the neuron indeed covary with the accumulated evidence and therefore increase across time, how can the effects of mechanisms 1 and 2 be disentangled? Relatedly, the independent noise models model each neuron separately and thereby include many more parameters, each informed by less data. Is it possible that the relatively poor cross-validation of the independent noise model may be a consequence of the overfitting of the independent noise model?

Thank you for this important observation. Please see our response to the essential revisions above which addresses this issue. In short, although it is true that firing rates increase with time (with accumulating evidence) they do so in a way that depends on the stimulus, and so just as often as they increase with time, they decrease.

Regarding the poor cross-validation of the independent noise model, we apologize for confusion here — both the shared and independent noise model have exactly the same number of parameters. They only differ in that the latent process for a trial contains unique noise instantiation per trial for the independent noise model and the same instantiating for the shared model. The number of parameters is the same. See above for our response to this issue, and how the manuscript was modified in light of this confusion.

Another related question is how robust the parameter recovery properties of these models are under a wider range of data-generating parameter settings. I greatly appreciate the inclusion of a parameter recovery study (Figure S1C) using a single synthetic dataset, but it could be made even stronger by simulating multiple datasets with a wider range of parameter settings. Such a simulation study would help understand how robust and reliable the estimated parameters of all models are. Similarly, it would be helpful if also the \theta_{y} parameters are shown, which aren't shown in Figure S1C.

We agree that understanding the model fitting behavior under a wider set of parameter settings is valuable. We fit our model to additional sets of parameter settings and included an additional supplemental figure (Figure 1 — figure supplement 2) to illustrate these results. In short, we found that parameter recovery was robust across the different parameter settings we tested. We also updated Figure S1C with the neural parameters. We included the following in the Results to note that parameter recovery was robust:

“We verified that our method was able to recover the parameters that generated synthetic physiologically-relevant spiking and choices data (Figure 1 — figure supplement 1), and that parameter recovery was robust across a range of parameter values (Figure 1 — figure supplement 2)).” 

An aspect of the paper that initially raised confusion with me is that the models fit on the choice data and stimulus information alone, make different predictions for the evidence accumulation dynamics in different regions (e.g., Figure 5A, 6A) and also led to different best-fitting parameters in different regions (Figure S9A). It took me a while to realize that this is due to the data being pooled across different rats and sessions - as such, the behavioral choice data are not the same across regions, and neither is the resulting fit models. This could easily be clarified by adding a few notes in the captions of the relevant figures.

Thanks for pointing this out. We agree that this tends to be a point of confusion, and we have added clarification prior to Fig 3, where the choice model is first introduced:

“We stress that because of this, each fitted choice model uses different behavioral choice data, and thus the fitted parameters vary from fitted model to fitted model.”

Combined, this manuscript represents an interesting and welcome contribution to an ongoing debate on the neural dynamics of decision-making across different brain regions. It also introduced new joint modelling techniques that can be used in the field and raised new questions on how the concurrent activity of neurons across different brain regions combined leads to behavior.

We appreciate the very generous views on our work!

metaphor the narrator compares the old man¨s eye to that of the vulture, using this metaphor to emphasize his revulsion towards it.

Repetition the narrator repeats key phrases like the eye and rid myself to reinforce his obsession.

hyperbole - this exaggerates the narrator΅s heightened senses and paranoia

The repetition of the sound creates a rhythmic, unsettling quality

Personification - the narrator personifies his desire to rid myself of the eye as if it is a separate entity

Author response:

eLife assessment

This useful study reports on the discovery of an antimicrobial agent that kills Neisseria gonorrhoeae. Sensitivity is attributed to a combination of DedA assisted uptake of oxydifficidin into the cytoplasm and the presence of a oxydifficidin-sensitive RplL ribosomal protein. Due to the narrow scope, the broader antibacterial spectrum remains unclear and therefore the evidence supporting the conclusions is incomplete with key methods and data lacking. This work will be of interest to microbiologists and synthetic biologists.

General comment about narrow scope: The broader antibacterial spectrum of oxydifficidin has been reported previously (S B Zimmerman et al., 1987). The main focus of this study is on its previously unreported potent anti-gonococcal activity and mode of action. While it is true that broad-spectrum antibiotics have historically played a role in effectively controlling a wide range of infections, we and others believe that narrow-spectrum antibiotics have an overlooked importance in addressing bacterial infections. Their advantage lies in their ability to target specific pathogens without markedly disrupting the human microbiota.

We are troubled by the statement that our paper is narrow in scope and that evidence supporting our conclusions is incomplete. We do not feel the reviews as presented substantiate drawing this conclusion about our work.

Public Reviews:

Reviewer #1 (Public Review):

Summary:

Kan et al. report the serendipitous discovery of a Bacillus amyloliquefaciens strain that kills N. gonorrhoeae. They use TnSeq to identify that the anti-gonococcal agent is oxydifficidin and show that it acts at the ribosome and that one of the dedA gene products in N. gonorrhoeae MS11 is important for moving the oxydifficidin across the membrane.

Strengths:

This is an impressive amount of work, moving from a serendipitous observation through TnSeq to characterize the mechanism by which Oxydifficidin works.

Weaknesses:

(1) There are important gaps in the manuscript's methods.

The requested additions to the method describing bacterial sequencing and anti-gonococcal activity screening will be made. However, we do not think the absence of these generic methods reduces the significance of our findings.

(2) The work should evaluate antibiotics relevant to N. gonorrhoeae.

(1) It is not clear to us why reevaluating the activity of well characterized antibiotics against known gonorrhoeae clinical strains would add value to this manuscript. The activity of clinically relevant antibiotics against antibiotic-resistant N. gonorrhoeae clinical isolates is well described in the literature. Our use of antibiotics in this study was intended to aid in the identification of oxydifficidin’s mode of action. This is true for both Tables 1 and 2.

(2) If the reviewer insists, we would be happy to include MIC data for the following clinically relevant antibiotics: ceftriaxone (cephalosporin/beta-lactam), gentamicin (aminoglycoside), azithromycin (macrolide), and ciprofloxacin (fluoroquinolone).

(3) The genetic diversity of dedA and rplL in N. gonorrhoeae is not clear, neither is it clear whether oxydifficidin is active against more relevant strains and species than tested so far.

(1) We thank the reviewer for this suggestion. We aligned the DedA sequence from strain MS11 with DedA proteins from 220 N. gonorrhoeae strains that have high-quality assemblies in NCBI. The result showed that there are no amino acid changes in this protein. Using the same method, we observed several single amino acid changes in RplL. This included changes at A64, G25 and S82 in 4 strains with one change per strain. These sites differ from R76 and K84, where we identified changes that provide resistance to oxydifficidin. Notably, in a similar search of representative Escherichia, Chlamydia, Vibrio, and Pseudomonas NCBI deposited genomes, we did not identify changes in RplL at position R76 or K84.

(2) While the usefulness of screening more clinically relevant antibiotics against clinical isolates as suggested in comment 2 was not clear to us, we agree that screening these strains for oxydifficidin activity would be beneficial. We have ordered Neisseria gonorrhoeae strain AR1280, AR1281 (CDC), and Neisseria meningitidis ATCC 13090. They will be tested when they arrive.

Reviewer #2 (Public Review):

Summary:

Kan et al. present the discovery of oxydifficidin as a potential antimicrobial against N. gonorrhoeae, including multi-drug resistant strains. The authors show the role of DedA flippase-assisted uptake and the specificity of RplL in the mechanism of action for oxydifficidin. This novel mode of action could potentially offer a new therapeutic avenue, providing a critical addition to the limited arsenal of antibiotics effective against gonorrhea.

