Research Area: Human Disease

MIT Down syndrome researchers work on ways to ensure a healthy lifespan

An Alana Down Syndrome Center webinar, co-sponsored by the Massachusetts Down Syndrome Congress, presented numerous MIT studies that all share the goal of improving health throughout life for people with trisomy 21.

David Orenstein | The Picower Institute for Learning and Memory
April 24, 2025

In recent decades the life expectancy of people with Down syndrome has surged past 60 years, so the focus of research at the Alana Down Syndrome Center at MIT has been to make sure people can enjoy the best health during that increasing timeframe.

“A person with Down syndrome can live a long and happy life,” said Rosalind Mott Firenze, scientific director of the center founded at MIT in 2019 with a gift from the Alana Foundation. “So the question is now how do we improve health and maximize ability through the years? It’s no longer about lifespan, but about healthspan.”

Firenze and three of the center’s Alana Fellows scientists spoke during a webinar, hosted on April 17th, where they described the center’s work toward that goal. An audience of 99 people signed up to hear the webinar titled “Building a Better Tomorrow for Down Syndrome Through Research and Technology,” with many viewers hailing from the Massachusetts Down Syndrome Congress, which co-sponsored the event.

The research they presented covered ways to potentially improve health from stages before birth to adulthood in areas such as brain function, heart development, and sleep quality.

Boosting brain waves

One of the center’s most important areas of research involves testing whether boosting the power of a particular frequency of brain activity—“gamma” brain waves of 40Hz—can improve brain development and function. The lab of the center’s Director Li-Huei Tsai, Picower Professor in The Picower Institute for Learning and Memory and the Department of Brain and Cognitive Sciences, uses light that flickers and sound that clicks 40 times a second to increase that rhythm in the brain. In early studies of people with Alzheimer’s disease, which is a major health risk for people with Down syndrome, the non-invasive approach has proved safe, and appears to improve memory while preventing brain cells from dying. The reason it works appears to be because it promotes a healthy response among many types of brain cells.

Working with mice that genetically model Down syndrome, Alana Fellow Dong Shin Park has been using the sensory stimulation technology to study whether the healthy cellular response can affect brain development in a fetus while a mother is pregnant. In ongoing research, he said, he’s finding that exposing pregnant mice to the light and sound appears to improve fetal brain development and brain function in the pups after they are born.

In his research, Postdoctoral Associate Md. Rezaul Islam worked with 40Hz sensory stimulation and Down syndrome model mice at a much later stage in life—when they are adult aged. Together with former Tsai Lab member Brennan Jackson, he found that when the mice were exposed to the light and sound, their memory improved. The underlying reason seemed to be an increase not only in new connections among their brain cells, but also an increase in the generation of new ones. The research, currently online as a preprint, is set to publish in a peer-reviewed journal very soon.

Firenze said the Tsai lab has also begun to test the sensory stimulation in human adults with Down syndrome. In that testing, which is led by Dr. Diane Chan, it is proving safe and well tolerated, so the lab is hoping to do a year-long study with volunteers to see if the stimulation can delay or prevent the onset of Alzheimer’s disease.

Studying cells

Many Alana Center researchers are studying other aspects of the biology of cells in Down syndrome to improve healthspan. Leah Borden, an Alana Fellow in the lab of Biology Professor Laurie Boyer, is studying differences in heart development. Using advanced cultures of human heart tissues grown from trisomy 21 donors, she is finding that tissue tends to be stiffer than in cultures made from people without the third chromosome copy. The stiffness, she hypothesizes, might affect cellular function and migration during development, contributing to some of the heart defects that are common in the Down syndrome population.

Firenze pointed to several other advanced cell biology studies going on in the center. Researchers in the lab of Computer Science Professor Manolis Kellis, for instance, have used machine learning and single cell RNA sequencing to map the gene expression of more than 130,000 cells in the brains of people with or without Down syndrome to understand differences in their biology.

Researchers the lab of Y. Eva Tan Professor Edward Boyden, meanwhile, are using advanced tissue imaging techniques to look into the anatomy of cells in mice, Firenze said. They are finding differences in the structures of key organelles called mitochondria that provide cells with energy.

And in 2022, Firenze recalled, Tsai’s lab published a study showing that brain cells in Down syndrome mice exhibited a genome-wide disruption in how genes are expressed, leading them to take on a more senescent, or aged-like, state.

Striving for better sleep

One other theme of the Alana Center’s research that Firenze highlighted focuses on ways to understand and improve sleep for people with Down syndrome. In mouse studies in Tsai’s lab, they’ve begun to measure sleep differences between model and neurotypical mice to understand more about the nature of sleep disruptions.

“Sleep is different and we need to address this because it’s a key factor in your health,” Firenze said.

Firenze also highlighted how the Alana Center has collaborated with MIT’s Desphande Center for Technological Innovation to help advance a new device for treating sleep apnea in people with Down syndrome. Led by Mechanical Engineering Associate Professor Ellen Roche, the ZzAlign device improves on current technology by creating a custom-fit oral prosthesis accompanied by just a small tube to provide the needed air pressure to stabilize mouth muscles and prevent obstruction of the airway.

Through many examples of research projects aimed at improving brain and heart health and enhancing sleep, the webinar presented how MIT’s Alana Down Syndrome Center is working to advance the healthspan of people with Down syndrome.

 

New study reveals how cleft lip and cleft palate can arise

MIT biologists have found that defects in some transfer RNA molecules can lead to the formation of these common conditions.

Anne Trafton | MIT News
April 17, 2025

Cleft lip and cleft palate are among the most common birth defects, occurring in about one in 1,050 births in the United States. These defects, which appear when the tissues that form the lip or the roof of the mouth do not join completely, are believed to be caused by a mix of genetic and environmental factors.

In a new study, MIT biologists have discovered how a genetic variant often found in people with these facial malformations leads to the development of cleft lip and cleft palate.

Their findings suggest that the variant diminishes cells’ supply of transfer RNA, a molecule that is critical for assembling proteins. When this happens, embryonic face cells are unable to fuse to form the lip and roof of the mouth.

“Until now, no one had made the connection that we made. This particular gene was known to be part of the complex involved in the splicing of transfer RNA, but it wasn’t clear that it played such a crucial role for this process and for facial development. Without the gene, known as DDX1, certain transfer RNA can no longer bring amino acids to the ribosome to make new proteins. If the cells can’t process these tRNAs properly, then the ribosomes can’t make protein anymore,” says Michaela Bartusel, an MIT research scientist and the lead author of the study.

Eliezer Calo, an associate professor of biology at MIT, is the senior author of the paper, which appears today in the American Journal of Human Genetics.

Genetic variants

Cleft lip and cleft palate, also known as orofacial clefts, can be caused by genetic mutations, but in many cases, there is no known genetic cause.

“The mechanism for the development of these orofacial clefts is unclear, mostly because they are known to be impacted by both genetic and environmental factors,” Calo says. “Trying to pinpoint what might be affected has been very challenging in this context.”

To discover genetic factors that influence a particular disease, scientists often perform genome-wide association studies (GWAS), which can reveal variants that are found more often in people who have a particular disease than in people who don’t.

