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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.

Sebastian Lourido awarded highest alumni honor from alma mater

Whitehead Institute Member Sebastian Lourido receives the Tulane 2025 Science and Engineering Outstanding Alumni Award for Professional Excellence

Whitehead Institute
April 11, 2025

The Lourido laboratory at Whitehead Institute studies the developmental transitions and molecular pathways that the single cell parasite Toxoplasma gondii (T. gondii ), uses to infect its host, causing toxoplasmosis.They combine several approaches that span phospho-proteomics, chemical-genetics, and genome editing to investigate the unique biology of these organisms and identify specific features that can be targeted to treat infections of T. gondii and related parasites.

Lourido, who is also an associate professor of biology at Massachusetts Institute of Technology, originally joined Whitehead Institute as a Whitehead Fellow in 2012, a program that allows promising MD or PhD graduates to initiate their own research program in lieu of a traditional postdoctoral fellowship. “Sebastian’s demonstrated excellence as a young investigator underscores the importance of investing in the next generation of scientists and scientific leaders,” says Ruth Lehmann, Whitehead Institute’s President and Director.

After receiving both a BS in Cell and Molecular Biology and a BFA in Studio Art, Lourido went on to pursue graduate work at Washington University in St. Louis. In addition to this honor, Lourido has also been the recipient of other awards including the NIH Director’s Early Independence Award and the 2024 William Trager Award from the American Society of Tropical Medicine and Hygiene and was recognized as one of the Burroughs Wellcome Fund’s Investigators in the Pathogenesis of Infectious Disease.

At the core of problem-solving

Stuart Levine ’97, director of MIT’s BioMicro Center, keeps departmental researchers at the forefront of systems biology.

Samantha Edelen | Department of Biology
March 19, 2025

As director of the MIT BioMicro Center (BMC), Stuart Levine ’97 wholeheartedly embraces the variety of challenges he tackles each day. One of over 50 core facilities providing shared resources across the Institute, the BMC supplies integrated high-throughput genomics, single-cell and spatial transcriptomic analysis, bioinformatics support, and data management to researchers across MIT.

“Every day is a different day,” Levine says, “there are always new problems, new challenges, and the technology is continuing to move at an incredible pace.” After more than 15 years in the role, Levine is grateful that the breadth of his work allows him to seek solutions for so many scientific problems.

By combining bioinformatics expertise with biotech relationships and a focus on maximizing the impact of the center’s work, Levine brings the broad range of skills required to match the diversity of questions asked by researchers in MIT’s Department of Biology.

Expansive expertise

Biology first appealed to Levine as an MIT undergraduate taking class 7.012 (Introduction to Biology), thanks to the charisma of instructors Professor Eric Lander and Amgen Professor Emerita Nancy Hopkins. After earning his PhD in biochemistry from Harvard University and Massachusetts General Hospital, Levine returned to MIT for postdoctoral work with Professor Richard Young, core member at the Whitehead Institute for Biomedical Research.

In the Young Lab, Levine found his calling as an informaticist and ultimately decided to stay at MIT. Here, his work has a wide-ranging impact: the BMC serves over 100 labs annually, from the the Computer Science and Artificial Intelligence Laboratory and the departments of Brain and Cognitive Sciences; Earth, Atmospheric and Planetary Sciences; Chemical Engineering; Mechanical Engineering; and, of course, Biology.

“It’s a fun way to think about science,” Levine says, noting that he applies his knowledge and streamlines workflows across these many disciplines by “truly and deeply understanding the instrumentation complexities.”

This depth of understanding and experience allows Levine to lead what longtime colleague Professor Laurie Boyer describes as “a state-of-the-art core that has served so many faculty and provides key training opportunities for all.” He and his team work with cutting-edge, finely tuned scientific instruments that generate vast amounts of bioinformatics data, then use powerful computational tools to store, organize, and visualize the data collected, contributing to research on topics ranging from host-parasite interactions to proposed tools for NASA’s planetary protection policy.

