Dr. Francis Collins
Decoding Heart-Brain Talk to Prevent Sudden Cardiac Deaths
Posted on by Dr. Francis Collins
As a cardiac electrophysiologist, Deeptankar DeMazumder has worked for years with people at risk for sudden cardiac arrest (SCA). Despite the latest medical advances, less than 10 percent of individuals stricken with an SCA will survive this highly dangerous condition in which irregular heart rhythms, or arrhythmias, cause the heart suddenly to stop beating.
In his role as a physician, DeMazumder keeps a tight focus on the electrical activity in their hearts, doing his best to prevent this potentially fatal event. In his other role, as a scientist at the University of Cincinnati College of Medicine, DeMazumber is also driven by a life-saving aspiration: finding ways to identify at-risk individuals with much greater accuracy than currently possible—and to develop better ways of protecting them from SCAs. He recently received a 2020 NIH Director’s New Innovator Award to pursue one of his promising ideas.
SCAs happen without warning and can cause death within minutes. Poor heart function and abnormal heart rhythms are important risk factors, but it’s not possible today to predict reliably who will have an SCA. However, doctors already routinely capture a wealth of information in electrical signals from the heart using electrocardiograms (ECGs). They also frequently use electroencephalograms (EEGs) to capture electrical activity in the brain.
DeMazumder’s innovative leap is to look at these heart and brain signals jointly, as well as in new ways, during sleep. According to the physician-scientist, sleep is a good time to search for SCA signatures in the electrical crosstalk between the heart and the brain because many other aspects of brain activity quiet down. He also thinks it’s important to pay special attention to what happens to the body’s electrical signals during sleep because most sudden cardiac deaths happen early in the waking hours, for reasons that aren’t well understood.
He has promising preliminary evidence from both animal models and humans suggesting that signatures within heart and brain signals are unique predictors of sudden death, even in people who appear healthy [1]. DeMazumder has already begun developing a set of artificial intelligence algorithms for jointly deciphering waveform signals from the heart, brain, and other body signals [2,3]. These new algorithms associate the waveform signals with a wealth of information available in electronic health records to improve upon the algorithm’s ability to predict catastrophic illness.
DeMazumder credits his curiosity about what he calls the “art and science of healing” to his early childhood experiences and his family’s dedication to community service in India. It taught him to appreciate the human condition, and he has integrated this life-long awareness into his Western medical training and his growing interest in computer science.
For centuries, humans have talked about how true flourishing needs both head and heart. In DeMazumder’s view, science is just beginning to understand the central role of heart-brain conversations in our health. As he continues to capture and interpret these conversations through his NIH-supported work, he hopes to find ways to identify individuals who don’t appear to have serious heart disease but may nevertheless be at high risk for SCA. In the meantime, he will continue to do all he can for the patients in his care.
References:
[1] Mitochondrial ROS drive sudden cardiac death and chronic proteome remodeling in heart failure. Dey S, DeMazumder D, Sidor A, Foster DB, O’Rourke B. Circ Res. 2018;123(3):356-371.
[2] Entropy of cardiac repolarization predicts ventricular arrhythmias and mortality in patients receiving an implantable cardioverter-defibrillator for primary prevention of sudden death. DeMazumder D, Limpitikul WB, Dorante M, et al. Europace. 2016;18(12):1818-1828.
[3] Dynamic analysis of cardiac rhythms for discriminating atrial fibrillation from lethal ventricular arrhythmias. DeMazumder D, Lake DE, Cheng A, et al. Circ Arrhythm Electrophysiol. 2013;6(3):555-561.
Links:
Sudden Cardiac Arrest (National Heart, Lung, and Blood Institute/NIH)
Deeptankar DeMazumder (University of Cincinnati College of Medicine)
DeMazumder Project Information (NIH RePORTER)
NIH Director’s New Innovator Award (Common Fund)
NIH Support: National Heart, Lung, and Blood Institute; Common Fund
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
Expressing My Gratitude for a Job Well Done
Posted on by Dr. Francis Collins
A Thumbs Up for New NIH Research Center
Posted on by Dr. Francis Collins
The Hidden Beauty of Intestinal Villi
Posted on by Dr. Francis Collins
The human small intestine, though modest in diameter and folded compactly to fit into the abdomen, is anything but small. It measures on average about 20 feet from end to end and plays a big role in the gastrointestinal tract, breaking down food and drink from the stomach to absorb the water and nutrients.
