Researcher: Feng Zhang

How is the McGovern Institute using AI?

by Jennifer Michalowski |

This story also appears in our Fall 2026 BrainScan newsletter.

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The tools of artificial intelligence (AI) are enabling McGovern researchers to ask new kinds of questions and solve more complex problems — and that’s leading to new insights into how the brain works and how mental illness might be more effectively managed.

“I can’t think of any facet [of brain research] where there isn’t an AI-based tool or technology that helps us,” says McGovern Institute Senior Research Scientist Satrajit Ghosh. Ghosh thinks today’s AI technology is particularly powerful in the early stages of research, when researchers are figuring out which questions are important and how to answer them. Historically, this has meant deep reading of the scientific literature — but existing knowledge can be overlooked when people don’t know where to look for it.

“The entire scientific workflow — from ideation to research to execution to dissemination — now has AI tools and technologies that can help.”
– Satra Ghosh

Large language models like Gemini or ChatGPT can pull information and ideas from sources across the internet, connecting users with resources they might not consider accessing on their own. “We get caught in our tracks and grooves that are hard to step outside of,” Ghosh says. “There are still biases in these tools, but I think overall they’re in a much more flexible space than humans typically are.”

McGovern Investigator Feng Zhang adds that by accelerating this vital phase of doing science, AI tools are broadening the scope of what an individual lab can explore. “We’ve never been short on good questions — what’s new is that we can chase them the moment they occur to us, and follow them as far as the data goes,” says Zhang, who is the James and Patricia Poitras Professor of Neuroscience at MIT.

Testing ideas faster

Once a plan is in place, researchers can often go from idea to execution much faster with the help of AI. McGovern Investigators Nancy Kanwisher and Josh McDermott say they have begun using AI models to pilot experiments, refining their approach before working with human study participants. They even use AI to generate images or sounds that their subjects will hear or see during their studies, designing stimuli to uncover specific aspects of the brain’s response.

The models they use are far more reliable representations of human perception than researchers were able to build just a few years ago. And with major advances in AI’s ability to generate computer code, models that took months or years to piece together can now be generated in days.

McDermott, who is the Uncas (1923) and Helen Whitaker Professor of Brain and Cognitive Sciences at MIT, uses an array of models to investigate different aspects of auditory processing in the brain. His team has models that recognize speech, models that localize sounds, and models that detect changes in pitch. “I fantasized about doing these things when I was a grad student, and it just wasn’t possible,” he says. “We were always trying to build working models of things, and it never worked. Now it’s really possible, and it’s just opening huge numbers of doors.”

Using a computational model of the auditory system, Josh McDermott has shown how the brain can selectively focus attention on one voice among many in a noisy environment — shedding light on a longstanding neuroscientific phenomenon known as the “cocktail party problem.” Photo: Steph Stevens

For McDermott, one priority is finding ways to develop better devices to help people hear. Computational models can facilitate that by enabling experimentation on a scale that is not possible with human subjects, allowing researchers to explore thousands of different conditions or rapidly test the consequences of modifying a hearing aid algorithm in different ways.

AI models also allow researchers to explore conditions they can’t test in the real world. McDermott’s lab, for example, has studied how hearing might work in a world where sounds behave differently, helping reveal which aspects of perception were shaped by the environment in which our ancestors evolved. And Kanwisher says models help overcome the limitations of experiments with humans, where the tools for monitoring and manipulating brain activity are imprecise and researchers have no control over their subjects’ life experiences.

That’s particularly valuable for studying cognitive functions that are uniquely human. “If you want to study things like language, or mathematical cognition, or theory of mind, it’s pretty hard to do in a mouse or even a monkey,” says McGovern Investigator and Associate Professor of Brain and Cognitive Sciences Evelina Fedorenko, who studies how the brain creates and understands language. AI models, on the other hand, can be trained to mimic these abilities. And what scientists like Fedorenko are learning is that models that behave similarly to humans often process information in ways that are similar to the human brain.

“If you’re in the business of studying sensory systems, like I am, it’s totally changed the way that we do science.” – Josh McDermott

Kanwisher, the Walter A. Rosenblith Professor of Cognitive Neuroscience at MIT, and her colleagues have used AI to explore how and why brains have networks specialized for the processing of certain kinds of objects. Her team and others have tasked AI models with processing visual information, training them with an assortment of images without further information or instruction. Remarkably, such models develop specific responses for certain categories of images: faces, places, bodies, and words — all of which Kanwisher’s team has shown are preferentially processed by specific regions in the human brain.

Kanwisher says that was surprising, because cognitive scientists have reasoned that human brains likely evolved these selective regions to optimize the way perceptual information is later used by the brain. But that doesn’t explain why AI responds to these categories with the same selectivity.

“The AI models don’t use face images to engage social cognition or place images to engage navigation,” she says. “They don’t do a thing with them, and yet they pull out the same categories as important.” That doesn’t rule out previous ideas about human brains — but, Kanwisher says, “It’s proof that you can have these shockingly similar things for very different reasons.” Her group is now exploring how the models’ training experience shapes the development of selective regions — something that cannot be done in humans.

Building better tools

AI is also helping McGovern scientists build physical tools, both to allow researchers to study the brain in new ways and to get better treatments to patients.

“Molecular syringes” (above), engineered by the AI tool AlphaFold in the Zhang lab, can deliver therapeutic cargo into specific cells in the body. These nanosyringes may lead to safer and more effective treatments for a variety of conditions, including cancer. Image: Joseph Kreitz, Feng Zhang

In Feng Zhang and Ed Boyden’s labs, researchers are using an AI tool called AlphaFold to design proteins with specific properties. Zhang’s lab is searching for proteins that can deliver therapies to specific brain cells, while Boyden’s team is developing tools for monitoring signaling in the brain.

