Research Area: Cell Biology

Pioneering a deeper understanding of metabolism
Merrill Meadow | Whitehead Institute
March 23, 2022

Metabolism is the sum of life-sustaining chemical reactions occurring in cells and across whole organisms. The human genome codes for thousands of metabolic enzymes, and specific metabolic pathways play significant roles in many biological processes—from breaking down food to release energy, to normal proliferation and differentiation of cells, to pathologies underlying diabetes, cancer, and other diseases.

For decades, Whitehead Institute researchers have helped both to clarify how metabolism works in healthy states and to identify how metabolic processes gone awry contribute to diseases. Among Whitehead Founding Member Harvey Lodish’s wide-ranging accomplishments, for example, are the identification of genes and proteins involved in development of insulin resistance and stress responses in fat cells. His lab explored the hormones controlling fatty acid and glucose metabolism, broadening understanding of obesity and type 2 diabetes. In 1995, the lab cloned adiponectin, a hormone made exclusively by fat cells. A long series of studies has shown that adiponectin causes muscle to burn fatty acids faster – so they are not stored as fat – and increases the metabolism of the sugar glucose. More recently the lab identified and characterized types of RNAs that are specifically expressed in fat cells – including a microRNA unique to brown fat, which burns rather than stores fatty acids. In addition, former Member David Sabatini’s discovery of the mTOR protein and his subsequent work elaborating many ways in which the mTOR pathway affects cells function has proven to be fundamental to understanding the relationship between metabolism and an array of diseases.

Today, Institute researchers continue to pioneer a deeper understanding of how metabolic processes contribute to health and disease – with long-term implications that could range from new treatments for obesity and type 2 diabetes to methods for slowing the aging process. Here are a few examples of Whitehead Institute scientists’ creative and pioneering work in the field of metabolism.

Understanding hibernation and torpor 

Research inspiration comes in many forms. For example, Whitehead Institute Member Siniša Hrvatin – who joined the faculty in January 2022 from Harvard Medical School (HMS) – was inspired to pursue his current research by science-fiction tales about suspended animation for long-term space travel. And during graduate school, he realized that the ability of some mammals to enter a state of greatly reduced metabolism – such as occurs in hibernation –  was a mild but real-world form of suspended animation.

Hrvatin’s doctoral research in Doug Melton’s lab at Harvard University focused primarily on stem cell biology. But his subsequent postdoctoral research positions at Massachusetts Institute of Technology (MIT) and HMS enabled him to begin exploring the mechanisms and impact of reduced metabolic states in mammals. The timing was serendipitous, too, because he was able to use the growing array of genetic tools that were becoming available – and create some new tools of his own as well.

“To survive extreme environments, many animals have evolved the ability to profoundly decrease metabolic rate and body temperature and enter states of dormancy, such as hibernation and torpor,” Hrvatin says. Hibernating animals enter repeated states of significantly reduced metabolic activity, each lasting days to weeks. By comparison, daily torpor is shorter, with animals entering repeated periods of lower-than-normal metabolic activity lasting several hours.

Hrvatin’s lab studies the mysteries of how animals and their cells initiate, regulate, and survive these adaptations. “Our long-term goal is to determine if these adaptations can be harnessed to create therapeutic applications for humans.” He and his team are focusing on three broad questions regarding the mechanisms underlying torpor in mice and hibernation in hamsters.

First: How do the animals’ brains initiate and regulate the metabolic processes involved in this process? During his postdoctoral research,  Hrvatin published details of his discovery of neurons involved in the regulation of mouse torpor. “Now we are investigating how these torpor-regulating neurons receive information about the body’s energy-state,” he explains, “and studying how these neurons then drive a decrease of  metabolic rate and body temperature throughout the body.”

Second: How do individual cells – and their genomes – adapt to extreme or changing environments; and how do these adaptations differ between types of organisms?

“Cells from hibernating organisms ranging from rodents to bears have evolved the ability to survive extreme cold temperatures for many weeks to months,” Hrvatin notes. “We are using genetic screens to identify species-specific mechanisms of tolerance to extreme cold. Then we will explore whether these mechanisms can be induced in non-hibernating organisms – potentially to provide health benefits.”

Third: Can we deliberately and specifically slow down tissue damage, disease progression, and or aging in cells and whole organisms by inducing torpor or hibernation – or facets of those states? It has long been known that hibernating animals live longer than closely related non-hibernators; that cancer cells do not replicate during hibernation; and that cold can help protect neurons from the effects of loss of oxygen. However, the cellular mechanisms underlying these phenomena remain largely unknown. Hrvatin’s lab will induce a long-term hibernation-like state in mice and natural hibernation in hamsters, and study how those states affect processes such as tissue repair, cancer progression, and aging.

“In the lab, if you take many human cell types and put them in a cold environment they die, but cells from hibernators survive,” Hrvatin notes. “We’re fascinated by the cellular processes underlying those survival capacities. As a starting place, we are using novel CRISPR screening approaches to help us identify the genomic mechanisms involved.”

And then? “Ultimately, we hope to take on the biggest question: Is it possible to transfer some of those survival abilities to humans?

Solving a mitochondrial conundrum

When Whitehead Institute postdoctoral researcher Jessica Spinelli was studying cancer metabolism in graduate school, she became interested in what seemed to be a scientific paradox regarding mitochondria, the cell’s energy-producing organelles: Mitochondria are believed to be important for tumor growth; but they generally need oxygen to function, and substantial portions of tumors have very low oxygen levels. Pursuing research in the lab of former Whitehead Institute Member David Sabatini, Spinelli sought to understand how those facts fit together and whether mitochondria could somehow adapt to function with limited oxygen levels.

