DOI: 10.1007/s43440-024-00614-4

Resource: FlowJo (RRID:SCR_008520)

Curator: @scibot

SciCrunch record: RRID:SCR_008520

---

What is this?

DOI: 10.1007/s43440-024-00614-4

Resource: (BD Biosciences Cat# 561012, RRID:AB_2034024)

Curator: @scibot

SciCrunch record: RRID:AB_2034024

---

What is this?

DOI: 10.1007/s43440-024-00614-4

Resource: (BD Biosciences Cat# 556463, RRID:AB_2869075)

Curator: @scibot

SciCrunch record: RRID:AB_2869075

---

What is this?

DOI: 10.1007/s43440-024-00614-4

Resource: GraphPad Prism (RRID:SCR_002798)

Curator: @scibot

SciCrunch record: RRID:SCR_002798

---

What is this?

DOI: 10.1007/s43440-024-00614-4

Resource: (DSMZ Cat# ACC-582, RRID:CVCL_1844)

Curator: @scibot

SciCrunch record: RRID:CVCL_1844

---

What is this?

DOI: 10.1007/s43440-024-00614-4

Resource: (JCRB Cat# IFO50038, RRID:CVCL_0007)

Curator: @scibot

SciCrunch record: RRID:CVCL_0007

---

What is this?

DOI: 10.1007/s43440-024-00614-4

Resource: (KCB Cat# KCB 90029YJ, RRID:CVCL_0004)

Curator: @scibot

SciCrunch record: RRID:CVCL_0004

---

What is this?

DOI: 10.1007/s11060-024-04740-0

Resource: (ATCC Cat# CRL-1635, RRID:CVCL_0839)

Curator: @scibot

SciCrunch record: RRID:CVCL_0839

---

What is this?

DOI: 10.1038/s41388-024-03081-6

Resource: GraphPad Prism (RRID:SCR_002798)

Curator: @scibot

SciCrunch record: RRID:SCR_002798

---

What is this?

DOI: 10.1038/s41388-024-03081-6

Resource: (Promega Cat# W4021, RRID:AB_430834)

Curator: @scibot

SciCrunch record: RRID:AB_430834

---

What is this?

DOI: 10.1038/s41388-024-03081-6

Resource: (Cell Signaling Technology Cat# 5625, RRID:AB_10699459)

Curator: @scibot

SciCrunch record: RRID:AB_10699459

---

What is this?

DOI: 10.1038/s41388-024-03081-6

Resource: (Cell Signaling Technology Cat# 9491, RRID:AB_2068144)

Curator: @scibot

SciCrunch record: RRID:AB_2068144

---

What is this?

DOI: 10.1038/s41388-024-03081-6

Resource: (Cell Signaling Technology Cat# 4132, RRID:AB_2071197)

Curator: @scibot

SciCrunch record: RRID:AB_2071197

---

What is this?

DOI: 10.1038/s41388-024-03081-6

Resource: (Cell Signaling Technology Cat# 2978, RRID:AB_2259616)

Curator: @scibot

SciCrunch record: RRID:AB_2259616

---

What is this?

DOI: 10.1038/s41388-024-03081-6

Resource: (Cell Signaling Technology Cat# 12790, RRID:AB_2631166)

Curator: @scibot

SciCrunch record: RRID:AB_2631166

---

What is this?

DOI: 10.1038/s41388-024-03081-6

Resource: (Cell Signaling Technology Cat# 4654, RRID:AB_10614011)

Curator: @scibot

SciCrunch record: RRID:AB_10614011

---

What is this?

DOI: 10.1038/s41388-024-03081-6

Resource: (Santa Cruz Biotechnology Cat# sc-271384, RRID:AB_10609119)

Curator: @scibot

SciCrunch record: RRID:AB_10609119

---

What is this?

DOI: 10.1038/s41388-024-03081-6

Resource: (Cell Signaling Technology Cat# 2546, RRID:AB_2276129)

Curator: @scibot

SciCrunch record: RRID:AB_2276129

---

What is this?

DOI: 10.1038/s41388-024-03081-6

Resource: (Santa Cruz Biotechnology Cat# sc-393941 AF594, RRID:AB_2924720)

Curator: @scibot

SciCrunch record: RRID:AB_2924720

---

What is this?

DOI: 10.1038/s41388-024-03081-6

Resource: (Thermo Fisher Scientific Cat# PA5-16417, RRID:AB_10989504)

Curator: @scibot

SciCrunch record: RRID:AB_10989504

---

What is this?

DOI: 10.1038/s41388-024-03081-6

Resource: (Cell Signaling Technology Cat# 92470, RRID:AB_2800187)

Curator: @scibot

SciCrunch record: RRID:AB_2800187

---

What is this?

DOI: 10.1038/s41388-024-03081-6

Resource: (Cell Signaling Technology Cat# 2118, RRID:AB_561053)

Curator: @scibot

SciCrunch record: RRID:AB_561053

---

What is this?

DOI: 10.1038/s41388-024-03081-6

Resource: (Cell Signaling Technology Cat# 4873, RRID:AB_2119694)

Curator: @scibot

SciCrunch record: RRID:AB_2119694

---

What is this?

DOI: 10.1038/s44161-024-00487-z

Resource: FlowJo (RRID:SCR_008520)

Curator: @scibot

SciCrunch record: RRID:SCR_008520

---

What is this?

DOI: 10.1038/s44161-024-00487-z

Resource: (Abcam Cat# ab14917, RRID:AB_301509)

Curator: @AniH

SciCrunch record: RRID:AB_301509

Curator comments:

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What is this?

DOI: 10.1007/s12010-024-04986-4

Resource: (ATCC Cat# CRL-1446, RRID:CVCL_0286)

Curator: @scibot

SciCrunch record: RRID:CVCL_0286

---

What is this?

DOI: 10.1007/s00280-024-04688-y

Resource: (RRID:CVCL_C9CV)

Curator: @scibot

SciCrunch record: RRID:CVCL_C9CV

---

What is this?

DOI: 10.1007/s00280-024-04688-y

Resource: GraphPad Prism (RRID:SCR_002798)

Curator: @scibot

SciCrunch record: RRID:SCR_002798

---

What is this?

DOI: 10.1007/s00280-024-04688-y

Resource: (ATCC Cat# CRL-1873, RRID:CVCL_0093)

Curator: @scibot

SciCrunch record: RRID:CVCL_0093

---

What is this?

DOI: 10.1007/s00280-024-04688-y

Resource: (IZSLER Cat# BS TCL 237, RRID:CVCL_8792)

Curator: @scibot

SciCrunch record: RRID:CVCL_8792

---

What is this?

DOI: 10.1007/s00280-024-04688-y

Resource: (ICLC Cat# HTL00002, RRID:CVCL_0511)

Curator: @scibot

SciCrunch record: RRID:CVCL_0511

---

What is this?

DOI: 10.1007/s00280-024-04688-y

Resource: (BCBC Cat# 4142,RRID:BCBC_4142)

Curator: @scibot

SciCrunch record: RRID:BCBC_4142

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What is this?

DOI: 10.1007/s00259-024-06796-6

Resource: R Project for Statistical Computing (RRID:SCR_001905)

Curator: @scibot

SciCrunch record: RRID:SCR_001905

---

What is this?

DOI: 10.1007/s00259-024-06796-6

Resource: BrainNet Viewer (RRID:SCR_009446)

Curator: @scibot

SciCrunch record: RRID:SCR_009446

---

What is this?

DOI: 10.1007/s00259-024-06796-6

Resource: MarsBaR region of interest toolbox for SPM (RRID:SCR_009605)

Curator: @scibot

SciCrunch record: RRID:SCR_009605

---

What is this?

DOI: 10.1007/s00259-024-06796-6

Resource: AAL (RRID:SCR_003550)

Curator: @scibot

SciCrunch record: RRID:SCR_003550

---

What is this?

DOI: 10.1007/s00259-024-06796-6

Resource: SCR_024416

Curator: @scibot

SciCrunch record: RRID:SCR_024416

---

What is this?

DOI: 10.1007/s00259-024-06796-6

Resource: SPM (RRID:SCR_007037)

Curator: @scibot

SciCrunch record: RRID:SCR_007037

---

What is this?

DOI: 10.1007/s00259-024-06796-6

Resource: Connectivity Toolbox (RRID:SCR_009550)

Curator: @scibot

SciCrunch record: RRID:SCR_009550

---

What is this?

DOI: 10.1007/s00259-024-06796-6

Resource: MRIcroGL (RRID:SCR_024413)

Curator: @scibot

SciCrunch record: RRID:SCR_024413

---

What is this?

DOI: 10.1038/s41401-024-01317-5

Resource: RRID:Addgene_34615

Curator: @scibot

SciCrunch record: RRID:Addgene_34615

---

What is this?

DOI: 10.1007/s10529-024-03502-2

Resource: RRID:Addgene_60621

Curator: @scibot

SciCrunch record: RRID:Addgene_60621

---

What is this?

DOI: 10.1038/s41596-024-01004-z

Resource: (Cell Signaling Technology Cat# 2118, RRID:AB_561053)

Curator: @scibot

SciCrunch record: RRID:AB_561053

---

What is this?

DOI: 10.1038/s41596-024-01004-z

Resource: (Cell Signaling Technology Cat# 5032, RRID:AB_10548197)

Curator: @scibot

SciCrunch record: RRID:AB_10548197

---

What is this?

DOI: 10.1038/s41596-024-01004-z

Resource: (Abcam Cat# ab109524, RRID:AB_10859793)

Curator: @scibot

SciCrunch record: RRID:AB_10859793

---

What is this?

DOI: 10.1038/s41596-024-01004-z

Resource: (Sigma-Aldrich Cat# F3165, RRID:AB_259529)

Curator: @scibot

SciCrunch record: RRID:AB_259529

---

What is this?

DOI: 10.1038/s41596-024-01004-z

Resource: (Santa Cruz Biotechnology Cat# sc-514238, RRID:AB_2813859)

Curator: @scibot

SciCrunch record: RRID:AB_2813859

---

What is this?

DOI: 10.1038/s41596-024-01004-z

Resource: (Atlas Antibodies Cat# HPA044968, RRID:AB_2679160)

Curator: @scibot

SciCrunch record: RRID:AB_2679160

---

What is this?

DOI: 10.1038/s41596-024-01004-z

Resource: (Santa Cruz Biotechnology Cat# sc-73614, RRID:AB_1131294)

Curator: @scibot

SciCrunch record: RRID:AB_1131294

---

What is this?

DOI: 10.1038/s41596-024-01004-z

Resource: (Santa Cruz Biotechnology Cat# sc-47778, RRID:AB_626632)

Curator: @scibot

SciCrunch record: RRID:AB_626632

---

What is this?

DOI: 10.1038/s41596-024-01004-z

Resource: (Cell Signaling Technology Cat# 4499, RRID:AB_10544537)

Curator: @scibot

SciCrunch record: RRID:AB_10544537

---

What is this?

DOI: 10.1038/s41596-024-01004-z

Resource: (BCRC Cat# 60005, RRID:CVCL_0030)

Curator: @scibot

SciCrunch record: RRID:CVCL_0030

---

What is this?

DOI: 10.1038/s41596-024-01004-z

Resource: (RRID:CVCL_Y019)

Curator: @scibot

SciCrunch record: RRID:CVCL_Y019

---

What is this?

DOI: 10.1007/s10641-024-01562-x

Resource: SCR_025283

Curator: @scibot

SciCrunch record: RRID:SCR_025283

---

What is this?

DOI: 10.1007/s10641-024-01562-x

Resource: CLC Genomics Workbench (RRID:SCR_011853)

Curator: @scibot

SciCrunch record: RRID:SCR_011853

---

What is this?

DOI: 10.1007/s13402-024-00963-5

Resource: GraphPad Prism (RRID:SCR_002798)

Curator: @scibot

SciCrunch record: RRID:SCR_002798

---

What is this?

DOI: 10.1007/s13402-024-00963-5

Resource: ImageJ (RRID:SCR_003070)

Curator: @scibot

SciCrunch record: RRID:SCR_003070

---

What is this?

DOI: 10.1007/s13402-024-00963-5

Resource: GraphPad Prism (RRID:SCR_002798)

Curator: @AniH

SciCrunch record: RRID:SCR_002798

---

What is this?

DOI: 10.1186/s40543-024-00449-x

Resource: (NCI-DTP Cat# MCF7, RRID:CVCL_0031)

Curator: @scibot

SciCrunch record: RRID:CVCL_0031

---

What is this?

DOI: 10.1007/s00018-024-05293-1

Resource: (NIH-ARP Cat# 2188-324, RRID:CVCL_0022)

Curator: @scibot

SciCrunch record: RRID:CVCL_0022

---

What is this?

DOI: 10.1007/s00018-024-05293-1

Resource: (ECACC Cat# 88062428, RRID:CVCL_0131)

Curator: @scibot

SciCrunch record: RRID:CVCL_0131

---

What is this?

DOI: 10.1007/s11427-024-2583-6

Resource: AB_2856286

Curator: @scibot

SciCrunch record: RRID:AB_2856286

---

What is this?

DOI: 10.1113/JP284099

Resource: AB_3076525

Curator: @AniH

SciCrunch record: RRID:AB_3076525

---

What is this?

DOI: 10.1113/JP284099

Resource: (Abcam Cat# ab64693, RRID:AB_1523910)

Curator: @AniH

SciCrunch record: RRID:AB_1523910

---

What is this?

DOI: 10.1113/JP284099

Resource: (Abcam Cat# ab49999, RRID:AB_881438)

Curator: @AniH

SciCrunch record: RRID:AB_881438

---

What is this?

DOI: 10.1113/JP284099

Resource: (Covance Cat# PCK-591P-100, RRID:AB_291542)

Curator: @AniH

SciCrunch record: RRID:AB_291542

---

What is this?

DOI: 10.1113/JP284099

Resource: (Thermo Fisher Scientific Cat# PA1-28664, RRID:AB_10990162)

Curator: @AniH

SciCrunch record: RRID:AB_10990162

---

What is this?

