The National Academy of Medicine recently announced the election of more than 90 members during its annual meeting, including MIT faculty members Matthew Vander Heiden and Fan Wang, along with five MIT alumni.
Election to the National Academy of Medicine (NAM) is considered one of the highest honors in the fields of health and medicine and recognizes individuals who have demonstrated outstanding professional achievement and commitment to service.
Matthew Vander Heiden is the director of the Koch Institute for Integrative Cancer Research at MIT, a Lester Wolfe Professor of Molecular Biology, and a member of the Broad Institute of MIT and Harvard. His research explores how cancer cells reprogram their metabolism to fuel tumor growth and has provided key insights into metabolic pathways that support cancer progression, with implications for developing new therapeutic strategies. The National Academy of Medicine recognized Vander Heiden for his contributions to “the development of approved therapies for cancer and anemia” and his role as a “thought leader in understanding metabolic phenotypes and their relations to disease pathogenesis.”
Vander Heiden earned his MD and PhD from the University of Chicago and completed his clinical training in internal medicine and medical oncology at the Brigham and Women’s Hospital and the Dana-Farber Cancer Institute. After postdoctoral research at Harvard Medical School, Vander Heiden joined the faculty of the MIT Department of Biology and the Koch Institute in 2010. He is also a practicing oncologist and instructor in medicine at Dana-Farber Cancer Institute and Harvard Medical School.
Fan Wang is a professor of brain and cognitive sciences, an investigator at the McGovern Institute, and director of the K. Lisa Yang and Hock E. Tan Center for Molecular Therapeutics at MIT. Wang’s research focuses on the neural circuits governing the bidirectional interactions between the brain and body. She is specifically interested in the circuits that control the sensory and emotional aspects of pain and addiction, as well as the sensory and motor circuits that work together to execute behaviors such as eating, drinking, and moving. The National Academy of Medicine has recognized her body of work for “providing the foundational knowledge to develop new therapies to treat chronic pain and movement disorders.”
Before coming to MIT in 2021, Wang obtained her PhD from Columbia University and received her postdoctoral training at the University of California at San Francisco and Stanford University. She became a faculty member at Duke University in 2003 and was later appointed the Morris N. Broad Professor of Neurobiology. Wang is also a member of the American Academy of Arts and Sciences and she continues to make important contributions to the neural mechanisms underlying general anesthesia, pain perception, and movement control.
MIT alumni who were elected to the NAM for 2024 include:
Leemore Dafny PhD ’01 (Economics);
David Huang ’85 MS ’89 (Electrical Engineering and Computer Science) PhD ’93 Medical Engineering and Medical Physics);
Nola M. Hylton ’79 (Chemical Engineering);
Mark R. Prausnitz PhD ’94 (Chemical Engineering); and
Konstantina M. Stankovic ’92 (Biology and Physics) PhD ’98 (Speech and Hearing Bioscience and Technology)
Established originally as the Institute of Medicine in 1970 by the National Academy of Sciences, the National Academy of Medicine addresses critical issues in health, science, medicine, and related policy and inspires positive actions across sectors.
“This class of new members represents the most exceptional researchers and leaders in health and medicine, who have made significant breakthroughs, led the response to major public health challenges, and advanced health equity,” said National Academy of Medicine President Victor J. Dzau. “Their expertise will be necessary to supporting NAM’s work to address the pressing health and scientific challenges we face today.”
When an election result is disputed, people who are skeptical about the outcome may be swayed by figures of authority who come down on one side or the other. Those figures can be independent monitors, political figures, or news organizations. However, these “debunking” efforts don’t always have the desired effect, and in some cases, they can lead people to cling more tightly to their original position.
Neuroscientists and political scientists at MIT and the University of California at Berkeley have now created a computational model that analyzes the factors that help to determine whether debunking efforts will persuade people to change their beliefs about the legitimacy of an election. Their findings suggest that while debunking fails much of the time, it can be successful under the right conditions.
For instance, the model showed that successful debunking is more likely if people are less certain of their original beliefs and if they believe the authority is unbiased or strongly motivated by a desire for accuracy. It also helps when an authority comes out in support of a result that goes against a bias they are perceived to hold: for example, Fox News declaring that Joseph R. Biden had won in Arizona in the 2020 U.S. presidential election.
“When people see an act of debunking, they treat it as a human action and understand it the way they understand human actions — that is, as something somebody did for their own reasons,” says Rebecca Saxe, the John W. Jarve Professor of Brain and Cognitive Sciences, a member of MIT’s McGovern Institute for Brain Research, and the senior author of the study. “We’ve used a very simple, general model of how people understand other people’s actions, and found that that’s all you need to describe this complex phenomenon.”
The findings could have implications as the United States prepares for the presidential election taking place on Nov. 5, as they help to reveal the conditions that would be most likely to result in people accepting the election outcome.
MIT graduate student Setayesh Radkani is the lead author of the paper, which appears today in a special election-themed issue of the journal PNAS Nexus. Marika Landau-Wells PhD ’18, a former MIT postdoc who is now an assistant professor of political science at the University of California at Berkeley, is also an author of the study.
Modeling motivation
In their work on election debunking, the MIT team took a novel approach, building on Saxe’s extensive work studying “theory of mind” — how people think about the thoughts and motivations of other people.
As part of her PhD thesis, Radkani has been developing a computational model of the cognitive processes that occur when people see others being punished by an authority. Not everyone interprets punitive actions the same way, depending on their previous beliefs about the action and the authority. Some may see the authority as acting legitimately to punish an act that was wrong, while others may see an authority overreaching to issue an unjust punishment.
Last year, after participating in an MIT workshop on the topic of polarization in societies, Saxe and Radkani had the idea to apply the model to how people react to an authority attempting to sway their political beliefs. They enlisted Landau-Wells, who received her PhD in political science before working as a postdoc in Saxe’s lab, to join their effort, and Landau suggested applying the model to debunking of beliefs regarding the legitimacy of an election result.
The computational model created by Radkani is based on Bayesian inference, which allows the model to continually update its predictions of people’s beliefs as they receive new information. This approach treats debunking as an action that a person undertakes for his or her own reasons. People who observe the authority’s statement then make their own interpretation of why the person said what they did. Based on that interpretation, people may or may not change their own beliefs about the election result.
Additionally, the model does not assume that any beliefs are necessarily incorrect or that any group of people is acting irrationally.
“The only assumption that we made is that there are two groups in the society that differ in their perspectives about a topic: One of them thinks that the election was stolen and the other group doesn’t,” Radkani says. “Other than that, these groups are similar. They share their beliefs about the authority — what the different motives of the authority are and how motivated the authority is by each of those motives.”
