Research Area: Cell Biology

Cells are known by the company they keep
Eva Frederick
March 2, 2021

In the paper, published online March 1 in the journal Cell Metabolism, researchers at Whitehead Institute and the Morgridge Institute for Research performed CRISPR-based genetic screens of cells cultured in either traditional media or a new physiologic medium previously designed in the Sabatini Lab at Whitehead Institute designed to more closely reflect the nutrient composition of human blood. The screen revealed that different genes became essential for survival and reproduction in the various conditions.

“This work underscores the importance of using more human-like, physiologically relevant media for culturing human cancer cell lines,” said Whitehead Institute Member and co-senior author David Sabatini, who is also a professor of biology at the Massachusetts Institute of Technology and an investigator of the Howard Hughes Medical Institute. “The information we can learn from screens in human plasma-like media — or media designed to mimic other fluids throughout the body — may inform new therapeutic methods down the line.”

The widespread use of a human plasma-like medium could open the door for many researchers to conduct experiments in the lab that could have more relevance to human disease, but without complicated methods or prohibitive costs.

“Medium composition is both relatively accessible and quite flexible,” said co-senior author Jason Cantor, an Investigator at the Morgridge Institute for Research and an assistant professor of biochemistry at the University of Wisconsin-Madison, and a former postdoc in Sabatini’s lab. “Not all researchers have access to specialized tissue culture incubators, nor can everyone easily pursue some of the more complex 3D and co-culture methods, but most can get their hands on a bottle of media.”

The big screen

The idea that different environmental conditions may lead to different genes being essential is not a new one. “People have done this in microorganisms, and shown that if you throw [bacteria] into different growth conditions — the contributions of different genes to cell fitness can change,” Cantor said.

With this reasoning in mind — that medium composition could affect which genes become necessary for human cells to grow — the researchers set up screens to identify essential genes in a single leukemia cell line in different kinds of culture media. One batch was grown in a traditional medium, and another cultured in the lab’s new medium called Human Plasma-Like Medium, or HPLM, which has a metabolic composition more reflective of that in human blood.

The approach they used — called a CRISPR screen —  takes advantage of CRISPR-Cas9 gene editing technology to systematically snip and knock out genes across the genome, with the goal of creating a population of cells in which every possible gene knockout is represented. The expression of some genes is essential to survival, and cells grow substantially slower or die when those genes are deleted. Other cells may have trouble functioning, and some may grow even faster. Once the pooled cells have had a chance to grow and proliferate, researchers sequence the genetic material of the entire population to determine which genes were critical for survival within the given screen.

Once they completed the initial screens, the researchers identified hundreds of genes that were conditionally essential — that is, necessary for cell growth in one medium versus another. Interestingly, these medium-dependent essential genes collectively had roles in a number of different biological processes.

To determine how much these genes were dependent on the type of cells studied, the researchers then ran similar screens across a panel of human blood cancer cell lines, and then pursued follow-up work to understand why certain genes were identified as conditionally essential.

Ultimately, they uncovered the underlying gene-nutrient interactions, and specifically for these hit genes, traced the effects to availability of certain metabolites — the nutrients and small molecules necessary for metabolism — that are uniquely defined in HPLM versus the traditional media.

The next steps

CRISPR screens can help scientists identify potential drug targets and map out important cellular interactions to inform cancer therapies. “There are so many ways that people use CRISPR screens right now,” said Cantor. “What this study is showing is that the availability metabolites can have a major impact on which genes are important for cell growth, and so I think there are a lot of implications here in terms of how these types of screens could be performed in the future in order to potentially increase the fidelity of what we see in the lab and what might happen in the body.”

The research also establishes more nuanced relationships between cells’ genes and their environment. “What this allows us to do down the line, theoretically, is to tune how important a gene is — how important the encoded protein is — by manipulating metabolite levels in the blood,” said Cantor. “That’s one of our bigger-picture ideas.”

In the future, these relationships could inform cancer treatments. For example, if scientists could “tune” the importance of a specific gene for cancer cell growth, then the protein encoded by that gene could become a more promising drug target — in effect, tricking cancer cells into revealing possible context-dependent vulnerabilities. “The idea of targeting metabolites to treat cancer isn’t itself new — in fact, it [underlies] a well-established anti-cancer therapeutic enzyme still in use today — but I think our work maybe enables us to look for ways to couple this type of approach with other targeted therapies.”