DOI: 10.1113/JP284099

Resource: (GE Healthcare Cat# NA934, RRID:AB_772206)

Curator: @AniH

SciCrunch record: RRID:AB_772206

---

What is this?

DOI: 10.1113/JP284099

Resource: (Abcam Cat# ab9484, RRID:AB_307274)

Curator: @AniH

SciCrunch record: RRID:AB_307274

---

What is this?

DOI: 10.1113/JP284099

Resource: (Millipore Cat# MAB360, RRID:AB_11212597)

Curator: @AniH

SciCrunch record: RRID:AB_11212597

---

What is this?

DOI: 10.1113/JP284099

Resource: ProLong. Fluoromount G Mounting Medium (RRID:SCR_015961)

Curator: @AniH

SciCrunch record: RRID:SCR_015961

---

What is this?

DOI: 10.1113/JP284099

Resource: (Thermo Fisher Scientific Cat# H3569, RRID:AB_2651133)

Curator: @AniH

SciCrunch record: RRID:AB_2651133

---

What is this?

DOI: 10.1113/JP284099

Resource: (Abcam Cat# ab27236, RRID:AB_954457)

Curator: @AniH

SciCrunch record: RRID:AB_954457

---

What is this?

DOI: 10.1002/smll.202400300

Resource: (MGI Cat# 3581193,RRID:MGI:3581193)

Curator: @AniH

SciCrunch record: RRID:MGI:3581193

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What is this?

DOI: 10.1002/advs.202401423

Resource: University of California at San Diego Electron Microscopy Core Facility (RRID:SCR_022039)

Curator: @AniH

SciCrunch record: RRID:SCR_022039

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What is this?

DOI: 10.1155/2024/8863925

Resource: GraphPad Prism (RRID:SCR_002798)

Curator: @AniH

SciCrunch record: RRID:SCR_002798

---

What is this?

DOI: 10.1007/s10456-024-09935-7

Resource: (Molecular Probes Cat# L21411, RRID:AB_2314665)

Curator: @AniH

SciCrunch record: RRID:AB_2314665

---

What is this?

DOI: 10.1038/s41467-024-49455-y

Resource: (MGI Cat# 5658686,RRID:MGI:5658686)

Curator: @AniH

SciCrunch record: RRID:MGI:5658686

---

What is this?

DOI: 10.1038/s41467-024-49455-y

Resource: (MGI Cat# 5652524,RRID:MGI:5652524)

Curator: @AniH

SciCrunch record: RRID:MGI:5652524

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What is this?

DOI: 10.1038/s41590-024-01880-3

Resource: (IMSR Cat# JAX_000664,RRID:IMSR_JAX:000664)

Curator: @AniH

SciCrunch record: RRID:IMSR_JAX:000664

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What is this?

DOI: 10.1007/s00210-024-03146-z

Resource: AB11006367

Curator: @AniH

SciCrunch record: RRID:AB11006367

---

What is this?

DOI: 10.1007/s00210-024-03146-z

Resource: AB2936876

Curator: @AniH

SciCrunch record: RRID:AB2936876

---

What is this?

DOI: 10.1007/s00335-024-10046-3

Resource: (IMSR Cat# JAX_000664,RRID:IMSR_JAX:000664)

Curator: @AniH

SciCrunch record: RRID:IMSR_JAX:000664

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What is this?

Why?

Reminds me of the relationship between eukaryotes and mitochondria

I currently work for the Illinois Department of Human Services as a caseworker. I read to the client their rights and responsibilities during each interview, whether it is an initial application or renewal. One of their rights is that the agency will treat the client and their family members with dignity and respect and will not discriminate against any of mentioned factors. Clients must be aware of their rights and identify when they have experienced discrimination. It is important to inform individuals that they can speak up if they have experienced discrimination.

That was my question, how does the professional organization measure social workers delivery of socially just/advocacy work?

typo ↓

プログラム

typo ↓

リスト内包表記

presumably this is referring to apple's cross-functional hierarchy, which indeed leads to a denser network than a normal top-down hierarchy

Fascinating claim. Is this referring to NZS (non-zero sum)? Help your community, and the community will help you. Basically claiming direct and indirect reciprocity work better in the modern age than ever before due to information transparency.

But intangibles like honor and trustworthiness aren't visible online?

Is this still "innovation" or just "rapid deployment of good ideas my friends had"? I agree this captures what matters, but I think the language is starting to sound like an advertisement.

Not obviously true. Larger groups may run into groupthink problems more often and it's more difficult to cultivate obsessive passion in large groups.

Reminds me of Benchmark VC

Should be removed to emphasize that the number of nodes greatly limits the number of project streams available to an org.

Says who? Needs citation. Also, "style bias" isn't a central term in network theory according to wikipedia. This is a business school appropriation.

crazyy

NOOOOOOOOOO

nah they kinda have chemistry...

??

blood children firsst im sorry...

HELP THE VOICEEE

now why would you touch herr

death

IT IS BEST

and he's not

ok so EVERYONE knows

PLS DO

i feel so bad that she lost her wolf

insulted him but still somehwat cares (stretching it)

ok so she knows

thats kinda harsh

shes kinda real for that

soo hot

Does this mean pesticides are a human made product that impact insects in the same capacity as allelochmeicals and secondary metabolites?

I didn't see any evidence pointing to the commonalities between human genes and fruit fly gene sequence, is this part of the reasoning why the species can be used for this purpose?

This is extremely interesting. I wonder what this means for the future of the species as it seems a greater concentration of larger males are mating with females. Are the mother's genetics what keep a variety of sizes present within a population?

I wonder is this attributes to domestication of fruit flies even if this is not a term used in relation to the species.

I am a bit confused by this. Does the placement of eggs impact natural selections placed upon D. melanogaster?

This is extremely important to cultivating D. melanogaster in a lab environment and points to the species adaptability to thrive in a variety of conditions.

can this be used to reflect people's natural resistance to certain diseases?

kinda surprising that they could use something as small as these flies to study gut microbes, I wonder what type of tools they use to study this.

When it comes to model organisms, a short generation time is essential that way we can quickly see the results of potential crosses, another model organism that has a short generation time is the zebrafish

eLife assessment

This important work significantly advances the field of computational modelling of genome organisation through the development of OpenNucleome. The evidence supporting the tool's effectiveness is compelling, as the authors compare their predictions with experimental data. It is anticipated that OpenNucleome will attract significant interest from the biophysics and genomics communities.

Reviewer #1 (Public Review):

Summary:

In this paper the authors develop a comprehensive program to investigate the organization of chromosome structures at 100 kb resolution. It is extremely well executed. The authors have thought through all aspects of the problem. The resulting software will be most useful to the community. Interestingly they capture many experimental observations accurately. I have very little complaints.

Strengths:

A lot of details are provided. The success of the method is well illustrated. Software is easily available,

Weaknesses:

The number of parameters in the energy function is very large. Any justification? Could they simply be the functions?

What would the modification be if the resolution is increased?

They should state that the extracted physical values are scale dependent. Example, viscosity.

Reviewer #2 (Public Review):

Summary:

In this work, Lao et al. develop an open-source software (OpenNucleome) for GPU-accelerated molecular dynamics simulation of the human nucleus accounting for chromatin, nucleoli, nuclear speckles, etc. Using this, the authors investigate the steady-state organization and dynamics of many of the nuclear components.

Strengths:

This is a comprehensive open-source tool to study several aspects of the nucleus, including chromatin organization, interactions with lamins and organization, and interactions with nuclear speckles and nucleoli. The model is built carefully, accounting for several important factors and optimizing the parameters iteratively to achieve experimentally known results. Authors have simulated the entire genome at 100kb resolution (which is a very good resolution to simulate and study the entire diploid genome) and predict several static quantities such as the radius of gyration and radial positions of all chromosomes, and time-dependent quantities like the mean-square displacement of important genomic regions.

Weaknesses:

One weakness of the model is that it has several parameters. Some of them are constrained by the experiments. However, the role of every parameter is not clear in the manuscript.

Reviewer #3 (Public Review):

Summary:

The authors present OpenNucleome, a computational tool for simulating the structure and dynamics of the human nucleus. The software models nuclear components, including chromosomes and nuclear bodies, and incorporates GPU acceleration for potential performance gains. The authors aim to advance the understanding of nuclear organization by providing a tool that aligns with experimental data and is accessible to the genome architecture research community.

Strengths:

OpenNucleome provides a model of the nucleus, contributing to the advancement of computational biology.<br /> Utilizing GPU acceleration with OpenMM may offer potential performance improvements.

Weaknesses:

It could still take advantage of clearer explanations regarding the generation and usage of input and output files and compatibility with other tools.

Author response:

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

Reviewer 1:

Comment 0: In this paper, the authors develop a comprehensive program to investigate the organization of chromosome structures at 100 kb resolution. It is extremely well executed. The authors have thought through all aspects of the problem. The resulting software will be most useful to the community. Interestingly they capture many experimental observations accurately.

I have very few complaints.

We appreciate the reviewer’s strong assessment of the paper’s significance, novelty, and broad interest, and we thank them for the detailed suggestions and comments.

Comment 1: The number of parameters in the energy function is very large. Is there any justification for this? Could they simplify the functions?

We extend our gratitude to the reviewer for their insightful remarks. The parameters within our model can be categorized into two groups: those governing chromosome-chromosome interactions and those governing chromosome-nuclear landmark interactions.

In terms of chromosome-chromosome interactions, the parameter count is relatively modest compared to the vast amount of Hi-C data available. For instance, while the whole-genome Hi-C matrix at the 100KB resolution encompasses approximately 303212 contacts, our model comprises merely six parameters for interactions among different compartments, along with 1000 parameters for the ideal potential. As outlined in the supporting information, the ideal potential is contingent upon sequence separation, with 1000 chosen to encompass bead separations of up to 100MB. While it is theoretically plausible to reduce the number of parameters by assuming interactions cease beyond a certain sequence separation, determining this scale a priori presents a challenge.

During the parameterization process, we observed that interchromosomal contacts predicted solely based on compartmental interactions inadequately mirrored Hi-C data. Consequently, we introduced 231 additional parameters to more accurately capture interactions between distinct pairs of autosomes. These interactions may stem from factors such as non-coding RNA or proteins not explicable by simple, non-specific compartmental interactions.

Regarding parameters concerning chromosome-nuclear landmark interactions, we have 30321 parameters for speckles and 30321 for the nuclear lamina. To streamline the model, we opted to assign a unique parameter to each chromatin bead. However, it is conceivable that many chromatin beads share a similar mechanism for interacting with nuclear lamina or speckles, potentially allowing for a common parameter assignment. Nonetheless, implementing such simplification necessitates a deeper mechanistic understanding of chromosome-nuclear landmark interactions, an aspect currently lacking.

As our comprehension of nuclear organization progresses, the interpretability of parameter counts may improve, facilitating their reduction.

Comment 2: What would the modification be if the resolution is increased?

To increase the resolution of chromatin, we can in principle keep the same energy function as defined in Eq. S6. In this case, we only need to carry out further parameter optimization.

However, transitioning to higher resolutions may unveil additional features not readily apparent at 100kb. Notably, chromatin loops with an average size of 200kb or smaller have been identified in high-resolution Hi-C data [1]. To effectively capture these loops, new terms in the energy function must be incorporated. For instance, Qi and Zhang [2] employed additional contact potentials between CTCF sites to account for loop formation. Alternatively, an explicit loop-extrusion process could be introduced to model loop formation more accurately.

Comment 3: They should state that the extracted physical values are scale-dependent. For example, viscosity.

We thank the reviewer for the comment and would like to clarify that our model does not predict the viscosity. The nucleoplasmic viscosity was set as 1Pa · s to produce a diffusion coefficient that reproduces experimental value. The exact value for the nucleoplasmic viscosity is still rather controversial, and our selected value falls in the range of reported experimental values from 10−1Pa·s to 102Pa · s.

We have modified the main text to clarify the calculation of the diffusion coefficient.

“The exponent and the diffusion coefficient Dα = (27±11)×10−4μm2 · s−α both match well with the experimental values [cite], upon setting the nucleoplasmic viscosity as 1Pa · s (see Supporting Information Section: Mapping the reduced time unit to real time for more details).”

Reviewer 2:

Comment 0: In this work, Lao et al. develop an open-source software (OpenNucleome) for GPU-accelerated molecular dynamics simulation of the human nucleus accounting for chromatin, nucleoli, nuclear speckles, etc. Using this, the authors investigate the steady-state organization and dynamics of many of the nuclear components.

We thank the reviewer for summary of our work.

Comment 1: The authors could introduce a table having every parameter and the optimal parameter value used. This would greatly help the reader.

We would like to point out that model parameters are indeed provided in Table S1, S2, S3, S4, and Fig. S7. In these tables, we further provided details on how the parameters were determined.

Given the large number of parameters for the ideal potential (1000), we opted to plot it rather than listing out all the numbers. We added three new figures to plot the interaction parameters between chromosomes, between chromosomes and speckles, and between chromosomes and the nuclear lamina. Numerical values can be found online in the GitHub repository (parameters).

Comment 2: How many total beads are simulated? Do all beads have the same size?

The total number of the coarse-grained beads is 70542, including 60642 chromatin beads, 300 nucleolus beads, 1600 speckle beads, and 8000 nuclear lamina beads. The radius of the chromatin, nucleolus, and speckle beads is 0.25, while that of the lamina bead is 0.5. More information of the size and number of the beads are discussed in the Section: Components of the whole nucleus model.

Comment 3: In Equation S17, what is the 3rd and 4th powers mean? What necessitates it?

The potential defined in Equation S17 follows the definition of class2 bond in the LAMMPS package (LAMMPS docs). Compared to a typical harmonic potential, the presence of higher order terms produces sharper increase in the energy at large distances (Author response image 1). This essentially reduces the flucatuation of bond length in simulations.

Author response image 1.