The researchers modeled more than 200 different scenarios in which an authority attempts to debunk a belief held by one group regarding the validity of an election outcome.
Each time they ran the model, the researchers altered the certainty levels of each group’s original beliefs, and they also varied the groups’ perceptions of the motivations of the authority. In some cases, groups believed the authority was motivated by promoting accuracy, and in others they did not. The researchers also altered the groups’ perceptions of whether the authority was biased toward a particular viewpoint, and how strongly the groups believed in those perceptions.
Building consensus
In each scenario, the researchers used the model to predict how each group would respond to a series of five statements made by an authority trying to convince them that the election had been legitimate. The researchers found that in most of the scenarios they looked at, beliefs remained polarized and in some cases became even further polarized. This polarization could also extend to new topics unrelated to the original context of the election, the researchers found.
However, under some circumstances, the debunking was successful, and beliefs converged on an accepted outcome. This was more likely to happen when people were initially more uncertain about their original beliefs.
“When people are very, very certain, they become hard to move. So, in essence, a lot of this authority debunking doesn’t matter,” Landau-Wells says. “However, there are a lot of people who are in this uncertain band. They have doubts, but they don’t have firm beliefs. One of the lessons from this paper is that we’re in a space where the model says you can affect people’s beliefs and move them towards true things.”
Another factor that can lead to belief convergence is if people believe that the authority is unbiased and highly motivated by accuracy. Even more persuasive is when an authority makes a claim that goes against their perceived bias — for instance, Republican governors stating that elections in their states had been fair even though the Democratic candidate won.
As the 2024 presidential election approaches, grassroots efforts have been made to train nonpartisan election observers who can vouch for whether an election was legitimate. These types of organizations may be well-positioned to help sway people who might have doubts about the election’s legitimacy, the researchers say.
“They’re trying to train to people to be independent, unbiased, and committed to the truth of the outcome more than anything else. Those are the types of entities that you want. We want them to succeed in being seen as independent. We want them to succeed as being seen as truthful, because in this space of uncertainty, those are the voices that can move people toward an accurate outcome,” Landau-Wells says.
The research was funded, in part, by the Patrick J. McGovern Foundation and the Guggenheim Foundation.
A classical way to image nanoscale structures in cells is with high-powered, expensive super-resolution microscopes. As an alternative, MIT researchers have developed a way to expand tissue before imaging it — a technique that allows them to achieve nanoscale resolution with a conventional light microscope.
In the newest version of this technique, the researchers have made it possible to expand tissue 20-fold in a single step. This simple, inexpensive method could pave the way for nearly any biology lab to perform nanoscale imaging.
“This democratizes imaging,” says Laura Kiessling, the Novartis Professor of Chemistry at MIT and a member of the Broad Institute of MIT and Harvard and MIT’s Koch Institute for Integrative Cancer Research. “Without this method, if you want to see things with a high resolution, you have to use very expensive microscopes. What this new technique allows you to do is see things that you couldn’t normally see with standard microscopes. It drives down the cost of imaging because you can see nanoscale things without the need for a specialized facility.”
At the resolution achieved by this technique, which is around 20 nanometers, scientists can see organelles inside cells, as well as clusters of proteins.
“Twenty-fold expansion gets you into the realm that biological molecules operate in. The building blocks of life are nanoscale things: biomolecules, genes, and gene products,” says Edward Boyden, the Y. Eva Tan Professor in Neurotechnology at MIT; a professor of biological engineering, media arts and sciences, and brain and cognitive sciences; a Howard Hughes Medical Institute investigator; and a member of MIT’s McGovern Institute for Brain Research and Koch Institute for Integrative Cancer Research.
Boyden and Kiessling are the senior authors of the new study, which appears today in Nature Methods. MIT graduate student Shiwei Wang and Tay Won Shin PhD ’23 are the lead authors of the paper.
A single expansion
Boyden’s lab invented expansion microscopy in 2015. The technique requires embedding tissue into an absorbent polymer and breaking apart the proteins that normally hold tissue together. When water is added, the gel swells and pulls biomolecules apart from each other.
The original version of this technique, which expanded tissue about fourfold, allowed researchers to obtain images with a resolution of around 70 nanometers. In 2017, Boyden’s lab modified the process to include a second expansion step, achieving an overall 20-fold expansion. This enables even higher resolution, but the process is more complicated.
“We’ve developed several 20-fold expansion technologies in the past, but they require multiple expansion steps,” Boyden says. “If you could do that amount of expansion in a single step, that could simplify things quite a bit.”
With 20-fold expansion, researchers can get down to a resolution of about 20 nanometers, using a conventional light microscope. This allows them see cell structures like microtubules and mitochondria, as well as clusters of proteins.
In the new study, the researchers set out to perform 20-fold expansion with only a single step. This meant that they had to find a gel that was both extremely absorbent and mechanically stable, so that it wouldn’t fall apart when expanded 20-fold.
To achieve that, they used a gel assembled from N,N-dimethylacrylamide (DMAA) and sodium acrylate. Unlike previous expansion gels that rely on adding another molecule to form crosslinks between the polymer strands, this gel forms crosslinks spontaneously and exhibits strong mechanical properties. Such gel components previously had been used in expansion microscopy protocols, but the resulting gels could expand only about tenfold. The MIT team optimized the gel and the polymerization process to make the gel more robust, and to allow for 20-fold expansion.
To further stabilize the gel and enhance its reproducibility, the researchers removed oxygen from the polymer solution prior to gelation, which prevents side reactions that interfere with crosslinking. This step requires running nitrogen gas through the polymer solution, which replaces most of the oxygen in the system.
Once the gel is formed, select bonds in the proteins that hold the tissue together are broken and water is added to make the gel expand. After the expansion is performed, target proteins in tissue can be labeled and imaged.
“This approach may require more sample preparation compared to other super-resolution techniques, but it’s much simpler when it comes to the actual imaging process, especially for 3D imaging,” Shin says. “We document the step-by-step protocol in the manuscript so that readers can go through it easily.”
Imaging tiny structures
Using this technique, the researchers were able to image many tiny structures within brain cells, including structures called synaptic nanocolumns. These are clusters of proteins that are arranged in a specific way at neuronal synapses, allowing neurons to communicate with each other via secretion of neurotransmitters such as dopamine.
In studies of cancer cells, the researchers also imaged microtubules — hollow tubes that help give cells their structure and play important roles in cell division. They were also able to see mitochondria (organelles that generate energy) and even the organization of individual nuclear pore complexes (clusters of proteins that control access to the cell nucleus).