Recently, Spinelli and colleagues published an answer to the conundrum – one that could inform research into medical conditions including ischemia, diabetes and cancer. In a Science paper for which Spinelli was first author, the team demonstrated that when cells are deprived of oxygen, a molecule called fumarate can serve as a substitute and enable mitochondria to continue functioning.

As Spinelli explains, humans need oxygen molecules for the cellular respiration process that takes place in our cells’ mitochondria. In this process – called the electron transport chain – electrons are passed along in a sort of cellular relay race that, ultimately, allows the cell to create the energy needed to perform its vital functions. Usually, oxygen is necessary to keep that process operating.

Using mass spectrometry to measure the quantities of molecules produced through cellular respiration in varied conditions, Spinelli and the team found that cells deprived of oxygen had a high level of succinate molecules, which form when electrons are added to a molecule called fumarate. “From this, we hypothesized that the accumulation of succinate in low-oxygen environments is caused by mitochondria using fumarate as a substitute for oxygen’s role in the electron transport chain,” Spinelli explains. “That could explain how mitochondria function with relatively little oxygen.” The next step was to test that hypothesis in mice, and those studies provided several interesting findings: Only mitochondria in kidney, liver, and brain tissues could use fumarate in the electron transport chain. And even in normal conditions, mitochondria in these tissues used both fumarate and oxygen to function – shifting to rely more heavily on fumarate when oxygen was reduced. In contrast, heart and skeletal muscle mitochondria made minimal use of fumarate and did not function well with limited oxygen.

“We foresee some exciting work ahead, learning exactly how this process is regulated in different tissues,” Spinelli says, “and, especially, in solid tumor cancers, where oxygen levels vary between regions.”

Seeking a more accurate model of diabetes

Max Friesen, a postdoctoral researcher in the lab of Whitehead Institute Founding Member Rudolf Jaenisch, studies the role of cell metabolism in type 2 diabetes (T2D). An increasingly prevalent disease that affects millions of people around the world, T2D is hard to study in the lab. This has made it very challenging for scientists to detail the cellular mechanisms through which it develops – and therefore to create effective therapeutics.

“It has always been very hard to model T2D, because metabolism differs greatly between species,” Friesen says. “That fact leads to complications when we use animal models to study this disease. Mice, for example, have much higher metabolism and faster heart rates than humans. As a result, researchers have developed many approaches that cure diabetes in mice but that fail in humans.” Nor do most in vitro culture systems—cells in a dish—effectively recapitulate the disease.

But, building on Jaenisch’s pioneering success in developing disease models derived from human stem cells, Friesen is working to create a much more accurate in vitro system for studying diabetes. His goal is to make human stem cell-derived tissues that function as they would in the human body, closely recapitulating what happens when an individual develops diabetes. Currently, Friesen is differentiating human stem cells into metabolic tissues such as liver and adipocytes (fat). He has improved current differentiation protocols by adapting these cells to a culture medium that is much closer to the environment they see in the human body. Serendipitously, the process also makes the cells responsive to insulin at levels that are present in the human bloodstream. “This serves as a great model of a healthy cell that we can then turn into a disease model by exposing the cell to diabetic hyperinsulinemia,” Friesen says.

These advances should enable him to gain a better understanding of how metabolic pathways – such as the insulin signaling pathway – function in a diabetic model versus a healthy control model. “My hope is that our new models will enable us to figure out how dietary insulin resistance develops, and then identify a therapeutic intervention that blocks that disease-causing process,” he explains. “It would be fantastic to help alleviate this growing global health burden.”

Yukiko Yamashita, unraveler of stem cells’ secrets

The MIT biologist’s research has shed light on the immortality of germline cells and the function of “junk DNA.”

Anne Trafton | MIT News Office
March 22, 2022

When cells divide, they usually generate two identical daughter cells. However, there are some important exceptions to this rule: When stem cells divide, they often produce one differentiated cell along with another stem cell, to maintain the pool of stem cells.

Yukiko Yamashita has spent much of her career exploring how these “asymmetrical” cell divisions occur. These processes are critically important not only for cells to develop into different types of tissue, but also for germline cells such as eggs and sperm to maintain their viability from generation to generation.

“We came from our parents’ germ cells, who used to be also single cells who came from the germ cells of their parents, who used to be single cells that came from their parents, and so on. That means our existence can be tracked through the history of multicellular life,” Yamashita says. “How germ cells manage to not go extinct, while our somatic cells cannot last that long, is a fascinating question.”

Yamashita, who began her faculty career at the University of Michigan, joined MIT and the Whitehead Institute in 2020, as the inaugural holder of the Susan Lindquist Chair for Women in Science and a professor in the Department of Biology. She was drawn to MIT, she says, by the eagerness to explore new ideas that she found among other scientists.

“When I visited MIT, I really enjoyed talking to people here,” she says. “They are very curious, and they are very open to unconventional ideas. I realized I would have a lot of fun if I came here.”

Exploring paradoxes

Before she even knew what a scientist was, Yamashita knew that she wanted to be one.

“My father was an admirer of Albert Einstein, so because of that, I grew up thinking that the pursuit of the truth is the best thing you could do with your life,” she recalls. “At the age of 2 or 3, I didn’t know there was such a thing as a professor, or such a thing as a scientist, but I thought doing science was probably the coolest thing I could do.”

Yamashita majored in biology at Kyoto University and then stayed to pursue her PhD, studying how cells make exact copies of themselves when they divide. As a postdoc at Stanford University, she became interested in the exceptions to that carefully orchestrated process, and began to study how cells undergo divisions that produce daughter cells that are not identical. This kind of asymmetric division is critical for multicellular organisms, which begin life as a single cell that eventually differentiates into many types of tissue.

