Archive for the ‘Media Control’ Category

Safer social media means putting control in the hands of users – The Drum

The news that the watchdog Ofcom will have increased powers over social media platforms to act over harmful content has been met with a mixture of excitement and concern in the UK. Users, advertisers and social media firms themselves are asking how these powers will impact the platforms, the content shared on them, and how we interact with them every day. Right now we have no answer to that question.

However, we can begin to think hard about what the impact of Ofcoms regulatory powers over social media might be be that on users, advertisers, or public safety.

Over the last few years, we have seen social media firms take significant steps to eliminate bad actors, manipulative political content, and hate speech from their platforms. From Twitter experimenting with new solutions to remove toxicity from the platform to Instagram and Facebooks experiments to hide likes, the platforms have shown how serious they are about making their environments healthier and happier for their users.

Recently, Facebook stated that it has an army of digital police, made up of algorithms and AI, working alongside humans to create a safer and more transparent online environment. While this all sounds like a lot, its clearly not enough. Thats why Ofcom taking a more powerful role in keeping people safe online determining what content is harmful and how it should be handled can only be a positive step as part of a shared responsibility model.

Many politicians and business owners have been asking how big the role of regulators should be in determining what content is harmful and what is merely controversial. While its hard to define clear cut lines when it comes to how harmful a piece of content is, it is possible to educate users and the public as a whole about behaviours on social media. While technology and regulation can help, its only by teaching people to use social media responsibly that we stand a chance of limiting harmful content in the online world for good.

The platforms themselves could be the place to start. Gently educating users, particularly younger users, about how to behave on social media is a step in the right direction. Putting their money where their mouth is and launching a global campaign on this could be a big push that the industry badly needs. But the onus isnt on the platforms alone. Education initiatives do exist, for example, Safer Internet Day. A European initiative celebrated annually, Safer Internet Day aims to teach users about topics from cyberbullying to social media. Education systems, if they arent already, should also be including social media behaviour in their curriculum and governments should be encouraging this. In short, collaboration between the platforms, the educators and the governments is the right way forward.

Unlike with media such as radio, TV and print, the attempts made to regulate the early days of the internet didnt meet with much success. Even today innocent searches on the internet can expose users to content that makes them feel upset and confused, or has even worse effects.

Given the scale and open nature of the internet, cracking down on harmful content is an uphill battle for regulators and for users. However, where we have seen some success is in the implementation of greater user controls. By giving users control over the content they see, whether, through ad blockers, parental controls or URL filtering, the internet has become a safer, healthier place for users.

The same could easily be true for social media. If users were given more control over the content they and their children can see, the social world would undoubtedly feel like a safe place to inhabit. This shouldnt negatively impact brands rather, it would encourage brands to be more careful and inclusive with the content they share. Harmful stereotyping could be one example of creative advertising that this level of user control could stamp out. This can only be a good thing.

Yih-Choung Teh, group director of strategy and research at Ofcom, said that for most people the risks of social media "are still outweighed by the huge benefits of the internet. And while most internet users favour tighter rules in some areas, particularly social media, people also recognise the importance of protecting free speech which is one of the internets great strengths."

The move to give Ofcom greater power has the potential to see governments and platforms working hand-in-hand to remove harmful content and toxicity from the social sphere. We work with many of the largest brands in the world, and we know they get value from social media for reaching and engaging with their audiences. It has a positive impact on their business in countless ways, but no brand wants this to come at a cost of their brand reputation, customer loyalty, or worse. Brands want to be sure that they are investing their ad budgets into safe and trustworthy platforms, which are free from harm and toxicity.

While we shouldnt expect anything to change overnight, this move by Ofcom is a step in the right direction for both users and advertisers. Anything that makes social media platforms safer and more engaging is a win-win, both for the people using them and for businesses advertising on them.

Yuval Ben-Itzhak is chief executive officer of Socialbakers.

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Safer social media means putting control in the hands of users - The Drum

Facebook Tests Easier Access to Chronological News Feed and Other Feed Sorting Options – Social Media Today

Facebook's infamous News Feed algorithm has helped the platform maximize engagement, and boost time spent in-app - but it's also long been a major bugbear for many users.

The complex algorithm, which aims to show users more content that they're likely to be interested in, uses a range of factors in its equations, including when something is posted, how often you interact with the creator, what type of post it is, and more.

That means that users generally see posts that are seeing higher levels of engagement appear closer to the top of their feeds. But is that a better experience?

Would you be better off just seeing a feed of allthe posts from all the people and Pages that you've chosen to follow in reverse chronological order - ala Facebook pre-2013?

You might soon be able to get a better perspective on this - this week, reverse engineering expert Jane Manchun Wong has uncovered a new Facebook test which would provide a simplified toggle that you could use to switch across to different variations of your News Feed.

As you can see in these images, the test provides three different variations of the News Feed in separate tabs, which you would be able to access along the top of the feed list.

Those three versions are:

To be clear, the new listing options would not add anything to Facebook, functionally. Users can already access a reverse chronological News Feed by switching to most recent via the 'More' listing in the app (left image below) or clicking on the three dots beside 'News Feed' in the right-hand column on desktop.

Those setting can't be saved, however, so whenever you do switch this, it will default back to the algorithm feed next time you log-in.

You can also view your 'Already Seen' listing via this URL:http://www.facebook.com/seen

This new option would make it easier to access both, which could be a welcome change. Facebook has confirmed that the option is being tested internally, though it says it has no plans to roll it out to the public at this stage.

But then again, it might not be as great as many users would hope.

While calls for less algorithm intervention constantly resonate through every platform which uses such, when people have been provided with the capacity to switch the algorithm off, most users haven't bothered to do so.

Twitter announced an option to easily switch between 'Latest' and 'Top' tweets back in November 2018, enabling users to essentially turn off its algorithm sorting (which it rolled out in 2016). But Twitter says that even with the option, most users have stuck with the algorithm-defined listing, while on-platform engagement has continued to rise, underlining its beneficial impact.

Facebook, too, has experimented with alternate feed options. Back in 2016, Facebook tried a similar process of tabbed News Feed listings, separated by topic, in order to boost engagement.

That didn't really work out, and Facebook abandoned the experiment before it got too far.

As noted, while the rumblings of dissatisfaction with algorithm-defined feeds are ever-present on every network which has implemented such, when provided with an alternative, most users don't bother changing their behavior.

Maybe, a reverse-chronological feed switch on Facebook would be different -but I wouldn't count on it.

Still, it is interesting to note that this is an aspect that Facebook is exploring, which would suggest that this is still an element where it believes it can generate more engagement. The introduction of an alternate, chronological feed also aligns with the broader social media shift towards giving users more control, and enabling them more specific choice over their feeds, as opposed to hiding the back-end processes and showing them what the system thinks they'll want - even if they don't realize it.

For example, Instagram recently rolled out a new option which enables users to see which accounts they interact with the least, which could show them which people and profiles they should unfollow to improve their experience.

That's almost like a new level of trust from the platform - in the past, the apps have taken much of this type of control away, or hidden such insight from view, with the implication seemingly being that the algorithm knows better, and you should just trust that.

Now, with people more educated on how social platforms work, and what following certain people and pages means for their feeds, users are a little more discerning in their following habits. You can see this specifically on Instagram - the old 'follow for follow' trick, for example, isn't as effective on 'the gram' because people don't as readily add others on the platform as they did on Facebook and Twitter.

And because people are now more discerning, and more conscious of what they're allowing into their feeds, the need for algorithm dictation reduces - which is why platforms may now feel more comfortable allowing users access to tools like this, because the impact of them making such a switch is less than it would have been in the past anyway.

Or, as noted, they know that most people simply won't bother.

Either way, it's an interesting experiment, and it could change Facebook usage habits, if implemented. We'll keep you updated on any progress.

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Facebook Tests Easier Access to Chronological News Feed and Other Feed Sorting Options - Social Media Today

Public trust in data could have helped China contain the coronavirus | TheHill – The Hill

In less than two months, the coronavirus has spread from the Chinese city of Wuhan to 25 countries, prompting the World Health Organization to declare a global health emergency. In reaction, the Chinese Communist Party swiftly restricted travel and shut down major transportation systems a feat many believe possible due to the authoritarian control the government has over its citizens.

While these efforts are aggressive and undemocratic, they fall within the scope of fairly well-known epidemic planning. However, many new data-driven techniques that could have stemmed, or at least slowed, the spread of the coronavirus has not been deployed. One reason for this is these methods rely on crowd-sourcing data, which to be accurate, often depends on public trust in the governments use of citizen information. Something absent in China.

For example, New York University professor Daniel B. Neill, and MIT Lincoln Labs researcher, Mallory Noble, have developed a new tool called pre-syndromic surveillance that uses machine learning to comb through the de-identified emergency room and social media data to discover outbreaks that do not correspond with known illnesses. Recently, the team successfully piloted the technique in New York Citys Department of Health and Mental Hygiene.

Neill refers to the system as a public health safety net because by aggregating and analyzing the actual description of new symptoms from patients, first responders, and citizens, they are better able to identify and manage new disease patterns.

Traditional epidemiologists rely on past cases and patterns to make public health decisions, but some new diseases behave differently, comments Neill. This technique allows us to identify and respond to something new.

Similarly, tools like Flu Near You and Pandemic Pulse developed by Boston Childrens Hospital integrate Twitter and Google Search data to detect biothreats. Like Neill and Nobles pre-syndromic surveillance approach, these tools utilize natural language processing engineering lingo that just means reading what people actually say or write down.

Both teams won the Department Homeland Securitys 2018 Hidden Signals Challenge to use public data to identify emerging biothreats.

But for any of this to work, citizens must trust these digital platforms wont be used to harm them.

