Research Area: Computational Biology

The cartographer of cells

Aviv Regev helped pioneer single-cell genomics. Now she’s cochairing a massive effort to map the trillions of cells in the human body. Biology will never be the same.

Sam Apple | MIT Technology Review
August 23, 2018

Last October, Aviv Regev spoke to a gathering of international scientists at Israel’s Weizmann Institute of Science. For Regev, a computational and systems biologist at the Broad Institute of MIT and Harvard, the gathering was also a homecoming of sorts. Regev earned her PhD from nearby Tel Aviv University in 2002. Now, 15 years later, she was back to discuss one of the most ambitious projects in the history of biology.

The project, the Human Cell Atlas, aims to create a reference map that categorizes all the approximately 37 trillion cells that make up a human. The Human Cell Atlas is often compared to the Human Genome Project, the monumental scientific collaboration that gave us a complete readout of human DNA, or what might be considered the unabridged cookbook for human life. In a sense, the atlas is a continuation of that project’s work. But while the same DNA cookbook is found in every cell, each cell type reads only some of the recipes—that is, it expresses only certain genes, following their DNA instructions to produce the proteins that carry out a cell’s activities. The promise of the Human Cell Atlas is to reveal which specific genes are expressed in every cell type, and where the cells expressing those genes can be found.

Speaking to her colleagues at the meeting in Israel, Regev, who is cochairing the Human Cell Atlas Organizing Committee with Sarah Teichmann of the Wellcome Trust Sanger Institute, displayed the no-nonsense demeanor you might expect of someone at the helm of a massive scientific undertaking. The project had been under way for a year, and Regev, an MIT biology professor who is also chair of the faculty of the Broad and director of its Klarman Cell Observatory and Cell Circuits Program, was reviewing a newly published white paper detailing how the Human Cell Atlas is expected to change the way we diagnose, monitor, and treat disease.

As Regev made her way through the white paper, the possibilities began to seem almost endless. At the most basic level, as a reference map detailing the genes expressed by each different type of healthy cell, the Human Cell Atlas will make it easier to identify how gene expression and signaling go awry in the case of disease. The same map could also help drug developers avoid toxic side effects: researchers targeting a gene that’s harmful in one part of the body would know if the same gene is playing a vital role in another. And because the atlas is expected to reveal many new types of cells, it could also add much more sensitivity to a type of standard blood test, which simply counts different subsets of immune cells. Likewise, looking at individual intestinal cells might provide new insights into the specific cells responsible for inflammation and food allergies. And a better understanding of types of neurons could have far-reaching implications for brain science.

The final product, Regev says, will amount to nothing less than a “periodic table of our cells,” a tool that is designed not to answer one specific question but to make countless new discoveries possible. Eric Lander, the founding director and president of the Broad Institute and a member of the Human Cell Atlas Organizing Committee, likens it to genomics. “People thought at the beginning they might use genomics for this application or that application,” he says. “Nothing has failed to be transformed by genomics, and nothing will fail to be transformed by having a cell atlas.”

Cellular circuits

Regev’s interest in cells began at Tel Aviv University, where she was one of just 15 or so entering students in a highly selective program that gave them the freedom to take high-level courses in any subject. “You could go your first day as a freshman and decide to take a graduate class in political science,” she says.

Regev took a genetics class her first semester and got hooked on the computational challenge of finding order in the complex, interconnected networks of proteins and genes within each cell. She pursued that topic for her doctoral work, characterizing living systems in a mathematical language that had been designed to describe computer processes. As she finished her doctorate in 2002, she was accepted into a program at Harvard’s Bauer Center for Genomics Research that allowed her to start her own lab without first training as a postdoc.

Not long after, Lander, who’d begun his own career as a mathematician after studying algebraic coding theory and combinatorial mathematics at Oxford, was searching for star talent for the newly created Broad Institute, whose mission is to use genomics to study human disease and help advance its treatment. He first met Regev at a lunch at the Bauer Center during which the fellows took turns speaking about their research for five to 10 minutes. “By the time we got all the way around the table I had written down ‘Hire Aviv Regev,’” he recalls.

Convinced by Lander to join the Broad after “many cups of tea” at Cafe Algiers in Harvard Square, Regev continued to apply computational approaches to study the mind-bogglingly complicated machinery of the cell. A single cell is made up of millions of molecules that are in constant conversation as they work together to do all the things cells need to do: divide, grow, repair internal damage, and, in the case of immune cells, signal other cells about threats. Inside the nucleus, the DNA is transcribed into RNA. That in turn gives rise to proteins, the molecules that do the work inside a cell. Meanwhile, proteins on the surface of the cell are constantly receiving molecular messages from outside—glucose is available, an invader has arrived. These must be relayed back to proteins in the nucleus, which will respond by transcribing other DNA, giving rise to new proteins and still more signaling networks.

“It’s like a complex computer that is made of these many, many different parts that are interacting with each other and telling each other what to do,” says Regev. The protein signaling networks are like “circuits”—and you can think about the cell “almost like a wiring diagram,” she says. But using computational approaches to understand their activity first requires gathering an enormous amount of data, which Regev has long done through RNA sequencing. Unlike DNA sequencing, she says, it can tell her which genes are actually being expressed, so it offers a far more dynamic picture of a cell in action. But simply sequencing the RNA of the cells she’s studying can tell her only so much. To understand how the circuits change under different circumstances, Regev subjects cells to different stimuli, such as hormones or pathogens, to see how the resulting protein signals change.

Next comes what she calls “the modeling step”—creating algorithms that try to decipher the most likely sequence of molecular events following a stimulus. And just as someone might study a computer by cutting out circuits and seeing how that changes the machine’s operation, Regev tests her model by seeing if it can predict what will happen when she silences specific genes and then exposes the cells to the same stimulus.

In a 2009 study, Regev and her team examined how exposure to molecular components of pathogens like bacteria, viruses, or fungi affected the circuitry of the immune system’s dendritic cells. She turned to a technique known as RNA interference (she now uses CRISPR), which allowed her to systematically shut genes down. Then she looked at which genes were expressed to determine how the cells’ response changed in each case. Her team singled out 100 different genes that were involved in regulating the response to the pathogens—some of which weren’t previously known to be involved in immune function. The study, published in Science, generated headlines. But according to longtime colleague Dana Pe’er, now chair of computational and systems biology at the Sloan Kettering Institute at the Memorial Sloan Kettering Cancer Center and a member of the Human Cell Atlas Organizing Committee, what really sets Regev apart is the elegance of her work. Regev, says Pe’er, “has a rare, innate ability of seeing complex biology and simplifying it and formalizing it into beautiful, abstract, describable principles.”