Those studies led to a discovery that helped to overturn previous theories about the role of so-called junk DNA. These sequences, which make up most of the genome, were thought to be essentially useless because they don’t code for any proteins. To Yamashita, it seemed paradoxical that cells would carry so much DNA that wasn’t serving any purpose.

“I couldn’t really believe that huge amount of our DNA is junk, because every time a cell divides, it still has the burden of replicating that junk,” she says. “So, my lab started studying the function of that junk, and then we realized it is a really important part of the chromosome.”

In human cells, the genome is stored on 23 pairs of chromosomes. Keeping all of those chromosomes together is critical to cells’ ability to copy genes when they are needed. Over several years, Yamashita and her colleagues at the University of Michigan, and then at MIT, discovered that stretches of junk DNA act like bar codes, labeling each chromosome and helping them bind to proteins that bundle chromosomes together within the cell nucleus.

Without those barcodes, chromosomes scatter and start to leak out of the cell’s nucleus. Another intriguing observation regarding these stretches of junk DNA was that they have much greater variability between different species than protein-coding regions of DNA. By crossing two different species of fruit flies, Yamashita showed that in cells of the hybrid offspring flies, chromosomes leak out just as they would if they lost their barcodes, suggesting that the codes are specific to each species.

“We think that might be one of the big reasons why different species become incompatible, because they don’t have the right information to bundle all of their chromosomes together into one place,” Yamashita says.

Stem cell longevity

Yamashita’s interest in stem cells also led her to study how germline cells (the cells that give rise to eggs and sperm cells) maintain their viability so much longer than regular body cells across generations. In typical animal cells, one factor that contributes to age-related decline is loss of genetic sequences that encode genes that cells use continuously, such as genes for ribosomal RNAs.

A typical human cell may have hundreds of copies of these critical genes, but as cells age, they lose some of them. For germline cells, this can be detrimental because if the numbers get too low, the cells can no longer form viable daughter cells.

Yamashita and her colleagues found that germline cells overcome this by tearing sections of DNA out of one daughter cell during cell division and transferring them to the other daughter cell. That way, one daughter cell has the full complement of those genes restored, while the other cell is sacrificed.

That wasteful strategy would likely be too extravagant to work for all cells in the body, but for the small population of germline cells, the tradeoff is worthwhile, Yamashita says.

“If skin cells did that kind of thing, where every time you make one cell, you are essentially trashing the other one, you couldn’t afford it. You would be wasting too many resources,” she says. “Germ cells are not critical for viability of an organism. You have the luxury to put many resources into them but then let only half of the cells recover.”

An ‘oracle’ for predicting the evolution of gene regulation

Researchers created a mathematical framework to examine the genome and detect signatures of natural selection, deciphering the evolutionary past and future of non-coding DNA.

Raleigh McElvery
March 9, 2022

Despite the sheer number of genes that each human cell contains, these so-called “coding” DNA sequences comprise just 1% of our entire genome. The remaining 99% is made up of “non-coding” DNA — which, unlike coding DNA, does not carry the instructions to build proteins.

One vital function of this non-coding DNA, also called “regulatory” DNA, is to help turn genes on and off, controlling how much (if any) of a protein is made. Over time, as cells replicate their DNA to grow and divide, mutations often crop up in these non-coding regions — sometimes tweaking their function and changing the way they control gene expression. Many of these mutations are trivial, and some are even beneficial. Occasionally, though, they can be associated with increased risk of common diseases, such as type 2 diabetes, or more life-threatening ones, including cancer.

To better understand the repercussions of such mutations, researchers have been hard at work on mathematical maps that allow them to look at an organism’s genome, predict which genes will be expressed, and determine how that expression will affect the organism’s observable traits. These maps, called fitness landscapes, were conceptualized roughly a century ago to understand how genetic makeup influences one common measure of organismal fitness in particular: reproductive success. Early fitness landscapes were very simple, often focusing on a limited number of mutations. Much richer data sets are now available, but researchers still require additional tools to characterize and visualize such complex data. This ability would not only facilitate a better understanding of how individual genes have evolved over time, but would also help to predict what sequence and expression changes might occur in the future.

In a new study published on March 9 in Nature, a team of scientists has developed a framework for studying the fitness landscapes of regulatory DNA. They created a neural network model that, when trained on hundreds of millions of experimental measurements, was capable of predicting how changes to these non-coding sequences in yeast affected gene expression. They also devised a unique way of representing the landscapes in two dimensions, making it easy to understand the past and forecast the future evolution of non-coding sequences in organisms beyond yeast — and even design custom gene expression patterns for gene therapies and industrial applications.

“We now have an ‘oracle’ that can be queried to ask: What if we tried all possible mutations of this sequence? Or, what new sequence should we design to give us a desired expression?” says Aviv Regev, a professor of biology at MIT (on leave), core member of the Broad Institute of Harvard and MIT (on leave), head of Genentech Research and Early Development, and the study’s senior author. “Scientists can now use the model for their own evolutionary question or scenario, and for other problems like making sequences that control gene expression in desired ways. I am also excited about the possibilities for machine learning researchers interested in interpretability; they can ask their questions in reverse, to better understand the underlying biology.”

Prior to this study, many researchers had simply trained their models on known mutations (or slight variations thereof) that exist in nature. However, Regev’s team wanted to go a step further by creating their own unbiased models capable of predicting an organism’s fitness and gene expression based on any possible DNA sequence — even sequences they’d never seen before. This would also enable researchers to use such models to engineer cells for pharmaceutical purposes, including new treatments for cancer and autoimmune disorders.

