Research Area: Computational Biology

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.

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.” 

A new approach to modeling complex biological systems

MIT engineers’ new model could help researchers glean insights from genomic data and other huge datasets. This is potentially critical to researchers who study any kind of complex biological system, according to senior author Douglas Lauffenburger.

Anne Trafton | MIT News
November 5, 2024

Over the past two decades, new technologies have helped scientists generate a vast amount of biological data. Large-scale experiments in genomics, transcriptomics, proteomics, and cytometry can produce enormous quantities of data from a given cellular or multicellular system.

However, making sense of this information is not always easy. This is especially true when trying to analyze complex systems such as the cascade of interactions that occur when the immune system encounters a foreign pathogen.

MIT biological engineers have now developed a new computational method for extracting useful information from these datasets. Using their new technique, they showed that they could unravel a series of interactions that determine how the immune system responds to tuberculosis vaccination and subsequent infection.

This strategy could be useful to vaccine developers and to researchers who study any kind of complex biological system, says Douglas Lauffenburger, the Ford Professor of Engineering in the departments of Biological Engineering, Biology, and Chemical Engineering.

“We’ve landed on a computational modeling framework that allows prediction of effects of perturbations in a highly complex system, including multiple scales and many different types of components,” says Lauffenburger, the senior author of the new study.

Shu Wang, a former MIT postdoc who is now an assistant professor at the University of Toronto, and Amy Myers, a research manager in the lab of University of Pittsburgh School of Medicine Professor JoAnne Flynn, are the lead authors of a new paper on the work, which appears today in the journal Cell Systems.

Modeling complex systems

When studying complex biological systems such as the immune system, scientists can extract many different types of data. Sequencing cell genomes tells them which gene variants a cell carries, while analyzing messenger RNA transcripts tells them which genes are being expressed in a given cell. Using proteomics, researchers can measure the proteins found in a cell or biological system, and cytometry allows them to quantify a myriad of cell types present.

Using computational approaches such as machine learning, scientists can use this data to train models to predict a specific output based on a given set of inputs — for example, whether a vaccine will generate a robust immune response. However, that type of modeling doesn’t reveal anything about the steps that happen in between the input and the output.

“That AI approach can be really useful for clinical medical purposes, but it’s not very useful for understanding biology, because usually you’re interested in everything that’s happening between the inputs and outputs,” Lauffenburger says. “What are the mechanisms that actually generate outputs from inputs?”

To create models that can identify the inner workings of complex biological systems, the researchers turned to a type of model known as a probabilistic graphical network. These models represent each measured variable as a node, generating maps of how each node is connected to the others.

Probabilistic graphical networks are often used for applications such as speech recognition and computer vision, but they have not been widely used in biology.

Lauffenburger’s lab has previously used this type of model to analyze intracellular signaling pathways, which required analyzing just one kind of data. To adapt this approach to analyze many datasets at once, the researchers applied a mathematical technique that can filter out any correlations between variables that are not directly affecting each other. This technique, known as graphical lasso, is an adaptation of the method often used in machine learning models to strip away results that are likely due to noise.

“With correlation-based network models generally, one of the problems that can arise is that everything seems to be influenced by everything else, so you have to figure out how to strip down to the most essential interactions,” Lauffenburger says. “Using probabilistic graphical network frameworks, one can really boil down to the things that are most likely to be direct and throw out the things that are most likely to be indirect.”

Mechanism of vaccination

To test their modeling approach, the researchers used data from studies of a tuberculosis vaccine. This vaccine, known as BCG, is an attenuated form of Mycobacterium bovis. It is used in many countries where TB is common but isn’t always effective, and its protection can weaken over time.

In hopes of developing more effective TB protection, researchers have been testing whether delivering the BCG vaccine intravenously or by inhalation might provoke a better immune response than injecting it. Those studies, performed in animals, found that the vaccine did work much better when given intravenously. In the MIT study, Lauffenburger and his colleagues attempted to discover the mechanism behind this success.

The data that the researchers examined in this study included measurements of about 200 variables, including levels of cytokines, antibodies, and different types of immune cells, from about 30 animals.

The measurements were taken before vaccination, after vaccination, and after TB infection. By analyzing the data using their new modeling approach, the MIT team was able to determine the steps needed to generate a strong immune response. They showed that the vaccine stimulates a subset of T cells, which produce a cytokine that activates a set of B cells that generate antibodies targeting the bacterium.

