Skip to main content

cancer

How One Change to The Coronavirus Spike Influences Infectivity

Posted on by

electron micrograph of COVID-19 viruses
Caption: Spike proteins (blue) crown SARS-CoV-2, the virus that causes COVID-19. Once the virus enters humans, the spike protein is decorated with sugars that attach to some of its amino acids, forming O-glycans. Loss of key O-glycans may facilitate viral spread to human cells. Credit: National Institute of Allergy and Infectious Diseases, NIH

Since joining NIH, I’ve held a number of different leadership positions. But there is one position that thankfully has remained constant for me: lab chief. I run my own research laboratory at NIH’s National Institute of Dental and Craniofacial Research (NIDCR).

My lab studies a biochemical process called O-glycosylation. It’s fundamental to life and fascinating to study. Our cells are often adorned with a variety of carbohydrate sugars. O-glycosylation refers to the biochemical process through which these sugar molecules, either found at the cell surface or secreted, get added to proteins. The presence or absence of these sugars on certain proteins plays fundamental roles in normal tissue development and first-line human immunity. It also is associated with various diseases, including cancer.

Our lab recently joined a team of NIH scientists led by my NIDCR colleague Kelly Ten Hagen to demonstrate how O-glycosylation can influence SARS-CoV-2, the coronavirus that causes COVID-19, and its ability to fuse to cells, which is a key step in infecting them. In fact, our data, published in the journal Proceedings of the National Academy of Sciences, indicate that some variants, seem to have mutated to exploit the process to their advantage [1].

The work builds on the virus’s reliance on the spike proteins that crown its outer surface to attach to human cells. Once there, the spike protein must be activated to fuse and launch an infection. That happens when enzymes produced by our own cells make a series of cuts, or cleavages, to the spike protein.

The first cut comes from an enzyme called furin. We and others had earlier evidence that O-glycosylation can affect the way furin makes those cuts. That got us thinking: Could O-glycosylation influence the interaction between furin and the spike protein? The furin cleavage area of the viral spike was indeed adorned with sugars, and their presence or absence might influence spike activation by furin.

We also noticed the Alpha and Delta variants carry a mutation that removes the amino acid proline in a specific spot. That was intriguing because we knew from earlier work that enzymes called GALNTs, which are responsible for adding bulky sugar molecules to proteins, prefer prolines near O-glycosylation sites.

It also suggested that loss of proline in the new variants could mean decreased O-glycosylation, which might then influence the degree of furin cleavage and SARS-CoV-2’s ability to enter cells. I should note that the recent Omicron variant was not examined in the current study.

After detailed studies in fruit fly and mammalian cells, we demonstrated in the original SARS-CoV-2 virus that O-glycosylation of the spike protein decreases furin cleavage. Further experiments then showed that the GALNT1 enzyme adds sugars to the spike protein and this addition limits the ability of furin to make the needed cuts and activate the spike protein.

Importantly, the spike protein change found in the Alpha and Delta variants lowers GALNT1 activity, making it easier for furin to start its activating cuts. It suggests that glycosylation of the viral spike by GALNT1 may limit infection with the original virus, and that the Alpha and Delta variant mutation at least partially overcomes this effect, to potentially make the virus more infectious.

Building on these studies, our teams looked for evidence of GALNT1 in the respiratory tracts of healthy human volunteers. We found that the enzyme is indeed abundantly expressed in those cells. Interestingly, those same cells also express the ACE2 receptor, which SARS-CoV-2 depends on to infect human cells.

It’s also worth noting here that the Omicron variant carries the very same spike mutation that we studied in Alpha and Delta. Omicron also has another nearby change that might further alter O-glycosylation and cleavage of the spike protein by furin. The Ten Hagen lab is looking into these leads to learn how this region in Omicron affects spike glycosylation and, ultimately, the ability of this devastating virus to infect human cells and spread.

Reference:

[1] Furin cleavage of the SARS-CoV-2 spike is modulated by O-glycosylation. Zhang L, Mann M, Syed Z, Reynolds HM, Tian E, Samara NL, Zeldin DC, Tabak LA, Ten Hagen KG. PNAS. 2021 Nov 23;118(47).

