Tyler O'Neal, Staff Editor INDUSTRY

Using MD simulations, MIT chemists show how molecular clusters in the nucleus interact with chromosomes

A new study finds the clusters form small, stable droplets and may give the genome a gel-like structure

A cell stores all of its genetic material in its nucleus, in the form of chromosomes, but that’s not all that’s tucked away in there. The nucleus is also home to small bodies called nucleoli — clusters of proteins and RNA that help build ribosomes.

Using supercomputer simulations, MIT chemists have now discovered how these bodies interact with chromosomes in the nucleus, and how those interactions help the nucleoli exist as stable droplets within the nucleus.

Their findings also suggest that chromatin-nuclear body interactions lead the genome to take on a gel-like structure, which helps to promote stable interactions between the genome and transcription types of machinery. These interactions help control gene expression.

“This model has inspired us to think that the genome may have gel-like features that could help the system encode important contacts and help further translate those contacts into functional outputs,” says Bin Zhang, the Pfizer-Laubach Career Development Associate Professor of Chemistry at MIT, an associate member of the Broad Institute of Harvard and MIT, and the senior leader of the study. MIT graduate student Yifeng Qi is the head of the study.

Modeling droplets

Much of Zhang’s research focuses on modeling the three-dimensional structure of the genome and analyzing how that structure influences gene regulation.

In the new study, he wanted to extend his modeling to include nucleoli. These small bodies, which break down at the beginning of cell division and then re-form later in the process, consisting of more than a thousand different molecules of RNA and proteins. One of the key functions of the nucleoli is to produce ribosomal RNA, a component of ribosomes.

Recent studies have suggested that nucleoli exist as multiple liquid droplets. This was puzzling because, under normal conditions, multiple droplets should eventually fuse into one large droplet, to minimize the surface tension of the system, Zhang says.

“That’s where the problem gets interesting, because in the nucleus, somehow those multiple droplets can remain stable across an entire cell cycle, over about 24 hours,” he says.

To explore this phenomenon, Zhang and Qi used a technique called molecular dynamics simulation, which can model how a molecular system changes over time. At the beginning of the simulation, the proteins and RNA that make up the nucleoli are randomly distributed throughout the nucleus, and the simulation tracks how they gradually form small droplets.

In their supercomputer simulation, the researchers also included chromatin, the substance that makes up chromosomes and includes proteins as well as DNA. Using data from previous experiments that analyzed the structure of chromosomes, the MIT team calculated the interaction energy of individual chromosomes, which allowed them to provide realistic representations of 3D genome structures.

Using this model, the researchers were able to observe how nucleoli droplets form. They found that if they modeled the nucleolar components on their own, with no chromatin, they would eventually fuse into one large droplet, as expected. However, once chromatin was introduced into the model, the researchers found that the nucleoli formed multiple droplets, just as they do in living cells.

The researchers also discovered why that happens: The nucleoli droplets become tethered to certain regions of the chromatin, and once that happens, the chromatin acts as a drag that prevents the nucleoli from fusing.

“Those forces essentially arrest the system into those small droplets and hinder them from fusing together,” Zhang says. “Our study is the first to highlight the importance of this chromatin network that could significantly slow down the fusion and arrest the system in its droplet state.”

Gene control

The nucleoli are not the only small structures found in the nucleus — others include nuclear speckles and the nuclear lamina, an envelope that surrounds the genome and can bind to chromatin. Zhang’s group is now working on modeling the contributions of these nuclear structures, and their initial findings suggest that they help to give the genome more gel-like properties, Zhang says.

“This coupling that we have observed between chromatin and nuclear bodies is not specific to the nucleoli. It’s general to other nuclear bodies as well,” he says. “This nuclear body concentration will fundamentally change the dynamics of the genome organization and will very likely turn the genome from a liquid to a gel.”

This gel-like state would make it easier for different regions of the chromatin to interact with each other than if the structure existed in a liquid state, he says. Maintaining stable interactions between distant regions of the genome is important because genes are often controlled by stretches of chromatin that are physically distant from them.

University of Copenhagen's Lorenzen implements new algo on Computerome 2 to predict COVID-related ICU resource use, saves lives

Tyler O'Neal, Staff Editor INDUSTRY

The COVID-19 pandemic is on the rise in many European countries, and hospitals worldwide are under maximum pressure. 

Now, an innovative algorithm will help alleviate pressure whenever hospitals are confronted by new waves of COVID. Researchers from the University of Copenhagen, among others, have developed the algorithm, which can predict the course of COVID patients' illnesses with how many of them will be highly likely or unlikely to require intensive care or ventilation.

This is important for the allocation of staff across the hospitals in for example Denmark explains one of the study's authors. 

"If we can see that we’ll have capacity issues five days out because too many beds are taken at Rigshospitalet, for example, we can plan better and divert patients to hospitals with more space and staffing. As such, our algorithm has the potential to save lives," explains Stephan Lorenzen, a postdoc at the University of Copenhagen’s Department of Computer Science. Getty Images

The algorithm uses individual patient data from Sundhedsplatform (the National Health Platform) including information about a patient’s gender, age, medications, BMI, whether they smoke or not, blood pressure, and more.

This allows the algorithm to predict how many patients, within a one-to-fifteen day time frame, will need intensive care in the form of, for example, ventilators and constant monitoring by nurses and doctors.

Along with colleagues at the University of Copenhagen, as well as researchers at Rigshospitalet and Bispebjerg Hospital, Lorenzen developed the new algorithm based on health data from 42,526 Danish patients who tested positive for the coronavirus between March 2020 and May 2021.

Predicts the number of intensive care patients with 90 percent accuracy

Traditionally, researchers have used regression models to predict Covid-related hospital admissions. However, these models haven’t taken individual disease histories, age, gender, and other factors into account.