Staying ahead of the curve

With a scientist directing the core, the BMC aims to enable researchers to “take the best advantage of systems biology methods,” says Levine. These methods use advanced research technologies to do things like prepare large sets of DNA and RNA for sequencing, read DNA and RNA sequences from single cells, and localize gene expression to specific tissues.

Levine presents a lightweight, clear rectangle about the width of a cell phone and the length of a VHS cassette.

“This is a flow cell that can do 20 human genomes to clinical significance in two days — 8 billion reads,” he says. “There are newer instruments with several times that capacity available as well.”

The vast majority of research labs do not need that kind of power, but the Institute, and its researchers as a whole, certainly do. Levine emphasizes that “the ROI [return on investment] for supporting shared resources is extremely high because whatever support we receive impacts not just one lab, but all of the labs we support. Keeping MIT’s shared resources at the bleeding edge of science is critical to our ability to make a difference in the world.”

To stay at the edge of research technology, Levine maintains company relationships, while his scientific understanding allows him to educate researchers on what is possible in the space of modern systems biology. Altogether, these attributes enable Levine to help his researcher clients “push the limits of what is achievable.”

The man behind the machines

Each core facility operates like a small business, offering specialized services to a diverse client base across academic and industry research, according to Amy Keating, Jay A. Stein (1968) Professor of Biology and head of the Department of Biology. She explains that “the PhD-level education and scientific and technological expertise of MIT’s core directors are critical to the success of life science research at MIT and beyond.”

While Levine clearly has the education and expertise, the success of the BMC “business” is also in part due to his tenacity and focus on results for the core’s users.

He was recognized by the Institute with the MIT Infinite Mile Award in 2015 and the MIT Excellence Award in 2017, for which one nominator wrote, “What makes Stuart’s leadership of the BMC truly invaluable to the MIT community is his unwavering dedication to producing high-quality data and his steadfast persistence in tackling any type of troubleshooting needed for a project. These attributes, fostered by Stuart, permeate the entire culture of the BMC.”

“He puts researchers and their research first, whether providing education, technical services, general tech support, or networking to collaborators outside of MIT,” says Noelani Kamelamela, lab manager of the BMC. “It’s all in service to users and their projects.”

Tucked into the far back corner of the BMC lab space, Levine’s office is a fitting symbol of his humility. While his guidance and knowledge sit at the center of what elevates the BMC beyond technical support, he himself sits away from the spotlight, resolutely supporting others to advance science.

“Stuart has always been the person, often behind the scenes, that pushes great science, ideas, and people forward,” Boyer says. “His knowledge and advice have truly allowed us to be at the leading edge in our work.”

Manipulating time with torpor

New research from the Hrvatin Lab recently published in Nature Aging indicates that inducing a hibernation-like state in mice slows down epigenetic changes that accompany aging.

Shafaq Zia | Whitehead Institute
March 7, 2025

Surviving extreme conditions in nature is no easy feat. Many species of mammals rely on special adaptations called daily torpor and hibernation to endure periods of scarcity. These states of dormancy are marked by a significant drop in body temperature, low metabolic activity, and reduced food intake—all of which help the animal conserve energy until conditions become favorable again.

The lab of Whitehead Institute Member Siniša Hrvatin studies daily torpor, which lasts several hours, and its longer counterpart, hibernation, in order to understand their effects on tissue damage, disease progression, and aging. In their latest study, published in Nature Aging on March 7, first author Lorna Jayne, Hrvatin, and colleagues show that inducing a prolonged torpor-like state in mice slows down epigenetic changes that accompany aging.

“Aging is a complex phenomenon that we’re just starting to unravel,” says Hrvatin, who is also an assistant professor of biology at Massachusetts Institute of Technology. “Although the full relationship between torpor and aging remains unclear, our findings point to decreased body temperature as the central driver of this anti-aging effect.”

Tampering with the biological clock

Aging is a universal process, but scientists have long struggled to find a reliable metric for measuring it. Traditional clocks fall short because biological age doesn’t always align with chronology—cells and tissues in different organisms age at varying rates.