Links:

COVID-19 Research (NIH)

Kelly Ten Hagen (National Institute of Dental and Craniofacial Research/NIH)

Lawrence Tabak (NIDCR)

NIH Support: National Institute of Dental and Craniofacial Research


Teaching the Immune System to Attack Cancer with Greater Precision

Posted on by

Needle in a vial. Cancer cell in the background
Credit: PhotobyTawat/Shutterstock/Tom Deerink, National Institute of General Medical Sciences, NIH

To protect humans from COVID-19, the Pfizer and Moderna mRNA vaccines program human cells to translate the injected synthetic messenger RNA into the coronavirus spike protein, which then primes the immune system to arm itself against future appearances of that protein. It turns out that the immune system can also be trained to spot and attack distinctive proteins on cancer cells, killing them and leaving healthy cells potentially untouched.

While these precision cancer vaccines remain experimental, researchers continue to make basic discoveries that move the field forward. That includes a recent NIH-funded study in mice that helps to refine the selection of protein targets on tumors as a way to boost the immune response [1]. To enable this boost, the researchers first had to discover a possible solution to a longstanding challenge in developing precision cancer vaccines: T cell exhaustion.

The term refers to the immune system’s complement of T cells and their capacity to learn to recognize foreign proteins, also known as neoantigens, and attack them on cancer cells to shrink tumors. But these responding T cells can exhaust themselves attacking tumors, limiting the immune response and making its benefits short-lived.

In this latest study, published in the journal Cell, Tyler Jacks and Megan Burger, Massachusetts Institute of Technology, Cambridge, help to explain this phenomenon of T cell exhaustion. The researchers found in mice with lung tumors that the immune system initially responds as it should. It produces lots of T cells that target many different cancer-specific proteins.

Yet there’s a problem: various subsets of T cells get in each other’s way. They compete until, eventually, one of those subsets becomes the dominant T cell type. Even when those dominant T cells grow exhausted, they still remain in the tumor and keep out other T cells, which might otherwise attack different neoantigens in the cancer.

Building on this basic discovery, the researchers came up with a strategy for developing cancer vaccines that can “awaken” T cells and reinvigorate the body’s natural cancer-fighting abilities. The strategy might seem counterintuitive. The researchers vaccinated mice with neoantigens that provide a weak but encouraging signal to the immune cells responsible for presenting the distinctive cancer protein target, or antigen, to T cells. It’s those T cells that tend to get suppressed in competition with other T cells.

When the researchers vaccinated the mice with one of those neoantigens, the otherwise suppressed T cells grew in numbers and better targeted the tumor. What’s more, the tumors shrank by more than 25 percent on average.

Research on this new strategy remains in its early stages. The researchers hope to learn if this approach to cancer vaccines might work even better when used in combination with immunotherapy drugs, which unleash the immune system against cancer in other ways.

It’s also possible that the recent and revolutionary success of mRNA vaccines for preventing COVID-19 actually could help. An important advantage of mRNA is that it’s easy for researchers to synthesize once they know the specific nucleic acid sequence of a protein target, and they can even combine different mRNA sequences to make a multiplex vaccine that primes the immune system to recognize multiple neoantigens. Now that we’ve seen how well mRNA vaccines work to prompt a desired immune response against COVID-19, this same technology can be used to speed the development and testing of future vaccines, including those designed precisely to fight cancer.

Reference:

[1] Antigen dominance hierarchies shape TCF1+ progenitor CD8 T cell phenotypes in tumors. Burger ML, Cruz AM, Crossland GE, Gaglia G, Ritch CC, Blatt SE, Bhutkar A, Canner D, Kienka T, Tavana SZ, Barandiaran AL, Garmilla A, Schenkel JM, Hillman M, de Los Rios Kobara I, Li A, Jaeger AM, Hwang WL, Westcott PMK, Manos MP, Holovatska MM, Hodi FS, Regev A, Santagata S, Jacks T. Cell. 2021 Sep 16;184(19):4996-5014.e26.

Links:

Cancer Treatment Vaccines (National Cancer Institute/NIH)

The Jacks Lab (Massachusetts Institute of Technology, Cambridge)

NIH Support: National Cancer Institute; National Heart, Lung, and Blood Institute


New Microscope Technique Provides Real-Time 3D Views

Posted on by

Most of the “cool” videos shared on my blog are borne of countless hours behind a microscope. Researchers must move a biological sample through a microscope’s focus, slowly acquiring hundreds of high-res 2D snapshots, one painstaking snap at a time. Afterwards, sophisticated computer software takes this ordered “stack” of images, calculates how the object would look from different perspectives, and later displays them as 3D views of life that can be streamed as short videos.

