From summer internships as an undergraduate studying neuroscience at the University of Notre Dame, Sadie Zacharek developed interests in areas ranging from neuroimaging to developmental psychopathologies, from basic-science research to clinical translation. When she interviewed with John Gabrieli, the Grover Hermann Professor of Health Sciences and Technology and Cognitive Neuroscience, for a position in his lab as a graduate fellow, everything came together.
“The brain provides a window not only into dysfunction but also into response to treatment,” she says. “John and I both wanted to explore how we might use neuroimaging as a step toward personalized medicine.”
Zacharek joined the Gabrieli lab in 2020 and currently holds the Sheldon and Janet Razin’59 Fellowship for 2023-2024. In the Gabrieli lab, she has been designing and helping launch studies focusing on the neural mechanisms driving childhood depression and social anxiety disorder with the aim of developing strategies to predict which treatments will be most effective for individual patients.
Helping children and adults
“Depression in children is hugely understudied,” says Zacharek. “Most of the research has focused on adult and adolescent depression.” But the clinical presentation differs in the two groups, she says. “In children, irritability can be the primary presenting symptom rather than melancholy.” To get to the root of childhood depression, she is exploring both the brain basis of the disorder and how the parent-child relationship might influence symptoms. “Parents help children develop their emotion-regulation skills,” she says. “Knowing the underlying mechanisms could, in family-focused therapy, help them turn a ‘downward spiral’ into irritability, into an ‘upward spiral,’ away from it.”
The studies she is conducting include functional magnetic resonance imaging (fMRI) of children to explore their brain responses to positive and negative stimuli, fMRI of both the child and parent to compare maps of their brains’ functional connectivity, and magnetic resonance spectroscopy to explore the neurochemical environment of both, including quantities of neurometabolites that indicate inflammation (higher levels have been found to correlate with depressive pathology).
“If we could find a normative range for neurochemicals and then see how far someone has deviated in depression, or a neural signature of elevated activity in a brain region, that could serve as a biomarker for future interventions,” she says. “Such a biomarker would be especially relevant for children given that they are less able to articulately convey their symptoms or internal experience.”
“The brain provides a window not only into dysfunction but also into response to treatment.” – Sadie Zacharek
Social anxiety disorder is a chronic and disabling condition that affects about 7.1 percent of U.S. adults. Treatment usually involves cognitive behavior therapy (CBT), and then, if there is limited response, the addition of a selective serotonin reuptake inhibitor (SSRI), as an anxiolytic.
But what if research could reveal the key neurocircuitry of social anxiety disorder as well as changes associated with treatment? That could open the door to predicting treatment outcome.
Zacharek is collecting neuroimaging data, as well as clinical assessments, from participants. The participants diagnosed with social anxiety disorder will then undergo 12 weeks of group CBT, followed by more data collection, and then individual CBT for 12 weeks plus an SSRI for those who do not benefit from the group CBT. The results from those two time points will help determine the best treatment for each person.
“We hope to build a predictive model that could enable clinicians to scan a new patient and select the optimal treatment,” says Zacharek. “John’s many long-standing relationships with clinicians in this area make all of these translational studies possible.”
Genetic engineer Joseph Kreitz looks to the microscopic world for inspiration in Feng Zhang’s lab at the McGovern Institute. Photo: Steph Steve
In their quest to deepen their understanding of the brain, McGovern scientists take inspiration wherever it comes — and sometimes it comes from surprising sources. To develop new tools for research and innovative strategies for treating disease, they’ve drawn on proteins that organisms have been making for billions of years as well as sophisticated materials engineered for modern technology.
For McGovern investigator Feng Zhang, the natural world provides a rich source of molecules with remarkable and potentially useful functions.
Zhang is one of the pioneers of CRISPR, a programmable system for gene editing that is built from the components of a bacterial adaptive immune system. Scientists worldwide use CRISPR to modify genetic sequences in their labs, and many CRISPR-based therapies, which aim to treat disease through gene editing, are now in development. Meanwhile, Zhang and his team have continued to explore CRISPR-like systems beyond the bacteria in which they were originally discovered.
Turning to nature
This year, the search for evolutionarily related systems led Zhang’s team to a set of enzymes made by more complex organisms, including single-celled algae and hard-shell clams. Like the enzymes that power CRISPR, these newly discovered enzymes, called Fanzors, can be directed to cut DNA at specific sites by programming an RNA molecule as a guide.
Rhiannon Macrae, a scientific advisor in Zhang’s lab, says the discovery was surprising because Fanzors don’t seem to play the same role in immunity that CRISPR systems do. In fact, she says it’s not clear what Fanzors do at all. But as programmable gene editors, Fanzors might have an important advantage over current CRISPR tools — particularly for clinical applications. “Fanzor proteins are much smaller than the workhorse CRISPR tool, Cas9,” Macrae says. “This really matters when you actually want to be able to use one of these tools in a patient, because the bigger the tool, the harder it is to package and deliver to patients’ cells.”
Cryo-EM map of a Fanzor protein (gray, yellow, light blue, and pink) in complex with ωRNA (purple) and its target DNA (red). Non-target DNA strand in blue. Image: Zhang lab
Zhang’s team has thought a lot about how to get therapies to patients’ cells, and size is only one consideration. They’ve also been looking for ways to direct drugs, gene-editing tools, or other therapies to specific cells and tissues in the body. One of the lab’s leading strategies comes from another unexpected natural source: a microscopic syringe produced by certain insect-infecting bacteria.
In their search for an efficient system for targeted drug delivery, Zhang and graduate student Joseph Kreitz first considered the injection systems of bacteria-infecting viruses: needle-like structures that pierce the outer membrane of their host to deliver their own genetic material. But these viral injection systems can’t easily be freed from the rest of the virus.
Then Zhang learned that some bacteria have injection systems of their own, which they release inside their hosts after packing them with toxins. They reengineered the bacterial syringe, devising a delivery system that works on human cells. Their current system can be programmed to inject proteins — including those used for gene editing — directly into specified cell types. With further development, Zhang hopes it will work with other types of therapies, as well.
