Research Area: Cell Biology

Locally produced proteins help mitochondria function

One of the ways that cells ensure proteins end up where they're needed is creating them at that location, through a process called localized translation. New research from the Weissman Lab has expanded our understanding localized translation at mitochondria and sheds light on the organizational principles of genes and the proteins they encode.

Greta Friar | Whitehead Institute
August 27, 2025

Now, Weissman, who is also a professor of biology at the Massachusetts Institute of Technology and an HHMI Investigator, and postdoc in his lab Jingchuan Luo have expanded our knowledge of localized translation at mitochondria, structures that generate energy for the cell. In a paper published in Cell on August 27, they share a new tool, LOCL-TL, for studying localized translation in close detail, and describe the discoveries it enabled about two classes of proteins that are locally translated at mitochondria.

The importance of localized translation at mitochondria relates to their unusual origin. Mitochondria were once bacteria that lived within our ancestors’ cells. Over time the bacteria lost their autonomy and became part of the larger cells, which included migrating most of their genes into the larger cell’s genome in the nucleus. Cells evolved processes to ensure that proteins needed by mitochondria that are encoded in genes in the larger cell’s genome get transported to the mitochondria. Mitochondria retain a few genes in their own genome, so production of proteins from the mitochondrial genome and that of the larger cell’s genome must be coordinated to avoid mismatched production of mitochondrial parts. Localized translation may help cells to manage the interplay between mitochondrial and nuclear protein production—among other purposes.

How to detect local protein production

For a protein to be made, genetic code stored in DNA is read into RNA, and then the RNA is read or translated by a ribosome, a cellular machine that builds a protein according to the RNA code. Weissman’s lab previously developed a method to study localized translation by tagging ribosomes near a structure of interest, and then capturing the tagged ribosomes in action and observing the proteins they are making. This approach, called proximity-specific ribosome profiling, allows researchers to see what proteins are being made where in the cell. The challenge that Luo faced was how to tweak this method to capture only ribosomes at work near mitochondria.

Ribosomes work quickly, so a ribosome that gets tagged while making a protein at the mitochondria can move on to making other proteins elsewhere in the cell in a matter of minutes. The only way researchers can guarantee that the ribosomes they capture are still working on proteins made near the mitochondria is if the experiment happens very quickly.

Weissman and colleagues had previously solved this time sensitivity problem in yeast cells with a ribosome-tagging tool called BirA that is activated by the presence of the molecule biotin. BirA is fused to the cellular structure of interest, and tags ribosomes it can touch—but only once activated. Researchers keep the cell depleted of biotin until they are ready to capture the ribosomes, to limit the time when tagging occurs. However, this approach does not work with mitochondria in mammalian cells because they need biotin to function normally, so it cannot be depleted.

Luo and Weissman adapted the existing tool to respond to blue light instead of biotin. The new tool, LOV-BirA, is fused to the mitochondria’s outer membrane. Cells are kept in the dark until the researchers are ready. Then they expose the cells to blue light, activating LOV-BirA to tag ribosomes. They give it a few minutes and then quickly extract the ribosomes. This approach proved very accurate at capturing only ribosomes working at mitochondria.

The researchers then used a method originally developed by the Weissman lab to extract the sections of RNA inside of the ribosomes. This allows them to see exactly how far along in the process of making a protein the ribosome is when captured, which can reveal whether the entire protein is made at the mitochondria, or whether it is partly produced elsewhere and only gets completed at the mitochondria.

“One advantage of our tool is the granularity it provides,” Luo says. “Being able to see what section of the protein is locally translated helps us understand more about how localized translation is regulated, which can then allow us to understand its dysregulation in disease and to control localized translation in future studies.”

Two protein groups are made at mitochondria

Using these approaches, the researchers found that about twenty percent of the genes needed in mitochondria that are located in the main cellular genome are locally translated at mitochondria. These proteins can be divided into two distinct groups with different evolutionary histories and mechanisms for localized translation.

One group consists of relatively long proteins, each containing more than 400 amino acids or protein building blocks. These proteins tend to be of bacterial origin—present in the ancestor of mitochondria—and they are locally translated in both mammalian and yeast cells, suggesting that their localized translation has been maintained through a long evolutionary history.

Like many mitochondrial proteins encoded in the nucleus, these proteins contain a mitochondrial targeting sequence (MTS), a zip code that tells the cell where to bring them. The researchers discovered that most proteins containing an MTS also contain a nearby inhibitory sequence that prevents transportation until they are done being made. This group of locally translated proteins lacks the inhibitory sequence, so they are brought to the mitochondria during their production.

Production of these longer proteins begins anywhere in the cell, and then after approximately the first 250 amino acids are made, they get transported to the mitochondria. While the rest of the protein gets made, it is simultaneously fed into a channel that brings it inside the mitochondria. This ties up the channel for a long time, limiting import of other proteins, so cells can only afford to do this simultaneous production and import for select proteins. The researchers hypothesize that these bacterial-origin proteins are given priority as an ancient mechanism to ensure that they are accurately produced and placed within mitochondria.

The second locally translated group consists of short proteins, each less than 200 amino acids long. These proteins are more recently evolved, and correspondingly, the researchers found that the mechanism for their localized translation is not shared by yeast. Their mitochondrial recruitment happens at the RNA level. Two sequences within regulatory sections of each RNA molecule that do not encode the final protein instead code for the cell’s machinery to recruit the RNAs to the mitochondria.

The researchers searched for molecules that might be involved in this recruitment, and identified the RNA binding protein AKAP1, which exists at mitochondria. When they eliminated AKAP1, the short proteins were translated indiscriminately around the cell. This provided an opportunity to learn more about the effects of localized translation, by seeing what happens in its absence. When the short proteins were not locally translated, this led to the loss of various mitochondrial proteins, including those involved in oxidative phosphorylation, our cells’ main energy generation pathway.

In future research, Weissman and Luo will delve deeper into how localized translation affects mitochondrial function and dysfunction in disease. The researchers also intend to use LOCL-TL to study localized translation in other cellular processes, including in relation to embryonic development, neural plasticity, and disease.

“This approach should be broadly applicable to different cellular structures and cell types, providing many opportunities to understand how localized translation contributes to biological processes,” Weissman says. “We’re particularly interested in what we can learn about the roles it may play in diseases including neurodegeneration, cardiovascular diseases, and cancers.”

