"The line means that the southern wind is acting like a trumpet, announcing its own purpose, and its whistling through the leaves is a sign of an incoming storm — a metaphor for the foreshadowing of political and military trouble in the play." I love this analogy. The word choice is perfect here.

BCKG Info

BCKG Info

BCKG

BCKG Info

BCKG Info

BCKG Info

PRO

enlace roto y no hay figura

Analyze or determine.

Given

Given

determine

determine

determine

determine

analyze

analyze

extraneous \)Thedomainis\(

Equations given are:

You should have chosen which notation to use for the domain. Based on how the others were done, even though this is simpler to look at, I would have expected (-inf,-1)U(-1,1)U(1, inf)

if, when?

not sure why this is set off with commas.

A function can have one y-intercept at most.

equation

an equation

equation

equation

equation

equations

Equations

equation

Why are we calling equations formulas?

of. It belongs to it, it's not for it.

I never remember learning set-builder notation other than recently.

I remember finding domain and range, but defining that by solving for x is new to me.

Determine the i. Doman and ii. Range of each of the following functions.

Evaluate f(1) and f(a+h) for f(x)=x^2-3x+5. Why use unnecessary commas.

Weird extra bracket stuff in answer.

Evaluate the function at

equation

identify

identify

set A in domain D.

So regarding the picture above, if x in domain {1, 2, 3, 4} equals 2, then f(2)=4 in range {2, 4, 6}.

a function of

I don't remember a lot of math about sets and elements.

called points

by formulas, are you saying that you will derive them and show how they are built so that we can learn modeling for analysis?

no guiones

no guiones

no guiones

??

y el árbol?

numerar las tablas

malla

no guiones

no guiones

malla

no guiones

no guiones

no guiones

no guiones

explicr porqué se alinea con el artículo

cambiar por coma

??

règle le vocabulaire

point lien pas de cote

C'est important exam

??

y la imagen??

cambia a un párrafo de enlace con el capítulo anterior porque está desconectado

quitar numeración

ajustar

ajustar

ajustar

espacio

Supporting Evidence for first reason. Rapid deforestation and Climate Change

The untold consequences for tampering with nature will come back to bite us if we aren't considerate and meticulous.

Suggestion: What about the Dodo?

If we have the power to resurrect a species, does the practice of conservation become irrelavent?

My Opinion- Does humanity not owe these creatures to continue existing after the careless destruction of its population due to our intervention.

Biased Source

"-world, one that could have unforeseen, long-lasting effects." - Oppossitions reason

Supporters reason

Emotionally driven Quote/evidence meant to spur upset the audience.

Apart from having to deal with language barriers, another issue immigrants have to face is being aware of scams or scammers.

Coming into a new country, millions struggle or avoid going for financial advice due to lack of being proficient in english.

• Personal Google AI subscriptions use saved Gemini chat history by default to train generative AI models and improve Google services.
• Having a paid subscription does not exempt users from having their conversation history utilized for model training.
• Transcripts may be selected for review by human contractors, with Google explicitly advising users not to submit confidential or sensitive information.
• Chats evaluated by human reviewers are retained for up to three years and are not removed even if a user deletes their Gemini activity.
• The only method to prevent training is disabling "Keep Activity" in settings, which forces Gemini to delete chat sessions after 72 hours.

Hacker News Discussion

• Industry-wide practice: Commenters emphasize that frontier AI labs routinely log interactions, retain flagged sessions for safety moderation, and utilize human reviewers.
• Data flywheel economics: Several participants argue that consumer AI subscription fees are subsidized primarily because user interactions provide high-value training data and real-world workflows.
• Ecosystem data exposure: Users express concern over Gemini's workspace integrations (Gmail, Google Docs, Photos), questioning whether context pulled from personal accounts becomes vulnerable to human review.
• Legal and compliance skepticism: The community debates how "safety" clauses in Terms of Service and ambiguous GDPR interpretations allow providers to retain and analyze interaction data with minimal consumer recourse.

I think the authors made the research easy to understand by using examples like late-night emails and requests for leave. Their main point seems to be that employees cannot fix this culture just by managing their time better. Managers also have to change what gets rewarded. I’m still wondering how employees could push for those changes when their manager believes constant availability is necessary.

This made me think differently about Netflix’s unlimited vacation policy. It sounds supportive, but employees might hesitate to use it if everyone around them keeps working. It connects to concertive control when coworkers develop and enforce expectations about what counts as dedication. A written policy allowing time off may not mean much if the team makes taking it feel unacceptable.

I like this suggestion because being present for more hours does not necessarily mean someone is contributing more. As a student balancing work and classes, I can see why having clear goals would be more helpful than feeling like I always need to be available. Still, managers would need to make sure those goals are realistic, or the pressure could stay the same.

Calling someone less professional for requesting paternity leave shows how language can pressure employees to put work first. This connects to feminist approaches because it raises questions about whose life fits the organization’s expectations. The example also shows that expectations about caregiving affect men too. Being a committed parent should not automatically make someone seem less committed to their job.

This challenged the idea that working longer automatically means doing better work. These employees were getting strong reviews while making time for other things, but they felt they had to hide that. It seems like the organization was rewarding the appearance of constant dedication instead of learning from how these employees actually got their work done.

This connects to hegemony in Chapter 6. Employees can start accepting constant availability as normal, even when it hurts them. What stood out to me is how people might judge coworkers for setting boundaries because they have already sacrificed their own time. That helps keep the expectation going without managers having to enforce it directly.

Gringasho: conocido como el sicario más joven

Within one of her first appearances, this line already sets up Olivia as a more peculiar character than the others in the cast. Even if it is Malvolio that is marveling here, the fact that she enjoys the company of someone that others find so annoying is already a point of interest.

This line represents the play as a whole quite fantastically. Every likable character uses their wits in some way or another, while Malvolio ends up being the foolish wit that falsely thinks he is far beyond the knowledge of the others in the cast. The likable character all end up in quite comedic scenarios, though, so they could be likened to fools.

This line can be seen as a direct juxtaposition between Maria and Feste. The amusing Feste believes an appreciation for color is healthy, while Maria believes they stem from negativity. While it may not necessarily contribute towards Maria’s outward characterization, it may be a part of her smaller nuances as a character.

This is so dehumanizing

This is just so annoying to see.

Here Hughes uses a metaphor to compare greedy people to parasites or "leeches". By using this metaphor there is an understanding that the avarice of the higher class is exploitative and unwanted. As the lines go on it is clear that Hughes full message is about how the exploited must take back the America which they have worked so hard for. Hughes is promoting the idea of this lower, working class ridding themselves of the parasites, reclaiming the land, and rejoicing in their declaration over America.

In this moment Hughes makes a claim that America belongs to those "who made America". Not the white men who sat on porches or those who stood and watched the auctions. Not the white settlers who murdered Native Americans in mass or the people who refuse to support the poor and less fortunate. But rather Hughes is arguing that America was made by those who gave blood, sweat, and tears for the very foundations and growth of the nation. Hence why he says, "The land thats mine-the poor man's, Indians, Negro's, ME-Who made America."

Here, Hughes promotes a message of hope for change in America. When Hughes writes "America was never America to me", he is discussing how marginalized groups have never felt the promise of America. However, in his following lines he shows promise in achieving those feelings and how those feelings are simply something that hasn't happened yet. Rather than spewing hate and rejecting the nation, he chooses to make an oath to believe and work for Americas change for the better.

Hughes uses situational irony to portray the hypocrisy in the "homeland of the free" being built on the backs of people who were denied freedom and held in slavery and captivity. By doing so he places emphasis on how while others profited on the benefits of freedom in America, many lower class people and minorities, especially enslaved people, were subject to the opposite of what was to be expected in a nation dubbed "land of the free".

Estas dibujando en la pared????

That's fucking awesome.

The teachers were already bilingual. And, there was no bilingual program in the school yet.

Two solitudes premise: The idea that bilinguals store their languages separately in mind. (False)

https://www.etsy.com/listing/4592924862/large-index-card-planner-organizer

Calling an algorithm a "series of rules" makes it sound completely neutral. But in reality, those specific rules control what information millions of people see every single day. It makes me wonder if social media companies should be forced to tell us exactly how their systems decide what to show us.

I've had experiences with instagram and youtube recommending content that matched my interests, even when i hadn't searched for them. A good example of this is about AI and other technologies. After watching a few videos about AI and technology, my fyp started giving me new research, tech products, and related content that I found interesting. One thing that was also interesting was that I noticed recommendations improved over time as I interacted with more posts.

There have been some instances while scrolling on social media websites like tiktok or instagram have yielded exactly the kind of content in chunks that I was looking for. I could engage with a single video I quite enjoyed for just enough time that afterwards, whether in their respective search engines or just a feed, The content mirrored it quite well. I would stay on things for just long enough that it would curate exactly the tangential media influences as well.

I’m not sure this would technically be considered social media since it’s a app for shopping, but I’ve noticed how Amazon is always recommending stuff to me based off my previous purchases or things I’ve looked up. The starting page will either have more variants of things I have searched for or it have stuff that would pair well with my previous purchases. Amazon also automatically bundled item with something I am looking to purchase. I would say this is more of a good experience because it doesn’t feel like stalking of any sort it’s simply using my purchase and search history, and a lot of the time the same information from other users buying the same things, to find more items related to my interests.

This topic really got my attention because I easily find myself in YouTube or instagram that I thought I would spend only 5 minutes but after a while, an hour has passed. I think these recommendation algorithms makes those social media platforms really addictive and make us to spend more time on it. It recommends the stuffs that I am interested so in result, I spend more time on it.

An interesting source I found from Google talking about how YouTube's recommendations work: https://support.google.com/youtube/answer/16089387

Youtube apparently recommends videos based on a combination of their interests and behavior. The algorithm considers factors like watch history, search history, likes, dislikes, and watch time. All of these work together to predict which videos someone might enjoy

One interesting detail I learned was that YouTube doesn't just measure how long someone watches a video, but also uses satisfaction surveys to determine whether users enjoyed the content too, which helps with distinguishing high performing videos from low performing videos. I previously thought that YouTube gathers all their recommendation info from how people watch the videos themselves, opposed to manual surveys.

What stands out about this source is that it is the owner of Twitter openly admitting the algorithm rewards hate engagement, and treating it like a funny quirk, instead of a problem. It backs up the chapter's point about pile-ons: the system doesn't separate liking something from being angry at it, it just sees interaction.

I think it's crazy being able to search people up with their phone numbers and that it's required to make an account with a phone number. This reminds me of the school directory and how a couple people have found my number through there, texting me, even though I have never given them my phone number myself. People can have malicious intent, so knowing this, it makes me wary.

This source is about how facebook users will have to use their phone number to sign up for the account, and then facebook will publicly post the users phone number for everyone to see. This is something we have seen a lot in this class where users private information will be posted publicly without them knowing.