“Almost like a roadmap or a subway map, you could find what were really the most important paths. Even though a lot of other things in the immune system were changing one way or another, they were really off the critical path and didn’t matter so much,” Lauffenburger says.

The researchers then used the model to make predictions for how a specific disruption, such as suppressing a subset of immune cells, would affect the system. The model predicted that if B cells were nearly eliminated, there would be little impact on the vaccine response, and experiments showed that prediction was correct.

This modeling approach could be used by vaccine developers to predict the effect their vaccines may have, and to make tweaks that would improve them before testing them in humans. Lauffenburger’s lab is now using the model to study the mechanism of a malaria vaccine that has been given to children in Kenya, Ghana, and Malawi over the past few years.

“The advantage of this computational approach is that it filters out many biological targets that only indirectly influence the outcome and identifies those that directly regulate the response. Then it’s possible to predict how therapeutically altering those biological targets would change the response. This is significant because it provides the basis for future vaccine and trial designs that are more data driven,” says Kathryn Miller-Jensen, a professor of biomedical engineering at Yale University, who was not involved in the study.

Lauffenburger’s lab is also using this type of modeling to study the tumor microenvironment, which contains many types of immune cells and cancerous cells, in hopes of predicting how tumors might respond to different kinds of treatment.

The research was funded by the National Institute of Allergy and Infectious Diseases.

Sauer & Davis Lab News Brief: structures of molecular woodchippers reveal mechanism for versatility

Rest in pieces: deconstructing polypeptide degradation machinery

Lillian Eden | Department of Biology
November 12, 2024

Research from the Sauer and Davis Labs in the Department of Biology at MIT shows that conformational changes contribute to the specificity of “molecular woodchippers” 

Degradation is a crucial process for maintaining protein homeostasis by culling excess or damaged proteins whose components can then be recycled. It is also a highly regulated process—for good reason. A cell could potentially waste many resources if the degradation machinery destroys proteins it shouldn’t. 

One of the major pathways for protein degradation in bacteria and eukaryotic mitochondria involves a molecular machine called ClpXP. ClpXP is made up of two components: a star-shaped structure made up of six subunits called ClpX that engages and unfolds proteins tagged for degradation, and an associated barrel-shaped enzyme, called ClpP, that chemically breaks up proteins into small pieces called peptides. 

ClpXP is incredibly adaptable and is often compared to a woodchipper — able to take in materials and spit out their broken-down components. Thanks to biochemical experiments, this molecular degradation machine is known to be able to break down hundreds of different proteins in the cell regardless of physical or chemical properties such as size, shape, or charge. ClpX uses energy from ATP hydrolysis to unfold proteins before they are threaded through its central channel, referred to as the axial channel, and into the degradation chamber of ClpP.

In three papers, one in PNAS and two in Nature Communications, researchers from the Department of Biology at MIT have expanded our understanding of how this molecular machinery engages with, unfolds, and degrades proteins — and how that machinery refrains, by design, from unfolding proteins not tagged for degradation. 

Alireza Ghanbarpour, until recently a postdoc in the Sauer Lab and Davis Lab and first author on all three papers, began with a simple question: given the vast repertoire of potential substrates — that is, proteins to be degraded — how is ClpXP so specific?

Ghanbarpour — now an assistant professor in the Department of Biochemistry and Molecular Biology at Washington University School of Medicine in St. Louis — found that the answer to this question lies in conformational changes in the molecular machine as it engages with an ill-fated protein. 

Reverse Engineering using Structural Insights

Ghanbarpour approached the question of ClpXP’s versatility by characterizing conformational changes of the molecular machine using a technique called cryogenic electron microscopy. In cryo-EM, sample particles are frozen in solution, and images are collected; algorithms then create 3D renderings from the 2D images.

“It’s really useful to generate different structures in different conditions and then put them together until you know how a machine works,” he says. “I love structural biology, and these molecular machines make fascinating targets for structural work and biochemistry. Their structural plasticity and precise functions offer exciting opportunities to understand how nature leverages enzyme conformations to generate novel functions and tightly regulate protein degradation within the cell.”

Inside the cell, these proteases do not work alone but instead work together with “adaptor” proteins, which can promote — or inhibit — degradation by ClpXP. One of the adaptor proteins that promotes degradation by ClpXP is SspB. 

In E. coli and most other bacteria, ClpXP and SspB interact with a tag called ssrA that is added to incomplete proteins when their biosynthesis on ribosomes stalls. 

