Study: Firms often use automation to control certain workers’ wages

When we hear about automation and artificial intelligence replacing jobs, it may seem like a tsunami of technology is going to wipe out workers broadly, in the name of greater efficiency. But a study co-authored by an MIT economist shows markedly different dynamics in the U.S. since 1980. 

Rather than implement automation in pursuit of maximal productivity, firms have often used automation to replace employees who specifically receive a “wage premium,” earning higher salaries than other comparable workers. In practice, that means automation has frequently reduced the earnings of non-college-educated workers who had obtained better salaries than most employees with similar qualifications. 

This finding has at least two big implications. For one thing, automation has affected the growth in U.S. income inequality even more than many observers realize. At the same time, automation has yielded a mediocre productivity boost, plausibly due to the focus of firms on controlling wages rather than finding more tech-driven ways to enhance efficiency and long-term growth.

“There has been an inefficient targeting of automation,” says MIT’s Daron Acemoglu, co-author of a published paper detailing the study’s results. “The higher the wage of the worker in a particular industry or occupation or task, the more attractive automation becomes to firms.” In theory, he notes, firms could automate efficiently. But they have not, by emphasizing it as a tool for shedding salaries, which helps their own internal short-term numbers without building an optimal path for growth.

The study estimates that automation is responsible for 52 percent of the growth in income inequality from 1980 to 2016, and that about 10 percentage points derive specifically from firms replacing workers who had been earning a wage premium. This inefficient targeting of certain employees has offset 60-90 percent of the productivity gains from automation during the time period.

“It’s one of the possible reasons productivity improvements have been relatively muted in the U.S., despite the fact that we’ve had an amazing number of new patents, and an amazing number of new technologies,” Acemoglu says. “Then you look at the productivity statistics, and they are fairly pitiful.”

The paper, “Automation and Rent Dissipation: Implications for Wages, Inequality, and Productivity,” appears in the May print issue of the Quarterly Journal of Economics. The authors are Acemoglu, who is an Institute Professor at MIT; and Pascual Restrepo, an associate professor of economics at Yale University.

Inequality implications

Dating back to the 2010s, Acemoglu and Restrepo have combined to conduct many studies about automation and its effects on employment, wages, productivity, and firm growth. In general, their findings have suggested that the effects of automation on the workforce after 1980 are more significant than many other scholars have believed. 

To conduct the current study, the researchers used data from many sources, including U.S. Census Bureau statistics, data from the bureau’s American Community Survey, industry numbers, and more. Acemoglu and Restrepo analyzed 500 detailed demographic groups, sorted by five levels of education, as well as gender, age, and ethnic background. The study links this information to an analysis of changes in 49 U.S. industries, for a granular look at the way automation affected the workforce. 

Ultimately, the analysis allowed the scholars to estimate not just the overall amount of jobs erased due to automation, but how much of that consisted of firms very specifically trying to remove the wage premium accruing to some of their workers. 

Among other findings, the study shows that within groups of workers affected by automation, the biggest effects occur for workers in the 70th-95th percentile of the salary range, indicating that higher-earning employees bear much of the brunt of this process. 

And as the analysis indicates, about one-fifth of the overall growth in income inequality is attributable to this sole factor.

“I think that is a big number,” says Acemoglu, who shared the 2024 Nobel Prize in economic sciences with his longtime collaborators Simon Johnson of MIT and James Robinson of the University of Chicago.

He adds: “Automation, of course, is an engine of economic growth and we’re going to use it, but it does create very large inequalities between capital and labor, and between different labor groups, and hence it may have been a much bigger contributor to the increase in inequality in the United States over the last several decades.” 

The productivity puzzle

The study also illuminates a basic choice for firm managers, but one that gets overlooked. Imagine a type of automation — call-center technology, for instance — that might actually be inefficient for a business. Even so, firm managers have incentive to adopt it, reduce wages, and oversee a less productive business with increased net profits.

Writ large, some version of this seems to have been happening to the U.S. economy since 1980: Greater profitability is not the same as increased productivity.

“Those two things are different,” says Acemoglu. “You can reduce costs while reducing productivity.” 

Indeed, the current study by Acemoglu and Restrepo calls to mind an observation by the late MIT economist Robert M. Solow, who in 1987 wrote, “You can see the computer age everywhere but in the productivity statistics.” 

In that vein, Acemoglu observes, “If managers can reduce productivity by 1 percent but increase profits, many of them might be happy with that. It depends on their priorities and values. So the other important implication of our paper is that good automation at the margins is being bundled with not-so-good automation.” 

To be clear, the study does not necessarily imply that less automation is always better. Certain types of automation can boost productivity and feed a virtuous cycle in which a firm makes more money and hires more workers. 