Luo et al. “Proximity-specific ribosome profiling reveals the logic of localized mitochondrial translation.” Cell, August 27, 2025. https://doi.org/10.1016/j.cell.2025.08.002

Mapping cells in time and space: a new tool reveals a detailed history of tumor growth

Weissman and colleagues have developed an advanced lineage tracing tool that not only captures an accurate family tree of cell divisions, but also combines that with spatial information: identifying where each cell ends up within a tissue.

Greta Friar | Whitehead Institute
July 24, 2025

All life is connected in a vast family tree. Every organism exists in relationship to its ancestors, descendants, and cousins, and the path between any two individuals can be traced. The same is true of cells within organisms—each of the trillions of cells in the human body is produced through successive divisions from a fertilized egg, and can all be related to one another through a cellular family tree. In simpler organisms such as the worm C. elegans, this cellular family tree has been fully mapped, but the cellular family tree of a human is many times larger and more complex.

In the past, Whitehead Institute Member Jonathan Weissman and other researchers have developed lineage tracing methods to track and reconstruct the family trees of cell divisions in model organisms in order to understand more about the relationships between cells and how they assemble into tissues, organs, and—in some cases—tumors. These methods could help to answer many questions about how organisms develop and diseases like cancer are initiated and progress.

Now, Weissman and colleagues have developed an advanced lineage tracing tool that not only captures an accurate family tree of cell divisions, but also combines that with spatial information: identifying where each cell ends up within a tissue. The researchers used their tool, PEtracer, to observe the growth of metastatic tumors in mice. Combining lineage tracing and spatial data provided the researchers with a detailed view of how elements intrinsic to the cancer cells and from their environments influenced tumor growth, as Weissman and postdocs in his lab Luke Koblan, Kathryn Yost, and Pu Zheng, and graduate student William Colgan share in a paper published in the journal Science on July 24.

“Developing this tool required combining diverse skillsets through the sort of ambitious interdisciplinary collaboration that’s only possible at a place like Whitehead Institute,” says Weissman, who is also a professor of biology at the Massachusetts Institute of Technology and an HHMI Investigator. “Luke came in with an expertise in genetic engineering, Pu in imaging, Katie in cancer biology, and William in computation but the real key to their success was their ability to work together to build PEtracer.”

“Understanding how cells move in time and space is an important way to look at biology, and here we were able to see both of those things in high resolution. The idea is that by understanding both a cell’s past and where it ends up, you can see how different factors throughout its life influenced its behaviors. In this study we use these approaches to look at tumor growth, though in principle we can now begin to apply these tools to study other biology of interest like embryonic development,” Koblan says.

Designing a tool to track cells in space and time

PEtracer tracks cells’ lineages by repeatedly adding short, predetermined codes to the DNA of cells over time. Each piece of code, called a lineage tracing mark, is made up of 5 bases, the building blocks of DNA. These marks are inserted using a gene editing technology called prime editing, which directly rewrites stretches of DNA with minimal undesired byproducts. Over time, each cell acquires more lineage tracing marks, while also maintaining the marks of its ancestors. The researchers can then compare cells’ combinations of marks to figure out relationships and reconstruct the family tree.

“We used computational modeling to design the tool from first principles, to make sure that it was highly accurate, and compatible with imaging technology. We ran many simulations to land on the optimal parameters for a new lineage tracing tool, and then engineered our system to fit those parameters,” Colgan says.

When the tissue—in this case, a tumor growing in the lung of a mouse—had sufficiently grown, the researchers collected these tissues and used advanced imaging approaches to look at each cell’s lineage relationship to other cells via the lineage tracing marks, along with its spatial position within the imaged tissue and its identity (as determined by the levels of different RNAs expressed in each cell). PEtracer is compatible with both imaging approaches and sequencing methods that capture genetic information from single cells.

“Making it possible to collect and analyze all of this data from the imaging was a large challenge,” Zheng says. “What’s particularly exciting to me is not just that we were able to collect terabytes of data, but that we designed the project to collect data that we knew we could use to answer important questions and drive biological discovery.”

Reconstructing the history of a tumor

Combining the lineage tracing, gene expression, and spatial data let the researchers understand how the tumor grew. They could tell how closely related neighboring cells are and compare their traits. Using this approach, the researchers found that the tumors they were analyzing were made up of four distinct modules, or neighborhoods, of cells.

The tumor cells closest to the lung, the most nutrient-dense region, were the most fit, meaning their lineage history indicated the highest rate of cell division over time. Fitness in cancer cells tends to correlate to how aggressively tumors will grow.

The cells at the “leading edge” of the tumor, the far side from the lung, were more diverse and not as fit. Below the leading edge was a low-oxygen neighborhood of cells that might once have been leading edge cells, now trapped in a less desirable spot. Between these cells and the lung-adjacent cells was the tumor core, a region with both living and dead cells as well as cellular debris.

The researchers found that cancer cells across the family tree were equally likely to end up in most of the regions, with the exception of the lung adjacent region, where a few branches of the family tree dominated. This suggests that the cancer cells’ differing traits were heavily influenced by their environments, or the conditions in their local neighborhoods, rather than their family history. Further evidence of this point was that expression of certain fitness-related genes, such as Fgf1/Fgfbp1, correlated to a cell’s location rather than its ancestry. However, lung adjacent cells also had inherited traits that gave them an edge, including expression of the fitness-related gene Cldn4­—showing that family history influenced outcomes as well.

These findings demonstrate how cancer growth is influenced both by factors intrinsic to certain lineages of cancer cells and by environmental factors that shape the behavior of cancer cells exposed to them.

“By looking at so many dimensions of the tumor in concert, we could gain insights that would not have been possible with a more limited view,” Yost says. “Being able to characterize different populations of cells within a tumor will enable researchers to develop therapies that target the most aggressive populations more effectively.”

“Now that we’ve done the hard work of designing the tool, we’re excited to apply it to look at all sorts of questions in health and disease, in embryonic development, and across other model species, with an eye toward understanding important problems in human health,” Koblan says. “The data we collect will also be useful for training AI models of cellular behavior. We’re excited to share this technology with other researchers and see what we all can discover.”

Luke W. Koblan, Kathryn E. Yost, Pu Zheng, William N. Colgan, Matthew G. Jones, Dian Yang, Arhan Kumar, Jaspreet Sandhu, Alexandra Schnell, Dawei Sun, Can Ergen, Reuben A. Saunders, Xiaowei Zhuang, William E. Allen, Nir Yosef, Jonathan S. Weissman. “High-resolution spatial mapping of cell state and lineage dynamics in vivo with PEtracer.” Science, online July 24, 2025. https://doi.org/10.1126/science.adx3800

Putting liver cells in context: new method combines imaging and sequencing to study gene function in living tissue

Researchers in the Weissman Lab have developed a powerful approach that simultaneously measures how genetic changes such as turning off individual genes affect both gene expression and cell structure in intact liver tissue, with the goal of discovering how genes control organ function and disease.