The tagging process frees up the ribosome to make more proteins, but creates a problem: incomplete proteins are prone to aggregation, which could be detrimental to cellular health and can lead to disease. By interacting with the degradation tag, ClpXP and SspB help to ensure the degradation of these incomplete proteins. Understanding this process and how it may go awry may open therapeutic avenues in the future.

“It wasn’t clear how certain adapters were interacting with the substrate and the molecular machines during substrate delivery,” Ghanbarpour notes. “My recent structure reveals that the adapter engages with the enzyme, reaching deep into the axial channel to deliver the substrate.” 

Ghanbarpour and colleagues showed that ClpX engages with both the SspB adaptor and the ssrA degradation tag of an ill-fated protein at the same time. Surprisingly, they also found that this interaction occurs while the upper part of the axial channel through ClpX is closed — in fact, the closed channel allows ClpX to contact both the tag and the adaptor simultaneously.

This result was surprising, according to senior author and Salvador E. Luria Professor of Biology Robert Sauer, whose lab has been working on understanding this molecular machine for more than two decades: it was unclear whether the channel through ClpX closes in response to a substrate interaction, or if the channel is always closed until it opens to pass an unfolded protein down to ClpP to be degraded.

Preventing Rogue Degradation

Throughout this project, Ghanbarpour was co-advised by structural biologist and Associate Professor of Biology Joey Davis and collaborated with members of the Davis Lab to better understand the conformational changes that allow these molecular machines to function. Using a cryo-EM analysis approach developed in the Davis lab called CryoDRGN, the researchers showed that there is an equilibrium between ClpXP in the open and closed states: it’s usually closed but is open in about 10% of the particles in their samples. 

The closed state is almost identical to the conformation ClpXP assumes when it is engaged with an ssrA-tagged substrate and the SspB adaptor. 

To better understand the biological significance of this equilibrium, Ghanbarpour created a mutant of ClpXP that is always in the open position. Compared to normal ClpXP, the mutant degraded some proteins lacking obvious degradation tags faster but degraded ssrA-tagged proteins more slowly. 

According to Ghanbarpour, these results indicate that the closed channel improves ClpXP’s ability to efficiently engage tagged proteins meant to be degraded, whereas the open channel allows more “promiscuous” degradation. 

Pausing the Process

The next question Ghanbarpour wanted to answer was what this molecular machine looks like while engaged with a protein it is attempting to unfold. To do that, he created a substrate with a highly stable protein attached to the degradation tag that is initially pulled into ClpX, but then dramatically slows protein unfolding and degradation.

In the structures where the degradation process stalls, Ghanbarpour found that the degradation tag was pulled far into the molecular machine—through ClpX and into ClpP—and the folded protein part of the substrate was pulled tightly against the axial channel of ClpX. 

The opening of the axial channel, called the axial pore, is made up of looping protein structures called RKH loops. These flexible loops were found to play roles both in recognizing the ssrA degradation tag and in how substrates or the SspB adaptor interact with or are pulled against the channel during degradation. 

The flexibility of these RKH loops allows ClpX to interact with a large number of different proteins and adapters, and these results clarify some previous biochemical and mutational studies of interactions between the substrate and ClpXP. 

Although Ghanbarpour’s recent work focused on just one adaptor and degradation tag, he noted there are many more targets — ClpXP is something akin to a Swiss army knife for breaking down polypeptide chains. 

The way those other substrates interact with ClpXP could differ from the structures solved with the SspB adaptor and ssrA tag. It also stands to reason that the way ClpXP reacts to each substrate may be unique. For example, given that ClpX is occasionally in an open state, some substrates may engage with ClpXP only while it’s in an open conformation. 

In his new position at Washington University, Ghanbarpour intends to continue exploring how ClpXP and other molecular machines locate their target substrates and interact with adaptors, shedding light on how cells regulate protein degradation and maintain protein homeostasis.

The structures Ghanbarpour solved involved free-floating protein degradation machinery, but membrane-bound degradation machinery also exists. The membrane-bound version’s structure and conformational adaptions potentially differ from the structures Ghanbarpour found in his previous three papers. Indeed, in a recent preprint, Ghanbarpour worked on the cryo-EM structure of a nautilus shell-shaped protein assembly that seems to control membrane-bound degradation machinery. This assembly plays a critical role in regulating protein degradation within the bacterial inner membrane.