“At our core, we are a basic cell biology lab,” added Nicholas Rossiter, a technician in Cantor’s lab and the first author of the study. “But whenever you’re studying basic cell biology, there’s the potential to translate it into therapeutic strategy. Our plan is just to keep on chugging along in our lab and studying how exactly cell biology can be influenced by these environmental factors. We do the basics, and then there will hopefully be some auspicious findings that can be carried on into therapeutics.”

Seychelle Vos investigates how the genome is organized so it can fit inside the cell — and how that careful organization affects gene expression.

February 24, 2021

The Davis and Berger labs combined cryo-electron microscopy and machine learning to visualize molecules in 3D.

February 4, 2021
Machine-learning model helps determine protein structures

New technique reveals many possible conformations that a protein may take.

Anne Trafton | MIT News Office
February 4, 2021

Cryo-electron microscopy (cryo-EM) allows scientists to produce high-resolution, three-dimensional images of tiny molecules such as proteins. This technique works best for imaging proteins that exist in only one conformation, but MIT researchers have now developed a machine-learning algorithm that helps them identify multiple possible structures that a protein can take.

Unlike AI techniques that aim to predict protein structure from sequence data alone, protein structure can also be experimentally determined using cryo-EM, which produces hundreds of thousands, or even millions, of two-dimensional images of protein samples frozen in a thin layer of ice. Computer algorithms then piece together these images, taken from different angles, into a three-dimensional representation of the protein in a process termed reconstruction.

In a Nature Methods paper, the MIT researchers report a new AI-based software for reconstructing multiple structures and motions of the imaged protein — a major goal in the protein science community. Instead of using the traditional representation of protein structure as electron-scattering intensities on a 3D lattice, which is impractical for modeling multiple structures, the researchers introduced a new neural network architecture that can efficiently generate the full ensemble of structures in a single model.

“With the broad representation power of neural networks, we can extract structural information from noisy images and visualize detailed movements of macromolecular machines,” says Ellen Zhong, an MIT graduate student and the lead author of the paper.

With their software, they discovered protein motions from imaging datasets where only a single static 3D structure was originally identified. They also visualized large-scale flexible motions of the spliceosome — a protein complex that coordinates the splicing of the protein coding sequences of transcribed RNA.

“Our idea was to try to use machine-learning techniques to better capture the underlying structural heterogeneity, and to allow us to inspect the variety of structural states that are present in a sample,” says Joseph Davis, the Whitehead Career Development Assistant Professor in MIT’s Department of Biology.

Davis and Bonnie Berger, the Simons Professor of Mathematics at MIT and head of the Computation and Biology group at the Computer Science and Artificial Intelligence Laboratory, are the senior authors of the study, which appears today in Nature Methods. MIT postdoc Tristan Bepler is also an author of the paper.

Visualizing a multistep process

The researchers demonstrated the utility of their new approach by analyzing structures that form during the process of assembling ribosomes — the cell organelles responsible for reading messenger RNA and translating it into proteins. Davis began studying the structure of ribosomes while a postdoc at the Scripps Research Institute. Ribosomes have two major subunits, each of which contains many individual proteins that are assembled in a multistep process.

To study the steps of ribosome assembly in detail, Davis stalled the process at different points and then took electron microscope images of the resulting structures. At some points, blocking assembly resulted in accumulation of just a single structure, suggesting that there is only one way for that step to occur. However, blocking other points resulted in many different structures, suggesting that the assembly could occur in a variety of ways.

Because some of these experiments generated so many different protein structures, traditional cryo-EM reconstruction tools did not work well to determine what those structures were.

“In general, it’s an extremely challenging problem to try to figure out how many states you have when you have a mixture of particles,” Davis says.

After starting his lab at MIT in 2017, he teamed up with Berger to use machine learning to develop a model that can use the two-dimensional images produced by cryo-EM to generate all of the three-dimensional structures found in the original sample.

In the new Nature Methods study, the researchers demonstrated the power of the technique by using it to identify a new ribosomal state that hadn’t been seen before. Previous studies had suggested that as a ribosome is assembled, large structural elements, which are akin to the foundation for a building, form first. Only after this foundation is formed are the “active sites” of the ribosome, which read messenger RNA and synthesize proteins, added to the structure.

In the new study, however, the researchers found that in a very small subset of ribosomes, about 1 percent, a structure that is normally added at the end actually appears before assembly of the foundation. To account for that, Davis hypothesizes that it might be too energetically expensive for cells to ensure that every single ribosome is assembled in the correct order.

“The cells are likely evolved to find a balance between what they can tolerate, which is maybe a small percentage of these types of potentially deleterious structures, and what it would cost to completely remove them from the assembly pathway,” he says.

