50 years of innovation

The exhibition provides a glimpse into the many contributors and advancements that have defined MIT’s cancer research history since the founding of the Center for Cancer Research in 1974. When the National Cancer Act was passed in 1971, very little was understood about the biology of cancer, and it aimed to deepen our understanding of cancer and develop better strategies for the prevention, detection, and treatment of the disease. MIT embraced this call to action, establishing a center where many leading biologists tackled cancer’s fundamental questions. Building on this foundation, the Koch Institute opened its doors in 2011, housing engineers and life scientists from many fields under one roof to accelerate progress against cancer in novel and transformative ways.

In the 13 years since, the Koch Institute’s collaborative and interdisciplinary approach to cancer research has yielded significant advances in our understanding of the underlying biology of cancer and allowed for the translation of these discoveries into meaningful patient impacts. Over 120 spin-out companies—many headquartered nearby in the Kendall Square area—have their roots in Koch Institute research, with nearly half having advanced their technologies to clinical trials or commercial applications. The Koch Institute’s collaborative approach extends beyond its labs: principal investigators often form partnerships with colleagues at world-renowned medical centers, bridging the gap between discovery and clinical impact.

Current Koch Institute Director Matthew Vander Heiden, also a practicing oncologist at the Dana-Farber Cancer Institute, is driven by patient stories.

“It is never lost on us that the work we do in the lab is important to change the reality of cancer for patients,” he says. “We are constantly motivated by the urgent need to translate our research and improve outcomes for those impacted by cancer.”

Symbols of progress

The items on display as part of Object Lessons take viewers on a journey through five decades of MIT cancer research, from the pioneering days of Salvador Luria, founding director of the Center for Cancer Research, to some of the Koch Institute’s newest investigators including Francisco Sánchez-Rivera, Eisen and Chang Career Development Professor and an assistant professor of biology, and Jessica Stark, Underwood-Prescott Career Development Professor and an assistant professor of biological engineering and chemical engineering.

Among the standout pieces is a humble yet iconic object: Salvador Luria’s ceramic mug, emblazoned with “Luria’s broth.” Lysogeny broth, often called—apocryphally—Luria Broth, is a medium for growing bacteria. Still in use today, the recipe was first published in 1951 by a research associate in Luria’s lab. The artifact, on loan from the MIT Museum, symbolizes the foundational years of the Center for Cancer Research and serves as a reminder of Luria’s influence as an early visionary. His work set the stage for a new era of biological inquiry that would shape cancer research at MIT for generations.

Visitors can explore firsthand how the Koch Institute continues to build on the legacy of its predecessors, translating decades of knowledge into new tools and therapies that have the potential to transform patient care and cancer research.

For instance, the PCR machine designed in the Horvitz Lab in the 1980s made genetic manipulation of cells easier, and gene sequencing faster and more cost-effective. At the time of its commercialization, this groundbreaking benchtop unit marked a major leap forward. In the decades since, technological advances have allowed for the visualization of DNA and biological processes at a much smaller scale, as demonstrated by the handheld BioBits® imaging device developed by Stark and on display next door to the Horvitz panel.

 “We created BioBits kits to address a need for increased equity in STEM education,” Stark says. “By making hands-on biology education approachable and affordable, BioBits kits are helping inspire and empower the next generation of scientists.”

While the exhibition showcases scientific discoveries and marvels of engineering, it also aims to underscore the human element of cancer research through personally significant items, such as a messenger bag and Seq-Well device belonging to Alex Shalek, J. W. Kieckhefer Professor in the Institute for Medical Engineering and Science and the Department of Chemistry.

Shalek investigates the molecular differences between individual cells, developing mobile RNA-sequencing devices. He could often be seen toting the bag around the Boston area, and worldwide as he perfected and shared his technology with collaborators near and far. Through his work, Shalek has helped to make single cell sequencing accessible for labs in more than 30 countries across six continents.

“The KI seamlessly brings together students, staff, clinicians, and faculty across multiple different disciplines to collaboratively derive transformative insights into cancer,” Shalek says. “To me, these sorts of partnerships are the best part about being at MIT.”