AlphaFold predicts the structure of biomolecules, such as how a sequence of amino acids will fold into a three-dimensional protein. This eliminates much of the trial-and-error stage of protein engineering, speeding scientists’ ability to find molecules that interact with biological systems in the ways that they want.

Boyden, who is the Y. Eva Tan Professor in Neurotechnology at MIT, envisions another powerful AI tool: one that predicts how a brain works from its structure. The idea, he says, is to use machine learning to associate detailed brain maps with physiology and circuit function.

Building such a model would require vast amounts of data, but Boyden says the tools and tissue samples needed for large-scale structural analyses are available.

He envisions a kind of “compiler” built from molecular maps of brain cells and tissues — one that could help neuroscientists understand circuit function and eventually predict the effects of medications and other therapies.

Decoding data

Once an experiment is complete, data analysis begins — and AI is enabling scientists to find insights in datasets that, until recently, were too large and complex to make sense of.

AI tools have been invaluable, for instance, in creating detailed wiring diagrams, or connectomes, which many neuroscientists see as critical guides for brain research. That work, which involves tracing neurons across millions of microscope images, relies heavily on computer vision.

Without AI, piecing together the circuitry of an entire brain would take lifetimes. Remarkably, scientists now have access to a fully mapped connectome for the brain of a fruit fly, whose neurons make more than 50 million connections with one another.

A consortium of scientists, including McGovern Investigator Sven Dorkenwald, created a complete connectome of the fly brain (above) using computer vision algorithms and hundreds of human proofreaders. It is the largest, most complete neuronal map ever produced by scientists. Image: FlyWire Consortium, Sven Dorkenwald

McGovern Investigator Tomaso Poggio and his team are using AI to interact with that connectome by running simulations of circuit function, testing for a motion-detecting circuit that Poggio first proposed in theoretical work he did in the 1980s. Until recently, Poggio, who is the Eugene McDermott Professor Emeritus of Brain and Cognitive Sciences at MIT, had no way to find it.

Personalizing mental health

Other McGovern researchers are deploying AI to find ways to improve the diagnosis and treatment of brain disorders. They are finding that AI can pick up on signals that can be difficult for clinicians to detect — sometimes years before diagnosis.

Working with Satra Ghosh, John Gabrieli’s team found that a machine learning model could predict from clinical data which children and teenagers would later be diagnosed with bipolar disorder. With other collaborators, Ghosh has found that AI can recognize autism-related behaviors in parents’ videos of their toddlers, and he trained an AI model to detect changes in voice and speech patterns associated with conditions like Alzheimer’s and Parkinson’s disease. With further development, tools like these could facilitate earlier diagnosis and intervention for people who need it.

Researchers in Gabrieli’s lab think AI can also enable a more personalized approach to treating mental illness. It’s difficult to predict who will respond to a particular medication or therapy, leaving doctors and patients with a trial-and-error approach to treatment. Gabrieli’s team is betting that machine learning algorithms can find patterns hidden within complex clinical datasets that can enable more informed treatment decisions.

By merging machine-learning approaches with clinical and neuroimaging data, Gun Ahn, a graduate student in John Gabrieli’s lab, is pioneering precision-based methods that could help heal mental illness more quickly and effectively than current strategies.

In a recent success, Gun Ahn, a graduate student on Gabrieli’s team, followed up on a study in which the group found that a daily mindfulness practice meaningfully reduced anxiety for about 50 percent of autistic adults. No single factor separated those who benefitted from those who did not, so the team used machine learning to analyze demographic data and patient questionnaires. Their model was able to predict, with 80 percent accuracy, who responded to the practice.

This finding could help patients decide whether mindfulness is worth trying, particularly if it can one day be paired with predictions about alternative treatments. Gabrieli’s lab is taking a similar approach to predict patients’ response to medications for attention-deficit/hyperactivity disorder, hoping to help people bypass ineffective options and quickly find what works for them.

Tailoring interventions to a patient’s personal biology and experience has long been a goal in psychiatry — but the knowledge clinicians need to achieve this has been elusive. “For about 10 to 15 years, people have been talking about the incredible value that precision psychiatry would have,” says Gabrieli, who is the Grover Hermann Professor of Health Sciences and Technology at MIT. “This is, to me, the most promising scientific approach that could be widely usable.”

AI could even help clinicians move beyond formal diagnoses to understanding and treating people more holistically. Ahn imagines a model trained broadly, with data from a wide swath of patients.

Moving the field forward

Science isn’t done once the data has been analyzed: sharing knowledge is essential, too. Poggio, for example, integrated AI into the writing of a new book, “Brains Minds Machines: The Mystery of Human Intelligence, The Enigmas of the Artificial,” even crediting Gemini and ChatGPT as coauthors. He says the tools helped him organize his thinking and offload tedious tasks.

Looking ahead, most McGovern scientists say AI technologies are advancing so rapidly, their future impacts on neuroscience are difficult to predict. Could AI one day replace human researchers altogether? Some think it’s possible — at least in some areas.

“The scary prospect is that AI is going to put humans out of business. We’re definitely not close to that now, but nobody can predict the future,” says McDermott. “Physically doing experiments may be something that continues to be the domain of humans. But deciding what experiments to run and what they mean, and analyzing all the data? It’s unclear whether humans are still going to be the most efficient way to do that.”

Others are more skeptical. “It’s hard for AI to generate truly creative new ideas,” Boyden says. “I haven’t seen that happen yet.”

