Category: News Briefs
Axolotls can regrow whole body parts, from tails and limbs to even parts of their brain and spine, making them fascinating research subjects, and their unique looks have been captured in art and culture in their native Mexico and beyond. Recently, Peter Reddien’s lab has added axolotls to their list of regenerative specimens with a research project led by graduate student Conor McMann.
April 4, 2024
Endowed chairs are generally created through philanthropic gifts from individual donors, organizations, or groups of donors honoring a specific person. The chairs — of which the Institute currently has five — provide steady, predictable funding to support investigations in Members’ labs, including: Whitehead Institute Member Iain Cheeseman, who — in addition to being a professor of biology at Massachusetts Institute of Technology (MIT) — holds the Margaret and Herman Sokol Chair in Biomedical Research; Yukiko Yamashita — Whitehead Institute Member, professor of biology at MIT, and Howard Hughes Medical Institute Investigator — the inaugural incumbent of the Susan Lindquist Chair for Women in Science; Jonathan Weissman — Professor of Biology and Whitehead Institute Core Member and HHMI Investigator — is the inaugural holder of the Landon T. Clay Professor of Biology Chair. In 2020, Mary Gehring — Professor of Biology, Graduate Officer, and Core Member of the whitehead Institute Core Member and David Baltimore Chair in Biomedical Research, Whitehead Institute was named the inaugural holder of the Clay Career Development Chair. In 2023, Gehring was succeeded by Sebastian Lourido, associate professor of Biology and Core Member of the Whitehead Institute.
April 2, 2024
How do new species emerge over time? The Yamashita Lab studies the role of "junk" DNA in making two related species reproductively incompatible.
March 20, 2024
Whitehead Institute researchers including those in the Page Lab and Corradin Lab are investigating the role of X and Y chromosomes beyond sex determination, paying close attention to conditions that mostly — or distinctly — affect females, and mentoring the next generation of researchers to challenge the status quo for a better world.
Shafaq Zia | Whitehead Institute
March 12, 2024
With the new technique, MIT researchers hope to identify mutations that could be targeted with new cancer therapies.
Anne Trafton | MIT News
March 12, 2024
Tumors can carry mutations in hundreds of different genes, and each of those genes may be mutated in different ways — some mutations simply replace one DNA nucleotide with another, while others insert or delete larger sections of DNA.
Until now, there has been no way to quickly and easily screen each of those mutations in their natural setting to see what role they may play in the development, progression, and treatment response of a tumor. Using a variant of CRISPR genome-editing known as prime editing, MIT researchers have now come up with a way to screen those mutations much more easily.
The researchers demonstrated their technique by screening cells with more than 1,000 different mutations of the tumor suppressor gene p53, all of which have been seen in cancer patients. This method, which is easier and faster than any existing approach, and edits the genome rather than introducing an artificial version of the mutant gene, revealed that some p53 mutations are more harmful than previously thought.
This technique could also be applied to many other cancer genes, the researchers say, and could eventually be used for precision medicine, to determine how an individual patient’s tumor will respond to a particular treatment.
“In one experiment, you can generate thousands of genotypes that are seen in cancer patients, and immediately test whether one or more of those genotypes are sensitive or resistant to any type of therapy that you’re interested in using,” says Francisco Sanchez-Rivera, an MIT assistant professor of biology, a member of the Koch Institute for Integrative Cancer Research, and the senior author of the study.
MIT graduate student Samuel Gould is the lead author of the paper, which appears today in Nature Biotechnology.
Editing cells
The new technique builds on research that Sanchez-Rivera began 10 years ago as an MIT graduate student. At that time, working with Tyler Jacks, the David H. Koch Professor of Biology, and then-postdoc Thales Papagiannakopoulos, Sanchez-Rivera developed a way to use CRISPR genome-editing to introduce into mice genetic mutations linked to lung cancer.
In that study, the researchers showed that they could delete genes that are often lost in lung tumor cells, and the resulting tumors were similar to naturally arising tumors with those mutations. However, this technique did not allow for the creation of point mutations (substitutions of one nucleotide for another) or insertions.