To accomplish this goal, Eeshit Dhaval Vaishnav, a graduate student at MIT and co-first author, Carl de Boer, now an assistant professor at the University of British Columbia, and their colleagues created a neural network model to predict gene expression. They trained it on a dataset generated by inserting millions of totally random non-coding DNA sequences into yeast, and observing how each random sequence affected gene expression. They focused on a particular subset of non-coding DNA sequences called promoters, which serve as binding sites for proteins that can switch nearby genes on or off.

“This work highlights what possibilities open up when we design new kinds of experiments to generate the right data to train models,” Regev says. “In the broader sense, I believe these kinds of approaches will be important for many problems — like understanding genetic variants in regulatory regions that confer disease risk in the human genome, but also for predicting the impact of combinations of mutations, or designing new molecules.”

Regev, Vaishnav, de Boer, and their coauthors went on to test their model’s predictive abilities in a variety of ways, in order to show how it could help demystify the evolutionary past — and possible future — of certain promoters. “Creating an accurate model was certainly an accomplishment, but, to me, it was really just a starting point,” Vaishnav explains.

First, to determine whether their model could help with synthetic biology applications like producing antibiotics, enzymes, and food, the researchers practiced using it to design promoters that could generate desired expression levels for any gene of interest. They then scoured other scientific papers to identify fundamental evolutionary questions, in order to see if their model could help answer them. The team even went so far as to feed their model a real-world population data set from one existing study, which contained genetic information from yeast strains around the world. In doing so, they were able to delineate thousands of years of past selection pressures that sculpted the genomes of today’s yeast.

But, in order to create a powerful tool that could probe any genome, the researchers knew they’d need to find a way to forecast the evolution of non-coding sequences even without such a comprehensive population data set. To address this goal, Vaishnav and his colleagues devised a computational technique that allowed them to plot the predictions from their framework onto a two-dimensional graph. This helped them show, in a remarkably simple manner, how any non-coding DNA sequence would affect gene expression and fitness, without needing to conduct any time-consuming experiments at the lab bench.

“One of the unsolved problems in fitness landscapes was that we didn’t have an approach for visualizing them in a way that meaningfully captured the evolutionary properties of sequences,” Vaishnav explains. “I really wanted to find a way to fill that gap, and contribute to the longstanding vision of creating a complete fitness landscape.”

Martin Taylor, a professor of genetics at the University of Edinburgh’s Medical Research Council Human Genetics Unit who was not involved in the research, says the study shows that artificial intelligence can not only predict the effect of regulatory DNA changes, but also reveal the underlying principles that govern millions of years of evolution.

Despite the fact that the model was trained on just a fraction of yeast regulatory DNA in a few growth conditions, he’s impressed that it’s capable of making such useful predictions about the evolution of gene regulation in mammals.

“There are obvious near-term applications, such as the custom design of regulatory DNA for yeast in brewing, baking, and biotechnology,” he explains. “But extensions of this work could also help identify disease mutations in human regulatory DNA that are currently difficult to find and largely overlooked in the clinic. This work suggests there is a bright future for AI models of gene regulation trained on richer, more complex, and more diverse data sets.”

Even before the study was formally published, Vaishnav began receiving queries from other researchers hoping to use the model to devise non-coding DNA sequences for use in gene therapies.

“People have been studying regulatory evolution and fitness landscapes for decades now,” Vaishnav says. “I think our framework will go a long way in answering fundamental, open questions about the evolution and evolvability of gene regulatory DNA — and even help us design biological sequences for exciting new applications.”

Sometimes science takes a village
Greta Friar | Whitehead Institute
February 17, 2022

Alexandra Navarro, a graduate student in Whitehead Institute Member Iain Cheeseman’s lab, was studying the gene for CENPR, a protein related to cell division—the Cheeseman lab’s research focus—when she came across something interesting: another molecule hidden in CENPR’s genetic code. The hidden molecule is a peptide only 37 amino acids long, too small to show up in most surveys of the cell. It gets created only when the genetic code for CENPR is translated from an offset start and stopping place—essentially, when a cell reads the instructions for making CENPR in a different way. The Cheeseman lab has become very interested in these sorts of hidden molecules, which they have found lurking in a number of other molecules’ genetic codes. Navarro began studying the peptide as a side project during slow periods in her main research on cell division proteins. However, as her research on the peptide progressed, Navarro eventually found herself unsure of how to proceed. CENPR belongs in the centromere, a part of the cell necessary for cell division, but the alternative peptide ends up in the Golgi, a structure that helps to modify molecules and prep them for delivery to different destinations. In other words, the peptide had nothing to do with the part of the cell that Navarro and Cheeseman typically study.

Usually when Navarro comes across something outside of her area of expertise, she will consult with her lab mates, others in Whitehead Institute, or nearby collaborators. However, none of her usual collaborators’ research focuses on the Golgi, so this time Cheeseman suggested that Navarro share what they had found and ask for input from as wide a circle of researchers as possible—on the internet. Often, researchers guard their work in progress carefully, reluctant to share it lest they be scooped, which means someone else publishes a paper on the same topic first. In the competitive world of academic research, where publishing papers is a key part of getting jobs, tenure, and future funding, the specter of scooping can loom large. But science is also an inherently collaborative practice, with scientists contributing droplets of discovery to a shared pool of knowledge, so that new findings can be built upon what came before. Cheeseman is a board member of ASAPbio (Accelerating Science and Publication in biology), a nonprofit that promotes open communication, the use of preprints, and transparent peer review in the life sciences. Researchers like Cheeseman believe that if science adopts more transparent and collaborative practices, such as more frequently and widely sharing research in progress, this will benefit both the people involved and the quality of the science, and will speed up the search for discoveries with the potential to positively impact humankind. But how helpful are such “open science” practices in reality? Navarro and Cheeseman had the perfect opportunity to find out.