Magnetic imaging
In McGovern Associate Investigator Alan Jasanoff’s lab, researchers are designing sensors that can track the activity of specific neurons or molecules in the brain, using magnetic resonance imaging (MRI) or related forms of non-invasive imaging. These tools are essential for understanding how the brain’s cells and circuits work together to process information. “We want to give MRI a suite of metaphorical colors: sensitivities that enable us to dissect the different kinds of mechanistically significant contributors to neural activity,” he explains.
Jasanoff can tick off a list of molecules with notable roles in biology and industry that his lab has repurposed to glean more information from brain imaging. These include manganese — a metal once used to tint ancient glass; nitric oxide synthase — the enzyme that causes blushing; and iron oxide nanoparticles — tiny magnets that enable compact data storage inside computers. But Jasanoff says none of these should be considered out of place in the imaging world. “Most are pretty logical choices,” he says. “They all do different things and we use them in pretty different ways, but they are either magnetic or interact with magnetic molecules to serve our purposes for brain imaging.”
Manganese, a metal that interacts weakly with magnetic fields, is a key component in new MRI sensors being developed in Alan Jasanoff’s lab at the McGovern Institute.
The enzyme nitric oxide synthase, for example, plays an important role in most functional MRI scans. The enzyme produces nitric oxide, which causes blood vessels to expand. This can bring a blush to the cheeks, but in the brain, it increases blood flow to bring more oxygen to busy neurons. MRI can detect this change because it is sensitive to the magnetic properties of blood.
By using blood flow as a proxy for neural activity, functional MRI scans light up active regions of the brain, but they can’t pinpoint the activity of specific cells. So Jasanoff and his team devised a more informative MRI sensor by reengineering nitric oxide synthase. Their modified enzyme, which they call NOSTIC, can be introduced into a select group of cells, where it will produce nitric oxide in response to neural activity — triggering increased blood flow and strengthening the local MRI signal. Researchers can deliver it to specific kinds of brain cells, or they can deliver it exclusively to neurons that communicate directly with one another. Then they can watch for an elevated MRI signal when those cells fire. This lets them see how information flows through the brain and tie specific cells to particular tasks.
Miranda Dawson, a graduate student in Jasanoff’s lab, is using NOSTIC to study the brain circuits that fuel addiction. She’s interested in the involvement of a brain region called the insula, which may mediate the physical sensations that people with addiction experience during drug cravings or withdrawal. With NOSTIC, Dawson can follow how the insula communicates to other parts of the brain as a rat experiences these MITstages of addiction. “We give our sensor to the insula, and then it projects to anatomically connected brain regions,” she explains. “So we’re able to delineate what circuits are being activated at different points in the addiction cycle.”
Miranda Dawson uses her lab’s novel MRI sensor, NOSTIC, to illuminate the brain circuits involved in fentanyl craving and withdrawal. Photo: Steph Stevens; MRI scan: Nan Li, Souparno Ghosh, Jasanoff lab
Mining biodiversity
McGovern investigators know that good ideas and useful tools can come from anywhere. Sometimes, the key to harnessing those tools is simply recognizing their potential. But there are also opportunities for a more deliberate approach to finding them.
McGovern Investigator Ed Boyden is leading a program that aims to accelerate the discovery of valuable natural products. Called the Biodiversity Network (BioNet), the project is collecting biospecimens from around the world and systematically analyzing them, looking for molecular tools that could be applied to major challenges in science and medicine, from brain research to organ preservation. “The idea behind BioNet,” Boyden explains, “is rather than wait for chance to give us these discoveries, can we go look for them on purpose?”
In 2022, nine MIT faculty were granted tenure in the School of Science:
Gloria Choi examines the interaction of the immune system with the brain and the effects of that interaction on neurodevelopment, behavior, and mood. She also studies how social behaviors are regulated according to sensory stimuli, context, internal state, and physiological status, and how these factors modulate neural circuit function via a combinatorial code of classic neuromodulators and immune-derived cytokines. Choi joined the Department of Brain and Cognitive Sciences after a postdoc at Columbia University. She received her bachelor’s degree from the University of California at Berkeley, and her PhD from Caltech. Choi is also an investigator in The Picower Institute for Learning and Memory.
Nikta Fakhri develops experimental tools and conceptual frameworks to uncover laws governing fluctuations, order, and self-organization in active systems. Such frameworks provide powerful insight into dynamics of nonequilibrium living systems across scales, from the emergence of thermodynamic arrow of time to spatiotemporal organization of signaling protein patterns and discovery of odd elasticity. Fakhri joined the Department of Physics in 2015 following a postdoc at University of Göttingen. She completed her undergraduate degree at Sharif University of Technology and her PhD at Rice University.
Geobiologist Greg Fournier uses a combination of molecular phylogeny insights and geologic records to study major events in planetary history, with the hope of furthering our understanding of the co-evolution of life and environment. Recently, his team developed a new technique to analyze multiple gene evolutionary histories and estimated that photosynthesis evolved between 3.4 and 2.9 billion years ago. Fournier joined the Department of Earth, Atmospheric and Planetary Sciences in 2014 after working as a postdoc at the University of Connecticut and as a NASA Postdoctoral Program Fellow in MIT’s Department of Civil and Environmental Engineering. He earned his BA from Dartmouth College in 2001 and his PhD in genetics and genomics from the University of Connecticut in 2009.
Daniel Harlow researches black holes and cosmology, viewed through the lens of quantum gravity and quantum field theory. His work generates new insights into quantum information, quantum field theory, and gravity. Harlow joined the Department of Physics in 2017 following postdocs at Princeton University and Harvard University. He obtained a BA in physics and mathematics from Columbia University in 2006 and a PhD in physics from Stanford University in 2012. He is also a researcher in the Center for Theoretical Physics.
A biophysicist, Gene-Wei Li studies how bacteria optimize the levels of proteins they produce at both mechanistic and systems levels. His lab focuses on design principles of transcription, translation, and RNA maturation. Li joined the Department of Biology in 2015 after completing a postdoc at the University of California at San Francisco. He earned an BS in physics from National Tsinghua University in 2004 and a PhD in physics from Harvard University in 2010.