But this video is different. It was created by what’s called a multi-angle projection imaging system. This new optical device requires just a few camera snapshots and two mirrors to image a biological sample from multiple angles at once. Because the device eliminates the time-consuming process of acquiring individual image slices, it’s up to 100 times faster than current technologies and doesn’t require computer software to construct the movie. The kicker is that the video can be displayed in real time, which isn’t possible with existing image-stacking methods.

The video here shows two human melanoma cells, rotating several times between overhead and side views. You can see large amounts of the protein PI3K (brighter orange hues indicate higher concentrations), which helps some cancer cells divide and move around. Near the cell’s perimeter are small, dynamic surface protrusions. PI3K in these “blebs” is thought to help tumor cells navigate and survive in foreign tissues as the tumor spreads to other organs, a process known as metastasis.

The new multi-angle projection imaging system optical device was described in a paper published recently in the journal Nature Methods [1]. It was created by Reto Fiolka and Kevin Dean at the University of Texas Southwestern Medical Center, Dallas.

Like most technology, this device is complicated. Rather than the microscope and camera doing all the work, as is customary, two mirrors within the microscope play a starring role. During a camera exposure, these mirrors rotate ever so slightly and warp the acquired image in such a way that successive, unique perspectives of the sample magically come into view. By changing the amount of warp, the sample appears to rotate in real-time. As such, each view shown in the video requires only one camera snapshot, instead of acquiring hundreds of slices in a conventional scheme.

The concept traces to computer science and an algorithm called the shear warp transform method. It’s used to observe 3D objects from different perspectives on a 2D computer monitor. Fiolka, Dean, and team found they could implement a similar algorithm optically for use with a microscope. What’s more, their multi-angle projection imaging system is easy-to-use, inexpensive, and can be converted for use on any camera-based microscope.

The researchers have used the device to view samples spanning a range of sizes: from mitochondria and other tiny organelles inside cells to the beating heart of a young zebrafish. And, as the video shows, it has been applied to study cancer and other human diseases.

In a neat, but also scientifically valuable twist, the new optical method can generate a virtual reality view of a sample. Any microscope user wearing the appropriately colored 3D glasses immediately sees the objects.

While virtual reality viewing of cellular life might sound like a gimmick, Fiolka and Dean believe that it will help researchers use their current microscopes to see any sample in 3D—offering the chance to find rare and potentially important biological events much faster than is possible with even the most advanced microscopes today.

Fiolka, Dean, and team are still just getting started. Because the method analyzes tissue very quickly within a single image frame, they say it will enable scientists to observe the fastest events in biology, such as the movement of calcium throughout a neuron—or even a whole bundle of neurons at once. For neuroscientists trying to understand the brain, that’s a movie they will really want to see.

Reference:

[1] Real-time multi-angle projection imaging of biological dynamics. Chang BJ, Manton JD, Sapoznik E, Pohlkamp T, Terrones TS, Welf ES, Murali VS, Roudot P, Hake K, Whitehead L, York AG, Dean KM, Fiolka R. Nat Methods. 2021 Jul;18(7):829-834.

Links:

Metastatic Cancer: When Cancer Spreads (National Cancer Institute)

Fiolka Lab (University of Texas Southwestern Medical Center, Dallas)

Dean Lab (University of Texas Southwestern)

Microscopy Innovation Lab (University of Texas Southwestern)

NIH Support: National Cancer Institute; National Institute of General Medical Sciences


Artificial Intelligence Accurately Predicts RNA Structures, Too

Posted on by

A mechanical claw grabs molecular models
Credit: Camille L.L. Townshend

Researchers recently showed that a computer could “learn” from many examples of protein folding to predict the 3D structure of proteins with great speed and precision. Now a recent study in the journal Science shows that a computer also can predict the 3D shapes of RNA molecules [1]. This includes the mRNA that codes for proteins and the non-coding RNA that performs a range of cellular functions.

This work marks an important basic science advance. RNA therapeutics—from COVID-19 vaccines to cancer drugs—have already benefited millions of people and will help many more in the future. Now, the ability to predict RNA shapes quickly and accurately on a computer will help to accelerate understanding these critical molecules and expand their healthcare uses.