I found it interesting that Twitter wanted to make conversations healthier, but the article shows that it is much harder than simply identifying dirt words. The article also explains that sarcasm and context can make it difficult for an algorithm to decide whether a conversation is actually unhealthy.It makes me wonder whether social-media companies can create moderation tools without misunderstanding users’ real intentions.

The pervasiveness of personal data extraction from private entities is really astounding. To create a profile of an individual that matches certain archetypes of folks so efficiently scares me. To that point, marketing organizes people like that in the first place purely for the sake of keeping you around and buying their product faster. Regardless of the intent of the individual, information can be still be gleamed. It's weird that there's so much crossover between target and criminal profiling.

According to the article, cable news is more likely to divide people on political issues than social media or online news. An interesting thing I learned was that about 17% of Americans were politically polarized because they watched TV news, which was three to four times as many as those who read news online. The researchers also found that cable news viewers tend to stick with sources that match their political opinions. This implies that social media is not always the primary source of political echo chambers, and that traditional media may have an even greater influence.

I was surprised that TikTok's success might have more to do with its design than its algorithm. Being able to swipe past videos so easily makes the app feel like it knows what we like. It makes me wonder how much time we spend scrolling without realizing it.

Systemic bias refers to a system that, due to its structural design, has evolved over time to produce biased recommendations. It is not a matter of individual bias, but rather results from algorithms that intentionally lower the visibility of certain communities or content types. When I thought about this, it reminded me of the “information bubble” on TikTok, where the algorithm only recommends news, politics, military topics, and philosophy that you like—it will never recommend anything that falls outside your preferences.

Translated with DeepL.com (free version)

I chose this source from its title because I was interested in what ways people defend that the earth is flat, other than what I’ve already heard. It’s expands more on people beliefs and the information that they choose to trust and how the influences their understanding of the world. I thought it was a good example of why it’s so important to find reliable sources and evidence before believing something, and this has only become more the in modern time with the use of ai.

This article speaks about how Facebook made it necessary for account users to enter their phone number when setting up an account. That's normal when you think about verification. However, the platform misuses this information by continuing its privacy breaches. Furthermore, other people who have access to your phone number can look at your account. It can show targeted ads. There is a whole list of privacy breaches this platform has been accused of.

This article shows that Facebook does not let user to opt-out their phone number with Facebook. I think this can bring a privacy concerns, especially when server gets hacked, leaking people's private information. I think it will be better if Facebook let people to opt-out their phone number, even if the original purpose was for two factor authentication.

This source is a sarcastic posted on X in 2019 that got nine thousand reposts, 29 thousand likes and 48 comments. The post and comments are about how when there are structural issues people are told to work on themselves and instead of fixing this underlying issue.

This source tells a story of how targets algorithm was able to see a pattern in shoppers purcahse history. They noticed a trend with a customers shopping that was connected to alot of pregnant women. So they decided to mail coupons adressed to the girl because they believed she was pregant due to the purchase history. The father didnt appreciate them mailing that because she was a high school student only to find out afterwards she was pregnant.

This article talks about an algorithm that Target has. Based on what someone is buying and how much and how far apart the transactions are, basically target can tell if you’re pregnant. They assign a pregnancy score to you and will send you baby pamphlets in the mail. The article talks about a father who received his daughter’s target mail suggesting various baby things. He went target and complained. It seemed that target was able to find his daughter was pregnant well before him. He later visited and issued an apology.

This part surprised me the most. Usually, when someone posts a bad take, the instinct is to call it out, and it feels like the responsible thing to do. But if the algorithm reads every angry quote tweet as engagement, then calling it out just helps it reach more people. You see the same thing with ragebait on TikTok and instagram, where some creators post intentionally dumb stuff because they know the hate comments will push it.

It is kind of like how I always see the same idea on TikTok of how bad the car made by Huawei is. Never see the video say the good side about it. When the test of it’s breaking of break paddle, I saw the video when it did not went viral.

Personally, I feel like I get people who no longer are a part of my life recommended to me the most. I think it's funny, but also sometimes a reminder that I don't want to have. I think that while it can do good in the sense it connects you with people who share many mutuals as you, it can also bring harm depending on who is recommended. It's interesting how those recommendations are made.

Most importantly is to evaluate their ads, like if sent a drug to fat loss to people think they are fat. They might under think the issue for their body and take the drug without consideration.

I think it's interesting how clickbait and trolling can get more attention because people react to them. Just because a post gets a lot of engagement doesn't mean it's good information. I think social media platforms should be more careful about what they recommend.

I have concerns about this algorithm because, as algorithms become more advanced and more opaque, users will only be shown recommendations for things they already like, and they won’t see anything at all about things they don’t know or don’t want to see at all.

I think that the platform has to balance user safety and freedom of expression when their algorithms are recommending things. There should be ways for people to opt out of algorithms and also more protection for children who are on the platforms.

I think it's interesting how much of TikTok's success might be down to it's design and not so much it's actual recommendation algorithm. The ability to fast-forward through videos makes it easier for users to find content they enjoy with little effort. Which leads me to wonder if other platforms could improve their recommendations by simplifying the process for users to dismiss content they're not interested in.

https://www.omegasonics.com/<br /> Omegasonics

Recommended by Lucas Dul as something he uses in his shop.

Individual analysis is something I didn't consider before and is super surprising to me. I did not know how tech companies will focus on the behavior of users and use it to keep people hooked on the app.

This section made me realize that recommendation algorithms can shape people’s experiences online, even when users think they are making completely independent choices. I agree that it is unfair to blame individual users for problems that may come from the system. This really makes me wonder how social-media companies can be held responsible when their algorithms create biased results.

This makes me think about today's social media, which is full of bots, hateful speech, and overall negativity. I believe it's very easy for those running these platforms to avoid this, but they choose not to, as it results in more user engagement.

Sometimes spending time with loved ones makes time nonexistant.

I like this because it shows that even the small things matter.

The opening line here really paints a picture and I can imagine how the beetles sound.

For example, diamond is extremely hard, while graphite is very soft, even though both are made of carbon.

Molecular compounds generally have lower melting and boiling points than ionic compounds.

I thought this line was interesting because I interpreted it as that like men, women, and animals are all equals.

I liked this cause it shows how time and weather can make time feel different. The line "It was evening all afternoon" particularly stood out because it paints a picture of how time is standing still.

BODY organized a lot like this:

I. The introduction (usually one paragraph) 1. Introduces the texts to be synthesized: (i) Gives the title of each source (following the citation guidelines of whatever style<br /> sheet you are using);<br /> (ii) Provides the name of each author; (ii) Sometimes also provides pertinent background information about the authors, about the texts to be summarized or about the general topic from which the texts are drawn.

INTRODUCTION organized a lot like this:

I. The introduction (usually one paragraph) 1. Contains a one-sentence statement that sums up the focus of your synthesis. 2. Also introduces the texts to be synthesized: (i) Gives the title of each source (following the citation guidelines of whatever style<br /> sheet you are using);<br /> (ii) Provides the name of each author; (ii) Sometimes also provides pertinent background information about the authors, about the texts to be summarized, or about the general topic from which the texts are drawn.

Background synthesis: Doesn't need an argument, simply an overview of the current state of information and discourse.

Thesis-Driven Synthesis: Combines info to support a larger point.

Synthesis searches for links between materials for the purpose of constructing a thesis or theory.

CHAPTER 33: SYNTHESIS FOR RESEARCH WRITING

via Lucas Dul (aka u/Lucasdul2) at https://www.reddit.com/r/typewriters/comments/1xairdl/comment/19rpjcvqm7v9e/?context=1&screen_view_count=2

Puts about a dozen machines a month through their ultrasonic cleaner. Doesn't rely on it solely due to need for quality control.

https://www.crystal-clean.com/service/parts-cleaners/<br /> Crystal Clean

via https://www.reddit.com/r/typewriters/comments/1xnv3xz/comment/19s5otatkk481/

What do you mean "for every group"? Are you saying for each of the five groups above the median, it is exactly this, including 2.5% yearly GDP growth?

Labor force participation?

What does "here" mean? This language is too dense to follow. What is meant by "access limited support conclusions"? What are "individual majority assertions"?

"survey" is vague not quite correct here - I don't think it's really a survey in the conventional sense is it?

Is the reading for pleasure considered highly effective for improving standardized test scores, performance in class, or something else entirely?

Is this statement implying that the coursework students perform in schools has little impact on performance in standardized tests? Or is this statement implying that this is just for homework?

I find this to be very interesting. From what I have seen, teachers often consider quiet and polite students who do not participate in learning to be good students. When understanding the concept of a good student, should we consider the students good learners if they do not learn at all? Is it enough to just respect the rules but not participate?

fairness

dependent on cultural factors, humans arent necessarily terrible at these calculations, they have been trained to do the very opposite, avoiding the possibility to improve upon nonverbal skillsets -either due to religious practice, social acceptance, or moral compass

It is very interesting to see how different cultures interpret interaction. One culture may be based entirely around social, economic, or educational hierarchy while other cultures do the opposite.

time as a hidden length.

It has an acceleration, at the least, and in the circle it has a second-degree acceleration (three t's in the denominator). So Newton's algorithm must fail. If it works at all, it can only work on a geometric circle, where time is not passing. But a kinematic circle is not equivalent physically or mathematically to a geometric circle, so the calculus must be reworked. AD and DC are perpendicular. BC is the gravity vector, which is "inward."

The length of the line BC then represents the centripetal acceleration of our pencil over the interval AC. Those two motions or velocities happen over the same interval, so the two times superimpose. Whatever time it took us to draw the radius initially, is the same time it now took us to go from A to C. Therefore we have measured the circumference using the radius.

we may assign the acceleration a to the line segment BC. Furthermore, if a = v2/2r, and AB = r, then r = v, and a = r/2. Consulting the diagram above again, that is also 2BC = CO. What this means is that we have a new way to find a centripetal acceleration, currently called gravity. The equation a = r/2 gives us a total acceleration over 1/8th of the orbit, so the total acceleration over the entire orbit is 4r.

https://milesmathis.com/vel5.jpg

We defined local extrema occurring on an open interval, and theorem 3.1.1 does not state this explicitly, which does not infer an absolute maximum and absolute minimum on a closed interval...???

