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M. Imakaev/G. Fudenberg/N. Naumova/J. Dekker/L. Mirny

DNA loops help to keep local regions of the genome together.

Leonid Mirny swivels in his office chair and grabs the power cord for his laptop. He practically bounces in his seat as he threads the cable through his fingers, creating a doughnut-sized loop. It's a dynamic process of motors constantly extruding loops! says Mirny, a biophysicist here at the Massachusetts Institute of Technology in Cambridge.

Mirny's excitement isn't about keeping computer accessories orderly. Rather, he's talking about a central organizing principle of the genome how roughly 2 metres of DNA can be squeezed into nearly every cell of the human body without getting tangled up like last year's Christmas lights.

He argues that DNA is constantly being slipped through ring-like motor proteins to make loops. This process, called loop extrusion, helps to keep local regions of DNA together, disentangling them from other parts of the genome and even giving shape and structure to the chromosomes.

Scientists have bandied about similar hypotheses for decades, but Mirny's model, and a similar one championed by Erez Lieberman Aiden, a geneticist at Baylor College of Medicine in Houston, Texas, add a new level of molecular detail at a time of explosive growth for research into the 3D structure of the genome. The models neatly explain the data flowing from high-profile projects on how different parts of the genome interact physically which is why they've garnered so much attention.

But these simple explanations are not without controversy. Although it has become increasingly clear that genome looping regulates gene expression, possibly contributing to cell development and diseases such as cancer, the predictions of the models go beyond what anyone has ever seen experimentally.

For one thing, the identity of the molecular machine that forms the loops remains a mystery. If the leading protein candidate acted like a motor, as Mirny proposes, it would guzzle energy faster than it has ever been seen to do. As a physicist friend of mine tells me, 'This is kind of the Higgs boson of your field', says Mirny; it explains one of the deepest mysteries of genome biology, but could take years to prove.

And although Mirny's model is extremely similar to Lieberman Aiden's and the differences esoteric sorting out which is right is more than a matter of tying up loose ends. If Mirny is correct, it's a complete revolution in DNA enzymology, says Kim Nasmyth, a leading chromosome researcher at the University of Oxford, UK. What's actually powering the loop formation, he adds, has got to be the biggest problem in genome biology right now.

Geneticists have known for more than three decades that the genome forms loops, bringing regulatory elements into close proximity with genes that they control. But it was unclear how these loops formed.

Several researchers have independently put forward versions of loop extrusion over the years. The first was Arthur Riggs, a geneticist at the Beckman Research Institute of City of Hope in Duarte, California, who first proposed what he called DNA reeling in an overlooked 1990 report1. Yet it's Nasmyth who is most commonly credited with originating the concept.

As he tells it, the idea came to him in 2000, after a day spent mountain climbing in the Italian Alps. He and his colleagues had recently discovered the ring-like shape of cohesin2, a protein complex best known for helping to separate copies of chromosomes during cell division. As Nasmyth fiddled with his climbing gear, it dawned on him that chromosomes might be actively threaded through cohesin, or the related complex condensin, in much the same way as the ropes looped through his carabiners. It appeared to explain everything, he says.

Nasmyth described the idea in a few paragraphs in a massive, 73-page review article3. Nobody took notice whatsoever, he says not even John Marko, a biophysicist at Northwestern University in Evanston, Illinois, who more than a decade later developed a mathematical model that complemented Nasmyth's verbal argument4.

Mirny joined this loop-modelling club around five years ago. He wanted to explain data sets compiled by biologist Job Dekker, a frequent collaborator at the University of Massachusetts Medical School in Worcester. Dekker had been looking at physical interactions between different spots on chromosomes using a technique called Hi-C, in which scientists sequence bits of DNA that are close to one another and produce a map of each chromosome, usually depicted as a fractal-like chessboard. The darkest squares along the main diagonal represent spots of closest interaction.

The Hi-C snapshots that Dekker and his collaborators had taken revealed distinct compartmentalized loops, with interactions happening in discrete blocks of DNA between 200,000 and 1 million letters long5.