But currently, Acemoglu believes, the complexities of automation are not yet recognized clearly enough. Perhaps seeing the broad historical pattern of U.S. automation, since 1980, will help people better grasp the tradeoffs involved — and not just economists, but firm managers, workers, and technologists. 

“The important thing is whether it becomes incorporated into people’s thinking and where we land in terms of the overall holistic assessment of automation, in terms of inequality, productivity and labor market effects,” Acemoglu says. “So we hope this study moves the dial there.”

Or, as he concludes, “We could be missing out on potentially even better productivity gains by calibrating the type and extent of automation more carefully, and in a more productivity-enhancing way. It’s all a choice, 100 percent.”

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MIT BrainTrust supports neighbors living with brain injuries

Since 1998, members of MIT’s BrainTrust club have helped Boston-area residents with brain injuries or other neurological disorders through their buddy program. The organization’s members also visit patients in nursing homes suffering from neurological issues.

BrainTrust is one of the founding chapters of Synapse National, an organization created by MIT alumna Alissa Totman ’13. Synapse’s goal is to provide social support for individuals living with brain injuries and to educate and inspire student leaders in the field of brain injury.

“Learning directly from individuals who had experienced brain injury during my time in BrainTrust gave me an appreciation of the gaps in resources and opportunities for improvement in brain injury care, which ultimately motivated me to pursue a career in brain injury medicine. My experience in BrainTrust continues to shape my approach to patient care and my professional goal of improving access to specialized care for individuals with brain injury by serving as a consulting provider in the acute care hospital, as well as by training the next generation of leaders in the field,” says Totman.

The club’s president, junior Karie Shen, who is pursuing a double major in biology (Course 7) and brain and cognitive science (Course 9), says, “BrainTrust is a student-run service organization that provides support for individuals with brain injury and other neurological disorders. I joined BrainTrust because it seemed like the perfect intersection of community service and neuroscience, and I care about these two things deeply.”

BrainTrust volunteers participate in training and then are paired with a local buddy who has experienced a brain injury. Members can also spend time on the weekends with patients in nursing homes who have dementia, Alzheimer’s disease, or who have had a stroke.

Shen, along with Elizabeth Zhang, president of the MIT Pre-Med Society, recently developed a program that allows BrainTrust members to visit patients in hospice. “It’s an experience that is deeply valuable for students. We work through a third-party organization called Compassus. Because the pairing process is HIPAA-protected, our role as BrainTrust executive members is to recruit students and connect them with the hospice volunteer coordinator for training. We also provide funding for transportation, generously supported by the UA Community Service Committee,” says Shen.

Shen, who plans to go to medical school and specialize in neurology, neuro-oncology, or geriatric medicine when she completes her degree, finds the experience rewarding, at times difficult, but also offers a glimpse into the reality of working with people with brain injuries.

“Visiting the people in hospice or a nursing home is hard. I’ve seen residents cry for no apparent reason that the nurses or I can understand. But I have also come to understand that caring for a patient’s quality of life and dignity is equally important. What I came to realize is that my presence itself mattered. That perspective has shaped how I think about the kind of physician I want to become,” says Shen.

First-year student Jordan Lacsamana heard about the club during Campus Preview Weekend and was immediately interested. Lacsamana, who will major in brain and cognitive sciences, is a volunteer in the Buddy Program and meets with her buddy at least once a month.

“I joined the club because it aligned with my interests academically, but I also wanted to support someone in the Boston community. I’m pre-med, and I’m interested in surgery, possibly neurosurgery or cardiovascular surgery. But I also think it’s nice to have someone outside of MIT to talk with. It’s great to learn more about them and have that one-on-one friendship, which really is the goal,” says Lacsamana.

Lacsamana says she enjoys spending time with Amanda, her buddy, and exploring Boston and Harvard Square, meeting for coffee or meals, and getting as much out of the relationship as Amanda does.

“I see her as a mentor because coming to Boston from Dallas was such a big change, so I’ve also been able to look to her for advice. But I think one of the great things about the program is that you get to learn more about them as an individual, instead of seeing them as just a person with an injury,” says Lacsamana.

“Many of our brain injury buddies simply enjoy being around students, staying connected to what we are learning and doing. Some have been with the club for years, even upwards of a decade, and still keep up with former student members long after they graduate. It is really wonderful to see how BrainTrust has created this web of friendships between people who would otherwise never have met,” says Shen.

“Amanda has stayed in touch with her former buddy since she graduated from MIT and is going to her wedding,” says Lacsamana. “I think it’s a testament to how amazing this program is at forming those connections.”

MIT students who seek real-world opportunities in fields such as cognitive science, health care, medicine, and cognitive/neurological prosthetics, or who want to help a local resident, can join BrainTrust. Email braintrust-exec@mit.edu for more information.