Viral proteins

The researchers are now using this technique to study the coronavirus spike protein, which is the viral protein that binds to receptors on human cells and allows them to enter cells. The receptor binding domain (RBD) of the spike protein has three subunits, each of which can point either up or down.

“For me, watching the pandemic unfold over the past year has emphasized how important front-line antiviral drugs will be in battling similar viruses, which are likely to emerge in the future. As we start to think about how one might develop small molecule compounds to force all of the RBDs into the ‘down’ state so that they can’t interact with human cells, understanding exactly what the ‘up’ state looks like and how much conformational flexibility there is will be informative for drug design. We hope our new technique can reveal these sorts of structural details,” Davis says.

The research was funded by the National Science Foundation Graduate Research Fellowship Program, the National Institutes of Health, and the MIT Jameel Clinic for Machine Learning and Health. This work was supported by MIT Satori computation cluster hosted at the MGHPCC.

A new database of potential antibiotic targets
Raleigh McElvery
January 20, 2021

Many cells, including bacteria, are covered in a sugar-rich coating that protects their membrane and internal components. These sugars are often joined to other macromolecules, like proteins or lipids, to form glycoconjugates. The glycoconjugates that encrust bacteria help prevent them from “popping” under environmental stress, and facilitate host-pathogen interactions. Because the sugary layer perpetuates survival and virulence, researchers are looking for ways to create chinks in this microbial armor — or better yet, to prevent it from being made in the first place.

Glycoconjugates are built by many enzymes working in close succession at the cell membrane. One enzyme family, comprised of phosphoglycosyl transferases (PGTs), is responsible for catalyzing the first step in the assembly line. Of this large enzyme family, one subtype in particular stands out: “monotopic” PGTs, which are unique to bacteria and could serve as antibiotic targets. If researchers can develop drugs that inhibit monoPGTs, the sugar armor wouldn’t be built and noxious bacteria could be easier to defeat.

A new PNAS study co-authored by Professor of Biology and Chemistry, Barbara Imperiali, highlights the diversity and significance of these potential drug targets. Imperiali teamed up with graduate student Katherine O’Toole and Professor of Chemistry Karen Allen from Boston University to categorize over 38,000 different monoPGTs, compiling this information into the first database of its kind.

“We’ve taken an enzyme family that was once considered quirky and insignificant, and demonstrated that it’s actually very prevalent,” Imperiali says. “Hopefully the database will help us better understand these enzymes, their molecular pathways, and the human pathogens they support.”

Imperiali and her colleagues used sequence analysis of known monoPGTs to define a “signature” amino acid sequence. They leveraged this signature to identify the entire superfamily of monoPGTs amidst the 63,152 sequences downloaded from an online portal, which they then clustered into closely-related subtypes. The researchers also created a family tree, which included over 100 monoPGTs from diverse bacterial species. Imperiali hopes others will take advantage of this new information to pinpoint monoPGTs in pathogens of interest, and explore similarities and differences in related microbes and their enzymes.

The researchers’ analyses also revealed strange, new proteins that appeared to include two enzymes in one — a monoPGT fused to one of the other enzymes that typically play a separate role in the same sugar-modifying pathway. “It’s essentially one protein with two functions,” Imperiali explains. These fusion enzymes could reveal which enzymes “talk” to one another and work sequentially during the glycoconjugate-building process, she adds, revealing the complicated chain of events that creates the bacterial sugar shield.

The team even found cases where one monoPGT was fused to a member of a different PGT family — polytopic PGTs (polyPGTs). MonoPGTs and polyPGTs are involved in different pathways that each build glycoconjugates, so having a dual-function protein could allow cells to easily switch between mechanisms. Bacterial cells lack the organizational compartments that human and other eukaryotic cells have, so perhaps these fusion enzymes help exert control and order at different points in the cell cycle, Imperiali speculates. At the moment, though, the hybrid PGTs remain an evolutionary mystery.

While some researchers parse these ancient puzzles, others may use the database to inspire new drugs to combat antibiotic resistance. “At the end of the day,” Imperiali says, “we’ve shed light on a set of enzymes that could become pivotal therapeutic targets.”

RNA molecules are masters of their own destiny
Eva Frederick | Whitehead Institute
December 16, 2020

At any given moment in the human body, in about 30 trillion cells, DNA is being “read” into molecules of messenger RNA, the intermediary step between DNA and proteins, in a process called transcription.