To solve this dilemma, scientists have turned to studying molecular processes that are common to aging across many species. This, in the past decade, has led to the development of epigenetic clocks, new computational tools that can estimate an organism’s age by analyzing the accumulation of epigenetic marks in cells over time.

Think of epigenetic marks as tiny chemical tags that cling either to the DNA itself or to the proteins, called histones, around which the DNA is wrapped. Histones act like spools, allowing long strands of DNA to coil around them, much like thread around a bobbin. When epigenetic tags are added to histones, they can compact the DNA, preventing genetic information from being read, or loosen it, making the information more accessible. When epigenetic tags attach directly to DNA, they can alter how the proteins that “read” a gene bind to the DNA.

While it’s unclear if epigenetic marks are a cause or consequence of aging, this much is evident: these marks change over an organism’s lifespan, altering how genes are turned on or off, without modifying the underlying DNA sequence. These changes have enabled researchers to track the biological age of individual cells and tissues using dedicated epigenetic clocks.

In nature, states of stasis like hibernation and daily torpor help animals survive by conserving energy and avoiding predators. But now, emerging research in marmots and bats hints that hibernation may also slow down epigenetic aging, prompting researchers to explore whether there’s a deeper connection between prolonged bouts of torpor and longevity.

However, investigating this link has been challenging, as the mechanisms that trigger, regulate, and sustain torpor remain largely unknown. In 2020, Hrvatin and colleagues made a breakthrough by identifying neurons in a specific region of the mouse hypothalamus, known as the avMLPA, which act as core regulators of torpor.

“This is when we realized that we could leverage this system to induce torpor and explore mechanistically how the state of torpor might have beneficial effects on aging,” says Jayne. “You can imagine how difficult it is to study this in natural hibernators because of accessibility and the lack of tools to manipulate them in sophisticated ways.”

The age-old mystery

The researchers began by injecting adeno-associated virus in mice, a gene delivery vehicle that enables scientists to introduce new genetic material into target cells. They employed this technology to instruct neurons in the mice’s avMLPA region to produce a special receptor called Gq-DREADD, which does not respond to the brain’s natural signals but can be chemically activated by a drug. When the researchers administered this drug to the mice, it bound to the Gq-DREADD receptors, activating the torpor-regulating neurons and triggering a drop in the animals’ body temperature.

However, to investigate the effects of torpor on longevity, the researchers needed to maintain these mice in a torpor-like state for days to weeks. To achieve this, the mice were continuously administered the drug through drinking water.

The mice were kept in a torpor-like state with periodic bouts of arousal for a total of nine months. The researchers measured the blood epigenetic age of these mice at the 3-, 6-, and 9-month marks using the mammalian blood epigenetic clock. By the 9-month mark, the torpor-like state had reduced blood epigenetic aging in these mice by approximately 37%, making them biologically three months younger than their control counterparts.

To further assess the effects of torpor on aging,  the group evaluated these mice using the mouse clinical frailty index, which includes measurements like tail stiffening, gait, and spinal deformity that are commonly associated with aging. As expected, mice in the torpor-like state had a lower frailty index compared to the controls.

With the anti-aging effects of the torpor-like state established, the researchers sought to understand how each of the key factors underlying torpor—decreased body temperature, low metabolic activity, and reduced food intake—contributed to longevity.

To isolate the effects of reduced metabolic rate, the researchers induced a torpor-like state in mice, while maintaining the animal’s normal body temperature. After three months, the blood epigenetic age of these mice was similar to that of the control group, suggesting that low metabolic rate alone does not slow down epigenetic aging.

Next, Hrvatin and colleagues isolated the impact of low caloric intake on blood epigenetic aging by restricting the food intake of mice in the torpor-like state, while maintaining their normal body temperature. After three months, these mice were a similar blood epigenetic age as the control group.

When both low metabolic rate and reduced food intake were combined, the mice still exhibited higher blood epigenetic aging after three months compared to mice in the torpor state with low body temperature. These findings, combined, led the researchers to conclude that neither low metabolic rate nor reduced caloric intake alone are sufficient to slow down blood epigenetic aging. Instead, a drop in body temperature is necessary for the anti-aging effects of torpor.