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Method for stress-testing cloud computing algorithms helps avoid network failures

Researchers from MIT and elsewhere have developed a more user-friendly and efficient method to help networking engineers identify potential system failures before they cause major problems, like a cloud service outage that leaves millions of users unable to access applications. 

The technique uncovers hidden blind spots that might cause a shortcut algorithm to fail unexpectedly when it is deployed. 

This new approach can identify worse-case scenarios that an engineer might miss if they use a traditional method that compares an algorithm against a set of human-designed past test cases. It is also less labor-intensive than other verification tools that require engineers to rewrite an algorithm in a complex mathematical code each time they want to test it.

Instead of needing a mathematical reformulation, the new method reads the algorithm’s source code directly and automatically searches for worse-case scenarios that lead to the highest level of underperformance.

By helping engineers quickly and easily stress-test a networking algorithm before deployment, the method could catch failure modes that might otherwise only appear in a real outage. The technique could also be used to analyze the risks of deploying AI-generated code.

“We need to have good tools to measure the worse-case scenario performance of our algorithms so we know what could happen before we put them into production. This is an easy-to-use tool that can be plugged into current systems so we can find the best algorithm to use and ensure the worse-case scenarios are identified in advance,” says Pantea Karimi, an electrical engineering and computer science (EECS) graduate student and lead author of a paper on this new technique. 

She is joined on the paper by senior authors Mohammad Alizadeh, an associate professor of EECS and a member of the Computer Science and Artificial Intelligence Laboratory (CSAIL); and Behnaz Arzani, a principal researcher at Microsoft Research; along with Ryan Beckett, Siva Kesava Reddy Karkarla, and Pooria Namyar, researchers at Microsoft Research; and Santiago Segarra, a professor at Rice University. The research will be presented at the USENIX Symposium on Networked Systems Design and Implementation. 

Assessing algorithms

In large systems like cloud servers, the tried-and-true algorithms that route data from one place to another or are often too computationally intensive to run in a feasible amount of time. 

So, engineers and researchers develop suboptimal algorithms called heuristics that can run much faster. However, there could be unexpected but plausible circumstances that will cause a heuristic to underperform or fail when deployed.

A heuristic can route millions of data requests across a cloud network in seconds, but under the wrong conditions — like an unusual traffic pattern or a sudden spike in demand — the shortcut can break down in ways the designer never anticipated.

When these problems occur, a company may have no choice but to drop some requests that can’t be processed. 

The firm could also deliberately allocate more resources in advance to head-off a potential disaster, leading to higher overall costs and wasted electricity from underutilization.

“This is really bad for a company because, either way, they are going to lose a lot of money. If this particular scenario hasn’t happened before and was never tested, how would a developer know in advance before it happens?” Karimi says.

Stress-testing heuristics typically involves running a new algorithm in simulation using a set of human-designed test cases and manually comparing the performance with a previous algorithm. But this is time-consuming and can leave blind spots if an engineer doesn’t know to test for certain situations.

Alternatively, engineers could use a verification tool to evaluate the performance of their heuristic more systematically. However, these tools require the engineer to encode the algorithm into a complex, mathematical formula that can take days to flesh out. The process, which doesn’t work for every type of heuristic, must be repeated each time the engineer changes the code.

Instead, the researchers developed a more user-friendly and efficient verification tool, called MetaEase, that analyzes the heuristic’s existing implementation code directly to identify the biggest risks of deploying it.

“This would reduce the friction of using these heuristic analysis tools,” Karimi says.

She began this work during an internship at Microsoft Research, where the team previously developed MetaOpt, a heuristic analyzer that requires engineers to rewrite their algorithms as formal optimization models. MetaEase grew out of the desire to remove that barrier.

Maximizing the gap

MetaEase is driven by two key innovations. First, it uses a technique called symbolic execution to map out the different decision points in the heuristic's code. These are places where the algorithm might behave differently depending on the input.

This technique produces a set of representative starting points, each corresponding to a distinct behavior the heuristic could exhibit.

Second, from these starting points, MetaEase utilizes a guided search to systematically move toward inputs that make the heuristic perform as poorly as possible, compared to the optimal algorithm.

In machine learning, for instance, an input could be a set of user queries to an AI chatbot at a given time.

“In this way, we have exploited every possible heuristic behavior and used special techniques to move in the direction where we think the performance gap is going to increase,” Karimi explains.

In the end, MetaEase identifies the input that maximizes the performance gap between the heuristic and an optimal benchmark.

With this information, a heuristic developer could inspect the input to understand what went wrong and incorporate safeguards that will prevent the problem from happening during deployment.

In simulated experiments, MetaEase often identified inputs with larger performance gaps than traditional methods — pinpointing more catastrophic worse-case scenarios. And it did so much more efficiently. 