These 'topologically associating domains', or TADs, are a bit like the carriages on a crowded train. People can move about and bump into each other in the same carriage, but they can't interact with passengers in adjacent carriages unless they slip between the end doors. The human genome may be 3 billion nucleotides long, but most interactions happen locally, within TADs.

Mirny and his team had been labouring for more than a year to explain TAD formation using computer simulations. Then, as luck would have it, Mirny happened to attend a conference at which Marko spoke about his then-unpublished model of loop extrusion. (Marko coined the term, which remains in use today.) It was the missing piece of Mirny's puzzle. The researchers gave loop extrusion a try, and it worked. The physical act of forming the loops kept the local domains well organized. The model reproduced many of the finer-scale features of the Hi-C maps.

When Mirny and his colleagues posted their finished manuscript on the bioRxiv preprint server in August 2015, they were careful to describe the model in terms of a generic loop-extruding factor. But the paper didn't shy away from speculating as to its identity: cohesin was the driving force behind the looping process for cells not in the middle of dividing, when chromosomes are loosely packed6. Condensin, they argued in a later paper, served this role during cell division, when the chromosomes are tightly wound7.

A key clue was the protein CTCF, which was known to interact with cohesin at the base of each loop of uncondensed chromosomes. For a long time, researchers had assumed that loops form on DNA when these CTCF proteins bump into one another at random and lock together. But if any two CTCF proteins could pair, why did loops form only locally, and not between distant sites?

Mirny's model assumes that CTCFs act as stop signs for cohesin. If cohesin stops extruding DNA only when it hits CTCFs on each side of a growing loop, it will naturally bring the proteins together.

But singling out cohesin was a big leap of faith, says biophysicist Geoff Fudenberg, who did his PhD in Mirny's lab and is now at the University of California, San Francisco. No one has seen these motors doing these things in living cells or even in vitro, he says. But we see all of these different features of the data that line up and can be unified under this principle.

Experiments had shown, for example, that reducing the amount of cohesin in a cell results in the formation of fewer loops8. Overactive cohesin creates so many loops that chromosomes smush up into structures that resemble tiny worms9.

The authors of these studies had trouble making sense of their results. Then came Mirny's paper on bioRxiv. It was the first time that a preprint has really changed the way people were thinking about stuff in this field, says Matthias Merkenschlager, a cell biologist at the MRC London Institute of Medical Sciences. (Mirny's team eventually published the work in May 2016, in Cell Reports6.)

Lieberman Aiden says that the idea of loop extrusion first dawned on him during a conference call in March 2015. He and his former mentor, geneticist Eric Lander of the Broad Institute in Cambridge, Massachusetts, had published some of the most detailed, high-resolution Hi-C maps of the human genome available at the time10.

During his conference call, Lieberman Aiden was trying to explain a curious phenomenon in his data. Almost all the CTCF landing sites that anchored loops had the same orientation. What he realized was that CTCF, as a stop sign for extrusion, had inherent directionality. And just as motorists race through intersections with stop signs facing away from them, so a loop-extruding factor goes through CTCF sites unless the stop sign is facing the right way.

His lab tested the model by systematically deleting and flipping CTCF-binding sites, and remapping the chromosomes with Hi-C. Time and again, the data fitted the model. The team sent its paper for review in July 2015 and published the findings three months later11.

Mirny's August 2015 bioRxiv paper didn't have the same level of experimental validation, but it did include computer simulations to explain the directional bias of CTCF. In fact, both models make essentially the same predictions, leading some onlookers to speculate on whether Mirny seeded the idea. Lieberman Aiden insists that he came up with his model independently. We submitted our paper before I ever saw their manuscript, he says.

There are some tiny differences. The cartoons Mirny uses to describe his model seem to suggest that one cohesin ring does the extruding, whereas Lieberman Aiden's contains two rings, connected like a pair of handcuffs (see 'The taming of the tangles'). Suzana Hadjur, a cell biologist at University College London, calls this mechanistic nuance absolutely fundamental to determining cohesin's role in the extrusion process.

Nik Spencer/Nature

Neither Lieberman Aiden nor Mirny say they have a strong opinion on whether the system uses one ring or two, but they do differ on cohesin's central contribution to loop formation. Mirny maintains that the protein is the power source for looping, whereas Lieberman Aiden summarily dismisses this idea. Cohesin is a big doughnut, he says. It doesn't do that much. It can open and close, but we are very, very confident that cohesin itself is not a motor.