Scientists have a pretty good idea of how transcription gets started: proteins called RNA polymerases are recruited to specific regions of the DNA molecules and begin skimming their way down the strand, synthesizing mRNA molecules as they go. But part of this process is less well understood: how does the cell know when to stop transcribing?

Now, new work from the labs of Whitehead Institute Member Richard Young, also a professor of biology at Massachusetts Institute of Technology (MIT), and Arup K. Chakraborty, professor of chemical engineering, physics and chemistry at MIT, suggests that RNA molecules themselves are responsible for regulating their formation through a feedback loop. Too few RNA molecules, and the cell initiates transcription to create more. Then, at a certain threshold, too many RNA molecules cause transcription to draw to a halt.

The research, published in Cell on December 16, represents a collaboration between biologists and physicists, and provides some insight into the potential roles of the thousands of RNAs that are not translated into any proteins, called noncoding RNAs, which are common in mammals and have mystified scientists for decades.

A question of condensates

Previous work in Young’s lab has focused on transcriptional condensates, small cellular droplets that bring together the molecules needed to transcribe DNA to RNA. Scientists in the lab discovered the transcriptional droplets in 2018, noticing that they typically formed when transcription began and dissolved a few seconds or minutes later when the process was finished.

The researchers wondered if the force that governed the dissolution of the transcriptional condensates could be related to the chemical properties of the RNA they produced — specifically, its highly negative charge. If this were the case, it would be the latest example of cellular processes being regulated via a feedback mechanism — an elegant, efficient system used in the cell to control biological functions such as red blood cell production and DNA repair.

As an initial test, the researchers used an in vitro experiment to test whether the amount of RNA had an effect on condensate formation. They found that within the range of physiological levels observed in cells, low levels of RNA encouraged droplet formation and high levels of RNA discouraged it.

Thinking outside the biology box 

With these results in mind, Young Lab postdocs and co-first authors Ozgur Oksuz and Jon Henninger teamed up with physicist and co-first author Krishna Shrinivas, a graduate student in Arup Chakraborty’s lab, to investigate what physical forces were at play.

Shrinivas proposed that the team build a computational model to study the physical and chemical interactions between actively transcribed RNA and condensates formed by transcriptional proteins. The goal of the model was not to simply reproduce existing results, but to create a platform with which to test a variety of situations.

“The way most people study these kinds of problems is to take mixtures of molecules in a test tube, shake it and see what happens,” Shrinivas said. “That is as far away from what happens in a cell as one can imagine. Our thought was, ‘Can we try to study this problem in its biological context, which is this out-of-equilibrium, complex process?’”

Studying the problem from a physics perspective allowed the researchers to take a step back from traditional biology methods. “As a biologist, it’s difficult to come up with new hypotheses, new approaches to understanding how things work from available data,” Henninger said. “You can do screens, you can identify new players, new proteins, new RNAs that may be involved in a process, but you’re still limited by our classical understanding of how all these things interact. Whereas when talking with a physicist, you’re in this theoretical space extending beyond what the data can currently give you. Physicists love to think about how something would behave, given certain parameters.”

Once the model was complete, the researchers could ask it questions about situations that may arise in cells — for instance, what happens to condensates when RNAs of different lengths are produced at different rates as time ensues? — and then follow it up with an experiment at the lab bench. “We ended up with a very nice convergence of model and experiment,” Henninger said. “To me, it’s like the model helps distill the simplest features of this type of system, and then you can do more predictive experiments in cells to see if it fits that model.”

The charge is in charge

Through a series of modeling and experiments at the lab bench, the researchers were able to confirm their hypothesis that the effect of RNA on transcription is due to RNAs molecules’ highly negative charge. Furthermore, it was predicted that initial low levels of RNA enhance and subsequent higher levels dissolve condensates formed by transcriptional proteins. Because the charge is carried by the RNAs’ phosphate backbone, the effective charge of a given RNA molecule is directly proportional to its length.

In order to test this finding in a living cell, the researchers engineered mouse embryonic stem cells to have glowing condensates, then treated them with a chemical to disrupt the elongation phase of transcription. Consistent with the model’s predictions, the resulting dearth of condensate-dissolving RNA molecules increased the size and lifetime of condensates in the cell. Conversely, when the researchers engineered cells to induce the production of extra RNAs, transcriptional condensates at these sites dissolved. “These results highlight the importance of understanding how non-equilibrium feedback mechanisms regulate the functions of the biomolecular condensates present in cells,” said Chakraborty.