From smoothies to fruit salad

There are lots of empty coffee mugs in Regev’s office at the Broad Institute, but very little in the way of decoration. She approaches her science with a businesslike efficiency. “There are many brilliant people,” says Lander. “She’s a brilliant person who can get things done.”

In the fast-changing arena of genomics (“2015 in my field is considered ancient history,” she says), she is known for making the most of the latest innovations—and for helping to spur the next ones. For years, she and others in the field struggled with a dirty secret of RNA sequencing: though its promise has always been precision—the power of knowing the exact code—the techniques produced results that were unspecific. Every cell has only a minuscule amount of RNA. For sequencing purposes, the RNA from millions of cells had to be pooled together. Bulk RNA sequencing left researchers with what she likens to a smoothie. Once it’s blended together, there’s no way to distinguish all the fruits—or in this case, the RNA from individual cells—that went into it. What researchers needed was something more like a fruit salad, a way to separate all the blueberries, raspberries, and blackberries.

In 2011, working with Broad Institute colleague Joshua Levin, PhD ’92, and postdocs Alex Shalek, now at MIT’s Institute for Medical Engineering and Science, and Rahul Satija, now at the New York Genome Center, Regev managed to obtain enough RNA from a single cell to sequence it. To test the method, they sequenced 18 individual dendritic cells from the bone marrow of a mouse. The cells were all obtained in the same way and were expected to be the same type. But to the researchers’ amazement, they were expressing different genes and could be classified into two distinct subtypes. It was like finding out the smoothie you’d been drinking for years had ingredients you’d never known about.

Regev and her colleagues weren’t the only ones figuring out how to sequence a single cell with such sensitivity, nor were they the very first to succeed. Other labs were making similar advances at approximately the same time, each using its own technology and algorithms. And they all faced the same problem: isolating and extracting enough RNA from individual cells was time consuming and expensive. Regev and her colleagues had spent many thousands of dollars to sequence only 18 cells. If the body was full of rare, undiscovered cells, it was going to take an extraordinarily long time to find them.

Skip ahead seven years and the cost of single-cell RNA sequencing is down to only pennies per cell. A critical breakthrough was Drop-Seq, a new technology developed by researchers at Harvard and the Broad Institute, including Regev and members of her lab. The device embeds individual cells into distinct oil droplets with a tiny “bar-coded” bead. When the cell is broken apart for sequencing, some of its RNA attaches to the bead in its droplet. This allows researchers to analyze thousands at once without getting their genetic material mixed up.

Cell theory 2.0

When cell theory was first proposed by German scientists some 180 years ago, it was hard to fathom that our tissues are built from “individual elementary units,” as Theodor Schwann, one of the two scientists credited with the theory, described cells. But it soon became a central tenet of biology, and over the decades and centuries, cells began to give up their secrets. Microscopes improved; new staining and sorting techniques became available. With each advance, new distinctions became possible. Muscle cells could be distinguished from neurons, and then categorized again as smooth or skeletal muscle cells. Cells, it became clear, were all fundamentally similar but came in different forms that had different properties.

By the 21st century, 200 to 300 major cell types had been identified. And while biologists have long recognized that the true number of cell types must be higher, the extent of their diversity is only now coming into full focus, thanks in large part to single-cell RNA sequencing. Regev says that the immune system alone can now be divided into more than 200 cell types and that even our retinas have 100 or more distinct types of neurons. She and her colleagues have discovered several of them.

The idea that knowing so much more about our cells could lead to medical breakthroughs is no longer hypothetical. By sequencing the RNA of individual cancer cells in recent years—“Every cell is an experiment now,” she says—she has found remarkable differences between the cells of a single tumor, even when they have the same mutations. (Last year that work led to Memorial Sloan Kettering’s Paul Marks Prize for Cancer Research.) She found that while some cancers are thought to develop resistance to therapy, a subset of melanoma cells were resistant from the start. And she discovered that two types of brain cancer, oligodendroglioma and astrocytoma, harbor the same cancer stem cells, which could have important implications for how they’re treated.

The excitement in the field has become tangible as more new cell types have been found. And yet Regev realized that if the aim was comprehensive knowledge, the approach needed to be coordinated. If each lab were to rely on its own techniques, it would be hard to standardize the computational tools and the resulting data. The new studies were producing “very nice glimmers of light,” Regev says—“a thing here, a thing there.” But she wanted to make sure those findings could be connected.Regev has also been busily mapping cells from the immune system, brain, gut, and elsewhere. She is not alone. Other labs have started their own mapping projects, each tackling a different part of the body. Last year researchers at the University of Washington attempted to classify every cell type in the microscopic worm C. elegans. “Every single field in biology is saying, ‘Of course we have to look at single-cell resolution,’” says Lander. “How did we ever imagine we were going to solve a problem without single-cell resolution?”

Regev began to advocate creating something more unified: a map that would allow researchers to chart gene expression and cell types across the entire body. Sarah Teichmann had been thinking along the same lines. When she reached out to Regev in late 2015 about the possibility of joining forces, Regev immediately said yes.

A Google Maps for our cells

The Human Cell Atlas is a collaboration among hundreds of biologists, technologists, and software engineers across the globe. Results from single-cell RNA sequencing will be combined with other data points to provide a comprehensive catalogue of all human cells.

But the many researchers involved won’t simply be compiling spreadsheets listing different cell types. The atlas will also reveal where the cells are located in the body, how many there are, what forms they can take, even the developmental history of different cell types as they differentiated from stem cells. And all of this will be made accessible through a data coordination platform and a rich visual interface that Regev compares to Google Maps. It will allow users to zoom in to the molecular level of our cells, but zooming out to the level of tissues and organs will be important too. As a 2017 overview of the Human Cell Atlas by the project’s organizing committee noted, an atlas “is a map that aims to show the relationships among its elements.” Just as corresponding coastlines seen in an atlas of Earth offer visual evidence of continental drift, compiling all the data about our cells in one place could reveal relationships among cells, tissues, and organs, including some that are entirely unexpected. And just as the periodic table made it possible to predict the existence of elements yet to be observed, the Human Cell Atlas, Regev says, could help us predict the existence of cells that haven’t been found.

The plan is not to sequence all 37 trillion cells but to sample from every part of the body. As Regev talks about the project, her enthusiasm evident, she digs up a slide to demonstrate how effective sampling can be. The slide, first only an empty frame of white, begins to fill in, pixel by pixel, with specks of blue and yellow. Soon, even though many of the pixels haven’t yet been filled, the image on the screen is unmistakable: it is Van Gogh’s Starry Night. Likewise, Regev explains, the Human Cell Atlas can give a complete picture even if not every single cell has been sequenced.