It was also able to analyze a recent networking heuristic that no state-of-the-art method could handle.

In the future, the researchers want to enhance MetaEase so it can process additional types of types of data, like categorical inputs. They also want to improve the scalability of their method and adapt MetaEase to evaluate more complex heuristics.

“Reasoning about the worst-case performance of deployed heuristics is a hard and longstanding problem. MetaEase makes tangible progress by analyzing heuristics directly from source code, eliminating the need for formal models that have historically limited who can use such analysis tools. I was pleasantly surprised that it handles non-convex and randomized heuristics by combining symbolic execution with gradient-based search in a practical and effective way,” says Ratul Mahajan of the University of Washington Paul G. Allen School of Computer Science and Engineering, who was not involved with this research.

This research was funded, in part, by a Microsoft Research internship and the U.S. National Science Foundation (NSF).

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MIT marks first Robert R. Taylor Day with Tuskegee University

On April 10, MIT marked its first official Robert R. Taylor Day with a program centered on the life and work of Robert Robinson Taylor (Class of 1892), the Institute’s first Black graduate and the first academically trained Black architect in the United States.

After graduating from MIT, Taylor joined Tuskegee Institute (now Tuskegee University), where he designed campus buildings, developed a curriculum, and helped establish an approach to architectural education grounded in making and community life — an orientation that continues to shape the relationship between MIT and Tuskegee today. 

Taylor returned to MIT on April 10, 1911, to speak at the 50th anniversary of the Institute’s founding — the date now observed as Robert R. Taylor Day. Reflecting on his education, he credited MIT with the “methods and plans” he carried to Tuskegee Institute. “Certainly the spirit,” he said, was found “in the love of doing things correctly, of putting logical ways of thinking into the humblest task … to build up the immediate community in which the persons live.”

One hundred fifteen years later, at the MIT Museum, students and faculty gathered around Taylor’s original thesis, “A Soldiers Home.” The work was presented alongside archival materials from Taylor’s time at MIT by Jonathan Duval, assistant curator of architecture and design. Rather than framing Taylor as a distant historical figure, the encounter with the work itself — its drawings, assumptions, and ambitions — set the terms for the day, bringing forward not only his accomplishments but the ideas and methods that continue to inform teaching and collaboration today. Attendees then gathered for a lunch-and-learn session including a hybrid panel involving MIT and Tuskegee University faculty. 

“It is so important to continue to develop the MIT-Tuskegee relationship begun by Robert R. Taylor,” says Kwesi Daniels, associate professor and head of the architecture department at Tuskegee University. “MIT students are provided an opportunity to experience the campus Taylor designed and his ethos of social architecture. For the Tuskegee students, they are able to appreciate the foundation Taylor received at MIT. The engagement epitomizes the ‘mind and hand’ philosophy of MIT and the head, hand, heart philosophy of Tuskegee.”

An ongoing exchange

Student and faculty exchanges, launched by the architecture departments at both institutions, have extended these connections in recent years. MIT students travel to Tuskegee for work in historic preservation and community engagement, sampling Daniels’ scanning and drone equipment, while Tuskegee students come to MIT to engage with digital fabrication and entrepreneurship.

For Nicholas de Monchaux, professor and head of the Department of Architecture at MIT, the relationship reflects continuity. “We are not uniting. We’re reuniting,” he says. “This year’s celebration should really be seen as the kickoff of a year of reflecting on Robert Taylor’s legacy and imagining what the day, and his legacy, can become over time.”

The day’s program — the vision for which originally emerged from a suggestion made by MIT literature professor Joshua Bennett during a meeting at Tuskegee with de Monchaux, Daniels, and Tuskegee President Mark Brown — moved into a broader effort among faculty and collaborators across architecture, history, and the humanities. As Bennett put it, “The primary aim of Robert R. Taylor Day is to lift up not only Taylor’s accomplishments, but his ideas — and the fact that his ideas live on in those of us who have inherited his legacy.”

That emphasis is also visible in the dedicated coursework and research that has accompanied the exchange since 2022. In class 4.s12 (Brick x Brick: Drawing a Particular Survey), taught by Carrie Norman, assistant professor in architecture at MIT, students document buildings on the Tuskegee campus through measured drawings and archival interpretation. Working from limited historical material, they reconstruct both form and intent.

“My role has been to structure this work pedagogically,” Norman says, “guiding students in methods of close looking, measured drawing, and archival interpretation.” She describes Taylor’s work as “an ongoing research agenda,” adding that “the broader aim is not only to deepen engagement with Taylor’s legacy, but to build on it through new forms of design research.”