Whitehead Institute
June 12, 2025

 

However, capturing both the “visuals and sound” of biological data, such as gene expression and cell structure data, from the same cells requires researchers to develop new approaches. They also have to make sure that the data they capture accurately reflects what happens in living organisms, including how cells interact with each other and their environments.

Whitehead Institute and Harvard University researchers have taken on these challenges and developed Perturb-Multimodal (Perturb-Multi), a powerful new approach that simultaneously measures how genetic changes such as turning off individual genes affect both gene expression and cell structure in intact liver tissue. The method, described in Cell on June 12, aims to accelerate discovery of how genes control organ function and disease.

The research team, led by Whitehead Institute Member Jonathan Weissman and then-graduate student in his lab Reuben Saunders, along with Xiaowei Zhuang, the David B. Arnold Professor of Science at Harvard University, and then-postdoc in her lab Will Allen, created a system that can test hundreds of different genetic modifications within a single mouse liver while capturing multiple types of data from the same cells.

“Understanding how our organs work requires looking at many different aspects of cell biology at once,” Saunders says. “With Perturb-Multi, we can see how turning off specific genes changes not just what other genes are active, but also how proteins are distributed within cells, how cellular structures are organized, and where cells are located in the tissue. It’s like having multiple specialized microscopes all focused on the same experiment.”

“This approach accelerates discovery by both allowing us to test the functions of many different genes at once, and then for each gene, allowing us to measure many different functional outputs or cell properties at once—and we do that in intact tissue from animals,” says Zhuang, who is also an HHMI Investigator.

A more efficient approach to genetic studies

Traditional genetic studies in mice often turn off one gene in an animal, and then observe what changes in that gene’s absence to learn about what the gene does. The researchers designed their approach to turn off hundreds of different genes across a single liver, while still only turning off one gene per cell—using what is known as a mosaic approach. This allowed them to study the roles of hundreds of individual genes at once in a single animal. The researchers then collected diverse types of data from cells across the same liver to get a full picture of the consequences of turning off the genes.

“Each cell serves as its own experiment, and because all the cells are in the same animal, we eliminate the variability that comes from comparing different mice,” Saunders says. “Every cell experiences the same physiological conditions, diet, and environment, making our comparisons much more precise.”

“The challenge we faced was that tissues, to perform their functions, rely on thousands of genes, expressed in many different cells, working together. Each gene, in turn, can control many aspects of a cell’s function. Testing these hundreds of genes in mice using current methods would be extremely slow and expensive—near impossible in practice,” Allen says.

Revealing new biology through combined measurements

The team applied Perturb-Multi to study genetic controls of liver physiology and function. Their study led to discoveries in three important aspects of liver biology: fat accumulation in liver cells—a precursor to liver disease; stress responses; and hepatocyte zonation (how liver cells specialize, assuming different traits and functions, based on their location within the liver).

“Overcoming the inherent complexity of biology in living animals required developing new tools that bridge multiple disciplines – including, in this case, genomics, imaging, and AI,” Allen says.

One striking finding emerged from studying genes that, when disrupted, cause fat accumulation in liver cells. The imaging data revealed that four different genes all led to similar fat droplet accumulation, but the sequencing data showed they did so through three completely different mechanisms.

“Without combining imaging and sequencing, we would have missed this complexity entirely,” Saunders says. “The imaging told us which genes affect fat accumulation, while the sequencing revealed whether this was due to increased fat production, cellular stress, or other pathways. This kind of mechanistic insight could be crucial for developing targeted therapies for fatty liver disease.”

The researchers also discovered new regulators of liver cell zonation. Unexpectedly, the newly discovered regulators include genes involved in modifying the extracellular matrix—the scaffolding between cells. “We found that cells can change their specialized functions without physically moving to a different zone,” Saunders says. “This suggests that liver cell identity is more flexible than previously thought.”

Technical innovation enables new science

Developing Perturb-Multi required solving several technical challenges. The team created new methods for preserving the content of interest in cells—RNA and proteins—during tissue processing, for collecting many types of imaging data and single-cell gene expression data from tissue samples that have been fixed with a preservative, and for integrating multiple types of data from the same cells.

“Overcoming the inherent complexity of biology in living animals required developing new tools that bridge multiple disciplines – including, in this case, genomics, imaging, and AI,” Allen says.

The two components of Perturb-Multi—the imaging and sequencing assays—together, applied to the same tissue, provide insights that are unattainable through either assay alone.

“Each component had to work perfectly while not interfering with the others,” says Weissman, who is also a professor of biology at the Massachusetts Institute of Technology and an HHMI Investigator. “The technical development took considerable effort, but the payoff is a system that can reveal biology we simply couldn’t see before.”

Expanding to new organs and other contexts

The researchers plan to expand Perturb-Multi to other organs, including the brain, and to study how genetic changes affect organ function under different conditions like disease states or dietary changes.

“Without combining imaging and sequencing, we would have missed this complexity entirely,” Saunders says.

“We’re also excited about using the data we generate to train machine learning models,” adds Saunders. “With enough examples of how genetic changes affect cells, we could eventually predict the effects of mutations without having to test them experimentally—a ‘virtual cell’ that could accelerate both research and drug development.”

“Perturbation data are critical for training such AI models and the paucity of existing perturbation data represents a major hindrance in such ‘virtual cell’ efforts,” Zhuang says. “We hope Perturb-Multi will fill this gap by accelerating the collection of perturbation data.”

The approach is designed to be scalable, with the potential for genome-wide studies that test thousands of genes simultaneously. As sequencing and imaging technologies continue to improve, the researchers anticipate that Perturb-Multi will become even more powerful and accessible to the broader research community.

“Our goal is to keep scaling up. We plan to do genome-wide perturbations, study different physiological conditions, and look at different organs,” says Weissman. “That we can now collect so many types of data from so many cells, at speed, is going to be critical for building AI models like virtual cells, and I think it’s going to help us answer previously unsolvable questions about health and disease.”

Notes

Reuben A. Saunders, William E. Allen, Xingjie Pan, Jaspreet Sandhu, Jiaqi Lu, Thomas K. Lau, Karina Smolyar, Zuri A. Sullivan, Catherine Dulac, Jonathan S. Weissman, Xiaowei Zhuang. “Perturb-Multimodal: a Platform for Pooled Genetic Screens with Sequencing and Imaging in Intact Mammalian Tissue.” Cell, June 12, 2025. DOI: 10.1016/j.cell.2025.05.022.