Comparison between the Class2 potential (defined in Eq. S17) and the Harmonic potential (K(r − r0)2, with K = 20 and r0 = 0.5).

Comment 4: What do the X-axis and Y-axis numbers in Figure 5A and 5B mean? What are their units?

We apologize for the lack of clarify in our original figure. In Fig. 5A, the X and Y axis depicts the simulated and experimental radius of gyration (Rg) for individual chromosomes, as indicated in the title of the figure. Similarly, in Fig. 5B, the X and Y axis depicts the simulated and experimental radial position of individual chromosomes.

We have converted the chromosome Rg values into reduced units and labeled the corresponding axes in the updated figure (Fig. 5). The normalized radial position is unitless and its detailed definition is included in the supporting information Section: Computing simulated normalized chromosome radial positions. We updated the figure caption to provide an explicit reference to the SI text.

Reviewer 3:

Comment 0: In this work, the authors present the development of OpenNucleome, a software for simulating the structure and dynamics of the human nucleus. It provides a detailed model of nuclear components such as chromosomes and nuclear bodies, and uses GPU acceleration for better performance based on the OpenMM package. The work also shows the model’s accuracy in comparisons with experimental data and highlights the utility in the understanding of nuclear organization. While I consider this work a good tool for the genome architecture scientific community, I have some comments and questions that could further clarify the usage of this tool and help potential users. I also have a few questions that would help to clarify the technique and results and some suggestions for references.

We appreciate the reviewer’s strong assessment of the paper’s significance, novelty, and broad interest, and we thank them for the detailed suggestions and comments.

Comment 1: Could the authors elaborate on what they consider to be ’well-established and easily adoptable modeling tools’?

By well established, we meant that models that have been extensively validated and verified, and are highly regarded by the community.

By easily adoptable, we meant that tools that are well documented and can be relatively easily learned by new groups without help from the developers.

We have revised the text to clarify our meaning.

“Despite the progress made in computational modeling, the absence of well-documented software with easy-to-follow tutorials pose a challenge.”

Comment 2: Recognizing the value of a diverse range of tools in the community, the Open-MiChroM tool is also an open-source platform built on top of OpenMM. The documentation shows various modeling approaches and many tutorials that contain different approaches besides the MiChroM energy function. How does OpenNucleome compare in terms of facilitating crossvalidation and user accessibility? The two tools seem to be complementary, which is a gain to the field. I recommend adding one or two sentences in the matter. Also, while navigating the OpenNucleome GitHub, I have not found the tutorials mentioned in the text. I also consider a barrier in the process of generating necessary input files. I would suggest expanding the tutorials and documentation to help potential users.

We thank the reviewer for the excellent comments. We agree that while many of the tutorials were included in the original package, they were not as clearly documented. We have revised them extensively to to now present:

• A tutorial for optimizing chromosome chromosome interactions.

• A tutorial for optimizing chromosome nuclear landmark interactions.

• A tutorial for building initial configurations.

• A tutorial for relaxing the initial configurations.

• A tutorial for selecting the initial configurations.

• A tutorial for setting up performing Langevin dynamics simulations.

• A tutorial for setting up performing Brownian dynamics simulations.

• A tutorial for setting up performing simulations with deformed nucleus.

• A tutorial for analyzing simulation trajectories.

• A tutorial for introducing new features to the model.

These tutorials and our well-documented and open source code (https://zhanggroup-mitchemistry.github.io/OpenNucleome) should significantly promote user accessibility. Our inclusion of python scripts for analyzing simulation trajectorials shall allow users to compute various quantities for evaluating and comparing model quality.

We added a new paragraph in the Section: Conclusions and Dicussion of the main text to compare OpenNucleosome with existing software for genome modeling.

“Our software enhances the capabilities of existing genome simulation tools [cite]. Specifically, OpenNucleome aligns with the design principles of Open-MiChroM [cite], prioritizing open-source accessibility while expanding simulation capabilities to the entire nucleus. Similar to software from the Alber lab [cite], OpenNucleome offers highresolution genome organization that faithfully reproduces a diverse range of experimental data. Furthermore, beyond static structures, OpenNucleome facilitates dynamic simulations with explicit representations of various nuclear condensates, akin to the model developed by [citet].”

Comment 3: Lastly, I would appreciate it if the authors could expand their definition of ’standardized practices’.

We apologize for any confusion caused. By ”standardized practices,” we refer to the fact that different groups often employ unique procedures for structural modeling. These procedures differ in the representation of chromosomes, the nucleus environment, and the algorithms for parameter optimization. This absence of a consensus on the optimal practices for genome modeling can be daunting for newcomers to the field.

We have revised the text to the following to avoid confusion:

“Many research groups develop their own independent software, which complicates crossvalidation and hinders the establishment of best practices for genome modeling [3–5].”

Comment 4: On page 7, the authors refer to the SI Section: Components of the whole nucleus model for further details. Could the authors provide more information on the simulated density of nuclear bodies? Is there experimental data available that details the ratio of chromatin to other nuclear components, which was used as a reference in the simulation?

We thank the reviewer for the comment. Imaging studies have provided quantitative measures about the size and number of various nuclear bodies. For example, there are 2 ∼ 5 nucleoli per nucleus, with the typical size RNo ≈ 0.5μm [6–10]. In the review by Spector and Lamond [11], the authors showed that there are 20 ∼ 50 speckles, with the typical size RSp ≈ 0.3μm. We used these numbers to guide our simulation of nuclear bodies. These information was mentioned in the Section: Chromosomes as beads on the string polymers of the supporting information.

The chromatin density is fixed by the average size of chromatin bead and the nucleus size. We chose the size of chromatin based on imaging studies as detailed in the Subsection: Mapping chromatin bead size to real unit of the supporting information. Upon fixing the bead size, the chromatin volume is determined.

Comment 5: In the statement, ’the ideal potential is only applied for beads from the same chromosome to approximate the effect of loop extrusion by Cohesin molecules for chromosome compaction and territory formation,’ it would be helpful if the authors could clarify the scope of this potential. Specifically, the code indicates that the variable ’dend ideal’ is set at 1000, suggesting an interaction along a 100Mb polymer chain at a resolution of 100Kb per bead. Could the authors elaborate on their motivation for the Cohesin complex’s activity having a significant effect over such long distances within the polymer chain?

We thank the reviewer for the insight comment. They are correct that the ideal potential was introduced to capture chromosome folding beyond the interactions between compartments, including loop extrusion. Practically, we parameterized the ideal potential such that the simulated average contact probabilities as a function of sequence separation match the experimental values. The reviewer is correct that beyond a specific value of sequence separation, one would expect the impact of loop extrusion on chromosome folding should be negligible, due to Cohesin dissociation. Correspondingly, the interaction potential should be zero at large sequence separations.

However, it is important to note that the precise separation scale cannot be known a priori. We chose 100Mb as a conservative estimation. However, as we can see from Fig. S7, our parameterization scheme indeed produced interaction parameters are mainly zero at large sequence separations. Interesting, the scale at which the potential approaches 0 (∼ 500KB), indeed agree with the estimated length traveled by Cohesin molecules before dissociation [12].

Comment 6: On pages 8 and 9, the authors discuss the optimization process. However, in reviewing the code and documentation available on the GitHub page, I could not find specific sections related to the optimization procedure described in the paper. In this context, I have a few questions: Could the authors provide more details or direct me to the parts of the documentation and the text/SI that address the optimization procedure used in their study? Additional clarification on the cost/objective function employed during the optimization process would be highly beneficial, as this was not readily apparent in the text.

We thank the reviewer for the comment. We revised the SI to include the definition of the cost function for the Adam optimizer.

“During the optimization process, our aim was to minimize the disparity between experimental findings and simulated data. To achieve this, we defined the cost function as follows:

where the index i iterates over all the constraints defined in Eq. S28.”

The detailed optimization procedure was included in the SI as quoted below

“The details of the algorithm for parameter optimization are as follows

(1) Starting with a set of values for and we performed 50 independent 3-million-step long MD simulations to obtain an ensemble of nuclear configurations. The 500K steps of each trajectory are discarded

as equilibration. We collected the configurations at every 2000 simulation steps from the rest of the simulation trajectories to compute the ensemble averages defined on the left-hand side of Eq. S13.

(2) Check the convergence of the optimization by calculating the percentage of error

defined as . The summation over i includes all the average contact probabilities defined in Eq. S28.

(3) If the error is less than a tolerance value etol, the optimization has converged, and we stop the simulations. Otherwise, we update the parameters, α, using the Adam optimizer [13]. With the new parameter values, we return to step one and restart the iteration.”

Previously, the optimization code was included as part of the analysis folder. To avoid confusion and improve readability, a separate folder named optimization has been created. This folder provides the Adam optimization of chromosome-chromosome interactions (chr-chr optimization) and chromosome-nuclear landmarks interactions (chr-NL optimization).

Comment 7: What was the motivation for choosing the Adam algorithm for optimization? Adam is designed for training on stochastic objective functions. Could the authors elucidate on the ’stochastic’ aspect of their function to be optimized? Why the Adam algorithm was considered the most appropriate choice for this application?

We thank the reviewer for the comment. As defined in Eq. R1, the cost function measures the difference between the simulated constraints with corresponding experimental values. The estimation of simulation values, by averaging over an ensemble of chromosome configurations, is inherently noisy and stochastic. Exact ensemble averages can only be achieved with unlimited samples obtained from infinite long simulations.

In the past, we have used the Newton’s method for parameterization, and the detailed algorithm can be found in the SI of Ref. 14. However, we found that Adam is more efficient as it is a first-order approximation method. The Newton’s method, on the other hand, is second-order approximation method and requires estimation of the Hessian matrix. When the number of constraints is large, as is in our case, the computational cost for estimating the Hessian matrix can be significant. Another advantage of the Adam algorithm lies in its adjustment of the learning rate along the optimization to further speedup convergence.

Comment 8: The authors mention that examples of setting up simulations, parameter optimization, and introducing new features are provided in the GitHub repository. However, I was unable to locate these examples. Could the authors guide me to these specific resources or consider adding them if they are not currently available?

We thank the reviewer for the comment. We have improved the GitHub repository and all the tutorials can be found using the links provided in Response to Comment 2.

Comment 9: Furthermore, the paper states that ’a configuration file that provides the position of individual particles in the PDB file format is needed to initialize the simulations.’ It would be beneficial for new users if the authors could elaborate on how this file is generated. And all other input files in general. Detailing the procedures for a new user to run their system using OpenNucleome would be helpful.

We thank the reviewer for the comment. The procedure for generating initial configurations was explained in the SI Section: Initial configurations for simulations and quoted below.

“We first created a total of 1000 configurations for the genome by sequentially generating the conformation of each one of the 46 chromosomes as follows. For a given chromosome, we start by placing the first bead at the center (origin) of the nucleus. The positions of the following beads, i, were determined from the (i − 1)-th bead as . v is a normalized random vector, and 0.5 was selected as the bond length between neighboring beads. To produce globular chromosome conformations, we rejected vectors, v, that led to bead positions with distance from the center larger than 4σ. Upon creating the conformation of a chromosome i, we shift its center of mass to a value ri com determined as follows. We first compute a mean radial distance, with the following equation

where Di is the average value of Lamin B DamID profile for chromosome i. Dhi and Dlo represent the highest and lowest average DamID values of all chromosomes, and 6σ and 2σ represent the upper and lower bound in radial positions for chromosomes. As shown in Fig. S6, the average Lamin B DamID profiles are highly correlated with normalized chromosome radial positions as reported by DNA MERFISH [cite], supporting their use as a proxy for estimating normalized chromosome radial positions. We then select as a uniformly distributed random variable within the range . Without loss of generality, we randomly chose the directions for shifting all 46 chromosomes.

We further relaxed the 1000 configurations to build more realistic genome structures. Following an energy minimization process, one-million-step molecular dynamics (MD) simulations were performed starting from each configuration. Simulations were performed with the following energy function

where UGenome is defined as in Eq. S7. UG-La is the excluded volume potential between chromosomes and lamina, i.e, only the second term in Eq. S24. Parameters in UGenome were from a preliminary optimization. The end configurations of the MD simulations were collected to build the final configuration ensemble (FCE).”

The tutorial for preparing initial configurations can be found at this link.

Comment 10: In the section discussing the correlation between simulated and experimental contact maps, as referenced in Figure 4A and Figure S2, the authors mention a high degree of correlation. Could the authors specify the exact value of this correlation and explain the method used for its computation? Considering that comparing two Hi-C matrices involves a large number of data points, it would be helpful to know if all data points were included in this analysis.

We have updated Fig 4A and S2 to include Pearson correlation coefficients next to the contact maps. The reviewer is correct in that all the non-redundant data points of the contact maps are included in computing the correlation coefficients.

For improved clarity, we added a new section in the supporting information to detail the calculations. The section is titled Computing Pearson correlation coefficients between experimental and simulated contact maps, and the relevant text is quoted below.

“We computed the Pearson correlation coefficients (PCC) between experimental and simulated contact maps in Fig. 4A and Fig. S2 as

xi and yi represent the experimental and simulated contact probabilities, and n is the total number of data points. Only non-redundant data points, i.e., half of the pairwise contacts, are used in the PCC calculation.”

Comment 11: In addition, the author said: ”Moreover, the simulated and experimental average contact probabilities between pairs of chromosomes agree well, and the Pearson correlation coefficient between the two datasets reaches 0.89.” How does this correlation behave when not accounting for polymer compaction or scaling? An analysis presenting the correlation as a function of genomic distance would be interesting.

Author response image 2.

Pearson correlation coefficient between experimental and simulated contact probabilities as a function of the sequence separation within specific chromosomes. For each chromosome, we first gathered a set of experimental contacts alongside a matching set of simulated ones for genomic pairs within a particular separation range. The Pearson correlation coefficient at the corresponding sequence separation was then determined using Equation R4. We limited the calculations to half of the chromosome length to ensure the availability of sufficient data.