Strengths:

This study underscores the potential of revisiting natural products for antibiotic discovery of modern-day-concerning pathogens and highlights a new target mechanism that could inform future drug development. Indeed there is a recent growing body of research utilizing AI and predictive computational informatics to revisit potential antimicrobial agents and metabolites from cultured bacterial species. The discovery of oxydifficidin interaction with RplL and its DedA-assisted uptake mechanism opens new research directions in understanding and combating antibiotic-resistant N. gonorrhoeae. Methodologically, the study is rigorous employing various experimental techniques such as genome sequencing, bioassay-guided fractionation, LCMS, NMR, and Tn-mutagenesis.

Weaknesses:

The scope is somewhat narrow, focusing primarily on N. gonorrhoeae. This limits the generalizability of the findings and leaves questions about its broader antibacterial spectrum. Moreover, while the study demonstrates the in vitro effectiveness of oxydifficidin, there is a lack of in vivo validation (i.e., animal models) for assessing pre-clinical potential of oxydifficidin. Potential SNPs within dedA or RplL raise concerns about how quickly resistance could emerge in clinical settings.

(1) Spectrum/narrow scope: The broader antibacterial spectrum of oxydifficidin has been reported previously (S B Zimmerman et al., 1987). The focus of this study is on its previously unreported potent anti-gonococcal activity and its mode of action. While it is true that broad-spectrum antibiotics have historically played a role in effectively controlling a wide range of infections, we and others believe that narrow-spectrum antibiotics have an overlooked importance in addressing bacterial infections. Their advantage lies in their ability to target specific pathogens without markedly disrupting the human microbiota.

(2) Animal models: We acknowledge the reviewer’s insight regarding the importance of in vivo validation to enhance oxydifficidin’s pre-clinical potential. However, due to the labor-intensive process needed to isolate oxydifficidin, obtaining a sufficient quantity for animal studies is beyond the scope of this study. Our future work will focus on optimizing the yield of oxydifficidin and developing a topical mouse model for subsequent investigations.

(3) Potential SNPs: Please see our response to Reviewer #1’s comment 3. We acknowledge that potential SNPs within dedA and rplL raise concerns regarding clinical resistance, which is a common issue for protein-targeting antibiotics. Yet, as pointed out in the manuscript, obtaining mutants in the lab was a very low yield endeavor.

Reviewer #3 (Public Review):

Summary:

The authors have shown that oxydifficidin is a potent inhibitor of Neisseria gonorrhoeae. They were able to identify the target of action to rplL and showed that resistance could occur via mutation in the DedA flippase and RplL.

Strengths:

This was a very thorough and clearly argued set of experiments that supported their conclusions.

Weaknesses:

There was no obvious weakness in the experimental design. Although it is promising that the DedA mutations resulted in attenuation of fitness, it remains an open question whether secondary rounds of mutation could overcome this selective disadvantage which was untried in this study.

We thank the reviewer for the positive comment. We agree that investigating factors that could compensate for the fitness attenuation caused by DedA mutation would enhance our understanding of the role of DedA.

eLife assessment

This study provides valuable new insights into the trade-offs associated with the evolution of drug resistance in the yeast S. cerevisiae, based on a solid approach to evolving and phenotyping hundreds of independent strains. The authors identify distinct phenotypic clusters, defined by their growth across defined conditions, which suggest that tradeoffs are diverse but at the same time could be limited to a few classes according to the underlying resistance mechanisms. The methodologies used align with the current state-of-the-art, and the data and analysis are solid as they broadly support the claims, with only a few minor weaknesses remaining after revision. This work will interest molecular biologists working on the evolution of new phenotypes and microbiologists studying multi-drug therapy.

Reviewer #1 (Public Review):

Summary:

In their manuscript, Schmidlin, Apodaca et al try to answer fundamental questions about the evolution of new phenotypes and the trade-offs associated with this process. As a model, they use yeast resistance to two drugs, fluconazole and radicicol. They use barcoded libraries of isogenic yeasts to evolve thousands of strains in 12 different environments. They then measure the fitness of evolved strains in all environments and use these measurements to enumerate patterns in fitness trade-offs. They identify only six major clusters corresponding to different trade-off profiles, suggesting the vast genotypic landscape of evolved mutants translates to a highly constrained phenotypic space. They sequence over a hundred evolved strains and find that mutations in the same gene can result in different phenotypic profiles.

Overall, the authors deploy innovative methods to scale up experimental evolution experiments, and in many aspects of their approach tried to minimize experimental variation.

Weaknesses:

(1) The main objective of the authors is to characterize the extent of phenotypic diversity in terms of resistance trade-offs between fluconazole and radicicol. To minimize noise in the measurement of relative fitness, the authors only included strains with at least 500 barcode counts across all time points in all 12 experimental conditions, resulting in a set of 774 lineages passing this threshold. As the authors remark, this will bias their datasets for lineages with high fitness in all 12 environments, as all these strains must be fit enough to maintain a high abundance. One of the main observations of the authors is phenotypic space is constrained to a few clusters of roughly similar relative fitness patterns, giving hope that such clusters could be enumerated and considered to design antimicrobial treatment strategies. However, by excluding all lineages that fit in only one or a few environments, they conceal much of the diversity that might exist in terms of trade-offs and set up an inclusion threshold that might present only a small fraction of phenotypic space with characteristics consistent with generalist resistance mechanisms or broadly increased fitness. The general conclusions of the authors regarding the evolution of trade-offs might thus be more focused on multi-drug resistant phenotypes.

(2) Most large-scale pooled competition assays using barcodes are usually stopped after ~25 to avoid noise due to the emergence of secondary mutations. The authors measure fitness across ~40 generations, which is almost the same number of generations as in the evolution experiment. This raises the possibility of secondary mutations biasing abundance values, which would not have been detected by the whole genome sequencing as it was performed before the competition assay. Previous studies approximated the fraction of lineages that could be overtaken by secondary mutations (Venkataram and Dunn et al 2016). In their calculations, Venkataram and Dunn et al defined adaptive mutations in their data as having a selection coefficient of 5% and highly adaptive mutations at around 10%. From this and an estimation of the mutation rate, they estimate that the fraction of lineages overtaken by adaptive mutations is negligible (10^4) after 32 generations. However, the effects on fitness observed by the authors here tend to be much stronger than 5-10%, with relative fitness advantages above 1 and often reaching 2. This could result in a much higher chance of lineages being overtaken at 40 generations.

(3) The approach used by the authors to identify and visualize clusters of phenotypes among lineages does not seem to consider the uncertainty in the measurement of their relative fitness. As can be seen from Figure S4, the inter-replicate difference in measured fitness can often be quite large. From these graphs, it is also possible to see that some of the fitness measurements do not correlate linearly (ex.: Med Flu, Hi Rad Low Flu), meaning that taking the average of both replicates might not be the best approach. Because the clustering approach used does not seem to take this variability into account, it becomes difficult to evaluate the strength of the clustering, especially because the UMAP projection does not include any representation of uncertainty around the position of lineages.

(4) The authors make the decision to use UMAP and a Gaussian mixed model as well as validation data to identify unique clusters, which is one of their main objectives. The choice of 7 clusters as the cutoff for the multiple Gaussian model is not well explained. Based on Figure S6A, BIC starts leveling off at 6 clusters, not 7, and going to 8 clusters would provide the same reduction as going from 6 to 7. This choice also appears arbitrary in Figure S6B, where BIC levels off at 9 clusters when only highly abundant lineages are considered. All of the data presented in the validations is presented to fit within the 6 clusters structure but does not include evidence against alternative scenarios for additional relevant clusters as might be suggested by Figure S6.