Related work has contributed to a recent exhibition on the Tuskegee Chapel at the National Building Museum, curated by Helen Bechtel of the Yale School of Architecture. Building on research conducted in Norman’s course, students developed large-scale models that form part of the exhibition. New 3D fabrications use a limited set of archival materials to reconstruct the chapel originally designed by Taylor as the first electrified building in Alabama’s Macon County, which was destroyed by fire in 1957.

Looking ahead

Timothy Hyde, professor in the MIT Department of Architecture, has also been involved in the ongoing MIT–Tuskegee collaboration and in efforts to situate Taylor’s work within a broader historical context. He notes that Taylor’s training at MIT helped shape the curriculum he later developed at Tuskegee. “The other influence I would like to mention is the city of Boston itself,” Hyde adds. “Boston was a prosperous city with a wealth of civic architecture that Taylor would have seen and studied.” 

A documentary project on Taylor’s life, supported by the MIT Human Insight Collaborative and led by Hyde and historian Christopher Capozzola, senior associate dean for MIT Open Learning, is currently in development.

For some students, these encounters shape longer trajectories. As an undergraduate at Tuskegee, Myles Sampson participated in the MIT Summer Research Program (MSRP), where he began to connect architecture with a growing interest in computation. He later enrolled in MIT’s Master of Science in Architecture Studies (SMArchS) computation program, working with Professor Larry Sass, who introduced him to robotic fabrication.

“I never looked back,” Sampson says. “Without that hands-on research experience, I would never have looked past contemporary architectural practice.” He is now pursuing a doctorate in computational design at Carnegie Mellon University, focused on the role of automation in architecture and construction.

Sampson contributed significant work to the National Building Museum’s exhibition. His installation, Brick Parable, brings together historical reference and robotic construction. As de Monchaux notes, the project reflects the long arc of Taylor’s legacy: “bricks were fired by students as part of Taylor’s training program … Myles [Sampson]’s piece, made with a robotic assembly of bricks, explores the architectural idea of the chapel in contemporary form.”

For Daniels, the continued circulation of students between the two institutions remains central. Viewing Taylor’s thesis in particular offers a shared point of reference. “Whether the student is from Tuskegee or MIT, they are able to appreciate the quality of work Taylor completed as a student,” he says, “and how he built on that work by creating a college campus, beginning at age 25.”

Across these activities, Taylor’s work is approached not as a fixed legacy, but as a set of methods and commitments that continue to be tested. As Catherine Armwood, dean of Tuskegee University Robert R. Taylor School of Architecture and Construction Science, describes it: “While our students leverage [the design and entrepreneurship program] MITdesignX to turn architectural concepts into social enterprises through advanced fabrication and venture mentorship, MIT students come to Tuskegee for an immersion in historic preservation. By surveying buildings handcrafted by our founding students, they learn a legacy of self-reliance and community impact that can’t be found anywhere else,” Armwood says. “Together, we are bridging technical innovation with deep-rooted heritage to train a new generation of visionary leaders.” 

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Biologist Joey Davis explores how cells build complex structures

Ribosomes, the cellular machines that assemble proteins, are made from dozens of proteins and RNA molecules. Putting all of those pieces together is a complex puzzle — one that MIT Associate Professor Joey Davis PhD ’10 revels in trying to solve.

Understanding how these structures form and later break down could help researchers learn more about how disruptions of these fundamental processes can lead to disease. But, as Davis points out, it’s also an interesting biological question.

“Our long-term goal is to really understand how the natural world assembles these huge complexes rapidly and efficiently. It’s a fundamentally interesting question to think about how these things get put together,” he says.

His work has helped reveal that unlike building a house, which happens in a prescribed sequence of steps — pouring the foundation, building the frame, putting on the roof, then doing electrical and plumbing work — ribosomes can be assembled in a more flexible way. Cells can even skip an assembly step and then come back to it later.

“In these natural systems, it seems like the assembly pathways are much more dynamic and flexible,” he says. “It appears that evolution has selected pathways that aren’t strictly ordered in the way we would think about an assembly line, where you always put in one component, then the next, and then the next. We’re excited to understand the selective advantages of such approaches.”

A love of discovery

Davis’ interest in how things are put together developed early in life, inspired by his father, a carpenter who framed houses. During the mid-1980s, the family moved from Colorado to Southern California, where his father worked in construction during a housing boom there.

“I was always interested in building things, which I think probably came from being around my dad and other builders,” Davis says.

As an undergraduate at the University of California at Berkeley, where he majored in computer science and biological engineering, Davis’ interests turned toward smaller scales, in the realm of cells and molecules. During his junior year, he started working in the lab of chemistry professor Michael Marletta, who studies molecular-level biological interactions.