If a patient believes what they tell an emergency room attendee or what they say on social media may lead to aggressive quarantines or other harmful actions by their government, its less likely they will be honest. And without reliable information, none of these new systems work.

The "New York Times" reported that Chinese authorities went to great lengths to hide the disease from the public by silencing doctors and others for raising red flags and even closed a food market thought to be the source of the outbreak but told residents it was due to renovations.

While those activities surely slowed authorities ability to work with citizens to respond to the disease, the new machine learning techniques outlined by Neill and others work illuminates an even more important flaw in the Chinese strategy the possibility that some new technologies may be democracy-oriented in that they simply work better in open societies.

At a minimum, the demand for trust and transparency with citizen data represents an irony for the Chinese Communist Party. The governments expansive powers are often seen as a scary but effective vehicle to monitor citizens, such as through new facial recognition technologies.

But there is another way to look at it: If advances in crowd-sourced, citizen communication depend on accurate and complete information, which in turn relies on public trust, then places like China will struggle to fully take advantage of these technologies.

The coronavirus spread so quickly because it is a new virus that behaves differently than what weve seen before and known methods of disease control were caught off-guard. But new crowd-sourced technologies can help us adapt quicker. Lack of public trust hurts those efforts.

Coronavirus is the latest global epidemic, but it wont be the last. The lack of greater trust and transparency from the government of the worlds most populated country risks lives in China and across the globe.

Scott Andes is the executive director of theBlock Center for Technology and Societyat Carnegie Mellon University. Scott was a Fellow at the Brookings Institution and his research focuses on the economic and social impact of new technology.

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Public trust in data could have helped China contain the coronavirus | TheHill - The Hill

How much can parents really control how their kids turn out? – The Boston Globe

In 2009, while awaiting Olivias arrival, my wife, Andrea, and I constructed a parenting plan that included the following set of screen-time rules: zero screen time. It wasnt especially nuanced, but it pretty well summed up our concerns about how watching television and using computers and apps can negatively affect the developing brain. And anyway, zero screen time was what the American Academy of Pediatricsthe gold standard as far as were concernedwas recommending for kids younger than 2. We tacked on an extra six months, just to be safe. So until Olivia was 2, televisions and other screens could not be used in our house while she was awake.

When her younger sister, Violet, came along a few years later, we imposed the same rule. If she was awake, the screens were dark. Even today, the girls are allowed to watch television only on weekends. The Internet is used exclusively for schoolwork, and only while supervised. And phones? The only time our daughters are allowed to pick one up is for calls or FaceTime with relatives. (Were not totally crazy, by the way: We recently bought a video game system that the girls can use during their allotted screen-time sessionsalbeit to play dance and soccer games that get them off the couch.)

Screen use is just one part of our parenting plan. Like many parents, we have invested a lot of time and energy researching strategies for just about everything in our kids livesfrom their sleep schedules and diets to their ratio of planned activities to free time. Its early, and things can change, but for now theyre both doing well in school, have developed good friendships, and, generally speaking, are happy and well adjusted. So far, so good.

This start was, of course, precisely what wed hoped for. But something happened around the time of Violets birth that started me down a path that has led me to some rather unexpected questions.

From the moment Violet began to display a personality, it was evident that she shared many traits with her big sister, and with her parents. But there were elements that clearly belonged to her alone. From the earliest age, she enjoyed attracting attention. She lit up when she could get people to coo or laugh. Such was her delight in knocking over block towers or trashing teddy bear tea parties that she earned the family nickname Destructo. All of these delightfully extroverted qualities made her unique in our family, and I marveled at how that could be possible. She was being raised with the same plan as her sister. Where had these traits come from?

And just like that, I was tumbling down a rabbit hole. I began to question just how much our parenting had to do with the people our daughters were becoming. The more I thought about it, the more the answer seemed inevitable: not much at all. I started bringing the subject up with family and friends. I think only 25 percent of who Olivia and Violet are has anything to do with how were raising them, Id say. The rest is just random chance. Its luck. This notion, as it happened, did not go over well. Some parents rejected it outright, others got legitimately angry. No one (my wife included) entirely agreed with me.

But I kept poking around, looking for what science had to offer. It turns out that researchers have been circling around this very question for a very long time, and, I was startled to discover, their conclusions are rather unambiguous. If anything, according to the research, my 25 percent estimate had been vastly overestimating the influence of our parenting.

__________

THERE ARE AS MANY PARENTING APPROACHES out there as diet plans. Perhaps youve encountered one or two yourself: attachment parenting, slow parenting, tiger parenting, free-range parenting, helicopter parenting, snowplow parenting . . . OK, those last two are actually snarky diagnoses of parenting behaviors, but you get the idea. An entire industry has sprouted to promote methods for raising super fantastic kids, with more announced seemingly every day. Is it possible that all of it, all the books and blogs and podcasts, all of the expert advice, is simply bunk? How much does parenting actually matter when it comes to long-term outcomes for our childrento the personalities they develop, to their success, sense of fulfillment, and overall happiness? I wanted to find out.

Which gets us back to Olivia and her requests for a phone. The first time, I laughed it off. So around Christmastime she started asking instead for an iPod touch, which is basically a phone in sheeps clothing. When I laughed again, she started demanding to know when, exactly, shed be able to get a phone. How about in eighth grade? she asked, mentioning the organization Wait Until 8th, which encourages parents to take the pledge to hold off on phones until their kids reach that grade. The idea of an eighth-grader with a phone struck me as absurd.

Over the next few days, though, I found myself returning to that exchange. Why couldnt Olivia have a phone in eighth grade? When would we feel comfortable with her having one? What were we afraid would happen? In turning these questions over in my mind, I kept coming back to what research suggests about parenting: That most of who our children become is determined not by parenting, but by their genes, with their peer groups playing an important supporting role. (I find this argument persuasive, as Ill explain later, but its important to note that there are tragic exceptions, including children who experience trauma, abuse, and neglect in their many forms.)

But if I truly believe this, if our kids truly were born not as blank slates to be filled by Andrea and me with our parentingif they came into this world already programmed to be the people they are for the most part going to becomethen what could having a phone possibly do to change the arc of Olivias life in some awful way? Not much, it would seem. But a phone?

Struggling to reconcile my fears with what the research is telling me, a single, unavoidable question begins to form in my mind: Does parenting actually matter at all?

__________

IF YOURE LOOKING FOR INSIGHT into just how muchor how littleparenting affects childhood development, a good place to start is with the Harvard cognitive psychologist Steven Pinker. In the 1990s, Pinker helped bring to prominence the work of the psychology researcher Judith Rich Harris. In 1995, Harris caused a stir when she published an article in Psychological Review that began with this stark claim: Do parents have any important long-term effects on the development of their childs personality? This article examines the evidence and concludes that the answer is no.

Pinker was moved enough by the pieces challenge to his own assumptions that he wrote the forward to the groundbreaking work Harris published three years later, The Nurture Assumption: Why Children Turn Out the Way They Do. The bookwhich has been called an utterly persuasive assault on virtually every tenet of child developmentargues that genes and peers have a far greater influence than parents on the adult that a child will become. When Pinker released The Blank Slate: The Modern Denial of Human Nature, in 2002, he included a chapter in the bestseller that touched on some of the themes found in Harriss work.

By the time Harris passed away, in 2018, she and Pinker had become friends. So on a blustery afternoon in January, I take the elevator up to the ninth floor of Harvards William James Hall, where Pinker has his offices. Pinker specializes in psycholinguistics and visual cognition, studying, among other things, how language is acquired, but hes also a kind of public intellectual whose informative, often entertaining, and sometimes controversial opinions are regularly featured in the media. On the day I visit, we sit down in the office of one his research assistants because a documentary film crew is busily setting up in his office for an interview about rationality and emotion in the history of science.

In The Blank Slate, Pinker writes that about half of the variation in intelligence, personality, and life outcomes is influenced by genes. I ask him if parenting is what accounts for the other half.

There are environmental effects, he says. But this is an important point that Harris makes: Environmental effects must not be equated with parenting effects. There is also the culture, and when we talk about kids, culture equals peer group, pretty much. A social circle, in other words, helps shape a kids personality and developmental outcomes, but a parent for the most part does not. There is evidence that parenting can have some early effect, he says, but it gets diluted over time, and whatever effects of the families there are tend to peter out as the children get older. Difficult to believe? Pinker can point to exhaustive studies that confirm as much.

Whats sometimes called the first law of behavioral genetics is that all behavioral traits are partly heritable, he says. The second law is that the effects of families are far weaker than the effects of genes. This explains why, for instance, identical twins brought up in different homes have been consistently shown to wind up quite similar to each other. And why, statistically speaking, adopted kids who arent biological siblings but are brought up in the same home turn out to be not very similar at all.

Theres still another contributor to child outcomesa unique environment thats covered by the third law of behavioral geneticsbut that one is less clear. One of the few things researchers can say for sure about the third law is that its not related to parenting. It just refers to what we cant explain, Pinker says. There are, however, some theories about these mystery influences. One of them, Pinker says, is that there are mutations in the genome after conception that will differ, even between twins, and make us all different from one another in a way thats not predictable either from our parents or from our upbringing.

From Pinkers perspective, there just isnt much evidence that the strategies parents employ while raising their kids wind up mattering very much. Harris understood this. Her argument, Pinker says, is that if you were to switch the kids around within a [specific] neighborhoodthat is, transplant them to other familiesyoud see very little, if any, long-term difference.

This is a staggering contention that can be difficult to acceptwhich may explain some of the criticism that The Blank Slate received upon publication. (It received plenty of acclaim, too, including being named a finalist for the Pulitzer Prize.) Nearly two decades later, Pinker seems to still find the criticism annoying. A lot of the critiques were certainly ignorant, including coming from many so-called experts in child psychology, he says. They just could not wrap their minds around the idea that genes mattered. They would say, Well, what about all the brilliant parents who have such brilliant children? Like, not getting the point: Yes, but they shared the [parents] genes.