(5) Large-scale barcode sequencing assays can often be noisy and are generally validated using growth curves or competition assays. Reconstructing some of the specific mutants they identified to validate their phenotypes would also have been a good addition. If the phenotypic clusters identified cannot be reproduced outside of the sequencing assay, then their relevance are they as a model for multi-drug resistance scenarios might be reduced.

Reviewer #1 (Public Review):

Summary:

In their manuscript, Schmidlin, Apodaca, et al try to answer fundamental questions about the evolution of new phenotypes and the trade-offs associated with this process. As a model, they use yeast resistance to two drugs, fluconazole and radicicol. They use barcoded libraries of isogenic yeasts to evolve thousands of strains in 12 different environments. They then measure the fitness of evolved strains in all environments and use these measurements to examine patterns in fitness trade-offs. They identify only six major clusters corresponding to different trade-off profiles, suggesting the vast genotypic landscape of evolved mutants translates to a highly constrained phenotypic space. They sequence over a hundred evolved strains and find that mutations in the same gene can result in different phenotypic profiles.

Overall, the authors deploy innovative methods to scale up experimental evolution experiments, and in many aspects of their approach tried to minimize experimental variation.

Weaknesses:

(1) One of the objectives of the authors is to characterize the extent of phenotypic diversity in terms of resistance trade-offs between fluconazole and radicicol. To minimize noise in the measurement of relative fitness, the authors only included strains with at least 500 barcode counts across all time points in all 12 experimental conditions, resulting in a set of 774 lineages passing this threshold. This corresponds to a very small fraction of the starting set of ~21 000 lineages that were combined after experimental evolution for fitness measurements. As the authors briefly remark, this will bias their datasets for lineages with high fitness in all 12 environments, as all these strains must be fit enough to maintain a high abundance. One of the main observations of the authors is phenotypic space is constrained to a few clusters of roughly similar relative fitness patterns, giving hope that such clusters could be enumerated and considered to design antimicrobial treatment strategies. However, by excluding all lineages that fit in only one or a few environments, they conceal much of the diversity that might exist in terms of trade-offs and set up an inclusion threshold that might present only a small fraction of phenotypic space with characteristics consistent with generalist resistance mechanisms or broadly increased fitness. This has important implications regarding the general conclusions of the authors regarding the evolution of trade-offs.

(2) Most large-scale pooled competition assays using barcodes are usually stopped after ~25 to avoid noise due to the emergence of secondary mutations. The authors measure fitness across ~40 generations, which is almost the same number of generations as in the evolution experiment. This raises the possibility of secondary mutations biasing abundance values, which would not have been detected by the whole genome sequencing as it was performed before the competition assay.

(3) The approach used by the authors to identify and visualize clusters of phenotypes among lineages does not seem to consider the uncertainty in the measurement of their relative fitness. As can be seen from Figure S4, the inter-replicate difference in measured fitness can often be quite large. From these graphs, it is also possible to see that some of the fitness measurements do not correlate linearly (ex.: Med Flu, Hi Rad Low Flu), meaning that taking the average of both replicates might not be the best approach. Because the clustering approach used does not seem to take this variability into account, it becomes difficult to evaluate the strength of the clustering, especially because the UMAP projection does not include any representation of uncertainty around the position of lineages. This might paint a misleading picture where clusters appear well separate and well defined but are in fact much fuzzier, which would impact the conclusion that the phenotypic space is constricted.

(4) The authors make the decision to use UMAP and a gaussian mixed model to cluster and represent the different fitness landscapes of their lineages of interest. Their approach has many caveats. First, compared to PCA, the axis does not provide any information about the actual dissimilarities between clusters. Using PCA would have allowed a better understanding of the amount of variance explained by components that separate clusters, as well as more interpretable components. Second, the advantages of dimensional reduction are not clear. In the competition experiment, 11/12 conditions (all but the no drug, no DMSO conditions) can be mapped to only three dimensions: concentration of fluconazole, concentration of radicicol, and relative fitness. Each lineage would have its own fitness landscape as defined by the plane formed by relative fitness values in this space, which can then be examined and compared between lineages. Third, the choice of 7 clusters as the cutoff for the multiple Gaussian model is not well explained. Based on Figure S6A, BIC starts leveling off at 6 clusters, not 7, and going to 8 clusters would provide the same reduction as going from 6 to 7. This choice also appears arbitrary in Figure S6B, where BIC levels off at 9 clusters when only highly abundant lineages are considered. This directly contradicts the statement in the main text that clusters are robust to noise, as more a stringent inclusion threshold appears to increase and not decrease the optimal number of clusters. Additional criteria to BIC could have been used to help choose the optimal number of clusters or even if mixed Gaussian modeling is appropriate for this dataset.

(5) Large-scale barcode sequencing assays can often be noisy and are generally validated using growth curves or competition assays. Having these types of results would help support the accuracy of the main assay in the manuscript and thus better support the claims of the authors.

Reviewer #2 (Public Review):

Summary:

Schmidlin & Apodaca et al. aim to distinguish mutants that resist drugs via different mechanisms by examining fitness tradeoffs across hundreds of fluconazole-resistant yeast strains. They barcoded a collection of fluconazole-resistant isolates and evolved them in different environments with a view to having relevance for evolutionary theory, medicine, and genotype-phenotype mapping.

Strengths:

There are multiple strengths to this paper, the first of which is pointing out how much work has gone into it; the quality of the experiments (the thought process, the data, the figures) is excellent. Here, the authors seek to induce mutations in multiple environments, which is a really large-scale task. I particularly like the attention paid to isolates with are resistant to low concentrations of FLU. So often these are overlooked in favour of those conferring MIC values >64/128 etc. What was seen is different genotype and fitness profiles. I think there's a wealth of information here that will actually be of interest to more than just the fields mentioned (evolutionary medicine/theory).

Weaknesses:

Not picking up low fitness lineages - which the authors discuss and provide a rationale as to why. I can completely see how this has occurred during this research, and whilst it is a shame I do not think this takes away from the findings of this paper. Maybe in the next one!

In the abstract the authors focus on 'tradeoffs' yet in the discussion they say the purpose of the study is to see how many different mechanisms of FLU resistance may exist (lines 679-680), followed up by "We distinguish mutants that likely act via different mechanisms by identifying those with different fitness tradeoffs across 12 environments". Whilst I do see their point, and this is entirely feasible, I would like a bit more explanation around this (perhaps in the intro) to help lay-readers make this jump. The remainder of my comments on 'weaknesses' are relatively fixable, I think:

In the introduction I struggle to see how this body of research fits in with the current literature, as the literature cited is a hodge-podge of bacterial and fungal evolution studies, which are very different! So example, the authors state "previous work suggests that mutants with different fitness tradeoffs may affect fitness through different molecular mechanisms" (lines 129-131) and then cite three papers, only one of which is a fungal research output. However, the next sentence focuses solely on literature from fungal research. Citing bacterial work as a foundation is fine, but as you're using yeast for this I think tailoring the introduction more to what is and isn't known in fungi would be more appropriate. It would also be great to then circle back around and mention monotherapy vs combination drug therapy for fungal infections as a rationale for this study. The study seems to be focused on FLU-resistant mutants, which is the first-line drug of choice, but many (yeast) infections have acquired resistance to this and combination therapy is the norm.