A selfish gene unlike any other

Certain genes are “selfish," cheating the rules of inheritance to increase their chances of being transmitted. Researchers in the Yamashita Lab have uncovered a unique "self-limiting" mechanism keeping the selfish gene Stellate in check

Shafaq Zia | Whitehead Institute
May 7, 2025

When a species reproduces, typically, each parent passes on one of their two versions, or alleles, of a given gene to their offspring. But not all alleles play fair in their quest to be passed onto future generations.

Certain alleles, called meiotic drivers, are “selfish”—they cheat the rules of inheritance to increase their chances of being transmitted, often at the expense of the organism’s fitness.

The lab of Whitehead Institute Member Yukiko Yamashita investigates how genetic information is transmitted across generations through the germline—cells that give rise to egg and sperm. Now, Yamashita and first author Xuefeng Meng, a graduate student in the Yamashita Lab, have discovered a meiotic driver that operates differently from previously known drivers.

The researchers’ findings, published online in Science Advances on May 7, reveal that the Stellate (Ste) gene—which has multiple copies located close to one another—on the X chromosome in Drosophila melanogaster, a fruit fly species, is a meiotic driver that biases the transmission of the X chromosome. However, it also has a unique “self-limiting” mechanism that helps preserve the organism’s ability to have male offspring.

“This mechanism is an inherent remedy to the gene’s selfish drive,” says Yamashita, who is also a professor of biology at Massachusetts Institute of Technology and an investigator of the Howard Hughes Medical Institute. “Without it, the gene could severely skew the sex ratio in a population and drive the species to extinction—a paradox that has been recognized for a long time.”

Fatal success

Meiosis is a key process underlying sexual reproduction. This is when cells from the germline undergo two rounds of specialized cell division—meiosis I and meiosis II—to form gametes (egg and sperm cells). In males, this typically results in an equal number of X-bearing and Y-bearing sperm, which ensures an equal chance of having a male or female offspring.

Meiotic drivers located on sex chromosomes can skew this sex ratio by selectively destroying gametes that do not carry the driver allele. Among them is the meiotic driver Ste.

In male germline cells of fruit flies, Ste is kept in check by small RNA molecules, called piRNAs, produced by Suppressor of Stellate (Su(Ste)) located on the Y chromosome. These RNA molecules recruit special proteins to silence Ste RNA. This prevents the production of Ste protein that would otherwise disrupt the development of Y-bearing sperm, which helps maintain the organism’s ability to have male offspring.

“But the suppressing mechanism isn’t foolproof,” Meng explains. “When the meiotic driver and its suppressor are located on different chromosomes, they can get separated during reproduction, leaving the driver unchecked in the next generation.”

A skewed sex ratio toward females offers a short-term advantage: having more females than males could increase a population’s reproductive potential. But in the long run, the meiotic driver risks fatal success—driving the species toward extinction through depletion of males.

Interestingly, prior research suggests that un-silencing Ste only modestly skews a population’s sex ratio, even in the absence of the suppressor, unlike other meiotic drivers that almost exclusively produce females in the progeny. Could another mechanism be at play, keeping Ste’s selfish drive in check?

Practicing self-restraint

To explore this intriguing possibility, researchers in the Yamashita Lab began by examining the process of sperm development. Under moderate Ste expression, pre-meiotic germ cell development and meiosis proceeded normally but defects in sperm development began to emerge soon after. Specifically, a subset of spermatids—immature sperm cells produced after meiosis—failed to incorporate essential DNA-packaging proteins called protamines, which are required to preserve the integrity of genetic information in sperm.

To confirm if the spermatids impacted were predominantly those that carried the Y chromosome, the researchers used an imaging technique called immunofluorescence staining, which uses antibodies to attach fluorescent molecules to a protein of interest, making it glow. They combined this with a technique called FISH (fluorescence in-situ hybridization), which tags the X and Y chromosomes with fluorescent markers, allowing researchers to distinguish between cells that will become X-bearing or Y-bearing following meiosis.

Indeed, the team found that while Ste protein is present in all spermatocytes before meiosis I, it unevenly divides between the two daughter cells—a phenomenon called asymmetric segregation—during meiosis I and gets concentrated in Y-bearing spermatids, eventually inducing DNA-packaging defects in these spermatids.

These findings clarified Ste’s role as a meiotic driver but the researchers still wondered why expression of Ste only led to a moderate sex ratio distortion. The answer soon became clear when they observed Ste undergo another round of asymmetric segregation during meiosis II. This meant that even if a secondary spermatocyte inherited Ste protein after meiosis I, only half of the spermatids produced in this round of cell division ended up retaining the protein. Hence, only half of the Y-bearing spermatids were going to be killed off.

“This self-limiting mechanism is the ultimate solution to the driver-suppressor separation problem,” says Yamashita. “But the idea is so unconventional that had it been proposed as just a theory, without the evidence we have now, it would’ve been completely dismissed.”

These findings have solved some questions and raised others: Unlike female meiosis, which is known to be asymmetrical, male meiosis has traditionally been considered symmetrical. Does the unequal segregation of Ste suggest there’s an unknown asymmetry in male meiosis? Do meiotic drivers like Ste trigger this asymmetry, or do they simply exploit it to limit their selfish drive?

Answering them is the next big step for Yamashita and her colleagues. “This could fundamentally change our understanding of male meiosis,” she says. “The best moments in science are when textbook knowledge is challenged and it turns out to have been tunnel vision.”

Manipulating time with torpor

New research from the Hrvatin Lab recently published in Nature Aging indicates that inducing a hibernation-like state in mice slows down epigenetic changes that accompany aging.

Shafaq Zia | Whitehead Institute
March 7, 2025

Surviving extreme conditions in nature is no easy feat. Many species of mammals rely on special adaptations called daily torpor and hibernation to endure periods of scarcity. These states of dormancy are marked by a significant drop in body temperature, low metabolic activity, and reduced food intake—all of which help the animal conserve energy until conditions become favorable again.

The lab of Whitehead Institute Member Siniša Hrvatin studies daily torpor, which lasts several hours, and its longer counterpart, hibernation, in order to understand their effects on tissue damage, disease progression, and aging. In their latest study, published in Nature Aging on March 7, first author Lorna Jayne, Hrvatin, and colleagues show that inducing a prolonged torpor-like state in mice slows down epigenetic changes that accompany aging.