__________

MUCH OF THE RESEARCH Pinker cites has come from the study of twins. Researchers have been conducting these kinds of studies for generations with the goal, generally speaking, of figuring out just how much of a particular trait is influenced by our genes and how much has to do with something else.

These questions seem to be of intense interest to researchers because there have been a lot of twin studies. In 2015, a team co-led by the Dutch behavioral geneticist Tinca Polderman made international headlines with the release of a meta-analysis of essentially every such study published between 1958 and 2012. That meant analyzing the results of 2,748 research projects50 or so per yearinvolving more than 14 million pairs of twins (some pairs may have participated in multiple studies). The team compiled the findings and created a database that allows anyone to easily look up what five decades of research shows about the genetic component of 28 different functional trait domains.

A researcher I talked to called the analysis one of the most important papers published in the last 50 years in science. So I decide to call up Polderman, an assistant professor at Vrije Universiteit Amsterdam, and find out what the research actually tells us.

I start by asking Polderman to explain how twin tests work. We have two types of twins, she says, monozygotic twins and dizygotic twins. Monozygotic twins are genetically identical, while dizygotic twinswhat we know as fraternalare, on average, 50 percent identical, just like any other siblings. Since both types grow up in the same family, the shared environment is controlled. Twin researchers choose a traitsay depression, IQ, or blood pressuremeasure it in twin pairs of each type, and then average the results. For traits in which identical twins score very similarly and fraternal ones do not, she explains, researchers can conclude that genes play an outsize role. When the difference between the two types of twins is less pronounced, the trait may be more influenced by environmental factors.

I ask Polderman what the studies reveal about the influence of parenting. For shared environmental influencesthe technical term that encompasses parentingwe find very, very limited evidence, she replies.

There is evidence that the shared environment can contribute to things like criminal behavior and educational attainment, Polderman continues, but Im not sure whether its parenting, or whether, for instance, its the shared neighborhood, or poverty, or anything else you could speculate about. Its likewise possible that parenting contributes to early performance on school and IQ tests. But again, that may well be telling us more about socioeconomics than anything else. It can be a matter of money, what parents can pay, she says. It can also be a matter of providing books in the house at that point in time, or providing educational television programs, maybe. Or, nowadays, educational games.

Polderman tells me that despite all her work studying twins, she wasnt prepared for just how little her own parenting would shape her two sons, who are now adults. I thought, actually, that the influence of parenting would be quite substantial on their behavior, she says. Instead, her experience tracked exactly with what she was seeing in her twin studies, especially when it came to their different attitudes toward their schoolwork. So I think, in the end, that the influence of parents is, on this kind of thing, quite limited.

Of course, theres another way to look at this. Every parent understands the feelings of overwhelming guilt that often accompany the raising of kids. Each failure, public tantrum, and refusal to eat vegetables can feel like a referendum on your fitness to bring up children, and even your worth as a human being. But Polderman no longer makes judgments when, for instance, she sees a friend struggling with a screaming child on the supermarket floor. I think they are excellent parents, she says. Its just, its in the children. And its not the parents fault, or the parents ... reward when it turns out well.

So what the hell is the point of being a parent? I ask, exasperated. Whats our job here? Is it just to keep our kids alive?

I think your task as a parent is to create an environment for your child that is as safe and as stable and as predictable as possible, she says. Within that environment, a child can develop most optimally. Because I think at extreme conditions, like neglect or abuseor traumatic life eventswill surely also have quite a big impact on child development.

And there it wasthe fear, underlying everything, of a life-altering traumatic event. It was absurd to think that giving Olivia a phone would qualify as one, yet isnt that what Ive been imagining? Am I actually going to cause long-term harm to her in some way if she has a phone? Im asking myself as much as Polderman, and I answer my own question: No. Im not. So, why am I still so resistant? Why are we still so resistant as parentsthe evidence here is overwhelming, isnt it? That its luck? That its not really what were doing as parents?

Well, in a way, yes, I can only agree on that, Polderman replies, thankfully taking my outburst in stride. Well, still, of course, I think it might matter. She says sitting around with a phone or laptop all day was the kind of thing that could lead to obesity, for instance.

At last! Some evidence that keeping a phone away from Olivia is a good ideaand that at least something Ill do as a parent can overcome genes.

That would have been a good time to hang up the phone, but somethings nagging at me and I cant. I finally ask, Was obesity one of the traits that you looked at in the twin study?

I think so, yes, Polderman replies. Im just quickly looking at our website. I hear the clicking of computer keys as she searches her database. OK, here I have it, she says. Weight maintenance functions. Yeah, so, about 75 percent of the difference is explained by genetic differences. I see that the shared environment is measured quite low, again, around 10 percent. So its mainly genetic.

__________

SO WHERE DO I COME DOWN these days on the great nature-nurture debate? Im fairly certain that theres really no debate at all. When it comes to the development of Olivia and Violet, the die was largely cast before they were even born. But that, I have decided, is not the same as saying that parenting doesnt matter. I am confident that it does, but not in the ways that we sometimes assume.

No, I do not believe that we have much ability to shape the adults that our daughters will become. But, as Steven Pinker told me, citing Judith Rich Harris, Its a mistake to think that if parents cant shape their childrens personalities, [that] they have no effect on their children. No one thinks that they can shape the personality of their spouse, but no one would say it doesnt matter how I treat my husband or wife. Of course it matters how you treat them. It matters to the quality of your relationship.

Theres something else, too, and this one is coming not from any research Ive found. In my experience, the most powerful thing in life, more powerful than fear, than love, than even genes, is choice. Andrea and I cant really make it any more or less likely that our daughters will surround themselves with supportive people, or pursue fulfilling careers, or use money wisely, or eat right. But its my beliefOK, its my hopethat a childhood overflowing with love, support, respect, concern, communication, and structure can inform our daughters future decisions. We may not be able to lead them to our hoped-for outcomes, but we can demonstrate that the options exist. And once you know a thing is possible, it becomes a choice you can make.

Im saying that DNA is not destiny, and also that I now understand that a phone will neither warp Olivias brain nor damage her prospects. But shes still not getting one anytime soon.

___________________

John Wolfson is editor of Boston College Magazine. Send comments to magazine@globe.com.

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How much can parents really control how their kids turn out? - The Boston Globe

LATS kinasemediated CTCF phosphorylation and selective loss of genomic binding – Science Advances

INTRODUCTION

Metazoan interphase chromosomes are partitioned into discrete megabase-sized topologically associating domains (TADs) that exhibit highly increased contact frequencies within themselves (13). At higher resolutions, TADs are typically composed of smaller loop structures, referred to as insulated neighborhoods (4). The chromatin domain boundaries are strongly enriched for the binding of the architectural protein CCCTC-binding factor (CTCF) (5, 6), which is pivotal in organizing chromatin topology by establishing chromatin loops (7). The chromatin architectural domains act as insulated units that spatially constrain transcription regulatory circuits (4, 8). Interactions between genes and their regulatory elements generally are confined within the same CTCF-mediated loop domains. CTCF-anchored domain boundaries function as barriers to insulate adjacent chromatin domains. Experimental disruption of CTCF binding at loop anchors alters domain insulation and local gene expression (917). In cancer, somatic mutations occur recurrently at insulated neighborhood boundaries (11, 13, 18). DNA hypermethylation occurs at CTCF-binding sites at domain anchors in certain gliomas (12). These genetic and epigenetic events perturb CTCF DNA binding, impair the insulation between topological domains, and allow outside enhancers to illegitimately contact and activate otherwise inactive proto-oncogenes located inside the domains (12, 13). These studies underscore the functional importance of chromatin topological organization in transcriptional regulation.

While the genome topology is generally stable (4, 8), a subset of chromatin architectural domains undergo marked remodeling during the cell differentiation or reprogramming process, which is accompanied by substantial gene expression changes (1924). Moreover, CTCF genomic binding and interphase chromatin topological organization are mostly abolished during mitosis (2527). The molecular mechanisms underlying three-dimensional (3D) genome dynamics during cell differentiation and cell cycle remain to be elucidated. Furthermore, external signals trigger rapid transcriptional responses in cells. Changes in chromatin spatial organization affect gene expression, yet it is largely elusive whether and, if so, how CTCF-mediated 3D genome architecture may reshape in response to outside signals.

The canonical Hippo-LATS signaling pathway is an evolutionarily conserved central regulator of cell proliferation, apoptosis, organ size, tissue homeostasis, and tumorigenesis (28, 29). The core to this pathway in mammals is a kinase cascade in which the MST1/2 kinases (orthologs of Drosophila Hippo) phosphorylate and activate the LATS1/2 kinases. LATS1/2 kinases are activated by a wide variety of stress signals (30). Active LATS1/2 phosphorylate the major downstream effectors, YAP and its paralog TAZ, and prompt their sequestration in the cytoplasm via 14-3-3 binding, leading to apoptosis and growth arrest in cells. In the absence of LATS activation, YAP/TAZ are unphosphorylated and localized in the nucleus, where they associate with the TEA domain (TEAD) family of transcription factors to coactivate a set of genes that promote cell growth and survival. Dysregulation of LATS signaling causes overgrowth and tumorigenesis (3133). YAP/TAZ are pervasively activated in human malignancies and are essential for the initiation or growth of most solid tumors (29, 34).