Methods: Line 769 - which yeast? I haven't even seen mention of which species is being used in this study; different yeast employ different mechanisms of adaptation for resistance, so could greatly impact the results seen. This could help with some background context if the species is mentioned (although I assume S. cerevisiae). In which case, should aneuploidy be considered as a mechanism? This is mentioned briefly on line 556, but with all the sequencing data acquired this could be checked quickly?

I think the authors could be bolder and try and link this to other (pathogenic) yeasts. What are the implications of this work on say, Candida infections?

Author response:

We thank you for the opportunity to provide a concise response. The criticisms are accurately summarized in the eLife assessment:

the study fails to engage prior literature that has extensively examined the impact of variance in offspring number, implying that some of the paradoxes presented might be resolved within existing frameworks.

The essence of our study is to propose the adoption of the Haldane model of genetic drift, based on the branching process, in lieu of the Wright-Fisher (WF) model, based on sampling, usually binomial.  In addition to some extensions of the Haldane model, we present 4 paradoxes that cannot be resolved by the WF model. The reviews suggest that some of the paradoxes could be resolved by the WF model, if we engage prior literature sufficiently.

We certainly could not review all the literature on genetic drift as there must be thousands of them. Nevertheless, the literature we do not cover is based on the WF model, which has the general properties that all modifications of the WF model share.  (We should note that all such modifications share the sampling aspect of the WF model. To model such sampling, N is imposed from outside of the model, rather than self-generating within the model.  Most important, these modifications are mathematically valid but biologically untenable, as will be elaborated below. Thus, in concept, the WF and Haldane models are fundamentally different.)

In short, our proposal is general with the key point that the WF model cannot resolve these (and many other) paradoxes.  The reviewers disagree (apparently only partially) and we shall be specific in our response below.

We shall first present the 4th paradox, which is about multi-copy gene systems (such as rRNA genes and viruses, see the companion paper). Viruses evolve both within and between hosts. In both stages, there are severe bottlenecks.  How does one address the genetic drift in viral evolution? How can we model the effective population sizes both within- and between- hosts?  The inability of the WF model in dealing with such multi-copy gene systems may explain the difficulties in accounting for the SARS-CoV-2 evolution. Given the small number of virions transmitted between hosts, drift is strong which we have shown by using the Haldane model (Ruan, Luo, et al. 2021; Ruan, Wen, et al. 2021; Hou, et al. 2023). 

As the reviewers suggest, it is possible to modify the WF model to account for some of these paradoxes. However, the modifications are often mathematically convenient but biologically dubious. Much of the debate is about the progeny number, K.  (We shall use haploid model for this purpose but diploidy does not pose a problem as stated in the main text.) The modifications relax the constraint of V(k) = E(k) inherent in the WF sampling.  One would then ask how V(k) can be different from E(k) in the WF sampling even though it is mathematically feasible (but biologically dubious)?  Kimura and Crow (1963) may be the first to offer a biological explanation.  If one reads it carefully, Kimura's modification is to make the WF model like the Haldane model. Then, why don't we use the Haldane model in the first place by having two parameters, E(k) and V(k), instead of the one-parameter WF model?

The Haldane model is conceptually simpler. It allows the variation in population size, N, to be generated from within the model, rather than artificially imposed from outside of the model.  This brings us to the first paradox, the density-dependent Haldane model. When N is increasing exponentially as in bacterial or yeast cultures, there is almost no drift when N is very low and drift becomes intense as N grows to near the carrying capacity.  We do not see how the WF model can resolve this paradox, which can otherwise be resolved by the Haldane model.

The second and third paradoxes are about how much mathematical models of population genetic can be detached from biological mechanisms. The second paradox about sex chromosomes is rooted in the realization of V(k) ≠ E(k).  Since E(k) is the same between sexes but V(k) is different, how does the WF sampling give rise to V(k) ≠ E(k)? We are asking a biological question that troubled Kimura and Crow (1963) alluded above. The third paradox is acknowledged by two reviewers. Genetic drift manifested in the fixation probability of an advantageous mutation is 2s/V(k).  It is thus strange that the fundamental parameter of drift in the WF model, N (or Ne), is missing.  In the Haldane model, drift is determined by V(k) with N being a scaling factor; hence 2s/V(k) makes perfect biological sense,

We now answer the obvious question: If the model is fundamentally about the Haldane model, why do we call it the WF-Haldane model? The reason is that most results obtained by the WF model are pretty good approximations and the branching process may not need to constantly re-derive the results.  At least, one can use the WF results to see how well they fit into the Haldane model. In our earlier study (Chen, et al. (2017); Fig. 3), we show that the approximations can be very good in many (or most) settings.

We would like to use the modern analogy of gas-engine cars vs. electric-motor ones. The Haldane model and the WF model are as fundamentally different concepts as the driving mechanisms of gas-powered vs electric cars.  The old model is now facing many problems and the fixes are often not possible.  Some fixes are so complicated that one starts thinking about simpler solutions. The reservations are that we have invested so much in the old models which might be wasted by the switch. However, we are suggesting the integration of the WF and Haldane models. In this sense, the WF model has had many contributions which the new model gratefully inherits. This is true with the legacy of gas-engine cars inherited by EVs.

The editors also issue the instruction: while the modified model yields intriguing theoretical predictions, the simulations and empirical analyses are incomplete to support the authors' claims. 

We are thankful to the editors and reviewers for the thoughtful comments and constructive criticisms. We also appreciate the publishing philosophy of eLife that allows exchanges, debates and improvements, which are the true spirits of science publishing.

References for the provisional author responses

Chen Y, Tong D, Wu CI. 2017. A New Formulation of Random Genetic Drift and Its Application to the Evolution of Cell Populations. Mol. Biol. Evol. 34:2057-2064.

Hou M, Shi J, Gong Z, Wen H, Lan Y, Deng X, Fan Q, Li J, Jiang M, Tang X, et al. 2023. Intra- vs. Interhost Evolution of SARS-CoV-2 Driven by Uncorrelated Selection-The Evolution Thwarted. Mol. Biol. Evol. 40.

Kimura M, Crow JF. 1963. The measurement of effective population number. Evolution:279-288.

Ruan Y, Luo Z, Tang X, Li G, Wen H, He X, Lu X, Lu J, Wu CI. 2021. On the founder effect in COVID-19 outbreaks: how many infected travelers may have started them all? Natl. Sci. Rev. 8:nwaa246.

Ruan Y, Wen H, He X, Wu CI. 2021. A theoretical exploration of the origin and early evolution of a pandemic. Sci Bull (Beijing) 66:1022-1029.

Review comments

eLife assessment 

This study presents a useful modification of a standard model of genetic drift by incorporating variance in offspring numbers, claiming to address several paradoxes in molecular evolution.

It is unfortunate that the study fails to engage prior literature that has extensively examined the impact of variance in offspring number, implying that some of the paradoxes presented might be resolved within existing frameworks.

We do not believe that the paradoxes can be resolved.

In addition, while the modified model yields intriguing theoretical predictions, the simulations and empirical analyses are incomplete to support the authors' claims. 

Public Reviews: 

Reviewer #1 (Public Review): 

Summary: 

The authors present a theoretical treatment of what they term the "Wright-Fisher-Haldane" model, a claimed modification of the standard model of genetic drift that accounts for variability in offspring number, and argue that it resolves a number of paradoxes in molecular evolution. Ultimately, I found this manuscript quite strange.

The notion of effective population size as inversely related to the variance in offspring number is well known in the literature, and not exclusive to Haldane's branching process treatment. However, I found the authors' point about variance in offspring changing over the course of, e.g. exponential growth fairly interesting, and I'm not sure I'd seen that pointed out before.

