basic science
Chipping Away at the Causes of Polycystic Kidney Disease
Posted on by Lawrence Tabak, D.D.S., Ph.D.
It’s often said that two is better than one. That’s true whether driving across the country, renovating a kitchen, or looking for a misplaced set of car keys. But a recent study shows this old saying also applies for modeling a kidney disease with two very complementary, cutting-edge technologies: an organoid, a living miniaturized organ grown in a laboratory dish; and an “organ-on-a-chip,” silicon chips specially engineered to mimic the 3D tissue structure and basic biology of a human body organ.
Using this one-two approach at the lab bench, the researchers modeled in just a few weeks different aspects of the fluid-filled cysts that form in polycystic kidney disease (PKD), a common cause of kidney failure. This is impossible to do in real-time in humans for a variety of technical reasons.
These powerful technologies revealed that blood glucose plays a role in causing the cysts. They also showed the cysts form via a different biological mechanism than previously thought. These new leads, if confirmed, offer a whole new way of thinking about PKD cysts, and more exciting, how to prevent or slow the disease in millions of people worldwide.
These latest findings, published in the journal Nature Communications, come from Benjamin Freedman and colleagues at the University of Washington School of Medicine, Seattle [1]. While much is known about the genetic causes of PKD, Freedman and team realized there’s much still much to learn about the basics of how cysts form in the kidney’s tiny tubes, or tubules, that help to filter toxins out of the bloodstream.
Each human kidney has millions of tubules, and in people with PKD, some of them expand gradually and abnormally to form sacs of fluid that researchers liken to water balloons. These sacs, or cysts, crowd out healthy tissue, leading over time to reduced kidney function and, in some instances, complete kidney failure.
To understand cyst formation better, Freedman’s team and others have invented methods to grow human kidney organoids, complete with a system of internal tubules. Impressively, organoids made from cells carrying mutations known to cause PKD develop cysts, just as people with these same mutations do. When suspended in fluid, the organoids also develop telltale signs of PKD even more dramatically, showing they are sensitive to changes in their environments.
At any given moment, about a quarter of all the fluids in the body pass through the kidneys, and this constant flow was missing from the organoid. That’s when Freedman and colleagues turned to their other modeling tool: a kidney-on-a-chip.
These more complex 3D models, containing living kidney cells, aim to mimic more fully the kidney and its environment. They also contain a network of microfluidic channels to replicate the natural flow of fluids in a living kidney. Combining PKD organoids with kidney-on-a-chip technology provided the best of both worlds.
Their studies found that exposing PKD organoid-on-a-chip models to a solution including water, glucose, amino acids, and other nutrients caused cysts to expand more quickly than they otherwise would. However, the cysts don’t develop from fluids that the kidneys outwardly secrete, as long thought. The new findings reveal just the opposite. The PKD cysts arise and grow as the kidney tissue works to retain most of the fluids that constantly pass through them.
They also found out why: the cysts were absorbing glucose and taking in water from the fluid passing over them, causing the cysts to expand. Although scientists had known that kidneys absorb glucose, they’d never connected this process to the formation of cysts in PKD.
In further studies, the scientists gave fluorescently labeled glucose to mice with PKD and could see that kidney cysts in the animals also took up glucose. The researchers think that the tubules are taking in fluid in the mice just as they do in the organoids.
Understanding the mechanisms of PKD can point to new ways to treat it. Indeed, the research team showed adding compounds that block the transport of glucose also prevented cyst growth. Freedman notes that glucose transport inhibitors (flozins), a class of oral drugs now used to treat diabetes, are in development for other types of kidney disease. He said the new findings suggest glucose transport inhibitors might have benefits for treating PKD, too.
There’s much more work to do. But the hope is that these new insights into PKD biology will lead to promising ways to prevent or treat this genetic condition that now threatens the lives of far too many loved ones in so many families.
This two-is-better-than-one approach is just an example of the ways in which NIH-supported efforts in tissue chips are evolving to better model human disease. That includes NIH’s National Center for Advancing Translational Science’s Tissue Chip for Drug Screening program, which is enabling promising new approaches to study human diseases affecting organ systems throughout the body.