Here, we identified CTCF as a previously unidentified substrate of the LATS kinases. Active LATS directly phosphorylated CTCF in the zinc finger (ZF) linker regions and impaired its DNA binding activity. Genome-wide CTCF DNA binding profiling revealed that LATS-activating cellular stress did not cause widespread loss of CTCF DNA binding in the genome, but rather selectively dissociated it from a small subset of its genomic binding sites, which were enriched for anchors of chromatin domains containing YAP target genes. The stress-induced CTCF phosphorylation and dissociation from DNA were dependent on LATS. Locus-specific loss of CTCF occupancy disrupted local chromatin domains and decreased expression of YAP target genes that were located inside them. Therefore, the study uncovers signal-responsive plasticity of the 3D genome architecture and identifies CTCF ZF linker phosphorylation as the critical underlying mechanism.

CTCF contains 11 ZFs, and the central ZFs 4 to 7 are responsible for its binding to the core sequence motif that is present in the vast majority of the known CTCF-binding sites (35). For multi-ZF transcription factors, optimal DNA binding requires cooperative binding of adjacent ZFs, and the linkers that connect neighboring ZFs play an important structural role in stabilizing protein-DNA interactions (36). The ZF linkers are often phosphorylated during mitosis, and these events correlate with loss of DNA binding activity and mitotic chromosomal eviction (3739). Large-scale phosphoproteomic analysis of mitotic cells identified mitosis-preferential phosphorylation events in >1000 proteins, including phosphorylation of human CTCF ZF linkers, in particular threonine (T) 374 (in the linker between ZF4 and ZF5) and serine (S) 402 (in the linker between ZF5 and ZF6) (Fig. 1A) (40). Concurrently, CTCF is excluded from mitotic chromosomes (41). Subsequent global proteomics studies detected phosphorylation of CTCF ZF linkers in asynchronous cells as well (www.phosphosite.org/proteinAction.action?id=1155&showAllSites=true). We reasoned that CTCF ZF linker phosphorylation may also occur in interphase cells and represent a mechanism for signal-responsive inactivation of CTCF DNA binding. However, kinases that mediate CTCF ZF linker phosphorylation and the significance of these phosphorylation events in CTCF-mediated 3D genome organization are not defined.

(A) Phosphorylation sites in CTCF ZF linkers match the LATS kinase phosphorylation motif (HxRxxS/T). (B) Direct phosphorylation of CTCF ZF linkers by LATS in vitro. Recombinant glutathione S-transferase (GST) and GST-CTCF (amino acids 351 to 410) wild-type (WT) or T374E/S402E mutant (Mut) fusion proteins were incubated with active LATS2 kinase and ATP, followed by immunoblotting with antibodies against phospho-RxxS/T. GST proteins were stained with Coomassie dye. (C) LATS kinases induce CTCF ZF linker phosphorylation in cells. Full-length Flag-tagged WT or T374E/S402E Mut CTCF was transfected into HEK293 cells with LATS1 and/or MST2, followed by immunoprecipitation with anti-Flag antibodies and immunoblotting for phosphorylation of RxxS/T. (D to F) Cellular stress stimulates CTCF ZF linker phosphorylation. MCF7 cells stably expressing Flag-CTCF were cultured in glucose-free [Glu()] media supplemented with 2-DG (D), in serum-free [serum()] media (E), or in suspension (F) for 1 day. Flag-CTCF proteins were immunoprecipitated with anti-Flag antibodies and immunoblotted for phosphorylation of RxxS/T or S402. (G) Energy stress induces endogenous CTCF phosphorylation in a LATS-dependent manner. MCF7 cells were infected with lentiviral vector pLKO or short hairpin RNA (shRNA) targeting LATS2 (shLATS2). The knockdown efficiency was determined by real-time reverse transcription polymerase chain reaction (RT-PCR). Cells were cultured in glucose-free media or in suspension for 1 day and subjected to immunoprecipitation with anti-CTCF antibodies and immunoblotting for phospho-RxxS/T. For quantification, the intensity of phospho-CTCF versus total CTCF in control samples are set at 1.0 (basal levels). The numbers under the blots are fold induction over basal levels. (H) LATS nuclear translocation under stress. HCT116 cells were cultured in normal or glucose-free media for 1 day and subjected to immunofluorescence analysis for subcellular localization of indicated proteins. DNA was stained blue with DAPI (4,6-diamidino-2-phenylindole).

The phosphorylation sites T374 and S402 in the CTCF ZF linker regions match the consensus phosphorylation motif (HxRxxS/T) for LATS kinases (Fig. 1A) (42, 43). To test whether LATS kinases were able to phosphorylate CTCF, we purified recombinant glutathione S-transferase (GST) fusion proteins that contained wild-type (WT) CTCF ZF linkers or a mutant CTCF, in which T374 and S402 were substituted with glutamate (E). After incubation with active recombinant LATS2 kinase, the WT CTCF protein fragment was phosphorylated at the RxxS/T sites, while T374E/S402E mutant CTCF was not phosphorylated (Fig. 1B), demonstrating that LATS can directly phosphorylate CTCF ZF linkers in vitro.

To examine whether LATS could phosphorylate CTCF in cells, we cotransfected Flag-tagged full-length CTCF into human embryonic kidney (HEK) 293 cells with LATS1 and/or MST2 (fig. S1A), followed by immunoprecipitation with anti-Flag antibodies and immunoblotting for phosphorylation of the RxxS/T sites. LATS activation in transfected cells required the upstream MST kinases (44). Consistently, expression of either LATS1 or MST2 alone caused little phosphorylation of CTCF, but coexpression of both kinases induced strong phosphorylation of CTCF at the RxxS/T sites (Fig. 1C). Mutations at T374 and S402 of CTCF largely abrogated this phosphorylation (Fig. 1C), suggesting that T374 and S402 are the major LATS-mediated phosphorylation sites in CTCF. We developed an antibody specifically recognizing phosphorylated S402 of human CTCF. When probed with this antibody, CTCF was shown to be phosphorylated in cells expressing both LATS1 and MST2 (fig. S1B). Together, the results indicate that activated LATS kinases phosphorylate CTCF primarily at T374 and S402 in cells.

LATS kinases are activated by a wide range of signals, including energy stress, serum starvation, and cell detachment (30). We analyzed whether activation of endogenous LATS kinases may induce CTCF ZF linker phosphorylation. Energy stress such as glucose deprivation reportedly induced LATS activation and YAP phosphorylation (4547). We stably expressed Flag-tagged CTCF in MCF7 breast cancer cells through lentiviral transduction. When the cells were switched to glucose-free media, YAP S127 (the major LATS phosphorylation site) became phosphorylated (fig. S1C), indicative of LATS activation. Further treatment with 2-deoxy-d-glucose (2-DG), a glucose analog that competitively inhibits glycolysis, caused even more robust YAP S127 phosphorylation (fig. S1D). When Flag-CTCF proteins were immunoprecipitated from the cells under this energy stress, they exhibited increased phosphorylation at the RxxS/T sites (Fig. 1D), although the exact fraction of phosphorylated CTCF remains to be determined.

Serum starvation activated LATS kinases, leading to YAP phosphorylation (48). MCF7 cells stably expressing Flag-CTCF were cultured in serum-free media. LATS kinases were activated, as evidenced by YAP S127 phosphorylation (fig. S1E). We immunoprecipitated Flag-CTCF proteins and found that their phosphorylation at the RxxS/T sites was markedly enhanced in serum-starved cells compared with cells cultured in serum-supplemented media (Fig. 1E).

Cell detachment caused reorganization of cytoskeleton and activation of the LATS kinases (49). Detachment of MCF7 cells stably expressing Flag-CTCF increased CTCF phosphorylation at the RxxS/T sites, including the S402 residue as detected by the phospho-S402specific antibody (Fig. 1F). Collectively, these results show that LATS-activating signals stimulate CTCF ZF linker phosphorylation.

We further asked whether endogenous CTCF proteins were phosphorylated in response to cellular stress and whether this phosphorylation was LATS dependent. LATS1 and LATS2 share a high degree of homology and functional overlap but exhibit different expression patterns (50). According to transcriptomics analysis (e.g., GSE112295), LATS2 expression is much higher than LATS1 in MCF7 cells. To deplete most of the activity of LATS, we transduced MCF7 cells with lentiviral short hairpin RNA (shRNA) targeting LATS2 (Fig. 1G), followed by glucose starvation or cell detachment. Endogenous CTCF proteins were immunoprecipitated with anti-CTCF antibodies and immunoblotted for phosphorylation at the RxxS/T sites. In empty vectorinfected control cells, glucose starvation or cell detachment markedly stimulated endogenous CTCF ZF linker phosphorylation (Fig. 1G). CTCF protein abundance was steady under these conditions (Fig. 1G), implying that phosphorylation may not affect CTCF protein stability. LATS depletion did not affect the basal levels of CTCF phosphorylation in cells under normal conditions but essentially abolished glucose starvation or cell detachmentinduced CTCF ZF linker phosphorylation (Fig. 1G). The results suggest that LATS is required for cellular stressinduced phosphorylation of endogenous CTCF ZF linkers.

Our study identified CTCF as a previously unknown substrate of the LATS kinases. However, CTCF is a nuclear protein, while LATS kinases are generally cytoplasmic (28, 29). To investigate where in cells LATS may phosphorylate CTCF, we determined subcellular localization of LATS, YAP, and CTCF in cells under energy stress. We found an antibody against LATS1 that was suitable for immunofluorescence analysis. LATS1 was relatively abundant in HCT116 colon cancer cells (according to the Cancer Cell Line Encyclopedia). Under normal culture conditions, LATS1 was detected predominantly in the cytoplasm, whereas YAP and CTCF were exclusively nuclear (Fig. 1H). In cells under glucose starvation, LATS1 was accumulated in the nucleus in the majority of cells, whereas YAP mostly translocated to the cytoplasm, and CTCF remained nuclear (Fig. 1H). It was known that phosphorylation of YAP generated docking sites for 14-3-3, which promoted YAP cytoplasmic localization (44, 51). In contrast, based on the sequence, phosphorylation of CTCF ZF linkers (T374 and S402) does not create binding sites for 14-3-3. The results suggest that stress-activated LATS moves into the nucleus, where it phosphorylates nuclear substrates YAP and CTCF.