We thank the reviewer for the comment. The analysis presenting the correlation as a function of genomic distance (sequence separation) for each chromosome is shown in Figure S12 and also included in the SI. While the correlation coefficients decreases at larger separation, the values around 0.5 is quite reasonable and comparable to results obtained using Open-Michrom.

We also computed the correlation of whole genome contact maps after excluding intra-chromosomal contacts. The PCC decreased from 0.89 to 0.4. Again, the correlation coefficient is quite reasonable considering that these contacts are purely predicted by the compartmental interactions and were not directly optimized.

Comment 12: I recommend using the web-server that is familiar to the authors to benchmark the OpenNucleome tool/model: ”3DGenBench: A Web-Server to Benchmark Computational Models for 3D Genomics.” Nucleic Acids Research, vol. 50, no. W1, July 2022, pp. W4-12.

We appreciate the reviewer’s suggestion. Unfortunately, the website is no longer active during the time of the revision. However, as detailed in Response to comment 11, we used the one of the popular metrics to exclude polymer compact effect and evaluate the agreement between simulation and experiments.

Comment 13: Regarding the comparison of simulation results with microscopy data from reference 34. Given their different resolutions and data point/space groupings, how do the authors align these datasets? Could the authors describe how they performed this comparison? How were the radial positions calculated in both the simulations and experiments? Since the data from reference 34 indicates a non-globular shape of the nucleus; how did this factor into the calculation of radial distributions?

We thank the reviewer for the comment and apologize for the confusion. First, the average properties we examined, including radial positions and interchromosomal contacts, were averaged over all genomic loci. Therefore, they are independent of data resolution.

Secondly, instead of calculating the absolute radial positions, which are subject to variations in nucleus shape and size, we defined the normalized radial positions. They measure the ratio between the distance from the nucleus center to the chromosome center and the distance from the nucleus center to the lamina. This definition was frequently used in prior imaging studies to measure chromosome radial positions.

The calculation of the simulated normalized radial positions and the experimental normalized radial positions are discussed in the Section: Computing simulated normalized chromosome radial positions

“For a given chromosome i, we first determined its center of mass position denoted as Ci. Starting from the center of the nucleus, O, we extend the the vector vOC to identify the intersection point with the nuclear lamina as Pi. The normalized chromosome radial position i is then defined as , where ||·|| represents the L2 norm.

and Section: Computing experimental normalized chromosome radial positions.

“We followed the same procedure outlined in Section: Computing simulated normalized chromosome radial positions to compute the experimental values. To determine the center of the nucleus using DNA MERFISH data, we used the algorithm, minimum volume enclosing ellipsoid (MVEE)[15], to fit an ellipsoid for each genome structure. The optimal ellipsoid defined as is obtained by optimizing subjecting to the constraint that . xi correspond to the list of chromatin positions determined experimentally.”

Comment 14: In the sentence: ”It is evident that telomeres exhibit anomalous subdiffusive motion.” I recommend mentioning the work ”Di Pierro, Michele, et al., ”Anomalous Diffusion, Spatial Coherence, and Viscoelasticity from the Energy Landscape of Human Chromosomes.” Proceedings of the National Academy of Sciences, vol. 115, no. 30, July 2018, pp. 7753-58.”.

We have revised the sentence to include the citation as follows.

“In line with previous research [cite], telomeres display anomalous subdiffusive motion. When fitted with the equation , these trajectories yield a spectrum of α values, with a peak around 0.59.”

Comment 15: Regarding the observation that ’chromosomes appear arrested and no significant changes in their radial positions are observed over timescales comparable to the cell cycle,’ could the authors provide more details on the calculations or analyses that led to this conclusion? Specifically, information on the equilibration/relaxation time of chromosome territories relative to rearrangements within a cell cycle would be interesting.

Our conclusion here was mostly based on the time trace of normalized radial positions shown in Figure 6A of the main text. Over the timescale of an entire cell cycle (24 hours), the relatively little to no changes in the radial positions supports glassy dynamics of chromosomes. We further determined the mean squared displacement (MSD) for chromosome center of masses. As shown in the left panel of Fig. S12, the MSDs are much smaller than the average size of chromosomes (see Rg values in Fig. 5A), supporting arrested dynamics.

We further computed the auto-correlation function of the normalized chromosome radial position as

where t indexes over the trajectory frames and ¯r is the mean position. As shown in Fig. S12, the positions are not completely decorrelated over 10 hours, again supporting slow dynamics. It would be interesting to examine the relaxation timescale more closely in future studies.

Comment 16: The authors also comment on the SI ”Section: Initial configurations for simulations provides more details on preparing the 1000 initial configurations.” and related to reference 34 mentioning that ”the average Lamin B DamID profiles are highly correlated with chromosome radial positions as reported by DNA MERFISH”. How do the authors account for situations where homologous chromosomes are neighbors or have an interacting interface? Ref. 34 indicates that distinguishing between these scenarios can be challenging, potentially leading to ’invalid distributions’ that are filtered out. Clarification on how such cases were handled in the simulations would be helpful.

We would like to first clarify that when comparing with experimental data, we averaged over the homologous chromosomes to obtain haploid data. We added the following text in the manuscript to emphasize this point

“Given that the majority of experimental data were analyzed for the haploid genome, we adopted a similar approach by averaging over paternal and maternal chromosomes to facilitate direct comparison. More details on data analysis can be found in the Supporting Information Section: Details of simulation data analysis.”

Furthermore, we used the processed DNA MERFISH data from the Zhuang lab, which unambiguously assigns a chromosome ID to each data point. Therefore, the issue mentioned by the reviewer is not present in the procssed data. In our simulations, since we keep track of the explicit connection between genomic segments, the trace of individual chromosomes can be determined for any configuration. Therefore, there is no ambiguity in terms of simulation data.

Comment 17: When discussing the interaction with nuclear lamina and nuclear envelop deformation, I suggest mentioning the following studies: The already cited ref 52 and ”Contessoto, Vin´ıcius G., et al. ”Interphase Chromosomes of the Aedes Aegypti Mosquito Are Liquid Crystalline and Can Sense Mechanical Cues.” Nature Communications, vol. 14, no. 1, Jan. 2023, p. 326.”

We updated the text to include the suggested reference.

“Numerous studies have highlighted the remarkable influence of nuclear shape on the positioning of chromosomes and the regulation of gene expression [16, 17].”

Comment 18: The authors state that ’Tutorials in the format of Python Scripts with extensive documentation are provided to facilitate the adoption of the model by the community.’ However, as I mentioned, the documentation appears to be limited, and the available tutorials could benefit from further expansion. I suggest that the authors consider enhancing these resources to better assist users in adopting and understanding the model.

As detailed in the Response to Comment 2, we have updated the GitHub repository to better document the included Jupyter notebooks and tutorials.

Comment 19: In the Methods section, the authors discuss using Langevin dynamics for certain simulations and Brownian dynamics for others. Could the authors provide more detailed reasoning behind the choice of these different dynamics for different aspects of the simulation? Furthermore, it would be insightful to know how the results might vary if only one of these dynamics was utilized throughout the study. Such clarification would help in understanding the implications of these methodological choices on the outcomes of the simulations.

We thank the reviewer for the comment. As detailed in the supporting information Section: Mapping the Reduced Time Unit to Real Time, the Brownian dynamics simulations provide a rigorous mapping to the biological timescale. By choosing a specific value for the nucleoplasmic viscosity, we determined the time unit in simulations as τ = 0.65s. With this time conversion, the simulated diffusion coefficients of telomeres match well with experimental values. Therefore, Brownian dynamics simulations are recommended for computing time dependent quantities and the large damping coefficients mimics the complex nuclear environment well.

On the other hand, the large damping coefficient slows down the configuration relaxation of the system significantly. For computing equilibrium statistical properties, it is useful to use a small coefficient and the Langevin integrator with large time steps to facilitate conformational relaxation.

References

[1] Rao, S. S.; Huntley, M. H.; Durand, N. C.; Stamenova, E. K.; Bochkov, I. D.; Robinson, J. T.; Sanborn, A. L.; Machol, I.; Omer, A. D.; Lander, E. S.; others A 3D map of the human genome at kilobase resolution reveals principles of chromatin looping. Cell 2014, 159, 1665–1680.

[2] Qi, Y.; Zhang, B. Predicting three-dimensional genome organization with chromatin states. PLoS computational biology 2019, 15, e1007024.

[3] Yildirim, A.; Hua, N.; Boninsegna, L.; Zhan, Y.; Polles, G.; Gong, K.; Hao, S.; Li, W.; Zhou, X. J.; Alber, F. Evaluating the role of the nuclear microenvironment in gene function by population-based modeling. Nature Structural & Molecular Biology 2023, 1–14.

[4] Junior, A. B. O.; Contessoto, V. G.; Mello, M. F.; Onuchic, J. N. A scalable computational approach for simulating complexes of multiple chromosomes. Journal of molecular biology 2021, 433, 166700.

[5] Fujishiro, S.; Sasai, M. Generation of dynamic three-dimensional genome structure through phase separation of chromatin. Proceedings of the National Academy of Sciences 2022, 119, e2109838119.

[6] Caragine, C. M.; Haley, S. C.; Zidovska, A. Nucleolar dynamics and interactions with nucleoplasm in living cells. Elife 2019, 8, e47533.

[7] Brangwynne, C. P.; Mitchison, T. J.; Hyman, A. A. Active liquid-like behavior of nucleoli determines their size and shape in Xenopus laevis oocytes. Proceedings of the National Academy of Sciences 2011, 108, 4334–4339.

[8] Farley, K. I.; Surovtseva, Y.; Merkel, J.; Baserga, S. J. Determinants of mammalian nucleolar architecture. Chromosoma 2015, 124, 323–331.

[9] Qi, Y.; Zhang, B. Chromatin network retards nucleoli coalescence. Nature Communications 2021, 12, 6824.

[10] Caragine, C. M.; Haley, S. C.; Zidovska, A. Surface fluctuations and coalescence of nucleolar droplets in the human cell nucleus. Physical review letters 2018, 121, 148101.

[11] Spector, D. L.; Lamond, A. I. Nuclear speckles. Cold Spring Harbor perspectives in biology 2011, 3, a000646.

[12] Banigan, E. J.; Mirny, L. A. Loop extrusion: theory meets single-molecule experiments. Current opinion in cell biology 2020, 64, 124–138.

[13] Kingma, D. P.; Ba, J. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 2014,

[14] Zhang, B.; Wolynes, P. G. Topology, structures, and energy landscapes of human chromosomes. Proceedings of the National Academy of Sciences 2015, 112, 6062–6067.

[15] Moshtagh, N.; others Minimum volume enclosing ellipsoid. Convex optimization 2005, 111, 1–9.

[16] Brahmachari, S.; Contessoto, V. G.; Di Pierro, M.; Onuchic, J. N. Shaping the genome via lengthwise compaction, phase separation, and lamina adhesion. Nucleic Acids Res. 2022, 50, 1–14.

[17] Contessoto, V. G.; Dudchenko, O.; Aiden, E. L.; Wolynes, P. G.; Onuchic, J. N.; Di Pierro, M. Interphase chromosomes of the Aedes aegypti mosquito are liquid crystalline and can sense mechanical cues. Nature Communications 2023, 14, 326.

like a homeschooling collection? Orrr what?

two of the requirements forbid "all" references by libraries and municipalities - as in you can't have only references from California or California references aren't counted?

This line tells the daughter specific ways to love a man, implying there are certain steps to follow. It suggests trying different methods if one way doesn't work. This advice makes love seem like a set of tasks to complete rather than something natural and genuine.

This advice tells the daughter to act a certain way around men to protect her reputation. It talks about the need for women to be careful about how they present themselves to men that they don't know.

This instruction shows the focus on household chores and doing things the right way. The details highlight the importance of routine in teaching the daughter how to do things properly.

I take this line as a message from the mother basically saying to not let your efforts go to waste. Do not become a woman that won't be allowed to access the very resources that keeps her alive.

This line can be interpreted in different ways, but the way I take it is that it portrays the inevitable fate of a woman during these times (1970s). If a woman behaves in an orderly fashion, she will never be seen as a "slut" and hopefully never gets taken advantage of.

This quote, amongst many others, describe how the woman is forced to learn certain practices. It seems like it follows the theme of a message from a mother to a daughter; the mother is teaching her daughter how to do this and how to do that.

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Infopiguła:

Liczba Polaków zarabiających ponad 10 tys. zł / mc wzrosła w 2022 r. o 51% rdr., do 1,5 mln osób. Na koniec 2022 r. było w Polsce 90 tys. milionerów dolarowych, 10% mniej niż rok wcześniej.

Pensje zarabiających ponad 10 tys. wzrosły o 10% do ok. 375 mld zł. Głównym czynnikiem była wysoka inflacja, ale też napływ Ukraińców z dobrymi zarobkami w korporacjach.

Liczba osób z zarobkami 20-50 tys. zł / mies. wzrosła o 38% do 440 tys., tych z zarobkami między 50 a 83 tys. zł wzrosła o 1% do 84 tys., a osób z zarobkami ponad 83 tys. zł (czyli 1 mln rocznie) spadła o 5% do 35 tys. O prawie 2% wzrosła liczba osób z majątkiem ponad 50 mln $.