For orofacial clefts, some of the genetic variants that have regularly turned up in GWAS appeared to be in a region of DNA that doesn’t code for proteins. In this study, the MIT team set out to figure out how variants in this region might influence the development of facial malformations.

Their studies revealed that these variants are located in an enhancer region called e2p24.2. Enhancers are segments of DNA that interact with protein-coding genes, helping to activate them by binding to transcription factors that turn on gene expression.

The researchers found that this region is in close proximity to three genes, suggesting that it may control the expression of those genes. One of those genes had already been ruled out as contributing to facial malformations, and another had already been shown to have a connection. In this study, the researchers focused on the third gene, which is known as DDX1.

DDX1, it turned out, is necessary for splicing transfer RNA (tRNA) molecules, which play a critical role in protein synthesis. Each transfer RNA molecule transports a specific amino acid to the ribosome — a cell structure that strings amino acids together to form proteins, based on the instructions carried by messenger RNA.

While there are about 400 different tRNAs found in the human genome, only a fraction of those tRNAs require splicing, and those are the tRNAs most affected by the loss of DDX1. These tRNAs transport four different amino acids, and the researchers hypothesize that these four amino acids may be particularly abundant in proteins that embryonic cells that form the face need to develop properly.

When the ribosomes need one of those four amino acids, but none of them are available, the ribosome can stall, and the protein doesn’t get made.

The researchers are now exploring which proteins might be most affected by the loss of those amino acids. They also plan to investigate what happens inside cells when the ribosomes stall, in hopes of identifying a stress signal that could potentially be blocked and help cells survive.

Malfunctioning tRNA

While this is the first study to link tRNA to craniofacial malformations, previous studies have shown that mutations that impair ribosome formation can also lead to similar defects. Studies have also shown that disruptions of tRNA synthesis — caused by mutations in the enzymes that attach amino acids to tRNA, or in proteins involved in an earlier step in tRNA splicing — can lead to neurodevelopmental disorders.

“Defects in other components of the tRNA pathway have been shown to be associated with neurodevelopmental disease,” Calo says. “One interesting parallel between these two is that the cells that form the face are coming from the same place as the cells that form the neurons, so it seems that these particular cells are very susceptible to tRNA defects.”

The researchers now hope to explore whether environmental factors linked to orofacial birth defects also influence tRNA function. Some of their preliminary work has found that oxidative stress — a buildup of harmful free radicals — can lead to fragmentation of tRNA molecules. Oxidative stress can occur in embryonic cells upon exposure to ethanol, as in fetal alcohol syndrome, or if the mother develops gestational diabetes.

“I think it is worth looking for mutations that might be causing this on the genetic side of things, but then also in the future, we would expand this into which environmental factors have the same effects on tRNA function, and then see which precautions might be able to prevent any effects on tRNAs,” Bartusel says.

The research was funded by the National Science Foundation Graduate Research Program, the National Cancer Institute, the National Institute of General Medical Sciences, and the Pew Charitable Trusts.

Restoring healthy gene expression with programmable therapeutics

CAMP4 Therapeutics is targeting regulatory RNA, whose role in gene expression was first described by co-founder and MIT Professor Richard Young.

Zach Winn | MIT News
April 16, 2025

Many diseases are caused by dysfunctional gene expression that leads to too much or too little of a given protein. Efforts to cure those diseases include everything from editing genes to inserting new genetic snippets into cells to injecting the missing proteins directly into patients.

CAMP4 is taking a different approach. The company is targeting a lesser-known player in the regulation of gene expression known as regulatory RNA. CAMP4 co-founder and MIT Professor Richard Young has shown that by interacting with molecules called transcription factors, regulatory RNA plays an important role in controlling how genes are expressed. CAMP4’s therapeutics target regulatory RNA to increase the production of proteins and put patients’ levels back into healthy ranges.

The company’s approach holds promise for treating diseases caused by defects in gene expression, such as metabolic diseases, heart conditions, and neurological disorders. Targeting regulatory RNAs as opposed to genes could also offer more precise treatments than existing approaches.

“If I just want to fix a single gene’s defective protein output, I don’t want to introduce something that makes that protein at high, uncontrolled amounts,” says Young, who is also a core member of the Whitehead Institute. “That’s a huge advantage of our approach: It’s more like a correction than sledgehammer.”

CAMP4’s lead drug candidate targets urea cycle disorders (UCDs), a class of chronic conditions caused by a genetic defect that limits the body’s ability to metabolize and excrete ammonia. A phase 1 clinical trial has shown CAMP4’s treatment is safe and tolerable for humans, and in preclinical studies the company has shown its approach can be used to target specific regulatory RNA in the cells of humans with UCDs to restore gene expression to healthy levels.

“This has the potential to treat very severe symptoms associated with UCDs,” says Young, who co-founded CAMP4 with cancer genetics expert Leonard Zon, a professor at Harvard Medical School. “These diseases can be very damaging to tissues and causes a lot of pain and distress. Even a small effect in gene expression could have a huge benefit to patients, who are generally young.”

Mapping out new therapeutics

Young, who has been a professor at MIT since 1984, has spent decades studying how genes are regulated. It’s long been known that molecules called transcription factors, which orchestrate gene expression, bind to DNA and proteins. Research published in Young’s lab uncovered a previously unknown way in which transcription factors can also bind to RNA. The finding indicated RNA plays an underappreciated role in controlling gene expression.

CAMP4 was founded in 2016 with the initial idea of mapping out the signaling pathways that govern the expression of genes linked to various diseases. But as Young’s lab discovered and then began to characterize the role of regulatory RNA in gene expression around 2020, the company pivoted to focus on targeting regulatory RNA using therapeutic molecules known as antisense oligonucleotides (ASOs), which have been used for years to target specific messenger RNA sequences.

CAMP4 began mapping the active regulatory RNAs associated with the expression of every protein-coding gene and built a database, which it calls its RAP Platform, that helps it quickly identify regulatory RNAs to target  specific diseases and select ASOs that will most effectively bind to those RNAs.

Today, CAMP4 is using its platform to develop therapeutic candidates it believes can restore healthy protein levels to patients.

“The company has always been focused on modulating gene expression,” says CAMP4 Chief Financial Officer Kelly Gold MBA ’09. “At the simplest level, the foundation of many diseases is too much or too little of something being produced by the body. That is what our approach aims to correct.”

Accelerating impact

CAMP4 is starting by going after diseases of the liver and the central nervous system, where the safety and efficacy of ASOs has already been proven. Young believes correcting genetic expression without modulating the genes themselves will be a powerful approach to treating a range of complex diseases.

“Genetics is a powerful indicator of where a deficiency lies and how you might reverse that problem,” Young says. “There are many syndromes where we don’t have a complete understanding of the underlying mechanism of disease. But when a mutation clearly affects the output of a gene, you can now make a drug that can treat the disease without that complete understanding.”

As the company continues mapping the regulatory RNAs associated with every gene, Gold hopes CAMP4 can eventually minimize its reliance on wet-lab work and lean more heavily on machine learning to leverage its growing database and quickly identify regRNA targets for every disease it wants to treat.