Michael McDonald focuses on the evolution of galaxies and clusters of galaxies, and the role that environment plays in dictating this evolution. This research involves the discovery and study of the most distant assemblies of galaxies alongside analyses of the complex interplay between gas, galaxies, and black holes in the closest, most massive systems. McDonald joined the Department of Physics and the Kavli Institute for Astrophysics and Space Research in 2015 after three years as a Hubble Fellow, also at MIT. He obtained his BS and MS degrees in physics at Queen’s University, and his PhD in astronomy at the University of Maryland in College Park.
Gabriela Schlau-Cohen combines tools from chemistry, optics, biology, and microscopy to develop new approaches to probe dynamics. Her group focuses on dynamics in membrane proteins, particularly photosynthetic light-harvesting systems that are of interest for sustainable energy applications. Following a postdoc at Stanford University, Schlau-Cohen joined the Department of Chemistry faculty in 2015. She earned a bachelor’s degree in chemical physics from Brown University in 2003 followed by a PhD in chemistry at the University of California at Berkeley.
Phiala Shanahan’s research interests are focused around theoretical nuclear and particle physics. In particular, she works to understand the structure and interactions of hadrons and nuclei from the fundamental degrees of freedom encoded in the Standard Model of particle physics. After a postdoc at MIT and a joint position as an assistant professor at the College of William and Mary and senior staff scientist at the Thomas Jefferson National Accelerator Facility, Shanahan returned to the Department of Physics as faculty in 2018. She obtained her BS from the University of Adelaide in 2012 and her PhD, also from the University of Adelaide, in 2015.
Omer Yilmaz explores the impact of dietary interventions on stem cells, the immune system, and cancer within the intestine. By better understanding how intestinal stem cells adapt to diverse diets, his group hopes to identify and develop new strategies that prevent and reduce the growth of cancers involving the intestinal tract. Yilmaz joined the Department of Biology in 2014 and is now also a member of Koch Institute for Integrative Cancer Research. After receiving his BS from the University of Michigan in 1999 and his PhD and MD from University of Michigan Medical School in 2008, he was a resident in anatomic pathology at Massachusetts General Hospital and Harvard Medical School until 2013.
In 2023, five MIT faculty were granted tenure in the School of Science:
Physicist Riccardo Comin explores the novel phases of matter that can be found in electronic solids with strong interactions, also known as quantum materials. His group employs a combination of synthesis, scattering, and spectroscopy to obtain a comprehensive picture of these emergent phenomena, including superconductivity, (anti)ferromagnetism, spin-density-waves, charge order, ferroelectricity, and orbital order. Comin joined the Department of Physics in 2016 after postdoctoral work at the University of Toronto. He completed his undergraduate studies at the Universita’ degli Studi di Trieste in Italy, where he also obtained a MS in physics in 2009. Later, he pursued doctoral studies at the University of British Columbia, Canada, earning a PhD in 2013.
Netta Engelhardt researches the dynamics of black holes in quantum gravity and uses holography to study the interplay between gravity and quantum information. Her primary focus is on the black hole information paradox, that black holes seem to be destroying information that, according to quantum physics, cannot be destroyed. Engelhardt was a postdoc at Princeton University and a member of the Princeton Gravity Initiative prior to joining the Department of Physics in 2019. She received her BS in physics and mathematics from Brandeis University and her PhD in physics from the University of California at Santa Barbara. Engelhardt is a researcher in the Center for Theoretical Physics and the Black Hole Initiative at Harvard University.
Mark Harnett studies how the biophysical features of individual neurons endow neural circuits with the ability to process information and perform the complex computations that underlie behavior. As part of this work, his lab was the first to describe the physiological properties of human dendrites. He joined the Department of Brain and Cognitive Sciences and the McGovern Institute for Brain Research in 2015. Prior, he was a postdoc at the Howard Hughes Medical Institute’s Janelia Research Campus. He received his BA in biology from Reed College in Portland, Oregon and his PhD in neuroscience from the University of Texas at Austin.
Or Hen investigates quantum chromodynamic effects in the nuclear medium and the interplay between partonic and nucleonic degrees of freedom in nuclei. Specifically, Hen utilizes high-energy scattering of electron, neutrino, photon, proton and ion off atomic nuclei to study short-range correlations: temporal fluctuations of high-density, high-momentum, nucleon clusters in nuclei with important implications for nuclear, particle, atomic, and astrophysics. Hen was an MIT Pappalardo Fellow in the Department of Physics from 2015 to 2017 before joining the faculty in 2017. He received his undergraduate degree in physics and computer engineering from the Hebrew University and earned his PhD in experimental physics at Tel Aviv University.
Sebastian Lourido is interested in learning about the vulnerabilities of parasites in order to develop treatments for infectious diseases and expand our understanding of eukaryotic diversity. His lab studies many important human pathogens, including Toxoplasma gondii, to model features conserved throughout the phylum. Lourido was a Whitehead Fellow at the Whitehead Institute for Biomedical Research until 2017, when he joined the Department of Biology and became a Whitehead Member. He earned his BS from Tulane University in 2004 and his PhD from Washington University in St. Louis in 2012.
Twenty-four individuals and one team were awarded MIT Excellence Awards — the highest awards for staff at the Institute — at a well-attended and energetic ceremony the afternoon of June 8 in Kresge Auditorium. In addition to the Excellence Awards, two community members were honored with the Collier Medal and Staff Award for Distinction in Service.
The Excellence Awards, Collier Medal, and Staff Award for Distinction in Service recognize the extraordinary dedication of staff and community members who represent all areas of the Institute, both on campus and at the Lincoln Laboratory.
The Collier Medal honors the memory of Officer Sean Collier, who gave his life protecting and serving the MIT community, and celebrates an individual or group whose actions demonstrate the importance of community. The Staff Award for Distinction in Service, now in its second year, is presented to a staff member whose service to the Institute results in a positive lasting impact on the community.
