Research Area: Biochemistry, Biophysics, and Structural Biology

Eva Frederick | Whitehead Institute

July 28, 2020

In order to grow and thrive, cells need sugar. A repertoire of cellular mechanisms turn unwieldy molecules of glucose and fructose into versatile building blocks for making useful molecules such as lipids, and energy to fuel necessary processes in the cell. But for any of these things to happen, the cells need to sense when sugars are present in the first place — and scientists are still unraveling how they do it.

Now, in a new paper online July 27 in Nature Metabolism, researchers in the lab of Whitehead Institute Member David Sabatini, identify a key molecule that signals to the cell’s growth-triggering complex mTORC1 when there is sugar to be had, leading to a metabolic response. “This discovery puts us another step closer to understanding the biology of mTORC1 and its effects on cellular growth and metabolism,” said Sabatini, who is also a professor of biology at Massachusetts Institute of Technology and investigator with the Howard Hughes Medical Institute.

mTORC1 — short for “mechanistic target of rapamycin complex 1” — is a complex of proteins involved in regulating cell growth and metabolism. Jose Orozco, a fifth-year M.D./Ph.D student in Sabatini’s lab, describes mTORC1 as a sort of cellular licensing board. In order for other parts of the cell to grow and create new products, they must first be “approved” by mTORC1. If there are enough building blocks in the cell to create a certain product, mTORC1 will add a phosphate group to the appropriate “builders” — a signal that allows the building to begin.

“The builders in this case are metabolic pathways responsible for the creation of proteins, the regulation of nucleotides, regulation of glycolysis, regulation of fatty acid synthesis,” he says. “None of these builders can sense everything. But mTORC1 can, and it makes this sort of unified decision for the cell, ‘Yes, we have everything we need to grow.’”

One essential component for cellular building is glucose. That means mTORC1 has a sweet tooth by necessity: the complex is only active when there is enough glucose in the cell. When there’s glucose to go around, mTORC1 is “on” and binds to a lysosome, a structure that serves as the cell’s “digestive system”, where it perches to perform its phosphorylation duties. When a cell is starved for glucose, the complex falls off the lysosome, inactive.

Since the early 2010s, scientists have known one way that mTOR proteins sense glucose: when there is no glucose available, the cell inhibits the action of mTORC1 through a pathway involving the protein AMPK. But another study suggested that even without AMPK, mTORC1 can still sense an absence of glucose. “I think a lot of people had written it off as ‘Oh, [the signal] must just be AMPK,’” Orozco says. “But when we tested that hypothesis, we showed that even cells that didn’t have any AMPK were still able to sense glucose availability. That was the observation that started this project.”

To find the mysterious second sugar-sensing process, Orozco and colleagues created cells in which the known signalling protein AMPK was out of the picture. Using these modified cells, they began looking for specific traits of the glucose molecule that might be triggering the response. The team found that sugars that could be broken down by the cell, such as mannose, glucosamine and fructose, were able to activate mTORC1. Non-metabolizable sugars had no effect.

This suggested that the signaling molecule was not glucose itself, but something produced when glucose is taken apart during glycolysis — the biochemical process that breaks down the sugar into usable building blocks. With this in mind, the researchers next combed step by step through glycolysis products to see which ones could be the signal molecule.

The team identified a step of glycolysis that seemed to be key, zeroing  in on a glycolysis product called dihydroxyacetone phosphate, or DHAP. Even in the complete absence of glucose, the researchers could turn on mTORC1 by adding DHAP.

It is difficult to prove exactly why the cell relies on DHAP as a signal, but Orozco has some ideas. For one thing, DHAP later goes on to serve as the backbone of lipids, which are built by a process controlled by mTORC1 — so it would make sense that mTORC1 would respond to its presence or absence. Also, DHAP levels are extremely sensitive to changes in the amount of cellular glucose, more so than any other glycolysis intermediary. Also, DHAP is a product of both glucose and fructose, which are both important sugars in the human diet.

In the future, the team hopes to understand more. “We don’t know the biochemical details of how DHAP [conveys its message],” Orozco says. “We don’t know the sensor, we don’t know what proteins bind it, and we don’t know if that causes conformational changes in [associated proteins]. That we sort of leave as the enticing next question that we want to tackle.”