Reference:
[1] Glucose absorption drives cystogenesis in a human organoid-on-chip model of polycystic kidney disease. Li SR, Gulieva RE, Helms L, Cruz NM, Vincent T, Fu H, Himmelfarb J, Freedman BS. Nat Commun. 2022 Dec 23;13(1):7918.
Links:
Polycystic Kidney Disease (National Institute of Diabetes and Digestive and Kidney Diseases/NIH)
Your Kidneys & How They Work (NIDDK)
Freedman Lab (University of Washington, Seattle)
Tissue Chip for Drug Screening (National Center for Advancing Translational Sciences/NIH)
NIH Support: National Center for Advancing Translational Sciences; National Institute of Diabetes and Digestive and Kidney Diseases; National Heart, Lung, and Blood Institute
The Chemistry Clicked: Two NIH-Supported Researchers Win 2022 Nobel Prize in Chemistry
Posted on by Lawrence Tabak, D.D.S., Ph.D.
Through the years, NIH has supported a total of 169 researchers who have received or shared 101 Nobel Prizes. That’s quite a testament to the world-leading science that NIH pursues and its continued impact on improving human health and well-being.
Those numbers include the news late last week that the 2022 Nobel Prize in Chemistry was shared by two long-time grantees for their work on a transformative scientific approach known as “click chemistry.” This form of chemistry has made it possible for researchers to snap together, like LEGO pieces, molecular building blocks to form hybrid biomolecules, often with easy-to-track imaging agents attached. Not only has click chemistry expanded our ability to explore the molecular underpinnings of a wide range of biological processes, but it has provided us with new tools for developing drugs, diagnostics, and a wide array of “smart” materials.
For K. Barry Sharpless, Scripps Research, La Jolla, CA, October 5, 2022 marked the second time that he’s received an early-morning congratulatory call from The Royal Swedish Academy of Sciences. The first such call came in 2001, when Sharpless got the news that he was a co-winner of the Nobel Prize in Chemistry for his discovery of asymmetric catalytic reactions.
This time around, Sharpless was recognized for his groundbreaking studies in the mid-1990s with click chemistry, a term that he coined himself. His initial work established click chemistry as a fast-and-reliable way to attach molecules of interest in the lab [1]. He and co-recipient Morten Meldal, University of Copenhagen, Denmark, who is not funded by NIH, then independently introduced a copper-catalyzed click that further refined the chemistry and helped popularize it across biology and the material sciences [2,3].
For Carolyn R. Bertozzi of Stanford University, Palo Alto, CA, it is her first Nobel. Bertozzi was recognized for expanding the use of click chemistry with so-called bioorthogonal chemistry, which is a copper-free version of the approach that can be used inside living cells without the risk of metal-associated toxicities [4,5].
Bertozzi’s work has been especially interesting to me because of her focus on glycans, which I’ve studied throughout my career. Glycans are the carbohydrate molecules that coat the surfaces of our cells and most secreted proteins. They are essential to life, and, in higher organisms, play fundamental roles in basic processes such as metabolism, immunity, and cellular communication.
Glycans also remain poorly understood, largely because, until recently, they have been so difficult for basic scientists to study with traditional techniques. That has changed with development of new tools to study glycans and the enzymes that assemble them. My long-time collaborator, Kelly Ten Hagen, a senior investigator at NIH’s National Institute of Dental and Craniofacial Research, and I collaborated with Carolyn on identifying small molecules that inhibit the enzyme responsible for the first step in mucin-type O-glycosylation [6]
In the early 2000s, Bertozzi and her team introduced bioorthogonal chemistry, which enabled researchers to label glycans and visualize them in a range of cells and living organisms. Her team’s pioneering approach quickly became an essential tool in basic science labs around the world that study glycans, leading to a number of stunning discoveries that would have otherwise been difficult or impossible.