In addition to its trials in urea cycle disorders, the company plans to launch key preclinical safety studies for a candidate targeting seizure disorders with a genetic basis, this year. And as the company continues exploring drug development efforts around the thousands of genetic diseases where increasing protein levels are can have a meaningful impact, it’s also considering collaborating with others to accelerate its impact.

“I can conceive of companies using a platform like this to go after many targets, where partners fund the clinical trials and use CAMP4 as an engine to target any disease where there’s a suspicion that gene upregulation or downregulation is the way to go,” Young says.

Helping the immune system attack tumors

Stefani Spranger is working to discover why some cancers don’t respond to immunotherapy, in hopes of making them more vulnerable to it.

Anne Trafton | MIT News
February 26, 2025

In addition to patrolling the body for foreign invaders, the immune system also hunts down and destroys cells that have become cancerous or precancerous. However, some cancer cells end up evading this surveillance and growing into tumors.

Once established, tumor cells often send out immunosuppressive signals, which leads T cells to become “exhausted” and unable to attack the tumor. In recent years, some cancer immunotherapy drugs have shown great success in rejuvenating those T cells so they can begin attacking tumors again.

While this approach has proven effective against cancers such as melanoma, it doesn’t work as well for others, including lung and ovarian cancer. MIT Associate Professor Stefani Spranger is trying to figure out how those tumors are able to suppress immune responses, in hopes of finding new ways to galvanize T cells into attacking them.

“We really want to understand why our immune system fails to recognize cancer,” Spranger says. “And I’m most excited about the really hard-to-treat cancers because I think that’s where we can make the biggest leaps.”

Her work has led to a better understanding of the factors that control T-cell responses to tumors, and raised the possibility of improving those responses through vaccination or treatment with immune-stimulating molecules called cytokines.

“We’re working on understanding what exactly the problem is, and then collaborating with engineers to find a good solution,” she says.

Jumpstarting T cells

As a student in Germany, where students often have to choose their college major while still in high school, Spranger envisioned going into the pharmaceutical industry and chose to major in biology. At Ludwig Maximilian University in Munich, her course of study began with classical biology subjects such as botany and zoology, and she began to doubt her choice. But, once she began taking courses in cell biology and immunology, her interest was revived and she continued into a biology graduate program at the university.

During a paper discussion class early in her graduate school program, Spranger was assigned to a Science paper on a promising new immunotherapy treatment for melanoma. This strategy involves isolating tumor-infiltrating T-cells during surgery, growing them into large numbers, and then returning them to the patient. For more than 50 percent of those patients, the tumors were completely eliminated.

“To me, that changed the world,” Spranger recalls. “You can take the patient’s own immune system, not really do all that much to it, and then the cancer goes away.”

Spranger completed her PhD studies in a lab that worked on further developing that approach, known as adoptive T-cell transfer therapy. At that point, she still was leaning toward going into pharma, but after finishing her PhD in 2011, her husband, also a biologist, convinced her that they should both apply for postdoc positions in the United States.

They ended up at the University of Chicago, where Spranger worked in a lab that studies how the immune system responds to tumors. There, she discovered that while melanoma is usually very responsive to immunotherapy, there is a small fraction of melanoma patients whose T cells don’t respond to the therapy at all. That got her interested in trying to figure out why the immune system doesn’t always respond to cancer the way that it should, and in finding ways to jumpstart it.

During her postdoc, Spranger also discovered that she enjoyed mentoring students, which she hadn’t done as a graduate student in Germany. That experience drew her away from going into the pharmaceutical industry, in favor of a career in academia.

“I had my first mentoring teaching experience having an undergrad in the lab, and seeing that person grow as a scientist, from barely asking questions to running full experiments and coming up with hypotheses, changed how I approached science and my view of what academia should be for,” she says.

Modeling the immune system

When applying for faculty jobs, Spranger was drawn to MIT by the collaborative environment of MIT and its Koch Institute for Integrative Cancer Research, which offered the chance to collaborate with a large community of engineers who work in the field of immunology.

“That community is so vibrant, and it’s amazing to be a part of it,” she says.

Building on the research she had done as a postdoc, Spranger wanted to explore why some tumors respond well to immunotherapy, while others do not. For many of her early studies, she used a mouse model of non-small-cell lung cancer. In human patients, the majority of these tumors do not respond well to immunotherapy.

“We build model systems that resemble each of the different subsets of non-responsive non-small cell lung cancer, and we’re trying to really drill down to the mechanism of why the immune system is not appropriately responding,” she says.

As part of that work, she has investigated why the immune system behaves differently in different types of tissue. While immunotherapy drugs called checkpoint inhibitors can stimulate a strong T-cell response in the skin, they don’t do nearly as much in the lung. However, Spranger has shown that T cell responses in the lung can be improved when immune molecules called cytokines are also given along with the checkpoint inhibitor.

Those cytokines work, in part, by activating dendritic cells — a class of immune cells that help to initiate immune responses, including activation of T cells.

“Dendritic cells are the conductor for the orchestra of all the T cells, although they’re a very sparse cell population,” Spranger says. “They can communicate which type of danger they sense from stressed cells and then instruct the T cells on what they have to do and where they have to go.”

Spranger’s lab is now beginning to study other types of tumors that don’t respond at all to immunotherapy, including ovarian cancer and glioblastoma. Both the brain and the peritoneal cavity appear to suppress T-cell responses to tumors, and Spranger hopes to figure out how to overcome that immunosuppression.

“We’re specifically focusing on ovarian cancer and glioblastoma, because nothing’s working right now for those cancers,” she says. “We want to understand what we have to do in those sites to induce a really good anti-tumor immune response.”

Taking the pulse of sex differences in the heart

Work led by Talukdar and Page Lab postdoc Lukáš Chmátal shows that there are differences in how healthy male and female heart cells—specifically, cardiomyocytes, the muscle cells responsible for making the heart beat—generate energy.

Greta Friar | Whitehead Institute
February 18, 2025

Heart disease is the number one killer of men and women, but it often presents differently depending on sex. There are sex differences in the incidence, outcomes, and age of onset of different types of heart problems. Some of these differences can be explained by social factors—for example, women experience less-well recognized symptoms when having heart attacks, and so may take longer to be diagnosed and treated—but others are likely influenced by underlying differences in biology. Whitehead Institute Member David Page and colleagues have now identified some of these underlying biological differences in healthy male and female hearts, which may contribute to the observed differences in disease.

“My sense is that clinicians tend to think that sex differences in heart disease are due to differences in behavior,” says Harvard-MIT MD-PhD student Maya Talukdar, a graduate student in Page’s lab. “Behavioral factors do contribute, but even when you control for them, you still see sex differences. This implies that there are more basic physiological differences driving them.”

Page, who is also an HHMI Investigator and a professor of biology at the Massachusetts Institute of Technology, and members of his lab study the underlying biology of sex differences in health and disease, and recently they have turned their attention to the heart. In a paper published on February 17 in the women’s health edition of the journal Circulation, work led by Talukdar and Page lab postdoc Lukáš Chmátal shows that there are differences in how healthy male and female heart cells—specifically, cardiomyocytes, the muscle cells responsible for making the heart beat—generate energy.