“The function of these proteases goes beyond simply degrading damaged proteins. They also target transcription factors, regulatory proteins, and proteins that don’t exist in normal conditions,” he says. “My new lab is particularly interested in understanding how cells use these proteases and their accessory adaptors, both under normal and stress conditions, to reshape the proteome and support recovery from cellular distress.”

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.

Cancer biologists discover a new mechanism for an old drug

Study reveals the drug, 5-fluorouracil, acts differently in different types of cancer — a finding that could help researchers design better drug combinations.

Anne Trafton | MIT News
October 7, 2024

Since the 1950s, a chemotherapy drug known as 5-fluorouracil has been used to treat many types of cancer, including blood cancers and cancers of the digestive tract.

Doctors have long believed that this drug works by damaging the building blocks of DNA. However, a new study from MIT has found that in cancers of the colon and other gastrointestinal cancers, it actually kills cells by interfering with RNA synthesis.

The findings could have a significant effect on how doctors treat many cancer patients. Usually, 5-fluorouracil is given in combination with chemotherapy drugs that damage DNA, but the new study found that for colon cancer, this combination does not achieve the synergistic effects that were hoped for. Instead, combining 5-FU with drugs that affect RNA synthesis could make it more effective in patients with GI cancers, the researchers say.

“Our work is the most definitive study to date showing that RNA incorporation of the drug, leading to an RNA damage response, is responsible for how the drug works in GI cancers,” says Michael Yaffe, a David H. Koch Professor of Science at MIT, the director of the MIT Center for Precision Cancer Medicine, and a member of MIT’s Koch Institute for Integrative Cancer Research. “Textbooks implicate the DNA effects of the drug as the mechanism in all cancer types, but our data shows that RNA damage is what’s really important for the types of tumors, like GI cancers, where the drug is used clinically.”

Yaffe, the senior author of the new study, hopes to plan clinical trials of 5-fluorouracil with drugs that would enhance its RNA-damaging effects and kill cancer cells more effectively.

Jung-Kuei Chen, a Koch Institute research scientist, and Karl Merrick, a former MIT postdoc, are the lead authors of the paper, which appears today in Cell Reports Medicine.

An unexpected mechanism

Clinicians use 5-fluorouracil (5-FU) as a first-line drug for colon, rectal, and pancreatic cancers. It’s usually given in combination with oxaliplatin or irinotecan, which damage DNA in cancer cells. The combination was thought to be effective because 5-FU can disrupt the synthesis of DNA nucleotides. Without those building blocks, cells with damaged DNA wouldn’t be able to efficiently repair the damage and would undergo cell death.

Yaffe’s lab, which studies cell signaling pathways, wanted to further explore the underlying mechanisms of how these drug combinations preferentially kill cancer cells.

The researchers began by testing 5-FU in combination with oxaliplatin or irinotecan in colon cancer cells grown in the lab. To their surprise, they found that not only were the drugs not synergistic, in many cases they were less effective at killing cancer cells than what one would expect by simply adding together the effects of 5-FU or the DNA-damaging drug given alone.

“One would have expected that these combinations to cause synergistic cancer cell death because you are targeting two different aspects of a shared process: breaking DNA, and making nucleotides,” Yaffe says. “Karl looked at a dozen colon cancer cell lines, and not only were the drugs not synergistic, in most cases they were antagonistic. One drug seemed to be undoing what the other drug was doing.”

Yaffe’s lab then teamed up with Adam Palmer, an assistant professor of pharmacology at the University of North Carolina School of Medicine, who specializes in analyzing data from clinical trials. Palmer’s research group examined data from colon cancer patients who had been on one or more of these drugs and showed that the drugs did not show synergistic effects on survival in most patients.

“This confirmed that when you give these combinations to people, it’s not generally true that the drugs are actually working together in a beneficial way within an individual patient,” Yaffe says. “Instead, it appears that one drug in the combination works well for some patients while another drug in the combination works well in other patients. We just cannot yet predict which drug by itself is best for which patient, so everyone gets the combination.”

These results led the researchers to wonder just how 5-FU was working, if not by disrupting DNA repair. Studies in yeast and mammalian cells had shown that the drug also gets incorporated into RNA nucleotides, but there has been dispute over how much this RNA damage contributes to the drug’s toxic effects on cancer cells.