That's enough of this book for me.

local

This point is moot.

are defined as

local

local

local

local

local

local

local

local

local

what about the local extrema at x=0?

local

local

local

these are not synonyms: you want local

local

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

Learn more at Review Commons

Reply to the reviewers

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

Summary: The manuscript by Obaji et al investigates the role of TFIIIC and RNA Pol III in the recruitment of SMC protein containing complexes (condensin IDC, condensin I, and cohesion), as well as their role in remodeling the 3D genome in C. elegans. Connection between TFIIIC, condensin, and 3D genome organization have been seen in other organisms, several cited by the authors. This works extends the idea to C. elegans. The authors perform a series of ChIP-seq and Hi-C experiments after depletion of TFIIIC or a subunit of RNA Pol III. The observed impacts are relatively minor, but TFIIIC does appear to play a role in recruiting condensin and cohesin to a subset of their binding sites, and it also plays a role in shaping the 3D genome. However, there does not seem to be the same level cooperation between SMC complexes and TFIIIC at tRNA genes as was previously described in yeast (Hausler et al 2006, Genes and Development). The paper is written in a very short format, and as a result some details, at times essential information (such as the antibodies used in the experiment) are missing. So some aspects of the work were hard to evaluate. Overall, the study describes some interesting findings, but the significance of the work is limited by the relatively minor effects observed.

The experiments were well done. My expertise is not in Hi-C data analysis, so I cannot comment on the quality of those aspects of the paper. Most of my concerns relate the how the experiments were described and how conclusions were drawn. Some of the conclusions should be qualified, but I am not sure if additional experiments would make the conclusions any stronger.*

* Major comments: 1. The information on what antibodies were used in the ChIP-seq experiments are missing. tftc-3 and rpc-1 were tagged with degron::GFP. Did the authors use anti-GFP antibodies? For other proteins analyzed, such as TFTC-5 and TBP-1, what antibodies were used? Are these antibodies validated/high quality?*

• *The antibodies used for each data set is provided in the column labeled “Antibody” in the Supplemental File 1, ChIP tab. The anti-GFP antibody is ab290 from Abcam. TFTC-5, TBP-1 antibodies are from modENCODE used in PMID: 31918747 and PMID: 24011592. We added this information in the Methods section.

* To show that the tag did not interfere with gene function, the authors assessed lethality in the tagged strains. The data shown on Supplemental Figure 2 shows unusually high level of lethality in the no tag control. Only 20-40 eggs were counted for each data point. I would recommend counting more, and if the lethality remains in the no tag control, I would worry about the starting wild type strain having some background mutations.*

• *We thank the reviewer for pointing this out. The lethality shown in the plot is likely due to technical variability, miscounting embryos and worms crawling to the edge of the plate. Students include the control strain every time, and perform the counting for at least three biological replicates to account for the technical variability within the experimenter. As we do not observe dead embryos on our maintenance plates of no tag control, we think the issue is likely technical, rather than background mutations.

* After validating the strains and determining depletion conditions, the authors first analyze 3D genome organization. Compartmentalization weakens a bit, more obvious after 22 hours of TFTC-3 depletion, at which point they see increased interactions between the arms and the center of chromosomes. Additionally, a small increase in insulation is observed at TAD boundaries on the X chromosome. These data are described on Fig 3, and in general support the conclusions. In the legend, the authors mention to B-B, A-A, A-B. It was unclear what they refer to.*

• The A and B refer to the two compartments that the principal component analysis assigns each genomic bin to. At the scale of chromosomal domains, A compartments are designated to be the chromosome centers, and B compartments correspond to the two arms. The C. elegans* genome also compartmentalizes at a smaller scale, which is apparent when the compartment analysis is performed using Hi-C data from the arms and centers separately. This smaller scale compartmentalization correlates with transcription and is antagonized by DNA loop extrusion (PMID: 36331876). At the smaller scale, A compartments correspond to active chromatin based on overlap with histone modifications that mark active chromatin. B compartments are inactive/repressed. For both large and small scale compartmentalization, A-A means the interactions between the genomic bins both assigned to the A compartment. A-B means interactions between a pair of genomic bins with one assigned to A and the other to B. We edited the Figure legend to read “PCA analysis segregates the genome into large-scale A and B compartments. A compartments are located mostly towards chromosome centers and B the arms.”

* Next, the authors investigate impacts on condensin IDC binding. After 1 hour of TFTC-3 depletion, condensin IDC binding is reduced at a subset of sites that overlap with TFTC-3 sites (Fig 4). The authors refer to these as tRNA genes. While many of them are likely tRNA genes, not necessarily all. Either check whether they are all tRNA genes, or refer to them as TFTC-3 binding sites.*

• *Thank you for pointing out the mismatch between the text and figure. We edited the subheading to read “TFIIIC regulates condensin I-DC binding to TFIIIC sites including tRNA genes”. We indeed analyzed all TFIIIC peaks. In the revised figure, we splitted the TFIIIC peaks that are tRNA genes, which confirms the effect on tRNA genes (Supplemental Figure 4).

* For one of these sites, the authors scramble the box A/B DNA sequence. Please explain how this experiment was done. Was it by replacing the endogenous sequence using crispr? In any case, in this mutant, TFTC-3, RPC-1 and DPY-27 binding all decreased. *

• *The sequence of the box A and B motifs were mutated using CRISPR/Cas9 and homologous recombination. The resulting mutant sequence with highlighted changes are provided in the Supplemental File 1 “strains” tab.

On Fig 4 B, binding seems to be decreased at many additional sites as well. Why is binding decreased at additional sites?

• *Thank you for pointing to these additional sites. We found out that while constructing the mutant genome, we mistakenly used a genome sequence with an unrelated insertion (400 bp at chromosome X 16215493) which is ~14.4 Mb downstream of the scramble mutation site. This shifted the coordinates starting from ~16.2 to the 17.7 Mb, which is the end of the chromosome, thus systematically reducing ChIP average signal for the TFIIIC sites in this region. The genome version is corrected and the data is re-plotted in Figure 4B.

* While not necessarily easy to answer, what I am most curious about is whether the change in condensin IDC binding at a subset of sites is actually biologically relevant. Is there an impact on dosage compensation?*

We have been curious about this question as well. We performed mRNA-seq upon TFTC-3 depletion. mRNA-seq detects upregulation of X chromosomes due to subtle defects in dosage compensation (example TOP-1 and TOP-1 depletion from our lab PMID: 36302374). We asked if TFTC-3 depletion causes derepression of the X chromosomes. To answer, we test if the change in X chromosomal gene expression is within or outside the variation observed for autosomes. The new Figure 3G and the associated supplemental panels show that while TFIIIC depletion does cause differential mRNA expression, the effect is not specific to the X chromosome, thus we cannot claim a role on dosage compensation. We think that TFIIIC binding at the rex sites is a consequence of condensin I-DC bridging tRNA genes to the rex sites as two anchors of a condensin I-DC mediated DNA loop (the cartoon in Figure 3D and the revised model figure in Figure 7C tries to make this point).

* While the experiments concerning condensin IDC binding were done after 1 hour of depletion (after 1 hr of TFTC-3 depletion RPC-1 is still bound), the experiments with cohesin were done after 22 hours of depletion (when RPC-1 binding is impacted). Therefore, the authors can conclude that condensin IDC binding is impacted by TFTC-3, but not RPC-1, but the same cannot be said for cohesin (Fig 5). The authors seem to imply that only TFCT-3 is involved and not RPC-1. In fact, data for 1 hr of TFTC-3 depletion is not shown. *

• *Our data show that TFIIIC regulates SMC-3 but does not definitely rule out RPC-1 role and we removed claims of such. However, at tRNA genes, where both RPC-1 and TFTC-3 bind highly, the effect size of RPC-1 depletion on SMC-3 binding is lower than that of TFTC-3 (Figure 5B), thus our original conclusion is likely to be correct. We have revised the text to read:

“We should note that 22 hr TFTC-3 depletion reduces RNA Pol III. However the magnitude of SMC reduction upon TFTC-3 depletion is greater than that of RPC-1 (Figure 5B), thus supporting a specific role for TFIIIC in cohesin regulation. In addition, SMC-3 binding at RNA Pol III only sites (without TFIIIC) is significantly lower compared to TFIIIC+, PolIII+ sites and ETCs (Figure 5F), supporting the interpretation that TFIIIC is required for cohesin localization.”

Addiotionally, the title of the section and the title of figure refer to these sites as tRNA genes, but tRNAs are not even mentioned in the text of this section. Are these in fact exclusively tRNA genes, or are they TFTC-3 binding sites.

• *Please see our response to comment 4 as we marked the tRNA genes in example regions and split the classes of TFIIIC binding sites as being tRNA genes and not.

* In a very brief section, the authors describe the final finding, clustering of tRNA genes, which seems to depend on TFTC-3, but not RPC-1 or condensin or cohesin. While the rest of the paper investigates how TFTC-3 impacts condensin and cohesin, this data shows that condensin and cohesin are not working with TFIIIC in this process. The data is data, and I am not arguing with it, but the story does seem more disconnected than the title implies.*

• *Yes, it is fair to point out the disconnect. We do not want to imply a connection in the title, but we want to state the two main functions of TFIIIC: regulating SMC binding and regulating 3D contacts of tRNA genes. I think we do a reasonable job explaining that these two observations may be connected but, we cannot find a direct support for or rule out SMCs mediating tRNA genes clustering. Redundancy may be the reason, because TFIIIC regulates multiple SMC complexes. To deplete all SMCs simultaneously is not trivial, because the efficiency of auxin mediated depletion differs between different targets due to rate of protein turnover, and the level of depletion need to be characterized individually and combinatorially.

*

Minor comments: 1. TFTC-3 depletion seems quite effective, RPC-1 depletion a little less so. It should be mentioned in the text. Also, on Fig 1C, please explain the signal that remains after depletion. (The explanation is in the legend for Figure 2-it should be explained earlier.)*

• Auxin inducible depletion was done in a tissue-specific manner, using eft-3* promoter to drive TIR1 expression in soma. In early-stage L3 worms, most cells are postmitotic and there are a handful of germ cells (~10-30) that start to proliferate and do not express TIR1. This corresponds to ~2-4% of total cells, and is observed as remaining GFP signal in the belly of the worm upon 1 hr auxin depletion. Even if 100% depletion is achieved in the soma, some protein is expected to remain in the western blot and ChIP-seq.

Thus, to compare TFTC-3 and RPC-1 depletion, we quantified the level of GFP signal reduction in the head of worms. The baseline GFP signal is lower for TFTC-3 compared to RPC-1, thus making comparison difficult, but normalizing the signals to the average of the initial condition (no auxin control) showed roughly similar level of depletion (Supplemental Figure 2B).

The following sentences describes the tissue specific depletion: “By choosing eft-3 driven TIR1 expression, depletion is limited to the somatic cells in L3 larvae while the few germ cells are not affected.”.... “Soma-specific depletion was validated by the reduction of the GFP signal across the body except the few mitotic germ cells in early L3 larvae (Figure 1C).”

* Supplemental Figure 3 legend refers to distribution of peak categories and cartoon interpretation that are not shown on the figure.*

• *Supplemental Figure 3 legend is corrected by removing text referring to a cartoon. The peak distributions are plotted on the lower left of each chromosome.