For now at least, today’s tools need oversight and guidance. To get the most out of them, users need experience and expertise. “They still have hallucinations from time to time,” Poggio warns. “One has to be careful.”

Nonetheless, today’s tools are tremendously empowering. They’re not just getting more powerful; they’re also getting more accessible. “These models are becoming exciting because they’re becoming usable by everyone, not just specialists,” Zhang says. “And when more people can do more, the whole field moves faster.”

RareNet Symposium 2026

by Julie Pryor |

On June 9, the McGovern Institute convened leaders in science, biotechnology, and patient advocacy for RareNet 2026, a first-of-its-kind symposium aimed at dismantling the barriers between laboratory discovery and life-changing treatments for rare brain disorders.

Over 300 million people worldwide live with rare disorders—most affecting the brain and nervous system. Yet the vast majority lack an approved therapy. The Rare Brain Disorders Nexus (RareNet) was established at the McGovern Institute in 2025 by MIT alums Ana Méndez ’91 and Rajeev Jayavant ’86, (EE ’88, SM ’88) to address this need. Led by Guoping Feng, the James W. (1963) and Patricia T. Poitras Professor of Neuroscience at MIT, RareNet draws together expertise from the MIT community and beyond to expedite the path from lab to clinic.

MIT President Sally Kornbluth set the tone at the inaugural RareNet symposium, thanking founders Méndez and Jayavant for helping MIT focus on this important challenge: “I look forward to watching RareNet dissolve needless barriers, accelerate timelines, and bring new hope to millions of patients and their families for whom hope is long overdue.”

MIT President Sally Kornbluth (third from left) with RareNet founders Rajeev Jayavant (far left) and Ana Méndez (center) at the June 9 symposium at the McGovern Institute. Also pictured are the founders’ son Neal (second from left), RareNet Director Guoping Feng, RareNet Scientific Advisor Xian Gao, and McGovern Institute Director Robert Desimone. Photo: Steph Stevens

RareNet’s collaborative vision came into focus at the symposium, where more than a dozen leading neuroscientists, biotech innovators, and patient advocates shared the podium. The scientific program spanned the full translational pathway, from fundamental discovery to clinical development.

Feng Zhang (McGovern Institute, MIT; HHMI; Broad Institute), Katherine High (RhyGaze AG; Rockefeller University), Kiran Musunuru (University of Pennsylvania), and Timothy Yu (Boston Children’s Hospital; Harvard Medical School) discussed emerging genetic medicines–including genome editing, gene therapy, and individualized therapeutic strategies–and the challenges involved in bringing them safely to patients.

Kevin Bender (University of California San Francisco), Christopher Walsh (Boston Children’s Hospital; Harvard Medical School), Sonia Vallabh (Broad Institute; MGH; Harvard Medical School), and Joseph Buxbaum (Icahn School of Medicine at Mount Sinai) explored how insights into disease mechanisms, human genetics, and patient-derived data are advancing research in neurodevelopmental disorders, autism, and prion disease.

Representatives from the Sturge-Weber and FOXP1 communities–including Karen Ball, Matt Shirley, and Samit Dasgupta–demonstrated how patient foundations can help define research priorities, build essential resources, and drive promising discoveries toward meaningful treatments.

Also among the day’s speakers was Monica Coenraads, who transformed her child’s rare disease into a powerful research initiative. When her daughter Chelsea was diagnosed with Rett syndrome in 1998 at age two, Coenraads faced an uncertain future. Today, as founder and CEO of the Rett Syndrome Research Trust, she is helping to rewrite that story for other families. In a compelling talk with John Sinnamon, Director of Research at RSRT, Coenraads shared both the scientific breakthroughs reshaping Rett syndrome treatment and the deeply personal journey that sparked it all.

“Monica and John’s talks capture the vital connection between patients and families, cutting-edge research, and translational innovation,” says RareNet Executive Director Xian Gao. “It’s a powerful reminder that behind every research breakthrough is a human story demanding progress.”

Feng Zhang inducted into the National Inventors Hall of Fame

by Julie Pryor |

Fifteen innovation pioneers, including McGovern Investigator Feng Zhang, have been inducted into the 2026 class of the National Inventors Hall of Fame. Zhang is being recognized for his innovations in gene editing and for sharing his resources and expertise broadly with the global scientific community.

In addition to his appointment at the McGovern Institute, Zhang is the James and Patricia Poitras Professor of Neuroscience at MIT and has joint appointments in the departments of Brain and Cognitive Sciences and Biological Engineering. He is also an investigator at the McGovern Institute for Brain Research at MIT, an investigator in the Howard Hughes Medical Institute, and co-director of the K. Lisa Yang and Hock E. Tan Center for Molecular Therapeutics at MIT.

“The National Inventors Hall of Fame is committed to illuminating the legacies of world-changing inventors and creating opportunities for the next generation to learn from these innovative role models,” said Monica Jones, Chief Executive Officer of the National Inventors Hall of Fame. “The inventors in our 2026 class have made contributions in fields as varied as semiconductor technology and portable inhalers. Induction into the Hall of Fame honors the significance of these advances, which have enhanced our daily lives and well-being.”

Zhang has invented transformative technologies to improve human health, including first demonstrating the use of engineered CRISPR-Cas9 systems for genome editing in human cells. He has co-founded several companies to commercialize these technologies. Through the nonprofit repository Addgene, by 2023 over 75,000 samples of Zhang’s reagents had been shared with researchers in more than 79 countries. He also has trained scientists from around the world in online research forums, in his workshops and in his lab.

