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mRNA Vaccines May Pack More Persistent Punch Against COVID-19 Than Thought

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Many people, including me, have experienced a sense of gratitude and relief after receiving the new COVID-19 mRNA vaccines. But all of us are also wondering how long the vaccines will remain protective against SARS-CoV-2, the coronavirus responsible for COVID-19.

Earlier this year, clinical trials of the Moderna and Pfizer-BioNTech vaccines indicated that both immunizations appeared to protect for at least six months. Now, a study in the journal Nature provides some hopeful news that these mRNA vaccines may be protective even longer [1].

In the new study, researchers monitored key immune cells in the lymph nodes of a group of people who received both doses of the Pfizer-BioNTech mRNA vaccine. The work consistently found hallmarks of a strong, persistent immune response against SARS-CoV-2 that could be protective for years to come.

Though more research is needed, the findings add evidence that people who received mRNA COVID-19 vaccines may not need an additional “booster” shot for quite some time, unless SARS-CoV-2 evolves into new forms, or variants, that can evade this vaccine-induced immunity. That’s why it remains so critical that more Americans get vaccinated not only to protect themselves and their loved ones, but to help stop the virus’s spread in their communities and thereby reduce its ability to mutate.

The new study was conducted by an NIH-supported research team led by Jackson Turner, Jane O’Halloran, Rachel Presti, and Ali Ellebedy at Washington University School of Medicine, St. Louis. That work builds upon the group’s previous findings that people who survived COVID-19 had immune cells residing in their bone marrow for at least eight months after the infection that could recognize SARS-CoV-2 [2]. The researchers wanted to see if similar, persistent immunity existed in people who hadn’t come down with COVID-19 but who were immunized with an mRNA vaccine.

To find out, Ellebedy and team recruited 14 healthy adults who were scheduled to receive both doses of the Pfizer-BioNTech vaccine. Three weeks after their first dose of vaccine, the volunteers underwent a lymph node biopsy, primarily from nodes in the armpit. Similar biopsies were repeated at four, five, seven, and 15 weeks after the first vaccine dose.

The lymph nodes are where the human immune system establishes so-called germinal centers, which function as “training camps” that teach immature immune cells to recognize new disease threats and attack them with acquired efficiency. In this case, the “threat” is the spike protein of SARS-COV-2 encoded by the vaccine.

By the 15-week mark, all of the participants sampled continued to have active germinal centers in their lymph nodes. These centers produced an army of cells trained to remember the spike protein, along with other types of cells, including antibody-producing plasmablasts, that were locked and loaded to neutralize this key protein. In fact, Ellebedy noted that even after the study ended at 15 weeks, he and his team continued to find no signs of germinal center activity slowing down in the lymph nodes of the vaccinated volunteers.

Ellebedy said the immune response observed in his team’s study appears so robust and persistent that he thinks that it could last for years. The researcher based his assessment on the fact that germinal center reactions that persist for several months or longer usually indicate an extremely vigorous immune response that culminates in the production of large numbers of long-lasting immune cells, called memory B cells. Some memory B cells can survive for years or even decades, which gives them the capacity to respond multiple times to the same infectious agent.

This study raises some really important issues for which we still don’t have complete answers: What is the most reliable correlate of immunity from COVID-19 vaccines? Are circulating spike protein antibodies (the easiest to measure) the best indicator? Do we need to know what’s happening in the lymph nodes? What about the T cells that are responsible for cell-mediated immunity?

If you follow the news, you may have seen a bit of a dust-up in the last week on this topic. Pfizer announced the need for a booster shot has become more apparent, based on serum antibodies. Meanwhile, the Food and Drug Administration and Centers for Disease Control and Prevention said such a conclusion would be premature, since vaccine protection looks really good right now, including for the delta variant that has all of us concerned.

We’ve still got a lot more to learn about the immunity generated by the mRNA vaccines. But this study—one of the first in humans to provide direct evidence of germinal center activity after mRNA vaccination—is a good place to continue the discussion.