Nonetheless, I don't think the authors' modeling, simulations, or empirical data analysis are sufficient to justify their claims. 

Weaknesses: 

I have several outstanding issues. First of all, the authors really do not engage with the literature regarding different notions of an effective population. Most strikingly, the authors don't talk about Cannings models at all, which are a broad class of models with non-Poisson offspring distributions that nonetheless converge to the standard Wright-Fisher diffusion under many circumstances, and to "jumpy" diffusions/coalescents otherwise (see e.g. Mohle 1998, Sagitov (2003), Der et al (2011), etc.). Moreover, there is extensive literature on effective population sizes in populations whose sizes vary with time, such as Sano et al (2004) and Sjodin et al (2005).

Of course in many cases here the discussion is under neutrality, but it seems like the authors really need to engage with this literature more. 

The most interesting part of the manuscript, I think, is the discussion of the Density Dependent Haldane model (DDH). However, I feel like I did not fully understand some of the derivation presented in this section, which might be my own fault. For instance, I can't tell if Equation 5 is a result or an assumption - when I attempted a naive derivation of Equation 5, I obtained E(K_t) = 1 + r/c*(c-n)*dt. It's unclear where the parameter z comes from, for example. Similarly, is equation 6 a derivation or an assumption? Finally, I'm not 100% sure how to interpret equation 7. I that a variance effective size at time t? Is it possible to obtain something like a coalescent Ne or an expected number of segregating sites or something from this? 

Similarly, I don't understand their simulations. I expected that the authors would do individual-based simulations under a stochastic model of logistic growth, and show that you naturally get variance in offspring number that changes over time. But it seems that they simply used their equations 5 and 6 to fix those values. Moreover, I don't understand how they enforce population regulation in their simulations---is N_t random and determined by the (independent) draws from K_t for each individual? In that case, there's no "interaction" between individuals (except abstractly, since logistic growth arises from a model that assumes interactions between individuals). This seems problematic for their model, which is essentially motivated by the fact that early during logistic growth, there are basically no interactions, and later there are, which increases variance in reproduction. But their simulations assume no interactions throughout! 

The authors also attempt to show that changing variance in reproductive success occurs naturally during exponential growth using a yeast experiment. However, the authors are not counting the offspring of individual yeast during growth (which I'm sure is quite hard). Instead, they use an equation that estimates the variance in offspring number based on the observed population size, as shown in the section "Estimation of V(K) and E(K) in yeast cells". This is fairly clever, however, I am not sure it is right, because the authors neglect covariance in offspring between individuals. My attempt at this derivation assumes that I_t | I_{t-1} = \sum_{I=1}^{I_{t-1}} K_{i,t-1} where K_{i,t-1} is the number of offspring of individual i at time t-1. Then, for example, E(V(I_t | I_{t-1})) = E(V(\sum_{i=1}^{I_{t-1}} K_{i,t-1})) = E(I_{t-1})V(K_{t-1}) + E(I_{k-1}(I_{k-1}-1))*Cov(K_{i,t-1},K_{j,t-1}). The authors have the first term, but not the second, and I'm not sure the second can be neglected (in fact, I believe it's the second term that's actually important, as early on during growth there is very little covariance because resources aren't constrained, but at carrying capacity, an individual having offspring means that another individuals has to have fewer offspring - this is the whole notion of exchangeability, also neglected in this manuscript). As such, I don't believe that their analysis of the empirical data supports their claim. 

Thus, while I think there are some interesting ideas in this manuscript, I believe it has some fundamental issues:

first, it fails to engage thoroughly with the literature on a very important topic that has been studied extensively. Second, I do not believe their simulations are appropriate to show what they want to show. And finally, I don't think their empirical analysis shows what they want to show. 

References: 

Möhle M. Robustness results for the coalescent. Journal of Applied Probability. 1998;35(2):438-447. doi:10.1239/jap/1032192859 

Sagitov S. Convergence to the coalescent with simultaneous multiple mergers. Journal of Applied Probability. 2003;40(4):839-854. doi:10.1239/jap/1067436085 

Der, Ricky, Charles L. Epstein, and Joshua B. Plotkin. "Generalized population models and the nature of genetic drift." Theoretical population biology 80.2 (2011): 80-99 

Sano, Akinori, Akinobu Shimizu, and Masaru Iizuka. "Coalescent process with fluctuating population size and its effective size." Theoretical population biology 65.1 (2004): 39-48 

Sjodin, P., et al. "On the meaning and existence of an effective population size." Genetics 169.2 (2005): 1061-1070 

Reviewer #2 (Public Review): 

Summary: 

This theoretical paper examines genetic drift in scenarios deviating from the standard Wright-Fisher model. The authors discuss Haldane's branching process model, highlighting that the variance in reproductive success equates to genetic drift. By integrating the Wright-Fisher model with the Haldane model, the authors derive theoretical results that resolve paradoxes related to effective population size. 

Strengths: 

The most significant and compelling result from this paper is perhaps that the probability of fixing a new beneficial mutation is 2s/V(K). This is an intriguing and potentially generalizable discovery that could be applied to many different study systems. 

The authors also made a lot of effort to connect theory with various real-world examples, such as genetic diversity in sex chromosomes and reproductive variance across different species. 

Weaknesses: 

One way to define effective population size is by the inverse of the coalescent rate. This is where the geometric mean of Ne comes from. If Ne is defined this way, many of the paradoxes mentioned seem to resolve naturally. If we take this approach, one could easily show that a large N population can still have a low coalescent rate depending on the reproduction model. However, the authors did not discuss Ne in light of the coalescent theory. This is surprising given that Eldon and Wakeley's 2006 paper is cited in the introduction, and the multiple mergers coalescent was introduced to explain the discrepancy between census size and effective population size, superspreaders, and reproduction variance - that said, there is no explicit discussion or introduction of the multiple mergers coalescent. 

The Wright-Fisher model is often treated as a special case of the Cannings 1974 model, which incorporates the variance in reproductive success. This model should be discussed. It is unclear to me whether the results here have to be explained by the newly introduced WFH model, or could have been explained by the existing Cannings model. 

The abstract makes it difficult to discern the main focus of the paper. It spends most of the space introducing "paradoxes". 

The standard Wright-Fisher model makes several assumptions, including hermaphroditism, non-overlapping generations, random mating, and no selection. It will be more helpful to clarify which assumptions are being violated in each tested scenario, as V(K) is often not the only assumption being violated. For example, the logistic growth model assumes no cell death at the exponential growth phase, so it also violates the assumption about non-overlapping generations. 

The theory and data regarding sex chromosomes do not align. The fact that \hat{alpha'} can be negative does not make sense. The authors claim that a negative \hat{alpha'} is equivalent to infinity, but why is that? It is also unclear how theta is defined. It seems to me that one should take the first principle approach e.g., define theta as pairwise genetic diversity, and start with deriving the expected pair-wise coalescence time under the MMC model, rather than starting with assuming theta = 4Neu. Overall, the theory in this section is not well supported by the data, and the explanation is insufficient. 