“While some cancer patients have deletions in certain genes, the vast majority of mutations that cancer patients have in their tumors also include point mutations or small insertions,” Sanchez-Rivera says.
Since then, David Liu, a professor in the Harvard University Department of Chemistry and Chemical Biology and a core institute member of the Broad Institute, has developed new CRISPR-based genome editing technologies that can generate additional types of mutations more easily. With base editing, developed in 2016, researchers can engineer point mutations, but not all possible point mutations. In 2019, Liu, who is also an author of the Nature Biotechnology study, developed a technique called prime editing, which enables any kind of point mutation to be introduced, as well as insertions and deletions.
“Prime editing in theory solves one of the major challenges with earlier forms of CRISPR-based editing, which is that it allows you to engineer virtually any type of mutation,” Sanchez-Rivera says.
When they began working on this project, Sanchez-Rivera and Gould calculated that if performed successfully, prime editing could be used to generate more than 99 percent of all small mutations seen in cancer patients.
However, to achieve that, they needed to find a way to optimize the editing efficiency of the CRISPR-based system. The prime editing guide RNAs (pegRNAs) used to direct CRISPR enzymes to cut the genome in certain spots have varying levels of efficiency, which leads to “noise” in the data from pegRNAs that simply aren’t generating the correct target mutation. The MIT team devised a way to reduce that noise by using synthetic target sites to help them calculate how efficiently each guide RNA that they tested was working.
“We can design multiple prime-editing guide RNAs with different design properties, and then we get an empirical measurement of how efficient each of those pegRNAs is. It tells us what percentage of the time each pegRNA is actually introducing the correct edit,” Gould says.
Analyzing mutations
The researchers demonstrated their technique using p53, a gene that is mutated in more than half of all cancer patients. From a dataset that includes sequencing information from more than 40,000 patients, the researchers identified more than 1,000 different mutations that can occur in p53.
“We wanted to focus on p53 because it’s the most commonly mutated gene in human cancers, but only the most frequent variants in p53 have really been deeply studied. There are many variants in p53 that remain understudied,” Gould says.
Using their new method, the researchers introduced p53 mutations in human lung adenocarcinoma cells, then measured the survival rates of these cells, allowing them to determine each mutation’s effect on cell fitness.
Among their findings, they showed that some p53 mutations promoted cell growth more than had been previously thought. These mutations, which prevent the p53 protein from forming a tetramer — an assembly of four p53 proteins — had been studied before, using a technique that involves inserting artificial copies of a mutated p53 gene into a cell.
Those studies found that these mutations did not confer any survival advantage to cancer cells. However, when the MIT team introduced those same mutations using the new prime editing technique, they found that the mutation prevented the tetramer from forming, allowing the cells to survive. Based on the studies done using overexpression of artificial p53 DNA, those mutations would have been classified as benign, while the new work shows that under more natural circumstances, they are not.
“This is a case where you could only observe these variant-induced phenotypes if you’re engineering the variants in their natural context and not with these more artificial systems,” Gould says. “This is just one example, but it speaks to a broader principle that we’re going to be able to access novel biology using these new genome-editing technologies.”
Because it is difficult to reactivate tumor suppressor genes, there are few drugs that target p53, but the researchers now plan to investigate mutations found in other cancer-linked genes, in hopes of discovering potential cancer therapies that could target those mutations. They also hope that the technique could one day enable personalized approaches to treating tumors.
“With the advent of sequencing technologies in the clinic, we’ll be able to use this genetic information to tailor therapies for patients suffering from tumors that have a defined genetic makeup,” Sanchez-Rivera says. “This approach based on prime editing has the potential to change everything.”
The research was funded, in part, by the National Institute of General Medical Sciences, an MIT School of Science Fellowship in Cancer Research, a Howard Hughes Medical Institute Hanna Gray Fellowship, the V Foundation for Cancer Research, a National Cancer Institute Cancer Center Support Grant, the Ludwig Center at MIT, a Koch Institute Frontier Award, the MIT Research Support Committee, and the Koch Institute Support (core) Grant from the National Cancer Institute.
For a brief period during embryonic development, cells must rely on messenger RNAs provided by the maternal genome. In Developmental Cell, Bartel Lab members detail how cells regulate this limited supply of genetic material.