To do the sequencing, Regev and Teichmann have welcomed and recruited experts in each different tissue type. Though expected to take years, the project is moving ahead rapidly with such backers as NIH, the EU, the Wellcome Trust, the Manton Foundation, and the Chan Zuckerberg Initiative, which pledged to spend $3 billion to battle disease over the next decade; this year alone it will fund 85 Human Cell Atlas grants. Early results are already pouring in. In March, Swedish researchers working on cells related to human development announced they had sequenced 250,000 individual cells. In May, a team at the Broad made a data set of more than 500,000 immune cells available on a preview site. The goal, Regev says, is for researchers everywhere to be able to use the open-source platform of the Human Cell Atlas to perform joint analyses.

Plenty of challenges remain before the atlas can become a reality. New visualization software must be developed. Sequencing and computational approaches will need to be standardized across a huge number of labs. Conceptual issues, such as what distinguishes one cell type from another, have to be worked through. But the community behind the Human Cell Atlas—including more than 800 individuals as of June—has no shortage of motivation.

One of Regev’s own recent studies, published in August in Nature, is perhaps the best example of how the project could change biology. In mapping cells of the lungs, Regev and Jay Rajagopal’s lab at Massachusetts General Hospital found a new, very rare cell type that primarily expresses a gene linked to cystic fibrosis. Regev now thinks that these rare cells probably play a key role in the disease. More surprising yet, researchers had previously thought that a different cell type was expressing the gene.

“Imagine if somebody wanted to do gene therapy,” Regev says. “You have to fix the gene, but you have to fix it in the right cell.” The Human Cell Atlas could help researchers identify the right cell and understand how the gene in question is regulated by that cell’s extraordinarily complicated molecular networks.

For Regev, the importance of the Human Cell Atlas goes beyond its promise to revolutionize biology and medicine. As she once put it, without an atlas of our cells, “we don’t really know what we’re made of.”

Researchers discover new type of lung cell, critical insights for cystic fibrosis

A comprehensive single-cell analysis of airway cells in mice, validated in human tissue, reveals molecular details critical to understanding lung disease.

Karen Zusi
August 1, 2018

Researchers have identified a rare cell type in airway tissue, previously uncharacterized in the scientific literature, that appears to play a key role in the biology of cystic fibrosis. Using new technologies that enable scientists to study gene expression in thousands of individual cells, the team comprehensively analyzed the airway in mice and validated the results in human tissue.

Led by researchers from the Broad Institute of MIT and Harvard and Massachusetts General Hospital (MGH), the molecular survey also characterized gene expression patterns for other new cell subtypes. The work expands scientific and clinical understanding of lung biology, with broad implications for all diseases of the airway — including asthma, chronic obstructive pulmonary disease, and bronchitis.

Jayaraj Rajagopal, a physician in the Pulmonary and Critical Care Unit at MGH, associate member at the Broad Institute, and a Howard Hughes Medical Institute (HHMI) faculty scholar, and Broad core institute member Aviv Regev, director of the Klarman Cell Observatory at the Broad Institute, professor of biology at MIT, and an HHMI investigator, supervised the research. Daniel Montoro, a graduate student in Rajagopal’s lab, and postdoctoral fellows Adam Haber and Moshe Biton in the Regev lab are co-first authors on the paper published today in Nature.

“We have the framework now for a new cellular narrative of lung disease,” said Rajagopal, who is also a professor at Harvard Medical School and a principal faculty member at the Harvard Stem Cell Institute. “We’ve uncovered a whole distribution of cell types that seem to be functionally relevant. What’s more, genes associated with complex lung diseases can now be linked to specific cells that we’ve characterized. The data are starting to change the way we think about lung diseases like cystic fibrosis and asthma.”

“With single-cell sequencing technology, and dedicated efforts to map cell types in different tissues, we’re making new discoveries — new cells that we didn’t know existed, cell subtypes that are rare or haven’t been noticed before, even in systems that have been studied for decades,” said Regev, who is also co-chair of the international Human Cell Atlas consortium. “And for some of these, understanding and characterizing them sheds new light immediately on what’s happening inside the tissue.”

Using single-cell RNA sequencing, the researchers analyzed tens of thousands of cells from the mouse airway, mapping the physical locations of cell types and creating a cellular “atlas” of the tissue. They also developed a new method called pulse-seq to monitor development of cell types from their progenitors in the mouse airway. The findings were validated in human tissue.

One extremely rare cell type, making up roughly one percent of the cell population in mice and humans, appeared radically different from other known cells in the dataset. The team dubbed this cell the “pulmonary ionocyte” because its gene expression pattern was similar to ionocytes — specialized cells that regulate ion transport and hydration in fish gills and frog skin.

Strikingly, at levels higher than any other cell type, these ionocytes expressed the gene CFTR — which, when mutated, causes cystic fibrosis in humans. CFTR is critical for airway function, and for decades researchers and clinicians have assumed that it is frequently expressed at low levels in ciliated cells, a common cell type spread throughout the entire airway.

But according to the new data, the majority of CFTR expression occurs in only a few cells, which researchers didn’t even know existed until now.

When the researchers disrupted a critical molecular process in pulmonary ionocytes in mice, they observed the onset of key features associated with cystic fibrosis — most notably, the formation of dense mucus. This finding underscores how important these cells are to airway-surface regulation.

“Cystic fibrosis is an amazingly well-studied disease, and we’re still discovering completely new biology that may alter the way we approach it,” said Rajagopal. “At first, we couldn’t believe that the majority of CFTR expression was located in these rare cells, but the graduate students and postdocs on this project really brought us along with their data.”

The results may also have implications for developing targeted cystic fibrosis therapies, according to the team. For example, a gene therapy that corrects for a mutation in CFTRwould need to be delivered to the right cells, and a cell atlas of the tissue could provide a reference map to guide that process.

The study further highlighted where other disease-associated genes are expressed in the airway. For example, asthma development has been previously linked with a gene that encodes a sensor for rhinoviruses, and the data now indicate that this gene is expressed by ciliated cells. Another gene linked with asthma is expressed in tuft cells, which separated into at least two groups — one that senses chemicals in the airway and one that produces inflammation. The results suggest that a whole ensemble of cells may be responsible for different aspects of asthma.

Using the pulse-seq assay, the researchers tracked how the newly characterized cells and subtypes in the mouse airway develop. They demonstrated that mature cells in the airway arise from a common progenitor: the basal cells. The team also discovered a previously undescribed cellular structure in the tissue. These structures, which the researchers called “hillocks,” are unique zones of rapid cell turnover, and their function is not yet understood.