The 2023 MIT Excellence Award recipients and their award categories are:
Sustaining MIT: Erin Genereux; Rachida Kernis; J. Bradley Morrison, and the Tip Box Recycling Team (John R. Collins, Michael A. DeBerio, Normand J. Desrochers III, Mitchell S. Galanek, David M. Pavone, Ryan Samz, Rosario Silvestri, and Lu Zhong);
Innovative Solutions: Abram Barrett, Nicole H. W. Henning
Bringing Out the Best: Patty Eames, Suzy Maholchic Nelson
Serving Our Community: Mahnaz El-Kouedi, Kara Flyg, Timothy J. Meunier, Marie A. Stuppard, Roslyn R. Wesley
Embracing Diversity, Equity, and Inclusion: Farrah A. Belizaire
Outstanding Contributor: Diane Ballestas, Robert J. Bicchieri, Lindsey Megan Charles, Benoit Desbiolles, Dennis C. Hamel, Heather Anne Holland, Gregory L. Long, Linda Mar, Mary Ellen Sinkus, Sarah E. Willis, and Phyl A. Winn
The 2023 Collier Medal recipient was Martin Eric William Nisser, a graduate student fellow in the Department of Electrical Engineering and Computer Science/Computer Science and Artificial Intelligence Laboratory and the School of Engineering/MIT Schwarzman College of Computing.
The 2023 recipient of the Staff Award for Distinction in Service was Kimberly A. Haberlin, chief of staff in the Chancellor’s Office.
Presenters included President Sally Kornbluth; Vice President for Human Resources Ramona Allen; Provost Cynthia Barnhart; School of Engineering Dean Anantha Chandrakasan; MIT Police Chief John DiFava and MIT Police Captain Andrew Turco; Institute Community and Equity Officer John Dozier; Lincoln Laboratory Director Eric Evans; and Chancellor Melissa Nobles. As always, an animated and supportive audience with signs, pompoms, and glow bracelets filled the auditorium with cheers for the honorees.
Visit the MIT Human Resources website for more information about the award categories, selection process, recipients, and to view the archive video of the event.
The lab of Edward Boyden, the Y. Eva Tan Professor in Neurotechnology, has developed a powerful technology called Expansion Revealing (ExR) that makes visible molecular structures that were previously too hidden to be seen with even the most powerful microscopes. It “reveals” the nanoscale alterations in synapses, neural wiring, and other molecular assemblies using ordinary lab microscopes. It does so this way: Inside a cell, proteins and other molecules are often tightly packed together. These dense clusters can be difficult to image because the fluorescent labels used to make them visible can’t wedge themselves between the molecules. ExR “de-crowds” the molecules by expanding the cell using a chemical process, making the molecules accessible to fluorescent tags.
Jinyoung Kang is a J. Douglas Tan Postdoctoral Fellow in the Boyden and Feng labs. Photo: Steph Stevens
“This technology can be used to answer a lot of biological questions about dysfunction in synaptic proteins, which are involved in neurodegenerative diseases,” says Jinyoung Kang, a J. Douglas Tan Postdoctoral Fellow in the labs of Boyden and Guoping Feng, the James W. (1963) and Patricia T. Poitras Professor of Brain and Cognitive Sciences. “Until now, there has been no tool to visualize synapses very well at nanoscale.”
Over the past year, the Boyden team has been using ExR to explore the underlying mechanisms of brain disorders, including autism spectrum disorder (ASD) and Alzheimer’s disease. Since the method can be applied iteratively, Boyden imagines it may one day succeed in creating a 100-fold magnification of molecular structures.
“Using earlier technology, researchers may be missing entire categories of molecular phenomena, both functional and dysfunctional,” says Boyden. “It’s critical to bring these nanostructures into view so that we can identify potential targets for new therapeutics that can restore functional molecular arrangements.”
The team is applying ExR to the study of mutant-animal-model brain slices to expose complex synapse 3D nanoarchitecture and configuration. Among their questions: How do synapses differ when mutations that cause autism and other neurological conditions are present?
Using the new technology, Kang and her collaborator Menglong Zeng characterized the molecular architecture of excitatory synapses on parvalbumin interneurons, cells that drastically influence the downstream effects of neuronal signaling and ultimately change cognitive behaviors. They discovered condensed AMPAR clustering in parvalbumin interneurons is essential for normal brain function. The next step is to explore their role in the function of parvalbumin interneurons, which are vulnerable to stressors and have been implicated in brain disorders including autism and Alzheimer’s disease.
The researchers are now investigating whether ExR can reveal abnormal protein nanostructures in SHANK3 knockout mice and marmosets. Mutations in the SHANK3 gene lead to one of the most severe types of ASD, Phelan-McDermid syndrome, which accounts for about 2 percent of all ASD patients with intellectual disability.
A team of researchers led by Feng Zhang at the McGovern Institute and the Broad Institute of MIT and Harvard has uncovered the first programmable RNA-guided system in eukaryotes — organisms that include fungi, plants, and animals.
In a study in Nature, the team describes how the system is based on a protein called Fanzor. They showed that Fanzor proteins use RNA as a guide to target DNA precisely, and that Fanzors can be reprogrammed to edit the genome of human cells. The compact Fanzor systems have the potential to be more easily delivered to cells and tissues as therapeutics than CRISPR/Cas systems, and further refinements to improve their targeting efficiency could make them a valuable new technology for human genome editing.
CRISPR/Cas was first discovered in prokaryotes (bacteria and other single-cell organisms that lack nuclei) and scientists including Zhang’s lab have long wondered whether similar systems exist in eukaryotes. The new study demonstrates that RNA-guided DNA-cutting mechanisms are present across all kingdoms of life.
“This new system is another way to make precise changes in human cells, complementing the genome editing tools we already have.” — Feng Zhang
“CRISPR-based systems are widely used and powerful because they can be easily reprogrammed to target different sites in the genome,” said Zhang, senior author on the study and a core institute member at the Broad, an investigator at MIT’s McGovern Institute, the James and Patricia Poitras Professor of Neuroscience at MIT, and a Howard Hughes Medical Institute investigator. “This new system is another way to make precise changes in human cells, complementing the genome editing tools we already have.”
Searching the domains of life
A major aim of the Zhang lab is to develop genetic medicines using systems that can modulate human cells by targeting specific genes and processes. “A number of years ago, we started to ask, ‘What is there beyond CRISPR, and are there other RNA-programmable systems out there in nature?’” said Zhang.
McGovern Investigator Feng Zhang in his lab.