Although the exact mechanisms linking low body temperature and epigenetic aging are unclear, the team hypothesizes that it may involve the cell cycle, which regulates how cells grow and divide: lower body temperatures can potentially slow down cellular processes, including DNA replication and mitosis. This, over time, may impact cell turnover and aging. With further research, the Hrvatin Lab aims to explore this link in greater depth and shed light on the lingering mystery.

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.”

MIT biologists discover a new type of control over RNA splicing

They identified proteins that influence splicing of about half of all human introns, allowing for more complex types of gene regulation.

Anne Trafton | MIT News
February 20, 2025

RNA splicing is a cellular process that is critical for gene expression. After genes are copied from DNA into messenger RNA, portions of the RNA that don’t code for proteins, called introns, are cut out and the coding portions are spliced back together.

This process is controlled by a large protein-RNA complex called the spliceosome. MIT biologists have now discovered a new layer of regulation that helps to determine which sites on the messenger RNA molecule the spliceosome will target.

The research team discovered that this type of regulation, which appears to influence the expression of about half of all human genes, is found throughout the animal kingdom, as well as in plants. The findings suggest that the control of RNA splicing, a process that is fundamental to gene expression, is more complex than previously known.

“Splicing in more complex organisms, like humans, is more complicated than it is in some model organisms like yeast, even though it’s a very conserved molecular process. There are bells and whistles on the human spliceosome that allow it to process specific introns more efficiently. One of the advantages of a system like this may be that it allows more complex types of gene regulation,” says Connor Kenny, an MIT graduate student and the lead author of the study.

Christopher Burge, the Uncas and Helen Whitaker Professor of Biology at MIT, is the senior author of the study, which appears today in Nature Communications.

Building proteins

RNA splicing, a process discovered in the late 1970s, allows cells to precisely control the content of the mRNA transcripts that carry the instructions for building proteins.

Each mRNA transcript contains coding regions, known as exons, and noncoding regions, known as introns. They also include sites that act as signals for where splicing should occur, allowing the cell to assemble the correct sequence for a desired protein. This process enables a single gene to produce multiple proteins; over evolutionary timescales, splicing can also change the size and content of genes and proteins, when different exons become included or excluded.

The spliceosome, which forms on introns, is composed of proteins and noncoding RNAs called small nuclear RNAs (snRNAs). In the first step of spliceosome assembly, an snRNA molecule known as U1 snRNA binds to the 5’ splice site at the beginning of the intron. Until now, it had been thought that the binding strength between the 5’ splice site and the U1 snRNA was the most important determinant of whether an intron would be spliced out of the mRNA transcript.

In the new study, the MIT team discovered that a family of proteins called LUC7 also helps to determine whether splicing will occur, but only for a subset of introns — in human cells, up to 50 percent.

Before this study, it was known that LUC7 proteins associate with U1 snRNA, but the exact function wasn’t clear. There are three different LUC7 proteins in human cells, and Kenny’s experiments revealed that two of these proteins interact specifically with one type of 5’ splice site, which the researchers called “right-handed.” A third human LUC7 protein interacts with a different type, which the researchers call “left-handed.”

The researchers found that about half of human introns contain a right- or left-handed site, while the other half do not appear to be controlled by interaction with LUC7 proteins. This type of control appears to add another layer of regulation that helps remove specific introns more efficiently, the researchers say.

“The paper shows that these two different 5’ splice site subclasses exist and can be regulated independently of one another,” Kenny says. “Some of these core splicing processes are actually more complex than we previously appreciated, which warrants more careful examination of what we believe to be true about these highly conserved molecular processes.”

“Complex splicing machinery”

Previous work has shown that mutation or deletion of one of the LUC7 proteins that bind to right-handed splice sites is linked to blood cancers, including about 10 percent of acute myeloid leukemias (AMLs). In this study, the researchers found that AMLs that lost a copy of the LUC7L2 gene have inefficient splicing of right-handed splice sites. These cancers also developed the same type of altered metabolism seen in earlier work.