“My mother would always emphasize that I should choose to do something useful for the world; to live a life that is meaningful and is adding something to the world, rather than just consuming from the world,” Zhang says. “That has been one of the strongest guiding factors for me.”

In partnership with the United States Patent and Trademark Office (USPTO), the Hall of Fame will honor Zhang and the other 2026 inductees on May 7 at an event in Washington DC.

New study suggests a way to rejuvenate the immune system

by Anne Trafton |

As people age, their immune system function declines. T cell populations become smaller and can’t react to pathogens as quickly, making people more susceptible to a variety of infections.

To try to overcome that decline, researchers at MIT and the Broad Institute have found a way to temporarily program cells in the liver to improve T-cell function. This reprogramming can compensate for the age-related decline of the thymus, where T cell maturation normally occurs.

Using mRNA to deliver three key factors that usually promote T-cell survival, the researchers were able to rejuvenate the immune systems of mice. Aged mice that received the treatment showed much larger and more diverse T cell populations in response to vaccination, and they also responded better to cancer immunotherapy treatments. Their findings are published in the December 17 issue of the journal Nature.

If developed for use in patients, this type of treatment could help people lead healthier lives as they age, the researchers say.

“If we can restore something essential like the immune system, hopefully we can help people stay free of disease for a longer span of their life,” says Feng Zhang, the James and Patricia Poitras Professor of Neuroscience at MIT, who has joint appointments in the departments of Brain and Cognitive Sciences and Biological Engineering.

Zhang, who is also an investigator at the McGovern Institute for Brain Research at MIT, a core institute member at the Broad Institute of MIT and Harvard, an investigator in the Howard Hughes Medical Institute, and co-director of the K. Lisa Yang and Hock E. Tan Center for Molecular Therapeutics at MIT, is the senior author of the new study. Former MIT postdoc Mirco Friedrich is the lead author of the paper, which appears today in Nature.

A temporary factory

The thymus, a small organ located in front of the heart, plays a critical role in T-cell development. Within the thymus, immature T cells go through a checkpoint process that ensures a diverse repertoire of T cells. The thymus also secretes cytokines and growth factors that help T cells to survive.

However, starting in early adulthood, the thymus begins to shrink. This process, known as thymic involution, leads to a decline in the production of new T cells. By the age of approximately 75, the thymus is greatly reduced.

“As we get older, the immune system begins to decline. We wanted to think about how can we maintain this kind of immune protection for a longer period of time, and that’s what led us to think about what we can do to boost immunity,” Friedrich says.

Previous work on rejuvenating the immune system has focused on delivering T cell growth factors into the bloodstream, but that can have harmful side effects. Researchers are also exploring the possibility of using transplanted stem cells to help regrow functional tissue in the thymus.

The MIT team took a different approach: They wanted to see if they could create a temporary “factory” in the body that would generate the T-cell-stimulating signals that are normally produced by the thymus.

“Our approach is more of a synthetic approach,” Zhang says. “We’re engineering the body to mimic thymic factor secretion.”

For their factory location, they settled on the liver, for several reasons. First, the liver has a high capacity for producing proteins, even in old age. Also, it’s easier to deliver mRNA to the liver than to most other organs of the body. The liver was also an appealing target because all of the body’s circulating blood has to flow through it, including T cells.

To create their factory, the researchers identified three immune cues that are important for T-cell maturation. They encoded these three factors into mRNA sequences that could be delivered by lipid nanoparticles. When injected into the bloodstream, these particles accumulate in the liver and the mRNA is taken up by hepatocytes, which begin to manufacture the proteins encoded by the mRNA.

The factors that the researchers delivered are DLL1, FLT-3, and IL-7, which help immature progenitor T cells mature into fully differentiated T cells.

Immune rejuvenation

Tests in mice revealed a variety of beneficial effects. First, the researchers injected the mRNA particles into 18-month-old mice, equivalent to humans in their 50s. Because mRNA is short-lived, the researchers gave the mice multiple injections over four weeks to maintain a steady production by the liver.

After this treatment, T cell populations showed significant increases in size and function.

The researchers then tested whether the treatment could enhance the animals’ response to vaccination. They vaccinated the mice with ovalbumin, a protein found in egg whites that is commonly used to study how the immune system responds to a specific antigen. In 18-month-old mice that received the mRNA treatment before vaccination, the researchers found that the population of cytotoxic T-cells specific to ovalbumin doubled, compared to mice of the same age that did not receive the mRNA treatment.

The mRNA treatment can also boost the immune system’s response to cancer immunotherapy, the researchers found. They delivered the mRNA treatment to 18-month-old mice, who were then implanted with tumors and treated with a checkpoint inhibitor drug. This drug, which targets the protein PD-L1, is designed to help take the brakes off the immune system and stimulate T cells to attack tumor cells.

Mice that received the treatment showed much higher survival rates and longer lifespan that those that received the checkpoint inhibitor drug but not the mRNA treatment.

The researchers found that all three factors were necessary to induce this immune enhancement; none could achieve all aspects of it on their own. They now plan to study the treatment in other animal models and to identify additional signaling factors that may further enhance immune system function. They also hope to study how the treatment affects other immune cells, including B cells.

Other authors of the paper include Julie Pham, Jiakun Tian, Hongyu Chen, Jiahao Huang, Niklas Kehl, Sophia Liu, Blake Lash, Fei Chen, Xiao Wang, and Rhiannon Macrae.

The research was funded, in part, by the Howard Hughes Medical Institute, the K. Lisa Yang Brain-Body Center, part of the Yang Tan Collective at MIT, Broad Institute Programmable Therapeutics Gift Donors, the Pershing Square Foundation, J. and P. Poitras, and an EMBO Postdoctoral Fellowship.