“The atlas that we’ve created is already starting to drastically re-shape our understanding of airway and lung biology,” said Regev. “And, for this and other organ systems being studied at the single-cell level, we’ll have to drape everything we know on top of this new cellular diversity to understand human health and disease.”

Funding for this study was provided in part by the Klarman Cell Observatory at the Broad Institute, Manton Foundation, HHMI, New York Stem Cell Foundation, Harvard Stem Cell Institute, Human Frontiers Science Program, and National Institutes of Health.

Paper(s) cited:

Montoro DT, Haber AL, Biton M et al. A revised airway epithelial hierarchy includes CFTR-expressing ionocytes. Nature. Online August 1, 2018. DOI: 10.1038/s41586-018-0393-7

Decoding RNA-protein interactions

Scientists leverage one step, unbiased method to characterize the binding preferences of more than 70 human RNA-binding proteins.

Raleigh McElvery
June 7, 2018

Thanks to continued advances in genetic sequencing, scientists have identified virtually every A, T, C, and G nucleotide in our genetic code. But to fully understand how the human genome encodes us, we need to go one step further, mapping the function of each base. That is the goal of the Encyclopedia of DNA Elements (ENCODE) project, funded by the National Human Genome Research Institute and launched on the heels of the Human Genome Project in 2003. Although much has already been accomplished — mapping protein-DNA interactions and the inheritance of different epigenetic states — understanding the function of a DNA sequence also requires deciphering the purpose of the RNAs encoded by it, as well as which proteins bind to those RNAs.

Such RNA-binding proteins (RBPs) regulate gene expression by controlling various post-transcriptional processes — directing where the RNAs go in the cell, how stable they are, and which proteins will be synthesized. Yet these vital RNA-protein relationships remain difficult to catalog, since most of the necessary experiments are arduous to complete and difficult to interpret accurately.

In a new study, a team of MIT biologists and their collaborators describes the binding specificity of 78 human RBPs, using a one-step, unbiased method that efficiently and precisely determines the spectrum of RNA sequences and structures these proteins prefer. Their findings suggest that RBPs don’t just recognize specific RNA segments, but are often influenced by contextual features as well — like the folded structures of the RNA in question, or the nucleotides flanking the RNA-binding sequence.

“RNA is never naked in the cell because there are always proteins binding, guiding, and modifying it,” says Christopher Burge, director of the Computational and Systems Biology PhD Program, professor of biology and biological engineering, extramural member of the Koch Institute for Integrative Cancer Research, associate member of the Broad Institute of MIT and Harvard, and senior author of the study. “If you really want to understand post-transcriptional gene regulation, then you need to characterize those interactions. Here, we take advantage of deep sequencing to give a more nuanced picture of exactly what RNAs the proteins bind and where.”

MIT postdoc Daniel Dominguez, former graduate student Peter Freese, and current graduate student Maria Alexis are the lead authors of the study, which is part of the ENCODE project and appears in Molecular Cell on June 7.

A method for the madness

From the moment an RNA is born, it is coated by RBPs that control nearly every aspect of its lifecycle. RBPs generally contain a binding domain, a three-dimensional folded structure that can attach to a specific nucleotide sequence on the RNA called a motif. Because there are over 1,500 different RBPs found in the human genome, the biologists needed a way to systematically determine which of those proteins bound to which RNA motifs.

After considering a number of different approaches to analyze RNA-protein interactions both directly in the cell (in vivo) and isolated in a test tube (in vitro), the biologists settled on an in vitro method known as RNA Bind-n-Seq (RBNS), developed four years ago by former Burge lab postdoc and co-author Nicole Lambert.

Although Lambert had previously tested only a small subset of proteins, RBNS surpassed other approaches because it was a quantitative method that revealed both low and high affinity RNA-protein interactions, required only a single procedural step, and screened nearly every possible RNA motif. This new study improved the assay’s throughput, systematically exploring the binding specificities of more than 70 human RBPs at a high resolution.

“Even with that initial small sample, it was clear RBNS was the way to go, and over the last three-and-a-half years we’ve been gradually building on this approach,” Dominguez says. “Since a single RBP can select from billions of unique RNA molecules, our approach gives you a lot more power to detect the all those possible targets, taking into account RNA secondary structure and contextual features. It’s an extremely deep and detailed assay.”

First, the researchers purified the human RBPs, mixing them with randomly-generated synthetic RNAs roughly 20 nucleotides long, which represented virtually all the RNAs an RBP could bind to. Next, they extracted the RBPs along with their bound RNAs and sequenced them. With the help of their collaborators from the University of California at San Diego and University of Connecticut Health, the team conducted additional assays to glean what these RNA-protein interactions might look like in an actual cell, and infer the cellular function of the RBPs.

The researchers expected most RBPs to bind to a unique RNA motif, but to their surprise they found the opposite: Many of the proteins, regardless of structural class, seemed to prefer similar short, unfolded nucleotide sequence motifs.

“Human cells express hundreds of thousands of distinct transcripts, so you might think that each RBP would bind a slightly different RNA sequence in order to distinguish between targets,” Alexis says. “In fact, one might assume that having distinct RBP motifs would ensure maximum flexibility. But, as it turns out, nature has built in substantial redundancy; multiple proteins seem to bind the same short, linear sequences.”

Redundant motifs with distinct targets and functions

This overlap in RBP binding preference suggested to the scientists that there must be some other indicator besides the sequence of the motif that signaled RBPs which RNA to target. Those signals, it turned out, stemmed from the spacing of the motifs as well as which nucleotide bases flank its binding sites. For the less common RBPs that targeted non-linear RNA sequences, the precise way the RNA folded also seemed to influence binding specificity.

The obvious question, then, is: Why might RBPs have evolved to rely on contextual features instead of just giving them distinct motifs?

Accessibility seems like one of the more plausible arguments. The researchers reasoned that linear RNA segments are physically easier to reach because they are not obstructed by other RNA strands, and they found that more accessible motifs are more likely to be bound. Another possibility is that having many proteins target the same motif creates some inter-protein competition. If one protein increases RNA stability and another decreases it, whichever binds the strongest will prevent the other from binding at all, enabling more pronounced changes in gene activity between cells or cell states. In other scenarios, proteins with similar functions that target the same motif could provide redundancy to ensure that regulation occurs in the cell.

“It’s definitely a difficult question, and one that we may never truly be able to answer,” Dominguez says. “As RBPs duplicated over evolutionary time, perhaps altering recognition of the contextual features around the RNA motif was easier than changing the entire RNA motif. And that would give new opportunities for RBPs to select different cellular targets.”

This study marks one of the first in vitro contributions to the ENCODE Project. While in vivo assays reveal information specific to the particular cell line or tissue in which they were conducted, RBNS will help define the basic rules of RNA-protein interactions — so fundamental they are likely to apply across many cell types and tissues.