Mitotic phosphorylation of ZF linkers correlated with exclusion of ZF proteins from mitotic chromosomes, and phosphomimetic substitutions at ZF linkers abolished DNA binding in vitro (37, 38, 52, 53). The crystal structure of the human CTCF DNA binding domain in complex with DNA was recently solved (54). Although neither T374 nor S402 is in direct contact with DNA, phosphorylation of either residue may reduce the overall electrostatic attraction between CTCF and DNA. In addition, both T374 and S402 are adjacent to residues that directly coordinate zinc ions. Their phosphorylation may partially distort the zinc-binding sites, therefore decreasing DNA binding.

We verified whether phosphorylation-mimicking mutations of ZF linker phosphorylation sites (T374 and S402) in CTCF impaired its DNA binding in vitro and in cells. We first purified WT and phosphomimetic mutant CTCF proteins that were transiently expressed in HEK293 cells and incubated them with DNA fragments containing a CTCF-binding site from the AXL gene. WT CTCF protein displayed the strongest binding activity, single T374E or S402E mutant showed reduced DNA binding, and the T374E/S402E double mutant exhibited the weakest affinity for DNA (fig. S2A). This observation is consistent with a previous report that phosphorylation of two ZF linkers causes stronger loss of DNA binding activity than phosphorylation of a single linker (52). We then stably expressed Flag-tagged WT and phosphomimetic mutant CTCF in MCF7 cells via lentiviral transduction (fig. S2B), followed by chromatin immunoprecipitation (ChIP) analysis of exogenous CTCF binding at the AXL locus with anti-Flag antibodies. Single phosphomimetic mutation at either T374 or S402 substantially decreased CTCF binding, while simultaneous substitutions at both sites maximally reduced CTCF binding (fig. S2B). Therefore, phosphomimetic mutations in CTCF ZF linkers diminish the DNA binding activity of CTCF, implying that ZF linker phosphorylation may disable CTCF from binding to DNA.

The impact of external signals on CTCF genomic binding in cells remains poorly understood. LATS kinases phosphorylate CTCF in the ZF linkers and impair its DNA binding activity. However, it was unknown how CTCF occupancy in the genome might be altered by LATS signaling. To address this question, we conducted the CUT&RUNsequencing (seq) assay (55) to map genome-wide CTCF DNA binding in cells under normal versus glucose starvation conditions in two biological replicates (fig. S3A). To facilitate the identification of sites exhibiting differential binding, we used a stringent threshold for peak calling and identified 11,880 CTCF-binding sites in cells in normal media and 11,792 sites in cells in glucose-free media (Fig. 2A). The identified CTCF-bound regions were highly enriched for the known consensus binding motif for CTCF (fig. S3B). Under glucose starvation, CTCF occupancy at most of its genomic targets (10,507) was not altered, but 1363 sites exhibited >2-fold decreased CTCF binding and 1275 sites displayed >2-fold increased CTCF binding (Fig. 2, A and B). Peak annotation, which by default assigns peaks to the nearest transcription start sites (TSS), identified 744 genes that were closest to the 1363 sites showing decreased CTCF binding, and Gene Ontology (GO) pathway analysis revealed that they were most significantly associated with the Hippo-LATS signaling pathway (Fig. 2A and table S1). Sites with increased CTCF binding corresponded to 715 genes, which were most significantly associated with mTOR (mammalian target of rapamycin) signaling (Fig. 2A and table S1). We noticed that representative YAP target genes downstream of LATS signaling (e.g., AMOTL2, AXL, CRY1, GLI2) were all among the 744 genes displaying decreased CTCF binding under glucose starvation (Fig. 2B). We, thus, analyzed YAP ChIP-sequencing (ChIP-seq) data (GSM2859577) to identify its genomic target genes in MCF7 cells. It turned out that 191 of the 744 genes (25.7%) were potential direct genomic targets of YAP. Together, the results demonstrate that energy stress can rearrange CTCF binding in the genome. In particular, with respect to inactivation of CTCF DNA binding, the LATS-activating signal does not cause global loss of CTCF genomic binding but rather selectively dissociates CTCF from specific subsets of genomic sites that are mostly associated with LATS signaling and highly enrich YAP target genes.

(A) Genome-wide mapping of CTCF DNA binding in MCF7 cells in normal (control) and glucose-free [Glu()] media. Under glucose starvation, 1363 and 1275 genomic sites show decreased and increased CTCF binding (>2-fold), respectively. Right: GO pathway analysis of genes associated with CTCF-binding sites. AMPK, adenosine monophosphateactivated protein kinase. (B) Heatmap of log2-transformed CTCF-binding signals at genomic sites exhibiting differential binding under glucose starvation [from (A)]. Representative YAP target genes that are closest to the sites with decreased CTCF binding are indicated. (C) Glucose starvation specifically reduces CTCF binding at domain boundaries surrounding YAP target genes (AMOTL2, AXL, and PFKFB3) (highlighted in green), whereas sites outside these chromatin loops are not affected. ChIA-PET interactions of CTCF in MCF7 cells under normal conditions are shown beneath the CTCF genomic binding profiles. The anchors of CTCF loops generally overlap with CTCF-binding peaks. Genomic positions are shown on top.

To understand how alterations in CTCF genomic binding were related to gene expression, we analyzed microarray gene expression profiling of MCF7 cells under glucose starvation (56). Nearly 600 genes were significantly down-regulated by energy stress (fig. S4A), and they were most significantly associated with the Hippo-LATS signaling pathway (fig. S4B). Representative YAP target genes all displayed reduced expression (fig. S4A). Among the 744 genes that are closest to the genomic sites showing decreased CTCF binding under stress, 293 of them (39%) were down-regulated but very few (3%) were up-regulated (fig. S4C). In addition, 103 of the 191 potential YAP genomic targets were down-regulated. Conversely, among the 715 genes that are closest to sites with increased CTCF binding, 259 (36%) were up-regulated (fig. S4C). Therefore, differential CTCF genomic binding due to energy stress correlates with differential expression of nearby genes. Under energy stress, YAP target genes lose/decrease nearby CTCF binding and concomitantly are down-regulated.

CTCF mediates the formation of insulated neighborhoods (4). CTCF-anchored chromatin looping interactions in MCF7 cells have been mapped by paired-end tag sequencing (ChIA-PET) analysis (57, 58). To determine how CTCF genomic binding may modulate gene expression, we aligned the differential CTCF-binding sites to CTCF-mediated loop anchors. Among the 1363 sites showing decreased CTCF binding under stress, 821 (60%) were overlapping with loop anchors, and these loops contained 1875 genes. Similarly, 681 of the 1275 sites (53%) with increased CTCF binding also overlapped with loop anchors, and 1657 genes were located in these loops. Among the 597 down-regulated genes under energy stress, 276 were located in chromatin loops whose anchors displayed decreased CTCF binding (198 of them shared the same loop with at least another down-regulated gene), and only 19 were in loops with increased CTCF binding at anchors (fig. S4D). Among the 511 up-regulated genes under energy stress, 225 resided in loops with increased CTCF binding at anchors (162 genes were located in the same loop with at least another up-regulated gene), and 22 were in loops with decreased CTCF binding at anchors (fig. S4D). The results suggest that CTCF-anchored chromatin loops may positively regulate genes inside them.

Because LATS-mediated phosphorylation disrupted CTCF DNA binding and YAP target genes lost CTCF binding under stress, we particularly examined CTCF genomic occupancy at representative YAP target genes. At the AMOTL2 locus, CTCF-binding peaks generally overlapped with anchors of CTCF-associated chromatin loops, and CTCF occupancy at anchors of a loop domain containing this gene was markedly decreased by glucose starvation (Fig. 2C). Similarly, CTCF binding at the borders of chromatin loops accommodating other YAP target genes (e.g., AXL, BCL2L1, GLI2, LATS2, PFKFB3, TEAD4) also strongly declined in cells under energy stress (Fig. 2C and fig. S5). In notable contrast, CTCF binding in the same genomic regions but outside the chromatin domains containing YAP targets was not affected by glucose starvation (Fig. 2C and fig. S5). The proangiogenic gene vascular endothelial growth factor A (VEGFA) is located in CTCF-anchored chromatin loops (fig. S5). We previously showed that CTCF acts as an enhancer blocker at this locus (59). VEGFA is not a YAP target gene, and CTCF binding at loop anchors surrounding VEGFA was insensitive to glucose starvation (fig. S5). The results suggest that LATS-activating energy stress reduces CTCF binding preferentially at the anchors of chromatin domains containing YAP target genes.

We performed standard ChIP assays to validate the loss of CTCF binding at YAP target genes in cells under stress. MCF7 cells were cultured in normal or glucose-free media and subsequently subjected to ChIP analysis of endogenous CTCF with anti-CTCF antibodies. CTCF binding at the anchors of chromatin loops containing representative YAP target genes was substantially decreased by glucose deprivation (Fig. 3A). Expression of YAP target genes was also reduced (fig. S6A). For comparison, CTCF binding at the VEGFA locus and VEGFA expression were not altered by energy stress (Fig. 3A and fig. S6A). As cell detachment promoted CTCF ZF linker phosphorylation (Fig. 1), CTCF binding at YAP target genes in MCF7 cells was reduced in detached cells, compared with its binding at these loci in attached cells (Fig. 3B). The decrease in CTCF binding was accompanied by reduced expression of YAP target genes (fig. S6B). By contrast, CTCF binding at the VEGFA gene or VEGFA expression was not affected by cell detachment (Fig. 3B and fig. S6B). Overall, the results confirm that LATS-activating stress signals impede CTCF binding selectively at YAP target genes and down-regulate their expression.