“Aging is a complex phenomenon that we’re just starting to unravel,” says Hrvatin, who is also an assistant professor of biology at Massachusetts Institute of Technology. “Although the full relationship between torpor and aging remains unclear, our findings point to decreased body temperature as the central driver of this anti-aging effect.”

Tampering with the biological clock

Aging is a universal process, but scientists have long struggled to find a reliable metric for measuring it. Traditional clocks fall short because biological age doesn’t always align with chronology—cells and tissues in different organisms age at varying rates.

To solve this dilemma, scientists have turned to studying molecular processes that are common to aging across many species. This, in the past decade, has led to the development of epigenetic clocks, new computational tools that can estimate an organism’s age by analyzing the accumulation of epigenetic marks in cells over time.

Think of epigenetic marks as tiny chemical tags that cling either to the DNA itself or to the proteins, called histones, around which the DNA is wrapped. Histones act like spools, allowing long strands of DNA to coil around them, much like thread around a bobbin. When epigenetic tags are added to histones, they can compact the DNA, preventing genetic information from being read, or loosen it, making the information more accessible. When epigenetic tags attach directly to DNA, they can alter how the proteins that “read” a gene bind to the DNA.

While it’s unclear if epigenetic marks are a cause or consequence of aging, this much is evident: these marks change over an organism’s lifespan, altering how genes are turned on or off, without modifying the underlying DNA sequence. These changes have enabled researchers to track the biological age of individual cells and tissues using dedicated epigenetic clocks.

In nature, states of stasis like hibernation and daily torpor help animals survive by conserving energy and avoiding predators. But now, emerging research in marmots and bats hints that hibernation may also slow down epigenetic aging, prompting researchers to explore whether there’s a deeper connection between prolonged bouts of torpor and longevity.

However, investigating this link has been challenging, as the mechanisms that trigger, regulate, and sustain torpor remain largely unknown. In 2020, Hrvatin and colleagues made a breakthrough by identifying neurons in a specific region of the mouse hypothalamus, known as the avMLPA, which act as core regulators of torpor.

“This is when we realized that we could leverage this system to induce torpor and explore mechanistically how the state of torpor might have beneficial effects on aging,” says Jayne. “You can imagine how difficult it is to study this in natural hibernators because of accessibility and the lack of tools to manipulate them in sophisticated ways.”

The age-old mystery

The researchers began by injecting adeno-associated virus in mice, a gene delivery vehicle that enables scientists to introduce new genetic material into target cells. They employed this technology to instruct neurons in the mice’s avMLPA region to produce a special receptor called Gq-DREADD, which does not respond to the brain’s natural signals but can be chemically activated by a drug. When the researchers administered this drug to the mice, it bound to the Gq-DREADD receptors, activating the torpor-regulating neurons and triggering a drop in the animals’ body temperature.

However, to investigate the effects of torpor on longevity, the researchers needed to maintain these mice in a torpor-like state for days to weeks. To achieve this, the mice were continuously administered the drug through drinking water.

The mice were kept in a torpor-like state with periodic bouts of arousal for a total of nine months. The researchers measured the blood epigenetic age of these mice at the 3-, 6-, and 9-month marks using the mammalian blood epigenetic clock. By the 9-month mark, the torpor-like state had reduced blood epigenetic aging in these mice by approximately 37%, making them biologically three months younger than their control counterparts.

To further assess the effects of torpor on aging,  the group evaluated these mice using the mouse clinical frailty index, which includes measurements like tail stiffening, gait, and spinal deformity that are commonly associated with aging. As expected, mice in the torpor-like state had a lower frailty index compared to the controls.

With the anti-aging effects of the torpor-like state established, the researchers sought to understand how each of the key factors underlying torpor—decreased body temperature, low metabolic activity, and reduced food intake—contributed to longevity.

To isolate the effects of reduced metabolic rate, the researchers induced a torpor-like state in mice, while maintaining the animal’s normal body temperature. After three months, the blood epigenetic age of these mice was similar to that of the control group, suggesting that low metabolic rate alone does not slow down epigenetic aging.

Next, Hrvatin and colleagues isolated the impact of low caloric intake on blood epigenetic aging by restricting the food intake of mice in the torpor-like state, while maintaining their normal body temperature. After three months, these mice were a similar blood epigenetic age as the control group.

When both low metabolic rate and reduced food intake were combined, the mice still exhibited higher blood epigenetic aging after three months compared to mice in the torpor state with low body temperature. These findings, combined, led the researchers to conclude that neither low metabolic rate nor reduced caloric intake alone are sufficient to slow down blood epigenetic aging. Instead, a drop in body temperature is necessary for the anti-aging effects of torpor.

Although the exact mechanisms linking low body temperature and epigenetic aging are unclear, the team hypothesizes that it may involve the cell cycle, which regulates how cells grow and divide: lower body temperatures can potentially slow down cellular processes, including DNA replication and mitosis. This, over time, may impact cell turnover and aging. With further research, the Hrvatin Lab aims to explore this link in greater depth and shed light on the lingering mystery.

Kingdoms collide as bacteria and cells form captivating connections

Studying the pathogen R. parkeri, researchers discovered the first evidence of extensive and stable interkingdom contacts between a pathogen and a eukaryotic organelle.

Lillian Eden | Department of Biology
January 24, 2025

In biology textbooks, the endoplasmic reticulum is often portrayed as a distinct, compact organelle near the nucleus, and is commonly known to be responsible for protein trafficking and secretion. In reality, the ER is vast and dynamic, spread throughout the cell and able to establish contact and communication with and between other organelles. These membrane contacts regulate processes as diverse as fat metabolism, sugar metabolism, and immune responses.

Exploring how pathogens manipulate and hijack essential processes to promote their own life cycles can reveal much about fundamental cellular functions and provide insight into viable treatment options for understudied pathogens.

New research from the Lamason Lab in the Department of Biology at MIT recently published in the Journal of Cell Biology has shown that Rickettsia parkeri, a bacterial pathogen that lives freely in the cytosol, can interact in an extensive and stable way with the rough endoplasmic reticulum, forming previously unseen contacts with the organelle.

It’s the first known example of a direct interkingdom contact site between an intracellular bacterial pathogen and a eukaryotic membrane.

The Lamason Lab studies R. parkeri as a model for infection of the more virulent Rickettsia rickettsii. R. rickettsii, carried and transmitted by ticks, causes Rocky Mountain Spotted Fever. Left untreated, the infection can cause symptoms as severe as organ failure and death.