Instead, he suspects that some other factor is pushing cohesin around, and many in the field agree. Claire Wyman, a molecular biophysicist at Erasmus University Medical Centre in Rotterdam, the Netherlands, points out that cohesin is only known to consume small amounts of energy for clasping and releasing DNA, so it's a stretch to think of it motoring along the chromosome at the speeds required for Mirny's model to work. I'm willing to concede that it's possible, she says. But the Magic 8-Ball would say that, 'All signs point to no'.

One group of proteins that might be doing the pushing is the RNA polymerases, the enzymes that create RNA from a DNA template. In a study online in Nature this week12, Jan-Michael Peters, a chromosome biologist at the Research Institute of Molecular Pathology in Vienna, and his colleagues show that RNA polymerases can move cohesin over long distances on the genome as they transcribe genes into RNA. RNA polymerases are one type of motor that could contribute to loop extrusion, Peters says. But, he adds, the data indicate that it cannot be the only force at play.

Frank Uhlmann, a biochemist at the Francis Crick Institute in London, offers an alternative that doesn't require a motor protein at all. In his view, a cohesin complex might slide along DNA randomly until it hits a CTCF site and creates a loop. This model requires only nearby strands of DNA to interact randomly which is much more probable, Uhlmann says. We do not need to make any assumptions about activities that we don't have experimental evidence for.

Researchers are trying to gather experimental evidence for one model or another. At the Lawrence Livermore National Laboratory in California, for example, biophysicist Aleksandr Noy is attempting to watch loop extrusion in action in a test tube. He throws in just three ingredients: DNA, some ATP to provide energy, and the bacterial equivalent of cohesin and condensin, a protein complex known as SMC.

We see evidence of DNA being compacted into these kinds of flowers with loops, says Noy, who is collaborating with Mirny on the project. That suggests that SMC and by extension cohesin might have a motor function. But then again, it might not. The truth is that we just don't know at this point, Noy says.

The experiment that perhaps comes the closest to showing cohesin acting as a motor was published in February13. David Rudner, a bacterial cell biologist at Harvard Medical School in Boston, Massachusetts, and his colleagues made time-lapse Hi-C maps of the bacterium Bacillus subtilis that reveal SMC zipping along the chromosome and creating a loop at a rate of more than 50,000 DNA letters per minute. This tempo is on par with what researchers estimate would be necessary for Mirny's model to work in human cells as well.

Rudner hasn't yet proved that SMC uses ATP to make that happen. But, he says, he's close and he would be shocked if cohesin worked differently in human cells.

For now, the debate rages about what cohesin is, or is not, doing inside the cell and many researchers, including Doug Koshland, a cell biologist at the University of California, Berkeley, insist that a healthy dose of scepticism is still warranted when it comes to Mirny's idea. I am worried that the simplicity and elegance of the loop-extrusion model is already filling textbooks, coronated long before its time, he says.

And although it may seem an academic dispute among specialists, Mirny notes that if it his model is correct, it will have real-world implications. In cancer, for instance, cohesin is frequently mutated and CTCF sites altered. Defective versions of cohesin have also been implicated in several rare human developmental disorders. If the loop-extruding process is to blame, says Mirny, then perhaps a better understanding of the motor could help fix the problem.

But his main interest remains more fundamental. He just wants to understand why DNA is configured in the way it is. And although his model assumes a lot of things about cohesin, Mirny says, The problem is that I don't know any other way to explain the formation of these loops.

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DNA's secret weapon against knots and tangles - Nature.com

Ask John Novembre to recall a fun time, and he might tell you about a recent weeklong hackathon. He and his students and postdocs set aside their daily obligations to stay up late eating takeout and crunching data.

This isnt to say that Novembre, a computational biologist, is purely a computer geek (although hell admit thats part of his identity). Yes, his whiteboard-lined walls at the University of Chicago are adorned with symbols and graphs and equations embryos of the clever algorithms and computational tricks that allow him to wrest meaning from some of the largest genomic data sets in the world. But thats just a glimpse of what he does.