Greta Friar | Whitehead Institute
March 6, 2024
Exploring the cellular neighborhood
Alison Biester | Department of Biology
March 12, 2024
New software allows scientists to model shapeshifting proteins in native cellular environments
Cells rely on complex molecular machines composed of protein assemblies to perform essential functions such as energy production, gene expression, and protein synthesis. To better understand how these machines work, scientists capture snapshots of them by isolating proteins from cells and using various methods to determine their structures. However, isolating proteins from cells also removes them from the context of their native environment, including protein interaction partners and cellular location.
Recently, cryogenic electron tomography (cryo-ET) has emerged as a way to observe proteins in their native environment by imaging frozen cells at different angles to obtain three-dimensional structural information. This approach is exciting because it allows researchers to directly observe how and where proteins associate with each other, revealing the cellular neighborhood of those interactions within the cell.
With the technology available to image proteins in their native environment, graduate student Barrett Powell wondered if he could take it one step further: what if molecular machines could be observed in action? In a paper published today in Nature Methods, Powell describes the method he developed, called tomoDRGN, for modeling structural differences of proteins in cryo-ET data that arise from protein motions or proteins binding to different interaction partners. These variations are known as structural heterogeneity.
Although Powell had joined the Davis Lab as an experimental scientist, he recognized the potential impact of computational approaches in understanding structural heterogeneity within a cell. Previously, the Davis Lab developed a related methodology named cryoDRGN to understand structural heterogeneity in purified samples. As Powell and Associate Professor of Biology Joey Davis saw cryo-ET rising in prominence in the field, Powell took on the challenge of reimagining this framework to work in cells.
When solving structures with purified samples, each particle is imaged only once. By contrast, cryo-ET data is collected by imaging each particle more than 40 times from different angles. That meant tomoDRGN needed to be able to merge the information from more than 40 images, which was where the project hit a roadblock: the amount of data led to an information overload.
To address the information overload, Powell successfully rebuilt the cryoDRGN model to prioritize only the highest-quality data. When imaging the same particle multiple times, radiation damage occurs. The images acquired earlier, therefore, tend to be of higher quality because the particles are less damaged.
“By excluding some of the lower quality data, the results were actually better than using all of the data–and the computational performance was substantially faster,” Powell says.
Just as Powell was beginning work on testing his model, he had a stroke of luck: the authors of a groundbreaking new study that visualized, for the first time, ribosomes inside cells at near-atomic resolution, shared their raw data on the Electric Microscopy Public Image Archive (EMPIAR). This dataset was an exemplary test case for Powell, through which he demonstrated that tomoDRGN could uncover structural heterogeneity within cryo-ET data.
According to Powell, one exciting result is what tomoDRGN found surrounding a subset of ribosomes in the EMPIAR dataset. Some of the ribosomal particles were associated with a bacterial cell membrane and engaged in a process called cotranslational translocation. This occurs when a protein is being simultaneously synthesized and transported across a membrane. Researchers can use this result to make new hypotheses about how the ribosome functions with other protein machinery integral to transporting proteins outside of the cell, now guided by a structure of the complex in its native environment.
After seeing that tomoDRGN could resolve structural heterogeneity from a structurally diverse dataset, Powell was curious: how small of a population could tomoDRGN identify? For that test, he chose a protein named apoferritin which is a commonly used benchmark for cryo-ET and is often treated as structurally homogeneous. Ferritin is a protein used for iron storage and is referred to as apoferritin when it lacks iron.
Surprisingly, in addition to the expected particles, tomoDRGN revealed a minor population of ferritin particles–with iron bound–making up just 2% of the dataset that was not previously reported. This result further demonstrated tomoDRGN’s ability to identify structural states that occur so infrequently that they would be averaged out with traditional analysis tools.
Powell and other members of the Davis Lab are excited to see how tomoDRGN can be applied to further ribosomal studies and to other systems. Davis works on understanding how cells assemble, regulate, and degrade molecular machines, so the next steps include exploring ribosome biogenesis within cells in greater detail using this new tool.
“What are the possible states that we may be losing during purification?” Davis says. “Perhaps more excitingly, we can look at how they localize within the cell and what partners and protein complexes they may be interacting with.”