“Understanding how the loss of this LUC7 protein in some AMLs alters splicing could help in the design of therapies that exploit these splicing differences to treat AML,” Burge says. “There are also small molecule drugs for other diseases such as spinal muscular atrophy that stabilize the interaction between U1 snRNA and specific 5’ splice sites. So the knowledge that particular LUC7 proteins influence these interactions at specific splice sites could aid in improving the specificity of this class of small molecules.”

Working with a lab led by Sascha Laubinger, a professor at Martin Luther University Halle-Wittenberg, the researchers found that introns in plants also have right- and left-handed 5’ splice sites that are regulated by Luc7 proteins.

The researchers’ analysis suggests that this type of splicing arose in a common ancestor of plants, animals, and fungi, but it was lost from fungi soon after they diverged from plants and animals.

“A lot what we know about how splicing works and what are the core components actually comes from relatively old yeast genetics work,” Kenny says. “What we see is that humans and plants tend to have more complex splicing machinery, with additional components that can regulate different introns independently.”

The researchers now plan to further analyze the structures formed by the interactions of Luc7 proteins with mRNA and the rest of the spliceosome, which could help them figure out in more detail how different forms of Luc7 bind to different 5’ splice sites.

The research was funded by the U.S. National Institutes of Health and the German Research Foundation.

A planarian’s guide to growing a new head

Researchers at the Whitehead Institute have described a pathyway by which planarians, freshwater flatworms with spectacular regenerative capabilities, can restore large portions of their nervous system, even regenerating a new head with a fully functional brain.

Shafaq Zia | Whitehead Institute
February 6, 2025

Cut off any part of this worm’s body and it will regrow. This is the spectacular yet mysterious regenerative ability of freshwater flatworms known as planarians. The lab of Whitehead Institute Member Peter Reddien investigates the principles underlying this remarkable feat. In their latest study, published in PLOS Genetics on February 6, first author staff scientist M. Lucila Scimone, Reddien, and colleagues describe how planarians restore large portions of their nervous system—even regenerating a new head with a fully functional brain—by manipulating a signaling pathway.

This pathway, called the Delta-Notch signaling pathway, enables neurons to guide the differentiation of a class of progenitors—immature cells that will differentiate into specialized types—into glia, the non-neuronal cells that support and protect neurons. The mechanism ensures that the spatial pattern and relative numbers of neurons and glia at a given location are precisely restored following injury.

“This process allows planarians to regenerate neural circuits more efficiently because glial cells form only where needed, rather than being produced broadly within the body and later eliminated,” said Reddien, who is also a professor of biology at Massachusetts Institute of Technology and an Investigator with the Howard Hughes Medical Institute.

Coordinating regeneration

Multiple cell types work together to form a functional human brain. These include neurons and a more abundant group of cells called glial cells—astrocytes, microglia, and oligodendrocytes. Although glial cells are not the fundamental units of the nervous system, they perform critical functions in maintaining the connections between neurons, called synapses, clearing away dead cells and other debris, and regulating neurotransmitter levels, effectively holding the nervous system together like glue. A few years ago, Reddien and colleagues discovered cells in planarians that looked like glial cells and performed similar neuro-supportive functions. This led to the first characterization of glial cells in planarians in 2016.

Unlike in mammals where the same set of neural progenitors give rise to both neurons and glia, glial cells in planarians originate from a separate, specialized group of progenitors. These progenitors, called phagocytic progenitors, can not only give rise to glial cells but also pigment cells that determine the worm’s coloration, as well as other, lesser understood cell types.

Why neurons and glia in planarians originate from distinct progenitors—and what factors ultimately determine the differentiation of phagocytic progenitors into glia—are questions that still puzzled Reddien and team members. Then, a study showing that planarian neurons regenerate before glia formation led the researchers to wonder whether a signaling mechanism between neurons and phagocytic progenitors guides the specification of glia in planarians.