* Figure 7 shows some new analysis described in the discussion. It should be in the results section.

*

We added a final paragraph to the results section titled: TFIIIC promotes 3D DNA contacts between tRNA genes located on different chromosomes.

“Although subtle, we also detected an enrichment of Hi-C contacts between TFIIIC bound tRNA genes on different chromosomes, which did not reduce upon single depletion of cohesin or condensin (Figure 7A). We reasoned that the TFIIIC dependent trans contacts between tRNA genes reflect interphase organization of the genome in postmitotic somatic cells. Indeed, Hi-C data derived from mixed developmental stage C. elegans embryos lacked the enrichment of trans contacts between TFIIIC bound tRNA genes (Figure 7B).”

*

Reviewer #1 (Significance (Required)):

Significance: My enthusiasm is a bit limited due to the minor impacts observed. Condensin and cohesin binding is only impacted at a subset of binding sites and it is unclear whether the changes affects condensin and cohesin function. Similarly, 3D genome organization did not change that much, and condensin and cohesin are not even involved in tRNA clustering. As a result, the study does not advance the knowledge in the field by a lot.*

*

Audience: the study will be interesting to scientists in the 3D genome organization field, especially as a point of comparison to other systems.

My expertise is in chromosome structure and organization and condensin biology.*

• *

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

General Writing Comments • Given that the strongest and most novel finding is TFIIIC's role in condensin/cohesin recruitment and boundary formation, the Introduction and Abstract would benefit from earlier framing around why SMC complexes localize where they do (i.e., boundary-element biology), rather than leading primarily with the transcriptional background of TFIIIC.*

• Thank you for the suggestion and we agree that the introduction can be written by leading with SMCs. However, by explaining that TFIIIC is a recruiter of RNA Pol III first, we emphasize the need for a robust evaluation of TFIIIC function in SMC regulation in multicellular organisms. Much of the field has accepted correlations from genomic experiments in humans, drosophila and worm, as strong evidence for TFIIIC role in metazoan genome organization. However, a direct test to distinguish TFIIIC function from RNA Pol III had not been done. Our study used the auxin inducible system in C. elegans* to accomplish this.

• * In the Discussion, consider leading with the strongest, most novel results (SMC recruitment, tRNA gene 3D contacts) before moving to the more incremental arm/center compartmentalization findings.*

* Science Comments HOT-site behavior vs. direct regulatory function. Because TFIIIC/ETC sites can behave as generically hyper-occupied regions in other organisms, the authors should more explicitly distinguish the possibility that TFIIIC directly regulates SMC recruitment from the alternative that TFIIIC and SMC complexes are both independently drawn to a shared class of highly accessible chromatin. Or that TFIIIC is generating a HOT site. A discussion of HOT sites vs TFIIIC sites would be helpful.*

• Although TFIIIC/ETC sites are hyper occupied in yeast, a vast majority of TFIIIC/ETC sites are not hyper occupied in C. elegans; 71 out of 914 TFIIIC peaks overlap with a HOT site and 70/422 HOT sites overlap with a TFIIIC peak. The reviewer’s question is: whether TFIIIC and SMC complexes are independently drawn to a class of highly accessible chromatin. We answered this question by focusing on condensin I-DC, which is restricted to the X chromosome, thus autosomal HOT sites serve as a control. We show that TFTC-3 binding at the X chromosomal condensin I-DC binding sites, including the strong rex* sites, are greater than that of HOT sites across other chromosomes (Supplemental figure 3E). Thus, our data is consistent for a specific interaction of TFIIIC with condensin I-DC rather than each independently being attracted to HOT sites. We added the following text to the result section: “We reasoned that if TFIIIC binds to rex sites due their properties as a HOT site, the level of TFIIIC enrichment at the rex sites should be comparable to other HOT sites across the genome. This was not the case (Supplemental figure 3E), thus further supporting the specific interaction between TFIIIC binding and condensin I-DC.”

* Possible chromatin-opening role for TFIIIC at the 1 hr timepoint. Along the same lines. At 1 hr TFTC-3 depletion, condensin binding is lost specifically at sites where TFIIIC and DPY-27 binding overlap, while DPY-27 is retained at other, non-overlapping sites. This raises the possibility that TFIIIC helps maintain locally accessible chromatin that permits condensin binding, rather than (or in addition to) direct recruitment via protein-protein interaction. ATAC-seq under the same depletion conditions could help distinguish these possibilities.*

• It is possible that TFIIIC mediates nucleosome removal from tRNA genes and this helps with SMC complex binding. However, our data argues that accessibility is not sufficient*. While RNA Pol III+/TFIIIC- sites are more accessible than RNA Pol III -/TFIIIC+ sites, SMC binding is higher at RNA Pol III -/TFIIIC+ sites, as shown in new Figure 5F and added the text:

“Next, we considered the possibility that TFIIIC bound sites are more accessible thus show greater ChIP-seq signal. Instead, we found that ATAC-seq coverage was higher for RNA Pol III-only sites compared to TFIIIC-only sites, which is opposite of the pattern observed for SMC-3 (Figure 5F). Together, our results in Figure 4 and 5 are consistent with a specific function for TFIIIC in mediating SMC complex localization.”

* More minor comments: 1. Quantify depletion efficiency. The claim of GFP signal loss (Fig. 1C) is currently supported only by representative images - please quantify mean GFP intensity per worm (auxin vs. no-auxin) across a sample of animals. Similarly, please quantify Western blot band intensity (Fig. 1B, normalized to tubulin, across replicates) rather than relying on visual inspection of a single blot.*

Please note that the depletion is accomplished by driving the expression of TIR1 by using eft-3 promoter, thus expressing in somatic cells. Please see our response to the reviewer 1 minor comments #1. We quantified the level of GFP signal reduction in the head of worms. The baseline GFP signal is lower for TFTC-3 compared to RPC-1, thus making comparison difficult, but normalized signals with and without auxin showed roughly similar levels of depletion (Supplemental Figure 2B).

* Genome-wide confirmation that RPC-1 depletion leaves TFIIIC binding intact. Figure 4C shows TFTC-3 signal is retained at one representative locus after 1 hr RPC-1 depletion, but this is not shown genome-wide. Since the claim of an RNA Pol III-independent function for TFIIIC rests on RPC-1 depletion not affecting TFIIIC binding, please provide a genome-wide comparison (e.g., a heatmap or violin plot analogous to Fig. 1D) confirming this.*

The claim of an RNA Pol III independent role for TFIIIC is supported by 1) TFIIIC does not require RNA Pol III for binding in vitro (PMID: 23063749). In vivo, RNA Pol III and TFIIIC each have independent localization sites, thus their binding does not require each other (Supplemental Figure 1). 2) RNA Pol III only sites show lower SMC binding compared to sites where TFIIIC binds (Figure 5F), thus RNA Pol III cannot be the major regulator of SMCs. 3) 1 hr RPC-1 depletion substantially affecting TFIIIC is inconsistent with our results that TFTC-3 but not RPC-1 depletion regulates condensin I-DC binding and tRNA gene clustering.

* Reconcile genome-wide vs. locus-specific contact-frequency effects. The claim of "minimal impact on average contact frequency" (Supp. Fig. 3A) rests on a genome-wide P(s)/slope plot, which by construction averages over all loci and distances and could mask spatially localized effects. This appears to be in some tension with Fig. 3B/C and Supp. Fig. 3B, which show a visibly stronger arm-center mixing signal specifically at 22 hr TFTC-3 depletion. A quantitative summary distinguishing a modest genome-wide shift from a strong, spatially restricted effect diluted by averaging would strengthen this section.*

• *Hi-C measures relative contact frequencies, a quantitative summary must be accomplished by fitting a model to the data. While models are useful, they also make a lot of assumptions. As a result, the field has presented distinct models that fit HiC data (e.g. both loop extrusion and non loop extrusion models produce SMC mediated loops).

Our lab would like to take a genetic approach to address the mechanisms by which arm-center organization is changed upon RPC-1/TFTC-3 depletion. For example, do nuclear pores which interact with both TFIIIC/RNA Pol III explain their common role in chromosome arm/center organization?

* o Related: please provide statistics on the apparent partial rebound in B-compartment strength after 22 hr auxin treatment (Fig. 3B, bottom-right saddle values) even as A-compartment strength remains reduced.*

• *Thank you for bringing our attention to this partial rebound. This was a product of the first eigen vector not correctly assigning the arm/center compartments to A and B for in the TFTC-3 22 hour depletion data. In the revised version, we standardized the compartment designation, by using the no-tag control EV1 to define the A and B compartments in all samples. This showed that there is no partial rebound in B compartment strength. The Figure 3B is updated and data from replicates are provided in Supplemental Figure 3B. We also provide a statistical test of the compartmentalization in Supplemental Figure 3D.

* Direct comparison between the scramble and depletion experiments (Figure 4). The Box A/B scramble experiment (Fig. 4A-B) and the TFTC-3 auxin depletion experiment (Fig. 4C-D) are presented as parallel lines of evidence but are never directly compared quantitatively. Is the reduction in DPY-27 binding at the scrambled locus comparable in magnitude to the reduction at TFTC-3-depletion-affected peaks genome-wide? A side-by-side comparison, or at least a statement about this, would strengthen the claim that both perturbations report on the same mechanism.*

• *The ChIP-seq scores from depletion and the scramble mutation cannot be compared to each other directly, because ChIP-seq measures relative binding, rather than absolute (unless very careful spike-in corrections are done (PMID: 39271835). Immunoprecipitation enriches DNA, thus in knockdown conditions, it may inflate the level of binding. This is why we refrain from making statements about absolute changes, and instead limit our definitive conclusions based on relative comparisons.

For instance, we can definitively state that TFTC-3 depletion specifically reduces SMC binding at sites that overlap with TFIIIC relative to sites that do not overlap with TFIIIC (Figure 4D and 5B). Similarly, scramble mutation specifically reduces binding, relative to the other sites that are not mutated (Figure 4B).

* Single-locus scope of the scramble experiment. The Box A/B scramble was performed at only one tRNA gene (F56C3.t1). Please discuss whether this result is expected to generalize across tRNA genes broadly, or consider testing a second, independent locus to rule out gene-specific effects.*

• The scramble experiment at one locus supports the observation from tens of other loci in the wild type condition, where SMC complexes do not bind to unexpressed tRNA genes *(Supplemental Figure 4A). Thus, the conclusion that TFIIIC regulates SMC binding comes from 1) endogenous condition - where TFIIIC presence is a requirement for observing SMC localization, 2) scramble experiment - where boxA/B mutation eliminate binding of TFIIIC and SMC 3) depletion experiment - where TFTC-3 depletion reduce SMC specifically at TFIIIC binding sites compared to non-TFIIIC dependent SMC binding sites.