Confirmation of this feedback mechanism might help answer a long-standing mystery of the mammalian genome: the purpose of non-coding RNAs, which make up a large portion of genetic material. “While we know a lot about how proteins work, there are tens of thousands of noncoding RNA species, and we don’t know the functions of most of these molecules,” said Young. “The finding that RNA molecules can regulate transcriptional condensates makes us wonder if many of the noncoding species just function locally to tune gene expression throughout the genome. Then this giant mystery of what all these RNAs do has a potential solution.”

The researchers are optimistic that understanding this new role for RNA in the cell could inform therapies for a wide range of diseases. “Some diseases are actually caused by increased or decreased expression of a single gene,” said Oksuz, a co-first author. “We now know that if you modulate the levels of RNA, you have a predictable effect on condensates. So you could hypothetically tune up or down the expression of a disease gene to restore the expression — and possibly restore the phenotype — that you want, in order to treat a disease.”

Young added that a deeper understanding of RNA behavior could inform therapeutics more generally. In the last 10 years, a variety of drugs have been developed that directly target RNA successfully. “RNA is an important target,” Young said. “Understanding mechanistically how RNA molecules regulate gene expression bridges the gap between gene dysregulation in disease and new therapeutic approaches that target RNA.”

A research tool of a different color
Greta Friar | Whitehead Institute
November 18, 2020

Melanosomes are the organelles, or structures, inside our cells, that produce melanin, the molecule that gives our skin, hair and eyes their color. Melanosomes produce several different forms of melanin, including black/brown coloration and yellow/red coloration, and the many variations in levels at which each coloration can be produced in an individual generate the wide variety of skin, hair, and eye colors in the world.

Many genes that have been associated with skin color encode proteins that are active in melanosomes, but their specific functions are unknown, leaving gaps in researchers’ understanding of the underlying biology of skin color. In order to help researchers get a more detailed understanding of melanosome biology, Whitehead Institute Member David Sabatini’s lab has developed a tool, called MelanoIP, with which researchers can rapidly and specifically isolate melanosomes from the cell and analyze their contents. Using this tool, researchers can uncover the identity of the proteins at work there and explain mechanistically how genetic variation contributes to differences in skin color. In research published in Nature on November 18, Sabatini and graduate student Charles Hank Adelmann unveil MelanoIP and explain how they used it to crack the identity of melanosome protein MFSD12.

MelanoIP is the latest in a series of tools based on a method that Sabatini, who is also a professor of biology at Massachusetts Institute of Technology and an investigator with the Howard Hughes Medical Institute, and collaborators developed to rapidly extract specific organelles from the cell for investigation. Sabatini and former graduate student Walter Chen first developed the method to isolate mitochondria. The process starts with researchers creating a tag that localizes to the organelle type of interest. Then they expose the contents of the whole cell to beads covered in antibodies that latch onto the tags, which pull the organelles with them when they are collected. The lab has since adapted this process to use on lysosomes, the recycling centers of the cell, and peroxisomes, organelles important in several metabolic processes—and now, melanosomes.

The first melanosome protein that Sabatini and Adelmann turned their attention to, MFSD12, was known to be linked to the production of red coloration or pheomelanin. When MFSD12 is suppressed, this leads to darker skin color in humans and mice, because the melanosomes are generating brown/black melanin but not any of the lighter red melanin. However, MFSD12’s exact role was unknown. Using MelanoIP, Adelmann discovered that MFSD12 is required for the import of the amino acid cysteine into melanosomes, which is a necessary component in red melanin synthesis. Adelmann’s research suggests that MFSD12 is itself the transporter, but further work is needed to confirm whether it works alone or in conjunction with other molecules.

One reason that the Sabatini lab picked the melanosome as the next organelle to apply their IP toolkit to is because of its close relation to the lysosome, one of the organelles for which the lab had already built such a tool. This close relation proved relevant in Adelmann’s research on MFSD12, when he discovered that the protein is also required for the transport of cysteine into lysosomes. People with the rare genetic disorder cystinosis are affected by the buildup of cystine, another form of cysteine, in lysosomes. Adelmann found that by inhibiting MFSD12, and preventing cysteine from entering lysosomes, he could reverse the buildup of cystine in cells with the genetic mutation linked to cystinosis, suggesting a potential therapeutic use for MFSD12 inhibitors.

Adelmann is now turning his attention to cracking the identity of more of the proteins active in melanosomes and uncovering more of the biology underlying variation in skin color.

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Written by Greta Friar

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Adelmann, Charles H. et al. “MFSD12 mediates the import of cysteine into melanosomes and lysosomes.” Nature, Nov. 18, 2020. DOI: 10.1038/s41586-020-2937-x