Born into a military family, Novembre grew up moving from place to place, spending three years in Uruguay, where his mother is from. His early exposure to human differences I loved geography, I loved languages, I loved history morphed into a deep curiosity about evolution and genetic diversity. His work reflects a broad ambition: to understand how populations vary in time and space. He studies how humanitys genetic code shifts and mixes as groups expand, shrink, migrate, mingle, evolve and die out.

His mathematical chops have served him well in this endeavor. His innovative analyses and new ways of visualizing complex data reveal the genetic signatures of ancestry and the surprising connections between genes and geography. In 2015, at the age of 37, Novembre won a MacArthur fellowship the so-called genius grant which recognizes talented individuals who have shown extraordinary originality and promise for important future advances based on a track record of significant accomplishment.

Yet for all his accolades, Novembre comes across as genuinely humble, quoting colleagues papers the way English majors quote Dickinson and Yeats. Hanging above his desk, within eyeshot of his dry-erase brainstorms, is an old map he thinks is from the 1600s a constant reminder that any human attempt to model the world will always be a very imperfect representation.

Quanta Magazinespoke with Novembre about the motivations behind his work, the challenges of using DNA to interpret the past, and the racial legacy of genetics research. An edited and condensed version of the conversation follows.

QUANTA MAGAZINE: What got you thinking about genetic diversity as a computational problem?

JOHN NOVEMBRE: For me, the path starts pretty far back. In high school, I was a bit of a computer programming nerd. But in my classes, I was learning about the genetic code, which was completely mesmerizing. Then in college, I got a chance to do a summer research internship at Stanford, where I heard a talk by a student who had interned in Luigi Luca Cavalli-Sforzas lab. What they do what theyve become famous for is to look at variations in human genes, how theyre distributed across the globe, and what they can tell us about human history. That was fascinating to me.

I went back to my home campus, and I found a lab working on the population genetics of Quercus gambelii, the Gambel oak. I learned just how difficult a lot of the analysis tools were to use, and how much math and computation is involved in analyzing genetic data. All of a sudden I realized, Wait a minute. Heres this thing I really love programming so why dont I combine these two passions? My day-to-day activity became tinkering with computers, but my larger end is something that intellectually fascinates me, which is understanding genetic variation and how it changes through time.

Early in your career, you made waves by uncovering deficiencies in a common statistical tool known as principal component analysis (PCA). How did this discovery further your work in genetics?

What PCA does is, it takes an individuals genetic data and boils it down to just a few numbers. In learning about how this method works its strengths and its weaknesses I understood that the patterns it produces could reflect spatial structure in population data.

I was hoping to get access to genetic data from a region of the world where theres dense sampling, so that I could see what variation looks like at a continuous scale, where populations kind of blend into one another. And it turned out I was very lucky in that I got invited to join a collaboration with Carlos Bustamante, [then] at Cornell, to analyze one of the largest collections of [genomic data] being applied to human populations. The full data set was 3,192 European individuals. A large fraction of the sample had answered an ancestry questionnaire to say where their grandparents came from, and based on that, we saw we had samples from roughly 37 different origins across Europe.

So what did you learn?

When we applied PCA, right away we saw this major pattern: There was a striking resemblance between where individuals are located in genetic space and their geography where their grandparents came from. Thats really remarkable given how closely related human individuals are. Most geneticists wouldnt have thought you could tease apart very fine-scale structure within continental scales.

How fine-scale are we talking about?

Lets say I took an individual and hid their geographic location and then tried to put them back on a map. How well could I do? When we did this, we could often get within a few hundred kilometers. Even when we looked at German-speaking Swiss versus French-speaking Swiss versus Italian-speaking Swiss, we could see shifts in the genetic distribution.

Im surprised that my grandparents geographic coordinates could have such a notable effect on my genetics, given how often humans migrate. How do you explain this influence?

This is something I want to stress: The effect on your genetics is actually incredibly small. Its just that were looking at so many locations in the genome that we can pick up very small effects. This is the magic of big data: Very subtle patterns become detectable. So its not that where your grandparents live has a huge impact on your genetics. Its actually a very, very minor effect. But when you have hundreds of thousands of measurements, you can start to pick out that an individual seems to come from one location versus another.