“The heart is a hard-working pump, and heart failure often involves an energy crisis in which the heart can’t summon enough energy to pump blood fast enough to meet the body’s needs,” says Page. “What is intriguing about our current findings and their relationship to heart disease is that we’ve discovered sex differences in the generation of energy in cardiomyocytes, and this likely sets up males and females differently for an encounter with heart failure.”

Page and colleagues began their work by looking for sex differences in healthy hearts because they hypothesize that these impact sex differences in heart disease. Differences in baseline biology in the healthy state often affect outcomes when challenged by disease; for example, people with one copy of the sickle cell trait are more resistant to malaria, certain versions of the HLA gene are linked to slower progression of HIV, and variants of certain genes may protect against developing dementia.

Identifying baseline traits in the heart and figuring out how they interact with heart disease could not only reveal more about heart disease, but could also lead to new therapeutic strategies. If one group has a trait that naturally protects them against heart disease, then researchers can potentially develop medical therapies that induce or recreate that protective feature in others. In such a manner, Page and colleagues hope that their work to identify baseline sex differences could ultimately contribute to advances in prevention and treatment of heart disease.

The new work takes the first step by identifying relevant baseline sex differences. The researchers combined their expertise in sex differences with heart expertise provided by co-authors Christine Seidman, a Harvard Medical School professor and director of the Cardiovascular Genetics Center at Brigham and Women’s Hospital; Harvard Medical School Professor Jonathan Seidman; and Zoltan Arany, a professor and director of the Cardiovascular Metabolism Program at the University of Pennsylvania.

Along with providing heart expertise, the Seidmans and Arany provided data collected from healthy hearts. Gaining access to healthy heart tissue is difficult, and so the researchers felt fortunate to be able to perform new analyses on existing datasets that had not previously been looked at in the context of sex differences. The researchers also used data from the publicly available Genotype-Tissue Expression Project. Collectively, the datasets provided information on bulk and single cell gene expression, as well as metabolomics, of heart tissue—and in particular, of cardiomyocytes.

The researchers searched these datasets for differences between male and female hearts, and found evidence that female cardiomyocytes have higher activity of the primary pathway for energy generation than male cardiomyocytes. Fatty acid oxidation (FAO) is the pathway that produces most of the energy that powers the heart, in the form of the energy molecule ATP. The researchers found that many genes involved in FAO have higher expression levels in female cardiomyocytes. Metabolomic data reinforced these findings by showing that female hearts had greater flux of free fatty acids, the molecules used in FAO, and that female hearts used more free fatty acids than did males in the generation of ATP.

Altogether, these findings show that there are fundamental differences in how female and male hearts generate energy to pump blood. Further experiments are needed to explore whether these differences contribute to the sex differences seen in heart disease. The researchers suspect that an association is likely, because energy production is essential to heart function and failure.

In the meantime, Page and his lab members continue to investigate the biology underlying sex differences in tissues and organs throughout the body.

“We have a lot to learn about the molecular origins of sex differences in health and disease,” Chmátal says. “What’s exciting to me is that the knowledge that comes from these basic science discoveries could lead to treatments that benefit men and women, as well as to policy changes that take sex differences into account when determining how doctors are trained and patients are diagnosed and treated.”

AI model deciphers the code in proteins that tells them where to go

Whitehead Institute and CSAIL researchers created a machine-learning model to predict and generate protein localization, with implications for understanding and remedying disease.

Greta Friar | Whitehead Institute
February 13, 2025

Proteins are the workhorses that keep our cells running, and there are many thousands of types of proteins in our cells, each performing a specialized function. Researchers have long known that the structure of a protein determines what it can do. More recently, researchers are coming to appreciate that a protein’s localization is also critical for its function. Cells are full of compartments that help to organize their many denizens. Along with the well-known organelles that adorn the pages of biology textbooks, these spaces also include a variety of dynamic, membrane-less compartments that concentrate certain molecules together to perform shared functions. Knowing where a given protein localizes, and who it co-localizes with, can therefore be useful for better understanding that protein and its role in the healthy or diseased cell, but researchers have lacked a systematic way to predict this information.

Meanwhile, protein structure has been studied for over half-a-century, culminating in the artificial intelligence tool AlphaFold, which can predict protein structure from a protein’s amino acid code, the linear string of building blocks within it that folds to create its structure. AlphaFold and models like it have become widely used tools in research.

Proteins also contain regions of amino acids that do not fold into a fixed structure, but are instead important for helping proteins join dynamic compartments in the cell. MIT Professor Richard Young and colleagues wondered whether the code in those regions could be used to predict protein localization in the same way that other regions are used to predict structure. Other researchers have discovered some protein sequences that code for protein localization, and some have begun developing predictive models for protein localization. However, researchers did not know whether a protein’s localization to any dynamic compartment could be predicted based on its sequence, nor did they have a comparable tool to AlphaFold for predicting localization.

Now, Young, also member of the Whitehead Institute for Biological Research; Young lab postdoc Henry Kilgore; Regina Barzilay, the School of Engineering Distinguished Professor for AI and Health at MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL); and colleagues have built such a model, which they call ProtGPS. In a paper published on Feb. 6 in the journal Science, with first authors Kilgore and Barzilay lab graduate students Itamar Chinn, Peter Mikhael, and Ilan Mitnikov, the cross-disciplinary team debuts their model. The researchers show that ProtGPS can predict to which of 12 known types of compartments a protein will localize, as well as whether a disease-associated mutation will change that localization. Additionally, the research team developed a generative algorithm that can design novel proteins to localize to specific compartments.

“My hope is that this is a first step towards a powerful platform that enables people studying proteins to do their research,” Young says, “and that it helps us understand how humans develop into the complex organisms that they are, how mutations disrupt those natural processes, and how to generate therapeutic hypotheses and design drugs to treat dysfunction in a cell.”

The researchers also validated many of the model’s predictions with experimental tests in cells.

“It really excited me to be able to go from computational design all the way to trying these things in the lab,” Barzilay says. “There are a lot of exciting papers in this area of AI, but 99.9 percent of those never get tested in real systems. Thanks to our collaboration with the Young lab, we were able to test, and really learn how well our algorithm is doing.”

Developing the model

The researchers trained and tested ProtGPS on two batches of proteins with known localizations. They found that it could correctly predict where proteins end up with high accuracy. The researchers also tested how well ProtGPS could predict changes in protein localization based on disease-associated mutations within a protein. Many mutations — changes to the sequence for a gene and its corresponding protein — have been found to contribute to or cause disease based on association studies, but the ways in which the mutations lead to disease symptoms remain unknown.

Figuring out the mechanism for how a mutation contributes to disease is important because then researchers can develop therapies to fix that mechanism, preventing or treating the disease. Young and colleagues suspected that many disease-associated mutations might contribute to disease by changing protein localization. For example, a mutation could make a protein unable to join a compartment containing essential partners.