The research was funded by the National Institutes of Health ENCODE Project, an NIH/NIGMS grant, the National Defense Science and Engineering Graduate Fellowship, Kirschstein National Research Service Award, Burroughs Wellcome Postdoctoral Fund, and an NIH Individual Postdoctoral Fellowship.

Network of diverse noncoding RNAs acts in the brain

Scientists identify the first known network consisting of three types of regulatory RNAs.

Nicole Giese Rura | Whitehead Institute
June 7, 2018

Scientists at MIT’s Whitehead Institute have identified a highly conserved network of noncoding RNAs acting in the mammalian brain. While gene regulatory networks are well described, this is the first documented regulatory network comprised of three types of noncoding RNA: microRNA, long noncoding RNA, and circular RNA. The finding, which is described online this week in the journal Cell, expands our understanding of how several noncoding RNAs can interact to regulate each other.

This sophisticated network, which is conserved in placental mammals, intrigued Whitehead Member David Bartel, whose lab identified it.

“It has been quite an adventure to unravel the different elements of this network,” says Bartel, who is also a professor of biology at MIT and investigator with the Howard Hughes Medical Institute. “When we removed the long noncoding RNA, we saw huge increases in the microRNA, which, with the help of a second microRNA turned out to reduce the levels of the circular RNA.”

RNA may be best known for acting as a template during protein production, but most RNA molecules in the cell do not actually code for proteins. Many play fundamental roles in the splicing and translation of protein-coding RNAs, whereas others play regulatory roles. MicroRNAs, as the name would suggest, are small, about 22 nucleotides (nucleotides are the building blocks of RNA); long noncoding RNAs (lncRNAs) are longer than 200 nucleotides; and circular RNAs (circRNAs) are looped RNAs formed by atypical splicing of either lncRNAs or protein-coding RNAs. These three types of noncoding RNAs have been shown previously to be vital for controlling protein-coding gene expression, and in some instances their dysregulation is linked to cancer or other diseases.

Previous work by Bartel and Whitehead member and MIT Professor Hazel Sive identified hundreds of lncRNAs conserved in vertebrate animals, including Cyrano, which contains an unusual binding site for the microRNA miR-7.

In the current research, Ben Kleaveland, a postdoc in Bartel’s lab and first author of the Cell paper, delves into Cyrano’s function in mice. His results are surprising: a regulatory network centered on four noncoding RNAs — a lncRNA, a circRNA, and two microRNAs — acting in mammalian neurons. The network employs multiple interactions between these noncoding RNAs to ultimately ensure that the levels of one microRNA, miR-7, are kept extremely low and the levels of one circRNA, Cdr1as, are kept high.

Several aspects of this highly tuned network are unique. The lncRNA Cyrano targets miR-7 for degradation. Cyrano is exceptionally efficient, and in some cells, reduces miR-7 by an astounding 98 percent — a stronger effect than scientists have ever documented for this phenomenon, called target RNA-directed microRNA degradation. In the described network, unchecked miR-7 indirectly leads to degradation of the circRNA Cdr1as. CircRNAs such as this one are usually highly stable because the RNA degradation machinery needs to latch onto the end of an RNA molecule before the machinery can operate. In the case of Cdr1as, the circRNA contains a prodigious number of sites that can interact with miR-7: 130 in mice and 73 in humans. As these sites are bound by miR-7, another microRNA, miR-671, springs into action and directs slicing of the Cdr1as. This renders Cdr1as vulnerable to degradation.

The network’s precise function still eludes researchers, but evidence suggests that it may be important in brain function. All four components of the network are enriched in the brain, particularly in neurons, and recently, Cdr1as has been reported to influence neuronal activity in mice.

“We’re in the early stages of understanding this network, and there’s so much left to discover,” Kleaveland says. “Our current hypothesis is that Cdr1as is not only regulated by miR-7 but also facilitates miR-7 function by delivering this microRNA to neuronal synapses.”

This work was supported by the National Institutes of Health and the Howard Hughes Medical Institute.

Computing changes in cell fate

Meena Chakraborty ’19 has spent two years in the lab of Nobel Prize winner Philip Sharp, combining computer science and wet lab techniques to study the impact of microRNAs on gene expression.

Raleigh McElvery
May 2, 2018

When Meena Chakraborty was eleven years old, her parents took her to South Africa to show her what life was like outside her hometown of Lexington, Massachusetts. The trip was first and foremost a family vacation, but what struck Chakraborty, now a junior at MIT, was neither the sights nor safaris, but their visit to a children’s hospital. Looking back, she identifies that experience as the catalyst that spurred her current career path, centered on three years of biology research with implications for human health.

“I remember being astounded that the patients there were my age,” she says. “I had all these things in my life to look forward to, while they were fighting HIV and might not survive. That’s when I started thinking that I could do something to counter disease, and studying biology seemed like the best way to do that.”

Up until that point, she’d intended to be a writer. So when it came time to choose a college, she initially shied away from MIT, fearing it would be too “tech-focused.”

“Even though I was primarily interested in biology, I still wanted diversity in terms of the academic subjects and the people around me,” she says. “But it became clear that MIT really encourages you to step outside your major. Every undergrad has to complete a Humanities, Arts, or Social Sciences concentration, and I chose philosophy. Those classes have become a staple of my undergrad experience, and allowed me to keep in touch with my love for writing while still focusing on my science.”

Given her propensity for math, she declared Course 6-7 (Computer Science and Molecular Biology), as a means to develop analytical tools to decipher large data sets and better understand biological systems. The summer after her freshman year, she had her first chance to marry these two skills in a real-world setting: she began working in the lab of Nobel Prize winner Philip Sharp, located in the Koch Institute for Integrative Cancer Research.

This was her first foray into computational biology, but it wasn’t her first time at the Koch — she’d shadowed two graduates students in the Irvine lab for a summer as a junior in high school. This time, though, as an undergraduate, she was assigned her own project, under the guidance of postdoctoral fellow Salil Garg. Together, they’ve studied a type of RNA known as microRNA (miRNA) for the past two years.

Messenger RNA (mRNA) — perhaps the most well-known of the RNAs — constitutes the intermediate step between DNA and the final product of gene expression: the protein. In contrast, miRNAs are never translated into proteins. Instead, they bind to complementary sequences in target mRNAs, preventing those mRNAs from being turned into proteins, and blocking gene expression.