Two years ago, Zhang lab members discovered a class of RNA-programmable systems in prokaryotes called OMEGAs, which are often linked with transposable elements, or “jumping genes”, in bacterial genomes and likely gave rise to CRISPR/Cas systems. That work also highlighted similarities between prokaryotic OMEGA systems and Fanzor proteins in eukaryotes, suggesting that the Fanzor enzymes might also use an RNA-guided mechanism to target and cut DNA.
In the new study, the researchers continued their study of RNA-guided systems by isolating Fanzors from fungi, algae, and amoeba species, in addition to a clam known as the Northern Quahog. Co-first author Makoto Saito of the Zhang lab led the biochemical characterization of the Fanzor proteins, showing that they are DNA-cutting endonuclease enzymes that use nearby non-coding RNAs known as ωRNAs to target particular sites in the genome. It is the first time this mechanism has been found in eukaryotes, such as animals.
Unlike CRISPR proteins, Fanzor enzymes are encoded in the eukaryotic genome within transposable elements and the team’s phylogenetic analysis suggests that the Fanzor genes have migrated from bacteria to eukaryotes through so-called horizontal gene transfer.
“These OMEGA systems are more ancestral to CRISPR and they are among the most abundant proteins on the planet, so it makes sense that they have been able to hop back and forth between prokaryotes and eukaryotes,” said Saito.
To explore Fanzor’s potential as a genome editing tool, the researchers demonstrated that it can generate insertions and deletions at targeted genome sites within human cells. The researchers found the Fanzor system to initially be less efficient at snipping DNA than CRISPR/Cas systems, but by systematic engineering, they introduced a combination of mutations into the protein that increased its activity 10-fold. Additionally, unlike some CRISPR systems and the OMEGA protein TnpB, the team found that a fungal-derived Fanzor protein did not exhibit “collateral activity,” where an RNA-guided enzyme cleaves its DNA target as well as degrading nearby DNA or RNA. The results suggest that Fanzors could potentially be developed as efficient genome editors.
Co-first author Peiyu Xu led an effort to analyze the molecular structure of the Fanzor/ωRNA complex and illustrate how it latches onto DNA to cut it. Fanzor shares structural similarities with its prokaryotic counterpart CRISPR-Cas12 protein, but the interaction between the ωRNA and the catalytic domains of Fanzor is more extensive, suggesting that the ωRNA might play a role in the catalytic reactions. “We are excited about these structural insights for helping us further engineer and optimize Fanzor for improved efficiency and precision as a genome editor,” said Xu.
Like CRISPR-based systems, the Fanzor system can be easily reprogrammed to target specific genome sites, and Zhang said it could one day be developed into a powerful new genome editing technology for research and therapeutic applications. The abundance of RNA-guided endonucleases like Fanzors further expands the number of OMEGA systems known across kingdoms of life and suggests that there are more yet to be found.
“Nature is amazing. There’s so much diversity,” said Zhang. “There are probably more RNA-programmable systems out there, and we’re continuing to explore and will hopefully discover more.”
The paper’s other authors include Guilhem Faure, Samantha Maguire, Soumya Kannan, Han Altae-Tran, Sam Vo, AnAn Desimone, and Rhiannon Macrae.
Support for this work was provided by the Howard Hughes Medical Institute; Poitras Center for Psychiatric Disorders Research at MIT; K. Lisa Yang and Hock E. Tan Molecular Therapeutics Center at MIT; Broad Institute Programmable Therapeutics Gift Donors; The Pershing Square Foundation, William Ackman, and Neri Oxman; James and Patricia Poitras; BT Charitable Foundation; Asness Family Foundation; Kenneth C. Griffin; the Phillips family; David Cheng; Robert Metcalfe; and Hugo Shong.
The brain and the digestive tract are in constant communication, relaying signals that help to control feeding and other behaviors. This extensive communication network also influences our mental state and has been implicated in many neurological disorders.
MIT engineers have designed a new technology for probing those connections. Using fibers embedded with a variety of sensors, as well as light sources for optogenetic stimulation, the researchers have shown that they can control neural circuits connecting the gut and the brain, in mice.
In a new study, the researchers demonstrated that they could induce feelings of fullness or reward-seeking behavior in mice by manipulating cells of the intestine. In future work, they hope to explore some of the correlations that have been observed between digestive health and neurological conditions such as autism and Parkinson’s disease.
“The exciting thing here is that we now have technology that can drive gut function and behaviors such as feeding. More importantly, we have the ability to start accessing the crosstalk between the gut and the brain with the millisecond precision of optogenetics, and we can do it in behaving animals,” says Polina Anikeeva, the Matoula S. Salapatas Professor in Materials Science and Engineering, a professor of brain and cognitive sciences, director of the K. Lisa Yang Brain-Body Center, associate director of MIT’s Research Laboratory of Electronics, and a member of MIT’s McGovern Institute for Brain Research.
McGovern Institute Associate Investigator Polina Anikeeva in her lab. Photo: Steph Stevens
Anikeeva is the senior author of the new study, which appears today in Nature Biotechnology. The paper’s lead authors are MIT graduate student Atharva Sahasrabudhe, Duke University postdoc Laura Rupprecht, MIT postdoc Sirma Orguc, and former MIT postdoc Tural Khudiyev.
The brain-body connection
Last year, the McGovern Institute launched the K. Lisa Yang Brain-Body Center to study the interplay between the brain and other organs of the body. Research at the center focuses on illuminating how these interactions help to shape behavior and overall health, with a goal of developing future therapies for a variety of diseases.
“There’s continuous, bidirectional crosstalk between the body and the brain,” Anikeeva says. “For a long time, we thought the brain is a tyrant that sends output into the organs and controls everything. But now we know there’s a lot of feedback back into the brain, and this feedback potentially controls some of the functions that we have previously attributed exclusively to the central neural control.”
As part of the center’s work, Anikeeva set out to probe the signals that pass between the brain and the nervous system of the gut, also called the enteric nervous system. Sensory cells in the gut influence hunger and satiety via both the neuronal communication and hormone release.
Untangling those hormonal and neural effects has been difficult because there hasn’t been a good way to rapidly measure the neuronal signals, which occur within milliseconds.
