Posted on by Lawrence Tabak, D.D.S., Ph.D.
The COVID-19 pandemic continues to present considerable public health challenges in the United States and around the globe. One of the most puzzling is why many people who get over an initial and often relatively mild COVID illness later develop new and potentially debilitating symptoms. These symptoms run the gamut including fatigue, shortness of breath, brain fog, anxiety, and gastrointestinal trouble.
People understandably want answers to help them manage this complex condition referred to as Long COVID syndrome. But because Long COVID is so variable from person to person, it’s extremely difficult to work backwards and determine what these people had in common that might have made them susceptible to Long COVID. The variability also makes it difficult to identify all those who have Long COVID, whether they realize it or not. But a recent study, published in the journal Lancet Digital Health, shows that a well-trained computer and its artificial intelligence can help.
Researchers found that computers, after scanning thousands of electronic health records (EHRs) from people with Long COVID, could reliably make the call. The results, though still preliminary and in need of further validation, point the way to developing a fast, easy-to-use computer algorithm to help determine whether a person with a positive COVID test is likely to battle Long COVID.
In this groundbreaking study, NIH-supported researchers led by Emily Pfaff, University of North Carolina, Chapel Hill, and Melissa Haendel, the University of Colorado Anschutz Medical Campus, Aurora, relied on machine learning. In machine learning, a computer sifts through vast amounts of data to look for patterns. One reason machine learning is so powerful is that it doesn’t require humans to tell the computer which features it should look for. As such, machine learning can pick up on subtle patterns that people would otherwise miss.
In this case, Pfaff, Haendel, and team decided to “train” their computer on EHRs from people who had reported a COVID-19 infection. (The records are de-identified to protect patient privacy.) The researchers found just what they needed in the National COVID Cohort Collaborative (N3C), a national, publicly available data resource sponsored by NIH’s National Center for Advancing Translational Sciences. It is part of NIH’s Researching COVID to Enhance Recovery (RECOVER) initiative, which aims to improve understanding of Long COVID.
The researchers defined a group of more than 1.5 million adults in N3C who either had been diagnosed with COVID-19 or had a record of a positive COVID-19 test at least 90 days prior. Next, they examined common features, including any doctor visits, diagnoses, or medications, from the group’s roughly 100,000 adults.
They fed that EHR data into a computer, along with health information from almost 600 patients who’d been seen at a Long COVID clinic. They developed three machine learning models: one to identify potential long COVID patients across the whole dataset and two others that focused separately on people who had or hadn’t been hospitalized.
All three models proved effective for identifying people with potential Long-COVID. Each of the models had an 85 percent or better discrimination threshold, indicating they are highly accurate. That’s important because, once researchers can identify those with Long COVID in a large database of people such as N3C, they can begin to ask and answer many critical questions about any differences in an individual’s risk factors or treatment that might explain why some get Long COVID and others don’t.
This new study is also an excellent example of N3C’s goal to assemble data from EHRs that enable researchers around the world to get rapid answers and seek effective interventions for COVID-19, including its long-term health effects. It’s also made important progress toward the urgent goal of the RECOVER initiative to identify people with or at risk for Long COVID who may be eligible to participate in clinical trials of promising new treatment approaches.
Long COVID remains a puzzling public health challenge. Another recent NIH study published in the journal Annals of Internal Medicine set out to identify people with symptoms of Long COVID, most of whom had recovered from mild-to-moderate COVID-19 . More than half had signs of Long COVID. But, despite extensive testing, the NIH researchers were unable to pinpoint any underlying cause of the Long COVID symptoms in most cases.
So if you’d like to help researchers solve this puzzle, RECOVER is now enrolling adults and kids—including those who have and have not had COVID—at more than 80 study sites around the country.
 Identifying who has long COVID in the USA: a machine learning approach using N3C data. Pfaff ER, Girvin AT, Bennett TD, Bhatia A, Brooks IM, Deer RR, Dekermanjian JP, Jolley SE, Kahn MG, Kostka K, McMurry JA, Moffitt R, Walden A, Chute CG, Haendel MA; N3C Consortium. Lancet Digit Health. 2022 May 16:S2589-7500(22)00048-6.