This miRNA-directed silencing is widespread and complex. In some cases, miRNAs silence single genes. In others, multiple miRNAs coordinate to turn groups of genes on and off in concert, thereby controlling entire sets of genes that interact with one another.  For example, two years ago, Chakraborty’s mentor used computational methods to pinpoint a group of poorly expressed, understudied “nonclassical” miRNAs that appear to coordinate the expression of pluripotency genes. Pluripotency gene levels dictate the behavior and fate of embryonic stem cells — non-specialized cells awaiting instructions to “differentiate” and assume a particular cell type (skin cell, blood cell, neuron, and so on).

Chakraborty then used a technique known as fluorescence-activated cell sorting (FACS) to determine how nonclassical miRNAs affect gene expression in embryonic stem cells. She used a FACS assay to detect miRNA activity, engineering special DNA and inserting it into mouse embryonic stem cells. The DNA contained two genes: one encoding a red fluorescent protein with a place for miRNAs to bind, and another that makes a blue fluorescent protein and lacks this miRNA attachment site. When the miRNA binds to the gene expressing the red fluorescing protein, it is silenced, and the cell makes fewer red proteins compared to blue ones, whose production remains unhindered.

“We know when miRNAs are active, they will reduce the expression of the red florescent protein, but not the blue one,” she says. “And that’s precisely what we’ve seen with these nonclassical miRNAs, suggesting that they are active in the cell.”

Chakraborty is excited about what this finding could mean for cancer research. A growing number of studies have shown that some cancers arise when miRNAs fail to help embryonic stem cells interconvert between cell states.

Although she spends anywhere from four to 20 hours a week in lab, Chakraborty hasn’t lost sight of her extracurriculars. As co-president of the Biology Undergraduate Student Association, she serves as a liaison between biology students and faculty, coordinating events to connect the two. As the discussion chair for the Effective Altruism Club, she promotes dialogue between student club members regarding charities — how these organizations can maximize their donations, and how the public should decide which ones to support. As a volunteer for the non-profit Help at Your Door, she inputs grocery lists from senior citizens and disabled individuals into a computer program, and then coordinates with community members to deliver the specified order.

Last summer, she was accepted into the Johnson & Johnson UROP Scholars Program, joining approximately 20 fellow undergraduate women in STEM research at MIT during the summer term. Her cohort attended faculty presentations, workshops, and networking events geared towards post-graduate careers in the sciences.

“I really appreciated that program, because I think a lot of women are afraid of science due to societal norms,” she says. “I remember originally thinking I wouldn’t be good at computer science or math, and now here I am combining both skills with wet lab techniques in my research.”

Most recently, Chakraborty was a recipient of the 2017-2018 Barry Goldwater Scholarship Award, selected from a nationwide field of candidates nominated by university faculty. She will also remain on campus this coming summer to conduct faculty-mentored research as part of the MIT Amgen Scholars Program.

After she graduates in 2019, Chakraborty intends to pursue a PhD in a biology-related discipline, perhaps computational biology. After that, the options are endless — professor, consultant, research scientist. She’s still weighing the possibilities, and doesn’t seem too concerned about selecting one just yet.

“I know I’m going in the right direction, because it hits me every time I finish a challenging assignment or whenever I figure out a new approach in the lab,” she says. “When I complete a task like that with the help of friends and mentors, there’s this sense of pride and a feeling that I can’t believe how much I’ve learned in just once semester. The way my brain considers problems and finds solutions is just so different from the way it was three years ago when I first started out as a freshman.”

Photo credit: Raleigh McElvery
Single-cell database to propel biological studies

Whitehead team analyzes transcriptomes for roughly 70,000 cells in planarians, creates publicly available database to drive further research.

Nicole Davis | Whitehead Institute
April 20, 2018

A team at Whitehead Institute and MIT has harnessed single-cell technologies to analyze over 65,000 cells from the regenerative planarian flatworm, Schmidtea mediterranea, revealing the complete suite of actives genes (or “transcriptome”) for practically every type of cell in a complete organism. This transcriptome atlas represents a treasure trove of biological information on planarians, which is the subject of intense study in part because of its unique ability to regrow lost or damaged body parts. As described in the April 19 advance online issue of the journal Science, this new, publicly available resource has already fueled important discoveries, including the identification of novel planarian cell types, the characterization of key transition states as cells mature from one type to another, and the identity of new genes that could impart positional cues from muscles cells — a critical component of tissue regeneration.

“We’re really at the beginning of an amazing era,” says senior author Peter Reddien, a member of Whitehead Institute, professor of biology at MIT, and investigator with the Howard Hughes Medical Institute. “Just as genome sequences became indispensable resources for studying the biology of countless organisms, analyzing the transcriptomes of every cell type will become another fundamental tool — not just for planarians, but for many different organisms.”

The ability to systematically reveal which genes in the genome are active within an individual cell flows from a critical technology known as single-cell RNA sequencing. Recent advances in the technique have dramatically reduced the per-cell cost, making it feasible for a single laboratory to analyze a suitably large number of cells to capture the cell type diversity present in complex, multi-cellular organisms.

Reddien has maintained a careful eye on the technology from its earliest days because he believed it offered an ideal way to unravel planarian biology. “Planarians are relatively simple, so it would be theoretically possible for us to capture every cell type. Yet they still have a sufficiently large number of cells — including types we know little or even nothing about,” he explains. “And because of the unusual aspects of planarian biology — essentially, adults maintain developmental information and progenitor cells that in other organisms might be present transiently only in embryos — we could capture information about mature cells, progenitor cells, and information guiding cell decisions by sampling just one stage, the adult.”

Two and a half years ago, Reddien and his colleagues — led by first author Christopher Fincher, a graduate student in Reddien’s laboratory — set out to apply single-cell RNA sequencing systematically to planarians. The group isolated individual cells from five regions of the animal and gathered data from a total of 66,783 cells. The results include transcriptomes for rare cell types, such as those that comprise on the order of 10 cells out of an adult animal that consists of roughly 500,000 to 1 million cells.

In addition, the researchers uncovered some cell types that have yet to be described in planarians, as well cell types common to many organisms, making the atlas a valuable tool across the scientific community. “We identified many cells that were present widely throughout the animal, but had not been previously identified. This surprising finding highlights the great value of this approach in identifying new cells, a method that could be applied widely to many understudied organisms,” Fincher says.

“One main important aspect of our transcriptome atlas is its utility for the scientific community,” Reddien says. “Because many of the cell types present in planarians emerged long ago in evolution, similar cells still exist today in various organisms across the planet. That means these cell types and the genes active within them can be studied using this resource.”

The Whitehead team also conducted some preliminary analyses of their atlas, which they’ve dubbed “Planarian Digiworm.” For example, they were able to discern in the transcriptome data a variety of transition states that reflect the progression of stem cells into more specialized, differentiated cell types. Some of these cellular transition states have been previously analyzed in painstaking detail, thereby providing an important validation of the team’s approach.