“We needed a device that didn’t exist. So, we decided to make it,” says Atharva Sahasrabudhe.
“To be able to perform gut optogenetics and then measure the effects on brain function and behavior, which requires millisecond precision, we needed a device that didn’t exist. So, we decided to make it,” says Sahasrabudhe, who led the development of the gut and brain probes.
The electronic interface that the researchers designed consists of flexible fibers that can carry out a variety of functions and can be inserted into the organs of interest. To create the fibers, Sahasrabudhe used a technique called thermal drawing, which allowed him to create polymer filaments, about as thin as a human hair, that can be embedded with electrodes and temperature sensors.
The filaments also carry microscale light-emitting devices that can be used to optogenetically stimulate cells, and microfluidic channels that can be used to deliver drugs.
The mechanical properties of the fibers can be tailored for use in different parts of the body. For the brain, the researchers created stiffer fibers that could be threaded deep into the brain. For digestive organs such as the intestine, they designed more delicate rubbery fibers that do not damage the lining of the organs but are still sturdy enough to withstand the harsh environment of the digestive tract.
“To study the interaction between the brain and the body, it is necessary to develop technologies that can interface with organs of interest as well as the brain at the same time, while recording physiological signals with high signal-to-noise ratio,” Sahasrabudhe says. “We also need to be able to selectively stimulate different cell types in both organs in mice so that we can test their behaviors and perform causal analyses of these circuits.”
The fibers are also designed so that they can be controlled wirelessly, using an external control circuit that can be temporarily affixed to the animal during an experiment. This wireless control circuit was developed by Orguc, a Schmidt Science Fellow, and Harrison Allen ’20, MEng ’22, who were co-advised between the Anikeeva lab and the lab of Anantha Chandrakasan, dean of MIT’s School of Engineering and the Vannevar Bush Professor of Electrical Engineering and Computer Science.
Driving behavior
Using this interface, the researchers performed a series of experiments to show that they could influence behavior through manipulation of the gut as well as the brain.
First, they used the fibers to deliver optogenetic stimulation to a part of the brain called the ventral tegmental area (VTA), which releases dopamine. They placed mice in a cage with three chambers, and when the mice entered one particular chamber, the researchers activated the dopamine neurons. The resulting dopamine burst made the mice more likely to return to that chamber in search of the dopamine reward.
Then, the researchers tried to see if they could also induce that reward-seeking behavior by influencing the gut. To do that, they used fibers in the gut to release sucrose, which also activated dopamine release in the brain and prompted the animals to seek out the chamber they were in when sucrose was delivered.
Next, working with colleagues from Duke University, the researchers found they could induce the same reward-seeking behavior by skipping the sucrose and optogenetically stimulating nerve endings in the gut that provide input to the vagus nerve, which controls digestion and other bodily functions.
Duke University postdoc Laura Rupprecht, MIT graduate student Atharva Sahasrabudhe, and MIT postdoc Sirma Orguc holding their engineered flexible fiber in Polina Anikeeva’s lab at MIT. Photo: Courtesy of the researchers
“Again, we got this place preference behavior that people have previously seen with stimulation in the brain, but now we are not touching the brain. We are just stimulating the gut, and we are observing control of central function from the periphery,” Anikeeva says.
Sahasrabudhe worked closely with Rupprecht, a postdoc in Professor Diego Bohorquez’ group at Duke, to test the fibers’ ability to control feeding behaviors. They found that the devices could optogenetically stimulate cells that produce cholecystokinin, a hormone that promotes satiety. When this hormone release was activated, the animals’ appetites were suppressed, even though they had been fasting for several hours. The researchers also demonstrated a similar effect when they stimulated cells that produce a peptide called PYY, which normally curbs appetite after very rich foods are consumed.
The researchers now plan to use this interface to study neurological conditions that are believed to have a gut-brain connection. For instance, studies have shown that autistic children are far more likely than their peers to be diagnosed with GI dysfunction, while anxiety and irritable bowel syndrome share genetic risks.
“We can now begin asking, are those coincidences, or is there a connection between the gut and the brain? And maybe there is an opportunity for us to tap into those gut-brain circuits to begin managing some of those conditions by manipulating the peripheral circuits in a way that does not directly ‘touch’ the brain and is less invasive,” Anikeeva says.
The research was funded, in part, by the Hock E. Tan and K. Lisa Yang Center for Autism Research and the K. Lisa Yang Brain-Body Center, the National Institute of Neurological Disorders and Stroke, the National Science Foundation (NSF) Center for Materials Science and Engineering, the NSF Center for Neurotechnology, the National Center for Complementary and Integrative Health, a National Institutes of Health Director’s Pioneer Award, the National Institute of Mental Health, and the National Institute of Diabetes and Digestive and Kidney Diseases.
MIT scientists have developed tiny, soft-bodied robots that can be controlled with a weak magnet. The robots, formed from rubbery magnetic spirals, can be programmed to walk, crawl, swim—all in response to a simple, easy-to-apply magnetic field.
“This is the first time this has been done, to be able to control three-dimensional locomotion of robots with a one-dimensional magnetic field,” says McGovern associate investigator Polina Anikeeva, whose team reported on the magnetic robots June 3, 2023, in the journal Advanced Materials. “And because they are predominantly composed of polymer and polymers are soft, you don’t need a very large magnetic field to activate them. It’s actually a really tiny magnetic field that drives these robots,” says Anikeeva, who is also the Matoula S. Salapatas Professor in Materials Science and Engineering and a professor of brain and cognitive sciences at MIT, as well as the associate director of MIT’s Research Laboratory of Electronics and director of MIT’s K. Lisa Yang Brain-Body Center.
McGovern Institute Associate Investigator Polina Anikeeva in her lab. Photo: Steph Stevens
The new robots are well suited to transport cargo through confined spaces and their rubber bodies are gentle on fragile environments, opening the possibility that the technology could be developed for biomedical applications. Anikeeva and her team have made their robots millimeters long, but she says the same approach could be used to produce much smaller robots.