What are your thoughts on the ethics of commercial ancestry tests?

I advise for Ancestry.com their DNA branch so Im very sensitive to the challenges of communicating results. On the one hand, projects like our genetic map of Europe show the tremendous potential and power of these tools for learning about ancestry. But then theres also the immense complexity of it: What does it really mean to talk about where an individual is from? We can talk about where our parents and our grandparents are from, or we can go very far back into the past when we all came from Africa. And we can have different ideas about origin, in terms of geographic location versus some kind of cultural or ethnic population.

Id say were still in the early days of really nailing this problem of using genetic data from today to interpret the past. Were still facing the complexity of real biological systems and populations, which resist some of our attempts to use very simple models of history.

In what ways has your work influenced how you think about race?

Its very clear that genetics research has a difficult and dark history. But its been exciting to be part of a new generation doing this kind of work in a time when diversity is much more appreciated and understood and valued and when we have the data to make it even more clear just how poorly conceived racial worldviews have been.

Are you thinking of a particular example?

A very powerful one for me was being part of some of the first teams to look at genome-wide data taken from multiple human populations. You can sort the genome by what regions vary the most across human populations and then ask, OK, what genes are near those locations, and what do we know about them?

If you do this exercise, you will see, at the very extreme top of the list, variants that are involved in skin pigmentation, in eye color, in hair color. So its an empirical fact that the things we use to see differences in each other are outliers in the human genome. Your average set of genes in the human genome is much more similar globally.

You analyzed the first whole-genome sequences of three gray wolf species and compared them to the genomes of three dog species. What did you discover?

That was a big surprise. We were thinking we might find that all three of the dog lineages are most closely related to one of the three wolf lineages. They might all be related to the Israeli wolf, for instance, because maybe dogs were domesticated in the Middle East. Or maybe there were two domestications of dogs, and the dingo would be related to the Chinese wolf while the basenji was related to the Croatian wolf.

But what we saw was that the three dogs were most closely related to each other but not embedded within the genealogy of the three wolves. Our hypothesis is that there was a wolf lineage that dogs were domesticated from that has since gone extinct. The storys gotten incredibly complicated, and I think the final chapters not written yet.

Saverio Truglia for Quanta Magazine

Video:John Novembre explains how he uses genomic data to map human history.

Are you a dog person?

Not particularly, no. I would say my motivation was primarily to try to solve this larger challenge for the whole field, which is: How do we use DNA sequences today as a record of the past? You can swap out the species names for me, and its still interesting. Its still a fun problem.

How has your approach to analyzing genetic data evolved over time?

Theres been increasing movement in my work toward data visualization. Your eye can actually process a large amount of information and interpret complex patterns. With the right visualization tools, you gain a more direct and intuitive understanding of the major features of the data and how they reflect biological processes.

Can you give an example?

One of the tools weve developed is a method that tells us where in a landscape there is more or less gene flow in other words, how individuals are moving between populations. Our analysis infers areas where theres more genetic difference than youd expect per unit of geographic distance, and other areas where theres less. So were able to produce a geographic map that is colored in brown and blue to represent areas of low and high migration.

For example, we looked at genetic data from more than 1,000 elephants sampled across Africa. With our approach, you feed in the data with no prior knowledge, and you get this map of migration rates. You see this brown barrier down Central Africa marking where theres low migration and a blue corridor in the east where theres high migration. Of course, if you know the ecology, you understand: Oh! Thats the forest elephants on one side and the savannah elephants on the other.

Have you applied this method to other populations?

Yes. When we run it on the human data from Europe, for instance, we see it infers a brown area of reduced migration between the U.K. and France, representing essentially the English Channel. We see a lot of blue high migration in the North Sea because of historical connections there, such as Viking contacts between Scandinavia and the U.K. Then we see brown diffusely around Switzerland and Austria, which we think represents the Alps.

Did you get any puzzling results, such as areas of low or high migration that dont seem to jibe with the landscape?

Im more surprised by how often the genetics do align with the geological features. You take a bunch of living individuals and extract a molecule from their bodies and start comparing them to one another, and you can see that the Alps are a feature of our planet. Its kind of wild.