In addition, Reddien and his colleagues knew from their own prior, extensive research that there is positional information encoded in adult planarian muscle — information that is required not only for the general maintenance of adult tissues but also for the regeneration of lost or damaged tissue. Based on the activity pattern of known genes, they could determine roughly which positions the cells had occupied in the intact animal, and then sort through those cells’ transcriptomes to identify new genes that are candidates for transmitting positional information.

“There are an unlimited number of directions that can now be taken with these data,” Reddien says. “We plan to extend our initial work, using further single-cell analyses, and also to mine the transcriptome atlas for addressing important questions in regenerative biology. We hope many other investigators find this to be a very valuable resource, too.”

This work was supported by the National Institutes of Health, the Howard Hughes Medical Institute, and the Eleanor Schwartz Charitable Foundation.

Scientists find different cell types contain the same enzyme ratios

New discovery suggests that all life may share a common design principle.

Justin Chen | Department of Biology
March 29, 2018

By studying bacteria and yeast, researchers at MIT have discovered that vastly different types of cells still share fundamental similarities, conserved across species and refined over time. More specifically, these cells contain the same proportion of specialized proteins, known as enzymes, which coordinate chemical reactions within the cell.

To grow and divide, cells rely on a unique mixture of enzymes that perform millions of chemical reactions per second. Many enzymes, working in relay, perform a linked series of chemical reactions called a “pathway,” where the products of one chemical reaction are the starting materials for the next. By making many incremental changes to molecules, enzymes in a pathway perform vital functions such as turning nutrients into energy or duplicating DNA.

For decades, scientists wondered whether the relative amounts of enzymes in a pathway were tightly controlled in order to better coordinate their chemical reactions. Now, researchers have demonstrated that cells not only produce precise amounts of enzymes, but that evolutionary pressure selects for a preferred ratio of enzymes. In this way, enzymes behave like ingredients of a cake that must be combined in the correct proportions and all life may share the same enzyme recipe.

“We still don’t know why this combination of enzymes is ideal,” says Gene-Wei Li, assistant professor of biology at MIT, “but this question opens up an entirely new field of biology that we’re calling systems level optimization of pathways. In this discipline, researchers would study how different enzymes and pathways behave within the complex environment of the cell.”

Li is the senior author of the study, which appears online in the journal Cell on March 29, and in print on April 19. The paper’s lead author, Jean-Benoît Lalanne, is a graduate student in the MIT Department of Physics.

An unexpected observation

For more than 100 years, biologists have studied enzymes by watching them catalyze chemical reactions in test tubes, and — more recently — using X-rays to observe their molecular structure.

And yet, despite years of work describing individual proteins in great detail, scientists still don’t understand many of the basic properties of enzymes within the cell. For example, it is not yet possible to predict the optimal amount of enzyme a cell should make to maximize its chance of survival.

The calculation is tricky because the answer depends not only on the specific function of the enzyme, but also how its actions may have a ripple effect on other chemical reactions and enzymes within the cell.

“Even if we know exactly what an enzyme does,” Li says, “we still don’t have a sense for how much of that protein the cell will make. Thinking about biochemical pathways is even more complicated. If we gave biochemists three enzymes in a pathway that, for example, break down sugar into energy, they would probably not know how to mix the proteins at the proper ratios to optimize the reaction.”

The study of the relative amounts of substances — including proteins — is known as “stoichiometry.” To investigate the stoichiometry of enzymes in different types of cells, Li and his colleagues analyzed three different species of bacteria — Escherichia coli, Bacillus subtilis, and Vibrio natriegens — as well as the budding yeast Saccharomyces cerevisiae. Among these cells, scientists compared the amount of enzymes in 21 pathways responsible for a variety of tasks including repairing DNA, constructing fatty acids, and converting sugar to energy. Because these species of yeast and bacteria have evolved to live in different environments and have different cellular structures, such as the presence or lack of a nucleus, researchers were surprised to find that all four cells types had nearly identical enzyme stoichiometry in all pathways examined.

Li’s team followed up their unexpected results by detailing how bacteria achieve a consistent enzyme stoichiometry. Cells control enzyme production by regulating two processes. The first, transcription, converts the information contained in a strand of DNA into many copies of messenger RNA (mRNA). The second, translation, occurs as ribosomes decode the mRNAs to construct proteins. By analyzing transcription across all three bacterial species, Li’s team discovered that the different bacteria produced varying amounts of mRNA encoding for enzymes in a pathway.

Different amounts of mRNA theoretically lead to differences in protein production, but the researchers found instead that the cells adjusted their rates of translation to compensate for changes in transcription. Cells that produced more mRNA slowed their rates of protein synthesis, while cells that produced less mRNA increased the speed of protein synthesis. Thanks to this compensation, the stoichiometry of enzymes remained constant across the different bacteria.

“It is remarkable that E. coli and B. subtilis need the same relative amount of the corresponding proteins, as seen by the compensatory variations in transcription and translation efficiencies,” says Johan Elf, professor of physical biology at Uppsala University in Sweden. “These results raise interesting questions about how enzyme production in different cells have evolved.”

“Examining bacterial gene clusters was really striking,” lead author Lalanne says. “Over a long evolutionary history, these genes have shifted positions, mutated into different sequences, and been bombarded by mobile pieces of DNA that randomly insert themselves into the genome. Despite all this, the bacteria have compensated for these changes by adjusting translation to maintain the stoichiometry of their enzymes. This suggests that evolutionary forces, which we don’t yet understand, have shaped cells to have the same enzyme stoichiometry.”

Searching for the stoichiometry regulating human health

In the future, Li and his colleagues will test whether their findings in bacteria and yeast extend to humans. Because unicellular and multicellular organisms manage energy and nutrients differently, and experience different selection pressures, researchers are not sure what they will discover.

“Perhaps there will be enzymes whose stoichiometry varies, and a smaller subset of enzymes whose levels are more conserved,” Li says. “This would indicate that the human body is sensitive to changes in specific enzymes that could make good drug targets. But we won’t know until we look.”

Beyond the human body, Li and his team believe that it is possible to find simplicity underlying the complex bustle of molecules within all cells. Like other mathematical patterns in nature, such as the the spiral of seashells or the branching pattern of trees, the stoichiometry of enzymes may be a widespread design principle of life.

The research was funded by the National Institutes of Health, Pew Biomedical Scholars Program, Sloan Research Fellowship, Searle Scholars Program, National Sciences and Engineering Research Council of Canada, Howard Hughes Medical Institute, National Science Foundation, Helen Hay Whitney Foundation, Jane Coffin Childs Memorial Fund, and the Smith Family Foundation.