Around the corner from Shalek’s display, visitors will find an object that serves as a stark reminder of the real people impacted by Koch Institute research: Steven Keating’s SM’12, PhD ’16 3D-printed model of his own brain tumor. Keating, who passed away in 2019, became a fierce advocate for the rights of patients to their medical data, and came to know Vander Heiden through his pursuit to become an expert on his tumor type, IDH-mutant glioma. In the years since, Vander Heiden’s work has contributed to a new therapy to treat Steven’s tumor type. In 2024, the drug, called vorasidenib, gained FDA approval, providing the first therapeutic breakthrough for Keating’s cancer in more than 20 years.

As the Koch Institute looks to the future, Object Lessons stands as a celebration of the people, the science, and the culture that have defined MIT’s first half-century of breakthroughs and contributions to the field of cancer research.

“Working in the uniquely collaborative environment of the Koch Institute and MIT, I am confident that we will continue to unlock key insights in the fight against cancer,” says Vander Heiden. “Our community is poised to embark on our next 50 years with the same passion and innovation that has carried us this far.”

Object Lessons will be on view in the Koch Institute Public Galleries. Visit Monday through Friday, 9 a.m. to 5 p.m., to see the exhibit up close.

The post KI Gallery Exhibit: Artifacts from a half century of cancer research appeared first on MIT Department of Biology.

Immune checkpoint blockade (ICB) therapies can be very effective against some cancers by helping the immune system recognize cancer cells that are masquerading as healthy cells.

The post A blueprint for better cancer immunotherapies appeared first on MIT Department of Biology.

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.

The post A new approach to modeling complex biological systems appeared first on MIT Department of Biology.

Doctors have long believed that this drug works by damaging the building blocks of DNA. However, a new study from MIT has found that in cancers of the colon and other gastrointestinal cancers, it actually kills cells by interfering with RNA synthesis.

The findings could have a significant effect on how doctors treat many cancer patients. Usually, 5-fluorouracil is given in combination with chemotherapy drugs that damage DNA, but the new study found that for colon cancer, this combination does not achieve the synergistic effects that were hoped for. Instead, combining 5-FU with drugs that affect RNA synthesis could make it more effective in patients with GI cancers, the researchers say.

“Our work is the most definitive study to date showing that RNA incorporation of the drug, leading to an RNA damage response, is responsible for how the drug works in GI cancers,” says Michael Yaffe, a David H. Koch Professor of Science at MIT, the director of the MIT Center for Precision Cancer Medicine, and a member of MIT’s Koch Institute for Integrative Cancer Research. “Textbooks implicate the DNA effects of the drug as the mechanism in all cancer types, but our data shows that RNA damage is what’s really important for the types of tumors, like GI cancers, where the drug is used clinically.”

Yaffe, the senior author of the new study, hopes to plan clinical trials of 5-fluorouracil with drugs that would enhance its RNA-damaging effects and kill cancer cells more effectively.

Jung-Kuei Chen, a Koch Institute research scientist, and Karl Merrick, a former MIT postdoc, are the lead authors of the paper, which appears today in Cell Reports Medicine.

An unexpected mechanism

Clinicians use 5-fluorouracil (5-FU) as a first-line drug for colon, rectal, and pancreatic cancers. It’s usually given in combination with oxaliplatin or irinotecan, which damage DNA in cancer cells. The combination was thought to be effective because 5-FU can disrupt the synthesis of DNA nucleotides. Without those building blocks, cells with damaged DNA wouldn’t be able to efficiently repair the damage and would undergo cell death.

Yaffe’s lab, which studies cell signaling pathways, wanted to further explore the underlying mechanisms of how these drug combinations preferentially kill cancer cells.

The researchers began by testing 5-FU in combination with oxaliplatin or irinotecan in colon cancer cells grown in the lab. To their surprise, they found that not only were the drugs not synergistic, in many cases they were less effective at killing cancer cells than what one would expect by simply adding together the effects of 5-FU or the DNA-damaging drug given alone.