At the moment, studying the glucose sensing pathway is purely foundational research. But while there are no clear applications yet, surprises could lurk just around the corner. “Targeting nutrient sensing in mTOR has shown some promise in, of all things, regulating depression and mood,” Orozco says. “That’s interesting, and we don’t really understand why that is the case. How is glucose targeting going to be important? We don’t know yet. But we think it has a lot of potential.”

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Written by Eva Frederick

Citation:

Orozco, J.M., Krawczyk, P.A., Scaria, S.M. et al. Dihydroxyacetone phosphate signals glucose availability to mTORC1. Nat Metab (2020). https://doi.org/10.1038/s42255-020-0250-5

A biophysicist employs super-resolution microscopy to peer inside living cells and witness never-before-seen phenomena.

Raleigh McElvery | Department of Biology

July 23, 2020

How do cells use physics to carry out biological processes? Biophysicist Ibrahim Cissé explores this fundamental question in his interdisciplinary laboratory, leveraging super-resolution microscopy to probe the properties of living matter. As a postdoc in 2013, he discovered that RNA polymerase II, a critical protein in gene expression, forms fleeting (“transient”) clusters with similar molecules in order to transcribe DNA into RNA. He joined the Department of Physics in 2014, and was recently granted tenure and a joint appointment in biology. He sat down to discuss how his physics training led him to rewrite the textbook on biology.

Q: How does your work revise conventional models describing how RNA polymerases carry out their cellular duties?

A: My interest in biology has always been curiosity-driven. As a physicist reading biology textbooks, I thought that transcription — the process by which DNA is made into RNA — was fully understood. It’s so basic, and the textbooks write about it with such confidence. Come to find out, most of what we know about the cell nucleus, where gene expression starts, comes from people studying these processes outside the cell, inside a test tube. I started to wonder: Do we actually know how they work in a living cell?

The textbook models say that when a specific gene is being activated, RNA polymerase and dozens of other molecules are recruited to the DNA to begin transcription. If you don’t look closely enough, the polymerases appear to be uniformly distributed and acting randomly throughout the nucleus. However, my single-molecule and “super-resolution” microscopy methods allowed me to see something different when I looked inside live cells: polymerase clusters, which are very dynamic. In the mid-’90s, people had observed similar clusters in so-called “fixed” cells that were chemically frozen. But these findings were dismissed as possible artifacts of the fixation procedure. However, when we saw these same protein clusters in living cells that were not treated with harsh chemicals, it suggested that the textbook explanation may be incomplete.

Q: How has your background in physics given you a unique perspective on the mechanics of living cells?

A: When I arrived at the University of Illinois at Urbana-Champaign to begin my PhD in physics, I hadn’t enrolled in a biology class since high school. I was really taken with the interdisciplinary work of one physics professor, Taekjip Ha, who became my PhD mentor. He had developed single-molecule fluorescence resonance energy transfer techniques, to study with unprecedented sensitivity when two biomolecules are close to each other and monitor the distance between them in real time.

Taekjip graciously accepted me into his lab despite my limited biology background, and I never looked back. His work mirrored my interest in condensed matter physics, but the material we were looking at wasn’t from the inanimate world, it was living matter.

Between 2006 and 2008, as I was working on my PhD, super-resolution microscopy really took off from the single-molecule microscopes I used in grad school. It was a natural progression, in my mind, to learn cell biology during my postdoc fellowship at École Normale Supérieure in Paris, and to try to visualize weak and transient interactions directly in living cells using single-molecule and super-resolution imaging. You could now pinpoint molecules with nanometer accuracy; you could “turn on” and “off” molecules to observe them individually and ensure there was no overlap between those that were side-by-side.

Thanks to these new techniques, we saw clusters of RNA polymerases in living cells for the first time during my postdoc, and I pushed the technique further to reveal the cluster dynamics. But the fact that you had to turn individual molecules on and off made it really hard to see these clusters assembling or disassembling. I didn’t want to trade temporal resolution for spatial resolution. So I came up with an approach called Time-Correlated Photoactivated Localization Microscopy (tcPALM). It allowed us to measure the lifetimes of these ephemeral polymerase clusters, and we found that they last just a few seconds.

Once I arrived at MIT, we wanted to test whether the clusters could be fleeting but still biologically relevant. We pushed a dual-color super-resolution technique where we correlated the clusters with gene activity. With RNA live-imaging experts at Howard Hughes Medical Institute’s Janelia Research Campus, Brian English and Tim Lionnet, and my postdoc, Wonki Cho, we found that roughly 80 to 100 polymerases form a cluster on a gene where transcription is about to start. Although the cluster is only there for a few seconds, that’s enough time to load a handful of polymerases and generate “bursts” of RNA transcription. In fact, there was a linear correlation between the clusters’ transient dynamics and the number of messenger RNAs made in each burst.