The power of preprints

Navarro and Cheeseman wrote up what they knew so far–they had found a hidden peptide that localizes exclusively to the Golgi, and it stays there throughout the cell cycle–as a “preprint in progress,” an incomplete draft of a paper that acknowledges there is more to come. In December 2020, they posted the preprint in progress to bioRxiv, a website that serves as a repository for biology preprints, or papers that have not yet

been published. The site was inspired by arXiv, a similar repository launched in 1991 to provide free and easy access to research in math, physics, computer science, and similar fields. arXiv has become a central hub for research in these fields, with an average of 10-15,000 submissions and 30 million downloads per month. The biology fields were slower to create such a hub: BioRxiv launched in 2013. In December, 2021, it received around 3,000 submissions and 2.3 million downloads.

Navarro and Cheeseman’s decision to post a preprint in progress to bioRvix is not common practice, but a lot of researchers have started posting preprints that resemble the final paper closer to publication. Some journals even require it. This type of early sharing has many benefits: contrary to the fear that sharing research before publication will lead to scooping, it allows researchers to stake a claim sooner by making their work public record pre-publication. Preprints enable researchers to show off their most current work during the narrow windows of the academic job cycle. This can be particularly crucial for early career researchers whose biggest project to date—such as graduate thesis work—is still in publication limbo. Preprints also allow new ideas and knowledge to get out into the world sooner, the better to inspire other researchers. Findings that seemed minor at first have provided the key insight for someone else’s major discovery throughout biology’s history. The sooner research is shared, the sooner it can be built upon to develop important advances, like new medicines or a better model of how a disease spreads.

Navarro and Cheeseman weren’t expecting their discovery to have that kind of major impact, but they knew the peptide could be useful to researchers studying the Golgi. The peptide is small and doesn’t disrupt any functions in the cell. Researchers can attach fluorescent proteins to it that make the Golgi glow in imaging. These traits make the peptide a useful potential tool. Since Navarro and Cheeseman posted the preprint, multiple researchers have reached out about using the peptide.

However, the main goal of posting a preprint in progress, as opposed to a polished preprint, is to ask for input to further the research. The morning after the researchers put their preprint on bioRxiv, Cheeseman shared it on Twitter and asked for feedback. Other researchers soon shared the tweet further, and responses started flooding in. Some researchers simply commented that they found the project interesting, which was reassuring for Cheeseman and Navarro.

“It was nice to see that we weren’t the only ones who thought this thing we found was really cool,” Navarro says. “It gave me a lot of motivation to keep moving on with this project.”

Then, some researchers had specific questions and ideas. The topic that seemed of greatest interest was how the peptide ends up at the Golgi, followed by where exactly in the Golgi it ends up. Researchers suggested online tools that might help predict answers to these questions. They proposed different mechanisms that might be involved.

Navarro used these suggestions to design a new series of experiments, in order to better characterize how the peptide associates with the Golgi. She found out that the peptide attaches to the Golgi’s outer-facing membrane. She started developing an understanding of which of peptide’s 37 amino acids were necessary for Golgi localization, and so was able to narrow in on a 14-amino acid sequence within the peptide that was sufficient for this localization.

Her next question was what specific mechanisms were driving the peptide’s Golgi localization. Navarro had a good lead for one mechanism: the evidence and outside input suggested that after the peptide was created, it likely underwent a modification that gave it a sticky tag to anchor it to the Golgi. What would be the best experiments to confirm this mechanism and determine the other mechanisms involved? Navarro and Cheeseman decided it was time to check back in with the crowd online.

Narrowing in on answers

Navarro and Cheeseman updated their preprint with their new findings, and invited further feedback. This time, they had a specific ask: how to test whether the peptide has the modification they suspected. They received suggestions: a probe, an inhibitor. They also received some unexpected feedback that took them in a new direction. Harmit Malik, professor and associate director of the basic sciences division at Fred Hutch, studies the evolutionary changes that occur in genes. Malik found the peptide interesting enough to dig into its evolutionary history across primates. He emailed Cheeseman and Navarro his findings. Versions of the peptide existed in many primates, and some of the variations between species affected where the peptide ended up. This was a rich new vein of inquiry for Navarro to follow in order to pinpoint exactly which parts of the sequence were necessary for Golgi localization, and the researchers might never have come across it if they had not sought input online.

Guided by the latest set of suggestions, Navarro resumed work on the project. She found evidence that the peptide does undergo the suspected modification. She winnowed down to a 10-amino acid sequence within the peptide that appears to be the minimal sequence necessary for this type of Golgi localization. Navarro and Cheeseman rewrote the paper, adding the discovery of a minimal Golgi targeting sequence—basically a postal code that marks a molecule’s destination as the Golgi. They posted a third version of the preprint in September, 2021. This time, Cheeseman did not ask Twitter for feedback: the paper may undergo more changes, but it now contains a complete research story.

The changing face of science

Based on their experience, would Cheeseman and Navarro recommend sharing preprints in progress? The answer is a resounding yes—if the project is a good fit. Both agree that for projects like this, where the subject is outside the expertise of a researcher’s usual circle of collaborators, asking the wider scientific community for help can be extremely valuable.

“I often share my research with other people at Whitehead Institute, and other cell division researchers at conferences, but this process allowed me to share it with people who work in different scientific areas, with whom I would not normally engage,” Navarro says.

Cheeseman hopes that sharing hubs like bioRxiv will develop ways for even larger and more diverse groups of scientists to connect.