For clinical researchers, click chemistry has emerged as a workhorse in drug discovery and the improved targeting of cancer chemotherapies and other small-molecule drugs. The approach also is being used to improve delivery of antibody-based therapies and to create new biomaterials. Meanwhile, in the material sciences, click chemistry has been used to solve a number of problems in working with polymers and to expand their industrial uses.
Click chemistry is an excellent example of how advances in basic science can build the foundation for a wide range of practical applications, including those aimed at improving human health. It also highlights the value of strong, sustained public funding for fundamental research, and NIH is proud to have supported Sharpless continuously since 1975 and Bertozzi since 1999. I send my sincere congratulations to both of these most-deserving scientists.
References:
[1] Click Chemistry: Diverse chemical function from a few good reactions. Kolb, HC, Finn, MG, Sharpless, KB. Angew. Chem. Int. Ed. 2001, 40 (11), 2004–2021
[2] A stepwise huisgen cycloaddition process: Copper(I)-catalyzed regioselective “Llgation” of azides and terminal alkynes. Rostovtsev VV, Green LG, Fokin VV, Sharpless KB. Angew. Chem. Int. Ed. 2002, 41 (14), 2596–2599.
[3] Peptidotriazoles on solid phase: [1,2,3]-Triazoles by regiospecific copper(I)-catalyzed 1,3-dipolar cycloadditions of terminal alkynes to azides. Tornøe CW, Sengeløv H, Meldal M. J. Org. Chem. 2002, 67 (9), 3057–3064.
[4] A strain-promoted [3 + 2] azide−alkyne cycloaddition for covalent modification of biomolecules in living systems. Agard NJ, Prescher JA, Bertozzi CR. J. Am. Chem. Soc. 2004, 126 (46), 15046–15047
[5] In vivo imaging of membrane associated glycans in developing zebrafish. Laughlin ST, Baskin JM, Amacher SL, Bertozzi CR. Science 2008, 320 (5876), 664–667.
[6] Small molecule inhibitors of mucin-type O-glycosylation from a uridine-based library. Hang, HC, Yu, C, Ten Hagen, KG, Tian, E, Winans, KA, Tabak, LA, Bertozzi, Chem Biol. 2004 Jul;11(7):1009-1016.
Links:
The Nobel Prize in Chemistry 2022 (The Royal Swedish Academy of Sciences, Stockholm)
Video: Announcement of the 2022 Nobel Prize in Chemistry (YouTube)
Click Chemistry and Bioorthogonal Chemistry (The Royal Swedish Academy of Sciences)
Sharpless Lab (Scripps Research, La Jolla, CA)
Bertozzi Group (Stanford University, Palo Alto, CA)
NIH Support:
K. Barry Sharpless: National Institute of General Medical Sciences
Carolyn R. Bertozzi: National Cancer Institute; National Institute of Allergy and Infectious Diseases; National Institute of General Medical Sciences
Artificial Intelligence Accurately Predicts Protein Folding
Posted on by Dr. Francis Collins
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
Celebrating the Fourth with Neuroscience Fireworks
Posted on by Dr. Francis Collins
There’s so much to celebrate about our country this Fourth of July. That includes giving thanks to all those healthcare providers who have put themselves in harm’s way to staff the ERs, hospital wards, and ICUs to care for those afflicted with COVID-19, and also for everyone who worked so diligently to develop, test, and distribute COVID-19 vaccines.
These “shots of hope,” created with rigorous science and in record time, are making it possible for a great many Americans to gather safely once again with family and friends. So, if you’re vaccinated (and I really hope you are—because these vaccines have been proven safe and highly effective), fire up the grill, crank up the music, and get ready to show your true red, white, and blue colors. My wife and I—both fully vaccinated—intend to do just that!
To help get the celebration rolling, I’d like to share a couple minutes of some pretty amazing biological fireworks. While the track of a John Philip Sousa march is added just for fun, what you see in the video above is the result of some very serious neuroscience research that is scientifically, as well as visually, breath taking. Credit for this work goes to an NIH-supported team that includes Ricardo Azevedo and Sunil Gandhi, at the Center for the Neurobiology of Learning and Memory, University of California, Irvine, and their collaborator Damian Wheeler, Translucence Biosystems, Irvine, CA. Azevedo is also an NIH National Research Service Award fellow and a Medical Scientist Training Program trainee with Gandhi.