Wang is now using this technique to image carbohydrates known as glycans, which are found on cell surfaces and help control cells’ interactions with their environment. This method could also be used to image tumor cells, allowing scientists to glimpse how proteins are organized within those cells, much more easily than has previously been possible.
The researchers envision that any biology lab should be able to use this technique at a low cost since it relies on standard, off-the-shelf chemicals and common equipment such confocal microscopes and glove bags, which most labs already have or can easily access.
“Our hope is that with this new technology, any conventional biology lab can use this protocol with their existing microscopes, allowing them to approach resolution that can only be achieved with very specialized and costly state-of-the-art microscopes,” Wang says.
The research was funded, in part, by the U.S. National Institutes of Health, an MIT Presidential Graduate Fellowship, U.S. National Science Foundation Graduate Research Fellowship grants, Open Philanthropy, Good Ventures, the Howard Hughes Medical Institute, Lisa Yang, Ashar Aziz, and the European Research Council.
Novel magnetic nanodiscs could provide a much less invasive way of stimulating parts of the brain, paving the way for stimulation therapies without implants or genetic modification, MIT researchers report.
The scientists envision that the tiny discs, which are about 250 nanometers across (about 1/500 the width of a human hair), would be injected directly into the desired location in the brain. From there, they could be activated at any time simply by applying a magnetic field outside the body. The new particles could quickly find applications in biomedical research, and eventually, after sufficient testing, might be applied to clinical uses.
The development of these nanoparticles is described in the journal Nature Nanotechnology, in a paper by Polina Anikeeva, a professor in MIT’s departments of Materials Science and Engineering and Brain and Cognitive Sciences, graduate student Ye Ji Kim, and 17 others at MIT and in Germany.
Deep brain stimulation (DBS) is a common clinical procedure that uses electrodes implanted in the target brain regions to treat symptoms of neurological and psychiatric conditions such as Parkinson’s disease and obsessive-compulsive disorder. Despite its efficacy, the surgical difficulty and clinical complications associated with DBS limit the number of cases where such an invasive procedure is warranted. The new nanodiscs could provide a much more benign way of achieving the same results.
Over the past decade other implant-free methods of producing brain stimulation have been developed. However, these approaches were often limited by their spatial resolution or ability to target deep regions. For the past decade, Anikeeva’s Bioelectronics group as well as others in the field used magnetic nanomaterials to transduce remote magnetic signals into brain stimulation. However, these magnetic methods relied on genetic modifications and can’t be used in humans.
Since all nerve cells are sensitive to electrical signals, Kim, a graduate student in Anikeeva’s group, hypothesized that a magnetoelectric nanomaterial that can efficiently convert magnetization into electrical potential could offer a path toward remote magnetic brain stimulation. Creating a nanoscale magnetoelectric material was, however, a formidable challenge.
Kim synthesized novel magnetoelectric nanodiscs and collaborated with Noah Kent, a postdoc in Anikeeva’s lab with a background in physics who is a second author of the study, to understand the properties of these particles.
The structure of the new nanodiscs consists of a two-layer magnetic core and a piezoelectric shell. The magnetic core is magnetostrictive, which means it changes shape when magnetized. This deformation then induces strain in the piezoelectric shell which produces a varying electrical polarization. Through the combination of the two effects, these composite particles can deliver electrical pulses to neurons when exposed to magnetic fields.
One key to the discs’ effectiveness is their disc shape. Previous attempts to use magnetic nanoparticles had used spherical particles, but the magnetoelectric effect was very weak, says Kim. This anisotropy enhances magnetostriction by over a 1000-fold, adds Kent.
The team first added their nanodiscs to cultured neurons, which allowed then to activate these cells on demand with short pulses of magnetic field. This stimulation did not require any genetic modification.
They then injected small droplets of magnetoelectric nanodiscs solution into specific regions of the brains of mice. Then, simply turning on a relatively weak electromagnet nearby triggered the particles to release a tiny jolt of electricity in that brain region. The stimulation could be switched on and off remotely by the switching of the electromagnet. That electrical stimulation “had an impact on neuron activity and on behavior,” Kim says.
The team found that the magnetoelectric nanodiscs could stimulate a deep brain region, the ventral tegmental area, that is associated with feelings of reward.
The team also stimulated another brain area, the subthalamic nucleus, associated with motor control. “This is the region where electrodes typically get implanted to manage Parkinson’s disease,” Kim explains. The researchers were able to successfully demonstrate the modulation of motor control through the particles. Specifically, by injecting nanodiscs only in one hemisphere, the researchers could induce rotations in healthy mice by applying magnetic field.
The nanodiscs could trigger the neuronal activity comparable with conventional implanted electrodes delivering mild electrical stimulation. The authors achieved subsecond temporal precision for neural stimulation with their method yet observed significantly reduced foreign body responses as compared to the electrodes, potentially allowing for even safer deep brain stimulation.
The multilayered chemical composition and physical shape and size of the new multilayered nanodiscs is what made precise stimulation possible.
While the researchers successfully increased the magnetostrictive effect, the second part of the process, converting the magnetic effect into an electrical output, still needs more work, Anikeeva says. While the magnetic response was a thousand times greater, the conversion to an electric impulse was only four times greater than with conventional spherical particles.
“This massive enhancement of a thousand times didn’t completely translate into the magnetoelectric enhancement,” says Kim. “That’s where a lot of the future work will be focused, on making sure that the thousand times amplification in magnetostriction can be converted into a thousand times amplification in the magnetoelectric coupling.”
What the team found, in terms of the way the particles’ shapes affects their magnetostriction, was quite unexpected. “It’s kind of a new thing that just appeared when we tried to figure out why these particles worked so well,” says Kent.
Anikeeva adds: “Yes, it’s a record-breaking particle, but it’s not as record-breaking as it could be.” That remains a topic for further work, but the team has ideas about how to make further progress.
While these nanodiscs could in principle already be applied to basic research using animal models, to translate them to clinical use in humans would require several more steps, including large-scale safety studies, “which is something academic researchers are not necessarily most well-positioned to do,” Anikeeva says. “When we find that these particles are really useful in a particular clinical context, then we imagine that there will be a pathway for them to undergo more rigorous large animal safety studies.”