If researchers are hesitant to use an open science approach, Cheeseman and Navarro recommend testing the waters by starting with a lower stakes project. In this case, Navarro’s Golgi paper was a side project, something of personal interest but not integral to her career. Having had a positive experience using an open approach on this project, Cheeseman and Navarro agree they would be comfortable using such an approach again in the future.

“I wouldn’t suggest sharing a preprint in progress for every paper, but I think constructive opportunities are more plentiful than researchers may realize,” Cheeseman says.

In general, Cheeseman thinks, the biology field needs to re-envision how its science gets shared.

“The idea that one size fits all, that everything needs to be a multi-figure paper in a high impact journal, is just not compatible with the way that people do research,” Cheeseman says. “We need to get flexible and explore and value scholarship in every form.”

As for the peptide paper? Regardless of where it ends up, Cheeseman and Navarro consider their open science experiment a success. By sharing their research and asking for input, they gained insights, research tools, and points of view that took the project from a curious finding to a rich understanding of the mechanisms behind Golgi localization. Their early realization that the peptide functions outside of their region of expertise could have been a dead end. But by being open about what they were working on and what sort of guidance they needed, the researchers were able to overcome that hurdle and decode their mystery peptide, with a little help from the wider scientific community.

Blending machine learning and biology to predict cell fates and other changes
Greta Friar | Whitehead Institute
February 1, 2022

Imagine a ball thrown in the air: it curves up, then down, tracing an arc to a point on the ground some distance away. The path of the ball can be described with a simple mathematical equation, and if you know the equation, you can figure out where the ball is going to land. Biological systems tend to be harder to forecast, but Whitehead Institute Member Jonathan Weissman, postdoc in his lab Xiaojie Qiu, and collaborators at the University of Pittsburgh School of Medicine are working on making the path taken by cells as predictable as the arc of a ball. Rather than looking at how cells move through space, they are considering how cells change with time.

Weissman, Qiu, and collaborators Jianhua Xing, professor of computational and systems biology at the University of Pittsburgh School of Medicine, and Xing lab graduate student Yan Zhang have built a machine learning framework that can define the mathematical equations describing a cell’s trajectory from one state to another, such as its development from a stem cell into one of several different types of mature cell. The framework, called dynamo, can also be used to figure out the underlying mechanisms—the specific cocktail of gene activity—driving changes in the cell. Researchers could potentially use these insights to manipulate cells into taking one path instead of another, a common goal in biomedical research and regenerative medicine.  

The researchers describe dynamo in a paper published in the journal Cell on February 1. They explain the framework’s many analytical capabilities and use it to help understand mechanisms of human blood cell production, such as why one type of blood cell forms first (appears more rapidly than others).

“Our goal is to move towards a more quantitative version of single cell biology,” Qiu says. “We want to be able to map how a cell changes in relation to the interplay of regulatory genes as accurately as an astronomer can chart a planet’s movement in relation to gravity, and then we want to understand and be able to control those changes.”

How to map a cell’s future journey

 Dynamo uses data from many individual cells to come up with its equations. The main information that it requires is how the expression of different genes in a cell changes from moment to moment. The researchers estimate this by looking at changes in the amount of RNA over time, because RNA is a measurable product of gene expression. In the same way that knowing the starting position and velocity of a ball is necessary to understand the arc it will follow, researchers use the starting levels of RNAs and how those RNA levels are changing to predict the path of the cell. However, calculating changes in the amount of RNA from single cell sequencing data is challenging, because sequencing only measures RNA once. Researchers must then use clues like RNA-being-made at the time of sequencing and equations for RNA turnover to estimate how RNA levels were changing. Qiu and colleagues had to improve on previous methods in several ways in order to get clean enough measurements for dynamo to work. In particular, they used a recently developed experimental method that tags new RNA to distinguish it from old RNA, and combined this with sophisticated mathematical modeling, to overcome limitations of older estimation approaches.

The researchers’ next challenge was to move from observing cells at discrete points in time to a continuous picture of how cells change. The difference is like switching from a map showing only landmarks to a map that shows the uninterrupted landscape, making it possible to trace the paths between landmarks. Led by Qiu and Zhang, the group used machine learning to reveal continuous functions that define these spaces. 

“There have been tremendous advances in methods for broadly profiling transcriptomes and other ‘omic’ information with single-cell resolution. The analytical tools for exploring these data, however, to date have been descriptive instead of predictive. With a continuous function, you can start to do things that weren’t possible with just accurately sampled cells at different states. For example, you can ask: if I changed one transcription factor, how is it going to change the expression of the other genes?” says Weissman, who is also a professor of biology at the Massachusetts Institute of Technology (MIT), a member of the Koch Institute for Integrative Biology Research at MIT, and an investigator of the Howard Hughes Medical Institute.

Dynamo can visualize these functions by turning them into math-based maps. The terrain of each map is determined by factors like the relative expression of key genes. A cell’s starting place on the map is determined by its current gene expression dynamics. Once you know where the cell starts, you can trace the path from that spot to find out where the cell will end up.

The researchers confirmed dynamo’s cell fate predictions by testing it against cloned cells–cells that share the same genetics and ancestry. One of two nearly-identical clones would be sequenced while the other clone went on to differentiate. Dynamo’s predictions for what would have happened to each sequenced cell matched what happened to its clone.