“One would have expected that these combinations to cause synergistic cancer cell death because you are targeting two different aspects of a shared process: breaking DNA, and making nucleotides,” Yaffe says. “Karl looked at a dozen colon cancer cell lines, and not only were the drugs not synergistic, in most cases they were antagonistic. One drug seemed to be undoing what the other drug was doing.”

Yaffe’s lab then teamed up with Adam Palmer, an assistant professor of pharmacology at the University of North Carolina School of Medicine, who specializes in analyzing data from clinical trials. Palmer’s research group examined data from colon cancer patients who had been on one or more of these drugs and showed that the drugs did not show synergistic effects on survival in most patients.

“This confirmed that when you give these combinations to people, it’s not generally true that the drugs are actually working together in a beneficial way within an individual patient,” Yaffe says. “Instead, it appears that one drug in the combination works well for some patients while another drug in the combination works well in other patients. We just cannot yet predict which drug by itself is best for which patient, so everyone gets the combination.”

These results led the researchers to wonder just how 5-FU was working, if not by disrupting DNA repair. Studies in yeast and mammalian cells had shown that the drug also gets incorporated into RNA nucleotides, but there has been dispute over how much this RNA damage contributes to the drug’s toxic effects on cancer cells.

Inside cells, 5-FU is broken down into two different metabolites. One of these gets incorporated into DNA nucleotides, and other into RNA nucleotides. In studies of colon cancer cells, the researchers found that the metabolite that interferes with RNA was much more effective at killing colon cancer cells than the one that disrupts DNA.

That RNA damage appears to primarily affect ribosomal RNA, a molecule that forms part of the ribosome — a cell organelle responsible for assembling new proteins. If cells can’t form new ribosomes, they can’t produce enough proteins to function. Additionally, the lack of undamaged ribosomal RNA causes cells to destroy a large set of proteins that normally bind up the RNA to make new functional ribosomes.

The researchers are now exploring how this ribosomal RNA damage leads cells to under programmed cell death, or apoptosis. They hypothesize that sensing of the damaged RNAs within cell structures called lysosomes somehow triggers an apoptotic signal.

“My lab is very interested in trying to understand the signaling events during disruption of ribosome biogenesis, particularly in GI cancers and even some ovarian cancers, that cause the cells to die. Somehow, they must be monitoring the quality control of new ribosome synthesis, which somehow is connected to the death pathway machinery,” Yaffe says.

New combinations

The findings suggest that drugs that stimulate ribosome production could work together with 5-FU to make a highly synergistic combination. In their study, the researchers showed that a molecule that inhibits KDM2A, a suppressor of ribosome production, helped to boost the rate of cell death in colon cancer cells treated with 5-FU.

The findings also suggest a possible explanation for why combining 5-FU with a DNA-damaging drug often makes both drugs less effective. Some DNA damaging drugs send a signal to the cell to stop making new ribosomes, which would negate 5-FU’s effect on RNA. A better approach may be to give each drug a few days apart, which would give patients the potential benefits of each drug, without having them cancel each other out.

“Importantly, our data doesn’t say that these combination therapies are wrong. We know they’re effective clinically. It just says that if you adjust how you give these drugs, you could potentially make those therapies even better, with relatively minor changes in the timing of when the drugs are given,” Yaffe says.

He is now hoping to work with collaborators at other institutions to run a phase 2 or 3 clinical trial in which patients receive the drugs on an altered schedule.

“A trial is clearly needed to look for efficacy, but it should be straightforward to initiate because these are already clinically accepted drugs that form the standard of care for GI cancers. All we’re doing is changing the timing with which we give them,” he says.

The researchers also hope that their work could lead to the identification of biomarkers that predict which patients’ tumors will be more susceptible to drug combinations that include 5-FU. One such biomarker could be RNA polymerase I, which is active when cells are producing a lot of ribosomal RNA.

The research was funded by the Damon Runyon Cancer Research Fund, a Ludwig Center at MIT Fellowship, the National Institutes of Health, the Ovarian Cancer Research Fund, the Holloway Foundation, and the STARR Cancer Consortium.