Engineering magnetic robots
Anikeeva says that until now, magnetic robots have moved in response to moving magnetic fields. She explains that for these models, “if you want your robot to walk, your magnet walks with it. If you want it to rotate, you rotate your magnet.” That limits the settings in which such robots might be deployed. “If you are trying to operate in a really constrained environment, a moving magnet may not be the safest solution. You want to be able to have a stationary instrument that just applies magnetic field to the whole sample,” she explains.
Youngbin Lee, a former graduate student in Anikeeva’s lab, engineered a solution to this problem. The robots he developed in Anikeeva’s lab are not uniformly magnetized. Instead, they are strategically magnetized in different zones and directions so a single magnetic field can enable a movement-driving profile of magnetic forces.
Before they are magnetized, however, the flexible, lightweight bodies of the robots must be fabricated. Lee starts this process with two kinds of rubber, each with a different stiffness. These are sandwiched together, then heated and stretched into a long, thin fiber. Because of the two materials’ different properties, one of the rubbers retains its elasticity through this stretching process, but the other deforms and cannot return to its original size. So when the strain is released, one layer of the fiber contracts, tugging on the other side and pulling the whole thing into a tight coil. Anikeeva says the helical fiber is modeled after the twisty tendrils of a cucumber plant, which spiral when one layer of cells loses water and contracts faster than a second layer.
A third material—one whose particles have the potential to become magnetic—is incorporated in a channel that runs through the rubbery fiber. So once the spiral has been made, a magnetization pattern that enables a particular type of movement can be introduced.
“Youngbin thought very carefully about how to magnetize our robots to make them able to move just as he programmed them to move,” Anikeeva says. “He made calculations to determine how to establish such a profile of forces on it when we apply a magnetic field that it will actually start walking or crawling.”
To form a caterpillar-like crawling robot, for example, the helical fiber is shaped into gentle undulations, and then the body, head, and tail are magnetized so that a magnetic field applied perpendicular to the robot’s plane of motion will cause the body to compress. When the field is reduced to zero, the compression is released, and the crawling robot stretches. Together, these movements propel the robot forward. Another robot in which two foot-like helical fibers are connected with a joint is magnetized in a pattern that enables a movement more like walking.
Biomedical potential
This precise magnetization process generates a program for each robot and ensures that that once the robots are made, they are simple to control. A weak magnetic field activates each robot’s program and drives its particular type of movement. A single magnetic field can even send multiple robots moving in opposite directions, if they have been programmed to do so. The team found that one minor manipulation of the magnetic field has a useful effect: With the flip of a switch to reverse the field, a cargo-carrying robot can be made to gently shake and release its payload.
Anikeeva says she can imagine these soft-bodied robots—whose straightforward production will be easy to scale up—delivering materials through narrow pipes or even inside the human body. For example, they might carry a drug through narrow blood vessels, releasing it exactly where it is needed. She says the magnetically-actuated devices have biomedical potential beyond robots as well, and might one day be incorporated into artificial muscles or materials that support tissue regeneration.
Maedbh King came to MIT to make a difference in mental health. As a postdoctoral fellow in the K. Lisa Yang Integrative Computational Neuroscience (ICoN) Center, she is building computer models aimed at helping clinicians improve diagnosis and treatment, especially for young people with neurodevelopmental and psychiatric disorders.
Tapping two large patient-data sources, King is working to analyze critical biological and behavioral information to better categorize patients’ mental health conditions, including autism spectrum disorder, attention-deficit hyperactivity disorder (ADHD), anxiety, and suicidal thoughts — and to provide more predictive approaches to addressing them. Her strategy reflects the center’s commitment to a holistic understanding of human brain function using theoretical and computa-tional neuroscience.
“Today, treatment decisions for psychiatric disorders are derived entirely from symptoms, which leaves clinicians and patients trying one treatment and, if it doesn’t work, trying another,” says King. “I hope to help change that.”
King grew up in Dublin, Ireland, and studied psychology in college; gained neuroimaging and programming skills while earning a master’s degree from Western University in Canada; and received her doctorate from the University of California, Berkeley, where she built maps and models of the human brain. In fall 2022, King joined the lab of Satrajit Ghosh, a McGovern Institute principal research scientist whose team uses neuroimaging, speech communication, and machine learning to improve assessments and treatments for mental health and neurological disorders.
Big-data insights
King is pursuing several projects using the Healthy Brain Network, a landmark mental health study of children and adolescents in New York City. She and lab colleagues are extracting data from cognitive and other assessments — such as language patterns, favorite school subjects, and family mental illness history — from roughly 4,000 participants to provide more-nuanced understanding of their neurodevelopmental disorders, such as autism or ADHD.
“Computational models are powerful. They can identify patterns that can’t be obtained with the human eye through electronic records,” says King.
With this database, one can develop “very rich clinical profiles of these young people,” including their challenges and adaptive strengths, King explains. “We’re interested in placing these participants within a spectrum of symptoms, rather than just providing a binary label of, ‘has this disorder’ or ‘doesn’t have it.’ It’s an effort to subtype based on these phenotypic assessments.”
In other research, King is developing tools to detect risk factors for suicide among adolescents. Working with psychiatrists at Children’s Hospital of Philadelphia, she is using detailed questionnaires from some 20,000 youths who visited the hospital’s emergency department over several years; about one-tenth had tried to take their own lives. The questionnaires collect information about demographics, lifestyle, relationships, and other aspects of patients’ lives.
“One of the big questions the physicians want to answer is, Are there any risk predictors we can identify that can ultimately prevent, or at least mitigate, future suicide attempts?” King says. “Computational models are powerful. They can identify patterns that can’t be obtained with the human eye through electronic records.”
King is passionate about producing findings to help practitioners, whether they’re clinicians, teachers, parents, or policy makers, and the populations they’re studying. “This applied work,” she says, “should be communicated in a way that can be useful.
From cameras to self-driving cars, many of today’s technologies depend on artificial intelligence (AI) to extract meaning from visual information. Today’s AI technology has artificial neural networks at its core, and most of the time we can trust these AI computer vision systems to see things the way we do — but sometimes they falter. According to MIT and IBM Research scientists, one way to improve computer vision is to instruct the artificial neural networks that they rely on to deliberately mimic the way the brain’s biological neural network processes visual images.