{Alpha and alpha' can both be negative.  X^2 = 0.47 would yield x = -0.7}

Reviewer #3 (Public Review): 

Summary: 

Ruan and colleagues consider a branching process model (in their terminology the "Haldane model") and the most basic Wright-Fisher model. They convincingly show that offspring distributions are usually non-Poissonian (as opposed to what's assumed in the Wright-Fisher model), and can depend on short-term ecological dynamics (e.g., variance in offspring number may be smaller during exponential growth). The authors discuss branching processes and the Wright-Fisher model in the context of 3 "paradoxes": (1) how Ne depends on N might depend on population dynamics; (2) how Ne is different on the X chromosome, the Y chromosome, and the autosomes, and these differences do match the expectations base on simple counts of the number of chromosomes in the populations; (3) how genetic drift interacts with selection. The authors provide some theoretical explanations for the role of variance in the offspring distribution in each of these three paradoxes. They also perform some experiments to directly measure the variance in offspring number, as well as perform some analyses of published data. 

Strengths: 

(1) The theoretical results are well-described and easy to follow. 

(2) The analyses of different variances in offspring number (both experimentally and analyzing public data) are convincing that non-Poissonian offspring distributions are the norm. 

(3) The point that this variance can change as the population size (or population dynamics) change is also very interesting and important to keep in mind. 

(4) I enjoyed the Density-Dependent Haldane model. It was a nice example of the decoupling of census size and effective size. 

Weaknesses: 

(1) I am not convinced that these types of effects cannot just be absorbed into some time-varying Ne and still be well-modeled by the Wright-Fisher process. 

(2) Along these lines, there is well-established literature showing that a broad class of processes (a large subset of Cannings' Exchangeable Models) converge to the Wright-Fisher diffusion, even those with non-Poissonian offspring distributions (e.g., Mohle and Sagitov 2001). E.g., equation (4) in Mohle and Sagitov 2001 shows that in such cases the "coalescent Ne" should be (N-1) / Var(K), essentially matching equation (3) in the present paper. 

(3) Beyond this, I would imagine that branching processes with heavy-tailed offspring distributions could result in deviations that are not well captured by the authors' WFH model. In this case, the processes are known to converge (backward-in-time) to Lambda or Xi coalescents (e.g., Eldon and Wakely 2006 or again in Mohle and Sagitov 2001 and subsequent papers), which have well-defined forward-in-time processes. 

(4) These results that Ne in the Wright-Fisher process might not be related to N in any straightforward (or even one-to-one) way are well-known (e.g., Neher and Hallatschek 2012; Spence, Kamm, and Song 2016; Matuszewski, Hildebrandt, Achaz, and Jensen 2018; Rice, Novembre, and Desai 2018; the work of Lounès Chikhi on how Ne can be affected by population structure; etc...) 

(5) I was also missing some discussion of the relationship between the branching process and the Wright-Fisher model (or more generally Cannings' Exchangeable Models) when conditioning on the total population size. In particular, if the offspring distribution is Poisson, then conditioned on the total population size, the branching process is identical to the Wright-Fisher model. 

(6) In the discussion, it is claimed that the last glacial maximum could have caused the bottleneck observed in human populations currently residing outside of Africa. Compelling evidence has been amassed that this bottleneck is due to serial founder events associated with the out-of-Africa migration (see e.g., Henn, Cavalli-Sforza, and Feldman 2012 for an older review - subsequent work has only strengthened this view). For me, a more compelling example of changes in carrying capacity would be the advent of agriculture ~11kya and other more recent technological advances. 

Recommendations for the authors: 

Reviewing Editor Comments: 

The reviewers recognize the value of this model and some of the findings, particularly results from the density-dependent Haldane model. However, they expressed considerable concerns with the model and overall framing of this manuscript.

First, all reviewers pointed out that the manuscript does not sufficiently engage with the extensive literature on various models of effective population size and genetic drift, notably lacking discussion on Cannings models and related works.

Second, there is a disproportionate discussion on the paradoxes, yet some of the paradoxes might already be resolved within current theoretical frameworks. All three reviewers found the modeling and simulation of the yeast growth experiment hard to follow or lacking justification for certain choices. The analysis approach of sex chromosomes is also questioned. 

The reviewers recommend a more thorough review of relevant prior literature to better contextualize their findings. The authors need to clarify and/or modify their derivations and simulations of the yeast growth experiment to address the identified caveats and ensure robustness. Additionally, the empirical analysis of the sex chromosome should be revisited, considering alternative scenarios rather than relying solely on the MSE, which only provides a superficial solution. Furthermore, the manuscript's overall framing should be adjusted to emphasize the conclusions drawn from the WFH model, rather than focusing on the "unresolved paradoxes", as some of these may be more readily explained by existing frameworks. Please see the reviewers' overall assessment and specific comments. 

Reviewer #2 (Recommendations For The Authors): 

In the introduction -- "Genetic drift is simply V(K)" -- this is a very strong statement. You can say it is inversely proportional to V(K), but drift is often defined based on changes in allele frequency. 

Page 3 line 86. "sexes is a sufficient explanation."--> "sex could be a sufficient explanation" 

The strongest line of new results is about 2s/V(K). Perhaps, the paper could put more emphasis on this part and demonstrate the generality of this result with a different example. 

The math notations in the supplement are not intuitive. e.g., using i_k and j_k as probabilities. I also recommend using E[X] and V[X]for expectation and variance rather than \italic{E(X)} to improve the readability of many equations. 

Eq A6, A7, While I manage to follow, P_{10}(t) and P_{10} are not defined anywhere in the text. 

Supplement page 7, the term "probability of fixation" is confusing in a branching model. 

E.q. A 28. It is unclear eq. A.1 could be used here directly. Some justification would be nice. 

Supplement page 17. "the biological meaning of negative..". There is no clear justification for this claim. As a reader, I don't have any intuition as to why that is the case.

hi

eLife assessment

This paper describes an important advance in an in vitro neural culture system to generate mature, functional, diverse, and geometrically consistent cultures, in a 384-well format with defined dimensions and the absence of the necrotic core, which persists for up to 300 days. The well-based format and conserved geometry make it a promising tool for arrayed screening studies. Some of the evidence is incomplete and could benefit from a more direct head-to-head comparison with more standard culture methods and standardization of cell seeding density as well as further data on reproducibility in each well and for each cell line.

Author response:

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

eLife assessment:

Franke et al. explore and characterize the color response properties in the mouse primary visual cortex, revealing specific color opponent encoding strategies across the visual field. The data is solid; however, the evidence supporting some conclusions is incomplete. In its current form, the paper makes a useful contribution to how color is coded in mouse V1. Significance would be enhanced with some additional analyses and a clearer discussion of the limitations of the data presented.

We thank the reviewers for appreciating our manuscript. We have rewritten the conclusions of the paper to be more conservative and now more explicitly focus on color processing in mouse V1, rather than comparing V1 to the retina. Additionally, we discuss the limitations of our approach in detail in the Discussion section. Finally, we have addressed all comments from the reviewers below.

Referee 1 (Remarks to the Author):

In this study, Franke et al. explore and characterize color response properties across primary visual cortex, revealing specific color opponent encoding strategies across the visual field. The authors use awake 2P imaging to define the spectral response properties of visual interneurons in layer 2/3. They find that opponent responses are more pronounced at photopic light levels, and that diversity in color opponent responses exists across the visual field, with green ON/ UV OFF responses more strongly represented in the upper visual field. This is argued to be relevant for the detection of certain features that are more salient when using chromatic space, possibly due to noise reduction. In the revised version, Franke et al. have addressed the potential pitfalls in the discussion, which is an important point for the non-expert reader. Thus, this study provides a solid characterization of the color properties of V1 and is a valuable addition to visual neuroscience research.

My remaining concerns are based more on the interpretation. I’m still not convinced by the statement "This type of color-opponency in the receptive field center of V1 neurons was not present in the receptive field center of retinal ganglion cells and, therefore, is likely computed by integrating center and surround information downstream of the retina." and I would suggest rewording it in the abstract.