The post Cancer biologists discover a new mechanism for an old drug appeared first on MIT Department of Biology.

BSG-MSRP-Bio student Yeongseo Son breathed new life into her love of science over the summer in the Spranger Lab studying immune responses in the lung in the Department of Biology at MIT.

When Yeongseo Son was initially invited to join the Spranger Lab as part of the Bernard S. and Sophie G. Gould MIT Research Program in Biology, she thought the email was spam. Having grown up in the South for most of her life, she had never pictured herself at MIT.

Back home at the University of Georgia, Son studies neutrophils, a kind of innate immune cell that serves as the body’s first line of defense against foreign pathogens. After taking a graduate-level course on immunology last semester, Son realized she needed to increase her basic understanding of the broad discipline.

“I knew that coming to work with Professor Spranger would give me a chance to work on cancer immunology and T cell biology, two really cool and important fields I haven’t been exposed to,” Son says.

It took several attempts from the Senior Lecturer and BSG-MSRP-Bio program coordinator Mandana Sassanfar to reach her before Son accepted.  

“Before I arrived, I was worried it would be too intense or that I wouldn’t fit in,” Son says. “I couldn’t have been more wrong: yes, the work is challenging, but everyone is here because they truly love science.”

Vexing Viruses

In the lab of Stefani Spranger, Associate Professor in the Department of Biology and Intramural Faculty of the Koch Institute for Integrative Cancer Research, Son was first tasked with a seemingly simple second project: growing a new strain of influenza to infect mice that had recently recovered from another strain. 

This quest involved multiple steps, such as culturing cells, infecting the cells with the virus, and measuring how lethal it is to host cells, working with a strain that her lab hadn’t used before.   

To test the strength of the virus, the virus is mixed with host cells in order to infect them. Then the host cells are placed on a layer of agar, a gelatinous substance that provides nutrients for the host cells. When a virus-infected cell dies, it creates a hole in the layer of cells called a plaque. The number of plaques is recorded to determine the virus’s titer, or frequency. 

Son excitedly executed her plaque assay after breezing through the first two steps. The next day, to her surprise and disappointment, all her cells — including the negative control — had died. 

“The first time it failed, I was crushed because I had written the protocol over and over,” Son says. 

That initial disappointment, however, turned into excitement to solve the problem. She worked closely with her mentor, Postdoc Taylor Heim, who helped motivate her to keep trying to figure out what had gone wrong.

Son spent weeks designing a process to effectively titer the virus. She laid out a plan of action to assess what could be toxic to the cells and systematically tested each component of the protocol that could affect the growth of her strain of influenza. 

It took Son four attempts before she had a eureka moment: the success of her cell cultures depended on the precise measurement of just one reagent. 

Too much of the reagent meant the cells would all die on arrival, but just a little bit, and they would survive. It took Son three more attempts — seven experiments in total — to fully ensure the success of the assay.

Throughout this process, and despite her many failures, Son realized she finds troubleshooting very enjoyable. Each failure was unique and crucial for her eventual success.  

“I’m making a difference — I’m figuring something out that can really help with future experiments,” Son says. “That moment of success is why I gained such confidence in being a scientist.”

Lighting Up the Lungs

In the Spranger Lab, Son’s other summer project focused on the respiratory system. She was examining a type of specialized cell called resident memory CD8+ T cells in the lungs and lymph nodes of mice infected with influenza. These specialized T cells gain a kind of memory of how to fight off a virus and remain in the lungs and lung-draining lymph node tissues long after the tissues have overcome the immune challenge of something like influenza. 

Son’s postdoctoral student mentor Taylor Heim is especially interested in the potential of these cells for cancer immunotherapy.

To better understand how the resident memory T cell populations change over time, Son and Heim conducted a time-point experiment in which mice were studied at different points after being infected with influenza. They do this by injecting antibodies into the mouse’s bloodstream after infection, which mark any immune cells circulating in the blood, allowing the researchers to gauge if the cells are recruited to help fight a virus.