MCF7 cells were cultured in normal or glucose-free media (A) or in suspension (B) for 1 day, followed by ChIP analysis with anti-CTCF antibodies to determine CTCF binding at the indicated genes. The E-cadherin (CDH1) promoter served as a negative control. Data are represented as mean SD. IgG, immunoglobulin G.

To test whether loss of CTCF DNA binding contributed to down-regulation of YAP targets, we depleted CTCF in MCF7 cells by lentiviral shRNAs (59). This led to decreased expression of various YAP target genes (fig. S7A). Similar depletion of CTCF in other cancer cell lines (HCT116 and A549) also down-regulated YAP targets (fig. S7A). The results suggest that CTCF is required to sustain YAP target gene expression.

To unravel how CTCF may support YAP target genes, we examined CTCF DNA binding and chromatin looping at their genomic loci. On the basis of ChIA-PET data, CTCF-binding sites around YAP target genes interacted with each other to form chromosomal loops, and representative YAP target genes along with TEAD/YAP binding peaks are enclosed inside these loops (Fig. 4A). These looping configurations match the pattern of insulated neighborhoods (4). Chromatin domains containing YAP target gene AMOTL2, PFKFB3, or BCL2L1 were enriched for the active histone mark H3 lysine 27 acetylation (H3K27ac) compared with the adjacent regions (Fig. 4A).

(A) Insulated neighborhoods at the active YAP target gene loci (AMOTL2, PFKFB3, and BCL2L1). CTCF ChIA-PET interactions are displayed below the ChIP-seq profiles of H3K27 acetylation (H3K27ac), YAP, TEAD4, CTCF, and cohesin (Rad21). All epigenomics data except YAP binding (derived from MDA-MB-231 cells) were generated in MCF7 cells. Domain anchors are highlighted in green. (B) CRISPR-Cas9mediated deletion of the CTCF-binding site (underlined) at the PFKFB3 promoter in two clones (KO3 and KO12) of MCF7 cells. (C) CTCF DNA binding is required for PFKFB3 expression. WT, KO3, and KO12 MCF7 cells were subjected to ChIP analysis for CTCF binding at PFKFB3 (left) and to RT-PCR analysis for PFKFB3 expression (right). IgG served as antibody control. Data are represented as mean SD.

PFKFB3 is a key player in tumor metabolism (60, 61). To determine whether CTCF binding at the domain anchors was important for YAP target gene expression, we used CRISPR-Cas9 to introduce small deletions at the CTCF-binding site upstream of the PFKFB3 promoter in MCF7 cells (Fig. 4B). The YAP/TEAD binding site at the PFKFB3 promoter is downstream of the CTCF site and remained intact in the mutant cells (fig. S7B). ChIP analysis confirmed that ablation of the CTCF site strongly down-regulated CTCF occupancy at the PFKFB3 promoter in two independent mutant clones (Fig. 4C). PFKFB3 expression substantially decreased in the mutant cells (Fig. 4C), suggesting that CTCF binding at the loop anchor is required for PFKFB3 expression. However, because this CTCF site is proximal to the TSS, we cannot exclude the possibility that CTCF may act as a direct transcriptional activator of this gene.

As LATS was required for stress-stimulated CTCF ZF linker phosphorylation (Fig. 1G), we further verified whether loss of CTCF DNA binding at YAP target genes due to energy stress was dependent on LATS. We cultured vector (pLKO)infected control and LATS2-depleted MCF7 cells in normal or glucose-free media (Fig. 1G) and performed ChIP analysis of CTCF genomic binding at YAP target genes. In normal media, depletion of LATS2 in MCF7 cells did not increase CTCF DNA binding (Fig. 5A and fig. S8), implying that LATS is essentially inactive under this condition. Glucose starvation substantially reduced CTCF binding at YAP target genes in control cells, but this effect was largely blocked by LATS depletion (Fig. 5A and fig. S8). The result suggests that energy stresscaused dissociation of CTCF from YAP target genes is LATS dependent.

(A) Control (pLKO), LATS2-, or YAP-depleted MCF7 cells were cultured in glucose-free media for 1 day, followed by ChIP analysis of CTCF binding at indicated loci. (B) LATS is recruited to the YAP/TEAD binding sites under stress. MCF7 cells stably expressing Flag-LATS1 were cultured in glucose-free media at indicated times. Binding of Flag-LATS1 at the YAP/TEAD sites of indicated YAP target genes (VEGFA served as a negative control) was examined by ChIP analysis with anti-Flag antibodies (IgG as antibody control). (C) Depletion of YAP in MCF7 cells down-regulates its target genes. MCF7 cells were infected with lentiviral control (pLKO) or shRNA targeting YAP (shYAP). Knockdown efficiency and YAP target gene expression were determined by quantitative RT-PCR. (D and E) YAP is required for CTCF ZF linker phosphorylation induced by glucose starvation (D) or cell detachment (E). Control and YAP-depleted MCF7 cells were cultured in glucose-free media or in suspension for 1 day. Endogenous CTCF proteins were immunoprecipitated with anti-CTCF antibodies and immunoblotted for RxxS/T phosphorylation. Cell lysates were immunoblotted for YAP. Data are represented as mean SD. *P < 0.05.

While LATS is required for CTCF ZF linker phosphorylation and loss of DNA binding under stress, it remains elusive how LATS signaling selectively disrupts CTCF DNA binding at specific genomic sites, in particular at the loop anchors surrounding YAP target genes. Because YAP is associated with genomic DNA sites (i.e., primarily TEAD binding sites) inside these chromatin domains, and LATS physically interacts with YAP (42, 62), we postulated that LATS is recruited to these specific genomic regions via interactions with YAP. Although YAP and CTCF generally bind to separate genomic sites due to frequent intradomain chromatin contacts (4, 8), LATS can reach and phosphorylate CTCF that anchors the same domains.

We, thus, examined whether LATS was recruited to YAP/TEAD-binding sites at YAP target genes. We stably expressed Flag-tagged LATS1 in MCF7 cells by lentiviral transduction. Since LATS exhibited dynamic subcellular localization, we examined its potential DNA binding at different time points following stress. The cells were starved in glucose-free media for various periods, followed by ChIP analysis with anti-Flag antibodies. Flag-LATS1 was markedly enriched at the YAP/TEAD sites around representative YAP target genes specifically in cells under glucose starvation, with peak binding observed at 4 to 8 hours after starvation (Fig. 5B). The result suggests that LATS is recruited to genomic sites around YAP target genes under stress.

As YAP may be critical for recruiting LATS, we tested whether YAP deficiency decreased CTCF ZF linker phosphorylation induced by LATS-activating cellular stress. We depleted YAP in MCF7 cells with lentiviral shRNA (Fig. 5C). Depletion of YAP expectedly decreased its target gene expression (Fig. 5C). Control and YAP-depleted cells were then subjected to glucose starvation or cell detachment, followed by immunoprecipitation of endogenous CTCF and immunoblotting for RxxS/T phosphorylation. YAP depletion largely prevented CTCF ZF linker phosphorylation induced by energy starvation (Fig. 5D) or cell detachment (Fig. 5E). Therefore, YAP critically facilitates stress-induced CTCF ZF linker phosphorylation.

We next investigated whether YAP is required for dissociation of CTCF at YAP target genes under energy stress. Control and YAP-depleted MCF7 cells were cultured in normal or glucose-free media, and subsequently subjected to ChIP analysis of CTCF genomic binding. Depletion of YAP, which reduced YAP target gene expression, had no effect on CTCF DNA binding at YAP target genes in cells cultured in normal media (Fig. 5A and fig. S8). When cells were under glucose starvation, CTCF binding at YAP target genes was diminished in control cells but was largely maintained in YAP-depleted cells (Fig. 5A and fig. S8). Together, these results suggest that YAP is required for LATS-dependent CTCF phosphorylation and loss of DNA binding at YAP target genes.

It is poorly understood whether and how CTCF-mediated genome topology may be regulated by external signals. CTCF is absolutely required for 3D genome organization (7). Because cellular stress caused loss of CTCF DNA binding at selected genomic loci such as YAP target genes, we verified whether corresponding 3D chromatin interactions were disrupted. On the basis of ChIA-PET analysis, CTCF anchors a nearly 200-kb chromatin loop spanning the PFKFB3 locus (Figs. 4A and 6A). We performed quantitative chromosome conformation capture (3C) assays to measure the contact frequency between the loop anchors in MCF7 cells and detected a specific polymerase chain reaction (PCR) product derived from the two interacting CTCF sites (verified by DNA sequencing) (Fig. 6A). As a negative control, a primer from the PFKFB3 3 untranslated region (3UTR) failed to generate specific PCR products when combined with the 3C primer from the PFKFB3 5 region. The result confirmed the specific interaction between the two CTCF-binding sites flanking PFKFB3. When MCF7 cells were under glucose starvation, consistent with diminished CTCF binding at this locus, the interaction between these two CTCF sites was substantially decreased (Fig. 6A), demonstrating a loss of CTCF-mediated chromatin looping. By contrast, in the same 3C library samples, CTCF-associated chromatin loop surrounding the VEGFA gene (figs. S5 and S9A) was not influenced by glucose starvation (fig. S9B), which is consistent with the persistent CTCF DNA binding at this locus under energy stress (fig. S5).