Q: What is it like to be a physicist working with biologists?

A: Even though I joined MIT as a physics hire, I was lucky enough to get lab space in Building 68 alongside amazing biologists. They were the perfect people to talk to about my crazy ideas. And it turned out that renowned researchers like Rick Young and Phil Sharp actually had similar theories. They had genomic evidence for clusters of gene regulators, which they call “super enhancers,” that we all thought could relate to what my lab was seeing. That’s led to hours of exciting discussions between our labs, and has evolved into one of my most rewarding collaborations — and revealed that clusters associate as tiny transcriptional condensates with properties of liquid droplets.

Now, students and postdocs in my lab are wondering about the clusters’ functions and mechanisms of action, and whether protein clustering extends beyond transcription. For instance, clustering could explain some aspects of neurodegeneration. One perplexing idea that came out of this work is that perhaps it gets harder for our cells to clear protein condensates as we age, leading to Parkinson’s, Alzheimer’s, and other diseases. It’s becoming clearer that physics may be just as important as biology for understanding how cells work. The physics of how condensates and droplets form in the inanimate world is increasingly helpful in determining how living cells can evolve to regulate the same process for specific biological functions like transcription. Nature uses physics in much more elaborate ways than we initially anticipated.

In the lab, Biology Professor Amy Keating researches the interactions of proteins with a mix of modeling and synthetic lab work and diverse minds

School of Science

June 11, 2020

Almost everything in biology is a multistep process, from the metabolization of carbohydrates and fats as fuel to information transcription from DNA and RNA. Without proteins and their interactions, cells couldn’t perform any of these biological tasks. But how do proteins establish their individual roles? And how do they interact with each other?  These questions drive Professor Amy Keating’s research, and both lab experiments and computational modeling are helping her reveal the mysteries behind the basic functions of life.

In Keating’s field of research, as with most areas of science, the use of artificial intelligence is a relatively new – and growing – trend. “It’s pretty scary how fast new methods in machine learning are changing the landscape,” says Keating, who holds appointments in both the Department of Biology and the Department of Biological Engineering. “I think that we will see a disruptive change in protein modeling over the coming years.” She has found that incorporating basic machine learning methods in her own work has generated some success in uncovering how protein sequences determine their interactions.

However, there are limits to using only computational modeling due to the complexities of protein-protein interaction and a general need for empirical data to calibrate the models. Her lab group integrates computation with biological engineering in a laboratory setting. Keating’s team often starts by using computational modeling to narrow down their search from a massive collection of protein structure models. This step limits their output from an effectively infinite space (~10³⁰) to something on the order of 10⁶ potential promising molecules that can be experimentally tested. They can feed the results of experiments into other algorithms that help designate the specific features of the protein that prove important. This process is cyclical, and Keating emphasizes that experimental efforts are crucial for improving the success rate of this kind of work. That is where the lab comes in. There, they do what the computer cannot: they build proteins.

With the disruption of the COVID-19 crisis the Keating lab has focused their attention on computational projects, as well as on reviewing the literature and writing up papers and theses. The members are also using their time at home to brainstorm and plan their research. “We are having multiple group meetings per week by Zoom, including a ‘Keating Group Idea Lab,’ at which everyone throws out ideas, ranging from practical suggestions about current projects to out-there new concepts, for group discussion,” says Keating. “We are confident that we can use this time productively, to advance our science, even as we make long lists of things that we are eager to do as soon as we can get back into the laboratory.”

A topic of current interest to Keating and her group members is interactions among proteins with “short linear motifs” or SLiMs, which are abundant –more than one hundred thousand such motifs are thought to exist in one human. One family of these SLiM-binding proteins regulates movement of cells within the body and is implicated in the spread of cancer cells to a secondary location (metastasis). The lab’s novel mini-protein and peptide designs aim to disrupt these protein interactions and could be useful for eventually disrupting and treating cancer and other diseases.