The team included researchers affiliated with MIT’s departments of Materials Science and Engineering, Electrical Engineering and Computer Science, Chemistry, and Brain and Cognitive Sciences; the Research Laboratory of Electronics; the McGovern Institute for Brain Research; and the Koch Institute for Integrative Cancer Research; and from the Friedrich-Alexander University of Erlangen, Germany. The work was supported, in part, by the National Institutes of Health, the National Center for Complementary and Integrative Health, the National Institute for Neurological Disorders and Stroke, the McGovern Institute for Brain Research, and the K. Lisa Yang and Hock E. Tan Center for Molecular Therapeutics in Neuroscience.
One of the brain’s most celebrated qualities is its adaptability. Changes to neural circuits, whose connections are continually adjusted as we experience and interact with the world, are key to how we learn. But to keep knowledge and memories intact, some parts of the circuitry must be resistant to this constant change.
“Brains have figured out how to navigate this landscape of balancing between stability and flexibility, so that you can have new learning and you can have lifelong memory,” says neuroscientist Mark Harnett, an investigator at MIT’s McGovern Institute.
In the August 27, 2024 of the journal Cell Reports, Harnett and his team show how individual neurons can contribute to both parts of this vital duality. By studying the synapses through which pyramidal neurons in the brain’s sensory cortex communicate, they have learned how the cells preserve their understanding of some of the world’s most fundamental features, while also maintaining the flexibility they need to adapt to a changing world.
McGovern Institute Investigator Mark Harnett. Photo: Adam Glanzman
Visual connections
Pyramidal neurons receive input from other neurons via thousands of connection points. Early in life, these synapses are extremely malleable; their strength can shift as a young animal takes in visual information and learns to interpret it. Most remain adaptable into adulthood, but Harnett’s team discovered that some of the cells’ synapses lose their flexibility when the animals are less than a month old. Having both stable and flexible synapses means these neurons can combine input from different sources to use visual information in flexible ways.
A confocal image of a mouse brain showing dLGN neurons in pink. Image: Courtney Yaeger, Mark Harnett.
Postdoctoral fellow Courtney Yaeger took a close look at these unusually stable synapses, which cluster together along a narrow region of the elaborately branched pyramidal cells. She was interested in the connections through which the cells receive primary visual information, so she traced their connections with neurons in a vision-processing center of the brain’s thalamus called the dorsal lateral geniculate nucleus (dLGN).
The long extensions through which a neuron receives signals from other cells are called dendrites, and they branch of from the main body of the cell into a tree-like structure. Spiny protrusions along the dendrites form the synapses that connect pyramidal neurons to other cells. Yaeger’s experiments showed that connections from the dLGN all led to a defined region of the pyramidal cells—a tight band within what she describes as the trunk of the dendritic tree.
Yaeger found several ways in which synapses in this region— formally known as the apical oblique dendrite domain—differ from other synapses on the same cells. “They’re not actually that far away from each other, but they have completely different properties,” she says.
Stable synapses
In one set of experiments, Yaeger activated synapses on the pyramidal neurons and measured the effect on the cells’ electrical potential. Changes to a neuron’s electrical potential generate the impulses the cells use to communicate with one another. It is common for a synapse’s electrical effects to amplify when synapses nearby are also activated. But when signals were delivered to the apical oblique dendrite domain, each one had the same effect, no matter how many synapses were stimulated. Synapses there don’t interact with one another at all, Harnett says. “They just do what they do. No matter what their neighbors are doing, they all just do kind of the same thing.”
Representative oblique (top) and basal (bottom) dendrites from the same Layer 5 pyramidal neuron imaged across 7 days. Transient spines are labeled with yellow arrowheads the day before disappearance. Image: Courtney Yaeger, Mark Harnett.
The team was also able to visualize the molecular contents of individual synapses. This revealed a surprising lack of a certain kind of neurotransmitter receptor, called NMDA receptors, in the apical oblique dendrites. That was notable because of NMDA receptors’ role in mediating changes in the brain. “Generally when we think about any kind of learning and memory and plasticity, it’s NMDA receptors that do it,” Harnett says. “That is the by far most common substrate of learning and memory in all brains.”
When Yaeger stimulated the apical oblique synapses with electricity, generating patterns of activity that would strengthen most synapses, the team discovered a consequence of the limited presence of NMDA receptors. The synapses’ strength did not change. “There’s no activity-dependent plasticity going on there, as far as we have tested,” Yaeger says.
That makes sense, the researchers say, because the cells’ connections from the thalamus relay primary visual information detected by the eyes. It is through these connections that the brain learns to recognize basic visual features like shapes and lines.
“These synapses are basically a robust, high fidelity readout of this visual information,” Harnett explains. “That’s what they’re conveying, and it’s not context sensitive. So it doesn’t matter how many other synapses are active, they just do exactly what they’re going to do, and you can’t modify them up and down based on activity. So they’re very, very stable.”
“You actually don’t want those to be plastic,” adds Yaeger.
“Can you imagine going to sleep and then forgetting what a vertical line looks like? That would be disastrous.” – Courtney Yaeger
By conducting the same experiments in mice of different ages, the researchers determined that the synapses that connect pyramidal neurons to the thalamus become stable a few weeks after young mice first open their eyes. By that point, Harnett says, they have learned everything they need to learn. On the other hand, if mice spend the first weeks of their lives in the dark, the synapses never stabilize—further evidence that the transition depends on visual experience.
The team’s findings not only help explain how the brain balances flexibility and stability, they could help researchers teach artificial intelligence how to do the same thing. Harnett says artificial neural networks are notoriously bad at this: When an artificial neural network that does something well is trained to do something new, it almost always experiences “catastrophic forgetting” and can no longer perform its original task. Harnett’s team is exploring how they can use what they’ve learned about real brains to overcome this problem in artificial networks.
Placebos are inert treatments, generally not expected to impact biological pathways or improve a person’s physical health. But time and again, some patients report that they feel better after taking a placebo. Increasingly, doctors and scientists are recognizing that rather than dismissing placebos as mere trickery, they may be able to help patients by harnessing their power.
To maximize the impact of the placebo effect and design reliable therapeutic strategies, researchers need a better understanding of how it works. Now, with a new animal model developed by scientists at the McGovern Institute, they will be able to investigate the neural circuits that underlie placebos’ ability to elicit pain relief.
“The brain and body interaction has a lot of potential, in a way that we don’t fully understand,” says McGovern investigator Fan Wang. “I really think there needs to be more of a push to understand placebo effect, in pain and probably in many other conditions. Now we have a strong model to probe the circuit mechanism.”