* Figure/text mismatch for autosomal DPY-26 binding (Figure 5). The statement "DPY-26 binding to autosomes was weak" (p. 11) is illustrated by Fig. 5C, which shows an X-chromosomal region, not an autosome (autosomal DPY-26/SMC-3 tracks appear only in Fig. 5A). Please point the reader to the correct panel, or add a clearly labeled autosomal example alongside the X-chromosomal one so both are shown side by side.*

• *Thank you for pointing out the disconnect. The quantification for the autosomal data is shown in Figure 5D. We edited the sentence as follows: “As expected, DPY-26 binding to autosomes was weak in largely interphase cells of L3 larvae, thus resulting in fewer peaks compared to the X chromosome (Figure 5D).“

* Replicate-level concordance for Hi-C. All Hi-C data are presented as merged replicates; replicate-level correlation is not shown. A supplementary figure comparing the two biological replicates (e.g., stratum-adjusted correlation or scatterplots of key metrics such as insulation score or compartment strength) would let readers judge how robust the reported effects are across replicates.*

• *We provide the key figures in the paper plotted separately for the biological replicates in Supplemental Figure 3B and Supplemental Figure 5, and present statistical tests with the replicates included in Supplemental Figure 3D, Figure 3E, Figure 7A.

* Derepression as a potential mechanism. Neither the tRNA promoter scramble nor TFTC-3 depletion is accompanied by expression data. Since transcriptional derepression of genes near the scrambled motif or near TFIIIC-bound sites could confound the interpretation of reduced condensin/cohesin binding, even limited expression data (qRT-PCR at a few loci, or RNA-seq) would help rule this out. Otherwise, please discuss this caveat in the results.*

We performed mRNA-seq analysis upon 1 hr and 22 hr TFTC-3 depletion. If TFIIIC locally regulated RNA Pol II transcription, there should be a correlation between differential gene expression and distance to a TFIIIC binding site. Although there were some differentially expressed genes close to TFIIIC peaks, the correlation was not statistically significant (Figure 5E). Indeed, acute depletion of TFTC-3 had limited effect on mRNA expression (Supplemental Figure 3F). Thus, the TFTC-3 effect on SMC binding and tRNA gene clustering is unlikely to be through RNA Pol II transcription. We added the following text:

“To rule out the local control of SMC binding by TFIIIC across the genome is indirect through regulation of the neighboring genes, we analyzed mRNA-seq changes upon 1 hr and 22 hr depletion of TFTC-3 as a function of distance to the nearest TFIIIC binding sites. We did not observe a statistically significant correlation (Figure 5E). There were genes close to TFIIIC binding sites that were affected by TFTC-3 or RPC-1 depletion (Supplemental Figure 4C, Supplemental File 1 DESeq tab). However, this effect was not widespread and thus the local effect of TFIIIC and RNA Pol III on RNA Pol II transcription is limited and does not explain the observations presented in Figure 4D and Figure 5B.”

* Quantification of interchromosomal tRNA gene contacts. The intrachromosomal tRNA-tRNA contact enrichment is quantified with paired statistics (Fig. 6B), but the interchromosomal (trans) pileups shown in Fig. 7A are presented only qualitatively. Since the trans-chromosomal contacts are central to the argument against a pure loop-extrusion model, a matching quantification and statistical test would substantially strengthen this claim.*

• For trans contacts, we merged TFIIIC-bound tRNA genes into 1kb clusters to match the HiC resolution. We then extracted Hi-C contacts for every inter-chromosomal pair of clusters. A majority of these pairwise contacts were 0, thus for each tRNA gene/cluster containing bin, we calculated the average of all its trans *pairwise contacts with other tRNA gene/cluster containing bins. Differences between conditions were assessed with a nested ANOVA testing whether the variation between conditions exceeded variation between replicates followed by a post hoc t-test against the control using the ANOVA error term with Bonferroni correction. The effect of TFIIIC depletion was statistically significant, while SMC depletions were not. The results are presented in revised Figure 7A with replicates separated.

* Basis for attributing reduced Hi-C fountains to "indirect effects." Supp. Fig. 4B attributes the loss of cohesin-mediated fountain signal after 22 hr TFTC-3 depletion to indirect effects of prolonged depletion, but no data directly distinguishes direct from indirect causes. Please either soften this interpretive claim or provide supporting evidence (e.g., a comparison against the 1 hr TFTC-3 depletion timepoint, which does not show this loss).*

• *The data for the 1hr depletion of TFTC-3 is shown in the same Figure panel (Supplemental Figure 4B). To help readers assess the intensity of the Hi-C contact frequency, we averaged contact frequency within a 60 degree cone centered at the fountain origins, and plotted as a line for individual replicates of relevant data sets in Supplemental Figure 4B.

* Reliance on an external dataset for condensin I/II clustering. The individual contributions of condensin I (DPY-26 TEV) and condensin II (KLE-2 TEV) to tRNA gene clustering (Fig. 6D-E) are assessed using a previously published dataset (Das et al., 2024) rather than the authors' own degron system. Please confirm that depletion timing/developmental stage are comparable enough to the rest of the study for direct comparison.*

• The time at which the Hi-C are performed is L3 larvae for both studies. Das et al 2024 depletes target protein by heat shock activation of TEV mediated cleavage, which starts at the L1 stage, ~24 hours before the Hi-C is performed. In Das et al. 2024, COH-1 degradation showed reduced Hi-C contacts forming fountains, which was also the case for our 1 hr auxin induced SMC-3 degradation (Kim et al 2025). Similarly, DPY-26 degradation produced the expected X chromosomal 3D organization changes in Das et al. 2024, which is the case using an sdc-2* mutant+RNAi in Crane et al. 2015. Thus we believe the data are comparable to a reasonable extent.

In addition, the higher than average contact frequencies between TFIIIC bound tRNA genes in our Hi-C experiments and the Meister lab data, confirm the replicability of the observation that TFIIIC bound tRNA genes have higher than expected contacts in 3D.

* Additional Points Requiring Clarification • Viability and developmental timing of depletion. The strains remain viable without auxin, with depletion restricted to the soma; some GFP signal and germ cell division are still observed at the 2-cell stage under auxin. Please clarify what this residual signal implies for interpreting phenotypes at the earliest depletion timepoints - could early developmental effects be underestimated if depletion is incomplete at this stage?*

• *The lack of depletion in the germline and early embryogenesis is by design, because we did not want to potentially affect the germline and early development, as TFIIIC and RNA Pol III are needed for tRNA expression. Rather, we aimed to directly test the interphase functions in 3D genome regulation. Thus we expressed TIR1 under the control of the eft3 promoter, which is a strong somatic promoter that is not expressed in the germline. We clarified the text as follows: “We endogenously tagged tftc-3 and rpc-1 at the C terminus with degron-GFP and crossed to obtain homozygous strains expressing the auxin receptor TIR1 under the control of a soma-specific promoter peft-3 (Zhang et al., 2015) (Figure 1A). By choosing eft-3 driven TIR1 expression, depletion is limited to the somatic cells in L3 larvae while the few germ cells that are present are not affected.”

• * Nature of the rex site overlapping a tRNA gene cluster (Figure 3D). One strong rex site sits near a cluster of four tRNA genes with higher TFTC-3 than RPC-1 binding. Since tRNA genes and rex sites are both classic examples of highly-occupied regions, the authors should discuss whether this overlap reflects a specific functional relationship between condensin I-DC recruitment and TFIIIC, or a more generic co-occurrence at hyper-bound loci - and whether this particular locus is representative of the other 16 strong rex sites analyzed in Fig. 3E.*

• We show TFIIIC ChIP-seq signal at 17 strong rex and non-rex HOT sites in Supplemental Figure 3E, which supports the specific enrichment of TFIIIC at the rex sites above other HOT sites, including those on the X chromosomes. This supports the conclusion that the binding reflects a specific binding relationship *between condensin I-DC and TFIIIC and is unlikely to be a generic co-occurance at hyper-bound loci. The following text is added:

“In previous work, we noted that rex elements coincide with high occupancy target (HOT) sites defined by a high number of transcription factors localizing to a subset of genomic sites by ChIP (Albritton et al., 2017). We reasoned that if TFIIIC binds to rex sites due their properties as a HOT site, the level of TFIIIC enrichment at the rex sites should be comparable to other HOT sites across the genome. This was not the case (Supplemental figure 3E), thus further supporting the specific interaction between TFIIIC binding and condensin I-DC.”

• * Strength of the DPY-27/TFTC-3 correlation. The correlation between DPY-27 and TFTC-3 ChIP enrichment across the X chromosome (r = 0.82, Fig. 3F) is notably strong relative to the RPC-1 correlation (r = 0.45) and, in our view, deserves more prominent discussion in the main text as one of the more compelling quantitative links supporting the model.*

We thank the reviewer for their suggestion and added the following text to highlight the finding. “In S. pombe, the TFIIIB subunit TBP physically interacts with condensin and localizes it to tRNA genes (Iwasaki et al., 2015). In human cell extracts, condensin II physically interacts with the TFIIIC subunits (Yuen et al., 2017). Our analysis of the X-specific condensin I-DC provides strong evidence for a specific interaction between an SMC complex with TFIIIC and not RNA Pol III. First, the correlation between DPY-27 and TFTC-3 ChIP enrichment across the X chromosome (r = 0.82) is stronger than that of the RPC-1 (r = 0.45), thus quantitatively linking TFIIIC binding to that of condensin I-DC. Second, unlike TFIIIC, RNA Pol III depletion does not reduce condensin I-DC localization at the X chromosomal tRNA genes (Figure 4D). Together, these observations support an evolutionary conserved function for the general initiation factors, rather than RNA Polymerase III, in regulating SMC complex binding to tRNA genes.”

• * Normalization for insulation score comparisons. Figure 3E reports Wilcoxon rank-sum p-values comparing insulation scores across conditions, but it is not stated whether a spike-in or other cross-condition normalization was used for the underlying Hi-C/ChIP data, and no per-site error bars or replicate spread are shown. Please clarify the normalization approach and, if possible, show replicate-level variability underlying these scores.*

Hi-C data was normalized using the ICE (iterative correction and eigenvector decomposition) method from Imakaev et al. Nature Methods from Mirny lab. Raw observed counts are generated in Juicer and Cooltools add ICE balancing. Broadly, the contacts of each genomic bin are balanced, meaning Hi-C matrices reflect the “relative” contact frequency of each genomic region with the rest of the genome, and is not a measure of “absolute” contact frequency. Spike in approaches are not a validated normalization technique in Hi-C. There is indeed variability in the insulation scores between replicates. A linear mixed-effects model with strain/depletion conditions as a fixed effect and rex sites as shared conditions, did not show statistical significance for an effect of TFTC-3 depletion on average insulation score across all rex sites (figure 3E). We edited the text accordingly and edited the section subheading to read “TFIIIC binding positively correlates with condensin I-DC on the X chromosomes”

• * Reference line for Figure 4B. A diagonal line with slope 1 would help readers visually assess the degree of deviation from a 1:1 relationship between wild-type and scramble-mutant ChIP enrichment, and should be added to this panel.*

• *We added the line to Figure 4B.