Son’s work this summer goes deeper, examining proteins known as cytokines that enable the immune system to combat germs or other substances that can harm an organism. 

Son used a genetically modified mouse to track the production of interferon-gamma, IFN‐γ. IFN‐γ is a cytokine that plays a key role in regulating immune responses, often helping fight off infection and cancer. Son found evidence that resident memory T cells produce this cytokine in both the lungs and lung-draining lymph nodes. 

The goal of this research is to one day use the information collected on resident memory CD8+ T cell populations and cytokine expression to help systematically target cancerous cells that appear in the body.

“Yeongseo has helped us pioneer a system to track how these cells move within tissues of living mice,” Spranger explains. “By using this approach, we will be able to understand how they are affecting cancer development and how cancer is affecting them, and that’s pretty exciting.”

Learning Outside the Lab

The BSG-MSRP-Bio program also gave Son near-constant access to faculty from across the biology department, both through extracurricular offerings such as dinner seminars and journal clubs as well as departmental retreats. 

She’s also sat down with professors individually and heard more about their stories and research as part of her podcast Let’s Talk Chemistry. Nobel Laureate Phil Sharp, whose office is on the same floor as the Spranger Lab, joined the show after Son dropped by his office to introduce herself. Son learned more about his discoveries in RNA splicing and the behind-the-scenes details of his Nobel Prize ceremonies. 

At MIT, Son has found a welcoming community of enthusiastic scientists working towards common goals, especially in her lab. Every day, members of the Spranger Lab actively seek each other out to have lunch together, and she feels right at home with them.

“I realized that yes, the people in this community are intensely passionate about their work, but they’re also multi-dimensional with a ton of different interests,” Son says. “One of the graduate students in my lab even gave me tennis lessons, and I’m already a better player because of it.”

As she returns to her studies in Georgia and begins the process of applying to graduate schools, Son is excited about her future in science. Armed with new knowledge, confidence, and community, she’s ready for whatever curveball her career in science will throw her next.

Want to know more about our BSG-MSRP-Bio Students? Read more testimonials and stories here.

The post BSG-MSRP-Bio student profile: Yeongseo Son, Spranger Lab appeared first on MIT Department of Biology.

The prize was established in 2021 to recognize graduate students in the life sciences or biomedical research from institutions outside the United States who embody Dr. Amon’s infectious enthusiasm for discovery science.

Both of this year’s winners work to unravel the fundamental biology of chromatin, the densely structured complex of DNA, RNA, and proteins that makes up a cell’s genetic material.

Uzonyi is pursuing her PhD at the Weizmann Institute of Science in Israel under the supervision of Schraga Schwartz and Yonatan Stelzer. In her thesis, Uzonyi focuses on deciphering the principles of RNA editing code via large-scale systematic probing.

Henneberg is a doctoral candidate in the Department of Molecular Machines and Signaling, at the Max Planck Institute of Biochemistry in Germany, works under the supervision of Professor Brenda Schulman and Professor Matthias Mann. For his research project, he probes active ubiquitin E3 ligase networks within cells. He works on the development of probes targeting active ubiquitin E3 ligases within cells and utilizing them in mass spectrometry-based workflows to explore the response of these ligase networks to cellular signaling pathways and therapeutics.

This fall, Anna Uzonyi and Lukas Teoman Henneberg, will visit the Koch Institute. The MIT community and Amon Lab alumni are invited to attend their scientific presentations on Thursday, November 14 at 2:00 p.m. in the Luria Auditorium, followed by a 3:30 p.m. reception in the KI Galleries.

Uzonyi will present on “Inosine and m6A: Deciphering the deposition and function of adenosine modifications” and Henneberg will present on “Capturing active cellular destroyers: Probing dynamic ubiquitin E3 ligase networks.“

The post 2024 Angelika Amon Young Scientist award winners announced appeared first on MIT Department of Biology.