As discussed previously and now nicely added to the discussion, it is difficult to make a direct comparison given the different stimulus types used to characterize the retina and V1 recordings and the different levels of adaptation in both tissues. I will leave this point to the discussion, which allows for a more nuanced description of the phenomenon. Why do I think this is important? In the introduction, the authors argue that "the discrepancy [of previous studies] may be due to differences in stimulus design or light levels." However, while different light levels can be tested in V1, this cannot be done properly in the retina with 2P experiments. To address this, one would have to examine color-opponency in RGC terminals in vivo, which is beyond the scope of this study. Addressing these latter points directly in the discussion would, in my opinion, only strengthen the study.

We thank the reviewer for the feedback. We removed the sentence mentioned by the reviewer from the abstract, as well as from the summary of our results in the Introduction. Additionally, we now phrase the interpretation of the retinal results more conservatively and specifically highlight in the Discussion that comparing ex-vivo retinal to in-vivo cortical data is challenging. With these changes, we believe that the focus of the paper is explicitly defined to be on the neuronal representation of color in mouse visual cortex, rather than on the comparison of retinal and cortical color processing.

Minor:

In the abstract, the second sentence says that we already know the mechanisms in primates.

Unfortunately, I do not think this is true. First, primates refers to an order with several species, which might have adaptations to their color-processing. Second, I’m aware of several characterizations in "primates" that have led to convincing models (as referenced), but in my opinion, this is far from a true understanding the mechanisms, especially since very little is known about foveal color processing due to the difficulties of these experiments. Similarly in the introduction. "Primates" is indirectly defined as a species. Perhaps some rewording is needed here as well, since we know how different cone distributions can be in rodents (see Peichl’s work).

Thanks. We have reworded the Abstract and Introduction towards indicating that many studies have been performed in primate species, without suggesting that the mechanisms are described.

The legend in Fig. 2 has a "Fig. ???"

Fixed.

Referee 2 (Remarks to the Author):

Franke et al. characterize the representation of color in the primary visual cortex of mice, highlighting how this changes across the visual field. Using calcium imaging in awake, head-fixed mice, they characterize the properties of V1 neurons (layer 2/3) using a large center-surround stimulation where green and ultra-violet colors were presented in random combinations. Clustering of responses revealed a set of functional cell-types based on their preference to different combinations of green and UV in their center and surround. These functional types were demonstrated to have different spatial distributions across V1, including one neuronal type (Green-ON/UV-OFF) that was much more prominent in the posterior V1 (i.e. upper visual field). Modelling work suggests that these neurons likely support the detection of predator-like objects in the sky.

Strengths: The large-scale single-cell resolution imaging used in this work allows the authors to map the responses of individual neurons across large regions of the visual cortex. Combining this large dataset with clustering analysis enabled the authors to group V1 neurons into distinct functional cell types and demonstrate their relative distribution in the upper and lower visual fields. Modelling work demonstrated the different capacity of each functional type to detect objects in the sky, providing insight into the ethological relevance of color opponent neurons in V1.

We thank the reviewer for appreciating our study.

Weaknesses: While the study presents convincing evidence about the asymmetric distribution of color-opponent neurons in V1, the paper would greatly benefit from a more in-depth discussion of the caveats related to the conclusions drawn about their origin. This is particularly relevant regarding the conclusion drawn about the contribution of color opponent neurons in the retina. The mismatch between retinal color opponency and V1 color opponency could imply that this feature is not solely inherited from the retina, however, there are other plausible explanations that are not discussed here. Direct evidence for this statement remains weak.

Thanks for this comment. We removed the retinal findings from the abstract, as well as from the summary of our results in the Introduction. In addition, we now phrase the interpretation of the retinal results more conservatively and specifically highlight in the Discussion that comparing ex-vivo retinal to in-vivo cortical data is challenging. With these changes, we believe that the focus of the paper is explicitly defined to be on the neuronal representation of color in mouse visual cortex, rather than on the comparison of retinal and cortical color processing.

In addition, the paper would benefit from adding explicit neuron counts or percentages to the quadrants of each of the density plots in Figures 2-5. The variance explained by the principal components does not capture the percentage of color opponent cells. Additionally, there appear to be some remaining errors in the figure legend and labels that have not been addressed (e.g. ’??’ in Fig 2 legend).

Thank you for this suggestion. We believe that adding the numbers or percentages to the figure panels would make them too crowded. Instead, we have now mentioned in the Results section and the legends that the percentages of variance explained by the color (off-diagonal) and luminance axis (diagonal) correlate with the number of neurons located in the color (top left and bottom right) and luminance contrast quadrants (top right and bottom left), respectively. Together with the number of neurons in each plot stated in the legends and the scale bar indicating the number of neurons per gray level, we hope this approach provides clarity for the reader to interpret the panels. Additionally, we have fixed the broken reference in the legend of Fig. 2.

Overall, this study will be a valuable resource for researchers studying color vision, cortical processing, and the processing of ethologically relevant information. It provides a useful basis for future work on the origin of color opponency in V1 and its ethological relevance.

General Suggestions:

-  Please add possible caveats of using ETA method to the discussion section. For example, it is unclear to what extent ON/OFF cells are being overlooked by using ETA method.

We now discuss the limitations of the ETA approach in the Discussion section.

- The caveats of using the percentage of variance explained in the retina as evidence against V1 solely inheriting color-opponency from retinal output neurons are not adequately addressed. For example, could the mismatch in explained variance of the color axis between V1 and RGCs be explained by a subset of non-color opponent RGCs projecting elsewhere (not dLGN-V1) or that color opponent cells project to a larger number of neurons in V1 than non-color opponent cells? We suggest adding a paragraph to the discussion to address this issue.

We have removed these conclusions from the paper, more carefully interpret the retinal results and mention that comparing ex-vivo retina data with in-vivo cortical data is challenging.

- Please clarify how the different response types shown in Figure 5e-f lead to differences in noise detection and thereby differences in predator discriminability. For example, why does Gon/UVoff not respond to the noise scene while Goff/UVoff does?

We added this to the Results section.

- Please clarify the relationship between ETA amplitude, neural response probability, and neural response amplitude. For example, do color-opponent cells have equal absolute neural response amplitudes to the different colors?

Thank you for bringing up this point. The ETA is obtained by summing the stimulus sequences that elicit an event (i.e., response), weighted by the amplitude of the response. Consequently, the absolute amplitude of the ETA correlates with the calcium amplitude. Importantly, the ETA amplitudes of different stimulus conditions are comparable because they were estimated on the same normalized calcium trace. Therefore, comparing the absolute amplitudes of ETAs of color-opponent neurons reveals the response magnitude of the cells to different colors. We have now included this information in the Results section.

Abstract: - "more than a third of neurons in mouse V1 are color-opponent in their receptive field center". It is unclear what data supports this statement. Can you please provide a statement in the manuscript that supports this directly using the number of neurons?

We added the following sentence to the Results section: Nevertheless, a substantial fraction of neurons (33.1%) preferred color-opponent stimuli and scattered along the off-diagonal in the upper left and lower right quadrants, especially for the RF center.

Figure 2: - There is a ?? in the figure legend. Which figure should this refer to? - please provide explicit neuron counts/percentages for each quadrant in b.