FOSTERING MULTIPLE INTERACTIONS

Currently, Keating’s research team consists of six students who have backgrounds in almost as many different cultures. Her students’ diversity, which stems not just from different focuses in formal training but also from life experiences, is integral to their success, according to Keating. She wishes that more women like herself and members of underrepresented minority groups who love STEM would consider pursuing academic careers. “It’s hard work, but it’s very rewarding,” she entices. The best thing about being a faculty member, she believes, is having a team of bright minds who contribute unique ideas and insights to a problem and provide information beyond her own areas of expertise.

“I learn facts that they know and I do not. I learn interesting ways of thinking about science and also ways of doing science,” she says, noting that novel ideas in methodology lead to advances in research. “I’ve learned a lot of things about computer science from my students. I’m happy that one of my former biology students is [now] a professor of computer science,” she admits, appreciating his expertise as a benefit in frequent collaborations. “I love that students at MIT question everything.” Keating’s ever-expanding knowledge builds on top of a diverse background gleaned during her time as a student.

Keating’s bachelor’s degree from Harvard University is in physics. During her PhD at University of California, Los Angeles, she shifted to chemistry — specifically computational physical organic chemistry. When browsing for a postdoctoral position, she discovered the work of former MIT Department of Biology faculty and Whitehead Institute member Peter Kim and joined him. She maintained her interest in computation as a tool for biological research, concurrently co-advised by MIT Professor of Electrical Engineering and Computer Science Bruce Tidor. It was somewhat down to chance that her academic job search led her to MIT. “I certainly never thought I would be a biology professor, especially at MIT,” she remarks of her convoluted career path through the wide world of science.

But it is an unexpected result for which Keating is grateful. “My undergrad self would have been surprised by the MIT School of Science,” she muses, which makes MIT “so much more than ‘just’ the world’s best engineering school.” That is something of a common misconception about the Institute, she feels. “I think a lot of people outside of MIT don’t know how outstanding our basic science programs are.” Keating is a part of the strong science education at MIT, which is constantly adapting to keep up with the digital age, which led to her receiving the most recent Fund for the Future of Science Award.

“I was thrilled, and pretty surprised, to receive the award; my fantastic colleagues in the School of Science are not people that you want to be competing with.” This support is invaluable to her research on the foundations of biological interactions, and to ensure a robust team that has what it needs to develop important advances.  The curious minds with which she collaborates are equally as invaluable.

“The people at MIT are amazingly smart, curious, and focused on things that I value,” Keating adds, “like good ideas, intellectual rigor, discovering new things, and education.”

This article appeared in the Summer 2020 issue of Science@MIT

Greta Friar | Whitehead Institute

May 27, 2020

In order to understand our biology, researchers need to investigate not only what cells are doing, but also more specifically what is happening inside of cells at the level of organelles, the specialized structures that perform unique tasks to keep the cell functioning. However, most methods for analysis take place at the level of the whole cell. Because a specific organelle might make up only a fraction of an already microscopic cell’s contents, “background noise” from other cellular components can drown out useful information about the organelle being studied, such as changes in the organelle’s protein or metabolite levels in response to different conditions.

Whitehead Institute Member David Sabatini and Walter Chen, a former graduate student in Sabatini’s lab and now a pediatrics resident at Boston Children’s Hospital and Boston Medical Center and a postdoctoral researcher at Harvard Medical School, developed in recent years a method for isolating organelles for analysis that outstrips previous methods in its ability to purify organelles both rapidly and specifically. They first applied the method to mitochondria, the energy-generating organelles known as the “powerhouses of the cell,” and published their study in Cell in 2016. Subsequently, former Sabatini lab postdoctoral researcher Monther Abu-Remaileh and graduate student Gregory Wyant applied the method to lysosomes, the recycling plants of cells that break down cell parts for reuse, as described in the journal Science in 2017. In collaboration with former Sabatini lab postdoctoral researcher Kivanc Birsoy, Sabatini and Chen next developed a way to use the mitochondrial method in mice, as described in PNAS in 2019. Now, in a paper published in iScience on May 22, Sabatini, Chen, and graduate student Jordan Ray have extended the method for use on peroxisomes, organelles that play essential roles in human physiology.

“It’s gratifying to see this toolkit expand so we can use it to gain insight into the nuances of these organelles’ biology,” Sabatini says.

Using their organellar immunoprecipitation techniques, the researchers have uncovered previously unknown aspects of mitochondrial biology, including changes in metabolites during diverse states of mitochondrial function. They also uncovered new aspects of lysosomal biology, including how nutrient starvation affects the exchange of amino acids between the organelle and the rest of the cell. Their methods could help researchers gain new insights into diseases in which mitochondria or lysosomes are affected, such as mitochondrial respiratory chain disorders, lysosomal storage diseases, and Parkinson’s Disease. Now that Sabatini, Chen, and Ray have extended the method to peroxisomes, it could also be used to learn more about peroxisome-linked disorders.

