Mucus and the coronavirus

As the lethal COVID-19 coronavirus propagates around the globe, we know a sneeze, a cough or simply touching a surface with the virus can spread the infection.

What researchers don’t know is exactly the role different compositions of mucus, the slimy substance on human tissue, play in the transmission and infection of coronaviruses. Nor do they know why some people known as “super-spreaders” will spread the disease more than others. But University of Utah biomedical engineering assistant professor Jessica R. Kramer is now researching how mucus plays a part in transferring coronaviruses from person to person.

“Not everyone spreads the disease equally. The quality of their mucus may be part of the explanation,” Kramer says. “One person may sneeze and transmit it to another person, and another may not, and that is not well understood.”

She has received a one-year, $200,000 Rapid Response Research (RAPID) grant from the National Science Foundation for the research.

PHOTO CREDIT: Dan Hixson/University of Utah College of Engineering

University of Utah biomedical engineering assistant professor Jessica R. Kramer has received a new grant to research how mucins, the slimy substance in human tissue, plays a role in spreading coronaviruses such as COVID-19.

Understanding how different compositions of the proteins that make up mucus spread coronaviruses could help identify those who are “super-spreaders” as well as those who could be more vulnerable to becoming infected, says Kramer. That could lead to faster, more accurate data on who will spread the virus or more effective quarantine measures for high-risk populations. The nation’s epidemiologists have said since the arrival of COVID-19 that accurate testing to know where the infection is growing is a key factor to containing its spread.

Kramer and her team will create different forms of synthetic mucins, the proteins that make up mucus, and test them with non-hazardous versions of coronaviruses. COVID-19, which is the cause of the worldwide pandemic, is a novel coronavirus that by the end of March has so far killed more than 37,000 people since it was first discovered late last year. But it is only one of many forms of coronaviruses.

Kramer will use special aerosols in a closed environment to simulate coughing to help determine how different mucins carry the virus through the air. She will also test the viability of the virus when it lands on a surface based on the mucins that carry it. Her lab will also examine how mucin composition on the next victim’s mouth, eyes or lungs affects whether the virus makes it through the mucus into their cells to replicate.

The composition of mucus changes from person to person based on their genetics, environmental factors, or their lifestyle such as whether the person smokes or what their diet is. “It’s important that people understand that it’s not only the amount of mucus that is a factor but how the molecular composition is different,” she says.

Kramer’s lab at the University of Utah has been creating synthetic mucins and more recently studying how mucins and bacteria interact with each other. She says researching how mucins interact with viruses is a natural extension of this work.

Kramer’s award is the second NSF RAPID grant to be given to U researchers related to the spread of the COVID-19 coronavirus. Michael Vershinin and Saveez Saffarian of the U’s Department of Physics & Astronomy will study how the structure of the coronavirus withstands changes in humidity and temperature and under what conditions the virus falls apart.

Find original post here.

COVID-19 antibody tests can guide public health, policy decisions

It’s too soon to use COVID-19 antibody testing to issue “immunity passports,” antibody tests that are available today. But they are good enough to inform public health decisions about relaxing social distancing interventions, says an international group of infectious disease and public health experts in Science Immunology today.

“We don’t need to wait for the perfect test to monitor populations,” says University of Utah Health infectious disease physician-researcher Daniel Leung. “We can use what we have if we go in with our eyes open.” Leung is the corresponding author on the editorial together with specialists from seven different countries and leading public health institutions in the U.S., including Johns Hopkins Bloomberg School of Public Health, Harvard School of Public Health, University of California, San Francisco and Pennsylvania State University.

Today’s tests are ready for populations, not people

Some have suggested that detecting antibodies to SARS-CoV-2—the coronavirus that causes COVID-19—become the basis of “immunity passports” that enable people to return to work, school, or travel. Yet facts indicate that it is premature to take that step. Scientists have yet to determine whether the antibodies or perhaps a threshold level of antibodies, protect a person from being re-infected. In addition, there are multiple antibody tests, none with the levels of specificity needed to declare someone immune.

In short, we are far from being at a place where a positive antibody test guarantees that a person cannot get COVID-19 nor spread it to someone else, say Leung and colleagues. And the stakes are too high to risk getting it wrong.

Regardless, these same tests are good enough to monitor the spread of COVID-19 in populations. “There is no need to throw out the baby with the bathwater,” Leung says. “We can use serological testing at the population level to get valuable information about transmission and the impact of interventions—and we don’t need a perfect serology test to do it.”

Understanding trends such as where outbreaks are occurring and which regions are quiet, along with the characteristics of who is getting ill and who is protected, can provide information to guide policy. Is a specific state or county ready to ease restrictions? Are students safe to go back to school? Do certain populations need extra protection?

Fine-tuning existing tests to meet different needs

One reason many of today’s tests can work for public-level decisions is that they do not just provide black and white answers. Instead, their parameters can be adjusted to fit different needs. One of these characteristics is specificity—how well a test detects antibodies to SARS-CoV-2 and not to antibodies against other coronaviruses. The other is sensitivity—the minimum level of antibodies someone must have in their blood in order to test positive.

In general, there is a tradeoff between the two. Adjusting a test to prioritize sensitivity makes it not as specific, and making a test more specific makes it less sensitive. But, according to the editorial, it’s OK to sacrifice one for the other in order to answer certain questions.

Take the situation in a rural countryside where relatively few people per capita have had COVID-19. In that setting, a test with high sensitivity and low specificity would not be optimal. These characteristics could easily result in the same number of people testing positive who never had COVID-19 as the number of people who really are positive. In this situation, the results would be practically meaningless.