Rickettsia is difficult to study because it is an obligate pathogen, meaning it can only live and reproduce inside living cells, much like a virus. Researchers must get creative to parse out fundamental questions and molecular players in the R. parkeri life cycle, and much remains unclear about how R. parkeri spreads.

Detour to the junction

First author Yamilex Acevedo-Sánchez, a BSG-MSRP-Bio program alum and a graduate student at the time, stumbled across the ER and R. parkeri interactions while trying to observe Rickettsia reaching a cell junction.

The current model for Rickettsia infection involves R. parkeri spreading cell to cell by traveling to the specialized contact sites between cells and being engulfed by the neighboring cell in order to spread. Listeria monocytogenes, which the Lamason Lab also studies, uses actin tails to forcefully propel itself into a neighboring cell. By contrast, R. parkeri can form an actin tail, but loses it before reaching the cell junction. Somehow, R. parkeri is still able to spread to neighboring cells.

After an MIT seminar about the ER’s lesser-known functions, Acevedo-Sánchez developed a cell line to observe whether Rickettsia might be spreading to neighboring cells by hitching a ride on the ER to reach the cell junction.

Instead, she saw an unexpectedly high percentage of R. parkeri surrounded and enveloped by the ER, at a distance of about 55 nanometers. This distance is significant because membrane contacts for interorganelle communication in eukaryotic cells form connections from 10-80 nanometers wide. The researchers ruled out that what they saw was not an immune response, and the sections of the ER interacting with the R. parkeri were still connected to the wider network of the ER.

“I’m of the mind that if you want to learn new biology, just look at cells,” Acevedo-Sánchez says. “Manipulating the organelle that establishes contact with other organelles could be a great way for a pathogen to gain control during infection.”

The stable connections were unexpected because the ER is constantly breaking and reforming connections, lasting seconds or minutes. It was surprising to see the ER stably associating around the bacteria. As a cytosolic pathogen that exists freely in the cytosol of the cells it infects, it was also unexpected to see R. parkeri surrounded by a membrane at all.

Small margins

Acevedo-Sánchez collaborated with the Center for Nanoscale Systems at Harvard University to view her initial observations at higher resolution using focused ion beam scanning electron microscopy. FIB-SEM involves taking a sample of cells and blasting them with a focused ion beam in order to shave off a section of the block of cells. With each layer, a high-resolution image is taken. The result of this process is a stack of images.

From there, Acevedo-Sánchez marked what different areas of the images were — such as the mitochondria, Rickettsia, or the ER — and a program called ORS Dragonfly, a machine learning program, sorted through the thousand or so images to identify those categories. That information was then used to create 3D models of the samples.

Acevedo-Sánchez noted that less than 5 percent of R. parkeri formed connections with the ER — but small quantities of certain characteristics are known to be critical for R. parkeri infection. R. parkeri can exist in two states: motile, with an actin tail, and nonmotile, without it. In mutants unable to form actin tails, R. parkeri are unable to progress to adjacent cells — but in nonmutants, the percentage of R. parkeri that have tails starts at about 2 percent in early infection and never exceeds 15 percent at the height of it.

The ER only interacts with nonmotile R. parkeri, and those interactions increased 25-fold in mutants that couldn’t form tails.

Creating connections

Co-authors Acevedo-Sánchez, Patrick Woida, and Caroline Anderson also investigated possible ways the connections with the ER are mediated. VAP proteins, which mediate ER interactions with other organelles, are known to be co-opted by other pathogens during infection.

During infection by R. parkeri, VAP proteins were recruited to the bacteria; when VAP proteins were knocked out, the frequency of interactions between R. parkeri and the ER decreased, indicating R. parkeri may be taking advantage of these cellular mechanisms for its own purposes during infection.

Although Acevedo-Sánchez now works as a senior scientist at AbbVie, the Lamason Lab is continuing the work of exploring the molecular players that may be involved, how these interactions are mediated, and whether the contacts affect the host or bacteria’s life cycle.

Senior author and associate professor of biology Rebecca Lamason noted that these potential interactions are particularly interesting because bacteria and mitochondria are thought to have evolved from a common ancestor. The Lamason Lab has been exploring whether R. parkeri could form the same membrane contacts that mitochondria do, although they haven’t proven that yet. So far, R. parkeri is the only cytosolic pathogen that has been observed behaving this way.

“It’s not just bacteria accidentally bumping into the ER. These interactions are extremely stable. The ER is clearly extensively wrapping around the bacterium, and is still connected to the ER network,” Lamason says. “It seems like it has a purpose — what that purpose is remains a mystery.”

A new approach to modeling complex biological systems

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

Anne Trafton | MIT News
November 5, 2024

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

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

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

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

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

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

Modeling complex systems

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

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

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

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

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

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

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

Mechanism of vaccination

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

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

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

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

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

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

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

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

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

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

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

Rest in pieces: deconstructing polypeptide degradation machinery

Lillian Eden | Department of Biology
November 12, 2024

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

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

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

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

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

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

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

Reverse Engineering using Structural Insights

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

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

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

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

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

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

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

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

Preventing Rogue Degradation

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

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

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

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

Pausing the Process

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

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

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

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

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

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

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

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

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

An elegant switch regulates production of protein variants during cell division

Cells make variants of thousands of proteins. These variants are not produced indiscriminately, but rather through precise regulatory mechanisms that can meet rapidly changing needs of the cell according to new research from the Cheeseman Lab.

Greta Friar | Whitehead Institute
October 18, 2024

Our cells contain thousands of proteins that have gone largely undetected and unstudied until recent years: these are variants of known proteins, which cells can make when their protein-building machinery interacts differently with the same stretch of genetic code. These protein variants have typically been overlooked as occasional accidents of gene expression, but researchers including Whitehead Institute Member Iain Cheeseman are discovering that they are actually abundant and can play important roles in cell functions. Researchers in Cheeseman’s lab are studying individual protein variants to learn more about them and their roles in health and disease, but they also wanted to understand broader patterns of protein variant production: how do cells control when to make one variant of a protein versus another, and what are the consequences of such switches?

Cheeseman, who is also a professor of biology at the Massachusetts Institute of Technology, and graduate student in his lab Jimmy Ly have now identified how cells switch to a different pattern of protein variant production during mitosis, or cell division. In research published in the journal Nature on October 23, they show that this broad regulatory switch helps cells survive paused cell divisions that can sometimes occur in healthy humans or be triggered by certain chemotherapy treatments. The work confirms that cells make variants of thousands of proteins, and also demonstrates that cells do not do so indiscriminately. Rather, cells use precise regulatory mechanisms to switch between different patterns of protein variant production, in order to rapidly tailor the proteins available to fit the changing needs of the cell.