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MDMA-assisted therapy for severe PTSD: a randomized, double-blind, placebo-controlled phase 3 study

• Jennifer M. Mitchell, 
• Michael Bogenschutz, 
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• Sarah Kleiman, 
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• Marcela Ot'alora G., 
• Wael Garas, 
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• Sukhpreet S. Klaire, 
• Bessel van der Kolk, 
• Keren Tzarfaty, 
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• Ray Worthy, 
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• Rick Doblin 

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Nature Medicine volume 27, pages1025--1033 (2021)Cite this article

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Matters Arising to this article was published on 11 October 2021

Matters Arising to this article was published on 11 October 2021

Abstract

Post-traumatic stress disorder (PTSD) presents a major public health problem for which currently available treatments are modestly effective. We report the findings of a randomized, double-blind, placebo-controlled, multi-site phase 3 clinical trial (NCT03537014) to test the efficacy and safety of 3,4-methylenedioxymethamphetamine (MDMA)-assisted therapy for the treatment of patients with severe PTSD, including those with common comorbidities such as dissociation, depression, a history of alcohol and substance use disorders, and childhood trauma. After psychiatric medication washout, participants (n = 90) were randomized 1:1 to receive manualized therapy with MDMA or with placebo, combined with three preparatory and nine integrative therapy sessions. PTSD symptoms, measured with the Clinician-Administered PTSD Scale for DSM-5 (CAPS-5, the primary endpoint), and functional impairment, measured with the Sheehan Disability Scale (SDS, the secondary endpoint) were assessed at baseline and at 2 months after the last experimental session. Adverse events and suicidality were tracked throughout the study. MDMA was found to induce significant and robust attenuation in CAPS-5 score compared with placebo (P < 0.0001, d = 0.91) and to significantly decrease the SDS total score (P = 0.0116, d = 0.43). The mean change in CAPS-5 scores in participants completing treatment was -24.4 (s.d. 11.6) in the MDMA group and -13.9 (s.d. 11.5) in the placebo group. MDMA did not induce adverse events of abuse potential, suicidality or QT prolongation. These data indicate that, compared with manualized therapy with inactive placebo, MDMA-assisted therapy is highly efficacious in individuals with severe PTSD, and treatment is safe and well-tolerated, even in those with comorbidities. We conclude that MDMA-assisted therapy represents a potential breakthrough treatment that merits expedited clinical evaluation.

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Main

PTSD is a common and debilitating condition with immeasurable social and economic costs that affects the lives of hundreds of millions of people annually. There are a number of environmental and biological risk factors that contribute to the development and maintenance of PTSD1, and poor PTSD treatment outcomes are associated with several comorbid conditions that include childhood trauma2, alcohol and substance use disorders3, depression4, suicidal ideation5 and dissociation6. It is therefore imperative to identify a therapeutic that is beneficial in those individuals with the comorbidities that typically confer treatment resistance.

The selective serotonin reuptake inhibitors (SSRIs) sertraline and paroxetine are Food and Drug Administration (FDA)-approved first-line therapeutics for the treatment of PTSD. However, an estimated 40--60% of patients do not respond to these compounds7. Likewise, although evidenced-based trauma-focused psychotherapies such as prolonged exposure and cognitive behavioral therapy are considered to be the gold standard treatments for PTSD8, many participants fail to respond or continue to have significant symptoms, and dropout rates are high9,10. Novel cost-effective therapeutics are therefore desperately needed11.

The substituted amphetamine 3,4-methylenedioxymethamphetamine (MDMA) induces serotonin release by binding primarily to presynaptic serotonin transporters12. MDMA has been shown to enhance fear memory extinction, modulate fear memory reconsolidation (possibly through an oxytocin-dependent mechanism), and bolster social behavior in animal models13,14. Pooled analysis of six phase 2 trials of MDMA-assisted therapy for PTSD have now shown promising safety and efficacy findings15.

Here, we assess the efficacy and safety of MDMA-assisted therapy in individuals with severe PTSD. Participants were given three doses of MDMA or placebo in a controlled clinical environment and in the presence of a trained therapy team. Primary and secondary outcome measures (CAPS-5 and SDS, respectively) were assessed by a centralized pool of blinded, independent diagnostic assessors. MDMA-assisted therapy for PTSD was granted an FDA Breakthrough Therapy designation, and the protocol and statistical analysis plan (SAP) were developed in conjunction with the FDA16.

Results

Demographics

Participants were recruited from 7 November 2018 to 26 May 2020, with the last participant visit conducted on 21 August 2020. A total of 345 participants were assessed for eligibility, 131 were enrolled, 91 were confirmed for randomization (United States, n = 77; Canada, n = 9; Israel, n = 5), and 46 were randomized to MDMA and 44 to placebo (Fig. 1).

Fig. 1: Procedure timeline and study flow diagram.

a, Procedure timeline. Following the screening procedures and medication taper, participants attended a total of three preparatory sessions, three experimental sessions, nine integration sessions and four endpoint assessments (T1--4) over 18 weeks, concluding with a final study-termination visit. IR, independent rater; T, timepoint of endpoint assessment; T1, baseline; T2, after the first experimental session; T3, after the second experimental session; T4, 18 weeks after baseline. b, CONSORT diagram indicating participant numbers and disposition through the course of the trial.

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Study arms were not significantly different in terms of race, ethnicity, sex, age, dissociative subtype, disability or CAPS-5 score (Table 1). The mean duration of PTSD diagnosis was 14.8 (s.d. 11.6) years and 13.2 (s.d. 11.4) years in the MDMA and placebo groups, respectively. Of note, six participants in the MDMA group and 13 participants in the placebo group had the dissociative subtype according to CAPS-5 score.

Table 1 Demographics and baseline characteristics

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Efficacy

MDMA significantly attenuated PTSD symptomology, as shown by the change in CAPS-5 total severity score from baseline to 18 weeks after baseline. Mixed model repeated measure (MMRM) analysis of the de jure estimand (that is, the effects of the drug if taken as directed) showed a significant difference in treatment arms (n = 89 (MDMA n = 46), P < 0.0001, between-group difference = 11.9, 95% confidence interval (CI) = 6.3--17.4, d.f. = 71) (Fig. 2a). MMRM sensitivity analysis of the de facto estimand (that is, the effects of the drug if taken as assigned, regardless of adherence) showed a significant difference in treatment arms (n = 90, P < 0.0001, d.f. = 72).

Fig. 2: Measures of MDMA efficacy in the MDMA-assisted therapy group and the placebo group.

a, Change in CAPS-5 total severity score from T1 to T4 (P < 0.0001, d = 0.91, n = 89 (MDMA n = 46)), as a measure of the primary outcome. Primary analysis was completed using least square means from an MMRM model. b, Change in SDS total score from T1 to T4 (P = 0.0116, d = 0.43, n = 89 (MDMA n = 46)), as a measure of the secondary outcome. Primary analysis was completed using least square means from an MMRM model. c, Change in BDI-II score from T1 to study termination (t = -3.11, P = 0.0026, n = 81 (MDMA n = 42)), as a measure of the exploratory outcome. Data are presented as mean and s.e.m.

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The mean change in CAPS-5 scores from baseline to 18 weeks after baseline in the completers (per protocol set) was -24.4 (s.d. 11.6) (n = 42) in the MDMA-assisted therapy group compared with -13.9 (s.d. 11.5) (n = 37) in the placebo with therapy group.

The effect size of the MDMA-assisted therapy treatment compared with placebo with therapy was d = 0.91 (95% CI = 0.44--1.37, pooled s.d. = 11.55) in the de jure estimand and d = 0.97 (95% CI = 0.51--1.42) in the de facto estimand. When the within-group treatment effect (which included the effect of the supportive therapy that was administered in both arms) was compared between the MDMA and placebo groups, the effect size was 2.1 (95% CI = -5.6 to 1.4) in the MDMA group and 1.2 (95% CI = -4.9 to 2.5) in the placebo group.

Over the same period, MDMA significantly reduced clinician-rated functional impairment as assessed with the SDS. MMRM analysis of the de jure estimand showed a significant difference in treatment arms (n = 89 (MDMA n = 46), P = 0.0116, d.f. = 71, effect size = 0.43, 95% CI = -0.01 to 0.88, pooled s.d. = 2.53) (Fig. 2b). The mean change in SDS scores from baseline to 18 weeks after baseline in the completers was -3.1 (s.d. 2.6) (n = 42) in the MDMA-assisted therapy group and -2.0 (s.d. 2.4) (n = 37) in the placebo with therapy group.

MDMA was equally effective in participants with comorbidities that are often associated with treatment resistance. Participants with the dissociative subtype of PTSD who received MDMA-assisted therapy had significant symptom reduction on the CAPS-5 (mean MDMA Δ = -30.8 (s.d. 9.0), mean placebo Δ = -12.8 (s.d. 12.8)), and this was similar to that in their counterparts with non-dissociative PTSD (mean MDMA Δ = -23.6 (s.d. 11.7), mean placebo Δ = -14.3 (s.d. 11.2)). The benefit of MDMA therapy was not modulated by history of alcohol use disorder, history of substance use disorder, overnight stay or severe childhood trauma. Results were consistent across all 15 study sites with no effect by study site (P = 0.1003). In MMRM analysis there was no obvious impact of SSRI history on effectiveness of MDMA (Supplementary Table 2).

MDMA therapy was effective in an exploratory endpoint analysis of the reduction of depression symptoms (using the Beck Depression Inventory II (BDI-II)) from baseline to study termination of the de jure estimand (mean MDMA Δ = -19.7 (s.d. 14.0), n = 42; mean placebo Δ = -10.8 (s.d. 11.3), n = 39; t = -3.11, P = 0.0026, d.f. = 79, effect size = 0.67, 95% CI = 0.22--1.12) (Fig. 2c).

Clinically significant improvement (a decrease of ≥10 points on the CAPS-5), loss of diagnosis (specific diagnostic measure on the CAPS-5), and remission (loss of diagnosis and a total CAPS-5 score ≤ 11) were each tracked. At the primary study endpoint (18 weeks after baseline), 28 of 42 (67%) of the participants in the MDMA group no longer met the diagnostic criteria for PTSD, compared with 12 of 37 (32%) of those in the placebo group after three sessions. Additionally, 14 of 42 participants in the MDMA group (33%) and 2 of 37 participants in the placebo group (5%) met the criteria for remission after three sessions (Fig. 3).

Fig. 3: Treatment response and remission for MDMA and placebo groups as a percentage of total participants randomized to each arm (MDMA, n = 46; placebo, n = 44).

Responders (clinically significant improvement, defined as a ≥10-point decrease on CAPS-5), loss of diagnosis (specific diagnostic measure on CAPS-5), and remission (loss of diagnosis and a total CAPS-5 score of ≤11) were tracked in both groups. Non-response is defined as a <10-point decrease on CAPS-5. Withdrawal is defined as a post-randomization early termination.

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Safety

Treatment-emergent adverse events (TEAEs, adverse events that occurred during the treatment period from the first experimental session to the last integration session) that were more prevalent in the MDMA study arm were typically transient, mild to moderate in severity, and included muscle tightness, decreased appetite, nausea, hyperhidrosis and feeling cold (Supplementary Table 3). Importantly, no increase in adverse events related to suicidality was observed in the MDMA group. A transient increase in vital signs (systolic and diastolic blood pressure and heart rate) was observed in the MDMA group (Supplementary Table 4). Two participants in the MDMA group had a transient increase in body temperature to 38.1 °C: one had an increase after the second MDMA session, and one had an increase after the second and third MDMA sessions.

Two participants, both randomized to the placebo group, reported three serious adverse events (SAEs) during the trial. One participant in the placebo group reported two SAEs of suicidal behavior during the trial, and another participant in the placebo group reported one SAE of suicidal ideation that led to self-hospitalization. Five participants in the placebo group and three participants in the MDMA group reported adverse events of special interest (AESIs) of suicidal ideation, suicidal behavior or self-harm in the context of suicidal ideation. One participant in the placebo group reported two cardiovascular AESIs in which underlying cardiac etiology could not be ruled out (Table 2). One participant randomized to the MDMA group chose to discontinue participation due to being triggered by the CAPS-5 assessments and to an adverse event of depressed mood following an experimental session; this participant met the criterion as a non-responder, which was defined as having a less than 10-point decrease in CAPS-5 score. MDMA sessions were not otherwise followed by a lowering of mood.

Table 2 Participants with treatment-emergent SAEs and AESIs

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Suicidality was tracked throughout the study using the Columbia Suicide Severity Rating Scale (C-SSRS) at each study visit. More than 90% of participants reported suicidal ideation in their lifetime, and 17 of 46 participants (37%) in the MDMA group and 14 of 44 participants (32%) in the placebo group reported suicidal ideation at baseline. Although the number of participants who reported suicidal ideation varied throughout the visits, prevalence never exceeded baseline and was not exacerbated in the MDMA group. Serious suicidal ideation (a score of 4 or 5 on the C-SSRS) was minimal during the study and occurred almost entirely in the placebo arm (Fig. 4).

Fig. 4: Number of participants reporting the presence of suicidal ideation as measured with the C-SSRS at each visit and separated by treatment group.

C-SSRS ideation scores range from 0 (no ideation) to 5. A C-SSRS ideation score of 4 or 5 is termed 'serious ideation'. The number of participants endorsing any positive ideation (>0) is shown by the colored bars and noted in the table below the graph. The number of participants endorsing serious ideation is given in parentheses in the table.

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Discussion

Here, we demonstrate that three doses of MDMA given in conjunction with manualized therapy over the course of 18 weeks results in a significant and robust attenuation of PTSD symptoms and functional impairment as assessed using the CAPS-5 and SDS, respectively. MDMA also significantly mitigated depressive symptoms as assessed using the BDI-II. Of note, MDMA did not increase the occurrence of suicidality during the study.

These data illustrate the potential benefit of MDMA-assisted therapy for PTSD over the FDA-approved first-line pharmacotherapies sertraline and paroxetine, which have both exhibited smaller effect sizes in pivotal studies16. Previous comparison of change in CAPS score between sertraline and placebo showed effect sizes of 0.31 and 0.37 (ref. 16). Similarly, comparison of change in CAPS score between paroxetine and placebo showed effect sizes of 0.56, 0.45 and 0.09 (ref. 16). By contrast, the effect size of 0.91 demonstrated in this study between MDMA-assisted therapy and placebo with therapy was larger than that for any other previously identified PTSD pharmacotherapy16,17,18. To directly assess superiority, a head-to-head comparison of MDMA-assisted therapy with SSRIs for PTSD would be needed. Although the present study tested the effects of MDMA using a model in which both treatment groups received supportive therapy, participants who received MDMA and supportive therapy (d = 2.1) had greater improvement in PTSD change scores compared with those who received placebo with supportive therapy (d = 1.2), suggesting that MDMA enhanced the effects of supportive therapy. In clinical practice, both MDMA and supportive therapy will be components of this PTSD treatment.