In the lab, Davis investigated how enzymes that contain heme are able to preferentially bind to either oxygen or nitric oxide, two gases that are very similar in structure. That work kindled a love of studying the natural world and pursuing discoveries in fundamental science.

“Being in the Marletta lab and seeing students and postdocs that were really passionate about these problems had a big impact on me,” Davis says. “The goal was to understand the fundamentals of how molecular discrimination works, and the idea of discovery for the sake of discovery was thrilling.”

After graduating from Berkeley, Davis spent another year working in Marletta’s lab, and then a year working odd jobs, before heading to MIT to pursue a PhD in biology. There, he worked with Professor Bob Sauer, now emeritus, who studied the relationship between protein structure and function, with a particular focus on the molecular machines that degrade or remodel proteins.

Davis’ thesis research centered on enzymes called AAA proteases, which remove damaged proteins from cellular membranes and send them to cell organelles that break them down. In addition to studying the structure and function of the proteases, Davis worked on ways to engineer them to tag specific proteins for destruction.

That work led him into synthetic biology, which he used to develop genetic parts that drive production of proteins of interest. Some of those parts ended up being used by the biotech startup Ginkgo Bioworks, where Davis took a job as a senior scientist after graduating.

Working at Ginkgo Bioworks allowed Davis to stay in Boston while his partner finished her PhD. The couple then moved back to California, where Davis worked as a postdoc at Scripps Research, which was home to one of the first direct electron detection cameras for cryo-electron microscopy (cryo-EM). These detectors allow researchers to generate structures with near atomic resolution. At Scripps, Davis began using them to study ribosomes as they were being assembled.

Peering into the ribosome

After joining the MIT faculty in 2017, Davis continued his work on ribosomes and assembled a lab group that includes students from a variety of backgrounds who work together to develop new ways to explore biological phenomena.

“I have a mix of method developers and biologists in the group, and the work from each of them informs each other,” Davis says. “My lab goes back and forth between building sets of tools to answer biological questions, and then as we’re answering those questions, it motivates the next generation of tool development.”

During ribosome assembly, RNA molecules fold themselves into the correct shapes, creating docking sites for proteins to attach. Then, more RNA molecules come in and fold themselves into the structure.

“It’s a beautifully coupled process by which the cell folds hundreds of RNA helices and binds on the order of 50 proteins, and it does it in two minutes from start to finish. E. coli does this 100,000 times per hour, and it’s amazing how rapid and efficient the process is,” Davis says.

Cryo-EM allows scientists to capture this process in minute detail. It can be used to take hundreds of thousands of two-dimensional images of ribosome samples frozen in a thin layer of ice, from different angles. Computer algorithms then piece together these images into a three-dimensional representation of the ribosome.

To gain insight into how ribosomes are assembled, researchers can stall the process at different points and then analyze the resulting structures. In 2021, Davis’s lab developed a new method called CryoDRGN, which uses neural networks to analyze cryo-EM data and generate the full ensemble of structures that were present in the sample.

This work has shown that when certain steps of ribosome assembly are blocked, many different structures result, suggesting that the assembly can occur in a variety of ways.

In future work, Davis aims to dramatically increase the throughput of cryo-EM to generate datasets of protein structures that could help improve the AI-based models that are now used to predict protein structures.

“There are still huge swaths of sequence space that these models are very poor at predicting, but if we could collect data on those sequences en masse, that could potentially serve as key training data for a next-generation protein structure prediction method that could fill out that space,” he says.

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Rett syndrome study highlights potential for personalized treatments

Although many studies approach the developmental disorder Rett syndrome as a single condition arising from general loss of function in the gene MECP2, a new study by neuroscientists in The Picower Institute for Learning and Memory at MIT shows that two different mutations of the gene caused many distinct abnormalities in lab cultures. Moreover, correcting key differences made by each mutation required different treatments.

“Individual mutations matter,” says Mriganka Sur, senior author of the new open-accdess study in Nature Communications and the Newton Professor in the Picower Institute and the Department of Brain and Cognitive Sciences. “This is an approach to personalizing treatment, even for a single-gene disorder.”

The study employed advanced 3D human brain tissue cultures called “organoids” or “minibrains” derived from skin cells or blood cells donated by Rett syndrome patients with each mutation. Lead author Tatsuya Osaki, a Picower Institute research scientist, says that the organoids’ ability to model the specific consequences of each mutation enabled him to gain mutation-specific insights that haven’t emerged in prior studies, where scientists just knocked out MECP2 overall. The organoids also provided a novel opportunity to understand how each mutation affected different cell types and their interactions.

Distinct effects

More than 800 mutations in MECP2 can cause Rett syndrome, but just eight account for more than 60 percent of cases. Sur and Osaki chose one of these, R306C, which involves a difference of just one DNA base pair (916C>T), because it represents 7-8 percent of Rett syndrome cases. The other mutation they chose, V247X, is much more rare and severe because it cuts off production of the gene’s protein product by a single DNA base deletion (705Gdel), leaving the protein not just errant, but incomplete.