Also anything but small is the total surface area of the organ’s inner lining, where millions of U-shaped folds in the mucosal tissue triple the available space to absorb the water and nutrients that keep our bodies nourished. If these folds, packed with finger-like absorptive cells called villi, were flattened out, they would be the size of a tennis court!
That’s what makes this this microscopic image so interesting. It shows in cross section the symmetrical pattern of the villi (its cells outlined by yellow) that pack these folds. Each cell’s nucleus contains DNA (teal), and the villi themselves are fringed by thousands of tiny bristles, called microvilli (magenta), which are too small to see individually here. Collectively, microvilli make up an absorptive surface, called the brush border, where digested nutrients in the fluid passing through the intestine can enter cells via transport channels.
Amy Engevik, a researcher at the Medical University of South Carolina, Charleston, took this snapshot to show what a healthy intestinal cellular landscape looks like in a young mouse. The Engevik lab studies the dynamic movement of ions, water, and proteins in the intestine—a process that goes wrong in humans born with a rare disorder called microvillus inclusion disease (MVID).
MVID causes chronic gastrointestinal problems in newborn babies, due to defects in a protein that transports various cellular components. Because they cannot properly absorb nutrition from food, these tiny patients require intravenous feeding almost immediately, which carries a high risk for sepsis and intestinal injury.
Engevik and her team study this disease using a mouse model that replicates many of the characteristics of the disorder in humans [1]. Interestingly, when Engevik gets together with her family, she isn’t the only one talking about MVID and villi. Her two sisters, Mindy and Kristen, also study the basic science of gastrointestinal disorders! Instead of sibling rivalry, though, this close alliance has strengthened the quality of her research, says Amy, who is the middle child.
Beyond advancing science and nurturing sisterhood in science, Engevik’s work also captured the fancy of the judges for the Federation of American Societies for Experimental Biology’s annual BioArt Scientific Image and Video Competition. Her image was one of 10 winners announced in December 2020.
Because multiple models are useful for understanding fundamentals of diseases like MVID, Engevik has also developed a large-animal model (pig) that has many features of the human disease [2]. She hopes that her efforts will help to improve our understanding of MVID and other digestive diseases, as well as lead to new, potentially life-saving treatments for babies suffering from MVID.
References:
[1] Loss of MYO5B Leads to reductions in Na+ absorption with maintenance of CFTR-dependent Cl- secretion in enterocytes. Engevik AC, Kaji I, Engevik MA, Meyer AR, Weis VG, Goldstein A, Hess MW, Müller T, Koepsell H, Dudeja PK, Tyska M, Huber LA, Shub MD, Ameen N, Goldenring JR. Gastroenterology. 2018 Dec;155(6):1883-1897.e10.
[2] Editing myosin VB gene to create porcine model of microvillus inclusion disease, with microvillus-lined inclusions and alterations in sodium transporters. Engevik AC, Coutts AW, Kaji I, Rodriguez P, Ongaratto F, Saqui-Salces M, Medida RL, Meyer AR, Kolobova E, Engevik MA, Williams JA, Shub MD, Carlson DF, Melkamu T, Goldenring JR. Gastroenterology. 2020 Jun;158(8):2236-2249.e9.
Links:
Microvillus inclusion disease (Genetic and Rare Diseases Center/NIH)
Digestive Diseases (National Institute of Diabetes and Digestive and Kidney Diseases/NIH)
Amy Engevik (Medical University of South Carolina, Charleston)
Podcast: A Tale of Three Sisters featuring Drs. Mindy, Amy, and Kristen Engevik (The Immunology Podcast, April 29, 2021)
BioArt Scientific Image and Video Competition (Federation of American Societies for Experimental Biology, Bethesda, MD)
NIH Support: National Institute of Diabetes and Digestive and Kidney Diseases
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