[1] SARS-CoV-2 mRNA vaccines induce persistent human germinal centre responses. Turner JS, O’Halloran JA, Kalaidina E, Kim W, Schmitz AJ, Zhou JQ, Lei T, Thapa M, Chen RE, Case JB, Amanat F, Rauseo AM, Haile A, Xie X, Klebert MK, Suessen T, Middleton WD, Shi PY, Krammer F, Teefey SA, Diamond MS, Presti RM, Ellebedy AH. Nature. 2021 Jun 28. [Online ahead of print]

[2] SARS-CoV-2 infection induces long-lived bone marrow plasma cells in humans. Turner JS, Kim W, Kalaidina E, Goss CW, Rauseo AM, Schmitz AJ, Hansen L, Haile A, Klebert MK, Pusic I, O’Halloran JA, Presti RM, Ellebedy AH. Nature. 2021 May 24. [Online ahead of print]


COVID-19 Research (NIH)

Ellebedy Lab (Washington University, St. Louis)

NIH Support: National Institute of Allergy and Infectious Diseases; National Center for Advancing Translational Sciences

New Metric Identifies Coronavirus Hotspots in Real Time

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High viral load found from PCR helps to predict hotspots

During the pandemic, it’s been critical to track in real time where the coronavirus is spreading at home and abroad. But it’s often hard for public health officials to know whether changes in the reported number of COVID-19 cases over time truly reflect the spread of the virus or whether they are confounded by changes in testing levels or lags in the reporting of results.

Now, NIH-funded researchers have discovered a clever workaround to detect more accurately where COVID-19 hotspots are emerging. As published in the journal Science, the new approach focuses on the actual amount of virus present in a positive COVID diagnostic test [1], not just whether the test is positive or negative. What’s even better is these data on a person’s “viral load” are readily available from polymerase chain reaction, or PCR, tests that are the “gold standard” for detecting SARS-CoV-2, the virus responsible for COVID-19. In fact, if you’ve been tested for COVID-19, there’s a good chance you’ve had a PCR-based test.

Here’s how a PCR test for COVID-19 works. After a person provides a nasal swab or saliva sample, any genetic material in the sample is extracted and prepared for the PCR machine. It uses special nucleic acid primers that, if any genetic material from SARS-CoV-2 is present, will make millions more copies of them and result in a positive test result. PCR is an enzymatic reaction that works by running many cycles of heating and cooling; each cycle results in doubling of the genetic material present in the original sample.

But it turns out that PCR can go beyond a simple “yes” or “no” test result. It’s also possible to get some sense of how much coronavirus is present in a positive sample based on the number of cycles required to make enough copies of its genetic material to get the “yes” result. This measure is known as the “cycle threshold,” or Ct, value.

When a sample is run with lots of virus in it, the PCR machine doesn’t need to make so many cycles to reach detectable levels—and the Ct value is considered low. But, when the virus is barely present in a sample, the machine needs to run more cycles before it will reach the threshold for detection. In this case, the Ct value is high. This makes the Ct metric a bit counterintuitive: low Ct means a high level of infection, and high Ct means a low level of infection.

In the new study, researchers in Michael Mina’s lab, Harvard T. H. Chan School of Public Health, Boston, including James Hay and Lee Kennedy-Shaffer, wanted to use Ct values to understand better the overall trajectory of the spread of SARS-CoV-2. Their idea was a little out of the box, since Ct values weren’t being factored into a diagnostic testing process that was set up to give people a yes-or-no answer about COVID-19 status. In fact, Ct values were often discarded.

The team members had a hunch that the amount of virus in patient samples would vary based on whether an outbreak is increasing or declining. Their reasoning was that during an outbreak, when SARS-CoV-2 is spreading rapidly through a community, a larger proportion of infected individuals will have recently contracted the virus than when it is spreading more slowly. The researchers also knew that the virus reaches its peak level in humans soon after infection (generally a couple of days before symptoms begin), and then falls to very low but still detectable levels over the course of weeks or sometimes even months. So, when viral load within samples is highest—and Ct values are lowest—it suggests an outbreak of SARS-CoV-2 is underway. As an outbreak slows and cases fall, viral loads should fall and Ct values rise.

The researchers found that just 30 positive PCR test results on a single day were enough to give an accurate real-time estimate of the growth rate of SARS-CoV-2 infections based on Ct values. With Ct values from multiple time points, it was possible to reconstruct the epidemic curve and estimate the true number of people infected. They found that even Ct values collected from a single location at a single point in time could provide extremely valuable information about the growth or decline of an outbreak.

The findings suggest that these data can now be captured and put to good use as a key metric for decision-making and gauging the success of the pandemic response going forward. It’s also important to note that the value of these data are not unique to COVID-19 and the ongoing pandemic. It appears this can be extremely useful new way to monitor the course of other viral outbreaks, now and in the future, in a way that’s less susceptible to the vagaries of testing. The hope is that this will mean even greater success in capturing viral outbreaks and mobilizing resources in real time to the places where they are most needed.