Feng Zhang elected to EMBO membership

by Julie Pryor |

The European Molecular Biology Organization (EMBO), a professional non-profit organization dedicated to promoting international research in life sciences, announced its new members today. Among the 69 new members recognized for their outstanding achievements is Feng Zhang, the James and Patricia Poitras Professor of Neuroscience at MIT and an investigator at the McGovern Institute.

Zhang, who is also a core member of the Broad Institute, a professor of brain and cognitive sciences and biological engineering at MIT, and a Howard Hughes Medical Institute investigator, is a molecular biologist focused on improving human health. He played an integral role in pioneering the use of CRISPR-Cas9 for genome editing in human cells, including working with Stuart Orkin to develop Casgevy, the first CRISPR-based therapeutic approved for clinical use. His team is currently discovering new ways to modify cellular function and activity—including the restoration of diseased, stressed, or aged cells to a more healthful state.

Zhang has been elected to EMBO as an associate member, where he joins a community of more than 2,100 international life scientists that have demonstrated research excellence in their fields.

“A major strength of EMBO lies in the excellence and dedication of its members,” says EMBO Director Fiona Watt. “Science thrives on global collaboration, and the annual election of the new EMBO members and associate members brings fresh energy and inspiration to our community. We are honoured to welcome this remarkable group of scientists to the EMBO Membership. Their ideas and contributions will enrich the organization and help advance the life sciences internationally.”

The 60 new EMBO members in 2025 are based in 18 member states of the EMBC, the intergovernmental organization that funds the main EMBO programs and activities. The nine new EMBO associate members, including Zhang, are based in six countries outside Europe. In total, 29 (42%) of the new members are women and 40 (58%) are men.

The new members will be formally welcomed at the next EMBO Members’ Meeting in Heidelberg, Germany, on 22-24 October 2025.

Rational engineering generates a compact new tool for gene therapy

by Jennifer Michalowski |

Scientists at the McGovern Institute and the Broad Institute of MIT and Harvard have reengineered a compact RNA-guided enzyme they found in bacteria into an efficient, programmable editor of human DNA. The protein they created, called NovaIscB, can be adapted to make precise changes to the genetic code, modulate the activity of specific genes, or carry out other editing tasks. Because its small size simplifies delivery to cells, NovaIscB’s developers say it is a promising candidate for developing gene therapies to treat or prevent disease.

The study was led by McGovern Institute investigator Feng Zhang, who is also the James and Patricia Poitras Professor of Neuroscience at MIT, a Howard Hughes Medical Institute investigator, and a core member of the Broad Institute. Zhang and his team reported their work today in the journal Nature Biotechnology.

Compact tools

NovaIscB is derived from a bacterial DNA cutter that belongs to a family of proteins called IscBs, which Zhang’s lab discovered in 2021. IscBs are a type of OMEGA system, the evolutionary ancestors to Cas9, which is part of the bacterial CRISPR system that Zhang and others have developed into powerful genome-editing tools. Like Cas9, IscB enzymes cut DNA at sites specified by an RNA guide. By reprogramming that guide, researchers can redirect the enzymes to target sequences of their choosing.

IscBs had caught the team’s attention not only because they share key features of CRISPR’s DNA-cutting Cas9, but also because they are a third of its size. That would be an advantage for potential gene therapies: Compact tools are easier to deliver to cells, and with a small enzyme, researchers would have more flexibility to tinker, potentially adding new functionalities without creating tools that were too bulky for clinical use.

From their initial studies of IscBs, researchers in Zhang’s lab knew that some members of the family could cut DNA targets in human cells. None of the bacterial proteins worked well enough to be deployed therapeutically, however: The team would have to modify an IscB to ensure it could edit targets in human cells efficiently without disturbing the rest of the genome.

To begin that engineering process, Soumya Kannan, a graduate student in Zhang’s lab who is now a junior fellow at the Harvard Society of Fellows, and postdoctoral fellow Shiyou Zhu first searched for an IscB that would make good starting point. They tested nearly 400 different IscB enzymes that can be found in bacteria. Ten were capable of editing DNA in human cells.

Even the most active of those would need to be enhanced to make it a useful genome editing tool. The challenge would be increasing the enzyme’s activity, but only at the sequences specified by its RNA guide. If the enzyme became more active, but indiscriminately so, it would cut DNA in unintended places. “The key is to balance the improvement of both activity and specificity at the same time,” explains Zhu.

Zhu notes that bacterial IscBs are directed to their target sequences by relatively short RNA guides, which makes it difficult to restrict the enzyme’s activity to a specific part of the genome. If an IscB could be engineered to accommodate a longer guide, it would be less likely to act on sequences beyond its intended target.

To optimize IscB for human genome editing, the team leveraged information that graduate student Han Altae-Tran, who is now a postdoctoral fellow at the University of Washington, had learned about the diversity of bacterial IscBs and how they evolved. For instance, the researchers noted that IscBs that worked in human cells included a segment they called REC, which was absent in other IscBs. They suspected the enzyme might need that segment to interact with the DNA in human cells. When they took a closer look at the region, structural modeling suggested that by slightly expanding part of the protein, REC might also enable IscBs to recognize longer RNA guides.

Based on these observations, the team experimented with swapping in parts of REC domains from different IscBs and Cas9s, evaluating how each change impacted the protein’s function. Guided by their understanding of how IscBs and Cas9s interact with both DNA and their RNA guides, the researchers made additional changes, aiming to optimize both efficiency and specificity.

In the end, they generated a protein they called NovaIscB, which was over 100 times more active in human cells than the IscB they had started with and that had demonstrated good specificity for its targets.