The team’s video starts off with 3D, colorized renderings of a mouse brain at cellular resolution. About 25 seconds in, the video flashes to a bundle of nerve fibers called the fornix. Thanks to the wonders of fluorescent labeling combined with “tissue-clearing” and other innovative technologies, you can clearly see the round cell bodies of individual neurons, along with the long, arm-like axons that they use to send out signals and connect with other neurons to form signaling circuits. The human brain has nearly 100 trillion of these circuits and, when activated, they process incoming sensory information and provide outputs that lead to our thoughts, words, feelings, and actions.
As shown in the video, the nerve fibers of the fornix provide a major output pathway from the hippocampus, a region of the brain involved in memory. Next, we travel to the brain’s neocortex, the outermost part of the brain that’s responsible for complex behaviors, and then move on to explore an intricate structure called the corticospinal tract, which carries motor commands to the spinal cord. The final stop is the olfactory tubercle —towards the base of the frontal lobe—a key player in odor processing and motivated behaviors.
Azevedo and his colleagues imaged the brain in this video in about 40 minutes using their imaging platform called the Translucence Biosystems’ Mesoscale Imaging System™. This process starts with a tissue-clearing method that eliminates light-scattering lipids, leaving the mouse brain transparent. From there, advanced light-sheet microscopy makes thin optical sections of the tissue, and 3D data processing algorithms reconstruct the image to high resolution.
Using this platform, researchers can take brain-wide snapshots of neuronal activity linked to a specific behavior. They can also use it to trace neural circuits that span various regions of the brain, allowing them to form new hypotheses about the brain’s connectivity and how such connectivity contributes to memory and behavior.
The video that you see here is a special, extended version of the team’s first-place video from the NIH-supported BRAIN Initiative’s 2020 “Show Us Your BRAINS!” imaging contest. Because of the great potential of this next-generation technology, Translucence Biosystems has received Small Business Innovation Research grants from NIH’s National Institute of Mental Health to disseminate its “brain-clearing” imaging technology to the neuroscience community.
As more researchers try out this innovative approach, one can only imagine how much more data will be generated to enhance our understanding of how the brain functions in health and disease. That is what will be truly spectacular for everyone working on new and better ways to help people suffering from Alzheimer’s disease, Parkinson’s disease, schizophrenia, autism, epilepsy, traumatic brain injury, depression, and so many other neurological and psychiatric disorders.
Wishing all of you a happy and healthy July Fourth!
Links:
Brain Research Through Advancing Innovative Neurotechnologies® (BRAIN) Initiative (NIH)
NIH National Research Service Award
Medical Scientist Training Program (National Institute of General Medical Sciences/NIH)
Small Business Innovation Research and Small Business Technology Transfer (NIH)
Translucence Biosystems (Irvine, CA)
Sunil Gandhi (University of California, Irvine)
Ricardo Azevedo (University of California, Irvine)
Video: iDISCO-cleared whole brain from a Thy1-GFP mouse (Translucence Biosystems)
Show Us Your BRAINs! Photo & Video Contest (Brain Initiative/NIH)
NIH Support: National Institute of Mental Health; National Eye Institute
Protein Mapping Study Reveals Valuable Clues for COVID-19 Drug Development
Posted on by Dr. Francis Collins
One way to fight COVID-19 is with drugs that directly target SARS-CoV-2, the novel coronavirus that causes the disease. That’s the strategy employed by remdesivir, the only antiviral drug currently authorized by the U.S. Food and Drug Administration to treat COVID-19. Another promising strategy is drugs that target the proteins within human cells that the virus needs to infect, multiply, and spread.
With the aim of developing such protein-targeted antiviral drugs, a large, international team of researchers, funded in part by the NIH, has precisely and exhaustively mapped all of the interactions that take place between SARS-CoV-2 proteins and the human proteins found within infected host cells. They did the same for the related coronaviruses: SARS-CoV-1, the virus responsible for outbreaks of Severe Acute Respiratory Syndrome (SARS), which ended in 2004; and MERS-CoV, the virus that causes the now-rare Middle East Respiratory Syndrome (MERS).