Context-dependent placebo effect
McGovern Investigator Fan Wang. Photo: Caitliin Cunningham
In the September 5, 2024, issue of the journal Current Biology, Wang and her team report that they have elicited strong placebo pain relief in mice by activating pain-suppressing neurons in the brain while the mice are in a specific environment—thereby teaching the animals that they feel better when they are in that context. Following their training, placing the mice in that environment alone is enough to suppress pain. The team’s experiments, which were funded by the National Institutes of Health, the K. Lisa Yang Brain-Body Center and the K. Lisa Yang and Hock E. Tan Center for Molecular Therapeutics within MIT’s Yang Tan Collective show that this context-dependent placebo effect relieves both acute and chronic pain.
Context is critical for the placebo effect. While a pill can help a patient feel better when they expect it to, even if it is made only of sugar or starch, it seems to be not just the pill that sets up those expectations, but the entire scenario in which the pill is taken. For example, being in a hospital and interacting with doctors can contribute to a patient’s perception of care, and these social and environmental factors can make a placebo effect more probable.
Postdoctoral fellows Bin Chen and Nitsan Goldstein used visual and textural cues to define a specific place. Then they activated pain-suppressing neurons in the brain while the animals were in this “pain-relief box.” Those pain-suppressing neurons, which Wang’s lab discovered a few years ago, are located in an emotion-processing center of the brain called the central amygdala. By expressing light-sensitive channels in these neurons, the researchers were able to suppress pain with light in the pain-relief box and leave the neurons inactive when mice were in a control box.
Animals learned to prefer the pain-relief box to other environments. And when the researchers tested their response to potentially painful stimuli after they had made that association, they found the mice were less sensitive while they were there. “Just by being in the context that they had associated with pain suppression, we saw that reduced pain—even though we weren’t actually activating those [pain-suppressing] neurons,” Goldstein explains.
Acute and chronic pain relief
Some scientists have been able to elicit placebo pain relief in rodents by treating the animals with morphine, linking environmental cues to the pain suppression caused by the drugs similar to the way Wang’s team did by directly activating pain-suppressing neurons. This drug-based approach works best for setting up expectations of relief for acute pain; its placebo effect is short-lived and mostly ineffective against chronic pain. So Wang, Chen, and Goldstein were particularly pleased to find that their engineered placebo effect was effective for relieving both acute and chronic pain.
In their experiments, animals experiencing a chemotherapy-induced hypersensitivity to touch exhibited a preference for the pain relief box as much as animals who were exposed to a chemical that induces acute pain, days after their initial conditioning. Once there, their chemotherapy-induced pain sensitivity was eliminated; they exhibited no more sensitivity to painful stimuli than they had prior to receiving chemotherapy.
One of the biggest surprises came when the researchers turned their attention back to the pain-suppressing neurons in the central amygdala that they had used to trigger pain relief. They suspected that those neurons might be reactivated when mice returned to the pain-relief box. Instead, they found that after the initial conditioning period, those neurons remained quiet. “These neurons are not reactivated, yet the mice appear to be no longer in pain,” Wang says. “So it suggests this memory of feeling well is transferred somewhere else.”
Goldstein adds that there must be a pain-suppressing neural circuit somewhere that is activated by pain-relief-associated contexts—and the team’s new placebo model sets researchers up to investigate those pathways. A deeper understanding of that circuitry could enable clinicians to deploy the placebo effect—alone or in combination with active treatments—to better manage patients’ pain in the future.
When you arrive in a new city, every outing can be an exploration. You may know your way to a few places, but only if you follow a specific route. As you wander around a bit, get lost a few times, and familiarize yourself with some landmarks and where they are relative to each other, your brain develops a cognitive map of the space. You learn how things are laid out, and navigating gets easier.
It takes a lot to generate a useful mental map. “You have to understand the structure of relationships in the world,” says McGovern Investigator Mehrdad Jazayeri. “You need learning and experience to construct clever representations. The advantage is that when you have them, the world is an easier place to deal with.”
Indeed, Jazayeri says, internal models like these are the core of intelligent behavior.
Mehrdad Jazayeri (right) and graduate student Jack Gabel sit inside a rig designed to probe the brain’s ability to solve real-world problems with internal models. Photo: Steph Stevens
Many McGovern scientists see these cognitive maps as windows into their biggest questions about the brain: how it represents the external world, how it lets us learn and adapt, and how it forms and reconstructs memories. Researchers are learning that cells and strategies that the brain uses to understand the layout of a space also help track other kinds of structures in the world, too — from variations in sound to sequences of events. By studying how neurons behave as animals navigate their environments, McGovern researchers also expect to deepen their understanding of other important cognitive functions as well.
Decoding spatial maps
McGovern Investigator Ila Fiete builds theoretical models that help explain how spatial maps are formed in the brain. Previous research has shown that “place cells” and “grid cells” are place-sensitive neurons in the brain’s hippocampus and entorhinal cortex whose firing patterns help an animal map out a space. As an animal becomes familiar with its environment, subsets of these cells become tied to specific locations, firing only when the animal is in them.
The brain’s ability to navigate the world is made possible by a brain circuit that includes the hippocampus (above), entorhinal cortex, and retrosplenial cortex. The firing pattern of “grid cells” and “place cells” in this circuit help form mental representations, or cognitive maps, of the external world. These brain regions are also among the first areas to be affected in people with Alzheimer’s, who often have trouble navigating. Image: Qian Chen, Guoping Feng
Fiete’s models have shown how these circuits can integrate information about movement, like signals from the muscles and vestibular system that change as an animal moves around, to calculate and update its estimate of an animal’s position in space. Fiete suspects the cells that do this can use the same strategy to keep track of other kinds of movement or change.
Mapping a space is about understanding where things are in relationship to one another, says Jazayeri, and tracking relationships is useful for modeling many kinds of structure in the world. For example, the hippocampus and entorhinal cortex are also closely linked to episodic memory, which keeps track of the connections between events and experiences.
“These brain areas are thought to be critical for learning relationships,” Jazayeri says.
Navigating virtual worlds
A key feature of cognitive maps is that they enable us to make predictions and respond to new situations without relying on immediate sensory cues. In a study published in Nature this June, Jazayeri and Fiete saw evidence of the brain’s ability to call up an internal model of an abstract domain: they watched neurons in the brain’s entorhinal cortex register a sequence of images, even when they were hidden from view.
Ila Fiete and postdoc Sarthak Chandra (right) develop theoretical models to study the brain. Photo: Steph Stevens
We can remember the layout of our home from far away or plan a walk through the neighborhood without stepping outside — so it may come as no surprise that the brain can call up its internal model in the absence of movement or sensory inputs. Indeed, previous research has shown that the circuits that encode physical space also encode abstract spaces like auditory sound sequences. But these experiments were performed in the presence of the stimuli, and Jazayeri and his team wanted to know whether simply imagining movement through an abstract domain may also evoke the same cognitive maps.