Researchers led by James DiCarlo, the director of MIT’s Quest for Intelligence and member of the MIT-IBM Watson AI Lab, have made a computer vision model more robust by training it to work like a part of the brain that humans and other primates rely on for object recognition. This May, at the International Conference on Learning Representations (ICLR), the team reported that when they trained an artificial neural network using neural activity patterns in the brain’s inferior temporal (IT) cortex, the artificial neural network was more robustly able to identify objects in images than a model that lacked that neural training. And the model’s interpretations of images more closely matched what humans saw, even when images included minor distortions that made the task more difficult.
Comparing neural circuits
McGovern Investigator and Director of MIT Quest for Intelligence, James DiCarlo. Photo: Justin Knight
Many of the artificial neural networks used for computer vision already resemble the multi-layered brain circuits that process visual information in humans and other primates. Like the brain, they use neuron-like units that work together to process information. As they are trained for a particular task, these layered components collectively and progressively process the visual information to complete the task — determining for example, that an image depicts a bear or a car or a tree.
DiCarlo and others previously found that when such deep-learning computer vision systems establish efficient ways to solve visual problems, they end up with artificial circuits that work similarly to the neural circuits that process visual information in our own brains. That is, they turn out to be surprisingly good scientific models of the neural mechanisms underlying primate and human vision.
That resemblance is helping neuroscientists deepen their understanding of the brain. By demonstrating ways visual information can be processed to make sense of images, computational models suggest hypotheses about how the brain might accomplish the same task. As developers continue to refine computer vision models, neuroscientists have found new ideas to explore in their own work.
“As vision systems get better at performing in the real world, some of them turn out to be more human-like in their internal processing. That’s useful from an understanding biology point of view,” says DiCarlo, who is also a professor of brain and cognitive sciences and an investigator at the McGovern Institute.
Engineering more brain-like AI
While their potential is promising, computer vision systems are not yet perfect models of human vision. DiCarlo suspected one way to improve computer vision may be to incorporate specific brain-like features into these models.
To test this idea, he and his collaborators built a computer vision model using neural data previously collected from vision-processing neurons in the monkey IT cortex — a key part of the primate ventral visual pathway involved in the recognition of objects — while the animals viewed various images. More specifically, Joel Dapello, a Harvard graduate student and former MIT-IBM Watson AI Lab intern, and Kohitij Kar, Assistant Professor, Canada Research Chair (Visual Neuroscience) at York University and visiting scientist at MIT, in collaboration with David Cox, IBM Research’s VP for AI Models and IBM director of the MIT-IBM Watson AI Lab, and other researchers at IBM Research and MIT, asked an artificial neural network to emulate the behavior of these primate vision-processing neurons while the network learned to identify objects in a standard computer vision task.
“In effect, we said to the network, ‘please solve this standard computer vision task, but please also make the function of one of your inside simulated “neural” layers be as similar as possible to the function of the corresponding biological neural layer,’” DiCarlo explains. “We asked it to do both of those things as best it could.” This forced the artificial neural circuits to find a different way to process visual information than the standard, computer vision approach, he says.
After training the artificial model with biological data, DiCarlo’s team compared its activity to a similarly-sized neural network model trained without neural data, using the standard approach for computer vision. They found that the new, biologically-informed model IT layer was – as instructed — a better match for IT neural data. That is, for every image tested, the population of artificial IT neurons in the model responded more similarly to the corresponding population of biological IT neurons.
“Everybody gets something out of the exciting virtuous cycle between natural/biological intelligence and artificial intelligence,” DiCarlo says.
The researchers also found that the model IT was also a better match to IT neural data collected from another monkey, even though the model had never seen data from that animal, and even when that comparison was evaluated on that monkey’s IT responses to new images. This indicated that the team’s new, “neurally-aligned” computer model may be an improved model of the neurobiological function of the primate IT cortex — an interesting finding, given that it was previously unknown whether the amount of neural data that can be currently collected from the primate visual system is capable of directly guiding model development.
With their new computer model in hand, the team asked whether the “IT neural alignment” procedure also leads to any changes in the overall behavioral performance of the model. Indeed, they found that the neurally-aligned model was more human-like in its behavior — it tended to succeed in correctly categorizing objects in images for which humans also succeed, and it tended to fail when humans also fail.
Adversarial attacks
The team also found that the neurally-aligned model was more resistant to “adversarial attacks” that developers use to test computer vision and AI systems. In computer vision, adversarial attacks introduce small distortions into images that are meant to mislead an artificial neural network.
“Say that you have an image that the model identifies as a cat. Because you have the knowledge of the internal workings of the model, you can then design very small changes in the image so that the model suddenly thinks it’s no longer a cat,” DiCarlo explains.
These minor distortions don’t typically fool humans, but computer vision models struggle with these alterations. A person who looks at the subtly distorted cat still reliably and robustly reports that it’s a cat. But standard computer vision models are more likely to mistake the cat for a dog, or even a tree.
“There must be some internal differences in the way our brains process images that lead to our vision being more resistant to those kinds of attacks,” DiCarlo says. And indeed, the team found that when they made their model more neurally-aligned, it became more robust, correctly identifying more images in the face of adversarial attacks. The model could still be fooled by stronger “attacks,” but so can people, DiCarlo says. His team is now exploring the limits of adversarial robustness in humans.
A few years ago, DiCarlo’s team found they could also improve a model’s resistance to adversarial attacks by designing the first layer of the artificial network to emulate the early visual processing layer in the brain. One key next step is to combine such approaches — making new models that are simultaneously neurally-aligned at multiple visual processing layers.
The new work is further evidence that an exchange of ideas between neuroscience and computer science can drive progress in both fields. “Everybody gets something out of the exciting virtuous cycle between natural/biological intelligence and artificial intelligence,” DiCarlo says. “In this case, computer vision and AI researchers get new ways to achieve robustness and neuroscientists and cognitive scientists get more accurate mechanistic models of human vision.”
This work was supported by the MIT-IBM Watson AI Lab, Semiconductor Research Corporation, DARPA, the Massachusetts Institute of Technology Shoemaker Fellowship, Office of Naval Research, the Simons Foundation, and Canada Research Chair Program.