Biologists’ new peptide could fight many cancers

Drug that targets a key cancer protein could combat leukemia and other types of cancer.

Anne Trafton | MIT News Office
January 15, 2018

MIT biologists have designed a new peptide that can disrupt a key protein that many types of cancers, including some forms of lymphoma, leukemia, and breast cancer, need to survive.

The new peptide targets a protein called Mcl-1, which helps cancer cells avoid the cellular suicide that is usually induced by DNA damage. By blocking Mcl-1, the peptide can force cancer cells to undergo programmed cell death.

“Some cancer cells are very dependent on Mcl-1, which is the last line of defense keeping the cell from dying. It’s a very attractive target,” says Amy Keating, an MIT professor of biology and one of the senior authors of the study.

Peptides, or small protein fragments, are often too unstable to use as drugs, but in this study, the researchers also developed a way to stabilize the molecules and help them get into target cells.

Loren Walensky, a professor of pediatrics at Harvard Medical School and a physician at Dana-Farber Cancer Institute, is also a senior author of the study, which appears in the Proceedings of the National Academy of Sciences the week of Jan. 15. Researchers in the lab of Anthony Letai, an associate professor of medicine at Harvard Medical School and Dana-Farber, were also involved in the study, and the paper’s lead author is MIT postdoc Raheleh Rezaei Araghi.

A promising target

Mcl-1 belongs to a family of five proteins that play roles in controlling programmed cell death, or apoptosis. Each of these proteins has been found to be overactive in different types of cancer. These proteins form what is called an “apoptotic blockade,” meaning that cells cannot undergo apoptosis, even when they experience DNA damage that would normally trigger cell death. This allows cancer cells to survive and proliferate unchecked, and appears to be an important way that cells become resistant to chemotherapy drugs that damage DNA.

“Cancer cells have many strategies to stay alive, and Mcl-1 is an important factor for a lot of acute myeloid leukemias and lymphomas and some solid tissue cancers like breast cancers. Expression of Mcl-1 is upregulated in many cancers, and it was seen to be upregulated as a resistance factor to chemotherapies,” Keating says.

Many pharmaceutical companies have tried to develop drugs that target Mcl-1, but this has been difficult because the interaction between Mcl-1 and its target protein occurs in a long stretch of 20 to 25 amino acids, which is difficult to block with the small molecules typically used as drugs.

Peptide drugs, on the other hand, can be designed to bind tightly with Mcl-1, preventing it from interacting with its natural binding partner in the cell. Keating’s lab spent many years designing peptides that would bind to the section of Mcl-1 involved in this interaction — but not to other members of the protein family.

Once they came up with some promising candidates, they encountered another obstacle, which is the difficulty of getting peptides to enter cells.

“We were exploring ways of developing peptides that bind selectively, and we were very successful at that, but then we confronted the problem that our short, 23-residue peptides are not promising therapeutic candidates primarily because they cannot get into cells,” Keating says.

To try to overcome this, she teamed up with Walensky’s lab, which had previously shown that “stapling” these small peptides can make them more stable and help them get into cells. These staples, which consist of hydrocarbons that form crosslinks within the peptides, can induce normally floppy proteins to assume a more stable helical structure.

Keating and colleagues created about 40 variants of their Mcl-1-blocking peptides, with staples in different positions. By testing all of these, they identified one location in the peptide where putting a staple not only improves the molecule’s stability and helps it get into cells, but also makes it bind even more tightly to Mcl-1.

“The original goal of the staple was to get the peptide into the cell, but it turns out the staple can also enhance the binding and enhance the specificity,” Keating says. “We weren’t expecting that.”

Killing cancer cells

The researchers tested their top two Mcl-1 inhibitors in cancer cells that are dependent on Mcl-1 for survival. They found that the inhibitors were able to kill these cancer cells on their own, without any additional drugs. They also found that the Mcl-1 inhibitors were very selective and did not kill cells that rely on other members of the protein family.

Keating says that more testing is needed to determine how effective the drugs might be in combating specific cancers, whether the drugs would be most effective in combination with others or on their own, and whether they should be used as first-line drugs or when cancers become resistant to other drugs.

“Our goal has been to do enough proof-of-principle that people will accept that stapled peptides can get into cells and act on important targets. The question now is whether there might be any animal studies done with our peptide that would provide further validation,” she says.

Joshua Kritzer, an associate professor of chemistry at Tufts University, says the study offers evidence that the stapled peptide approach is worth pursuing and could lead to new drugs that interfere with specific protein interactions.

“There have been a lot of biologists and biochemists studying essential interactions of proteins, with the justification that with more understanding of them, we would be able to develop drugs that inhibit them. This work now shows a direct line from biochemical and biophysical understanding of protein interactions to an inhibitor,” says Kritzer, who was not involved in the research.

Keating’s lab is also designing peptides that could interfere with other relatives of Mcl-1, including one called Bfl-1, which has been less studied than the other members of the family but is also involved in blocking apoptosis.

The research was funded by the Koch Institute Dana-Farber Bridge Project and the National Institutes of Health.

Douglas Lauffenburger

Education

  • PhD, 1979, University of Minnesota
  • BS, 1975, Chemical Engineering, University of Illinois, Urbana-Champaign

Research Summary

The Lauffenburger laboratory emphasizes integration of experimental and mathematical/computational analysis approaches, toward development and validation of predictive models for physiologically-relevant behavior in terms of underlying molecular and molecular network properties. Our work has been recognized as providing contributions fostering the interface of bioengineering, quantitative cell biology, and systems biology. Our main focus has been on fundamental aspects of cell dysregulation, complemented by translational efforts in identifying and testing new therapeutic ideas. Applications addressed have chiefly resided in various types of cancer (including breast, colon, lung, and pancreatic cancers along with leukemias and lymphomas), inflammatory pathologies (such as endometriosis, Crohn’s disease, colitis, rheumatoid arthritis, and Alzheimer’s disease), and the immune system (mainly for vaccines against pathogens such as HIV, malaria, and tuberculosis). We have increasingly emphasized complex tissue contexts, including mouse models, human subjects, and tissue-engineered micro-physiological systems platforms in association with outstanding collaborators. From our laboratory have come more than 100 doctoral and postdoctoral trainees. Many hold faculty positions at academic institutions in the USA, Canada, and Europe; others have gone on to research positions in biotechnology and pharmaceutical companies; and others yet have moved into policy and government agency careers.

Awards

  • Bernard M. Gordon Prize for Innovation in Engineering and Technology Education, National Academy of Engineering, 2021
  • American Association for the Advancement of Science, Member, 2019
  • American Academy of Arts and Sciences, Fellow, 2001
  • John Simon Guggenheim Memorial Foundation, Guggenheim Fellowship, 1989