A plethora of hidden proteins

Hw can our cells contain unknown proteins? In high school biology classes, students learn the rule that each gene codes for exactly one protein, such that if you know an organism’s genetic code, you should know every protein it can make. In fact, there are instead many genes that code for multiple proteins. For a protein to be made, first the genetic code for it is copied from DNA into a messenger RNA (mRNA). Then, a ribosome, the cellular machine that follows the instructions in genetic code to build a protein, locates the coding sequence within the mRNA by scanning for the start codon, a sequence of the three bases A, U, and G – bases are the chemical building blocks of RNA, abbreviated as A, U, C, and G. The ribosome recognizes the AUG start codon as the place to begin following instructions, and builds a protein based on the genetic sequence from there through to another trio of bases called a stop codon. However, one way that different versions of a protein can be produced is that a ribosome may begin reading the instructions from multiple different starting points.

Sometimes, a ribosome may miss the first AUG start codon and skip ahead to another AUG somewhere in the middle of the gene’s code, creating a truncated version of the protein. Sometimes, a ribosome may treat a similar trio of bases, such as CUG or GUG, as a start codon. This can cause it to begin earlier, creating a protein based on an extended genetic sequence. These possibilities mean that cells contain thousands more different proteins, or variants of proteins, than are represented by the dogma of one gene, one protein.

In order to understand protein variant production, the researchers—in collaboration with researchers from Whitehead Institute Member David Bartel’s lab–used a method that let them carefully track ribosomes to compare which start sites ribosomes tended to use. They looked at start site selection during mitosis versus during the rest of the cell cycle and found that a dramatic shift in use occurred for thousands of start sites. Specifically, the researchers found that during mitosis, ribosome scanning becomes more stringent. The ribosome will only begin making proteins at AUG sequences, and even then, only at AUGs that have preferable sequences of bases surrounding them—known as a strong Kozak context. This increased selectivity does not always lead to the familiar version of the protein being made during mitosis; sometimes the first AUG start codon has a weak Kozak context, so a truncated protein gets made from an AUG start codon with a stronger Kozak context that lies within the gene.

“Coming into this project, we knew very little about protein production during mitosis—for a long time, people didn’t think much protein production happened in mitosis at all,” Ly says. “It was satisfying to show not only that it is occurring, but that there’s a shift in which proteins are being made—and that this shift is important for cellular viability.”

How cells switch between protein variant programs

The researchers next identified how the switch to increased stringency is initiated during mitosis. They discovered that the key player is a protein called eIF1, which is one of many partners that can pair with ribosomes to help them select their start site. In particular, increased eIF1 pairing with ribosomes causes the ribosomes to be more stringent in their start codon selection, inhibiting the usage of non-AUG initiation sites or sites with weak Kozak contexts.

During mitosis, ribosome pairing with eIF1 increases sharply, leading to the shift in stringency. This change in pairing rate during mitosis puzzled the researchers: ribosomes and their partners, including eIF1, all typically reside together in the main body of the cell—where ribosomes make proteins—so they should be able to pair freely at any time. The researchers looked for other molecules in the same location that could be altering how ribosomes and eIF1 interact during different parts of the cell cycle, but they couldn’t find anything. Eventually, the researchers realized that the answer to the puzzle lay in a separate location: the nucleus.

They found that cells maintain a large pool of eIF1 inside of the nucleus, locked away from the ribosomes. Then, during cell division, the wall of the nucleus dissolves, mixing its contents with the rest of the cell. This is necessary for the dividing cell to divvy up its DNA, but it also releases the pool of eIF1 to pair with ribosomes, increasing stringency. At the end of mitosis, the nucleus reforms and eIF1 is re-incorporated into the nucleus of each of the two daughter cells, and the cells return to a less stringent program.

“The explanation for increased interaction between eIF1 and ribosomes during mitosis had really stumped us, and so when I saw eIF1 localizing to the nucleus, that was a really exciting ‘aha’ moment,” Ly says. “Discovering this mechanism of nuclear release during mitosis was unexpected, and it’s interesting to think about how else cells might be using it.”

Consequences of increased stringency for the cell

Once the researchers understood the how, they then wanted to understand the why? What they discovered is that when cells have no nuclear pool of eIF1, and so no change in stringency during mitosis, they are more likely to die during mitosis. In particular, these cells fare poorly during mitotic arrest, a state in which cells get stuck in mitosis for hours or even days–much longer than typical mitosis. Arrest occurs when cells detect a possible cell division error and so halt their division until the error is corrected or the cell dies.

One effect of increased stringency during mitosis is related to mitochondria, which are required for energy production in many cell types and are therefore required for maintaining viability. Cells stuck in mitotic arrest need energy to keep them going through this unexpected delay. The researchers found that increased stringency during mitosis led to an increase in the production of important mitochondrial proteins, boosting the cells’ energy supply to get them through arrest.

Increased stringency also gives cells the tools they need to escape arrest, even if they haven’t fixed the error that caused them to pause division. In a Nature paper in 2023, Cheeseman and then-postdoc in his lab Mary-Jane Tsang showed that when cells build up enough of the truncated version of a protein called CDC20, they can escape arrest. Ly’s work adds to this story by showing that the nuclear release of eIF1 increases stringency, leading to more production of truncated CDC20 during mitosis, which explains how cells build up enough of this protein variant during mitosis to trigger their escape. These findings may have important potential implications for some cancer chemotherapy strategies.

Some chemotherapies work by trapping cancer cells in mitotic arrest until they die. Cheeseman, Tsang, and Ly’s work collectively shows that when cancer cells lack sufficient truncated CDC20—as can occur in the absence of nuclear eIF1—the cells cannot escape arrest and so are killed off by these chemotherapies at higher rates. These results could be used to improve the efficacy of antimitotic chemotherapy drugs.

The switch in protein variant production that the researchers found affects thousands of proteins. These newly identified protein variants serve as a foundation for many future projects in the lab.

As the researchers continue to examine the consequences of this switch to stringency during mitosis, they are also searching for other cases in which cells regulate protein variant production outside of mitosis. For example, the researchers are interested in how this switch in stringency affects fertility; immature egg cells spend a long time in a form of arrested cell division without an intact nucleus, and Ly observed eIF1 in the nucleus of the immature female eggs.