 A longitudinal study of COVID-19 sequelae and immunity: baseline findings. Sneller MC, Liang CJ, Marques AR, Chung JY, Shanbhag SM, Fontana JR, Raza H, Okeke O, Dewar RL, Higgins BP, Tolstenko K, Kwan RW, Gittens KR, Seamon CA, McCormack G, Shaw JS, Okpali GM, Law M, Trihemasava K, Kennedy BD, Shi V, Justement JS, Buckner CM, Blazkova J, Moir S, Chun TW, Lane HC. Ann Intern Med. 2022 May 24:M21-4905.
COVID-19 Research (NIH)
National COVID Cohort Collaborative (N3C) (National Center for Advancing Translational Sciences/NIH)
Emily Pfaff (University of North Carolina, Chapel Hill)
Melissa Haendel (University of Colorado, Aurora)
NIH Support: National Center for Advancing Translational Sciences; National Institute of General Medical Sciences; National Institute of Allergy and Infectious Diseases
As COVID-19 rapidly expanded throughout the world in April 2020, many in the biomedical technology community voiced significant concerns about the lack of available diagnostic tests. At that time, testing for SARS-CoV-2, the coronavirus that causes COVID-19, was conducted exclusively in clinical laboratories by order of a health-care provider. “Over the counter” (OTC) tests did not exist, and low complexity point of care (POC) platforms were rare. Fewer than 8 million tests were performed in the U.S. that month, and it was clear that we needed a radical transformation to make tests faster and more accessible.
By February 2022, driven by the Omicron variant surge, U.S. capacity had increased to a new record of more than 1.2 billion tests in a single month. Remarkably, the overwhelming majority of these—more than 85 percent—were “rapid tests” conducted in home and POC settings.
The story behind this practice-changing, “test-at-home” transformation is deeply rooted in technologic and manufacturing innovation. The NIH’s National Institute of Biomedical Imaging and Bioengineering (NIBIB), working collaboratively with multiple partners across NIH, government, academia, and the private sector, has been privileged to play a leading role in this effort via the Rapid Acceleration of Diagnostics (RADx®) initiative. On this two-year anniversary of RADx, we take a brief look back at its formation, impact, and potential for future growth.
On April 24, 2020, Congress recognized that testing was an urgent national need and appropriated $1.5 billion to NIH via an emergency supplement . The goal was to substantially increase the number, type, and availability of diagnostic tests in only five to six months. Since the “normal” commercialization cycle for this type of diagnostic technology is typically more than five years, we needed an entirely new approach . . . fast.
The RADx initiative was launched just five days after that challenging Congressional directive . Four NIH RADx programs were eventually created to support technology development and delivery, with the goal of matching test performance with community needs .The first two programs, RADx Tech and RADx Advanced Technology Platforms (ATP), were developed by NIBIB and focused on innovation for rapidly creating, scaling up, and deploying new technologies.
RADx Tech is built around NIBIB’s Point of Care Technologies Research Network (POCTRN) and includes core activities for technology review, test validation, clinical studies, regulatory authorization, and test deployment. Overall, the RADx Tech network includes approximately 900 participants from government, academia, and the private sector with unique capabilities and resources designed to decrease inherent risk and guide technologies from design and development to fully disseminated commercial products.
At the core of RADx Tech operations is the “innovation funnel” rapid review process, popularized as a shark tank . A total of 824 complete applications were submitted during two open calls in a four-month period, beginning April 2020 and during a one-month period in June 2021. Forty-seven projects received phase 1 funding to validate and lower the inherent risk of developing these technologies. Meanwhile, 50 companies received phase 2 contracts to support FDA authorization studies and manufacturing expansion 
Beyond test development, RADx Tech has evolved to become a key contributor to the U.S. COVID-19 response. The RADx Independent Test Assessment Program (ITAP) was launched in October 2021 to accelerate regulatory authorization of new tests as a joint effort with the Food and Drug Administration (FDA) . The ITAP acquires analytical and clinical performance data and works closely with FDA and manufacturers to shave weeks to months off the time it normally takes to receive Emergency Use Authorization (EUA).