This distance can make it difficult to map the complex interactions between genes and enhancers. To overcome that, MIT researchers have invented a new technique that allows them to observe the timing of gene and enhancer activation in a cell. When a gene is turned on around the same time as a particular enhancer, it strongly suggests the enhancer is controlling that gene.

Learning more about which enhancers control which genes, in different types of cells, could help researchers identify potential drug targets for genetic disorders. Genomic studies have identified mutations in many non-protein-coding regions that are linked to a variety of diseases. Could these be unknown enhancers?

“When people start using genetic technology to identify regions of chromosomes that have disease information, most of those sites don’t correspond to genes. We suspect they correspond to these enhancers, which can be quite distant from a promoter, so it’s very important to be able to identify these enhancers,” says Phillip Sharp, an MIT Institute Professor Emeritus and member of MIT’s Koch Institute for Integrative Cancer Research.

Sharp is the senior author of the new study, which appears today in Nature. MIT Research Assistant D.B. Jay Mahat is the lead author of the paper.

Hunting for eRNA

Less than 2 percent of the human genome consists of protein-coding genes. The rest of the genome includes many elements that control when and how those genes are expressed. Enhancers, which are thought to turn genes on by coming into physical contact with gene promoter regions through transiently forming a complex, were discovered about 45 years ago.

More recently, in 2010, researchers discovered that these enhancers are transcribed into RNA molecules, known as enhancer RNA or eRNA. Scientists suspect that this transcription occurs when the enhancers are actively interacting with their target genes. This raised the possibility that measuring eRNA transcription levels could help researchers determine when an enhancer is active, as well as which genes it’s targeting.

“That information is extraordinarily important in understanding how development occurs, and in understanding how cancers change their regulatory programs and activate processes that lead to de-differentiation and metastatic growth,” Mahat says.

However, this kind of mapping has proven difficult to perform because eRNA is produced in very small quantities and does not last long in the cell. Additionally, eRNA lacks a modification known as a poly-A tail, which is the “hook” that most techniques use to pull RNA out of a cell.

One way to capture eRNA is to add a nucleotide to cells that halts transcription when incorporated into RNA. These nucleotides also contain a tag called biotin that can be used to fish the RNA out of a cell. However, this current technique only works on large pools of cells and doesn’t give information about individual cells.

While brainstorming ideas for new ways to capture eRNA, Mahat and Sharp considered using click chemistry, a technique that can be used to join two molecules together if they are each tagged with “click handles” that can react together.

The researchers designed nucleotides labeled with one click handle, and once these nucleotides are incorporated into growing eRNA strands, the strands can be fished out with a tag containing the complementary handle. This allowed the researchers to capture eRNA and then purify, amplify, and sequence it. Some RNA is lost at each step, but Mahat estimates that they can successfully pull out about 10 percent of the eRNA from a given cell.

Using this technique, the researchers obtained a snapshot of the enhancers and genes that are being actively transcribed at a given time in a cell.

“You want to be able to determine, in every cell, the activation of transcription from regulatory elements and from their corresponding gene. And this has to be done in a single cell because that’s where you can detect synchrony or asynchrony between regulatory elements and genes,” Mahat says.

Timing of gene expression

Demonstrating their technique in mouse embryonic stem cells, the researchers found that they could calculate approximately when a particular region starts to be transcribed, based on the length of the RNA strand and the speed of the polymerase (the enzyme responsible for transcription) — that is, how far the polymerase transcribes per second. This allowed them to determine which genes and enhancers were being transcribed around the same time.

The researchers used this approach to determine the timing of the expression of cell cycle genes in more detail than has previously been possible. They were also able to confirm several sets of known gene-enhancer pairs and generated a list of about 50,000 possible enhancer-gene pairs that they can now try to verify.

Learning which enhancers control which genes would prove valuable in developing new treatments for diseases with a genetic basis. Last year, the U.S. Food and Drug Administration approved the first gene therapy treatment for sickle cell anemia, which works by interfering with an enhancer that results in activation of a fetal globin gene, reducing the production of sickled blood cells.