Previous research on MDMA for PTSD has suggested that those with a recent history of SSRI treatment may not respond as robustly to MDMA18. Given that 65.5% of participants in the current trial have a lifetime history of SSRI use, it is difficult to separate the ramifications of long-term SSRI treatment from the effects of treatment resistance. However, there was no obvious effect of previous SSRI use on therapeutic efficacy in this trial. Similarly, although years of PTSD diagnosis or age of onset may affect treatment efficacy, no obvious relationship was seen here between duration or onset of PTSD diagnosis and treatment efficacy.

Serotonin and the serotonin transporter are of particular importance in the generation, consolidation, retrieval and reconsolidation of fear memories19,20. Reduced serotonin transporter levels (which result in greater amounts of extracellular serotonin) have been shown to predict propensity to develop PTSD21, increase fear and anxiety-related behaviors22, and induce greater amygdalar blood oxygenation level-dependent (BOLD) activity in response to fearful images23. There is extensive serotonergic innervation of the amygdala, and amygdalar serotonin levels have been shown to increase following exposure to stressful and fear-inducing stimuli24. MDMA enhances the extinction of fear memories in mice through increased expression of brain-derived neurotrophic factor in the amygdala, and human neuroimaging studies have demonstrated that MDMA is associated with attenuated amygdalar BOLD activity during presentation of negative emotional stimuli25. Together these data suggest that MDMA may exert its therapeutic effects through a well-conserved mechanism of amygdalar serotonergic function that regulates fear-based behaviors and contributes to the maintenance of PTSD. Perhaps by reopening an oxytocin-dependent critical period of neuroplasticity that typically closes after adolescence15, MDMA may facilitate the processing and release of particularly intractable, potentially developmental, fear-related memories.

It is intriguing to speculate that the pharmacological properties of MDMA, when combined with therapy, may produce a 'window of tolerance,' in which participants are able to revisit and process traumatic content without becoming overwhelmed or encumbered by hyperarousal and dissociative symptoms26. MDMA-assisted therapy may facilitate recall of negative or threatening memories with greater self-compassion27 and less PTSD-related shame and anger28. Additionally, the acute prosocial and interpersonal effects of MDMA25,29 may support the quality of the therapeutic alliance, a potentially important factor relating to PTSD treatment adherence30 and outcome31. Indeed, clinicians have suggested that "MDMA may catalyze therapeutic processing by allowing patients to stay emotionally engaged while revisiting traumatic experiences without becoming overwhelmed"32.

Given that PTSD is a strong predictor of disability in both veteran and community populations33, it is promising to note that the robust reduction in PTSD and depressive symptoms identified here is complemented by a significant improvement in SDS score (for example, work and/or school, social and family functioning). Approximately 4.7 million US veterans report a service-related disability[34](https://www.nature.com/articles/s41591-021-01336-3#ref-CR34 "Bureau of Labor and Statistics. Employment Situation of Veterans---2020. News release, 18 March 2021; https://www.bls.gov/news.release/pdf/vet.pdf

            "), costing the US government approximately $73 billion per year[35](https://www.nature.com/articles/s41591-021-01336-3#ref-CR35 "Congressional Budget Office. Possible Higher Spending Paths for Veterans' Benefits (2018);
              https://www.cbo.gov/publication/54881

            "). Identification of a PTSD treatment that could improve social and family functioning and ameliorate impairment across a broad range of environmental contexts could provide major medical cost savings, in addition to improving the quality of life for veterans and others affected by this disorder.

PTSD is a particularly persistent and incapacitating condition when expressed in conjunction with other disorders of mood and affect. In the present study, perhaps most compelling are the data indicating efficacy in participants with chronic and severe PTSD, and the associated comorbidities including childhood trauma, depression, suicidality, history of alcohol and substance use disorders, and dissociation, because these groups are all typically considered treatment resistant2,3,4,5,6. Given that more than 80% of those assigned a PTSD diagnosis have at least one comorbid disorder3, the identification of a therapy that is effective in those with complicated PTSD and dual diagnoses could greatly improve PTSD treatment. Additional studies should therefore be conducted to evaluate the safety and efficacy of MDMA-assisted therapy for PTSD in those with specific comorbidities.

Although recent research suggests that dissociative subtype PTSD is difficult to treat36, participants with the dissociative subtype who received MDMA-assisted therapy had significant symptom reduction that was at least similar to that of their counterparts with non-dissociative PTSD. Given that this covariate was significant, it warrants further study. Furthermore, given that other treatments for PTSD are not consistently effective for those with the dissociative subtype, these data, if replicated, would indicate an important novel therapeutic niche for MDMA-assisted therapy for typically hard-to-treat populations.

Importantly, there were no major safety issues reported in the MDMA arm of this study. Although abuse potential, cardiovascular risk and suicidality were recorded as AESIs, MDMA was not shown to induce or potentiate any of these conditions. In addition, although there was often a transient increase in blood pressure during MDMA sessions, this was expected based on phase 2 data and previous studies in healthy volunteers37. These data suggest that MDMA has an equivalent, if not better, safety profile compared with that of first-line SSRIs for the treatment of PTSD, which are known to carry a low risk of QT interval prolongation38.

There are several limitations to the current trial. First, due to the coronavirus disease 2019 (COVID-19) pandemic, the participant population is smaller than originally planned. However, given the power noted in this study, it is unlikely that population size was an impediment. Second, the population is relatively homogeneous and lacks racial and ethnic diversity, which should be addressed in future trials. Third, this report describes the findings of a short-term pre-specified primary outcome, 2 months after the last experimental session and 5 weeks since the final integrative therapy session; long-term follow-up data from this controlled trial will be collected to assess durability of treatment. Fourth, safety data were by necessity collected by site therapists, perhaps limiting the blinding of the data. To eliminate this effect on the primary and secondary outcome measures, all efficacy data were collected by blinded, independent raters. Last, given the subjective effects of MDMA, the blinding of participants was also challenging and possibly led to expectation effects14. However, although blinding was not formally assessed during the study, when participants were contacted to be informed of their treatment assignment at the time of study unblinding it became apparent that at least 10% had inaccurately guessed their treatment arm. Although anecdotal, at least 7 of 44 participants in the placebo group (15.9%) inaccurately believed that they had received MDMA, and at least 2 of 46 participants in the MDMA group (4.3%) inaccurately believed that they had received placebo.

We may soon be confronted with the potentially enormous economic and social repercussions of PTSD, exacerbated by the COVID-19 pandemic. Overwhelmingly high rates of psychological and mental health impairment could be with us for years to come and are likely to impart a considerable emotional and economic burden. Novel PTSD therapeutics are desperately needed, especially for those for whom comorbidities confer treatment resistance.

In summary, MDMA-assisted therapy induces rapid onset of treatment efficacy, even in those with severe PTSD, and in those with associated comorbidities including dissociative PTSD, depression, history of alcohol and substance use disorders, and childhood trauma. Not only is MDMA-assisted therapy efficacious in individuals with severe PTSD, but it may also provide improved patient safety. Compared with current first-line pharmacological and behavioral therapies, MDMA-assisted therapy has the potential to dramatically transform treatment for PTSD and should be expeditiously evaluated for clinical use.

LE - 218. Encarnado, conserva o Espírito algum vestígio das percepções que teve e dos conhecimentos que adquiriu nas existências anteriores? “Guarda vaga lembrança, que lhe dá o que se chama ideias inatas.” a) Não é, então, quimérica a teoria das ideias inatas? “Não; os conhecimentos adquiridos em cada existência não mais se perdem. Liberto da matéria, o Espírito sempre os tem presentes. Durante a encarnação, esquece-os em parte, momentânea.

Gemini Skynet GPT and it's related series of current ... Mistral, ORCA and specifically Tesla (who has spent significant seed funding to build something connecting "VIN #s" and SOCSECNUM "individuals" to the ... RHL of Kula Shaer's Govinda and "like plugging Wikipedia into our heads and becoming ... all knowing, or ..."

MT Anderson wrote a dystopicish novel and series called "Feed" which was also made into a silver screen work or television series related to the idea that "advertisements" as propaganda would be a significant issue with the "Lowell truth table layers" as in ....

Plugging Google into your heads instead of Wikipedia, and the entire first page of what you are reading/thinking/understanding being paid-in-place-"bold this is an advertisement" links that are ... like how you get everything for free.

It is probably central to Wikipedia's literal religious adverse atttitude towards "advertisements" and while I think it looks like a "waste of money, considering the possibility that ads can be clearly placed in a separate non central and obvious location, like a commercial" it is the kind of statement that coearly reinforces the importance of things like Facebook's "Community Standards" as a movement that is designed to ensure that we don't lose our "rights to free speech and truth"

Facebook has spent significant development effort building what is currecntly the most robust "fact checking" system that the world has, a consoritum of independent fact checers that serve as a good framework for how to "mechanical turk" the verification of truth that I envisioned being fundamental to the "halo system"

Vérifier comment fonctionne la bibliothèque de Laval

https://www.liberation.fr/environnement/climat/la-france-baisse-de-53-ses-emissions-de-gaz-a-effet-de-serre-au-premier-trimestre-20240626_AJIXJEV5HFB6VAN7WKMH6IPOUI/

https://taz.de/Tiefseebergbau/!6016627/

https://taz.de/Werbung-mit-klimaneutral-Label/!6016630/

https://www.repubblica.it/italia/2024/06/26/news/caldo_africano_quando_torna_il_mondo_a_50_gradi-423289939/

https://www.repubblica.it/economia/2024/06/26/news/edison_eolico_galleggiante_sicilia_blunova-423303234/

All about Sainsburys bank. https://sainsburys-online.org/sainsburys-bank/

https://www.theguardian.com/environment/article/2024/jun/26/air-freight-greenhouse-gas-emissions-increase-post-pandemic-economy

https://www.theguardian.com/environment/article/2024/jun/26/sarah-finch-climate-activism-uk-supreme-court-win

https://www.theguardian.com/environment/article/2024/jun/26/sarah-finch-climate-activism-uk-supreme-court-win

eLife assessment

This important work provides another layer of regulatory mechanism for TGF-beta signaling activity. The evidence supports the involvement of microtubules as a reservoir of Smad2/3, however, additional evidence to convincingly demonstrate the functional involvement of Rudhira in this process is highly appreciated. The work will be of broad interest to developmental biologists in general and molecular biologists in the field of growth factor signaling.

Reviewer #1 (Public Review):

Summary

This manuscript aimed to study the role of Rudhira (also known as Breast Carcinoma Amplified Sequence 3), an endothelium-restricted microtubules-associated protein, in regulating of TGFβ signaling. The authors demonstrate that Rudhira is a critical signaling modulator for TGFβ signaling by releasing Smad2/3 from cytoskeletal microtubules and how Rudhira is a Smad2/3 target gene. Taken together, the authors provide a model of how Rudhira contributes to TGFβ signaling activity to stabilize the microtubules, which is essential for vascular development.

Strengths

The study used different methods and techniques to achieve aims and support conclusions, such as Gene Ontology analysis, functional analysis in culture, immunostaining analysis, and proximity ligation assay. This study provides an unappreciated additional layer of TGFβ signaling activity regulation after ligand-receptor interaction.

Weaknesses

(1) It is unclear how current findings provide a better understanding of Rudhira KO mice, which the authors published some years ago.<br /> (2) Why do they use HEK cells instead of SVEC cells in Figure 2 and 4 experiments?<br /> (3) A model shown in Figure 5E needs improvement to grasp their findings easily.

Reviewer #2 (Public Review):

Summary:

It was first reported in 2000 that Smad2/3/4 are sequestered to microtubules in resting cells and TGF-β stimulation releases Smad2/3/4 from microtubules, allowing activation of the Smad signaling pathway. Although the finding was subsequently confirmed in a few papers, the underlying mechanism has not been explored. In the present study, the authors found that Rudhira/breast carcinoma amplified sequence 3 is involved in the release of Smad2/3 from microtubules in response to TGF-β stimulation. Rudhira is also induced by TGF-β and is probably involved in the stabilization of microtubules in the delayed phase after TGF-β stimulation. Therefore, Rudhira has two important functions downstream of TGF-β in the early as well as delayed phase.

Strengths:

This work aimed to address an unsolved question on one of the earliest events after TGF-β stimulation. Based on loss-of-function experiments, the authors identified a novel and potentially important player, Rudhira, in the signal transmission of TGF-β,

Weaknesses:

The authors have identified a key player that triggers Smad2/3 released from microtubules after TGF-β stimulation probably via its association with microtubules. This is an important first step for understanding the regulation of Smad signaling, but underlying mechanisms as well as upstream and downstream events largely remain to be elucidated.

(1) The process of how Rudhira causes the release of Smad proteins from microtubules remains unclear. The statement that "Rudhira-MT association is essential for the activation and release of Smad2/3 from MTs" (lines 33-34) is not directly supported by experimental data.

(2) The process of how Rudhira is mobilized to microtubules in response to TGF-β remains unclear.

(3) After Rudhira releases Smad proteins from microtubules, Rudhira stabilizes microtubules. The process of how cells return to a resting state and recover their responsiveness to TGF-β remains unclear.

This reviewer is also afraid that some of the biochemical data lack appropriate controls and are not convincing enough.

Author response:

eLife assessment:

This important work provides another layer of regulatory mechanism for TGF-beta signaling activity. The evidence supports the involvement of microtubules as a reservoir of Smad2/3, however, additional evidence to convincingly demonstrate the functional involvement of Rudhira in this process is highly appreciated. The work will be of broad interest to developmental biologists in general and molecular biologists in the field of growth factor signaling.