In organoids cultured for three months, each mutation produced some common but also sometimes distinct consequences compared to control organoids with non-mutated MECP2. For many of their experiments, the team used “three-photon” microscopes capable of cellular-level resolution all the way through the organoids’ approximate 1 millimeter thickness, resolving both their structure (via “third-harmonic generation” imaging), and the live activity patterns of their neurons (via calcium fluorescence).

For instance, the scientists observed that the V247X organoids exhibited several structural differences from their controls — they were larger and had different thicknesses of various layers — but the R306C ones were much more like their controls. Organoids harboring either mutation exhibited less-developed axon projections from their neurons, compared to their control comparators.

Looking at properties of neural activity and connectivity in the organoids, the scientists found some similar deficits across both mutations. Both showed reduced spiking activity and synchronicity between neurons compared to in their controls.

But when the scientists looked at other properties, the organoids started to diverge from each other. In particular, an indication of the efficiency of their network structure called “small-world propensity” (SWP) was decreased in R306C organoids, and increased in V247X ones, compared to controls. This means that both mutations altered the development of typical network structures for information processing, but in different directions.

To ensure that their results were meaningful for Rett syndrome patients, the team collaborated with Charles Nelson at Boston Children’s Hospital, whose team measured EEG in several children with different Rett mutations. Although the sample was small, the researchers measured indications that the SWP property in the EEG readings was altered in the volunteers, much like in the organoids.

Finally, by labeling excitatory neurons to flash in one color and inhibitory neurons to flash in a different color, the scientists were able to see that connectivity between the different neural types differed significantly from controls in the V247X organoids.

Treatment tests

All the testing showed that each mutation caused several changes in organoid structure, activity, and connectivity, and that the deviations were often particular to the specific mutation.

To understand how these differences emerged, and how they might be corrected, Sur and Osaki’s team turned to examining how the cells in each kind of organoid might be expressing their genes differently than controls. Differences in gene expression often lead to alterations of key molecular pathways in cells that can disrupt their activity and function. Analysis with a technique called single cell RNA sequencing indeed yielded hundreds of differences in each organoid type, where some genes were expressed more than in controls while others were underexpressed.

For instance, the analyses revealed that in R306C organoids a gene called HDAC2 was overexpressed. That protein is known for repressing expression of other genes. Meanwhile, in the V247X organoids, the scientists found reduced expression of genes for some receptors of the inhibitory neurotransmitter GABA. These organoids also showed defects in the function of astrocyte cells, which support many aspects of neural function.

Organoids with either mutation also exhibited aberrations in molecular pathways that enable the development of circuit connections between neurons, called synapses.

Given the specific defects they observed, the scientists decided to treat the organoids with a drug that can inhibit HDAC2 activity and another that increases GABA’s efficacy. The HDAC2 inhibitor restored neuronal activity and SWP to normal levels in the R306C organoids, and the GABA “agonist” baclofen restored SWP to control levels in the V247X organoids.

Osaki notes each of the treatment drugs has already been studied in other disease contexts, meaning they are well-understood drugs that could be repurposed.

Now that the researchers have developed an organoid platform for dissecting individual mutations’ consequences, identifying both their roots and testing treatments, they plan to apply it to studying four more mutations, Sur says, comparing all of them against a standardized control organoid.

In addition to Sur, Osaki, and Nelson, the paper’s other authors are Chloe Delepine, Yuma Osako, Devorah Kranz, April Levin, and Michela Fagiolini.

The National Institutes of Health, a MURI grant, The Freedom Together Foundation, and the Simons Foundation provided support for the research.

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It took 40 years for technology to catch up to this zipper design

In 1985, the Innovative Design Fund placed an ad in Scientific American offering up to $10,000 to support clever prototypes for clothing, home decor, and textiles. William Freeman PhD ’92, then an electrical engineer at Polaroid and now an MIT professor, saw it and submitted a novel idea: a three-sided zipper. Instead of fastening pants, it’d be like a switch that seamlessly flips chairs, tents, and purses between soft and rigid states, making them easier to pack and put together.

Freeman’s blueprint was much like a regular zipper, except triangular. On each side, he nailed a belt to connect narrow wooden “teeth” together. A slider wrapping around the device could be moved up to fasten the three strips into place, straightening them into a triangular tube. His proposal was rejected, but Freeman patented his prototype and stored it in his garage in the hopes it might come in handy one day.