"Our algorithm is based on more detailed data than other models. This means that we can predict the number of patients who will be admitted to intensive care units or who need a ventilator within five days with over 90 percent accuracy," states Stephan Lorenzen.

The algorithm provides extremely accurate predictions for the likely number of intensive care patients for up to ten days.

"We make better predictions than comparable models because we are able to more accurately map the potential need for ventilators and 24-hour intensive care for up to ten days. Precision decreases slightly beyond that, similar to that of the existing algorithmic models used to predict the course of illness in Covid cases," he elaborates.

In principle, the algorithm is ready to be deployed in Danish hospitals. As such, the researchers are about to begin discussions with relevant health professionals.

"We have shown that data can be used for so incredibly much. And, that we in Denmark, are lucky to have so much health information to draw from. Hopefully, our new algorithm can help our hospitals avoid Covid overload when a new wave of the illness hits," concludes Stephan Lorenzen.

What distinguishes the new algorithm from others

Most existing algorithms in the field do not take the gender, age, and medical history of individuals into account. They look at the number of hospitalized COVID patients in need of intensive care on any given day. Based on this, along with mortality and new infection data, existing models try to predict how many people will be hospitalized tomorrow.

"For example, typical models cannot distinguish between younger or older people. Whether there are five people who are 80-years-old or more hospitalized, or five 25-year-old patients, has a major impact on the prediction in relation to what the probability of hospitalization is. Our new algorithm accounts for this," says Stephan Lorenzen.

Ethical considerations

  • The new algorithm uses health data approved for use under section 42 d of the Danish Health Act.
  • Data is processed on Computerome 2, a secure supercomputer for personal data, and under the permission of the Danish Patient Safety Authority, data owners, and other relevant authorities.
  • The Danish Council on Ethics has approved the study and the regional executive boards have approved the use of data.

Using GIZMO massively parallel, multiphysics code, Wisconsin scientists discover the Magellanic Stream is five times closer than previously thought

Tyler O'Neal, Staff Editor INDUSTRY

Our galaxy is not alone. Swirling around the Milky Way are several smaller, dwarf galaxies — the biggest of which are the Small and Large Magellanic Clouds, visible in the night sky of the Southern Hemisphere. COLIN LEGG / SCOTT LUCCHINI A view of the gas in the Magellanic System as it would appear in the night sky. This image, taken directly from the numerical simulations, has been modified slightly for aesthetics.

During their dance around the Milky Way over billions of years, the Magellanic Clouds’ gravity has ripped from each of them an enormous arc of gas — the Magellanic Stream. The stream helps tell the history of how the Milky Way and its closest galaxies came to be and what their future looks like.

New astronomical models developed by scientists at the University of Wisconsin–Madison and the Space Telescope Science Institute recreate the birth of the Magellanic Stream over the last 3.5 billion years. Using the latest data on the structure of the gas, the researchers discovered that the stream is maybe five times closer to Earth than previously thought.  

The findings suggest that the stream may collide with the Milky Way far sooner than expected, helping fuel new star formation in our galaxy.

“The Magellanic Stream origin has been a big mystery for the last 50 years. We proposed a new solution with our models,” says Scott Lucchini, a graduate student in physics at UW–Madison and lead author of the paper. “The surprising part was that the models brought the stream much closer to the Milky Way.”

The new models also provide a precise prediction of where to find the stream’s stars. These stars would have been ripped from their parent galaxies with the rest of the stream’s gas, but only a few have been tentatively identified. Future telescope observations might finally spot the stars and confirm the new reconstruction of the stream’s origin is correct.

“It’s shifting the paradigm of the stream,” says Lucchini. “Some have thought the stars are too faint to see because they’re too far away. But we now see that the stream is basically at the outer part of the disk of the Milky Way.”

That’s close enough to spot, says Elena D’Onghia, a professor of astronomy at UW–Madison, and supervisor of the project. “With the current facilities, we should be able to find the stars. That’s exciting,” she says.

Lucchini, D’Onghia, and Space Telescope Science Institute scientist Andrew Fox published their findings in The Astrophysical Journal Letters on Nov. 8.

The latest work was based both on fresh data and different assumptions about the history of the Magellanic Clouds and Stream. In 2020, the research team predicted that the stream is enveloped by a large corona of warm gas. So, they plugged this new corona into their simulations, while also accounting for a new model of the dwarf galaxies that suggests they have a relatively brief history of orbiting one another — a mere 3 billion years or so.

“Adding the corona to the problem changed the orbital history of the clouds,” Lucchini explains.

In this new recreation, as the dwarf galaxies were captured by the Milky Way, the Small Magellanic Cloud orbited around the Large Magellanic Cloud in the opposite direction than previously thought. As the orbiting dwarf galaxies stripped gas from one another, they produced the Magellanic Stream.

The opposite-direction orbit pushed and pulled the stream so it arced toward Earth, rather than stretching farther away into intergalactic space. The stream’s closest approach is likely to be just 20 kiloparsecs from Earth, or about 65,000 light-years away. The clouds themselves sit between 55 and 60 kiloparsecs away.

“The revised distance changes our understanding of the stream. It means our estimates of many of the stream’s properties, such as mass and density, will need to be revised,” says Fox.

If the stream is this close, then it likely has just one-fifth the mass previously thought. The closest approach of the stream also means this gas will start merging with the Milky Way in about 50 million years, providing the fresh material needed to jump-start the birth of new stars in the galaxy.

The stars in the Magellanic Stream itself have eluded researchers for decades. But the new study suggests that perhaps they were simply looking in the wrong place.

“This model tells us exactly where the stars should be,” says D’Onghia.