Inside cells, 5-FU is broken down into two different metabolites. One of these gets incorporated into DNA nucleotides, and other into RNA nucleotides. In studies of colon cancer cells, the researchers found that the metabolite that interferes with RNA was much more effective at killing colon cancer cells than the one that disrupts DNA.

That RNA damage appears to primarily affect ribosomal RNA, a molecule that forms part of the ribosome — a cell organelle responsible for assembling new proteins. If cells can’t form new ribosomes, they can’t produce enough proteins to function. Additionally, the lack of undamaged ribosomal RNA causes cells to destroy a large set of proteins that normally bind up the RNA to make new functional ribosomes.

The researchers are now exploring how this ribosomal RNA damage leads cells to under programmed cell death, or apoptosis. They hypothesize that sensing of the damaged RNAs within cell structures called lysosomes somehow triggers an apoptotic signal.

“My lab is very interested in trying to understand the signaling events during disruption of ribosome biogenesis, particularly in GI cancers and even some ovarian cancers, that cause the cells to die. Somehow, they must be monitoring the quality control of new ribosome synthesis, which somehow is connected to the death pathway machinery,” Yaffe says.

New combinations

The findings suggest that drugs that stimulate ribosome production could work together with 5-FU to make a highly synergistic combination. In their study, the researchers showed that a molecule that inhibits KDM2A, a suppressor of ribosome production, helped to boost the rate of cell death in colon cancer cells treated with 5-FU.

The findings also suggest a possible explanation for why combining 5-FU with a DNA-damaging drug often makes both drugs less effective. Some DNA damaging drugs send a signal to the cell to stop making new ribosomes, which would negate 5-FU’s effect on RNA. A better approach may be to give each drug a few days apart, which would give patients the potential benefits of each drug, without having them cancel each other out.

“Importantly, our data doesn’t say that these combination therapies are wrong. We know they’re effective clinically. It just says that if you adjust how you give these drugs, you could potentially make those therapies even better, with relatively minor changes in the timing of when the drugs are given,” Yaffe says.

He is now hoping to work with collaborators at other institutions to run a phase 2 or 3 clinical trial in which patients receive the drugs on an altered schedule.

“A trial is clearly needed to look for efficacy, but it should be straightforward to initiate because these are already clinically accepted drugs that form the standard of care for GI cancers. All we’re doing is changing the timing with which we give them,” he says.

The researchers also hope that their work could lead to the identification of biomarkers that predict which patients’ tumors will be more susceptible to drug combinations that include 5-FU. One such biomarker could be RNA polymerase I, which is active when cells are producing a lot of ribosomal RNA.

The research was funded by the Damon Runyon Cancer Research Fund, a Ludwig Center at MIT Fellowship, the National Institutes of Health, the Ovarian Cancer Research Fund, the Holloway Foundation, and the STARR Cancer Consortium.

In immune cells, X marks the spot(s)

By researching the effects of sex chromosomes on two types of immune cells, researchers in the Page Lab explore the biological underpinnings of sex biases in immunity and autoimmune disease

Greta Friar | Whitehead Institute
August 6, 2024

There are many known sex differences in health and disease: cases in which either men or women are more likely to get a disease, experience a symptom, or have a certain drug side effect. Some of these sex differences are caused by social and environmental factors: for example, when men smoked more than women, men were more likely to develop lung cancer. However, some have biological underpinnings. For example, men are more likely to be red-green colorblind because the relevant gene is on the X chromosome, of which men with XY chromosomes have no backup copy for a dysfunctional version.

Often, the specific factors contributing to a sex difference are hard to tease apart; there may not be a simple way to tell what is caused by sex chromosomes versus sex hormones versus environment. To address this question, researchers in Whitehead Institute Member David Page’s lab previously developed an approach to identify the contributions of the sex chromosomes to sex differences. Now, Page and former postdoc in his lab Laura Blanton have built on that work by measuring the effects of the sex chromosomes on two types of immune cells. The work, published in the journal Cell Genomics on August 6, shows that sex chromosome gene expression is consistent across cell types, but that its effects are cell type specific.

Sex differences are common in the function and dysfunction of our immune system. Examples include the typically weaker male immune response to pathogens and vaccines, and the female-biased frequency of autoimmune diseases. Page and Blanton’s work in immune cells examines several genes that have been implicated in such sex differences.