(A and B) 3C analyses of CTCF-anchored chromatin looping at the PFKFB3 gene in MCF7 cells under glucose starvation (A) and at the AXL gene in MCF7 cells following cell detachment (B). Top: Schematics of CTCF-anchored insulated neighborhoods at the PFKFB3 (A) and AXL (B) loci. Primers and only restriction enzyme sites used in 3C analysis are shown. Middle: Quantitative 3C analysis by real-time PCR. PCR analysis with primers 3 and 4 (for AXL) also compares the digestion and ligation efficiencies. Data are represented as mean SD. Bottom: Partial sequence of the 3C PCR products shows the junction containing the Hind III site AAGCTT (underlined) (A) or the Eco RI site GAATTC (underlined) from primers 1 and 4 (B). (C) LATS kinases selectively disrupt CTCF-mediated insulated neighborhoods. YAP target genes (e.g., PFKFB3) are located within CTCF-mediated insulated neighborhoods. Under conditions of high nutrient availability, LATS kinases are inactive, and unphosphorylated YAP binds to its target genes including PFKFB3 (mainly via TEAD) to activate their transcription in the context of insulated neighborhoods. The resulting high PFKFB3 expression expedites glycolysis and cell proliferation. Under energy starvation, LATS kinases are activated, translocate into the nucleus, and physically associate with YAP that is already bound to DNA. Although the YAP binding sites may be distant from the CTCF sites, due to high intradomain interactions, YAP-associated LATS can contact CTCF proteins that anchor the same chromatin domains. Subsequently, LATS kinases phosphorylate YAP and CTCF, dissociating both factors from DNA. Loss of CTCF DNA binding disassembles local insulated neighborhoods. These changes down-regulate YAP target genes including PFKFB3, thereby impeding glycolysis and cell proliferation. For insulated neighborhoods that do not attract LATS (e.g., lacking YAP binding), CTCF DNA binding and chromatin looping remain unaltered under energy stress.

We also examined the effect of cell detachment on chromatin looping at the AXL locus (Figs. 2C and 6B). The 3C analysis validated the existence of a loop structure between the two CTCF-binding sites flanking AXL, which were 60 kb apart, in MCF7 cells under normal culture condition (Fig. 6B). This looping conformation was considerably reduced by cell detachment (Fig. 6B). In contrast, the chromatin loop at the VEGFA locus was preserved in matrix-detached cells (fig. S9C). Together, cellular stress may selectively disrupt CTCF-anchored chromatin domains encompassing YAP target genes (Fig. 6C).

The vast majority of genes in the genome, along with their regulatory elements, are accommodated in CTCF-organized insulated neighborhoods (4). This chromatin topological organization represents a fundamental principle underlying mammalian gene transcription (4, 8). However, it is largely elusive whether and how the CTCF-mediated 3D genome architecture may be rapidly remodeled by environmental signals to modulate gene expression. Here, we identified CTCF as a novel substrate of the LATS kinases. LATS can directly phosphorylate CTCF in the ZF linker regions and disable its DNA binding activity. LATS-activating cellular stress causes CTCF phosphorylation and selective dissociation from a subset of genomic sites in a LATS-dependent manner. These genomic sites are most significantly associated with LATS signaling and highly enriched for insulated neighborhood boundaries flanking YAP target genes. Loss of CTCF DNA binding at these sites disrupts corresponding insulated neighborhoods. We, thus, propose the following scenario for cellular stressinduced 3D genome remodeling (Fig. 6C): Under normal culture condition (e.g., with sufficient nutrients), LATS kinases are cytoplasmic and inactive, while YAP is nuclear and binds to its genomic targets. In response to stress signals, LATS kinases are activated and translocate into the nucleus. Through physical interactions with chromatin-associated YAP, LATS kinases are recruited preferentially to insulated neighborhoods containing YAP target genes. Although the CTCF-binding sites and TEAD/YAP-binding sites in the genome are typically separate from each other on linear DNA, due to the high frequency of intradomain chromatin interactions (4, 8), LATS kinases associated with the YAP sites are able to contact and phosphorylate CTCF proteins that anchor the same topological chromatin domains, leading to dissociation of CTCF from DNA and consequent disruption of corresponding insulated neighborhoods. By contrast, insulated neighborhoods that are unable to attract LATS kinases remain unperturbed. Therefore, LATS signaling does not globally block CTCF DNA binding but rather selectively disrupts CTCF occupancy only at a subset of CTCF genomic binding sites, especially the anchors of insulated neighborhoods containing YAP target genes (Fig. 6C). The locus-specific effect specifically affects genes that are concentrated in the LATS signaling pathway. While this model remains to be further validated, external signals may cause certain transcription factors to interfere with CTCF for its DNA binding or DNA hypermethylation at selected CTCF-binding sites, resulting in dissociation of CTCF from DNA. In addition, it is also unclear how some genomic sites gain CTCF binding under stress.

Given the pivotal role of CTCF in chromatin topological organization (7), CTCF ZF linker phosphorylation may represent a general mechanism for inactivation of CTCF and dynamic remodeling of the 3D genome in interphase cells in quick response to external signals. The ZF linkers of CTCF potentially match phosphorylation motifs of various kinases. It is possible that other kinases, when activated by their cognate upstream signals, are recruited to selected genomic loci surrounding their target genes and phosphorylate CTCF proteins that anchor local insulated neighborhoods. This leads to locus-specific loss of CTCF DNA binding and disassembly of chromatin domains, thus eliciting signal-specific transcriptional responses. Overall, the study suggests that interphase genome topological architecture can respond dynamically and rapidly to environmental cues through signal-induced CTCF ZF linker phosphorylation and consequent loss of DNA binding at specific genomic sites. The evolutionarily conserved LATS signaling pathway plays an essential role in mitotic exit (63) and modulates CTCF binding only at a minority subset of genomic sites; therefore, LATS kinases are unlikely to be responsible for CTCF phosphorylation and global exclusion from chromatin during early mitosis.

Activation of LATS signaling dissociates CTCF from a subset of genomic sites and reduces expression of local genes. The locus-specific loss of CTCF DNA binding and chromatin looping is unlikely a secondary effect of diminished expression of these genes. In YAP-depleted cells, YAP target genes are down-regulated (Fig. 5C), but CTCF occupancy at the boundaries of chromatin domains containing them is unaltered (Fig. 5A), suggesting that CTCF binding at these sites is independent of YAP or expression of its target genes. Chromatin topological organization is independent of the transcriptional states of genes located inside the chromatin domains (64). By contrast, depletion of CTCF or ablation of CTCF binding decreases YAP target gene expression (Fig. 4 and fig. S7), suggesting that CTCF plays an essential regulatory role in the transcription of the LATS signaling pathwayresponsive genes. Together, the results suggest that CTCF-mediated insulated neighborhoods may be required for activation of YAP target genes. Disruption of this domain organization due to loss of CTCF DNA binding may contribute to the down-regulation of YAP targets.

YAP is a transcriptional coactivator and a main effector of LATS signaling. Inactivation of YAP by LATS-mediated phosphorylation is sufficient to down-regulate its target genes. Why does LATS signaling also disrupt CTCF DNA binding and chromatin looping at YAP targets? It is conceivable that CTCF-mediated insulated neighborhoods may provide a conducive epigenetic environment to facilitate YAP-dependent transcriptional activation. Simultaneous loss of both YAP and CTCF not only ensures down-regulation of YAP targets but may also achieve a more durable transcriptional response than inactivation of YAP alone. Therefore, both YAP-associated enhancers and CTCF-mediated chromatin architectural organization may be important for YAP target gene expression. In this regard, in addition to inactivating YAP, LATS kinases may down-regulate downstream target genes, in part, through CTCF phosphorylation and consequent 3D genome architectural remodeling.

The HEK293, MCF7 breast cancer cells, and A549 lung cancer cells were cultured in Dulbeccos modified Eagles medium (DMEM) supplemented with 10% fetal bovine serum (FBS). HCT116 colon cancer cells were cultured in McCoys 5A medium with 10% FBS. Cell detachment was achieved by culturing cells in polyHEMA (2-hydroxyethyl methacrylate)coated dishes. Where indicated, the following drugs were used: phosphatase inhibitors (10 mM sodium fluoride, 10 mM sodium pyrophosphate) and 2-DG (25 mM) were from Sigma. Glucose-free DMEM was from Thermo Fisher Scientific. The following antibodies were obtained commercially: anti-CTCF (Cell Signaling Technology, #3418), antiphospho-RxxS/T motif (Cell Signaling Technology, #9614), anti-LATS1 (Cell Signaling Technology, #3477), anti-MST2 (Cell Signaling Technology, #3952), anti-YAP (Cell Signaling Technology, #14074), antiphospho-YAP (S127) (Cell Signaling Technology, #13008), anti-Flag (Sigma, #F1804), and anti-tubulin (Sigma, #T9026). Rabbit polyclonal antiphospho-CTCF (S402) antibodies were custom generated using a specific phosphopeptide (GenScript).

Lentiviral shRNAs targeting human CTCF, LATS1, LATS2, YAP, and PFKFB3 were obtained from the pLKO.1-based TRC (The RNAi consortium) library (Addgene, #10878). Cells were infected with lentivirus and followed by puromycin selection. For reverse transcription PCR (RT-PCR), cells were lysed in TRIzol reagent (Invitrogen, #15596026), followed by total RNA purification. Reverse transcription of RNA was conducted using Moloney murine leukemia virus reverse transcriptase with random primers. Gene expression was determined by real-time quantitative PCR (qPCR) with the SYBR Green PCR Kit (Applied Biosystems, #4309155). Data were normalized against -actin.