To test the entorhinal cortex’s ability to do this, Jazayeri and his team designed an experiment where animals had to “mentally” navigate through a previously explored, but now invisible, sequence of images. Working with Fiete, they found that the neurons that had become responsive to particular images in the visible sequence would also fire when mentally navigating the sequence in which images were hidden from view — suggesting the animal was conjuring a representation of the image in its mind.
Ila Fiete has shown that the brain generates a one-dimensional ring of neural activity that acts as a compass. Here, head direction is indicated by color. Image: Ila Fiete
“You see these neurons in the entorhinal cortex undergo very clear dynamic patterns that are in correspondence with what we think the animal might be thinking at the time,” Jazayeri says. “They are updating themselves without any change out there in the world.”
The team then incorporated their data into a computational model to explore how neural circuits might form a mental model of abstract sequences. Their artificial circuit showed that the external inputs (eg., image sequences) become associated with internal models through a simple associative learning rule in which neurons that fire together, wire together. This model suggests that imagined movement could update the internal representations, and the learned association of these internal representations with external inputs might enable a recall of the corresponding inputs even when they are absent.
More broadly, Fiete’s research on cognitive mapping in the hippocampus is leading to some interesting predictions: “One of the conclusions we’re coming to in my group is that when you reconstruct a memory, the area that’s driving that reconstruction is the entorhinal cortex and hippocampus but the reconstruction may happen in the sensory periphery, using the representations that played a role in experiencing that stimulus in the first place,” Fiete explains. “So when I reconstruct an image, I’m likely using my visual cortex to do that reconstruction, driven by the hippocampal complex.” Signals from the entorhinal cortex to the visual cortex during navigation could help an animal visualize landmarks and find its way, even when those landmarks are not visible in the external world.
Landmark coding
Near the entorhinal cortex is the retrosplenial cortex, another brain area that seems to be important for navigation. It is positioned to integrate visual signals with information about the body’s position and movement through space. Both the retrosplenial cortex and the entorhinal cortex are among the first areas impacted by Alzheimer’s disease; spatial disorientation and navigation difficulties may be consequences of their degeneration.
Researchers suspect the retrosplenial cortex may be key to letting an animal know not just where something is, but also how to get there. McGovern Investigator Mark Harnett explains that to generate a cognitive map that can be used to navigate, an animal must understand not just where objects or other cues are in relationship to itself, but also where they are in relationship to each other.
In a study reported in eLife in 2020, Harnett and colleagues may have glimpsed both of these kinds of representations of space inside the brain. They watched neurons there light up as mice ran on a treadmill and tracked the passage of a virtual environment. As the mice became familiar with the landscape and learned where they were likely to find a reward, activity in the retrosplenial cortex changed.
Lukas Fischer, a Harnett lab postdoc, operates a rig designed to study how mice navigate a virtual environment. Photo: Justin Knight
“What we found was this representation started off sort of crude and mostly about what the animal was doing. And then eventually it became more about the task, the landscape, and the reward,” Harnett says.
Harnett’s team has since begun investigating how the retrosplenial cortex enables more complex spatial reasoning. They designed an experiment in which mice must understand many spatial relationships to access a treat. The experimental setup requires mice to consider the location of reward ports, the center of their environment, and their own viewing angle. Most of the time, they succeed. “They have to really do some triangulation, and the retrosplenial cortex seems to be critical for that,” Harnett says.
When the team monitored neural activity during the task, they found evidence that when an animal wasn’t quite sure where to go, its brain held on to multiple spatial hypotheses at the same time, until new information ruled one out.
Fiete, who has worked with Harnett to explore how neural circuits can execute this kind of spatial reasoning, points out that Jazayeri’s team has observed similar reasoning in animals that must make decisions based on temporarily ambiguous auditory cues. “In both cases, animals are able to hold multiple hypotheses in mind and do the inference,” she says. “Mark’s found that the retrosplenial cortex contains all the signals necessary to do that reasoning.”
Beyond spatial reasoning
As his team learns more about the how the brain creates and uses cognitive maps, Harnett hopes activity in the retrosplenial cortex will shed light on a fundamental aspect of the brain’s organization. The retrosplenial cortex doesn’t just receive information from the brain’s vision-processing center, it also sends signals back. He suspects these may direct the visual cortex to relay information that is particularly pertinent to forming or using a meaningful cognitive map.
“The brain’s navigation system is a beautiful playground.” – Ila Fiete
This kind of connectivity, where parts of the brain that carry out complex cognitive processing send signals back to regions that handle simpler functions, is common in the brain. Figuring out why is a key pursuit in Harnett’s lab. “I want to use that as a model for thinking about the larger cortical computations, because you see this kind of motif repeated in a lot of ways, and it’s likely key for understanding how learning works,” he says.
Fiete is particularly interested in unpacking the common set of principles that allow cell circuits to generate maps of both our physical environment and our abstract experiences. What is it about this set of brain areas and circuits that, on the one hand, permits specific map-building computations, and, on the other hand, generalizes across physical space and abstract experience?
“The brain’s navigation system is a beautiful playground,” she says, “and an amazing system in which to investigate all of these questions.”
Using functional magnetic resonance imaging (fMRI), neuroscientists have identified several regions of the brain that are responsible for processing language. However, discovering the specific functions of neurons in those regions has proven difficult because fMRI, which measures changes in blood flow, doesn’t have high enough resolution to reveal what small populations of neurons are doing.
Now, using a more precise technique that involves recording electrical activity directly from the brain, MIT neuroscientists have identified different clusters of neurons that appear to process different amounts of linguistic context. These “temporal windows” range from just one word up to about six words.
The temporal windows may reflect different functions for each population, the researchers say. Populations with shorter windows may analyze the meanings of individual words, while those with longer windows may interpret more complex meanings created when words are strung together.
“This is the first time we see clear heterogeneity within the language network,” says Evelina Fedorenko, an associate professor of neuroscience at MIT. “Across dozens of fMRI experiments, these brain areas all seem to do the same thing, but it’s a large, distributed network, so there’s got to be some structure there. This is the first clear demonstration that there is structure, but the different neural populations are spatially interleaved so we can’t see these distinctions with fMRI.”
Fedorenko, who is also a member of MIT’s McGovern Institute for Brain Research, is the senior author of the study, which appears today in Nature Human Behavior. MIT postdoc Tamar Regev and Harvard University graduate student Colton Casto are the lead authors of the paper.