Developing a method to measure sex chromosome influence

The approach that the researchers used is based on several facts about sex chromosomes. Firstly, although females typically have two X chromosomes and males typically have one X and one Y, there are people with rare combinations of sex chromosomes, who have anywhere from 1-5 X chromosomes and 0-4 Y chromosomes. Secondly, there are two types of X chromosome: The active X chromosome (Xa) and the inactive X chromosome (Xi). They are genetically identical, but many of the genes on Xi are either switched off or have their expression level dialed way down.

Xa does not really function as a sex chromosome since everyone in the world has exactly one Xa regardless of their sex. In people with more than one X chromosome, any additional X chromosomes are always Xi. Furthermore, Page and Blanton’s research demonstrates that Xa responds to gene expression by Xi and Y—the sex chromosomes—in the same manner as do the other 22 pairs of non-sex chromosomes—the autosomes.

With these facts in mind, the researchers collected cells from donors with different combinations of sex chromosomes. Then they measured the expression of every gene in these cells, across the donor population, and observed how the expression of each gene changed with the addition of each Xi or Y chromosome.

This approach was first shared in a Cell Genomics paper by Page and former postdoc Adrianna San Roman in 2023. They had cultured two types of cells, fibroblasts and lymphoblastoid cell lines, from donor tissue samples. They found that the effects of Xi and Y were modular—each additional chromosome changed gene expression by about the same amount. This approach allowed the researchers to identify which genes are sensitive to regulation by the sex chromosomes, and to measure the strength of the effect for each responsive gene.

In that and a following paper, Page and San Roman looked at how Xi and Y affect gene expression from Xa and the autosomes. Blanton expanded the study of Xi and Y by using the same approach in two types of immune cells, monocytes and CD4+ T cells, taken directly from donors’ blood. Studying cells taken directly from the body, rather than cells cultured in the lab, enabled the researchers to confirm that their observations applied in both conditions.

In all three papers, the researchers found that the sex chromosomes have significant effects on the expression levels of many genes that are active throughout the body. They also identified a particular pair of genes as driving much of this effect in all four cell types. The genes, ZFX and ZFY, found on the X and Y chromosomes respectively, are transcription factors that can dial up the expression of other genes. The pair originates from the same ancestral gene, and although they have grown slightly apart since the X and Y chromosomes diverged, they still perform the same gene regulatory function. The researchers found that they tended to affect expression of the same gene targets by similar though not identical amounts.

In other words, the presence of either sex chromosome causes roughly the same effect on expression of autosomal and Xa genes. This similarity makes sense: carefully calibrated gene regulation is necessary in every body, and so each sex chromosome must maintain that function. It does, however, make it harder to spot the cases in which sex chromosomes contribute to sex differences in health and disease.

“Sex differences in health and disease could stem from the rare instances in which one gene responds very differently to Xi versus Y—we found cases where that occurs,” Blanton says. “They could also stem from subtle differences in the gene expression changes caused by Xi and Y that build up into larger effects downstream.”

Blanton then combined her and San Roman’s data in order to look at how the effects of sex chromosome dosage—how many Xs or Ys are in a cell—compared across all four cell types.

The effects of sex chromosomes on immune cells

 Blanton found that gene expression from the sex chromosomes was consistent across all four cell types. The exceptions to this rule were always X chromosome genes that are only expressed on Xa, and so could be regulated by Xi and Y in the way that autosomal genes are. This contrasts with speculation that different genes on Xi might be silenced in different cells.

However, each cell type had a distinct response to this identical sex chromosome gene expression. Different biological pathways were affected, or the same biological pathway could be affected in the opposite direction. Key immune cell processes affected by sex chromosome dosage in either monocytes or T cells included production of immune system proteins, signaling, and inflammatory response.

The cell type specific responses were due to different genes responding to the sex chromosomes in each cell type. The researchers do not yet know the mechanism causing the same gene to respond to sex chromosome dosage in one cell type but not another. One possibility is that access to the genes is blocked in some of the cell types. Regions of DNA can become tightly packed so that a gene, or a DNA region that regulates the gene, becomes inaccessible to transcription factors such as ZFX and ZFY, and so they cannot affect the gene’s expression. Another possibility is that the genes might require specific partner molecules in order for their expression level to increase, and that these partners may be present in one cell type but not the other.

Blanton also measured how X chromosome dosage affected T cells in their inactive state, when there is no perceived immune threat, versus their activated state, when they begin to produce an immune response and replicate themselves. Increases in X chromosome dosage led to heightened activation, with increased expression of genes related to proliferation. This finding highlights the importance of looking at how sex chromosomes affect not just different cell types, but cells in different states or scenarios.