They tested this hypothesis by feeding ProtGOS more than 200,000 proteins with disease-associated mutations, and then asking it to both predict where those mutated proteins would localize and measure how much its prediction changed for a given protein from the normal to the mutated version. A large shift in the prediction indicates a likely change in localization.

The researchers found many cases in which a disease-associated mutation appeared to change a protein’s localization. They tested 20 examples in cells, using fluorescence to compare where in the cell a normal protein and the mutated version of it ended up. The experiments confirmed ProtGPS’s predictions. Altogether, the findings support the researchers’ suspicion that mis-localization may be an underappreciated mechanism of disease, and demonstrate the value of ProtGPS as a tool for understanding disease and identifying new therapeutic avenues.

“The cell is such a complicated system, with so many components and complex networks of interactions,” Mitnikov says. “It’s super interesting to think that with this approach, we can perturb the system, see the outcome of that, and so drive discovery of mechanisms in the cell, or even develop therapeutics based on that.”

The researchers hope that others begin using ProtGPS in the same way that they use predictive structural models like AlphaFold, advancing various projects on protein function, dysfunction, and disease.

Moving beyond prediction to novel generation

The researchers were excited about the possible uses of their prediction model, but they also wanted their model to go beyond predicting localizations of existing proteins, and allow them to design completely new proteins. The goal was for the model to make up entirely new amino acid sequences that, when formed in a cell, would localize to a desired location. Generating a novel protein that can actually accomplish a function — in this case, the function of localizing to a specific cellular compartment — is incredibly difficult. In order to improve their model’s chances of success, the researchers constrained their algorithm to only design proteins like those found in nature. This is an approach commonly used in drug design, for logical reasons; nature has had billions of years to figure out which protein sequences work well and which do not.

Because of the collaboration with the Young lab, the machine learning team was able to test whether their protein generator worked. The model had good results. In one round, it generated 10 proteins intended to localize to the nucleolus. When the researchers tested these proteins in the cell, they found that four of them strongly localized to the nucleolus, and others may have had slight biases toward that location as well.

“The collaboration between our labs has been so generative for all of us,” Mikhael says. “We’ve learned how to speak each other’s languages, in our case learned a lot about how cells work, and by having the chance to experimentally test our model, we’ve been able to figure out what we need to do to actually make the model work, and then make it work better.”

Being able to generate functional proteins in this way could improve researchers’ ability to develop therapies. For example, if a drug must interact with a target that localizes within a certain compartment, then researchers could use this model to design a drug to also localize there. This should make the drug more effective and decrease side effects, since the drug will spend more time engaging with its target and less time interacting with other molecules, causing off-target effects.

The machine learning team members are enthused about the prospect of using what they have learned from this collaboration to design novel proteins with other functions beyond localization, which would expand the possibilities for therapeutic design and other applications.

“A lot of papers show they can design a protein that can be expressed in a cell, but not that the protein has a particular function,” Chinn says. “We actually had functional protein design, and a relatively huge success rate compared to other generative models. That’s really exciting to us, and something we would like to build on.”

All of the researchers involved see ProtGPS as an exciting beginning. They anticipate that their tool will be used to learn more about the roles of localization in protein function and mis-localization in disease. In addition, they are interested in expanding the model’s localization predictions to include more types of compartments, testing more therapeutic hypotheses, and designing increasingly functional proteins for therapies or other applications.

“Now that we know that this protein code for localization exists, and that machine learning models can make sense of that code and even create functional proteins using its logic, that opens up the door for so many potential studies and applications,” Kilgore says.

Cellular interactions help explain vascular complications due to COVID-19 virus infection

Whitehead Institute Founding Member Rudolf Jaenisch and colleagues have found that cellular interactions help explain how SARS-CoV-2, the virus that causes COVID-19, could have such significant vascular complications, including blood clots, heart attacks, and strokes.

Greta Friar | Whitehead Institute
December 31, 2024

COVID-19 is a respiratory disease primarily affecting the lungs. However, the SARS-CoV-2 virus that causes COVID-19 surprised doctors and scientists by triggering an unusually large percentage of patients to experience vascular complications – issues related to blood flow, such as blood clots, heart attacks, and strokes.

Whitehead Institute Founding Member Rudolf Jaenisch and colleagues wanted to understand how this respiratory virus could have such significant vascular effects. They used pluripotent stem cells to generate three relevant vascular and perivascular cell types—cells that surround and help maintain blood vessels—so they could closely observe the effects of SARS-CoV-2 on the cells. Instead of using existing methods to generate the cells, the researchers developed a new approach, providing them with fresh insights into the mechanisms by which the virus causes vascular problems. The researchers found that SARS-CoV-2 primarily infects perivascular cells and that signals from these infected cells are sufficient to cause dysfunction in neighboring vascular cells, even when the vascular cells are not themselves infected. In a paper published in the journal Nature Communications on December 30, Jaenisch, postdoc in his lab Alexsia Richards, Harvard University Professor and Wyss Institute for Biologically Inspired Engineering Member David Mooney, and then-postdoc in the Jaenisch and Mooney labs Andrew Khalil share their findings and present a scalable stem cell-derived model system with which to study vascular cell biology and test medical therapies.

A new problem requires a new approach

When the COVID-19 pandemic began, Richards, a virologist, quickly pivoted her focus to SARS-CoV-2. Khalil, a bioengineer, had already been working on a new approach to generate vascular cells. The researchers realized that a collaboration could provide Richards with the research tool she needed and Khalil with an important research question to which his tool could be applied.

The three cell types that Khalil’s approach generated were endothelial cells, the vascular cells that form the lining of blood vessels; and smooth muscle cells and pericytes, perivascular cells that surround blood vessels and provide them with structure and maintenance, among other functions. Khalil’s biggest innovation was to generate all three cell types in the same media—the mixture of nutrients and signaling molecules in which stem cell-derived cells are grown.

The combination of signals in the media determines the final cell type into which a stem cell will mature, so it is much easier to grow each cell type separately in specially tailored media than to find a mixture that works for all three. Typically, Richards explains, virologists will generate a desired cell type using the easiest method, which means growing each cell type and then observing the effects of viral infection on it in isolation. However, this approach can limit results in several ways. Firstly, it can make it challenging to distinguish the differences in how cell types react to a virus from the differences caused by the cells being grown in different media.

“By making these cells under identical conditions, we could see in much higher resolution the effects of the virus on these different cell populations, and that was essential in order to form a strong hypothesis of the mechanisms of vascular symptom risk and progression,” Khalil says.

Secondly, infecting isolated cell types with a virus does not accurately represent what happens in the body, where cells are in constant communication as they react to viral exposure. Indeed, Richards’ and Khalil’s work ultimately revealed that the communication between infected and uninfected cell types plays a critical role in the vascular effects of COVID-19.

“The field of virology often overlooks the importance of considering how cells influence other cells and designing models to reflect that,” Richards says. “Cells do not get infected in isolation, and the value of our model is that it allows us to observe what’s happening between cells during infection.”