Kannan and Zhu constructed and screened hundreds of new IscBs before arriving at NovaIscB—and every change they made to the original protein was strategic. Their efforts were guided by their team’s knowledge of IscBs’ natural evolution as well as predictions of how each alteration would impact the protein’s structure, made using an artificial intelligence tool called AlphaFold2. Compared to traditional methods of introducing random changes into a protein and screening for their effects, this rational engineering approach greatly accelerated the team’s ability to identify a protein with the features they were looking for.

The team demonstrated that NovaIscB is a good scaffold for a variety of genome editing tools. “It biochemically functions very similarly to Cas9, and that makes it easy to port over tools that were already optimized with the Cas9 scaffold,” Kannan says. With different modifications, the researchers used NovaIscB to replace specific letters of the DNA code in human cells and to change the activity of targeted genes.

Importantly, the NovaIscB-based tools are compact enough to be easily packaged inside a single adeno-associated virus (AAV)—the vector most commonly used to safely deliver gene therapy to patients. Because they are bulkier, tools developed using Cas9 can require a more complicated delivery strategy.

Demonstrating NovaIscB’s potential for therapeutic use, Zhang’s team created a tool called OMEGAoff that adds chemical markers to DNA to dial down the activity of specific genes. They programmed OMEGAoff to repress a gene involved in cholesterol regulation, then used AAV to deliver the system to the livers of mice, leading to lasting reductions in cholesterol levels in the animals’ blood.

The team expects that NovaIscB can be used to target genome editing tools to most human genes, and look forward to seeing how other labs deploy the new technology. They also hope others will adopt their evolution-guided approach to rational protein engineering. “Nature has such diversity and its systems have different advantages and disadvantages,” Zhu says. “By learning about that natural diversity, we can make the systems we are trying to engineer better and better.”

This study was funded in part by the K. Lisa Yang and Hock E. Tan Center for Molecular Therapeutics at MIT, Broad Institute Programmable Therapeutics Gift Donors, Pershing Square Foundation, William Ackman, Neri Oxman, the Phillips family, and J. and P. Poitras.

An ancient RNA-guided system could simplify delivery of gene editing therapies

by Jennifer Michalowski |

A vast search of natural diversity has led scientists at MIT’s McGovern Institute and the Broad Institute of MIT and Harvard to uncover ancient systems with potential to expand the genome editing toolbox. These systems, which the researchers call TIGR (Tandem Interspaced Guide RNA) systems, use RNA to guide them to specific sites on DNA. TIGR systems can be reprogrammed to target any DNA sequence of interest, and they have distinct functional modules that can act on the targeted DNA. In addition to its modularity, TIGR is very compact compared to other RNA-guided systems, like CRISPR, which is a major advantage for delivering it in a therapeutic context.

These findings are reported online February 27, 2025 in the journal Science.

“This is a very versatile RNA-guided system with a lot of diverse functionalities,” says Feng Zhang, the James and Patricia Poitras Professor of Neuroscience at MIT who led the research. The TIGR-associated (Tas) proteins that Zhang’s team found share a characteristic RNA-binding component that interacts with an RNA guide that directs it to a specific site in the genome. Some cut the DNA at that site, using an adjacent DNA-cutting segment of the protein. That modularity could facilitate tool development, allowing researchers to swap useful new features into natural Tas proteins.

“Nature is pretty incredible,” said Zhang who is also an investigator at the McGovern Institute and the Howard Hughes Medical Institute, a core member of the Broad Institute, a professor of brain and cognitive sciences and biological engineering at MIT, and co-director of the K. Lisa Yang and Hock E. Tan Center for Molecular Therapeutics at MIT. “It’s got a tremendous amount of diversity, and we have been exploring that natural diversity to find new biological mechanisms and harnessing them for different applications to manipulate biological processes,” he says. Previously, Zhang’s team adapted bacterial CRISPR systems into gene editing tools that have transformed modern biology. His team has also found a variety of programmable proteins, both from CRISPR systems and beyond.

In their new work, to find novel programmable systems, the team began by zeroing in a structural feature of the CRISPR Cas9 protein that binds to the enzyme’s RNA guide. That is a key feature that has made Cas9 such a powerful tool: “Being RNA-guided makes it relatively easy to reprogram, because we know how RNA binds to other DNA or other RNA,” Zhang explains. His team searched hundreds of millions of biological proteins with known or predicted structures, looking for any that shared a similar domain. To find more distantly related proteins, they used an iterative process: from Cas9, they identified a protein called IS110, which had previously been shown by others to bind RNA. They then zeroed in on the structural features of IS110 that enable RNA binding and repeated their search.

At this point, the search had turned up so many distantly related proteins that they team turned to artificial intelligence to make sense of the list. “When you are doing iterative, deep mining, the resulting hits can be so diverse that they are difficult to analyze using standard phylogenetic methods, which rely on conserved sequence,” explains Guilhem Faure, a computational biologist in Zhang’s lab. With a protein large language model, the team was able to cluster the proteins they had found into groups according to their likely evolutionarily relationships. One group set apart from the rest, and its members were particularly intriguing because they were encoded by genes with regularly spaced repetitive sequences reminiscent of an essential component of CRISPR systems. These were the TIGR-Tas systems.

Zhang’s team discovered >20,000 different Tas proteins, mostly occurring in bacteria-infecting viruses. Sequences within each gene’s repetitive region—its TIGR arrays—encode an RNA guide that interacts with the RNA-binding part of the protein. In some, the RNA-binding region is adjacent to a DNA-cutting part of the protein. Others appear to bind to other proteins, which suggests they might help direct those proteins to DNA targets.