The goal, as reported in the journal Science, was to use these protein “interactomes” to uncover vulnerabilities shared by all three coronaviruses. The hope is that the newfound knowledge about these shared proteins—and the pathways to which they belong—will inform efforts to develop new kinds of broad-spectrum antiviral therapeutics for use in the current and future coronavirus outbreaks.
Facilitated by the Quantitative Biosciences Institute Research Group, the team, which included David E. Gordon and Nevan Krogan, University of California, San Francisco, and hundreds of other scientists from around the world, successfully mapped nearly 400 protein-protein interactions between SARS-CoV-2 and human proteins.
You can see one of these interactions in the video above. The video starts out with an image of the Orf9b protein of SARS-CoV-2, which normally consists of two linked molecules (blue and orange). But researchers discovered that Orf9b dissociates into a single molecule (orange) when it interacts with the human protein TOM70 (teal). Through detailed structural analysis using cryo-electron microscopy (cryo-EM), the team went on to predict that this interaction may disrupt a key interaction between TOM70 and another human protein called HSP90.
While further study is needed to understand all the details and their implications, it suggests that this interaction may alter important aspects of the human immune response, including blocking interferon signals that are crucial for sounding the alarm to prevent serious illness. While there is no drug immediately available to target Orf9b or TOM70, the findings point to this interaction as a potentially valuable target for treating COVID-19 and other diseases caused by coronaviruses.
This is just one intriguing example out of 389 interactions between SARS-CoV-2 and human proteins uncovered in the new study. The researchers also identified 366 interactions between human and SARS-CoV-1 proteins and 296 for MERS-CoV. They were especially interested in shared interactions that take place between certain human proteins and the corresponding proteins in all three coronaviruses.
To learn more about the significance of these protein-protein interactions, the researchers conducted a series of studies to find out how disrupting each of the human proteins influences SARS-CoV-2’s ability to infect human cells. These studies narrowed the list to 73 human proteins that the virus depends on to replicate.
Among them were the receptor for an inflammatory signaling molecule called IL-17, which has been suggested as an indicator of COVID-19 severity. Two other human proteins—PGES-2 and SIGMAR1—were of particular interest because they are targets of existing drugs, including the anti-inflammatory indomethacin for PGES-2 and antipsychotics like haloperidol for SIGMAR1.
To connect the molecular-level data to existing clinical information for people with COVID-19, the researchers looked to medical billing data for nearly 740,000 Americans treated for COVID-19. They then zeroed in on those individuals who also happened to have been treated with drugs targeting PGES-2 or SIGMAR1. And the results were quite striking.
They found that COVID-19 patients taking indomethacin were less likely than those taking an anti-inflammatory that doesn’t target PGES-2 to require treatment at a hospital. Similarly, COVID-19 patients taking antipsychotic drugs like haloperidol that target SIGMAR1 were half as likely as those taking other types of antipsychotic drugs to require mechanical ventilation.
More research is needed before we can think of testing these or similar drugs against COVID-19 in human clinical trials. Yet these findings provide a remarkable demonstration of how basic molecular and structural biological findings can be combined with clinical data to yield valuable new clues for treating COVID-19 and other viral illnesses, perhaps by repurposing existing drugs. Not only is NIH-supported basic science essential for addressing the challenges of the current pandemic, it is building a strong foundation of fundamental knowledge that will make us better prepared to deal with infectious disease threats in the future.
Reference:
[1] Comparative host-coronavirus protein interaction networks reveal pan-viral disease mechanisms. Gordon DE et al. Science. 2020 Oct 15:eabe9403.
Links:
Coronavirus (COVID-19) (NIH)
Krogan Lab (University of California, San Francisco)
NIH Support: National Institute of Allergy and Infectious Diseases; National Institute of Neurological Disorders and Stroke; National Institute of General Medical Sciences
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