Nearly 40 years later, MIT Computer Science and Artificial Intelligence Laboratory (CSAIL) researchers wanted to revive the project to create items with “tunable stiffness.” Prior attempts to adjust that weren’t easily reversible or required manual assembly, so CSAIL built an automated design tool and adaptable fastener called the “Y-zipper.” The scientists’ software program helps users customize three-sided zippers, which it then builds on its own in a 3D printer using plastics. These devices can be attached or embedded into camping equipment, medical gear, robots, and art installations for more convenient assembly.

“A regular zipper is great for closing up flat objects, like a jacket, but Freeman ideated something more dynamic. Using current fabrication technology, his mechanism can transform more complex items,” says MIT postdoc and CSAIL researcher Jiaji Li, who is a lead author on an open-access paper presenting the project. “We’ve developed a process that builds objects you can rapidly shift from flexible to rigid, and you can be confident they’ll work in the real world.”

Why zippers?

Users can customize how the fasteners look when they’re zipped up in CSAIL’s software program; they can select the length of each strip, as well as the direction and angle at which they’ll bend. They can also choose from one of four motion “primitives” to select how the zipper will appear when it’s zipped up: straight, bent (similar to an arch), coiled (resembling a spring), or twisted (looks like screws).

The Y-zipper that results will appear to “shape-shift” in the real world. When unzipped, it can look like a squid with three sprawling tentacles, and when you close it up, it becomes a more compact structure (like a rod, for instance). This flexibility could be useful when you’re traveling — take pitching a tent, for example. The process can take up to six minutes to do alone, but with the Y-zipper’s help, it can be done in one minute and 20 seconds. You simply attach each arm to a side of the tent, supporting the structure from the top so that the zipper seemingly pops the canopy into place. 

This seamless transition could also unlock more flexible wearables, often useful in medical scenarios. The team wrapped the Y-zipper around a wrist cast, so that a user could loosen it during the day, and zip it up at night to prevent further injuries. In turn, a seemingly stiff device can be made more comfortable, adjusting to a patient’s needs.

The system can also aid users in crafting technology that moves at the push of a button. One can attach a motor to the Y-zipper after fabrication to automate the zipping process, which helps build things like an adaptive robotic quadruped. The robot could potentially change the size of its legs, tightening up into taller limbs and unzipping when it needs to be lower to the ground. Eventually, such rapid adjustments could help the robot explore the uneven terrain of places like canyons or forests. Actuated Y-zippers can also build dynamic art installations — for example, the team created a long, winding flower that “bloomed” thanks to a static motor zipping up the device.

Mastering the material

While Li and his colleagues saw the creative potential of the Y-zipper, it wasn’t yet clear how durable it would be. Could they sustain daily use?

The team ran a series of stress tests to find out. First, they evaluated the strength and flexibility of polylactic acid (PLA) and thermoplastic polyurethane (TPU), two plastics commonly used in 3D printing. Using a machine that bent the Y-zippers down, they found that PLA could handle heavier loads, while TPU was more pliable.

In another experiment, CSAIL researchers used an actuator to continuously open and close the Y-zipper to see how long it’d take to snap. Some 18,000 cycles of zipping and unzipping later, they finally broke. Y-zipper’s secret to durability, according to 3D simulations: its elastic structure, which helps distribute the stress of heavy loads.

Despite these findings, Li envisions an even more durable three-sided zipper using stronger materials, like metal. They may also make the zippers bigger for larger-scale projects, but that’s not yet possible with their current 3D printing platform.

Jiaji also notes that some applications remain unexplored, like space exploration, wherein Y-zipper’s tentacles could be built into a spacecraft to grab nearby rock samples. Likewise, the zippers could be embedded into structures that can be assembled rapidly, helping relief workers quickly set up shelters or medical tents during natural disasters and rescues.

“Reimagining an everyday zipper to tackle 3D morphological transitions is a brilliant approach to dynamic assembly,” says Zhejiang University assistant professor Guanyun Wang, who wasn’t involved in the paper. “More importantly, it effectively bridges the gap between soft and rigid states, offering a highly scalable and innovative fabrication approach that will greatly benefit the future design of embodied intelligence.”

Li and Freeman wrote the paper with Tianjin University PhD student Xiang Chang and MIT CSAIL colleagues: PhD student Maxine Perroni-Scharf; undergraduate Dingning Cao; recent visiting researchers Mingming Li (Zhejiang University), Jeremy Mrzyglocki (Technical University of Munich), and Takumi Yamamoto (Keio University); and MIT Associate Professor Stefanie Mueller, who is a CSAIL principal investigator and senior author on the work. Their research was supported, in part, by a postdoctoral research fellowship from Zhejiang University and the MIT-GIST Program.

The researchers’ work was presented at the ACM’s ​​Computer-Human Interaction (CHI) conference on Human Factors in Computing Systems in April.

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