“As we learn what pathways the sex chromosomes influence in each cell type, we can begin to make sense of the contributions of the sex chromosomes to each cell type’s functions and its roles in disease,” Blanton says.

Although Page and Blanton found that the presence of an Xi or Y chromosome had very similar effects on most genes, the researchers did identify one interesting case in which response to X and Y differed. FCG2RB is a gene involved in immunity that has been implicated in and thought to contribute to the female bias in developing systemic lupus erythematosus (SLE). Blanton found that unlike most genes, FCGR2B is sensitive to X and not Y chromosome dosage. This strengthens the case that higher expression of FCGR2B could be driving the SLE female bias.

“FCGR2B provides a promising opportunity to study the contributions of the sex chromosomes to a sex bias in disease, and to learn more about the biology of a chronic disease that affects many people around the world,” Page says.

In other cases, the researchers found that genes which have been suspected to contribute to female bias in disease did not have a strong response to X chromosome dosage. For example, TLR7 is thought to contribute to female bias in developing autoimmunity, and CD40LG is thought to contribute to female bias in developing lupus. Neither of the genes showed increased expression as X chromosome dosage increased. This suggests that other mechanisms may be driving the sex bias in these cases.

Because of the limited pool of donors, the researchers were not able to identify every gene that responds to sex chromosome dosage, and future research may uncover more sex-chromosome-sensitive genes of interest. Meanwhile, the Page lab continues to investigate the sex chromosomes’ shared role as regulators of gene expression throughout the body.

“We’ve got to recalibrate our thinking from the view that X and Y are mainly involved in differentiating males and females, to understanding that they also have largely shared functions that are important throughout the body,” Page says. “At the same time, I think that uncovering the biology of Xi is going to be incredibly important for understanding women’s health and sex differences in health and disease.”

Gene silencing tool has a need for speed

Small changes in the molecular machines that carry out RNA interference can lead to big differences in the efficacy of gene silencing. These new findings from the Bartel Lab have implications for the design of gene-silencing therapeutics.

Greta Friar | Whitehead Institute
July 17, 2024

RNA interference (RNAi) is a process that many organisms, including humans, use to decrease the activity of target RNAs in cells by triggering their degradation or slicing them in half. If the target is a messenger RNA, the intermediary between gene and protein, then RNAi can decrease or completely silence expression of the gene. Researchers figured out how to tailor RNAi to target different RNAs, and since then it has been used as a research tool to silence genes of interest. RNAi is also used in a growing number of therapeutics to silence genes that contribute to disease.

However, researchers still do not understand some of the biochemistry underlying RNAi. Slight differences in the design of the RNAi machinery can lead to big differences in how effective it is at decreasing gene expression. Through trial and error, researchers have worked out guidelines for making the most effective RNAi tools without understanding exactly why they work. However, Whitehead Institute Member David Bartel and graduate student in his lab Peter Wang have now dug deeper to figure out the mechanics of the main cellular machine involved in RNAi. The researchers’ findings, shared in Molecular Cell on July 17, not only provide explanations for some of the known rules for RNAi tool design, but also provide new insights that could improve future designs.

Slicing speed is highly variable

The cellular machine that carries out RNAi has two main parts. One is a guide RNA, a tiny RNA typically only 22 bases or nucleotides long. RNA, like DNA, is made of four possible bases, although RNA has the base uracil (U) instead of the DNA base thymine (T). RNA bases bind to each other in certain pairings—guanines (G) pair to cytosines (C) and adenines (A) pair to U’s—and the sequence of bases in the guide RNA corresponds to a complementary sequence within the target RNA. When the guide RNA comes across a target, the corresponding bases pair up, binding the RNAs. Then the other part of the RNAi machine, an Argonaute protein bound to the guide RNA, can slice the target RNA in half or trigger the cell to break it down more gradually.

In humans, AGO2 is the Argonaute protein that is best at slicing. Only a couple dozen RNA targets actually get sliced, but these few targets play essential roles in processes such as neuron signal control and accurate body shape formation. Slicing is also important for RNAi tools and therapeutics.

In order for AGO2 to slice its target, the target must be in the exact right position. As the guide and target RNAs bind together, they go through a series of motions to ultimately form a double helix. Only in that configuration can AGO2 slice the target.

Researchers had assumed that AGO2 slices through different target RNAs at roughly the same rate, because most research into this process used the same few guide RNAs. These guide RNAs happen to have similar features, and so similar slicing kinetics—but they turn out not to be representative of most guide RNAs.