[1] Estimating epidemiologic dynamics from cross-sectional viral load distributions. Hay JA, Kennedy-Shaffer L, Kanjilal S, Lennon NJ, Gabriel SB, Lipsitch M, Mina MJ. Science. 2021 Jun 3.


COVID-19 Research (NIH)

Michael Mina (Harvard T. H. Chan School of Public Health, Boston)

NIH Support: Common Fund, National Institute of General Medical Sciences; National Cancer Institute

How Immunity Generated from COVID-19 Vaccines Differs from an Infection

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Orginal viral spike is shown binding to antibody from vaccine and from infection. Variant spikes only bind to antibody from vaccine.

A key issue as we move closer to ending the pandemic is determining more precisely how long people exposed to SARS-CoV-2, the COVID-19 virus, will make neutralizing antibodies against this dangerous coronavirus. Finding the answer is also potentially complicated with new SARS-CoV-2 “variants of concern” appearing around the world that could find ways to evade acquired immunity, increasing the chances of new outbreaks.

Now, a new NIH-supported study shows that the answer to this question will vary based on how an individual’s antibodies against SARS-CoV-2 were generated: over the course of a naturally acquired infection or from a COVID-19 vaccine. The new evidence shows that protective antibodies generated in response to an mRNA vaccine will target a broader range of SARS-CoV-2 variants carrying “single letter” changes in a key portion of their spike protein compared to antibodies acquired from an infection.

These results add to evidence that people with acquired immunity may have differing levels of protection to emerging SARS-CoV-2 variants. More importantly, the data provide further documentation that those who’ve had and recovered from a COVID-19 infection still stand to benefit from getting vaccinated.

These latest findings come from Jesse Bloom, Allison Greaney, and their team at Fred Hutchinson Cancer Research Center, Seattle. In an earlier study, this same team focused on the receptor binding domain (RBD), a key region of the spike protein that studs SARS-CoV-2’s outer surface. This RBD is especially important because the virus uses this part of its spike protein to anchor to another protein called ACE2 on human cells before infecting them. That makes RBD a prime target for both naturally acquired antibodies and those generated by vaccines. Using a method called deep mutational scanning, the Seattle group’s previous study mapped out all possible mutations in the RBD that would change the ability of the virus to bind ACE2 and/or for RBD-directed antibodies to strike their targets.

In their new study, published in the journal Science Translational Medicine, Bloom, Greaney, and colleagues looked again to the thousands of possible RBD variants to understand how antibodies might be expected to hit their targets there [1]. This time, they wanted to explore any differences between RBD-directed antibodies based on how they were acquired.

Again, they turned to deep mutational scanning. First, they created libraries of all 3,800 possible RBD single amino acid mutants and exposed the libraries to samples taken from vaccinated individuals and unvaccinated individuals who’d been previously infected. All vaccinated individuals had received two doses of the Moderna mRNA vaccine. This vaccine works by prompting a person’s cells to produce the spike protein, thereby launching an immune response and the production of antibodies.

By closely examining the results, the researchers uncovered important differences between acquired immunity in people who’d been vaccinated and unvaccinated people who’d been previously infected with SARS-CoV-2. Specifically, antibodies elicited by the mRNA vaccine were more focused to the RBD compared to antibodies elicited by an infection, which more often targeted other portions of the spike protein. Importantly, the vaccine-elicited antibodies targeted a broader range of places on the RBD than those elicited by natural infection.

These findings suggest that natural immunity and vaccine-generated immunity to SARS-CoV-2 will differ in how they recognize new viral variants. What’s more, antibodies acquired with the help of a vaccine may be more likely to target new SARS-CoV-2 variants potently, even when the variants carry new mutations in the RBD.

It’s not entirely clear why these differences in vaccine- and infection-elicited antibody responses exist. In both cases, RBD-directed antibodies are acquired from the immune system’s recognition and response to viral spike proteins. The Seattle team suggests these differences may arise because the vaccine presents the viral protein in slightly different conformations.

Also, it’s possible that mRNA delivery may change the way antigens are presented to the immune system, leading to differences in the antibodies that get produced. A third difference is that natural infection only exposes the body to the virus in the respiratory tract (unless the illness is very severe), while the vaccine is delivered to muscle, where the immune system may have an even better chance of seeing it and responding vigorously.

Whatever the underlying reasons turn out to be, it’s important to consider that humans are routinely infected and re-infected with other common coronaviruses, which are responsible for the common cold. It’s not at all unusual to catch a cold from seasonal coronaviruses year after year. That’s at least in part because those viruses tend to evolve to escape acquired immunity, much as SARS-CoV-2 is now in the process of doing.