“Cells have axes of control that they use to quickly make broad changes in gene expression,” Cheeseman says. “Several of these are central to controlling cell division—for example, the role of phosphorylation as a regulatory switch in mitosis has been well studied. Our work identifies another axis of control, and we’re excited to discover more about when and how cells make use of it.”

Pursuing the secrets of a stealthy parasite

By unraveling the genetic pathways that help Toxoplasma gondii persist in human cells, Sebastian Lourido hopes to find new ways to treat toxoplasmosis.

Anne Trafton | MIT News
August 25, 2024

Toxoplasma gondii, the parasite that causes toxoplasmosis, is believed to infect as much as one-third of the world’s population. Many of those people have no symptoms, but the parasite can remain dormant for years and later reawaken to cause disease in anyone who becomes immunocompromised.

Why this single-celled parasite is so widespread, and what triggers it to reemerge, are questions that intrigue Sebastian Lourido, an associate professor of biology at MIT and member of the Whitehead Institute for Biomedical Research. In his lab, research is unraveling the genetic pathways that help to keep the parasite in a dormant state, and the factors that lead it to burst free from that state.

“One of the missions of my lab to improve our ability to manipulate the parasite genome, and to do that at a scale that allows us to ask questions about the functions of many genes, or even the entire genome, in a variety of contexts,” Lourido says.

There are drugs that can treat the acute symptoms of Toxoplasma infection, which include headache, fever, and inflammation of the heart and lungs. However, once the parasite enters the dormant stage, those drugs don’t affect it. Lourido hopes that his lab’s work will lead to potential new treatments for this stage, as well as drugs that could combat similar parasites such as a tickborne parasite known as Babesia, which is becoming more common in New England.

“There are a lot of people who are affected by these parasites, and parasitology often doesn’t get the attention that it deserves at the highest levels of research. It’s really important to bring the latest scientific advances, the latest tools, and the latest concepts to the field of parasitology,” Lourido says.

A fascination with microbiology

As a child in Cali, Colombia, Lourido was enthralled by what he could see through the microscopes at his mother’s medical genetics lab at the University of Valle del Cauca. His father ran the family’s farm and also worked in government, at one point serving as interim governor of the state.

“From my mom, I was exposed to the ideas of gene expression and the influence of genetics on biology, and I think that really sparked an early interest in understanding biology at a fundamental level,” Lourido says. “On the other hand, my dad was in agriculture, and so there were other influences there around how the environment shapes biology.”

Lourido decided to go to college in the United States, in part because at the time, in the early 2000s, Colombia was experiencing a surge in violence. He was also drawn to the idea of attending a liberal arts college, where he could study both science and art. He ended up going to Tulane University, where he double-majored in fine arts and cell and molecular biology.

As an artist, Lourido focused on printmaking and painting. One area he especially enjoyed was stone lithography, which involves etching images on large blocks of limestone with oil-based inks, treating the images with chemicals, and then transferring the images onto paper using a large press.

“I ended up doing a lot of printmaking, which I think attracted me because it felt like a mode of expression that leveraged different techniques and technical elements,” he says.

At the same time, he worked in a biology lab that studied Daphnia, tiny crustaceans found in fresh water that have helped scientists learn about how organisms can develop new traits in response to changes to their environment. As an undergraduate, he helped develop ways to use viruses to introduce new genes into Daphnia. By the time he graduated from Tulane, Lourido had decided to go into science rather than art.

“I had really fallen in love with lab science as an undergrad. I loved the freedom and the creativity that came from it, the ability to work in teams and to build on ideas, to not have to completely reinvent the entire system, but really be able to develop it over a longer period of time,” he says.

After graduating from college, Lourido spent two years in Germany, working at the Max Planck Institute for Infection Biology. In Arturo Zychlinksy’s lab, Lourido studied two bacteria known as Shigella and Salmonella, which can cause severe illnesses, including diarrhea. His studies there helped to reveal how these bacteria get into cells and how they modify the host cells’ own pathways to help them replicate inside cells.

As a graduate student at Washington University in St. Louis, Lourido worked in several labs focusing on different aspects of microbiology, including virology and bacteriology, but eventually ended up working with David Sibley, a prominent researcher specializing in Toxoplasma.

“I had not thought much about Toxoplasma before going to graduate school,” Lourido recalls. “I was pretty unaware of parasitology in general, despite some undergrad courses, which honestly very superficially treated the subject. What I liked about it was here was a system where we knew so little — organisms that are so different from the textbook models of eukaryotic cells.”

Toxoplasma gondii belongs to a group of parasites known as apicomplexans — a type of protozoans that can cause a variety of diseases. After infecting a human host, Toxoplasma gondii can hide from the immune system for decades, usually in cysts found in the brain or muscles. Lourido found the organism especially intriguing because as a 17-year-old, he had been diagnosed with toxoplasmosis. His only symptom was swollen glands, but doctors found that his blood contained antibodies against Toxoplasma.

“It is really fascinating that in all of these people, about a quarter to a third of the world’s population, the parasite persists. Chances are I still have live parasites somewhere in my body, and if I became immunocompromised, it would become a big problem. They would start replicating in an uncontrolled fashion,” he says.

A transformative approach

One of the challenges in studying Toxoplasma is that the organism’s genetics are very different from those of either bacteria or other eukaryotes such as yeast and mammals. That makes it harder to study parasitic gene functions by mutating or knocking out the genes.

Because of that difficulty, it took Lourido his entire graduate career to study the functions of just a couple of Toxoplasma genes. After finishing his PhD, he started his own lab as a fellow at the Whitehead Institute and began working on ways to study the Toxoplasma genome at a larger scale, using the CRISPR genome-editing technique.

With CRISPR, scientists can systematically knock out every gene in the genome and then study how each missing gene affects parasite function and survival.

“Through the adaptation of CRISPR to Toxoplasma, we’ve been able to survey the entire parasite genome. That has been transformative,” says Lourido, who became a Whitehead member and MIT faculty member in 2017. “Since its original application in 2016, we’ve been able to uncover mechanisms of drug resistance and susceptibility, trace metabolic pathways, and explore many other aspects of parasite biology.”

Using CRISPR-based screens, Lourido’s lab has identified a regulatory gene called BFD1 that appears to drive the expression of genes that the parasite needs for long-term survival within a host. His lab has also revealed many of the molecular steps required for the parasite to shift between active and dormant states.

“We’re actively working to understand how environmental inputs end up guiding the parasite in one direction or another,” Lourido says. “They seem to preferentially go into those chronic stages in certain cells like neurons or muscle cells, and they proliferate more exuberantly in the acute phase when nutrient conditions are appropriate or when there are low levels of immunity in the host.”