The MIT team is now applying this approach to other types of cells, with a focus on autoimmune diseases. Working with researchers at Boston Children’s Hospital, they are exploring immune cell mutations that have been linked to lupus, many of which are found in non-coding regions of the genome.

“It’s not clear which genes are affected by these mutations, so we are beginning to tease apart the genes these putative enhancers might be regulating, and in what cell types these enhancers are active,” Mahat says. “This is a tool for creating gene-to-enhancer maps, which are fundamental in understanding the biology, and also a foundation for understanding disease.”

The findings of this study also offer evidence for a theory that Sharp has recently developed, along with MIT professors Richard Young and Arup Chakraborty, that gene transcription is controlled by membraneless droplets known as condensates. These condensates are made of large clusters of enzymes and RNA, which Sharp suggests may include eRNA produced at enhancer sites.

“We picture that the communication between an enhancer and a promoter is a condensate-type, transient structure, and RNA is part of that. This is an important piece of work in building the understanding of how RNAs from enhancers could be active,” he says.

The research was funded by the National Cancer Institute, the National Institutes of Health, and the Emerald Foundation Postdoctoral Transition Award.

The post New technique reveals how gene transcription is coordinated in cells appeared first on MIT Department of Biology.

Culminating 25 years of research, MIT, Harvard University, and Yale University scientists and collaborators have unveiled a comprehensive atlas of human tyrosine kinases — enzymes that regulate a wide variety of cellular activities — and their binding sites.

The addition of tyrosine kinases to a previously published dataset from the same group now completes a free, publicly available atlas of all human kinases and their specific binding sites on proteins, which together orchestrate fundamental cell processes such as growth, cell division, and metabolism.

Now, researchers can use data from mass spectrometry, a common laboratory technique, to identify the kinases involved in normal and dysregulated cell signaling in human tissue, such as during inflammation or cancer progression.

“I am most excited about being able to apply this to individual patients’ tumors and learn about the signaling states of cancer and heterogeneity of that signaling,” says Michael Yaffe, who is the David H. Koch Professor of Science at MIT, the director of the MIT Center for Precision Cancer Medicine, a member of MIT’s Koch Institute for Integrative Cancer Research, and a senior author of the new study. “This could reveal new druggable targets or novel combination therapies.”

The study, published in Nature, is the product of a long-standing collaboration with senior authors Lewis Cantley at Harvard Medical School and Dana-Farber Cancer Institute, Benjamin Turk at Yale School of Medicine, and Jared Johnson at Weill Cornell Medical College.

The paper’s lead authors are Tomer Yaron-Barir at Columbia University Irving Medical Center, and MIT’s Brian Joughin, with contributions from Kontstantin Krismer, Mina Takegami, and Pau Creixell.

Kinase kingdom

Human cells are governed by a network of diverse protein kinases that alter the properties of other proteins by adding or removing chemical compounds called phosphate groups. Phosphate groups are small but powerful: When attached to proteins, they can turn proteins on or off, or even dramatically change their function. Identifying which of the almost 400 human kinases phosphorylate a specific protein at a particular site on the protein was traditionally a lengthy, laborious process.

Beginning in the mid 1990s, the Cantley laboratory developed a method using a library of small peptides to identify the optimal amino acid sequence — called a motif, similar to a scannable barcode — that a kinase targets on its substrate proteins for the addition of a phosphate group. Over the ensuing years, Yaffe, Turk, and Johnson, all of whom spent time as postdocs in the Cantley lab, made seminal advancements in the technique, increasing its throughput, accuracy, and utility.

Johnson led a massive experimental effort exposing batches of kinases to these peptide libraries and observed which kinases phosphorylated which subsets of peptides. In a corresponding Nature paper published in January 2023, the team mapped more than 300 serine/threonine kinases, the other main type of protein kinase, to their motifs. In the current paper, they complete the human “kinome” by successfully mapping 93 tyrosine kinases to their corresponding motifs.