Public Reviews:

Reviewer #1 (Public Review):

Summary

This manuscript aimed to study the role of Rudhira (also known as Breast Carcinoma Amplified Sequence 3), an endothelium-restricted microtubules-associated protein, in regulating of TGFβ signaling. The authors demonstrate that Rudhira is a critical signaling modulator for TGFβ signaling by releasing Smad2/3 from cytoskeletal microtubules and how Rudhira is a Smad2/3 target gene. Taken together, the authors provide a model of how Rudhira contributes to TGFβ signaling activity to stabilize the microtubules, which is essential for vascular development.

Strengths

The study used different methods and techniques to achieve aims and support conclusions, such as Gene Ontology analysis, functional analysis in culture, immunostaining analysis, and proximity ligation assay. This study provides an unappreciated additional layer of TGFβ signaling activity regulation after ligand-receptor interaction.

We thank the reviewer for acknowledging the importance of our study and providing a clear summary of our findings.

Weaknesses

(1) It is unclear how current findings provide a better understanding of Rudhira KO mice, which the authors published some years ago.

Our previous study demonstrated that Rudhira KO mice have a predominantly developmental cardiovascular phenotype that phenocopies TGFβ loss of function (Shetty, Joshi et al., 2018). Additionally, we found that at the molecular level, Rudhira regulates cytoskeletal organization (Jain et al., 2012; Joshi and Inamdar, 2019). Our current study builds upon these previous findings, showing an essential role of Rudhira in maintaining TGFβ signaling and controlling the microtubule cytoskeleton during vascular development. On one hand Rudhira regulates TGFβ signaling by promoting the release of Smads from microtubules, while on the other, Rudhira is a TGFβ target essential for stabilizing microtubules. Thus, our current study provides a molecular basis for Rudhira function in cardiovascular development.

(2) Why do they use HEK cells instead of SVEC cells in Figure 2 and 4 experiments?

Our earlier studies have characterized the role of Rudhira in detail using both loss and gain of function methods in multiple cell types (Jain et al., 2012; Shetty, Joshi et al., 2018; Joshi and Inamdar, 2019). As endothelial cells are particularly difficult to transfect, and because the function of Rudhira in promoting cell migration is conserved in HEK cells, it was practical and relevant to perform these experiments in HEK cells (Figures 2 and 4E).

(3) A model shown in Figure 5E needs improvement to grasp their findings easily.

We have modified Figure 5E for clarity.

Reviewer #2 (Public Review):

Summary

It was first reported in 2000 that Smad2/3/4 are sequestered to microtubules in resting cells and TGF-β stimulation releases Smad2/3/4 from microtubules, allowing activation of the Smad signaling pathway. Although the finding was subsequently confirmed in a few papers, the underlying mechanism has not been explored. In the present study, the authors found that Rudhira/breast carcinoma amplified sequence 3 is involved in the release of Smad2/3 from microtubules in response to TGF-β stimulation. Rudhira is also induced by TGF-β and is probably involved in the stabilization of microtubules in the delayed phase after TGF-β stimulation. Therefore, Rudhira has two important functions downstream of TGF-β in the early as well as delayed phase.

Strengths:

This work aimed to address an unsolved question on one of the earliest events after TGF-β stimulation. Based on loss-of-function experiments, the authors identified a novel and potentially important player, Rudhira, in the signal transmission of TGF-β.

We thank the reviewer for the critical evaluation and appreciation of our findings.

Weaknesses:

The authors have identified a key player that triggers Smad2/3 released from microtubules after TGF-β stimulation probably via its association with microtubules. This is an important first step for understanding the regulation of Smad signaling, but underlying mechanisms as well as upstream and downstream events largely remain to be elucidated.

We acknowledge that the mechanisms regulating cytoskeletal control of Smad signaling are far from clear, but these are out of scope of this manuscript. This manuscript rather focuses on Rudhira/Bcas3 as a pivot to understand vascular TGFβ signaling and microtubule connections.

(1) The process of how Rudhira causes the release of Smad proteins from microtubules remains unclear. The statement that "Rudhira-MT association is essential for the activation and release of Smad2/3 from MTs" (lines 33-34) is not directly supported by experimental data.

We agree with the reviewer’s comment. Although we provide evidence that the loss of Rudhira (and thereby deduced loss of Rudhira-MT association) prevents release of Smad2/3 from MTs (Fig 3C), it does not confirm the requirement of Rudhira-MT association for this. In light of this, we have modified the statement to ‘Rudhira associates with MTs and is essential for the activation and release of Smad2/3 from MTs”.

(2) The process of how Rudhira is mobilized to microtubules in response to TGF-β remains unclear.

Our previous study showed that Rudhira associates with microtubules, and preferentially binds to stable microtubules (Jain et al., 2012; Joshi and Inamdar, 2019). Since TGFβ stimulation is known to stabilize microtubules, we hypothesize that TGFβ stimulation increases Rudhira binding to stable microtubules. We have mentioned this in our revised manuscript.

(3) After Rudhira releases Smad proteins from microtubules, Rudhira stabilizes microtubules. The process of how cells return to a resting state and recover their responsiveness to TGF-β remains unclear.

We show that dissociation of Smads from microtubules is an early response and stabilization of microtubules is a late TGFβ response. However, we agree that the sequence of these molecular events has not been characterized in-depth in this or any other study, making it difficult to assign causal roles (eg. whether release of Smads from MTs is a pre-requisite for MT stabilization by Rudhira) or reversibility. However, the TGFβ pathway is auto regulatory, leading to increased turnover of receptors and Smads and increased expression of inhibitory Smads, which may recover responsiveness to TGFβ. Additionally, the still short turnover time of stable microtubules (several minutes to hours) may also promote quick return to resting state.

We have discussed this in our revised manuscript.

Author response:

eLife assessment

This important study provides new insight into the dynamics that underlie the development of therapy resistance in prostate cancer by revealing that divergent tumor evolutionary paths occur in response to different treatment timing and that these converge on common resistance mechanisms. The use of barcoded lineage tracing and characterization of isolated tumor clonal populations provides compelling evidence supporting the importance of clonal dynamics in a tumor ecosystem for treatment resistance. Several open questions remain, however, raising the possibility of alternative interpretations of the data set in its current form. Overall, the findings deepen our understanding of prostate cancer evolution and hold promising implications for how drug resistance can be addressed or prevented.

We are pleased the reviewers found our work reporting distinct evolutionary paths to resistance based on timing of treatment to be important and supported by compelling evidence.  We also acknowledge the need for additional work to clarify some details, particularly regarding the mechanism of clonal cooperativity as a catalyst of resistance.

Public Reviews:

Reviewer #1 (Public Review):

Summary:

Lee, Eugine et al. use in vivo barcoded lineage tracing to investigate the evolutionary paths to androgen receptor signaling inhibition (ARSI) resistance in two different prostate cancer clinical scenario models: measurable disease and minimal residual disease. Using two prostate cancer cell lines, LNCaP/AR and CWR22PC, the authors find that in their minimal residual disease models, the outgrowth of pre-existing resistant clones gives rise to ARSI-resistant tumors. Interestingly, in their measurable disease model or post-engraftment ARSI setting, these pre-existing resistant clones are depleted and rather a subset of clones that give rise to the treatment of naïve tumors adapt to ARSI treatment and are enriched in resistant tumors. For the LNCaP/AR cell line, characterization of pre-existing resistant clones in treatment naïve and ARSI treatment settings reveal increased baseline androgen receptor transcriptional output as well as baseline upregulation of glucocorticoid receptor (GR) as the primary driver of pre-existing resistance. Similarly, the authors found induction of high GR expression over long-term ARSI treatment in ARSI-sensitive clones for adaptive resistance to ARSI. For CWR22Pc cells, HER3/NRG1 signaling was the primary driver for ARSI resistance in both measurable disease and minimal residual disease models. Not only were these findings consistent with the authors' previous reports of GR and NRG1/Her3 as the molecular drivers of ARSI resistance in LNCaP/AR and CWR22Pc, respectively, but also demonstrate conserved resistance mechanisms despite pre-existing or adaptive evolutionary paths to resistance. Lastly, the authors show adaptive ARSI resistance is dependent on interclonal cooperation, where the presence of pre-existing resistant clones or "helper" clones is required to promote adaptive resistance in ARSI-sensitive clones.

Strengths:

The authors employ DNA barcoding, powerful a tool already demonstrated by others to track the clonal evolution of tumor populations during resistance development, to study the effects of the timing of therapy as a variable on resistance evolution. The authors use barcoding in two cell line models of prostate cancer in two clinical disease scenarios to demonstrate divergent evolutionary paths converging on common resistant mechanisms. By painstakingly isolating clones with barcodes of interest to generate clonal cell lines from the treatment of naïve cell populations, the authors are able to not only characterize pre-existing resistance but also show cooperativity between resistant and drug-sensitive populations for adaptive resistance.

Weaknesses:

While the finding that different evolutionary paths result in common molecular drivers of ARSI resistance is novel and unexpected, this work primarily confirms the authors' previous published work identifying the resistance mechanisms in these cell lines. The impact of the work would be greater with additional studies understanding the specific molecular/genetic mechanisms by which cells become resistant or cooperate within a population to give rise to resistant population subclones.

We agree that additional insights into the mechanism of adaptive resistant and the role of cell-cell cooperativity are clear next steps for this work. We propose to do so through single cell characterization (RNA-seq, ATAC-seq) of tumor evolution in a time course experiment where we can track each clone using expressed barcodes. This will allow us to explore the dynamics of interaction between the "adaptable" and "helper" clones. Unfortunately, the barcode methodology used in this initial report is DNA-based; therefore, a follow-up study using a transcribable barcode library is needed to address these fascinating questions.

This study would also benefit from additional explanation or exploration of why the two resistance driver pathways described (GR and NRG1/Her3) are cell line specific and if there are genetic or molecular backgrounds in which specific resistance signaling is more likely to be the predominant driver of resistance.

In the case of NRG1/HER3 pathway mediated resistance, we know that this mechanism requires that the PTEN/PIK3CA pathway be wildtype.  This is the case for the CWR22Pc model described in the manuscript. Furthermore, we have data showing that PTEN deletion in these cells rescues the phenotype, meaning that CWR22Pc cells with PTEN deletion are no longer dependent on NRG1/HER3 signaling for ARSI resistance.

In contrast, LNCaP/AR cells are PTEN null at baseline and therefore must evolve alternative mechanisms of ARSI resistance. Since our initial identification of the GR mechanism, we and others have extended the finding to additional models (VCaP, LAPC4) (PMID: 24315100; PMID: 28191869). Another recent insight is the importance of RB1 and TP53 status in maintenance of luminal lineage identity during ARSI therapy, and the recognition of lineage plasticity as a resistance mechanism in cell lines/tumor models that lack these two tumor suppressors. In summary, baseline genetics clearly plays a role in which ARSI resistance pathway is  likely to emerge. We will clarify this point in the revision with additional discussion.

Reviewer #2 (Public Review):

Summary

The authors aimed to characterise the evolutionary dynamics that occur during the resistance to androgen receptor signalling inhibition, and how this differs in established tumours vs. residual disease, in prostate cancer. By using a barcoding method, they aimed to both characterise the distribution of clones that support therapy resistance in these settings, while also then being able to isolate said clones from the pre-graft population via single-cell cloning to characterise the mechanisms of resistance and dependency on cooperativity.

While, interestingly, the timing of combination therapies has been shown to be critical to avoid cross-resistance, the timing of therapy has not been specifically considered as a factor dictating resistance pathways. Additionally, the role of residual disease and dormant populations in driving relapse is of increasing interest, yet a lot remains to be understood of these populations. The question of whether different clinical manifestations of therapy resistance follow similar evolutionary pathways to resistance is therefore interesting and relevant for the field.

The methods applied are elegant and the body of work is substantial. The proposed divergent evolutionary pathways pose interesting questions, and the findings on cooperativity provide insight. However, whether the model truly reflects minimal residual disease to the extent that the authors suggest may limit the relevance of the findings at this stage. Certain patterns in the DNA barcoding results also call into question whether the results fully support the strong claims of the authors, or whether alternative explanations could exist. While the potential to isolate individual clones in the pre-graft setting is a great strength of the method applied and the isolation of these clones is a huge body of work in itself, the limited number of clones that could be isolated also somewhat limits the validation of the findings.

Strengths

Very relevant and interesting question, clear clinical relevance, applying elegant methods that hold the potential to provide a novel understanding of multiple aspects of therapy resistance, through from evolutionary patterns to intracellular and cooperative mechanisms of resistance.

The text is clearly written, logical, and the structure is easy to follow.

Weaknesses

(1) The extent to which the model used truly mimics residual disease

The main conclusions of the paper are built upon results using a model for minimal residual disease. However, the extent to which this truly recapitulates minimal residual disease, particularly with regard to their focus on the timings of therapy, could be discussed further. If in the clinical setting residual disease occurs following the existence of a tumour and its microenvironment, there might be many aspects of the process that are missed when coinciding treatment with engraftment of a xenograft tumour with pre-castration. If any characterisation of the minimal residual disease was possible (such as histologically or through RNA sequencing), this may help demonstrate in what ways this model recapitulates minimal residual disease.

We appreciate the reviewer's feedback on this point and acknowledge that the pre-ARSI setting used in our studies is not precisely identical to minimal residual disease (MRD) seen clinically, where a patient typically undergoes primary treatment (radical prostatectomy surgery or local radiotherapy) then relapses with distant disease from micrometastases that were not initially detectable.  Having uncovered a key difference in the path to resistance using our pre-ARSI model, we believe our data provide a strong rationale to invest additional effort in designing newer MRD models that more closely mimic the clinical scenario, perhaps through surgical resection of a primary tumor that could “seed” micrometatases prior to therapy. We will highlight this aspect in our revised manuscript and provide clarity on the limitations and scope of our study.

(2) Whether the observed enrichment of pre-resistant clones is truly that

The authors strongly make the case that their barcoding experiments provide evidence for pre-existing resistance in the context of minimal residual disease. However, it seems that the clones enriched in the ARSIR tumours are consistently the most enriched clones in the pregraft. Is it possible that the high selective pressure in the pre-engraftment ARSI condition simply leads to an enrichment of the most populous clones from the pregraft? Whereas in the control setting, the reduced selective pressure at the point of engraftment allows for a wider variety of clones to establish in the tumour?