We fixed the figure reference. We believe that adding the numbers or percentages to the figure panels would make them too crowded. Instead, we have now mentioned in the Results section and the legends that the percentages of variance explained by the color (off-diagonal) and luminance axis (diagonal) correlate with the number of neurons located in the color (top left and bottom right) and luminance contrast quadrants (top right and bottom left), respectively. Together with the number of neurons in each plot stated in the legends and the scale bar indicating the number of neurons per gray level, we hope this approach provides clarity for the reader to interpret the panels.

Figure 3: - Fig 3: Color scheme makes it very difficult to differentiate the different conditions, especially when printed.

Thanks we changed the color scheme.

- Add explicit neuron counts/percentages for each quadrant in b.

See above.

Figure 4: - Add explicit neuron counts/percentages for each quadrant in b.

See above.

Figure 5: - Add explicit neuron counts/percentages for each quadrant in c.

See above.

Methods: - "we modeled each response type to have a square RF with 10 degrees visual angle in diameter". There appears to be a mismatch between this statement and Figure 5e where 18 degrees is reported.

Thanks we fixed that.

Referee 3 (Remarks to the Author):

This paper studies chromatic coding in mouse primary visual cortex. Calcium responses of a large collection of cells are measured in response to a simple spot stimulus. These responses are used to estimate chromatic tuning properties - specifically sensitivity to UV and green stimuli presented in a large central spot or a larger still surrounding region. Cells are divided based on their responses to these stimuli into luminance or chromatic sensitive groups. The results are interesting and many aspects of the experiments and conclusions are well done; several technical concerns, however, limit the support for several main conclusions,

Limitations of stimulus choice The paper relies on responses to a large (37.5 degree diameter) modulated spot and surround region. This spot is considerably larger than the receptive fields of both V1 cells and retinal ganglion cells (it is twice the area of the average V1 receptive field). As a result, the spot itself is very likely to strongly activate both center and surround mechanisms, and responses of cells are likely to depend on where the receptive fields are located within the spot

(and, e.g., how much of the true neural surround samples the center spot vs the surround region). Most importantly, the surrounds of most of the recorded cells will be strongly activated by the central spot. This brings into question statements in the paper about selective activation of center and surround (e.g. page 2, right column). This in turn raises questions about several subsequent analyses that rely on selective center and surround activation.

Thank you for this comment. A similar point was raised by a reviewer in the first round of revision. We agree with the reviewers that it is critical to discuss both the rationale behind our stimulus design and its limitations to facilitate better interpretation by the reader.

To be able to record from many V1 neurons simultaneously, we used a stimulus size of 37.5 degree visual angle in diameter, which is slightly larger than center RFs of single V1 neurons (between 20 - 30 degrees visual angle depending on the stimulus, see here). The disadvantage of this approach is that the stimulus is only roughly centered on the neurons’ center RFs. To reduce the impact of potential stimulus misalignment on our results, we used the following steps: { For each recording, we positioned the monitor such that the mean RF across all neurons lies within the center of the stimulus field of view.

We confirmed that this procedure results in good stimulus alignment for the large majority of recorded neurons within individual recording fields by using a sparse noise stimulus (Suppl. Fig. 1a-c). Specifically, we found that for 83% of tested neurons, more than two thirds of their center RF, determined by the sparse noise stimulus, overlapped with the center spot of the color noise stimulus.

For analysis, we excluded neurons without a significant center STA, which may be caused by misalignment of the stimulus.

Together, we believe these points strongly suggest that the center spot and the surround annulus of the noise stimulus predominantly drive center (i.e. classical RF) and surround (i.e. extraclassical RF), respectively, of the recorded V1 neurons. This is further supported by the fact that color response types identified using an automated clustering method were robust across mice (Suppl. Fig. 6c), indicating consistent stimulus centering.

Nevertheless, we cannot exclude the possibility that the stimulus was misaligned for a subset of the recorded neurons used in our analysis. We agree with the reviewer that such misalignment might have caused the center stimulus to partially activate the surround. To further address this issue beyond the controls we have already implemented, one could compare the results of our approach with an approach that centers the stimulus on individual neurons. However, we believe that performing these additional experiments is beyond the scope of the current study.

To acknowledge the experimental limitations of our study and the concerns brought up by the reviewer, we have added the steps we perform to reduce the effects of stimulus misalignment in the Results section and discuss the problem of stimulus alignment in the Discussion in a separate section. With this, we believe our manuscript explains both the rationale behind our stimulus design as well as important limitations of the approach.

Comparison with retina A key conclusion of the paper is that the chromatic tuning in V1 is not inherited from retinal ganglion cells. This conclusion comes from comparing chromatic tuning in a previously-collected data set from retina with the present results. But the retina recordings were made using a considerably smaller spot, and hence it is not clear that the comparison made in the paper is accurate. For example, the stimulus used for the V1 experiments almost certainly strongly stimulates both center and surround of retinal ganglion cells. The text focuses on color opponency in the receptive field centers of retinal ganglion cells, but center-surround opponency seems at least as relevant for such large spots. This issue needs to be described more clearly and earlier in the paper.

Thanks for this comment. We removed the retinal findings from the abstract, as well as from the summary of our results in the Introduction. In addition, we now phrase the interpretation of the retinal results more conservatively and specifically highlight in the Discussion that comparing ex-vivo retinal to in-vivo cortical data is challenging. With these changes, we believe that the focus of the paper is explicitly defined to be on the neuronal representation of color in mouse visual cortex, rather than on the comparison of retinal and cortical color processing.

Limitations associated with ETA analysis One of the reviewers in the previous round of reviews raised the concern that the ETA analysis may not accurately capture responses of cells with nonlinear receptive field properties such as On/Off cells. This possibility and whether it is a concern should be discussed.

Thanks for this comment. We now discuss the limitation of using an ETA analysis in the

Discussion section.

Discrimination performance poor Discriminability of color or luminance is used as a measure of population coding. The discrimination performance appears to be quite poor - with 500-1000 neurons needed to reliably distinguish light from dark or green from UV. Intuitively I would expect that a single cell would provide such discrimination. Is this intuition wrong? If not, how do we interpret the discrimination analyses?

Thank you for raising this point. The plots in Fig. 2c (and Figs. 3-5) show discriminability in bits, with the discrimination accuracy in % highlighted by the dotted horizontal lines. For 500 neurons, the discriminability is approx. 0.8 bits, corresponding to 95% accuracy. Even for 50 neurons, the accuracy is significantly above chance level. We now mention in the legends that the dotted lines indicate decoding accuracy in %.

Version 5 of this preprint has been peer-reviewed and recommended by Peer Community in Zoology.<br /> See the peer reviews and the recommendation.

based on power relations

something non-fixed

I do not think it is just Oaxacans who think that mental health is for crazies. A lot of people in the hispanic culture also believe or do not believe that mental health is a thing.

Micro aspects of everyday life influenced by digital world.

It starts mattering when you think of taking a break from the Internet or even your smartphone.

Compared to the US, India has mere 31.7% smartphone users,

Daily routine of students and working people.

Prozac is still a very popular drug for depression. Not only is it the most prescribed, but you can catch a commercial every time on day time tv. It is one of the medicines that has the most propaganda.

The world could become more educated on trans people. I know there is a lot of information that would help people understand how to address them better as well as be more mindful

We live in a society where sex rules. A lot of things are sexualized, and women are over sexualized to the point where they are not seen as people but items.

I don't get the origin of item 4 of this list.

Coming from the Latin community - I can say that mental illnesses are not recognized. A lot of the time they are pinned to nervous attacks or anxiety and even still, anxiety is not a real thing.

I do believe that people usually feel distress in different types of ways - panic attacks, sadness, anger. Large events that can also contribute to internal dysfunctions can also be too much of something, like winning the lottery.