Next, by creating and using advanced computational tools, Yaron-Barir, Krismer, Joughin, Takegami, and Yaffe tested whether the results were predictive of real proteins, and whether the results might reveal unknown signaling events in normal and cancer cells. By analyzing phosphoproteomic data from mass spectrometry to reveal phosphorylation patterns in cells, their atlas accurately predicted tyrosine kinase activity in previously studied cell signaling pathways.

For example, using recently published phosphoproteomic data of human lung cancer cells treated with two targeted drugs, the atlas identified that treatment with erlotinib, a known inhibitor of the protein EGFR, downregulated sites matching a motif for EGFR. Treatment with afatinib, a known HER2 inhibitor, downregulated sites matching the HER2 motif. Unexpectedly, afatinib treatment also upregulated the motif for the tyrosine kinase MET, a finding that helps explain patient data linking MET activity to afatinib drug resistance.

Actionable results

There are two key ways researchers can use the new atlas. First, for a protein of interest that is being phosphorylated, the atlas can be used to narrow down hundreds of kinases to a short list of candidates likely to be involved. “The predictions that come from using this will still need to be validated experimentally, but it’s a huge step forward in making clear predictions that can be tested,” says Yaffe.

Second, the atlas makes phosphoproteomic data more useful and actionable. In the past, researchers might gather phosphoproteomic data from a tissue sample, but it was difficult to know what that data was saying or how to best use it to guide next steps in research. Now, that data can be used to predict which kinases are upregulated or downregulated and therefore which cellular signaling pathways are active or not.

“We now have a new tool now to interpret those large datasets, a Rosetta Stone for phosphoproteomics,” says Yaffe. “It is going to be particularly helpful for turning this type of disease data into actionable items.”

In the context of cancer, phosophoproteomic data from a patient’s tumor biopsy could be used to help doctors quickly identify which kinases and cell signaling pathways are involved in cancer expansion or drug resistance, then use that knowledge to target those pathways with appropriate drug therapy or combination therapy.

Yaffe’s lab and their colleagues at the National Institutes of Health are now using the atlas to seek out new insights into difficult cancers, including appendiceal cancer and neuroendocrine tumors. While many cancers have been shown to have a strong genetic component, such as the genes BRCA1 and BRCA2 in breast cancer, other cancers are not associated with any known genetic cause. “We’re using this atlas to interrogate these tumors that don’t seem to have a clear genetic driver to see if we can identify kinases that are driving cancer progression,” he says.

Biological insights

In addition to completing the human kinase atlas, the team made two biological discoveries in their recent study. First, they identified three main classes of phosphorylation motifs, or barcodes, for tyrosine kinases. The first class is motifs that map to multiple kinases, suggesting that numerous signaling pathways converge to phosphorylate a protein boasting that motif. The second class is motifs with a one-to-one match between motif and kinase, in which only a specific kinase will activate a protein with that motif. This came as a partial surprise, as tyrosine kinases have been thought to have minimal specificity by some in the field.

The final class includes motifs for which there is no clear match to one of the 78 classical tyrosine kinases. This class includes motifs that match to 15 atypical tyrosine kinases known to also phosphorylate serine or threonine residues. “This means that there’s a subset of kinases that we didn’t recognize that are actually playing an important role,” says Yaffe. It also indicates there may be other mechanisms besides motifs alone that affect how a kinase interacts with a protein.

The team also discovered that tyrosine kinase motifs are tightly conserved between humans and the worm species C. elegans, despite the species being separated by more than 600 million years of evolution. In other words, a worm kinase and its human homologue are phosphorylating essentially the same motif. That sequence preservation suggests that tyrosine kinases are highly critical to signaling pathways in all multicellular organisms, and any small change would be harmful to an organism.

The research was funded by the Charles and Marjorie Holloway Foundation, the MIT Center for Precision Cancer Medicine, the Koch Institute Frontier Research Program via L. Scott Ritterbush, the Leukemia and Lymphoma Society, the National Institutes of Health, Cancer Research UK, the Brain Tumour Charity, and the Koch Institute Support (core) grant from the National Cancer Institute.

The post “Rosetta Stone” of cell signaling could expedite precision cancer medicine appeared first on MIT Department of Biology.