Viral infection of smooth muscle cells has broader, indirect effects

When the researchers exposed their cells to SARS-CoV-2, the smooth muscle cells and pericytes became infected—the former at especially high levels, and this infection resulted in strong inflammatory gene expression—but the endothelial cells resisted infection. Endothelial cells did show some response to viral exposure, likely due to interactions with proteins on the virus’ surface. Typically, endothelial cells press tightly together to form a firm barrier that keeps blood inside of blood vessels and prevents viruses from getting out. When exposed to SARS-CoV-2, the junctions between endothelial cells appeared to weaken slightly. The cells also had increased levels of reactive oxygen species, which are damaging byproducts of certain cellular processes.

However, big changes in endothelial cells only occurred after the cells were exposed to infected smooth muscle cells. This triggered high levels of inflammatory signaling within the endothelial cells. It led to changes in the expression of many genes relevant to immune response. Some of the genes affected were involved in coagulation pathways, which thicken blood and so can cause blood clots and related vascular events. The junctions between endothelial cells experienced much more significant weakening after exposure to infected smooth muscle cells, which would lead to blood leakage and viral spread. All of these changes occurred without SARS-CoV-2 ever infecting the endothelial cells.

This work shows that viral infection of smooth muscle cells, and their resultant signaling to endothelial cells, is the lynchpin in the vascular damage caused by SARS-CoV-2. This would not have been apparent if the researchers had not been able to observe the cells interacting with each other.

Clinical relevance of stem cell results

The effects that the researchers observed were consistent with patient data. Some of the genes whose expression changed in their stem cell-derived model had been identified as markers of high risk for vascular complications in COVID-19 patients with severe infections. Additionally, the researchers found that a later strain of SARS-CoV-2, an Omicron variant, had much weaker effects on the vascular and perivascular cells than did the original viral strain. This is consistent with the reduced levels of vascular complications seen in COVID-19 patients infected with recent strains.

Having identified smooth muscle cells as the main site of SARS-Cov-2 infection in the vascular system, the researchers next used their model system to test one drug’s ability to prevent infection of smooth muscle cells. They found that the drug, N, N-Dimethyl-D-erythro-sphingosine, could reduce infection of the cell type without harming smooth muscle or endothelial cells. Although preventing vascular complications of COVID-19 is not as pressing a need with current viral strains, the researchers see this experiment as proof that their stem cell model could be used for future drug development. New coronaviruses and other pathogens are frequently evolving, and when a future virus causes vascular complications, this model could be used to quickly test drugs to find potential therapies while the need is still high. The model system could also be used to answer other questions about vascular cells, how these cells interact, and how they respond to viruses.

“By integrating bioengineering strategies into the analysis of a fundamental question in viral pathology, we addressed important practical challenges in modeling human disease in culture and gained new insights into SARS-CoV-2 infection,” Mooney says.

“Our interdisciplinary approach allowed us to develop an improved stem cell model for infection of the vasculature,” says Jaenisch, who is also a professor of biology at the Massachusetts Institute of Technology. “Our lab is already applying this model to other questions of interest, and we hope that it can be a valuable tool for other researchers.”

Cellular traffic congestion in chronic diseases suggests new therapeutic targets

Many chronic diseases have a common denominator that could be driving their dysfunction: reduced protein mobility, which in turn reduces protein function. A new paper from the Young Lab describes this pervasive mobility defect.

Greta Friar | Whitehead Institute
November 26, 2024

Chronic diseases like type 2 diabetes and inflammatory disorders have a huge impact on humanity. They are a leading cause of disease burden and deaths around the globe, are physically and economically taxing, and the number of people with such diseases is growing.

Treating chronic disease has proven difficult because there is not one simple cause, like a single gene mutation, that a treatment could target. At least, that’s how it has appeared to scientists. However, research from Whitehead Institute Member Richard Young and colleagues, published in the journal Cell on November 27, reveals that many chronic diseases have a common denominator that could be driving their dysfunction: reduced protein mobility. What this means is that around half of all proteins active in cells slow their movement when cells are in a chronic disease state, reducing the proteins’ functions. The researchers’ findings suggest that protein mobility may be a linchpin for decreased cellular function in chronic disease, making it a promising therapeutic target.

In this paper, Young and colleagues in his lab, including postdoc Alessandra Dall’Agnese, graduate students Shannon Moreno and Ming Zheng, and research scientist Tong Ihn Lee, describe their discovery of this common mobility defect, which they call proteolethargy; explain what causes the defect and how it leads to dysfunction in cells; and propose a new therapeutic hypothesis for treating chronic diseases.

“I’m excited about what this work could mean for patients,” says Dall’Agnese. “My hope is that this will lead to a new class of drugs that restore protein mobility, which could help people with many different diseases that all have this mechanism as a common denominator.”

“This work was a collaborative, interdisciplinary effort that brought together biologists, physicists, chemists, computer scientists and physician-scientists,” Lee says. “Combining that expertise is a strength of the Young lab. Studying the problem from different viewpoints really helped us think about how this mechanism might work and how it could change our understanding of the pathology of chronic disease.”

Commuter delays cause work stoppages in the cell

How do proteins moving more slowly through a cell lead to widespread and significant cellular dysfunction? Dall’Agnese explains that every cell is like a tiny city, with proteins as the workers who keep everything running. Proteins have to commute in dense traffic in the cell, traveling from where they are created to where they work. The faster their commute, the more work they get done. Now, imagine a city that starts experiencing traffic jams along all the roads. Stores don’t open on time, groceries are stuck in transit, meetings are postponed. Essentially all operations in the city are slowed.

The slow down of operations in cells experiencing reduced protein mobility follows a similar progression. Normally, most proteins zip around the cell bumping into other molecules until they locate the molecule they work with or act on. The slower a protein moves, the fewer other molecules it will reach, and so the less likely it will be able to do its job. Young and colleagues found that such protein slow-downs lead to measurable reductions in the functional output of the proteins. When many proteins fail to get their jobs done in time, cells begin to experience a variety of problems—as they are known to do in chronic diseases.

Discovering the protein mobility problem

Young and colleagues first suspected that cells affected in chronic disease might have a protein mobility problem after observing changes in the behavior of the insulin receptor, a signaling protein that reacts to the presence of insulin and causes cells to take in sugar from blood. In people with diabetes, cells become less responsive to insulin — a state called insulin resistance — causing too much sugar to remain in the blood. In research published on insulin receptors in Nature Communications in 2022, Young and colleagues reported that insulin receptor mobility might be relevant to diabetes.

Knowing that many cellular functions are altered in diabetes, the researchers considered the possibility that altered protein mobility might somehow affect many proteins in cells. To test this hypothesis, they studied proteins involved in a broad range of cellular functions, including MED1, a protein involved in gene expression; HP1α, a protein involved in gene silencing; FIB1, a protein involved in production of ribosomes; and SRSF2, a protein involved in splicing of messenger RNA. They used single-molecule tracking and other methods to measure how each of those proteins moves in healthy cells and in cells in disease states. All but one of the proteins showed reduced mobility (about 20-35%) in the disease cells.