Wang paired AGO2 with a larger variety of guide RNAs and measured the rate at which each AGO2-guide RNA complex sliced its targets. He found big differences. Whereas the commonly used guide RNAs might differ in their slicing rate by 2-fold, the larger pool of guide RNAs differed by as much as 250-fold. The slicing rates were often much slower than the researchers expected. Previously, researchers thought that all targets could be sliced relatively quickly, so the rate wasn’t considered as a limiting factor – other parts of the process were thought to determine the overall pace – but Wang found that slicing can sometimes be the slowest step.

“The important consideration is whether the slicing rate is faster or slower than other processes in the cell,” Wang says. “We found that for many guide RNAs, the slicing rate was the limiting factor. As such, it impacted the efficacy.”

The slower AGO2 is to slice targets, the more messenger RNAs will remain intact to be made into protein, meaning that the corresponding gene will continue being expressed. The researchers observed this in action: the guide RNAs with slower slicing rates decreased target gene expression by less than the faster ones.

Small changes lead to big differences in slicing rate

Next, the researchers explored what could be causing such big differences in slicing rate between guide RNAs. They mutated guide RNAs to swap out single bases along the guide RNA’s sequence—say, switching the 10th base in the sequence from a C to an A—and measured how this changed the slicing rate.

“The important consideration is whether the slicing rate is faster or slower than other processes in the cell,” Wang says. “We found that for many guide RNAs, the slicing rate was the limiting factor. As such, it impacted the efficacy.”

The researchers found that slicing rate increased when the base at position 7 was an A or a U. The bases A and U pair more weakly than C and G. The researchers found that having a weak A-U pair at that position, or a fully mismatched pair at position 6 or 7, may allow a kink to form in the double helix shape that actually makes the target easier to slice. Wang also found that slicing rate increases with certain substitutions at the 10th and the 17th base positions, although the researchers could not yet determine possible underlying mechanisms.

These observations correspond to existing recommendations for RNAi design, such as not using a G at position 7. The new work demonstrates that the reason these recommendations work is because they affect the slicing rate, and, in the case of position 7, the new work further identifies the specific mechanism at play.

Interplay between regions matters

People designing synthetic guide RNAs thought that the bases at the tail end, past the 16th position, were not very important. This is because in the case of the most commonly used guide RNAs, the target will be rapidly cleaved even if all of the tail end positions are mismatches that cannot pair.

However, Wang and Bartel found that the identity of the tail end bases are only irrelevant in a specific scenario that happens to be true of the most commonly used guide RNAs: when the bases in the center of the guide RNA (positions 9-12) are strong-pairing Cs and Gs. When the center pairings are weak, then the tail end bases need to be perfect matches to the target RNA. The researchers found that guide RNAs could have up to a 600-fold difference in tolerance for tail end mismatches based on the strength of their central pairings.

The reason for this difference has to do with the final set of motions that the two RNAs must perform in order to assume their final double helix shape. A perfectly paired tail end makes it easier for the RNAs to complete these motions. However, a strong enough center can pull the RNAs into the double helix even if the tail ends are not ideally suited for doing so.

The observation that weak central pairing requires perfect or near perfect tail end matches could provide a useful new guideline for designing synthetic RNAs. Any guide RNA runs the risk of sometimes binding other messenger RNAs that are similar enough to the intended target RNA. In the case of a therapy, this off-target binding can lead to negative side effects. Bartel and Wang suggest that researchers could design guide RNAs with weak centers, which would require more perfect pairing in the tail end, so that the guide RNA will be less likely to bind non-target RNAs; only the perfect pairing of the target’s RNA sequence would suffice.

Altogether, Wang and Bartel’s findings explain how small differences between guide RNAs can make such large differences in the efficacy of RNAi, providing a rationale for the long-standing RNAi design guidelines. Some of the findings even suggest new guidelines that could help with future synthetic guide RNA designs.

“Discovering the interplay between the center and tail end of the guide RNA was unexpected and satisfying,” says Bartel, who is also a professor at the Massachusetts Institute of Technology and a Howard Hughes Medical Investigator. “It explains why, even though the guidelines suggested that tail-end sequence doesn’t matter, the target RNAs that are sliced in our cells do have pairing to the tail end. This observation could prove useful to reduce off-target effects in RNAi therapeutics.”