• * tRNA gene positions relative to ChIP signal (Figure 4C). Please mark the positions of tRNA genes directly on this genome browser panel, since the spatial relationship between the TFTC-3/RPC-1/DPY-27 tracks and the underlying tRNA genes is central to the interpretation of this representative locus.*

• *Thank you for pointing this out. We updated Figure 4C, Figure 5 A&C marking the tRNA genes.

• * Loop vs. compartment-level nature of tRNA gene interactions. It remains unclear whether the reported tRNA gene contacts (Fig. 6, 7) reflect discrete, loop-like interactions or a broader compartment-level clustering phenomenon. Given that these possibilities imply different underlying mechanisms, the authors should state explicitly which interpretation their data best support, or acknowledge that both may contribute.*

• *Our data is best explained with a model combining both, and we tried to clarify in the revised Figure 7C model panel legend:

“(C) Since both TFIIIC and RNA Pol III mediate interaction of tRNA genes with the nuclear pore (Ikegami & Lieb, 2013) and reduce contacts between chromosome arm and centers, nuclear pore tethering may contribute to the 3D compartmentalization of chromosome arm and centers. Our data showed that TFIIIC is required for localization of cohesin and condensin complexes to tRNA genes. TFIIIC is also required for 3D contacts between TFIIIC bound tRNA genes. The lack of prominent Hi-C loops between tRNA genes argue that the contacts must be infrequent, thus heterogeneous within the population of cells. We propose that (i) TFIIIC acts as a dynamic barrier to loop extrusion, thus localizing SMC complexes at a fraction of tRNA genes in a given cell. This conclusion is supported by TFTC-3 mediating condensin and cohesin binding to TFIIIC sites, and the specific enrichment of TFTC-3 at rex sites, as a result of TFIIIC being at one of the two anchors of a condensin I-DC mediated DNA loop. We also propose that (ii) TFIIIC promotes 3D contacts between tRNA genes by protein-protein interactions through compartmentalization of the interphase genome. This conclusion is supported by the presence of tRNA gene contacts between different chromosomes and DPY-26 depletion not reducing tRNA contact frequency on the X chromosomes. “

• *

• * Explicit mechanistic model for TFIIIC-SMC recruitment. The manuscript would benefit from a clearer statement of which mechanistic model is favored: (i) TFIIIC acts as a loop-extrusion barrier/anchor, with tRNA gene interactions arising in cis (proximal or distal) or in trans as extrusion products; or (ii) TFIIIC recruits SMC complexes to sites independent of loop extrusion. The current data (weak but genome-wide insulation effects, plus trans contacts and DPY-26 TEV results that argue against extrusion alone) suggest a hybrid model - making this explicit would help readers evaluate the proposed self-organization model in Figure 7C.*

• Our study supports a hybrid model including two mechanisms for TFIIIC mediated promotion of 3D contacts between tRNA genes: (i) TFIIIC is a weak barrier to DNA loop extrusion by multiple SMC complexes. Thus, TFIIIC mediates 3D contacts in cis by positioning tRNA genes at the anchor of SMC mediated DNA loops. We do not favor the recruitment model, because of two reasons. First, TFIIIC likely ChIPs to the rex sites because condensin I-DC mediated DNA loop anchors TFIIIC next to the rex sites. Thus loop extrusion is consistent with the strong positive correlation between TFIIIC binding and condensin I-DC. Second, if TFIIIC recruited condensin I-DC, we expected the X chromosome expression to increase significantly upon TFTC-3 depletion, which was not the case (Figure 3G). (ii) TFIIIC mediates compartmentalization of tRNA genes in trans*. Our analysis shows that DPY-26 cleavage does not reduce contacts between tRNA genes on the X chromosomes. Thus loop extrusion alone fails to explain TFIIIC function in 3D clustering of tRNA genes. Thus we also have to include a loop extrusion independent mechanism. We tried to clarify this model in Figure 7 and its legend (see previous response).

• * Statistical comparison of RNA Pol III recycling correlations. The claim that snoRNA genes show less efficient Pol III recycling than tRNA/ncRNA genes rests on comparing three Pearson correlation coefficients (0.36 tRNA, 0.38 ncRNA, 0.52 snoRNA; Supp. Fig. 2C) without a formal statistical test for whether these correlations differ significantly from one another.*

We performed an Analysis of Covariance (ANCOVA) with an interaction test to ask whether the relationship (slope) between RPC-1 binding and TFTC-3 depletion differed between the three groups of genes. Less RNA Pol III recycling at the snoRNA genes was statistically significant; snoRNA vs tRNA p=0.0062 , snoRNA vs ncRNA p=0.0347, tRNA vs ncRNA= 0.9095. The supplemental figure is updated to contain the slope of the correlations, and figure legend contains the results of the statistical test. *

Reviewer #2 (Significance (Required)):

This manuscript investigates whether TFIIIC regulates SMC complex (cohesin, condensin I, condensin I-DC) binding and 3D genome organization independent of its canonical role in recruiting RNA Pol III. Using auxin-inducible degradation of TFTC-3 (TFIIIC subunit) and RPC-1 (Pol III catalytic subunit) in C. elegans, the authors show that TFIIIC, but not Pol III, is required for cohesin and condensin binding at tRNA genes, and that TFIIIC specifically promotes long-range 3D contacts between distant tRNA genes. Depletion of individual SMC complexes does not eliminate these tRNA gene contacts, suggesting either redundancy among SMC complexes or an SMC-independent mechanism. The work is a well-designed and largely convincing dissection of a long-standing correlative hypothesis, separating the transcriptional and structural roles of TFIIIC.

The findings are exciting, since little is known about positioning SMC complexes in C. elegans, and more broadly, the relationship between TFIIIC and SMC in other systems. While correlations have been seen, I know only one paper showing reliance of condensin (II) and TFIIIC by the Gerton lab, but this was a broad knock down, not the elegant scramble experiments shown here. In addition, this paper broadens the relationship to other SMC complexes. I do have some questions/clarifications.

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

The manuscript by Obaji et al entitled "TFIIIC regulates SMC complex binding and 3D contacts between tRNA genes" is a detailed molecular genetic characterization of the role of tRNA genes in the organization of the C. elegans genome. The manuscript builds on data from numerous organisms. The data show that tRNA genes function as chromosome organizing hubs in worms. The strength of this manuscript is the thoroughness of the experiments and the careful and accurate description of the results without any hyperbole. The authors use auxin to induce depletion of essential protein subunits of TFIIIC, polIII and SMC. They follow the effect using western blots. They show significant depletion of these proteins within 1h. However, the western blots are not quantitative. They should titrate known quantities of purified proteins as standards in order to quantify depletion.*

• *The depletion is performed by a somatic promoter driven TIR-1 in full L3 larvae, with ~1-3% germ cells. We quantified the level of depletion in somatic cells using GFP expression in nuclei at the head. Please see our response to the reviewer 1 minor comments #1. We quantified the level of GFP signal reduction in the head of worms. The baseline GFP signal is lower for TFTC-3 compared to RPC-1, thus making comparison difficult, but normalized signals with and without auxin showed roughly similar levels of depletion (Supplemental Figure 2B).

* Following depletion, the authors use ChIP Seq to map binding of these proteins at sites in the genome. The authors may wish to analyze the DNA sequence motifs that remain bound by TFIIIC following depletion for 1 hour compared to sequences that lose TFIIIC binding upon depletion. The same analysis should be performed for sites that remain bound after 22h. What are the chromatin signatures at these sites (from previous ChIP-Seq data). This analysis could highlight high-affinity sites for TFIIIC or specific chromatin signatures that aid in TFIIIC binding and residence time in chromatin.*

• *While we achieve effective depletion of target proteins by driving TIR-1 under eft-3 promoter, the level of depletion is a function of the amount of p-eft3 driven TIR1 in each cell cell type. Thus the proposed analysis is more suited to a single cell organism or a tissue culture system. Nevertheless, we were able to capture interpretable differences between broad classes of TFIIIC targets, as shown in Supplemental Figure 2D where snoRNA promoters have a higher RPC-1 turnover (less efficient recycling) compared to the tRNA genes.

* In Figure3 the authors analyze 3D contacts using HiC. Depletion of TFIIIC had minimal effects on average contact frequency. This is to be expected given that most contact sites are unlikely to be influenced by TFIIIC. There were subtle differences in separation on arms and centers upon depletion that appear to have a greater effect on smaller chromosomes. Is the density of TFIIIC sites greater on smaller chromosomes? This result is similar to data in yeast that show that tRNA genes have local effects on chromosome function rather than global effects.*

• *There was no obvious correlation between the density of TFIIIC sites (see different classes of peaks marked for each chromosome on the left) and the chromosome-domain level as shown in Supplemental Figure 3.

* In Fig3D they analyze "insulation" on the X chromosome at a specific rex site and show that there are subtle changes. *

It is unclear if these are statistically significant and/or functionally important.

Indeed, the effect on the insulation score was limited to the rex site near the tRNA gene cluster in Figure 3D, and there was no statistical difference between average insulation score across all other rex sites (Figure 3E). We asked if the TFIIIC function at the rex sites is to recruit condensin I-DC to the X chromosome. Subtle changes in condensin I-DC binding results in derepression of X chromosome expression as measured by mRNA-seq (PMID: 36302374). We did not detect an X chromosome specific effect in TFTC-3 knockdown (Figure 3G). Thus we conclude that TFIIIC contributes to insulation at the example rex site by being the anchor of a local DNA loop that connects a TFIIIC site to the rex site (Cartoon in Figure 3D). We reason that the insulation score, calculated from local contacts, increases as a result of fewer DNA loops on the left side of the rex site, rather than TFIIIC functioning directly as an insulator at the rex site. We added the following text:

“To understand if TFIIIC binding at the rex sites contribute to condensin I-DC recruitment, we performed mRNA-seq upon TFTC-3 depletion (Supplemental figure 3F). Depletion of proteins that regulate condensin I-DC binding results in derepression of the X chromosomes within one hour of auxin incubation (Morao et al., 2022). We compared 1 hr depletion of TFTC-3 to no auxin control, and 22 hr depletion of TFTC-3 to 22 hr auxin no tag control, which reports on the combined effect of the degron tag and auxin inducible depletion of TFTC-3. The statistically significant differences on average mRNA abundance from different chromosomes was not X chromosome specific (Figure 3G), supporting our conclusion that TFIIIC binding at rex sites is a consequence of condensin I-DC activity on the X chromosomes.”