The first step to unravel this mystery was to look at the Notch signaling pathway, which is known to play a crucial role in the development of neurons and glia in other organisms, and determine its role in planarian glia regeneration. To do this, the researchers used RNA interference (RNAi)—a technique that decreases or completely silences the expression of genes—to turn off key genes involved in the Notch pathway and amputated the planarian’s head. It turned out Notch signaling is essential for glia regeneration and maintenance in planarians—no glial cells were found in the animal following RNAi, while the differentiation of other types of phagocytic cells was unaffected.

Of the different Notch signaling pathway components the researchers tested, turning of the genes notch-1, delta-2, and suppressor of hairless produced this phenotype. Interestingly, the signaling molecules Delta-2 was found on the surface of neurons, whereas Notch-1 was expressed in phagocytic progenitors.

With these findings in hand, the researchers hypothesized that interaction between Delta-2 on neurons and Notch-1 on phagocytic progenitors could be governing the final fate determination of glial cells in planarians.

To test the hypothesis, the researchers transplanted eyes either from planarians lacking the notch-1 gene or from planarians lacking the delta-2 gene into wild-type animals and assessed the formation of glial cells around the transplant site. They observed that glial cells still formed around the notch-1 deficient eyes, as notch-1 was still active in the glial progenitors of the host wild-type animal. However, no glial cells formed around the delta-2 deficient eyes, even with the Notch signaling pathway intact in phagocytic progenitors, confirming that delta-2 in the photoreceptor neurons is required for the differentiation of phagocytic progenitors into glia near the eye.

“This experiment really showed us that you have two faces of the same coin—one is the phagocytic progenitors expressing Notch-1, and one is the neurons expressing Delta-2—working together to guide the specification of glia in the organism,”said Scimone.

The researchers have named this phenomenon coordinated regeneration, as it allows neurons to influence the pattern and number of glia at specific locations without the need for a separate mechanism to adjust the relative numbers of neurons and glia.

The group is now interested in investigating whether the same phenomenon might also be involved in the regeneration of other tissue types.

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.

A sum of their parts

Researchers in the Department of Biology at MIT use an AI-driven approach to computationally predict short amino acid sequences that can bind to or inhibit a target, with a potential for great impact on fundamental biological research and therapeutic applications.

Lillian Eden | Department of Biology
February 6, 2025

All biological function is dependent on how different proteins interact with each other. Protein-protein interactions facilitate everything from transcribing DNA and controlling cell division to higher-level functions in complex organisms.

Much remains unclear about how these functions are orchestrated on the molecular level, however, and how proteins interact with each other — either with other proteins or with copies of themselves. 

Recent findings have revealed that small protein fragments have a lot of functional potential. Even though they are incomplete pieces, short stretches of amino acids can still bind to interfaces of a target protein, recapitulating native interactions. Through this process, they can alter that protein’s function or disrupt its interactions with other proteins. 

Protein fragments could therefore empower both basic research on protein interactions and cellular processes and could potentially have therapeutic applications. 

Recently published in Proceedings of the National Academy of Sciences, a new computational method developed in the Department of Biology at MIT builds on existing AI models to computationally predict protein fragments that can bind to and inhibit full-length proteins in E. coli. Theoretically, this tool could lead to genetically encodable inhibitors against any protein. 

The work was done in the lab of Associate Professor of Biology and HHMI Investigator Gene-Wei Li in collaboration with the lab of Jay A. Stein (1968) Professor of Biology, Professor of Biological Engineering and Department Head Amy Keating.

Leveraging Machine Learning

The program, called FragFold, leverages AlphaFold, an AI model that has led to phenomenal advancements in biology in recent years due to its ability to predict protein folding and protein interactions. 

The goal of the project was to predict fragment inhibitors, which is a novel application of AlphaFold. The researchers on this project confirmed experimentally that more than half of FragFold’s predictions for binding or inhibition were accurate, even when researchers had no previous structural data on the mechanisms of those interactions. 

“Our results suggest that this is a generalizable approach to find binding modes that are likely to inhibit protein function, including for novel protein targets, and you can use these predictions as a starting point for further experiments,” says co-first and corresponding author Andrew Savinov, a postdoc in the Li Lab. “We can really apply this to proteins without known functions, without known interactions, without even known structures, and we can put some credence in these models we’re developing.”