However, the same test can be used if it is tuned for that situation. This can be done by designating a higher cutoff and saying that a test does not count as positive unless it has a stronger signal. Doing so lowers the false positive rate by increasing specificity. In this scenario, positive tests are more likely to be truly positive—and that data can be safely used to monitor that population.

On the other hand, an urban setting where higher proportions of the population have been infected would do better with a test prioritized for higher sensitivity. That would provide a better snapshot of the spread of COVID-19 by capturing a greater segment of the population.

“While we should certainly collect these data, we need to make sure the right studies are put in place so we can meaningfully interpret these data for individuals and for populations,” says Andrew Azman, assistant scientist at Johns Hopkins Bloomberg School of Public Health.

Additional studies will only make the results of antibody testing more informative. The editorial stipulates that we still need to understand whether antibodies remain in the body for months or years, what levels of antibodies provide immunity, and how responses might differ in people who had various severities of infection or who have other medical conditions.

Equally as important as leveraging the technologies at hand, the authors say, is building an infrastructure that allows states and countries to share protocols, standardize methods, share results, and coordinate activities. This would not only improve the response to the current pandemic but could build a foundation for monitoring other infectious diseases, including influenza, cholera, malaria, and future pandemics.

The knowledge gained now may help re-frame the future, they say. “The current crisis presents an opportunity to rethink how health systems generate and use surveillance data and how to harness the power of serological tests and seroepidemiology.”

Find original post here.

In addition to Leung, co-authors are Juliet Bryant, Andrew Azman, Matthew Ferrari, Benjamin Arnold, Maciej Boni, Yap Boum, Kyla Hayford, Francisco Luquero, Michael Mina, Isabel Rodriguez-Barraquer, Joseph Wu, Djibril Wade and Guy Vernet. In addition to the institutions mentioned, collaborators come from Fondation Mérieux, Médecins Sans Frontières, Epicentre in Yaounde and Paris, University of Hong Kong, IRESSEF, Dakar and Institut Pasteur de Bangui.

The editorial was published as “Serology for SARS-CoV-2: apprehensions, opportunities, and the path forward.”

People who live together may not get COVID-19 together

If anyone I live with were to get COVID-19, I would be resigned to the idea that I would get it too. After all, my family breathes the same air without wearing masks and touches the same doorknobs, day in and day out. And yet, it’s not necessarily a foregone conclusion that members of a shared household will share the virus.

Once one person becomes infected, there is a 12% likelihood that someone they are living with will become infected too, according to the University of Utah’s Utah HERO phase one study. Reports from China (also here) indicate that what we’re seeing in Utah is similar to what’s happening elsewhere in the world.

Scientists arrived at the number by performing antibody tests on more than 8,000 Utahns in randomly chosen households across four counties in the state. A positive test indicates that a person has had COVID-19 sometime in the past. Among households where at least one person tested positive, the scientists calculated the proportion of remaining members who also had antibodies.

It’s thought that, typically, when two or more people in a household test positive, one passed the virus to the other. But in some cases, they each may have been infected by someone outside the household. Either way, the frequency that two or more people in a household test positive is lower than one might expect considering how quickly COVID-19 is spreading all around us.

“You might think, ‘Wow if I’m in a household with an infected person, I’m a goner,’” says U of U Health epidemiologist Matthew Samore. “But that’s just not true. The interesting thing is, what are the implications?”

Superspreader or super unlucky?

Close examination of COVID-19 cases worldwide has already taught us that new coronavirus infection spreads more readily when people are close to one another indoors for long periods of time. That characterizes the living condition of many homes—and yet spread from one person in a household to another fails to happen about 88% of the time. What, then, could make the difference between who is likely to spread the virus and who is not?

Perhaps you’ve heard of the term “superspreaders”? One explanation, says Samore and his colleague Damon Toth, is that there is a large variability in infectiousness, and certain conditions are conducive to spreading the virus to large numbers of people. There is support for the idea that a relatively small proportion of people could be responsible for much of the spread of the disease. Research suggests that 10% to 20% of infected people were responsible for 80% of the cases examined.

“If we can understand the factors that make people superspreaders, or that make people minimally infectious, then we can create better policies that better control spread,” Samore says. What could those factors be? Scientists are trying to answer the question, but several possibilities exist.

Biological differences that lead infected people to shed more virus—and for longer periods of time—could boost their infectivity. Characteristics like these could come from being in a certain age group, from changes in the immune system and from other reasons scientists have yet to uncover.

The surrounding environment presents another set of conditions that could make a big difference. Imagine that households with a large number of people in tight quarters, multigenerational families or poor ventilation could be particularly conducive to spreading the disease.

Whether superspreading conditions are the main culprit or transmission mainly comes from many mild spreading events, chances are spreading happens more frequently outside the home. An infected person is more likely to encounter a greater number of people in the community, and a superspreader can potentially infect dozens of others. That suggests that community spread—rather than household transmission—may be a main driver of the pandemic.

Regardless, Toth points out that it is still important to take precautions at home if you know that someone you live with either has COVID-19 or has come in close contact with someone who does. Utah HERO has found that about 1% of Utahns tested positive for antibodies, meaning that about 99% were still susceptible to the disease.

“Twelve percent is still a pretty high risk if you have someone coming home with the virus,” Toth says. “It is a lot higher than before you had someone in your household who is positive.”

Find original post here.