* In Figure4A and 4B, the authors mutate the TFIIIC binding site at a specific tRNA gene on the X chromosome. This mutation eliminated TFIIIC binding but only reduced Dpy27 binding. Furthermore, the authors also investigated Dpy27 binding upon TFIIIC depletion and determined that depletion reduced Dpy27 binding at TFIIIC sites. These data suggest that TFIIIC may not be involved in "recruiting" Dpy27 but in stabilizing the binding at specific sites after it has been recruited.*

• *We agree with this interpretation. As for the mechanism of “stabilization”, in the model we propose that TFIIIC is a weak barrier to DNA loop extrusion, thus condensin/cohesin “pauses” at the TFIIIC occupied sites.

* In figure6 the authors deplete TFIIIC or SMC3 or Dpy26 and analyze tRNA contacts via HiC. They show that TFIIIC plays a role in the long-range contacts but are not cohesion/condensin dependent. This is interesting and needs to be highlighted. Is it possible that they utilize other proteins such as nuclear pore proteins.*

• *While both TFIIIC and RNA Pol III subunits physically interact with the nuclear pore proteins, only TFIIIC regulates SMC complex binding. Therefore, we do not favor the nuclear pore idea for SMC regulation. However, we do agree that the mechanism of TFIIIC being a barrier may be through changing DNA topology or anchoring the DNA at another nuclear structure that prevents the loop extruding SMC complexes to bypass the TFIIIC bound locus.

* One weakness of this manuscript is a lack of discussion on the conserved mechanisms that tRNA genes use in 3D organization of genomes across species from yeast to flies to worms to mammals. The discussion needs expanding highlighting commonalities in mechanism across species.

*

We summarized mechanisms of tRNA gene clustering in different species in the final section of the revised discussion. To our knowledge, tRNA gene clustering was not shown outside yeast species.

* Reviewer #3 (Significance (Required)):

The manuscript builds on data from numerous organisms. The data show that tRNA genes function as chromosome organizing hubs in worms. The strength of this manuscript is the thoroughness of the experiments and the careful and accurate description of the results without any hyperbole. The weakness of this manuscript is a lack of discussion on the conserved mechanisms that tRNA genes use in 3D organization of genomes across species from budding yeast to fission yeast to flies to worms to mammals. The discussion needs expanding highlighting commonalities in mechanism across species.*

• *

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Referee #3

Evidence, reproducibility and clarity

The manuscript by Obaji et al entitled "TFIIIC regulates SMC complex binding and 3D contacts between tRNA genes" is a detailed molecular genetic characterization of the role of tRNA genes in the organization of the C. elegans genome. The manuscript builds on data from numerous organisms. The data show that tRNA genes function as chromosome organizing hubs in worms. The strength of this manuscript is the thoroughness of the experiments and the careful and accurate description of the results without any hyperbole.

The authors use auxin to induce depletion of essential protein subunits of TFIIIC, polIII and SMC. They follow the effect using western blots. They show significant depletion of these proteins within 1h. However, the western blots are not quantitative. They should titrate known quantities of purified proteins as standards in order to quantify depletion. Following depletion, the authors use ChIP Seq to map binding of these proteins at sites in the genome. The authors may wish to analyze the DNA sequence motifs that remain bound by TFIIIC following depletion for 1 hour compared to sequences that lose TFIIIC binding upon depletion. The same analysis should be performed for sites that remain bound after 22h. What are the chromatin signatures at these sites (from previous ChIP-Seq data). This analysis could highlight high-affinity sites for TFIIIC or specific chromatin signatures that aid in TFIIIC binding and residence time in chromatin. In Figure3 the authors analyze 3D contacts using HiC. Depletion of TFIIIC had minimal effects on average contact frequency. This is to be expected given that most contact sites are unlikely to be influenced by TFIIIC. There were subtle differences in separation on arms and centers upon depletion that appear to have a greater effect on smaller chromosomes. Is the density of TFIIIC sites greater on smaller chromosomes? This result is similar to data in yeast that show that tRNA genes have local effects on chromosome function rather than global effects. In Fig3D they analyze "insulation" on the X chromosome at a specific rex site and show that there are subtle changes. It is unclear if these are statistically significant and/or functionally important.

In Figure4A and 4B, the authors mutate the TFIIIC binding site at a specific tRNA gene on the X chromosome. This mutation eliminated TFIIIC binding but only reduced Dpy27 binding. Furthermore, the authors also investigated Dpy27 binding upon TFIIIC depletion and determined that depletion reduced Dpy27 binding at TFIIIC sites. These data suggest that TFIIIC may not be involved in "recruiting" Dpy27 but in stabilizing the binding at specific sites after it has been recruited.

In figure6 the authors deplete TFIIIC or SMC3 or Dpy26 and analyze tRNA contacts via HiC. They show that TFIIIC plays a role in the long-range contacts but are not cohesion/condensin dependent. This is interesting and needs to be highlighted. Is it possible that they utilize other proteins such as nuclear pore proteins. One weakness of this manuscript is a lack of discussion on the conserved mechanisms that tRNA genes use in 3D organization of genomes across species from yeast to flies to worms to mammals. The discussion needs expanding highlighting commonalities in mechanism across species.

Significance

The manuscript builds on data from numerous organisms. The data show that tRNA genes function as chromosome organizing hubs in worms. The strength of this manuscript is the thoroughness of the experiments and the careful and accurate description of the results without any hyperbole. The weakness of this manuscript is a lack of discussion on the conserved mechanisms that tRNA genes use in 3D organization of genomes across species from budding yeast to fission yeast to flies to worms to mammals. The discussion needs expanding highlighting commonalities in mechanism across species.

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

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Referee #2

Evidence, reproducibility and clarity

General Writing Comments

• Given that the strongest and most novel finding is TFIIIC's role in condensin/cohesin recruitment and boundary formation, the Introduction and Abstract would benefit from earlier framing around why SMC complexes localize where they do (i.e., boundary-element biology), rather than leading primarily with the transcriptional background of TFIIIC.
• In the Discussion, consider leading with the strongest, most novel results (SMC recruitment, tRNA gene 3D contacts) before moving to the more incremental arm/center compartmentalization findings.

Science Comments

HOT-site behavior vs. direct regulatory function. Because TFIIIC/ETC sites can behave as generically hyper-occupied regions in other organisms, the authors should more explicitly distinguish the possibility that TFIIIC directly regulates SMC recruitment from the alternative that TFIIIC and SMC complexes are both independently drawn to a shared class of highly accessible chromatin. Or that TFIIIC is generating a HOT site. A discussion of HOT sites vs TFIIIC sites would be helpful. Possible chromatin-opening role for TFIIIC at the 1 hr timepoint. Along the same lines. At 1 hr TFTC-3 depletion, condensin binding is lost specifically at sites where TFIIIC and DPY-27 binding overlap, while DPY-27 is retained at other, non-overlapping sites. This raises the possibility that TFIIIC helps maintain locally accessible chromatin that permits condensin binding, rather than (or in addition to) direct recruitment via protein-protein interaction. ATAC-seq under the same depletion conditions could help distinguish these possibilities.

More minor comments:

1. Quantify depletion efficiency. The claim of GFP signal loss (Fig. 1C) is currently supported only by representative images - please quantify mean GFP intensity per worm (auxin vs. no-auxin) across a sample of animals. Similarly, please quantify Western blot band intensity (Fig. 1B, normalized to tubulin, across replicates) rather than relying on visual inspection of a single blot.
2. Genome-wide confirmation that RPC-1 depletion leaves TFIIIC binding intact. Figure 4C shows TFTC-3 signal is retained at one representative locus after 1 hr RPC-1 depletion, but this is not shown genome-wide. Since the claim of an RNA Pol III-independent function for TFIIIC rests on RPC-1 depletion not affecting TFIIIC binding, please provide a genome-wide comparison (e.g., a heatmap or violin plot analogous to Fig. 1D) confirming this.
3. Reconcile genome-wide vs. locus-specific contact-frequency effects. The claim of "minimal impact on average contact frequency" (Supp. Fig. 3A) rests on a genome-wide P(s)/slope plot, which by construction averages over all loci and distances and could mask spatially localized effects. This appears to be in some tension with Fig. 3B/C and Supp. Fig. 3B, which show a visibly stronger arm-center mixing signal specifically at 22 hr TFTC-3 depletion. A quantitative summary distinguishing a modest genome-wide shift from a strong, spatially restricted effect diluted by averaging would strengthen this section. o Related: please provide statistics on the apparent partial rebound in B-compartment strength after 22 hr auxin treatment (Fig. 3B, bottom-right saddle values) even as A-compartment strength remains reduced.
4. Direct comparison between the scramble and depletion experiments (Figure 4). The Box A/B scramble experiment (Fig. 4A-B) and the TFTC-3 auxin depletion experiment (Fig. 4C-D) are presented as parallel lines of evidence but are never directly compared quantitatively. Is the reduction in DPY-27 binding at the scrambled locus comparable in magnitude to the reduction at TFTC-3-depletion-affected peaks genome-wide? A side-by-side comparison, or at least a statement about this, would strengthen the claim that both perturbations report on the same mechanism.
5. Single-locus scope of the scramble experiment. The Box A/B scramble was performed at only one tRNA gene (F56C3.t1). Please discuss whether this result is expected to generalize across tRNA genes broadly, or consider testing a second, independent locus to rule out gene-specific effects.
6. Figure/text mismatch for autosomal DPY-26 binding (Figure 5). The statement "DPY-26 binding to autosomes was weak" (p. 11) is illustrated by Fig. 5C, which shows an X-chromosomal region, not an autosome (autosomal DPY-26/SMC-3 tracks appear only in Fig. 5A). Please point the reader to the correct panel, or add a clearly labeled autosomal example alongside the X-chromosomal one so both are shown side by side.
7. Replicate-level concordance for Hi-C. All Hi-C data are presented as merged replicates; replicate-level correlation is not shown. A supplementary figure comparing the two biological replicates (e.g., stratum-adjusted correlation or scatterplots of key metrics such as insulation score or compartment strength) would let readers judge how robust the reported effects are across replicates.
8. Derepression as a potential mechanism. Neither the tRNA promoter scramble nor TFTC-3 depletion is accompanied by expression data. Since transcriptional derepression of genes near the scrambled motif or near TFIIIC-bound sites could confound the interpretation of reduced condensin/cohesin binding, even limited expression data (qRT-PCR at a few loci, or RNA-seq) would help rule this out. Otherwise, please discuss this caveat in the results.
9. Quantification of interchromosomal tRNA gene contacts. The intrachromosomal tRNA-tRNA contact enrichment is quantified with paired statistics (Fig. 6B), but the interchromosomal (trans) pileups shown in Fig. 7A are presented only qualitatively. Since the trans-chromosomal contacts are central to the argument against a pure loop-extrusion model, a matching quantification and statistical test would substantially strengthen this claim.
10. Basis for attributing reduced Hi-C fountains to "indirect effects." Supp. Fig. 4B attributes the loss of cohesin-mediated fountain signal after 22 hr TFTC-3 depletion to indirect effects of prolonged depletion, but no data directly distinguishes direct from indirect causes. Please either soften this interpretive claim or provide supporting evidence (e.g., a comparison against the 1 hr TFTC-3 depletion timepoint, which does not show this loss).
11. Reliance on an external dataset for condensin I/II clustering. The individual contributions of condensin I (DPY-26 TEV) and condensin II (KLE-2 TEV) to tRNA gene clustering (Fig. 6D-E) are assessed using a previously published dataset (Das et al., 2024) rather than the authors' own degron system. Please confirm that depletion timing/developmental stage are comparable enough to the rest of the study for direct comparison.