Like proteins, the shapes of single-stranded RNA molecules are important for their ability to function properly inside cells. Yet far less is known about these RNA structures and the rules that determine their precise shapes. The RNA elements (bases) can form internal hydrogen-bonded pairs, but the number of possible combinations of pairings is almost astronomical for any RNA molecule with more than a few dozen bases.

In hopes of moving the field forward, a team led by Stephan Eismann and Raphael Townshend in the lab of Ron Dror, Stanford University, Palo Alto, CA, looked to a machine learning approach known as deep learning. It is inspired by how our own brain’s neural networks process information, learning to focus on some details but not others.

In deep learning, computers look for patterns in data. As they begin to “see” complex relationships, some connections in the network are strengthened while others are weakened.

One of the things that makes deep learning so powerful is it doesn’t rely on any preconceived notions. It also can pick up on important features and patterns that humans can’t possibly detect. But, as successful as this approach has been in solving many different kinds of problems, it has primarily been applied to areas of biology, such as protein folding, in which lots of data were available for researchers to train the computers.

That’s not the case with RNA molecules. To work around this problem, Dror’s team designed a neural network they call ARES. (No, it’s not the Greek god of war. It’s short for Atomic Rotationally Equivariant Scorer.)

To start, the researchers trained ARES on just 18 small RNA molecules for which structures had been experimentally determined. They gave ARES these structural models specified only by their atomic structure and chemical elements.

The next test was to see if ARES could determine from this small training set the best structural model for RNA sequences it had never seen before. The researchers put it to the test with RNA molecules whose structures had been determined more recently.

ARES, however, doesn’t come up with the structures itself. Instead, the researchers give ARES a sequence and at least 1,500 possible 3D structures it might take, all generated using another computer program. Based on patterns in the training set, ARES scores each of the possible structures to find the one it predicts is closest to the actual structure. Remarkably, it does this without being provided any prior information about features important for determining RNA shapes, such as nucleotides, steric constraints, and hydrogen bonds.

It turns out that ARES consistently outperforms humans and all other previous methods to produce the best results. In fact, it outperformed at least nine other methods to come out on top in a community-wide RNA-puzzles contest. It also can make predictions about RNA molecules that are significantly larger and more complex than those upon which it was trained.

The success of ARES and this deep learning approach will help to elucidate RNA molecules with potentially important implications for health and disease. It’s another compelling example of how deep learning promises to solve many other problems in structural biology, chemistry, and the material sciences when—at the outset—very little is known.

Reference:

[1] Geometric deep learning of RNA structure. Townshend RJL, Eismann S, Watkins AM, Rangan R, Karelina M, Das R, Dror RO. Science. 2021 Aug 27;373(6558):1047-1051.

Links:

Structural Biology (National Institute of General Medical Sciences/NIH)

The Structures of Life (National Institute of General Medical Sciences/NIH)

RNA Biology (NIH)

RNA Puzzles

Dror Lab (Stanford University, Palo Alto, CA)

NIH Support: National Cancer Institute; National Institute of General Medical Sciences


Artificial Intelligence Accurately Predicts Protein Folding

Posted on by

Caption: Researchers used artificial intelligence to map hundreds of new protein structures, including this 3D view of human interleukin-12 (blue) bound to its receptor (purple). Credit: Ian Haydon, University of Washington Institute for Protein Design, Seattle

Proteins are the workhorses of the cell. Mapping the precise shapes of the most important of these workhorses helps to unlock their life-supporting functions or, in the case of disease, potential for dysfunction. While the amino acid sequence of a protein provides the basis for its 3D structure, deducing the atom-by-atom map from principles of quantum mechanics has been beyond the ability of computer programs—until now. 

In a recent study in the journal Science, researchers reported they have developed artificial intelligence approaches for predicting the three-dimensional structure of proteins in record time, based solely on their one-dimensional amino acid sequences [1]. This groundbreaking approach will not only aid researchers in the lab, but guide drug developers in coming up with safer and more effective ways to treat and prevent disease.

This new NIH-supported advance is now freely available to scientists around the world. In fact, it has already helped to solve especially challenging protein structures in cases where experimental data were lacking and other modeling methods hadn’t been enough to get a final answer. It also can now provide key structural information about proteins for which more time-consuming and costly imaging data are not yet available.