The reviewer raises an important point about enrichment of ARSI resistance clones in the pregraft but we do not believe that explains the subsequent in vivo data for the following reasons:

(1) The two most enriched clones in the Pre-ARSIR tumors are the second and third the most enriched clones in pre-graft, not first (Supplementary figure 1E). If the clones were enriched in resistant tumors based on their abundance in starting population, we expect to find the most enriched clone in the tumor.

(2) By varying the androgen concentration in the pregraft culture media, we could selectively deplete or enrich the same clones enriched in the Pre-ARSIR tumors in vivo, indicating the enrichment of these clones in the resistant tumors is unlikely to be solely based on their relative frequency in the pregraft (Supplementary figure 2).

We will clarify these points in the revised manuscript.

Additionally, is there the possibility that the clones highly enriched in the pregraft are in fact a heterogeneous group of cells bearing the same barcode due to stochastic events in the process of viral transduction? Addressing these questions would greatly improve the study.

The barcode library was deep sequenced to confirm even distribution of the barcodes before it was transferred from Novartis (PMID: 258491301) and we intentionally used a low multiplicity of infection (MOI) to generate barcode lines to ensure single copy insertion. That said, we cannot entirely rule out the possibility that the second and third most enriched clones in the pregraft originated from the same ancestral clone and subsequently acquired two different barcodes.  We will clarify this point in the revised manuscript.

(3) The robustness of the subsequent work based on 1-2 pre-resistant clones

While appreciating the volume of work involved in isolating and culturing individual pre-resistant clones, given the previous point, the conclusions would benefit from very robust validations with these single-cell clones. There are only two clones, and the results seem to focus more on one than the other, for which the data is less convincing. For instance, the Enz IC50 data, which in the case for pre-ARSI R2 is restricted to the supplementary, compares the clones A-D. In Figure S8 B, pre-ARSI R2 is compared to clone B, which is, of the four clones shown in the main figure when compared to R1, the one with the lowest Enz IC50. Therefore, while the resistant clones seem to have a significantly higher Enz IC50, comparing both clones to clones A-D may not have achieved this significance. It would also be useful to know how abundant the resistant clones were in the original barcode experiments.

We acknowledge that studies relying on 1-2 biological samples indeed have limitations. Given our extensive prior work into the role of GR in the development of ARSI resistance (and that of other labs), we focused on demonstrating that both pre-ARSIR1 and pre-ARSIR2 clones exhibit pre-existing GR expression and are primed to further upregulate GR levels under ARSI conditions, thereby relying on GR function to sustain resistance. Given the redundancy of resistant mechanisms of the two clones, we made efforts to isolate additional clones enriched in Pre-ARSIR tumors. However, despite our attempts, we were unable to identify further clones. Pre-ARSIR1 and pre-ARSIR2 are second and third most enriched clones in pre-graft (2.1% and 1.7% respectively).

(4) The logic used in the final section requires further explanation

In the final section, the authors suggest that a pre-ARSIR clone is able to cooperate with a pre-Intact clone to aid adaptive ARSI resistance. If this is true, then could it not be that rare, pre-resistant clones support adaptive resistance in established tumours? And, therefore, the mechanism underlying resistance could be through pre-existing resistant clones in both settings. The work would benefit from a discussion to clarify this discrepancy in the interpretation of the findings. This is particularly necessary given the strong wording the authors use regarding their findings, such as that they have provided 'conclusive evidence' for acquired resistance.

We agree that rare, pre-resistant clones could support adaptive resistance (and therefore resistance in this adaptive setting could, technically be called “pre-existing”) but it is critical to recognize that these rare, pre-resistant “helper” clones are vastly outnumbered by pre-Intact clones that “acquire” resistance through their “help.” We find this to be fascinating biology and we will clarify this logic in the resubmission, as well as future experimental approaches to unravel the mechanism.

eLife assessment

This important study provides new insight into the dynamics that underlie the development of therapy resistance in prostate cancer by revealing that divergent tumor evolutionary paths occur in response to different treatment timing and that these converge on common resistance mechanisms. The use of barcoded lineage tracing and characterization of isolated tumor clonal populations provides compelling evidence supporting the importance of clonal dynamics in a tumor ecosystem for treatment resistance. Several open questions remain, however, raising the possibility of alternative interpretations of the data set in its current form. Overall, the findings deepen our understanding of prostate cancer evolution and hold promising implications for how drug resistance can be addressed or prevented.

Reviewer #1 (Public Review):

Summary:

Lee, Eugine et al. use in vivo barcoded lineage tracing to investigate the evolutionary paths to androgen receptor signaling inhibition (ARSI) resistance in two different prostate cancer clinical scenario models: measurable disease and minimal residual disease. Using two prostate cancer cell lines, LNCaP/AR and CWR22PC, the authors find that in their minimal residual disease models, the outgrowth of pre-existing resistant clones gives rise to ARSI-resistant tumors. Interestingly, in their measurable disease model or post-engraftment ARSI setting, these pre-existing resistant clones are depleted and rather a subset of clones that give rise to the treatment of naïve tumors adapt to ARSI treatment and are enriched in resistant tumors. For the LNCaP/AR cell line, characterization of pre-existing resistant clones in treatment naïve and ARSI treatment settings reveal increased baseline androgen receptor transcriptional output as well as baseline upregulation of glucocorticoid receptor (GR) as the primary driver of pre-existing resistance. Similarly, the authors found induction of high GR expression over long-term ARSI treatment in ARSI-sensitive clones for adaptive resistance to ARSI. For CWR22Pc cells, HER3/NRG1 signaling was the primary driver for ARSI resistance in both measurable disease and minimal residual disease models. Not only were these findings consistent with the authors' previous reports of GR and NRG1/Her3 as the molecular drivers of ARSI resistance in LNCaP/AR and CWR22Pc, respectively, but also demonstrate conserved resistance mechanisms despite pre-existing or adaptive evolutionary paths to resistance. Lastly, the authors show adaptive ARSI resistance is dependent on interclonal cooperation, where the presence of pre-existing resistant clones or "helper" clones is required to promote adaptive resistance in ARSI-sensitive clones.

Strengths:

The authors employ DNA barcoding, powerful a tool already demonstrated by others to track the clonal evolution of tumor populations during resistance development, to study the effects of the timing of therapy as a variable on resistance evolution. The authors use barcoding in two cell line models of prostate cancer in two clinical disease scenarios to demonstrate divergent evolutionary paths converging on common resistant mechanisms. By painstakingly isolating clones with barcodes of interest to generate clonal cell lines from the treatment of naïve cell populations, the authors are able to not only characterize pre-existing resistance but also show cooperativity between resistant and drug-sensitive populations for adaptive resistance.

Weaknesses:

While the finding that different evolutionary paths result in common molecular drivers of ARSI resistance is novel and unexpected, this work primarily confirms the authors' previous published work identifying the resistance mechanisms in these cell lines. The impact of the work would be greater with additional studies understanding the specific molecular/genetic mechanisms by which cells become resistant or cooperate within a population to give rise to resistant population subclones.

This study would also benefit from additional explanation or exploration of why the two resistance driver pathways described (GR and NRG1/Her3) are cell line specific and if there are genetic or molecular backgrounds in which specific resistance signaling is more likely to be the predominant driver of resistance.

Reviewer #2 (Public Review):

Summary

The authors aimed to characterise the evolutionary dynamics that occur during the resistance to androgen receptor signalling inhibition, and how this differs in established tumours vs. residual disease, in prostate cancer. By using a barcoding method, they aimed to both characterise the distribution of clones that support therapy resistance in these settings, while also then being able to isolate said clones from the pre-graft population via single-cell cloning to characterise the mechanisms of resistance and dependency on cooperativity.

While, interestingly, the timing of combination therapies has been shown to be critical to avoid cross-resistance, the timing of therapy has not been specifically considered as a factor dictating resistance pathways. Additionally, the role of residual disease and dormant populations in driving relapse is of increasing interest, yet a lot remains to be understood of these populations. The question of whether different clinical manifestations of therapy resistance follow similar evolutionary pathways to resistance is therefore interesting and relevant for the field.

The methods applied are elegant and the body of work is substantial. The proposed divergent evolutionary pathways pose interesting questions, and the findings on cooperativity provide insight. However, whether the model truly reflects minimal residual disease to the extent that the authors suggest may limit the relevance of the findings at this stage. Certain patterns in the DNA barcoding results also call into question whether the results fully support the strong claims of the authors, or whether alternative explanations could exist. While the potential to isolate individual clones in the pre-graft setting is a great strength of the method applied and the isolation of these clones is a huge body of work in itself, the limited number of clones that could be isolated also somewhat limits the validation of the findings.

Strengths

• Very relevant and interesting question, clear clinical relevance, applying elegant methods that hold the potential to provide a novel understanding of multiple aspects of therapy resistance, through from evolutionary patterns to intracellular and cooperative mechanisms of resistance.

• The text is clearly written, logical, and the structure is easy to follow.

Weaknesses

(1) The extent to which the model used truly mimics residual disease

The main conclusions of the paper are built upon results using a model for minimal residual disease. However, the extent to which this truly recapitulates minimal residual disease, particularly with regard to their focus on the timings of therapy, could be discussed further. If in the clinical setting residual disease occurs following the existence of a tumour and its microenvironment, there might be many aspects of the process that are missed when coinciding treatment with engraftment of a xenograft tumour with pre-castration. If any characterisation of the minimal residual disease was possible (such as histologically or through RNA sequencing), this may help demonstrate in what ways this model recapitulates minimal residual disease.

(2) Whether the observed enrichment of pre-resistant clones is truly that

The authors strongly make the case that their barcoding experiments provide evidence for pre-existing resistance in the context of minimal residual disease. However, it seems that the clones enriched in the ARSIR tumours are consistently the most enriched clones in the pregraft. Is it possible that the high selective pressure in the pre-engraftment ARSI condition simply leads to an enrichment of the most populous clones from the pregraft? Whereas in the control setting, the reduced selective pressure at the point of engraftment allows for a wider variety of clones to establish in the tumour? Additionally, is there the possibility that the clones highly enriched in the pregraft are in fact a heterogeneous group of cells bearing the same barcode due to stochastic events in the process of viral transduction? Addressing these questions would greatly improve the study.

(3) The robustness of the subsequent work based on 1-2 pre-resistant clones

While appreciating the volume of work involved in isolating and culturing individual pre-resistant clones, given the previous point, the conclusions would benefit from very robust validations with these single-cell clones. There are only two clones, and the results seem to focus more on one than the other, for which the data is less convincing. For instance, the Enz IC50 data, which in the case for pre-ARSI R2 is restricted to the supplementary, compares the clones A-D. In Figure S8 B, pre-ARSI R2 is compared to clone B, which is, of the four clones shown in the main figure when compared to R1, the one with the lowest Enz IC50. Therefore, while the resistant clones seem to have a significantly higher Enz IC50, comparing both clones to clones A-D may not have achieved this significance. It would also be useful to know how abundant the resistant clones were in the original barcode experiments.

(4) The logic used in the final section requires further explanation

In the final section, the authors suggest that a pre-ARSIR clone is able to cooperate with a pre-Intact clone to aid adaptive ARSI resistance. If this is true, then could it not be that rare, pre-resistant clones support adaptive resistance in established tumours? And, therefore, the mechanism underlying resistance could be through pre-existing resistant clones in both settings. The work would benefit from a discussion to clarify this discrepancy in the interpretation of the findings. This is particularly necessary given the strong wording the authors use regarding their findings, such as that they have provided 'conclusive evidence' for acquired resistance.

Chapter 1

The development of biomimetic (synthetic methods which mimic biochemical processes) technology which interact with the wound site during early stages of key tissue repair can create a controlled microenvironment that would provide instruction and guidance to reestablish structural normalcy - or regeneration.

Scientists demonstrated limb regrowth and functional restoration of an American clawed frog's amputated hindlimb. The scientists administered a multi-drug, pro-regerative treatment delivered by a wearable bioreactor. The scientists were able to regenerate tissues of skin, bone, vasculature, and nerves.

American Clawed Frog - has the potential to regenerate limbs at a young age, but later in life does not/has the same capabilities as humans

Human application once science is refined

MUSIC REF

Boa tarde, a tod@s!

Será mesmo assim? Ou será um mito?

Saudações académicas

Music Library

put another way—conviction becomes certainty over time (if u continue to pursue the thing ur doing and things are going well)

eLife assessment

This valuable study demonstrates that genomic insertion of a G4-containing sequence can be sufficient to induce chromosome loops and alter gene expression. The evidence supporting the conclusions is convincing. Effects were shown by Hi-C as well as qPCR for chromatin modifications and expression, and the specificity of the effects was controlled by mutating the G4-containing sequence or treating with LNA probes to abolish G4 structure formation. The work will be of interest to researchers working on chromatin organization and gene regulation.

Reviewer #1 (Public Review):

In this manuscript, Chowdhury and co-workers provide interesting data to support the role of G4-structures in promoting chromatin looping and long-range DNA interactions. The authors achieve this by artificially inserting a G4-containing sequence in an isolated region of the genome using CRISPR-Cas9 and comparing it to a control sequence that does not contain G4 structures. Based on the data provided, the authors can conclude that G4-insertion promotes long-range interactions (measured by Hi-C) and affects gene expression (measured by qPCR) as well as chromatin remodelling (measured by ChIP of specific histone markers).

In this revised version of the manuscript, G4 formation of the inserted sequence was validated by ChIP-qPCR, and the same G4-containing sequence was inserted at a second locus, and similar, though not identical, effects on chromatin and gene expression were observed.

Strengths:

This is the first attempt to connect genomics datasets of G4s and HiC with gene expression.<br /> The use of Cas9 to artificially insert a G4 is also very elegant.