Additional Points Requiring Clarification

• Viability and developmental timing of depletion. The strains remain viable without auxin, with depletion restricted to the soma; some GFP signal and germ cell division are still observed at the 2-cell stage under auxin. Please clarify what this residual signal implies for interpreting phenotypes at the earliest depletion timepoints - could early developmental effects be underestimated if depletion is incomplete at this stage?
• Nature of the rex site overlapping a tRNA gene cluster (Figure 3D). One strong rex site sits near a cluster of four tRNA genes with higher TFTC-3 than RPC-1 binding. Since tRNA genes and rex sites are both classic examples of highly-occupied regions, the authors should discuss whether this overlap reflects a specific functional relationship between condensin I-DC recruitment and TFIIIC, or a more generic co-occurrence at hyper-bound loci - and whether this particular locus is representative of the other 16 strong rex sites analyzed in Fig. 3E.
• Strength of the DPY-27/TFTC-3 correlation. The correlation between DPY-27 and TFTC-3 ChIP enrichment across the X chromosome (r = 0.82, Fig. 3F) is notably strong relative to the RPC-1 correlation (r = 0.45) and, in our view, deserves more prominent discussion in the main text as one of the more compelling quantitative links supporting the model.
• Normalization for insulation score comparisons. Figure 3E reports Wilcoxon rank-sum p-values comparing insulation scores across conditions, but it is not stated whether a spike-in or other cross-condition normalization was used for the underlying Hi-C/ChIP data, and no per-site error bars or replicate spread are shown. Please clarify the normalization approach and, if possible, show replicate-level variability underlying these scores.
• Reference line for Figure 4B. A diagonal line with slope 1 would help readers visually assess the degree of deviation from a 1:1 relationship between wild-type and scramble-mutant ChIP enrichment, and should be added to this panel.
• tRNA gene positions relative to ChIP signal (Figure 4C). Please mark the positions of tRNA genes directly on this genome browser panel, since the spatial relationship between the TFTC-3/RPC-1/DPY-27 tracks and the underlying tRNA genes is central to the interpretation of this representative locus.
• Loop vs. compartment-level nature of tRNA gene interactions. It remains unclear whether the reported tRNA gene contacts (Fig. 6, 7) reflect discrete, loop-like interactions or a broader compartment-level clustering phenomenon. Given that these possibilities imply different underlying mechanisms, the authors should state explicitly which interpretation their data best support, or acknowledge that both may contribute.
• Explicit mechanistic model for TFIIIC-SMC recruitment. The manuscript would benefit from a clearer statement of which mechanistic model is favored: (i) TFIIIC acts as a loop-extrusion barrier/anchor, with tRNA gene interactions arising in cis (proximal or distal) or in trans as extrusion products; or (ii) TFIIIC recruits SMC complexes to sites independent of loop extrusion. The current data (weak but genome-wide insulation effects, plus trans contacts and DPY-26 TEV results that argue against extrusion alone) suggest a hybrid model - making this explicit would help readers evaluate the proposed self-organization model in Figure 7C.
• Statistical comparison of RNA Pol III recycling correlations. The claim that snoRNA genes show less efficient Pol III recycling than tRNA/ncRNA genes rests on comparing three Pearson correlation coefficients (0.36 tRNA, 0.38 ncRNA, 0.52 snoRNA; Supp. Fig. 2C) without a formal statistical test for whether these correlations differ significantly from one another.

Significance

This manuscript investigates whether TFIIIC regulates SMC complex (cohesin, condensin I, condensin I-DC) binding and 3D genome organization independent of its canonical role in recruiting RNA Pol III. Using auxin-inducible degradation of TFTC-3 (TFIIIC subunit) and RPC-1 (Pol III catalytic subunit) in C. elegans, the authors show that TFIIIC, but not Pol III, is required for cohesin and condensin binding at tRNA genes, and that TFIIIC specifically promotes long-range 3D contacts between distant tRNA genes. Depletion of individual SMC complexes does not eliminate these tRNA gene contacts, suggesting either redundancy among SMC complexes or an SMC-independent mechanism. The work is a well-designed and largely convincing dissection of a long-standing correlative hypothesis, separating the transcriptional and structural roles of TFIIIC.

The findings are exciting, since little is known about positioning SMC complexes in C. elegans, and more broadly, the relationship between TFIIIC and SMC in other systems. While correlations have been seen, I know only one paper showing reliance of condensin (II) and TFIIIC by the Gerton lab, but this was a broad knock down, not the elegant scramble experiments shown here. In addition, this paper broadens the relationship to other SMC complexes. I do have some questions/clarifications.

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

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Referee #1

Evidence, reproducibility and clarity

Summary: The manuscript by Obaji et al investigates the role of TFIIIC and RNA Pol III in the recruitment of SMC protein containing complexes (condensin IDC, condensin I, and cohesion), as well as their role in remodeling the 3D genome in C. elegans. Connection between TFIIIC, condensin, and 3D genome organization have been seen in other organisms, several cited by the authors. This works extends the idea to C. elegans. The authors perform a series of ChIP-seq and Hi-C experiments after depletion of TFIIIC or a subunit of RNA Pol III. The observed impacts are relatively minor, but TFIIIC does appear to play a role in recruiting condensin and cohesin to a subset of their binding sites, and it also plays a role in shaping the 3D genome. However, there does not seem to be the same level cooperation between SMC complexes and TFIIIC at tRNA genes as was previously described in yeast (Hausler et al 2006, Genes and Development). The paper is written in a very short format, and as a result some details, at times essential information (such as the antibodies used in the experiment) are missing. So some aspects of the work were hard to evaluate. Overall, the study describes some interesting findings, but the significance of the work is limited by the relatively minor effects observed.

The experiments were well done. My expertise is not in Hi-C data analysis, so I cannot comment on the quality of those aspects of the paper. Most of my concerns relate the how the experiments were described and how conclusions were drawn. Some of the conclusions should be qualified, but I am not sure if additional experiments would make the conclusions any stronger.

Major comments:

1. The information on what antibodies were used in the ChIP-seq experiments are missing. tftc-3 and rpc-1 were tagged with degron::GFP. Did the authors use anti-GFP antibodies? For other proteins analyzed, such as TFTC-5 and TBP-1, what antibodies were used? Are these antibodies validated/high quality?
2. To show that the tag did not interfere with gene function, the authors assessed lethality in the tagged strains. The data shown on Supplemental Figure 2 shows unusually high level of lethality in the no tag control. Only 20-40 eggs were counted for each data point. I would recommend counting more, and if the lethality remains in the no tag control, I would worry about the starting wild type strain having some background mutations.
3. After validating the strains and determining depletion conditions, the authors first analyze 3D genome organization. Compartmentalization weakens a bit, more obvious after 22 hours of TFTC-3 depletion, at which point they see increased interactions between the arms and the center of chromosomes. Additionally, a small increase in insulation is observed at TAD boundaries on the X chromosome. These data are described on Fig 3, and in general support the conclusions. In the legend, the authors mention to B-B, A-A, A-B. It was unclear what they refer to.
4. Next, the authors investigate impacts on condensin IDC binding. After 1 hour of TFTC-3 depletion, condensin IDC binding is reduced at a subset of sites that overlap with TFTC-3 sites (Fig 4). The authors refer to these as tRNA genes. While many of them are likely tRNA genes, not necessarily all. Either check whether they are all tRNA genes, or refer to them as TFTC-3 binding sites.
5. For one of these sites, the authors scramble the box A/B DNA sequence. Please explain how this experiment was done. Was it by replacing the endogenous sequence using crispr? In any case, in this mutant, TFTC-3, RPC-1 and DPY-27 binding all decreased. On Fig 4 B, binding seems to be decreased at many additional sites as well. Why is binding decreased at additional sites?
6. While not necessarily easy to answer, what I am most curious about is whether the change in condensin IDC binding at a subset of sites is actually biologically relevant. Is there an impact on dosage compensation?
7. While the experiments concerning condensin IDC binding were done after 1 hour of depletion (after 1 hr of TFTC-3 depletion RPC-1 is still bound), the experiments with cohesin were done after 22 hours of depletion (when RPC-1 binding is impacted). Therefore, the authors can conclude that condensin IDC binding is impacted by TFTC-3, but not RPC-1, but the same cannot be said for cohesin (Fig 5). The authors seem to imply that only TFCT-3 is involved and not RPC-1. In fact, data for 1 hr of TFTC-3 depletion is not shown. Addiotionally, the title of the section and the title of figure refer to these sites as tRNA genes, but tRNAs are not even mentioned in the text of this section. Are these in fact exclusively tRNA genes, or are they TFTC-3 binding sites.
8. In a very brief section, the authors describe the final finding, clustering of tRNA genes, which seems to depend on TFTC-3, but not RPC-1 or condensin or cohesin. While the rest of the paper investigates how TFTC-3 impacts condensin and cohesin, this data shows that condensin and cohesin are not working with TFIIIC in this process. The data is data, and I am not arguing with it, but the story does seem more disconnected than the title implies.

Minor comments:

1. TFTC-3 depletion seems quite effective, RPC-1 depletion a little less so. It should be mentioned in the text. Also, on Fig 1C, please explain the signal that remains after depletion. (The explanation is in the legend for Figure 2-it should be explained earlier.)
2. Supplemental Figure 3 legend refers to distribution of peak categories and cartoon interpretation that are not shown on the figure.
3. Figure 7 shows some new analysis described in the discussion. It should be in the results section.

Significance

My enthusiasm is a bit limited due to the minor impacts observed. Condensin and cohesin binding is only impacted at a subset of binding sites and it is unclear whether the changes affects condensin and cohesin function. Similarly, 3D genome organization did not change that much, and condensin and cohesin are not even involved in tRNA clustering. As a result, the study does not advance the knowledge in the field by a lot.

Audience: the study will be interesting to scientists in the 3D genome organization field, especially as a point of comparison to other systems.

My expertise is in chromosome structure and organization and condensin biology.

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