The RADx Tech program also created a Variant Task Force to monitor the performance of tests against each new coronavirus “variant of concern” that emerges. This helps to ensure that marketed tests continue to remain effective. Other innovative RADx Tech projects include Say Yes! Covid Test, the first online free OTC test distribution program, and Project Rosa, which conducts real-time variant tracking across the country .
RADx Tech, by any measure, has exceeded even the most-optimistic expectations. In two years, RADx Tech-supported companies have received 44 EUAs and added approximately 2 billion tests and test products to the U.S. capacity. These remarkable numbers have steadily increased from more than16 million tests in September 2020, just five months after the program was established .
RADx Tech has also made significant contributions to the distribution of 1 billion free OTC tests via the government site, COVID.gov/tests. It has also provided critical guidance on serial testing and variants that have improved test performance and changed regulatory practice [9,10]. In addition, the RADx Mobile Application Reporting System (RADx MARS) reduces barriers to test reporting and test-to-treat strategies’ The latter offers immediate treatment options via telehealth or a POC location whenever a positive test result is reported. Finally, the When to Test website provides critical guidance on when and how to test for individuals, groups, and communities.
As we look to the future, RADx Tech has enormous potential to impact the U.S. response to other pathogens, diseases, and future pandemics. Major challenges going forward include improving home tests to work as well as lab platforms and building digital health networks for capturing and reporting test results to public health officials .
A recent editorial published in the journal Nature Biotechnology noted, “RADx has spawned a phalanx of diagnostic products to market in just 12 months. Its long-term impact on point of care, at-home, and population testing may be even more profound .” We are now poised to advance a new wave of precision medicine that’s led by innovative diagnostic technologies. It represents a unique opportunity to emerge stronger from the pandemic and achieve long-term impact.
 NIH mobilizes national innovation initiative for COVID-19 diagnostics, NIH news release, April 29, 2020.
 Rapid scaling up of Covid-19 diagnostic testing in the United States—The NIH RADx Initiative. Tromberg BJ, Schwetz TA, Pérez-Stable EJ, Hodes RJ, Woychik RP, Bright RA, Fleurence RL, Collins FS. N Engl J Med. 2020 Sep 10;383(11):1071-1077.
 We need more covid-19 tests. We propose a ‘shark tank’ to get us there. Alexander L. and Blunt R., Washington Post, April 20, 2020.
 RADx® Tech/ATP dashboard, National Institute of Biomedical Imaging and Bioengineering, NIH.
 New HHS actions add to Biden Administration efforts to increase access to easy-to-use over-the-counter COVID-19 tests. U.S. Department of Health and Human Services Press Office, October 25, 2021.
 A method for variant agnostic detection of SARS-CoV-2, rapid monitoring of circulating variants, detection of mutations of biological significance, and early detection of emergent variants such as Omicron. Lai E, et al. medRxiV preprint, January 9, 2022.
 Longitudinal assessment of diagnostic test performance over the course of acute SARS-CoV-2 infection. Smith RL, et al. J Infect Dis. 2021 Sep 17;224(6):976-982.
 Comparison of rapid antigen tests’ performance between Delta (B.1.61.7; AY.X) and Omicron (B.1.1.529; BA1) variants of SARS-CoV-2: Secondary analysis from a serial home self-testing study. Soni A, et al. MedRxiv preprint, March 2, 2022.
 Reporting COVID-19 self-test results: The next frontier. Health Affairs, Juluru K., et al. Health Affairs, February 11, 2022.
 Radical solutions. Nat Biotechnol. 2021 Apr;39(4):391.
Get Free At-Home COVID Tests (COVID.gov)
When to Test (Consortia for Improving Medicine with Innovation & Technology, Boston)
RADx Programs (NIH)
RADx® Tech and ATP Programs (National Institute of Biomedical Imaging and Biomedical Engineering/NIH)
[Note: Acting NIH Director Lawrence Tabak has asked the heads of NIH’s Institutes and Centers (ICs) to contribute occasional guest posts to the blog to highlight some of the interesting science that they support and conduct. This is the eighth in the series of NIH IC guest posts that will run until a new permanent NIH director is in place.]