“I’m excited that we were able to transfer physics-based insight and methodology, which are commonly used to understand the single-molecule processes like gene transcription in normal cells, to a disease context and show that they can be used to uncover unexpected mechanisms of disease,” Zheng says. “This work shows how the random walk of proteins in cells is linked to disease pathology.”

Moreno concurs: “In school, we’re taught to consider changes in protein structure or DNA sequences when looking for causes of disease, but we’ve demonstrated that those are not the only contributing factors. If you only consider a static picture of a protein or a cell, you miss out on discovering these changes that only appear when molecules are in motion.”

 Can’t commute across the cell, I’m all tied up right now

Next, the researchers needed to determine what was causing the proteins to slow down. They suspected that the defect had to do with an increase in cells of the level of reactive oxygen species (ROS), molecules that are highly prone to interfering with other molecules and their chemical reactions. Many types of chronic-disease-associated triggers, such as higher sugar or fat levels, certain toxins, and inflammatory signals, lead to an increase in ROS, also known as an increase in oxidative stress. The researchers measured the mobility of the proteins again, in cells that had high levels of ROS and were not otherwise in a disease state, and saw comparable mobility defects, suggesting that oxidative stress was to blame for the protein mobility defect.

The final part of the puzzle was why some, but not all, proteins slow down in the presence of ROS. SRSF2 was the only one of the proteins that was unaffected in the experiments, and it had one clear difference from the others: its surface did not contain any cysteines, an amino acid building block of many proteins. Cysteines are especially susceptible to interference from ROS because it will cause them to bond to other cysteines. When this bonding occurs between two protein molecules, it slows them down because the two proteins cannot move through the cell as quickly as either protein alone.

About half of the proteins in our cells contain surface cysteines, so this single protein mobility defect can impact many different cellular pathways. This makes sense when one considers the diversity of dysfunctions that appear in cells of people with chronic diseases: dysfunctions in cell signaling, metabolic processes, gene expression and gene silencing, and more. All of these processes rely on the efficient functioning of proteins—including the diverse proteins studied by the researchers. Young and colleagues performed several experiments to confirm that decreased protein mobility does in fact decrease a protein’s function. For example, they found that when an insulin receptor experiences decreased mobility, it acts less efficiently on IRS1, a molecule to which it usually adds a phosphate group.

From understanding a mechanism to treating a disease

Discovering that decreased protein mobility in the presence of oxidative stress could be driving many of the symptoms of chronic disease provides opportunities to develop therapies to rescue protein mobility. In the course of their experiments, the researchers treated cells with an antioxidant drug—something that reduces ROS—called N-acetyl cysteine and saw that this partially restored protein mobility.

The researchers are pursuing a variety of follow ups to this work, including the search for drugs that safely and efficiently reduce ROS and restore protein mobility. They developed an assay that can be used to screen drugs to see if they restore protein mobility by comparing each drug’s effect on a simple biomarker with surface cysteines to one without. They are also looking into other diseases that may involve protein mobility, and are exploring the role of reduced protein mobility in aging.

“The complex biology of chronic diseases has made it challenging to come up with effective therapeutic hypotheses,” says Young, who is also a professor of biology at the Massachusetts Institute of Technology. “The discovery that diverse disease-associated stimuli all induce a common feature, proteolethargy, and that this feature could contribute to much of the dysregulation that we see in chronic disease, is something that I hope will be a real game changer for developing drugs that work across the spectrum of chronic diseases.”

A blueprint for better cancer immunotherapies

By examining antigen architectures, MIT researchers built a therapeutic cancer vaccine that may improve tumor response to immune checkpoint blockade treatments.

Bendta Schroeder | Koch Institute
November 25, 2024

Immune checkpoint blockade (ICB) therapies can be very effective against some cancers by helping the immune system recognize cancer cells that are masquerading as healthy cells.

T cells are built to recognize specific pathogens or cancer cells, which they identify from the short fragments of proteins presented on their surface. These fragments are often referred to as antigens. Healthy cells will will not have the same short fragments or antigens on their surface, and thus will be spared from attack.

Even with cancer-associated antigens studding their surfaces, tumor cells can still escape attack by presenting a checkpoint protein, which is built to turn off the T cell. Immune checkpoint blockade therapies bind to these “off-switch” proteins and allow the T cell to attack.

Researchers have established that how cancer-associated antigens are distributed throughout a tumor determines how it will respond to checkpoint therapies. Tumors with the same antigen signal across most of its cells respond well, but heterogeneous tumors with subpopulations of cells that each have different antigens, do not. The overwhelming majority of tumors fall into the latter category and are characterized by heterogenous antigen expression. Because the mechanisms behind antigen distribution and tumor response are poorly understood, efforts to improve ICB therapy response in heterogenous tumors have been hindered.

In a new study, MIT researchers analyzed antigen expression patterns and associated T cell responses to better understand why patients with heterogenous tumors respond poorly to ICB therapies. In addition to identifying specific antigen architectures that determine how immune systems respond to tumors, the team developed an RNA-based vaccine that, when combined with ICB therapies, was effective at controlling tumors in mouse models of lung cancer.

Stefani Spranger, associate professor of biology and member of MIT’s Koch Institute for Integrative Cancer Research, is the senior author of the study, appearing recently in the Journal for Immunotherapy of Cancer. Other contributors include Koch Institute colleague Forest White, the Ned C. (1949) and Janet Bemis Rice Professor and professor of biological engineering at MIT, and Darrell Irvine, professor of immunology and microbiology at Scripps Research Institute and a former member of the Koch Institute.

While RNA vaccines are being evaluated in clinical trials, current practice of antigen selection is based on the predicted stability of antigens on the surface of tumor cells.

“It’s not so black-and-white,” says Spranger. “Even antigens that don’t make the numerical cut-off could be really valuable targets. Instead of just focusing on the numbers, we need to look inside the complex interplays between antigen hierarchies to uncover new and important therapeutic strategies.”

Spranger and her team created mouse models of lung cancer with a number of different and well-defined expression patterns of cancer-associated antigens in order to analyze how each antigen impacts T cell response. They created both “clonal” tumors, with the same antigen expression pattern across cells, and “subclonal” tumors that represent a heterogenous mix of tumor cell subpopulations expressing different antigens. In each type of tumor, they tested different combinations of antigens with strong or weak binding affinity to MHC.

The researchers found that the keys to immune response were how widespread an antigen is expressed across a tumor, what other antigens are expressed at the same time, and the relative binding strength and other characteristics of antigens expressed by multiple cell populations in the tumor

As expected, mouse models with clonal tumors were able to mount an immune response sufficient to control tumor growth when treated with ICB therapy, no matter which combinations of weak or strong antigens were present. However, the team discovered that the relative strength of antigens present resulted in dynamics of competition and synergy between T cell populations, mediated by immune recognition specialists called cross-presenting dendritic cells in tumor-draining lymph nodes. In pairings of two weak or two strong antigens, one resulting T cell population would be reduced through competition. In pairings of weak and strong antigens, overall T cell response was enhanced.