Zhang and his team experimented with dozens of Tas proteins, demonstrating that some can be programmed to make targeted cuts to DNA in human cells. As they think about developing TIGR-Tas systems into programmable tools, the researchers are encouraged by features that could make those tools particularly flexible and precise.

They note that CRISPR systems can only be directed to segments of DNA that are flanked by short motifs known as PAMs (protospacer adjacent motifs). TIGR Tas proteins, in contrast, have no such requirement. “This means theoretically, any site in the genome should be targetable,” says scientific advisor Rhiannon Macrae. The team’s experiments also show that TIGR systems have what Faure calls a “dual-guide system,” interacting with both strands of the DNA double helix to home in on their target sequences, which should ensure they act only where they are directed by their RNA guide. What’s more, Tas proteins are compact—a quarter of the size Cas9 on average—making them easier to deliver, which could overcome a major obstacle to therapeutic deployment of gene editing tools.

Excited by their discovery, Zhang’s team is now investigating the natural role of TIGR systems in viruses as well as how they can be adapted for research or therapeutics. They have determined the molecular structure of one of the Tas proteins they found to work in human cells, and will use that information to guide their efforts to make it more efficient. Additionally, they note connections between TIGR-Tas systems and certain RNA-processing proteins in human cells. “I think there’s more there to study in terms of what some of those relationships may be, and it may help us better understand how these systems are used in humans,” Zhang says.

This work was supported by the Helen Hay Whitney Foundation, Howard Hughes Medical Institute, K. Lisa Yang and Hock E. Tan Center for Molecular Therapeutics, Broad Institute Programmable Therapeutics Gift Donors, Pershing Square Foundation, William Ackman, and Neri Oxman, the Phillips family, J. and P. Poitras, and the BT Charitable Foundation.

Scientists engineer CRISPR enzymes that evade the immune system

by Allessandra DiCorato | Broad Institute |

The core components of CRISPR-based genome-editing therapies are bacterial proteins called nucleases that can stimulate unwanted immune responses in people, increasing the chances of side effects and making these therapies potentially less effective.

Researchers at the Broad Institute of MIT and Harvard and Cyrus Biotechnology have now engineered two CRISPR nucleases, Cas9 and Cas12, to mask them from the immune system. The team identified protein sequences on each nuclease that trigger the immune system and used computational modeling to design new versions that evade immune recognition. The engineered enzymes had similar gene-editing efficiency and reduced immune responses compared to standard nucleases in mice.

Appearing today in Nature Communications, the findings could help pave the way for safer, more efficient gene therapies. The study was led by Feng Zhang, a core institute member at the Broad and an Investigator at the McGovern Institute for Brain Research at MIT.

“As CRISPR therapies enter the clinic, there is a growing need to ensure that these tools are as safe as possible, and this work tackles one aspect of that challenge,” said Zhang, who is also a co-director of the K. Lisa Yang and Hock E. Tan Center for Molecular Therapeutics, the James and Patricia Poitras Professor of Neuroscience, and a professor at MIT. He is an Investigator at the Howard Hughes Medical Institute.

Rumya Raghavan, a graduate student in Zhang’s lab when the study began, and Mirco Julian Friedrich, a postdoctoral scholar in Zhang’s lab, were co-first authors on the study.

“People have known for a while that Cas9 causes an immune response, but we wanted to pinpoint which parts of the protein were being recognized by the immune system and then engineer the proteins to get rid of those parts while retaining its function,” said Raghavan.

“Our goal was to use this information to create not only a safer therapy, but one that is potentially even more effective because it is not being eliminated by the immune system before it can do its job,” added Friedrich.

In search of immune triggers

Many CRISPR-based therapies use nucleases derived from bacteria. About 80 percent of people have pre-existing immunity to these proteins through everyday exposure to these bacteria, but scientists didn’t know which parts of the nucleases the immune system recognized.

To find out, Zhang’s team used a specialized type of mass spectrometry to identify and analyze the Cas9 and Cas 12 protein fragments recognized by immune cells. For each of two nucleases — Cas9 from Streptococcus pyogenes and Cas12 from Staphylococcus aureus — they identified three short sequences, about eight amino acids long, that evoked an immune response. They then partnered with Cyrus Biotechnology, a company co-founded by University of Washington biochemist David Baker that develops structure-based computational tools to design proteins that evade the immune response. After Zhang’s team identified immunogenic sequences in Cas9 and Cas12, Cyrus used these computational approaches to design versions of the nucleases that did not include the immune-triggering sequences.

Zhang’s lab used prediction software to validate that the new nucleases were less likely to trigger immune responses. Next, the team engineered a panel of new nucleases informed by these predictions and tested the most promising candidates in human cells and in mice that were genetically modified to bear key components of the human immune system. In both cases, they found that the engineered enzymes resulted in significantly reduced immune responses compared to the original nucleases, but still cut DNA at the same efficiency.

Minimally immunogenic nucleases are just one part of safer gene therapies, Zhang’s team says. In the future, they hope their methods may also help scientists design delivery vehicles to evade the immune system.

This study was funded in part by the Poitras Center for Psychiatric Disorders Research, the K. Lisa. Yang and Hock E. Tan Center for Molecular Therapeutics in Neuroscience and the Hock E. Tan and K. Lisa Yang Center for Autism Research at MIT.

Feng Zhang awarded 2024 National Medal of Technology

by Julie Pryor |

This post is adapted from an MIT News story.