Temporal windows
Functional MRI, which has helped scientists learn a great deal about the roles of different parts of the brain, works by measuring changes in blood flow in the brain. These measurements act as a proxy of neural activity during a particular task. However, each “voxel,” or three-dimensional chunk, of an fMRI image represents hundreds of thousands to millions of neurons and sums up activity across about two seconds, so it can’t reveal fine-grained detail about what those neurons are doing.
One way to get more detailed information about neural function is to record electrical activity using electrodes implanted in the brain. These data are hard to come by because this procedure is done only in patients who are already undergoing surgery for a neurological condition such as severe epilepsy.
“It can take a few years to get enough data for a task because these patients are relatively rare, and in a given patient electrodes are implanted in idiosyncratic locations based on clinical needs, so it takes a while to assemble a dataset with sufficient coverage of some target part of the cortex. But these data, of course, are the best kind of data we can get from human brains: You know exactly where you are spatially and you have very fine-grained temporal information,” Fedorenko says.
In a 2016 study, Fedorenko reported using this approach to study the language processing regions of six people. Electrical activity was recorded while the participants read four different types of language stimuli: complete sentences, lists of words, lists of non-words, and “jabberwocky” sentences — sentences that have grammatical structure but are made of nonsense words.
Those data showed that in some neural populations in language processing regions, activity would gradually build up over a period of several words, when the participants were reading sentences. However, this did not happen when they read lists of words, lists of nonwords, of Jabberwocky sentences.
In the new study, Regev and Casto went back to those data and analyzed the temporal response profiles in greater detail. In their original dataset, they had recordings of electrical activity from 177 language-responsive electrodes across the six patients. Conservative estimates suggest that each electrode represents an average of activity from about 200,000 neurons. They also obtained new data from a second set of 16 patients, which included recordings from another 362 language-responsive electrodes.
When the researchers analyzed these data, they found that in some of the neural populations, activity would fluctuate up and down with each word. In others, however, activity would build up over multiple words before falling again, and yet others would show a steady buildup of neural activity over longer spans of words.
By comparing their data with predictions made by a computational model that the researchers designed to process stimuli with different temporal windows, the researchers found that neural populations from language processing areas could be divided into three clusters. These clusters represent temporal windows of either one, four, or six words.
“It really looks like these neural populations integrate information across different timescales along the sentence,” Regev says.
Processing words and meaning
These differences in temporal window size would have been impossible to see using fMRI, the researchers say.
“At the resolution of fMRI, we don’t see much heterogeneity within language-responsive regions. If you localize in individual participants the voxels in their brain that are most responsive to language, you find that their responses to sentences, word lists, jabberwocky sentences and non-word lists are highly similar,” Casto says.
The researchers were also able to determine the anatomical locations where these clusters were found. Neural populations with the shortest temporal window were found predominantly in the posterior temporal lobe, though some were also found in the frontal or anterior temporal lobes. Neural populations from the two other clusters, with longer temporal windows, were spread more evenly throughout the temporal and frontal lobes.
Fedorenko’s lab now plans to study whether these timescales correspond to different functions. One possibility is that the shortest timescale populations may be processing the meanings of a single word, while those with longer timescales interpret the meanings represented by multiple words.
“We already know that in the language network, there is sensitivity to how words go together and to the meanings of individual words,” Regev says. “So that could potentially map to what we’re finding, where the longest timescale is sensitive to things like syntax or relationships between words, and maybe the shortest timescale is more sensitive to features of single words or parts of them.”
The research was funded by the Zuckerman-CHE STEM Leadership Program, the Poitras Center for Psychiatric Disorders Research, the Kempner Institute for the Study of Natural and Artificial Intelligence at Harvard University, the U.S. National Institutes of Health, an American Epilepsy Society Research and Training Fellowship, the McDonnell Center for Systems Neuroscience, Fondazione Neurone, the McGovern Institute, MIT’s Department of Brain and Cognitive Sciences, and the Simons Center for the Social Brain.
The U.S. Department of Defense (DoD) has announced three MIT professors among the members of the 2024 class of the Vannevar Bush Faculty Fellowship (VBFF). The fellowship is the DoD’s flagship single-investigator award for research, inviting the nation’s most talented researchers to pursue ambitious ideas that defy conventional boundaries.
Domitilla Del Vecchio, professor of mechanical engineering and the Grover M. Hermann Professor in Health Sciences & Technology; Mehrdad Jazayeri, professor of brain and cognitive sciences and an investigator at the McGovern Institute for Brain Research; and Themistoklis Sapsis, the William I. Koch Professor of Mechanical Engineering and director of the Center for Ocean Engineering are among the 11 university scientists and engineers chosen for this year’s fellowship class. They join an elite group of approximately 50 fellows from previous class years.
“The Vannevar Bush Faculty Fellowship is more than a prestigious program,” said Bindu Nair, director of the Basic Research Office in the Office of the Under Secretary of Defense for Research and Engineering, in a press release. “It’s a beacon for tenured faculty embarking on groundbreaking ‘blue sky’ research.”
Research topics
Each fellow receives up to $3 million over a five-year term to pursue cutting-edge projects. Research topics in this year’s class span a range of disciplines, including materials science, cognitive neuroscience, quantum information sciences, and applied mathematics. While pursuing individual research endeavors, Fellows also leverage the unique opportunity to collaborate directly with DoD laboratories, fostering a valuable exchange of knowledge and expertise.
Del Vecchio, whose research interests include control and dynamical systems theory and systems and synthetic biology, will investigate the molecular underpinnings of analog epigenetic cell memory, then use what they learn to “establish unprecedented engineering capabilities for creating self-organizing and reconfigurable multicellular systems with graded cell fates.”
“With this fellowship, we will be able to explore the limits to which we can leverage analog memory to create multicellular systems that autonomously organize in permanent, but reprogrammable, gradients of cell fates and can be used for creating next-generation tissues and organoids with dramatically increased sophistication,” she says, honored to have been selected.
Jazayeri wants to understand how the brain gives rise to cognitive and emotional intelligence. The engineering systems being built today lack the hallmarks of human intelligence, explains Jazayeri. They neither learn quickly nor generalize their knowledge flexibly. They don’t feel emotions or have emotional intelligence.
Jazayeri plans to use the VBFF award to integrate ideas from cognitive science, neuroscience, and machine learning with experimental data in humans, animals, and computer models to develop a computational understanding of cognitive and emotional intelligence.
“I’m honored and humbled to be selected and excited to tackle some of the most challenging questions at the intersection of neuroscience and AI,” he says.