Whitehead Institute Member Jonathan Weissman and colleagues used large-scale systematic genetic screens to identify the molecules and pathways that populate the mitochondrial surface with important and diverse signaling proteins. They deciphered the logic by which the cell ensures the proper delivery of these proteins. These findings may have important implications for understanding the impact on health and disease when these processes go awry.
Greta Friar | Whitehead Institute
February 26, 2024
How do cells respond to disruptions in splicing?
Lillian Eden | Department of Biology
March 4, 2024
New research from the Calo Lab in the Department of Biology has identified the protein Mdm2 generating a form that activates a cascade of cellular stress responses when splicing is disrupted.
To create proteins, DNA is transcribed into RNA, and that RNA is then “translated” into protein. Between the creation of the RNA and the translation to protein is often a step called splicing. During splicing, segments called introns are removed, and the remaining pieces, called exons, are joined together to form the blueprint for translation. By splicing together different exons, the cell can create different proteins from the same section of genetic code. When splicing goes awry, it can lead to diseases and cancers.
New research recently published in Disease Models & Mechanisms from the Calo Lab in the Department of Biology at MIT has identified the mechanism for how cells respond to disruptions in splicing, which involves activating a cellular stress response. The stress response, once activated, causes widespread effects, including changes to cell metabolism.
Researchers have discovered cellular stress responses for other core cellular processes, such as ribosome biogenesis. However, this is the first time researchers have identified how cells respond to perturbing the splicing process.
A particular protein acts as a kind of canary in a coal mine: Mdm2, which responds to a broad range of splicing disruptions. Mdm2 does not cause a stress response by itself. Rather, Mdm2 is itself spliced differently in response to splicing disruptions. Downstream, the alternative splicing of Mdm2 leads to the activation of a protein called p53, which is known to orchestrate a cascade of responses to stress.
Researchers have long wondered why some cell types seem more sensitive to splicing disruptions than others. For example, some disorders caused by mutations in proteins that perform RNA splicing, despite affecting the whole organism, induce more noticeable changes in tissues derived from the neural crest—a collection of stem cells that contributes to the formation of the face, jaw, retinas, limbs, and heart during development. Certain splicing inhibitors have also increased the effectiveness of some cancer treatments, but the mechanism is unknown.
One of the p53-induced stress responses includes changing the metabolism of cells and how they use sugars, which may explain why some cells are more sensitive to splicing disruptions than others. Inhibiting glycolysis, the reactions that extract energy from glucose, can affect how cells divide and migrate.
The way cells divide and migrate is critical during development; in experiments, zebrafish treated with glycolysis inhibitors exhibited similar changes to craniofacial features as those where splicing was disrupted. Cancerous cells, too, are known to require high levels of sugar metabolism and, therefore, may be especially sensitive to treatments that induce changes in the splicing pathway.
The researchers knocked down genes to mimic milder splicing disruptions instead of knocking them out entirely. Splicing is so essential that knocking out the splicing machinery can lead to extreme responses like cell death. In organismal models like zebrafish, those severe phenotypes don’t accurately reflect how splicing disruptions present in human diseases.
First author Jade Varineau, a graduate student in the Calo lab, was drawn to the project because it allowed her to explore what was happening at the RNA and cellular level while also observing how splicing perturbations were affecting the whole organism.
“I think this data can help us reframe the way we think about diseases and cancers that are impacted by splicing—that a treatment that works for one may work for another because all the symptoms may stem from the same cellular response,” Varineau says.
Although the results indicate how cells broadly respond to splicing perturbations, the mechanism for how disruptions in splicing induce the alternate splicing of Mdm2 remains unclear. Senior author Eliezer Calo says the lab is also exploring how splicing mechanisms may be altered for things like cancer. Their work, he says, opens the door for further exploration of cell-type specificity of genetic disorders and improvements in cancer treatments using splicing inhibitors.
“We know that the sensor is encoded in the gene Mdm2—what are the molecules that allow Mdm2 to act as a sensor, and how does the sensor malfunction for things like cancer?” Calo says. “The next step is to find out how the sensor works.”