Moving from math to biological insight and non-trivial predictions

With a continuous function for a cell’s path over time determined, dynamo can then gain insights into the underlying biological mechanisms. Calculating derivatives of the function provides a wealth of information, for example by allowing researchers to determine the functional relationships between genes—whether and how they regulate each other. Calculating acceleration can show that a gene’s expression is growing or shrinking quickly even when its current level is low, and can be used to reveal which genes play key roles in determining a cell’s fate very early in the cell’s trajectory. The researchers tested their tools on blood cells, which have a large and branching differentiation tree. Together with blood cell expert Vijay Sankaran of Boston Children’s Hospital, the Dana-Farber Cancer Institute, Harvard Medical School, and Broad Institute of MIT and Harvard, and Eric Lander of Broad Institute, they found that dynamo accurately mapped blood cell differentiation and confirmed a recent finding that one type of blood cell, megakaryocytes, forms earlier than others. Dynamo also discovered the mechanism behind this early differentiation: the gene that drives megakaryocyte differentiation, FLI1, can self-activate, and because of this is present at relatively high levels early on in progenitor cells. This predisposes the progenitors to differentiate into megakaryocytes first.

The researchers hope that dynamo could not only help them understand how cells transition from one state to another, but also guide researchers in controlling this. To this end, dynamo includes tools to simulate how cells will change based on different manipulations, and a method to find the most efficient path from one cell state to another. These tools provide a powerful framework for researchers to predict how to optimally reprogram any cell type to another, a fundamental challenge in stem cell biology and regenerative medicine, as well as to generate hypotheses of how other genetic changes will alter cells’ fate. There are a variety of possible applications.

“If we devise a set of equations that can describe how genes within a cell regulate each other, we can computationally describe how to transform terminally differentiated cells into stem cells, or predict how a cancer cell may respond to various combinations of drugs that would be impractical to test experimentally,” Xing says.

Dynamo’s computational modeling can be used to predict the most likely path that a cell will follow when reprogramming one cell type to another, as well as the path that a cell will take after specific genetic manipulations. 

Dynamo moves beyond merely descriptive and statistical analyses of single cell sequencing data to derive a predictive theory of cell fate transitions. The dynamo toolset can provide deep insights into how cells change over time, hopefully making cells’ trajectories as predictable for researchers as the arc of a ball, and therefore also as easy to change as switching up a pitch.

3 Questions: Kristin Knouse on the liver’s regenerative capabilities

The clinically-trained cell biologist exploits the liver’s unique capacities in search of new medical applications.

Grace van Deelen | Department of Biology
December 15, 2021

Why is the liver the only human organ that can regenerate? How does it know when it’s been injured? What can our understanding of the liver contribute to regenerative medicine? These are just some of the questions that new assistant professor of biology Kristin Knouse and her lab members are asking in their research at the Koch Institute for Integrative Cancer Research. Knouse sat down to discuss why the liver is so unique, what lessons we might learn from the organ, and what its regeneration might teach us about cancer.

Q: Your lab is interested in questions about how body tissues sense and respond to damage. What is it about the liver that makes it a good tool to model those questions?

A: I’ve always felt that we, as scientists, have so much to gain from treasuring nature’s exceptions, because those exceptions can shine light onto a completely unknown area of biology and provide building blocks to confer such novelty to other systems. When it comes to organ regeneration in mammals, the liver is that exception. It is the only solid organ that can completely regenerate itself. You can damage or remove over 75 percent of the liver and the organ will completely regenerate in a matter of weeks. The liver therefore contains the instructions for how to regenerate a solid organ; however, we have yet to access and interpret those instructions. If we could fully understand how the liver is able to regenerate itself, perhaps one day we could coax other solid organs to do the same.

There are some things we already know about liver regeneration, such as when it begins, what genes are expressed, and how long it takes. However, we still don’t understand why the liver can regenerate but other organs cannot. Why is it that these fully differentiated liver cells — cells that have already assumed specialized roles in the liver — can re-enter the cell cycle and regenerate the organ? We don’t have a molecular explanation for this. Our lab is working to answer this fundamental question of cell and organ biology and apply our discoveries to unlock new approaches for regenerative medicine. In this regard, I don’t necessarily consider myself exclusively a liver biologist, but rather someone who is leveraging the liver to address this much broader biological problem.

Q: As an MD/PhD student, you conducted your graduate research in the lab of the late Professor Angelika Amon here at MIT. How did your work in her lab lead to an interest in studying the liver’s regenerative capacities?

A: What was incredible about being in Angelika’s lab was that she had an interest in almost everything and gave me tremendous independence in what I pursued. I began my graduate research in her lab with an interest in cell division, and I was doing experiments to observe how cells from different mammalian tissues divide. I was isolating cells from different mouse tissues and then studying them in culture. In doing that, I found that when the cells were isolated and grown in a dish they could not segregate their chromosomes properly, suggesting that the tissue environment was essential for accurate cell division. In order to further study and compare these two different contexts — cells in a tissue versus cells in culture — I was keen to study a tissue in which I could observe a lot of cells undergoing cell division at the same time.

So I thought back to my time in medical school, and I remembered that the liver has the ability to completely regenerate itself. With a single surgery to remove part of the liver, I could stimulate millions of cells to divide. I therefore began exploiting liver regeneration as a means of studying chromosome segregation in tissue. But as I continued to perform surgeries on mice and watch the liver rapidly regenerate itself, I couldn’t help but become absolutely fascinated by this exceptional biological process. It was that fascination with this incredibly unique but poorly understood phenomenon — alongside the realization that there was a huge, unmet medical need in the area of regeneration — that convinced me to dedicate my career to studying this.

Q: What kinds of clinical applications might a better understanding of organ regeneration lead to, and what role do you see your lab playing in that research?

A: The most proximal medical application for our work is to confer regenerative capacity to organs that are currently non-regenerative. As we begin to achieve a molecular understanding of how and why the liver can regenerate, we put ourselves in a powerful position to identify and surmount the barriers to regeneration in non-regenerative tissues, such as the heart and nervous system. By answering these complementary questions, we bring ourselves closer to the possibility that, one day, if someone has a heart attack or a spinal cord injury, we could deliver a therapy that stimulates the tissue to regenerate itself. I realize that may sound like a moonshot now, but I don’t think any problem is insurmountable so long as it can be broken down into a series of tractable questions.