One example is FtsZ, a protein that is key for cell division. It is well-studied but contains a region that is intrinsically disordered and, therefore, especially challenging to study. Disordered proteins are dynamic, and their functional interactions are very likely fleeting — occurring so briefly that current structural biology tools can’t capture a single structure or interaction. 

The researchers leveraged FragFold to explore the activity of fragments of FtsZ, including fragments of the intrinsically disordered region, to identify several new binding interactions with various proteins. This leap in understanding confirms and expands upon previous experiments measuring FtsZ’s biological activity. 

This progress is significant in part because it was made without solving the disordered region’s structure, and because it exhibits the potential power of FragFold.

“This is one example of how AlphaFold is fundamentally changing how we can study molecular and cell biology,” Keating says. “Creative applications of AI methods, such as our work on FragFold, open up unexpected capabilities and new research directions.”

Inhibition, and beyond

The researchers accomplished these predictions by computationally fragmenting each protein and then modeling how those fragments would bind to interaction partners they thought were relevant.

They compared the maps of predicted binding across the entire sequence to the effects of those same fragments in living cells, determined using high-throughput experimental measurements in which millions of cells each produce one type of protein fragment. 

AlphaFold uses co-evolutionary information to predict folding, and typically evaluates the evolutionary history of proteins using something called multiple sequence alignments for every single prediction run. The MSAs are critical, but are a bottleneck for large-scale predictions — they can take a prohibitive amount of time and computational power. 

For FragFold, the researchers instead pre-calculated the MSA for a full-length protein once and used that result to guide the predictions for each fragment of that full-length protein. 

Savinov, together with Keating Lab alum Sebastian Swanson, PhD ‘23, predicted inhibitory fragments of a diverse set of proteins in addition to FtsZ. Among the interactions they explored was a complex between lipopolysaccharide transport proteins LptF and LptG. A protein fragment of LptG inhibited this interaction, presumably disrupting the delivery of lipopolysaccharide, which is a crucial component of the E. coli outer cell membrane essential for cellular fitness.

“The big surprise was that we can predict binding with such high accuracy and, in fact, often predict binding that corresponds to inhibition,” Savinov says. “For every protein we’ve looked at, we’ve been able to find inhibitors.”

The researchers initially focused on protein fragments as inhibitors because whether a fragment could block an essential function in cells is a relatively simple outcome to measure systematically. Looking forward, Savinov is also interested in exploring fragment function outside inhibition, such as fragments that can stabilize the protein they bind to, enhance or alter its function, or trigger protein degradation. 

Design, in principle 

This research is a starting point for developing a systemic understanding of cellular design principles, and what elements deep-learning models may be drawing on to make accurate predictions. 

“There’s a broader, further-reaching goal that we’re building towards,” Savinov says. “Now that we can predict them, can we use the data we have from predictions and experiments to pull out the salient features to figure out what AlphaFold has actually learned about what makes a good inhibitor?” 

Savinov and collaborators also delved further into how protein fragments bind, exploring other protein interactions and mutating specific residues to see how those interactions change how the fragment interacts with its target. 

Experimentally examining the behavior of thousands of mutated fragments within cells, an approach known as deep mutational scanning, revealed key amino acids that are responsible for inhibition. In some cases, the mutated fragments were even more potent inhibitors than their natural, full-length sequences. 

“Unlike previous methods, we are not limited to identifying fragments in experimental structural data,” says Swanson. “The core strength of this work is the interplay between high-throughput experimental inhibition data and the predicted structural models: the experimental data guides us towards the fragments that are particularly interesting, while the structural models predicted by FragFold provide a specific, testable hypothesis for how the fragments function on a molecular level.”

Savinov is excited about the future of this approach and its myriad applications.

“By creating compact, genetically encodable binders, FragFold opens a wide range of possibilities to manipulate protein function,” Li agrees. “We can imagine delivering functionalized fragments that can modify native proteins, change their subcellular localization, and even reprogram them to create new tools for studying cell biology and treating diseases.”