DEVELOPING A POTENT METHOD

The researchers’ method is based on “organellar immunoprecipitation,” which utilizes antibodies, immune system proteins that recognize specific perceived threats that they are supposed to bind to and help remove from the body. The researchers create a custom tag for each type of organelle by taking an epitope, the section of a typical perceived threat that antibodies recognize and bind to, and fusing it to a protein that is known to localize to the membrane of the organelle of interest, so the tag will attach to the organelle. The cells containing these tagged organelles are first broken up to release all of the cell’s contents, and then put in solution with tiny magnetic beads covered in the aforementioned antibodies. The antibodies on the beads latch onto the tagged organelles. A magnet is then used to collect all of the beads and separate the bound organelles from the rest of the cellular material, while contaminants are washed away. The resulting isolated organelles can subsequently be analyzed using a variety of methods that look at the organelles’ metabolites, lipids, and proteins.

With their method, Chen and Sabatini have developed an organellar isolation technique that is both rapid and specific, qualities that prior methods have typically lacked. The workflow that Chen and Sabatini developed is fast—this new iteration for peroxisomes takes only 10 minutes to isolate the tagged organelles once they have been released from cells. Speed is important because the natural profile of the organelles’ metabolites and proteins begins to change once they are released from the cell, and the longer the process takes, the less the results will reflect the organelle’s native state.

“We’re interested in studying the metabolic contents of organelles, which can be labile over the course of an isolation,” Chen says. “Because of their speed and specificity, these methods allow us to not only better assess the original metabolic profile of a specific organelle but also study proteins that may have more transient interactions with the organelle, which is very exciting.”

PEROXISOMES TAKE THE LIMELIGHT

Peroxisomes are organelles that are important for multiple metabolic processes and contribute to a number of essential biological functions, such as producing the insulating myelin sheaths for neurons. Defects in peroxisomal function are found in various genetic disorders in children and have been implicated in neurodegenerative diseases as well. However, compared to other organelles such as mitochondria, peroxisomes are relatively understudied. Being able to get a close-up look at the contents of peroxisomes may provide insights into important and previously unappreciated biology. Importantly, in contrast to traditional ways of isolating peroxisomes, the new method that Sabatini, Chen, and Ray have developed is not only fast and specific, but also reproducible and easy to use.

“Peroxisomal biology is quite fascinating, and there are a lot of outstanding questions about how they are formed, how they mature, and what their role is in disease that hopefully this tool can help elucidate,” Ray says.

An exciting next step may be to adapt the peroxisome isolation method so it can be used in a mammaliam model organism, such as mice, something the researchers have already done with the mitochondrial version.

“Using this method in animals could be especially helpful for studying peroxisomes because peroxisomes participate in a variety of functions that are essential on an organismal rather than cellular level,” Chen says. Going forward, Chen is interested in using the method to profile the contents of peroxisomes in specific cell types across a panel of different mammalian organs.

While Chen sets out to discover what unknown biology the peroxisome isolation method can reveal, researchers in Sabatini’s lab are busy working on another project: extending the method to even more organelles.

Written by Greta Friar

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David Sabatini’s primary affiliation is with Whitehead Institute for Biomedical Research, where his laboratory is located and all his research is conducted. He is also a Howard Hughes Medical Institute investigator and a professor of biology at Massachusetts Institute of Technology.

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Citations:

G. Jordan Ray, Elizabeth A. Boydston, Emily Shortt, Gregory A. Wyant, Sebastian Lourido, Walter W. Chen, David M. Sabatini,  “A PEROXO-Tag Enables Rapid Isolation of Peroxisomes from Human Cells,” iScience, May 22, 2020.

Bayraktar et al., “MITO-Tag Mice enable rapid isolation and multimodal profiling of mitochondria from specific cell types in vivo,” PNAS, Jan 2, 2019.

Abu-Remaileh et al., “Lysosomal metabolomics reveals V-ATPase- and mTOR-dependent regulation of amino acid efflux from lysosomes,” Science, Nov 10, 2017.

Chen et al., “Absolute quantification of matrix metabolites reveals the dynamics of mitochondrial metabolism,” Cell, August 25, 2016.