How have the questions youre researching changed from when you started out?

The data types have changed, and the scale of the data has changed. For my Ph.D., I worked on trying to learn what I could from a single genetic variant that was observed in 71 populations. Now its completely routine to have data sets with millions of variants. I couldnt have imagined that wed be where we are today back then. So thats an incredible game-changer, but the core question is still the same: How do we use mathematics and statistical models to interpret population genetic data?

What are the big remaining problems you hope to solve?

I think one holy grail is a method that would allow you to infer how migration rates and population sizes change through time and space. It would be a very complete description of a population and its demographic history.

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A Map of Human History, Hidden in DNA - Quanta Magazine

Paragon NanoLabs specializes in DNA phenotyping, which is the process of predicting physical appearance and ancestry from unidentified DNA evidence. The result is called a Snapshot prediction of what a person is likely to look like.(Photo: Mesa Police Department)

UsingDNA technology, Mesa police investigators have come up with a composite of a man in a Mesa sexual assault who has been at large since early January, police said.

On Jan. 9, a man entered a home in the 500 block of EastBroadway between 2 and 4 a.m., Mesa police said.

The man then sexually assaulted a 4-year-old in the home while the child's family was asleep, police said.

The man was described by witnesses as Hispanic, 18-30 years old, short build, with short brown curly hair, police said.

Using DNA evidence that was collected at the scene, investigatorssought the services of Paragon NanoLabs.

The companyspecializes in DNA phenotyping, which isthe process of predicting physical appearance and ancestry from unidentified DNA evidence.The result is called a Snapshot prediction of what a person is likely to look like.

An artist's sketch of a man wanted in a Mesa sexual assault.(Photo: Mesa Police Department)

Through using DNA evidence from this investigation, a Snapshot composite was produced depicting what the manmay have looked like at 25 years old with an average body-mass indexof 22, police said.His genomic ancestry matched a Latino with some African-American mixture, police said.

Snapshot composites are scientific approximations of appearance based on DNA and are not likely to be exact replicas of appearance, police said.

Silent Witness is rewarding up to $5,000 for information leading to the arrest and/orindictment of the suspect.

Anyone with information is asked to call the Mesa Police Department at 480-644-2211 or Silent Witness at 480-WITNESS.

Read or Share this story: http://azc.cc/2oX7g3V

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Mesa police use DNA technology to create composite sketch of man wanted in sex assault - AZCentral.com

GASTONIA, N.C. (WBTV) Attorneys for convicted murderer Mark Carver got some good news from a judge in Gaston County Thursday after he ordered new DNA testing in the case.

Carver, convicted in the death of UNC Charlotte student Ira Yarmolenko, did not appear in court.

Judge David Lee requested five law enforcement officers who were involved in the case be contacted and asked to give DNA samples. Carvers defense team has argued that several investigators may have contaminated the crime scene.

We believe that all the evidence used against Mr. Carver has been discredited, said Chris Mumma, Executive Director of North Carolina Center on Actual Innocence. I would like to see those officers voluntarily give their DNA because there is no harm in giving it.

Judge Lee did not order the DNA be taken from the officers but did state that if the officers did not want to give the samples, additional hearings may be required.

Gaston County District Attorney Locke Bell argues that several of the law enforcement officials brought up in court had no connection or contact with Carver. Therefore, he said that contamination would be nearly impossible.

There has to be some sort of connection, or it just keeps going and going, said Bell in court. If that is not the case, what we would have to do is go back to where Carver had breakfast and take the DNA of the waitress.

The judge also ordered that the state must re-test the electronic DNA evidence used in the trial under the current protocol. Mumma says that protocol was available during the trial.

I am expecting to see exactly what our experts have seen. They have submitted reports that say it never would have been reported that Mark Carvers DNA was on that car, said Mumma.

Bell argued that the test that was done was accurate.

The new way of doing it does not say the old way was wrong, said Bell.

Both attorneys promised to share their complete files in the case before any hearing concerning a new trial.

He is innocent. We know that he is innocent. Everyone that knows him knows Mark is a great guy, said one of Carvers family members. The truth will come out, he will be found innocent.

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Judge allows new DNA testing in murder of NC college student - WNCN