***

Feng Zhang, the James and Patricia Poitras Professor of Neuroscience at MIT and an Investigator at the McGovern Institute, has won the National Medal of Technology and Innovation, the nation’s highest recognition for scientists and engineers. The prestigious award recognizes “American innovators whose vision, intellect, creativity, and determination have strengthened America’s economy and improved our quality of life.”

Zhang, who is also a professor of brain and cognitive sciences and biological engineering at MIT, a core member of the Broad Institute of MIT and Harvard, and an investigator with the Howard Hughes Medical Institute, was recognized for his work developing molecular tools, including the CRISPR genome-editing system, that have accelerated biomedical research and led to the first FDA-approved gene editing therapy.

This year, the White House awarded the National Medal of Science to 14 recipients and named nine individual awardees of the National Medal of Technology and Innovation, along with two organizations. Zhang is among four MIT faculty members who were awarded the nation’s highest honors for exemplary achievement and leadership in science and technology.

Designing molecular tools

Zhang, who earned his undergraduate degree from Harvard University in 2004, has contributed to the development of multiple molecular tools to accelerate the understanding of human disease. While a graduate student at Stanford University, from which he received his PhD in 2009, Zhang worked in the lab of Professor Karl Deisseroth. There, he worked on a protein called channelrhodopsin, which he and Deisseroth believed held potential for engineering mammalian cells to respond to light.

The resulting technique, known as optogenetics, is now used widely used in neuroscience and other fields. By engineering neurons to express light-sensitive proteins such as channelrhodopsin, researchers can either stimulate or silence the cells’ electrical impulses by shining different wavelengths of light on them. This has allowed for detailed study of the roles of specific populations of neurons in the brain, and the mapping of neural circuits that control a variety of behaviors.

In 2011, about a month after joining the MIT faculty, Zhang attended a talk by Harvard Medical School Professor Michael Gilmore, who studies the pathogenic bacterium Enteroccocus. The scientist mentioned that these bacteria protect themselves from viruses with DNA-cutting enzymes known as nucleases, which are part of a defense system known as CRISPR.

“I had no idea what CRISPR was, but I was interested in nucleases,” Zhang told MIT News in 2016. “I went to look up CRISPR, and that’s when I realized you might be able to engineer it for use for genome editing.”

In January 2013, Zhang and members of his lab reported that they had successfully used CRISPR to edit genes in mammalian cells. The CRISPR system includes a nuclease called Cas9, which can be directed to cut a specific genetic target by RNA molecules known as guide strands.

Since then, scientists in fields from medicine to plant biology have used CRISPR to study gene function and modify faulty genes that cause disease. More recently, Zhang’s lab has devised many enhancements to the original CRISPR system, such as making the targeting more precise and preventing unintended cuts in the wrong locations. In 2023, the FDA approved Casgevy, a CRISPR gene therapy based on Zhang’s discoveries, for the treatment of sickle cell disease and beta thalassemia.

The National Medal of Technology and Innovation was established in 1980 and is administered for the White House by the U.S. Department of Commerce’s Patent and Trademark Office. The award recognizes those who have made lasting contributions to America’s competitiveness and quality of life and helped strengthen the nation’s technological workforce.

The promise of gene therapy

by Robert Desimone |

Portrait of Bob Desimone wearing a suit and tie.
McGovern Institute Director Robert Desimone. Photo: Steph Stevens

As we start 2024, I hope you can join me in celebrating a historic recent advance: the FDA approval of Casgevy, a bold new treatment for devastating sickle cell disease and the world’s first approved CRISPR gene therapy.

Developed by Vertex Pharmaceuticals and CRISPR Therapeutics, we are proud to share that this pioneering therapy licenses the CRISPR discoveries of McGovern scientist and Poitras Professor of Neuroscience Feng Zhang.

It is amazing to think that Feng’s breakthrough work adapting CRISPR-Cas9 for genome editing in eukaryotic cells was published only 11 years ago today in Science.

Incredibly, CRISPR-Cas9 rapidly transitioned from proof-of-concept experiments to an approved treatment in just over a decade.

McGovern scientists are determined to maintain the momentum!

 

Incredibly, CRISPR-Cas9 rapidly transitioned from proof-of-concept experiments to an approved treatment in just over a decade.

Our labs are creating new gene therapies that are already in clinical trials or preparing to enroll patients in trials. For instance, Feng Zhang’s team has developed therapies currently in clinical trials for lymphoblastic leukemia and beta thalassemia, while another McGovern researcher, Guoping Feng, the Poitras Professor of Brain and Cognitive Sciences at MIT, has made advancements that lay the groundwork for a new gene therapy to treat a severe form of autism spectrum disorder. It is expected to enter clinical trials later this year. Moreover, McGovern fellows Omar Abudayyeh and Jonathan Gootenberg created programmable genomic tools that are now licensed for use in monogenic liver diseases and autoimmune disorders.

These exciting innovations stem from your steadfast support of our high-risk, high-reward research. Your generosity is enabling our scientists to pursue basic research in other areas with potential therapeutic applications in the future, such as mechanisms of pain, addiction, the connections between the brain and gut, the workings of memory and attention, and the bi-directional influence of artificial intelligence on brain research. All of this fundamental research is being fueled by major new advances in technology, many of them developed here.

As we enter a new year filled with anticipation following our inaugural gene therapy, I want to express my heartfelt gratitude for your invaluable support in advancing our research programs. Your role in pushing our research to new heights is valued by all faculty, students, and researchers at the McGovern Institute. We can’t wait to share our continued progress with you.

Thank you again for partnering with us to make great scientific achievements possible.

With appreciation and best wishes,

Robert Desimone, PhD
Director, McGovern Institute
Doris and Don Berkey Professor of Neuroscience, MIT