The good news so far is that, unlike the situation for the common cold, we have now developed multiple COVID-19 vaccines. The evidence continues to suggest that acquired immunity from vaccines still offers substantial protection against the new variants now circulating around the globe.

The hope is that acquired immunity from the vaccines will indeed produce long-lasting protection against SARS-CoV-2 and bring an end to the pandemic. These new findings point encouragingly in that direction. They also serve as an important reminder to roll up your sleeve for the vaccine if you haven’t already done so, whether or not you’ve had COVID-19. Our best hope of winning this contest with the virus is to get as many people immunized now as possible. That will save lives, and reduce the likelihood of even more variants appearing that might evade protection from the current vaccines.


[1] Antibodies elicited by mRNA-1273 vaccination bind more broadly to the receptor binding domain than do those from SARS-CoV-2 infection. Greaney AJ, Loes AN, Gentles LE, Crawford KHD, Starr TN, Malone KD, Chu HY, Bloom JD. Sci Transl Med. 2021 Jun 8.


COVID-19 Research (NIH)

Bloom Lab (Fred Hutchinson Cancer Research Center, Seattle)

NIH Support: National Institute of Allergy and Infectious Diseases

How COVID-19 Can Lead to Diabetes

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Human abdominal anatomy with highlighted pancreas. Cluster of Infected Beta Cells with cornaviruses are in foreground.

Along with the pneumonia, blood clots, and other serious health concerns caused by SARS-CoV-2, the COVID-19 virus, some studies have also identified another troubling connection. Some people can develop diabetes after an acute COVID-19 infection.

What’s going on? Two new NIH-supported studies, now available as pre-proofs in the journal Cell Metabolism [1,2], help to answer this important question, confirming that SARS-CoV-2 can target and impair the body’s insulin-producing cells.

Type 1 diabetes occurs when beta cells in the pancreas don’t secrete enough insulin to allow the body to metabolize food optimally after a meal. As a result of this insulin insufficiency, blood glucose levels go up, the hallmark of diabetes.

Earlier lab studies had suggested that SARS-CoV-2 can infect human beta cells [3]. They also showed that this dangerous virus can replicate in these insulin-producing beta cells, to make more copies of itself and spread to other cells [4].

The latest work builds on these earlier studies to discover more about the connection between COVID-19 and diabetes. The work involved two independent NIH-funded teams, one led by Peter Jackson, Stanford University School of Medicine, Palo Alto, CA, and the other by Shuibing Chen, Weill Cornell Medicine, New York. I’m actually among the co-authors on the study by the Chen team, as some of the studies were conducted in my lab at NIH’s National Human Genome Research Institute, Bethesda, MD.

Both studies confirmed infection of pancreatic beta cells in autopsy samples from people who died of COVID-19. Additional studies by the Jackson team suggest that the coronavirus may preferentially infect the insulin-producing beta cells.

This also makes biological sense. Beta cells and other cell types in the pancreas express the ACE2 receptor protein, the TMPRSS2 enzyme protein, and neuropilin 1 (NRP1), all of which SARS-CoV-2 depends upon to enter and infect human cells. Indeed, the Chen team saw signs of the coronavirus in both insulin-producing beta cells and several other pancreatic cell types in the studies of autopsied pancreatic tissue.

The new findings also show that the coronavirus infection changes the function of islets—the pancreatic tissue that contains beta cells. Both teams report evidence that infection with SARS-CoV-2 leads to reduced production and release of insulin from pancreatic islet tissue. The Jackson team also found that the infection leads directly to the death of some of those all-important beta cells. Encouragingly, they showed this could avoided by blocking NRP1.

In addition to the loss of beta cells, the infection also appears to change the fate of the surviving cells. Chen’s team performed single-cell analysis to get a careful look at changes in the gene activity within pancreatic cells following SARS-CoV-2 infection. These studies showed that beta cells go through a process of transdifferentiation, in which they appeared to get reprogrammed.

In this process, the cells begin producing less insulin and more glucagon, a hormone that encourages glycogen in the liver to be broken down into glucose. They also began producing higher levels of a digestive enzyme called trypsin 1. Importantly, they also showed that this transdifferentiation process could be reversed by a chemical (called trans-ISRIB) known to reduce an important cellular response to stress.