“I am humbled to be included in such a select group,” echoes Sapsis, who will use the grant to research new algorithms and theory designed for the efficient computation of extreme event probabilities and precursors, and for the design of mitigation strategies in complex dynamical systems.
Examples of Sapsis’s work include risk quantification for extreme events in human-made systems; climate events, such as heat waves, and their effect on interconnected systems like food supply chains; and also “mission-critical algorithmic problems such as search and path planning operations for extreme anomalies,” he explains.
VBFF impact
Named for Vannevar Bush PhD 1916, an influential inventor, engineer, former professor, and dean of the School of Engineering at MIT, the highly competitive fellowship, formerly known as the National Security Science and Engineering Faculty Fellowship, aims to advance transformative, university-based fundamental research. Bush served as the director of the U.S. Office of Scientific Research and Development, and organized and led American science and technology during World War II.
“The outcomes of VBFF-funded research have transformed entire disciplines, birthed novel fields, and challenged established theories and perspectives,” said Nair. “By contributing their insights to DoD leadership and engaging with the broader national security community, they enrich collective understanding and help the United States leap ahead in global technology competition.”
Four others with MIT ties were also honored: Jonathan Abraham, graduate of the Harvard/MIT MD-PhD Program; Dmitriy Aronov PhD ’10; Vijay Sankaran, graduate of the Harvard/MIT MD-PhD Program; and Steven McCarroll, institute member of the Broad Institute of MIT and Harvard.
Every three years, HHMI selects roughly two dozen new investigators who have significantly impacted their chosen disciplines to receive a substantial and completely discretionary grant. This funding can be reviewed and renewed indefinitely. The award, which totals roughly $11 million per investigator over the next seven years, enables scientists to continue working at their current institution, paying their full salary while providing financial support for researchers to be flexible enough to go wherever their scientific inquiries take them.
Of the almost 1,000 applicants this year, 26 investigators were selected for their ability to push the boundaries of science and for their efforts to create highly inclusive and collaborative research environments.
“When scientists create environments in which others can thrive, we all benefit,” says HHMI president Erin O’Shea. “These newest HHMI Investigators are extraordinary, not only because of their outstanding research endeavors but also because they mentor and empower the next generation of scientists to work alongside them at the cutting edge.”
Steven Flavell
Steven Flavell, associate professor of brain and cognitive sciences and investigator in the Picower Institute for Learning and Memory, seeks to uncover the neural mechanisms that generate the internal states of the brain, for example, different motivational and arousal states. Working in the model organism, the C. elegans worm, the lab has used genetic, systems, and computational approaches to relate neural activity across the brain to precise features of the animal’s behavior. In addition, they have mapped out the anatomical and functional organization of the serotonin system, mapping out how it modulates the internal state of C. elegans. As a newly named HHMI Investigator, Flavell will pursue research that he hopes will build a foundational understanding of how internal states arise and influence behavior in nervous systems in general. The work will employ brain-wide neural recordings, computational modeling, expansive research on neuromodulatory system organization, and studies of how the synaptic wiring of the nervous system constrains an animal’s ability to generate different internal states.
“I think that it should be possible to define the basis of internal states in C. elegans in concrete terms,” Flavell says. “If we can build a thread of understanding from the molecular architecture of neuromodulatory systems, to changes in brain-wide activity, to state-dependent changes in behavior, then I think we’ll be in a much better place as a field to think about the basis of brain states in more complex animals.”
Mary Gehring
Mary Gehring, professor of biology and core member and David Baltimore Chair in Biomedical Research at the Whitehead Institute for Biomedical Research, studies how plant epigenetics modulates plant growth and development, with a long-term goal of uncovering the essential genetic and epigenetic elements of plant seed biology. Ultimately, the Gehring Lab’s work provides the scientific foundations for engineering alternative modes of seed development and improving plant resiliency at a time when worldwide agriculture is in a uniquely precarious position due to climate changes.
The Gehring Lab uses genetic, genomic, computational, synthetic, and evolutionary approaches to explore heritable traits by investigating repetitive sequences, DNA methylation, and chromatin structure. The lab primarily uses the model plant A. thaliana, a member of the mustard family and the first plant to have its genome sequenced.
“I’m pleased that HHMI has been expanding its support for plant biology, and gratified that our lab will benefit from its generous support,” Gehring says. “The appointment gives us the freedom to step back, take a fresh look at the scientific opportunities before us, and pursue the ones that most interest us. And that’s a very exciting prospect.”
Mehrdad Jazayeri
Mehrdad Jazayeri, a professor of brain and cognitive sciences and an investigator at the McGovern Institute for Brain Research, studies how physiological processes in the brain give rise to the abilities of the mind. Work in the Jazayeri Lab brings together ideas from cognitive science, neuroscience, and machine learning with experimental data in humans, animals, and computer models to develop a computational understanding of how the brain creates internal representations, or models, of the external world.
Before coming to MIT in 2013, Jazayeri received his BS in electrical engineering, majoring in telecommunications, from Sharif University of Technology in Tehran, Iran. He completed his MS in physiology at the University of Toronto and his PhD in neuroscience at New York University.
With his appointment to HHMI, Jazayeri plans to explore how the brain enables rapid learning and flexible behavior — central aspects of intelligence that have been difficult to study using traditional neuroscience approaches.
“This is a recognition of my lab’s past accomplishments and the promise of the exciting research we want to embark on,” he says. “I am looking forward to engaging with this wonderful community and making new friends and colleagues while we elevate our science to the next level.”
Gene-Wei Li
Gene-Wei Li, associate professor of biology, has been working on quantifying the amount of proteins cells produce and how protein synthesis is orchestrated within the cell since opening his lab at MIT in 2015.
Li, whose background is in physics, credits the lab’s findings to the skills and communication among his research team, allowing them to explore the unexpected questions that arise in the lab.
For example, two of his graduate student researchers found that the coordination between transcription and translation fundamentally differs between the model organisms E. coli and B. subtilis. In B. subtilis, the ribosome lags far behind RNA polymerase, a process the lab termed “runaway transcription.” The discovery revealed that this kind of uncoupling between transcription and translation is widespread across many species of bacteria, a study that contradicted the long-standing dogma of molecular biology that the machinery of protein synthesis and RNA polymerase work side-by-side in all bacteria.
The support from HHMI enables Li and his team the flexibility to pursue the basic research that leads to discoveries at their discretion.
“Having this award allows us to be bold and to do things at a scale that wasn’t possible before,” Li says. “The discovery of runaway transcription is a great example. We didn’t have a traditional grant for that.”