Posted on by Lawrence Tabak, D.D.S., Ph.D.
The NIH continues to support the development of some very innovative therapies to control SARS-CoV-2, the coronavirus that causes COVID-19. One innovative idea involves a molecular decoy to thwart the coronavirus.
How’s that? The decoy is a specially engineered protein particle that mimics the 3D structure of the ACE2 receptor, a protein on the surface of our cells that the virus’s spike proteins bind to as the first step in causing an infection.
The idea is when these ACE2 decoys are administered therapeutically, they will stick to the spike proteins that crown the coronavirus (see image above). With its spikes covered tightly in decoy, SARS-CoV-2 has a more-limited ability to attach to the real ACE2 and infect our cells.
Recently, the researchers published their initial results in the journal Nature Chemical Biology, and the early data look promising . They found in mouse models of severe COVID-19 that intravenous infusion of an engineered ACE2 decoy prevented lung damage and death. Though more study is needed, the researchers say the decoy therapy could potentially be delivered directly to the lungs through an inhaler and used alone or in combination with other COVID-19 treatments.
The findings come from a research team at the University of Illinois Chicago team, led by Asrar Malik and Jalees Rehman, working in close collaboration with their colleagues at the University of Illinois Urbana-Champaign. The researchers had been intrigued by an earlier clinical trial testing the ACE2 decoy strategy . However, in this earlier attempt, the clinical trial found no reduction in mortality. The ACE2 drug candidate, which is soluble and degrades in the body, also proved ineffective in neutralizing the virus.
Rather than give up on the idea, the UIC team decided to give it a try. They engineered a new soluble version of ACE2 that structurally might work better as a decoy than the original one. Their version of ACE2, which includes three changes in the protein’s amino acid building blocks, binds the SARS-CoV-2 spike protein much more tightly. In the lab, it also appeared to neutralize the virus as well as monoclonal antibodies used to treat COVID-19.
To put it to the test, they conducted studies in mice. Normal mice don’t get sick from SARS-CoV-2 because the viral spike can’t bind well to the mouse version of the ACE2 receptor. So, the researchers did their studies in a mouse that carries the human ACE2 and develops a severe acute respiratory syndrome somewhat similar to that seen in humans with severe COVID-19.
In their studies, using both the original viral isolate from Washington State and the Gamma variant (P.1) first detected in Brazil, they found that infected mice infused with their therapeutic ACE2 protein had much lower mortality and showed few signs of severe acute respiratory syndrome. While the protein worked against both versions of the virus, infection with the more aggressive Gamma variant required earlier treatment. The treated mice also regained their appetite and weight, suggesting that they were making a recovery.
Further studies showed that the decoy bound to spike proteins from every variant tested, including Alpha, Beta, Delta and Epsilon. (Omicron wasn’t yet available at the time of the study.) In fact, the decoy bound just as well, if not better, to new variants compared to the original virus.
The researchers will continue their preclinical work. If all goes well, they hope to move their ACE2 decoy into a clinical trial. What’s especially promising about this approach is it could be used in combination with treatments that work in other ways, such as by preventing virus that’s already infected cells from growing or limiting an excessive and damaging immune response to the infection.
Last week, more than 17,500 people in the United States were hospitalized with severe COVID-19. We’ve got to continue to do all we can to save lives, and it will take lots of innovative ideas, like this ACE2 decoy, to put us in a better position to beat this virus once and for all.
 Engineered ACE2 decoy mitigates lung injury and death induced by SARS-CoV-2 variants.
Zhang L, Dutta S, Xiong S, Chan M, Chan KK, Fan TM, Bailey KL, Lindeblad M, Cooper LM, Rong L, Gugliuzza AF, Shukla D, Procko E, Rehman J, Malik AB. Nat Chem Biol. 2022 Jan 19.
COVID-19 Research (NIH)
Asrar Malik (University of Illinois Chicago)
Jalees Rehman (University of Illinois Chicago)
NIH Support: National Heart, Lung, and Blood Institute; National Institute of Allergy and Infectious Diseases
Posted on by Lawrence Tabak, D.D.S., Ph.D.
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 .
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.
 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).
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