The new work comes from a group led by David Baker and Minkyung Baek, University of Washington, Seattle, Institute for Protein Design. Over the course of the pandemic, Baker’s team has been working hard to design promising COVID-19 therapeutics. They’ve also been working to design proteins that might offer promising new ways to treat cancer and other conditions. As part of this effort, they’ve developed new computational approaches for determining precisely how a chain of amino acids, which are the building blocks of proteins, will fold up in space to form a finished protein.

But the ability to predict a protein’s precise structure or shape from its sequence alone had proven to be a difficult problem to solve despite decades of effort. In search of a solution, research teams from around the world have come together every two years since 1994 at the Critical Assessment of Structure Prediction (CASP) meetings. At these gatherings, teams compete against each other with the goal of developing computational methods and software capable of predicting any of nature’s 200 million or more protein structures from sequences alone with the greatest accuracy.

Last year, a London-based company called DeepMind shook up the structural biology world with their entry into CASP called AlphaFold. (AlphaFold was one of Science’s 2020 Breakthroughs of the Year.) They showed that their artificial intelligence approach—which took advantage of the 170,000 proteins with known structures in a reiterative process called deep learning—could predict protein structure with amazing accuracy. In fact, it could predict most protein structures almost as accurately as other high-resolution protein mapping techniques, including today’s go-to strategies of X-ray crystallography and cryo-EM.

The DeepMind performance showed what was possible, but because the advances were made by a world-leading deep learning company, the details on how it worked weren’t made publicly available at the time. The findings left Baker, Baek, and others eager to learn more and to see if they could replicate the impressive predictive ability of AlphaFold outside of such a well-resourced company.

In the new work, Baker and Baek’s team has made stunning progress—using only a fraction of the computational processing power and time required by AlphaFold. The new software, called RoseTTAFold, also relies on a deep learning approach. In deep learning, computers look for patterns in large collections of data. As they begin to recognize complex relationships, some connections in the network are strengthened while others are weakened. The finished network is typically composed of multiple information-processing layers, which operate on the data to return a result—in this case, a protein structure.

Given the complexity of the problem, instead of using a single neural network, RoseTTAFold relies on three. The three-track neural network integrates and simultaneously processes one-dimensional protein sequence information, two-dimensional information about the distance between amino acids, and three-dimensional atomic structure all at once. Information from these separate tracks flows back and forth to generate accurate models of proteins rapidly from sequence information alone, including structures in complex with other proteins.

As soon as the researchers had what they thought was a reasonable working approach to solve protein structures, they began sharing it with their structural biologist colleagues. In many cases, it became immediately clear that RoseTTAFold worked remarkably well. What’s more, it has been put to work to solve challenging structural biology problems that had vexed scientists for many years with earlier methods.

RoseTTAFold already has solved hundreds of new protein structures, many of which represent poorly understood human proteins. The 3D rendering of a complex showing a human protein called interleukin-12 in complex with its receptor (above image) is just one example. The researchers have generated other structures directly relevant to human health, including some that are related to lipid metabolism, inflammatory conditions, and cancer. The program is now available on the web and has been downloaded by dozens of research teams around the world.

Cryo-EM and other experimental mapping methods will remain essential to solve protein structures in the lab. But with the artificial intelligence advances demonstrated by RoseTTAFold and AlphaFold, which has now also been released in an open-source version and reported in the journal Nature [2], researchers now can make the critical protein structure predictions at their desktops. This newfound ability will be a boon to basic science studies and has great potential to speed life-saving therapeutic advances.

References:

[1] Accurate prediction of protein structures and interactions using a three-track neural network. Baek M, DiMaio F, Anishchenko I, Dauparas J, Grishin NV, Adams PD, Read RJ, Baker D., et al. Science. 2021 Jul 15:eabj8754.

[2] Highly accurate protein structure prediction with AlphaFold. Jumper J, Evans R, Pritzel A, Green T, Senior AW, Kavukcuoglu K, Kohli P, Hassabis D. et al. Nature. 2021 Jul 15.

Links:

Structural Biology (National Institute of General Medical Sciences/NIH)

The Structures of Life (NIGMS)

Baker Lab (University of Washington, Seattle)

CASP 14 (University of California, Davis)

NIH Support: National Institute of Allergy and Infectious Diseases; National Institute of General Medical Sciences


Next Page