The consequences of this transdifferentiation of beta cells aren’t yet clear, but would be predicted to worsen insulin deficiency and raise blood glucose levels. More study is needed to understand how SARS-CoV-2 reaches the pancreas and what role the immune system might play in the resulting damage. Above all, this work provides yet another reminder of the importance of protecting yourself, your family members, and your community from COVID-19 by getting vaccinated if you haven’t already—and encouraging your loved ones to do the same.


[1] SARS-CoV-2 infection induces beta cell transdifferentiation. Tang et al. Cell Metab 2021 May 19;S1550-4131(21)00232-1.

[2] SARS-CoV-2 infects human pancreatic beta cells and elicits beta cell impairment. Wu et al. Cell Metab. 2021 May 18;S1550-4131(21)00230-8.

[3] A human pluripotent stem cell-based platform to study SARS-CoV-2 tropism and model virus infection in human cells and organoids. Yang L, Han Y, Nilsson-Payant BE, Evans T, Schwartz RE, Chen S, et al. Cell Stem Cell. 2020 Jul 2;27(1):125-136.e7.

[4] SARS-CoV-2 infects and replicates in cells of the human endocrine and exocrine pancreas. Müller JA, Groß R, Conzelmann C, Münch J, Heller S, Kleger A, et al. Nat Metab. 2021 Feb;3(2):149-165.


COVID-19 Research (NIH)

Type 1 Diabetes (National Institute of Diabetes, Digestive and Kidney Disorders/NIH)

Jackson Lab (Stanford Medicine, Palo Alto, CA)

Shuibing Chen Laboratory (Weill Cornell Medicine, New York City)

NIH Support: National Institute of Diabetes and Digestive and Kidney Diseases; National Human Genome Research Institute; National Institute of General Medical Sciences; National Cancer Institute; National Institute of Allergy and Infectious Diseases; Eunice Kennedy Shriver National Institute of Child Health and Human Development

U.K. Study Shows Power of Digital Contact Tracing for COVID-19

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COVID-19 cases in the United Kindom. Hands hold a smart phone with the NHS COVID-19 app
Credit: Adapted from Getty Image and Wymant C, Nature, 2021

There’s been much interest in using digital technology to help contain the spread of COVID-19 in our communities. The idea is to make available opt-in smart phone apps that create a log of other apps operating on the phones of nearby participants. If a participant tests positive for COVID-19 and enters the result, the app will then send automatic alerts to those phones—and participants—who recently came into close proximity with them.

In theory, digital tracing would be much faster and more efficient than the challenging detective work involved in traditional contract tracing. But many have wondered how well such an opt-in system would work in practice. A recent paper, published in the journal Nature, shows that a COVID-19 digital tracing app worked quite well in the United Kingdom [1].

The research comes from Christophe Fraser, Oxford University, and his colleagues in the U.K. The team studied the NHS COVID-19 app, the National Health Service’s digital tracing smart phone app for England and Wales. Launched in September 2020, the app has been downloaded onto 21 million devices and used regularly by about half of eligible smart phone users, ages 16 and older. That’s 16.5 million of 33.7 million people, or more than a quarter of the total population of England and Wales.

From the end of September through December 2020, the app sent about 1.7 million exposure notifications. That’s 4.4 on average for every person with COVID-19 who opted-in to the digital tracing app.

The researchers estimate that around 6 percent of app users who received notifications of close contact with a positive case went on to test positive themselves. That’s similar to what’s been observed in traditional contact tracing.

Next, they used two different approaches to construct mathematical and statistical models to determine how likely it was that a notified contact, if infected, would quarantine in a timely manner. Though the two approaches arrived at somewhat different answers, their combined outputs suggest that the app may have stopped anywhere from 200,000 to 900,000 infections in just three months. This means that roughly one case was averted for each COVID-19 case that consented to having their contacts notified through the app.

Of course, these apps are only as good as the total number of people who download and use them faithfully. They estimate that for every 1 percent increase in app users, the number of COVID-19 cases could be reduced by another 1 or 2 percent. While those numbers might sound small, they can be quite significant when one considers the devastating impact that COVID-19 continues to have on the lives and livelihoods of people all around the world.


[1] The epidemiological impact of the NHS COVID-19 App. Wymant C, Ferretti L, Tsallis D, Charalambides M, Abeler-Dörner L, Bonsall D, Hinch R, Kendall M, Milsom L, Ayres M, Holmes C, Briers M, Fraser C. Nature. 2021 May 12.


COVID-19 Research (NIH)


Christophe Fraser (Oxford University, UK)

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