Guide RNA target sequences were designed using online software TargetFinder (http://crispr.mit.edu/) and cloned into Bsm BIdigested lentiCRISPRv2 (Addgene, 52961) (65). Following lentiviral production, MCF7 cells were infected and selected with puromycin. Single clones were picked and genotyped by genomic DNA PCR. PCR products were cloned into the pGEM-T Easy vector (Promega, A1360) and sequenced.

The ChIP assay was conducted as previously described (66). Briefly, cells were cross-linked with 1% formaldehyde for 10 min. The reaction was stopped by 0.125 M glycine solution. Cross-linked cells were washed in 1 phosphate-buffered saline (PBS) buffer and collected. Cell pellets were washed several times in washing buffer [0.25% Triton X-100, 10 mM EDTA, 0.5 mM EGTA, and 10 mM tris (pH 8.0)] and resuspended in sonication buffer [1 mM EDTA, 0.5 mM EGTA, and 10 mM tris (pH 8.0)], mixed with glass beads, and then subjected to the sonication process. The sonicated samples were diluted with ChIP buffer [0.01% SDS, 1.0% Triton X-100, 1.0 mM EDTA, 20 mM tris (pH 8.0), 150 mM NaCl] and incubated with antibodies against CTCF, YAP, or Flag. The immunoprecipitates were subjected to a series of washing steps to remove nonspecific binding materials. After reverse cross-linking, DNA was purified and then analyzed by real-time qPCR. Final results represent percentage of input chromatin, and error bars indicate SD from triplicate experiments.

GST fusion proteins CTCF WT and mutant (T374E and S402E) (ZF4-ZF5: amino acids 351 to 410) were generated by PCR, cloned into GST tag bacterial expression vector pGEX-KG, and verified by DNA sequencing and then transformed into BL21 competent cells for protein expression. Protein expression was induced using 0.1 mM isopropyl -d-1-thiogalactopyranoside and subsequent incubation at 18C overnight. Protein samples were purified using glutathione-conjugated sepharose and stored at 80C until use. The homogeneity and concentration of the proteins were estimated by SDSpolyacrylamide gel electrophoresis (PAGE) and Coomassie blue staining with bovine serum albumin (BSA) as a standard control.

Briefly, the phosphorylation reactions contained 20 mM tris (pH 7.5), 20 mM Hepes (pH 7.5), 5 mM -glycerophosphate, 1 mM dithiothreitol, 2 mM Na3VO4, 100 mM NaCl, 10 mM MgCl2, 0.1 mM adenosine triphosphate (ATP), LATS2 kinase (SignalChem, L02-11G), 100 ng of recombinant GST and GST-CTCF (WT or mutant) purified from bacteria, and were incubated at 30C for 1 hour. The reaction mixture was separated by SDS-PAGE, and phosphorylated proteins were detected by Western blotting with anti-RXXpS/T antibody.

Flag tagged-CTCF (WT and mutant) proteins from transfected HEK293 cells were obtained by immunoprecipitation with anti-Flag antibodies, followed by elution with Flag peptides. The purified proteins were then incubated with biotin-labeled double-stranded DNA oligos [~70 base pairs (bp)] carrying a CTCF-binding site from the AXL gene and then pulled down with reaction buffer [50 mM tris (pH 8.5), 50 mM KCl, 5 mM MgCl2, 0.5% BSA, and 5% glycerol] containing 0.1% NP-40 for 1 hour at 4C, and then mixed with streptavidin magnetic beads. The beads were washed extensively with the reaction buffer and were analyzed by SDS-PAGE and Western blotting with anti-CTCF antibody. Western blotting followed standard molecular biology procedures. Quantifications of Western blots were performed with ImageJ and reflected the relative amounts as a ratio of phospho-CTCF protein band relative to the lanes loading control.

A modified 3C assay protocol was conducted as described previously (67, 68). Briefly, 1 107 cells were washed in cold PBS buffer. Cells were cross-linked with a final concentration of 2% formaldehyde for 10 min at room temperature, and cross-linking was stopped with glycine (final concentration, 125 mM). Nuclei were collected from the cross-linked cells and then digested with Eco RI or Hind III at 37C overnight. The restriction enzymes were heat inactivated, and the reaction mixture was diluted in the ligation buffer to favor intramolecular ligation of cross-linked chromatin segments, and the DNA was subjected to ligation with T4 DNA ligase at 16C for 3 days. The ligation reaction mixtures were incubated overnight at 65C with the reverse buffer containing proteinase K (final concentrations at 200 g/ml) to reverse the cross-links and digest the proteins. After the cross-links were reversed, DNA was purified by phenol chloroform extraction and ethanol precipitated. 3C yields a genome-wide ligation product library in which each ligation product corresponds to a specific interaction between the two corresponding loci. The frequency with which a specific 3C ligation product occurs in the library is a measure of the frequency with which the loci are sufficiently close in space to be cross-linked. Real-time PCR amplification with primers across the restriction sites in the specific 3C ligation products was carried out to quantify the frequency with which the loci interact. Loading controls represent total DNA concentrations between 3C library samples (using PCR primers that do not amplify across the restriction sites used during the 3C assays). The PCR products were also analyzed by agarose gel electrophoresis, purified, and verified by DNA sequencing.

MCF7 cells were cultured in normal or glucose-free media for 24 hours, and 6 106 cells were subjected to the CUT&RUN assay with anti-CTCF antibodies (Cell Signaling Technology, 3418) (55). Genomic DNA fragments (200 to 600 bp) were recovered from agarose gels. Library preparation and high-throughput sequencing were conducted by the Genomic Services Laboratory at HudsonAlpha. In replicate experiments, libraries were prepared using Illuminas TruSeq ChIP Sample Preparation Kit (IP-202-1012) according to the manufacturers instructions. The quality of the library was checked with Agilent TapeStation. Final libraries were subjected to paired-end sequencing of 100-bp length on an Illumina HiSeq 2500 (30 to 40 million reads for each sample). The obtained genome-wide CTCF-binding data were deposited in the National Center for Biotechnology Information, NIH (NCBI) Gene Expression Omnibus (GEO; GSE114319).

ChIP-seq datasets were obtained from NCBI GEO, using the following GEO Series accession numbers: TEAD4 (GSM1010860), YAP (GSE66081) (69), CTCF (GSM1010734), Rad21 (GSM1010791) (70), and H3K27ac (GSM2483409). CTCF ChIA-PET interactions were from GSM970215 (57, 58). Except YAP binding (derived from MDA-MB-231 cells), all datasets were generated in MCF7 cells. ChIP-seq and CUT&RUN-seq raw reads were trimmed with bbduk.sh (removal of adapters and low-quality reads) (https://jgi.doe.gov/data-and-tools/bbtools/bb-tools-user-guide/bbduk-guide/) and aligned to human genome (hg19) using Bowtie2 (71) with parameters (-n 1 -m 1 -p 8), and the quality of the trimmed data was evaluated by FastQC program (www.bioinformatics.babraham.ac.uk/projects/fastqc/). Peaks were identified using the MACS2 program (72) with parameters (high stringent cutoff q value <0.01) and annotated with the command annotatePeaks.pl from the HOMER package (73) and GREAT (74). By default, annotatePeaks.pl assigns peaks to the nearest TSS. Genome browser tracks were created with the genomeCoverageBed command in BEDTools (75) and normalized such that each value represents the read count per kilobase pair per million mapped and filtered reads, and data tracks of visualization were normalized to the number of fragments falling within all peaks for each sample (76). BamCoverage was used to generate the bigWig file of fragment or read coverages, and bamCompare was used to compare the difference between these two normalized BAM files (e.g., log2ratio) based on the number of mapped reads, including control and experimental datasets (76). All sequencing tracks were visualized in the Intergrative Genomics Viewer genome browsers (77). The de novo motif analysis was performed by the findmotifsgenome.pl from the HOMER motif discovery algorithm (73). Phyper function (Python) in R package was used to calculate the overlap genes correlation and significance. Pearsons correlation coefficient and Pearsons 2 test were carried out to calculate overall similarity between the replicates of RNA sequencing and ChIP-seq. DEseq2 (Benjamini-Hochberg adjusted P < 0.1; FoldChange>2; DEseq method) was also performed to find the differential binding sites between two peak files, including control and experimental (78). Principal components analysis shows clustering differences of the samples using a small number of principal components according to differentially binding sites (79). GO analysis of differentially binding sites (stress experimental condition versus control condition) for CTCF CUT&RUN-seq was carried out with the Database for Annotation, Visualization and Integrated Discovery (DAVID) web tool (https://david.ncifcrf.gov/, Version 6.8) with the adjusted P value <1 103 (80, 81).

All statistical analyses were performed using Microsoft Excel. Data are shown in dot plots or histograms as mean SD. Statistical analysis between different experimental groups was determined by two-tailed Students t tests. In general, for samples with low variation, three to five biological replicates per condition were analyzed in each experiment, including RT-qPCR, ChIP-qPCR, and Western blotting. A value of P < 0.05 is considered to be statistically significant.

Acknowledgments: We are grateful to S. Henikoff (Fred Hutchinson Cancer Research Center, Seattle, Washington 98109, USA) for providing the CUT&RUN-seq protocol and reagents. Funding: No external funding was received for this submission. Author contributions: H.L., Q.Y., Y.L., M.L., and J.L. performed experimental work and analyzed the data. H.L. performed the bioinformatics analysis. M. Tang and D.Z. provided technical support. T.S.X., L.W., M. Tan, Y.R., and J.B. offered advice on experiments or structural and informatics analyses. J.L. conceived the project and wrote the manuscript. Competing interests: The authors declare that they have no competing interests. Data and materials availability: All data needed to evaluate the conclusions in the paper are present in the paper and/or the Supplementary Materials. CTCF CUT&RUN-seq data were deposited in the NCBI GEO (GSE114319). Additional data related to this paper may be requested from the authors.

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