In subclonal tumors, with different cell populations emitting different antigen signals, competition rather than synergy was the rule, regardless of antigen combination. Tumors with a subclonal cell population expressing a strong antigen would be well-controlled under ICB treatment at first, but eventually parts of the tumor lacking the strong antigen began to grow and developed the ability evade immune attack and resist ICB therapy.

Incorporating these insights, the researchers then designed an RNA-based vaccine to be delivered in combination with ICB treatment with the goal of strengthening immune responses suppressed by antigen-driven dynamics. Strikingly, they found that no matter the binding affinity or other characteristics of the antigen targeted, the vaccine-ICB therapy combination was able to control tumors in mouse models. The widespread availability of an antigen across tumor cells determined the vaccine’s success, even if that antigen was associated with weak immune response.

Analysis of clinical data across tumor types showed that the vaccine-ICB therapy combination may be an effective strategy for treating patients with tumors with high heterogeneity. Patterns of antigen architectures in patient tumors correlated with T cell synergy or competition in mice models and determined responsiveness to ICB in cancer patients. In future work with the Irvine laboratory at the Scripps Research Institute, the Spranger laboratory will further optimize the vaccine with the aim of testing the therapy strategy in the clinic.

Establishing boundaries of the genetic kind

The pseudoautosomal region (PAR) is a critical area on the Y chromosome that swaps genetic information with the X chromosome. Recent research from the Page Lab reaffirms the location of PAR and offers a refined understanding of where crossover events occur.

Shafaq Zia | Whitehead Institute
October 14, 2024

At first, the X and the Y sex chromosomes seemed like an unlikely pair. But then, researchers, including Whitehead Institute Member David Page, began finding clues that suggested otherwise: identical DNA sequences on the X and Y chromosomes.

Soon, it became clear that the tips of the X and Y chromosomes join together in a tight embrace, swapping genetic material during the process of sperm production from immature male germ cells. This limited area of genetic exchange between the two sex chromosomes is called the pseudoautosomal region (PAR).

But science is an iterative process—a continuous cycle of questioning, testing, and revising knowledge. Last fall, what had long been considered well established in genetics was called into question when new research suggested that the boundary of the PAR might be half a million base pairs away from the accepted location. Given that a typical human gene is about tens of thousands of base pairs, this length would potentially span multiple genes on the X and Y chromosomes, raising serious concerns about the accuracy and validity of decades of scientific literature.

Fortunately, new work from Page, research scientist Daniel Winston Bellott, and colleagues—published Oct. 14 in the American Journal of Human Genetics—offers clarity. In this study, the group re-examines the size of the PAR using sequencing data presented by outside researchers in their 2023 work, alongside decades of genomic resources, and single-cell sequencing of human sperm. Their findings confirm that the location of the boundary to the PAR, as identified by scientists in 1989, still holds true.

“If one is interested in understanding sex differences in health and disease, the boundary of the pseudoautosomal region is arguably the most fundamental landmark in the genome,” says Page, who is also a professor of biology at the Massachusetts Institute of Technology and an Investigator with Howard Hughes Medical Institute. “Had this boundary been multiple genes off, the field would have been shaken to its foundations.”

Dance of the chromosomes

The X and Y chromosomes evolved from an ancestral pair of chromosomes with identical structures. Over time, the Y chromosome degenerated drastically, losing hundreds of functional genes. Despite their differences, the X and Y chromosomes come together during a special type of cell division called male meiosis, which produces sperm cells.

This process begins with the tips of the sex chromosomes aligning side by side like two strands of rope. As the X and Y chromosomes embrace each other, enzymes create breaks in the DNA. These breaks are repaired using the opposite chromosome as a template, linking the X and Y together. About half of the time, an entire segment of DNA, which often contains multiple genes, will cross over onto the opposite chromosome.

The genetic exchange, called recombination, concludes with the X and Y chromosomes being pulled apart to opposite ends of the dividing cell, ensuring that each chromosome ends up in a different daughter cell. “This intricate dance of the X and Y chromosomes is essential to a sperm getting either an X or a Y—not both, and not neither,” says Page.

This way when the sperm—carrying either an X or a Y—fuses with the egg—carrying an X—during fertilization, the resulting zygote has the right number of chromosomes and a mix of genetic material from both parents.

But that’s not all. The swapping of DNA during recombination also allows for the chromosomes to have the same genes but with slight variations. These unique combinations of genetic material across sex chromosomes are key to genetic diversity within a species, enabling it to survive, adapt, and reproduce successfully.

Beyond the region of recombination, the Y chromosome contains genes that are important for sex determination, for sperm production, and for general cellular functioning. The primary sex-defining gene, SRY, which triggers the development of an embryo into a male, is located only 10,000 bases from the boundary of the PAR.

Advancing together

To determine whether the location of this critical boundary on the human sex chromosomes—where they stop crossing over during meiosis and become X-specific or Y-specific—had been misidentified for over three decades, researchers began by comparing publicly-available DNA sequences from the X and the Y chromosomes of seven primate species: humans, chimpanzees, gorillas, orangutans, siamangs, rhesus macaques, and colobus monkeys.

Based on the patterns of crossover between the X and the Y chromosomes of these species, the researchers constructed an evolutionary tree. Upon analyzing how DNA sequences close to and distant from the PAR boundary group together across species, the researchers found a substitution mutation—where a letter in a long string of letters is swapped for a different one—in the DNA of the human X and Y chromosomes. This change was also present in the chimpanzee Y chromosome, suggesting that the mutation originally occurred in the last common ancestor of humans and chimpanzees and was then transferred to the human X chromosome.

“These alignments between various primates allowed us to observe where the X and the Y chromosomes have preserved identity over millions of years and where they have diverged,” says Bellott. “That [pseudoautosomal] boundary has remained unchanged for 25 million years.”

Next, the group studied crossover events in living humans using a vast dataset of single-cell sequencing of sperm samples. They found 795 sperm with clear swapping of genetic material somewhere between the originally proposed boundary of the PAR and the newly-proposed 2023 boundary.

Once these analyses confirmed that the original location of the PAR boundary remains valid, Page and his team turned their attention to data from the 2023 study that contested this 1989 finding. The researchers focused on 10 male genomes assembled by the outside group, which contained contiguous sequences from the PAR.

Since substitutions on the Y chromosome typically occur at a steady rate, but in the PAR, changes on the X chromosome can transfer to the Y through recombination, the researchers compared the DNA sequences from the ten genomes to determine whether they followed the expected steady rate of change or if they varied.

The team found that close to the originally proposed PAR boundary, the DNA sequences changed at a steady rate. But further away from the boundary, the rate of change varied, suggesting that crossover events likely occurred in this region. Furthermore, the group identified several shared genetic differences between the X and the Y chromosomes of these genomes, which demonstrates that recombination has occurred even closer to the PAR boundary than scientists observed in 1989.

“Ironically, instead of contradicting the original boundary, the 2023 work has helped us refine the location of crossover to an even narrower area near the boundary,” says Page.

Thanks to the efforts of Page’s group at Whitehead Institute, our understanding of the PAR is clearer than ever, and business can go on as usual for researchers investigating sex differences in health and disease.