Beyond regenerative medicine, I believe our work studying liver regeneration also has implications for cancer. At first glance this may seem counterintuitive, as rapid regrowth is the exact opposite of what we want cancer cells to do. However, the reality is that the majority of cancer-related deaths are attributable not to the rapidly proliferating cells that constitute primary tumors, but rather to the cells that disperse from the primary tumor and lie dormant for years before manifesting as metastatic disease and creating another tumor. These dormant cells evade most of the cancer therapies designed to target rapidly proliferating cells. If you think about it, these dormant cells are not unlike the liver: they are quiet for months, maybe years, and then suddenly awaken. I hope that as we start to understand more about the liver, we might learn how to target these dormant cancer cells, prevent metastatic disease, and thereby offer lasting cancer cures.

How some tissues can “breathe” without oxygen
Eva Frederick | Whitehead Institute
December 2, 2021

Humans need oxygen molecules for a process called cellular respiration, which takes place in our cells’ mitochondria. Through a series of reactions called the electron transport chain, electrons are passed along in a sort of cellular relay race, allowing the cell to create ATP, the molecule that gives our cells energy to complete their vital functions.

At the end of this chain, two electrons remain, which are typically passed off to oxygen, the “terminal electron acceptor.” This completes the reaction and allows the process to continue with more electrons entering the electron transport chain.

In the past, however, scientists have noticed that cells are able to maintain some functions of the electron transport chain, even in the absence of oxygen. “This indicated that mitochondria could actually have partial function, even when oxygen is not the electron acceptor,” said Whitehead Institute postdoctoral researcher Jessica Spinelli. “We wanted to understand, how does this work? How are mitochondria capable of maintaining these electron inputs when oxygen is not the terminal electron acceptor?”

In a paper published December 2 in the journal Science, Whitehead Institute scientists and collaborators led by Spinelli have found the answer to these questions. Their research shows that when cells are deprived of oxygen, another molecule called fumarate can step in and serve as a terminal electron acceptor to enable mitochondrial function in this environment. The research, which was completed in the laboratory of former Whitehead Member David Sabatini, answers a long-standing mystery in the field of cellular metabolism, and could potentially inform research into diseases that cause low oxygen levels in tissues, including ischemia, diabetes and cancer.

A new runner in the cellular relay

The researchers began their investigation into how cells can maintain mitochondrial function without oxygen by using mass spectrometry to measure the quantities of molecules called metabolites that are produced through cellular respiration in both normal and low-oxygen conditions. When cells were deprived of oxygen, researchers noticed a high level of a molecule called succinate.

When you add electrons to oxygen at the end of the electron transport chain, it picks up two protons and becomes water. When you add electrons to fumarate, it becomes succinate. “This led us to think that maybe this accumulation of succinate that’s occurring could actually be caused by fumarate being used as an electron acceptor, and that this reaction could explain the maintenance of mitochondrial functions in hypoxia,” Spinelli said.

Usually, the fumarate-succinate reaction runs the other direction in cells — a protein complex called the SDH complex takes away electrons from succinate, leaving fumarate. For the opposite to happen, the SDH complex would need to be running in reverse. “Although the SDH complex is known to catalyze some fumarate reduction, it was thought that it was thermodynamically impossible for this SDH complex to undergo a net reversal,” Spinelli said. “Fumarate is used as an electron acceptor in lower eukaryotes, but they use a totally different enzyme and electron carrier, and mammals are not known to possess either of these.”

Through a series of assays, however, the researchers were able to ascertain that this complex was indeed running in reverse in cultured cells, largely due to accumulation of a molecule called ubiquinol, which the researchers observed to build up under low-oxygen conditions.

With their hypothesis confirmed, “We wanted to get back to our initial question and ask, does that net reversal of the SDH complex maintain mitochondrial functions which are happening when cells are exposed to hypoxia?” said Spinelli. “So, we knocked out SDH complex and then we demonstrated through a number of means that loss of both oxygen and fumarate as an electron acceptors was sufficient to [bring the electron transport chain to a halt].”

All this work was done in cultured cells, so the next step for Spinelli and collaborators was to study whether fumarate could serve as a terminal electron acceptor in mouse models.

When they tried this, the team uncovered something interesting: some, but not all, of the mice’s tissues were able to successfully reverse the activity of the SDH complex and perform mitochondrial functions using fumarate as a terminal electron acceptor.

“What was really cool to see is that there were three tissues  — the kidney, the liver, and the brain — which on a bulk tissue scale, are operating the SDH complex in a backwards direction, even at physiological oxygen levels,” said Spinelli. In other words, even in normal conditions, these tissues were reducing both fumarate and oxygen to maintain their functions, and when deprived of oxygen, fumarate could take over as a terminal electron acceptor.

In contrast, tissues such as the heart and the skeletal muscle are able to perform minimal fumarate reduction without reversing the SDH complex, but not to the extent that they could effectively retain mitochondrial function when deprived of oxygen.

“We think there’s a lot of exciting work downstream of this to figure out how exactly this process is regulated differently in different tissues — and understanding in disease settings whether the SDH complex is operating in the net reverse direction,” Spinelli said.

In particular, Spinelli is interested in studying the behavior of the SDH complex in cancer cells.
“Certain regions of solid tumors have very low levels of oxygen, and certain regions have high levels of oxygen,” Spinelli said. “It’s interesting to think about how those cells are surviving in that microenvironment — are they using fumarate as an electron acceptor to enable cell survival?”